def run(self):
"""
2007-03-20
2007-04-03
"""
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
if self.draw_only:
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(self.output_fname)
data_matrix = Numeric.array(data_matrix)
else:
(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(self.input_fname)
snp_acc_ls = header[2:]
strain_id2index = self.get_id2index(curs, self.strain_info_table, strain_acc_list)
snp_id2index = self.get_id2index(curs, self.snp_locus_table, snp_acc_ls)
from dbSNP2data import dbSNP2data
dbSNP2data_instance = dbSNP2data(report=self.report)
data_matrix = dbSNP2data_instance.get_data_matrix(curs, strain_id2index, snp_id2index, nt2number, self.data_table, need_heterozygous_call=1)
FilterStrainSNPMatrix_instance.write_data_matrix(data_matrix, self.output_fname, header, strain_acc_list, category_list)
heterozygous_data_matrix, coarse_data_matrix = self.get_heterozygous_and_coarse_data_matrix(data_matrix)
self.displayDataMatrix(heterozygous_data_matrix, title='heterozygous_data_matrix, 5-10=hetero, else=0')
self.displayDataMatrix(coarse_data_matrix, title='coarse_data_matrix, 0=NA, 1=h**o, 2=hetero')
raw_input("enter")
def run(self):
"""
2007-10-11
"""
import MySQLdb
conn = MySQLdb.connect(db=self.dbname, host=self.hostname)
curs = conn.cursor()
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(
self.input_fname)
if self.debug:
import pdb
pdb.set_trace()
identity_pair_ls = self.construct_identity_pair_ls(
strain_acc_list, header, data_matrix)
g = self.construct_graph_out_of_identity_pair(identity_pair_ls)
g = self.expand_g_with_singleton_strain_id_ls(g, strain_acc_list)
cc_id2clique_id_ls, clique_id2ecotype_id_ls = self.compute_components_and_cliques(
g)
if self.commit:
self.create_identity_table(curs, self.identity_table)
self.create_component2clique_table(curs,
self.component2clique_table)
self.create_clique2ecotype_table(curs, self.clique2ecotype_table)
self.submit_identity_pairs(curs, g, self.identity_table)
self.submit_cc_id2clique_id_ls(curs, cc_id2clique_id_ls,
self.component2clique_table)
self.submit_clique_id2ecotype_id_ls(curs, clique_id2ecotype_id_ls,
self.clique2ecotype_table)
def shuffleMatrixSNPColumn_in_chrom_position_order(input_fname, curs,
snps_table, output_fname):
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(
input_fname)
snp_acc_list = header[2:]
snp_acc2col_index = {}
new_snp_acc_list = []
curs.execute(
"select snpid, chromosome, position from %s order by chromosome, position"
% (snps_table))
rows = curs.fetchall()
for row in rows:
snpid, chromosome, position = row
snp_acc2col_index[snpid] = len(snp_acc2col_index)
new_snp_acc_list.append(snpid)
import numpy
old_matrix = numpy.array(data_matrix)
new_matrix = numpy.zeros(old_matrix.shape, numpy.integer)
for j in range(old_matrix.shape[1]):
snp_acc = snp_acc_list[j]
col_index = snp_acc2col_index[snp_acc]
new_matrix[:, col_index] = old_matrix[:, j]
header = header[:2] + new_snp_acc_list
FilterStrainSNPMatrix_instance.write_data_matrix(new_matrix, output_fname,
header, strain_acc_list,
category_list)
def run(self): """ 2007-10-11 """ import MySQLdb conn = MySQLdb.connect(db=self.dbname,host=self.hostname) curs = conn.cursor() from FilterStrainSNPMatrix import FilterStrainSNPMatrix FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix() header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(self.input_fname) if self.debug: import pdb pdb.set_trace() identity_pair_ls = self.construct_identity_pair_ls(strain_acc_list, header, data_matrix) g = self.construct_graph_out_of_identity_pair(identity_pair_ls) g = self.expand_g_with_singleton_strain_id_ls(g, strain_acc_list) cc_id2clique_id_ls, clique_id2ecotype_id_ls = self.compute_components_and_cliques(g) if self.commit: self.create_identity_table(curs, self.identity_table) self.create_component2clique_table(curs, self.component2clique_table) self.create_clique2ecotype_table(curs, self.clique2ecotype_table) self.submit_identity_pairs(curs, g, self.identity_table) self.submit_cc_id2clique_id_ls(curs, cc_id2clique_id_ls, self.component2clique_table) self.submit_clique_id2ecotype_id_ls(curs, clique_id2ecotype_id_ls, self.clique2ecotype_table)
def cmp192StrainsBorevitsAndNordborgData(borevitz_data_fname,
nordborg_data_fname):
"""
2007-10-09
compare between borevitz and nordborg data of 192 strains
"""
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
borevitz_header, borevitz_strain_acc_list, borevitz_category_list, borevitz_data_matrix = FilterStrainSNPMatrix_instance.read_data(
borevitz_data_fname)
nordborg_header, nordborg_strain_acc_list, nordborg_category_list, nordborg_data_matrix = FilterStrainSNPMatrix_instance.read_data(
nordborg_data_fname, turn_into_integer=0)
#for nordborg data
accession_name2index = {}
for i in range(len(nordborg_category_list)):
accession_name = nordborg_category_list[i]
accession_name2index[accession_name] = i
nativename_missing_in_nordborg_alignment_ls = []
for nativename in borevitz_category_list:
if nativename not in accession_name2index:
nativename_missing_in_nordborg_alignment_ls.append(nativename)
print 'nativename_missing_in_nordborg_alignment_ls:', nativename_missing_in_nordborg_alignment_ls
sys.stderr.write(
"Comparing 192 strains' data from borevitz lab and nordborg 2010 ...")
