def solver(medium, s, Uh, V, alpha, domain):
#==============================================================================
# Load the receiver coordinates and recording times from the data directory
datadir = np.load('datadir.npz')
times = np.load(str(datadir['recordingTimes']))
receiverPoints = np.load(str(datadir['receivers']))
# Compute length of time step.
# This parameter is used for FFT shifting and time windowing
dt = times[1] - times[0]
# Load the windowing parameters for the receiver and time axes of
# the 3D data array
if Path('window.npz').exists():
windowDict = np.load('window.npz')
# Time window parameters (with units of time)
tstart = windowDict['tstart']
tstop = windowDict['tstop']
# Convert time window parameters to corresponding array indices
tstart = int(round(tstart / dt))
tstop = int(round(tstop / dt))
tstep = windowDict['tstep']
# Receiver window parameters
rstart = windowDict['rstart']
rstop = windowDict['rstop']
rstep = windowDict['rstep']
else:
# Set default window parameters if user did
# not specify window parameters.
# Time window parameters (integers corresponding to indices in an array)
tstart = 0
tstop = len(times)
tstep = 1
# Receiver window parameters
rstart = 0
rstop = receiverPoints.shape[0]
rstep = 1
# Slice the recording times according to the time window parameters
# to create a time window array
tinterval = np.arange(tstart, tstop, tstep)
times = times[tinterval]
T = times[-1] - times[0]
times = np.linspace(-T, T, 2 * len(times) - 1)
# Slice the receiverPoints array according to the receiver window parameters
rinterval = np.arange(rstart, rstop, rstep)
receiverPoints = receiverPoints[rinterval, :]
Nr = receiverPoints.shape[0]
Nt = len(times) # number of samples in time window
# Get information about the pulse function used to
# generate the interrogating wave (These parameters are
# used to help Vezda decide if it needs to recompute the
# test functions in the case the user changes these parameters.)
velocity = pulseFun.velocity # only used if medium == constant
peakFreq = pulseFun.peakFreq # peak frequency
peakTime = pulseFun.peakTime # time at which the pulse amplitude is maximum
# Used for getting time and frequency units
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
else:
plotParams = default_params()
# Get machine precision
eps = np.finfo(float).eps # about 2e-16 (used in division
# so we never divide by zero)
#==============================================================================
# Load the user-specified space-time sampling grid
try:
samplingGrid = np.load('samplingGrid.npz')
except FileNotFoundError:
samplingGrid = None
if samplingGrid is None:
sys.exit(
textwrap.dedent('''
A space-time sampling grid needs to be set up before running the
\'vzsolve\' command. Enter:
vzgrid --help
from the command-line for more information on how to set up a
sampling grid.
'''))
if 'z' not in samplingGrid:
# Apply linear sampling method to three-dimensional space-time
x = samplingGrid['x']
y = samplingGrid['y']
tau = samplingGrid['tau']
z = None
# Get number of sampling points in space and time
Nx = len(x)
Ny = len(y)
X, Y = np.meshgrid(x, y, indexing='ij')
# Initialize the Histogram for storing images at each sampling point in time.
# Initialize the Image (time-integrated Histogram with respect to L2 norm)
Image = np.zeros(X.shape)
if medium == 'constant':
# Vezda will compute free-space test functions over the space-time
# sampling grid via function calls to 'FundamentalSolutions.py'. This is
# much more efficient than applying a forward and inverse FFT pair to
# shift the test functions in time corresponding to different sampling
# points in time. FFT pairs are only used when medium == variable.
pulse = lambda t: pulseFun.pulse(t)
# Previously computed test functions and parameters from pulseFun module
# are stored in 'VZTestFuncs.npz'. If the current space-time sampling grid
# and pulseFun module parameters are consistent with those stored in
# 'VZTestFuncs.npz', then Vezda will load the previously computed test
# functions. Otherwise, Vezda will recompute the test functions. This reduces
# computational cost by only computing test functions when necessary.
if Path('VZTestFuncs.npz').exists():
print(
'\nDetected that free-space test functions have already been computed...'
)
print(
'Checking consistency with current space-time sampling grid and pulse function...'
)
TFDict = np.load('VZTestFuncs.npz')
if samplingIsCurrent(TFDict, receiverPoints, times, velocity,
tau, x, y, z, peakFreq, peakTime):
print('Moving forward to imaging algorithm...')
TFarray = TFDict['TFarray']
else:
if tau[0] != 0:
tu = plotParams['tu']
if tu != '':
print(
'Recomputing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Recomputing test functions for focusing time %0.2f...'
% (tau[0]))
TFarray, samplingPoints = sampleSpace(
receiverPoints, times - tau[0], velocity, x, y, z,
pulse)
else:
print('Recomputing test functions...')
TFarray, samplingPoints = sampleSpace(
receiverPoints, times, velocity, x, y, z, pulse)
np.savez('VZTestFuncs.npz',
TFarray=TFarray,
time=times,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
tau=tau,
samplingPoints=samplingPoints)
else:
print(
'\nComputing free-space test functions for the current space-time sampling grid...'
)
if tau[0] != 0:
tu = plotParams['tu']
if tu != '':
print(
'Computing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Computing test functions for focusing time %0.2f...'
% (tau[0]))
TFarray, samplingPoints = sampleSpace(
receiverPoints, times - tau[0], velocity, x, y, z,
pulse)
else:
TFarray, samplingPoints = sampleSpace(
receiverPoints, times, velocity, x, y, z, pulse)
np.savez('VZTestFuncs.npz',
TFarray=TFarray,
time=times,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
tau=tau,
samplingPoints=samplingPoints)
#==============================================================================
if domain == 'freq':
# Transform test functions into the frequency domain and bandpass for efficient solution
# to near-field equation
print('Transforming test functions to the frequency domain...')
N = nextPow2(Nt)
TFarray = np.fft.rfft(TFarray, n=N, axis=1)
if plotParams['fmax'] is None:
freqs = np.fft.rfftfreq(N, tstep * dt)
plotParams['fmax'] = np.max(freqs)
pickle.dump(plotParams, open('plotParams.pkl', 'wb'),
pickle.HIGHEST_PROTOCOL)
# Apply the frequency window
fmin = plotParams['fmin']
fmax = plotParams['fmax']
fu = plotParams['fu'] # frequency units (e.g., Hz)
if fu != '':
print('Applying bandpass filter: [%0.2f %s, %0.2f %s]' %
(fmin, fu, fmax, fu))
else:
print('Applying bandpass filter: [%0.2f, %0.2f]' %
(fmin, fmax))
df = 1.0 / (N * tstep * dt)
startIndex = int(round(fmin / df))
stopIndex = int(round(fmax / df))
finterval = np.arange(startIndex, stopIndex, 1)
TFarray = TFarray[:, finterval, :]
N = TFarray.shape[1]
#==============================================================================
# Solve the near-field equation for each sampling point
print('Localizing the source...')
# Compute the Tikhonov-regularized solution to the near-field equation N * phi = tf.
# 'tf' is a test function
# 'alpha' is the regularization parameter
# 'phi_alpha' is the regularized solution given 'alpha'
k = 0 # counter for spatial sampling points
for ix in trange(Nx, desc='Solving system'):
for iy in range(Ny):
tf = np.reshape(TFarray[:, :, k], (N * Nr, 1))
phi_alpha = Tikhonov(Uh, s, V, tf, alpha)
Image[ix, iy] = 1.0 / (norm(phi_alpha) + eps)
k += 1
Imin = np.min(Image)
Imax = np.max(Image)
Image = (Image - Imin) / (Imax - Imin + eps)
elif medium == 'variable':
if 'testFuncs' in datadir:
# Load the user-provided test functions
TFarray = np.load(str(datadir['testFuncs']))
# Apply the receiver/time windows, if any
TFarray = TFarray[rinterval, :, :]
TFarray = TFarray[:, tinterval, :]
#==============================================================================
if domain == 'freq':
# Transform test functions into the frequency domain and bandpass for efficient solution
# to near-field equation
print(
'Transforming test functions to the frequency domain...'
)
N = nextPow2(Nt)
TFarray = np.fft.rfft(TFarray, n=N, axis=1)
if plotParams['fmax'] is None:
freqs = np.fft.rfftfreq(N, tstep * dt)
plotParams['fmax'] = np.max(freqs)
pickle.dump(plotParams, open('plotParams.pkl', 'wb'),
pickle.HIGHEST_PROTOCOL)
# Apply the frequency window
fmin = plotParams['fmin']
fmax = plotParams['fmax']
fu = plotParams['fu'] # frequency units (e.g., Hz)
if fu != '':
print(
'Applying bandpass filter: [%0.2f %s, %0.2f %s]' %
(fmin, fu, fmax, fu))
else:
print('Applying bandpass filter: [%0.2f, %0.2f]' %
(fmin, fmax))
df = 1.0 / (N * tstep * dt)
startIndex = int(round(fmin / df))
stopIndex = int(round(fmax / df))
finterval = np.arange(startIndex, stopIndex, 1)
TFarray = TFarray[:, finterval, :]
N = TFarray.shape[1]
# Load the sampling points
samplingPoints = np.load(str(datadir['samplingPoints']))
else:
sys.exit(
textwrap.dedent('''
FileNotFoundError: Attempted to load file containing test
functions, but no such file exists. If a file exists containing
the test functions, run:
'vzdata --path=<path/to/data/>'
and specify the file containing the test functions when prompted.
Otherwise, specify 'no' when asked if a file containing the test
functions exists.
'''))
userResponded = False
print(
textwrap.dedent('''
In what order was the sampling grid spanned to compute the test functions?
Enter 'xy' if for each x, loop over y. (Default)
Enter 'yx' if for each y, loop over x.
Enter 'q/quit' to abort the calculation.
'''))
while userResponded == False:
order = input('Order: ')
if order == '' or order == 'xy':
print('Proceeding with order \'xy\'...')
print('Localizing the source...')
# Compute the Tikhonov-regularized solution to the near-field equation N * phi = tf.
# 'tf' is a test function
# 'alpha' is the regularization parameter
# 'phi_alpha' is the regularized solution given 'alpha'
k = 0 # counter for spatial sampling points
for ix in trange(Nx, desc='Solving system'):
for iy in range(Ny):
tf = np.reshape(TFarray[:, :, k], (N * Nr, 1))
phi_alpha = Tikhonov(Uh, s, V, tf, alpha)
Image[ix, iy] = 1.0 / (norm(phi_alpha) + eps)
k += 1
Imin = np.min(Image)
Imax = np.max(Image)
Image = (Image - Imin) / (Imax - Imin + eps)
userResponded = True
break
elif order == 'yx':
print('Proceeding with order \'yx\'...')
print('Localizing the source...')
# Compute the Tikhonov-regularized solution to the near-field equation N * phi = tf.
# 'tf' is a test function
# 'alpha' is the regularization parameter
# 'phi_alpha' is the regularized solution given 'alpha'
k = 0 # counter for spatial sampling points
for iy in trange(Ny, desc='Solving system'):
for ix in range(Nx):
tf = np.reshape(TFarray[:, :, k], (N * Nr, 1))
phi_alpha = Tikhonov(Uh, s, V, tf, alpha)
Image[ix, iy] = 1.0 / (norm(phi_alpha) + eps)
k += 1
Imin = np.min(Image)
Imax = np.max(Image)
Image = (Image - Imin) / (Imax - Imin + eps)
userResponded = True
break
elif order == 'q' or order == 'quit':
sys.exit('Aborting calculation.')
else:
print(
textwrap.dedent('''
Invalid response. Please enter one of the following:
Enter 'xy' if for each x, loop over y. (Default)
Enter 'yx' if for each y, loop over x.
Enter 'q/quit' to abort the calculation.
'''))
if domain == 'freq':
np.savez('imageNFE.npz', Image=Image, alpha=alpha, X=X, Y=Y)
else:
np.savez('imageNFE.npz',
Image=Image,
alpha=alpha,
X=X,
Y=Y,
tau=tau)
#==============================================================================
else:
# Apply linear sampling method to four-dimensional space-time
x = samplingGrid['x']
y = samplingGrid['y']
z = samplingGrid['z']
tau = samplingGrid['tau']
# Get number of sampling points in space and time
Nx = len(x)
Ny = len(y)
Nz = len(z)
X, Y, Z = np.meshgrid(x, y, z, indexing='ij')
# Initialize the Histogram for storing images at each sampling point in time.
# Initialize the Image (time-integrated Histogram with respect to L2 norm)
Image = np.zeros(X.shape)
if medium == 'constant':
# Vezda will compute free-space test functions over the space-time
# sampling grid via function calls to 'FundamentalSolutions.py'. This is
# much more efficient than applying a forward and inverse FFT pair to
# shift the test functions in time corresponding to different sampling
# points in time. FFT pairs are only used when medium == variable.
pulse = lambda t: pulseFun.pulse(t)
# Previously computed test functions and parameters from pulseFun module
# are stored in 'VZTestFuncs.npz'. If the current space-time sampling grid
# and pulseFun module parameters are consistent with those stored in
# 'VZTestFuncs.npz', then Vezda will load the previously computed test
# functions. Otherwise, Vezda will recompute the test functions. This reduces
# computational cost by only computing test functions when necessary.
if Path('VZTestFuncs.npz').exists():
print(
'\nDetected that free-space test functions have already been computed...'
)
print(
'Checking consistency with current space-time sampling grid and pulse function...'
)
TFDict = np.load('VZTestFuncs.npz')
if samplingIsCurrent(TFDict, receiverPoints, times, velocity,
tau, x, y, z, peakFreq, peakTime):
print('Moving forward to imaging algorithm...')
TFarray = TFDict['TFarray']
else:
if tau[0] != 0:
tu = plotParams['tu']
if tu != '':
print(
'Recomputing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Recomputing test functions for focusing time %0.2f...'
% (tau[0]))
TFarray, samplingPoints = sampleSpace(
receiverPoints, times - tau[0], velocity, x, y, z,
pulse)
else:
print('Recomputing test functions...')
TFarray, samplingPoints = sampleSpace(
receiverPoints, times, velocity, x, y, z, pulse)
np.savez('VZTestFuncs.npz',
TFarray=TFarray,
time=times,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
z=z,
tau=tau,
samplingPoints=samplingPoints)
else:
print(
'\nComputing free-space test functions for the current space-time sampling grid...'
)
if tau[0] != 0:
tu = plotParams['tu']
if tu != '':
print(
'Computing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Computing test functions for focusing time %0.2f...'
% (tau[0]))
TFarray, samplingPoints = sampleSpace(
receiverPoints, times - tau[0], velocity, x, y, z,
pulse)
else:
TFarray, samplingPoints = sampleSpace(
receiverPoints, times, velocity, x, y, z, pulse)
np.savez('VZTestFuncs.npz',
TFarray=TFarray,
time=times,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
z=z,
tau=tau,
samplingPoints=samplingPoints)
#==============================================================================
if domain == 'freq':
# Transform test functions into the frequency domain and bandpass for efficient solution
# to near-field equation
print('Transforming test functions to the frequency domain...')
N = nextPow2(Nt)
TFarray = np.fft.rfft(TFarray, n=N, axis=1)
if plotParams['fmax'] is None:
freqs = np.fft.rfftfreq(N, tstep * dt)
plotParams['fmax'] = np.max(freqs)
pickle.dump(plotParams, open('plotParams.pkl', 'wb'),
pickle.HIGHEST_PROTOCOL)
# Apply the frequency window
fmin = plotParams['fmin']
fmax = plotParams['fmax']
fu = plotParams['fu'] # frequency units (e.g., Hz)
if fu != '':
print('Applying bandpass filter: [%0.2f %s, %0.2f %s]' %
(fmin, fu, fmax, fu))
else:
print('Applying bandpass filter: [%0.2f, %0.2f]' %
(fmin, fmax))
df = 1.0 / (N * tstep * dt)
startIndex = int(round(fmin / df))
stopIndex = int(round(fmax / df))
finterval = np.arange(startIndex, stopIndex, 1)
TFarray = TFarray[:, finterval, :]
N = TFarray.shape[1]
#==============================================================================
# Solve the near-field equation for each sampling point
print('Localizing the source...')
# Compute the Tikhonov-regularized solution to the near-field equation N * phi = tf.
# 'tf' is a test function
# 'alpha' is the regularization parameter
# 'phi_alpha' is the regularized solution given 'alpha'
k = 0 # counter for spatial sampling points
for ix in trange(Nx, desc='Solving system'):
for iy in range(Ny):
for iz in range(Nz):
tf = np.reshape(TFarray[:, :, k], (N * Nr, 1))
phi_alpha = Tikhonov(Uh, s, V, tf, alpha)
Image[ix, iy, iz] = 1.0 / (norm(phi_alpha) + eps)
k += 1
Imin = np.min(Image)
Imax = np.max(Image)
Image = (Image - Imin) / (Imax - Imin + eps)
elif medium == 'variable':
if 'testFuncs' in datadir:
# Load the user-provided test functions
TFarray = np.load(str(datadir['testFuncs']))
# Apply the receiver/time windows, if any
TFarray = TFarray[rinterval, :, :]
TFarray = TFarray[:, tinterval, :]
#==============================================================================
if domain == 'freq':
# Transform test functions into the frequency domain and bandpass for efficient solution
# to near-field equation
print(
'Transforming test functions to the frequency domain...'
)
N = nextPow2(Nt)
TFarray = np.fft.rfft(TFarray, n=N, axis=1)
if plotParams['fmax'] is None:
freqs = np.fft.rfftfreq(N, tstep * dt)
plotParams['fmax'] = np.max(freqs)
pickle.dump(plotParams, open('plotParams.pkl', 'wb'),
pickle.HIGHEST_PROTOCOL)
# Apply the frequency window
fmin = plotParams['fmin']
fmax = plotParams['fmax']
fu = plotParams['fu'] # frequency units (e.g., Hz)
if fu != '':
print(
'Applying bandpass filter: [%0.2f %s, %0.2f %s]' %
(fmin, fu, fmax, fu))
else:
print('Applying bandpass filter: [%0.2f, %0.2f]' %
(fmin, fmax))
df = 1.0 / (N * tstep * dt)
startIndex = int(round(fmin / df))
stopIndex = int(round(fmax / df))
finterval = np.arange(startIndex, stopIndex, 1)
TFarray = TFarray[:, finterval, :]
N = TFarray.shape[1]
# Load the sampling points
samplingPoints = np.load(str(datadir['samplingPoints']))
else:
sys.exit(
textwrap.dedent('''
FileNotFoundError: Attempted to load file containing test
functions, but no such file exists. If a file exists containing
the test functions, run:
'vzdata --path=<path/to/data/>'
and specify the file containing the test functions when prompted.
Otherwise, specify 'no' when asked if a file containing the test
functions exists.
'''))
userResponded = False
print(
textwrap.dedent('''
In what order was the sampling grid spanned to compute the test functions?
Enter 'xyz' if for each x, for each y, loop over z. (Default)
Enter 'xzy' if for each x, for each z, loop over y.
Enter 'yxz' if for each y, for each x, loop over z.
Enter 'yzx' if for each y, for each z, loop over x.
Enter 'zxy' if for each z, for each x, loop over y.
Enter 'zyx' if for each z, for each y, loop over x
Enter 'q/quit' to abort the calculation.
'''))
while userResponded == False:
order = input('Order: ')
if order == '' or order == 'xyz':
print('Proceeding with order \'xyz\'...')
print('Localizing the source...')
# Compute the Tikhonov-regularized solution to the near-field equation N * phi = tf.
# 'tf' is a test function
# 'alpha' is the regularization parameter
# 'phi_alpha' is the regularized solution given 'alpha'
k = 0 # counter for spatial sampling points
for ix in trange(Nx, desc='Solving system'):
for iy in range(Ny):
for iz in range(Nz):
tf = np.reshape(TFarray[:, :, k], (N * Nr, 1))
phi_alpha = Tikhonov(Uh, s, V, tf, alpha)
Image[ix, iy,
iz] = 1.0 / (norm(phi_alpha) + eps)
k += 1
Imin = np.min(Image)
Imax = np.max(Image)
Image = (Image - Imin) / (Imax - Imin + eps)
userResponded = True
break
elif order == 'xzy':
print('Proceeding with order \'xzy\'...')
print('Localizing the source...')
