Format examples

examples/density_plot.py

@@ -42,15 +42,15 @@
 PACKAGE_DIR = Path(__file__).resolve().parent.parent
 
 
-def extract_density(population, plane='xy', bins=100, neurite_type=NeuriteType.basal_dendrite):
+def extract_density(population, plane="xy", bins=100, neurite_type=NeuriteType.basal_dendrite):
     """Extracts the 2d histogram of the center
     coordinates of segments in the selected plane.
     """
     segment_midpoints = np.array(
-        get_feat('segment_midpoints', population, neurite_type=neurite_type)
+        get_feat("segment_midpoints", population, neurite_type=neurite_type)
     )
-    horiz = segment_midpoints[:, 'xyz'.index(plane[0])]
-    vert = segment_midpoints[:, 'xyz'.index(plane[1])]
+    horiz = segment_midpoints[:, "xyz".index(plane[0])]
+    vert = segment_midpoints[:, "xyz".index(plane[1])]
     return np.histogram2d(np.array(horiz), np.array(vert), bins=(bins, bins))
 
 
@@ -60,10 +60,10 @@ def plot_density(
     new_fig=True,
     subplot=111,
     levels=None,
-    plane='xy',
-    colorlabel='Nodes per unit area',
+    plane="xy",
+    colorlabel="Nodes per unit area",
     labelfontsize=16,
-    color_map='Reds',
+    color_map="Reds",
     no_colorbar=False,
     threshold=0.01,
     neurite_type=NeuriteType.basal_dendrite,
@@ -82,7 +82,7 @@ def plot_density(
     H2 = np.ma.masked_array(H1, mask)
 
     colormap = mpl.cm.get_cmap(color_map).copy()
-    colormap.set_bad(color='white', alpha=None)
+    colormap.set_bad(color="white", alpha=None)
 
     plots = ax.contourf(
         (xedges1[:-1] + xedges1[1:]) / 2,
@@ -96,9 +96,9 @@ def plot_density(
         cbar = plt.colorbar(plots)
         cbar.ax.set_ylabel(colorlabel, fontsize=labelfontsize)
 
-    kwargs['title'] = kwargs.get('title', '')
-    kwargs['xlabel'] = kwargs.get('xlabel', plane[0])
-    kwargs['ylabel'] = kwargs.get('ylabel', plane[1])
+    kwargs["title"] = kwargs.get("title", "")
+    kwargs["xlabel"] = kwargs.get("xlabel", plane[0])
+    kwargs["ylabel"] = kwargs.get("ylabel", plane[1])
 
     return matplotlib_utils.plot_style(fig=fig, ax=ax, **kwargs)
 
@@ -109,10 +109,10 @@ def plot_neuron_on_density(
     new_fig=True,
     subplot=111,
     levels=None,
-    plane='xy',
-    colorlabel='Nodes per unit area',
+    plane="xy",
+    colorlabel="Nodes per unit area",
     labelfontsize=16,
-    color_map='Reds',
+    color_map="Reds",
     no_colorbar=False,
     threshold=0.01,
     neurite_type=NeuriteType.basal_dendrite,

examples/end_to_end_distance.py

@@ -55,8 +55,8 @@ def make_end_to_end_distance_plot(nb_segments, end_to_end_distance, neurite_type
     plt.figure()
     plt.plot(nb_segments, end_to_end_distance)
     plt.title(neurite_type)
-    plt.xlabel('Number of segments')
-    plt.ylabel('End-to-end distance')
+    plt.xlabel("Number of segments")
+    plt.ylabel("End-to-end distance")
     # uncomment to show
     # plt.show()
 
@@ -79,24 +79,24 @@ def _dist(seg):
 
 def main():
     #  load a neuron from an SWC file
-    filename = Path(PACKAGE_DIR, 'tests/data/swc/Neuron_3_random_walker_branches.swc')
+    filename = Path(PACKAGE_DIR, "tests/data/swc/Neuron_3_random_walker_branches.swc")
     m = nm.load_morphology(filename)
 
