Improvements and new plots

tmd/Population/Population.py

@@ -13,9 +13,26 @@ def __init__(self, name='Pop'):
         '''
         self.neurons = []
         self.name = name
-        self.apicals = []
-        self.axons = []
-        self.basals = []
+
+    @property
+    def axons(self):
+        return [a for n in self.neurons for a in n.axon]
+
+    @property
+    def apicals(self):
+        return [a for n in self.neurons for a in n.apical]
+
+    @property
+    def basals(self):
+        return [a for n in self.neurons for a in n.basal]
+
+    @property
+    def neurites(self):
+        return self.apicals + self.axons + self.basals
+
+    @property
+    def dendrites(self):
+        return self.apicals + self.basals
 
     def append_neuron(self, new_neuron):
         """

tmd/Topology/analysis.py

@@ -174,3 +174,15 @@ def get_image_max_diff(Z1, Z2):
     diff = np.max(np.abs(img2 - img1))
 
     return diff
+
+
+def transform_to_length(ph, direction=False):
+    '''Transforms a persistence diagram into
+    a (start_point, length) equivalent diagram.
+    '''
+    if not direction:
+        return [[i[0], np.abs(i[1] - i[0])] for i in ph]
+    else:
+        return [[i[0], i[1] - i[0]] for i in ph]
+
+

tmd/io/io.py

@@ -108,8 +108,4 @@ def load_population(input_directory, tree_types=None):
         pop.append_neuron(load_neuron(os.path.join(input_directory, i),
                                       tree_types=tree_types))
 
-    pop.apicals = [ap for n in pop.neurons for ap in n.apical]
-    pop.axons = [ax for n in pop.neurons for ax in n.axon]
-    pop.basals = [bas for n in pop.neurons for bas in n.basal]
-
     return pop

view/plot.py

@@ -123,7 +123,7 @@ def ph_diagram(ph, new_fig=True, subplot=False, color='b', alpha=1.0, **kwargs):
     return _cm.plot_style(fig=fig, ax=ax, **kwargs)
 
 
-def gaussian_kernel(ph, new_fig=True, subplot=111, xlims=None, ylims=None, **kwargs):
+def gaussian_kernel(ph, new_fig=True, subplot=111, xlims=None, ylims=None, norm_factor=None, **kwargs):
     '''Plots the gaussian kernel
        of the ph diagram that is given.
     '''
@@ -144,7 +144,10 @@ def gaussian_kernel(ph, new_fig=True, subplot=111, xlims=None, ylims=None, **kwa
     kernel = stats.gaussian_kde(values)
     positions = _np.vstack([X.ravel(), Y.ravel()])
     Z = _np.reshape(kernel(positions).T, X.shape)
-    Zn = Z / _np.max(Z)
+
+    if norm_factor is None:
+        norm_factor = _np.max(Z)
+    Zn = Z / norm_factor
 
     fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
 
@@ -156,7 +159,8 @@ def gaussian_kernel(ph, new_fig=True, subplot=111, xlims=None, ylims=None, **kwa
     return Z, _cm.plot_style(fig=fig, ax=ax, **kwargs)
 
 
-def ph_image(ph, new_fig=True, subplot=111, xlims=None, ylims=None, masked=False, cmap=_cm.plt.cm.jet, **kwargs):
+def ph_image(ph, new_fig=True, subplot=111, xlims=None, ylims=None, masked=False,
+             norm_factor=None, cmap=_cm.plt.cm.jet, **kwargs):
     '''Plots the gaussian kernel
        of the ph diagram that is given.
     '''
@@ -173,7 +177,10 @@ def ph_image(ph, new_fig=True, subplot=111, xlims=None, ylims=None, masked=False
     kernel = stats.gaussian_kde(values)
     positions = _np.vstack([X.ravel(), Y.ravel()])
     Z = _np.reshape(kernel(positions).T, X.shape)
-    Zn = Z / _np.max(Z)
+
+    if norm_factor is None:
+        norm_factor = _np.max(Z)
+    Zn = Z / norm_factor
 
     fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
 
@@ -462,3 +469,24 @@ def plot_average(ph_list, new_fig=True, subplot=111, xlims=None, ylims=None, bin
 
