%pip install semantic-link-labs

import sempy_labs as labs
from sempy_labs import lakehouse as lake
from sempy_labs import directlake
import sempy_labs.report as rep

dataset_name = ''
workspace_name = None

labs.vertipaq_analyzer(dataset=dataset_name, workspace=workspace_name)

labs.vertipaq_analyzer(dataset=dataset_name, workspace=workspace_name, export='zip')

labs.vertipaq_analyzer(dataset=dataset_name, workspace=workspace_name, export='table')

labs.import_vertipaq_analyzer(folder_path='', file_name='')

labs.run_model_bpa(dataset=dataset_name, workspace=workspace_name)

labs.run_model_bpa(dataset=dataset_name, workspace=workspace_name, export=True)

labs.run_model_bpa(dataset=dataset_name, workspace=workspace_name, language='italian')

import sempy
sempy.fabric._client._utils._init_analysis_services()
import Microsoft.AnalysisServices.Tabular as TOM
import pandas as pd

dataset_name = ''
workspace_name = ''

rules = pd.DataFrame(
    [
        (
            "Performance",
            "Table",
            "Warning",
            "Rule name...",
            lambda obj, tom: tom.is_calculated_table(table_name=obj.Name),
            'Rule description...',
            '',
        ),
        (
            "Performance",
            "Column",
            "Warning",
            "Do not use floating point data types",
            lambda obj, tom: obj.DataType == TOM.DataType.Double,
            'The "Double" floating point data type should be avoided, as it can result in unpredictable roundoff errors and decreased performance in certain scenarios. Use "Int64" or "Decimal" where appropriate (but note that "Decimal" is limited to 4 digits after the decimal sign).',
        )
    ],
    columns=[
            "Category",
            "Scope",
            "Severity",
            "Rule Name",
            "Expression",
            "Description",
            "URL",
        ],
)

labs.run_model_bpa(dataset=dataset_name, workspace=workspace_name, rules=rules)

labs.translate_semantic_model(dataset=dataset_name, workspace=workspace_name, languages=['italian', 'japanese', 'hindi'], exclude_characters='_')

lake.get_lakehouse_tables(lakehouse=None, workspace=None, extended=True, count_rows=False)

lake.get_lakehouse_tables(lakehouse=None, workspace=None, extended=True, count_rows=False, export=True)

directlake.check_fallback_reason(dataset=dataset_name, workspace=workspace_name)

lake.optimize_lakehouse_tables(tables=['', ''], lakehouse=None, workspace=None)

directlake.warm_direct_lake_cache_isresident(dataset=dataset_name, workspace=workspace_name)

directlake.warm_direct_lake_cache_perspective(dataset=dataset_name, workspace=workspace_name, perspective='', add_dependencies=True)