semvec/pypi-packages huggingface dataset:
LongitudinalDifferenceInDifferencesPy 1.0.2
Runs a difference in differences analysis in Python.
License: MIT

<a href="https://www.youtube.com/watch?v=J7q2H8aB8bQ"><img src="https://github.com/microsoft/LongitudinalDifferenceInDifferencesPy/blob/master/Images/youtubeScreenshot.PNG" /></a>

# Introduction 
It's critical to know if the work we are doing is helping the team reach its organizational goals. The problem is that there are potentially many other effects at play. One analysis method for doing this is called a difference in differences analysis. If I had done nothing, then what would have happened to the numbers I care about or "key performance indicators" (KPI) after people started using my product.

It's not always feasible (e.g. due to cost or lack of necessary infrastructure) or ethical (e.g. randomly assigning a cancer-causing drug to a subgroup of the population sample) to run a fully randomized experiment when building a product. If you're looking to measure impact on some quantitative KPIs and you can't run experiments/"randomized control trials"/"AB Testing", then this type of analysis is for you.

Thanks to Sol Sadeghi (Cosine Data Science team @ Microsoft) for informing me about this analysis technique and helping with the initial code review!

# Case Study

Example of how this tool was used inside of Microsoft to show the (5+%) impact of a product on internal/"1st party" developer productivity within the Windows division at Microsoft coming soon.

# Getting Started
[Our documentation is available on this repository's wiki.](https://github.com/microsoft/LongitudinalDifferenceInDifferencesPy/wiki)


# Demo

To see a full demonstration of the tool with some example data, 
1. Navigate to the `example` directory.
2. Run the following command:
```python
python exampleDIDAnalysis.py
```



# Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
libxmplite 1.3
python bindings for the libxmp-lite modplayer library (protracker, screamtracker, ...)
License: MIT
Keywords: sound,audio,playback,tracker,module,protracker,Amiga

[![saythanks](https://img.shields.io/badge/say-thanks-ff69b4.svg)](https://saythanks.io/to/irmen)
[![Latest Version](https://img.shields.io/pypi/v/libxmplite.svg)](https://pypi.python.org/pypi/libxmplite/)


# Python libxmp-lite  modplayer

This module (pun intented) provides a Python interface to the
libxmp-lite (a cut-down version of [libxmp](https://github.com/cmatsuoka/libxmp) library.
The libxmp-lite library is linked into the extenson module, no additional
fiddling and installation is required. 

*Requires Python 3.5 or newer.  Also works on pypy3 (because it uses cffi).* 

The library is primarily distributed in source form so you need a C compiler to build and install this
(note: the setup script takes care of the actual compilation process, no need to worry about compiling things yourself).
For Linux and Mac this shouldn't be a problem. For Windows users, if the correct binary install
is not available on pypi, you'll have to get it to compile as well which may be a bit of a hassle 
on this platform. You have to make sure that the required tools that allow you to compile Python extension modules
are installed (Visual Studio or the VC++ build tools).
 
Software license for these Python bindings: MIT

Software license for the libxmp library: LGPL


## Example

### Most basic mod decoding

```python
import libxmplite

print("Supported module formats: ", libxmplite.get_formats())

xmp = libxmplite.Xmp()
xmp.load("amiga.mod")
xmp.start(44100)

info = xmp.module_info()    # grab name, comment, number of patterns, ....

frame_info = xmp.play_frame()

# ... process the frame buffer bytes ...
# ... repeat until satisfied

xmp.release()
```

There's also a ``xmp.play_buffer()`` method that is more suited to be integrated
into an async pull API. It's used in the "modplay" example (which utilizes my
other library, [miniaudio](https://github.com/irmen/pyminiaudio), for sound playback).


There's also extensive documentation for the underlying [libxmp API](https://github.com/cmatsuoka/libxmp/blob/master/docs/libxmp.rst).
jupyter-server-widget 0.1.2
Jupyter Notebook %%magics and Widget to start and stop servers from a Cell
License: MIT
Keywords: ipython

# Jupyter Server Widget

Jupyter Notebook %%magics and Widget to start and stop servers from a Cell


Usage
------

Load extension inside a Jupyter notebook:

```
%load_ext jupyterserverwidget
```

Add server commands with cell magic:

```
%server myserver1 --args myargs
```

```
%%servers
myserver1 --args myargs
myserver2 --args myargs
```

Click buttons to start them, click again to stop.


Examples
---------

![Screenshot](screenshot.png "Examples")


Installation
------------

Install and activate dependencies:

- [ipywidgets](https://github.com/jupyter-widgets/ipywidgets)

Then:

    $ pip install jupyter_server_widget
onevox 0.3.0rc3
Library for adding well described noise to images.
License: MIT

oneVoxel
=========

Small library for adding well described noise to images, with a command-line
utility specifically for application on Nifti formatted images.


Installation
------------

Simple! Just open your favourite terminal and type:

::

    $ pip install onevox


Alongside installing the oneVoxel package, this will also ensure the
dependencies are installed: ``numpy``, ``scipy``, ``nibabel``, and ``nilearn``.

For building this in Docker, you can run the following command:

::

    $ docker build -t gkiar/onevox:local --network host .


Usage
-----

From within Python this library can used to apply noise to arbitrary images:

::

    [1]: import numpy as np
    [2]: np.random.seed(1234)
    [3]: data = np.random.random((10,10,10))  # Create data matrix
    [4]: mask = data[:,:,0] > 0.4  # Define mask as values higher than 0.4 in the first 2D slice

    [5]: from onevoxel import noise  # Load noise utils

    [6]: # Generate noise locations from image and mask 
    [7]: loc = noise.generate_noise_params(data, mask, erode=0, mode='independent')
    [8]: loc
    [(3, 6, 0),
     (4, 4, 1),
     (9, 0, 2),
     (7, 9, 3),
     (1, 2, 4),
     (3, 1, 5),
     (0, 7, 6),
     (9, 0, 7),
     (3, 4, 8),
     (1, 9, 9)]

    [9]: # Apply noise to the image and verify it's in the right spot
    [10]: noisy_data, noisy_hash = noise.apply_noise_params(data, loc, scale=True, intensity=0.01)
    [11]: sorted(list(zip(*np.where(noisy_data != data))), key=lambda elem: elem[2]) == loc
    True


Contributing
------------

Excited by the project and want to get involved?! *Please* check out our
`contributing guide <./CONTRIBUTING.md>`__, and look through the
`issues <https://github.com/gkiar/onevoxel/issues/>`__ to start seeing where
you can lend a hand. We look forward to approving your amazing contributions!