From becea1a33e364655127656fa7672359f483546be Mon Sep 17 00:00:00 2001
From: IvanARashid <ivan52x53@gmail.com>
Date: Fri, 3 Jan 2025 16:28:26 +0100
Subject: [PATCH 1/4] Fixed bug

---
 src/original/IAR_LundUniversity/ivim_fit_method_biexp.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/original/IAR_LundUniversity/ivim_fit_method_biexp.py b/src/original/IAR_LundUniversity/ivim_fit_method_biexp.py
index 735ab0b8..e855bba6 100644
--- a/src/original/IAR_LundUniversity/ivim_fit_method_biexp.py
+++ b/src/original/IAR_LundUniversity/ivim_fit_method_biexp.py
@@ -61,13 +61,13 @@ def ivim_model(self, b, S0, f, D_star, D):
     
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0.005, 0), (np.inf, 1, 0.1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
             
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.03, 0.001)
         else:
             self.initial_guess = initial_guess

From 9dfe0af2d0fae133d7ef7f8dd8ed4d7b811f1bc4 Mon Sep 17 00:00:00 2001
From: IvanARashid <ivan52x53@gmail.com>
Date: Wed, 8 Jan 2025 09:44:21 +0100
Subject: [PATCH 2/4] Bugfixes for bounds and initial guesses

---
 src/original/IAR_LundUniversity/ivim_fit_method_linear.py   | 6 +++---
 .../IAR_LundUniversity/ivim_fit_method_modified_mix.py      | 2 +-
 .../IAR_LundUniversity/ivim_fit_method_modified_topopro.py  | 2 +-
 .../IAR_LundUniversity/ivim_fit_method_segmented_2step.py   | 6 +++---
 .../IAR_LundUniversity/ivim_fit_method_segmented_3step.py   | 6 +++---
 src/original/IAR_LundUniversity/ivim_fit_method_sivim.py    | 6 +++---
 .../IAR_LundUniversity/ivim_fit_method_subtracted.py        | 6 +++---
 7 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/src/original/IAR_LundUniversity/ivim_fit_method_linear.py b/src/original/IAR_LundUniversity/ivim_fit_method_linear.py
index 25b98fd1..69fd168e 100644
--- a/src/original/IAR_LundUniversity/ivim_fit_method_linear.py
+++ b/src/original/IAR_LundUniversity/ivim_fit_method_linear.py
@@ -83,13 +83,13 @@ def sivim_model(self, b, S0, f, D):
             
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0), (np.inf, 1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
             
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.001)
         else:
             self.initial_guess = initial_guess
diff --git a/src/original/IAR_LundUniversity/ivim_fit_method_modified_mix.py b/src/original/IAR_LundUniversity/ivim_fit_method_modified_mix.py
index 8b6bb7e3..1dde9a08 100644
--- a/src/original/IAR_LundUniversity/ivim_fit_method_modified_mix.py
+++ b/src/original/IAR_LundUniversity/ivim_fit_method_modified_mix.py
@@ -78,7 +78,7 @@ def __init__(self, gtab, bounds=None, maxiter=10, xtol=1e-8, rescale_units=False
         if gtab.bvals[-1] >= 10:
             self.bvals = gtab.bvals/1000
             
-        if bounds == None:
+        if bounds is None:
             # Bounds expressed as (lower bound, upper bound) for [f, D*, D].
             self.bounds = np.array([(0, 1), (5, 100), (0, 4)])
         elif (bounds[0][1] <= 1) or rescale_units: # Realistically, if mm2/s units are used, D* bound is <= 1
diff --git a/src/original/IAR_LundUniversity/ivim_fit_method_modified_topopro.py b/src/original/IAR_LundUniversity/ivim_fit_method_modified_topopro.py
index 587d00f8..7e0816d1 100644
--- a/src/original/IAR_LundUniversity/ivim_fit_method_modified_topopro.py
+++ b/src/original/IAR_LundUniversity/ivim_fit_method_modified_topopro.py
@@ -76,7 +76,7 @@ def __init__(self, gtab, bounds=[[0, 0.005, 1e-5], [1, 0.1, 0.004]], \
         # and are thus not changed.
         if gtab.bvals[-1] >= 10:
             self.bvals = gtab.bvals/1000
-        if bounds == None:
+        if bounds is None:
             # Bounds expressed as (lower bound, upper bound) for [f, D*, D].
             self.bounds = np.array([(0, 1), (5, 100), (0, 4)])
         elif (bounds[0][1] <= 1) or rescale_units: # Realistically, if mm2/s units are used, D* bound is <= 1
diff --git a/src/original/IAR_LundUniversity/ivim_fit_method_segmented_2step.py b/src/original/IAR_LundUniversity/ivim_fit_method_segmented_2step.py
index 382f06ea..eebe1265 100644
--- a/src/original/IAR_LundUniversity/ivim_fit_method_segmented_2step.py
+++ b/src/original/IAR_LundUniversity/ivim_fit_method_segmented_2step.py
@@ -86,13 +86,13 @@ def ivim_signal(self, b, S0, f, D_star, D):
     
