used get function when accessing dictionaries. updated error message …

…handling.

src/sempy_labs/__init__.py

@@ -34,7 +34,7 @@
     # list_sqlendpoints,
     # list_tables,
     list_warehouses,
-    # list_workspace_role_assignments,
+    list_workspace_role_assignments,
     create_warehouse,
     update_item,
 )
@@ -113,7 +113,7 @@
     #'list_sqlendpoints',
     #'list_tables',
     "list_warehouses",
-    #'list_workspace_role_assignments',
+    'list_workspace_role_assignments',
     "create_warehouse",
     "update_item",
     "create_abfss_path",

src/sempy_labs/_ai.py

@@ -14,6 +14,7 @@ def optimize_semantic_model(dataset: str, workspace: Optional[str] = None):
     from ._model_bpa import run_model_bpa
     from .directlake._fallback import check_fallback_reason
     from ._helper_functions import format_dax_object_name
+    from .tom import connect_semantic_model
 
     modelBPA = run_model_bpa(
         dataset=dataset, workspace=workspace, return_dataframe=True
@@ -78,10 +79,7 @@ def generate_measure_descriptions(
 
     validModels = ["gpt-35-turbo", "gpt-35-turbo-16k", "gpt-4"]
     if gpt_model not in validModels:
-        print(
-            f"{icons.red_dot} The '{gpt_model}' model is not a valid model. Enter a gpt_model from this list: {validModels}."
-        )
-        return
+        raise ValueError(f"{icons.red_dot} The '{gpt_model}' model is not a valid model. Enter a gpt_model from this list: {validModels}.")
 
     dfM = fabric.list_measures(dataset=dataset, workspace=workspace)
 
@@ -116,8 +114,8 @@ def generate_measure_descriptions(
     )
 
     # Update the model to use the new descriptions
-    tom_server = fabric.create_tom_server(readonly=False, workspace=workspace)
-    m = tom_server.Databases.GetByName(dataset).Model
+    #with connect_semantic_model(dataset=dataset, workspace=workspace, readonly=False) as tom:
+
 
     # for t in m.Tables:
     # tName = t.Name
@@ -173,48 +171,33 @@ def generate_aggs(
     numericTypes = ["Int64", "Double", "Decimal"]
 
     if any(value not in aggTypes for value in columns.values()):
-        print(
-            f"{icons.red_dot} Invalid aggregation type(s) have been specified in the 'columns' parameter. Valid aggregation types: {aggTypes}."
-        )
-        return
+        raise ValueError(f"{icons.red_dot} Invalid aggregation type(s) have been specified in the 'columns' parameter. Valid aggregation types: {aggTypes}.")
 
     dfC = fabric.list_columns(dataset=dataset, workspace=workspace)
     dfP = fabric.list_partitions(dataset=dataset, workspace=workspace)
     dfM = fabric.list_measures(dataset=dataset, workspace=workspace)
     dfR = fabric.list_relationships(dataset=dataset, workspace=workspace)
     if not any(r["Mode"] == "DirectLake" for i, r in dfP.iterrows()):
-        print(
-            f"{icons.red_dot} The '{dataset}' semantic model within the '{workspace}' workspace is not in Direct Lake mode. This function is only relevant for Direct Lake semantic models."
-        )
-        return
-
+        raise ValueError(f"{icons.red_dot} The '{dataset}' semantic model within the '{workspace}' workspace is not in Direct Lake mode. This function is only relevant for Direct Lake semantic models.")
+
     dfC_filtT = dfC[dfC["Table Name"] == table_name]
 
     if len(dfC_filtT) == 0:
-        print(
-            f"{icons.red_dot} The '{table_name}' table does not exist in the '{dataset}' semantic model within the '{workspace}' workspace."
-        )
-        return
+        raise ValueError(f"{icons.red_dot} The '{table_name}' table does not exist in the '{dataset}' semantic model within the '{workspace}' workspace.")
 
     dfC_filt = dfC[
         (dfC["Table Name"] == table_name) & (dfC["Column Name"].isin(columnValues))
     ]
 
     if len(columns) != len(dfC_filt):
-        print(
-            f"{icons.red_dot} Columns listed in '{columnValues}' do not exist in the '{table_name}' table in the '{dataset}' semantic model within the '{workspace}' workspace."
-        )
-        return
+        raise ValueError(f"{icons.red_dot} Columns listed in '{columnValues}' do not exist in the '{table_name}' table in the '{dataset}' semantic model within the '{workspace}' workspace.")
 
