def toRecArray(self,returnType='RealImag'):
'''
Function that returns a numpy.recarray for a SimpegMT impedance data object.
:param str returnType: Switches between returning a rec array where the impedance is split to real and imaginary ('RealImag') or is a complex ('Complex')
'''
# Define the record fields
dtRI = [('freq',float),('x',float),('y',float),('z',float),('zxxr',float),('zxxi',float),('zxyr',float),('zxyi',float),
('zyxr',float),('zyxi',float),('zyyr',float),('zyyi',float),('tzxr',float),('tzxi',float),('tzyr',float),('tzyi',float)]
dtCP = [('freq',float),('x',float),('y',float),('z',float),('zxx',complex),('zxy',complex),('zyx',complex),('zyy',complex),('tzx',complex),('tzy',complex)]
impList = ['zxxr','zxxi','zxyr','zxyi','zyxr','zyxi','zyyr','zyyi']
for src in self.survey.srcList:
# Temp array for all the receivers of the source.
# Note: needs to be written more generally, using diffterent rxTypes and not all the data at the locaitons
# Assume the same locs for all RX
locs = src.rxList[0].locs
if locs.shape[1] == 1:
locs = np.hstack((np.array([[0.0,0.0]]),locs))
elif locs.shape[1] == 2:
locs = np.hstack((np.array([[0.0]]),locs))
tArrRec = np.concatenate((src.freq*np.ones((locs.shape[0],1)),locs,np.nan*np.ones((locs.shape[0],12))),axis=1).view(dtRI)
# np.array([(src.freq,rx.locs[0,0],rx.locs[0,1],rx.locs[0,2],np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ) for rx in src.rxList],dtype=dtRI)
# Get the type and the value for the DataMT object as a list
typeList = [[rx.rxType.replace('z1d','zyx'),self[src,rx]] for rx in src.rxList]
# Insert the values to the temp array
for nr,(key,val) in enumerate(typeList):
tArrRec[key] = mkvc(val,2)
# Masked array
mArrRec = np.ma.MaskedArray(rec2ndarr(tArrRec),mask=np.isnan(rec2ndarr(tArrRec))).view(dtype=tArrRec.dtype)
# Unique freq and loc of the masked array
uniFLmarr = np.unique(mArrRec[['freq','x','y','z']]).copy()
try:
outTemp = recFunc.stack_arrays((outTemp,mArrRec))
#outTemp = np.concatenate((outTemp,dataBlock),axis=0)
except NameError as e:
outTemp = mArrRec
if 'RealImag' in returnType:
outArr = outTemp
elif 'Complex' in returnType:
# Add the real and imaginary to a complex number
outArr = np.empty(outTemp.shape,dtype=dtCP)
for comp in ['freq','x','y','z']:
outArr[comp] = outTemp[comp].copy()
for comp in ['zxx','zxy','zyx','zyy','tzx','tzy']:
outArr[comp] = outTemp[comp+'r'].copy() + 1j*outTemp[comp+'i'].copy()
else:
raise NotImplementedError('{:s} is not implemented, as to be RealImag or Complex.')
# Return
return outArr
def fromRecArray(cls, recArray, srcType='primary'):
"""
Class method that reads in a numpy record array to MTdata object.
Only imports the impedance data.
"""
if srcType == 'primary':
src = simpegMT.SurveyMT.srcMT_polxy_1Dprimary
elif srcType == 'total':
src = sdsimpegMT.SurveyMT.srcMT_polxy_1DhomotD
else:
raise NotImplementedError(
'{:s} is not a valid source type for MTdata')
# Find all the frequencies in recArray
uniFreq = np.unique(recArray['freq'])
srcList = []
dataList = []
for freq in uniFreq:
# Initiate rxList
rxList = []
# Find that data for freq
dFreq = recArray[recArray['freq'] == freq].copy()
# Find the impedance rxTypes in the recArray.
rxTypes = [
comp for comp in recArray.dtype.names
if (len(comp) == 4 or len(comp) == 3) and 'z' in comp
]
for rxType in rxTypes:
# Find index of not nan values in rxType
notNaNind = ~np.isnan(dFreq[rxType])
if np.any(
notNaNind): # Make sure that there is any data to add.
locs = rec2ndarr(dFreq[['x', 'y', 'z']][notNaNind].copy())
if dFreq[rxType].dtype.name in 'complex128':
rxList.append(
simpegMT.SurveyMT.RxMT(locs, rxType + 'r'))
dataList.append(dFreq[rxType][notNaNind].real.copy())
rxList.append(
simpegMT.SurveyMT.RxMT(locs, rxType + 'i'))
dataList.append(dFreq[rxType][notNaNind].imag.copy())
else:
rxList.append(simpegMT.SurveyMT.RxMT(locs, rxType))
dataList.append(dFreq[rxType][notNaNind].copy())
srcList.append(src(rxList, freq))
# Make a survey
survey = simpegMT.SurveyMT.SurveyMT(srcList)
dataVec = np.hstack(dataList)
return cls(survey, dataVec)
def fromRecArray(cls, recArray, srcType='primary'):
"""
Class method that reads in a numpy record array to MTdata object.
