semvec/pypi-packages huggingface dataset:
ephysio 1.0.13
Tools for reading ephys data

# ephysio
Python code for reading ephys files

## openEphysIO
This module provides convenient access to continuous and event data saved by OpenEphys. For instance:

    from ephysio import openEphysIO
    import matplotlib.pyplot as plt
    
    ldr = openEphysIO.Loader("/path/to/data")
    
    # first probe
    strm = ldr.spikestreams()[0] 
    
    # time x channel map of the data
    dat = ldr.data(strm) 
    
    # sampling rate in Hz
    fs_Hz = ldr.samplingrate(strm) 
    STIMMARKERCHANNEL = 2
    
    # time stamps of events in channel 2 on the NIDAQ, translated to neuropixel time
    evts = ldr.nidaqevents()[STIMMARKERCHANNEL] 
    CHANNEL = 100
    STIMNO = 10
    
    # Start time of event #10
    s0 = evts[STIMNO, 0] 
    
    # prepare to receive 100 ms of data
    ds = np.arange(int(.1*fs_Hz)) 
    
    # 100 ms of data following the stimulus 
    response = dat[s0:s0+ds, CHANNEL] 
    
    # plot data with time in ms on x-axis
    plt.plot(ds*1000/fs_Hz, response) 

Much more documentation is included in the module itself. Most users will want to use the Loader class rather than the various other functions.

## Credits

Developed and tested by DAW and Frank Lanfranchi.
sarracen 1.3.1
SPH analysis and visualization
Requires Python: >=3.8

Sarracen
========

A Python library for smoothed particle hydrodynamics (SPH) analysis and visualization.

About
-----

Sarracen is built upon the pandas, Matplotlib and NumPy libraries. It can load SPH particle data into a pandas DataFrame object that has been extended to allow for rendering of particle data and interpolation of particles to fixed grids. Additionally, this allows for access to the rich landscape of Python scientific and statistical libraries. All SPH functions offer multi-threaded CPU and CUDA implementations. Our intended application is for astrophysical SPH data. 

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

The latest stable release and associated dependencies can be installed from PyPi:

    pip install sarracen

This is the recommended way to install Sarracen.

To install the latest development snapshot, install using this GitHub repository. Either clone the repository and add it to your path so that it can be imported, or install directly through pip:

    pip install git+https://github.com/ttricco/sarracen.git

Documentation
-------------

Sarracen's documentation is hosted online at [https://sarracen.readthedocs.io](https://sarracen.readthedocs.io).

Contributing
------------

Contributions are welcomed and appreciated. Here are some ways to get involved:
- Submitting bug reports.
- Feature requests or suggestions.
- Improving the documentation or providing examples.
- Writing code to add optimizations or new features.

Please use the [GitHub issue tracker](https://github.com/ttricco/sarracen/issues) to raise any bugs or to submit feature
requests. If something does not work as you might expect, please let us know. If there are features that you feel are
missing, please let us know.

Code submissions should be submitted as a pull request. Make sure that all existing unit tests successfully pass, and 
please add any new unit tests that are relevant. Documentation changes should also be submitted as a pull request.

If you are stuck or need help, [raising an issue](https://github.com/ttricco/sarracen/issues) is a good place to start. 
This helps us keep common issues in public view. Feel free to also [email](mailto:tstricco@mun.ca) with questions.

Please note that we adhere to a [code of conduct](CODE_OF_CONDUCT.md).

Citation
--------

Please cite the paper if you use Sarracen within your work. Sarracen is published with the Journal of Open Source Software (DOI: [10.21105/joss.05263](https://doi.org/10.21105/joss.05263)).

```
@ARTICLE{Sarracen,
       author = {{Harris}, Andrew and {Tricco}, Terrence},
        title = "{Sarracen: a Python package for analysis and visualization of smoothed particle hydrodynamics data}",
      journal = {The Journal of Open Source Software},
     keywords = {smoothed particle hydrodynamics, data visualization, Python, data science, Jupyter Notebook, astrophysics, astronomy},
         year = 2023,
        month = jun,
       volume = {8},
       number = {86},
          eid = {5263},
        pages = {5263},
          doi = {10.21105/joss.05263},
}
```
eldar-tic-tac-toe 2.0.1
Tic Tac Toe with Hard AI, colors and tests
Requires Python: >=3.8

# 🎮 Tic Tac Toe

Features:
- Easy & Hard AI (Minimax)
- Emoji + Colors
- CLI
- Tested with pytest

## Install
pip install eldar-tic-tac-toe

## Play
tic-tac-toe --ai hard
GaiaXPy 2.1.4
Utilities to handle BP/RP (XP) Gaia low-resolution spectra as delivered via the Archive.
Requires Python: >=3.10

BP/RP (often shortened as XP) spectra became available for the first time in Gaia Data Release 3 (DR3). In their first release, only source mean spectra are available: these are spectra that have been generated from a number of single observations of the same object. Epoch spectra, i.e. spectra consisting of one single observation, will become available in future releases.

