address review: revert unrelated changes, keep only core bug fixes

Author: Devguru-codes

.gitignore

@@ -42,14 +42,3 @@ phantoms/MR_XCAT_qMRI/*.json
 phantoms/MR_XCAT_qMRI/*.txt
 tests/IVIMmodels/unit_tests/models
 models
-
-# Custom additions for testing & local setups
-ivim_test_venv/
-venv/
-.env/
-env/
-check_zenodo.py
-*test_output*.txt
-.vscode/
-.DS_Store
-Thumbs.db

src/standardized/IAR_LU_segmented_2step.py

@@ -61,13 +61,13 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             bvec = np.zeros((self.bvalues.size, 3))
             bvec[:,2] = 1
             gtab = gradient_table(self.bvalues, 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]]]
+        
+            # Adapt the initial guess to the format needed for the algorithm
+            initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
 
-            # Convert dict bounds/initial_guess to list-of-lists as expected by IvimModelSegmented2Step
-            bounds_list = [[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]]]
-            initial_guess_list = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
-
-            self.IAR_algorithm = IvimModelSegmented2Step(gtab, bounds=bounds_list, initial_guess=initial_guess_list, b_threshold=self.thresholds)
+            self.IAR_algorithm = IvimModelSegmented2Step(gtab, bounds=bounds, initial_guess=initial_guess, b_threshold=self.thresholds)
         else:
             self.IAR_algorithm = None
 
@@ -116,4 +116,4 @@ def ivim_fit(self, signals, bvalues, thresholds=None, **kwargs):
         results["Dp"] = fit_results.model_params[2]
         results["D"] = fit_results.model_params[3]
 
-        return results
+        return results

src/standardized/IAR_LU_subtracted.py

@@ -60,12 +60,14 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             bvec[:,2] = 1
             gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
 
-            # Convert dict bounds/initial_guess to list-of-lists as expected by IvimModelSubtracted
-            bounds_list = [[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]]]
-            initial_guess_list = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
-
-            self.IAR_algorithm = IvimModelSubtracted(gtab, bounds=bounds_list, initial_guess=initial_guess_list)
+            # 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]], \
+                        [self.bounds["S0"][1], self.bounds["f"][1], self.bounds["Dp"][1], self.bounds["D"][1]]]
+            
+            # Adapt the initial guess to the format needed for the algorithm
+            initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
+            
+            self.IAR_algorithm = IvimModelSubtracted(gtab, bounds=bounds, initial_guess=initial_guess)
         else:
             self.IAR_algorithm = None
 
@@ -111,4 +113,4 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         results["Dp"] = fit_results.model_params[2]
         results["D"] = fit_results.model_params[3]
 
-        return results
+        return results

src/standardized/PV_MUMC_biexp.py

@@ -81,7 +81,7 @@ def ivim_fit(self, signals, bvalues=None):
             self.thresholds = 200
 
         # Default fallback parameters (D, f, Dp) used if the optimizer fails
-        DEFAULT_PARAMS = [0.001, 0.1, 0.01]
+        DEFAULT_PARAMS = [0.003, 0.1, 0.05]
 
         try:
             fit_results = self.PV_algorithm(bvalues, signals, bounds=bounds, cutoff=self.thresholds)