acc_name_pair_ls = []
borevitz_dist_ls = []
nordborg_dist_ls = []
no_of_borevits_strains = len(borevitz_strain_acc_list)
no_of_valid_nordborg_pairs_ls = []
for i in range(no_of_borevits_strains):
for j in range(i + 1, no_of_borevits_strains):
acc_name1 = borevitz_category_list[i]
acc_name2 = borevitz_category_list[j]
if acc_name1 in accession_name2index and acc_name2 in accession_name2index:
borevitz_dist, no_of_valid_pairs = calBinaryDistanceBetTwoNumericVectors(
borevitz_data_matrix[i], borevitz_data_matrix[j])
nordborg_dist, no_of_valid_pairs = calBinaryDistanceBetTwoAlignmentVectors(
nordborg_data_matrix[accession_name2index[acc_name1]],
nordborg_data_matrix[accession_name2index[acc_name2]])
borevitz_dist_ls.append(borevitz_dist)
nordborg_dist_ls.append(nordborg_dist)
acc_name_pair_ls.append((acc_name1, acc_name2))
no_of_valid_nordborg_pairs_ls.append(no_of_valid_pairs)
sys.stderr.write("Done.\n")
return acc_name_pair_ls, borevitz_dist_ls, nordborg_dist_ls, no_of_valid_nordborg_pairs_ls
def run(self):
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(self.input_fname)
data_matrix = Numeric.array(data_matrix)
locus_allele_prob_vector = self.cal_locus_allele_prob_vector(data_matrix)
locus_heterozygous_prob_vector = self.cal_locus_heterozygous_prob_vector(locus_allele_prob_vector)
locus_heterozygous_prob_matrix = self.cal_locus_heterozygous_prob_matrix(locus_heterozygous_prob_vector, self.max_selfing_generation)
selfing_generation_ls = self.cal_selfing_generation_prob(data_matrix, locus_heterozygous_prob_vector, strain_acc_list, category_list, locus_heterozygous_prob_matrix, self.output_fname)
import pylab
pylab.clf()
pylab.hist(selfing_generation_ls, 20)
pylab.title("hist of selfing generations")
pylab.show()
def run(self):
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(
self.input_fname)
data_matrix = Numeric.array(data_matrix)
locus_allele_prob_vector = self.cal_locus_allele_prob_vector(
data_matrix)
locus_heterozygous_prob_vector = self.cal_locus_heterozygous_prob_vector(
locus_allele_prob_vector)
locus_heterozygous_prob_matrix = self.cal_locus_heterozygous_prob_matrix(
locus_heterozygous_prob_vector, self.max_selfing_generation)
selfing_generation_ls = self.cal_selfing_generation_prob(
data_matrix, locus_heterozygous_prob_vector, strain_acc_list,
category_list, locus_heterozygous_prob_matrix, self.output_fname)
import pylab
pylab.clf()
pylab.hist(selfing_generation_ls, 20)
pylab.title("hist of selfing generations")
pylab.show()
def find_2010_accession_id_for_old_2010_x_149snp_matrix(input_fname, curs, accession_table='at.accession'):
"""
2007-11-05 whether the names can still be matched to entries in accession_table.
"""
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(input_fname)
strain_acc_accession_id_ls = []
strain_acc_match_failed_ls = []
for strain_acc in category_list:
curs.execute("select id from %s where name='%s'"%(accession_table, strain_acc))
rows = curs.fetchall()
if rows:
accession_id = rows[0][0]
strain_acc_accession_id_ls.append([strain_acc, accession_id])
else:
accession_id = ''
strain_acc_accession_id_ls.append([strain_acc])
strain_acc_match_failed_ls.append(strain_acc)
print '%s\t%s'%(strain_acc, accession_id)
return strain_acc_accession_id_ls, strain_acc_match_failed_ls
def shuffleMatrixSNPColumn_in_chrom_position_order(input_fname, curs, snps_table, output_fname):
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(input_fname)
snp_acc_list = header[2:]
snp_acc2col_index = {}
new_snp_acc_list = []
curs.execute("select snpid, chromosome, position from %s order by chromosome, position"%(snps_table))
rows = curs.fetchall()
for row in rows:
snpid, chromosome, position = row
snp_acc2col_index[snpid] = len(snp_acc2col_index)
new_snp_acc_list.append(snpid)
import numpy
old_matrix = numpy.array(data_matrix)
new_matrix = numpy.zeros(old_matrix.shape, numpy.integer)
for j in range(old_matrix.shape[1]):
snp_acc = snp_acc_list[j]
col_index = snp_acc2col_index[snp_acc]
new_matrix[:,col_index] = old_matrix[:,j]
header = header[:2] + new_snp_acc_list
FilterStrainSNPMatrix_instance.write_data_matrix(new_matrix, output_fname, header, strain_acc_list, category_list)
def find_2010_accession_id_for_old_2010_x_149snp_matrix(
input_fname, curs, accession_table='at.accession'):
"""
2007-11-05 whether the names can still be matched to entries in accession_table.
"""
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(
input_fname)
strain_acc_accession_id_ls = []
strain_acc_match_failed_ls = []
for strain_acc in category_list:
curs.execute("select id from %s where name='%s'" %
(accession_table, strain_acc))
rows = curs.fetchall()
if rows:
accession_id = rows[0][0]
strain_acc_accession_id_ls.append([strain_acc, accession_id])
else:
accession_id = ''
strain_acc_accession_id_ls.append([strain_acc])
strain_acc_match_failed_ls.append(strain_acc)
print '%s\t%s' % (strain_acc, accession_id)
return strain_acc_accession_id_ls, strain_acc_match_failed_ls
def run(self):
import MySQLdb
conn = MySQLdb.connect(db=self.dbname,host=self.hostname, user = self.user, passwd = self.passwd)
curs = conn.cursor()
from dbSNP2data import dbSNP2data
dbSNP2data_instance = dbSNP2data(user=self.user, passwd=self.passwd, output_fname='whatever')
snp_id2index, snp_id_list, snp_id2info = dbSNP2data_instance.get_snp_id2index_m(curs, self.input_table, self.snp_locus_table)
#strain_id2index, strain_id_list
strain_id2index, strain_id_list, nativename2strain_id, strain_id2acc, strain_id2category = dbSNP2data_instance.get_strain_id2index_m(curs, self.input_table, self.strain_info_table)
#2008-06-02 stuff returned by get_strain_id2index_m is totally changed.