# Compute the Tikhonov-regularized solution to the near-field equation N * phi = tf.
# 'tf' is a test function
# 'alpha' is the regularization parameter
# 'phi_alpha' is the regularized solution given 'alpha'
k = 0 # counter for spatial sampling points
for ix in trange(Nx, desc='Solving system'):
for iz in range(Nz):
for iy in range(Ny):
tf = np.reshape(TFarray[:, :, k], (N * Nr, 1))
phi_alpha = Tikhonov(Uh, s, V, tf, alpha)
Image[ix, iy,
iz] = 1.0 / (norm(phi_alpha) + eps)
k += 1
Imin = np.min(Image)
Imax = np.max(Image)
Image = (Image - Imin) / (Imax - Imin + eps)
userResponded = True
break
elif order == 'yxz':
print('Proceeding with order \'yxz\'...')
print('Localizing the source...')
# Compute the Tikhonov-regularized solution to the near-field equation N * phi = tf.
# 'tf' is a test function
# 'alpha' is the regularization parameter
# 'phi_alpha' is the regularized solution given 'alpha'
k = 0 # counter for spatial sampling points
for iy in trange(Ny, desc='Solving system'):
for ix in range(Nx):
for iz in range(Nz):
tf = np.reshape(TFarray[:, :, k], (N * Nr, 1))
phi_alpha = Tikhonov(Uh, s, V, tf, alpha)
Image[ix, iy,
iz] = 1.0 / (norm(phi_alpha) + eps)
k += 1
Imin = np.min(Image)
Imax = np.max(Image)
Image = (Image - Imin) / (Imax - Imin + eps)
userResponded = True
break
elif order == 'yzx':
print('Proceeding with order \'yzx\'...')
print('Localizing the source...')
# Compute the Tikhonov-regularized solution to the near-field equation N * phi = tf.
# 'tf' is a test function
# 'alpha' is the regularization parameter
# 'phi_alpha' is the regularized solution given 'alpha'
k = 0 # counter for spatial sampling points
for iy in trange(Ny, desc='Solving system'):
for iz in range(Nz):
for ix in range(Nx):
tf = np.reshape(TFarray[:, :, k], (N * Nr, 1))
phi_alpha = Tikhonov(Uh, s, V, tf, alpha)
Image[ix, iy,
iz] = 1.0 / (norm(phi_alpha) + eps)
k += 1
Imin = np.min(Image)
Imax = np.max(Image)
Image = (Image - Imin) / (Imax - Imin + eps)
userResponded = True
break
elif order == 'zxy':
print('Proceeding with order \'zxy\'...')
print('Localizing the source...')
# Compute the Tikhonov-regularized solution to the near-field equation N * phi = tf.
# 'tf' is a test function
# 'alpha' is the regularization parameter
# 'phi_alpha' is the regularized solution given 'alpha'
k = 0 # counter for spatial sampling points
for iz in trange(Nz, desc='Solving system'):
for ix in range(Nx):
for iy in range(Ny):
tf = np.reshape(TFarray[:, :, k], (N * Nr, 1))
phi_alpha = Tikhonov(Uh, s, V, tf, alpha)
Image[ix, iy,
iz] = 1.0 / (norm(phi_alpha) + eps)
k += 1
Imin = np.min(Image)
Imax = np.max(Image)
Image = (Image - Imin) / (Imax - Imin + eps)
userResponded = True
break
elif order == 'zyx':
print('Proceeding with order \'zyx\'...')
print('Localizing the source...')
# Compute the Tikhonov-regularized solution to the near-field equation N * phi = tf.
# 'tf' is a test function
# 'alpha' is the regularization parameter
# 'phi_alpha' is the regularized solution given 'alpha'
k = 0 # counter for spatial sampling points
for iz in trange(Nz, desc='Solving system'):
for iy in range(Ny):
for ix in range(Nx):
tf = np.reshape(TFarray[:, :, k], (N * Nr, 1))
phi_alpha = Tikhonov(Uh, s, V, tf, alpha)
Image[ix, iy,
iz] = 1.0 / (norm(phi_alpha) + eps)
k += 1
Imin = np.min(Image)
Imax = np.max(Image)
Image = (Image - Imin) / (Imax - Imin + eps)
userResponded = True
break
elif order == 'q' or order == 'quit':
sys.exit('Aborting calculation.')
else:
print(
textwrap.dedent('''
Invalid response. Please enter one of the following:
Enter 'xyz' if for each x, for each y, loop over z. (Default)
Enter 'xzy' if for each x, for each z, loop over y.
Enter 'yxz' if for each y, for each x, loop over z.
Enter 'yzx' if for each y, for each z, loop over x.
Enter 'zxy' if for each z, for each x, loop over y.
Enter 'zyx' if for each z, for each y, loop over x
Enter 'q/quit' to abort the calculation.
'''))
if domain == 'freq':
np.savez('imageNFE.npz', Image=Image, alpha=alpha, X=X, Y=Y, Z=Z)
else:
np.savez('imageNFE.npz',
Image=Image,
alpha=alpha,
X=X,
Y=Y,
Z=Z,
tau=tau)
def cli():
parser = argparse.ArgumentParser()
parser.add_argument(
'--fmin',
type=float,
help='Specify the minimum frequency component of the noise.')
parser.add_argument(
'--fmax',
type=float,
help='Specify the maximum frequency component of the noise.')
parser.add_argument(
'--snr',
type=float,
help='''Specify the desired signal-to-noise ratio. Must be a positive
real number.''')
args = parser.parse_args()
#==============================================================================
try:
Dict = np.load('noisyData.npz')
fmin = Dict['fmin']
fmax = Dict['fmax']
snr = Dict['snr']
except FileNotFoundError:
fmin, fmax, snr = None, None, None
# Used for getting frequency units
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
else:
plotParams = default_params()
if all(v is None for v in [args.fmin, args.fmax, args.snr]):
# if no arguments are passed
if all(v is None for v in [fmin, fmax, snr]):
# and no parameters have been assigned values
sys.exit(
textwrap.dedent('''
No noise has been added to the data.
'''))
else:
# print fmin, fmax, snr and exit
fu = plotParams['fu']
if fu != '':
sys.exit(
textwrap.dedent('''
Band-limited white noise has already been added to the data:
Minimum frequency: {:0.2f} {}
Maximum frequency: {:0.2f} {}
Signal-to-noise ratio: {:0.2f}
'''.format(fmin, fu, fmax, fu, snr)))
else:
sys.exit(
textwrap.dedent('''
Band-limited white noise has already been added to the data:
Minimum frequency: {:0.2f}
Maximum frequency: {:0.2f}
Signal-to-noise ratio: {:0.2f}
'''.format(fmin, fmax, snr)))
elif all(v is not None for v in [args.fmin, args.fmax, args.snr]):
# if all arguments were passed
if args.fmax < args.fmin:
sys.exit(
textwrap.dedent('''
RelationError: The maximum frequency component of the nosie must be greater
than or equal to the mininum frequency component.
'''))
elif args.fmin <= 0:
sys.exit(
textwrap.dedent('''
ValueError: The minimum frequency component of the noise must be strictly positive.
'''))
elif args.snr <= 0:
sys.exit(
textwrap.dedent('''
ValueError: The signal-to-noise ratio (SNR) must be strictly positive.
'''))
fmin = args.fmin
fmax = args.fmax
snr = args.snr
fu = plotParams['fu']
if fu != '':
print(
textwrap.dedent('''
Adding band-limited white noise:
Minimum frequency: {:0.2f} {}
Maximum frequency: {:0.2f} {}
Signal-to-noise ratio: {:0.2f}
'''.format(fmin, fu, fmax, fu, snr)))
else:
print(
textwrap.dedent('''
Adding band-limited white noise:
Minimum frequency: {:0.2f}
Maximum frequency: {:0.2f}
Signal-to-noise ratio: {:0.2f}
'''.format(fmin, fmax, snr)))
# Load the 3D data array and recording times from data directory
datadir = np.load('datadir.npz')
recordedData = np.load(str(datadir['recordedData']))
recordingTimes = np.load(str(datadir['recordingTimes']))
dt = recordingTimes[1] - recordingTimes[0]
noisyData = add_noise(recordedData, dt, fmin, fmax, snr)
np.savez('noisyData.npz',
noisyData=noisyData,
fmin=fmin,
fmax=fmax,
snr=snr)
else:
sys.exit(
textwrap.dedent('''
Error: All command-line arguments \'--fmin\', \'--fmax\', and \'--snr\' must
be used when parameterizing the noise.
'''))
def cli():
parser = argparse.ArgumentParser()
parser.add_argument('--data', action='store_true',
help='Plot the recorded data. (Default)')
parser.add_argument('--testfunc', action='store_true',
help='Plot the simulated test functions.')
parser.add_argument('--tu', type=str,
help='Specify the time units (e.g., \'s\' or \'ms\').')
parser.add_argument('--au', type=str,
help='Specify the amplitude units (e.g., \'m\' or \'mm\').')
parser.add_argument('--pclip', type=float,
help='''Specify the percentage (0-1) of the peak amplitude to display. This
parameter is used for pcolormesh plots only. Default is set to 1.''')
parser.add_argument('--title', type=str,
help='''Specify a title for the wiggle plot. Default title is
\'Data\' if \'--data\' is passed and 'Test Function' if \'--testfunc\'
is passed.''')
parser.add_argument('--format', '-f', type=str, default='pdf', choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help='''Specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument('--map', action='store_true',
help='''Plot a map of the receiver and source/sampling point locations. The current
source/sampling point will be highlighted. The boundary of the scatterer will also
be shown if available.''')
parser.add_argument('--mode', type=str, choices=['light', 'dark'], required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
#==============================================================================
# if a plotParams.pkl file already exists, load relevant parameters
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
# update parameters for wiggle plots based on passed arguments
if args.mode is not None:
plotParams['view_mode'] = args.mode
if args.tu is not None:
plotParams['tu'] = args.tu
if args.au is not None:
plotParams['au'] = args.au
if args.pclip is not None:
if args.pclip >= 0 and args.pclip <= 1:
plotParams['pclip'] = args.pclip
else:
print(textwrap.dedent(
'''
Warning: Invalid value passed to argument \'--pclip\'. Value must be
between 0 and 1.
'''))
if args.title is not None:
if args.data:
plotParams['data_title'] = args.title
elif args.testfunc:
plotParams['tf_title'] = args.title
else: # create a plotParams dictionary file with default values
plotParams = default_params()
# update parameters for wiggle plots based on passed arguments
if args.mode is not None:
plotParams['view_mode'] = args.mode
if args.tu is not None:
plotParams['tu'] = args.tu
if args.au is not None:
plotParams['au'] = args.au
if args.title is not None:
if args.data:
plotParams['data_title'] = args.title
elif args.testfunc:
plotParams['tf_title'] = args.title
pickle.dump(plotParams, open('plotParams.pkl', 'wb'), pickle.HIGHEST_PROTOCOL)
#==============================================================================
# Load the relevant data to plot
datadir = np.load('datadir.npz')
receiverPoints = np.load(str(datadir['receivers']))
time = np.load(str(datadir['recordingTimes']))
# Apply any user-specified windows
rinterval, tinterval, tstep, dt, sinterval = get_user_windows()
receiverPoints = receiverPoints[rinterval, :]
time = time[tinterval]
# Load the scatterer boundary, if it exists
if 'scatterer' in datadir:
scatterer = np.load(str(datadir['scatterer']))
else:
scatterer = None
if all(v is True for v in [args.data, args.testfunc]):
# User specified both data and testfuncs for plotting
# Send error message and exit.
sys.exit(textwrap.dedent(
'''
Error: Cannot plot both recorded data and simulated test functions. Use
vzwiggles --data
to plot the recorded data or
vzwiggles --testfuncs
to plot the simulated test functions.
'''))
elif all(v is not True for v in [args.data, args.testfunc]):
# User did not specify which wiggles to plot.
# Plot recorded data by default.
# load the 3D data array into variable 'X'
# X[receiver, time, source]
wiggleType = 'data'
X = load_data(domain='time', verbose=True)
if 'sources' in datadir:
sourcePoints = np.load(str(datadir['sources']))
sourcePoints = sourcePoints[sinterval, :]
else:
sourcePoints = None
elif args.data:
# load the 3D data array into variable 'X'
# X[receiver, time, source]
wiggleType = 'data'
X = load_data(domain='time', verbose=True)
if 'sources' in datadir:
sourcePoints = np.load(str(datadir['sources']))
sourcePoints = sourcePoints[sinterval, :]
else:
sourcePoints = None
elif args.testfunc:
wiggleType = 'testfunc'
# Update time to convolution times
T = time[-1] - time[0]
time = np.linspace(-T, T, 2 * len(time) - 1)
if 'testFuncs' not in datadir and not Path('VZTestFuncs.npz').exists():
X, sourcePoints = load_test_funcs(domain='time', medium='constant',
verbose=True, return_sampling_points=True)
if 'testFuncs' in datadir and not Path('VZTestFuncs.npz').exists():
X, sourcePoints = load_test_funcs(domain='time', medium='variable',
verbose=True, return_sampling_points=True)
# Pad time axis with zeros to length of convolution 2Nt-1
npad = ((0, 0), (X.shape[1] - 1, 0), (0, 0))
X = np.pad(X, pad_width=npad, mode='constant', constant_values=0)
elif not 'testFuncs' in datadir and Path('VZTestFuncs.npz').exists():
X, sourcePoints = load_test_funcs(domain='time', medium='constant',
verbose=True, return_sampling_points=True)
elif 'testFuncs' in datadir and Path('VZTestFuncs.npz').exists():
userResponded = False
print(textwrap.dedent(
'''
Two files are available containing simulated test functions.
Enter '1' to view the user-provided test functions. (Default)
Enter '2' to view the test functions computed by Vezda.
Enter 'q/quit' to exit.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
X, sourcePoints = load_test_funcs(domain='time', medium='variable',
verbose=True, return_sampling_points=True)
# Pad time axis with zeros to length of convolution 2Nt-1
npad = ((0, 0), (X.shape[1] - 1, 0), (0, 0))
X = np.pad(X, pad_width=npad, mode='constant', constant_values=0)
userResponded = True
break
elif answer == '2':
X, sourcePoints = load_test_funcs(domain='time', medium='constant',
verbose=True, return_sampling_points=True)
userResponded = True
break
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.')
else:
print('Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.')
#==============================================================================
# increment source/recording interval and receiver interval to be consistent
# with one-based indexing (i.e., count from one instead of zero)
sinterval += 1
rinterval += 1
Ns = X.shape[2]
remove_keymap_conflicts({'left', 'right', 'up', 'down', 'save'})
if args.map:
fig, ax1, ax2 = setFigure(num_axes=2, mode=plotParams['view_mode'],
ax2_dim=receiverPoints.shape[1])
ax1.volume = X
ax1.index = Ns // 2
title = wave_title(ax1.index, sinterval, sourcePoints, wiggleType, plotParams)
plotWiggles(ax1, X[:, :, ax1.index], time, rinterval, receiverPoints, title, wiggleType, plotParams)
ax2.index = ax1.index
plotMap(ax2, ax2.index, receiverPoints, sourcePoints, scatterer, wiggleType, plotParams)
plt.tight_layout()
fig.canvas.mpl_connect('key_press_event', lambda event: process_key_waves(event, time, rinterval, sinterval,
receiverPoints, sourcePoints, Ns, scatterer,
args.map, wiggleType, plotParams))
else:
fig, ax = setFigure(num_axes=1, mode=plotParams['view_mode'])
ax.volume = X
ax.index = Ns // 2
title = wave_title(ax.index, sinterval, sourcePoints, wiggleType, plotParams)
plotWiggles(ax, X[:, :, ax.index], time, rinterval, receiverPoints, title, wiggleType, plotParams)
plt.tight_layout()
fig.canvas.mpl_connect('key_press_event', lambda event: process_key_waves(event, time, rinterval, sinterval,
receiverPoints, sourcePoints, Ns, scatterer,
args.map, wiggleType, plotParams))
plt.show()
def cli():
parser = argparse.ArgumentParser()
parser.add_argument(
'--nfe',
action='store_true',
help='''Plot the image obtained by solving the near-field equation.''')
parser.add_argument(
'--lse',
action='store_true',
help=
'''Plot the image obtained by solving the Lippmann-Schwinger equation.'''
)
parser.add_argument(
'--movie',
action='store_true',
help='Save a three-dimensional figure as a rotating frame.')
parser.add_argument('--isolevel',
type=float,
default=None,
help='''Specify the contour level of the isosurface for
three-dimensional visualizations. Level must be between 0 and 1.'''
)
parser.add_argument(
'--format',
'-f',
type=str,
default=None,
choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help=
'''specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument('--xlabel',
type=str,
default=None,
help='''specify a label for the x-axis.''')
parser.add_argument('--ylabel',
type=str,
default=None,
help='''specify a label for the y-axis.''')
parser.add_argument('--zlabel',
type=str,
default=None,
help='''specify a label for the z-axis.''')
parser.add_argument(
'--xu',
type=str,
default=None,
help='Specify the units for the x-axis (e.g., \'m\' or \'km\').')
parser.add_argument(
'--yu',
type=str,
default=None,
help='Specify the units for the y-axis (e.g., \'m\' or \'km\').')
parser.add_argument(
'--zu',
type=str,
default=None,
help='Specify the units for the z-axis (e.g., \'m\' or \'km\').')