     # Check if doing sum/count/min/max etc. on a non-number column
     for col, agg in columns.items():
         dfC_col = dfC_filt[dfC_filt["Column Name"] == col]
         dataType = dfC_col["Data Type"].iloc[0]
         if agg in aggTypesAggregate and dataType not in numericTypes:
-            print(
-                f"{icons.red_dot} The '{col}' column in the '{table_name}' table is of '{dataType}' data type. Only columns of '{numericTypes}' data types can be aggregated as '{aggTypesAggregate}' aggregation types."
-            )
-            return
+            raise ValueError(f"{icons.red_dot} The '{col}' column in the '{table_name}' table is of '{dataType}' data type. Only columns of '{numericTypes}' data types can be aggregated as '{aggTypesAggregate}' aggregation types.")            
 
     # Create/update lakehouse delta agg table
     aggSuffix = "_agg"
@@ -230,10 +213,7 @@ def generate_aggs(
     dfI_filt = dfI[(dfI["Id"] == sqlEndpointId)]
 
     if len(dfI_filt) == 0:
-        print(
-            f"{icons.red_dot} The lakehouse (SQL Endpoint) used by the '{dataset}' semantic model does not reside in the '{lakehouse_workspace}' workspace. Please update the lakehouse_workspace parameter."
-        )
-        return
+        raise ValueError(f"{icons.red_dot} The lakehouse (SQL Endpoint) used by the '{dataset}' semantic model does not reside in the '{lakehouse_workspace}' workspace. Please update the lakehouse_workspace parameter.")
 
     lakehouseName = dfI_filt["Display Name"].iloc[0]
     lakehouse_id = resolve_lakehouse_id(
@@ -284,7 +264,7 @@ def generate_aggs(
     # Create/update semantic model agg table
     tom_server = fabric.create_tom_server(readonly=False, workspace=workspace)
     m = tom_server.Databases.GetByName(dataset).Model
-    f"\n{icons.in_progress} Updating the '{dataset}' semantic model..."
+    print(f"\n{icons.in_progress} Updating the '{dataset}' semantic model...")
     dfC_agg = dfC[dfC["Table Name"] == aggTableName]
 
     if len(dfC_agg) == 0:

src/sempy_labs/_clear_cache.py

@@ -20,9 +20,7 @@ def clear_cache(dataset: str, workspace: Optional[str] = None):
         or if no lakehouse attached, resolves to the workspace of the notebook.
     """
 
-    if workspace is None:
-        workspace_id = fabric.get_workspace_id()
-        workspace = fabric.resolve_workspace_name(workspace_id)
+    workspace = fabric.resolve_workspace_name(workspace)
 
     datasetID = resolve_dataset_id(dataset=dataset, workspace=workspace)
 

