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镜像自地址 https://gitee.com/sunhf/gprMax.git 已同步 2025-08-06 04:26:52 +08:00
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Consolidated min/max functions into a single function. Added more robust checking of output names when reading output files.

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Craig Warren
2016-08-15 12:10:33 +01:00
父节点 6af67d02ac
当前提交 0fbfb74882
共有 1 个文件被更改，包括 77 次插入 和 108 次删除
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185
user_libs/optimisation_taguchi/fitness_functions.py 普通文件 -> 可执行文件
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@@ -18,88 +18,56 @@ np.seterr(divide='ignore')
from gprMax.exceptions import GeneralError
"""This module contains fitness metric functions that can be used with the Taguchi optimisation method.
All fitness functions must take two arguments and return a single fitness value.
The first argument should be the name of the output file
All fitness functions must take two arguments and return a single fitness value.
The first argument should be the name of the output file
The second argument is a dictionary which can contain any number of additional arguments, e.g. names (IDs) of outputs (rxs) from input file
"""
def minvalue(filename, args):
def min_max_value(filename, args):
"""Minimum value from a response.
Args:
filename (str): Name of output file
args (dict): 'outputs' key with a list of names (IDs) of outputs (rxs) from input file
args (dict): 'type' key with string 'min', 'max' or 'absmax'; 'outputs' key with a list of names (IDs) of outputs (rxs) from input file
Returns:
minvalue (float): Magnitude of minimum value from specific outputs
value (float): Minimum, maximum, or absolute maximum value from specific outputs
"""
f = h5py.File(filename, 'r')
nrx = f.attrs['nrx']
value = 0
outputsused = False
for rx in range(1, nrx + 1):
output = f['/rxs/rx' + str(rx) + '/']
if output.attrs['Name'] in args['outputs']:
outputname = list(output.keys())[0]
minvalue = np.amin(output[outputname])
if args['type'] == 'min':
value += np.abs(np.amin(output[outputname]))
elif args['type'] == 'max':
value += np.amax(output[outputname])
elif args['type'] == 'absmax':
value += np.amax(np.abs(output[outputname]))
else:
raise GeneralError('type must be either min, max, or absmax')
outputsused = True
return np.abs(minvalue)
# Check in case no outputs where found
if not outputsused:
raise GeneralError('No outputs matching {} were found'.format(args['outputs']))
def maxvalue(filename, args):
"""Maximum value from a response.
Args:
filename (str): Name of output file
args (dict): 'outputs' key with a list of names (IDs) of outputs (rxs) from input file
Returns:
maxvalue (float): Maximum value from specific outputs
"""
f = h5py.File(filename, 'r')
nrx = f.attrs['nrx']
for rx in range(1, nrx + 1):
output = f['/rxs/rx' + str(rx) + '/']
if output.attrs['Name'] in args['outputs']:
outputname = list(output.keys())[0]
maxvalue = np.amax(output[outputname])
return maxvalue
def maxabsvalue(filename, args):
"""Maximum absolute value from a response.
Args:
filename (str): Name of output file
args (dict): 'outputs' key with a list of names (IDs) of outputs (rxs) from input file
Returns:
maxabsvalue (float): Maximum absolute value from specific outputs
"""
f = h5py.File(filename, 'r')
nrx = f.attrs['nrx']
for rx in range(1, nrx + 1):
output = f['/rxs/rx' + str(rx) + '/']
if output.attrs['Name'] in args['outputs']:
outputname = list(output.keys())[0]
maxabsvalue = np.amax(np.abs(output[outputname]))
return maxabsvalue
return value
def xcorr(filename, args):
"""Maximum value of a cross-correlation between a response and a reference response.
Args:
filename (str): Name of output file
args (dict): 'refresp' key with path & filename of reference response (time, amp) stored in a text file; 'outputs' key with a list of names (IDs) of outputs (rxs) from input file
Returns:
xcorrmax (float): Maximum value from specific outputs
"""
@@ -107,8 +75,8 @@ def xcorr(filename, args):
# Load (from text file) the reference response. See if file exists at specified path and if not try input file directory
refrespfile = os.path.abspath(args['refresp'])
if not os.path.isfile(refrespfile):
raise GeneralError('Cannot load reference response at {}'.format(refrespfile))
with open(refresp, 'r') as f:
raise GeneralError('Cannot load reference response from {}'.format(refrespfile))
with open(refrespfile, 'r') as f:
refdata = np.loadtxt(f)
reftime = refdata[:,0] * 1e-9
refresp = refdata[:,1]
@@ -117,19 +85,25 @@ def xcorr(filename, args):
f = h5py.File(filename, 'r')
nrx = f.attrs['nrx']
modeltime = np.arange(0, f.attrs['dt'] * f.attrs['Iterations'], f.attrs['dt'])
outputsused = False
for rx in range(1, nrx + 1):
output = f['/rxs/rx' + str(rx) + '/']
if output.attrs['Name'] in args['outputs']:
outputname = list(output.keys())[0]
modelresp = output[outputname]
# Convert field value (V/m) to voltage
# Convert electric field value (V/m) to voltage (V)
if outputname == 'Ex':
modelresp *= -f.attrs['dx, dy, dz'][0]
elif outputname == 'Ey':
modelresp *= -f.attrs['dx, dy, dz'][1]
elif outputname == 'Ez':
modelresp *= -f.attrs['dx, dy, dz'][2]
outputsused = True
# Check in case no outputs where found
if not outputsused:
raise GeneralError('No outputs matching {} were found'.format(args['outputs']))
# Normalise reference respose and response from output file
# refresp /= np.amax(np.abs(refresp))
@@ -162,7 +136,7 @@ def xcorr(filename, args):
# Calculate cross-correlation
xcorr = signal.correlate(refresp, modelresp)
# Set any NaNs to zero
xcorr = np.nan_to_num(xcorr)
@@ -179,11 +153,11 @@ def xcorr(filename, args):
def min_sum_diffs(filename, args):
"""Sum of the differences (in dB) between responses and a reference response.
