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https://gitee.com/sunhf/gprMax.git
已同步 2025-08-08 07:24:19 +08:00
Added more options for checking PMLs
这个提交包含在：
Craig Warren
93
testing/diff_output_files.py
普通文件
93
testing/diff_output_files.py
普通文件
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# Copyright (C) 2015-2023: The University of Edinburgh, United Kingdom
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# Authors: Craig Warren, Antonis Giannopoulos, and John Hartley
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#
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# This file is part of gprMax.
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#
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# gprMax is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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#
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# gprMax is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License
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# along with gprMax. If not, see <http://www.gnu.org/licenses/>.
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import logging
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from pathlib import Path
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import h5py
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import numpy as np
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logger = logging.getLogger(__name__)
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def diff_output_files(filename1, filename2):
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"""Calculates differences between two output files.
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Args:
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filename1: string of filename (including path) of output file 1.
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filename2: string of filename (including path) of output file 2.
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Returns:
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time: numpy array containing time.
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datadiffs: numpy array containing power (dB) of differences.
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"""
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file1 = h5py.File(Path(filename1), "r")
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file2 = h5py.File(Path(filename2), "r")
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# Path to receivers in files
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path = "rxs/rx1/"
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# Get available field output component names
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outputs1 = list(file1[path].keys())
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outputs2 = list(file2[path].keys())
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if outputs1 != outputs2:
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logger.exception("Field output components are not the same in each file")
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raise ValueError
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# Check that type of float used to store fields matches
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floattype1 = file1[path + outputs1[0]].dtype
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floattype2 = file2[path + outputs2[0]].dtype
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if floattype1 != floattype2:
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logger.warning(
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f"Type of floating point number in test model ({file1[path + outputs1[0]].dtype}) "
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f"does not match type in reference solution ({file2[path + outputs2[0]].dtype})\n"
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)
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# Arrays for storing time
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time1 = np.zeros((file1.attrs["Iterations"]), dtype=floattype1)
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time1 = np.linspace(0, (file1.attrs["Iterations"] - 1), num=file1.attrs["Iterations"])
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time2 = np.zeros((file2.attrs["Iterations"]), dtype=floattype2)
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time2 = np.linspace(0, (file2.attrs["Iterations"] - 1), num=file2.attrs["Iterations"])
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# Arrays for storing field data
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data1 = np.zeros((file1.attrs["Iterations"], len(outputs1)), dtype=floattype1)
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data2 = np.zeros((file2.attrs["Iterations"], len(outputs2)), dtype=floattype2)
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for ID, name in enumerate(outputs1):
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data1[:, ID] = file1[path + str(name)][:]
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data2[:, ID] = file2[path + str(name)][:]
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if np.any(np.isnan(data1[:, ID])) or np.any(np.isnan(data2[:, ID])):
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logger.exception("Data contains NaNs")
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raise ValueError
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file1.close()
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file2.close()
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# Diffs
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datadiffs = np.zeros(data1.shape, dtype=np.float64)
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for i in range(len(outputs2)):
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maxi = np.amax(np.abs(data1[:, i]))
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datadiffs[:, i] = np.divide(
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np.abs(data2[:, i] - data1[:, i]), maxi, out=np.zeros_like(data1[:, i]), where=maxi != 0
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) # Replace any division by zero with zero
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# Calculate power (ignore warning from taking a log of any zero values)
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with np.errstate(divide="ignore"):
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datadiffs[:, i] = 20 * np.log10(datadiffs[:, i])
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# Replace any NaNs or Infs from zero division
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datadiffs[:, i][np.invert(np.isfinite(datadiffs[:, i]))] = 0
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return time1, datadiffs
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@@ -1,135 +0,0 @@
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# Copyright (C) 2015-2023: The University of Edinburgh, United Kingdom
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# Authors: Craig Warren, Antonis Giannopoulos, and John Hartley
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#
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# This file is part of gprMax.
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#
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# gprMax is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
|
|
||||||
# the Free Software Foundation, either version 3 of the License, or
|
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# (at your option) any later version.
|
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#
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# gprMax is distributed in the hope that it will be useful,
|
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
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# GNU General Public License for more details.
|
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#
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# You should have received a copy of the GNU General Public License
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# along with gprMax. If not, see <http://www.gnu.org/licenses/>.
