def test_brute_force_search_manual_grids():
# create a parameter to estimate
params = (10, 10)
# we need to define some search bounds
grid_1 = utils.grid_slice(5, 15, 5)
grid_2 = utils.grid_slice(5, 15, 5)
grids = (
grid_1,
grid_2,
)
bounds = ()
# set the verbose level 0 is silent, 1 is final estimate, 2 is each iteration
verbose = 0
# create a simple function to transform the parameters
func = lambda freq, offset: np.sin(
np.linspace(0, 1, 1000) * 2 * np.pi * freq) + offset
# create a "response"
response = func(*params)
# get the ball-park estimate
p0 = utils.brute_force_search(response, utils.error_function, func, grids,
bounds)
# assert that the estimate is equal to the parameter
npt.assert_equal(params, p0[0])
def fit_popeye(bars, dat):
stimulus = generate_stimulus(bars)
model = generate_model(stimulus)
# set search grid
x_grid = grid_slice(-10, 10, 5)
y_grid = grid_slice(-10, 10, 5)
s_grid = grid_slice(0.25, 5.25, 5)
# set search bounds
x_bound = (-12.0, 12.0)
y_bound = (-12.0, 12.0)
s_bound = (0.001, 12.0)
b_bound = (1e-8, None)
m_bound = (None, None)
# loop over each voxel and set up a GaussianFit object
grids = (x_grid, y_grid, s_grid,)
bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
# fit the response
fit = GaussianFit(model, dat, grids, bounds)
return fit
def test_brute_force_search_manual_grids():
# create a parameter to estimate
params = (10,10)
# we need to define some search bounds
grid_1 = utils.grid_slice(5,15,5)
grid_2 = utils.grid_slice(5,15,5)
grids = (grid_1,grid_2,)
bounds = ()
# set the verbose level 0 is silent, 1 is final estimate, 2 is each iteration
verbose = 0
# create a simple function to transform the parameters
func = lambda freq, offset: np.sin( np.linspace(0,1,1000) * 2 * np.pi * freq) + offset
# create a "response"
response = func(*params)
# get the ball-park estimate
p0 = utils.brute_force_search(response, utils.error_function, func, grids, bounds)
# assert that the estimate is equal to the parameter
npt.assert_equal(params, p0[0])
def test_resurrect_model():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.arange(0,360,90)
thetas = np.insert(thetas,0,-1)
thetas = np.append(thetas,-1)
num_blank_steps = 20
num_bar_steps = 20
ecc = 10
tr_length = 1.5
frames_per_tr = 1.0
scale_factor = 0.50
pixels_across = 100
pixels_down = 100
dtype = ctypes.c_int16
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance, screen_width,
thetas, num_bar_steps, num_blank_steps, ecc, clip=0.01)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width, scale_factor, tr_length, dtype)
# set cache grids
x_grid = utils.grid_slice(-10, 10, 5)
y_grid = utils.grid_slice(-10, 10, 5)
s_grid = utils.grid_slice(0.55,5.25, 5)
grids = (x_grid, y_grid, s_grid,)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.spm_hrf)
model.hrf_delay = 0
model.mask_size = 5
# seed rng
np.random.seed(4932)
# cache the model
cache = model.cache_model(grids, ncpus=3)
# resurrect cached model
cached_model_path = '/tmp/og_cached_model.pkl'
model = og.GaussianModel(stimulus, utils.double_gamma_hrf, cached_model_path=cached_model_path)
model.hrf_delay = 0
model.mask_size = 5
# make sure the same
nt.assert_true(np.sum([c[0] for c in cache] - model.cached_model_timeseries) == 0)
nt.assert_true(np.sum([c[1] for c in cache] - model.cached_model_parameters) == 0)
def test_cache_model_slice():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.arange(0,360,90)
thetas = np.insert(thetas,0,-1)
thetas = np.append(thetas,-1)
num_blank_steps = 20
num_bar_steps = 20
ecc = 10
tr_length = 1.5
frames_per_tr = 1.0
scale_factor = 0.50
pixels_across = 100
pixels_down = 100
dtype = ctypes.c_int16
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance, screen_width,
thetas, num_bar_steps, num_blank_steps, ecc, clip=0.01)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width, scale_factor, tr_length, dtype)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.spm_hrf)
model.hrf_delay = 0
model.mask_size = 5
# set cache grids
x_grid = utils.grid_slice(-10, 10, 5)
y_grid = utils.grid_slice(-10, 10, 5)
s_grid = utils.grid_slice(0.55,5.25, 5)
grids = (x_grid, y_grid, s_grid,)
# seed rng
np.random.seed(4932)
# cache the pRF model
cache = model.cache_model(grids, ncpus=3)
# save it out
pickle.dump(cache, open('/tmp/og_cached_model.pkl','wb'))
# make sure its the right size
cached_model = pickle.load(open('/tmp/og_cached_model.pkl','rb'))
nt.assert_equal(np.sum([c[0] for c in cache]),np.sum([c[0] for c in cached_model]))
def test_grid_slice():
# test this case
from_1 = 5
to_1 = 15
from_2 = 0
to_2 = 2
Ns = 5
# set a parameter to estimate
params = (10, 1)
# see if we properly tile the parameter space for Ns=2
grid_1 = utils.grid_slice(from_1, to_1, Ns)
grid_2 = utils.grid_slice(from_2, to_2, Ns)
grids = (grid_1, grid_2)
# unbounded
bounds = ()
# create a simple function to generate a response from the parameter
func = lambda freq, offset: np.sin(
np.linspace(0, 1, 1000) * 2 * np.pi * freq) + offset
# create a "response"
response = func(*params)
# get the ball-park estimate
p0 = utils.brute_force_search(response, utils.error_function, func, grids,
bounds)
# make sure we fit right
npt.assert_equal(params, p0[0])
# make sure we sliced it right
npt.assert_equal(p0[2][0].min(), from_1)
npt.assert_equal(p0[2][0].max(), to_1)
npt.assert_equal(p0[2][1].min(), from_2)
npt.assert_equal(p0[2][1].max(), to_2)
def test_grid_slice():
# test this case
from_1 = 5
to_1 = 15
from_2 = 0
to_2 = 2
Ns=5
# set a parameter to estimate
params = (10,1)
# see if we properly tile the parameter space for Ns=2
grid_1 = utils.grid_slice(from_1, to_1, Ns)
grid_2 = utils.grid_slice(from_2, to_2, Ns)
grids = (grid_1, grid_2)
# unbounded
bounds = ()
# create a simple function to generate a response from the parameter
func = lambda freq,offset: np.sin( np.linspace(0,1,1000) * 2 * np.pi * freq) + offset
# create a "response"
response = func(*params)
# get the ball-park estimate
p0 = utils.brute_force_search(response, utils.error_function, func, grids, bounds)
# make sure we fit right
npt.assert_equal(params, p0[0])
# make sure we sliced it right
npt.assert_equal(p0[2][0].min(),from_1)
npt.assert_equal(p0[2][0].max(),to_1)
npt.assert_equal(p0[2][1].min(),from_2)
npt.assert_equal(p0[2][1].max(),to_2)
def test_xval():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.array([-1, 0, 90, 180, 270, -1])
num_blank_steps = 30
num_bar_steps = 30
ecc = 10
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 0.10
pixels_down = 100
pixels_across = 100
dtype = ctypes.c_int16
voxel_index = (1,2,3)
auto_fit = True
verbose = 1
# rng
np.random.seed(2764932)
