"""Stimuli from Robinson, Hammon & de Sa (2007)
https://doi.org/10.1016/j.visres.2007.02.017
This module reproduces all of the stimuli used by Robinson,
Hammon & de Sa (2007) as they were provided to the model described
in that paper.
Each stimulus is provided by a separate function,
a full list can be found as stimupy.papers.RHS2007.__all__
The output of each of these functions is a stimulus dictionary.
For a visual representation of all the stimuli and their mask,
simply run this module as a script:
$ python stimuli/papers/RHS2007.py
Attributes
----------
__all__ (list of str): list of all stimulus-functions
that are exported by this module when executing
>>> from stimupy.papers.RHS2007 import *
References
----------
Robinson, A. E., Hammon, P. S., & de Sa, V. R. (2007).
Explaining brightness illusions
using spatial filtering and local response normalization.
Vision Research, 47(12), 1631-1644.
https://doi.org/10.1016/j.visres.2007.02.017
"""
import numpy as np
import stimupy
from stimupy.utils import (
flip_dict,
pad_dict_by_visual_size,
pad_dict_to_shape,
pad_dict_to_visual_size,
resolution,
rotate_dict,
stack_dicts,
)
__all__ = [
"WE_thick",
"WE_thin_wide",
"WE_dual",
"WE_anderson",
"WE_howe",
"WE_zigzag",
"WE_radial_thick_small",
"WE_radial_thick",
"WE_radial_thin_small",
"WE_radial_thin",
"WE_circular1",
"WE_circular05",
"WE_circular025",
"grating_induction",
"sbc_large",
"sbc_small",
"todorovic_equal",
"todorovic_in_large",
"todorovic_in_small",
"todorovic_out",
"checkerboard_016",
"checkerboard_094",
"checkerboard_21",
"corrugated_mondrian",
"benary_cross",
"todorovic_benary1_2",
"todorovic_benary3_4",
"todorovic_benary1_2_3_4",
"bullseye_thin",
"bullseye_thick",
]
VISEXTENT = (32.0, 32.0)
PPD = 32
v1, v2, v3 = 0.0, 0.5, 1.0
def gen_all(ppd=PPD, pad=True, skip=False):
stims = {} # save the stimulus-dicts in a larger dict, with name as key
for stim_name in __all__:
print(f"Generating RHS2007.{stim_name}")
# Get a reference to the actual function
func = globals()[stim_name]
try:
stim = func(ppd=ppd, pad=pad)
# Accumulate
stims[stim_name] = stim
except NotImplementedError as e:
if not skip:
raise e
# Skip stimuli that aren't implemented
print("-- not implemented")
pass
return stims
[docs]def WE_thick(ppd=PPD, pad=True):
"""White stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1a.
Grating size: 12x16 deg
Grating frequency: 0.25 cpd
Target size: 4x2 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (1999).
A multiscale spatial filtering account
of the White effect, simultaneous brightness contrast and grating induction.
Vision Research, 39, 4361-4377.
White, M. (1979).
A new effect of pattern on perceived lightness.
Perception, 8, 413-416.
"""
height, width = 12.0, 16.0
params = {
"ppd": ppd,
"frequency": 4.0 / width,
"intensity_bars": (v3, v1),
"intensity_target": v2,
"target_indices": (3, 6),
"target_heights": 4.0,
"period": "even",
}
stim = stimupy.whites.white(
visual_size=(height, width),
**params,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 4.18,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def WE_thin_wide(ppd=PPD, pad=True):
"""White stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1b.
Grating size: 12x16 deg
Grating frequency: 0.5 cpd
Target size: 2x1 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (1999).
A multiscale spatial filtering account
of the White effect, simultaneous brightness contrast and grating induction.
Vision Research, 39, 4361-4377.
White, M. (1979).
A new effect of pattern on perceived lightness. Perception, 8, 413-416.
"""
height, width = 12.0, 16.0
params = {
"ppd": ppd,
"frequency": 8.0 / width,
"intensity_bars": (v1, v3),
"intensity_target": v2,
"target_indices": (4, 13),
"target_heights": 2.0,
"period": "even",
}
stim = stimupy.whites.white(
visual_size=(height, width),
**params,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 4.6,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def WE_dual(ppd=PPD, pad=True):
"""Dual White stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1c.
