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pyramid_sparse.py
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#!/usr/bin/evn python
# -*- coding: utf-8 -*-
__author__ = 'hervemn'
import os
import encodings
import shutil
import h5py
import sys
import numpy as np
from progressbar import ProgressBar
import time
from fragment import basic_fragment as B_frag
from matplotlib import pyplot as plt
import scipy.sparse as sp
import string
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f, 1):
pass
return i
def build_and_filter(base_folder, size_pyramid, factor):
""" build fragments pyramids for multi scale analysis and remove high sparsity fragments"""
min_bin_per_contig = 1
fact_sub_sampling = factor
###########################################
all_pyramid_folder = os.path.join(base_folder, 'pyramids')
if not(os.path.exists(all_pyramid_folder)):
os.mkdir(all_pyramid_folder)
init_pyramid_folder = os.path.join(all_pyramid_folder, 'pyramid_' + str(1) + '_no_thresh')
if not(os.path.exists(init_pyramid_folder)):
init_size_pyramid = 1
build(base_folder, init_size_pyramid, factor, min_bin_per_contig,)
init_pyramid_folder_level_0 = os.path.join(init_pyramid_folder, "level_0")
contig_info = os.path.join(init_pyramid_folder_level_0, '0_contig_info.txt')
fragments_list = os.path.join(init_pyramid_folder_level_0, '0_fragments_list.txt')
init_abs_fragments_contacts = os.path.join(init_pyramid_folder_level_0, '0_abs_frag_contacts.txt')
biases_init_abs_fragments_contacts = os.path.join(base_folder,'abs_fragments_contacts_weighted_biases.txt')
shutil.move(init_abs_fragments_contacts,biases_init_abs_fragments_contacts)
abs_contact_2_coo_file(biases_init_abs_fragments_contacts,init_abs_fragments_contacts)
###########################################
init_pyramid_file = os.path.join(init_pyramid_folder, "pyramid.hdf5")
###########################################
pyramid_folder = os.path.join(all_pyramid_folder, 'pyramid_' + str(size_pyramid) + '_thresh_auto')
if not(os.path.exists(pyramid_folder)):
os.mkdir(pyramid_folder)
level = 0
pyramid_level_folder = os.path.join(pyramid_folder, "level_" + str(level))
if not(os.path.exists(pyramid_level_folder)):
os.mkdir(pyramid_level_folder)
current_contig_info = os.path.join(pyramid_level_folder, str(level) + "_contig_info.txt")
current_frag_list = os.path.join(pyramid_level_folder, str(level) + "_fragments_list.txt")
current_abs_fragments_contacts = os.path.join(pyramid_level_folder, str(level) + "_abs_frag_contacts.txt")
if not(os.path.exists(current_contig_info) and os.path.exists(current_frag_list) and os.path.exists(current_abs_fragments_contacts)):
# if not(os.path.exists(current_contig_info) and os.path.exists(current_frag_list)) :
###########################################
print "start filtering"
pyramid_0 = h5py.File(init_pyramid_file)
thresh = remove_problematic_fragments(contig_info, fragments_list, init_abs_fragments_contacts,
current_contig_info, current_frag_list,
current_abs_fragments_contacts, pyramid_0)
pyramid_0.close()
###########################################
else:
print "filtering already done..."
hdf5_pyramid_file = os.path.join(pyramid_folder, "pyramid.hdf5")
# print "the file is : ", hdf5_pyramid_file
encode_sys_file = sys.getfilesystemencoding()
# if encode_sys_file == None:
# print "No encoding system"
# else:
# print "Yesss it is bitch!!"
pyramid_handle = h5py.File(hdf5_pyramid_file)
pyramid_level_folder = os.path.join(pyramid_folder,"level_"+str(level))
level_pyramid = str(level)+"_"
sub_2_super_frag_index_file = os.path.join(pyramid_level_folder,level_pyramid+"sub_2_super_index_frag.txt")
for level in xrange(0, size_pyramid):
pyramid_level_folder = os.path.join(pyramid_folder,"level_"+str(level))
if not(os.path.exists(pyramid_level_folder)):
os.mkdir(pyramid_level_folder)
level_pyramid = str(level)+"_"
new_contig_list_file = os.path.join(pyramid_level_folder,level_pyramid+"contig_info.txt")
new_fragments_list_file = os.path.join(pyramid_level_folder,level_pyramid+"fragments_list.txt")
new_abs_fragments_contacts_file = os.path.join(pyramid_level_folder,level_pyramid+"abs_frag_contacts.txt")
if level>0:
if os.path.exists(new_contig_list_file) and os.path.exists(new_fragments_list_file) and os.path.exists(new_abs_fragments_contacts_file) \
and os.path.exists((sub_2_super_frag_index_file)):
# if os.path.exists(new_contig_list_file) and os.path.exists(new_fragments_list_file) and os.path.exists((sub_2_super_frag_index_file)):
print "level already built"
nfrags = file_len(new_fragments_list_file) - 1
else: # this should never append !!!
print "writing new_files.."
nfrags = subsample_data_set(current_contig_info, current_frag_list,fact_sub_sampling,current_abs_fragments_contacts,
new_abs_fragments_contacts_file,min_bin_per_contig,
new_contig_list_file,new_fragments_list_file,sub_2_super_frag_index_file)
else:
if os.path.exists(new_contig_list_file) and os.path.exists(new_fragments_list_file) and os.path.exists(new_abs_fragments_contacts_file):
# if os.path.exists(new_contig_list_file) and os.path.exists(new_fragments_list_file) :
print "level already built..."
