def __init__(self, opts):
#---+----|----+----|----+----|----+----|----+----|----+----|----+----|
"""
Initializes ParamSummarizer object.
"""
CommonFunctions.__init__(self, opts)
def __init__(self, opts):
#---+----|----+----|----+----|----+----|----+----|----+----|----+----|
"""
Initializes MCMCImpl object by assigning supplied phycas object
to a data member variable.
"""
CommonFunctions.__init__(self, opts)
self.opts = opts
self.sitelikef = None
def __init__(self, opts):
#---+----|----+----|----+----|----+----|----+----|----+----|----+----|
"""
Initializes ScriptGenImpl object.
"""
CommonFunctions.__init__(self, opts)
self.script_filename = None
self.scriptf = None
self.sampledata_filename = None
self.samplef = None
def __init__(self, opts):
#---+----|----+----|----+----|----+----|----+----|----+----|----+----|
"""
Initializes RefDistImpl object.
"""
CommonFunctions.__init__(self, opts)
self.skip = None
self.epsilon = None
self.rooted = None
self.refdistf = None
self.rooted_trees = False
self.out_refdist_file_name = None
self.min_sample_size = 1
def __init__(self, opts):
#---+----|----+----|----+----|----+----|----+----|----+----|----+----|
"""
Initializes the SimulateImpl object by assigning supplied phycas object
to a data member variable.
"""
CommonFunctions.__init__(self, opts)
self.starting_tree_source = None
self.starting_tree = None
#self.ntax = None
self.sim_model_tree = None
self.data_matrix = None
self.nchar = opts.nchar
def __init__(self, opts):
#---+----|----+----|----+----|----+----|----+----|----+----|----+----|
"""
Initializes the LikeImpl object by assigning supplied phycas object
to a data member variable.
"""
CommonFunctions.__init__(self, opts)
self.starting_tree = None
self.taxon_labels = None
self.data_matrix = None
self.ntax = None
self.nchar = None
self.reader = NexusReader()
self.npatterns = [] # Will hold the actual number of patterns for each subset after data file has been read
def __init__(self, opts):
# ---+----|----+----|----+----|----+----|----+----|----+----|----+----|
"""
Initializes TreeSummarizer object by assigning supplied phycas object
to a data member variable.
"""
CommonFunctions.__init__(self, opts)
self.r = self._getLot()
eld = self.edgelen_dist
self._current = 0
self.trees = []
if eld is None:
if self.distribution.lower() != "yule":
raise ValueError("edgelen_dist must be set if the tree simulator's distribution is not Yule")
if self.speciation_rate is None:
raise ValueError(
"edgelen_dist or the speciation_rate must be set to generate trees from the Yule process"
)
else:
eld.setLot(self.r)
self.stdout.debugging("Generating %s" % str(self))
def __init__(self):
CommonFunctions.__init__(self, None)
self.pdf_splits_to_plot = None
# Variables associated with PDF tree drawing (used in pdftree() function)
# The 14 standard fonts guaranteed to be available in all PDF consumer applications:
# Times-Roman Helvetica Courier Symbol
# Times-Bold Helvetica-Bold Courier-Bold ZapfDingbats
# Times-Italic Helvetica-Oblique Courier-Oblique
# Times-BoldItalic Helvetica-BoldOblique Courier-BoldOblique
self.pdf_filename = 'trees.pdf' # Set to desired name of pdf file to create
self.pdf_edge_support_file = None # File containing PAUP* output with table of support values; if specified, the support values will be shown on trees plotted
self.pdf_tip_label_font = 'Times-Italic' # Font used for tip node names; should be one of the 14 standard fonts listed above
self.pdf_tip_label_height = 12 # Height in points of tip node name font
self.pdf_plot_label_font = 'Helvetica' # Font used for plot axis labels; should be one of the 14 standard fonts listed above
self.pdf_plot_label_height = 12 # Height in points of plot axis label font
self.pdf_title_font = 'Helvetica' # Font used for scalebar text; should be one of the 14 standard fonts listed above
self.pdf_title_height = 14 # Height in points of scalebar text font
self.pdf_scalebar_position = 'bottom' # Valid values are 'top', 'bottom' or None
self.pdf_scalebar_label_font = 'Helvetica' # Font used for scalebar text; should be one of the 14 standard fonts listed above
self.pdf_scalebar_label_height = 10 # Height in points of scalebar text font
self.pdf_support_label_font = 'Times-Roman' # Font used for edge support values; should be one of the 14 standard fonts listed above
self.pdf_support_label_height = 8 # Height in points of edge support font
self.pdf_support_as_percent = True # If True, support values will be shown as percentages (e.g. 93.1) rather than proportions (e.g. 0.931)
self.pdf_support_decimals = 1 # The number of decimal places shown in support values (e.g. to get 93.7, specify 1; to round up to 94, specify 0)
self.pdf_ladderize = 'right' # Valid values are 'right', 'left' or None
self.pdf_page_width = 8.5 # Page width in inches
self.pdf_page_height = 11.0 # Page length in inches
self.pdf_line_width = 1.0 # Width of lines representing edges in the tree
self.pdf_left_margin = 1.0 # Left margin in inches (1 inch = 72 points)
self.pdf_right_margin = 1.0 # Right margin in inches (1 inch = 72 points)
self.pdf_top_margin = 1.0 # Top margin in inches (1 inch = 72 points)
self.pdf_bottom_margin = 1.0 # Bottom margin in inches (1 inch = 72 points)
self.keep_xy_proportional = True # If True, vertical dimension of each tree in a collection will be kept proportional to its horizontal dimension
self.keep_tip_labels_proportional = True # If True, tip label height will be kept commensurate with size of tree for each tree in a printed collection (smaller trees will have smaller tip labels)
self.pdf_treefile = None # Set to tree file name if you want to make one pdf file with each tree from tree file on a separate page
self.pdf_newick = None # Set to the tree description to print if only want to save one tree to a pdf file
self.pdf_outgroup_taxon = None # Set to taxon name of tip serving as the outgroup for display rooting purposes (note: at this time outgroup can consist of just one taxon)
def __init__(self, opts):
#---+----|----+----|----+----|----+----|----+----|----+----|----+----|
"""
Gathers information relevant to Gelfand-Ghosh calculations from the
parent GG object.