Grating sizes: 6x8 deg
Grating frequency: 0.5 cpd
Target size: 2x1 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
"""
height, width = 6.0, 8.0
params = {
"ppd": ppd,
"frequency": 4.0 / width,
"intensity_bars": (v3, v1),
"intensity_target": v2,
"target_indices": (3, 6),
"target_heights": 2.0,
"period": "even",
}
stim1 = stimupy.whites.white(
visual_size=(height, width),
**params,
)
stim2 = stimupy.whites.white(
visual_size=(height, width),
**params,
)
stim2 = rotate_dict(stim2, 3)
if pad:
shape = np.array(resolution.shape_from_visual_size_ppd(VISEXTENT, ppd)) / (1, 2)
else:
shape = np.max([stim1["img"].shape, stim2["img"].shape], axis=1)
# Pad and stack
stim1 = pad_dict_to_shape(stim1, shape, pad_value=v2)
stim2 = pad_dict_to_shape(stim2, shape, pad_value=v2)
stim = stack_dicts(stim1, stim2, keep_mask_indices=False)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
[docs]def WE_anderson(ppd=PPD, pad=True):
"""Anderson variation of White stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1d.
Grating size: 16x16 deg
Grating frequency: 0.5 cpd
Target size: 3.2x1 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Anderson, B. L. (2001).
Contrasting theories of White's illusion. Perception, 30, 1499-1501
Blakeslee, B., Pasieka, W., & McCourt, M. E. (2005).
Oriented multiscale spatial filtering and contrast normalization:
a parsimonious model of brightness induction in a continuum of stimuli
including White, Howe and simultaneous brightness contrast.
Vision Research, 45, 607-615.
"""
height, width = 16.0, 16.0
params = {
"ppd": ppd,
"frequency": 8.0 / width,
"intensity_bars": (v3, v1),
"intensity_target": v2,
"target_indices_top": (6,),
"target_indices_bottom": (11,),
"target_center_offset": height / 10.0,
"target_height": height / 5.0,
"intensity_stripes": (v1, v3),
"stripe_center_offset": height / 5.0,
"stripe_height": height / 5.0,
"period": "even",
}
stim = stimupy.whites.anderson(
visual_size=(height, width),
**params,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 6.43,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def WE_howe(ppd=PPD, pad=True):
"""Howe variation of White stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1e.
Grating size: 16x16 deg
Grating frequency: 0.5 cpd
Target size: 3.2x1 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., Pasieka, W., & McCourt, M. E. (2005).
Oriented multiscale spatial filtering and contrast normalization:
a parsimonious model of brightness induction in a continuum
of stimuli including White, Howe and simultaneous brightness contrast.
Vision Research, 45, 607-615.
Howe, P. D. L. (2001).
A comment on the Anderson (1997), the Todorovic (1997),
and the Ross and Pessoa (2000) explanations of White's effect.
Perception, 30, 1023-1026
"""
height, width = 16.0, 16.0
params = {
"ppd": ppd,
"frequency": 8.0 / width,
"intensity_bars": (v3, v1),
"intensity_target": v2,
"target_indices_top": (6,),
"target_indices_bottom": (11,),
"target_center_offset": height / 5.0,
"target_height": height / 5.0,
"intensity_stripes": (v1, v3),
"period": "even",
}
stim = stimupy.whites.howe(
visual_size=(height, width),
**params,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 0.0,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def WE_zigzag(ppd=PPD, pad=True):
"""Wedding cake stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1f.
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Clifford, C. W. G., & Spehar, B. (2003).
Using colour to disambiguate contrast and assimilation in White's effect.
