read_data.py

import pandas as pd
import plotly.graph_objects as go

# Read the CSV file
data_file = 'data_with_multiple_reference_points.csv'
df = pd.read_csv(data_file)

# Identify reference points
# Reference points are rows where Azimuth (rad) = 0, Elevation (rad) = 0
reference_indices = df[(df['Azimuth (rad)'] == 0) & (df['Elevation (rad)'] == 0)].index

# Initialize the 3D scatter plot
fig = go.Figure()

# Loop through each reference point and plot its data
for i in range(len(reference_indices)):
    # Get the start and end indices for this reference point's group
    start_idx = reference_indices[i]
    end_idx = reference_indices[i + 1] if i + 1 < len(reference_indices) else len(df)
    
    # Extract reference point and generated points
    reference_point = df.iloc[start_idx]
    generated_points = df.iloc[start_idx + 1:end_idx]

    # Plot the reference point
    fig.add_trace(go.Scatter3d(
        x=[reference_point['X']],
        y=[reference_point['Y']],
        z=[reference_point['Z']],
        mode='markers',
        marker=dict(size=8, color='red'),
        name=f'Reference Point {i + 1}'
    ))

    # Plot the generated points
    fig.add_trace(go.Scatter3d(
        x=generated_points['X'],
        y=generated_points['Y'],
        z=generated_points['Z'],
        mode='markers',
        marker=dict(size=4),
        name=f'Generated Points {i + 1}'
    ))

# Update layout for better interaction
fig.update_layout(
    title='3D Points from CSV (Multiple Reference Points)',
    scene=dict(
        xaxis_title='X-axis',
        yaxis_title='Y-axis',
        zaxis_title='Z-axis',
        aspectmode='cube'  # Ensure equal scaling
    )
)

# Show the plot
fig.show()