Quantify Intermediate

creation_stats.txt

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+Statistics for intermediate.out:
+  Mean: 0.08 ms
+  Median: 0.06 ms
+  Standard Deviation: 0.05 ms
+  Min: 0.007012 ms
+  Max: 0.420355 ms
+--------------------------------------------------
+Statistics for rrt.out:
+  Mean: 0.20 ms
+  Median: 0.17 ms
+  Standard Deviation: 0.07 ms
+  Min: 0.006336 ms
+  Max: 0.499003 ms

datavis.py

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+import re
+import matplotlib.pyplot as plt
+import numpy as np
+
+def extract_times(file_path):
+    """
+    Extracts time values from a file where each line is in the format:
+    'CreatePath::intermediate() Time: <number> <unit>'
+    Supports both 'ns' and 's' units.
+    """
+    times = []
+    units = []  # List to keep track of units (ns or s)
+
+    with open(file_path, 'r') as f:
+        for line in f:
+            # Use regex to extract the time value and its unit (ns or s)
+            match = re.search(r'Time:\s*([\d\.]+)\s*(ms|s)', line)
+            if match:
+                time_value = float(match.group(1))
+                unit = match.group(2)
+
+                # Store the time and unit in their respective lists
+                times.append(time_value)
+                units.append(unit)
+
+    return times, units
+
+def truncate_times(times, threshold=0.5):
+    """
+    Truncates times that are greater than the specified threshold.
+    Default threshold is set to 400,000.
+    """
+    return [time for time in times if time <= threshold]
+
+def calculate_stats(times):
+    """
+    Calculates and returns basic statistics for the time values:
+    mean, median, standard deviation, minimum, and maximum.
+    """
+    mean = np.mean(times)
+    median = np.median(times)
+    std_dev = np.std(times)
+    min_val = np.min(times)
+    max_val = np.max(times)
+
+    return mean, median, std_dev, min_val, max_val
+
+def plot_distribution(times, units, title, subplot_idx, x_min, x_max):
+    """
+    Plots the distribution of the times using a histogram.
+    Now the x-axis is scaled consistently for both plots.
+    """
+    plt.subplot(1, 2, subplot_idx)  # Create a subplot (1 row, 2 columns)
+
+    # Increase the number of bins for finer granularity
+    bins = 50  # Increase bins to get more granularity in the lower end of the distribution
+
+    # Plot histogram
+    density, bins, _ = plt.hist(times, bins=bins, density=True, color='blue', alpha=0.7, edgecolor='black')
+
+    # Set the x-axis limits to be the same for both plots
+    plt.xlim(x_min, x_max)
+
+    # Determine the unit for x-axis label
+    if units[0] == 'ms':
+        x_label = "Time (ms)"
+    elif units[0] == 's':
+        x_label = "Time (s)"
+
+    plt.title(title)
+    plt.xlabel(x_label)
+    plt.ylabel("Density")
+    plt.legend()
+
+def print_stats(title, times, units):
+    """
+    Prints the statistics for a given dataset.
+    """
+    mean, median, std_dev, min_val, max_val = calculate_stats(times)
+
+    # Display the unit (ns or s) for reference
+    unit_str = "ms" if units[0] == 'ms' else "s"
+
+    print(f"Statistics for {title}:")
+    print(f"  Mean: {mean:.2f} {unit_str}")
+    print(f"  Median: {median:.2f} {unit_str}")
+    print(f"  Standard Deviation: {std_dev:.2f} {unit_str}")
+    print(f"  Min: {min_val} {unit_str}")
+    print(f"  Max: {max_val} {unit_str}")
+    print("-" * 50)
+
+def main():
+    # File paths
+    file_path_intermediate = 'intermediate_creation.out'
+    file_path_rrt = 'rrt_creation.out'
+
+    # Extract times and units from both files
+    times_intermediate, units_intermediate = extract_times(file_path_intermediate)
+    times_rrt, units_rrt = extract_times(file_path_rrt)
+
+    if (units_intermediate[0] == 'ms'):
+    # Truncate times greater than 400000
+        times_intermediate = truncate_times(times_intermediate)
+        times_rrt = truncate_times(times_rrt)
+
+    # If both files have valid times, determine the common x-axis limits
+    if times_intermediate and times_rrt:
+        # Find the global min and max values across both time datasets
+        x_min = min(min(times_intermediate), min(times_rrt))
+        x_max = max(max(times_intermediate), max(times_rrt))
+    else:
+        x_min, x_max = 0, 1  # Default values if no valid data found
+
+    # Create a figure for the two plots
+    plt.figure(figsize=(14, 6))
+
+    # Plot and calculate statistics for intermediate.out
+    if times_intermediate:
+        plot_distribution(times_intermediate, units_intermediate, f"Distribution of {file_path_intermediate}", 1, x_min, x_max)
+        print_stats("intermediate.out", times_intermediate, units_intermediate)
+    else:
+        print(f"No valid times found in {file_path_intermediate}.")
+
+    # Plot and calculate statistics for rrt.out
+    if times_rrt:
+        plot_distribution(times_rrt, units_rrt, f"Distribution of {file_path_rrt}", 2, x_min, x_max)
+        print_stats("rrt.out", times_rrt, units_rrt)
+    else:
+        print(f"No valid times found in {file_path_rrt}.")
+
+    # Show the plots
+    plt.tight_layout()  # Adjust layout to prevent overlap
+    plt.show()
+
+if __name__ == '__main__':
+    main()