semvec/pypi-packages huggingface dataset:
silero-advanced 1.0.0
Silero-Advanced - это улучшенный модуль Silero.
Requires Python: >=3.7
Keywords: python silero

# 👋 Приветствуем в Silero-Advanced!
Silero-Advanced - это улучшенный модуль Silero.
# 🤔 В чем отличие?
Оригинальная Silero не может озвучивать цифры и некоторые символы. В этом модуле данная проблема решена.
# ⬇️ Установка
```py
pip install silero-advanced
```
# 💻 Использование
Модуль имеет 3 функции:<br>

• Синтезировать<br>
• Синтезировать и воспризвести<br>
• Синтезировать и сохранить аудио в формате wav<br>

Пример 1 функции:
```py
import time
import sounddevice as sd
from silero_advanced import synthesis

synthesis = synthesis()
audio = synthesis.synthesis("Привет, Мир! Я умею озвучивать цифры и символы! Например: 2 + 2")

sd.play(audio, synthesis.sample_rate)
time.sleep(len(audio) / synthesis.sample_rate)
sd.stop()
```

Пример 2 функции:
```py
from silero_advanced import synthesis

synthesis = synthesis()
synthesis.synthesisAndPlay("Привет, Мир! Я умею озвучивать цифры и символы! Например: 2 + 2")
```

Пример 3 функции:
```py
from silero_advanced import synthesis

synthesis = synthesis()
synthesis.synthesisAndSaveWav("Привет, Мир! Я умею озвучивать цифры и символы! Например: 2 + 2")
# Файл будет сохранен в корневом каталоге
```
# 🙇 Спасибо всем авторам библиотек, что были использованы в данном проекте:
<a href="https://silero.ai/">Silero</a><br>
<a href="https://github.com/seriyps/ru_number_to_text">Sergey Prokhorov</a>
strstyle 1.1.8
python package to handle colorful outputs
Requires Python: >=3.7

style
=====

Install
-------

::

    $ pip install strstyle


Usage
-----

.. code:: py

    import strstyle

    print(strstyle.red('Hello', strstyle.bold('world') + '!'))


API
---

strstyle.\ ``strstyle*[.strstyle](*objects, sep=' ')``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Chain `strstyle <#strstyles>`__ and call the last one as a method with an argument. Order doesn't matter, and later strstyles
take precedence in case of a conflict, e.g. ``strstyle.red.yellow.green`` is equivalent to ``strstyle.green``. strstyles can
be nested.

Multiple arguments will be separated by ``sep``, a space by default.

strstyle.\ ``enabled``
~~~~~~~~~~~~~~~~~~~

Color support is automatically detected, but can also be changed manually.

- set ``strstyle.enabled`` to ``True`` or ``False``
- use the command line parameter ``--color`` or ``--no-color``


strstyles
------

+---------------------+-------------------------------------+-------------------------------------------+
| Modifiers           | Colors                              | Background colors                         |
+=====================+===============+=====================+==================+========================+
| - ``bold``          | - ``black``   | - ``light_black``   | - ``on_black``   | - ``on_light_black``   |
| - ``dim``           | - ``red``     | - ``light_red``     | - ``on_red``     | - ``on_light_red``     |
| - ``italic``        | - ``green``   | - ``light_green``   | - ``on_green``   | - ``on_light_green``   |
| - ``underline``     | - ``yellow``  | - ``light_yellow``  | - ``on_yellow``  | - ``on_light_yellow``  |
| - ``inverse``       | - ``blue``    | - ``light_blue``    | - ``on_blue``    | - ``on_light_blue``    |
| - ``hidden``        | - ``magenta`` | - ``light_magenta`` | - ``on_magenta`` | - ``on_light_magenta`` |
| - ``strikethrough`` | - ``cyan``    | - ``light_cyan``    | - ``on_cyan``    | - ``on_light_cyan``    |
|                     | - ``white``   | - ``light_white``   | - ``on_white``   | - ``on_light_white``   |
+---------------------+---------------+---------------------+------------------+------------------------+
cram2fastq 0.0.5
Download and convert cram-to-fastq from irods.

# cram2fastq
[![](https://img.shields.io/pypi/v/cram2fastq?logo=PyPI)](https://pypi.org/project/cram2fastq/)

A python script to retrieve and convert crams from irods to fastq files. For internal use within the sanger HPC environment.

## Installation
```bash
pip install cram2fastq
# or
pip install git+https://github.com/clatworthylab/cram2fastq.git
```

Before using the tool, check that:

1) samtools is available on your `$PATH`. If not:
```bash
export PATH=/nfs/team297/kt16/Softwares/samtools-1.11/bin:$PATH
```

2) `REF_PATH` is set as well. If not:
```bash
export REF_PATH=/lustre/scratch117/core/sciops_repository/cram_cache/%2s/%2s/%s:/lustre/scratch118/core/sciops_repository/cram_cache/%2s/%2s/%s:URL=http:://refcache.dnapipelines.sanger.ac.uk::8000/%s
```

If setting it up for the first time, just do this once:
```bash
echo 'export PATH=/nfs/team297/kt16/Softwares/samtools-1.11/bin:$PATH' >> ~/.bashrc
echo 'export REF_PATH=/lustre/scratch117/core/sciops_repository/cram_cache/%2s/%2s/%s:/lustre/scratch118/core/sciops_repository/cram_cache/%2s/%2s/%s:URL=http:://refcache.dnapipelines.sanger.ac.uk::8000/%s' >> ~/.bashrc
source ~/.bashrc
````

## Instructions
```bash
usage: cram2fastq.py [-h] [--meta META] [--study STUDY] [--outpath OUTPATH] [--bulk] [--bsub] [--DNAP] [--queue QUEUE] [--ncpu NCPU] [--mem MEM] [--dryrun]

