[ { "path": ".github/FUNDING.yml", "content": "# These are supported funding model platforms\n\ngithub: # Replace with up to 4 GitHub Sponsors-enabled usernames e.g., [user1, user2]\npatreon: # Replace with a single Patreon username\nopen_collective: cadcad1\nko_fi: # Replace with a single Ko-fi username\ntidelift: # Replace with a single Tidelift platform-name/package-name e.g., npm/babel\ncommunity_bridge: # Replace with a single Community Bridge project-name e.g., cloud-foundry\nliberapay: # Replace with a single Liberapay username\nissuehunt: # Replace with a single IssueHunt username\notechie: # Replace with a single Otechie username\nlfx_crowdfunding: # Replace with a single LFX Crowdfunding project-name e.g., cloud-foundry\ncustom: # Replace with up to 4 custom sponsorship URLs e.g., ['link1', 'link2']\n" }, { "path": ".github/workflows/cadcad-ci.yml", "content": "# This workflow will install Python dependencies and run tests with multiple versions of Python\n\nname: cadCAD CI\n\non:\n push:\n branches: [ \"master\" ]\n pull_request:\n branches: [ \"master\" ]\n workflow_dispatch:\n\npermissions:\n contents: read\n\njobs:\n windows-build:\n if: false\n \n continue-on-error: true\n\n runs-on: windows-latest\n\n strategy:\n matrix:\n python-version: [\"3.9\", \"3.10\", \"3.11\", \"3.12\"]\n\n steps:\n - uses: actions/checkout@v4\n\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v5\n with:\n python-version: ${{ matrix.python-version }}\n cache: 'pip'\n\n - name: Display Python version\n run: python -c \"import sys; print(sys.version)\"\n\n - name: Install test and build dependencies\n run: |\n python -m pip install --upgrade pip\n python -m pip install jupyter\n pip install -r requirements.txt\n\n - name: Build cadCAD\n shell: cmd\n run: |\n python setup.py bdist_wheel\n for %%x in (\"dist\\*.whl\") do python -m pip install %%x --force-reinstall\n\n - name: Run tests\n run: |\n python -m pytest\n\n build:\n continue-on-error: true\n\n strategy:\n matrix:\n python-version: [\"3.9\", \"3.10\", \"3.11\", \"3.12\"]\n os: [ubuntu-latest, macos-latest]\n\n runs-on: ${{ matrix.os }}\n\n steps:\n - uses: actions/checkout@v4\n\n - name: Set up Python ${{ matrix.python-version }}\n uses: actions/setup-python@v5\n with:\n python-version: ${{ matrix.python-version }}\n cache: 'pip'\n\n - name: Display Python version\n run: python -c \"import sys; print(sys.version)\"\n\n - name: Install test and build dependencies\n run: |\n python -m pip install --upgrade pip\n python -m pip install jupyter\n pip install -r requirements.txt\n\n - name: Build cadCAD\n run: |\n python setup.py bdist_wheel\n python -m pip install dist/*.whl --force-reinstall\n\n - name: Run tests\n run: |\n python -m pytest\n" }, { "path": ".github/workflows/cadcad-publish.yml", "content": "# This workflow will publish the cadCAD library to PyPI when a release is created\n\nname: cadCAD Publish\n\non:\n release:\n types: [published]\n\njobs:\n pypi-publish:\n name: Upload release to PyPI\n\n if: startsWith(github.ref, 'refs/tags/')\n\n runs-on: ubuntu-latest\n\n environment:\n name: pypi\n url: https://pypi.org/p/cadCAD\n\n permissions:\n id-token: write\n\n steps:\n - uses: actions/checkout@v4\n\n - name: Set up Python\n uses: actions/setup-python@v5\n with:\n python-version: '3.12'\n\n - name: Install dependencies\n run: |\n python -m pip install --upgrade pip\n pip install -r requirements.txt\n\n - name: Build cadCAD\n run: |\n python setup.py bdist_wheel\n\n - name: Publish package\n uses: pypa/gh-action-pypi-publish@release/v1\n" }, { "path": ".gitignore", "content": "*.idea\n*.vscode\n*.ipynb_checkpoints\n*.DS_Store\n*.eggs\n*.pytest_cache\n*.mypy_cache\n*.csv\n*.egg-info\n*.sqlite3\n*.pyc\n*venv\n*external_data\n*sys_exec.py\n*/__pycache__\n*.DS_Store\n*notes.*\n*announcement.md\n*dist.py\n*/poc\n\n__pycache__\nPipfile\nPipfile.lock\nbuild\nresults\nresult\nnotebooks\nclient_work\nmonkeytype\nvenv\nnotes.*\nannouncement.md\ntesting/tests/a_b_tests/venv_0_4_23\n\n\nsimulations/tickets\nsimulations/validation\nsimulations/poc/local/\nsimulations/regression_tests/poc\nsimulations/regression_tests/poc_configs\ndist/*\ntesting/tests/import_cadCAD.nbconvert.ipynb\ntesting/tests/cadCAD_memory_address.json\n" }, { "path": "AUTHORS.txt", "content": "Authors\n=======\n\ncadCAD was originally implemented by Joshua E. Jodesty and designed by Michael Zargham, Markus B. Koch, and\nMatthew V. Barlin in 2018. Since then, upgrades and improvements were further committed by Danilo L. Bernardineli, Tyler D. Mace\nand Emanuel Lima.\n \n\n\nCurrent Project Maintainers:\n- Emanuel Lima @emanuellima1\n- Michael Zargham @mzargham\n\n\nActive and Past Contributors:\n- Joshua E. Jodesty @JEJodesty\n- Markus B. Koch @markusbkoch\n- Matthew V. Barlin @matttyb80\n- Michael Zargham @mzargham\n- Danilo L. Bernardineli @danlessa\n- Tyler D. Mace @tylerdmace\n- Emanuel Lima @emanuellima1\n- Zixuan Zhang @zixuanzh\n- Charles Rice @charles-rice\n- Benjamin Scholz @BenSchZA\n\n\nWe’d also like to thank:\n- Andrew Clark @aclarkData\n- Nick Hirannet @nick-phl-7\n- Jonathan Gabler \n- Harry Goodnight @\n- Charlie Hoppes\n- Nikhil Jamdade\n- Chris Frazier\n- Griff Green @GriffGreen\n- Isaac @eenti\n- Jonny Dubowsky @jonnydubowsky\n- Sem Brestels @sembrestels\n- @fjribi\n- Andrea Maria Piana @cammellos\n" }, { "path": "CHANGELOG.md", "content": "# Changelog:\n\n## 0.5.3 - April 19, 2024\n\n### Bugs fixed\n\n- Solve issue on which nested initial states are being copied from one run to another ([#357](https://github.com/cadCAD-org/cadCAD/pull/357));\n- Created add_parameter_labels ([#360](https://github.com/cadCAD-org/cadCAD/pull/360));\n- Add supress_print attribute to the Executor class for silencing prints and tqdm ([#361](https://github.com/cadCAD-org/cadCAD/pull/361));\n\n## 0.5.2 - April 11, 2024\n\n### Bugs fixed\n\n- fix: easy supports list/array/sequence type as parameter ([#348](https://github.com/cadCAD-org/cadCAD/pull/348));\n- Update setup.py to include tqdm package ([#342](https://github.com/cadCAD-org/cadCAD/pull/342));\n- Debug M dictionairy unpacking in utils ([#343](https://github.com/cadCAD-org/cadCAD/pull/343));\n- Add test for counting the parameters argument inside policies and SUFs ([#345](https://github.com/cadCAD-org/cadCAD/pull/345));\n- Fix sweep_cartesian_product such that it works nicely with easy_run ([#355](https://github.com/cadCAD-org/cadCAD/pull/355));\n- Update easy_run.py ([#356](https://github.com/cadCAD-org/cadCAD/pull/356));\n\n## 0.5.1 - December 21, 2023\n\n### Bugs fixed\n\n- Fixed a bug where the single_proc mode would result in a missing simulation key ([#332](https://github.com/cadCAD-org/cadCAD/issues/332));\n- Fixed a bug where the single_proc mode would not return results ([#335](https://github.com/cadCAD-org/cadCAD/issues/335));\n- Fixed a wrong parameter order when doing single runs ([#337](https://github.com/cadCAD-org/cadCAD/issues/337)).\n- Added a Windows CI pipeline;\n\n## 0.5.0 - December 20, 2023\n\n### New User Features\n\n- Added toggle for enabling users to deactivate deepcopying. This is done by passing an additional object on the `ExecutionContext`, eg. `ExecutionContext(mode, additional_objs={'deepcopy_off': True})`\n\n### New Submodules\n\n- A collection of type annotations for encapsuling `cadCAD` projects is now implemented through the `cadCAD.types` submodules\n- Added `cadCAD.tools` as a submodule, which is originated from the `cadCAD_tools` Python package. This submodule contains several helper functions for making the simulation experience more straightforward as well as a collection of performance profiling tools.\n- Added `cadCAD.diagram` as a submodule, which is originated from the `cadCAD_diagram` Python package. This submodule contains functions for programatically generating block diagrams from existing models.\n- More informative error messages when policies and SUFs are wrongly implemented. (Issues #288 and #258)\n\n### Backend Improvements\n\n- Merged repo with the `cadCAD_legacy_devel`, which includes performance improvements. In particular, simulations will start up faster due to code optimizations.\n- `cadCAD` now uses `pytest` as the testing framework. This was made possible by isolating the existing tests and wrapping them into functions.\n\n### Fixes\n\n- cadCAD is now Python 3.10+ compatible (Issue #306 and #301)\n- Proper support for `ExecutionMode.single_mode` (Issue #253 and #254)\n\n## September 28, 2021\n### New Features:\n* **ver. ≥ `0.4.28`:**\n * #### [Experiments](documentation#experiments)\n * #### System Model Configuration\n * Configurations (`cadCAD.utils.Configuration`'s) are now accessed via the `configs` member of `cadCAD.configuration.Experiment`.\n [Example:](documentation#experiments) `cadCAD.configuration.Experiment().configs`\n * `cadCAD.configs` has been re-included for backwards compatibility and has been assigned `cadCAD.experiment.configs`\n * #### Experiments\n * `cadCAD.configuration.Experiment()` is unique representation of an experiment of one or more configured System\n Models.\n * The `cadCAD` module now contains a default Experiment object `cadCAD.experiment` as an instantiation of\n `cadCAD.configuration.Experiment()`\n * An `Experiment`'s `append_model` method stores multiple system model `Configuration`'s for simulation\n execution within `cadCAD.configuration.Experiment().configs`.\n `cadCAD.configuration.Experiment().model_ids` contains system model labels and/or indexes for\n `cadCAD.configuration.Experiment().configs`\n * The `Experiment`'s `append_model` method is defined with `model_id` parameter that accepts a system model\n label.\n * If duplicate `model_id`'s are specified, an index is appended to the label after the `@` symbol.\n (Example: `cadCAD.configuration.Experiment().model_ids = ['sys_model', 'sys_model@1', 'sys_model@2', ...]`)\n * If `model_id`'s are not specified or duplicate, the label is auto-generated as a string indicating the\n system model index within `cadCAD.configuration.Experiment().configs`.\n (Example of unspecified system models at indexes 1, 3, and 4:\n `cadCAD.configuration.Experiment().model_ids = ['sys_model', '1', 'sys_model@2', '3', '4', ...]`)\n * #### [Upgrade Guide:](https://github.com/cadCAD-org/cadCAD/blob/master/documentation/cadCAD-v0.4.28-Model-Upgrade-Guide.md) specific to feature changes / additions\n\n\n## August 25, 2021\n### New Features:\n* **ver. ≥ `0.4.27`:**\n * #### [Experiments](documentation#experiments)\n * #### System Model Configurations\n * Configurations (`cadCAD.utils.Configuration`'s) as are no longer a part of the `cadCAD` module\n (as `cadCAD.configs`) and are now accessed via the `configs` member of `cadCAD.configuration.Experiment`.\n [Example:](documentation#experiments) `cadCAD.configuration.Experiment().configs`\n * #### Experiments\n * `cadCAD.configuration.Experiment` is unique representation of an experiment of one or more configured System\n Models. An `Experiment`'s `append_model` method stores multiple system model `Configuration`'s for simulation\n execution.\n * The `Experiment`'s `append_model` method requires a `model_id` parameter that is auto-created as `'sys_model_#'`.\n * **Requirements:**\n * Users must use different `model_id`'s when appending multiple System Model Configurations.\n * Users can no longer use the `config_list` method of `cadCAD.configuration.Experiment`\n * **Backwards Compatibility:** The `append_model` method of `cadCAD.configuration.Experiment` can also be used as\n the `append_configs` method.\n * Removed [Nix](https://nixos.org/)\n * #### [Upgrade Guide:](https://github.com/cadCAD-org/cadCAD/blob/master/documentation/cadCAD-v0.4.27-Model-Upgrade-Guide.md) specific to feature changes / additions\n* **Fixes:**\n * [#248](https://github.com/cadCAD-org/cadCAD/issues/248)\n * The previous release was returning partial results. An A/B test for this has been included and will be for\n future releases\n * [#242](https://github.com/cadCAD-org/cadCAD/issues/242)\n * Parallelized simulations enabled with the re-inclusion of `ProcessPool`.\n * [#257](https://github.com/cadCAD-org/cadCAD/issues/257)\n * ValueError for runs accepted by the `cadCAD.configuration.Experiment().append_model` via the `sim_configs` no\n longer gives mis-leading error message if catching a non-related ValueError\n * [#252](https://github.com/cadCAD-org/cadCAD/issues/252)\n * Jupyter lab and Jupyter notebook recognises cadCAD module\n\n\n## September 22, 2020\n#### [Multi - System Model Execution](https://github.com/cadCAD-org/cadCAD/blob/master/documentation/Simulation_Execution.md#multiple-simulation-execution)\n* **ver. ≥ `0.4.23`:**\n * **Hot-Fix:** [#203](https://github.com/cadCAD-org/cadCAD/pull/203) (**No Breaking Changes**)\n * Multi - System Model simulation results will no longer return truncated results (exclude the results of the last\n `cadCAD.configuration.Configuration` appended to `cadCAD.configs`).\n * Issue: [#195](https://github.com/cadCAD-org/cadCAD/issues/195)\n * Parameter Sweep value `M` (Params) requires up to a maximum of 2 distinct lengths\n\n\n## August 5, 2020\n#### [Experiments](documentation#experiments)\n* `cadCAD.configuration.Experiment` (Alpha) is in development and needed to be released to support the implementation of\nweb applications and proprietary feature extensions. It is intended to represent a unique identifier of an experiment of\none or more configured System Models. For this reason, `append_configs` is a method of\n`cadCAD.configuration.Experiment`. As of now it does not support multi - system model simulation because configurations\nare still appended globally despite `append_config` being a method of Experiment.\n\n##### Examples:\n* **ver. ≥ `0.4.22`:**\n ```python\n from cadCAD.configuration import Experiment\n exp = Experiment()\n exp.append_configs(...)\n ```\n* **ver. `0.3.1`:** *Deprecated*\n ```python\n from cadCAD.configuration import append_configs\n append_configs(...)\n ```\n\n\n## June 22, 2020\n* Bug Fix: Multiprocessing error for Windows\n\n\n## June 19, 2020\n\n## New Features:\n#### [Local Execution Mode (Default)](documentation/Simulation_Execution.md#simulation-execution-modes)\n* Local Execution Mode (Default): Implicit parallelization of Monte-Carlo / Stochastic simulations (Automatically\nselects Multi-Threaded Mode if simulations are configured for more than a single run)\n * **Backwards Compatibility:** `cadCAD.engine.ExecutionMode` accepts legacy execution modes from ver. `0.3.1`\n\n##### Examples:\n* **ver. ≥ `0.4.22`:**\n ```python\n from cadCAD.engine import ExecutionMode, ExecutionContext\n exec_mode = ExecutionMode()\n local_ctx = ExecutionContext(context=exec_mode.local_mode)\n ```\n* **ver. `0.3.1`:**\n\n Multi-Threaded:\n ```python\n from cadCAD.engine import ExecutionMode, ExecutionContext\n\n exec_mode = ExecutionMode()\n single_ctx = ExecutionContext(context=exec_mode.multi_proc)\n ```\n\n Single-Thread:\n ```python\n from cadCAD.engine import ExecutionMode, ExecutionContext\n\n exec_mode = ExecutionMode()\n multi_ctx = ExecutionContext(context=exec_mode.single_proc)\n ```\n\n\n#### cadCAD Post-Processing Enhancements / Modifications\n* \t[**Single Result Dataset**](documentation/Simulation_Execution.md#4-execute-simulation--produce-system-event-dataset)\nas a 2 dimensional `list`\n * Returns a single dataset instead of multiple datasets per Monte Carlo simulation as in `0.3.1`:\n * New System Metrics as dataset attributes:\n * **Simulation** (Alpha) is a unique identifier being developed to represent Experiments as stated above and\n will be renamed accordingly\n * **Subset** is a unique identifier of Monte-Carlo simulations produced by parameter sweeps\n * Note: Returning a single dataset was originally specified during the project’s inception instead of multiple per\n simulation\n\n##### Examples:\n* **ver. ≥ `0.4.22`:**\n ```python\n import pandas as pd\n from tabulate import tabulate\n from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n import system_model_A, system_model_B\n from cadCAD import configs\n\n exec_mode = ExecutionMode()\n local_ctx = ExecutionContext(context=exec_mode.local_mode)\n simulation = Executor(exec_context=local_ctx, configs=configs)\n raw_result, sys_model, _ = simulation.execute()\n\n result = pd.DataFrame(raw_result)\n print(tabulate(result, headers='keys', tablefmt='psql'))\n ```\n Results:\n ```\n +----+------------+-----------+----+---------------------+------------+--------+-----+---------+----------+\n | | s1 | s2 | s3 | timestamp | simulation | subset | run | substep | timestep |\n |----+------------+-----------+----+---------------------+------------+--------+-----+---------+----------|\n | 0 | 0.0 | 0.0 | 1 | 2018-10-01 15:16:24 | 0 | 0 | 1 | 0 | 0 |\n | 1 | 1.0 | 4 | 5 | 2018-10-01 15:16:25 | 0 | 0 | 1 | 1 | 1 |\n | 2 | 2.0 | 6 | 5 | 2018-10-01 15:16:25 | 0 | 0 | 1 | 2 | 1 |\n | 3 | 3.0 | [ 30 300] | 5 | 2018-10-01 15:16:25 | 0 | 0 | 1 | 3 | 1 |\n | 4 | 0 | 0 | 1 | 2018-10-01 15:16:24 | 1 | 0 | 1 | 0 | 0 |\n | 5 | 1 | 0 | 5 | 2018-10-01 15:16:25 | 1 | 0 | 1 | 1 | 1 |\n | 6 | a | 0 | 5 | 2018-10-01 15:16:25 | 1 | 0 | 1 | 2 | 1 |\n | 7 | ['c', 'd'] | [ 30 300] | 5 | 2018-10-01 15:16:25 | 1 | 0 | 1 | 3 | 1 |\n +----+------------+-----------+----+---------------------+------------+--------+-----+---------+----------+\n ```\n* **ver. `0.3.1`:**\n ```python\n import pandas as pd\n from tabulate import tabulate\n from cadCAD.engine import ExecutionMode, ExecutionContext, Executor\n import system_model_A, system_model_B\n from cadCAD import configs\n\n exec_mode = ExecutionMode()\n\n multi_ctx = ExecutionContext(context=exec_mode.multi_proc)\n simulation = Executor(exec_context=multi_ctx, configs=configs)\n\n i = 0\n config_names = ['sys_model_A', 'sys_model_B']\n for raw_result, _ in simulation.execute():\n result = pd.DataFrame(raw_result)\n print()\n print(f\"{config_names[i]} Result: System Events DataFrame:\")\n print(tabulate(result, headers='keys', tablefmt='psql'))\n print()\n i += 1\n ```\n Results:\n ```\n +----+------------+-----------+----+---------------------+-----+---------+----------+\n | | s1 | s2 | s3 | timestamp | run | substep | timestep |\n |----+------------+-----------+----+---------------------+-----+---------+----------|\n | 0 | 0.0 | 0.0 | 1 | 2018-10-01 15:16:24 | 1 | 0 | 0 |\n | 1 | 1.0 | 4 | 5 | 2018-10-01 15:16:25 | 1 | 1 | 1 |\n | 2 | 2.0 | 6 | 5 | 2018-10-01 15:16:25 | 1 | 2 | 1 |\n | 3 | 3.0 | [ 30 300] | 5 | 2018-10-01 15:16:25 | 1 | 3 | 1 |\n | 4 | 0 | 0 | 1 | 2018-10-01 15:16:24 | 1 | 0 | 0 |\n | 5 | 1 | 0 | 5 | 2018-10-01 15:16:25 | 1 | 1 | 1 |\n | 6 | a | 0 | 5 | 2018-10-01 15:16:25 | 1 | 2 | 1 |\n | 7 | ['c', 'd'] | [ 30 300] | 5 | 2018-10-01 15:16:25 | 1 | 3 | 1 |\n +----+------------+-----------+----+---------------------+-----+---------+----------+\n ```\n\n* **Flattened Configuration list:** The `cadCAD.configs` (System Model Configuration) `list` has been **temporarily**\nflattened to contain single run `cadCAD.configuration.Configuration` objects to both fault-tolerant simulation and\nelastic workloads. This functionality will be restored in a subsequent release by a class that returns\n`cadCAD.configs`'s original representation in ver. `0.3.1`.\n * The conversion utilities have been provided to restore its original representation of configurations with\n runs >= 1\n * [System Configuration Conversions](documentation/System_Configuration.md)\n * Configuration as list of Configuration Objects (as in ver. `0.3.1`)\n * New: System Configuration as a Pandas DataFrame\n * New: System Configuration as list of Dictionaries\n\n##### Examples:\n* Notes:\n * `configs` is temporarily returned in a flattened format and reformatted into its intended format\n * `Configuration` objects at `0x10790e470` and `0x1143dd630` are reconstituted into objects at `0x10790e7b8`\n and `0x116268908` respectively.\n* **ver. ≥ `0.4.22`:**\n ```python\n from pprint import pprint\n from documentation.examples import sys_model_A, sys_model_B\n from cadCAD.configuration.utils import configs_as_objs, configs_as_dataframe, configs_as_dicts\n from cadCAD import configs\n\n flattened_configs = configs\n\n print('Flattened Format: Temporary')\n pprint(flattened_configs)\n print()\n\n print('Intended Format:')\n intended_configs = configs_as_objs(flattened_configs)\n pprint(intended_configs)\n print()\n ```\n\n Result:\n ```bash\n Flattened Format: Temporary\n [,\n ,\n ,\n ]\n\n Intended Format:\n [,\n ]\n ```\n\n\n#### Expandable state and policy update parameter space:\n* Enables the development of feature enhancements that involve the use of additional parameters without requiring users\nto modify their update parameters spaces when upgrading to newer versions. For this reason state / policy update\nexamples in documentation include an additional `**kwargs` parameter.\n * [State Updates](documentation/README.md#state-update-functions)\n * [Policy Updates](documentation/README.md#state-update-functions)\n\n##### Examples:\n* **ver. ≥ `0.4.22`:**\n ```python\n def state_update(_params, substep, sH, s, _input, **kwargs):\n ...\n return 'state_variable_name', new_value\n\n def policy(_params, substep, sH, s, **kwargs):\n ...\n return {'signal_1': value_1, ..., 'signal_N': value_N}\n ```\n* **ver. `0.3.1`:**\n\n ```python\n def state_update(_params, substep, sH, s, _input):\n ...\n return 'state_variable_name', new_value\n\n def policy(_params, substep, sH, s):\n ...\n return {'signal_1': value_1, ..., 'signal_N': value_N}\n ```\n\n\n## May 29, 2020\n* Packaging: Add [Nix](https://nixos.org/) derivation and shell for local development and distribution of cadCAD package\nusing Nix. Nix is a powerful package manager for Linux and other Unix systems that makes package management reliable and\nreproducible, allowing you to share your development and build environments across different machines.\n" }, { "path": "CONTRIBUTING.md", "content": "# Contributing to cadCAD\n\n:+1::tada: First off, thanks for taking the time to contribute! :tada::+1:\n\nThe following is a set of guidelines for contributing to cadCAD. \nUse your best judgment, and feel free to propose changes to this document in a pull request.\n\n### Pull Requests:\n\nPull Request (PR) presented as \"->\".\n\nGeneral Template:\n\n`user:branch -> org:staging`\n\nContributing a new feature:\n\n`user:feature -> org:staging`\n\nContributing to an existing feature:\n\n`user:feature -> org:feature`\n\n### General Advise for Forked Repositories:\n1. `git pull fork staging`\n2. `git checkout -b feature` (new feature)\n3. Apply your awesomeness! (Commit Often)\n4. `git push fork feature`\n5. Apply a rebase/merge strategy you're comfortable with (Recommended Below).\n6. Submit PR from `user:staging` into `org:staging`\n7. PR is queued for review\n8. PR Reviewed (Update necessary if rejected)\n9. PR Approved (There may be circumstances delaying the merge.)\n10. Your contribution merged into next feature release on `org:master`\n\n### Recommended Strategy: [Rebase](https://git-scm.com/book/en/v2/Git-Branching-Rebasing)\n1. Add cadCAD-org/cadCAD as remote within you forked project locally.\n2. `git checkout remote/master`\n3. `git pull remote master`\n4. `git checkout your_branch`\n5. `git rebase master`\n6. Resolve merge conflicts (while leveraging rebase commands)\n7. `git push fork your_branch`\n\nThanks! :heart:\n" }, { "path": "LICENSE.txt", "content": "MIT License\n\nCopyright (c) 2018-2020 BlockScience\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE." }, { "path": "README.md", "content": "```\n ___________ ____\n ________ __ ___/ / ____/ | / __ \\\n / ___/ __` / __ / / / /| | / / / /\n/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\nby cadCAD ver. 0.5.3\n======================================\n Complex Adaptive Dynamics\n o i e\n m d s\n p e i\n u d g\n t n\n e\n r\n```\n***cadCAD*** is a Python package that assists in the processes of designing, testing and validating complex systems\nthrough simulation, with support for Monte Carlo methods, A/B testing and parameter sweeping.\n\n# Getting Started\n\n#### Change Log: [ver. 0.5.3](CHANGELOG.md)\n\n## 0. Pre-installation Virtual Environments with [`venv`](https://docs.python.org/3/library/venv.html) (Optional):\nIf you wish to create an easy to use virtual environment to install cadCAD within, please use python's built in `venv` package.\n\n***Create** a virtual environment:*\n```bash\n$ python3 -m venv ~/cadcad\n```\n\n***Activate** an existing virtual environment:*\n```bash\n$ source ~/cadcad/bin/activate\n(cadcad) $\n```\n\n***Deactivate** virtual environment:*\n```bash\n(cadcad) $ deactivate\n$\n```\n\n## 1. Installation:\nRequires [>= Python 3.9.0](https://www.python.org/downloads/)\n\n**Option A:** Install Using **[pip](https://pypi.org/project/cadCAD/)**\n```bash\npip3 install cadCAD\n```\n\n**Option B:** Build From Source\n```\npip3 install -r requirements.txt\npython3 setup.py sdist bdist_wheel\npip3 install dist/*.whl\n```\n\n## 2. Documentation:\n* [Simulation Configuration](documentation/README.md)\n* [Simulation Execution](documentation/Simulation_Execution.md)\n* [Policy Aggregation](documentation/Policy_Aggregation.md)\n* [Parameter Sweep](documentation/System_Model_Parameter_Sweep.md)\n* [Display System Model Configurations](documentation/System_Configuration.md)\n\n## 3. Connect:\n* Website: https://www.cadcad.org\n* Discord: https://discord.gg/DX9uH8m4qY\n* Twitter: https://twitter.com/cadcad_org\n* Forum: https://community.cadcad.org\n* Github: https://github.com/cadCAD-org\n* Telegram: https://t.me/cadcad_org\n\n## 4. Contribute to this repository:\nFollow [this document](CONTRIBUTING.md) to start contributing!