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps, num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width, scale_factor, tr_length, dtype)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.spm_hrf)
model.hrf_delay = 0
# generate a random pRF estimate
x = -5.24
y = 2.58
sigma = 1.24
beta = 2.5
baseline = -0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-10, 10, 5)
y_grid = utils.grid_slice(-10, 10, 5)
s_grid = utils.grid_slice(0.5, 3.25, 5)
# set search bounds
x_bound = (-12.0,12.0)
y_bound = (-12.0,12.0)
s_bound = (0.001,12.0)
b_bound = (1e-8,None)
m_bound = (None,None)
# loop over each voxel and set up a GaussianFit object
grids = (x_grid, y_grid, s_grid,)
bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
# pack multiple "runs"
data = np.vstack((data,data))
# make it a singular "voxel"
data = np.reshape(data, (1,data.shape[0],data.shape[1]))
# set bootstraps and resamples
bootstraps = 2
kfolds = 2
# make fodder
bundle = utils.xval_bundle(bootstraps, kfolds, og.GaussianFit, model, data, grids, bounds, np.tile((1,2,3),(3,1)))
# test
for b in bundle:
fit = utils.parallel_xval(b)
npt.assert_almost_equal(fit.rss,0)
npt.assert_equal(fit.cod, 100.0)
npt.assert_(np.all(fit.tst_data == fit.trn_data))
npt.assert_(np.all(fit.tst_idx != fit.trn_idx))
def test_og_fit():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.arange(0, 360, 90)
thetas = np.insert(thetas, 0, -1)
thetas = np.append(thetas, -1)
num_blank_steps = 30
num_bar_steps = 30
ecc = 12
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 1.0
pixels_across = 100
pixels_down = 100
dtype = ctypes.c_int16
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps,
num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width,
scale_factor, tr_length, dtype)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.spm_hrf)
model.hrf_delay = 0
model.mask_size = 6
# generate a random pRF estimate
x = -5.24
y = 2.58
sigma = 1.24
beta = 2.5
baseline = -0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-10, 10, 5)
y_grid = utils.grid_slice(-10, 10, 5)
s_grid = utils.grid_slice(0.25, 5.25, 5)
# set search bounds
x_bound = (-12.0, 12.0)
y_bound = (-12.0, 12.0)
s_bound = (0.001, 12.0)
b_bound = (1e-8, None)
m_bound = (None, None)
# loop over each voxel and set up a GaussianFit object
grids = (
x_grid,
y_grid,
s_grid,
)
bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
# fit the response
fit = og.GaussianFit(model, data, grids, bounds)
# coarse fit
ballpark = [-5., 5., 2.75]
npt.assert_almost_equal((fit.x0, fit.y0, fit.s0), ballpark)
# the baseline/beta should be 0/1 when regressed data vs. estimate
(m, b) = np.polyfit(fit.scaled_ballpark_prediction, data, 1)
npt.assert_almost_equal(m, 1.0)
npt.assert_almost_equal(b, 0.0)
# assert equivalence
npt.assert_almost_equal(fit.x, x)
npt.assert_almost_equal(fit.y, y)
npt.assert_almost_equal(fit.sigma, sigma)
npt.assert_almost_equal(fit.beta, beta)
# test receptive field
rf = generate_og_receptive_field(x, y, sigma, fit.model.stimulus.deg_x,
fit.model.stimulus.deg_y)
rf /= (2 * np.pi * sigma**2) * 1 / np.diff(model.stimulus.deg_x[0, 0:2])**2
npt.assert_almost_equal(np.round(rf.sum()),
np.round(fit.receptive_field.sum()))
# test model == fit RF
npt.assert_almost_equal(
np.round(fit.model.generate_receptive_field(x, y, sigma).sum()),
np.round(fit.receptive_field.sum()))
def test_recast_estimation_results():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.arange(0,360,45)
num_blank_steps = 0
num_bar_steps = 30
ecc = 10
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 0.10
pixels_down = 100
pixels_across = 100
dtype = ctypes.c_int16
voxel_index = (1,2,3)
auto_fit = True
verbose = 1
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps, num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width, scale_factor, tr_length, dtype)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.spm_hrf)
model.hrf_delay = 0
# generate a random pRF estimate
x = -5.24
y = 2.58
sigma = 1.24
beta = 2.5
baseline = -0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-5,4,5)
y_grid = utils.grid_slice(-5,7,5)
s_grid = utils.grid_slice(1/stimulus.ppd,5.25,5)
b_grid = utils.grid_slice(0.1,4.0,5)
# set search bounds
x_bound = (-12.0,12.0)
y_bound = (-12.0,12.0)
s_bound = (1/stimulus.ppd,12.0)
b_bound = (1e-8,1e2)
m_bound = (None,None)
# loop over each voxel and set up a GaussianFit object
grids = (x_grid, y_grid, s_grid,)
bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
# create 3 voxels of data
all_data = np.array([data,data,data])
indices = [(0,0,0),(0,0,1),(0,0,2)]
# bundle the voxels
bundle = utils.multiprocess_bundle(og.GaussianFit, model, all_data, grids, bounds, indices)
# run analysis
with sharedmem.Pool(np=3) as pool:
output = pool.map(utils.parallel_fit, bundle)
# create grid parent
arr = np.zeros((1,1,3))
grid_parent = nibabel.Nifti1Image(arr,np.eye(4,4))
# recast the estimation results
nif = utils.recast_estimation_results(output, grid_parent)
dat = nif.get_data()
# assert equivalence
npt.assert_almost_equal(np.mean(dat[...,0]), x)
npt.assert_almost_equal(np.mean(dat[...,1]), y)
npt.assert_almost_equal(np.mean(dat[...,2]), sigma)
npt.assert_almost_equal(np.mean(dat[...,3]), beta)
npt.assert_almost_equal(np.mean(dat[...,4]), baseline)
# recast the estimation results - OVERLOADED
nif = utils.recast_estimation_results(output, grid_parent, True)
dat = nif.get_data()
# assert equivalence
npt.assert_almost_equal(np.mean(dat[...,0]), np.arctan2(y,x),2)
npt.assert_almost_equal(np.mean(dat[...,1]), np.sqrt(x**2+y**2),2)
npt.assert_almost_equal(np.mean(dat[...,2]), sigma)
npt.assert_almost_equal(np.mean(dat[...,3]), beta)
npt.assert_almost_equal(np.mean(dat[...,4]), baseline)
def test_strf_fit():
viewing_distance = 38
screen_width = 25
thetas = np.tile(np.arange(0,360,90),2)
thetas = np.insert(thetas,0,-1)
thetas = np.append(thetas,-1)
num_blank_steps = 20
num_bar_steps = 20
ecc = 10
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 0.50
pixels_down = 200
pixels_across = 200
dtype = ctypes.c_int16
Ns = 3
voxel_index = (1,2,3)
auto_fit = True
verbose = 1
projector_hz = 480
tau = 0.00875
mask_size = 5
hrf = 0.25
# create the sweeping bar stimulus in memory
stim = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps, num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(stim, viewing_distance, screen_width, scale_factor, tr_length, dtype)
stimulus.fps = projector_hz
flicker_vec = np.zeros_like(stim[0,0,:]).astype('uint8')
flicker_vec[1*20:5*20] = 1
flicker_vec[5*20:9*20] = 2
stimulus.flicker_vec = flicker_vec
stimulus.flicker_hz = [10,20]
# initialize the gaussian model
model = strf.SpatioTemporalModel(stimulus, utils.spm_hrf)
model.tau = tau
model.hrf_delay = hrf
model.mask_size = mask_size
# generate a random pRF estimate
x = -2.24
y = 1.58
sigma = 1.23
