semvec/pypi-packages huggingface dataset:
matplotlib-radar 0.2.0
Radar chart for matplotlib
Requires Python: >=3.11

# matplotlib-radar

[![Version](https://img.shields.io/pypi/v/matplotlib-radar)](https://pypi.org/project/matplotlib-radar/)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/matplotlib-radar)
[![codecov](https://codecov.io/gh/harryhaller001/matplotlib-radar/graph/badge.svg?token=WO0CGOVJOH)](https://codecov.io/gh/harryhaller001/matplotlib-radar)

Feature-rich generation of radar chart based on matplotlib.

## Quick start

Install package with `pip install matplotlib-radar` or `uv add matplotlib-radar`.

Example code to generate radar chart:

```python
from matplotlib_radar import radar_chart
import numpy as np

radar_chart(
    label=["A", "B", "C", "D", "E"],
    data={"Sample 1": np.random.rand(5), "Sample 2": np.random.rand(5), "Sample 3": np.random.rand(5)},
    rotation=5,
    title="Radar chart with multiple samples",
)
```


![Example radar chart](./docs/source/_static/image/example-radar-chart.jpg)

For more plotting examples, check out the vignette notebook in the documentation.

## Development

```bash
# Clone repo
git clone https://github.com/harryhaller001/matplotlib-radar
cd matplotlib-radar

# Setup virtualenv
uv venv

# Install dependencies and setup precommit hook
make install

# Run all checks
make format
make testing
make typing
make build
```

## License

MIT license
pkoffee 0.1.0
Coffee productivity analysis with statistical modeling
Requires Python: >=3.12

# PKoffee - Coffee Productivity Analysis

A Conda package implemented in python for analyzing the relationship between coffee consumption and productivity through statistical modeling and visualization.

Project inspired by _Le Café - Oldelaf_ for the S3 School 2026.

[![Le Café - Oldelaf - on YouTube](http://img.youtube.com/vi/UGtKGX8B9hU/0.jpg)](http://www.youtube.com/watch?v=UGtKGX8B9hU "Le Café - Oldelaf")


## Features

- **Data Analysis**: Load and analyze coffee consumption vs productivity datasets
- **Multiple Models**: Fit various mathematical models (quadratic, Michaelis-Menten, logistic, peak functions)
- **Visualization**: Beautiful violin plots with model overlays using Seaborn
- **Model Comparison**: Automatic R² scoring and ranking of models
- **Type-Safe**: Full type hints for better IDE support and code quality

## Installation

```bash
pixi install
```

## Quick Start

```python
from pathlib import Path
from pkoffee.data import load_csv
from pkoffee.productivity_analysis import fit_all_models
from pkoffee.visualization import plot_models

# Load your data
data = load_csv(Path("coffee_productivity.csv"))

# Fit models
fitted_models = fit_all_models(data)

# Create visualization
plot_models(data, fitted_models, output_path=Path("results.png"))
```

## Command Line Interface

```bash
# Analyze
cd analysis && pixi shell
# fit models and save to toml
pkoffee analyze --data-file coffee_productivity.csv --output fitted_models.toml --show-rankings
# plot models predictions
pkoffee plot --data-file coffee_productivity.csv --models fitted_models.toml --output analysis.png --y-max 7
```

## Project Structure

```
pkoffee-sol/
├── src/pkoffee/
│   ├── __init__.py               # Python package
│   ├── cli.py                    # Command line interface
│   ├── data.py                   # Data loading utilities
│   ├── fit_model.py              # Model definition and fitting
│   ├── fit_model_io.py           # Model writing/reading to file
│   ├── log.py                    # Logging utilities
│   ├── metrics.py                # Model evaluation metrics
│   ├── parametric_function.py    # Mathematical models as parametric functions
│   ├── productivity_analysis.py  # Coffee analysis
│   └── visualization.py          # Plotting functions
├── tests/
│   ├── test_data.py
│   ├── test_fit_model.py
│   ├── test_fit_model_io.py
│   ├── test_metrics.py
│   └── test_parametric_function.py
├── README.md
├── pixi.toml
├── pixi.lock
└── pyproject.toml
```

## Models Implemented

1. **Quadratic**: `f(x) = a₀ + a₁x + a₂x²`
2. **Michaelis-Menten (Saturating)**: `f(x) = y₀ + Vₘₐₓ·x/(K + x)`
3. **Logistic**: `f(x) = y₀ + L/(1 + e^(-k(x - x₀)))`
4. **Peak**: `f(x) = a·x·e^(-x/b)`
5. **Peak2**: `f(x) = a·x²·e^(-x/b)`

