added icons to more functions, fixed regex issues, renamed run_dax fu…

…nction name, fixed update_directlake_partitionentity parameters.

src/sempy_labs/__init__.py

@@ -5,7 +5,7 @@
 # create_connection_vnet,
 # create_connection_on_prem
 # )
-from sempy_labs._dax import run_dax
+from sempy_labs._dax import evaluate_dax_impersonation
 from sempy_labs._generate_semantic_model import (
     create_blank_semantic_model,
     create_semantic_model_from_bim,
@@ -89,7 +89,7 @@
     # create_connection_cloud,
     # create_connection_vnet,
     # create_connection_on_prem,
-    "run_dax",
+    "evaluate_dax_impersonation",
     "create_blank_semantic_model",
     "create_semantic_model_from_bim",
     #'deploy_semantic_model',

src/sempy_labs/_ai.py

@@ -6,7 +6,7 @@
 from pyspark.sql import SparkSession
 from typing import List, Optional, Union
 from IPython.display import display
-
+import sempy_labs._icons as icons
 
 def optimize_semantic_model(dataset: str, workspace: Optional[str] = None):
 
@@ -39,7 +39,7 @@ def optimize_semantic_model(dataset: str, workspace: Optional[str] = None):
 
         if len(fallback_filt) > 0:
             print(
-                f"The '{dataset}' semantic model is a Direct Lake semantic model which contains views. Since views always fall back to DirectQuery, it is recommended to only use lakehouse tables and not views."
+                f"{icons.yellow_dot} The '{dataset}' semantic model is a Direct Lake semantic model which contains views. Since views always fall back to DirectQuery, it is recommended to only use lakehouse tables and not views."
             )
 
     # Potential model reduction estimate
@@ -56,11 +56,11 @@ def optimize_semantic_model(dataset: str, workspace: Optional[str] = None):
         totSize = df["Total Size"].sum()
         if len(df_filt) > 0:
             print(
-                f"Potential savings of {totSize} bytes from following the '{rule}' rule."
+                f"{icons.yellow_dot} Potential savings of {totSize} bytes from following the '{rule}' rule."
             )
             display(df_filt)
         else:
-            print(f"The '{rule}' rule has been followed.")
+            print(f"{icons.green_dot} The '{rule}' rule has been followed.")
 
 
 def generate_measure_descriptions(
@@ -78,7 +78,7 @@ def generate_measure_descriptions(
     validModels = ["gpt-35-turbo", "gpt-35-turbo-16k", "gpt-4"]
     if gpt_model not in validModels:
         print(
-            f"The '{gpt_model}' model is not a valid model. Enter a gpt_model from this list: {validModels}."
+            f"{icons.red_dot} The '{gpt_model}' model is not a valid model. Enter a gpt_model from this list: {validModels}."
         )
         return
 
@@ -173,7 +173,7 @@ def generate_aggs(
 
     if any(value not in aggTypes for value in columns.values()):
         print(
-            f"Invalid aggregation type(s) have been specified in the 'columns' parameter. Valid aggregation types: {aggTypes}."
+            f"{icons.red_dot} Invalid aggregation type(s) have been specified in the 'columns' parameter. Valid aggregation types: {aggTypes}."
         )
         return
 
@@ -183,15 +183,15 @@ def generate_aggs(
     dfR = fabric.list_relationships(dataset=dataset, workspace=workspace)
     if not any(r["Mode"] == "DirectLake" for i, r in dfP.iterrows()):
         print(
-            f"The '{dataset}' semantic model within the '{workspace}' workspace is not in Direct Lake mode. This function is only relevant for Direct Lake semantic models."
+            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
 
     dfC_filtT = dfC[dfC["Table Name"] == table_name]
 
     if len(dfC_filtT) == 0:
         print(
-            f"The '{table_name}' table does not exist in the '{dataset}' semantic model within the '{workspace}' workspace."
+            f"{icons.red_dot} The '{table_name}' table does not exist in the '{dataset}' semantic model within the '{workspace}' workspace."
         )
         return
 
