semvec/pypi-packages huggingface dataset:
multi-slit-solar-explorer 0.0.3
A model of the optical design for the Multi-slit Solar Explorer (MUSE).
Requires Python: >=3.10

# multi-slit-solar-explorer

[![tests](https://github.com/sun-data/multi-slit-solar-explorer/actions/workflows/tests.yml/badge.svg)](https://github.com/sun-data/multi-slit-solar-explorer/actions/workflows/tests.yml)
[![codecov](https://codecov.io/gh/sun-data/multi-slit-solar-explorer/graph/badge.svg?token=tBcex8q72g)](https://codecov.io/gh/sun-data/multi-slit-solar-explorer)
[![Black](https://github.com/sun-data/multi-slit-solar-explorer/actions/workflows/black.yml/badge.svg)](https://github.com/sun-data/multi-slit-solar-explorer/actions/workflows/black.yml)
[![Ruff](https://github.com/sun-data/multi-slit-solar-explorer/actions/workflows/ruff.yml/badge.svg)](https://github.com/sun-data/multi-slit-solar-explorer/actions/workflows/ruff.yml)
[![Documentation Status](https://readthedocs.com/projects/sun-data-multi-slit-solar-explorer/badge/?version=latest&token=04d5b4b840efc110f0858920dcef5f0c7d420092904885488a4518e1621df298)](https://sun-data-multi-slit-solar-explorer.readthedocs-hosted.com/en/latest/?badge=latest)
[![PyPI version](https://badge.fury.io/py/multi-slit-solar-explorer.svg)](https://badge.fury.io/py/multi-slit-solar-explorer)

A Python library which models the optical system of the Multi-Slit Solar Explorer, a NASA satellite which measures the spectrum of the solar corona over a wide field of view with high spatial, spectral, and temporal resolution.
tvb-kernels 0.5
TVB computational kernels
Requires Python: >=3.8

# tvb_kernels

This is a library of computational kernels for TVB.

## scope

in order of priority

- [x] sparse delay coupling functions
- [ ] fused heun neural mass model step functions
- [x] random number generation
- [ ] neural ODE
- [ ] bold / tavg monitors
- [x] small matmul suitable for neural ODE

which will then be used internally by TVB.

## building

No stable versions are available, so users can install from
source with `pip install .` or to just get a wheel `pip wheel .`

## dev

for best experience
```
apt-get/brew install clangd clang-tidy
pip install -U pip
pip install -U python-lsp-server[all] cmake-language-server nanobind scikit-build-core[pyproject]
pip install --no-build-isolation -Ceditable.rebuild=true -ve .
```

for the plain makefile workflow,
```
bear -- make -B test
```
 
## roadmap

- improve api (ownership, checking etc)
- variants
  - single
  - batches
  - spatialized parameters
- cuda ports for Jax, CuPy and Torch users 
- mex functions?
- scalable components
  - domains
  - projections
  - small vm to automate inner loop
muramasa-kyoto 0.7.0
A package for plasmid DNA sequence anomaly detection
Requires Python: >=3.6

# Muramasa Kyoto

Muramasa Kyoto is a Python package for detecting anomalies in plasmid DNA sequences using K-means clustering and Mahalanobis distance.

## Installation

You can install Muramasa Kyoto using pip:pip install muramasa_kyoto

## Usage

Here's a basic example of how to use Muramasa Kyoto:

```python
from muramasa_kyoto import feature_extraction, anomaly_detection

# Extract features from your plasmid sequences
feature_extraction.create_feature_file('input.csv', 'features.csv')

# Load the features
import pandas as pd
features = pd.read_csv('features.csv')

# Create and train the anomaly detector
detector = anomaly_detection.AnomalyDetector()
detector.fit(features)

# Predict anomalies
predictions = detector.predict(features)

# Get feature contributions for anomalies
contributions = detector.get_feature_contributions(features[predictions['is_anomaly']])