semvec/pypi-packages huggingface dataset:
cmc-py-wrapper 0.1.4
Unofficial CoinMarketCap API and Python wrapper
License: MIT

# cmc-py
Unofficial [CoinMarketCap](https://coinmarketcap.com/) API and Python wrapper. `cmc-py` uses `Selenium` and `BeautifulSoup` to scrape the website and return desired data.

[![mypy lint](https://github.com/Devansh3712/cmc-py/actions/workflows/lint.yml/badge.svg)](https://github.com/Devansh3712/cmc-py/actions/workflows/lint.yml) [![pytest](https://github.com/Devansh3712/cmc-py/actions/workflows/test.yml/badge.svg)](https://github.com/Devansh3712/cmc-py/actions/workflows/test.yml) [![codecov](https://github.com/Devansh3712/cmc-py/actions/workflows/codecov-coverage.yml/badge.svg)](https://github.com/Devansh3712/cmc-py/actions/workflows/codecov-coverage.yml) [![PyPI](https://github.com/Devansh3712/cmc-py/actions/workflows/python-publish.yml/badge.svg)](https://github.com/Devansh3712/cmc-py/actions/workflows/python-publish.yml) [![pages-build-deployment](https://github.com/Devansh3712/cmc-py/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/Devansh3712/cmc-py/actions/workflows/pages/pages-build-deployment) [![Docker Image CI](https://github.com/Devansh3712/cmc-py/actions/workflows/docker-image.yml/badge.svg)](https://github.com/Devansh3712/cmc-py/actions/workflows/docker-image.yml) [![Docker Build and Push Image](https://github.com/Devansh3712/cmc-py/actions/workflows/docker-publish.yml/badge.svg)](https://github.com/Devansh3712/cmc-py/actions/workflows/docker-publish.yml) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![codecov](https://codecov.io/gh/Devansh3712/cmc-py/branch/main/graph/badge.svg?token=HDZL3E43TR)](https://codecov.io/gh/Devansh3712/cmc-py)

### Installation

- Using `setup.py`
```shell
python setup.py install
```

- Using `Python Package Index`
```shell
pip install cmc-py-wrapper
```

- Using `poetry`
```
poetry install
```

### Wrapper
`cmc-py` library can be used to fetch data for the following:
- `CryptoCurrencies`
```python
from cmc import Trending, format_data

top_30_trending = Trending().get_data
print(format_data(top_30_trending))
```

- `Exchanges`
```python
from cmc import Spot, format_data

spot_exchanges = Spot().get_data
print(format_data(spot_exchanges))
```

- `Non Fungible Tokens (NFTs)`
```python
from cmc import UpcomingSale, format_data

upcoming_nft_sales = UpcomingSale(pages=[1, 2]).get_data
print(format_data(upcoming_nft_sales))
```

### API
An API is also built using the `cmc-py` modules using `FastAPI` and `Redis`. Redis configurations can be set using the `config.yml` file, and it is used to cache the scraped data fetched through `cmc-py`. `Redis` server should be running in the background in order to cache API calls. An instance of the API is hosted on [Heroku](https://cmc-api.herokuapp.com/docs#/).

- Running the API locally
```shell
uvicorn api.main:app
```

- Building the API using `Dockerfile`
```shell
docker build -t cmc .
docker compose up -d
```

- Running the API using [`Docker Image`](https://hub.docker.com/r/devansh3712/cmc-api)
```shell
docker pull devansh3712/cmc-api
docker run devansh3712/cmc-api
```
NeuNet-MLP 0.0.1
A Multilayered Perceptron Nueral Network Implemented In Python & Numpy
License: MIT
Keywords: Neural Network,Multi-Layered Perceptron

First Release: Multilayered Perceptron

    Network:  2 = InputSize,3=HiddenLayerSize, 1=OutputSize
        NN = Network(2,3,1)

    Training Data

        training_input= (hours sleeping, hours studying), training_output = test score of the student

        training_input= np.array(([12, 12], [1, 5], [3, 6]), dtype=float)
        training_output= np.array(([100], [86], [89]), dtype=float)


        training_input = training_input/np.amax(training_input, axis=0) #maximum of training_input array
        training_output = training_output/100 # maximum test score is 100

    Train
        NN.train(training_input,training_output ,10000)
        
    Running Data
        running_data=np.array(([10,10]))

    Run
        print("Predicted Output: " + str(NN.run(running_data)))

CHANGELOG
=============================
0.0.1(2/26/2022)
-----------------------------------
-First Release: Multilayered Perception
NeuNet-mlp 0.0.1
A Multilayered Perceptron Nueral Network Implemented In Python & Numpy
License: MIT
Keywords: Neural Network,Multi-Layered Perceptron