GaiaXPy is a Python library to facilitate handling Gaia BP/RP spectra as distributed from the Gaia archive.

To learn more about the package click [here](https://gaiaxpy.readthedocs.io/en/latest/description.html).

You can also check the [homepage](https://gaia-dpci.github.io/GaiaXPy-website/) for documentation, tutorials, and resources.

## Licence
[BSD 3-Clause](https://github.com/gaia-dpci/GaiaXPy/blob/main/LICENCE)
ooipy 1.2.5
toolbox for downloading and analyzing OOI hydrophone data
Requires Python: >=3.8

# OOIPY
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) ![unit test](https://github.com/Ocean-Data-Lab/ooipy/actions/workflows/unit_test.yml/badge.svg) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4276862.svg)](https://doi.org/10.5281/zenodo.4276862) [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/Ocean-Data-Lab/ooipy/master.svg)](https://results.pre-commit.ci/latest/github/Ocean-Data-Lab/ooipy/master) [![Documentation](https://readthedocs.org/projects/ooipy/badge/?version=latest)](https://ooipy.readthedocs.io/en/latest/?badge=latest)

<img src='imgs/OOIPY_Logo.png' align="right" width=150>

<!-- SPHINX-START -->

OOIPY is a python toolbox designed to aid in scientific analysis of Ocean Observatories Initiative (OOI) data. Some data (such as broadband hydrophone data) is not available through the OOI API (M2M). This package is designed to help with the acquiring of datasets from the OOI Raw Data Server. Additionally, tools to analyze the data, such as spectrogram and power spectral density plotting are also provided.
gnuhealth-control 5.0.2
GNU Health HIS installer and instance administration tool
Requires Python: <3.15,>=3.10
Keywords: health, hospital, HIS, eHealth, gnuhealth

.. SPDX-FileCopyrightText: 2008-2025 Luis Falcón <falcon@gnuhealth.org>
.. SPDX-FileCopyrightText: 2011-2025 GNU Solidario <health@gnusolidario.org>
..
.. SPDX-License-Identifier: CC-BY-SA-4.0 

The GNU Health HIS Installer and Manager
========================================

The gnuhealth-control is a single-entry point to install, setup and manage
the GNU Health Hospital Management System

Installation
------------
GNU Health Control application can be installed with pip::

 $ pip install --upgrade gnuhealth-control


Technology
----------
The GNU Health Control is a self-contained curses Python application.


Development
-----------
The development of gnuhealth-control will be done at Codeberg.

The development mailing list is at health-dev@gnu.org 
General questions can be done on health@gnu.org mailing list.

Note: You need to subscribe before posting or the messages will be automatically
discarded. More at: https://docs.gnuhealth.org/his/support.html

Homepage
--------
https://www.gnuhealth.org


Documentation
-------------

https://docs.gnuhealth.org
eth-typing 6.0.0b1
eth-typing: Common type annotations for ethereum python packages
Requires Python: <4,>=3.10
Keywords: ethereum

# eth-typing

[![Join the conversation on Discord](https://img.shields.io/discord/809793915578089484?color=blue&label=chat&logo=discord&logoColor=white)](https://discord.gg/GHryRvPB84)
[![Build Status](https://circleci.com/gh/ethereum/eth-typing.svg?style=shield)](https://circleci.com/gh/ethereum/eth-typing)
[![PyPI version](https://badge.fury.io/py/eth-typing.svg)](https://badge.fury.io/py/eth-typing)
[![Python versions](https://img.shields.io/pypi/pyversions/eth-typing.svg)](https://pypi.python.org/pypi/eth-typing)
[![Docs build](https://readthedocs.org/projects/eth-typing/badge/?version=latest)](https://eth-typing.readthedocs.io/en/latest/?badge=latest)

Common type annotations for ethereum python packages.

Read the [documentation](https://eth-typing.readthedocs.io/).

View the [change log](https://eth-typing.readthedocs.io/en/latest/release_notes.html).

