semvec/pypi-packages huggingface dataset:
django-public-admin 0.0.6
A public read-only version of the Django Admin
License: MIT
Requires Python: <4.0,>=3.9

[![PyPI](https://img.shields.io/pypi/v/django-public-admin)](https://pypi.org/project/django-public-admin/) [![Documentation Status](https://readthedocs.org/projects/django-public-admin/badge/?version=latest)](https://django-public-admin.readthedocs.io/en/latest/?badge=latest) [![PyPI - Python Version](https://img.shields.io/pypi/pyversions/django-public-admin)](https://pypi.org/project/django-public-admin/) [![PyPI - Django Version](https://img.shields.io/pypi/djversions/django-public-admin)](https://pypi.org/project/django-public-admin/)

# Django Public Admin


A public and read-only version of the [Django Admin](https://docs.djangoproject.com/en/3.0/ref/contrib/admin/). A drop-in replacement for Django's native `AdminSite` and `ModelAdmin` for publicly accessible data. Check the [documentation](https://django-public-admin.readthedocs.io/en/latest/?badge=latest) for more information on how to use it.

## Contributing

We use `tox` to Run tests with Python 3.6, 3.7 and 3.8, and with Django 2 and 3. Also we use Black and `flake8`:

```console
$ poetry install
$ poetry run tox
```

### Docs

To build the docs we use [Sphinx](https://www.sphinx-doc.org/en/):

```
$ poetry run sphinx-build docs docs/_build/
```

Them just jump to `docs/_build/index.html`.

## License & Credits

This package is licensed under [MIT license](/LICENSE) and acknowledge [Serenata de Amor](https://github.com/okfn-brasil/serenata-de-amor) (© [Open Knowledge Brasil](https://br.okfn.org) and, previously, © [Data Science Brigade](https://github.com/datasciencebr)).
mime-utils 0.1.1
Some reusable libraries
License: MIT
Requires Python: >=3.8

# Mime Utils

An package containing misc helper libraries for projects

* oauth2user - Support user interaction when logging inn with Oauth2

## Installation

Run the following to install:

```python
pip install mime-utils
```

## Usage

```python
from oauth2user import Oauth2User

TODO: Fill out here
```

# Developing Mime Utils

To install Mime Utils, along with the tools you need to develop and run tests, run the following in your virtualenv:

```bash
$ pip install -e .[dev]
```
prayer-times-calculator-offline 1.0.3
Prayer Times Calculator - Offline
License: MIT

# Prayer Times Calculator - Offline

Not designed as a general purpose prayer time library. This is meant as a cloud-free, drop-in replacement for [`prayer-times-calculator`](https://github.com/uchagani/prayer-times-calculator), which in turn is used by the [Islamic Prayer Times HomeAssistant integration](https://www.home-assistant.io/integrations/islamic_prayer_times/).


Equivalence to the `prayer-times-calculator` library was checked by emperical comparison of 50,000 requests (each with randomized parameters). The original intention was to replicate the output exactly, however please note the following exceptions where `prayer-times-calculator-offline` will return a different result than `prayer-times-calculator` for the same call:

* Asr calculation is stable; at time of writing, AlAdhan's API uses the [API server's local time](https://github.com/islamic-network/prayer-times/blob/f7ed3a2467fbced252a0914c8b202c8b61ca58b9/src/PrayerTimes/PrayerTimes.php#L538) as part of the Asr calculation, causing the returned time to drift as the server's day progresses.
* ISO8601 date components are correct; at time of writing, AlAdhan's API sometime returns ISO8601 timestamps with correct times but incorrect dates.
  * E.g. [this API call](https://api.aladhan.com/v1/timings/28-06-2042?latitude=78.76131061889032&longitude=72.35356426766558&method=21&iso8601=true&school=1&midnightMode=1&latitudeAdjustmentMethod=3&tune=1%2C3%2C-1%2C5%2C4%2C4%2C-2%2C-2%2C1) where all timestamps are returned with the same date component, making Isha appear to happen before Maghrib and Fajr appear to happen after Sunrise
* Portugal Maghrib tuning applied correctly; ~At time of writing, AlAdhan's API maps the [Asr tuning value to Maghrib.](https://github.com/islamic-network/api.aladhan.com/blob/f3d57f7c7b54f5bb9592efdb984fbbea41093f82/api/Utils/PrayerTimesHelper.php#L306)~ Since [fixed in API](https://github.com/islamic-network/api.aladhan.com/pull/75)
* Local time output (`iso8601=False`) is not supported, as it is not required for use in HomeAssistant (which deals only with UTC internally)
* 'None' is supported as a high-latitude adjustment method
* Angles of 0 are supported for the "custom" calculation method; at time of writing, AlAdhan's API silently ignores such angles and uses a different default angle in their place.
* Floating point & trig operations take place on your machine, and may vary slightly from the AlAdhan API server's implementations; there may be 1-minute differences if these computational variations span a rounding break. Hijri date calculation may also be affected, including the 30-minute Isha offset during Ramadan when using the Makkah calculation method.