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0.005, 0), (np.inf, 1, 0.1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
             
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.03, 0.001)
         else:
             self.initial_guess = initial_guess
diff --git a/src/original/IAR_LundUniversity/ivim_fit_method_segmented_3step.py b/src/original/IAR_LundUniversity/ivim_fit_method_segmented_3step.py
index de60269c..3a2f675e 100644
--- a/src/original/IAR_LundUniversity/ivim_fit_method_segmented_3step.py
+++ b/src/original/IAR_LundUniversity/ivim_fit_method_segmented_3step.py
@@ -105,13 +105,13 @@ def ivim_signal(self, b, S0, f, D_star, D):
     
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0.005, 0), (np.inf, 1, 0.1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
             
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.03, 0.001)
         else:
             self.initial_guess = initial_guess
diff --git a/src/original/IAR_LundUniversity/ivim_fit_method_sivim.py b/src/original/IAR_LundUniversity/ivim_fit_method_sivim.py
index 8fe1800b..3b0ec9ab 100644
--- a/src/original/IAR_LundUniversity/ivim_fit_method_sivim.py
+++ b/src/original/IAR_LundUniversity/ivim_fit_method_sivim.py
@@ -77,13 +77,13 @@ def sivim_model(self, b, S0, f, D):
             
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0), (np.inf, 1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
             
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.001)
         else:
             self.initial_guess = initial_guess
diff --git a/src/original/IAR_LundUniversity/ivim_fit_method_subtracted.py b/src/original/IAR_LundUniversity/ivim_fit_method_subtracted.py
index be514805..0780a97a 100644
--- a/src/original/IAR_LundUniversity/ivim_fit_method_subtracted.py
+++ b/src/original/IAR_LundUniversity/ivim_fit_method_subtracted.py
@@ -93,13 +93,13 @@ def perfusion_signal(self, b, S0, D_star):
     
     def set_bounds(self, bounds):
         # Use this function for fits that uses curve_fit
-        if bounds == None:
+        if bounds is None:
             self.bounds = np.array([(0, 0, 0.005, 0), (np.inf, 1, 0.1, 0.004)])
         else:
-            self.bounds = np.array([(0, *bounds[0]), (np.inf, *bounds[1])])
+            self.bounds = np.array([bounds[0], bounds[1]])
             
     def set_initial_guess(self, initial_guess):
-        if initial_guess == None:
+        if initial_guess is None:
             self.initial_guess = (1, 0.2, 0.03, 0.001)
         else:
             self.initial_guess = initial_guess

From c7c4376d0a06bb2d170b33c71ade3c0423ee7418 Mon Sep 17 00:00:00 2001
From: IvanARashid <ivan52x53@gmail.com>
Date: Fri, 7 Feb 2025 13:46:10 +0100
Subject: [PATCH 3/4] Added support for initial guesses, bounds, and thresholds
 where applicable

