semvec/pypi-packages huggingface dataset:
cmd-watcher 0.0.1
A Python package to watch for command outputs and store them to a file for further analysis
Requires Python: >=3.6

# Command Watcher [![Python Badge](https://img.shields.io/badge/-Python-00000??style=flat-square&logo=python&logoColor=FFD43B&color=informational)](https://www.python.org/downloads/)  [![Downloads](https://pepy.tech/badge/hideme)](https://pepy.tech/project/cmd-watcher) [![Downloads](https://pepy.tech/badge/hideme/week)](https://pepy.tech/project/cmd-watcher/week)

cmd-watcher is a python tool to watch the output of a given command until certain conditions given by the user are satisfied. The conditions can be
1. There is a change in output
2. When the time limit exceeds

```
pip install cmd-watcher
```

## Usage

Format of proxies returned by **HideMe** is as shown below
```bash
$ python3 watcher.py -d DELAY [-o OUTPUT_FILE] [-t TIMEOUT] [-s] <cmd>

Command Watcher Tool

optional arguments:
  -h, --help            show this help message and exit
  -d DELAY, --delay DELAY
                        How long to wait until next execution
  -o OUTPUT_FILE, --output OUTPUT_FILE
                        File where the output should be stored
  -t TIMEOUT, --timeout TIMEOUT
                        For how many second should i watch
  -s, --stop            Pass this option if you want to stop checking whenever
                        there is a difference in output
```
If you want to store the command outputs to a file then pass the --output/-o with the file name

Example:
```bash
$ python3 watcher.py -d 2 -o ping_check.txt ping google.com
```
Above usage will execute the "ping google.com" command every 2 seconds and stores the output into file ping_check.txt

If you want to stop the execution when there is a change in the output then pass --stop/-s option

Example:
```bash
$ python3 watcher.py -d 2 -o ping_check.txt -s ping google.com
```
raisin 3.0.5
Simple parallel, distributed and cluster computing
Keywords: parallel,parallelisation,distributed,cluster work,cluster computing,serialization,serializeclient,server

﻿
***************************************
Raisin: To perform cluster work easily!
***************************************

.. Pour la syntaxe voir: https://deusyss.developpez.com/tutoriels/Python/SphinxDoc/

Project Philosophy
^^^^^^^^^^^^^^^^^^

| The main aim of project\ *raisin*\  is to \ **share physical resources**\  of your laptop with a community.
| In counterpart, you can \ **benefit from the community resources**\ .
| There are 2 sides in this project:

1. Resources usage
------------------

| The \ *raisin*\  API wants to be as close as possible to the 'threading' and 'multiprocessing' python APIs.
| The advantage in using \ *raisin*\  rather than 'threading' or 'multiprocessing' is that the computing power is greatly increased (depending on the number of connected resources).
| Though \ *raisin*\  is based on 'multiprocessing' module - that splits tasks among the resources of a single computer - it also shares the load over the different machines in the network. Everything is automatically and intelligently orchestrated.

\ *raisin*\  wants to be \ **as simple as possible**\ . That’s why the code analysis and the resources management are automated. It also uses a bunch of classes and functions default parameters that are suitable for most usages.

| However, you can tune \ *raisin*\  behavior as you want since all these parameters are \ **fully customizable**\ .
| \ *raisin*\  is a multi-OS module 100% written in python in order to keep installation reliable and simple.

In a future version, \ *raisin*\  will be able to perform automatic parallelization, a little like 'pydron'.

2. Resources sharing
--------------------

| To be able to use community resources, you must give in return!
| That’s why, when \ *raisin*\  is installed as a python package, you have to install the 'application' part.
| The minimum to do is to start a grape server that will be listening to execute the tasks requested by itself or by other users on the network. To do this, run the following command: ``python -m raisin server``

Installation
^^^^^^^^^^^^

| To install the module, you have to go through pypi: ``pip install raisin``.
| Once the module is installed, execute ``python -m raisin install`` to create the configuration file.
| For the uninstallation, it is enough to execute ``python -m raisin uninstall && pip uninstall raisin``.

Basic example
^^^^^^^^^^^^^

.. code:: python

    >>> import raisin
    >>> f = lambda x: x**2 # a costly function
    >>> list(raisin.imap_unordered(f, range(3), context=True))
    [Result(0), Result(1), Result(4)]
    >>>


* See the `documentation <http://raisin-docs.ddns.net/>`_ for more details and examples.
todoterm 1.0.3
A simple TODO command line application
Requires Python: >=3.7

# todoterm

A simple TODO command line application.


## Installation

    python -m pip install todoterm


## Usage

First create a sqlite database file with the following command:

    python -m todoterm init


This creates a sqlite database file named **.todo** in the current working directory.
Todoterm always modifies the database in the current working directory.


Next add new tasks with the following command:

    python -m todoterm add "Task description here..."


List tasks with the **list** - command:

    python -m todoterm list


Delete tasks with the **del** - command:

    python -m todoterm del 1


Mark tasks as done with the **done** - command:

    python -m todoterm done 1


Undo tasks with the **undo** - command:

    python -m todoterm done 1


The del, done and undo commands require atleast one integer value as input.
These integers are the IDs of the modified tasks. You can see them in the output of the
list command. To get all available options for each command, use option **--help**.