def initial(self):
# create sample points
self.nullMask = self.readmap(self.inputfolder + '/nullmask')
self.oneMask = self.readmap(self.inputfolder + '/onemask')
# load a map with random uniform values
self.uniformMap = self.readmap(self.inputfolder + '/uniform')
# AT SOME POINT WITH STOCHASTIC INPUT
# in that case land use should not include urban
self.landuse = self.readmap(self.inputfolder + '/init_lu90')
self.landuse00 = self.readmap(self.inputfolder + '/init_lu00')
self.landuse06 = self.readmap(self.inputfolder + '/init_lu06')
self.landuse12 = self.readmap(self.inputfolder + '/init_lu12')
self.landuse18 = self.readmap(self.inputfolder + '/init_lu18')
self.initialUrb = self.landuse == 1
self.roads = self.readmap(self.inputfolder + '/roads')
self.noGoMap = cover(self.readmap(self.inputfolder + '/nogo'), \
boolean(self.nullMask))
self.zones = readmap(self.inputfolder + '/zones')
self.samplePoints = self.readmap(self.inputfolder + '/sampPoint')
self.sumStats = parameters.getSumStats()
self.yieldMap = scalar(self.oneMask)
# List of landuse types in order of 'who gets to choose first'
self.landUseList = parameters.getLandUseList()
self.relatedTypeDict = parameters.getRelatedTypeDict()
# Input values from parameters file
self.suitFactorDict = parameters.getSuitFactorDict()
self.variableSuperDict = parameters.getVariableSuperDict()
self.noGoLanduseList = parameters.getNoGoLanduseTypes()
# Uniform map of small numbers, used to avoid equal suitabilities.
# The same uniform map is applied in each iteration.
self.noise = self.uniformMap # noise added as a uniform map created in the create_initial_maps.py
# This part used to be the initial
# Set seeds to be able to reproduce results
random.seed(10)
np.random.seed(10)
setrandomseed(10)
# Create the 'overall' landuse class
## self.environment = uncertainty.getInitialLandUseMap(self.landuse)
self.environment = self.landuse
self.landUse = LandUse(self.landUseList, self.nullMask)
self.landUse.setInitialEnvironment(self.environment)
# Create an object for every landuse type in the list
self.landUse.createLandUseTypeObjects(self.relatedTypeDict, \
self.suitFactorDict, \
self.weightDict, \
self.variableSuperDict, \
self.noise)
# Static suitability factors
self.landUse.determineNoGoAreas(self.noGoMap, self.noGoLanduseList)
self.landUse.loadDistanceMaps()
self.landUse.calculateStaticSuitabilityMaps(self.yieldMap)
import pickle import os import metrics import numpy as np import parameters import calibrate from pcraster.framework import * #### Script to read in the metrics saved as the result of the LU_urb.py script. #### Metrics are transformed into an array # Work directory: work_dir = parameters.getWorkDir() # Get metrics metricNames = parameters.getSumStats() # Get the number of parameter iterations and number of time step defined in the parameter.py script nrOfTimesteps = parameters.getNrTimesteps() numberOfIterations = parameters.getNumberofIterations() iterations = range(1, numberOfIterations + 1, 1) timeSteps = range(1, nrOfTimesteps + 1, 1) # Get the observed time steps. Time steps relate to the year of the CLC data, where 1990 was time step 0. obsSampleNumbers = [1] #range(1,20+1,1) <- for stochastic model obsTimeSteps = parameters.getObsTimesteps() # Read the reference files refArray = parameters.getColFiles() # Path to the folder with the metrics stored country = parameters.getCountryName()
import pickle import os import metrics import numpy as np import parameters ##from pcraster.framework import * ##from numba import njit from scipy.spatial import distance #### Script to find calibrate LU model # Work directory: work_dir = parameters.getWorkDir() # Get metrics metricList = parameters.getSumStats() locationalMetric = parameters.getLocationalAccuracyMetric() all_metrices = metricList+locationalMetric # Get case studies case_studies = parameters.getCaseStudies() # Get calibration scenarios scenarios = parameters.getCalibrationScenarios() # Get the number of parameter iterations and number of time step defined in the parameter.py script nrOfTimesteps=parameters.getNrTimesteps() numberOfIterations = parameters.getNumberofIterations() iterations = range(1, numberOfIterations+1, 1) timeSteps=range(1,nrOfTimesteps+1,1) # Get the observed time steps. Time steps relate to the year of the CLC data, where 1990 was time step 0. obsSampleNumbers = [1] #range(1,20+1,1) <- for stochastic model