Journal of Vision, 3, 294a.
https://doi.org/10.1167/3.9.294
"""
i1, i2 = 1, 2
stim = stimupy.wedding_cakes.wedding_cake(
visual_size=(16.0, 17.0),
ppd=PPD,
L_size=(4.0, 4, 1.0),
target_height=2.0,
target_indices1=((i1, -1), (i1, 0), (i1, 1), (i1, 2)),
target_indices2=((i2, 0), (i2, 1), (i2, 2), (i2, 3)),
intensity_bars=(1.0, 0.0),
intensity_target=0.5,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
return stim
[docs]def WE_radial_thick_small(ppd=PPD, pad=True):
"""Radial White stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1g.
Circle size: 16x16 deg
Number of segments: 14
Target length: 4 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Anstis, S. (2003).
White's effect in brightness & color.
Online Demonstration
"""
n_segments = 14
params = {
"ppd": ppd,
"n_segments": n_segments,
"rotation": 90 - (90 / (n_segments / 2)),
"target_indices": (1, 8),
"target_width": 4.0,
"target_center": 4.5,
"intensity_segments": (v1, v3),
"intensity_background": v2,
"intensity_target": v2,
}
stim = stimupy.whites.angular(
visual_size=(16, 16),
**params,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
[docs]def WE_radial_thick(ppd=PPD, pad=True):
"""Radial White stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1h.
Circle size: 24x24 deg
Number of segments: 18
Target length: 4 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Anstis, S. (2003).
White's effect in brightness & color.
Online Demonstration
"""
n_segments = 18
params = {
"ppd": ppd,
"n_segments": n_segments,
"rotation": 90 - (90 / (n_segments / 2)),
"target_indices": (1, 10),
"target_width": 4.0,
"target_center": 6.0,
"intensity_segments": (v1, v3),
"intensity_background": v2,
"intensity_target": v2,
}
stim = stimupy.whites.angular(
visual_size=(24, 24),
**params,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
[docs]def WE_radial_thin_small(ppd=PPD, pad=True):
"""Radial White stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1i.
Circle size: 16x16 deg
Number of segments: 26
Target length: 2 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Anstis, S. (2003).
White's effect in brightness & color.
Online Demonstration
"""
n_segments = 26
params = {
"ppd": ppd,
"n_segments": n_segments,
"rotation": 90 - (90 / (n_segments / 2)),
"target_indices": (1, 14),
"target_width": 2.0,
"target_center": 4.0,
"intensity_segments": (v1, v3),
"intensity_background": v2,
"intensity_target": v2,
}
stim = stimupy.whites.angular(
visual_size=(16, 16),
**params,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
[docs]def WE_radial_thin(ppd=PPD, pad=True):
"""Radial White stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1j.
Circle size: 24x24 deg
Number of segments: 42
Target length: 2 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Anstis, S. (2003).
White's effect in brightness & color.
Online Demonstration
"""
n_segments = 42
params = {
"ppd": ppd,
"n_segments": n_segments,
"rotation": 90 - (90 / (n_segments / 2)),
"target_indices": (1, 22),
"target_width": 2.0,
"target_center": 6.0,
"intensity_segments": (v1, v3),
"intensity_background": v2,
"intensity_target": v2,
}
stim = stimupy.whites.angular(
visual_size=(24, 24),
**params,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
[docs]def WE_circular1(ppd=PPD, pad=True):
"""Ring (or circular White) stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1k.
Circle size: 16x16 deg
Ring frequency: 0.5 cpd
Ring width: 1 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Howe, P. D. L. (2005).
White's effect:
removing the junctions but preserving the strength of the illusion.
Perception, 34, 557-564.
"""
height, width = 16.0, 16.0
params = {
"ppd": ppd,
"frequency": 8.0 / height,
"target_indices": 5,
"intensity_background": v2,
"intensity_target": v2,
"clip": True,
}
stim1 = stimupy.whites.radial(
visual_size=(height, width),
intensity_rings=(v1, v3),
**params,
)
stim2 = stimupy.whites.radial(
visual_size=(height, width),
intensity_rings=(v3, v1),
**params,
)
stim = stack_dicts(stim1, stim2)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
[docs]def WE_circular05(ppd=PPD, pad=True):
"""Ring (or circular White) stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1l.
Circle size: 16x16 deg
Ring frequency: 1 cpd
Ring width: 0.5 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Howe, P. D. L. (2005).
