def main(args):
arg_dict = parse_arguments(args)
if not confirm_arguments(arg_dict):
if args[0].split(os.path.sep)[-1] == "schemaenergy.py":
print_usage(args)
return
# Flags and values
print_E = False
print_m = False
output_file = sys.stdout
# Inputs:
# The alignment/fragment file name.
msa_file = arg_dict[ARG_MULTIPLE_SEQUENCE_ALIGNMENT_FILE]
if arg_dict.has_key(ARG_PRINT_E):
print_E = True
if arg_dict.has_key(ARG_PRINT_M):
print_m = True
# Read the alignment file to create a list of parents.
# The parents will appear in the list in the order in which they appear in the file.
parent_list = schema.readMultipleSequenceAlignmentFile(file(msa_file, 'r'))
parents = [p for (k,p) in parent_list]
crossovers = schema.readCrossoverFile(file(arg_dict[ARG_CROSSOVER_FILE], 'r'))
fragments = schema.getFragments(crossovers, parents[0])
# Get the contacts
pdb_contacts = schema.readContactFile(file(arg_dict[ARG_CONTACT_FILE], 'r'))
contacts = schema.getSCHEMAContactsWithCrossovers(pdb_contacts, parents, crossovers)
if arg_dict.has_key(ARG_OUTPUT_FILE):
output_file = file(arg_dict[ARG_OUTPUT_FILE], 'w')
# Now, what does the user want?
output_string = '%s'
output_file.write('# chimera')
if print_E:
output_string += '\t%d'
output_file.write('\tE')
if print_m:
output_string += '\t%d'
output_file.write('\tm')
output_string += '\n'
output_file.write('\n')
if arg_dict.has_key(ARG_CHIMERAS): # Print values for chimeras
chimeras = arg_dict[ARG_CHIMERAS]
# Could be a) a chimera, b) a list of chimeras, or c) a file of chimeras.
if type(chimeras) is list:
# It's a list of chimeras
for chimera_blocks in chimeras:
outputEnergies(chimera_blocks, contacts, fragments, parents, output_file, output_string, print_E, print_m)
elif os.path.isfile(chimeras):
# It's a file of chimeras
for line in file(chimeras,'r').readlines():
chimera_blocks = line.strip()
outputEnergies(chimera_blocks, contacts, fragments, parents, output_file, output_string, print_E, print_m)
else:
# It's a single chimera sequence
chimera_blocks = chimeras
outputEnergies(chimera_blocks, contacts, fragments, parents, output_file, output_string, print_E, print_m)
else:
# Enumerates all possible chimeras and their disruption and mutation values.
p = len(parents)
n = len(fragments)
Es = []
ms = []
for i in xrange(len(parents)**len(fragments)):
# The next two lines turn i into a chimera block pattern
# (e.g., 0 -> '11111111', 1 -> '11111112', 2 -> '11111113'...)
n2c = schema.base(i,p)
chimera_blocks = ''.join(['1']*(n-len(n2c))+['%d'%(int(x)+1,) for x in n2c])
(E, m) = outputEnergies(chimera_blocks, contacts, fragments, parents, output_file, output_string, print_E, print_m)
if (print_E):
Es.append(E)
if (print_m):
ms.append(m)
if (print_E):
mean_str = "# Average disruption <E> = %1.4f\n" % schema.mean(Es)
output_file.write(mean_str)
if (print_m):
mean_str = "# Average mutation <m> = %1.4f\n" % schema.mean(ms)
output_file.write(mean_str)
if arg_dict.has_key(ARG_OUTPUT_FILE):
output_file.close()
def main(args):
arg_dict = parse_arguments(args)
if not confirm_arguments(arg_dict):
if args[0].split(os.path.sep)[-1] == "schemarandom.py":
print_usage(args)
return
# Flags and values
print_E = False
print_m = False
# Inputs:
# The alignment/fragment file name.
msa_file = arg_dict[ARG_MULTIPLE_SEQUENCE_ALIGNMENT_FILE]