weight = 0.90
beta = 1.0
baseline = -0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, weight, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-8.0,7.0,5)
y_grid = utils.grid_slice(-8.0,7.0,5)
s_grid = utils.grid_slice(0.75,3.0,5)
w_grid = utils.grid_slice(0.05,0.95,5)
# set search bounds
x_bound = (-10,10)
y_bound = (-10,10)
s_bound = (1/stimulus.ppd,10)
w_bound = (1e-8,1.0)
b_bound = (1e-8,1e5)
u_bound = (None, None)
# loop over each voxel and set up a GaussianFit object
grids = (x_grid, y_grid, s_grid, w_grid,)
bounds = (x_bound, y_bound, s_bound, w_bound, b_bound, u_bound)
# fit the response
fit = strf.SpatioTemporalFit(model, data, grids, bounds)
# coarse fit
ballpark = [-0.5,
3.25,
2.4375,
0.94999999999999996,
1.0292,
-0.24999999999999992]
npt.assert_almost_equal((fit.x0,fit.y0,fit.sigma0,fit.weight0,fit.beta0,fit.baseline0),ballpark,4)
# fine fit
npt.assert_almost_equal(fit.x, x, 2)
npt.assert_almost_equal(fit.y, y, 2)
npt.assert_almost_equal(fit.sigma, sigma, 2)
npt.assert_almost_equal(fit.weight, weight, 2)
npt.assert_almost_equal(fit.beta, beta, 2)
npt.assert_almost_equal(fit.baseline, baseline, 2)
# overloaded
npt.assert_almost_equal(fit.overloaded_estimate, [ 2.5272803, 2.7411676, 1.23 , 0.9 , 1. , -0.25 ], 2)
m_rf = fit.model.m_rf(fit.model.tau)
p_rf = fit.model.p_rf(fit.model.tau)
npt.assert_almost_equal(simps(np.abs(m_rf)),simps(p_rf),2)
# responses
m_resp = fit.model.generate_m_resp(fit.model.tau)
p_resp = fit.model.generate_p_resp(fit.model.tau)
npt.assert_(np.max(m_resp,0)[0]<np.max(m_resp,0)[1])
npt.assert_(np.max(p_resp,0)[0]>np.max(p_resp,0)[1])
# amps
npt.assert_(fit.model.m_amp[0]<fit.model.m_amp[1])
npt.assert_(fit.model.p_amp[0]>fit.model.p_amp[1])
# receptive field
rf = generate_og_receptive_field(x, y, sigma, fit.model.stimulus.deg_x, fit.model.stimulus.deg_y)
rf /= (2 * np.pi * sigma**2) * 1/np.diff(model.stimulus.deg_x[0,0:2])**2
npt.assert_almost_equal(np.round(rf.sum()), np.round(fit.receptive_field.sum()))
# test model == fit RF
npt.assert_almost_equal(np.round(fit.model.generate_receptive_field(x,y,sigma).sum()), np.round(fit.receptive_field.sum()))
def test_auditory_hrf_fit():
# stimulus features
duration = 30 # seconds
Fs = int(44100/2) # Hz
lo_freq = 200.0 # Hz
hi_freq = 10000.0 # Hz
tr_length = 1.0 # seconds
clip_number = 0 # TRs
dtype = ctypes.c_double
# fit settings
auto_fit = True
verbose = 1
debug = False
Ns = 10
# generate auditory stimulus
time = np.linspace(0,duration,duration*Fs)
ch = chirp(time, lo_freq, duration, hi_freq, method='logarithmic')
signal = np.tile(np.concatenate((ch,ch[::-1])),5)
blank = np.zeros((30*Fs))
signal = np.concatenate((blank,signal,blank),-1)
# instantiate an instance of the Stimulus class
stimulus = AuditoryStimulus(signal, Fs, tr_length, dtype) ### stimulus
# initialize the gaussian model
model = aud.AuditoryModel(stimulus, utils.spm_hrf) ### model
model.hrf_delay = 0
# invent pRF estimate
center_freq_hz = 987
sigma_hz = 123
center_freq = np.log10(center_freq_hz)
sigma = np.log10(sigma_hz)
hrf_delay = 1.25
beta = 2.4
baseline = 0.59
# generate data
data = model.generate_prediction(center_freq, sigma, hrf_delay, beta, baseline)
# search grids
c_grid = utils.grid_slice(np.log10(300), np.log10(1000), Ns)
s_grid = utils.grid_slice(np.log10(100), np.log10(500), Ns)
h_grid = utils.grid_slice(-1,1,Ns)
grids = (c_grid, s_grid, h_grid,)
# search bounds
c_bound = (np.log10(lo_freq), np.log10(hi_freq))
s_bound = (np.log10(50), np.log10(hi_freq))
h_bound = (-2,2)
b_bound = (1e-8, None)
m_bound = (None,None)
bounds = (c_bound, s_bound, h_bound, b_bound, m_bound)
# fit it
fit = aud.AuditoryFit(model, data, grids, bounds, Ns=Ns)
# grid fit
npt.assert_almost_equal(fit.center_freq0, 3)
npt.assert_almost_equal(fit.sigma0, 2)
npt.assert_almost_equal(fit.hrf0, 1.2222222222222223)
npt.assert_almost_equal(fit.beta0, 2.3404365192849017)
npt.assert_almost_equal(fit.baseline0, 1.416)
# final fit
npt.assert_almost_equal(fit.center_freq, center_freq)
npt.assert_almost_equal(fit.sigma, sigma)
npt.assert_almost_equal(fit.beta, beta)
npt.assert_almost_equal(fit.baseline, baseline)
npt.assert_almost_equal(fit.center_freq_hz, center_freq_hz)
# test receptive field
rf = np.exp(-((10**fit.model.stimulus.freqs-10**fit.center_freq)**2)/(2*(10**fit.sigma)**2))
rf /= (10**fit.sigma*np.sqrt(2*np.pi))
npt.assert_almost_equal(np.round(rf.sum()), np.round(fit.receptive_field.sum()))
# test model == fit RF
rf = np.exp(-((fit.model.stimulus.freqs-fit.center_freq)**2)/(2*fit.sigma**2))
rf /= (fit.sigma*np.sqrt(2*np.pi))
npt.assert_almost_equal(np.round(rf.sum()), np.round(fit.receptive_field_log10.sum()))
def test_dog():
# stimulus features
viewing_distance = 31
screen_width = 41
thetas = np.arange(0,360,90)
# thetas = np.insert(thetas,0,-1)
# thetas = np.append(thetas,-1)
num_blank_steps = 0
num_bar_steps = 30
ecc = 10
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 0.50
pixels_down = 100
pixels_across = 100
dtype = ctypes.c_int16
auto_fit = True
verbose = 0
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps, num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width, scale_factor, tr_length, dtype)
# initialize the gaussian model
model = dog.DifferenceOfGaussiansModel(stimulus, utils.spm_hrf)
model.hrf_delay = 0
model.mask_size = 20
# set the pRF params
x = 2.2
y = 2.5
sigma = 0.90
sigma_ratio = 1.5
volume_ratio = 0.5
beta = 0.25
baseline = -0.10
# create "data"
data = model.generate_prediction(x, y, sigma, sigma_ratio, volume_ratio, beta, baseline)
# set up the grids
x_grid = utils.grid_slice(-5,5,4)
y_grid = utils.grid_slice(-5,5,4)
s_grid = utils.grid_slice(1/stimulus.ppd0*1.10,3.5,4)
sr_grid = utils.grid_slice(1.0,2.0,4)
vr_grid = utils.grid_slice(0.10,0.90,4)
grids = (x_grid, y_grid, s_grid, sr_grid, vr_grid,)
# set up the bounds
x_bound = (-ecc,ecc)
y_bound = (-ecc,ecc)
s_bound = (1/stimulus.ppd,5)
sr_bound = (1.0,None)
vr_bound = (1e-8,1.0)
bounds = (x_bound, y_bound, s_bound, sr_bound, vr_bound,)
# fit it
fit = dog.DifferenceOfGaussiansFit(model, data, grids, bounds)
# coarse fit
ballpark = [1.666666666666667,
1.666666666666667,
2.8243187483428391,
1.9999999999999998,
0.10000000000000001,
0.3639449,
-0.025000000000000022]
npt.assert_almost_equal((fit.x0,fit.y0,fit.s0,fit.sr0,fit.vr0, fit.beta0, fit.baseline0), ballpark)
# fine fit
npt.assert_almost_equal(fit.x, x, 2)
npt.assert_almost_equal(fit.y, y, 2)
npt.assert_almost_equal(fit.sigma, sigma, 2)
npt.assert_almost_equal(fit.sigma_ratio, sigma_ratio, 1)
npt.assert_almost_equal(fit.volume_ratio, volume_ratio, 1)
# test the RF
rf = fit.model.receptive_field(*fit.estimate[0:-2])
est = fit.estimate[0:-2].copy()
rf_new = fit.model.receptive_field(*est)
