In [1]:

    

%matplotlib inline

from matplotlib import pyplot as plt
import matplotlib.mlab as mlab
import csv
from scipy.stats import norm
import numpy as np
import scipy.stats as stats
import numpy


    

In [2]:

    

data = open('../data/data.csv', 'r').readlines()
fieldnames = ['x', 'y', 'z', 'unmasked', 'synapses']
reader = csv.reader(data)
reader.next()

rows = [[int(col) for col in row] for row in reader]


    

In [3]:

    

sorted_x = sorted(list(set([r[0] for r in rows])))
sorted_y = sorted(list(set([r[1] for r in rows])))
sorted_z = sorted(list(set([r[2] for r in rows])))

x = list([r[0] for r in rows])
y = list([r[1] for r in rows])
z = list([r[2] for r in rows])


    

In [4]:

    

real_volume = numpy.zeros((len(sorted_x), len(sorted_y), len(sorted_z)))
for r in rows:
    real_volume[sorted_x.index(r[0]), sorted_y.index(r[1]), sorted_z.index(r[2])] = r[-1]


    

In [5]:

    

XYPlane = numpy.zeros((len(x), len(y)))
XZPlane = numpy.zeros((len(x), len(z)))
YZPlane = numpy.zeros((len(y), len(z)))

for row in rows:
    XYPlane[row[0], row[1]] += row[-1]
    XZPlane[row[0], row[2]] += row[-1]
    YZPlane[row[1], row[2]] += row[-1]


    

In [6]:

    

import scipy.signal as sig


    

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AutoXYPlane = sig.correlate2d(YZPlane,YZPlane)


    

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AutoXZPlane = sig.correlate2d(XZPlane,XZPlane)


    

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AutoYZPlane = sig.correlate2d(YZPlane,YZPlane)


    

In [7]:

    

XYPlaneShrunk = numpy.zeros((len(sorted_x), len(sorted_y)))
XZPlaneShrunk = numpy.zeros((len(sorted_x), len(sorted_z)))
YZPlaneShrunk = numpy.zeros((len(sorted_y), len(sorted_z)))

for r in rows:
    XYPlaneShrunk[sorted_x.index(r[0]), sorted_y.index(r[1])] += r[-1]
    XZPlaneShrunk[sorted_x.index(r[0]), sorted_z.index(r[2])] += r[-1]
    YZPlaneShrunk[sorted_y.index(r[1]), sorted_z.index(r[2])] += r[-1]


    

In [15]:

    

AutoXYPlane = sig.correlate2d(XYPlaneShrunk,XYPlaneShrunk)
AutoXZPlane = sig.correlate2d(XZPlaneShrunk,XZPlaneShrunk)
AutoYZPlane = sig.correlate2d(YZPlaneShrunk,YZPlaneShrunk)


plt.imshow(AutoXYPlane, interpolation='nearest', cmap = 'hot')
plt.gca().set_aspect('equal', adjustable='box')
plt.colorbar()
plt.show()

plt.imshow(AutoXZPlane, interpolation='nearest', cmap = 'hot')
plt.colorbar()
plt.axis('equal')
plt.show()

plt.imshow(AutoYZPlane, interpolation='nearest', cmap = 'hot')
plt.colorbar()
plt.show()


    

In [10]:

    

import pandas as pd
import seaborn as sns


    

In [46]:

    

frame = pd.DataFrame(AutoXYPlane)
ax = plt.axes()
sns.heatmap(frame, ax = ax)

ax.set_title('XY Plane 2D Spatial AutoCorrelation',fontsize = 20)
ax.set_xticks(np.arange(0, 101, 20))
ax.set_yticks(np.arange(0, 201, 20))
plt.xlabel("Y axis", fontsize = 20)
plt.ylabel("X axis", fontsize = 20)
plt.show()


    

In [48]:

    

frame = pd.DataFrame(AutoXZPlane)
ax = plt.axes()
sns.heatmap(frame, ax = ax)
ax.set_yticks(np.arange(0, 201, 20))
ax.set_title('XZ Plane 2D Spatial AutoCorrelation',fontsize = 20)
plt.xlabel("Z axis", fontsize = 20)
plt.ylabel("X axis", fontsize = 20)
plt.show()


    

In [49]:

    

frame = pd.DataFrame(AutoYZPlane)
ax = plt.axes()
sns.heatmap(frame, ax = ax)
ax.set_yticks(np.arange(0, 101, 20))
ax.set_title('YZ Plane 2D Spatial AutoCorrelation',fontsize = 20)
plt.xlabel("Z axis", fontsize = 20)
plt.ylabel("Y axis", fontsize = 20)
plt.show()


    

In [83]:

    

#Try auto correlation per Y layers

for i in sorted_y:
    Plane = numpy.zeros((len(sorted_x), len(sorted_z)))
    for r in rows:
        if r[1] == i:
            Plane[sorted_x.index(r[0]), sorted_z.index(r[2])] += r[-1]
    
    AutoPlane = sig.correlate2d(Plane,Plane)

    frame = pd.DataFrame(AutoPlane)
    sns.heatmap(frame)
    plt.show()


    

In [86]:

    

#Take into account unmasked!

XYPlaneShrunk = numpy.zeros((len(sorted_x), len(sorted_y)))
XZPlaneShrunk = numpy.zeros((len(sorted_x), len(sorted_z)))
YZPlaneShrunk = numpy.zeros((len(sorted_y), len(sorted_z)))

for r in rows:
    if r[-1] != 0:
        XYPlaneShrunk[sorted_x.index(r[0]), sorted_y.index(r[1])] += float(r[-1])/r[-2]
        XZPlaneShrunk[sorted_x.index(r[0]), sorted_z.index(r[2])] += float(r[-1])/r[-2]
        YZPlaneShrunk[sorted_y.index(r[1]), sorted_z.index(r[2])] += float(r[-1])/r[-2]


    

In [87]:

    

AutoXYPlane = sig.correlate2d(XYPlaneShrunk,XYPlaneShrunk)
AutoXZPlane = sig.correlate2d(XZPlaneShrunk,XZPlaneShrunk)
AutoYZPlane = sig.correlate2d(YZPlaneShrunk,YZPlaneShrunk)


plt.imshow(AutoXYPlane, interpolation='nearest', cmap = 'hot')
plt.colorbar()
plt.show()

plt.imshow(AutoXZPlane, interpolation='nearest', cmap = 'hot')
plt.colorbar()
plt.show()

plt.imshow(AutoYZPlane, interpolation='nearest', cmap = 'hot')
plt.colorbar()
plt.show()


    

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