added list_item_job_instances, ran black

src/sempy_labs/_dax.py

@@ -260,7 +260,10 @@ def get_dax_query_memory_size(
     (dataset_name, dataset_id) = resolve_dataset_name_and_id(dataset, workspace_id)
 
     df = get_dax_query_dependencies(
-        dataset=dataset_id, workspace=workspace_id, dax_string=dax_string, put_in_memory=True
+        dataset=dataset_id,
+        workspace=workspace_id,
+        dax_string=dax_string,
+        put_in_memory=True,
     )
 
     return df["Total Size"].sum()

src/sempy_labs/_environments.py

@@ -10,8 +10,11 @@
 from sempy.fabric.exceptions import FabricHTTPException
 from uuid import UUID
 
+
 def create_environment(
-    environment: str, description: Optional[str] = None, workspace: Optional[str | UUID] = None
+    environment: str,
+    description: Optional[str] = None,
+    workspace: Optional[str | UUID] = None,
 ):
     """
     Creates a Fabric environment.

src/sempy_labs/_generate_semantic_model.py

@@ -273,18 +273,25 @@ def deploy_semantic_model(
         If set to True, overwrites the existing semantic model in the workspace if it exists.
     """
 
-    (source_workspace_name, source_workspace_id) = resolve_workspace_name_and_id(source_workspace)
+    (source_workspace_name, source_workspace_id) = resolve_workspace_name_and_id(
+        source_workspace
+    )
 
     if target_workspace is None:
         target_workspace_name = source_workspace_name
         target_workspace_id = fabric.resolve_workspace_id(target_workspace_name)
     else:
-        (target_workspace_name, target_workspace_id) = resolve_workspace_name_and_id(target_workspace)
+        (target_workspace_name, target_workspace_id) = resolve_workspace_name_and_id(
+            target_workspace
+        )
 
     if target_dataset is None:
         target_dataset = source_dataset
 
-    if target_dataset == source_dataset and target_workspace_name == source_workspace_name:
+    if (
+        target_dataset == source_dataset
+        and target_workspace_name == source_workspace_name
+    ):
         raise ValueError(
             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."
@@ -350,7 +357,10 @@ def get_semantic_model_bim(
     (dataset_name, dataset_id) = resolve_dataset_name_and_id(dataset, workspace_id)
 
     bimJson = get_semantic_model_definition(
-        dataset=dataset_id, workspace=workspace_id, format="TMSL", return_dataframe=False
+        dataset=dataset_id,
+        workspace=workspace_id,
+        format="TMSL",
+        return_dataframe=False,
     )
 
     if save_to_file_name is not None:
@@ -444,7 +454,9 @@ def get_semantic_model_definition(
         return decoded_parts
 
 
-def get_semantic_model_size(dataset: str | UUID, workspace: Optional[str | UUID] = None):
+def get_semantic_model_size(
+    dataset: str | UUID, workspace: Optional[str | UUID] = None
+):
     """
     Gets size of the semantic model in bytes.
 

src/sempy_labs/_git.py

@@ -255,7 +255,9 @@ def initialize_git_connection(workspace: Optional[str | UUID] = None) -> str:
 
 
 def commit_to_git(
-    comment: str, item_ids: str | List[str] = None, workspace: Optional[str | UUID] = None
+    comment: str,
+    item_ids: str | List[str] = None,
+    workspace: Optional[str | UUID] = None,
 ):
     """
     Commits all or a selection of items within a workspace to Git.

src/sempy_labs/_helper_functions.py

@@ -148,7 +148,9 @@ def resolve_report_name(report_id: UUID, workspace: Optional[str | UUID] = None)
     )
 
 
-def resolve_item_name_and_id(item: str | UUID, type: Optional[str] = None, workspace: Optional[str | UUID] = None) -> Tuple[str, UUID]:
+def resolve_item_name_and_id(
+    item: str | UUID, type: Optional[str] = None, workspace: Optional[str | UUID] = None
+) -> Tuple[str, UUID]:
 
