def ssa_denoise_recon(st, p, flow, fhigh): """ SSA method, that de-noises the data given in stream by a rank reduction of the singular values of the Hankel matrix, created from the data in st and the sampling interval of the traces, to p. :param st: Stream of data :type st: :param dt: sampling interval :type dt: float :param p: number of singular values used to reconstuct the data :type p: int :param flow: min freq. in the data in Hz :type flow: float :param fhigh: max freq. in the data in Hz :type fhigh: float Example st = stream dt = st[0].stats.delta p = 4 flow = 1 fhigh = 250 st_ssa = ssa_denoise_recon(st, dt, p, flow, fhigh) """ st_tmp = st.copy() data = stream2array(st_tmp) dt = st_tmp[0].stats.delta data_ssa = fx_ssa(data,dt,p,flow,fhigh) st_ssa = array2stream(data_ssa, st_tmp) return st_ssa
def plot_sigma(sigma, stream, fs=20, ylimit=None):
si_stream = array2stream(sigma, stream)
plot(si_stream[0], ylabel='sigma', yticks=True, fs=fs, ylimit=ylimit)
return
def fk_reconstruct(st, slopes=[-10,10], deltaslope=0.05, slopepicking=False, smoothpicks=False, dist=0.5, maskshape=['boxcar',None],
method='denoise', solver="iterative", mu=5e-2, tol=1e-12, fulloutput=False, peakinput=False, alpha=0.9):
"""
This functions reconstructs missing signals in the f-k domain, using the original data,
including gaps, filled with zeros, and its Mask-array (see makeMask, and slope_distribution.
If all traces are avaiable it is a useful method to de-noise the data.
Uses the following cost function to minimize:
J = ||dv - T FHmtx2D Yw Dv ||^{2}_{2} + mu^2 ||Dv||^{2}_{2}
J := Cost function
dv:= Column-wise-ordered long vector of the 2D signal d (columns: t-domain, rows: x-domain)
DV:= Column-wise-ordered long vector of the f-k-spectrum D ( columns: f-domain, rows: k-domain)
Yw := Diagonal matrix built from the column-wise-ordered long vector of Mask
T := Sampling matrix which maps the fully sampled desired seismic data to the available samples.
For de-noising problems T = I (identity matrix)
mu := Trade-off parameter between misfit and model norm
Minimizing is done via a method of the LSMR solver, de-noising (1-2 iterations), reconstruction(8-10) iterations.
T FHmtx2D Yw Dv will be formed to one matrix A, so at the end the equation system that will be solved has the form:
| A | | dv |
| | * Dv = | |
| mu * I | | 0 |
:param st: Stream with missing traces, to be reconstructed or complete stream to be de-noised
:type st: obspy.core.stream.Stream
:param slopes: Range of slopes to investigate for mask-function
:type slopes: list
:param deltaslope: stepsize inbetween slopes.
:type deltaslope: float
:param slopepicking: If True peaks of slopedistribution can be picked by hand.
:type slopepicking: bool
:param smoothpicks: Determines the smoothing of the Slopedistribution, default off. If enabled the distribution ist smoothened by
convoluting it with a boxcar of size smoothpicks.
:type smoothpicks: int
:param dist: Minimum distance inbetween maximum picks.
:type dist: float
:param maskshape: maskshape[0] describes the shape of the lobes of the mask. Possible inputs are:
-boxcar (default)
-taper
-butterworth
maskshape[1] is an additional attribute to the shape of taper and butterworth, for:
-taper: maskshape[1] = slope of sides
-butterworth: maskshape[1] = number of poles
e.g.: maskshape['taper', 2] produces a symmetric taper with slope of side = 2.
:type maskshape: list
:param method: Desired fk-method, options are 'denoise' and 'interpolate'
:type method: string
:param solver: Solver used for method. Options are 'lsqr' and 'iterative'.
If method is 'denoise' only the iterative solver is used.
:type solver: string
:param mu: Damping parameter for the solver
:type mu: float
:param tol: Tolerance for solver to abort iteration.
