semvec/pypi-packages huggingface dataset:
tabulatepdf 0.1.1
A CLI for tabula-py
Requires Python: >=3.6

### A CLI for [tabula-py](https://github.com/chezou/tabula-py)

#### Installation

Install using pip:
```shell
pip install tabulatepdf
```
Install from Github
```shell
git clone https://github.com/Weizhang2017/tabulatepdf
cd tabulatepdf 
python setup.py install
```

#### CLI

```shell
usage: tabulatepdf [-h] --pdf pdf [--output output] [--page page]

extract tables from pdf

optional arguments:
  -h, --help       show this help message and exit
  --pdf pdf        pdf file to extract tables from
  --output output  output filename, default: output.csv
  --page page      page number of pdf to extract from, default: 1
```

###### Simple Example:

```shell
tabulatepdf --pdf sample.pdf --output output.csv --page 10
table saved to output.csv
```
git-processor 0.7
Process and parse through git log statistic
Keywords: git git_processor data science

Git Processor
=============

A git log processor for better stats.

View the demo `git processor demo <https://sylhare.github.io/git-processor/>`_.


Setup
-----

Install the library with:

.. code:: bash

    # From pypi
    python3 -m pip install git_processor


To use it, run the ``generate_git_logs.sh`` in where you have all your repository.
It will create a ``stats.txt`` file.

.. code:: python

    import os
    from git_processor.data import Projects

    p = Projects(os.path.abspath("stats.txt"))  # Process the git log stats
    p.clean_up_names()                          # Get all similar names as one
    p.df                                        # Get the created dataframe
    p.total()                                   # Total commits per user



Let's use jupyter to display the information.

.. code:: bash

    pyhton3 -m pip install jupyter
    cd jupyter/
    jupyter notebook

Once you are in the jupyter notebook, you can display the data and plot the stats.
Check in the ``jupyter/`` folder, you can reuse the demo and get your stats in one go.
Just click on ``Run All Cell``, or go along with <kbd>shift</kbd> + <kbd>enter</kbd> to run them individually.


Testing
-------

To find out more info about the testing configuration, check out the
``tox.ini`` file.

.. code:: bash

   # Run the test suite
   tox
   # Run the linter:
   tox -e lint


Local Installation
------------------

Using a virtual environment:

.. code:: bash

    # From pypi
    python3 -m pip install virtualenv

Then set it up and install the package locally

.. code:: bash

   # Create the virtual environment
   python3 -m venv `pwd`/env
   # Activate it
   source env/bin/activate
   # Install from local
   (env) python3 -m pip install .
rubricprocessor 0.0.2
Process Blackboard rubric archives
Requires Python: >=3.6

# Rubric Processor
 Blackboard Rubric processor


This python package processes [Blackboard](https://www.blackboard.com/en-apac) rubric export archives, and produces a XLSX file with tabs for each of the rubrics. Each tab has the **latest** score (both weighted and raw) for every student


The processor can be run in 3 different modes
* `cli` - call with parameters for a single rubric export
* `gui` - a pyqt5 gui to allow the user to select and process files from the ui
* `batch` - process multiple rubric exports as specified in a control file (csv format - expects "zipfile" and "gradecentre" columns to be present)
pyopendnp3 0.1.0
pyopendnp3 -- python binding for opendnp3

# pyopendnp3

This project is a minor fork of the great
[pydnp3](https://github.com/ChargePoint/pydnp3) library, which provides Python
bindings for OpenDNP3.

Why fork? Because pydnp3 only provides pip-friendly wheels for Python 2
and Python 2.7 has reached end of life. This fork aims to provide pip-friendly
wheels for Python 3 available on PyPI.

While the name of the project is pyopendnp3, module names are unchanged and
thus code that worked with pydnp3 should work with no changes.

This project currently produces wheels for the following platforms:

* Windows
  * Python 3.5 x86-64
  * Python 3.6 x86-64
  * Python 3.7 x86-64
  * Python 3.8 x86-64

The remainder of this README is reproduced verbatim from pydnp3.

Python bindings for the [opendnp3](https://github.com/automatak/dnp3) library,  an open source
implementation of the [DNP3](http://ww.dnp.org) protocol stack written in C++14.

Note:  This is a work in progress.  See [Issues](http://github.com/Kisensum/pydnp3/issues) for things we know about and feel free to add your own.

**Supported Platforms:** Linux, MacOS

## Dependencies
To build the library from source, you must have:

* A toolchain with a C++14 compiler
* CMake >= 2.8.12 (https://cmake.org/download/)

This repository includes two repositories as submodules (under `deps/`):

* dnp3 (https://github.com/automatak/dnp3)
* pybind11 (https://github.com/Kisensum/pybind11) - This is a fork containing a minor patch
required to compile some of the pydnp3 wrapper code. It will be replaced with pybind11 proper
when the issue is resolved.

## Build & Install
At the moment, this library must be built from source:
```
    $ clone --recursive http://github.com/Kisensum/pydnp3
    $ cd pydnp3
    $ python setup.py install
```


## Documentation

pydnp3 is a thin wrapper around most all of the opendnp3 classes.  Documentation for the opendnp3
classes is available at [automatak](https://www.automatak.com/opendnp3/#documentation).

Use python's help to discover the available wrapper classes and functions.  For example,

```
> import pydnp3
> help (pydnp3.opendnp3)
Help on module pydnp3.opendnp3 in pydnp3:

NAME
    pydnp3.opendnp3 - Bindings for opendnp3 namespace

FILE
    (built-in)

CLASSES
    pybind11_builtins.pybind11_object(__builtin__.object)
        AnalogCommandEvent
        AnalogInfo
            AnalogSpec
...
```
nemo-tts 0.9.0
Collection of Neural Modules for Speech Synthesis

NeMo TTS Collection: Neural Modules for Speech Synthesis
========================================================

**Models**

The NeMo TTS collection currently has modules for building
1. Tacotron 2
2. Waveglow

See the documentation and tutorials for ``nemo_tts`` [here](https://nvidia.github.io/NeMo/tts/intro.html).
nemo-nlp 0.9.0
Collection of Neural Modules for Natural Language Processing

NeMo NLP Collection: Neural Modules for Natural Language Processing
===================================================================

**Models**

    * Neural Machine Translation
    * BERT
    * Intent Classification and Slot filling
    * Named Entity Recognition

See the documentation and tutorials for ``nemo_nlp`` [here](https://nvidia.github.io/NeMo/nlp/intro.html).
nemo-asr 0.9.0
Collection of Neural Modules for Speech Recognition

NeMo ASR Collection: Neural Modules for Automatic Speech Recognition
====================================================================

**Models**

The NeMo ASR collection currently has modules for building
1. All Jasper convolutional models:
..* Jasper DR 10x5
..* Jasper 15x5 Seperable
..* Jasper Mini
2. Jasper Encoder + Seq2seq Decoder

See the documentation and tutorials for ``nemo_asr`` [here](https://nvidia.github.io/NeMo/asr/intro.html).