\n" }, { "path": "ascii_art.py", "content": "production = r'''\n __________ ____\n ________ __ _____/ ____/ | / __ \\\n / ___/ __` / __ / / / /| | / / / /\n/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\nby BlockScience\n======================================\n Complex Adaptive Dynamics \n o i e\n m d s\n p e i\n u d g\n t n\n e\n r\n'''\n\ntext = r'''\nComplex Adaptive Dynamics \no i e\nm d s\np e i\nu d g\nt n\ne\nr\n\n'''\n\nblock_letters = r'''\n __________ ____\n ________ __ _____/ ____/ | / __ \\\n / ___/ __` / __ / / / /| | / / / /\n/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\nby BlockScience\n'''\n" }, { "path": "cadCAD/__init__.py", "content": "import os\n\nimport dill\n\nfrom cadCAD.configuration import Experiment\n\nname = \"cadCAD\"\nversion = \"0.5.3\"\nexperiment = Experiment()\nconfigs = experiment.configs\n\nif os.name == \"nt\":\n dill.settings[\"recurse\"] = True\n\nlogo = r\"\"\"\n ___________ ____\n ________ __ ___/ / ____/ | / __ \\\n / ___/ __` / __ / / / /| | / / / /\n/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\n\\___/\\__,_/\\__,_/\\____/_/ |_/_____/\nby cadCAD\n\"\"\"\n" }, { "path": "cadCAD/configuration/__init__.py", "content": "from typing import Dict, Callable, List, Tuple\nfrom pandas.core.frame import DataFrame # type: ignore\nfrom datetime import datetime\nfrom collections import deque\nfrom copy import deepcopy\nimport pandas as pd # type: ignore\n\nfrom cadCAD.utils import key_filter\nfrom cadCAD.configuration.utils import exo_update_per_ts, configs_as_objs\nfrom cadCAD.configuration.utils.depreciationHandler import sanitize_partial_state_updates, sanitize_config\n\n\nclass Configuration():\n def __init__(self, user_id, model_id, subset_id, subset_window, sim_config={}, initial_state={}, seeds={}, env_processes={},\n exogenous_states={}, partial_state_update_blocks={}, policy_ops=[lambda a, b: a + b],\n session_id=0, simulation_id=0, run_id=1, experiment_id=0, exp_window=deque([0, None], 2),\n exp_creation_ts=None, **kwargs\n ) -> None:\n self.sim_config = sim_config\n self.initial_state = initial_state\n self.seeds = seeds\n self.env_processes = env_processes\n self.exogenous_states = exogenous_states\n self.partial_state_update_blocks = partial_state_update_blocks\n self.policy_ops = policy_ops\n self.kwargs = kwargs\n\n self.session_id = session_id # essentially config id\n\n self.experiment_id = experiment_id\n self.user_id = user_id\n self.model_id = model_id\n self.exp_creation_ts = exp_creation_ts\n\n self.labeled_jobs = {}\n self.simulation_id = simulation_id\n self.subset_id = subset_id\n self.run_id = run_id\n\n self.exp_window = exp_window\n self.subset_window = subset_window\n\n sanitize_config(self)\n\n\nclass Experiment(object):\n def __init__(self):\n self.exp_creation_ts = str(datetime.utcnow())\n self.configs = []\n self.sys_configs = []\n\n self.model_job_map, self.model_job_counts = {}, {}\n self.model_ids = list(self.model_job_map.keys())\n self.model_id_queue = []\n\n self.exp_id = 0\n self.simulation_id = -1\n self.subset_id = 0\n self.exp_window = deque([self.exp_id, None], 2)\n self.subset_window = deque([self.subset_id, None], 2)\n\n def append_model(\n self,\n user_id='cadCAD_user',\n model_id=None,\n sim_configs={}, initial_state={}, seeds={}, raw_exogenous_states={}, env_processes={},\n partial_state_update_blocks={}, policy_ops=[lambda a, b: a + b], _exo_update_per_ts: bool = True, **kwargs\n # config_list=deepcopy(global_configs)\n ) -> None:\n _sim_configs = deepcopy(sim_configs)\n # self.configs = config_list\n self.simulation_id += 1\n\n try:\n max_runs = _sim_configs[0]['N']\n except KeyError:\n max_runs = _sim_configs['N']\n\n if _exo_update_per_ts is True:\n exogenous_states = exo_update_per_ts(raw_exogenous_states)\n else:\n exogenous_states = raw_exogenous_states\n\n if isinstance(_sim_configs, dict):\n _sim_configs = [_sim_configs]\n\n sim_cnt_local = 0\n new_sim_configs = []\n for subset_id, t in enumerate(list(zip(_sim_configs, list(range(len(_sim_configs)))))):\n sim_config = t[0]\n sim_config['subset_id'] = subset_id\n sim_config['subset_window'] = self.subset_window\n N: int = sim_config['N']\n for n in range(0, N):\n sim_config['simulation_id'] = self.simulation_id\n sim_config['run_id'] = n\n sim_config['N'] = 1\n new_sim_configs.append((sim_config.copy()))\n sim_cnt_local += 1\n\n if model_id == None:\n new_model_id = str(len(self.model_ids))\n if new_model_id in self.model_ids:\n model_id = f\"model@{len(self.model_ids)}\"\n else:\n model_id = str(new_model_id)\n elif model_id != None:\n if model_id in self.model_ids:\n model_id = f\"{model_id}@{len(self.model_ids)}\"\n else:\n model_id = str(model_id)\n\n run_id = 0\n new_model_ids, new_configs = [], []\n for sim_config in new_sim_configs:\n subset_id = sim_config['subset_id']\n sim_config['N'] = run_id + 1\n if max_runs == 1:\n sim_config['run_id'] = run_id\n elif max_runs >= 1:\n if run_id >= max_runs:\n sim_config['N'] = run_id - (max_runs - 1)\n\n self.exp_window = self.exp_window.copy()\n config = Configuration(\n exp_creation_ts=self.exp_creation_ts,\n\n sim_config=sim_config,\n initial_state=deepcopy(initial_state),\n seeds=seeds,\n exogenous_states=exogenous_states,\n env_processes=env_processes,\n partial_state_update_blocks=partial_state_update_blocks,\n policy_ops=policy_ops,\n\n # session_id=session_id,\n user_id=user_id,\n model_id=model_id,\n session_id=f\"{user_id}={sim_config['simulation_id']}_{sim_config['run_id']}\",\n\n experiment_id=self.exp_id,\n simulation_id=self.simulation_id,\n subset_id=subset_id,\n run_id=sim_config['run_id'],\n\n exp_window=self.exp_window,\n subset_window=self.subset_window\n )\n\n # self.configs.append(config)\n new_configs.append(config)\n new_model_ids.append(model_id)\n run_id += 1\n self.configs += new_configs\n self.model_id_queue += new_model_ids\n self.exp_id += 1\n self.exp_window.appendleft(self.exp_id)\n self.sys_configs += configs_as_objs(new_configs)\n\n unique_new_model_ids = list(set(new_model_ids))\n new_model_job_list = [(model_id, []) for model_id in unique_new_model_ids]\n for model_id, v in new_model_job_list:\n if model_id not in self.model_ids:\n self.model_job_map[model_id] = v\n self.model_ids.append(model_id)\n else:\n except_str = f\"\"\"\n Error: Duplicate model_id in Experiment - \\'{model_id}\\' in {self.model_ids}\n -- Specify unique model_id for each use of `.append_model` per `Experiment()`\n \"\"\"\n raise Exception(except_str)\n\n for model_id, job in list(zip(new_model_ids, new_configs)):\n self.model_job_map[model_id].append(job)\n\n self.model_job_counts = dict([(k, len(v)) for k, v in self.model_job_map.items()])\n\n append_configs = append_model\n\n\nclass Identity:\n def __init__(self, policy_id: Dict[str, int] = {'identity': 0}) -> None:\n self.beh_id_return_val = policy_id\n\n def p_identity(self, var_dict, sub_step, sL, s, **kwargs):\n return self.beh_id_return_val\n\n def policy_identity(self, k: str) -> callable:\n return self.p_identity\n\n def no_state_identity(self, var_dict, sub_step, sL, s, _input, **kwargs):\n return None\n\n def state_identity(self, k: str) -> callable:\n return lambda var_dict, sub_step, sL, s, _input, **kwargs: (k, s[k])\n\n # state_identity = cloudpickle.dumps(state_identity)\n\n def apply_identity_funcs(self,\n identity: callable,\n df: DataFrame,\n cols: List[str]) -> DataFrame:\n \"\"\"\n Apply the identity on each df column, using its self value as the\n argument.\n \"\"\"\n fill_values = {col: identity(col) for col in cols}\n filled_df = df.fillna(fill_values)\n return filled_df\n\n\nclass Processor:\n def __init__(self, id: Identity = Identity()) -> None:\n self.id = id\n self.p_identity = id.p_identity\n self.policy_identity = id.policy_identity\n self.no_state_identity = id.no_state_identity\n self.state_identity = id.state_identity\n self.apply_identity_funcs = id.apply_identity_funcs\n\n def create_matrix_field(self, partial_state_updates, key: str) -> DataFrame:\n if key == 'variables':\n identity = self.state_identity\n elif key == 'policies':\n identity = self.policy_identity\n\n df = pd.DataFrame(key_filter(partial_state_updates, key))\n filled_df = self.apply_identity_funcs(identity, df, list(df.columns))\n if len(filled_df) > 0:\n return filled_df\n else:\n return pd.DataFrame({'empty': []})\n\n def generate_config(self, initial_state, partial_state_updates, exo_proc\n ) -> List[tuple[list[callable], List[callable]]]:\n\n def no_update_handler(bdf, sdf):\n if (bdf.empty == False) and (sdf.empty == True):\n bdf_values = bdf.values.tolist()\n sdf_values = [[self.no_state_identity] * len(bdf_values) for m in range(len(partial_state_updates))]\n return sdf_values, bdf_values\n elif (bdf.empty == True) and (sdf.empty == False):\n sdf_values = sdf.values.tolist()\n bdf_values = [[self.p_identity] * len(sdf_values) for m in range(len(partial_state_updates))]\n return sdf_values, bdf_values\n else:\n sdf_values = sdf.values.tolist()\n bdf_values = bdf.values.tolist()\n return sdf_values, bdf_values\n\n def only_ep_handler(state_dict):\n sdf_functions = [\n lambda var_dict, sub_step, sL, s, _input, **kwargs: (k, v) for k, v in zip(state_dict.keys(), state_dict.values())\n ]\n sdf_values = [sdf_functions]\n bdf_values = [[self.p_identity] * len(sdf_values)]\n return sdf_values, bdf_values\n\n if len(partial_state_updates) != 0:\n # backwards compatibility\n partial_state_updates = sanitize_partial_state_updates(partial_state_updates)\n\n bdf = self.create_matrix_field(partial_state_updates, 'policies')\n sdf = self.create_matrix_field(partial_state_updates, 'variables')\n sdf_values, bdf_values = no_update_handler(bdf, sdf)\n zipped_list = list(zip(sdf_values, bdf_values))\n else:\n sdf_values, bdf_values = only_ep_handler(initial_state)\n zipped_list = list(zip(sdf_values, bdf_values))\n\n return list(map(lambda x: (x[0] + exo_proc, x[1]), zipped_list))" }, { "path": "cadCAD/configuration/utils/__init__.py", "content": "import pandas as pd # type: ignore\nfrom datetime import datetime, timedelta\nfrom collections import Counter\nfrom functools import reduce\nfrom funcy import curry # type: ignore\nfrom cadCAD.types import *\nfrom typing import Union, Dict, List\n\nfrom cadCAD.configuration.utils.depreciationHandler import sanitize_partial_state_updates\nfrom cadCAD.utils import dict_filter, contains_type, flatten_tabulated_dict, tabulate_dict\n\n\nclass TensorFieldReport:\n def __init__(self, config_proc):\n self.config_proc = config_proc\n\n def create_tensor_field(self, partial_state_updates, exo_proc, keys=['policies', 'variables']):\n\n partial_state_updates = sanitize_partial_state_updates(partial_state_updates) # Temporary\n\n dfs = [self.config_proc.create_matrix_field(partial_state_updates, k) for k in keys]\n df = pd.concat(dfs, axis=1)\n for es, i in zip(exo_proc, range(len(exo_proc))):\n df['es' + str(i + 1)] = es\n df['m'] = df.index + 1\n return df\n\n\ndef configs_as_spec(configs):\n sim_ids = list(map(lambda x: x.simulation_id, configs))\n # sim_ids = [config.simulation_id for config in configs]\n sim_id_counts = list(Counter(sim_ids).values())\n IDed_configs = list(zip(sim_ids, configs))\n del sim_ids\n selected_IDed_configs = dict(IDed_configs)\n del IDed_configs\n counted_IDs_configs = list(zip(sim_id_counts, selected_IDed_configs.values()))\n del sim_id_counts, selected_IDed_configs\n for runs, config in counted_IDs_configs:\n config.sim_config['N'] = runs\n return counted_IDs_configs\n\n\ndef configs_as_objs(configs):\n counted_IDs_configs = configs_as_spec(configs)\n new_configs = list(map(lambda x: x[1], counted_IDs_configs))\n del counted_IDs_configs\n return new_configs\n\n\ndef configs_as_dicts(configs):\n counted_IDs_configs = configs_as_spec(configs)\n new_configs = list(map(lambda x: x[1].__dict__, counted_IDs_configs))\n del counted_IDs_configs\n return new_configs\n\n\ndef configs_as_dataframe(configs):\n new_configs = configs_as_dicts(configs)\n configs_df = pd.DataFrame(new_configs)\n del new_configs\n configs_df_columns = list(configs_df.columns)\n header_cols = ['session_id', 'user_id', 'simulation_id', 'run_id']\n for col in header_cols:\n configs_df_columns.remove(col)\n return configs_df[header_cols + configs_df_columns]\n\n## System Model\n\ndef state_update(y, x):\n return lambda var_dict, sub_step, sL, s, _input, **kwargs: (y, x)\n\ndef policy(y, x):\n return lambda _g, step, sL, s, **kwargs: {y: x}\n\ndef bound_norm_random(rng, low, high):\n res = rng.normal((high+low)/2, (high-low)/6)\n if res < low or res > high:\n res = bound_norm_random(rng, low, high)\n # return Decimal(res)\n return float(res)\n\n\ntstep_delta = timedelta(days=0, minutes=0, seconds=30)\ndef time_step(dt_str, dt_format='%Y-%m-%d %H:%M:%S', _timedelta = tstep_delta):\n dt = datetime.strptime(dt_str, dt_format)\n t = dt + _timedelta\n return t.strftime(dt_format)\n\n\nep_t_delta = timedelta(days=0, minutes=0, seconds=1)\ndef ep_time_step(s_condition, dt_str, fromat_str='%Y-%m-%d %H:%M:%S', _timedelta = ep_t_delta):\n if s_condition:\n return time_step(dt_str, fromat_str, _timedelta)\n else:\n return dt_str\n\n\ndef exo_update_per_ts(ep):\n # @curried\n def ep_decorator(f, y, var_dict, sub_step, sL, s, _input, **kwargs):\n if s['substep'] + 1 == 1:\n return f(var_dict, sub_step, sL, s, _input, **kwargs)\n else:\n return y, s[y]\n\n return {es: ep_decorator(f, es) for es, f in ep.items()} # type: ignore\n\n\ndef trigger_condition(s, pre_conditions, cond_opp):\n condition_bools = [s[field] in precondition_values for field, precondition_values in pre_conditions.items()]\n return reduce(cond_opp, condition_bools)\n\n\ndef apply_state_condition(pre_conditions, cond_opp, y, f, _g, step, sL, s, _input, **kwargs):\n def state_scope_tuner(f):\n lenf = f.__code__.co_argcount\n if lenf == 5:\n return f(_g, step, sL, s, _input)\n elif lenf == 6:\n return f(_g, step, sL, s, _input, **kwargs)\n\n if trigger_condition(s, pre_conditions, cond_opp):\n return state_scope_tuner(f)\n else:\n return y, s[y]\n\n\ndef var_trigger(y, f, pre_conditions, cond_op):\n return lambda _g, step, sL, s, _input, **kwargs: \\\n apply_state_condition(pre_conditions, cond_op, y, f, _g, step, sL, s, _input, **kwargs)\n\n\ndef var_substep_trigger(substeps):\n def trigger(end_substep, y, f):\n pre_conditions = {'substep': substeps}\n cond_opp = lambda a, b: a and b\n return var_trigger(y, f, pre_conditions, cond_opp)\n\n return lambda y, f: curry(trigger)(substeps)(y)(f)\n\n\ndef env_trigger(end_substep):\n def trigger(end_substep, trigger_field, trigger_vals, funct_list):\n def env_update(state_dict, sweep_dict, target_value):\n state_dict_copy = state_dict.copy()\n # Use supstep to simulate current sysMetrics\n if state_dict_copy['substep'] == end_substep:\n state_dict_copy['timestep'] = state_dict_copy['timestep'] + 1\n\n if state_dict_copy[trigger_field] in trigger_vals:\n for g in funct_list:\n target_value = g(sweep_dict, target_value)\n\n del state_dict_copy\n return target_value\n\n return env_update\n\n return lambda trigger_field, trigger_vals, funct_list: \\\n curry(trigger)(end_substep)(trigger_field)(trigger_vals)(funct_list)\n\n\ndef config_sim(config_dict: ConfigurationDict):\n\n if \"N\" in config_dict:\n if config_dict[\"N\"] <= 0:\n raise ValueError(\"'N' must be > 0\")\n else:\n pass\n else:\n raise KeyError(\"The 'sim_configs' dictionary must contain the key 'N' (# of Monte Carlo Runs)\")\n\n if \"T\" not in config_dict:\n raise KeyError(\"The 'sim_configs' dictionary must contain the key 'T' (Timestep Iterator)\")\n else:\n if \"M\" in config_dict:\n params = config_dict['M']\n\n param_values_length = {key: len(value) if type(value) == list else 0 \n for key, value in params.items()}\n param_values_length_set = set(param_values_length.values())\n distinct_param_value_lengths = len(param_values_length_set)\n\n if distinct_param_value_lengths > 2:\n raise Exception('When sweeping, `M` list lengths should either be 1 and/or equal. More than two distinct lengths are not allowed')\n elif (distinct_param_value_lengths == 1) and (0 in param_values_length_set):\n return config_dict\n elif (distinct_param_value_lengths == 1) or (1 in param_values_length_set):\n return [{**config_dict, \"M\": M} \n\n for M in flatten_tabulated_dict(tabulate_dict(params))]\n else:\n raise Exception('When sweeping, `M` list lengths should either be 1 and/or equal. ')\n\n else:\n config_dict[\"M\"] = [{}]\n return config_dict\n\n\ndef psub_list(psu_block, psu_steps):\n return [psu_block[psu] for psu in psu_steps]\n\n\ndef psub(policies, state_updates):\n return {\n 'policies': policies,\n 'states': state_updates\n }\n\n\ndef genereate_psubs(policy_grid, states_grid, policies, state_updates):\n PSUBS = []\n for policy_ids, state_list in zip(policy_grid, states_grid):\n filtered_policies = {k: v for (k, v) in policies.items() if k in policy_ids}\n filtered_state_updates = {k: v for (k, v) in state_updates.items() if k in state_list}\n PSUBS.append(psub(filtered_policies, filtered_state_updates))\n\n return PSUBS\n\n\ndef access_block(state_history, target_field, psu_block_offset, exculsion_list=[]):\n exculsion_list += [target_field]\n\n def filter_history(key_list, block):\n filter = lambda key_list: \\\n lambda d: {k: v for k, v in d.items() if k not in key_list}\n return list(map(filter(key_list), block))\n\n if psu_block_offset < -1:\n if len(state_history) >= abs(psu_block_offset):\n return filter_history(exculsion_list, state_history[psu_block_offset])\n else:\n return []\n elif psu_block_offset == -1:\n return filter_history(exculsion_list, state_history[psu_block_offset])\n else:\n return []\n\n## Parameter Sweep\ndef partial_state_sweep_filter(state_field, partial_state_updates):\n partial_state_dict = dict([(k, v[state_field]) for k, v in partial_state_updates.items()])\n return dict([\n (k, dict_filter(v, lambda v: isinstance(v, list))) for k, v in partial_state_dict.items()\n if contains_type(list(v.values()), list)\n ])\n\n\ndef state_sweep_filter(raw_exogenous_states):\n return dict([(k, v) for k, v in raw_exogenous_states.items() if isinstance(v, list)])\n\n\n# @curried\ndef sweep_partial_states(_type, in_config):\n configs = []\n # filtered_mech_states\n filtered_partial_states = partial_state_sweep_filter(_type, in_config.partial_state_update_blocks)\n if len(filtered_partial_states) > 0:\n for partial_state, state_dict in filtered_partial_states.items():\n for state, state_funcs in state_dict.items():\n for f in state_funcs:\n config = in_config.copy()\n config.partial_state_updates[partial_state][_type][state] = f\n configs.append(config)\n del config\n else:\n configs = [in_config]\n\n return configs\n\n# @curried\ndef sweep_states(state_type, states, in_config):\n configs = []\n filtered_states = state_sweep_filter(states)\n if len(filtered_states) > 0:\n for state, state_funcs in filtered_states.items():\n for f in state_funcs:\n config = in_config.copy()\n exploded_states = states.copy()\n exploded_states[state] = f\n if state_type == 'exogenous':\n config.exogenous_states = exploded_states\n elif state_type == 'environmental':\n config.env_processes = exploded_states\n configs.append(config)\n del config, exploded_states\n else:\n configs = [in_config]\n\n return configs\n" }, { "path": "cadCAD/configuration/utils/depreciationHandler.py", "content": "from copy import deepcopy\n\n\ndef sanitize_config(config):\n for key, value in config.kwargs.items():\n if key == 'state_dict':\n config.initial_state = value\n elif key == 'seed':\n config.seeds = value\n elif key == 'mechanisms':\n config.partial_state_update_blocks = value\n\n if config.initial_state == {}:\n raise Exception('The initial conditions of the system have not been set')\n\n\ndef sanitize_partial_state_updates(partial_state_updates):\n new_partial_state_updates = partial_state_updates.copy()\n def rename_keys(d):\n if 'behaviors' in d:\n d['policies'] = d.pop('behaviors')\n\n if 'states' in d:\n d['variables'] = d.pop('states')\n\n if isinstance(new_partial_state_updates, list):\n for v in new_partial_state_updates:\n rename_keys(v)\n elif isinstance(new_partial_state_updates, dict):\n for k, v in new_partial_state_updates.items():\n rename_keys(v)\n\n del partial_state_updates\n return new_partial_state_updates\n" }, { "path": "cadCAD/configuration/utils/policyAggregation.py", "content": "def get_base_value(x):\n if isinstance(x, str):\n return ''\n elif isinstance(x, int):\n return 0\n elif isinstance(x, list):\n return []\n else:\n return 0\n\n\ndef policy_to_dict(v):\n return dict(list(zip(map(lambda n: 'p' + str(n + 1), list(range(len(v)))), v)))\n\n\nadd = lambda a, b: a + b\n\n\n# @curried\n# def foldr_dict_vals(f, d):\n# return foldr(f)(list(d.values()))\n#\n#\n# def sum_dict_values():\n# return foldr_dict_vals(add)\n\n\n# @curried\n# def dict_op(f, d1, d2):\n# def set_base_value(target_dict, source_dict, key):\n# if key not in target_dict:\n# return get_base_value(source_dict[key])\n# else:\n# return target_dict[key]\n#\n# key_set = set(list(d1.keys()) + list(d2.keys()))\n#\n# return {k: f(set_base_value(d1, d2, k), set_base_value(d2, d1, k)) for k in key_set}\n#\n# def dict_elemwise_sum():\n# return dict_op(add)\n" }, { "path": "cadCAD/configuration/utils/userDefinedObject.py", "content": "from collections import namedtuple\nfrom inspect import getmembers, ismethod\nfrom pandas.core.frame import DataFrame\n\nfrom cadCAD.utils import SilentDF\n\n\ndef val_switch(v):\n if isinstance(v, DataFrame) is True:\n return SilentDF(v)\n else:\n return v\n\n\nclass udcView(object):\n def __init__(self, d, masked_members):\n self.__dict__ = d\n self.masked_members = masked_members\n\n def __repr__(self):\n members = {}\n variables = {\n k: val_switch(v) for k, v in self.__dict__.items()\n if str(type(v)) != \"\" and k not in self.masked_members # and isinstance(v, DataFrame) is not True\n }\n members['methods'] = [k for k, v in self.__dict__.items() if str(type(v)) == \"\"]\n\n members.update(variables)\n return f\"{members}\"\n\n\nclass udcBroker(object):\n def __init__(self, obj, function_filter=['__init__']):\n d = {}\n funcs = dict(getmembers(obj, ismethod))\n filtered_functions = {k: v for k, v in funcs.items() if k not in function_filter}\n d['obj'] = obj\n d.update(vars(obj)) # somehow is enough\n d.update(filtered_functions)\n\n self.members_dict = d\n\n def get_members(self):\n return self.members_dict\n\n def get_view(self, masked_members):\n return udcView(self.members_dict, masked_members)\n\n def get_namedtuple(self):\n return namedtuple(\"Hydra\", self.members_dict.keys())(*self.members_dict.values())\n\n\ndef UDO(udo, masked_members=['obj']):\n return udcBroker(udo).get_view(masked_members)\n\n\ndef udoPipe(obj_view):\n return UDO(obj_view.obj, obj_view.masked_members)\n" }, { "path": "cadCAD/diagram/__init__.py", "content": "from cadCAD.diagram.config_diagram import diagram, diagram_from_config" }, { "path": "cadCAD/diagram/config_diagram.py", "content": "from graphviz import Digraph\nimport inspect\nimport re\n\n\n### Inspect functions\n\n\ndef extract_var_key(raw_line: str, var_id: str) -> str:\n \"\"\"\n Extract the key from an line in the form \"dict['key']\" or\n \"dict.get('key', *args)\".\n \"\"\"\n line = raw_line.strip()[len(var_id) :]\n state_var = \"\"\n if line[0] == \"[\":\n state_var = line[2:-2]\n elif line[0:4] == \".get\":\n call = line.split(\"(\")[1]\n call = call.split(\")\")[0]\n call = call.strip()\n sep = \"'\"\n if call[0] == '\"':\n sep = '\"'\n state_var = [el for el in call.split(sep) if len(el) > 0][0]\n return state_var\n\n\ndef extract_vars_from_source(source: str, var_id: str) -> set:\n \"\"\"\n Extract keys from an source code that consumes an dict with\n var_id name.\n \"\"\"\n regex = (\n r\"((\"\n + var_id\n + r\"\\[(\\'|\\\")\\w+(\\'|\\\")\\])|(\"\n + var_id\n + r\".get\\((\\'|\\\")\\w+(\\'|\\\")[A-z,\\s\\\"\\']*\\)))\"\n )\n\n matches = re.findall(regex, source)\n lines = [match[0] for match in matches]\n state_vars = set([extract_var_key(line, var_id) for line in lines])\n return state_vars\n\n\ndef extract_keys(f: callable) -> dict:\n \"\"\"\n\n \"\"\"\n src = inspect.getsource(f)\n params = inspect.signature(f)\n params_key = list(params.parameters)[0]\n state_key = list(params.parameters)[3]\n output = {\n \"state\": extract_vars_from_source(src, state_key),\n \"params\": extract_vars_from_source(src, params_key),\n }\n return output\n\n\ndef relate_psub(psub: dict) -> dict:\n \"\"\"\n Given an dict describing an Partial State Update block, this functions\n generates an dict with three keys: 'params' and 'state' which are sets\n containing all unique parameters and state variables for the PSUB,\n and 'map' for doing an more detailed map.\n \"\"\"\n psub_relation = {\"map\": {}, \"params\": set(), \"state\": set()}\n unique_params = set()\n unique_vars = set()\n keys = [\"policies\", \"variables\"]\n for key in keys:\n type_functions = psub.get(key, {})\n type_keys = {k: extract_keys(v) for k, v in type_functions.items()}\n params_list = [v.get(\"params\", set()) for v in type_keys.values()]\n vars_list = [v.get(\"state\", set()) for v in type_keys.values()]\n if len(params_list) > 0:\n params = set.union(*params_list)\n else:\n params = set()\n if len(vars_list) > 0:\n vars = set.union(*vars_list)\n else:\n vars = set()\n psub_relation[\"params\"] = psub_relation[\"params\"].union(params)\n psub_relation[\"state\"] = psub_relation[\"state\"].union(vars)\n psub_relation[\"map\"][key] = type_keys\n return psub_relation\n\n\ndef generate_relations(psubs) -> list:\n \"\"\"\n Generates an list of dicts,\n\n \"\"\"\n psub_relations = [relate_psub(psub) for psub in psubs]\n return psub_relations\n\n\n### Diagram functions\n\n\ndef generate_time_graph() -> Digraph:\n time_graph = Digraph(\"cluster_timestep\", engine=\"dot\")\n time_graph.attr(style=\"filled\", bgcolor=\"pink\", dpi=\"50\", rankdir=\"LR\")\n return time_graph\n\n\ndef generate_variables_cluster(variables: dict, i: int, suffix=\"\") -> Digraph:\n state_graph = Digraph(\"cluster_variables_{}{}\".format(i, suffix))\n state_graph.attr(style=\"filled, dashed\", label=\"State\", fillcolor=\"skyblue\")\n for key, value in variables.items():\n name = \"variable_{}_{}{}\".format(key, i, suffix)\n description = \"{} ({})\".format(key, type(value).__name__)\n state_graph.node(\n name,\n description,\n shape=\"cylinder\",\n style=\"filled, solid\",\n fillcolor=\"honeydew\",\n ),\n return state_graph\n\n\ndef generate_params_cluster(params: dict, i: int) -> Digraph:\n params_graph = Digraph(\"cluster_params_{}\".format(i))\n params_graph.attr(style=\"filled, dashed\", label=\"Parameters\", fillcolor=\"skyblue\")\n for key, value in params.items():\n name = \"param_{}_{}\".format(key, i)\n description = \"{} ({})\".format(key, type(value).__name__)\n params_graph.node(\n name,\n description,\n shape=\"cylinder\",\n style=\"filled, solid\",\n fillcolor=\"honeydew\",\n ),\n return params_graph\n\n\ndef generate_psub_graph(i: int):\n psub_graph = Digraph(\"cluster_psub_{}\".format(i))\n psub_graph.attr(\n style=\"filled, dashed\",\n label=f\"Partial State Update Block #{i}\",\n fillcolor=\"thistle\",\n center=\"true\",\n )\n return psub_graph\n\n\ndef relate_params(graph: Digraph, params, i, origin=-1) -> Digraph:\n for param in params:\n dst = \"param_{}_{}\".format(param, i)\n src = \"param_{}_{}\".format(param, origin)\n graph.edge(src, dst)\n return graph\n\n\ndef generate_policies_cluster(policies: dict, i: int, psub_graph) -> Digraph:\n policy_graph = Digraph(\"cluster_policy_{}\".format(i))\n policy_graph.attr(label=\"Policies\")\n policy_graph.node(\n \"agg_{}\".format(i),\n \"Aggregation\",\n shape=\"circle\",\n style=\"filled,bold\",\n fillcolor=\"greenyellow\",\n width=\"1\",\n )\n for key, value in policies.items():\n name = \"policy_{}_{}\".format(key, i)\n description = \"{} ({})\".format(key, value.__name__)\n policy_graph.node(\n name,\n description,\n style=\"filled, bold\",\n fillcolor=\"palegreen\",\n shape=\"cds\",\n height=\"1\",\n width=\"1\",\n )\n psub_graph.edge(name, \"agg_{}\".format(i))\n return psub_graph.subgraph(policy_graph)\n\n\ndef relate(\n graph, relations, i, src_prefix, dst_prefix, suffix=\"\", reverse=False\n) -> Digraph:\n for key, value in relations.items():\n dst = \"{}_{}_{}\".format(dst_prefix, key, i)\n for param in value:\n src = \"{}_{}_{}{}\".format(src_prefix, param, i, suffix)\n if reverse:\n graph.edge(dst, src)\n else:\n graph.edge(src, dst)\n return graph\n\n\ndef generate_sufs_cluster(sufs: dict, i: int, psub_graph, agg=False) -> Digraph:\n suf_graph = Digraph(\"cluster_suf_{}\".format(i))\n suf_graph.attr(label=\"State Update Functions\")\n for key, value in sufs.items():\n name = \"suf_{}_{}\".format(key, i)\n description = \"{} ({})\".format(key, value.__name__)\n suf_graph.node(\n name,\n description,\n style=\"filled, bold\",\n fillcolor=\"red\",\n shape=\"cds\",\n height=\"1\",\n width=\"1\",\n )\n if agg:\n psub_graph.edge(\"agg_{}\".format(i), name)\n return psub_graph.subgraph(suf_graph)\n\n\ndef relate_params_to_sufs(graph, sufs, i) -> Digraph:\n for key, value in sufs.items():\n dst = \"suf_{}_{}\".format(key, i)\n for param in value:\n src = \"param_{}_{}\".format(param, i)\n graph.edge(src, dst)\n return graph\n\n\ndef diagram(initial_state, params, psubs):\n \"\"\"\n Generates an diagram for an cadCAD configuration object.\n \"\"\"\n relations = generate_relations(psubs)\n time_graph = generate_time_graph()\n for i_psub, psub in enumerate(psubs):\n psub_graph = generate_psub_graph(i_psub)\n\n # Parameters\n psub_params = relations[i_psub].get(\"params\", set())\n psub_params = {k: params.get(k, None) for k in psub_params}\n psub_vars = relations[i_psub].get(\"state\", set())\n psub_vars = {k: initial_state.get(k, None) for k in psub_vars}\n psub_graph.subgraph(generate_params_cluster(psub_params, i_psub))\n psub_graph.subgraph(generate_variables_cluster(psub_vars, i_psub))\n # psub_graph = relate_params(psub_graph, psub_params, i)\n\n # Policies\n policies = psub.get(\"policies\", {})\n psub_map = relations[i_psub].get(\"map\", {})\n policy_map = psub_map.get(\"policies\", {})\n policy_inputs = {\n policy: relation[\"state\"] for policy, relation in policy_map.items()\n }\n policy_params = {\n policy: relation[\"params\"] for policy, relation in policy_map.items()\n }\n list_of_inputs = list(policy_inputs.values())\n if len(list_of_inputs) > 0:\n agg = True\n inputs = set.union(*list_of_inputs)\n inputs = {k: initial_state.get(k, None) for k in inputs}\n psub_graph.subgraph(generate_policies_cluster(policies, i_psub, psub_graph))\n psub_graph = relate(psub_graph, policy_params, i_psub, \"param\", \"policy\")\n psub_graph = relate(psub_graph, policy_inputs, i_psub, \"variable\", \"policy\")\n else:\n agg = False\n\n # SUFs\n sufs = psub.get(\"variables\", {})\n suf_map = psub_map.get(\"variables\", {})\n sufs_inputs = {\n policy: relation[\"state\"] for policy, relation in suf_map.items()\n }\n sufs_params = {\n policy: relation[\"params\"] for policy, relation in suf_map.items()\n }\n list_of_inputs = list(sufs_inputs.values())\n if len(list_of_inputs) > 0:\n inputs = set.union(*list_of_inputs)\n inputs = {k: initial_state.get(k, None) for k in inputs}\n generate_sufs_cluster(sufs, i_psub, psub_graph, agg=agg)\n psub_graph = relate(psub_graph, sufs_params, i_psub, \"param\", \"suf\")\n psub_graph = relate(psub_graph, sufs_inputs, i_psub, \"variable\", \"suf\")\n\n time_graph.subgraph(psub_graph)\n return time_graph\n\n\ndef diagram_from_config(config):\n initial_state = config.initial_state\n params = config.sim_config[\"M\"]\n psubs = config.partial_state_update_blocks\n return diagram(initial_state, params, psubs)\n" }, { "path": "cadCAD/engine/__init__.py", "content": "from time import time\nfrom typing import Callable, Dict, List, Any, Tuple, Union, Sequence, Mapping\nfrom tqdm.auto import tqdm\n\nfrom cadCAD.utils import flatten\nfrom cadCAD.utils.execution import print_exec_info\nfrom cadCAD.configuration import Configuration, Processor\nfrom cadCAD.configuration.utils import TensorFieldReport, configs_as_objs, configs_as_dicts\nfrom cadCAD.engine.simulation import Executor as SimExecutor\nfrom cadCAD.engine.execution import single_proc_exec, parallelize_simulations, local_simulations\nfrom cadCAD.types import *\n\nVarDictType = Dict[str, List[object]]\nStatesListsType = List[dict[str, object]]\nConfigsType = List[tuple[list[callable], List[callable]]]\nEnvProcessesType = Dict[str, callable]\n\n\nclass ExecutionMode:\n local_mode = 'local_proc'\n multi_mode = 'multi_proc'\n distributed = 'dist_proc'\n single_mode = 'single_proc'\n # Backwards compatible modes below\n single_proc = 'single_proc'\n multi_proc = 'multi_proc'\n\n\ndef auto_mode_switcher(config_amt: int):\n try:\n if config_amt == 1:\n return ExecutionMode.single_mode, single_proc_exec\n elif (config_amt > 1):\n return ExecutionMode.multi_mode, parallelize_simulations\n except AttributeError:\n if config_amt < 1:\n raise ValueError('N must be >= 1!')