ecotype_id2row_index = {}
for strain_id, acc in strain_id2acc.iteritems():
row_index = strain_id2index[strain_id]
ecotype_id2row_index[acc] = row_index
#strain_id2acc, strain_id2category = dbSNP2data_instance.get_strain_id_info_m(curs, strain_id_list, self.strain_info_table)
snp_id2acc = dbSNP2data_instance.get_snp_id_info_m(curs, snp_id_list, self.snp_locus_table)
data_matrix = dbSNP2data_instance.get_data_matrix_m(curs, strain_id2index, snp_id2index, nt2number, self.input_table, need_heterozygous_call=1)
from OutputPopulation import OutputPopulation
popid2ecotypeid_ls = OutputPopulation.get_popid2ecotypeid_ls(curs, self.population_table)
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
from RemoveBadSNPs import RemoveBadSNPs
RemoveBadSNPs_instance = RemoveBadSNPs()
popid2strain_id_snp_id_ls = {}
for popid, ecotypeid_ls in popid2ecotypeid_ls.iteritems():
if len(ecotypeid_ls)>=self.min_no_of_strains_per_pop:
sys.stderr.write("Population %s\n"%popid)
sub_data_matrix, new_ecotypeid_ls = self.create_sub_data_matrix(popid, data_matrix, ecotypeid_ls, ecotype_id2row_index)
if len(new_ecotypeid_ls)>=self.min_no_of_strains_per_pop:
sys.stderr.write("\tPopulation %s has %s strains\n"%(popid, len(new_ecotypeid_ls)))
strain_id_selected, snp_id_selected = self.cleanup_one_population(FilterStrainSNPMatrix_instance, RemoveBadSNPs_instance, sub_data_matrix, new_ecotypeid_ls, snp_id_list, self.min_no_of_strains_per_pop, self.row_cutoff, self.col_cutoff, self.min_log_prob)
if strain_id_selected and snp_id_selected:
popid2strain_id_snp_id_ls[popid] = [strain_id_selected, snp_id_selected]
if self.commit:
self.create_popid2snpid_table(curs, self.output_table)
self.mark_strain_id_selected(curs, popid2strain_id_snp_id_ls, self.population_table)
self.submit_popid2snpid_list(curs, popid2strain_id_snp_id_ls, self.population_table, self.output_table)
conn.commit()
def cmp192StrainsBorevitsAndNordborgData(borevitz_data_fname, nordborg_data_fname):
"""
2007-10-09
compare between borevitz and nordborg data of 192 strains
"""
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
borevitz_header, borevitz_strain_acc_list, borevitz_category_list, borevitz_data_matrix = FilterStrainSNPMatrix_instance.read_data(borevitz_data_fname)
nordborg_header, nordborg_strain_acc_list, nordborg_category_list, nordborg_data_matrix = FilterStrainSNPMatrix_instance.read_data(nordborg_data_fname, turn_into_integer=0)
#for nordborg data
accession_name2index = {}
for i in range(len(nordborg_category_list)):
accession_name = nordborg_category_list[i]
accession_name2index[accession_name] = i
nativename_missing_in_nordborg_alignment_ls = []
for nativename in borevitz_category_list:
if nativename not in accession_name2index:
nativename_missing_in_nordborg_alignment_ls.append(nativename)
print 'nativename_missing_in_nordborg_alignment_ls:', nativename_missing_in_nordborg_alignment_ls
sys.stderr.write("Comparing 192 strains' data from borevitz lab and nordborg 2010 ...")
acc_name_pair_ls = []
borevitz_dist_ls = []
nordborg_dist_ls = []
no_of_borevits_strains = len(borevitz_strain_acc_list)
no_of_valid_nordborg_pairs_ls = []
for i in range(no_of_borevits_strains):
for j in range(i+1, no_of_borevits_strains):
acc_name1 = borevitz_category_list[i]
acc_name2 = borevitz_category_list[j]
if acc_name1 in accession_name2index and acc_name2 in accession_name2index:
borevitz_dist, no_of_valid_pairs = calBinaryDistanceBetTwoNumericVectors(borevitz_data_matrix[i], borevitz_data_matrix[j])
nordborg_dist, no_of_valid_pairs = calBinaryDistanceBetTwoAlignmentVectors(nordborg_data_matrix[accession_name2index[acc_name1]], nordborg_data_matrix[accession_name2index[acc_name2]])
borevitz_dist_ls.append(borevitz_dist)
nordborg_dist_ls.append(nordborg_dist)
acc_name_pair_ls.append((acc_name1, acc_name2))
no_of_valid_nordborg_pairs_ls.append(no_of_valid_pairs)
sys.stderr.write("Done.\n")
return acc_name_pair_ls, borevitz_dist_ls, nordborg_dist_ls, no_of_valid_nordborg_pairs_ls
def run(self):
"""
2007-03-20
2007-04-03
"""
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
if self.draw_only:
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(
self.output_fname)
data_matrix = Numeric.array(data_matrix)
else:
(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(
self.input_fname)
snp_acc_ls = header[2:]
strain_id2index = self.get_id2index(curs, self.strain_info_table,
strain_acc_list)
snp_id2index = self.get_id2index(curs, self.snp_locus_table,
snp_acc_ls)
from dbSNP2data import dbSNP2data
dbSNP2data_instance = dbSNP2data(report=self.report)
data_matrix = dbSNP2data_instance.get_data_matrix(
curs,
strain_id2index,
snp_id2index,
nt2number,
self.data_table,
need_heterozygous_call=1)
FilterStrainSNPMatrix_instance.write_data_matrix(
data_matrix, self.output_fname, header, strain_acc_list,
category_list)
heterozygous_data_matrix, coarse_data_matrix = self.get_heterozygous_and_coarse_data_matrix(
data_matrix)
self.displayDataMatrix(
heterozygous_data_matrix,
title='heterozygous_data_matrix, 5-10=hetero, else=0')
self.displayDataMatrix(
coarse_data_matrix,
title='coarse_data_matrix, 0=NA, 1=h**o, 2=hetero')
raw_input("enter")
def run(self):
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
import MySQLdb
#conn = MySQLdb.connect(db="stock",host='natural.uchicago.edu', user='******', passwd='iamhereatusc')
conn = MySQLdb.connect(db=self.dbname, host=self.hostname)
