semvec/pypi-packages huggingface dataset:
OrzTinyPNG 0.0.5
A tool for compress image such as png and jpg using TinyPNG API
Requires Python: >=3, <4

# OrzTinyPNG

This is a simple example package. You can use

## Usage

```bash
$ pip install OrzTinyPNG
$ tiny -h
```

[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content.

# How to Publish

[publish tutorial](https://packaging.python.org/tutorials/packaging-projects/)

# Choose you license

[Choose a License for your self](https://choosealicense.com)

# 参考

- [TinyPNG 官网](https://tinypng.com/)
- [TinyPNG API Key](https://tinypng.com/developers)
- [TinyPNG API Ref](https://tinypng.com/developers/reference)
- [TinyPNG Python API Doc](https://tinypng.com/developers/reference/python)
winstats 0.80
A simple Windows status retrieval module with no additional dependencies.



Windows Stats
===============

A simple Windows status retrieval module with no additional
dependencies.  Wraps common system status queries from Windows' ugly C-API:

* Memory stats
* Performance stats
* Disk info, types, and stats
* Performance Data Counters (aka PerfMon)

Supports Python 2.6 and above; WindowsXP and above.

*Note:* Relicensed as LGPL v3 (or later).


Install
-------------

::

    D:\> pip install winstats


Use
-----

**Setup**::

    import winstats

    # optional
    import locale
    locale.setlocale(locale.LC_ALL, '')
    fmt = lambda n: locale.format('%d', n, True)

**Memory Stats**::

    meminfo = winstats.get_mem_info()
    print '    Total: %s b' % fmt(meminfo.TotalPhys)
    print '    usage: %s%%' % fmt(meminfo.MemoryLoad)
    print

**Performance Stats**::

    pinfo = winstats.get_perf_info()
    print '    Cache: %s p' % fmt(pinfo.SystemCache)
    print '    Cache: %s b' % fmt(pinfo.SystemCacheBytes)
    print

**Disk Stats**::

    drives = winstats.get_drives()
    drive = drives[0]
    fsinfo = winstats.get_fs_usage(drive)
    vinfo = winstats.get_vol_info(drive)

    print '    Disks:', ', '.join(drives)
    print '    %s:\\' % drive
    print '        Name:', vinfo.name
    print '        Type:', vinfo.fstype
    print '        Total:', fmt(fsinfo.total)
    print '        Used: ', fmt(fsinfo.used)
    print '        Free: ', fmt(fsinfo.free)
    print

**Perfmon - Performance Counters**::

    # take a second snapshot 100ms after the first:
    usage = winstats.get_perf_data(r'\Processor(_Total)\% Processor Time',
                                   fmt='double', delay=100)
    print '    CPU Usage: %.02f %%' % usage

    # query multiple at once:
    counters = [ r'\Paging File(_Total)\% Usage', r'\Memory\Available MBytes']
    results = winstats.get_perf_data(counters, fmts='double large'.split())
    print '    Pagefile Usage: %.2f %%, Mem Avail: %s MB' % results


Results
---------

The examples above are built into the module, and double as a minimal test
suite::

    D:\> python.exe -m winstats

    Memory Stats:
        Total: 536,330,240 b
        usage: 31%

    Performance Stats:
        Cache: 35,921 p
        Cache: 147,132,416 b

    Disk Stats:
        Disks: C, D, O
        C:\
            Name: System
            Type: NTFS
            Total: 10,725,732,352
            Used:  3,160,956,928
            Free:  7,564,775,424

    PerfMon queries:
        CPU Usage: 10.00 %
        Pagefile Usage: 0.55 %, Mem Avail: 347 MB

And more ...
starcoder 0.0.2
StarCoder is an unsupervised deep learning model tailored for exploratory empirical research in the humanities.

StarCoder is essentially a generator that combines autoencoder and graph-convolutional mechanisms with the open set of neural architectures to build end-to-end models of entity-relationship schemas.  By adopting intuitive JSON for all I/O, and using reconstruction loss as the objective, it allows researchers from other fields to apply it to their specific areas, while ML researchers can continue to augment it with new techniques.
parallelprocs 0.1.5
Run command lines via (GNU) parallel

# parallelprocs

Run commands via GNU Parallel or something like it.

# Synopsis

````
$ cat test.py
#!/usr/bin/env python

from parallelprocs import run

try:
    run(['echo foo', 'echo bar', 'echo baz'], verbose=True)
except Exception as e:
    print(e)
$ ./test.py
Running job (# jobs = 3)
bar
baz
foo
````

# Description

The module exposes a single function `run` that takes the following arguments:

## Required Arguments:

commands [str]: commands

## Keyword Options

* parallel (str): path to "parallel", default "which('parallel')"
* num_procs (int): num of concurrent processes, default "0" to use all CPUs
* verbose (bool): print messages to sys.stderr, default "False"
* halt (int): number of failed jobs to trigger halt, default "0" for no halt

# Author

Ken Youens-Clark <kyclark@gmail.com>