semvec/pypi-packages huggingface dataset:
pynpdc 0.31.0
Python lib to interact with the NPDC stack of the Norwegian Polar Institute
Requires Python: >=3.10

# pynpdc

`pynpdc` is a library for accessing the
[Norwegian Polar Data Centre](https://data.npolar.no/) using Python3. It
provides clients with simple methods for logging in and out as well as fetching
and manipulating datasets, attachments, records, and labels.

It is based on the following REST APIs:

- [NPDC Auth API (Komainu)](https://docs.data.npolar.no/auth/)
- [NPDC Data API (Kinko)](https://docs.data.npolar.no/api/)

## Getting started

Use

```
pip3 install pynpdc
```

to install `pynpdc` into your project.

## Jupyter

If you want to run the examples, install:

```sh
pip install jupyter urllib3 black[jupyter]
```

_(`urllib3` helps to get rid of the InsecureRequestWarning when you deal with
staging entrypoints. `black[jupyter]` will add Jupityer formatting abilities to
black)_

Then execute

```sh
jupyter lab
```

to run the user interface. The examples are found in the folder
`jupyter-notebooks`.
investigraph 0.8.0
etl pipeline for investigations with follow the money data
Requires Python: <3.14,>=3.11

[![investigraph on pypi](https://img.shields.io/pypi/v/investigraph)](https://pypi.org/project/investigraph/)
[![Python test and package](https://github.com/dataresearchcenter/investigraph-etl/actions/workflows/python.yml/badge.svg)](https://github.com/dataresearchcenter/investigraph-etl/actions/workflows/python.yml)
[![Build docker container](https://github.com/dataresearchcenter/investigraph-etl/actions/workflows/build-docker.yml/badge.svg)](https://github.com/dataresearchcenter/investigraph-etl/actions/workflows/build-docker.yml)
[![pre-commit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit)](https://github.com/pre-commit/pre-commit)
[![Coverage Status](https://coveralls.io/repos/github/dataresearchcenter/investigraph-etl/badge.svg?branch=main)](https://coveralls.io/github/dataresearchcenter/investigraph-etl?branch=main)
[![MIT License](https://img.shields.io/pypi/l/investigraph)](./LICENSE)

# investigraph

**An ETL framework for building datasets in the [FollowTheMoney](https://followthemoney.tech) format for investigative journalism and research.**

[Documentation](https://dataresearchcenter.github.io/investigraph/)

[Tutorial](https://dataresearchcenter.github.io/investigraph/tutorial/)

## installation

`investigraph` requires at least **Python 3.11**

    pip install investigraph

## example datasets

There is a dedicated [repo](https://github.com/dataresearchcenter/investigraph-datasets) for example datasets built with investigraph.

## deployment

### docker

`docker-compose.yml` for local development / testing, use `docker-compose.prod.yml` as a starting point for a production setup. [More instructions here](https://dataresearchcenter.github.io/investigraph/deployment/)

## run locally

Install app and dependencies (use a virtualenv):

    pip install investigraph

Or, e.g. when using [poetry](https://python-poetry.org/):

    poetry add investigraph

After installation, `investigraph` as a command should be available:

    investigraph --help

Quick run a local dataset definition:

    investigraph run -c ./path/to/config.yml

## development

This package is using [poetry](https://python-poetry.org/) for packaging and dependencies management, so first [install it](https://python-poetry.org/docs/#installation).

Clone investigraph repository to a local destination.

Within the root directory, run

    poetry install --with dev

This installs a few development dependencies, including [pre-commit](https://pre-commit.com/) which needs to be registered:

    poetry run pre-commit install

Before creating a commit, this checks for correct code formatting (isort, black) and some other useful stuff (see: `.pre-commit-config.yaml`)

### test

    make test

## supported by

[Media Tech Lab Bayern batch #3](https://github.com/media-tech-lab)

<a href="https://www.media-lab.de/en/programs/media-tech-lab">
    <img src="https://raw.githubusercontent.com/media-tech-lab/.github/main/assets/mtl-powered-by.png" width="240" title="Media Tech Lab powered by logo">
</a>
lssfind 0.1.1
Python implementation of LSSFind and LocalLSSFind
Requires Python: >=3.12

# LSSFind

Python implementation of LSSFind and LocalLSSFind.

## Installation

This package is available at PyPI and can be installed using
```bash
pip install lssfind
```

## Usage

The main algorithms of LSSFind and LocalLSSFind are implemented as `get_prevalent_interactions` and `get_sample_interactions` respectively. They can be used with Random Forest from scikit-learn.

```python
from lssfind import get_prevalent_interactions, get_sample_interactions
from sklearn.ensemble import RandomForestRegressor
from sklearn.datasets import make_regression
X, y = make_regression(n_features=4, n_informative=2, random_state=0, shuffle=False)
rf = RandomForestRegressor()
rf.fit(X, y)
get_prevalent_interactions(rf, impurity_decrease_threshold=1., min_weight=0.5)
get_sample_interactions(rf, impurity_decrease_threshold=1., testpoints=[[0, 0, 0, 0]], min_weight_dwp=0.5, min_weight_pp=0.25)
```

## Literature

For theoretical background about LSSFind, see
> M. Behr, Y. Wang, X. Li, & B. Yu, Provable Boolean interaction recovery from tree ensemble obtained via random forests, *Proc. Natl. Acad. Sci. U.S.A.* 119 (22) e2118636119, https://doi.org/10.1073/pnas.2118636119 (2022).

