def run():
defaults = PaperDefaults()
#David's globals
size = 51
csize = 9
npoints = 32
scale = 2.0
cval = 0.5
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))
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['size'] = size
extra_vars['csize'] = csize
extra_vars['vals'] = vals
extra_vars['figure_name'] = 'tbp'
extra_vars['return_var'] = 'O'
optimize_model(x, gt, extra_vars, defaults)
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'
optimize_model(x,gt,extra_vars,defaults)
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
nr = 17
npoints = 32 // 2
ncontrasts = 5
# experiment parameters
# generate stimuli
##################
im = []
for k in range(nr):
im_ = sp.zeros((size, size)) + sp.nan
im_[size // 2 - k:size // 2 + k + 1,
size // 2 - k:size // 2 + k + 1] = 0.5
im.append(im_)
im = sp.array(im)
# generate populations
######################
contrasts = sp.linspace(1., 0., ncontrasts, endpoint=False)[::-1]
# contrasts = sp.logspace(-2, 0., ncontrasts)
x = sp.array([
utils.get_population(xim_, 'gaussian', npoints=npoints) for xim_ in im
])
ax = [c * x for c in contrasts]
cx = np.concatenate(ax[:], axis=0)
# Experimental data
extra_vars = {}
extra_vars['size'] = size
extra_vars['npoints'] = npoints
extra_vars['nr'] = nr
extra_vars['stimsizes'] = 2 * sp.arange(nr) + 1
extra_vars['ssn'] = defaults._DEFAULT_PARAMETERS['ssn']
extra_vars['ssf'] = defaults._DEFAULT_PARAMETERS['ssf']
extra_vars['hp_file'] = os.path.join(defaults._FIGURES, 'best_hps.npz')
extra_vars['figure_name'] = 'size_tuning'
extra_vars['return_var'] = 'O'
extra_vars['contrasts'] = contrasts
extra_vars['curvecols'] = sns.cubehelix_palette(ncontrasts)
extra_vars['curvelabs'] = [
'Single-cell response at contrast %g' % (cst, ) for cst in contrasts
]
optimize_model(cx, None, extra_vars, defaults)
