semvec/pypi-packages huggingface dataset:
semtag 0.3.3
A tool for managing semantic version tags in git repositories
Requires Python: >=3.8
Keywords: git, tags, semantic-versioning, semver, version-control

# Semantic Version GIT Tagger

A pretty trivial python script to easily manage git tags with semantic versioning (semver.org)  

## Usage

```bash
semtag [options]
```

### Options

<!-- OPTIONS:START -->
```
  -h, --help         show this help message and exit
  -v, --verbose      Verbosity (-v for INFO, -vv for DEBUG)
  -b, --by BY        Increment by a specific number
  -p, --patch        Increment patch version (x.x.PATCH)
  -m, --minor        Increment minor version (x.MINOR.0)
  -M, --major        Increment major version (MAJOR.0.0)
  -l, --label LABEL  Add label to the version (e.g., -l rc1 creates
                     1.0.0-rc1). Used alone, adds label to current version
  -a, --msg MSG      Annotated tags message
  -u, --push         Push the new tag to remote repository
  -U, --pushall      Push all local tags to remote repository
  -n, --no-fetch     Do not fetch tags from remote before operation
```
<!-- OPTIONS:END -->

### Examples

```bash
# Increment minor version and push to origin (1.0.0 -> 1.1.0)
semtag -mu

# Increment major version (1.0.0 -> 2.0.0)
semtag -M

# Increment patch version (1.0.0 -> 1.0.5)
semtag -u -p -b 5

# Increment patch and add label (1.0.0 -> 1.0.1-rc1)
semtag -u -pl rc1
```

## Installation

Using pip  (preferred)
```bash
pip install semtag
```

Using Homebrew (Mac)

```bash
brew install mateuszmikrut/tap/semtag
```

Using .deb package

<!-- VERSION:DEB:START -->
```bash
VERSION="0.3.2"
curl -LO "https://github.com/mateuszmikrut/semtag/releases/download/${VERSION}/semtag-${VERSION}-1_amd64.deb"
sudo dpkg -i "semtag-${VERSION}-1_amd64.deb"
```
<!-- VERSION:DEB:END -->

Using .rpm package

<!-- VERSION:RPM:START -->
```bash
VERSION="0.3.2"
sudo dnf install https://github.com/mateuszmikrut/semtag/releases/download/${VERSION}/semtag-${VERSION}-1.x86_64.rpm
```
<!-- VERSION:RPM:END -->

From source
```bash
git clone https://github.com/mateuszmikrut/semtag.git
cd semtag
python -m venv venv
source ./venv/bin/activate
pip install -r requirements.txt
python ./semtag.py
```

## Releases

https://github.com/mateuszmikrut/semtag/releases/ 

## Supported Version Formats

The script supports semantic versioning with the following formats:
- `v1.0.0` (with 'v' prefix)
- `1.0.0` (without prefix)
- `1.0.0-rc1` (with prerelease label)

When incrementing versions, prerelease labels are automatically removed.
syllabify 0.5.0
Automatically convert plain text into phonemes (US English pronunciation) and syllabify
Requires Python: >=3.10
Keywords: phonetics, syllabification, linguistics, cmu, pronunciation

# Syllabify

[![PyPI](https://img.shields.io/pypi/v/syllabify.svg)](https://pypi.org/project/syllabify/)
[![Python 3.x](https://img.shields.io/pypi/pyversions/syllabify.svg?logo=python&logoColor=white)](https://pypi.org/project/syllabify/)
[![License](https://img.shields.io/github/license/eoleedi/TimeTree-Exporter)](https://github.com/eoleedi/TimeTree-Exporter/blob/main/LICENSE)

Automatically convert plain text into phonemes (US English pronunciation) and syllabify.

