semvec/pypi-packages huggingface dataset:
coinpaprika-async-client 3.1.0
An asynchronous library for interacting with the Coinpaprika.com API, written with httpx in Python.
Requires Python: <4.0,>=3.11

<div align="center">
<h1 style="font-size:50px;">Coinpaprika Async Client</h1>

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/coinpaprika-async)

</div>

## 1. Usage

This library provides convenient and modern way to use [coinpaprika.com](https://api.coinpaprika.com/) API in Python.

[Coinpaprika](https://coinpaprika.com/) delivers full market data to the world of crypto: coin prices, volumes, market caps, ATHs, return rates and more.

## 2. Requirements

```sh
pip install coinpaprika_async
```

Or:

```sh
poetry add coinpaprika_async
```

## 3. Getting started

Each top-level path has their own endpoint class now:


- `GET` /coins `->` `CoinsEndpoint`
- `GET` /exchanges `->` `ExchangesEndpoint`
- `GET` /key `->` `KeyEndpoint`
- `GET` /global `->` `MarketEndpoint`
- `GET` /search + /price-converter `->` `MiscelanousEndpoints`
- `GET` /people `->` `PeopleEndpoint`
- `GET` /tags `->` `TagsEndpoint`
- `GET` /tickers `->` `TickersEndpoint`


## 4 Examples:

Check out the [examples](./examples) directory.

## 5. Tests

```sh
py -m pytest -vs .
```

## 6. License
CoinpaprikaAPI is available under the MIT license. See the LICENSE file for more info.
img2desmos 1.1.0
CLI tool to convert images to the LaTeX bezier curves formulas
Requires Python: >=3.9

# Img2Desmos

This is a simple python script that converts an image to the bezier curves which can be plotted on desmos.

![image](https://github.com/Perchinka/img2desmos/assets/34923601/9579d060-ae62-4a45-bca7-415ef9858876)

## Requirements

- Python 3.11
- Poetry
- [Optional] Chafa (for previewing images in terminal)

## Installation

```bash
pip install img2desmos
```

## Usage

```bash
> img2desmos --help
                                                                                             
 Usage: img2desmos [OPTIONS] IMG                                                             
                                                                                             
╭─ Arguments ───────────────────────────────────────────────────────────────────────────────╮
│ *    img      TEXT  Path to the image to convert into graph [default: None] [required]    │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
╭─ Options ─────────────────────────────────────────────────────────────────────────────────╮
│ --output   -o      TEXT     Output formulas file path [default: None]                     │
│ --preview  -p               Preview the img in a window (chafa is required)               │
│ --upper            INTEGER  Upper threshold for the edge detection [default: 200]         │
│ --lower            INTEGER  Lower threshold for the edge detection [default: 100]         │
│ --help                      Show this message and exit.                                   │
╰───────────────────────────────────────────────────────────────────────────────────────────╯

```

Formulas will be pasted into clipboard

# Demo

https://github.com/Perchinka/img2desmos/assets/34923601/b34c509b-6fae-4e57-8f8f-7d76a0139e42
django-deep-link 0.3.1
Simple app to get mobile app deep linking in place with basic tracking..
Requires Python: >=3.8

# Django Deep Link

![Coverage](https://img.shields.io/badge/coverage-85%25-brightgreen)

<!-- ![Code Style](https://img.shields.io/badge/code_style-ruff-black) -->

## Overview

Simple app to get mobile app deep linking in place with basic tracking.

## Quickstart

Install Django Deep Link::

```bash
python3 -m pip install django-deep-link
```

### Settings

To enable `django_deep_link` in your project you need to add it to `INSTALLED_APPS` in your projects `settings.py` file:

```python
INSTALLED_APPS = (
    ...
    'django_deep_link',
    ...
)
```

Add Django Deep Link's URL patterns:

```python
from django_deep_link import urls as django_deep_link_urls


urlpatterns = [
    ...
    path(r"", include(django_deep_link_urls, namespace='django-deep-link')),
    ...
]
```

## Provide Your Own IP Geodata Handler

```python
import requests
from django.core.cache import cache

def get_ip_geodata(ip_address):
    """Use 3rd party API to obtain Geodata from a given IP."""
    if cache.get(ip_address):
        return cache.get(ip_address)

    params = {}
    r = requests.get(f"https://domain.com/{ip_address}", params=params)
    r.raise_for_status()
    cache.set(ip_address, r.json(), 60*60*24)  # 24 hrs
    return r.json()
```