White's effect:
removing the junctions but preserving the strength of the illusion.
Perception, 34, 557-564.
"""
height, width = 16.0, 16.0
params = {
"ppd": ppd,
"frequency": 16.0 / height,
"target_indices": 11,
"intensity_background": v2,
"intensity_target": v2,
"clip": True,
}
stim1 = stimupy.whites.radial(
visual_size=(height, width),
intensity_rings=(v1, v3),
**params,
)
stim2 = stimupy.whites.radial(
visual_size=(height, width),
intensity_rings=(v3, v1),
**params,
)
stim = stack_dicts(stim1, stim2)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
[docs]def WE_circular025(ppd=PPD, pad=True):
"""Ring (or circular White) stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1m.
Circle size: 16x16 deg
Ring frequency: 2 cpd
Ring width: 0.25 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Howe, P. D. L. (2005).
White's effect:
removing the junctions but preserving the strength of the illusion.
Perception, 34, 557-564.
"""
height, width = 16.0, 16.0
params = {
"ppd": ppd,
"frequency": 32.0 / height,
"target_indices": 23,
"intensity_background": v2,
"intensity_target": v2,
"clip": True,
}
stim1 = stimupy.whites.radial(
visual_size=(height, width),
intensity_rings=(v1, v3),
**params,
)
stim2 = stimupy.whites.radial(
visual_size=(height, width),
intensity_rings=(v3, v1),
**params,
)
stim = stack_dicts(stim1, stim2)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
[docs]def grating_induction(ppd=PPD, pad=True):
"""Grating induction as shown in Robinson, Hammon, & de Sa (2007) Fig 1n.
Circle size: 12x16 deg
Grating frequency: 0.25 cpd
Target size: 1x16 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (1999).
A multiscale spatial filtering account
of the White effect, simultaneous brightness contrast and grating induction.
Vision Research, 39, 4361-4377.
McCourt, M. E. (1982).
A spatial frequency dependent grating-induction effect.
Vision Research, 22, 119-134.
https://doi.org/10.1016/0042-6989(82)90173-0
"""
height, width = 12.0, 16.0
params = {
"ppd": ppd,
"frequency": 4.0 / width,
"target_width": 1.0,
"intensities": (0.0, 1.0),
"intensity_target": (0.5),
"period": "ignore",
}
stim = stimupy.gratings.grating_induction(
visual_size=(height, width),
**params,
)
mask1 = np.where(stim["target_mask"] % 2, 1, 2)
mask2 = np.where(stim["target_mask"] == 0, 0, 1)
stim["target_mask"] = (mask1 * mask2).astype(int)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 6.23,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def sbc_large(ppd=PPD, pad=True):
"""Simultaneous brightness contrast as shown in Robinson, Hammon, & de Sa (2007) Fig 1o.
Stimulus size: 13x31 deg
Target sizes: 3x3 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (1999).
A multiscale spatial filtering account
of the White effect, simultaneous brightness contrast and grating induction.
Vision Research, 39, 4361-4377.
"""
stim = stimupy.sbcs.basic_two_sided(
visual_size=(13.0, 31.0),
ppd=ppd,
target_size=3.0,
intensity_target=v2,
intensity_background=(0.0, 1.0),
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 11.35,
}
stim.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return stim
[docs]def sbc_small(ppd=PPD, pad=True):
"""Simultaneous brightness contrast as shown in Robinson, Hammon, & de Sa (2007) Fig 1p.
Stimulus size: 13x31 deg
Target sizes: 1x1 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (1999).
A multiscale spatial filtering account
of the White effect, simultaneous brightness contrast and grating induction.
Vision Research, 39, 4361-4377.
"""
stim = stimupy.sbcs.basic_two_sided(
visual_size=(13.0, 31.0),
ppd=ppd,
target_size=1.0,
intensity_background=(0.0, 1.0),
intensity_target=v2,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 19.78,
}
stim.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return stim
[docs]def todorovic_equal(ppd=PPD, pad=True):
"""Todorovic stimulus - equal as shown in Robinson, Hammon, & de Sa (2007) Fig 1q.
