Update app.py

app.py

@@ -51,10 +51,10 @@ import imports_utils
 import speckle_utils
 import data_utils
 
-from config import landuseDatabaseId , streamId,  dmBranchName, dmCommitId, luBranchName, luCommitId
+from config import landuseDatabaseId , streamId,  dmBranchName, dmCommitId, luBranchName, luCommitId, distanceMatrixActivityNodes
 from imports_utils import speckleToken
 from imports_utils import fetchDistanceMatrices
-from config import distanceMatrixActivityNodes
+from config import useSpeckleData
 #from config import distanceMatrixTransportStops
 
 
@@ -62,41 +62,53 @@ if speckleToken is None:
     raise Exception("Speckle token not found")
 else:
     print("Speckle token found successfully!")
+
+if useSpeckleData:
     
     CLIENT = SpeckleClient(host="https://speckle.xyz/")
     account = get_default_account()
     CLIENT.authenticate_with_token(token=speckleToken)
     
-    streamDistanceMatrices = speckle_utils.getSpeckleStream(streamId,dmBranchName,CLIENT, dmCommitId)
-    matrices = fetchDistanceMatrices (streamDistanceMatrices)
-    streamLanduses = speckle_utils.getSpeckleStream(streamId,luBranchName,CLIENT, luCommitId)
-    streamData = streamLanduses["@Data"]["@{0}"]
+    landuses, matrices = getDataFromSpeckle(
+	speckleToken,
+	streamId,
+	dmBranchName, 
+    luBranchName)
+         
+    #streamDistanceMatrices = speckle_utils.getSpeckleStream(streamId,dmBranchName,CLIENT, dmCommitId)
+    #matrices = fetchDistanceMatrices (streamDistanceMatrices)
+    #streamLanduses = speckle_utils.getSpeckleStream(streamId,luBranchName,CLIENT, luCommitId)
+    #streamData = streamLanduses["@Data"]["@{0}"]
     
-    df_speckle_lu = speckle_utils.get_dataframe(streamData, return_original_df=False)
-    df_lu = df_speckle_lu.copy()
-    df_lu = df_lu.astype(str)
-    df_lu =  df_lu.set_index("uuid", drop=False)  # variable, uuid as default
+    #df_speckle_lu = speckle_utils.get_dataframe(streamData, return_original_df=False)
+    #df_lu = df_speckle_lu.copy()
+    #df_lu = df_lu.astype(str)
+    #df_lu =  df_lu.set_index("uuid", drop=False)  # variable, uuid as default
 
     df_dm = matrices[distanceMatrixActivityNodes]
-    
     df_dm_dict = df_dm.to_dict('index')
- 
-    
+     
     # Replace infinity with 10000 and NaN values with 0, then convert to integers
     df_dm = df_dm.replace([np.inf, -np.inf], 10000).fillna(0)
     df_dm = df_dm.apply(pd.to_numeric, errors='coerce')
     df_dm = df_dm.round(0).astype(int)
         
     mask_connected = df_dm.index.tolist()
-    
+
+    """
     lu_columns = []   # provided by user? or prefix
     for name in df_lu.columns:
       if name.startswith("lu+"):
         lu_columns.append(name)
+
+    """
+    df_lu_filtered = df_lu.loc[mask_connected]
     
-    df_lu_filtered = df_lu[lu_columns].loc[mask_connected]
-    df_lu_filtered.columns = [col.replace('lu+', '') for col in df_lu_filtered.columns]
-    df_lu_filtered.columns = [col.replace('ASSETS+', '') for col in df_lu_filtered.columns]
+    #df_lu_filtered.columns = [col.replace('lu+', '') for col in df_lu_filtered.columns]
+
+    mergeAssetNonAssetLanduse = True
+    if mergeAssetNonAssetLanduse:
+        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')
@@ -106,8 +118,6 @@ else:
     
 
 
-
-
 def test(input_json):
     print("Received input")
     # Parse the input JSON string
@@ -121,6 +131,8 @@ def test(input_json):
 
     from config import useGrasshopperData 
 
+    useGrasshopperData = inputs['input']["useGrasshopper"]    # fetch grasshoper data or not
+    
     if useGrasshopperData == True: # grasshopper input
         matrix = inputs['input']["matrix"]
         landuses = inputs['input']["landuse_areas"]    # fetch grasshoper data or not