nfrags = file_len(new_fragments_list_file) - 1
try:
status = pyramid_handle.attrs[str(level)] == "done"
except KeyError:
pyramid_handle.attrs[str(level)] = "pending"
status = False
if not(status):
print "Start filling the pyramid"
# level_to_fill = pyramid_handle.create_dataset(str(level), (nfrags,nfrags), 'i')
# fill_pyramid_level(level_to_fill,new_abs_fragments_contacts_file, size_chunk,nfrags)
fill_sparse_pyramid_level(pyramid_handle, level, new_abs_fragments_contacts_file, nfrags)
pyramid_handle.attrs[str(level)] = "done"
################################################
current_frag_list = new_fragments_list_file
current_contig_info = new_contig_list_file
current_abs_fragments_contacts = new_abs_fragments_contacts_file
sub_2_super_frag_index_file = os.path.join(pyramid_level_folder,level_pyramid+"sub_2_super_index_frag.txt")
print "pyramid built."
pyramid_handle.close()
###############################################
obj_pyramid = pyramid(pyramid_folder, size_pyramid,)
return obj_pyramid
def build(base_folder,size_pyramid, factor, min_bin_per_contig):
""" build fragments pyramids for multi scale analysis """
fact_sub_sampling = factor
contig_info = os.path.join(base_folder,'info_contigs.txt')
fragments_list = os.path.join(base_folder,'fragments_list.txt')
init_abs_fragments_contacts = os.path.join(base_folder,'abs_fragments_contacts_weighted.txt')
biases_init_abs_fragments_contacts = os.path.join(base_folder,'abs_fragments_contacts_weighted_biases.txt')
shutil.move(init_abs_fragments_contacts,biases_init_abs_fragments_contacts)
abs_contact_2_coo_file(biases_init_abs_fragments_contacts,init_abs_fragments_contacts)
all_pyramid_folder = os.path.join(base_folder,'pyramids')
pyramid_folder = os.path.join(all_pyramid_folder,'pyramid_'+str(size_pyramid)+'_no_thresh')
if not(os.path.exists(all_pyramid_folder)):
os.mkdir(all_pyramid_folder)
if not(os.path.exists(pyramid_folder)):
os.mkdir(pyramid_folder)
hdf5_pyramid_file = os.path.join(pyramid_folder,"pyramid.hdf5")
pyramid_handle = h5py.File(hdf5_pyramid_file)
level = 0
pyramid_level_folder = os.path.join(pyramid_folder,"level_"+str(level))
if not(os.path.exists(pyramid_level_folder)):
os.mkdir(pyramid_level_folder)
current_contig_info = os.path.join(pyramid_level_folder,str(level)+"_contig_info.txt")
current_frag_list = os.path.join(pyramid_level_folder,str(level)+"_fragments_list.txt")
current_abs_fragments_contacts = os.path.join(pyramid_level_folder,str(level)+"_abs_frag_contacts.txt")
for level in xrange(0,size_pyramid):
pyramid_level_folder = os.path.join(pyramid_folder,"level_"+str(level))
if not(os.path.exists(pyramid_level_folder)):
os.mkdir(pyramid_level_folder)
level_pyramid = str(level)+"_"
if level == 0:
shutil.copyfile(contig_info,current_contig_info)
shutil.copyfile(init_abs_fragments_contacts,current_abs_fragments_contacts)
nfrags = init_frag_list(fragments_list,current_frag_list)
new_abs_fragments_contacts_file = current_abs_fragments_contacts
new_contig_list_file = current_contig_info
new_fragments_list_file = current_frag_list
sub_2_super_frag_index_file = os.path.join(pyramid_level_folder,level_pyramid+"sub_2_super_index_frag.txt")
else:
new_contig_list_file = os.path.join(pyramid_level_folder,level_pyramid+"contig_info.txt")
new_fragments_list_file = os.path.join(pyramid_level_folder,level_pyramid+"fragments_list.txt")
new_abs_fragments_contacts_file = os.path.join(pyramid_level_folder,level_pyramid+"abs_frag_contacts.txt")
if os.path.exists(new_contig_list_file) and os.path.exists(new_fragments_list_file) and os.path.exists(new_abs_fragments_contacts_file) \
and os.path.exists(sub_2_super_frag_index_file):
print "level already built..."
nfrags = file_len(new_fragments_list_file) - 1
else:
print "writing new_files.."
nfrags = subsample_data_set(current_contig_info, current_frag_list,fact_sub_sampling,current_abs_fragments_contacts,
new_abs_fragments_contacts_file,min_bin_per_contig,
new_contig_list_file,new_fragments_list_file,sub_2_super_frag_index_file)
################################################
try:
status = pyramid_handle.attrs[str(level)] == "done"
except KeyError:
pyramid_handle.attrs[str(level)] = "pending"
status = False
if not(status):
print "Start filling the pyramid"
fill_sparse_pyramid_level(pyramid_handle, level, new_abs_fragments_contacts_file, nfrags)
pyramid_handle.attrs[str(level)] = "done"
################################################
current_frag_list = new_fragments_list_file
current_contig_info = new_contig_list_file
current_abs_fragments_contacts = new_abs_fragments_contacts_file
sub_2_super_frag_index_file = os.path.join(pyramid_level_folder,level_pyramid+"sub_2_super_index_frag.txt")
print "pyramid built."
