math.Skewness#

Returns skewness of 1xN vector

Parameters

• _data_vector Matrix 1xN vector

math.Variance#

Returns variance of 1xN vector

Parameters

• _data_vector Matrix 1xN vector

math.Std#

Returns standard deviation of 1xN vector

Parameters

• _data_vector Matrix 1xN vector

math.GatherDescriptiveStats#

Returns suite of statistical information for a 1xN vector

Parameters

• _data_vector Matrix 1xN vector

math.GetIC#

Computes small-sample AIC

Parameters

• logl
• params
• samples

math.HolmBonferroniCorrection#

Returns Holm-Bonferroni corrected p-values

Parameters

• ps Dict a key -> p-value (or null, for not done)

math.BenjaminiHochbergFDR#

Returns Benjamini-Hochberg corrected q-values (FDR)

Parameters

• ps Dict a key -> p-value (or null, for not done)

math.minNormalizedRange#

Returns the range normalized to the lowest value

Returns number

math.DoLRT#

Computes the LRT and p-value

Parameters

• lognull
• logalt
• df

math.Sum#

Computes sum of 1xN vector

Parameters

• _data_vector Matrix 1xN vector

math.Median#

Returns median average of 1xN vector

Parameters

• _data_vector Matrix 1xN vector

math.Int#

Converts a float to integer by rounding

Parameters

• float

math.Mean#

Returns mean average of 1xN vector

Parameters

• _data_vector Matrix 1xN vector

math.Kurtosis#

Returns kurtosis of 1xN vector

Parameters

• _data_vector Matrix 1xN vector

matrix.Symmetrize#

upper triangle gets copied to lower triangle for non-square matrices, the largest square minor is symmetrized

Parameters

• mx Matrix matrix to perform transform on

Returns any nothing

#

Parameters

• strings Dict/Matrix set of strings to partition (if Dict, will use the set of VALUES)
• rex Dict/Matrix string matrix of regular expressions (if Dict, will use the set of VALUES)

Returns Dict "reg-exp" : "set of matched strings" (as dict) PLUS a special key ("") which did not match any regular expression

regexp.Split#

Parameters

• string String
• re String the regular expression

Returns Dictionary a dictionary containing split strings

regexp.Replace#

Parameters

• string String
• re String search for this expression
• repl String replace each occurence of re with repl

Returns Dictionary a dictionary containing split strings

regexp.Find#

Parameters

• string String
• re String the regular expression

Returns String the portion of the string that is matching

regexp.FindSubexpressions#

Parameters

• string String
• re String the regular expression

Returns AssociativeList all matched RE subsexpressions

regexp.PartitionByRegularExpressions#

Parameters

• strings Dict/Matrix set of strings to partition (if Dict, will use the set of VALUES)
• rex Dict/Matrix string matrix of regular expressions ((if Dict, will use the set of VALUES)

Returns Dict "reg-exp" : "set of matched strings" (as dict) PLUS a special key ("") which did not match any regular expression

models.binary.generic.Time#

Parameters

• option does nothing

Returns any default time

models.binary.generic.DefineQMatrix#

Parameters

• modelSpec Dictionary
• namespace String

models.codon.generate_stencil#

Generates a branch length extraction stencil for synonymous and non-synonymous rates

Parameters

• type String terms.genetic_code.synonymous or terms.genetic_code.nonsynonymous or callback
• model Dictionary the model object

Returns Matrix the appropriate NxN matrix stencil

models.codon.generic.DefineQMatrix#

Parameters

• modelSpect
• namespace

#

return complete differences between two codons *

models.codon.MapCode#

Parameters

• genetic_code String

Returns Dictionary the sense, stop, and translation-table for the genetic code

models.DNA.generic.Time#

Parameters

• option does nothing

Returns any default time

models.DNA.generic.DefineQMatrix#

Parameters

• modelSpec Dictionary
• namespace String

frequencies.empirical.binary#

Sets model's equilibrium frequency estimator to ML for binary data

Parameters

• model Dictionary
• namespace String
• datafilter DataSetFilter

Returns Dictionary updated model

frequencies.empirical.binary#

Sets model's equilibrium frequency estimator to Binary chatacter 2x1 estimator

Parameters

• model Dictionary
• namespace String does nothing
• datafilter DataSetFilter does nothing