"""
CommonFunctions.__init__(self, opts)
self.nsamples = 0
self.last_error = None
self.bincountsf = None # filename used for bin counts file
self.postpredf = None # filename used for post pred. files
self.starting_tree_source = None
self.gg_bin_patterns = opts.gg_bin_patterns
self.gg_num_bins = opts.gg_num_bins
self.pfile = None
self.tfile = None
self.model = None
self.model_type = None
self.data_matrix = None
self.likelihood = None
self.ntax = 0
self.nchar = 0
self.npatterns = 0
self.params = None
self.trees = None
self.is_invariable_sites_model = False
self.is_discrete_gamma_model = False
self.num_edgelen_hyperparams = 0
self.stored_trees = None # list of trees built from tree definitions in the trees file
self.taxon_labels = None # list of taxon labels from the data matrix
self.param_file_lines = None # list of lines from the params file (header lines excluded)
self.param_headers = None # list of parameter headers from the param file (self.param_names is a subset of this list)
self.tree_objects = None # dictionary in which keys are unique tree identifiers, and values are tree objects
self.parameters = None # dictionary in which keys are unique tree identifiers, and values are lists
# e.g. self.parameters[tree_id][j] = {'rAG': -4.2432, 'freqC': -2.3243, ...}
# where tree_id is a list of split representations uniquely identifying a particular tree
# and j is the index of the (j+1)th sample pertaining to that tree. The keys in the
# dictionary (e.g. 'rAG') are parameter names and the values are transformed parameter values
self.edge_lengths = None # dictionary in which keys are unique tree identifiers, and values are lists
# e.g. self.edge_lengths[tree_id][j] = {'-**-**--': -1.2345, '-*****--':-2.14225, ...}
# where tree_id is a list of split representations uniquely identifying a particular tree
# and j is the index of the (j+1)th sample from that tree. The keys in the dictionary (e.g. '-**-**--')
# are string representations of splits and the values are log-transformed edge lengths
self.sample_size = None # self.sample_size[tid] is the sample size (after skip samples have been removed) for tree tid
self.log_like = None # list of log likelihood values gleaned from the params file
self.log_prior = None # list of log prior values gleaned from the params file
self.log_posterior = None # list of log posterior values gleaned from the params file
# initialize quantities used in Gelfand-Ghosh calculations
self.gg_simdata = likelihood.SimData() # temporary container used to hold nascent posterior predictive simulation results until they have been analyzed
self.gg_y = likelihood.SimData() # observed dataset
self.gg_mu = likelihood.SimData() # mean of all posterior predictive datasets
self.minbins = None
self.gg_binned_simdata = None # if self.gg_bin_patterns is True, this vector of floats is used to summarize the counts in self.gg_simdata
self.gg_binned_y = None # if self.gg_bin_patterns is True, this vector of floats is used instead of self.gg_y
if self.gg_num_bins == 7:
self.minbins = True
self.gg_binned_mu = [0.0]*7
elif self.gg_num_bins == 15:
self.minbins = False
self.gg_binned_mu = [0.0]*15
else:
assert False, 'gg_num_bins must be set to either 7 or 15 (%d was specified in this case)' % self.gg_num_bins
self.gg_a = [] # vector of compromise actions (one for each k in kvalues)
self.gg_npatterns = [] # vector containing the number of patterns in each posterior predictive dataset
self.gg_t = [] # vector of t values computed from posterior predictive datasets
self.gg_t_y = 0.0 # t for original dataset
self.gg_t_mean = 0.0 # mean of t over all posterior predictive datasets
self.gg_t_mu = 0.0 # t of mean over all posterior predictive datasets
self.gg_t_a = [] # vector of t values computed from compromise action (one for each k in kvalues)
self.gg_Pm = 0.0 # penalty component (same for all k)
self.gg_Gm = [] # vector of goodness-of-fit components (one for each k in kvalues)
self.gg_Dm = [] # vector of overall measures (one for each k in kvalues)
self.gg_num_post_pred_reps = 0.0 # counts total number of posterior predictive simulations performed
self.gg_total = 0
