def test_run_trajectory_analysis_wdiff(self):
"""Correctly computes the window difference method"""
obs = run_trajectory_analysis(self.ord_res, self.metadata_map,
trajectory_categories=self.categories,
sort_category=self.sort_by,
algorithm='wdiff', window_size=3)
exp_control_group = GroupResults('Control', np.array([-2.5790341819,
-2.0166764661,
-3.0360721941,
0.]),
-1.9079457105,
{'mean': -1.9079457105,
'std': 1.1592139913}, None)
exp_fast_group = GroupResults('Fast', np.array([11.2291654905,
3.9163741156,
4.4943507388]),
6.5466301150,
{'mean': 6.5466301150,
'std': 3.3194494926},
"Cannot calculate the first difference "
"with a window of size (3).")
exp_treatment = CategoryResults('Treatment', 0.0103976830,
[exp_control_group, exp_fast_group],
None)
exp = GradientANOVAResults('wdiff', False, [exp_treatment])
self.assert_gradientANOVA_results_almost_equal(obs, exp)
def test_run_trajectory_analysis_trajectory(self):
"""Correctly computes the trajectory method"""
obs = run_trajectory_analysis(self.ord_res,
self.metadata_map,
trajectory_categories=self.categories,
sort_category=self.sort_by,
algorithm='trajectory')
exp_control_group = GroupResults(
'Control',
np.array([8.6681963576, 7.0962717982, 7.1036434615, 4.0675712674]),
6.73392072123, {'2-norm': 13.874494152}, None)
exp_fast_group = GroupResults(
'Fast', np.array([11.2291654905, 3.9163741156, 4.4943507388]),
6.5466301150, {'2-norm': 12.713431181}, None)
exp_treatment = CategoryResults('Treatment', 0.9374500147,
[exp_control_group, exp_fast_group],
None)
exp = GradientANOVAResults('trajectory', False, [exp_treatment])
self.assert_gradientANOVA_results_almost_equal(obs, exp)
def test_run_trajectory_analysis_diff(self):
"""Correctly computes the first difference method"""
obs = run_trajectory_analysis(self.ord_res, self.metadata_map,
trajectory_categories=self.categories,
sort_category=self.sort_by,
algorithm='diff')
exp_control_group = GroupResults('Control', np.array([-1.5719245594,
0.0073716633,
-3.0360721941]),
-1.5335416967,
{'mean': -1.5335416967,
'std': 1.2427771485}, None)
exp_fast_group = GroupResults('Fast', np.array([-7.3127913749,
0.5779766231]),
-3.3674073758,
{'mean': -3.3674073758,
'std': 3.9453839990}, None)
exp_treatment = CategoryResults('Treatment', 0.6015260608,
[exp_control_group, exp_fast_group],
None)
exp = GradientANOVAResults('diff', False, [exp_treatment])
self.assert_gradientANOVA_results_almost_equal(obs, exp)
def test_run_trajectory_analysis_trajectory(self):
"""Correctly computes the trajectory method"""
obs = run_trajectory_analysis(self.ord_res, self.metadata_map,
trajectory_categories=self.categories,
sort_category=self.sort_by,
algorithm='trajectory')
exp_control_group = GroupResults('Control', np.array([8.6681963576,
7.0962717982,
7.1036434615,
4.0675712674]),
6.73392072123,
{'2-norm': 13.874494152}, None)
exp_fast_group = GroupResults('Fast', np.array([11.2291654905,
3.9163741156,
4.4943507388]),
6.5466301150,
{'2-norm': 12.713431181}, None)
exp_treatment = CategoryResults('Treatment', 0.9374500147,
[exp_control_group, exp_fast_group],
None)
exp = GradientANOVAResults('trajectory', False, [exp_treatment])
self.assert_gradientANOVA_results_almost_equal(obs, exp)
def test_run_trajectory_analysis_avg(self):
"""Correctly computes the avg method"""
obs = run_trajectory_analysis(self.ord_res,
self.metadata_map,
trajectory_categories=self.categories)
exp_control_group = GroupResults(
'Control',
np.array([
2.3694943596755276, 3.3716388181385781, 5.4452089176253367,
4.5704258453173559, 4.4972603724478377
]), 4.05080566264, {'avg': 4.0508056626409275}, None)
exp_fast_group = GroupResults(
'Fast',
np.array([
7.2220488239279126, 4.2726021564374372, 1.1169097274372082,
4.02717600030876
]), 4.15968417703, {'avg': 4.1596841770278292}, None)
exp_treatment = CategoryResults('Treatment', 0.93311555,
[exp_control_group, exp_fast_group],
None)
exp = GradientANOVAResults('avg', False, [exp_treatment])
