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Flag Aggregation KPI Builder

FlagAggKPIBuilder

Bases: KPIBuilder[FlagAggKPIDefinition]

Fluent builder for creating FlagAggregation KPI definitions in bulk.

Provides a clean, declarative API for specifying multiple KPI definitions at once. Particularly useful for creating large numbers of KPIs with different combinations of flags and aggregations.

Example:

>>> builder = FlagAggKPIBuilder()
>>> definitions = (
...     builder
...     .for_flags(['BZ.market_price', 'BZ.net_position'])
...     .with_aggregations([Aggregations.Mean, Aggregations.Max, Aggregations.Min])
...     .for_all_objects()
...     .build()
... )
>>> len(definitions)  # 2 flags × 3 aggs = 6

All methods return self for chaining.

Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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class FlagAggKPIBuilder(KPIBuilder[FlagAggKPIDefinition]):
    """
    Fluent builder for creating FlagAggregation KPI definitions in bulk.

    Provides a clean, declarative API for specifying multiple KPI definitions
    at once. Particularly useful for creating large numbers of KPIs with
    different combinations of flags and aggregations.

    Example:

        >>> builder = FlagAggKPIBuilder()
        >>> definitions = (
        ...     builder
        ...     .for_flags(['BZ.market_price', 'BZ.net_position'])
        ...     .with_aggregations([Aggregations.Mean, Aggregations.Max, Aggregations.Min])
        ...     .for_all_objects()
        ...     .build()
        ... )
        >>> len(definitions)  # 2 flags × 3 aggs = 6

    All methods return self for chaining.
    """

    def __init__(self):
        """Initialize builder with default values."""
        super().__init__()
        self._flags: list[FlagTypeProtocol] = []
        self._aggregations: list[Aggregation] = []
        self._objects: list[Hashable] | Literal['auto'] = 'auto'
        self._model_flags: dict[FlagTypeProtocol, FlagTypeProtocol] = {}

    def for_flag(self, flag: FlagTypeProtocol) -> FlagAggKPIBuilder:
        """
        Set a single flag.

        Args:
            flag: Flag (e.g., 'BZ.Results.market_price')

        Returns:
            Self for chaining
        """
        self._flags = [flag]
        return self

    def for_flags(self, flags: list[FlagTypeProtocol]) -> FlagAggKPIBuilder:
        """
        Set multiple flags.

        Args:
            flags: List of flags

        Returns:
            Self for chaining
        """
        self._flags = flags
        return self

    def with_aggregation(self, agg: Aggregation) -> FlagAggKPIBuilder:
        """
        Set a single aggregation.

        Args:
            agg: Aggregation function

        Returns:
            Self for chaining
        """
        self._aggregations = [agg]
        return self

    def with_aggregations(self, aggs: list[Aggregation]) -> FlagAggKPIBuilder:
        """
        Set multiple aggregations.

        Args:
            aggs: List of aggregation functions

        Returns:
            Self for chaining
        """
        self._aggregations = aggs
        return self

    def for_all_objects(self) -> FlagAggKPIBuilder:
        """
        Auto-discover objects from data.

        Objects will be detected from DataFrame columns when
        generate_kpis() is called.

        Returns:
            Self for chaining
        """
        self._objects = 'auto'
        return self

    def for_objects_with_model_properties(
            self,
            properties: dict[str, Any] | None = None,
            query_expr: str | None = None,
            filter_funcs: dict[str, Callable[[Any], bool]] | None = None
    ) -> 'FlagAggKPIBuilder':
        """
        Filter objects based on model properties during KPI generation.

        During KPI generation, the model DataFrame is fetched and filtered using the
        specified conditions. Only objects that:
        1. Pass all filter conditions, AND
        2. Exist in both model and flag DataFrames
        will be included in the generated KPIs.

        Three modes of operation (can be combined with AND logic):
            1. Property filters: Exact match or list membership
            2. Query expression: Pandas query string evaluated on model DataFrame
            3. Filter functions: Custom functions applied to property columns

        All conditions across all modes are combined with AND logic.

