def extract_ph_neuron(neuron, feature='radial_distances', output_file=None,
neurite_type='all', sort=False, **kwargs):
'''Extracts persistent homology from tree'''
ph = get_ph_neuron(neuron, feature=feature, neurite_type='all', **kwargs)
if sort:
sort_ph(ph)
else:
p = ph
if output_file is None:
output_file = 'PH_' + neuron.name + '_' + neurite_type + '.txt'
write_ph(p, output_file)
def extract_ph(tree, feature='radial_distances', output_file='test.txt',
sort=False, **kwargs):
'''Extracts persistent homology from tree'''
ph = get_persistence_diagram(tree, feature=feature, **kwargs)
if sort:
p = sort_ph(ph)
else:
p = ph
write_ph(p, output_file)
def barcode(ph,
new_fig=True,
subplot=False,
color='b',
linewidth=1.2,
**kwargs):
"""
Generates a 2d figure (barcode) of the persistent homology
of a tree as it has been computed by
Topology.get_persistent_homology method.
"""
# Initialization of matplotlib figure and axes.
fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
ph_sort = analysis.sort_ph(ph)
for ip, p in enumerate(ph_sort):
ax.plot(p[:2], [ip, ip], c=color, linewidth=linewidth)
kwargs['title'] = kwargs.get('title', 'Persistence barcode')
kwargs['xlabel'] = kwargs.get('xlabel',
'Lifetime: radial distance from soma')
_cm.plt.ylim([-1, len(ph_sort)])
return _cm.plot_style(fig=fig, ax=ax, **kwargs)