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modules/deeplearning/cloud_fraction_fcn_viirs.py

@@ -1042,6 +1042,28 @@ def analyze_5cat(file):
     return cm_0_1, cm_1_2, cm_0_2, [acc_0, acc_1, acc_2], [recall_0, recall_1, recall_2],\
         [precision_0, precision_1, precision_2], [mcc_0, mcc_1, mcc_2], lbls, pred
 
+# from util.plot_cm import *
+# from sklearn.metrics import confusion_matrix
+# import numpy as np
+# tup = np.load('/Users/tomrink/cld_frac_viirs.npy', allow_pickle=True)
+# lbls = tup[0]
+# pred = tup[1]
+# cld_prob = tup[2]
+# from util.plot import plot_image
+# cm = confusion_matrix(lbls.flatten(), pred.flatten())
+# plot_confusion_matrix(cm, ['CLR', '1/4', '1/2', '3/4', 'CLD'], normalize=True, axis=0)
+
+# from deeplearning.cloud_fraction_fcn_viirs import run_evaluate_static
+# run_evaluate_static('/Users/tomrink/clavrx_VNP02IMG.A2019306.1912.001.2019307003236.uwssec.nc',
+# '/Users/tomrink/cld_frac_A2019306.1912', '/Users/tomrink/tf_model_cld_frac_viirs/run-20230421193944/')
+# import numpy as np
+# tup = np.load('/Users/tomrink/cld_frac_A2019306.1912.npy', allow_pickle=True)
+# cfrac = tup[0]
+# bt = tup[1]
+# refl = tup[2]
+# cp = tup[3]
+# from util.plot import plot_image
+
 
 if __name__ == "__main__":
     nn = SRCNN()