pyramid_handle.close()
###############################################
# obj_pyramid = pyramid(pyramid_folder,size_pyramid,use_gpu,default_level,use_gpu)
#
# return obj_pyramid
def abs_contact_2_coo_file(abs_contact_file, coo_file):
print "here we go"
p = ProgressBar('green', width=20, block='▣', empty='□')
sparse_dict = dict()
h = open(abs_contact_file, "r")
all_lines = h.readlines()
n_lines = len(all_lines)
#index start at
for i in range(1, n_lines):
pt = np.float32(i)/n_lines
if i%10**6 == 0:
p.render(pt * 100, 'step %s\nProcessing...\nDescription: convert dense file to COO sparse data.' % i)
line = all_lines[i]
dat = line.split()
mates = [int(dat[0]), int(dat[1])]
mates.sort()
f1 = mates[0] - 1
f2 = mates[1] - 1
if f1 in sparse_dict:
if f2 in sparse_dict[f1]:
sparse_dict[f1][f2] += 1
else:
sparse_dict[f1][f2] = 1
else:
sparse_dict[f1] = dict()
sparse_dict[f1][f2] = 1
keys = sparse_dict.keys()
keys.sort()
h.close()
h_coo = open(coo_file, 'w')
h_coo.write("%s\t%s\t%s\n"%('id_frag_a','id_frag_b','n_contact'))
for fa in keys:
d_fb = sparse_dict[fa]
keys_b = d_fb.keys()
keys_b.sort()
for fb in keys_b:
nc = d_fb[fb]
h_coo.write("%s\t%s\t%s\n"%(str(fa), str(fb), str(nc)))
h_coo.close()
print "Done."
def fill_sparse_pyramid_level(pyramid_handle, level, contact_file, nfrags):
print "here we go"
p = ProgressBar('green', width=20, block='▣', empty='□')
sparse_dict = dict()
h = open(contact_file, "r")
all_lines = h.readlines()
n_lines = len(all_lines)
#index start at
for i in range(1, n_lines):
pt = np.float32(i)/n_lines
if i%10**6 == 0:
p.render(pt * 100, 'step %s\nProcessing...\nDescription: loading sparse data into hdf5.' % i)
line = all_lines[i]
dat = line.split()
mates = [int(dat[0]), int(dat[1])]
nc = int(dat[2])
mates.sort()
f1 = mates[0]
f2 = mates[1]
if f1 in sparse_dict:
if f2 in sparse_dict[f1]:
sparse_dict[f1][f2] += nc
else:
sparse_dict[f1][f2] = nc
else:
sparse_dict[f1] = dict()
sparse_dict[f1][f2] = nc
keys = sparse_dict.keys()
keys.sort()
out_r = []
out_c = []
out_d = []
for r in keys:
data = sparse_dict[r]
for c in data.keys():
out_r.append(r)
out_c.append(c)
out_d.append(data[c])
n_on_pxls = len(out_d)
level_hdf5 = pyramid_handle.create_group(str(level))
data_2_sparse = level_hdf5.create_dataset('data', (3, n_on_pxls), 'i')
data_nfrags = level_hdf5.create_dataset('nfrags', (1, 1), 'i')
np_csr = np.zeros((3, n_on_pxls), dtype=np.int32)
np_csr[0, :] = out_r
np_csr[1, :] = out_c
np_csr[2, :] = out_d
data_2_sparse[0, :] = out_r
data_2_sparse[1, :] = out_c
data_2_sparse[2, :] = out_d
data_nfrags[:] = nfrags
print "Done."
