semvec/pypi-packages huggingface dataset:
d3net-unofficial 0.0.1
Unofficial Python package of D3Net implementation by Sony Research AI.
Requires Python: >=3.6

# D3Net (Music Source Separation) for Spleeter Web

**This is a modified version of the [official D3Net repo](https://github.com/sony/ai-research-code/blob/master/d3net/music-source-separation/) made to be compatible with [Spleeter Web](https://github.com/JeffreyCA/spleeter-web)!**

This is inference code for D3Net based music source separation.

## Quick Music Source Separation Demo by D3Net

From the Colab link below, you can try using D3Net to generate and listen to separated audio sources of your audio music file. Please give it a try!

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sony/ai-research-code/blob/master/d3net/music-source-separation/D3Net-MSS.ipynb)

## Getting started

## Prerequisites
* nnabla 
* librosa
* pydub
* numpy
* soundfile
* yaml

## Inference: Music source separation with pretrained model

Download the pre-trained D3Net model for Music Source Separation [here](https://nnabla.org/pretrained-models/ai-research-code/d3net/mss/d3net-mss.h5).

Run the below inference command for a sample audio file `test.wav` in current directory:
```python
 python ./separate.py -i ./test.wav -o output/ -m d3net-mss.h5 -c cudnn
 ```
Arguments:  
-i : Input files. (Any audio format files supported by FFMPEG.)  
-o : Output directory. (Output folder path to save separated instruments)  
-m : Model file. (Pre-trained model)  
-c : Context. (Extension modules : `cpu` or `cudnn`)

## Training: Train the music source separation model from scratch (**coming soon**)
Generic-model-class 0.0.3
A fast general purpose Monte Carlo simulation of radiation transport.
Requires Python: >=3.6

# Monty Carlo

This is a thesis project, which is yet in development and not ready for packaging. Nevertheless, I uploaded this placeholder package so that the name is not taken in the meantime.

The first version might be available as early as jully. 



## What to expect

### Customizability 

 
### Speed

Although it is a python module this package is written in a happy mix of Python, [Cython](https://cython.org/), C and C++. A notable example of a package that also does this is [Numpy](https://github.com/numpy/numpy). Most of the initialization and pretty much all the programming user interface is in Python, so while setting up your simulation or handling the results of it, you'll be dealing with Python. However, from the moment you tell MontyCarlo to start simulating, it leaves the world of Python and starts running optimized C code. Each language is therefore placed strategically so that it can play to its strenghts.


### Fun

Using the power of [vtk](https://vtk.org/) through the wonderful work of [mayavi](https://pypi.org/project/mayavi/) remarkable visualizations are easy in Monty Carlo. 

50keV electrons in water (secondary particles off):

![Electrons in Water ](https://user-images.githubusercontent.com/63464503/110106080-20e4bc00-7da1-11eb-953c-d5904ff196f1.png)


10MeV electrons in water (primary in red, secondary photon in green)

![image](https://user-images.githubusercontent.com/63464503/110102562-d9f4c780-7d9c-11eb-8f70-20f3b26d3503.png)




![SSSS250k](https://user-images.githubusercontent.com/63464503/110109261-14626280-7da5-11eb-8f0b-cd46bf08fca0.png)
pensimpy 1.2.5
The Python implementation of IndPenSim
Requires Python: >=3.8

![alt text](figures/logo_light.png "Logo Title Text 1")

**Development Status:** As of 08/2020 PenSimPy is under active maintenance (expect bug fixes and updates). 
Please follow, star, and fork to get the latest functions.
# PenSimPy ![](https://img.shields.io/badge/python-3.8.0-orange) ![](https://img.shields.io/badge/c%2B%2B-11-blue)
PenSimPy is a Python version of [IndPenSim](http://www.industrialpenicillinsimulation.com/), which simulates the industrial-scale Penicillium chrysogenum fermentation. 
This simulator is based on `c++` solving the ODE functions so as to achieve a faster performance than the MATLAB version. 

PenSimPy aims for recording those key features such as `pH`, `temperature`, `vessel volume`, `vessel weight`, `penicillin concentration`, 
`dissolved oxygen concentration`, `sugar feed rate`, `soil bean feed rate`, `aeration rate`, `back pressure`, `water`, `acid`, 
`phenylacetic acid flow-rate`, `Oxygen in percent in off-gas`, and the Raman spectroscopy data throughout the whole process. A conventionally used 
Sequential Batch Control strategy with PID control is presented with example codes and all the recipe's feed can be manually updated. 

Furthermore, we incorporate Reinforcement Learning to improve the penicillin gain and present them as example codes.
A web application based on PenSimPy can be found [here](http://quartic.ddns.net:8000/).

Installation
============
PenSimPy relies on [fastodeint](https://github.com/Quarticai/fastodeint), a python bindings for an ODE solver implemented in C++. Unfortunely, it's not available as a PyPI package at this point, so you need to follow the installation instruction [here](https://github.com/Quarticai/fastodeint/blob/master/README.md) to build it yourself.
Once fastodeint is installed, you can install PenSimPy by the following command
```
pip install pensimpy
```
Examples
============
See the `examples` directory
- see [examples/batch_generation.py](pensimpy/examples/batch_generation.py) to generate
batch and Raman data with self-designed recipes
- see [examples/reinforcement_learning_boilerplate.py](pensimpy/examples/reinforcement_learning_boilerplate.py) to 
simulate penicillin yield process with reinforcement learning