semvec/pypi-packages huggingface dataset:
seissolxdmfwriter 0.4.6
A python writer for SeisSol xdmf output
Requires Python: >=3.6
Keywords: SeisSol

seissolxdmfwriter
=================
A python module to write seissol outputs readable by paraview.

```python
import seissolxdmfwriter as sxw
import seissolxdmf as sx
import numpy as np

fn = "test-fault.xdmf"
# Read data from input file using seissolxdmf
sx = sx.seissolxdmf(fn)
geom = sx.ReadGeometry()
connect = sx.ReadConnect()
dt = sx.ReadTimeStep()
outputTimes = sx.ReadTimes()

SRs = sx.ReadData("SRs")
SRd = sx.ReadData("SRd")
SR = np.sqrt(SRs**2 + SRd**2)

# Write the 0,4 and 8th times steps of array SRs and SR in SRtest-fault.xdmf/SRtest-fault.h5
dictTime = {outputTimes[i]: i for i in [0, 4, 8]}
sxw.write(
    "test-fault",
    geom,
    connect,
    {"SRs": SRs, "SR": SR},
    dictTime,
    reduce_precision=True,
    backend="hdf5",
)

# Finally, the module can be use to write data directly from seissolxdmf, limiting
# the memory requirements

sxw.write_from_seissol_output(
    'test-fault-sx',
    sx,
    ['SRs', 'SRd','fault-tag', 'partition'],
    [3,4],
    reduce_precision=True,
    backend="hdf5",
    compression_level=4,
)

```

The module also encapsulates `seissol_output_extractor`, which can be used to extract and process data from SeisSol output files, allowing selection of variables, time steps, spatial ranges, and output format.
Here is an example of use:

```bash
# extracts PSR, Vr and partition, at 2nd and 4th time steps and at simulation time 0.5, from test-fault.xdmf and write into test_new-fault.xdmf
# use seissol_output_extractor --h for additionnal info about the arguments
seissol_output_extractor test-fault.xdmf --time "i2,i4,0.5" --variable PSR Vr partition --add2prefix "_new"
```
cool-seq-tool 0.15.4
Common Operation on Lots of Sequences Tool
Requires Python: >=3.11

<h1 align="center">
Cool-Seq-Tool
</h1>

[![image](https://img.shields.io/pypi/v/cool-seq-tool.svg)](https://pypi.python.org/pypi/cool-seq-tool) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.14007783.svg)](https://doi.org/10.5281/zenodo.14007783) [![image](https://img.shields.io/pypi/l/cool-seq-tool.svg)](https://pypi.python.org/pypi/cool-seq-tool) [![image](https://img.shields.io/pypi/pyversions/cool-seq-tool.svg)](https://pypi.python.org/pypi/cool-seq-tool) [![Actions status](https://github.com/genomicmedlab/cool-seq-tool/actions/workflows/checks.yaml/badge.svg)](https://github.com/genomicmedlab/cool-seq-tool/actions/checks.yaml)

---

**[Documentation](https://coolseqtool.readthedocs.io/stable/)** · [Installation](https://coolseqtool.readthedocs.io/stable/install.html) · [Usage](https://coolseqtool.readthedocs.io/stable/usage.html) · [API reference](https://coolseqtool.readthedocs.io/stable/reference/index.html)

---

## Overview

<!-- description -->
The Common Operations On Lots-Of Sequences Tool, **Cool-Seq-Tool**, provides:

- A Pythonic API on top of sequence data of interest to tertiary analysis tools, including mappings between gene names and transcripts, [MANE transcript](https://www.ncbi.nlm.nih.gov/refseq/MANE/) descriptions, and transcript alignment data from the [Universal Transcript Archive](https://github.com/biocommons/uta)
- Augmented access to the [SeqRepo](https://github.com/biocommons/biocommons.seqrepo) database, including multiple additional methods and tools
- Mapping tools, including a transcript selection algorithm for selecting a representative transcript defined [here](https://coolseqtool.readthedocs.io/stable/transcript_selection.html), that combine the above to support translation between references sequences, annotation layers, and transcripts
<!-- /description -->

---

## Install

Cool-Seq-Tool is available on [PyPI](https://pypi.org/project/cool-seq-tool)

```shell
python3 -m pip install cool-seq-tool
```

See the [installation instructions](https://coolseqtool.readthedocs.io/stable/install.html) in the documentation for a description of dependency setup requirements.

