semvec/pypi-packages huggingface dataset:
video-funnel 0.1.4
Use multiple connections to request the video, then feed the combined data to the player.
License: MIT
Requires Python: >=3.6
Keywords: Online multi-threaded video play

.. raw:: html

   <p align="center">

.. raw:: html

   </p>

.. raw:: html

   <h1 align="center">

Video Funnel

.. raw:: html

   </h1>

.. raw:: html

   <p align="center">

.. raw:: html

   </p>

.. raw:: html

   <p align="center">

让你在线看视频也能达到多线程下载的速度

.. raw:: html

   </p>

--------------

马上使用：
^^^^^^^^^^

1. 从 `PyPI <https://pypi.python.org/pypi/video_funnel>`__ 安装：

.. code:: bash

    $ pip(3) install --user video_funnel
    # or
    $ sudo pip(3) install video_funnel

2. 启动 ``video_funnel`` 的服务器：

.. code:: bash

    $ vf http://tulip.ink/test.mp4 &
    * Listening at port 8080 ...

3. 用 ``mpv`` 播放：

.. code:: bash

    $ mpv --quiet http://localhost:8080

``--quiet`` 参数是为了避免 mpv 的状态栏与 video_funnel
的进度条混杂在一起。

动机：
^^^^^^

众所周知，百度网盘之类产品的视频在线播放非常模糊，下载吧又限速，于是我写了
`aiodl <https://github.com/cshuaimin/aiodl>`__ 这个下载器，通过
`EX-百度云盘 <https://github.com/gxvv/ex-baiduyunpan/>`__
获取的直链来“多线程”下载。可是每次都要下载完才能看又十分不爽，直接用 mpv
之类的播放器播放直链又因为限速的原因根本没法看，遂有了本项目。

实现思路：
^^^^^^^^^^

1. 先将视频按照一定大小分块。块的大小根据视频的清晰度而异，以下载完一个块后视频可以播放为准。可通过命令行参数
   ``--block-size/-b`` 来指定，默认为 8MB 。
2. 对于上一步中的一个块，再次分块——为区别改叫切片，启动多个协程来下载这些切片，以实现“多线程”提速的目的。块和切片大小一起决定了有多少个连接在同时下载。切片的大小通过
   ``--piece-size/-p`` 来指定，默认为 1MB 。
3. 一个块中的切片全部下载完后，就可以将数据传给播放器了。当播放器播放这一块的时候，回到第
   2 步下载下一块数据。为节省内存，设置了在内存中最多存在 2
   个下载完而又没有传给播放器的块。

一些细节：
^^^^^^^^^^

1. 该如何把数据传给播放器呢？我最初的设想是通过标准输出，这样简单好写。但
   stdio 是无法 seek 的，这就意味着你只能从视频的开头看起，无法快进 :P
   如你所见，现在的解决方案是用 HTTP
   协议与播放器传输数据。需要快进的时候播放器发送 HTTP Range
   请求，video_funnel
   将请求中的范围经过分块、切片后“多线程”下载。但这样就又带来了两个问题：

   1. 需要播放器支持从 URL 播放。mplayer、mpv
      之类的命令行播放器大多都支持，但一些 Windows 的播放器就不得而知了
      :P 不过可以使用 HTML 的 video 标签在浏览器播放。
   2. 怎么就没有处理 Range 请求的包啊，自己处理很麻烦的好吗～

2. 由于下载的部分是用异步 IO 写的，与播放器交互的服务器部分就不能使用
   Flask 之类阻塞的框架了，幸好 aiohttp 居然同时支持客户端和服务端。

3. 说起来简单，实际写起来处处是坑啊 :(
carbonate-utils 0.4.0
Tools for tools for managing federated carbon clusters.
License: MIT
Keywords: graphite carbon carbonate

# Carbonate Utils

Utilities to plumb carbonate tools together.
Depends on the master (and yet unreleased) branches of whisper, carbonate and carbon.

