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GRSL/MERIS_dataset_generation/meris_case_1.py

+import numpy as np
+import os
+import library as lib
+
+
+from pathlib import Path
+
+CURRENT_DIR = Path(__file__).parent
+
+#mu and sigmas refer to the underlying normal distribution, not the distribution itself
+
+wl_array=[412.5,442.5,490,510,560,620,665,681.25]
+
+n_samples=650
+
+C_mu=-0.86
+X_mu=-1.21
+Y_mu=-1.75
+
+C_sigma=0.3
+X_sigma=0.3
+Y_sigma=0.3
+
+corr_CX=0.8
+corr_CY=0.8
+
+##########################################à
+
+mu=np.array([C_mu,X_mu,Y_mu])
+cov=np.zeros((len(mu),len(mu)))
+
+
+cov[0,0]=C_sigma
+cov[1,1]=X_sigma
+cov[2,2]=Y_sigma
+
+
+
+cov_CX=corr_CX*C_sigma*X_sigma
+cov_CY=corr_CY*C_sigma*Y_sigma
+
+#######################################
+
+cov[0,1]=cov_CX
+cov[1,0]=cov_CX
+
+cov[0,2]=cov_CY
+cov[2,0]=cov_CY
+
+###################################
+
+generator=np.random.default_rng()
+
+samples=generator.multivariate_normal(mean=mu,cov=cov,size=n_samples)
+
+oap=np.power(10,samples)
+
+#%%
+
+################################
+
+R=np.zeros((oap.shape[0],len(wl_array)))
+
+for i in range(R.shape[0]):
+    C,X,Y=oap[i,:]
+    
+    R[i,:]=lib.get_reflectance_vector(wl_array, C, X, Y)
+    
+
+
+
+C_array=oap[:,0]
+
+save_path=CURRENT_DIR / 'generated_dataset'
+os.makedirs(save_path, exist_ok=True)
+
+
+
+np.save(save_path / 'target.npy',C_array)
+np.save(save_path / 'input.npy',R)
+np.save(save_path / 'oap.npy',oap)
+