Implement checks for fulfilment of algorithm requirements in the OsipiBase class

.gitignore

@@ -25,4 +25,6 @@ nosetests.xml
 coverage.xml
 *.pyc
 phantoms/MR_XCAT_qMRI/*.json
-phantoms/MR_XCAT_qMRI/*.txt
+phantoms/MR_XCAT_qMRI/*.txt
+osipivenv/
+

src/standardized wip/IAR_LU_linear.py

@@ -39,6 +39,10 @@ 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
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -61,6 +65,11 @@ def ivim_fit(self, signals, bvalues=None):
         Returns:
             _type_: _description_
         """
+
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         if self.IAR_algorithm is None:
             if bvalues is None:

src/standardized/ETP_SRI_LinearFitting.py

@@ -41,7 +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=bvalues, thresholds=thresholds, bounds=bounds, initial_guess=initial_guess)
         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')
@@ -54,8 +54,12 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.ETP_stats = stats
 
         # Check the inputs
-
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
+
     def ivim_fit(self, signals, bvalues=None, linear_fit_option=False, **kwargs):
         """Perform the IVIM fit
 
@@ -67,6 +71,11 @@ def ivim_fit(self, signals, bvalues=None, linear_fit_option=False, **kwargs):
         Returns:
             _type_: _description_
         """
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+
         signals[signals<0.0000001]=0.0000001
         if bvalues is None:
             bvalues = self.bvalues

src/standardized/IAR_LU_biexp.py

@@ -47,6 +47,10 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.use_bounds = False
         self.use_initial_guess = False
         # Check the inputs
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -69,7 +73,12 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         Returns:
             _type_: _description_
         """
-
+
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+
         if self.IAR_algorithm is None:
             if bvalues is None:
                 bvalues = self.bvalues
@@ -102,6 +111,11 @@ def ivim_fit_full_volume(self, signals, bvalues, **kwargs):
             _type_: _description_
         """
 
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+
         if self.IAR_algorithm is None:
             if bvalues is None:
                 bvalues = self.bvalues

src/standardized/IAR_LU_modified_mix.py

@@ -47,6 +47,10 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.use_bounds = False
         self.use_initial_guess = False
         # Check the inputs
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -70,6 +74,11 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             _type_: _description_
         """
 
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+
         if self.IAR_algorithm is None:
             if bvalues is None:
                 bvalues = self.bvalues

src/standardized/IAR_LU_modified_topopro.py

@@ -47,6 +47,10 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.use_bounds = False
         self.use_initial_guess = False
         # Check the inputs
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -70,6 +74,11 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             _type_: _description_
         """
 
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+
         if self.IAR_algorithm is None:
             if bvalues is None:
                 bvalues = self.bvalues

src/standardized/IAR_LU_segmented_2step.py

@@ -47,6 +47,10 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.use_bounds = False
         self.use_initial_guess = False
         # Check the inputs
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -70,6 +74,11 @@ def ivim_fit(self, signals, bvalues, thresholds=None, **kwargs):
             _type_: _description_
         """
         print(thresholds)
+
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         if self.IAR_algorithm is None:
             if bvalues is None:

src/standardized/IAR_LU_segmented_3step.py

@@ -47,6 +47,10 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.use_bounds = False
         self.use_initial_guess = False
         # Check the inputs
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -70,6 +74,11 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             _type_: _description_
         """
 
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+
         if self.IAR_algorithm is None:
             if bvalues is None:
                 bvalues = self.bvalues

src/standardized/IAR_LU_subtracted.py

@@ -47,6 +47,10 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.use_bounds = False
         self.use_initial_guess = False
         # Check the inputs
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         # Initialize the algorithm
         if self.bvalues is not None:
@@ -70,6 +74,11 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             _type_: _description_
         """
 
