def run():
defaults = PaperDefaults()
#David's globals
_DEFAULT_TILTEFFECT_DEGPERPIX = .25 # <OToole77>
_DEFAULT_TILTEFFECT_SIZE = 51 #101
_DEFAULT_TILTEFFECT_CSIZE = iround(2. / _DEFAULT_TILTEFFECT_DEGPERPIX)
_DEFAULT_TILTEFFECT_SSIZE = iround(8. / _DEFAULT_TILTEFFECT_DEGPERPIX)
_DEFAULT_TILTEFFECT_CVAL = .5
_DEFAULT_TILTEFFECT_SVALS = np.linspace(0.0, 0.5, 10)
_DEFAULT_TILTEFFECT_SCALES = {'ow77': 0.40, 'ms79': 0.60} #0.45
_DEFAULT_TILTEFFECT_NPOINTS = 25 #100
_DEFAULT_TILTEFFECT_DECODER_TYPE = 'circular_vote'
_DEFAULT_TILTEFFECT_CSV = {
'ow77': os.path.join(defaults._DATADIR, 'OW_fig4_Black.csv'),
'ms79': os.path.join(defaults._DATADIR, 'MS1979.csv'),
}
# experiment parameters
cpt = (_DEFAULT_TILTEFFECT_SIZE // 2, _DEFAULT_TILTEFFECT_SIZE // 2)
spt = (_DEFAULT_TILTEFFECT_SIZE // 2,
_DEFAULT_TILTEFFECT_SIZE // 2 + _DEFAULT_TILTEFFECT_CSIZE)
dt_in = _DEFAULT_TILTEFFECT_CVAL - _DEFAULT_TILTEFFECT_SVALS
# simulate populations
im = sp.array([[
stim.get_center_nfsurrounds(size=_DEFAULT_TILTEFFECT_SIZE,
csize=_DEFAULT_TILTEFFECT_CSIZE,
nsize=_DEFAULT_TILTEFFECT_CSIZE,
fsize=_DEFAULT_TILTEFFECT_SSIZE,
cval=_DEFAULT_TILTEFFECT_CVAL,
nval=_DEFAULT_TILTEFFECT_CVAL,
fval=sval,
bgval=sp.nan)
] for sval in _DEFAULT_TILTEFFECT_SVALS])
# get shifts for model for both papers, and from digitized data
sortidx = sp.argsort(dt_in) # re-order in increasing angular differences
# O'Toole and Wenderoth (1977)
_, ds_ow77_paper_y = sp.genfromtxt(_DEFAULT_TILTEFFECT_CSV['ow77'],
delimiter=',').T
extra_vars = {}
extra_vars['scale'] = _DEFAULT_TILTEFFECT_SCALES['ow77']
extra_vars['decoder'] = _DEFAULT_TILTEFFECT_DECODER_TYPE
extra_vars['npoints'] = _DEFAULT_TILTEFFECT_NPOINTS
extra_vars['npoints'] = _DEFAULT_TILTEFFECT_NPOINTS
extra_vars['cval'] = _DEFAULT_TILTEFFECT_CVAL
extra_vars['sortidx'] = sortidx
extra_vars['cpt'] = cpt
extra_vars['spt'] = spt
extra_vars['sval'] = sval
extra_vars['kind'] = 'circular'
extra_vars['figure_name'] = 'f3a'
extra_vars['return_var'] = 'O'
extra_vars['hp_file'] = os.path.join(defaults._FIGURES, 'best_hps.npz')
optimize_model(im, ds_ow77_paper_y, extra_vars, defaults)
def run():
defaults = PaperDefaults()
#David's globals
_DEFAULT_KW97_TILTEFFECT_DEGPERPIX = .45 # <OToole77>
_DEFAULT_TILTEFFECT_SIZE = 101 #101
_DEFAULT_KW97_TILTEFFECT_CSIZE = iround(3.6 /
_DEFAULT_KW97_TILTEFFECT_DEGPERPIX)
_DEFAULT_KW97_TILTEFFECT_NSIZE = iround(5.4 /
_DEFAULT_KW97_TILTEFFECT_DEGPERPIX)
_DEFAULT_KW97_TILTEFFECT_FSIZE = iround(10.7 /
_DEFAULT_KW97_TILTEFFECT_DEGPERPIX)
_DEFAULT_TILTEFFECT_CVAL = .5
_DEFAULT_TILTEFFECT_SVALS = np.linspace(0.0, 0.5, 10)
_DEFAULT_KW97_TILTEFFECT_SCALE = 1.25
_DEFAULT_TILTEFFECT_NPOINTS = 25 #100
_DEFAULT_TILTEFFECT_CIRCULAR = True
