semvec/pypi-packages huggingface dataset:
jupyter-module-loader 0.0.1
A python package that allows importing Jupyter notebooks as python modules
Requires Python: >=3.6

# Jupyter notebooks module loader
Allows to import Jupyter notebooks as if there were regular python modules.

The code is based on the following example from Jupyter's documentation:  
https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Importing%20Notebooks.html

## Installation
```bash
pip install jupyter-module-loader
```

## Usage
Suppose a project has the following files structure:
```text
src/
├── main.py
└── my_notebook.ipynb
```
Let `my_notebook.ipynb` have a cell containing following code:
```python
    def fn():
        ...
```
Within `main.py` it is possible to import `my_notebook.ipynb` like so:
```python
    import jupyter_module_loader
    jupyter_module_loader.register()

    import my_notebook
    my_notebook.fn() # Calls fn() defined within the notebook above
```
logtweet 0.1.4
Create a tweet based on a #100DaysOfCode log message
Requires Python: >=3.6

# LogTweet

Create a tweet based on a #100DaysOfCode log message. 


## Usage
The log url can be configured. The log has to have a format like my log, which you can find at [https://log100days.lpld.io/log.md](https://log100days.lpld.io/log.md). 

My log is based on the [original #100DaysOfCode log repo](https://github.com/kallaway/100-days-of-code/blob/master/log.md). 
This is a markdown log. 
Have created a little Flask app that converts my log to an HTML site using the `markdown2` package. But other Markdown converters should work similar. 

Once you have an HTML document with `h2` day headers and `h3` sections for "Today's Progress" and "Link(s)", you can point the tool at that URL and generate a Tweet from it. 

To actually enable the tweeting, you need to create a Twitter developer account and get an API key, API secret, Access Token and Access Token Secret. 

The tweet will also contain the first link that you define in the "Link(s)" section under today's log. 
To save some space the link is shortened. 
By default, I use my own link shortener. 

If you want to, you can also use the Bit.ly service. 
Their links might be a bit shorter and provide some engagement/tracking info.
To use the Bitly service, you need to generate an API key through them and add it to the config (see below). 


### Options

If you want to create a tweet for a different day than today, you can do so with the `--offset` command line flag. 
The offset is defined in integer days relative to today. 
So to generate a tweet for yesterday use `-o -1`. 

If you want to suppress the actual tweeting and only see the message in the console, use the `--testmode` command line flag.

## Installation
I recommend [`pipx`](https://pipxproject.github.io/pipx/) to install python scripts and other tools in isolated virtual environments. This keeps the you platform python clean and you don't have to worry about activating a particular virtual environment to use a tool/script. 

```shell
pipx install logtweet
```

This way you will have a clean environment and the tool still available on the command line.

But if you want to, you should be able to install it with `pip install logtweet`.

## Configuration

You need a configuration file for the script to work. 
The `config.ini` can either be in the current working directory or in `~/.config/logtweet/`. 

Because I can not figure out how to define a "post-install hook" that is run by `pip` I can not generate an example config at the defined location. 
You can find an example config on [GitHub](https://github.com/tbrlpld/100daysofweb-with-python-course/blob/master/work/078-twitter-bot/config.ini.example).

In that config file you define the URL where your log can be found and the API keys and access tokens that are needed for Twitter and Bit.ly.


## Development

Install with 
```shell
python -m pip install -e ".[develop]"
```

This installs the app dependencies as well as tools to develop and distribute the package.
networking-calico 3.12.0
This project contains documentation and code for integrating Project Calico networking into OpenStack/Neutron.

========================
Team and repository tags
========================

.. image:: http://governance.openstack.org/badges/networking-calico.svg
    :target: http://governance.openstack.org/reference/tags/index.html

.. Change things from this point on

============
Introduction
============

This project contains code for integrating Project Calico's networking into
OpenStack/Neutron.

Calico is an open source solution for virtual networking in cloud data centers,
developed principally by Tigera and released under the Apache 2.0 License.

For general documentation about Calico, including how to get started with a
Calico/OpenStack installation, and how to use various OpenStack/Neutron
features with Calico, please see http://docs.projectcalico.org/master.