:type tol: float
:param fulloutput: If True, the function additionally outputs FH, dv, Dv, Ts and Yw
:type fulloutput: bool
:param peakinput: Chosen peaks of the distribution, insert here if the peaks are not to be meant to recalculated
:type peakinput: np.ndarray
###### returns:
:param st_rec: Stream with reconstructed signals on the missing traces
:type st_rec: obspy.core.stream.Stream
## if fulloutput=True
:param st_rec: Stream with reconstructed signals on the missing traces
:type st_rec: obspy.core.stream.Stream
:param FH: 2DiFFT-matrix for column-wise ordered longvector of the f-k spectrum
:type FH: scipy.sparse.csc.csc_matrix
:param dv: Column-wise ordered longvector of the t-x data
:type dv: numpy.ndarray
:param Dv: Column-wise ordered longvector of the f-k spectrum of dv
:type Dv: numpy.ndarray
:param Ts: Sampling-matrix, which maps desired to available data
:type Ts: scipy.sparse.dia.dia_matrix
:param Yw: Diagonal matrix constructed of the column-wise ordered longvector of the mask-matrix
:type Yw: scipy.sparse.dia.dia_matrix
Example:
from obspy import read as read_st
import sipy
stream = read_st("../data/synthetics_uniform/SUNEW.QHD")
#Example around PP.
stream_org = st.copy()
d = bowpy.util.array_util.stream2array(stream_org)
ArrayData = np.zeros((d.shape[0], 300))
for i, trace in enumerate(d):
ArrayData[i,:]=trace[400:700]
stream = bowpy.util.array_util.array2stream(ArrayData, stream_org)
dssa = bowpy.filter.fk.fk_reconstruct(stream, mu=5e-2, method='interpolate')
stream_ssa = bowpy.util.array_util.array2stream(dssa, stream)
bowpy.util.fkutil.plot(stream_ssa)
Author: S. Schneider, 2016
Reference: Mostafa Naghizadeh, Seismic data interpolation and de-noising in the frequency-wavenumber
domain, 2012, GEOPHYSICS
"""
# Prepare data.
st_tmp = st.copy()
ArrayData = stream2array(st_tmp, normalize=False)
ADT = ArrayData.copy().transpose()
fkData = np.fft.fft2(ArrayData)
fkDT = np.fft.fft2(ADT)
# Look for missing Traces
recon_list = []
for i, trace in enumerate(st_tmp):
try:
if trace.stats.zerotrace == 'True':
recon_list.append(i)
except AttributeError:
if sum(trace.data) == 0. :
recon_list.append(i)
except:
continue
print(recon_list)
# Calculate mask-function W.
try:
if peakinput.any():
peaks = peakinput
except:
print("Calculating slope distribution...\n")
M, prange, peaks = slope_distribution(fkData, slopes, deltaslope, peakpick=None, mindist=dist, smoothing=smoothpicks, interactive=slopepicking)
if fulloutput:
kin = 'n'
while kin in ('n', 'N'):
plt.figure()
plt.title('Magnitude-Distribution')
plt.xlabel('Slope in fk-domain')
plt.ylabel('Magnitude of slope')
plt.plot(prange, M)
plt.plot(peaks[0], peaks[1]/peaks[1].max()*M.max(), 'ro')
plt.show()
kin = raw_input("Use picks? (y/n) \n")
if kin in ['y' , 'Y']:
print("Using picks, continue \n")
elif kin in ['n', 'N']:
print("Don't use picks, please re-pick \n")
M, prange, peaks = slope_distribution(fkData, slopes, deltaslope, peakpick=None, mindist=dist, smoothing=smoothpicks, interactive=True)
print("Creating mask function with %i significant linear events \n" % len(peaks[0]) )
W = makeMask(fkData, peaks[0], maskshape)
# If fulloutput is desired, a bunch of messages and user interaction appears.
if fulloutput:
plt.figure()
plt.subplot(3,1,1)
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.title("fk-spectrum")
plt.imshow(abs(np.fft.fftshift(fkData)), aspect='auto', interpolation='none')
plt.subplot(3,1,2)
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.title("Mask-function")
plt.imshow(np.fft.fftshift(W), aspect='auto', interpolation='none')
plt.subplot(3,1,3)
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.title("Applied mask-function")
plt.imshow(abs(np.fft.fftshift(W*fkData)), aspect='auto', interpolation='none')
plt.show()
kin = raw_input("Use Mask? (y/n) \n")
if kin in ['y' , 'Y']:
print("Using Mask, continue \n")
elif kin in ['n', 'N']:
msg="Don't use Mask, exit"
raise IOError(msg)
