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Merged
1 commit merged into from
Jul 2, 2024
Merged

used get function when accessing dictionaries. updated error message … #19

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4 changes: 2 additions & 2 deletions src/sempy_labs/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -34,7 +34,7 @@
# list_sqlendpoints,
# list_tables,
list_warehouses,
# list_workspace_role_assignments,
list_workspace_role_assignments,
create_warehouse,
update_item,
)
Expand Down Expand Up @@ -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",
Expand Down
44 changes: 12 additions & 32 deletions src/sempy_labs/_ai.py
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Expand Up @@ -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
Expand Down Expand Up @@ -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)

Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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"
Expand All @@ -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(
Expand Down Expand Up @@ -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:
Expand Down
4 changes: 1 addition & 3 deletions src/sempy_labs/_clear_cache.py
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Expand Up @@ -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)

Expand Down
64 changes: 32 additions & 32 deletions src/sempy_labs/_connections.py
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Expand Up @@ -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)

Expand Down Expand Up @@ -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)

Expand Down Expand Up @@ -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)

Expand Down
19 changes: 6 additions & 13 deletions src/sempy_labs/_generate_semantic_model.py
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Expand Up @@ -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"""
{{
Expand Down Expand Up @@ -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": {}}
Expand Down Expand Up @@ -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:
Expand All @@ -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())

Expand Down Expand Up @@ -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
Expand Down
15 changes: 3 additions & 12 deletions src/sempy_labs/_helper_functions.py
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Expand Up @@ -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(" ", "_")

Expand Down Expand Up @@ -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

Expand Down
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