Add testing of example scripts (#1041) (#1096)

examples/boxplot.py

@@ -27,11 +27,17 @@
 # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 """Box Plot function for multiple morphs."""
+from pathlib import Path
 
+from neurom import load_morphologies
 from neurom.view import matplotlib_utils
+from neurom.features import get
 
 
-def boxplot(neurons, feature, new_fig=True, subplot=False):
+PACKAGE_DIR = Path(__file__).resolve().parent.parent
+
+
+def boxplot(neurons, feature, new_fig=True, subplot=111):
     """Plot a histogram of the selected feature for the population of morphologies.
     Plots x-axis versus y-axis on a scatter|histogram|binned values plot.
 
@@ -52,12 +58,26 @@ def boxplot(neurons, feature, new_fig=True, subplot=False):
         Default is False, which returns a matplotlib figure object. If True,
         returns a matplotlib axis object, for use as a subplot.
     """
-    feature_values = [getattr(neu, 'get_' + feature)() for neu in neurons]
+    feature_values = [get(feature, neuron) for neuron in neurons]
 
     _, ax = matplotlib_utils.get_figure(new_fig=new_fig, subplot=subplot)
 
     ax.boxplot(feature_values)
 
-    x_labels = ['neuron_id' for _ in neurons]
+    x_labels = [neuron.name for neuron in neurons]
 
     ax.set_xticklabels(x_labels)
+
+    # uncomment below to show image
+    # pylab.show()
+
+
+def main():
+
+    morphology_directory =  Path(PACKAGE_DIR, "tests/data/valid_set")
+    neurons = load_morphologies(morphology_directory)
+    boxplot(neurons, "section_lengths")
+
+
+if __name__ == "__main__":
+    main()

examples/density_plot.py

@@ -27,20 +27,28 @@
 # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 """Example for generating density plots."""
+from pathlib import Path
 
 import pylab as plt
+import matplotlib as mpl
 import numpy as np
 
 from neurom import get as get_feat
 from neurom.view import matplotlib_utils, matplotlib_impl
 from neurom.core.types import NeuriteType
 
+from neurom import load_morphologies
+
+PACKAGE_DIR = Path(__file__).resolve().parent.parent
+
 
 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 = get_feat('segment_midpoints', population, neurite_type=neurite_type)
+    segment_midpoints = np.array(
+	get_feat('segment_midpoints', population, neurite_type=neurite_type)
+    )
     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))
@@ -62,12 +70,13 @@ def plot_density(population,  # pylint: disable=too-many-arguments, too-many-loc
     mask = H1 < threshold  # mask = H1==0
     H2 = np.ma.masked_array(H1, mask)
 
-    getattr(plt.cm, color_map).set_bad(color='white', alpha=None)
+    colormap = mpl.cm.get_cmap(color_map).copy()
+    colormap.set_bad(color='white', alpha=None)
 
     plots = ax.contourf((xedges1[:-1] + xedges1[1:]) / 2,
                         (yedges1[:-1] + yedges1[1:]) / 2,
                         np.transpose(H2), # / np.max(H2),
-                        cmap=getattr(plt.cm, color_map), levels=levels)
+                        cmap=colormap, levels=levels)
 
     if not no_colorbar:
         cbar = plt.colorbar(plots)
@@ -91,9 +100,23 @@ def plot_neuron_on_density(population, # pylint: disable=too-many-arguments
     """
     _, ax = matplotlib_utils.get_figure(new_fig=new_fig)
 
-    matplotlib_impl.plot_tree(population.neurites[0], ax)
+    ref_neuron = population[0]
+    matplotlib_impl.plot_tree(ref_neuron.neurites[0], ax)
 
     return plot_density(population, plane=plane, bins=bins, new_fig=False, subplot=subplot,
                         colorlabel=colorlabel, labelfontsize=labelfontsize, levels=levels,
                         color_map=color_map, no_colorbar=no_colorbar, threshold=threshold,
                         neurite_type=neurite_type, **kwargs)
+
+
+def main():
+
+    morphology_directory = Path(PACKAGE_DIR, "tests/data/valid_set")
+    neurons = load_morphologies(morphology_directory)
+
+    plot_density(neurons)
+    plot_neuron_on_density(neurons)
+
+
+if __name__ == "__main__":
+    main()

examples/end_to_end_distance.py

@@ -28,13 +28,17 @@
 # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 """Calculate and plot end-to-end distance of neurites."""
+from pathlib import Path
 
 import neurom as nm
 from neurom import morphmath
 import numpy as np
 import matplotlib.pyplot as plt
 
 
+PACKAGE_DIR = Path(__file__).resolve().parent.parent
+
+
 def path_end_to_end_distance(neurite):
     """Calculate and return end-to-end-distance of a given neurite."""
     trunk = neurite.root_node.points[0]
@@ -54,7 +58,8 @@ def make_end_to_end_distance_plot(nb_segments, end_to_end_distance, neurite_type
     plt.title(neurite_type)
     plt.xlabel('Number of segments')
     plt.ylabel('End-to-end distance')
-    plt.show()
+    # uncomment to show
+    #plt.show()
 
 
 def calculate_and_plot_end_to_end_distance(neurite):
@@ -71,9 +76,9 @@ def _dist(seg):
                                   end_to_end_distance, neurite.type)
 
