class PyMsBayesTestCase(unittest.TestCase):
def set_up(self):
self.temp_fs = TempFileSystem(parent=package_paths.test_path(),
prefix='PyMsBayesTestTemp-')
self.test_id = 'pymsbayes-' + random_str()
def tear_down(self):
self.register_file_system()
self.temp_fs.purge()
self.assertEqual(FileStream.open_files, set())
def get_test_path(self, parent=None, prefix='temp'):
return self.temp_fs.get_file_path(parent=parent, prefix=prefix)
def get_test_subdir(self, parent=None, prefix='temp'):
return self.temp_fs.create_subdir(parent=parent, prefix=prefix)
def register_file(self, path):
self.temp_fs._register_file(path)
def register_dir(self, path):
self.temp_fs._register_dir(path)
def register_file_system(self):
_LOG.debug('registering test file system...')
for path, dirs, files, in os.walk(self.temp_fs.base_dir):
for f in files:
if f.startswith(self.test_id):
self.register_file(os.path.join(path, f))
for d in dirs:
if d.startswith(self.test_id):
self.register_dir(os.path.join(path, d))
def _exe_script(self,
script_name,
args,
stdout=None,
stderr=None,
return_code=0):
script_path = package_paths.script_path(script_name)
if isinstance(args, str):
arg_list = args.split()
else:
arg_list = args
arg_list = [str(x) for x in arg_list]
cmd = [sys.executable, script_path] + arg_list
_LOG.debug('Invocation:\n\t{0}'.format(' '.join(cmd)))
p = subprocess.Popen(cmd,
shell=False,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
o, e = p.communicate()
exit_code = p.wait()
if exit_code != return_code:
_LOG.error("exit code {0} did not match {1}".format(
exit_code, return_code))
_LOG.error("here is the stdout:\n{0}".format(o))
_LOG.error("here is the stderr:\n{0}".format(e))
self.assertEqual(exit_code, return_code)
if stdout != None:
if o != stdout:
_LOG.error("std out did not match expected:\n{0}".format(o))
self.assertEqual(o, stdout)
if stderr != None:
if e != stderr:
_LOG.error("std error did not match expected:\n{0}".format(e))
self.assertEqual(e, stderr)
def get_expected_indices(self,
num_pairs,
dummy_column=True,
parameters_reported=True):
num_summary_params = 4
if _CV_INCLUDED:
num_summary_params += 1
num_params = 4 * num_pairs
num_default_stats = 4 * num_pairs
start = 0
if dummy_column:
start = 1
param_indices = range(start, start + num_summary_params)
start += num_summary_params
if parameters_reported:
param_indices += range(start, start + num_params)
start += num_params
stat_indices = range(start, start + num_default_stats)
return param_indices, stat_indices
def prior_file_is_valid(self,
prior_path,
num_of_samples,
num_of_columns=None):
try:
prior_file = open(prior_path, 'rU')
except:
_LOG.error('prior invalid: could not open prior path {0}'.format(
prior_path))
return False
nrows = 0
for i, line in enumerate(prior_file):
if nrows == 0 and HEADER_PATTERN.match(line):
pass
else:
nrows += 1
if not num_of_columns:
num_of_columns = len(line.strip().split())
ncols = len(line.strip().split())
if num_of_columns != ncols:
_LOG.error('prior invalid: num of columns at line {0} is {1} '
'NOT {2}'.format(i + 1, ncols, num_of_columns))
return False
prior_file.close()
if num_of_samples != nrows:
_LOG.error('prior invalid: num of rows is {0} NOT {1}'.format(
nrows, num_of_samples))
return False
return True
def get_number_of_lines(self, path):
f, close = process_file_arg(path)
count = 0
for l in f:
count += 1
if close:
f.close()
return count
def get_number_of_header_lines(self, path):
f, close = process_file_arg(path)
count = 0
for l in f:
if HEADER_PATTERN.match(l.strip()):
count += 1
if close:
f.close()
return count
def parse_python_config(self, path):
return ConfigObj(path)
def get_config_from_msbayes_workers(self, msbayes_workers):
cfgs = [MsBayesConfig(w.config_path) for w in msbayes_workers]
self.assertSameConfigs(cfgs)
return cfgs[0]
def assertSameConfigs(self, cfgs):
configs = list(cfgs)
c1 = configs.pop(0)
for c2 in cfgs:
self.assertEqual(c1.time_in_subs_per_site,
c2.time_in_subs_per_site)
self.assertEqual(c1.npairs, c2.npairs)
self.assertEqual(c1.implementation, c2.implementation)
self.assertEqual(c1.div_model_prior, c2.div_model_prior)
self.assertEqual(c1.bottle_proportion_shared,
c2.bottle_proportion_shared)
self.assertEqual(c1.theta_parameters, c2.theta_parameters)
self.assertEqual(c1.taxa, c2.taxa)
self.assertTrue(c1.sample_table.equals(c2.sample_table))
if c1.psi:
self.assertSameDistributions(c1.psi, c2.psi)
else:
self.assertEqual(c1.psi, c2.psi)
if c1.tau:
self.assertSameDistributions(c1.tau, c2.tau)
else:
self.assertEqual(c1.tau, c2.tau)
if c1.theta:
self.assertSameDistributions(c1.theta, c2.theta)
else:
self.assertEqual(c1.theta, c2.theta)
if c1.a_theta:
self.assertSameDistributions(c1.a_theta, c2.a_theta)
else:
self.assertEqual(c1.a_theta, c2.a_theta)
if c1.d_theta:
self.assertSameDistributions(c1.d_theta, c2.d_theta)
else:
self.assertEqual(c1.d_theta, c2.d_theta)
if c1.recombination:
self.assertSameDistributions(c1.recombination,
c2.recombination)
else:
self.assertEqual(c1.recombination, c2.recombination)
if c1.migration:
self.assertSameDistributions(c1.migration, c2.migration)
else:
self.assertEqual(c1.migration, c2.migration)
if c1.dpp_concentration:
self.assertSameDistributions(c1.dpp_concentration,
c2.dpp_concentration)
else:
self.assertEqual(c1.dpp_concentration, c2.dpp_concentration)
if c1.bottle_proportion:
