def load_from_file(self, tree_fname=None, virus_fname = None): if tree_fname is None: tree_fname = self.intermediate_tree_fname if os.path.isfile(tree_fname): self.tree = json_to_dendropy(read_json(tree_fname)) if virus_fname is None: virus_fname = self.clean_virus_fname if os.path.isfile(virus_fname): self.viruses = read_json(virus_fname) if os.path.isfile(self.frequency_fname): self.frequencies = read_json(self.frequency_fname)
def main(tree, viruses):
from seq_util import json_to_Bio_alignment
from tree_util import json_to_dendropy
print "--- Ancestral inference at " + time.strftime("%H:%M:%S") + " ---"
aln = json_to_Bio_alignment(viruses)
print "--- Set-up ancestral inference at " + time.strftime("%H:%M:%S") + " ---"
anc_seq = ancestral_sequences(tree, aln, seqtype='str')
anc_seq.calc_ancestral_sequences()
anc_seq.cleanup_tree()
out_fname = "data/tree_ancestral.json"
return json_to_dendropy(anc_seq.T.seed_node)
def main(tree, viruses):
from seq_util import json_to_Bio_alignment
from tree_util import json_to_dendropy
print "--- Ancestral inference at " + time.strftime("%H:%M:%S") + " ---"
aln = json_to_Bio_alignment(viruses)
print "--- Set-up ancestral inference at " + time.strftime("%H:%M:%S") + " ---"
anc_seq = ancestral_sequences(tree, aln, seqtype='str')
anc_seq.calc_ancestral_sequences()
anc_seq.cleanup_tree()
out_fname = "data/tree_ancestral.json"
return json_to_dendropy(anc_seq.T.seed_node)
def test(params):
from io_util import read_json
from tree_util import json_to_dendropy, to_Biopython, color_BioTree_by_attribute
from Bio import Phylo
tree_fname = 'data/tree_refine_10y_50v.json'
tree = json_to_dendropy(read_json(tree_fname))
fm = fitness_model(tree, predictors=params['predictors'], verbose=2)
fm.predict(niter=params['niter'])
#btree = to_Biopython(tree)
#color_BioTree_by_attribute(btree, 'fitness')
#Phylo.draw(btree, label_func=lambda x:'')
return fm
def test(params): from io_util import read_json from tree_util import json_to_dendropy, to_Biopython, color_BioTree_by_attribute from Bio import Phylo tree_fname='data/tree_refine_10y_50v.json' tree = json_to_dendropy(read_json(tree_fname)) fm = fitness_model(tree, predictors = params['predictors'], verbose=2) fm.predict(niter = params['niter']) #btree = to_Biopython(tree) #color_BioTree_by_attribute(btree, 'fitness') #Phylo.draw(btree, label_func=lambda x:'') return fm
def test():
from Bio import Phylo
tree = json_to_dendropy(read_json('auspice/tree.json'))
print "calculate local branching index"
T2 = get_average_T2(tree, 365)
tau = T2 * 2**-4
print "avg pairwise distance:", T2
print "memory time scale:", tau
calc_delta_LBI(tree, tau, datetime.datetime(2014, 1, 1))
bioTree = to_Biopython(tree)
color_BioTree_by_attribute(bioTree, 'date')
Phylo.draw(bioTree)
def main():
print "--- Tree LBI at " + time.strftime("%H:%M:%S") + " ---"
tree = json_to_dendropy(read_json('data/tree_refine.json'))
print "calculate local branching index"
T2 = get_average_T2(tree, 365)
tau = T2 * 2**-4
print "avg pairwise distance:", T2
print "memory time scale:", tau
calc_LBI(tree, tau=tau)
write_json(dendropy_to_json(tree.seed_node), "data/tree_LBI.json")
def main(params):
import time
from io_util import read_json
from io_util import write_json
from tree_util import json_to_dendropy, dendropy_to_json
print "--- Start fitness model optimization at " + time.strftime("%H:%M:%S") + " ---"
tree_fname = "data/tree_refine.json"
tree = json_to_dendropy(read_json(tree_fname))
fm = fitness_model(tree, predictors=params["predictors"], verbose=1)
fm.predict(niter=params["niter"])
out_fname = "data/tree_fitness.json"
write_json(dendropy_to_json(tree.seed_node), out_fname)
return out_fname
def main(params):
import time
from io_util import read_json
from io_util import write_json
from tree_util import json_to_dendropy, dendropy_to_json
print "--- Start fitness model optimization at " + time.strftime("%H:%M:%S") + " ---"
tree_fname='data/tree_refine.json'
tree = json_to_dendropy(read_json(tree_fname))
fm = fitness_model(tree, predictors = params['predictors'], verbose=1)
fm.predict(niter = params['niter'])
out_fname = "tree_fitness.json"
write_json(dendropy_to_json(tree.seed_node), out_fname)
return out_fname
def main(in_fname='data/tree_refine.json', tree=True):
print "--- Mutational tolerance at " + time.strftime("%H:%M:%S") + " ---"
viruses = read_json(in_fname)
if tree:
viruses = json_to_dendropy(viruses)
assign_fitness(viruses)
if tree:
out_fname = "data/tree_tolerance.json"
write_json(dendropy_to_json(viruses.seed_node), out_fname)
else:
out_fname = "data/virus_tolerance.json"
write_json(viruses, out_fname)
return out_fname, viruses
def main(in_fname='tree_refine.json', tree=True):
print "--- Mutational tolerance at " + time.strftime("%H:%M:%S") + " ---"
viruses = read_json(in_fname)
if tree:
viruses = json_to_dendropy(viruses)
assign_fitness(viruses)
if tree:
out_fname = "tree_tolerance.json"
write_json(dendropy_to_json(viruses.seed_node), out_fname)
else:
out_fname = "virus_tolerance.json"
write_json(viruses, out_fname)
return out_fname, viruses
def main(tree_fname = 'data/tree_refine.json'): print "--- Testing predictor evaluations ---" tree = json_to_dendropy(read_json(tree_fname)) print "Calculating epitope distances" calc_epitope_distance(tree) print "Calculating nonepitope distances" calc_nonepitope_distance(tree) print "Calculating LBI" # calc_LBI(tree) print "Writing decorated tree" out_fname = "data/tree_predictors.json" write_json(dendropy_to_json(tree.seed_node), out_fname) return out_fname
