semvec/pypi-packages huggingface dataset:
aiohere 2.2.0
Asynchronous Python client for the HERE API
License: MIT
Requires Python: >=3.9
Keywords: aiohttp,asyncio,here

# aiohere

Asynchronous Python client for the HERE API

Based on [herepy](https://github.com/abdullahselek/HerePy)

[![GitHub Actions](https://github.com/eifinger/aiohere/workflows/CI/badge.svg)](https://github.com/eifinger/aiohere/actions?workflow=CI)
[![PyPi](https://img.shields.io/pypi/v/aiohere.svg)](https://pypi.python.org/pypi/aiohere)
[![PyPi](https://img.shields.io/pypi/l/aiohere.svg)](https://github.com/eifinger/aiohere/blob/master/LICENSE)
[![codecov](https://codecov.io/gh/eifinger/aiohere/branch/master/graph/badge.svg)](https://codecov.io/gh/eifinger/aiohere)
[![Downloads](https://pepy.tech/badge/aiohere)](https://pepy.tech/project/aiohere)

## Installation

```bash
pip install aiohere
```

## Usage

```python
from aiohere import AioHere, WeatherProductType

import asyncio

API_KEY = ""


async def main():
    """Show example how to get weather forecast for your location."""
    async with AioHere(api_key=API_KEY) as aiohere:
        response = await aiohere.weather_for_coordinates(
            latitude=49.9836187,
            longitude=8.2329145,
            products=[WeatherProductType.FORECAST_7DAYS_SIMPLE],
        )
        lowTemperature = response["dailyForecasts"][0]["forecasts"][0]["lowTemperature"]
        highTemperature = response["dailyForecasts"][0]["forecasts"][0][
            "highTemperature"
        ]
        weekday = response["dailyForecasts"][0]["forecasts"][0]["weekday"]

        print(
            f"Temperature on {weekday} will be between {lowTemperature}°C and {highTemperature}°C"
        )


if __name__ == "__main__":
    loop = asyncio.new_event_loop()
    loop.run_until_complete(main())
```

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pymlimageutils 0.1.0
This package is useful for checking image data quality, separating image and label files, and splitting datasets.
License: MIT
Requires Python: >=3.8,<4.0
Keywords: machine learning,utilities

## mlutils
This package is useful for checking image data quality, separating image and label files, and splitting datasets.

#### Requirements
The package requires the following dependencies to be installed:
   - os
   - PIL (Python Imaging Library)
   - cv2 (OpenCV)
   - random
   - shutil

#### Installation
you can install mlutils via pip:
``
pip install mlutils
``
#### Usage
##### Check for corrupted images
Use the check_corrupted_images_in_directory(dir_in) function to check for corrupted images in a directory. It returns the number of corrupted images found.
```python
from mlutils import check_corrupted_images_in_directory
check_corrupted_images_in_directory("/path/to/images/folder")
```
#####Check image naming conventions
Use the check_naming_conventions(dir_in) function to check and fix image naming conventions in a directory. This function renames images with upper-case file extensions to lower-case and changes "jpeg" to "jpg" file extensions.
```python
from mlutils import check_naming_conventions
check_naming_conventions("/path/to/images/folder")
```
####Detect and fix images with a premature ending
Use the detect_and_fix_premature_ending(dir_path) function to detect and fix images with a premature ending in a directory. It returns the number of fixed images.
```python
from mlutils import detect_and_fix_premature_ending
detect_and_fix_premature_ending("/path/to/images/folder")
```
####Separate and copy files
Use the separate_and_copy_files(folders, dest_main_folder) function to separate image and label files from multiple folders and copy them to a new destination folder.
```python
from mlutils import separate_and_copy_files
folders = ["/path/to/1st/images/folder", "/path/to/2nd/images/folder"]
dest_main_folder = "/path/to/destination/folder"
separate_and_copy_files(folders, dest_main_folder)
```
####Split a dataset
Use the split_dataset(images_path, labels_path, img_val_path, label_val_path, split_fraction) function to split a dataset into training and validation sets. The function moves a fraction of images and their corresponding label files to validation folders.
```python
from mlutils import split_dataset
images_path = "/path/to/training/images"
labels_path = "/path/to/training/labels"
img_val_path = "/path/to/validation/images"
label_val_path = "/path/to/validation/labels"
split_fraction = 0.2 # 20% of the data will be moved to validation set
split_dataset(images_path, labels_path, img_val_path, label_val_path, split_fraction)
```
data-repos-cars 1.0.0
A cars dataset included in the DataRepos namespace package
License: MIT
Requires Python: >=3.7
Keywords: namespace,data,cars

