Data Visualization

The homodyne package provides comprehensive plotting capabilities for both experimental and simulated C₂ correlation function data. This guide covers the visualization tools available for data validation, analysis results, and theoretical predictions.

Overview 

The plotting functionality in homodyne supports two main visualization workflows:

Experimental Data Visualization: Validate and inspect experimental correlation data
Simulated Data Visualization: Display theoretical predictions and fitted results with scaling transformations

Both workflows generate publication-quality heatmaps with detailed statistical analysis and customizable parameters.

Experimental Data Plotting 

The --plot-experimental-data functionality allows you to visualize experimental correlation data without performing fitting, making it ideal for data quality assessment and validation.

Basic Usage 

# Basic experimental data plotting
homodyne --plot-experimental-data --config experiment.json

# With verbose output for debugging
homodyne --plot-experimental-data --config experiment.json --verbose

# Quiet mode for batch processing
homodyne --plot-experimental-data --config experiment.json --quiet

Supported Data Formats 

HDF5 Files

The primary format for experimental XPCS data using the PyXPCS viewer library:

{
  "experimental_data": {
    "data_file_name": "your_experiment.hdf",
    "exchange_key": "exchange",
    "data_folder_path": "./data/experiment/"
  }
}

Features: - Automatic loading via PyXPCS viewer - Exchange key specification for HDF5 structure navigation - Support for multi-angle experimental datasets

NPZ Files

Pre-processed correlation data in NumPy compressed format:

File Structure: - c2_data: 3D array with shape (n_phi, n_t1, n_t2) - t1, t2: Time coordinate arrays - phi_angles: Angular coordinates in degrees

import numpy as np

# Example NPZ file structure
data = np.load("experimental_c2_data.npz")
c2_data = data["c2_data"]          # Shape: (4, 60, 60) for 4 angles
t1 = data["t1"]                    # Time array for first correlation axis
t2 = data["t2"]                    # Time array for second correlation axis
phi_angles = data["phi_angles"]    # Angular coordinates [0, 45, 90, 135]

Output Structure 

Experimental data plots are saved to ./homodyne_results/exp_data/:

./homodyne_results/exp_data/
├── data_validation_phi_0.png      # Phi = 0° analysis
├── data_validation_phi_45.png     # Phi = 45° analysis
├── data_validation_phi_90.png     # Phi = 90° analysis
└── data_validation_phi_135.png    # Phi = 135° analysis

Plot Layout: - 2-column layout: Streamlined visualization - Left column: C₂ heatmap showing c₂(t₁, t₂) - Right column: Statistical summaries and quality metrics - Removed elements: Diagonal and cross-section plots for cleaner presentation

Multi-Angle Handling 

For experiments with multiple phi angles, each angle is plotted individually:

# Automatically handles multiple phi angles
homodyne --plot-experimental-data --config multi_angle_experiment.json

Benefits: - Individual optimization of color scaling per angle - Clear visualization of angle-dependent scattering patterns - Statistical analysis specific to each scattering geometry

Simulated Data Plotting 

The --plot-simulated-data functionality displays theoretical predictions and fitted correlation functions with customizable scaling transformations.

Basic Usage 

# Basic simulated data plotting
homodyne --plot-simulated-data --config theory.json

# With custom scaling parameters
homodyne --plot-simulated-data --config theory.json --contrast 0.3 --offset 1.2

# Override phi angles from command line
homodyne --plot-simulated-data --config theory.json --phi-angles 0,45,90,135

Scaling Transformation 

The core feature of simulated data plotting is the scaling transformation:

\[c_{2,\text{fitted}} = \text{contrast} \times c_{2,\text{theoretical}} + \text{offset}\]

Parameters: - contrast: Scaling factor (default: 1.0, no scaling) - offset: Baseline offset (default: 0.0, no offset) - phi-angles: Comma-separated angles in degrees

Physical Interpretation: - Contrast: Accounts for experimental signal strength and instrumental factors - Offset: Baseline correlation level from incoherent scattering and background

Command-Line Arguments 

Scaling Parameters

# Custom contrast and offset
--contrast 0.25 --offset 1.1

# Default values (no scaling)
--contrast 1.0 --offset 0.0

Phi Angles Override

# Standard angles
--phi-angles 0,45,90,135

# High-resolution angular sampling
--phi-angles 0,15,30,45,60,75,90,105,120,135,150,165

# Specific angles of interest
--phi-angles 0,90,180

Note: Command-line --phi-angles overrides config file specifications

Data File Requirements 

Required NPZ Files

Theoretical Data (theoretical_c2_data.npz): - Contains pure theoretical predictions from model - Used as input for scaling transformation

Fitted Data (fitted_c2_data.npz): - Contains scaled theoretical data matching experimental conditions - Result of applying scaling transformation

Array Structure:

# Both files should contain:
{
  "c2_data": array,        # Shape: (n_phi, n_t1, n_t2)
  "t1": array,             # Time coordinate array
  "t2": array,             # Time coordinate array
  "phi_angles": array      # Angular coordinates in degrees
}

