Implement checks for fulfilment of algorithm requirements in the OsipiBase class

.gitignore

@@ -25,4 +25,5 @@ nosetests.xml
 coverage.xml
 *.pyc
 phantoms/MR_XCAT_qMRI/*.json
-phantoms/MR_XCAT_qMRI/*.txt
+phantoms/MR_XCAT_qMRI/*.txt
+

src/standardized wip/IAR_LU_linear.py

@@ -38,8 +38,6 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         """
         super(IAR_LU_linear, self).__init__(bvalues, thresholds, bounds, initial_guess)
 
-        # Check the inputs
-
         # Initialize the algorithm
         if self.bvalues is not None:
             bvec = np.zeros((self.bvalues.size, 3))

src/standardized/ETP_SRI_LinearFitting.py

@@ -41,7 +41,6 @@ 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)
         if bounds is not None:
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
@@ -52,10 +51,8 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         # defined with initials?
         self.ETP_weighting = weighting
         self.ETP_stats = stats
-
-        # Check the inputs
-
 
+
     def ivim_fit(self, signals, bvalues=None, linear_fit_option=False, **kwargs):
         """Perform the IVIM fit
 
@@ -67,6 +64,7 @@ def ivim_fit(self, signals, bvalues=None, linear_fit_option=False, **kwargs):
         Returns:
             _type_: _description_
         """
+
         signals[signals<0.0000001]=0.0000001
         if bvalues is None:
             bvalues = self.bvalues

src/standardized/IAR_LU_biexp.py

@@ -46,7 +46,6 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
         self.use_bounds = False
         self.use_initial_guess = False
-        # Check the inputs
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -69,7 +68,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         Returns:
             _type_: _description_
         """
-        
+
         if self.IAR_algorithm is None:
             if bvalues is None:
                 bvalues = self.bvalues
@@ -101,7 +100,7 @@ def ivim_fit_full_volume(self, signals, bvalues, **kwargs):
         Returns:
             _type_: _description_
         """
-        
+
         if self.IAR_algorithm is None:
             if bvalues is None:
                 bvalues = self.bvalues

src/standardized/IAR_LU_modified_mix.py

@@ -46,7 +46,6 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
         self.use_bounds = False
         self.use_initial_guess = False
-        # Check the inputs
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -69,7 +68,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         Returns:
             _type_: _description_
         """
-        
+
         if self.IAR_algorithm is None:
             if bvalues is None:
                 bvalues = self.bvalues

src/standardized/IAR_LU_modified_topopro.py

@@ -46,7 +46,6 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
         self.use_bounds = False
         self.use_initial_guess = False
-        # Check the inputs
 
         # Initialize the algorithm
         if self.bvalues is not None:

src/standardized/IAR_LU_segmented_2step.py

@@ -46,7 +46,6 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
         self.use_bounds = False
         self.use_initial_guess = False
-        # Check the inputs
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -70,6 +69,7 @@ def ivim_fit(self, signals, bvalues, thresholds=None, **kwargs):
             _type_: _description_
         """
         print(thresholds)
+
 
         if self.IAR_algorithm is None:
             if bvalues is None:

src/standardized/IAR_LU_segmented_3step.py

@@ -46,7 +46,6 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
         self.use_bounds = False
         self.use_initial_guess = False
-        # Check the inputs
 
         # Initialize the algorithm
         if self.bvalues is not None:

src/standardized/IAR_LU_subtracted.py

@@ -46,7 +46,6 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
         self.use_bounds = False
         self.use_initial_guess = False
-        # Check the inputs
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -70,6 +69,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             _type_: _description_
         """
 
+
         if self.IAR_algorithm is None:
             if bvalues is None:
                 bvalues = self.bvalues

src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py

@@ -19,8 +19,7 @@ class OGC_AmsterdamUMC_Bayesian_biexp(OsipiBase):
 
