def mC1P_bab(mat, matb = bm.BinaryMatrix(), mat_rem = bm.BinaryMatrix()): ms = c1p.make_intersect_components(mat) # intersect components j = 1 # iterator for tracing for m in ms: print str(j) + '/' + str(len(ms)) + ' ', sys.stdout.flush() j += 1 # sort matrix to get heuristic as first answer m.sort() # branch and bound # optimal rows to remove to make compnonent mC1P rows = bab.branch_and_bound(m, False, MC1PTester(m)) rows.sort() for i in xrange(len(rows) - 1, -1, -1): row = m.get_row_info(rows[i]) # row to remove mat_rem.add_row_info(row) m.remove_row(rows[i]) #endfor # collect usable rows into the C1P matrix for r in m._rows: matb.add_row_info(r) #endfor print '' #endfor return matb, mat_rem
def C1P_bab(mat, matb = bm.BinaryMatrix(), mat_rem = bm.BinaryMatrix()): ms = c1p.split_matrix(mat) # split matrices j = 1 # iterator for tracing for m in ms: print str(j) + '/' + str(len(ms)) j += 1 # sort matrix to get heuristic as first answer m.sort() # branch and bound # optimal rows to remove to make compnonent C1P rows = bab.branch_and_bound(m, False, BABTester(m._height)) rows.sort() for i in xrange(len(rows) - 1, -1, -1): row = m.get_row_info(rows[i]) # row to remove mat_rem.add_row_info(row) m.remove_row(rows[i]) #endfor # collect usable rows into the C1P matrix for r in m._rows: matb.add_row_info(r) #endfor print '' #endfor return matb, mat_rem
j = 1 # iterator for tracing
del mat
for m in ms:
print str(j) + '/' + str(len(ms))
j += 1
# sort matrix to get heuristic as first answer
m.sort()
# branch and bound
# optimal rows to remove to make compnonent C1P
rows = bab.branch_and_bound(m, prop, BABTester(m._height))
rows.sort()
for i in xrange(len(rows) - 1, -1, -1):
row = m.get_row_info(rows[i]) # row to remove
mat_rem.add_row_info(row)
m.remove_row(rows[i])
#endfor
# collect usable rows into the C1P matrix
for r in m._rows:
matb.add_row_info(r)
#endfor
mat_rem = bm.BinaryMatrix() # rows removed
j = 1 # iterator for tracing
for m in ms:
print str(j) + '/' + str(len(ms)) + ' ',
sys.stdout.flush()
j += 1
# sort matrix to get heuristic as first answer
m.sort()
# branch and bound
# optimal rows to remove to make compnonent mC1P
rows = bab.branch_and_bound(m, prop, MC1PTester(m))
rows.sort()
for i in xrange(len(rows) - 1, -1, -1):
row = m.get_row_info(rows[i]) # row to remove
mat_rem.add_row_info(row)
m.remove_row(rows[i])
#endfor
# collect usable rows into the C1P matrix
for r in m._rows:
matb.add_row_info(r)
#endfor
def do_c1p(self, suffix, message, make_C1P, compute_PQRtree, m=False):
acs_c1p = self.c1p_dir + "/" + self.output_prefix + "ACS_C1P_" + suffix # C1P matrix file
acs_discarded = self.c1p_dir + "/" + self.output_prefix + "ACS_DISC_" + suffix # matrix of removed rows file
pq_tree = self.cars_dir + "/" + self.output_prefix + "PQTREE_" + suffix # PQ-tree file
if self.markers_doubled:
pq_tree_doubled = self.cars_dir + "/" + self.output_prefix + "PQTREE_DOUBLED_" + suffix # PQ-tree file
#endif
if not self.quiet:
print("----> Making (weighted)ACS matrix C1P (" + message + "): " +
self.acs_file + " " + acs_discarded)
#endif
if m:
if make_C1P == "heuristic":
#m = bm.BinaryMatrix() # matrix
mat_rem = bm.BinaryMatrix() # rows discarded
#m.from_file(self.acs_file)