value_1 = np.sqrt(simps(simps(rf)))
value_2 = np.sqrt(simps(simps(rf_new)))
nt.assert_almost_equal(value_1, value_2)
# polar coordinates
npt.assert_almost_equal([fit.theta,fit.rho],[np.arctan2(y,x),np.sqrt(x**2+y**2)], 5)
def test_bounded_amplitude_failure():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.arange(0, 360, 90)
thetas = np.insert(thetas, 0, -1)
thetas = np.append(thetas, -1)
num_blank_steps = 30
num_bar_steps = 30
ecc = 12
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 1.0
pixels_across = 100
pixels_down = 100
dtype = ctypes.c_int16
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps,
num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width,
scale_factor, tr_length, dtype)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.double_gamma_hrf,
utils.percent_change)
model.hrf_delay = 0
model.mask_size = 6
# generate a random pRF estimate
x = -5.24
y = 2.58
sigma = 1.24
beta = -0.25
baseline = 0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-10, 10, 5)
y_grid = utils.grid_slice(-10, 10, 5)
s_grid = utils.grid_slice(0.25, 5.25, 5)
# set search bounds
x_bound = (-12.0, 12.0)
y_bound = (-12.0, 12.0)
s_bound = (0.001, 12.0)
b_bound = (1e-8, None)
m_bound = (None, None)
# loop over each voxel and set up a GaussianFit object
grids = (
x_grid,
y_grid,
s_grid,
)
bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
# fit the response
fit = og.GaussianFit(model, data, grids, bounds)
nt.assert_true(fit.model.bounded_amplitude)
nt.assert_true(fit.slope > 0)
nt.assert_true(fit.beta0 > 0)
nt.assert_true(fit.beta > 0)
nt.assert_true(fit.beta != beta)
def test_negative_og_fit():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.arange(0,360,90)
thetas = np.insert(thetas,0,-1)
thetas = np.append(thetas,-1)
num_blank_steps = 30
num_bar_steps = 30
ecc = 12
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 1.0
pixels_across = 100
pixels_down = 100
dtype = ctypes.c_int16
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps, num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width, scale_factor, tr_length, dtype)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.double_gamma_hrf, utils.percent_change)
model.hrf_delay = 0
model.mask_size = 6
# generate a random pRF estimate
x = -5.24
y = 2.58
sigma = 1.24
beta = -0.25
baseline = 0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-10,10,5)
y_grid = utils.grid_slice(-10,10,5)
s_grid = utils.grid_slice (0.25,5.25,5)
# set search bounds
x_bound = (-12.0,12.0)
y_bound = (-12.0,12.0)
s_bound = (0.001,12.0)
b_bound = (None,None)
m_bound = (None,None)
# loop over each voxel and set up a GaussianFit object
grids = (x_grid, y_grid, s_grid,)
bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
# fit the response
fit = og.GaussianFit(model, data, grids, bounds)
# coarse fit
ballpark = [-5.0, 5.0, 2.75, -0.27940915461573274, -0.062499999999999993]
npt.assert_almost_equal((fit.x0, fit.y0, fit.s0, fit.beta0, fit.baseline0), ballpark)
# assert equivalence
npt.assert_almost_equal(fit.x, x, 2)
npt.assert_almost_equal(fit.y, y, 2)
npt.assert_almost_equal(fit.sigma, sigma, 2)
npt.assert_almost_equal(fit.beta, beta, 2)
nt.assert_false(fit.model.bounded_amplitude)
nt.assert_true(fit.slope<0)
nt.assert_true(fit.beta0<0)
nt.assert_true(fit.beta<0)
# test receptive field
rf = generate_og_receptive_field(x, y, sigma, fit.model.stimulus.deg_x, fit.model.stimulus.deg_y)
rf /= (2 * np.pi * sigma**2) * 1/np.diff(model.stimulus.deg_x[0,0:2])**2
npt.assert_almost_equal(np.round(rf.sum()), np.round(fit.receptive_field.sum()))
# test model == fit RF
npt.assert_almost_equal(np.round(fit.model.generate_receptive_field(x,y,sigma).sum()), np.round(fit.receptive_field.sum()))
def test_bounded_amplitude_failure():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.arange(0,360,90)
thetas = np.insert(thetas,0,-1)
thetas = np.append(thetas,-1)
num_blank_steps = 30
num_bar_steps = 30
ecc = 12
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 1.0
pixels_across = 100
pixels_down = 100
dtype = ctypes.c_int16
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps, num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width, scale_factor, tr_length, dtype)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.double_gamma_hrf, utils.percent_change)
model.hrf_delay = 0
model.mask_size = 6
# generate a random pRF estimate
x = -5.24
y = 2.58
sigma = 1.24
beta = -0.25
baseline = 0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-10,10,5)
y_grid = utils.grid_slice(-10,10,5)
s_grid = utils.grid_slice (0.25,5.25,5)
# set search bounds
x_bound = (-12.0,12.0)
y_bound = (-12.0,12.0)
s_bound = (0.001,12.0)
b_bound = (1e-8,None)
m_bound = (None,None)
# loop over each voxel and set up a GaussianFit object
grids = (x_grid, y_grid, s_grid,)
bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
# fit the response
fit = og.GaussianFit(model, data, grids, bounds)
nt.assert_true(fit.model.bounded_amplitude)
nt.assert_true(fit.slope>0)
nt.assert_true(fit.beta0>0)
nt.assert_true(fit.beta>0)
nt.assert_true(fit.beta != beta)
# def test_og_nuisance_fit():
#
# # stimulus features
# viewing_distance = 38
# screen_width = 25
# thetas = np.arange(0,360,90)
# thetas = np.insert(thetas,0,-1)
# thetas = np.append(thetas,-1)
# num_blank_steps = 30
# num_bar_steps = 30
# ecc = 12
# tr_length = 1.0
# frames_per_tr = 1.0
# scale_factor = 1.0
# pixels_across = 200
# pixels_down = 200
# dtype = ctypes.c_int16
#
# # create the sweeping bar stimulus in memory
# bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
# screen_width, thetas, num_bar_steps, num_blank_steps, ecc)
#
# # create an instance of the Stimulus class
# stimulus = VisualStimulus(bar, viewing_distance, screen_width, scale_factor, tr_length, dtype)
#
# # initialize the gaussian model
# model = og.GaussianModel(stimulus, utils.double_gamma_hrf)
# model.hrf_delay = 0
#
# # generate a random pRF estimate
# x = -5.24
# y = 2.58
# sigma = 0.98
# beta = 2.5
# baseline = 0.0
#
# # create the "data"
# data = model.generate_prediction(x, y, sigma, beta, baseline)
#
# # create nuisance signal
# step = np.zeros(len(data))
# step[30:-30] = 1
#
# # add to data
# data += step
#
# # create design matrix
# nuisance = sm.add_constant(step)
#
# # recreate model with nuisance
# model = og.GaussianModel(stimulus, utils.double_gamma_hrf, nuisance)
# model.hrf_delay = 0
#
# # set search grid
# x_grid = (-7,7)
# y_grid = (-7,7)
# s_grid = (0.25,3.25)
#
# # set search bounds
# x_bound = (-10.0,10.0)
# y_bound = (-10.0,10.0)
# s_bound = (0.001,10.0)
# b_bound = (1e-8,None)
# m_bound = (None, None)
#
# # loop over each voxel and set up a GaussianFit object
# grids = (x_grid, y_grid, s_grid,)
# bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
#
# # fit the response
# fit = og.GaussianFit(model, data, grids, bounds, Ns=3)
#
# # assert equivalence
# nt.assert_almost_equal(fit.x, x, 1)
# nt.assert_almost_equal(fit.y, y, 1)
# nt.assert_almost_equal(fit.sigma, sigma, 1)
# nt.assert_almost_equal(fit.beta, beta, 1)
def test_strf_css_fit():
