def compute(in_file, n_vols, out_file):
print('reading stimuli')
st = read_prf_stimuli(n_vols)
print('making model')
model, grids, bounds = make_model(st)
nii = load(in_file)
data = nii.get_data()
indices = array(list(ndindex(data.shape[:3])))
x = data.reshape((-1, data.shape[-1]))
good_voxels = x.mean(axis=1) > 2500
x = x[good_voxels, :]
indices = indices[good_voxels, :]
bundle = utils.multiprocess_bundle(og.GaussianFit,
model,
x,
grids,
bounds,
indices,
auto_fit=True,
verbose=1,
Ns=3)
print('starting computation')
with Pool(40) as pool:
output = pool.map(utils.parallel_fit, bundle)
nif = utils.recast_estimation_results(output, nii)
nif.to_filename(out_file)
def test_parallel_fit_manual_grids():
# 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.double_gamma_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 = slice(-5, 4, 5)
y_grid = slice(-5, 7, 5)
s_grid = slice(1 / stimulus.ppd, 5.25, 5)
b_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)
# make 3 voxels
all_data = np.array([data, data, data])
num_voxels = data.shape[0]
indices = [(1, 2, 3)] * 3
# 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)
# assert equivalence
for fit in output:
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)
npt.assert_almost_equal(fit.baseline, baseline, 2)
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_parallel_fit_manual_grids():
# 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.double_gamma_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 = slice(-5,4,5)
y_grid = slice(-5,7,5)
s_grid = slice(1/stimulus.ppd,5.25,5)
b_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)
# make 3 voxels
all_data = np.array([data,data,data])
num_voxels = data.shape[0]
indices = [(1,2,3)]*3
# 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)
# assert equivalence
for fit in output:
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)
npt.assert_almost_equal(fit.baseline, baseline, 2)
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)
############################################################################################################################################
#
# actual fitting
#
############################################################################################################################################
voxel_indices = [(xx, 0, 0) for xx in np.arange(data.shape[1])]
print("starting fitting of {subject}, hemi {hemi}".format(subject=subject,
hemi=hemi))
bundle = utils.multiprocess_bundle(Fit=css.CompressiveSpatialSummationFit,
model=css_model,
data=data.T,
grids=css_grids,
bounds=css_bounds,
indices=voxel_indices,
auto_fit=True,
verbose=1,
Ns=12)
# run analysis
pool = multiprocessing.Pool(N_PROCS)
output = pool.map(utils.parallel_fit, bundle)
for fit in output:
estimates[:6, fit.voxel_index[0]] = fit.estimate
estimates[6, fit.voxel_index[0]] = fit.rsquared
# try to free up memory by closing the pool and joining them with the main thread
pool.close()
zorder=2)
plt.xticks(fontsize=16)
plt.yticks(fontsize=16)
plt.xlabel('Time', fontsize=18)
plt.ylabel('Amplitude', fontsize=18)
plt.xlim(0, len(fit.data))
plt.legend(loc=0)
## multiprocess 3 voxels
data = [data, data, data]
indices = ([1, 2, 3], [4, 6, 5], [7, 8, 9])
bundle = utils.multiprocess_bundle(og.GaussianFit,
model,
data,
grids,
bounds,
indices,
auto_fit=True,
verbose=1,
Ns=3)
## run
print("popeye will analyze %d voxels across %d cores" % (len(bundle), 3))
with sharedmem.Pool(np=3) as pool:
t1 = datetime.datetime.now()
output = pool.map(utils.parallel_fit, bundle)
t2 = datetime.datetime.now()
delta = t2 - t1
print("popeye multiprocessing finished in %s.%s seconds" %
(delta.seconds, delta.microseconds))