## Development

```bash
# Activate the environment
pixi shell
# use development dependencies like jupyter notebooks/ipython
jupyterlab
```
#### Tests
```bash
# Run tests
pixi run test
```

#### Linting
```bash
# Linting
pixi run lint
pixi run format
```

## License

MIT License
datajudge 1.13.0
datajudge allows to assess  whether data from database complies with referenceinformation.
Requires Python: >=3.10
Keywords: test, databases, validation

# datajudge

[![CI](https://github.com/Quantco/datajudge/actions/workflows/ci.yaml/badge.svg)](https://github.com/Quantco/datajudge/actions/workflows/ci.yaml)
[![Documentation Status](https://readthedocs.org/projects/datajudge/badge/?version=latest)](https://datajudge.readthedocs.io/en/latest/?badge=latest)
[![Conda-forge](https://img.shields.io/conda/vn/conda-forge/datajudge?logoColor=white&logo=conda-forge)](https://anaconda.org/conda-forge/datajudge)
[![PypiVersion](https://img.shields.io/pypi/v/datajudge.svg?logo=pypi&logoColor=white)](https://pypi.org/project/datajudge)
[![codecov.io](https://codecov.io/github/QuantCo/datajudge/coverage.svg?branch=main)](https://codecov.io/github/QuantCo/datajudge?branch=main)

Express and test specifications against data from database.

[Documentation](https://datajudge.readthedocs.io/en/latest/index.html)

# Supported backends

![PostgreSQL](https://img.shields.io/badge/PostgreSQL-4169E1?logo=postgresql&logoColor=white)
![Microsoft SQL Server](https://img.shields.io/badge/SQL_Server-CC2927?logo=microsoftsqlserver&logoColor=white)
![Snowflake](https://img.shields.io/badge/Snowflake-29B5E8?logo=snowflake&logoColor=white)
![Google BigQuery](https://img.shields.io/badge/BigQuery-669DF6?logo=googlebigquery&logoColor=white)
![DuckDB](https://img.shields.io/badge/DuckDB-FFF000?logo=duckdb&logoColor=black)

# Usage

`datajudge` can either be installed via pypi with `pip install datajudge` or via conda-forge with `conda install datajudge -c conda-forge`.

Please refer to the [Getting Started](https://datajudge.readthedocs.io/en/latest/getting-started/) section of our documentation for details.

Expressing an expectations between different tables from a database may look as such:

```python
from datajudge import BetweenRequirement

companies_between_req = BetweenRequirement.from_tables(
    db_name1="example",
    table_name1="companies",
    db_name2="example",
    table_name2="companies_archive",
)

companies_between_req.add_row_superset_constraint(
    columns1=["name"], columns2=["name"], constant_max_missing_fraction=0
)
```
topsis-vansh-102303137 1.0.1
TOPSIS implementation for MCDM problems

TOPSIS Python Package

Project-1 (UCS654)

Submitted by: Vansh Garg
Roll No: 102303137
Group: 3C15

Project Description:

topsis-vansh-102303137 is a Python package for solving Multiple Criteria Decision Making (MCDM) problems using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method.

This package helps in ranking alternatives based on their distance from the ideal best and ideal worst solutions.

Installation

Install the package using pip:
pip install topsis-vansh-102303137

Usage

Run the package directly from the command line:
topsis <input_file.csv> <weights> <impacts>


Example:
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+"

Weights and impacts can also be passed without quotes:
topsis sample.csv 0.25,0.25,0.25,0.25 +,+,-,+

⚠️ Important:
If your input contains spaces, always enclose it in double quotes " ".

Input File Format:
1. Input file must be a CSV file
2. First column → Alternative name (identifier)
3. Remaining columns → Numeric criteria only
4. No missing or categorical values allowed

Sample CSV (sample.csv):
Model,Storage,Camera,Price,Looks
M1,16,12,250,5
M2,16,8,200,3
M3,32,16,300,4
M4,32,8,275,4
M5,16,16,225,2

Parameters
Weights Vector
    Must be numeric
    Length = number of criteria

Example:
0.25,0.25,0.25,0.25


Impacts Vector
    Use + for benefit criteria
    Use - for cost criteria

Example:
+,+,-,+

Output
The program generates a CSV file with:
Topsis Score → Relative closeness to ideal solution
Rank → Higher score gets better rank

Sample Output:
      Topsis Score   Rank
1     0.534277        3
2     0.308368        5
3     0.691632        1
4     0.534737        2
5     0.401046        4

Notes
Higher Topsis Score indicates a better alternative
Rank 1 represents the best option
Input validation is performed for:

Missing values
Incorrect weights/impacts length
Invalid impact symbols
skelmis-docx 2.6.0
Create, read, and update Microsoft Word .docx files.
Requires Python: >=3.10
Keywords: Docx

# python-docx

*python-docx* is a Python library for reading, creating, and updating Microsoft Word 2007+ (.docx) files.

