changed spherical_mean to _spherical_mean

dmipy/core/fitted_modeling_framework.py

@@ -165,7 +165,7 @@ def predict(self, acquisition_scheme=None, S0=None, mask=None):
         if mask is None:
             mask = self.mask
 
-        if self.model.spherical_mean:
+        if self.model._spherical_mean:
             N_samples = len(acquisition_scheme.shell_bvalues)
         else:
             N_samples = len(acquisition_scheme.bvalues)
@@ -181,7 +181,7 @@ def predict(self, acquisition_scheme=None, S0=None, mask=None):
 
     def R2_coefficient_of_determination(self, data):
         "Calculates the R-squared of the model fit."
-        if self.model.spherical_mean:
+        if self.model._spherical_mean:
             Nshells = len(self.model.scheme.shell_bvalues)
             data_ = np.zeros(np.r_[data.shape[:-1], Nshells])
             for pos in zip(*np.where(self.mask)):
@@ -200,7 +200,7 @@ def R2_coefficient_of_determination(self, data):
 
     def mean_squared_error(self, data):
         "Calculates the mean squared error of the model fit."
-        if self.model.spherical_mean:
+        if self.model._spherical_mean:
             Nshells = len(self.model.scheme.shell_bvalues)
             data_ = np.zeros(np.r_[data.shape[:-1], Nshells])
             for pos in zip(*np.where(self.mask)):

dmipy/core/modeling_framework.py

@@ -290,12 +290,13 @@ def _prepare_model_properties(self):
         """Checks that spherical mean and regular models cannot be optimized
         together, and whether the model can estimate a Fiber Orientation
         Distribution (FOD)."""
-        models_spherical_mean = [model.spherical_mean for model in self.models]
+        models_spherical_mean = [
+            model._spherical_mean for model in self.models]
         if len(np.unique(models_spherical_mean)) > 1:
             msg = "Cannot mix spherical mean and non-spherical mean models. "
             msg = "Current model selection is {}".format(self.models)
             raise ValueError(msg)
-        self.spherical_mean = np.all(models_spherical_mean)
+        self._spherical_mean = np.all(models_spherical_mean)
         self.fod_available = False
         for model in self.models:
             try:
@@ -648,7 +649,7 @@ def fit(self, acquisition_scheme, data, parameter_initial_guess=None,
 
         # if the models are spherical mean based then estimate the
         # spherical mean of the data.
-        if self.spherical_mean:
+        if self._spherical_mean:
             data_to_fit = np.zeros(
                 np.r_[data_.shape[:-1],
                       self.scheme.unique_dwi_indices.max() + 1])
@@ -721,7 +722,7 @@ def simulate_signal(self, acquisition_scheme, model_parameters_array):
             The simulated signal of the microstructure model.
         """
         Ndata = acquisition_scheme.number_of_measurements
-        if self.spherical_mean:
+        if self._spherical_mean:
             Ndata = len(acquisition_scheme.shell_bvalues)
         x0 = model_parameters_array
 

dmipy/distributions/distribute_models.py

@@ -10,7 +10,7 @@
 
 class DistributedModel:
     "Contains various properties of distributed models."
-    spherical_mean = False
+    _spherical_mean = False
 
     def _check_for_double_model_class_instances(self):
         "Checks if there are no models with the same class instantiation."

dmipy/distributions/distributions.py

@@ -84,7 +84,7 @@ class SD1Watson(ModelProperties):
         'mu': np.r_[1., 1.],
         'odi': 1.,
     }
-    spherical_mean = False
+    _spherical_mean = False
 
     def __init__(self, mu=None, odi=None):
         self.mu = mu
@@ -182,7 +182,7 @@ class SD2Bingham(ModelProperties):
         'odi': 1.,
         'beta_fraction': 1.
     }
-    spherical_mean = False
+    _spherical_mean = False
 
     def __init__(self, mu=None, psi=None, odi=None, beta_fraction=None):
         self.mu = mu
@@ -346,7 +346,7 @@ class DD1GammaDistribution(ModelProperties):
         'alpha': 1.,
         'beta': BETA_SCALING,
     }
-    spherical_mean = False
+    _spherical_mean = False
 
     def __init__(self, alpha=None, beta=None, Nsteps=30,
                  normalization='standard'):

dmipy/signal_models/capped_cylinder_models.py

@@ -42,7 +42,7 @@ class CC3CappedCylinderCallaghanApproximation(ModelProperties):
         53-59.
     """
 