\n\n\nclass ExecutionContext:\n def __init__(self, context=ExecutionMode.local_mode, method=None, additional_objs=None) -> None:\n self.name = context\n self.additional_objs = additional_objs\n if context == 'local_proc':\n self.method = local_simulations\n elif context == 'single_proc':\n self.method = single_proc_exec\n elif context == 'multi_proc':\n self.method = parallelize_simulations\n elif context == 'dist_proc':\n def distroduce_proc(\n simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, SimIDs, RunIDs,\n ExpIDs,\n SubsetIDs,\n SubsetWindows,\n configured_n, # exec_method,\n sc, additional_objs=additional_objs\n ):\n return method(\n simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, SimIDs, RunIDs,\n ExpIDs,\n SubsetIDs,\n SubsetWindows,\n configured_n, # exec_method,\n sc, additional_objs\n )\n\n self.method = distroduce_proc\n\n\nclass Executor:\n def __init__(self,\n exec_context: ExecutionContext, configs: List[Configuration], sc=None, empty_return=False, supress_print=False\n ) -> None:\n self.sc = sc\n self.SimExecutor = SimExecutor\n self.exec_method = exec_context.method\n self.exec_context = exec_context.name\n self.additional_objs = exec_context.additional_objs\n self.configs = configs\n self.empty_return = empty_return\n self.supress_print = supress_print\n\n def execute(self) -> Tuple[object, object, Dict[str, object]]:\n if self.empty_return is True:\n return [], [], []\n\n config_proc = Processor()\n create_tensor_field = TensorFieldReport(\n config_proc).create_tensor_field\n\n sessions = []\n var_dict_list, states_lists = [], []\n Ts, Ns, SimIDs, RunIDs = [], [], [], []\n ExpIDs, ExpWindows, SubsetIDs, SubsetWindows = [], [], [], []\n eps, configs_structs, env_processes_list = [], [], []\n partial_state_updates, sim_executors = [], []\n config_idx = 0\n\n # Execution Info\n if self.supress_print is False:\n print_exec_info(self.exec_context, configs_as_objs(self.configs))\n\n t1 = time()\n for x in tqdm(self.configs,\n total=len(self.configs),\n desc=\"Initializing configurations\",\n disable=self.supress_print):\n sessions.append(\n {\n 'user_id': x.user_id, 'experiment_id': x.experiment_id, 'session_id': x.session_id,\n 'simulation_id': x.simulation_id, 'run_id': x.run_id,\n 'subset_id': x.subset_id, 'subset_window': x.subset_window\n }\n )\n Ts.append(x.sim_config['T'])\n Ns.append(x.sim_config['N'])\n\n ExpIDs.append(x.experiment_id)\n ExpWindows.append(x.exp_window)\n SimIDs.append(x.simulation_id)\n SubsetIDs.append(x.subset_id)\n RunIDs.append(x.run_id)\n SubsetWindows.append(x.subset_window)\n\n var_dict_list.append(x.sim_config['M'])\n states_lists.append([x.initial_state])\n eps.append(list(x.exogenous_states.values()))\n configs_structs.append(config_proc.generate_config(\n x.initial_state, x.partial_state_update_blocks, eps[config_idx]))\n env_processes_list.append(x.env_processes)\n partial_state_updates.append(x.partial_state_update_blocks)\n sim_executors.append(SimExecutor(x.policy_ops).simulation)\n\n config_idx += 1\n\n remote_threshold = 100\n config_amt = len(self.configs)\n\n def get_final_dist_results(simulations: List[StateHistory],\n psus: List[StateUpdateBlocks],\n eps,\n sessions: List[SessionDict]):\n tensor_fields = [create_tensor_field(\n psu, ep) for psu, ep in list(zip(psus, eps))]\n return simulations, tensor_fields, sessions\n\n def get_final_results(simulations: List[StateHistory],\n psus: List[StateUpdateBlocks],\n eps,\n sessions: List[SessionDict],\n remote_threshold: int):\n \n # if list of lists of lists of dicts: do flatten\n # if list of dicts: do not flatetn\n # else raise error\n\n\n init: bool = isinstance(simulations, Sequence)\n failed_1 = False\n failed_2 = False\n \n try:\n init: bool = isinstance(simulations, Sequence)\n dont_flatten = init & isinstance(simulations[0], Mapping)\n do_flatten = not dont_flatten\n except:\n failed_1 = True\n do_flatten = True\n\n try:\n do_flatten = init & isinstance(simulations[0], Sequence)\n do_flatten &= isinstance(simulations[0][0], Sequence)\n do_flatten &= isinstance(simulations[0][0][0], Mapping)\n except:\n failed_2 = True\n do_flatten = False\n\n if failed_1 and failed_2:\n raise ValueError('Invalid simulation results (Executor output is not list[dict] or list[list[list[dict]]])')\n\n\n flat_timesteps, tensor_fields = [], []\n for sim_result, psu, ep in tqdm(list(zip(simulations, psus, eps)),\n total=len(simulations),\n desc='Flattening results',\n disable=self.supress_print):\n if do_flatten:\n flat_timesteps.append(flatten(sim_result))\n tensor_fields.append(create_tensor_field(psu, ep))\n \n if do_flatten:\n flat_simulations = flatten(flat_timesteps)\n else:\n flat_simulations = simulations\n\n return flat_simulations, tensor_fields, sessions\n\n final_result = None\n original_N = len(configs_as_dicts(self.configs))\n if self.exec_context != ExecutionMode.distributed:\n # Consider Legacy Support\n if self.exec_context == ExecutionMode.local_mode:\n self.exec_context, self.exec_method = auto_mode_switcher(\n config_amt)\n elif self.exec_context == ExecutionMode.single_mode or self.exec_context == ExecutionMode.single_proc:\n self.exec_context, self.exec_method = ExecutionMode.single_mode, single_proc_exec\n elif self.exec_context == ExecutionMode.multi_mode or self.exec_context == ExecutionMode.multi_proc:\n if config_amt == 1:\n raise ValueError(\"Multi mode must have at least 2 configs\")\n else:\n self.exec_context, self.exec_method = ExecutionMode.multi_mode, parallelize_simulations\n else:\n raise ValueError(\"Invalid execution mode specified\")\n\n if self.supress_print is False:\n print(\"Execution Method: \" + self.exec_method.__name__)\n \n simulations_results = self.exec_method(\n sim_executors, var_dict_list, states_lists, configs_structs, env_processes_list, Ts, SimIDs, RunIDs,\n ExpIDs, SubsetIDs, SubsetWindows, original_N, self.additional_objs\n )\n\n final_result = get_final_results(\n simulations_results, partial_state_updates, eps, sessions, remote_threshold)\n elif self.exec_context == ExecutionMode.distributed:\n if self.supress_print is False:\n print(\"Execution Method: \" + self.exec_method.__name__)\n simulations_results = self.exec_method(\n sim_executors, var_dict_list, states_lists, configs_structs, env_processes_list, Ts,\n SimIDs, RunIDs, ExpIDs, SubsetIDs, SubsetWindows, original_N, self.sc\n )\n final_result = get_final_dist_results(\n simulations_results, partial_state_updates, eps, sessions)\n\n t2 = time()\n if self.supress_print is False:\n print(f\"Total execution time: {t2 - t1 :.2f}s\")\n\n return final_result\n" }, { "path": "cadCAD/engine/execution.py", "content": "from typing import Callable, Dict, List, Any, Tuple, Sequence\nfrom pathos.multiprocessing import ProcessPool # type: ignore\nfrom collections import Counter\nfrom cadCAD.types import *\nfrom cadCAD.utils import flatten\n\nVarDictType = Dict[str, List[object]]\nStatesListsType = List[dict[str, object]]\nConfigsType = List[tuple[list[callable], List[callable]]]\nEnvProcessesType = Dict[str, callable]\n\n\ndef single_proc_exec(\n simulation_execs: Sequence[ExecutorFunction],\n var_dict_list: Union[Sequence[Parameters], Parameters],\n states_lists: Sequence[StateHistory],\n configs_structs: Sequence[StateUpdateBlocks],\n env_processes_list: Sequence[EnvProcesses],\n Ts: Sequence[TimeSeq],\n SimIDs: Sequence[SimulationID],\n Ns: Sequence[Run],\n ExpIDs: Sequence[int],\n SubsetIDs: Sequence[SubsetID],\n SubsetWindows: Sequence[SubsetWindow],\n configured_n: Sequence[N_Runs],\n additional_objs=None\n) -> List:\n \n \n if not isinstance(var_dict_list, Sequence):\n var_dict_list = list([var_dict_list])\n\n raw_params = (\n simulation_execs, states_lists, configs_structs, env_processes_list,\n Ts, SimIDs, Ns, SubsetIDs, SubsetWindows, var_dict_list)\n \n results: List = []\n for raw_param in zip(*raw_params):\n simulation_exec, states_list, config, env_processes, T, sim_id, N, subset_id, subset_window, var_dict = raw_param\n result = simulation_exec(\n var_dict, states_list, config, env_processes, T, sim_id, N, subset_id, subset_window, configured_n, additional_objs\n )\n results.append(flatten(result))\n return flatten(results)\n\ndef parallelize_simulations(\n simulation_execs: List[ExecutorFunction],\n var_dict_list: List[Parameters],\n states_lists: List[StateHistory],\n configs_structs: List[StateUpdateBlocks],\n env_processes_list: List[EnvProcesses],\n Ts: List[TimeSeq],\n SimIDs: List[SimulationID],\n Ns: List[Run],\n ExpIDs: List[int],\n SubsetIDs: List[SubsetID],\n SubsetWindows: List[SubsetWindow],\n configured_n: List[N_Runs],\n additional_objs=None\n):\n\n params = list(\n zip(\n simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list,\n Ts, SimIDs, Ns, SubsetIDs, SubsetWindows\n )\n )\n\n len_configs_structs = len(configs_structs)\n\n unique_runs = Counter(SimIDs)\n sim_count = max(unique_runs.values())\n highest_divisor = int(len_configs_structs / sim_count)\n\n new_configs_structs, new_params = [], []\n for count in range(len(params)):\n if count == 0:\n new_params.append(\n params[count: highest_divisor]\n )\n new_configs_structs.append(\n configs_structs[count: highest_divisor]\n )\n elif count > 0:\n new_params.append(\n params[count * highest_divisor: (count + 1) * highest_divisor]\n )\n new_configs_structs.append(\n configs_structs[count * highest_divisor: (count + 1) * highest_divisor]\n )\n\n def process_executor(params):\n if len_configs_structs > 1:\n with ProcessPool(processes=len_configs_structs) as pp:\n results = pp.map(\n lambda t: t[0](t[1], t[2], t[3], t[4], t[5], t[6], t[7], t[8], t[9], configured_n), params\n )\n else:\n t = params[0]\n results = t[0](t[1], t[2], t[3], t[4], t[5], t[6], t[7], t[8], t[9], configured_n)\n return results\n\n results = flatten(list(map(lambda params: process_executor(params), new_params)))\n\n return results\n\n\ndef local_simulations(\n simulation_execs: List[ExecutorFunction],\n var_dict_list: List[Parameters],\n states_lists: List[StateHistory],\n configs_structs: List[StateUpdateBlocks],\n env_processes_list: List[EnvProcesses],\n Ts: List[TimeSeq],\n SimIDs: List[SimulationID],\n Ns: List[Run],\n ExpIDs: List[int],\n SubsetIDs: List[SubsetID],\n SubsetWindows: List[SubsetWindow],\n configured_n: List[N_Runs],\n additional_objs=None\n ):\n config_amt = len(configs_structs)\n\n if config_amt == 1: # and configured_n != 1\n return single_proc_exec(\n simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list,\n Ts, SimIDs, Ns, ExpIDs, SubsetIDs, SubsetWindows, configured_n, additional_objs\n )\n elif config_amt > 1: # and configured_n != 1\n return parallelize_simulations(\n simulation_execs, var_dict_list, states_lists, configs_structs, env_processes_list,\n Ts, SimIDs, Ns, ExpIDs, SubsetIDs, SubsetWindows, configured_n, additional_objs\n )\n # elif config_amt > 1 and configured_n == 1:\n" }, { "path": "cadCAD/engine/simulation.py", "content": "from typing import Any, Callable, Dict, List, Tuple\nfrom copy import deepcopy, copy\nfrom functools import reduce\nfrom funcy import curry # type: ignore\n\nfrom cadCAD.utils import flatten\nfrom cadCAD.engine.utils import engine_exception\nfrom cadCAD.types import *\n\nid_exception: callable = curry(engine_exception)(KeyError)(KeyError)(None)\n\nAggregator = Callable[[object, object], object]\n\n\ndef policy_scope_tuner(args: tuple,\n additional_objs: object,\n f: PolicyFunction) -> dict:\n \"\"\"\n Execute cadCAD policy function.\n \"\"\"\n (sweep_dict, sub_step, sL, s) = args\n if additional_objs is None:\n return f(sweep_dict, sub_step, sL, s)\n else:\n return f(sweep_dict, sub_step, sL, s, additional_objs)\n\n\ndef compose(init_reduction_funct: Aggregator,\n funct_list: List[Aggregator],\n val_list: dict) -> object:\n \"\"\"\n Reduce the nested policy input dict into a simple one.\n \"\"\"\n result, i = None, 0\n def composition(x): return [reduce(\n init_reduction_funct, x.values())] + funct_list\n for g in composition(val_list):\n if i == 0:\n result = g\n i = 1\n else:\n result = g(result)\n return result\n\nclass Executor:\n def __init__(\n self,\n policy_ops,\n policy_update_exception: callable = id_exception,\n state_update_exception: callable = id_exception\n ) -> None:\n\n self.policy_ops = policy_ops\n self.state_update_exception = state_update_exception\n self.policy_update_exception = policy_update_exception\n\n def get_policy_input(\n self,\n sweep_dict: Dict[str, List[object]],\n sub_step: int,\n sL: List[dict[str, object]],\n s: Dict[str, object],\n funcs: List[callable],\n additional_objs\n ) -> Dict[str, object]:\n \"\"\"\n Retrieves the Policy Input for usage on State Update Functions\n\n Arguments:\n sweep_dict - System Parameters\n sub_step - Execution order in regards to PSUBs\n sL - History of the variables state\n s - Current variables state\n funcs - list of cadCAD Policies to be executed\n \"\"\"\n\n ops = self.policy_ops\n\n args = (sweep_dict, sub_step, sL, s)\n def execute_policy(f: PolicyFunction) -> dict:\n return policy_scope_tuner(args, additional_objs, f)\n\n col_results: List[PolicyOutput] = map(execute_policy, funcs)\n # Create a nested dict containing all results combination\n # new_dict[policy_input][policy_ordinal] = policy_input_value\n new_dict: dict = {}\n for i, col_result in enumerate(col_results):\n for label, value in col_result.items():\n if label not in new_dict.keys():\n new_dict[label] = {}\n else:\n pass\n new_dict[label][i] = value\n\n # Aggregator functions\n ops_head, *ops_tail = ops\n\n # Function for aggregating a combination of policy inputs\n # for the same signal\n def f(val_list):\n return compose(init_reduction_funct=ops_head,\n funct_list=ops_tail,\n val_list=val_list)\n\n # Generate dict to be consumed by SUFs\n policy_input = {\n label: f(val_list)\n for label, val_list\n in new_dict.items()}\n\n return policy_input\n\n def apply_env_proc(\n self,\n sweep_dict,\n env_processes: Dict[str, callable],\n state_dict: Dict[str, object]\n ) -> Dict[str, object]:\n\n def env_composition(target_field, state_dict, target_value):\n function_type = type(lambda x: x)\n env_update = env_processes[target_field]\n if isinstance(env_update, list):\n for f in env_update:\n target_value = f(sweep_dict, target_value)\n elif isinstance(env_update, function_type):\n target_value = env_update(state_dict, sweep_dict, target_value)\n else:\n target_value = env_update\n\n return target_value\n\n filtered_state_dict = {\n k: v for k, v in state_dict.items() if k in env_processes.keys()}\n env_proc_dict = {\n target_field: env_composition(\n target_field, state_dict, target_value)\n for target_field, target_value in filtered_state_dict.items()\n }\n\n for k, v in env_proc_dict.items():\n state_dict[k] = v\n\n return state_dict\n\n # mech_step\n def partial_state_update(\n self,\n sweep_dict: Parameters,\n sub_step: Substep,\n sL: List[State],\n sH: StateHistory,\n state_funcs: List[StateUpdateFunction],\n policy_funcs: List[PolicyFunction],\n env_processes: EnvProcesses,\n time_step: int,\n run: int,\n additional_objs\n ) -> List[dict[str, object]]:\n\n if type(additional_objs) == dict:\n if additional_objs.get('deepcopy_off', False) == True:\n last_in_obj = copy(sL[-1])\n if len(additional_objs) == 1:\n additional_objs = None \n # XXX: drop the additional objects if only used for deepcopy\n # toggling.\n else:\n last_in_obj = deepcopy(sL[-1])\n else:\n last_in_obj = deepcopy(sL[-1])\n \n _input: Dict[str, Any] = self.policy_update_exception(\n self.get_policy_input(sweep_dict, sub_step, sH, last_in_obj, policy_funcs, additional_objs)\n )\n\n def generate_record(state_funcs):\n def state_scope_tuner(f):\n lenf = f.__code__.co_argcount\n if lenf == 5:\n return self.state_update_exception(f(sweep_dict, sub_step, sH, last_in_obj, _input))\n elif lenf == 6:\n return self.state_update_exception(f(sweep_dict, sub_step, sH, last_in_obj, _input, additional_objs))\n for f in state_funcs:\n yield state_scope_tuner(f)\n\n def transfer_missing_fields(source, destination):\n for k in source:\n if k not in destination:\n destination[k] = source[k]\n del source \n return destination\n\n try:\n new_state_vars = dict(generate_record(state_funcs))\n except (ValueError, TypeError):\n raise ValueError(\"There is a State Update Function which is not returning an proper tuple\")\n\n\n last_in_copy: Dict[str, Any] = transfer_missing_fields(last_in_obj, new_state_vars)\n last_in_copy: Dict[str, Any] = self.apply_env_proc(sweep_dict, env_processes, last_in_copy)\n last_in_copy['substep'], last_in_copy['timestep'], last_in_copy['run'] = sub_step, time_step, run\n\n sL.append(last_in_copy)\n del last_in_copy\n\n return sL\n\n # mech_pipeline - state_update_block\n def state_update_pipeline(\n self,\n sweep_dict: Dict[str, List[object]],\n simulation_list,\n configs: List[tuple[list[callable], List[callable]]],\n env_processes: Dict[str, callable],\n time_step: int,\n run: int,\n additional_objs\n ) -> List[dict[str, object]]:\n\n sub_step = 0\n states_list_copy: List[dict[str, object]] = tuple(simulation_list[-1])\n genesis_states: Dict[str, object] = states_list_copy[-1].copy()\n# genesis_states: Dict[str, object] = states_list_copy[-1]\n\n if len(states_list_copy) == 1:\n genesis_states['substep'] = sub_step\n\n del states_list_copy\n states_list: List[dict[str, object]] = [genesis_states]\n\n sub_step += 1\n for [s_conf, p_conf] in configs:\n states_list: List[dict[str, object]] = self.partial_state_update(\n sweep_dict, sub_step, states_list, simulation_list, s_conf, p_conf, env_processes, time_step, run,\n additional_objs\n )\n sub_step += 1\n\n time_step += 1\n\n return states_list\n\n # state_update_pipeline\n def run_pipeline(\n self,\n sweep_dict: Dict[str, List[object]],\n states_list: List[dict[str, object]],\n configs: List[tuple[list[callable], List[callable]]],\n env_processes: Dict[str, callable],\n time_seq: range,\n run: int,\n additional_objs\n ) -> List[list[dict[str, object]]]:\n time_seq: List[int] = [x + 1 for x in time_seq]\n simulation_list: List[list[dict[str, object]]] = [states_list]\n\n for time_step in time_seq:\n pipe_run: List[dict[str, object]] = self.state_update_pipeline(\n sweep_dict, simulation_list, configs, env_processes, time_step, run, additional_objs\n )\n _, *pipe_run = pipe_run\n simulation_list.append(pipe_run)\n\n return simulation_list\n\n def simulation(\n self,\n sweep_dict: SweepableParameters,\n states_list: StateHistory,\n configs,\n env_processes: EnvProcesses,\n time_seq: TimeSeq,\n simulation_id: SimulationID,\n run: int,\n subset_id: SubsetID,\n subset_window: SubsetWindow,\n configured_N: int,\n # remote_ind\n additional_objs: Union[None, Dict]=None\n ):\n run += 1\n subset_window.appendleft(subset_id)\n\n def execute_run(sweep_dict, states_list, configs, env_processes, time_seq, _run) -> List[dict[str, object]]:\n def generate_init_sys_metrics(genesis_states_list, sim_id, _subset_id, _run, _subset_window):\n for D in genesis_states_list:\n d = D.copy()\n d['simulation'], d['subset'], d['run'], d['substep'], d['timestep'] = \\\n sim_id, _subset_id, _run, 0, 0\n yield d\n\n states_list_copy: List[dict[str, object]] = list(\n generate_init_sys_metrics(\n tuple(states_list), simulation_id, subset_id, run, subset_window)\n )\n\n first_timestep_per_run: List[dict[str, object]] = self.run_pipeline(\n sweep_dict, states_list_copy, configs, env_processes, time_seq, run, additional_objs\n )\n del states_list_copy\n\n return first_timestep_per_run\n\n pipe_run = flatten(\n [execute_run(sweep_dict, states_list, configs,\n env_processes, time_seq, run)]\n )\n\n return pipe_run\n" }, { "path": "cadCAD/engine/utils.py", "content": "from datetime import datetime\n\n\ndef datetime_range(start, end, delta, dt_format='%Y-%m-%d %H:%M:%S'):\n reverse_head = end\n [start, end] = [datetime.strptime(x, dt_format) for x in [start, end]]\n\n def _datetime_range(start, end, delta):\n current = start\n while current < end:\n yield current\n current += delta\n\n reverse_tail = [dt.strftime(dt_format) for dt in _datetime_range(start, end, delta)]\n return reverse_tail + [reverse_head]\n\n\ndef last_index(l):\n return len(l)-1\n\n\ndef retrieve_state(l, offset):\n return l[last_index(l) + offset + 1]\n\n\n# @curried\ndef engine_exception(ErrorType, error_message, exception_function, try_function):\n try:\n return try_function\n except ErrorType:\n print(error_message)\n return exception_function\n" }, { "path": "cadCAD/tools/__init__.py", "content": "from cadCAD.tools.execution import easy_run\nfrom cadCAD.tools.profiling import profile_run\nfrom cadCAD.tools.utils import generic_suf, add_parameter_labels\n" }, { "path": "cadCAD/tools/execution/__init__.py", "content": "from cadCAD.tools.execution.easy_run import easy_run" }, { "path": "cadCAD/tools/execution/easy_run.py", "content": "import inspect\nimport types\nfrom typing import Dict, Union\n\nimport pandas as pd # type: ignore\nfrom cadCAD.configuration import Experiment\nfrom cadCAD.configuration.utils import config_sim\nfrom cadCAD.engine import ExecutionContext, ExecutionMode, Executor\n\n\ndef describe_or_return(v: object) -> object:\n \"\"\"\n Thanks @LinuxIsCool!\n \"\"\"\n if isinstance(v, types.FunctionType):\n return f'function: {v.__name__}'\n elif isinstance(v, types.LambdaType) and v.__name__ == '':\n return f'lambda: {inspect.signature(v)}'\n elif isinstance(v, list):\n return str(v)\n else:\n return v\n\n\ndef select_M_dict(M_dict: Dict[str, object], keys: set) -> Dict[str, object]:\n \"\"\"\n Thanks @LinuxIsCool!\n \"\"\"\n return {k: describe_or_return(v) for k, v in M_dict.items() if k in keys}\n\n\ndef select_config_M_dict(configs: list, i: int, keys: set) -> Dict[str, object]:\n return select_M_dict(configs[i].sim_config['M'], keys)\n\n\ndef easy_run(\n state_variables,\n params,\n psubs,\n N_timesteps,\n N_samples,\n use_label=False,\n assign_params: Union[bool, set] = True,\n drop_substeps=True,\n exec_mode='local',\n deepcopy_off=False,\n supress_print=False\n) -> pd.DataFrame:\n \"\"\"\n Run cadCAD simulations without headaches.\n \"\"\"\n\n # Set-up sim_config\n simulation_parameters = {'N': N_samples,\n 'T': range(N_timesteps), 'M': params}\n sim_config = config_sim(simulation_parameters) # type: ignore\n\n # Create a new experiment\n exp = Experiment()\n exp.append_configs(\n sim_configs=sim_config,\n initial_state=state_variables,\n partial_state_update_blocks=psubs,\n )\n configs = exp.configs\n\n # Set-up cadCAD executor\n if exec_mode == 'local':\n _exec_mode = ExecutionMode().local_mode\n elif exec_mode == 'single':\n _exec_mode = ExecutionMode().single_mode\n exec_context = ExecutionContext(_exec_mode, additional_objs={'deepcopy_off': deepcopy_off})\n executor = Executor(exec_context=exec_context, configs=configs, supress_print=supress_print)\n\n # Execute the cadCAD experiment\n (records, tensor_field, _) = executor.execute()\n\n # Parse the output as a pandas DataFrame\n df = pd.DataFrame(records)\n\n if drop_substeps == True:\n # Drop all intermediate substeps\n first_ind = (df.substep == 0) & (df.timestep == 0)\n last_ind = df.substep == max(df.substep)\n inds_to_drop = first_ind | last_ind\n df = df.loc[inds_to_drop].drop(columns=['substep'])\n else:\n pass\n\n if assign_params == False:\n pass\n else:\n M_dict = configs[0].sim_config['M']\n params_set = set(M_dict.keys())\n\n if assign_params == True:\n pass\n else:\n params_set &= assign_params # type: ignore\n\n # Logic for getting the assign params criteria\n if type(assign_params) is list:\n selected_params = set(assign_params) & params_set # type: ignore\n elif type(assign_params) is set:\n selected_params = assign_params & params_set\n else:\n selected_params = params_set\n # Attribute parameters to each row*\n params_dict = select_config_M_dict(configs, 0, selected_params)\n\n # Handles all cases of parameter types including list\n for key, value in params_dict.items():\n df[key] = df.apply(lambda _: value, axis=1)\n\n for i, (_, n_df) in enumerate(df.groupby(['simulation', 'subset', 'run'])):\n params_dict = select_config_M_dict(configs, i, selected_params)\n for key, value in params_dict.items():\n df.loc[n_df.index, key] = df.loc[n_df.index].apply(\n lambda _: value, axis=1)\n\n # Based on Vitor Marthendal (@marthendalnunes) snippet\n if use_label == True:\n psub_map = {\n order + 1: psub.get('label', '') for (order, psub) in enumerate(psubs)\n }\n psub_map[0] = 'Initial State'\n df['substep_label'] = df.substep.map(psub_map)\n\n return df\n" }, { "path": "cadCAD/tools/preparation.py", "content": "from typing import Mapping\nfrom cadCAD.tools.execution import easy_run\nfrom pandas import DataFrame # type: ignore\nfrom cadCAD.types import *\nfrom cadCAD.tools.types import *\nfrom itertools import product\nfrom dataclasses import dataclass\n\n\ndef sweep_cartesian_product(sweep_params: SweepableParameters) -> SweepableParameters:\n \"\"\"\n Makes a cartesian product from dictionary values.\n This is useful for plugging inside the sys_params dict, like:\n ```python\n sweep_params = {'a': [0.1, 0.2], 'b': [1, 2]}\n product_sweep \n sys_params = {**cartesian_product_sweep(sweep_params),\n 'c': [0.1]}\n ```\n Usage:\n >>> sweep_params = {'a': [0.1, 0.2], 'b': [1, 2]}\n >>> cartesian_product_sweep(sweep_params)\n {'a': [0.1, 0.1, 0.2, 0.2], 'b': [1, 2, 1, 2]}\n \"\"\"\n cartesian_product = product(*sweep_params.values())\n transpose_cartesian_product = zip(*cartesian_product)\n zipped_sweep_params = zip(sweep_params.keys(), transpose_cartesian_product)\n sweep_dict = dict(zipped_sweep_params)\n sweep_dict = {k: list(v) for k, v in sweep_dict.items()}\n return sweep_dict\n\n\ndef prepare_params(params: SystemParameters,\n cartesian_sweep: bool = False) -> Mapping[str, List[object]]:\n simple_params = {k: [v.value]\n for k, v in params.items()\n if type(v) is Param}\n\n sweep_params: SweepableParameters = {k: v.value\n for k, v in params.items()\n if type(v) is ParamSweep}\n if cartesian_sweep is True:\n sweep_params = sweep_cartesian_product(sweep_params)\n else:\n pass\n\n cleaned_params = {**simple_params, **sweep_params}\n return cleaned_params\n\n\ndef prepare_state(state: InitialState) -> Mapping[str, object]:\n cleaned_state = {k: v.value\n for k, v in state.items()}\n return cleaned_state\n\n\n@dataclass\nclass ConfigurationWrapper():\n initial_state: InitialState\n params: SystemParameters\n timestep_block: StateUpdateBlocks\n timesteps: int\n samples: int\n\n def run(self, *args, **kwargs) -> DataFrame:\n output = easy_run(prepare_state(self.initial_state),\n prepare_params(self.params),\n self.timestep_block,\n self.timesteps,\n self.samples,\n *args,\n **kwargs)\n return output\n" }, { "path": "cadCAD/tools/profiling/__init__.py", "content": "from cadCAD.tools.profiling.profile_run import profile_run" }, { "path": "cadCAD/tools/profiling/profile_run.py", "content": "from typing import Dict\nfrom cadCAD.tools import easy_run\nfrom cadCAD.types import StateUpdateBlocks, Parameters, State, StateUpdateBlock\nfrom time import time\nimport pandas as pd # type: ignore\n\n\ndef MEASURE_TIME_SUF(p, s, h, v, p_i): return ('run_time', time())\n\n\nMEASURING_BLOCK: StateUpdateBlock = {\n 'label': 'Time Measure',\n 'policies': {},\n 'variables': {\n 'run_time': MEASURE_TIME_SUF\n }\n} # type: ignore\n\n\ndef profile_psubs(psubs: StateUpdateBlocks, profile_substeps=True) -> StateUpdateBlocks:\n \"\"\"\n Updates a TimestepBlock so that a time measuring function is added.