parser.add_argument(
'--colormap',
type=str,
default=None,
choices=['viridis', 'plasma', 'inferno', 'magma', 'cividis'],
help=
'specify a perceptually uniform sequential colormap. Default is \'magma\''
)
parser.add_argument(
'--colorbar',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'y/yes/true\' to plot colorbar. Default is \'n/no/false\'')
parser.add_argument(
'--invert_xaxis',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'y/yes/true\' to invert x-axis. Default is \'n/no/false\'')
parser.add_argument(
'--invert_yaxis',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'y/yes/true\' to invert y-axis. Default is \'n/no/false\'')
parser.add_argument(
'--invert_zaxis',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'y/yes/true\' to invert z-axis. Default is \'n/no/false\'')
parser.add_argument(
'--show_scatterer',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'y/yes/true\' to show scatterer boundary. Default is \'n/no/false\''
)
parser.add_argument(
'--show_sources',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help='specify \'n/no/false\' to hide sources. Default is \'y/yes/true\''
)
parser.add_argument(
'--show_receivers',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'n/no/false\' to hide receivers. Default is \'y/yes/true\'')
parser.add_argument(
'--mode',
type=str,
choices=['light', 'dark'],
required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
#==============================================================================
# if a plotParams.pkl file already exists, load relevant parameters
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
# for both wiggle plots and image plots
if args.format is not None:
plotParams['pltformat'] = args.format
if args.mode is not None:
plotParams['view_mode'] = args.mode
# for image/map plots
if args.isolevel is not None:
plotParams['isolevel'] = args.isolevel
if args.xlabel is not None:
plotParams['xlabel'] = args.xlabel
if args.ylabel is not None:
plotParams['ylabel'] = args.ylabel
if args.zlabel is not None:
plotParams['zlabel'] = args.zlabel
#==============================================================================
# handle units here
if args.xu is not None:
plotParams['xu'] = args.xu
if args.yu is not None:
plotParams['yu'] = args.yu
if args.zu is not None:
plotParams['zu'] = args.zu
#==============================================================================
if args.colormap is not None:
plotParams['colormap'] = args.colormap
#==============================================================================
if args.colorbar is not None:
if args.colorbar == 'n' or args.colorbar == 'no' or args.colorbar == 'false':
plotParams['colorbar'] = False
elif args.colorbar == 'y' or args.colorbar == 'yes' or args.colorbar == 'true':
plotParams['colorbar'] = True
#==============================================================================
if args.invert_xaxis is not None:
if args.invert_xaxis == 'n' or args.invert_xaxis == 'no' or args.invert_xaxis == 'false':
plotParams['invert_xaxis'] = False
elif args.invert_xaxis == 'y' or args.invert_xaxis == 'yes' or args.invert_xaxis == 'true':
plotParams['invert_xaxis'] = True
#==============================================================================
if args.invert_yaxis is not None:
if args.invert_yaxis == 'n' or args.invert_yaxis == 'no' or args.invert_yaxis == 'false':
plotParams['invert_yaxis'] = False
elif args.invert_yaxis == 'y' or args.invert_yaxis == 'yes' or args.invert_yaxis == 'true':
plotParams['invert_yaxis'] = True
#==============================================================================
if args.invert_zaxis is not None:
if args.invert_zaxis == 'n' or args.invert_zaxis == 'no' or args.invert_zaxis == 'false':
plotParams['invert_zaxis'] = False
elif args.invert_zaxis == 'y' or args.invert_zaxis == 'yes' or args.invert_zaxis == 'true':
plotParams['invert_zaxis'] = True
#==============================================================================
if args.show_scatterer is not None:
if args.show_scatterer == 'n' or args.show_scatterer == 'no' or args.show_scatterer == 'false':
plotParams['show_scatterer'] = False
elif args.show_scatterer == 'y' or args.show_scatterer == 'yes' or args.show_scatterer == 'true':
plotParams['show_scatterer'] = True
#==============================================================================
if args.show_sources is not None:
if args.show_sources == 'n' or args.show_sources == 'no' or args.show_sources == 'false':
plotParams['show_sources'] = False
elif args.show_sources == 'y' or args.show_sources == 'yes' or args.show_sources == 'true':
plotParams['show_sources'] = True
#==============================================================================
if args.show_receivers is not None:
if args.show_receivers == 'n' or args.show_receivers == 'no' or args.show_receivers == 'false':
plotParams['show_receivers'] = False
elif args.show_receivers == 'y' or args.show_receivers == 'yes' or args.show_receivers == 'true':
plotParams['show_receivers'] = True
#==============================================================================
else: # create a plotParams dictionary file with default values
plotParams = default_params()
# updated parameters based on passed arguments
#for both image and wiggle plots
if args.format is not None:
plotParams['pltformat'] = args.format
# for image/map plots
if args.isolevel is not None:
plotParams['isolevel'] = args.isolevel
if args.colormap is not None:
plotParams['colormap'] = args.colormap
if args.colorbar is not None:
if args.colorbar == 'n' or args.colorbar == 'no' or args.colorbar == 'false':
plotParams['colorbar'] = False
elif args.colorbar == 'y' or args.colorbar == 'yes' or args.colorbar == 'true':
plotParams['colorbar'] = True
if args.xlabel is not None:
plotParams['xlabel'] = args.xlabel
if args.ylabel is not None:
plotParams['ylabel'] = args.ylabel
if args.zlabel is not None:
plotParams['zlabel'] = args.zlabel
#==============================================================================
# update units
if args.xu is not None:
plotParams['xu'] = args.xu
if args.yu is not None:
plotParams['yu'] = args.yu
if args.zu is not None:
plotParams['zu'] = args.zu
#==============================================================================
if args.invert_xaxis is not None:
if args.invert_xaxis == 'n' or args.invert_xaxis == 'no' or args.invert_xaxis == 'false':
plotParams['invert_xaxis'] = False
elif args.invert_xaxis == 'y' or args.invert_xaxis == 'yes' or args.invert_xaxis == 'true':
plotParams['invert_xaxis'] = True
#==============================================================================
if args.invert_yaxis is not None:
if args.invert_yaxis == 'n' or args.invert_yaxis == 'no' or args.invert_yaxis == 'false':
plotParams['invert_yaxis'] = False
elif args.invert_yaxis == 'y' or args.invert_yaxis == 'yes' or args.invert_yaxis == 'true':
plotParams['invert_yaxis'] = True
#==============================================================================
if args.invert_zaxis is not None:
if args.invert_zaxis == 'n' or args.invert_zaxis == 'no' or args.invert_zaxis == 'false':
plotParams['invert_zaxis'] = False
elif args.invert_zaxis == 'y' or args.invert_zaxis == 'yes' or args.invert_zaxis == 'true':
plotParams['invert_zaxis'] = True
#==============================================================================
if args.show_scatterer is not None:
if args.show_scatterer == 'n' or args.show_scatterer == 'no' or args.show_scatterer == 'false':
plotParams['show_scatterer'] = False
elif args.show_scatterer == 'y' or args.show_scatterer == 'yes' or args.show_scatterer == 'true':
plotParams['show_scatterer'] = True
#==============================================================================
if args.show_sources is not None:
if args.show_sources == 'n' or args.show_sources == 'no' or args.show_sources == 'false':
plotParams['show_sources'] = False
elif args.show_sources == 'y' or args.show_sources == 'yes' or args.show_sources == 'true':
plotParams['show_sources'] = True
#==============================================================================
if args.show_receivers is not None:
if args.show_receivers == 'n' or args.show_receivers == 'no' or args.show_receivers == 'false':
plotParams['show_receivers'] = False
elif args.show_receivers == 'y' or args.show_receivers == 'yes' or args.show_receivers == 'true':
plotParams['show_receivers'] = True
pickle.dump(plotParams, open('plotParams.pkl', 'wb'),
pickle.HIGHEST_PROTOCOL)
#==============================================================================
# load the shape of the scatterer and source/receiver locations
datadir = np.load('datadir.npz')
receiverPoints = np.load(str(datadir['receivers']))
if 'sources' in datadir:
sourcePoints = np.load(str(datadir['sources']))
else:
sourcePoints = None
if 'scatterer' in datadir and plotParams['show_scatterer']:
scatterer = np.load(str(datadir['scatterer']))
else:
scatterer = None
if plotParams['show_scatterer']:
print(
textwrap.dedent('''
Warning: Attempted to load the file containing the scatterer coordinates,
but no such file exists. If a file exists containing the scatterer
points, run:
'vzdata --path=<path/to/data/>'
and specify the file containing the scatterer points when prompted. Otherwise,
specify 'no' when asked if a file containing the scatterer points exists.
'''))
if Path('window.npz').exists():
windowDict = np.load('window.npz')
# Apply the receiver window
rstart = windowDict['rstart']
rstop = windowDict['rstop']
rstep = windowDict['rstep']
rinterval = np.arange(rstart, rstop, rstep)
receiverPoints = receiverPoints[rinterval, :]
if sourcePoints is not None:
# Apply the source window
sstart = windowDict['sstart']
sstop = windowDict['sstop']
sstep = windowDict['sstep']
sinterval = np.arange(sstart, sstop, sstep)
sourcePoints = sourcePoints[sinterval, :]
#==============================================================================
# Load the user-specified sampling grid
if 'samplingGrid' in datadir:
samplingGrid = np.load(str(datadir['samplingGrid']))
else:
try:
samplingGrid = np.load('samplingGrid.npz')
except FileNotFoundError:
samplingGrid = None
if samplingGrid is None:
sys.exit(
textwrap.dedent('''
A sampling grid needs to be set up before it can be plotted.
Enter:
vzgrid --help
from the command-line for more information on how to set up a
sampling grid.
'''))
x = samplingGrid['x']
y = samplingGrid['y']
tau = samplingGrid['tau']
if 'z' not in samplingGrid:
X, Y = np.meshgrid(x, y, indexing='ij')
Z = None
else:
z = samplingGrid['z']
X, Y, Z = np.meshgrid(x, y, z, indexing='ij')
#==============================================================================
if Path('imageNFE.npz').exists() and not Path('imageLSE.npz').exists():
if args.lse:
sys.exit(
textwrap.dedent('''
PlotError: User requested to plot an image obtained by solving
the Lippmann-Schwinger equation (LSE), but no such image exists.
'''))
# plot the image obtained by solving the near-field equaiton (NFE)
Dict = np.load('imageNFE.npz')
flag = 'NFE'
fig, ax = plotImage(Dict, X, Y, Z, tau, plotParams, flag, args.movie)
elif not Path('imageNFE.npz').exists() and Path('imageLSE.npz').exists():
if args.nfe:
sys.exit(
textwrap.dedent('''
PlotError: User requested to plot an image obtained by solving
the near-field equation (NFE), but no such image exists.
'''))
# plot the image obtained by solving the Lippmann-Schwinger equation (LSE)
Dict = np.load('imageLSE.npz')
flag = 'LSE'
fig, ax = plotImage(Dict, X, Y, Z, tau, plotParams, flag)
elif Path('imageNFE.npz').exists() and Path('imageLSE.npz').exists():
if args.nfe and not args.lse:
# plot the image obtained by solving the near-field equaiton (NFE)
Dict = np.load('imageNFE.npz')
flag = 'NFE'
fig, ax = plotImage(Dict, X, Y, Z, tau, plotParams, flag,
args.movie)
elif not args.nfe and args.lse:
# plot the image obtained by solving the Lippmann-Schwinger equation (LSE)
Dict = np.load('imageLSE.npz')
flag = 'LSE'
fig, ax = plotImage(Dict, X, Y, Z, tau, plotParams, flag)
elif args.nfe and args.lse:
sys.exit(
textwrap.dedent('''
PlotError: Please specify only one of the arguments \'--nfe\' or \'--lse\' to
view the corresponding image.'''))
else:
flag = ''
print(
textwrap.dedent('''
Images obtained by solving both NFE and LSE are available. Enter:
vzimage --nfe
to view the image obtained by solving NFE or
vzimage --lse
to view the image obtained by solving LSE.
'''))
else:
flag = ''
if args.nfe:
print(
'Warning: An image obtained by solving the near-field equation (NFE) does not exist.'
)
elif args.lse:
print(
'Warning: An image obtained by solving the Lippmann-Schwinger equation (LSE) does not exist.'
)
elif args.nfe and args.lse:
print(
textwrap.dedent('''
Warning: An image has not yet been obtained by solving either the
near-field equation (NFE) or the Lippmann-Schwinger equation (LSE).
'''))
try:
ax
except NameError:
fig, ax = setFigure(num_axes=1,
mode=plotParams['view_mode'],
ax1_dim=receiverPoints.shape[1])
plotMap(ax, None, receiverPoints, sourcePoints, scatterer, 'data',
plotParams)
#==============================================================================
pltformat = plotParams['pltformat']
fig.savefig('image' + flag + '.' + pltformat,
format=pltformat,
bbox_inches='tight',
facecolor=fig.get_facecolor(),
transparent=True)
plt.show()
def cli():
parser = argparse.ArgumentParser()
parser.add_argument(
'--nfe',
action='store_true',
help='''Plot the image obtained by solving the near-field equation.''')
parser.add_argument(
'--lse',
action='store_true',
help=
'''Plot the image obtained by solving the Lippmann-Schwinger equation.'''
)
parser.add_argument(
'--spacetime',
action='store_true',
help='''Plot the support of the source function in space-time.''')
parser.add_argument(
'--movie',
action='store_true',
help='''Save the space-time figure as a rotating frame (2D space)
or as a time-lapse structure (3D space).''')
parser.add_argument('--isolevel',
type=float,
default=None,
help='''Specify the contour level of the isosurface for
three-dimensional visualizations. Level must be between 0 and 1.'''
)
parser.add_argument(
'--format',
'-f',
type=str,
default=None,
choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help=
'''specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument('--xlabel',
type=str,
default=None,
help='''specify a label for the x-axis.''')
parser.add_argument('--ylabel',
type=str,
default=None,
help='''specify a label for the y-axis.''')
parser.add_argument('--zlabel',
type=str,
default=None,
help='''specify a label for the z-axis.''')
parser.add_argument(
'--xu',
type=str,
default=None,
help='Specify the units for the x-axis (e.g., \'m\' or \'km\').')
parser.add_argument(
'--yu',
type=str,
default=None,
help='Specify the units for the y-axis (e.g., \'m\' or \'km\').')
parser.add_argument(
'--zu',
type=str,
default=None,
help='Specify the units for the z-axis (e.g., \'m\' or \'km\').')
parser.add_argument(
'--colormap',
type=str,
default=None,
choices=['viridis', 'plasma', 'inferno', 'magma', 'cividis'],
help=
'specify a perceptually uniform sequential colormap. Default is \'magma\''
)
parser.add_argument(
'--colorbar',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'y/yes/true\' to plot colorbar. Default is \'n/no/false\'')
parser.add_argument(
'--invert_xaxis',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'y/yes/true\' to invert x-axis. Default is \'n/no/false\'')
parser.add_argument(
'--invert_yaxis',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'y/yes/true\' to invert y-axis. Default is \'n/no/false\'')
parser.add_argument(
'--invert_zaxis',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'y/yes/true\' to invert z-axis. Default is \'n/no/false\'')
parser.add_argument(
'--show_scatterer',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'y/yes/true\' to show scatterer boundary. Default is \'n/no/false\''
)
parser.add_argument(
'--show_sources',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help='specify \'n/no/false\' to hide sources. Default is \'y/yes/true\''
)
parser.add_argument(
'--show_receivers',
type=str,
default=None,
choices=['n', 'no', 'false', 'y', 'yes', 'true'],
help=
'specify \'n/no/false\' to hide receivers. Default is \'y/yes/true\'')
parser.add_argument(
'--mode',
type=str,
choices=['light', 'dark'],
required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
#==============================================================================
# if a plotParams.pkl file already exists, load relevant parameters
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
# for both wiggle plots and image plots
if args.format is not None:
plotParams['pltformat'] = args.format
if args.mode is not None:
plotParams['view_mode'] = args.mode
# for image/map plots
if args.isolevel is not None:
plotParams['isolevel'] = args.isolevel
if args.xlabel is not None:
plotParams['xlabel'] = args.xlabel
if args.ylabel is not None:
plotParams['ylabel'] = args.ylabel
if args.zlabel is not None:
plotParams['zlabel'] = args.zlabel
#==============================================================================
# handle units here
if args.xu is not None:
plotParams['xu'] = args.xu
if args.yu is not None:
plotParams['yu'] = args.yu
if args.zu is not None:
plotParams['zu'] = args.zu
#==============================================================================
if args.colormap is not None:
plotParams['colormap'] = args.colormap
#==============================================================================
if args.colorbar is not None:
if args.colorbar == 'n' or args.colorbar == 'no' or args.colorbar == 'false':
plotParams['colorbar'] = False
elif args.colorbar == 'y' or args.colorbar == 'yes' or args.colorbar == 'true':
plotParams['colorbar'] = True
#==============================================================================
if args.invert_xaxis is not None:
if args.invert_xaxis == 'n' or args.invert_xaxis == 'no' or args.invert_xaxis == 'false':
plotParams['invert_xaxis'] = False
elif args.invert_xaxis == 'y' or args.invert_xaxis == 'yes' or args.invert_xaxis == 'true':
plotParams['invert_xaxis'] = True
#==============================================================================
if args.invert_yaxis is not None:
if args.invert_yaxis == 'n' or args.invert_yaxis == 'no' or args.invert_yaxis == 'false':
plotParams['invert_yaxis'] = False
elif args.invert_yaxis == 'y' or args.invert_yaxis == 'yes' or args.invert_yaxis == 'true':
plotParams['invert_yaxis'] = True
#==============================================================================
if args.invert_zaxis is not None:
if args.invert_zaxis == 'n' or args.invert_zaxis == 'no' or args.invert_zaxis == 'false':
plotParams['invert_zaxis'] = False
elif args.invert_zaxis == 'y' or args.invert_zaxis == 'yes' or args.invert_zaxis == 'true':
plotParams['invert_zaxis'] = True
#==============================================================================
if args.show_scatterer is not None:
if args.show_scatterer == 'n' or args.show_scatterer == 'no' or args.show_scatterer == 'false':
plotParams['show_scatterer'] = False
elif args.show_scatterer == 'y' or args.show_scatterer == 'yes' or args.show_scatterer == 'true':
plotParams['show_scatterer'] = True
#==============================================================================
if args.show_sources is not None:
if args.show_sources == 'n' or args.show_sources == 'no' or args.show_sources == 'false':
plotParams['show_sources'] = False
elif args.show_sources == 'y' or args.show_sources == 'yes' or args.show_sources == 'true':
plotParams['show_sources'] = True
#==============================================================================
if args.show_receivers is not None:
if args.show_receivers == 'n' or args.show_receivers == 'no' or args.show_receivers == 'false':
plotParams['show_receivers'] = False
elif args.show_receivers == 'y' or args.show_receivers == 'yes' or args.show_receivers == 'true':
plotParams['show_receivers'] = True
#==============================================================================
else: # create a plotParams dictionary file with default values
plotParams = default_params()
# updated parameters based on passed arguments
#for both image and wiggle plots
if args.format is not None:
plotParams['pltformat'] = args.format
# for image/map plots
if args.isolevel is not None:
plotParams['isolevel'] = args.isolevel
if args.colormap is not None:
plotParams['colormap'] = args.colormap
if args.colorbar is not None:
if args.colorbar == 'n' or args.colorbar == 'no' or args.colorbar == 'false':
plotParams['colorbar'] = False
elif args.colorbar == 'y' or args.colorbar == 'yes' or args.colorbar == 'true':
plotParams['colorbar'] = True
if args.xlabel is not None:
plotParams['xlabel'] = args.xlabel
if args.ylabel is not None:
plotParams['ylabel'] = args.ylabel
if args.zlabel is not None:
plotParams['zlabel'] = args.zlabel
#==============================================================================
# update units
if args.xu is not None:
plotParams['xu'] = args.xu
if args.yu is not None:
plotParams['yu'] = args.yu
if args.zu is not None:
plotParams['zu'] = args.zu
#==============================================================================
if args.invert_xaxis is not None:
if args.invert_xaxis == 'n' or args.invert_xaxis == 'no' or args.invert_xaxis == 'false':
plotParams['invert_xaxis'] = False
elif args.invert_xaxis == 'y' or args.invert_xaxis == 'yes' or args.invert_xaxis == 'true':
plotParams['invert_xaxis'] = True
#==============================================================================
if args.invert_yaxis is not None:
if args.invert_yaxis == 'n' or args.invert_yaxis == 'no' or args.invert_yaxis == 'false':
plotParams['invert_yaxis'] = False
elif args.invert_yaxis == 'y' or args.invert_yaxis == 'yes' or args.invert_yaxis == 'true':
plotParams['invert_yaxis'] = True
#==============================================================================
if args.invert_zaxis is not None:
if args.invert_zaxis == 'n' or args.invert_zaxis == 'no' or args.invert_zaxis == 'false':
plotParams['invert_zaxis'] = False
elif args.invert_zaxis == 'y' or args.invert_zaxis == 'yes' or args.invert_zaxis == 'true':
plotParams['invert_zaxis'] = True
#==============================================================================
if args.show_scatterer is not None:
if args.show_scatterer == 'n' or args.show_scatterer == 'no' or args.show_scatterer == 'false':
plotParams['show_scatterer'] = False
elif args.show_scatterer == 'y' or args.show_scatterer == 'yes' or args.show_scatterer == 'true':
plotParams['show_scatterer'] = True
#==============================================================================
if args.show_sources is not None:
if args.show_sources == 'n' or args.show_sources == 'no' or args.show_sources == 'false':
plotParams['show_sources'] = False
elif args.show_sources == 'y' or args.show_sources == 'yes' or args.show_sources == 'true':
plotParams['show_sources'] = True
#==============================================================================
if args.show_receivers is not None:
if args.show_receivers == 'n' or args.show_receivers == 'no' or args.show_receivers == 'false':
plotParams['show_receivers'] = False
elif args.show_receivers == 'y' or args.show_receivers == 'yes' or args.show_receivers == 'true':
plotParams['show_receivers'] = True
pickle.dump(plotParams, open('plotParams.pkl', 'wb'),
pickle.HIGHEST_PROTOCOL)
#==============================================================================
# load the shape of the scatterer and source/receiver locations
datadir = np.load('datadir.npz')
receiverPoints = np.load(str(datadir['receivers']))
if 'sources' in datadir:
sourcePoints = np.load(str(datadir['sources']))
else:
sourcePoints = None
if 'scatterer' in datadir and plotParams['show_scatterer']:
scatterer = np.load(str(datadir['scatterer']))
else:
scatterer = None
if plotParams['show_scatterer']:
print(
textwrap.dedent('''
Warning: Attempted to load the file containing the scatterer coordinates,
but no such file exists. If a file exists containing the scatterer
points, run:
'vzdata --path=<path/to/data/>'
and specify the file containing the scatterer points when prompted. Otherwise,
specify 'no' when asked if a file containing the scatterer points exists.
