semvec/pypi-packages huggingface dataset:
flask-url-map-serializer 0.0.1
Dumps the URL map of a flask app to a JSON file
License: MIT

This package adds a ``urls_to_json`` command to the ``flask``
CLI that dumps the URL map of the flask app to a JSON file.

It also provides the ``flask_url_map_serializer`` module containing
the following functions which convert rules from the URL map to a
JSON-serializable dict:

- ``dump_url_rule(url_map, endpoint)``
- ``dump_url_map(url_map)```
solotop-random-user-agent 0.69.1
A package to get random user agents based filters provided by user
License: MIT

Random User Agents
==================

Random User Agents is a python library that provides list of user agents,
from a collection of more than 326,000+ user agents, based on filters.

Some of the filter names which can be passed to `UserAgent()` are listed below:

    operating_systems : [
        UNIX, LINUX, WINDOWS, MAC, ...
    ]

    hardware_types : [        MOBILE, COMPUTER, SERVER, ...
    ]

    software_types : [
        WEB_BROWSER, BOT__CRAWLER, BOT__ANALYSER, ...
    ]

    software_names : [
       EDGE, CHROME, CHROMIUM, ANDROID, FIREFOX, OPERA, ...
    ]

    software_engines : [
       BLINK, GECKO, WEBKIT, ...
    ]

    popularity : [
        POPULAR, COMMON, AVERAGE, ....
    ]

*All filters are available in random_user_agent.params*

Installation
------------

You can install random_useragent by running the following command:

    pip install solotop999_random_user_agent

Or you can download direct from [Github](https://github.com/solotop999/solotop_random_user_agent) and install it manually.

UsageTo get 100 user agents of browser `chrome` based on operating systems `windows` or `linux`
-----------------------------------------------------------------------

```python
    from random_user_agent.user_agent import UserAgent
    from random_user_agent.params import SoftwareName, OperatingSystem


    # you can also import SoftwareEngine, HardwareType, SoftwareType, Popularity from random_user_agent.params
    # you can also set number of user agents required by providing `limit` as parameter
  
    software_names = [SoftwareName.CHROME.value]
    operating_systems = [OperatingSystem.WINDOWS.value, OperatingSystem.LINUX.value]   
  
    user_agent_rotator = UserAgent(software_names=software_names, operating_systems=operating_systems, limit=100)

    # Get list of user agents.
    user_agents = user_agent_rotator.get_user_agents()

    # Get Random User Agent String.
    user_agent = user_agent_rotator.get_random_user_agent()

```

License
-------

The MIT License (MIT). Please see [License File](https://github.com/Luqman-Ud-Din/random_user_agent/blob/master/LICENSE) for more information.

Special thanks to @Luqman-Ud-Din.

---
solotop999-random-user-agent 0.69.0
A package to get random user agents based filters provided by user
License: MIT

Random User Agents
==================

Random User Agents is a python library that provides list of user agents,
from a collection of more than 326,000+ user agents, based on filters.

Some of the filter names which can be passed to `UserAgent()` are listed below:

    operating_systems : [
        UNIX, LINUX, WINDOWS, MAC, ...
    ]

    hardware_types : [
        MOBILE, COMPUTER, SERVER, ...
    ]

    software_types : [
        WEB_BROWSER, BOT__CRAWLER, BOT__ANALYSER, ...
    ]

    software_names : [
       EDGE, CHROME, CHROMIUM, ANDROID, FIREFOX, OPERA, ...
    ]

    software_engines : [
       BLINK, GECKO, WEBKIT, ...
    ]
    
    popularity : [
        POPULAR, COMMON, AVERAGE, ....
    ]


*All filters are available in random_user_agent.params*


Installation
------------

You can install random_useragent by running the following command:

    pip install random_user_agent

Or you can download direct from [Github](https://github.com/Luqman-Ud-Din/random_user_agent) and install it manually.


Usage
-----

To get 100 user agents of browser `chrome` based on operating systems `windows` or `linux`


```python
    from random_user_agent.user_agent import UserAgent
    from random_user_agent.params import SoftwareName, OperatingSystem


    # you can also import SoftwareEngine, HardwareType, SoftwareType, Popularity from random_user_agent.params
    # you can also set number of user agents required by providing `limit` as parameter
    
    software_names = [SoftwareName.CHROME.value]
    operating_systems = [OperatingSystem.WINDOWS.value, OperatingSystem.LINUX.value]   
    
    user_agent_rotator = UserAgent(software_names=software_names, operating_systems=operating_systems, limit=100)

    # Get list of user agents.
    user_agents = user_agent_rotator.get_user_agents()

    # Get Random User Agent String.
    user_agent = user_agent_rotator.get_random_user_agent()

```

License
-------
The MIT License (MIT). Please see [License File](https://github.com/Luqman-Ud-Din/random_user_agent/blob/master/LICENSE) for more information.


User Agents Source
-------
special thanks to [whatismybrowser](https://developers.whatismybrowser.com/) for providing real user agents.
mcreep 1.1.6
Fitting of various pre-defined models to creep data.
License: MIT
Requires Python: >=3.6

MCREEP
------

MCREEP package fits various pre-defined models to creep data.

The package can process both tensile and indentation creep measuremenents <br>
in such a way that the results from the two methods are comparable.

