Module parameteriser¶

parameteriser ¶

Classes:

MhData –

Data log from metaheuristic calibration processes

Functions:

add_to_hypercube –

Add entries to a hypercube
add_transform –

Create a parameteriser for which parameter transformations can be defined
apply_sys_config –

Apply a model configuration to a simulation
as_py_structure –

Try to convert an external pointer to a native python representation
backtransform –

Get the parameteriser values in the untransformed space
bound_values –
bound_values_df –

min/max bound a column in a data frame
concatenate_parameterisers –

Concatenate hypercubes to a single parameteriser
create_multisite_obj_parameteriser –

Builds a parameteriser usable with a multisite multiobjective calculator.
create_muskingum_param_constraints –

Create a parameteriser with Muskingum-type constraints. Given an existing parameteriser, create a wrapper that adds constraints on two of its parameters.
create_parameter_sampler –
create_parameteriser –

Create a SWIFT parameteriser
create_sce_optim_swift –

Build an SCE optimiser for a SWIFT model
create_sce_termination_wila –

Create a type of termination criteria suitable for the SCE algorithm.
evaluate_score_for_parameters –

Computes the value of an objective for a given set of parameters
example_parameteriser –

Get examples of typical parameterisers
execute_optimisation –

Launch an optimization task, as defined by the object passed as an argument
extract_optimisation_log –

Extract the logger from a parameter extimator (optimiser or related)
feasible_muskingum_bounds –

[summary]
filtered_parameters –

Wrap a parameteriser in a filter that can hide some parameters
get_best_score –

Gets the best score in a population for a given objective
get_default_sce_parameters –

[summary]
get_logger_content –

Gets logger content on an optimiser, recorded detail of the optimisation process for post-optimisation analysis.
get_marginal_termination –

Create an termination criterion based on the rate of marginal fitness improvement
get_max_iteration_termination –

Create an termination criterion based on the number of objective evaluations
get_max_runtime_termination –

Create an termination criterion based on the wall clock runtime
get_score_at_index –

Get an objective scores in a vector thereof
hide_parameters –

Hide some parameters (from the outside e.g. optimisers) in a filter parameteriser
is_hypercube –

Is the object a native parameteriser that can be cast as a hypercube?
is_sampler_seeding –

Is the argument a native object that is a seeded candidate parameteriser factory
is_score –

OBJECTIVE_SCORES_WILA_PTR
is_set_of_scores –

VEC_OBJECTIVE_SCORES_PTR
linear_parameteriser –

Create a scaled linear parameteriser, tying by a linear transformation a (resp several) model state to a (resp several) parameter values
linear_parameteriser_from –

Create a scaled linear parameteriser, tying by a linear transformation a (resp several) model state to a (resp several) parameter values
listify –
make_state_init_parameteriser –

[summary]
mk_optim_log –
num_free_parameters –
parameteriser_as_dataframe –

Convert an external object hypercube parameteriser to a pandas data frame
parameteriser_for_score –

Gets the parameteriser for a score
parameters_for –
scores_as_dataframe –

Convert objective scores to a pandas data frame representation
set_calibration_logger –

Sets logging on an optimiser, so as to record a detail of the optimisation process for post-optimisation analysis.
set_hypercube –

Set the properties of a hypercube parameteriser
set_max_parameter_value –

Sets the maximum value of a model parameter value
set_min_parameter_value –

Sets the minimum value of a model parameter value
set_parameter_value –

Sets the value of a model parameter value
show_parameters –

Show some parameters (from the outside e.g. optimisers) in a filter parameteriser
sort_by_score –

Sort objective scores according to one of the objective values
subcatchment_parameteriser –

Create a parameteriser that gets applied to a subset of a whole catchment
wrap_transform –

Create a parameteriser for which parameter transformations can be defined.