\n \"\"\"\n new_timestep_block: StateUpdateBlocks = []\n new_timestep_block.append(MEASURING_BLOCK)\n if profile_substeps is True:\n for psub in psubs:\n new_timestep_block.append(psub)\n new_timestep_block.append(MEASURING_BLOCK)\n else:\n pass\n return new_timestep_block\n\n\ndef profile_run(state_variables: State,\n params: Parameters,\n psubs: StateUpdateBlocks,\n *args,\n profile_substeps=True,\n **kwargs) -> pd.DataFrame:\n\n if profile_substeps is True:\n kwargs.update(drop_substeps=False)\n\n new_psubs = profile_psubs(psubs, profile_substeps)\n state_variables.update({'run_time': None})\n\n return easy_run(state_variables,\n params,\n new_psubs,\n *args,\n **kwargs)\n" }, { "path": "cadCAD/tools/profiling/visualizations.py", "content": "from tqdm.auto import tqdm\nimport pandas as pd\nimport plotly.express as px\nimport numpy as np\n\n\ndef visualize_elapsed_time_per_ts(df: pd.DataFrame, relative=False) -> None:\n indexes = ['simulation', 'run', 'timestep', 'substep']\n\n z_df = df.set_index(indexes)\n first_time = z_df.query(\n 'timestep == 1 & substep == 1').reset_index([-1, -2]).run_time\n s = (z_df.run_time - first_time)\n s.name = 'time_since_start'\n\n z_df = z_df.join(s)\n s = z_df.groupby(indexes[:-1]).time_since_start.max()\n\n fig_df = s.reset_index()\n if relative is True:\n s = fig_df.groupby(indexes[:-2]).time_since_start.diff()\n s.name = 'psub_duration'\n fig_df = fig_df.join(s)\n\n y_col = 'psub_duration'\n else:\n y_col = 'time_since_start'\n \n fig = px.box(fig_df,\n x='timestep',\n y=y_col)\n\n return fig\n\n\ndef visualize_substep_impact(df: pd.DataFrame, relative=True, **kwargs) -> None:\n indexes = ['simulation', 'run', 'timestep', 'substep']\n\n new_df = df.copy()\n new_df = new_df.assign(psub_time=np.nan).set_index(indexes)\n\n # Calculate the run time associated with PSUBs\n for ind, gg_df in tqdm(df.query('substep > 0').groupby(indexes[:-1])):\n g_df = gg_df.reset_index()\n N_rows = len(g_df)\n substep_rows = list(range(N_rows))[1:-1:2]\n\n for substep_row in substep_rows:\n t1 = g_df.run_time[substep_row - 1]\n t2 = g_df.run_time[substep_row + 1]\n dt = t2 - t1\n g_df.loc[substep_row, 'psub_time'] = dt\n g_df = g_df.set_index(indexes)\n new_df.loc[g_df.index, 'psub_time'] = g_df.psub_time\n\n fig_df = new_df.reset_index().dropna(subset=['psub_time'])\n\n\n if 'substep_label' in fig_df.columns:\n x_col = 'substep_label'\n else:\n x_col = 'substep'\n fig_df[x_col] = fig_df[x_col] / 2\n\n if relative is True:\n fig_df = fig_df.assign(relative_psub_time=fig_df.groupby(indexes[:-1]).psub_time.apply(lambda x: x / x.sum()))\n y_col = 'relative_psub_time'\n else:\n y_col = 'psub_time'\n\n inds = fig_df[y_col] < fig_df[y_col].quantile(0.95)\n inds &= fig_df[y_col] > fig_df[y_col].quantile(0.05)\n\n fig = px.box(fig_df[inds],\n x=x_col,\n y=y_col,\n **kwargs)\n\n return fig\n" }, { "path": "cadCAD/tools/types.py", "content": "from typing import NamedTuple, Tuple, Dict, Union, List\n\nclass InitialValue(NamedTuple):\n value: object\n type: type\n\n\nclass Param(NamedTuple):\n value: object\n type: type\n\n\nclass ParamSweep(NamedTuple):\n value: List[object]\n type: type\n\nInitialState = Dict[str, InitialValue]\nSystemParameters = Dict[str, Union[Param, ParamSweep]]" }, { "path": "cadCAD/tools/utils.py", "content": "from cadCAD.types import *\nimport pandas as pd\n\n\ndef generic_suf(variable: str, signal: str = \"\") -> StateUpdateFunction:\n \"\"\"\n Generate a State Update Function that assigns the signal value to the\n given variable. By default, the signal has the same identifier as the\n variable.\n \"\"\"\n if signal is \"\":\n signal = variable\n else:\n pass\n\n def suf(_1, _2, _3, _4, signals: PolicyOutput) -> StateUpdateTuple:\n return (variable, signals[signal])\n\n return suf\n\n\ndef add_parameter_labels(configs: list, df: pd.DataFrame) -> pd.DataFrame:\n \"\"\"Utility function to add the parameters to a dataframe after processing\n\n Args:\n configs (list): The configurations of the simulations\n df (pd.DataFrame): Simulation dataframe\n\n Returns:\n pd.DataFrame: Simulation dataframe with labels\n \"\"\"\n\n # Find the relevant parameters\n sim_params = pd.DataFrame([x.sim_config[\"M\"] for x in configs])\n sim_params[[\"subset\", \"simulation\", \"run\"]] = [\n [x.subset_id, x.simulation_id, x.run_id] for x in configs\n ]\n # Fix because run_id is 0 indexed, but cadCAD dataframe is 1 indexed for runs\n sim_params[\"run\"] += 1\n\n # Join\n sim_params = sim_params.set_index([\"subset\", \"simulation\", \"run\"])\n df = df.join(sim_params, on=[\"subset\", \"simulation\", \"run\"])\n return df\n" }, { "path": "cadCAD/types.py", "content": "from typing import TypedDict, Callable, Union, Dict, List, Tuple, Iterable\nfrom collections import deque\n\nState = Dict[str, object]\nParameters = Dict[str, object]\nSweepableParameters = Dict[str, List[object]]\nSubstep = int\nStateHistory = List[List[State]]\nPolicyOutput = Dict[str, object]\nStateVariable = object\n\nPolicyFunction = Callable[[Parameters, Substep, StateHistory, State], PolicyOutput]\nStateUpdateFunction = Callable[[Parameters, Substep, StateHistory, State, PolicyOutput], Tuple[str, StateVariable]]\n\nclass StateUpdateBlock(TypedDict):\n policies: Dict[str, PolicyFunction]\n variables: Dict[str, StateUpdateFunction]\n\n\nStateUpdateBlocks = List[StateUpdateBlock]\n\nclass ConfigurationDict(TypedDict):\n T: Iterable # Generator for the timestep variable\n N: int # Number of MC Runs\n M: Union[Parameters, SweepableParameters] # Parameters / List of Parameter to Sweep\n\nTargetValue = object\nEnvProcess: Callable[[State, SweepableParameters, TargetValue], TargetValue]\nEnvProcesses = Dict[str, Callable]\nTimeSeq = Iterable\nSimulationID = int\nRun = int\nSubsetID = int\nSubsetWindow = Iterable\nN_Runs = int\n\nExecutorFunction = Callable[[Parameters, StateHistory, StateUpdateBlocks, EnvProcesses, TimeSeq, SimulationID, Run, SubsetID, SubsetWindow, N_Runs], object]\nExecutionParameter = Tuple[ExecutorFunction, Parameters, StateHistory, StateUpdateBlocks, EnvProcesses, TimeSeq, SimulationID, Run, SubsetID, SubsetWindow, N_Runs]\n\n\nclass SessionDict(TypedDict):\n user_id: str\n experiment_id: int\n session_id: str\n simulation_id: int\n run_id: int\n subset_id: int\n subset_window: deque\n" }, { "path": "cadCAD/utils/__init__.py", "content": "from functools import reduce\nfrom collections import defaultdict\nfrom itertools import product\nimport warnings\nfrom typing import Union\nfrom cadCAD.types import *\nfrom typing import List, Dict, Union\n\nimport functools\nimport operator\n\nfrom pandas import DataFrame # type: ignore\n\n\nclass SilentDF(DataFrame):\n def __repr__(self):\n return str(hex(id(DataFrame)))\n\n\ndef append_dict(dict: dict, new_dict: dict) -> dict:\n \"\"\"\n >>> append_dict({1: 2, 3: 4}, {3: 5})\n {1: 2, 3: 5}\n \"\"\"\n dict.update(new_dict)\n return dict\n\n\ndef arrange_cols(df: DataFrame, reverse=False) -> DataFrame:\n \"\"\"\n Reorders `df` columns so that the variable order is `session_metrics`\n followed by `sys_metrics` and `results_cols`\n \"\"\"\n session_metrics = ['session_id', 'user_id', 'simulation_id', 'run_id']\n sys_metrics = ['run', 'timestep', 'substep']\n result_cols = list(set(df.columns) - set(session_metrics) - set(sys_metrics))\n result_cols.sort(reverse=reverse)\n return df[session_metrics + sys_metrics + result_cols]\n\n\nclass IndexCounter:\n def __init__(self):\n self.i = 0\n\n def __call__(self):\n self.i += 1\n return self.i\n\n\ndef compose(*functions: Tuple[callable]) -> object:\n return reduce(lambda f, g: lambda x: f(g(x)), functions, lambda x: x)\n\n\ndef pipe(x: object) -> object:\n return x\n\n\ndef print_pipe(x: object) -> object:\n print(x)\n return x\n\n\ndef tupalize(k: object, vs: Union[list, dict]):\n \"\"\"\n >>> tupalize(1, 1)\n [(1, 1)]\n >>> tupalize(1, [2, 3])\n [(1, 2), (1, 3)]\n \"\"\"\n l = []\n if isinstance(vs, list):\n for v in vs:\n l.append((k, v))\n else:\n l.append((k, vs))\n return l\n\ndef flattenDict(l: dict) -> list:\n \"\"\"\n >>> flattenDict({1: [1, 2, 3], 4: 5})\n [{1: 1, 4: 5}, {1: 2, 4: 5}, {1: 3, 4: 5}]\n \"\"\"\n flat_list = [tupalize(k, vs) for k, vs in l.items()]\n flat_dict = [dict(items) for items in product(*flat_list)]\n return flat_dict\n\n\ndef flatten(l: Union[list, dict]):\n if isinstance(l, list):\n return functools.reduce(operator.iconcat, l, [])\n elif isinstance(l, dict):\n return flattenDict(l)\n\n\ndef flatMap(f, collection):\n return flatten(list(map(f, collection)))\n\n\ndef dict_filter(dictionary, condition):\n return dict([(k, v) for k, v in dictionary.items() if condition(v)])\n\n\ndef get_max_dict_val_len(g: Dict[str, List[int]]) -> int:\n return len(max(g.values(), key=len))\n\n\ndef tabulate_dict(d: Dict[str, List[int]]) -> Dict[str, List[int]]:\n max_len = get_max_dict_val_len(d)\n _d = {}\n for k, vl in d.items():\n if len(vl) != max_len:\n _d[k] = vl + list([vl[-1]] * (max_len-1))\n else:\n _d[k] = vl\n\n return _d\n\n\ndef flatten_tabulated_dict(d: Dict[str, List[int]]) -> List[dict[str, int]]:\n max_len = get_max_dict_val_len(d)\n dl: List[dict] = [{} for i in range(max_len)]\n\n for k, vl in d.items():\n for v, i in zip(vl, list(range(len(vl)))):\n dl[i][k] = v\n\n return dl\n\n\ndef contains_type(_collection, type):\n return any(isinstance(x, type) for x in _collection)\n\n\ndef drop_right(l, n):\n return l[:len(l) - n]\n\n\ndef key_filter(l, keyname):\n if (type(l) == list):\n return [v[keyname] for v in l]\n # Keeping support to dictionaries for backwards compatibility\n # Should be removed in the future\n warnings.warn(\n \"The use of a dictionary to describe Partial State Update Blocks will be deprecated. Use a list instead.\",\n FutureWarning)\n return [v[keyname] for k, v in l.items()]\n\n\ndef groupByKey(l):\n d = defaultdict(list)\n for key, value in l:\n d[key].append(value)\n return list(dict(d).items()).pop()\n\n\n# @curried\ndef rename(new_name, f):\n f.__name__ = new_name\n return f\n\n\ndef curry_pot(f, *argv):\n sweep_ind = f.__name__[0:5] == 'sweep'\n arg_len = len(argv)\n if sweep_ind is True and arg_len == 4:\n return f(argv[0])(argv[1])(argv[2])(argv[3])\n elif sweep_ind is False and arg_len == 4:\n return f(argv[0], argv[1], argv[2], argv[3])\n elif sweep_ind is True and arg_len == 3:\n return f(argv[0])(argv[1])(argv[2])\n elif sweep_ind is False and arg_len == 3:\n return f(argv[0], argv[1], argv[2])\n else:\n raise TypeError('curry_pot() needs 3 or 4 positional arguments')\n" }, { "path": "cadCAD/utils/execution.py", "content": "from pprint import pprint\n\nfrom cadCAD import logo, version\nfrom cadCAD.utils import flatten\n\n\ndef print_exec_info(exec_context, configs):\n print(logo)\n print(f'cadCAD Version: {version}')\n print(f'Execution Mode: {exec_context}')\n models = len(configs)\n sim_strs, run_vals, timestep_vals, params, sub_states = [], [], [], [], set()\n for i, config in enumerate(configs):\n sim_config = config.sim_config\n n_n = sim_config['N']\n n_t = len(sim_config['T'])\n n_m = len(sim_config['M'])\n n_s = len(config.initial_state)\n run_vals.append(n_n)\n for timestep in [*sim_config['T']]:\n timestep_vals.append(timestep)\n if type(sim_config['M']) is dict:\n params.append(n_m)\n else:\n n_m = 0\n for state_key in list(config.initial_state.keys()):\n sub_states.add(state_key)\n\n sim_strs.append(f' Simulation {i}: (Timesteps, Params, Runs, Sub-States) = ({n_t}, {n_m}, {n_n}, {n_s})')\n\n timesteps = len(timestep_vals)\n if sum(params) != 0:\n param_count = sum(params)\n else:\n param_count = 0\n runs = sum(run_vals)\n init_states = len(sub_states)\n\n print(\"Simulation Dimensions:\")\n print(\n f'Entire Simulation: (Models, Unique Timesteps, Params, Total Runs, Sub-States) = ({models}, {timesteps}, {param_count}, {runs}, {init_states})'\n )\n for sim_str in sim_strs:\n print(sim_str)\n" }, { "path": "cadCAD/utils/jupyter.py", "content": "def get_home_dir(user):\n return f\"s3://jupyterbackups/jupyter/{user}/\"\n\ndef set_write_path(sc, user, datafolder_path):\n return get_home_dir(user) + datafolder_path +f'_{sc.applicationId}'\n" }, { "path": "cadCAD/utils/sys_config.py", "content": "from funcy import curry\nfrom cadCAD.configuration.utils import ep_time_step, time_step\n\n\ndef increment(y, incr_by):\n return lambda _g, step, sL, s, _input, **kwargs: (y, s[y] + incr_by)\n\n\ndef track(y):\n return lambda _g, step, sL, s, _input, **kwargs: (y, s[y].x)\n\n\ndef simple_state_update(y, x):\n return lambda _g, step, sH, s, _input, **kwargs: (y, x)\n\n\ndef simple_policy_update(y):\n return lambda _g, step, sH, s, **kwargs: y\n\n\ndef update_timestamp(y, timedelta, format):\n return lambda _g, step, sL, s, _input, **kwargs: (\n y,\n ep_time_step(s, dt_str=s[y], fromat_str=format, _timedelta=timedelta)\n )\n\n\ndef apply(f, y: str, incr_by: int):\n return lambda _g, step, sL, s, _input, **kwargs: (y, curry(f)(s[y])(incr_by))\n\n\ndef add(y: str, incr_by):\n return apply(lambda a, b: a + b, y, incr_by)\n\n\ndef increment_state_by_int(y: str, incr_by: int):\n return lambda _g, step, sL, s, _input, **kwargs: (y, s[y] + incr_by)\n\n\ndef s(y, x):\n return lambda _g, step, sH, s, _input, **kwargs: (y, x)\n\n\ndef time_model(y, substeps, time_delta, ts_format='%Y-%m-%d %H:%M:%S'):\n def apply_incriment_condition(s):\n if s['substep'] == 0 or s['substep'] == substeps:\n return y, time_step(dt_str=s[y], dt_format=ts_format, _timedelta=time_delta)\n else:\n return y, s[y]\n return lambda _g, step, sL, s, _input, **kwargs: apply_incriment_condition(s)\n" }, { "path": "documentation/Historically_State_Access.md", "content": "Historical State Access (DEPRECATED)\n==\n#### Motivation\nThe current state (values of state variables) is accessed through the `s` list. When the user requires previous state \nvariable values, they may be accessed through the state history list, `sH`. Accessing the state history should be \nimplemented without creating unintended feedback loops on the current state.\n\nThe 3rd parameter of state and policy update functions (labeled as `sH` of type `list[list[dict]]`) provides access to \npast Partial State Update Block (PSUB) given a negative offset number. `access_block` is used to access past PSUBs \n(`list[dict]`) from `sH`. For example, an offset of `-2` denotes the second to last PSUB.\n\n#### Exclusion list\nCreate a list of states to exclude from the reported PSU.\n```python\nexclusion_list = [\n 'nonexistent', 'last_x', '2nd_to_last_x', '3rd_to_last_x', '4th_to_last_x'\n]\n```\n##### Example Policy Updates\n###### Last partial state update\n```python\ndef last_update(_params, substep, sH, s, **kwargs):\n return {\"last_x\": access_block(\n state_history=sH,\n target_field=\"last_x\", # Add a field to the exclusion list\n psu_block_offset=-1,\n exculsion_list=exclusion_list\n )\n }\n```\n* Note: Although `target_field` adding a field to the exclusion may seem redundant, it is useful in the case of the \nexclusion list being empty while the `target_field` is assigned to a state or a policy key.\n##### Define State Updates\n###### 2nd to last partial state update\n```python\ndef second2last_update(_params, substep, sH, s, **kwargs):\n return {\"2nd_to_last_x\": access_block(sH, \"2nd_to_last_x\", -2, exclusion_list)}\n```\n\n\n###### 3rd to last partial state update\n```python\ndef third_to_last_x(_params, substep, sH, s, _input, **kwargs):\n return '3rd_to_last_x', access_block(sH, \"3rd_to_last_x\", -3, exclusion_list)\n```\n###### 4rd to last partial state update\n```python\ndef fourth_to_last_x(_params, substep, sH, s, _input, **kwargs):\n return '4th_to_last_x', access_block(sH, \"4th_to_last_x\", -4, exclusion_list)\n```\n###### Non-exsistent partial state update\n* `psu_block_offset >= 0` doesn't exist\n```python\ndef nonexistent(_params, substep, sH, s, _input, **kwargs):\n return 'nonexistent', access_block(sH, \"nonexistent\", 0, exclusion_list)\n```\n\n#### [Example Simulation:](examples/historical_state_access.py)\n\n\n#### Example Output:\n###### State History\n```\n+----+-------+-----------+------------+-----+\n| | run | substep | timestep | x |\n|----+-------+-----------+------------+-----|\n| 0 | 1 | 0 | 0 | 0 |\n| 1 | 1 | 1 | 1 | 1 |\n| 2 | 1 | 2 | 1 | 2 |\n| 3 | 1 | 3 | 1 | 3 |\n| 4 | 1 | 1 | 2 | 4 |\n| 5 | 1 | 2 | 2 | 5 |\n| 6 | 1 | 3 | 2 | 6 |\n| 7 | 1 | 1 | 3 | 7 |\n| 8 | 1 | 2 | 3 | 8 |\n| 9 | 1 | 3 | 3 | 9 |\n+----+-------+-----------+------------+-----+\n```\n###### Accessed State History: \nExample: `last_x`\n```\n+----+-----------------------------------------------------------------------------------------------------------------------------------------------------+\n| | last_x |\n|----+-----------------------------------------------------------------------------------------------------------------------------------------------------|\n| 0 | [] |\n| 1 | [{'x': 0, 'run': 1, 'substep': 0, 'timestep': 0}] |\n| 2 | [{'x': 0, 'run': 1, 'substep': 0, 'timestep': 0}] |\n| 3 | [{'x': 0, 'run': 1, 'substep': 0, 'timestep': 0}] |\n| 4 | [{'x': 1, 'run': 1, 'substep': 1, 'timestep': 1}, {'x': 2, 'run': 1, 'substep': 2, 'timestep': 1}, {'x': 3, 'run': 1, 'substep': 3, 'timestep': 1}] |\n| 5 | [{'x': 1, 'run': 1, 'substep': 1, 'timestep': 1}, {'x': 2, 'run': 1, 'substep': 2, 'timestep': 1}, {'x': 3, 'run': 1, 'substep': 3, 'timestep': 1}] |\n| 6 | [{'x': 1, 'run': 1, 'substep': 1, 'timestep': 1}, {'x': 2, 'run': 1, 'substep': 2, 'timestep': 1}, {'x': 3, 'run': 1, 'substep': 3, 'timestep': 1}] |\n| 7 | [{'x': 4, 'run': 1, 'substep': 1, 'timestep': 2}, {'x': 5, 'run': 1, 'substep': 2, 'timestep': 2}, {'x': 6, 'run': 1, 'substep': 3, 'timestep': 2}] |\n| 8 | [{'x': 4, 'run': 1, 'substep': 1, 'timestep': 2}, {'x': 5, 'run': 1, 'substep': 2, 'timestep': 2}, {'x': 6, 'run': 1, 'substep': 3, 'timestep': 2}] |\n| 9 | [{'x': 4, 'run': 1, 'substep': 1, 'timestep': 2}, {'x': 5, 'run': 1, 'substep': 2, 'timestep': 2}, {'x': 6, 'run': 1, 'substep': 3, 'timestep': 2}] |\n+----+-----------------------------------------------------------------------------------------------------------------------------------------------------+\n```" }, { "path": "documentation/Policy_Aggregation.md", "content": "Policy Aggregation\n==\n\nFor each Partial State Update, multiple policy dictionaries are aggregated into a single dictionary to be inputted into \nall state functions using an initial reduction function and optional subsequent map functions. \n\n#### Aggregate Function Composition:\n```python\n# Reduce Function\nadd = lambda a, b: a + b # Used to add policy values of the same key\n# Map Function\nmult_by_2 = lambda y: y * 2 # Used to multiply all policy values by 2\npolicy_ops=[add, mult_by_2]\n```\n\n##### Example Policy Updates per Partial State Update (PSU)\n```python\ndef p1_psu1(_params, step, sH, s, **kwargs):\n return {'policy1': 1}\ndef p2_psu1(_params, step, sH, s, **kwargs):\n return {'policy2': 2}\n```\n* `add` not applicable due to lack of redundant policies\n* `mult_by_2` applied to all policies\n* Result: `{'policy1': 2, 'policy2': 4}`\n\n```python\ndef p1_psu2(_params, step, sH, s, **kwargs):\n return {'policy1': 2, 'policy2': 2}\ndef p2_psu2(_params, step, sH, s, **kwargs):\n return {'policy1': 2, 'policy2': 2}\n```\n* `add` applicable due to redundant policies\n* `mult_by_2` applied to all policies\n* Result: `{'policy1': 8, 'policy2': 8}`\n\n```python\ndef p1_psu3(_params, step, sH, s, **kwargs):\n return {'policy1': 1, 'policy2': 2, 'policy3': 3}\ndef p2_psu3(_params, step, sH, s, **kwargs):\n return {'policy1': 1, 'policy2': 2, 'policy3': 3}\n```\n* `add` applicable due to redundant policies\n* `mult_by_2` applied to all policies\n* Result: `{'policy1': 4, 'policy2': 8, 'policy3': 12}`\n\n#### Aggregate Policies using functions\n```python\nfrom cadCAD.configuration import Experiment\n\nexp = Experiment()\nexp.append_model(\n sim_configs=???,\n initial_state=???,\n partial_state_update_blocks=???,\n policy_ops=[add, mult_by_2] # Default: [lambda a, b: a + b]\n)\n```\n\n#### Example\n##### * [System Model Configuration](examples/policy_aggregation.py)\n##### * Simulation Results:\n```\n+----+---------------------------------------------+-------+------+-----------+------------+\n| | policies | run | s1 | substep | timestep |\n|----+---------------------------------------------+-------+------+-----------+------------|\n| 0 | {} | 1 | 0 | 0 | 0 |\n| 1 | {'policy1': 2, 'policy2': 4} | 1 | 1 | 1 | 1 |\n| 2 | {'policy1': 8, 'policy2': 8} | 1 | 2 | 2 | 1 |\n| 3 | {'policy3': 12, 'policy1': 4, 'policy2': 8} | 1 | 3 | 3 | 1 |\n| 4 | {'policy1': 2, 'policy2': 4} | 1 | 4 | 1 | 2 |\n| 5 | {'policy1': 8, 'policy2': 8} | 1 | 5 | 2 | 2 |\n| 6 | {'policy3': 12, 'policy1': 4, 'policy2': 8} | 1 | 6 | 3 | 2 |\n| 7 | {'policy1': 2, 'policy2': 4} | 1 | 7 | 1 | 3 |\n| 8 | {'policy1': 8, 'policy2': 8} | 1 | 8 | 2 | 3 |\n| 9 | {'policy3': 12, 'policy1': 4, 'policy2': 8} | 1 | 9 | 3 | 3 |\n+----+---------------------------------------------+-------+------+-----------+------------+\n```\n" }, { "path": "documentation/README.md", "content": "Simulation Configuration\n==\n\n## Introduction\n\nGiven a **Simulation Configuration**, cadCAD produces datasets that represent the evolution of the state of a system \nover [discrete time](https://en.wikipedia.org/wiki/Discrete_time_and_continuous_time#Discrete_time). The state of the \nsystem is described by a set of [State Variables](#State-Variables). The dynamic of the system is described by \n[Policy Functions](#Policy-Functions) and [State Update Functions](#State-Update-Functions), which are evaluated by \ncadCAD according to the definitions set by the user in [Partial State Update Blocks](#Partial-State-Update-Blocks).\n\nA Simulation Configuration is comprised of a [System Model](#System-Model) and a set of [Simulation Properties](#Simulation-Properties).\n\n### Experiments\n`cadCAD.configuration.Experiment` is a unique representation of an experiment of one or more configured System Models.\nThe `append_model` method of `Experiment` appends a System Model configurations, each representing a single `run`.\n\n```python\nfrom cadCAD.configuration import Experiment\n\nexp = Experiment()\nexp.append_model(\n model_id = ..., # OPTIONAL: System Model label\n initial_state = ..., # System Model\n partial_state_update_blocks = ..., # System Model\n policy_ops = ..., # System Model\n sim_configs = ..., # Simulation Properties\n user_id = ..., # OPTIONAL: Configuration User ID\n)\n```\nParameters: `append_model`\n* **model_id** : str - OPTIONAL: System Model label\n* **initial_state** : _dict_ - [State Variables](#State-Variables) and their initial values\n* **partial_state_update_blocks** : List[dict[dict]] - list of [Partial State Update Blocks](#Partial-State-Update-Blocks)\n* **policy_ops** : List[functions] - See [Policy Aggregation](Policy_Aggregation.md) \n* **sim_configs** - See [System Model Parameter Sweep](System_Model_Parameter_Sweep.md)\n* **user_id** : str - OPTIONAL: Configuration User ID\n\n## Simulation Properties\n\nSimulation properties are passed to `append_model` in the `sim_configs` parameter. To construct this parameter, we \nuse the `config_sim` function in `cadCAD.configuration.utils`\n\n```python\nfrom cadCAD.configuration.utils import config_sim\nfrom cadCAD.configuration import Experiment\n\nsim_config_dict = {\n \"N\": ...,\n \"T\": range(...),\n \"M\": ...\n}\n\nc = config_sim(sim_config_dict)\n\nexp = Experiment()\nexp.append_model(\n ...\n sim_configs = c # Simulation Properties\n)\n```\n\n### T - Simulation Length\nComputer simulations run in discrete time:\n\n>Discrete time views values of variables as occurring at distinct, separate \"points in time\", or equivalently as being \nunchanged throughout each non-zero region of time (\"time period\")—that is, time is viewed as a discrete variable. (...) \nThis view of time corresponds to a digital clock that gives a fixed reading of 10:37 for a while, and then jumps to a \nnew fixed reading of 10:38, etc. \n([source: Wikipedia](https://en.wikipedia.org/wiki/Discrete_time_and_continuous_time#Discrete_time))\n\nAs is common in many simulation tools, in cadCAD too we refer to each discrete unit of time as a **timestep**. cadCAD \nincrements a \"time counter\", and at each step it updates the state variables according to the equations that describe \nthe system.\n\nThe main simulation property that the user must set when creating a Simulation Configuration is the number of timesteps \nin the simulation. In other words, for how long do they want to simulate the system that has been modeled.\n\n### N - Number of Runs\n\ncadCAD facilitates running multiple simulations of the same system sequentially, reporting the results of all those \nruns in a single dataset. This is especially helpful for running \n[Monte Carlo Simulations](https://github.com/cadCAD-org/demos/blob/master/tutorials/robots_and_marbles/robot-marbles-part-4/robot-marbles-part-4.ipynb).\n\n### M - Parameters of the System\n\nParameters of the system, passed to the state update functions and the policy functions in the `params` parameter are \ndefined here. See [System Model Parameter Sweep](System_Model_Parameter_Sweep.md) for more information.\n\n## System Model\nThe System Model describes the system that will be simulated in cadCAD. It is comprised of a set of \n[State Variables](##State-Variables) and the [State Update Functions](#State-Update-Functions) that determine the \nevolution of the state of the system over time. [Policy Functions](#Policy-Functions) (representations of user policies \nor internal system control policies) may also be part of a System Model.\n\n### State Variables\n>A state variable is one of the set of variables that are used to describe the mathematical \"state\" of a dynamical \nsystem. Intuitively, the state of a system describes enough about the system to determine its future behaviour in the \nabsence of any external forces affecting the system. ([source: Wikipedia](https://en.wikipedia.org/wiki/State_variable))\n\ncadCAD can handle state variables of any Python data type, including custom classes. It is up to the user of cadCAD to \ndetermine the state variables needed to **sufficiently and accurately** describe the system they are interested in.\n\nState Variables are passed to `append_model` along with its initial values, as a Python `dict` where the `dict_keys` \nare the names of the variables and the `dict_values` are their initial values.\n\n```python\nfrom cadCAD.configuration import Experiment\n\ngenesis_states = {\n 'state_variable_1': 0,\n 'state_variable_2': 0,\n 'state_variable_3': 1.5,\n 'timestamp': '2019-01-01 00:00:00'\n}\n\nexp = Experiment()\nexp.append_model(\n initial_state = genesis_states,\n ...\n)\n```\n### State Update Functions\nState Update Functions represent equations according to which the state variables change over time. Each state update \nfunction must return a tuple containing a string with the name of the state variable being updated and its new value. \nEach state update function can only modify a single state variable. The general structure of a state update function is:\n```python\ndef state_update_function_A(_params, substep, sH, s, _input, **kwargs):\n ...\n return 'state_variable_name', new_value\n```\nParameters:\n* **_params** : _dict_ - [System parameters](System_Model_Parameter_Sweep.md)\n* **substep** : _int_ - Current [substep](#Substep)\n* **sH** : _list[list[dict_]] - Historical values of all state variables for the simulation. See \n[Historical State Access (DEPRECATED)](Historically_State_Access.md) for details\n* **s** : _dict_ - Current state of the system, where the `dict_keys` are the names of the state variables and the \n`dict_values` are their current values.\n* **_input** : _dict_ - Aggregation of the signals of all policy functions in the current \n[Partial State Update Block](#Partial-State-Update-Block)\n* **\\*\\*kwargs** - State Update feature extensions \n\nReturn:\n* _tuple_ containing a string with the name of the state variable being updated and its new value.\n \nState update functions should not modify any of the parameters passed to it, as those are mutable Python objects that \ncadCAD relies on in order to run the simulation according to the specifications.\n\n### Policy Functions\nA Policy Function computes one or more signals to be passed to [State Update Functions](#State-Update-Functions) \n(via the _\\_input_ parameter). Read \n[this article](https://github.com/cadCAD-org/demos/blob/master/tutorials/robots_and_marbles/robot-marbles-part-2/robot-marbles-part-2.ipynb) \nfor details on why and when to use policy functions.\n\n\n\nThe general structure of a policy function is:\n```python\ndef policy_function_1(_params, substep, sH, s, **kwargs):\n ...\n return {'signal_1': value_1, ..., 'signal_N': value_N}\n```\nParameters:\n* **_params** : _dict_ - [System parameters](System_Model_Parameter_Sweep.md)\n* **substep** : _int_ - Current [substep](#Substep)\n* **sH** : _list[list[dict_]] - Historical values of all state variables for the simulation. See \n[Historical State Access (DEPRECATED)](Historically_State_Access.md) for details\n* **s** : _dict_ - Current state of the system, where the `dict_keys` are the names of the state variables and the \n`dict_values` are their current values.\n* **\\*\\*kwargs** - Policy Update feature extensions \n \nReturn:\n* _dict_ of signals to be passed to the state update functions in the same \n[Partial State Update Block](#Partial-State-Update-Blocks)\n\nPolicy functions should not modify any of the parameters passed to it, as those are mutable Python objects that cadCAD \nrelies on in order to run the simulation according to the specifications.\n\nAt each [Partial State Update Block](#Partial-State-Update-Blocks) (PSUB), the `dicts` returned by all policy functions \nwithin that PSUB dictionaries are aggregated into a single `dict` using an initial reduction function \n(a key-wise operation, default: `dic1['keyA'] + dic2['keyA']`) and optional subsequent map functions. The resulting \naggregated `dict` is then passed as the `_input` parameter to the state update functions in that PSUB. For more \ninformation on how to modify the aggregation method, see [Policy Aggregation](Policy_Aggregation.md).\n\n### Partial State Update Blocks\n\nA **Partial State Update Block** (PSUB) is a set of State Update Functions and Policy Functions such that State Update \nFunctions in the set are independent from each other and Policies in the set are independent from each other and from \nthe State Update Functions in the set. In other words, if a state variable is updated in a PSUB, its new value cannot \nimpact the State Update Functions and Policy Functions in that PSUB - only those in the next PSUB.