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
 
 
@@ -236,22 +236,42 @@ class AISTrajectory(AISPoints):
                                       node_size=0):
         nb_channels = 1
 
-        if bounding_box != 'fit':
-            raise ValueError("feature not implemented")
         positions = self.df[['longitude', 'latitude']].to_numpy()
-        min_lon, max_lon = (min(positions[:, 0]), max(positions[:, 0]))
-        min_lat, max_lat = (min(positions[:, 1]), max(positions[:, 1]))
-        if min_lat == max_lat:
-            min_lat -= 1
-            max_lat += 1
-        if min_lon == max_lon:
-            min_lon -= 1
-            max_lon += 1
+        if bounding_box == 'fit':
+            lower_lon, upper_lon = (min(positions[:, 0]), max(positions[:, 0]))
+            lower_lat, upper_lat = (min(positions[:, 1]), max(positions[:, 1]))
+        elif bounding_box == 'centered':
+            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]))
+
+            distance_to_center = max(center_lon - min_lon, max_lon - center_lon, center_lat - min_lat,
+                                     max_lat - center_lat)
+
+            upper_lat = center_lat + distance_to_center
+            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}")
+
+        if lower_lat == upper_lat:
+            lower_lat -= 1
+            upper_lat += 1
+        if lower_lon == upper_lon:
+            lower_lon -= 1
+            upper_lon += 1
 
         if features is None:
             data = np.zeros((height, width, nb_channels), dtype=np.uint8)
             for longitude, latitude in positions:
-                x_coord, y_coord = get_coord(latitude, longitude, height, width, min_lat, max_lat, min_lon, max_lon)
+                x_coord, y_coord = get_coord(latitude, longitude, height, width, lower_lat, upper_lat, lower_lon,
+                                             upper_lon)
 
                 x_lower_bound = max(0, x_coord - node_size)
                 x_upper_bound = min(height - 1, x_coord + node_size)
@@ -265,30 +285,38 @@ class AISTrajectory(AISPoints):
             if link:
                 lon, lat = positions[0, 0], positions[0, 1]
                 for longitude, latitude in positions[1:]:
-                    x_prv, y_prev = get_coord(lat, lon, height, width, min_lat, max_lat, min_lon, max_lon)
-                    x_nxt, y_nxt = get_coord(latitude, longitude, height, width, min_lat, max_lat, min_lon, max_lon)
+                    x_prv, y_prev = get_coord(lat, lon, height, width, lower_lat, upper_lat, lower_lon, upper_lon)
+                    x_nxt, y_nxt = get_coord(latitude, longitude, height, width, lower_lat, upper_lat, lower_lon,
+                                             upper_lon)
 
                     lon, lat = longitude, latitude
                     for x, y in bresenham(x_prv, y_prev, x_nxt, y_nxt):
                         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]
                 longitude = pos[0]
-                x_coord, y_coord = get_coord(latitude, longitude, height, width, min_lat, max_lat, min_lon, max_lon)
+                x_coord, y_coord = get_coord(latitude, longitude, height, width, lower_lat, upper_lat, lower_lon,
+                                             upper_lon)
                 value = __get_image_value__(f, bounds)
                 x_lower_bound = max(0, x_coord - node_size)
                 x_upper_bound = min(height - 1, x_coord + node_size)
@@ -307,8 +335,9 @@ class AISTrajectory(AISPoints):
                 for pos, f in zip(positions[1:], features_vectors[1:]):
                     latitude = pos[1]
                     longitude = pos[0]
-                    x_prv, y_prev = get_coord(lat, lon, height, width, min_lat, max_lat, min_lon, max_lon)
-                    x_nxt, y_nxt = get_coord(latitude, longitude, height, width, min_lat, max_lat, min_lon, max_lon)
+                    x_prv, y_prev = get_coord(lat, lon, height, width, lower_lat, upper_lat, lower_lon, upper_lon)
+                    x_nxt, y_nxt = get_coord(latitude, longitude, height, width, lower_lat, upper_lat, lower_lon,
+                                             upper_lon)
                     lon, lat = longitude, latitude
                     for x, y in bresenham(x_prv, y_prev, x_nxt, y_nxt):
                         for i, v in enumerate(value):