Attributes:

INTERCEPT_COL –
MAX_VALUE_COL –
MIN_VALUE_COL –
PARAM_NAME_COL –
SCALING_VAR_NAME_COL –
STATE_NAME_COL –
VALUE_COL –

INTERCEPT_COL `module-attribute` ¶

INTERCEPT_COL = 'intercept'

MAX_VALUE_COL `module-attribute` ¶

MAX_VALUE_COL = 'max_value'

MIN_VALUE_COL `module-attribute` ¶

MIN_VALUE_COL = 'min_value'

PARAM_NAME_COL `module-attribute` ¶

PARAM_NAME_COL = 'param_name'

SCALING_VAR_NAME_COL `module-attribute` ¶

SCALING_VAR_NAME_COL = 'scaling_var_name'

STATE_NAME_COL `module-attribute` ¶

STATE_NAME_COL = 'state_name'

VALUE_COL `module-attribute` ¶

VALUE_COL = 'value'

MhData ¶

MhData(data: DataFrame, fitness: str = 'NSE', messages: str = 'Message', categories: str = 'Category')

Data log from metaheuristic calibration processes

Methods:

bound_fitness –

Return a copy of the log data with the fitness measure bound by min/max limits
facet_plot –

Facet plot of parameter value evolution, facetted by a category.
rename_columns –

Rename the columns of the data log according to a mapping.
subset_by_message –

Subset the log by filtering the 'Message' column by a regexp pattern
subset_by_pattern –

Subset the log by filtering an attribute by a regexp pattern

Attributes:

data (DataFrame) –

The inner data of this data log

data `property` ¶

data: DataFrame

The inner data of this data log

bound_fitness ¶

bound_fitness(obj_lims: Sequence[float] = None) -> DataFrame

Return a copy of the log data with the fitness measure bound by min/max limits

Parameters:

obj_lims (Sequence[float], default: None ) –

min/max limits, length 2. Defaults to None.

Returns:

DataFrame –

pd.DataFrame: log data with bound fitness

facet_plot ¶

facet_plot(y: str, facet_category: str = 'Message', col_wrap: int = 3, x: str = 'PointNumber', fig_width_in=15, fig_heigth_in=10)

Facet plot of parameter value evolution, facetted by a category.

This method requires the package seaborn to be installed.

Parameters:

y (str) –

variable name (model parameter) to use for the y-axis, e.g. "x4" for GR4J
facet_category (str, default: 'Message' ) –

Data attribute to use to facet. Defaults to "Message".
col_wrap (int, default: 3 ) –

Max number of columns in the plot. Defaults to 3.
x (str, default: 'PointNumber' ) –

variable name (calibration iteration, or model parameter) to use for the x-axis. Defaults to "PointNumber".
fig_width_in (int, default: 15 ) –

figure width in inches. Defaults to 15.
fig_heigth_in (int, default: 10 ) –

figure height in inches. Defaults to 10.

Returns:

FacetGrid –

The plot to display

rename_columns ¶

rename_columns(colnames_map: Dict[str, str]) -> None

Rename the columns of the data log according to a mapping.

This is handy for instance to change fully qualified parameter names such as 'subarea.Wolf_Creek.x1' to just 'x1' to produce more legible plots.

Parameters:

colnames_map (Dict[str, str]) –

mapping

subset_by_message ¶

subset_by_message(pattern: str = 'Initial.*|Reflec.*|Contrac.*|Add.*') -> MhData

Subset the log by filtering the 'Message' column by a regexp pattern

Parameters:

pattern (str, default: 'Initial.*|Reflec.*|Contrac.*|Add.*' ) –

regexp pattern, filter definition

Returns:

Any ( MhData ) –

New MhData object with subset data

subset_by_pattern ¶

subset_by_pattern(colname: str, pattern: str) -> MhData

Subset the log by filtering an attribute by a regexp pattern

Parameters:

colname (str) –

column name to filter on
pattern (str) –

regexp pattern, filter definition

Returns:

Any ( MhData ) –

New MhData object with subset data

add_to_hypercube ¶

add_to_hypercube(parameteriser, specs)

Add entries to a hypercube

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it
specs (DataFrame) –

An optional data frame description of the parameter set, with at least columns Name, Min, Max, Value.

add_transform ¶

add_transform(parameteriser: TransformParameteriser, param_name: str, inner_param_name: str, transform_id: str, a=1.0, b=0.0)

Create a parameteriser for which parameter transformations can be defined

This allows to define e.g. a virtual parameter log_X instead of calibrating on the parameter X.

