semvec/pypi-packages huggingface dataset:
restapisdktest 1.6.8
Python client library for RESTAPI SDK

# Getting Started with RESTAPI SDK

## Install the Rest API SDK

To install the SDK, run the following command at the command line:
    
    pip install thoughtspot-rest-api-sdk

## Initialize the API Client

**_Note:_** Documentation for the client can be found [here.](https://github.com/thoughtspot/rest-api-sdk/blob/main/Python/doc/client.md)

The following parameters are configurable for the API Client:

| Parameter | Type | Description |
|  --- | --- | --- |
| `content_type` | `string` | body content type for post request<br>*Default*: `'application/json'` |
| `accept_language` | `string` | response format<br>*Default*: `'application/json'` |
| `access_token` | `string` | The OAuth 2.0 Access Token to use for API requests. |
| `base_url` | `string` | *Default*: `'https://localhost:443'` |
| `environment` | Environment | The API environment. <br> **Default: `Environment.PRODUCTION`** |
| `http_client_instance` | `HttpClient` | The Http Client passed from the sdk user for making requests |
| `override_http_client_configuration` | `bool` | The value which determines to override properties of the passed Http Client from the sdk user |
| `timeout` | `float` | The value to use for connection timeout. <br> **Default: 60** |
| `max_retries` | `int` | The number of times to retry an endpoint call if it fails. <br> **Default: 0** |
| `backoff_factor` | `float` | A backoff factor to apply between attempts after the second try. <br> **Default: 2** |
| `retry_statuses` | `Array of int` | The http statuses on which retry is to be done. <br> **Default: [408, 413, 429, 500, 502, 503, 504, 521, 522, 524, 408, 413, 429, 500, 502, 503, 504, 521, 522, 524]** |
| `retry_methods` | `Array of string` | The http methods on which retry is to be done. <br> **Default: ['GET', 'PUT', 'GET', 'PUT']** |

The API client can be initialized as follows:

```python
from restapisdk.restapisdk_client import RestapisdkClient
from restapisdk.configuration import Environment

client = RestapisdkClient(
    content_type='application/json',
    accept_language='application/json',
    access_token='AccessToken',
    environment=Environment.PRODUCTION,
    base_url = 'https://localhost:443',)
```

## Authorization

This API uses `OAuth 2 Bearer token`.

## Guide
[API Reference](https://github.com/thoughtspot/rest-api-sdk/blob/main/Python/doc/README.md)

## Live Playground
We recommend that you browse through the [code playground](https://try-everywhere.thoughtspot.cloud/v2/#/everywhere/api/rest/playgroundV2) before you start constructing your API requests. The playground offers an interactive portal with comprehensive information about the API endpoints, request and response workflows.
nfractal 0.3.0
Create Fractals Using Complex-Valued Neural Networks!

# Neural Fractal
A Library for Visual Exploration of Dynamical Systems defined by Neural Networks

[Home Page](https://amirabbasasadi.github.io/neural-fractal/)  
![A fractal Generated by Neural Fractal](https://user-images.githubusercontent.com/8543469/148009825-7c22991e-e33f-4efb-be6f-69db5fc15001.png)  
The dynamical system that generated the above fractal has been explained in the documentation.  

## Features
- Define Dynamical Systems Using Complex-Valued Neural Networks
- GPU Support for Accelerated Sampling and Rendering
- Pseudo-Coloring Utilities
- Built on Top of PyTorch

## About the package
Neural Fractal has been developed for exploring the properties of dynamical systems defined by neural networks and specially complex-valued neural networks. Fractals are visualizations of Chaos. They have infinite self-similar patterns. One way to generate fractals is by applying a function repeatedly on a set of points and keeping the points that do not diverge to infinity. Interestingly, Even dynamical systems constructed by simple functions in this way can generate amazing fractals. But what happens if instead of a simple function we use a neural network? Repeatedly applying a neural network is equivalent to a recurrent neural network which is able to model complicated non-linear dynamical systems. Reservoir computing has demonstrated even completely random RNNs can construct strange and interesting dynamics. This package is an attempt to explore the strange and beautiful world of fractals

## Installation
```
pip install nfractal
```

## Quick Start
See the [Home Page](https://amirabbasasadi.github.io/neural-fractal/)


## Features Under Development
- Automatic pseudo-coloring
- Gnerating zoom animations

## Main Contributors
- Amirabbas Asadi, Independet AI and computer science researcher