Args:
filename (str): Name of output file
args (dict): 'refresp' key with path & filename of reference response; 'outputs' key with a list of names (IDs) of outputs (rxs) from input file
Returns:
diffdB (float): Sum of the differences (in dB) between responses and a reference response
"""
@@ -200,7 +174,7 @@ def min_sum_diffs(filename, args):
# Load (from gprMax output file) the response
f = h5py.File(filename, 'r')
nrx = f.attrs['nrx']
diffdB = 0
outputs = 0
for rx in range(1, nrx + 1):
@@ -214,20 +188,24 @@ def min_sum_diffs(filename, args):
diffdB += tmp
outputs += 1
# Check in case no outputs where found
if outputs = 0:
raise GeneralError('No outputs matching {} were found'.format(args['outputs']))
return diffdB / outputs
def compactness(filename, args):
"""A measure of the compactness of a time domain signal.
Args:
filename (str): Name of output file
args (dict): 'outputs' key with a list of names (IDs) of outputs (rxs) from input file
Returns:
compactness (float): Compactness value of signal
"""
f = h5py.File(filename, 'r')
nrx = f.attrs['nrx']
dt = f.attrs['dt']
@@ -235,6 +213,7 @@ def compactness(filename, args):
time = np.linspace(0, 1, iterations)
time *= (iterations * dt)
outputsused = False
for rx in range(1, nrx + 1):
output = f['/rxs/rx' + str(rx) + '/']
if output.attrs['Name'] in args['outputs']:
@@ -249,14 +228,14 @@ def compactness(filename, args):
maxtab, mintab = peakdet(outputdata, delta)
peaks = maxtab + mintab
peaks.sort()
# Remove any peaks smaller than a threshold
thresholdpeak = 1e-3
peaks = [peak for peak in peaks if np.abs(outputdata[peak]) > thresholdpeak]
# Amplitude ratio of the 1st to 3rd peak - hopefully be a measure of a compact envelope
compactness = np.abs(outputdata[peaks[0]]) / np.abs(outputdata[peaks[2]])
# # Percentage of maximum value to measure compactness of signal
# durationthreshold = 2
# # Check if there is a peak/trough smaller than threshold
@@ -267,8 +246,11 @@ def compactness(filename, args):
# time2threshold = time[peaks[durationthresholdexist[0]]]
# compactness = time2threshold - time[min(peaks)]
return compactness
# Check in case no outputs where found
if not outputsused:
raise GeneralError('No outputs matching {} were found'.format(args['outputs']))
return compactness
######################################
@@ -277,78 +259,78 @@ def compactness(filename, args):
def spectral_centroid(x, samplerate):
"""Calculate the spectral centroid of a signal.
Args:
x (float): 1D array containing time domain signal
samplerate (float): Sample rate of signal (Hz)
Returns:
centroid (float): Weighted mean of the frequencies present in the signal
"""
magnitudes = np.abs(np.fft.rfft(x))
length = len(x)
# Positive frequencies
freqs = np.abs(np.fft.fftfreq(length, 1.0/samplerate)[:length//2+1])
centroid = np.sum(magnitudes*freqs) / np.sum(magnitudes)
return centroid
def zero_crossings(x):
"""Find location of zero crossings in 1D data array.
Args:
x (float): 1D array
Returns:
indexzeros (int): Array of indices of zero crossing locations
"""
pos = x > 0
npos = ~pos
indexzeros = ((pos[:-1] & npos[1:]) | (npos[:-1] & pos[1:])).nonzero()[0]
return indexzeros
def peakdet(v, delta, x = None):
"""Detect peaks and troughs in a vector (adapted from MATLAB script at http://billauer.co.il/peakdet.html).
"""Detect peaks and troughs in a vector (adapted from MATLAB script at http://billauer.co.il/peakdet.html).
A point is considered a maximum peak if it has the maximal value, and was preceded (to the left) by a value lower by delta.
Eli Billauer, 3.4.05 (Explicitly not copyrighted).
This function is released to the public domain; Any use is allowed.
Args:
v (float): 1D array
delta (float): threshold for determining peaks/troughs
Returns:
maxtab, mintab (list): Indices of peak/trough locations
"""
maxtab = []
mintab = []
if x is None:
x = np.arange(len(v))
v = np.asarray(v)
if len(v) != len(x):
raise GeneralError('Input vectors v and x must have same length')
if not np.isscalar(delta):
raise GeneralError('Input argument delta must be a scalar')
if delta <= 0:
raise GeneralError('Input argument delta must be positive')
mn, mx = np.Inf, -np.Inf
mnpos, mxpos = np.NaN, np.NaN
lookformax = True
for i in np.arange(len(v)):
this = v[i]
if this > mx:
@@ -357,7 +339,7 @@ def peakdet(v, delta, x = None):
if this < mn:
mn = this
mnpos = x[i]
if lookformax:
if this < mx-delta:
if int(mxpos) != 0:
@@ -374,16 +356,3 @@ def peakdet(v, delta, x = None):
lookformax = True
return maxtab, mintab