# Read the alignment file to create a list of parents.
# The parents will appear in the list in the order in which they appear in the file.
parent_list = schema.readMultipleSequenceAlignmentFile(file(msa_file, "r"))
parents = [p for (k, p) in parent_list]
# Get the contacts
pdb_contacts = schema.readContactFile(file(arg_dict[ARG_CONTACT_FILE], "r"))
# Establish connection to output, either file or, if no output file is
# specified, to standard output.
if arg_dict.has_key(ARG_OUTPUT_FILE):
output_file = file(arg_dict[ARG_OUTPUT_FILE], "w")
else:
output_file = sys.stdout
# Get the number of libraries to evaluate.
if arg_dict.has_key(ARG_NUM_LIBRARIES):
num_libraries = int(arg_dict[ARG_NUM_LIBRARIES])
else:
num_libraries = int(1e3)
# Get the minimum fragment size.
if arg_dict.has_key(ARG_MIN_FRAGMENT_SIZE):
min_length = int(arg_dict[ARG_MIN_FRAGMENT_SIZE])
else:
min_length = 4
# Get the number of fragments -- one more than the number of crossovers.
num_fragments = int(arg_dict[ARG_NUM_CROSSOVERS]) + 1
num_parents = len(parents)
library_size = num_parents ** num_fragments
if arg_dict.has_key(ARG_MAX_CHIMERAS_PER_LIBRARY):
max_chimeras = min(library_size, int(arg_dict[ARG_MAX_CHIMERAS_PER_LIBRARY]))
else:
max_chimeras = library_size
if arg_dict.has_key(ARG_RANDOM_SEED):
random.seed(int(arg_dict[ARG_RANDOM_SEED]))
# Make libraries consistent with RASPP
(new_parents, identical_sites) = raspp.collapse_parents(parents)
if len(new_parents[0]) < num_fragments * min_length:
error_msg = (
"Minimum diversity length of %d is too large.\n%d "
+ "fragments with diversity %d cannot be found in a "
+ "sequence of length %d (with identities removed). Aborting..."
)
print error_msg % (min_length, num_fragments, min_length, len(parents[0]))
return
start_time = time.clock()
output_file.write("# <E>\t<m>\tcrossover points\n")
random_crossovers = []
for libnum in range(num_libraries):
crossovers = schema.generateRandomCrossovers(len(new_parents[0]), num_fragments - 1, min_length)
crossovers = raspp.translate_collapsed_indices(crossovers, identical_sites)
random_crossovers.append(crossovers)
for crossovers in random_crossovers:
fragments = schema.getFragments(crossovers, parents[0])
filtered_contacts = schema.getSCHEMAContactsWithCrossovers(pdb_contacts, parents, crossovers)
all_chimeras = []
if max_chimeras < library_size:
# Assemble a random sample of chimeras, with replacement
for n_chim in range(max_chimeras):
chim_index = random.randint(0, library_size - 1)
n2c = schema.base(chim_index, num_parents)
chimera_blocks = "".join(["1"] * (num_fragments - len(n2c)) + ["%d" % (int(x) + 1,) for x in n2c])
all_chimeras.append(chimera_blocks)
else: # We'll be covering all chimeras in the library; might as well get a good sample.
# The number of parents and fragments specifies all possible chimeras, regardless of
# crossover point positions, so pre-generate all chimeras.
max_chimeras = library_size
for i in range(library_size):
