Changed how args and kwargs are passed through osipi_fit

Author: IvanARashid

src/standardized/IAR_LU_biexp.py

@@ -51,7 +51,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             self.IAR_algorithm = None
 
 
-    def ivim_fit(self, signals, bvalues=None):
+    def ivim_fit(self, signals, bvalues):
         """Perform the IVIM fit
 
         Args:

src/standardized/IAR_LU_segmented_2step.py

@@ -28,7 +28,7 @@ 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?
 
-    def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
+    def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None):
         """
             Everything this algorithm requires should be implemented here.
             Number of segmentation thresholds, bounds, etc.
@@ -51,7 +51,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             self.IAR_algorithm = None
 
 
-    def ivim_fit(self, signals, bvalues=None):
+    def ivim_fit(self, signals, bvalues, thresholds=None):
         """Perform the IVIM fit
 
         Args:
@@ -61,6 +61,7 @@ def ivim_fit(self, signals, bvalues=None):
         Returns:
             _type_: _description_
         """
+        print(thresholds)
 
         if self.IAR_algorithm is None:
             if bvalues is None:

src/standardized/IAR_LU_subtracted.py

@@ -28,7 +28,7 @@ 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?
 
-    def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
+    def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None):
         """
             Everything this algorithm requires should be implemented here.
             Number of segmentation thresholds, bounds, etc.
@@ -51,7 +51,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             self.IAR_algorithm = None
 
 
-    def ivim_fit(self, signals, bvalues=None):
+    def ivim_fit(self, signals, bvalues):
         """Perform the IVIM fit
 
         Args:

src/standardized/PV_MUMC_biexp.py

@@ -42,31 +42,32 @@ def osipi_initiate_algorithm(self, algorithm, **kwargs):
         self.__class__ = getattr(importlib.import_module(import_path), algorithm)
         self.__init__(**kwargs)
 
-    def osipi_fit(self, data=None, bvalues=None, thresholds=None, bounds=None, initial_guess=None, **kwargs):
+    #def osipi_fit(self, data=None, bvalues=None, thresholds=None, bounds=None, initial_guess=None, **kwargs):
+    def osipi_fit(self, data, bvalues, **kwargs):
         """Fits the data with the bvalues
         Returns [S0, f, Dstar, D]
         """
 
         # We should first check whether the attributes in the __init__ are not None
         # Then check if they are input here, if they are, these should overwrite the attributes
         use_bvalues = bvalues if bvalues is not None else self.bvalues
-        use_thresholds = thresholds if self.bvalues is None else self.thresholds
-        use_bounds = bounds if self.bounds is None else self.bounds
-        use_initial_guess = initial_guess if self.initial_guess is None else self.initial_guess
+        #use_thresholds = thresholds if self.thresholds is None else self.thresholds
+        #use_bounds = bounds if self.bounds is None else self.bounds
+        #use_initial_guess = initial_guess if self.initial_guess is None else self.initial_guess
 
         # Make sure we don't make arrays of None's
         if use_bvalues is not None: use_bvalues = np.asarray(use_bvalues) 
-        if use_thresholds is not None: use_thresholds = np.asarray(use_thresholds) 
-        if use_bounds is not None: use_bounds = np.asarray(use_bounds) 
-        if use_initial_guess is not None: use_initial_guess = np.asarray(use_initial_guess) 
-        kwargs["bvalues"] = use_bvalues
+        #if use_thresholds is not None: use_thresholds = np.asarray(use_thresholds) 
+        #if use_bounds is not None: use_bounds = np.asarray(use_bounds) 
+        #if use_initial_guess is not None: use_initial_guess = np.asarray(use_initial_guess) 
+        #kwargs["bvalues"] = use_bvalues
 
         #args = [data, use_bvalues, use_thresholds]
-        args = [data, use_thresholds]
-        if self.required_bounds or self.required_bounds_optional:
-            args.append(use_bounds)
-        if self.required_initial_guess or self.required_initial_guess_optional:
-            args.append(use_initial_guess)
+        args = [data, use_bvalues]
+        #if self.required_bounds or self.required_bounds_optional:
+            #args.append(use_bounds)
+        #if self.required_initial_guess or self.required_initial_guess_optional:
+            #args.append(use_initial_guess)
 
         # Run a check_requirements method that makes sure that these inputs fulfil the requirements
 
@@ -76,7 +77,7 @@ def osipi_fit(self, data=None, bvalues=None, thresholds=None, bounds=None, initi
         # Then we pass everything into the ivim_fit method which performs the fit according to the algorithm
 
         #args = [data, use_bvalues, use_initial_guess, use_bounds, use_thresholds]
-        args = [arg for arg in args if arg is not None]
+        #args = [arg for arg in args if arg is not None]
         results = self.ivim_fit(*args, **kwargs)
 
         #self.parameter_estimates = self.ivim_fit(data, bvalues)

src/wrappers/OsipiBase.py

@@ -2,8 +2,9 @@
 import os
 from pathlib import Path
 #from src.standardized.ETP_SRI_LinearFitting import ETP_SRI_LinearFitting
-#from src.standardized.IAR_LU_biexp import IAR_LU_biexp
-from src.standardized.PV_MUMC_biexp import PV_MUMC_biexp
+from src.standardized.IAR_LU_biexp import IAR_LU_biexp
+from src.standardized.IAR_LU_segmented_2step import IAR_LU_segmented_2step
+#from src.standardized.PV_MUMC_biexp import PV_MUMC_biexp
 
 ## Simple test code... 
 # Used to just do a test run of an algorithm during development
@@ -25,7 +26,7 @@ def ivim_model(b, S0=1, f=0.1, Dstar=0.01, D=0.001):
 
 #model1 = ETP_SRI_LinearFitting(thresholds=[200])
 #model2 = IAR_LU_biexp()
-model2 = PV_MUMC_biexp()
+model2 = IAR_LU_segmented_2step()
 
 #dev_test_run(model1, linear_fit_option=True)
-dev_test_run(model2)
+dev_test_run(model2, thresholds="Arbitrary kwarg for ivim_fit")