semvec/pypi-packages huggingface dataset:
food-python 0.1.0
A framework for out-of-distribution and anomaly detection
Requires Python: >=3.6.0

# food
A framework for out-of-distribution and anomaly detection.

## Installation
From the root directory run

```
  pip install .
```
or
```
  python setup.py install [--user]
```

## Usage
```
from food import AngleSoftmax
```

## Requirements
 * torch>=1.3.0
 * numpy>=1.16.0
 * cv2>=4.1.0
 * to be filled
proust 0.1.2
Online time series analysis
Requires Python: >=3.5
Keywords: timeseries,time,series,analysis

# Proust
Online time series analysis

[![pypi](https://img.shields.io/pypi/v/proust)](https://pypi.org/project/proust/)
[![build](https://github.com/danielsuo/proust/workflows/build/badge.svg)](https://github.com/danielsuo/proust/actions)
[![coverage](https://img.shields.io/codecov/c/github/danielsuo/proust)](https://codecov.io/github/danielsuo/proust)
[![readthedocs](https://img.shields.io/readthedocs/proust)](https://readthedocs.org/projects/proust/)
[![doc coverage](https://raw.githubusercontent.com/danielsuo/proust/master/.github/badges/docstring_coverage.svg?sanitize=true)](https://readthedocs.org/projects/proust/)
[![tests](https://img.shields.io/azure-devops/tests/danielsuo/proust/1?compact_message)](https://dev.azure.com/danielsuo/proust/_build?definitionId=1&_a=summary)
[![smells](https://sonarcloud.io/api/project_badges/measure?project=danielsuo_proust&metric=code_smells)](https://sonarcloud.io/dashboard?id=danielsuo_proust)

## Setting up a development environment
[Insert typical language about using some python environment]. Within that
environment, run the following:

```python
# Install editable version of code
pip install -e .

# Install testing dependencies
pip install -e .[testing]

# Install pre-commit hooks
pre-commit install
```

## Contributing
We use the `pre-commit` tools to automatically lint (and fix, where possible)
for Python warnings, errors, and style. This runs automatically on `git commit`
and will either pass with no issue, make changes to files, and / or ask you to
make fixes. If the tests don't pass, the commit fails (a good thing! Keeps
history clean).

### Testing
We also use `pytest`, which we encourage you to run before contributing (`pytest
-n auto` for parallelized testing).

### Documentation
To update the doc coverage badge, run
```bash
docstring-coverage -b .github/badges/docstring_coverage.svg .
```

### Versioning
To update the version, run
```bash
bumpversion [major|minor|patch]
```
longleding-ocr-engine-sdk 0.1.3
Longleding OCR Engine SDK
Requires Python: >=3.6

Longleding Operation OCR Engine SDK

# Supported Python Versions

Python >= 3.6

# Installation

longleding-ocr-engine-sdk is available for Linux, macOS, and Windows.

```shell script
$ pip install longleding-ocr-engine-sdk
```

# Basic Usage

```python
# -*- coding: utf-8 -*-
import ocr_engine

ocr_engine_endpoint = "localhost:80"
ocr_engine_src = "demo"
ocr_engine.init_service(endpoint=ocr_engine_endpoint, src=ocr_engine_src)

if __name__ == '__main__':
    print(ocr_engine.ocr_with_image_oss_path('upload/some_image.jpg'))
    print(ocr_engine.ocr_with_image_url('https://some.domain.com/some_image.jpg'))
    with open('some_image.jpg', 'rb') as f:
        img = f.read()
        print(ocr_engine.ocr_with_image(img))

```
celestial-tools 0.0.7
A collection of tools for updating embedded software images
Requires Python: >=3.5

# celestial_tools
[![CircleCI](https://circleci.com/gh/PseudoDesign/celestial_tools.svg?style=svg)](https://circleci.com/gh/PseudoDesign/celestial_tools)
[![codecov](https://codecov.io/gh/PseudoDesign/celestial_tools/branch/master/graph/badge.svg)](https://codecov.io/gh/PseudoDesign/celestial_tools)
[![Documentation Status](https://readthedocs.org/projects/celestial-tools/badge/?version=latest)](https://celestial-tools.readthedocs.io/en/latest/?badge=latest)


See the above links for documentation.
clump 0.0.5
Easy-to-use package for grouping, tagging and finding similar documents, using advanced natural language processing techniques
Requires Python: >=3.6

# clump

Easy-to-use library for grouping, tagging and finding similar documents, using advanced natural language processing techniques.

The initial purpose of this package is for finding similar or related documents. The typical use case for this is news apps that want to display related content for a particular news story.

Many developers don’t have the time to invest in learning the best practices for this, so this module provides a simple package for loading all the content to consider and then a function that given a passage of text, will find all the related stories.

## How to use:

Simply install the library with pip and import the VectorSpaceModel 

```python
from clumb import VectorSpaceModel
training_docs = ['Some pieces of text', 'More text']

# build the model
model = VectorSpaceModel(training_docs)

# find the three most similar documents
similar = model.find_similar('Another text', n=3)
print(similar)

```

## Current Limitations

* Small contextual consideration.
* Performance on large datasets 

## Road Map

* Document clustering
* Automatic tagging
* Topic classification
* Performance improvements by pre clustering documents into large groups, then searching just the similar clusters.