semvec/pypi-packages huggingface dataset:
nys-constants 0.1.0
Core constants and enums for the Noyes system
Requires Python: >=3.8

# NysConstants

Core constants and enums for the Noyes system.

## Installation

```bash
pip install nys_constants
```

## Usage

```python
from nys_constants.nys_constants import (
    RequestType,
    RequestStatus,
    JobType,
    JobStatus,
    TriggerStatus,
    SortOrder,
    EntityType,
    LogLevel,
    OnEmptySKUAction,
    MeasurementUnit,
    StorageModuleType,
    LevelStatus,
    StorageStatus
)

# Use the enums
request_type = RequestType.FULFILLMENT
job_status = JobStatus.EXECUTING
```

## Development

This package is part of the Noyes monorepo. To install in development mode:

```bash
pip install -e .
```
fastapi-filters 0.3.1
fastapi-filters
Requires Python: <4.0,>=3.10

<h1 align="center">
<img alt="logo" src="https://raw.githubusercontent.com/uriyyo/fastapi-filters/main/logo.png">
</h1>

<div align="center">
<img alt="license" src="https://img.shields.io/badge/License-MIT-lightgrey">
<img alt="test" src="https://github.com/uriyyo/fastapi-filters/workflows/Test/badge.svg">
<img alt="codecov" src="https://codecov.io/gh/uriyyo/fastapi-filters/branch/main/graph/badge.svg?token=QqIqDQ7FZi">
<a href="https://pepy.tech/project/fastapi-filters"><img alt="downloads" src="https://pepy.tech/badge/fastapi-filters"></a>
<a href="https://pypi.org/project/fastapi-filters"><img alt="pypi" src="https://img.shields.io/pypi/v/fastapi-filters"></a>
<img alt="black" src="https://img.shields.io/badge/code%20style-black-000000.svg">
</div>

## Introduction

`fastapi-filters` is a library that provides filtering/sorting feature for [FastAPI](https://fastapi.tiangolo.com/)
applications.

----

## Installation

```bash
pip install fastapi-filters
```

## Quickstart

To create filters you need either define them manually using `create_filters` function or automatically generate them
based on model using `create_filters_from_model` function.

```py
from typing import List

from fastapi import FastAPI, Depends
from pydantic import BaseModel, Field

# import all you need from fastapi-filters
from fastapi_filters import create_filters, create_filters_from_model, FilterValues

app = FastAPI()  # create FastAPI app


class UserOut(BaseModel):  # define your model
    name: str = Field(..., example="Steve")
    surname: str = Field(..., example="Rogers")
    age: int = Field(..., example=102)


@app.get("/users")
async def get_users_manual_filters(
    # manually define filters
    filters: FilterValues = Depends(create_filters(name=str, surname=str, age=int)),
) -> List[UserOut]:
    pass


@app.get("/users")
async def get_users_auto_filters(
    # or automatically generate filters from pydantic model
    filters: FilterValues = Depends(create_filters_from_model(UserOut)),
) -> List[UserOut]:
    pass
```

Currently, `fastapi-filters` supports `SQLAlchemy` integration.

```py
from fastapi_filters.ext.sqlalchemy import apply_filters


@app.get("/users")
async def get_users(
    db: AsyncSession = Depends(get_db),
    filters: FilterValues = Depends(create_filters_from_model(UserOut)),
) -> List[UserOut]:
    query = apply_filters(select(UserOut), filters)
    return (await db.scalars(query)).all()
```
tadasets 0.2.2
Great data sets for Topological Data Analysis.
Keywords: topological data analysis, data sets, test data, algebraic topology

[![PyPI version](https://badge.fury.io/py/tadasets.svg)](https://badge.fury.io/py/tadasets)
![PyPI - Downloads](https://img.shields.io/pypi/dm/tadasets)
[![Codecov](https://codecov.io/gh/scikit-tda/tadasets/branch/master/graph/badge.svg)](https://codecov.io/gh/scikit-tda/tadasets)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

This package provides some nice utilities for creating and loading data sets
that are useful for Topological Data Analysis. Currently, we provide various
synthetic data sets with particular topological features.

# Setup

Installation is as easy as

```
pip install tadasets
```

# Usage

The shape constructors are exposed in a functional interface, each returning a numpy array containing data sampled on the object. Available objects include

- torus
- d-sphere
- swiss roll
- infinity sign
- eyeglasses

Each shape can be embedded in arbitrary ambient dimension by supplying the `ambient` argument. Additionally, noise can be added to the shape through the `noise` argument.

```python
import tadasets

torus = tadasets.torus(n=2000, c=2, a=1, ambient=200, noise=0.2)
swiss_roll = tadasets.swiss_roll(n=2000, r=4, ambient=10, noise=1.2)
dsphere = tadasets.dsphere(n=1000, d=12, r=3.14, ambient=14, noise=0.14)
infty_sign = tadasets.infty_sign(n=3000, noise=0.1)
eyeglasses = tadasets.eyeglasses(n=1000, r1=1, r2=2, neck_size=.5, noise=0.1, ambient=10)
```

## Contributions

We welcome contributions of all shapes and sizes. There are lots of opportunities for potential projects, so please get in touch if you would like to help out. Everything from an implementation of your favorite distance, notebooks, examples, and documentation are all equally valuable so please don’t feel you can’t contribute.

If you have ideas for new shapes or features, please do suggest them in an issue and submit a pull request!

To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.
mkvinfo 0.0.3
Python library for probing matroska files with mkvmerge.
Requires Python: >=3.10

# mkvinfo

[![PyPI - Version](https://img.shields.io/pypi/v/mkvinfo?link=https%3A%2F%2Fpypi.org%2Fproject%2Fmkvinfo%2F)](https://pypi.org/project/mkvinfo/)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mkvinfo)
![License](https://img.shields.io/github/license/Ravencentric/mkvinfo)
![PyPI - Types](https://img.shields.io/pypi/types/mkvinfo)

![GitHub Build Workflow Status](https://img.shields.io/github/actions/workflow/status/Ravencentric/mkvinfo/release.yml)
![GitHub Tests Workflow Status](https://img.shields.io/github/actions/workflow/status/ravencentric/mkvinfo/tests.yml?label=tests)
[![codecov](https://codecov.io/gh/Ravencentric/mkvinfo/graph/badge.svg?token=96UDXZHP41)](https://codecov.io/gh/Ravencentric/mkvinfo)

Python library for probing [matroska](https://www.matroska.org/index.html) files with [`mkvmerge`](https://mkvtoolnix.download/doc/mkvmerge.html).

## Installation

`mkvinfo` is available on [PyPI](https://pypi.org/project/mkvinfo/), so you can simply use [pip](https://github.com/pypa/pip) to install it.

```sh
pip install mkvinfo
```

## Usage

```py
from mkvinfo import MKVInfo

mkv = MKVInfo.from_file("./Big Buck Bunny, Sunflower version.mkv")

assert mkv.file_name == "Big Buck Bunny, Sunflower version.mkv"
assert mkv.container.properties.title == "Big Buck Bunny, Sunflower version"
assert mkv.container.properties.writing_application == "mkvmerge v92.0 ('Everglow') 64-bit"

for track in mkv.tracks:
    print(f"{track.id} - {track.codec}")
    #> 0 - AVC/H.264/MPEG-4p10
    #> 1 - MP3
    #> 2 - AC-3
```

Checkout the complete documentation [here](https://ravencentric.cc/mkvinfo/).

## License

Distributed under the [MIT](https://choosealicense.com/licenses/mit/) License. See [LICENSE](https://github.com/Ravencentric/mkvinfo/blob/main/LICENSE) for more information.