gprMax/testing/models_pmls/plot_pml_comparison.py

# Copyright (C) 2015-2023: The University of Edinburgh, United Kingdom
#                 Authors: Craig Warren, Antonis Giannopoulos, and John Hartley
#
# This file is part of gprMax.
#
# gprMax is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# gprMax is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with gprMax.  If not, see <http://www.gnu.org/licenses/>.

import itertools
import logging
from operator import add
from pathlib import Path

import h5py
import matplotlib.pyplot as plt
import numpy as np

logger = logging.getLogger(__name__)

# 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", "*"]

parts = Path(__file__).parts
path = "rxs/rx1/"
basename = "pml_3D_pec_plate"
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):
    # Open output file and read iterations
    fileref = h5py.File(Path(*parts[:-1], basename, basename + "_ref.h5"), "r")
    filetest = h5py.File(Path(*parts[:-1], basename, basename + str(x + 1) + ".h5"), "r")

    # Get available field output component names
    outputsref = list(fileref[path].keys())
    outputstest = list(filetest[path].keys())
    if outputsref != outputstest:
        logger.exception("Field output components do not match reference solution")
        raise ValueError

    # Check that type of float used to store fields matches
    if filetest[path + outputstest[0]].dtype != fileref[path + outputsref[0]].dtype:
        logger.warning(
            f"Type of floating point number in test model ({filetest[path + outputstest[0]].dtype}) "
            f"does not match type in reference solution ({fileref[path + outputsref[0]].dtype})\n"
        )
    floattyperef = fileref[path + outputsref[0]].dtype
    floattypetest = filetest[path + outputstest[0]].dtype
    # logger.info(f'Data type: {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])):
            logger.exception("Test data contains NaNs")
            raise ValueError

    fileref.close()
    filetest.close()

    # Diffs
    datadiffs = np.zeros(datatest.shape, dtype=np.float64)
    for i in range(len(outputstest)):
        maxi = np.amax(np.abs(dataref[:, i]))
        datadiffs[:, i] = np.divide(
            np.abs(datatest[:, i] - dataref[:, i]), maxi, out=np.zeros_like(dataref[:, i]), where=maxi != 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(f": {np.amax(datadiffs[start::, 1]):.1f} [dB]")
    logger.info(f"{model}: Max. error {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)