semvec/pypi-packages huggingface dataset:
cepcenv 0.2.2
CEPC software management toolkit
License: MIT
Keywords: CEPC

cepcenv
=======

CEPC software management toolkit


Install cepcenv
---------------

Install ``cepcenv`` with ``curl`` or ``wget`` ::

    curl -sSL http://cepcsoft.ihep.ac.cn/package/cepcenv/script/install.sh | sh -s [CEPCENV_DIR]

or ::

    wget -qO- http://cepcsoft.ihep.ac.cn/package/cepcenv/script/install.sh | sh -s [CEPCENV_DIR]

If ``CEPCENV_DIR`` is omitted, ``cepcenv`` will be installed in the
current directory.


Install cepcsoft with cepcenv
-----------------------------

Setup ``cepcenv``::

    . <CEPCENV_DIR>/setup.sh

List available CEPC software version::

    cepcenv ls-remote

Specify CEPC software version and where to install::

    cepcenv [-r <SOFTWARE_ROOT>] install <version>

If ``-r <SOFTWARE_ROOT>`` is not specified, everything will be
installed in the current directory.

Multiple versions could be installed under the same software root
directory.


Configuration
-------------

Create the configuration file if it does not exist::

    [ -e ~/.cepcenv.conf ] || cepcenv config -e > ~/.cepcenv.conf

Edit `~/.cepcenv.conf` and uncomment `software_root`::

    software_root: <SOFTWARE_ROOT>


Select cepcsoft Version
-----------------------

List local installed CEPC software version::

    cepcenv -r <SOFTWARE_ROOT> ls

Select local CEPC software version you would like to use::

    cepcenv -r <SOFTWARE_ROOT> use <version>
looo.py 2018.1
A code generator for array-based code on CPUs and GPUs
License: MIT

Loopy: Transformation-Based Generation of High-Performance CPU/GPU Code
=======================================================================

.. image:: https://gitlab.tiker.net/inducer/loopy/badges/master/pipeline.svg
   :target: https://gitlab.tiker.net/inducer/loopy/commits/master
.. image:: https://badge.fury.io/py/loo.py.png
  :target: http://pypi.python.org/pypi/loo.py

Loopy lets you easily generate the tedious, complicated code that is necessary
to get good performance out of GPUs and multi-core CPUs.
Loopy's core idea is that a computation should be described simply and then
*transformed* into a version that gets high performance. This transformation
takes place under user control, from within Python.

It can capture the following types of optimizations:

* Vector and multi-core parallelism in the OpenCL/CUDA model
* Data layout transformations (structure of arrays to array of structures)
* Loop unrolling
* Loop tiling with efficient handling of boundary cases
* Prefetching/copy optimizations
* Instruction level parallelism
* and many more

Loopy targets array-type computations, such as the following:

* dense linear algebra,
* convolutions,
* n-body interactions,
* PDE solvers, such as finite element, finite difference, and
  Fast-Multipole-type computations

It is not (and does not want to be) a general-purpose programming language.

Loopy is licensed under the liberal `MIT license
<http://en.wikipedia.org/wiki/MIT_License>`_ and free for commercial, academic,
and private use. All of Loopy's dependencies can be automatically installed from
the package index after using::

    pip install loo.py

In addition, Loopy is compatible with and enhances
`pyopencl <http://mathema.tician.de/software/pyopencl>`_.

---

Places on the web related to Loopy:

* `Python package index <http://pypi.python.org/pypi/loo.py>`_ (download releases) Note the extra '.' in the PyPI identifier!

* `Documentation <http://documen.tician.de/loopy>`_ (read how things work)
* `Github <http://github.com/inducer/loopy>`_ (get latest source code, file bugs)
* `Wiki <http://wiki.tiker.net/Loopy>`_ (read installation tips, get examples, read FAQ)
* `Homepage <http://mathema.tician.de/software/loopy>`_
strategies-manager 1.0.0
StrategiesManager
License: MIT

**StrategiesManager**

# 这是一个策略回测库，支持内外盘期货历史回测跟实时回测两种模式

# 例子：
```python
from strategies_manager import StrategiesManager

# 自定义操作函数
def myAction(data):
    """
    自定义操作
    实现自己的策略
    :param data: 行情数据，字典类型
    :return:
    """
    print(data)

# 创建实例
manager = StrategiesManager()
# 注册自定义函数
manager.registAction(myAction)

# 实时数据使用
manager.runRealTime(stock_code='NYMEX_F_CL_2010', ktype='1Min')

# 历史数据回测使用
manager.runHistory(stock_code='NYMEX_F_CL_2010', startTime='2020-06-07', endTime='2020-08-17', ktype='1Min')
```
dataray 0.1.1
a class of decorators helping inspect data
License: MIT
Requires Python: >=3.7,<4.0
Keywords: json,data,inspection

# DataRay

DataRay is a class of decorators helping detect the metadata of the data.

The motivation of this package is to help understand how your api request data looks like. Normally we can directly
check how the api request looks like via some UI platform such as postman etc. With this package, user may directly
get the data structure and some basic metadata of the requested data

## Features
* Json Structure
* Further feature coming soon


## Detect the Json Structure

```python
# here is your customer request function. Now it is supposed to return list or dict you are interested in looking into
from dataray import dataray

@ dataray.ray
def request_func(*args, **kwargs):
    ...
```
Then every time calling `request_func`, the json structure will be printed out.