def convert3Dto1Dobject(NSEMdata, rxType3D='yx'):
# 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']]))
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']]) -
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) #+ 0.01*np.linalg.norm(sur1D.dobs)
mtData1DList.append(dat1D)
# Return the the list of data.
return mtData1DList
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))
