Optimize path_distance computation: use path_distances_2

examples/distances_example.py

@@ -8,21 +8,22 @@
 phs2 = [tmd.methods.get_ph_neuron(n, neurite_type='basal') for n in pop2.neurons]
 
 # Normalize the limits
-xlims, ylims = tmd.analysis.define_limits(phs1 + phs2)
+xlims, ylims = tmd.analysis.get_limits(phs1 + phs2)
+
 # Create average images for populations
-imgs1 = [tmd.analysis.persistence_image_data(p, xlims=xlims, ylims=ylims) for p in phs1]
-IMG1 = tmd.analysis.average_image(phs1, xlims=xlims, ylims=ylims)
-imgs2 = [tmd.analysis.persistence_image_data(p, xlims=xlims, ylims=ylims) for p in phs2]
-IMG2 = tmd.analysis.average_image(phs2, xlims=xlims, ylims=ylims)
+imgs1 = [tmd.analysis.get_persistence_image_data(p, xlims=xlims, ylims=ylims) for p in phs1]
+IMG1 = tmd.analysis.get_average_persistence_image(phs1, xlims=xlims, ylims=ylims)
+imgs2 = [tmd.analysis.get_persistence_image_data(p, xlims=xlims, ylims=ylims) for p in phs2]
+IMG2 = tmd.analysis.get_average_persistence_image(phs2, xlims=xlims, ylims=ylims)
 
 # You can plot the images if you want to create pretty figures
-average_figure1 = view.plot.plot_img_basic(IMG1, title='', xlims=xlims, ylims=ylims, cmap=cm.jet)
-average_figure2 = view.plot.plot_img_basic(IMG2, title='', xlims=xlims, ylims=ylims, cmap=cm.jet)
+average_figure1 = view.common.plot_img_basic(IMG1, title='', xlims=xlims, ylims=ylims, cmap=cm.jet)
+average_figure2 = view.common.plot_img_basic(IMG2, title='', xlims=xlims, ylims=ylims, cmap=cm.jet)
 
 # Create the diffence between the two images
-DIMG = tmd.analysis.img_diff_data(IMG1, IMG2) # subtracts IMG2 from IMG1 so anything red IMG1 has more of it and anything blue IMG2 has more of it - or that's how it is supposed to be :)
+DIMG = tmd.analysis.get_image_diff_data(IMG1, IMG2) # subtracts IMG2 from IMG1 so anything red IMG1 has more of it and anything blue IMG2 has more of it - or that's how it is supposed to be :)
 
 # Plot the difference between them
-diff_image = view.plot.plot_img_basic(DIMG, vmin=-1.0, vmax=1.0) # vmin, vmax important to see changes
+diff_image = view.common.plot_img_basic(DIMG, vmin=-1.0, vmax=1.0) # vmin, vmax important to see changes
 # Quantify the absolute distance between the two averages
 dist = np.sum(np.abs(DIMG))

examples/extract_ph.py

@@ -1,7 +1,6 @@
 # Example to extract the persistence diagram from a neuronal tree
 
 # Step 1: Import the tmd module
-
 import tmd
 
 # Step 2: Load your morphology
@@ -22,22 +21,19 @@
 ph_basal = tmd.methods.get_ph_neuron(neu, neurite_type='basal')
 
 # Step 6: Plot the extracted topological data with three different ways
-import view
+from tmd.view import view, plot
 
 # Visualize the neuron
-view.view.neuron(neu)
+view.neuron(neu)
 
 # Visualize a selected neurite type or multiple of them
-view.view.neuron(neu, neurite_type=['apical'])
+view.neuron(neu, neurite_type=['apical'])
 
 # Visualize the persistence diagram
-view.plot.ph_diagram(ph_apical)
+plot.diagram(ph_apical)
 
 # Visualize the persistence barcode
-view.plot.barcode(ph_apical)
+plot.barcode(ph_apical)
 
 # Visualize the persistence image
-view.plot.ph_image(ph_apical)
-
-# Create an overview figure for the topology of a tree
-
+plot.persistence_image(ph_apical)

tmd/Tree/Tree.py

@@ -18,6 +18,7 @@ class Tree(object):
     from tmd.Tree.methods import get_point_radial_distances_time
     from tmd.Tree.methods import get_point_weighted_radial_distances
     from tmd.Tree.methods import get_point_path_distances
+    from tmd.Tree.methods import get_point_path_distances_2
     from tmd.Tree.methods import get_point_projection
     from tmd.Tree.methods import get_point_section_lengths
     from tmd.Tree.methods import get_point_section_branch_orders

tmd/Tree/methods.py

@@ -15,7 +15,6 @@ def _rd_w(p1, p2, w=(1., 1., 1.), normed=True):
     '''
     if normed:
         w = (_np.array(w) / _np.linalg.norm(w))
-
     return _np.dot(w, (_np.subtract(p1, p2)))
 
 
@@ -96,7 +95,7 @@ def get_segment_lengths(tree):
     return seg_len
 
 
-# Points features
+# Points features to be used for topological extraction
 def get_point_radial_distances(self, point=None, dim='xyz'):
     '''Tree method to get radial distances from a point.
     If point is None, the soma surface -defined by
@@ -180,6 +179,21 @@ def path_length(seg_id):
     return _np.array([path_length(i) for i in range(size(self))])
 
 
+def get_point_path_distances_2(self):
+    '''Tree method to get path distances from the root.
+    '''
+    import copy
+
+    seg_len = get_segment_lengths(self)
+    path_lengths = _np.append(0, copy.deepcopy(seg_len))
+    children = get_children(self)
+
+    for i in children.keys():     
+        path_lengths[children[i]] = path_lengths[children[i]] + path_lengths[i]
+
+    return path_lengths
+
+
 def get_point_section_lengths(self):
     '''Tree method to get section lengths.
     '''
@@ -365,6 +379,14 @@ def get_way_to_root(tree, sec_id=0):
     return way
 
 
+def get_children(tree):
+    '''Returns a dictionary of children
+       for each node of the tree
+    '''
+    from collections import OrderedDict
+    return OrderedDict({i: _np.where(tree.p == i)[0] for i in xrange(len(tree.p))})
+
+
 # PCA
 def get_pca(self, plane='xy', component=0):
     '''Returns the i-th principal