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Copy file name to clipboardExpand all lines: CONTRIBUTING.md
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Thank you for taking the time to contribute to **mesa-frames**! Since the project is still in its early stages, we warmly welcome contributions that will help shape its development. 🎉
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For a more general and comprehensive guide, please refer to [mesa's main contribution guidelines](https://github.com/projectmesa/mesa/blob/main/CONTRIBUTING.md). 📜
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For a more general and comprehensive guide, please refer to [mesa's main contribution guidelines](https://github.com/mesa/mesa/blob/main/CONTRIBUTING.md). 📜
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## Project Roadmap 🗺️
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Before contributing, we recommend reviewing our [roadmap](https://projectmesa.github.io/mesa-frames/roadmap/) file to understand the project's current priorities, upcoming features, and long-term vision. This will help ensure your contributions align with the project's direction.
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Before contributing, we recommend reviewing our [roadmap](https://mesa.github.io/mesa-frames/roadmap/) file to understand the project's current priorities, upcoming features, and long-term vision. This will help ensure your contributions align with the project's direction.
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## How to Contribute 💡
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No manual setup needed in these environments!
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For more details on runtime type checking, see the [Development Guidelines](https://projectmesa.github.io/mesa-frames/development/).
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For more details on runtime type checking, see the [Development Guidelines](https://mesa.github.io/mesa-frames/development/).
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# mesa-frames 🚀
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mesa-frames is an extension of the [mesa](https://github.com/projectmesa/mesa) framework, designed for complex simulations with thousands of agents. By storing agents in a DataFrame, mesa-frames significantly enhances the performance and scalability of mesa, while maintaining a similar syntax. mesa-frames allows for the use of [vectorized functions](https://stackoverflow.com/a/1422198) which significantly speeds up operations whenever simultaneous activation of agents is possible.
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mesa-frames is an extension of the [mesa](https://github.com/mesa/mesa) framework, designed for complex simulations with thousands of agents. By storing agents in a DataFrame, mesa-frames significantly enhances the performance and scalability of mesa, while maintaining a similar syntax. mesa-frames allows for the use of [vectorized functions](https://stackoverflow.com/a/1422198) which significantly speeds up operations whenever simultaneous activation of agents is possible.
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## Why DataFrames? 📊
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The following is a performance graph showing execution time using mesa and mesa-frames for the [Boltzmann Wealth model](https://mesa.readthedocs.io/en/stable/tutorials/intro_tutorial.html).
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([You can check the script used to generate the graph here](https://github.com/projectmesa/mesa-frames/blob/main/examples/boltzmann_wealth/performance_plot.py), but if you want to additionally compare vs Mesa, you have to uncomment `mesa_implementation` and its label)
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([You can check the script used to generate the graph here](https://github.com/mesa/mesa-frames/blob/main/examples/boltzmann_wealth/performance_plot.py), but if you want to additionally compare vs Mesa, you have to uncomment `mesa_implementation` and its label)
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## Installation
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To get started with mesa-frames, first clone the repository from GitHub:
[](https://colab.research.google.com/github/projectmesa/mesa-frames/blob/main/docs/general/user-guide/2_introductory-tutorial.ipynb)
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[](https://colab.research.google.com/github/mesa/mesa-frames/blob/main/docs/general/user-guide/2_introductory-tutorial.ipynb)
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**Note:** mesa-frames is currently in its early stages of development. As such, the usage patterns and API are subject to change. Breaking changes may be introduced. Reports of feedback and issues are encouraged.
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[You can find the API documentation here](https://projectmesa.github.io/mesa-frames/api).
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[You can find the API documentation here](https://mesa.github.io/mesa-frames/api).
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### Creation of an Agent
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See the License for the specific language governing permissions and
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limitations under the License.
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For the full license text, see the [LICENSE](https://github.com/projectmesa/mesa-frames/blob/main/LICENSE) file in the GitHub repository.
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For the full license text, see the [LICENSE](https://github.com/mesa/mesa-frames/blob/main/LICENSE) file in the GitHub repository.
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One of our major priorities was to move from pandas to polars as the primary dataframe backend. This transition was motivated by performance considerations.