---

## Usage

All Cool-Seq-Tool resources can be initialized by way of a top-level class instance:

```pycon
>>> from cool_seq_tool import CoolSeqTool
>>> from cool_seq_tool.schemas import AnnotationLayer, CoordinateType
>>> cst = CoolSeqTool()
>>> result = await cst.mane_transcript.get_mane_transcript(
...     "NP_004324.2",
...     599,
...     AnnotationLayer.PROTEIN,
...     coordinate_type=CoordinateType.INTER_RESIDUE,
... )
>>> result.gene, result.refseq, result.status
('EGFR', 'NM_005228.5', <TranscriptPriority.MANE_SELECT: 'mane_select'>)
```

---

## Feedback and contributing

We welcome bug reports, feature requests, and code contributions from users and interested collaborators. The [documentation](https://coolseqtool.readthedocs.io/stable/contributing.html) contains guidance for submitting feedback and contributing new code.
oakvar 2.12.25
A genomic variant analysis platform
Requires Python: >=3.8
Keywords: genomic variant analysis interpretation genome

.. image:: https://github.com/rkimoakbioinformatics/oakvar/raw/master/oakvar/gui/websubmit/images/logo.png
  :width: 300
  :alt: OakVar logo

OakVar: Genomic Variant Analysis Platform
*****************************************

Listen to Deep Dive into OakVar!: https://youtu.be/MNvDdL_p5s8

Mission
=======

* OakVar is a bridge between cutting-edge genomics research and everyday people.
* OakVar democratizes access to genomics knowledge for all.

What it does
============

* Annotate genomic variants with diverse annotation sources.
* Make databases of annotated variants.
* Query annotated variants with filter sets.
* Make reports in diverse formats.
* Visualize annotated variants with graphical user interface.
* Works via CLI and GUI.
* Easily develop, run, and distribute CLI and GUI genomics apps. OakVar acts as an operating system for genomics apps.
* Connect genomic data to AI/ML models.

Internship
===========================
The main project will be OakVar. Full remote work. Unpaid position (no funding yet). Please send your application to info@oakbioinformatics.com.

Resources
=========
Ready to start? `OakVar Documentation`_

.. _OakVar Documentation: https://docs.oakvar.com

`Subscribe to the mailing list`_: Be in the loop and notified of the new releases and news of OakVar. 

.. _Subscribe to the mailing list: https://dashboard.mailerlite.com/forms/21170/56038572068701589/share

Medium: https://medium.com/oakvar

Discord: https://discord.gg/wZfkTMKTjG 

Reddit: https://www.reddit.com/r/oakvar

Email: info@oakbioinformatics.com

Many Thanks to Our Sponsors
=================================

Organizational Sponsors
-----------------------

.. image:: https://dna-seq.github.io/dna-seq/just_dna_seq.png
   :width: 225
   :alt: Just DNA-Seq
   :target: https://github.com/dna-seq

Individual Sponsors
-------------------

.. image:: https://avatars.githubusercontent.com/u/110073399?v=4
   :width: 50
   :alt: RegenCenter
   :target: https://github.com/RegenCenter

OakVar is in CodeX
******************

.. image:: https://storage.oakvar.com/oakvar-public/codex.png
  :width: 400
  :alt: CodeX logo

.. _LICENSE: https://github.com/rkimoakbioinformatics/oakvar/blob/master/LICENSE
metamaska 0.0.1
malevolent payload classifier
Requires Python: >=3.13

#  μετάμάσκα - malevolent payload classifier
meta.mask can detect different types of malicious payloads like SQL injection, XSS, path-traversal, and command injection payloads.