Theoretical background about LocalLSSFind can be found in
> K. Vuk, N. A. Ihlo, & M. Behr, Provable Recovery of Locally Important Signed Features and Interactions from Random Forest, *arXiv*, https://arxiv.org/abs/2512.11081 (2025).

## Acknowledgement

Part of our implementation is based on Python code which was provided by Yu Wang
as part of iterative Random Forest, a project of the group of Bin Yu,
see https://github.com/Yu-Group/iterative-Random-Forest/blob/master/irf/utils.py
PyMMM 0.7.12
Python wrapper for MMM module
Requires Python: >=3.8

# PyMMM - Würth Elektronik Magnetic Modeling Module

Python wrapper over OpenMagnetics for flyback transformer and current transformer selection from Würth's catalog.

## Features

- **Energy-based magnetic filtering** - Quickly filter catalog by storable energy
- **Virtual magnetics** - Generate winding combinations from real products
- **Flyback/CT design** - Process design inputs for flyback and current transformers
- **Full simulation** - Core losses, winding losses, temperature rise calculations
- **Visualization** - SVG plots of cores, windings, and magnetic fields

## Installation

```bash
pip install wheelhouse/pymmm-*.whl
```

## Quick Start

```python
import PyMMM

# Load catalog
PyMMM.load_magnetics_from_file("flybacks.ndjson", expand=True)

# Define requirements
inputs = {
    "inputVoltage": {"minimum": 85, "maximum": 265},
    "operatingPoints": [{
        "outputVoltages": [12],
        "outputCurrents": [1],
        "switchingFrequency": 100000,
        "mode": "Discontinuous Conduction Mode"
    }]
}

# Filter by energy
PyMMM.compute_energy_cache(100, inputs)
min_energy = PyMMM.calculate_flyback_required_magnetic_energy(inputs)
refs = PyMMM.filter_magnetics_by_energy(min_energy, min_energy * 2)

# Get virtual magnetics
for ref in refs:
    result = PyMMM.calculate_virtual_magnetics_by_reference(ref, inputs, True)
    print(result["virtualMagnetics"])
```

## Building from Source

```bash
# Build wheel
python3 -m pipx run build

# Or with cibuildwheel for manylinux
cibuildwheel
```

## Documentation

See [llms.txt](llms.txt) for detailed API documentation and usage patterns.

## API Reference

Run `help(PyMMM)` or `help(PyMMM.function_name)` for function documentation.

### Key Functions

| Function | Description |
|----------|-------------|
| `load_magnetics_from_file` | Load catalog from NDJSON |
| `compute_energy_cache` | Precompute storable energy |
| `filter_magnetics_by_energy` | Filter by energy range |
| `calculate_virtual_magnetics_by_reference` | Generate winding combinations |
| `filter_magnetics_by_flow` | Score and rank magnetics |
| `devirtualize_mas_by_reference` | Map virtual to real product |
| `calculate_saturation_current` | Calculate saturation current |

## License

Proprietary - Würth Elektronik
aa-theme-xix 0.3.2
Theme for auth.legionofdeath.ru
Requires Python: >=3.11

# Alliance Auth XIX Theme

## Installation

```shell
sudo su allianceserver
cd
source /home/allianceserver/venv/auth/bin/activate
git clone https://gitlab.com/legion-of-xxdeathxx/aa-theme-xix.git
pip install -e aa-theme-xix/
```

Now open your `local.py` and add the following right below your `INSTALLED_APPS`:
```shell
nano myauth/myauth/settings/local.py
```
```python
# XIX Theme - https://gitlab.com/legion-of-xxdeathxx/aa-theme-xix
INSTALLED_APPS.insert(0, "aa_theme_xix")
```

After installation, run the command:
```shell
python /home/allianceserver/myauth/manage.py collectstatic --noinput
```

**Important**

If you are using [aa-gdpr](https://gitlab.com/tactical-supremacy/aa-gdpr), the template stuff needs to be **after** the `aa-gdpr`
entry, like this:

```python
# GDPR Compliance
INSTALLED_APPS.insert(0, "aagdpr")
AVOID_CDN = True


# XIX Theme - https://gitlab.com/legion-of-xxdeathxx/aa-theme-xix
INSTALLED_APPS.insert(0, "aa_theme_xix")
```


## Updating
```shell
sudo su allianceserver
cd
source /home/allianceserver/venv/auth/bin/activate
cd aa-theme-xix/
git pull
cd ..
pip install -e aa-theme-xix/
```

After updating, run the command:
```shell
python /home/allianceserver/myauth/manage.py collectstatic --noinput
```
Topsis-Sarang-102353017 0.0.4
Command line implementation of TOPSIS method

# Topsis-Sarang-102353017

This package implements the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method as a Python command-line tool. It is developed as part of an academic assignment.

## Installation

pip install Topsis-Sarang-102353017

## Usage

topsis <input_file> <weights> <impacts> <output_file>

## Example

topsis data.csv "1,1,1,1,1" "+,+,+,-,-" result.csv

## Input File Requirements

- Input file must be in CSV format
- The first column should contain alternative names
- Remaining columns must contain numeric values
- Minimum three columns are required

## Output

The output CSV file contains the TOPSIS score and rank for each alternative.

## Dependencies

pandas  
numpy  

## Author

Sarang Priani  
Roll Number: 102353017  

## PyPI Link

https://pypi.org/project/Topsis-Sarang-102353017/

## License

This project is created for academic purposes.