viewing_distance = 38
screen_width = 25
thetas = np.tile(np.arange(0,360,90),2)
num_blank_steps = 0
num_bar_steps = 30
ecc = 10
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 0.50
pixels_down = 100
pixels_across = 100
dtype = ctypes.c_int16
Ns = 3
voxel_index = (1,2,3)
auto_fit = True
verbose = 1
projector_hz = 480
tau = 0.00875
mask_size = 5
hrf = 0.25
# create the sweeping bar stimulus in memory
stim1 = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps, num_blank_steps, ecc, clip=0.33)
# create the sweeping bar stimulus in memory
stim2 = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps, num_blank_steps, ecc, clip=0.0001)
stim = np.concatenate((stim1,stim2),-1)
# create an instance of the Stimulus class
stimulus = VisualStimulus(stim, viewing_distance, screen_width, scale_factor, tr_length, dtype)
stimulus.fps = projector_hz
flicker_vec = np.zeros_like(stim1[0,0,:]).astype('uint8')
flicker_vec[1*20:5*20] = 1
flicker_vec[5*20:9*20] = 2
flicker_vec = np.tile(flicker_vec,2)
stimulus.flicker_vec = flicker_vec
stimulus.flicker_hz = [10,20,10,20]
# initialize the gaussian model
model = strf.SpatioTemporalModel(stimulus, utils.spm_hrf)
model.tau = tau
model.hrf_delay = hrf
model.mask_size = mask_size
# generate a random pRF estimate
x = -2.24
y = 1.58
sigma = 1.23
n = 0.90
weight = 0.95
beta = 0.88
baseline = -0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, n, weight, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-8.0,7.0,4)
y_grid = utils.grid_slice(-8.0,7.0,4)
s_grid = utils.grid_slice(0.75,3.0,4)
n_grid = utils.grid_slice(0.25,0.95,4)
w_grid = utils.grid_slice(0.25,0.95,4)
# set search bounds
x_bound = (-10,10)
y_bound = (-10,10)
s_bound = (1/stimulus.ppd,10)
n_bound = (1e-8,1.0-1e-8)
w_bound = (1e-8,1.0-1e-8)
b_bound = (1e-8,1e5)
u_bound = (None, None)
# loop over each voxel and set up a GaussianFit object
grids = (x_grid, y_grid, s_grid, n_grid, w_grid,)
bounds = (x_bound, y_bound, s_bound, n_bound, w_bound, b_bound, u_bound)
# fit the response
fit = strf.SpatioTemporalFit(model, data, grids, bounds)
# coarse fit
ballpark = [-3.0,
2.0,
1.5,
0.95,
0.95,
0.88574075,
-0.25]
npt.assert_almost_equal((fit.x0,fit.y0,fit.sigma0, fit.n0, fit.weight0,fit.beta0,fit.baseline0),ballpark)
# fine fit
npt.assert_almost_equal(fit.x, x, 2)
npt.assert_almost_equal(fit.y, y, 2)
npt.assert_almost_equal(fit.sigma, sigma, 1)
npt.assert_almost_equal(fit.n, n, 2)
npt.assert_almost_equal(fit.weight, weight, 2)
npt.assert_almost_equal(fit.beta, beta, 2)
npt.assert_almost_equal(fit.baseline, baseline, 2)
# overloaded
npt.assert_almost_equal(fit.overloaded_estimate,[2.5266437, 2.7390143, 1.3014282, 0.9004958, 0.9499708, 0.8801774], 2)
# rfs
m_rf = fit.model.m_rf(fit.model.tau)
p_rf = fit.model.p_rf(fit.model.tau)
npt.assert_almost_equal(simps(np.abs(m_rf)),simps(p_rf),5)
# responses
m_resp = fit.model.generate_m_resp(fit.model.tau)
p_resp = fit.model.generate_p_resp(fit.model.tau)
npt.assert_(np.max(m_resp,0)[0]<np.max(m_resp,0)[1])
npt.assert_(np.max(p_resp,0)[0]>np.max(p_resp,0)[1])
# amps
npt.assert_(fit.model.m_amp[0]<fit.model.m_amp[1])
npt.assert_(fit.model.p_amp[0]>fit.model.p_amp[1])
# receptive field
npt.assert_almost_equal(4.0, fit.receptive_field.sum())
def test_strf_hrf_fit():
viewing_distance = 38
screen_width = 25
thetas = np.tile(np.arange(0, 360, 90), 2)
thetas = np.insert(thetas, 0, -1)
thetas = np.append(thetas, -1)
num_blank_steps = 20
num_bar_steps = 20
ecc = 10
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 0.50
pixels_down = 200
pixels_across = 200
dtype = ctypes.c_int16
Ns = 3
voxel_index = (1, 2, 3)
auto_fit = True
verbose = 1
projector_hz = 480
tau = 0.00875
mask_size = 5
# create the sweeping bar stimulus in memory
stim = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps,
num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(stim, viewing_distance, screen_width,
scale_factor, tr_length, dtype)
stimulus.fps = projector_hz
flicker_vec = np.zeros_like(stim[0, 0, :]).astype('uint8')
flicker_vec[1 * 20:5 * 20] = 1
flicker_vec[5 * 20:9 * 20] = 2
stimulus.flicker_vec = flicker_vec
stimulus.flicker_hz = [10, 20]
# initialize the gaussian model
model = strf.SpatioTemporalModel(stimulus, utils.double_gamma_hrf)
model.tau = tau
model.mask_size = mask_size
# generate a random pRF estimate
x = -2.24
y = 1.58
sigma = 1.23
weight = 0.90
hrf_delay = -0.13
beta = 1.0
baseline = -0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, weight, hrf_delay, beta,
baseline)
# set search grid
x_grid = utils.grid_slice(-8.0, 7.0, 3)
y_grid = utils.grid_slice(-8.0, 7.0, 3)
s_grid = utils.grid_slice(0.75, 3.0, 3)
w_grid = utils.grid_slice(0.05, 0.95, 3)
h_grid = utils.grid_slice(-0.25, 0.25, 3)
# set search bounds
x_bound = (-10, 10)
y_bound = (-10, 10)
s_bound = (1 / stimulus.ppd, 10)
w_bound = (1e-8, 1.0)
b_bound = (1e-8, 1e5)
u_bound = (None, None)
h_bound = (-2.0, 2.0)
# loop over each voxel and set up a GaussianFit object
grids = (
x_grid,
y_grid,
s_grid,
w_grid,
h_grid,
)
bounds = (x_bound, y_bound, s_bound, w_bound, h_bound, b_bound, u_bound)
# fit the response
fit = strf.SpatioTemporalFit(model, data, grids, bounds)
# coarse fit
npt.assert_almost_equal((fit.x0, fit.y0, fit.sigma0, fit.weight0, fit.hrf0,
fit.beta0, fit.baseline0),
[-0.5, -0.5, 3., 0.95, -0.25, 1., 0.02], 2)
# fine fit
npt.assert_almost_equal(fit.x, x, 2)
npt.assert_almost_equal(fit.y, y, 2)
npt.assert_almost_equal(fit.sigma, sigma, 2)
npt.assert_almost_equal(fit.weight, weight, 2)
npt.assert_almost_equal(fit.beta, beta, 2)
npt.assert_almost_equal(fit.baseline, baseline, 2)
# overloaded
npt.assert_almost_equal(fit.overloaded_estimate,
[2.53, 2.74, 1.23, 0.9, 5.87, 1., -0.25], 2)
m_rf = fit.model.m_rf(fit.model.tau)
p_rf = fit.model.p_rf(fit.model.tau)
npt.assert_almost_equal(simps(np.abs(m_rf)), simps(p_rf), 5)
# responses
m_resp = fit.model.generate_m_resp(fit.model.tau)
p_resp = fit.model.generate_p_resp(fit.model.tau)
npt.assert_(np.max(m_resp, 0)[0] < np.max(m_resp, 0)[1])
npt.assert_(np.max(p_resp, 0)[0] > np.max(p_resp, 0)[1])
# amps
npt.assert_(fit.model.m_amp[0] < fit.model.m_amp[1])
npt.assert_(fit.model.p_amp[0] > fit.model.p_amp[1])
# receptive field
npt.assert_almost_equal(4.0, fit.receptive_field.sum())
def test_strf_css_fit():
viewing_distance = 38
screen_width = 25
thetas = np.tile(np.arange(0,360,90),2)
thetas = np.insert(thetas,0,-1)
thetas = np.append(thetas,-1)