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import itertools
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import logging
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from operator import add
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from pathlib import Path
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import h5py
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import matplotlib.pyplot as plt
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import numpy as np
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logger = logging.getLogger(__name__)
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# Create/setup plot figure
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# colors = ['#E60D30', '#5CB7C6', '#A21797', '#A3B347'] # Plot colours from http://tools.medialab.sciences-po.fr/iwanthue/index.php
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# colorIDs = ["#62a85b", "#9967c7", "#b3943f", "#6095cd", "#cb5c42", "#c95889"]
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colorIDs = ["#79c72e", "#5774ff", "#ff7c2c", "#4b4e80", "#d7004e", "#007545", "#ff83ec"]
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# colorIDs = ["#ba0044", "#b2d334", "#470055", "#185300", "#ff96b1", "#3e2700", "#0162a9", "#fdb786"]
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colors = itertools.cycle(colorIDs)
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# for i in range(2):
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# next(colors)
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lines = itertools.cycle(("--", ":", "-.", "-"))
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markers = ["o", "d", "^", "s", "*"]
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parts = Path(__file__).parts
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path = "rxs/rx1/"
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basename = "pml_3D_pec_plate"
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PMLIDs = ["CFS-PML", "HORIPML-1", "HORIPML-2", "MRIPML-1", "MRIPML-2"]
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maxerrors = []
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testmodels = ["pml_3D_pec_plate_" + s for s in PMLIDs]
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fig, ax = plt.subplots(
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subplot_kw=dict(xlabel="Iterations", ylabel="Error [dB]"), figsize=(20, 10), facecolor="w", edgecolor="w"
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)
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for x, model in enumerate(testmodels):
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# Open output file and read iterations
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fileref = h5py.File(Path(*parts[:-1], basename, basename + "_ref.h5"), "r")
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filetest = h5py.File(Path(*parts[:-1], basename, basename + str(x + 1) + ".h5"), "r")
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# Get available field output component names
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outputsref = list(fileref[path].keys())
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outputstest = list(filetest[path].keys())
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if outputsref != outputstest:
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logger.exception("Field output components do not match reference solution")
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raise ValueError
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# Check that type of float used to store fields matches
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if filetest[path + outputstest[0]].dtype != fileref[path + outputsref[0]].dtype:
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logger.warning(
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f"Type of floating point number in test model ({filetest[path + outputstest[0]].dtype}) "
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f"does not match type in reference solution ({fileref[path + outputsref[0]].dtype})\n"
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)
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floattyperef = fileref[path + outputsref[0]].dtype
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floattypetest = filetest[path + outputstest[0]].dtype
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# logger.info(f'Data type: {floattypetest}')
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# Arrays for storing time
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# timeref = np.zeros((fileref.attrs['Iterations']), dtype=floattyperef)
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# timeref = np.linspace(0, (fileref.attrs['Iterations'] - 1) * fileref.attrs['dt'], num=fileref.attrs['Iterations']) / 1e-9
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# timetest = np.zeros((filetest.attrs['Iterations']), dtype=floattypetest)
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# timetest = np.linspace(0, (filetest.attrs['Iterations'] - 1) * filetest.attrs['dt'], num=filetest.attrs['Iterations']) / 1e-9
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timeref = np.zeros((fileref.attrs["Iterations"]), dtype=floattyperef)
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timeref = np.linspace(0, (fileref.attrs["Iterations"] - 1), num=fileref.attrs["Iterations"])
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timetest = np.zeros((filetest.attrs["Iterations"]), dtype=floattypetest)
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timetest = np.linspace(0, (filetest.attrs["Iterations"] - 1), num=filetest.attrs["Iterations"])
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# Arrays for storing field data
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dataref = np.zeros((fileref.attrs["Iterations"], len(outputsref)), dtype=floattyperef)
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datatest = np.zeros((filetest.attrs["Iterations"], len(outputstest)), dtype=floattypetest)
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for ID, name in enumerate(outputsref):
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dataref[:, ID] = fileref[path + str(name)][:]
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datatest[:, ID] = filetest[path + str(name)][:]
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if np.any(np.isnan(datatest[:, ID])):
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logger.exception("Test data contains NaNs")
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raise ValueError
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fileref.close()
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filetest.close()
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# Diffs
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datadiffs = np.zeros(datatest.shape, dtype=np.float64)
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for i in range(len(outputstest)):
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maxi = np.amax(np.abs(dataref[:, i]))
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datadiffs[:, i] = np.divide(
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np.abs(datatest[:, i] - dataref[:, i]), maxi, out=np.zeros_like(dataref[:, i]), where=maxi != 0
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) # Replace any division by zero with zero
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# Calculate power (ignore warning from taking a log of any zero values)
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with np.errstate(divide="ignore"):
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datadiffs[:, i] = 20 * np.log10(datadiffs[:, i])
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# Replace any NaNs or Infs from zero division
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datadiffs[:, i][np.invert(np.isfinite(datadiffs[:, i]))] = 0
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# Print maximum error value
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start = 210
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maxerrors.append(f": {np.amax(datadiffs[start::, 1]):.1f} [dB]")
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logger.info(f"{model}: Max. error {maxerrors[x]}")
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# Plot diffs (select column to choose field component, 0-Ex, 1-Ey etc..)
