semvec/pypi-packages huggingface dataset:
ghenv 1.0.6
A CLI tool to manage GitHub environments.
Requires Python: >=3.10.0

# GitHub Env (GHENV)
#### Copy your .env into GitHub Secrets within seconds

#### Flow:
1. Checks if a .env exists at your current workdir, errors out if it doesn't.
2. Loads all the environment variables from the .env file.
3. Asks for the name of the repository you'd like to use.
4. Creates KV pairs of the environment variables and their values on GitHub secrets on your chosen repo.

#### Optional environment variables:
1. `GH_TOKEN` - GitHub token to use for authentication. If not provided, it will ask for a token.
2. `GH_REPO` - GitHub repository to use. If not provided, it will ask for a repository.
mrex 0.0.4
Magic Regex
Requires Python: >=3.9

# Magic Regex

Read and write regular expressions easily.

```python
import mrex

# Find
id_re = mrex.exactly("id: ").and_(mrex.DIGITS.group_as("id"))
id_str = id_re.find("id: 12345").group("id")
# id_str == "12345"

# Split
words = mrex.NON_CHARS.split("It's just a flesh wound...")
# words == ["It", "s", "just", "a", "flesh", "wound", ""]
```

# Installation

```bash
pip install mrex
```

#  Development

* Download source
* Install development dependencies: `flit install -s --deps develop`
* Format code: `black .`
* Run tests: `pytest`
* Bump version in [src/mrex/\_\_init\_\_.py](src/mrex/__init__.py)
* Build package: `flit build`
* Deploy: `flit publish`

# Thanks

Project influenced by [magic-regexp](https://github.com/danielroe/magic-regexp).
sp-piracicaba 0.0.18
Dados Espaciais do Plano Diretor de Piracicaba
Requires Python: >=3
Keywords: python,dados espaciais,geoprocessamento

# Plano Diretor de Piracicaba

- [GitHub](https://github.com/open-geodata/sp_piracicaba)
- [PyPi](https://pypi.org/project/sp-piracicaba/)

<br>

Em janeiro de 2021, peguei o arquivo _Mapa_1_2_3_4_macrozonas_zonas_PLC.dwg_ e extrai os seguintes _layers_:

- **Divisão Municipal** (_Divisa_Municipal.shp_), que contem o limite territorial do município;
- **Divisão Perímetro** (_Divisa_Perimetro.shp_), que contem o limite do perímetro urbano do município;
- **Divisão dos Bairros** (_Divisa_Abairramento.shp_), que contem o limite dos bairros inseriodo dentro do perímetro
  urbano do município;
- **Divisão Área Urbana e Rural** (_Divisa_UrbanoRural.shp_), que contem a divisão entre área rural e área urbana do
  município;
- **Macrozoneamento** (_Macrozonas.shp_), proposto pelo Plano Diretor

<br>

No arquivo não foi específicado o _datum_.

- Testei _datum_ SAD 69 e não ficou bom;
- Testei _datum_ Córrego Alegre. Ficou melhor e, portanto, é o que estou usando.

<br>

---

### Como Usar?

O repositório faz parte do projeto [**_OpenGeodata_**](https://pypi.org/project/open-geodata), que tem por objetivo compartilhar dados espaciais por meio de _packages_ do python.

```bash
# Install
pip3 install open-geodata --upgrade
pip3 install sp-piracicaba --upgrade
```

<br>

Uma vez instalado os pacotes, é possível listar e carregar os _datasets_ disponíveis, com os comandos abaixo.

```python
# List Datasets from package (dataframes and geodataframes)
geo.get_dataset_from_package('sp_piracicaba')

# Load Dataset from package
geo.load_dataset_from_package('sp_piracicaba', dataset_name)
```

<br>

---

### _TODO_

1. Atualizar os mapas conforme [IPPLAP – Instituto de Pesquisas e Planejamento de Piracicaba](http://www.ipplap.com.br/)
sp-bh-pcj-2020-2035 0.0.7
Dados Espaciais da Agência das Bacias PCJ, com informações apresentadas no Plano de Bacias 2020-2035
Requires Python: >=3
Keywords: python,dados espaciais,geoprocessamento

# Plano de Bacias PCJ 2020-2035

- [PyPi](https://pypi.org/project/sp-bh-pcj-2020-2035/)
- [GitHub](https://github.com/open-geodata/sp_bh_pcj-2020-2035)

<br>

A Agência das Bacias PCJ mantem um [ArcGIS Server](https://mapas.agenciapcj.org.br/arcgis/rest/services) possibilitando o consumo de dados espaciais.

Visando baixar todos os dados disponíveis, referente ao [Plano de Bacias 2020-2035](https://plano.agencia.baciaspcj.org.br/), em 20.05.2022 acessei todos os dados usando o repositório [ArcGIS REST API](https://github.com/open-geodata/arcgis_rest_api).