\n\n![](https://i.imgur.com/9rlX9TG.png)\n\nPartial State Update Blocks are passed to `append_model` as a list of Python `dicts` where the `dict_keys` are named \n`\"policies\"` and `\"variables\"` and the values are also Python `dicts` where the keys are the names of the policy and \nstate update functions and the values are the functions.\n\n```python\nfrom cadCAD.configuration import Experiment\n\nPSUBs = [\n {\n \"policies\": {\n \"b_1\": policy_function_1,\n ...\n \"b_J\": policy_function_J\n },\n \"variables\": {\n \"s_1\": state_update_function_1,\n ...\n \"s_K\": state_update_function_K\n }\n }, #PSUB_1,\n {...}, #PSUB_2,\n ...\n {...} #PSUB_M\n]\n\nexp = Experiment()\nexp.append_model(\n ...\n partial_state_update_blocks = PSUBs,\n ...\n)\n```\n\n#### Substep\nAt each timestep, cadCAD iterates over the `partial_state_update_blocks` list. For each Partial State Update Block, \ncadCAD returns a record containing the state of the system at the end of that PSUB. We refer to that subdivision of a \ntimestep as a `substep`.\n\n## Result Dataset\n\ncadCAD returns a dataset containing the evolution of the state variables defined by the user over time, with three `int` \nindexes:\n* `subset` - identifies the subset per sweepable parameter produced by a parameter sweep (ver. `0.3.1`'s result was \nmultiple datasets; A single dataset per sweepable parameter).\n* `run` - identifies the [run](#N-Number-of-Runs)\n* `timestep` - discrete unit of time (the total number of timesteps is defined by the user in the \n[T Simulation Parameter](#T-Simulation-Length))\n* `substep` - subdivision of timestep (the number of [substeps](#Substeps) is the same as the number of Partial State \nUpdate Blocks)\n* `simulation` - **Alpha: Ignore**\n\nTherefore, the total number of records in the resulting dataset is `N` x `T` x `len(partial_state_update_blocks)`\n\n#### [System Simulation Execution](Simulation_Execution.md)\n" }, { "path": "documentation/Simulation_Execution.md", "content": "# Simulation Execution\nSystem Simulations are executed with the execution engine executor (`cadCAD.engine.Executor`) given System Model \nConfigurations. There are multiple simulation Execution Modes and Execution Contexts.\n\n## Steps\n### 1. *Choose Execution Mode*\n#### Simulation Execution Modes:\n`cadCAD` executes a process per System Model Configuration and a thread per System Simulation.\n#### Class: `cadCAD.engine.ExecutionMode`\n#### Attributes:\n* **Local Mode (Default):** Automatically selects Single Threaded or Multi-Process Modes (Example: \n`cadCAD.engine.ExecutionMode().local_mode`).\n* **Single Threaded Mode:** A single threaded Execution Mode for a single System Model Configuration (Example: \n`cadCAD.engine.ExecutionMode().single_mode`).\n* **Multi-Process Mode:** Execution Mode for System Model Simulations which executes Multiple processes within \nmultiple processes per given System Model Configuration (Example: `cadCAD.engine.ExecutionMode().multi_mode`).\n\n### 2. *Create Execution Context using Execution Mode*\n```python\nfrom cadCAD.engine import ExecutionMode, ExecutionContext\nexec_mode = ExecutionMode()\nlocal_mode_ctx = ExecutionContext(context=exec_mode.local_mode)\n```\n\n### 3. *Create Simulation Executor*\n```python\nfrom cadCAD.engine import Executor\nfrom ... import exp # import of an instantiated of an `cadCAD.configuration.Experiment` object \nsimulation = Executor(exec_context=local_mode_ctx, configs=exp.configs)\n```\n\n### 4. *Execute Simulation & Produce System Event Dataset*\nA Simulation execution produces a System Event Dataset and the Tensor Field applied to initial states used to create it.\n\n```python\nimport pandas as pd\nraw_system_events, tensor_field, sessions = simulation.execute()\n\n# Simulation Result Types:\n# raw_system_events: List[dict] \n# tensor_field: pd.DataFrame\n\n# Result System Events DataFrame\nsimulation_result = pd.DataFrame(raw_system_events)\n```\n\n#### Example Tensor Field\n```\n+----+-----+--------------------------------+--------------------------------+\n| | m | b1 | s1 |\n|----+-----+--------------------------------+--------------------------------|\n| 0 | 1 | | |\n| 1 | 2 | | |\n| 2 | 3 | | |\n+----+-----+--------------------------------+--------------------------------+\n```\n\n#### Example Result: System Events DataFrame\n```\n+----+------+-----------+------------+--------+-----+---------+----------+\n| | s1 | s2 | simulation | subset | run | substep | timestep |\n|----+------+-----------|------------+--------+-----+---------+----------|\n| 0 | 0 | 0.0 | 0 | 0 | 1 | 0 | 0 |\n| 1 | 1 | 4 | 0 | 0 | 1 | 1 | 1 |\n| 2 | 2 | 6 | 0 | 0 | 1 | 2 | 1 |\n| 3 | 3 | [ 30 300] | 0 | 0 | 1 | 3 | 1 |\n| 4 | 0 | 0.0 | 1 | 0 | 1 | 0 | 0 |\n| 5 | 1 | 4 | 1 | 0 | 1 | 1 | 1 |\n| 6 | 2 | 6 | 1 | 0 | 1 | 2 | 1 |\n| 7 | 3 | [ 30 300] | 1 | 0 | 1 | 3 | 1 |\n+----+------+-----------+------------+--------+-----+---------+----------+\n```\n\n## Execution Examples:\n### Single Simulation Execution (Single Threaded Execution)\nExample System Model Configurations: \n* [System Model A](examples/sys_model_A.py)\n* [System Model B](examples/sys_model_B.py)\n\nExample Simulation Executions:\n* [System Model A](examples/sys_model_A_exec.py)\n* [System Model B](examples/sys_model_B_exec.py)\n\n```python\nimport pandas as pd\nfrom tabulate import tabulate\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom documentation.examples import sys_model_A\nfrom documentation.examples.sys_model_A import exp\n\nexec_mode = ExecutionMode()\n\n# Single Process Execution using a Single System Model Configuration:\n# sys_model_A\nlocal_mode_ctx = ExecutionContext(context=exec_mode.local_mode)\nsys_model_A_simulation = Executor(exec_context=local_mode_ctx, configs=exp.configs)\n\nsys_model_A_raw_result, sys_model_A_tensor_field, sessions = sys_model_A_simulation.execute()\nsys_model_A_result = pd.DataFrame(sys_model_A_raw_result)\nprint()\nprint(\"Tensor Field: sys_model_A\")\nprint(tabulate(sys_model_A_tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Result: System Events DataFrame\")\nprint(tabulate(sys_model_A_result, headers='keys', tablefmt='psql'))\nprint()\n```\n\n#### Multiple Simulation Execution\n##### Multi-Process / Threaded Execution\nExample System Model Configurations: \n* [System Model A](examples/sys_model_A.py)\n* [System Model B](examples/sys_model_B.py)\n\nExample Simulation Executions:\n* [System Model AB](examples/sys_model_AB_exec.py)\n\n```python\nimport pandas as pd\nfrom tabulate import tabulate\n\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom documentation.examples import sys_model_A, sys_model_B, system_model_AB_exp\n\nexec_mode = ExecutionMode()\n\n# # Multiple Processes Execution using Multiple System Model Configurations:\n# # sys_model_A & sys_model_B\nlocal_mode_ctx = ExecutionContext(context=exec_mode.local_mode)\nsys_model_AB_simulation = Executor(exec_context=local_mode_ctx, configs=system_model_AB_exp.configs)\n\nsys_model_AB_raw_result, sys_model_AB_tensor_field, sessions = sys_model_AB_simulation.execute()\nsys_model_AB_result = pd.DataFrame(sys_model_AB_raw_result)\nprint()\nprint(\"Tensor Field:\")\nprint(tabulate(sys_model_AB_tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Result: System Events DataFrame:\")\nprint(tabulate(sys_model_AB_result, headers='keys', tablefmt='psql'))\nprint()\n```\n\n##### System Model Parameter Sweep [Info](System_Model_Parameter_Sweep.md) \nExample: [Param Sweep](examples/param_sweep.py)\n\n```python\nimport pandas as pd\nfrom tabulate import tabulate\n\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom documentation.examples import param_sweep\nfrom documentation.examples.param_sweep import exp\n\nexec_mode = ExecutionMode()\nlocal_mode_ctx = ExecutionContext(context=exec_mode.local_mode)\nrun = Executor(exec_context=local_mode_ctx, configs=exp.configs)\n\nraw_result, tensor_field, sessions = run.execute()\nresult = pd.DataFrame(raw_result)\nprint()\nprint(\"Tensor Field:\")\nprint(tabulate(tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Output:\")\nprint(tabulate(result, headers='keys', tablefmt='psql'))\nprint()\n```\n" }, { "path": "documentation/System_Configuration.md", "content": "# Display System Model Configurations:\n\n## Announcement:\n\nSee [CHANGELOG](CHANGELOG.md)\n\nThe `cadCAD.configuration.Experiment().configs` (System Model Configurations) `list` has been flattened \nto contain single run `Configuration` objects. This functionality will be restored in a \nsubsequent release by a class that returns the original representation in ver. `0.3.1`.\n* The conversion utilities have been provided to restore its original representation of configurations with \nruns >= 1\n * System Configuration Conversions:\n * Configuration as list of Configuration Objects (as in ver. `0.3.1`) \n * New: System Configuration as a Pandas DataFrame\n * New: System Configuration as list of Dictionaries\n\n## Conversions\n##### Note: The following applies as a result of simulation execution\n\n#### Imports:\n```python\nfrom cadCAD.configuration.utils import configs_as_objs, configs_as_dataframe, configs_as_dicts\n```\n\n#### System Configurations as list of Configuration Objects\nExample:\n* `configs` is temporarily returned in a flattened format and reformatted into its intended format. \n* `Configuration` objects at `0x10790e470` and `0x1143dd630` are reconstituted into objects at `0x10790e7b8` \nand `0x116268908` respectively.\n```python\nfrom ... import exp # import of an instantiated `cadCAD.configuration.Experiment` object \n\nflattened_configs = exp.configs\n \nprint('Flattened Format: Temporary') \npprint(flattened_configs)\nprint()\n\nprint('Intended Format:')\nintended_configs = configs_as_objs(flattened_configs)\npprint(intended_configs)\nprint()\n\nprint(\"Object: cadCAD.configuration.Configuration(...).sim_config\")\npprint(intended_configs[0].sim_config)\nprint()\n```\nReturn:\n```\nFlattened Format: Temporary\n[,\n ,\n ,\n ]\n\nIntended Format:\n[,\n ]\n\nObject: cadCAD.configuration.Configuration(...).sim_config\n{'M': [{}],\n 'N': 2,\n 'T': range(0, 1),\n 'run_id': 1,\n 'simulation_id': 0,\n 'subset_id': 0,\n 'subset_window': deque([0, None], maxlen=2)}\n```\n\n#### System Configurations as a Pandas DataFrame\n```python\nflattened_configs = configs\nconfigs_df = configs_as_dataframe(configs)\nconfigs_df\n```\n\n#### System Configurations as list of Dictionaries\n```python\nconfigs_dicts: list = configs_as_dicts(configs)\npprint(configs_dicts[0]['sim_config'])\n```\nReturn:\n```\n{'env_processes': {'s3': [ at 0x7f8f9c99bd90>,\n at 0x7f8f9c9a11e0>],\n 's4': .trigger..env_update at 0x7f8f9c9a12f0>},\n 'exogenous_states': {},\n 'initial_state': {'s1': 0.0,\n 's2': 0.0,\n 's3': 1.0,\n 's4': 1.0,\n 'timestamp': '2018-10-01 15:16:24'},\n 'kwargs': {},\n 'partial_state_updates': [{'policies': {'p1': ,\n 'p2': },\n 'variables': {'s1': ,\n 's2': ,\n 's3': . at 0x7f8f9c99bae8>,\n 's4': . at 0x7f8f9c99bea0>,\n 'timestamp': . at 0x7f8f9c99b730>}},\n {'policies': {'p1': ,\n 'p2': },\n 'variables': {'s1': ,\n 's2': ,\n 's3': . at 0x7f8f9c99bbf8>,\n 's4': . at 0x7f8f9c9a1048>,\n 'timestamp': . at 0x7f8f9c99b840>}},\n {'policies': {'p1': ,\n 'p2': },\n 'variables': {'s1': ,\n 's2': ,\n 's3': . at 0x7f8f9c99bd08>,\n 's4': . at 0x7f8f9c9a1158>,\n 'timestamp': . at 0x7f8f9c99b950>}}],\n 'policy_ops': [ at 0x7f8f9cb39158>],\n 'run_id': 249,\n 'seeds': {'a': ,\n 'b': ,\n 'c': ,\n 'z': },\n 'session_id': 'cadCAD_user=0_249',\n 'sim_config': {'M': {'alpha': 1, 'beta': 2, 'gamma': 3, 'omega': 7},\n 'N': 250,\n 'T': range(0, 5000),\n 'run_id': 249,\n 'simulation_id': 0},\n 'simulation_id': 0,\n 'user_id': 'cadCAD_user'}\n```\n" }, { "path": "documentation/System_Model_Parameter_Sweep.md", "content": "System Model Parameter Sweep\n==\nParametrization of a System Model configuration that produces multiple configurations.\n\n##### Set Parameters\n*Note:* `params` values require up to a maximum of 2 distinct lengths\n```python\nparams = {\n 'alpha': [1],\n 'beta': [2, 5],\n 'gamma': [3, 4],\n 'omega': [7]\n}\n```\nThe parameters above produce 2 Runs per Simulation.\n* Run 1: \n * `alpha = 1`\n * `beta = 2`\n * `gamma = 3`\n * `omega = 7`\n* Run 2:\n * `alpha = 1`\n * `beta = 5`\n * `gamma = 4`\n * `omega = 7`\n\nAll parameters can also be set to include a single parameter each, which will result in a single simulation.\n\n##### Example State Updates\n\nPrevious State:\n`y = 0`\n\n```python\ndef state_update(_params, step, sH, s, _input, **kwargs):\n y = 'state'\n x = s['state'] + _params['alpha'] + _params['gamma']\n return y, x\n```\n* Updated State:\n * Simulation 1: `y = 4 = 0 + 1 + 3` \n * Simulation 2: `y = 5 = 0 + 1 + 4`\n\n##### Example Policy Updates\n```python\n# Internal States per Mechanism\ndef policies(_params, step, sH, s, **kwargs):\n return {'beta': _params['beta'], 'gamma': _params['gamma']}\n```\n* Simulation 1: `{'beta': 2, 'gamma': 3]}` \n* Simulation 2: `{'beta': 5, 'gamma': 4}`\n\n##### Configure Simulation\n```python\nfrom cadCAD.configuration.utils import config_sim\n\ng = {\n 'alpha': [1],\n 'beta': [2, 5],\n 'gamma': [3, 4],\n 'omega': [7]\n}\n\nsim_config = config_sim(\n {\n \"N\": 2,\n \"T\": range(5),\n \"M\": g,\n }\n)\n```\n#### Example\n##### * [System Model Configuration](examples/param_sweep.py)" }, { "path": "documentation/__init__.py", "content": "" }, { "path": "documentation/cadCAD-v0.4.23-Model-Upgrade-Guide.md", "content": "\n \n \n \n \n \n \n \n \n \n\n\n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n
\n Feature\n \n ver. 0.4.23\n \n ver. 0.3.1 (Deprecated)\n
\n
\n Experiments\n
\n 		\n
\nfrom cadCAD.configuration import Experiment\nexp = Experiment()\nexp.append_configs(...) \n
\n 		\n
\nfrom cadCAD.configuration import append_configs\nappend_configs(…)         \n
\n
\n
\n Local Execution Mode\n
\n 		\n
          \nfrom cadCAD.engine import ExecutionMode, ExecutionContext\nexec_mode = ExecutionMode()\nlocal_ctx = ExecutionContext(context=exec_mode.local_mode)         \n
\n 		\n

Multi-Threaded

\n 
\nfrom cadCAD.engine import ExecutionMode, ExecutionContext\nexec_mode = ExecutionMode()\nsingle_ctx = ExecutionContext(context=exec_mode.multi_proc)\n
\n

Single-Threaded

\n
\nfrom cadCAD.engine import ExecutionMode, ExecutionContext\nexec_mode = ExecutionMode()\nmulti_ctx = ExecutionContext(context=exec_mode.single_proc)\n
\n
\n
\n cadCAD Post-Processing Enhancements / Modifications\n
\n 		\n 
\nimport pandas as pd\nfrom tabulate import tabulate\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nimport system_model_A, system_model_B\n\nfrom cadCAD import configs\nexec_mode = ExecutionMode()\n\nlocal_ctx = ExecutionContext(context=exec_mode.local_mode)\nsimulation = Executor(exec_context=local_ctx, configs=configs)\nraw_result, sys_model, _ = simulation.execute()\nresult = pd.DataFrame(raw_result)\nprint(tabulate(result, headers='keys', tablefmt='psql'))\n
\n 		\n 
\n        \nimport pandas as pd\nfrom tabulate import tabulate\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nimport system_model_A, system_model_B\nfrom cadCAD import configs\n\nexec_mode = ExecutionMode()\nmulti_ctx = ExecutionContext(context=exec_mode.multi_proc)\nsimulation = Executor(exec_context=multi_ctx, configs=configs)\ni = 0\nconfig_names = ['sys_model_A', 'sys_model_B']\nfor raw_result, _ in simulation.execute():\nresult = pd.DataFrame(raw_result)\nprint()\nprint(f\"{config_names[i]} Result: System Events DataFrame:\")\nprint(tabulate(result, headers='keys', tablefmt='psql'))\nprint()\ni += 1\n           \n
\n
\n
\n Expandable state and policy update parameter\n
\n 		\n 
\ndef state_update(_params, substep, sH, s, _input, **kwargs):\n…\nreturn 'state_variable_name', new_value\ndef policy(_params, substep, sH, s, **kwargs):\n…\nreturn {'signal_1': value_1, …, 'signal_N': value_N}\n
\n 		\n 
     \ndef state_update(_params, substep, sH, s, _input):\n…\nreturn 'state_variable_name', new_value\ndef policy(_params, substep, sH, s):\n…\nreturn {'signal_1': value_1, …, 'signal_N': value_N}  \n
\n
\n" }, { "path": "documentation/cadCAD-v0.4.27-Model-Upgrade-Guide.md", "content": "\n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n
\n Feature\n \n ver. 0.4.27\n \n ver. 0.4.23\n
\n
\n Experiments & System Model Configurations\n
\n 		\n
\nfrom cadCAD.configuration import Experiment\n\nexp = Experiment()\nexp.append_model(\n    model_id = 'sys_model_1', # System Model\n    initial_state = ..., # System Model\n    partial_state_update_blocks = ..., # System Model\n    policy_ops = ..., # System Model\n    sim_configs = ..., # Simulation Properties\n)\nexp.append_model(\n    model_id = 'sys_model_2', # System Model\n    ...\n)\n\nconfigs = exp.configs\n
\n 		\n
\nfrom cadCAD import configs\nfrom cadCAD.configuration import Experiment\nexp = Experiment()\nexp.append_configs(...)       \n
\n
\n
\n cadCAD Post-Processing Modifications\n
\n 		\n 
\nimport pandas as pd\nfrom tabulate import tabulate\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom simulations.regression_tests.experiments import multi_exp\nfrom simulations.regression_tests.models import config_multi_1, config_multi_2\n\nexec_mode = ExecutionMode()\n\nlocal_proc_ctx = ExecutionContext(context=exec_mode.local_mode)\nrun = Executor(exec_context=local_proc_ctx, configs=multi_exp.configs)\n\nraw_result, tensor_fields, _ = run.execute()\nresult = pd.DataFrame(raw_result)\nprint(tabulate(tensor_fields[0], headers='keys', tablefmt='psql'))\nprint(tabulate(result, headers='keys', tablefmt='psql'))\n
\n 		\n 
\nimport pandas as pd\nfrom tabulate import tabulate\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nimport system_model_A, system_model_B\n\nfrom cadCAD import configs\nexec_mode = ExecutionMode()\n\nlocal_ctx = ExecutionContext(context=exec_mode.local_mode)\nsimulation = Executor(exec_context=local_ctx, configs=configs)\nraw_result, sys_model, _ = simulation.execute()\nresult = pd.DataFrame(raw_result)\nprint(tabulate(result, headers='keys', tablefmt='psql'))           \n
\n
\n" }, { "path": "documentation/cadCAD-v0.4.28-Model-Upgrade-Guide.md", "content": "\n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n
\n Feature\n \n ver. 0.4.28\n \n ver. 0.4.23\n
\n
\n Experiments & System Model Configurations\n
\n 		\n
\nfrom cadCAD.configuration import Experiment\n\nexp = Experiment()\nexp.append_model(\n    model_id = 'sys_model_1', # System Model - OPTIONAL\n    initial_state = ..., # System Model\n    partial_state_update_blocks = ..., # System Model\n    policy_ops = ..., # System Model\n    sim_configs = ..., # Simulation Properties\n)\nexp.append_model(...)\n\nconfigs = exp.configs\n
\n 		\n
\nfrom cadCAD import configs\nfrom cadCAD.configuration import Experiment\nexp = Experiment()\nexp.append_configs(...)       \n
\n
\n
\n cadCAD Post-Processing Modifications\n
\n 		\n 
\nimport pandas as pd\nfrom tabulate import tabulate\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom simulations.regression_tests.experiments import multi_exp\nfrom simulations.regression_tests.models import config_multi_1, config_multi_2\n\nexec_mode = ExecutionMode()\n\nlocal_proc_ctx = ExecutionContext(context=exec_mode.local_mode)\nrun = Executor(exec_context=local_proc_ctx, configs=multi_exp.configs)\n\nraw_result, tensor_fields, _ = run.execute()\nresult = pd.DataFrame(raw_result)\nprint(tabulate(tensor_fields[0], headers='keys', tablefmt='psql'))\nprint(tabulate(result, headers='keys', tablefmt='psql'))\n
\n 		\n 
\nimport pandas as pd\nfrom tabulate import tabulate\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nimport system_model_A, system_model_B\n\nfrom cadCAD import configs\nexec_mode = ExecutionMode()\n\nlocal_ctx = ExecutionContext(context=exec_mode.local_mode)\nsimulation = Executor(exec_context=local_ctx, configs=configs)\nraw_result, sys_model, _ = simulation.execute()\nresult = pd.DataFrame(raw_result)\nprint(tabulate(result, headers='keys', tablefmt='psql'))           \n
\n
\n" }, { "path": "documentation/examples/__init__.py", "content": "from cadCAD.configuration import Experiment\n\nsystem_model_AB_exp = Experiment()\n" }, { "path": "documentation/examples/cadCAD_diagram.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def policy_1(p, s, h, v):\\n\",\n \" return {\\\"pi_1\\\": v[\\\"var_1\\\"]}\\n\",\n \"\\n\",\n \"\\n\",\n \"def policy_2(p, s, h, v):\\n\",\n \" return {\\\"pi_1\\\": v[\\\"var_1\\\"], \\\"pi_2\\\": v[\\\"var_1\\\"] * v[\\\"var_2\\\"]}\\n\",\n \"\\n\",\n \"\\n\",\n \"def suf_1(p, s, h, v, pi):\\n\",\n \" return (\\\"var_1\\\", pi[\\\"pi_1\\\"])\\n\",\n \"\\n\",\n \"\\n\",\n \"def suf_2(p, s, h, v, pi):\\n\",\n \" return (\\\"var_2\\\", pi[\\\"pi_2\\\"])\\n\",\n \"\\n\",\n \"\\n\",\n \"psubs = [\\n\",\n \" {\\n\",\n \" \\\"label\\\": \\\"Test\\\",\\n\",\n \" \\\"policies\\\": {\\\"policy_1\\\": policy_1, \\\"policy_2\\\": policy_2},\\n\",\n \" \\\"variables\\\": {\\\"var_1\\\": suf_1, \\\"var_2\\\": suf_2},\\n\",\n \" }\\n\",\n \"]\\n\",\n \"\\n\",\n \"initial_state = {\\\"var_1\\\": 0, \\\"var_2\\\": 1}\\n\",\n \"\\n\",\n \"params = {\\\"param_1\\\": 0, \\\"param_2\\\": 1}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"sys.path.append('..')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/svg+xml\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"cluster_timestep\\n\",\n \"\\n\",\n \"\\n\",\n \"cluster_psub_0\\n\",\n \"\\n\",\n \"Partial State Update Block\\n\",\n \"\\n\",\n \"\\n\",\n \"cluster_variables_0\\n\",\n \"\\n\",\n \"State\\n\",\n \"\\n\",\n \"\\n\",\n \"cluster_policy_0\\n\",\n \"\\n\",\n \"Policies\\n\",\n \"\\n\",\n \"\\n\",\n \"cluster_suf_0\\n\",\n \"\\n\",\n \"State Update Functions\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"state_0\\n\",\n \"\\n\",\n \"\\n\",\n \"State 1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"policy_policy_1_0\\n\",\n \"\\n\",\n \"policy_1 (policy_1)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"state_0->policy_policy_1_0\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"policy_policy_2_0\\n\",\n \"\\n\",\n \"policy_2 (policy_2)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"state_0->policy_policy_2_0\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"variable_var_2_0\\n\",\n \"\\n\",\n \"\\n\",\n \"var_2 (int)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"variable_var_2_0->policy_policy_2_0\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"variable_var_1_0\\n\",\n \"\\n\",\n \"\\n\",\n \"var_1 (int)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"variable_var_1_0->policy_policy_1_0\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"variable_var_1_0->policy_policy_2_0\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"agg_0\\n\",\n \"\\n\",\n \"Aggregation\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"suf_var_1_0\\n\",\n \"\\n\",\n \"var_1 (suf_1)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"agg_0->suf_var_1_0\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"suf_var_2_0\\n\",\n \"\\n\",\n \"var_2 (suf_2)\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"agg_0->suf_var_2_0\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"policy_policy_1_0->agg_0\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"policy_policy_2_0->agg_0\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"state_1\\n\",\n \"\\n\",\n \"state_1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"suf_var_1_0->state_1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"suf_var_2_0->state_1\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from cadCAD_diagram.config_diagram import diagram\\n\",\n \"\\n\",\n \"diagram(initial_state, params, psubs)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.7.6\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 4\n}\n" }, { "path": "documentation/examples/cadCAD_tools_example.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" # cadCAD Easy Run for the Minimal Prey & Predator model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\\n\",\n \" from .autonotebook import tqdm as notebook_tqdm\\n\"\n ]\n }\n ],\n \"source\": [\n \"import plotly.express as px\\n\",\n \"import numpy as np\\n\",\n \"import sys\\n\",\n \"import seaborn as sns\\n\",\n \"from tqdm.auto import tqdm\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {},\n \"source\": [\n \" ## Minimal Prey and Predator Model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"TIMESTEPS = 100\\n\",\n \"SAMPLES = 10\\n\",\n \"\\n\",\n \"initial_conditions = {\\n\",\n \" 'prey_population': 100,\\n\",\n \" 'predator_population': 15\\n\",\n \"}\\n\",\n \"\\n\",\n \"params = {\\n\",\n \" \\\"prey_birth_rate\\\": [1.0],\\n\",\n \" \\\"predator_birth_rate\\\": [0.01],\\n\",\n \" \\\"predator_death_const\\\": [1.0],\\n\",\n \" \\\"prey_death_const\\\": [0.03],\\n\",\n \" # Precision of the simulation. Lower is more accurate / slower\\n\",\n \" \\\"dt\\\": [0.01, 0.1, 0.05]\\n\",\n \"}\\n\",\n \"\\n\",\n \"\\n\",\n \"def p_predator_births(params, step, sL, s):\\n\",\n \" dt = params['dt']\\n\",\n \" predator_population = s['predator_population']\\n\",\n \" prey_population = s['prey_population']\\n\",\n \" birth_fraction = params['predator_birth_rate'] + \\\\\\n\",\n \" np.random.random() * 0.0002\\n\",\n \" births = birth_fraction * prey_population * predator_population * dt\\n\",\n \" return {'add_to_predator_population': births}\\n\",\n \"\\n\",\n \"\\n\",\n \"def p_prey_births(params, step, sL, s):\\n\",\n \" dt = params['dt']\\n\",\n \" population = s['prey_population']\\n\",\n \" birth_fraction = params['prey_birth_rate'] + np.random.random() * 0.1\\n\",\n \" births = birth_fraction * population * dt\\n\",\n \" return {'add_to_prey_population': births}\\n\",\n \"\\n\",\n \"\\n\",\n \"def p_predator_deaths(params, step, sL, s):\\n\",\n \" dt = params['dt']\\n\",\n \" population = s['predator_population']\\n\",\n \" death_rate = params['predator_death_const'] + np.random.random() * 0.005\\n\",\n \" deaths = death_rate * population * dt\\n\",\n \" return {'add_to_predator_population': -1.0 * deaths}\\n\",\n \"\\n\",\n \"\\n\",\n \"def p_prey_deaths(params, step, sL, s):\\n\",\n \" dt = params['dt']\\n\",\n \" death_rate = params['prey_death_const'] + np.random.random() * 0.1\\n\",\n \" prey_population = s['prey_population']\\n\",\n \" predator_population = s['predator_population']\\n\",\n \" deaths = death_rate * prey_population * predator_population * dt\\n\",\n \" return {'add_to_prey_population': -1.0 * deaths}\\n\",\n \"\\n\",\n \"\\n\",\n \"def s_prey_population(params, step, sL, s, _input):\\n\",\n \" y = 'prey_population'\\n\",\n \" x = s['prey_population'] + _input['add_to_prey_population']\\n\",\n \" return (y, x)\\n\",\n \"\\n\",\n \"\\n\",\n \"def s_predator_population(params, step, sL, s, _input):\\n\",\n \" y = 'predator_population'\\n\",\n \" x = s['predator_population'] + _input['add_to_predator_population']\\n\",\n \" return (y, x)\\n\",\n \"\\n\",\n \"\\n\",\n \"partial_state_update_blocks = [\\n\",\n \" {\\n\",\n \" 'label': 'Lotka-Volterra Equations',\\n\",\n \" 'policies': {\\n\",\n \" 'predator_births': p_predator_births,\\n\",\n \" 'prey_births': p_prey_births,\\n\",\n \" 'predator_deaths': p_predator_deaths,\\n\",\n \" 'prey_deaths': p_prey_deaths,\\n\",\n \" },\\n\",\n \" 'variables': {\\n\",\n \" 'prey_population': s_prey_population,\\n\",\n \" 'predator_population': s_predator_population\\n\",\n \" }\\n\",\n \" },\\n\",\n \" {\\n\",\n \" 'label': 'Do Nothing',\\n\",\n \" 'policies': {\\n\",\n \" \\n\",\n \" },\\n\",\n \" 'variables': {\\n\",\n \" \\n\",\n \" }\\n\",\n \" }\\n\",\n \"]\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import sys\\n\",\n \"sys.path.append(\\\"../..\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \" ___________ ____\\n\",\n \" ________ __ ___/ / ____/ | / __ \\\\\\n\",\n \" / ___/ __` / __ / / / /| | / / / /\\n\",\n \"/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\\n\",\n \"\\\\___/\\\\__,_/\\\\__,_/\\\\____/_/ |_/_____/\\n\",\n \"by cadCAD\\n\",\n \"\\n\",\n \"cadCAD Version: 0.4.28\\n\",\n \"Execution Mode: local_proc\\n\",\n \"Simulation Dimensions:\\n\",\n \"Entire Simulation: (Models, Unique Timesteps, Params, Total Runs, Sub-States) = (1, 100, 5, 30, 2)\\n\",\n \" Simulation 0: (Timesteps, Params, Runs, Sub-States) = (100, 5, 30, 2)\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Initializing configurations: 100%|██████████| 30/30 [00:00<00:00, 669.03it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Execution Method: parallelize_simulations\\n\",\n \"Execution Mode: parallelized\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Flattening results: 100%|██████████| 30/30 [00:00<00:00, 324.56it/s]\"\n ]\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Total execution time: 0.86s\\n\"\n ]\n },\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from cadCAD.tools import easy_run\\n\",\n \"\\n\",\n \"df = easy_run(initial_conditions,\\n\",\n \" params,\\n\",\n \" partial_state_update_blocks,\\n\",\n \" TIMESTEPS,\\n\",\n \" SAMPLES,\\n\",\n \" assign_params=True,\\n\",\n \" drop_substeps=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.plotly.v1+json\": {\n \"config\": {\n \"plotlyServerURL\": \"https://plot.ly\"\n },\n \"data\": [\n {\n \"hovertemplate\": \"color=1