---
 src/standardized/ETP_SRI_LinearFitting.py        |  9 +++++++--
 src/standardized/IAR_LU_biexp.py                 | 11 ++++++++---
 src/standardized/IAR_LU_modified_mix.py          |  9 +++++++--
 src/standardized/IAR_LU_modified_topopro.py      |  9 +++++++--
 src/standardized/IAR_LU_segmented_2step.py       | 14 +++++++++++---
 src/standardized/IAR_LU_segmented_3step.py       |  9 +++++++--
 src/standardized/IAR_LU_subtracted.py            |  9 +++++++--
 .../OGC_AmsterdamUMC_Bayesian_biexp.py           |  9 +++++++--
 src/standardized/OGC_AmsterdamUMC_biexp.py       |  5 +++++
 .../OGC_AmsterdamUMC_biexp_segmented.py          |  5 +++++
 src/standardized/OJ_GU_seg.py                    |  5 +++++
 src/standardized/PV_MUMC_biexp.py                | 16 +++++++++++++---
 src/standardized/PvH_KB_NKI_IVIMfit.py           |  7 ++++++-
 13 files changed, 95 insertions(+), 22 deletions(-)

diff --git a/src/standardized/ETP_SRI_LinearFitting.py b/src/standardized/ETP_SRI_LinearFitting.py
index a5d45c55..41aa1665 100644
--- a/src/standardized/ETP_SRI_LinearFitting.py
+++ b/src/standardized/ETP_SRI_LinearFitting.py
@@ -27,6 +27,11 @@ class ETP_SRI_LinearFitting(OsipiBase):
     required_initial_guess_optional = False
     accepted_dimensions = 1
     # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
+
+    # Supported inputs in the standardized class
+    supported_bounds = False
+    supported_initial_guess = False
+    supported_thresholds = True
     
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
         """
@@ -36,7 +41,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             Our OsipiBase object could contain functions that compare the inputs with
             the requirements.
         """
-        super(ETP_SRI_LinearFitting, self).__init__(bvalues, thresholds, bounds, initial_guess)
+        super(ETP_SRI_LinearFitting, self).__init__(bvalues=bvalues, thresholds=thresholds, bounds=bounds, initial_guess=initial_guess)
         
         # Could be a good idea to have all the submission-specfic variable be 
         # defined with initials?
@@ -67,7 +72,7 @@ def ivim_fit(self, signals, bvalues=None, linear_fit_option=False, **kwargs):
         
         results = {}
         if linear_fit_option:
-            f, Dstar = ETP_object.linear_fit(bvalues, signals)
+            f, Dstar = ETP_object.linear_fit(bvalues, signals, self.ETP_weighting, self.ETP_stats)
 
             results["f"] = f
             results["D*"] = Dstar
diff --git a/src/standardized/IAR_LU_biexp.py b/src/standardized/IAR_LU_biexp.py
index 4f4f7231..3192b6ca 100644
--- a/src/standardized/IAR_LU_biexp.py
+++ b/src/standardized/IAR_LU_biexp.py
@@ -27,6 +27,11 @@ class IAR_LU_biexp(OsipiBase):
     required_initial_guess = False
     required_initial_guess_optional = True
     accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
+
+    # Supported inputs in the standardized class
+    supported_bounds = True
+    supported_initial_guess = True
+    supported_thresholds = False
     
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
         """
@@ -46,7 +51,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             bvec[:,2] = 1
             gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelBiExp(gtab)
+            self.IAR_algorithm = IvimModelBiExp(gtab, bounds=self.bounds, initial_guess=self.initial_guess)
         else:
             self.IAR_algorithm = None
         
@@ -72,7 +77,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             bvec[:,2] = 1
             gtab = gradient_table(bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelBiExp(gtab)
+            self.IAR_algorithm = IvimModelBiExp(gtab, bounds=self.bounds, initial_guess=self.initial_guess)
             
         fit_results = self.IAR_algorithm.fit(signals)
         
@@ -104,7 +109,7 @@ def ivim_fit_full_volume(self, signals, bvalues, **kwargs):
             bvec[:,2] = 1
             gtab = gradient_table(bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelBiExp(gtab)
+            self.IAR_algorithm = IvimModelBiExp(gtab, bounds=self.bounds, initial_guess=self.initial_guess)
             
         fit_results = self.IAR_algorithm.fit(signals)
         
diff --git a/src/standardized/IAR_LU_modified_mix.py b/src/standardized/IAR_LU_modified_mix.py
index 9571a569..eb667971 100644
--- a/src/standardized/IAR_LU_modified_mix.py
+++ b/src/standardized/IAR_LU_modified_mix.py
@@ -28,6 +28,11 @@ class IAR_LU_modified_mix(OsipiBase):
     required_initial_guess_optional = True
     accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
     