'''))
if Path('window.npz').exists():
windowDict = np.load('window.npz')
# Apply the receiver window
rstart = windowDict['rstart']
rstop = windowDict['rstop']
rstep = windowDict['rstep']
rinterval = np.arange(rstart, rstop, rstep)
receiverPoints = receiverPoints[rinterval, :]
if sourcePoints is not None:
# Apply the source window
sstart = windowDict['sstart']
sstop = windowDict['sstop']
sstep = windowDict['sstep']
sinterval = np.arange(sstart, sstop, sstep)
sourcePoints = sourcePoints[sinterval, :]
#==============================================================================
if Path('imageNFE.npz').exists() and not Path('imageLSE.npz').exists():
if args.lse:
sys.exit(
textwrap.dedent('''
PlotError: User requested to plot an image obtained by solving
the Lippmann-Schwinger equation (LSE), but no such image exists.
'''))
# plot the image obtained by solving the near-field equaiton (NFE)
Dict = np.load('imageNFE.npz')
X = Dict['X']
Y = Dict['Y']
if 'Z' in Dict:
Z = Dict['Z']
else:
Z = None
if 'tau' in Dict:
tau = Dict['tau']
Ntau = len(tau)
else:
tau = None
alpha = Dict['alpha']
flag = 'NFE'
fig1, ax1, *otherImages = plotImage(Dict, plotParams, flag,
args.spacetime, args.movie)
if otherImages:
if len(otherImages) == 2:
fig2, ax2 = otherImages
elif len(otherImages) == 4:
fig2, ax2, fig3, ax3 = otherImages
elif not Path('imageNFE.npz').exists() and Path('imageLSE.npz').exists():
if args.nfe:
sys.exit(
textwrap.dedent('''
PlotError: User requested to plot an image obtained by solving
the near-field equation (NFE), but no such image exists.
'''))
# plot the image obtained by solving the Lippmann-Schwinger equation (LSE)
Dict = np.load('imageLSE.npz')
flag = 'LSE'
fig1, ax1 = plotImage(Dict, plotParams, flag)
elif Path('imageNFE.npz').exists() and Path('imageLSE.npz').exists():
if args.nfe and not args.lse:
# plot the image obtained by solving the near-field equaiton (NFE)
Dict = np.load('imageNFE.npz')
X = Dict['X']
Y = Dict['Y']
if 'Z' in Dict:
Z = Dict['Z']
else:
Z = None
if 'tau' in Dict:
tau = Dict['tau']
Ntau = len(tau)
else:
tau = None
alpha = Dict['alpha']
flag = 'NFE'
fig1, ax1, *otherImages = plotImage(Dict, plotParams, flag,
args.spacetime, args.movie)
if otherImages:
if len(otherImages) == 2:
fig2, ax2 = otherImages
elif len(otherImages) == 4:
fig2, ax2, fig3, ax3 = otherImages
elif not args.nfe and args.lse:
# plot the image obtained by solving the Lippmann-Schwinger equation (LSE)
Dict = np.load('imageLSE.npz')
flag = 'LSE'
fig1, ax1 = plotImage(Dict, plotParams, flag)
elif args.nfe and args.lse:
sys.exit(
textwrap.dedent('''
PlotError: Please specify only one of the arguments \'--nfe\' or \'--lse\' to
view the corresponding image.'''))
else:
sys.exit(
textwrap.dedent('''
Images obtained by solving both NFE and LSE are available. Enter:
vzimage --nfe
to view the image obtained by solving NFE or
vzimage --lse
to view the image obtained by solving LSE.
'''))
else:
flag = ''
if args.nfe:
print(
'Warning: An image obtained by solving the near-field equation (NFE) does not exist.'
)
elif args.lse:
print(
'Warning: An image obtained by solving the Lippmann-Schwinger equation (LSE) does not exist.'
)
elif args.nfe and args.lse:
print(
textwrap.dedent('''
Warning: An image has not yet been obtained by solving either the
near-field equation (NFE) or the Lippmann-Schwinger equation (LSE).
'''))
try:
ax1
except NameError:
fig1, ax1 = setFigure(num_axes=1,
mode=plotParams['view_mode'],
ax1_dim=receiverPoints.shape[1])
plotMap(ax1, None, receiverPoints, sourcePoints, scatterer, 'data',
plotParams)
#==============================================================================
pltformat = plotParams['pltformat']
fig1.savefig('image' + flag + '.' + pltformat,
format=pltformat,
bbox_inches='tight',
facecolor=fig1.get_facecolor(),
transparent=True)
if flag == 'NFE' and args.spacetime:
remove_keymap_conflicts({'left', 'right', 'up', 'down', 'save'})
if Z is None:
try:
fig3.savefig('spacetime' + flag + '.' + pltformat,
format=pltformat,
bbox_inches='tight',
facecolor=fig3.get_facecolor(),
transparent=True)
fig2.canvas.mpl_connect(
'key_press_event', lambda event: process_key_images(
event, plotParams, alpha, X, Y, Z, Ntau, tau))
except NameError:
print(
'\nSpace-time reconstruction is not available for a single sampling point in time.\n'
)
else:
try:
fig2.savefig('spacetime' + flag + '.' + pltformat,
format=pltformat,
bbox_inches='tight',
facecolor=fig2.get_facecolor(),
transparent=True)
fig2.canvas.mpl_connect(
'key_press_event', lambda event: process_key_images(
event, plotParams, alpha, X, Y, Z, Ntau, tau))
except NameError:
print(
'\nSpace-time reconstruction is not available for a single sampling point in time.\n'
)
plt.show()
def cli():
parser = argparse.ArgumentParser()
parser.add_argument('--data',
action='store_true',
help='Plot the recorded data. (Default)')
parser.add_argument('--testfunc',
action='store_true',
help='Plot the simulated test functions.')
parser.add_argument('--tu',
type=str,
help='Specify the time units (e.g., \'s\' or \'ms\').')
parser.add_argument(
'--au',
type=str,
help='Specify the amplitude units (e.g., \'m\' or \'mm\').')
parser.add_argument(
'--pclip',
type=float,
help=
'''Specify the percentage (0-1) of the peak amplitude to display. This
parameter is used for pcolormesh plots only. Default is set to 1.'''
)
parser.add_argument(
'--title',
type=str,
help='''Specify a title for the wiggle plot. Default title is
\'Data\' if \'--data\' is passed and 'Test Function' if \'--testfunc\'
is passed.''')
parser.add_argument(
'--format',
'-f',
type=str,
default='pdf',
choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help=
'''Specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument(
'--map',
action='store_true',
help=
'''Plot a map of the receiver and source/sampling point locations. The current
source/sampling point will be highlighted. The boundary of the scatterer will also
be shown if available.''')
parser.add_argument(
'--mode',
type=str,
choices=['light', 'dark'],
required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
#==============================================================================
# if a plotParams.pkl file already exists, load relevant parameters
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
# update parameters for wiggle plots based on passed arguments
if args.mode is not None:
plotParams['view_mode'] = args.mode
if args.tu is not None:
plotParams['tu'] = args.tu
if args.au is not None:
plotParams['au'] = args.au
if args.pclip is not None:
if args.pclip >= 0 and args.pclip <= 1:
plotParams['pclip'] = args.pclip
else:
print(
textwrap.dedent('''
Warning: Invalid value passed to argument \'--pclip\'. Value must be
between 0 and 1.
'''))
if args.title is not None:
if args.data:
plotParams['data_title'] = args.title
elif args.testfunc:
plotParams['tf_title'] = args.title
else: # create a plotParams dictionary file with default values
plotParams = default_params()
# update parameters for wiggle plots based on passed arguments
if args.mode is not None:
plotParams['view_mode'] = args.mode
if args.tu is not None:
plotParams['tu'] = args.tu
if args.au is not None:
plotParams['au'] = args.au
if args.title is not None:
if args.data:
plotParams['data_title'] = args.title
elif args.testfunc:
plotParams['tf_title'] = args.title
pickle.dump(plotParams, open('plotParams.pkl', 'wb'),
pickle.HIGHEST_PROTOCOL)
#==============================================================================
# Load the relevant data to plot
datadir = np.load('datadir.npz')
receiverPoints = np.load(str(datadir['receivers']))
recordingTimes = np.load(str(datadir['recordingTimes']))
dt = recordingTimes[1] - recordingTimes[0]
if 'scatterer' in datadir:
scatterer = np.load(str(datadir['scatterer']))
else:
scatterer = None
if Path('window.npz').exists():
windowDict = np.load('window.npz')
# Apply the receiver window
rstart = windowDict['rstart']
rstop = windowDict['rstop']
rstep = windowDict['rstep']
# Apply the time window
tstart = windowDict['tstart']
tstop = windowDict['tstop']
tstep = windowDict['tstep']
# Convert time window parameters to corresponding array indices
Tstart = int(round(tstart / dt))
Tstop = int(round(tstop / dt))
else:
rstart = 0
rstop = receiverPoints.shape[0]
rstep = 1
tstart = recordingTimes[0]
tstop = recordingTimes[-1]
Tstart = 0
Tstop = len(recordingTimes)
tstep = 1
rinterval = np.arange(rstart, rstop, rstep)
receiverPoints = receiverPoints[rinterval, :]
tinterval = np.arange(Tstart, Tstop, tstep)
if all(v is True for v in [args.data, args.testfunc]):
# User specified both data and testfuncs for plotting
# Send error message and exit.
sys.exit(
textwrap.dedent('''
Error: Cannot plot both recorded data and simulated test functions. Use
vzwiggles --data
to plot the recorded data or
vzwiggles --testfuncs
to plot the simulated test functions.
'''))
elif all(v is not True for v in [args.data, args.testfunc]):
# User did not specify which wiggles to plot.
# Plot recorded data by default.
# load the 3D data array into variable 'X'
# X[receiver, time, source]
wiggleType = 'data'
if Path('noisyData.npz').exists():
userResponded = False
print(
textwrap.dedent('''
Detected that band-limited noise has been added to the data array.
Would you like to plot the noisy data? ([y]/n)
Enter 'q/quit' exit the program.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == 'y' or answer == 'yes':
print('Proceeding with plot of noisy data...')
# read in the noisy data array
X = np.load('noisyData.npz')['noisyData']
userResponded = True
elif answer == 'n' or answer == 'no':
print('Proceeding with plot of noise-free data...')
# read in the recorded data array
X = np.load(str(datadir['recordedData']))
userResponded = True
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print(
'Invalid response. Please enter \'y/yes\', \'n\no\', or \'q/quit\'.'
)
else:
# read in the recorded data array
X = np.load(str(datadir['recordedData']))
time = recordingTimes
if 'sources' in datadir:
sourcePoints = np.load(str(datadir['sources']))
else:
sourcePoints = None
X = X[rinterval, :, :]
elif args.data:
# load the 3D data array into variable 'X'
# X[receiver, time, source]
wiggleType = 'data'
if Path('noisyData.npz').exists():
userResponded = False
print(
textwrap.dedent('''
Detected that band-limited noise has been added to the data array.
Would you like to plot the noisy data? ([y]/n)
Enter 'q/quit' exit the program.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == 'y' or answer == 'yes':
print('Proceeding with plot of noisy data...')
# read in the noisy data array
X = np.load('noisyData.npz')['noisyData']
userResponded = True
elif answer == 'n' or answer == 'no':
print('Proceeding with plot of noise-free data...')
# read in the recorded data array
X = np.load(str(datadir['recordedData']))
userResponded = True
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print(
'Invalid response. Please enter \'y/yes\', \'n\no\', or \'q/quit\'.'
)
else:
# read in the recorded data array
X = np.load(str(datadir['recordedData']))
time = recordingTimes
if 'sources' in datadir:
sourcePoints = np.load(str(datadir['sources']))
else:
sourcePoints = None
X = X[rinterval, :, :]
elif args.testfunc:
wiggleType = 'testfunc'
if 'testFuncs' in datadir and not Path('VZTestFuncs.npz').exists():
X = np.load(str(datadir['testFuncs']))
time = np.load(str(datadir['convolutionTimes']))
sourcePoints = np.load(str(datadir['samplingPoints']))
X = X[rinterval, :, :]
elif not 'testFuncs' in datadir and Path('VZTestFuncs.npz').exists():
print(
'\nDetected that free-space test functions have already been computed...'
)
print(
'Checking consistency with current space-time sampling grid...'
)
TFDict = np.load('VZTestFuncs.npz')
samplingGrid = np.load('samplingGrid.npz')
x = samplingGrid['x']
y = samplingGrid['y']
if 'z' in samplingGrid:
z = samplingGrid['z']
else:
z = None
tau = samplingGrid['tau']
pulse = lambda t: pulseFun.pulse(t)
velocity = pulseFun.velocity
peakFreq = pulseFun.peakFreq
peakTime = pulseFun.peakTime
# set up the convolution times based on the length of the recording time interval
time = recordingTimes[tinterval]
T = time[-1] - time[0]
time = np.linspace(-T, T, 2 * len(time) - 1)
if samplingIsCurrent(TFDict, receiverPoints, time, velocity, tau,
x, y, z, peakFreq, peakTime):
print('Moving forward to plot test functions...')
X = TFDict['TFarray']
sourcePoints = TFDict['samplingPoints']
else:
if tau[0] != 0:
tu = plotParams['tu']
if tu != '':
print(
'Recomputing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Recomputing test functions for focusing time %0.2f...'
% (tau[0]))
X, sourcePoints = sampleSpace(receiverPoints,
time - tau[0], velocity, x,
y, z, pulse)
else:
print('Recomputing test functions...')
X, sourcePoints = sampleSpace(receiverPoints, time,
velocity, x, y, z, pulse)
if z is None:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=time,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
tau=tau,
samplingPoints=sourcePoints)
else:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=time,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
z=z,
tau=tau,
samplingPoints=sourcePoints)
elif 'testFuncs' in datadir and Path('VZTestFuncs.npz').exists():
userResponded = False
print(
textwrap.dedent('''
Two files are available containing simulated test functions.
Enter '1' to view the user-provided test functions. (Default)
Enter '2' to view the test functions computed by Vezda.
Enter 'q/quit' to exit.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
X = np.load(str(datadir['testFuncs']))
time = np.load(str(datadir['convolutionTimes']))
sourcePoints = np.load(str(datadir['samplingPoints']))
X = X[rinterval, :, :]
userResponded = True
break
elif answer == '2':
print(
'\nDetected that free-space test functions have already been computed...'
)
print(
'Checking consistency with current spatial sampling grid...'
)
TFDict = np.load('VZTestFuncs.npz')
samplingGrid = np.load('samplingGrid.npz')
x = samplingGrid['x']
y = samplingGrid['y']
if 'z' in samplingGrid:
z = samplingGrid['z']
else:
z = None
tau = samplingGrid['tau']
pulse = lambda t: pulseFun.pulse(t)
velocity = pulseFun.velocity
peakFreq = pulseFun.peakFreq
peakTime = pulseFun.peakTime
# set up the convolution times based on the length of the recording time interval
time = recordingTimes[tinterval]
T = time[-1] - time[0]
time = np.linspace(-T, T, 2 * len(time) - 1)
if samplingIsCurrent(TFDict, receiverPoints, time,
velocity, tau, x, y, z, peakFreq,
peakTime):
print('Moving forward to plot test functions...')
X = TFDict['TFarray']
sourcePoints = TFDict['samplingPoints']
else:
if tau[0] != 0:
tu = plotParams['tu']
if tu != '':
print(
'Recomputing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Recomputing test functions for focusing time %0.2f...'
% (tau[0]))
X, sourcePoints = sampleSpace(
receiverPoints, time - tau[0], velocity, x, y,
z, pulse)
else:
print('Recomputing test functions...')
X, sourcePoints = sampleSpace(
receiverPoints, time, velocity, x, y, z, pulse)
if z is None:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=time,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
tau=tau,
samplingPoints=sourcePoints)
else:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=time,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
z=z,
tau=tau,
samplingPoints=sourcePoints)
userResponded = True
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.')
else:
print(
'Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.'
)
else:
print(
'\nComputing free-space test functions for the current space-time sampling grid...'
)
# set up the convolution times based on the length of the recording time interval
time = recordingTimes[tinterval]
T = time[-1] - time[0]
time = np.linspace(-T, T, 2 * len(time) - 1)
samplingGrid = np.load('samplingGrid.npz')
x = samplingGrid['x']
y = samplingGrid['y']
if 'z' in samplingGrid:
z = samplingGrid['z']
else:
z = None
tau = samplingGrid['tau']
pulse = lambda t: pulseFun.pulse(t)
velocity = pulseFun.velocity
peakFreq = pulseFun.peakFreq
peakTime = pulseFun.peakTime
if tau[0] != 0:
tu = plotParams['tu']
if tu != '':
print(
'Computing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Computing test functions for focusing time %0.2f...' %
(tau[0]))
X, sourcePoints = sampleSpace(receiverPoints, time - tau[0],
velocity, x, y, z, pulse)
else:
X, sourcePoints = sampleSpace(receiverPoints, time, velocity,
x, y, z, pulse)
if z is None:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=time,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
tau=tau,
samplingPoints=sourcePoints)
else:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=time,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
z=z,
tau=tau,
samplingPoints=sourcePoints)
#==============================================================================
if Path('window.npz').exists() and wiggleType == 'data':
t0 = tstart
tf = tstop
# Apply the source window
sstart = windowDict['sstart']
sstop = windowDict['sstop']
sstep = windowDict['sstep']
else:
t0 = time[0]
tf = time[-1]
sstart = 0
sstop = X.shape[2]
sstep = 1
sinterval = np.arange(sstart, sstop, sstep)
X = X[:, :, sinterval]
if sourcePoints is not None:
sourcePoints = sourcePoints[sinterval, :]
# increment source/recording interval and receiver interval to be consistent
# with one-based indexing (i.e., count from one instead of zero)
sinterval += 1
rinterval += 1
rstart += 1
Ns = X.shape[2]
remove_keymap_conflicts({'left', 'right', 'up', 'down', 'save'})
if args.map:
fig, ax1, ax2 = setFigure(num_axes=2,
mode=plotParams['view_mode'],
ax2_dim=receiverPoints.shape[1])
ax1.volume = X
ax1.index = Ns // 2
title = wave_title(ax1.index, sinterval, sourcePoints, wiggleType,
plotParams)
plotWiggles(ax1, X[:, :, ax1.index], time, t0, tf, rstart, rinterval,
receiverPoints, title, wiggleType, plotParams)
ax2.index = ax1.index
plotMap(ax2, ax2.index, receiverPoints, sourcePoints, scatterer,
wiggleType, plotParams)
plt.tight_layout()
fig.canvas.mpl_connect(
'key_press_event', lambda event: process_key_waves(
event, time, t0, tf, rstart, rinterval, sinterval,
receiverPoints, sourcePoints, Ns, scatterer, args.map,
wiggleType, plotParams))
else:
fig, ax = setFigure(num_axes=1, mode=plotParams['view_mode'])
ax.volume = X
ax.index = Ns // 2
title = wave_title(ax.index, sinterval, sourcePoints, wiggleType,
plotParams)
plotWiggles(ax, X[:, :, ax.index], time, t0, tf, rstart, rinterval,
receiverPoints, title, wiggleType, plotParams)
plt.tight_layout()
fig.canvas.mpl_connect(
'key_press_event', lambda event: process_key_waves(
event, time, t0, tf, rstart, rinterval, sinterval,
receiverPoints, sourcePoints, Ns, scatterer, args.map,
wiggleType, plotParams))
plt.show()
from pathlib import Path
import textwrap
from vezda.math_utils import nextPow2
from vezda.signal_utils import tukey_taper
from vezda.sampling_utils import samplingIsCurrent, compute_impulse_responses
from vezda.plot_utils import default_params
sys.path.append(os.getcwd())
import pulseFun
datadir = np.load('datadir.npz')
# Used for getting time and frequency units
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
else:
plotParams = default_params()
def load_data(domain, taper=False, verbose=False, skip_fft=False):
# load the recorded data
print('Loading recorded waveforms...')
if Path('noisyData.npz').exists():
userResponded = False
print(
textwrap.dedent('''
Detected that band-limited noise has been added to the data array.
