import numpy as np
import scipy.ndimage as img
class SpectrumPainter(object):
def __init__(self, Fs=1000000, T_line=0.005):
self.NFFT = 4096
self.Fs = Fs
self.T_line = T_line
@property
def repetitions(self):
return int(np.ceil(self.T_line * self.Fs / self.NFFT))
def convert_image(self, filename):
pic = img.imread(filename)
# Set FFT size to be double the image size so that the edge of the spectrum stays clear
# preventing some bandfilter artifacts
self.NFFT = 2*pic.shape[1]
# Repeat image lines until each one comes often enough to reach the desired line time
ffts = (np.flipud(np.repeat(pic[:, :, 0], self.repetitions, axis=0) / 16.)**2.) / 256.
# Embed image in center bins of the FFT
fftall = np.zeros((ffts.shape[0], self.NFFT))
startbin = int(self.NFFT/4)
fftall[:, startbin:(startbin+pic.shape[1])] = ffts
# Generate random phase vectors for the FFT bins, this is important to prevent high peaks in the output
# The phases won't be visible in the spectrum
phases = 2*np.pi*np.random.rand(*fftall.shape)
rffts = fftall * np.exp(1j*phases)
# Perform the FFT per image line, then concatenate them to form the final signal
timedata = np.fft.ifft(np.fft.ifftshift(rffts, axes=1), axis=1) / np.sqrt(float(self.NFFT))
linear = timedata.flatten()
linear = linear / np.max(np.abs(linear))
return linear
