per-datasets 0.0.4a1

A Python package for loading petroleum datasets

per-datasets

A Python package for loading reservoir datasets from API endpoints.

Installation

pip install per-datasets

Quick Start

Option 1: Using Global API Key (Recommended)

First, set your API key globally:

# Set API key globally (works across all projects)
per-datasets set-key "your_api_key_here"

# Or use interactive setup
per-datasets interactive

Then use in your Python code:

import per_datasets as pds

# Initialize without API key (uses global key)
pds.initialize()

# Load a random reservoir dataset
df_random = pds.reservoir.load_random()
print(f"Loaded dataset with shape: {df_random.shape}")

Option 2: Using API Key in Code

import per_datasets as pds

# Initialize with your API key
pds.initialize('your_api_key_here')

# Load a random reservoir dataset
df_random = pds.reservoir.load_random()
print(f"Loaded dataset with shape: {df_random.shape}")

Workflows

The package includes Dockerized workflows for common operations:

Available Workflows

1. Add Workflow - Adds two numbers together
2. Subtract Workflow - Subtracts one number from another
3. PINN Workflow - Trains a Physics-Informed Neural Network (Transformer-based)

Running Workflows in Python

You can run workflows directly in Python:

from per_datasets.workflows import add, subtract, pinn

# Run simple workflows
print(add(5, 3))       # 8
print(subtract(10, 4)) # 6

# Run PINN training workflow
results = pinn(epochs=50)
print(f"Final Loss: {results['final_loss']}")

# Visualize the loss history dynamically
from per_datasets import visual # Or use pds.visual if imported as pds
visual.line_plot(results, y='loss_history', title="PINN Training Loss")

Building Workflow Containers

# Build all workflow Docker images
./build_workflows.sh

# Or build individually
docker build -t perd-add-workflow -f per_datasets/workflows/add/Dockerfile .
docker build -t perd-subtract-workflow -f per_datasets/workflows/substract/Dockerfile .

Running Workflows

# Run add workflow
docker run --rm perd-add-workflow 5.2 3.8

# Run subtract workflow
docker run --rm perd-subtract-workflow 10.5 4.3

See per_datasets/workflows/README.md for more details.

Command Line Interface

The package includes a CLI for managing API keys globally:

# Set API key globally
per-datasets set-key "your_api_key_here"

# Check configuration status
per-datasets status

# Get stored API key (masked)
per-datasets get-key

# Remove API key
per-datasets remove-key

# Interactive setup
per-datasets interactive

# Clear all configuration
per-datasets clear

# Show help
per-datasets --help

Complete Usage Examples

import per_datasets as pds

# Initialize (uses global key if available)
pds.initialize()

# Load a random reservoir dataset
df_random = pds.reservoir.load_random()
print(f"Loaded dataset with shape: {df_random.shape}")

# Load a specific dataset by ID
df_specific = pds.reservoir.load('your_dataset_id')

# Get information about available datasets
info = pds.get_dataset_info()

API Reference

initialize(api_key=None)

Initialize the per_datasets module with API credentials.

Parameters:

• api_key (str, optional): The API key for authentication. If not provided, uses globally stored key.

Note: If no API key is provided and none is stored globally, raises a ValueError with instructions to set a global key.

load_random()

Loads a random reservoir model from the API endpoint and returns as pandas DataFrame.

Returns:

• pandas.DataFrame: A DataFrame containing the dataset

Configuration Management

The package stores configuration in ~/.per_datasets/config.json by default:

{
  "api_key": "your_api_key_here"
}

Benefits of Global Configuration:

• ✅ No API key in code: Keep sensitive keys out of your source code
• ✅ Cross-project: Use the same API key across multiple projects
• ✅ Secure: API keys are stored in user's home directory
• ✅ Override: Can still provide API key in code to override global setting
• ✅ Easy management: Use CLI commands to manage keys

Security Notes:

• API keys are stored in plain text in your home directory
• Only you can access the configuration file
• Consider using environment variables for production deployments

Dependencies

• requests>=2.25.1
• pandas>=1.3.0

License

MIT

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

Development

To set up the development environment:

git clone https://github.com/P-E-R-D/library-py.git
cd per-datasets
pip install -e .

Building and Publishing

Automatic Deployment (Recommended)

This package uses GitHub Actions for automatic deployment to PyPI:

1. Make your changes to the code
2. Update version numbers in per_datasets/__init__.py and pyproject.toml
3. Create a git tag with the new version:

   git tag v0.2.0
   git push origin v0.2.0
   

4. GitHub Actions automatically builds and uploads to PyPI!

See DEPLOYMENT.md for detailed setup instructions.

Manual Publishing

python -m build
twine upload dist/*