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Diffusion Motion correction paper (cluster_pipeline) Browse

R6289/histograms.py
cfea8427fe6ccluster_pipeline

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histograms.py
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from matplotlib import pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
import glob
path = '.'
n_frames = int(len(glob.glob1(path,"*.dat"))/2)
wx = np.genfromtxt('tmp201804201803wx_frame1.dat', delimiter=',')
wy = np.genfromtxt('tmp201804201803wy_frame1.dat', delimiter=',')
wx2 = np.square(wx)
wy2 = np.square(wy)
l2 = wx2 + wy2
l = np.sqrt(l2)
lengths = np.empty([n_frames,wx.size])
lengths[0,:] = l.flatten()
for i in range(1,n_frames):
wx = np.genfromtxt('tmp201804201803wx_frame{}.dat'.format(i+1), delimiter=',')
wy = np.genfromtxt('tmp201804201803wy_frame{}.dat'.format(i+1), delimiter=',')
wx2 = np.square(wx)
wy2 = np.square(wy)
l2 = wx2 + wy2
l = np.sqrt(l2)
lengths[i,:] = l.flatten()
plt.figure()
plt.hist(lengths.flatten())
plt.xlabel('Displacement [pixels]')
plt.ylabel('Frequency')
plt.show()
plt.figure()
plt.hist2d(lengths.flatten(),np.repeat(range(1,n_frames+1),wx.size),bins=9)
plt.show()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
hist, xedges, yedges = np.histogram2d(lengths.flatten(), np.repeat(range(1,n_frames+1),wx.size), bins=9, range=[[0, 50], [1, n_frames]])
xpos, ypos = np.meshgrid(xedges[:-1], yedges[:-1])
xpos = xpos.flatten('F')
ypos = ypos.flatten('F')
zpos = np.zeros_like(xpos)
dx = 2.5 * np.ones_like(zpos)
dy = (n_frames-1)/20*np.ones_like(zpos)
dz = hist.flatten()
ax.bar3d(xpos, ypos, zpos, dx, dy, dz, color='b', zsort='average')
plt.show()
plt.figure()
max_lengths = np.amax(lengths, axis=1)
plt.hist(max_lengths)
plt.show()
plt.figure()
mean_lengths = np.mean(lengths, axis=1)
plt.hist(mean_lengths)
plt.show()
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