rows = c1p.make_C1P(self.m) # rows to remove to make C1P
for j in xrange(len(rows) - 1, -1, -1):
mat_rem.add_row_info(m.get_row_info(rows[j]))
self.m.remove_row(rows[j])
#endfor
f = file(acs_c1p, 'w') # C1P matrix file
f.write(str(m))
f.close()
f = open(acs_discarded, 'w')
mat_rem.write(f.write)
f.close()
elif make_C1P == "circ_heuristic":
#m = bm.BinaryMatrix()
#m.from_file(self.acs_file)
mat2, mat_rem = cc1p.make_circC1P(self.m, 'max')
f = open(self.acs_c1p, 'w')
mat2.write(f.write)
f.close()
f = open(self.acs_discarded, 'w')
mat_rem.write(f.write)
f.close()
elif make_C1P == "branch_and_bound":
prop = False # True if using weights as probs
#mat = bm.BinaryMatrix() # matrix
#m.from_file(self.acs_file)
ms = c1p.split_matrix(self.m) # split matrices
matb = bm.BinaryMatrix() # C1P matrix
mat_rem = bm.BinaryMatrix() # rows removed
j = 1 # iterator for tracing
#del mat
for m in ms:
print str(j) + '/' + str(len(ms))
j += 1
# sort matrix to get heuristic as first answer
m.sort()
# branch and bound
# optimal rows to remove to make compnonent C1P
rows = bab.branch_and_bound(m, prop, BABTester(m._height))
rows.sort()
for i in xrange(len(rows) - 1, -1, -1):
row = self.m.get_row_info(rows[i]) # row to remove
mat_rem.add_row_info(row)
m.remove_row(rows[i])
#endfor
# collect usable rows into the C1P matrix
for r in m._rows:
matb.add_row_info(r)
#endfor
print ''
#endfor
f = file(acs_c1p, 'w')
f.write(str(matb))
f.close()
f = file(acs_discarded, 'w')
f.write(str(mat_rem))
f.close()
elif make_C1P == "circ_branch_and_bound":
#mat = bm.BinaryMatrix() # matrix
#mat.from_file(self.acs_file)
mode = 'max'
if mode == 'whole':
matb, mat_rem = circC1P_bab(self.m)
elif mode == 'max':
maxs = c1p.make_intersect_components(
self.m) # split matrices
matb = bm.BinaryMatrix() # C1P matrix
mat_rem = bm.BinaryMatrix() # rows removed
j = 1 # iterator for tracing
#del mat
for max_comp in maxs:
print 'Max:' + str(j) + '/' + str(len(maxs)) + ' '
j += 1
circC1P_bab(max_comp, matb, mat_rem)
#endfor
#endif
f = file(self.acs_c1p, 'w')
f.write(str(matb))
f.close()
f = file(self.acs_discarded, 'w')
f.write(str(mat_rem))
f.close()
elif make_C1P == "m_heuristic":
#m = bm.BinaryMatrix() # matrix
mat_rem = bm.BinaryMatrix() # rows discarded
#m.from_file(self.acs_file)
rows = mc1p.make_mC1P(m) # rows to remove to make C1P
for j in xrange(len(rows) - 1, -1, -1):
mat_rem.add_row_info(self.m.get_row_info(rows[j]))
self.m.remove_row(rows[j])
#endfor
f = file(self.acs_c1p, 'w') # C1P matrix file
f.write(str(self.m))
f.close()
f = open(self.acs_discarded, 'w')
mat_rem.write(f.write)
f.close()
elif make_C1P == "m_branch_and_bound":
prop = False
#mat = bm.BinaryMatrix() # matrix
rows_rem = [] # rows removed
#mat.from_file(self.acs_file)
ms = c1p.make_intersect_components(
self.m) # intersect components
matb = bm.BinaryMatrix() # mC1P matrix
mat_rem = bm.BinaryMatrix() # rows removed
j = 1 # iterator for tracing
for m in ms:
print str(j) + '/' + str(len(ms)) + ' ',
sys.stdout.flush()
j += 1
# sort matrix to get heuristic as first answer
self.m.sort()
# branch and bound
# optimal rows to remove to make compnonent mC1P
rows = bab.branch_and_bound(m, prop, MC1PTester(m))
rows.sort()
for i in xrange(len(rows) - 1, -1, -1):
row = m.get_row_info(rows[i]) # row to remove