# Checking for number of iteration and reconstruction behavior.
maxiter=None
interpol = False
if isinstance(method, str):
if method in ("denoise"):
maxiter = 2
recon_list = []
elif method in ("interpolate"):
maxiter = 10
interpol = True
elif isinstance(method, int):
maxiter=method
print("maximum %i" %maxiter)
if solver in ("lsqr", "leastsquares", "ilsmr", "iterative", "cg", "fmin"):
pocs = False
# To keep the order it would be better to transpose W to WT
# but for creation of Y, WT has to be transposed again,
# so this step can be skipped.
Y = W.reshape(1,W.size)[0]
Yw = sparse.diags(Y)
# Initialize arrays for cost-function.
dv = ADT.transpose().reshape(1, ADT.size)[0]
Dv = fkDT.transpose().reshape(1, fkDT.size)[0]
T = np.ones((ArrayData.shape[0], ArrayData.shape[1]))
T[recon_list] = 0.
T = T.reshape(1, T.size)[0]
Ts = sparse.diags(T)
# Create sparse-matrix with iFFT operations.
print("Creating iFFT2 operator as a %ix%i matrix ...\n" %(fkDT.shape[0]*fkDT.shape[1], fkDT.shape[0]*fkDT.shape[1]))
FH = create_iFFT2mtx(fkDT.shape[0], fkDT.shape[1])
print("... finished\n")
# Create model matrix A.
print("Creating sparse %ix%i matrix A ...\n" %(FH.shape[0], FH.shape[1]))
A = Ts.dot(FH.dot(Yw))
print("Starting reconstruction...\n")
if solver in ("lsqr", "leastsquares"):
print(" ...using iterative least-squares solver...\n")
x = sparse.linalg.lsqr(A, dv, mu, atol=tol, btol=tol, conlim=tol, iter_lim=maxiter)
print("istop = %i \n" % x[1])
print("Used iterations = %i \n" % x[2])
print("residual Norm ||x||_2 = %f \n " % x[8])
print("Misfit ||Ax - b||_2= %f \n" % x[4])
print("Condition number = %f \n" % x[6])
Dv_rec = x[0]
elif solver in ("ilsmr", "iterative"):
print(" ...using iterative LSMR solver...\n")
x = sparse.linalg.lsmr(A,dv,mu, atol=tol, btol=tol, conlim=tol, maxiter=maxiter)
print("istop = %i \n" % x[1])
print("Used iterations = %i \n" % x[2])
print("Misfit = %f \n " % x[3])
print("Modelnorm = %f \n" % x[4])
print("Condition number = %f \n" % x[5])
print("Norm of Dv = %f \n" % x[6])
Dv_rec = x[0]
elif solver in ("cg"):
A = Ts.dot(FH.dot(Yw))
Ah = A.conjugate().transpose()
madj = Ah.dot(dv)
E = mu * sparse.eye(A.shape[0])
B = A + E
Binv = sparse.linalg.inv(B)
x = sparse.linalg.cg(Binv, madj, maxiter=maxiter)
Dv_rec = x[0]
elif solver in ('fmin'):
A = Ts.dot(FH.dot(Yw))
global arg1
global arg2
global arg3
arg1 = dv
arg2 = A
arg3 = mu
def J(x):
COST = np.linalg.norm(arg1 - arg2.dot(x), 2)**2. + arg3*np.linalg.norm(x,2)**2.
return COST
Dv_rec = sp.optimize.fmin_cg(J, x0=Dv, maxiter=10)
data_rec = np.fft.ifft2(Dv_rec.reshape(fkData.shape)).real
elif solver in ("pocs"):
pocs=True
threshold = abs( (fkData*W.astype('complex').max()) )
for i in range(maxiter):
data_tmp = ArrayData.copy()
fkdata = np.fft.fft2(data_tmp) * W.astype('complex')
fkdata[ np.where(abs(fkdata) < threshold)] = 0. + 0j
threshold = threshold * alpha
#if i % 10 == 0.:
# plt.imshow(abs(fkdata), aspect='auto', interpolation='none')
# plt.savefig("%s.png" % i)
data_tmp = np.fft.ifft2(fkdata).real.copy()
ArrayData[recon_list] = data_tmp[recon_list]
data_rec = ArrayData.copy()
else:
print("No solver or method specified.")
