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Copy file name to clipboardExpand all lines: .github/ISSUE_TEMPLATE/new_pack_proposal.md
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-[ ] (OPTIONAL) Create a directory to place your examples, if applicable, by running `mkdir docs/examples/<your-pack-name>/<your-example-name>`.
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-[ ] (OPTIONAL) Copy your example scripts and data under the directory you just made. If you have multiple examples, house them under their own separate directory (i.e. `.../<your-pack-name>/example1` and `.../<your-pack-name>/example2`).
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-[ ] List your pack and its dependencies under `docs/source/available-packs.rst` using the same format as the other packs. In this same file, add a description of your pack (1-2 sentences is recommended).
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-[ ] Give yourself credit by listing your name and contributors to the pack you've created!
Copy file name to clipboardExpand all lines: .github/ISSUE_TEMPLATE/new_profile_proposal.md
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-[ ] Fork and clone this repository and cd to the top level by running `cd diffpy.cmi`.
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-[ ] Create a requirements file by running `touch requirements/profiles/<your-profile-name>.yml`. List all packs used in this profile under the header `packs:` in a bulleted list. All additional packages used (if any) should be listed under `extras:` in a similar fashion (see [this file](https://github.com/diffpy/diffpy.cmi/blob/main/requirements/profiles/all.yml) for reference). If no additional packages are required, add the `extras:` header but leave the entries empty.
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-[ ] List your profile and its dependencies under `docs/source/available-profiles.rst` using the same format as the other profiles. In this same file, add a description of your profile (1-2 sentences is recommended).
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-[ ] Give yourself credit by listing your name and contributors to the profile you've created!
Copy file name to clipboardExpand all lines: docs/source/index.rst
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``diffpy.cmi`` - Complex modeling infrastructure: a modular framework for multi-modal modeling of scientific data.
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diffpy.cmi is designed as an extensible complex modeling infrastructure. Users and developers can readily integrate novel data types and constraints into custom workflows. While widely used for advanced analysis of structural data, the framework is general and can be applied to any problem where model parameters are refined to fit calculated quantities to data.
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``diffpy.cmi`` is designed as an extensible complex modeling infrastructure.
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Users and developers can readily integrate novel data types and constraints into custom workflows.
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While widely used for advanced analysis of structural data, the framework is general and can be applied to any problem where
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model parameters are refined to fit data.
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diffpy.cmi is a community-driven project that supports Unix, Linux, macOS, and Windows platforms. It is designed to be used in Python scripts enabling flexible scripting and automation for advanced and reproducible workflows. Users are encouraged to leverage the software for their modeling needs and to contribute feedback, use cases, and extensions through the project community.
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``diffpy.cmi`` is a community-driven project that supports Unix, Linux, macOS, and Windows platforms.
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It is designed to be used in Python scripts enabling flexible scripting and automation for advanced and reproducible workflows.
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Users are encouraged to leverage the software for their modeling needs and to contribute feedback,
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use cases, and extensions through the project community.
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.. image:: ./img/diffpycmi_screenshot.png
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:alt:codecov-in-pr-comment
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:width:600px
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:align:center
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=======================================
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Community Use and Extensible Modeling
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The power of diffpy.cmi
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=======================================
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One of the key strengths of ``diffpy.cmi`` is its modular design, which allows the integration and fitting of *any* data type, not just PDFs.
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While its most commonly used functionality is in PDF modeling and multi-dataset fitting, **we actively encourage and support community-developed modules and workflows for any data types you see fit!**
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``diffpy.cmi`` is designed to be a **generalized regression engine** for any data type, enabling researchers to fit, analyze, and combine multiple sources of information in a unified framework.
<strong>diffpy.cmi can be used to fit ANY type of data!</strong>
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</div>
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One of the key strengths of ``diffpy.cmi`` is its modular design, which allows the integration and fitting of *any* data type.
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While its most commonly used functionality is in pair distribution function (PDF) modeling and multi-dataset fitting,
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**we actively encourage and support community-developed modules and workflows for any data types you see fit!**
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.. image:: ./img/cmi_problem_types.png
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:alt:codecov-in-pr-comment
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:width:500px
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:align:center
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To make ``diffpy.cmi`` easy to use and easy to contribute to, we've developed the concepts of **Packs** and **Profiles**.
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Please see below for more information on these concepts and how you can contribute your own extensions to the community!
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To make ``diffpy.cmi`` easy to use, we organize functionality into **Packs** and **Profiles**, allowing users to contribute their own modules and workflows seamlessly.
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Please see the :ref:`overview <overview>` for more information on these concepts and how you can contribute your own extensions to the community!
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===============
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Getting started
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===============
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To get started, please visit the :ref:`Getting started <installation>` page.
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To get started, please visit the :ref:`Getting started <installation>` section.
``diffpy.cmi`` is designed as an extensible complex modeling infrastructure. Users and developers can readily integrate novel data types and constraints into custom workflows. While widely used for advanced analysis of structural data, the framework is general and can be applied to any problem where model parameters are refined to fit calculated quantities to data.
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``diffpy.cmi`` is designed as an extensible complex modeling infrastructure.
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Users and developers can readily integrate novel data types and constraints into custom workflows.
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While widely used for advanced analysis of structural data, the framework is general and can be applied to any problem where
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model parameters are refined to fit data.
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``diffpy.cmi`` is a community-driven project that supports Unix, Linux, macOS, and Windows platforms. It is designed to be used in Python scripts enabling flexible scripting and automation for advanced and reproducible workflows. Users are encouraged to leverage the software for their modeling needs and to contribute feedback, use cases, and extensions through the project community.