_DEFAULT_TILTEFFECT_DECODER_TYPE = 'circular_vote'
csvfiles = [
os.path.join(defaults._DATADIR, 'KW97_GH.csv'),
os.path.join(defaults._DATADIR, 'KW97_JHK.csv'),
os.path.join(defaults._DATADIR, 'KW97_LL.csv'),
os.path.join(defaults._DATADIR, 'KW97_SJL.csv'),
]
# experiment parameters
cpt = (_DEFAULT_TILTEFFECT_SIZE // 2, _DEFAULT_TILTEFFECT_SIZE // 2)
spt = (_DEFAULT_TILTEFFECT_SIZE // 2,
_DEFAULT_TILTEFFECT_SIZE // 2 + _DEFAULT_KW97_TILTEFFECT_CSIZE)
dt_in = _DEFAULT_TILTEFFECT_CVAL - _DEFAULT_TILTEFFECT_SVALS
# simulate populations
im = sp.array([[
stim.get_center_nfsurrounds(size=_DEFAULT_TILTEFFECT_SIZE,
csize=_DEFAULT_KW97_TILTEFFECT_CSIZE,
nsize=_DEFAULT_KW97_TILTEFFECT_NSIZE,
fsize=_DEFAULT_KW97_TILTEFFECT_FSIZE,
cval=_DEFAULT_TILTEFFECT_CVAL,
nval=sp.nan,
fval=sval,
bgval=sp.nan)
] for sval in _DEFAULT_TILTEFFECT_SVALS])
# get shifts for model for both papers, and from digitized data
sortidx = sp.argsort(dt_in) # re-order in increasing angular differences
# O'Toole and Wenderoth (1977)
n_subjects = len(csvfiles)
ds_kw97_paper_x = sp.zeros((n_subjects, 9))
ds_kw97_paper_y = sp.zeros((n_subjects, 9))
for sidx, csv in enumerate(csvfiles):
ds_kw97_paper_x[sidx], ds_kw97_paper_y[sidx] = \
sp.genfromtxt(csv, delimiter=',').T
ds_kw97_paper_x = (ds_kw97_paper_x + 360.) % 360. - 45.
ds_kw97_paper_y = 45. - ds_kw97_paper_y
for sidx in range(n_subjects):
ds_kw97_paper_x[sidx] = ds_kw97_paper_x[sidx][sp.argsort(
ds_kw97_paper_x[sidx])]
extra_vars = {}
extra_vars['scale'] = _DEFAULT_KW97_TILTEFFECT_SCALE
extra_vars['decoder'] = _DEFAULT_TILTEFFECT_DECODER_TYPE
extra_vars['npoints'] = _DEFAULT_TILTEFFECT_NPOINTS
extra_vars['npoints'] = _DEFAULT_TILTEFFECT_NPOINTS
extra_vars['cval'] = _DEFAULT_TILTEFFECT_CVAL
extra_vars['sortidx'] = sortidx
extra_vars['cpt'] = cpt
extra_vars['spt'] = spt
extra_vars['sval'] = sval
extra_vars['kind'] = 'circular'
extra_vars['figure_name'] = 'f3b'
extra_vars['return_var'] = 'O'
extra_vars['hp_file'] = os.path.join(defaults._FIGURES, 'best_hps.npz')
adjusted_gt = signal.resample(np.mean(ds_kw97_paper_y, axis=0), 10)
optimize_model(im, adjusted_gt, extra_vars, defaults)
def run():
defaults = PaperDefaults()
#David's globals
size = 51
mpp = 0.76 # 0.76 # 1.11
scale = 0.23 # 0.23 # 0.22
csv_file_x = os.path.join(defaults._DATADIR, 'WL1987_corrected_X.csv')
csv_file_y = os.path.join(defaults._DATADIR, 'WL1987_corrected_Y.csv')
# experiment parameters
dd = (-150., 150.) # in seconds of arc
sec2u = lambda s: (s - dd[0]) / (dd[1] - dd[0])
u2sec = lambda u: u * (dd[1] - dd[0]) + dd[0]
min2pix = lambda m: iround(m / float(mpp))
npoints = 50
ndists = 10
dists = sp.linspace(0.0, 12., ndists)
lh, lw = 1, 4.
ph, pw = 2., 2.