+    # Supported inputs in the standardized class
+    supported_bounds = True
+    supported_initial_guess = False
+    supported_thresholds = False
+
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
         """
             Everything this algorithm requires should be implemented here.
@@ -46,7 +51,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             bvec[:,2] = 1
             gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelVP(gtab)
+            self.IAR_algorithm = IvimModelVP(gtab, bounds=self.bounds, rescale_results_to_mm2_s=True)
         else:
             self.IAR_algorithm = None
         
@@ -72,7 +77,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             bvec[:,2] = 1
             gtab = gradient_table(bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelVP(gtab, rescale_results_to_mm2_s=True)
+            self.IAR_algorithm = IvimModelVP(gtab, bounds=self.bounds, rescale_results_to_mm2_s=True)
             
         fit_results = self.IAR_algorithm.fit(signals)
         
diff --git a/src/standardized/IAR_LU_modified_topopro.py b/src/standardized/IAR_LU_modified_topopro.py
index 9e39fc20..5b7cb7a8 100644
--- a/src/standardized/IAR_LU_modified_topopro.py
+++ b/src/standardized/IAR_LU_modified_topopro.py
@@ -28,6 +28,11 @@ class IAR_LU_modified_topopro(OsipiBase):
     required_initial_guess_optional = True
     accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
     
+    # Supported inputs in the standardized class
+    supported_bounds = True
+    supported_initial_guess = False
+    supported_thresholds = False
+    
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
         """
             Everything this algorithm requires should be implemented here.
@@ -46,7 +51,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             bvec[:,2] = 1
             gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelTopoPro(gtab)
+            self.IAR_algorithm = IvimModelTopoPro(gtab, bounds=self.bounds, rescale_results_to_mm2_s=True)
         else:
             self.IAR_algorithm = None
         
@@ -72,7 +77,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             bvec[:,2] = 1
             gtab = gradient_table(bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelTopoPro(gtab, rescale_results_to_mm2_s=True)
+            self.IAR_algorithm = IvimModelTopoPro(gtab, bounds=self.bounds, rescale_results_to_mm2_s=True)
             
         fit_results = self.IAR_algorithm.fit(signals)
         
diff --git a/src/standardized/IAR_LU_segmented_2step.py b/src/standardized/IAR_LU_segmented_2step.py
index 3fb5cdd8..c0849ede 100644
--- a/src/standardized/IAR_LU_segmented_2step.py
+++ b/src/standardized/IAR_LU_segmented_2step.py
@@ -28,6 +28,11 @@ class IAR_LU_segmented_2step(OsipiBase):
     required_initial_guess_optional = True
     accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
     
+    # Supported inputs in the standardized class
+    supported_bounds = True
+    supported_initial_guess = True
+    supported_thresholds = True
+    
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None):
         """
             Everything this algorithm requires should be implemented here.
@@ -46,7 +51,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             bvec[:,2] = 1
             gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelSegmented2Step(gtab)
+            self.IAR_algorithm = IvimModelSegmented2Step(gtab, bounds=self.bounds, initial_guess=self.initial_guess, b_threshold=self.thresholds)
         else:
             self.IAR_algorithm = None
         
@@ -72,8 +77,11 @@ 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)
-            
-            self.IAR_algorithm = IvimModelSegmented2Step(gtab)
+
+            if self.thresholds is None:
+                self.thresholds = 200
+
+            self.IAR_algorithm = IvimModelSegmented2Step(gtab, bounds=self.bounds, initial_guess=self.initial_guess, b_threshold=self.thresholds)
             
         fit_results = self.IAR_algorithm.fit(signals)
         
diff --git a/src/standardized/IAR_LU_segmented_3step.py b/src/standardized/IAR_LU_segmented_3step.py
index 170b8ee0..390d86a5 100644
--- a/src/standardized/IAR_LU_segmented_3step.py
+++ b/src/standardized/IAR_LU_segmented_3step.py
@@ -28,6 +28,11 @@ class IAR_LU_segmented_3step(OsipiBase):
     required_initial_guess_optional = True
     accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
     