src/sempy_labs/_connections.py

@@ -60,16 +60,16 @@ def create_connection_cloud(
     if response.status_code == 200:
         o = response.json()
         new_data = {
-            "Connection Id": o["id"],
-            "Connection Name": o["name"],
-            "Connectivity Type": o["connectivityType"],
-            "Connection Type": o["connectionDetails"]["type"],
-            "Connection Path": o["connectionDetails"]["path"],
-            "Privacy Level": o["privacyLevel"],
-            "Credential Type": o["credentialDetails"]["credentialType"],
-            "Single Sign On Type": o["credentialDetails"]["singleSignOnType"],
-            "Connection Encryption": o["credentialDetails"]["connectionEncryption"],
-            "Skip Test Connection": o["credentialDetails"]["skipTestConnection"],
+            "Connection Id": o.get("id"),
+            "Connection Name": o.get("name"),
+            "Connectivity Type": o.get("connectivityType"),
+            "Connection Type": o.get("connectionDetails").get("type"),
+            "Connection Path": o.get("connectionDetails").get("path"),
+            "Privacy Level": o.get("privacyLevel"),
+            "Credential Type": o.get("credentialDetails").get("credentialType"),
+            "Single Sign On Type": o.get("credentialDetails").get("singleSignOnType"),
+            "Connection Encryption": o.get("credentialDetails").get("connectionEncryption"),
+            "Skip Test Connection": o.get("credentialDetails").get("skipTestConnection"),
         }
         df = pd.concat([df, pd.DataFrame(new_data, index=[0])], ignore_index=True)
 
@@ -135,17 +135,17 @@ def create_connection_on_prem(
     if response.status_code == 200:
         o = response.json()
         new_data = {
-            "Connection Id": o["id"],
-            "Connection Name": o["name"],
-            "Gateway ID": o["gatewayId"],
-            "Connectivity Type": o["connectivityType"],
-            "Connection Type": o["connectionDetails"]["type"],
-            "Connection Path": o["connectionDetails"]["path"],
-            "Privacy Level": o["privacyLevel"],
-            "Credential Type": o["credentialDetails"]["credentialType"],
-            "Single Sign On Type": o["credentialDetails"]["singleSignOnType"],
-            "Connection Encryption": o["credentialDetails"]["connectionEncryption"],
-            "Skip Test Connection": o["credentialDetails"]["skipTestConnection"],
+            "Connection Id": o.get("id"),
+            "Connection Name": o.get("name"),
+            "Gateway ID": o.get("gatewayId"),
+            "Connectivity Type": o.get("connectivityType"),
+            "Connection Type": o.get("connectionDetails").get("type"),
+            "Connection Path": o.get("connectionDetails").get("path"),
+            "Privacy Level": o.get("privacyLevel"),
+            "Credential Type": o.get("credentialDetails").get("credentialType"),
+            "Single Sign On Type": o.get("credentialDetails").get("singleSignOnType"),
+            "Connection Encryption": o.get("credentialDetails").get("connectionEncryption"),
+            "Skip Test Connection": o.get("credentialDetails").get("skipTestConnection"),
         }
         df = pd.concat([df, pd.DataFrame(new_data, index=[0])], ignore_index=True)
 
@@ -213,17 +213,17 @@ def create_connection_vnet(
     if response.status_code == 200:
         o = response.json()
         new_data = {
-            "Connection Id": o["id"],
-            "Connection Name": o["name"],
-            "Gateway ID": o["gatewayId"],
-            "Connectivity Type": o["connectivityType"],
-            "Connection Type": o["connectionDetails"]["type"],
-            "Connection Path": o["connectionDetails"]["path"],
-            "Privacy Level": o["privacyLevel"],
-            "Credential Type": o["credentialDetails"]["credentialType"],
-            "Single Sign On Type": o["credentialDetails"]["singleSignOnType"],
-            "Connection Encryption": o["credentialDetails"]["connectionEncryption"],
-            "Skip Test Connection": o["credentialDetails"]["skipTestConnection"],
+            "Connection Id": o.get("id"),
+            "Connection Name": o.get("name"),
+            "Gateway ID": o.get("gatewayId"),
+            "Connectivity Type": o.get("connectivityType"),
+            "Connection Type": o.get("connectionDetails").get("type"),
+            "Connection Path": o.get("connectionDetails").get("path"),
+            "Privacy Level": o.get("privacyLevel"),
+            "Credential Type": o.get("credentialDetails").get("credentialType"),
+            "Single Sign On Type": o.get("credentialDetails").get("singleSignOnType"),
+            "Connection Encryption": o.get("credentialDetails").get("connectionEncryption"),
+            "Skip Test Connection": o.get("credentialDetails").get("skipTestConnection"),
         }
         df = pd.concat([df, pd.DataFrame(new_data, index=[0])], ignore_index=True)
 

