def __init__(self, tm1_rest):
"""
:param tm1_rest: instance of RestService
"""
self._tm1_rest = tm1_rest
self.cells = CellService(tm1_rest)
self.elements = ElementService(tm1_rest)
def __init__(self, **kwargs):
self._tm1_rest = RestService(**kwargs)
# instantiate all Services
self.annotations = AnnotationService(self._tm1_rest)
self.cells = CellService(self._tm1_rest)
self.chores = ChoreService(self._tm1_rest)
self.cubes = CubeService(self._tm1_rest)
self.dimensions = DimensionService(self._tm1_rest)
self.elements = ElementService(self._tm1_rest)
self.hierarchies = HierarchyService(self._tm1_rest)
self.monitoring = MonitoringService(self._tm1_rest)
self.power_bi = PowerBiService(self._tm1_rest)
self.processes = ProcessService(self._tm1_rest)
self.security = SecurityService(self._tm1_rest)
self.server = ServerService(self._tm1_rest)
self.subsets = SubsetService(self._tm1_rest)
self.applications = ApplicationService(self._tm1_rest)
self.views = ViewService(self._tm1_rest)
def _instantiate_services(self):
self.annotations = AnnotationService(self._tm1_rest)
self.cells = CellService(self._tm1_rest)
self.chores = ChoreService(self._tm1_rest)
self.cubes = CubeService(self._tm1_rest)
self.dimensions = DimensionService(self._tm1_rest)
self.elements = ElementService(self._tm1_rest)
self.git = GitService(self._tm1_rest)
self.hierarchies = HierarchyService(self._tm1_rest)
self.monitoring = MonitoringService(self._tm1_rest)
self.power_bi = PowerBiService(self._tm1_rest)
self.processes = ProcessService(self._tm1_rest)
self.security = SecurityService(self._tm1_rest)
self.server = ServerService(self._tm1_rest)
self.subsets = SubsetService(self._tm1_rest)
self.applications = ApplicationService(self._tm1_rest)
self.views = ViewService(self._tm1_rest)
self.sandboxes = SandboxService(self._tm1_rest)
self.git = GitService(self._tm1_rest)
class PowerBiService:
def __init__(self, tm1_rest):
"""
:param tm1_rest: instance of RestService
"""
self._tm1_rest = tm1_rest
self.cells = CellService(tm1_rest)
self.elements = ElementService(tm1_rest)
@require_pandas
def execute_mdx(self, mdx, **kwargs) -> 'pd.DataFrame':
return self.cells.execute_mdx_dataframe_shaped(mdx, **kwargs)
@require_pandas
def execute_view(self, cube_name, view_name, private,
**kwargs) -> 'pd.DataFrame':
return self.cells.execute_view_dataframe_shaped(
cube_name, view_name, private, **kwargs)
@require_pandas
def get_member_properties(self,
dimension_name: str,
hierarchy_name: str,
member_selection: Iterable = None,
skip_consolidations: bool = True,
attributes: Iterable = None,
skip_parents: bool = False,
level_names=None,
parent_attribute: str = None) -> 'pd.DataFrame':
"""
:param dimension_name: Name of the dimension
:param hierarchy_name: Name of the hierarchy in the dimension
:param member_selection: Selection of members. Iterable or valid MDX string
:param skip_consolidations: Boolean flag to skip consolidations
:param attributes: Selection of attributes. Iterable. If None retrieve all.
:param level_names: List of labels for parent columns. If None use level names from TM1.
:param skip_parents: Boolean Flag to skip parent columns.
:param parent_attribute: Attribute to be displayed in parent columns. If None, parent name is used.