     (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
 
@@ -159,7 +161,9 @@ def resolve_item_name_and_id(item: str | UUID, type: Optional[str] = None, works
         )
     else:
         if type is None:
-            raise ValueError(f"{icons.warning} Must specify a 'type' if specifying a name as the 'item'.")
+            raise ValueError(
+                f"{icons.warning} Must specify a 'type' if specifying a name as the 'item'."
+            )
         item_name = item
         item_id = fabric.resolve_item_id(
             item_name=item, type=type, workspace=workspace_id
@@ -188,7 +192,9 @@ def resolve_dataset_name_and_id(
     return dataset_name, dataset_id
 
 
-def resolve_dataset_id(dataset: str | UUID, workspace: Optional[str | UUID] = None) -> UUID:
+def resolve_dataset_id(
+    dataset: str | UUID, workspace: Optional[str | UUID] = None
+) -> UUID:
     """
     Obtains the ID of the semantic model.
 
@@ -217,7 +223,9 @@ def resolve_dataset_id(dataset: str | UUID, workspace: Optional[str | UUID] = No
     return dataset_id
 
 
-def resolve_dataset_name(dataset_id: UUID, workspace: Optional[str | UUID] = None) -> str:
+def resolve_dataset_name(
+    dataset_id: UUID, workspace: Optional[str | UUID] = None
+) -> str:
     """
     Obtains the name of the semantic model.
 
@@ -271,7 +279,9 @@ def resolve_lakehouse_name(
     )
 
 
-def resolve_lakehouse_id(lakehouse: str, workspace: Optional[str | UUID] = None) -> UUID:
+def resolve_lakehouse_id(
+    lakehouse: str, workspace: Optional[str | UUID] = None
+) -> UUID:
     """
     Obtains the ID of the Fabric lakehouse.
 
@@ -559,7 +569,9 @@ def language_validate(language: str):
     return lang
 
 
-def resolve_workspace_name_and_id(workspace: Optional[str | UUID] = None) -> Tuple[str, str]:
+def resolve_workspace_name_and_id(
+    workspace: Optional[str | UUID] = None,
+) -> Tuple[str, str]:
     """
     Obtains the name and ID of the Fabric workspace.
 
@@ -609,7 +621,9 @@ def _decode_b64(file, format: Optional[str] = "utf-8"):
     return result
 
 
-def is_default_semantic_model(dataset: str, workspace: Optional[str | UUID] = None) -> bool:
+def is_default_semantic_model(
+    dataset: str, workspace: Optional[str | UUID] = None
+) -> bool:
     """
     Identifies whether a semantic model is a default semantic model.
 
@@ -716,7 +730,9 @@ def _add_part(target_dict, path, payload):
     target_dict["definition"]["parts"].append(part)
 
 
-def resolve_workspace_capacity(workspace: Optional[str | UUID] = None) -> Tuple[UUID, str]:
+def resolve_workspace_capacity(
+    workspace: Optional[str | UUID] = None,
+) -> Tuple[UUID, str]:
     """
     Obtains the capacity Id and capacity name for a given workspace.
 
@@ -1083,7 +1099,9 @@ def convert_to_alphanumeric_lowercase(input_string):
     return cleaned_string
 
 
-def resolve_environment_id(environment: str, workspace: Optional[str | UUID] = None) -> UUID:
+def resolve_environment_id(
+    environment: str, workspace: Optional[str | UUID] = None
+) -> UUID:
     """
     Obtains the environment Id for a given environment.
 
