pywrdrb.parameters.FfmpNycRunningAvgParameter#

class pywrdrb.parameters.FfmpNycRunningAvgParameter(model, node, max_avg_delivery, **kwargs)#

Enforces the NYC FFMP delivery constraint using a running average over time.

This custom Pywr parameter limits the amount of water that can be delivered from an NYC reservoir based on a long-term daily average (max_avg_delivery). It ensures that daily releases stay within the specified delivery budget and adjusts dynamically based on past deliveries.

node#

The node from which water is being delivered (usually an NYC reservoir).

Type:

pywr.Node

max_avg_delivery#

Maximum allowable average delivery volume per day.

Type:

float

max_delivery#

Array holding the daily remaining delivery allowance per scenario.

Type:

numpy.ndarray

timestep#

The number of days per timestep (usually 1 for daily models).

Type:

int

datetime#

The current model date used for enforcing reset logic.

Type:

pandas.Timestamp

setup()#

Allocates an array to store per-scenario delivery limits.

reset()#

Initializes delivery limits and datetime to model start.

value(timestep, scenario_index)#

Returns the remaining delivery volume for the given scenario.

after()#

Updates the running delivery limit based on the prior day’s release.

load(model, data)#

Class method to load the parameter from JSON/YAML model data.

__init__(model, node, max_avg_delivery, **kwargs)#

Initialize the FfmpNycRunningAvgParameter.

Parameters:

• model (pywr.Model) – The Pywr model instance.

• node (pywr.Node) – The node associated with this delivery parameter.

• max_avg_delivery (pywr.parameters.ConstantParameter) – The maximum daily average delivery limit for the FFMP constraint.

• **kwargs – Additional keyword arguments passed to the base Parameter class.

Notes

This parameter is typically used on NYC reservoir delivery nodes to ensure long-term delivery limits are not exceeded in the FFMP simulation framework.

Methods

__init__(model, node, max_avg_delivery, **kwargs)	Initialize the FfmpNycRunningAvgParameter.
after()	Update the delivery budget based on the previous timestep’s release.
before(self)
finish(self)
get_all_values(self)
get_constant_value(self)	Return a constant value.
get_double_lower_bounds(self)
get_double_upper_bounds(self)
get_double_variables(self)
get_integer_lower_bounds(self)
get_integer_upper_bounds(self)
get_integer_variables(self)
get_value(self, ScenarioIndex scenario_index)
load(model, data)	Load the parameter from model and configuration data.
register(cls)
reset()	Reset the delivery budget and internal clock for all scenarios.
set_double_variables(self, double[)
set_integer_variables(self, int[)
setup()	Allocate internal arrays used to track max delivery per scenario.
unregister(cls)
value(timestep, scenario_index)	Get the remaining delivery volume for the current timestep and scenario.

Attributes

children
comment	unicode
double_size	'int'
integer_size	'int'
is_constant
is_variable	'bool'
model
name
parents
size
tags	dict