# The next two lines turn i into a chimera block pattern
# (e.g., 0 -> '11111111', 1 -> '11111112', 2 -> '11111113'...)
n2c = schema.base(i, num_parents)
chimera_blocks = "".join(["1"] * (num_fragments - len(n2c)) + ["%d" % (int(x) + 1,) for x in n2c])
all_chimeras.append(chimera_blocks)
# Randomly assort the chimeras
random.shuffle(all_chimeras)
# Calculate average E and m for the library or subsample
E_values = []
m_values = []
for chim_index in range(max_chimeras):
chimera_blocks = all_chimeras[chim_index]
E = schema.getChimeraDisruption(chimera_blocks, filtered_contacts, fragments, parents)
m = schema.getChimeraShortestDistance(chimera_blocks, fragments, parents)
E_values.append(E)
m_values.append(m)
average_E = schema.mean(E_values)
average_m = schema.mean(m_values)
xover_pat = "%d " * len(crossovers)
xover_str = xover_pat % tuple(crossovers)
output_file.write(("%1.4f\t%1.4f\t%s\n") % (average_E, average_m, xover_str))
output_file.flush()
total_time = time.clock() - start_time
output_file.write(
"# Finished in %1.2f seconds (%d libraries, %d chimeras)\n"
% (total_time, num_libraries, num_libraries * max_chimeras)
)
if arg_dict.has_key(ARG_OUTPUT_FILE):
output_file.close()
def main(args):
arg_dict = parse_arguments(args)
if not confirm_arguments(arg_dict):
if args[0].split(os.path.sep)[-1] == "schemaenergy.py":
print_usage(args)
return
# Flags and values
print_E = False
print_m = False
output_file = sys.stdout
# Inputs:
# The alignment/fragment file name.
msa_file = arg_dict[ARG_MULTIPLE_SEQUENCE_ALIGNMENT_FILE]
if arg_dict.has_key(ARG_PRINT_E):
print_E = True
if arg_dict.has_key(ARG_PRINT_M):
print_m = True
# Read the alignment file to create a list of parents.
# The parents will appear in the list in the order in which they appear in the file.
parent_list = schema.readMultipleSequenceAlignmentFile(file(msa_file, 'r'))
parents = [p for (k, p) in parent_list]
crossovers = schema.readCrossoverFile(
file(arg_dict[ARG_CROSSOVER_FILE], 'r'))
fragments = schema.getFragments(crossovers, parents[0])
# Get the contacts
pdb_contacts = schema.readContactFile(file(arg_dict[ARG_CONTACT_FILE],
'r'))
contacts = schema.getSCHEMAContactsWithCrossovers(pdb_contacts, parents,
crossovers)
if arg_dict.has_key(ARG_OUTPUT_FILE):
output_file = file(arg_dict[ARG_OUTPUT_FILE], 'w')
# Now, what does the user want?
output_string = '%s'
output_file.write('# chimera')
if print_E:
output_string += '\t%d'
output_file.write('\tE')
if print_m:
output_string += '\t%d'
output_file.write('\tm')
output_string += '\n'
output_file.write('\n')
if arg_dict.has_key(ARG_CHIMERAS): # Print values for chimeras
chimeras = arg_dict[ARG_CHIMERAS]
# Could be a) a chimera, b) a list of chimeras, or c) a file of chimeras.
if type(chimeras) is list:
# It's a list of chimeras
for chimera_blocks in chimeras:
outputEnergies(chimera_blocks, contacts, fragments, parents,
output_file, output_string, print_E, print_m)
elif os.path.isfile(chimeras):
# It's a file of chimeras
for line in file(chimeras, 'r').readlines():
chimera_blocks = line.strip()
outputEnergies(chimera_blocks, contacts, fragments, parents,
output_file, output_string, print_E, print_m)
else:
# It's a single chimera sequence
chimera_blocks = chimeras
outputEnergies(chimera_blocks, contacts, fragments, parents,
output_file, output_string, print_E, print_m)
else:
# Enumerates all possible chimeras and their disruption and mutation values.
p = len(parents)
n = len(fragments)
Es = []
ms = []
for i in xrange(len(parents)**len(fragments)):
# The next two lines turn i into a chimera block pattern
# (e.g., 0 -> '11111111', 1 -> '11111112', 2 -> '11111113'...)
n2c = schema.base(i, p)
chimera_blocks = ''.join(['1'] * (n - len(n2c)) +
['%d' % (int(x) + 1, ) for x in n2c])
(E, m) = outputEnergies(chimera_blocks, contacts, fragments,
parents, output_file, output_string,
print_E, print_m)
if (print_E):
Es.append(E)
if (print_m):
ms.append(m)
if (print_E):
mean_str = "# Average disruption <E> = %1.4f\n" % schema.mean(Es)
output_file.write(mean_str)
if (print_m):
mean_str = "# Average mutation <m> = %1.4f\n" % schema.mean(ms)
output_file.write(mean_str)
if arg_dict.has_key(ARG_OUTPUT_FILE):
output_file.close()