semvec/pypi-packages huggingface dataset:
AIPyS 0.1.3
AI Powered Photoswitchable Screen
Requires Python: >=3.8


---

# AI Powered Photoswitchable Screen (AIPyS) Version 2

![AIPyS Logo](https://github.com/gkanfer/AI-PS/raw/master/logoAIPS.png)

## Introduction

AIPyS V2 is an AI-driven platform enhancing the capabilities of photoswitchable genetic CRISPR screen technology. Utilizing advanced algorithms like **U-net** and **cGAN** for segmentation and employing Bayesian inference for differential sgRNA abundance analysis, AIPyS offers precise detection of single cells and subcellular phenotypes in microscopy images. It integrates Numpy, scikit-image, and scipy for parametric object detection and leverages the PyMC3 library for statistical modeling. For interactive data exploration and visualization, the platform is deployed online via Plotly-Dash.

For detailed insights, visit the [Documentation](https://gkanfer.github.io/AIPyS/).

## Quick Installation Guide

AIPyS supports Windows environments and necessitates Python 3.8. For seamless operation with machine learning components like PyTorch and Cellpose, please align CUDA and cuDNN versions meticulously.

- **Conda Installation**: Conveniently install using the provided `environment.yml` to configure both Python and CUDA/cuDNN dependencies accurately.
    ```bash
    conda env create -f environment.yml
    conda activate aipys_env
    ```

- **PIP Installation**: For environments where Conda is unavailable, use pip while ensuring correct CUDA/cuDNN configurations.
    ```bash
    pip install AIPySPro
    ```

Check installation:
```bash
aipys --version
```

## Highlighted Features

### Segmentation and Analysis
- **Parametric Segmentation**: Enhances R-based code for effective segmentation using scikit-image.
- **Deep Learning Segmentation**: Incorporates U-net and cGAN models for cutting-edge segmentation accuracy.
- **Granularity Analysis and Classification**: Utilizes logistic regression and CNN classifiers trained on meticulously segmented cell images for precise phenotype classification.

### Deployment and Integration
- **Nikon-nis Elements Integration**: Employs AIPyS for advanced image processing, offering streamlined deployment capabilities for Nikon-nis Elements jobs module.
- **Interactive Data Visualization**: Leverages Plotly-Dash for an immersive data visualization experience, allowing users to interactively explore analysis outcomes.

### Bayesian Model Training for Granularity Analysis
- Utilizes Bayesian inference to train models capable of discerning intricate subcellular phenotypes, contributing significantly to the understanding and characterization of genetic modifications impacting cell morphology.

---
topsis-tarndeep-singh-102117210 1.0
A package for performing TOPSIS analysis
Keywords: python, topsis


# Topsis
# Submitted by: TARNDEEP SINGH
# Roll no.: 102117210 
This package implements the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for multi-criteria decision-making.  
Link to Assn:(https://github.com/psrana/Assignment-Topsis)  
 

Steps:  
  ~Loading the data and converting it to .csv file format  
  ~Checking for conditions given in problem statement 
  ~Normalising the data & calculating seperation measures   
  ~Getting seperation & topsis score for each row and ranking based on score  
  ~Appending new columns into dataset & saving into result file
jaraco.media 4.0.1
DVD and other multimedia tools
Requires Python: >=3.8

.. image:: https://img.shields.io/pypi/v/jaraco.media.svg
   :target: https://pypi.org/project/jaraco.media

.. image:: https://img.shields.io/pypi/pyversions/jaraco.media.svg

.. image:: https://github.com/jaraco/jaraco.media/actions/workflows/main.yml/badge.svg
   :target: https://github.com/jaraco/jaraco.media/actions?query=workflow%3A%22tests%22
   :alt: tests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v2.json
    :target: https://github.com/astral-sh/ruff
    :alt: Ruff

.. .. image:: https://readthedocs.org/projects/PROJECT_RTD/badge/?version=latest
..    :target: https://PROJECT_RTD.readthedocs.io/en/latest/?badge=latest

.. image:: https://img.shields.io/badge/skeleton-2024-informational
   :target: https://blog.jaraco.com/skeleton


concat
------

FFmpeg provides a routine to
`concatenate media files <https://trac.ffmpeg.org/wiki/Concatenate>`_.
Unfortunately, the UI for that routine is so bad that even ffmpeg
provides multiple, platform-specific techniques to generate the input.
``jaraco.media.compat``, in contrast, takes a number of input files
and optionally an output file and input filter, creates the input file
in its required syntax and then runs ffmpeg against that file. Example::

    $ python -m jaraco.media.concat /Volumes/drone/DCIM/100MEDIA/DJI_*.MP4 -i '(17|18|19)' -o /Volumes/Public/Flights/2022-02-20.mp4


srt-concat
----------

Concatenate SRT files, updating the offsets based on the durations
of their associated media files. Example::

    $ python -m jaraco.media.srt-concat /Volumes/Drone/DCIM/100MEDIA/DJI_00*.SRT -i '(17|18|19)' -o /Volumes/Public/Flights/2022-02-20.srt


splice
------

Extract timestamps from a video.

    $ python -m jaraco.media.splice infile.mp4 outfile.mp4 00:00:00-01:32:46 01:47:20-01:49:17

Troubleshooting
---------------

If you see "libaacs not initialized!" or "aacs_open() failed",
remember that you have to re-register with the latest
beta key each month.

See `this blog
<http://drbobtechblog.com/handbrake-can-use-makemkv-to-automatically-process-blu-ray-discs-heres-how/>`_
for details.