semvec/pypi-packages huggingface dataset:
flexi-datareader 1.0.2
A versatile library for reading datasets from multiple formats and generating synthetic datasets.
Requires Python: >=3.6

# Flexi DataReader

**Flexi DataReader** is a powerful and versatile Python library for working with datasets in multiple formats. It simplifies the process of reading files, generating synthetic datasets, and loading predefined datasets from scikit-learn.

---

## ðŸŒŸ Features

- Automatically detects and processes files in various formats:
  - CSV
  - JSON
  - Excel (`.xlsx`)
  - MATLAB (`.mat`)
  - Parquet
- Generates synthetic datasets for machine learning using scikit-learn:
  - Classification
  - Regression
  - Clustering
- Loads predefined datasets from scikit-learn:
  - Iris, Digits, California Housing, and more.
- Robust error handling for missing files, invalid data, and unsupported formats.

---

## ðŸš€ Installation

Install the library directly from PyPI:

```bash
pip install flexi-datareader
```
To install the latest development version from GitHub

```bash
pip install git+https://github.com/YousofLHC/flexi-datareader.git
```
okipy 1.2.0
Minimal, typed, functional and dynamic test library.
Requires Python: >=3.9
Keywords: test, functional, library, testing, dynamic, minimal

# Oki.py

Minimal, typed, functional and dynamic test library.

## Usage

```py
def main():
    suite = Suite()

    @suite.test()
    def adds_correctly(ctx):
        assert 2 + 2 == 4

    @suite.test()
    def adds_incorrectly(ctx):
        print("this must work")
        assert 2 + 2 == 5

    @suite.test()
    def it_doesnt_fail(ctx):
        print("hope this doesnt fail ...")
        print("oh, this will fail", file=sys.stderr)
        raise Exception("Expected failure.")

    @suite.test()
    def it_fails(ctx):
        with ctx.assert_raises():
            raise Exception("Expected failure.")

    suite.run()
```

Results in this output when run :

![test suite output screenshot](https://git.barnulf.net/mb/okipy/media/branch/master/assets/output.png)

It is also possible to write inline tests :

```py

def add(a, b):
    return a + b


@test()
def it_works(ctx):
    pass

```

![test inline output screenshot](https://git.barnulf.net/mb/okipy/media/branch/master/assets/inline.png)
ollamaocr-python 0.1.1
ollamaocr with Llama vision
Requires Python: >=3.7

# Ollama OCR

A Python-based OCR tool leveraging the **Llama 3.2-Vision** model for highly accurate text recognition from images, preserving original formatting and structure.

## Features
- 🚀 **High Accuracy**: Text recognition powered by the **Llama 3.2-Vision** model.
- 📝 **Preserves Formatting**: Maintains the original structure and layout of the recognized text.
- 🖼️ **Wide Format Support**: Works with image formats such as `.jpg`, `.jpeg`, and `.png`.
- ⚡️ **Customizable Output**: Returns results in either **Markdown** or **JSON** format.
- 💪 **Robust Error Handling**: Ensures smooth processing with clear error messages for unsupported formats or invalid configurations.

---

## System Requirements
- **Python** 3.8 or higher
- **Ollama Server** running locally
- **Llama 3.2-Vision** model installed

### Prerequisites
1. Ensure the **Ollama server** is running before using the tool.
2. Download and configure the **Llama 3.2-Vision** model for OCR tasks.

---

## Instalation

```
pip install ollamaocr-python
```

---
## Usage
Basic Usage

```
from ollamaocr import OllamaOCR

# Initialize the OCR tool
ocr = OllamaOCR()

# Perform OCR in Markdown format
markdown_result = ocr.perform_ocr("path/to/image.jpg", output_format="markdown")
print(markdown_result)

# Perform OCR in JSON format
json_result = ocr.perform_ocr("path/to/image.jpg", output_format="json")
print(json_result)
```

## Error Handling
The class provides comprehensive error handling for unsupported formats or invalid configurations:

```
from ollamaocr import OllamaOCR

ocr = OllamaOCR()

try:
    result = ocr.perform_ocr("invalid_file.bmp", output_format="markdown")
except ValueError as e:
    print(f"Error: {e}")
```

## Customizable Prompts
Modify the prompts used for OCR to suit specific requirements:

- Markdown Prompt: Preserves formatting in Markdown structure.
- JSON Prompt: Outputs results in JSON format.


## Limitations
Currently supports only .jpg, .jpeg, and .png image formats.
Requires the Ollama server to be running locally with the Llama 3.2-Vision model installed.

## License
This project is licensed under the MIT License.
cd_sem 0.1.2
SEM image analysis
Requires Python: >=3.12

# CD-SEM
This is a package for analysing SEM images to extract PSD informaiton for use in other
data analysis pipelines.

The current beta Version 0.1 is as close to a direct port over from the Mathematica
notebook written by Ricardo Ruiz. Analysis methods have not yet been imported.

This project is funded by the CHiPPS EFRC at Berkeley National Laboratory.

## Use
Use the 'RunThis_CD_SEM_Analysis.ipynb' file to run the analysis of your SEM (*.tif) image.