Returns Dictionary updated model

frequencies.empirical.protein#

Sets model's equilibrium frequency estimator to protein 20x1 estimator

Parameters

• model Dictionary
• namespace String
• datafilter DataSetFilter

Returns Dictionary updated model

frequencies.empirical.protein#

Sets model's equilibrium frequency estimator to ML for protein data

Parameters

• model Dictionary
• namespace String
• datafilter DataSetFilter

Returns Dictionary updated model

frequencies.empirical.protein#

Multiplies nucleotide frequencies into the empirical codon estimate

Parameters

• model Dictionary
• __estimates Matrix 4x3 matrix of frequency estimates

Returns Dictionary updated model

frequencies.equal#

Sets model's equilibrium frequency estimator to equal frequencies

Parameters

• model Dictionary
• namespace String does nothing
• datafilter DataSetFilter does nothing

Returns Dictionary updated model

frequencies.empirical.F3x4#

Parameters

• model Dictionary
• namespace String
• datafilter DataSetFilter

Returns Dictionary updated model

frequencies.empirical.F1x4#

Parameters

• model Dictionary
• namespace String
• datafilter DataSetFilter

Returns Dictionary updated model

frequencies.empirical.corrected.CF3x4#

Parameters

• model Dictionary
• namespace String
• datafilter DataSetFilter

Returns Dictionary updated model

frequencies.mle#

To be implemented

Parameters

• model
• namespace
• datafilter

frequencies.empirical.nucleotide#

Sets model's equilibrium frequency estimator to Nucleotide 4x1 estimator

Parameters

• model Dictionary
• namespace String
• datafilter DataSetFilter

Returns Dictionary updated model

frequencies.empirical.nucleotide#

Compute equilibrium frequencies at run-time using Q inversion

Parameters

• model Dictionary
• namespace String
• datafilter DataSetFilter

Returns Dictionary updated model

#

This is an function that computes stationary frequencies of Markov process based on its
rate matrix

Parameters

• Q Matrix matrix

model.ApplyToBranch#

Parameters

• model_id
• tree
• branch

model.define_from_components#

Parameters

• id {String} Name of of Model
• q {Matrix} instantaneous transition matrix
• evf {Matrix} the equilibrium frequencies
• canonical Number matrix exponential

Examples

q = {{*,t,kappa*t,t}
{t,*,t,t*kappa}
{t*kappa,t,*,t}
{t,t*kappa,t,*}};
evf =  {{0.4}{0.3}{0.2}{0.1}};
model.define_from_components(q,evf,1);


Returns any nothing, sets a global {Model}

Parameters

• model_spec
• id
• tag

Parameters

• model_spec
• id
• tag

model.generic.GetLocalParameter#

Parameters

• model_spec
• tag

model.generic.GetLocalParameter#

Parameters

• model Model

Returns String

model.generic.GetGlobalParameter#

Parameters

• model_spec
• tag

model.generic.get_a_parameter#

Parameters

• model_spec
• tag
• type

model.generic.get_rate_variation#

Parameters

• model_spec
• tag
• type

model.generic.DefineModel#

Parameters

• model_spec
• id
• arguments
• data_filter
• estimator_type

model.generic.DefineMixtureModel#

Parameters

• model_spec
• id
• arguments
• data_filter
• estimator_type

model.ApplyModelToTree#

Parameters

• id {String}
• tree
• model_list
• rules

models.generic.ConstrainBranchLength#

Parameters

• model Model
• or AssociativeList {Number} value
• parameter String

Returns any the number of constraints generated (0 or 1)

models.generic.SetBranchLength#

Parameters

• model Model
• or AssociativeList {Number} value
• parameter String

Returns any the number of constraints generated (0 or 1)

models.generic.AttachFilter#

Parameters

• model Model
• filter DataSetFilter

Returns any 0

model.Dimension#

Parameters

• model Model

Returns Matrix dimensions of model

model.parameters.Local#

Parameters

• model Model

Returns Matrix local parameters

model.parameters.Global#

Parameters

• model Model

Returns Matrix global parameters

model.MatchAlphabets#

Parameters

• a1 Matrix first alphabet to compare
• a2 Matrix second alphabet to compare

Returns Number 1 if they are equal, 0 if they are not

#

Parameters

• models Dict list of model objects
• filter RegEx only apply to parameters matching this expression

Returns Dict the set of constraints applied

#

Parameters

• model Dict first alphabet to compare

Returns String the branch length expression string

model.GetParameters_RegExp#

Parameters

• model {String} - model ID
• re {String} - regular expression

Returns any a dictionary of global model parameters that match a regexp

parameters.DeclareCategory#

Parameters

• def Dict category definition components

Parameters

Returns any n/d

parameters.SetValue#

Sets value of passed parameter id

Parameters

• id String id of parameter to set value to
• value Number value to set

Returns any nothing

parameters.SetLocalValue#

Sets value of passed parameter tree.branch.id

Parameters

• tree String id of tree
• branch String id of branch
• id String id of parameter to set value to
• value Number value to set

