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import os
import pandas as pd
import math
import pickle
import pprint
pp = pprint.PrettyPrinter(indent=4)
# For phylogeny parsing
# !pip install opentree
from opentree import OT
# !pip install ete3
from ete3 import Tree, PhyloTree
# Constants
Fix_Tree = True
format_ = 1 #8
class Phylogeny:
# Phylogeny class for Fish dataset
# If node_ids is None, it assumes that the tree already exists. Otherwise, you have to pass node_ids (i.e., list of species names).
def __init__(self, filePath, node_ids=None, verbose=False):
# filenames for phylo tree and cached mapping ottid-speciesname
cleaned_fine_tree_fileName = "cleaned_metadata.tre"
name_conversion_file = "name_conversion.pkl"
self.ott_ids = []
self.ott_id_dict = {}
self.node_ids = node_ids
self.treeFileNameAndPath = os.path.join(filePath, cleaned_fine_tree_fileName)
self.conversionFileNameAndPath = os.path.join(filePath, name_conversion_file)
self.total_distance = -1 # -1 means we never calculated it before.
self.distance_matrix = {}
self.species_groups_within_relative_distance = {}
self.get_ott_ids(node_ids, verbose=verbose)
self.get_tree(self.treeFileNameAndPath)
self.get_total_distance()
# Given two species names, get the phylo distance between them
def get_distance(self, species1, species2):
d= None
if self.distance_matrix[species1][species2] == -1:
if species1 == species2:
return 0
ott_id1 = 'ott' + str(self.ott_id_dict[species1])
ott_id2 = 'ott' + str(self.ott_id_dict[species2])
d = self.tree.get_distance(ott_id1, ott_id2)
self.distance_matrix[species1][species2] = d
else:
d = self.distance_matrix[species1][species2]
return d
# relative_distance = 0 => species node itself
# relative_distance = 1 => all species
def get_siblings_by_name(self, species, relative_distance, verbose=False):
self.get_species_groups(relative_distance, verbose)
for species_group in self.species_groups_within_relative_distance[relative_distance]:
if species in species_group:
return species_group
raise species+" was not found in " + self.species_groups_within_relative_distance[relative_distance]
def get_parent_by_name(self, species, relative_distance, verbose=False):
ott_id = 'ott' + str(self.ott_id_dict[species])
parent = self.get_parent_by_ottid(ott_id, relative_distance, verbose)
return parent
def get_distance_between_parents(self, species1, species2, relative_distance):
parent1 = self.get_parent_by_name(species1, relative_distance)
parent2 = self.get_parent_by_name(species2, relative_distance)
return self.tree.get_distance(parent1, parent2)
def get_species_groups(self, relative_distance, verbose=False):
if relative_distance not in self.species_groups_within_relative_distance.keys():
groups = {}
for species in self.getLabelList():
parent_node = self.get_parent_by_name(species, relative_distance, verbose)
parent = parent_node.name
if parent not in groups.keys():
groups[parent] = [species]
else:
groups[parent].append(species)
self.species_groups_within_relative_distance[relative_distance] = groups.values()
if verbose:
print("At relative_distance", relative_distance, ", the groups are:", groups.values())
return self.species_groups_within_relative_distance[relative_distance]
def getLabelList(self):
return list(self.node_ids)
# ------- privete functions
def get_total_distance(self):
if self.node_ids is None:
self.node_ids = self.ott_id_dict.keys()
self.init_distance_matrix()