curs = conn.cursor()
if self.debug:
import pdb
pdb.set_trace()
nt_number2diff_matrix_index = self.get_nt_number2diff_matrix_index(
nt2number)
SNPpos2col_index, snpid2col_index, snp_acc_ls, snp_index2snp_info_ls = self.setup_SNP_dstruc(
curs, self.snp_locus_table)
ecotype_id2accession_id, ecotype_id2row_index, ecotype_id2info_ls, ecotype_id_ls, accession_id2row_index, accession_id_ls, accession_id2ecotype_id_ls = self.setup_accession_ecotype_dstruc(
curs, self.accession2ecotype_table, self.ecotype_table,
self.calls_table)
ecotype_X_snp_matrix, ecotype_X_snp_matrix_touched = self.get_ecotype_X_snp_matrix(
curs, ecotype_id2row_index, snpid2col_index, self.calls_table)
if self.sub_justin_output_fname:
header = ['ecotype_id', 'ecotype_id'] + snp_acc_ls
FilterStrainSNPMatrix_instance.write_data_matrix(
ecotype_X_snp_matrix, self.sub_justin_output_fname, header,
ecotype_id_ls, ecotype_id_ls)
alignment_id2positions_to_be_checked_ls, alignment_id2start = self.get_alignment_id2positions_to_be_checked_ls(
curs, self.alignment_table)
accession_X_snp_matrix, accession_X_snp_matrix_touched, snp_index2alignment_id = self.get_accession_X_snp_matrix(
curs, accession_id2row_index, SNPpos2col_index,
self.sequence_table, self.alignment_table,
alignment_id2positions_to_be_checked_ls)
if self.output_fname:
header = ['accession_id', 'accession_id'] + snp_acc_ls
FilterStrainSNPMatrix_instance.write_data_matrix(
accession_X_snp_matrix, self.output_fname, header,
accession_id_ls, accession_id_ls)
summary_diff_matrix_ls, diff_details_ls = self.cmp_two_matricies(
accession_X_snp_matrix,
accession_X_snp_matrix_touched,
ecotype_X_snp_matrix,
ecotype_X_snp_matrix_touched,
nt_number2diff_matrix_index,
ecotype_id2accession_id,
ecotype_id2row_index,
accession_id2row_index,
diff_details_ls_type=2)
print "diff_matrix_touched_accession_vs_touched_ecotype"
print summary_diff_matrix_ls[0]
print "diff_matrix_touched_accession_vs_untouched_ecotype"
print summary_diff_matrix_ls[1]
print "diff_matrix_untouched_accession_vs_touched_ecotype"
print summary_diff_matrix_ls[2]
print "diff_matrix_untouched_accession_vs_untouched_ecotype"
print summary_diff_matrix_ls[3]
summary_diff_matrix_caption_ls = [
'PCR-tried vs sequenom-tried', 'PCR-tried vs sequenom-untried',
'PCR-untried vs sequenom-tried', 'PCR-untried vs sequenom-untried'
]
if self.latex_output_fname:
outf = open(self.latex_output_fname, 'w')
outf.write(
'\\section{2010 PCR versus sequenom. summary} \\label{section_summary}\n'
)
for i in range(len(summary_diff_matrix_ls)):
from pymodule.latex import outputMatrixInLatexTable
wrapped_diff_matrix = self.wrap_diff_matrix_with_row_col_names(
summary_diff_matrix_ls[i])
table_label = 'table_dm%s' % i
outf.write(
outputMatrixInLatexTable(wrapped_diff_matrix,
summary_diff_matrix_caption_ls[i],
table_label))
table_no = i
#output the whole diff_details_ls
outf.write(
'\\section{Real Mismatches between pcr and sequenom (deletion/NA excluded)} \\label{section_real_mismatch}\n'
)
diff_details_ls = self.beautify_snp_diff_details_ls(
diff_details_ls, ecotype_id2info_ls, snp_index2snp_info_ls,
alignment_id2start, snp_index2alignment_id)
table_label = 'table_dm%s' % table_no
caption = 'mismatches between pcr and sequenom data (deletion/NA excluded, sorted by accession id)'
outf.write(
outputMatrixInLatexTable(diff_details_ls,
caption,
table_label,
header_ls=[
'nativename', 'stkparent',
'ecotype_id', 'duplicate',
'accession_id', 'SNP',
'chromosome', 'position',
'alignment_id', 'alignment_start',
'pcr_call', 'sequenom_call'
]))
#Strain-wise comparison
outf.write(
'\\section{2010 PCR versus sequenom for each strain} \\label{section_strain_wise}\n'
)
accession_id_ls.sort()
for accession_id in accession_id_ls:
ecotype_id_ls = accession_id2ecotype_id_ls[accession_id]
outf.write(
'\\subsection{strain %s(accession id=%s)}\n' %
(ecotype_id2info_ls[ecotype_id_ls[0]][0], accession_id))
for ecotype_id in ecotype_id_ls:
outf.write(
'\\subsubsection{corresponding ecotype %s(stkparent=%s, ecotype id=%s, duplicate=%s)}\n'
% (ecotype_id2info_ls[ecotype_id][0],
ecotype_id2info_ls[ecotype_id][1], ecotype_id[0],
ecotype_id[1]))
e_row_index = ecotype_id2row_index[ecotype_id]
a_row_index = accession_id2row_index[accession_id]
diff_matrix_ls, diff_details_ls = self.cmp_two_lists(
accession_X_snp_matrix[a_row_index, :],
accession_X_snp_matrix_touched[a_row_index, :],
ecotype_X_snp_matrix[e_row_index, :],
ecotype_X_snp_matrix_touched[e_row_index, :],
nt_number2diff_matrix_index)
wrapped_diff_matrix = self.wrap_diff_matrix_with_row_col_names(
diff_matrix_ls[0])
table_no += 1
table_label = 'table_dm%s' % table_no
caption = 'accession id=%s vs ecotype id=%s, duplicate=%s(nativename=%s, stockparent=%s)' % (
accession_id, ecotype_id[0], ecotype_id[1],
ecotype_id2info_ls[ecotype_id][0],
ecotype_id2info_ls[ecotype_id][1])
outf.write(
outputMatrixInLatexTable(wrapped_diff_matrix, caption,
table_label))
if diff_details_ls:
diff_details_ls = self.beautify_diff_details_ls(
diff_details_ls, snp_index2snp_info_ls,
alignment_id2start, snp_index2alignment_id)
table_no += 1
table_label = 'table_dm%s' % table_no
caption = 'detailed difference for accession id=%s vs ecotype id=%s, duplicate=%s' % (
accession_id, ecotype_id[0], ecotype_id[1])
outf.write(
outputMatrixInLatexTable(
diff_details_ls,
caption,
table_label,
header_ls=[
