semvec/pypi-packages huggingface dataset:
algorithmics-utils 0.1.1
Python module to learn

# Algorithmics-test-utils
Utilities for testing different algorithms in the University of Tartu Algorithmics course.

## Install Rust from rustup.rs

Visit [rustup](https://rustup.rs) site and install Rust on your system.

## Create pyvenv
```
python -m venv venv
```

## Activate pyvenv
```
/venv/Scripts/activate
```

## Install requirements
```
pip install -r requirements.txt
```

## Generate package
```
maturin develop
```
llm-autoscoring 0.2.0
A library for AutoScoring LLM extraction processes


# LLM AutoScoring Library

This library provides a comprehensive set of tools for evaluating the performance of Language Model (LLM) extraction processes. It includes various scoring mechanisms and an easy-to-use evaluation framework.

## Installation

```bash
pip install llm_autoscoring
```

## Usage

Here's a quick example of how to use the library:

```python
from llm_autoscoring import Evaluator, ExactMatchScorer, RegexScorer, MultiSelectScorer, OpenAIScorer

# Define scorers
scorers = {
    "name": ExactMatchScorer(),
    "date": RegexScorer(r"\d{4}-\d{2}-\d{2}"),
    "fruits": MultiSelectScorer(),
    "description": OpenAIScorer("your-api-key-here")
}

# Create evaluator
evaluator = Evaluator(scorers)

# Define reference and candidate data
references = {
    "name": "John Doe",
    "date": "2023-05-15",
    "fruits": "apple,banana,cherry",
    "description": "A tall man with brown hair"
}
candidates = {
    "name": "John Doe",
    "date": "2023-05-15",
    "fruits": "apple,cherry,date",
    "description": "A man of average height with dark hair"
}

# Evaluate
results = evaluator.evaluate(references, candidates)
print(results)
```

## Available Scorers

- `ExactMatchScorer`: Checks for exact matches between reference and candidate strings.
- `RegexScorer`: Checks if the candidate string matches a given regular expression pattern.
- `OpenAIScorer`: Uses OpenAI's API to judge the similarity between reference and candidate strings.
- `SemanticScorer`: Computes semantic similarity between reference and candidate strings using spaCy.
- `F1Scorer`: Calculates the F1 score for text comparison.
- `DateScorer`: Checks if dates match, with flexible parsing.
- `MultiSelectScorer`: Evaluates multi-select fields, providing F1 score and detailed metrics.
RaincloudPlot 0.1.0
A package for creating raincloud plots
Requires Python: >=3.6

# RaincloudPlot

RaincloudPlot is a Python package that provides a convenient function to create raincloud plotsâ€”a combination of density plots, box plots, and strip plots that provide a detailed visualization of data distributions.

## Features

- **Raincloud Plots**: Visualize the distribution of your data in a detailed and intuitive way.
- **Customization Options**: Easily customize the plot with options for titles, labels, colors, orientation, and more.
- **Thunder Mode**: Connect the means of each group with a point plot to emphasize differences between groups.

## Installation

You can install the RaincloudPlot package directly from PyPI:

```sh
pip install RaincloudPlot
```

## Usage

Here's how you can use the `create_raincloud_plot` function:

```python
import pandas as pd
from RaincloudPlot import create_raincloud_plot

# Sample DataFrame
df = pd.DataFrame({
    'city': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix'],
    'temp': [75, 80, 72, 90, 85]
})

# Create a raincloud plot
create_raincloud_plot(
    df=df, 
    x='city', 
    y='temp', 
    title='Raincloud Plot of Temperatures by City', 
    xlabel='City', 
    ylabel='Temperature (Â°F)', 
    thunder=True
)
```

## Parameters

- **df**: `DataFrame` containing the data to be plotted.
- **x**: Column name for the x-axis data.
- **y**: Column name for the y-axis data.
- **title**: Title of the plot.
- **xlabel**: Label for the x-axis (defaults to the `x` column name).
- **ylabel**: Label for the y-axis (defaults to the `y` column name).
- **palette**: Color palette for the plot (default: `'Set2'`).
- **alpha**: Transparency level for the plot elements (default: `0.6`).
- **orient**: Orientation of the plot, `'h'` for horizontal or `'v'` for vertical (default: `'h'`).
- **figsize**: Size of the figure (default: `(10, 10)`).
- **thunder**: Whether to connect the means of each group with a point plot (default: `False`).

## Example

```python
create_raincloud_plot(
    df=df, 
    x='city', 
    y='temp', 
    title='Raincloud Plot of Temperatures by City', 
    xlabel='City', 
    ylabel='Temperature (Â°F)', 
    thunder=True
)
```

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

## Contact

- **Name**: Yosri Ben Halima
- **Email**: yosri.benhalima@ept.ucar.tn
- **GitHub**: [Yosri-Ben-Halima](https://github.com/Yosri-Ben-Halima)
karyoplot 0.0.3
karyoplot: annotations along the genome

# karyoplot

## What is karyoplot ?

``karyoplot`` is a python package developed to draw simple karyoplot/ideogram with a simple configuration file like Circos [1], without any lines of code like karyoploteR [2]. 
``karyoplot`` deals with conventional bioinformatics formats (.bed, .fasta, .gff, .gtf, .vcf, ...) to avoid file manipulations before drawing. 

## How does it work ?

``karyoplot`` takes as an argument a configuration file with different sections which will have a sequence Fasta file as reference for ideograms. Every plot to draw on reference sequences is described in a track section with link to associated data and drawing options.
``karyoplot`` is mainly based on ``matplotlib`` for graph rendering and pysam for bioinformatics file format parsing.

``karyoplot`` is under developpement, currently supported format are:

* fasta
* bed

available soon:

* gff
* vcf
* bam
* wig/bigWig

## Install

**create your virtual env in a dedicated directory**
```
python3 -m venv $HOME/myvenvs/karyoplot
```
**set up the new env**
```
source $HOME/myvenvs/karyoplot/bin/activate
```
**install karyoplot**
``` 
pip install karyoplot 
```
**enjoy**
```
karyoplot -h
```
**to leave the env**
```
deactivate
```

## Usage

**Simple usage**

```
karyoplot config.ini -o genome_karyo.png -v 2
```

The `config.ini` file is a configuration file in Microsoft Windows INI style, readable by the [configparser](https://docs.python.org/3/library/configparser.html) Python module.
Some sections are expected with defined options to customize your plot. See the associated documentation [here](https://bioger.pages.mia.inra.fr/karyoplot/) for full options and required sections/options.

![karyoplot](https://forgemia.inra.fr/bioger/karyoplot/raw/dev/img/typical_karyoplot.png)

## Support
For any request please contact nicolas.lapalu[at]inrae.fr or adeline.simon[at]inrae.fr

## Authors and acknowledgment

* Rosanne Phebe
* Nicolas Lapalu
* Adeline Simon

## License
licensed under GNU General Public License v3.0 


## References

* [1] Krzywinski, M. et al. Circos: an Information Aesthetic for Comparative Genomics. Genome Res (2009) 19:1639-1645
* [2] Bernat Gel & Eduard Serra. (2017). karyoploteR: an R/Bioconductor package to plot customizable genomes displaying arbitrary data. Bioinformatics, 31–33. doi:10.1093/bioinformatics/btx346