splitp package

Submodules

splitp.nx_tree module

class splitp.nx_tree.NXTree(newickString, name=None, numStates=4, taxa_ordering=None)

Bases: object

A rooted phylogenetic tree.

A rooted phylogenetic tree consisting of a collection of node objects and an adjacency matrix

Attributes:

num_nodes: The number of nodes in the tree num_bases: The size of the character set for the phylogeny nodes: A list of node objects representing the nodes in the tree adjM: An adjacency matrix describing the connections within the tree initDist: The initial distribution of states/characters at the root of the tree.

add_node(n, branch_length=0, in_node=None)

Adds a new node to the tree

Args:

n: The node object to add into the tree. in_node: The name of the parent node, default is None and is used for the root.

adjacency_matrix()

all_singular_values(M)

all_splits(trivial=False, only_balance=None, randomise=False)

build_JC_matrix(l)

build_K2ST_matrix(transition, transversion)

draw_from_multinomial(LT, n)

Use a given table of probabilities from getLikelihoods() and draw from its distribution

evolve_pattern()

false_splits(only_balance=None, randomise=False)

fast_signed_sum_subflattening(split, data_dict, bundle=None, labels=None)

A faster version of signed sum subflattening. Requires a data dictionary and can be supplied with a bundle of re-usable information to reduce the number of calls to the multiplications function.

fast_sparse_flattening(split, table, format='dok')

A faster version of signed sum subflattening. Requires a data dictionary and can be supplied with a bundle of re-usable information to reduce the number of calls to the multiplications function.

flattening(split, table)

Build a flattening matrix from a split

Args:

split: A string representing the split to build the flattening from table: A pandas data-frame table with site pattern `probabilities’

Returns:

A flattening matrix as a data-frame (to provide labels)

generate_alignment(sequence_length)

get_descendants(n, return_iter=False)

Returns a list of children/descendents of a given node

get_num_taxa()

Returns the number of taxa/leaf-nodes in the tree

get_parent(n)

Returns the parent node for a given node

get_pattern_probabilities()

Returns a full table of site-pattern probabilities (binary character set)

get_root(return_index=True)

Returns the root node

hartigan_algorithm(pattern)

index_of_node(node_name)

is_leaf(n_index_or_name)

Determines whether a node is a leaf node from it’s index.

is_root(n_index_or_name)

Determines whether a node is a root node from it’s index.

node_index(n)

node_name(index)

nodes()

nodes_list()

num_nodes()

parsimony_score(pattern)

Calculate a parsimony score for a site pattern or split

Args:

pattern: A string representing a site pattern or split

Returns:

A parsimony score for the given split or site pattern

rate_matrix(model)

reassign_all_transition_matrices(matrix)

scale_TR_rate_matrix(Q, return_scale_factor=False)

signed_sum_subflattening(split, data_table)

sparse_flattening(split, table, format='dok')

sparse_subflattening(split, data_table, as_dense_array=False)

split_score(matrix, return_singular_values=False, force_frob_norm_on_dense=False, data_table_for_frob_norm=None)

subflattening(Ft, specialState='T', type=(1, 1))

Creates a subflattening from a transformed flattening data frame

subflattening_alt(F, S=None, returnLRMats=False)

svd_error(M)

“Returns the SVD for a given matrix (All but two/four largest SVs)

transformed_flattening(F, S=None)

Creates a transformed flattening from a flattening data frame

true_splits(include_trivial=False)

Returns set of all true splits in the tree.

splitp.parsers module

splitp.parsers.json_to_newick(json_dict, namestring='id', lengthstring='branch_length', childrenstring='children')

splitp.parsers.newick_to_json(newick_string, namestring='id', lengthstring='branch_length', childrenstring='children', generate_names=False)

Parses a newick string (example “((A,B),C)”) into JSON format accepted by NetworkX

Args:

newick_string: The newick string to convert to JSON. Names, branch lengths and named

internal nodes accepted. If no names are given, an ID is generated for the name if generate_names=True.

namestring: The label for node name to use in the JSON (default “id”). lengthstring: The label for branch length to use in the JSON (default “branch_length”). childrenstring: The label for children arrays to use in the JSON (default “children”). generate_names: Whether or not to generate names if none are given

splitp.squangles module

class splitp.squangles.SquangleEvaluator

Bases: object

evaluate_polynomial(poly, data)

get_flattening(DTable)

get_polynomials()

read_polynomial(filename)

test_pattern_conversion()

to_int(pattern)

to_pattern(integer)

transformed_prob_dist(prob_dist)

splitp.tree_helper_functions module

splitp.tree_helper_functions.all_splits(num_taxa, trivial=False, only_balance=None, randomise=False)

Generates all splits as string-representations

Args:

num_taxa: The number of taxa on the tree. trivial: Whether or not to calculate trivial splits, default True. only_trivial: Whether to ONLY create trivial splits, default False.

Returns:

A list of string-representations of splits (using ‘|’-notation)

splitp.tree_helper_functions.balanced_newick_tree(num_taxa)

splitp.tree_helper_functions.check_splits_representatives(split_reps, orbits)

splitp.tree_helper_functions.frob_norm(matrix, data_table=None)

Calculates the Frobenius Norm for a given matrix

splitp.tree_helper_functions.get_balance(s, asTuple=False)

Returns a string formatted ‘X|X’ which describes the balance of a given split string

splitp.tree_helper_functions.get_pattern_counts(alignment, asNumpyArray=False)

splitp.tree_helper_functions.is_sparse(matrix)

splitp.tree_helper_functions.make_substitution_matrix(subs_prob, k)

splitp.tree_helper_functions.normalised(scores)

splitp.tree_helper_functions.pattern_counts_to_probs(patterns, seqLen)

splitp.tree_helper_functions.pattern_probs_from_alignment(pathToFile, check=True)

splitp.tree_helper_functions.read_alignment_from_file(pathToFile, check=True)

splitp.tree_helper_functions.scaled_h_matrix(_lambda)

splitp.tree_helper_functions.scores_to_weights(scores)

splitp.tree_helper_functions.split_equivalence_classes(splits, group)

Breaks up list of splits into equivalence classes under the canonical action of the specified group.

Module contents