Returns any nothing

parameters.SetValues#

Sets value of passed parameter id

Parameters

• desc dict -> {id : id, mle : value}

Returns any nothing

parameters.ConstrainMeanOfSet#

Ensures that the mean of parameters in a set is maintained

Parameters

• set Dict/Matrix list of variable ids
• weights Dict/Matrix weights to apply
• mean Number desired mean
• namespace String desired mean

Returns any nothing

parameters.UnconstrainParameterSet#

Parameters

• lf LikelihoodFunction the likelihood function to operate on
• set Matrix set of parameters to unconstrain

Returns any nothing

parameters.Mean#

Returns mean of values

Parameters

• values Matrix values to return mean of
• weights Matrix weights to multiply values by
• d Number does nothing

Returns Number mean

parameters.Quote#

Quotes the argument

Parameters

• arg String string to be quoted

Returns String string in quotes

parameters.AppendMultiplicativeTerm#

Parameters

• expression String the matrix to modify
• term String the multiplier to append

Returns String (expression) * (term)

Parameters

• matrix Matrix matrix to scale
• term Number scalar to multiply matrix by
• do_empties Number if element matrix is empty, fill with term

Returns Matrix New matrix

parameters.StringMatrixToFormulas#

Parameters

• id String matrix to scale
• matrix Matrix if element matrix is empty, fill with term

Returns any nothing

parameters.GenerateAttributedNames#

Parameters

• prefix String
• attributes Dictionary
• delimiter String

parameters.GenerateSequentialNames#

Parameters

Returns Matrix 1 x row vector of generated names

parameters.GetRange#

Parameters

• id

Returns any variable range

parameters.SetRange#

Parameters

• id
• ranges

Returns any nothing

parameters.DeclareGlobal#

Parameters

• id String
• cache Matrix

Returns any nothing

parameters.IsIndependent#

Check if parameter is independent

Parameters

• parameter id of parameter to check

Returns Bool TRUE if independent, FALSE otherwise

parameters.SetConstraint#

sets constraint on parameter

Parameters

• or String {AssociativeList} id - id(s) of parameter(s) to set constraint on
• value Number the constraint to set on the parameter
• global_tag String the global namespace of the parameter

Returns any nothing

parameters.SetProprtionalConstraint#

constrain x to be x := C * (current value of x)

Parameters

• id String of parameter(s) to set constraint on
• scaler String variable - the ID of the 'C' scaler variable above; could also be an expression

Returns any nothing

parameters.ConstrainSets#

constraint set of parameters

Parameters

• set1 AssociativeList -
• set2 AssociativeList -

Returns any nothing

parameters.RemoveConstraint#

Removes a constraint from a parameter

Parameters

• id String id of parameter to remove constraint from

Returns any nothing

parameters.helper.copy_definitions#

Copies definitions from target to source

Parameters

• target Dictionary the target dictionary
• source Dictionary the source element to copy to target

Returns any nothing

pparameters.SetStickBreakingDistribution#

Parameters

• parameters AssociativeList
• values Matrix

Returns any nothing

pparameters.GetStickBreakingDistribution#

Parameters

• parameters AssociativeList

Returns Matrix computed distribution (Nx2)

parameters.helper.stick_breaking#

Parameters

• parameters AssociativeList
• initial_values Matrix

Returns any weights

parameters.helper.dump_matrix#

Prints matrix to screen

Parameters

• matrix Matrix

Returns any nothing

parameters.helper.tree_lengths_to_initial_values#

Sets tree lengths to initial values

Parameters

• dict a [0 to N-1] dictrionary of tree objects
• type codon or nucleotide

Returns Dictionary dictionary of initial branch lengths

parameters.GetProfileCI#

Profiles likelihood function based on covariance precision level

Parameters

• id String covariance parameter id
• lf LikelihoodFunction likelihood function to profile
• null-null Number Covariance precision level

Returns Dictionary a dictionary containing profiling information

parameters.ExportParameterDefinition#

Geneate an HBL string needed to define a parameter

Parameters

• id String the name of the parameter to export;