Would you like to use the noisy data? ([y]/n)
Enter 'q/quit' exit the program.
'''))
while userResponded == False:
def cli():
parser = argparse.ArgumentParser()
parser.add_argument(
'--format',
'-f',
type=str,
default='pdf',
choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help=
'''specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument(
'--mode',
type=str,
choices=['light', 'dark'],
required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
#==============================================================================
def process_key_picard(event, tstart, tstop, rinterval, receiverPoints,
scatterer, args, recordingTimes):
if args.map:
fig = event.canvas.figure
ax1 = fig.axes[0]
ax2 = fig.axes[1]
if event.key == 'left' or event.key == 'down':
previous_slice(ax1, tstart, tstop, rinterval, args,
recordingTimes, receiverPoints)
previous_source(ax2, tstart, tstop, rinterval, args,
recordingTimes, receiverPoints)
elif event.key == 'right' or event.key == 'up':
next_slice(ax1, tstart, tstop, rinterval, args, recordingTimes,
receiverPoints)
next_source(ax1, tstart, tstop, rinterval, args,
recordingTimes, receiverPoints)
else:
fig = event.canvas.figure
ax = fig.axes[0]
if event.key == 'left' or event.key == 'down':
previous_slice(ax, tstart, tstop, rinterval, args,
recordingTimes, receiverPoints)
elif event.key == 'right' or event.key == 'up':
next_slice(ax, tstart, tstop, rinterval, args, recordingTimes,
receiverPoints)
fig.canvas.draw()
#==============================================================================
try:
s = np.load('singularValues.npy')
U = np.load('leftVectors.npy')
except FileNotFoundError:
s = None
U = None
if any(v is None for v in [s, U]):
sys.exit(
textwrap.dedent('''
A singular-value decomposition needs to be computed before any
spectral analysis can be performed. Enter:
vzsvd --help
from the command line for more information on how to compute
a singular-value decomposition.
'''))
try:
TFDict = np.load('VZTestFuncs.npz')
except FileNotFoundError:
TFDict = None
if TFDict is None:
sys.exit(textwrap.dedent('''
'''))
k = s.size
s = np.reshape(s, (k, 1))
# Compute coefficients for Picard plot
TFarray = TFDict['TFarray']
TF = TFarray[:, :, 0, 0]
Nr, Nt = TF.shape
b = np.reshape(TF, (Nt * Nr, 1))
c = np.abs(U.T @ b)
d = np.divide(c, s)
#==============================================================================
datadir = np.load('datadir.npz')
receiverPoints = np.load(str(datadir['receivers']))
sourcePoints = np.load(str(datadir['sources']))
if 'scatterer' in datadir:
scatterer = np.load(str(datadir['scatterer']))
else:
scatterer = None
if Path('window.npz').exists():
windowDict = np.load('window.npz')
# Set the receiver window for receiverPoints
rstart = windowDict['rstart']
rstop = windowDict['rstop']
rstep = windowDict['rstep']
# Set the source window for sourcePoints
sstart = windowDict['sstart']
sstop = windowDict['sstop']
sstep = windowDict['sstep']
else:
rstart = 0
rstop = Nr
rstep = 1
sstart = 0
sstop = sourcePoints.shape[0]
sstep = 1
# pltrstart is used to plot the correct receivers for
# the simulated test function computed by Vezda
pltrstart = rstart
pltsstart = sstart
rinterval = np.arange(rstart, rstop, rstep)
receiverPoints = receiverPoints[rinterval, :]
sinterval = np.arange(sstart, sstop, sstep)
sourcePoints = sourcePoints[sinterval, :]
#==============================================================================
remove_keymap_conflicts({'left', 'right', 'up', 'down', 'save'})
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
else:
plotParams = default_params()
if args.mode is not None:
plotParams['view_mode'] = args.mode
pickle.dump(plotParams, open('plotParams.pkl', 'wb'),
pickle.HIGHEST_PROTOCOL)
fig, ax = setFigure(num_axes=1, mode=plotParams['view_mode'])
# ax1.index = k // 2
# PicardPlot(ax1, s, c, d)
# if receiverPoints.shape[1] == 2:
# ax2 = fig.add_subplot(122)
# elif receiverPoints.shape[1] == 3:
# ax2 = fig.add_subplot(122, projection='3d')
# ax2.index = ax1.index
# map_plot(ax2, ax2.index, args, rinterval, receiverPoints, sourcePoints, scatterer)
# plt.tight_layout()
# #fig.canvas.mpl_connect('key_press_event', lambda event: process_key(event, tstart, tstop, rinterval,
# # receiverPoints, sourcePoints, scatterer,
# # args, recordingTimes))
ax.index = k // 2
PicardPlot(ax, s, c, d)
plt.tight_layout()
fig.savefig('Picard.' + args.format,
format=args.format,
bbox_inches='tight')
plt.show()
def cli():
parser = argparse.ArgumentParser()
parser.add_argument('--nfo', action='store_true',
help='''Plot the singular-value decomposition of the
near-field operator (NFO).''')
parser.add_argument('--lso', action='store_true',
help='''Plot the singular-value decomposition of the
Lippmann-Schwinger operator (LSO).''')
parser.add_argument('--format', '-f', type=str, default='pdf', choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help='''Specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument('--mode', type=str, choices=['light', 'dark'], required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
# See if an SVD already exists. If so, attempt to load it...
if args.nfo and not args.lso:
operatorName = 'near-field operator'
filename = 'NFO_SVD.npz'
elif not args.nfo and args.lso:
operatorName = 'Lippmann-Schwinger operator'
filename = 'LSO_SVD.npz'
elif args.nfo and args.lso:
sys.exit(textwrap.dedent(
'''
UsageError: Please specify only one of the arguments \'--nfo\' or \'--lso\'.
'''))
else:
sys.exit(textwrap.dedent(
'''
For which operator would you like to plot a singular-value decomposition?
Enter:
vzsvd --nfo
for the near-field operator or
vzsvd --lso
for the Lippmann-Schwinger operator.
'''))
try:
U, s, Vh = load_svd(filename)
except IOError:
sys.exit(textwrap.dedent(
'''
A singular-value decomposition of the {s} does not exist.
'''.format(s=operatorName)))
#==============================================================================
# Read in data files
#==============================================================================
datadir = np.load('datadir.npz')
receiverPoints = np.load(str(datadir['receivers']))
recordingTimes = np.load(str(datadir['recordingTimes']))
# Apply user-specified windows
rinterval, tinterval, tstep, dt, sinterval = get_user_windows()
receiverPoints = receiverPoints[rinterval, :]
recordingTimes = recordingTimes[tinterval]
# Load appropriate source points and source window
if args.nfo: # Near-field operator
if 'sources' in datadir:
sourcePoints = np.load(str(datadir['sources']))
sourcePoints = sourcePoints[sinterval, :]
else:
sourcePoints = None
else:
# if args.lso (Lippmann-Schwinger operator)
# in the case of the Lippmann-Schwinger operator, 'sourcePoints'
# correspond to sampling points, which should always exist.
if 'testFuncs' in datadir:
sourcePoints = np.load(str(datadir['samplingPoints']))
elif Path('VZTestFuncs.npz').exists():
TFDict = np.load('VZTestFuncs.npz')
sourcePoints = TFDict['samplingPoints']
else:
sys.exit(textwrap.dedent(
'''
Error: A sampling grid must exist and test functions computed
before a singular-value decomposition of the Lippmann-Schwinger
operator can be computed or plotted.
'''))
# update sinterval for test functions
sinterval = np.arange(0, sourcePoints.shape[0], 1)
# increment receiver/source intervals to be consistent with
# one-based indexing (i.e., count from one instead of zero)
rinterval += 1
sinterval += 1
#==============================================================================
# Determine whether to plot SVD in time domain or frequency domain
#==============================================================================
if np.issubdtype(U.dtype, np.complexfloating):
domain = 'freq'
else:
domain = 'time'
# Load plot parameters
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
else:
plotParams = default_params()
Nr = receiverPoints.shape[0]
Nt = len(recordingTimes)
k = len(s)
if domain == 'freq':
# plot singular vectors in frequency domain
N = nextPow2(2 * Nt)
freqs = np.fft.rfftfreq(N, tstep * dt)
if plotParams['fmax'] is None:
plotParams['fmax'] = np.max(freqs)
# Apply the frequency window
fmin = plotParams['fmin']
fmax = plotParams['fmax']
df = 1.0 / (N * tstep * dt)
startIndex = int(round(fmin / df))
stopIndex = int(round(fmax / df))
finterval = np.arange(startIndex, stopIndex, 1)
freqs = freqs[finterval]
M = len(freqs)
Ns = int(Vh.shape[1] / M)
U = U.toarray().reshape((Nr, M, k))
V = Vh.getH().toarray().reshape((Ns, M, k))
else: # domain == 'time'
M = 2 * Nt - 1
Ns = int(Vh.shape[1] / M)
U = U.reshape((Nr, M, k))
V = Vh.T.reshape((Ns, M, k))
T = recordingTimes[-1] - recordingTimes[0]
times = np.linspace(-T, T, M)
if args.mode is not None:
plotParams['view_mode'] = args.mode
pickle.dump(plotParams, open('plotParams.pkl', 'wb'), pickle.HIGHEST_PROTOCOL)
remove_keymap_conflicts({'left', 'right', 'up', 'down', 'save'})
if domain == 'freq':
# plot the left singular vectors
fig_lvec, ax_lvec_r, ax_lvec_i = setFigure(num_axes=2, mode=plotParams['view_mode'])
ax_lvec_r.volume = U.real
ax_lvec_i.volume = U.imag
ax_lvec_r.index = 0
ax_lvec_i.index = 0
fig_lvec.suptitle('Left-Singular Vector', color=ax_lvec_r.titlecolor, fontsize=16)
fig_lvec.subplots_adjust(bottom=0.27, top=0.86)
leftTitle_r = vector_title('left', ax_lvec_r.index + 1, 'real')
leftTitle_i = vector_title('left', ax_lvec_i.index + 1, 'imag')
for ax, title in zip([ax_lvec_r, ax_lvec_i], [leftTitle_r, leftTitle_i]):
left_im = plotFreqVectors(ax, ax.volume[:, :, ax.index], freqs, rinterval,
receiverPoints, title, 'left', plotParams)
lp0 = ax_lvec_r.get_position().get_points().flatten()
lp1 = ax_lvec_i.get_position().get_points().flatten()
left_cax = fig_lvec.add_axes([lp0[0], 0.12, lp1[2]-lp0[0], 0.03])
lcbar = fig_lvec.colorbar(left_im, left_cax, orientation='horizontal')
lcbar.outline.set_edgecolor(ax_lvec_r.cbaredgecolor)
lcbar.ax.tick_params(axis='x', colors=ax_lvec_r.labelcolor)
lcbar.ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
lcbar.set_label('Amplitude',
labelpad=5, rotation=0, fontsize=12, color=ax_lvec_r.labelcolor)
fig_lvec.canvas.mpl_connect('key_press_event', lambda event: process_key_vectors(event, freqs, rinterval, sinterval,
receiverPoints, sourcePoints, plotParams,
'cmplx_left'))
# plot the right singular vectors
fig_rvec, ax_rvec_r, ax_rvec_i = setFigure(num_axes=2, mode=plotParams['view_mode'])
ax_rvec_r.volume = V.real
ax_rvec_i.volume = V.imag
ax_rvec_r.index = 0
ax_rvec_i.index = 0
fig_rvec.suptitle('Right-Singular Vector', color=ax_rvec_r.titlecolor, fontsize=16)
fig_rvec.subplots_adjust(bottom=0.27, top=0.86)
rightTitle_r = vector_title('right', ax_rvec_r.index + 1, 'real')
rightTitle_i = vector_title('right', ax_rvec_i.index + 1, 'imag')
for ax, title in zip([ax_rvec_r, ax_rvec_i], [rightTitle_r, rightTitle_i]):
right_im = plotFreqVectors(ax, ax.volume[:, :, ax.index], freqs, sinterval,
sourcePoints, title, 'right', plotParams)
rp0 = ax_rvec_r.get_position().get_points().flatten()
rp1 = ax_rvec_i.get_position().get_points().flatten()
right_cax = fig_rvec.add_axes([rp0[0], 0.12, rp1[2]-rp0[0], 0.03])
rcbar = fig_rvec.colorbar(right_im, right_cax, orientation='horizontal')
rcbar.outline.set_edgecolor(ax_rvec_r.cbaredgecolor)
rcbar.ax.tick_params(axis='x', colors=ax_rvec_r.labelcolor)
rcbar.ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
rcbar.set_label('Amplitude',
labelpad=5, rotation=0, fontsize=12, color=ax_lvec_r.labelcolor)
fig_rvec.canvas.mpl_connect('key_press_event', lambda event: process_key_vectors(event, freqs, rinterval, sinterval,
receiverPoints, sourcePoints, plotParams,
'cmplx_right'))
else:
# domain == 'time'
fig_vec, ax_lvec, ax_rvec = setFigure(num_axes=2, mode=plotParams['view_mode'])
ax_lvec.volume = U
ax_lvec.index = 0
leftTitle = vector_title('left', ax_lvec.index + 1)
plotWiggles(ax_lvec, ax_lvec.volume[:, :, ax_lvec.index], times, rinterval,
receiverPoints, leftTitle, 'left', plotParams)
ax_rvec.volume = V
ax_rvec.index = 0
rightTitle = vector_title('right', ax_rvec.index + 1)
plotWiggles(ax_rvec, ax_rvec.volume[:, :, ax_rvec.index], times, sinterval,
sourcePoints, rightTitle, 'right', plotParams)
fig_vec.tight_layout()
fig_vec.canvas.mpl_connect('key_press_event', lambda event: process_key_vectors(event, times, rinterval, sinterval,
receiverPoints, sourcePoints, plotParams))
#==============================================================================
# plot the singular values
# figure and axis for singular values
fig_vals, ax_vals = setFigure(num_axes=1, mode=plotParams['view_mode'])
n = np.arange(1, k + 1, 1)
kappa = s[0] / s[-1] # condition number = max(s) / min(s)
ax_vals.plot(n, s, '.', clip_on=False, markersize=9, label=r'Condition Number: %0.1e' %(kappa), color=ax_vals.pointcolor)
ax_vals.set_xlabel('n', color=ax_vals.labelcolor)
ax_vals.set_ylabel('$\sigma_n$', color=ax_vals.labelcolor)
legend = ax_vals.legend(title='Singular Values', loc='upper center', bbox_to_anchor=(0.5, 1.25),
markerscale=0, handlelength=0, handletextpad=0, fancybox=True, shadow=True,
fontsize='large')
legend.get_title().set_fontsize('large')
ax_vals.set_xlim([1, k])
ax_vals.set_ylim(bottom=0)
ax_vals.locator_params(axis='y', nticks=6)
ax_vals.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
fig_vals.tight_layout()
fig_vals.savefig('singularValues.' + args.format, format=args.format, bbox_inches='tight', facecolor=fig_vals.get_facecolor())
plt.show()
def cli():
parser = argparse.ArgumentParser()
parser.add_argument('--data', action='store_true',
help='Plot the frequency spectrum of the recorded data. (Default)')
parser.add_argument('--testfunc', action='store_true',
help='Plot the frequency spectrum of the simulated test functions.')
parser.add_argument('--power', action='store_true',
help='''Plot the mean power spectrum of the input signals. Default is to plot the
mean amplitude spectrum of the Fourier transform.''')
parser.add_argument('--fmin', type=float,
help='Specify the minimum frequency of the amplitude/power spectrum plot. Default is set to 0.')
parser.add_argument('--fmax', type=float,
help='''Specify the maximum frequency of the amplitude/power spectrum plot. Default is set to the
maximum frequency bin based on the length of the time signal.''')
parser.add_argument('--fu', type=str,
help='Specify the frequency units (e.g., Hz)')
parser.add_argument('--format', '-f', type=str, default='pdf', choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help='''specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument('--mode', type=str, choices=['light', 'dark'], required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
#==============================================================================
# Get time window parameters
tinterval, tstep, dt = get_user_windows()[1:4]
datadir = np.load('datadir.npz')
recordingTimes = np.load(str(datadir['recordingTimes']))
recordingTimes = recordingTimes[tinterval]
# Used for getting time and frequency units
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
else:
plotParams = default_params()
if all(v is True for v in [args.data, args.testfunc]):
sys.exit(textwrap.dedent(
'''
Error: Cannot plot frequency spectrum of both recorded data and
simulated test functions. Use
vzspectra --data
to plot the frequency spectrum of the recorded data or
vzspectra --testfuncs
to plot the frequency spectrum of the simulated test functions.
'''))
elif (args.data and not args.testfunc) or all(v is not True for v in [args.data, args.testfunc]):
# default is to plot spectra of data if user does not specify either args.data or args.testfunc
X = load_data(domain='time', verbose=True)
elif not args.data and args.testfunc:
if 'testFuncs' not in datadir and not Path('VZTestFuncs.npz').exists():
X = load_test_funcs(domain='time', medium='constant', verbose=True)
elif 'testFuncs' in datadir and not Path('VZTestFuncs.npz').exists():
X = load_test_funcs(domain='time', medium='variable', verbose=True)
elif not 'testFuncs' in datadir and Path('VZTestFuncs.npz').exists():
X = load_test_funcs(domain='time', medium='constant', verbose=True)
elif 'testFuncs' in datadir and Path('VZTestFuncs.npz').exists():
userResponded = False
print(textwrap.dedent(
'''
Two files are available containing simulated test functions.
Enter '1' to view the user-provided test functions. (Default)
Enter '2' to view the test functions computed by Vezda.
Enter 'q/quit' to exit.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
X = load_test_funcs(domain='time', medium='variable', verbose=True)
userResponded = True
break
elif answer == '2':
X = load_test_funcs(domain='time', medium='constant', verbose=True)
userResponded = True
break
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.')
else:
print('Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.')
#==============================================================================
# compute spectra
freqs, amplitudes = compute_spectrum(X, tstep * dt, args.power)
if args.power:
plotLabel = 'power'
plotParams['freq_title'] = 'Mean Power Spectrum'
plotParams['freq_ylabel'] = 'Power'
else:
plotLabel = 'amplitude'
plotParams['freq_title'] = 'Mean Amplitude Spectrum'
plotParams['freq_ylabel'] = 'Amplitude'
if args.data or all(v is not True for v in [args.data, args.testfunc]):
plotParams['freq_title'] += ' [' + plotParams['data_title'] + ']'
elif args.testfunc:
plotParams['freq_title'] += ' [' + plotParams['tf_title'] + 's]'
if args.fmin is not None:
if args.fmin >= 0:
if args.fmax is not None:
if args.fmax > args.fmin:
plotParams['fmin'] = args.fmin
plotParams['fmax'] = args.fmax
else:
sys.exit(textwrap.dedent(
'''
RelationError: The maximum frequency of the %s spectrum plot must
be greater than the mininum frequency.
