semvec/pypi-packages huggingface dataset:
dwtls 1.0.2
A library of trainable and fixed discrete wavelet transform layers
License: MIT
Requires Python: >=3.7

# dwtls: Discrete Wavelet Transform LayerS

[![PyPI version](https://badge.fury.io/py/dwtls.svg)](https://badge.fury.io/py/dwtls)
| [**Paper**](https://doi.org/10.1109/TASLP.2021.3072496)
| [**Demo**](https://tomohikonakamura.github.io/Tomohiko-Nakamura/demo/MRDLA/)

`dwtls` is a downsampling/pooling layer library of discrete wavelet transform (DWT) layers with fixed and trainable wavelets presented in [1].
The use of DWT has an anti-aliasing filter and the perferct reconstruction property, at least either of which is lacked in conventional pooling layers.
The two properties enables the DWT layers to propagate entire information of an input feature.  

The library includes the following layers.
- DWT layers with fixed wavelets (e.g., Haar, CDF22, CDF26, CDF15, and DD4 wavelets)
- DWT layers with trainable wavelets (trainable DWT layers)
- Weight-normalized trainable DWT layers
The library works with Python>=3.7 and pytorch>=1.0.

## Installation
`dwtls` can be installed with:
```bash
pip install dwtls
```
or 
```bash
git clone git@github.com:TomohikoNakamura/dwtls.git
pip install -e dwtls
```

## How to use
All DWT layers are implemented as subclasses of `torch.nn.Module` of PyTorch, so we can use them in many DNNs, using PyTorch scripts.
We prepare [a short tutorial notebook](examples/tutorial.ipynb), which makes it easy to use our DWT layers in your codes.  
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/TomohikoNakamura/dwtls/blob/master/examples/tutorial.ipynb)

## Citation
If you use `dwtls` in your work, please cite:

```bibtex
@article{TNakamura202104IEEEACMTASLP,
    author={Nakamura, Tomohiko and Kozuka, Shihori and Saruwatari, Hiroshi},
    title = {Time-Domain Audio Source Separation with Neural Networks Based on Multiresolution Analysis},
    journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
    volume=29,
    pages={1687--1701},
    month=apr,
    year=2021,
    doi={10.1109/TASLP.2021.3072496},
}
```

## References
[1] Tomohiko Nakamura, Shihori Kozuka, and Hiroshi Saruwatari, “Time-Domain Audio Source Separation with Neural Networks Based on Multiresolution Analysis,” IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 29, pp. 1687–1701, Apr. 2021. [pdf](https://doi.org/10.1109/TASLP.2021.3072496), [demo](https://tomohikonakamura.github.io/Tomohiko-Nakamura/demo/MRDLA/)

## License
`dwtls` is MIT licensed (see [LICENSE](LICENSE) file).
handy-dandy 0.1.3
A few handy tools for your python program!
License: MIT
Requires Python: >=3.8,<4.0

# Handy Dandy
*A collection of handy programs for your python project.*


### Output validator
Handy dandy's validate decorator can make sure that 
functions return the type that you want them to. 

```py
from handy_dandy import validator as val

@val.string # expecting function to return string
def foo(x):
	return x

y = foo(5)
```


## Timer
Records how long a function takes to run.
```py
from handy_dandy import timer

@timer
def foo():
	pass
```

## System
system function. Returns the computer's os (win32, win64, mac, linux).

## Recorder
Records the amount of times it has been ran. This can be helpful when you want to see how many times multiple functions have been ran durring the program.

```py
>>> from handy_dandy import Recorder
>>> x = Recorder('f', 's')
>>> x.click('f')
1
>>> x.clicks()
{'f': 1, 's': 0}
>>> x.click()
>>> x.clicks()
{'f': 2, 's': 1}
```

## Design
I made a bunch of text design programs for this package, inspired by [this](https://replit.com/talk/learn/Enhancing-Python-projects/142183). They're really self-explanitory, so i'll just give a list of the function names:
- scroll_clear()
- underline(text, color='white')
- bold(text, color='white')
- italic(text, color='white)
- color(text, color)
- ascii(text)p