+    # Supported inputs in the standardized class
+    supported_bounds = True
+    supported_initial_guess = True
+    supported_thresholds = False
+    
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
         """
             Everything this algorithm requires should be implemented here.
@@ -46,7 +51,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             bvec[:,2] = 1
             gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelSegmented3Step(gtab)
+            self.IAR_algorithm = IvimModelSegmented3Step(gtab, bounds=self.bounds, initial_guess=self.initial_guess)
         else:
             self.IAR_algorithm = None
         
@@ -72,7 +77,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             bvec[:,2] = 1
             gtab = gradient_table(bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelSegmented3Step(gtab)
+            self.IAR_algorithm = IvimModelSegmented3Step(gtab, bounds=self.bounds, initial_guess=self.initial_guess)
             
         fit_results = self.IAR_algorithm.fit(signals)
         
diff --git a/src/standardized/IAR_LU_subtracted.py b/src/standardized/IAR_LU_subtracted.py
index d5508f22..19dd3ec4 100644
--- a/src/standardized/IAR_LU_subtracted.py
+++ b/src/standardized/IAR_LU_subtracted.py
@@ -28,6 +28,11 @@ class IAR_LU_subtracted(OsipiBase):
     required_initial_guess_optional = True
     accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
     
+    # Supported inputs in the standardized class
+    supported_bounds = True
+    supported_initial_guess = True
+    supported_thresholds = False
+    
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None):
         """
             Everything this algorithm requires should be implemented here.
@@ -46,7 +51,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             bvec[:,2] = 1
             gtab = gradient_table(self.bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelSubtracted(gtab)
+            self.IAR_algorithm = IvimModelSubtracted(gtab, bounds=self.bounds, initial_guess=self.initial_guess)
         else:
             self.IAR_algorithm = None
         
@@ -72,7 +77,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             bvec[:,2] = 1
             gtab = gradient_table(bvalues, bvec, b0_threshold=0)
             
-            self.IAR_algorithm = IvimModelSubtracted(gtab)
+            self.IAR_algorithm = IvimModelSubtracted(gtab, bounds=self.bounds, initial_guess=self.initial_guess)
             
         fit_results = self.IAR_algorithm.fit(signals)
         
diff --git a/src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py b/src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py
index 1b8c8f94..430bddbf 100644
--- a/src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py
+++ b/src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py
@@ -28,6 +28,11 @@ class OGC_AmsterdamUMC_Bayesian_biexp(OsipiBase):
     accepted_dimensions = 1  # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
     accepts_priors = True
 
+    # Supported inputs in the standardized class
+    supported_bounds = True
+    supported_initial_guess = True
+    supported_thresholds = True
+
     def __init__(self, bvalues=None, thresholds=None, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=True, prior_in=None):
         """
             Everything this algorithm requires should be implemented here.
@@ -39,7 +44,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=([0, 0, 0.005, 0.7],[0.
             Args:
                  datain is a 2D array with values of D, f, D* (and S0) that will form the prior.
         """
-        super(OGC_AmsterdamUMC_Bayesian_biexp, self).__init__(bvalues, bounds, initial_guess) #, fitS0, prior_in)
+        super(OGC_AmsterdamUMC_Bayesian_biexp, self).__init__(bvalues=bvalues, bounds=bounds, thresholds=thresholds, initial_guess=initial_guess) #, fitS0, prior_in)
         self.OGC_algorithm = fit_bayesian
         self.initialize(bounds, initial_guess, fitS0, prior_in)
 
@@ -74,7 +79,7 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
         if initial_guess is not None and len(initial_guess) == 4:
             self.initial_guess = initial_guess
         bvalues=np.array(bvalues)
-        fit_results = fit_segmented(bvalues, signals, bounds=self.bounds, cutoff=150, p0=self.initial_guess)
+        fit_results = fit_segmented(bvalues, signals, bounds=self.bounds, cutoff=self.thresholds, p0=self.initial_guess)
         fit_results=fit_results+(1,)
         fit_results = self.OGC_algorithm(bvalues, signals, self.neg_log_prior, x0=fit_results, fitS0=self.fitS0)
 
diff --git a/src/standardized/OGC_AmsterdamUMC_biexp.py b/src/standardized/OGC_AmsterdamUMC_biexp.py
index 66b9e6c0..ea59b885 100644
--- a/src/standardized/OGC_AmsterdamUMC_biexp.py
+++ b/src/standardized/OGC_AmsterdamUMC_biexp.py
@@ -27,6 +27,11 @@ class OGC_AmsterdamUMC_biexp(OsipiBase):
     required_initial_guess_optional = True
     accepted_dimensions = 1  # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
 
