def single_object_beta(self,
otu_table,
metric,
tree_string,
missing_sams=None):
""" running single_file_beta should give same result using --rows"""
if missing_sams is None:
missing_sams = []
metrics = list_known_nonphylogenetic_metrics()
metrics.extend(list_known_phylogenetic_metrics())
# new metrics that don't trivially parallelize must be dealt with
# carefully
warnings.filterwarnings(
'ignore', 'dissimilarity binary_dist_chisq is\
not parallelized, calculating the whole matrix...')
warnings.filterwarnings(
'ignore', 'dissimilarity dist_chisq is not\
parallelized, calculating the whole matrix...')
warnings.filterwarnings(
'ignore', 'dissimilarity dist_gower is not\
parallelized, calculating the whole matrix...')
warnings.filterwarnings(
'ignore', 'dissimilarity dist_hellinger is\
not parallelized, calculating the whole matrix...')
warnings.filterwarnings('ignore', 'unifrac had no information for\
sample M*')
# self.files_to_remove.extend([input_path,tree_path])
# self.folders_to_remove.append(output_dir)
# os.mkdir(output_dir+'/ft/')
for metric in metrics:
# do it
beta_out = single_object_beta(otu_table,
metric,
tree_string,
rowids=None,
full_tree=False)
sams, dmtx = parse_distmat(beta_out)
# do it by rows
for i in range(len(sams)):
if sams[i] in missing_sams:
continue
rows = sams[i]
# row_outname = output_dir + '/' + metric + '_' +\
# in_fname
r_out = single_object_beta(otu_table,
metric,
tree_string,
rowids=rows,
full_tree=False)
col_sams, row_sams, row_dmtx = parse_matrix(r_out)
self.assertEqual(row_dmtx.shape,
(len(rows.split(',')), len(sams)))
# make sure rows same as full
for j in range(len(rows.split(','))):
for k in range(len(sams)):
row_v1 = row_dmtx[j, k]
full_v1 =\
dmtx[sams.index(row_sams[j]),
sams.index(col_sams[k])]
npt.assert_almost_equal(row_v1, full_v1)
# full tree run:
if 'full_tree' in str(metric).lower():
continue
# do it by rows with full tree
for i in range(len(sams)):
if sams[i] in missing_sams:
continue
rows = sams[i]
#~ row_outname = output_dir + '/ft/' + metric + '_' +\
#~ in_fname
r_out = single_object_beta(otu_table,
metric,
tree_string,
rowids=None,
full_tree=True)
col_sams, row_sams, row_dmtx = parse_matrix(r_out)
self.assertEqual(row_dmtx.shape,
(len(rows.split(',')), len(sams)))
# make sure rows same as full
for j in range(len(rows.split(','))):
for k in range(len(sams)):
row_v1 = row_dmtx[j, k]
full_v1 =\
dmtx[sams.index(row_sams[j]),
sams.index(col_sams[k])]
npt.assert_almost_equal(row_v1, full_v1)
# do it with full tree
r_out = single_object_beta(otu_table,
metric,
tree_string,
rowids=None,
full_tree=True)
sams_ft, dmtx_ft = parse_distmat(r_out)
self.assertEqual(sams_ft, sams)
npt.assert_almost_equal(dmtx_ft, dmtx)
def single_object_beta(self, otu_table, metric, tree_string,
missing_sams=None):
""" running single_file_beta should give same result using --rows"""
if missing_sams==None:
missing_sams = []
# setup
#input_path = get_tmp_filename()
#in_fname = os.path.split(input_path)[1]
#f = open(input_path,'w')
#f.write(otu_table_string)
#f.close()
#tree_path = get_tmp_filename()
#f = open(tree_path,'w')
#f.write(tree_string)
#f.close()
metrics = list_known_nonphylogenetic_metrics()
metrics.extend(list_known_phylogenetic_metrics())
#output_dir = get_tmp_filename(suffix = '')
#os.mkdir(output_dir)
# new metrics that don't trivially parallelize must be dealt with
# carefully
warnings.filterwarnings('ignore','dissimilarity binary_dist_chisq is\
not parallelized, calculating the whole matrix...')
warnings.filterwarnings('ignore','dissimilarity dist_chisq is not\
parallelized, calculating the whole matrix...')
warnings.filterwarnings('ignore','dissimilarity dist_gower is not\
parallelized, calculating the whole matrix...')
warnings.filterwarnings('ignore','dissimilarity dist_hellinger is\
not parallelized, calculating the whole matrix...')
