#!/usr/bin/env python

# Copyright (c) 2015, Ecole Polytechnique Federale de Lausanne, Blue Brain Project
# All rights reserved.
#
# This file is part of NeuroM <https://github.com/BlueBrain/NeuroM>
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#     1. Redistributions of source code must retain the above copyright
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#        notice, this list of conditions and the following disclaimer in the
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#     3. Neither the name of the copyright holder nor the names of
#        its contributors may be used to endorse or promote products
#        derived from this software without specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
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'''Plot a selection of features from a morphology population'''

from neurom import ezy
from neurom.analysis import morphtree as mt
from collections import defaultdict
import json
import numpy as np
import matplotlib.pyplot as plt


nrns = ezy.load_neurons('../Synthesizer/build/L23MC/')
sim_params = json.load(open('../Synthesizer/data/L23MC.json'))


# Neurite types of interest
NEURITES_ = (ezy.TreeType.axon,
             ezy.TreeType.apical_dendrite,
             ezy.TreeType.basal_dendrite)

# map feature names to functors that get us arrays of that
# feature, for a given tree type
GET_FEATURE = {
    'trunk_azimuth': lambda nrn, typ: [mt.trunk_azimuth(n, nrn.soma)
                                       for n in nrn.neurites if n.type == typ],
    'trunk_elevation': lambda nrn, typ: [mt.trunk_elevation(n, nrn.soma)
                                         for n in nrn.neurites if n.type == typ]
}

# For now we use all the features in the map
FEATURES = GET_FEATURE.keys()

# data structure to store results
stuff = defaultdict(lambda: defaultdict(list))

# unpack data into arrays
for nrn in nrns:
    for t in NEURITES_:
        for feat in FEATURES:
            stuff[feat][str(t).split('.')[1]].extend(
                GET_FEATURE[feat](nrn, t)
            )

# load histograms, distribution parameter sets and figures into arrays.
# To plot figures, do
# plots[i].show()

histos = []
dists = []
plots = []

for feat, d in stuff.iteritems():
    for typ, data in d.iteritems():
        dist = sim_params['components'][typ][feat]
        dists.append(dist)
        print 'Type = %s, Feature = %s, Distribution = %s' % (typ, feat, dist)
        # print 'DATA', data

        num_bins = 100
        histo = np.histogram(data, num_bins, normed=True)
        # print 'HEIGHT', histo[0]
        # print 'BINS', histo[1]
        histos.append(histo)
        fig = plt.figure()
        ax = fig.add_subplot(111)
        ax.hist(histo[0], bins=histo[1])
        ax.set_title('%s (%s)' % (feat, typ))
        plots.append(fig)

@@ -1841,16 +1841,61 @@
 e as mt%0A
+from neurom.view import common as view_utils%0A
 from col
@@ -1957,40 +1957,8 @@
  np%0A
-import matplotlib.pyplot as plt%0A
 %0A%0Anr
@@ -3599,53 +3599,59 @@
  fig
- = plt.figure()%0A        ax = fig.add_
+, ax = view_utils.get_figure(new_fig=True, 
 subplot
-(
+=
 111)

Simplify feature plotter