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StandardPlot Package - Testing Version

This is a local testing version of the StandardPlot package that provides consistent plotting configuration for data analysis.

Your Custom Configuration

  • Figure size: 14 × 10.5 cm (5.51 × 4.13 inches)
  • Font: Bitstream Vera Sans Mono (monospace), size 11 for labels, size 9 for legends
  • Background: #b0d8e3 (light blue-green)
  • Borders: Full border around plots (all spines visible)
  • Ticks: Only on labeled axes (bottom and left)
  • Grid: Disabled
  • Colors:
    • Primary: #0e3768 (dark blue)
    • Highlight: #f1f2f6 (light gray)
    • Dark: #555555 (medium gray)
    • Other: #efe897 (light yellow)

Testing Instructions

1. Setup Dependencies

cd C:\Users\steve\claude\standardplot
python setup_test.py

2. Run Tests

python test_standardplot.py

This will:

  • Test package imports
  • Verify configuration is applied correctly
  • Test all plotting functions
  • Create sample plots in ./figures/test_output/
  • Check font availability
  • Verify style consistency

3. Try Examples

python example_usage.py

This will:

  • Create various example plots demonstrating the package
  • Show different plot types (line, bar, scatter, histograms, subplots)
  • Demonstrate color usage
  • Save examples to ./figures/examples/

Usage Examples

# Import the package
from standardplot import StandardPlots, plot_saver, plot_config

# Create a line plot with automatic styling
fig, ax = StandardPlots.line_plot(
    data=df, x='time', y='value',
    xlabel='Time (hours)', ylabel='Temperature (°C)'
)

# Save with consistent settings
plot_saver.save_plot(fig, 'temperature_analysis', subfolder='results')

# Access colors from your palette
primary_color = plot_config.get_color('primary')  # #0e3768
colors = plot_config.get_palette(4)  # Get 4 colors from palette

File Structure

standardplot/
├── __init__.py           # Package initialization and imports
├── config.py            # Configuration management and matplotlib settings
├── utils.py             # Plotting utility functions and PlotSaver
├── test_standardplot.py # Comprehensive test suite
├── example_usage.py     # Usage examples and demonstrations
├── setup_test.py        # Dependency setup for testing
└── README.md           # This file

Expected Test Results

When you run the tests, you should see:

  • ✅ All imports work correctly
  • ✅ Configuration is applied (figure size, colors, font, etc.)
  • ✅ Basic plotting functions create properly styled plots
  • ✅ Color system works
  • ✅ Subplot functionality works
  • ⚠️ Font warning (if Bitstream Vera Sans Mono isn't available - this is OK)

Troubleshooting

Font Issues: If you don't have Bitstream Vera Sans Mono installed, the system will fall back to the default monospace font. This is expected and won't break functionality.

Import Errors: Run setup_test.py to install missing dependencies.

Permission Errors: Make sure you have write permissions in the directory for creating the figures/ folder.

Global Installation

Once testing is successful, you can install globally using the quick_install.py script that will make this available in all Python environments.

What Makes This Different

Unlike standard matplotlib, this package:

  • Automatically applies your exact specifications
  • Provides consistent color management
  • Includes utility functions for common plot types
  • Handles file saving with organized folder structure
  • Works the same way across all your projects

The goal is to eliminate the repetitive formatting work while maintaining full flexibility when you need it.