def test_AreaAnalyst(self):
aa = AreaAnalyst(self.r1, self.r1)
raster = aa.getChangeMap()
band = raster.getBand(1)
assert_array_equal(band, self.r1r1)
# Masked raster
aa = AreaAnalyst(self.r2, self.r2)
raster = aa.getChangeMap()
band = raster.getBand(1)
assert_array_equal(band, self.r2r2)
def test_AreaAnalyst(self):
aa = AreaAnalyst(self.r1, self.r1)
raster = aa.getChangeMap()
band = raster.getBand(1)
assert_array_equal(band, self.r1r1)
# Masked raster
aa = AreaAnalyst(self.r2, self.r2)
raster = aa.getChangeMap()
band = raster.getBand(1)
assert_array_equal(band, self.r2r2)
def sim(self):
"""
Make 1 iteracion of simulation.
"""
# TODO: eleminate AreaAnalyst.getChangeMap() from the process
transition = self.crosstable.getCrosstable()
prediction = self.getPrediction()
state = self.getState()
new_state = state.getBand(1).copy() # New states (the result of simulation) will be stored there.
analyst = AreaAnalyst(state, prediction)
classes = analyst.classes
changes = analyst.getChangeMap().getBand(1)
# Make transition between classes according to
# number of moved pixel in crosstable
self.rangeChanged.emit(self.tr("Simulation process %p%"), len(classes) ** 2 - len(classes))
for initClass in classes:
for finalClass in classes:
if initClass == finalClass:
continue
# TODO: Calculate number of pixels to be moved via TransitoionMatrix and state raster
n = transition.getTransition(
initClass, finalClass
) # Number of pixels to be moved (constant count now).
# Find n appropriate places for transition initClass -> finalClass
class_code = analyst.encode(initClass, finalClass)
places = changes == class_code # Array of places where transitions initClass -> finalClass are occured
placesCount = np.sum(places)
if placesCount < n:
self.logMessage.emit(
self.tr("There are more transitions in the transition matrix, then the model have found")
)
n = placesCount
confidence = self.getConfidence().getBand(1)
confidence = (
confidence * places
) # The higher is number in cell, the higer is probability of transition in the cell
indices = []
for i in range(n):
index = np.unravel_index(
confidence.argmax(), confidence.shape
) # Select the cell with biggest probability
indices.append(index)
confidence[index] = -1 # Mark the cell to prevent second selection
# Now "indices" contains indices of the appropriate places,
# make transition initClass -> finalClass
for index in indices:
new_state[index] = finalClass
self.updateProgress.emit()
result = Raster()
result.create([new_state], state.getGeodata())
self.state = result
self.updatePrediction(result)
self.processFinished.emit()
def main(initRaster, finalRaster, factors):
print 'Start Reading Init Data...', clock()
initRaster = Raster(initRaster)
finalRaster = Raster(finalRaster)
factors = [Raster(rasterName) for rasterName in factors]
print 'Finish Reading Init Data', clock(), '\n'
print "Start Making CrossTable...", clock()
crosstab = CrossTableManager(initRaster, finalRaster)
#print crosstab.getTransitionStat()
print "Finish Making CrossTable", clock(), '\n'
# Create and Train Analyst
print 'Start creating AreaAnalyst...', clock()
analyst = AreaAnalyst(initRaster, finalRaster)
print 'Finish creating AreaAnalyst ...', clock(), '\n'
print 'Start Making Change Map...', clock()
analyst = AreaAnalyst(initRaster, finalRaster)
changeMap = analyst.getChangeMap()
print 'Finish Making Change Map', clock(), '\n'
#~ # Create and Train ANN Model
