def __init__(self,
model,
data,
grids,
bounds,
Ns,
tr_length,
voxel_index=(1, 2, 3),
auto_fit=True,
verbose=0):
PopulationFit.__init__(self, model, data, grids, bounds, Ns, tr_length,
voxel_index, auto_fit, verbose)
if self.auto_fit:
self.start = time.clock()
try:
self.ballpark
self.estimate
self.OLS
self.finish = time.clock()
if self.verbose:
print(self.msg)
except:
self.finish = time.clock()
if self.verbose:
print(self.errmsg)
def __init__(self,
model,
data,
grids,
bounds,
voxel_index=(1, 2, 3),
Ns=None,
auto_fit=True,
verbose=0):
PopulationFit.__init__(self, model, data, grids, bounds, voxel_index,
Ns, auto_fit, verbose)
def __init__(self, model, data, grids, bounds, Ns, tr_length,
voxel_index=(1,2,3), auto_fit=True, verbose=0):
PopulationFit.__init__(self, model, data, grids, bounds, Ns, tr_length, voxel_index, auto_fit, verbose)
if self.auto_fit:
self.start = time.clock()
try:
self.ballpark;
self.estimate;
self.OLS;
self.finish = time.clock()
if self.verbose:
print(self.msg)
except:
self.finish = time.clock()
if self.verbose:
print(self.errmsg)
def __init__(self, model, data, grids, bounds,
voxel_index=(1,2,3), Ns=None, auto_fit=True, verbose=0):
r"""
A class containing tools for fitting the Difference of Gaussians pRF model.
The `DifferenceOfGaussiansFit` class houses all the fitting tool that
are associated with estimating a pRF model. The `GaussianFit` takes a
`DifferenceOfGaussiansModel` instance `model` and a time-series `data`.
In addition, extent and sampling-rate of a brute-force grid-search is set
with `grids` and `Ns`. Use `bounds` to set limits on the search space for
each parameter.
Paramaters
----------
model : `DifferenceOfGaussiansModel` class instance
An object representing the CSS model.
data : ndarray
An array containing the measured BOLD signal of a single voxel.
grids : tuple
A tuple indicating the search space for the brute-force grid-search.
The tuple contains pairs of upper and lower bounds for exploring a
given dimension. For example `grids=((-10,10),(0,5),)` will
search the first dimension from -10 to 10 and the second from 0 to 5.
These values cannot be `None`.
For more information, see `scipy.optimize.brute`.
bounds : tuple
A tuple containing the upper and lower bounds for each parameter
in `parameters`. If a parameter is not bounded, simply use
`None`. For example, `fit_bounds=((0,None),(-10,10),)` would
bound the first parameter to be any positive number while the
second parameter would be bounded between -10 and 10.
Ns : int
Number of samples per stimulus dimension to sample during the ballpark search.
For more information, see `scipy.optimize.brute`.
voxel_index : tuple
A tuple containing the index of the voxel being modeled. The
fitting procedure does not require a voxel index, but
collating the results across many voxels will does require voxel
indices. With voxel indices, the brain volume can be reconstructed
using the newly computed model estimates.
auto_fit : bool
A flag for automatically running the fitting procedures once the
`GaussianFit` object is instantiated.
verbose : int
0 = silent
1 = print the final solution of an error-minimization
2 = print each error-minimization step
"""
PopulationFit.__init__(self, model, data, grids, bounds,
voxel_index, Ns, auto_fit, verbose)
def __init__(self, model, data, grids, bounds,
voxel_index=(1,2,3), Ns=None, auto_fit=True, verbose=0):
r"""
A class containing tools for fitting the 1D Gaussian pRF model.
The `AuditoryFit` class houses all the fitting tool that are associated with
estimatinga pRF model. The `PopulationFit` takes a `AuditoryModel` instance
`model` and a time-series `data`. In addition, extent and sampling-rate of a
brute-force grid-search is set with `grids` and `Ns`. Use `bounds` to set
limits on the search space for each parameter.
Paramaters
----------
model : `AuditoryModel` class instance
An object representing the 1D Gaussian model.
data : ndarray
An array containing the measured BOLD signal of a single voxel.
grids : tuple
A tuple indicating the search space for the brute-force grid-search.
The tuple contains pairs of upper and lower bounds for exploring a
given dimension. For example `grids=((-10,10),(0,5),)` will
search the first dimension from -10 to 10 and the second from 0 to 5.
These values cannot be `None`.
For more information, see `scipy.optimize.brute`.
bounds : tuple
A tuple containing the upper and lower bounds for each parameter
in `parameters`. If a parameter is not bounded, simply use
`None`. For example, `fit_bounds=((0,None),(-10,10),)` would
bound the first parameter to be any positive number while the
second parameter would be bounded between -10 and 10.
Ns : int
Number of samples per stimulus dimension to sample during the ballpark search.
For more information, see `scipy.optimize.brute`.
voxel_index : tuple
A tuple containing the index of the voxel being modeled. The
fitting procedure does not require a voxel index, but
collating the results across many voxels will does require voxel
indices. With voxel indices, the brain volume can be reconstructed
using the newly computed model estimates.
auto_fit : bool
A flag for automatically running the fitting procedures once the
`GaussianFit` object is instantiated.
verbose : int
0 = silent
1 = print the final solution of an error-minimization
2 = print each error-minimization step
"""
# invoke the base class
PopulationFit.__init__(self, model, data, grids, bounds,
voxel_index, Ns, auto_fit, verbose)
def __init__(self, model, data, grids, bounds, voxel_index=(1, 2, 3), Ns=None, auto_fit=True, verbose=0):
PopulationFit.__init__(self, model, data, grids, bounds, voxel_index, Ns, auto_fit, verbose)
def __init__(self, model, data, search_bounds, fit_bounds, tr_length,
voxel_index=(1,2,3), auto_fit=True, verbose=True, uncorrected_rval=0.0):
"""
A Gaussian population receptive field model [1]_.