'snp', 'chromosome', 'position',
'alignment_id', 'alignment_start',
'pcr_call', 'sequenom_call'
]))
#SNP-wise comparison
outf.write(
'\\section{2010 PCR versus sequenom for each SNP} \\label{section_snp_wise}\n'
)
for snp_column in range(accession_X_snp_matrix.shape[1]):
snp_acc, chromosome, position = snp_index2snp_info_ls[
snp_column]
alignment_id = snp_index2alignment_id[snp_column]
alignment_start = alignment_id2start[alignment_id]
outf.write(
'\\subsection{SNP %s(chrom=%s, pos=%s, alignment id=%s, alignment start=%s)}\n'
% (snp_acc, chromosome, position, alignment_id,
alignment_start))
diff_matrix_ls, diff_details_ls = self.cmp_two_matricies(
accession_X_snp_matrix,
accession_X_snp_matrix_touched,
ecotype_X_snp_matrix,
ecotype_X_snp_matrix_touched,
nt_number2diff_matrix_index,
ecotype_id2accession_id,
ecotype_id2row_index,
accession_id2row_index,
snp_column=snp_column,
diff_details_ls_type=1)
wrapped_diff_matrix = self.wrap_diff_matrix_with_row_col_names(
diff_matrix_ls[0])
table_no += 1
table_label = 'table_dm%s' % table_no
caption = 'SNP %s(chromosome=%s, position=%s, alignment id=%s, alignment start=%s)' % (
snp_acc, chromosome, position, alignment_id,
alignment_start)
outf.write(
outputMatrixInLatexTable(wrapped_diff_matrix, caption,
table_label))
if diff_details_ls:
diff_details_ls = self.beautify_snp_diff_details_ls(
diff_details_ls, ecotype_id2info_ls)
table_no += 1
table_label = 'table_dm%s' % table_no
caption = 'detailed difference for SNP %s' % (snp_acc)
header_ls = [
'nativename', 'stkparent', 'ecotype_id', 'duplicate',
'accession_id', 'pcr_call', 'sequenom_call'
]
outf.write(
outputMatrixInLatexTable(diff_details_ls, caption,
table_label, header_ls))
del outf
def run(self):
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
import MySQLdb
#conn = MySQLdb.connect(db="stock",host='natural.uchicago.edu', user='******', passwd='iamhereatusc')
conn = MySQLdb.connect(db=self.dbname,host=self.hostname)
curs = conn.cursor()
if self.debug:
import pdb
pdb.set_trace()
nt_number2diff_matrix_index = self.get_nt_number2diff_matrix_index(nt2number)
SNPpos2col_index, snpid2col_index, snp_acc_ls, snp_index2snp_info_ls = self.setup_SNP_dstruc(curs, self.snp_locus_table)
ecotype_id2accession_id, ecotype_id2row_index, ecotype_id2info_ls, ecotype_id_ls, accession_id2row_index, accession_id_ls, accession_id2ecotype_id_ls = self.setup_accession_ecotype_dstruc(curs, self.accession2ecotype_table, self.ecotype_table, self.calls_table)
ecotype_X_snp_matrix, ecotype_X_snp_matrix_touched = self.get_ecotype_X_snp_matrix(curs, ecotype_id2row_index, snpid2col_index, self.calls_table)
if self.sub_justin_output_fname:
header = ['ecotype_id', 'ecotype_id'] + snp_acc_ls
FilterStrainSNPMatrix_instance.write_data_matrix(ecotype_X_snp_matrix, self.sub_justin_output_fname, header, ecotype_id_ls, ecotype_id_ls)
alignment_id2positions_to_be_checked_ls, alignment_id2start = self.get_alignment_id2positions_to_be_checked_ls(curs, self.alignment_table)
accession_X_snp_matrix, accession_X_snp_matrix_touched, snp_index2alignment_id= self.get_accession_X_snp_matrix(curs, accession_id2row_index, SNPpos2col_index, self.sequence_table, self.alignment_table, alignment_id2positions_to_be_checked_ls)
if self.output_fname:
header = ['accession_id', 'accession_id'] + snp_acc_ls
FilterStrainSNPMatrix_instance.write_data_matrix(accession_X_snp_matrix, self.output_fname, header, accession_id_ls, accession_id_ls)
summary_diff_matrix_ls, diff_details_ls = self.cmp_two_matricies(accession_X_snp_matrix, accession_X_snp_matrix_touched, ecotype_X_snp_matrix, ecotype_X_snp_matrix_touched, nt_number2diff_matrix_index, ecotype_id2accession_id, ecotype_id2row_index, accession_id2row_index, diff_details_ls_type=2)
print "diff_matrix_touched_accession_vs_touched_ecotype"
print summary_diff_matrix_ls[0]
print "diff_matrix_touched_accession_vs_untouched_ecotype"
print summary_diff_matrix_ls[1]
print "diff_matrix_untouched_accession_vs_touched_ecotype"
print summary_diff_matrix_ls[2]
print "diff_matrix_untouched_accession_vs_untouched_ecotype"
print summary_diff_matrix_ls[3]
summary_diff_matrix_caption_ls = ['PCR-tried vs sequenom-tried', 'PCR-tried vs sequenom-untried', 'PCR-untried vs sequenom-tried', 'PCR-untried vs sequenom-untried']
if self.latex_output_fname:
outf = open(self.latex_output_fname, 'w')
outf.write('\\section{2010 PCR versus sequenom. summary} \\label{section_summary}\n')
for i in range(len(summary_diff_matrix_ls)):
from pymodule.latex import outputMatrixInLatexTable
wrapped_diff_matrix = self.wrap_diff_matrix_with_row_col_names(summary_diff_matrix_ls[i])
table_label = 'table_dm%s'%i
outf.write(outputMatrixInLatexTable(wrapped_diff_matrix, summary_diff_matrix_caption_ls[i], table_label))
table_no = i
#output the whole diff_details_ls
outf.write('\\section{Real Mismatches between pcr and sequenom (deletion/NA excluded)} \\label{section_real_mismatch}\n')
diff_details_ls = self.beautify_snp_diff_details_ls(diff_details_ls, ecotype_id2info_ls, snp_index2snp_info_ls, alignment_id2start, snp_index2alignment_id)
table_label = 'table_dm%s'%table_no
caption = 'mismatches between pcr and sequenom data (deletion/NA excluded, sorted by accession id)'
outf.write(outputMatrixInLatexTable(diff_details_ls, caption, table_label, header_ls=['nativename', 'stkparent', 'ecotype_id', 'duplicate', 'accession_id', 'SNP', 'chromosome', 'position', 'alignment_id', 'alignment_start', 'pcr_call', 'sequenom_call']))