num_blank_steps = 20
num_bar_steps = 20
ecc = 10
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 0.50
pixels_down = 100
pixels_across = 100
dtype = ctypes.c_int16
Ns = 3
voxel_index = (1,2,3)
auto_fit = True
verbose = 1
projector_hz = 480
tau = 0.00875
mask_size = 5
hrf = 0.25
# create the sweeping bar stimulus in memory
stim1 = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps, num_blank_steps, ecc, clip=0.33)
# create the sweeping bar stimulus in memory
stim2 = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps, num_blank_steps, ecc, clip=0.0001)
stim = np.concatenate((stim1,stim2),-1)
# create an instance of the Stimulus class
stimulus = VisualStimulus(stim, viewing_distance, screen_width, scale_factor, tr_length, dtype)
stimulus.fps = projector_hz
flicker_vec = np.zeros_like(stim1[0,0,:]).astype('uint8')
flicker_vec[1*20:5*20] = 1
flicker_vec[5*20:9*20] = 2
flicker_vec = np.tile(flicker_vec,2)
stimulus.flicker_vec = flicker_vec
stimulus.flicker_hz = [10,20,10,20]
# initialize the gaussian model
model = strf.SpatioTemporalModel(stimulus, utils.double_gamma_hrf)
model.tau = tau
model.hrf_delay = hrf
model.mask_size = mask_size
# generate a random pRF estimate
x = -2.24
y = 1.58
sigma = 1.23
n = 0.90
weight = 0.95
beta = 1.0
baseline = 0
# create the "data"
data = model.generate_prediction(x, y, sigma, n, weight, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-8.0,7.0,3)
y_grid = utils.grid_slice(-8.0,7.0,3)
s_grid = utils.grid_slice(0.75,3.0,3)
n_grid = utils.grid_slice(0.25,0.95,3)
w_grid = utils.grid_slice(0.25,0.95,3)
# set search bounds
x_bound = (-10,10)
y_bound = (-10,10)
s_bound = (1/stimulus.ppd,10)
n_bound = (1e-8,1.0-1e-8)
w_bound = (1e-8,1.0-1e-8)
b_bound = (1e-8,1e5)
u_bound = (None, None)
# loop over each voxel and set up a GaussianFit object
grids = (x_grid, y_grid, s_grid, n_grid, w_grid,)
bounds = (x_bound, y_bound, s_bound, n_bound, w_bound, b_bound, u_bound)
# fit the response
fit = strf.SpatioTemporalFit(model, data, grids, bounds)
# coarse fit
npt.assert_almost_equal((fit.x0,fit.y0,fit.sigma0, fit.n0, fit.weight0,fit.beta0,fit.baseline0),[-0.5 , -0.5 , 1.875, 0.95 , 0.95 , 1. , 0. ])
# fine fit
npt.assert_almost_equal(fit.x, x, 1)
npt.assert_almost_equal(fit.y, y, 1)
npt.assert_almost_equal(fit.sigma, sigma, 1)
npt.assert_almost_equal(fit.n, n, 1)
npt.assert_almost_equal(fit.weight, weight, 1)
npt.assert_almost_equal(fit.beta, beta, 1)
npt.assert_almost_equal(fit.baseline, baseline, 1)
# overloaded
npt.assert_almost_equal(fit.overloaded_estimate, [2.5259863707822303,
2.7330681871539069,
1.3062396482386418,
0.9011492100931614,
0.94990930073215352,
1.0005707740082497],4)
# rfs
m_rf = fit.model.m_rf(fit.model.tau)
p_rf = fit.model.p_rf(fit.model.tau)
npt.assert_almost_equal(simps(np.abs(m_rf)),simps(p_rf),5)
# responses
m_resp = fit.model.generate_m_resp(fit.model.tau)
p_resp = fit.model.generate_p_resp(fit.model.tau)
npt.assert_(np.max(m_resp,0)[0]<np.max(m_resp,0)[1])
npt.assert_(np.max(p_resp,0)[0]>np.max(p_resp,0)[1])
# amps
npt.assert_(fit.model.m_amp[0]<fit.model.m_amp[1])
npt.assert_(fit.model.p_amp[0]>fit.model.p_amp[1])
# receptive field
npt.assert_almost_equal(4.0, fit.receptive_field.sum())
def test_dog():
# stimulus features
viewing_distance = 31
screen_width = 41
thetas = np.arange(0, 360, 90)
# thetas = np.insert(thetas,0,-1)
# thetas = np.append(thetas,-1)
num_blank_steps = 0
num_bar_steps = 30
ecc = 10
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 0.50
pixels_down = 100
pixels_across = 100
dtype = ctypes.c_int16
auto_fit = True
verbose = 0
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps,
num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width,
scale_factor, tr_length, dtype)
# initialize the gaussian model
model = dog.DifferenceOfGaussiansModel(stimulus, utils.spm_hrf)
model.hrf_delay = 0
model.mask_size = 20
# set the pRF params
x = 2.2
y = 2.5
sigma = 0.90
sigma_ratio = 1.5
volume_ratio = 0.5
beta = 0.25
baseline = -0.10
# create "data"
data = model.generate_prediction(x, y, sigma, sigma_ratio, volume_ratio,
beta, baseline)
# set up the grids
x_grid = utils.grid_slice(-5, 5, 4)
y_grid = utils.grid_slice(-5, 5, 4)
s_grid = utils.grid_slice(1 / stimulus.ppd0 * 1.10, 3.5, 4)
sr_grid = utils.grid_slice(1.0, 2.0, 4)
vr_grid = utils.grid_slice(0.10, 0.90, 4)
grids = (
x_grid,
y_grid,
s_grid,
sr_grid,
vr_grid,
)
# set up the bounds
x_bound = (-ecc, ecc)
y_bound = (-ecc, ecc)
s_bound = (1 / stimulus.ppd, 5)
sr_bound = (1.0, None)
vr_bound = (1e-8, 1.0)
bounds = (
x_bound,
y_bound,
s_bound,
sr_bound,
vr_bound,
)
# fit it
fit = dog.DifferenceOfGaussiansFit(model, data, grids, bounds)
# coarse fit
ballpark = [
1.666666666666667, 1.666666666666667, 2.8243187483428391,
1.9999999999999998, 0.10000000000000001
]
npt.assert_almost_equal((fit.x0, fit.y0, fit.s0, fit.sr0, fit.vr0),
ballpark)
# the baseline/beta should be 0/1 when regressed data vs. estimate
(m, b) = np.polyfit(fit.scaled_ballpark_prediction, data, 1)
npt.assert_almost_equal(m, 1.0)
npt.assert_almost_equal(b, 0.0)
# fine fit
npt.assert_almost_equal(fit.x, x, 2)
npt.assert_almost_equal(fit.y, y, 2)
npt.assert_almost_equal(fit.sigma, sigma, 2)
npt.assert_almost_equal(fit.sigma_ratio, sigma_ratio, 1)
npt.assert_almost_equal(fit.volume_ratio, volume_ratio, 1)
# test the RF
rf = fit.model.receptive_field(*fit.estimate[0:-2])
est = fit.estimate[0:-2].copy()
rf_new = fit.model.receptive_field(*est)
value_1 = np.sqrt(simps(simps(rf)))
value_2 = np.sqrt(simps(simps(rf_new)))
nt.assert_almost_equal(value_1, value_2)
# polar coordinates
npt.assert_almost_equal(
[fit.theta, fit.rho],
[np.arctan2(y, x), np.sqrt(x**2 + y**2)], 4)
def test_bootstrap():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.array([-1, 0, 90, 180, 270, -1])
num_blank_steps = 30
num_bar_steps = 30
ecc = 10
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 0.10
pixels_down = 100
pixels_across = 100
dtype = ctypes.c_int16
voxel_index = (1, 2, 3)
auto_fit = True
verbose = 1
# rng
np.random.seed(2764932)
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps,
num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width,
scale_factor, tr_length, dtype)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.spm_hrf)
model.hrf_delay = 0
# generate a random pRF estimate
x = -5.24
y = 2.58
sigma = 1.24
beta = 2.5
baseline = -0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-10, 10, 5)
y_grid = utils.grid_slice(-10, 10, 5)
s_grid = utils.grid_slice(0.5, 3.25, 5)
# set search bounds
x_bound = (-12.0, 12.0)
y_bound = (-12.0, 12.0)
s_bound = (0.001, 12.0)
b_bound = (1e-8, None)
m_bound = (None, None)
# loop over each voxel and set up a GaussianFit object
grids = (
x_grid,
y_grid,
