vector_tolerance_analysis/tolerance_analysis.py at main · Nihar2082/vector_tolerance_analysis · GitHub

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# # tolerance_analysis.py

# import numpy as np
# import matplotlib.pyplot as plt
# from config import *

# def simulate_tolerances():
#     np.random.seed(42)
#     tailgate_tol = np.random.normal(0, TAILGATE_TOL, NUM_SAMPLES)
#     window_tol = np.random.normal(0, WINDOW_TOL, NUM_SAMPLES)

#     final_tailgate_w = TAILGATE_WIDTH + tailgate_tol
#     final_window_w = WINDOW_WIDTH + window_tol

#     final_tailgate_h = TAILGATE_HEIGHT + tailgate_tol
#     final_window_h = WINDOW_HEIGHT + window_tol

#     gap_w = final_tailgate_w - final_window_w
#     gap_h = final_tailgate_h - final_window_h

#     return gap_w, gap_h

# def plot_gap_histograms(gap_w, gap_h):
#     plt.figure(figsize=(10, 5))

#     plt.subplot(1, 2, 1)
#     plt.hist(gap_w, bins=30, alpha=0.7, color='blue', edgecolor='black')
#     plt.axvline(x=2*NOMINAL_GAP, color='red', linestyle='dashed', label="Nominal Gap (20 mm)")
#     plt.xlabel("Gap in Width (mm)")
#     plt.ylabel("Frequency")
#     plt.title("Tolerance Analysis - Width Gap")
#     plt.legend()

#     plt.subplot(1, 2, 2)
#     plt.hist(gap_h, bins=30, alpha=0.7, color='green', edgecolor='black')
#     plt.axvline(x=2*NOMINAL_GAP, color='red', linestyle='dashed', label="Nominal Gap (20 mm)")
#     plt.xlabel("Gap in Height (mm)")
#     plt.ylabel("Frequency")
#     plt.title("Tolerance Analysis - Height Gap")
#     plt.legend()

#     plt.tight_layout()
#     plt.show()
# ===========================
# tolerance_analysis.py
# ===========================
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from config import *

def simulate_tolerances():
    np.random.seed(42)
    tailgate_tol = np.random.normal(0, TAILGATE_TOL, NUM_SAMPLES)
    window_tol = np.random.normal(0, WINDOW_TOL, NUM_SAMPLES)

    final_tailgate_w = TAILGATE_WIDTH + tailgate_tol
    final_window_w = WINDOW_WIDTH + window_tol

    final_tailgate_h = TAILGATE_HEIGHT + tailgate_tol
    final_window_h = WINDOW_HEIGHT + window_tol

    gap_w = final_tailgate_w - final_window_w
    gap_h = final_tailgate_h - final_window_h

    # Print sample data
    print("Tailgate Tolerances (sample):", tailgate_tol[:10])
    print("Window Tolerances (sample):", window_tol[:10])
    print("Gap Width (sample):", gap_w[:10])
    print("Gap Height (sample):", gap_h[:10])

    # Save to CSV
    df = pd.DataFrame({
        'tailgate_tol': tailgate_tol,
        'window_tol': window_tol,
        'gap_width': gap_w,
        'gap_height': gap_h
    })
    df.to_csv("tolerance_data.csv", index=False)

    return gap_w, gap_h

def plot_gap_histograms(gap_w, gap_h):
    plt.figure(figsize=(10, 5))

    plt.subplot(1, 2, 1)
    plt.hist(gap_w, bins=30, alpha=0.7, color='blue', edgecolor='black')
    plt.axvline(x=2*NOMINAL_GAP, color='red', linestyle='dashed', label="Nominal Gap (20 mm)")
    plt.xlabel("Gap in Width (mm)")
    plt.ylabel("Frequency")
    plt.title("Tolerance Analysis - Width Gap")
    plt.legend()

    plt.subplot(1, 2, 2)
    plt.hist(gap_h, bins=30, alpha=0.7, color='green', edgecolor='black')
    plt.axvline(x=2*NOMINAL_GAP, color='red', linestyle='dashed', label="Nominal Gap (20 mm)")
    plt.xlabel("Gap in Height (mm)")
    plt.ylabel("Frequency")
    plt.title("Tolerance Analysis - Height Gap")
    plt.legend()

    plt.tight_layout()
    plt.show()