Merge pull request #1063 from sahmed95/issue-1061

Fixes #1061 : Changed all S0 to 1.0

dipy/data/__init__.py

@@ -313,7 +313,7 @@ def dsi_deconv_voxels():
             for iz in range(2):
                 data[ix, iy, iz], dirs = SticksAndBall(gtab,
                                                        d=0.0015,
-                                                       S0=100,
+                                                       S0=1.,
                                                        angles=[(0, 0),
                                                                (90, 0)],
                                                        fractions=[50, 50],

dipy/reconst/csdeconv.py

@@ -180,7 +180,7 @@ def fit(self, data):
         return SphHarmFit(self, shm_coeff, None)
 
 
-    def predict(self, sh_coeff, gtab=None, S0=1):
+    def predict(self, sh_coeff, gtab=None, S0=1.):
         """Compute a signal prediction given spherical harmonic coefficients
         for the provided GradientTable class instance.
 
@@ -951,7 +951,7 @@ def recursive_response(gtab, data, mask=None, sh_order=8, peak_thr=0.01,
            the fiber response function for spherical deconvolution of
            diffusion MRI data.
     """
-    S0 = 1
+    S0 = 1.
     evals = fa_trace_to_lambdas(init_fa, init_trace)
     res_obj = (evals, S0)
 

dipy/reconst/dki.py

@@ -889,7 +889,7 @@ def axial_kurtosis(dki_params, min_kurtosis=0, max_kurtosis=3):
     return AK.reshape(outshape)
 
 
-def dki_prediction(dki_params, gtab, S0=150):
+def dki_prediction(dki_params, gtab, S0=1.):
     """ Predict a signal given diffusion kurtosis imaging parameters.
 
     Parameters
@@ -1038,7 +1038,7 @@ def fit(self, data, mask=None):
 
         return DiffusionKurtosisFit(self, dki_params)
 
-    def predict(self, dki_params, S0=1):
+    def predict(self, dki_params, S0=1.):
         """ Predict a signal for this DKI model class instance given
         parameters.
 
@@ -1263,7 +1263,7 @@ def rk(self, min_kurtosis=0, max_kurtosis=3):
         """
         return radial_kurtosis(self.model_params, min_kurtosis, max_kurtosis)
 
-    def predict(self, gtab, S0=1):
+    def predict(self, gtab, S0=1.):
         r""" Given a DKI model fit, predict the signal on the vertices of a
         gradient table
 

dipy/reconst/dti.py

@@ -807,7 +807,7 @@ def fit(self, data, mask=None):
 
         return TensorFit(self, dti_params)
 
-    def predict(self, dti_params, S0=1):
+    def predict(self, dti_params, S0=1.):
         """
         Predict a signal for this TensorModel class instance given parameters.
 
@@ -1133,7 +1133,7 @@ def adc(self, sphere):
         """
         return apparent_diffusion_coef(self.quadratic_form, sphere)
 
-    def predict(self, gtab, S0=1, step=None):
+    def predict(self, gtab, S0=1., step=None):
         r"""
         Given a model fit, predict the signal on the vertices of a sphere
 

dipy/reconst/mapmri.py

@@ -108,7 +108,8 @@ def __init__(self,
         >>> data, gtab = dsi_voxels()
         >>> sphere = get_sphere('symmetric724')
         >>> from dipy.sims.voxel import SticksAndBall
-        >>> data, golden_directions = SticksAndBall(gtab, d=0.0015, S0=1, angles=[(0, 0), (90, 0)], fractions=[50, 50], snr=None)
+        >>> data, golden_directions = SticksAndBall(gtab, d=0.0015, S0=100,
+        ... angles=[(0, 0), (90, 0)], fractions=[50, 50], snr=None)
         >>> from dipy.reconst.mapmri import MapmriModel
         >>> radial_order = 4
         >>> map_model = MapmriModel(gtab, radial_order=radial_order)
@@ -326,7 +327,7 @@ def rtop(self):
                     i, 2]) / 2.0) * self._mapmri_coef[i] * Bm[i]
         return const * rtop
 
