Update Doors_Schedule.py

Doors_Schedule.py

@@ -223,6 +223,8 @@ def get_selected_columns(dfs, user_patterns):
    
     #clmn_name = map_user_input_to_standard_labels(user_patterns)
     #if len(clmn_name) < len(user_patterns):
+    if len(user_patterns) == 2:
+      clmn_name = ["door_id", "door_type"] 
     if len(user_patterns) == 4:
       clmn_name = ["door_id", "door_type", "width", "height"]
     if len(user_patterns) == 3:
@@ -333,6 +335,9 @@ def get_similar_colors(selected_columns_new):
       col_dict = defaultdict(lambda: {'values': [], 'color': None, 'widths': []})
   else:
       col_dict = defaultdict(lambda: {'values': [], 'color': None, 'widths': [], 'heights': []})
+      if selected_columns_new.shape[1] == 2:
+        col_dict = defaultdict(lambda: {'values': [], 'color': None})
+
 
   for _, row in selected_columns_new.iterrows():
       key = row['door_type']
@@ -340,29 +345,41 @@ def get_similar_colors(selected_columns_new):
       if 'structural_opening' in selected_columns_new.columns:
         col_dict[key]['widths'].append(row['structural_opening'])  # Add structural opening
       else:
-        col_dict[key]['widths'].append(row['width'])  # Assuming 'widht' is a typo for 'width'
-        col_dict[key]['heights'].append(row['height'])
+        if selected_columns_new.shape[1] > 2:
+          col_dict[key]['widths'].append(row['width'])  # Assuming 'widht' is a typo for 'width'
+          col_dict[key]['heights'].append(row['height'])
       col_dict[key]['color'] = key_colors[key]  # Assign the unique RGB color
 
   # Convert defaultdict to a normal dictionary
   col_dict = dict(col_dict)
   return col_dict
-
+    
 def get_flattened_tuples_list(col_dict):
     tuples_list = []
+
     for key, values_dict in col_dict.items():
-        if 'heights' in values_dict:  # Case where 'heights' exists
+        if 'heights' in values_dict and 'widths' in values_dict:
+            # Case: Both widths and heights present
             tuples_list.append([
-                (value, width, height, values_dict["color"]) 
+                (value, width, height, values_dict["color"])
                 for value, width, height in zip(values_dict['values'], values_dict['widths'], values_dict['heights'])
             ])
-        else:  # Case without 'heights'
+        elif 'widths' in values_dict:
+            # Case: Only widths present
             tuples_list.append([
-                (value, width, values_dict["color"]) 
+                (value, width, values_dict["color"])
                 for value, width in zip(values_dict['values'], values_dict['widths'])
             ])
+        else:
+            # Case: Neither widths nor heights
+            tuples_list.append([
+                (value, values_dict["color"])
+                for value in values_dict['values']
+            ])
 
+    # Flatten the list of lists
     flattened_list = [item for sublist in tuples_list for item in sublist]
+
     return flattened_list
 
 def find_text_in_plan(label, x):
@@ -380,6 +397,14 @@ def find_text_in_plan(label, x):
 def get_word_locations_plan(flattened_list, plan_texts):
   locations = []
   not_found = []
+
+  if len(flattened_list[0]) == 2:
+    for lbl, clr in flattened_list:
+      location,worz, txt_pt = find_text_in_plan(lbl, plan_texts)
+      if len(location) ==0:
+        not_found.append(lbl)
+      locations.append((location, lbl, clr))
+
   if len(flattened_list[0]) == 3:
     for lbl, w, clr in flattened_list:
       location,worz, txt_pt = find_text_in_plan(lbl, plan_texts)
@@ -393,7 +418,7 @@ def get_word_locations_plan(flattened_list, plan_texts):
         not_found.append(lbl)
       locations.append((location, lbl, clr, w, h))
   return locations, not_found
-
+    
 def get_repeated_labels(locations):
   seen_labels = set()
   repeated_labels = set()
@@ -408,8 +433,18 @@ def get_repeated_labels(locations):
 
 def get_cleaned_data(locations):
   processed = defaultdict(int)
-  
+
   new_data = []
+  if len(locations[0]) == 3:
+    for coords, label, color in locations:
+        if len(coords)>1:
+          index = processed[label] % len(coords)  # Round-robin indexing
+          new_coord = [coords[index]]  # Pick the correct coordinate
+          new_data.append((new_coord, label, color))
+          processed[label] += 1  # Move to the next coordinate for this label
+        if len(coords)==1:
+          new_data.append((coords, label, color))
+
   if len(locations[0]) == 4:
     for coords, label, color, w in locations:
         if len(coords)>1:
@@ -428,7 +463,7 @@ def get_cleaned_data(locations):
           processed[label] += 1  # Move to the next coordinate for this label
         if len(coords)==1:
           new_data.append((coords, label, color, w, h))
-  
+
   return new_data
 
 '''def get_width_info_tobeprinted(new_data):
@@ -620,6 +655,8 @@ def mainRun(schedule, plan, searcharray):
       width_info_tobeprinted = get_width_info_tobeprinted(new_data)
       cleaned_width = get_cleaned_width(width_info_tobeprinted)
       widths = get_widths_bb_format(cleaned_width, kelma)
+  if selected_columns_new.shape[1] == 2:
+      widths = []
   final_pdf_bytes= process_pdf(plan, "final_output_width.pdf", new_data, widths)