| Name | cml-pam JSON |
| Version |
0.3.2
JSON |
| download |
| home_page | None |
| Summary | The Population activity Modeller (PAM) is a python API for activity sequence modelling. |
| upload_time | 2024-04-05 21:28:18 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | MIT |
| keywords |
agent-based modelling
sequence model
matsim
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<!--- the "--8<--" html comments define what part of the README to add to the index page of the documentation -->
<!--- --8<-- [start:docs] -->
# Population Activity Modeller
[](https://arup-group.github.io/pam)
[](https://medium.com/arupcitymodelling/pandemic-activity-modifier-intro-3d2dccbc716e)
PAM is a python library for population **activity sequence** modelling. Example use cases:
- **Read** an existing population then **write** to a new format.
- **Modify** an existing population, for example to model activity locations.
- **Create** your own activity-based model.
PAM supports common travel and activity formats, including MATSim.
## Activity Sequences?
Population **activity sequences** (sometimes called **activity plans**) are used to model the activities (where and when people are at home, work, education and so on) and associated travel of a population:
Activity sequences are used by transport planners to model travel demand, but can also be used in other domains, such as for virus transmission or energy use modelling.
## Brief History
PAM was originally built and shared to rapidly modify existing activity models to respond to pandemic lock-down scenarios.
This functionality used a **read-modify-write** pattern. Where modifications are made by applying **policies**. Example policies might be (a) infected persons quarantine at home, (b) only critical workers travel to work, and (c) everyone shops locally.
## Features
### Activity Modelling
In addition to the original **read-modify-write** pattern and functionality, PAM has modules for:
- location modelling
- discretionary activity modelling
- mode choice modelling
- facility sampling
- vehicle ownership
More generally the core PAM data structure and modules can be used as a library to support your own use cases, including building your own activity-based model.
### MATSim
PAM fully supports the [MATSim](https://www.matsim.org/) population/plans format. This includes vehicles, unselected plans, leg routes and leg attributes. A core use case of PAM is to ***read-modify-write*** *experienced plans* from MATSim. This can allow new MATSim scenarios to be *"warm started"* from existing scenarios, significantly reducing MATSim compute time.
<!--- --8<-- [end:docs] -->
## Documentation
For more detailed instructions, see our [documentation](https://arup-group.github.io/pam/latest).
## Installation
To install PAM, we recommend using the [mamba](https://mamba.readthedocs.io/en/latest/index.html) package manager:
### As a user
<!--- --8<-- [start:docs-install-user] -->
``` shell
mamba create -n pam -c conda-forge -c city-modelling-lab cml-pam
mamba activate pam
```
<!--- --8<-- [end:docs-install-user] -->
### As a developer
<!--- --8<-- [start:docs-install-dev] -->
``` shell
git clone git@github.com:arup-group/pam.git
cd pam
mamba create -n pam -c conda-forge -c city-modelling-lab --file requirements/base.txt --file requirements/dev.txt
mamba activate pam
pip install --no-deps -e .
```
<!--- --8<-- [end:docs-install-dev] -->
### Installing with pip
Installing directly with pip as a user (`pip install cml-pam`) or as a developer (`pip install -e '.[dev]'`) is also possible, but you will need the `libgdal` & `libspatialindex` geospatial non-python libraries pre-installed.
For more detailed instructions, see our [documentation](https://arup-group.github.io/pam/latest/installation/).
## Contributing
There are many ways to make both technical and non-technical contributions to PAM.
Before making contributions to the PAM source code, see our contribution guidelines and follow the [development install instructions](#as-a-developer).
If you are using `pip` to install PAM instead of the recommended `mamba`, you can install the optional test and documentation libraries using the `dev` option, i.e., `pip install -e '.[dev]'`
If you plan to make changes to the code then please make regular use of the following tools to verify the codebase while you work:
- `pre-commit`: run `pre-commit install` in your command line to load inbuilt checks that will run every time you commit your changes.
The checks are: 1. check no large files have been staged, 2. lint python files for major errors, 3. format python files to conform with the [pep8 standard](https://peps.python.org/pep-0008/).
You can also run these checks yourself at any time to ensure staged changes are clean by simple calling `pre-commit`.
- `pytest` - run the unit test suite, check test coverage, and test that the example notebooks successfully run.
- `pytest -p memray -m "high_mem" --no-cov` (not available on Windows) - after installing memray (`mamba install memray pytest-memray`), test that memory and time performance does not exceed benchmarks.
For more information, see our [documentation](https://arup-group.github.io/pam/latest/contributing/coding/).
## Building the documentation
If you are unable to access the online documentation, you can build the documentation locally.
First, [install a development environment of PAM](https://arup-group.github.io/pam/latest/contributing/coding/), then deploy the documentation using [mike](https://github.com/jimporter/mike):
```
mike deploy 0.2
mike serve
```
Then you can view the documentation in a browser at <http://localhost:8000/>.
## Credits
This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [arup-group/cookiecutter-pypackage](https://github.com/arup-group/cookiecutter-pypackage) project template.