* The creep data:
    - A text file with two columns.
    - The 1st column = time, the 2nd column = deformation.
	- Deformation = strain (for tensile) or penetration depth (for indentation).
* The pre-defined models within the pagkage are:
    - Simple empirical models = Power law, Nutting's law.
    - Phenomenological models = elasto-visco-plastic models = EVP models.
* The theory and models are described in the open-acces publication:
	- Materials 16 (2023) 834.
	  [https://doi.org/10.3390/ma16020834](https://doi.org/10.3390/ma16020834)
	- If you use MCREEP in your research, **please cite** the publication above.

Quick start
-----------
1. Install MCREEP: `pip install mcreep`
2. Download the [demo package](https://mirekslouf.github.io/mcreep/docs),
   unzip it, and follow the instructions in *readme* file.
3. Basically, you just run the unzipped PY-script and see the results.
	- The PY-script should run anywhere, but the recommended environment is
      [Spyder](https://www.spyder-ide.org).
	- The output (in Spyder) should look like in this 
      [PrintScreen](https://mirekslouf.github.io/mcreep/docs/images/mcreep_printscreen.png).

Installation, documentation and examples
----------------------------------------
* MCREEP is available at
  [PyPI](https://pypi.org/project/mcreep/)
  &rArr; installation: `pip install mcreep`
* Home page of the project is at
  [GitHub](https://github.com/mirekslouf/mcreep/) 
  and [GitHub pages](https://mirekslouf.github.io/mcreep/).
* [Docs and examples](https://mirekslouf.github.io/mcreep/docs/)
  are summarized at GitHub pages.
* All modules, classes and functions are documented by docstrings.
* [Full documentation](https://mirekslouf.github.io/mcreep/docs/pdoc.html/index.html)
  was auto-generated from docstrings by [PDoc](https://pdoc.dev).

Versions of MCREEP
------------------
* Version 1.0 = finalized, working for both tensile and indentation creep
* Version 1.1 = a few important improvements, better documentation
fablab-lib 0.2.6
A package for communicating with PLC via industrial protocols such as OPC UA, Modbus, etc.         and logging data to a CSV file.
License: MIT
Requires Python: >=3.7

# fablab_lib

## Purpose

fablab_lib is a package designed to facilitate communication with Programmable Logic Controllers (PLCs) through industrial protocols.

## Features

- Enables seamless interaction with PLCs.
- Provides an interface for sending and receiving data.
- Supports various industrial protocols for communication.

## Installation

You can install fablab_lib via pip:
This project is licensed under the MIT License - see the [LICENSE](LICENSE.txt) file for details.

To install the 'fablab_lib' package, you can use the following command:
```pip install fablab_lib```

## Note

To install this lib in Raspberry Pi (Python >= 3.7), you need install following libs before:

```sudo apt-get install python3-pandas```

```sudo apt-get install python3-numpy```
quantscript 0.1.0
A package for calculating statistical data in finance
License: MIT

# Financial Analysis Scripts

This repository contains a collection of Python scripts for financial analysis. The scripts cover various aspects such as Cointegration Testing, Duration Calculation, and Statistical Features computation.

## Scripts

### CointegrationTest

The `CointegrationTest` script is designed for conducting cointegration tests on financial time series data. Cointegration is a statistical property that allows two or more time series to move together over time. This script provides functionalities for performing cointegration tests and analyzing the results.

### Duration

The `Duration` script focuses on calculating the duration of financial instruments. Duration is a measure of the sensitivity of the price of a bond or other debt instrument to a change in interest rates. This script allows users to compute the duration of financial instruments based on given parameters.

### StatisticalFeatures

The `StatisticalFeatures` script is dedicated to computing various statistical features of financial datasets. This includes measures such as mean, standard deviation, skewness, and kurtosis. These statistical features provide insights into the distribution and characteristics of the data.

## Usage

To use these scripts, follow these general steps:

git clone https://github.com/tonyyang1223/financial-analysis-scripts.git
cd financial-analysis-scripts
pdf-chatter 0.1.5
LLM powered Q&A over extracted PDF text
License: MIT
Requires Python: >=3.9

# PDF Chatter

Question Answering over PDFs using Nougat-OCR and GPT-4.

## Getting Started

### Prerequisites

- Python 3.9 or later
- a NVIDIA GPU with CUDA support
- environment variable `OPENAI_API_KEY` set to your OpenAI API key

### Installation

```bash
pip install pdf-chatter
```

### Usage

```bash
pdf-chatter path/to/pdf
```

which opens a REPL where you can ask questions, and GPT-4 will answer them based on the content of the PDF.

> Note: pdf-chatter will save a .mmd (multi-markdown) next to the target pdf. This contains the extracted text from the PDF, and is used as a cache so the same PDF doesn't need to be re-processed every time you run pdf-chatter.

Additionally you can run the summarize command to get a summary of the PDF before entering the REPL.

```bash
pdf-summarize path/to/pdf
```

### Example

<!-- ![pdf-chatter-demo](https://raw.githubusercontent.com/david-andrew/pdf-chatter/master/assets/pdf-chatter-demo.gif) -->
<img src="https://david-andrew.github.io/_next/static/media/pdf-chatter-demo.6b4a0e5f.gif" width="100%">

### Tips & Notes

- Nougat-OCR doesn't extract images, so any questions about images in the document will not be answered
- Nougart-OCR works best on documents similar to scientific papers, reports, etc.

## How it works

1. Extract text from the PDF using [Nougat-OCR](https://facebookresearch.github.io/nougat/)
2. The entire document is fed to [GPT-4](https://openai.com/gpt-4) as part of its chat history via the OpenAI API
3. A [simple REPL](https://pypi.org/project/easyrepl/) collects the user's questions and feeds them to GPT-4, which streams the answer back.