Merge pull request #90 from OSIPI/wrapper_dev

Support for bounds, initial guess, and threhold inputs

Author: oliverchampion

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

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

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

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

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

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

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

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

src/original/IAR_LundUniversity/simple_test_of_fits.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

src/standardized/ETP_SRI_LinearFitting.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)
 

src/standardized/IAR_LU_biexp.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)
 

src/standardized/IAR_LU_modified_mix.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)