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level, fill_value, limit, tolerance)\\u001b[0m\\n\\u001b[1;32m 5520\\u001b[0m \\u001b[38;5;66;03m# perform the reindex on the axes\\u001b[39;00m\\n\\u001b[0;32m-> 5521\\u001b[0m \\u001b[38;5;28;01mreturn\\u001b[39;00m \\u001b[38;5;28mself\\u001b[39m\\u001b[38;5;241m.\\u001b[39m_reindex_axes(\\n\\u001b[1;32m 5522\\u001b[0m axes, level, limit, tolerance, method, fill_value, copy\\n\\u001b[1;32m 5523\\u001b[0m )\\u001b[38;5;241m.\\u001b[39m__finalize__(\\u001b[38;5;28mself\\u001b[39m, method\\u001b[38;5;241m=\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mreindex\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m)\\n\",\n \"File \\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/pandas/core/generic.py:5544\\u001b[0m, in \\u001b[0;36mNDFrame._reindex_axes\\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\\u001b[0m\\n\\u001b[1;32m 5543\\u001b[0m ax \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;28mself\\u001b[39m\\u001b[38;5;241m.\\u001b[39m_get_axis(a)\\n\\u001b[0;32m-> 5544\\u001b[0m new_index, indexer \\u001b[38;5;241m=\\u001b[39m ax\\u001b[38;5;241m.\\u001b[39mreindex(\\n\\u001b[1;32m 5545\\u001b[0m labels, level\\u001b[38;5;241m=\\u001b[39mlevel, limit\\u001b[38;5;241m=\\u001b[39mlimit, tolerance\\u001b[38;5;241m=\\u001b[39mtolerance, method\\u001b[38;5;241m=\\u001b[39mmethod\\n\\u001b[1;32m 5546\\u001b[0m )\\n\\u001b[1;32m 5548\\u001b[0m axis \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;28mself\\u001b[39m\\u001b[38;5;241m.\\u001b[39m_get_axis_number(a)\\n\",\n \"File \\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/pandas/core/indexes/base.py:4437\\u001b[0m, in \\u001b[0;36mIndex.reindex\\u001b[0;34m(self, target, method, level, limit, tolerance)\\u001b[0m\\n\\u001b[1;32m 4435\\u001b[0m indexer, _ \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;28mself\\u001b[39m\\u001b[38;5;241m.\\u001b[39mget_indexer_non_unique(target)\\n\\u001b[0;32m-> 4437\\u001b[0m target \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;28mself\\u001b[39m\\u001b[38;5;241m.\\u001b[39m_wrap_reindex_result(target, indexer, preserve_names)\\n\\u001b[1;32m 4438\\u001b[0m \\u001b[38;5;28;01mreturn\\u001b[39;00m target, indexer\\n\",\n \"File \\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/pandas/core/indexes/multi.py:2602\\u001b[0m, in \\u001b[0;36mMultiIndex._wrap_reindex_result\\u001b[0;34m(self, target, indexer, preserve_names)\\u001b[0m\\n\\u001b[1;32m 2601\\u001b[0m \\u001b[38;5;28;01mtry\\u001b[39;00m:\\n\\u001b[0;32m-> 2602\\u001b[0m target \\u001b[38;5;241m=\\u001b[39m MultiIndex\\u001b[38;5;241m.\\u001b[39mfrom_tuples(target)\\n\\u001b[1;32m 2603\\u001b[0m \\u001b[38;5;28;01mexcept\\u001b[39;00m \\u001b[38;5;167;01mTypeError\\u001b[39;00m:\\n\\u001b[1;32m 2604\\u001b[0m \\u001b[38;5;66;03m# not all tuples, see test_constructor_dict_multiindex_reindex_flat\\u001b[39;00m\\n\",\n \"File \\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/pandas/core/indexes/multi.py:220\\u001b[0m, in \\u001b[0;36mnames_compat..new_meth\\u001b[0;34m(self_or_cls, *args, **kwargs)\\u001b[0m\\n\\u001b[1;32m 218\\u001b[0m kwargs[\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mnames\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m kwargs\\u001b[38;5;241m.\\u001b[39mpop(\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mname\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m)\\n\\u001b[0;32m--> 220\\u001b[0m \\u001b[38;5;28;01mreturn\\u001b[39;00m meth(self_or_cls, \\u001b[38;5;241m*\\u001b[39margs, \\u001b[38;5;241m*\\u001b[39m\\u001b[38;5;241m*\\u001b[39mkwargs)\\n\",\n \"File \\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/pandas/core/indexes/multi.py:615\\u001b[0m, in \\u001b[0;36mMultiIndex.from_tuples\\u001b[0;34m(cls, tuples, sortorder, names)\\u001b[0m\\n\\u001b[1;32m 613\\u001b[0m tuples \\u001b[38;5;241m=\\u001b[39m np\\u001b[38;5;241m.\\u001b[39masarray(tuples\\u001b[38;5;241m.\\u001b[39m_values)\\n\\u001b[0;32m--> 615\\u001b[0m arrays \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;28mlist\\u001b[39m(lib\\u001b[38;5;241m.\\u001b[39mtuples_to_object_array(tuples)\\u001b[38;5;241m.\\u001b[39mT)\\n\\u001b[1;32m 616\\u001b[0m \\u001b[38;5;28;01melif\\u001b[39;00m \\u001b[38;5;28misinstance\\u001b[39m(tuples, \\u001b[38;5;28mlist\\u001b[39m):\\n\",\n \"File \\u001b[0;32mlib.pyx:2977\\u001b[0m, in \\u001b[0;36mpandas._libs.lib.tuples_to_object_array\\u001b[0;34m()\\u001b[0m\\n\",\n \"\\u001b[0;31mValueError\\u001b[0m: Buffer dtype mismatch, expected 'Python object' but got 'long'\",\n \"\\nThe above exception was the direct cause of the following exception:\\n\",\n \"\\u001b[0;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"\\u001b[0;32m/var/folders/y4/dvc6qr5j30s3frwztjt5shg40000gn/T/ipykernel_61363/3498303338.py\\u001b[0m in \\u001b[0;36m?\\u001b[0;34m()\\u001b[0m\\n\\u001b[1;32m 1\\u001b[0m \\u001b[0;32mfrom\\u001b[0m \\u001b[0mcadCAD_tools\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mprofiling\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mvisualizations\\u001b[0m \\u001b[0;32mimport\\u001b[0m \\u001b[0mvisualize_substep_impact\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 2\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m----> 3\\u001b[0;31m \\u001b[0mvisualize_substep_impact\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mdf\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mrelative\\u001b[0m\\u001b[0;34m=\\u001b[0m\\u001b[0;32mTrue\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\",\n \"\\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/cadCAD_tools/profiling/visualizations.py\\u001b[0m in \\u001b[0;36m?\\u001b[0;34m(df, relative, **kwargs)\\u001b[0m\\n\\u001b[1;32m 61\\u001b[0m \\u001b[0mx_col\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0;34m'substep'\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 62\\u001b[0m \\u001b[0mfig_df\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mx_col\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mfig_df\\u001b[0m\\u001b[0;34m[\\u001b[0m\\u001b[0mx_col\\u001b[0m\\u001b[0;34m]\\u001b[0m \\u001b[0;34m/\\u001b[0m \\u001b[0;36m2\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 63\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 64\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mrelative\\u001b[0m \\u001b[0;32mis\\u001b[0m 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\\u001b[0mSeries\\u001b[0m\\u001b[0;34m/\\u001b[0m\\u001b[0mTimeSeries\\u001b[0m \\u001b[0mwill\\u001b[0m \\u001b[0mbe\\u001b[0m \\u001b[0mconformed\\u001b[0m \\u001b[0mto\\u001b[0m \\u001b[0mthe\\u001b[0m \\u001b[0mDataFrames\\u001b[0m \\u001b[0mindex\\u001b[0m \\u001b[0mto\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4298\\u001b[0m \\u001b[0mensure\\u001b[0m \\u001b[0mhomogeneity\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4299\\u001b[0m \\\"\\\"\\\"\\n\\u001b[0;32m-> 4300\\u001b[0;31m \\u001b[0mvalue\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mrefs\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0m_sanitize_column\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mvalue\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 4301\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 4302\\u001b[0m if (\\n\\u001b[1;32m 4303\\u001b[0m \\u001b[0mkey\\u001b[0m \\u001b[0;32min\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mcolumns\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/pandas/core/frame.py\\u001b[0m in \\u001b[0;36m?\\u001b[0;34m(self, value)\\u001b[0m\\n\\u001b[1;32m 5032\\u001b[0m \\u001b[0;32massert\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0misinstance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mvalue\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mDataFrame\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5033\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mis_dict_like\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mvalue\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5034\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0misinstance\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mvalue\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mSeries\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5035\\u001b[0m \\u001b[0mvalue\\u001b[0m \\u001b[0;34m=\\u001b[0m \\u001b[0mSeries\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mvalue\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m-> 5036\\u001b[0;31m \\u001b[0;32mreturn\\u001b[0m \\u001b[0m_reindex_for_setitem\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mvalue\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mindex\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0m\\u001b[1;32m 5037\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5038\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0mis_list_like\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mvalue\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 5039\\u001b[0m \\u001b[0mcom\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mrequire_length_match\\u001b[0m\\u001b[0;34m(\\u001b[0m\\u001b[0mvalue\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0mself\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mindex\\u001b[0m\\u001b[0;34m)\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/lib/python3.11/site-packages/pandas/core/frame.py\\u001b[0m in \\u001b[0;36m?\\u001b[0;34m(value, index)\\u001b[0m\\n\\u001b[1;32m 12307\\u001b[0m \\u001b[0;32mif\\u001b[0m \\u001b[0;32mnot\\u001b[0m \\u001b[0mvalue\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mindex\\u001b[0m\\u001b[0;34m.\\u001b[0m\\u001b[0mis_unique\\u001b[0m\\u001b[0;34m:\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 12308\\u001b[0m \\u001b[0;31m# duplicate axis\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 12309\\u001b[0m \\u001b[0;32mraise\\u001b[0m \\u001b[0merr\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 12310\\u001b[0m \\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[0;32m> 12311\\u001b[0;31m raise TypeError(\\n\\u001b[0m\\u001b[1;32m 12312\\u001b[0m \\u001b[0;34m\\\"incompatible index of inserted column with frame index\\\"\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\\u001b[1;32m 12313\\u001b[0m ) from err\\n\\u001b[1;32m 12314\\u001b[0m \\u001b[0;32mreturn\\u001b[0m \\u001b[0mreindexed_value\\u001b[0m\\u001b[0;34m,\\u001b[0m \\u001b[0;32mNone\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0;34m\\u001b[0m\\u001b[0m\\n\",\n \"\\u001b[0;31mTypeError\\u001b[0m: incompatible index of inserted column with frame index\"\n ]\n }\n ],\n \"source\": [\n \"from cadCAD_tools.profiling.visualizations import visualize_substep_impact\\n\",\n \"\\n\",\n \"visualize_substep_impact(df, relative=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"c7a8382fce234affa682e5be26089cba\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"HBox(children=(FloatProgress(value=0.0, max=600.0), HTML(value='')))\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n },\n {\n \"data\": {\n \"application/vnd.plotly.v1+json\": {\n \"config\": {\n \"plotlyServerURL\": 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\"white\",\n \"linecolor\": \"white\",\n \"ticks\": \"\"\n },\n \"bgcolor\": \"#E5ECF6\",\n \"radialaxis\": {\n \"gridcolor\": \"white\",\n \"linecolor\": \"white\",\n \"ticks\": \"\"\n }\n },\n \"scene\": {\n \"xaxis\": {\n \"backgroundcolor\": \"#E5ECF6\",\n \"gridcolor\": \"white\",\n \"gridwidth\": 2,\n \"linecolor\": \"white\",\n \"showbackground\": true,\n \"ticks\": \"\",\n \"zerolinecolor\": \"white\"\n },\n \"yaxis\": {\n \"backgroundcolor\": \"#E5ECF6\",\n \"gridcolor\": \"white\",\n \"gridwidth\": 2,\n \"linecolor\": \"white\",\n \"showbackground\": true,\n \"ticks\": \"\",\n \"zerolinecolor\": \"white\"\n },\n \"zaxis\": {\n \"backgroundcolor\": \"#E5ECF6\",\n \"gridcolor\": \"white\",\n \"gridwidth\": 2,\n \"linecolor\": \"white\",\n \"showbackground\": true,\n \"ticks\": \"\",\n \"zerolinecolor\": \"white\"\n }\n },\n \"shapedefaults\": {\n \"line\": {\n \"color\": \"#2a3f5f\"\n }\n },\n \"ternary\": {\n \"aaxis\": {\n \"gridcolor\": \"white\",\n \"linecolor\": \"white\",\n \"ticks\": \"\"\n },\n \"baxis\": {\n \"gridcolor\": \"white\",\n \"linecolor\": \"white\",\n \"ticks\": \"\"\n },\n \"bgcolor\": \"#E5ECF6\",\n \"caxis\": {\n \"gridcolor\": \"white\",\n \"linecolor\": \"white\",\n \"ticks\": \"\"\n }\n },\n \"title\": {\n \"x\": 0.05\n },\n \"xaxis\": {\n \"automargin\": true,\n \"gridcolor\": \"white\",\n \"linecolor\": \"white\",\n \"ticks\": \"\",\n \"title\": {\n \"standoff\": 15\n },\n \"zerolinecolor\": \"white\",\n \"zerolinewidth\": 2\n },\n \"yaxis\": {\n \"automargin\": true,\n \"gridcolor\": \"white\",\n \"linecolor\": \"white\",\n \"ticks\": \"\",\n \"title\": {\n \"standoff\": 15\n },\n \"zerolinecolor\": \"white\",\n \"zerolinewidth\": 2\n }\n }\n },\n \"xaxis\": {\n \"anchor\": \"y\",\n \"domain\": [\n 0,\n 1\n ],\n \"title\": {\n \"text\": \"substep_label\"\n }\n },\n \"yaxis\": {\n \"anchor\": \"x\",\n \"domain\": [\n 0,\n 1\n ],\n \"title\": {\n \"text\": \"psub_time\"\n }\n }\n }\n },\n \"text/html\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"visualize_substep_impact(df, relative=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n" }, { "path": "documentation/examples/example_1.py", "content": "from pprint import pprint\n\nimport pandas as pd\nfrom tabulate import tabulate\n\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom documentation.examples import sys_model_A, sys_model_B, system_model_AB_exp\nfrom documentation.examples.sys_model_A import exp\n\nexec_mode = ExecutionMode()\n\n# Single Process Execution using a Single System Model Configuration:\n# sys_model_A\nlocal_proc_ctx = ExecutionContext(context=exec_mode.local_mode)\nsys_model_A_simulation = Executor(exec_context=local_proc_ctx, configs=exp.configs)\n\nsys_model_A_raw_result, sys_model_A_tensor_field, sessions = sys_model_A_simulation.execute()\nsys_model_A_result = pd.DataFrame(sys_model_A_raw_result)\nprint()\nprint(\"Tensor Field: sys_model_A\")\nprint(tabulate(sys_model_A_tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Result: System Events DataFrame\")\nprint(tabulate(sys_model_A_result, headers='keys', tablefmt='psql'))\nprint()\n\n# # Multiple Processes Execution using Multiple System Model Configurations:\n# # sys_model_A & sys_model_B\nmulti_proc_ctx = ExecutionContext(context=exec_mode.multi_proc)\nsys_model_AB_simulation = Executor(exec_context=multi_proc_ctx, configs=system_model_AB_exp.configs)\n\nsys_model_AB_raw_result, sys_model_AB_tensor_field, sessions = sys_model_AB_simulation.execute()\nprint()\nsys_model_AB_result = pd.DataFrame(sys_model_AB_raw_result)\nprint()\nprint(f\"Tensor Field:\")\nprint(tabulate(sys_model_AB_tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Result: System Events DataFrame:\")\nprint(tabulate(sys_model_AB_result, headers='keys', tablefmt='psql'))\nprint()\n" }, { "path": "documentation/examples/historical_state_access.py", "content": "import pandas as pd\nfrom tabulate import tabulate\n\nfrom cadCAD.configuration.utils import config_sim, access_block\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom cadCAD.configuration import Experiment\n\npolicies, variables = {}, {}\nexclusion_list = ['nonexsistant', 'last_x', '2nd_to_last_x', '3rd_to_last_x', '4th_to_last_x']\n\n# Policies per Mechanism\n\n# state_history, target_field, psu_block_offset, exculsion_list\ndef last_update(_g, substep, sH, s):\n return {\"last_x\": access_block(\n state_history=sH,\n target_field=\"last_x\",\n psu_block_offset=-1,\n exculsion_list=exclusion_list\n )\n }\npolicies[\"last_x\"] = last_update\n\ndef second2last_update(_g, substep, sH, s):\n return {\"2nd_to_last_x\": access_block(sH, \"2nd_to_last_x\", -2, exclusion_list)}\npolicies[\"2nd_to_last_x\"] = second2last_update\n\n\n# Internal States per Mechanism\n\n# WARNING: DO NOT delete elements from sH\ndef add(y, x):\n return lambda _g, substep, sH, s, _input: (y, s[y] + x)\nvariables['x'] = add('x', 1)\n\n# last_partial_state_update_block\ndef nonexsistant(_g, substep, sH, s, _input):\n return 'nonexsistant', access_block(sH, \"nonexsistant\", 0, exclusion_list)\nvariables['nonexsistant'] = nonexsistant\n\n# last_partial_state_update_block\ndef last_x(_g, substep, sH, s, _input):\n return 'last_x', _input[\"last_x\"]\nvariables['last_x'] = last_x\n\n# 2nd to last partial state update block\ndef second_to_last_x(_g, substep, sH, s, _input):\n return '2nd_to_last_x', _input[\"2nd_to_last_x\"]\nvariables['2nd_to_last_x'] = second_to_last_x\n\n# 3rd to last partial state update block\ndef third_to_last_x(_g, substep, sH, s, _input):\n return '3rd_to_last_x', access_block(sH, \"3rd_to_last_x\", -3, exclusion_list)\nvariables['3rd_to_last_x'] = third_to_last_x\n\n# 4th to last partial state update block\ndef fourth_to_last_x(_g, substep, sH, s, _input):\n return '4th_to_last_x', access_block(sH, \"4th_to_last_x\", -4, exclusion_list)\nvariables['4th_to_last_x'] = fourth_to_last_x\n\n\ngenesis_states = {\n 'x': 0,\n 'nonexsistant': [],\n 'last_x': [],\n '2nd_to_last_x': [],\n '3rd_to_last_x': [],\n '4th_to_last_x': []\n}\n\nPSUB = {\n \"policies\": policies,\n \"variables\": variables\n}\n\npsubs = {\n \"PSUB1\": PSUB,\n \"PSUB2\": PSUB,\n \"PSUB3\": PSUB\n}\n\nsim_config = config_sim(\n {\n \"N\": 1,\n \"T\": range(3),\n }\n)\n\nexp = Experiment()\nexp.append_model(\n model_id='hist_state_access',\n sim_configs=sim_config,\n initial_state=genesis_states,\n partial_state_update_blocks=psubs\n)\n\nexec_mode = ExecutionMode()\nlocal_proc_ctx = ExecutionContext(context=exec_mode.local_mode)\nrun = Executor(exec_context=local_proc_ctx, configs=exp.configs)\n\nraw_result, tensor_field, sessions = run.execute()\nresult = pd.DataFrame(raw_result)\ncols = ['run','substep','timestep','x','nonexsistant','last_x','2nd_to_last_x','3rd_to_last_x','4th_to_last_x']\nresult = result[cols]\n\nprint()\nprint(\"Tensor Field:\")\nprint(tabulate(tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Output:\")\nprint(tabulate(result, headers='keys', tablefmt='psql'))\nprint()\n" }, { "path": "documentation/examples/param_sweep.py", "content": "import pprint\n\nimport pandas as pd\nfrom tabulate import tabulate\n\nfrom cadCAD.configuration.utils import env_trigger, var_substep_trigger, config_sim, psub_list\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom cadCAD.configuration import Experiment\n\npp = pprint.PrettyPrinter(indent=4)\n\n\ndef some_function(x):\n return x\n\n\ng: Dict[str, List[int]] = {\n 'alpha': [1],\n 'beta': [2, 5],\n 'gamma': [3, 4],\n 'omega': [some_function]\n}\n\npsu_steps = ['1', '2', '3']\nsystem_substeps = len(psu_steps)\nvar_timestep_trigger = var_substep_trigger([0, system_substeps])\nenv_timestep_trigger = env_trigger(system_substeps)\nenv_process = {}\n\n\n# Policies\ndef gamma(_params, step, sH, s):\n return {'gamma': _params['gamma']}\n\n\ndef omega(_params, step, sH, s):\n return {'omega': _params['omega'](7)}\n\n\n# Internal States\ndef alpha(_params, step, sH, s, _input):\n return 'alpha', _params['alpha']\n\ndef alpha_plus_gamma(_params, step, sH, s, _input):\n return 'alpha_plus_gamma', _params['alpha'] + _params['gamma']\n\n\ndef beta(_params, step, sH, s, _input):\n return 'beta', _params['beta']\n\n\ndef policies(_params, step, sH, s, _input):\n return 'policies', _input\n\n\ndef sweeped(_params, step, sH, s, _input):\n return 'sweeped', {'beta': _params['beta'], 'gamma': _params['gamma']}\n\n\ngenesis_states = {\n 'alpha_plus_gamma': 0,\n 'alpha': 0,\n 'beta': 0,\n 'policies': {},\n 'sweeped': {}\n}\n\nenv_process['sweeped'] = env_timestep_trigger(trigger_field='timestep', trigger_vals=[5], funct_list=[lambda _g, x: _g['beta']])\n\nsim_config = config_sim(\n {\n \"N\": 2,\n \"T\": range(5),\n \"M\": g,\n }\n)\n\npsu_block = {k: {\"policies\": {}, \"variables\": {}} for k in psu_steps}\nfor m in psu_steps:\n psu_block[m]['policies']['gamma'] = gamma\n psu_block[m]['policies']['omega'] = omega\n psu_block[m][\"variables\"]['alpha'] = alpha_plus_gamma\n psu_block[m][\"variables\"]['alpha_plus_gamma'] = alpha\n psu_block[m][\"variables\"]['beta'] = beta\n psu_block[m]['variables']['policies'] = policies\n psu_block[m][\"variables\"]['sweeped'] = var_timestep_trigger(y='sweeped', f=sweeped)\n\npsubs = psub_list(psu_block, psu_steps)\nprint()\npp.pprint(psu_block)\nprint()\n\nexp = Experiment()\nexp.append_model(\n model_id='param_sweep',\n sim_configs=sim_config,\n initial_state=genesis_states,\n env_processes=env_process,\n partial_state_update_blocks=psubs\n)\n\nexec_mode = ExecutionMode()\nlocal_proc_ctx = ExecutionContext(context=exec_mode.local_mode)\nrun = Executor(exec_context=local_proc_ctx, configs=exp.configs)\n\nraw_result, tensor_field, sessions = run.execute()\nresult = pd.DataFrame(raw_result)\nprint()\nprint(\"Tensor Field:\")\nprint(tabulate(tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Output:\")\nprint(tabulate(result, headers='keys', tablefmt='psql'))\nprint()\n" }, { "path": "documentation/examples/policy_aggregation.py", "content": "import pandas as pd\nfrom tabulate import tabulate\n\nfrom cadCAD.configuration.utils import config_sim\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom cadCAD.configuration import Experiment\n\n# Policies per Mechanism\ndef p1m1(_g, step, sH, s):\n return {'policy1': 1}\ndef p2m1(_g, step, sH, s):\n return {'policy2': 2}\n\ndef p1m2(_g, step, sH, s):\n return {'policy1': 2, 'policy2': 2}\ndef p2m2(_g, step, sH, s):\n return {'policy1': 2, 'policy2': 2}\n\ndef p1m3(_g, step, sH, s):\n return {'policy1': 1, 'policy2': 2, 'policy3': 3}\ndef p2m3(_g, step, sH, s):\n return {'policy1': 1, 'policy2': 2, 'policy3': 3}\n\n\n# Internal States per Mechanism\ndef add(y, x):\n return lambda _g, step, sH, s, _input: (y, s[y] + x)\n\ndef policies(_g, step, sH, s, _input):\n y = 'policies'\n x = _input\n return (y, x)\n\n\n# Genesis States\ngenesis_states = {\n 'policies': {},\n 's1': 0\n}\n\nvariables = {\n 's1': add('s1', 1),\n \"policies\": policies\n}\n\npsubs = {\n \"m1\": {\n \"policies\": {\n \"p1\": p1m1,\n \"p2\": p2m1\n },\n \"variables\": variables\n },\n \"m2\": {\n \"policies\": {\n \"p1\": p1m2,\n \"p2\": p2m2\n },\n \"variables\": variables\n },\n \"m3\": {\n \"policies\": {\n \"p1\": p1m3,\n \"p2\": p2m3\n },\n \"variables\": variables\n }\n}\n\n\nsim_config = config_sim(\n {\n \"N\": 1,\n \"T\": range(3),\n }\n)\n\nexp = Experiment()\nexp.append_model(\n model_id='policy_agg',\n sim_configs=sim_config,\n initial_state=genesis_states,\n partial_state_update_blocks=psubs,\n policy_ops=[lambda a, b: a + b, lambda y: y * 2] # Default: lambda a, b: a + b\n)\n\nexec_mode = ExecutionMode()\nlocal_proc_ctx = ExecutionContext(context=exec_mode.local_mode)\nrun = Executor(exec_context=local_proc_ctx, configs=exp.configs)\n\nraw_result, tensor_field, sessions = run.execute()\nresult = pd.DataFrame(raw_result)\n\nprint()\nprint(\"Tensor Field:\")\nprint(tabulate(tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Output:\")\nprint(tabulate(result, headers='keys', tablefmt='psql'))\nprint()\n" }, { "path": "documentation/examples/sys_model_A.py", "content": "import numpy as np\nfrom datetime import timedelta\n\nfrom cadCAD.configuration.utils import bound_norm_random, config_sim, time_step, env_trigger\nfrom cadCAD.configuration import Experiment\nfrom documentation.examples import system_model_AB_exp\n\nseeds = {\n 'z': np.random.RandomState(1),\n 'a': np.random.RandomState(2),\n 'b': np.random.RandomState(3),\n 'c': np.random.RandomState(4)\n}\n\n\n# Policies per Mechanism\ndef p1m1(_g, step, sH, s):\n return {'param1': 1}\ndef p2m1(_g, step, sH, s):\n return {'param1': 1, 'param2': 4}\n\ndef p1m2(_g, step, sH, s):\n return {'param1': 'a', 'param2': 2}\ndef p2m2(_g, step, sH, s):\n return {'param1': 'b', 'param2': 4}\n\ndef p1m3(_g, step, sH, s):\n return {'param1': ['c'], 'param2': np.array([10, 100])}\ndef p2m3(_g, step, sH, s):\n return {'param1': ['d'], 'param2': np.array([20, 200])}\n\n\n# Internal States per Mechanism\ndef s1m1(_g, step, sH, s, _input):\n y = 's1'\n x = s['s1'] + 1\n return (y, x)\ndef s2m1(_g, step, sH, s, _input):\n y = 's2'\n x = _input['param2']\n return (y, x)\n\ndef s1m2(_g, step, sH, s, _input):\n y = 's1'\n x = s['s1'] + 1\n return (y, x)\ndef s2m2(_g, step, sH, s, _input):\n y = 's2'\n x = _input['param2']\n return (y, x)\n\ndef s1m3(_g, step, sH, s, _input):\n y = 's1'\n x = s['s1'] + 1\n return (y, x)\ndef s2m3(_g, step, sH, s, _input):\n y = 's2'\n x = _input['param2']\n return (y, x)\n\ndef policies(_g, step, sH, s, _input):\n y = 'policies'\n x = _input\n return (y, x)\n\n\n# Exogenous States\nproc_one_coef_A = 0.7\nproc_one_coef_B = 1.3\n\ndef es3(_g, step, sH, s, _input):\n y = 's3'\n x = s['s3'] * bound_norm_random(seeds['a'], proc_one_coef_A, proc_one_coef_B)\n return (y, x)\n\ndef es4(_g, step, sH, s, _input):\n y = 's4'\n x = s['s4'] * bound_norm_random(seeds['b'], proc_one_coef_A, proc_one_coef_B)\n return (y, x)\n\ndef update_timestamp(_g, step, sH, s, _input):\n y = 'timestamp'\n return y, time_step(dt_str=s[y], dt_format='%Y-%m-%d %H:%M:%S', _timedelta=timedelta(days=0, minutes=0, seconds=1))\n\n\n# Genesis States\ngenesis_states = {\n 's1': 0.0,\n 's2': 0.0,\n 's3': 1.0,\n 's4': 1.0,\n 'timestamp': '2018-10-01 15:16:24'\n}\n\n\n# Environment Process\ntrigger_timestamps = ['2018-10-01 15:16:25', '2018-10-01 15:16:27', '2018-10-01 15:16:29']\nenv_processes = {\n \"s3\": [lambda _g, x: 5],\n \"s4\": env_trigger(3)(trigger_field='timestamp', trigger_vals=trigger_timestamps, funct_list=[lambda _g, x: 10])\n}\n\n\npsubs = [\n {\n \"policies\": {\n \"b1\": p1m1,\n \"b2\": p2m1\n },\n \"variables\": {\n \"s1\": s1m1,\n \"s2\": s2m1,\n \"s3\": es3,\n \"s4\": es4,\n \"timestamp\": update_timestamp\n }\n },\n {\n \"policies\": {\n \"b1\": p1m2,\n \"b2\": p2m2\n },\n \"variables\": {\n \"s1\": s1m2,\n \"s2\": s2m2,\n # \"s3\": es3p1,\n # \"s4\": es4p2,\n }\n },\n {\n \"policies\": {\n \"b1\": p1m3,\n \"b2\": p2m3\n },\n \"variables\": {\n \"s1\": s1m3,\n \"s2\": s2m3,\n # \"s3\": es3p1,\n # \"s4\": es4p2,\n }\n }\n]\n\n\nsim_config = config_sim(\n {\n \"N\": 2,\n \"T\": range(1),\n }\n)\n\nexp = Experiment()\nexp.append_model(\n model_id='sys_model_A',\n sim_configs=sim_config,\n initial_state=genesis_states,\n env_processes=env_processes,\n partial_state_update_blocks=psubs,\n policy_ops=[lambda a, b: a + b]\n)\n\nsystem_model_AB_exp.append_model(\n model_id='sys_model_A',\n sim_configs=sim_config,\n initial_state=genesis_states,\n env_processes=env_processes,\n partial_state_update_blocks=psubs,\n policy_ops=[lambda a, b: a + b]\n)\n" }, { "path": "documentation/examples/sys_model_AB_exec.py", "content": "import pandas as pd\nfrom tabulate import tabulate\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom documentation.examples import sys_model_A, sys_model_B, system_model_AB_exp\n\nexec_mode = ExecutionMode()\n\n# Multiple Processes Execution using Multiple System Model Configurations:\nlocal_proc_ctx = ExecutionContext(context=exec_mode.local_mode)\nsys_model_AB_simulation = Executor(exec_context=local_proc_ctx, configs=system_model_AB_exp.configs)\n\ni = 0\nconfig_names = ['sys_model_A', 'sys_model_B']\nsys_model_AB_raw_result, sys_model_AB_tensor_field, sessions = sys_model_AB_simulation.execute()\nsys_model_AB_result = pd.DataFrame(sys_model_AB_raw_result)\nprint()\nprint(f\"Tensor Field:\")\nprint(tabulate(sys_model_AB_tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Result: System Events DataFrame:\")\nprint(tabulate(sys_model_AB_result, headers='keys', tablefmt='psql'))\nprint()\n" }, { "path": "documentation/examples/sys_model_A_exec.py", "content": "import pandas as pd\nfrom tabulate import tabulate\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom documentation.examples import sys_model_A\nfrom documentation.examples.sys_model_A import exp\n\nexec_mode = ExecutionMode()\n\n# Single Process Execution using a Single System Model Configuration:\nlocal_proc_ctx = ExecutionContext(context=exec_mode.local_mode)\nsys_model_A_simulation = Executor(exec_context=local_proc_ctx, configs=exp.configs)\n\nsys_model_A_raw_result, sys_model_A_tensor_field, sessions = sys_model_A_simulation.execute()\nsys_model_A_result = pd.DataFrame(sys_model_A_raw_result)\nprint()\nprint(\"Tensor Field: sys_model_A\")\nprint(tabulate(sys_model_A_tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Result: System Events DataFrame\")\nprint(tabulate(sys_model_A_result, headers='keys', tablefmt='psql'))\nprint()\n" }, { "path": "documentation/examples/sys_model_B.py", "content": "import numpy as np\nfrom datetime import timedelta\n\nfrom cadCAD.configuration.utils import bound_norm_random, config_sim, env_trigger, time_step\nfrom cadCAD.configuration import Experiment\nfrom documentation.examples import system_model_AB_exp\n\nseeds = {\n 'z': np.random.RandomState(1),\n 'a': np.random.RandomState(2),\n 'b': np.random.RandomState(3),\n 'c': np.random.RandomState(3)\n}\n\n\n# Policies per Mechanism\ndef p1m1(_g, step, sH, s):\n return {'param1': 1}\ndef