semvec/pypi-packages huggingface dataset:
pymugen 1.2.1
Management about FASTA and VCF files.
License: MIT
Keywords: VCF,FASTA,Genomics

pymugen
  -------

  This module allows to manage data about genomic sequences, specificaly getting sequences from a FASTA file.

  The module's other functionality is the ability to get sequences with a mutation from a VCF file and transform it to another representation.


  Installation
  ------------

  Install pymugen with python

  ``pip3 install pymugen``

  Usage/Examples
  --------------

  Fasta example

  ::

    from pymugen.fasta import FastaReader

    fasta = FastaReader("fasta.fa")

    chromosome = fasta['chr1']
    sequence = chromosome.sequence(10, 5, 5)

    sequence[1]
    # A

    sequence[:]
    # CATGCATGCAT

    str(sequence)
    # C-A-T-G-C,A,T-G-C-A-T

    chromosome[5: 16]
    # CATGCATGCAT


  VCF example

  ::

    from pymugen.transformers import ExtendedTransformer
    from pymugen.fasta import FastaReader

    vcf = ExtendedTransformer("vcf.vcf", "fasta.fa")

    fasta = FastaReader("fasta.fa")

    chromosome = fasta['chr1']
    sequence = chromosome.sequence(10, 5, 5)

    mutated = vcf.method(sequence, "CGT")

    str(mutated)
    # 'A|w-q-r-e-w,s-d-f,v-c-x-z-v'

    mutated[:]
    # 'wqrewsdfvcxzv'
data-steps 0.3.3
Simple tool for pandas data transformation
License: MIT
Requires Python: >=3.7

# data-steps

This projects provides a minmal framework to
organize data transformations in pandas.

It is intended to be used in both notebooks
and code files.

The main idea is to provide a simple decorator
syntax that is easy to maintains when data
transfromation steps get changed or added
throughout the project. A prime example
is data cleaning where only later in the project
some required cleaning steps become apparent.

## Features

After wrapping a pandas DataFrame in a `DataSteps`
class. The following features are available.

- register data transformations with the instances `.step`
    decorator
- get an overview of the registered steps with `.steps`
- inspect the original data the fully transformed data
    and any partially transformed data in between
- change parameters of registered steps
- interactively redefine or deactivate steps in jupyter notebooks
- register steps that return secondary results, i.e. the main result is passed alon
    the pipeline, whereas the secondary result is stored seperately
- convert data steps pipelines to strings that can more easily be integrated into a non-eda code-base

## Usage Example

Wrap your data in an instance

```python
from data_steps import DataSteps

data = DataSteps(my_pandas_df)

#register transformation steps

@data.step
def data_transformation(df):
    #transfromation steps
    ...
    return transformed_df

@data.step
def transform_with_parameters(df,param1,param2=4):
    #transfromation steps
    ...
    return transformed_df

#access original data
data.original

#set or update transformation parameters
data.update_step_kwargs('transform_with_parameters',{'param1':10})

#access data after all transformation steps
data.transformed


#get an overview of the registered steps
data.steps

#only execute some steps to help debugging transformations
data.partial_transform(0)
```


# History

## 0.0.1 (2021-01-31)

- First release on PyPi.

## 0.1.0 (2021-02-11)

- Changed step decorator to work in bare format,
  i.e. `<instance>.step` instead of `<instance>.step()`

## 0.2.0 (2021-05-02)

- support for additional arguments in steps

## 0.3.0 (2021-05-30)

- support for exporting a datasteps pipeline as a string
- Enable steps to contain side results next to the transformed data. These could be summaries for diagnostics or plots for an intermediate result

### 0.3.1 (2021-05-30)
- bugfixes

### 0.3.2 (2021-05-30)
- bugfixes

### 0.3.3 (2021-08-01)

- Downgraded python requirements to 3.7 to enable google colab support by default.

## Possible extensions

No Concrete plans at the moment but feel free to open enhancement issues on github
anglo 0.0.1b0
🪐 A modern lightweight micro web framework for Python 3.
License: MIT

<h1 align="center">
  <img src="https://github.com/serumstudio/anglo/raw/main/.images/anglo.png" height="250" alt="anglo">
</h1>

<h4 align="center">🪐 A modern lightweight micro web framework for Python 3.</h4>

<p align="center">
  <a href="#about">About</a> | 
  <a href="#installation">Installation</a> | 
  <a href="#usage">Usage</a> | 
  <a href="#features">Features</a> | 
  <a href="#contributors">Contributors</a> |
  <a href="#license">License</a>
</p>

# About

> **Next**: [Installation](https://github.com/serumstudio/anglo#installation) 

**Anglo** is an *open source* framework use for deploying and developing, **web applications** <br>
with modern and lightweight integration. It was created to build web applications easily and reliable.

It also comes with alot of different [features](https://github.com/serumstudio/anglo#features) that you may want to check out. Anglo was mainly <br>
built at the top of **WSGI Environment**. It serves as a full wrapper for WSGI that makes your <br>
production easier. The API wrapper for Anglo is simmilar to famous web framework, **Flask and <br>
Django.**

| [Learn More](https://serum.studio/anglo)|
|-------|


<div align="right" id="installation">
  <h1> Installation </h1>
</div>

# Usage

<div align="right" id="features">
  <h1> Features </h1>
</div>

# Contributors

<div align="right" id="license">
  <h1> License </h1>
  <p> Anglo is license under <a href="https://github.com/serumstudio/anglo/blob/main/LICENSE">MIT</a> </p>
</div>