optional arguments:
  -h, --help         show this help message and exit
  --meta META        txt/csv file containing the SANGER SAMPLE IDS as per manifest as a separate line for each sample.
  --study STUDY      Study ID. This will be the name of the output folder.
  --outpath OUTPATH  Path to the directory holding the converted files.
  --bulk             If passed, assume file is bulk data rather than 10x data.
  --bsub             If passed, submits as job to bsub.
  --DNAP             If passed, treats samples as created using semiautomated pipeline from DNAP (i.e. same ID for GEX/TCR/BCR). Output will be separated as folders.
  --queue QUEUE      bsub queue. Only works if --bsub is passed.
  --ncpu NCPU        bsub ncpu. Only works if --bsub is passed.
  --mem MEM          bsub memory. Only works if --bsub is passed.
  --dryrun           If passed, prints command rather than actually run.
```

After installation, it is as easy as doing:
```bash
cram2fastq.py --meta sampleids.txt --study test --outpath /path/to/folder --bulk
```

Adding the `--bsub` option will submit this as a job if you have many samples to process.
```bash
cram2fastq.py --meta sampleids.txt --study test --outpath /path/to/folder --bulk --bsub
```

`sampleids.txt` is simply a single column `.txt` or `.csv` file with the sanger sample ids (no header).
The IDs should correspond to `SANGER SAMPLE ID` column in the manifest.

For example:
```
SangerSampleID00000001
SangerSampleID00000002
SangerSampleID00000003
```

## Output
Once it is all finished, a folder (with the name as whatever you provide for `--study`) will be created under `--outpath` with the appropriate `.cram` files converted to `.fastq.gz` files.
job-generator 1.0.3
A package to generate jobs for a given set of parameters
Requires Python: >=3.7

# Resume and Cover Letter Generator

This project is a software development project that utilizes web scraping, machine learning APIs, and HTML templates to generate a resume and cover letter based on a job posting. The purpose of this project is to automate the process of job application by generating a personalized resume and cover letter in a matter of seconds.

## Table of Contents

- [Resume and Cover Letter Generator](#resume-and-cover-letter-generator)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [Usage](#usage)
  - [Contributing](#contributing)

## Installation

1. Clone the repository to your local machine.
2. Install the required packages by running `pip install -r requirements.txt`.
3. Create a `.env.local` file and add the following variables:
   ```
   API_KEY=<your_api_key>
   ```
   Note: You will need to obtain an API key from the machine learning API provider.
4. Run `python main.py` to start the application.

## Usage

This project is currently limited to a command line tool. The program is controlled by a number of flags.

- `-h` or `--help`: Displays the help menu.
- `-o` or `--output`: Determines if files will be output.
- `-v` or `--verbose`: Determines if the program will print the output.
- `-r` or `--resume`: Determines if a resume will be generated.
- `-l` or `--letter`: Determines if a cover letter will be generated.
- `-p` or `--posting`: Determines if a job posting will be generated.

## Contributing

Contributions are always welcome! If you have any ideas or suggestions, please create a pull request.

<!-- ## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details. -->