center_disp = 0.0
flanker_disp = -33.3
mp0 = size // 2
# Need to scale up the ecrfs
defaults._DEFAULT_PARAMETERS[
'srf'] = defaults._DEFAULT_PARAMETERS['srf'] * 2 - 1
defaults._DEFAULT_PARAMETERS[
'ssn'] = defaults._DEFAULT_PARAMETERS['ssn'] * 2 - 1
defaults._DEFAULT_PARAMETERS[
'ssf'] = defaults._DEFAULT_PARAMETERS['ssf'] * 2 - 1
# simulate populations
im = get_wl87_stim(size=size,
dists=min2pix(dists),
cval=sec2u(center_disp),
sval=sec2u(flanker_disp),
ch=min2pix(lh),
cw=min2pix(lw),
sh=min2pix(ph),
sw=min2pix(pw))
# Get ground truth data
paper_data_x = sp.genfromtxt(csv_file_x, delimiter=',')
paper_data_y = sp.genfromtxt(csv_file_y, delimiter=',') * -1
paper_fit_y = sfit(sp.linspace(dists.min(), dists.max(), 100),
paper_data_x,
sp.nanmean(paper_data_y, axis=0),
k=2,
t=[5.])
paper_fit_y = paper_fit_y[np.round(
np.linspace(0, paper_fit_y.shape[0] - 1, ndists)).astype(int)]
extra_vars = {}
extra_vars['scale'] = scale
extra_vars['kind'] = 'gaussian'
extra_vars['decoder'] = 'circular_vote'
extra_vars['npoints'] = npoints
extra_vars['cval'] = sec2u(center_disp)
extra_vars['sval'] = sec2u(flanker_disp)
extra_vars['figure_name'] = 'f5'
extra_vars['u2sec'] = u2sec
extra_vars['min2pix'] = min2pix
extra_vars['dists'] = dists
extra_vars['flanker_disp'] = flanker_disp
extra_vars['mp0'] = mp0
extra_vars['lh'] = lh
extra_vars['pw'] = pw
extra_vars['size'] = size
extra_vars['gt_x'] = paper_data_x
extra_vars['return_var'] = 'O'
extra_vars['hp_file'] = os.path.join(defaults._FIGURES, 'best_hps.npz')
optimize_model(im, paper_fit_y, extra_vars, defaults)
def run(make_stims=False):
defaults = PaperDefaults()
#David's globals
test_colors = ['orange', 'turquoise']
size = 77
csize = 3
ssize = None
_MINPERPIX = 2.
_DEFAULT_SM2003_CSIZE_MIN = 6.0
_DEFAULT_SM2003_SIZE_MIN = 153.0
_DEFAULT_SM2003_CPDS = sp.array([0., 1., 2., 3.3, 5., 10.]) # <from paper>
_DEFAULT_SM2003_CPMS = _DEFAULT_SM2003_CPDS / 60.
_DEFAULT_SM2003_CPSS = iround(1 + _DEFAULT_SM2003_CPMS * \
(_DEFAULT_SM2003_SIZE_MIN - _DEFAULT_SM2003_CSIZE_MIN)/2.0) # <realistic>
_DEFAULT_SM2003_CSIZE = iround(_DEFAULT_SM2003_CSIZE_MIN / _MINPERPIX)
cpss = _DEFAULT_SM2003_CPSS
#Use color parameters
defaults._DEFAULT_PARAMETERS['continuous'] = False
defaults._DEFAULT_PARAMETERS['srf'] = _DEFAULT_SM2003_CSIZE
csvfiles = {
'ObsML': {
'PL': defaults._DATADIR + '/SM2003_Fig5_ObsML_PL.csv',
'LP': defaults._DATADIR + '/SM2003_Fig5_ObsML_LP.csv',
},
'ObsPM': {
'PL': defaults._DATADIR + '/SM2003_Fig5_ObsPM_PL.csv',
'LP': defaults._DATADIR + '/SM2003_Fig5_ObsPM_LP.csv',
},
}
_DEFAULT_SM2003_COLORS_RGB = {
'orange': sp.array([.99, .65, .47]),
'purple': sp.array([.65, .54, .77]),
# adjusted to have Y=.30 #sp.array([.60, .49, .71]),
'purple_alt': sp.array([.60, .48, .70]),
'lime': sp.array([.56, .60, .49]),
# adjusted to have Y=.30 #sp.array([.61, .65, .53]),
'lime_alt': sp.array([.51, .61, .53]),
'turquoise': sp.array([.44, .78, .73]),
'neutral': sp.array([.66, .66, .66]),
'lilac': sp.array([.89, .62, .80]),
'citrus': sp.array([.67, 1.0, .50]),
'EEW': sp.array([.58, .58, .58])
}