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ax.plot(timeref[start::], datadiffs[start::, 1], color=next(colors), lw=2, ls=next(lines), label=model)
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ax.set_xticks(np.arange(0, 2200, step=100))
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ax.set_xlim([0, 2100])
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ax.set_yticks(np.arange(-160, 0, step=20))
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ax.set_ylim([-160, -20])
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ax.set_axisbelow(True)
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ax.grid(color=(0.75, 0.75, 0.75), linestyle="dashed")
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mylegend = list(map(add, PMLIDs, maxerrors))
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legend = ax.legend(mylegend, loc=1, fontsize=14)
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frame = legend.get_frame()
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frame.set_edgecolor("white")
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frame.set_alpha(0)
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plt.show()
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# Save a PDF/PNG of the figure
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# fig.savefig(basepath + '.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
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# fig.savefig(savename + '.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
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@@ -0,0 +1,80 @@
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# Copyright (C) 2015-2023: The University of Edinburgh, United Kingdom
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# Authors: Craig Warren, Antonis Giannopoulos, and John Hartley
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|
#
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|
# 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
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|
from pathlib import Path
|
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import matplotlib.pyplot as plt
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import numpy as np
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from testing.diff_output_files import diff_output_files
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logger = logging.getLogger(__name__)
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|
# 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"]
|
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colors = itertools.cycle(colorIDs)
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# for i in range(2):
|
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# next(colors)
|
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lines = itertools.cycle(("--", ":", "-.", "-"))
|
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markers = ["o", "d", "^", "s", "*"]
|
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|
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fn = Path(__file__)
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basename = "pml_3D_pec_plate"
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PMLIDs = ["CFS-PML", "HORIPML-1", "HORIPML-2", "MRIPML-1", "MRIPML-2"]
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maxerrors = []
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testmodels = [basename + "_" + s for s in PMLIDs]
|
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fig, ax = plt.subplots(
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subplot_kw=dict(xlabel="Iterations", ylabel="Error [dB]"), figsize=(20, 10), facecolor="w", edgecolor="w"
|
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)
|
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for x, model in enumerate(testmodels):
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time, datadiffs = diff_output_files(fn.parent.joinpath(basename + "_ref.h5"),
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fn.parent.joinpath(basename + str(x + 1) + ".h5"))
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||||||
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# Print maximum error value
|
||||||
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start = 210
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maxerrors.append(f": {np.amax(datadiffs[start::, 1]):.1f} [dB]")
|
||||||
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logger.info(f"{model}: Max. error {maxerrors[x]}")
|
||||||
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|
||||||
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# Plot diffs (select column to choose field component, 0-Ex, 1-Ey etc..)