return
if interpol:
st_rec = st.copy()
for i in recon_list:
st_rec[i].data = data_rec[i,:]
st_rec[i].stats.zerotrace = 'reconstructed'
else:
st_rec = array2stream(data_rec, st)
if fulloutput and not pocs:
return st_rec, FH, dv, Dv, Dv_rec, Ts, Yw, W
else:
return st_rec
def pocs_recon(st, maxiter=None, alpha=None, dmethod='reconstruct', method='linear', beta=None, peaks=None, maskshape=None,
dt=None, p=None, flow=None, fhigh=None, slidingwindow=False, alpha_i_test=False, st_org=None, plotfeedback=False):
"""
This functions reconstructs missing signals in the f-k domain, using the original data,
including gaps, filled with zeros. It applies the projection onto convex sets (pocs) algorithm in
2D.
Reference: 3D interpolation of irregular data with a POCS algorithm, Abma & Kabir, 2006
:param st:
:type st:
:param maxiter:
:type maxiter:
:param nol: Number of loops
:type nol:
returns:
:param st_rec:
:type st_rec:
"""
if not maxiter and not alpha and not alpha_i_test:
raise IOError('One of maxiter, alpha or alpha_i_test has to be chosen')
if alpha_i_test and not st_org:
raise IOError('For alpha_i_test an orignal stream is needed')
st_tmp = st.copy()
ArrayData = stream2array(st_tmp, normalize=True)
recon_list = []
if dmethod in ('reconstruct'):
for i, trace in enumerate(st_tmp):
try:
if trace.stats.zerotrace in ['True']:
recon_list.append(i)
except AttributeError:
if sum(trace.data) == 0. :
recon_list.append(i)
except:
continue
noft = recon_list
elif dmethod in ('denoise', 'de-noise'):
noft = range(ArrayData.shape[0])
if alpha_i_test:
ADref = stream2array(st_org)
if ADref.shape != ArrayData.shape:
raise IOError('Shapes of reference stream and reconstructed stream differ!')
alpha_range = np.linspace(50,99,11)/100.
i_range = np.flipud(np.arange(5,50))
test_length = float(alpha_range.size * i_range.size)
curr_step = 1.
alpha = 0.
maxiter = max(i_range)
Qmax = 0.
for i in i_range:
for a in alpha_range:
ADrec = pocs(ArrayData, i, noft, a, beta, method, dmethod, peaks, maskshape, dt, p, flow, fhigh, slidingwindow, plotfeedback=plotfeedback)
Q = 10.*np.log( np.linalg.norm(ADref,2)**2. / np.linalg.norm(ADref - ADrec,2)**2. )
if Q >= Qmax: # and maxiter > i:
alpha = a
maxiter = i
Qmax = Q
progress = 100. * (curr_step / test_length)
curr_step += 1.
print ('Progress of alpha-i test: %i %%, current Q: %f, current Qmax: %f' % ( int(progress),Q ,Qmax ), end='\r')
sys.stdout.flush()
ADfinal = pocs(ArrayData, maxiter, noft, alpha, beta, method, dmethod, peaks, maskshape, dt, p, flow, fhigh, slidingwindow)
else:
ADfinal = pocs(ArrayData, maxiter, noft, alpha, beta, method, dmethod, peaks, maskshape, dt, p, flow, fhigh, slidingwindow, plotfeedback=plotfeedback)
#datap = ADfinal.copy()
st_rec = array2stream(ADfinal, st)
st_rec.normalize()
if alpha_i_test:
for trace in st_rec:
trace.stats.pocs = {'alpha': alpha, 'iteration': maxiter, 'Q': Qmax}
else:
for trace in st_rec:
trace.stats.pocs = {'alpha': alpha, 'iteration': maxiter}
for trace in noft:
st_rec[trace].stats.recon = True
return st_rec
def fk_filter(st, inv=None, event=None, ftype='eliminate',
fshape=['butterworth', 2, 2], phase=None, polygon=4,
normalize=True, stack=False, slopes=[-3, 3], deltaslope=0.05,
slopepicking=False, smoothpicks=False, dist=0.5,
maskshape=['boxcar', None], order=4., peakinput=False,
eval_mean=1, fs=25):
"""
Import stream, the function applies an 2D FFT, removes a certain window
around the desired phase to surpress a slownessvalue corresponding to a
wavenumber and applies an 2d iFFT. To fill the gap between uneven
distributed stations use array_util.gaps_fill_zeros(). A method to
interpolate the signals in the fk-domain is beeing build, also a method
using a norm minimization method.