     # print mean end-to-end distance per neurite type
     print(
-        'Mean end-to-end distance for axons: ',
+        "Mean end-to-end distance for axons: ",
         mean_end_to_end_dist(n for n in m.neurites if n.type == nm.AXON),
     )
     print(
-        'Mean end-to-end distance for basal dendrites: ',
+        "Mean end-to-end distance for basal dendrites: ",
         mean_end_to_end_dist(n for n in m.neurites if n.type == nm.BASAL_DENDRITE),
     )
     print(
-        'Mean end-to-end distance for apical dendrites: ',
+        "Mean end-to-end distance for apical dendrites: ",
         mean_end_to_end_dist(n for n in m.neurites if n.type == nm.APICAL_DENDRITE),
     )
 
-    print('End-to-end distance per neurite (nb segments, end-to-end distance, neurite type):')
+    print("End-to-end distance per neurite (nb segments, end-to-end distance, neurite type):")
     for nrte in m.neurites:
         # plot end-to-end distance for increasingly larger parts of neurite
         calculate_and_plot_end_to_end_distance(nrte)
@@ -108,5 +108,5 @@ def main():
         )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

examples/extract_distribution.py

@@ -66,8 +66,8 @@ def main():
         find_optimal_distribution(population_directory, "section_lengths")
     )
 
-    print(json.dumps(result, indent=2, separators=(',', ': '), cls=NeuromJSON))
+    print(json.dumps(result, indent=2, separators=(",", ": "), cls=NeuromJSON))
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

examples/features_graph_table.py

@@ -41,7 +41,7 @@
 def stylize(ax, name, feature):
     """Stylization modifications to the plots"""
     ax.set_ylabel(feature)
-    ax.set_title(name, fontsize='small')
+    ax.set_title(name, fontsize="small")
 
 
 def histogram(neuron, feature, ax, bins=15, normed=True, cumulative=False):
@@ -104,5 +104,5 @@ def main():
     )
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

examples/get_features.py

@@ -51,12 +51,12 @@ def stats(data):
             min and max of data
     """
     return {
-        'len': len(data),
-        'mean': np.mean(data),
-        'sum': np.sum(data),
-        'std': np.std(data),
-        'min': np.min(data),
-        'max': np.max(data),
+        "len": len(data),
+        "mean": np.mean(data),
+        "sum": np.sum(data),
+        "std": np.std(data),
+        "min": np.min(data),
+        "max": np.max(data),
     }
 
 
@@ -66,15 +66,15 @@ def pprint_stats(data):
 
 
 def main():
-    filename = Path(PACKAGE_DIR, 'tests/data/swc/Neuron.swc')
+    filename = Path(PACKAGE_DIR, "tests/data/swc/Neuron.swc")
 
     #  load a neuron from an SWC file
     m = nm.load_morphology(filename)
 
     # Get some soma information
     # Soma radius and surface area
-    print("Soma radius", nm.get('soma_radius', m))
-    print("Soma surface area", nm.get('soma_surface_area', m))
+    print("Soma radius", nm.get("soma_radius", m))
+    print("Soma surface area", nm.get("soma_surface_area", m))
 
     # Get information about neurites
     # Most neurite data can be queried for a particular type of neurite.
@@ -85,48 +85,48 @@ def main():
     # to warm up...
 
     # number of neurites
-    print('Number of neurites (all):', nm.get('number_of_neurites', m))
+    print("Number of neurites (all):", nm.get("number_of_neurites", m))
     print(
-        'Number of neurites (axons):',
-        nm.get('number_of_neurites', m, neurite_type=nm.NeuriteType.axon),
+        "Number of neurites (axons):",
+        nm.get("number_of_neurites", m, neurite_type=nm.NeuriteType.axon),
     )
     print(
-        'Number of neurites (apical dendrites):',
-        nm.get('number_of_neurites', m, neurite_type=nm.NeuriteType.apical_dendrite),
+        "Number of neurites (apical dendrites):",
+        nm.get("number_of_neurites", m, neurite_type=nm.NeuriteType.apical_dendrite),
     )
     print(
-        'Number of neurites (basal dendrites):',
-        nm.get('number_of_neurites', m, neurite_type=nm.NeuriteType.basal_dendrite),
+        "Number of neurites (basal dendrites):",
+        nm.get("number_of_neurites", m, neurite_type=nm.NeuriteType.basal_dendrite),
     )
 