def init_frag_list(fragment_list,new_frag_list):
""" adapt the original frag list to fit the build function requirements """
handle_frag_list = open(fragment_list,'r')
handle_new_frag_list = open(new_frag_list,'w')
handle_new_frag_list.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n"%('id', 'chrom', 'start_pos', 'end_pos', 'size',
'gc_content', 'accu_frag', 'frag_start', 'frag_end'))
handle_frag_list.readline()
i = 0
while 1:
line_frag = handle_frag_list.readline()
if not line_frag:
handle_frag_list.close()
handle_new_frag_list.close()
break
i += 1
data = line_frag.split('\t')
id_init = data[0]
contig_name = data[1]
start_pos = data[2]
end_pos = data[3]
length_kb = data[4]
gc_content = str(float(data[5]))
accu_frag = str(1)
frag_start = id_init
frag_end = id_init
handle_new_frag_list.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n"%(id_init,contig_name,start_pos,end_pos,length_kb,gc_content,accu_frag,frag_start,frag_end))
return i
def subsample_data_set(contig_info, fragments_list, fact_sub_sample, abs_fragments_contacts, new_abs_fragments_contacts_file,
min_bin_per_contig,
new_contig_list_file,new_fragments_list_file, old_2_new_file):
import numpy as np
print "fact sub sampling = ", fact_sub_sample
print "minimum bin numer per contig = ", min_bin_per_contig
if fact_sub_sample <= 1:
print "subsampling : nothing to do"
shutil.copy(fragments_list,new_fragments_list_file)
shutil.copy(contig_info,new_contig_list_file)
shutil.copy(abs_fragments_contacts,new_abs_fragments_contacts_file)
nfrags = file_len(fragments_list) - 1
handle_old_2_new = open(old_2_new_file,'w')
handle_old_2_new.write("%s\t%s\n"%("current_id","super_id"))
for ind in xrange(0,nfrags):
curr_id = str(ind + 1)
super_id = curr_id
handle_old_2_new.write("%s\t%s\n"%(curr_id,super_id))
handle_old_2_new.close()
else:
print "subsampling : start"
old_2_new_frags = dict()
spec_new_frags = dict()
handle_new_contigs_list = open(new_contig_list_file,'w')
handle_new_contigs_list.write('%s\t%s\t%s\t%s\n' % ('contig','length_kb','n_frags','cumul_length'))
new_abs_id_frag = 0
id_frag_abs = 0
##### reading contig info !!!! #######################################
handle_contig_info = open(contig_info, 'r')
handle_contig_info.readline()
sum_length_contigs = 0
while 1:
line_contig = handle_contig_info.readline()
if not line_contig:
handle_contig_info.close()
handle_new_contigs_list.close()
break
data = line_contig.split('\t')
init_contig = data[0]
id_frag_start = 1
id_frag_end = int(data[2])
length_kb = data[1]
orientation = 'w'
condition_sub_sample = (id_frag_end / np.float32(fact_sub_sample) ) >= min_bin_per_contig and not (fact_sub_sample ==1)
accu_frag = 0
new_rel_id_frag = 0
id_frag_rel = 0
sum_length_contigs += id_frag_end
if condition_sub_sample:
for arbind in range(0,id_frag_end ):
# for id_frag_rel in range(1,id_frag_end+1 ):
id_frag_rel += 1
id_frag_abs += 1
if id_frag_rel%fact_sub_sample == 1:
accu_frag = 0
new_abs_id_frag += 1
new_rel_id_frag += 1
spec_new_frags[new_abs_id_frag] = dict()
spec_new_frags[new_abs_id_frag]['frag_start'] = id_frag_abs
spec_new_frags[new_abs_id_frag]['frag_end'] = id_frag_abs
accu_frag += 1
old_2_new_frags[id_frag_abs] = new_abs_id_frag
spec_new_frags[new_abs_id_frag]['accu_frag'] = accu_frag
spec_new_frags[new_abs_id_frag]['id_rel'] = new_rel_id_frag
spec_new_frags[new_abs_id_frag]['init_contig'] = init_contig
spec_new_frags[new_abs_id_frag]['gc_content'] = []
spec_new_frags[new_abs_id_frag]['size'] = []
spec_new_frags[new_abs_id_frag]['frag_end'] = id_frag_abs
else:
for arbind in xrange(0,id_frag_end ):
id_frag_abs += 1
new_abs_id_frag += 1
new_rel_id_frag += 1
id_frag_rel += 1
old_2_new_frags[id_frag_abs] = new_abs_id_frag
spec_new_frags[new_abs_id_frag] = {'frag_start':id_frag_abs,'frag_end':id_frag_abs,'accu_frag' : 1 ,
'init_contig' : init_contig,'gc_content':[],'size':[],'id_rel':new_rel_id_frag}
handle_new_contigs_list.write('%s\t%s\t%s\t%s\n' % (init_contig,length_kb,new_rel_id_frag,new_abs_id_frag-new_rel_id_frag))
# write new fragments list
print "size matrix before sub sampling = ",id_frag_abs
print "size matrix after sub sampling = ",new_abs_id_frag
print "sum length contigs = ",sum_length_contigs
##### reading fragments list !!!! #######################################
handle_fragments_list = open(fragments_list,'r')
handle_fragments_list.readline()
id_abs = 0
while 1:
line_fragments = handle_fragments_list.readline()
if not line_fragments:
handle_fragments_list.close()
break
id_abs += 1
# print id_abs
data = line_fragments.split('\t')
id_init = int(data[0])
contig_name = data[1]
start_pos = int(data[2])
end_pos = int(data[3])
length_kb = int(data[4])
gc_content = float(data[5])
np_id_abs = id_abs
curr_id = id_init
init_frag_start = int(data[7])
init_frag_end = int(data[8])
id_new = old_2_new_frags[id_abs]
spec_new_frags[id_new]['gc_content'].append(gc_content)
# spec_new_frags[id_new]['size'].append(length_kb)
if id_abs == spec_new_frags[id_new]['frag_start']:
spec_new_frags[id_new]['start_pos'] = start_pos
spec_new_frags[id_new]['init_frag_start'] = init_frag_start # coord level 0
if id_abs == spec_new_frags[id_new]['frag_end']:
spec_new_frags[id_new]['end_pos'] = end_pos
spec_new_frags[id_new]['size'] = end_pos - spec_new_frags[id_new]['start_pos']
spec_new_frags[id_new]['init_frag_end'] = init_frag_end # coord level 0
print id_abs
keys_new_frags = spec_new_frags.keys()
keys_new_frags.sort()
handle_new_fragments_list = open(new_fragments_list_file,'w')
handle_new_fragments_list.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n"%('id','chrom','start_pos','end_pos','size',
'gc_content','accu_frag','init_frag_start','init_frag_end',
'sub_frag_start','sub_frag_end'))
# print "id problem",spec_new_frags[1]
nfrags = len(keys_new_frags)
print "nfrags = ",nfrags
for new_frag in keys_new_frags:
id = str(spec_new_frags[new_frag]['id_rel'])
gc_content = np.array(spec_new_frags[new_frag]['gc_content']).mean()
size = spec_new_frags[new_frag]['size']
start_pos = spec_new_frags[new_frag]['start_pos']
end_pos = spec_new_frags[new_frag]['end_pos']
chrom = spec_new_frags[new_frag]['init_contig']
init_frag_start = spec_new_frags[new_frag]['init_frag_start']
init_frag_end = spec_new_frags[new_frag]['init_frag_end']
sub_frag_start = spec_new_frags[new_frag]['frag_start']
sub_frag_end = spec_new_frags[new_frag]['frag_end']
accu_frag = str( int(init_frag_end) - int(init_frag_start) +1 )
##########################
handle_new_fragments_list.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n"%(id,chrom,start_pos,end_pos,size,
gc_content,accu_frag,init_frag_start,init_frag_end,
sub_frag_start,sub_frag_end))
##########################
handle_new_fragments_list.close()
print "new fragments list written..."
