def resampleNSEMdataAtFreq(NSEMdata, freqs):
"""
Function to resample NSEMdata at set of frequencies
"""
# Make a rec array
NSEMrec = NSEMdata.toRecArray().data
# Find unique locations
uniLoc = np.unique(NSEMrec[['x','y','z']])
uniFreq = NSEMdata.survey.freqs
# Get the comps
dNames = NSEMrec.dtype
# Loop over all the locations and interpolate
for loc in uniLoc:
# Find the index of the station
ind = np.sqrt(np.sum((rec_to_ndarr(NSEMrec[['x','y','z']]) - rec_to_ndarr(loc))**2,axis=1)) < 1. # Find dist of 1 m accuracy
# Make a temporary recArray and interpolate all the components
tArrRec = np.concatenate((simpeg.mkvc(freqs,2),np.ones((len(freqs),1))*rec_to_ndarr(loc),np.nan*np.ones((len(freqs),12))),axis=1).view(dNames)
for comp in ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi','tzxr','tzxi','tzyr','tzyi']:
int1d = sciint.interp1d(NSEMrec[ind]['freq'],NSEMrec[ind][comp],bounds_error=False)
tArrRec[comp] = simpeg.mkvc(int1d(freqs),2)
# Join together
try:
outRecArr = recFunc.stack_arrays((outRecArr,tArrRec))
except NameError:
outRecArr = tArrRec
# Make the NSEMdata and return
return Data.fromRecArray(outRecArr)
def rotateData(NSEMdata, rotAngle):
'''
Function that rotates clockwist by rotAngle (- negative for a counter-clockwise rotation)
'''
recData = NSEMdata.toRecArray('Complex')
impData = rec_to_ndarr(recData[['zxx', 'zxy', 'zyx', 'zyy']], complex)
# Make the rotation matrix
# c,s,zxx,zxy,zyx,zyy = sympy.symbols('c,s,zxx,zxy,zyx,zyy')
# rotM = sympy.Matrix([[c,-s],[s, c]])
# zM = sympy.Matrix([[zxx,zxy],[zyx,zyy]])
# rotM*zM*rotM.T
# [c*(c*zxx - s*zyx) - s*(c*zxy - s*zyy), c*(c*zxy - s*zyy) + s*(c*zxx - s*zyx)],
# [c*(c*zyx + s*zxx) - s*(c*zyy + s*zxy), c*(c*zyy + s*zxy) + s*(c*zyx + s*zxx)]])
s = np.sin(-np.deg2rad(rotAngle))
c = np.cos(-np.deg2rad(rotAngle))
rotMat = np.array([[c, -s], [s, c]])
rotData = (rotMat.dot(impData.reshape(-1, 2, 2).dot(rotMat.T))).transpose(
1, 0, 2).reshape(-1, 4)
outRec = recData.copy()
for nr, comp in enumerate(['zxx', 'zxy', 'zyx', 'zyy']):
outRec[comp] = rotData[:, nr]
return Data.fromRecArray(outRec)
def rotateData(NSEMdata, rotAngle):
'''
Function that rotates clockwist by rotAngle (- negative for a counter-clockwise rotation)
'''
recData = NSEMdata.toRecArray('Complex')
impData = rec_to_ndarr(recData[['zxx','zxy','zyx','zyy']],complex)
# Make the rotation matrix
# c,s,zxx,zxy,zyx,zyy = sympy.symbols('c,s,zxx,zxy,zyx,zyy')
# rotM = sympy.Matrix([[c,-s],[s, c]])
# zM = sympy.Matrix([[zxx,zxy],[zyx,zyy]])
# rotM*zM*rotM.T
# [c*(c*zxx - s*zyx) - s*(c*zxy - s*zyy), c*(c*zxy - s*zyy) + s*(c*zxx - s*zyx)],
# [c*(c*zyx + s*zxx) - s*(c*zyy + s*zxy), c*(c*zyy + s*zxy) + s*(c*zyx + s*zxx)]])
s = np.sin(-np.deg2rad(rotAngle))
c = np.cos(-np.deg2rad(rotAngle))
rotMat = np.array([[c,-s],[s,c]])
rotData = (rotMat.dot(impData.reshape(-1,2,2).dot(rotMat.T))).transpose(1,0,2).reshape(-1,4)
outRec = recData.copy()
for nr,comp in enumerate(['zxx','zxy','zyx','zyy']):
outRec[comp] = rotData[:,nr]
return Data.fromRecArray(outRec)
def resample_data(NSEMdata, locs='All', freqs='All', rxs='All', verbose=False):
"""
Function that selects locations from all the receivers in the survey
(uses the numerator location as a reference). Also gives the option
of selecting frequencies and receiver.
