semvec/pypi-packages huggingface dataset:
pyimgtool 0.0.1a8
Tool to help prep images for web sharing
Requires Python: >=3.6

# pyimgtool

Image compression/watermarking using pillow. Useful for photographers to prep files for web sharing.

## Features

- Resize/thumbnail images to maximum or minimum dimensions
- Image watermarking
- Text watermarking
- JPEG compression

## Install

Simply run `pip install pyimgtool`  

## Todo

- Automatically find best region for watermarking
- Set text watermark color based on target region
- Create specific print sizes at 300 dpi
  - Add padding if image is smaller than print size
happer 0.1.1
Library for applying haplotypes to reference DNA sequences

# happer

Daniel Standage, 2018  
https://github.com/bioforensics/happer

**happer** is a minimal Python library for generating complete haplotype sequences.
Given a reference sequence and haplotype alleles annotated in BED format, happer will mutate the reference and produce sequences to match the specified haplotypes.


## Installation

To install:

```bash
pip3 install happer
```

To make sure the package installed correctly:

```bash
pip3 install pytest
py.test --pyargs happer
```

happer requires Python version 3.


## Usage

Reference sequences must be provided in Fasta format, and haplotype alleles must be specified in BED format as follows.
Alleles corresponding to different haplotypes at the locus are separated by a `|` character, so for example a diploid individual should have 2 `|`-separated alleles annotated at each locus, while a tetraploid would have 4 alleles.
In the example below, the `CCGA` alleles are phased and represent one haplotype, while the `TATG` alleles are phased and represent another haplotype.

```txt
#SeqID    Start  End     Alleles
chr1     38827  38828   C|T
chr1     59288  59289   C|A
chr2     24771  24772   G|T
chr4     201191 201192  A|G
```

To invoke happer from the command line:

```
[standage@lappy ~]$ happer --out haploseqs.fasta refr.fasta alleles.bed
```

To invoke happer directly in Python:

```python
>>> import happer
>>> seqfile = open('refr.fasta', 'r')
>>> alleles = open('alleles.bed', 'r')
>>> for label, haploseq in happer.mutate.mutate(seqfile, alleles):
...     # do whatever you'd like with the sequences
```
vsbio 0.1.0
Collection of simple bioinformatic tools.

sbio
======
Collection of simple Bioinformatic tools.

fastq_quality
------
Script usage:  
`fastq_quality.py -r1 *R1*.fastq.gz -r2 *R2*.fastq.gz`  
`fastq_quality.py -h`

Module usage:  
`from sbio.fastq_quality import FASTQ_Quality`  
`fq = FASTQ_Quality(read1, read2, sampling_number)`  
`fq.run()`  

Files must be .gz zipped  
Paired reads must contain _R1 or _R2 in file name  

Along with the FASTQ file and sample name 5 FASTQ attributes are obtained:
>file_size --> FASTQ file size (human readable)
>total_read_count --> Total read count within each FASTQ file  
>sampling_size  --> Random analyzed read count  
>length_mean --> Average read length  
>read_average --> Average read quality  
>reads_gt_q30 --> Read counts with an average quality greater than 30  

Note: when calculating percent of reads above Q30 use reads_gt_q30/sampling_size  

After ran object will contain nested dot notation for each read, fq.read1.fastq --> 'sample_S25_L001_R1.fastq.gz'
sbio 0.0.7
Collection of simple bioinformatic tools.

sbio
======
Collection of simple Bioinformatic tools.

fastq_quality
------
Script usage:  
`fastq_quality.py -r1 *R1*.fastq.gz -r2 *R2*.fastq.gz`  
`fastq_quality.py -h`

Module usage:  
`from sbio.fastq_quality import FASTQ_Quality`  
`fq = FASTQ_Quality(read1, read2, sampling_number)`  
`fq.run()`  

Files must be .gz zipped  
Paired reads must contain _R1 or _R2 in file name  

Along with the FASTQ file and sample name 5 FASTQ attributes are obtained:
>file_size --> FASTQ file size (human readable)
>total_read_count --> Total read count within each FASTQ file  
>sampling_size  --> Random analyzed read count  
>length_mean --> Average read length  
>read_average --> Average read quality  
>reads_gt_q30 --> Read counts with an average quality greater than 30  

Note: when calculating percent of reads above Q30 use reads_gt_q30/sampling_size  

After ran object will contain nested dot notation for each read, fq.read1.fastq --> 'sample_S25_L001_R1.fastq.gz'