semvec/pypi-packages huggingface dataset:
magic-tasks 1.0.0
A client library for interacting with the task server.
Requires Python: >=3.8

# client-lib

A Python client library for interacting with the task server API.

## Installation

```bash
pip install .
```

## Usage

```python
from client_lib import connect_session, create_normal_task

session = connect_session()
api_key = session.create_user()
session.login_server(api_key)
task = create_normal_task("My Task", "echo hello", everyday=True)
session.sub(task, email="your@email.com")
```
jobberr 0.1.0
run python job

# Jobber 

A lightweight command-line tool to list and run Python files within directories.

## Usage

```bash
jobber <command> <directory>
```
Commands:
- list: Recursively lists all subdirectories and files starting from <directory>.
- run: Executes all Python files directly inside <directory>.
> Note: run does not traverse into subdirectories.

```python
# List contents of the current directory
jobber list .

# List contents of the directory A
jobber list A

# Run Python files in the directory A
jobber run A

# Run Python files in the subdirectory A/D
jobber run .\A\D\
```
rulekit 2.1.24.2
Comprehensive suite for rule-based learning
Requires Python: >=3.9

[![Coverage Status](https://adaa-polsl.github.io/RuleKit-python/badges/coverage-badge.svg?dummy=8484744)](https://adaa-polsl.github.io/RuleKit-python/reports/coverage/index.html)
[![Tests Status](https://adaa-polsl.github.io/RuleKit-python/badges/test-badge.svg?dummy=8484744)](https://adaa-polsl.github.io/RuleKit-python/reports/junit/report.html)
[![Flake8 Status](https://adaa-polsl.github.io/RuleKit-python/badges/flake8-badge.svg?dummy=8484744)](https://adaa-polsl.github.io/RuleKit-python/reports/flake8/index.html)
[![PyPI](https://img.shields.io/pypi/v/rulekit?label=pypi%20package)](https://pypi.org/project/rulekit/)
[![PyPI - Downloads](https://img.shields.io/pypi/dm/rulekit)](https://pypi.org/project/rulekit/)

# Rulekit

This package is python wrapper for [RuleKit](https://github.com/adaa-polsl/RuleKit) library - a versatile tool for rule learning. 
 
Based on a sequential covering induction algorithm, it is suitable for classification, regression, and survival problems.
 
## Installation
 
```bash
pip install rulekit
```

> **NOTE**: 
This package is a wrapper for Java library, and requires Java Development Kit version 8 or later to be installed on the computer. Both Open JDK and Oracle implementations are supported.

If you don’t have JDK installed on your computer you can quickly set it up using install-jdk package.
```
pip install install-jdk
```
```python
import jdk
jdk.install('11', jre=True)
```
 
## Running tests
 
If you're running tests for the first time (or you want to update existing tests resources) you need to download tests resources from RuleKit repository. You can do it by running:
```
python tests/resources.py download
```
Runing tests:    
In directory where `setup.py` file exists.
```
python -m unittest discover ./tests
```
 
## Sample usage
 
```python
from sklearn.datasets import load_iris

from rulekit.classification import RuleClassifier
 
X, y = load_iris(return_X_y=True)
 
clf = RuleClassifier()
clf.fit(X, y)
prediction = clf.predict(X)
 
print(prediction)
```
 
## Documentation
 
Full documentation is available [here](https://adaa-polsl.github.io/RuleKit-python/)

## Licensing

The software is publicly available under [GNU AGPL-3.0](https://github.com/adaa-polsl/RuleKit-python/blob/main/LICENSE) license. Any derivative work obtained under this license must be licensed under the AGPL if this derivative work is distributed to a third party. For commercial projects that require the ability to distribute RuleKit code as part of a program that cannot be distributed under the AGPL, it may be possible to obtain an appropriate license from the authors.
pyNNST 0.2
Definition of non-stationary index for time-series

Index of non-stationarity
---------------------------------------------
.. image:: https://zenodo.org/badge/243275920.svg
   :target: https://zenodo.org/badge/latestdoi/243275920

Obtaining non-stationary index for time-series.

Installing this package
-----------------------

Use `pip` to install it by:

.. code-block:: console

    $ pip install pyNNST


Simple examples
---------------

Here is a simple example on how to use the code:

.. code-block:: python

    # Import packages 
    import pyNNST
    import numpy as np

    # Define a sample signal x
    T = 20                                # Time length of x
    fs = 400                              # Sampling frequency of x
    dt = 1 / fs                           # Time between discreete signal values
    x = np.random.rand(T * fs)            # Signal
    time = np.linspace(0, T - dt, T * fs) # Time vector
    std = np.std(x, ddof = 1)             # Standard deviation of x
    mean = np.mean(x)                     # Mean value of x

    # Class initialization
    example = pyNNST.nnst(x, nperseg = 100, noverlap = 0, confidence = 95)
    
    # Compute the run test for non-stationarity
    example.idns() 
    outcome = example.get_outcome()  # Get the results of the test as a string
    index = example.get_index()      # Get the index of non-stationarity
    limits = example.get_limits()    # Get the limits outside of which the signal is non-stationary


Reference:

Non-stationarity index in vibration fatigue: Theoretical and experimental research; L. Capponi, M. Cesnik, J. Slavic, F. Cianetti, M. Boltezar; International Journal of Fatigue 104, 221-230
https://www.sciencedirect.com/science/article/abs/pii/S014211231730316X
todoman 4.6.0
A simple icalendar-based todo manager.
Requires Python: >=3.9
Keywords: todo, task, icalendar, cli

Todoman
=======

`CI status <https://builds.sr.ht/~whynothugo/todoman/commits/main>`_
| `Coverage <https://codecov.io/gh/pimutils/todoman>`_
| `Documentation <https://todoman.rtfd.org/>`_
| `ISC Licence <https://github.com/pimutils/todoman/blob/main/LICENCE>`_

Todoman is a simple, standards-based, cli todo (aka: task) manager. Todos
are stored into `icalendar <https://tools.ietf.org/html/rfc5545>`_ files, which
means you can sync them via `CalDAV <http://en.wikipedia.org/wiki/CalDAV>`_
using, for example, `vdirsyncer <https://vdirsyncer.readthedocs.org/>`_.

Todoman is now part of the ``pimutils`` project, and is hosted at `GitHub
<https://github.com/pimutils/todoman>`_.

Todoman should run fine on any Unix-like OS. It's been tested on GNU/Linux,
BSD, and macOS.  We do not support windows, and very basic testing seems to
indicate it does not work.

Feel free to join the IRC channel: #pimutils on irc.libera.chat.

Features
--------

* Listing, editing and creating todos.
* Todos are read from individual ics files from the configured directory. This
  matches the `vdir <https://vdirsyncer.readthedocs.org/en/latest/vdir.html>`_
  specification.
* There's support for the most common TODO features for now (summary,
  description, location, due date and priority).
* Todoman should run on any major operating system (except Windows).
* Unsupported fields may not be shown but are *never* deleted or altered.

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

For detailed usage, configuration and contributing documentation, please
consult the latest version of the manual at readthedocs_.

.. _readthedocs: https://todoman.readthedocs.org/

LICENCE
-------

Todoman is licensed under the ISC licence. See LICENCE for details.