Image of rat pyramidal cell layer 2/3 for comapeing to the NETMORPH

In [1]:

    

import numpy as np
import McNeuron
import matplotlib.pyplot as plt
from copy import deepcopy
%matplotlib inline
#np.random.seed(0)
L_neuron = McNeuron.visualize.get_all_path("/Volumes/Arch/Projects/Computational Anatomy/pyramidal/de koninck/")
#L_neuron = McNeuron.visualize.get_all_path("/Volumes/Arch/Projects/Computational Anatomy/badea, ganglia cell, retina mouse/")


    

In [90]:

    

i = 45
n = McNeuron.Neuron(file_format = 'swc', input_file=L_neuron[i])
#inter2 = Inter2.subsample_given_n_node(400)
McNeuron.visualize.plot_2D(n, dpi = 120, size = 5, show_width= False,  background = .01,line_width=2)


    

In [91]:

    

McNeuron.visualize.plot_dedrite_tree(n)


    

In [54]:

    

#n.show_features()


    

In [ ]:

    

McNeuron.visualize.plot_2D(n.subsample_given_n_node(100),size = 5)


    

In [ ]:

    

L_neuron = McNeuron.visualize.get_all_path("/Volumes/Arch/Projects/Computational Anatomy/pyramidal/de koninck/")
B = np.zeros(len(L_neuron))
for i in range(len(L_neuron)):
    n = McNeuron.Neuron(file_format = 'swc', input_file=L_neuron[i])
    B[i] = sum(n.branch_order[1:]==2)
    print i


    

In [92]:

    

B = McNeuron.visualize.important_node_full_matrix(n)
I = np.sort(B.sum(axis=1))
(F,G) = np.unique(I, return_counts=True)
plt.plot(G)


    

    
Out[92]:





[<matplotlib.lines.Line2D at 0x10b8b2dd0>]






    

In [93]:

    

plt.imshow(B)


    

    
Out[93]:





<matplotlib.image.AxesImage at 0x10da64310>






    

In [94]:

    

from numpy.linalg import inv
P = np.eye(B.shape[0]) - inv(B)
plt.imshow(P)


    

    
Out[94]:





<matplotlib.image.AxesImage at 0x1092f2150>






    

In [96]:

    

P.sum(axis=0)


    

    
Out[96]:





array([ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,
        2.,  2.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,
        0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

In [100]:

    

(ends,) = np.where(P.sum(axis=0)==2)
I = B[np.ix_(ends,ends)].sum(axis=1)
(F,G) = np.unique(I, return_counts=True)
plt.plot(G)


    

    
Out[100]:





[<matplotlib.lines.Line2D at 0x10d86d050>]






    

In [66]:

    

B[np.ix_(ends,ends)]


    

    
Out[66]:





array([[ 1.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  1.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  1., ...,  0.,  0.,  0.],
       ..., 
       [ 0.,  0.,  0., ...,  1.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  1.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  1.]])

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