def __call__(model: modeltools.Model) -> None:
con = model.parameters.control.fastaccess
der = model.parameters.derived.fastaccess
flu = model.sequences.fluxes.fastaccess
counter = 0
for idx in range(der.nmbinputs):
if not modelutils.isnan(flu.inputs[idx]):
counter += 1
if counter == con.minnmbinputs:
break
else:
flu.actualfactor = con.defaultfactor
flu.actualconstant = con.defaultconstant
return
d_mean_height = model.return_mean_v1(con.inputheights, flu.inputs,
der.nmbinputs)
d_mean_inputs = model.return_mean_v1(flu.inputs, flu.inputs,
der.nmbinputs)
d_nominator = 0.0
d_denominator = 0.0
for idx in range(der.nmbinputs):
if not modelutils.isnan(flu.inputs[idx]):
d_temp = con.inputheights[idx] - d_mean_height
d_nominator += d_temp * (flu.inputs[idx] - d_mean_inputs)
d_denominator += d_temp * d_temp
if d_denominator > 0.0:
flu.actualfactor = d_nominator / d_denominator
flu.actualconstant = d_mean_inputs - flu.actualfactor * d_mean_height
else:
flu.actualfactor = con.defaultfactor
flu.actualconstant = con.defaultconstant
return
def __call__(model: modeltools.Model, inputs: Vector,
outputs: Vector) -> None:
con = model.parameters.control.fastaccess
der = model.parameters.derived.fastaccess
for idx_out in range(der.nmboutputs):
d_sumweights = 0.0
d_sumvalues = 0.0
d_sumvalues_inf = 0.0
counter_inf = 0
for idx_try in range(con.maxnmbinputs):
idx_in = der.proximityorder[idx_out, idx_try]
if not modelutils.isnan(inputs[idx_in]):
if modelutils.isinf(der.weights[idx_out, idx_try]):
d_sumvalues_inf += inputs[idx_in]
counter_inf += 1
else:
d_sumweights += der.weights[idx_out, idx_try]
d_sumvalues += der.weights[idx_out,
idx_try] * inputs[idx_in]
if counter_inf:
outputs[idx_out] = d_sumvalues_inf / counter_inf
elif d_sumweights:
outputs[idx_out] = d_sumvalues / d_sumweights
else:
outputs[idx_out] = modelutils.nan
def __call__(model: modeltools.Model) -> None:
con = model.parameters.control.fastaccess
der = model.parameters.derived.fastaccess
flu = model.sequences.fluxes.fastaccess
for idx_out in range(der.nmboutputs):
for idx_try in range(con.maxnmbinputs):
idx_in = der.proximityorder[idx_out, idx_try]
flu.outputs[idx_out] = flu.inputs[idx_in]
if not modelutils.isnan(flu.outputs[idx_out]):
break
def __call__(
model: modeltools.Model,
values: Vector[float],
mask: Vector[float],
number: int,
) -> float:
counter = 0
d_result = 0.0
for idx in range(number):
if not modelutils.isnan(mask[idx]):
counter += 1
d_result += values[idx]
if counter > 0:
return d_result / counter
return modelutils.nan
def __call__(model: modeltools.SegmentModel) -> None:
sol = model.parameters.solver.fastaccess
fac = model.sequences.factors.fastaccess
i = model.idx_segment
d_wl = fac.referencewaterlevel[i]
if modelutils.isnan(d_wl) or modelutils.isinf(d_wl):
d_min, d_max = 0.0, 2.0
elif d_wl <= 0.001:
d_min, d_max = 0.0, 0.01
else:
d_min, d_max = 0.9 * d_wl, 1.1 * d_wl
fac.referencewaterlevel[i] = model.pegasusreferencewaterlevel.find_x(
d_min,
d_max,
0.0,
1000.0,
sol.tolerancewaterlevel,
sol.tolerancedischarge,
100,
)