@@ -201,7 +201,7 @@ def generate_aggs(
 
     if len(columns) != len(dfC_filt):
         print(
-            f"Columns listed in '{columnValues}' do not exist in the '{table_name}' table in the '{dataset}' semantic model within the '{workspace}' workspace."
+            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
 
@@ -211,7 +211,7 @@ def generate_aggs(
         dataType = dfC_col["Data Type"].iloc[0]
         if agg in aggTypesAggregate and dataType not in numericTypes:
             print(
-                f"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."
+                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
 
@@ -230,7 +230,7 @@ def generate_aggs(
 
     if len(dfI_filt) == 0:
         print(
-            f"The lakehouse (SQL Endpoint) used by the '{dataset}' semantic model does not reside in the '{lakehouse_workspace}' workspace. Please update the lakehouse_workspace parameter."
+            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
 
@@ -278,16 +278,16 @@ def generate_aggs(
         delta_table_name=aggLakeTName,
     )
     spark_df.write.mode("overwrite").format("delta").save(aggFilePath)
-    f"The '{aggLakeTName}' table has been created/updated in the lakehouse."
+    f"{icons.green_dot} The '{aggLakeTName}' table has been created/updated in the lakehouse."
 
     # Create/update semantic model agg table
     tom_server = fabric.create_tom_server(readonly=False, workspace=workspace)
     m = tom_server.Databases.GetByName(dataset).Model
-    f"\nUpdating the '{dataset}' semantic model..."
+    f"\n{icons.in_progress} Updating the '{dataset}' semantic model..."
     dfC_agg = dfC[dfC["Table Name"] == aggTableName]
 
     if len(dfC_agg) == 0:
-        print(f"Creating the '{aggTableName}' table...")
+        print(f"{icons.in_progress} Creating the '{aggTableName}' table...")
         exp = m.Expressions["DatabaseQuery"]
         tbl = TOM.Table()
         tbl.Name = aggTableName
@@ -318,15 +318,15 @@ def generate_aggs(
 
             tbl.Columns.Add(col)
             print(
-                f"The '{aggTableName}'[{cName}] column has been added to the '{dataset}' semantic model."
+                f"{icons.green_dot} The '{aggTableName}'[{cName}] column has been added to the '{dataset}' semantic model."
             )
 
         m.Tables.Add(tbl)
         print(
-            f"The '{aggTableName}' table has been added to the '{dataset}' semantic model."
+            f"{icons.green_dot} The '{aggTableName}' table has been added to the '{dataset}' semantic model."
         )
     else:
-        print(f"Updating the '{aggTableName}' table's columns...")
+        print(f"{icons.in_progress} Updating the '{aggTableName}' table's columns...")
         # Remove existing columns
         for t in m.Tables:
             tName = t.Name
@@ -347,12 +347,12 @@ def generate_aggs(
             col.DataType = System.Enum.Parse(TOM.DataType, dType)
 
             m.Tables[aggTableName].Columns.Add(col)
-            print(f"The '{aggTableName}'[{cName}] column has been added.")
+            print(f"{icons.green_dot} The '{aggTableName}'[{cName}] column has been added.")
 
     # Create relationships
     relMap = {"m": "Many", "1": "One", "0": "None"}
 
-    print(f"\nGenerating necessary relationships...")
+    print(f"\n{icons.in_progress} Generating necessary relationships...")
     for i, r in dfR.iterrows():
         fromTable = r["From Table"]
         fromColumn = r["From Column"]
@@ -384,27 +384,27 @@ def generate_aggs(
                 rel.FromColumn = m.Tables[aggTableName].Columns[fromColumn]
                 m.Relationships.Add(rel)
                 print(
-                    f"'{aggTableName}'[{fromColumn}] -> '{toTable}'[{toColumn}] relationship has been added."
+                    f"{icons.green_dot} '{aggTableName}'[{fromColumn}] -> '{toTable}'[{toColumn}] relationship has been added."
                 )
             except:
                 print(
-                    f"'{aggTableName}'[{fromColumn}] -> '{toTable}'[{toColumn}] relationship has not been created."
+                    f"{icons.red_dot} '{aggTableName}'[{fromColumn}] -> '{toTable}'[{toColumn}] relationship has not been created."
                 )
         elif toTable == table_name:
             try:
                 rel.ToColumn = m.Tables[aggTableName].Columns[toColumn]
                 m.Relationships.Add(rel)
                 print(
-                    f"'{fromTable}'[{fromColumn}] -> '{aggTableName}'[{toColumn}] relationship has been added."
+                    f"{icons.green_dot} '{fromTable}'[{fromColumn}] -> '{aggTableName}'[{toColumn}] relationship has been added."
                 )
             except:
                 print(
-                    f"'{fromTable}'[{fromColumn}] -> '{aggTableName}'[{toColumn}] relationship has not been created."
+                    f"{icons.red_dot} '{fromTable}'[{fromColumn}] -> '{aggTableName}'[{toColumn}] relationship has not been created."
                 )
     f"Relationship creation is complete."
 