param st: Stream
type st: obspy.core.stream.Stream
param inv: inventory
type inv: obspy.station.inventory.Inventory
param event: Event
type event: obspy.core.event.Event
param ftype: type of method, default is 'eliminate-polygon',
possible inputs are:
-eliminate
-extract
-eliminate-polygon
-extract-polygon
-mask
-fk
type ftype: string
param fshape: fshape[0] describes the shape of the fk-filter in case of
ftype is 'eliminate' or 'extract'. Possible inputs are:
-spike (default)
-boxcar
-taper
-butterworth
fshape[1] is an additional attribute to the shape of taper
and butterworth, for:
-taper: fshape[1] = slope of sides
-butterworth: fshape[1] = number of poles
fshape[3] describes the length of the filter shape,
respectivly wavenumber corner points around k=0,
e.g.: fshape['taper', 2, 4] produces a symmetric taper with
slope of side = 2, where the signal is reduced about
50% at k=+-2
type fshape: list
param phase: name of the phase to be investigated
type phase: string
param polygon: number of vertices of polygon for fk filter, only needed
if ftype is set to eliminate-polygon or extract-polygon.
Default is 12.
type polygon: int
param normalize: normalize data to 1
type normalize: bool
param SSA: Force SSA algorithm or let it check, default:False
type SSA: bool
param eval_mean: number of linear events used to calculate the average of
the area in the fk domain.
returns: stream_filtered, the filtered stream.
References: Yilmaz, Thomas
Author: S. Schneider 2016
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published
by the Free Software Foundation, either version 3 of the License, or
any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details: http://www.gnu.org/licenses/
"""
# Convert format and prepare Variables.
# Check for Data type of variables.
if not type(st) == Stream:
print("Wrong input type of stream, must be obspy.core.stream.Stream")
raise TypeError
if len(fshape) == 1:
fshape = [fshape[0], None, None]
st_tmp = st.copy()
ArrayData = stream2array(st_tmp, normalize)
ix = ArrayData.shape[0]
iK = int(math.pow(2, nextpow2(ix)))
try:
yinfo = epidist2nparray(attach_epidist2coords(inv, event, st_tmp))
dx = (yinfo.max() - yinfo.min() + 1) / yinfo.size
k_axis = np.fft.fftfreq(iK, dx)
except:
try:
ymax = st_tmp[0].stats.distance
ymin = st_tmp[0].stats.distance
for trace in st_tmp:
if trace.stats.distance > ymax:
ymax = trace.stats.distance
if trace.stats.distance < ymin:
ymin = trace.stats.distance
dx = (ymax - ymin + 1) / len(st_tmp)
k_axis = np.fft.fftfreq(iK, dx)
except:
print("\nNo inventory or event-information found. \nContinue without specific distance and wavenumber information.")
yinfo=None
dx=None
k_axis=None
it = ArrayData.shape[1]
iF = int(math.pow(2,nextpow2(it)))
dt = st_tmp[0].stats.delta
f_axis = np.fft.fftfreq(iF,dt)
# Calc mean diff of each epidist entry if it is reasonable
# do a partial stack and apply filter.
"""
2D Frequency-Space / Wavenumber-Frequency Filter #########################################################
"""
# 2D f-k Transformation
# Note array_fk has f on the x-axis and k on the y-axis!!!
# For interaction the conj.-transposed Array is shown!!!
# Decide when to use SSA to fill the gaps, calc mean distance of each epidist entry
# if it differs too much --> SSA
if ftype in ("eliminate"):
if phase:
if not isinstance(event, Event) and not isinstance(inv, Inventory):
msg='For alignment on phase calculation inventory and event information is needed, not found.'
