Transform 2D Irregular Grid Data to Perfect Visualizations

Transform irregular grid data into smooth visualizations using Python. Learn how to convert scattered sensor data into clean heatmaps, with step-by-step implementation of different interpolation methods.

The Challenge

Problem Statement:
– Temperature sensors distributed irregularly across a city
– Uneven spacing between data points
– Need smooth visualization without artifacts
– Convert irregular grid data to regular grid

Code Implementation

Core Interpolation Function:

def interpolate_data(x, y, values, new_size, method='linear'):
    # Flatten coordinates
    points = np.column_stack((x.flatten(), y.flatten()))
    
    # Create regular grid
    x_new = np.linspace(x.min(), x.max(), new_size)
    y_new = np.linspace(y.min(), y.max(), new_size)
    X_new, Y_new = np.meshgrid(x_new, y_new)
    
    # Interpolate
    values_new = griddata(points, values.flatten(), 
                         (X_new, Y_new), method=method)
    
    return X_new, Y_new, values_new

Key Findings

Method Comparison:
– Linear interpolation: Most stable, no artifacts
– Cubic interpolation: Holes in data, especially with irregular spacing
– RBF thin plate: Better than cubic but still shows holes
– RBF multiquadric: Worse performance, multiple holes

Resolution Impact:
– 20 points: Poor quality, significant artifacts
– 100 points: Improved clarity
– 200 points: Optimal balance of quality/performance
– 400 points: Minimal additional improvement

Interactive Visualization

Using ipywidgets for Dynamic Testing:
– Adjust grid resolution on the fly
– Switch between interpolation methods
– Compare results across different datasets
– Visual feedback for parameter optimization

Best Practices

For Production Code:
– Stick with linear interpolation for reliability
– Use 200 points as default resolution
– Test algorithm on multiple datasets
– Implement error handling for edge cases

Download Project Files

Get the complete code and example datasets here:

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