+    # Supported inputs in the standardized class
+    supported_bounds = True
+    supported_initial_guess = True
+    supported_thresholds = False
+
     def __init__(self, bvalues=None, thresholds=None, bounds=([0, 0, 0.005, 0.7],[0.005, 0.7, 0.2, 1.3]), initial_guess=None, fitS0=False):
         """
             Everything this algorithm requires should be implemented here.
diff --git a/src/standardized/OGC_AmsterdamUMC_biexp_segmented.py b/src/standardized/OGC_AmsterdamUMC_biexp_segmented.py
index c452f7b5..43ab1818 100644
--- a/src/standardized/OGC_AmsterdamUMC_biexp_segmented.py
+++ b/src/standardized/OGC_AmsterdamUMC_biexp_segmented.py
@@ -27,6 +27,11 @@ class OGC_AmsterdamUMC_biexp_segmented(OsipiBase):
     required_initial_guess_optional = True
     accepted_dimensions = 1  # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
 
+    # Supported inputs in the standardized class
+    supported_bounds = True
+    supported_initial_guess = True
+    supported_thresholds = True
+
     def __init__(self, bvalues=None, thresholds=150, bounds=([0, 0, 0.005],[0.005, 0.7, 0.2]), initial_guess=[0.001, 0.01, 0.01,1]):
         """
             Everything this algorithm requires should be implemented here.
diff --git a/src/standardized/OJ_GU_seg.py b/src/standardized/OJ_GU_seg.py
index af40be7a..bd55e63e 100644
--- a/src/standardized/OJ_GU_seg.py
+++ b/src/standardized/OJ_GU_seg.py
@@ -25,6 +25,11 @@ class OJ_GU_seg(OsipiBase):
     required_initial_guess = False
     required_initial_guess_optional = False
     accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
+
+    # Supported inputs in the standardized class
+    supported_bounds = False
+    supported_initial_guess = False
+    supported_thresholds = True
     
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
         """
diff --git a/src/standardized/PV_MUMC_biexp.py b/src/standardized/PV_MUMC_biexp.py
index f08ddd1c..0e10d5d3 100644
--- a/src/standardized/PV_MUMC_biexp.py
+++ b/src/standardized/PV_MUMC_biexp.py
@@ -22,6 +22,11 @@ class PV_MUMC_biexp(OsipiBase):
     required_initial_guess = False
     required_initial_guess_optional = True
     accepted_dimensions = 1 # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
+
+    # Supported inputs in the standardized class
+    supported_bounds = True
+    supported_initial_guess = False
+    supported_thresholds = True
     
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
         """
@@ -31,7 +36,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             Our OsipiBase object could contain functions that compare the inputs with
             the requirements.
         """
-        super(PV_MUMC_biexp, self).__init__(bvalues, None, bounds, None)
+        super(PV_MUMC_biexp, self).__init__(bvalues=bvalues, thresholds=thresholds, bounds=bounds, initial_guess=initial_guess)
         self.PV_algorithm = fit_least_squares
         
     
@@ -46,8 +51,13 @@ def ivim_fit(self, signals, bvalues=None):
             _type_: _description_
         """
         
-            
-        fit_results = self.PV_algorithm(bvalues, signals)
+        if self.thresholds is None:
+            self.thresholds = 200
+        
+        if self.bounds is None:
+            self.bounds = ([0.9, 0.0001, 0.0, 0.0025], [1.1, 0.003, 1, 0.2])
+
+        fit_results = self.PV_algorithm(bvalues, signals, bounds=self.bounds, cutoff=self.thresholds)
 
         results = {} 
         results["f"] = fit_results[1]
diff --git a/src/standardized/PvH_KB_NKI_IVIMfit.py b/src/standardized/PvH_KB_NKI_IVIMfit.py
index 52eea8d4..66e201dd 100644
--- a/src/standardized/PvH_KB_NKI_IVIMfit.py
+++ b/src/standardized/PvH_KB_NKI_IVIMfit.py
@@ -27,6 +27,11 @@ class PvH_KB_NKI_IVIMfit(OsipiBase):
     required_initial_guess_optional =False
     accepted_dimensions = 1  # Not sure how to define this for the number of accepted dimensions. Perhaps like the thresholds, at least and at most?
 