Returns String the string for an HBL definition of the parameter e.g. "global x := 2.3; x :> 1; x :< 3"; note that the definition will be NOT recursive, so if x depends on y and z, then y and z need to be exported separately

parameters.SetLocalModelParameters#

Copy local parameters from a node to 'template' variables

Parameters

• model Dict model description
• tree String id
• node String ide.g. set alpha = Tree.Node.alpha set beta = Tree.Node.beta

parameters.SetLocalModelParameters#

Set category variables to their mean values for branch length calculations

Parameters

• model Dict model description

parameters.ApplyNameSpace#

Applies a namespace to parameter ids

Parameters

parameters.ValidateID#

Given a set of strings, convert them to valid IDs that don't clash following renames

Parameters

• ids Array/Dict the ids to validate (should be unique), if dict, should be i -> ID

Returns Dictionary a dictionary containing old -> new id map

parameters.DeclareGlobalWithRanges#

Parameters

• id String variable id
• init Number initial value (could be None)
• lb Number lower bound (could be None)
• ub Number upper bound (could be None)

Returns any nothing

models.protein.generic.Time#

Parameters

• option does nothing

Returns any default time

models.protein.generic.DefineQMatrix#

Parameters

• modelSpec Dictionary
• namespace String

alignments.GetSequenceNames#

Get sequence names from existing dataset

Parameters

• dataset_name String name of dataset to get sequence names from

Returns Matrix list of sequence names

alignments.GetSequenceNames#

Get sequence from existing dataset by name

Parameters

• dataset_name String name of dataset to get sequence names from
• sequence_name (String | None) the name of the sequence to extract or None to set up the initial mapping

Returns String the corresponding sequence string

alignments.GetIthSequence#

Get i-th sequence name/value from an alignment

Parameters

• dataset_name String name of dataset to get sequence names from
• index String the name of the sequence to extract or None to set up the initial mapping

Returns Dict {"id" : sequence name, "sequence" : sequence data}

Reads dataset from a file path

Parameters

• code String name - name of the genetic code to load, or None to prompt

Returns Dictionary metadata pertaining to the genetic code

alignments.GetIthSequenceOriginalName#

Get i-th sequence name/value from an alignment; retrieve the original sequence name if available

Parameters

• dataset_name String name of dataset to get sequence names from
• index String the name of the sequence to extract or None to set up the initial mapping

Returns Dict {"id" : sequence name, "sequence" : sequence data}

alignments.GetAllSequences#

Get all sequences as "id" : "sequence" dictionary

Parameters

• dataset_name String name of dataset to get sequence names from

Returns Dict { id -> sequence}

Parameters

• dataset_name the name of the dataset you wish to use
• file_name path to file

Returns Dictionary r - metadata pertaining to the dataset

alignments.FilterType#

Determine what type of data are in the filter

Parameters

• filter_name String the name of the dataset filter object

Returns String one of the standard datatypes, or the alphabet string if it's non-standard

Read a protein dataset from file_name

Parameters

• dataset_name the name of the dataset you wish to use
• file_name path to file

Returns Dictionary r - metadata pertaining to the dataset

alignments.AlphabetType#

Categorize an alphabet for an alignment

Parameters

• alphabet the alphabet vector, e.g. fetched by GetDataInfo (alphabet, ..., "CHARACTERS");

Returns String one of standard alphabet types or None if unknown

alignments.EnsureMapping#

Ensure that name mapping is not None by creating a f(x)=x map if needed

Parameters

• dataset_name the name of the dataset you wish to use
• r Dictionary metadata pertaining to the dataset
• file_name path to file

Returns Dictionary r - metadata pertaining to the dataset

Parameters

• dataset_name String the name of the dataset you wish to use
• data String the data you wish to parse

Returns Dictionary r - metadata pertaining to the dataset

alignments.PromptForGeneticCodeAndAlignment#

Prompts user for genetic code and alignment

Parameters

• dataset_name String the name of the dataset you wish to use
• datafilter_name String the name of the dataset filter you wish to use

Returns Dictionary r - metadata pertaining to the dataset

Loads genetic code and alignment from file path

Parameters

• dataset_name String the name of the dataset you wish to use
• datafilter_name String the name of the dataset filter you wish to use
• path String path to file

Returns Dictionary r - metadata pertaining to the dataset

Creates codon dataset filter from data set

Parameters

• datafilter_name String the name of the dataset filter you wish to use
• dataset_name String the name of the existing dataset
• data_info Dictionary DataSet metadata information

Returns Dictionary updated data_info that includes the number of sites, dataset, and datafilter name