Stimulus size: 13x31 deg
Target size: 8x8 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (1999).
A multiscale spatial filtering account
of the White effect, simultaneous brightness contrast and grating induction.
Vision Research, 39, 4361-4377.
Pessoa, L., Baratoff, G., Neumann, H., & Todorovic, D. (1998).
Lightness and junctions: variations on White's display.
Investigative Ophthalmology and Visual Science (Supplement), 39, S159.
"""
params = {
"visual_size": (13.0, 15.5),
"ppd": ppd,
"cross_size": 8.0,
"cross_thickness": 1.6,
"intensity_target": v2,
}
stim1 = stimupy.todorovics.equal(
intensity_background=1.0,
intensity_covers=0.0,
**params,
)
stim2 = stimupy.todorovics.equal(
intensity_background=0.0,
intensity_covers=1.0,
**params,
)
stim = stack_dicts(stim1, stim2)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 2.2,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def todorovic_in_large(ppd=PPD, pad=True):
"""Todorovic stimulus - in as shown in Robinson, Hammon, & de Sa (2007) Fig 1r.
Stimulus size: 13x31 deg
Target size: 5.3x5.3 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (1999).
A multiscale spatial filtering account
of the White effect, simultaneous brightness contrast and grating induction.
Vision Research, 39, 4361-4377.
Todorovic, D. (1997).
Lightness and junctions.
Perception, 26, 379-395.
"""
params = {
"visual_size": (13.0, 15.5),
"ppd": ppd,
"target_size": 5.3125,
"covers_size": 3.2,
"covers_offset": 2.4,
"intensity_target": v2,
}
stim1 = stimupy.todorovics.rectangle(
intensity_background=1.0,
intensity_covers=0.0,
**params,
)
stim2 = stimupy.todorovics.rectangle(
intensity_background=0.0,
intensity_covers=1.0,
**params,
)
stim = stack_dicts(stim1, stim2)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 2.4,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def todorovic_in_small(ppd=PPD, pad=True):
"""Todorovic stimulus - in as shown in Robinson, Hammon, & de Sa (2007) Fig 1s.
Stimulus size: 13x31 deg
Target size: 3x3 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (1999).
A multiscale spatial filtering account
of the White effect, simultaneous brightness contrast and grating induction.
Vision Research, 39, 4361-4377.
Todorovic, D. (1997).
Lightness and junctions.
Perception, 26, 379-395.
"""
params = {
"visual_size": (13.0, 15.5),
"ppd": ppd,
"cross_size": 3.0,
"cross_thickness": 1.6,
"covers_size": 3.2,
"intensity_target": v2,
}
stim1 = stimupy.todorovics.cross(
intensity_background=1.0,
intensity_covers=0.0,
**params,
)
stim2 = stimupy.todorovics.cross(
intensity_background=0.0,
intensity_covers=1.0,
**params,
)
stim = stack_dicts(stim1, stim2)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 4.4,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def todorovic_out(ppd=PPD, pad=True):
"""Todorovic stimulus - out as shown in Robinson, Hammon, & de Sa (2007) Fig 1t.
Stimulus size: 13x31 deg
Target size: 9.4x9.4 deg
(note: in RHS2007, it says 8.7x8.7 deg, however that does not match with Fig 1t)
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (1999).
A multiscale spatial filtering account
of the White effect, simultaneous brightness contrast and grating induction.
Vision Research, 39, 4361-4377.
Pessoa, L., Baratoff, G., Neumann, H., & Todorovic, D. (1998).
Lightness and junctions: variations on White's display.
Investigative Ophthalmology and Visual Science (Supplement), 39, S159.
"""
params = {
"visual_size": (13.0, 15.5),
"ppd": ppd,
"cross_size": 9.2,
"cross_thickness": 1.6,
"covers_size": 3.2,
"intensity_target": v2,
}
stim1 = stimupy.todorovics.cross(
intensity_background=1.0,
intensity_covers=0.0,
**params,
)
stim2 = stimupy.todorovics.cross(
intensity_background=0.0,
intensity_covers=1.0,
**params,
)
stim = stack_dicts(stim1, stim2)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 1.53,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def checkerboard_016(ppd=PPD, pad=True):
"""Checkerboard stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1u.