+    # Supported inputs in the standardized class
+    supported_bounds = False
+    supported_initial_guess = False
+    supported_thresholds = False
+
     def __init__(self, bvalues=None, thresholds=None,bounds=None,initial_guess=None):
         """
             Everything this algorithm requires should be implemented here.
@@ -35,7 +40,7 @@ def __init__(self, bvalues=None, thresholds=None,bounds=None,initial_guess=None)
             Our OsipiBase object could contain functions that compare the inputs with
             the requirements.
         """
-        super(PvH_KB_NKI_IVIMfit, self).__init__(bvalues, thresholds,bounds,initial_guess)
+        super(PvH_KB_NKI_IVIMfit, self).__init__(bvalues=bvalues, thresholds=thresholds,bounds=bounds,initial_guess=initial_guess)
         self.NKI_algorithm = generate_IVIMmaps_standalone
 
 

From 3f36ddcbf94f0b0f068b00f7cbbedc6af0e5857a Mon Sep 17 00:00:00 2001
From: IvanARashid <ivan52x53@gmail.com>
Date: Fri, 7 Feb 2025 14:05:08 +0100
Subject: [PATCH 4/4] Delete, not relevant

---
 .../IAR_LundUniversity/simple_test_of_fits.py | 130 ------------------
 1 file changed, 130 deletions(-)
 delete mode 100644 src/original/IAR_LundUniversity/simple_test_of_fits.py

