Added functionality to better handle divide by zero error when taking log10.

user_libs/antenna_patterns/plot_fields.py

@@ -83,7 +83,14 @@ ax.annotate('Ground', xy=(np.deg2rad(270), 0), xytext=(8, -15), textcoords='offs
 for patt in range(0, len(radii)):
     pattplot = np.append(patterns[patt, :], patterns[patt, 0])  # Append start value to close circle
     pattplot = pattplot / np.max(np.max(patterns))  # Normalise, based on set of patterns
-    ax.plot(theta, 10 * np.log10(pattplot), label='{:.2f}m'.format(radii[patt]), marker='.', ms=6, lw=1.5)
+
+    # Calculate power (ignore warning from taking a log of any zero values)
+    with np.errstate(divide='ignore'):
+        power = 10 * np.log10(pattplot)
+    # Replace any NaNs or Infs from zero division
+    power[np.invert(np.isfinite(power))] = 0
+    
+    ax.plot(theta, power, label='{:.2f}m'.format(radii[patt]), marker='.', ms=6, lw=1.5)
 
 # Add Hertzian dipole plot
 # hertzplot1 = np.append(hertzian[0, :], hertzian[0, 0]) # Append start value to close circle

user_libs/optimisation_taguchi/fitness_functions.py

@@ -13,8 +13,6 @@ import matplotlib.pyplot as plt
 import numpy as np
 from scipy import signal
 
-np.seterr(divide='ignore')
-
 from gprMax.exceptions import GeneralError
 
 """This module contains fitness metric functions that can be used with the Taguchi optimisation method.
@@ -183,9 +181,14 @@ def min_sum_diffs(filename, args):
         if output.attrs['Name'] in args['outputs']:
             outputname = list(output.keys())[0]
             modelresp = np.array(output[outputname])
+
             # Calculate sum of differences
-            tmp = 20 * np.log10(np.abs(modelresp - refresp) / np.amax(np.abs(refresp)))
-            tmp = np.abs(np.sum(tmp[-np.isneginf(tmp)])) / len(tmp[-np.isneginf(tmp)])
+            with np.errstate(divide='ignore'): # Ignore warning from taking a log of any zero values
+                tmp = 20 * np.log10(np.abs(modelresp - refresp) / np.amax(np.abs(refresp)))
+            # Replace any NaNs or Infs from zero division
+            tmp[np.invert(np.isfinite(tmp))] = 0
+
+            tmp = np.abs(np.sum(tmp)) / len(tmp)
             diffdB += tmp
             outputs += 1