Parameters:

parameteriser (TransformParameteriser) –

A TransformParameteriser wrapper, or a type inheriting from it
param_name (str) –

the name of the meta-parameter. Note that it can be the same value as inner_param_name, but this is NOT recommended.
inner_param_name (str) –

the name of the parameter being transformed
transform_id (str) –

identifier for a known bijective univariate function
a (float, default: 1.0 ) –

parameter in Y = F(ax+b). Defaults to 1.0.
b (float, default: 0.0 ) –

parameter in Y = F(ax+b). Defaults to 0.0.

apply_sys_config ¶

apply_sys_config(parameteriser, simulation)

Apply a model configuration to a simulation

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it
simulation (Simulation) –

simulation

as_py_structure ¶

as_py_structure(x: Any)

Try to convert an external pointer to a native python representation

Parameters:

x (Any) –

object, presumably wrapper around an Xptr, to convert to a 'pure' python representation

Returns:

–

backtransform ¶

backtransform(parameteriser)

Get the parameteriser values in the untransformed space

Get the parameteriser values in the untransformed space, i.e. remove any transform added via wrapTransform. This allows to transform back e.g. from a virtual parameter log_X to the underlying model (or even virtual/meta) parameter X.

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it

Returns:

–

[HypercubeParameteriser]: The parameters definitions without the transforms (if there are any)

bound_values ¶

bound_values(x, lim: List = None)

bound_values_df ¶

bound_values_df(x, colname, lim=None)

min/max bound a column in a data frame

Parameters:

x ([type]) –

a data frame
colname ([type]) –

a character vector, name of the column to bound
lim ([type], default: None ) –

a num vector of the min/max limits to apply, for instance c(0, 1). Defaults to None.

Returns:

–

concatenate_parameterisers ¶

concatenate_parameterisers(*args: Sequence[HypercubeParameteriser], strategy: str = '') -> CompositeParameteriser

Concatenate hypercubes to a single parameteriser

Parameters:

strategy (str, default: '' ) –

The strategy to contatenate. Defaults to "", equivalent to "composite", the only available. May have other options in the future.

Returns:

CompositeParameteriser ( CompositeParameteriser ) –

A concatenated parameteriser

create_multisite_obj_parameteriser ¶

create_multisite_obj_parameteriser(func_parameterisers, func_identifiers, prefixes=None, mix_func_parameteriser=None, hydro_parameteriser=None)

Builds a parameteriser usable with a multisite multiobjective calculator.

This is an advanced topic; users may refer to this sample workflow

Parameters:

func_parameterisers ([type]) –

list of external pointers, parameterisers for each function of a multiobjective calculation.
func_identifiers ([type]) –

character, identifiers for each of the objectives defined in an multisite objective definition.
prefixes ([type], default: None ) –

Optional prefixes to use to disambiguate short parameter names used in each function of a multiobjective calculator.. Defaults to None.
mix_func_parameteriser ([type], default: None ) –

parameteriser, default None. (FUTURE) Optional parameteriser used in mixing the multiple objectives.. Defaults to None.
hydro_parameteriser ([type], default: None ) –

parameteriser, default None. Optional parameteriser applied to the simulation model.. Defaults to None.

Returns:

–

create_muskingum_param_constraints ¶

create_muskingum_param_constraints(inner_parameters, delta_t=1, param_name_k='K', param_name_x='X', simulation=None)

Create a parameteriser with Muskingum-type constraints. Given an existing parameteriser, create a wrapper that adds constraints on two of its parameters.

Parameters:

inner_parameters ([HypercubeParameteriser]) –

A SWIFT parameteriser object.
delta_t (int, default: 1 ) –

the simulation time step in HOURS. Defaults to 1.
param_name_k (str, default: 'K' ) –

the variable identifier to use for the delay parameter of the Muskingum routing. Defaults to "K".
param_name_x (str, default: 'X' ) –

the variable identifier to use for the attenuation parameter of the Muskingum routing. Defaults to "X".
simulation ([Simulation], default: None ) –

the model simulation from which link properties are inspected to define constraints. The links' parameters must already be set.. Defaults to None.

Raises:

ValueError –

[description]

Returns:

–

create_parameter_sampler ¶

create_parameter_sampler(seed, parameteriser, type: str)

Parameters:

seed ([type]) –

seed integer, the seed to use for the sampler
parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it
type (str) –

identifying a method such as 'urs' for uniform random sampling.