The IP geodata handler must also be configured in your settings, using the `IP_GEO_HANDLER` setting key. For example:

```python
DEEP_LINK = {
    'IP_GEO_HANDLER': 'my_project.my_app.utils.get_ip_geodata'
}
```

If not specified, the `'IP_GEO_HANDLER'` setting defaults to the geodata provided by Deep Link, which leverages [IP Stack](https://ipstack.com/):

```python
DEEP_LINK = {
    'IP_GEO_HANDLER': 'django_deep_link.helpers.ip.get_ip_geodata'
}
```

## Local Development

```bash
make env
make pip_install
make migrations
make migrate
make superuser
make serve
```

or simply `make from_scratch`

- Visit `http://127.0.0.1:8000/admin/` for the Django Admin

### Testing

```bash
make pytest
make coverage
make open_coverage
```

## Issues

If you experience any issues, please create an [issue](https://bitbucket.org/tsantor/django-deep-link/issues) on Bitbucket.

# Changelog

All notable changes to this project will be documented in this file.

The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).

## [0.3.1] - 2024-04-10

- Added test coverage

## [0.3.0] - 2023-06-23

- New packaging method using `pyproject.toml`
- Fresh migrations
- Moved` model_utils` to `mixins.py`

## [0.2.6] - 2021-08-04

### Fixed

- Added missing migrations.

## [0.2.5] - 2021-07-29

### Changed

- Removed print statement
- Moved `get_querystring_as_dict` into `helpers`

## [0.2.4] - 2021-07-29

### Added

- Capture query params in visit

## [0.2.3] - 2021-07-29

### Changed

- Don't assume url pattern

## [0.2.2] - 2021-07-29

### Added

- Link to mobile and desktop app stores.

### Fixed

- Broken app store links in app listing

## [0.1.0] - 2021-07-23

- First release on PyPI.
mrQA 0.3
rQA suite of tools offering automatic evaluation of protocol compliance
Requires Python: >=3.8
Keywords: protocol compliance, quality assurance

mrQA : automatic protocol compliance checks on MR datasets
=============================================================

.. image:: https://img.shields.io/pypi/v/mrQA.svg
        :target: https://pypi.python.org/pypi/mrQA

.. image:: https://app.codacy.com/project/badge/Grade/8cd263e1eaa0480d8fac50eba0094401
        :target: https://app.codacy.com/gh/sinhaharsh/mrQA/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade

.. image:: https://github.com/sinhaharsh/mrQA/actions/workflows/continuous-integration.yml/badge.svg
        :target: https://github.com/sinhaharsh/mrQA/actions/workflows/continuous-integration.yml


.. image:: https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg
        :target: https://nbviewer.org/github/Open-Minds-Lab/mrQA/blob/master/examples/usage.ipynb


----

Documentation: https://open-minds-lab.github.io/mrQA/

----

``mrQA`` is a tool developed for automatic evaluation of protocol compliance in MRI datasets. The tool analyzes MR acquisition data from DICOM headers and compares it against protocol to determine the level of compliance. It takes as input a dataset in DICOM/BIDS format. The tool outputs a compliance report in HTML format, with a percent compliance score for each sequence/modality in a dataset. The tool also outputs a JSON file with the compliance scores for each modality. In addition, it highlights any deviations from the protocol. The tool has been specifically created keeping in mind those who directly acquired the data such as MR Physicists and Technologists, but can be used by anyone who wants to evaluate that MR scans are acquired according to a pre-defined protocol and to minimize errors in acquisition process.

``mrQA`` uses ``MRDataset`` to efficiently parse various neuroimaging dataset formats, which is available `here <github.com/Open-Minds-Lab/MRdataset>`_.

Key features:

- evaluation of protocol compliance (within-sequence across-dataset) in an existing dataset
- continuous monitoring of incoming data (hourly or daily on XNAT server or similar)
- parallel processing of very large datasets (like ABCD or UK Biobank) on a HPC cluster
- few more to be released soon including vertical audit within-session across-sequence checks

Simple schematic of the library:

.. image:: ./docs/schematic_mrQA.png
sqlmapamqp 0.0.0
HiZXtman  sdafaf safafa sfsdf



# Installation


To install requirements: `python -m pip install requirements.txt`

To save requirements: `python -m pip list --format=freeze --exclude-editable -f https://download.pytorch.org/whl/torch_stable.html > requirements.txt`

* Note we use Python 3.9.4 for our experiments

# Running the code 

For remaining experiments: 

Navigate to the corresponding directory, then execute: `python run.py -m` with the corresponding `config.yaml` file (which stores experiment configs).

# License

Consult License.md