Visualization Features 

Color Scaling

Individual Angle Optimization: - vmin = min(c2_data) calculated separately for each phi angle - Maximizes contrast and visibility for each scattering geometry - Prevents saturation from dominant angular contributions

Clean Presentation: - No grid lines on heatmaps for professional appearance - Consistent colormap (viridis) across all visualizations - Publication-quality formatting and labeling

Configuration Integration 

Plotting Configuration 

The configuration file supports comprehensive plotting settings:

{
  "output_settings": {
    "plotting": {
      "experimental_data_plotting": {
        "enabled": true,
        "data_sources": {
          "hdf_files": {
            "supported": true,
            "loader": "pyxpcs.viewer",
            "exchange_key": "exchange"
          },
          "npz_files": {
            "supported": true,
            "format": "theoretical_c2_data.npz or fitted_c2_data.npz"
          }
        },
        "plot_layout": {
          "columns": 2,
          "include_diagonal_plots": false,
          "include_crosssection_plots": false
        },
        "multiple_phi_handling": {
          "plot_individually": true
        }
      },
      "simulated_data_plotting": {
        "enabled": true,
        "scaling_transformation": {
          "formula": "c2_fitted = contrast * c2_theoretical + offset",
          "default_contrast": 1.0,
          "default_offset": 0.0
        },
        "phi_angles_input": {
          "command_line_override": true,
          "format": "--phi-angles 0,45,90,135",
          "fallback_to_config": true
        },
        "heatmap_settings": {
          "colormap": "viridis",
          "color_scaling": {
            "per_angle_vmin": true
          },
          "remove_grid": true
        }
      }
    }
  }
}

Usage Examples 

Data Quality Assessment 

# Quick validation of experimental data
homodyne --plot-experimental-data --config experiment.json

# Detailed debugging output
homodyne --plot-experimental-data --config experiment.json --verbose

Look for: - Correlation values around 1.0-2.0 for proper c₂ functions - Clear time-dependent decay patterns - Consistent behavior across different phi angles - Absence of artifacts or discontinuities

Theoretical Prediction Visualization 

# Display raw theoretical predictions
homodyne --plot-simulated-data --config theory.json

# Scale predictions to match experimental conditions
homodyne --plot-simulated-data --config theory.json --contrast 0.3 --offset 1.1

# Custom angular sampling for detailed analysis
homodyne --plot-simulated-data --config theory.json --phi-angles 0,30,60,90,120,150

Model Validation Workflow 

# Step 1: Validate experimental data quality
homodyne --plot-experimental-data --config experiment.json --verbose

# Step 2: Generate theoretical predictions
homodyne --plot-simulated-data --config model.json --contrast 0.25

# Step 3: Run full analysis for comparison
homodyne --config combined.json --method classical --plot-c2-heatmaps

Workflow Benefits: - Systematic quality control before analysis - Visual validation of model predictions - Direct comparison between theory and experiment - Publication-ready visualization output

Best Practices 

Data Preparation 

Experimental Data: - Ensure proper HDF5 structure with exchange keys - Verify time arrays are properly spaced with consistent dt - Check phi angle coverage matches experimental geometry
Simulated Data: - Generate theoretical predictions with same time/angle sampling as experiment - Use appropriate scaling parameters based on experimental conditions - Validate array dimensions match expected structure

Visualization Quality 

Color Scaling: - Use per-angle vmin optimization for multi-angle datasets - Maintain consistent colormap (viridis) across all plots - Avoid saturated or unclear visualization ranges
Layout and Presentation: - Utilize 2-column layout for efficient space usage - Remove unnecessary grid lines for clean appearance - Include proper axis labels and units
Statistical Analysis: - Review quality metrics in statistical summary panels - Check for outliers or unexpected patterns - Validate correlation function ranges and decay behavior

Error Handling and Troubleshooting 

Common Issues 

Missing Data Files:

# Error: NPZ files not found
# Solution: Verify file paths in configuration
ls -la theoretical_c2_data.npz fitted_c2_data.npz

Dimension Mismatches:

# Check array dimensions
data = np.load("experimental_data.npz")
print(f"c2_data shape: {data['c2_data'].shape}")  # Expected: (n_phi, n_t1, n_t2)
print(f"phi_angles: {data['phi_angles']}")        # Expected: [0, 45, 90, 135]

Invalid Phi Angles:

# Error: Cannot parse phi angles
# Solution: Use proper comma-separated format
--phi-angles 0,45,90,135  # Correct
--phi-angles "0 45 90 135"  # Incorrect

Performance Considerations 

Large Datasets: - Use chunked loading for memory-efficient processing - Consider subsampling time arrays for initial visualization - Enable quiet mode for batch processing multiple files

Multi-Angle Analysis: - Parallel processing automatically handles multiple phi angles - Individual plots allow focused analysis of specific scattering geometries - Color scaling optimization improves visualization quality

This comprehensive plotting functionality provides researchers with powerful tools for data validation, theoretical prediction visualization, and publication-quality figure generation in XPCS analysis workflows.