def construct_bestfit_err_mat(self,
full_mat,
bestfit_full_mat,
paths,
plot=False):
r"""
Construct the average best fit matrix using the sum of the files, and
generate the corresponding labels using the paths provided.
(Helper function)
"""
full_mat_var = np.delete(full_mat, np.s_[5::4], axis=1)
bestfit_full_mat_var = np.delete(bestfit_full_mat, np.s_[5::4], axis=1)
full_mat_var_cov = np.cov(full_mat_var.T)
full_mat_diag = np.diag(full_mat_var_cov)
err = np.sqrt(full_mat_diag)
label = larch_score.generate_labels(paths)[0]
# bestFit = np.mean(bestfit_full_mat_var,axis=0)
bestFit = np.mean(full_mat_var, axis=0)
self.label = label
self.full_mat_diag = full_mat_diag
if plot:
plt.figure(figsize=(8, 5))
plt.xticks(np.arange(len(full_mat_diag)), label, rotation=70)
plt.bar(np.arange(len(full_mat_diag)), np.sqrt(full_mat_diag))
return bestFit, err
def test_generate_label_1(self):
"""
Test Labels with one label
"""
data = [1]
result_label = ['s02_1', 'e0', 'sigma_1', 'deltaR_1']
result_s02_label = []
result = larch_score.generate_labels(data)
self.assertEqual(result[0], result_label)
self.assertEqual(result[1], ['s02_1'])
self.assertEqual(result[2], ['sigma_1'])
self.assertEqual(result[3], ['deltaR_1'])
def test_generate_label_2(self):
"""
Test labels with multiple paths
"""
data = [1, 2]
result_label = [
's02_1', 'e0', 'sigma_1', 'deltaR_1', 's02_2', 'sigma_2',
'deltaR_2'
]
result_s02_label = []
result = larch_score.generate_labels(data)
self.assertEqual(result[0], result_label)
self.assertEqual(result[1], ['s02_1', 's02_2'])
self.assertEqual(result[2], ['sigma_1', 'sigma_2'])
self.assertEqual(result[3], ['deltaR_1', 'deltaR_2'])
def __init__(self, paths, dirs, params):
self.paths = paths
# Generate Label for plotting
self.label = larch_score.generate_labels(self.paths)[0]
self.params = params
self.dirs = dirs
self.num_paths = len(paths)
self.num_params = 3 * self.num_paths + 1
self.return_str = ""
if 'optimize' not in params:
self.params['optimize'] = False
if 'series' not in params:
self.series = False
else:
self.series = self.params['series']
self.series_index = self.params['series_index']