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

@@ -26,7 +26,7 @@ class IAR_LU_linear(OsipiBase):
     required_bounds_optional = True # Bounds may not be required but are optional
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, weighting=None, stats=False):
         """
@@ -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

@@ -25,8 +25,7 @@ class ETP_SRI_LinearFitting(OsipiBase):
     required_bounds_optional = True # Bounds may not be required but are optional
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
     # Supported inputs in the standardized class
     supported_bounds = False
@@ -41,7 +40,6 @@ 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 +50,8 @@ 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 +63,7 @@ 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

@@ -26,7 +26,7 @@ class IAR_LU_biexp(OsipiBase):
     required_bounds_optional = True # Bounds may not be required but are optional
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
     # Supported inputs in the standardized class
     supported_bounds = True
@@ -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

@@ -26,7 +26,7 @@ class IAR_LU_modified_mix(OsipiBase):
     required_bounds_optional = True # Bounds may not be required but are optional
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
     # Supported inputs in the standardized class
     supported_bounds = True
@@ -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

@@ -26,7 +26,7 @@ class IAR_LU_modified_topopro(OsipiBase):
     required_bounds_optional = True # Bounds may not be required but are optional
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
     # Supported inputs in the standardized class
     supported_bounds = True
@@ -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

@@ -26,7 +26,7 @@ class IAR_LU_segmented_2step(OsipiBase):
     required_bounds_optional = True # Bounds may not be required but are optional
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
     # Supported inputs in the standardized class
     supported_bounds = True
@@ -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

@@ -26,7 +26,7 @@ class IAR_LU_segmented_3step(OsipiBase):
     required_bounds_optional = True # Bounds may not be required but are optional
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
     # Supported inputs in the standardized class
     supported_bounds = True
@@ -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

@@ -26,7 +26,7 @@ class IAR_LU_subtracted(OsipiBase):
     required_bounds_optional = True # Bounds may not be required but are optional
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
     # Supported inputs in the standardized class
     supported_bounds = True
@@ -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

@@ -19,13 +19,12 @@ 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
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
     accepts_priors = True
 
 
@@ -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

@@ -19,13 +19,12 @@ 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
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
 
     # Supported inputs in the standardized class
@@ -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

@@ -19,13 +19,12 @@ 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
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
 
     # Supported inputs in the standardized class
@@ -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

@@ -24,7 +24,7 @@ class OJ_GU_seg(OsipiBase):
     required_bounds_optional = False # Bounds may not be required but are optional
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
     # Supported inputs in the standardized class
     supported_bounds = False
@@ -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/PV_MUMC_biexp.py

@@ -21,7 +21,7 @@ class PV_MUMC_biexp(OsipiBase):
     required_bounds_optional = True # Bounds may not be required but are optional
     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?
+    accepted_dimensions = (1,1) #(min dimension, max dimension)
 
     # Supported inputs in the standardized class
     supported_bounds = True

src/standardized/PvH_KB_NKI_IVIMfit.py

@@ -19,13 +19,12 @@ 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
     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?
+    accepted_dimensions = (1,1)  #(min dimension, max dimension)
 
     # Supported inputs in the standardized class
     supported_bounds = False