     # Create IF measure
-    f"\nCreating measure to check if the agg table can be used..."
+    f"\n{icons.in_progress} Creating measure to check if the agg table can be used..."
     aggChecker = "IF("
     dfR_filt = dfR[
         (dfR["From Table"] == table_name) & (~dfR["From Column"].isin(columnValues))
@@ -419,7 +419,7 @@ def generate_aggs(
     print(aggChecker)
 
     # Todo: add IFISFILTERED clause for columns
-    f"\n Creating the base measures in the agg table..."
+    f"\n{icons.in_progress} Creating the base measures in the agg table..."
     # Create base agg measures
     dep = fabric.evaluate_dax(
         dataset=dataset,

src/sempy_labs/_clear_cache.py

@@ -8,6 +8,7 @@
 def clear_cache(dataset: str, workspace: Optional[str] = None):
     """
     Clears the cache of a semantic model.
+    See `here <https://learn.microsoft.com/analysis-services/instances/clear-the-analysis-services-caches?view=asallproducts-allversions>`_ for documentation.
 
     Parameters
     ----------

src/sempy_labs/_dax.py

@@ -7,7 +7,7 @@
 
 
 @log
-def run_dax(
+def evaluate_dax_impersonation(
     dataset: str,
     dax_query: str,
     user_name: Optional[str] = None,

src/sempy_labs/_icons.py

@@ -2,3 +2,8 @@
 yellow_dot = "\U0001F7E1"
 red_dot = "\U0001F534"
 in_progress = "⌛"
+checked = "\u2611"
+unchecked = "\u2610"
+start_bold = "\033[1m"
+end_bold = "\033[0m"
+bullet = "\u2022"

src/sempy_labs/_list_functions.py

@@ -1308,7 +1308,7 @@ def list_kpis(dataset: str, workspace: Optional[str] = None):
             ]
         )
 
-        for t in tom.model.Tables:
+        for t in tom._model.Tables:
             for m in t.Measures:
                 if m.KPI is not None:
                     new_data = {

src/sempy_labs/_model_bpa.py

@@ -1008,7 +1008,7 @@ def run_model_bpa(
     dfM["Referenced By"].fillna(0, inplace=True)
     dfM["Referenced By"] = dfM["Referenced By"].fillna(0).astype(int)
 
-    pattern = "[^\( ][a-zA-Z0-9_()-]+\[[^\[]+\]|'[^']+'\[[^\[]+\]|\[[^\[]+\]"
+    pattern = r"[^\( ][a-zA-Z0-9_()-]+\[[^\[]+\]|'[^']+'\[[^\[]+\]|\[[^\[]+\]"
 
     dfM["Has Fully Qualified Measure Reference"] = False
     dfM["Has Unqualified Column Reference"] = False
@@ -1041,15 +1041,15 @@ def run_model_bpa(
 
