semvec/pypi-packages huggingface dataset:
ftscursor 0.5.8
A sqlite3 cursor with extra methods to support FTS3/4/5

# FTSCursor
A sqlite3 cursor with extra methods to support FTS3/4/5.

This package contains a subclass of the sqlite3.Cursor class that makes
it easy to perform (simple) full-text-search operations using the sqlite3
module.

See the included [example script](https://github.com/anthony-aylward/ftscursor/blob/master/ftscursor/example_script.py)
for an application of the FTSCursor class. Once `ftscursor` is installed, the
example script can be run from the command line by calling `ftscursor-example`.
For usage information, see `ftscursor-example -h`:

```
usage: ftscursor-example [-h]
                         <path/to/database.db> <table_name> <column_name>
                         [<column_name> ...] <query>

Create a sqlite3 FTS table in memory and perform a query

positional arguments:
  <path/to/database.db>
                        a sqlite database
  <table_name>          the table which will be indexed
  <column_name>         a column which will be indexed
  <query>               a FTS query

optional arguments:
  -h, --help            show this help message and exit
```
finrich 0.3.2
Calculate enrichment of genomic regions with fine-mapping signals

# finrich

Calculate enrichment of genomic regions with fine-mapping signals

## Installation

```sh
pip3 install finrich
```
or
```sh
pip3 install --user finrich
```

## Example
```
finrich --help
finrich finemap.bed regions.bed background.bed --permutations 1000 --processes 2
```
KILLERLIB 1
For developers
Keywords: KILLERLIB


<h1 align="center">KILLERLIB</h1>
<p align="center">It's a Usefull Project for Developers ... It checkers Emails Found Or Not Found</p>

<p align="center"> • DevVlopered The Lib BY ALAA7C • </p>


## Installing :
```
pip install KILLERLIB

```
```
INFO:  https://t.me/LegendsLIB
```
jpcovid-v2 0.0.1
A package for calculating time transition policy scores against COVID-19 in Japan
Requires Python: >=3.7

# jpcovid_v2

jpcovid_v2の目的は政策提案者の失敗を特定することです。

jpcovid_v2はcovid-19による各都道府県の死者数の総計をその国の人口で割ったスコアを時系列でグラフにし、そのスコアのグラフの傾きを示したものです。

そのスコアのグラフは連続値で単調増加関数です。そのため、急激にグラフが増加しているところで政策提案者が間違いを犯していることとなります。

したがって、このグラフの微分(傾き)を取れば、政策提案者の間違いをより明確に表すことができると考えました。

出力されたグラフの値が高いところほど、政策提案者はより深刻な間違いを犯していると考えることができます。

jpcovid_v2 は、インターネットを介して次のサイトから最新のデータをスクレイピングします。

https://www3.nhk.or.jp/n-data/opendata/coronavirus/nhk_news_covid19_prefectures_daily_data.csv

# jpcovid_v2のインストール方
$ pip install jpcovid_v2

# jpcovid_v2の実行方法
$ jpcovid_v2 <都道府県名>

$ jpcovid_v2 Tokyo Osaka

<img src="tokyo_osaka.png" width="400">

$ jpcovid_v2 Tokyo Tottori

<img src="tokyo_tottori.png" width="400">

$ jpcovid_v2 Osaka Niigata

<img src="osaka_niigata.png" width="400">

$ jpcovid_v2 Hokkaido Nara

<img src="hokkaido_nara.png" width="400">

$ jpcovid_v2 Niigata Fukui Shimane Tottori

<img src="niigata_fukui_shimane_tottori.png" width="400">
coloc 0.4.2
the COLOC method by Giambartolomei et al.

# coloc

An implementation of the COLOC method for colocalization of GWAS and/or eQTL
signals by [Giambartolomei et al](https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1004383).

# Installation

```sh
pip3 install coloc
```
or
```sh
pip3 install --user coloc
```

# Example usage

```python
from coloc import coloc, print_coloc_result
help(coloc)
print_coloc_result('not colocalized', *coloc([1, 4, 9], [9, 4, 1]))
```
```
not colocalized

PP0: [|||||               ] [ 0.26540194014704316 ]
PP1: [||||                ] [ 0.21657860877712054 ]
PP2: [||||                ] [ 0.21657860877712054 ]
PP3: [|||                 ] [ 0.17661198683565493 ]
PP4: [||                  ] [ 0.12482885546306076 ]
```
```python
print_coloc_result('colocalized', *coloc([1, 4, 9], [1, 4, 9]))
```
```
colocalized

PP0: [                    ] [ 0.0015168270421050444 ]
PP1: [                    ] [ 0.001237791593959775 ]
PP2: [                    ] [ 0.001237791593959775 ]
PP3: [                    ] [ 1.4094001056824932e-05 ]
PP4: [||||||||||||||||||| ] [ 0.9959934957689188 ]
```
chipseqpeaks 1.1.5
Easy management of ChIP-seq peak calling

# chipseqpeaks

A wrapper for MACS2 that abstracts out some things and makes it easier to use.
Thanks due to Joshua Chiou for inspiration and contributions.

> **Warning:** Before using chipseqpeaks, please make sure that one of the
> environment variables TMPDIR, TEMP, or TMP is set to an appropriate path.
> This is the only way to ensure MACS2 writes temporary files to the correct
> location, and failing to do so may cause errors on some systems. (ง •̀_•́)ง

## Installation

```sh
pip install chipseqpeaks
```

or

```sh
pip install --user chipseqpeaks
```

## Example API usage
```python
from chipseqpeaks import ChIPSeqPeaks
with ChIPSeqPeaks(<bytes object or path to BAM file>) as cp:
    cp.cleans_up = False
    cp.remove_blacklisted_peaks(<path/to/blacklist.bed>)
    cp.write(<output prefix>)
```

## Example command line usage

For help text, see:
```sh
chipseqpeaks-call -h
```
For ChIP-seq:
```sh
chipseqpeaks-call --control input.bam chip.bam
```

For ATAC-seq:
```sh
chipseqpeaks-call --atac-seq atac.bam
```