def plot_cluster_h5_group(h5_group, labels_kwargs=None, centroids_kwargs=None):
"""
Plots the cluster labels and mean response for each cluster
Parameters
----------
h5_group : h5py.Datagroup object
H5 group containing the labels and mean response
labels_kwargs : dict, optional
keyword arguments for the labels plot. NOT enabled yet.
centroids_kwargs : dict, optional
keyword arguments for the centroids plot. NOT enabled yet.
Returns
-------
fig_labels : figure handle
Figure containing the labels
fig_centroids : figure handle
Figure containing the centroids
"""
if not isinstance(h5_group, h5py.Group):
raise TypeError('h5_group should be a h5py.Group')
h5_labels = USIDataset(h5_group['Labels'])
h5_centroids = USIDataset(h5_group['Mean_Response'])
labels_mat = np.squeeze(h5_labels.get_n_dim_form())
if labels_mat.ndim > 3:
print('Unable to visualize 4 or more dimensional labels!')
if labels_mat.ndim == 1:
fig_labs, axis_labs = plt.subplots(figsize=(5.5, 5))
axis_labs.plot(h5_labels.get_pos_values(h5_labels.pos_dim_labels[0]), labels_mat)
axis_labs.set_xlabel(h5_labels.pos_dim_descriptors[0])
axis_labs.set_ylabel('Cluster index')
axis_labs.set_title(get_attr(h5_group, 'cluster_algorithm') + ' Labels')
elif labels_mat.ndim == 2:
fig_labs, axis_labs = plot_cluster_labels(labels_mat, num_clusters=h5_centroids.shape[0],
x_label=h5_labels.pos_dim_descriptors[0],
y_label=h5_labels.pos_dim_descriptors[1],
x_vec=h5_labels.get_pos_values(h5_labels.pos_dim_labels[0]),
y_vec=h5_labels.get_pos_values(h5_labels.pos_dim_labels[1]),
title=get_attr(h5_group, 'cluster_algorithm') + ' Labels')
# TODO: probably not a great idea to load the entire dataset to memory
centroids_mat = h5_centroids.get_n_dim_form()
if len(h5_centroids.spec_dim_labels) == 1:
legend_mode = 2
if h5_centroids.shape[0] < 6:
legend_mode = 1
fig_cent, axis_cent = plot_cluster_centroids(centroids_mat,
h5_centroids.get_spec_values(h5_centroids.spec_dim_labels[0]),
legend_mode=legend_mode,
x_label=h5_centroids.spec_dim_descriptors[0],
y_label=h5_centroids.data_descriptor,
overlayed=h5_centroids.shape[0] < 6,
title=get_attr(h5_group,
'cluster_algorithm') + ' Centroid',
amp_units=get_attr(h5_centroids, 'units'))
elif len(h5_centroids.spec_dim_labels) == 2:
# stack of spectrograms
if h5_centroids.dtype in [np.complex64, np.complex128, np.complex]:
fig_cent, axis_cent = plot_complex_spectra(centroids_mat, subtitle_prefix='Cluster',
title=get_attr(h5_group, 'cluster_algorithm') + ' Centroid',
x_label=h5_centroids.spec_dim_descriptors[0],
y_label=h5_centroids.spec_dim_descriptors[1],
amp_units=get_attr(h5_centroids, 'units'))
else:
fig_cent, axis_cent = plot_map_stack(centroids_mat, color_bar_mode='each', evenly_spaced=True,
title='Cluster',
heading=get_attr(h5_group,
'cluster_algorithm') + ' Centroid')
return fig_labs, fig_cent
def plot_cluster_h5_group(h5_group, labels_kwargs=None, centroids_kwargs=None):
"""
Plots the cluster labels and mean response for each cluster
Parameters
----------
h5_group : h5py.Datagroup object
H5 group containing the labels and mean response
labels_kwargs : dict, optional
keyword arguments for the labels plot. NOT enabled yet.
centroids_kwargs : dict, optional
keyword arguments for the centroids plot. NOT enabled yet.