:return: pandas DataFrame
"""
if not member_selection:
member_selection = f"{{ [{dimension_name}].[{hierarchy_name}].Members }}"
if skip_consolidations:
member_selection = f"{{ Tm1FilterByLevel({member_selection}, 0) }}"
if not isinstance(member_selection, str):
if isinstance(member_selection, Iterable):
member_selection = "{" + ",".join(
f"[{dimension_name}].[{member}]"
for member in member_selection) + "}"
else:
raise ValueError(
"Argument 'element_selection' must be None or str")
if not self.elements.attribute_cube_exists(dimension_name):
raise RuntimeError(self.elements.ELEMENT_ATTRIBUTES_PREFIX +
dimension_name + " cube must exist")
members = [
tupl[0]
for tupl in self.elements.execute_set_mdx(mdx=member_selection,
element_properties=None,
member_properties=(
"Name",
"UniqueName"),
parent_properties=None)
]
element_types = self.elements.get_element_types(
dimension_name=dimension_name,
hierarchy_name=hierarchy_name,
skip_consolidations=skip_consolidations)
df = pd.DataFrame(data=[(member["Name"], element_types[member["Name"]])
for member in members
if member["Name"] in element_types],
dtype=str,
columns=[dimension_name, 'Type'])
calculated_members_definition = list()
calculated_members_selection = list()
if not skip_parents:
levels = self.elements.get_levels_count(dimension_name,
hierarchy_name)
# potential custom parent names
if not level_names:
level_names = self.elements.get_level_names(dimension_name,
hierarchy_name,
descending=True)
for parent in range(1, levels, 1):
name_or_attribute = f"Properties('{parent_attribute}')" if parent_attribute else "Name"
member = f"""
MEMBER [{self.elements.ELEMENT_ATTRIBUTES_PREFIX + dimension_name}].[{level_names[parent]}]
AS [{dimension_name}].CurrentMember.{'Parent.' * parent}{name_or_attribute}
"""
calculated_members_definition.append(member)
calculated_members_selection.append(
f"[{self.elements.ELEMENT_ATTRIBUTES_PREFIX + dimension_name}].[{level_names[parent]}]"
)
if attributes is None:
column_selection = "{Tm1SubsetAll([" + self.elements.ELEMENT_ATTRIBUTES_PREFIX + dimension_name + "])}"
else:
column_selection = "{" + ",".join(
"[" + self.elements.ELEMENT_ATTRIBUTES_PREFIX +
dimension_name + "].[" + attribute + "]"
for attribute in attributes) + "}"
if calculated_members_selection:
column_selection = column_selection + " + {" + ",".join(
calculated_members_selection) + "}"
member_selection = ",".join(member["UniqueName"] for member in members)
mdx_with_block = ""
if calculated_members_definition:
mdx_with_block = "WITH " + " ".join(calculated_members_definition)
mdx = f"""
{mdx_with_block}
SELECT
{{ {member_selection} }} ON ROWS,
{{ {column_selection} }} ON COLUMNS
FROM [{self.elements.ELEMENT_ATTRIBUTES_PREFIX + dimension_name}]
"""
df_data = self.execute_mdx(mdx)
# shift levels to right hand side
if not skip_parents:
# skip max level (= leaves)
level_names = level_names[1:]
# iterative approach
for _ in level_names:
rows_to_shift = df_data[df_data[level_names[-1]] == ''].index
if rows_to_shift.empty:
break
df_data.iloc[rows_to_shift, -len(level_names):] = df_data.iloc[
rows_to_shift, -len(level_names):].shift(1, axis=1)
df_data.iloc[:,
-len(level_names):] = df_data.iloc[:,
-len(level_names
):].fillna('')
return pd.merge(df, df_data, on=dimension_name).drop_duplicates()
class PowerBiService:
def __init__(self, tm1_rest):
"""
:param tm1_rest: instance of RestService
"""
self._tm1_rest = tm1_rest
self.cells = CellService(tm1_rest)
self.elements = ElementService(tm1_rest)
def execute_mdx(self, mdx, **kwargs):
cellset_id = self.cells.create_cellset(mdx)
return self.cells.extract_cellset_power_bi(cellset_id, **kwargs)
def execute_view(self, cube_name, view_name, private, **kwargs):
cellset_id = self.cells.create_cellset_from_view(
cube_name, view_name, private)
return self.cells.extract_cellset_power_bi(cellset_id, **kwargs)
def get_member_properties(self,
dimension_name,
hierarchy_name,
member_selection=None,
skip_consolidations=True,
attributes=None,
skip_parents=False,
level_names=None):
"""
:param dimension_name: Name of the dimension
:param hierarchy_name: Name of the hierarchy in the dimension
:param member_selection: Selection of members. Iterable or valid MDX string
:param skip_consolidations: Boolean flag to skip consolidations
:param attributes: Selection of attributes. Iterable. If None retrieve all.
