def check_value_is_valid(self, value, is_constant):
if value.shape != (self.size, self.size):
raise ValueError(
"Wrong array shape %s for %s, expected (%s,%s)"
% (value.shape, self.name, self.size, self.size)
)
for part in value:
PartitionDefn.check_value_is_valid(self, part, is_constant)
def __init__(
self, default=None, name=None, dimensions=None, dimension=None, size=None, **kw
):
PartitionDefn.__init__(self, default, name, dimensions, dimension, size, **kw)
(dim_name, dim_cats) = self.internal_dimension
self.internal_dimensions = (dim_name, dim_name + "2")
self.array_template = DictArrayTemplate(dim_cats, dim_cats)
def make_forward_tree_defn(
subst_model, tree, bin_names, with_indel_params=True, kn=True
):
"""Pairwise Fwd"""
indel = make_indel_model_defn(with_indel_params, kn)
subst = subst_model.make_fundamental_param_controller_defns(bin_names)
leaf = NonParamDefn("leaf", dimensions=("edge",))
if len(bin_names) > 1:
switch = ProbabilityParamDefn("bin_switch", dimensions=["locus"])
bprobs = PartitionDefn(
[1.0 / len(bin_names) for bin in bin_names],
name="bprobs",
dimensions=["locus"],
dimension=("bin", bin_names),
)
edge_args = [switch, bprobs]
edge_defn_constructor = EdgeSumAndAlignDefnWithBins
else:
edge_args = []
edge_defn_constructor = EdgeSumAndAlignDefn
mprobs = subst["word_probs"]
bin_data = CalcDefn(BinData)(mprobs, indel, subst["Qd"])
bin_data = bin_data.across_dimension("bin", bin_names)
edge_args.extend(bin_data)
(top, scores) = _recursive_defns(
tree, subst, leaf, edge_defn_constructor, edge_args
)
defn = FwdDefn(top)
# defn = SumDefn(*scores)
return AnnotateFloatDefn(defn, top)
def make_motif_word_prob_defns(self):
monomer_probs = PartitionDefn(
name="psmprobs",
default=None,
dimensions=("locus", "position", "edge"),
dimension=("motif", tuple(self.get_input_alphabet())),
)
monomer_probs3 = monomer_probs.across_dimension(
"position", [str(i) for i in range(self.word_length)])
monomer_probs3 = CalcDefn(lambda *x: numpy.array(x),
name="mprobs")(*monomer_probs3)
word_probs = CalcDefn(self.calc_word_probs,
name="wprobs")(monomer_probs3)
mprobs_matrix = CalcDefn(self.calc_word_weight_matrix,
name="mprobs_matrix")(monomer_probs3)
return (monomer_probs, word_probs, mprobs_matrix)
def make_motif_probs_defn(self):
"""Makes the first part of a parameter controller definition for this
model, the calculation of motif probabilities"""
return PartitionDefn(
name="mprobs",
default=None,
dimensions=("locus", "edge"),
dimension=("motif", tuple(self.get_input_alphabet())),
)