def run():
defaults = PaperDefaults()
#David's globals
size = 51
npoints = 64
cval1 = 0.25
cval2 = 0.75
sval = 0.75
test_contrasts = sp.array([0., 8., 32.])
mask_contrasts = sp.array([0., 8., 32.])
# experiment parameters
idx1 = int(cval1 * npoints)
idx2 = int(cval2 * npoints)
# simulate populations
imc = stim.get_center_surround(size=size, csize=9, cval=cval1, sval=sp.nan)
ims = stim.get_center_surround(size=size, csize=9, cval=sp.nan, sval=sval)
x1 = utils.get_population(imc,
npoints=npoints,
kind='gaussian',
scale=0.1,
fdomain=(0, 1))
x2 = sp.roll(x1, int((cval2 - cval1) * npoints), axis=-3)
xs = utils.get_population(ims,
npoints=npoints,
kind='gaussian',
scale=0.1,
fdomain=(0, 1))
x = []
for k1 in test_contrasts:
for k2 in mask_contrasts:
x.append(k1 / 100. * x1 + k2 / 100. * x2)
x = sp.array(x) + sp.array([xs])
# Experimental data
extra_vars = {}
extra_vars['size'] = size
extra_vars['npoints'] = npoints
extra_vars['sval'] = sval
extra_vars['figure_name'] = 'cross_orientation_suppression'
extra_vars['return_var'] = 'O'
extra_vars['idx1'] = idx1
extra_vars['idx2'] = idx2
extra_vars['test_contrasts'] = test_contrasts
extra_vars['mask_contrasts'] = mask_contrasts
optimize_model(x, [], extra_vars, defaults)
def run():
defaults = PaperDefaults()
#David's globals
size = 51
csize = 5
npoints = 64
scale = 2.0
neuron_theta = 0.50
cval = 0.5
csvfiles = [ defaults._DATADIR + \
'/TB2015_Fig1B_%s.csv' % (s,) \
for s in range(-90, 90, 30) + ['CO']]
# experiment parameters
cvals = (sp.arange(-90, 90, 30) + 90.) / 180.
svals = sp.linspace(0.0, 1.0, 6).tolist() + [sp.nan]
neuron_thetas = sp.linspace(0.0, 1.0, npoints)
neuron_idx = sp.argmin(sp.absolute(neuron_thetas - neuron_theta))
stims = [
stim.get_center_surround(size=size, csize=csize, cval=cv, sval=sv)
for cv in cvals for sv in svals
]
x = sp.array([
model_utils.get_population(im,
npoints=npoints,
kind='circular',
scale=scale,
fdomain=(0, 1)) for im in stims
])
# [Array shapes]
# trott and born 2015 data
gt = get_gt(csvfiles)
extra_vars = {}
extra_vars['scale'] = scale
extra_vars['npoints'] = npoints
extra_vars['cval'] = cval
extra_vars['cvals'] = cvals
extra_vars['svals'] = svals
extra_vars['size'] = size
extra_vars['csize'] = csize
extra_vars['neuron_idx'] = neuron_idx
extra_vars['figure_name'] = 'tbtcso'
extra_vars['return_var'] = 'O'
extra_vars['hp_file'] = os.path.join(defaults._FIGURES, 'best_hps.npz')
optimize_model(x, gt, extra_vars, defaults)
def run():
defaults = PaperDefaults()
# David's globals
size = 51
csize = 9
npoints = 32
scale = 2.0
cval = 0.5
neuron_theta = 0.50
csvfiles = [[
defaults._DATADIR + '/TB2015_%i_%s.csv' % (i, s)
for i in range(-90, 90, 30)
] for s in ('PS', 'PO')]
# experiment parameters
ppop = {
'kind': 'circular',
'npoints': npoints,
'scale': scale,
'fdomain': (0, 1),
}
vals_ang = sp.array([-90., -60., -30., 0., 30., 60.])
vals = (vals_ang + 90.) / 180.
imc1 = stim.get_center_surround(size=size,
csize=csize,
cval=cval,
sval=sp.nan)
x1 = model_utils.get_population(imc1, **ppop)
x = sp.zeros((2, len(vals), npoints, size, size))
cvals = (sp.arange(-90, 90, 30) + 90.) / 180.
