pywr.recorders.NumpyArrayNodeDeficitRecorder
- class pywr.recorders.NumpyArrayNodeDeficitRecorder
Recorder for timeseries of deficit from a Node.
This class stores deficit from a specific node for each time-step of a simulation. The data is saved internally using a memory view. The data can be accessed through the data attribute or to_dataframe() method.
- Parameters:
- modelpywr.core.Model
- nodepywr.core.Node
Node instance to record.
- temporal_agg_funcstr or callable (default=”mean”)
Aggregation function used over time when computing a value per scenario. This can be used to return, for example, the median flow over a simulation. For aggregation over scenarios see the agg_func keyword argument.
See also
Notes
Deficit is calculated as the difference between max_flow and self.node.flow (i.e. the actual flow allocated during the time-step):
deficit = max_flow - actual_flow
- __init__(*args, **kwargs)
Methods
__init__(*args, **kwargs)after(self)aggregated_value(self)before(self)finish(self)is_constraint_violated(self)Returns true if the value from this Recorder violates its constraint bounds.
load(cls, model, data)register(cls)reset(self)setup(self)to_dataframe(self)Return a pandas.DataFrame of the recorder data
unregister(cls)values(self)Compute a value for each scenario using temporal_agg_func.
Attributes
agg_funcchildrencommentcomment: str
constraint_lower_boundsconstraint_upper_boundsdataepsilonepsilon: 'double'
factorfactor: 'float'
ignore_nanignore_nan: 'bool'
is_constraintReturns true if either upper or lower constraint bounds is defined.
is_double_bounded_constraintReturns true if upper and lower constraint bounds are both defined and not-equal to one another.
is_equality_constraintReturns true if upper and lower constraint bounds are both defined and equal to one another.
is_lower_bounded_constraintReturns true if lower constraint bounds is defined and upper constraint bounds is not.
is_objectiveis_upper_bounded_constraintReturns true if upper constraint bounds is defined and lower constraint bounds is not.
modelnamenodeparentstagstags: dict
temporal_agg_func