src/sempy_labs/_generate_semantic_model.py

@@ -32,12 +32,10 @@ def create_blank_semantic_model(
     """
 
     if workspace is None:
-        workspace_id = fabric.get_workspace_id()
-        workspace = fabric.resolve_workspace_name(workspace_id)
+        workspace = fabric.resolve_workspace_name()
 
     if compatibility_level < 1500:
-        print(f"{icons.red_dot} Compatiblity level must be at least 1500.")
-        return
+        raise ValueError(f"{icons.red_dot} Compatiblity level must be at least 1500.")
 
     tmsl = f"""
   {{
@@ -90,10 +88,7 @@ def create_semantic_model_from_bim(
     dfI_filt = dfI[(dfI["Display Name"] == dataset)]
 
     if len(dfI_filt) > 0:
-        print(
-            f"WARNING: '{dataset}' already exists as a semantic model in the '{workspace}' workspace."
-        )
-        return
+        raise ValueError(f"{icons.red_dot} '{dataset}' already exists as a semantic model in the '{workspace}' workspace.")
 
     client = fabric.FabricRestClient()
     defPBIDataset = {"version": "1.0", "settings": {}}
@@ -131,7 +126,7 @@ def conv_b64(file):
 
     if response.status_code == 201:
         print(
-            f"The '{dataset}' semantic model has been created within the '{workspace}' workspace."
+            f"{icons.green_dot} The '{dataset}' semantic model has been created within the '{workspace}' workspace."
         )
         print(response.json())
     elif response.status_code == 202:
@@ -144,7 +139,7 @@ def conv_b64(file):
             response_body = json.loads(response.content)
         response = client.get(f"/v1/operations/{operationId}/result")
         print(
-            f"The '{dataset}' semantic model has been created within the '{workspace}' workspace."
+            f"{icons.green_dot} The '{dataset}' semantic model has been created within the '{workspace}' workspace."
         )
         print(response.json())
 
@@ -178,9 +173,7 @@ def deploy_semantic_model(
 
     """
 
-    if workspace is None:
-        workspace_id = fabric.get_workspace_id()
-        workspace = fabric.resolve_workspace_name(workspace_id)
+    workspace = fabric.resolve_workspace_name(workspace)
 
     if new_dataset_workspace is None:
         new_dataset_workspace = workspace

src/sempy_labs/_helper_functions.py

@@ -420,16 +420,10 @@ def save_as_delta_table(
     write_mode = write_mode.lower()
 
     if write_mode not in writeModes:
-        print(
-            f"{icons.red_dot} Invalid 'write_type' parameter. Choose from one of the following values: {writeModes}."
-        )
-        return
+        raise ValueError(f"{icons.red_dot} Invalid 'write_type' parameter. Choose from one of the following values: {writeModes}.")
 
     if " " in delta_table_name:
-        print(
-            f"{icons.red_dot} Invalid 'delta_table_name'. Delta tables in the lakehouse cannot have spaces in their names."
-        )
-        return
+        raise ValueError(f"{icons.red_dot} Invalid 'delta_table_name'. Delta tables in the lakehouse cannot have spaces in their names.")
 
     dataframe.columns = dataframe.columns.str.replace(" ", "_")
 
@@ -476,10 +470,7 @@ def language_validate(language: str):
     elif len(df_filt2) == 1:
         lang = df_filt2["Language"].iloc[0]
     else:
-        print(
-            f"The '{language}' language is not a valid language code. Please refer to this link for a list of valid language codes: {url}."
-        )
-        return
+        raise ValueError(f"{icons.red_dot} The '{language}' language is not a valid language code. Please refer to this link for a list of valid language codes: {url}.")
 
     return lang