self.assertSameDistributions(c1.bottle_proportion,
c2.bottle_proportion)
else:
self.assertEqual(c1.bottle_proportion, c2.bottle_proportion)
def get_parameter_summaries_from_msbayes_workers(self,
msbayes_workers,
shuffle_taus=True):
msbayes_workers = list(msbayes_workers)
s = dict(
zip([i for i in msbayes_workers[0].parameter_indices], [
SampleSummarizer(tag=msbayes_workers[0].header[i])
for i in msbayes_workers[0].parameter_indices
]))
ncols = None
header = msbayes_workers[0].header
pi = msbayes_workers[0].parameter_indices
for w in msbayes_workers:
self.assertEqual(w.header, header)
self.assertEqual(w.parameter_indices, pi)
f = open(w.prior_path, 'rU')
for line_idx, row in enumerate(f):
if not ncols:
ncols = len(row.strip().split())
if HEADER_PATTERN.match(row.strip()):
continue
r = row.strip().split()
assert len(r) == ncols
if shuffle_taus: # because taus are sorted in prior files
psi_index = get_indices_of_patterns(
w.header, PSI_PATTERNS)[0]
tau_indices = get_indices_of_patterns(
w.header, TAU_PATTERNS)
psi = int(r[psi_index])
taus = [float(r[i]) for i in tau_indices]
self.assertEqual(psi, len(set(taus)))
random.shuffle(taus)
for n, i in enumerate(tau_indices):
s[i].add_sample(taus[n])
p_set = set(w.parameter_indices) - set(tau_indices)
p = sorted(list(p_set))
for i in p:
s[i].add_sample(float(r[i]))
else:
for i in w.parameter_indices:
s[i].add_sample(float(r[i]))
f.close()
return s
def assertPriorIsPrecise(self, msbayes_workers, places=2):
msbayes_workers = list(msbayes_workers)
self.assertWorkersFinished(msbayes_workers)
param_sums = self.get_parameter_summaries_from_msbayes_workers(
msbayes_workers)
sample_size = 0
for w in msbayes_workers:
sample_size += w.sample_size
for s in param_sums.itervalues():
self.assertEqual(s.n, sample_size)
cfg = self.get_config_from_msbayes_workers(msbayes_workers)
psi_indices = get_indices_of_patterns(msbayes_workers[0].header,
PSI_PATTERNS)
self.assertEqual(len(psi_indices), 1)
model_indices = get_indices_of_patterns(msbayes_workers[0].header,
MODEL_PATTERNS)
if not msbayes_workers[0].model_index is None:
self.assertEqual(len(model_indices), 1)
else:
self.assertEqual(len(model_indices), 0)
tau_indices = get_indices_of_patterns(msbayes_workers[0].header,
TAU_PATTERNS)
a_theta_indices = get_indices_of_patterns(msbayes_workers[0].header,
A_THETA_PATTERNS)
d_theta_indices = get_indices_of_patterns(msbayes_workers[0].header,
D_THETA_PATTERNS)
if msbayes_workers[0].report_parameters:
self.assertEqual(len(tau_indices), cfg.npairs)
self.assertEqual(len(a_theta_indices), cfg.npairs)
self.assertEqual(len(d_theta_indices), 2 * cfg.npairs)
else:
self.assertEqual(len(tau_indices), 0)
self.assertEqual(len(a_theta_indices), 0)
self.assertEqual(len(d_theta_indices), 0)
_LOG.debug('\n{0}\n'.format('\n'.join(
[str(param_sums[i]) for i in sorted(param_sums.iterkeys())])))
for i in psi_indices:
self.assertSampleIsFromDistribution(param_sums[i],
cfg.psi,
places=places)
for i in tau_indices:
self.assertSampleIsFromDistribution(param_sums[i],
cfg.tau,
places=places)
for i in a_theta_indices:
self.assertSampleIsFromDistribution(param_sums[i],
cfg.a_theta,
places=places)
for i in d_theta_indices:
self.assertSampleIsFromDistribution(param_sums[i],
cfg.d_theta,
mean_adj=cfg.theta.mean,
max_adj=cfg.theta.maximum,
compare_variance=False,
places=places)
def assertPriorIsAccurate(self, msbayes_workers, places=2):
msbayes_workers = list(msbayes_workers)
self.assertWorkersFinished(msbayes_workers)
pass
def assertPriorIsValid(self, msbayes_workers, places=2):
msbayes_workers = list(msbayes_workers)
self.assertWorkersFinished(msbayes_workers)
self.assertPriorIsPrecise(msbayes_workers, places=places)
self.assertPriorIsAccurate(msbayes_workers, places=places)
def assertWorkersFinished(self, msbayes_workers):
for w in msbayes_workers:
self.assertTrue(w.finished)
def assertSampleIsFromDistribution(self,
sample_sum,
dist,
places=2,
mean_adj=1,
max_adj=1,
compare_variance=True):
if isinstance(dist, probability.DiscreteUniformDistribution):
self.assertEqual(sample_sum.minimum, dist.minimum)
self.assertEqual(sample_sum.maximum, dist.maximum)
else:
if dist.minimum != float('-inf') or dist.minimum != float('inf'):
self.assertAlmostEqual(sample_sum.minimum, dist.minimum,
places)
if dist.maximum != float('-inf') or dist.maximum != float('inf'):
self.assertAlmostEqual(sample_sum.maximum,
dist.maximum * max_adj, places)
self.assertAlmostEqual(sample_sum.mean, dist.mean * mean_adj, places)
if compare_variance:
self.assertAlmostEqual(sample_sum.variance, dist.variance, places)
def assertApproxEqual(self, x, y, percent_tol=1e-6):
eq = (((abs(x - y) / ((abs(x) + abs(y)) / 2)) * 100) < percent_tol)
if not eq:
_LOG.error('x ({0}) and y ({1}) are not equal'.format(x, y))
self.assertTrue(eq)
def files_equal(self, f1, f2, exclude_line_endings=False):
equal = True
diffs = []
f1, c1 = process_file_arg(f1)
f2, c2 = process_file_arg(f2)
line = 0
f1_end = False
f2_end = False
lines_left = True
while True:
line += 1
if f1_end == False:
try:
l1 = f1.next()
except (StopIteration, EOFError):
f1_end = line
pass
if f2_end == False:
try:
l2 = f2.next()
except (StopIteration, EOFError):
f2_end = line
pass
if f1_end != False and f2_end != False:
break
if exclude_line_endings:
l1 = l1.strip()
l2 = l2.strip()
if f1_end == False and f2_end == False and l1 != l2:
diffs.append(line)
equal = False
if f1_end != f2_end:
mn = min([f1_end, f2_end])
mx = max([f1_end, f2_end])
diffs.extend(range(mn, mx + 1))
equal = False
assert len(diffs) == len(set(diffs))
if c1:
f1.close()
if c2:
f2.close()
return equal, diffs
def assertSameFiles(self, files, exclude_line_endings=False):
files = list(files)
all_equal = True
diffs = StringIO()
f1 = files.pop(0)
for f2 in files:
equal, diff_list = self.files_equal(f1, f2, exclude_line_endings)
if not equal:
all_equal = False
n1 = f1
if not isinstance(n1, str):
n1 = f1.name
n2 = f2
if not isinstance(n2, str):
n2 = f2.name
diffs.write('{0} and {1} differ at lines:\n\t{2}\n'.format(
n1, n2, ','.join([str(i) for i in diff_list])))
if not all_equal:
_LOG.error('files are not equal:\n{0}\n'.format(diffs.getvalue()))
self.assertTrue(all_equal)
def assertSameUnsortedFiles(self, files):
files = list(files)
all_equal = True
diffs = StringIO()
f1, close = process_file_arg(files.pop(0))
lines1 = sorted(f1.readlines())
for f in files:
f2, close2 = process_file_arg(f)
lines2 = sorted(f2.readlines())
if len(lines1) != len(lines2):
all_equal = False
diffs.write('{0} ({1}) and {2} ({3}) have different '
'number of lines\n'.format(f1.name, len(lines1),
f2.name, len(lines2)))
for i in range(min(len(lines1), len(lines2))):
if lines1[i].strip().split() != lines2[i].strip().split():
all_equal = False
diffs.write('{0} and {1} differ at sorted index '
'{2}\n'.format(f1.name, f2.name, i))
if close2:
f2.close()
if not all_equal:
_LOG.error('files are not equal after sorting:\n{0}\n'.format(
diffs.getvalue()))
self.assertTrue(all_equal)
if close:
f1.close()
def same_samples(self, sample1, sample2, places=4, num_mismatches=0):
if len(sample1) != len(sample2):
return False
for i in range(len(sample1)):
if round(float(sample1[i]) - float(sample2[i]), places) != 0:
if num_mismatches < 1:
return False
num_mismatches -= 1
return True
def assertSameSamples(self,
files,
columns_to_ignore=[],
header=True,
places=5,
num_mismatches_per_sample=0,
num_sample_mismatches=0):
files = list(files)
all_equal = True
diffs = StringIO()
f1, close = process_file_arg(files.pop(0))
f1_lines = f1.readlines()
indices = [
i for i in range(len(f1_lines[0].strip().split()))
if i not in columns_to_ignore
]
h1 = []
if header:
head = f1_lines.pop(0).strip().split()
h1 = [head[i] for i in indices]
lines1 = sorted(f1_lines)
for f in files:
f2, close2 = process_file_arg(f)
f2_lines = f2.readlines()
h2 = []
if header:
head = f2_lines.pop(0).strip().split()
h2 = [head[i] for i in indices]
if h1 != h2:
all_equal = False
diffs.write('{0} and {1} have different headers; not '
'comparing further\n'.format(f1.name, f2.name))
continue
lines2 = sorted(f2_lines)
if len(lines1) != len(lines2):
all_equal = False
diffs.write('{0} ({1}) and {2} ({3}) have different '
'number of lines\n'.format(f1.name, len(lines1),
f2.name, len(lines2)))
n_matches = 0
n_mismatches = 0
for l1 in lines1:
found = False
for l2 in lines2:
values1 = l1.strip().split()
values2 = l2.strip().split()
v1 = [float(values1[x]) for x in indices]
v2 = [float(values2[x]) for x in indices]
if self.same_samples(
v1,
v2,
places=places,
num_mismatches=num_mismatches_per_sample):
found = True
if found:
n_matches += 1
else:
n_mismatches += 1
if n_mismatches > 0:
if n_mismatches > num_sample_mismatches:
all_equal = False
diffs.write('{0} and {1}\nhave {2} mismatching samples and '
'share {3} samples\n'.format(
f1.name, f2.name, n_mismatches, n_matches))
if close2:
f2.close()
if diffs.getvalue() != '':
_LOG.error('files are not equal after sorting:\n{0}\n'.format(
diffs.getvalue()))
self.assertTrue(all_equal)
if close:
f1.close()
def assertSameDistributions(self, d1, d2):
self.assertEqual(d1.name, d2.name)
self.assertEqual(str(d1), str(d2))
self.assertEqual(d1.minimum, d2.minimum)
self.assertEqual(d1.maximum, d2.maximum)
self.assertEqual(d1.mean, d2.mean)
self.assertEqual(d1.variance, d2.variance)
def assertSameIntegerPartitions(self, integer_partitions):
ips = list(integer_partitions)
ip1 = ips.pop(0)
for ip2 in ips:
self.assertEqual(ip1._initialized, ip2._initialized)
self.assertEqual(ip1.n, ip2.n)
self.assertEqual(ip1.key, ip2.key)
self.assertEqual(ip1.integer_partition, ip2.integer_partition)
self.assertEqual(ip1._items, ip2._items)
def assertSamePartitions(self, partitions):
ps = list(partitions)
p1 = ps.pop(0)
for p2 in ps:
self.assertEqual(p1._initialized, p2._initialized)
self.assertEqual(p1.n, p2.n)
self.assertEqual(p1.key, p2.key)
self.assertEqual(p1.partition, p2.partition)
self.assertEqual(p1.values, p2.values)
class PyMsBayesTestCase(unittest.TestCase):
def set_up(self):
self.temp_fs = TempFileSystem(
parent = package_paths.test_path(),
prefix = 'PyMsBayesTestTemp-')
self.test_id = 'pymsbayes-' + random_str()
def tear_down(self):
self.register_file_system()
self.temp_fs.purge()
self.assertEqual(FileStream.open_files, set())
def get_test_path(self, parent=None, prefix='temp'):
return self.temp_fs.get_file_path(parent=parent, prefix=prefix)
def get_test_subdir(self, parent=None, prefix='temp'):
return self.temp_fs.create_subdir(parent=parent, prefix=prefix)
def register_file(self, path):
self.temp_fs._register_file(path)
def register_dir(self, path):
self.temp_fs._register_dir(path)
def register_file_system(self):
_LOG.debug('registering test file system...')
