class gamma_nmr_cyl(FreeParameterConfig):
init_value = 5
lower_bound = 1
upper_bound = 10
parameter_transform = SinSqrClampTransform()
sampling_proposal = GaussianProposal(1)
class Dt(FreeParameterConfig):
init_value = 1.7e-10
lower_bound = 0
upper_bound = 1.0e-8
parameter_transform = SinSqrClampTransform()
sampling_proposal = GaussianProposal(1e-14)
class gamma_beta(FreeParameterConfig):
init_value = 1
lower_bound = 1.0e-7
upper_bound = 3.0e-7
parameter_transform = SinSqrClampTransform()
sampling_proposal = GaussianProposal(1e-7)
class gamma_k(FreeParameterConfig):
init_value = 1
lower_bound = 0
upper_bound = 20
parameter_transform = SinSqrClampTransform()
sampling_proposal = GaussianProposal(1.0)
class kappa(FreeParameterConfig):
init_value = 1
lower_bound = 1e-5
upper_bound = 2 * np.pi
parameter_transform = CosSqrClampTransform()
sampling_proposal = GaussianProposal(0.01)
class s0(FreeParameterConfig):
init_value = 1e4
lower_bound = 1e-5
upper_bound = 1e10
parameter_transform = ClampTransform()
sampling_proposal = GaussianProposal(std=25.0)
class T2s(FreeParameterConfig):
init_value = 0.01
lower_bound = 0.0
upper_bound = 1.0
parameter_transform = ClampTransform()
sampling_proposal = GaussianProposal(0.0001)
class FreeParameterConfig(ParameterConfig):
"""The default config options for free parameters.
This sets the attribute type to free.
Attributes:
init_value (float): the initial value
fixed (boolean or ndarray of float): if this parameter is fixed or not. If not fixed this should
hold a reference to a value or a matrix
lower_bound (float): the lower bounds
upper_bound (float): the upper bounds
parameter_transform: the parameter transformation
sampling_proposal: the proposal function
sampling_prior: the prior function
sampling_statistics: the sampling statistic, used after the sampling
"""
type = 'free'
data_type = 'mot_float_type'
fixed = False
init_value = 0.03
lower_bound = 0.0
upper_bound = 4.0
parameter_transform = IdentityTransform()
sampling_proposal = GaussianProposal(1.0)
sampling_prior = UniformWithinBoundsPrior()
sampling_statistics = GaussianPSS()
def __init__(self):
r"""This is a collapsed form of the Beta PDF meant for use in Automatic Relevance Detection sampling.
In this prior the ``alpha`` parameter of the Beta prior is set to 1 which simplifies the equation.
The parameter ``beta`` is still free and can be changed as desired.
The implemented prior is:
.. math::
B(x; 1, \beta) = \beta * (1 - x)^{\beta - 1}
"""
from mot.model_building.cl_functions.parameters import FreeParameter
params = [
FreeParameter(SimpleCLDataType.from_string('mot_float_type'),
'beta',
False,
1,
1e-4,
1000,
sampling_prior=ReciprocalPrior(),
sampling_proposal=GaussianProposal(0.01))
]
body = '''
if(value < 0 || value > 1){
return 0;
}
return beta * pow(1 - value, beta - 1);
'''
super(ARDBeta, self).__init__(body, 'ard_beta_pdf', params)
def __init__(self):
"""This is a Gaussian prior meant for use in Automatic Relevance Detection sampling.
This uses a Gaussian prior with mean at zero and a standard deviation determined by the ``alpha`` parameter
with the relationship :math:`\sigma = 1/\\sqrt(\\alpha)`.
"""
from mot.model_building.cl_functions.parameters import FreeParameter
params = [
FreeParameter(SimpleCLDataType.from_string('mot_float_type'),
'alpha',
False,
8,
1e-5,
1e4,
sampling_prior=UniformWithinBoundsPrior(),
sampling_proposal=GaussianProposal(20))
]
body = '''
if(value < 0 || value > 1){
return 0;
}
mot_float_type sigma = 1.0/sqrt(alpha);
return exp(-pown(value, 2) / (2 * pown(sigma, 2))) / (sigma * sqrt(2 * M_PI));
'''
super(ARDGaussian, self).__init__(body, 'ard_beta_pdf', params)
class R(FreeParameterConfig):
init_value = 2.0e-6
lower_bound = 1e-6
upper_bound = 20e-6
parameter_transform = CosSqrClampTransform()
sampling_proposal = GaussianProposal(1e-6)
class R2s(FreeParameterConfig):
"""R2s = 1/T2s, for lineaR T2sDec or other models."""
init_value = 10
lower_bound = 1
upper_bound = 50.0
parameter_transform = ClampTransform()
sampling_proposal = GaussianProposal(0.0001)
class R2(FreeParameterConfig):
"""R2 = 1/T2, for linear T2Dec or other models."""
init_value = 5
lower_bound = 0.5
upper_bound = 500.0
parameter_transform = ClampTransform()
sampling_proposal = GaussianProposal(0.0001)
class R1(FreeParameterConfig):
"""R1 = 1/T1, for linear T1Dec or other models. """
init_value = 2
lower_bound = 0.25
upper_bound = 100.0
parameter_transform = ClampTransform()
sampling_proposal = GaussianProposal(0.0001)
class E1(FreeParameterConfig):
"""This parameter is defined *only* for linear decay T1 fitting in GRE data *with* TR constant.
