semvec/pypi-packages huggingface dataset:
hesong-ipsc-busnetcli 3.2.1.post1
Python wrapper for Hesong IPSC CTI Service data bus client
License: MIT
Requires Python: >=2.6,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*

hesong-ipsc-busnetcli
#####################
IPSC专用的消息总线客户端的Python包装。

它用于向 Python_ 开发者提供SmartBus的客户端功能。

Features
********

* 直接封装 `IPSC Data Bus` 的 `C` 语言客户端库 ipsc-bus-client_
* 采用 Python_ 标准库的 `ctypes` 进行 `C` 语言动态/共享库的封装。所以安装时不需要进行编译，理论上同时支持多种Python（如 `pypy`, `ironpython`, `jython`）运行时（只要该 Python_ 实现的标准库支持 `ctypes`）
* 完整的客户端功能包装,其功能基本上与 `C` 语言实现的客户端一对一。

.. warning::
    ipsc-bus-client_ 目前只提供了 ``Linux-x86_64`` 支持。

Install
*******

* 通过 pip_ 安装

  使用 pip_ 工具从 PyPI_ 安装

  .. code:: sh

    pip install --user hesong-ipsc-busnetcli

* 从源代码安装

  .. code:: sh

    git clone https://bitbucket.org/hesong-opensource/ipsc-bus-client-python.git
    cd ipsc-bus-client-python
    python setup.py install

.. _Python: http://www.python.org/
.. _PyPI: http://pypi.python.org/
.. _pip: http://pip.pypa.io/
.. _ipsc-bus-client: http://bitbucket.org/hesong-opensource/ipsc-bus-client

CHANGELOG
*********

3.2.1
=====

:Date: 2018-08-07

Change
------
* 去除 Python 3.0-3.3 的支持
* 重构 SphinxDocs 文档

Fix
---
* 自定义 Error 类构造函数的一处错误

Add
---
* pylintrc 配置文件

Optimize
--------
* 优化包结构
* 许多来自 linter 的优化

AUTHORS
*******

* Liu Xue Yan <liu_xue_yan@foxmail.com>
textmetrics 0.0.2
Automatic text metrics---BLEU, ROUGE, and METEOR, pllus extras like vocab and ngrams.
License: MIT
Keywords: text-metrics text metrics nlp bleu rouge meteor ngrams vocabulary vocab

# textmetrics

Automatic text metrics---BLEU, ROUGE, and METEOR, plus extras like vocab and
ngrams.

## Usage

```bash
# Compares each candidate (c) separately against all references (r).
python -m textmetrics.main c1.txt c2.txt --references r1.txt r2.txt r3.txt
```

## Installation

Requires:
- Perl (for BLEU)
- Java 1.8 (for METEOR)
- Python 3.6+

```bash
pip install textmetrics
```

## Features

- [x] BLEU
- [x] ROUGE
- [x] METEOR


## Notes

BLEU and METEOR use the refernce implementations (in Perl and Java,
respectively). We originally used the reference Perl implementation for ROUGE
as well, but it ran so slowly that we opted for a Python reimplementation
instead. (ROUGE's original Perl implementation is also more difficult to setup,
even with wrapper libraries.)

## Worklist

- [ ] pypi

- [ ] API support

- [ ] ROUGE crashes things if it decides there aren't sentences (e.g., run with
  README.md as input and reference)

- [ ] Add back in orig ROUGE for completeness (place behind switch)

- [ ] ngrams has divide by zero error. With two simple files (two lines each,
  same first line, differing second line) running with `2.txt --references
  1.txt 1.txt` triggered this divide by zero

- [ ] Demo for better README

- [ ] Tests

- [ ] Early check in each module for whether program runnable + nice error
  message (e.g., no java or bad version, no perl or bad version, etc.)


Note to self: I followed this guide for [packaging to
pypi](https://packaging.python.org/tutorials/packaging-projects/), and future
uploads will probably look like:

```bash
# (1) ensure tests pass

# (2) bump version in setup.py

# (3) commit + push to github

# (4) generate distribution
python setup.py sdist bdist_wheel

# (5) Upload
twine upload dist/*
```
open_worm_analysis_toolbox 3.1.2
Open Worm Analysis Toolbox
License: MIT
Keywords: C. elegans worm tracking

See https://github.com/openworm/open-worm-analysis-toolbox
[![GitHub license](https://img.shields.io/github/license/mashape/apistatus.svg)](LICENSE.md)
[![Travis-CI](https://travis-ci.org/openworm/open-worm-analysis-toolbox.svg?branch=master)](https://travis-ci.org/openworm/open-worm-analysis-toolbox)
[![BountySource](https://api.bountysource.com/badge/team?team_id=23852)](https://www.bountysource.com/teams/openworm)


| <img src="documentation/OpenWorm%20Analysis%20Toolbox%20logo.png" width="125"> | Open Worm Analysis Toolbox |
====================

The **Open Worm Analysis Toolbox** is a Python port of the Schafer Lab's [Worm Analysis Toolbox 1.3.4](http://www.mrc-lmb.cam.ac.uk/wormtracker/index.php?action=analysis).

It can be used to process videos of *C. elegans* into statistics so the behaviour of individual worms can be compared.

It is also the package used by the OpenWorm project to determine how closely its simulated worm behaves like real worms. It was started as a sub-project of the [OpenWorm project](https://github.com/openworm).

[OWAT is on PyPI](https://pypi.python.org/pypi/open_worm_analysis_toolbox), so to install, simply type:

```
pip install open_worm_analysis_toolbox
```

Contributors please see:

-   [Installation Guide](documentation/INSTALL.md)
-   [Installation Guide for OS X](documentation/INSTALL-OSX.md)

Usage Example
-------------

```Python
import open_worm_analysis_toolbox as mv

# Load a "basic" worm from a file
bw = mv.BasicWorm.from_schafer_file_factory("example_contour_and_skeleton_info.mat")
# Normalize the basic worm
nw = mv.NormalizedWorm.from_BasicWorm_factory(bw)
# Plot this normalized worm    
wp = mv.NormalizedWormPlottable(nw, interactive=False)
wp.show()
# Obtain features
wf = mv.WormFeatures(nw)
```

Later, if we have control worms, we can run statistics on our worm:

```Python
# Compute histograms
experiment_histograms = mv.HistogramManager([wf, wf])
control_histograms = mv.HistogramManager(control_worms)

# Compute statistics
stat = mv.StatisticsManager(experiment_histograms, control_histograms)

# Plot statistics for the first extended feature
stat[0].plot(ax=None, use_alternate_plot=True)

# Give an overall assessment of the worm's similarity to the control set
print("Nonparametric p and q values are %.2f and %.2f, respectively." %
      (stat.min_p_wilcoxon, stat.min_q_wilcoxon))
```