Rename imports_utils to imports_utils.py

imports_utils → imports_utils.py

@@ -133,15 +133,8 @@ def get_page_by_id(notion_db_pages, page_id):
 
 
 
-CLIENT = SpeckleClient(host="https://speckle.xyz/")
-CLIENT.authenticate_with_token(token=userdata.get('speckle_token'))
-
-
-notion = client_notion(auth=userdata.get('notion_token'))
-
-
-stream_id="ebcfc50abe"
-
+"""
+#   define variables
 
 #   MAIN DISTANCE MATRIX
 branch_name_dm = "graph_geometry/distance_matrix"
@@ -157,94 +150,105 @@ commit_id_lu = "13ae6cdd30"
 #   LIVABILITY DOMAINS ATTRIBUTES
 notion_lu_domains = "407c2fce664f4dde8940bb416780a86d"
 notion_domain_attributes = "01401b78420f4296a2449f587d4ed9c9"
+"""
+
+
+#def streamNotionDatabases (notionToken, landuseDatabaseId, subdomainAttributesDatabaseId):
+if notionToken:
+    notion = client_notion(auth=userdata.get(notionToken))
+    lu_attributes  = fetch_all_database_pages(notion, landuseDatabaseId)
+    livability_attributes  = fetch_all_database_pages(notion, subdomainAttributesDatabaseId)
+else:
+    print ("Notion token is not provided")
+
+
+
+def streamMatrices (speckleToken, stream_id, branch_name_dm, commit_id):
+    CLIENT = SpeckleClient(host="https://speckle.xyz/")
+    CLIENT.authenticate_with_token(token=userdata.get(speckleToken))
+    
+    #stream_id="ebcfc50abe"
+    stream_distance_matrices = speckle_utils.getSpeckleStream(stream_id,
+                                            branch_name_dm,
+                                            CLIENT,
+                                            commit_id = commit_id_dm)
+
+    return stream_distance_matrices
+
+
+
+def fetchDomainMapper (luAttributePages):
+        
+    lu_domain_mapper ={}
+    subdomains_unique = []
+    
+    for page in lu_attributes:
+      value_landuse = get_property_value(page, "LANDUSE")
+      value_subdomain = get_property_value(page, "SUBDOMAIN_LIVEABILITY")
+      if value_subdomain and value_landuse:
+        lu_domain_mapper[value_landuse] = value_subdomain
+      if value_subdomain != "":
+        subdomains_unique.append(value_subdomain)
+       
+    #subdomains_unique = list(set(subdomains_unique))
+    return lu_domain_mapper
+
+
+
+def fetchSubdomainMapper (livability_attributes):
+
+    attribute_mapper ={}
+    domains_unique = []
+    
+    for page in domain_attributes:
+        subdomain = get_property_value(page, "SUBDOMAIN_UNIQUE")
+        sqm_per_employee = get_property_value(page, "SQM PER EMPL")
+        thresholds = get_property_value(page, "MANHATTAN THRESHOLD")
+        max_points = get_property_value(page, "LIVABILITY MAX POINT")
+        domain = get_property_value(page, "DOMAIN")
+        if  thresholds:   
+            attribute_mapper[subdomain] = {
+            'sqmPerEmpl': [sqm_per_employee if sqm_per_employee != "" else 0],
+            'thresholds': thresholds,
+            'max_points': max_points,
+            'domain': [domain if domain != "" else 0]
+            }    
+        if domain != "":
+            domains_unique.append(domain)
+    
+    #domains_unique = list(set(domains_unique))
+    return attribute_mapper
+
+
+
+
+
+                                      
+def fetchDistanceMatrices (stream_distance_matrices):
+    
+    # navigate to list with speckle objects of interest
+    distance_matrices = {}
+    for distM in stream_distance_matrice["@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
 
 
 
-lu_attributes  = fetch_all_database_pages(notion, notion_lu_domains)
-
-
-
-domain_attributes  = fetch_all_database_pages(notion, notion_domain_attributes)
-
-
-
-lu_domain_mapper ={}
-
-subdomains_unique = []
-
-for page in lu_attributes:
-  value_landuse = get_property_value(page, "LANDUSE")
-  value_subdomain = get_property_value(page, "SUBDOMAIN_LIVEABILITY")
-  if value_subdomain and value_landuse:
-    lu_domain_mapper[value_landuse] = value_subdomain
-  if value_subdomain != "":
-    subdomains_unique.append(value_subdomain)
-
-subdomains_unique = list(set(subdomains_unique))
-
-
-
-
-
-attribute_mapper ={}
-
-domains_unique = []
-
-for page in domain_attributes:
-  subdomain = get_property_value(page, "SUBDOMAIN_UNIQUE")
-  sqm_per_employee = get_property_value(page, "SQM PER EMPL")
-  thresholds = get_property_value(page, "MANHATTAN THRESHOLD")
-  max_points = get_property_value(page, "LIVABILITY MAX POINT")
-  domain = get_property_value(page, "DOMAIN")
-  if  thresholds:   #domain !="Transportation" and
-    attribute_mapper[subdomain] = {
-    'sqmPerEmpl': [sqm_per_employee if sqm_per_employee != "" else 0],
-    'thresholds': thresholds,
-    'max_points': max_points,
-    'domain': [domain if domain != "" else 0]
-    }
-
-  if domain != "":
-      domains_unique.append(domain)
-
-domains_unique = list(set(domains_unique))
-
-
-attribute_mapper[subdomain] = [sqm_per_employee if sqm_per_employee != "" else 0, thresholds,max_points,domain if domain != "" else 0 ]
-
-
-
-
-
-
-
-stream_distance_matrice = speckle_utils.getSpeckleStream(stream_id,
-                                        branch_name_dm,
-                                        CLIENT,
-                                        commit_id = commit_id_dm)
-
-
-                                        
-
-# navigate to list with speckle objects of interest
-distance_matrices = {}
-for distM in stream_distance_matrice["@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
-
 
 df_dm_transport = distance_matrices[dm_transportStops]