Modified from [the repository](https://github.com/cainesap/syllabify) set up by Andrew Caines with some key changes, itemised below:

- Environment management using Poetry
- Python 3.10+ compatibility
- Easy to access class and function interfaces
- Use up-to-date cmudict versioned by [python-cmudict](https://github.com/prosegrinder/python-cmudict)

## Set up

Requires [Python 3](https://www.python.org/downloads) (Anthony Evans used Python 2: if that's what you prefer, see his repo).

```bash
pip install syllabify
```

## Usage

### Package interface

```python
from syllabify import syllabify
word = syllabify("linguistics")
print(word)
```

```text
L IH0 NG {onset: L, nucleus: IH0, coda: NG}
G W IH1 {onset: GW, nucleus: IH1, coda: empty}
S T IH0 K S {onset: ST, nucleus: IH0, coda: KS}
```

You can get the onset, nucleus, and coda of each syllable:

```python
from syllabify import syllabify

word = syllabify("linguistics")
for syllable in word.syllables:
    print(f"Onset: {syllable.onset}")
    print(f"Nucleus: {syllable.nucleus}")
    print(f"Coda: {syllable.coda}")
```

```text
Onset: L
Nucleus: IH0
Coda: NG
Onset: GW
Nucleus: IH1
Coda: empty
Onset: ST
Nucleus: IH0
Coda: KS
```

### Command line interface

One word at a time:

```bash
syllabify linguistics
```

```text
📝 Input: linguistics
🔤 Syllabification:

LIH0NG [L.IH0.NG] | ˈGWIH1 [GW.IH1.∅] | STIH0KS [ST.IH0.KS]

📖 Legend:
  [onset.nucleus.coda] = syllable structure
  ∅ = empty position
  ˈ = primary stress
  ˌ = secondary stress
  | = syllable boundary
```

Or several (space-separated):

```bash
syllabify colorless green ideas
```

```text
📝 Input: colorless green ideas
🔤 Syllabification:

  ˈKAH1 [K.AH1.∅] | LER0 [L.ER0.∅] | LAH0S [L.AH0.S]
  ˈGRIY1N [GR.IY1.N]
  AY0 [∅.AY0.∅] | ˈDIY1 [D.IY1.∅] | AH0Z [∅.AH0.Z]

📖 Legend:
  [onset.nucleus.coda] = syllable structure
  ∅ = empty position
  ˈ = primary stress
  ˌ = secondary stress
  | = syllable boundary
```
breathe-design 1.6.0
A Python library for interating with the Breathe Design api and analyzing and visualizing Breathe Design data
Requires Python: >=3.9

# Breathe Design

A python based api wrapper for running the breathe design model.

> **Registration Required**: To use the Breathe Design API, you must register for a free trial. You can sign up at [https://www.breathebatteries.com/breathe-design-free-trial](https://www.breathebatteries.com/breathe-design-free-trial).

## Installation steps

### In a new project or existing project

In the virtual environment in your new or existing project, simply install `breathe_design` with

```
pip install breathe_design
```

### Using this repo

If you want to work with the examples in this repo, follow these steps...

- Open your terminal or command prompt.
- Navigate to the directory or create new one where you want to setup.
- Run the following command to clone our GitHub repository.

  ```bash
  git clone https://github.com/BreatheBatteries/breathe_design.git .
  ```

To set up everything in one go, just run `.\setupEnvironment.ps1`.

That will create a virtual environment and install the requirements.

## Alternative manual setup

Alternatively, run following command to create the virtual environment:

```bash
python -m venv myvenv
```

Replace `myvenv` with the desired name for your virtual environment.

- Activate the virtual environment:

  - On **Windows**:
    ```cmd
    myvenv\Scripts\activate
    ```
  - On **macOS/Linux**:
    ```bash
    source myvenv/bin/activate
    ```

  After activating the virtual environment, you'll see `(myenv)` in your terminal prompt to indicate that the environment is active.

- Install the `breathe_design` package in the virtual environment:

  ```
  pip install breathe_design
  ```

# Examples

Check out the python notebooks in [docs/examples](docs/examples) for how to use the api.