mat_rem.add_row_info(row)
m.remove_row(rows[i])
#endfor
# collect usable rows into the C1P matrix
for r in self.m._rows:
matb.add_row_info(r)
#endfor
print ''
#endfor
f = file(acs_c1p, 'w')
f.write(str(matb))
f.close()
f = file(acs_discarded, 'w')
f.write(str(mat_rem))
f.close()
elif make_C1P == "m_branch_and_bound_both":
prop = False # True if using weights as probs
#mat = bm.BinaryMatrix() # matrix
#mat.from_file(self.acs_file)
matb, mat_rem = C1P_and_mC1P_bab(self.m)
f = file(acs_c1p, 'w')
f.write(str(matb))
f.close()
f = file(acs_discarded, 'w')
f.write(str(mat_rem))
f.close()
else:
if make_C1P == "heuristic":
#m = bm.BinaryMatrix() # matrix
mat_rem = bm.BinaryMatrix() # rows discarded
#m.from_file(self.acs_file)
rows = c1p.make_C1P(self.m) # rows to remove to make C1P
for j in xrange(len(rows) - 1, -1, -1):
mat_rem.add_row_info(m.get_row_info(rows[j]))
self.m.remove_row(rows[j])
#endfor
f = file(acs_c1p, 'w') # C1P matrix file
f.write(str(m))
f.close()
f = open(acs_discarded, 'w')
mat_rem.write(f.write)
f.close()
elif make_C1P == "circ_heuristic":
#m = bm.BinaryMatrix()
#m.from_file(self.acs_file)
mat2, mat_rem = cc1p.make_circC1P(self.m, 'max')
f = open(self.acs_c1p, 'w')
mat2.write(f.write)
f.close()
f = open(self.acs_discarded, 'w')
mat_rem.write(f.write)
f.close()
elif make_C1P == "branch_and_bound":
prop = False # True if using weights as probs
# mat = bm.BinaryMatrix() # matrix
# mat.from_file(self.acs_file)
ms = c1p.split_matrix(self.m) # split matrices
matb = bm.BinaryMatrix() # C1P matrix
mat_rem = bm.BinaryMatrix() # rows removed
j = 1 # iterator for tracing
del mat
for m in ms:
print str(j) + '/' + str(len(ms))
j += 1
# sort matrix to get heuristic as first answer
m.sort()
# branch and bound
# optimal rows to remove to make compnonent C1P
rows = bab.branch_and_bound(m, prop, BABTester(m._height))
rows.sort()
for i in xrange(len(rows) - 1, -1, -1):
row = m.get_row_info(rows[i]) # row to remove
mat_rem.add_row_info(row)
m.remove_row(rows[i])
#endfor
# collect usable rows into the C1P matrix
for r in m._rows:
matb.add_row_info(r)
#endfor
print ''
#endfor
f = file(acs_c1p, 'w')
f.write(str(matb))
f.close()
f = file(acs_discarded, 'w')
f.write(str(mat_rem))
f.close()
elif make_C1P == "circ_branch_and_bound":
# mat = bm.BinaryMatrix() # matrix
# mat.from_file(self.acs_file)
mode = 'whole'
if mode == 'whole':
matb, mat_rem = circC1P_bab(self.m)
elif mode == 'max':
maxs = c1p.make_intersect_components(
self.m) # split matrices
matb = bm.BinaryMatrix() # C1P matrix
mat_rem = bm.BinaryMatrix() # rows removed
j = 1 # iterator for tracing
# del mat
for max_comp in maxs:
print 'Max:' + str(j) + '/' + str(len(maxs)) + ' '
j += 1
circC1P_bab(max_comp, matb, mat_rem)
#endfor
#endif
f = file(self.acs_c1p, 'w')
f.write(str(matb))
f.close()
f = file(self.acs_discarded, 'w')
f.write(str(mat_rem))
f.close()
elif make_C1P == "m_heuristic":
#m = bm.BinaryMatrix() # matrix
mat_rem = bm.BinaryMatrix() # rows discarded
#m.from_file(self.acs_file)
rows = mc1p.make_mC1P(self.m) # rows to remove to make C1P
for j in xrange(len(rows) - 1, -1, -1):
mat_rem.add_row_info(m.get_row_info(rows[j]))
self.m.remove_row(rows[j])
#endfor
f = file(self.acs_c1p, 'w') # C1P matrix file