for path, dirs, files, in os.walk(self.temp_fs.base_dir):
for f in files:
if f.startswith(self.test_id):
self.register_file(os.path.join(path, f))
for d in dirs:
if d.startswith(self.test_id):
self.register_dir(os.path.join(path, d))
def _exe_script(self, script_name, args, stdout = None, stderr = None,
return_code = 0):
script_path = package_paths.script_path(script_name)
if isinstance(args, str):
arg_list = args.split()
else:
arg_list = args
arg_list = [str(x) for x in arg_list]
cmd = [sys.executable, script_path] + arg_list
_LOG.debug('Invocation:\n\t{0}'.format(' '.join(cmd)))
p = subprocess.Popen(cmd, shell=False, stdout=subprocess.PIPE,
stderr=subprocess.PIPE)
o, e = p.communicate()
exit_code = p.wait()
if exit_code != return_code:
_LOG.error("exit code {0} did not match {1}".format(exit_code,
return_code))
_LOG.error("here is the stdout:\n{0}".format(o))
_LOG.error("here is the stderr:\n{0}".format(e))
self.assertEqual(exit_code, return_code)
if stdout != None:
if o != stdout:
_LOG.error("std out did not match expected:\n{0}".format(o))
self.assertEqual(o, stdout)
if stderr != None:
if e != stderr:
_LOG.error("std error did not match expected:\n{0}".format(e))
self.assertEqual(e, stderr)
def get_expected_indices(self, num_pairs, dummy_column=True,
parameters_reported=True):
num_summary_params = 4
if _CV_INCLUDED:
num_summary_params += 1
num_params = 4*num_pairs
num_default_stats = 4*num_pairs
start = 0
if dummy_column:
start = 1
param_indices = range(start, start+num_summary_params)
start += num_summary_params
if parameters_reported:
param_indices += range(start, start+num_params)
start += num_params
stat_indices = range(start, start+num_default_stats)
return param_indices, stat_indices
def prior_file_is_valid(self, prior_path, num_of_samples,
num_of_columns=None):
try:
prior_file = open(prior_path, 'rU')
except:
_LOG.error('prior invalid: could not open prior path {0}'.format(
prior_path))
return False
nrows = 0
for i, line in enumerate(prior_file):
if nrows == 0 and HEADER_PATTERN.match(line):
pass
else:
nrows += 1
if not num_of_columns:
num_of_columns = len(line.strip().split())
ncols = len(line.strip().split())
if num_of_columns != ncols:
_LOG.error('prior invalid: num of columns at line {0} is {1} '
'NOT {2}'.format(i+1, ncols, num_of_columns))
return False
prior_file.close()
if num_of_samples != nrows:
_LOG.error('prior invalid: num of rows is {0} NOT {1}'.format(
nrows, num_of_samples))
return False
return True
def get_number_of_lines(self, path):
f, close = process_file_arg(path)
count = 0
for l in f:
count += 1
if close:
f.close()
return count
def get_number_of_header_lines(self, path):
f, close = process_file_arg(path)
count = 0
for l in f:
if HEADER_PATTERN.match(l.strip()):
count += 1
if close:
f.close()
return count
def parse_python_config(self, path):
return ConfigObj(path)
def get_config_from_msbayes_workers(self, msbayes_workers):
cfgs = [MsBayesConfig(w.config_path) for w in msbayes_workers]
self.assertSameConfigs(cfgs)
return cfgs[0]
def assertSameConfigs(self, cfgs):
configs = list(cfgs)
c1 = configs.pop(0)
for c2 in cfgs:
self.assertEqual(c1.time_in_subs_per_site,
c2.time_in_subs_per_site)
self.assertEqual(c1.npairs, c2.npairs)
self.assertEqual(c1.implementation, c2.implementation)
self.assertEqual(c1.div_model_prior, c2.div_model_prior)
self.assertEqual(c1.bottle_proportion_shared,
c2.bottle_proportion_shared)
self.assertEqual(c1.theta_parameters, c2.theta_parameters)
self.assertEqual(c1.taxa, c2.taxa)
self.assertTrue(c1.sample_table.equals(c2.sample_table))
if c1.psi:
self.assertSameDistributions(c1.psi, c2.psi)
else:
self.assertEqual(c1.psi, c2.psi)
if c1.tau:
self.assertSameDistributions(c1.tau, c2.tau)
else:
self.assertEqual(c1.tau, c2.tau)
if c1.theta:
self.assertSameDistributions(c1.theta, c2.theta)
else:
self.assertEqual(c1.theta, c2.theta)
if c1.a_theta:
self.assertSameDistributions(c1.a_theta, c2.a_theta)
else:
self.assertEqual(c1.a_theta, c2.a_theta)
if c1.d_theta:
self.assertSameDistributions(c1.d_theta, c2.d_theta)
else:
self.assertEqual(c1.d_theta, c2.d_theta)
if c1.recombination:
self.assertSameDistributions(c1.recombination, c2.recombination)
else:
self.assertEqual(c1.recombination, c2.recombination)
if c1.migration:
self.assertSameDistributions(c1.migration, c2.migration)
else:
self.assertEqual(c1.migration, c2.migration)
if c1.dpp_concentration:
self.assertSameDistributions(c1.dpp_concentration,
c2.dpp_concentration)
else:
self.assertEqual(c1.dpp_concentration, c2.dpp_concentration)
if c1.bottle_proportion:
self.assertSameDistributions(c1.bottle_proportion,
c2.bottle_proportion)
else:
self.assertEqual(c1.bottle_proportion, c2.bottle_proportion)
def get_parameter_summaries_from_msbayes_workers(self, msbayes_workers,
shuffle_taus=True):
msbayes_workers = list(msbayes_workers)
s = dict(zip(
[i for i in msbayes_workers[0].parameter_indices],
[SampleSummarizer(
tag=msbayes_workers[0].header[i]) for i in msbayes_workers[
0].parameter_indices]))
ncols = None
header = msbayes_workers[0].header
pi = msbayes_workers[0].parameter_indices
for w in msbayes_workers:
self.assertEqual(w.header, header)
self.assertEqual(w.parameter_indices, pi)
f = open(w.prior_path, 'rU')
for line_idx, row in enumerate(f):
if not ncols:
ncols = len(row.strip().split())
if HEADER_PATTERN.match(row.strip()):
continue
r = row.strip().split()
assert len(r) == ncols
if shuffle_taus: # because taus are sorted in prior files
psi_index = get_indices_of_patterns(w.header,
PSI_PATTERNS)[0]
tau_indices = get_indices_of_patterns(w.header,
