diff --git a/src/standardized/IAR_LU_biexp.py b/src/standardized/IAR_LU_biexp.py index 1ec3c16..60a0b14 100644 --- a/src/standardized/IAR_LU_biexp.py +++ b/src/standardized/IAR_LU_biexp.py @@ -59,7 +59,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non if self.bvalues is not None: bvec = np.zeros((self.bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(self.bvalues, bvec, b0_threshold=0) + gtab = gradient_table(self.bvalues, bvecs=bvec, b0_threshold=0) self.IAR_algorithm = IvimModelBiExp(gtab, bounds=self.bounds, initial_guess=self.initial_guess) else: diff --git a/src/standardized/IAR_LU_modified_mix.py b/src/standardized/IAR_LU_modified_mix.py index 4c9ec4a..3580f8b 100644 --- a/src/standardized/IAR_LU_modified_mix.py +++ b/src/standardized/IAR_LU_modified_mix.py @@ -58,7 +58,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non if self.bvalues is not None: bvec = np.zeros((self.bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(self.bvalues, bvec, b0_threshold=0) + gtab = gradient_table(self.bvalues, bvecs=bvec, b0_threshold=0) bounds = [[self.bounds["f"][0], self.bounds["Dp"][0]*1000, self.bounds["D"][0]*1000], [self.bounds["f"][1], self.bounds["Dp"][1]*1000, self.bounds["D"][1]*1000]] @@ -90,7 +90,7 @@ def ivim_fit(self, signals, bvalues, **kwargs): bvec = np.zeros((bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(bvalues, bvec, b0_threshold=0) + gtab = gradient_table(bvalues, bvecs=bvec, b0_threshold=0) self.IAR_algorithm = IvimModelVP(gtab, bounds=bounds, rescale_results_to_mm2_s=True) diff --git a/src/standardized/IAR_LU_modified_topopro.py b/src/standardized/IAR_LU_modified_topopro.py index 26ab56f..caad456 100644 --- a/src/standardized/IAR_LU_modified_topopro.py +++ b/src/standardized/IAR_LU_modified_topopro.py @@ -61,7 +61,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non if self.bvalues is not None: bvec = np.zeros((self.bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(self.bvalues, bvec, b0_threshold=0) + gtab = gradient_table(self.bvalues, bvecs=bvec, b0_threshold=0) bounds = [[self.bounds["f"][0], self.bounds["Dp"][0]*1000, self.bounds["D"][0]*1000], [self.bounds["f"][1], self.bounds["Dp"][1]*1000, self.bounds["D"][1]*1000]] @@ -92,7 +92,7 @@ def ivim_fit(self, signals, bvalues, **kwargs): bvec = np.zeros((bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(bvalues, bvec, b0_threshold=0) + gtab = gradient_table(bvalues, bvecs=bvec, b0_threshold=0) self.IAR_algorithm = IvimModelTopoPro(gtab, bounds=bounds, rescale_results_to_mm2_s=True) diff --git a/src/standardized/IAR_LU_segmented_2step.py b/src/standardized/IAR_LU_segmented_2step.py index a970889..9d1d414 100644 --- a/src/standardized/IAR_LU_segmented_2step.py +++ b/src/standardized/IAR_LU_segmented_2step.py @@ -60,7 +60,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non if self.bvalues is not None: bvec = np.zeros((self.bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(self.bvalues, bvec, b0_threshold=0) + gtab = gradient_table(self.bvalues, bvecs=bvec, b0_threshold=0) bounds = [[self.bounds["S0"][0], self.bounds["f"][0], self.bounds["Dp"][0], self.bounds["D"][0]], \ [self.bounds["S0"][1], self.bounds["f"][1], self.bounds["Dp"][1], self.bounds["D"][1]]] @@ -97,7 +97,7 @@ def ivim_fit(self, signals, bvalues, thresholds=None, **kwargs): bvec = np.zeros((bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(bvalues, bvec, b0_threshold=0) + gtab = gradient_table(bvalues, bvecs=bvec, b0_threshold=0) if self.thresholds is None: self.thresholds = 200 diff --git a/src/standardized/IAR_LU_segmented_3step.py b/src/standardized/IAR_LU_segmented_3step.py index 089cb17..84772ea 100644 --- a/src/standardized/IAR_LU_segmented_3step.py +++ b/src/standardized/IAR_LU_segmented_3step.py @@ -60,7 +60,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non if self.bvalues is not None: bvec = np.zeros((self.bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(self.bvalues, bvec, b0_threshold=0) + gtab = gradient_table(self.bvalues, bvecs=bvec, b0_threshold=0) # Adapt the bounds to the format needed for the algorithm bounds = [[self.bounds["S0"][0], self.bounds["f"][0], self.bounds["Dp"][0], self.bounds["D"][0]], \ @@ -99,7 +99,7 @@ def ivim_fit(self, signals, bvalues, **kwargs): bvec = np.zeros((bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(bvalues, bvec, b0_threshold=0) + gtab = gradient_table(bvalues, bvecs=bvec, b0_threshold=0) self.IAR_algorithm = IvimModelSegmented3Step(gtab, bounds=bounds, initial_guess=initial_guess) diff --git a/src/standardized/IAR_LU_subtracted.py b/src/standardized/IAR_LU_subtracted.py index 174c03a..d3a680c 100644 --- a/src/standardized/IAR_LU_subtracted.py +++ b/src/standardized/IAR_LU_subtracted.py @@ -58,7 +58,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non if self.bvalues is not None: bvec = np.zeros((self.bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(self.bvalues, bvec, b0_threshold=0) + gtab = gradient_table(self.bvalues, bvecs=bvec, b0_threshold=0) # Adapt the bounds to the format needed for the algorithm bounds = [[self.bounds["S0"][0], self.bounds["f"][0], self.bounds["Dp"][0], self.bounds["D"][0]], \ @@ -97,7 +97,7 @@ def ivim_fit(self, signals, bvalues, **kwargs): bvec = np.zeros((bvalues.size, 3)) bvec[:,2] = 1 - gtab = gradient_table(bvalues, bvec, b0_threshold=0) + gtab = gradient_table(bvalues, bvecs=bvec, b0_threshold=0) self.IAR_algorithm = IvimModelSubtracted(gtab, bounds=bounds, initial_guess=initial_guess)