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``diffpy.cmi`` is a community-driven project that supports Unix, Linux, macOS, and Windows platforms.
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It is designed to be used in Python scripts enabling flexible scripting and automation for advanced and reproducible workflows.
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Users are encouraged to leverage the software for their modeling needs and to contribute feedback,
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use cases, and extensions through the project community.
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.. image:: ./img/diffpycmi_screenshot.png
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.. image:: ./img/CMI-logo.png
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:alt:codecov-in-pr-comment
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:width:600px
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:align:center
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===========================
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The power of diffpy.cmi
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===========================
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``diffpy.cmi`` is designed to be a **generalized regression engine** for any data type, enabling researchers to fit, analyze, and combine multiple sources of information in a unified framework.
<strong>diffpy.cmi can be used to fit ANY type of data!</strong>
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</div>
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While its most commonly used functionality is in atomic pair distribution function (PDF) modeling and multi-dataset fitting,
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**we actively encourage and support community-developed modules and workflows for any data types you see fit!**
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.. image:: ./img/cmi_problem_types.png
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:alt:codecov-in-pr-comment
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:width:500px
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:align:center
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To make ``diffpy.cmi`` easy to use, we organize functionality into **Packs** and **Profiles**, allowing users to contribute their own modules and workflows seamlessly.
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To make ``diffpy.cmi`` easy to use, we organize functionality into **Packs** and **Profiles**,
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allowing users to contribute their own modules and workflows seamlessly.
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.. _whats-a-pack:
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-------------------------
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Have you built a new workflow you'd like to implement into ``diffpy.cmi``?
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We would love to **highlight your extension or application!**
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We would love to **highlight it here!**
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To share your work with the community, we ask you to follow these simple steps:
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1. **Open an Issue:** Navigate to the ``diffpy.cmi`` `issues page <https://github.com/diffpy/diffpy.cmi/issues>`_ on GitHub and click :guilabel:`New Issue`.
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Select the "New Pack Proposal" template. Fill out the template with the required fields and open the issue.
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Select the "New Pack Proposal" template. Fill out the template with the name of your pack and open the issue.
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.. image:: ./img/issue_template.png
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:alt:codecov-in-pr-comment
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:width:500px
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:align:center
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2. **Create a Pull Request:** Address all prompts and questions in the issue thread.
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Once all questions are answered and the pack is ready to be added to ``diffpy.cmi``,
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1. **Create a Pull Request:** Address all prompts in the issue thread and check all boxes.
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Once everything is complete and the pack is ready to be added to ``diffpy.cmi``,
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`create a pull request <https://github.com/diffpy/diffpy.cmi>`_ (PR) linking to the issue.
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The PR will be reviewed by the maintainers and merged once approved.
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3. **Be recognized:** Community contributions and new use cases will be showcased and credited here.
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2. **Be recognized:** Make sure to add your pack to the list of available packs in the documentation!
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Community contributions and new use cases will be showcased and credited here.
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===================
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What's a Profile?
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===================
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A **profile** is a pre-configured modeling workflow for a specific data type or analysis task within the ``diffpy.cmi`` framework.
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Profiles are designed to streamline the modeling process by providing users with ready-to-use configurations that can be easily
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adapted to their specific needs. Profiles are designed to be simpler than packs, as they do not require any new code development.
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A **profile** is a pre-configured compilation of packs and packages for a specific data type or analysis task within the ``diffpy.cmi`` framework.
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Profiles are designed to be easily installable configurations that bundle multiple packs and any additional dependencies
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required for a particular modeling task.
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For example, the profile ``all`` contains the ``plotting`` and ``pdf`` packs. Additionally, it contains the package ``ipykernel`` to enable Jupyter Notebook support.
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For example, the profile ``pdf-plus`` contains the ``plotting`` and ``pdf`` packs.
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Additionally, it contains the package ``ipykernel`` to enable Jupyter Notebook support.
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Why Profiles?
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-------------------
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How to contribute a Profile
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---------------------------
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Have you built a new modeling workflow that you'd like to share with the community?
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We would love to **highlight your extension or application!**
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Have you built a profile that you'd like to share with the community?
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We would love to **highlight it here!**
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To share your work with the community, we ask you to follow these simple steps:
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1. **Open an Issue:** Navigate to the ``diffpy.cmi`` `issues page <https://github.com/diffpy/diffpy.cmi/issues>`_ on GitHub and click :guilabel:`New Issue`.
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Select the "New Profile Proposal" template. Fill out the template with the required fields and open the issue.
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Select the "New Profile Proposal" template. Fill out the template with the name of your profile and open the issue.
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.. image:: ./img/issue_template.png
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:alt:codecov-in-pr-comment
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:width:500px
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:align:center
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2. **Create a Pull Request:** Address all prompts and questions in the issue thread.
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Once all questions are answered and the profile is ready to be added to ``diffpy.cmi``,
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2. **Create a Pull Request:** Address all prompts in the issue thread and check all boxes.
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Once everything is complete and the profile is ready to be added to ``diffpy.cmi``,
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`create a pull request <https://github.com/diffpy/diffpy.cmi>`_ (PR) linking to the issue.
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The PR will be reviewed by the maintainers and merged once approved.
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3. **Be recognized:** Community contributions and new use cases will be showcased and credited here.
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3. **Be recognized:** Make sure to add your profile to the list of available profiles in the documentation!
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Community contributions and new use cases will be showcased and credited here.
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