semvec/pypi-packages huggingface dataset:
video-injection-pygame 0.0.3
Inject mp4 videos into your pygame surface
Requires Python: >=3.6

<h1>PyGame Video Injection</h1>
<h2>You are probably looking on how to make it so you can easily 
play videos inside of your pygame project without facing any huge troubles
and reinstalling old pygame versions. So here is a great solution for you!
only compatable with mp4 files</h2>
<h2>Now, you can easily create a 'video surface' and simply blit it onto your screen</h2>
<pre>from video_pygame.video import VideoSurface</pre>
<pre>vs = VideoSurface(
    video_path='example.mp4',
    position=pygame.Vector2(0, 0,),
    size=(1000, 500)
)</pre>
<h2>You can move it by changing it's position parameter - vs.position.x = 123</h2>
<h2>You can check if it is playing or not by checking vs.playing</h2>
<h2>and to get current frame, just call the object it self - vs()<h2>
<h2>here are some function the library also has</h2>
<pre>VideoSurface().play() -> None: start playing video (audio included)
VideoSurface().stop() -> None: stop playing video and reset frame to zero (audio included)
VideoSurface().get_position() -> list: get current position of the video surface</pre>
<h2>Here is some deeper explanation you dont really have to know</h2>
<h4>So there is a data folder in the library, when you create a new video surface object
it should create a folder inside of this data called what your .mp4 file is named, if it already
exists we just leave it as it is. Next we add all of the frames of the video as .jpg files into this new
folder, if it was done before already we just leave it as it is again. And last but not least we check if 
the final result is corrupted, what that means is if all of the frames are in place. If the video has
730 frames, then you can only finish vs' initialization when there is a 'frame730.jpg' file in the folder, if there is not
then we clear all of the files currently being there and start initialization all over again and repeat the proccess
of creating .jpg files. So thats why it might take some time when creating a new vs object for the first time, but after
it has initialized for once, its going to be Way more faster next time you run your code. I am not going to explain how
video is being synced with its sound, which is stored as a .wav and converted to pygame.mixer.Sound object, and
everything i said is pretty much everything you have to know, though you can also go through the code - i left some notes there
but not much really</h4>
<h1>Thank you for choosing PyGame Video Injection :))</h1>
cni-tlbx 2.2.5
computational neuroimaging toolbox
Requires Python: >=3

## Files
This repository contains four files.
1. `PEA.py`     - class implementation of Fourier analysis of phase-encoded stimuli.
4. `HGR.py`     - class implementation of linear RF model based on hashed Gaussians.
3. `pRF.py`     - class implementation of population receptive field mapping.
4. `IRM.py`     - class implementation of input-referred model estimation.
5. `gadgets.py` - collection of auxiliary tools (used by main tools).
abhiml 0.0.4
A program to check the Variance Inflation Factor (VIF) and determine the multicollinearity
Keywords: VIF

A dataframe consist of columns. The columns can be of object datatype (categorical) or numeric datatype. 

But the problem is that a machine only understands Numbers.

Categorical encoding is a process of converting categories to numbers.

One of the most common categorical encoding technique is One Hot Encoding. 

But One-Hot Encoding results in a Dummy Variable Trap as the outcome of one variable can easily be predicted with the help of the remaining variables.

Dummy Variable Trap is a scenario in which variables are highly correlated to each other.

The Dummy Variable Trap leads to the problem known as multicollinearity. 

Multicollinearity occurs where there is a dependency between the independent features. 

Multicollinearity is a serious issue in machine learning models like Linear Regression and Logistic Regression.

One of the common ways to check for multicollinearity is the Variance Inflation Factor (VIF):

VIF=1, Very Less Multicollinearity
VIF<5, Moderate Multicollinearity
VIF>5, Extreme Multicollinearity

Installations Required:
1. import pandas as pd
2. python -m pip install statsmodels


Change Log
==========

0.0.4(22/06/2022)
------------------
- Fourth Release