semvec/pypi-packages huggingface dataset:
cpas-toolbox 1.0.0
Toolbox to evaluate categorical pose and shape estimation
Requires Python: >=3.7

# Categorical Pose and Shape Evaluation Toolbox
[![PyPI Release](https://github.com/roym899/pose_and_shape_evaluation/actions/workflows/publish_release.yaml/badge.svg)](https://github.com/roym899/pose_and_shape_evaluation/actions/workflows/publish_release.yaml) [![PyTest](https://github.com/roym899/pose_and_shape_evaluation/actions/workflows/pytest.yaml/badge.svg)](https://github.com/roym899/pose_and_shape_evaluation/actions/workflows/pytest.yaml) [![Docs](https://github.com/roym899/pose_and_shape_evaluation/actions/workflows/build_docs.yaml/badge.svg)](https://github.com/roym899/pose_and_shape_evaluation/actions/workflows/build_docs.yaml)

CPAS Toolbox is a package for evaluation of categorical pose and shape estimation methods. It contains metrics, datasets and methods.
Visit the [documentation](https://roym899.github.io/pose_and_shape_evaluation/) for detailed usage instructions and API reference.


## Installation
```bash
pip install cpas_toolbox
```

## Citation
If you find this library useful in your research, consider citing [our publication](https://arxiv.org/abs/2202.10346):
```
@article{bruns2022evaluation,
  title={On the Evaluation of {RGB-D}-based Categorical Pose and Shape Estimation},
  author={Bruns, Leonard and Jensfelt, Patric},
  journal={arXiv preprint arXiv:2202.10346},
  year={2022}
}
```

## Development
- Use `pip install -e .` to install the package in editable mode
- Use `pip install -r requirements-dev.txt` to install dev tools
- Use `pytest -rf --cov=cpas_toolbox --cov-report term-missing tests/` to run tests and check code coverage
the-calc-pack-Pranav76 0.0.4
This package helps you to carry out basic arithmetic operations
Requires Python: >=3.7

# thecalcpack
**This package hepls you carry out the four basic arithmetic operations**

# Getting started
## Installing the package
> pip install the-calc-pack-Pranav76

## Using the package
```
import the_calc_pack_Pranav76.clk as mm
print(mm.Your required function(a,b))
```


# Functions Provided
1. add(a,b)
**This function adds two arguments a and b and returns them.**

2. sub(a,b)
**This function subtracts two arguments a and b and returns them.**

3. mul(a,b)
**This function multiplies two arguments a and b and returns them.**

4. div(a,b)
**This functions divides two arguments a and b and returns them.**
comparingnumber-HarshDhas 1.0.1
Helps to find greatest and smallest number among three number
Requires Python: >=3.7

# compairingnumber
**This package help you to find greater and smaller number between three numbers

# Getting started
## Installing the Package
> py -m pip install --index-url https://pypi.org/simple/ comparingnumber_HarshDhas

## using the package
` from HDPACKAGES import python `

# working of package
**the user needs to give three number inputs as x,y,z . theen gives greatest and smallest number among three numbers.
smart-energy.green-energy-api 0.1.0
A python library to access the Awattar Energy Price API.
Requires Python: >=3.7
Keywords: green_energy,kfw,api,cloud

# smart_energy.green_energy_api

A python library to access the Awattar Api. Refer also to the [Awattar Energy Price API](https://www.awattar.de/). This repo provides functionality for:

- Getting Energy Price forecast forecast information.

## Installing the library locally

Python 3 is recommended for this project.

```bash
python -m pip install -e .
```

> **This is needed for the first time when working with the library/examples/tests.**

## Example usage

```bash
ENERGY_DATA_API_URL="https://api.awattar.de" python3 examples/simple.py
```

or

```python
from green_energy_api.green_energy_api import GreenEnergyApi

green_energy_api = GreenEnergyApi('provide_api_url')
# or you can define additional optional parameters
# green_energy_api = GreenEnergyApi('provide_api_url', timeout=10)

print(green_energy_api.get_electricity_price())
```

## Development

### Installing required pip packages

```bash
python pip install -r requirements.txt
pre-commit install -t pre-push
```

### Linting

```bash
pylint green_energy_api/*.py tests/*.py examples/*.py
```

### Unit testing

```bash
pytest tests/*.py

# show logs
pytest -o log_cli=true

# code coverage
pytest --durations=10 --cov-report term-missing --cov=green_energy_api tests
```
exapi 0.1.1
Async API for exchanges
Requires Python: >=3.11

