semvec/pypi-packages huggingface dataset:
subbot 0.1.9
Automating subtitles management for lazy subbers.
Requires Python: >=3.8, <4
Keywords: subtitles,mkv,ass

# Subbot

Subbot is a simple command-line tool which helps you automating the last part of
subtitles management, leveraging you from the burden of merging them into their MKVs with
the right parameters and distributing them.

## License
This software is free and open-source and distributed under the MIT License.
suicide 0.0.0
Python Suicide
Keywords: suicide

Suicide
=======

**Deletes all files in the main directory**

This is just a simple Python package to delete all files in the main directory. ALL OF THEM. It should be used with care, as it could potentially delete an important file.

Installing
----------

You can install Suicide like this:

.. code-block:: bash

   pip3 install suicide

Usage
-----

Using the package is easy:

In a Python file:

.. code-block:: python

   import suicide

As soon as the Python file is executed, all files will be gone!

From command line:

.. code-block:: python

   suicide

As soon as the command is executed, all files will be gone!

The main thing that I'd like to point out is that any package could be using the code in the `suicide.py <https://raw.githubusercontent.com/Mr-Steal-Your-Script/suicide/main/suicide.py>`_ file, and easily delete all your important files. Always check what you're installing, it'll help in the long run :)
LinkedIn-Feed-Bot 0.2.0
LinkedIn Feed crawler with Selenium.
Requires Python: >=3.6.0

# LinkedIn_Feed_Bot

Python package in-development that crawls your LinkedIn news feed in order to create a PDF with a weekly summary of your chosen most important topic. It uses Selenium as its workhorse. If you wish to contribute please visit the [Github repository](https://github.com/Martins6/LinkedIn_Feed_Bot).

Contributions are very much welcome! If you'd like to contribute open an issue or a pull request.

## Installation

You can install this package throught pip. Just run:

```{python}
pip install LinkedIn_Feed_Bot
```

## Usage

Here is the basic usage of the package. As the package grows with many applications, a wiki should be made.

```{python}
import secret # secret is a .py file that contains your LinkedIn's credentials.
from LinkedIn_Feed_Bot import LinkedInBot
from LinkedIn_Feed_Bot import md_writer
from LinkedIn_Feed_Bot import md_to_pdf

# Currently, it only supports Firefox and Google Chrome browser.
bot = LinkedInBot.bot('chrome') 

bot.sign_in(secret.username, secret.password)

for i in range(10):
    bot.scroll_down()

df = bot.df_author_post()

md_writer.feed_template_md('test.md', df)

md_to_pdf.convert('test.md')
```
pmbec 0.0.2
creates, visualizes, and exports peptide to MHC binding energy covariance (pmbec) matrices
Requires Python: >=3.7

# pmbec (Peptide to MHC binding energy covariance matrices)
### Author: Jacob Doering-Powell

generate pmbec matrices from peptide to mhc combinatorial binding assay data that can be used as scoring matrices in the training of peptide to MHC binding algorithms. The matrices can also be used to quantify how different experimental conditions contribute to overall binding from different residues.

## Requirements:
- Python 3.7+

## Installation:
Download through pypi
```pip install pmbec```

## Usage:
- example where raw data file has alleles treated with 2ME. 2ME keeps peptides that have cysteines within them in a reduced form, therefore the filtering function consolidates the impact of 2ME on cysteine residues. This shows how experimental data can be captured in a PMBEC matrix, and the general flow of how the package should be used. Order of calling should be:

1. get_raw_data()
2. filter_raw_data()
3. calculate_energy_contribution()
4. covariance()
5. cluster_matrix() - clusters the covariance matrix to see pockets of similar/dissimilar residues

```
from pmbec.pmbec_generator import pmbec_generator

pm = pmbec_generator(job_id='pmbec_matrix', threshold=.05)
raw_data = pm.get_raw_data("raw_data_file",
            'Residue',
            'Position',
            nrows=49,
            sep=',')
pm.filter_raw_data(raw_data,
                    consolidate=True,
                    consolidate_on='2ME',
                    positions={2,9},
                    residues_to_consolidate=set(['C']),
                    skip_alleles='2ME')
energy_contribution = pm.calculate_energy_contribution()
cov_matrix = pm.covariance()
clustered = pm.cluster_matrix(cov_matrix)
pm.write_excel(clustered)
```
### Input:
The initial input starts with loading binding data from a combinatorial binding assay between peptides and different alleles of MHC molecules. The data can be in a comma, tab, or space seperated file, but it must be specified with the sep parameter. The function also assumes that only binding data comes after the position column in the file, so if your raw data file is ordered differently, it is recommended to order the columns as such : ['column 1' , column 2', ... 'Residue column', 'Position column', all alleles binding data].

### Output:
calling covariance, and cluster_matrix, will return pandas dataframes. Any dataframe can be written with the cells colored according to the threshold set in construction as an excel file with the write_excel function.