svals = sp.linspace(0.0, 1.0, 6).tolist() + [sp.nan]
for vdx, v in enumerate(vals):
imc2 = stim.get_center_surround(size=size,
csize=csize,
cval=v,
sval=sp.nan)
ims = stim.get_center_surround(size=size,
csize=csize,
cval=sp.nan,
sval=v)
x2 = model_utils.get_population(imc2, **ppop)
xs = model_utils.get_population(ims, **ppop)
x[0, vdx] = (x1 + x2) / 2.
x[1, vdx] = (x1 + x2) / 2. + xs
x.shape = (2 * len(vals), npoints, size, size)
# trott and born 2015 data
gt = get_gt(npoints, csvfiles)
extra_vars = {}
extra_vars['scale'] = scale
extra_vars['npoints'] = npoints
extra_vars['cval'] = cval
extra_vars['cvals'] = cvals
extra_vars['svals'] = svals
extra_vars['size'] = size
extra_vars['csize'] = csize
extra_vars['vals'] = vals
extra_vars['neuron_theta'] = neuron_theta
extra_vars['figure_name'] = 'tbp'
extra_vars['return_var'] = 'O'
extra_vars['hp_file'] = os.path.join(defaults._FIGURES, 'best_hps.npz')
optimize_model(x, gt, extra_vars, defaults)
def create_stims(extra_vars):
extra_vars = Bunch(extra_vars)
out_dir = re.split('\.', extra_vars.f4_stimuli_file)[0]
if not os.path.exists(out_dir):
os.makedirs(out_dir)
#################
nc = len(extra_vars._DEFAULT_KW2015_SO_PARAMETERS['selected_channels'])
# build stimuli
###############
all_hues_dkls = sp.linspace(0.0,
2 * sp.pi,
extra_vars.n_train,
endpoint=False)
test_hues_dklS = all_hues_dkls[::extra_vars.n_train //
extra_vars.n_t_hues][:extra_vars.n_t_hues]
surr_hues_dklS = test_hues_dklS[::extra_vars.n_t_hues //
extra_vars.n_s_hues][:extra_vars.n_s_hues]
#sp.linspace(0.0, 2*sp.pi, n_s_hues, endpoint=False)
test_sat_dklS = 0.2
surr_sat_dklS = 0.16
isolum_el = 0.0 # elevation is 0 to get isolumination to background
stims_all_lms = sp.zeros(
(extra_vars.n_train, extra_vars.size, extra_vars.size, 3))
stims_ind_lms = sp.zeros((extra_vars.n_t_hues, extra_vars.n_s_hues,
extra_vars.size, extra_vars.size, 3))
pbar_counter = 0
pbar = pb((extra_vars.n_s_hues + 1) * extra_vars.n_train,
'Building isoluminant stimuli [all]')
for i, azt in enumerate(all_hues_dkls):
dklS_ = sp.array([test_sat_dklS, azt, isolum_el])
c_lms_ = dklC2lms(sph2cart(dklS_))
stims_all_lms[i, ...,
0] = stim.get_center_surround(size=extra_vars.size,
csize=extra_vars.csize,
cval=c_lms_[0],
sval=gray_lms[0])
stims_all_lms[i, ...,
1] = stim.get_center_surround(size=extra_vars.size,
csize=extra_vars.csize,
cval=c_lms_[1],
sval=gray_lms[1])
stims_all_lms[i, ...,
2] = stim.get_center_surround(size=extra_vars.size,
csize=extra_vars.csize,
cval=c_lms_[2],
sval=gray_lms[2])
pbar_counter += 1
pbar.update(pbar_counter)
pbar.finish()
pbar_counter = 0
pbar = pb((extra_vars.n_s_hues + 1) * extra_vars.n_t_hues,
'Building isoluminant stimuli [ind]')
for i, azt in enumerate(test_hues_dklS):
dklS_ = sp.array([test_sat_dklS, azt, isolum_el])
c_lms_ = dklC2lms(sph2cart(dklS_))
for j, azs in enumerate(surr_hues_dklS):
dklS_ = sp.array([surr_sat_dklS, azs, isolum_el])
s_lms_ = dklC2lms(sph2cart(dklS_))
stims_ind_lms[i, j, ...,
0] = stim.get_center_surround(size=extra_vars.size,
csize=extra_vars.csize,
cval=c_lms_[0],
sval=s_lms_[0])
stims_ind_lms[i, j, ...,
1] = stim.get_center_surround(size=extra_vars.size,
csize=extra_vars.csize,
cval=c_lms_[1],
sval=s_lms_[1])
stims_ind_lms[i, j, ...,
2] = stim.get_center_surround(size=extra_vars.size,
