import numpy as np
from stimupy.noises import pseudo_white_spectrum
from stimupy.utils import bandpass, resolution
from stimupy.utils.contrast_conversions import adapt_intensity_range
__all__ = [
"narrowband",
]
[docs]def narrowband(
visual_size=None,
ppd=None,
shape=None,
center_frequency=None,
bandwidth=None,
intensity_range=(0, 1),
pseudo_noise=False,
):
"""Draw narrowband 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
center_frequency : float
noise center frequency in cpd
bandwidth : float
bandwidth of the noise in octaves
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 center_frequency is None:
raise ValueError("narrowband() missing argument 'center_frequency' which is not 'None'")
if bandwidth is None:
raise ValueError("narrowband() missing argument 'bandwidth' 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")
bp = bandpass(
visual_size=visual_size, ppd=ppd, center_frequency=center_frequency, bandwidth=bandwidth
)["img"]
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))
# Filter white noise with bandpass filter
narrow_noise_fft = white_noise_fft * bp
# ifft
narrow_noise = np.fft.ifft2(np.fft.ifftshift(narrow_noise_fft))
narrow_noise = np.real(narrow_noise)
# Adjust intensity range:
narrow_noise = adapt_intensity_range(narrow_noise, intensity_range[0], intensity_range[1])
stim = {
"img": narrow_noise,
"noise_mask": None,
"visual_size": visual_size,
"ppd": ppd,
"shape": shape,
"center_frequency": center_frequency,
"bandwidth": bandwidth,
"pseudo_noise": pseudo_noise,
"intensity_range": [narrow_noise.min(), narrow_noise.max()],
}
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": 20,
}
default_params.update(kwargs)
# fmt: off
stimuli = {
"narrowbands_narrowband3": narrowband(**default_params, center_frequency=3, bandwidth=1),
"narrowbands_narrowband9": narrowband(**default_params, center_frequency=9, bandwidth=1),
}
# fmt: on
return stimuli
if __name__ == "__main__":
from stimupy.utils import plot_stimuli
stims = overview()
plot_stimuli(stims, mask=False, save=None)