     # number of sections
-    print('Number of sections:', nm.get('number_of_sections', m))
+    print("Number of sections:", nm.get("number_of_sections", m))
     print(
-        'Number of sections (axons):',
-        nm.get('number_of_sections', m, neurite_type=nm.NeuriteType.axon),
+        "Number of sections (axons):",
+        nm.get("number_of_sections", m, neurite_type=nm.NeuriteType.axon),
     )
     print(
-        'Number of sections (apical dendrites):',
-        nm.get('number_of_sections', m, neurite_type=nm.NeuriteType.apical_dendrite),
+        "Number of sections (apical dendrites):",
+        nm.get("number_of_sections", m, neurite_type=nm.NeuriteType.apical_dendrite),
     )
     print(
-        'Number of sections (basal dendrites):',
-        nm.get('number_of_sections', m, neurite_type=nm.NeuriteType.basal_dendrite),
+        "Number of sections (basal dendrites):",
+        nm.get("number_of_sections", m, neurite_type=nm.NeuriteType.basal_dendrite),
     )
 
     # number of sections per neurite
-    print('Number of sections per neurite:', nm.get('number_of_sections_per_neurite', m))
+    print("Number of sections per neurite:", nm.get("number_of_sections_per_neurite", m))
     print(
-        'Number of sections per neurite (axons):',
-        nm.get('number_of_sections_per_neurite', m, neurite_type=nm.NeuriteType.axon),
+        "Number of sections per neurite (axons):",
+        nm.get("number_of_sections_per_neurite", m, neurite_type=nm.NeuriteType.axon),
     )
     print(
-        'Number of sections per neurite (apical dendrites):',
-        nm.get('number_of_sections_per_neurite', m, neurite_type=nm.NeuriteType.apical_dendrite),
+        "Number of sections per neurite (apical dendrites):",
+        nm.get("number_of_sections_per_neurite", m, neurite_type=nm.NeuriteType.apical_dendrite),
     )
     print(
-        'Number of sections per neurite (basal dendrites):',
-        nm.get('number_of_sections_per_neurite', m, neurite_type=nm.NeuriteType.apical_dendrite),
+        "Number of sections per neurite (basal dendrites):",
+        nm.get("number_of_sections_per_neurite", m, neurite_type=nm.NeuriteType.apical_dendrite),
     )
 
     # OK, this is getting repetitive, so lets loop over valid neurite types.
@@ -135,42 +135,42 @@ def main():
 
     # Section lengths for all and different types of neurite
     for ttype in nm.NEURITE_TYPES:
-        sec_len = nm.get('section_lengths', m, neurite_type=ttype)
-        print('Section lengths (', ttype, '):', sep='')
+        sec_len = nm.get("section_lengths", m, neurite_type=ttype)
+        print("Section lengths (", ttype, "):", sep="")
         pprint_stats(sec_len)
 
     # Segment lengths for all and different types of neurite
     for ttype in nm.NEURITE_TYPES:
-        seg_len = nm.get('segment_lengths', m, neurite_type=ttype)
-        print('Segment lengths (', ttype, '):', sep='')
+        seg_len = nm.get("segment_lengths", m, neurite_type=ttype)
+        print("Segment lengths (", ttype, "):", sep="")
         pprint_stats(seg_len)
 
     # Section radial distances for all and different types of neurite
     # Careful! Here we need to pass tree type as a named argument
     for ttype in nm.NEURITE_TYPES:
-        sec_rad_dist = nm.get('section_radial_distances', m, neurite_type=ttype)
-        print('Section radial distance (', ttype, '):', sep='')
+        sec_rad_dist = nm.get("section_radial_distances", m, neurite_type=ttype)
+        print("Section radial distance (", ttype, "):", sep="")
         pprint_stats(sec_rad_dist)
 
     # Section path distances for all and different types of neurite
     # Careful! Here we need to pass tree type as a named argument
     for ttype in nm.NEURITE_TYPES:
-        sec_path_dist = nm.get('section_path_distances', m, neurite_type=ttype)
-        print('Section path distance (', ttype, '):', sep='')
+        sec_path_dist = nm.get("section_path_distances", m, neurite_type=ttype)
+        print("Section path distance (", ttype, "):", sep="")
         pprint_stats(sec_path_dist)
 