#Strain-wise comparison
outf.write('\\section{2010 PCR versus sequenom for each strain} \\label{section_strain_wise}\n')
accession_id_ls.sort()
for accession_id in accession_id_ls:
ecotype_id_ls = accession_id2ecotype_id_ls[accession_id]
outf.write('\\subsection{strain %s(accession id=%s)}\n'%(ecotype_id2info_ls[ecotype_id_ls[0]][0], accession_id))
for ecotype_id in ecotype_id_ls:
outf.write('\\subsubsection{corresponding ecotype %s(stkparent=%s, ecotype id=%s, duplicate=%s)}\n'%(ecotype_id2info_ls[ecotype_id][0], ecotype_id2info_ls[ecotype_id][1], ecotype_id[0], ecotype_id[1]))
e_row_index = ecotype_id2row_index[ecotype_id]
a_row_index = accession_id2row_index[accession_id]
diff_matrix_ls, diff_details_ls= self.cmp_two_lists(accession_X_snp_matrix[a_row_index,:], accession_X_snp_matrix_touched[a_row_index,:], ecotype_X_snp_matrix[e_row_index,:], ecotype_X_snp_matrix_touched[e_row_index,:], nt_number2diff_matrix_index)
wrapped_diff_matrix = self.wrap_diff_matrix_with_row_col_names(diff_matrix_ls[0])
table_no += 1
table_label = 'table_dm%s'%table_no
caption = 'accession id=%s vs ecotype id=%s, duplicate=%s(nativename=%s, stockparent=%s)'%(accession_id, ecotype_id[0], ecotype_id[1], ecotype_id2info_ls[ecotype_id][0], ecotype_id2info_ls[ecotype_id][1])
outf.write(outputMatrixInLatexTable(wrapped_diff_matrix, caption, table_label))
if diff_details_ls:
diff_details_ls = self.beautify_diff_details_ls(diff_details_ls, snp_index2snp_info_ls, alignment_id2start, snp_index2alignment_id)
table_no += 1
table_label = 'table_dm%s'%table_no
caption = 'detailed difference for accession id=%s vs ecotype id=%s, duplicate=%s'%(accession_id, ecotype_id[0], ecotype_id[1])
outf.write(outputMatrixInLatexTable(diff_details_ls, caption, table_label, header_ls=['snp', 'chromosome', 'position', 'alignment_id', 'alignment_start', 'pcr_call', 'sequenom_call']))
#SNP-wise comparison
outf.write('\\section{2010 PCR versus sequenom for each SNP} \\label{section_snp_wise}\n')
for snp_column in range(accession_X_snp_matrix.shape[1]):
snp_acc, chromosome, position = snp_index2snp_info_ls[snp_column]
alignment_id = snp_index2alignment_id[snp_column]
alignment_start = alignment_id2start[alignment_id]
outf.write('\\subsection{SNP %s(chrom=%s, pos=%s, alignment id=%s, alignment start=%s)}\n'%(snp_acc, chromosome, position, alignment_id, alignment_start))
diff_matrix_ls, diff_details_ls = self.cmp_two_matricies(accession_X_snp_matrix, accession_X_snp_matrix_touched, ecotype_X_snp_matrix, ecotype_X_snp_matrix_touched, nt_number2diff_matrix_index, ecotype_id2accession_id, ecotype_id2row_index, accession_id2row_index, snp_column=snp_column, diff_details_ls_type=1)
wrapped_diff_matrix = self.wrap_diff_matrix_with_row_col_names(diff_matrix_ls[0])
table_no += 1
table_label = 'table_dm%s'%table_no
caption = 'SNP %s(chromosome=%s, position=%s, alignment id=%s, alignment start=%s)'%(snp_acc, chromosome, position, alignment_id, alignment_start)
outf.write(outputMatrixInLatexTable(wrapped_diff_matrix, caption, table_label))
if diff_details_ls:
diff_details_ls = self.beautify_snp_diff_details_ls(diff_details_ls, ecotype_id2info_ls)
table_no += 1
table_label = 'table_dm%s'%table_no
caption = 'detailed difference for SNP %s'%(snp_acc)
header_ls = ['nativename', 'stkparent', 'ecotype_id', 'duplicate', 'accession_id', 'pcr_call', 'sequenom_call']
outf.write(outputMatrixInLatexTable(diff_details_ls, caption, table_label, header_ls))
del outf
def run(self):
"""
2007-03-29
2007-04-03
2007-05-01
--db_connect()
--FilterStrainSNPMatrix_instance.read_data()
if self.comparison_only:
--FilterStrainSNPMatrix_instance.read_data()
else:
--get_SNPpos2index()
--create_SNP_matrix_2010()
--get_align_length_from_fname()
--get_positions_to_be_checked_ls()
--get_align_matrix_from_fname()
--get_positions_to_be_checked_ls()
--get_mapping_info_regarding_strain_acc()
--shuffle_data_matrix_according_to_strain_acc_ls()
--FilterStrainSNPMatrix_instance.write_data_matrix()
--extract_sub_data_matrix()
if self.sub_justin_output_fname:
--FilterStrainSNPMatrix_instance.write_data_matrix()
--compare_two_SNP_matrix()
--outputDiffType()
"""
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, src_strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(self.input_fname)
if self.comparison_only:
header, strain_acc_ls, abbr_name_ls_sorted, SNP_matrix_2010_sorted = FilterStrainSNPMatrix_instance.read_data(self.output_fname)
SNP_matrix_2010_sorted = Numeric.array(SNP_matrix_2010_sorted)
else:
(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)
#extract data from alignment
snp_acc_ls = header[2:]
SNPpos2index = self.get_SNPpos2index(curs, snp_acc_ls, self.snp_locus_table)
abbr_name_ls, SNP_matrix_2010 = self.create_SNP_matrix_2010(SNPpos2index, self.data_dir_2010)
strain_acc_ls, strain_acc2abbr_name, strain_acc2index = self.get_mapping_info_regarding_strain_acc(curs, self.strain_info_table, self.strain_info_2010_table, abbr_name_ls)
SNP_matrix_2010_sorted = self.shuffle_data_matrix_according_to_strain_acc_ls(SNP_matrix_2010, strain_acc_ls, strain_acc2index)
abbr_name_ls_sorted = []
for strain_acc in strain_acc_ls:
abbr_name_ls_sorted.append(strain_acc2abbr_name[strain_acc])
FilterStrainSNPMatrix_instance.write_data_matrix(SNP_matrix_2010_sorted, self.output_fname, header, strain_acc_ls, abbr_name_ls_sorted)
