DM_numpy2odm.py

## @package python.DM_numpy2odm
## @brief unkown python script prefix=autobuild.swdvlp64.opals.distro.demo
# 
#   setting odm attributes from numpy objects
# 
#   The script uses the NumpyConverter functionality of pyDM. Each attribute must be provided in a separate numpy array
#   which are 'combined' to a single dictionary object (also see DM_odm2numpy.py)
# 
#   The code computes normalized echo width values (between 0 and 1) based on single echoes, which is a pragmatic way of comparing 
#   values of different laser scanners (different wave length, different foot print size, etc.)
#   (script works in python 2 and 3)
# 

from __future__ import print_function

from opals import pyDM
import sys
import numpy

odm = "fullwave.odm"  # odm test file

# pyDM.Datamanager.load parameter: filename(string), readOnly(bool) threadSafety(bool)
dm = pyDM.Datamanager.load(odm, False, False)
if not dm:
    print("Unable to open ODM '" + odm + "'")
    sys.exit(1)


# build layout for retrieving echo width values (subset of odm attributes)
lf = pyDM.AddInfoLayoutFactory()
type, inDM = lf.addColumn(dm, "EchoWidth",  True); assert inDM == True
type, inDM = lf.addColumn(dm, "NrOfEchos",  True); assert inDM == True
layoutRead = lf.getLayout()

lf.addColumn(pyDM.ColumnType.float_, "_normalizedEchoWidth")
layoutWrite = lf.getLayout()

print("Get odm echo width as numpy object...")
#create dictionary of numpy objects
numpyDict = pyDM.NumpyConverter.createNumpyDict(dm.sizePoint(),layoutRead,False)
print("len(numpyDict)=",len(numpyDict))
pointindex = dm.getPointIndex()

#fill numpy dictionary with all leafs of the ODM point index
rowIdx = 0
count = float(pointindex.sizeLeaf())
for idx,leaf in enumerate(pointindex.leafs()):
    print("%5.1f%% finished" % (idx/count*100.) )
    rowIdx += pyDM.NumpyConverter.fillNumpyDict(numpyDict,rowIdx,leaf)

print("100.0% finished.",rowIdx,"values have been converted")


print("\nCompute min max echo width for single echos using numpy...")
mask = numpyDict["NrOfEchos"] == 1                 # generate single echo mask
minValue = (numpyDict["EchoWidth"][mask]).min()
maxValue = (numpyDict["EchoWidth"][mask]).max()
print("\tmin=%.2f" % minValue)
print("\tmax=%.2f" % maxValue)

# compute parameter of linear transform function
k = 1./(maxValue-minValue)
d = -minValue*k
normalizedEchoWidth = numpyDict["EchoWidth"]*k+d

# check if linear transform function
minCheck = (normalizedEchoWidth[mask]).min(); assert abs(minCheck) < 1e-10
maxCheck = (normalizedEchoWidth[mask]).max(); assert abs(maxCheck-1) < 1e-10

#store normalized echo width values within the odm
storeDict = {}
storeDict["_normalizedEchoWidth"] = normalizedEchoWidth
print("\nStore normalised echo width values in ODM...")
for idx,leaf in enumerate(pointindex.leafs()):
    print("%5.1f%% finished" % (idx/count*100.) )
    
    # pyDM.NumpyConverter.setFromNumpyDict( numpyDict, translators, leaf, layout, filter = None)
    # for details on the function, please refer to the docu
    pyDM.NumpyConverter.setFromNumpyDict(storeDict, [], leaf, layoutWrite)   # we don't need translators in this case
    
    #mark leaf as changed/dirty that it will be written do disk again
    leaf.setChanged(True)

print("100.0% finished.")

print("\nSave odm...")
dm.save()
print("finished")