s_grid,
)
bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
# pack multiple "runs"
data = np.vstack((data, data, data))
# make it a singular "voxel"
data = np.reshape(data, (1, data.shape[0], data.shape[1]))
# set bootstraps and resamples
bootstraps = 2
resamples = np.array((2, ))
# make fodder
bundle = utils.bootstrap_bundle(bootstraps, resamples, og.GaussianFit,
model, data, grids, bounds,
np.tile((1, 2, 3), (bootstraps, 1)))
# test
for b in bundle:
fit = utils.parallel_bootstrap(b)
npt.assert_almost_equal(fit.rss, 0)
npt.assert_equal(fit.n_resamples, resamples[0])
npt.assert_equal(np.sum(fit.resamples),
np.sum(np.arange(resamples[0])))
def test_auditory_fit():
# stimulus features
duration = 30 # seconds
Fs = 44100 # Hz
lo_freq = 200.0 # Hz
hi_freq = 10000.0 # Hz
tr_length = 1.0 # seconds
clip_number = 0 # TRs
dtype = ctypes.c_double
# fit settings
auto_fit = True
verbose = 1
debug = False
Ns = 20
# generate auditory stimulus
time = np.linspace(0, duration, duration * Fs)
ch = chirp(time, lo_freq, duration, hi_freq, method='logarithmic')
signal = np.tile(np.concatenate((ch, ch[::-1])), 5)
blank = np.zeros((30 * Fs))
signal = np.concatenate((blank, signal, blank), -1)
# instantiate an instance of the Stimulus class
stimulus = AuditoryStimulus(signal, Fs, tr_length, dtype) ### stimulus
# initialize the gaussian model
model = aud.AuditoryModel(stimulus, utils.double_gamma_hrf) ### model
model.hrf_delay = 0
# invent pRF estimate
center_freq = np.log10(987)
sigma = np.log10(123)
beta = 1.0
baseline = 0.0
# generate data
data = model.generate_prediction(center_freq, sigma, beta, baseline)
# search grids
c_grid = utils.grid_slice(np.log10(300), np.log10(1000), Ns)
s_grid = utils.grid_slice(np.log10(100), np.log10(500), Ns)
grids = (
c_grid,
s_grid,
)
# search bounds
c_bound = (np.log10(lo_freq), np.log10(hi_freq))
s_bound = (np.log10(50), np.log10(hi_freq))
b_bound = (1e-8, None)
m_bound = (None, None)
bounds = (c_bound, s_bound, b_bound, m_bound)
# fit it
fit = aud.AuditoryFit(model, data, grids, bounds, Ns=Ns)
# assert equivalence
npt.assert_almost_equal(fit.center_freq, center_freq)
npt.assert_almost_equal(fit.sigma, sigma)
npt.assert_almost_equal(fit.beta, beta)
npt.assert_almost_equal(fit.baseline, baseline)
npt.assert_almost_equal(fit.center_freq0, 3)
npt.assert_almost_equal(fit.sigma0, 2.14715158)
npt.assert_almost_equal(fit.beta0, beta)
npt.assert_almost_equal(fit.baseline0, baseline)
npt.assert_almost_equal(fit.center_freq_hz, 987)
# test receptive field
rf = np.exp(-((10**fit.model.stimulus.freqs - 10**fit.center_freq)**2) /
(2 * (10**fit.sigma)**2))
rf /= (10**fit.sigma * np.sqrt(2 * np.pi))
npt.assert_almost_equal(np.round(rf.sum()),
np.round(fit.receptive_field.sum()))
# test model == fit RF
rf = np.exp(-((fit.model.stimulus.freqs - fit.center_freq)**2) /
(2 * fit.sigma**2))
rf /= (fit.sigma * np.sqrt(2 * np.pi))
npt.assert_almost_equal(np.round(rf.sum()),
np.round(fit.receptive_field_log10.sum()))
def test_recast_estimation_results():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.arange(0, 360, 45)
num_blank_steps = 0
num_bar_steps = 30
ecc = 10
tr_length = 1.0
frames_per_tr = 1.0
scale_factor = 0.10
pixels_down = 100
pixels_across = 100
dtype = ctypes.c_int16
voxel_index = (1, 2, 3)
auto_fit = True
verbose = 1
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps,
num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width,
scale_factor, tr_length, dtype)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.spm_hrf)
model.hrf_delay = 0
# generate a random pRF estimate
x = -5.24
y = 2.58
sigma = 1.24
beta = 2.5
baseline = -0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-5, 4, 5)
y_grid = utils.grid_slice(-5, 7, 5)
s_grid = utils.grid_slice(1 / stimulus.ppd, 5.25, 5)
b_grid = utils.grid_slice(0.1, 4.0, 5)
# set search bounds
x_bound = (-12.0, 12.0)
y_bound = (-12.0, 12.0)
s_bound = (1 / stimulus.ppd, 12.0)
b_bound = (1e-8, 1e2)
m_bound = (None, None)
# loop over each voxel and set up a GaussianFit object
grids = (
x_grid,
y_grid,
s_grid,
)
bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
# create 3 voxels of data
all_data = np.array([data, data, data])
indices = [(0, 0, 0), (0, 0, 1), (0, 0, 2)]
# bundle the voxels
bundle = utils.multiprocess_bundle(og.GaussianFit, model, all_data, grids,
bounds, indices)
# run analysis
with sharedmem.Pool(np=3) as pool:
output = pool.map(utils.parallel_fit, bundle)
# create grid parent
arr = np.zeros((1, 1, 3))
grid_parent = nibabel.Nifti1Image(arr, np.eye(4, 4))
# recast the estimation results
nif = utils.recast_estimation_results(output, grid_parent)
dat = nif.get_data()
# assert equivalence
npt.assert_almost_equal(np.mean(dat[..., 0]), x)
npt.assert_almost_equal(np.mean(dat[..., 1]), y)
npt.assert_almost_equal(np.mean(dat[..., 2]), sigma)
npt.assert_almost_equal(np.mean(dat[..., 3]), beta)
npt.assert_almost_equal(np.mean(dat[..., 4]), baseline)
# recast the estimation results - OVERLOADED
nif = utils.recast_estimation_results(output, grid_parent, True)
dat = nif.get_data()
# assert equivalence
npt.assert_almost_equal(np.mean(dat[..., 0]), np.arctan2(y, x), 2)
npt.assert_almost_equal(np.mean(dat[..., 1]), np.sqrt(x**2 + y**2), 2)
npt.assert_almost_equal(np.mean(dat[..., 2]), sigma)
npt.assert_almost_equal(np.mean(dat[..., 3]), beta)
npt.assert_almost_equal(np.mean(dat[..., 4]), baseline)
def test_resurrect_model():
# stimulus features
viewing_distance = 38
screen_width = 25
thetas = np.arange(0,360,90)
thetas = np.insert(thetas,0,-1)
thetas = np.append(thetas,-1)
num_blank_steps = 20
num_bar_steps = 20
ecc = 10
tr_length = 1.5
frames_per_tr = 1.0
scale_factor = 1.0
pixels_across = 100
pixels_down = 100
dtype = ctypes.c_int16
# create the sweeping bar stimulus in memory
bar = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance, screen_width,
thetas, num_bar_steps, num_blank_steps, ecc, clip=0.01)
# create an instance of the Stimulus class
stimulus = VisualStimulus(bar, viewing_distance, screen_width, scale_factor, tr_length, dtype)
# set cache grids
x_grid = utils.grid_slice(-10, 10, 5)
y_grid = utils.grid_slice(-10, 10, 5)
s_grid = utils.grid_slice(0.55,5.25, 5)
grids = (x_grid, y_grid, s_grid,)
# set search bounds
x_bound = (-12.0,12.0)
y_bound = (-12.0,12.0)
s_bound = (0.001,12.0)
b_bound = (1e-8,None)
m_bound = (None,None)
bounds = (x_bound, y_bound, s_bound, b_bound, m_bound)
# initialize the gaussian model
model = og.GaussianModel(stimulus, utils.double_gamma_hrf)
model.hrf_delay = 0
model.mask_size = 5
cache = model.cache_model(grids, ncpus=3)
# seed rng
np.random.seed(4932)
# pluck an estimate and create timeseries
x, y, sigma = cache[51][1]
beta = 1.25
baseline = 0.25