-    def predict(self, gtab, S0=1.0):
+    def predict(self, gtab, S0=1.):
         """
         Predict a signal for this MapmriModel class instance given a gradient
         table.

dipy/reconst/shore.py

@@ -157,7 +157,7 @@ def __init__(self,
         gtab = gradient_table(bvals, bvecs)
         from dipy.sims.voxel import SticksAndBall
         data, golden_directions = SticksAndBall(gtab, d=0.0015,
-                                                S0=1, angles=[(0, 0), (90, 0)],
+                                                S0=1., angles=[(0, 0), (90, 0)],
                                                 fractions=[50, 50], snr=None)
         from dipy.reconst.canal import ShoreModel
         radial_order = 4

dipy/sims/tests/test_voxel.py

@@ -307,7 +307,7 @@ def test_DKI_crossing_fibers_simulations():
     assert_array_almost_equal(dt, dt_ref)
     assert_array_almost_equal(kt, kt_ref)
     assert_array_almost_equal(signal,
-                              DKI_signal(gtab_2s, dt_ref, kt_ref, S0=100,
+                              DKI_signal(gtab_2s, dt_ref, kt_ref, S0=1.,
                                          snr=None),
                               decimal=5)
 

dipy/sims/voxel.py

@@ -133,7 +133,7 @@ def add_noise(signal, snr, S0, noise_type='rician'):
     return noise_adder[noise_type](signal, noise1, noise2)
 
 
-def sticks_and_ball(gtab, d=0.0015, S0=100, angles=[(0, 0), (90, 0)],
+def sticks_and_ball(gtab, d=0.0015, S0=1., angles=[(0, 0), (90, 0)],
                     fractions=[35, 35], snr=20):
     """ Simulate the signal for a Sticks & Ball model.
 
@@ -188,7 +188,7 @@ def sticks_and_ball(gtab, d=0.0015, S0=100, angles=[(0, 0), (90, 0)],
     return S, sticks
 
 
-def single_tensor(gtab, S0=1, evals=None, evecs=None, snr=None):
+def single_tensor(gtab, S0=1., evals=None, evecs=None, snr=None):
     """ Simulated Q-space signal with a single tensor.
 
     Parameters
@@ -244,7 +244,7 @@ def single_tensor(gtab, S0=1, evals=None, evecs=None, snr=None):
     return S.reshape(out_shape)
 
 
-def multi_tensor(gtab, mevals, S0=100, angles=[(0, 0), (90, 0)],
+def multi_tensor(gtab, mevals, S0=1., angles=[(0, 0), (90, 0)],
                  fractions=[50, 50], snr=20):
     r""" Simulate a Multi-Tensor signal.
 
@@ -305,7 +305,7 @@ def multi_tensor(gtab, mevals, S0=100, angles=[(0, 0), (90, 0)],
     return add_noise(S, snr, S0), sticks
 
 
-def multi_tensor_dki(gtab, mevals, S0=100, angles=[(90., 0.), (90., 0.)],
+def multi_tensor_dki(gtab, mevals, S0=1., angles=[(90., 0.), (90., 0.)],
                      fractions=[50, 50], snr=20):
     r""" Simulate the diffusion-weight signal, diffusion and kurtosis tensors
     based on the DKI model
@@ -481,7 +481,7 @@ def kurtosis_element(D_comps, frac, ind_i, ind_j, ind_k, ind_l, DT=None,
     return wijkl
 
 
-def DKI_signal(gtab, dt, kt, S0=150, snr=None):
+def DKI_signal(gtab, dt, kt, S0=1., snr=None):
     r""" Simulated signal based on the diffusion and diffusion kurtosis
     tensors of a single voxel. Simulations are preformed assuming the DKI
     model.