semvec/pypi-packages huggingface dataset:
aanalyticsact 0.0.1.47
adobe analytics library for Team ACT
Requires Python: >=3.6

Adobe Analytics for Team ACT v.0.0.1
==============

This module has been built to provide a better environment specifically for adobe analysts in Team ACT.</br></br>

This module itself would not contain any confidential information & nor is incapable of retrieving any data unless the correct API credential is given.
The guide of pulling data will be only provided internally, no inquires will be accepted from outsiders.

  
## Functionalities


* Retrieve Daily, Weekly, Monthly data and automatically dumps into the DB connected in their local machine.
* Capable up to 2nd breakdown.
* Automatically retrieves data of all Reporting Suites.
* Update Segments
* Create Segments


More functionalities will be added in sooner time.<br/><br/>
corpus-patterns 0.1.2
Building blocks for spacy Matcher patterns
Requires Python: >=3.11,<4.0

# corpus-patterns

![Github CI](https://github.com/justmars/corpus-patterns/actions/workflows/main.yml/badge.svg)

A preparatory utils library.

## Create a custom tokenizer

```py
from corpus_patterns import set_tokenizer

nlp = spacy.blank("en")
nlp.tokenizer = set_tokenizer(nlp)
```

The tokenizer:

1. Removes dashes from infixes
2. Adds prefix/suffix rules for parenthesis/brackets
3. Adds special exceptions to treat dotted text as a single token

Use with modified config file:

```py
@spacy.registry.tokenizers("test")  # type: ignore
def create_corpus_tokenizer():
    def create_tokenizer(nlp):
        return set_tokenizer(nlp)
    return create_tokenizer

nlp = spacy.load("en_core_web_sm", config={"nlp": {"tokenizer": {"@tokenizers": "test"}}},
)
```

## Add .jsonl files to directory

Each file will contain lines of spacy matcher patterns.

```py
from corpus_patterns import create_rules
from pathlib import Path

create_rules(folder=Path("location-here"))  # check directory
```

## Utils

1. `annotate_fragments()` - given an nlp object and some `*.txt` files, create a single annotation `*.jsonl` file
2. `extract_lines_from_txt_files()` - accepts an iterator of `*.txt` files and yields each line (after sorting the same and ensuring uniqueness of content).
3. `split_data()` - given a list of text strings, split the same into two groups and return a dictionary containing these groups based on the ratio provided (defaults to 0.80)
mutview 0.0.1
A command tool to view reads over a loci from bam file, like a lightweigth IGV
Requires Python: >=3.7

# MutView
MutView is a tool to view the reads cover a mutation in bam file, output file is html format. It's so fast and efficient, just like a lightweight IGV(Integrative Genomics Viewer).

## Requires
- python >= 3.7
- samtools

## Installation
```
pip install mutview
```
or
```
pip install git+https://github.com/TuBieJun/MutView.git
```

## Example use
```
mutview -s -n 250 -c 2 -p 25466919 -r path_of_ref_fasta -S path_of_samtools -b input_bam -o out.html
```

## Argument description
```
$ mutview
usage: mutview [-h] -p POS -c CHROM -b BAM -r REF -o OUT [-w WINDOW_SIZE] [-s]
               [-n NUM_READS] [-q BASE_Q] [-Q ALN_Q] [-f FONT] [-S SAMTOOLS]

optional arguments:
  -h, --help            show this help message and exit
  -p POS, --pos POS     the mutation position
  -c CHROM, --chrom CHROM
                        the chrom of mutation
  -b BAM, --bam BAM     the input bam file
  -r REF, --ref REF     the reference genome fasta file
  -o OUT, --out OUT     the output html file
  -w WINDOW_SIZE, --window_size WINDOW_SIZE
                        the window size of reference genome around the
                        mutation, default is 300
  -s, --sort            sort as base like igv, default is False
  -n NUM_READS, --num_reads NUM_READS
                        the max reads number to view default is 100
  -q BASE_Q, --base_q BASE_Q
                        the min base quality, reads with bq below the
                        specified threshold will be lowercases. default is 20.
  -Q ALN_Q, --aln_q ALN_Q
                        the min aln quality, bases with mq below the specified
                        threshold will be highlighted. default is 20.
  -f FONT, --font FONT  the the font of the out html file, it should be
                        monospaced font, default is consolas for windows, if
                        you view the out html file in mac, you need set the
                        value to Menlo or other monospaced fonts
  -S SAMTOOLS, --samtools SAMTOOLS
                        the samtools path, default is samtools


```

## Out html Description
- The bases that are inconsistent with the reference genome will be highlighted.
- The bases with quality below the specified threshold will be represented in lowercase letters.
- The background color of reads with alignment quality below the specified threshold will be highlighted.
- Indels are represented using the characters "I" and "_". 
- The count of each base type will be displayed at the top
- The left sidebar will display the name of the reads, pair information, and positive/negative strand information.

**Here is example out:**

![example](https://github.com/TuBieJun/MutView/raw/master/screenshots/example.png)
xbitdiff 0.2
A simple xarray extension for differential compression
Requires Python: >=3.8
Keywords: xarray,bitwise diff

# xbitdiff

We store lot of redundant information, and in the era of big data, those costs add up.
`xbitdiff` is a simple wrapper around `xarray` for differential compression.
Given two data sets---a source and a target---differential compression stores the source and the difference 
between the source and the target, known as a diff.
When coupled with compression, the size of the diff can be substantially smaller than the size of the target.
How well differential compression works depends on the correlation (redudancy) between the source and the target.
The name `xbitdiff` is an homage to `xbitinfo` but the packages are not directly related.

`xbitdiff` can be install using `pip`
```sh
pip install xbitdiff
```