warnings.filterwarnings('ignore','unifrac had no information for\
sample M*')
#self.files_to_remove.extend([input_path,tree_path])
#self.folders_to_remove.append(output_dir)
#os.mkdir(output_dir+'/ft/')
for metric in metrics:
# do it
beta_out = single_object_beta(otu_table, metric,
tree_string,rowids=None,
full_tree=False)
sams, dmtx = parse_distmat(beta_out)
# do it by rows
for i in range(len(sams)):
if sams[i] in missing_sams: continue
rows = sams[i]
#row_outname = output_dir + '/' + metric + '_' +\
#in_fname
r_out = single_object_beta(otu_table, metric,
tree_string,rowids=rows,
full_tree=False)
col_sams, row_sams, row_dmtx = parse_matrix(r_out)
self.assertEqual(row_dmtx.shape, (len(rows.split(',')),
len(sams)))
# make sure rows same as full
for j in range(len(rows.split(','))):
for k in range(len(sams)):
row_v1 = row_dmtx[j,k]
full_v1 =\
dmtx[sams.index(row_sams[j]),
sams.index(col_sams[k])]
self.assertFloatEqual(row_v1, full_v1)
### full tree run:
if 'full_tree' in str(metric).lower(): continue
# do it by rows with full tree
for i in range(len(sams)):
if sams[i] in missing_sams: continue
rows = sams[i]
#~ row_outname = output_dir + '/ft/' + metric + '_' +\
#~ in_fname
r_out = single_object_beta(otu_table, metric,
tree_string,rowids=None,
full_tree=True)
col_sams, row_sams, row_dmtx = parse_matrix(r_out)
self.assertEqual(row_dmtx.shape, (len(rows.split(',')),
len(sams)))
# make sure rows same as full
for j in range(len(rows.split(','))):
for k in range(len(sams)):
row_v1 = row_dmtx[j,k]
full_v1 =\
dmtx[sams.index(row_sams[j]),
sams.index(col_sams[k])]
self.assertFloatEqual(row_v1, full_v1)
# # do it with full tree
r_out = single_object_beta(otu_table, metric,
tree_string,rowids=None,
full_tree=True)
sams_ft, dmtx_ft = parse_distmat(r_out)
self.assertEqual(sams_ft, sams)
self.assertFloatEqual(dmtx_ft, dmtx)
def single_file_beta(self,
otu_table_string,
tree_string,
missing_sams=None,
use_metric_list=False):
""" running single_file_beta should give same result using --rows"""
if missing_sams is None:
missing_sams = []
# setup
fd, input_path = mkstemp(suffix='.txt')
os.close(fd)
in_fname = os.path.split(input_path)[1]
f = open(input_path, 'w')
f.write(otu_table_string)
f.close()
fd, tree_path = mkstemp(suffix='.tre')
os.close(fd)
f = open(tree_path, 'w')
f.write(tree_string)
f.close()
metrics = list_known_nonphylogenetic_metrics()
metrics.extend(list_known_phylogenetic_metrics())
output_dir = mkdtemp()
# new metrics that don't trivially parallelize must be dealt with
# carefully
warnings.filterwarnings(
'ignore', 'dissimilarity binary_dist_chisq is\
not parallelized, calculating the whole matrix...')
warnings.filterwarnings(
'ignore', 'dissimilarity dist_chisq is not\
parallelized, calculating the whole matrix...')
warnings.filterwarnings(
'ignore', 'dissimilarity dist_gower is not\
parallelized, calculating the whole matrix...')
warnings.filterwarnings(
'ignore', 'dissimilarity dist_hellinger is\
not parallelized, calculating the whole matrix...')
warnings.filterwarnings('ignore', 'unifrac had no information for\
sample M*')
self.files_to_remove.extend([input_path, tree_path])
self.folders_to_remove.append(output_dir)
os.mkdir(output_dir + '/ft/')
for metric in metrics:
# do it
if use_metric_list:
single_file_beta(input_path, [metric],
tree_path,
output_dir,
rowids=None)
else:
single_file_beta(input_path,
metric,
tree_path,
output_dir,
rowids=None)
sams, dmtx = parse_distmat(
open(output_dir + '/' + metric + '_' + in_fname))
# do it by rows
for i in range(len(sams)):
if sams[i] in missing_sams:
continue
rows = sams[i]
row_outname = output_dir + '/' + metric + '_' +\
in_fname
if use_metric_list:
single_file_beta(input_path, [metric],
tree_path,
output_dir,
rowids=rows)
else:
single_file_beta(input_path,
metric,
tree_path,
output_dir,
rowids=rows)
col_sams, row_sams, row_dmtx = parse_matrix(open(row_outname))
self.assertEqual(row_dmtx.shape,
(len(rows.split(',')), len(sams)))
# make sure rows same as full
for j in range(len(rows.split(','))):
for k in range(len(sams)):
row_v1 = row_dmtx[j, k]
full_v1 =\
dmtx[sams.index(row_sams[j]),
sams.index(col_sams[k])]
npt.assert_almost_equal(row_v1, full_v1)
# full tree run:
if 'full_tree' in str(metric).lower():
continue
# do it by rows with full tree
for i in range(len(sams)):
if sams[i] in missing_sams:
continue
rows = sams[i]
row_outname = output_dir + '/ft/' + metric + '_' +\
in_fname
if use_metric_list:
single_file_beta(input_path, [metric],