f.write(str(self.m))
f.close()
f = open(self.acs_discarded, 'w')
mat_rem.write(f.write)
f.close()
elif make_C1P == "m_branch_and_bound":
prop = False
# mat = bm.BinaryMatrix() # matrix
rows_rem = [] # rows removed
# mat.from_file(self.acs_file)
ms = c1p.make_intersect_components(
self.m) # intersect components
matb = bm.BinaryMatrix() # mC1P matrix
mat_rem = bm.BinaryMatrix() # rows removed
j = 1 # iterator for tracing
for m in ms:
print str(j) + '/' + str(len(ms)) + ' ',
sys.stdout.flush()
j += 1
# sort matrix to get heuristic as first answer
self.m.sort()
# branch and bound
# optimal rows to remove to make compnonent mC1P
rows = bab.branch_and_bound(m, prop, MC1PTester(m))
rows.sort()
for i in xrange(len(rows) - 1, -1, -1):
row = self.m.get_row_info(rows[i]) # row to remove
mat_rem.add_row_info(row)
m.remove_row(rows[i])
#endfor
# collect usable rows into the C1P matrix
for r in m._rows:
matb.add_row_info(r)
#endfor
print ''
#endfor
f = file(acs_c1p, 'w')
f.write(str(matb))
f.close()
f = file(acs_discarded, 'w')
f.write(str(mat_rem))
f.close()
elif make_C1P == "m_branch_and_bound_both":
prop = False # True if using weights as probs
matb, mat_rem = C1P_and_mC1P_bab(self.m)
f = file(acs_c1p, 'w')
f.write(str(matb))
f.close()
f = file(acs_discarded, 'w')
f.write(str(mat_rem))
f.close()
#endif
if not self.quiet:
print("----> Creating a PQ-tree: " + pq_tree)
#endif
if self.markers_doubled:
self.computePQRtree()
if not self.quiet:
print("----> Halving PQ-tree columns")
#endif
self.halvePQRtree(pq_tree_doubled, pq_tree)
else:
self.computePQRtree()
def C1P_and_mC1P_bab(mat, matb = bm.BinaryMatrix(), mat_rem = bm.BinaryMatrix()): ms = c1p.make_intersect_components(mat) # intersect components j = 1 # iterator for tracing for m_full in ms: print str(j) + '/' + str(len(ms)) + ' ', sys.stdout.flush() j += 1 m = bm.BinaryMatrix() m_tel = bm.BinaryMatrix() # split telomere rows and non telomere rows for row in m_full: if row._isT: m_tel.add_row_info(row) else: m.add_row_info(row) #endif #endfor # C1P bab print 'C1P' sys.stdout.flush() st = mat_rem._height # sort matrix to get heuristic as first answer m.sort() # branch and bound # optimal rows to remove to make compnonent mC1P rows = bab.branch_and_bound(m, False, MC1PTester(m)) rows.sort() for i in xrange(len(rows) - 1, -1, -1): row = m.get_row_info(rows[i]) # row to remove mat_rem.add_row_info(row) m.remove_row(rows[i]) #endfor # collect usable rows into the C1P matrix for r in m._rows: matb.add_row_info(r) #endfor # remove unused telomere rows for i in xrange(st, mat_rem._height): row = mat_rem.get_row(i) for j in xrange(m_tel._height): if m_tel.get_row(j) == row: m_tel.remove_row(j) break #endif #endfor #endfor # mC1P bab print 'telomeres' sys.stdout.flush() # sort matrix to get heuristic as first answer m_tel.sort() # branch and bound # optimal rows to remove to make compnonent mC1P rows = bab.branch_and_bound(m_tel, False, PQMC1PTester(m)) rows.sort() for i in xrange(len(rows) - 1, -1, -1): row = m_tel.get_row_info(rows[i]) # row to remove mat_rem.add_row_info(row) m_tel.remove_row(rows[i]) #endfor # collect usable rows into the C1P matrix for r in m_tel._rows: matb.add_row_info(r) #endfor print '' #endfor return matb, mat_rem