semvec/pypi-packages huggingface dataset:
tenncor 0.0.3
Tensor algebra module.
License: MIT
Requires Python: >=3.6

# Tenncor
[![Build Status](https://travis-ci.org/mingkaic/tenncor.svg?branch=master)](https://travis-ci.org/mingkaic/tenncor)
[![codecov](https://codecov.io/gh/mingkaic/tenncor/branch/master/graph/badge.svg)](https://codecov.io/gh/mingkaic/tenncor)

## Synopsis

Tenncor libraries help developers build and evaluate tensor equations and its derivatives.
A tensor is an N-dimensional container that organizes its content by some shape. An M by N matrix for instance, is a 2-dimensional tensor with a shape of [N, M] (according to Tenncor's x-y-z-... coordinate notation).

## Core Components

### [TEQ (Tensor EQuations)](teq/README_TEQ.md)

This module supplies syntax tree for equation and generates derivative.
Constraints to the equation is limited to each tensor's shape.

Tensor objects acts as the function graph scaffolding. Tensor scaffolding has the following actors:
- Session/Traveler access graph scaffolding.
- External Optimizer manipulates graph scaffolding.
- GradBuilder generates more graph scaffolding.

### [EIGEN (EIGEN wrapper)](eigen/README_EIGEN.md)

This module wraps eigen operators using TEQ shape and coordinate arguments.

Eigen objects hold the real data and provides API to manipulate the data.

### [ONNX (ONNX marshaller)](onnx/README_ONNX.md)

This module marshals any TEQ graph, but requires data serialization functors when saving and loading.

### [MARSH (MARSHable objects)](marsh/README_MARSH.md)

This module defines marshable objects used as attribute values

### [QUERY](query/README_QUERY.md)

This module looks up TEQ subgraphs according to structural pattern, attributes, variable shapes or labels

### [OPT (OPTimizer)](opt/README_OPT.md)

This module specifies graph optimization through TEQ subgraph Query.

### [ETEQ (Eigen TEQ)](eteq/README_ETEQ.md)

This module is implements basic operations for Tenncor's TEQ Tensor objects generated through pybinder.

Additionally, ETEQ also defines data format and (de)serialization methods required by PBM.

### [LAYR (LAYeR models)](layr/README_LAYR.md)

This module contains utility functions for common machine learning api

## Supplemental Components

### [DBG (Debug)](dbg/README_DBG.md)

This module is contains debug libraries for TEQ Graphs.

### General diagram

High-level diagram available: https://drive.google.com/file/d/1PrsFa7Duj4Whlu_m0lmFr5JGikGnU3gC/view?usp=sharing

## Generators

### GEN

This is a generic generator for creating files from dictionary of objects and extensible plugins

### EGEN

This is the generator for EIGEN/ETEQ module. Generated files include:
- opcode: which defines OPERATION enum, operator metadata, switch case macros, and Eigen operator creation
- type: which defines DATA TYPE enum, type metadata, and switch case macros
- api, and pyapi: which defines APIs in C++ and python (through Pybind11)

## Building

Tenncor uses bazel 0.28+. Building with bazel before 2.0 has duplicate symbols issues. Will investigate after C++ Module support

Download bazel: https://docs.bazel.build/versions/master/install.html
extract-bib 1.0.0
A small utility that uses BibTool to extract the references used in a Pandoc Markdown file.
License: MIT

# extract_bib

![Test](https://github.com/willbarton/extract_bib/workflows/Test/badge.svg)

A small utility that uses [BibTool](https://github.com/ge-ne/bibtool) to extract only the works cited in a Pandoc Markdown file from a BibTeX file.

If your workflow involves writing in Markdown and converting that Markdown to TeX with Pandoc, and you keep a large bibliography in a single BibTeX file, this utility will allow you to extract only the works cited in the Markdown file into a separate BibTeX file for distribution/portability.

It is intended as part of a transparent, plain text academic workflow.

It is based on [this ruby Gist](https://gist.github.com/dsanson/1383132).

## Installation

```shell
pip install extract_bib
```

## Usage


To extract the references in `great_paper.md` that you keep in your `~/Documents/references.bib` and place them in `great_paper.bib`, run:

```shell
extract_bib --bibtex-file ~/Documents/references.bib great_paper.md great_paper.bib
```
Firebird 0.0.2
The Firebird Python library provides a pythonic interface to the Firebird API.
License: MIT
Keywords: api firebird

# Firebird Python Library

The Firebird Python library provides a pythonic interface to the Firebird API.