print "..."
### be carefull : le dictionnaire est base sur
if not(abs_fragments_contacts == 'SIMU'):
print "update sparse contacts file..." # index 0 based
handle_new_abs_fragments_contacts = open(new_abs_fragments_contacts_file,'w')
handle_abs_fragments_contacts = open(abs_fragments_contacts,'r')
handle_new_abs_fragments_contacts.write("%s\t%s\t%s\n"%('id_frag_a','id_frag_b','n_contact'))
handle_abs_fragments_contacts.readline()
all_lines = handle_abs_fragments_contacts.readlines()
sparse_dict = dict()
for id_line in range(1, len(all_lines)):
line = all_lines[id_line]
dat = line.split('\t')
fa = int(dat[0])
fb = int(dat[1])
nc = int(dat[2])
new_abs_id_frag_a = old_2_new_frags[fa + 1] # old_2_new_frags 1-based index
new_abs_id_frag_b = old_2_new_frags[fb + 1] # old_2_new_frags 1-based index
mates = [new_abs_id_frag_a - 1, new_abs_id_frag_b - 1]
mates.sort()
f1 = mates[0]
f2 = mates[1]
if f1 in sparse_dict:
if f2 in sparse_dict[f1]:
sparse_dict[f1][f2] += nc
else:
sparse_dict[f1][f2] = nc
else:
sparse_dict[f1] = dict()
sparse_dict[f1][f2] = nc
keys = sparse_dict.keys()
keys.sort()
for fa in keys:
d_fb = sparse_dict[fa]
keys_b = d_fb.keys()
keys_b.sort()
for fb in keys_b:
nc = d_fb[fb]
handle_new_abs_fragments_contacts.write("%s\t%s\t%s\n"%(str(fa), str(fb), str(nc)))
handle_new_abs_fragments_contacts.close()
print("subsampling: done.")
handle_old_2_new = open(old_2_new_file,'w')
handle_old_2_new.write("%s\t%s\n"%("current_id","super_id"))
for ind in old_2_new_frags.keys():
curr_id = str(ind)
super_id = str(old_2_new_frags[ind])
handle_old_2_new.write("%s\t%s\n"%(curr_id,super_id))
handle_old_2_new.close()
return nfrags
## PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM #####
def remove_problematic_fragments(contig_info, fragments_list, abs_fragments_contacts,new_contig_list_file,
new_fragments_list_file, new_abs_fragments_contacts_file,pyramid):
import numpy as np
p = ProgressBar('blue', width=20, block='▣', empty='□')
# full_resolution = pyramid["0"]
level = pyramid["0"]
np_2_scipy_sparse = level['data']
nfrags = level['nfrags'][0]
print "nfrags = ", nfrags
sparse_mat_csr = sp.csr_matrix((np_2_scipy_sparse[2,:], np_2_scipy_sparse[0:2,:]), shape=(nfrags, nfrags))
sparse_mat_csc = sp.csc_matrix((np_2_scipy_sparse[2,:], np_2_scipy_sparse[0:2,:]), shape=(nfrags, nfrags))
np_nfrags = np.float32(nfrags)
collect_sparsity = []
step = 0
full_mat = sparse_mat_csr + sparse_mat_csr.transpose()
collect_sparsity = np.float32(np.diff(full_mat.indptr)) / np.float32(nfrags)
# for i in range(0, nfrags):
# v_r = sparse_mat_csr[i, :]
# v_c = sparse_mat_csc[i, :]
# non_zeros = v_r.nnz + v_c.nnz
# # sparsity = (np_nfrags - non_zeros)/np_nfrags
# sparsity = (non_zeros)/np_nfrags
# collect_sparsity.append(sparsity)
# step += 1
# if step%1000 == 0:
# pt = step * 100 / nfrags
# p.render(pt, 'step %s\nProcessing...\nDescription: computing sparsity per frag.' % step)
# collect_sparsity = np.array(collect_sparsity,dtype=np.float32)
mean_spars = collect_sparsity.mean()
std_spars = collect_sparsity.std()
max_spars = collect_sparsity.max()
print "n init frags = ", nfrags
print "mean sparsity = ", mean_spars
print "std sparsity = ", std_spars
print "max_sparsity = ", max_spars
# plt.figure()
# plt.plot(collect_sparsity)
# plt.figure()
# plt.hist(collect_sparsity, 100)
# plt.show()
# SENSITIVE PARAMETER ########
# thresh = max_spars + std_spars
thresh = mean_spars - 1.01 * std_spars # para g1
