semvec/pypi-packages huggingface dataset:
pytorchtime 0.1.2
A time series package for PyTorch
Keywords: python, torchtime

torchtime: time series library for PyTorch
==========================================
[![Downloads](https://static.pepy.tech/badge/pytorchtime)](https://pypi.org/project/pytorchtime)
[![Documentation](https://img.shields.io/badge/dynamic/json.svg?label=docs&url=https%3A%2F%2Fpypi.org%2Fpypi%2Fpytorchtime%2Fjson&query=%24.info.version&colorB=brightgreen&prefix=v)](https://pytorchtime.com/docs/stable)

![Torchtime Logo](https://github.com/VincentSch4rf/torchtime/raw/main/docs/source/_static/img/logo-dark.svg)

--------------------------------------------------------------------------------

The aim of torchtime is to apply [PyTorch](https://github.com/pytorch/pytorch) to
the time series domain. By supporting PyTorch, torchtime follows the same philosophy
of providing strong GPU acceleration, having a focus on trainable features through
the autograd system, and having consistent style (tensor names and dimension names).
Therefore, it is primarily a machine learning library and not a general signal
processing library. The benefits of PyTorch can be seen in torchtime through
having all the computations be through PyTorch operations which makes it easy
to use and feel like a natural extension.

- [Support time series I/O (Load files, Save files)](http://pytorchtime.com/docs/stable/)
  - Load a variety of time series formats, such as `ts`, `arff`, `dvi`, `dxd`, into a torch Tensor
- [Dataloaders for common time series datasets](http://pytorchtime.com/docs/stable/datasets.html)
- Common time series transforms
    - [Nan2Value, Normalization, Padding, Resample](http://pytorchtime.com/docs/stable/transforms.html)

Installation
------------

Please refer to https://pytorchtime.com/docs/stable/installation.html for installation and build process of torchtime.

API Reference
-------------

API Reference is located here: http://pytorchtime.com/docs/stable/

Contributing Guidelines
-----------------------

Please refer to [CONTRIBUTING.md](./CONTRIBUTING.md)

Citation
--------

If you find this package useful, please cite as:

```bibtex
@article{scharf2022torchtime,
    title={PyTorch Time: Bringing Deep Learning to Time Series Classification},
    author={Vincent Scharf},
    url={https://github.com/VincentSch4rf/torchtime},
    year={2022}
}
```

Disclaimer on Datasets
----------------------

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!
lupin-sonarcloud-report 1.0.2
Tool used to generate a report about the latest SonarCloud analysis of a project.
Requires Python: >=3.10

# lupin_sonarcloud_report

Tool used to generate a report about the latest SonarCloud analysis of a project.

### Generate Sonarcloud Report

Configure the following environment variables :

- CI_PROJECT_NAME
- CI_COMMIT_SHA
- SONAR_TOKEN

Execute command `sonarcloud-report`
fairclient 1.0.1
A FAIR Data Point client written in Python
Requires Python: >=3.8
Keywords: FDP

<!--
SPDX-FileCopyrightText: 2024 Stichting Health-RI

SPDX-License-Identifier: MIT
-->

# FAIR Data Point client

-----

[![PyPI - Version](https://img.shields.io/pypi/v/fairclient.svg)](https://pypi.org/project/fairclient)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/fairclient.svg)](https://pypi.org/project/fairclient)
[![Codecov](https://img.shields.io/codecov/c/github/health-ri/fairclient?logo=codecov)](https://app.codecov.io/github/Health-RI/fairclient)

A simple client for the REST API that a FAIR Data Point (FDP) provides. Specifically tuned for the
FDP reference implementation.

## Table of Contents

- [Installation](#installation)
- [License](#license)

## Installation

Installation can be done using standard pip:

```console
pip install fairclient
```

## Related packages

This package is complementary to [SeMPyRO](https://github.com/health-RI/sempyro), a Python library
to generate RDF and DCAT(-AP) metadata. The RDF output from SeMPyRO can be uploaded to an FDP.

For an example, see [img2catalog](https://github.com/Health-RI/img2catalog).

## License

`fairclient` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
EdgeSync360-EdgeHub-Edge-Python-SDK 1.0.2.1
EdgeSync360_EdgeHub_Edge_Python_SDK package allows developers to write Python applications which access the EdgeSync360/EdgeHub Platform via MQTT protocol.