#comparison
data_matrix = Numeric.array(data_matrix)
sub_data_matrix = self.extract_sub_data_matrix(src_strain_acc_list, data_matrix, strain_acc_ls)
if self.sub_justin_output_fname:
FilterStrainSNPMatrix_instance.write_data_matrix(sub_data_matrix, self.sub_justin_output_fname, header, strain_acc_ls, abbr_name_ls_sorted)
diff_matrix, diff_tag_dict, diff_tag2counter= self.compare_two_SNP_matrix(SNP_matrix_2010_sorted, sub_data_matrix)
if self.diff_output_fname:
self.outputDiffType(diff_matrix, SNP_matrix_2010_sorted, sub_data_matrix, diff_tag_dict, self.diff_type_to_be_outputted, abbr_name_ls_sorted, header[2:], self.diff_output_fname)
summary_result_ls = []
for tag, counter in diff_tag2counter.iteritems():
summary_result_ls.append('%s(%s):%s'%(tag, diff_tag_dict[tag], counter))
print '\t%s(%s)\t%s'%(tag, diff_tag_dict[tag], counter)
import pylab
pylab.clf()
diff_matrix_reverse = list(diff_matrix)
diff_matrix_reverse.reverse()
diff_matrix_reverse = Numeric.array(diff_matrix_reverse)
pylab.imshow(diff_matrix_reverse, interpolation='nearest')
pylab.title(' '.join(summary_result_ls))
pylab.colorbar()
pylab.show()
#2007-11-01 do something as CmpAccession2Ecotype.py
from CmpAccession2Ecotype import CmpAccession2Ecotype
CmpAccession2Ecotype_ins = CmpAccession2Ecotype()
nt_number2diff_matrix_index = CmpAccession2Ecotype_ins.get_nt_number2diff_matrix_index(nt2number)
dc_placeholder = dict(zip(range(sub_data_matrix.shape[0]), range(sub_data_matrix.shape[1])))
diff_matrix_ls = CmpAccession2Ecotype_ins.cmp_two_matricies(SNP_matrix_2010_sorted, sub_data_matrix, nt_number2diff_matrix_index, dc_placeholder, dc_placeholder, dc_placeholder)
print diff_matrix_ls
def run(self):
"""
2007-03-29
2007-04-03
2007-05-01
--db_connect()
--FilterStrainSNPMatrix_instance.read_data()
if self.comparison_only:
--FilterStrainSNPMatrix_instance.read_data()
else:
--get_SNPpos2index()
--create_SNP_matrix_2010()
--get_align_length_from_fname()
--get_positions_to_be_checked_ls()
--get_align_matrix_from_fname()
--get_positions_to_be_checked_ls()
--get_mapping_info_regarding_strain_acc()
--shuffle_data_matrix_according_to_strain_acc_ls()
--FilterStrainSNPMatrix_instance.write_data_matrix()
--extract_sub_data_matrix()
if self.sub_justin_output_fname:
--FilterStrainSNPMatrix_instance.write_data_matrix()
--compare_two_SNP_matrix()
--outputDiffType()
"""
from FilterStrainSNPMatrix import FilterStrainSNPMatrix
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, src_strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(
self.input_fname)
if self.comparison_only:
header, strain_acc_ls, abbr_name_ls_sorted, SNP_matrix_2010_sorted = FilterStrainSNPMatrix_instance.read_data(
self.output_fname)
SNP_matrix_2010_sorted = Numeric.array(SNP_matrix_2010_sorted)
else:
(conn, curs) = db_connect(self.hostname, self.dbname, self.schema)
#extract data from alignment
snp_acc_ls = header[2:]
SNPpos2index = self.get_SNPpos2index(curs, snp_acc_ls,
self.snp_locus_table)
abbr_name_ls, SNP_matrix_2010 = self.create_SNP_matrix_2010(
SNPpos2index, self.data_dir_2010)
strain_acc_ls, strain_acc2abbr_name, strain_acc2index = self.get_mapping_info_regarding_strain_acc(
curs, self.strain_info_table, self.strain_info_2010_table,
abbr_name_ls)
SNP_matrix_2010_sorted = self.shuffle_data_matrix_according_to_strain_acc_ls(
SNP_matrix_2010, strain_acc_ls, strain_acc2index)
abbr_name_ls_sorted = []
for strain_acc in strain_acc_ls:
abbr_name_ls_sorted.append(strain_acc2abbr_name[strain_acc])
FilterStrainSNPMatrix_instance.write_data_matrix(
SNP_matrix_2010_sorted, self.output_fname, header,
strain_acc_ls, abbr_name_ls_sorted)
#comparison
data_matrix = Numeric.array(data_matrix)
sub_data_matrix = self.extract_sub_data_matrix(src_strain_acc_list,
data_matrix,
strain_acc_ls)
if self.sub_justin_output_fname:
FilterStrainSNPMatrix_instance.write_data_matrix(
sub_data_matrix, self.sub_justin_output_fname, header,
strain_acc_ls, abbr_name_ls_sorted)
diff_matrix, diff_tag_dict, diff_tag2counter = self.compare_two_SNP_matrix(
SNP_matrix_2010_sorted, sub_data_matrix)
if self.diff_output_fname:
self.outputDiffType(diff_matrix, SNP_matrix_2010_sorted,
sub_data_matrix, diff_tag_dict,
self.diff_type_to_be_outputted,
abbr_name_ls_sorted, header[2:],
self.diff_output_fname)
summary_result_ls = []
for tag, counter in diff_tag2counter.iteritems():
summary_result_ls.append('%s(%s):%s' %
(tag, diff_tag_dict[tag], counter))
print '\t%s(%s)\t%s' % (tag, diff_tag_dict[tag], counter)
import pylab
pylab.clf()
diff_matrix_reverse = list(diff_matrix)
diff_matrix_reverse.reverse()
diff_matrix_reverse = Numeric.array(diff_matrix_reverse)
pylab.imshow(diff_matrix_reverse, interpolation='nearest')
pylab.title(' '.join(summary_result_ls))
pylab.colorbar()
pylab.show()
#2007-11-01 do something as CmpAccession2Ecotype.py
from CmpAccession2Ecotype import CmpAccession2Ecotype
CmpAccession2Ecotype_ins = CmpAccession2Ecotype()
nt_number2diff_matrix_index = CmpAccession2Ecotype_ins.get_nt_number2diff_matrix_index(
nt2number)
dc_placeholder = dict(
zip(range(sub_data_matrix.shape[0]),
range(sub_data_matrix.shape[1])))
diff_matrix_ls = CmpAccession2Ecotype_ins.cmp_two_matricies(
SNP_matrix_2010_sorted, sub_data_matrix,
nt_number2diff_matrix_index, dc_placeholder, dc_placeholder,
dc_placeholder)
print diff_matrix_ls
def run(self):
"""
2007-04-16
(rank==0)