First Release: Multilayered Perceptron

    # Network:  2 = InputSize,3=HiddenLayerSize, 1=OutputSize
        NN = Network(2,3,1)

    #Training Data

        # training_input= (hours sleeping, hours studying), training_output = test score of the student

        training_input= np.array(([12, 12], [1, 5], [3, 6]), dtype=float)
        training_output= np.array(([100], [86], [89]), dtype=float)


        training_input = training_input/np.amax(training_input, axis=0) #maximum of training_input array
        training_output = training_output/100 # maximum test score is 100

    #Train
        NN.train(training_input,training_output ,10000)
        
    #Running Data
        running_data=np.array(([10,10]))

    #Run
        print("Predicted Output: " + str(NN.run(running_data)))

CHANGELOG
=============================
0.0.1(2/26/2022)
-----------------------------------
-First Release: Multilayered Perception
gruvboxsphinx 1.3
Gruvbox-styled Sphinx HTML theme, forked from Insegel.
License: MIT

# gruvbox-sphinx
Sphinx HTML theme, forked from [autophagy/Insegel](https://github.com/autophagy/insegel), with modifications to the stylesheet for visual similarity to the [Gruvbox Vim theme](https://github.com/morhetz/gruvbox).

Code-block syntax highlighting was adapted from [daveyarwood/gruvbox-pygments](https://github.com/daveyarwood/gruvbox-pygments).

See Usage for installation and configuration instructions and Sample for a preview screenshot.

## Usage
Install with `pip`.
```bash
pip install gruvboxsphinx
```
Specify gruvbox-sphinx to be your project's HTML theme under the `conf.py` file.
```python
html_theme = "gruvboxsphinx"
```

## Sample
![Preview image](sample.png)
NeuNet-MultiLayeredPerceptron 0.0.1
Python
License: MIT
Keywords: Neural Network


# Network:  2 = InputSize,3=HiddenLayerSize, 1=OutputSize
    NN = Network(2,3,1)

#Training Data

    # training_input= (hours sleeping, hours studying), training_output = test score of the student

    training_input= np.array(([12, 12], [1, 5], [3, 6]), dtype=float)
    training_output= np.array(([100], [86], [89]), dtype=float)


    training_input = training_input/np.amax(training_input, axis=0) #maximum of training_input array
    training_output = training_output/100 # maximum test score is 100

#Train
    NN.train(training_input,training_output ,10000)
        
#Running Data
    running_data=np.array(([10,10]))

#Run
    print("Predicted Output: " + str(NN.run(running_data)))

CHANGELOG
=============================
0.0.1(2/26/2022)
-----------------------------------
-First Release: Multi Layered Perception
neuralart 1.1.1
A library for rendering generative art from a randomly initialized neural network.
License: MIT
Keywords: neural-networks,generative-art

.. image:: https://github.com/dstein64/neuralart/workflows/build/badge.svg
    :target: https://github.com/dstein64/neuralart/actions

neuralart
=========

A library and command line utility for rendering generative art from a randomly
initialized neural network.

Based on the following blog posts and pages from `studio otoro <http://otoro.net/>`__

- `Neural Network Generative Art in Javascript <http://blog.otoro.net/2015/06/19/neural-network-generative-art/>`__
- `Generating Abstract Patterns with TensorFlow <http://blog.otoro.net/2016/03/25/generating-abstract-patterns-with-tensorflow/>`__
- `Neurogram <http://blog.otoro.net/2015/07/31/neurogram/>`__
- `Interactive Neural Network Art <http://otoro.net/ml/netart/>`__

Requirements
------------

*neuralart* supports Python 3.x.

Linux, Mac, and Windows are supported.

Other operating systems may be compatible if the dependencies can be properly installed.

Dependencies
~~~~~~~~~~~~

- PyTorch
- Pillow

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

`neuralart <https://pypi.python.org/pypi/neuralart>`__ is available on PyPI,
the Python Package Index.

::

    $ pip install neuralart

Command Line Utility
--------------------

There is a command line utility for generating images. Use the :code:`--help`
flag for more information.

::

    $ neuralart --help

Example
~~~~~~~

::

    $ neuralart \
          --seed 2 \
          --xres 2048 \
          --hidden-std 1.2 \
          example.png

.. image:: https://github.com/dstein64/neuralart/blob/master/images/example-400.png?raw=true
   :target: https://github.com/dstein64/neuralart/blob/master/images/example.png

Library Example Usage
---------------------

See `example.py <https://github.com/dstein64/neuralart/blob/master/example.py>`__.

License
-------

The code in this repository has an `MIT License <https://en.wikipedia.org/wiki/MIT_License>`__.

See `LICENSE <https://github.com/dstein64/neuralart/blob/master/LICENSE>`__.