Delete main__1_.py

main__1_.py

-from scipy.optimize import least_squares
-import numpy as np
-
-# Measured distances with ±10 cm errors (a, b, c, d, e, f)
-measured_distances = [3905.12, 2236.07, 5000, 8000, 8994.27, 5590.17]
-
-# Updated initial guess for (x_B, y_B, x_C, y_C, y_D)
-# Use values that are reasonably close to the expected beacon positions.
-initial_guess = [2500, 3000, 4500, 5500, 8000]
-
-# Updated bounds for the variables
-# Allowing for a wide range, given the distances are in the thousands
-bounds = ([0, 0, 0, 0, 0], [10000, 10000, 10000, 10000, 10000])
-
-
-# Error function for least squares
-def error_function(variables, measured_distances):
-    x_B, y_B, x_C, y_C, y_D = variables
-    a_measured, b_measured, c_measured, d_measured, e_measured, f_measured = measured_distances
-
-    # Calculating residuals
-    residuals = []
-    r_a = np.sqrt(x_B**2 + y_B**2) - a_measured
-    r_b = np.sqrt((x_C - x_B)**2 + (y_C - y_B)**2) - b_measured
-    r_c = np.sqrt(x_C**2 + (y_C - y_D)**2) - c_measured
-    r_d = y_D - d_measured
-    r_e = np.sqrt(x_C**2 + y_C**2) - e_measured
-    r_f = np.sqrt(x_B**2 + (y_B - y_D)**2) - f_measured
-
-    residuals.extend([r_a, r_b, r_c, r_d, r_e, r_f])
-    return residuals
-
-
-# Run least squares optimization
-result = least_squares(
-    error_function,
-    initial_guess,
-    args=(measured_distances, ),
-    bounds=bounds,
-    loss='soft_l1'  # Using a robust loss function to deal with noise
-)
-
-# Extract optimized coordinates
-optimized_coords = result.x
-x_B, y_B, x_C, y_C, y_D = optimized_coords
-
-# Print the optimized coordinates
-print("Optimized coordinates:", optimized_coords)
-
-# Manually calculate distances based on optimized coordinates
-a_calculated = np.sqrt(x_B**2 + y_B**2)  # Distance from A to B
-b_calculated = np.sqrt((x_C - x_B)**2 + (y_C - y_B)**2)
-c_calculated = np.sqrt(x_C**2 + (y_C - y_D)**2)
-d_calculated = y_D  # Distance from A to D (since A is at (0,0) and D is at (0, y_D))
-e_calculated = np.sqrt(x_C**2 + y_C**2)
-f_calculated = np.sqrt(x_B**2 + (y_B - y_D)**2)
-
-# Verification with a tolerance to handle the measurement error (±10 cm)
-a_measured = 3905.12  # Example measured distance (in cm)
-b_measured = 2236.07
-c_measured = 5000
-d_measured = 8000  # Example measured distance (in cm)
-e_measured = 8994.27
-f_measured = 5590.17
-
-# # Check if calculated distances match measured distances within ±10 cm
-# assert np.isclose(
-#     a_calculated, a_measured, atol=10
-# ), f"a_calculated {a_calculated} differs from a_measured {a_measured}"
-# assert np.isclose(
-#     b_calculated, b_measured, atol=10
-# ), f"b_calculated {b_calculated} differs from b_measured {b_measured}"
-# assert np.isclose(
-#     c_calculated, c_measured, atol=10
-# ), f"c_calculated {c_calculated} differs from c_measured {c_measured}"
-# assert np.isclose(
-#     d_calculated, d_measured, atol=10
-# ), f"d_calculated {d_calculated} differs from d_measured {d_measured}"
-# assert np.isclose(
-#     e_calculated, e_measured, atol=10
-# ), f"e_calculated {e_calculated} differs from e_measured {e_measured}"
-# assert np.isclose(
-#     f_calculated, f_measured, atol=10
-# ), f"f_calculated {f_calculated} differs from f_measured {f_measured}"
-
-# print(
-#     "All calculated distances are within the acceptable tolerance of the measured distances."
-# )
-
-# Calculate and print the residuals after optimization
-residuals_after_optimization = error_function(optimized_coords,
-                                              measured_distances)
-print("Residuals after optimization:", residuals_after_optimization)