--get_chr_start_ls()
elif free_computing_nodes:
-- (receive data)
--mpi_synchronize()
(rank==0)
--input_node()
--input_handler()
elif free_computing_nodes:
--computing_node()
--computing_node_handler()
--identify_ancestry_with_min_jumps()
--initialize_score_trace_matrix()
--is_child_heterozygous_SNP_compatible_with_parents()
(for loop)
--identify_ancestry_of_one_chr_with_DP()
--is_child_heterozygous_SNP_compatible_with_parents()
--trace()
--recursive_trace()
else:
--output_node()
--output_node_handler()
"""
node_rank = self.communicator.rank
free_computing_nodes = range(1, self.communicator.size - 1) # exclude the 1st and last node
if node_rank == 0:
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(
self.input_fname
)
snp_acc_list = header[2:]
data_matrix = Numeric.array(data_matrix)
no_of_strains = data_matrix.shape[0]
(conn, curs) = db_connect(self.hostname, self.dbname, self.schema, password="******", user="******")
# 2007-09-17 send strain_acc_list to the output_node
strain_acc_list_pickle = cPickle.dumps(strain_acc_list, -1)
self.communicator.send(strain_acc_list_pickle, self.communicator.size - 1, 0)
chr_start_ls = self.get_chr_start_ls(curs, snp_acc_list, self.snp_locus_table)
chr_start_ls_pickle = cPickle.dumps(chr_start_ls, -1) # -1 means use the highest protocol
data_matrix_pickle = cPickle.dumps(data_matrix, -1)
for node in free_computing_nodes: # send it to the computing_node
self.communicator.send(chr_start_ls_pickle, node, 0)
self.communicator.send(data_matrix_pickle, node, 0)
elif node_rank in free_computing_nodes:
data, source, tag = self.communicator.receiveString(0, 0)
chr_start_ls = cPickle.loads(data) # take the data
data, source, tag = self.communicator.receiveString(0, 0)
data_matrix = cPickle.loads(data)
else:
data, source, tag = self.communicator.receiveString(0, 0)
strain_acc_list = cPickle.loads(data)
mpi_synchronize(self.communicator)
if node_rank == 0:
parameter_list = [no_of_strains]
self.input_node(self.communicator, parameter_list, free_computing_nodes, self.message_size, self.report)
elif node_rank in free_computing_nodes:
trio_arrangement_ls = [
[0, 1, 2],
[1, 2, 0],
[2, 0, 1],
] # three different ways to pick the parent-set and the child
parameter_list = [data_matrix, chr_start_ls, trio_arrangement_ls]
computing_node(self.communicator, parameter_list, self.computing_node_handler, report=self.report)
else:
writer = csv.writer(open(self.output_fname, "w"), delimiter="\t")
parameter_list = [writer, strain_acc_list]
output_node(self.communicator, free_computing_nodes, parameter_list, self.output_node_handler, self.report)
del writer
def run(self):
"""
2007-04-16
(rank==0)
--get_chr_start_ls()
elif free_computing_nodes:
-- (receive data)
--mpi_synchronize()
(rank==0)
--input_node()
--input_handler()
elif free_computing_nodes:
--computing_node()
--computing_node_handler()
--identify_ancestry_with_min_jumps()
--initialize_score_trace_matrix()
--is_child_heterozygous_SNP_compatible_with_parents()
(for loop)
--identify_ancestry_of_one_chr_with_DP()
--is_child_heterozygous_SNP_compatible_with_parents()
--trace()
--recursive_trace()
else:
--output_node()
--output_node_handler()
"""
node_rank = self.communicator.rank
free_computing_nodes = range(1, self.communicator.size -
1) #exclude the 1st and last node
if node_rank == 0:
FilterStrainSNPMatrix_instance = FilterStrainSNPMatrix()
header, strain_acc_list, category_list, data_matrix = FilterStrainSNPMatrix_instance.read_data(
self.input_fname)
snp_acc_list = header[2:]
data_matrix = Numeric.array(data_matrix)
no_of_strains = data_matrix.shape[0]
(conn, curs) = db_connect(self.hostname,
self.dbname,
self.schema,
password='******',
user='******')
#2007-09-17 send strain_acc_list to the output_node
strain_acc_list_pickle = cPickle.dumps(strain_acc_list, -1)
self.communicator.send(strain_acc_list_pickle,
self.communicator.size - 1, 0)
chr_start_ls = self.get_chr_start_ls(curs, snp_acc_list,
self.snp_locus_table)
chr_start_ls_pickle = cPickle.dumps(
chr_start_ls, -1) #-1 means use the highest protocol
data_matrix_pickle = cPickle.dumps(data_matrix, -1)
for node in free_computing_nodes: #send it to the computing_node
self.communicator.send(chr_start_ls_pickle, node, 0)
self.communicator.send(data_matrix_pickle, node, 0)
elif node_rank in free_computing_nodes:
data, source, tag = self.communicator.receiveString(0, 0)
chr_start_ls = cPickle.loads(data) #take the data
data, source, tag = self.communicator.receiveString(0, 0)
data_matrix = cPickle.loads(data)
else:
data, source, tag = self.communicator.receiveString(0, 0)
strain_acc_list = cPickle.loads(data)
mpi_synchronize(self.communicator)
if node_rank == 0:
parameter_list = [no_of_strains]
self.input_node(self.communicator, parameter_list, free_computing_nodes, self.message_size, \
self.report)
elif node_rank in free_computing_nodes:
trio_arrangement_ls = [[0, 1, 2], [1, 2, 0], [
2, 0, 1
]] #three different ways to pick the parent-set and the child
parameter_list = [data_matrix, chr_start_ls, trio_arrangement_ls]
computing_node(self.communicator,
parameter_list,
self.computing_node_handler,
report=self.report)
else:
writer = csv.writer(open(self.output_fname, 'w'), delimiter='\t')
parameter_list = [writer, strain_acc_list]
output_node(self.communicator, free_computing_nodes,
parameter_list, self.output_node_handler, self.report)
del writer