Reads dataset from a file path

Parameters

• dataset_name String name of variable you would like to set the dataset to
• path String path to alignment file

Returns Dictionary r - metadata pertaining to the dataset

Creates nucleotide dataset filter from file

Parameters

• file_name String The location of the alignment file
• datafilter_name String the name of the dataset filter you wish to use
• dataset_name String the name of the dataset you wish to use

Returns Dictionary updated data_info that includes the number of sites, dataset, and datafilter name

alignments.CompressDuplicateSequences#

Take an input filter, replace all identical sequences with a single copy Optionally, rename the sequences to indicate copy # by adding ':copies'

Parameters

• filter_in String The name of an existing filter
• filter_out String the name (to be created) of the filter where the compressed sequences will go)
• rename Bool if true, rename the sequences

Returns any the number of unique sequences

alignments.DefineFiltersForPartitions#

Defines filters for multiple partitions

Parameters

• partitions Matrix a row vector of dictionaries with partition information containing "name" and "filter-string" attributes
• source_data DataSet the existing dataset to partition

Returns Matrix filters - a list of newly created dataset filters

alignments.serialize_site_filter#

Parameters

• data_filter DataFilter
• site_index Number

Returns String a string representation of data_filter

Parameters

• dataset_name id of dataset

Returns Dictionary r - metadata pertaining to the dataset

Reads a codon dataset from a file path given a genetic code

Parameters

• dataset_name String name of variable you would like to set the dataset to
• path String path to alignment file
• code Matrix genetic code
• stop_codons String the list of stopcodons

Returns Dictionary r - metadata pertaining to the dataset

#

Parameters

• sequence String the string to translate
• offset Number start at this position (should be in {0,1,2})
• code Dictionary genetic code description (e.g. returned by alignments.LoadGeneticCode)

Returns String the amino-acid translation ('?' is used to represent ambiguities; 'X' - stop codons)

#

Parameters

• sequence String the string to translate
• offset Number start at this position (should be in {0,1,2})
• code Dictionary genetic code description (e.g. returned by alignments.LoadGeneticCode)
• code lookup resolution lookup dictionary

Returns Dict list of possible amino-acids (as dicts) at this position

#

Parameters

• sequence String the string to translate
• code Dictionary genetic code description (e.g. returned by alignments.LoadGeneticCode)
• code lookup resolution lookup dictionary

Returns Dict for each reading frame F in {0, 1, 2} returnsF -> { terms.data.sequence: translated sequence (always choose X if available, otherwise first sense resolution) terms.sense_codons : N, // number of sense A/A terms.stop_codons : N, // number of stop codons terms.terminal_stop : T/F // true if there is a terminal stop codon }

#

Parameters

• sequence String the string to translate

Returns String the amino-acid translation ('?' is used to represent ambiguities; 'X' - stop codons)

alignments.Extract_site_patterns#

Parameters

• data_filter DataFilter

Returns any for a data filter, returns a dictionary like this "pattern id": "sites" : sites (0-based) mapping to this pattern "is_constant" : T/F (is the site constant w/matching ambigs) "0":{ "sites":{ "0":0 }, "is_constant":0 },

    "1":{
"sites":{
"0":1
},
"is_constant":1
},...
"34":{
"sites":{
"0":34,
"1":113
},
"is_constant":1
},
...


#

Parameters

• sequence String the input sequence

Returns String the sequence with all gaps removed

#

Parameters

• sequence String the input sequence

Returns String the sequence with all gaps removed

#

Parameters

• codon_sequence String the codon sequence to map
• aa_sequence String the matching aligned a.a. sequence
• no Number more than this many mismatches - the codon sequence to map
• mapping Dict code ["terms.code.mapping"]

Examples

GCAAAATCATTAGGGACTATGGAAAACAGA
-AKSLGTMEN-R

maps to

---GCAAAATCATTAGGGACTATGGAAAAC---AGA


Returns String the mapped sequence

#

Parameters

• filter String filter name
• breakPoints Matrix locations of breakpoints (Nx1)
• trees Matrix tree strings for partitions (Nx1)
• file String write the result here
• isCodon Bool is the filter a codon filter?