Stimulus size: 6.25x15.9375 deg
Target size: 0.156x0.156 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (2004).
A unified theory of brightness contrast and assimilation
incorporating oriented multi-scale spatial filtering and contrast normalization.
Vision Research, 44, 2483-2503.
DeValois, R. L., & DeValois, K. K. (1988).
Spatial vision.
New York: Oxford University Press.
"""
nchecks_height, nchecks_width = 40, 102
target_row = nchecks_height // 2
params = {
"ppd": ppd,
"board_shape": (nchecks_height, nchecks_width),
"check_visual_size": (5 / 32, 5 / 32),
"target_indices": ((target_row, 16), (target_row, 85)),
"extend_targets": False,
"intensity_checks": (v3, v1),
"intensity_target": v2,
}
stim = stimupy.checkerboards.checkerboard(**params)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 7.46,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def checkerboard_094(ppd=PPD, pad=True):
"""Checkerboard stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1v.
Stimulus size: 6.5625x23.4375 deg
Target size: 0.938x0.938 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (2004).
A unified theory of brightness contrast and assimilation
incorporating oriented multi-scale spatial filtering and contrast normalization.
Vision Research, 44, 2483-2503.
DeValois, R. L., & DeValois, K. K. (1988).
Spatial vision.
New York: Oxford University Press.
"""
nchecks_height, nchecks_width = 7, 25
target_row = nchecks_height // 2
params = {
"ppd": ppd,
"board_shape": (nchecks_height, nchecks_width),
"check_visual_size": (30 / 32, 30 / 32),
"target_indices": ((target_row, 6), (target_row, 17)),
"extend_targets": False,
"intensity_checks": (v1, v3),
"intensity_target": v2,
}
stim = stimupy.checkerboards.checkerboard(**params)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 2.84,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def checkerboard_21(ppd=PPD, pad=True):
"""Checkerboard stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1w.
Stimulus size: 6.28125x20.9375 deg
Target size: 2.09x2.09 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (2004).
A unified theory of brightness contrast and assimilation
incorporating oriented multi-scale spatial filtering and contrast normalization.
Vision Research, 44, 2483-2503.
DeValois, R. L., & DeValois, K. K. (1988).
Spatial vision.
New York: Oxford University Press.
"""
nchecks_height, nchecks_width = 3, 10
target_row = nchecks_height // 2
params = {
"ppd": ppd,
"board_shape": (nchecks_height, nchecks_width),
"check_visual_size": (67 / 32, 67 / 32),
"target_indices": ((target_row, 2), (target_row, 7)),
"extend_targets": False,
"intensity_checks": (v1, v3),
"intensity_target": v2,
}
stim = stimupy.checkerboards.checkerboard(**params)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 5.67,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def corrugated_mondrian(ppd=PPD, pad=True):
"""Corrugated Mondrians as shown in Robinson, Hammon, & de Sa (2007) Fig 1x.
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (2001).
A multiscale spatial filtering account
of the Wertheimer-Benary effect and the corrugated Mondrian.
Vision Research, 41, 2487-2502.
Adelson, E. H. (1993).
Perceptual organization and the judgment of brightness.
Science, 262, 2042-2044.
https://doi.org/10.1126/science.8266102
"""
v1, v2, v3, v4 = 0.0, 0.4, 0.75, 1.0
values = (
(v3, v2, v3, v2, v3),
(v4, v3, v2, v3, v4),
(v3, v2, v3, v2, v3),
(v2, v1, v2, v1, v2),
(v3, v2, v3, v2, v3),
)
params = {
"visual_size": (5 * 2, 5 * 2 + 1),
"ppd": ppd,
"depths": (0.0, -1.0, 0.0, 1.0, 0.0),
"intensities": values,
"target_indices": ((1, 2), (3, 2)),
"intensity_background": 0.5,
}
stim = stimupy.mondrians.corrugated_mondrian(**params)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=0.5)
experimental_data = {
"effect_strength": 10.85,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v4),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def benary_cross(ppd=PPD, pad=True):
"""Benarys cross as shown in Robinson, Hammon, & de Sa (2007) Fig 1y.
