semvec/pypi-packages huggingface dataset:
nchash 0.1.5
A fast checksum function for netCDF files

=============================
nchash
=============================

A fast checksum function for netCDF files

.. image:: https://travis-ci.org/aidanheerdegen/nchash.svg?branch=master
  :target: https://travis-ci.org/aidanheerdegen/nchash
.. image:: https://circleci.com/gh/aidanheerdegen/nchash.svg?style=shield
  :target: https://circleci.com/gh/aidanheerdegen/nchash
.. image:: http://codecov.io/github/aidanheerdegen/nchash/coverage.svg?branch=master
  :target: http://codecov.io/github/aidanheerdegen/nchash?branch=master
.. image:: https://landscape.io/github/aidanheerdegen/nchash/master/landscape.svg?style=flat
  :target: https://landscape.io/github/aidanheerdegen/nchash/master
.. image:: https://codeclimate.com/github/aidanheerdegen/nchash/badges/gpa.svg
  :target: https://codeclimate.com/github/aidanheerdegen/nchash
.. image:: https://badge.fury.io/py/nchash.svg
  :target: https://pypi.python.org/pypi/nchash

.. content-marker-for-sphinx

A fast hashing function for netCDF files.

True hashing or checksum functions take longer the larger the file size, and hence don't scale
well for very large files.

The intention of this code was to create something that would be able to detect changes
in files as a "first pass" in a hierarchy of checksums. Full md5 or sha style checksums would 
still be required to robustly identify files.

The specific use case is verifying input files in ocean simulation codes, which are run, checkpointed
and then re-run in a continuous cycle. This allows for a quick integrity check on resubmission, which 
if failed can trigger a slower but more rigorous full checksum comparison.

The code hashes a dump of the netCDF header, the size and the modification time of the file
(internal hdf5 modification time in the case of netCDF4 formatted files).

This hash is unlikely to be robust over long time periods, as there may well be changes to the 
way ncdump outputs the header. But in the use-case envisaged this would trigger a more exhaustive
check, which if successful, could then regenerate a new nchash for fast checking.

Over short to medium timescales, and certainly within the time taken to re-run the simulation this
would be robust to changes. Particularly so for netCDF4 files which have an internal hdf5 modfication
time stamp. In addition, netCDF4 files that are compressed will likely change size with even small
changes to variables in the file.

Testing on an HPC system with fast disk access hashing is independent of size and takes 0.5-0.8s. Testing
has included file sizes up to 100GB.

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

Conda install::

    conda install -c coecms nchash

Pip install (into a virtual environment)::

    pip install nchash

---
Use
---

-------
Develop
-------

Development install::

    git checkout https://github.com/aidanheerdegen/nchash
    cd nchash
    conda env create -f conda/dev-environment.yml
    source activate nchash-dev
    pip install -e '.[dev]'

The `dev-environment.yml` file is for speeding up installs and installing
packages unavailable on pypi, `requirements.txt` is the source of truth for
dependencies.

Run tests::

    py.test
pytdx-async 1.62
A Python Interface to TDX protocol

Pytdx - Python tdx数据接口
==========================

|Build Status|

文档
----

用户文档会逐步转移到gitbook上，您可以在

https://rainx.gitbooks.io/pytdx/content/

阅读使用该api接口的详细文档。

概述
----

Pytdx 是一款纯Python语言开发的类似TradeX的行情数据接口的实现。

特点
----

-  纯python实现，无须引入动态连接库\ ``.dll/.so``\ 文件
-  支持\ ``python2.7+``/``3.5+``\ ， 以及全平台\ ``Windows/MacOS/Linux``
-  可以通过设置参数提供\ ``线程安全``\ 接口调用
-  实现\ ``心跳包``\ 机制,可以在长时间没有交互的情况下保持不断线
-  (试验）支持多连接构成的连接池机制，和failover处理机制，保证稳定性。
-  可以自定义的自动重连策略
-  (试验）支持异步行情接口 ，由 @JaysonAlbert 提供实现,
   详情查看\ *async*\ 分支

    pytdx将重点放在底层包的解析，对于更高层的封装，建议参考或者直接使用\ ```Quantaxis`` <https://github.com/yutiansut/QUANTAXIS/blob/master/QUANTAXIS/QAFetch/QATdx.py>`__\ 的实现..

安装
----

::

    pip install pytdx

接口实现
--------

标准行情 pytdx.hq
~~~~~~~~~~~~~~~~~

用于读取标准行情信息

扩展行情 pytdx.exhq
~~~~~~~~~~~~~~~~~~~

用于读取扩展行情（外盘，期权，期货等）

数据文件读取 pytdx.reader
~~~~~~~~~~~~~~~~~~~~~~~~~

用于读取行情软件导出的k线数据

pytdx.pool (试验性质)
~~~~~~~~~~~~~~~~~~~~~

用于实现备用连接池以及failover支持的行情接口

命令行
------

我们提供了方便命令行调试和导出数据的命令行工具 ``hqget``
以及\ ``hqreader`` 具体使用方法请参考这里。

缘起
----

因为之前TradeX的接口是使用Python扩展的方式调用C++代码实现的，功能上有诸多的限制，如只支持32位的Python，
不支持MacOS, Linux等， 无奈我自己使用的电脑是Mac系统,
服务器又是基于Linux的，所以只能自己重新实现一份。

声明
----

此代码用于个人对网络协议的研究和习作，不对外提供服务，任何人使用本代码遇到问题请自行解决，也可以在github提issue给我，但是我不保证能即时处理。
由于我们连接的是既有的行情软件兼容行情服务器，机构请不要使用此代码，对此造成的任何问题本人概不负责。

其它
----

欢迎对量化交易感兴趣的朋友互相交流，可以来我们的智矿社区看看
http://zhikuang.org

.. |Build Status| image:: https://travis-ci.org/rainx/pytdx.svg?branch=master
   :target: https://travis-ci.org/rainx/pytdx
pretty-json 1.2.0
make pretty json
Keywords: pretty,json

pretty-json
===========

make pretty json.

Documentation: https://github.com/devstuff-io/pretty-json


Installation
------------

.. code-block:: shell

   pip install pretty-json


Environment Variables
---------------------

``PJSON_OUTPUT_STYLE``
......................

**Default**: monokai

The pygments_ formatter used to make the json pretty.


Usage
-----

.. code-block:: python

   from pretty_json import format_json

   # format_json(content, style=OUTPUT_STYLE)
   print format_json({'key': 'value'})


**See and sample the available styles**

.. code-block:: shell

   python -c "from pretty_json import sample;sample('manni')"


Authors
-------

See contributors section on GitHub.


.. _pygments: http://pygments.org/docs/styles/