TAU_PATTERNS)
psi = int(r[psi_index])
taus = [float(r[i]) for i in tau_indices]
self.assertEqual(psi, len(set(taus)))
random.shuffle(taus)
for n, i in enumerate(tau_indices):
s[i].add_sample(taus[n])
p_set = set(w.parameter_indices) - set(tau_indices)
p = sorted(list(p_set))
for i in p:
s[i].add_sample(float(r[i]))
else:
for i in w.parameter_indices:
s[i].add_sample(float(r[i]))
f.close()
return s
def assertPriorIsPrecise(self, msbayes_workers, places=2):
msbayes_workers = list(msbayes_workers)
self.assertWorkersFinished(msbayes_workers)
param_sums = self.get_parameter_summaries_from_msbayes_workers(
msbayes_workers)
sample_size = 0
for w in msbayes_workers:
sample_size += w.sample_size
for s in param_sums.itervalues():
self.assertEqual(s.n, sample_size)
cfg = self.get_config_from_msbayes_workers(msbayes_workers)
psi_indices = get_indices_of_patterns(msbayes_workers[0].header,
PSI_PATTERNS)
self.assertEqual(len(psi_indices), 1)
model_indices = get_indices_of_patterns(msbayes_workers[0].header,
MODEL_PATTERNS)
if not msbayes_workers[0].model_index is None:
self.assertEqual(len(model_indices), 1)
else:
self.assertEqual(len(model_indices), 0)
tau_indices = get_indices_of_patterns(msbayes_workers[0].header,
TAU_PATTERNS)
a_theta_indices = get_indices_of_patterns(msbayes_workers[0].header,
A_THETA_PATTERNS)
d_theta_indices = get_indices_of_patterns(msbayes_workers[0].header,
D_THETA_PATTERNS)
if msbayes_workers[0].report_parameters:
self.assertEqual(len(tau_indices), cfg.npairs)
self.assertEqual(len(a_theta_indices), cfg.npairs)
self.assertEqual(len(d_theta_indices), 2*cfg.npairs)
else:
self.assertEqual(len(tau_indices), 0)
self.assertEqual(len(a_theta_indices), 0)
self.assertEqual(len(d_theta_indices), 0)
_LOG.debug('\n{0}\n'.format('\n'.join(
[str(param_sums[i]) for i in sorted(param_sums.iterkeys())])))
for i in psi_indices:
self.assertSampleIsFromDistribution(param_sums[i], cfg.psi,
places=places)
for i in tau_indices:
self.assertSampleIsFromDistribution(param_sums[i], cfg.tau,
places=places)
for i in a_theta_indices:
self.assertSampleIsFromDistribution(param_sums[i], cfg.a_theta,
places=places)
for i in d_theta_indices:
self.assertSampleIsFromDistribution(param_sums[i], cfg.d_theta,
mean_adj=cfg.theta.mean,
max_adj=cfg.theta.maximum,
compare_variance=False,
places=places)
def assertPriorIsAccurate(self, msbayes_workers, places=2):
msbayes_workers = list(msbayes_workers)
self.assertWorkersFinished(msbayes_workers)
pass
def assertPriorIsValid(self, msbayes_workers, places=2):
msbayes_workers = list(msbayes_workers)
self.assertWorkersFinished(msbayes_workers)
self.assertPriorIsPrecise(msbayes_workers, places=places)
self.assertPriorIsAccurate(msbayes_workers, places=places)
def assertWorkersFinished(self, msbayes_workers):
for w in msbayes_workers:
self.assertTrue(w.finished)
def assertSampleIsFromDistribution(self, sample_sum, dist, places=2,
mean_adj=1,
max_adj=1,
compare_variance=True):
if isinstance(dist, probability.DiscreteUniformDistribution):
self.assertEqual(sample_sum.minimum, dist.minimum)
self.assertEqual(sample_sum.maximum, dist.maximum)
else:
if dist.minimum != float('-inf') or dist.minimum != float('inf'):
self.assertAlmostEqual(sample_sum.minimum, dist.minimum, places)
if dist.maximum != float('-inf') or dist.maximum != float('inf'):
self.assertAlmostEqual(sample_sum.maximum, dist.maximum*max_adj, places)
self.assertAlmostEqual(sample_sum.mean, dist.mean*mean_adj, places)
if compare_variance:
self.assertAlmostEqual(sample_sum.variance, dist.variance, places)
def assertApproxEqual(self, x, y, percent_tol=1e-6):
eq = (((abs(x-y) / ((abs(x)+abs(y))/2))*100) < percent_tol)
if not eq:
_LOG.error('x ({0}) and y ({1}) are not equal'.format(x, y))
self.assertTrue(eq)
def files_equal(self, f1, f2, exclude_line_endings=False):
equal = True
diffs = []
f1, c1 = process_file_arg(f1)
f2, c2 = process_file_arg(f2)
line = 0
f1_end = False
f2_end = False
lines_left = True
while True:
line += 1
if f1_end == False:
try:
l1 = f1.next()
except (StopIteration, EOFError):
f1_end = line
pass
if f2_end == False:
try:
l2 = f2.next()
except (StopIteration, EOFError):
f2_end = line
pass
if f1_end != False and f2_end != False:
break
if exclude_line_endings:
l1 = l1.strip()
l2 = l2.strip()
if f1_end == False and f2_end == False and l1 != l2:
diffs.append(line)
equal = False
if f1_end != f2_end:
mn = min([f1_end, f2_end])
mx = max([f1_end, f2_end])
diffs.extend(range(mn, mx+1))
equal = False
assert len(diffs) == len(set(diffs))
if c1:
f1.close()
if c2:
f2.close()
return equal, diffs
def assertSameFiles(self, files, exclude_line_endings=False):
files = list(files)
all_equal = True
diffs = StringIO()
f1 = files.pop(0)
for f2 in files:
equal, diff_list = self.files_equal(f1, f2, exclude_line_endings)
if not equal:
all_equal = False
n1 = f1
if not isinstance(n1, str):
n1 = f1.name
n2 = f2
if not isinstance(n2, str):
n2 = f2.name
diffs.write('{0} and {1} differ at lines:\n\t{2}\n'.format(
n1, n2, ','.join([str(i) for i in diff_list])))
if not all_equal:
_LOG.error('files are not equal:\n{0}\n'.format(diffs.getvalue()))
self.assertTrue(all_equal)
def assertSameUnsortedFiles(self, files):
files = list(files)
all_equal = True
diffs = StringIO()
f1, close = process_file_arg(files.pop(0))
lines1 = sorted(f1.readlines())
for f in files:
f2, close2 = process_file_arg(f)
lines2 = sorted(f2.readlines())
if len(lines1) != len(lines2):
all_equal = False
diffs.write('{0} ({1}) and {2} ({3}) have different '
'number of lines\n'.format(f1.name, len(lines1),