|
||||||
|
ax.plot(time[start::], datadiffs[start::, 1], color=next(colors), lw=2, ls=next(lines), label=model)
|
||||||
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ax.set_xticks(np.arange(0, 2200, step=100))
|
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ax.set_xlim([0, 2100])
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ax.set_yticks(np.arange(-160, 0, step=20))
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ax.set_ylim([-160, -20])
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ax.set_axisbelow(True)
|
||||||
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ax.grid(color=(0.75, 0.75, 0.75), linestyle="dashed")
|
||||||
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mylegend = list(map(add, PMLIDs, maxerrors))
|
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legend = ax.legend(mylegend, loc=1, fontsize=14)
|
||||||
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frame = legend.get_frame()
|
||||||
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frame.set_edgecolor("white")
|
||||||
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frame.set_alpha(0)
|
||||||
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|
||||||
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plt.show()
|
||||||
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|
||||||
|
# Save a PDF/PNG of the figure
|
||||||
|
# fig.savefig(basepath + '.pdf', dpi=None, format='pdf', bbox_inches='tight', pad_inches=0.1)
|
||||||
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# fig.savefig(savename + '.png', dpi=150, format='png', bbox_inches='tight', pad_inches=0.1)
|
||||||
@@ -0,0 +1,75 @@
|
|||||||
|
# Copyright (C) 2015-2023: The University of Edinburgh, United Kingdom
|
||||||
|
# Authors: Craig Warren, Antonis Giannopoulos, and John Hartley
|
||||||
|
#
|
||||||
|
# This file is part of gprMax.
|
||||||
|
#
|
||||||
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# gprMax is free software: you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
|
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# the Free Software Foundation, either version 3 of the License, or
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||||||
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# (at your option) any later version.
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||||||
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#
|
||||||
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# gprMax is distributed in the hope that it will be useful,
|
||||||
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
|
||||||
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
|
||||||
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# GNU General Public License for more details.
|
||||||
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#
|
||||||
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# You should have received a copy of the GNU General Public License
|
||||||
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# along with gprMax. If not, see <http://www.gnu.org/licenses/>.
|
||||||
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||||||
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import itertools
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import logging
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||||||
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from operator import add
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from pathlib import Path
|
||||||
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||||||
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import matplotlib.pyplot as plt
|
||||||
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import numpy as np
|
||||||
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|
||||||
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from testing.diff_output_files import diff_output_files
|
||||||
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|
||||||
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logger = logging.getLogger(__name__)
|
||||||
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|
||||||
|
# Create/setup plot figure
|
||||||
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# Plot colours from http://tools.medialab.sciences-po.fr/iwanthue/index.php
|
||||||
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colorIDs = ["#79c72e", "#5774ff", "#ff7c2c", "#4b4e80", "#d7004e", "#007545", "#ff83ec"]
|
||||||
|
colors = itertools.cycle(colorIDs)
|
||||||
|
lines = itertools.cycle(("--", ":", "-.", "-"))
|
||||||
|
markers = ["o", "d", "^", "s", "*"]
|
||||||
|
|
||||||
|
fn = Path(__file__)
|
||||||
|
basename = "pml_basic"
|
||||||
|
PMLIDs = ["off", "x0", "y0", "z0", "xmax", "ymax", "zmax"]
|
||||||
|
maxerrors = []
|
||||||
|
|
||||||
|
for x in range(len(PMLIDs)):
|
||||||
|
file1 = fn.parent.joinpath(basename + str(x + 1) + "_CPU.h5")
|
||||||
|
file2 = fn.parent.joinpath(basename + str(x + 1) + "_GPU.h5")
|
||||||
|
time, datadiffs = diff_output_files(file1, file2)
|
||||||
|
|
||||||
|
# Print maximum error value
|
||||||
|
start = 0
|
||||||
|
maxerrors.append(f": {np.amax(datadiffs[start::, 1]):.1f} [dB]")
|
||||||
|
logger.info(f"{file1.name} - {file2.name}: Max. error {maxerrors[x]}")
|
||||||
|
|
||||||
|
fig, ax = plt.subplots(
|
||||||
|
subplot_kw=dict(xlabel="Iterations", ylabel="Error [dB]"), figsize=(20, 10), facecolor="w", edgecolor="w")
|
||||||
|
|
||||||
|
# Plot diffs (select column to choose field component, 0-Ex, 1-Ey etc..)
|
||||||
|
ax.plot(time[start::], datadiffs[start::, 1], color=next(colors), lw=2, ls=next(lines), label=f"{file1.name} - {file2.name}")
|
||||||
|
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)
|
||||||
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