[![pypi](https://img.shields.io/pypi/v/metamaska.svg)](https://pypi.org/project/metamaska/)
[![python](https://img.shields.io/pypi/pyversions/metamaska.svg)](https://pypi.org/project/metamaska/)
[![Build Status](https://github.com/happyhackingspace/metamaska/actions/workflows/ci.yml/badge.svg)](https://github.com/happyhackingspace/metamaska/actions/workflows/ci.yml)
[![codecov](https://codecov.io/gh/happyhackingspace/metamaska/branch/main/graphs/badge.svg)](https://codecov.io/github/happyhackingspace/metamaska)



* Documentation: <https://happyhackingspace.github.io/metamaska>
* GitHub: <https://github.com/happyhackingspace/metamaska>
* PyPI: <https://pypi.org/project/metamaska/>
* Free software: MIT


## TODO

* support more types
* support interoperable model formats, more at [here](https://scikit-learn.org/stable/model_persistence.html#interoperable-formats)

## Credits

- [Cookiecutter](https://github.com/audreyr/cookiecutter)
- [waynerv/cookiecutter-pypackage](https://github.com/waynerv/cookiecutter-pypackage)
- [ML-based-WAF](https://github.com/vladan-stojnic/ML-based-WAF)
- [WAF-A-MoLE](https://github.com/AvalZ/WAF-A-MoLE)
tierkreis-visualization 0.1.7
Browser based visualization tool for tierkreis
Requires Python: >=3.12

# Tierkreis visualizer

To run with fast api:

```bash
just serve
```

To apply changes made in the frontend

```bash
just prod serve
```

will bundle the js before running the serve.

## Dev server

To run a updating dev server you can run

```bash
uv run tkr-vis-dev
```

from the `tierkreis/tierkreis_visualization` and

```bash
npm run dev
```

from `tierkreis/tierkreis_visualization/frontend`.

## OpenAPI

To regenerated the openapi spec run

```bash
uv run tkr-vis-openapi
```

Running `npm run dev` afterward will automatically update the api stubs.
llg3d 3.2.0
A solver for the stochastic Landau-Lifshitz-Gilbert equation in 3D
Requires Python: >=3.10

# LLG3D: A solver for the stochastic Landau-Lifshitz-Gilbert equation in 3D

[![pipeline status](https://gitlab.math.unistra.fr/llg3d/llg3d/badges/main/pipeline.svg)](https://gitlab.math.unistra.fr/llg3d/llg3d/-/commits/main)
[![coverage report](https://gitlab.math.unistra.fr/llg3d/llg3d/badges/main/coverage.svg)](https://llg3d.pages.math.unistra.fr/llg3d/coverage)
[![Latest Release](https://gitlab.math.unistra.fr/llg3d/llg3d/-/badges/release.svg)](https://gitlab.math.unistra.fr/llg3d/llg3d/-/releases)
[![Doc](https://img.shields.io/badge/doc-sphinx-blue)](https://llg3d.pages.math.unistra.fr/llg3d/)
[![SWH](https://archive.softwareheritage.org/badge/origin/https://gitlab.math.unistra.fr/llg3d/llg3d/)](https://archive.softwareheritage.org/browse/origin/?origin_url=https://gitlab.math.unistra.fr/llg3d/llg3d)

LLG3D is written in Python and may run in parallel using MPI, OpenCL or JAX.

See the [documentation](https://llg3d.pages.math.unistra.fr/llg3d/).
porran 0.0.12
PORous RANdom crystal structure generation
Requires Python: >=3.8

# PORRAN

PORous structure RANdom generation. Can be used for substituting atoms in porous crystals like zeolites, or doping Metal-Organic Frameworks. Algorithms used for substitutions are based on the [ZEORAN program](https://github.com/promerma/zeoran) \[1\].

## Installation

### Install with pip

The simplest way to install PORRAN is through pip, which automatically gets the source code from PyPI:
```
pip install porran
```

### Install from source
It is also possible to install PORRAN directly from the source. This can be done using the commands bellow:
```
git clone https://github.com/marko-petkovic/porran.git
cd porran
pip install .
```

## Getting started
More examples will follow in the future

### Examples
- An example on using PORRAN to generate zeolite structures (MOR) with Al substitutions can be found [here](examples/zeolite_example.ipynb).
- Using PORRAN to generate MOFs functionalized with lithium-alkoxide: [mofs_example](examples/mofs_example.ipynb).

## References
\[1\] Romero-Marimon, P., Gutiérrez-Sevillano, J. J., & Calero, S. (2023). Adsorption of Carbon Dioxide in Non-Löwenstein Zeolites. *Chemistry of Materials*, 35(13), 5222-5231.