semvec/pypi-packages huggingface dataset:
codeoceansdk 1.0.0
A set of functions to interact with the Code Ocean API
Requires Python: >=3.10

![Code Ocean Logo](https://raw.githubusercontent.com/codeocean/branding/main/logo/CO_logo_135x72.png)
# codeoceanapi-py
Python api for capsules, data assets, etc. 

## Documentation
Documentation for the SDK can be found [here](https://codeocean.github.io/codeoceansdk-py/codeoceansdk.html) The documentation has been generated using pdoc by running the following command: 

`pdoc src/codeoceansdk/ -o docs/`

The original REST API documentation can be found in the [Code Ocean User Guide](https://docs.codeocean.com/user-guide/code-ocean-api)

## Version

This version is tested with the 2.13 release of the Code Ocean Platform.

## pytest

For pytests, please set the API_KEY environment variable.

In order to include slower tests include --slow as an argument to pytest

Install package as editable: 

`pip install --editable .`

Run pytest from codeoceansdk-py

`pytest`
sphinx-md 0.0.4
Sphinx extension to use with Recommonmark or MystParser to fix links to rst from md, links to md from rst, and links to embedded files and dirs.
Requires Python: >=3.6

# Sphinx Markdown Extension

This extension fixes or improves how Sphinx handles links related to Markdown
when it generates the HTML site. It assumes you are using the `recommonmark`
extension. It's a good idea to use `sphinx_markdown_tables` as well.

**Contents**

- [What it does](#what-it-does)
- [How to use it](#how-to-use-it)

## What it does

1. Markdown files: Converts references to Markdown files that include anchors.
   ``` md
   [configuration options](autotest.md#configuration-options)
   ```
2. reST files: Fixes explicit links to Markdown files.
   ``` rst
   `Google Cloud Engine <gce.md>`__
   ```
3. Markdown files: Fixes references to reST files.
   ``` md
   [Application examples](examples/readme.rst)
   ```
4. Markdown files: Fixes links to files and directories within the GitHub repo.
   ``` md
   [Makefile](/Makefile)
   [deploy/kustomize](/deploy/kustomize)
   ```
   Links to files can be fixed one of two ways, which can be set in the
   [conf.py](/conf.py).

   ``` python
   baseBranch = "devel"
   useGitHubURL = True
   commitSHA = getenv('GITHUB_SHA')
   githubBaseURL = "https://github.com/intelkevinputnam/pmem-csi/"
   ```

   If ``useGitHubURL`` is set to True, it will try to create links based on
   your ``githubBaseURL`` and the SHA for the commit to the GitHub repo
   determined by the GitHub workflow on merge). If there is no SHA available,
   it will use the value of ``baseBranch``.

   If ``useGitHubURL`` is set to False, it will copy the files to the HTML
   output directory and provide links to that location.

   NOTE: Links to files and directories should use absolute paths relative to
   the repo (see Makefile and deploy/kustomize above). This will work both for
   the Sphinx build and when viewing in the GitHub repo.

   Links to directories are always converted to links to the GitHub repository.

## How to use it

1. Install the sphinx_md extension:

   ``` bash
   pip3 install sphinx_md
   ```

2. Add `sphinx_md` to the extensions in your `conf.py`:

   ``` python
   extensions = ['sphinx_md', ...]
   ```

3. If you want to use GitHub commit links, add the entire code snippet to
   your `conf.py`:

   ``` python
   from os import getenv

   sphinx_md_useGitHubURL = True
   baseBranch = "devel"
   commitSHA = getenv('GITHUB_SHA')
   githubBaseURL = 'https://github.com/' + (getenv('GITHUB_REPOSITORY') or '<your_group/your_project>') + '/'
   githubFileURL = githubBaseURL + "blob/"
   githubDirURL = githubBaseURL + "tree/"
   if commitSHA:
       githubFileURL = githubFileURL + commitSHA + "/"
       githubDirURL = githubDirURL + commitSHA + "/"
   else:
       githubFileURL = githubFileURL + baseBranch + "/"
       githubDirURL = githubDirURL + baseBranch + "/"
   sphinx_md_githubFileURL = githubFileURL
   sphinx_md_githubDirURL = githubDirURL
   ```
pyoptmat 1.3.5
Statistical inference for material models
Requires Python: >=3.6
Keywords: materials inference modeling

# pyoptmat: statistical inference for material models 

[![Run test suite](https://github.com/Argonne-National-Laboratory/pyoptmat/actions/workflows/tests.yml/badge.svg?branch=master)](https://github.com/Argonne-National-Laboratory/pyoptmat/actions/workflows/tests.yml) [![Documentation Status](https://readthedocs.org/projects/pyoptmat/badge/?version=stable)](https://pyoptmat.readthedocs.io/en/stable/)

pyoptmat is a package for calibrating statistical material
models to data.  The package is based on [pytorch](https://pytorch.org/)
and [pyro](https://pyro.ai/) and provides a framework for using machine-learning
techniques to calibrate deterministic and statistical models against
experimental data.

