Update AGBD.py

AGBD.py

@@ -57,6 +57,47 @@ feature_dtype = {'s2_num_days': Value('int16'),
                 "solar_elev": Value('float32'),
                 "urban_prop":Value('uint8')}
 
+norm_values = {'ALOS_bands': {
+    'HH': {'mean': -10.381429, 'std': 8.561741, 'min': -83.0, 'max': 13.329468, 'p1': -19.542107, 'p99': -2.402588},
+    'HV': {'mean': -16.722847, 'std': 8.718428, 'min': -83.0, 'max': 11.688309, 'p1': -29.285168, 'p99': -8.773987}},
+               'S2_bands': {'B01': {'mean': 0.12478869, 'std': 0.024433358, 'min': 1e-04, 'max': 1.8808, 'p1': 0.0787,
+                                    'p99': 0.1946},
+                            'B02': {'mean': 0.13480005, 'std': 0.02822557, 'min': 1e-04, 'max': 2.1776, 'p1': 0.0925,
+                                    'p99': 0.2216},
+                            'B03': {'mean': 0.16031432, 'std': 0.032037303, 'min': 1e-04, 'max': 2.12, 'p1': 0.1035,
+                                    'p99': 0.2556},
+                            'B04': {'mean': 0.1532097, 'std': 0.038628064, 'min': 1e-04, 'max': 2.0032, 'p1': 0.1023,
+                                    'p99': 0.2816},
+                            'B05': {'mean': 0.20312776, 'std': 0.04205057, 'min': 0.0422, 'max': 1.7502, 'p1': 0.1178,
+                                    'p99': 0.319},
+                            'B06': {'mean': 0.32636437, 'std': 0.07139242, 'min': 0.0502, 'max': 1.7245, 'p1': 0.1633,
+                                    'p99': 0.519},
+                            'B07': {'mean': 0.36605212, 'std': 0.08555025, 'min': 0.0616, 'max': 1.7149, 'p1': 0.1776,
+                                    'p99': 0.6076},
+                            'B08': {'mean': 0.3811653, 'std': 0.092815965, 'min': 1e-04, 'max': 1.7488, 'p1': 0.1691,
+                                    'p99': 0.646},
+                            'B8A': {'mean': 0.3910436, 'std': 0.0896364, 'min': 0.055, 'max': 1.688, 'p1': 0.1871,
+                                    'p99': 0.6386},
+                            'B09': {'mean': 0.3910644, 'std': 0.0836445, 'min': 0.0012, 'max': 1.7915, 'p1': 0.2124,
+                                    'p99': 0.6241},
+                            'B11': {'mean': 0.2917373, 'std': 0.07472579, 'min': 0.0953, 'max': 1.648, 'p1': 0.1334,
+                                    'p99': 0.4827},
+                            'B12': {'mean': 0.21169408, 'std': 0.05880649, 'min': 0.0975, 'max': 1.6775, 'p1': 0.115,
+                                    'p99': 0.3872}},
+               'CH': {'ch': {'mean': 9.736144, 'std': 9.493601, 'min': 0.0, 'max': 61.0, 'p1': 0.0, 'p99': 38.0},
+                      'std': {'mean': 7.9882116, 'std': 4.549494, 'min': 0.0, 'max': 254.0, 'p1': 0.0, 'p99': 18.0}},
+               'DEM': {'mean': 604.63727, 'std': 588.02094, 'min': -82.0, 'max': 5205.0, 'p1': 507.0, 'p99': 450.0},
+               'Sentinel_metadata': {
+                   'S2_vegetation_score': {'mean': 89.168724, 'std': 17.17321, 'min': 20.0, 'max': 100.0, 'p1': 29.0,
+                                           'p99': 100.0},
+                   'S2_date': {'mean': 299.1638, 'std': 192.87402, 'min': -165.0, 'max': 623.0, 'p1': 253.0,
+                               'p99': 277.0}}, 'GEDI': {
+        'agbd': {'mean': 66.97266, 'std': 98.66588, 'min': 0.0, 'max': 499.99985, 'p1': 0.9703503, 'p99': 163.46234},
+        'agbd_se': {'mean': 8.360701, 'std': 4.211524, 'min': 2.981795, 'max': 25.041483, 'p1': 2.9830396,
+                    'p99': 8.612499},
+        'rh98': {'mean': 12.074685, 'std': 10.276359, 'min': -1.1200076, 'max': 111.990005, 'p1': 2.3599916,
+                 'p99': 6.9500012},
+        'date': {'mean': 361.7431, 'std': 175.37294, 'min': 0.0, 'max': 624.0, 'p1': 360.0, 'p99': 146.0}}}
 
 def encode_lat_lon(lat, lon):
     """
@@ -215,8 +256,6 @@ class NewDataset(datasets.GeneratorBasedBuilder):
         return super().as_streaming_dataset(split=split, base_path=base_path)
 
     def _info(self):
-        with open('statistics.pkl', 'rb') as f:
-            self.norm_values = pickle.load(f)
 
         all_features = {
             'input': datasets.Sequence(datasets.Sequence(datasets.Sequence(datasets.Value('float32')))),
@@ -251,14 +290,14 @@ class NewDataset(datasets.GeneratorBasedBuilder):
 
             else:
                 patch = np.asarray(d["input"])
-                patch[:12] = denormalize_bands(patch[:12], self.norm_values['S2_bands'],['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09','B11', 'B12'])
-                patch[12:14] = denormalize_bands(patch[12:14], self.norm_values['ALOS_bands'], ['HH', 'HV'])
-                patch[14] = denormalize_data(patch[14], self.norm_values['CH']['ch'])
-                patch[15] = denormalize_data(patch[15], self.norm_values['CH']['std'])
+                patch[:12] = denormalize_bands(patch[:12], norm_values['S2_bands'],['B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09','B11', 'B12'])
+                patch[12:14] = denormalize_bands(patch[12:14], norm_values['ALOS_bands'], ['HH', 'HV'])
+                patch[14] = denormalize_data(patch[14], norm_values['CH']['ch'])
+                patch[15] = denormalize_data(patch[15], norm_values['CH']['std'])
                 patch[16] = decode_lc(patch[16], 'cos')
                 patch[17] = decode_lc(patch[17], 'sin')
                 patch[18] = patch[18] * 100
-                patch[19] = denormalize_data(patch[19], self.norm_values['DEM'])
+                patch[19] = denormalize_data(patch[19], norm_values['DEM'])
 
 
             lat, lon = d["metadata"]["lat"],d["metadata"]["lon"]
@@ -277,3 +316,5 @@ class NewDataset(datasets.GeneratorBasedBuilder):
                 data[feat] = d["metadata"][feat]
 
             yield i, data
+
+