pywrdrb.parameters.LaggedReservoirRelease

class pywrdrb.parameters.LaggedReservoirRelease(model, lag, roll_mean_lag_outflow, roll_mean_lagMinus1_outflow, roll_mean_lag_spill, roll_mean_lagMinus1_spill, **kwargs)

Pywr doesnt have a parameter to return a previous (>1 timesteps) node flow or parameter value. But we can calculate release for N timesteps ago based on rolling avg parameters for N & (N-1) timesteps.

lag

number of timesteps ago

Type:

int

roll_mean_lag_outflow

rolling mean outflow parameter for N timesteps ago

Type:

Parameter

roll_mean_lagMinus1_outflow

rolling mean outflow parameter for (N-1) timesteps ago

Type:

Parameter

roll_mean_lag_spill

rolling mean spill parameter for N timesteps ago

Type:

Parameter

roll_mean_lagMinus1_spill

rolling mean spill parameter for (N-1) timesteps ago

Type:

Parameter

value(timestep, scenario_index)

returns the release value for N timesteps ago

load(model, data)

loads the parameter from the model dictionary

__init__(model, lag, roll_mean_lag_outflow, roll_mean_lagMinus1_outflow, roll_mean_lag_spill, roll_mean_lagMinus1_spill, **kwargs)

Methods

__init__(model, lag, roll_mean_lag_outflow, ...)
after(self)
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)	Setup the parameter.
register(cls)
reset(self)
set_double_variables(self, double[)
set_integer_variables(self, int[)
setup(self)
unregister(cls)
value(timestep, scenario_index)	value_lag = lag * rollmean_lag - (lag - 1) * rollmean_lagMinus1

Attributes

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