# EdgeSync360.EdgeHub.Edge.Python.SDK

EdgeSync360.EdgeHub.Edge.Python.SDK package allows developers to write Python applications which access the EdgeSync360/EdgeHub Platform via MQTT protocol.

- [EdgeSync360/EdgeHub](https://docs.wise-paas.advantech.com/en/Guides_and_API_References/Data_Acquisition/1579073100328245315)
optimization-problem-inspector 0.6.1
A tool for analysis of industrial optimization problems and their solutions
Requires Python: >=3.10

# optimization-problem-inspector

[![PyPI - Version](https://img.shields.io/pypi/v/optimization-problem-inspector.svg)](https://pypi.org/project/optimization-problem-inspector)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/optimization-problem-inspector.svg)](https://pypi.org/project/optimization-problem-inspector)
[![coverage report](https://repo.ijs.si/DIS-CI/optimization-problem-inspector/badges/main/coverage.svg)](https://repo.ijs.si/DIS-CI/optimization-problem-inspector/-/commits/main)
[![pipeline status](https://repo.ijs.si/DIS-CI/optimization-problem-inspector/badges/main/pipeline.svg)](https://repo.ijs.si/DIS-CI/optimization-problem-inspector/-/commits/main)

-----

**Table of Contents**

- [Installation](#installation)
- [Running GUI](#running-gui)
- [License](#license)

## Installation

### From PyPI

First create a new virtual environment and activate it or activate desired virtual environment.

Now install the `optimization-problem-inspector` from PyPI with 

```
pip install optimization-problem-inspector
```


### From source

You can also install the package from source. The project is hosted at:

- https://repo.ijs.si/DIS-CI/optimization-problem-inspector

Download the repo and cd to it. Create a fresh virtual environment with python 3.9 or higher, e.g.,

```bash
python -m venv .venv
```

Activate it with something like

```bash
# in Linux/Unix
source .venv/bin/activate
```

Now install the package with (use `-e` flag to make and editable install if desired)

```
pip install -e .
```

This will make the package available in the currently activated virtual environment.

#### Using `hatch`

You can also use [hatch](https://github.com/pypa/hatch) to manage dependencies and get some additional extra functionality. After cd-ing to the project directory, you can run

```bash
hatch run python

```

to start Python3 shell with `optimization-problem-inspector installed`.

## Running GUI

To run the `optimization-problem-inspector` GUI after installation process has completed, type command

```bash
opi-gui
```

You can also run the GUI with

```bash
python -m optimization_problem_inspector.app
```

> Note: the desired virtual environment with `optimization-problem-inspector` has to be activated.

> Note: use `python` or `python3` command, depending on what executables you have available for Python3.

## Additional guides

- [Development](./DEVELOPMENT.md)
- [OPI app administration](./OPI_ADMINISTRATION.md)

## License

`optimization-problem-inspector` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
mongodbautomationaibi 0.0.7
A python package for connecting with database.

# requirements_dev.txt we use for the testing
It makes it easier to install and manage dependencies for development and testing, separate from the dependencies required for production.

# difference between requirements_dev.txt and requirements.txt

requirements.txt is used to specify the dependencies required to run the production code of a Python project, while requirements_dev.txt is used to specify the dependencies required for development and testing purposes.

# tox.ini
We use if for the testing in the python package testing against different version of the python 

## how tox works tox enviornment creation
1. Install depedencies and packages 
2. Run commands
3. Its a combination of the (virtualenvwrapper and makefile)
4. It creates a .tox


# pyproject.toml
it is being used for configuration the python project it is a alternative of the setup.cfg file. its containts configuration related to the build system
such as the build tool used package name version author license and dependencies

# setup.cfg
In summary, setup.cfg is used by setuptools to configure the packaging and installation of a Python projec

# Testing python application
*types of testing*
1. Automated testing 
2. Manual testing

*Mode of testing*
1. Unit testing
2. Integration tests

*Testing frameworks*

1. pytest
2. unittest
3. robotframework
4. selenium
5. behave
6. doctest

# check with the code style formatting and syntax(coding standard)

1. pylint
2. flake8(it is best because it containt 3 library pylint pycodestyle mccabe)
3. pycodestyle