self.assert_gradientANOVA_results_almost_equal(obs, exp)
def test_run_trajectory_analysis_diff(self):
"""Correctly computes the first difference method"""
obs = run_trajectory_analysis(self.ord_res, self.metadata_map,
trajectory_categories=self.categories,
sort_category=self.sort_by,
algorithm='diff')
exp_control_group = GroupResults('Control', np.array([-1.5719245594,
0.0073716633,
-3.0360721941]),
-1.5335416967,
{'mean': -1.5335416967,
'std': 1.2427771485}, None)
exp_fast_group = GroupResults('Fast', np.array([-7.3127913749,
0.5779766231]),
-3.3674073758,
{'mean': -3.3674073758,
'std': 3.9453839990}, None)
exp_treatment = CategoryResults('Treatment', 0.6015260608,
[exp_control_group, exp_fast_group],
None)
exp = GradientANOVAResults('diff', False, [exp_treatment])
self.assert_gradientANOVA_results_almost_equal(obs, exp)
def test_run_trajectory_analysis_avg(self):
"""Correctly computes the avg method"""
obs = run_trajectory_analysis(self.ord_res, self.metadata_map,
trajectory_categories=self.categories)
exp_control_group = GroupResults('Control',
np.array([2.3694943596755276,
3.3716388181385781,
5.4452089176253367,
4.5704258453173559,
4.4972603724478377]),
4.05080566264,
{'avg': 4.0508056626409275}, None)
exp_fast_group = GroupResults('Fast', np.array([7.2220488239279126,
4.2726021564374372,
1.1169097274372082,
4.02717600030876]),
4.15968417703,
{'avg': 4.1596841770278292}, None)
exp_treatment = CategoryResults('Treatment', 0.93311555,
[exp_control_group, exp_fast_group],
None)
exp = GradientANOVAResults('avg', False, [exp_treatment])
self.assert_gradientANOVA_results_almost_equal(obs, exp)
if weighted:
if sort_by == 'SampleID':
option_parser.error("The weighting_vector can't be the SampleID, "
"please specify a numeric column in the "
"--sort_by option.")
elif not sort_category:
option_parser.error("To weight the output, provide a metadata "
"category using the --sort_by option")
if algorithm == 'wdiff':
if not window_size:
option_parser.error("You should provide --window_size when using "
"the wdiff algorithm")
if window_size < 1:
option_parser.error("--window_size should be a positive integer, "
"%d found" % window_size)
res = run_trajectory_analysis(ord_res, metamap, categories,
sort_category, algorithm, axes,
weighted, window_size)
if not exists(output_dir):
mkdir(output_dir)
trajectories_fp = join(output_dir, 'trajectories.txt')
trajectories_raw_fp = join(output_dir, 'trajectories_raw_values.txt')
with open(trajectories_fp, 'w') as out_f:
with open(trajectories_raw_fp, 'w') as raw_f:
res.to_files(out_f, raw_f)
def test_run_trajectory_analysis_error(self):
"""Raises an error if the algorithm is not recognized"""
with self.assertRaises(ValueError):
run_trajectory_analysis(self.ord_res, self.metadata_map,
algorithm='foo')
if weighted:
if sort_by == 'SampleID':
option_parser.error("The weighting_vector can't be the SampleID, "
"please specify a numeric column in the "
"--sort_by option.")
elif not sort_category:
option_parser.error("To weight the output, provide a metadata "
"category using the --sort_by option")
if algorithm == 'wdiff':
if not window_size:
option_parser.error("You should provide --window_size when using "
"the wdiff algorithm")
if window_size < 1:
option_parser.error("--window_size should be a positive integer, "
"%d found" % window_size)
res = run_trajectory_analysis(ord_res, metamap, categories, sort_category,
algorithm, axes, weighted, window_size)
if not exists(output_dir):
mkdir(output_dir)
trajectories_fp = join(output_dir, 'trajectories.txt')
trajectories_raw_fp = join(output_dir, 'trajectories_raw_values.txt')
with open(trajectories_fp, 'w') as out_f:
with open(trajectories_raw_fp, 'w') as raw_f:
res.to_files(out_f, raw_f)
def test_run_trajectory_analysis_error(self):
"""Raises an error if the algorithm is not recognized"""
with self.assertRaises(ValueError):
run_trajectory_analysis(self.ord_res,
self.metadata_map,
algorithm='foo')
