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
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)
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 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(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'
extra_vars['hp_file'] = os.path.join(defaults._FIGURES, 'best_hps.npz')
optimize_model(x, gt, extra_vars, defaults)
def run(initialize_model=False):
defaults = PaperDefaults()
#David's globals
_DEFAULT_KW2015_SO_PARAMETERS = {
'filters': {
'name': 'gabors',
'aspect_ratio': .6,
'sizes': sp.array([9]),
'spatial_frequencies': sp.array([[9.0]]),
'orientations': sp.arange(2) * sp.pi / 2,
'phases': sp.array([0]),
'with_center_surround': False,
'padding': 'reflect',
'corr': False,
'ndp': False
},
'model': {
'channels_so': ('R+G-', 'B+Y-', 'R+C-', 'Wh+Bl-', 'G+R-', 'Y+B-',
'C+R-', 'Bl+Wh-'),
'normalize':
False
},
'dnp_so': None,
'selected_channels': [0, 1, 3, 4, 5, 7],
'norm_channels': [0, 1, 3, 4, 5, 7]
}
size = 51
csize = 9
n_train = 32
n_t_hues = 16
n_s_hues = 16
csvfiles = [
defaults._DATADIR + '/KW2015_%i.csv' % (i, )
for i in range(0, 360, 45)
]
#Load data from experiments
kw2015_fig2_x = sp.zeros((len(csvfiles), 16))
kw2015_fig2_y = sp.zeros((len(csvfiles), 16))
for idx, csv in enumerate(csvfiles):
kw2015_fig2_x[idx], kw2015_fig2_y[idx] = \
sp.genfromtxt(csv, delimiter=',')[1:].T
# experiment stimuli
extra_vars = {}
extra_vars['_DEFAULT_KW2015_SO_PARAMETERS'] = _DEFAULT_KW2015_SO_PARAMETERS
extra_vars['_DEFAULT_FLOATX_NP'] = defaults._DEFAULT_FLOATX_NP
extra_vars['size'] = size
extra_vars['csize'] = csize
extra_vars['n_train'] = n_train
extra_vars['n_t_hues'] = n_t_hues
extra_vars['n_s_hues'] = n_s_hues
extra_vars['figure_name'] = 'f4'
extra_vars['gt_x'] = kw2015_fig2_x
extra_vars['f4_stimuli_file'] = defaults.f4_stimuli_file
extra_vars['return_var'] = 'I'
extra_vars['precalculated_x'] = True
extra_vars['aux_y'] = []
extra_vars['percent_reg_train'] = 80.
extra_vars['hp_file'] = os.path.join(defaults._FIGURES, 'best_hps.npz')
if initialize_model:
create_stims(extra_vars)
stim_files = np.load(extra_vars['f4_stimuli_file'])
extra_vars['stims_all_lms'] = stim_files['stims_all_lms']
#Run model
#cx.run(so_all, from_gpu=False)
#sx_all[:] = cx.Y.get()[:, :, size//2, size//2]
adj_gt = np.mean(kw2015_fig2_y, axis=0)
im = stim_files['so_ind'].reshape(
n_t_hues * n_s_hues,
len(_DEFAULT_KW2015_SO_PARAMETERS['norm_channels']), size, size)
extra_vars['aux_data'] = stim_files['so_all'].transpose(0, 2, 3, 1)
extra_vars['cs_hue_diff'] = stim_files['cs_hue_diff']
optimize_model(im, adj_gt, extra_vars, defaults)