refactor: remove redundant np.asarray/np.array on self.bvalues (PR #165 review feedback)

self.bvalues is already guaranteed to be np.ndarray from OsipiBase.__init__(),
so re-wrapping it with np.asarray() or np.array() is a no-op / unnecessary copy.

Addresses review feedback from @oliverchampion on PR #165.

Changes across 25 files:
- OsipiBase.osipi_fit(): use_bvalues = self.bvalues (was np.asarray)
- OsipiBase.osipi_simple_bias_and_RMSE_test(): bvalues = self.bvalues (was np.asarray)
- All 23 standardized algorithms: bvalues = self.bvalues (was np.array/np.asarray)

Author: Devguru-codes

src/standardized/ASD_MemorialSloanKettering_QAMPER_IVIM.py

@@ -80,7 +80,7 @@ def ivim_fit(self, signals, **kwargs):
 
         initial_guess = [self.initial_guess["D"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["S0"]]
 
-        bvalues=np.array(self.bvalues)
+        bvalues=self.bvalues
         LB = np.array(bounds[0])[[1,0,2,3]]
         UB = np.array(bounds[1])[[1,0,2,3]]
 

src/standardized/DT_IIITN_WLS.py

@@ -75,7 +75,7 @@ def ivim_fit(self, signals, **kwargs):
         Returns:
             dict: Dictionary with keys "D", "f", "Dp".
         """
-        bvalues = np.array(self.bvalues)
+        bvalues = self.bvalues
 
         # Use threshold as cutoff if available
         cutoff = 200

src/standardized/IAR_LU_biexp.py

@@ -87,7 +87,7 @@ def ivim_fit(self, signals, **kwargs):
         initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
 
         if self.IAR_algorithm is None:
-            bvalues = np.asarray(self.bvalues)
+            bvalues = self.bvalues
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1
@@ -120,14 +120,14 @@ def ivim_fit_full_volume(self, signals, **kwargs):
                        [self.bounds["S0"][1], self.bounds["f"][1], self.bounds["Dp"][1], self.bounds["D"][1]]]
         initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
         if self.IAR_algorithm is None:
-            bvalues = np.asarray(self.bvalues)
+            bvalues = self.bvalues
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1
             gtab = gradient_table(bvalues, bvecs=bvec, b0_threshold=0)
 
             self.IAR_algorithm = IvimModelBiExp(gtab, bounds=bounds, initial_guess=initial_guess)
-        bvalues = np.asarray(self.bvalues)
+        bvalues = self.bvalues
         b0_index = np.where(bvalues == 0)[0][0]
         mask = signals[...,b0_index]>0
         fit_results = self.IAR_algorithm.fit(signals, mask=mask)

src/standardized/IAR_LU_modified_mix.py

@@ -82,7 +82,7 @@ def ivim_fit(self, signals, **kwargs):
                   [self.bounds["f"][1], self.bounds["Dp"][1]*1000, self.bounds["D"][1]*1000]]
 
         if self.IAR_algorithm is None:
-            bvalues = np.asarray(self.bvalues)
+            bvalues = self.bvalues
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1

src/standardized/IAR_LU_modified_topopro.py

@@ -85,7 +85,7 @@ def ivim_fit(self, signals, **kwargs):
                   [self.bounds["f"][1], self.bounds["Dp"][1]*1000, self.bounds["D"][1]*1000]]
 
         if self.IAR_algorithm is None:
-            bvalues = np.asarray(self.bvalues)
+            bvalues = self.bvalues
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1

src/standardized/IAR_LU_segmented_2step.py

@@ -89,7 +89,7 @@ def ivim_fit(self, signals, **kwargs):
         initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
 
         if self.IAR_algorithm is None:
-            bvalues = np.asarray(self.bvalues)
+            bvalues = self.bvalues
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1

src/standardized/IAR_LU_segmented_3step.py

@@ -92,7 +92,7 @@ def ivim_fit(self, signals, **kwargs):
         initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
 
         if self.IAR_algorithm is None:
-            bvalues = np.asarray(self.bvalues)
+            bvalues = self.bvalues
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1

src/standardized/IAR_LU_subtracted.py

@@ -89,7 +89,7 @@ def ivim_fit(self, signals, **kwargs):
         initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
 
         if self.IAR_algorithm is None:
-            bvalues = np.asarray(self.bvalues)
+            bvalues = self.bvalues
 
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1

src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py

@@ -87,7 +87,7 @@ def ivim_fit(self, signals, initial_guess=None, **kwargs):
 
         initial_guess = [self.initial_guess["D"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["S0"]]
 
-        bvalues=np.array(self.bvalues)
+        bvalues=self.bvalues
 
         epsilon = 0.000001
         fit_results = fit_segmented(bvalues, signals, bounds=bounds, cutoff=self.thresholds, p0=initial_guess)
@@ -120,7 +120,7 @@ def ivim_fit_full_volume(self, signals, njobs=4, **kwargs):
         # Get index of b=0
         shape=np.shape(signals)
 
-        bvalues=np.array(self.bvalues)
+        bvalues=self.bvalues
         b0_index = np.where(bvalues == 0)[0][0]
         # Mask of voxels where signal at b=0 >= 0.5
         valid_mask = signals[..., b0_index] >= 0
@@ -132,7 +132,7 @@ def ivim_fit_full_volume(self, signals, njobs=4, **kwargs):
         normalization_factor = np.mean(signals[..., b0_indices],axis=-1)
         signals = signals / np.repeat(normalization_factor[...,np.newaxis],np.shape(signals)[-1],-1)
 
-        bvalues=np.array(self.bvalues)
+        bvalues=self.bvalues
 
         epsilon = 0.000001
         fit_results = np.array(fit_segmented_array(bvalues, signals, bounds=bounds, cutoff=self.thresholds, p0=initial_guess))

src/standardized/OGC_AmsterdamUMC_biexp.py

@@ -65,7 +65,7 @@ def ivim_fit(self, signals, **kwargs):
 
         initial_guess = [self.initial_guess["D"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["S0"]]
 
-        bvalues=np.array(self.bvalues)
+        bvalues=self.bvalues
         fit_results = self.OGC_algorithm(bvalues, signals, p0=initial_guess, bounds=bounds, fitS0=self.fitS0)
 
         results = {}