Overhaul of PMLs and introduction of new, optional MRIPML formulation.

tests/models_pmls/plot_pml_comparison.py

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+import itertools
+from operator import add
+import os
+import sys
+
+from colorama import init, Fore, Style
+init()
+import h5py
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Create/setup plot figure
+#colors = ['#E60D30', '#5CB7C6', '#A21797', '#A3B347'] # Plot colours from http://tools.medialab.sciences-po.fr/iwanthue/index.php
+#colorIDs = ["#62a85b", "#9967c7", "#b3943f", "#6095cd", "#cb5c42", "#c95889"]
+colorIDs = ["#79c72e", "#5774ff", "#ff7c2c", "#4b4e80", "#d7004e", "#007545", "#ff83ec"]
+#colorIDs = ["#ba0044", "#b2d334", "#470055", "#185300", "#ff96b1", "#3e2700", "#0162a9", "#fdb786"]
+colors = itertools.cycle(colorIDs)
+# for i in range(2):
+#     next(colors)
+lines = itertools.cycle(('--', ':', '-.', '-'))
+markers = ['o', 'd', '^', 's', '*']
+
+basepath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'pml_3D_pec_plate')
+path = 'rxs/rx1/'
+refmodel = 'pml_3D_pec_plate_ref'
+PMLIDs = ['CFS-PML', 'HORIPML-1', 'HORIPML-2', 'MRIPML-1', 'MRIPML-2']
+maxerrors = []
+testmodels = ['pml_3D_pec_plate_' + s for s in PMLIDs]
+
+fig, ax = plt.subplots(subplot_kw=dict(xlabel='Iterations', ylabel='Error [dB]'), figsize=(20, 10), facecolor='w', edgecolor='w')
+
+for x, model in enumerate(testmodels):
+    # Get output for model and reference files
+    fileref = h5py.File(os.path.join(basepath, refmodel + '.out'), 'r')
+    filetest = h5py.File(os.path.join(basepath, model + '.out'), 'r')
+
+    # Get available field output component names
+    outputsref = list(fileref[path].keys())
+    outputstest = list(filetest[path].keys())
+    if outputsref != outputstest:
+        raise GeneralError('Field output components do not match reference solution')
+
+    # Check that type of float used to store fields matches
+    if filetest[path + outputstest[0]].dtype != fileref[path + outputsref[0]].dtype:
+        print(Fore.RED + 'WARNING: Type of floating point number in test model ({}) does not match type in reference solution ({})\n'.format(filetest[path + outputstest[0]].dtype, fileref[path + outputsref[0]].dtype) + Style.RESET_ALL)
+    floattyperef = fileref[path + outputsref[0]].dtype
+    floattypetest = filetest[path + outputstest[0]].dtype
+    # print('Data type: {}'.format(floattypetest))
+
+    # Arrays for storing time
+    # timeref = np.zeros((fileref.attrs['Iterations']), dtype=floattyperef)
+    # timeref = np.linspace(0, (fileref.attrs['Iterations'] - 1) * fileref.attrs['dt'], num=fileref.attrs['Iterations']) / 1e-9
+    # timetest = np.zeros((filetest.attrs['Iterations']), dtype=floattypetest)
+    # timetest = np.linspace(0, (filetest.attrs['Iterations'] - 1) * filetest.attrs['dt'], num=filetest.attrs['Iterations']) / 1e-9
+    timeref = np.zeros((fileref.attrs['Iterations']), dtype=floattyperef)
+    timeref = np.linspace(0, (fileref.attrs['Iterations'] - 1), num=fileref.attrs['Iterations'])
+    timetest = np.zeros((filetest.attrs['Iterations']), dtype=floattypetest)
+    timetest = np.linspace(0, (filetest.attrs['Iterations'] - 1), num=filetest.attrs['Iterations'])
+
+    # Arrays for storing field data
+    dataref = np.zeros((fileref.attrs['Iterations'], len(outputsref)), dtype=floattyperef)
+    datatest = np.zeros((filetest.attrs['Iterations'], len(outputstest)), dtype=floattypetest)
+    for ID, name in enumerate(outputsref):
+        dataref[:, ID] = fileref[path + str(name)][:]
+        datatest[:, ID] = filetest[path + str(name)][:]
+        if np.any(np.isnan(datatest[:, ID])):
+            raise ValueError('Test data contains NaNs')
+
+    fileref.close()
+    filetest.close()
+
+    # Diffs
+    datadiffs = np.zeros(datatest.shape, dtype=np.float64)
+    for i in range(len(outputstest)):
+        max = np.amax(np.abs(dataref[:, i]))
+        datadiffs[:, i] = np.divide(np.abs(datatest[:, i] - dataref[:, i]), max, out=np.zeros_like(dataref[:, i]), where=max != 0)  # Replace any division by zero with zero
+
+        # Calculate power (ignore warning from taking a log of any zero values)
+        with np.errstate(divide='ignore'):
+            datadiffs[:, i] = 20 * np.log10(datadiffs[:, i])
+        # Replace any NaNs or Infs from zero division
+        datadiffs[:, i][np.invert(np.isfinite(datadiffs[:, i]))] = 0
+
+    # Print maximum error value
+    start = 210
+    maxerrors.append(': {:.1f} [dB]'.format(np.amax(datadiffs[start::, 1])))
+    print('{}: Max. error {}'.format(model, maxerrors[x]))
+
+    # Plot diffs (select column to choose field component, 0-Ex, 1-Ey etc..)
+    ax.plot(timeref[start::], datadiffs[start::, 1], color=next(colors), lw=2, ls=next(lines), label=model)
+    ax.set_xticks(np.arange(0, 2200, step=100))
+    ax.set_xlim([0, 2100])
+    ax.set_yticks(np.arange(-160, 0, step=20))
+    ax.set_ylim([-160, -20])
+    ax.set_axisbelow(True)
+    ax.grid(color=(0.75,0.75,0.75), linestyle='dashed')
+
+mylegend = list(map(add, PMLIDs, maxerrors))
+legend = ax.legend(mylegend, loc=1, fontsize=14)
+frame = legend.get_frame()
+frame.set_edgecolor('white')
+frame.set_alpha(0)
+
+plt.show()
+
+# Save a PDF/PNG of the figure
+fig.savefig(basepath + '.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
+#fig.savefig(savename + '.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)