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Optimized conversion codes

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Gudni Karl
2015-04-07 19:06:01 -07:00
parent 69966eae60
commit 25799f3680
1 changed files with 26 additions and 24 deletions
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simpegMT/SurveyMT.py
+26 -24
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@@ -1,6 +1,7 @@
from SimPEG import Survey, Utils, Problem, np, sp, mkvc
from scipy.constants import mu_0
import sys
from numpy.lib import recfunctions as recFunc
class RxMT(Survey.BaseRx):
@@ -251,7 +252,7 @@ class DataMT(Survey.Data):
# 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
tArrRec = np.concatenate((src.freq*np.ones((locs.shape[0],1)),locs,np.nan*np.ones((10,8))),axis=1).view(dtRI)
tArrRec = np.concatenate((src.freq*np.ones((locs.shape[0],1)),locs,np.nan*np.ones((locs.shape[0],8))),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,self[src,rx][0]] for rx in src.rxList]
@@ -262,30 +263,31 @@ class DataMT(Survey.Data):
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']])
try:
outTemp = recFunc.stack_arrays((outTemp,mArrRec))
#outTemp = np.concatenate((outTemp,dataBlock),axis=0)
except NameError as e:
outTemp = mArrRec
if 'RealImag' in returnType:
dt = dtRI
for uniFL in uniFLmarr:
mTemp = rec2ndarr(mArrRec[np.ma.where(mArrRec[['freq','x','y','z']].data == np.array(uniFL))][impList]).sum(axis=0)
dataBlock = mkvc(np.concatenate((rec2ndarr(uniFL),mTemp.data)),2).T
try:
outArr = np.concatenate((outArr,dataBlock),axis=0)
except NameError as e:
outArr = dataBlock
elif 'Complex' in returnType:
outArr = outTemp
if 'Complex' in returnType:
# Add the real and imaginary to a complex number
from numpy.lib import recfunctions as recFunc
dt = dtCP
for uniFL in uniFLmarr:
mTemp = mkvc(rec2ndarr(mArrRec[np.ma.where(mArrRec[['freq','x','y','z']].data == np.array(uniFL))][impList]).sum(axis=0),2).T
compBlock = np.sum(mTemp.data.reshape((4,2))*np.array([[1,1j],[1,1j],[1,1j],[1,1j]]),axis=1).copy().view(dt[4::])
dataBlock = mkvc(recFunc.merge_arrays((np.array(uniFL),compBlock),flatten=True),2).T
try:
outArr = recFunc.stack_arrays((outArr,dataBlock),usemask=False)
except NameError as e:
outArr = dataBlock
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']:
outArr[comp] = outTemp[comp+'r'].copy() + 1j*outTemp[comp+'i'].copy()
# for uniFL in uniFLmarr:
# mTemp = mkvc(rec2ndarr(mArrRec[np.ma.where(mArrRec[['freq','x','y','z']].data == np.array(uniFL))][impList]).sum(axis=0),2).T
# compBlock = np.sum(mTemp.data.reshape((4,2))*np.array([[1,1j],[1,1j],[1,1j],[1,1j]]),axis=1).copy().view(dt[4::])
# dataBlock = mkvc(recFunc.merge_arrays((np.array(uniFL),compBlock),flatten=True),2).T
# try:
# outArr = recFunc.stack_arrays((outArr,dataBlock),usemask=False)
# except NameError as e:
# outArr = dataBlock
# Return
if 'RealImag' in returnType:
return outArr.view(dt)
elif 'Complex' in returnType:
return outArr
return outArr