# thresh = max(mean_spars - 0.9 * std_spars,0) # para g1
# SENSITIVE PARAMETER ########
# list_fragments_problem = np.nonzero(collect_sparsity==0)[0]
list_fragments_problem = np.nonzero(collect_sparsity<=thresh)[0]
print "cleaning : start"
import numpy as np
print "number of fragments to remove = ", len(list_fragments_problem)
handle_new_fragments_list = open(new_fragments_list_file,'w')
handle_new_fragments_list.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n" % ('id', 'chrom', 'start_pos', 'end_pos',
'size', 'gc_content', 'accu_frag',
'frag_start', 'frag_end'))
# build np_id_2_frag dictionary
np_id_2_frag = get_frag_info_from_fil(fragments_list)
n_total_frags = len(np_id_2_frag.keys())
# build Contigs.init_contigs dictionary ( need to know the number of frags per contig)
init_contigs, list_init_contig = get_contig_info_from_file(contig_info)
prob_frag = dict()
for np_index in list_fragments_problem:
tmp_frag = np_id_2_frag[np_index]
id = tmp_frag['index'] + '-' + tmp_frag['init_contig']
prob_frag[id] = {'init_contig' : tmp_frag['init_contig'],'index' :tmp_frag['index']}
# print prob_frag
contig_info_dict = dict()
# list_init_contig = init_contigs.keys()
# list_init_contig.sort()
for chrom in list_init_contig:
contig_info_dict[chrom] ={'n_frags': init_contigs[chrom]['n_frags'],'n_new_frags':0,
'length_kb':0}
new_id_frag_rel = 0
new_id_frag_abs = 1
init_id_frag_abs = 0
old_2_new_frags = dict()
handle_fragments_list = open(fragments_list,'r')
handle_fragments_list.readline()
spec_new_frags = dict()
tmp_cumul = {'start_pos':0,'end_pos': 0,'chrom':0,'size':0,'accu_frag':0,'gc_content':[],'lock': False,'init_id_frags':[] ,'list_chrom':[]}
######################################
step = 0
p = ProgressBar('blue', width=20, block='▣', empty='□')
while 1:
line_fragment = handle_fragments_list.readline()
if not line_fragment:
if tmp_cumul['lock']:
for ele in tmp_cumul['init_id_frags']:
old_2_new_frags[ele] = 'destroyed'
new_id_frag_abs -= 1
handle_fragments_list.close()
handle_new_fragments_list.close()
break
step += 1
pt = step*100/n_total_frags
init_id_frag_abs += 1
data = line_fragment.split('\t')
id = int(data[0])
if id == 1:
new_id_frag_rel = 1
if not tmp_cumul['lock']:
new_id_frag_abs += 0
else:
for ele in tmp_cumul['init_id_frags']:
old_2_new_frags[ele] = 'destroyed'
tmp_cumul['gc_content'] = []
tmp_cumul['start_pos'] = 0
tmp_cumul['init_id_frags'] = []
tmp_cumul['list_chrom'] = []
tmp_cumul['frag_start'] = []
tmp_cumul['frag_end'] = []
tmp_cumul['size'] = 0 ###################### debug
chrom = data[1]
start_pos = data[2]
end_pos = data[3]
size = int(data[4])
gc_content = float(data[5])
accu_frag = int(data[6])
frag_start = int(data[7])
frag_end = int(data[8])
name_frag = str(id)+'-'+chrom
lock = prob_frag.has_key(name_frag)
tmp_cumul['chrom'] = chrom
tmp_cumul['list_chrom'].append(chrom)
tmp_cumul['end_pos'] = end_pos
tmp_cumul['size'] += size
tmp_cumul['accu_frag'] += accu_frag
tmp_cumul['lock'] = prob_frag.has_key(name_frag)
if size <= 1:
tmp_cumul['lock'] = True
tmp_cumul['frag_start'].append(frag_start)
tmp_cumul['frag_end'].append(frag_end)
tmp_cumul['gc_content'].append(gc_content)
tmp_cumul['init_id_frags'].append(init_id_frag_abs)
old_2_new_frags[init_id_frag_abs] = new_id_frag_abs
if not lock:
for ele in tmp_cumul['list_chrom']:
if not(ele == tmp_cumul['list_chrom'][0]):
print "warning problem hetero fragments!!!!!!!!!!!!!!!"
