#!/usr/bin/env python

# Copyright (c) 2015, Ecole Polytechnique Federale de Lausanne, Blue Brain Project
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# This file is part of NeuroM <https://github.com/BlueBrain/NeuroM>
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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 matplotlib.pyplot as plt

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


NEURITES_ = (ezy.TreeType.axon,
             ezy.TreeType.apical_dendrite,
             ezy.TreeType.basal_dendrite)

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]
}

FEATURES = GET_FEATURE.keys()

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)
            )

# Then access the arrays of azimuths with tr_azimuth[key]
# where the keys are string representations of the tree types.

for feat, d in stuff.iteritems():
    for typ, data in d.iteritems():
        print typ, feat
        print 'Params:', sim_params['components'][typ][feat]

        num_bins = 100
        n, bins, patches = plt.hist(data, num_bins, normed=1, facecolor='green', alpha=0.5)
        plt.show()

Add examples_plot_features.py