flexmeasures.data.schemas.reporting.pandas_reporter

Classes

class flexmeasures.data.schemas.reporting.pandas_reporter.PandasMethodCall(*, only: types.StrSequenceOrSet | None = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: dict | None = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: bool | types.StrSequenceOrSet = False, unknown: str | None = None)

class flexmeasures.data.schemas.reporting.pandas_reporter.PandasReporterConfigSchema(*, only: types.StrSequenceOrSet | None = None, exclude: types.StrSequenceOrSet = (), many: bool = False, context: dict | None = None, load_only: types.StrSequenceOrSet = (), dump_only: types.StrSequenceOrSet = (), partial: bool | types.StrSequenceOrSet = False, unknown: str | None = None)

This schema lists fields that can be used to describe sensors in the optimised portfolio

Example:

{

“input_sensors”[

{“sensor” : 1, “alias” : “df1”}

], “transformations” : [

{

“df_input” : “df1”, “df_output” : “df2”, “method” : “copy”

}, {

“df_input” : “df2”, “df_output” : “df2”, “method” : “sum”

}, {

“method” : “sum”, “kwargs” : {“axis” : 0}

}

], “final_df_output” : “df2”

validate_chaining(data, **kwargs)

This validator ensures that we are always given an input and that the final_df_output is computed.