contig_info_dict[chrom]['n_new_frags'] +=1
contig_info_dict[chrom]['length_kb'] += tmp_cumul['size']
# str_frag_start = str(min(tmp_cumul["frag_start"]))
# str_frag_end = str(max(tmp_cumul["frag_end"]))
str_frag_start = str(new_id_frag_rel)
str_frag_end = str(new_id_frag_rel)
spec_new_frags[new_id_frag_abs] = {'accu_frag':accu_frag,'gc_content':tmp_cumul['gc_content'],'chrom':chrom}
handle_new_fragments_list.write("%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\t%s\n"%(str(new_id_frag_rel),chrom,tmp_cumul['start_pos'],
tmp_cumul['end_pos'],str(tmp_cumul['size']),
str(np.array(tmp_cumul['gc_content']).mean()),tmp_cumul['accu_frag'],str_frag_start,str_frag_end))
tmp_cumul['start_pos'] = end_pos
tmp_cumul['end_pos'] = start_pos
tmp_cumul['size'] = 0
tmp_cumul['accu_frag'] =0
tmp_cumul['lock'] = prob_frag.has_key(name_frag)
tmp_cumul['chrom'] = chrom
tmp_cumul['gc_content'] = []
tmp_cumul['init_id_frags'] = []
tmp_cumul['list_chrom'] = []
tmp_cumul['frag_start'] = []
tmp_cumul['frag_end'] = []
new_id_frag_rel += 1
new_id_frag_abs += 1
else:
p.render(pt ,'step %s\nProcessing...\nDescription: removing bad fragments.' % step)
p.render(pt ,'step %s\nProcessing...\nDescription: removing bad fragments.' % step)
########################################################################################################################
print 'max new id = ', new_id_frag_abs
handle_new_contigs_list = open(new_contig_list_file,'w')
handle_new_contigs_list.write('%s\t%s\t%s\t%s\n' % ('contig','length_kb','n_frags','cumul_length'))
handle_contig_info = open(contig_info,'r')
handle_contig_info.readline()
cumul_length = 0
while 1:
line_contig = handle_contig_info.readline()
if not line_contig:
handle_contig_info.close()
handle_new_contigs_list.close()
break
data = line_contig.split('\t')
contig = data[0]
# length_kb = data[1]
length_kb = contig_info_dict[contig]['length_kb']
n_frags = contig_info_dict[contig]['n_new_frags']
if n_frags >0:
handle_new_contigs_list.write('%s\t%s\t%s\t%s\n' % (contig,str(length_kb),str(n_frags),str(cumul_length)))
cumul_length += n_frags
else:
print contig +' has been deleted...'
print "update contacts files..."
# write new contacts file
n_total_contacts = file_len(abs_fragments_contacts)
handle_new_abs_fragments_contacts = open(new_abs_fragments_contacts_file,'w')
handle_abs_fragments_contacts = open(abs_fragments_contacts,'r')
handle_new_abs_fragments_contacts.write("%s\t%s\t%s\n"%('id_frag_a','id_frag_b','n_contact'))
all_lines = handle_abs_fragments_contacts.readlines()
# for id_line_contacts in xrange(1, len(all_lines_contact)):
# line_contacts = all_lines_contact[id_line_contacts]
# data = line_contacts.split()
# w_size = data[2]
# w_gc = data[3]
# abs_id_frag_a = int(data[0])
# abs_id_frag_b = int(data[1])
# new_abs_id_frag_a = old_2_new_frags[abs_id_frag_a]
# new_abs_id_frag_b = old_2_new_frags[abs_id_frag_b]
# # step += 1
# # pt = np.int32(step)/n_total_contacts
# # p.render(pt * 100, 'step %s\nProcessing...\nDescription: updating contacts file.' % step)
# if not(new_abs_id_frag_a == 'destroyed' or new_abs_id_frag_b == 'destroyed'):
# w_sub_sample = (spec_new_frags[new_abs_id_frag_a]['accu_frag']* spec_new_frags[new_abs_id_frag_b]['accu_frag'])
# handle_new_abs_fragments_contacts.write("%s\t%s\t%s\t%s\t%s\n"%(str(new_abs_id_frag_a),str(new_abs_id_frag_b),
# w_size, w_gc, str(w_sub_sample)))
sparse_dict = dict()
for id_line in range(1, len(all_lines)):
line = all_lines[id_line]
dat = line.split('\t')
fa = int(dat[0])
fb = int(dat[1])
nc = int(dat[2])
new_abs_id_frag_a = old_2_new_frags[fa + 1] # old_2_new_frags 1-based index
new_abs_id_frag_b = old_2_new_frags[fb + 1] # old_2_new_frags 1-based index
if not(new_abs_id_frag_a == 'destroyed' or new_abs_id_frag_b == 'destroyed'):
mates = [new_abs_id_frag_a - 1, new_abs_id_frag_b - 1]
mates.sort()
f1 = mates[0]
f2 = mates[1]
if f1 in sparse_dict:
if f2 in sparse_dict[f1]:
sparse_dict[f1][f2] += nc
else:
sparse_dict[f1][f2] = nc
else:
sparse_dict[f1] = dict()
sparse_dict[f1][f2] = nc
keys = sparse_dict.keys()
keys.sort()
for fa in keys:
d_fb = sparse_dict[fa]
keys_b = d_fb.keys()
keys_b.sort()
for fb in keys_b:
nc = d_fb[fb]
handle_new_abs_fragments_contacts.write("%s\t%s\t%s\n"%(str(fa), str(fb), str(nc)))
handle_new_abs_fragments_contacts.close()
return thresh
## PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM PROBLEM #####
def get_contig_info_from_file(contig_info):
handle_contig_info = open(contig_info,'r')
handle_contig_info.readline()
