added list_capacities, fixes.

src/sempy_labs/__init__.py

@@ -9,14 +9,15 @@
 from sempy_labs._generate_semantic_model import (
     create_blank_semantic_model,
     create_semantic_model_from_bim,
-    # deploy_semantic_model,
+    deploy_semantic_model,
     get_semantic_model_bim,
 )
 from sempy_labs._list_functions import (
     delete_custom_pool,
     list_semantic_model_objects,
     list_shortcuts,
     get_object_level_security,
+    list_capacities,
     # list_annotations,
     # list_columns,
     list_dashboards,
@@ -108,7 +109,7 @@
     "evaluate_dax_impersonation",
     "create_blank_semantic_model",
     "create_semantic_model_from_bim",
-    #'deploy_semantic_model',
+    "deploy_semantic_model",
     "get_semantic_model_bim",
     "get_object_level_security",
     #'list_annotations',
@@ -179,4 +180,5 @@
     "update_workspace_user",
     "list_workspace_users",
     "assign_workspace_to_dataflow_storage",
+    "list_capacities",
 ]

src/sempy_labs/_generate_semantic_model.py

@@ -153,55 +153,63 @@ def conv_b64(file):
 
 
 def deploy_semantic_model(
-    dataset: str,
-    new_dataset: Optional[str] = None,
-    workspace: Optional[str] = None,
-    new_dataset_workspace: Optional[str] = None,
+    source_dataset: str,
+    source_workspace: Optional[str] = None,
+    target_dataset: Optional[str] = None,
+    target_workspace: Optional[str] = None,
+    refresh_target_dataset: Optional[bool] = True,
 ):
     """
     Deploys a semantic model based on an existing semantic model.
 
     Parameters
     ----------
-    dataset : str
+    source_dataset : str
         Name of the semantic model to deploy.
-    new_dataset: str
-        Name of the new semantic model to be created.
-    workspace : str, default=None
+    source_workspace : str, default=None
         The Fabric workspace name.
         Defaults to None which resolves to the workspace of the attached lakehouse
         or if no lakehouse attached, resolves to the workspace of the notebook.
-    new_dataset_workspace : str, default=None
+    target_dataset: str
+        Name of the new semantic model to be created.
+    target_workspace : str, default=None
         The Fabric workspace name in which the new semantic model will be deployed.
         Defaults to None which resolves to the workspace of the attached lakehouse
         or if no lakehouse attached, resolves to the workspace of the notebook.
+    refresh_target_dataset : bool, default=True
+        If set to True, this will initiate a full refresh of the target semantic model in the target workspace.
 
     Returns
     -------
 
     """
 
-    workspace = fabric.resolve_workspace_name(workspace)
+    from sempy_labs import refresh_semantic_model
 
-    if new_dataset_workspace is None:
-        new_dataset_workspace = workspace
+    source_workspace = fabric.resolve_workspace_name(source_workspace)
 
-    if new_dataset is None:
-        new_dataset = dataset
+    if target_workspace is None:
+        target_workspace = source_workspace
 
-    if new_dataset == dataset and new_dataset_workspace == workspace:
+    if target_dataset is None:
+        target_dataset = source_dataset
+
+    if target_dataset == source_dataset and target_workspace == source_workspace:
         print(
             f"{icons.red_dot} The 'dataset' and 'new_dataset' parameters have the same value. And, the 'workspace' and 'new_dataset_workspace' "
             f"parameters have the same value. At least one of these must be different. Please update the parameters."
         )
         return
 
-    bim = get_semantic_model_bim(dataset=dataset, workspace=workspace)
+    bim = get_semantic_model_bim(dataset=source_dataset, workspace=source_workspace)
 
     create_semantic_model_from_bim(
-        dataset=new_dataset, bim_file=bim, workspace=new_dataset_workspace
+        dataset=target_dataset, bim_file=bim, workspace=target_workspace
     )
 
