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

 import numbers
 import random
+from copy import copy
 
 import pandas as pd
 import numpy as np
@@ -8,7 +9,7 @@ from scipy.interpolate import interp1d
 
 from skais.utils.geography import great_circle, position_from_distance, get_coord
 from skais.ais.ais_points import AISPoints
-from skais.utils.geometry import bresenham
+from skais.utils.geometry import bresenham, dist_on_grid
 
 
 @jit(nopython=True)
@@ -87,7 +88,7 @@ def __get_bounding_box__(bounding_box, positions, ref_index):
         upper_lon = center_lon + distance_to_center
         lower_lon = center_lon - distance_to_center
     elif type(bounding_box) is list:
-        if type(bounding_box[0]) is not numbers.Number:
+        if not isinstance(bounding_box[0], numbers.Number):
             upper_lon = bounding_box[1][0]
             lower_lon = bounding_box[0][0]
             upper_lat = bounding_box[1][1]
@@ -130,22 +131,9 @@ def generate_points(data, positions, height, width, node_size, lower_lat, upper_
                 data[x, y] = [1]
 
 
-@jit(nopython=True)
-def generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon):
-    lon, lat = positions[0, 0], positions[0, 1]
-    for longitude, latitude in positions[1:]:
-        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]
-
-
 @jit(nopython=True)
 def generate_points_with_features(data, positions, features_vectors, bounds, node_size, height, width,
-                                  lower_lat, upper_lat, lower_lon, upper_lon, link):
+                                  lower_lat, upper_lat, lower_lon, upper_lon):
     for pos, f in zip(positions, features_vectors):
         latitude = pos[1]
         longitude = pos[0]
@@ -162,21 +150,52 @@ def generate_points_with_features(data, positions, features_vectors, bounds, nod
             for y in range(y_lower_bound, y_upper_bound + 1):
                 for i, v in enumerate(value):
                     data[x, y, i] = v
-    if link:
-        lon, lat = positions[0, 0], positions[0, 1]
-
-        value = __get_image_value__(features_vectors[0], bounds)
-        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, 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):
-                    data[x, y, i] = v
-            value = __get_image_value__(f, bounds)
+
+
+@jit(nopython=True)
+def generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon, values,
+                   link_type=None):
+    lon, lat = positions[0, 0], positions[0, 1]
+
+    current_value = values[0]
+    for pos, nxt_value in zip(positions[1:], values[1:]):
+        latitude = pos[1]
+        longitude = pos[0]
+        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
+
+        dist = dist_on_grid(x_prv, y_prev, x_nxt, y_nxt)
+        for x, y in bresenham(x_prv, y_prev, x_nxt, y_nxt):
+            dist_prev = dist_on_grid(x_prv, y_prev, x, y)
+            dist_next = dist_on_grid(x, y, x_nxt, y_nxt)
+
+            pixel_color = current_value * (1 - dist_prev / dist) + nxt_value * (1 - dist_next / dist)
+            for i in range(len(pixel_color)):
+                if nxt_value[i] == current_value[i]:
+                    data[x, y, i] = current_value[i]
+                elif link_type == 'interpolate':
+                    data[x, y, i] = pixel_color[i]
+                elif link_type == 'previous':
+                    data[x, y, i] = current_value[i]
+                elif link_type == 'next':
+                    data[x, y, i] = nxt_value[i]
+                elif link_type == 'solid':
+                    data[x, y, i] = 1
+        current_value = nxt_value
+
+
+@jit(nopython=True)
+def generate_values(features_vectors, bounds):
+    result = np.zeros(features_vectors.shape)
+    for i in range(len(features_vectors)):
+        features = features_vectors[i]
+        value = __get_image_value__(features, bounds)
+        for j in range(len(value)):
+            v = value[j]
+            result[i, j] = v
+    return result
 
 
 class AISTrajectory(AISPoints):
@@ -345,7 +364,7 @@ class AISTrajectory(AISPoints):
                 result.append((row['ts_sec'], current_label))
         return result
 
-    def generate_array_from_positions(self, height=256, width=256, link=True, bounding_box='fit', ref_index=-1,
+    def generate_array_from_positions(self, height=256, width=256, link=None, bounding_box='fit', ref_index=-1,
                                       features=None, node_size=0):
 
         positions = self.df[['longitude', 'latitude']].to_numpy()
@@ -358,11 +377,11 @@ class AISTrajectory(AISPoints):
         elif type(features) is list:
             features_vectors = self.df[features].to_numpy()
             for c in features_vectors.T:
-                bounds.append((0, max(c)))
+                bounds.append([0, max(c)])
         elif type(features) is str:
             features_vectors = self.df[[features]].to_numpy()
             for c in features_vectors.T:
-                bounds.append((0, max(c)))
+                bounds.append([0, max(c)])
         elif type(features) is dict:
             bounds = list(features.values())
             features_vectors = self.df[features.keys()].to_numpy()
@@ -371,18 +390,24 @@ class AISTrajectory(AISPoints):
 
         if features_vectors is not None:
             nb_channels = len(features_vectors.T)
+            bounds = np.array(bounds)
         else:
             nb_channels = 1
-        data = np.zeros((height, width, nb_channels), dtype=np.float)
+        data = np.zeros((height, width, nb_channels), dtype=float)
 
         if features_vectors is None:
 
             generate_points(data, positions, height, width, node_size, lower_lat, upper_lat, lower_lon, upper_lon)
 
-            if link:
-                generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon)
+            if link is not None:
+                generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon,
+                               np.ones((len(positions), 1)), link)
 
         else:
-            generate_points_with_features(data, positions, features_vectors, bounds, node_size, height, width,
-                                          lower_lat, upper_lat, lower_lon, upper_lon, link)
+            generate_points_with_features(data, positions, features_vectors, np.array(bounds), node_size, height, width,
+                                          lower_lat, upper_lat, lower_lon, upper_lon)
+            if link is not None:
+                generate_links(data, positions, height, width, lower_lat, upper_lat, lower_lon, upper_lon,
+                               generate_values(features_vectors, bounds), link)
+
         return data