Only imports the impedance data.
"""
if srcType=='primary':
src = simpegMT.SurveyMT.srcMT_polxy_1Dprimary
elif srcType=='total':
src = sdsimpegMT.SurveyMT.srcMT_polxy_1DhomotD
else:
raise NotImplementedError('{:s} is not a valid source type for MTdata')
# Find all the frequencies in recArray
uniFreq = np.unique(recArray['freq'])
srcList = []
dataList = []
for freq in uniFreq:
# Initiate rxList
rxList = []
# Find that data for freq
dFreq = recArray[recArray['freq'] == freq].copy()
# Find the impedance rxTypes in the recArray.
rxTypes = [ comp for comp in recArray.dtype.names if (len(comp)==4 or len(comp)==3) and 'z' in comp]
for rxType in rxTypes:
# Find index of not nan values in rxType
notNaNind = ~np.isnan(dFreq[rxType])
if np.any(notNaNind): # Make sure that there is any data to add.
locs = rec2ndarr(dFreq[['x','y','z']][notNaNind].copy())
if dFreq[rxType].dtype.name in 'complex128':
rxList.append(simpegMT.SurveyMT.RxMT(locs,rxType+'r'))
dataList.append(dFreq[rxType][notNaNind].real.copy())
rxList.append(simpegMT.SurveyMT.RxMT(locs,rxType+'i'))
dataList.append(dFreq[rxType][notNaNind].imag.copy())
else:
rxList.append(simpegMT.SurveyMT.RxMT(locs,rxType))
dataList.append(dFreq[rxType][notNaNind].copy())
srcList.append(src(rxList,freq))
# Make a survey
survey = simpegMT.SurveyMT.SurveyMT(srcList)
dataVec = np.hstack(dataList)
return cls(survey,dataVec)
def toRecArray(self, returnType='RealImag'):
'''
Function that returns a numpy.recarray for a SimpegMT impedance data object.
:param str returnType: Switches between returning a rec array where the impedance is split to real and imaginary ('RealImag') or is a complex ('Complex')
'''
# Define the record fields
dtRI = [('freq', float), ('x', float), ('y', float), ('z', float),
('zxxr', float), ('zxxi', float), ('zxyr', float),
('zxyi', float), ('zyxr', float), ('zyxi', float),
('zyyr', float), ('zyyi', float), ('tzxr', float),
('tzxi', float), ('tzyr', float), ('tzyi', float)]
dtCP = [('freq', float), ('x', float), ('y', float), ('z', float),
('zxx', complex), ('zxy', complex), ('zyx', complex),
('zyy', complex), ('tzx', complex), ('tzy', complex)]
impList = [
'zxxr', 'zxxi', 'zxyr', 'zxyi', 'zyxr', 'zyxi', 'zyyr', 'zyyi'
]
for src in self.survey.srcList:
# Temp array for all the receivers of the source.
# Note: needs to be written more generally, using diffterent rxTypes and not all the data at the locaitons
# Assume the same locs for all RX
locs = src.rxList[0].locs
if locs.shape[1] == 1:
locs = np.hstack((np.array([[0.0, 0.0]]), locs))
elif locs.shape[1] == 2:
locs = np.hstack((np.array([[0.0]]), locs))
tArrRec = np.concatenate((src.freq * np.ones(
(locs.shape[0], 1)), locs, np.nan * np.ones(
(locs.shape[0], 12))),
axis=1).view(dtRI)
# np.array([(src.freq,rx.locs[0,0],rx.locs[0,1],rx.locs[0,2],np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ,np.nan ) for rx in src.rxList],dtype=dtRI)
# Get the type and the value for the DataMT object as a list
typeList = [[rx.rxType.replace('z1d', 'zyx'), self[src, rx]]
for rx in src.rxList]
# Insert the values to the temp array
for nr, (key, val) in enumerate(typeList):
tArrRec[key] = mkvc(val, 2)
# Masked array
mArrRec = np.ma.MaskedArray(
rec2ndarr(tArrRec),
mask=np.isnan(rec2ndarr(tArrRec))).view(dtype=tArrRec.dtype)
# Unique freq and loc of the masked array
uniFLmarr = np.unique(mArrRec[['freq', 'x', 'y', 'z']]).copy()
try:
outTemp = recFunc.stack_arrays((outTemp, mArrRec))
#outTemp = np.concatenate((outTemp,dataBlock),axis=0)
except NameError as e:
outTemp = mArrRec
if 'RealImag' in returnType:
outArr = outTemp
elif 'Complex' in returnType:
# Add the real and imaginary to a complex number
outArr = np.empty(outTemp.shape, dtype=dtCP)
for comp in ['freq', 'x', 'y', 'z']:
outArr[comp] = outTemp[comp].copy()
for comp in ['zxx', 'zxy', 'zyx', 'zyy', 'tzx', 'tzy']:
outArr[comp] = outTemp[
comp + 'r'].copy() + 1j * outTemp[comp + 'i'].copy()
else:
raise NotImplementedError(
'{:s} is not implemented, as to be RealImag or Complex.')
# Return
return outArr