Returns
-------
fig_labels : figure handle
Figure containing the labels
fig_centroids : figure handle
Figure containing the centroids
"""
if not isinstance(h5_group, h5py.Group):
raise TypeError('h5_group should be a h5py.Group')
h5_labels = USIDataset(h5_group['Labels'])
h5_centroids = USIDataset(h5_group['Mean_Response'])
labels_mat = np.squeeze(h5_labels.get_n_dim_form())
if labels_mat.ndim > 3:
print('Unable to visualize 4 or more dimensional labels!')
if labels_mat.ndim == 1:
fig_labs, axis_labs = plt.subplots(figsize=(5.5, 5))
axis_labs.plot(h5_labels.get_pos_values(h5_labels.pos_dim_labels[0]),
labels_mat)
axis_labs.set_xlabel(h5_labels.pos_dim_descriptors[0])
axis_labs.set_ylabel('Cluster index')
axis_labs.set_title(
get_attr(h5_group, 'cluster_algorithm') + ' Labels')
elif labels_mat.ndim == 2:
fig_labs, axis_labs = plot_cluster_labels(
labels_mat,
num_clusters=h5_centroids.shape[0],
x_label=h5_labels.pos_dim_descriptors[0],
y_label=h5_labels.pos_dim_descriptors[1],
x_vec=h5_labels.get_pos_values(h5_labels.pos_dim_labels[0]),
y_vec=h5_labels.get_pos_values(h5_labels.pos_dim_labels[1]),
title=get_attr(h5_group, 'cluster_algorithm') + ' Labels')
# TODO: probably not a great idea to load the entire dataset to memory
centroids_mat = h5_centroids.get_n_dim_form()
if len(h5_centroids.spec_dim_labels) == 1:
legend_mode = 2
if h5_centroids.shape[0] < 6:
legend_mode = 1
fig_cent, axis_cent = plot_cluster_centroids(
centroids_mat,
h5_centroids.get_spec_values(h5_centroids.spec_dim_labels[0]),
legend_mode=legend_mode,
x_label=h5_centroids.spec_dim_descriptors[0],
y_label=h5_centroids.data_descriptor,
overlayed=h5_centroids.shape[0] < 6,
title=get_attr(h5_group, 'cluster_algorithm') + ' Centroid',
amp_units=get_attr(h5_centroids, 'units'))
elif len(h5_centroids.spec_dim_labels) == 2:
# stack of spectrograms
if h5_centroids.dtype in [np.complex64, np.complex128, np.complex]:
fig_cent, axis_cent = plot_complex_spectra(
centroids_mat,
subtitle_prefix='Cluster',
title=get_attr(h5_group, 'cluster_algorithm') + ' Centroid',
x_label=h5_centroids.spec_dim_descriptors[0],
y_label=h5_centroids.spec_dim_descriptors[1],
amp_units=get_attr(h5_centroids, 'units'))
else:
fig_cent, axis_cent = plot_map_stack(
centroids_mat,
color_bar_mode='each',
evenly_spaced=True,
title='Cluster',
heading=get_attr(h5_group, 'cluster_algorithm') + ' Centroid')
return fig_labs, fig_cent
def plot_cluster_centroids(centroids, x_vec, legend_mode=1, x_label=None, y_label=None, title=None, axis=None,
overlayed=True, amp_units=None, **kwargs):
"""
Parameters
----------
centroids : numpy.ndarray
2D array. Centroids of clusters
x_vec : numpy.ndarray
1D array. Vector against which the curves are plotted
legend_mode : int, optional. default = 1
Appearance of legend:
0 - inside the plot
1 - outside the plot on the right
else - colorbar instead of legend
x_label : str, optional, default = None
Label for x axis
y_label : str, optional, default = None
Label for y axis
title : str, optional, default = None
Title for the plot
axis : matplotlib.axes.Axes object, optional.
Axis to plot this image onto. Will create a new figure by default or will use this axis object to plot into
overlayed : bool, optional
If True - all curves will be plotted overlayed on a single plot. Else, curves will be plotted separately
amp_units : str, optional
Units for amplitude
kwargs : dict
will be passed on to plot_line_family(), plot_complex_spectra, plot_curves
Returns
-------
fig, axes
"""
if isinstance(centroids, (list, tuple)):
centroids = np.array(centroids)
if not isinstance(centroids, np.ndarray):
raise TypeError('centroids should be a numpy array')
if centroids.ndim != 2:
raise ValueError('centroids should be a 2D numpy array - i.e. - 1D spectra')
if not isinstance(x_vec, (list, tuple)):
x_vec = np.array(x_vec)
if not isinstance(x_vec, np.ndarray):
raise TypeError('x_vec should be a array-like')
if x_vec.ndim != 1:
raise ValueError('x_vec should be a 1D array')
if not isinstance(legend_mode, int):
raise TypeError('legend_mode should be an integer')
if axis is not None:
if not isinstance(axis, mpl.axes.Axes):
raise TypeError('axis must be a matplotlib.axes.Axes object')
if not isinstance(overlayed, bool):
raise TypeError('overlayed should be a boolean value')
if amp_units is not None:
if not (isinstance(amp_units, (str, unicode)) or
(isinstance(amp_units, np.ndarray) and amp_units.dtype.type == np.str_)):
raise TypeError('amp_units should be a str')
else:
amp_units = 'a.u.'