f2.name, len(lines2)))
for i in range(min(len(lines1), len(lines2))):
if lines1[i].strip().split() != lines2[i].strip().split():
all_equal = False
diffs.write('{0} and {1} differ at sorted index '
'{2}\n'.format(f1.name, f2.name, i))
if close2:
f2.close()
if not all_equal:
_LOG.error('files are not equal after sorting:\n{0}\n'.format(
diffs.getvalue()))
self.assertTrue(all_equal)
if close:
f1.close()
def same_samples(self, sample1, sample2, places = 4, num_mismatches = 0):
if len(sample1) != len(sample2):
return False
for i in range(len(sample1)):
if round(float(sample1[i]) - float(sample2[i]), places) != 0:
if num_mismatches < 1:
return False
num_mismatches -= 1
return True
def assertSameSamples(self, files, columns_to_ignore = [], header = True,
places = 5, num_mismatches_per_sample = 0,
num_sample_mismatches = 0):
files = list(files)
all_equal = True
diffs = StringIO()
f1, close = process_file_arg(files.pop(0))
f1_lines = f1.readlines()
indices = [i for i in range(len(
f1_lines[0].strip().split())) if i not in columns_to_ignore]
h1 = []
if header:
head = f1_lines.pop(0).strip().split()
h1 = [head[i] for i in indices]
lines1 = sorted(f1_lines)
for f in files:
f2, close2 = process_file_arg(f)
f2_lines = f2.readlines()
h2 = []
if header:
head = f2_lines.pop(0).strip().split()
h2 = [head[i] for i in indices]
if h1 != h2:
all_equal = False
diffs.write('{0} and {1} have different headers; not '
'comparing further\n'.format(
f1.name, f2.name))
continue
lines2 = sorted(f2_lines)
if len(lines1) != len(lines2):
all_equal = False
diffs.write('{0} ({1}) and {2} ({3}) have different '
'number of lines\n'.format(f1.name, len(lines1),
f2.name, len(lines2)))
n_matches = 0
n_mismatches = 0
for l1 in lines1:
found = False
for l2 in lines2:
values1 = l1.strip().split()
values2 = l2.strip().split()
v1 = [float(values1[x]) for x in indices]
v2 = [float(values2[x]) for x in indices]
if self.same_samples(v1, v2, places = places,
num_mismatches = num_mismatches_per_sample):
found = True
if found:
n_matches += 1
else:
n_mismatches += 1
if n_mismatches > 0:
if n_mismatches > num_sample_mismatches:
all_equal = False
diffs.write('{0} and {1}\nhave {2} mismatching samples and '
'share {3} samples\n'.format(
f1.name, f2.name, n_mismatches, n_matches))
if close2:
f2.close()
if diffs.getvalue() != '':
_LOG.error('files are not equal after sorting:\n{0}\n'.format(
diffs.getvalue()))
self.assertTrue(all_equal)
if close:
f1.close()
def assertSameDistributions(self, d1, d2):
self.assertEqual(d1.name, d2.name)
self.assertEqual(str(d1), str(d2))
self.assertEqual(d1.minimum, d2.minimum)
self.assertEqual(d1.maximum, d2.maximum)
self.assertEqual(d1.mean, d2.mean)
self.assertEqual(d1.variance, d2.variance)
def assertSameIntegerPartitions(self, integer_partitions):
ips = list(integer_partitions)
ip1 = ips.pop(0)
for ip2 in ips:
self.assertEqual(ip1._initialized, ip2._initialized)
self.assertEqual(ip1.n, ip2.n)
self.assertEqual(ip1.key, ip2.key)
self.assertEqual(ip1.integer_partition, ip2.integer_partition)
self.assertEqual(ip1._items, ip2._items)
def assertSamePartitions(self, partitions):
ps = list(partitions)
p1 = ps.pop(0)
for p2 in ps:
self.assertEqual(p1._initialized, p2._initialized)
self.assertEqual(p1.n, p2.n)
self.assertEqual(p1.key, p2.key)
self.assertEqual(p1.partition, p2.partition)
self.assertEqual(p1.values, p2.values)
def main_cli():
description = '{name} {version}'.format(**_program_info)
parser = argparse.ArgumentParser(description = description)
parser.add_argument('-c', '--config',
type = arg_is_config,
required = True,
help = ('msBayes config file to be used to generate saturation '
'plot.'))
parser.add_argument('-n', '--num-prior-samples',
action = 'store',
type = int,
default = 1000,
help = ('The number of prior samples to simulate for the '
'saturation plot.'))
parser.add_argument('--np',
action = 'store',
type = int,
default = multiprocessing.cpu_count(),
help = ('The maximum number of processes to run in parallel. The '
'default is the number of CPUs available on the machine.'))
parser.add_argument('-o', '--output-dir',
action = 'store',
type = arg_is_dir,
help = ('The directory in which all output files will be written. '
'The default is to use the directory of the first observed '
'config file.'))
parser.add_argument('--temp-dir',
action = 'store',
type = arg_is_dir,
help = ('A directory to temporarily stage files. The default is to '
'use the output directory.'))
parser.add_argument('-s', '--stat-prefixes',
nargs = '*',
type = str,
default = ['pi', 'pi.net', 'wattTheta', 'tajD.denom'],
help = ('Prefixes of summary statistics to use in the analyses. '
'The prefixes should be separated by spaces. '
'Default: `-s pi pi.net wattTheta tajD.denom`.'))
parser.add_argument('--vertical-lines',
nargs = '*',
type = float,
default = [],
help = ('Positions along x-axis where vertical lines are to be '
'drawn. Default is to draw no vertical lines.'))
parser.add_argument('--compress',
action = 'store_true',
help = 'Compress plot data file.')
parser.add_argument('--keep-temps',
action = 'store_true',
help = 'Keep all temporary files.')
parser.add_argument('--seed',
action = 'store',
type = int,
help = 'Random number seed to use for the analysis.')
parser.add_argument('--version',
action = 'version',
version = '%(prog)s ' + _program_info['version'],
help = 'Report version and exit.')
parser.add_argument('--quiet',
action = 'store_true',
help = 'Run without verbose messaging.')
parser.add_argument('--debug',
action = 'store_true',
help = 'Run in debugging mode.')
