def result(data, taxon_names, tree, sample_names, one_sample_name,
**kwargs):
""" wraps the fast_unifrac fn to return just a matrix, in correct order
sample_names: list of unique strings
"""
envs = make_envs_dict(data, sample_names, taxon_names)
try:
unifrac_res = fast_unifrac_one_sample(one_sample_name,
tree,
envs,
weighted=weighted,
metric=metric,
**kwargs)
except ValueError as e:
if 'one_sample_name not found' in str(e):
warnings.warn('unifrac had no information on sample ' +\
one_sample_name +\
". Distances involving that sample aren't meaningful")
unifrac_res = (numpy.array([0.0]), [one_sample_name]
) # self only
else:
raise e
dist_mtx = _reorder_unifrac_res_one_sample(unifrac_res, sample_names)
return dist_mtx
def result(data, taxon_names, tree, sample_names, one_sample_name,**kwargs):
""" wraps the fast_unifrac fn to return just a matrix, in correct order
sample_names: list of unique strings
"""
envs = make_envs_dict(data, sample_names, taxon_names)
unifrac_res = fast_unifrac_one_sample(one_sample_name,
tree, envs, weighted=weighted, metric=metric,**kwargs)
dist_mtx = _reorder_unifrac_res_one_sample(unifrac_res,
sample_names)
return dist_mtx
def test_fast_unifrac_one_sample2(self):
"""fu one sam should match whole weighted unifrac result, for env 'B'"""
# first get full unifrac matrix
res = fast_unifrac(self.t, self.env_counts, weighted=True)
dmtx, env_order = res['distance_matrix']
dmtx_vec = dmtx[env_order.index('B')]
dmtx_vec = dmtx_vec[argsort(env_order)]
# then get one sample unifrac vector
one_sam_dvec, one_sam_env_order = \
fast_unifrac_one_sample('B', self.t, self.env_counts,weighted=True)
one_sam_dvec = one_sam_dvec[argsort(one_sam_env_order)]
self.assertFloatEqual(one_sam_dvec, dmtx_vec)
def test_fast_unifrac_one_sample2(self):
"""fu one sam should match whole weighted unifrac result, for env 'B'"""
# first get full unifrac matrix
res = fast_unifrac(self.t, self.env_counts, weighted=True)
dmtx, env_order = res['distance_matrix']
dmtx_vec = dmtx[env_order.index('B')]
dmtx_vec = dmtx_vec[argsort(env_order)]
# then get one sample unifrac vector
one_sam_dvec, one_sam_env_order = \
fast_unifrac_one_sample('B', self.t, self.env_counts,weighted=True)
one_sam_dvec = one_sam_dvec[argsort(one_sam_env_order)]
self.assertFloatEqual(one_sam_dvec, dmtx_vec)
def result(data, taxon_names, tree, sample_names, one_sample_name):
""" wraps the fast_unifrac fn to return just a matrix, in correct order
sample_names: list of unique strings
"""
envs = make_envs_dict(data, sample_names, taxon_names)
unifrac_res = fast_unifrac_one_sample(one_sample_name,
tree,
envs,
weighted=weighted,
metric=metric)
dist_mtx = _reorder_unifrac_res_one_sample(unifrac_res, sample_names)
return dist_mtx
def test_fast_unifrac_one_sample3(self):
"""fu one sam should match missing env unifrac result, for env 'B'"""
# first get full unifrac matrix
res = fast_unifrac(self.t, self.missing_env_counts, weighted=False)
dmtx, env_order = res['distance_matrix']
dmtx_vec = dmtx[env_order.index('C')]
dmtx_vec = dmtx_vec[argsort(env_order)]
# then get one sample unifrac vector
one_sam_dvec, one_sam_env_order = \
fast_unifrac_one_sample('C', self.t,
self.missing_env_counts,weighted=False)
one_sam_dvec = one_sam_dvec[argsort(one_sam_env_order)]
self.assertFloatEqual(one_sam_dvec, dmtx_vec)
# and should raise valueerror when 'B'
self.assertRaises(ValueError, fast_unifrac_one_sample, 'B', self.t,
self.missing_env_counts,weighted=False)
def result(data, taxon_names, tree, sample_names, one_sample_name,**kwargs):
""" wraps the fast_unifrac fn to return just a matrix, in correct order
sample_names: list of unique strings
"""
envs = make_envs_dict(data, sample_names, taxon_names)
try:
unifrac_res = fast_unifrac_one_sample(one_sample_name,
tree, envs, weighted=weighted, metric=metric,**kwargs)
except ValueError as e:
if 'one_sample_name not found' in str(e):
warnings.warn('unifrac had no information on sample ' +\
one_sample_name +\
". Distances involving that sample aren't meaningful")
unifrac_res = (numpy.array([0.0]),[one_sample_name]) # self only
else:
raise e
dist_mtx = _reorder_unifrac_res_one_sample(unifrac_res,
sample_names)
return dist_mtx
def test_fast_unifrac_one_sample3(self):
"""fu one sam should match missing env unifrac result, for env 'B'"""
# first get full unifrac matrix
res = fast_unifrac(self.t, self.missing_env_counts, weighted=False)
dmtx, env_order = res['distance_matrix']
dmtx_vec = dmtx[env_order.index('C')]
dmtx_vec = dmtx_vec[argsort(env_order)]
# then get one sample unifrac vector
one_sam_dvec, one_sam_env_order = \
fast_unifrac_one_sample('C', self.t,
self.missing_env_counts,weighted=False)
one_sam_dvec = one_sam_dvec[argsort(one_sam_env_order)]
self.assertFloatEqual(one_sam_dvec, dmtx_vec)
# and should raise valueerror when 'B'
self.assertRaises(ValueError,
fast_unifrac_one_sample,
'B',
self.t,
self.missing_env_counts,
weighted=False)