init_contigs = dict()
list_contigs = []
while 1:
line_contig = handle_contig_info.readline()
if not line_contig:
handle_contig_info.close()
break
data = line_contig.split('\t')
chr = data[0]
list_contigs.append(chr)
length_kb = int(data[1])
n_frags = int(data[2])
cumul_length = int(data[3])
init_contigs[chr] = dict()
init_contigs[chr]["n_frags"] = n_frags
init_contigs[chr]["length_kb"] = length_kb
init_contigs[chr]["cumul_length"] = cumul_length
return init_contigs, list_contigs
def get_frag_info_from_fil(fragments_list):
handle_list_fragments = open(fragments_list,'r')
handle_list_fragments.readline()
fragments_info = dict()
id = 0
while 1:
line_contig = handle_list_fragments.readline()
if not line_contig:
handle_list_fragments.close()
break
data = line_contig.split('\t')
fragments_info[id] = dict()
fragments_info[id]["init_contig"] = data[1]
fragments_info[id]["index"] = data[0]
id += 1
return fragments_info
class pyramid():
def __init__(self,pyramid_folder,n_levels):
print "init pyramid"
self.pyramid_folder = pyramid_folder
self.n_levels = n_levels
pyramid_file = "pyramid.hdf5"
self.pyramid_file = os.path.join(pyramid_folder, pyramid_file)
self.data = h5py.File(self.pyramid_file)
self.spec_level = dict()
# self.default_level = default_level
self.struct_initiated = False
self.resol_F_s_kb = 3 # size bin in kb
self.dist_max_kb = 30 * 2 * self.resol_F_s_kb # length histo in kb
# self.resol_F_s_kb = 10 # size bin in kb
# self.dist_max_kb = 10 * 2 * self.resol_F_s_kb # length histo in kb
for i in xrange(0, n_levels):
level_folder = os.path.join(pyramid_folder,"level_"+str(i))
find_super_index = i<n_levels-1
self.spec_level[str(i)] = dict()
self.spec_level[str(i)]["level_folder"] = level_folder
self.spec_level[str(i)]["fragments_list_file"] = os.path.join(level_folder, str(i) + "_fragments_list.txt")
self.spec_level[str(i)]["contig_info_file"] = os.path.join(level_folder, str(i) + "_contig_info.txt")
frag_dictionary, contig_dictionary , list_contigs, list_contigs_id = self.build_frag_dictionnary(self.spec_level[str(i)]["fragments_list_file"],i)
if i == 0:
self.list_contigs_name = list_contigs
self.list_contigs_id = list_contigs_id
self.spec_level[str(i)]["fragments_dict"] = frag_dictionary
self.spec_level[str(i)]["contigs_dict"] = contig_dictionary
if find_super_index:
# print "update super index"
super_index_file = os.path.join(level_folder,str(i)+"_sub_2_super_index_frag.txt")
self.update_super_index(self.spec_level[str(i)]["fragments_dict"], super_index_file)
self.update_super_index_in_dict_contig(self.spec_level[str(i)]["fragments_dict"],
self.spec_level[str(i)]["contigs_dict"])
else:
for contig_id in self.spec_level[str(i)]["contigs_dict"].keys():
try:
t = int(contig_id)
except ValueError:
self.spec_level[str(i)]["contigs_dict"].pop(contig_id)
print "object created"
def close(self):
self.data.close()
def get_level(self, level_id):
lev = level(self,level_id)
return lev
def build_frag_dictionnary(self, fragments_list, level):
handle_list_fragments = open(fragments_list,'r')
handle_list_fragments.readline()
fragments_info = dict()
id = 1
contig_dict = dict()
id_contig = 0
list_contigs = []
list_contigs_id = []
while 1:
line_contig = handle_list_fragments.readline()
if not line_contig:
handle_list_fragments.close()
break
data = line_contig.split('\t')
curr_id = int(data[0])
tag = data[0] + "-" + data[1]
start_pos = int(data[2])
end_pos = int(data[3])
size = int(data[4])
gc_content = float(data[5])
n_accu_frags = int(data[6])
id_init_frag_start = int(data[7])
id_init_frag_end = int(data[8])
if level > 0:
id_sub_frag_start = int(data[9])
id_sub_frag_end = int(data[10])
else:
id_sub_frag_start = int(curr_id)
id_sub_frag_end = int(curr_id)
fragments_info[id] = dict()
contig_name = data[1]
fragments_info[id]["init_contig"] = contig_name
fragments_info[id]["index"] = int(curr_id)
fragments_info[id]["tag"] = tag
fragments_info[id]["start_pos(bp)"] = start_pos
fragments_info[id]["end_pos(bp)"] = end_pos
fragments_info[id]["size(bp)"] = size
fragments_info[id]["sub_low_index"] = id_sub_frag_start
fragments_info[id]["sub_high_index"] = id_sub_frag_end
fragments_info[id]["super_index"] = curr_id
fragments_info[id]["n_accu_frags"] = n_accu_frags
if not(contig_name in contig_dict):
id_contig += 1
list_contigs.append(contig_name)
list_contigs_id.append(id_contig)
contig_dict[contig_name] = dict()
contig_dict[contig_name]["frag"] = []
contig_dict[contig_name]["id_contig"] = id_contig
contig_dict[id_contig] = []