     # Algorithm requirements
     required_bvalues = 4
-    required_thresholds = [0,
-                           0]  # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
+    required_thresholds = [0,0]  # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
     required_bounds = False
     required_bounds_optional = True  # Bounds may not be required but are optional
     required_initial_guess = False
@@ -86,6 +85,9 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
         Returns:
             _type_: _description_
         """
+
+        if initial_guess is not None and len(initial_guess) == 4:
+            self.initial_guess = initial_guess
         bvalues=np.array(bvalues)
 
         epsilon = 0.000001
@@ -100,5 +102,6 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
         results["D"] = fit_results[0]
         results["f"] = fit_results[1]
         results["Dp"] = fit_results[2]
+        results["S0"] = fit_results[3]
 
         return results

src/standardized/OGC_AmsterdamUMC_biexp.py

@@ -19,8 +19,7 @@ class OGC_AmsterdamUMC_biexp(OsipiBase):
 
     # Algorithm requirements
     required_bvalues = 4
-    required_thresholds = [0,
-                           0]  # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
+    required_thresholds = [0,0]  # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
     required_bounds = False
     required_bounds_optional = True  # Bounds may not be required but are optional
     required_initial_guess = False
@@ -81,5 +80,6 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         results["D"] = fit_results[0]
         results["f"] = fit_results[1]
         results["Dp"] = fit_results[2]
+        results["S0"] = fit_results[3]
 
         return results

src/standardized/OGC_AmsterdamUMC_biexp_segmented.py

@@ -19,8 +19,7 @@ class OGC_AmsterdamUMC_biexp_segmented(OsipiBase):
 
     # Algorithm requirements
     required_bvalues = 4
-    required_thresholds = [1,
-                           1]  # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
+    required_thresholds = [1,1]  # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
     required_bounds = False
     required_bounds_optional = True  # Bounds may not be required but are optional
     required_initial_guess = False
@@ -74,13 +73,16 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         Returns:
             _type_: _description_
         """
-
+
+        if self.initial_guess is not None and len(self.initial_guess) == 4:
+            self.initial_guess = self.initial_guess
         bvalues=np.array(bvalues)
         fit_results = self.OGC_algorithm(bvalues, signals, bounds=self.bounds, cutoff=self.thresholds, p0=self.initial_guess)
 
         results = {}
         results["D"] = fit_results[0]
         results["f"] = fit_results[1]
         results["Dp"] = fit_results[2]
+        results["S0"] = fit_results[3]
 
         return results

src/standardized/OJ_GU_seg.py

@@ -44,7 +44,6 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
         self.use_bounds = False
         self.use_initial_guess = False
-        # Check the inputs
 
         # Initialize the algorithm
 

src/standardized/PvH_KB_NKI_IVIMfit.py

@@ -19,8 +19,7 @@ class PvH_KB_NKI_IVIMfit(OsipiBase):
 
     # Algorithm requirements
     required_bvalues = 4
-    required_thresholds = [0,
-                           0]  # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
+    required_thresholds = [0,0]  # Interval from "at least" to "at most", in case submissions allow a custom number of thresholds
     required_bounds = False
     required_bounds_optional = False  # Bounds may not be required but are optional
     required_initial_guess = False

src/wrappers/OsipiBase.py

@@ -16,6 +16,17 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.initial_guess = np.asarray(initial_guess) if initial_guess is not None else None
         self.use_bounds = True
         self.use_initial_guess = True
+
+        # Validate the inputs
+        if self.bvalues is not None:
+            self.osipi_check_required_bvalues()
+        if self.thresholds is not None:
+            self.osipi_check_required_thresholds()
+        if self.bounds is not None:
+            self.osipi_check_required_bounds()
+        if self.initial_guess is not None:
+            self.osipi_check_required_initial_guess()
+
         # If the user inputs an algorithm to OsipiBase, it is intereprete as initiating
         # an algorithm object with that name.
         if algorithm:
@@ -53,7 +64,8 @@ def osipi_fit(self, data, bvalues=None, **kwargs):
         """Fits the data with the bvalues
         Returns [S0, f, Dstar, D]
         """
-
+        self.osipi_validate_inputs()
+
         # 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
@@ -123,6 +135,8 @@ def osipi_fit_full_volume(self, data, bvalues=None, **kwargs):
         Returns:
             results (dict): Dict with key each containing an array which is a parametric map.
         """
+
+        self.osipi_validate_inputs()
 
         try:
             use_bvalues = bvalues if bvalues is not None else self.bvalues
@@ -201,69 +215,63 @@ def osipi_print_requirements(self):
                 else:
                     print(f"Initial guess required: {self.required_initial_guess} and is not optional")
 
