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fourierfilter.py
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fourierfilter.py
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"""
Optical lock-in detection
This script reads a (x,y,t) .dv file and lets the user select
a region of interest from which a reference waveform is
calculated.This reference is used to calculate the time-
correlation pixel-by-pixel. The final output the correlation-
weighed image is stored as a .tiff file.
"""
import matplotlib.pyplot as pyplot
# from matplotlib.widgets import RectangleSelector
import numpy as np
from filehandling import *
# filename = 'data/2015-09-A-C127_VimN205S_post20min_2x50nM_3_R3D.dv'
# filename = 'data/2015-09-A-C127_VimN205S_post20min_2x50nM_6_R3D.dv'
# filename = 'data/2015-09-A-C127_VimN205S_post20min_2x50nM_10_R3D.dv'
filename = 'data/2015-09-A-C127_VimN205S_post20min_2x50nM_9_R3D.dv'
start_java_bridge()
image4d = readfile(filename)
imagexyt = image4d[:, :, 0, :]
# 3D Fourier Transform
freq = np.fft.fftn(imagexyt, axes=(0, 1, 2))
# print np.max(freq)
# print imagexyt.shape
(nx, ny, nt) = freq.shape
freqslice = np.zeros((nx, ny, nt), dtype=np.complex)
for i in range(0, nt):
freqslice[:, :, i] = np.fft.ifft2(freq[:, :, i])
# freqslice = np.fft.fftshift(freqslice)
# writefile(filename[:-3]+'_FT2.tiff', np.abs(freqslice))
# freq = np.fft.fftshift(freq)
# freq = np.abs(freq)
# writefile(filename[:-3]+'_FT.tiff', freq)
# writefile(filename[:-3] + '_FT.tiff', imagexyt)
# writefile('test10_FT.tiff', a/3)
end_java_bridge()
" plotting "
# prepare for plotting
freqdisp = np.fft.fftshift(freq)
freqdisp = np.abs(freqdisp)
freqdisp = np.sum(freqdisp, axis=0)
freqdisp = np.log(freqdisp)
# freqslicedisp = np.fft.ifftshift(freqslice)
freqslicedisp = np.abs(freqslice[:, :, 0])
fig1 = pyplot.figure(1)
ax1 = fig1.add_subplot(121)
ax1.data = freqdisp
ax1.nt = nt
# ax1.set_ylabel('time')
ax2 = fig1.add_subplot(122)
ax2.slice = 0
ax2.data = freqslice
ax2.nt = nt
def plot_freq(ax):
ax.clear()
ax.imshow((ax.data).transpose(), cmap='hot')
ax.set_title('3D Fourier Transform (x-projection)')
ax.set_xlabel('y')
ax.set_ylabel('time frequency')
fmax = np.floor((ax.nt-1)/2.0)
print ax.get_yticks()
# idx_label = np.arange(fmin, fmax, 5).astype(str).tolist()
n = 20
idx_label = np.arange(0, fmax, n)
idx_label = np.append(-idx_label[-1:0:-1], idx_label).astype(str).tolist()
# idx = np.arange(0, ax.nt, 5).tolist()
step = ax.nt/len(idx_label)
idx = np.arange(step/2, ax.nt, step).tolist()
print idx
# print idx.astype(str)
ax.set_yticks(idx)
ax.set_yticklabels(idx_label)
# ax.set_yticks([0, 1.4, 88])
# # print idx[::10]
# pyplot.draw()
def plot_freqslice(ax):
idx = np.fft.fftshift(range(0, ax.nt))
fmin = -np.ceil((ax.nt-1)/2.0)
sliceidx = idx[-fmin+ax.slice]
freqslicedisp = np.abs(ax.data[:, :, sliceidx])
ax.clear()
ax.imshow(freqslicedisp, cmap='hot')
ax.set_title('Slice frequency: %.3f' % np.fft.fftfreq(ax.nt)[sliceidx])
pyplot.draw()
def onscroll(event):
ax = fig1.get_axes()[-1]
fmin = -np.ceil((ax.nt-1)/2.0)
fmax = np.floor((ax.nt-1)/2.0)
if (ax.slice + event.step) < fmin:
if ax.slice == fmin:
return
else:
ax.slice = fmin
elif fmax < (ax.slice + event.step):
if ax.slice == fmax:
return
else:
ax.slice = fmax
else:
ax.slice += event.step
plot_freqslice(ax)
plot_freq(ax1)
plot_freqslice(ax2)
cid = fig1.canvas.mpl_connect('scroll_event', onscroll)
pyplot.show()