qubx.modeling_utils.ModelingUtilsFace

Hosts a registry of generic model/data-based optimizers, and sub-panels which can use them.

init(self, name=`'Utils'`, global_name=`'QubX.Modeling.Utils'`)
(Constructor)

source code

x.__init__(...) initializes x; see help(type(x)) for signature

Parameters:

pos - e.g. gtk.POS_LEFT, for gtk.Notebook; or POS_DROPDOWN for a dropdown at top

Overrides: object.__init__

(inherited documentation)

add_optimizer(self, name, get_model_prep, get_data_prep, get_ll, optimize, OnLL, OnIteration, OnOptimized)

source code

Registers a model/data-based optimization method for sub-panel use.

@param name: string to identify the algorithm
@param get_model_prep: func(model=None, modeltree=None) -> model_prep
    model: qubx.model.QubModel
    modeltree: qubx.tree.Node containing QMF (only if no model provided)
    model_prep: object derived from model, ready for computation, with fields:
        .source: the input QubModel, if provided
        .model: a private QubModel copy
        .modeltree: QMF tree, even if not provided
    since rates change so much more often, my algorithms re-use the rest of the prep when possible, by defining these fields:
        .K0, .K1, .K2: numpy.matrix of rate constants
        .set_rates(K0, K1): updates .K0, .K1, and .model
        .set_rates2(K0, K1, K2): preferred newer form for pressure sensitivity
@param get_data_prep: func(segs, model_prep, wait=True, receiver=lambda dp: None) -> data_prep
    segs: list of qubx.data_types.SourceSeg
    model_prep: result of get_model_prep, since signal layout may depend on model stimuli
    wait: False to return immediately
    receiver: if wait, this function is called with data_prep when it's ready (on gobject thread)
    data_prep: object with prepared data, ready for computation, with fields:
        .segs: input the list of SourceSeg
        .model_prep: input prepared model
        and whatever else the algorithm needs, e.g. sampled or idealized data
@param get_ll: func(model_prep=None, data_prep=None, wait=True, receiver=lambda LL: None, **kw) -> LL
    evaluates the score for given model and data; fills them in from onscreen sources if not provided.
    wait: False to return immediately
    receiver: if wait == False: func(LL) called when computation is done
    **kw: other keywords to apply to properties before running; e.g. use_Peq=True
@param optimize: func(model_prep=None, data_prep=None, wait=True, receiver=lambda LL: None, ext_return=False, **kw) -> LL
    iterates, adjusting parameters to improve LL for given model and data; fills them in from onscreen sources if not provided.
    wait: False to return immediately
    receiver: if wait == False: func(result--see ext_return) called when computation is done
    ext_return: True to return the tuple (LL, iterations, grads, hessian); False to return LL
    **kw: other keywords to apply to properties before running; e.g. use_Peq=True
@param OnLL: L{WeakEvent}(LL) when get_ll completes
@param OnIteration: L{WeakEvent}(iterations, LL, grads) each iteration during opt
@param OnOptimized: L{WeakEvent}(iterations, LL, grads, hessian) when optimize completes

__explore_featured

Value:

['optimizers', 'optimizer', 'optimizer_name', 'add_optimizer']

Class ModelingUtilsFace

__init__(self, name='Utils', global_name='QubX.Modeling.Utils') (Constructor)

add_optimizer(self, name, get_model_prep, get_data_prep, get_ll, optimize, OnLL, OnIteration, OnOptimized)

__explore_featured

optimizer

optimizer_name

init(self, name=`'Utils'`, global_name=`'QubX.Modeling.Utils'`)
(Constructor)