+    def osipi_validate_inputs(self):
+        """Validates the inputs of the algorithm."""
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+        self.osipi_accepts_dimension(self.data.ndim)
+
     def osipi_accepted_dimensions(self):
         """The array of accepted dimensions
         e.g.
         (1D,   2D,   3D,    4D,    5D,    6D)
         (True, True, False, False, False, False)
         """
 
-        #return (False,) * 6
-        return True
+        return getattr(self, 'accepted_dimensions', (1, 3))
 
     def osipi_accepts_dimension(self, dim):
         """Query if the selection dimension is fittable"""
 
-        #accepted = self.accepted_dimensions()
-        #if dim < 0 or dim > len(accepted):
-            #return False
-        #return accepted[dim]
+        min_dim, max_dim = self.osipi_accepted_dimensions()
+        if not (min_dim <= dim <= max_dim):
+            raise ValueError(f"Dimension {dim}D not supported. Requires {min_dim}-{max_dim}D spatial data")
         return True
 
     def osipi_check_required_bvalues(self):
         """Checks if the input bvalues fulfil the algorithm requirements"""
-
-        #if self.bvalues.size < self.required_bvalues:
-            #print("Conformance error: Number of b-values.")
-            #return False
-        #else: 
-            #return True
+        if not hasattr(self, "required_bvalues"):
+            raise AttributeError("required_bvalues not defined for this algorithm")
+        if self.bvalues is None:
+            raise ValueError("bvalues are not provided")
+        if len(self.bvalues) < self.required_bvalues:
+            raise ValueError(f"Atleast {self.required_bvalues} are required, but only {len(self.bvalues)} were provided")
         return True
 
     def osipi_check_required_thresholds(self):
         """Checks if the number of input thresholds fulfil the algorithm requirements"""
-
-        #if (len(self.thresholds) < self.required_thresholds[0]) or (len(self.thresholds) > self.required_thresholds[1]):
-            #print("Conformance error: Number of thresholds.")
-            #return False
-        #else: 
-            #return True
+        if not hasattr(self, "required_thresholds"):
+            raise AttributeError("required_thresholds is not defined for this algorithm")
+        if self.thresholds is None:
+            raise ValueError("thresholds are not provided")
+        if len(self.thresholds) < self.required_thresholds[0] and len(self.thresholds) > self.required_thresholds[1]:
+            raise ValueError(f"Thresholds should be between {self.required_thresholds[0]} and {self.required_thresholds[1]} but {len(self.thresholds)} were provided")
         return True
 
     def osipi_check_required_bounds(self):
         """Checks if input bounds fulfil the algorithm requirements"""
-        #if self.required_bounds is True and self.bounds is None:
-            #print("Conformance error: Bounds.")
-            #return False
-        #else:
-            #return True
+        if self.required_bounds is False and self.bounds is None:
+            raise ValueError("bounds are required but not provided")
         return True
 
     def osipi_check_required_initial_guess(self):
         """Checks if input initial guess fulfil the algorithm requirements"""
-
-        #if self.required_initial_guess is True and self.initial_guess is None:
-            #print("Conformance error: Initial guess")
-            #return False
-        #else:
-            #return True
+        if self.required_initial_guess is False and self.initial_guess is None:
+            raise ValueError("initial_guess are required but not provided")
         return True
 
-
-    def osipi_check_required_bvalues():
-        """Minimum number of b-values required"""
-        pass
-
     def osipi_author():
         """Author identification"""
         return ''