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Now we should transition to using the lazily evaluated version of polars.
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**Related issues:**[#10: GPU integration: Dask, cuda (cudf) and RAPIDS (Polars)](https://github.com/projectmesa/mesa-frames/issues/10), [#89: Investigate using Ibis for the common interface library to any DF backend](https://github.com/projectmesa/mesa-frames/issues/89), [#52: Use of LazyFrames for Polars implementation](https://github.com/projectmesa/mesa-frames/issues/52)
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**Related issues:**[#10: GPU integration: Dask, cuda (cudf) and RAPIDS (Polars)](https://github.com/mesa/mesa-frames/issues/10), [#89: Investigate using Ibis for the common interface library to any DF backend](https://github.com/mesa/mesa-frames/issues/89), [#52: Use of LazyFrames for Polars implementation](https://github.com/mesa/mesa-frames/issues/52)
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#### Progress and Next Steps
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A critical aspect of agent-based models is efficiently managing concurrent agent movements, especially when multiple agents attempt to move to the same location simultaneously. We aim to implement abstractions that handle these concurrency conditions automatically.
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**Related issues:**[#108: Adding abstraction of optimal agent movement](https://github.com/projectmesa/mesa-frames/issues/108), [#48: Emulate RandomActivation with DataFrame.rolling](https://github.com/projectmesa/mesa-frames/issues/48)
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**Related issues:**[#108: Adding abstraction of optimal agent movement](https://github.com/mesa/mesa-frames/issues/108), [#48: Emulate RandomActivation with DataFrame.rolling](https://github.com/mesa/mesa-frames/issues/48)
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# Welcome to mesa-frames 🚀
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mesa-frames is an extension of the [mesa](https://github.com/projectmesa/mesa) framework, designed for complex simulations with thousands of agents. By storing agents in a DataFrame, mesa-frames significantly enhances the performance and scalability of mesa, while maintaining a similar syntax.
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mesa-frames is an extension of the [mesa](https://github.com/mesa/mesa) framework, designed for complex simulations with thousands of agents. By storing agents in a DataFrame, mesa-frames significantly enhances the performance and scalability of mesa, while maintaining a similar syntax.
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You can get a model which is multiple orders of magnitude faster based on the number of agents - the more agents, the faster the relative performance.
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Check out our performance graphs comparing mesa and mesa-frames for the [Boltzmann Wealth model](https://mesa.readthedocs.io/en/stable/tutorials/intro_tutorial.html):
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mesa-frames is in its early stages, and we welcome your feedback and contributions! Check out our [GitHub repository](https://github.com/projectmesa/mesa-frames) to get started.
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mesa-frames is in its early stages, and we welcome your feedback and contributions! Check out our [GitHub repository](https://github.com/mesa/mesa-frames) to get started.
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## License
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mesa-frames is available under the MIT License. See the [LICENSE](https://github.com/projectmesa/mesa-frames/blob/main/LICENSE) file for full details.
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mesa-frames is available under the MIT License. See the [LICENSE](https://github.com/mesa/mesa-frames/blob/main/LICENSE) file for full details.
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"id": "7ee055b2",
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"metadata": {},
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"source": [
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"[](https://colab.research.google.com/github/projectmesa/mesa-frames/blob/main/docs/general/user-guide/2_introductory-tutorial.ipynb)"
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"[](https://colab.research.google.com/github/mesa/mesa-frames/blob/main/docs/general/user-guide/2_introductory-tutorial.ipynb)"
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"source": [
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"# Data Collector Tutorial\n",
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"\n",
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"[](https://colab.research.google.com/github/projectmesa/mesa-frames/blob/main/docs/general/user-guide/4_datacollector.ipynb)\n",
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"[](https://colab.research.google.com/github/mesa/mesa-frames/blob/main/docs/general/user-guide/4_datacollector.ipynb)\n",
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"\n",
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"This notebook walks you through using the concrete `DataCollector` in `mesa-frames` to collect model- and agent-level data and write it to different storage backends: **memory, CSV, Parquet, S3, and PostgreSQL**.\n",
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