def plot_dft_abs_phase(signal: np.array.__class__,
frequency_discretization=1,
is_in_db=False):
spectrum = fftshift(fft(signal))
fft_frequencies = fftshift(
fftfreq(signal.shape[0], 1 / frequency_discretization))
abs_values = np.abs(spectrum)
if is_in_db:
abs_values = power_samples_in_db(abs_values)
plt.figure()
plt.subplot(211)
plt.plot(fft_frequencies, abs_values, color='b')
plt.ylabel('Amplitude [dB]', color='b')
plt.xlabel('Frequency')
plt.grid()
plt.subplot(212)
plt.plot(fft_frequencies, np.angle(spectrum), color='g')
plt.ylabel('Phase [rad]', color='g')
plt.xlabel('Frequency')
plt.grid()
plt.show()
def ktoi(data,axis=-1):
if (axis == -1):
ax = fth.arange(0,data.ndim)
else:
ax = axis
return fth.fftshift(ft.ifftn(fth.ifftshift(data,axes=ax),axes=ax),axes=ax)
def ktoi(data, axis=-1):
if (axis == -1):
ax = fth.arange(0, data.ndim)
else:
ax = axis
return fth.fftshift(ft.ifftn(fth.ifftshift(data, axes=ax), axes=ax),
axes=ax)
def plot_dft_real_image(signal: np.array.__class__,
frequency_discretization=1,
is_in_db=False):
spectrum = fftshift(fft(signal))
if is_in_db:
spectrum = power_samples_in_db(
np.real(spectrum)) + 1j * power_samples_in_db(np.imag(spectrum))
fft_frequencies = fftshift(
fftfreq(signal.shape[0], 1 / frequency_discretization))
plt.subplot(211)
plt.plot(fft_frequencies, np.real(spectrum))
plt.subplot(212)
plt.plot(fft_frequencies, np.imag(spectrum))
# plt.ion()
plt.show()
def power_spectrum(pref, peak_val=1.0):
"""
Computes the FFT power spectrum of the orientation preference.
"""
fft_spectrum = abs(fftshift(fft2(pref-0.5, s=None, axes=(-2,-1))))
fft_spectrum = 1 - fft_spectrum # Inverted spectrum by convention
zero_min_spectrum = fft_spectrum - fft_spectrum.min()
spectrum_range = fft_spectrum.max() - fft_spectrum.min()
normalized_spectrum = (peak_val * zero_min_spectrum) / spectrum_range
return normalized_spectrum
def power_spectrum(pref, peak_val=1.0):
"""
Computes the FFT power spectrum of the orientation preference.
"""
fft_spectrum = abs(fftshift(fft2(pref - 0.5, s=None, axes=(-2, -1))))
fft_spectrum = 1 - fft_spectrum # Inverted spectrum by convention
zero_min_spectrum = fft_spectrum - fft_spectrum.min()
spectrum_range = fft_spectrum.max() - fft_spectrum.min()
normalized_spectrum = (peak_val * zero_min_spectrum) / spectrum_range
return normalized_spectrum
def _process(self, sheetview):
cr = sheetview.cyclic_range
data = sheetview.data if cr is None else sheetview.data/cr
fft_spectrum = abs(fftshift(fft2(data - 0.5, s=None, axes=(-2, -1))))
fft_spectrum = 1 - fft_spectrum # Inverted spectrum by convention
zero_min_spectrum = fft_spectrum - fft_spectrum.min()
spectrum_range = fft_spectrum.max() - fft_spectrum.min()
normalized_spectrum = (self.p.peak_val * zero_min_spectrum) / spectrum_range
l, b, r, t = sheetview.bounds.lbrt()
density = sheetview.xdensity
bb = BoundingBox(radius=(density/2)/(r-l))
return [SheetView(normalized_spectrum, bb,
metadata=sheetview.metadata,
label=sheetview.label+' FFT Power Spectrum')]
def OR_power_spectrum(image, toarray=True, peak_val=1.0):
""" Taken from current topographica code. Applies FFT power
spectrum to hue channel of OR maps (ie orientation). Accepts
RGB images or arrays."""
# Duplicates the line of code in command/pylabplot.py and tkgui.
# Unfortunately, there is no sensible way to reuse the existing code
if not toarray: peak_val = 255
if not image.shape[0] % 2:
hue = image[:-1, :-1]
else:
hue = image
fft_spectrum = abs(fftshift(fft2(hue - 0.5, s=None, axes=(-2, -1))))
fft_spectrum = 1 - fft_spectrum # Inverted spectrum by convention
zero_min_spectrum = fft_spectrum - fft_spectrum.min()
spectrum_range = fft_spectrum.max() - fft_spectrum.min()
normalized_spectrum = (peak_val * zero_min_spectrum) / spectrum_range
if not toarray:
return Image.fromarray(normalized_spectrum.astype('uint8'), mode='L')
return normalized_spectrum
def __call__(self, data, **params):
p = ParamOverrides(self, params)
fft_plot = 1 - np.abs(fftshift(fft2(data - 0.5, s=None,
axes=(-2, -1))))
return super(fftplot, self).__call__(fft_plot, **p)
def __call__(self, data, **params):
p = ParamOverrides(self, params)
fft_plot = 1 - np.abs(fftshift(fft2(data - 0.5, s=None, axes=(-2, -1))))
return super(fftplot, self).__call__(fft_plot, **p)
# coding=gbk # coding£ºutf-8 ''' @Created on 2018Äê1ÔÂ26ÈÕ ÏÂÎç2:43:08 @author: Administrator ''' from myFunc import mfft, mifft from math import * from numpy import * # import numpy as np from matplotlib import * import matplotlib.pyplot as plt from numpy.dual import fft from numpy.fft.helper import fftshift from numpy.core.function_base import linspace x = linspace(-pi, pi, 100) print(type(x)) print(shape(x)) y = random.random(size=(1, 10)) print(type(y)) print(type(shape(y))) print(type(shape(y))) f = sin(x) F = fftshift(fft(f)) #F = mfft(f) plt.subplot(121) plt.plot(abs(F)) plt.show()
from numpy.fft.helper import fftfreq, fftshift from scipy.fftpack import ifft, ifftshift from scipy.signal.signaltools import convolve from WiSim.plot_utils import plot_dft_real_image, plot_dft_abs_phase from WiSim.signal_source import SignalSource from WiSim.utils import complex_randn, db_to_power # Construct signal frequency_discretization = 600 * 10 ** 6 period_discretization = 1 / frequency_discretization window_length = int(1024/2) fft_frequencies = fftshift(fftfreq(window_length, period_discretization)) signal_borders = (70 * 10 ** 6, 230 * 10 ** 6) signal_positions = (fft_frequencies > signal_borders[0]) & (fft_frequencies < signal_borders[1]) n_signal_positions = sum(signal_positions) random_component = complex_randn((n_signal_positions,)) coefficients = np.arange(1, random_component.shape[0] + 1) random_component *= coefficients signal = np.zeros((window_length,), dtype=np.complex128) signal[signal_positions] = random_component signal = ifft(ifftshift(signal)) # Adding noise to the signal