@@ -1182,7 +1200,9 @@ def _make_list_unique(my_list):
     return list(set(my_list))
 
 
-def _get_partition_map(dataset: str, workspace: Optional[str | UUID] = None) -> pd.DataFrame:
+def _get_partition_map(
+    dataset: str, workspace: Optional[str | UUID] = None
+) -> pd.DataFrame:
 
     (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
     (dataset_name, dataset_id) = resolve_dataset_name_and_id(dataset, workspace_id)

src/sempy_labs/_job_scheduler.py

@@ -0,0 +1,88 @@
+import sempy.fabric as fabric
+import pandas as pd
+from typing import Optional
+from sempy_labs._helper_functions import (
+    resolve_workspace_name_and_id,
+    resolve_item_name_and_id,
+    pagination,
+)
+from sempy.fabric.exceptions import FabricHTTPException
+from uuid import UUID
+
+
+def list_item_job_instances(
+    item: str | UUID, type: Optional[str] = None, workspace: Optional[str | UUID] = None
+) -> pd.DataFrame:
+    """
+    Returns a list of job instances for the specified item.
+
+    This is a wrapper function for the following API: `Job Scheduler - List Item Job Instances <https://learn.microsoft.com/rest/api/fabric/core/job-scheduler/list-item-job-instances>`_.
+
+    Parameters
+    ----------
+    item : str | UUID
+        The item name or ID
+    type : str, default=None
+        The item type. If specifying the item name as the item, the item type is required.
+    workspace : str | UUID, default=None
+        The Fabric workspace name or ID used by the lakehouse.
+        Defaults to None which resolves to the workspace of the attached lakehouse
+        or if no lakehouse attached, resolves to the workspace of the notebook.
+
+    Returns
+    -------
+    pandas.DataFrame
+        Shows a list of job instances for the specified item.
+    """
+
+    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)
+    (item_name, item_id) = resolve_item_name_and_id(
+        item=item, type=type, workspace=workspace
+    )
+
+    client = fabric.FabricRestClient()
+    response = client.get(
+        f"v1/workspaces/{workspace_id}/items/{item_id}/jobs/instances"
+    )
+
+    if response.status_code != 200:
+        raise FabricHTTPException(response)
+
+    df = pd.DataFrame(
+        columns=[
+            "Job Instance Id",
+            "Item Id",
+            "Job Type",
+            "Invoke Type",
+            "Status",
+            "Root Activity Id" "Start Time UTC",
+            "End Time UTC",
+            "Failure Reason",
+        ]
+    )
+
+    responses = pagination(client, response)
+
+    if not responses[0].get("value"):
+        return df
+
+    dfs = []
+    for r in responses:
+        for v in r.get("value", []):
+            new_data = {
+                "Job Instance Id": v.get("id"),
+                "Item Id": v.get("itemId"),
+                "Job Type": v.get("jobType"),
+                "Invoke Type": v.get("invokeType"),
+                "Status": v.get("status"),
+                "Root Activity Id": v.get("rootActivityId"),
+                "Start Time UTC": v.get("startTimeUtc"),
+                "End Time UTC": v.get("endTimeUtc"),
+                "Failure Reason": v.get("failureReason"),
+            }
+            dfs.append(pd.DataFrame(new_data, index=[0]))
+
+    if dfs:
+        df = pd.concat(dfs, ignore_index=True)
+
+    return df

src/sempy_labs/_list_functions.py

@@ -258,7 +258,9 @@ def list_tables(
     return df
 
 
-def list_annotations(dataset: str | UUID, workspace: Optional[str | UUID] = None) -> pd.DataFrame:
+def list_annotations(
+    dataset: str | UUID, workspace: Optional[str | UUID] = None
+) -> pd.DataFrame:
     """
     Shows a semantic model's annotations and their properties.
 
@@ -939,7 +941,9 @@ def parse_value(text):
     return dfR
 
 
-def list_kpis(dataset: str | UUID, workspace: Optional[str | UUID] = None) -> pd.DataFrame:
+def list_kpis(
+    dataset: str | UUID, workspace: Optional[str | UUID] = None
+) -> pd.DataFrame:
     """
     Shows a semantic model's KPIs and their properties.
 