        Args:
            properties: Dict of property names to values. Scalars for exact match,
                lists/sets for membership checks.
            query_expr: Pandas query expression (uses engine="python")
            filter_funcs: Dict of property names to filter functions applied column-wise

        Returns:
            Self for chaining

        Examples:

            # Property filter - exact match and list membership
            builder.for_objects_with_model_properties(
                properties={'country': 'DE', 'type': ['wind', 'solar']}
            )

            # Query expression
            builder.for_objects_with_model_properties(
                query_expr='country == "DE" and voltage_kV > 200'
            )

            # Custom filter function
            builder.for_objects_with_model_properties(
                filter_funcs={'voltage_kV': lambda x: x > 200}
            )

            # Combined - properties AND query
            builder.for_objects_with_model_properties(
                properties={'country': 'DE'},
                query_expr='voltage_kV > 200'
            )
        """
        self._objects = ModelPropertyFilter(
            properties=properties,
            query_expr=query_expr,
            filter_funcs=filter_funcs
        )
        return self

    def for_object(self, object: Hashable) -> FlagAggKPIBuilder:
        """
        Specify explicit object.

        Args:
            object: object name / ID

        Returns:
            Self for chaining
        """
        self._objects = [object]
        return self

    def for_objects(self, objects: list[Hashable]) -> FlagAggKPIBuilder:
        """
        Specify explicit object list.

        Args:
            objects: List of object names

        Returns:
            Self for chaining
        """
        self._objects = objects
        return self

    def with_model_flag(self, flag: FlagTypeProtocol, model_flag: FlagTypeProtocol) -> FlagAggKPIBuilder:
        """
        Set explicit model flag for a specific flag.

        Args:
            flag: Variable flag
            model_flag: Corresponding model flag

        Returns:
            Self for chaining
        """
        self._model_flags[flag] = model_flag
        return self

    def build(self) -> list[FlagAggKPIDefinition]:
        """
        Generate all KPI definitions from builder configuration.

        Creates the Cartesian product of flags × aggregations.

        Returns:
            List of FlagAggKPIDefinition instances

        Example:

            >>> builder = FlagAggKPIBuilder()
            >>> definitions = (
            ...     builder
            ...     .for_flags(['BZ.market_price', 'BZ.net_position'])
            ...     .with_aggregations([Aggregations.Mean, Aggregations.Max, Aggregations.Min])
            ...     .build()
            ... )
            >>> len(definitions)  # 2 flags × 3 aggs = 6
        """
        definitions = []

        for flag in self._flags:
            for agg in self._aggregations:
                definition = FlagAggKPIDefinition(
                    flag=flag,
                    aggregation=agg,
                    model_flag=self._model_flags.get(flag),
                    objects=self._objects,
                    name_prefix=self._name_prefix,
                    name_suffix=self._name_suffix,
                    custom_name=self._custom_name,
                    extra_attributes=self._extra_attributes,
                )
                definitions.append(definition)

        return definitions

__init__ 

__init__()

Initialize builder with default values.

Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def __init__(self):
    """Initialize builder with default values."""
    super().__init__()
    self._flags: list[FlagTypeProtocol] = []
    self._aggregations: list[Aggregation] = []
    self._objects: list[Hashable] | Literal['auto'] = 'auto'
    self._model_flags: dict[FlagTypeProtocol, FlagTypeProtocol] = {}

for_flag 

for_flag(flag: FlagTypeProtocol) -> FlagAggKPIBuilder

Set a single flag.

Parameters:

Name Type Description Default
flag FlagTypeProtocol

Flag (e.g., 'BZ.Results.market_price')

 required

Returns:

Type Description
FlagAggKPIBuilder

Self for chaining
Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def for_flag(self, flag: FlagTypeProtocol) -> FlagAggKPIBuilder:
    """
    Set a single flag.

    Args:
        flag: Flag (e.g., 'BZ.Results.market_price')

    Returns:
        Self for chaining
    """
    self._flags = [flag]
    return self

for_flags 

for_flags(flags: list[FlagTypeProtocol]) -> FlagAggKPIBuilder

Set multiple flags.

Parameters:

Name Type Description Default
flags list[FlagTypeProtocol]

List of flags

 required

Returns:

Type Description
FlagAggKPIBuilder

Self for chaining
Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def for_flags(self, flags: list[FlagTypeProtocol]) -> FlagAggKPIBuilder:
    """
    Set multiple flags.

    Args:
        flags: List of flags

    Returns:
        Self for chaining
    """
    self._flags = flags
    return self

with_aggregation 

with_aggregation(agg: Aggregation) -> FlagAggKPIBuilder

Set a single aggregation.

