%pip install semantic-link-labs

import sempy_labs as labs
from sempy_labs.tom import connect_semantic_model

dataset = '' # Enter dataset name
workspace = None # Enter workspace name

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    for t in tom.model.Tables:
        print(t.Name)

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    for t in tom.model.Tables:
        t.Name = t.Name.replace('_',' ')


with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    for c in tom.all_columns():
        c.Name = c.Name.replace('_',' ')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.add_measure(table_name='Internet Sales', measure_name='Sales Amount', expression="SUM('Internet Sales'[SalesAmount])")
    tom.add_measure(table_name='Internet Sales', measure_name='Order Quantity', expression="SUM('Internet Sales'[OrderQty])")    

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    for t in tom.model.Tables:
        if t.Name == 'Internet Sales':
            tom.add_measure(table_name=t.Name, measure_name='Sales Amount', expression="SUM('Internet Sales'[SalesAmount])")
            tom.add_measure(table_name=t.Name, measure_name='Order Quantity', expression="SUM('Internet Sales'[OrderQty])")

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.add_data_column(table_name='Product', column_name='Size Range', source_column='SizeRange', data_type='Int64')
    tom.add_data_column(table_name= 'Segment', column_name='Summary Segment', source_column='SummarySegment', data_type='String')

    tom.add_calculated_column(table_name='Internet Sales', column_name='GrossMargin', expression="'Internet Sales'[SalesAmount] - 'Internet Sales'[ProductCost]", data_type='Decimal')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    for t in tom.model.Tables:
        if t.Name == 'Product':
            tom.add_data_column(table_name=t.Name, column_name='Size Range', source_column='SizeRange', data_type='Int64')
        elif t.Name == 'Segment':
            tom.add_data_column(table_name = t.Name, column_name='Summary Segment', source_column='SummarySegment', data_type='String')
        elif t.Name == 'Internet Sales':
            tom.add_calculated_column(table_name=t.Name, column_name='GrossMargin', expression="'Internet Sales'[SalesAmount] - 'Internet Sales'[ProductCost]", data_type='Decimal')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.add_hierarchy(table_name='Geography', hierarchy_name='Geo Hierarchy', levels=['Continent', 'Country', 'State', 'City'])

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    for t in tom.model.Tables:
        if t.Name == 'Geography':
            tom.add_hierarchy(table_name=t.Name, hierarchy_name='Geo Hierarchy', levels=['Continent', 'Country', 'State', 'City'])

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.add_relationship(
        from_table='Internet Sales', from_column='ProductKey',
        to_table='Product', to_column ='ProductKey', 
        from_cardinality='Many', to_cardinality='One')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    table_name='Sales'
    tom.add_table(name=table_name)
    tom.add_m_partition(table_name=table_name, partition_name=table_name, expression='let....')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    table_name = 'Sales'
    tom.add_table(name=table_name)
    tom.add_entity_partition(table_name=table_name, entity_name=table_name)

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    table_name = 'Sales'
    tom.add_calculated_table(name=table_name, expression="DISTINCT('Product'[Color])")
    tom.add_calculated_table_column(table_name=table_name, column_name='Color', source_column="'Product[Color]", data_type='String')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.add_role(role_name='Reader')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.set_rls(
        role_name='Reader', 
        table_name='Product',
        filter_expression="'Dim Product'[Color] = \"Blue\""
    )

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    for r in tom.model.Roles:
        if r.Name == 'Reader':
            tom.set_rls(role_name=r.Name, table_name='Product', filter_expression="'Dim Product'[Color] = \"Blue\"")

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.set_ols(role_name='Reader', table_name='Product', column_name='Size', permission='None')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    for r in tom.model.Roles:
        if r.Name == 'Reader':
            for t in tom.model.Tables:
                if t.Name == 'Product':
                    tom.set_ols(role_name=r.Name, table_name=t.Name, column_name='Size', permission='None')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.add_calculation_group(name='MyCalcGroup')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.add_calculation_item(table_name='MyCalcGroup', calculation_item_name='YTD', expression="CALCULATE(SELECTEDMEASURE(), DATESYTD('Calendar'[CalendarDate]))")
    tom.add_calculation_item(table_name='MyCalcGroup', calculation_item_name='MTD', expression="CALCULATE(SELECTEDMEASURE(), DATESMTD('Calendar'[CalendarDate]))")

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    for t in tom.model.Tables:
        if t.Name == 'MyCalcGroup':
            tom.add_calculation_item(table_name=t.Name, calculation_item_name='YTD', expression="CALCULATE(SELECTEDMEASURE(), DATESYTD('Calendar'[CalendarDate]))")
            tom.add_calculation_item(table_name=t.Name, calculation_item_name='MTD', expression="CALCULATE(SELECTEDMEASURE(), DATESMTD('Calendar'[CalendarDate]))")

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.add_translation(language='it-IT')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.set_translation(object = tom.model.Tables['Product'], language='it-IT', property='Name', value='Produtto')

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.add_field_parameter(table_name='Parameter', objects="'Product'[Color], [Sales Amount], 'Geography'[Country]")

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    for t in tom.model.Tables:
        if t.Name == 'Product':
            tom.remove_object(object=t.Columns['Size'])
            tom.remove_object(object=t.Hierarchies['Product Hierarchy'])

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.remove_object(object=tom.model.Tables['Product'].Columns['Size'])
    tom.remove_object(object=tom.model.Tables['Product'].Hierarchies['Product Hierarchy'])

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    for c in tom.all_columns():
        print(c.Name)

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    for m in tom.all_measures():
        print(m.Name)

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    for p in tom.all_partitions():
        print(p.Name)

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    for h in tom.all_hierarchies():
        print(h.Name)

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    for ci in tom.all_calculation_items():
        print(ci.Name)

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    for l in tom.all_levels():
        print(l.Name)

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    for rls in tom.all_rls():
        print(rls.Name)

with connect_semantic_model(dataset=dataset, readonly=False, workspace=workspace) as tom:
    tom.set_vertipaq_annotations()

    for t in tom.model.Tables:
        rc = tom.row_count(object = t)
        print(f"{t.Name}  : {str(rc)}")
        for c in t.Columns:
            col_size = tom.total_size(object=c)
            print(labs.format_dax_object_name(t.Name, c.Name) + ' : ' + str(col_size))

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    for c in tom.all_columns():
        full_name = labs.format_dax_object_name(c.Parent.Name, c.Name)
        for h in tom.used_in_hierarchies(column = c):
            print(f"{full_name} : {h.Name}")

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    for c in tom.all_columns():
        full_name = labs.format_dax_object_name(c.Parent.Name, c.Name)
        for r in tom.used_in_relationships(object = c):
            rel_name = labs.create_relationship_name(r.FromTable.Name, r.FromColumn.Name, r.ToTable.Name, r.ToColumn.Name)
            print(f"{full_name} : {rel_name}")

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    for t in tom.model.Tables:
        for r in tom.used_in_relationships(object = t):
            rel_name = labs.create_relationship_name(r.FromTable.Name, r.FromColumn.Name, r.ToTable.Name, r.ToColumn.Name)
            print(f"{t.Name} : {rel_name}")

with connect_semantic_model(dataset=dataset, readonly=True, workspace=workspace) as tom:
    dep = labs.get_model_calc_dependencies(dataset = dataset, workspace=workspace)
    for o in tom.used_in_rls(object = tom.model.Tables['Product'].Columns['Color'], dependencies=dep):
        print(o.Name)