semvec/pypi-packages huggingface dataset:
mayya-connect 0.0.1
A python package for connecting with database.
License: MIT
Requires Python: >=3.7

# 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
scHPL 1.0.5
Hierarchical progressive learning pipeline for single-cell RNA-sequencing datasets
License: MIT
Requires Python: >=3.6

|PyPI| |PyPIDownloads| |Docs|

scHPL: Hierarchical progressive learning of cell identities in single-cell data
=========================================================================
.. raw:: html

 <img src="https://github.com/lcmmichielsen/scHPL/blob/master/docs/scHPL.png" width="820px" align="center">

We present a hierarchical progressive learning method which automatically finds relationships between cell populations across multiple datasets and uses this to construct a hierarchical classification tree. For each node in the tree either a linear SVM, kNN, or one-class SVM, which enables the detection of unknown populations, is trained. The trained classification tree can be used to predict the labels of a new unlabeled dataset. 

NOTE: scHPL is not a batch correction tool, we advise to align the datasets before matching the cell populations. We advise doing this with scVI or scArches (see section treeArches below).

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

scHPL requires Python 3.6 or higher. The easiest way to install scHPL is through the following command::

    pip install scHPL

General usage
---------------

The ```tutorial.ipynb``` notebook explains the basics of scHPL. The `vignette folder </vignettes>`_ contains notebooks to reproduce the inter-dataset experiments. See the `documentation <https://schpl.readthedocs.io/en/latest/>`_ for more information.

treeArches
-----------

treeArches is a framework around scHPL and `scArches <https://github.com/theislab/scarches>`_ to automatically build and update reference atlases and the classification tree. Examples can be found in the `treeArches reprodicibility Github <https://github.com/lcmmichielsen/treeArches-reproducibility>`_ and in this `notebook <https://github.com/theislab/scarches/blob/master/notebooks/treeArches_pbmc.ipynb>`_.

Datasets
---------

All datasets used are publicly available data and can be downloaded from Zenodo. The simulated data and aligned datasets used during the interdataset experiments can be downloaded from the `scHPL Zenodo <https://doi.org/10.5281/zenodo.4557712>`_. The filtered PBMC-FACS and AMB2018 dataset can be downloaded from the `scRNA-seq benchmark Zenodo <https://doi.org/10.5281/zenodo.3357167>`_

For citation and further information please refer to: `"Hierarchical progressive learning of cell identities in single-cell data" <https://www.nature.com/articles/s41467-021-23196-8>`_
 


.. |PyPI| image:: https://img.shields.io/pypi/v/scHPL.svg
   :target: https://pypi.org/project/scHPL

.. |PyPIDownloads| image:: https://static.pepy.tech/badge/scHPL
   :target: https://pepy.tech/project/scHPL

.. |Docs| image:: https://readthedocs.org/projects/schpl/badge/?version=latest
   :target: https://schpl.readthedocs.io
mnemonic 0.21
Implementation of Bitcoin BIP-0039
License: MIT
Requires Python: >=3.8.1

python-mnemonic
===============

.. image:: https://badge.fury.io/py/mnemonic.svg
    :target: https://badge.fury.io/py/mnemonic

Reference implementation of BIP-0039: Mnemonic code for generating
deterministic keys

Abstract
--------

This BIP describes the implementation of a mnemonic code or mnemonic sentence --
a group of easy to remember words -- for the generation of deterministic wallets.

It consists of two parts: generating the mnenomic, and converting it into a
binary seed. This seed can be later used to generate deterministic wallets using
BIP-0032 or similar methods.

BIP Paper
---------

See https://github.com/bitcoin/bips/blob/master/bip-0039.mediawiki
for full specification

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

To install this library and its dependencies use:

 ``pip install mnemonic``

Usage examples
--------------

Import library into python project via:

.. code-block:: python

   from mnemonic import Mnemonic

Initialize class instance, picking from available dictionaries:

- english
- chinese_simplified
- chinese_traditional
- french
- italian
- japanese
- korean
- spanish
- turkish
- czech
- portuguese

.. code-block:: python

   mnemo = Mnemonic(language)
   mnemo = Mnemonic("english")

Generate word list given the strength (128 - 256):

.. code-block:: python

   words = mnemo.generate(strength=256)

Given the word list and custom passphrase (empty in example), generate seed:

.. code-block:: python

   seed = mnemo.to_seed(words, passphrase="")

Given the word list, calculate original entropy:

.. code-block:: python

   entropy = mnemo.to_entropy(words)

Changelog
=========

.. default-role:: code

All notable changes to this project will be documented in this file.

The format is based on `Keep a Changelog`_, and this project adheres to
`Semantic Versioning`_.

`0.21`_ - 2024-01-05
--------------------

.. _0.21: https://github.com/trezor/python-mnemonic/compare/v0.20...v0.21

Added
~~~~~

- Czech and Portuguese wordlists
- Option to provide custom list of words instead of loading from built-in file

Changed
~~~~~~~

- Use `secrets` module for randomness
- Use English as a default language if none is provided
- Language detection is unambiguous even if some words are ambiguous
- Build system switched to Poetry

Removed
~~~~~~~

- Support for Python below 3.8 was dropped


`0.20`_ - 2021-07-27
---------------------

.. _0.20: https://github.com/trezor/python-mnemonic/compare/v0.19...v0.20

Added
~~~~~

- Type annotations
- Support for testnet private keys

Changed
~~~~~~~

- Project directory structure was cleaned up
- Language on the `Mnemonic` object is remembered instead of repeatedly detecting

Removed
~~~~~~~

- Support for Python 2.7 and 3.4 was dropped



0.19 - 2019-10-01
------------------

Added
~~~~~

- Start of changelog


.. _Keep a Changelog: https://keepachangelog.com/en/1.0.0/
.. _Semantic Versioning: https://semver.org/spec/v2.0.0.html