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+
         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
@@ -47,6 +46,10 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
                  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=bvalues, bounds=bounds, thresholds=thresholds, initial_guess=initial_guess) #, fitS0, prior_in)
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
         self.OGC_algorithm = fit_bayesian
         self.initialize(bounds, initial_guess, fitS0, prior_in)
         self.fit_segmented=fit_segmented
@@ -86,6 +89,13 @@ def ivim_fit(self, signals, bvalues, initial_guess=None, **kwargs):
         Returns:
             _type_: _description_
         """
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+
+        if initial_guess is not None and len(initial_guess) == 4:
+            self.initial_guess = initial_guess
         bvalues=np.array(bvalues)
 
         epsilon = 0.000001

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
@@ -43,6 +42,10 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         """
         #super(OGC_AmsterdamUMC_biexp, self).__init__(bvalues, bounds, initial_guess, fitS0)
         super(OGC_AmsterdamUMC_biexp, self).__init__(bvalues=bvalues, bounds=bounds, initial_guess=initial_guess)
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
         self.OGC_algorithm = fit_least_squares
         self.fitS0=fitS0
         self.initialize(bounds, initial_guess, fitS0)
@@ -73,6 +76,10 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         Returns:
             _type_: _description_
         """
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         bvalues=np.array(bvalues)
         fit_results = self.OGC_algorithm(bvalues, signals, p0=self.initial_guess, bounds=self.bounds, fitS0=self.fitS0)

src/standardized/OGC_AmsterdamUMC_biexp_segmented.py

@@ -42,6 +42,10 @@ def __init__(self, bvalues=None, thresholds=150, bounds=None, initial_guess=None
             the requirements.
         """
         super(OGC_AmsterdamUMC_biexp_segmented, self).__init__(bvalues, thresholds, bounds, initial_guess)
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
         self.OGC_algorithm = fit_segmented
         self.initialize(bounds, initial_guess, thresholds)
 
@@ -74,7 +78,13 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         Returns:
             _type_: _description_
         """
-
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+
+        if initial_guess is not None and len(initial_guess) == 4:
+            self.initial_guess = initial_guess
         bvalues=np.array(bvalues)
         fit_results = self.OGC_algorithm(bvalues, signals, bounds=self.bounds, cutoff=self.thresholds, p0=self.initial_guess)
 

src/standardized/OJ_GU_seg.py

@@ -45,6 +45,10 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.use_bounds = False
         self.use_initial_guess = False
         # Check the inputs
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         # Initialize the algorithm
 
@@ -59,6 +63,11 @@ def ivim_fit(self, signals, bvalues=None):
         Returns:
             _type_: _description_
         """
+
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
 
         if bvalues is None:
             bvalues = self.bvalues

src/standardized/PV_MUMC_biexp.py

@@ -37,6 +37,10 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             the requirements.
         """
         super(PV_MUMC_biexp, self).__init__(bvalues=bvalues, thresholds=thresholds, bounds=bounds, initial_guess=initial_guess)
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
         self.PV_algorithm = fit_least_squares
         if bounds is not None:
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
@@ -55,6 +59,11 @@ def ivim_fit(self, signals, bvalues=None):
             _type_: _description_
         """
 
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
+
         if self.thresholds is None:
             self.thresholds = 200
 

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
@@ -41,6 +40,10 @@ def __init__(self, bvalues=None, thresholds=None,bounds=None,initial_guess=None)
             the requirements.
         """
         super(PvH_KB_NKI_IVIMfit, self).__init__(bvalues=bvalues, thresholds=thresholds,bounds=bounds,initial_guess=initial_guess)
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
         self.NKI_algorithm = generate_IVIMmaps_standalone
         if bounds is not None:
             print('warning, bounds from wrapper are not (yet) used in this algorithm')
@@ -58,6 +61,10 @@ def ivim_fit(self, signals, bvalues=None):
         Returns:
             _type_: _description_
         """
+        self.osipi_check_required_bvalues()
+        self.osipi_check_required_thresholds()
+        self.osipi_check_required_bounds()
+        self.osipi_check_required_initial_guess()
         #bvalues = np.array(bvalues)
         bvalues = bvalues.tolist() #NKI code expects a list instead of nparray
         # reshape signal as the NKI code expects a 4D array