semvec/pypi-packages huggingface dataset:
kpindex 2.1.0
Very simple package for obtaining the kp index data

# kpindex
Very simple package for obtaining the planetary Kp index data (see 
https://www.gfz-potsdam.de/en/kp-index/ for more information)

## Installation

This package depends on the following:

* numpy
* RecarrayTools
* PyFileIO

which are all available on PyPI.

Installation is simple and can be done in one of four ways:

### Method 1

This method simply uses the Python `pip3` command to download this 
module and its dependencies:

```pip3 install kpindex --user``` 

### Method 2

This method uses the Python wheel on the "releases" page of this 
repository. Download the wheel, then isntall using `pip3`:

```pip3 install kpindex-0.0.1-py3-none-any.whl --user```

### Method 3

Don't trust my prepackaged stuff? OK, clone this repository and build
your own:

```
git clone https://github.com/mattkjames7/kpindex.git
cd kpindex
python3 setup.py bdist_wheel
pip3 install dist/kpindex-0.0.1-py3-none-any.whl --user
```

### Method 4

So you don't like wheels? Fine. Clone the repository and just move the
"kpindex" folder to your `$PYTHONPATH`.

## Post-Install

In order for the module to be able to download the Kp index data from
the FTP site, you will need to point it in the direction of a directory
where you have read and write access using the `$KPDATA_PATH`
environment variable. This can be done either by running the following
in the terminal before starting Python, or inserting it into your 
`~/.bashrc` file:

```
export KPDATA_PATH=/path/to/the/data
```

## Usage

Using this module is very simple: the first time you run it you will 
need to update the database (also when you think the database is out of 
date) e.g.

```python
import kpindex
kpindex.UpdateLocalData()
```

It may take a couple of minutes to download the data and convert it, 
then you are ready to read the data:

```python
data = kpindex.GetKp(Date)
```

where `Date` could be `None`, in which case ALL of the Kp indices ever
will be returned; `Date` could be a single date in the format yyyymmdd,
in which case only Kp indices fromt hat date will be returned; finally
it could be a two element array/list/tuple containing two dates, in this
case it will return all the indices from the start to the end date.


Enjoy!
scoremipsum 0.0.9
SCOREM - generate sportsball score data for testing and demos (aka scoremipsum)
Requires Python: >=3.9
Keywords: scoremipsum, score, sports, sportsball, generation

[![Pytests](
https://github.com/cjstaples/scorem-ipsum/actions/workflows/python-app.yml/badge.svg?branch=master
)](https://github.com/cjstaples/scorem-ipsum/actions/workflows/python-app.yml)
# scoremipsum
    SCOREMIPSUM

    Generate sports-like scores and statistics 
    for use in data testing or as content filler. 

# -------------------------------------------------------------------
# DISCLAIMER
    Versions of package [ scoremipsum ] below v1.0.0 are considered pre-release.
    Features are subject to revision and may change or be removed entirely.

    This code is not intended for any production use.
    May contain bugs or unexpected behavior - use at your own risk. 

    Please send feedback or code issues to Chuck Staples [ cjstaples@gmail.com ]
# -------------------------------------------------------------------

# features (planned for initial review / v0.0.x and release / v1.0)
    * display help
        - ops.help()
    * display available commands
        - ops.commands()
    * get list of supported sports (anyball, baseball, basketball, hockey, football)
        - ops.sports()
    * generate pseudo-random scores that are generally realistic for sport
        - adjust generated scores to reduce incidence of ties
    * produce game results object in JSON format 
    * get sample set of scores with minimal input
        - ops.game()
    * get basic set of scores for any supported sport with a minimal call
        - ops.game(gametype='football')
        - ops.game(gametype='hockey')

# features (planned for release / v1.1)
    . support input parameter for sport
    . support input parameter for number of games
 
# features (planned for release / v1.2)
    . run module via entry point
    . support command line options for gametype, number of games, schedule set
    . support schedule home / away values
HanTa 1.1.2
Hannover Tagger: Morphological Analysis and POS Tagging

# HanTa - The Hanover Tagger 

HanTa is a pure Python package for lemmatization and POS tagging of Dutch, English and German sentences. The approach is to some extent language indpendent and language models for more langauges will be added in future.

Lemmatization and POS tagging are based on the morphological analysis of a word. The morphological analysis is done by an Hidden Markov Model that tries to find the best sequence of morphemes underlying each word.

## Usage

First a model has to be loaded:

```python

from HanTa import HanoverTagger as ht

tagger_de = ht.HanoverTagger('morphmodel_ger.pgz')
tagger_nl = ht.HanoverTagger('morphmodel_dutch.pgz')
tagger_en = ht.HanoverTagger('morphmodel_en.pgz')

```
Now we have three methods to anaylze words and sentences:

```python
tagger_en.tag_word('eating')
```

will give a list of all possible parts of speech (PoS) for the word *eating* together with a probability score.

```python
tagger_en.tag_word('eating')
```

The function analyze gives the most likely PoS and the lemma (VBG and *eat* in the exmaple below).

```python
tagger_en.analyze('unhappiest')
```

Using various optional parameters we can get more information like e.g. a list of morphemes:

```python
tagger_nl.analyze('huishoudhulpje',taglevel=3)
```

The last call producses the following output:

```
('huishoudhulp', [('huis', 'N(soort,onz)'), ('houd', 'WW'), ('hulp', 'N(soort,zijd)'), ('je', 'SUF_DIM')], 'N(soort,ev,dim,onz,stan)')
```

The package also contains a simple trigram based PoS tagger, that uses the probabilities from the morphological analysis for unknown words (and infrequent words from he training data).


```python
import nltk
from pprint import pprint

sent = "Die Europawahl in den Niederlanden findet immer donnerstags statt."

words = nltk.word_tokenize(sent)
lemmata = tagger_de.tag_sent(words)
pprint(lemmata)
```

## Further reading
For more information refer to the following resources:

* The main documentation: The Hanover Tagger (Version 1.1.0) - Lemmatization, Morphological Analysis and POS Tagging in Python. Hannover, 2023 [Online Available](https://doi.org/10.25968/opus-2457)
* Demo.ipynb on GitHub (https://github.com/wartaal/HanTa)
* Original publication: Christian Wartena. A probabilistic morphology model for German lemmatization.
In *Proceedings of the 15th Conference on Natural Language Processing
(KONVENS 2019): Long Papers*, pages 40â€“49, Erlangen, Germany, 2019.
German Society for Computational Linguistics & Language Technology. [Online Available](https://doi.org/10.25968/opus-1527)
loftyfamily 0.2
A python library focused on providing up to date insights and information on Hazel Lofty
Requires Python: >=3.6

# My Python Library

This is a Python library that provides up-to-date insights and information on Hazel Lofty

## Installation

To install the library, you can use pip:

```     
pip install loftyfamily
```

## Usage

Here is a simple example of how to use the library:

```python
from loftyfamily import Hazel

# Create an instance of Hazel
hazel = Hazel()

# Get age
age = hazel.getage()
print(f"Hazel's age: {age}")

# Get employment status
employment_status = hazel.getemploymentstatus()
print(f"Hazel's employment status: {employment_status}")
```

## Contributing

If you would like to contribute to the library, please follow these steps:

1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make your changes and commit them (`git commit -m 'Add new feature'`).
4. Push to the branch (`git push origin feature-branch`).
5. Create a new Pull Request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.