# O'Toole and Wenderoth (1977)
# load digitzed data from original paper
########################################
shift_phase_paper = sp.array([
sp.genfromtxt(csvfiles['ObsML']['PL'], delimiter=',').T[1],
sp.genfromtxt(csvfiles['ObsPM']['PL'], delimiter=',').T[1]
])
shift_anti_paper = sp.array([
sp.genfromtxt(csvfiles['ObsML']['LP'], delimiter=',').T[1],
sp.genfromtxt(csvfiles['ObsPM']['LP'], delimiter=',').T[1]
])
shift_phase_paper = np.mean(shift_phase_paper, axis=0) #double check this
shift_anti_paper = np.mean(shift_anti_paper, axis=0)
gt = np.vstack((shift_phase_paper, shift_anti_paper))
#Also preload regs for postprocessing
regpath = os.path.join(defaults._WORKDIR,\
'ShevellMonnier2003.reg.pkl.std.srf%issn%issf%i' \
% (defaults._DEFAULT_PARAMETERS['srf'], defaults._DEFAULT_PARAMETERS['ssn'], defaults._DEFAULT_PARAMETERS['ssf']))
reg_X_SO = joblib.load(regpath)['reg_X_SO']
reg_Y_SO = joblib.load(regpath)['reg_Y_SO']
reg_Z_SO = joblib.load(regpath)['reg_Z_SO']
scaler_SO = joblib.load(regpath)['scaler_SO']
reg_X_SX = joblib.load(regpath)['reg_X_SX']
reg_Y_SX = joblib.load(regpath)['reg_Y_SX']
reg_Z_SX = joblib.load(regpath)['reg_Z_SX']
scaler_SX = joblib.load(regpath)['scaler_SX']
so2image = cutils.get_XYZ2RGB_predictor(reg_X_SO, reg_Y_SO, reg_Z_SO,
scaler_SO)
sx2image = cutils.get_XYZ2RGB_predictor(reg_X_SX, reg_Y_SX, reg_Z_SX,
scaler_SX)
#Add to a dictionary
extra_vars = {}
extra_vars['n_cps'] = len(cpss)
extra_vars['cpss'] = cpss
extra_vars['test_colors'] = test_colors
extra_vars['_DEFAULT_SM2003_COLORS_RGB'] = _DEFAULT_SM2003_COLORS_RGB
extra_vars['size'] = size
extra_vars['csize'] = csize
extra_vars['ssize'] = ssize
extra_vars['kind'] = 'circular'
extra_vars['so2image'] = so2image
extra_vars['sx2image'] = sx2image
extra_vars['n_col'] = len(test_colors)
extra_vars['figure_name'] = 'f7'
extra_vars['return_var'] = 'O'
extra_vars['hp_file'] = os.path.join(defaults._FIGURES, 'best_hps.npz')
# measure shift for phase & antiphase inducers, for each test
if make_stims:
create_stimuli(gt, extra_vars, defaults)
else:
optimize_model(gt, extra_vars, defaults)
def sfit(ptx_eval, ptx, pty, s=1., k=3, t=None):
""" Fit a bunch of points and return an evaluation vector """
assert len(ptx) == len(pty)
if t is None:
return UnivariateSpline(x=ptx, y=pty, s=s, k=k)(ptx_eval)
else:
return LSQUnivariateSpline(x=ptx, y=pty, t=t, k=k)(ptx_eval)
""" Better with center size = 7 and gamma = .25 """
#Figure 3c
_DEFAULT_TILTEFFECT_DEGPERPIX = .25 # <OToole77>
_DEFAULT_TILTEFFECT_SIZE = 51 #101
_DEFAULT_TILTEFFECT_CSIZE = iround(2. / _DEFAULT_TILTEFFECT_DEGPERPIX)
_DEFAULT_TILTEFFECT_SSIZE = iround(8. / _DEFAULT_TILTEFFECT_DEGPERPIX)
_DEFAULT_TILTEFFECT_CVAL = .5
_DEFAULT_TILTEFFECT_SVALS = sp.linspace(0.0, 0.5, 10)
_DEFAULT_TILTEFFECT_SCALES = {'ow77': 0.40, 'ms79': 0.60} #0.45
_DEFAULT_TILTEFFECT_NPOINTS = 25 #100
_DEFAULT_TILTEFFECT_CIRCULAR = True
_DEFAULT_TILTEFFECT_DECODER_TYPE = 'circular_vote'
_DEFAULT_TILTEFFECT_CSV = {
'ow77': os.path.join(DATADIR, 'OW_fig4_Black.csv'),
'ms79': os.path.join(DATADIR, 'MS1979.csv'),
}
_DEFAULT_VALUE_BW3DS_SIZE = 31
_DEFAULT_VALUE_BW3DS_CSIZE = 8
_DEFAULT_VALUE_BW3DS_CVAL = .5