Draft: Develop

Improvements:

• Normalization using AIS_Points now raise exception if given a bad dictionary

New features:

• Added choosing fields for generating images for adding channels
• Added centering of image on last point
• Added chosing bounding box of image
• Added selecting range of values for features

skais/ais/ais_trajectory.py

@@ -62,7 +62,7 @@ def apply_time_sequence(dat, time, func):
 def __get_image_value__(features, bounds):
     value = []
     for f, b in zip(features, bounds):
-        value.append(1 - (b[1] - f) / b[1])
+        value.append(1 - (b[1] - f - b[0]) / (b[1] - b[0]))
     return value
 
 
@@ -236,12 +236,11 @@ class AISTrajectory(AISPoints):
                                       node_size=0):
         nb_channels = 1
 
+        positions = self.df[['longitude', 'latitude']].to_numpy()
         if bounding_box == 'fit':
-            positions = self.df[['longitude', 'latitude']].to_numpy()
             lower_lon, upper_lon = (min(positions[:, 0]), max(positions[:, 0]))
             lower_lat, upper_lat = (min(positions[:, 1]), max(positions[:, 1]))
         elif bounding_box == 'centered':
-            positions = self.df[['longitude', 'latitude']].to_numpy()
             center_lon, center_lat = positions[-1]
             min_lon, max_lon = (min(positions[:, 0]), max(positions[:, 0]))
             min_lat, max_lat = (min(positions[:, 1]), max(positions[:, 1]))
@@ -253,7 +252,11 @@ class AISTrajectory(AISPoints):
             lower_lat = center_lat - distance_to_center
             upper_lon = center_lon + distance_to_center
             lower_lon = center_lon - distance_to_center
-
+        elif type(bounding_box) is list:
+            upper_lon = bounding_box[1][0]
+            lower_lon = bounding_box[0][0]
+            upper_lat = bounding_box[1][1]
+            lower_lat = bounding_box[0][1]
         else:
             raise ValueError(f"Option not supported: {bounding_box}")
 
@@ -291,17 +294,23 @@ class AISTrajectory(AISPoints):
                         data[x, y] = [1]
 
         else:
+            bounds = []
             if type(features) is list:
                 nb_channels = len(features)
+                features_vectors = self.df[features].to_numpy()
+                for c in features_vectors.T:
+                    bounds.append((0, max(c)))
             elif type(features) is str:
-                features = [features]
+                features_vectors = self.df[[features]].to_numpy()
+                for c in features_vectors.T:
+                    bounds.append((0, max(c)))
+            elif type(features) is dict:
+                bounds = list(features.values())
+                features_vectors = self.df[features.keys()].to_numpy()
             else:
                 raise TypeError("Type not supported")
             data = np.zeros((height, width, nb_channels), dtype=np.float)
-            features_vectors = self.df[features].to_numpy()
-            bounds = []
-            for c in features_vectors.T:
-                bounds.append((min(c), max(c)))
+
 
             for pos, f in zip(positions, features_vectors):
                 latitude = pos[1]