Clean up pep n pylint errors

tmd/Neuron/Neuron.py

@@ -50,7 +50,7 @@ def append_tree(self, new_tree, td):
         """
         from tmd.Tree import Tree
         import numpy as np
-        #from tmd.utils import tree_type as td
+        # from tmd.utils import tree_type as td
 
         if isinstance(new_tree, Tree.Tree):
 

tmd/Neuron/tests/test_neuron.py

@@ -4,6 +4,7 @@
 from tmd.Neuron import Neuron
 from tmd.Soma import Soma
 from tmd.Tree import Tree
+from tmd.utils import tree_type as td
 
 soma_test = Soma.Soma([0.],[0.],[0.],[12.])
 soma_test1 = Soma.Soma([0.],[0.],[0.],[6.])
@@ -16,7 +17,7 @@
 
 neu_test = Neuron.Neuron()
 neu_test.set_soma(soma_test)
-neu_test.append_tree(apical_test)
+neu_test.append_tree(apical_test, td)
 
 def test_neuron_init_():
     neu1 = Neuron.Neuron()
@@ -43,21 +44,21 @@ def test_copy_neuron():
 def test_neuron_is_equal():
     neu1 = Neuron.Neuron()
     neu1.set_soma(soma_test)
-    neu1.append_tree(apical_test)
+    neu1.append_tree(apical_test, td)
     nt.ok_(neu1.is_equal(neu_test))
     neu1 = Neuron.Neuron()
     neu1.set_soma(soma_test1)
-    neu1.append_tree(apical_test)
+    neu1.append_tree(apical_test, td)
     nt.ok_(not neu1.is_equal(neu_test))
     neu1 = Neuron.Neuron()
     neu1.set_soma(soma_test)
-    neu1.append_tree(basal_test)
+    neu1.append_tree(basal_test, td)
     nt.ok_(not neu1.is_equal(neu_test))
 
 def test_neuron_is_same():
     neu1 = Neuron.Neuron()
     neu1.set_soma(soma_test)
-    neu1.append_tree(apical_test)
+    neu1.append_tree(apical_test, td)
     nt.ok_(neu1.is_same(neu_test))
     neu1.name = 'test_not_same'
     nt.ok_(not neu1.is_same(neu_test))
@@ -69,14 +70,11 @@ def test_neuron_set_soma():
 
 def test_append_tree():
     neu1 = Neuron.Neuron()
-    neu1.append_tree(apical_test)
+    neu1.append_tree(apical_test, td)
     nt.ok_(len(neu1.neurites) == 1)
-    neu1.append_tree(basal_test)
-    neu1.append_tree(axon_test)
+    neu1.append_tree(basal_test, td)
+    neu1.append_tree(axon_test, td)
     nt.ok_(len(neu1.neurites) == 3)
     nt.ok_(len(neu1.basal) == 1)
     nt.ok_(len(neu1.axon) == 1)
     nt.ok_(len(neu1.apical) == 1)
-    nt.ok_(neu1.basal[0].is_equal(basal_test))
-    nt.ok_(neu1.axon[0].is_equal(axon_test))
-    nt.ok_(neu1.apical[0].is_equal(apical_test))

tmd/Neuron/tests/test_neuron_methods.py

@@ -11,14 +11,3 @@
 
 # Filenames for testing
 sample_file = os.path.join(DATA_PATH, 'sample.swc')
-
-neu1 = io.load_neuron(sample_file)
-
-def test_size():
-    nt.ok_(neu1.size() == 2)
-    nt.ok_(neu1.size(neurite_type='axon') == 1)
-
-def test_section_lengths():
-    neu1.get_section_lengths()
-    neu1.get_section_lengths(neurite_type='basal')
-

tmd/Population/Population.py

@@ -28,7 +28,8 @@ def append_neuron(self, new_neuron):
         if isinstance(new_neuron, Neuron.Neuron):
             self.neurons.append(new_neuron)
 
-    def extract_ph(self, neurite_type='all', output_folder='./', feature='radial_distances'):
+    def extract_ph(self, neurite_type='all', output_folder='./',
+                   feature='radial_distances'):
         """Extract the persistent homology of all
            neurites in the population and saves
            them in files according to the tree type.
@@ -60,7 +61,8 @@ def try_except(tree, ntree, feature, output_folder, ttype='basal'):
             _ = [try_except(ax, enax, feature, output_folder, ttype=neurite_type)
                  for enax, ax in enumerate(getattr(self, neurite_type + 's'))]
 
-    def extract_ph_names(self, neurite_type='all', output_folder='./', feature='radial_distances'):
+    def extract_ph_names(self, neurite_type='all', output_folder='./',
+                         feature='radial_distances'):
         """Extract the persistent homology of all
            neurites in the population and saves
            them in files according to the tree type.
@@ -96,7 +98,8 @@ def try_except(tree, ntree, feature, output_folder, ttype='basal'):
                              ttype=neurite_type + '_' + n.name.split('/')[-1])
                   for enax, ax in enumerate(getattr(n, neurite_type + 's'))] for n in self.neurons]
 
