你已经派生过 gprMax
镜像自地址
https://gitee.com/sunhf/gprMax.git
已同步 2025-08-07 15:10:13 +08:00
95 行
3.6 KiB
Python
95 行
3.6 KiB
Python
# 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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