Parameters:

Name Type Description Default
agg Aggregation

Aggregation function

 required

Returns:

Type Description
FlagAggKPIBuilder

Self for chaining
Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def with_aggregation(self, agg: Aggregation) -> FlagAggKPIBuilder:
    """
    Set a single aggregation.

    Args:
        agg: Aggregation function

    Returns:
        Self for chaining
    """
    self._aggregations = [agg]
    return self

with_aggregations 

with_aggregations(aggs: list[Aggregation]) -> FlagAggKPIBuilder

Set multiple aggregations.

Parameters:

Name Type Description Default
aggs list[Aggregation]

List of aggregation functions

 required

Returns:

Type Description
FlagAggKPIBuilder

Self for chaining
Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def with_aggregations(self, aggs: list[Aggregation]) -> FlagAggKPIBuilder:
    """
    Set multiple aggregations.

    Args:
        aggs: List of aggregation functions

    Returns:
        Self for chaining
    """
    self._aggregations = aggs
    return self

for_all_objects 

for_all_objects() -> FlagAggKPIBuilder

Auto-discover objects from data.

Objects will be detected from DataFrame columns when generate_kpis() is called.

Returns:

Type Description
FlagAggKPIBuilder

Self for chaining
Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def for_all_objects(self) -> FlagAggKPIBuilder:
    """
    Auto-discover objects from data.

    Objects will be detected from DataFrame columns when
    generate_kpis() is called.

    Returns:
        Self for chaining
    """
    self._objects = 'auto'
    return self

for_objects_with_model_properties 

for_objects_with_model_properties(properties: dict[str, Any] | None = None, query_expr: str | None = None, filter_funcs: dict[str, Callable[[Any], bool]] | None = None) -> 'FlagAggKPIBuilder'

Filter objects based on model properties during KPI generation.

During KPI generation, the model DataFrame is fetched and filtered using the specified conditions. Only objects that: 1. Pass all filter conditions, AND 2. Exist in both model and flag DataFrames will be included in the generated KPIs.

Three modes of operation (can be combined with AND logic): 1. Property filters: Exact match or list membership 2. Query expression: Pandas query string evaluated on model DataFrame 3. Filter functions: Custom functions applied to property columns

All conditions across all modes are combined with AND logic.

Parameters:

Name Type Description Default
properties dict[str, Any] | None

Dict of property names to values. Scalars for exact match, lists/sets for membership checks.

 None
query_expr str | None

Pandas query expression (uses engine="python")

 None
filter_funcs dict[str, Callable[[Any], bool]] | None

Dict of property names to filter functions applied column-wise

 None

Returns:

Type Description
'FlagAggKPIBuilder'

Self for chaining

Examples:

# Property filter - exact match and list membership
builder.for_objects_with_model_properties(
    properties={'country': 'DE', 'type': ['wind', 'solar']}
)

# Query expression
builder.for_objects_with_model_properties(
    query_expr='country == "DE" and voltage_kV > 200'
)

# Custom filter function
builder.for_objects_with_model_properties(
    filter_funcs={'voltage_kV': lambda x: x > 200}
)

# Combined - properties AND query
builder.for_objects_with_model_properties(
    properties={'country': 'DE'},
    query_expr='voltage_kV > 200'
)
Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def for_objects_with_model_properties(
        self,
        properties: dict[str, Any] | None = None,
        query_expr: str | None = None,
        filter_funcs: dict[str, Callable[[Any], bool]] | None = None
) -> 'FlagAggKPIBuilder':
    """
    Filter objects based on model properties during KPI generation.

    During KPI generation, the model DataFrame is fetched and filtered using the
    specified conditions. Only objects that:
    1. Pass all filter conditions, AND
    2. Exist in both model and flag DataFrames
    will be included in the generated KPIs.

    Three modes of operation (can be combined with AND logic):
        1. Property filters: Exact match or list membership
        2. Query expression: Pandas query string evaluated on model DataFrame
        3. Filter functions: Custom functions applied to property columns

    All conditions across all modes are combined with AND logic.

