gprMax/tests/models_pmls/plot_pml_comparison.py

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)