Tested manual fingerprinting for ferrite setup

BachelorThesis/Software/PyPrecisionGUI/ART.py

                      "color":"black",
                      "size":15}
 
-        plt.plot(art_info[:, 1], art_info[:, 2], color="lightgreen", label="ART System", linewidth=5, marker="x")
+        plt.plot(art_info[:, 1], art_info[:, 2], color="green", label="ART System", linewidth=5, marker="x")
 
         print("x=", art_info[:, 1])
         print("y=", art_info[:, 2])

BachelorThesis/Software/PyPrecisionGUI/localization.py

     def __init__(self):
         self.scale_factor = 0.003
         self.k_nearest = 15
-        self.table = np.load('fingerprinting_tables/Julian_1cm_precision_corrected_antenas.npy')
+        #self.table = np.load('fingerprinting_tables/Julian_1cm_precision_corrected_antenas.npy')    # corrected table made by Ihm (No ferrite)
+        #self.table = np.load('fingerprinting_tables/SmallManualFingerprint_Ferrite.npy') # manual fingerprinting table (Ferrite) [A bit off]
+        #self.table = np.load('fingerprinting_tables/Julian_BThesis_table2_switchedAnt5&6 and 7&8.npy')    # Switched Ant5<->6 and 7<->8 in Excel (No Ferrite) [Does not work!]
         self.data = np.load('recorded_data/current_recording.npy')
 
 
             Output: - position [type = np.array[3]]:
                         The estimated position of the object in this sample x,y,z
         """
-        #print("fv =", fv)
+        print("fv =", fv)
         feature_vector = fv * self.scale_factor
 
+        print("table=", self.table)
         repeated_feature_vector = np.square(self.table[:, 9:] - feature_vector)
+
         euclidean_distances = np.sum(repeated_feature_vector, 1)
         order = np.argsort(euclidean_distances)
 

BachelorThesis/Software/PyPrecisionGUI/main_script.py

 # Set Axis
 ax1.set_xlim([-0.02*mm, 0.52*mm])
 ax1.set_ylim([-0.02*mm, 0.52*mm])
-ax1.set_xticks(np.arange(0.0*mm, 0.55*mm, 0.05*mm))
-ax1.set_yticks(np.arange(0.0*mm, 0.55*mm, 0.05*mm))
+#ax1.set_xticks(np.arange(0.0*mm, 0.55*mm, 0.05*mm))
+#ax1.set_yticks(np.arange(0.0*mm, 0.55*mm, 0.05*mm))
 ax1.set_xlabel('X-Position of object [mm]', fontdict=font)
 ax1.set_ylabel('Y-Position of object [mm]', fontdict=font)
 ax1.grid(alpha=0.6)
-for label in ax1.xaxis.get_ticklabels():
-    label.set_rotation(90)
+#for label in ax1.xaxis.get_ticklabels():
+ #   label.set_rotation(90)
 
 ax2.set_ylim([0.1*mm, 0.6*mm])
 ax2.set_xlim([0.0*mm, 0.2*mm])
 
             pos_counter += 1
 
-    ax1.plot(art_info[:, 1], art_info[:, 2], color="Gold", label="ART System", linewidth=5, marker="x")
+    ax1.plot(art_info[:, 1], art_info[:, 2], color="green", label="ART System", linewidth=1)
 
     print("x=", art_info[:, 1])
     print("y=", art_info[:, 2])
 
 
 # Decide what scaling_factors and Recording
-scale_factors = np.arange(0.001, 0.0035, 0.0005)
-localization.k_nearest = 5
+#scale_factors = np.arange(0.001, 0.0035, 0.0005)
+scale_factors = [1]
+localization.k_nearest = 15
 
-Recording = 15  # which ART Recording to pick
+Recording = 5  # which ART Recording to pick
 
 
 for sf in scale_factors:
     positions = positions * mm     # convert from m to mm
 
     # Plot IndLoc Positions
-    ax1.plot(positions[:, 0], positions[:, 1], label="sf = " + str(sf), linewidth=2)
-    ax2.plot(positions[:, 2], positions[:, 1], label="sf = " + str(sf), linewidth=2)
+    ax1.plot(positions[:, 0], positions[:, 1], label="sf = " + str(sf), linewidth=1)
+    ax2.plot(positions[:, 2], positions[:, 1], label="sf = " + str(sf), linewidth=1)
 
 
 plot_art(Recording)