 
-if __name__ == '__main__':
+def main():
     #  load a neuron from an SWC file
-    filename = '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
@@ -92,3 +97,7 @@ def _dist(seg):
         # print (number of segments, end-to-end distance, neurite type)
         print(sum(len(s.points) - 1 for s in nrte.root_node.ipreorder()),
               path_end_to_end_distance(nrte), nrte.type)
+
+
+if __name__ == '__main__':
+    main()

examples/extract_distribution.py

@@ -31,52 +31,46 @@
 """Extract a distribution for the selected feature of the population of morphologies among
    the exponential, normal and uniform distribution, according to the minimum ks distance.
    """
+from pathlib import Path
 
 from itertools import chain
 import argparse
 import json
 
 import neurom as nm
 from neurom import stats
+from neurom.utils import NeuromJSON
 
 
-def parse_args():
-    """Parse command line arguments."""
-    parser = argparse.ArgumentParser(
-        description='Morphology fit distribution extractor',
-        epilog='Note: Outputs json of the optimal distribution \
-                and corresponding parameters.')
+PACKAGE_DIR = Path(__file__).resolve().parent.parent
 
-    parser.add_argument('datapath',
-                        help='Path to morphology data directory')
 
-    parser.add_argument('feature',
-                        help='Feature to be extracted with neurom.get')
-
-    return parser.parse_args()
-
-
-def extract_data(data_path, feature):
+def find_optimal_distribution(population_directory, feature):
     """Loads a list of morphologies, extracts feature
        and transforms the fitted distribution in the correct format.
        Returns the optimal distribution, corresponding parameters,
        minimun and maximum values.
     """
-    population = nm.load_morphologies(data_path)
+    population = nm.load_morphologies(population_directory)
 
     feature_data = [nm.get(feature, n) for n in population]
     feature_data = list(chain(*feature_data))
 
     return stats.optimal_distribution(feature_data)
 
 
-if __name__ == '__main__':
-    args = parse_args()
+def main():
+
+    population_directory = Path(PACKAGE_DIR, "tests/data/valid_set")
 
-    d_path = args.datapath
+    result = stats.fit_results_to_dict(
+	find_optimal_distribution(population_directory, "section_lengths")
+    )
 
-    feat = args.feature
+    print(json.dumps(
+	result, indent=2, separators=(',', ': '), cls=NeuromJSON
+    ))
 
-    _result = stats.fit_results_to_dict(extract_data(d_path, feat))
 
-    print(json.dumps(_result, indent=2, separators=(',', ': ')))
+if __name__ == '__main__':
+    main()

examples/features_graph_table.py

@@ -28,33 +28,14 @@
 # SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 """Example for comparison of the same feature of multiple cells."""
-import argparse
 from pathlib import Path
 
 import pylab as pl
 import neurom as nm
 from neurom.io.utils import get_morph_files
 
 
-def parse_args():
-    """Parse command line arguments."""
-    parser = argparse.ArgumentParser(description='Feature Comparison Between Different Cells')
-
-    parser.add_argument('-d',
-                        '--datapath',
-                        help='Data directory')
-
-    parser.add_argument('-o',
-                        '--odir',
-                        default='.',
-                        help='Output path')
-
-    parser.add_argument('-f',
-                        '--features',
-                        nargs='+',
-                        help='List features separated by spaces')
-
-    return parser.parse_args()
+PACKAGE_DIR = Path(__file__).resolve().parent.parent
 
 
 def stylize(ax, name, feature):
@@ -88,7 +69,7 @@ def histogram(neuron, feature, ax, bins=15, normed=True, cumulative=False):
 
     feature_values = nm.get(feature, neuron)
     # generate histogram
-    ax.hist(feature_values, bins=bins, cumulative=cumulative, normed=normed)
+    ax.hist(feature_values, bins=bins, cumulative=cumulative, density=normed)
 
 
 def plot_feature(feature, cell):
@@ -106,14 +87,25 @@ def plot_feature(feature, cell):
     return fig
 
 
-if __name__ == '__main__':
-    args = parse_args()
+def create_feature_plots(morphologies_dir, feature_list, output_dir):
 
-    for morph_file in get_morph_files(args.datapath):
+    for morph_file in get_morph_files(morphologies_dir):
         m = nm.load_morphology(morph_file)
 
-        for _feature in args.features:
-            f = plot_feature(_feature, m)
-            figname = "{0}_{1}.eps".format(_feature, m.name)
-            f.savefig(Path(args.odir, figname))
+        for feature_name in feature_list:
+            f = plot_feature(feature_name, m)
+            figname = f"{feature_name}_{m.name}.eps"
+            f.savefig(Path(output_dir, figname))
             pl.close(f)
+
+
+def main():
+    create_feature_plots(
+        morphologies_dir=Path(PACKAGE_DIR, "tests/data/valid_set"),
+	feature_list=["section_lengths"],
+	output_dir=".",
+    )
+
+
+if __name__ == '__main__':
+    main()

examples/get_features.py

@@ -33,13 +33,16 @@
 morphometrics functionality.
 
 """
+from pathlib import Path
 
-from __future__ import print_function
 from pprint import pprint
 import numpy as np
 import neurom as nm
 
 
+PACKAGE_DIR = Path(__file__).resolve().parent.parent
+
+
 def stats(data):
     """Dictionary with summary stats for data
 
@@ -60,9 +63,9 @@ def pprint_stats(data):
     pprint(stats(data))
 
 
-if __name__ == '__main__':
+def main():
 
-    filename = '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)
@@ -152,3 +155,7 @@ def pprint_stats(data):
         rem_bifangles = nm.get('remote_bifurcation_angles', m, neurite_type=ttype)
         print('Local bifurcation angles (', ttype, '):', sep='')
         pprint_stats(rem_bifangles)
+
+
+if __name__ == '__main__':
+    main()