semvec/pypi-packages huggingface dataset:
git-pluse 1.0.0
GitHub commit analyzer - analyze and visualize commit frequency
License: MIT
Requires Python: >=3.7
Keywords: github, git, commit, analysis, visualization

# git-pluse

A GitHub commit analyzer that fetches commit history and visualizes cumulative commits over time.

## Installation

```bash
pip install git-pluse
```

## Usage

### Basic usage - generate JSON output

```bash
git-pluse https://github.com/erquren/git-pluse
```

This will create a JSON file named `repo.json` containing detailed commit records organized by date.

### Generate visualization

```bash
git-pluse https://github.com/erquren/git-pluse show
```

This will create both a JSON file (`repo.json`) and a PNG chart (`repo.png`) showing cumulative commits.

## Features

- Fetches all commits from a GitHub repository
- Generates JSON output with commit details (user, message, sha, time) organized by date
- Visualizes cumulative commits over time with line chart
- Dynamic tick intervals based on actual project duration:
  - ≤ 30 days: daily ticks
  - ≤ 90 days: weekly ticks
  - ≤ 365 days: monthly ticks
  - ≤ 730 days: bi-monthly ticks
  - > 730 days: quarterly ticks

## JSON Output Format

```json
{
  "repo": "erquren/git-pluse",
  "url": "https://github.com/erquren/git-pluse",
  "total_commits": 1234,
  "commits_by_date": {
    "2024-01-01": [
      {
        "user": "John Doe",
        "message": "Initial commit",
        "sha": "abc123...",
        "time": "2024-01-01T12:00:00Z"
      }
    ]
  },
  "first_commit_date": "2024-01-01",
  "last_commit_date": "2024-12-31"
}
```

## Chart Features

- **X-axis**: Date
- **Y-axis**: Cumulative commit count
- Dynamic tick intervals based on project duration
- Clean line chart with data point markers
- Area fill for better visualization

## Requirements

- Python 3.7+
- requests
- matplotlib

## License

MIT License
piecewise-rational 1.0.0
Shape-preserving piecewise rational cubic interpolation (Delbourgo-Gregory algorithm)
License: MIT
Requires Python: <4.0,>=3.9
Keywords: interpolation, rational cubic, shape preserving, monotonic, convex

# piecewise-rational

Shape-preserving piecewise rational cubic interpolation using the Delbourgo-Gregory algorithm.

## Installation

```bash
pip install piecewise-rational
```

## Usage

```python
from piecewise_rational import rational_cubic_interpolation

# Interpolate at x=0.5 given endpoints and derivatives
x_l, x_r = 0.0, 1.0      # interval
y_l, y_r = 0.0, 1.0      # values at endpoints
d_l, d_r = 1.0, 1.0      # derivatives at endpoints
r = 0.0                   # control parameter

y = rational_cubic_interpolation(0.5, x_l, x_r, y_l, y_r, d_l, d_r, r)
```

## Features

- Shape-preserving interpolation (maintains monotonicity and convexity)
- Control parameters for fitting second derivatives
- High numerical precision

## References

- R. Delbourgo, J.A. Gregory, "Shape preserving piecewise rational interpolation",
  SIAM Journal on Scientific and Statistical Computing, 1985.
- Peter Jaeckel, "Let's Be Rational", www.jaeckel.org

## License

MIT License. Python implementation derived from Peter Jaeckel's LetsBeRational.
cody-special 1.0.0
High-precision error functions and normal distribution (Cody's algorithm)
License: MIT
Requires Python: <4.0,>=3.9
Keywords: error function, erf, erfc, normal distribution, cdf, statistics

# cody-special

High-precision error functions and normal distribution implementations based on W.J. Cody's rational Chebyshev approximations.

## Installation

```bash
pip install cody-special
```

## Usage

```python
from cody_special import erf_cody, erfc_cody, erfcx_cody
from cody_special import norm_pdf, norm_cdf, inverse_norm_cdf

# Error functions
erf_cody(1.0)      # ~ 0.8427
erfc_cody(1.0)     # ~ 0.1573
erfcx_cody(1.0)    # ~ 0.4276

# Normal distribution
norm_pdf(0.0)           # ~ 0.3989 (standard normal PDF)
norm_cdf(0.0)           # = 0.5 (standard normal CDF)
inverse_norm_cdf(0.5)   # = 0.0 (inverse CDF / quantile function)
```

## References

- W.J. Cody, "Rational Chebyshev approximations for the error function", Math. Comp., 1969, pp. 631-638.
- Peter Jaeckel, "Let's Be Rational", www.jaeckel.org

## License

MIT License. Original algorithm by W.J. Cody. Python implementation derived from Peter Jaeckel's LetsBeRational.