semvec/pypi-packages huggingface dataset:
ppfl 1.0.0
Python Package Finder Library

# PPF - Python Package Finder Library

This project is home to a command line interface used
to search PyPi for various projects.

Using this CLI is really simple, just type "pypi" and then
the name of your project.

```sh
pypi <project>
```

The command will be processed and will return the following fields:

* Name
* Version
* Description
* Author
* License
* Python Requirements
* Project URL
* Project URLs
* Classifiers

If the project doesn't qualify or provide for one of these fields,
they will be omitted. License names are only shown if they are under 100 characters,
as some maintainers like to include the LICENSE file content in the field, when
theoretically it should be just the license name. This limit is imposed to take better
care over the UI and aligned representation of data.

### Example Usage

```
pypi pathlib

>>>

pathlib | 1.0.1
~~~~~~~~~~~~~~~
Object-oriented filesystem paths

Author: Antoine Pitrou
License: MIT License
Project URL: https://pypi.org/project/pathlib/

Project URLs:
- Download: https://pypi.python.org/pypi/pathlib/
- Homepage: https://pathlib.readthedocs.org/

Classifiers:
- Topic :: Software Development :: Libraries
- Topic :: System :: Filesystems
- License :: OSI Approved :: MIT License
- Operating System :: OS Independent
- Intended Audience :: Developers
- Development Status :: 5 - Production/Stable
- Programming Language :: Python :: 2.6
- Programming Language :: Python :: 2.7
- Programming Language :: Python :: 3
- Programming Language :: Python :: 3.2
- Programming Language :: Python :: 3.3
- Programming Language :: Python :: 3.4
```

### Installation

Install through pip:

```sh
pip install ppfl
```

### Requirements

The ``requests`` module is required for this CLI.
lauetools-neural-network 0.1
GUI routine for Laue neural network training and prediction
Requires Python: >=3.7

A feed-forward neural network (FFNN) model to predict the HKL in single/multi-grain/multi-phase Laue patterns with high efficiency and accuracy is introduced. Laue micro-diffraction is a X-ray scattering technique for the determination of local structural parameters (strain, stress and orientation) in materials from microstructure mapping (2D and 3D). The use of a polychromatic beam offers many experimental advantages (no rotation of the sample, many diffraction spots, signal that can be used even in the presence of strong orientation disorder). However, in the case of polycrystalline microstructures, the determination of the structural parameters (lattice parameters) from the fine analysis of the relative position of the spots requires identifying unambiguously all the spots forming the Laue pattern corresponding to an individual crystal among all other spots. The indexation step of the data analysis must be reliable to input unambiguous experimental dataset to the structural model refinement (final step in analysis) and rapid since production rate of data on the synchrotron line can amount to several 10000s diffraction images per hour, each image (Laue patterns) being able to contain contribution from multi-grain/ multi-phase present in the probed volume of the material.
braille-art 0.0.3
Braille art generator
Requires Python: >=3

# braille-art

一个便利的盲文字符画生成工具

## 安装

```sh
pip install braille-art
```

## 用法

```sh
brailleart <你的图片路径>
```

另外，提供了以下开关：

- `-w`/`--width`, `-h`/`--height`: 控制图片的长/宽，如果只给其中一个值，则等比例缩放

- `-o`/`--output`: 输出盲文字符画到指定文件

当然也可以在python中引用：

```python
import brailleart as ba

ba.convert(your_path, optional_width, optional_height)
```

## 感谢

感谢[这篇文章](https://www.luogu.com.cn/blog/hejy/canvas)给出的盲文字符表和权值表。
glasswall-saas-email-transaction-downloader 1.0.0
Python script to download the contents of an Azure Blob Container to local storage.
Requires Python: >=3.6

# SaaS Email Transaction Downloader
Python script to download the contents of an Azure Blob Container to local storage. 

The tool will recurse through the File Ids to search for any nested containers. If it finds a nested container it will download the contents and place in a directory named after the file id.

# Usage
## Prerequisites 
- Python 3
- pip
- Azure Storage Blob package `pip install azure-storage-blob`

## Setting Connection String
Windows
```
setx AZURE_STORAGE_CONNECTION_STRING "<yourconnectionstring>"
```

Linux
```
export AZURE_STORAGE_CONNECTION_STRING="<yourconnectionstring>"
```

macOS
```
export AZURE_STORAGE_CONNECTION_STRING="<yourconnectionstring>"
```

## Running the tool
```
usage: container-downloader.py [-h] [-c CONTAINER] [-o OUTPUT]

optional arguments:
  -h, --help                             show this help message and exit
  -c CONTAINER, --container CONTAINER    Name of the Container to download
  -o OUTPUT, --output OUTPUT             Output Folder
```
pydependencygrapher 1.1.1
Draws dependency graphs.

pydependencygrapher
===================

Draws dependency graphs from a modified CONLL input

Usage example
-------------

.. code::

    cat example.conll | ./pydependencygrapher.py

Input example
-------------

.. code::

    #example
    Sentence: Foi Darwin ou foi a evolução?
    1	Foi	SER,IR	V	V	ppi-3s	0	ROOT	_	_
    2	Darwin	Darwin	PNM	PNM	_	1	PRD-ARG2	_	_
    3	ou	OU	CJ	CJ	_	4	CONJ	_	_
    4	foi	SER,IR	V	V	ppi-3s	1	COORD	_	_
    5	a	A	DA	DA	fs	6	SP	_	_
    6	evolução	EVOLUÇÃO	CN	CN	fs	4	PRD-ARG2	_	_
    7	?	?	PNT	PNT	_	1	PUNCT	_	_


Output example
--------------

.. image:: https://raw.githubusercontent.com/joaoantonioverdade/pydependencygrapher/master/example.png
   :alt: dependency graph with tags