Returns:

–

create_parameteriser ¶

create_parameteriser(type='Generic subareas', specs: DataFrame = None)

Create a SWIFT parameteriser

Parameters:

type (str, default: 'Generic subareas' ) –

A string identifying the (likely SWIFT-specific) type of parameteriser to use. Recognised types are (case insensitive) 'log-likelihood', 'generic', 'subareas', 'links', 'nodes' and 'muskingum'. Defaults to "Generic subareas".
specs (DataFrame, default: None ) –

An optional data frame description of the parameter set, with at least columns Name, Min, Max, Value. Defaults to None.

Returns:

–

[HypercubeParameteriser]: new parameteriser

create_sce_optim_swift ¶

create_sce_optim_swift(objective, termination_criterion, sce_params, population_initialiser)

Build an SCE optimiser for a SWIFT model

Parameters:

objective (ObjectiveEvaluator) –

an objective calculator
termination_criterion (SceTerminationCondition) –

An object that can be passed to SCE for testing the completion of the algorithm.
sce_params (dict) –

optional; parameters controlling the behavior of the SCE optimisers.
population_initialiser (CandidateFactorySeed) –

an S4 object 'ExternalObjRef' [package "cinterop"] with external pointer type HYPERCUBE_PTR or coercible to it, or a type of object that can seed a sampler i.e. coercible to a type CANDIDATE_FACTORY_SEED_WILA_PTR. If the argument is a hypercube, a uniform random sampler is created.

Returns:

–

create_sce_termination_wila ¶

create_sce_termination_wila(type: str, arguments: Sequence[str]) -> SceTerminationCondition

Create a type of termination criteria suitable for the SCE algorithm.

Parameters:

type (str) –

A type of termination criterion; currently at least "relative standard deviation" and "maximum evaluations" are valid options
arguments (Sequence[str]) –

Arguments, in string forms even for numeric values, options for the selected type.

Returns:

SceTerminationCondition ( SceTerminationCondition ) –

[description]

evaluate_score_for_parameters ¶

evaluate_score_for_parameters(objective, parameteriser)

Computes the value of an objective for a given set of parameters

Parameters:

objective ([type]) –

an objective calculator
parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it

Returns:

–

example_parameteriser ¶

example_parameteriser(type: str, strict=False)

Get examples of typical parameterisers

Parameters:

type (str) –

identifier for a type of parameteriser including 'log-likelihood'
strict (bool, default: False ) –

If True an error is raised if the type is not found, otherwise a dummy empty parameteriser is returned.. Defaults to False.

Returns:

–

execute_optimisation ¶

execute_optimisation(optimiser)

Launch an optimization task, as defined by the object passed as an argument

Parameters:

optimiser (Optimiser) –

the instance of the optimiser that has been created for the optimisation task about to be launched.

Returns:

–

extract_optimisation_log ¶

extract_optimisation_log(estimator, fitness_name='log.likelihood') -> MhData

Extract the logger from a parameter extimator (optimiser or related)

Parameters:

estimator (Optimiser) –

the optimiser instance
fitness_name (str, default: 'log.likelihood' ) –

name of the fitness function to extract. Defaults to "log.likelihood".

Returns:

MhData ( MhData ) –

an object with methods to analyse the optimisation log

feasible_muskingum_bounds ¶

feasible_muskingum_bounds(simulation: Simulation, delta_t_hours=1)

[summary]

Parameters:

simulation (Simulation) –

[description]
delta_t_hours (int, default: 1 ) –

[description]. Defaults to 1.

Returns:

–

filtered_parameters ¶

filtered_parameters(parameteriser)

Wrap a parameteriser in a filter that can hide some parameters

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it. A deep copy of the input is taken.

Returns:

–

get_best_score ¶

get_best_score(scores_population, score_name: str = '', convert_to_py: bool = False) -> Dict[str, Any] | ObjectiveScores

Gets the best score in a population for a given objective

Parameters:

scores_population ([type]) –

an S4 object 'ExternalObjRef' [package "cinterop"] with external pointer type VEC_OBJECTIVE_SCORES_PTR
score_name (str, default: '' ) –

name of the objective to use for sorting. Defaults to "NSE".
convert_to_py (bool, default: False ) –

should the returned score be converted to an R representation. Default False. Defaults to False.