# create "data"
data = cache[51][0]
# fit it
fit = og.GaussianFit(model, data, grids, bounds, verbose=0)
# assert
npt.assert_equal(fit.estimate,fit.ballpark)
# create "data"
data = model.generate_prediction(x,y,sigma,beta,baseline)
# fit it
fit = og.GaussianFit(model, data, grids, bounds, verbose=0)
# assert
npt.assert_almost_equal(np.sum(fit.scaled_ballpark_prediction-fit.data)**2,0)
def test_strf_2dcos_fit():
viewing_distance = 38
screen_width = 25
thetas = np.tile(np.arange(0, 360, 90), 2)
thetas = np.insert(thetas, 0, -1)
thetas = np.append(thetas, -1)
num_blank_steps = 0
num_bar_steps = 30
ecc = 10
tr_length = 1
frames_per_tr = 1
scale_factor = 1.0
pixels_down = 100
pixels_across = 100
dtype = ctypes.c_int16
Ns = 5
voxel_index = (1, 2, 3)
auto_fit = True
verbose = 1
projector_hz = 480
tau = 0.00875
mask_size = 5
hrf = 0.25
# create the sweeping bar stimulus in memory
stim = simulate_bar_stimulus(pixels_across, pixels_down, viewing_distance,
screen_width, thetas, num_bar_steps,
num_blank_steps, ecc)
# create an instance of the Stimulus class
stimulus = VisualStimulus(stim, viewing_distance, screen_width,
scale_factor, tr_length, dtype)
stimulus.fps = projector_hz
flicker_vec = np.zeros_like(stim[0, 0, :]).astype('uint8')
flicker_vec[1 * 20:5 * 20] = 1
flicker_vec[5 * 20:9 * 20] = 2
stimulus.flicker_vec = flicker_vec
stimulus.flicker_hz = [10, 20]
# initialize the gaussian model
model = strf.SpatioTemporalModel(stimulus, utils.spm_hrf)
model.tau = tau
model.hrf_delay = hrf
model.mask_size = mask_size
model.power = 0.7
# generate a random pRF estimate
x = -2.24
y = 1.58
sigma = 1.23
weight = 0.90
beta = 1.0
baseline = -0.25
# create the "data"
data = model.generate_prediction(x, y, sigma, weight, beta, baseline)
# set search grid
x_grid = utils.grid_slice(-8.0, 7.0, 5)
y_grid = utils.grid_slice(-8.0, 7.0, 5)
s_grid = utils.grid_slice(0.75, 3.0, 5)
w_grid = utils.grid_slice(0.05, 0.95, 5)
# set search bounds
x_bound = (-10, 10)
y_bound = (-10, 10)
s_bound = (1 / stimulus.ppd, 10)
w_bound = (1e-8, 1.0)
b_bound = (1e-8, 1e5)
u_bound = (None, None)
# loop over each voxel and set up a GaussianFit object
grids = (
x_grid,
y_grid,
s_grid,
w_grid,
)
bounds = (x_bound, y_bound, s_bound, w_bound, b_bound, u_bound)
# fit the response
fit = strf.SpatioTemporalFit(model, data, grids, bounds)
# coarse fit
ballpark = [
-0.5, 3.25, 3.0, 0.72499999999999998, 0.858317, -0.25000000000000011
]
npt.assert_almost_equal(
(fit.x0, fit.y0, fit.sigma0, fit.weight0, fit.beta0, fit.baseline0),
ballpark)
# fine fit
npt.assert_almost_equal(fit.x, x)
npt.assert_almost_equal(fit.y, y)
npt.assert_almost_equal(fit.sigma, sigma)
npt.assert_almost_equal(fit.weight, weight)
npt.assert_almost_equal(fit.beta, beta)
npt.assert_almost_equal(fit.baseline, baseline)
# overloaded
npt.assert_almost_equal(fit.overloaded_estimate, [
2.5272803327887128, 2.7411676344185993, 1.2300000000008835,
0.89999999999333258, 1.0000000000005003, -0.25000000000063088
])
m_rf = fit.model.m_rf(fit.model.tau)
p_rf = fit.model.p_rf(fit.model.tau)
npt.assert_almost_equal(simps(np.abs(m_rf)), simps(p_rf), 5)
# responses
m_resp = fit.model.generate_m_resp(fit.model.tau)
p_resp = fit.model.generate_p_resp(fit.model.tau)
npt.assert_(np.max(m_resp, 0)[0] < np.max(m_resp, 0)[1])
npt.assert_(np.max(p_resp, 0)[0] > np.max(p_resp, 0)[1])
# amps
npt.assert_(fit.model.m_amp[0] < fit.model.m_amp[1])
npt.assert_(fit.model.p_amp[0] > fit.model.p_amp[1])
# receptive field
rf = generate_2dcos_receptive_field(x, y, sigma, fit.model.power,
fit.model.stimulus.deg_x,
fit.model.stimulus.deg_y)
rf /= (2 * np.pi * sigma**2) * 1 / np.diff(model.stimulus.deg_x[0, 0:2])**2
npt.assert_almost_equal(np.round(rf.sum()),
np.round(fit.receptive_field.sum()))
# test model == fit RF
npt.assert_almost_equal(
np.round(fit.model.generate_receptive_field(x, y, sigma).sum()),
np.round(fit.receptive_field.sum()))
def test_auditory_hrf_fit():
# stimulus features
duration = 30 # seconds
Fs = int(44100 / 2) # Hz
lo_freq = 200.0 # Hz
hi_freq = 10000.0 # Hz
tr_length = 1.0 # seconds
clip_number = 0 # TRs
dtype = ctypes.c_double
# fit settings
auto_fit = True
verbose = 1
debug = False
Ns = 10
# generate auditory stimulus
time = np.linspace(0, duration, duration * Fs)
ch = chirp(time, lo_freq, duration, hi_freq, method='logarithmic')
signal = np.tile(np.concatenate((ch, ch[::-1])), 5)
blank = np.zeros((30 * Fs))
signal = np.concatenate((blank, signal, blank), -1)
# instantiate an instance of the Stimulus class
stimulus = AuditoryStimulus(signal, Fs, tr_length, dtype) ### stimulus
# initialize the gaussian model
model = aud.AuditoryModel(stimulus, utils.spm_hrf) ### model
model.hrf_delay = 0
# invent pRF estimate
center_freq_hz = 987
sigma_hz = 123
center_freq = np.log10(center_freq_hz)
sigma = np.log10(sigma_hz)
hrf_delay = 1.25
beta = 2.4
baseline = 0.59
# generate data
data = model.generate_prediction(center_freq, sigma, hrf_delay, beta,
baseline)
# search grids
c_grid = utils.grid_slice(np.log10(300), np.log10(1000), Ns)
s_grid = utils.grid_slice(np.log10(100), np.log10(500), Ns)
h_grid = utils.grid_slice(-1, 1, Ns)
grids = (
c_grid,
s_grid,
h_grid,
)
# search bounds
c_bound = (np.log10(lo_freq), np.log10(hi_freq))
s_bound = (np.log10(50), np.log10(hi_freq))
h_bound = (-2, 2)
b_bound = (1e-8, None)
m_bound = (None, None)
bounds = (c_bound, s_bound, h_bound, b_bound, m_bound)
# fit it
fit = aud.AuditoryFit(model, data, grids, bounds, Ns=Ns)
# grid fit
npt.assert_almost_equal(fit.center_freq0, 3)
npt.assert_almost_equal(fit.hrf0, 1.2222222222222223)
# test the sigma parameter against best possibility
grid_sigmas = np.arange(s_grid.start, s_grid.stop, s_grid.step)
best_sigma = grid_sigmas[np.argmin(np.abs(grid_sigmas - sigma))]
npt.assert_array_less(np.abs(fit.sigma0 - sigma), s_grid.step)
# the baseline/beta should be 0/1 when regressed data vs. estimate
(m, b) = np.polyfit(fit.scaled_ballpark_prediction, data, 1)
npt.assert_almost_equal(m, 1.0)
npt.assert_almost_equal(b, 0.0)
# final fit
npt.assert_almost_equal(fit.center_freq, center_freq)
npt.assert_almost_equal(fit.sigma, sigma)
npt.assert_almost_equal(fit.beta, beta)
npt.assert_almost_equal(fit.baseline, baseline)
npt.assert_almost_equal(fit.center_freq_hz, center_freq_hz)
# test receptive field
rf = np.exp(-((10**fit.model.stimulus.freqs - 10**fit.center_freq)**2) /
(2 * (10**fit.sigma)**2))
rf /= (10**fit.sigma * np.sqrt(2 * np.pi))
npt.assert_almost_equal(np.round(rf.sum()),
np.round(fit.receptive_field.sum()))
# test model == fit RF
rf = np.exp(-((fit.model.stimulus.freqs - fit.center_freq)**2) /
(2 * fit.sigma**2))
rf /= (fit.sigma * np.sqrt(2 * np.pi))
npt.assert_almost_equal(np.round(rf.sum()),
np.round(fit.receptive_field_log10.sum()))