semvec/pypi-packages huggingface dataset:
atap-corpus 0.1.15
Corpus mini-framework allowing for memory-efficient slicing and provides a standardised base corpus structure for the collection of ATAP tools. 
License: MIT
Requires Python: <3.13,>=3.10

<a href="https://atap.edu.au"><img src="https://www.atap.edu.au/atap-logo.png" width="125" height="50" align="right"></a>  
# ATAP Corpus

Provides a standardised base Corpus structure for ATAP tools.

Different Corpus can be sliced into subcorpus based on different criterias and will always return an subclass
instance of BaseCorpus.
The slicing criteria is flexible, it accepts a user defined function and comes with convenience slicing
operations layered on top of it out-of-the-box.
Subcorpus maintains a parent-child relationship with original corpus in a tree internally.

Corpus can also be serialised and deserialised which can be used to carry across different ATAP analytics notebooks.

```shell
pip install atap_corpus
```

[//]: # (### Extras: Viz:)

[//]: # ()
[//]: # (Out of the box, Corpus also comes with simple and quick visualisations such as word clouds, timelines etc.)

[//]: # ()
[//]: # (```shell)

[//]: # (pip install atap_corpus[viz])

[//]: # (```)

## Tests

To run all the unit tests, there is a script you can execute.

```shell
./scripts/run_tests.sh
```

This repo originated from Juxtorpus and is a decoupling effort.
Juxtorpus repo may be accessed [here](https://github.com/Sydney-Informatics-Hub/juxtorpus).
pysonde 0.0.7
Post-processing tool for atmospheric sounding data
License: MIT
Requires Python: >=3.9

# pySonde: converting radiosonde files to netcdf

pySonde converts Vaisala's radiosonde files (mwx) and Meteomodem soundings (cor) to netCDF4 and interpolates them if needed to a common height grid for easier processing.

## Setup
```
pip install pysonde
```

For development
```sh
# Install dependencies
pipenv install --dev

# Setup pre-commit and pre-push hooks
pipenv run pre-commit install -t pre-commit
pipenv run pre-commit install -t pre-push
```

## Usage

A few example files are automatically installed and can be used to test if the installation was successful

Unix:
```sh
sounding_converter -i examples/level0/BCO_20200126_224454.mwx -o "test_{direction}.nc" -c config/main.yaml
```

Windows:
```sh
sounding_converter.exe -i examples/level0/BCO_20200126_224454.mwx -o "test_{direction}.nc" -c config/main.yaml
```

The configuration of attributes, variable names and units of the input and output is done via yaml files in the `config` folder.

To post-process radiosoundings with pysonde and track the processing steps, a new repository should be created that only contains the `config` folder and its scripts. An additional bash script with the `sounding_converter` calls tracks the processing steps. The version used of pysonde is automatically inserted into the output files.

The [repository containing the processing setup for the circBrazil campaign](https://github.com/observingClouds/soundings_circbrazil) can serve as a template.
pycounts_pjlearn 0.1.0
Calculate word counts in a text file! A learning project of the book Python packages
License: MIT
Requires Python: >=3.9

# pycounts_pjlearn

Calculate word counts in a text file! A learning project of the book Python packages

## Installation

```bash
$ pip install pycounts_pjlearn
```

## Usage

`pycounts` can be used to count words in a text file and plot results
as follows:

```python
from pycounts.pycounts import count_words
from pycounts.plotting import plot_words
import matplotlib.pyplot as plt

file_path = "test.txt"  # path to your file
counts = count_words(file_path)
fig = plot_words(counts, n=10)
plt.show()
```

## 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

`pycounts_pjlearn` was created by Pan Ji. It is licensed under the terms of the MIT license.

## Credits

`pycounts_pjlearn` was created with [`cookiecutter`](https://cookiecutter.readthedocs.io/en/latest/) and the `py-pkgs-cookiecutter` [template](https://github.com/py-pkgs/py-pkgs-cookiecutter).