Examples

GCAAAATCATTAGGGACTATGGAAAACAGA
-AKSLGTMEN-R

maps to

---GCAAAATCATTAGGGACTATGGAAAAC---AGA


Returns String the mapped sequence

ancestral.Sequences#

Parameters

• ancestral_data Dictionary the dictionary returned by ancestral.build

Returns any { "Node Name" : {String} inferred ancestral sequence, }

ancestral.ComputeSubstitutionCounts#

Parameters

• ancestral_data Dictionary the dictionary returned by ancestral.build
• branch_filter Dictionary/Function/None now to determine the subset of branches to count on None -- all branches Dictionary -- all branches that appear as keys in this dict Function -- all branches on which the function (called with branch name) returns 1
• substitution_filter Function/None how to decide if the substitution should count None -- different characters (except anything vs a gap) yields a 1 Function -- callback (state1, state2, ancestral_data) will return the value
• site_filter Function/None how to decide which sites will be kept None -- at least one substitution Function -- callback (substitution vector) will T/F

Returns any { "Branches" : {Matrix Nx1} names of selected branches, "Sites" : {Matrix Sx1} indices of sites passing filter, "Counts" : {Matrix NxS} of substitution counts, }N = number of selected branches S = number of sites passing filter

ancestral.build#

Builds ancestral states

Parameters

• _lfID Number the likelihood function ID
• _lfComponentID Number -
• options options -

Returns any an integer index to reference the opaque structure for subsequent operations

distances.nucleotide.tn93#

Compute all pairwise distances between sequences in a data set

Parameters

• filter {String} - id of dataset
• freqs {Matrix/null} - if not null, use these nucleotide frequencies
• options {Dict/null} - options for ambiguity treatment

Returns Matrix r - pairwise TN93 distances

distances.nucleotide.p_distance#

Compute all pairwise percent distances between sequences in a data set

Parameters

• filter {String} - id of dataset
• options {Dict/null} - options for ambiguity treatment

Returns Matrix r - pairwise TN93 distances

estimators.FitCodonModel#

Parameters

• codon_data DataFilter
• tree Tree
• genetic_code String
• option Dictionary
• initial_values Dictionary

Returns any MGREV results

estimators.FitMGREV#

Parameters

• codon_data DataFilter
• tree Tree
• genetic_code String
• option Dictionary
• initial_values Dictionary

Returns any MGREV results

estimators.FitMGREV#

compute the asymptotic (chi^2) p-value for the LRT

Parameters

• alternative Number log likelihood for the alternative (more general model)
• Null Number log likelihood for the null
• df Number degrees of freedom

Returns any p-value

estimators.ComputeLF#

compute the current value of the log likelihood function

Parameters

• lfid String name of the function

Returns any log likelihood

estimators.CreateInitialGrid#

prepare a Dict object suitable for seeding initial LF values

Parameters

• values Dict : "parameter_id" -> {{initial values}} [row matrix], e.g. ... "busted.test.bsrel_mixture_aux_0": { {0.1, 0.25, 0.4, 0.55, 0.7, 0.85, 0.9} }, "busted.test.bsrel_mixture_aux_1": { {0.1, 0.25, 0.4, 0.55, 0.7, 0.85, 0.9} } ...
• N int how many points to sample
• init Dict/null if not null, taken to be the initial template for variables i.e. random draws will be one index change from this vector

Returns Dict like in{ "0":{ "busted.test.bsrel_mixture_aux_0":{ "ID":"busted.test.bsrel_mixture_aux_0", "MLE":0.4 }, "busted.test.bsrel_mixture_aux_1":{ "ID":"busted.test.bsrel_mixture_aux_1", "MLE":0.7 }, "busted.test.omega1":{ "ID":"busted.test.omega1", "MLE":0.01 }, "busted.test.omega2":{ "ID":"busted.test.omega2", "MLE":0.1 }, "busted.test.omega3":{ "ID":"busted.test.omega3", "MLE":1.5 } } ....

estimators.LHC#

prepare a Dict object suitable for seeding initial LF values based on Latin Hypercube Sampling

Parameters

• ranges Dict : "parameter_id" -> range, i.e. { lower_bound: 0, upper_bound: 1 }.
• samples Number : the # of samples to draw

estimators.SetGlobals#

Parameters

Returns any nothing

estimators.SetCategory#

Parameters

Returns any nothing

Parameters

estimators.ExtractBranchInformation#

Parameters

Returns Dictionary branch information

estimators.branch_lengths_in_string#

Parameters

Returns String branch lenghts in tree string

estimators.ExtractMLEs#

Parameters

• likelihood_function_id String
• model_descriptions String

Returns any results

estimators.ExtractMLEsOptions#

Parameters

• likelihood_function_id String
• model_descriptions String
• options Dict

Returns any results

estimators.TraverseLocalParameters#

Parameters

• likelihood_function_id String
• model_descriptions Dictionary
• callback String (tree, node, parameter_list)

