fix: use keyword bvecs= in gradient_table() to fix dipy deprecation warning

Author: Devguru-codes

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:

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)
 

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)
 

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

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)
 

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)