diff --git a/src/original/IAR_LundUniversity/simple_test_of_fits.py b/src/original/IAR_LundUniversity/simple_test_of_fits.py
deleted file mode 100644
index 4029b9dd..00000000
--- a/src/original/IAR_LundUniversity/simple_test_of_fits.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import numpy as np
-from dipy.core.gradients import gradient_table
-from scipy.stats import norm
-import matplotlib.pyplot as plt
-import scienceplots
-import ivim_fit_method_biexp
-import ivim_fit_method_subtracted
-import ivim_fit_method_sivim
-import ivim_fit_method_linear
-import ivim_fit_method_segmented_3step
-import ivim_fit_method_segmented_2step
-import ivim_fit_method_modified_mix
-import ivim_fit_method_modified_topopro
-
-plt.style.use(["science", "ieee"])
-
-def ivim_signal(b, S0, f, D_star, D):
-    return S0*(f*np.exp(-b*D_star) + (1-f)*np.exp(-b*D))
-
-def diffusion_signal(b, S0, f, D):
-    return S0*(1-f)*np.exp(-b*D)
-
-def generate_noise(loc, sigma):
-    real_component = norm.rvs(loc=loc, scale=sigma/loc)
-    imaginary_component = norm.rvs(loc=loc, scale=sigma/loc)
-    return np.absolute(complex(real_component, imaginary_component))
-
-def add_rician_noise(signal, SNR):
-    sigma = signal[-1]/SNR
-    # Sample real and imaginary noise components from gaussian distributions
-    # Use the last b-value as the SNR baseline in order to avoid the noise floor
-    noise = np.array([generate_noise(signal_value, sigma) for signal_value in signal])
-    
-    # Add the two components to the signal and take the magniutde of the result
-    noised_signal = signal + noise
-    noised_signal = np.absolute(noised_signal)
-
-    return noised_signal
-
-# Ground truth
-factor = 1
-S0 = 1
-f = 0.1
-D_star = 30e-3
-D = 1e-3
-rescale_units = False
-
-# Settings
-lower_bounds = (0, 5, 0)
-upper_bounds = (1, 100, 4)
-bounds_um = (lower_bounds, upper_bounds)
-
-lower_bounds = (0, 0.005, 0)
-upper_bounds = (1, 0.1, 0.004)
-bounds_mm = (lower_bounds, upper_bounds)
-initial_guess_mm = (1, 0.2, 0.03, 0.001)
-
-# Create gtab containing b-value informations
-bvals = np.array([0, 50, 240, 800])/factor
-bvals = np.array([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, \
-    150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800])
-bvals = np.array([0, 20, 40, 60, 80, 100, 150, 200, 300, 400, 500, 600, 700, 800])
-#bvals = np.array([0, 50, 240, 800])
-bvec = np.zeros((bvals.size, 3))
-bvec[:,2] = 1
-gtab = gradient_table(bvals, bvec, b0_threshold=0)
-
-# Signal
-signal = ivim_signal(bvals, S0, f, D_star, D)
-noised_signal = add_rician_noise(signal, 3)
-noised_signal /= noised_signal[0]
-
-noised_signal6 = add_rician_noise(signal, 6)
-noised_signal6 /= noised_signal6[0]
-
-
-
-
-
-
-# biexp fit
-biexp_model = ivim_fit_method_biexp.IvimModelBiExp(gtab, bounds=bounds_mm, initial_guess=initial_guess_mm, rescale_units=rescale_units)
-biexp_fit = biexp_model.fit(noised_signal)
-
-# sIVIM fit
-lower_bounds_sivim = (0, 0)
-upper_bounds_sivim = (1, 4/factor)
-bounds_mm_sivim = (lower_bounds_sivim, upper_bounds_sivim)
-initial_guess_mm_sivim = (1, 0.2, 0.001)
-sivim_model = ivim_fit_method_sivim.IvimModelsIVIM(gtab, b_threshold=0.2, bounds=bounds_mm_sivim, initial_guess=initial_guess_mm_sivim, rescale_units=rescale_units)
-sivim_fit = sivim_model.fit(noised_signal)
-
-# linear fit
-linear_model = ivim_fit_method_linear.IvimModelLinear(gtab, b_threshold=0.2, bounds=bounds_mm_sivim, rescale_units=rescale_units)
-linear_fit = linear_model.fit(noised_signal)
-
-# Subtracted fit (Le Bihan 2019)
-subtracted_model = ivim_fit_method_subtracted.IvimModelSubtracted(gtab, bounds=bounds_mm, initial_guess=initial_guess_mm, rescale_units=rescale_units)#, b_threshold_lower=0.2, b_threshold_upper=0.1)
-subtracted_fit = subtracted_model.fit(noised_signal)
-
-# Segmented fit (3 step) (DIPY)
-segmented_3step_model = ivim_fit_method_segmented_3step.IvimModelSegmented3Step(gtab, bounds=bounds_mm, initial_guess=initial_guess_mm, rescale_units=rescale_units)#, b_threshold_lower=0.2, b_threshold_upper=0.1)
-segmented_3step_fit = segmented_3step_model.fit(noised_signal)
-
-# Segmented fit (2 step) (Conventional method)
-segmented_2step_model = ivim_fit_method_segmented_2step.IvimModelSegmented2Step(gtab, bounds=bounds_mm, initial_guess=initial_guess_mm, rescale_units=rescale_units)#, b_threshold_lower=0.2)
-segmented_2step_fit = segmented_2step_model.fit(noised_signal)
-segmented_2step_fit6 = segmented_2step_model.fit(noised_signal6)
-
-# MIX (Farooq et al.)
-mix_model = ivim_fit_method_modified_mix.IvimModelVP(gtab, bounds=bounds_mm, rescale_units=rescale_units, rescale_results_to_mm2_s=True)
-mix_fit = mix_model.fit(noised_signal)
-mix_fit6 = mix_model.fit(noised_signal6)
-
-# TopoPro (Fadnavis et al.)
-topopro_model = ivim_fit_method_modified_topopro.IvimModelTopoPro(gtab, bounds=bounds_mm, rescale_units=rescale_units, rescale_results_to_mm2_s=True)
-topopro_fit = topopro_model.fit(noised_signal)
-topopro_fit6 = topopro_model.fit(noised_signal6)
-
-# Print estimates
-print(f"Bi-exponential: {biexp_fit.model_params}")
-print(f"Linear: {linear_fit.model_params}")
-print(f"sIVIM: {sivim_fit.model_params}")
-print(f"Subtracted: {subtracted_fit.model_params}")
-print(f"3-step segmented: {segmented_3step_fit.model_params}")
-print(f"2-step segmented: {segmented_2step_fit.model_params}")
-print(f"MIX: {mix_fit.model_params}")
-print(f"TopoPro: {topopro_fit.model_params}")
-
-