## Usage

Please refer to the [usage section of `prayer-times-calculator`](https://github.com/uchagani/prayer-times-calculator?tab=readme-ov-file#usage).
pycricketpred 0.1.1
A package for predicting and find interesting cricket related statistics!
License: MIT
Requires Python: <4.0,>=3.9

# pycricketpred
[![codecov](https://codecov.io/gh/DSCI-310-2024/pycricketpred/graph/badge.svg?token=vzmjekckdP)](https://codecov.io/gh/DSCI-310-2024/pycricketpred)

A package for predicting and find interesting cricket related statistics!

## Installation

```bash
$ pip install pycricketpred
```

## Usage

The `pycricketpred` package has 4 modules (`data_wrangling`, `data_cleaning`, `eda` and `modelling`) which perform a wide variety of functions on ball-by-ball cricket data, in the `json` format. 

```
from pycricketpred.data_wrangling import *
from pycricketpred.data_cleaning import *
from pycricketpred.eda import *
from pycricketpred.modelling import *

# parse through json files in a zipped archive and convert the data into parquet format

process_cricket_jsons('data/t20s_json', 'data/t20s_parquet')

# create majority dtype mapping dictionary and create a dataframe from the mapping

majority_mapping = determine_majority_dtypes(['2203.parquet', '21332.parquet'], 'data/t20s_parquet')
apply_dtypes_and_concatenate(['2203.parquet', '21332.parquet'], 'data/t20s_parquet', majority_mapping)

# create histograms for variable distribution, specifying width and height

vis_bar(cricket_df, 'over', 10, 20)

# Create and save histograms for wicket distribution across categories

hist_chart(cricket_df, 'over', 'chart1.png', 'images/')
```
and perform a wide range of modelling tasks, including splitting data, preprocessing, and creating and evaluating classification models using a confusion matrix.

## Contributing

Interested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.

## License

`pycricketpred` was created by DSCI 310 Group 11: Alex Lin, Jackson Siemens, Shruti Vijaykumar Seetharam, Hanlin Zhao. It is licensed under the terms of the MIT license.

## Credits

`pycricketpred` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).
scikeras 0.13.0
Scikit-Learn API wrapper for Keras.
License: MIT
Requires Python: <4,>=3.9.0
Keywords: keras, tensorflow, scikit-learn, deep-learning, python

# Scikit-Learn Wrapper for Keras

[![Build Status](https://github.com/adriangb/scikeras/workflows/Tests/badge.svg)](https://github.com/adriangb/scikeras/actions?query=workflow%3ATests+branch%3Amaster)
[![Coverage Status](https://codecov.io/gh/adriangb/scikeras/branch/master/graph/badge.svg)](https://codecov.io/gh/adriangb/scikeras)
[![Docs](https://github.com/adriangb/scikeras/workflows/Build%20Docs/badge.svg)](https://www.adriangb.com/scikeras/)

Scikit-Learn compatible wrappers for Keras Models.

## Why SciKeras

SciKeras is derived from and API compatible with the now deprecated / removed `tf.keras.wrappers.scikit_learn`.

An overview of the differences as compared to the TF wrappers can be found in our
[migration](https://www.adriangb.com/scikeras/stable/migration.html) guide.

## Installation

This package is available on PyPi:

```bash
# Tensorflow
pip install scikeras[tensorflow]
```

Note that `pip install scikeras[tensorflow]` is basically equivalent to `pip install scikeras tensorflow`
and is offered just for convenience. You can also install just SciKeras with
`pip install scikeras`, but you will need a version of tensorflow installed at
runtime or SciKeras will throw an error when you try to import it.

The current version of SciKeras depends on `scikit-learn>=1.4.1post1` and `Keras>=3.2.0`.

### Migrating from `keras.wrappers.scikit_learn`

Please see the [migration](https://www.adriangb.com/scikeras/stable/migration.html) section of our documentation.

## Documentation

Documentation is available at [https://www.adriangb.com/scikeras/](https://www.adriangb.com/scikeras/).

## Contributing

See [CONTRIBUTING.md](https://github.com/adriangb/scikeras/blob/master/CONTRIBUTING.md)