semvec/pypi-packages huggingface dataset:
mpi-list 0.3
A Distributed List API over MPI
License: MIT
Requires Python: >=3.6

========
mpi list
========

This package implements the `DFM` class.

The `DFM` is a useful abstraction for working with
lists distributed over a set of MPI ranks.
The acronym stands for distributed free monoid,
which is just a fancy way to say it's a list.

If you're familiar with spark, it's like an RDD,
but only holds a list.

Quick Start
===========

.. code-block::

    from mpi_list import Context, DFM

    C = Context() # calls MPI_Init via mpi4py

    # After each of the three lines below:
    #  1. each rank now has 1000//C.procs consecutive numbers
    #  2. each rank now has a list of strings
    #  3. only numbers containing a '2' remain
    dfm = C . iterates(1000) \
            . map(lambda i: f"String {i}") \
            . filter(lambda s: '2' in s)

    if C.rank == 0:
        # Caution! Uncommenting this will deadlock your program.
        # Collective calls must be called by all ranks!
        #print( dfm . head(10) )
        pass

    # This is OK, since all ranks now have 'ans'
    ans = dfm.head(10)
    if C.rank == 0:
        print( ans )

    ans = dfm . filter(lambda s: len(s) <= len("String nn")) \
              . collect()
    if ans is not None: # only rank 0 gets "collect"
        print( ans )

Launch your program with `mpirun python my_prog.py`.

If you're using a supercomputer, consider installing
`spindle <https://computing.llnl.gov/projects/spindle/software>`_,
and then use `spindle mpirun python my_prog.py`.

.. _pyscaffold-notes:

Note
====

This project has been set up using PyScaffold 4.0.1. For details and usage
information on PyScaffold see https://pyscaffold.org/.
maleo 0.0.7.0
Wrapper library for text cleansing, preprocessing in NLP
License: MIT
Keywords: nlp,text-processing,machine-learning

<img src="logo.png" alt="Maleo" width="150" height="150">

# Maleo
Wrapper library for text cleansing, preprocessing and POS Tagging in NLP

## Docs
https://jakartaresearch.github.io/maleo/

## Overview of features
    - Scanner : get insight about your text dataset (ex: number of chars, words, emojis, etc)
    - Remove hyperlink, punctuation, stopword, emoticon, etc
    - Extract hashtags, price from text
    - Convert email, phone number, date to <TAG>
    - Convert Indonesian slang to formal word
    - Convert emoji to word or <TAG>
    - Convert word to number
    - Predict Part-of-Speech (POS) tags

## Installation
```
pip install maleo
```

## Getting Started
```python
from maleo.wizard import Wizard
from maleo.pos_tag import POS

wiz = Wizard()
pos = POS()

wiz.scanner(df, 'text')
wiz.emoji_to_word(df.text)
wiz.slang_to_formal(df.text)

pos.predict('saya mau pergi beli makan siang dulu', output_pair=False)
```

## Universal POS tags
https://universaldependencies.org/u/pos/index.html

## Contributor:
- Ruben Stefanus
mc-downloader 0.0.5
A downloader tool for Minecraft servers from Mojang services.
License: MIT
Requires Python: >=3.6

# mc-downloader

This is a python package design to download Minecraft servers files from Mojang servers.

The downloader verify integrity from mojang servers after download.

If the files have already been downloaded, they are rechecked, and if necessary they are re-downloaded.

## Installation

You need to install the package from pypi repository.

```bash
pip install mc-downloader
```

## Utilisation example

```python
import os
import mcdwld

def main():
    """Download all servers."""
    downloads_directory = os.getcwd() + '/downloads/'
    versions = mcdwld.get_versions(mcdwld.MOJANG_MANIFEST_URL)
    mcdwld.download_versions(versions, downloads_directory)

if __name__ == '__main__':
    main()
```
speedtestjob 1.0.0
speedtest job.
License: MIT
Requires Python: >=3.6.0

这是一个定时自动测速的工具，用于了解你的VPS网络真实带宽。
它借助于speedtest.net，因此最终测试数据准确性依赖于speedtest.net的节点。另外，为了保证测试结果最接近真实情况，在测试期间qing不要占用网络带宽。

此工具主要实现了：
    启动后按照设置的时间间隔启动一轮网速测试。
    每一轮测速耗时与设置的测速节点多少有关。
    每一轮测试结束后，测试结果会保存到excel文件中。
    此工具会重复上述测试，直到达到停止时间。


设置和说明：
    
    日志位置：/var/log/speedtest_job/
    测试结果文件位置：/var/log/speedtest_job/


怎么安装：


怎么使用：