p2m1(_g, step, sH, s):\n return {'param2': 4}\n\ndef p1m2(_g, step, sH, s):\n return {'param1': 'a', 'param2': 2}\ndef p2m2(_g, step, sH, s):\n return {'param1': 'b', 'param2': 4}\n\ndef p1m3(_g, step, sH, s):\n return {'param1': ['c'], 'param2': np.array([10, 100])}\ndef p2m3(_g, step, sH, s):\n return {'param1': ['d'], 'param2': np.array([20, 200])}\n\n\n# Internal States per Mechanism\ndef s1m1(_g, step, sH, s, _input):\n y = 's1'\n x = _input['param1']\n return (y, x)\ndef s2m1(_g, step, sH, s, _input):\n y = 's2'\n x = _input['param2']\n return (y, x)\n\ndef s1m2(_g, step, sH, s, _input):\n y = 's1'\n x = _input['param1']\n return (y, x)\ndef s2m2(_g, step, sH, s, _input):\n y = 's2'\n x = _input['param2']\n return (y, x)\n\ndef s1m3(_g, step, sH, s, _input):\n y = 's1'\n x = _input['param1']\n return (y, x)\ndef s2m3(_g, step, sH, s, _input):\n y = 's2'\n x = _input['param2']\n return (y, x)\n\n\n# Exogenous States\nproc_one_coef_A = 0.7\nproc_one_coef_B = 1.3\n\ndef es3(_g, step, sH, s, _input):\n y = 's3'\n x = s['s3'] * bound_norm_random(seeds['a'], proc_one_coef_A, proc_one_coef_B)\n return (y, x)\n\ndef es4(_g, step, sH, s, _input):\n y = 's4'\n x = s['s4'] * bound_norm_random(seeds['b'], proc_one_coef_A, proc_one_coef_B)\n return (y, x)\n\ndef update_timestamp(_g, step, sH, s, _input):\n y = 'timestamp'\n return y, time_step(dt_str=s[y], dt_format='%Y-%m-%d %H:%M:%S', _timedelta=timedelta(days=0, minutes=0, seconds=1))\n\n\n# Genesis States\ngenesis_states = {\n 's1': 0,\n 's2': 0,\n 's3': 1,\n 's4': 1,\n 'timestamp': '2018-10-01 15:16:24'\n}\n\n\n# Environment Process\ntrigger_timestamps = ['2018-10-01 15:16:25', '2018-10-01 15:16:27', '2018-10-01 15:16:29']\nenv_processes = {\n \"s3\": [lambda _g, x: 5],\n \"s4\": env_trigger(3)(trigger_field='timestamp', trigger_vals=trigger_timestamps, funct_list=[lambda _g, x: 10])\n}\n\npsubs = [\n {\n \"policies\": {\n \"b1\": p1m1,\n # \"b2\": p2m1\n },\n \"states\": {\n \"s1\": s1m1,\n # \"s2\": s2m1\n \"s3\": es3,\n \"s4\": es4,\n \"timestep\": update_timestamp\n }\n },\n {\n \"policies\": {\n \"b1\": p1m2,\n # \"b2\": p2m2\n },\n \"states\": {\n \"s1\": s1m2,\n # \"s2\": s2m2\n }\n },\n {\n \"policies\": {\n \"b1\": p1m3,\n \"b2\": p2m3\n },\n \"states\": {\n \"s1\": s1m3,\n \"s2\": s2m3\n }\n }\n]\n\nsim_config = config_sim(\n {\n \"N\": 2,\n \"T\": range(5),\n }\n)\n\nexp = Experiment()\nexp.append_model(\n model_id='sys_model_B',\n sim_configs=sim_config,\n initial_state=genesis_states,\n env_processes=env_processes,\n partial_state_update_blocks=psubs\n)\n\nsystem_model_AB_exp.append_model(\n model_id='sys_model_B',\n sim_configs=sim_config,\n initial_state=genesis_states,\n env_processes=env_processes,\n partial_state_update_blocks=psubs,\n policy_ops=[lambda a, b: a + b]\n)\n" }, { "path": "documentation/examples/sys_model_B_exec.py", "content": "import pandas as pd\nfrom tabulate import tabulate\n# The following imports NEED to be in the exact order\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom documentation.examples import sys_model_B\nfrom documentation.examples.sys_model_B import exp\n\nexec_mode = ExecutionMode()\n\nprint(\"Simulation Execution: Single Configuration\")\nprint()\nlocal_proc_ctx = ExecutionContext(context=exec_mode.local_mode)\nrun = Executor(exec_context=local_proc_ctx, configs=exp.configs)\n\nraw_result, tensor_field, sessions = run.execute()\nresult = pd.DataFrame(raw_result)\nprint()\nprint(\"Tensor Field: sys_model_B\")\nprint(tabulate(tensor_field, headers='keys', tablefmt='psql'))\nprint(\"Output:\")\nprint(tabulate(result, headers='keys', tablefmt='psql'))\nprint()\n" }, { "path": "requirements.txt", "content": "-i https://pypi.org/simple\n\nparameterized>=0.7.4\npytest>=6.0.2\ntabulate>=0.8.7\nwheel>=0.38.1\npandas>=1.1.5\nfuncy>=1.16\ndill>=0.3.4\npathos>=0.2.8\nnumpy>=1.22.0\npytz>=2021.1\nsetuptools>=69.0.2\ngraphviz>=0.20.1\ntqdm>=4.65.0\npytest>=7.4.3" }, { "path": "setup.py", "content": "from setuptools import find_packages, setup\n\nshort_description = (\n \"cadCAD: a differential games based simulation software package for research, validation, and \\\n Computer Aided Design of economic systems\"\n)\n\nlong_description = \"\"\"\ncadCAD (complex adaptive systems computer-aided design) is a python based, unified modeling framework for stochastic\ndynamical systems and differential games for research, validation, and Computer Aided Design of economic systems created\nby BlockScience. It is capable of modeling systems at all levels of abstraction from Agent Based Modeling (ABM) to\nSystem Dynamics (SD), and enabling smooth integration of computational social science simulations with empirical data\nscience workflows.\n\nAn economic system is treated as a state-based model and defined through a set of endogenous and exogenous state\nvariables which are updated through mechanisms and environmental processes, respectively. Behavioral models, which may\nbe deterministic or stochastic, provide the evolution of the system within the action space of the mechanisms.\nMathematical formulations of these economic games treat agent utility as derived from the state rather than direct from\nan action, creating a rich, dynamic modeling framework. Simulations may be run with a range of initial conditions and\nparameters for states, behaviors, mechanisms, and environmental processes to understand and visualize network behavior\nunder various conditions. Support for A/B testing policies, Monte Carlo analysis, and other common numerical methods is\nprovided.\n\"\"\"\n\nname = \"cadCAD\"\nversion = \"0.5.3\"\n\nsetup(\n name=name,\n version=version,\n description=short_description,\n long_description=long_description,\n url=\"https://github.com/cadCAD-org/cadCAD\",\n author=\"cadCAD-org Developers\",\n author_email=\"info@block.science\",\n license=\"LICENSE.txt\",\n packages=find_packages(),\n install_requires=[\n \"pandas\",\n \"funcy\",\n \"dill\",\n \"pathos\",\n \"numpy\",\n \"pytz\",\n \"six\",\n \"tqdm\",\n ],\n python_requires=\">=3.9.0\",\n)\n" }, { "path": "testing/__init__.py", "content": "" }, { "path": "testing/experiments/__init__.py", "content": "from cadCAD.configuration import Experiment\n\nexp_param_sweep = Experiment()\nexp_policy_agg = Experiment()" }, { "path": "testing/generic_test.py", "content": "import unittest\nfrom functools import reduce\nfrom tabulate import tabulate\n\ndef generate_assertions_df(df, expected_results, target_cols, evaluations):\n test_names = []\n for eval_f in evaluations:\n def wrapped_eval(a, b):\n try:\n return eval_f(a, b)\n except KeyError:\n return True\n\n test_name = f\"{eval_f.__name__}_test\"\n test_names.append(test_name)\n df[test_name] = df.apply(\n lambda x: wrapped_eval(\n x.filter(items=target_cols).to_dict(),\n expected_results[(x['subset'], x['run'], x['timestep'], x['substep'])]\n ),\n axis=1\n )\n\n return df, test_names\n\n\ndef make_generic_test(params):\n class TestSequence(unittest.TestCase):\n def generic_test(self, tested_df, expected_reults, test_name):\n erroneous = tested_df[(tested_df[test_name] == False)]\n print(tabulate(tested_df, headers='keys', tablefmt='psql'))\n\n if erroneous.empty is False: # Or Entire df IS NOT erroneous\n for index, row in erroneous.iterrows():\n # expected = expected_reults[(row['simulation'], row['run'], row['timestep'], row['substep'])]\n expected = expected_reults[(row['subset'], row['run'], row['timestep'], row['substep'])]\n unexpected = {f\"invalid_{k}\": expected[k] for k in expected if k in row and expected[k] != row[k]}\n\n for key in unexpected.keys():\n erroneous[key] = None\n erroneous.at[index, key] = unexpected[key]\n # etc.\n\n self.assertTrue(reduce(lambda a, b: a and b, tested_df[test_name]))\n\n @parameterized.expand(params)\n def test_validation(self, name, result_df, expected_results, target_cols, evaluations):\n tested_df, test_names = generate_assertions_df(result_df, expected_results, target_cols, evaluations)\n\n for test_name in test_names:\n self.generic_test(tested_df, expected_results, test_name)\n\n return TestSequence\n" }, { "path": "testing/models/__init__.py", "content": "from cadCAD.configuration import Experiment\n\nexp = Experiment()" }, { "path": "testing/models/param_sweep.py", "content": "import pprint\n\nfrom cadCAD import experiment\nfrom cadCAD.configuration import Experiment\nfrom cadCAD.configuration.utils import env_trigger, var_substep_trigger, config_sim, psub_list\nfrom typing import Dict, List\n\npp = pprint.PrettyPrinter(indent=4)\n\ndef some_function(x):\n return x\n\n# Optional\n# dict must contain lists opf 2 distinct lengths\ng: Dict[str, List[int]] = {\n 'alpha': [1],\n 'beta': [2, some_function],\n 'gamma': [3, 4],\n # 'beta': [1],\n # 'gamma': [4],\n 'omega': [7]\n}\n\npsu_steps = ['m1', 'm2', 'm3']\nsystem_substeps = len(psu_steps)\nvar_timestep_trigger = var_substep_trigger([0, system_substeps])\nenv_timestep_trigger = env_trigger(system_substeps)\nenv_process = {}\n\n\n# ['s1', 's2', 's3', 's4']\n# Policies per Mechanism\ndef gamma(_g, step, sL, s, **kwargs):\n return {'gamma': _g['gamma']}\n\n\ndef omega(_g, step, sL, s, **kwargs):\n return {'omega': _g['omega']}\n\n\n# Internal States per Mechanism\ndef alpha(_g, step, sL, s, _input, **kwargs):\n return 'alpha', _g['alpha']\n\n\ndef beta(_g, step, sL, s, _input, **kwargs):\n return 'beta', _g['beta']\n\n\ndef policies(_g, step, sL, s, _input, **kwargs):\n return 'policies', _input\n\n\ndef sweeped(_g, step, sL, s, _input, **kwargs):\n return 'sweeped', {'beta': _g['beta'], 'gamma': _g['gamma']}\n\npsu_block = {k: {\"policies\": {}, \"states\": {}} for k in psu_steps}\nfor m in psu_steps:\n psu_block[m]['policies']['gamma'] = gamma\n psu_block[m]['policies']['omega'] = omega\n psu_block[m][\"states\"]['alpha'] = alpha\n psu_block[m][\"states\"]['beta'] = beta\n psu_block[m]['states']['policies'] = policies\n psu_block[m][\"states\"]['sweeped'] = var_timestep_trigger(y='sweeped', f=sweeped)\n\n\n# Genesis States\ngenesis_states = {\n 'alpha': 0,\n 'beta': 0,\n 'policies': {},\n 'sweeped': {}\n}\n\n# Environment Process\nenv_process['sweeped'] = env_timestep_trigger(trigger_field='timestep', trigger_vals=[5], funct_list=[lambda _g, x: _g['beta']])\n\n\nsim_config = config_sim(\n {\n \"N\": 2,\n \"T\": range(5),\n \"M\": g, # Optional\n }\n)\n\n# New Convention\npartial_state_update_blocks = psub_list(psu_block, psu_steps)\n\nexp = Experiment()\nexp.append_model(\n sim_configs=sim_config,\n initial_state=genesis_states,\n env_processes=env_process,\n partial_state_update_blocks=partial_state_update_blocks\n)\nexperiment.append_model(\n sim_configs=sim_config,\n initial_state=genesis_states,\n env_processes=env_process,\n partial_state_update_blocks=partial_state_update_blocks\n)\n" }, { "path": "testing/models/policy_aggregation.py", "content": "from cadCAD.configuration import Experiment\nfrom cadCAD.configuration.utils import config_sim\n\n# Policies per Mechanism\ndef p1m1(_g, step, sL, s, **kwargs):\n return {'policy1': 1}\ndef p2m1(_g, step, sL, s, **kwargs):\n return {'policy2': 2}\n\ndef p1m2(_g, step, sL, s, **kwargs):\n return {'policy1': 2, 'policy2': 2}\ndef p2m2(_g, step, sL, s, **kwargs):\n return {'policy1': 2, 'policy2': 2}\n\ndef p1m3(_g, step, sL, s, **kwargs):\n return {'policy1': 1, 'policy2': 2, 'policy3': 3}\ndef p2m3(_g, step, sL, s, **kwargs):\n return {'policy1': 1, 'policy2': 2, 'policy3': 3}\n\n\n# Internal States per Mechanism\ndef add(y, x):\n return lambda _g, step, sH, s, _input, **kwargs: (y, s[y] + x)\n\ndef policies(_g, step, sH, s, _input, **kwargs):\n y = 'policies'\n x = _input\n return (y, x)\n\n\n# Genesis States\ngenesis_states = {\n 'policies': {},\n 's1': 0\n}\n\nvariables = {\n 's1': add('s1', 1),\n \"policies\": policies\n}\n\npartial_state_update_block = {\n \"m1\": {\n \"policies\": {\n \"p1\": p1m1,\n \"p2\": p2m1\n },\n \"variables\": variables\n },\n \"m2\": {\n \"policies\": {\n \"p1\": p1m2,\n \"p2\": p2m2\n },\n \"variables\": variables\n },\n \"m3\": {\n \"policies\": {\n \"p1\": p1m3,\n \"p2\": p2m3\n },\n \"variables\": variables\n }\n}\n\n\nsim_config = config_sim(\n {\n \"N\": 1,\n \"T\": range(3),\n }\n)\n\nexp = Experiment()\nexp.append_model(\n sim_configs=sim_config,\n initial_state=genesis_states,\n partial_state_update_blocks=partial_state_update_block,\n policy_ops=[lambda a, b: a + b, lambda y: y * 2] # Default: lambda a, b: a + b\n)\n" }, { "path": "testing/results_comparison.py", "content": "import unittest\nimport pandas as pd # type: ignore\nfrom tabulate import tabulate # type: ignore\nfrom pandas._testing import assert_frame_equal # type: ignore\nfrom testing.utils import assertEqual\n\ndef compare_results_pytest(result_diff):\n val_error_status_ind = False not in result_diff.val_error_status.tolist()\n type_error_status_ind = False not in result_diff.type_error_status.tolist()\n df_out = None\n erroneous_indexes = None\n select_result_diff = None\n\n if (val_error_status_ind is False) or (type_error_status_ind is False):\n erroneous_indexes = list(result_diff.index[result_diff[\"val_error_status\"] == False])\n select_result_diff = result_diff.iloc[erroneous_indexes]\n df_out = tabulate(select_result_diff, headers='keys', tablefmt='psql')\n\n assertEqual(val_error_status_ind, True, \"Value Error\")\n assertEqual(type_error_status_ind, True, \"Type Error\")\n\ndef compare_results(result_diff):\n class CompareResults(unittest.TestCase):\n def setUp(self):\n result_diff.columns\n self.val_error_status_ind = False not in result_diff.val_error_status.tolist()\n self.type_error_status_ind = False not in result_diff.type_error_status.tolist()\n self.df_out = None\n self.erroneous_indexes = None\n self.select_result_diff = None\n\n if (self.val_error_status_ind is False) or (self.type_error_status_ind is False):\n self.erroneous_indexes = list(result_diff.index[result_diff[\"val_error_status\"] == False])\n self.select_result_diff = result_diff.iloc[self.erroneous_indexes]\n self.df_out = tabulate(self.select_result_diff, headers='keys', tablefmt='psql')\n\n def test_val_error_status(self):\n print(self.df_out)\n self.assertEqual(self.val_error_status_ind, True, \"Value Error\")\n\n def test_type_error_status(self):\n print(self.df_out)\n self.assertEqual(self.type_error_status_ind, True, \"Type Error\")\n\n return CompareResults\n\n\ndef dataframe_difference(df1, df2):\n def discrepancies(row):\n return {\n col: {'values': list(vals), 'type': [type(v) for v in vals]} for col, vals in row.items()\n if vals[0] != vals[1]\n }\n\n def val_error_status(val):\n if type(val) is dict and len(val) != 0:\n return False\n else:\n return True\n\n def type_error_status(val):\n if type(val) is dict and len(val) != 0:\n return list(set([v['type'][0] == v['type'][1] for k, v in val.items()]))[0]\n else:\n return True\n\n df1_cols, df2_cols = list(df1.columns), list(df2.columns)\n if set(df1_cols) == set(df2_cols) and df1.shape == df2.shape:\n df_equal_ind = True\n try:\n assert_frame_equal(df1, df2)\n except:\n df_equal_ind = False\n\n if df_equal_ind is False:\n data = [list(zip(df1[col], df2[col])) for col in df1_cols]\n\n df = pd.DataFrame(data).T\n df.columns = df1_cols\n df['discrepancies'] = df.apply(discrepancies, axis=1)\n\n result_df1 = df1.reset_index()\n discrepancies_df = df[['discrepancies']].reset_index()\n result_diff = result_df1.merge(discrepancies_df, how='left', on='index')\n result_diff['val_error_status'] = result_diff['discrepancies'].apply(val_error_status)\n result_diff['type_error_status'] = result_diff['discrepancies'].apply(type_error_status)\n\n return result_diff\n else:\n df1['discrepancies'] = None\n df1['val_error_status'] = True\n df1['type_error_status'] = True\n return df1\n else:\n df1_row_count, df1_col_count = df1.shape\n df2_row_count, df2_col_count = df2.shape\n raise Exception(f\"\"\"\n DataFrames have mismatched dimensions or columns:\n\n Columns:\n * df1: {df1_cols}\n * df2: {df2_cols} \n Shapes:\n * df1: \n * Row Count: {df1_row_count}\n * Column Count: {df1_col_count}\n * df2:\n * Row Count: {df2_row_count}\n * Column Count: {df2_col_count}\n \"\"\")\n" }, { "path": "testing/test_additional_objs.py", "content": "from typing import Dict, List\nfrom cadCAD.engine import Executor, ExecutionContext, ExecutionMode\nfrom cadCAD.configuration import Experiment\nfrom cadCAD.configuration.utils import env_trigger, var_substep_trigger, config_sim, psub_list\nfrom cadCAD.types import *\nimport pandas as pd # type: ignore\nimport types\nimport inspect\nimport pytest\n\ndef describe_or_return(v: object) -> object:\n \"\"\"\n Thanks @LinuxIsCool!\n \"\"\"\n if isinstance(v, types.FunctionType):\n return f'function: {v.__name__}'\n elif isinstance(v, types.LambdaType) and v.__name__ == '':\n return f'lambda: {inspect.signature(v)}'\n else:\n return v\n\n\ndef select_M_dict(M_dict: Dict[str, object], keys: set) -> Dict[str, object]:\n \"\"\"\n Thanks @LinuxIsCool!\n \"\"\"\n return {k: describe_or_return(v) for k, v in M_dict.items() if k in keys}\n\n\ndef select_config_M_dict(configs: list, i: int, keys: set) -> Dict[str, object]:\n return select_M_dict(configs[i].sim_config['M'], keys)\n\n\ndef drop_substeps(_df):\n first_ind = (_df.substep == 0) & (_df.timestep == 0)\n last_ind = _df.substep == max(_df.substep)\n inds_to_drop = first_ind | last_ind\n return _df.copy().loc[inds_to_drop].drop(columns=['substep'])\n\n\ndef assign_params(_df: pd.DataFrame, configs) -> pd.DataFrame:\n \"\"\"\n Based on `cadCAD-tools` package codebase, by @danlessa\n \"\"\"\n M_dict = configs[0].sim_config['M']\n params_set = set(M_dict.keys())\n selected_params = params_set\n\n # Attribute parameters to each row\n # 1. Assign the parameter set from the first row first, so that\n # columns are created\n first_param_dict = select_config_M_dict(configs, 0, selected_params)\n\n # 2. Attribute parameter on an (simulation, subset, run) basis\n df = _df.assign(**first_param_dict).copy()\n for i, (_, subset_df) in enumerate(df.groupby(['simulation', 'subset', 'run'])):\n df.loc[subset_df.index] = subset_df.assign(**select_config_M_dict(configs,\n i,\n selected_params))\n return df\n\n\n\n\nSWEEP_PARAMS: Dict[str, List] = {\n 'alpha': [1],\n 'beta': [lambda x: 2 * x, lambda x: x],\n 'gamma': [3, 4],\n 'omega': [7]\n }\n\nSINGLE_PARAMS: Dict[str, object] = {\n 'alpha': 1,\n 'beta': lambda x: x,\n 'gamma': 3,\n 'omega': 5\n }\n\n\ndef create_experiment(N_RUNS=2, N_TIMESTEPS=3, params: dict=SWEEP_PARAMS):\n psu_steps = ['m1', 'm2', 'm3']\n system_substeps = len(psu_steps)\n var_timestep_trigger = var_substep_trigger([0, system_substeps])\n env_timestep_trigger = env_trigger(system_substeps)\n env_process = {}\n\n\n # ['s1', 's2', 's3', 's4']\n # Policies per Mechanism\n def gamma(params: Parameters, substep: Substep, history: StateHistory, state: State, **kwargs):\n return {'gamma': params['gamma']}\n\n\n def omega(params: Parameters, substep: Substep, history: StateHistory, state: State, **kwarg):\n return {'omega': params['omega']}\n\n\n # Internal States per Mechanism\n def alpha(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'alpha_var', params['alpha']\n\n\n def beta(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'beta_var', params['beta']\n \n def gamma_var(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'gamma_var', params['gamma']\n \n def omega_var(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'omega_var', params['omega']\n\n\n def policies(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'policies', _input\n\n\n def sweeped(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'sweeped', {'beta': params['beta'], 'gamma': params['gamma']}\n\n psu_block: dict = {k: {\"policies\": {}, \"states\": {}} for k in psu_steps}\n for m in psu_steps:\n psu_block[m]['policies']['gamma'] = gamma\n psu_block[m]['policies']['omega'] = omega\n psu_block[m][\"states\"]['alpha_var'] = alpha\n psu_block[m][\"states\"]['beta_var'] = beta\n psu_block[m][\"states\"]['gamma_var'] = gamma_var\n psu_block[m][\"states\"]['omega_var'] = omega_var\n psu_block[m]['states']['policies'] = policies\n psu_block[m][\"states\"]['sweeped'] = var_timestep_trigger(y='sweeped', f=sweeped)\n\n\n # Genesis States\n genesis_states = {\n 'alpha_var': 0,\n 'beta_var': 0,\n 'gamma_var': 0,\n 'omega_var': 0,\n 'policies': {},\n 'sweeped': {}\n }\n\n # Environment Process\n env_process['sweeped'] = env_timestep_trigger(trigger_field='timestep', trigger_vals=[5], funct_list=[lambda _g, x: _g['beta']])\n\n sim_config = config_sim(\n {\n \"N\": N_RUNS,\n \"T\": range(N_TIMESTEPS),\n \"M\": params, # Optional\n }\n )\n\n # New Convention\n partial_state_update_blocks = psub_list(psu_block, psu_steps)\n\n exp = Experiment()\n exp.append_model(\n sim_configs=sim_config,\n initial_state=genesis_states,\n env_processes=env_process,\n partial_state_update_blocks=partial_state_update_blocks\n )\n return exp\n\n\ndef test_deepcopy_off():\n exp = create_experiment()\n mode = ExecutionMode().local_mode\n exec_context = ExecutionContext(mode, additional_objs={'deepcopy_off': True})\n executor = Executor(exec_context=exec_context, configs=exp.configs)\n (records, tensor_field, _) = executor.execute()\n df = drop_substeps(assign_params(pd.DataFrame(records), exp.configs))\n\n # XXX: parameters should always be of the same type. Else, the test will fail\n first_sim_config = exp.configs[0].sim_config['M']\n\n\n for (i, row) in df.iterrows():\n if row.timestep > 0:\n \n assert row['alpha_var'] == row['alpha']\n assert type(row['alpha_var']) == type(first_sim_config['alpha'])\n assert row['gamma_var'] == row['gamma']\n assert type(row['gamma_var']) == type(first_sim_config['gamma'])\n assert row['omega_var'] == row['omega']\n assert type(row['omega_var']) == type(first_sim_config['omega'])\n\n" }, { "path": "testing/test_arg_count.py", "content": "from typing import Dict, List\nfrom cadCAD.engine import Executor, ExecutionContext, ExecutionMode\nfrom cadCAD.configuration import Experiment\nfrom cadCAD.configuration.utils import env_trigger, var_substep_trigger, config_sim, psub_list\nfrom cadCAD.types import *\nimport pandas as pd # type: ignore\nimport types\nimport inspect\nimport pytest\n\ndef test_sufs():\n\n psubs = [\n {\n 'policies': {\n 'p_A': lambda _1, _2, _3, _4: {}\n },\n 'variables': {\n 'v_A': lambda _1, _2, _3, _4, _5: ('a', 1)\n }\n }\n ]\n\n initial_state = {\n 'v_A': None\n }\n\n params = {'p_A': [1]}\n\n N_t = 5\n N_r = 1\n\n sim_config = config_sim(\n {\n \"N\": N_r,\n \"T\": range(N_t),\n \"M\": params, # Optional\n }\n )\n\n exp = Experiment()\n exp.append_model(\n sim_configs=sim_config,\n initial_state=initial_state,\n partial_state_update_blocks=psubs\n )\n\n mode = ExecutionMode().local_mode\n exec_context = ExecutionContext(mode, additional_objs={'deepcopy_off': True})\n executor = Executor(exec_context=exec_context, configs=exp.configs)\n (records, tensor_field, _) = executor.execute()\n" }, { "path": "testing/test_param_count.py", "content": "from cadCAD.configuration import Experiment\nfrom cadCAD.configuration.utils import config_sim\nfrom cadCAD.engine import Executor, ExecutionContext, ExecutionMode\nimport pytest\n\n\nP_no_lst = {'pA': 1, 'pB': 2, 'pC': 3}\nP_single_lst = {'pA': [1], 'pB': [1], 'pC': [3]}\nP_single_swp = {'pA': [4, 5, 6], 'pB': [1], 'pC': [3]}\nP_all_swp = {'pA': [7, 8, 9], 'pB': [1, 2, 3], 'pC': [1, 2, 3]}\nP_all_but_one_swp = {'pA': [7, 8, 9], 'pB': [1, 2, 3], 'pC': [1]}\nPs = [P_no_lst, P_single_lst, P_single_swp, P_all_swp, P_all_but_one_swp]\n\nCONFIG_SIGNATURES_TO_TEST = [(3, 3, 3, 3, 3),\n (1, 3, 3, 3, 3),\n (3, 1, 3, 3, 3),\n (1, 1, 3, 3, 3),\n (3, 3, 1, 3, 3),\n (1, 3, 1, 3, 3),\n (1, 1, 1, 3, 3)]\n\n\ndef run_experiment(exp: Experiment, mode: str):\n exec_context = ExecutionContext(mode)\n executor = Executor(exec_context=exec_context, configs=exp.configs)\n (records, tensor_field, _) = executor.execute()\n return records\n\n\ndef param_count_test_suf_generator(provided_params):\n def s_test_param_count(params, _2, _3, _4, _5):\n assert params.keys() == provided_params.keys(), 'Params are not matching'\n return ('varA', None)\n return s_test_param_count\n\n\ndef param_count_test_policy_generator(provided_params):\n def p_test_param_count(params, _2, _3, _4):\n assert params.keys() == provided_params.keys(), 'Params are not matching'\n return {'sigA': None}\n return p_test_param_count\n\n\ndef create_experiments(N_simulations=3, N_sweeps=3, N_runs=3, N_timesteps=3, N_substeps=3, params={}) -> Experiment:\n\n INITIAL_STATE = {'varA': None}\n PSUBs = [{'policies': {'sigA': param_count_test_policy_generator(\n params)}, 'variables': {'varA': param_count_test_suf_generator(params)}}] * N_substeps\n\n SIM_CONFIG = config_sim(\n {\n \"N\": N_runs,\n \"T\": range(N_timesteps),\n \"M\": params, # Optional\n }\n )\n\n exp = Experiment()\n for i_sim in range(N_simulations):\n exp.append_model(\n sim_configs=SIM_CONFIG,\n initial_state=INITIAL_STATE,\n partial_state_update_blocks=PSUBs\n )\n return exp\n\n\ndef expected_rows(N_simulations, N_sweeps, N_runs, N_timesteps, N_substeps, P) -> int:\n return N_simulations * N_sweeps * N_runs * (N_timesteps * N_substeps + 1)\n\n\n@pytest.mark.parametrize(\"N_sim,N_sw,N_r,N_t,N_s\", CONFIG_SIGNATURES_TO_TEST)\n@pytest.mark.parametrize(\"P\", Ps)\ndef test_row_count_single(N_sim, N_sw, N_r, N_t, N_s, P):\n args = (N_sim, N_sw, N_r, N_t, N_s, P)\n len(run_experiment(create_experiments(*args), 'single_proc'))\n\n\n@pytest.mark.parametrize(\"N_sim,N_sw,N_r,N_t,N_s\", CONFIG_SIGNATURES_TO_TEST)\n@pytest.mark.parametrize(\"P\", Ps)\ndef test_row_count_local(N_sim, N_sw, N_r, N_t, N_s, P):\n args = (N_sim, N_sw, N_r, N_t, N_s, P)\n len(run_experiment(create_experiments(*args), 'local_proc'))\n" }, { "path": "testing/test_print.py", "content": "from cadCAD.configuration import Experiment\nfrom cadCAD.configuration.utils import config_sim\nfrom cadCAD.engine import Executor, ExecutionContext, ExecutionMode\nimport pytest\n\nP_no_lst = {'pA': 1, 'pB': 2, 'pC': 3}\nP_single_lst = {'pA': [1], 'pB': [1], 'pC': [3]}\nP_single_swp = {'pA': [4, 5, 6], 'pB': [1], 'pC': [3]}\nP_all_swp = {'pA': [7, 8, 9], 'pB': [1, 2, 3], 'pC': [1, 2, 3]}\nP_all_but_one_swp = {'pA': [7, 8, 9], 'pB': [1, 2, 3], 'pC': [1]}\nPs = [P_no_lst, P_single_lst, P_single_swp, P_all_swp, P_all_but_one_swp]\n\nCONFIG_SIGNATURES_TO_TEST = [(3, 3, 3, 3, 3),\n (1, 3, 3, 3, 3),\n (3, 1, 3, 3, 3),\n (1, 1, 3, 3, 3),\n (3, 3, 1, 3, 3),\n (1, 3, 1, 3, 3),\n (1, 1, 1, 3, 3)]\n\n\ndef run_experiment(exp: Experiment, mode: str, supress_print=False):\n exec_context = ExecutionContext(mode)\n executor = Executor(exec_context=exec_context, configs=exp.configs, supress_print=supress_print)\n (records, tensor_field, _) = executor.execute()\n return records\n\n\ndef param_count_test_suf_generator(provided_params):\n def s_test_param_count(params, _2, _3, _4, _5):\n assert params.keys() == provided_params.keys(), 'Params are not matching'\n return ('varA', None)\n return s_test_param_count\n\n\ndef param_count_test_policy_generator(provided_params):\n def p_test_param_count(params, _2, _3, _4):\n assert params.keys() == provided_params.keys(), 'Params are not matching'\n return {'sigA': None}\n return p_test_param_count\n\n\ndef create_experiments(N_simulations=3, N_sweeps=3, N_runs=3, N_timesteps=3, N_substeps=3, params={}) -> Experiment:\n\n INITIAL_STATE = {'varA': None}\n PSUBs = [{'policies': {'sigA': param_count_test_policy_generator(\n params)}, 'variables': {'varA': param_count_test_suf_generator(params)}}] * N_substeps\n\n SIM_CONFIG = config_sim(\n {\n \"N\": N_runs,\n \"T\": range(N_timesteps),\n \"M\": params, # Optional\n }\n )\n\n exp = Experiment()\n for i_sim in range(N_simulations):\n exp.append_model(\n sim_configs=SIM_CONFIG,\n initial_state=INITIAL_STATE,\n partial_state_update_blocks=PSUBs\n )\n return exp\n\n\n\ndef test_print(capfd):\n exp = run_experiment(create_experiments(N_simulations=3, N_sweeps=3, N_runs=3, N_timesteps=3, N_substeps=3, params={'a': 0}), 'single_proc', supress_print=False)\n out, err = capfd.readouterr()\n assert \" ___________ ____\\n ________ __ ___/ / ____/ | / __ \\\\\\n / ___/ __` / __ / / / /| | / / / /\\n/ /__/ /_/ / /_/ / /___/ ___ |/ /_/ /\\n\\\\___/\\\\__,_/\\\\__,_/\\\\____/_/ |_/_____/\\nby cadCAD\" in out\n assert 'Initializing configurations' in err\n\n exp = run_experiment(create_experiments(N_simulations=3, N_sweeps=3, N_runs=3, N_timesteps=3, N_substeps=3, params={'a': 0}), 'single_proc', supress_print=True)\n out, err = capfd.readouterr()\n assert out == ''\n assert err == ''" }, { "path": "testing/test_results_signature.py", "content": "from cadCAD.configuration import Experiment\nfrom cadCAD.configuration.utils import config_sim\nfrom cadCAD.engine import Executor, ExecutionContext, ExecutionMode\nimport pytest\nimport pandas as pd # type: ignore\nfrom typing import Dict, List\n\n# (N_simulations, N_sweeps, N_runs, N_timesteps, N_substeps)\n\n\nCONFIG_SIGNATURES_TO_TEST = [\n (1, 20, 5, 10, 5), (3, 3, 3, 3, 3), (1, 3, 3, 3, 3),\n (3, 1, 3, 3, 3), (1, 1, 3, 3, 3),\n (3, 3, 1, 3, 3), (1, 3, 1, 3, 3), (1, 1, 1, 3, 3)]\n\n\ndef run_experiment(exp: Experiment, mode: str) -> List[Dict]:\n exec_context = ExecutionContext(mode)\n executor = Executor(exec_context=exec_context, configs=exp.configs)\n (records, tensor_field, _) = executor.execute()\n return records\n\n\ndef create_experiments(N_simulations=3, N_sweeps=3, N_runs=3, N_timesteps=3, N_substeps=3) -> Experiment:\n\n INITIAL_STATE = {'varA': None}\n PSUBs = [{'policies': {}, 'variables': {}}] * N_substeps\n params = {'A': [None] * N_sweeps,\n 'B': [None]}\n\n SIM_CONFIG = config_sim(\n {\n \"N\": N_runs,\n \"T\": range(N_timesteps),\n \"M\": params, # Optional\n }\n )\n\n exp = Experiment()\n for i_sim in range(N_simulations):\n exp.append_model(\n sim_configs=SIM_CONFIG,\n initial_state=INITIAL_STATE,\n partial_state_update_blocks=PSUBs\n )\n return exp\n\n\ndef expected_rows(N_simulations, N_sweeps, N_runs, N_timesteps, N_substeps) -> int:\n return N_simulations * N_sweeps * N_runs * (N_timesteps * N_substeps + 1)\n\n\n@pytest.mark.parametrize(\"N_sim,N_sw,N_r,N_t,N_s\", CONFIG_SIGNATURES_TO_TEST)\ndef test_identifiers_value_counts_single(N_sim, N_sw, N_r, N_t, N_s):\n args = (N_sim, N_sw, N_r, N_t, N_s)\n results = run_experiment(create_experiments(*args), 'single_proc')\n df = pd.DataFrame(results).query(\"timestep > 0\")\n assert len(set(df.timestep.value_counts().values)) == 1\n assert len(set(df.subset.value_counts().values)) == 1\n assert len(set(df.run.value_counts().values)) == 1\n\n\n@pytest.mark.parametrize(\"N_sim,N_sw,N_r,N_t,N_s\", CONFIG_SIGNATURES_TO_TEST[:-1])\ndef test_identifiers_value_counts_multi(N_sim, N_sw, N_r, N_t, N_s):\n args = (N_sim, N_sw, N_r, N_t, N_s)\n results = run_experiment(create_experiments(*args), 'multi_proc')\n df = pd.DataFrame(results).query(\"timestep > 0\")\n assert len(set(df.timestep.value_counts().values)) == 1\n assert len(set(df.subset.value_counts().values)) == 1\n assert len(set(df.run.value_counts().values)) == 1\n" }, { "path": "testing/test_row_count.py", "content": "from cadCAD.configuration import Experiment\nfrom cadCAD.configuration.utils import config_sim\nfrom cadCAD.engine import Executor, ExecutionContext, ExecutionMode\nimport pytest\nimport pandas as pd # type: ignore\nfrom typing import Dict, List\n\nCONFIG_SIGNATURES_TO_TEST = [(3, 3, 3, 3, 3), (1, 3, 3, 3, 3),\n (3, 1, 3, 3, 3), (1, 1, 3, 3, 3),\n (3, 3, 1, 3, 3), (1, 3, 1, 3, 3), (1, 1, 1, 3, 3)]\n\ndef run_experiment(exp: Experiment, mode: str) -> List[Dict]:\n exec_context = ExecutionContext(mode)\n executor = Executor(exec_context=exec_context, configs=exp.configs)\n (records, tensor_field, _) = executor.execute()\n return records\n\n\ndef create_experiments(N_simulations=3, N_sweeps=3, N_runs=3, N_timesteps=3, N_substeps=3) -> Experiment:\n\n INITIAL_STATE = {'varA': None}\n PSUBs = [{'policies': {}, 'variables': {}}] * N_substeps\n params = {'A': [None] * N_sweeps,\n 'B': [None]}\n\n SIM_CONFIG = config_sim(\n {\n \"N\": N_runs,\n \"T\": range(N_timesteps),\n \"M\": params, # Optional\n }\n )\n\n exp = Experiment()\n for i_sim in range(N_simulations):\n exp.append_model(\n sim_configs=SIM_CONFIG,\n initial_state=INITIAL_STATE,\n partial_state_update_blocks=PSUBs\n )\n return exp\n\n\ndef expected_rows(N_simulations, N_sweeps, N_runs, N_timesteps, N_substeps) -> int:\n return N_simulations * N_sweeps * N_runs * (N_timesteps * N_substeps + 1)\n\n\n@pytest.mark.parametrize(\"N_sim,N_sw,N_r,N_t,N_s\", CONFIG_SIGNATURES_TO_TEST)\ndef test_row_count_single(N_sim, N_sw, N_r, N_t, N_s):\n args = (N_sim, N_sw, N_r, N_t, N_s)\n results = run_experiment(create_experiments(*args), 'single_proc')\n assert len(results) == expected_rows(*args)\n\n\n@pytest.mark.parametrize(\"N_sim,N_sw,N_r,N_t,N_s\", CONFIG_SIGNATURES_TO_TEST)\ndef test_row_count_multi(N_sim, N_sw, N_r, N_t, N_s):\n args = (N_sim, N_sw, N_r, N_t, N_s)\n\n if N_sim == 1 and N_sw == 1 and N_r == 1:\n with pytest.raises(ValueError) as e_info:\n assert len(run_experiment(create_experiments(*args), 'multi_proc')) == expected_rows(*args)\n else:\n assert len(run_experiment(create_experiments(*args), 'multi_proc')) == expected_rows(*args)\n\n@pytest.mark.parametrize(\"N_sim,N_sw,N_r,N_t,N_s\", CONFIG_SIGNATURES_TO_TEST)\ndef test_row_count_local(N_sim, N_sw, N_r, N_t, N_s):\n args = (N_sim, N_sw, N_r, N_t, N_s)\n assert len(run_experiment(create_experiments(*args), 'local_proc')) == expected_rows(*args)\n" }, { "path": "testing/test_runs.py", "content": "from typing import Dict, List, Optional\nfrom cadCAD.engine import Executor, ExecutionContext, ExecutionMode\nfrom cadCAD.configuration import Experiment\nfrom cadCAD.configuration.utils import env_trigger, var_substep_trigger, config_sim, psub_list\nfrom cadCAD.types import *\nimport pandas as pd # type: ignore\nimport types\nimport inspect\nimport pytest\nfrom pandas import DataFrame\n\n\ndef describe_or_return(v: object) -> object:\n \"\"\"\n Thanks @LinuxIsCool!\n \"\"\"\n if isinstance(v, types.FunctionType):\n return f'function: {v.__name__}'\n elif isinstance(v, types.LambdaType) and v.__name__ == '':\n return f'lambda: {inspect.signature(v)}'\n else:\n return v\n\n\ndef select_M_dict(M_dict: Dict[str, object], keys: set) -> Dict[str, object]:\n \"\"\"\n Thanks @LinuxIsCool!\n \"\"\"\n return {k: describe_or_return(v) for k, v in M_dict.items() if k in keys}\n\n\ndef select_config_M_dict(configs: list, i: int, keys: set) -> Dict[str, object]:\n return select_M_dict(configs[i].sim_config['M'], keys)\n\n\ndef drop_substeps(_df):\n first_ind = (_df.substep == 0) & (_df.timestep == 0)\n last_ind = _df.substep == max(_df.substep)\n inds_to_drop = first_ind | last_ind\n return _df.copy().loc[inds_to_drop].drop(columns=['substep'])\n\n\ndef assign_params(_df: pd.DataFrame, configs) -> pd.DataFrame:\n \"\"\"\n Based on `cadCAD-tools` package codebase, by @danlessa\n \"\"\"\n M_dict = configs[0].sim_config['M']\n params_set = set(M_dict.keys())\n selected_params = params_set\n\n # Attribute parameters to each row\n # 1. Assign the parameter set from the first row first, so that\n # columns are created\n first_param_dict = select_config_M_dict(configs, 0, selected_params)\n\n # 2. Attribute parameter on an (simulation, subset, run) basis\n df = _df.assign(**first_param_dict).copy()\n for i, (_, subset_df) in enumerate(df.groupby(['simulation', 'subset', 'run'])):\n df.loc[subset_df.index] = subset_df.assign(**select_config_M_dict(configs,\n i,\n selected_params))\n return df\n\n\nSWEEP_PARAMS: Dict[str, List] = {\n 'alpha': [1],\n 'beta': [lambda x: 2 * x, lambda x: x, lambda x: x / 2],\n 'gamma': [3, 4, 5],\n 'omega': [7]\n}\n\nSINGLE_PARAMS: Dict[str, object] = {\n 'alpha': 1,\n 'beta': lambda x: x,\n 'gamma': 3,\n 'omega': 5\n}\n\n\ndef create_experiment(N_RUNS=2, N_TIMESTEPS=3, params: dict = SWEEP_PARAMS):\n psu_steps = ['m1', 'm2', 'm3']\n system_substeps = len(psu_steps)\n var_timestep_trigger = var_substep_trigger([0, system_substeps])\n env_timestep_trigger = env_trigger(system_substeps)\n env_process = {}\n\n # ['s1', 's2', 's3', 's4']\n # Policies per Mechanism\n\n def gamma(params: Parameters, substep: Substep, history: StateHistory, state: State, **kwargs):\n return {'gamma': params['gamma']}\n\n def omega(params: Parameters, substep: Substep, history: StateHistory, state: State, **kwarg):\n return {'omega': params['omega']}\n\n # Internal States per Mechanism\n\n def alpha(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'alpha_var', params['alpha']\n\n def beta(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'beta_var', params['beta']\n\n def gamma_var(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'gamma_var', params['gamma']\n\n def omega_var(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'omega_var', params['omega']\n\n def policies(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'policies', _input\n\n def sweeped(params: Parameters, substep: Substep, history: StateHistory, state: State, _input: PolicyOutput, **kwargs):\n return 'sweeped', {'beta': params['beta'], 'gamma': params['gamma']}\n\n psu_block: dict = {k: {\"policies\": {}, \"states\": {}} for k in psu_steps}\n for m in psu_steps:\n psu_block[m]['policies']['gamma'] = gamma\n psu_block[m]['policies']['omega'] = omega\n psu_block[m][\"states\"]['alpha_var'] = alpha\n psu_block[m][\"states\"]['beta_var'] = beta\n psu_block[m][\"states\"]['gamma_var'] = gamma_var\n psu_block[m][\"states\"]['omega_var'] = omega_var\n psu_block[m]['states']['policies'] = policies\n psu_block[m][\"states\"]['sweeped'] = var_timestep_trigger(\n y='sweeped', f=sweeped)\n\n # Genesis States\n genesis_states = {\n 'alpha_var': 0,\n 'beta_var': 0,\n 'gamma_var': 0,\n 'omega_var': 0,\n 'policies': {},\n 'sweeped': {}\n }\n\n # Environment Process\n env_process['sweeped'] = env_timestep_trigger(trigger_field='timestep', trigger_vals=[\n 5], funct_list=[lambda _g, x: _g['beta']])\n\n sim_config = config_sim(\n {\n \"N\": N_RUNS,\n \"T\": range(N_TIMESTEPS),\n \"M\": params, # Optional\n }\n )\n\n # New Convention\n partial_state_update_blocks = psub_list(psu_block, psu_steps)\n\n exp = Experiment()\n exp.append_model(\n sim_configs=sim_config,\n initial_state=genesis_states,\n env_processes=env_process,\n partial_state_update_blocks=partial_state_update_blocks\n )\n return exp\n\n@pytest.mark.parametrize(\"mode\", [\"local_proc\", \"single_proc\", \"multi_proc\"])\ndef test_mc_sweep_experiment(mode):\n experiment_assertions(create_experiment(\n N_RUNS=2, N_TIMESTEPS=2, params=SWEEP_PARAMS), mode)\n\n@pytest.mark.parametrize(\"mode\", [\"local_proc\", \"single_proc\", \"multi_proc\"])\ndef test_unique_sweep_experiment(mode):\n experiment_assertions(create_experiment(\n N_RUNS=1, N_TIMESTEPS=2, params=SWEEP_PARAMS), mode)\n\n@pytest.mark.parametrize(\"mode\", [\"local_proc\", \"single_proc\", \"multi_proc\"])\ndef test_mc_single_experiment(mode):\n experiment_assertions(create_experiment(\n N_RUNS=2, N_TIMESTEPS=2, params=SINGLE_PARAMS), mode)\n\n@pytest.mark.parametrize(\"mode\", [\"local_proc\", \"single_proc\", \"multi_proc\"])\ndef test_unique_single_experiment(mode):\n if mode == \"multi_proc\":\n with pytest.raises(ValueError) as e_info:\n experiment_assertions(create_experiment(\n N_RUNS=1, N_TIMESTEPS=2, params=SINGLE_PARAMS), mode)\n else:\n experiment_assertions(create_experiment(\n N_RUNS=1, N_TIMESTEPS=2, params=SINGLE_PARAMS), mode)\n\n\ndef experiment_assertions(exp: Experiment, mode: Optional[str]=None) -> None:\n if mode == None:\n mode = ExecutionMode().local_mode\n exec_context = ExecutionContext(mode)\n executor = Executor(exec_context=exec_context, configs=exp.configs)\n (records, tensor_field, _) = executor.execute()\n\n df: DataFrame = assign_params(pd.DataFrame(records), exp.configs)\n df = drop_substeps(df)\n\n # XXX: parameters should always be of the same type. Else, the test will fail\n first_sim_config = exp.configs[0].sim_config['M']\n\n required_keys = {'simulation': int,\n 'run': int,\n 'subset': int,\n 'timestep': int}\n\n for (i, row) in df.iterrows():\n if row.timestep > 0:\n\n assert row['alpha_var'] == row['alpha']\n assert type(row['alpha_var']) == type(first_sim_config['alpha'])\n assert row['gamma_var'] == row['gamma']\n assert type(row['gamma_var']) == type(first_sim_config['gamma'])\n assert row['omega_var'] == row['omega']\n assert type(row['omega_var']) == type(first_sim_config['omega'])\n for k, v in required_keys.items():\n assert k in row\n assert type(row[k]) == v\n" }, { "path": "testing/tests/__init__.py", "content": "" }, { "path": "testing/tests/a_b_tests/0_4_23_record_count.json", "content": "{\"record_count\": 74}" }, { "path": "testing/tests/a_b_tests/multi_model_row_count_0_4_23.py", "content": "import json, pathlib, pandas as pd\n\nfrom cadCAD import configs\nfrom cadCAD.configuration import Experiment\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom testing.models import param_sweep, policy_aggregation\n\n\ndel configs[:]\nexp = Experiment()\nsys_model_A_id = \"sys_model_A\"\nexp.append_configs(\n sim_configs=param_sweep.sim_config,\n initial_state=param_sweep.genesis_states,\n env_processes=param_sweep.env_process,\n partial_state_update_blocks=param_sweep.partial_state_update_blocks\n)\nsys_model_B_id = \"sys_model_B\"\nexp.append_configs(\n sim_configs=param_sweep.sim_config,\n initial_state=param_sweep.genesis_states,\n env_processes=param_sweep.env_process,\n partial_state_update_blocks=param_sweep.partial_state_update_blocks\n)\nsys_model_C_id = \"sys_model_C\"\nexp.append_configs(\n sim_configs=policy_aggregation.sim_config,\n initial_state=policy_aggregation.genesis_states,\n partial_state_update_blocks=policy_aggregation.partial_state_update_block,\n policy_ops=[lambda a, b: a + b, lambda y: y * 2] # Default: lambda a, b: a + b\n)\n\nexec_mode = ExecutionMode()\nlocal_mode_ctx = ExecutionContext(context=exec_mode.local_mode)\nsimulation = Executor(exec_context=local_mode_ctx, configs=configs)\nraw_results, _, _ = simulation.execute()\n\nresults_df = pd.DataFrame(raw_results)\nresult_rows = len(results_df.index)\n\nfile_dir = pathlib.Path(__file__).parent.absolute()\nfile_path = f'{file_dir}/0_4_23_record_count.json'\nwith open(file_path, 'w') as json_file:\n record_count = {'record_count': result_rows}\n json.dump(record_count, json_file)\n" }, { "path": "testing/tests/append_mod_test.py", "content": "import unittest\nfrom copy import deepcopy\n\nfrom testing.models import exp\nfrom testing.models.param_sweep import sim_config as sim_config_a\nfrom testing.models.param_sweep import genesis_states as genesis_states_a\nfrom testing.models.param_sweep import env_process as env_process_a\nfrom testing.models.param_sweep import partial_state_update_blocks as psubs_a\n\ndef append_model_id(model_ids, sim_config, genesis_states, env_process, psubs):\n exp_copy = deepcopy(exp)\n for mod_id in model_ids:\n exp_copy.append_model(\n model_id=mod_id,\n sim_configs=sim_config,\n initial_state=genesis_states,\n env_processes=env_process,\n partial_state_update_blocks=psubs,\n policy_ops=[lambda a, b: a + b]\n )\n return exp_copy\n\n\nclass AppendModelTest(unittest.TestCase):\n def test_index_model_ids(self):\n no_id_exp = append_model_id(\n model_ids=[None] * 10,\n sim_config=sim_config_a,\n genesis_states=genesis_states_a,\n env_process=env_process_a,\n psubs=psubs_a\n )\n expected = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n self.assertEqual(no_id_exp.model_ids == expected, True, \"Incorrect Indexing of System Model IDs\")\n\n def test_same_model_ids(self):\n same_id_exp = append_model_id(\n model_ids=['sys_model'] * 10,\n sim_config=sim_config_a,\n genesis_states=genesis_states_a,\n env_process=env_process_a,\n psubs=psubs_a\n )\n expected = [\n 'sys_model', 'sys_model@1', 'sys_model@2', 'sys_model@3', 'sys_model@4', 'sys_model@5',\n 'sys_model@6', 'sys_model@7', 'sys_model@8', 'sys_model@9'\n ]\n self.assertEqual(same_id_exp.model_ids == expected, True, \"Incorrect Duplicate Indexing of System Model IDs\")\n\n def test_different_model_ids(self):\n diff_id_exp = append_model_id(\n model_ids=[f'sys_model_{i}' for i in list(range(10))],\n sim_config=sim_config_a,\n genesis_states=genesis_states_a,\n env_process=env_process_a,\n psubs=psubs_a\n )\n expected = [\n 'sys_model_0', 'sys_model_1', 'sys_model_2', 'sys_model_3', 'sys_model_4', 'sys_model_5',\n 'sys_model_6', 'sys_model_7', 'sys_model_8', 'sys_model_9'\n ]\n self.assertEqual(diff_id_exp.model_ids == expected, True, \"Incorrect Unique System Model IDs\")\n\n def test_mix_model_ids(self):\n mix_exp = append_model_id(\n model_ids=[None, 'sys_model_A', None, 'sys_model_B', 'model@3', 'model@3'],\n sim_config=sim_config_a,\n genesis_states=genesis_states_a,\n env_process=env_process_a,\n psubs=psubs_a\n )\n expected = ['0', 'sys_model_A', '2', 'sys_model_B', 'model@3', 'model@3@5']\n self.assertEqual(mix_exp.model_ids == expected, True, \"Incorrect System Model ID Mix\")\n\n\nif __name__ == '__main__':\n unittest.main()" }, { "path": "testing/tests/import_cadCAD.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"15b9b09a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import cadCAD, os, pathlib, json\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"1b54b99a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# dunder_file = os.path.abspath('')\\n\",\n \"# file_path = f'{dunder_file}/expected_results/cadCAD_memory_address.json'\\n\",\n \"file_path = f'{os.getcwd()}/cadCAD_memory_address.json'\\n\",\n \"with open(file_path, 'w') as json_file:\\n\",\n \" memory_address = {'memory_address': hex(id(cadCAD))}\\n\",\n \" json.dump(memory_address, json_file)\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.11.0\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "testing/tests/test_cadCAD_exp.py", "content": "import pandas as pd\nfrom tabulate import tabulate\nfrom testing.results_comparison import dataframe_difference, compare_results_pytest\nfrom cadCAD import configs\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nimport pytest\n\n\n@pytest.fixture\ndef empty_experiment():\n exec_mode = ExecutionMode()\n exec_ctx = ExecutionContext(context=exec_mode.local_mode)\n run = Executor(exec_context=exec_ctx, configs=configs)\n raw_result, _, _ = run.execute()\n\n result_df = pd.DataFrame(raw_result)\n expected_df = pd.read_pickle(\"expected_results/param_sweep_4.pkl\")\n return dataframe_difference(result_df, expected_df)\n\n\ndef test_experiment(empty_experiment):\n compare_results_pytest(empty_experiment)\n" }, { "path": "testing/tests/test_import_cadCAD_test.py", "content": "import os, subprocess, json\nfrom testing.utils import assertEqual\n\ndef test_jupyter_nbconvert_row_count():\n command = f'jupyter nbconvert --to=notebook --ExecutePreprocessor.enabled=True {os.getcwd()}/testing/tests/import_cadCAD.ipynb'\n process = subprocess.Popen(command.split(), stdout=subprocess.PIPE)\n process.communicate()\n json_path = f'{os.getcwd()}/testing/tests/cadCAD_memory_address.json'\n memory_address = json.load(open(json_path))['memory_address']\n assertEqual(type(memory_address) == str, True, \"cadCAD is not importable by jupyter server\")\n" }, { "path": "testing/tests/test_multi_model_row_count.py", "content": "import json\nimport os\nimport pandas as pd # type: ignore\nfrom cadCAD.configuration import Experiment\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom testing.models import param_sweep, policy_aggregation\nimport pytest\nfrom dataclasses import dataclass\nfrom testing.utils import assertEqual\n\n@dataclass\nclass MultiModelRowCountResults():\n expected_rows: object\n expected_rows_from_api: object\n result_rows: object\n param_sweep_df_rows: object\n policy_agg_df_rows: object\n sys_model_A_id: object\n sys_model_B_id: object\n sys_model_C_id: object\n model_A_rows: object\n model_B_rows: object\n model_C_rows: object\n\n@pytest.fixture\ndef multi_model_row_count():\n exp = Experiment()\n sys_model_A_id = \"sys_model_A\"\n exp.append_model(\n model_id=sys_model_A_id,\n sim_configs=param_sweep.sim_config,\n initial_state=param_sweep.genesis_states,\n env_processes=param_sweep.env_process,\n partial_state_update_blocks=param_sweep.partial_state_update_blocks\n )\n sys_model_B_id = \"sys_model_B\"\n exp.append_model(\n model_id=sys_model_B_id,\n sim_configs=param_sweep.sim_config,\n initial_state=param_sweep.genesis_states,\n env_processes=param_sweep.env_process,\n partial_state_update_blocks=param_sweep.partial_state_update_blocks\n )\n sys_model_C_id = \"sys_model_C\"\n exp.append_model(\n model_id=sys_model_C_id,\n sim_configs=policy_aggregation.sim_config,\n initial_state=policy_aggregation.genesis_states,\n partial_state_update_blocks=policy_aggregation.partial_state_update_block,\n policy_ops=[lambda a, b: a + b, lambda y: y * 2] # Default: lambda a, b: a + b\n )\n\n simulation = 3\n model_A_sweeps = len(param_sweep.sim_config)\n model_B_sweeps = len(param_sweep.sim_config)\n model_C_sweeps = 1\n # total_sweeps = model_A_sweeps + model_B_sweeps\n\n model_A_runs = param_sweep.sim_config[0]['N']\n model_B_runs = param_sweep.sim_config[0]['N']\n model_C_runs = policy_aggregation.sim_config['N']\n # total_runs = model_A_runs + model_B_runs\n\n model_A_timesteps = len(param_sweep.sim_config[0]['T'])\n model_B_timesteps = len(param_sweep.sim_config[0]['T'])\n model_C_timesteps = len(policy_aggregation.sim_config['T'])\n\n model_A_substeps = len(param_sweep.partial_state_update_blocks)\n model_B_substeps = len(param_sweep.partial_state_update_blocks)\n model_C_substeps = len(policy_aggregation.partial_state_update_block)\n # total_substeps = model_A_substeps + model_B_substeps\n\n model_A_init_rows = model_A_runs * model_A_sweeps\n model_B_init_rows = model_B_runs * model_B_sweeps\n model_C_init_rows = model_C_runs * 1\n model_A_rows = model_A_init_rows + (model_A_sweeps * (model_A_runs * model_A_timesteps * model_A_substeps))\n model_B_rows = model_B_init_rows + (model_B_sweeps * (model_B_runs * model_B_timesteps * model_B_substeps))\n model_C_rows = model_C_init_rows + (model_C_sweeps * (model_C_runs * model_C_timesteps * model_C_substeps))\n\n\n exec_mode = ExecutionMode()\n local_mode_ctx = ExecutionContext(context=exec_mode.local_mode)\n simulation = Executor(exec_context=local_mode_ctx, configs=exp.configs)\n raw_results, _, _ = simulation.execute()\n\n results_df = pd.DataFrame(raw_results)\n param_sweep_df = pd.read_pickle(\"expected_results/param_sweep_4.pkl\")\n policy_agg_df = pd.read_pickle(\"expected_results/policy_agg_4.pkl\")\n param_sweep_df_rows = len(param_sweep_df.index)\n policy_agg_df_rows = len(policy_agg_df.index)\n\n expected_rows = param_sweep_df_rows + param_sweep_df_rows + policy_agg_df_rows\n expected_rows_from_api = model_A_rows + model_B_rows + model_C_rows\n result_rows = len(results_df.index)\n return MultiModelRowCountResults(expected_rows,\n expected_rows_from_api,\n result_rows,\n param_sweep_df_rows,\n policy_agg_df_rows,\n sys_model_A_id,\n sys_model_B_id,\n sys_model_C_id,\n model_A_rows,\n model_B_rows,\n model_C_rows)\n\n\ndef test_row_count(multi_model_row_count: MultiModelRowCountResults):\n equal_row_count = multi_model_row_count.expected_rows == multi_model_row_count.expected_rows_from_api == multi_model_row_count.result_rows\n assertEqual(equal_row_count, True, \"Row Count Mismatch between Expected and Multi-Model simulation results\")\n\n \ndef test_row_count_from_api(multi_model_row_count: MultiModelRowCountResults):\n assertEqual(multi_model_row_count.expected_rows == multi_model_row_count.expected_rows_from_api, True, \"API not producing Expected simulation results\")\n\n \ndef test_row_count_from_results(multi_model_row_count: MultiModelRowCountResults):\n assertEqual(multi_model_row_count.expected_rows == multi_model_row_count.result_rows, True, \"Engine not producing Expected simulation results\")\n\n \ndef test_row_count_from_sys_model_A(multi_model_row_count: MultiModelRowCountResults):\n assertEqual(multi_model_row_count.model_A_rows == multi_model_row_count.param_sweep_df_rows, True, f\"{multi_model_row_count.sys_model_A_id}: Row Count Mismatch with Expected results\")\n\n \ndef test_row_count_from_sys_model_B(multi_model_row_count: MultiModelRowCountResults):\n assertEqual(multi_model_row_count.model_B_rows == multi_model_row_count.param_sweep_df_rows, True, f\"{multi_model_row_count.sys_model_B_id}: Row Count Mismatch with Expected results\")\n\n \ndef test_row_count_from_sys_model_C(multi_model_row_count: MultiModelRowCountResults):\n assertEqual(multi_model_row_count.model_C_rows == multi_model_row_count.policy_agg_df_rows, True, f\"{multi_model_row_count.sys_model_C_id}: Row Count Mismatch with Expected results\")\n\n \ndef test_a_b_row_count(multi_model_row_count: MultiModelRowCountResults):\n file_path = f'{os.getcwd()}/testing/tests/a_b_tests/0_4_23_record_count.json'\n record_count_0_4_23 = json.load(open(file_path))['record_count']\n record_count_current = multi_model_row_count.result_rows\n assertEqual(record_count_current > record_count_0_4_23, True, \"Invalid Row Count for current version\")\n\n" }, { "path": "testing/tests/test_param_sweep.py", "content": "from testing.models import param_sweep\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nimport pandas as pd # type: ignore\nfrom testing.results_comparison import dataframe_difference, compare_results_pytest\nimport pytest\n\n\n@pytest.fixture\ndef ParamSweep():\n exec_mode = ExecutionMode()\n exec_ctx = ExecutionContext(context=exec_mode.local_mode)\n run = Executor(exec_context=exec_ctx, configs=param_sweep.exp.configs)\n raw_result, _, _ = run.execute()\n\n result_df = pd.DataFrame(raw_result)\n expected_df = pd.read_pickle(\"expected_results/param_sweep_4.pkl\")\n result_diff = dataframe_difference(result_df, expected_df)\n return result_diff\n\ndef test_pytest_compare_results(ParamSweep):\n compare_results_pytest(ParamSweep)\n" }, { "path": "testing/tests/test_policy_aggregation.py", "content": "import pandas as pd # type: ignore\nfrom testing.models import policy_aggregation as policy_agg\nfrom testing.results_comparison import dataframe_difference\nfrom cadCAD.engine import ExecutionMode, ExecutionContext, Executor\nfrom testing.results_comparison import dataframe_difference, compare_results_pytest\nimport pytest\n\n@pytest.fixture\ndef PolicyAggregation():\n exec_mode = ExecutionMode()\n exec_ctx = ExecutionContext(context=exec_mode.local_mode)\n run = Executor(exec_context=exec_ctx, configs=policy_agg.exp.configs)\n raw_result, _, _ = run.execute()\n\n result_df = pd.DataFrame(raw_result)\n expected_df = pd.read_pickle(\"expected_results/policy_agg_4.pkl\")\n result_diff = dataframe_difference(result_df, expected_df)\n return result_diff\n\ndef test_pytest_compare_results(PolicyAggregation):\n compare_results_pytest(PolicyAggregation)\n\n" }, { "path": "testing/tests/test_run1psub0.py", "content": "import pandas as pd # type: ignore\nfrom testing.results_comparison import dataframe_difference, compare_results_pytest\nimport pytest\n\n\n@pytest.fixture\ndef Run1Psub0():\n expected_df = pd.read_pickle(\"expected_results/param_sweep_psub0_4.pkl\")\n param_sweep_df = pd.read_pickle(\"expected_results/param_sweep_4.pkl\")\n result_df = param_sweep_df[\n (param_sweep_df.index > 0) & (param_sweep_df['subset'] < 1) & (param_sweep_df['run'] == 1)\n ]\n result_diff = dataframe_difference(result_df, expected_df)\n return result_diff\n\ndef test_pytest_compare_results(Run1Psub0):\n compare_results_pytest(Run1Psub0)\n\n" }, { "path": "testing/tests/test_runs_not_zero.py", "content": "from cadCAD.configuration.utils import config_sim\nfrom testing.utils import assertEqual\n\ndef test_runs_not_zero():\n val_error_indicator = False\n try:\n sim_config = config_sim(\n {\n \"N\": 0,\n \"T\": range(5)\n }\n )\n except ValueError:\n val_error_indicator = True\n assertEqual(val_error_indicator, True, \"ValueError raised when runs (N) < 1\")\n" }, { "path": "testing/tests/test_timestep1psub0.py", "content": "import pandas as pd\nfrom testing.results_comparison import dataframe_difference, compare_results_pytest\nimport pytest\n\n\n@pytest.fixture\ndef Timestep1Psub0():\n expected_df = pd.read_pickle(\"expected_results/param_sweep_timestep1_4.pkl\")\n param_sweep_df = pd.read_pickle(\"expected_results/param_sweep_4.pkl\")\n result_df = param_sweep_df[\n (param_sweep_df.index > 0) &\n (param_sweep_df['subset'] < 1) &\n (param_sweep_df['timestep'] < 2) &\n (param_sweep_df['run'] == 1)\n ]\n return dataframe_difference(result_df, expected_df)\n\ndef test_timestep1psub0(Timestep1Psub0):\n compare_results_pytest(Timestep1Psub0)\n" }, { "path": "testing/tools/test_tools.py", "content": "from cadCAD.tools.execution import easy_run\n\n# def test_empty_easy_run():\n# state_variables = {\n# }\n#\n# params = {\n# }\n#\n# psubs = [\n# {\n# 'policies': {},\n# 'variables': {},\n# },\n# ]\n#\n# N_timesteps = 2\n#\n# N_samples = 1\n#\n# results = easy_run(\n# state_variables,\n# params,\n# psubs,\n# N_timesteps,\n# N_samples,\n# )\n# print(results)\n\n\n\ndef test_easy_run():\n state_variables = {\n 'a':0.5,\n }\n\n params = {\n 'c':[1, 2],\n 'd':[1, 2],\n }\n\n def p(params, substep, state_history, previous_state):\n a_delta = 1 - params['c'] * previous_state['a']\n return {'a_delta': a_delta}\n\n def s(params, substep, state_history, previous_state, policy_input):\n return 'a', previous_state['a'] + policy_input['a_delta']\n\n psubs = [\n {\n 'policies': {'p': p},\n 'variables': {'a': s},\n },\n ]\n\n N_timesteps = 2\n\n N_samples = 1\n\n results = easy_run(\n state_variables,\n params,\n psubs,\n N_timesteps,\n N_samples,\n )\n" }, { "path": "testing/utils.py", "content": "#\n# def record_generator(row, cols):\n# return {col: row[col] for col in cols}\n\ndef gen_metric_row(row, cols):\n return ((row['run'], row['timestep'], row['substep']), {col: row[col] for col in cols})\n\n# def gen_metric_row(row):\n# return ((row['run'], row['timestep'], row['substep']), {'s1': row['s1'], 'policies': row['policies']})\n\n# def gen_metric_row(row):\n# return {\n# 'run': row['run'],\n# 'timestep': row['timestep'],\n# 'substep': row['substep'],\n# 's1': row['s1'],\n# 'policies': row['policies']\n# }\n\ndef gen_metric_dict(df, cols):\n return dict([gen_metric_row(row, cols) for index, row in df.iterrows()])\n\n\n\ndef assertEqual(_1, _2, _3=None):\n if _3 == None:\n assert _1 == _2\n else:\n assert _1 == _2, _3" } ]