raise IOError(msg)
st_al = alignon(st_tmp, inv, event, phase)
ArrayData = stream2array(st_al, normalize)
array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
array_filtered_fk = line_set_zero(array_fk, shape=fshape)
else:
array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
array_filtered_fk = line_set_zero(array_fk, shape=fshape)
elif ftype in ("extract"):
if phase:
if not isinstance(event, Event) and not isinstance(inv, Inventory):
msg='For alignment on phase calculation inventory and event information is needed, not found.'
raise IOError(msg)
st_al = alignon(st_tmp, inv, event, phase)
ArrayData = stream2array(st_al, normalize)
array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
array_filtered_fk = line_cut(array_fk, shape=fshape)
else:
array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
array_filtered_fk = line_cut(array_fk, shape=fshape)
elif ftype in ("eliminate-polygon"):
array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
if phase:
if not isinstance(event, Event) and not isinstance(inv, Inventory):
msg='For alignment on phase calculation inventory and event information is needed, not found.'
raise IOError(msg)
st_al = alignon(st_tmp, inv, event, phase)
ArrayData = stream2array(st_al, normalize)
array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
array_filtered_fk = _fk_eliminate_polygon(array_fk, polygon, ylabel=r'frequency domain f in Hz', \
yticks=f_axis, xlabel=r'wavenumber domain k in $\frac{1}{^{\circ}}$', xticks=k_axis, eval_mean=eval_mean, fs=fs)
else:
array_filtered_fk = _fk_eliminate_polygon(array_fk, polygon, ylabel=r'frequency domain f in Hz', \
yticks=f_axis, xlabel=r'wavenumber domain k in $\frac{1}{^{\circ}}$', xticks=k_axis, eval_mean=eval_mean, fs=fs)
elif ftype in ("extract-polygon"):
array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
if phase:
if not isinstance(event, Event) and not isinstance(inv, Inventory):
msg='For alignment on phase calculation inventory and event information is needed, not found.'
raise IOError(msg)
st_al = alignon(st_tmp, inv, event, phase)
ArrayData = stream2array(st_al, normalize)
array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
array_filtered_fk = _fk_extract_polygon(array_fk, polygon, ylabel=r'frequency domain f in Hz', \
yticks=f_axis, xlabel=r'wavenumber domain k in $\frac{1}{^{\circ}}$', xticks=k_axis, eval_mean=eval_mean, fs=fs)
else:
array_filtered_fk = _fk_extract_polygon(array_fk, polygon, ylabel=r'frequency domain f in Hz', \
yticks=f_axis, xlabel=r'wavenumber domain k in $\frac{1}{^{\circ}}$', xticks=k_axis, eval_mean=eval_mean, fs=fs)
elif ftype in ("mask"):
array_fk = np.fft.fft2(ArrayData)
M, prange, peaks = slope_distribution(array_fk, slopes, deltaslope, peakpick=None, mindist=dist, smoothing=smoothpicks, interactive=slopepicking)
W = makeMask(array_fk, peaks[0], maskshape)
array_filtered_fk = array_fk * W
array_filtered = np.fft.ifft2(array_filtered_fk)
stream_filtered = array2stream(array_filtered, st_original=st.copy())
return stream_filtered, array_fk, W
elif ftype in ("fk"):
if phase:
if not isinstance(event, Event) and not isinstance(inv, Inventory):
msg='For alignment on phase calculation inventory and event information is needed, not found.'
raise IOError(msg)
st_al = alignon(st_tmp, inv, event, phase)
ArrayData = stream2array(st_al, normalize)
array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
### BUILD DOUBLE TAPER ###
#array_filtered_fk =
else:
array_fk = np.fft.fft2(ArrayData, s=(iK,iF))
### BUILD DOUBLE TAPER ###
#array_filtered_fk =
else:
print("No type of filter specified")
raise TypeError
array_filtered = np.fft.ifft2(array_filtered_fk, s=(iK,iF)).real
# Convert to Stream object.
array_filtered = array_filtered[0:ix, 0:it]
stream_filtered = array2stream(array_filtered, st_original=st.copy())
stream_filtered.normalize()
return stream_filtered