def test_io(self):
tempfilename = self.tempfilename
tempfile = masterslave.open(tempfilename, mode='w')
tempfile.write(str(masterslave.rank()) + '\n')
tempfile.flush()
contents = ''
with open(tempfilename) as stillopen:
contents = stillopen.read()
assert_in(str(masterslave.rank()) + '\n', contents)
tempfile.close()
with open(tempfilename) as nowclosed:
ranks = [int(s.strip()) for s in nowclosed]
assert_equal(set(ranks), set(range(masterslave.size())))
tempfile = masterslave.open(tempfilename, 'a')
tempfile.write(str(masterslave.rank()) + '\n')
tempfile.close()
with open(tempfilename) as nowclosed:
ranks = [int(s.strip()) for s in nowclosed]
assert_equal(len(ranks), masterslave.size() * 2)
assert_equal(set(ranks), set(range(masterslave.size())))
if masterslave.size() != 1:
masterslave._comm.Barrier()
def func(d1, d2):
return d1, d2, masterslave.rank()
def func(dummy):
return dummy, masterslave.rank()
def main():
import argparse
parser = argparse.ArgumentParser(description='Estimate expected number of \
substitutions under stationary and non-stationary models.')
parser.add_argument(
'-i',
'--input_directory',
required=True,
type=os.path.expanduser,
help='Directory containing .fasta or .fasta.gz alignments')
parser.add_argument('-o',
'--output_directory',
required=True,
type=os.path.expanduser,
help='Directory in which results will be stored')
parser.add_argument('-l',
'--log_file',
type=os.path.expanduser,
help='Defaults to stderr')
parser.add_argument('-L',
'--log_level',
default='INFO',
type=str,
help='Set logging level')
parser.add_argument('-f',
'--force_recalculation',
action='store_true',
help='Force recalculation of existing results')
parser.add_argument('-c',
'--codon_position',
type=int,
default=-1,
help='Only use this codon position in analysis')
parser.add_argument('-g',
'--global_optimisation',
action='store_true',
help='Use global optimisation when fitting models')
parser.add_argument('-R',
'--num_range',
type=lambda x: slice(*eval(x)),
default=None,
help='Range of files to work on')
parser.add_argument('-T',
'--triad_file',
type=os.path.expanduser,
help='Tab delimited list of triads to be processed')
parser.add_argument('-u',
'--param_limit',
type=float,
default=20,
help='Upper limit for substitution parameters')
parser.add_argument('-p',
'--pretty_print',
action='store_true',
help='Pretty print json output')
parser.add_argument('-F',
'--fitter',
type=lambda x: getattr(nest, x),
default='seq_fit',
help='Nest function to use for fitting')
parser.add_argument('-O',
'--outgroup',
type=str,
help='Outgroup to use for clock test fitting')
kw_args = vars(parser.parse_args())
setup_logging(**kw_args)
if masterslave.rank() == 0:
logging.info(kw_args)
logging.info(_versions)
results = masterslave.imap(partial(process_triad, **kw_args),
itriads(**kw_args))
if results:
output_results(results, **kw_args)
return 0
def main():
import argparse
parser = argparse.ArgumentParser(description='Estimate expected number of \
substitutions under stationary and non-stationary models.')
parser.add_argument('-o',
'--output_directory',
required=True,
type=os.path.expanduser,
help='Directory in which results will be stored')
parser.add_argument('-l',
'--log_file',
type=os.path.expanduser,
help='Defaults to stderr')
parser.add_argument('-L',
'--log_level',
default='INFO',
help='Set logging level')
parser.add_argument('-m',
'--mode',
default='s',
choices='sra',
help='Write mode (skip, replace, append)')
parser.add_argument('-R',
'--num_range',
type=lambda x: slice(*eval(x)),
default=None,
help='Range of files to work on')
parser.add_argument('-N',
'--num_reps',
type=int,
default=100,
help='Number of G stat samples')
parser.add_argument(
'-u',
'--param_limit',
type=float,
default=20,
help='Upper limit for length and substitution parameters')
parser.add_argument('-P',
'--model_pos',
default=-1,
type=int,
help='Model for which to produce G statistics')
parser.add_argument('-F',
'--fitter',
type=lambda x: getattr(nest, x),
default='seq_fit',
help='Nest function to use for fitting')
parser.add_argument('-O',
'--outgroup',
type=str,
help='Outgroup to use for clock test fitting')
kw_args = vars(parser.parse_args())
setup_logging(**kw_args)
if masterslave.rank() == 0:
logging.info(kw_args)
logging.info(_versions)
processor = partial(param_bootstrap, **kw_args)
results = masterslave.imap(processor, istats(**kw_args))
if results:
output_results(results, **kw_args)
return 0