Update google_sheet_Legend.py

google_sheet_Legend.py

@@ -312,22 +312,22 @@ def mapnametoLegend(McTName):
     # firstpart= re.split(r'[`\-=~!@#$%^&*()_+\[\]{};\'\\:"|<,./<>?]', McTName[i][0])
     print('kkk' ,McTName[i][2])
     if McTName[i][2].startswith('Area'):
-      if section.startswith('1.0') or section.startswith('3.2'):
+      if section.startswith('1.0') or section.startswith('3.2') or section.startswith('3.3'):
         rowvalue=5# column 5 
       ar=0
       unit='m2'
     if McTName[i][2].startswith('Perimeter'):
-      if section.startswith('1.0') or section.startswith('3.2'):
+      if section.startswith('1.0') or section.startswith('3.2') or section.startswith('3.3'):
         rowvalue=7# column 7
       ar=0   
       unit='m'
     if McTName[i][2].startswith('Length'):
-      if section.startswith('1.0') or section.startswith('3.2'):
+      if section.startswith('1.0') or section.startswith('3.2') or section.startswith('3.3'):
         rowvalue=9# column 7
       ar=0   
       unit='m'
     if McTName[i][2].startswith('Count'):
-      if section.startswith('1.0') or section.startswith('3.2'):
+      if section.startswith('1.0') or section.startswith('3.2') or section.startswith('3.3'):
         rowvalue=3# column 7
 
       ar=0   
@@ -348,9 +348,15 @@ def mapnametoLegend(McTName):
               # print('kjjjj',roww[j])
               ar+=float(worksheetw.cell((indices[j]+1 ,rowvalue)).value) 
           else:
-              item+=m + ' ,'
+              if section.startswith('3.2') or section.startswith('3.3'):
+                item+=m
+              else: 
+                item+=m + ' ,'
               print(item)
-          n= McTName[i][0] + ' ( '+ allgbnames[:-2] +' , ' + item[:-1]  + ' )  ' 
+          if section.startswith('3.2') or section.startswith('3.3'):
+            n= McTName[i][0] + ' ( '+ allgbnames[:-2]+ ' )  '
+          else:
+            n= McTName[i][0] + ' ( '+ allgbnames[:-2] +' , ' + item[:-1]  + ' )  '
     else:
       if McTName[i][1].startswith('text1'):
         name=McTName[i][1].removeprefix('text1')
@@ -478,19 +484,20 @@ def deletefromlegend(deletedrows, SimilarAreaDictionarycopy, section, areaPermAr
     items = []
     print('deletefromlegend',deletedrows)
     idx = 0
-    if section.startswith('1.0') or section.startswith('3.2'):
+    if section.startswith('1.0') or section.startswith('3.2') or section.startswith('3.3'):
         areaPermArr = ast.literal_eval(areaPermArr)
     myDict = eval(SimilarAreaDictionarycopy)
     SimilarAreaDictionarycopy = pd.DataFrame(myDict)
     strings = deletedrows['content']
     colors = deletedrows['color']
     indicies_toDelete=[]
-    print(colors)
+    # print(colors)
     # Define your tolerance value
     tolerance = 2 # Allowable tolerance for RGB differences
-    print(SimilarAreaDictionarycopy)
+    # print(SimilarAreaDictionarycopy)
     color_list = list(SimilarAreaDictionarycopy['Color'])  # Convert Index/Series to list
-    if section.startswith('1.0'):       
+    if section.startswith('1.0'):      
+        
        color_list = [ast.literal_eval(color) for color in color_list]
         
     for j in range(len(colors)):
@@ -503,19 +510,24 @@ def deletefromlegend(deletedrows, SimilarAreaDictionarycopy, section, areaPermAr
             existing_color = tuple(existing_color)  # Ensure it's a tuple for comparison
 