tree_path,
output_dir + '/ft/',
rowids=rows,
full_tree=True)
else:
single_file_beta(input_path,
metric,
tree_path,
output_dir + '/ft/',
rowids=rows,
full_tree=True)
col_sams, row_sams, row_dmtx = parse_matrix(open(row_outname))
self.assertEqual(row_dmtx.shape,
(len(rows.split(',')), len(sams)))
# make sure rows same as full
for j in range(len(rows.split(','))):
for k in range(len(sams)):
row_v1 = row_dmtx[j, k]
full_v1 =\
dmtx[sams.index(row_sams[j]),
sams.index(col_sams[k])]
npt.assert_almost_equal(row_v1, full_v1)
# do it with full tree
if use_metric_list:
single_file_beta(input_path, [metric],
tree_path,
output_dir + '/ft/',
rowids=None,
full_tree=True)
else:
single_file_beta(input_path,
metric,
tree_path,
output_dir + '/ft/',
rowids=None,
full_tree=True)
sams_ft, dmtx_ft = parse_distmat(
open(output_dir + '/ft/' + metric + '_' + in_fname))
self.assertEqual(sams_ft, sams)
npt.assert_almost_equal(dmtx_ft, dmtx)
def single_file_beta(
self, otu_table_string, tree_string, missing_sams=None,
use_metric_list=False):
""" running single_file_beta should give same result using --rows"""
if missing_sams is None:
missing_sams = []
# setup
fd, input_path = mkstemp(suffix='.txt')
close(fd)
in_fname = os.path.split(input_path)[1]
f = open(input_path, 'w')
f.write(otu_table_string)
f.close()
fd, tree_path = mkstemp(suffix='.tre')
close(fd)
f = open(tree_path, 'w')
f.write(tree_string)
f.close()
metrics = list_known_nonphylogenetic_metrics()
metrics.extend(list_known_phylogenetic_metrics())
output_dir = mkdtemp()
# new metrics that don't trivially parallelize must be dealt with
# carefully
warnings.filterwarnings('ignore', 'dissimilarity binary_dist_chisq is\
not parallelized, calculating the whole matrix...')
warnings.filterwarnings('ignore', 'dissimilarity dist_chisq is not\
parallelized, calculating the whole matrix...')
warnings.filterwarnings('ignore', 'dissimilarity dist_gower is not\
parallelized, calculating the whole matrix...')
warnings.filterwarnings('ignore', 'dissimilarity dist_hellinger is\
not parallelized, calculating the whole matrix...')
warnings.filterwarnings('ignore', 'unifrac had no information for\
sample M*')
self.files_to_remove.extend([input_path, tree_path])
self.folders_to_remove.append(output_dir)
os.mkdir(output_dir + '/ft/')
for metric in metrics:
# do it
if use_metric_list:
single_file_beta(input_path, [metric], tree_path, output_dir,
rowids=None)
else:
single_file_beta(input_path, metric, tree_path, output_dir,
rowids=None)
sams, dmtx = parse_distmat(open(output_dir + '/' +
metric + '_' + in_fname))
# do it by rows
for i in range(len(sams)):
if sams[i] in missing_sams:
continue
rows = sams[i]
row_outname = output_dir + '/' + metric + '_' +\
in_fname
if use_metric_list:
single_file_beta(input_path, [metric], tree_path,
output_dir, rowids=rows)
else:
single_file_beta(input_path, metric, tree_path, output_dir,
rowids=rows)
col_sams, row_sams, row_dmtx = parse_matrix(open(row_outname))
self.assertEqual(row_dmtx.shape, (len(rows.split(',')),
len(sams)))
# make sure rows same as full
for j in range(len(rows.split(','))):
for k in range(len(sams)):
row_v1 = row_dmtx[j, k]
full_v1 =\
dmtx[sams.index(row_sams[j]),
sams.index(col_sams[k])]
assert_almost_equal(row_v1, full_v1)
# full tree run:
if 'full_tree' in str(metric).lower():
continue
# do it by rows with full tree
for i in range(len(sams)):
if sams[i] in missing_sams:
continue
rows = sams[i]
row_outname = output_dir + '/ft/' + metric + '_' +\
in_fname
if use_metric_list:
single_file_beta(input_path, [metric], tree_path,
output_dir + '/ft/', rowids=rows, full_tree=True)
else:
single_file_beta(input_path, metric, tree_path,
output_dir + '/ft/', rowids=rows, full_tree=True)
col_sams, row_sams, row_dmtx = parse_matrix(open(row_outname))
self.assertEqual(row_dmtx.shape, (len(rows.split(',')),
len(sams)))
# make sure rows same as full
for j in range(len(rows.split(','))):
for k in range(len(sams)):
row_v1 = row_dmtx[j, k]
full_v1 =\
dmtx[sams.index(row_sams[j]),
sams.index(col_sams[k])]
assert_almost_equal(row_v1, full_v1)
# do it with full tree
if use_metric_list:
single_file_beta(input_path, [metric], tree_path,
output_dir + '/ft/', rowids=None, full_tree=True)
else:
single_file_beta(input_path, metric, tree_path,
output_dir + '/ft/', rowids=None, full_tree=True)
sams_ft, dmtx_ft = parse_distmat(open(output_dir + '/ft/' +
metric + '_' + in_fname))
self.assertEqual(sams_ft, sams)
assert_almost_equal(dmtx_ft, dmtx)
