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)
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)
def plot_cluster_labels(labels_mat, num_clusters=None, x_label=None, y_label=None, title=None, axis=None, **kwargs):
"""
Plots the cluster labels
Parameters
----------
labels_mat : numpy.ndarray
1D or 2D unsigned integer array containing the labels of the clusters
num_clusters : int, optional
Number of clusters
x_label : str, optional
Label for x axis
y_label : str, optional
Label for y axis
title : str, optional
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
kwargs : dict
will be passed on to plot() or plot_map()
Returns
-------
fig : matplotlib.pyplot.Figure object
figure object
axis : matplotlib.Axes object
axis object
"""
if not isinstance(labels_mat, np.ndarray):
raise TypeError('labels_mat should be numpy array')
if labels_mat.ndim > 2:
raise ValueError('labels_mat should be a 1D or 2D array')
if not isinstance(num_clusters, int):
raise TypeError('num_clusters 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 num_clusters is not None:
if not isinstance(num_clusters, int) or num_clusters < 1:
raise TypeError('num_clusters should be a positive integer')
else:
num_clusters = np.max(labels_mat)
if axis is None:
fig, axis = plt.subplots(figsize=kwargs.pop('figsize', (5.5, 5)))
else:
fig = None
if labels_mat.ndim > 1:
_, _ = plot_map(axis, labels_mat, clim=[0, num_clusters - 1], aspect='auto', show_xy_ticks=True,
cmap=discrete_cmap(num_clusters, kwargs.pop('cmap', default_cmap)), **kwargs)
else:
x_vec = kwargs.pop('x_vec', np.arange(labels_mat.size))
axis.plot(x_vec, labels_mat, **kwargs)
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
def plot_cluster_labels(labels_mat,
num_clusters=None,
x_label=None,
y_label=None,
title=None,
axis=None,
**kwargs):
"""
Plots the cluster labels
Parameters
----------
labels_mat : numpy.ndarray
1D or 2D unsigned integer array containing the labels of the clusters
num_clusters : int, optional
Number of clusters
x_label : str, optional
Label for x axis
y_label : str, optional
Label for y axis
title : str, optional
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
kwargs : dict
will be passed on to plot() or plot_map()
Returns
-------
fig : matplotlib.pyplot.Figure object
figure object
axis : matplotlib.Axes object
axis object
"""
if not isinstance(labels_mat, np.ndarray):
raise TypeError('labels_mat should be numpy array')
if labels_mat.ndim > 2:
raise ValueError('labels_mat should be a 1D or 2D array')
if not isinstance(num_clusters, int):
raise TypeError('num_clusters 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 num_clusters is not None:
if not isinstance(num_clusters, int) or num_clusters < 1:
raise TypeError('num_clusters should be a positive integer')
else:
num_clusters = np.max(labels_mat)
if axis is None:
fig, axis = plt.subplots(figsize=kwargs.pop('figsize', (5.5, 5)))
else:
fig = None
if labels_mat.ndim > 1:
_, _ = plot_map(axis,
labels_mat,
clim=[0, num_clusters - 1],
aspect='auto',
show_xy_ticks=True,
cmap=discrete_cmap(num_clusters,
kwargs.pop('cmap', default_cmap)),
**kwargs)
else:
x_vec = kwargs.pop('x_vec', np.arange(labels_mat.size))
axis.plot(x_vec, labels_mat, **kwargs)
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
def test_cmap_not_str(self):
with self.assertRaises(ValueError):
plot_utils.discrete_cmap(num_bins=1, cmap='hello')
def test_numbins_is_not_uint(self):
with self.assertRaises(TypeError):
plot_utils.discrete_cmap(num_bins='hello')