def plot_svd(h5_main, savefig=False, num_plots=16, **kwargs):
'''
Replots the SVD showing the skree, abundance maps, and eigenvectors.
If h5_main is a Dataset, it will default to the most recent SVD group from that
Dataset.
If h5_main is the results group, then it will plot the values for that group.
:param h5_main:
:type h5_main: USIDataset or h5py Dataset or h5py Group
:param savefig: Saves the figures to disk with some default names
:type savefig: bool, optional
:param num_plots: Default number of eigenvectors and abundance plots to show
:type num_plots: int
:param kwargs: keyword arguments for svd filtering
:type kwarrgs: dict, optional
'''
if isinstance(h5_main, h5py.Group):
_U = find_dataset(h5_main, 'U')[-1]
_V = find_dataset(h5_main, 'V')[-1]
units = 'arbitrary (a.u.)'
h5_spec_vals = np.arange(_V.shape[1])
h5_svd_group = _U.parent
else:
h5_svd_group = find_results_groups(h5_main, 'SVD')[-1]
units = h5_main.attrs['quantity']
h5_spec_vals = h5_main.get_spec_values('Time')
h5_U = h5_svd_group['U']
h5_V = h5_svd_group['V']
h5_S = h5_svd_group['S']
_U = USIDataset(h5_U)
[num_rows, num_cols] = _U.pos_dim_sizes
abun_maps = np.reshape(h5_U[:, :16], (num_rows, num_cols, -1))
eigen_vecs = h5_V[:16, :]
skree_sum = np.zeros(h5_S.shape)
for i in range(h5_S.shape[0]):
skree_sum[i] = np.sum(h5_S[:i]) / np.sum(h5_S)
plt.figure()
plt.plot(skree_sum, 'bo')
plt.title('Cumulative Variance')
plt.xlabel('Total Components')
plt.ylabel('Total variance ratio (a.u.)')
if savefig:
plt.savefig('Cumulative_variance_plot.png')
fig_skree, axes = plot_utils.plot_scree(h5_S, title='Scree plot')
fig_skree.tight_layout()
if savefig:
plt.savefig('Scree_plot.png')
fig_abun, axes = plot_utils.plot_map_stack(abun_maps,
num_comps=num_plots,
title='SVD Abundance Maps',
color_bar_mode='single',
cmap='inferno',
reverse_dims=True,
fig_mult=(3.5, 3.5),
facecolor='white',
**kwargs)
fig_abun.tight_layout()
if savefig:
plt.savefig('Abundance_maps.png')
fig_eigvec, axes = plot_utils.plot_curves(h5_spec_vals * 1e3,
eigen_vecs,
use_rainbow_plots=False,
x_label='Time (ms)',
y_label=units,
num_plots=num_plots,
subtitle_prefix='Component',
title='SVD Eigenvectors',
evenly_spaced=False,
**kwargs)
fig_eigvec.tight_layout()
if savefig:
plt.savefig('Eigenvectors.png')
return
def test_title_wrong(self):
scree = np.exp(-1 * np.arange(100))
with self.assertRaises(TypeError):
plot_utils.plot_scree(scree, title=1)
def test_scree_wrong(self):
scree = 'string'
with self.assertRaises(TypeError):
plot_utils.plot_scree(scree)
def test_scree_h5py_dataset(self):
h5_f = h5py.File('test.h5', 'a')
scree = h5_f.create_dataset("test", data=np.arange(1, 25))
plot_utils.plot_scree(scree)