cmap = kwargs.get('cmap', default_cmap)
num_clusters = centroids.shape[0]
def __overlay_curves(axis, curve_stack):
plot_line_family(axis, x_vec, curve_stack, label_prefix='Cluster', cmap=cmap)
if legend_mode == 0:
axis.legend(loc='best', fontsize=14)
elif legend_mode == 1:
axis.legend(loc='upper left', bbox_to_anchor=(1, 1), fontsize=14)
else:
sm = make_scalar_mappable(0, num_clusters - 1, cmap=discrete_cmap(num_clusters, cmap))
plt.colorbar(sm)
if overlayed:
if centroids.dtype in [np.complex64, np.complex128, np.complex]:
fig, axes = plt.subplots(nrows=2, figsize=kwargs.pop('figsize', (5.5, 2 * 5)))
for axis, func in zip(axes.flat, [np.abs, np.angle]):
__overlay_curves(axis, func(centroids))
for var, var_name, func in zip([y_label, y_label, x_label], ['y_label', 'y_label', 'x_label'],
[axes[1].set_ylabel, axes[1].set_xlabel]):
if var is not None:
if not isinstance(var, (str, unicode)):
raise TypeError(var_name + ' should be a string')
func(var)
if title is not None:
if not isinstance(title, (str, unicode)):
raise TypeError('title should be a string')
for axis, comp_name, units in zip(axes.flat, ['Amplitude', 'Phase'], [amp_units, 'rad']):
axis.set_title('{} - {} ({})'.format(title, comp_name, units))
else:
if axis is None:
fig, axis = plt.subplots(figsize=kwargs.pop('figsize', (5.5, 5)))
else:
fig = None
__overlay_curves(axis, centroids)
for var, var_name, func in zip([title, x_label, y_label], ['title', 'x_label', 'y_label'],
[axis.set_title, axis.set_xlabel, axis.set_ylabel]):
if var is not None:
if not isinstance(var, (str, unicode)):
raise TypeError(var_name + ' should be a string')
func(var)
if fig is not None:
fig.tight_layout()
return fig, axis
else:
if centroids.dtype in [np.complex64, np.complex128, np.complex]:
return plot_complex_spectra(centroids, x_vec=x_vec, title=title, x_label=x_label, y_label=y_label,
subtitle_prefix='Cluster', amp_units=amp_units, **kwargs)
else:
return plot_curves(x_vec, centroids, x_label=x_label, y_label=y_label, title=title,
subtitle_prefix='Cluster ', **kwargs)
def plot_cluster_centroids(centroids,
x_vec,
legend_mode=1,
x_label=None,
y_label=None,
title=None,
axis=None,
overlayed=True,
amp_units=None,
**kwargs):
"""
Parameters
----------
centroids : numpy.ndarray
2D array. Centroids of clusters
x_vec : numpy.ndarray
1D array. Vector against which the curves are plotted
legend_mode : int, optional. default = 1
Appearance of legend:
0 - inside the plot
1 - outside the plot on the right
else - colorbar instead of legend
x_label : str, optional, default = None
Label for x axis
y_label : str, optional, default = None
Label for y axis
title : str, optional, default = None
Title for the plot
axis : matplotlib.axes.Axes object, optional.