model = MlpManager(ns=1)
model.createMlp(initRaster, factors, changeMap, [10])
print 'Start Setting MLP Trainig Data...', clock()
model.setTrainingData(initRaster,
factors,
changeMap,
mode='Stratified',
samples=1000)
print 'Finish Setting Trainig Data', clock(), '\n'
print 'Start MLP Training...', clock()
model.train(20, valPercent=20)
print 'Finish Trainig', clock(), '\n'
# print 'Start ANN Prediction...', clock()
# predict = model.getPrediction(initRaster, factors, calcTransitions=True)
# confidence = model.getConfidence()
# potentials = model.getTransitionPotentials()
#~ # Create and Train LR Model
#~ model = LR(ns=0)
#~ print 'Start Setting LR Trainig Data...', clock()
#~ model.setState(initRaster)
#~ model.setFactors(factors)
#~ model.setOutput(changeMap)
#~ model.setMode('Stratified')
#~ model.setSamples(100)
#~ model.setTrainingData()
#~ print 'Finish Setting Trainig Data', clock(), '\n'
#~ print 'Start LR Training...', clock()
#~ model.train()
#~ print 'Finish Trainig', clock(), '\n'
#~
#~ print 'Start LR Prediction...', clock()
#~ predict = model.getPrediction(initRaster, factors, calcTransitions=True)
#~ print 'Finish LR Prediction...', clock(), '\n'
# Create and Train WoE Model
# print 'Start creating AreaAnalyst...', clock()
# analyst = AreaAnalyst(initRaster, finalRaster)
# print 'Finish creating AreaAnalyst ...', clock(), '\n'
# print 'Start creating WoE model...', clock()
# bins = {0: [[1000, 3000]], 1: [[200, 500, 1500]]}
# model = WoeManager(factors, analyst, bins= bins)
# model.train()
# print 'Finish creating WoE model...', clock(), '\n'
#~ # Create and Train MCE Model
#~ print 'Start creating MCE model...', clock()
#~ matrix = [
#~ [1, 6],
#~ [1.0/6, 1]
#~ ]
#~ model = MCE(factors, matrix, 2, 3, analyst)
#~ print 'Finish creating MCE model...', clock(), '\n'
# predict = model.getPrediction(initRaster, factors, calcTransitions=True)
# confidence = model.getConfidence()
# potentials = model.getTransitionPotentials()
# filename = 'predict.tif'
# confname = 'confidence.tif'
# trans_prefix='trans_'
# try:
# predict.save(filename)
# confidence.save(confname)
# if potentials != None:
# for k,v in potentials.iteritems():
# map = v.save(trans_prefix+str(k) + '.tif')
# finally:
# os.remove(filename)
# #pass
# print 'Finish Saving...', clock(), '\n'
# simulation
print 'Start Simulation...', clock()
simulator = Simulator(initRaster, factors, model, crosstab)
# Make 1 cycle of simulation:
simulator.setIterationCount(1)
simulator.simN()
monteCarloSim = simulator.getState() # Result of MonteCarlo simulation
errors = simulator.errorMap(finalRaster) # Risk class validation
riskFunct = simulator.getConfidence() # Risk function
try:
monteCarloSim.save('simulation_result.tiff')
errors.save('risk_validation.tiff')
riskFunct.save('risk_func.tiff')
finally:
pass
# os.remove('simulation_result.tiff')
# os.remove('risk_validation.tiff')
# os.remove('risk_func.tiff')
print 'Finish Simulation', clock(), '\n'
print 'Done', clock()
def main(initRaster, finalRaster, factors):
print 'Start Reading Init Data...', clock()
initRaster = Raster(initRaster)
finalRaster = Raster(finalRaster)
factors = [Raster(rasterName) for rasterName in factors]
print 'Finish Reading Init Data', clock(), '\n'
print "Start Making CrossTable...", clock()
crosstab = CrossTableManager(initRaster, finalRaster)
#print crosstab.getTransitionStat()