# Reporting Bugs

If you find a bug, or run into an error, please raise an issue on the GitHub account.
kyrgyz-normalizer 0.1.1
Кыргызча текст нормализатор NLP тапшырмалары үчүн / Kyrgyz text normalizer for NLP tasks
Requires Python: >=3.7
Keywords: kyrgyz, normalizer, nlp, text-processing, кыргызча

# kyrgyz-normalizer

Кыргызча текст нормализатор — NLP тапшырмалары үчүн.

Сандарды, даталарды, убакытты, акча бирдиктерин, аббревиатураларды жана башка форматтарды кыргыз тилинде сөзгө айландырат.

## Орнотуу / Installation

```bash
pip install kyrgyz-normalizer
```

## Колдонуу / Usage

```python
from kyrgyz_normalizer import normalize

# Сандар
normalize("2024-жылы 15 км жол курулду")
# → "эки миң жыйырма төртүнчү жылы он беш километр жол курулду"

# Акча
normalize("Баасы 1500 сом")
# → "Баасы бир миң беш жүз сом"

# Даталар
normalize("01.02.2024")
# → "эки миң жыйырма төртүнчү жыл биринчи февраль"

# Убакыт
normalize("Саат 12:30да башталат")
# → "Саат он эки отузда башталат"

# Аббревиатуралар
normalize("КР Президенти БУУнун жыйынында")
# → "кыргыз республикасы Президенти бириккен улуттар уюмунун жыйынында"
```

### KyrgyzTextNormalizer классы

```python
from kyrgyz_normalizer import KyrgyzTextNormalizer

normalizer = KyrgyzTextNormalizer()

# Негизги нормализация
normalizer.normalize("$500 доллар")

# Санды сөзгө
normalizer.number_to_words(2024)  # "эки миң жыйырма төрт"

# Иреттик сан
normalizer.number_to_ordinal(5)  # "бешинчи"

# Римские цифры
normalizer.roman_to_number("XIV")  # 14
```

## Мүмкүнчүлүктөр / Features

- Сандарды сөзгө айландыруу (0 — триллиондорго чейин)
- Иреттик сандар (биринчи, экинчи, ...)
- Даталар (01.02.2024, 2024-08-15, 30 Ноябрь 2025)
- Убакыт (12:30, саат 15.00, 18:30да)
- Акча бирдиктери (сом, $, €, ₽, ...)
- Өлчөм бирдиктери (км, кг, м², ГБ, ...)
- Кыргызча аббревиатуралар (КР, БУУ, ЖЧК, ...)
- Англисче аббревиатуралар (IT, AI, GPS, ...)
- Телефон номерлери (+996, 0XXX)
- Римские цифры (XIV → он төртүнчү)
- Математикалык амалдар (5+3=8)
- Сингармонизм (кыргыз тилинин үндөшүүк закону)
- Пайыздар, бөлчөктөр, диапазондор

## Лицензия / License

MIT
dataframely 2.7.0
A declarative, polars-native data frame validation library
Requires Python: >=3.10

<!-- LOGO -->
<br />

<div align="center">

  <h3 align="center">
  <code>dataframely</code> — A declarative, 🐻‍❄️-native data frame validation library
  </h3>

[![CI](https://img.shields.io/github/actions/workflow/status/quantco/dataframely/ci.yml?style=flat-square&branch=main)](https://github.com/quantco/dataframely/actions/workflows/ci.yml)
[![Nightly CI](https://img.shields.io/github/actions/workflow/status/quantco/dataframely/nightly.yml?style=flat-square&branch=main)](https://github.com/quantco/dataframely/actions/workflows/nightly.yml)
[![conda-forge](https://img.shields.io/conda/vn/conda-forge/dataframely?logoColor=white&logo=conda-forge&style=flat-square)](https://prefix.dev/channels/conda-forge/packages/dataframely)
[![pypi-version](https://img.shields.io/pypi/v/dataframely.svg?logo=pypi&logoColor=white&style=flat-square)](https://pypi.org/project/dataframely)
[![python-version](https://img.shields.io/pypi/pyversions/dataframely?logoColor=white&logo=python&style=flat-square)](https://pypi.org/project/dataframely)
[![codecov](https://codecov.io/gh/Quantco/dataframely/graph/badge.svg?token=QOvhS7Zri2)](https://codecov.io/gh/Quantco/dataframely)