args = parser.parse_args()
##########################################################################
## handle args
from pymsbayes.utils.messaging import (LoggingControl,
InfoLogger)
LoggingControl.set_logging_level("INFO")
if args.quiet:
LoggingControl.set_logging_level("WARNING")
if args.debug:
LoggingControl.set_logging_level("DEBUG")
log = LoggingControl.get_logger(__name__)
from pymsbayes.workers import MsBayesWorker
from pymsbayes.utils.parsing import (get_patterns_from_prefixes,
DEFAULT_STAT_PATTERNS, get_stats_by_time, dict_line_iter)
from pymsbayes.manager import Manager
from pymsbayes.utils.tempfs import TempFileSystem
from pymsbayes.utils import probability
from pymsbayes.utils.functions import long_division
from pymsbayes.config import MsBayesConfig
from pymsbayes.utils import GLOBAL_RNG, MSBAYES_SORT_INDEX, ToolPathManager
from pymsbayes.fileio import process_file_arg
from pymsbayes.plotting import MATPLOTLIB_AVAILABLE, SaturationPlotGrid
MSBAYES_SORT_INDEX.set_index(0)
# get full paths to tools
msbayes_path = ToolPathManager.get_tool_full_path('msbayes.pl')
dpp_msbayes_path = ToolPathManager.get_tool_full_path('dpp-msbayes.pl')
if not args.output_dir:
args.output_dir = os.path.dirname(args.config)
info = InfoLogger(os.path.join(args.output_dir, 'pymsbayes-info.txt'))
stats_by_time_path = os.path.join(args.output_dir, 'stats-by-time.txt')
if args.compress:
stats_by_time_path += '.gz'
plot_path = os.path.join(args.output_dir, 'saturation-plot.pdf')
if not args.temp_dir:
args.temp_dir = args.output_dir
temp_fs = TempFileSystem(parent=args.temp_dir, prefix='temp-files-')
args.stat_prefixes = [s.rstrip('.') for s in args.stat_prefixes]
stat_patterns = get_patterns_from_prefixes(
[s + '.' for s in args.stat_prefixes],
ignore_case=True)
if not args.seed:
args.seed = random.randint(1, 999999999)
GLOBAL_RNG.seed(args.seed)
compress_level = None
if args.compress:
compress_level = 9
cfg = MsBayesConfig(args.config)
num_taxon_pairs = cfg.npairs
cfg.div_model_prior = 'constrained'
cfg.psi = probability.DiscreteUniformDistribution(num_taxon_pairs,
num_taxon_pairs)
config_path = temp_fs.get_file_path(prefix='cfg-')
cfg.write(config_path)
info.write('[pymsbayes]', log.info)
info.write('\tprogram_name = {name}'.format(**_program_info), log.info)
info.write('\tversion = {version}'.format(**_program_info), log.info)
info.write('\tinvocation = {0!r}'.format(' '.join(sys.argv)), log.info)
info.write('\toutput_directory = {0!r}'.format(args.output_dir), log.info)
info.write('\ttemp_directory = {0!r}'.format(temp_fs.base_dir), log.info)
info.write('\tsort_index = {0}'.format(
MSBAYES_SORT_INDEX.current_value()), log.info)
info.write('\tstat_patterns = {0!r}'.format(
', '.join([p.pattern for p in stat_patterns])), log.info)
info.write('\tseed = {0}'.format(args.seed), log.info)
info.write('\tnum_prior_samples = {0}'.format(args.num_prior_samples),
log.info)
info.write('\tstats_by_time_path = {0!r}'.format(stats_by_time_path),
log.info)
info.write('\t[[tool_paths]]', log.info)
info.write('\t\tdpp_msbayes = {0}'.format(dpp_msbayes_path), log.info)
info.write('\t\tmsbayes = {0}'.format(msbayes_path), log.info)
info.write('\t[[config]]', log.debug)
info.write('{0}'.format(str(cfg)), log.debug)
##########################################################################
## begin analysis --- generate samples
start_time = datetime.datetime.now()
if args.np > args.num_prior_samples:
args.np = args.num_prior_samples
batch_size, remainder = long_division(args.num_prior_samples, args.np)
schema = 'abctoolbox'
workers = []
for i in range(args.np):
sample_size = batch_size
if i == (args.np - 1):
sample_size += remainder
w = MsBayesWorker(
temp_fs = temp_fs,
sample_size = sample_size,
config_path = config_path,
report_parameters = True,
schema = schema,
include_header = True,
stat_patterns = stat_patterns,
write_stats_file = False)
workers.append(w)
log.info('Generating samples...')
workers = Manager.run_workers(
workers = workers,
num_processors = args.np)
log.info('Parsing samples...')
stats_by_time = get_stats_by_time([w.prior_path for w in workers])
stat_keys = stats_by_time.keys()
stat_keys.remove('PRI.t')
for prefix in args.stat_prefixes:
if not prefix in stat_keys:
raise Exception('stat prefix {0!r} not found in simulated stats:'
'\n\t{1}'.format(prefix, ', '.join(stat_keys)))
header = ['PRI.t'] + args.stat_prefixes
log.info('Writing stats-by-time matrix...')
out, close = process_file_arg(stats_by_time_path, 'w',
compresslevel = compress_level)
for row in dict_line_iter(stats_by_time, sep = '\t', header = header):
out.write(row)
if close:
out.close()
log.info('Creating plots...')
if not MATPLOTLIB_AVAILABLE:
log.warning(
'`matplotlib` could not be imported, so the plot can not be\n'
'produced. The data to create the plot can be found in:\n\t'
'{0!r}'.format(stats_by_time_path))
else:
y_labels = {'pi': r'$\pi$',
'pi.net': r'$\pi_{net}$',
'wattTheta': r'$\theta_W$',
'tajD.denom': r'$SD(\pi - \theta_W)$'}
spg = SaturationPlotGrid(stats_by_time,
x_key = 'PRI.t',
y_keys = args.stat_prefixes,
y_labels = y_labels,
num_columns = 2,
vertical_line_positions = args.vertical_lines)
fig = spg.create_grid()
fig.savefig(plot_path)
stop_time = datetime.datetime.now()
log.info('Done!')
info.write('\t[[run_stats]]', log.info)
info.write('\t\tstart_time = {0}'.format(str(start_time)), log.info)
info.write('\t\tstop_time = {0}'.format(str(stop_time)), log.info)
info.write('\t\ttotal_duration = {0}'.format(str(stop_time - start_time)),
log.info)
if not args.keep_temps:
log.debug('purging temps...')
temp_fs.purge()
def main_cli():
description = '{name} {version}'.format(**_program_info)
parser = argparse.ArgumentParser(description=description)
parser.add_argument(
'-c',
'--config',
type=arg_is_config,
required=True,
help=('msBayes config file to be used to generate saturation '
'plot.'))
parser.add_argument(
'-n',
'--num-prior-samples',
action='store',
type=int,
default=1000,
help=('The number of prior samples to simulate for the '
'saturation plot.'))
parser.add_argument(
'--np',
action='store',
type=int,
default=multiprocessing.cpu_count(),
help=('The maximum number of processes to run in parallel. The '
'default is the number of CPUs available on the machine.'))
parser.add_argument(
'-o',
'--output-dir',
action='store',
type=arg_is_dir,
help=('The directory in which all output files will be written. '
'The default is to use the directory of the first observed '
'config file.'))
parser.add_argument(
'--temp-dir',
action='store',
type=arg_is_dir,
help=('A directory to temporarily stage files. The default is to '
'use the output directory.'))
parser.add_argument(
'-s',
'--stat-prefixes',
nargs='*',
type=str,
default=['pi', 'pi.net', 'wattTheta', 'tajD.denom'],
help=('Prefixes of summary statistics to use in the analyses. '
'The prefixes should be separated by spaces. '
'Default: `-s pi pi.net wattTheta tajD.denom`.'))
parser.add_argument(
'--vertical-lines',
nargs='*',
type=float,
default=[],
help=('Positions along x-axis where vertical lines are to be '
'drawn. Default is to draw no vertical lines.'))
parser.add_argument('--compress',
action='store_true',
help='Compress plot data file.')
parser.add_argument('--keep-temps',
action='store_true',
help='Keep all temporary files.')
parser.add_argument('--seed',
action='store',
type=int,
help='Random number seed to use for the analysis.')
parser.add_argument('--version',
action='version',
version='%(prog)s ' + _program_info['version'],
help='Report version and exit.')