**Required**
:param SimPEG.EM.NSEM.Data NSEMdata: NSEM data object to process
**Optional**
:param numpy.ndarray locs: receiver locations to use (default is 'All' locations)
:param numpy.ndarray freqs: frequencies to use (default is 'All' frequencies))
:param string rxs: list of receiver sting types to use (default is 'All' types)
Can be any componation of ['zxx','zxy','zyx','zyy','tzx','tzy']
"""
# Initiate new objects
new_srcList = []
data_list = []
std_list = []
floor_list = []
# Sort out input frequencies
if locs is 'All':
locations = NSEMdata._unique_locations()
elif isinstance(locs, np.ndarray):
locations = locs
else:
raise IOError('Incorrect input type for locs. \n' +
'Can be \'All\' or ndarray ')
# Sort out input frequencies
if freqs is 'All':
frequencies = NSEMdata.survey.freqs
elif isinstance(freqs, np.ndarray):
frequencies = freqs
elif isinstance(freqs, list):
frequencies = np.array(freqs)
else:
raise IOError('Incorrect input type for freqs. \n' +
'Can be \'All\'; ndarray or a list')
# Sort out input rxs
if rxs is 'All':
rx_comp = True
elif isinstance(rxs, list):
rx_comp = []
for rxT in rxs:
if 'z' in rxT[0]:
rxtype = Point_impedance3D
elif 't' in rxT[0]:
rxtype = Point_tipper3D
else:
raise IOError('Unknown rx type string')
orient = rxT[1:3]
rx_comp.append((rxtype, orient))
else:
raise IOError('Incorrect input type for rxs. \n' +
'Can be \'All\' or a list')
# Filter the data
for src in NSEMdata.survey.srcList:
if src.freq in frequencies:
new_rxList = []
for rx in src.rxList:
if rx_comp is True or np.any([(isinstance(rx, ct)
and rx.orientation in co)
for (ct, co) in rx_comp]):
if len(rx.locs.shape) == 3:
ind_loc = np.sum(np.concatenate(
[(np.sqrt(
np.sum((rx.locs[:, :, 0] - location)**2,
axis=1)) < 0.1).reshape(-1, 1)
for location in locations],
axis=1),
axis=1,
dtype=bool)
new_locs = rx.locs[ind_loc, :, :]
else:
ind_loc = np.sum(np.concatenate(
[(np.sqrt(
np.sum((rx.locs[:, :] - location)**2, axis=1))
< 0.1).reshape(-1, 1) for location in locations],
axis=1),
axis=1,
dtype=bool)
new_locs = rx.locs[ind_loc, :]
new_rx = type(rx)
new_rxList.append(
new_rx(new_locs, rx.orientation, rx.component))
data_list.append(NSEMdata[src, rx][ind_loc])
try:
std_list.append(
NSEMdata.standard_deviation[src, rx][ind_loc])
floor_list.append(NSEMdata.floor[src, rx][ind_loc])
except Exception as e:
if verbose:
print('No standard deviation or floor assigned')
new_src = type(src)
new_srcList.append(new_src(new_rxList, src.freq))
survey = Survey(new_srcList)
if std_list or floor_list:
return Data(survey, np.concatenate(data_list),
np.concatenate(std_list), np.concatenate(floor_list))
else:
return Data(survey, np.concatenate(data_list))
def convert3Dto1Dobject(NSEMdata, rxType3D='yx'):
"""
Function that converts a 3D NSEMdata of a list of
1D NSEMdata objects for running 1D inversions for.
**Required**
:param SimPEG.EM.NSEM.Data NSEMdata: NSEM data object to process
**Optional**
:param string rxType3D: component of the NSEMdata to use
Can be 'xy', 'yx' or 'det'
"""
# Find the unique locations
# Need to find the locations
recDataTemp = NSEMdata.toRecArray().data.flatten()
# Check if survey.std has been assigned.
## NEED TO: write this...
# Calculte and add the DET of the tensor to the recArray
if 'det' in rxType3D:
Zon = ((recDataTemp['zxxr'] + 1j * recDataTemp['zxxi']) *
(recDataTemp['zyyr'] + 1j * recDataTemp['zyyi']))
Zoff = ((recDataTemp['zxyr'] + 1j * recDataTemp['zxyi']) *
(recDataTemp['zyxr'] + 1j * recDataTemp['zyxi']))
det = np.sqrt(Zon - Zoff)
recData = recFunc.append_fields(recDataTemp, ['zdetr', 'zdeti'],
[det.real, det.imag])
else:
recData = recDataTemp
uniLocs = rec_to_ndarr(np.unique(recData[['x', 'y', 'z']].copy()))
mtData1DList = []
if 'xy' in rxType3D:
corr = -1
# Shift the data to comply with the quadtrature of the 1d problem
else:
corr = 1
for loc in uniLocs:
# Make the receiver list
rx1DList = []
rx1DList.append(Point_impedance1D(simpeg.mkvc(loc, 2).T, 'real'))
rx1DList.append(Point_impedance1D(simpeg.mkvc(loc, 2).T, 'imag'))
# Source list
locrecData = recData[np.sqrt(
np.sum((rec_to_ndarr(recData[['x', 'y', 'z']].copy()) - loc)**2,
axis=1)) < 1e-5]
dat1DList = []
src1DList = []
for freq in locrecData['freq']:
src1DList.append(Planewave_xy_1Dprimary(rx1DList, freq))
for comp in ['r', 'i']:
dat1DList.append(
corr *
locrecData[rxType3D + comp][locrecData['freq'] == freq])
# Make the survey
sur1D = Survey(src1DList)
# Make the data
dataVec = np.hstack(dat1DList)
dat1D = Data(sur1D, dataVec)
sur1D.dobs = dataVec
# Need to take NSEMdata.survey.std and split it as well.
std = 0.05
sur1D.std = np.abs(sur1D.dobs * std)
mtData1DList.append(dat1D)
# Return the the list of data.
return mtData1DList