''' %(plotLabel)))
else:
fmax = plotParams['fmax']
if fmax > args.fmin:
plotParams['fmin'] = args.fmin
else:
sys.exit(textwrap.dedent(
'''
RelationError: The specified minimum frequency of the %s spectrum
plot must be less than the maximum frequency.
''' %(plotLabel)))
else:
sys.exit(textwrap.dedent(
'''
ValueError: The specified minimum frequency of the %s spectrum
plot must be nonnegative.
''' %(plotLabel)))
#===============================================================================
if args.fmax is not None:
if args.fmin is not None:
if args.fmin >= 0:
if args.fmax > args.fmin:
plotParams['fmin'] = args.fmin
plotParams['fmax'] = args.fmax
else:
sys.exit(textwrap.dedent(
'''
RelationError: The maximum frequency of the %s spectrum plot must
be greater than the mininum frequency.
''' %(plotLabel)))
else:
sys.exit(textwrap.dedent(
'''
ValueError: The specified minimum frequency of the %s spectrum
plot must be nonnegative.
''' %(plotLabel)))
else:
fmin = plotParams['fmin']
if args.fmax > fmin:
plotParams['fmax'] = args.fmax
else:
sys.exit(textwrap.dedent(
'''
RelationError: The specified maximum frequency of the %s spectrum
plot must be greater than the minimum frequency.
''' %(plotLabel)))
elif plotParams['fmax'] is None:
plotParams['fmax'] = np.max(freqs)
#===================================================================================
if args.fu is not None:
plotParams['fu'] = args.fu
if args.mode is not None:
plotParams['view_mode'] = args.mode
pickle.dump(plotParams, open('plotParams.pkl', 'wb'), pickle.HIGHEST_PROTOCOL)
fig, ax = setFigure(num_axes=1, mode=plotParams['view_mode'])
ax.plot(freqs, amplitudes, color=ax.linecolor, linewidth=ax.linewidth)
ax.set_title(plotParams['freq_title'], color=ax.titlecolor)
# get frequency units from plotParams
fu = plotParams['fu']
fmin = plotParams['fmin']
fmax = plotParams['fmax']
if fu != '':
ax.set_xlabel('Frequency (%s)' %(fu), color=ax.labelcolor)
else:
ax.set_xlabel('Frequency', color=ax.labelcolor)
ax.set_ylabel(plotParams['freq_ylabel'], color=ax.labelcolor)
ax.set_xlim([fmin, fmax])
ax.set_ylim(bottom=0)
ax.fill_between(freqs, 0, amplitudes, where=(amplitudes > 0), color='m', alpha=ax.alpha)
ax.locator_params(axis='y', nticks=6)
ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.tight_layout()
fig.savefig(plotLabel + 'Spectrum.' + args.format, format=args.format, bbox_inches='tight', facecolor=fig.get_facecolor())
plt.show()
def cli():
parser = argparse.ArgumentParser()
parser.add_argument(
'--data',
action='store_true',
help='Plot the frequency spectrum of the recorded data. (Default)')
parser.add_argument(
'--testfunc',
action='store_true',
help='Plot the frequency spectrum of the simulated test functions.')
parser.add_argument(
'--power',
action='store_true',
help=
'''Plot the mean power spectrum of the input signals. Default is to plot the
mean amplitude spectrum of the Fourier transform.''')
parser.add_argument(
'--fmin',
type=float,
help=
'Specify the minimum frequency of the amplitude/power spectrum plot. Default is set to 0.'
)
parser.add_argument(
'--fmax',
type=float,
help=
'''Specify the maximum frequency of the amplitude/power spectrum plot. Default is set to the
maximum frequency bin based on the length of the time signal.'''
)
parser.add_argument('--fu',
type=str,
help='Specify the frequency units (e.g., Hz)')
parser.add_argument(
'--format',
'-f',
type=str,
default='pdf',
choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help=
'''specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument(
'--mode',
type=str,
choices=['light', 'dark'],
required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
#==============================================================================
# Load the recording times from the data directory
datadir = np.load('datadir.npz')
receiverPoints = np.load(str(datadir['receivers']))
recordingTimes = np.load(str(datadir['recordingTimes']))
dt = recordingTimes[1] - recordingTimes[0]
if Path('window.npz').exists():
windowDict = np.load('window.npz')
# Apply the receiver window
rstart = windowDict['rstart']
rstop = windowDict['rstop']
rstep = windowDict['rstep']
# Apply the time window
tstart = windowDict['tstart']
tstop = windowDict['tstop']
tstep = windowDict['tstep']
# Convert time window parameters to corresponding array indices
Tstart = int(round(tstart / dt))
Tstop = int(round(tstop / dt))
else:
rstart = 0
rstop = receiverPoints.shape[0]
rstep = 1
tstart = recordingTimes[0]
tstop = recordingTimes[-1]
Tstart = 0
Tstop = len(recordingTimes)
tstep = 1
# Apply the receiver window
rinterval = np.arange(rstart, rstop, rstep)
receiverPoints = receiverPoints[rinterval, :]
# Apply the time window
tinterval = np.arange(Tstart, Tstop, tstep)
recordingTimes = recordingTimes[tinterval]
# Used for getting time and frequency units
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
else:
plotParams = default_params()
if all(v is True for v in [args.data, args.testfunc]):
sys.exit(
textwrap.dedent('''
Error: Cannot plot frequency spectrum of both recorded data and
simulated test functions. Use
vzspectra --data
to plot the frequency spectrum of the recorded data or
vzspectra --testfuncs
to plot the frequency spectrum of the simulated test functions.
'''))
elif (args.data and not args.testfunc) or all(
v is not True for v in [args.data, args.testfunc]):
# default is to plot spectra of data if user does not specify either args.data or args.testfunc
if Path('noisyData.npz').exists():
userResponded = False
print(
textwrap.dedent('''
Detected that band-limited noise has been added to the data array.
Would you like to plot the amplitude/power spectrum of the noisy data? ([y]/n)
Enter 'q/quit' exit the program.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == 'y' or answer == 'yes':
print('Proceeding with noisy data...')
# read in the noisy data array
noisy = True
X = np.load('noisyData.npz')['noisyData']
userResponded = True
elif answer == 'n' or answer == 'no':
print('Proceeding with noise-free data...')
# read in the recorded data array
noisy = False
X = np.load(str(datadir['recordedData']))
userResponded = True
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print(
'Invalid response. Please enter \'y/yes\', \'n\no\', or \'q/quit\'.'
)
else:
# read in the recorded data array
noisy = False
X = np.load(str(datadir['recordedData']))
# Load the windowing parameters for the receiver and time axes of
# the 3D data array
if Path('window.npz').exists():
print('Detected user-specified window:\n')
# For display/printing purposes, count receivers with one-based
# indexing. This amounts to incrementing the rstart parameter by 1
print('window @ receivers : start =', rstart + 1)
print('window @ receivers : stop =', rstop)
print('window @ receivers : step =', rstep, '\n')
tu = plotParams['tu']
if tu != '':
print('window @ time : start = %0.2f %s' % (tstart, tu))
print('window @ time : stop = %0.2f %s' % (tstop, tu))
else:
print('window @ time : start =', tstart)
print('window @ time : stop =', tstop)
print('window @ time : step =', tstep, '\n')
# Apply the source window
slabel = windowDict['slabel']
sstart = windowDict['sstart']
sstop = windowDict['sstop']
sstep = windowDict['sstep']
sinterval = np.arange(sstart, sstop, sstep)
# For display/printing purposes, count recordings/sources with one-based
# indexing. This amounts to incrementing the sstart parameter by 1
print('window @ %s : start = %s' % (slabel, sstart + 1))
print('window @ %s : stop = %s' % (slabel, sstop))
print('window @ %s : step = %s\n' % (slabel, sstep))
print('Applying window to data volume...')
X = X[rinterval, :, :]
X = X[:, tinterval, :]
X = X[:, :, sinterval]
# Apply tapered cosine (Tukey) window to time signals.
# This ensures the fast fourier transform (FFT) used in
# the definition of the matrix-vector product below is
# acting on a function that is continuous at its edges.
Nt = X.shape[1]
peakFreq = pulseFun.peakFreq
# Np : Number of samples in the dominant period T = 1 / peakFreq
Np = int(round(1 / (tstep * dt * peakFreq)))
# alpha is set to taper over 6 of the dominant period of the
# pulse function (3 periods from each end of the signal)
alpha = 6 * Np / Nt
print('Tapering time signals with Tukey window: %d' %
(int(round(alpha * 100))) + '%')
TukeyWindow = tukey(Nt, alpha)
X *= TukeyWindow[None, :, None]
elif not args.data and args.testfunc:
if Path('samplingGrid.npz').exists():
samplingGrid = np.load('samplingGrid.npz')
x = samplingGrid['x']
y = samplingGrid['y']
tau = samplingGrid['tau']
if 'z' in samplingGrid:
z = samplingGrid['z']
else:
z = None
else:
sys.exit(
textwrap.dedent('''
A sampling grid needs to be set up and test functions
computed before their Fourier spectrum can be plotted.
Enter:
vzgrid --help
from the command-line for more information on how to set up a
sampling grid.
'''))
pulse = lambda t: pulseFun.pulse(t)
velocity = pulseFun.velocity
peakFreq = pulseFun.peakFreq
peakTime = pulseFun.peakTime
if Path('VZTestFuncs.npz').exists():
print(
'\nDetected that free-space test functions have already been computed...'
)
print(
'Checking consistency with current space-time sampling grid...'
)
TFDict = np.load('VZTestFuncs.npz')
if samplingIsCurrent(TFDict, receiverPoints, recordingTimes,
velocity, tau, x, y, z, peakFreq, peakTime):
X = TFDict['TFarray']
sourcePoints = TFDict['samplingPoints']
else:
print('Recomputing test functions...')
if tau[0] != 0:
tu = plotParams['tu']
if tu != '':
print(
'Recomputing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Recomputing test functions for focusing time %0.2f...'
% (tau[0]))
X, sourcePoints = sampleSpace(receiverPoints,
recordingTimes - tau[0],
velocity, x, y, z, pulse)
else:
X, sourcePoints = sampleSpace(receiverPoints,
recordingTimes, velocity, x,
y, z, pulse)
if z is None:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=recordingTimes,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
tau=tau,
samplingPoints=sourcePoints)
else:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=recordingTimes,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
z=z,
tau=tau,
samplingPoints=sourcePoints)
else:
print(
'\nComputing free-space test functions for the current space-time sampling grid...'
)
if tau[0] != 0:
if tu != '':
print(
'Computing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Computing test functions for focusing time %0.2f...' %
(tau[0]))
X, sourcePoints = sampleSpace(receiverPoints,
recordingTimes - tau[0],
velocity, x, y, z, pulse)
else:
X, sourcePoints = sampleSpace(receiverPoints, recordingTimes,
velocity, x, y, z, pulse)
if z is None:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=recordingTimes,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
tau=tau,
samplingPoints=sourcePoints)
else:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=recordingTimes,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
z=z,
tau=tau,
samplingPoints=sourcePoints)
#==============================================================================
# compute spectra
freqs, amplitudes = compute_spectrum(X, tstep * dt, args.power)
if args.power:
plotLabel = 'power'
plotParams['freq_title'] = 'Mean Power Spectrum'
plotParams['freq_ylabel'] = 'Power'
else:
plotLabel = 'amplitude'
plotParams['freq_title'] = 'Mean Amplitude Spectrum'
plotParams['freq_ylabel'] = 'Amplitude'
if args.data or all(v is not True for v in [args.data, args.testfunc]):
if noisy:
plotParams[
'freq_title'] += ' [Noisy ' + plotParams['data_title'] + ']'
else:
plotParams['freq_title'] += ' [' + plotParams['data_title'] + ']'
elif args.testfunc:
plotParams['freq_title'] += ' [' + plotParams['tf_title'] + 's]'
if args.fmin is not None:
if args.fmin >= 0:
if args.fmax is not None:
if args.fmax > args.fmin:
plotParams['fmin'] = args.fmin
plotParams['fmax'] = args.fmax
else:
sys.exit(
textwrap.dedent('''
RelationError: The maximum frequency of the %s spectrum plot must
be greater than the mininum frequency.
''' % (plotLabel)))
else:
fmax = plotParams['fmax']
if fmax > args.fmin:
plotParams['fmin'] = args.fmin
else:
sys.exit(
textwrap.dedent('''
RelationError: The specified minimum frequency of the %s spectrum
plot must be less than the maximum frequency.
''' % (plotLabel)))
else:
sys.exit(
textwrap.dedent('''
ValueError: The specified minimum frequency of the %s spectrum
plot must be nonnegative.
''' % (plotLabel)))
#===============================================================================
if args.fmax is not None:
if args.fmin is not None:
if args.fmin >= 0:
if args.fmax > args.fmin:
plotParams['fmin'] = args.fmin
plotParams['fmax'] = args.fmax
else:
sys.exit(
textwrap.dedent('''
RelationError: The maximum frequency of the %s spectrum plot must
be greater than the mininum frequency.
''' % (plotLabel)))
else:
sys.exit(
textwrap.dedent('''
ValueError: The specified minimum frequency of the %s spectrum
plot must be nonnegative.
''' % (plotLabel)))
else:
fmin = plotParams['fmin']
if args.fmax > fmin:
plotParams['fmax'] = args.fmax
else:
sys.exit(
textwrap.dedent('''
RelationError: The specified maximum frequency of the %s spectrum
plot must be greater than the minimum frequency.
''' % (plotLabel)))
elif plotParams['fmax'] is None:
plotParams['fmax'] = np.max(freqs)
#===================================================================================
if args.fu is not None:
plotParams['fu'] = args.fu
if args.mode is not None:
plotParams['view_mode'] = args.mode
pickle.dump(plotParams, open('plotParams.pkl', 'wb'),
pickle.HIGHEST_PROTOCOL)
fig, ax = setFigure(num_axes=1, mode=plotParams['view_mode'])
ax.plot(freqs, amplitudes, color=ax.linecolor, linewidth=ax.linewidth)
ax.set_title(plotParams['freq_title'], color=ax.titlecolor)
# get frequency units from plotParams
fu = plotParams['fu']
fmin = plotParams['fmin']
fmax = plotParams['fmax']
if fu != '':
ax.set_xlabel('Frequency (%s)' % (fu), color=ax.labelcolor)
else:
ax.set_xlabel('Frequency', color=ax.labelcolor)
ax.set_ylabel(plotParams['freq_ylabel'], color=ax.labelcolor)
ax.set_xlim([fmin, fmax])
ax.set_ylim(bottom=0)
ax.fill_between(freqs,
0,
amplitudes,
where=(amplitudes > 0),
color='m',
alpha=ax.alpha)
ax.locator_params(axis='y', nticks=6)
ax.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.tight_layout()
fig.savefig(plotLabel + 'Spectrum.' + args.format,
format=args.format,
bbox_inches='tight',
facecolor=fig.get_facecolor())
plt.show()
def cli():
parser = argparse.ArgumentParser()
parser.add_argument(
'--nfo',
action='store_true',
help='''Compute or plot the singular-value decomposition of the
near-field operator (NFO).''')
parser.add_argument(
'--lso',
action='store_true',
help='''Compute or plot the singular-value decomposition of the
Lippmann-Schwinger operator (LSO).''')
parser.add_argument(
'--numVals',
'-k',
type=int,
help='''Specify the number of singular values/vectors to compute.
Must a positive integer between 1 and the order of the square
input matrix.''')
parser.add_argument(
'--domain',
'-d',
type=str,
choices=['time', 'freq'],
help='''Specify whether to compute the singular-value decomposition in
the time domain or frequency domain. Default is set to frequency domain
for faster, more accurate performance.''')
parser.add_argument(
'--plot',
'-p',
action='store_true',
help='''Plot the computed singular values and vectors.''')
parser.add_argument(
'--format',
'-f',
type=str,
default='pdf',
choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help=
'''Specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument(
'--mode',
type=str,
choices=['light', 'dark'],
required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
if args.nfo and not args.lso:
operatorType = 'near-field operator'
inputType = 'data'
try:
SVD = np.load('NFO_SVD.npz')
s = SVD['s']
Uh = SVD['Uh']
V = SVD['V']
domain = SVD['domain']
except FileNotFoundError:
s, Uh, V, domain = None, None, None, 'freq'
elif not args.nfo and args.lso:
operatorType = 'Lippmann-Schwinger operator'
inputType = 'test functions'
try:
SVD = np.load('LSO_SVD.npz')
s = SVD['s']
Uh = SVD['Uh']
V = SVD['V']
domain = SVD['domain']
except FileNotFoundError:
s, Uh, V, domain = None, None, None, 'freq'
elif args.nfo and args.lso:
sys.exit(
textwrap.dedent('''
UsageError: Please specify only one of the arguments \'--nfo\' or \'--lso\'.
'''))
else:
sys.exit(
textwrap.dedent('''
For which operator would you like to compute or plot a singular-value decomposition?
Enter:
vzsvd --nfo
for the near-field operator or
vzsvd --lso
for the Lippmann-Schwinger operator.
'''))
#==============================================================================
# if an SVD already exists...
if any(v is not None for v in
[s, Uh, V]) and args.numVals is not None and args.plot is True:
if args.numVals >= 1 and args.numVals == len(s):
userResponded = False
print(
textwrap.dedent('''
A singular-value decomposition of the {s} for {n} values/vectors already exists.
What would you like to do?
Enter '1' to specify a new number of values/vectors to compute. (Default)
Enter '2' to recompute a singular-value decomposition for {n} values/vectors.
Enter 'q/quit' to exit.
'''.format(s=operatorType, n=args.numVals)))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
k = int(
input(
'Please specify the number of singular values/vectors to compute: '
))
if isValid(k):
print('Proceeding with numVals = %s...' % (k))
userResponded = True
computeSVD = True
break
else:
break
elif answer == '2':
k = args.numVals
print(
'Recomputing SVD of the %s for %s singular values/vectors...'
% (operatorType, k))
userResponded = True
computeSVD = True
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print(
'Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.'
)
elif args.numVals >= 1 and args.numVals != len(s):
k = args.numVals
computeSVD = True
elif args.numVals < 1:
userResponded = False
print(
textwrap.dedent('''
ValueError: Argument '-k/--numVals' must be a positive integer
between 1 and the order of the square input matrix. The parameter will
be set to the default value of 6.
What would you like to do?
Enter '1' to specify a value of the parameter. (Default)
Enter '2' to proceed with the default value.
Enter 'q/quit' exit the program.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
k = int(
input(
'Please specify the number of singular values/vectors to compute: '
))
if isValid(k):
print('Proceeding with numVals = %s...' % (k))
userResponded = True
computeSVD = True
break
else:
break
elif answer == '2':
k = 6
print('Proceeding with the default value numVals = %s...' %
(k))
computeSVD = True
userResponded = True
break
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print(
'Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.'
)
elif all(v is not None for v in
[s, Uh, V]) and args.numVals is None and args.plot is True:
computeSVD = False
elif all(v is not None for v in
[s, Uh, V]) and args.numVals is not None and args.plot is False:
if args.numVals >= 1 and args.numVals == len(s):
userResponded = False
print(
textwrap.dedent('''
A singular-value decomposition of the {s} for {n} values/vectors already exists.
What would you like to do?
Enter '1' to specify a new number of values/vectors to compute. (Default)
Enter '2' to recompute a singular-value decomposition for {n} values/vectors.
Enter 'q/quit' to exit.
'''.format(s=operatorType, n=args.numVals)))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
k = int(
input(
'Please specify the number of singular values/vectors to compute: '
))
if isValid(k):
print('Proceeding with numVals = %s...' % (k))
userResponded = True
computeSVD = True
break
else:
break
elif answer == '2':
k = args.numVals
print(
'Recomputing SVD of the %s for %s singular values/vectors...'
% (operatorType, k))
userResponded = True
computeSVD = True
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print(
'Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.'