# Exapi (Exchange API)

## Install pyenv

[pyenv-installer](https://github.com/pyenv/pyenv-installer)

```shell script
curl https://pyenv.run | bash
```

Add to ~/.profile
```shell script
if [ -d "$HOME/.pyenv" ] ; then
    export PYENV_ROOT="$HOME/.pyenv"
    command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"
    eval "$(pyenv init -)"
    eval "$(pyenv virtualenv-init -)"
fi
```

Update
```shell script
pyenv update
```

## Install python 3.11

[pyenv](https://github.com/pyenv/pyenv)

```shell script
pyenv install 3.11.1
pyenv global 3.11.1
```

## Getting Started

### Update pip
```shell script
python3 -m pip install --upgrade pip
python3 -m pip install --upgrade setuptools
```

### Creating a virtual environment

```shell script
rm -rf venv
python3 -m venv venv
```

### Install the module in development mode
```shell script
python3 -m pip install -Ue '.[dev]'
```

## Build

```shell script
python3 -m build
# OR
hatch build
```

### Authenticate with the Package Registry

Edit the ```~/.pypirc``` file and add:

```shell
[pypi]
  username = __token__
  password = pypi-TOKEN
```

### Deploy to pypi

```shell script
python3 -m twine upload --repository pypi dist/*
```


### Clear ```__pycache__```

```shell script
py3clean .
```

## Read:
- [Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
- [hatchling](https://hatch.pypa.io/latest/config/build/)
- [GitLab PyPi Repository](https://docs.gitlab.com/ee/user/packages/pypi_repository/)
- [Packaging Python Projects](https://packaging.python.org/tutorials/packaging-projects/#creating-setup-py)
- [Virtual environments](https://packaging.python.org/guides/installing-using-pip-and-virtual-environments/)
medical-waveforms 0.1.5
Preprocessing and analysis of physiological waveforms
Requires Python: >=3.8

![Run tests workflow](https://github.com/UCL-Chimera/medical-waveforms/actions/workflows/run_tests.yml/badge.svg) ![Linting workflow](https://github.com/UCL-Chimera/medical-waveforms/actions/workflows/lint.yml/badge.svg)

# medical-waveforms

**medical-waveforms** is a Python package for preprocessing and analysis of physiological waveforms.

This package currently focuses on:

- Splitting waveforms into individual cycles (e.g. splitting a respiratory waveform into individual breaths)
- Extracting features from individual cycles
- Rule-based signal quality assessment


## Installation

Install with:

```
pip install medical-waveforms
```

The package is tested on Python 3.8 - 3.11.


## Getting started

See the [tutorial notebook](https://github.com/UCL-Chimera/medical-waveforms/blob/main/examples/tutorial.ipynb) for a general introduction to using the package.

The [signal quality assessment notebook](https://github.com/UCL-Chimera/medical-waveforms/blob/main/examples/signal_quality.ipynb) demonstrates customisation of the signal quality assessment process.

These tutorials currently focus on arterial blood pressure waveforms, but can be adapted to other physiological waveforms.


## Contributing to this project

Contributions are very welcome! Please see [CONTRIBUTING.md](https://github.com/UCL-Chimera/medical-waveforms/blob/main/CONTRIBUTING.md) to get started.


## Acknowledgements

Our signal quality assessment pipeline is adapted from that used in the excellent [PhysioNet Cardiovascular Signal Toolbox](https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox). Many thanks to its [contributors](https://github.com/cliffordlab/PhysioNet-Cardiovascular-Signal-Toolbox/graphs/contributors).
modelhouse 0.1.4
Easy model distribution for inference.

# modelhouse

## Why?

**The purpose of `modelhouse` is to make it easy to distribute models to workers on cloud.**

With `modelhouse`, you can store and load your models from different storage backends (Google Cloud Storage, AWS S3, local). `modelhouse` also provides model caching, which can significantly reduce model loading time while keeping memory utilization low.

## Installation

```pip install modelhouse```

## Documentation
For documentation, please visit https://modelhouse.readthedocs.io/en/latest/

## Getting started
TODO