This repository exists as a fork of [the official repo](https://github.com/python-openxml/python-docx) as I needed features and quality of life improvements.

Key differences at a glance:
- Supporting multiple numbered lists within a document ([1](https://skelmis-docx.readthedocs.io/en/latest/api/text.html#skelmis.docx.text.paragraph.Paragraph.restart_numbering), [2](https://skelmis-docx.readthedocs.io/en/latest/api/document.html#skelmis.docx.document.Document.configure_styles_for_numbered_lists))
- Supporting TOC updates within the package without the need to open the document manually ([1](https://skelmis-docx.readthedocs.io/en/latest/api/utility.html#skelmis.docx.utility.update_toc), [2](https://skelmis-docx.readthedocs.io/en/latest/api/utility.html#skelmis.docx.utility.export_libre_macro))
- Supporting floating images within documents ([1](https://skelmis-docx.readthedocs.io/en/latest/api/text.html#skelmis.docx.text.run.Run.add_float_picture))
- Supporting the ability to transform word documents into PDF's ([1](https://skelmis-docx.readthedocs.io/en/latest/api/utility.html#skelmis.docx.utility.document_to_pdf))
- Horizontal rules + paragraph bounding boxes / borders ([1](https://skelmis-docx.readthedocs.io/en/latest/api/text.html#skelmis.docx.text.paragraph.Paragraph.insert_horizontal_rule), [2](https://skelmis-docx.readthedocs.io/en/latest/api/text.html#skelmis.docx.text.paragraph.Paragraph.draw_paragraph_border))
- External hyperlinks ([1](https://skelmis-docx.readthedocs.io/en/latest/api/text.html#skelmis.docx.text.paragraph.Paragraph.add_external_hyperlink))
- Internal hyperlinks (Linking to bookmarks) ([1](https://skelmis-docx.readthedocs.io/en/latest/api/text.html#skelmis.docx.text.paragraph.Paragraph.add_internal_hyperlink))
- Creating bookmarks ([1](https://skelmis-docx.readthedocs.io/en/latest/api/text.html#skelmis.docx.text.paragraph.Paragraph.add_bookmark))
- The ability to insert a customisable Table of Contents (ToC) ([1](https://skelmis-docx.readthedocs.io/en/latest/api/text.html#skelmis.docx.text.paragraph.Paragraph.insert_table_of_contents))

## Installation

```
pip install skelmis-docx
```

## Example

```python
>>> from skelmis.docx import Document

>>> document = Document()
>>> document.add_paragraph("It was a dark and stormy night.")
<docx.text.paragraph.Paragraph object at 0x10f19e760>
>>> document.save("dark-and-stormy.docx")

>>> document = Document("dark-and-stormy.docx")
>>> document.paragraphs[0].text
'It was a dark and stormy night.'
```

More information is available in the [documentation](https://skelmis-docx.readthedocs.io/en/latest/)
netbox-gateways 0.9.2
Manage simple prefix default gateways


# NetBox Gateways Plugin
[Netbox](https://github.com/netbox-community/netbox) plugin  to Manage simple default gateways for prefixes.

## Compatibility

|      Netbox Version       |   Plugin Version    |
|---------------------------|-------|
| Netbox 4.5                | 0.9.0 |
| Netbox 4.3                | 0.8.0 |
| Netbox 4.2                | 0.7.0 |
| NetBox 4.1                | 0.6.0 |
| NetBox 4.0                | 0.5.0 |
| NetBox 3.2                | 0.4.2 |

## Installation

1. The plugin is available as a Python package in pypi and can be installed with pip  

```
pip install netbox-gateways
```
2. Enable the plugin in /opt/netbox/netbox/netbox/configuration.py:
```
PLUGINS = ['netbox_gateways']
```

3. Add `netbox-gateways` to your local_requirements.txt to ensure plugin is re-installed during a future upgrade or execution of .upgrade.sh script

```
echo netbox-gateways >> /opt/netbox/local_requirements.txt
```

4. Run Migrate

```
python3 netbox/manage.py migrate
```

## Screenshots

Prefix View
![Prefix View](/docs/images/prefix_view_gw.png)

Gateways
![Gateways](docs/images/gateways_table_view.png)

Add New Gateway
![Add New Gateway](/docs/images/add_new_gw.png)