foxes.algorithms.iterative.models.convergence¶

Classes¶

`ConvCrit`	Abstract base class for convergence criteria
`ConvCritList`	Combines multiple convergence criteria.
`ConvVarDelta`	Requires convergence of a selection of variables.
`DefaultConv`	Default convergence criteria.

Module Contents¶

class foxes.algorithms.iterative.models.convergence.ConvCrit(name: str | None = None)[source]¶

Abstract base class for convergence criteria

Parameters:¶

name: The convergence criteria name

abstract check_converged(algo: foxes.core.algorithm.Algorithm, prev_results: xarray.Dataset | None, results: xarray.Dataset, verbosity: int = 0) → bool[source]¶

Check convergence criteria.

Parameters:¶

algo: The calculation algorithm
prev_results: The farm results of previous iteration, or None if first
results: The farm results of current iteration
verbosity: The verbosity level, 0 = silent

Returns:¶

convergence: Convergence flag, true if converged

disable_subsets(no_subs: bool = True) → None[source]¶

Disable subset state selection in iterative algorithm.

This is needed if the convergence criterion requires all states to be calculated in each iteration.

Parameters:¶

no_subs: Disable subsets flag

property conv_states : numpy.ndarray | None¶

Get the convergence state per state.

Returns:¶

conv_states: The convergence state per state

property deltas : dict[str, float] | None¶

Get the most recent evaluation deltas.

Returns:¶

deltas: The most recent evaluation deltas

name¶

property no_subs : bool¶

Get the disable subsets flag.

Returns:¶

no_subs: Disable subsets flag

class foxes.algorithms.iterative.models.convergence.ConvCritList(crits: list[ConvCrit] = [], name: str | None = None)[source]¶

Bases: ConvCrit

Combines multiple convergence criteria.

Parameters:¶

crits: The criteria
name: The convergence criteria name

add_crit(crit: ConvCrit) → None[source]¶

Add a convergence criterion

Parameters:¶

crit: The criterion

check_converged(algo: foxes.core.algorithm.Algorithm, prev_results: xarray.Dataset | None, results: xarray.Dataset, verbosity: int = 0) → bool[source]¶

Check convergence criteria.

Parameters:¶

algo: The calculation algorithm
prev_results: The farm results of previous iteration, or None if first
results: The farm results of current iteration
verbosity: The verbosity level, 0 = silent

Returns:¶

convergence: Convergence flag, true if converged

disable_subsets(no_subs: bool = True) → None¶

Disable subset state selection in iterative algorithm.

This is needed if the convergence criterion requires all states to be calculated in each iteration.

Parameters:¶

no_subs: Disable subsets flag

property conv_states : numpy.ndarray | None¶

Get the convergence state per state.

Returns:¶

conv_states: The convergence state per state

crits = []¶

property deltas : dict[str, float] | None¶

Get the most recent evaluation deltas.

Returns:¶

deltas: The most recent evaluation deltas

property failed : ConvCrit | None¶

name¶

property no_subs : bool¶

Get the disable subsets flag.

Returns:¶

no_subs: Disable subsets flag

class foxes.algorithms.iterative.models.convergence.ConvVarDelta(limits: dict[str, float], wd_vars: list[str] | None = None, name: str | None = None)[source]¶

Bases: ConvCrit

Requires convergence of a selection of variables.

Parameters:¶

limits: The convergence limits. Keys: variables str, values are convergence thresholds
wd_vars: The wind direction type variables (unit deg)
name: The convergence criteria name

check_converged(algo: foxes.core.algorithm.Algorithm, prev_results: xarray.Dataset | None, results: xarray.Dataset, verbosity: int = 0) → bool[source]¶

Check convergence criteria.

Parameters:¶

algo: The calculation algorithm
prev_results: The farm results of previous iteration, or None if first
results: The farm results of current iteration
verbosity: The verbosity level, 0 = silent

Returns:¶

convergence: Convergence flag, true if converged

disable_subsets(no_subs: bool = True) → None¶

Disable subset state selection in iterative algorithm.

This is needed if the convergence criterion requires all states to be calculated in each iteration.

Parameters:¶

no_subs: Disable subsets flag

property conv_states : numpy.ndarray | None¶

Get the convergence state per state.

Returns:¶

conv_states: The convergence state per state

property deltas : dict[str, float] | None¶

Get the most recent evaluation deltas.

Returns:¶

deltas: The most recent evaluation deltas

limits¶

name¶

property no_subs : bool¶

Get the disable subsets flag.

Returns:¶

no_subs: Disable subsets flag

class foxes.algorithms.iterative.models.convergence.DefaultConv[source]¶

Bases: ConvVarDelta

Default convergence criteria.

Constructor.

check_converged(algo: foxes.core.algorithm.Algorithm, prev_results: xarray.Dataset | None, results: xarray.Dataset, verbosity: int = 0) → bool¶

Check convergence criteria.

Parameters:¶

algo: The calculation algorithm
prev_results: The farm results of previous iteration, or None if first
results: The farm results of current iteration
verbosity: The verbosity level, 0 = silent

Returns:¶

convergence: Convergence flag, true if converged

disable_subsets(no_subs: bool = True) → None¶

Disable subset state selection in iterative algorithm.

This is needed if the convergence criterion requires all states to be calculated in each iteration.

Parameters:¶

no_subs: Disable subsets flag

property conv_states : numpy.ndarray | None¶

Get the convergence state per state.

Returns:¶

conv_states: The convergence state per state

property deltas : dict[str, float] | None¶

Get the most recent evaluation deltas.

Returns:¶

deltas: The most recent evaluation deltas

limits¶

name¶

property no_subs : bool¶

Get the disable subsets flag.

Returns:¶

no_subs: Disable subsets flag