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src/c/o/cogent-1.5.3/cogent/evolve/models.py   cogent(Download)
def GTR(**kw):
    """General Time Reversible nucleotide substitution model."""
    return substitution_model.Nucleotide(
            motif_probs = None,
            do_scaling = True,

src/p/y/pycogent-HEAD/cogent/evolve/models.py   pycogent(Download)
def GTR(**kw):
    """General Time Reversible nucleotide substitution model."""
    return substitution_model.Nucleotide(
            motif_probs = None,
            do_scaling = True,

src/c/o/cogent-1.5.3/tests/test_evolve/test_scale_rules.py   cogent(Download)
    def _makeModel(self, do_scaling, predicates, scale_rules=[]):
        return substitution_model.Nucleotide(
            do_scaling=do_scaling, equal_motif_probs=True, 
            model_gaps=False, predicates=predicates, scales=scale_rules)
 

src/p/y/pycogent-HEAD/tests/test_evolve/test_scale_rules.py   pycogent(Download)
    def _makeModel(self, do_scaling, predicates, scale_rules=[]):
        return substitution_model.Nucleotide(
            do_scaling=do_scaling, equal_motif_probs=True, 
            model_gaps=False, predicates=predicates, scales=scale_rules)
 

src/c/o/cogent-1.5.3/tests/test_evolve/test_parameter_controller.py   cogent(Download)
        self.tree = LoadTree(treestring='((a,b),(c,d),e);')
        self.model = cogent.evolve.substitution_model.Nucleotide(
            do_scaling=True, equal_motif_probs=True, model_gaps=True)
 
    def test_scoped_local(self):
        model = cogent.evolve.substitution_model.Nucleotide(
                do_scaling=True, equal_motif_probs=True, model_gaps=True,
    def test_setMotifProbs(self):
        """Mprobs supplied to the parameter controller"""
        model = cogent.evolve.substitution_model.Nucleotide(
            model_gaps=True, motif_probs=None)
        lf = model.makeLikelihoodFunction(self.tree, 
    def test_setMultiLocus(self):
        """2 loci each with own mprobs"""
        model = cogent.evolve.substitution_model.Nucleotide(motif_probs=None)
        lf = model.makeLikelihoodFunction(self.tree, 
                motif_probs_from_align=False, loci=["a", "b"])
    def test_complex_parameter_rules(self):
            # This test has many local minima and so does not cope
            # with changes to optimiser details.
        model = cogent.evolve.substitution_model.Nucleotide(
                do_scaling=True, equal_motif_probs=True, model_gaps=True,

src/p/y/pycogent-HEAD/tests/test_evolve/test_parameter_controller.py   pycogent(Download)
        self.tree = LoadTree(treestring='((a,b),(c,d),e);')
        self.model = cogent.evolve.substitution_model.Nucleotide(
            do_scaling=True, equal_motif_probs=True, model_gaps=True)
 
    def test_scoped_local(self):
        model = cogent.evolve.substitution_model.Nucleotide(
                do_scaling=True, equal_motif_probs=True, model_gaps=True,
    def test_setMotifProbs(self):
        """Mprobs supplied to the parameter controller"""
        model = cogent.evolve.substitution_model.Nucleotide(
            model_gaps=True, motif_probs=None)
        lf = model.makeLikelihoodFunction(self.tree, 
    def test_setMultiLocus(self):
        """2 loci each with own mprobs"""
        model = cogent.evolve.substitution_model.Nucleotide(motif_probs=None)
        lf = model.makeLikelihoodFunction(self.tree, 
                motif_probs_from_align=False, loci=["a", "b"])
    def test_complex_parameter_rules(self):
            # This test has many local minima and so does not cope
            # with changes to optimiser details.
        model = cogent.evolve.substitution_model.Nucleotide(
                do_scaling=True, equal_motif_probs=True, model_gaps=True,

src/c/o/cogent-1.5.3/tests/test_evolve/test_newq.py   cogent(Download)
 
from cogent import LoadSeqs, DNA, LoadTree, LoadTable
from cogent.evolve.substitution_model import Nucleotide, General, \
                                                GeneralStationary
from cogent.evolve.discrete_markov import DiscreteSubstitutionModel
    def test_newQ_is_nuc_process(self):
        """newQ is an extension of an independent nucleotide process"""
        nuc = Nucleotide(motif_probs = self.asymm_nuc_probs)
        new_di = Nucleotide(motif_length=2, mprob_model='monomer',
            motif_probs = self.asymm_root_probs)
    def test_lf_display(self):
        """str of likelihood functions should not fail"""
        for (dummy, model) in self.ordered_by_complexity:
            di = Nucleotide(motif_length=2, mprob_model=model)
            di.adaptMotifProbs(self.cond_root_probs, auto=True)
    def test_get_statistics(self):
        """get statistics should correctly apply arguments"""
        for (mprobs, model) in self.ordered_by_complexity:
            di = Nucleotide(motif_length=2, motif_probs=mprobs, 
                    mprob_model=model)

src/p/y/pycogent-HEAD/tests/test_evolve/test_newq.py   pycogent(Download)
 
from cogent import LoadSeqs, DNA, LoadTree, LoadTable
from cogent.evolve.substitution_model import Nucleotide, General, \
                                                GeneralStationary
from cogent.evolve.discrete_markov import DiscreteSubstitutionModel
    def test_newQ_is_nuc_process(self):
        """newQ is an extension of an independent nucleotide process"""
        nuc = Nucleotide(motif_probs = self.asymm_nuc_probs)
        new_di = Nucleotide(motif_length=2, mprob_model='monomer',
            motif_probs = self.asymm_root_probs)
    def test_lf_display(self):
        """str of likelihood functions should not fail"""
        for (dummy, model) in self.ordered_by_complexity:
            di = Nucleotide(motif_length=2, mprob_model=model)
            di.adaptMotifProbs(self.cond_root_probs, auto=True)
    def test_get_statistics(self):
        """get statistics should correctly apply arguments"""
        for (mprobs, model) in self.ordered_by_complexity:
            di = Nucleotide(motif_length=2, motif_probs=mprobs, 
                    mprob_model=model)

src/c/o/cogent-1.5.3/cogent/evolve/solved_models.py   cogent(Download)
"""
 
from cogent.evolve.substitution_model import Nucleotide, CalcDefn
from cogent.evolve.predicate import MotifChange
from cogent.maths.matrix_exponentiation import FastExponentiator
class PredefinedNucleotide(Nucleotide):
    _default_expm_setting = None
 
    # Instead of providing calcExchangeabilityMatrix this subclass overrrides
    # makeContinuousPsubDefn to bypass the Q / Qd step.

src/p/y/pycogent-HEAD/cogent/evolve/solved_models.py   pycogent(Download)
"""
 
from cogent.evolve.substitution_model import Nucleotide, CalcDefn
from cogent.evolve.predicate import MotifChange
from cogent.maths.matrix_exponentiation import FastExponentiator
class PredefinedNucleotide(Nucleotide):
    _default_expm_setting = None
 
    # Instead of providing calcExchangeabilityMatrix this subclass overrrides
    # makeContinuousPsubDefn to bypass the Q / Qd step.

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