parser.add_argument('--quiet',
action='store_true',
help='Run without verbose messaging.')
parser.add_argument('--debug',
action='store_true',
help='Run in debugging mode.')
args = parser.parse_args()
##########################################################################
## handle args
from pymsbayes.utils.messaging import (LoggingControl, InfoLogger)
LoggingControl.set_logging_level("INFO")
if args.quiet:
LoggingControl.set_logging_level("WARNING")
if args.debug:
LoggingControl.set_logging_level("DEBUG")
log = LoggingControl.get_logger(__name__)
from pymsbayes.workers import MsBayesWorker
from pymsbayes.utils.parsing import (get_patterns_from_prefixes,
DEFAULT_STAT_PATTERNS,
get_stats_by_time, dict_line_iter)
from pymsbayes.manager import Manager
from pymsbayes.utils.tempfs import TempFileSystem
from pymsbayes.utils import probability
from pymsbayes.utils.functions import long_division
from pymsbayes.config import MsBayesConfig
from pymsbayes.utils import GLOBAL_RNG, MSBAYES_SORT_INDEX, ToolPathManager
from pymsbayes.fileio import process_file_arg
from pymsbayes.plotting import MATPLOTLIB_AVAILABLE, SaturationPlotGrid
MSBAYES_SORT_INDEX.set_index(0)
# get full paths to tools
msbayes_path = ToolPathManager.get_tool_full_path('msbayes.pl')
dpp_msbayes_path = ToolPathManager.get_tool_full_path('dpp-msbayes.pl')
if not args.output_dir:
args.output_dir = os.path.dirname(args.config)
info = InfoLogger(os.path.join(args.output_dir, 'pymsbayes-info.txt'))
stats_by_time_path = os.path.join(args.output_dir, 'stats-by-time.txt')
if args.compress:
stats_by_time_path += '.gz'
plot_path = os.path.join(args.output_dir, 'saturation-plot.pdf')
if not args.temp_dir:
args.temp_dir = args.output_dir
temp_fs = TempFileSystem(parent=args.temp_dir, prefix='temp-files-')
args.stat_prefixes = [s.rstrip('.') for s in args.stat_prefixes]
stat_patterns = get_patterns_from_prefixes(
[s + '.' for s in args.stat_prefixes], ignore_case=True)
if not args.seed:
args.seed = random.randint(1, 999999999)
GLOBAL_RNG.seed(args.seed)
compress_level = None
if args.compress:
compress_level = 9
cfg = MsBayesConfig(args.config)
num_taxon_pairs = cfg.npairs
cfg.div_model_prior = 'constrained'
cfg.psi = probability.DiscreteUniformDistribution(num_taxon_pairs,
num_taxon_pairs)
config_path = temp_fs.get_file_path(prefix='cfg-')
cfg.write(config_path)
info.write('[pymsbayes]', log.info)
info.write('\tprogram_name = {name}'.format(**_program_info), log.info)
info.write('\tversion = {version}'.format(**_program_info), log.info)
info.write('\tinvocation = {0!r}'.format(' '.join(sys.argv)), log.info)
info.write('\toutput_directory = {0!r}'.format(args.output_dir), log.info)
info.write('\ttemp_directory = {0!r}'.format(temp_fs.base_dir), log.info)
info.write('\tsort_index = {0}'.format(MSBAYES_SORT_INDEX.current_value()),
log.info)
info.write(
'\tstat_patterns = {0!r}'.format(', '.join(
[p.pattern for p in stat_patterns])), log.info)
info.write('\tseed = {0}'.format(args.seed), log.info)
info.write('\tnum_prior_samples = {0}'.format(args.num_prior_samples),
log.info)
info.write('\tstats_by_time_path = {0!r}'.format(stats_by_time_path),
log.info)
info.write('\t[[tool_paths]]', log.info)
info.write('\t\tdpp_msbayes = {0}'.format(dpp_msbayes_path), log.info)
info.write('\t\tmsbayes = {0}'.format(msbayes_path), log.info)
info.write('\t[[config]]', log.debug)
info.write('{0}'.format(str(cfg)), log.debug)
##########################################################################
## begin analysis --- generate samples
start_time = datetime.datetime.now()
if args.np > args.num_prior_samples:
args.np = args.num_prior_samples
batch_size, remainder = long_division(args.num_prior_samples, args.np)
schema = 'abctoolbox'
workers = []
for i in range(args.np):
sample_size = batch_size
if i == (args.np - 1):
sample_size += remainder
w = MsBayesWorker(temp_fs=temp_fs,
sample_size=sample_size,
config_path=config_path,
report_parameters=True,
schema=schema,
include_header=True,
stat_patterns=stat_patterns,
write_stats_file=False)
workers.append(w)
log.info('Generating samples...')
workers = Manager.run_workers(workers=workers, num_processors=args.np)
log.info('Parsing samples...')
stats_by_time = get_stats_by_time([w.prior_path for w in workers])
stat_keys = stats_by_time.keys()
stat_keys.remove('PRI.t')
for prefix in args.stat_prefixes:
if not prefix in stat_keys:
raise Exception('stat prefix {0!r} not found in simulated stats:'
'\n\t{1}'.format(prefix, ', '.join(stat_keys)))
header = ['PRI.t'] + args.stat_prefixes
log.info('Writing stats-by-time matrix...')
out, close = process_file_arg(stats_by_time_path,
'w',
compresslevel=compress_level)
for row in dict_line_iter(stats_by_time, sep='\t', header=header):
out.write(row)
if close:
out.close()
log.info('Creating plots...')
if not MATPLOTLIB_AVAILABLE:
log.warning(
'`matplotlib` could not be imported, so the plot can not be\n'
'produced. The data to create the plot can be found in:\n\t'
'{0!r}'.format(stats_by_time_path))
else:
y_labels = {
'pi': r'$\pi$',
'pi.net': r'$\pi_{net}$',
'wattTheta': r'$\theta_W$',
'tajD.denom': r'$SD(\pi - \theta_W)$'
}
spg = SaturationPlotGrid(stats_by_time,
x_key='PRI.t',
y_keys=args.stat_prefixes,
y_labels=y_labels,
num_columns=2,
vertical_line_positions=args.vertical_lines)
fig = spg.create_grid()
fig.savefig(plot_path)
stop_time = datetime.datetime.now()
log.info('Done!')
info.write('\t[[run_stats]]', log.info)
info.write('\t\tstart_time = {0}'.format(str(start_time)), log.info)
info.write('\t\tstop_time = {0}'.format(str(stop_time)), log.info)
info.write('\t\ttotal_duration = {0}'.format(str(stop_time - start_time)),
log.info)
if not args.keep_temps:
log.debug('purging temps...')
temp_fs.purge()