Paramaters
----------
data : ndarray
An array containing the measured BOLD signal.
model : `GaussianModel` class instance containing the representation
of the visual stimulus.
search_bounds : tuple
A tuple indicating the search space for the brute-force grid-search.
The tuple contains pairs of upper and lower bounds for exploring a
given dimension. For example `fit_bounds=((-10,10),(0,5),)` will
search the first dimension from -10 to 10 and the second from 0 to 5.
These values cannot be None.
For more information, see `scipy.optimize.brute`.
fit_bounds : tuple
A tuple containing the upper and lower bounds for each parameter
in `parameters`. If a parameter is not bounded, simply use
`None`. For example, `fit_bounds=((0,None),(-10,10),)` would
bound the first parameter to be any positive number while the
second parameter would be bounded between -10 and 10.
tr_length : float
The length of the repetition time in seconds.
voxel_index : tuple
A tuple containing the index of the voxel being modeled. The
fitting procedure does not require a voxel index, but
collating the results across many voxels will does require voxel
indices. With voxel indices, the brain volume can be reconstructed
using the newly computed model estimates.
auto-fit : bool
A flag for automatically running the fitting procedures once the
`GaussianFit` object is instantiated.
verbose : bool
A flag for printing some summary information about the model estiamte
after the fitting procedures have completed.
References
----------
.. [1] Dumoulin SO, Wandell BA. (2008) Population receptive field
estimates in human visual cortex. NeuroImage 39:647-660
"""
PopulationFit.__init__(self, model, data)
self.search_bounds = search_bounds
self.fit_bounds = fit_bounds
self.tr_length = tr_length
self.voxel_index = voxel_index
self.auto_fit = auto_fit
self.verbose = verbose
self.uncorrected_rval = uncorrected_rval
if self.auto_fit:
tic = time.clock()
self.ballpark_estimate;
if self.coarse_fit_stats[2] > self.uncorrected_rval:
self.estimate;
self.fit_stats;
self.rss;
toc = time.clock()
msg = ("VOXEL=(%.03d,%.03d,%.03d) TIME=%.03d RVAL=%.02f"
%(self.voxel_index[0],
self.voxel_index[1],
self.voxel_index[2],
toc-tic,
self.fit_stats[2]))
else:
toc = time.clock()
msg = ("VOXEL=(%.03d,%.03d,%.03d) TIME=%.03d COARSE_RVAL=%.02f"
%(self.voxel_index[0],
self.voxel_index[1],
self.voxel_index[2],
toc-tic,
self.coarse_fit_stats[2]))
if self.verbose:
print(msg)
def __init__(self, og_fit, fit_bounds, auto_fit=True, verbose=True, uncorrected_rval=0.0):
"""
A Gaussian population receptive field model [1]_.
Paramaters
----------
og_fit : `GaussianFit` class instance
A `GaussianFit` object. This object does not have to be fitted already,
as the fit will be performed inside the `DifferenceOfGaussiansFit` in any
case. The `GaussianFit` is used as a seed for the `DifferenceOfGaussiansFit`
search.
fit_bounds : tuple
A tuple containing the upper and lower bounds for each parameter
in `parameters`. If a parameter is not bounded, simply use
`None`. For example, `fit_bounds=((0,None),(-10,10),)` would
bound the first parameter to be any positive number while the
second parameter would be bounded between -10 and 10.
auto-fit : bool
A flag for automatically running the fitting procedures once the
`GaussianFit` object is instantiated.
verbose : bool
A flag for printing some summary information about the model estiamte
after the fitting procedures have completed.
References
----------
.. [1] Dumoulin SO, Wandell BA. (2008) Population receptive field
estimates in human visual cortex. NeuroImage 39:647-660
"""
PopulationFit.__init__(self, og_fit.model, og_fit.data)
self.og_fit = og_fit
self.fit_bounds = fit_bounds
self.tr_length = self.og_fit.tr_length
self.voxel_index = self.og_fit.voxel_index
self.auto_fit = auto_fit
self.verbose = verbose
self.uncorrected_rval = uncorrected_rval
if self.auto_fit:
tic = time.clock()
self.og_fit.estimate
if self.og_fit.fit_stats[2] > self.uncorrected_rval:
self.estimate;
self.fit_stats;
toc = time.clock()
msg = ("VOXEL=(%.03d,%.03d,%.03d) TIME=%.03d OG-RVAL=%.02f DoG-RVAL=%.02f"
%(self.voxel_index[0],
self.voxel_index[1],
self.voxel_index[2],
toc-tic,
self.og_fit.fit_stats[2],
self.fit_stats[2]))
else:
toc = time.clock()
msg = ("VOXEL=(%.03d,%.03d,%.03d) TIME=%.03d OG-RVAL=%.02f"
%(self.voxel_index[0],
self.voxel_index[1],
self.voxel_index[2],
toc-tic,
self.og_fit.fit_stats[2]))
if self.verbose:
print(msg)