Returns:

Dict[str, Any] | ObjectiveScores –

get_default_sce_parameters ¶

get_default_sce_parameters()

[summary]

Returns:

–

get_logger_content ¶

get_logger_content(optimiser: DeletableCffiNativeHandle, add_numbering: bool = False) -> DataFrame

Gets logger content on an optimiser, recorded detail of the optimisation process for post-optimisation analysis.

Parameters:

optimiser ([type]) –

the instance of the optimiser that has been created for the optimisation task about to be launched.
add_numbering (bool, default: False ) –

Add an explicit column for numbering the lines of the log. Defaults to False.

Returns:

DataFrame –

pd.DataFrame: The data log of the optimiser

get_marginal_termination ¶

get_marginal_termination(tolerance=1e-06, cutoff_no_improvement=10, max_hours=0.05)

Create an termination criterion based on the rate of marginal fitness improvement

Parameters:

tolerance ([type], default: 1e-06 ) –

the increment in the objective below which the improvement is considered negligible. Defaults to 1e-06.
cutoff_no_improvement (int, default: 10 ) –

the maximum number of successive times the algorithm fails to improve the objective function.. Defaults to 10.
max_hours (float, default: 0.05 ) –

the maximum wall time runtime for the optimisation. Defaults to 0.05.

Returns:

–

get_max_iteration_termination ¶

get_max_iteration_termination(max_iterations=1000)

Create an termination criterion based on the number of objective evaluations

Parameters:

max_iterations (int, default: 1000 ) –

number of iterations, which, if less than total count of optim objective evaluations, defines optim termination.. Defaults to 1000.

Returns:

–

get_max_runtime_termination ¶

get_max_runtime_termination(max_hours=0.05)

Create an termination criterion based on the wall clock runtime

Parameters:

max_hours (float, default: 0.05 ) –

the maximum wall time runtime in hours for the optimisation. Defaults to 0.05.

Returns:

–

get_score_at_index ¶

get_score_at_index(scores_population, index: int)

Get an objective scores in a vector thereof

Parameters:

scores_population ([type]) –

an S4 object 'ExternalObjRef' [package "cinterop"] with external pointer type VEC_OBJECTIVE_SCORES_PTR
index (int) –

one-based index in the population

Returns:

–

hide_parameters ¶

hide_parameters(parameteriser, patterns, regex=False, starts_with=False, strict=False)

Hide some parameters (from the outside e.g. optimisers) in a filter parameteriser

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it
patterns ([type]) –

character, one or more pattern to match and hide matching parameters. Match according to other parameters.
regex (bool, default: False ) –

logical, defaults False, should the patterns be used as regular expressions.. Defaults to False.
starts_with (bool, default: False ) –

logical, defaults False. Ignored if regex is True. Should the patterns be used as starting strings in the parameter names.. Defaults to False.
strict (bool, default: False ) –

logical, default False. Used only if regex and starts_with are False. If True, raises an error if one of the "patterns" has no exact match in the parameters.. Defaults to False.

is_hypercube ¶

is_hypercube(p_set: CffiNativeHandle)

Is the object a native parameteriser that can be cast as a hypercube?

Parameters:

p_set (CffiNativeHandle) –

[description]

Returns:

–

is_sampler_seeding ¶

is_sampler_seeding(obj: CffiNativeHandle)

Is the argument a native object that is a seeded candidate parameteriser factory

Parameters:

obj (CffiNativeHandle) –

[description]

Returns:

–

is_score ¶

is_score(x)

OBJECTIVE_SCORES_WILA_PTR

Parameters:

x ([type]) –

[description]

Returns:

–

is_set_of_scores ¶

is_set_of_scores(x)

VEC_OBJECTIVE_SCORES_PTR

Parameters:

x ([type]) –

[description]

Returns:

–

linear_parameteriser ¶

linear_parameteriser(param_name: VecStr, state_name: VecStr, scaling_var_name: VecStr, min_p_val: VecNum, max_p_val: VecNum, value: VecNum, selector_type: str = 'subareas', intercept: VecNum = 0.0)

Create a scaled linear parameteriser, tying by a linear transformation a (resp several) model state to a (resp several) parameter values

This allows to define tied parameters where pval = a * modelStateVal + intercept. The intent in particular is to define virtual model parameters such as initial store value as a fraction of the maximum storage capacity.