         dfM_filt = dfM[
             dfM["Measure Expression"].str.contains(
-                "(?i)USERELATIONSHIP\s*\(\s*'*"
-                + fromTable
-                + "'*\["
-                + fromColumn
-                + "\]\s*,\s*'*"
-                + toTable
-                + "'*\["
-                + toColumn
-                + "\]",
+                r"(?i)USERELATIONSHIP\s*\(\s*'*"
+                + re.escape(fromTable)
+                + r"'*\["
+                + re.escape(fromColumn)
+                + r"\]\s*,\s*'*"
+                + re.escape(toTable)
+                + r"'*\["
+                + re.escape(toColumn)
+                + r"\]",
                 regex=True,
             )
         ]

src/sempy_labs/_one_lake_integration.py

@@ -3,7 +3,7 @@
 from typing import Optional
 from sempy._utils._log import log
 from sempy_labs._helper_functions import resolve_workspace_name_and_id
-
+import sempy_labs._icons as icons
 
 @log
 def export_model_to_onelake(
@@ -42,7 +42,7 @@ def export_model_to_onelake(
 
     if len(dfD_filt) == 0:
         print(
-            f"The '{dataset}' semantic model does not exist in the '{workspace}' workspace."
+            f"{icons.red_dot} The '{dataset}' semantic model does not exist in the '{workspace}' workspace."
         )
         return
 
@@ -64,11 +64,11 @@ def export_model_to_onelake(
     try:
         fabric.execute_tmsl(script=tmsl, workspace=workspace)
         print(
-            f"The '{dataset}' semantic model's tables have been exported as delta tables to the '{workspace}' workspace.\n"
+            f"{icons.green_dot} The '{dataset}' semantic model's tables have been exported as delta tables to the '{workspace}' workspace.\n"
         )
     except:
         print(
-            f"ERROR: The '{dataset}' semantic model's tables have not been exported as delta tables to the '{workspace}' workspace."
+            f"{icons.red_dot} The '{dataset}' semantic model's tables have not been exported as delta tables to the '{workspace}' workspace."
         )
         print(
             f"Make sure you enable OneLake integration for the '{dataset}' semantic model. Follow the instructions here: https://learn.microsoft.com/power-bi/enterprise/onelake-integration-overview#enable-onelake-integration"
@@ -83,14 +83,14 @@ def export_model_to_onelake(
 
         if len(dfI_filt) == 0:
             print(
-                f"The '{destination_lakehouse}' lakehouse does not exist within the '{destination_workspace}' workspace."
+                f"{icons.red_dot} The '{destination_lakehouse}' lakehouse does not exist within the '{destination_workspace}' workspace."
             )
             # Create lakehouse
             destination_lakehouse_id = fabric.create_lakehouse(
                 display_name=destination_lakehouse, workspace=destination_workspace
             )
             print(
-                f"The '{destination_lakehouse}' lakehouse has been created within the '{destination_workspace}' workspace.\n"
+                f"{icons.green_dot} The '{destination_lakehouse}' lakehouse has been created within the '{destination_workspace}' workspace.\n"
             )
         else:
             destination_lakehouse_id = dfI_filt["Id"].iloc[0]
@@ -122,7 +122,7 @@ def export_model_to_onelake(
 
         client = fabric.FabricRestClient()
 
-        print("Creating shortcuts...\n")
+        print(f"{icons.in_progress} Creating shortcuts...\n")
         for tableName in tables:
             tablePath = "Tables/" + tableName
             shortcutName = tableName.replace(" ", "")
@@ -145,11 +145,11 @@ def export_model_to_onelake(
                 )
                 if response.status_code == 201:
                     print(
-                        f"\u2022 The shortcut '{shortcutName}' was created in the '{destination_lakehouse}' lakehouse within the '{destination_workspace}' workspace. It is based on the '{tableName}' table in the '{dataset}' semantic model within the '{workspace}' workspace.\n"
+                        f"{icons.bullet} The shortcut '{shortcutName}' was created in the '{destination_lakehouse}' lakehouse within the '{destination_workspace}' workspace. It is based on the '{tableName}' table in the '{dataset}' semantic model within the '{workspace}' workspace.\n"
                     )
                 else:
                     print(response.status_code)
             except:
                 print(
-                    f"ERROR: Failed to create a shortcut for the '{tableName}' table."
+                    f"{icons.red_dot} Failed to create a shortcut for the '{tableName}' table."
                 )