In [97]:
%matplotlib inline
import matplotlib.mlab as mlab
from matplotlib import pyplot as plt
from scipy.stats import norm
import csv
import numpy as np
from sklearn.neighbors.kde import KernelDensity as kde
import seaborn as sns
import pandas as pd
In [87]:
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 [88]:
unmaskedSynapses = ([r[-1] for r in rows if r[-2] != 0])
unmaskedSynapsesNoZero = ([r[-1] for r in rows if r[-2] != 0 if r[-1] !=0])
In [102]:
#including zeros
plt.hist(unmaskedSynapses, bins=50)
plt.show()
In [103]:
unmaskedSynapses = np.asarray(unmaskedSynapses)
sns.kdeplot(unmaskedSynapses)
Out[103]:
In [91]:
#unmaskedSynapsesArr = np.asarray(unmaskedSynapses)
#unmaskedSynapsesArr.reshape(1,-1)
#kde = KernelDensity(kernel='gaussian', bandwidth=0.2).fit(unmaskedSynapsesArr)
In [92]:
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]
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])))
volume = np.ndarray((len(sorted_x), len(sorted_y), len(sorted_z)))
for row in rows:
if row[-1] != 0:
volume[sorted_x.index(row[0]), sorted_y.index(row[1]), sorted_z.index(row[2])] = row[-1]
In [93]:
plt.imshow(np.amax(volume, axis=2), interpolation='nearest')
plt.show()
In [94]:
#datas = np.amax(volume, axis=2)
#print type(datas)
#for i in len(datas):
#for j in len(datas[i]):
# dframe.append(x=i, y=j,value = )
#print np.amax(volume, axis=2)
#d = pd.DataFrame(data, columns=list('xy'))
#d = pd.DataFrame(data=data[0:,0:],index=data[1:,0], columns=data[0,1:])
#print d
#sns.jointplot(x="x", y="y", data=d, kind="kde");
In [108]:
plt.hist(unmaskedSynapsesNoZero, bins=50)
(mu, sigma) = norm.fit(unmaskedSynapsesNoZero)
print mu, sigma
y = mlab.normpdf( 50, mu, sigma)
plt.plot(100, y, 'r--', linewidth=2)
plt.show()
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