semvec/pypi-packages huggingface dataset:
mathipy 0.1.1
Multimodal item feature extraction for K-12 math assessment
Requires Python: >=3.9
Keywords: math, assessment, education, readability, k-12

<img src="https://raw.githubusercontent.com/mshin77/mathipy/main/docs_src/_static/logo.svg" alt="MathiPy Logo" align="right" width="220px"/>

[![PyPI version](https://img.shields.io/pypi/v/mathipy)](https://pypi.org/project/mathipy/)
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[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)

Multimodal item feature extraction for K-12 math assessment. Analyze readability with math-aware normalization via [textstat](https://github.com/textstat/textstat) and [NLTK](https://www.nltk.org/), classify math content by [Common Core State Standards for Mathematics](https://www.thecorestandards.org/Math/) domain, estimate cognitive load components, extract visual complexity features from images using [OpenCV](https://opencv.org/) and [Pillow](https://pillow.readthedocs.io/), and perform multimodal optical character recognition (OCR) through [Gemini](https://ai.google.dev/) and [OpenAI](https://platform.openai.com/) vision APIs.

## Installation

```bash
pip install mathipy
```

With optional dependencies:

```bash
pip install mathipy[nlp]        # readability (textstat, nltk)
pip install mathipy[vision]     # visual analysis (opencv, pillow)
pip install mathipy[ocr]        # OCR via vision LLMs (httpx)
pip install mathipy[documents]  # document parsing (python-docx, pdfplumber)
pip install mathipy[all]        # all features
```

From GitHub:

```bash
pip install git+https://github.com/mshin77/mathipy.git[all]
```

## Getting Started

See [Quick Start](https://mshin77.github.io/mathipy/getting-started.html) and [Analyzing Math Items](https://mshin77.github.io/mathipy/vignettes/naep-demo.html) for tutorials.

## Citation

- Shin, M. (2026). *MathiPy: Multimodal item feature extraction for K-12 math assessment* (Python package version 0.1.1) [Computer software]. <https://github.com/mshin77/mathipy>
pytides-py3 0.8.0
조석 분석 및 예측을 위한 Python 라이브러리 (Tidal analysis and prediction library)
Requires Python: <3.14,>=3.11

# pytides-py3

> Tidal analysis and prediction library for Python 3.11+.
> An improved fork of the original [pytides](https://github.com/sam-cox/pytides), updated for NumPy 2.x and modern SciPy.

## Installation

```bash
pip install pytides-py3
```

Or with UV:

```bash
uv add pytides-py3
```

## Quick Start

```python
from datetime import datetime, timedelta
from pytidespy3 import Tide, constituent

# Build a model from known constituents
model = Tide(
    constituents=[constituent._M2, constituent._S2, constituent._K1],
    amplitudes=[1.0, 0.5, 0.3],
    phases=[0.0, 90.0, 180.0],
)

# Predict tidal heights
times = [datetime(2023, 1, 1) + timedelta(hours=i) for i in range(24)]
heights = model.at(times)

# Get high/low water times
highs = list(model.highs(datetime(2023, 1, 1), datetime(2023, 1, 8)))
lows = list(model.lows(datetime(2023, 1, 1), datetime(2023, 1, 8)))

# Decompose observed data into harmonic constituents
fitted = Tide.decompose(heights=observed_heights, t=observed_times)
```

## Main Features

- Tidal analysis and prediction via Schureman's harmonic constituent method
- 37 NOAA harmonic constituents (M2, S2, K1, O1, N2, etc.)
- Robust fitting: NaN/inf auto-removal, weighted least squares, robust loss functions (`huber`, `soft_l1`, `cauchy`, `arctan`)
- High/low water prediction with `highs()`, `lows()`, `extrema()`
- Tide classification: form number-based (semidiurnal, mixed, diurnal)

## Requirements

- Python >= 3.11, < 3.14
- NumPy >= 2.3.1
- SciPy >= 1.16.0

## Development

```bash
uv sync
uv run python -m unittest discover -s tests -v
uv build
```

## References

- Original pytides: https://github.com/sam-cox/pytides
- Schureman, P. (1958). __Manual of Harmonic Analysis and Prediction of Tides__. U.S. Coast and Geodetic Survey, Special Publication No. 98.
    - [Link1](https://tidesandcurrents.noaa.gov/publications/SpecialPubNo98.pdf)
- Meeus, J. (1991). __Astronomical Algorithms__. Willmann-Bell.

## License

MIT