def createGstatRealizations(setOfRealizations, nameCommandFile, nameOutMapList):
# number of realizations required
nSim=len(setOfRealizations)
# open template gstat script and replace
#print nameCommandFile
gstatTemplate=file(nameCommandFile + '.gst','r')
gstatTemplateString=gstatTemplate.read()
gstatTemplate.close()
gstatString=string.replace(gstatTemplateString,'NSIM',str(nSim))
gstatFile=file('tmpGstat.gst','w')
gstatFile.write(gstatString)
gstatFile.close()
# run gstat
os.system('gstat tmpGstat.gst')
os.remove('tmpGstat.gst')
# rename files
i=1
for realization in setOfRealizations:
#print realization
item=0
for name in nameOutMapList:
gstatOutputFileName=generateNameT('g_' + name,i)
#print gstatOutputFileName, realization[item]
shutil.move(gstatOutputFileName,realization[item])
item=item+1
i = i + 1
def createtimeseries(names, nameExtension, locations,sampleNumbers,timeSteps):
if deterministicInput(sampleNumbers):
for name in names:
tssFile = file(name + nameExtension + '.tss', "w")
tssFile.write("timeseries scalar\n")
tssFile.write("2\n")
tssFile.write("timestep\n")
tssFile.write("%s\n" % (name))
for step in timeSteps:
timeseriesValue=mapmaximum(ifthen(locations,generateNameT(name,step)))
value, valid = cellvalue(timeseriesValue, 1, 1); assert valid
tssFile.write("%d %g\n" % (step, value))
tssFile.close()
else:
for name in names:
for sample in sampleNumbers:
tssFile = file(generateNameS("%s%s.tss" % (name,nameExtension), sample), "w")
tssFile.write("timeseries scalar\n")
tssFile.write("2\n")
tssFile.write("timestep\n")
tssFile.write("%s\n" % (name))
for step in timeSteps:
timeseriesValue=mapmaximum(ifthen(locations,generateNameST(name,sample,step)))
value, valid = cellvalue(timeseriesValue, 1, 1); assert valid
tssFile.write("%d %g\n" % (step, value))
tssFile.close()
def createtimeseries(names, nameExtension, locations,sampleNumbers,timeSteps):
if deterministicInput(sampleNumbers):
for name in names:
tssFile = file(name + nameExtension + '.tss', "w")
tssFile.write("timeseries scalar\n")
tssFile.write("2\n")
tssFile.write("timestep\n")
tssFile.write("%s\n" % (name))
for step in timeSteps:
timeseriesValue=mapmaximum(ifthen(locations,generateNameT(name,step)))
value, valid = cellvalue(timeseriesValue, 1, 1); assert valid
tssFile.write("%d %g\n" % (step, value))
tssFile.close()
else:
for name in names:
for sample in sampleNumbers:
tssFile = file(generateNameS("%s%s.tss" % (name,nameExtension), sample), "w")
tssFile.write("timeseries scalar\n")
tssFile.write("2\n")
tssFile.write("timestep\n")
tssFile.write("%s\n" % (name))
for step in timeSteps:
timeseriesValue=mapmaximum(ifthen(locations,generateNameST(name,sample,step)))
value, valid = cellvalue(timeseriesValue, 1, 1); assert valid
tssFile.write("%d %g\n" % (step, value))
tssFile.close()
def createGstatRealizations(setOfRealizations, nameCommandFile, nameOutMapList):
# number of realizations required
nSim=len(setOfRealizations)
# open template gstat script and replace
#print nameCommandFile
gstatTemplate=file(nameCommandFile + '.gst','r')
gstatTemplateString=gstatTemplate.read()
gstatTemplate.close()
gstatString=string.replace(gstatTemplateString,'NSIM',str(nSim))
gstatFile=file('tmpGstat.gst','w')
gstatFile.write(gstatString)
gstatFile.close()
# run gstat
os.system('gstat tmpGstat.gst')
os.remove('tmpGstat.gst')
# rename files
i=1
for realization in setOfRealizations:
#print realization
item=0
for name in nameOutMapList:
gstatOutputFileName=generateNameT('g_' + name,i)
#print gstatOutputFileName, realization[item]
shutil.move(gstatOutputFileName,realization[item])
item=item+1
i = i + 1
def readDeterministic(self, name):
if self._userModel()._inPremc() or self._userModel()._inPostmc() or self._userModel()._inInitial():
newName = name + ".map"
else:
newName = generateNameT(name, self._userModel().currentTimeStep())
import pcraster
return pcraster.readmap(newName)
def readDeterministic(self, name):
if self._userModel()._inPremc() or self._userModel()._inPostmc(
) or self._userModel()._inInitial():
newName = name + ".map"
else:
newName = generateNameT(name, self._userModel().currentTimeStep())
import pcraster
return pcraster.readmap(newName)
def reportState(self, variable, variableName):
"""
Report a map into the state variable directory.
"""
sample = str(self.currentSampleNumber())
if re.search(".map", variableName):
filename = variableName
else:
filename = frameworkBase.generateNameT(variableName, self.currentTimeStep())
name = os.path.join(sample, "stateVar", filename)
pcraster.report(variable, name)
def readDeterministic(self, name):
"""
Read deterministic data from disk.
Returns the map of the current time step from the current working directory.
"""
if self._userModel()._inPremc() or self._inPostmc() or self._inInitial():
newName = name + ".map"
else:
newName = frameworkBase.generateNameT(name, self._userModel().currentTimeStep())
import pcraster
return pcraster.readmap(newName)
def readState(self, variableName):
"""
Read a state variable map.