[![What's a Python Namespace Package, and What's It For?](https://raw.githubusercontent.com/realpython/data-repos/main/namespace_package.jpg)](https://realpython.com/python-namespace-package/)

# DataReposCars - A Cars Dataset for DataRepos

This package provides an example cars-dataset for [DataRepos](https://pypi.org/project/data-repos/).

To use DataReposCars, you should install it into your virtual environment:

```shell
(venv) $ python -m pip install data-repos-cars
```

You can then read the data as a regular DataRepos dataset:

```python
>>> from data_repos import read
>>> read.data("cars")
...
```

Learn more about DataRepos, DataReposCars, and namespace packages in [What's a Python Namespace Package, and What's It For?](https://realpython.com/python-namespace-package/)
data-repos 1.0.0
A namespace package for data files
License: MIT
Requires Python: >=3.7
Keywords: namespace,data

[![What's a Python Namespace Package, and What's It For?](https://raw.githubusercontent.com/realpython/data-repos/main/namespace_package.jpg)](https://realpython.com/python-namespace-package/)

# DataRepos - A Namespace Package for Data Files

This package provides a namespace intended for data files.

It lets you read data files from a common package, but with the actual data distributed over multiple packages.

For example, if you have various data files that are very large (on the order of hundreds of megabytes or larger), then you can divide the data files into different packages, but they'll be callable from the same namespace in your Python code.

To use DataRepos, you should install it into your virtual environment:

```shell
(venv) $ python -m pip install data-repos
```

You can now import DataRepos as `data_repos`.

## Read Data Files

DataRepos provides a `read()` function that you can use to read data files. The data are returned as a [pandas](https://realpython.com/pandas-python-explore-dataset/) DataFrame:

```python
>>> from data_repos import read
>>> read.data("countries")
              country   population
0             Austria      8840521
1              Canada     37057765
2                Cuba     11338138
3  Dominican Republic     10627165
4             Germany     82905782
5              Norway      5311916
...
```

## Install Data Files

You can install other data files from PyPI with pip. Other cooperating [DataRepos](https://pypi.org/search/?q=data%2Drepos) packages can be installed and will integrate smoothly:

```shell
(venv) $ python -m pip install data-repos-cars
```

You can then read the cars dataset with the exact same syntax:

```python
>>> from data_repos import read
>>> read.data("cars")
...
```

Because DataRepos is a namespace package, it can be extended on the fly.

## Available Data Files

Two datasets are included as examples:

- **`iris`**: The classical [Iris dataset](https://en.wikipedia.org/wiki/Iris_flower_data_set), originally [published](https://doi.org/10.1111/j.1469-1809.1936.tb02137.x) by Ronald Fisher in 1936
- **`countries`**: Countries and their population, collected by [Samayo](https://github.com/samayo/country-json)

You can read these files with `read.data("iris")` and `read.data("countries")`, respectively.

## Add Your Own Data Files

You can also add your own data files by storing them in a folder named `data_repos` that's on Python's path.

See examples of how to do this and learn more about namespace packages in [What's a Python Namespace Package, and What's It For?](https://realpython.com/python-namespace-package/)