change: a little more organized

server/data_processing/area_processing.py

+import math
+from itertools import groupby
 from math import pi, cos
 
 EARTH = 6378.137  # Radius of the earth in kilometer
 METER = (1 / ((2 * pi / 360) * EARTH)) / 1000  # 1 meter in degree
 
+
+# check if lidar points is within range of the area selected
+def inArea(position, areaRange):
+    y, x, _ = position # position to be checked
+    if (abs(areaRange[0][0]) > x > abs(areaRange[1][0])) and (abs(areaRange[0][1]) > abs(y) > abs(areaRange[1][1])):
+        return True
+    else:
+        return False
+
+# find distance between two points
+def distance(point1, point2):
+    y1, x1 = point1
+    y2, x2 = point2
+    return math.sqrt(abs(y2 - y1)**2 + abs(x2 - x1)**2)
+
+# find the closest point in json list
+def closest_points(point, list, coords_taken):
+    closest_point_found = None
+    closest_dist = float('inf')
+    for current_point in list:
+        dist = distance(point, current_point['properties']['sub_div_center'][0])
+        if dist < closest_dist and current_point not in coords_taken:
+            closest_dist = dist
+            closest_point_found = current_point
+    return closest_point_found
+
+# Calculation of height in an area from las files
+def find_height(points):
+    print(points, " 5 ")
+    height_differences = [] # final container for height data
+
+    # sort the points
+    sorted_coords = sorted(points, key=lambda coord: (coord[0], coord[1]))
+
+    # group the sorted points that has the same xy- coordinates together
+    groupCoords = [list(group) for key, group in groupby(sorted_coords, key=lambda coord: (coord[0], coord[1]))]
+
+    # loop through the groups to find the difference in height
+    for group in groupCoords:
+        if len(group) < 2 or group[0] == group[1]:
+            continue # jump over iteration if there is only one coordinate or lidar registered the same point twice
+
+        # find max and min height
+        min_height = min(coords[2] for coords in group)
+        max_height = max(coords[2] for coords in group)
+
+        # difference between them
+        difference = max_height - min_height
+        height_differences.append(difference)
+
+    # list of thickness in an area
+    return height_differences
+
 def calculate_corners(lat, lng, area_offset):
     """Calculate corners of polygon based on a center coordinate
 

server/data_processing/process_lidar_data.py

@@ -3,11 +3,10 @@ import laspy
 import json
 import math
 import utm # src: https://github.com/Turbo87/utm
-from itertools import groupby
-from server.data_processing.area_processing import calculate_corners, define_gridareas
+from server.data_processing.area_processing import (calculate_corners, define_gridareas, inArea,
+                                                    find_height, closest_points)
 
-
-# hard coded files for test data
+# hard coded files for test datas
 lazData_path = ["server/example_lidar_data/ot_N_000005_1.laz", "server/example_lidar_data/ot_N_000033_1.laz"]
 
 # Info about data
@@ -30,58 +29,6 @@ def about_laz_file():
 
     return [las.header.version, las.header.point_count, las.header.scale, las.header.offset]
 
-# check if lidar points is within range of the area selected
-def inArea(position, areaRange):
-    y, x, _ = position # position to be checked
-    if (abs(areaRange[0][0]) > x > abs(areaRange[1][0])) and (abs(areaRange[0][1]) > abs(y) > abs(areaRange[1][1])):
-        return True
-    else:
-        return False
-
-# find distance between two points
-def distance(point1, point2):
-    y1, x1 = point1
-    y2, x2 = point2
-    return math.sqrt(abs(y2 - y1)**2 + abs(x2 - x1)**2)
-
-# find the closest point in json list
-def closest_points(point, list, coords_taken):
-    closest_point_found = None
-    closest_dist = float('inf')
-    for current_point in list:
-        dist = distance(point, current_point['properties']['sub_div_center'][0])
-        if dist < closest_dist and current_point not in coords_taken:
-            closest_dist = dist
-            closest_point_found = current_point
-    return closest_point_found
-
-# Calculation of height in an area from las files
-def find_height(points):
-    print(points, " 5 ")
-    height_differences = [] # final container for height data
-
-    # sort the points
-    sorted_coords = sorted(points, key=lambda coord: (coord[0], coord[1]))
-
-    # group the sorted points that has the same xy- coordinates together
-    groupCoords = [list(group) for key, group in groupby(sorted_coords, key=lambda coord: (coord[0], coord[1]))]
-
-    # loop through the groups to find the difference in height
-    for group in groupCoords:
-        if len(group) < 2 or group[0] == group[1]:
-            continue # jump over iteration if there is only one coordinate or lidar registered the same point twice
-
-        # find max and min height
-        min_height = min(coords[2] for coords in group)
-        max_height = max(coords[2] for coords in group)
-
-        # difference between them
-        difference = max_height - min_height
-        height_differences.append(difference)
-
-    # list of thickness in an area
-    return height_differences
-
 # find the height of an area based on the coordinates of it's center
 # and it's affiliations (subId and groupId) (soon to be implemented
 def calculate_area_data(center, body_of_water):