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'experimental'
 
     def __init__(

dmipy/signal_models/cylinder_models.py

@@ -59,7 +59,7 @@ class C1Stick(ModelProperties):
         'mu': np.r_[1., 1.],
         'lambda_par': DIFFUSIVITY_SCALING
     }
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'other'
 
     def __init__(self, mu=None, lambda_par=None):
@@ -156,7 +156,7 @@ class C2CylinderSodermanApproximation(ModelProperties):
         'lambda_par': DIFFUSIVITY_SCALING,
         'diameter': DIAMETER_SCALING
     }
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'cylinder'
 
     def __init__(
@@ -291,7 +291,7 @@ class C3CylinderCallaghanApproximation(ModelProperties):
         'lambda_par': DIFFUSIVITY_SCALING,
         'diameter': DIAMETER_SCALING
     }
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'cylinder'
 
     def __init__(
@@ -456,7 +456,7 @@ class C4CylinderGaussianPhaseApproximation(ModelProperties):
         'lambda_par': DIFFUSIVITY_SCALING,
         'diameter': DIAMETER_SCALING
     }
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'cylinder'
     _CYLINDER_TRASCENDENTAL_ROOTS = np.sort(special.jnp_zeros(1, 100))
 

dmipy/signal_models/gaussian_models.py

@@ -55,7 +55,7 @@ class G1Ball(ModelProperties):
     _parameter_scales = {
         'lambda_iso': DIFFUSIVITY_SCALING
     }
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'other'
 
     def __init__(self, lambda_iso=None):
@@ -120,7 +120,7 @@ class G2Zeppelin(ModelProperties):
         'lambda_par': DIFFUSIVITY_SCALING,
         'lambda_perp': DIFFUSIVITY_SCALING
     }
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'other'
 
     def __init__(self, mu=None, lambda_par=None, lambda_perp=None):
@@ -224,7 +224,7 @@ class G3RestrictedZeppelin(ModelProperties):
         'lambda_perp': DIFFUSIVITY_SCALING,
         'A': A_SCALING
     }
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'other'
 
     def __init__(self, mu=None, lambda_par=None, lambda_inf=None, A=None):

dmipy/signal_models/plane_models.py

@@ -35,7 +35,7 @@ class P3PlaneCallaghanApproximation(ModelProperties):
         'diameter': DIAMETER_SCALING
     }
 
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'plane'
 
     def __init__(

dmipy/signal_models/sphere_models.py

@@ -20,7 +20,7 @@ class S1Dot(ModelProperties):
     }
     _parameter_scales = {
     }
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'other'
 
     def __call__(self, acquisition_scheme, **kwargs):
@@ -67,7 +67,7 @@ class S2SphereSodermanApproximation(ModelProperties):
     _parameter_scales = {
         'diameter': DIAMETER_SCALING
     }
-    spherical_mean = False
+    _spherical_mean = False
     _model_type = 'sphere'
 
     def __init__(self, diameter=None):

dmipy/signal_models/spherical_mean_models.py

@@ -38,7 +38,7 @@ class C1StickSphericalMean(ModelProperties):
     _parameter_scales = {
         'lambda_par': DIFFUSIVITY_SCALING,
     }
-    spherical_mean = True
+    _spherical_mean = True
     _model_type = 'other'
 
     def __init__(self, lambda_par=None):
@@ -101,7 +101,7 @@ class G2ZeppelinSphericalMean(ModelProperties):
         'lambda_par': DIFFUSIVITY_SCALING,
         'lambda_perp': DIFFUSIVITY_SCALING
     }
-    spherical_mean = True
+    _spherical_mean = True
     _model_type = 'other'
 
     def __init__(self, lambda_par=None, lambda_perp=None):
@@ -175,7 +175,7 @@ class G3RestrictedZeppelinSphericalMean(ModelProperties):
         'lambda_inf': DIFFUSIVITY_SCALING,
         'A': A_SCALING
     }
-    spherical_mean = True
+    _spherical_mean = True
     _model_type = 'other'
 
     def __init__(self, lambda_par=None, lambda_inf=None, A=None):