![ArcGIS Server](https://i.imgur.com/ZAKT1bE.png)

O enderenço com os parâmetros, que utilizei, esta apresentado abaixo:

- https://mapas.agenciapcj.org.br/arcgis/rest/services/Planos/PlanoBacias_CRF/FeatureServer/49/query?where=1%3D1&objectIds=1&time=&geometry=&geometryType=esriGeometryEnvelope&inSR=&spatialRel=esriSpatialRelIntersects&distance=&units=esriSRUnit_Foot&relationParam=&outFields=*&returnGeometry=true&maxAllowableOffset=&geometryPrecision=&outSR=&havingClause=&gdbVersion=&historicMoment=&returnDistinctValues=false&returnIdsOnly=false&returnCountOnly=false&returnExtentOnly=false&orderByFields=&groupByFieldsForStatistics=&outStatistics=&returnZ=false&returnM=false&multipatchOption=xyFootprint&resultOffset=&resultRecordCount=&returnTrueCurves=false&returnExceededLimitFeatures=false&quantizationParameters=&returnCentroid=false&sqlFormat=none&resultType=&featureEncoding=esriDefault&datumTransformation=&f=geojson

<br>

Notei também que a Agência PCJ mantem uma [API Swagger](https://ssd.baciaspcj.org.br/api/index.html) com disponibilização de dados.

<br>

---

### Como Usar?

O repositório faz parte do projeto [**_OpenGeodata_**](https://pypi.org/project/open-geodata), que tem por objetivo compartilhar dados espaciais por meio de _packages_ do python.

```bash
# Install
pip3 install open-geodata --upgrade
pip3 install sp-bh-pcj-2020-2035 --upgrade
```

<br>

Uma vez instalado os pacotes, é possível listar e carregar os _datasets_ disponíveis, com os comandos abaixo.

```python
# List Datasets from package (dataframes and geodataframes)
geo.get_dataset_from_package('sp_bh_pcj_2020_2035')

# Load Dataset from package
geo.load_dataset_from_package('sp_bh_pcj_2020_2035', dataset_name)
```

<br>

---

### Observação

Existem três _layers_ que não inseri no repositório, visto apresentarem mais de 25mb (limite do GitHub). a saber:

- inventario florestal 2020 - bacia pcj - poligonos.7z
- uso da terra - poligonos.7z
- área de preservação permanente - poligonos.7z

<br>

---

### _TODO_

1. Definir _layers_
jansevakernel 1.0.0
A jupyter eva kernel.
Requires Python: >=3.7

# jansevakernel

![Logo](https://raw.githubusercontent.com/jans-code/jansevakernel/main/jansevakernel/logo-svg.svg)

A jupyter kernel implementation of [eva](https://github.com/nerdypepper/eva).

## Installation

- install eva (i.e. from the Arch community repository)
- get the kernel module via pip
- `pip install jansevakernel`
- then install kernelspec
- `jansevakernel`

## Dev Installation

- download/clone this project
- open shell in project folder
- `pip install -e ./`
- `jupyter kernelspec install --user jansevakernel`

## Uninstall

- `jupyter kernelspec uninstall jansevakernel`
- `pip uninstall jansevakernel`
maria-easy 0.0.1
This python library is an easy way to use mariaDB.
Keywords: mariadb development easy

# Maria Easy

This python library is an easy way to use mariaDB.

## Installation

To install the latest version use the following command:

    pip install maria-easy

## Usage

    from maria_easy.connector import Connector
    conn = Connector(user='root', password='pass', host=mariadb-server, database='test')
    conn.execute('create table oloc (a INT) ;')
    conn.execute('insert into oloc values (42) ;')
    conn.execute('SELECT * from oloc;')

## License

ISC

## Author information

This code was created in 2023 by Olivier Locard.
cghla 0.2.3.1
Tools for finding HLA alleles for WGS/RNAseq samples
Requires Python: >=3.9

# cghla

A set of tools that are useful for finding the MHC/HLA alleles for a given
sample using WGS, WES, or RNAseq data. In particular, these tools are designed for
the identification of MHC alleles to use in identification of novel peptides that can
be presented by MHC Class I proteins for finding presented neoepitopes in cancer.
As such, the specificity of the results are designed only for this purpose.

