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
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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]
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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])))
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for i in sorted_x:
unmasked = ([r[-2] for r in rows if r[0] == i])
mean = np.mean(unmasked)
variance = np.var(unmasked)
plt.hist(unmasked, bins=50)
plt.title("Layer " + str(i))
plt.show()
print "Layer " + str(i) + " has a mean: " + str(mean) + " and variance: " + str(variance)
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for i in sorted_y:
unmasked = ([r[-2] for r in rows if r[1] == i])
mean = np.mean(unmasked)
variance = np.var(unmasked)
plt.hist(unmasked, bins=50)
plt.title("Layer " + str(i))
plt.show()
print "Layer " + str(i) + " has a mean: " + str(mean) + " and variance: " + str(variance)
In [6]:
for i in sorted_z:
unmasked = ([r[-2] for r in rows if r[2] == i])
mean = np.mean(unmasked)
variance = np.var(unmasked)
plt.hist(unmasked, bins=50)
plt.title("Layer " + str(i))
plt.show()
print "Layer " + str(i) + " has a mean: " + str(mean) + " and variance: " + str(variance)
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volume = numpy.ndarray((max(sorted_x)+1, max(sorted_z)+1))
for row in rows:
volume[row[0], row[2]] += float(row[-2])
#count = 0
#for i in range(0,max(sorted_x)): #to iterate between 10 to 20
# for j in range(0,max(sorted_z)):
# if volume[i ,j] != 0:
# print i, j, volume[i ,j]
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import pandas as pd
import seaborn as sns
import numpy as np
volume = np.log(volume+1)
df = pd.DataFrame(volume)
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ax = sns.heatmap(df, yticklabels=False, xticklabels=False)
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vol = np.zeros((len(sorted_x), len(sorted_y), len(sorted_z)))
for r in rows:
vol[sorted_x.index(r[0]), sorted_y.index(r[1]), sorted_z.index(r[2])] = r[-2]
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yflat = np.amax(vol, axis=1)
frame_y = pd.DataFrame(yflat)
sns.heatmap(frame_y)
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In [19]:
zflat = np.amax(vol, axis=2)
frame_z = pd.DataFrame(zflat)
sns.heatmap(frame_z)
Out[19]:
In [20]:
xflat = np.amax(vol, axis=0)
frame_x = pd.DataFrame(xflat)
sns.heatmap(frame_x)
Out[20]:
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