def corrSuitabilityMaps():
#### Script to find corelation between suitbility maps
for country in case_studies:
suitDir = os.path.join(os.getcwd(), 'results', 'figures',
'suitability_maps')
workDir = parameters.getWorkDir()
resultFolder = os.path.join(workDir, 'results', country, 'metrics')
fig_dir = os.path.join(os.getcwd(), 'results', 'figures')
pointFile = 'sampPointNr.col'
pointDir = os.path.join(os.getcwd(), 'input_data', country, pointFile)
file_list = os.listdir(suitDir)
file_list = [x for x in file_list if x.startswith(country)]
maps = [
os.path.join(os.getcwd(), 'results', 'figures', 'suitability_maps',
f) for f in file_list
]
maps_pairs = [(file_list[i], file_list[j])
for i in range(len(file_list))
for j in range(i + 1, len(file_list))]
maps_pairs = [(x[8:], y[8:]) for x, y in maps_pairs]
arrays = []
for a_map in maps:
arrays.append(metrics.map2Array(a_map, pointDir))
pairs = [(arrays[i], arrays[j]) for i in range(len(arrays))
for j in range(i + 1, len(arrays))]
for i, pair in enumerate(pairs):
# Check for NaNs
mask = np.isnan(pair[0]) | np.isnan(pair[1])
# Mask, to remove NaNs
x = pair[0][~mask]
y = pair[1][~mask]
print(x.shape, y.shape)
print(np.corrcoef(x, y))
# Plot
plt.scatter(x, y, s=0.1)
a_string = 'correlation coefficient between '+\
maps_pairs[i][0]+' and '+maps_pairs[i][1]+': '+str(np.corrcoef(x,y)[0,1])
plt.title(a_string)
plt.xlabel(maps_pairs[i][0])
plt.ylabel(maps_pairs[i][1])
aPath = os.path.join(
fig_dir,
country + '_' + maps_pairs[i][0] + '_' + maps_pairs[i][1])
plt.savefig(os.path.join(resultFolder, aPath))
plt.close('all')
print(country, 'done')
# -*- coding: cp1252 -*- 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