## The Tools

### carbonate-sync

```bash
$ carbonate-sync --help
usage: carbonate-sync [-h] [-c CONFIG_FILE] [-C CLUSTER] [-S REMOTE_CLUSTER]
                      [-n NODE] [-b BATCH_SIZE] [--ssh-options SSH_OPTIONS]
                      [--rsync-options RSYNC_OPTIONS] [--temp-dir TEMP_DIR]
                      [--exclude EXCLUDE] [--start-time START_TIME]
                      [--end-time END_TIME] [--overwrite OVERWRITE]

carbonate-sync

optional arguments:
  -h, --help            show this help message and exit
  -c CONFIG_FILE, --config-file CONFIG_FILE
                        Config file to use (default:
                        /opt/graphite/conf/carbonate.conf)
  -C CLUSTER, --cluster CLUSTER
                        Cluster name (default: main)
  -S REMOTE_CLUSTER, --remote-cluster REMOTE_CLUSTER
                        Remote cluster name (default: None)
  -n NODE, --node NODE  Name of the local node (same as in carbonate.conf).
                        (default: coulomier)
  -b BATCH_SIZE, --batch-size BATCH_SIZE
                        Batch size for fetching metrics. (default: 1000)
  --ssh-options SSH_OPTIONS
                        Pass option(s) to ssh. Make sure to use "--ssh-
                        options=" if option starts with '-' (default: -o
                        StrictHostKeyChecking=no -o
                        UserKnownHostsFile=/dev/null -o Compression=no)
  --rsync-options RSYNC_OPTIONS
                        Pass option(s) to rsync. Make sure to use "--rsync-
                        options=" if option starts with '-' (default:
                        --archive --sparse --update --modify-window=100800)
  --temp-dir TEMP_DIR   Temporary dir (default: /tmp)
  --exclude EXCLUDE     Comma separated regexp of paths to exclude (slow).
                        (default: ^carbon\.)
  --start-time START_TIME
                        Sync data more recent than this time. (default: 0)
  --end-time END_TIME   Sync data older than this time (default: now).
                        (default: 1465420874.04)
  --overwrite OVERWRITE
                        Overwrite local data with remote data (default:
                        false). (default: False)
```

# Authors

 * Brice Arnould <b.arnould@criteo.com>
 * Corentin Chary <c.chary@criteo.com>

# License and warnings

These tools should be considered beta quality right now.
Tests exist for most functionality, but there is still significant work to be done to make them bullet-proof.

The code is available under the MIT license.
mrpy 1.1.0
An efficient alternative halo mass function distribution
License: MIT
Keywords: halo mass function bayesian gamma

mrpy
====
.. image:: https://travis-ci.org/steven-murray/mrpy.png?branch=master
		:target: https://travis-ci.org/steven-murray/mrpy
.. image:: https://coveralls.io/repos/steven-murray/mrpy/badge.svg?branch=master&service=github
        :target: https://coveralls.io/github/steven-murray/mrpy?branch=master
.. image:: https://api.codacy.com/project/badge/Grade/e5d5d9b72d024bd09e24ea833745c6da
        :target: https://www.codacy.com/app/steven-murray/mrpy?utm_source=github.com&amp;utm_medium=referral&amp;utm_content=steven-murray/mrpy&amp;utm_campaign=Badge_Grade

A Python package for calculations with the MRP parameterisation of the Halo Mass Function.

See Murray, Robotham, Power 2016 (in prep.) for more details on what the MRP is.


Quick Start
-----------

Installation
++++++++++++
Required packages are `numpy`, `scipy`, `mpmath` (for incomplete gamma functions),
and `cached_property`.
These should be automatically installed when installing `mrpy`.

To use the MCMC fitting features, `emcee` and `pystan` are needed. These are *not*
installed automatically.

The simplest way to install is ``pip install mrpy``. This should install the required
dependencies automatically.

To get the bleeding edge, use ``pip install git+git://github.com/steven-murray/mrpy.git``.

If for some reason ``pip`` is not an option, manually download the github
repo and use ``python setup.py install``.

Getting Started
+++++++++++++++
There's a lot of things that you can do with `mrpy`. What you require will depend on the problem at hand. We recommend
looking at some of the examples, and the API itself for how to use the code.

Documentation
+++++++++++++
`Read the docs <http://mrpy.readthedocs.org>`_.


Features
--------
With `mrpy` you can:

- Calculate basic statistics of the truncated generalised gamma distribution (TGGD) with the `TGGD` class: mean,
  mode, variance, skewness, pdf, cdf, generate random variates etc.
- Generate MRP quantities with the `MRP` class: differential number counts, cumulative number counts, various methods
  for generating normalisations.
- Generate the MRP-based halo mass function as a function of physical parameters via the `mrp_b13` function.
- Fit MRP parameters to data in the form of arbitrary curves with the `get_fit_curve` function.
- Fit MRP parameters to data in the form of a sample of variates with the `SimFit` class: simulation data is supported
  with extra efficiency, simulation suites fitted simultaneously is also supported, arbitrary priors on parameters,
  log-normal uncertainties on variates supported.
- Calculate analytic hessians, jacobians at any point (including the solution of a fit).
- Use alternate parameterisations of the same form via the `reparameterise` module.
- Work with a special entirely analytic model to understand the effects of various parameters in the `analytic_model` module.

Examples
--------
There are several examples featured in the ``docs/examples`` directory of the github repository. These can also be found
in the official documentation.