+    if refresh_target_dataset:
+        refresh_semantic_model(dataset=target_dataset, workspace=target_workspace)
+
 
 def get_semantic_model_bim(
     dataset: str,

src/sempy_labs/_list_functions.py

@@ -11,6 +11,7 @@
 from pyspark.sql import SparkSession
 from typing import Optional
 import sempy_labs._icons as icons
+from sempy.fabric.exceptions import FabricHTTPException
 
 
 def get_object_level_security(
@@ -1835,12 +1836,11 @@ def update_custom_pool(
         f"/v1/workspaces/{workspace_id}/spark/pools", json=request_body
     )
 
-    if response.status_code == 200:
-        print(
-            f"{icons.green_dot} The '{pool_name}' spark pool within the '{workspace}' workspace has been updated."
-        )
-    else:
-        raise ValueError(f"{icons.red_dot} {response.status_code}")
+    if response.status_code != 200:
+        raise FabricHTTPException(response)
+    print(
+        f"{icons.green_dot} The '{pool_name}' spark pool within the '{workspace}' workspace has been updated."
+    )
 
 
 def delete_custom_pool(pool_name: str, workspace: Optional[str | None] = None):
@@ -1874,12 +1874,11 @@ def delete_custom_pool(pool_name: str, workspace: Optional[str | None] = None):
     client = fabric.FabricRestClient()
     response = client.delete(f"/v1/workspaces/{workspace_id}/spark/pools/{poolId}")
 
-    if response.status_code == 200:
-        print(
-            f"{icons.green_dot} The '{pool_name}' spark pool has been deleted from the '{workspace}' workspace."
-        )
-    else:
-        print(f"{icons.red_dot} {response.status_code}")
+    if response.status_code != 200:
+        raise FabricHTTPException(response)
+    print(
+        f"{icons.green_dot} The '{pool_name}' spark pool has been deleted from the '{workspace}' workspace."
+    )
 
 
 def assign_workspace_to_capacity(capacity_name: str, workspace: Optional[str] = None):
@@ -1899,11 +1898,10 @@ def assign_workspace_to_capacity(capacity_name: str, workspace: Optional[str] =
     -------
     """
 
-    # https://learn.microsoft.com/en-us/rest/api/fabric/core/workspaces/assign-to-capacity?tabs=HTTP
     (workspace, workspace_id) = resolve_workspace_name_and_id(workspace)
 
     dfC = fabric.list_capacities()
-    dfC_filt = dfC[dfC["Name"] == capacity_name]
+    dfC_filt = dfC[dfC["Display Name"] == capacity_name]
     capacity_id = dfC_filt["Id"].iloc[0]
 
     request_body = {"capacityId": capacity_id}
@@ -2315,3 +2313,37 @@ def assign_workspace_to_dataflow_storage(
         )
     else:
         print(f"{icons.red_dot} {response.status_code}")
+
+
+def list_capacities() -> pd.DataFrame:
+    """
+    Shows the capacities and their properties.
+
+    Parameters
+    ----------
+
+    Returns
+    -------
+    pandas.DataFrame
+        A pandas dataframe showing the capacities and their properties
+    """
+
+    df = pd.DataFrame(
+        columns=["Id", "Display Name", "Sku", "Region", "State", "Admins"]
+    )
+
+    client = fabric.PowerBIRestClient()
+    response = client.get("/v1.0/myorg/capacities")
+
+    for i in response.json()["value"]:
+        new_data = {
+            "Id": i.get("id", {}).lower(),
+            "Display Name": i.get("displayName", {}),
+            "Sku": i.get("sku", {}),
+            "Region": i.get("region", {}),
+            "State": i.get("state", {}),
+            "Admins": [i.get("admins", [])],
+        }
+        df = pd.concat([df, pd.DataFrame(new_data, index=[0])], ignore_index=True)
+
+    return df

src/sempy_labs/_model_bpa_rules.py

@@ -1,3 +1,4 @@
+import sempy
 import sempy.fabric as fabric
 import pandas as pd
 import re
@@ -30,6 +31,7 @@ def model_bpa_rules(
         A pandas dataframe containing the default rules for the run_model_bpa function.
     """
 
+    sempy.fabric._client._utils._init_analysis_services()
     import Microsoft.AnalysisServices.Tabular as TOM
 
     workspace = fabric.resolve_workspace_name(workspace)