In [23]:
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
%matplotlib inline
import urllib2
import numpy as np
import matplotlib as mpl
mpl.rcParams['font.size'] = 25
mpl.rc('font',family='Times New Roman')
np.set_printoptions(precision=3, suppress=True)
url = ('https://raw.githubusercontent.com/Upward-Spiral-Science'
'/data/master/syn-density/output.csv')
data = urllib2.urlopen(url)
csv = np.genfromtxt(data, delimiter=",")[1:]
def check_condition(row):
if row[3] == 0:
return False
return True
a = np.apply_along_axis(check_condition, 1, csv)
a = np.where(a == True)[0]
nonZeroMask = csv[a, :]
synDividedMask = np.divide(nonZeroMask[:,4],nonZeroMask[:,3])
synDividedMask = synDividedMask * (64**3)
accurateDataT = np.vstack((nonZeroMask[:,0],nonZeroMask[:,1],nonZeroMask[:,2],synDividedMask))
accurateData = accurateDataT.T
cleaned = accurateData[accurateData[:,0] >= 409]
cleaned = cleaned[cleaned[:,0] <= 3529]
cleaned = cleaned[cleaned[:,1] >= 1564]
cleaned = cleaned[cleaned[:,1] <= 3124]
In [27]:
import math
divisionsx = np.unique(cleaned[:,0])
meanx = np.zeros((2,len(divisionsx)))
divisionsy = np.unique(cleaned[:,1])
meany = np.zeros((2,len(divisionsy)))
divisionsz = np.unique(cleaned[:,2])
meanz = np.zeros((2,len(divisionsz)))
maxlen = np.amax([len(divisionsx),len(divisionsy),len(divisionsz)])
xstep = np.divide(maxlen,len(divisionsx))
ystep = 2
zstep = 8
counter = 0
for d in divisionsx:
slicex = cleaned[cleaned[:,0] == d]
meanx[0,counter] = (counter)*xstep
meanx[1,counter] = np.mean(slicex[:,3])
counter += 1
counter = 0
for d in divisionsy:
slicey = cleaned[cleaned[:,1] == d]
meany[0,counter] = (counter)*ystep
meany[1,counter] = np.mean(slicey[:,3])
counter += 1
counter = 0
for d in divisionsz:
slicez = cleaned[cleaned[:,2] == d]
meanz[0,counter] = (counter)*zstep
meanz[1,counter] = np.mean(slicez[:,3])
counter += 1
#plot it
allmean = [301] * maxlen
fig,ax = plt.subplots()
ax.set_ylim([150,375])
ax.set_title('Avg. Synapses in all Axes')
ax.set_yticks([150,225,300,375])
ax.set_xticks([0,20,40,60,80])
ax.set_xlabel('x,y,z coordinate standardized to 0')
ax.set_ylabel('synaptic density')
ax.plot(allmean,label='whole data set',color='black')
ax.plot(meanx[0,:],meanx[1,:],label='x',color='blue')
ax.plot(meany[0,:],meany[1,:],label='y',color='red')
ax.plot(meanz[0,:],meanz[1,:],label='z',color='magenta')
legend = ax.legend(loc='lower left',fontsize=15)
plt.subplots_adjust(bottom = 0.2)
plt.subplots_adjust(left = 0.2)
plt.savefig('Fig7a.pdf', format='pdf', dpi=1000)
In [30]:
mean = meany[1,:]
mean1 = mean[:-1]
mean2 = mean[1:]
meandiff = mean2 - mean1
print meandiff
fig,ax = plt.subplots()
ax.plot(meandiff)
ax.plot([0] * len(meandiff))
ax.set_xlabel('y slice')
ax.set_ylabel('change in syn/unmasked')
ax.set_title('Change in Synapses: y')
ax.set_xticks([0,10,20,30,40])
ax.set_yticks([-30,-15,0,15])
plt.subplots_adjust(left = 0.2)
plt.subplots_adjust(bottom = 0.2)
plt.savefig('Fig7b.pdf', format='pdf', dpi=1000)
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