semvec/pypi-packages huggingface dataset:
rokas-data-manipulation 0.1.4
A package for common data manipulation operations in Python
Requires Python: <4.0,>=3.9

# Data Manipulation

A package for common data manipulation operations in Python.

## Features

- **Transpose 2D Matrix**: Transpose a given 2D matrix.
- **Generate 1D Windows**: Create time series windows from a 1D array.
- **2D Convolution**: Perform 2D cross-correlation.

## Installation

### Prerequisites

- Python 3.9 or higher
- [Poetry](https://python-poetry.org/docs/#installation)

### Setting Up the Project

1. **Clone the repository**:

   ```
   git clone https://github.com/TuringCollegeSubmissions/rgaldi-DE2v2.1.5.git
2. **Install dependencies**:
   ```
   poetry install
3. **Activate the virtual environment**:
   ``` 
   poetry shell

## **Usage**
   ```
   from rokas_data_manipulation import transpose2d, window1d, convolution2d
   
   # Example usage
   matrix = [[1, 2, 3], [4, 5, 6]]
   transposed = transpose2d(matrix)
   print(transposed)
   
   array = [1, 2, 3, 4, 5, 6, 7]
   windows = window1d(array, size=2, shift=2, stride=2)
   print(windows) 
   
   input_matrix = np.array([
    [1, 2, 3],
    [4, 5, 6],
    [7, 8, 9]
    ])
   kernel = np.array([
    [1, 0],
    [0, -1]
    ])
   convolution = convolution2d(input_matrix, kernel, stride = 1)
   print(convolution)
   ```
### **Testing**:
   ```
   poetry run pytest
   ```
streamlit-client-timezone 0.1.5
Streamlit component that gets the client's timezone.
Requires Python: >=3.7

# Streamlit Client Timezone
Streamlit custom component to get the client's timezone as a tzinfo object. 

## Download
```bash
pip install streamlit-client-timezone
```
## Usage
```python
import streamlit as st
from datetime import datetime

from client_timezone import client_timezone

timezone = client_timezone(key="client-timezone")

time_loaded = datetime.now(tz=timezone)
```
cialign 1.1.4
Toolkit for cleaning and interpreting multiple sequence alignments

![CI](https://github.com/KatyBrown/CIAlign/actions/workflows/main.yml/badge.svg)
# CIAlign
CIAlign documentation is now available via [ReadTheDocs](https://cialign.readthedocs.io/en/latest/)

## Installation

**Requirements**

* python >= 3.6
* matplotlib >= 2.1.1
* numpy >= 1.16.3
* scipy >= 1.3.0

The easiest way to install CIAlign is using conda or pip3.

**Conda**

`conda install -c bioconda cialign`

[link](https://anaconda.org/bioconda/cialign)

**pip3**
`pip3 install cialign`

[link](https://pypi.org/project/cialign/)

**Download**
The current release of CIAlign can also be downloaded directly using [this link](https://github.com/KatyBrown/CIAlign/releases/latest),

If you download the package directly, you will also need to add the CIAlign directory to your PATH environment variable as described [here](https://gist.github.com/nex3/c395b2f8fd4b02068be37c961301caa7)

## Summary
CIAlign allows the user to:


**Clean**

* Remove sources of noise from an MSA 
  * Remove sequences above a threshold level percentage of divergence from the majority.
  * Remove insertions which are not present in the majority of sequences.
  * Crop poorly aligned  sequence ends.
  * Remove short sequences below a threshold number of bases or amino acids.
  * Remove columns containing only gaps.
  * Remove either end of an alignment where columns don't meet a minimum identity threshold and coverage level.

**Visualise**

* Visualise alignments.
  * Generate image files summarising the alignment.
  * Label these images to show how CIAlign has affected the alignment.
  * Draw sequence logos
  * Plot alignment statistics - visualise coverage and conservation at each position in the alignment.


**Interpret**

* Generate consensus sequences.
* Generate position frequency, position probability and position weight matrices
* Format these matrices to be used as input for the BLAMM and MEME motif analysis tools.
* Generate a similarity matrix showing the percentage identity between each sequence pair.
  
**Edit**

* Extract a section of the alignment.
* Unalign the alignment.
* Replace U with T, or T with U in a nucleotide alignment.

CIAlign is designed to be highly customisable, allowing users to specify exactly which functions to run and which settings to use.

It is also transparent, generating a clear log file and alignment markup showing exactly how the alignment has changed and what has been removed by which function.

## Citation
If you found CIAlign useful, please cite: 

Tumescheit C, Firth AE, Brown K. 2022. CIAlign: A highly customisable command line tool to clean, interpret and visualise multiple sequence alignments. PeerJ 10:e12983 https://doi.org/10.7717/peerj.12983

## Mailing List
Sign up here for updates when a new feature is added to CIAlign
pharmcat-runner 0.0.16
Installs, runs, and parsers PharmCAT using Pharmacoscan input.

# PharmCAT Runner

The PharmCAT Runner tool provides a simplified pipeline for installing, running, and parsing PharmCAT outputs.

## Dependencies
1. bcftools/tabix/bgzip - 1.18+
1. PLINK - Required for Hardy Weinburg and sample call rate QC methods
1. Python 3.9+ - Optional but highly recommended

## Installation

Installation can be acheived using pip or any other package manager of your choosing. It is highly recommended that installation is done with Python 3.9+. Some conda environments can conflict with installation of a new verison.
```sh
pip install pharmcat_runner 
```

## Usage
```sh
# Install PharmCAT and all required dependencies. If an installation already exists, must pass --overwrite to update
python3 -m pharmcat_runner install

# Standardize VCFs
python3 -m pharmcat_runner standardize --files "data/*.vcf"

# Run optional QC steps followed by PharmCAT
python3 -m pharmcat_runner haplotype --files "data/*.vcf" --hwe 0.001 --variant_call_rate 0.95 --sample_call_rate 0.95

# Parse PharmCAT output
# Original Pharmacoscan VCFs are required for accurate reporting of CYP2D6 copy number
# Sex ids are required for accurate reporting of G6PD
# Warnings are reported if either of these are not supplied
python3 -m pharmcat_runner parser --files "results/pharmcat_output/*.json" --vcfs "data/*.vcf" --sex_ids sex_ids.txt
```

## sex_ids file
This file is required for accurate reporting of G6PD genotypes and phenotypes. This should be a tab delimited file without a header. The two columns should be sample id and predicted sex (M-Male, F-Female, O-Other). The sample ids in this file must match the sample ids in the pharmcat output
|   |   |
|---|---|
|1234 | M |
|5678 | F |
|1357 | O |

## Bugs and desired features
Please report any bugs and/or desired features to either the github issues page or to andrew.haddad@pitt.edu