Minor corrections

tmd/io/io.py

@@ -73,7 +73,10 @@ def load_neuron(input_file, line_delimiter='\n', soma_type=None,
         data = read_h5(input_file=input_file, remove_duplicates=remove_duplicates)
         neuron = Neuron.Neuron(name=input_file.replace('.h5', ''))
 
-    soma_ids = _np.where(_np.transpose(data)[1] == soma_index)[0]
+    try:
+        soma_ids = _np.where(_np.transpose(data)[1] == soma_index)[0]
+    except IndexError:
+        raise LoadNeuronError
 
     # Extract soma information from swc
     soma = Soma.Soma(x=_np.transpose(data)[SWC_DCT['x']][soma_ids],

tmd/view/plot.py

@@ -86,14 +86,15 @@ def diagram(ph, new_fig=True, subplot=False, color='b', alpha=1.0,
 
 def persistence_image(ph, new_fig=True, subplot=111, xlims=None, ylims=None,
                       masked=False, colorbar=False, norm_factor=None, threshold=0.01,
-                      vmin=None, vmax=None, cmap=jet_map, **kwargs):
+                      vmin=None, vmax=None, cmap=jet_map, bw_method=None, **kwargs):
     '''Plots the gaussian kernel
        of the ph diagram that is given.
     '''
     if xlims is None or xlims is None:
         xlims, ylims = analysis.get_limits(ph, coll=False)
 
-    Zn = analysis.get_persistence_image_data(ph, norm_factor=norm_factor, xlims=xlims, ylims=ylims)
+    Zn = analysis.get_persistence_image_data(ph, norm_factor=norm_factor, bw_method=bw_method,
+                                             xlims=xlims, ylims=ylims)
     fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
 
     if masked:
@@ -150,12 +151,13 @@ def persistence_image_add(Z2, Z1, new_fig=True, subplot=111, xlims=None, ylims=N
     return _cm.plot_style(fig=fig, ax=ax, **kwargs)
 
 
-def persistence_image_average(ph_list, new_fig=True, subplot=111, xlims=None, ylims=None,
-                              norm_factor=1.0, vmin=None, vmax=None, cmap=jet_map, **kwargs):
+def persistence_image_average(ph_list, new_fig=True, subplot=111, xlims=None,
+                              ylims=None, norm_factor=1.0, vmin=None, vmax=None,
+                              cmap=jet_map, weighted=False, **kwargs):
     """Merges a list of ph diagrams and plots their respective average image.
     """
     av_imgs = analysis.get_average_persistence_image(ph_list, xlims=xlims, ylims=ylims,
-                                                     norm_factor=norm_factor)
+                                                     norm_factor=norm_factor, weighted=weighted)
     if xlims is None or xlims is None:
         xlims, ylims = analysis.get_limits(ph_list)
 

tmd/view/view.py

@@ -64,7 +64,7 @@ def _seg_2d(seg, x_add=0.0, y_add=0.0):
     if _get_default('diameter', **kwargs):
 
         scale = _get_default('diameter_scale', **kwargs)
-        linewidth = [2 * d * scale for d in tr.d]
+        linewidth = [d * scale for d in tr.d]
 
     treecolor = _cm.get_color(_get_default('treecolor', **kwargs),
                               _utils.tree_type[tr.get_type()])
@@ -122,7 +122,7 @@ def _seg_2d(seg, x_add=0.0, y_add=0.0):
     if _get_default('diameter', **kwargs):
 
         scale = _get_default('diameter_scale', **kwargs)
-        linewidth = [2 * d * scale for d in tr.d]
+        linewidth = [d * scale for d in tr.d]
 
     if tr.get_type() not in _utils.tree_type:
         treecolor = 'black'
@@ -195,7 +195,7 @@ def soma(sm, plane='xy', new_fig=True, subplot=False, hadd=0.0, vadd=0.0, **kwar
 
 
 def neuron(nrn, plane='xy', new_fig=True, subplot=False, hadd=0.0, vadd=0.0,
-           neurite_type='all', rotation=None, nosoma=False, **kwargs):
+           neurite_type='all', rotation=None, nosoma=False, new_axes=True, **kwargs):
     '''Generates a 2d figure of the neuron,
     that contains a soma and a list of trees.
 
@@ -262,7 +262,7 @@ def neuron(nrn, plane='xy', new_fig=True, subplot=False, hadd=0.0, vadd=0.0,
         return None, 'No such plane found! Please select one of: xy, xz, yx, yz, zx, zy.'
 
     # Initialization of matplotlib figure and axes.
-    fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot)
+    fig, ax = _cm.get_figure(new_fig=new_fig, subplot=subplot, new_axes=new_axes)
 
     kwargs['new_fig'] = False
     kwargs['subplot'] = subplot
@@ -277,7 +277,6 @@ def neuron(nrn, plane='xy', new_fig=True, subplot=False, hadd=0.0, vadd=0.0,
 
     if rotation == 'apical':
         angle = _np.arctan2(nrn.apical[0].get_pca()[0], nrn.apical[0].get_pca()[1])
-    elif isinstance(rotation, list):
         angle = _np.arctan2(rotation[1], rotation[0])
 
     if neurite_type == 'all':
@@ -507,7 +506,7 @@ def _seg_3d(seg):
     if _get_default('diameter', **kwargs):
 
         scale = _get_default('diameter_scale', **kwargs)
-        linewidth = [2 * d * scale for d in tr.d]
+        linewidth = [d * scale for d in tr.d]
 
     treecolor = _cm.get_color(_get_default('treecolor', **kwargs),
                               _utils.tree_type[tr.get_type()])
@@ -578,7 +577,7 @@ def _seg_3d(seg):
     if _get_default('diameter', **kwargs):
 
         scale = _get_default('diameter_scale', **kwargs)
-        linewidth = [2 * d * scale for d in tr.d]
+        linewidth = [d * scale for d in tr.d]
 
     treecolor = _cm.get_color(_get_default('treecolor', **kwargs),
                               _utils.tree_type[tr.get_type()])
@@ -782,7 +781,7 @@ def population3d(pop, new_fig=True, new_axes=True, subplot=False, **kwargs):
 # pylint: disable=too-many-locals
 def density_cloud(obj, new_fig=True, subplot=111, new_axes=True, neurite_type='all',
                   bins=100, plane='xy', color_map=blues_map, alpha=0.8,
-                  centered=True, colorbar=True, **kwargs):
+                  centered=True, colorbar=True, plot_neuron=False, **kwargs):
     """
     View the neuron morphologies of a population as a density cloud.
     """
@@ -817,6 +816,16 @@ def density_cloud(obj, new_fig=True, subplot=111, new_axes=True, neurite_type='a
                         _np.transpose(H2), cmap=color_map,
                         alhpa=alpha)
 
+    kwargs['new_fig'] = False
+    kwargs['subplot'] = subplot
+
+    if plot_neuron:
+        nrn = obj.neurons[0]
+        h, v, _ = nrn.soma.get_center()
+        soma(nrn.soma, plane='xy', hadd=-h, vadd=-v, **kwargs)
+        for temp_tree in getattr(nrn, ntypes):
+            tree(temp_tree, plane='xy', hadd=-h, vadd=-v, treecolor='r', **kwargs)
+
     if colorbar:
         _cm.plt.colorbar(plots)
     # soma(neu.soma, new_fig=False)