# For one time, measure distance from all leaves down to root. They all should be equal.
# Save the value and reuse it.
if self.total_distance==-1:
for leaf in self.tree.iter_leaves():
total_distance = self.tree.get_distance(leaf) # gets distance to rootprint
assert math.isclose(self.total_distance, total_distance) or self.total_distance==-1
self.total_distance = total_distance
return self.total_distance
def init_distance_matrix(self):
for i in self.node_ids:
self.distance_matrix[i] = {}
for j in self.node_ids:
self.distance_matrix[i][j] = -1
def get_parent_by_ottid(self, ott_id, relative_distance, verbose=False):
abs_distance = relative_distance*self.total_distance
species_node = self.tree.search_nodes(name=ott_id)[0]
if verbose:
print('distance to ancestor: ', abs_distance, ". relaive distance: ", relative_distance)
# keep going up till distance exceeds abs_distance
distance = 0
parent = species_node
while distance < abs_distance:
if parent.up is None:
break
parent = parent.up
distance = self.tree.get_distance(parent, species_node)
return parent
# return ott_id_list
# node_ids: list of taxa
# returns: corresponding list of ott_ids
def get_ott_ids(self, node_ids, verbose=False):
if not os.path.exists(self.conversionFileNameAndPath):
if node_ids is None:
raise TypeError('No existing ottid-speciesnames found. node_ids should be a list of species names.')
if verbose:
print('Included taxonomy: ', node_ids, len(node_ids))
df2 = pd.DataFrame(columns=['in csv', 'in response', 'Same?'])
# Get the matches
resp = OT.tnrs_match(node_ids, do_approximate_matching=True)
matches = resp.response_dict['results']
unmatched_names = resp.response_dict['unmatched_names']
# Get the corresponding ott_ids
ott_ids = set()
ott_id_dict={}
assert len(unmatched_names)==0 # everything is matched!
for match_array in matches:
match_array_matches = match_array['matches']
assert len(match_array_matches)==1, match_array['name'] + " has too many matches" + str(list(map(lambda x: x['matched_name'], match_array_matches))) # we have a single unambiguous match!
first_match = match_array_matches[0]
ott_id = first_match['taxon']['ott_id']
ott_ids.add(ott_id)
if verbose:
#some original and matched names are not exactly the same. Not a bug
df2 = df2.append({'in csv':match_array['name'], 'in response': first_match['matched_name'], 'Same?': match_array['name'] == first_match['matched_name']}, ignore_index=True)
ott_id_dict[match_array['name']] = ott_id
ott_ids = list(ott_ids)
if verbose:
print(df2[df2['Same?']== False])
pp.pprint(ott_id_dict)
with open(self.conversionFileNameAndPath, 'wb') as f:
pickle.dump([ott_ids, ott_id_dict], f)
else:
with open(self.conversionFileNameAndPath, 'rb') as f:
ott_ids, ott_id_dict = pickle.load(f)
self.ott_ids = ott_ids
self.ott_id_dict = ott_id_dict
print(self.ott_id_dict)
def fix_tree(self, treeFileNameAndPath):
tree = PhyloTree(treeFileNameAndPath, format=format_)
# Special case for Fish dataset: Fix Esox Americanus.
D = tree.search_nodes(name="mrcaott47023ott496121")[0]
D.name = "ott496115"
tree.write(format=format_, outfile=treeFileNameAndPath)
def get_tree(self, treeFileNameAndPath):
if not os.path.exists(treeFileNameAndPath):
output = OT.synth_induced_tree(ott_ids=self.ott_ids, ignore_unknown_ids=False, label_format='id') # name_and_id ott_ids=list(ott_ids),
output.tree.write(path = treeFileNameAndPath, schema = "newick")
if Fix_Tree:
self.fix_tree(treeFileNameAndPath)
self.tree = PhyloTree(treeFileNameAndPath, format=format_)
class PhylogenyCUB:
# Phylogeny class for CUB dataset
def __init__(self, filePath, node_ids=None, verbose=False):
# cleaned_fine_tree_fileName = "1_tree-consensus-Hacket-AllSpecies.phy"
# cleaned_fine_tree_fileName = "1_tree-consensus-Hacket-AllSpecies-cub-names.phy"
cleaned_fine_tree_fileName = "1_tree-consensus-Hacket-27Species-cub-names.phy"
self.node_ids = node_ids
self.treeFileNameAndPath = os.path.join(filePath, cleaned_fine_tree_fileName)
self.total_distance = -1 # -1 means we never calculated it before.