print "Finish Making CrossTable", clock(), '\n'
# Create and Train Analyst
print 'Start creating AreaAnalyst...', clock()
analyst = AreaAnalyst(initRaster, finalRaster)
print 'Finish creating AreaAnalyst ...', clock(), '\n'
print 'Start Making Change Map...', clock()
analyst = AreaAnalyst(initRaster,finalRaster)
changeMap = analyst.getChangeMap()
print 'Finish Making Change Map', clock(), '\n'
#~ # Create and Train ANN Model
model = MlpManager(ns=1)
model.createMlp(initRaster, factors, changeMap, [10])
print 'Start Setting MLP Trainig Data...', clock()
model.setTrainingData(initRaster, factors, changeMap, mode='Stratified', samples=1000)
print 'Finish Setting Trainig Data', clock(), '\n'
print 'Start MLP Training...', clock()
model.train(20, valPercent=20)
print 'Finish Trainig', clock(), '\n'
# print 'Start ANN Prediction...', clock()
# predict = model.getPrediction(initRaster, factors, calcTransitions=True)
# confidence = model.getConfidence()
# potentials = model.getTransitionPotentials()
#~ # Create and Train LR Model
#~ model = LR(ns=0)
#~ print 'Start Setting LR Trainig Data...', clock()
#~ model.setState(initRaster)
#~ model.setFactors(factors)
#~ model.setOutput(changeMap)
#~ model.setMode('Stratified')
#~ model.setSamples(100)
#~ model.setTrainingData()
#~ print 'Finish Setting Trainig Data', clock(), '\n'
#~ print 'Start LR Training...', clock()
#~ model.train()
#~ print 'Finish Trainig', clock(), '\n'
#~
#~ print 'Start LR Prediction...', clock()
#~ predict = model.getPrediction(initRaster, factors, calcTransitions=True)
#~ print 'Finish LR Prediction...', clock(), '\n'
# Create and Train WoE Model
# print 'Start creating AreaAnalyst...', clock()
# analyst = AreaAnalyst(initRaster, finalRaster)
# print 'Finish creating AreaAnalyst ...', clock(), '\n'
# print 'Start creating WoE model...', clock()
# bins = {0: [[1000, 3000]], 1: [[200, 500, 1500]]}
# model = WoeManager(factors, analyst, bins= bins)
# model.train()
# print 'Finish creating WoE model...', clock(), '\n'
#~ # Create and Train MCE Model
#~ print 'Start creating MCE model...', clock()
#~ matrix = [
#~ [1, 6],
#~ [1.0/6, 1]
#~ ]
#~ model = MCE(factors, matrix, 2, 3, analyst)
#~ print 'Finish creating MCE model...', clock(), '\n'
# predict = model.getPrediction(initRaster, factors, calcTransitions=True)
# confidence = model.getConfidence()
# potentials = model.getTransitionPotentials()
# filename = 'predict.tif'
# confname = 'confidence.tif'
# trans_prefix='trans_'
# try:
# predict.save(filename)
# confidence.save(confname)
# if potentials != None:
# for k,v in potentials.iteritems():
# map = v.save(trans_prefix+str(k) + '.tif')
# finally:
# os.remove(filename)
# #pass
# print 'Finish Saving...', clock(), '\n'
# simulation
print 'Start Simulation...', clock()
simulator = Simulator(initRaster, factors, model, crosstab)
# Make 1 cycle of simulation:
simulator.setIterationCount(1)
simulator.simN()
monteCarloSim = simulator.getState() # Result of MonteCarlo simulation
errors = simulator.errorMap(finalRaster) # Risk class validation
riskFunct = simulator.getConfidence() # Risk function
try:
monteCarloSim.save('simulation_result.tiff')
errors.save('risk_validation.tiff')
riskFunct.save('risk_func.tiff')
finally:
pass
# os.remove('simulation_result.tiff')
# os.remove('risk_validation.tiff')
# os.remove('risk_func.tiff')
print 'Finish Simulation', clock(), '\n'
print 'Done', clock()