</div>

## 🗂 Table of Contents

- [Introduction](#-introduction)
- [Installation](#-installation)
- [Usage](#-usage)

## 📖 Introduction

Dataframely is a Python package to validate the schema and content of [`polars`](https://pola.rs/) data frames. Its
purpose is to make data pipelines more robust by ensuring that data meets expectations and more readable by adding
schema information to data frame type hints.

## 💿 Installation

You can install `dataframely` using your favorite package manager, e.g., `pixi` or `pip`:

```bash
pixi add dataframely
pip install dataframely
```

## 🎯 Usage

### Defining a data frame schema

```python
import dataframely as dy
import polars as pl

class HouseSchema(dy.Schema):
    zip_code = dy.String(nullable=False, min_length=3)
    num_bedrooms = dy.UInt8(nullable=False)
    num_bathrooms = dy.UInt8(nullable=False)
    price = dy.Float64(nullable=False)

    @dy.rule()
    def reasonable_bathroom_to_bedroom_ratio(cls) -> pl.Expr:
        ratio = pl.col("num_bathrooms") / pl.col("num_bedrooms")
        return (ratio >= 1 / 3) & (ratio <= 3)

    @dy.rule(group_by=["zip_code"])
    def minimum_zip_code_count(cls) -> pl.Expr:
        return pl.len() >= 2
```

### Validating data against schema

```python

import polars as pl

df = pl.DataFrame({
    "zip_code": ["01234", "01234", "1", "213", "123", "213"],
    "num_bedrooms": [2, 2, 1, None, None, 2],
    "num_bathrooms": [1, 2, 1, 1, 0, 8],
    "price": [100_000, 110_000, 50_000, 80_000, 60_000, 160_000]
})

# Validate the data and cast columns to expected types
validated_df: dy.DataFrame[HouseSchema] = HouseSchema.validate(df, cast=True)
```

See more advanced usage examples in the [documentation](https://dataframely.readthedocs.io/stable/).
umep-reqs 2.9
UMEP requirements package
Requires Python: >=3.7

# umep-reqs
A minimal Python package outlining UMEP dependencies.

## Installation

```bash
pip install umep-reqs
```

## Version Management

This package uses `setuptools_scm` for automatic versioning based on Git tags. The package version is automatically determined from the latest Git tag.

### How to Update Dependencies

When a new version of SuPy or other dependencies is released:

1. **Update the dependency version** in `pyproject.toml`:
   ```toml
   dependencies = [
       "supy==2025.6.2.dev0",  # Update this version number
       "numba==0.59.0",
       # ... other dependencies
   ]
   ```

2. **Commit your changes**:
   ```bash
   git add pyproject.toml
   git commit -m "Update SuPy to version X.X.X"
   ```

3. **Create a new release tag**:
   ```bash
   git tag 2.6  # Use the next version number
   git push origin main
   git push origin 2.6
   ```

4. **The CI/CD pipeline will automatically**:
   - Build the package with the new dependencies
   - Publish to Test PyPI (for all pushes)
   - Publish to PyPI (for tagged releases)
   - Create a GitHub release

### Important Notes

- **DO NOT** manually update version numbers in code - they are managed by Git tags
- All dependency updates should be made in `pyproject.toml`
- The `setup.py` file has been removed in favour of modern `pyproject.toml` configuration
- Version numbers follow semantic versioning (MAJOR.MINOR.PATCH)