@@ -1554,7 +1558,9 @@ def list_semantic_model_object_report_usage(
         final_df["Object"] = format_dax_object_name(
             final_df["Table Name"], final_df["Object Name"]
         )
-        dfC = fabric.list_columns(dataset=dataset_id, workspace=workspace_id, extended=True)
+        dfC = fabric.list_columns(
+            dataset=dataset_id, workspace=workspace_id, extended=True
+        )
         dfC["Object"] = format_dax_object_name(dfC["Table Name"], dfC["Column Name"])
         final_df = pd.merge(
             final_df,

src/sempy_labs/_managed_private_endpoints.py

@@ -66,7 +66,9 @@ def create_managed_private_endpoint(
     )
 
 
-def list_managed_private_endpoints(workspace: Optional[str | UUID] = None) -> pd.DataFrame:
+def list_managed_private_endpoints(
+    workspace: Optional[str | UUID] = None,
+) -> pd.DataFrame:
     """
     Shows the managed private endpoints within a workspace.
 

src/sempy_labs/_mirrored_databases.py

@@ -112,7 +112,9 @@ def create_mirrored_database(
     )
 
 
-def delete_mirrored_database(mirrored_database: str, workspace: Optional[str | UUID] = None):
+def delete_mirrored_database(
+    mirrored_database: str, workspace: Optional[str | UUID] = None
+):
     """
     Deletes a mirrored database.
 

src/sempy_labs/_notebooks.py

@@ -34,7 +34,9 @@ def _get_notebook_definition_base(
     return pd.json_normalize(result["definition"]["parts"])
 
 
-def _get_notebook_type(notebook_name: str, workspace: Optional[str | UUID] = None) -> str:
+def _get_notebook_type(
+    notebook_name: str, workspace: Optional[str | UUID] = None
+) -> str:
 
     df_items = _get_notebook_definition_base(
         notebook_name=notebook_name, workspace=workspace

src/sempy_labs/_query_scale_out.py

@@ -141,7 +141,9 @@ def qso_sync_status(
         return df, dfRep
 
 
-def disable_qso(dataset: str | UUID, workspace: Optional[str | UUID] = None) -> pd.DataFrame:
+def disable_qso(
+    dataset: str | UUID, workspace: Optional[str | UUID] = None
+) -> pd.DataFrame:
     """
     Sets the max read-only replicas to 0, disabling query scale out.
 

src/sempy_labs/_refresh_semantic_model.py

@@ -277,7 +277,9 @@ def display_trace_logs(trace, partition_map, widget, title, stop=False):
 
 @log
 def cancel_dataset_refresh(
-    dataset: str | UUID, request_id: Optional[str] = None, workspace: Optional[str | UUID] = None
+    dataset: str | UUID,
+    request_id: Optional[str] = None,
+    workspace: Optional[str | UUID] = None,
 ):
     """
     Cancels the refresh of a semantic model which was executed via the `Enhanced Refresh API <https://learn.microsoft.com/power-bi/connect-data/asynchronous-refresh>`_
@@ -323,7 +325,9 @@ def cancel_dataset_refresh(
 
 
 def get_semantic_model_refresh_history(
-    dataset: str | UUID, request_id: Optional[str] = None, workspace: Optional[str | UUID] = None
+    dataset: str | UUID,
+    request_id: Optional[str] = None,
+    workspace: Optional[str | UUID] = None,
 ) -> pd.DataFrame:
     """
     Obtains the semantic model refresh history (refreshes executed via the Enhanced Refresh API).

src/sempy_labs/_sql.py

@@ -8,6 +8,7 @@
 from sempy_labs._helper_functions import resolve_warehouse_id, resolve_lakehouse_id
 from uuid import UUID
 
+
 def _bytes2mswin_bstr(value: bytes) -> bytes:
     """Convert a sequence of bytes into a (MS-Windows) BSTR (as bytes).