Added a cross-correlation fitness metric.

user_libs/optimisations/taguchi_fitness.py

@@ -6,6 +6,7 @@
 # Please use the attribution at http://dx.doi.org/10.1190/1.3548506
 
 import numpy as np
+from scipy import signal
 import h5py
 
 """This module contains fitness metric functions that can be used with the Taguchi optimisation method.
@@ -16,12 +17,12 @@ import h5py
 """
 
 
-def fitness_max(filename, outputnames):
+def fitness_max(filename, args):
     """Return the maximum value from a specific output in the input file.
         
     Args:
         filename (str): Name of output file
-        outputnames (list): Names (IDs) of outputs (rxs) from input file
+        args (dict): 'outputs' key with a list of names (IDs) of outputs (rxs) from input file
         
     Returns:
         maxvalue (float): Maximum value from specific outputs
@@ -32,13 +33,81 @@ def fitness_max(filename, outputnames):
 
     for rx in range(1, nrx + 1):
         tmp = f['/rxs/rx' + str(rx) + '/']
-        if tmp.attrs['Name'] in outputnames:
+        if tmp.attrs['Name'] in args['outputs']:
             fieldname = list(tmp.keys())[0]
             maxvalue = np.amax(tmp[fieldname])
 
     return maxvalue
 
+
+def fitness_xcorr(filename, args):
+    """Return the maximum value from a specific output in the input file.
+        
+    Args:
+        filename (str): Name of output file
+        args (dict): 'refresp' key with path & filename of reference signal (time, amp) stored in a text file; 'outputs' key with a list of names (IDs) of outputs (rxs) from input file
+        
+    Returns:
+        xcorrmax (float): Maximum value from specific outputs
+    """
+
+    # Load and normalise the reference response
+    with open(args['refresp'], 'r') as f:
+        refdata = np.loadtxt(f)
+    reftime = refdata[:,0]
+    refresp = refdata[:,1]
+    refresp /= np.amax(np.abs(refresp))
+
+    # Load response from output file
+    f = h5py.File(filename, 'r')
+    nrx = f.attrs['nrx']
+    modeltime = np.arange(0, f.attrs['dt'] * f.attrs['Iterations'], f.attrs['dt'])
     
+    for rx in range(1, nrx + 1):
+        tmp = f['/rxs/rx' + str(rx) + '/']
+        if tmp.attrs['Name'] in args['outputs']:
+            fieldname = list(tmp.keys())[0]
+            modelresp = tmp[fieldname]
+            # Convert field value (V/m) to voltage
+            if fieldname == 'Ex':
+                modelresp *= -1 * f.attrs['dx, dy, dz'][0]
+            elif fieldname == 'Ey':
+                modelresp *= -1 * f.attrs['dx, dy, dz'][1]
+            if fieldname == 'Ez':
+                modelresp *= -1 * f.attrs['dx, dy, dz'][2]
+
+    # Normalise respose from output file
+    modelresp /= np.amax(np.abs(modelresp))
+
+    # Make both responses the same length in time
+    if reftime[-1] > modeltime[-1]:
+        reftime = np.arange(0, f.attrs['dt'] * f.attrs['Iterations'], reftime[-1] / len(reftime))
+        refresp = refresp[0:len(reftime)]
+    elif modeltime[-1] > reftime[-1]:
+        modeltime = np.arange(0, reftime[-1], f.attrs['dt'])
+        modelresp = modelresp[0:len(modeltime)]
+
+    # Resample the response with the lower sampling rate
+    if len(modeltime) > len(reftime):
+        reftime = signal.resample(reftime, len(modeltime))
+        refresp = signal.resample(refresp, len(modelresp))
+    elif len(reftime) > len(modeltime):
+        modeltime = signal.resample(modeltime, len(reftime))
+        modelresp = signal.resample(modelresp, len(refresp))
+
+    # Calculate cross-correlation
+    xcorr = signal.correlate(refresp, modelresp)
+    xcorrmax = np.amax(xcorr)
+
+    return xcorrmax
+
+
+
+
+
+
+
+