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utils/routing/distances.py

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+import numpy as np
+import pandas as pd
+import ast 
+from ast import literal_eval
+
+
+def distance_picking(Loc1, Loc2, y_low, y_high):
+    '''Calculate Picker Route Distance between two locations'''
+	# Start Point
+    x1, y1 = Loc1[0], Loc1[1]
+    # End Point
+    x2, y2 = Loc2[0], Loc2[1]
+    # Distance x-axis
+    distance_x = abs(x2 - x1)
+    # Distance y-axis
+    if x1 == x2:
+        distance_y1 = abs(y2 - y1)
+        distance_y2 = distance_y1
+    else:
+        distance_y1 = (y_high - y1) + (y_high - y2)
+        distance_y2 = (y1 - y_low) + (y2 - y_low)
+    # Minimum distance on y-axis 
+    distance_y = min(distance_y1, distance_y2)
+    # Total distance
+    distance = distance_x + distance_y
+    return int(distance)
+
+def next_location(start_loc, list_locs, y_low, y_high):
+    '''Find closest next location'''
+    # Distance to every next points candidate
+    list_dist = [distance_picking(start_loc, i, y_low, y_high) for i in list_locs]
+    # Minimum Distance 
+    distance_next = min(list_dist)
+    # Location of minimum distance
+    index_min = list_dist.index(min(list_dist))
+    next_loc = list_locs[index_min] 
+    list_locs.remove(next_loc) 
+    return list_locs, start_loc, next_loc, distance_next
+
+
+def centroid(list_in):
+    '''Centroid function'''
+    x, y = [p[0] for p in list_in], [p[1] for p in list_in]
+    centroid = [round(sum(x) / len(list_in),2), round(sum(y) / len(list_in), 2)]
+    return centroid
+
+ 
+def centroid_mapping(df_multi):
+    '''Mapping Centroids'''
+    # Mapping multi
+    df_multi['Coord'] = df_multi['Coord'].apply(literal_eval)
+    # Group coordinates per order
+    df_group = pd.DataFrame(df_multi.groupby(['OrderNumber'])['Coord'].apply(list)).reset_index()
+    # Calculate Centroid
+    df_group['Coord_Centroid'] = df_group['Coord'].apply(centroid)
+    # Dictionnary for mapping
+    list_order, list_coord = list(df_group.OrderNumber.values), list(df_group.Coord_Centroid.values)
+    dict_coord = dict(zip(list_order, list_coord))
+    # Final mapping
+    df_multi['Coord_Cluster'] = df_multi['OrderNumber'].map(dict_coord).astype(str)
+    df_multi['Coord'] = df_multi['Coord'].astype(str)
+    return df_multi
+
+def distance_picking_cluster(point1, point2):
+
+    y_low, y_high = 5.5, 50 
+    # Start Point
+    x1, y1 = point1[0], point1[1]
+    # End Point
+    x2, y2 = point2[0], point2[1]
+    # Distance x-axis
+    distance_x = abs(x2 - x1)
+    # Distance y-axis
+    if x1 == x2:
+        distance_y1 = abs(y2 - y1)
+        distance_y2 = distance_y1
+    else:
+        distance_y1 = (y_high - y1) + (y_high - y2)
+        distance_y2 = (y1 - y_low) + (y2 - y_low)
+    # Minimum distance on y-axis 
+    distance_y = min(distance_y1, distance_y2)
+    # Total distance
+    distance = distance_x + distance_y
+    return distance