 
     return Zn, _cm.plot_style(fig=fig, ax=ax, **kwargs)
+
+
+def start_length_plot(ph, direction=False, new_fig=True, subplot=False, color='b', alpha=1.0, **kwargs):
+    '''Plots the transformed ph diagram that
+    represents lengths and starting points of
+    a component.
+    '''
+    ph_transformed = _tm.analysis.transform_to_length(ph, direction=direction)
+
+    # Initialization of matplotlib figure and axes.
+    fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
+
+    for p in ph:
+
+        ax.scatter(p[0], p[1], c=color, edgecolors='black', alpha=alpha)
+
+    kwargs['title'] = kwargs.get('title', 'Transformed Persistence diagram')
+    kwargs['xlabel'] = kwargs.get('xlabel', 'Start of the component')
+    kwargs['ylabel'] = kwargs.get('ylabel', 'Length of the component')
+
+    return _cm.plot_style(fig=fig, ax=ax, **kwargs)

view/view.py

@@ -20,6 +20,40 @@ def _get_default(variable, **kwargs):
     return kwargs.get(variable, default[variable])
 
 
+
+def _get_polar_data(population, neurite_type='neurites', bins=20):
+    '''Extracts the data to plot the polar length distribution
+    of a neuron or a population of neurons.
+    '''
+
+    def seg_angle(seg):
+        '''mean x, y coordinates of a segment'''
+        mean_x = _np.mean([seg[0][0], seg[1][0]])
+        mean_y = _np.mean([seg[0][1], seg[1][1]])
+        return _np.arctan2(mean_y, mean_x)
+
+    segs = []
+    for tr in getattr(population, neurite_type):
+     segs = segs + tr.get_segments()
+
+    angles = _np.array([seg_angle(s) for s in segs])
+
+    lens = []
+    for tr in getattr(population, neurite_type):
+        lens = lens + tr.get_segment_lengths().tolist()
+    lens = _np.array(lens)
+
+    step = 2 * _np.pi / bins
+    ranges = [[i * step - _np.pi, (i + 1) * step - _np.pi] for i in xrange(bins)]
+
+    results = []
+
+    for r in ranges:
+        results.append(r + [_np.sum(lens[_np.where((angles > r[0]) & (angles < r[1]))[0]])])
+
+    return results
+
+
 def trunk(tr, plane='xy', new_fig=True, subplot=False, hadd=0.0, vadd=0.0, N=10, **kwargs):
     '''Generates a 2d figure of the trunk = first N segments of the tree.
 
@@ -1350,3 +1384,42 @@ def density_cloud(obj, new_fig=True, subplot=111, new_axes=True,
     soma(neu.soma, new_fig=False)
 
     return _cm.plot_style(fig=fig, ax=ax, **kwargs)
+
+
+def polar_plot(population, neurite_type='neurites', bins=20):
+    '''
+    '''
+    input_data = _get_polar_data(population, neurite_type=neurite_type, bins=bins)
+
+    fig = _cm.plt.figure()
+    ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
+
+    theta = _np.array(input_data)[:,0]
+    radii = _np.array(input_data)[:,2] / _np.max(input_data)
+    width = 2 * _np.pi / len(input_data)
+    bars = ax.bar(theta, radii, width=width, bottom=0.0, alpha=0.8)
+
+
+def polar_plot_custom_color(population, bins=25, apical_color='purple', basal_color='r',
+                            edgecolor=None, alpha=0.8):
+    '''
+    '''
+    fig = _cm.plt.figure()
+    ax = fig.add_axes([0.1, 0.1, 0.8, 0.8], polar=True)
+
+    input_data1 = _get_polar_data(population, neurite_type='basals', bins=bins)
+    input_data2 = _get_polar_data(population, neurite_type='apicals', bins=bins)
+
+    maximum = _np.max(_np.array(input_data1)[:,2].tolist() + _np.array(input_data2)[:,2].tolist())
+
+    theta = _np.array(input_data1)[:,0]
+    radii = _np.array(input_data1)[:,2] / maximum
+    width = 2 * _np.pi / len(input_data1)
+    bars = ax.bar(theta, radii, width=width, edgecolor=edgecolor,
+                  bottom=0.0, alpha=alpha, color=basal_color)
+
+    theta = _np.array(input_data2)[:,0]
+    radii = _np.array(input_data2)[:,2] / maximum
+    width = 2 * _np.pi / len(input_data2)
+    bars = ax.bar(theta, radii, width=width, edgecolor=edgecolor,
+                  bottom=0.0, alpha=alpha, color=apical_color)