A “material model” is mathematically a parameterized system of ordinary 
differential equations which, integrated through the experimental conditions, 
returns some simulated output that can be compared to the test measurements.
pyoptmat uses Bayesian inference with the pyro package to find statistical
distributions of the model  parameters to explain the variation in the 
experimental data.

As an example, consider a collection of tension test data on several samples 
of a material. The test measurements have some variation caused by 
manufacturing variability and uncertainty in the experimental controls and 
measurements.

![Example of fitting a statistical model to data](doc/sphinx/figures/demonstration.png)

pyoptmat aims to make training a statistical model to capture these 
variations easy. The image shows the results of training a simple material 
model to the test data. The trained statistical model captures the 
variability in the experimental data and can then be used to translate 
this uncertainty to models of engineering components. Transferring 
uncertainty quantified in experimental measurements to predictions of 
uncertainty in engineering applications is the main reason pyoptmat was 
developed.

The software is provided under an [MIT license](LICENSE).  Full
documentation is available [here](https://pyoptmat.readthedocs.io).
omniindex 0.1.15
A Python package for interacting with the OmniIndex Homomorphic Encrypted Blockchain API

## Project description

# OmniIndex Python Library

The OmniIndex Python library provides convenient access to the OmniIndex API from applications written in the Python language. It includes a pre-defined set of classes for API resources that initialize themselves dynamically from API responses which makes it compatible with a wide range of versions of the OmniIndex API.

You can find usage examples for the OmniIndex Python library in our [Python Library reference](https://omniindex.readthedocs.io/) which includes code samples etc
For just the [API reference](https://app.swaggerhub.com/apis-docs/OmniIndexAPI/OmniIndex/1.0.0)

## Installation

You don't need this source code unless you want to modify the package. If you just want to use the package, just run:

```bash
pip install --upgrade omniindex
```

Install from source with:

```bash
pip setup.py install
```

## Usage

The library needs to be configured with your account's secret keys which are available from your account onboarding packet. Set them as the OMNIINDEX_API_PASSWORD environment variable before using the library:
We would recommed you set these for

* OMNIINDEX_API_PASSWORD
* OMNIINDEX_API_USER
* OMNIINDEX_API_NODE
* OMNIINDEX_API_TYPE
* OMNIINDEX_API_UNIT
* OMNIINDEX_API_BASE_URL

Linux / bash etc

```bash
export OMNIINDEX_API_PASSWORD='sk-...'
```

Windows

```bash
setx OMNIINDEX_API_PASSWORD 'sk-...'
```
kicad-vault 0.0.2
A program to manage kicad BOMs, with vendor integration.
Requires Python: >=3.7

![](img/logo.png)
Kicad Vault is a Python based part management systems. It will automatically hunt for KiCad projects and generate a webserver highlighting components, their current prices and availability. It is currently only available with digikey integration. I've pared down the software, it simply will create a CSV file with prices of parts. In the future I will expand it to have full web server functionality.

## Getting setup
Requires KiCad 7.0+ for the `kicad-cli` export utility. This should come native in Linux/OSX, in Windows you need to make sure you have access to the app though the command line.

Start [here](https://developer.digikey.com/) to get some digikey api credentials.

Install and setup environment variables. Leave this in your .bashrc. If you're using Windows you need to add these to your environment variables.
``` bash
export DIGIKEY_CLIENT_ID="client_id"
export DIGIKEY_CLIENT_SECRET'="client_secret"
```

## Running
``` bash
pip install kicad-vault
kicad-vault <your_file>.kicad_sch --field MPN
```
Where "MPN" is the field in KiCad where you give the part number.
jellow-names 0.0.1
Package for maintaining centralized goverance of data for company data extraction
Requires Python: >=3.11

# jelloow-names

Package for maintaining centralized governance of data for company data extraction

## Deployment to PyPi

Add credential to `~/.pypirc` file.

```bash
echo "[pypi]
username = __token__
password = $PIPY_TOKEN" >> ~/.pypirc
```

Create and activate a virtual environment.

```bash
python -m venv venv
python.exe -m pip install --upgrade pip

#linux / mac
source venv/bin/activate

#windows
venv\Scripts\Activate.ps1

pip install -r requirements.txt
```

Build the package and then upload it to PyPi.

```bash
python -m build
python -m twine upload --repository testpypi dist/*
```