semvec/pypi-packages huggingface dataset:
mpec-extract 0.1.6
A library to extract from CEPAC and STOP output files
Requires Python: >=3.9


Note: make sure that twine distribution is <= 6.0.1 to be compatible with pyproject.toml.

Note: out_data files need to be regenerated with the current library structure in place (i.e. the module, class, and 
function definitions). So the library needs to be created then used to generate new out_data files, before others can 
use it. (I have already done this for the current library structure, but if it changes, the out_data might need to be 
regenerated.) See Developer's Notes below. 

-----

### Commands to build 

Remember to update the version number in the pyproject.toml file, and delete any old distributions. The twine upload 
will not work if there's any old versions around.

``` py -m build ```

This should create a dist folder with a tar.gz and a wheel build. 

### Commands to upload to PyPi or TestPyPi

``` py -m twine upload --repository pypi dist/*```

``` py -m twine upload --repository testpypi dist/*```

Note you'll need to create a PyPi and/or TestPyPi account and an API access token, and provide the token to twine, either via the 
commandline or via a .pypirc file. PyPi and TestPyPi are separate: they will require separate accounts and separate access tokens. 

If you choose to use a .pypirc file, an example is provided in this repo, but it needs to be placed in your local home 
directory, not the project directory!

### Commands to install the package

In general, you can find the correct command easily on the package webpage: 
- TestPyPi: https://test.pypi.org/project/mpec-extract/
- PyPi: https://pypi.org/project/mpec-extract/

The command should be something like: 

- Test PyPi: ``` pip install -i https://test.pypi.org/simple/ mpec-extract ```

- PyPi: ``` pip install mpec-extract ```

## Developer's Notes 

### Generating .out_data files 

This is what the error might look like for an out of date out_data file: 

```
Error in py_call_impl(callable, call_args$unnamed, call_args$named) : 
  ModuleNotFoundError: No module named 'm_cohorts'
```

You need to go to the MCT repo to recreate the out_data files. As of 8/20/25, the script is in a separate branch of the 
MCT repo, called extract_library. The script is called generate_data_file.py. That repo should already include the newer
files, marked with "_new". Also, they should be in the MPEC Model Resources folder for now. 

The reason the out_data files need to be regenerated is because when the out_data file is pickled, it will save
references to the module and class whose data it is saving. The old files are saving references to m_cohorts.CohortData 
and m_cohorts.GenericMonth, which is not ok because the pickle file needs to be loaded via the import_data_file() 
function in the extract_backend package, which cannot access m_cohorts.py. The data file needs to be saved within the 
structure of the package.
prism-pruner 0.0.8
Prism Pruner
Requires Python: >=3.12

# Prism Pruner

[![License](https://img.shields.io/github/license/ntampellini/prism_pruner)](https://github.com/ntampellini/prism_pruner/blob/master/LICENSE)
[![Powered by: Pixi](https://img.shields.io/badge/Powered_by-Pixi-facc15)](https://pixi.sh)
[![Code style: ruff](https://img.shields.io/badge/code%20style-ruff-000000.svg)](https://github.com/astral-sh/ruff)
[![GitHub Workflow Status](https://img.shields.io/github/actions/workflow/status/ntampellini/prism_pruner/test.yml?branch=master&logo=github-actions)](https://github.com/ntampellini/prism_pruner/actions/)
[![Codecov](https://img.shields.io/codecov/c/github/ntampellini/prism_pruner)](https://codecov.io/gh/ntampellini/prism_pruner)
[![PyPI - Version](https://img.shields.io/pypi/v/prism_pruner)](https://pypi.org/project/prism-pruner/)

<p align="center">

  <img src="examples/prism_logo.png" alt="PRISM logo" class="center" width="500"/>

</p>

PRISM (Pruning Interface for Similar Molecules) is the modular similarity pruning code originally from [FIRECODE](https://github.com/ntampellini/FIRECODE/tree/main), in a polished standalone package. It filters out duplicate structures from conformational ensembles, leaving behind non-redundant states.

The code implements a cached, iterative, divide-and-conquer approach on increasingly large subsets of the ensemble and removes duplicates as assessed by one of three metrics:
- Relative deviation of the moments of inertia on the principal axes
- Heavy-atom RMSD and maximum deviation
- Rotamer-corrected heavy-atom RMSD and maximum deviation

## Installation
The package is distributed through PyPI.

    pip install prism_pruner

## Usage
The main pruning functions are in prism_pruner.pruning, and a wrapper that chains up to all three is also available. The functions return the pruned ensemble structures and the relative boolean mask.