    Args:
        properties: Dict of property names to values. Scalars for exact match,
            lists/sets for membership checks.
        query_expr: Pandas query expression (uses engine="python")
        filter_funcs: Dict of property names to filter functions applied column-wise

    Returns:
        Self for chaining

    Examples:

        # Property filter - exact match and list membership
        builder.for_objects_with_model_properties(
            properties={'country': 'DE', 'type': ['wind', 'solar']}
        )

        # Query expression
        builder.for_objects_with_model_properties(
            query_expr='country == "DE" and voltage_kV > 200'
        )

        # Custom filter function
        builder.for_objects_with_model_properties(
            filter_funcs={'voltage_kV': lambda x: x > 200}
        )

        # Combined - properties AND query
        builder.for_objects_with_model_properties(
            properties={'country': 'DE'},
            query_expr='voltage_kV > 200'
        )
    """
    self._objects = ModelPropertyFilter(
        properties=properties,
        query_expr=query_expr,
        filter_funcs=filter_funcs
    )
    return self

for_object 

for_object(object: Hashable) -> FlagAggKPIBuilder

Specify explicit object.

Parameters:

Name Type Description Default
object Hashable

object name / ID

 required

Returns:

Type Description
FlagAggKPIBuilder

Self for chaining
Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def for_object(self, object: Hashable) -> FlagAggKPIBuilder:
    """
    Specify explicit object.

    Args:
        object: object name / ID

    Returns:
        Self for chaining
    """
    self._objects = [object]
    return self

for_objects 

for_objects(objects: list[Hashable]) -> FlagAggKPIBuilder

Specify explicit object list.

Parameters:

Name Type Description Default
objects list[Hashable]

List of object names

 required

Returns:

Type Description
FlagAggKPIBuilder

Self for chaining
Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def for_objects(self, objects: list[Hashable]) -> FlagAggKPIBuilder:
    """
    Specify explicit object list.

    Args:
        objects: List of object names

    Returns:
        Self for chaining
    """
    self._objects = objects
    return self

with_model_flag 

with_model_flag(flag: FlagTypeProtocol, model_flag: FlagTypeProtocol) -> FlagAggKPIBuilder

Set explicit model flag for a specific flag.

Parameters:

Name Type Description Default
flag FlagTypeProtocol

Variable flag

 required
model_flag FlagTypeProtocol

Corresponding model flag

 required

Returns:

Type Description
FlagAggKPIBuilder

Self for chaining
Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def with_model_flag(self, flag: FlagTypeProtocol, model_flag: FlagTypeProtocol) -> FlagAggKPIBuilder:
    """
    Set explicit model flag for a specific flag.

    Args:
        flag: Variable flag
        model_flag: Corresponding model flag

    Returns:
        Self for chaining
    """
    self._model_flags[flag] = model_flag
    return self

build 

build() -> list[FlagAggKPIDefinition]

Generate all KPI definitions from builder configuration.

Creates the Cartesian product of flags × aggregations.

Returns:

Type Description
list[FlagAggKPIDefinition]

List of FlagAggKPIDefinition instances

Example:

>>> builder = FlagAggKPIBuilder()
>>> definitions = (
...     builder
...     .for_flags(['BZ.market_price', 'BZ.net_position'])
...     .with_aggregations([Aggregations.Mean, Aggregations.Max, Aggregations.Min])
...     .build()
... )
>>> len(definitions)  # 2 flags × 3 aggs = 6
Source code in submodules/mesqual/mesqual/kpis/builders/flag_agg_builder.py 
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def build(self) -> list[FlagAggKPIDefinition]:
    """
    Generate all KPI definitions from builder configuration.

    Creates the Cartesian product of flags × aggregations.

    Returns:
        List of FlagAggKPIDefinition instances

    Example:

        >>> builder = FlagAggKPIBuilder()
        >>> definitions = (
        ...     builder
        ...     .for_flags(['BZ.market_price', 'BZ.net_position'])
        ...     .with_aggregations([Aggregations.Mean, Aggregations.Max, Aggregations.Min])
        ...     .build()
        ... )
        >>> len(definitions)  # 2 flags × 3 aggs = 6
    """
    definitions = []

    for flag in self._flags:
        for agg in self._aggregations:
            definition = FlagAggKPIDefinition(
                flag=flag,
                aggregation=agg,
                model_flag=self._model_flags.get(flag),
                objects=self._objects,
                name_prefix=self._name_prefix,
                name_suffix=self._name_suffix,
                custom_name=self._custom_name,
                extra_attributes=self._extra_attributes,
            )
            definitions.append(definition)

    return definitions