In [1]:

    

import ndio.remote.neurodata as neurodata
nd = neurodata()


    

In [2]:

    

token_mito = "kasthuri2015_ramon_v4"
channel_mito = 'mitochondria'


    

In [3]:

    

'''
Mitochondria count
A count of the total number of mitochondria within the bounds (694×1794, 1750×2460, 1004×1379).

'''

import numpy as np
from datetime import datetime

startTime = datetime.now()

# Get cylinder 1 mask

token_green = 'kat11greencylinder'
token_red = 'kat11redcylinder'
token_mojo = 'kat11mojocylinder'
channel = 'annotation'
res = 3

pixel_dim = 0.003*(2**res)*0.003*(2**res)*0.030  #can get from LIMS
imsize = nd.get_image_size(token_red, resolution=res)
offset = nd.get_image_offset(token_red, resolution=res)

# Known bounds - TODO get from LIMS

xbox = [694,1794]; 
ybox = [1750, 2460];
zbox = [1004, 1379];

# Get mitochondria in "core region"
mask = nd.get_volume(token_red, channel, xbox[0],xbox[1],ybox[0],ybox[1],zbox[0],zbox[1],resolution=res)
# count unique mitochondria - TODO - split apart different labels


    

In [4]:

    

mito = nd.get_volume(token_mito, channel_mito, xbox[0],xbox[1],ybox[0],ybox[1],zbox[0],zbox[1],resolution=res)

# Mask volume
mito.cutout[mask.cutout == 0] = 0


    

In [5]:

    

mito_id = np.unique(mito.cutout) - 1 #to account for bg

import pylab as plt
%matplotlib inline
plt.imshow(mito.cutout[:,:,100].T)
plt.show()

print 'Total number of mitochondria in cylinder 1 is: ' + str(len(mito_id))


    

    













    




Total number of mitochondria in cylinder 1 is: 650

In [6]:

    

# Get all dendrite source data and mask it (following code examples above)

import ndio.ramon as ramon
import time
import ndio

start = time.time()

token = 'kasthuri2015_ramon_v4'
channel = 'neurons'
res = 3

id_segment = nd.get_ramon_ids(token, channel, ramon_type=ramon.RAMONSegment)
id_neuron = nd.get_ramon_ids(token, channel, ramon_type=ramon.RAMONNeuron)
print 'Number of RAMON segments (unique objects) is: ' + str(len(id_segment))
print 'In the 3 volume cylinder we identified {} (RAMON) neurons'.format(len(id_neuron))

# Retrieve metadata for all objects

# Get all segments
channel = 'neurons'
segAll = nd.get_ramon(token,channel,id_segment)
print '\ntime elapsed so far: ' + str(time.time()-start)


# Mask masked volume for 1) inhib dendrites  2) excitatory dendrites - see claim 12
# count mito in 1) and 2) by voxel and by percentage


    

    




Number of RAMON segments (unique objects) is: 3945
In the 3 volume cylinder we identified 1907 (RAMON) neurons

time elapsed so far: 263.5589571

In [7]:

    

'''
Count Dendrite Things
'''

# TODO:  Maybe count difference based on cylinder of interest
dendrite_neurites = []
dendrite_neurons =  []
dendrite_excitatory_neurites = []
dendrite_smooth_neurites = []
spine = 0
spine_id = []
for x in segAll:
    if x.segmentclass == 2:
        dendrite_neurites.append(x.id)
        dendrite_neurons.append(x.neuron)
        
        if x.kvpairs['segment_subtype'] == 'spiny':
            dendrite_excitatory_neurites.append(x.id)
            
        if x.kvpairs['segment_subtype'] == 'smooth':
            dendrite_smooth_neurites.append(x.id)

    if x.kvpairs['is_spine'] is '1':
        spine += 1
        spine_id.append(x.id)


    

In [8]:

    

segdata = nd.get_cutout(token, channel, xbox[0],xbox[1],ybox[0],ybox[1],zbox[0],zbox[1],resolution=res)
def ismember(A, B):
    return [ np.sum(a == B) for a in A ]

segAll[mask == 0] = 0
excitatory_mask = ismember(dendrite_excitatory_neurites,segdata)
inhibitory_mask = ismember(dendrite_smooth_neurites,segdata)
spine_mask = ismember(spine_id,segAll)


    

    




/Users/graywr1/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py:3: UnicodeWarning: Unicode equal comparison failed to convert both arguments to Unicode - interpreting them as being unequal
  app.launch_new_instance()

In [9]:

    

print spine_mask
'''
Mitochondria occupy twice as much volume in inhibitory dendrites than in excitatory dendrites
(only in cylinder 1)
'''

plt.imshow(segdata[:,:,100].T)
plt.show()

'''
Only very rarely (n = 3/1,425) do mitochondria reside in dendritic spines
'''

# Get all spines (see claim 12)
# Get all data and mask it (following code examples above)
# Mask masked volume for spines
# For each spine is there a mito?  How many total spines?  

#print '3/1425'


    

    




[]






    













    
Out[9]:





'\nOnly very rarely (n = 3/1,425) do mitochondria reside in dendritic spines\n'

In [10]:

    

print "There are {} mitochondria total in the annotated volume.".format(len(mito_id))


    

    




There are 650 mitochondria total in the annotated volume.

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