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (2001).
A multiscale spatial filtering account
of the Wertheimer-Benary effect and the corrugated Mondrian.
Vision Research, 41, 2487-2502.
Benary, W. (1924).
Beobachtungen zu einem Experiment über Helligkeitskontrast.
Psychologische Forschung, 5, 131-142.
https://doi.org/10.1007/BF00402398
"""
params = {
"ppd": ppd,
"cross_thickness": 4.0,
"target_size": 2.5,
"intensity_background": v3,
"intensity_cross": v1,
"intensity_target": v2,
}
stim = stimupy.benarys.cross_triangles(
visual_size=(13, 23),
**params,
)
stim = flip_dict(stim)
# Switch target indices
mask1 = np.where(stim["target_mask"] == 1, 1, 0)
mask2 = np.where(stim["target_mask"] == 2, 1, 0)
stim["target_mask"] = (mask1 * 2 + mask2).astype(int)
if pad:
stim = pad_dict_by_visual_size(stim, ((0.0, 0.0), (4.0, 4.0)), ppd, pad_value=v3)
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
experimental_data = {
"effect_strength": 9.2,
}
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
experimental_data=experimental_data,
)
return {**stim, **params}
[docs]def todorovic_benary1_2(ppd=PPD, pad=True):
"""Todorovic variation of Benarys cross as shown in Robinson, Hammon, & de Sa (2007) Fig 1z.
Only contains the first two targets (1-2).
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (2001).
A multiscale spatial filtering account
of the Wertheimer-Benary effect and the corrugated Mondrian.
Vision Research, 41, 2487-2502.
Todorovic, D. (1997).
Lightness and junctions.
Perception, 26, 379-395.
"""
# Generate full stim
stim = todorovic_benary1_2_3_4(ppd=ppd, pad=pad)
# Remove unused targets
stim["img"] = np.where(stim["target_mask"] == 3, v3, stim["img"])
stim["img"] = np.where(stim["target_mask"] == 4, v1, stim["img"])
stim["target_mask"] = np.where(stim["target_mask"] > 2, 0, stim["target_mask"])
stim["target_type"] = stim["target_type"][:2]
stim["target_rotation"] = stim["target_rotation"][:2]
stim["target_x"] = stim["target_x"][:2]
stim["target_y"] = stim["target_y"][:2]
# Add experimental data
stim["effect_strength"] = 11.96
return stim
[docs]def todorovic_benary3_4(ppd=PPD, pad=True):
"""Todorovic variation of Benarys cross as shown in Robinson, Hammon, & de Sa (2007) Fig 1z.
Only contains the last two targets (3-4).
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (2001).
A multiscale spatial filtering account
of the Wertheimer-Benary effect and the corrugated Mondrian.
Vision Research, 41, 2487-2502.
Todorovic, D. (1997).
Lightness and junctions.
Perception, 26, 379-395.
"""
# Generate full stim
stim = todorovic_benary1_2_3_4(ppd=ppd, pad=pad)
# Remove unused targets
stim["img"] = np.where(stim["target_mask"] == 1, v3, stim["img"])
stim["img"] = np.where(stim["target_mask"] == 2, v1, stim["img"])
stim["target_mask"] = np.where(stim["target_mask"] < 3, 0, stim["target_mask"])
stim["target_mask"] = np.where(
stim["target_mask"], stim["target_mask"] - 2, stim["target_mask"]
)
stim["target_type"] = stim["target_type"][2:]
stim["target_rotation"] = stim["target_rotation"][2:]
stim["target_x"] = stim["target_x"][2:]
stim["target_y"] = stim["target_y"][2:]
# Add experimental data
stim["effect_strength"] = 9.55
return stim
[docs]def todorovic_benary1_2_3_4(ppd=PPD, pad=True):
"""Todorovic variation of Benarys cross as shown in Robinson, Hammon, & de Sa (2007) Fig 1z.
Contains all four targets (1-2-3-4).
