treeflow.model.phylo_model module

treeflow.model.phylo_model.parse_model(model: Dict[str, object] | str) → Tuple[str, Dict[str, object | Dict[str, object]] | None]

exception treeflow.model.phylo_model.PhyloModelParseError

Bases: ValueError

add_note(): Exception.add_note(note) – add a note to the exception

args

with_traceback(): Exception.with_traceback(tb) – set self.__traceback__ to tb and return self.

class treeflow.model.phylo_model.PhyloModel(model_dict: Dict[str, str | Dict[str, object]])

Bases: object

Class to represent the configuration of a basic phylogenetic model

classmethod check_model_dict(model_dict: Dict[str, str | Dict[str, object]])

all_params() → Dict[str, object]

free_params() → Dict[str, Dict[str, object]]

relaxed_clock() → bool

treeflow.model.phylo_model.is_scalar_or_vector_of_type(value: object, numeric_type: Type)

treeflow.model.phylo_model.constant(value: object) → Tensor | object

treeflow.model.phylo_model.get_prior(var_name: str, dist_name: str, params: Dict[str, Tensor | object]) → Distribution

treeflow.model.phylo_model.get_params(params: Dict[str, object | Dict[str, object]] | None) → Generator[Root, Tensor, Tuple[Dict[str, Tensor | object], bool]]

Get parameters for part of the model. Builds prior distributions or converts literals to Tensor constants.

Returns:

dict – Dictionary mapping parameter names to values
has_root – Whether one of the parameters is a root node

treeflow.model.phylo_model.wrap_in_root_if_needed(dist: Distribution, has_root_param: bool) → Distribution | Root

treeflow.model.phylo_model.get_tree_model(tree_model: str, tree_model_params: Dict[str, object], has_root_param: bool, initial_tree: TensorflowRootedTree, var_name: str = 'tree') → Generator[Distribution | Root, TensorflowRootedTree, TensorflowRootedTree]

treeflow.model.phylo_model.get_subst_model(subst_model: str) → EigendecompositionSubstitutionModel

treeflow.model.phylo_model.get_subst_model_params(subst_model: str, params: Dict[str, object | Dict[str, object]] | None, float_dtype: DType = tf.float64, include_jc_frequencies: bool = True) → Generator[Distribution, Tensor, Tuple[Dict[str, Tensor | object], bool]]

treeflow.model.phylo_model.get_strict_clock_rates(clock_rate: Tensor)

treeflow.model.phylo_model.get_relaxed_lognormal_clock_rate_distribution(branch_rate_loc: Tensor, branch_rate_scale: Tensor, has_root_param: bool, initial_tree: TensorflowRootedTree) → Distribution

treeflow.model.phylo_model.get_clock_model_rates(clock_model: str, clock_model_params: Dict[str, object], has_root_param: bool, initial_tree: TensorflowRootedTree) → Generator[Distribution, Tensor, Tensor]

treeflow.model.phylo_model.get_discrete_gamma_site_rate_distribution(category_count: Tensor, site_gamma_shape: Tensor) → DiscretizedDistribution

treeflow.model.phylo_model.get_discrete_weibull_site_rate_distribution(category_count: Tensor, site_weibull_concentration: Tensor, site_weibull_scale: Tensor | None = None) → DiscretizedDistribution

treeflow.model.phylo_model.get_site_rate_distribution(site_model, site_model_params) → FiniteDiscreteDistribution

treeflow.model.phylo_model.get_sequence_distribution(alignment: Alignment, tree: TensorflowRootedTree, subst_model: EigendecompositionSubstitutionModel, subst_model_params: Dict[str, Tensor], site_model: str, site_model_params: Dict[str, object], clock_model_rates: Tensor, pattern_counts: Tensor | None = None) → Distribution

treeflow.model.phylo_model.phylo_model_to_joint_distribution(model: PhyloModel, initial_tree: TensorflowRootedTree, initial_alignment: Alignment, pattern_counts: Tensor | None = None) → JointDistributionCoroutine