From 3c6933cfbf2a4b01a996f7403b8564338983d8d2 Mon Sep 17 00:00:00 2001
From: jlKronos01 <joelleungyancheuk@gmail.com>
Date: Wed, 18 Dec 2024 19:36:28 +0000
Subject: [PATCH] Revert "Update beacons_D_origin.py"
This reverts commit 5bf2422af4e8c8cedefab65bc7b6250f2702b6f7, reversing
changes made to 3e664451199012c2aebb769d07d7d670a4ce8fa3.
---
.../Beacons_tag_position/beacons_D_origin.py | 25 +------------------
1 file changed, 1 insertion(+), 24 deletions(-)
diff --git a/Wireless_Communication/UWB/Beacons_tag_position/beacons_D_origin.py b/Wireless_Communication/UWB/Beacons_tag_position/beacons_D_origin.py
index 1a8b4324..eda0501d 100644
--- a/Wireless_Communication/UWB/Beacons_tag_position/beacons_D_origin.py
+++ b/Wireless_Communication/UWB/Beacons_tag_position/beacons_D_origin.py
@@ -2,17 +2,10 @@ from scipy.optimize import least_squares
import numpy as np
# Measured distances arrive in order: A, E, D, B, F, C
-<<<<<<< HEAD
measured_distances = [1496.692877, 1477.462413, 677.287532, 947.921123, 1358.796385, 922.593196]
# Introduce ±10 cm of noise into the measurements
noise_level = 0 # Set to zero for no noise
-=======
-measured_distances = [1496.223841, 1503.259376, 677.756567, 945.575945, 1419.301988, 923.531267 ]
-
-# Introduce ±10 cm of noise
-noise_level =0
->>>>>>> 06a45f31e79242c1c4b0f08ce03d5ddf58789e64
measured_distances_noisy = measured_distances + np.random.uniform(-noise_level, noise_level, size=len(measured_distances))
# Automatically generate a robust initial guess
@@ -29,7 +22,6 @@ def generate_initial_guess(measured_distances):
return [x_B, y_B, x_C, y_C, y_A]
-<<<<<<< HEAD
# Generate bounds based on measured distances
def generate_bounds(measured_distances):
min_dist = min(measured_distances)
@@ -51,16 +43,6 @@ def generate_bounds(measured_distances):
max_dist * 1.5 # y_A upper bound
]
return lower_bound, upper_bound
-=======
-# Generate the initial guess
-initial_guess = generate_initial_guess(measured_distances_noisy)
-
-# Simplified uniform bounds for all variables
-bounds = ([-2000, -2000, -2000, -2000, -2000], [2000, 2000, 2000, 0, 2000])
-
-# Ensure the initial guess is within bounds
-initial_guess = np.clip(initial_guess, bounds[0], bounds[1])
->>>>>>> 06a45f31e79242c1c4b0f08ce03d5ddf58789e64
# Define the error function
def error_function(variables, measured):
@@ -103,7 +85,7 @@ result_noisy = least_squares(
error_function,
initial_guess,
args=(measured_distances_noisy,),
- bounds=bounds,
+ bounds=(lower_bounds, upper_bounds),
loss='soft_l1'
)
@@ -112,11 +94,6 @@ optimized_coords_noisy = result_noisy.x
x_B, y_B, x_C, y_C, y_A = optimized_coords_noisy
print("Optimized coordinates with noise:", optimized_coords_noisy)
-<<<<<<< HEAD
# Calculate and print residuals
residuals_noisy = error_function(optimized_coords_noisy, measured_distances_noisy)[:-1] # Ignore penalty
print("\nResiduals with noisy measurements:", residuals_noisy)
-=======
-residuals_noisy = error_function(optimized_coords_noisy, measured_distances_noisy)[:-1] # Ignore penalty in residuals
-print("Residuals with noisy measurements:", residuals_noisy)
->>>>>>> 06a45f31e79242c1c4b0f08ce03d5ddf58789e64
--
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