* Free software: Apache license
* Documentation: http://docs.projectcalico.org/master
* Source: http://opendev.org/openstack/networking-calico
* Bugs: http://bugs.launchpad.net/networking-calico
braviarc-homeassistant-dev 0.3.8
bravia tv controller for home-assistant
Requires Python: >=3.6

# BraviaRC Python Package

Installation
------------

-  ``pip3 install git+https://github.com/dcnielsen90/braviarc.git``

About
=====

``BraviaRC`` is a Python library for remote communication with Sony Bravia TVs 2013 and newer
(http://info.tvsideview.sony.net/en_ww/home_device.html#bravia)

Requirements
============

Python 3.3 or 3.4 is required.

Usage
=====

```python

#new instance for TV at 192.168.1.25
braviarc = BraviaRC('192.168.1.25')

#connect to the instance (or register)
pin = '1878'
braviarc.connect(pin, 'my_device_id', 'my device name')

#check connection
if braviarc.is_connected():

  #get power status
  power_status = braviarc.get_power_status()
  print (power_status)

  #get playing info
  playing_content = braviarc.get_playing_info()

  #print current playing channel
  print (playing_content.get('title'))

  #get volume info
  volume_info = braviarc.get_volume_info()

  #print current volume
  print (volume_info.get('volume'))

  #change channel
  braviarc.play_content(uri)
  
  #get app list
  app_info = braviarc.load_app_list()
  print (app_info)
  
  #start a given app
  braviarc.start_app("Netflix")

  #turn off the TV
  braviarc.turn_off()
welllogspy 0.1.1.6
WellLogs is a package to visualize Oil And Gas Well logs in order to make interpretetion and petrophysics analysis
Requires Python: >=3.6

# WellLogs - Python

## Introduction.
This package is being designed among others to provide Petroleum Engineering tools in a modern programming language. This package is part of a project which proposes to make basic but powerful engineering software packages that cover the main topics of the Oil and Gas development phases which could be applied to any case study by suitable engineers.

There are five topics in which the project is going to be focused on:

<br>-Geoscience* (Current Package)
<br>-Reservoir
<br>-Production
<br>-Economics
<br>-Integration

<br> The package will always be in permanent development and open to suggestions to enhance the program. As the code has been written so far by a code enthusiastic Petroleum Engineer I hope to learn as much as possible to get better and usefull programs.

## WellLogspy Description
WellLogspy is a package to visualize Oil And Gas Well logs in order to make interpretetion and petrophysics analysis via Matplotlib package.

<br> The visualization of logs is based on '''tracks''' subpackage. Each log track is implemented through a single function that plot the desired data. For example, the Gr Track is composed generally by GammaRay and Sp logs, so grtrack function can plot the GammaRay and SP logs with optional features like add Well tops (Formations or Units), Gr clean and Gr shale to Vshale estimations, etc...

<br> The petrphysics analysis is contained in the '''petrophysics''' subpackage which allows you to estimate the basic petrophysics properties such like Shale volume, effective porosity, water saturation, permeability and flow capacity. A Montecarlo simulation can be implemented for uncertainty analysis on the original resources in place.
fundamental 0.1.1.18
Data frame downloader for fundamental financial

fundamental:  Dataframe for fundamental financials
==================================================

It is a Python 3 library for generating stock fundamental data through
YahooFinance. The library is primarily based on yfinance package.
fundamental requires only list of tickers input from the user and easily
generate dataframe for fundamental ratio analysis, relative value ratio
analysis and industry relative analysis.

Data inclusion
--------------

-  financials for recent 3 years. (income statement, balance sheet, cash
   flow statement)
-  calendar year end financials for comparision.
-  TTM financial by aggregate recent 4 quarters data
-  Basic share data for each fiscal year end and recent fiscal quarter
   end
-  price data for each fiscal year end and recent fiscal quarter end
- It takes about 10 minutes to create a dataframe for 100 stocks.

Usage
-----

::
   # pip install fundamental

   from fundamental import fundamental

   symlist=['GOOG','MSFT','FB']

   df = fundamental.get_df_list(symlist)

   https://github.com/steven2K19/fundamental

Limitation
----------

-  slow internet connection would lead scraping error and the program
   will auto try 3 times.