)
elif args.numVals >= 1 and args.numVals != len(s):
k = args.numVals
computeSVD = True
elif args.numVals < 1:
userResponded = False
print(
textwrap.dedent('''
ValueError: Argument '-k/--numVals' must be a positive integer
between 1 and the order of the square input matrix. The parameter will
be set to the default value of 6.
What would you like to do?
Enter '1' to specify a value of the parameter. (Default)
Enter '2' to proceed with the default value.
Enter 'q/quit' exit the program.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
k = int(
input(
'Please specify the number of singular values/vectors to compute: '
))
if isValid(k):
print('Proceeding with numVals = %s...' % (k))
userResponded = True
computeSVD = True
break
else:
break
elif answer == '2':
k = 6
print('Proceeding with the default value numVals = %s...' %
(k))
computeSVD = True
userResponded = True
break
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print(
'Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.'
)
elif all(v is not None for v in
[s, Uh, V]) and args.numVals is None and args.plot is False:
sys.exit(
textwrap.dedent('''
No action specified. A singular-value decomposition of the %s
for %s values/vectors already exists. Please specify at least one of '-k/--numVals'
or '-p/--plot' arguments with 'vzsvd' command.
''' % (operatorType, len(s))))
#==============================================================================
# if an SVD does not already exist...
elif any(v is None for v in
[s, Uh, V]) and args.numVals is not None and args.plot is True:
if args.numVals >= 1:
computeSVD = True
k = args.numVals
elif args.numVals < 1:
userResponded = False
print(
textwrap.dedent('''
ValueError: Argument '-k/--numVals' must be a positive integer
between 1 and the order of the square input matrix. The parameter will
be set to the default value of 6.
What would you like to do?
Enter '1' to specify a value of the parameter. (Default)
Enter '2' to proceed with the default value.
Enter 'q/quit' exit the program.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
k = int(
input(
'Please specify the number of singular values/vectors to compute: '
))
if isValid(k):
print('Proceeding with numVals = %s...' % (k))
userResponded = True
computeSVD = True
break
else:
break
elif answer == '2':
k = 6
print('Proceeding with the default value numVals = %s...' %
(k))
computeSVD = True
userResponded = True
break
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print(
'Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.'
)
elif any(v is None for v in
[s, Uh, V]) and args.numVals is None and args.plot is True:
userResponded = False
print(
textwrap.dedent('''
PlotError: A singular-value decomposition of the {s} does not exist. A plot will be
generated after a singular-value decomposition has been computed.
Enter '1' to specify a number of singular values/vectors to compute. (Default)
Enter 'q/quit' to exit.
'''.format(s=operatorType)))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
k = int(
input(
'Please specify the number of singular values/vectors to compute: '
))
if isValid(k):
print('Proceeding with numVals = %s...' % (k))
userResponded = True
computeSVD = True
break
else:
break
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print('Invalid response. Please enter \'1\', or \'q/quit\'.')
elif any(v is None for v in
[s, Uh, V]) and args.numVals is not None and args.plot is False:
if args.numVals >= 1:
k = args.numVals
computeSVD = True
elif args.numVals < 1:
userResponded = False
print(
textwrap.dedent('''
ValueError: Argument '-k/--numVals' must be a positive integer
between 1 and the order of the square input matrix. The parameter will
be set to the default value of 6.
What would you like to do?
Enter '1' to specify a value of the parameter. (Default)
Enter '2' to proceed with the default value.
Enter 'q/quit' exit the program.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
k = int(
input(
'Please specify the number of singular values/vectors to compute: '
))
if isValid(k):
print('Proceeding with numVals = %s...' % (k))
userResponded = True
computeSVD = True
break
else:
break
elif answer == '2':
k = 6
print('Proceeding with the default value numVals = %s...' %
(k))
computeSVD = True
userResponded = True
break
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print(
'Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.'
)
elif any(v is None for v in
[s, Uh, V]) and args.numVals is None and args.plot is False:
sys.exit(
textwrap.dedent('''
Nothing to be done. A singular-value decomposition of the {s} does not exist.
Please specify at least one of '-k/--numVals' or '-p/--plot'
arguments with 'vzsvd' command.
'''.format(s=operatorType)))
#==============================================================================
# Read in data files
datadir = np.load('datadir.npz')
receiverPoints = np.load(str(datadir['receivers']))
recordingTimes = np.load(str(datadir['recordingTimes']))
dt = recordingTimes[1] - recordingTimes[0]
if Path('window.npz').exists():
windowDict = np.load('window.npz')
# Apply the receiver window
rstart = windowDict['rstart']
rstop = windowDict['rstop']
rstep = windowDict['rstep']
# Apply the time window
tstart = windowDict['tstart']
tstop = windowDict['tstop']
tstep = windowDict['tstep']
# Convert time window parameters to corresponding array indices
Tstart = int(round(tstart / dt))
Tstop = int(round(tstop / dt))
else:
rstart = 0
rstop = receiverPoints.shape[0]
rstep = 1
tstart = recordingTimes[0]
tstop = recordingTimes[-1]
Tstart = 0
Tstop = len(recordingTimes)
tstep = 1
# Apply the receiver window
rinterval = np.arange(rstart, rstop, rstep)
receiverPoints = receiverPoints[rinterval, :]
# Apply the time window
tinterval = np.arange(Tstart, Tstop, tstep)
recordingTimes = recordingTimes[tinterval]
# Used for getting time and frequency units
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
else:
plotParams = default_params()
if computeSVD:
# get time units for printing time windows or time shifts
tu = plotParams['tu']
if args.nfo:
if Path('noisyData.npz').exists():
userResponded = False
print(
textwrap.dedent('''
Detected that band-limited noise has been added to the data array.
Would you like to compute an SVD of the noisy data? ([y]/n)
Enter 'q/quit' exit the program.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == 'y' or answer == 'yes':
print(
'Proceeding with singular-value decomposition using noisy data...'
)
# read in the noisy data array
X = np.load('noisyData.npz')['noisyData']
userResponded = True
elif answer == 'n' or answer == 'no':
print(
'Proceeding with singular-value decomposition using noise-free data...'
)
# read in the recorded data array
X = np.load(str(datadir['recordedData']))
userResponded = True
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.\n')
else:
print(
'Invalid response. Please enter \'y/yes\', \'n\no\', or \'q/quit\'.'
)
else:
# read in the recorded data array
X = np.load(str(datadir['recordedData']))
if Path('window.npz').exists():
print('Detected user-specified window:\n')
# For display/printing purposes, count receivers with one-based
# indexing. This amounts to incrementing the rstart parameter by 1
print('window @ receivers : start =', rstart + 1)
print('window @ receivers : stop =', rstop)
print('window @ receivers : step =', rstep, '\n')
if tu != '':
print('window @ time : start = %0.2f %s' % (tstart, tu))
print('window @ time : stop = %0.2f %s' % (tstop, tu))
else:
print('window @ time : start =', tstart)
print('window @ time : stop =', tstop)
print('window @ time : step =', tstep, '\n')
# Apply the source window
slabel = windowDict['slabel']
sstart = windowDict['sstart']
sstop = windowDict['sstop']
sstep = windowDict['sstep']
sinterval = np.arange(sstart, sstop, sstep)
# For display/printing purposes, count recordings/sources with one-based
# indexing. This amounts to incrementing the sstart parameter by 1
print('window @ %s : start = %s' % (slabel, sstart + 1))
print('window @ %s : stop = %s' % (slabel, sstop))
print('window @ %s : step = %s\n' % (slabel, sstep))
print('Applying window to data volume...')
X = X[rinterval, :, :]
X = X[:, tinterval, :]
X = X[:, :, sinterval]
Nr, Nt, Ns = X.shape
# Apply tapered cosine (Tukey) window to time signals.
# This ensures the fast fourier transform (FFT) used in
# the definition of the matrix-vector product below is
# acting on a function that is continuous at its edges.
peakFreq = pulseFun.peakFreq
# Np : Number of samples in the dominant period T = 1 / peakFreq
Np = int(round(1 / (tstep * dt * peakFreq)))
# alpha is set to taper over 6 of the dominant period of the
# pulse function (3 periods from each end of the signal)
alpha = 6 * Np / Nt
print('Tapering time signals with Tukey window: %d' %
(int(round(alpha * 100))) + '%')
TukeyWindow = tukey(Nt, alpha)
X *= TukeyWindow[None, :, None]
else:
Nr, Nt, Ns = X.shape
elif args.lso:
if Path('samplingGrid.npz').exists():
samplingGrid = np.load('samplingGrid.npz')
x = samplingGrid['x']
y = samplingGrid['y']
tau = samplingGrid['tau']
if 'z' in samplingGrid:
z = samplingGrid['z']
else:
z = None
else:
sys.exit(
textwrap.dedent('''
A sampling grid needs to be set up before computing a
singular-value decomposition of the %s.
Enter:
vzgrid --help
from the command-line for more information on how to set up a
sampling grid.
''' % (operatorType)))
pulse = lambda t: pulseFun.pulse(t)
velocity = pulseFun.velocity
peakFreq = pulseFun.peakFreq
peakTime = pulseFun.peakTime
if Path('VZTestFuncs.npz').exists():
print(
'\nDetected that free-space test functions have already been computed...'
)
print(
'Checking consistency with current space-time sampling grid...'
)
TFDict = np.load('VZTestFuncs.npz')
if samplingIsCurrent(TFDict, receiverPoints, recordingTimes,
velocity, tau, x, y, z, peakFreq,
peakTime):
X = TFDict['TFarray']
sourcePoints = TFDict['samplingPoints']
print('Moving forward to SVD...')
else:
print('Recomputing test functions...')
# set up the convolution times based on length of recording time interval
T = recordingTimes[-1] - recordingTimes[0]
convolutionTimes = np.linspace(-T, T,
2 * len(recordingTimes) - 1)
if tau[0] != 0:
if tu != '':
print(
'Recomputing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Recomputing test functions for focusing time %0.2f...'
% (tau[0]))
X, sourcePoints = sampleSpace(
receiverPoints, convolutionTimes - tau[0],
velocity, x, y, z, pulse)
else:
X, sourcePoints = sampleSpace(receiverPoints,
convolutionTimes,
velocity, x, y, z, pulse)
if z is None:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=recordingTimes,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
tau=tau,
samplingPoints=sourcePoints)
else:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=recordingTimes,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
z=z,
tau=tau,
samplingPoints=sourcePoints)
else:
print(
'\nComputing free-space test functions for the current space-time sampling grid...'
)
if tau[0] != 0:
if tu != '':
print(
'Computing test functions for focusing time %0.2f %s...'
% (tau[0], tu))
else:
print(
'Computing test functions for focusing time %0.2f...'
% (tau[0]))
X, sourcePoints = sampleSpace(receiverPoints,
recordingTimes - tau[0],
velocity, x, y, z, pulse)
else:
X, sourcePoints = sampleSpace(receiverPoints,
recordingTimes, velocity, x,
y, z, pulse)
if z is None:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=recordingTimes,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
tau=tau,
samplingPoints=sourcePoints)
else:
np.savez('VZTestFuncs.npz',
TFarray=X,
time=recordingTimes,
receivers=receiverPoints,
peakFreq=peakFreq,
peakTime=peakTime,
velocity=velocity,
x=x,
y=y,
z=z,
tau=tau,
samplingPoints=sourcePoints)
Nr, Nt, Ns = X.shape
#==============================================================================
if args.domain is not None:
domain = args.domain
if domain == 'freq':
# Transform convolutional operator into frequency domain and bandpass for efficient SVD
print('Transforming %s to the frequency domain...' % (inputType))
N = nextPow2(2 * Nt)
X = np.fft.rfft(X, n=N, axis=1)
if plotParams['fmax'] is None:
freqs = np.fft.rfftfreq(N, tstep * dt)
plotParams['fmax'] = np.max(freqs)
# Apply the frequency window
fmin = plotParams['fmin']
fmax = plotParams['fmax']
fu = plotParams['fu'] # frequency units (e.g., Hz)
if fu != '':
print('Applying bandpass filter: [%0.2f %s, %0.2f %s]' %
(fmin, fu, fmax, fu))
else:
print('Applying bandpass filter: [%0.2f, %0.2f]' %
(fmin, fmax))
df = 1.0 / (N * tstep * dt)
startIndex = int(round(fmin / df))
stopIndex = int(round(fmax / df))
finterval = np.arange(startIndex, stopIndex, 1)
X = X[:, finterval, :]
#==============================================================================
# Compute the k largest singular values (which='LM') of the operator A
# Singular values are elements of the vector 's'
# Left singular vectors are columns of 'U'
# Right singular vectors are columns of 'V'
A = asConvolutionalOperator(X)
if k == 1:
print('Computing SVD of the %s for 1 singular value/vector...' %
(operatorType))
else:
print('Computing SVD of the %s for %s singular values/vectors...' %
(operatorType, k))
startTime = time.time()
U, s, Vh = svds(A, k, which='LM')
endTime = time.time()
print('Elapsed time:', humanReadable(endTime - startTime), '\n')
# sort the singular values and corresponding vectors in descending order
# (i.e., largest to smallest)
index = s.argsort()[::-1]
s = s[index]
Uh = U[:, index].conj().T
V = Vh[index, :].conj().T
# Write binary output with numpy
if args.nfo:
np.savez('NFO_SVD.npz', s=s, Uh=Uh, V=V, domain=domain)
elif args.lso:
np.savez('LSO_SVD.npz', s=s, Uh=Uh, V=V, domain=domain)
#==============================================================================
if args.plot and all(v is not None for v in [s, Uh, V]):
Nr = receiverPoints.shape[0]
Nt = len(recordingTimes)
try:
k
except NameError:
k = len(s)
if args.domain is not None and domain != args.domain:
if domain == 'freq':
s1 = 'time'
s2 = 'frequency'
else:
s1 = 'frequency'
s2 = 'time'
sys.exit(
textwrap.dedent('''
Error: Attempted to plot the singular-value decomposition in the %s
domain, but the decomposition was computed in the %s domain.
''' % (s1, s2)))
if domain == 'freq':
# plot singular vectors in frequency domain
N = nextPow2(2 * Nt)
freqs = np.fft.rfftfreq(N, tstep * dt)
if plotParams['fmax'] is None:
plotParams['fmax'] = np.max(freqs)
# Apply the frequency window
fmin = plotParams['fmin']
fmax = plotParams['fmax']
df = 1.0 / (N * tstep * dt)
startIndex = int(round(fmin / df))
stopIndex = int(round(fmax / df))
finterval = np.arange(startIndex, stopIndex, 1)
freqs = freqs[finterval]
fmax = freqs[-1]
M = len(freqs)
Ns = int(V.shape[0] / M)
U = np.reshape(Uh.conj().T, (Nr, M, k))
V = np.reshape(V, (Ns, M, k))
else: # domain == 'time'
M = 2 * Nt - 1
Ns = int(V.shape[0] / M)
U = np.reshape(Uh.T, (Nr, M, k))
V = np.reshape(V, (Ns, M, k))
T = recordingTimes[-1] - recordingTimes[0]
times = np.linspace(-T, T, M)
if args.nfo: # Near-field operator
try:
sinterval
except NameError:
if Path('window.npz').exists():
sstart = windowDict['sstart']
sstop = windowDict['sstop']
sstep = windowDict['sstep']
else:
sstart = 0
sstop = Ns
sstep = 1
sinterval = np.arange(sstart, sstop, sstep)
if 'sources' in datadir:
sourcePoints = np.load(str(datadir['sources']))
sourcePoints = sourcePoints[sinterval, :]
else:
sourcePoints = None
else:
# if args.lso (Lippmann-Schwinger operator)
# in the case of the Lippmann-Schwinger operator, 'sourcePoints'
# correspond to sampling points, which should always exist.
try:
sourcePoints
except NameError:
if Path('VZTestFuncs.npz').exists():
TFDict = np.load('VZTestFuncs.npz')
sourcePoints = TFDict['samplingPoints']
else:
sys.exit(
textwrap.dedent('''
Error: A sampling grid must exist and test functions computed
before a singular-value decomposition of the Lippmann-Schwinger
operator can be computed or plotted.
'''))
sstart = 0
sstop = sourcePoints.shape[0]
sstep = 1
sinterval = np.arange(sstart, sstop, sstep)
# increment source/recording interval and receiver interval to be consistent
# with one-based indexing (i.e., count from one instead of zero)
sinterval += 1
rinterval += 1
rstart += 1
sstart += 1
if args.mode is not None:
plotParams['view_mode'] = args.mode
pickle.dump(plotParams, open('plotParams.pkl', 'wb'),
pickle.HIGHEST_PROTOCOL)
remove_keymap_conflicts({'left', 'right', 'up', 'down', 'save'})
if domain == 'freq':
# plot the left singular vectors
fig_lvec, ax_lvec_r, ax_lvec_i = setFigure(
num_axes=2, mode=plotParams['view_mode'])
ax_lvec_r.volume = U.real
ax_lvec_i.volume = U.imag
ax_lvec_r.index = 0
ax_lvec_i.index = 0
fig_lvec.suptitle('Left-Singular Vector',
color=ax_lvec_r.titlecolor,
fontsize=16)
fig_lvec.subplots_adjust(bottom=0.27, top=0.86)
leftTitle_r = vector_title('left', ax_lvec_r.index + 1, 'real')
leftTitle_i = vector_title('left', ax_lvec_i.index + 1, 'imag')
for ax, title in zip([ax_lvec_r, ax_lvec_i],
[leftTitle_r, leftTitle_i]):
left_im = plotFreqVectors(ax, ax.volume[:, :, ax.index], freqs,
fmin, fmax, rstart, rinterval,
receiverPoints, title, 'left',
plotParams)
lp0 = ax_lvec_r.get_position().get_points().flatten()
lp1 = ax_lvec_i.get_position().get_points().flatten()
left_cax = fig_lvec.add_axes([lp0[0], 0.12, lp1[2] - lp0[0], 0.03])
lcbar = fig_lvec.colorbar(left_im,
left_cax,
orientation='horizontal')
lcbar.outline.set_edgecolor(ax_lvec_r.cbaredgecolor)
lcbar.ax.tick_params(axis='x', colors=ax_lvec_r.labelcolor)
lcbar.ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
lcbar.set_label('Amplitude',
labelpad=5,
rotation=0,
fontsize=12,
color=ax_lvec_r.labelcolor)
fig_lvec.canvas.mpl_connect(
'key_press_event', lambda event: process_key_vectors(
event, freqs, fmin, fmax, rstart, sstart, rinterval,
sinterval, receiverPoints, sourcePoints, plotParams,
'cmplx_left'))
# plot the right singular vectors
fig_rvec, ax_rvec_r, ax_rvec_i = setFigure(
num_axes=2, mode=plotParams['view_mode'])
ax_rvec_r.volume = V.real
ax_rvec_i.volume = V.imag
ax_rvec_r.index = 0
ax_rvec_i.index = 0
fig_rvec.suptitle('Right-Singular Vector',
color=ax_rvec_r.titlecolor,
fontsize=16)
fig_rvec.subplots_adjust(bottom=0.27, top=0.86)
rightTitle_r = vector_title('right', ax_rvec_r.index + 1, 'real')
rightTitle_i = vector_title('right', ax_rvec_i.index + 1, 'imag')
for ax, title in zip([ax_rvec_r, ax_rvec_i],
[rightTitle_r, rightTitle_i]):
right_im = plotFreqVectors(ax, ax.volume[:, :, ax.index],
freqs, fmin, fmax, sstart,
sinterval, sourcePoints, title,
'right', plotParams)
rp0 = ax_rvec_r.get_position().get_points().flatten()
rp1 = ax_rvec_i.get_position().get_points().flatten()
right_cax = fig_rvec.add_axes(
[rp0[0], 0.12, rp1[2] - rp0[0], 0.03])
rcbar = fig_rvec.colorbar(right_im,
right_cax,
orientation='horizontal')
rcbar.outline.set_edgecolor(ax_rvec_r.cbaredgecolor)
rcbar.ax.tick_params(axis='x', colors=ax_rvec_r.labelcolor)
rcbar.ax.yaxis.set_major_formatter(FormatStrFormatter('%.1f'))
rcbar.set_label('Amplitude',
labelpad=5,
rotation=0,
fontsize=12,
color=ax_lvec_r.labelcolor)
fig_rvec.canvas.mpl_connect(
'key_press_event', lambda event: process_key_vectors(
event, freqs, fmin, fmax, rstart, sstart, rinterval,
sinterval, receiverPoints, sourcePoints, plotParams,
'cmplx_right'))
else:
# domain == 'time'
fig_vec, ax_lvec, ax_rvec = setFigure(num_axes=2,
mode=plotParams['view_mode'])
ax_lvec.volume = U
ax_lvec.index = 0
leftTitle = vector_title('left', ax_lvec.index + 1)
plotWiggles(ax_lvec, ax_lvec.volume[:, :, ax_lvec.index], times,
-T, T, rstart, rinterval, receiverPoints, leftTitle,
'left', plotParams)
ax_rvec.volume = V
ax_rvec.index = 0
rightTitle = vector_title('right', ax_rvec.index + 1)
plotWiggles(ax_rvec, ax_rvec.volume[:, :, ax_rvec.index], times,
-T, T, sstart, sinterval, sourcePoints, rightTitle,
'right', plotParams)
fig_vec.tight_layout()
fig_vec.canvas.mpl_connect(
'key_press_event', lambda event: process_key_vectors(
event, times, -T, T, rstart, sstart, rinterval, sinterval,
receiverPoints, sourcePoints, plotParams))
#==============================================================================
# plot the singular values
# figure and axis for singular values
fig_vals, ax_vals = setFigure(num_axes=1, mode=plotParams['view_mode'])
n = np.arange(1, k + 1, 1)
kappa = s[0] / s[-1] # condition number = max(s) / min(s)
ax_vals.plot(n,
s,
'.',
clip_on=False,
markersize=9,
label=r'Condition Number: %0.1e' % (kappa),
color=ax_vals.pointcolor)
ax_vals.set_xlabel('n', color=ax_vals.labelcolor)
ax_vals.set_ylabel('$\sigma_n$', color=ax_vals.labelcolor)
legend = ax_vals.legend(title='Singular Values',
loc='upper center',
bbox_to_anchor=(0.5, 1.25),
markerscale=0,
handlelength=0,
handletextpad=0,
fancybox=True,
shadow=True,
fontsize='large')
legend.get_title().set_fontsize('large')
ax_vals.set_xlim([1, k])
ax_vals.set_ylim(bottom=0)
ax_vals.locator_params(axis='y', nticks=6)
ax_vals.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
fig_vals.tight_layout()
fig_vals.savefig('singularValues.' + args.format,
format=args.format,
bbox_inches='tight',
facecolor=fig_vals.get_facecolor())
plt.show()
def cli():
parser = argparse.ArgumentParser()
parser.add_argument('--data', action='store_true',
help='Plot the recorded data. (Default)')
parser.add_argument('--impulse', action='store_true',
help='Plot the simulated impulse responses.')
