semvec/pypi-packages huggingface dataset:
mim-solvers 0.2.0
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
Requires Python: >=3.8

# mim_solvers
Implementation of efficient numerical optimal control solvers.
In particular, the Sequential Quadratic Programming (SQP) solver described in [this paper](https://laas.hal.science/hal-04330251) solves nonlinear constrained OCPs efficiently by leveraging sparsity.

All the solvers are implemented based on the API of [Crocoddyl](https://github.com/loco-3d/crocoddyl/tree/devel) (v3).
In other words, our solvers take as input a `crocoddyl.ShootingProblem`.

Examples on how to use the solvers can be found in the `examples` directory.

# Dependencies
- [Pinocchio](https://github.com/stack-of-tasks/pinocchio) (rigid-body dynamics computations)
- [Crocoddyl](https://github.com/loco-3d/crocoddyl) (optimal control library)
- [ProxSuite](https://github.com/Simple-Robotics/proxsuite) (quadratic programming) (optional)

# Installation

  ## Using conda

`conda install mim-solvers --channel conda-forge`


  ## Using CMake
`git clone --recursive https://github.com/machines-in-motion/mim_solvers.git`

`cd mim_solvers && mkdir build && cd build`

`cmake .. [-DCMAKE_BUILD_TYPE=Release] [-DCMAKE_INSTALL_PREFIX=...]`

`make [-j6] && make install`

You can also run unittests using `ctest` and benchmarks using `./benchmarks/ur5` or `./benchmarks/solo12` from the build directory.


# Contributors

-   [Armand Jordana](https://github.com/ajordana) (NYU): main developer and manager of the project
-   [Sébastien Kleff](https://github.com/skleff1994) (NYU): main developer and manager of the project
-   [Avadesh Meduri](https://github.com/avadesh02) (NYU): main developer and manager of the project
-   [Ludovic Righetti](https://engineering.nyu.edu/faculty/ludovic-righetti) (NYU): project instructor
-   [Justin Carpentier](https://jcarpent.github.io) (INRIA): project instructor
-   [Nicolas Mansard](http://projects.laas.fr/gepetto/index.php/Members/NicolasMansard) (LAAS-CNRS): project instructor
-   [Yann de Mont-Marin](https://github.com/ymontmarin) (INRIA): Conda integration and support
-   [Louis Montaut](https://github.com/lmontaut) (INRIA): CMake support
-   [Guilhem Saurel](https://github.com/nim65s) (LAAS-CNRS): CMake & pip packaging support
-   [Jianghan ZHang](https://github.com/JianghanZHang) (NYU): features extension
f2p 0.1.1
FITS 2D/3D physical size analyzer
Requires Python: >=3.8

fits2physical
=============

Overview
--------

fits2physical is a Python package to analyze FITS files, compute pixel and physical sizes, and generate plots.

Installation
------------

Install via pip (after PyPI release) or GitHub:

.. code-block:: bash

    pip install f2p
    # or directly from GitHub
    pip install git+https://github.com/siwakotiutsav/fits2physical.git

Usage
-----

Analyze a FITS file and get dimensions:

.. code-block:: python

    import f2p
    df, fig = f2p.analyze("../data/ngc253/NGC253_sky_v1_17_1_ch3-shortmediumlong_s3d.fits",
                           distance_mpc=3.5)
    print(df)
    fig.show()  # Optional: display figure

Expected output:

.. code-block:: text

       Quantity     Pixels                    Angular               Physical
    0    Length  85.529646   17.10592943977466 arcsec  290.26160171739974 pc
    1   Breadth  50.442038  10.088407695872492 arcsec  171.18493250494006 pc
    2  Diagonal  97.406365   19.48127334931105 arcsec   330.5675745813207 pc

Figure
------

Here’s the figure generated by the analysis:

.. image:: docs/_static/example_plot.png
    :alt: FITS Analysis Figure
    :align: center
    :width: 80%
gcell 0.1.1
A Python library that represents different entities in a cell
Requires Python: >=3.10

# gcell

[![Documentation Status](https://readthedocs.org/projects/gcell/badge/?version=latest)](https://gcell.readthedocs.io/en/latest/)

`gcell` is a comprehensive toolkit for genomic data analysis, focusing on cell type-specific regulatory analysis, DNA sequence manipulation, protein structure prediction, and pathway analysis. It integrates various modules to facilitate the study of different aspects of gene expression regulation.

# News
- I dropped `graphviz` and `pygraphviz` dependency due to complexity of maintain installing across different platform. It only affects the network layout for drawing causal graph. `nx.spring_layout(G)` is used instead of `nx.nx_agraph.graphviz_layout(G)` now, which is uglier unforturnately.
- Feature: Now you can load pre-infered cell types on getdemo website easily:
```python
from gcell.cell.celltype import GETDemoLoader
g = GETDemoLoader()
print(g.available_celltypes) # this gives you a list of cell type names
g.load_celltype('Plasma Cell')
```
- Fix: `zarr` has been limited to `<3.0.0` to avoid s3 problem


## Goal
The long term goal of this package is to create a open-source, community-involved python-centric playground/tool-set for future AI Agent to discover new biology through predictive model.


## Key Modules

- **Celltype**: Tools for cell type analysis, including gene expression and motif analysis. Basis for `get_model` interpretation analysis.
- **DNA**: Functions for DNA sequence manipulation, motif scanning, and track visualization.
- **RNA**: Classes for handling GENCODE gene annotations and GTF files.
- **Protein**: Functionality for protein domain analysis (Uniprot, InterPro) and AlphaFold2 predictions parsing as well as retrieve protein-protein interaction networks from the STRING database..
- **Pathway**: Tools for pathway (GO, KEGG, Reactome, etc.) analysis using gprofiler.


## Installation

```
pip install git+https://github.com/GET-Foundation/gcell.git@main
```

## License

`gcell` is open-source software licensed under the MIT License. See the LICENSE file for more details.
pydicom3 3.1.0
A pure Python package for reading and writing DICOM data
Requires Python: >=3.10
Keywords: dicom, python, medical, imaging

# Pydicom3
Pydicom3 is a vendored fork of the [pydicom](https://github.com/pydicom/pydicom) package, 
with a few changes made to increase error tolerance so as to allow for the ingestion of a wider range of 
DICOM files we receive from data vendors (which may be malformed but salvageable).

For any documentation needs, please refer to the `pydicom` package.