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Blakeslee, B., & McCourt, M. E. (2001).
A multiscale spatial filtering account
of the Wertheimer-Benary effect and the corrugated Mondrian.
Vision Research, 41, 2487-2502.
Todorovic, D. (1997).
Lightness and junctions.
Perception, 26, 379-395.
"""
params = {
"ppd": ppd,
"L_width": 2.5,
"target_size": 2.5,
"target_type": ("t", "t", "t", "t"),
"target_rotation": (0.0, 45.0, 225.0, 180.0),
"target_x": (2.5, 9.5, 18.0, 26.0),
"target_y": (4.0, 6.5, 6.5 - np.sqrt(12.5) / 2.0 + 1 / ppd, 6.5),
"intensity_background": v3,
"intensity_cross": v1,
"intensity_target": v2,
}
stim = stimupy.benarys.todorovic_generalized(
visual_size=(13.0, 31.0),
**params,
)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
[docs]def bullseye_thin(ppd=PPD, pad=True):
"""Bullseye stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1aa.
Target size: 0.608x0.608 deg
Ring widths: 0.122 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Bindman, D., & Chubb, C. (2004).
Brightness assimilation in bullseye displays.
Vision Research, 44, 309-319.
https://doi.org/10.1016/S0042-6989(03)00430-9
"""
radii = np.array([0.304, 0.426, 0.548, 0.670, 0.792])
params = {
"visual_size": 0.792 * 2,
"ppd": ppd,
"radii": radii,
"intensity_target": v2,
"origin": "mean",
}
stim1 = stimupy.bullseyes.rectangular_generalized(
**params,
intensity_frames=(v1, v3),
)
stim2 = stimupy.bullseyes.rectangular_generalized(
**params,
intensity_frames=(v3, v1),
)
# Individual padding
if pad:
stim1 = pad_dict_to_visual_size(stim1, np.array(VISEXTENT) / 2, ppd, pad_value=v2)
stim2 = pad_dict_to_visual_size(stim2, np.array(VISEXTENT) / 2, ppd, pad_value=v2)
stim = stack_dicts(stim1, stim2)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
[docs]def bullseye_thick(ppd=PPD, pad=True):
"""Bullseye stimulus as shown in Robinson, Hammon, & de Sa (2007) Fig 1bb.
Target size: 0.608x0.608 deg
Ring widths: 0.243 deg
Parameters
----------
ppd : int
Resolution of stimulus in pixels per degree. (default: 32)
pad : bool
If True, include padding to 32x32 deg (default: True)
Returns
-------
dict of str
dict with the stimulus (key: "img") and target mask (key: "target_mask")
and additional keys containing stimulus parameters
References
----------
Bindman, D., & Chubb, C. (2004).
Brightness assimilation in bullseye displays.
Vision Research, 44, 309-319.
https://doi.org/10.1016/S0042-6989(03)00430-9
"""
radii = np.array([0.304, 0.547, 0.790, 1.033, 1.276])
params = {
"visual_size": 1.276 * 2,
"ppd": ppd,
"radii": radii,
"intensity_target": v2,
"origin": "mean",
}
stim1 = stimupy.bullseyes.rectangular_generalized(
**params,
intensity_frames=(v1, v3),
)
stim2 = stimupy.bullseyes.rectangular_generalized(
**params,
intensity_frames=(v3, v1),
)
# Individual padding
if pad:
stim1 = pad_dict_to_visual_size(stim1, np.array(VISEXTENT) / 2, ppd, pad_value=v2)
stim2 = pad_dict_to_visual_size(stim2, np.array(VISEXTENT) / 2, ppd, pad_value=v2)
stim = stack_dicts(stim1, stim2)
if pad:
stim = pad_dict_to_visual_size(stim, VISEXTENT, ppd, pad_value=v2)
params.update(
visual_size=np.array(stim["img"].shape) / ppd,
shape=stim["img"].shape,
intensity_range=(v1, v3),
)
return {**stim, **params}
if __name__ == "__main__":
from stimupy.utils import plot_stimuli
stims = gen_all(pad=True, skip=True)
plot_stimuli(stims, mask=True)
