import numpy as np
from stimupy.noises import pseudo_white_spectrum
from stimupy.utils import resolution
from stimupy.utils.contrast_conversions import adapt_intensity_range
__all__ = [
"one_over_f",
"pink",
"brown",
]
[docs]def one_over_f(
visual_size=None,
ppd=None,
shape=None,
exponent=None,
intensity_range=(0, 1),
pseudo_noise=False,
):
"""Draw 1 / (f**exponent) noise texture
Parameters
----------
visual_size : Sequence[Number, Number], Number, or None (default)
visual size [height, width] of grating, in degrees
ppd : Sequence[Number, Number], Number, or None (default)
pixels per degree [vertical, horizontal]
shape : Sequence[Number, Number], Number, or None (default)
shape [height, width] of grating, in pixels
exponent
exponent used to create 1 / (f**exponent) noise
intensity_range : Sequence[Number, Number]
minimum and maximum intensity value; default: (0, 1).
be aware that not every instance has mean=(max-min)/2.
pseudo_noise : bool
if True, generate pseudo-random noise with ideal power spectrum.
Returns
-------
dict[str, Any]
dict with the stimulus (key: "img"),
and additional keys containing stimulus parameters
"""
if exponent is None:
raise ValueError("one_over_f() missing argument 'exponent' which is not 'None'")
# Resolve resolution
shape, visual_size, ppd = resolution.resolve(shape=shape, visual_size=visual_size, ppd=ppd)
if len(np.unique(ppd)) > 1:
raise ValueError("ppd should be equal in x and y direction")
# Prepare spatial frequency axes and create bandpass filter:
fy = np.fft.fftshift(np.fft.fftfreq(shape[0], d=1.0 / np.unique(ppd)))
fx = np.fft.fftshift(np.fft.fftfreq(shape[1], d=1.0 / np.unique(ppd)))
Fx, Fy = np.meshgrid(fx, fy)
# Create 2d array with 1 / (f**exponent)
f = np.sqrt(Fy**2.0 + Fx**2.0)
f = f**exponent
f[f == 0.0] = 1.0 # Prevent division by zero (DC is zero anyways)
if pseudo_noise:
# Create white noise with frequency amplitude of 1 everywhere
white_noise_fft = pseudo_white_spectrum(shape)
else:
# Create white noise and fft
white_noise = np.random.rand(*shape) * 2.0 - 1.0
white_noise_fft = np.fft.fftshift(np.fft.fft2(white_noise))
# Create 1/f noise:
noise_fft = white_noise_fft / f
# ifft
noise = np.fft.ifft2(np.fft.ifftshift(noise_fft))
noise = np.real(noise)
# Adjust intensity range:
noise = adapt_intensity_range(noise, intensity_range[0], intensity_range[1])
stim = {
"img": noise,
"noise_mask": None,
"visual_size": visual_size,
"ppd": ppd,
"shape": shape,
"exponent": exponent,
"pseudo_noise": pseudo_noise,
"intensity_range": [noise.min(), noise.max()],
}
return stim
[docs]def pink(
visual_size=None,
ppd=None,
shape=None,
intensity_range=(0, 1),
pseudo_noise=False,
):
"""Draw pink (1 / f) noise texture
Parameters
----------
visual_size : Sequence[Number, Number], Number, or None (default)
visual size [height, width] of grating, in degrees
ppd : Sequence[Number, Number], Number, or None (default)
pixels per degree [vertical, horizontal]
shape : Sequence[Number, Number], Number, or None (default)
shape [height, width] of grating, in pixels
intensity_range : Sequence[Number, Number]
minimum and maximum intensity value; default: (0, 1).
be aware that not every instance has mean=(max-min)/2.
pseudo_noise : bool
if True, generate pseudo-random noise with ideal power spectrum
Returns
-------
A stimulus dictionary with the noise array ['img']
"""
stim = one_over_f(
visual_size=visual_size,
ppd=ppd,
exponent=1.0,
intensity_range=intensity_range,
pseudo_noise=pseudo_noise,
)
return stim
[docs]def brown(
visual_size=None,
ppd=None,
shape=None,
intensity_range=(0, 1),
pseudo_noise=False,
):
"""Draw brown (1 / (f**2.0)) noise texture
Parameters
----------
visual_size : Sequence[Number, Number], Number, or None (default)
visual size [height, width] of grating, in degrees
ppd : Sequence[Number, Number], Number, or None (default)
pixels per degree [vertical, horizontal]
shape : Sequence[Number, Number], Number, or None (default)
shape [height, width] of grating, in pixels
intensity_range : Sequence[Number, Number]
minimum and maximum intensity value; default: (0, 1)
be aware that not every instance has mean=(max-min)/2.
pseudo_noise : bool
if True, generate pseudo-random noise with ideal power spectrum
Returns
-------
A stimulus dictionary with the noise array ['img']
"""
stim = one_over_f(
visual_size=visual_size,
ppd=ppd,
exponent=2.0,
intensity_range=intensity_range,
pseudo_noise=pseudo_noise,
)
return stim
def overview(**kwargs):
"""Generate example stimuli from this module
Returns
-------
stims : dict
dict with all stimuli containing individual stimulus dicts.
"""
default_params = {
"visual_size": 10,
"ppd": 10,
}
default_params.update(kwargs)
# fmt: off
stimuli = {
"naturals_one-over-f": one_over_f(**default_params, exponent=0.5),
"naturals_pink": pink(**default_params),
"naturals_brown": brown(**default_params),
}
# fmt: on
return stimuli
if __name__ == "__main__":
from stimupy.utils import plot_stimuli
stims = overview()
plot_stimuli(stims, mask=False, save=None)