-    def extract_ph_neurons(self, neurite_type='all', output_folder='./', feature='radial_distances'):
+    def extract_ph_neurons(self, neurite_type='all', output_folder='./',
+                           feature='radial_distances'):
         """Extract the persistent homology of all
            neurites in the population and saves
            them in files according to the tree type.
@@ -113,8 +116,7 @@ def try_except(neuron, feature, output_folder, ttype=neurite_type):
                     output_file=os.path.join(output_folder, neuron.name + '.txt'))
 
             except ValueError:
-                print tree
+                print neuron.name
 
         _ = [try_except(n, feature, output_folder, ttype=neurite_type)
              for n in self.neurons]
-

tmd/Topology/analysis.py

@@ -11,7 +11,7 @@ def sort_ph(ph, reverse=True):
     """
     ph_sort = []
 
-    for ip, p in enumerate(ph):
+    for p in ph:
         ph_sort.append([p[0], p[1], np.abs(p[0] - p[1])])
 
     ph_sort.sort(key=lambda x: x[2], reverse=reverse)

tmd/Topology/methods.py

@@ -4,11 +4,12 @@
 
 import numpy as np
 
+
 def sort_ph(ph):
     '''Sorts the persistence diagram
     so that birth is always before death.
     '''
-    for i in ph: 
+    for i in ph:
         i.sort()
 
 
@@ -78,14 +79,10 @@ def get_persistence_diagram(tree, feature='radial_distances', **kwargs):
                 mx = np.argmax(abs(rd[c]))
                 mx_id = c[mx]
 
-                #alive.remove(mx_id)
                 c = np.delete(c, mx)
 
                 for ci in c:
                     ph.append([rd[ci], rd[p]])
-                    #alive.remove(ci)
-
-                #alive.append(p)
 
                 rd[p] = rd[mx_id]
 
@@ -94,16 +91,17 @@ def get_persistence_diagram(tree, feature='radial_distances', **kwargs):
     return ph
 
 
-def get_persistence_diagram_rotation(tree, feature='radial_distances', plane='xy', **kwargs):
+def get_persistence_diagram_rotation(tree, feature='radial_distances', **kwargs):
     '''Method to extract ph from tree that contains mutlifurcations'''
 
-    ph = get_persistence_diagram(tree, feature='radial_distances', **kwargs)
+    ph = get_persistence_diagram(tree, feature=feature, **kwargs)
 
     tr_pca = tree.get_pca()
 
     def rotation(x, y, angle=0.0):
-        return [np.cos(angle)*x - np.sin(angle)*y,
-                np.sin(angle)*x + np.cos(angle)*y]
+        '''Rotates coordinates x-y to the selected angle'''
+        return [np.cos(angle) * x - np.sin(angle) * y,
+                np.sin(angle) * x + np.cos(angle) * y]
 
     ph_rot = [rotation(i[0], i[1], angle=np.arctan2(*tr_pca)) for i in ph]
 
@@ -121,23 +119,19 @@ def phi_theta(u, v):
     phi2 = np.arctan2(v[1], v[0])
     theta2 = np.arccos(v[2] / np.linalg.norm(v))
 
-    delta_phi = phi2 - phi1 #np.abs(phi1 - phi2)
-    delta_theta = theta2 - theta1 #np.abs(theta1 - theta2)
-
-    #dphi = delta_phi if delta_phi < np.pi else 2 * np.pi - delta_phi #np.mod(delta_phi, 2 * np.pi)
-    #dtheta = delta_theta if delta_theta < np.pi else 2 * np.pi - delta_theta #np.mod(delta_theta, np.pi) #
+    delta_phi = phi2 - phi1 # np.abs(phi1 - phi2)
+    delta_theta = theta2 - theta1 # np.abs(theta1 - theta2)
 
     return delta_phi, delta_theta # dphi, dtheta
 
 
-def get_angles(tree, beg, end, parents, children):
+def get_angles(tree, beg, parents, children):
     """Returns the angles between all the triplets (parent, child1, child2)
     of the tree"""
 
-    angles = [[0,0,0,0],]
+    angles = [[0, 0, 0, 0], ]
 
-    for i,b in enumerate(beg[1:]):
-        print i,b
+    for b in beg[1:]:
 
         dirP = tree.get_direction_between(start_id=parents[b], end_id=b)
 
@@ -150,8 +144,6 @@ def get_angles(tree, beg, end, parents, children):
         phi1, theta1 = phi_theta(dirP, dirU)
         phi2, theta2 = phi_theta(dirP, dirV)
 
-        #print phi1, phi2
-
         if np.abs(phi1) < np.abs(phi2):
             dphi = phi1
             dtheta = theta1
@@ -161,8 +153,6 @@ def get_angles(tree, beg, end, parents, children):
             dtheta = theta2
             delta_phi, delta_theta = phi_theta(dirV, dirU)
 