These tools implement a particular workflow that assumes you have the following:

    bwa
    samtools
    ngsutilsj

Additionally, the tools are built to work with data from the IPD-IMGT/HLA database
which is available from: https://hla.alleles.org

Finally, final prediction can use MHC peptide binding affinities from mhcFlurry which
are available here: https://openvax.github.io/mhcflurry-motifs/mhcflurry.allele_sequences.csv

The basic workflow can be described as:

## WGS
1) Find the location of HLA alleles in the reference genome.
2) Extract flanking sequence from the reference genome, and adding this sequence
   to the allele sequences
3) Extraction of mapped reads to the HLA locus in an already aligned BAM file
4) Re-alignment of these reads (optionally with unmapped reads as well) to the 
   now extended flanking HLA allele sequences
5) Score HLA allele pairs (maternal/paternal) to find the most likely genotypes
   for the individual.
6) Predict the most likely genotypes

## RNAseq
The RNAseq/WES workflow is similar, but instead of using the `*_gen.fasta` versions
of the IPD-IMGTR/HLA sequences you can use the `*_nuc.fasta` versions. When using these
coding mRNA sequence, you can skip steps 1 and 2. And instead of extracting a sub-set
of reads, you can align the raw FASTQ sequence. The scoring and prediction steps remain
the same.
mk-blender-scr 1.0.8
Create a Python script for Blender from Atoms of ASE.
Keywords: blender ase


====================================================
化学構造をBlender上で作成するためのライブラリ
====================================================

| cif,xyz,VASPの構造ファイルをBeldnerに取り込むためのライブラリ
| Blender上で動作するPythonスクリプトを生成する.

Webアプリ版
------------------------------------
基本的には ``mk-blender-scr`` をインストールせずともWebアプリのみを使用すれば良い.

| `アプリはこちら <https://shimi-lab-makeblenderscriptapp-app-vrendi.streamlit.app/>`_
| `MakeBlenderScriptApp(GitHub) <https://github.com/shimi-lab/MakeBlenderScriptApp>`_

インストール
-----------------------

>>> pip install mk-blender-scr

ドキュメント
-------------------------
`ドキュメントはこちら <https://shimi-lab.github.io/mk-blender-scr_Document/>`_ .

基本的な使用方法
-------------------------

  .. code-block:: python

      from mk_blender_src import create,BallAndStick,SpaceFilling,Stick,Animation
      from ase.io import read

      atoms = read("CONTCAR") # CONTCAR is vasp-format file
      create("BallAndStick.py",BallAndStick(atoms))
      
`BallAndStick.py` をBlender上で実行すると,Ball-Stickスタイルの3Dモデルが作成される.

スタイルは

- BallAndStick (球棒)
- SpaceFilling (空間充填)
- Stick (棒)
- Animation (アニメーション(空間充填で再現される))

より詳細な使用方法は `ドキュメント <https://shimi-lab.github.io/mk-blender-scr_Document/>`_ を参照する.


インポート可能な構造ファイル
------------------------------

| ASEのAtomsオブジェクトを作成する必要があるため,VASP(POSCAR,CONTCAR), xyz, cif等を読み込むことができる.
| 詳細なフォーマットは以下を参照
| https://wiki.fysik.dtu.dk/ase/ase/io/io.html

Pythonバージョン
------------------

Python3.8,Python3.10での動作確認済

依存ライブラリ
----------------

| 以下の条件下で安定的な動作が確認されている.
| nglview==3.0.3の場合,ipywidgetsは8.0以下である必要があることが知られている.

- `ase==3.22.1 <https://wiki.fysik.dtu.dk/ase/>`_
- `Jinja2==3.1.2 <https://jinja.palletsprojects.com/en/3.1.x/>`_
- `ipywidgets==7.7.2 <https://ipywidgets.readthedocs.io/en/stable/index.html>`_
- `nglview==3.0.3 <https://pypi.org/project/nglview/>`_

Blenderバージョン
--------------------
Blender2.9, Blender3.0 での動作確認済
timepilot 0.12
A simple Python library for timezone formatting
Requires Python: >=3.6



# TimePilot Lib 311x-012

TimePilot is a simple Python 3.11 library that helps you work with timezones and format them in different ways. With TimePilot, you can display the current time in a specific timezone or format a range of dates and times to appear in a particular way.

## Installation

You can easily install TimePilot using pip. Simply run the following command:

```python
pip install timepilot
```

## How to Use

Here's an example of how you can use TimePilot to display the current time in the Eastern Timezone:

```python
import timepilot

# Specify the timezone and formatting option you want to use
time_zone = "US/Eastern"
time_format = "hour_minute"

# Get the current time in the specified timezone and format
current_time = timepilot.get_current_time(time_zone, time_format)

# Print out the current time
print(f"The current time in {time_zone} is {current_time}")
```

We should get an output of something like this:

    The current time in US/Eastern is 3:30 PM

## Available Formatting Options
TimePilot provides several formatting options for displaying timezones, including:
- `hour_minute`: displays the hour and minute (e.g. 3:30 PM)
- `hour_minute_second`: displays the hour, minute, and second (e.g. 3:30:45 PM)
- `minute_second`: displays the minute and second (e.g. 30:45)
- `hour`: displays the hour (e.g. 3 PM)
- `second`: displays the second (e.g. 45)

You can specify the formatting option when calling get_current_time or format_datetime.

## Version

The current version of TimePilot is 311x-012, indicating that it is compatible with Python 3.11 and all sub-versions and the library version is 0.1.

# License
TimePilot is released under the MIT License. You can find the license information in the LICENSE file.