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- import os
- import matplotlib.pyplot as plt
- import numpy as np
- from quantities import Hz, mm, dimensionless
- from neo.core import CircularRegionOfInterest
- from neo.io import TiffIO
- import elephant as el
- data_path = os.path.expanduser("~/Data/WaveScalES/LENS/170110_mouse2_deep/t1")
- # loading data
- data = TiffIO(data_path).read(units=dimensionless, sampling_rate=25 * Hz,
- spatial_scale=0.05 * mm)
- images = data[0].segments[0].imagesequences[0]
- images /= images.max()
- plt.subplot(2, 2, 1)
- plt.imshow(images[100], cmap='gray')
- plt.title("Original image (frame 100)")
- # preprocessing
- background = np.mean(images, axis=0)
- preprocessed_images = images - background
- plt.subplot(2, 2, 2)
- plt.imshow(preprocessed_images[100], cmap='gray')
- plt.title("Subtracted background (frame 100)")
- # defining ROI and extracting signal
- roi = CircularRegionOfInterest(x=50, y=50, radius=10)
- circle = plt.Circle(roi.centre, roi.radius, color='b', fill=False)
- ax = plt.gca()
- ax.add_artist(circle)
- central_signal = preprocessed_images.signal_from_region(roi)[0]
- plt.subplot(2, 2, 3)
- plt.plot(central_signal.times, central_signal, lw=0.8)
- plt.title("Mean signal from ROI")
- plt.xlabel("Time [s]")
- # calculating power spectrum
- freqs, psd = el.spectral.welch_psd(central_signal,
- fs=central_signal.sampling_rate,
- freq_res=0.1 * Hz,
- overlap=0.8)
- plt.subplot(2, 2, 4)
- plt.plot(freqs, np.mean(psd, axis=0), lw=0.8)
- plt.title("Average power spectrum")
- plt.xlabel("frequency [Hz]")
- plt.ylabel("Fourier signal")
- plt.tight_layout()
- plt.show() # see Figure 2
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