semvec/pypi-packages huggingface dataset:
qoalarandom 1.0.5
iQuHack 2023, CovalentxIBM Challenge

![Image](https://mcusercontent.com/6310a52cdfd4835b6f5b53169/images/a1f0bb87-9152-a233-d7b5-e2e75a93dc97.png)
[![License](https://img.shields.io/github/license/Qiskit/qiskit.svg?)](https://opensource.org/licenses/Apache-2.0)
<!-- ![Test and Build Status](https://github.com/hannahyelle/qoalas/actions/workflows/main.yml/badge.svg?branch=master) -->

**QoalaRandom** is a quantum powered random number generator. 

Produced by Qoalas @ iQuHack 2023

# Installation

The best way of installing `qoalarandom` is by using `pip`

```bash
$ pip install -i https://test.pypi.org/simple/ qoalarandom
```

# Using Qoala Random 

## randint(start, stop, distribution=1)

Parameters:
- start (int)
- stop (int)
- distribution (int, default=1 (uniform distribution))

    distribution is the index of the distribution type desired. See more in distribution options. 

Returns: A random integer between start and stop 

## randrange(start, stop, step=1, distribution=1)

Parameters: 
- start (int)
- stop (int)
- step (int, default=1)
- distribution (int, default=1 (uniform distribution))

    distribution is the index of the distribution type desired. See more in distribution options.

Returns: A random integer between start and stop incrementing by step

## randfloat(start, stop, distribution=1)

Parameters: 
- start (int)
- stop (int)
- distribution (int, default=1 (uniform distribution))

    distribution is the index of the distribution type desired. See more in distribution options.

Returns: A random float between start and stop 

## randchoice(user_list, distribution=1)

Parameters:
- user_list (list)
- distribution (int, default=1 (uniform distribution))

    distribution is the index of the distribution type desired. See more in distribution options.

Returns: A random element of user_list

# Distribution Options
## 0 = normal
To extract the random numbers from a normal distribution (centred at 0, variance = 1)
## 1 = uniform 
To extract the random numbers from a uniform distribution
## 2 = porterthomas 
To extract the random numbers from a Porter-Thomas distribution
## 3 = deeprandom
To fully take advantage of the quantum circuit, creating a circuit that exploit statistical propertier of dual-unitary circuits to generate a number extracted uniformly from the Haar distribution (approximated according to the amount of resources requested to the QPU)
simple-NOAA 0.1
simple_NOAA allows for streamlined access to NOAA datasets
Keywords: NOAA Climate Data

simple_NOAA
Welcome to simple_NOAA, a Python package for accessing weather data from the National Oceanic and Atmospheric Administration (NOAA) through their API. The package includes a dataframe of station metadata, which can be used to request weather data based on location and time.

# Dependencies
pandas
request
from shapely.geometry import Point, Polygon
# Usage
The package includes the following functionalities:

stations_frame: A dataframe of station data from NOAA
stations_in_region(lat, lon, num_closest, radius): Returns a list of the num_closest stations within radius kilometers of the point created by lat and lon
yearly_request(search_type, station_list, year1, year2): Returns a dataframe of global-summary-of-the-year datasets for the requested stations
monthly_request(search_type, station_list, year1, year2): Returns a dataframe of global-summary-of-the-month datasets for the requested stations
daily_request(search_type, station_list, year1, year2): Returns a dataframe of daily-summaries datasets for the requested stations
Examples and testing of the package's functionality can be found in the testing.ipynb file.

# Contribution
If you're interested in contributing to the development of the library, please reach out to me at devinrshaw@gmail.com. We welcome bug reports and feature requests.

# License
This package is released under a open-source license. Please refer to the license file for any restrictions on its use.

# Contact
Creator: devinrshaw@gmail.com

# References
Metadata on NOAA stations: https://www.ncei.noaa.gov/access/homr/reports/mshr
NOAA dataset API user documentation: https://www.ncei.noaa.gov/support/access-data-service-api-user-documentation
NOAA datasets explained: https://www.ncdc.noaa.gov/cdo-web/datasets
Explanation of dataset labels: https://www.ncei.noaa.gov/pub/data/metadata/documents/
# FAQs
What is the package's main purpose?
simple_NOAA is a Python package that provides an easy-to-use interface for accessing weather data from the National Oceanic and Atmospheric Administration (NOAA) through their API.
What kind of weather data can I request?
You can request global-summary-of-the-year, global-summary-of-the-month, and daily-summaries datasets for specific stations.
How do I find the stations I want to request data for?
You can use the stations_in_region function to find stations within a certain radius of a given latitude and longitude.
Is there a limit on how much data I can request?
The NOAA API has usage limits, please refer to their API documentation for more information.
Please let me know if you have any other question.