parser.add_argument('--medium', type=str, default='constant', choices=['constant', 'variable'],
help='''Specify whether the background medium is constant or variable
(inhomogeneous). If argument is set to 'constant', the velocity defined in
the required 'pulsesFun.py' file is used. Default is set to 'constant'.''')
parser.add_argument('--tu', type=str,
help='Specify the time units (e.g., \'s\' or \'ms\').')
parser.add_argument('--au', type=str,
help='Specify the amplitude units (e.g., \'m\' or \'mm\').')
parser.add_argument('--colormap', type=str, default=None, choices=['grays', 'seismic', 'native'],
help='specify a colormap for wiggle plots. Default is \'grays\'.')
parser.add_argument('--pclip', type=float,
help='''Specify the percentage (0-1) of the peak amplitude to display. This
parameter is used for pcolormesh plots only. Default is set to 1.''')
parser.add_argument('--title', type=str,
help='''Specify a title for the wiggle plot. Default title is
\'Data\' if \'--data\' is passed and 'Impulse Response' if \'--impulse\'
is passed.''')
parser.add_argument('--format', '-f', type=str, default='pdf', choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help='''Specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument('--map', action='store_true',
help='''Plot a map of the receiver and source/search point locations. The current
source/search point will be highlighted. The boundary of the scatterer will also
be shown if available.''')
parser.add_argument('--mode', type=str, choices=['light', 'dark'], required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
#==============================================================================
# if a plotParams.pkl file already exists, load relevant parameters
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
# update parameters for wiggle plots based on passed arguments
if args.mode is not None:
plotParams['view_mode'] = args.mode
if args.tu is not None:
plotParams['tu'] = args.tu
if args.au is not None:
plotParams['au'] = args.au
if args.colormap is not None:
plotParams['wiggle_colormap'] = args.colormap
if args.pclip is not None:
if args.pclip >= 0 and args.pclip <= 1:
plotParams['pclip'] = args.pclip
else:
print(textwrap.dedent(
'''
Warning: Invalid value passed to argument \'--pclip\'. Value must be
between 0 and 1.
'''))
if args.title is not None:
if args.data:
plotParams['data_title'] = args.title
elif args.impulse:
plotParams['tf_title'] = args.title
else: # create a plotParams dictionary file with default values
plotParams = default_params()
# update parameters for wiggle plots based on passed arguments
if args.mode is not None:
plotParams['view_mode'] = args.mode
if args.tu is not None:
plotParams['tu'] = args.tu
if args.au is not None:
plotParams['au'] = args.au
if args.colormap is not None:
plotParams['wiggle_colormap'] = args.colormap
if args.title is not None:
if args.data:
plotParams['data_title'] = args.title
elif args.impulse:
plotParams['tf_title'] = args.title
pickle.dump(plotParams, open('plotParams.pkl', 'wb'), pickle.HIGHEST_PROTOCOL)
#==============================================================================
# Load the relevant data to plot
datadir = np.load('datadir.npz')
receiverPoints = np.load(str(datadir['receivers']))
time = np.load(str(datadir['recordingTimes']))
# Apply any user-specified windows
receiverNumbers, tinterval, tstep, dt, sourceNumbers = get_user_windows()
receiverPoints = receiverPoints[receiverNumbers, :]
time = time[tinterval]
if 'sources' in datadir:
sourcePoints = np.load(str(datadir['sources']))
sourcePoints = sourcePoints[sourceNumbers, :]
# Check for source-receiver reciprocity
reciprocalNumbers = get_unique_indices(sourcePoints, receiverPoints)
reciprocalNumbers = np.asarray(reciprocalNumbers, dtype=np.int)
if len(reciprocalNumbers) > 0:
newReceivers = sourcePoints[reciprocalNumbers, :]
reciprocity = True
else:
reciprocity = False
else:
reciprocity = False
sourcePoints = None
# Load the scatterer boundary, if it exists
if 'scatterer' in datadir:
scatterer = np.load(str(datadir['scatterer']))
else:
scatterer = None
if all(v is True for v in [args.data, args.impulse]):
# User specified both data and impulse response for plotting
# Send error message and exit.
sys.exit(textwrap.dedent(
'''
Error: Cannot plot both recorded data and simulated impulse responses. Use
vzwiggles --data
to plot the recorded data or
vzwiggles --impulse
to plot the simulated impulse responses.
'''))
elif all(v is not True for v in [args.data, args.impulse]) or args.data:
# If user did not specify which wiggles to plot, plot recorded data by default.
# load the 3D data array into variable 'X'
# X[receiver, time, source]
wiggleType = 'data'
X = load_data(domain='time', verbose=True)
if reciprocity:
Nr = len(receiverNumbers)
Ns = len(sourceNumbers)
M = len(reciprocalNumbers)
XR = X[-M:, :, -Nr:]
X = X[:Nr, :, :Ns]
reciprocalNumbers += 1
ER = Experiment(XR, time, reciprocalNumbers, newReceivers,
receiverNumbers, receiverPoints, wiggleType)
else:
ER = None
elif args.impulse:
wiggleType = 'impulse'
# Update time to convolution times
T = time[-1] - time[0]
time = np.linspace(-T, T, 2 * len(time) - 1)
X, sourcePoints = load_impulse_responses(domain='time', medium=args.medium,
verbose=True, return_search_points=True)
# Update sourceNumbers to match search points
sourceNumbers = np.arange(sourcePoints.shape[0])
if reciprocity:
Nr = len(receiverNumbers)
M = len(reciprocalNumbers)
XR = X[-M:, :, :]
X = X[:Nr, :, :]
reciprocalNumbers += 1
ER = Experiment(XR, time, reciprocalNumbers, newReceivers,
sourceNumbers, sourcePoints, wiggleType)
else:
ER = None
#==============================================================================
# increment source/receiver numbers to be consistent with
# one-based indexing (i.e., count from one instead of zero)
sourceNumbers += 1
receiverNumbers += 1
E = Experiment(X, time, receiverNumbers, receiverPoints,
sourceNumbers, sourcePoints, wiggleType)
p = Plotter(E, ER)
p.plot(scatterer, plotParams, args.map)
plt.show()
def cli():
parser = argparse.ArgumentParser()
parser.add_argument('--data', action='store_true',
help='Plot the frequency spectra of the recorded data. (Default)')
parser.add_argument('--impulse', action='store_true',
help='Plot the frequency spectra of the simulated impulse responses.')
parser.add_argument('--scaling', '-s', type=str, default='amp', choices=['amp', 'pow', 'psd'],
help='''Specify the scaling of the spectrum. Choose from amplitude ('amp'), power ('pow'),
or power spectral density ('psd') using Welch's method. Default is set to 'amp'.''')
parser.add_argument('--nseg', type=int,
help='''Specify the approximate number of segments into which the time signal will be partitioned.
Only used if scaling is set to 'psd'. Increasing the number of segments increases computational
cost and the accuracy of the PSD estimate, but decreases frequency resolution. Default is set to 20.''')
parser.add_argument('--fmin', type=float,
help='Specify the minimum frequency of the amplitude/power spectrum plot. Default is set to 0.')
parser.add_argument('--fmax', type=float,
help='''Specify the maximum frequency of the amplitude/power spectrum plot. Default is set to the
maximum frequency bin based on the length of the time signal.''')
parser.add_argument('--au', type=str,
help='Specify the amplitude units (e.g., Pa)')
parser.add_argument('--fu', type=str,
help='Specify the frequency units (e.g., Hz)')
parser.add_argument('--format', '-f', type=str, default='pdf', choices=['png', 'pdf', 'ps', 'eps', 'svg'],
help='''specify the image format of the saved file. Accepted formats are png, pdf,
ps, eps, and svg. Default format is set to pdf.''')
parser.add_argument('--mode', type=str, choices=['light', 'dark'], required=False,
help='''Specify whether to view plots in light mode for daytime viewing
or dark mode for nighttime viewing.
Mode must be either \'light\' or \'dark\'.''')
args = parser.parse_args()
#==============================================================================
# Get time window parameters
tinterval, tstep, dt = get_user_windows()[1:4]
datadir = np.load('datadir.npz')
recordingTimes = np.load(str(datadir['recordingTimes']))
recordingTimes = recordingTimes[tinterval]
# Used for getting time and frequency units
if Path('plotParams.pkl').exists():
plotParams = pickle.load(open('plotParams.pkl', 'rb'))
else:
plotParams = default_params()
if all(v is True for v in [args.data, args.impulse]):
X = load_data(domain='time', verbose=True)
Xlabel = plotParams['data_title']
Xcolor = 'm'
if 'testFuncs' not in datadir and not Path('VZImpulseResponses.npz').exists():
X2 = load_impulse_responses(domain='time', medium='constant', verbose=True)
X2label = plotParams['impulse_title']
X2color = 'c'
elif 'testFuncs' in datadir and not Path('VZImpulseResponses.npz').exists():
X2 = load_impulse_responses(domain='time', medium='variable', verbose=True)
X2label = plotParams['impulse_title']
X2color = 'c'
elif not 'testFuncs' in datadir and Path('VZImpulseResponses.npz').exists():
X2 = load_impulse_responses(domain='time', medium='constant', verbose=True)
X2label = plotParams['impulse_title']
X2color = 'c'
elif 'testFuncs' in datadir and Path('VZImpulseResponses.npz').exists():
userResponded = False
print(textwrap.dedent(
'''
Two files are available containing simulated impulse responses.
Enter '1' to view the user-provided impulse responses. (Default)
Enter '2' to view the impulse responses computed by Vezda.
Enter 'q/quit' to exit.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
X2 = load_impulse_responses(domain='time', medium='variable', verbose=True)
X2label = plotParams['impulse_title']
X2color = 'c'
userResponded = True
break
elif answer == '2':
X2 = load_impulse_responses(domain='time', medium='constant', verbose=True)
X2label = plotParams['impulse_title']
X2color = 'c'
userResponded = True
break
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.')
else:
print('Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.')
elif (args.data and not args.impulse) or all(v is not True for v in [args.data, args.impulse]):
# default is to plot spectra of data if user does not specify either args.data or args.impulse
X = load_data(domain='time', verbose=True)
Xlabel = plotParams['data_title']
Xcolor = 'm'
X2 = None
elif not args.data and args.impulse:
if 'testFuncs' not in datadir and not Path('VZImpulseResponses.npz').exists():
X = load_impulse_responses(domain='time', medium='constant', verbose=True)
Xlabel = plotParams['impulse_title']
Xcolor = 'c'
elif 'testFuncs' in datadir and not Path('VZImpulseResponses.npz').exists():
X = load_impulse_responses(domain='time', medium='variable', verbose=True)
Xlabel = plotParams['impulse_title']
Xcolor = 'c'
elif not 'testFuncs' in datadir and Path('VZImpulseResponses.npz').exists():
X = load_impulse_responses(domain='time', medium='constant', verbose=True)
Xlabel = plotParams['impulse_title']
Xcolor = 'c'
elif 'testFuncs' in datadir and Path('VZImpulseResponses.npz').exists():
userResponded = False
print(textwrap.dedent(
'''
Two files are available containing simulated impulse responses.
Enter '1' to view the user-provided impulse responses. (Default)
Enter '2' to view the impulse responses computed by Vezda.
Enter 'q/quit' to exit.
'''))
while userResponded == False:
answer = input('Action: ')
if answer == '' or answer == '1':
X = load_impulse_responses(domain='time', medium='variable', verbose=True)
Xlabel = plotParams['impulse_title']
Xcolor = 'c'
userResponded = True
break
elif answer == '2':
X = load_impulse_responses(domain='time', medium='constant', verbose=True)
Xlabel = plotParams['impulse_title']
Xcolor = 'c'
userResponded = True
break
elif answer == 'q' or answer == 'quit':
sys.exit('Exiting program.')
else:
print('Invalid response. Please enter \'1\', \'2\', or \'q/quit\'.')
X2 = None
#==============================================================================
# compute spectra
if args.nseg is not None:
if args.nseg >= 1:
nseg = args.nseg
else:
sys.exit(textwrap.dedent(
'''
Error: Optional argument '--nseg' must be greater than or equal to one.
'''))
else:
# if args.nseg is None
nseg = 1
freqs, A = compute_spectra(X, tstep * dt, scaling=args.scaling, nseg=nseg)
if X2 is not None:
Nt = X.shape[1]
X2 = X2[:, -Nt:, :]
freqs2, A2 = compute_spectra(X2, tstep * dt, scaling=args.scaling, nseg=nseg)
else:
freqs2 = None
A2 = None
if args.au is not None:
plotParams['au'] = args.au
if args.fu is not None:
plotParams['fu'] = args.fu
au = plotParams['au']
fu = plotParams['fu']
if args.scaling == 'amp':
plotLabel = 'amplitude'
plotParams['freq_title'] = 'Mean Amplitude Spectrum'
if au != '':
plotParams['freq_ylabel'] = 'Amplitude (%s)' %(au)
else:
plotParams['freq_ylabel'] = 'Amplitude'
elif args.scaling == 'pow':
plotLabel = 'power'
plotParams['freq_title'] = 'Mean Power Spectrum'
if au != '':
plotParams['freq_ylabel'] = 'Power (%s)' %(au + '$^2$')
else:
plotParams['freq_ylabel'] = 'Power'
elif args.scaling == 'psd':
plotLabel = 'psd'
plotParams['freq_title'] = 'Mean Power Spectral Density'
if au != '' and fu != '':
plotParams['freq_ylabel'] = 'Power/Frequency (%s)' %(au + '$^2/$' + fu)
else:
plotParams['freq_ylabel'] = 'Power/Frequency'
if args.fmin is not None:
if args.fmin >= 0:
if args.fmax is not None:
if args.fmax > args.fmin:
plotParams['fmin'] = args.fmin
plotParams['fmax'] = args.fmax
else:
sys.exit(textwrap.dedent(
'''
RelationError: The maximum frequency of the %s spectrum plot must
be greater than the mininum frequency.
''' %(plotLabel)))
else:
fmax = plotParams['fmax']
if fmax > args.fmin:
plotParams['fmin'] = args.fmin
else:
sys.exit(textwrap.dedent(
'''
RelationError: The specified minimum frequency of the %s spectrum
plot must be less than the maximum frequency.
''' %(plotLabel)))
else:
sys.exit(textwrap.dedent(
'''
ValueError: The specified minimum frequency of the %s spectrum
plot must be nonnegative.
''' %(plotLabel)))
#===============================================================================
if args.fmax is not None:
if args.fmin is not None:
if args.fmin >= 0:
if args.fmax > args.fmin:
plotParams['fmin'] = args.fmin
plotParams['fmax'] = args.fmax
else:
sys.exit(textwrap.dedent(
'''
RelationError: The maximum frequency of the %s spectrum plot must
be greater than the mininum frequency.
''' %(plotLabel)))
else:
sys.exit(textwrap.dedent(
'''
ValueError: The specified minimum frequency of the %s spectrum
plot must be nonnegative.
''' %(plotLabel)))
else:
fmin = plotParams['fmin']
if args.fmax > fmin:
plotParams['fmax'] = args.fmax
else:
sys.exit(textwrap.dedent(
'''
RelationError: The specified maximum frequency of the %s spectrum
plot must be greater than the minimum frequency.
''' %(plotLabel)))
elif plotParams['fmax'] is None:
plotParams['fmax'] = np.max(freqs)
#===================================================================================
if args.mode is not None:
plotParams['view_mode'] = args.mode
pickle.dump(plotParams, open('plotParams.pkl', 'wb'), pickle.HIGHEST_PROTOCOL)
fig, ax = setFigure(num_axes=1, mode=plotParams['view_mode'])
if args.scaling == 'psd':
plotscale = 'log'
else:
plotscale = 'linear'
gradient_fill(freqs, A, fill_color=Xcolor, ax=ax, scale=plotscale, zorder=2)
handles, labels = [], []
handles.append(Line2D([0], [0], color=Xcolor, lw=4))
labels.append(Xlabel)
if all(v is not None for v in [freqs2, A2]):
gradient_fill(freqs2, A2, fill_color=X2color, ax=ax, scale=plotscale, zorder=1)
handles.append(Line2D([0], [0], color=X2color, lw=4))
labels.append(X2label)
ax.legend(handles, labels, fancybox=True, framealpha=1, shadow=True, loc='upper right')
ax.set_title(plotParams['freq_title'], color=ax.titlecolor)
fmin = plotParams['fmin']
fmax = plotParams['fmax']
if fu != '':
ax.set_xlabel('Frequency (%s)' %(fu), color=ax.labelcolor)
else:
ax.set_xlabel('Frequency', color=ax.labelcolor)
ax.set_ylabel(plotParams['freq_ylabel'], color=ax.labelcolor)
ax.set_xlim([fmin, fmax])
if args.scaling != 'psd':
ax.set_ylim(bottom=0)
ax.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
plt.tight_layout()
fig.savefig(plotLabel + 'Spectrum.' + args.format, format=args.format, bbox_inches='tight', facecolor=fig.get_facecolor())
plt.show()
