Update imports_utils.py

imports_utils.py

@@ -172,8 +172,114 @@ def fetchSubdomainMapper (livabilityAttributePages):
     return attribute_mapper
 
 
+# --------------------------------------------------------------------------------------------- #
+
+
+def getDataFromSpeckle(
+	speckleToken,
+	streamID,
+	matrixBranchName, 
+    landuseBranchName, 
+	matrixComitID="", 
+	landuseComitID="",     
+	pathToData = ["@Data", "@{0}"]
+	uuidColumn = "uuid", 
+	landuseColumns="lu+",
+	mergeAssetWithNonAssetLanduse = True
+
+		):
+	
+
+    if landuseBranchName:
+        streamLanduses = speckle_utils.getSpeckleStream(streamId,luBranchName,CLIENT, luCommitId)
+        streamData = streamLanduses["@Data"]["@{0}"]
+        
+        dfLanduses = speckle_utils.get_dataframe(streamData, return_original_df=False)
+        dfLanduses =  dfLanduses.set_index("uuid", drop=False)  # variable, uuid as default
+        
+    	if type(landuseColumns) == type("s"):
+    		# extract landuse columns with "landuseColumns"
+            landuse_columns = []   # provided by user? or prefix
+            for name in df_lu.columns:
+              if name.startswith(landuseColumns):
+                landuse_columns.append(name) 
+            
+    	elif type(landuseColumns) == type([]):
+    		#assmuming the user provided a lsit of columns 
+    		landuse_columns = landuseColumns
+
+        dfLanduses_filtered = df_lu[landuse_columns]
+        dfLanduses_filtered.columns = [col.replace('lu+', '') for col in df_lu_filtered.columns]
+
+        """
+        
+        if mergeAssetWithNonAssetLanduse:
+            df_lu_filtered.columns = [col.replace('ASSETS+', '') for col in df_lu_filtered.columns]
+    
+        df_lu_filtered = df_lu_filtered.replace([np.inf, -np.inf], 10000).fillna(0)
+        df_lu_filtered = df_lu_filtered.apply(pd.to_numeric, errors='coerce')
+        df_lu_filtered = df_lu_filtered.astype(int)
+        df_lu_filtered = df_lu_filtered.T.groupby(level=0).sum().T
+        """
+
+
+
+    # -------------------------- stream matrices ------------------------------ #
+
+
+    if matrixBranchName: 
+        streamObj = speckle_utils.getSpeckleStream(streamId,dmBranchName,CLIENT, dmCommitId)
+
+        matrices = {}
+        isDict = False     
+        try:
+            data_part = streamObj["@Data"]["@{0}"]
+            for matrix in data_part:
+                # Find the matrix name
+                matrix_name = next((attr for attr in dir(matrix) if "matrix" in attr), None)
+     
+                if not matrix_name:
+                    continue
+     
+                matrix_data = getattr(matrix, matrix_name)
+                originUUID = matrix_data["@originUUID"]
+                destinationUUID = matrix_data["@destinationUUID"]
+     
+                processed_rows = []
+                for chunk in matrix_data["@chunks"]:
+                    for row in chunk["@rows"]:
+                        processed_rows.append(row["@row"])
+     
+                matrix_array = np.array(processed_rows)
+                matrix_df = pd.DataFrame(matrix_array, index=originUUID, columns=destinationUUID)
+                matrices[matrix_name] = matrix_df
+        except KeyError:
+            data_part = streamObj["@Data"].__dict__
+            print(data_part.keys())
+     
+            for k, v in data_part.items():
+                if "matrix" in k:
+                    matrix_name = k
+                    matrix_data = v
+                    originUUID = matrix_data["@originUUID"]
+                    destinationUUID = matrix_data["@destinationUUID"]
+     
+                    processed_rows = []
+                    for chunk in matrix_data["@chunks"]:
+                        for row in chunk["@rows"]:
+                            processed_rows.append(row["@row"])
+     
+                    matrix_array = np.array(processed_rows)
+                    matrix_df = pd.DataFrame(matrix_array, index=originUUID, columns=destinationUUID)
+                    matrices[matrix_name] = matrix_df
+     
+
+
+    return dfLanduses_filtered, matrices
+
 
-                
+
+"""
 def fetchDistanceMatrices (streamObj):
 
     matrices = {}
@@ -227,35 +333,6 @@ def fetchDistanceMatrices (streamObj):
     
 """
 
-def fetchDistanceMatrices (stream_distance_matrices):
-    
-    # navigate to list with speckle objects of interest
-    distance_matrices = {}
-
-    destUUID = matrixObj.__getitem__('@destinationUUID')
-    oriUUID = matrixObj.__getitem__('@originUUID')
-    chunks = matrixObj.__getitem__('@chunks')
-    
-    for distM in stream_distance_matrices["@Data"]['@{0}']:
-      for kk in distM.__dict__.keys():
-        try:
-          if kk.split("+")[1].startswith("distance_matrix"):
-            distance_matrix_dict = json.loads(distM[kk])
-            origin_ids = distance_matrix_dict["origin_uuid"]
-            destination_ids = distance_matrix_dict["destination_uuid"]
-            distance_matrix =  distance_matrix_dict["matrix"]
-              
-            # Convert the distance matrix to a DataFrame
-            df_distances = pd.DataFrame(distance_matrix, index=origin_ids, columns=destination_ids)
-    
-            # i want to add the index & colum names to dist_m_csv
-            #distance_matrices[kk]  = dist_m_csv[kk]
-            distance_matrices[kk] = df_distances
-        except:
-          pass
-
-    return distance_matrices
-"""