Args:

param_name (VecStr): the name of the meta-parameter. Note that it can be the same value as inner_param_name without interference, though this may be confusing a choice.
state_name (VecStr): the name of the model state to modify, based on the value of the meta-parameter and the state found in 'scalingVarName'
scaling_var_name (VecStr): the name of the parameter for each subarea model, to which to apply the area scaled value.
min_p_val (VecNum): minimum value allowed for the meta-parameter
max_p_val (VecNum): minimum value allowed for the meta-parameter
value (VecNum): value for the meta parameter.
selector_type (str, optional): an identifier to define to which catchment element(s) the parameteriser will be applied. Defaults to "subareas".
intercept (VecNum, optional): intercepts in the linear relationship(s). Defaults to 0.0.

Returns:

–

[ScalingParameteriser]: new ScalingParameteriser

linear_parameteriser_from ¶

linear_parameteriser_from(data_frame: DataFrame, selector_type: str = 'subareas')

Create a scaled linear parameteriser, tying by a linear transformation a (resp several) model state to a (resp several) parameter values

This allows to define tied parameters where pval = a * modelStateVal + intercept. The intent in particular is to define virtual model parameters such as initial store value as a fraction of the maximum storage capacity. Args: data_frame (pd.DataFrame): data frame with columns "param_name", "state_name", "scaling_var_name", "min_value", "max_value", "value", "intercept", selector_type (str, optional): [description]. Defaults to "subareas".

Returns:

–

listify ¶

listify(*args)

make_state_init_parameteriser ¶

make_state_init_parameteriser(parameteriser)

[summary]

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it

Returns:

–

[StateInitParameteriser]: new state initialisation parameteriser

mk_optim_log ¶

mk_optim_log(log_dataframe: DataFrame, fitness='NSE', messages='Message', categories='Category')

num_free_parameters ¶

num_free_parameters(parameteriser) -> int

parameteriser_as_dataframe ¶

parameteriser_as_dataframe(parameteriser)

Convert an external object hypercube parameteriser to a pandas data frame

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it

Returns:

–

[type]: [a data frame]

parameteriser_for_score ¶

parameteriser_for_score(score: ObjectiveScores)

Gets the parameteriser for a score

Parameters:

score ([type]) –

[description]

Returns:

–

parameters_for ¶

parameters_for(model_id: str, as_pandas_df=False)

scores_as_dataframe ¶

scores_as_dataframe(scores_population)

Convert objective scores to a pandas data frame representation

Parameters:

scores_population ([type]) –

[description]

Returns:

–

set_calibration_logger ¶

set_calibration_logger(optimiser, type='')

Sets logging on an optimiser, so as to record a detail of the optimisation process for post-optimisation analysis.

Parameters:

optimiser ([type]) –

[description]
type (str, default: '' ) –

[description]. Defaults to "".

Returns:

–

set_hypercube ¶

set_hypercube(parameteriser: HypercubeParameteriser, specs: DataFrame)

Set the properties of a hypercube parameteriser

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it
specs (DataFrame) –

An optional data frame description of the parameter set, with at least columns Name, Min, Max, Value.

set_max_parameter_value ¶

set_max_parameter_value(parameteriser, variable_name, value)

Sets the maximum value of a model parameter value

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it
variable_name (str or iterable of str) –

model variable state identifier(s)
value (numeric or iterable of numeric) –

value(s)

set_min_parameter_value ¶

set_min_parameter_value(parameteriser, variable_name, value)

Sets the minimum value of a model parameter value

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it
variable_name (str or iterable of str) –

model variable state identifier(s)
value (numeric or iterable of numeric) –

value(s)

set_parameter_value ¶

set_parameter_value(parameteriser, variable_name, value)

Sets the value of a model parameter value

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it
variable_name (str or iterable of str) –

model variable state identifier(s)
value (numeric or iterable of numeric) –

value(s)

show_parameters ¶

show_parameters(parameteriser, patterns, regex=False, starts_with=False)

Show some parameters (from the outside e.g. optimisers) in a filter parameteriser

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it
patterns ([type]) –

character, one or more pattern to match and show matching parameters. Match according to other parameters
regex (bool, default: False ) –

should the patterns be used as regular expressions. Defaults to False.
starts_with (bool, default: False ) –

should the patterns be used as starting strings in the parameter names. Defaults to False.

sort_by_score ¶

sort_by_score(scores_population: VectorObjectiveScores, score_name: str = '')

Sort objective scores according to one of the objective values

Parameters:

scores_population (VectorObjectiveScores) –

an S4 object 'ExternalObjRef' [package "cinterop"] with external pointer type VEC_OBJECTIVE_SCORES_PTR
score_name (str, default: '' ) –

name of the objective to use for sorting. Defaults to "", meaning assume single objective and sort by that.

