semvec/pypi-packages huggingface dataset:
sphinxcontrib.divparams 0.0.6
Converts parameter tables in HTML documentation generated by Sphinx into <div>'s.
License: MIT
Keywords: helpers

Parameters list as `<div>`'s in HTML generated by Sphinx
========================================================

This package is a simple Sphinx extension named `divparams`, which was created
to bring another style to the lists of function parameters and return values in
HTML files generated by Sphinx.

To make use of the dirty hack this extension provides, first, install its
package from PyPI:

.. code-block:: bash

    $ sudo pip install sphinxcontrib.divparams

and then import it into your Sphinx's `conf.py` and add its parameters to the
lists of extensions, templates directories, and static directories:

.. code-block:: python

    import sphinxcontrib.divparams as divparams
    extensions       = [..., 'sphinxcontrib.divparams']
    templates_path   = [..., divparams.get_templates_path()]
    html_static_path = [..., divparams.get_static_path()]

    divparams_enable_postprocessing = True

See following links for more information. There is a broader description and
a couple of nice pictures on the standalone documentation page.

- **Repository:**    https://github.com/firegurafiku/sphinxcontrib-divparams
- **PyPI:**          https://pypi.python.org/pypi/sphinxcontrib.divparams
- **Documentation:** https://pythonhosted.org/sphinxcontrib.divparams/
- **Travis-CI:**     https://travis-ci.org/firegurafiku/sphinxcontrib-divparams
- **Reddit thread:** https://www.reddit.com/r/Python/comments/3se07g
- **Original SO question:** http://stackoverflow.com/questions/33296824
objectsinpython 0.9
Interface to Objects In Space from CircuitPython embedded hardware
License: MIT
Requires Python: >=3.6

Objects In Python
=================

Interface to [Objects In Space][ois] for [CircuitPython][] embedded hardware.

[![build status](https://travis-ci.org/jreese/objectsinpython.svg?branch=master)](https://travis-ci.org/jreese/objectsinpython)
[![version](https://img.shields.io/pypi/v/objectsinpython.svg)](https://pypi.org/project/objectsinpython)
[![license](https://img.shields.io/pypi/l/objectsinpython.svg)](https://github.com/jreese/objectsinpython/blob/master/LICENSE)
[![code style](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)


Install
-------

Download the [compiled module][builds] and copy them to your board. Note there are two
versions: "full" or regular for boards with more memory (eg, M4/samd51) and "tiny" for
boards with less memory (eg, M0/samd21). When using the tiny build, be sure to rename
the module to `oip.mpy`.

```bash
$ cp -X oip.mpy /Volumes/CIRCUITPY/oip.mpy
```

OR

```bash
$ cp -X oip-tiny.mpy /Volumes/CIRCUITPY/oip.mpy
```


Overview
--------

Objects In Python uses a simple, event-based API to execute your functions when buttons
are pressed or game values update. Getting started is as easy as importing the module,
creating the interface, and starting the connection:

```python
from oip import OIP

oip = OIP()
...
oip.start()
```

Turn lights on or off when game state changes: 

```python
@oip.on("IFF_ACTIVE")
def iff_active(now, value):
    pixels[0] = BLUE if value else RED
```

Connect buttons to game commands:

```python
@oip.press(board.BUTTON_A)
def thrust_while_holding(now, value):
    oip.execute("BURN_MAIN_ENGINE" if value else "STOP_MAIN_ENGINE")
```

With the full build, use helper classes to mitigate typoes:

```python
@oip.on(Boolean.IFF_ACTIVE)
def iff_active(now, value):
    ...

@oip.press(board.BUTTON_A)
def thrust_while_holding(now, value):
    oip.execute(Command.BURN_MAIN_ENGINE if value else Command.STOP_MAIN_ENGINE)
```

Check out the [example projects][examples] for more ideas.


License
-------

Objects In Python is copyright [John Reese](https://jreese.sh), and licensed under the
MIT license.  I am providing code in this repository to you under an open source
license.  This is my personal repository; the license you receive to my code
is from me and not from my employer. See the `LICENSE` file for details.

[ois]: http://objectsgame.com/
[CircuitPython]: https://circuitpython.org
[builds]: https://github.com/jreese/objectsinpython/releases/latest
[examples]: https://github.com/jreese/objectsinpython/tree/master/examples
smartalloc 0.0.1
Resource allocation using an SMT solver.
License: MIT

# SMarTalloc

A collection of utility functions for using Microsoft's Z3 theorem prover to
find the maximal allocation of limited resources given a prioritized list of
discrete tasks to be executed.

Resources and Tasks
-------------------
``smartalloc`` is designed to be as thin of a wrapper around the Z3 library as
possible, so it does not define classes called "Resource" or "Task."

Instead, Z3 primitives, such as Int or Real, are considered the resources, and
tasks that they are allocated for are represented by (collections of) Z3
constraints.

Allocation
----------
The main workhorse of ``smartalloc`` is the ``allocate()`` function.  It takes
two arguments: the intrinsic constraints on the resources, and a list of the
constraints required for each task.

Intrinsic resource constraints are applied immediately, and if the system
already has no solution then an exception will be raised.  After this, the
constraints for each task are added to the system.  If the system is still
satisfiable then the task is considered "worked" and the next task will be
added.  If the system is not satisfiable, that task will be removed from the
system before moving to the next.  All tasks provided will be attempted to be
added to the system; the ordering of the tasks determines which tasks get to
claim their necessary resources first.

The ``allocate()`` function returns a tuple of two items.  The first is the
allocation description object (in the form of a Z3 model).  The value of a
resource variable can be retrieved from it by using it like a dictionary with
the resource variable as the key.  The second is a list of the indices of the
tasks from the input are "worked" in the allocation result.
SPEMM 1.1.9
TEST DEMO
License: MIT

# Speech Enhancement Evaluation Metrics

计算语音增强相关的评价指标，计算结果会被保存为 Excel 表格。

## Usage

安装依赖：

```shell
# PESQ
git clone https://github.com/vBaiCai/python-pesq.git
cd python-pesq
python setup.py install

# STOI
pip install pystoi

# tqdm
pip install tqdm

# Librosa
pip install librosa

# tablib
pip install tablib
```

使用方法：

```shell
Speech Enhancement Evaluation Metrics

optional arguments:
  -h, --help            
                        show this help message and exit
  --nosiy_dir NOSIY_DIR
                        带噪语音目录
  --denosiy_dir DENOSIY_DIR
                        降噪语音的目录
  --clean_dir CLEAN_DIR
                        纯净语音的目录
  --output_path OUTPUT_PATH
                        评价指标存储的全路径，必须以拓展名 .xls 结尾
  --limit LIMIT         
                        被测试语音的数量。默认为0，表示不限制数量
  --offset OFFSET       
                        从某个索引位置开始计算评价指标，默认为0，表示从索引为 0 的语音开始计算
  --sr SR               
                        语音文件的采样率
```

例如：

```shell
python main.py --nosiy_dir /media/imucs/DataDisk/haoxiang/Release/speech_enhancement/release_-5_0_30_50/test/noisy/ --denosiy_dir ../se_-5_0_30_50_VCC/output/ --clean_dir /media/imucs/DataDisk/haoxiang/Release/speech_enhancement/release_-5_0_30_50/test/clean
```

## ToDo

- [x] 实现测试 stoi 和 metric 的评价功能
- [x] 生成表格