This parameter is also defined in the SSFP equation. However, in SSFP this parameter is from the protocol (!)
E1 = exp( -TR / T1 ).
After estimation of this parameter, T1 can be recovered by applying the next equation:
-TR / log( E1 ).
"""
init_value = 0.37
lower_bound = 0.0
upper_bound = 1.0
parameter_transform = ClampTransform()
sampling_proposal = GaussianProposal(0.0001)
def __init__(self,
data_type,
name,
fixed,
value,
lower_bound,
upper_bound,
parameter_transform=None,
sampling_proposal=None,
sampling_prior=None,
sampling_statistics=None):
"""This are the kind of parameters that are generally meant to be optimized.
These parameters may optionally be fixed to a value or list of values for all voxels.
Args:
data_type (mot.cl_data_type.SimpleCLDataType): the data type expected by this parameter
name (str): The name of this parameter
fixed (boolean): If this parameter is fixed to the value given
value (double or ndarray): A single value for all voxels or a list of values for each voxel
lower_bound (double): The lower bound of this parameter
upper_bound (double): The upper bound of this parameter
parameter_transform (AbstractTransformation): The parameter transformation function
sampling_proposal (ParameterProposal): The proposal function for use in model sampling
sampling_prior (ParameterPrior): The prior function for use in model sampling
sampling_statistics (ParameterSampleStatistics): The statistic functions used to get
statistics out of the samples
Attributes:
value (number or ndarray): The value of this state
lower_bound (number or ndarray): The lower bound
upper_bound (number or ndarray): The upper bound
fixed (boolean): If this free parameter is fixed to its value.
parameter_transform (AbstractTransformation): The parameter transformation (codec information)
sampling_proposal (ParameterProposal): The proposal function for use in model sampling
sampling_prior (ParameterPrior): The prior function for use in model sampling
sampling_statistics (ParameterSampleStatistics): The statistic functions used to get
statistics out of the samples
"""
super(FreeParameter, self).__init__(data_type, name)
self._value = value
self._lower_bound = lower_bound
self._upper_bound = upper_bound
self._fixed = fixed
self._parameter_transform = parameter_transform or IdentityTransform()
self._sampling_proposal = sampling_proposal or GaussianProposal(1.0)
self._sampling_prior = sampling_prior or UniformWithinBoundsPrior()
self._sampling_statistics = sampling_statistics or GaussianPSS()
def __init__(self, name='Weight', value=0.5, lower_bound=0.0, upper_bound=1.0, parameter_kwargs=None):
"""Implements Scalar model function to add the semantics of representing a Weight.
Some of the code checks for type Weight, be sure to use this model function if you want to represent a Weight.
A weight is meant to be a model volume fraction.
Args:
name (str): The name of the model
value (number or ndarray): The initial value for the single free parameter of this function.
lower_bound (number or ndarray): The initial lower bound for the single free parameter of this function.
upper_bound (number or ndarray): The initial upper bound for the single free parameter of this function.
"""
parameter_settings = dict(parameter_transform=CosSqrClampTransform(),
sampling_proposal=GaussianProposal(0.01),
sampling_prior=UniformWithinBoundsPrior())
parameter_settings.update(parameter_kwargs or {})
super(Weight, self).__init__(name=name, param_name='w', value=value, lower_bound=lower_bound,
upper_bound=upper_bound, parameter_kwargs=parameter_settings)
def __init__(self, name='Scalar', param_name='s', value=0.0, lower_bound=0.0, upper_bound=float('inf'),
parameter_kwargs=None):
"""A Scalar model function to be used during optimization.
Args:
name (str): The name of the model
value (number or ndarray): The initial value for the single free parameter of this function.
lower_bound (number or ndarray): The initial lower bound for the single free parameter of this function.
upper_bound (number or ndarray): The initial upper bound for the single free parameter of this function.
parameter_kwargs (dict): additional settings for the parameter initialization
"""
parameter_settings = dict(parameter_transform=ClampTransform(),
sampling_proposal=GaussianProposal(1.0))
parameter_settings.update(parameter_kwargs or {})
super(Scalar, self).__init__(
name,
'cmScalar',
(FreeParameter(SimpleCLDataType.from_string('mot_float_type'), param_name,
False, value, lower_bound, upper_bound, **parameter_settings),))
