semvec/pypi-packages huggingface dataset:
mlllearnzebra 0.1.0
View machine learning algorithms as code
Requires Python: >=3.7

 # ðŸ“¦ MLLearn

*MLLearn* is a Python package that prints code implementations of machine learning algorithms straight to your terminal â€” perfect for learning, reference, or even flexing your own custom algorithms ðŸ˜Ž.

---

## ðŸš€ Installation

Install it locally:

```bash
pip install .
context-enriched-chunking 0.1.3
A text chunking strategy that keeps document title and section as headers for each chunk.
Requires Python: >=3.6

# Context-Enriched Chunking

A text chunking strategy that keeps document title and section as headers for each chunk.

```python
from context_enriched_chunking import ContextEnrichedChunking

text = "Any text"
splitter = ContextEnrichedChunking(section_max_words=10, chunk_size=1000, chunk_overlap=100)
splitter.split_text(text, title)
```

Prompt para geração de palavras-chave:

```
Você é um assistente de inteligência artificial que extrai palavras-chave de uma resposta, e essas palavras-chave devem conter as informações mais importantes da resposta, sem as quais a resposta estaria incorreta.
Todas as palavras-chave devem ser substrings contidas na resposta.
É necessário gerar um mínimo de uma e um máximo de quatro palavras-chave.
As palavras-chave devem estar em uma única linha separadas por ;
Pergunta: {question}
Resposta: {answer}
Palavras-chave: 
```

.env

```
OPENAI_API_KEY=
LLM_EVALUATOR=gpt-4o-mini
RAGAS_DO_NOT_TRACK=true
```

python3 evaluate/generate_embeddings.py cec,recursive,token,semantic

python3 evaluate/generate_answers.py gpt-4o-mini-2024-07-18,deepseek-r1:8b,llama3.1:8b [--n_questions=20] [--overwrite]

python3 evaluate/generate_answers.py gpt-4o,llama3-1-8b-instruct-v1,llama3-1-405b-instruct-v1,llama3-1-70b-instruct-v1 => ATENÇÃO REMOVI 70B

gpt, ollama, or evaluate/custom_llm.load_custom_llm(model_name)

python3 evaluate/evaluate.py [--overwrite]

# Publicando

editar pyproject.toml e setup.py com nova versão

pip install -e .

python3 -m build
twine upload dist/*
vibe-pentesterlab 2.0.1
AI-Assisted Offensive Security Framework
Requires Python: >=3.8

# Vibe PentesterLab v2.0.0 🚀

**Author:** Prajeesh KC
AI-powered, plugin-extensible pentesting lab.

## Features
- XSS / SQLi / SSRF / JWT / Subdomain / Dirsearch / Masscan
- AI Reasoning & Suggestions
- Flask + Streamlit GUI
- API Key Intelligence (Shodan, VT, Censys)
- PDF + Markdown Reports
- Kali Tool Integration

## Usage
```
hpc-scripts 0.1.0
A short description of the project.
Requires Python: >=3.8

# Brainglobe array batch script file creation

Using the following function will write an array job file and will also create
a file called commands.txt with a list of all the commands that will be run when the array script is launched.

Its probably important not to edit this file while the job is running.

Existing output directories are skipped.

Rawdata directory is assumed to be in NIU neuroblueprint format: https://neuroblueprint.neuroinformatics.dev/latest/specification.html 

probably the only things that matter are:

- rawdata folder contains a folder for each mouse
- derivatives data is the same as the rawdata folder but with /rawdata/ replaced by /derivatives/ (changing this might break things)
- whole brain images can be kept in a different location to the "rawdata" and this should work as long as the mouse folder names match.. 


Go to hpc_scripts/slurm_config and set the parameters according to your need. Make sure to edit the email to be your email




```
slurm_params = {
    "time_limit": "3-0:0", # 3 days, 0 hours, 0 minutes
    "n_jobs": 10,
    "n_jobs_at_a_time": 4,
    "user_email": "",
    "memory_limit": 60,

}

```


Look in hpc_scripts/brainglobe/brainreg_array_job_constructor.py and edit the arguments in the main() function. Also the paths to the data should be changed to yours.

```

def main():
    atlas = "allen_mouse_10um"
    overwrite_existing=False
    rawdata_directory = Path("/path/to/rawdata/")   
    serial2p_directory_raw = Path(""/path/to/whole_brains/")
    array_job_outpath=""/path/to/batch_scripts/" 


```


## Running scripts

To run scripts you should submit jobs via slurm. For this you will need to have your python code
installed remotely and you will need a batch script, written in bash, that will be used to call jobs on
the HPC.

First SSH to the swc network.

```ssh user@ssh.swc.ucl.ac.uk```

SSH again to the hpc.

```ssh user@hpc-gw2```

Then call the sbatch command on the batch script file.

```sbatch /path/to/batch_script.sh```
mllearn-zebra 0.1.0
View machine learning algorithms as code
Requires Python: >=3.7

 # ðŸ“¦ MLLearn

*MLLearn* is a Python package that prints code implementations of machine learning algorithms straight to your terminal â€” perfect for learning, reference, or even flexing your own custom algorithms ðŸ˜Ž.

---

## ðŸš€ Installation

Install it locally:

```bash
pip install .
prmtools 0.1.0
A toolkit for PRM (Parallel Reaction Monitoring) data analysis
Requires Python: >=3.7

# prmtools

A toolkit for PRM (Parallel Reaction Monitoring) data analysis in Python.

## Features
- Fragment extraction
- Peak quality assessment
- EIC and MS2 plotting
- Results consolidation
- Command-line interface

## Installation

```bash
pip install .
```

## Usage

```bash
prmtools --input <input_file_or_dir> --output <output_dir>
```

## Structure
- `prmtools/` - Core package modules
- `prmtools/cli.py` - Command-line interface
- `setup.py` - Packaging script

## Requirements
- numpy
- pandas
- matplotlib
- scipy
- openpyxl

## License
MIT