Axis to plot this image onto. Will create a new figure by default or will use this axis object to plot into
overlayed : bool, optional
If True - all curves will be plotted overlayed on a single plot. Else, curves will be plotted separately
amp_units : str, optional
Units for amplitude
kwargs : dict
will be passed on to plot_line_family(), plot_complex_spectra, plot_curves
Returns
-------
fig, axes
"""
if isinstance(centroids, (list, tuple)):
centroids = np.array(centroids)
if not isinstance(centroids, np.ndarray):
raise TypeError('centroids should be a numpy array')
if centroids.ndim != 2:
raise ValueError(
'centroids should be a 2D numpy array - i.e. - 1D spectra')
if not isinstance(x_vec, (list, tuple)):
x_vec = np.array(x_vec)
if not isinstance(x_vec, np.ndarray):
raise TypeError('x_vec should be a array-like')
if x_vec.ndim != 1:
raise ValueError('x_vec should be a 1D array')
if not isinstance(legend_mode, int):
raise TypeError('legend_mode should be an integer')
if axis is not None:
if not isinstance(axis, mpl.axes.Axes):
raise TypeError('axis must be a matplotlib.axes.Axes object')
if not isinstance(overlayed, bool):
raise TypeError('overlayed should be a boolean value')
if amp_units is not None:
if not (isinstance(amp_units, (str, unicode)) or
(isinstance(amp_units, np.ndarray)
and amp_units.dtype.type == np.str_)):
raise TypeError('amp_units should be a str')
else:
amp_units = 'a.u.'
cmap = kwargs.get('cmap', default_cmap)
num_clusters = centroids.shape[0]
def __overlay_curves(axis, curve_stack):
plot_line_family(axis,
x_vec,
curve_stack,
label_prefix='Cluster',
cmap=cmap)
if legend_mode == 0:
axis.legend(loc='best', fontsize=14)
elif legend_mode == 1:
axis.legend(loc='upper left', bbox_to_anchor=(1, 1), fontsize=14)
else:
sm = make_scalar_mappable(0,
num_clusters - 1,
cmap=discrete_cmap(num_clusters, cmap))
plt.colorbar(sm)
if overlayed:
if centroids.dtype in [np.complex64, np.complex128, np.complex]:
fig, axes = plt.subplots(nrows=2,
figsize=kwargs.pop(
'figsize', (5.5, 2 * 5)))
for axis, func in zip(axes.flat, [np.abs, np.angle]):
__overlay_curves(axis, func(centroids))
for var, var_name, func in zip(
[y_label, y_label, x_label], ['y_label', 'y_label', 'x_label'],
[axes[1].set_ylabel, axes[1].set_xlabel]):
if var is not None:
if not isinstance(var, (str, unicode)):
raise TypeError(var_name + ' should be a string')
func(var)
if title is not None:
if not isinstance(title, (str, unicode)):
raise TypeError('title should be a string')
for axis, comp_name, units in zip(axes.flat,
['Amplitude', 'Phase'],
[amp_units, 'rad']):
axis.set_title('{} - {} ({})'.format(
title, comp_name, units))
else:
if axis is None:
fig, axis = plt.subplots(
figsize=kwargs.pop('figsize', (5.5, 5)))
else:
fig = None
__overlay_curves(axis, centroids)
for var, var_name, func in zip(
[title, x_label, y_label], ['title', 'x_label', 'y_label'],
[axis.set_title, axis.set_xlabel, axis.set_ylabel]):
if var is not None:
if not isinstance(var, (str, unicode)):
raise TypeError(var_name + ' should be a string')
func(var)
if fig is not None:
fig.tight_layout()
return fig, axis
else:
if centroids.dtype in [np.complex64, np.complex128, np.complex]:
return plot_complex_spectra(centroids,
x_vec=x_vec,
title=title,
x_label=x_label,
y_label=y_label,
subtitle_prefix='Cluster',
amp_units=amp_units,
**kwargs)
else:
return plot_curves(x_vec,
centroids,
x_label=x_label,
y_label=y_label,
title=title,
subtitle_prefix='Cluster ',
**kwargs)
def test_not_stdevs(self):
frequencies = 2**np.arange(4)
image_stack = [self.get_complex_2d_image(freq) for freq in frequencies]
with self.assertRaises(TypeError):
plot_utils.plot_complex_spectra(np.array(image_stack), stdevs=-1)
def test_is_x_vec(self):
frequencies = 2**np.arange(4)
image_stack = [self.get_complex_2d_image(freq) for freq in frequencies]
ran_arr = np.zeros_like(image_stack)
with self.assertRaises(ValueError):
plot_utils.plot_complex_spectra(np.array(image_stack), ran_arr)
def test_is_not_dim_x_vec(self):
frequencies = 2**np.arange(4)
image_stack = [self.get_complex_2d_image(freq) for freq in frequencies]
with self.assertRaises(ValueError):
plot_utils.plot_complex_spectra(np.array(image_stack), [1])
def test_not_map_stack(self):
with self.assertRaises(TypeError):
plot_utils.plot_complex_spectra('wrongthing')