semvec/pypi-packages huggingface dataset:
bank1 0.0.1
Say bank

# bank
This is an example project demonstrating how to publish a python module to pypi.

##installation

Run the following to install

'''python
pip install bank
'''

##usage

'''python
from bank import deposit_amount
from bank import withdraw_amount
from bank import home_loan
from bank import personal_loan
from bank import vehicle_loan
from bank import home_loan
# if you want deposit a amount
dep_amount=deposit_amount.dep_amount(10000,10000000)
print(dep_amount)
# if you want withdraw a amount
with_amount=withdraw_amount.with_amount(1000,10000)
print(with_amount)

#home_loan
h_loan=home_loan.hm_loan(10000)
print(h_loan)
#personal_loan
per_loan=personal_loan.per_loan(1000)
print(per_loan)
#vehicle_loan

v_loan=vehicle_loan.ve_loan(10000)
print(v_loan)

'''
'''python
pip install -e .[dev]
Bytes-Converter 1.0.0
This library contains digital storage unit converter
Requires Python: >= 3.6

This is a **Python library** which Converts **Digital Storage** Units Like **KB** [KiloByte], **MB** [MegaByte] etc. into each or the Other very Efficiently with must Precision and Efficiency.

It has one **Class**, which is:

Class **UnitConversion**: 

```
This Module is the Subpackage of the library which does the work and will convert the Digital Units. kotha bolna
Usage: from Bytes_Converter import UnitConversion as uc
# Say, you will convert from GB to MB 
uc.gb_to_mb(196)

Note: It will return None if bound to a variable. The input should be given in <class 'int'> or <class 'float'>. 
TypeError will be raised if any other data type is used as input, if you use any good text editor like VS Code or any IDE it will show the references while you call the method/function. 
 
```
neurommsig-epilepsy 0.0.1
Mechanistic pathway knowledge on Epilepsy encoded in Biological Expression Language
Requires Python: >=3.6

NeuroMMSig Epilepsy
===================
Mechanistic pathway knowledge on Epilepsy encoded in Biological Expression
Language.

Installation
------------
To install the ``neurommsig_epilepsy`` python package for programmatic access
to the BEL files in this repository, use the following code in your shell:

.. code-block:: sh

    $ pip install neurommsig_epilepsy

To install the ``neurommsig_epilepsy`` python package for programmatic access
to the BEL files in this repository, use the following code in your shell:

.. code-block:: sh

    $ git clone https://github.com/neurommsig-epilepsy/neurommsig-epilepsy.git
    $ cd neurommsig_epilepsy
    $ pip install -e .

To optionally re-compile all of the BEL files, use

.. code-block:: sh

    $ neurommsig-epilepsy compile

Usage
-----
To get the knowledge and summarize it, use:

.. code-block:: python

    import neurommsig_epilepsy

    graph = neurommsig_epilepsy.get_graph()
    graph.summarize()
hemekg 0.0.1
Mechanistic knowledge about heme and hemolytic disorders in Biological Expression Language with PyBEL
Requires Python: >=3.6

HemeKG
======
Assembling and modeling the knowledge around heme pathogenicity and pathway dysregulation in the context of hemolytic
disorders. The data hosted in this repo was curated by `Farah Humayun <https://github.com/Fahumayun>`_ in context of her
Master's Thesis. The curated documents are located in the ``hemekg`` directory.

Citation
--------
If you find HemeKG useful in your work, please consider citing:

.. [1] Humayun, F., *et al.* (2020). `A computational approach for mapping heme biology in the context of hemolytic
       disorders <https://doi.org/10.3389/fbioe.2020.00074>`_. *Front. Bioeng. Biotechnol.* 8:74.

Installation
------------
To install the ``hemekg`` python package for programmatic access to
the BEL files in this repository, use the following code in your shell:

.. code-block:: sh

    $ git clone https://github.com/hemekg/hemekg.git
    $ cd hemekg
    $ pip install -e .

Commands
--------
To see all the commands, simply run:

.. code-block:: sh

    $ hemekg

Usage
-----
To get the BEL graph, use the following code:

.. code-block:: python

    >>> import hemekg
    >>> graph = hemekg.get_graph()
    >>> graph.summarize()

Annotations
~~~~~~~~~~~
Auxiliary annotations are located in the hemekg/annotations directory.
These include:

1. TimePoint.
2. Species.
3. Concentration.
amazon-affiliate-url 0.1.4
amazon_affiliate_url
Requires Python: >=3.5

# amazon_affiliate_url

## Install

~~~~shell
pip install amazon_affiliate_url
or
pip3 install amazon_affiliate_url
~~~~

## Usage

~~~~python
from amazon_affiliate_url import AmazonAffiliateUrl, Country

ASIN = 'SOME_ASIN'
TAG =  'YOUR_AFFILIATE_TAG'
BITLY_TOKEN = 'YOUR_BITLY_TOKEN'

print(
    AmazonAffiliateUrl.url_cls(
        asin_or_url=ASIN,
        affiliate_tag=TAG,
        # bitly_token=BITLY_TOKEN,
        # shorten_url=True,
        country=Country.Netherlands
    )
)
~~~~