self.distance_matrix = {}
self.species_groups_within_relative_distance = {}
self.get_tree(self.treeFileNameAndPath)
self.get_total_distance()
# Given two species names, get the phylo distance between them
def get_distance(self, species1, species2):
d= None
if self.distance_matrix[species1][species2] == -1:
if species1 == species2:
return 0
d = self.tree.get_distance(species1, species2)
self.distance_matrix[species1][species2] = d
else:
d = self.distance_matrix[species1][species2]
return d
# relative_distance = 0 => species node itself
# relative_distance = 1 => all species
def get_siblings_by_name(self, species, relative_distance, verbose=False):
#NOTE: This implementation was causing inconsistencies since finding the parent.get_leaves() was not equivalent to get_species_groups
# ott_id = 'ott' + str(self.ott_id_dict[species])
# return self.get_siblings_by_ottid(ott_id, relative_distance, get_ottids, verbose)
self.get_species_groups(relative_distance, verbose)
for species_group in self.species_groups_within_relative_distance[relative_distance]:
if species in species_group:
return species_group
raise species+" was not found in " + self.species_groups_within_relative_distance[relative_distance]
def get_parent_by_name(self, species, relative_distance, verbose=False):
abs_distance = relative_distance*self.total_distance
species_node = self.tree.search_nodes(name=species)[0]
if verbose:
print('distance to ancestor: ', abs_distance, ". relaive distance: ", relative_distance)
# keep going up till distance exceeds abs_distance
distance = 0
parent = species_node
while distance < abs_distance:
if parent.up is None:
break
parent = parent.up
distance = self.tree.get_distance(parent, species_node)
return parent
def get_distance_between_parents(self, species1, species2, relative_distance):
parent1 = self.get_parent_by_name(species1, relative_distance)
parent2 = self.get_parent_by_name(species2, relative_distance)
return self.tree.get_distance(parent1, parent2)
def get_species_groups(self, relative_distance, verbose=False):
if relative_distance not in self.species_groups_within_relative_distance.keys():
groups = {}
for species in self.getLabelList():
parent_node = self.get_parent_by_name(species, relative_distance, verbose)
parent = parent_node.name
if parent not in groups.keys():
groups[parent] = [species]
else:
groups[parent].append(species)
self.species_groups_within_relative_distance[relative_distance] = groups.values()
if verbose:
print("At relative_distance", relative_distance, ", the groups are:", groups.values())
return self.species_groups_within_relative_distance[relative_distance]
def getLabelList(self):
return list(self.node_ids)
# ------- privete functions
def get_total_distance(self):
if self.node_ids is None:
self.node_ids = sorted([leaf.name for leaf in self.tree.iter_leaves()])
self.init_distance_matrix()
# maximum distance between root and lead node taken as total distance
leaf_to_root_distances = [self.tree.get_distance(leaf) for leaf in self.tree.iter_leaves()]
self.total_distance = max(leaf_to_root_distances)
return self.total_distance
def init_distance_matrix(self):
for i in self.node_ids:
self.distance_matrix[i] = {}
for j in self.node_ids:
self.distance_matrix[i][j] = -1
def get_tree(self, treeFileNameAndPath):
# if not os.path.exists(treeFileNameAndPath):
# output = OT.synth_induced_tree(ott_ids=self.ott_ids, ignore_unknown_ids=False, label_format='id') # name_and_id ott_ids=list(ott_ids),
# output.tree.write(path = treeFileNameAndPath, schema = "newick")
self.tree = PhyloTree(treeFileNameAndPath, format=format_)
# setting a dummy name to the internal nodes if it is unnamed
for i, node in enumerate(self.tree.traverse("postorder")):
if not len(node.name) > 0:
node.name = str(i) |