#

Parameters

• tree_name String
• model_descriptions Dictionary
• initial_values Matrix
• branch_length_conditions

Returns any number of constrained parameters;

#

Parameters

• likelihood_function_id String
• model_descriptions Dictionary
• initial_values Matrix
• branch_length_conditions

Returns any estimators.ApplyExistingEstimates.df_correction - Abs(estimators.ApplyExistingEstimates.keep_track_of_proportional_scalers);

estimators.GetGlobalMLE#

Parameters

• results Dictionary
• tag String

Returns any None

estimators.FitExistingLF#

Fits a LikelihoodFunction

Parameters

• model_map

Returns any LF results

estimators.FitLF#

Makes a likelihood function object with the desired parameters

Parameters

• data_filters_list Matrix a vector of {DataFilter}s
• tree_list Matrix a vector of {Tree}s
• model_map
• initial_values

Returns any LF results

estimators.FitLF#

Fits a LikelihoodFunction

Parameters

• data_filters_list Matrix a vector of {DataFilter}s
• tree_list Matrix a vector of {Tree}s
• model_map
• initial_values

Returns any LF results

estimators.GetBranchEstimates#

Parameters

• results Dictionary
• partiton_index Number
• node_name String

Returns any None

estimators.TakeLFStateSnapshot#

Parameters

Returns Dict parameter -> {"MLE" : value, "constraint" : string (if present)}

estimators.GetGlobalMLE_RegExp#

Extract global scope parameter estimates that match a regular expression

Parameters

• results Dictionary
• regular String expression to match

Returns Dict parameter description : value (could be empty)

estimators.FitSingleModel_Ext#

Parameters

• data_filter DataFilter
• tree Tree
• model Dict
• initial_values Matrix
• run_options Dict

Returns any results

estimators.FitGTR_Ext#

Parameters

• data_filter DataFilter
• tree Tree
• initial_values Matrix
• run_options Dict

Returns any results

estimators.FitGTR#

Parameters

• data_filter DataFilter
• tree Tree
• initial_values Matrix

Returns any results

genetic_code.DefineCodonToAAMapping#

genetic_code.DefineCodonToAAMapping

Parameters

• code AssociativeList the nucleotide to codon mapping

Returns any returns ordered codon to amino acid map

genetic_code.DefineCodonToAAMapping#

genetic_code.DefineIntegerToAAMapping

Parameters

• code AssociativeList the nucleotide to codon mapping
• only_sense Boolean if true, do not include stop codons in mapping

Returns any returns an integer to AA map { "0" : "K" .. "60" : "F" }

genetic_code.IsStop#

genetic_code.IsStop

Parameters

• codon Number (a number between 0 and 63 in AAA...TTT encoding)
• code Number the genetic code

Returns any whether or not the codon is a stop codon

genetic_code.DefineCodonToAAGivenCode#

genetic_code.DefineCodonToAAGivenCode

Parameters

• code AssociativeList the nucleotide to codon mapping

Returns any the amino acid map

trees.infer.NJ#

Infer a neighbor joining tree from a data filter using a specific distance

Parameters

• datafilter String the ID of an existing datafilter
• null null/String/Matrix , null : use the default distance calculation appropriate for the datatype String : a callback which takes (filter_id, seq1, seq2) arguments and returns d(seq1,seq2) Matrix : a precomputed distance matrix (same order of rows/column as datafilter names)

Returns String inferred tree string

trees.GetTreeString#

Looks for a newick tree in an alignment file

Parameters

• look_for_newick_tree (String | Bool) If a string, sanitizes and returns the string. If TRUE, search the alignment file for a newick tree. If FALSE, the user will be prompted for a nwk tree file.

Returns String a newick tree string

trees._matrix2string#

Convert a matrix representation of a tree into Newick

Parameters

• matrix_form Matrix : Kx3 matrix; each row represents ?
• N Number : number of leaves
• names Matrix/Dict : leaf index -> name
• do_lengths Bool : include branch lengths in the output

Returns String newick tree string

trees.PartitionTree#

Partitions a tree by assigning nodes to either being internal or leaf

Parameters

• avl Dictionary an AVL representation of the tree to be partitioned

Returns any nothing

Loads an annatotaed tree topology from a newick tree

Parameters

• look_for_newick_tree (String | Bool) If a string, sanitizes and returns the string. If TRUE, search the alignment file for a newick tree. If FALSE, the user will be prompted for a nwk tree file.