:param level_names: List of labels for parent columns. If None use level names from TM1.
:param skip_parents: Boolean Flag to skip parent columns.
:return: pandas DataFrame
"""
if not member_selection:
member_selection = f"{{ [{dimension_name}].[{hierarchy_name}].Members }}"
if skip_consolidations:
member_selection = f"{{ Tm1FilterByLevel({member_selection}, 0) }}"
if not isinstance(member_selection, str):
if isinstance(member_selection, Iterable):
member_selection = "{" + ",".join(
f"[{dimension_name}].[{member}]"
for member in member_selection) + "}"
else:
raise ValueError(
"Argument 'element_selection' must be None or str")
if not self.elements.attribute_cube_exists(dimension_name):
raise RuntimeError(self.elements.ELEMENT_ATTRIBUTES_PREFIX +
dimension_name + " cube must exist")
members = [
tupl[0]
for tupl in self.elements.execute_set_mdx(mdx=member_selection,
element_properties=None,
member_properties=(
"Name",
"UniqueName"),
parent_properties=None)
]
element_types = self.elements.get_element_types(
dimension_name=dimension_name,
hierarchy_name=hierarchy_name,
skip_consolidations=skip_consolidations)
df = pd.DataFrame(data=[(member["Name"], element_types[member["Name"]])
for member in members
if member["Name"] in element_types],
dtype=str,
columns=[dimension_name, 'Type'])
calculated_members_definition = list()
calculated_members_selection = list()
if not skip_parents:
levels = self.elements.get_levels_count(dimension_name,
hierarchy_name)
# potential custom parent names
if not level_names:
level_names = self.elements.get_level_names(
dimension_name, hierarchy_name)
for parent in range(1, levels, 1):
calculated_members_definition.append(f"""
MEMBER [{self.elements.ELEMENT_ATTRIBUTES_PREFIX + dimension_name}].[{level_names[parent]}]
AS [{dimension_name}].CurrentMember.{'Parent.' * parent}Name
""")
calculated_members_selection.append(
f"[{self.elements.ELEMENT_ATTRIBUTES_PREFIX + dimension_name}].[{level_names[parent]}]"
)
if attributes is None:
column_selection = "{Tm1SubsetAll([" + self.elements.ELEMENT_ATTRIBUTES_PREFIX + dimension_name + "])}"
else:
column_selection = "{" + ",".join(
"[" + self.elements.ELEMENT_ATTRIBUTES_PREFIX +
dimension_name + "].[" + attribute + "]"
for attribute in attributes) + "}"
if calculated_members_selection:
column_selection = column_selection + " + {" + ",".join(
calculated_members_selection) + "}"
member_selection = ",".join(member["UniqueName"] for member in members)
mdx_with_block = ""
if calculated_members_definition:
mdx_with_block = "WITH " + " ".join(calculated_members_definition)
mdx = f"""
{mdx_with_block}
SELECT
{{ {member_selection} }} ON ROWS,
{{ {column_selection} }} ON COLUMNS
FROM [{self.elements.ELEMENT_ATTRIBUTES_PREFIX + dimension_name}]
"""
df_data = self.execute_mdx(mdx, element_unique_names=False)
return pd.merge(df, df_data, on=dimension_name)