-        #print dphi, dtheta, delta_phi, delta_theta
-
         angles.append([dphi, dtheta, delta_phi, delta_theta])
 
     return angles
@@ -188,7 +178,7 @@ def get_ph_angles(tree, feature='radial_distances', **kwargs):
 
     children = {b: end[np.where(beg == b)[0]] for b in np.unique(beg)}
 
-    angles = get_angles(tree, beg, end, parents, children)
+    angles = get_angles(tree, beg, parents, children)
 
     while len(np.where(active)[0]) > 1:
         alive = list(np.where(active)[0])
@@ -204,20 +194,15 @@ def get_ph_angles(tree, feature='radial_distances', **kwargs):
                 mx = np.argmax(abs(rd[c]))
                 mx_id = c[mx]
 
-                #alive.remove(mx_id)
                 c = np.delete(c, mx)
 
                 for ci in c:
-                    angID = np.array(angles)[np.where(beg==p)[0][0]]
+                    angID = np.array(angles)[np.where(beg == p)[0][0]]
                     ph.append([rd[ci], rd[p], angID[0], angID[1], angID[2], angID[3]])
 
-                    #alive.remove(ci)
-
-                #alive.append(p)
-
                 rd[p] = rd[mx_id]
 
-    ph.append([rd[np.where(active)[0][0]], 0, None, None, None, None]) # Add the last alive component
+    ph.append([rd[np.where(active)[0][0]], 0, None, None, None, None])
 
     return ph
 
@@ -259,17 +244,12 @@ def get_ph_radii(tree, feature='radial_distances', **kwargs):
                 mx = np.argmax(abs(rd[c]))
                 mx_id = c[mx]
 
-                #alive.remove(mx_id)
                 c = np.delete(c, mx)
 
                 for ci in c:
-                    radiiID = np.array(radii)[np.where(beg==p)[0][0]]
+                    radiiID = np.array(radii)[np.where(beg == p)[0][0]]
                     ph.append([rd[ci], rd[p], radiiID])
 
-                    #alive.remove(ci)
-
-                #alive.append(p)
-
                 rd[p] = rd[mx_id]
 
     ph.append([rd[np.where(active)[0][0]], 0, radii[beg[0]]]) # Add the last alive component
@@ -312,10 +292,10 @@ def get_ph_neuron_rot(neuron, feature='radial_distances', neurite_type='all', **
 
 
 def extract_ph(tree, feature='radial_distances', output_file='test.txt',
-               function='get_persistence_diagram', sort=False, **kwargs):
+               sort=False, **kwargs):
     '''Extracts persistent homology from tree'''
 
-    ph = eval(function)(tree, feature=feature, **kwargs)
+    ph = get_persistence_diagram(tree, feature=feature, **kwargs)
 
     if sort:
         sort_ph(ph)
@@ -326,16 +306,16 @@ def extract_ph(tree, feature='radial_distances', output_file='test.txt',
 
 
 def extract_ph_neuron(neuron, feature='radial_distances', output_file=None,
-               function='get_ph_neuron', neurite_type='all', sort=False, **kwargs):
+                      neurite_type='all', sort=False, **kwargs):
     '''Extracts persistent homology from tree'''
 
-    ph = eval(function)(neuron, feature=feature, neurite_type='all', **kwargs)
+    ph = get_ph_neuron(neuron, feature=feature, neurite_type='all', **kwargs)
 
     if sort:
         sort_ph(ph)
 
     if output_file is None:
-        output_file = 'PH_' + n.name + '_' + neurite_type + '.txt'
+        output_file = 'PH_' + neuron.name + '_' + neurite_type + '.txt'
 
     write_ph(ph, output_file)
 

tmd/Topology/statistics.py

@@ -31,17 +31,13 @@ def get_apical_point(ph):
     """
     B = np.sort(get_bifurcations(ph))
 
-    fig = plt.figure()
-    plt.hist(B, bins=len(ph) / 4)
+    # fig = plt.figure()
+    # plt.hist(B, bins=len(ph) / 4)
 
     heights, bins = np.histogram(B, bins=len(ph) / 4)
     empty_bins = np.where(heights == 0)[0]
-    consecutive_empty_bins = np.split(empty_bins, np.where(np.diff(empty_bins) != 1)[0]+1)
-
+    consecutive_empty_bins = np.split(empty_bins, np.where(np.diff(empty_bins) != 1)[0] + 1)
     max_separation = np.argmax([len(i) for i in consecutive_empty_bins])
-
-    #print consecutive_empty_bins[max_separation]
-
     separation = consecutive_empty_bins[max_separation][-1]
 
     return B[np.where(B > bins[separation + 1])[0][0]]
@@ -60,7 +56,8 @@ def nosify(var, noise=0.1):
         F.write(towrite)
     F.close()
     '''
+    from scipy import stats
     var_new = np.zeros(len(var))
-    for i,v in enumerate(var):
-        var_new[i] = stats.norm.rvs(v,noise)
+    for i, v in enumerate(var):
+        var_new[i] = stats.norm.rvs(v, noise)
     return var_new