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"description": "<!--- the \"--8<--\" html comments define what part of the README to add to the index page of the documentation -->\n<!--- --8<-- [start:docs] -->\n\n\n\n# Population Activity Modeller\n\n\n[](https://arup-group.github.io/pam)\n[](https://medium.com/arupcitymodelling/pandemic-activity-modifier-intro-3d2dccbc716e)\n\nPAM is a python library for population **activity sequence** modelling. Example use cases:\n\n- **Read** an existing population then **write** to a new format.\n- **Modify** an existing population, for example to model activity locations.\n- **Create** your own activity-based model.\n\nPAM supports common travel and activity formats, including MATSim.\n\n## Activity Sequences?\n\nPopulation **activity sequences** (sometimes called **activity plans**) are used to model the activities (where and when people are at home, work, education and so on) and associated travel of a population:\n\n \n\nActivity sequences are used by transport planners to model travel demand, but can also be used in other domains, such as for virus transmission or energy use modelling.\n\n## Brief History\n\nPAM was originally built and shared to rapidly modify existing activity models to respond to pandemic lock-down scenarios.\n\n \n\nThis functionality used a **read-modify-write** pattern. Where modifications are made by applying **policies**. Example policies might be (a) infected persons quarantine at home, (b) only critical workers travel to work, and (c) everyone shops locally.\n\n\n\n## Features\n\n### Activity Modelling\n\nIn addition to the original **read-modify-write** pattern and functionality, PAM has modules for:\n\n- location modelling\n- discretionary activity modelling\n- mode choice modelling\n- facility sampling\n- vehicle ownership\n\nMore generally the core PAM data structure and modules can be used as a library to support your own use cases, including building your own activity-based model.\n\n### MATSim\n\nPAM fully supports the [MATSim](https://www.matsim.org/) population/plans format. This includes vehicles, unselected plans, leg routes and leg attributes. A core use case of PAM is to ***read-modify-write*** *experienced plans* from MATSim. This can allow new MATSim scenarios to be *\"warm started\"* from existing scenarios, significantly reducing MATSim compute time.\n\n<!--- --8<-- [end:docs] -->\n## Documentation\n\nFor more detailed instructions, see our [documentation](https://arup-group.github.io/pam/latest).\n\n## Installation\n\nTo install PAM, we recommend using the [mamba](https://mamba.readthedocs.io/en/latest/index.html) package manager:\n\n### As a user\n<!--- --8<-- [start:docs-install-user] -->\n``` shell\nmamba create -n pam -c conda-forge -c city-modelling-lab cml-pam\nmamba activate pam\n```\n<!--- --8<-- [end:docs-install-user] -->\n### As a developer\n<!--- --8<-- [start:docs-install-dev] -->\n``` shell\ngit clone git@github.com:arup-group/pam.git\ncd pam\nmamba create -n pam -c conda-forge -c city-modelling-lab --file requirements/base.txt --file requirements/dev.txt\nmamba activate pam\npip install --no-deps -e .\n```\n<!--- --8<-- [end:docs-install-dev] -->\n\n### Installing with pip\n\nInstalling directly with pip as a user (`pip install cml-pam`) or as a developer (`pip install -e '.[dev]'`) is also possible, but you will need the `libgdal` & `libspatialindex` geospatial non-python libraries pre-installed.\n\nFor more detailed instructions, see our [documentation](https://arup-group.github.io/pam/latest/installation/).\n\n## Contributing\n\nThere are many ways to make both technical and non-technical contributions to PAM.\nBefore making contributions to the PAM source code, see our contribution guidelines and follow the [development install instructions](#as-a-developer).\n\nIf you are using `pip` to install PAM instead of the recommended `mamba`, you can install the optional test and documentation libraries using the `dev` option, i.e., `pip install -e '.[dev]'`\n\nIf you plan to make changes to the code then please make regular use of the following tools to verify the codebase while you work:\n\n- `pre-commit`: run `pre-commit install` in your command line to load inbuilt checks that will run every time you commit your changes.\nThe checks are: 1. check no large files have been staged, 2. lint python files for major errors, 3. format python files to conform with the [pep8 standard](https://peps.python.org/pep-0008/).\nYou can also run these checks yourself at any time to ensure staged changes are clean by simple calling `pre-commit`.\n- `pytest` - run the unit test suite, check test coverage, and test that the example notebooks successfully run.\n- `pytest -p memray -m \"high_mem\" --no-cov` (not available on Windows) - after installing memray (`mamba install memray pytest-memray`), test that memory and time performance does not exceed benchmarks.\n\nFor more information, see our [documentation](https://arup-group.github.io/pam/latest/contributing/coding/).\n\n## Building the documentation\n\nIf you are unable to access the online documentation, you can build the documentation locally.\nFirst, [install a development environment of PAM](https://arup-group.github.io/pam/latest/contributing/coding/), then deploy the documentation using [mike](https://github.com/jimporter/mike):\n\n```\nmike deploy 0.2\nmike serve\n```\n\nThen you can view the documentation in a browser at <http://localhost:8000/>.\n\n## Credits\n\nThis package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [arup-group/cookiecutter-pypackage](https://github.com/arup-group/cookiecutter-pypackage) project template.\n",
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