```python
from prism_pruner.conformer_ensemble import ConformerEnsemble
from prism_pruner.pruner import prune

ensemble = ConformerEnsemble.from_xyz("ensemble.xyz")

ensemble.coords.shape # (1086, 136, 3)

pruned, mask = prune(
    ensemble.coords,
    ensemble.atoms,

    # the third pruning routine can be
    # slow and is often not necessary,
    # so it's off by default
    rot_corr_rmsd_pruning=False,

    debugfunction=print,
)

pruned.shape # (387, 136, 3)
mask.shape # (1086,)
# where pruned is ensemble.coords[mask]
```
For additional performance, it is also possible to read/provide energies to only evaluate the similarity of structures that are energetically close.

For additional usage, see the [examples folder](https://github.com/ntampellini/prism_pruner/tree/master/examples).

## Credits
This package was created with [Cookiecutter](https://github.com/audreyr/cookiecutter) and the [jevandezande/pixi-cookiecutter](https://github.com/jevandezande/pixi-cookiecutter) project template.
open-mpic-core 6.3.2
Library of core (common) MPIC functionality for Open MPIC in Python.
Requires Python: >=3.11

# open-mpic-core-python

A Python implementation of the Open MPIC core library which can be adapted to various transports or deployment environments.

## Overview

The `open-mpic-core` library is a "common code" or "core" library for Open MPIC, written in Python. It is designed to be included in Python projects that wrap this code in something that can be invoked to leverage it. 

For example:
- The [aws-lambda-python](https://github.com/open-mpic/aws-lambda-python) GitHub project utilizes this library as part of an AWS Lambda-based MPIC implementation.
- The [open-mpic-containers](https://github.com/open-mpic/open-mpic-containers) GitHub project uses this library to implement the key MPIC components (coordinator, CAA checker, DCV checker) as FastAPI-based Docker images that can be deployed as desired.

## API Specification

The `open-mpic-core` library utilizes object definitions which are based on the Open MPIC API Specification. This codebase is versioned to correspond to the Open MPIC API Specification version on which it is based. MPIC implementations based on this library should therefore be expected to conform to the corresponding Open MPIC API specification.

## Installation

To include the `open-mpic-core` library in your project, add it as a dependency in your `pyproject.toml` file:

```toml
[project]
dependencies = [
  "open-mpic-core"
]
```

Alternatively, you can install it using pip:

```sh
pip install open-mpic-core
```

## Usage

Here is an example of how to use the `open-mpic-core` library in your project:

```python
from open_mpic_core.mpic_caa_checker import MpicCaaChecker
from open_mpic_core.common_domain import CaaCheckRequest, CaaCheckResponse

# Create a CAA check request
caa_request = CaaCheckRequest(
    caa_check_parameters=...  # Fill in the required parameters
)

# Perform the CAA check
checker = MpicCaaChecker()
caa_response = await checker.check_caa(caa_request)

# Process the response
if caa_response.check_passed:
    print("CAA check passed")
else:
    print("CAA check failed")
```

## Contributing

Contributions are welcome! Please see the [contributing guidelines](CONTRIBUTING.md) for more information.

## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

## Links

- [Documentation](https://github.com/open-mpic/open-mpic-core-python)
- [Issues](https://github.com/open-mpic/open-mpic-core-python/issues)
- [Source](https://github.com/open-mpic/open-mpic-core-python)

## Authors

- Henry Birge-Lee
- Grace Cimaszewski
- Dmitry Sharkov