semvec/pypi-packages huggingface dataset:
noisemaker 0.0.1
Infinite Scoped Deterministic Fractal Noise
License: MIT
Keywords: fractal,noise,generation,infinite

# NoiseMaker

Olsen Noise algorithm for infinite scoped deterministic fractal noise. The noise is deterministic so it's perfectly for tiling and returning to the same postion and having the same regenerated noise. The algorithm is infinite and scoped so only enough values as needed are generated.

```python
from PIL import Image
from noisemaker import noise
Image.fromarray(noise((250, 100), (0, 0), transpose=True)).save("noise0.png")
Image.fromarray(noise((250, 100), (0, 100), transpose=True)).save("noise1.png")
Image.fromarray(noise((250, 100), (0, 200), transpose=True)).save("noise2.png")
```

The `noise()` algorithm requires only the first shape parameter. The `transpose` flag is to covert it into an image ready format.

# Tiling

* ![noise0](https://user-images.githubusercontent.com/3302478/101229669-34737000-3656-11eb-9820-2e18fae18918.png)
* ![noise1](https://user-images.githubusercontent.com/3302478/101229676-4523e600-3656-11eb-8ce1-74062438f93b.png)
* ![noise2](https://user-images.githubusercontent.com/3302478/101229681-48b76d00-3656-11eb-8641-8cb1cdd680ee.png)

Note these are three different images. Simply adjacent in the requested space, so they stack.

# Box Kernel (Default)

```python
from noisemaker import noise
from PIL import Image
Image.fromarray(noise(500, 500, iteration=5)).save("noise-5.png")
```

![noise-5](https://user-images.githubusercontent.com/3302478/101246313-5c4eec00-36c7-11eb-9d4f-49e4d080ddca.png)


# Gaussian Kernel

There is also a gaussian kernel availible, which is about a third the blur factor of the default box blur:

```python
from noisemaker import noise, GAUSSIAN
from PIL import Image
Image.fromarray(noise((500, 500), iteration=5, kernel=GAUSSIAN)).save("noise-5g.png")
```
![noise-5g](https://user-images.githubusercontent.com/3302478/101246608-47735800-36c9-11eb-8d56-0ac4b4432dbd.png)
telebot-utils 0.0.8
Utils for pyTelegramBotAPI module
License: MIT
Keywords: telebot,utils,chat,bot

# Telebot Utils 🤖

Collection of utils for pyTelegramBotAPI to develop awesome Telegram Bots.

## Installation

To install the package run the following command in your terminal:

```
pip install telebot-utils
```

## Usage

To use the utilities that the telebot-utils package provide you need to import it in your script:

```python
import telebot_utils
```

## License

[LICENSE.md](LICENSE.md)

## Contributing

See [CONTRIBUTING.md](CONTRIBUTING.md)
uppaalpy 0.0.4
UPPAAL wrapper for Python
License: MIT
Requires Python: >=3.8

# uppaal-py
UPPAAL wrapper for Python. Currently supports reading from and writing to .xml files. Works with Python >= 3.8.

## Dependencies
* [NetworkX](https://github.com/networkx/networkx)

## Installation
Via pip:
```
pip install uppaal-py
```

Or you can just install the dependencies and copy the dir `uppaalpy` into your project.

## Usage
```Python
>>> from uppaalpy import nta
>>> my_nta = nta.NTA.fromXML('test.xml')
>>> my_nta.to_file('test_new.xml', pretty = True)
```

## License
[MIT](https://mit-license.org/)
olsapy 2.0.1
a package for orthogonal linear separation analysis (OLSA)
License: MIT
Requires Python: >=3.6
Keywords: omics,bioinformatics,transcriptome,chemoinformatics

# OLSApy

***

## OLSApy: Orthogonal Linear Separation Analysis in Python

***
* OLSA is an analysis method of omics data to decompose the complex effects of a perturbagen into basic components.  
* OLSApy is a package for OLSA in python.  
* OLSA can be applied to any kinds of omics data such as RNA-seq, proteome, and so on.  

***

## Dependency
* python >= 3.6
* requirements: numpy, pandas, scipy

***

## Setup
```pip install olsapy```  
For conda, please use conda skeleton  

***

## Usage

    
    from olsapy import olsa
    
    df = <a dataframe of interest (feature x sample)>
    
    ### calculation
    dat = olsa.OLSA()
    dat.load_df(df)
    dat.olsa(accumulation=0.6) # accumulation determines the vectors subjected to varimax rotation
    rsm,rvm,ts,contribution = dat.get_res()
    ### visualization
    dat.plot(focus=[<sample names of interest>])
    
    ### visualization of already prepared data
    dat.plot(rsm,focus=[<sample names of interest>])
    

***

## Licence
This software is released under the MIT License, see LICENSE.  

***

## Authors
Setsuo Kinoshita, Shotaro Maedera, and Tadahaya Mizuno  

***

## References
* Sci Rep. 2019 Feb 12;9(1):1824. doi: 10.1038/s41598-019-38528-4.  
* http://www.ilincs.org/ilincs/  

***

## Bug Report
If you would like to report any bugs about olsapy, don't hesitate to create an issue on github here, or email me:  
* tadahaya@gmail.com  
* tadahaya@mol.f.u-tokyo.ac.jp
SecondOrderElec 0.0.4
A python module to work with continuous-time system. You will find various second order electronic filters models.
License: MIT
Requires Python: >=3.6

# Electronic Lib for second order system

![](https://img.shields.io/travis/com/slashformotion/SecondOrderElec/master?label=test%20%28master%29) ![PyPI - Downloads](https://img.shields.io/pypi/dm/SecondOrderElec) ![PyPI](https://img.shields.io/pypi/v/SecondOrderElec) [![Documentation Status](https://readthedocs.org/projects/secondorderelec/badge/?version=latest)](https://secondorderelec.readthedocs.io/en/latest/?badge=latest) 


A simple python library for the analysis of second order system.

## Getting Started

### Dependancies

* numpy
* matplotlib
* scipy

### Installing

Simple:
```bash
pip install SecondOrderElec
```

### Documentation

It's on [readthedocs](https://secondorderelec.readthedocs.io/en/latest/).

### Exemples

In this library you will find four main models for second order filters: 

- Low-Pass(```SecondOrderElec.LP```)
- High-Pass (```SecondOrderElec.HP```
- Band-Pass (```SecondOrderElec.BP```)
- Notch (```SecondOrderElec.Notch```)

```Python
from SecondOrderElec import LP
import numpy as np

w = np.logspace(1,3,1000)

filter_instance = LP(T0=1, m=1.1, w0=6000)
t,s = filter_instance.freqresp(w=w, plot=True)
```
![Module](static/img/fresqresp_LP_module.svg) ![Argument](static/img/fresqresp_LP_arg.svg)



You will find others exemples [here](https://github.com/slashformotion/SecondOrderElec/tree/master/exemples).

### Help

Create an issue [here](https://github.com/slashformotion/SecondOrderElec/issues).

### Acknowledgments

Most of the work is from [Vincent Choqueuse](https://github.com/vincentchoqueuse).

### Maintainers

- [@vincentchocqueuse](https://github.com/vincentchoqueuse)
- [@slashformotion](https://github.com/slashformotion)

### Licence

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.