Returns:

VectorObjectiveScores –

an S4 object 'ExternalObjRef' [package "cinterop"] with external pointer type VEC_OBJECTIVE_SCORES_PTR

subcatchment_parameteriser ¶

subcatchment_parameteriser(parameteriser, subcatchment)

Create a parameteriser that gets applied to a subset of a whole catchment

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it
subcatchment (Simulation) –

the subcatchment, subset of a whole catchment, identifying which elements (subareas, nodes, links) will be parameterised by the new parameteriser. Other catchment elements are untouched.

Returns:

–

[HypercubeParameteriser]: New parameteriser whose application is limited to the subcatchment.

Examples:

>>> sub_cats = simulation.split_to_subcatchments(["node.node_7", "node.node_10"])
>>> sc = sub_cats["node.node_7"]
>>> p = sp.create_parameteriser('generic subarea')
>>> p.add_parameter_to_hypercube("x1", 32, 1, 6e3)
>>> sp = p.subcatchment_parameteriser(sc)
>>> sp.apply_sys_config(simulation)

wrap_transform ¶

wrap_transform(parameteriser)

Create a parameteriser for which parameter transformations can be defined.

This allows to define e.g. a virtual parameter log_X instead of calibrating on the parameter X.

Parameters:

parameteriser (HypercubeParameteriser) –

A HypercubeParameteriser wrapper, or a type inheriting from it

Returns:

TransformParameteriser –

A new parameteriser (TransformParameteriser) which has methods to define parameter transforms

Module parameteriser¶

parameteriser ¶

INTERCEPT_COL module-attribute ¶

MAX_VALUE_COL module-attribute ¶

MIN_VALUE_COL module-attribute ¶

PARAM_NAME_COL module-attribute ¶

SCALING_VAR_NAME_COL module-attribute ¶

STATE_NAME_COL module-attribute ¶

VALUE_COL module-attribute ¶

MhData ¶

data property ¶

bound_fitness ¶

facet_plot ¶

rename_columns ¶

subset_by_message ¶

subset_by_pattern ¶

add_to_hypercube ¶

add_transform ¶

apply_sys_config ¶

as_py_structure ¶

backtransform ¶

bound_values ¶

bound_values_df ¶

concatenate_parameterisers ¶

create_multisite_obj_parameteriser ¶

create_muskingum_param_constraints ¶

create_parameter_sampler ¶

create_parameteriser ¶

create_sce_optim_swift ¶

create_sce_termination_wila ¶

evaluate_score_for_parameters ¶

example_parameteriser ¶

execute_optimisation ¶

extract_optimisation_log ¶

feasible_muskingum_bounds ¶

filtered_parameters ¶

get_best_score ¶

get_default_sce_parameters ¶

get_logger_content ¶

get_marginal_termination ¶

get_max_iteration_termination ¶

get_max_runtime_termination ¶

get_score_at_index ¶

hide_parameters ¶

is_hypercube ¶

is_sampler_seeding ¶

is_score ¶

is_set_of_scores ¶

linear_parameteriser ¶

linear_parameteriser_from ¶

listify ¶

make_state_init_parameteriser ¶

mk_optim_log ¶

num_free_parameters ¶

parameteriser_as_dataframe ¶

parameteriser_for_score ¶

parameters_for ¶

scores_as_dataframe ¶

set_calibration_logger ¶

set_hypercube ¶

set_max_parameter_value ¶

set_min_parameter_value ¶

set_parameter_value ¶

show_parameters ¶

sort_by_score ¶

subcatchment_parameteriser ¶

wrap_transform ¶

INTERCEPT_COL `module-attribute` ¶

MAX_VALUE_COL `module-attribute` ¶

MIN_VALUE_COL `module-attribute` ¶

PARAM_NAME_COL `module-attribute` ¶

SCALING_VAR_NAME_COL `module-attribute` ¶

STATE_NAME_COL `module-attribute` ¶

VALUE_COL `module-attribute` ¶

data `property` ¶