csize=extra_vars.csize,
cval=c_lms_[2],
sval=s_lms_[2])
pbar_counter += 1
pbar.update(pbar_counter)
pbar.finish()
# compute vanilla SO features for those stimuli
###############################################
so_all = sp.zeros(
(extra_vars.n_train, nc, extra_vars.size, extra_vars.size))
so_ind = sp.zeros((extra_vars.n_t_hues, extra_vars.n_s_hues, nc,
extra_vars.size, extra_vars.size))
pbar = pb(extra_vars.n_train, 'Computing SO features [all]')
for idx in range(extra_vars.n_train):
so_all[idx] = GET_SO(stims_all_lms[idx], extra_vars._DEFAULT_FLOATX_NP,
extra_vars._DEFAULT_KW2015_SO_PARAMETERS)
pbar.update(idx)
pbar.finish()
pbar = pb(extra_vars.n_t_hues * extra_vars.n_s_hues,
'Computing SO features [ind]')
for idx in range(extra_vars.n_t_hues):
for jdx in range(extra_vars.n_s_hues):
so_ind[idx, jdx] = GET_SO(stims_ind_lms[idx, jdx],
extra_vars._DEFAULT_FLOATX_NP,
extra_vars._DEFAULT_KW2015_SO_PARAMETERS)
pbar.update(jdx + idx * extra_vars.n_s_hues)
pbar.finish()
so_ind = so_ind.reshape(extra_vars.n_t_hues * extra_vars.n_s_hues, nc,
extra_vars.size, extra_vars.size)
#Final ops
cs_hue_diff = da2ha(test_hues_dklS.reshape(extra_vars.n_t_hues, 1) - \
surr_hues_dklS.reshape(1, extra_vars.n_s_hues))
cs_hue_diff *= (180 / sp.pi)
np.savez(extra_vars.f4_stimuli_file,
so_all=so_all,
so_ind=so_ind,
stims_all_lms=stims_all_lms,
cs_hue_diff=cs_hue_diff)
def run(hps=None):
defaults = PaperDefaults()
#David's globals
size=51
csize=9
npoints=37
scale=1.
_DEFAULT_BWC_CSV_CTS = sp.array([0.0, .06, .12, .25, .50]) * 100
csvfiles=sp.array([[os.path.join(defaults._DATADIR, 'BWC2009_%i_%i.csv' \
% (i, j)) for i in _DEFAULT_BWC_CSV_CTS] for j in _DEFAULT_BWC_CSV_CTS]).T
# experiment parameters
im = sp.array([
stim.get_center_surround(
size=size, csize=csize, cval=.25, sval=sp.nan),
stim.get_center_surround(
size=size, csize=csize, cval=.75, sval=sp.nan)])
# populations for vertical (masking) and horizontal (driving) stimuli
#####################################################################
xv = model_utils.get_population(im[0],
kind='circular', npoints=npoints, scale=scale)
xh = model_utils.get_population(im[1],
kind='circular', npoints=npoints, scale=scale)
# superimposed populations
##########################
v_contrasts = [0.0, .06, .12, .25, .50]
h_contrasts = [0.0, .06, .12, .25, .50]
nv, nh = len(v_contrasts), len(h_contrasts)
x = sp.array([[h*xh + v*xv for h in h_contrasts] for v in v_contrasts])
x.shape = (nv * nh,) + x.shape[2:]
# busse and wade data
t_paper = sp.zeros((nv, nh, 13))
y_paper = sp.zeros((nv, nh, 13))
for idx in range(nv):
for jdx in range(nh):
t_paper[idx, jdx], y_paper[idx, jdx] = \
sp.genfromtxt(csvfiles[idx, jdx], delimiter=',').T
res_y_paper = sp.zeros((y_paper.shape[0],y_paper.shape[1],npoints))
for r in range(y_paper.shape[0]):
for c in range(y_paper.shape[1]):
res_y_paper[r,c,:] = sp.signal.resample(y_paper[r,c,:],npoints)
gt = [t_paper,res_y_paper]
extra_vars = {}
extra_vars['scale'] = scale
extra_vars['npoints'] = npoints
extra_vars['size'] = size
extra_vars['csize'] = csize
extra_vars['nv'] = nv
extra_vars['nh'] = nh
extra_vars['figure_name'] = 'bw'
extra_vars['return_var'] = 'O'
defaults = adjust_parameters(defaults,hps)
optimize_model(x,gt,extra_vars,defaults)