Returns Dictionary an annotated tree

Parameters

• dataset_name

Returns Dictionary an annotated tree

Loads a tree topology with node name label mappings and annotations from a list of partitions

Parameters

• partitions Matrix a 1xN vector of partition names, typically retrieved from the partitions key in the dictionary returned from an alignments parser function.
• mapping Dictionary a mapping of node names to labels

Examples

hky85_nucdata_info = alignments.ReadNucleotideAlignment(file_name, "hky85.nuc_data", "hky85.nuc_filter");
name_mapping = {
"HUMAN":"HUMAN",
"CHIMP":"CHIMP",
"BABOON":"BABOON",
"RHMONKEY":"RHMONKEY",
"COW":"COW",
"PIG":"PIG",
"HORSE":"HORSE",
"CAT":"CAT",
"MOUSE":"MOUSE",
"RAT":"RAT"
};
=>
{
"0":{
"name":"default",
"filter-string":"",
"tree": *    }
}


Returns Dictionary of matched partitions

trees.branch_names#

Parameters

• tree
• respect_case

Returns any result

trees.RootTree#

Parameters

• tree_info Dict
• root String on this node (or prompt if empty)

Returns any a {Dictionary} (same as ExtractTreeInfo) for the rerooted tree

trees.ExtractTreeInfo#

Parameters

• tree_string String

Returns any a {Dictionary} of the following tree information :- newick string - newick string with branch lengths - annotated string - model map - internal leaves - list of models

trees.HasBranchLengths#

Parameters

• tree Dictionary information object (e.g. as returned by LoadAnnotatedTopology)

Returns any a {Boolean} to indicate whether the tree has a valid branch length array

trees.BootstrapSupport#

Parameters

• tree Dictionary information object (e.g. as returned by LoadAnnotatedTopology)

Returns any a {Dictionary} (could be empty or partially filled) with "node name" -> bootstrap support

trees.GetBranchCount#

Gets total branch count of supplied tree

Parameters

• tree_string String

Returns Number total branch count

trees.SortedBranchLengths#

Sorts branch lengths in a descending order

Parameters

• tree_string String

Returns Number total branch count

trees.BranchNames#

Return branch names

Parameters

• tree_string String

Returns Matrix 1xN sorted branch names

trees.ParsimonyLabel#

Compute branch labeling using parsimony

Parameters

• tree String ID
• leaf Dict -> label labels may be missing for some of the leaves to induce partial labeling of the tree

Returns Dict {"score" : value, "labels" : Internal Branch -> label}

trees.ParsimonyLabel#

Annotate a tree string with using user-specified labels

Parameters

• tree String ID
• node_name Dict/String -> label OR (if string) (node_name) => name + annotation
• a String pair of characters to enclose the label in
• node_name Dict/String -> length OR (if string) (node_name) => length annotation

Returns String annotated string

trees.ConjunctionLabel#

Compute branch labeling using conjunction, i.e. node N is labeled 'X' iff all of the nodes that are in the subtree rooted at 'N' are also labeled 'N'

Parameters

• tree String ID
• leaf Dict -> label labels may be missing for some of the leaves to induce partial labeling of the tree

Returns Dict {"labeled" : # of labeled internal nodes, "labels" : Internal Branch -> label}

trees.GenerateSymmetricTree#

Generate a symmetric binary tree on N leaves (only perfectly symmetric if N is a power of 2)

Parameters

• N Number : number of leavers
• rooted Bool : whether the tree is rooted
• branch_name None/String : if a string, then it is assumed to be a function with an integer argument (node index) that generates branch names default is to use numeric names
• branch_length None/String : if a string, then it is assumed to be a function with no arguments that generates branch lengths

Returns String Newick tree string

trees.GenerateSymmetricTree#

Generate a ladder tree on N leaves

Parameters

• N Number : number of leavers
• rooted Bool : whether the tree is rooted
• branch_name None/String : if a string, then it is assumed to be a function with an integer argument (node index) that generates branch names default is to use numeric names
• branch_length None/String : if a string, then it is assumed to be a function with no arguments that generates branch lengths

Returns String Newick tree string

trees.GenerateRandomTree#

Generate a RANDOM tree on N leaves

Parameters

• N Number : number of leavers
• rooted Bool : whether the tree is rooted
• branch_name None/String : if a string, then it is assumed to be a function with an integer argument (node index) that generates branch names default is to use numeric names
• branch_length None/String : if a string, then it is assumed to be a function with no arguments that generates branch lengths

Returns String Newick tree string