## Installation

```sh
python -m pip install eth-typing
```
bazooka 1.3.1
Reliable HTTP client

Bazooka - reliable HTTP client
==============================

![tests workflow](https://github.com/infraguys/bazooka/actions/workflows/tests.yaml/badge.svg)

Overview
--------

Bazooka is a reliable and efficient HTTP client for Python that provides
features such as retries out of the box, full compatibility with the
`requests` library, and customizable logging options.

Key Features
------------

*   **retries**: Bazooka includes built-in retry logic to handle temporary network failures or 5XX server errors.
*   **full compatibility**: Bazooka is designed to work seamlessly with the `requests` library, allowing for easy integration into existing projects.
*   **explicit automatic error handling**: by default client raises exception if status code isn't 2xx.
*   **curl-like customizable Logging**: Bazooka offers flexible logging options, including duration logging and sensitive logging support.

Example Usage
-------------

### Basic Example

```python
import bazooka

client = bazooka.Client()
print(client.get('http://example.com').json())
```

### Using Correlation IDs

(Usable to match bazooka requests with your business logic logs)

```python
from bazooka import correlation

client = bazooka.Client(correlation_id='my_correlation_id')
print(client.get('http://example.com').json())
```

### Raised Exceptions on 4xx Errors

```python
import bazooka
from bazooka import exceptions

client = bazooka.Client()

try:
    response = client.get('http://example.com', timeout=10)
except exceptions.NotFoundError:
    # process 404 error
    pass
except exceptions.ConflictError:
   # process 409 error
    pass
except exceptions.BaseHTTPException as e:
   # process other HTTP errors
    if e.code in range(400, 500):
        print(f"4xx Error: {e}")
    else:
        raise
```

Getting Started
---------------

To get started with Bazooka, install it via pip:

```bash
pip install bazooka
```
bareqr 0.1.1
Bare headless QR code generator
Requires Python: >=3.12

# bareqr - Bare headless QR code generator

**bareqr** was derived from [python-qrcode](https://github.com/lincolnloop/python-qrcode). I removed all dependencies, console scripts, renderers,
and made it headless.

**bareqr** is:

-   stateless
-   threadsafe
-   PEP-8 compliant
-   fully typed

## Main features

There is just one feature: produce the matrix.

```python
from bareqr import qrcode
from pprint import pprint

qr = qrcode("1234")

pprint(qr.rows)
>>>
[[1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1],
 [1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1],
 [1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1],
 [1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1],
 ...
 [1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0]]

```

**bareqr** won't automatically split data to chunks of different types.
You may split data manually:

```python
qr = qrcode("1234", "HELLO", "world")
```

For each chunk **bareqr** will choose the correct data type: number, alphanumeric or bytes.

The automatic splitting is available as a separate utility:

```python
from bareqr import optimal_chunks

chunks = optimal_chunks("1234HELLOworld", min_chunk=4)
qr = qrcode(*chunks)
```

`qrcode` has an internal global cache of precomputed blank QR codes.
In **bareqr** this cache is external and opt-in:

```python
cache = {}
qr1 = qrcode("1234", blanks_cache=cache)
qr2 = qrcode("5678", blanks_cache=cache)
```

## Extra features

Ok, in fact **bareqr** is not completely headless.
There are two utils to render QR as ascii or png. Treat them as handy extras.

```python
from bareqr import qrcode, as_ascii, as_png
qr = qrcode("1234")

asc = as_ascii(qr, border=2, invert=True)
for row in asc:
    print(row)

png = as_png(qr, border=2, scale=4)
with open("qr.png", "wb") as f:
    f.write(png)

```

The png encoder is custom to avoid extra dependencies.
tiktalik 2.0.0
Python SDK for the Tiktalik Computing service
Requires Python: >=3.10
Keywords: SDK, Tiktalik, VPS

# Tiktalik Cloud Computing Platform SDK for Python

Tiktalik SDK for Python enables developers using the Python programming
language to access core features of the [Tiktalik Cloud Computing](http://www.tiktalik.com) Platform.

The SDK features:

 * retrieving detailed information on user's instances: recent operations,
   running state, hourly cost etc.
 * core operations on an instance: creating, starting, stopping, starting backup
 * adding and removing network interfaces
 * listing and manipulating backups
 * listing and manipulating networks
 
 More cool features coming soon!

## Installation

Use pip to install the latest stable version:

`$ pip install tiktalik`

## Requirements

 * Python >=3.10
 * no additional modules are required.

## Documentation

See [http://www.tiktalik.com/api](http://www.tiktalik.com/api) for documentation and tutorials.