"""
sample = str(self.currentSampleNumber())
if re.search(".map", variableName):
filename = variableName
else:
timestep = self.firstTimeStep() - 1
filename = frameworkBase.generateNameT(variableName, timestep)
name = os.path.join(sample, "stateVar", filename)
return pcraster.readmap(name)
def reportState(self, variable, variableName):
"""
Report a map into the state variable directory.
"""
sample = str(self.currentSampleNumber())
if re.search(".map", variableName):
filename = variableName
else:
filename = frameworkBase.generateNameT(variableName,
self.currentTimeStep())
name = os.path.join(sample, "stateVar", filename)
pcraster.report(variable, name)
def readState(self, variableName):
"""
Read a state variable map.
"""
sample = str(self.currentSampleNumber())
if re.search(".map", variableName):
filename = variableName
else:
timestep = self.firstTimeStep() - 1
filename = frameworkBase.generateNameT(variableName, timestep)
name = os.path.join(sample, "stateVar", filename)
return pcraster.readmap(name)
def mcaveragevariance(names,sampleNumbers, timeSteps):
if staticInput(timeSteps):
for name in names:
mean=average(name + '.map', sampleNumbers)
var=variance(name + '.map', sampleNumbers)
minimum=sampleMin(name + '.map', sampleNumbers)
maximum=sampleMax(name + '.map', sampleNumbers)
#std=stddev(name + '.map', sampleNumbers)
report(mean, name + '-ave.map')
report(var, name + '-var.map')
report(minimum, name + '-min.map')
report(maximum, name + '-max.map')
report(sqrt(var)/mean, name + '-err.map')
else:
nrSamples=scalar(len(sampleNumbers))
for name in names:
for step in timeSteps:
var=variance(generateNameT(name,step),sampleNumbers)
mean=average(generateNameT(name,step), sampleNumbers)
report(mean, generateNameT(name + '-ave', step))
report(var, generateNameT(name + '-var', step))
report(sqrt(var)/mean, generateNameT(name + '-err', step))
def timeseries(name, timeSteps, row, col):
mask = numpy.zeros(len(timeSteps)).astype(numpy.bool_)
steps = numpy.zeros(len(timeSteps)).astype(numpy.int32)
array = numpy.zeros(len(timeSteps)).astype(numpy.float32)
i = 0
while i < len(timeSteps):
filename = generateNameT(name, timeSteps[i])
steps[i] = timeSteps[i]
array[i], mask[i] = readFieldCell(filename, row, col)
i += 1
steps = numpy.compress(numpy.logical_not(mask), steps)
array = numpy.compress(numpy.logical_not(mask), array)
return steps, array
def readDeterministic(self, name):
"""
Read deterministic data from disk.
Returns the map of the file with current time step, from the current
working directory.
"""
if self._inPremc() or self._inPostmc() or self._inInitial():
newName = name + ".map"
else:
newName = frameworkBase.generateNameT(name, self.currentTimeStep())
return pcraster.readmap(newName)
def timeseries(name, timeSteps, row, col):
mask = numpy.zeros(len(timeSteps)).astype(numpy.bool_)
steps = numpy.zeros(len(timeSteps)).astype(numpy.int32)
array = numpy.zeros(len(timeSteps)).astype(numpy.float32)
i = 0
while i < len(timeSteps):
filename = generateNameT(name, timeSteps[i])
steps[i] = timeSteps[i]
array[i], mask[i] = readFieldCell(filename, row, col)
i += 1
steps = numpy.compress(numpy.logical_not(mask), steps)
array = numpy.compress(numpy.logical_not(mask), array)
return steps, array
def mcaveragevariance(names,sampleNumbers, timeSteps):
if staticInput(timeSteps):
for name in names:
mean=average(name + '.map', sampleNumbers)
var=variance(name + '.map', sampleNumbers)
minimum=sampleMin(name + '.map', sampleNumbers)
maximum=sampleMax(name + '.map', sampleNumbers)
#std=stddev(name + '.map', sampleNumbers)
report(mean, name + '-ave.map')
report(var, name + '-var.map')
report(minimum, name + '-min.map')
report(maximum, name + '-max.map')
report(sqrt(var)/mean, name + '-err.map')
else:
nrSamples=scalar(len(sampleNumbers))
for name in names:
for step in timeSteps:
var=variance(generateNameT(name,step),sampleNumbers)
mean=average(generateNameT(name,step), sampleNumbers)
report(mean, generateNameT(name + '-ave', step))
report(var, generateNameT(name + '-var', step))
report(sqrt(var)/mean, generateNameT(name + '-err', step))
def mcpercentiles(names, percentiles, sampleNumbers, timeSteps):
if staticInput(timeSteps):
for name in names:
results = percentile(name + ".map", sampleNumbers, percentiles)
for i in range(len(percentiles)):
report(results[i], "%s_%s.map" % (name, percentiles[i]))
else:
for name in names:
for step in timeSteps:
results = percentile(generateNameT(name, step), sampleNumbers,
percentiles)
assert len(results) == len(percentiles)
for i in range(len(percentiles)):
report(results[i],
"%s_%d_%s.map" % (name, step, percentiles[i]))
def mcpercentiles(
names,
percentiles,
sampleNumbers,
timeSteps):
if staticInput(timeSteps):
for name in names:
results = percentile(name + ".map", sampleNumbers, percentiles)
for i in range(len(percentiles)):
report(results[i], "%s_%s.map" % (name, percentiles[i]))
else:
for name in names:
for step in timeSteps:
results = percentile(generateNameT(name, step), sampleNumbers,
percentiles)
assert len(results) == len(percentiles)
for i in range(len(percentiles)):
report(results[i], "%s_%d_%s.map" % (name, step, percentiles[i]))