                
-            print('eee',existing_color,color)
+            # print('eee',existing_color,color)
             if is_color_within_tolerance(existing_color, color, tolerance):
                 print(f'Color {color} found close to {existing_color} at index {idx}')
                 found = True
-                print(strings[j])
+                # print(strings[j])
                 matchA = re.search(r"Area=(\d+\.\d+)", strings[j])
                 matchP = re.search(r"Perimeter=(\d+\.\d+)", strings[j])
                 matchL = re.search(r"Length=(\d+\.\d+)", strings[j])
+                if matchA:
+                  print('matchingcontentAA ',float(matchA.group(1)),int(idx))
+                if matchP:
+                   print('matchingcontentPP ',float(matchP.group(1)),int(idx))
                 comment = SimilarAreaDictionarycopy.iloc[int(idx), SimilarAreaDictionarycopy.columns.get_loc('Comments')]
                 occ = SimilarAreaDictionarycopy.iloc[int(idx), SimilarAreaDictionarycopy.columns.get_loc('Occurences')]
 
                 # Only subtract area if the area value is found
                 if matchA:
+                    print(' SimilarAreaDictionaryArea',  float(matchA.group(1)),   SimilarAreaDictionarycopy.iloc[int(idx), SimilarAreaDictionarycopy.columns.get_loc('Total Area')])
                     SimilarAreaDictionarycopy.iloc[int(idx), SimilarAreaDictionarycopy.columns.get_loc('Total Area')] =SimilarAreaDictionarycopy.iloc[int(idx), SimilarAreaDictionarycopy.columns.get_loc('Total Area')] - float(matchA.group(1))
                     # Update area occurrences
                     if pd.notna(comment) and 'Area' in str(comment):
@@ -535,6 +547,7 @@ def deletefromlegend(deletedrows, SimilarAreaDictionarycopy, section, areaPermAr
                       area_occurrences = int(matches[0])
                       perimeter_occurrences = int(matches[1]) -1
                     else:       
+
                       area_occurrences = int(SimilarAreaDictionarycopy.iloc[int(idx), SimilarAreaDictionarycopy.columns.get_loc('Occurences')]) 
                       perimeter_occurrences = SimilarAreaDictionarycopy.iloc[int(idx), SimilarAreaDictionarycopy.columns.get_loc('Occurences')]-1
                       # print(area_occurrences,perimeter_occurrences)
@@ -571,6 +584,7 @@ def deletefromlegend(deletedrows, SimilarAreaDictionarycopy, section, areaPermAr
         if not found:
             print(f'Color {color} not found within tolerance')
     print('indicies_toDelete',indicies_toDelete)
+
     SimilarAreaDictionarycopy.drop(index=indicies_toDelete, axis=0, inplace=True)
     print('SimilarAreaDictionarycopy',SimilarAreaDictionarycopy)
     return SimilarAreaDictionarycopy
@@ -691,18 +705,18 @@ def delete3_2(deletedrows,dictionary):
       df_doors.loc[df_doors['Type'] == 'Single Doors', 'Quantity'] = str(new_value)
 
       # Print the updated DataFrame to verify the change
-      print(df_doors)
+      # print(df_doors)
     elif 'double' in str(newitem):
       current_value = df_doors.loc[df_doors['Type'] == 'Double Doors', 'Quantity'].values[0]
-      print(current_value)
+      # print(current_value)
       # Step 2: Subtract one from the current value
       new_value = int(current_value) - 1
-      print(new_value)
+      # print(new_value)
       # Step 3: Update the DataFrame with the new value
       df_doors.loc[df_doors['Type'] == 'Double Doors', 'Quantity'] = str(new_value)
 
       # Print the updated DataFrame to verify the change
-      print(df_doors)
+      # print(df_doors)
   df_doors = df_doors[['Type', 'Quantity']]
   return df_doors
 
@@ -719,6 +733,7 @@ def deletemarkups(list1, dbPath, path):
     list1 = pd.DataFrame(myDict1)
 
     dbxTeam = tsadropboxretrieval.ADR_Access_DropboxTeam('user')
+    
     md, res = dbxTeam.files_download(path=dbPath + path)
     data = res.content
     doc = fitz.open("pdf", data)