## Installation

```
pip install firebird
```

## Requirements

- Python 3.7+


# Usage

```Python

import firebird


client = firebird.Client('your_api_key...')

# Fetch a number
number = firebird.resource.Number.one(client, '...')

```
kobra 0.3.1
Wrapper for twine & python setup.py
License: MIT
Requires Python: >=3
Keywords: kobra tool

# 🐍 Kobra
Wrapper for `twine` &amp; `python setup.py`. The name of this module is not an error but a combination of the words king and cobra.
## 💾 Install
Installing the project by using pip:
```
pip install kobra
```
If you have \*nix such a system (Android, Mac OS, Free BSD, Linux) you may not have the last python in your terminal, so use:
```
pip3 install kobra
```  
## ⚙️ Functions
Part of the functionality is copying from `python setup.py`.
### 💾 Install
Installing the module from source code:
```
kobra
```
or:
```
kobra install
```
### 🧑‍💻 Develop
A command for developers to use the module folder as the main:
```
kobra develop
```
It is desirable to use when changing locales and configs of your module, which are in separate files.
### 📦 Publish
With this command, you can change the version of your module, create a build with `python setup.py sdist` and release to pypi.org:
```
kobra publish
```
For the command to work in your module there must be a file `__init__.py`, for example the following:
```python
__version__ = "0.2.1"
```
You can also use the version from `__init__.py` in your `setup.py`:
```python
from setuptools import setup
from <modulename> import __version__

setup(
    ...
    version=__version__
    ...
)
```
unclutterer 0.0.3
unclutterer is a library that helps you clean your plotly figures.. This library is inspired by the book Storytelling with Data by Cole Nussbaumer Knaflic (https://www.kobo.com/us/en/ebook/storytelling-with-data).
License: MIT
Requires Python: >=3.6

# Unclutterer

Unclutterer is a library that helps you clean your plotly figures. 
This library was inspired by the book "Storytelling with Data by Cole
Nussbaumer Knaflic (https://www.kobo.com/us/en/ebook/storytelling-with-data).

## Installation

```shell script
pip install unclutterer
```

## Testing

To test this project run:

```shell script
pytest
```

## Notebooks for examples

You can use this to see how the library modifies the plots. We are using 
[unnotebook](http://www.unnotebook.com/) to plot the examples. 

1. Build and push `notebook` image:

```bash
docker build . -t unclutterer
```

2. Run notebook

```shell script
docker-compose up notebook
```

or 

```bash
docker run --rm -it \
    -v /Users/rigo/Documents/Projects/notebooks/stock-predictions:/notebooks \
    -p 8899:8899 unclutterer
```


## Usage

...

## Deploying pip library

Build the pip library package to deploy to pip:

```shell script
python3 setup.py sdist bdist_wheel
```

Publish to pip. You can follow steps [here](https://docs.gitlab.com/ee/user/packages/pypi_repository/) 

Note that you will need to install twine and register your pypi. Usually in the file
`~/.pypirc`

```shell script
python3 -m twine upload --repository pypi dist/*
```
scrapezone-python-sdk 1.0.5
Official client SDK of Scrapezone
License: MIT

<p align="center">
    <a href="https://scrapezone.com/"><img src="https://app.scrapezone.com/img/logo.svg" alt="Scrapezone Logo" width="300" height="60"></a>
  </a>
</p>

<h2 align="center">
  Getting Data Was Never Easier
</h2>

<p align="center">
Forget about proxies, servers, and IP addresses. Just get the data you need.
</p>

Google and eCommerce HTML Scraper
Send a request with up to 1,000 URLs and receive the raw, unblocked HTML files.

## Quick Start

1. Create a new account at: https://app.scrapezone.com
2. Copy your scrape username and password.
3. Start getting the data you need.

## Example request:

```
from scrapezone_client import ScrapezoneClient
from decouple import config

username = config('SCRAPEZONE_USERNAME')
password = config('SCRAPEZONE_PASSWORD')

scrapezoneClient = ScrapezoneClient(username, password)

results = scrapezoneClient.scrape(
    {'scraper_name': 'amazon_product_display', 'query': [
        'https://amazon.com/dp/B01LSUQSB0',
        'https://amazon.com/dp/B084K5HNCB'
    ]})

print(results)
```

Full documentation can be found [Here](https://github.com/Scrapezone/documentation).