     # Local bifurcation angles for all and different types of neurite
     for ttype in nm.NEURITE_TYPES:
-        local_bifangles = nm.get('local_bifurcation_angles', m, neurite_type=ttype)
-        print('Local bifurcation angles (', ttype, '):', sep='')
+        local_bifangles = nm.get("local_bifurcation_angles", m, neurite_type=ttype)
+        print("Local bifurcation angles (", ttype, "):", sep="")
         pprint_stats(local_bifangles)
 
     # Remote bifurcation angles for all and different types of neurite
     for ttype in nm.NEURITE_TYPES:
-        rem_bifangles = nm.get('remote_bifurcation_angles', m, neurite_type=ttype)
-        print('Local bifurcation angles (', ttype, '):', sep='')
+        rem_bifangles = nm.get("remote_bifurcation_angles", m, neurite_type=ttype)
+        print("Local bifurcation angles (", ttype, "):", sep="")
         pprint_stats(rem_bifangles)
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()

examples/histogram.py

@@ -76,24 +76,24 @@ def histogram(neurons, feature, new_fig=True, subplot=111, normed=False, **kwarg
             figure file.
     """
 
-    bins = kwargs.get('bins', 25)
-    cumulative = kwargs.get('cumulative', False)
+    bins = kwargs.get("bins", 25)
+    cumulative = kwargs.get("cumulative", False)
 
     fig, ax = matplotlib_utils.get_figure(new_fig=new_fig, subplot=subplot)
 
-    kwargs['xlabel'] = kwargs.get('xlabel', feature)
+    kwargs["xlabel"] = kwargs.get("xlabel", feature)
 
-    kwargs['ylabel'] = kwargs.get('ylabel', feature + ' fraction')
+    kwargs["ylabel"] = kwargs.get("ylabel", feature + " fraction")
 
-    kwargs['title'] = kwargs.get('title', feature + ' histogram')
+    kwargs["title"] = kwargs.get("title", feature + " histogram")
 
     feature_values = [neurom.features.get(feature, neu) for neu in neurons]
 
     neu_labels = [neu.name for neu in neurons]
 
     ax.hist(feature_values, bins=bins, cumulative=cumulative, label=neu_labels, density=normed)
 
-    kwargs['no_legend'] = len(neu_labels) == 1
+    kwargs["no_legend"] = len(neu_labels) == 1
 
     return matplotlib_utils.plot_style(fig=fig, ax=ax, **kwargs)
 
@@ -149,16 +149,16 @@ def population_histogram(pops, feature, new_fig=True, normed=False, subplot=111,
             figure file.
     """
 
-    bins = kwargs.get('bins', 25)
-    cumulative = kwargs.get('cumulative', False)
+    bins = kwargs.get("bins", 25)
+    cumulative = kwargs.get("cumulative", False)
 
     fig, ax = matplotlib_utils.get_figure(new_fig=new_fig, subplot=subplot)
 
-    kwargs['xlabel'] = kwargs.get('xlabel', feature)
+    kwargs["xlabel"] = kwargs.get("xlabel", feature)
 
-    kwargs['ylabel'] = kwargs.get('ylabel', feature + ' fraction')
+    kwargs["ylabel"] = kwargs.get("ylabel", feature + " fraction")
 
-    kwargs['title'] = kwargs.get('title', feature + ' histogram')
+    kwargs["title"] = kwargs.get("title", feature + " histogram")
 
     pops_feature_values = population_feature_values(pops, feature)
 
@@ -168,7 +168,7 @@ def population_histogram(pops, feature, new_fig=True, normed=False, subplot=111,
         pops_feature_values, bins=bins, cumulative=cumulative, label=pops_labels, density=normed
     )
 
-    kwargs['no_legend'] = len(pops_labels) == 1
+    kwargs["no_legend"] = len(pops_labels) == 1
 
     return matplotlib_utils.plot_style(fig=fig, ax=ax, **kwargs)