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.gitignore
vendored
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.gitignore
vendored
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[Ss]cripts
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[Ss]cripts
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pyvenv.cfg
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pyvenv.cfg
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.venv
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.venv
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pip-selfcheck.json
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pip-selfcheck.json
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
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# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
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# User-specific stuff
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.idea/**/workspace.xml
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.idea/**/tasks.xml
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.idea/**/usage.statistics.xml
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.idea/**/dictionaries
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.idea/**/shelf
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# AWS User-specific
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.idea/**/aws.xml
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# Generated files
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.idea/**/contentModel.xml
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# Sensitive or high-churn files
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.idea/**/dataSources/
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.idea/**/dataSources.ids
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.idea/**/dataSources.local.xml
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.idea/**/sqlDataSources.xml
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.idea/**/dynamic.xml
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.idea/**/uiDesigner.xml
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.idea/**/dbnavigator.xml
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# Gradle
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.idea/**/gradle.xml
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.idea/**/libraries
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# Gradle and Maven with auto-import
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# When using Gradle or Maven with auto-import, you should exclude module files,
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# since they will be recreated, and may cause churn. Uncomment if using
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# auto-import.
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# .idea/artifacts
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# .idea/compiler.xml
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# .idea/jarRepositories.xml
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# .idea/modules.xml
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# .idea/*.iml
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# .idea/modules
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# *.iml
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# *.ipr
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# CMake
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cmake-build-*/
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# Mongo Explorer plugin
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.idea/**/mongoSettings.xml
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# File-based project format
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*.iws
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# IntelliJ
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out/
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# mpeltonen/sbt-idea plugin
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.idea_modules/
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# JIRA plugin
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atlassian-ide-plugin.xml
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# Cursive Clojure plugin
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.idea/replstate.xml
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# SonarLint plugin
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.idea/sonarlint/
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# Crashlytics plugin (for Android Studio and IntelliJ)
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com_crashlytics_export_strings.xml
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crashlytics.properties
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crashlytics-build.properties
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fabric.properties
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# Editor-based Rest Client
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.idea/httpRequests
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# Android studio 3.1+ serialized cache file
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.idea/caches/build_file_checksums.ser
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@ -0,0 +1,8 @@
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from mesa import Agent, Model
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class ForkliftAgent(Agent):
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def __init__(self, unique_id, model):
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super().__init__(unique_id, model)
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print("Created forklift Agent with ID: {}".format(unique_id))
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from mesa.space import MultiGrid
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from mesa.space import MultiGrid
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from mesa.datacollection import DataCollector
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from mesa.datacollection import DataCollector
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from ForkliftAgent import ForkliftAgent
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def compute_gini(model):
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agent_wealths = [agent.wealth for agent in model.schedule.agents]
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x = sorted(agent_wealths)
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N = model.num_agents
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B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))
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return (1 + (1 / N) - 2 * B)
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class MoneyModel(Model):
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class ForkliftModel(Model):
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"""A model with some number of agents."""
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def __init__(self, N, width, height):
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def __init__(self, width, height):
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self.num_agents = N
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self.num_agents = 1
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self.running = True
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self.running = True
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self.grid = MultiGrid(height, width, True)
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self.grid = MultiGrid(height, width, True)
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self.schedule = RandomActivation(self)
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self.schedule = RandomActivation(self)
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self.datacollector = DataCollector(
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# self.datacollector = DataCollector(
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model_reporters={"Gini": compute_gini},
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# model_reporters={"Gini": compute_gini},
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agent_reporters={"Wealth": lambda a: a.wealth}
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# agent_reporters={"Wealth": lambda a: a.wealth}
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)
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# )
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# Create agents
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# Create agents
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for i in range(self.num_agents):
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for i in range(self.num_agents):
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a = MoneyAgent(i, self)
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a = ForkliftAgent(1, self)
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self.schedule.add(a)
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self.schedule.add(a)
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# Add the agent to a random grid cell
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# Add the agent to a random grid cell
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x = random.randrange(self.grid.width)
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x = random.randrange(self.grid.width)
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self.grid.place_agent(a, (x, y))
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self.grid.place_agent(a, (x, y))
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def step(self):
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def step(self):
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self.datacollector.collect(self)
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# self.datacollector.collect(self)
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self.schedule.step()
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self.schedule.step()
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def run_model(self, n):
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for i in range(n):
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self.step()
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class MoneyAgent(Agent):
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""" An agent with fixed initial wealth."""
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def __init__(self, unique_id, model):
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super().__init__(unique_id, model)
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self.wealth = 1
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def move(self):
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possible_steps = self.model.grid.get_neighborhood(
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self.pos, moore=True, include_center=False
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)
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new_position = random.choice(possible_steps)
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self.model.grid.move_agent(self, new_position)
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def give_money(self):
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cellmates = self.model.grid.get_cell_list_contents([self.pos])
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if len(cellmates) > 1:
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other = random.choice(cellmates)
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other.wealth += 1
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self.wealth -= 1
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def step(self):
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self.move()
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if self.wealth > 0:
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self.give_money()
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# instalujemy ze środowiskiem venv
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# następnie wykonujemy komendy:
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```
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pip install mesa
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pip install -r https://raw.githubusercontent.com/projectmesa/mesa/main/examples/boltzmann_wealth_model/requirements.txt
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```
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main.py
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from mesa.visualization.modules import CanvasGrid
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from mesa.visualization.ModularVisualization import ModularServer
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from ForkliftModel import ForkliftModel
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def agent_portrayal(agent):
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portrayal = {"Shape": "image.png", "scale": 1.0, "Layer": 0}
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return portrayal
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grid = CanvasGrid(agent_portrayal, 10, 10, 512, 512)
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server = ModularServer(ForkliftModel,
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[grid],
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"Automatyczny Wózek Widłowy",
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{"width": 10, "height": 10})
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server.port = 8888
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server.launch()
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@ -0,0 +1,4 @@
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jupyter
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matplotlib
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mesa
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numpy
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