Merge branch 'main' into preproc-main

IvanARashid · web-flow · commit 640e62e4af69 · 2026-05-06T20:54:20.000+02:00
diff --git a/.github/workflows/unit_test.yml b/.github/workflows/unit_test.yml
@@ -31,9 +31,12 @@ jobs:
           python -m pip install --upgrade pip
           pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
           pip install -r requirements.txt
+      - name: Install libomp (macOS)
+        if: runner.os == 'macOS'
+        run: brew install libomp
       - name: Test with pytest
         run: |
           pip install pytest pytest-cov
-          python -m pytest --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml --cov-report=html -r w
+          python -m pytest --doctest-modules --junitxml=junit/test-results.xml --cov=. --cov-report=xml --cov-report=html -r w ${{ runner.os == 'macOS' && '-k "not test_deep_learning_algorithms"' || '' }}
           python -m pytest --doctest-modules --junitxml=junit/test-results-brain.xml --cov=. --cov-report=xml --cov-report=html --dataFile tests/IVIMmodels/unit_tests/generic_brain.json -r w -k test_ivim_fit_saved
 
diff --git a/conftest.py b/conftest.py
@@ -19,14 +19,14 @@ def pytest_addoption(parser):
     )
     parser.addoption(
         "--ricianNoise",
+        action="store_true",
         default=False,
-        type=bool,
         help="Use Rician noise, non-rician is gaussian",
     )
     parser.addoption(
         "--usePrior",
+        action="store_true",
         default=False,
-        type=bool,
         help="Use a prior where accepted",
     )
     parser.addoption(
diff --git a/src/standardized/ASD_MemorialSloanKettering_QAMPER_IVIM.py b/src/standardized/ASD_MemorialSloanKettering_QAMPER_IVIM.py
@@ -1,4 +1,5 @@
 from src.wrappers.OsipiBase import OsipiBase
+import warnings
 import numpy as np
 import matlab.engine
 
@@ -47,7 +48,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.use_initial_guess = {"f" : True, "D" : True, "Dp" : True, "S0" : True}
 
         if eng is None:
-            print('initiating matlab; this may take some time. For repeated testing one could use the optional input eng as an already initiated matlab engine')
+            warnings.warn('Initiating MATLAB; this may take some time. For repeated testing one could use the optional input eng as an already initiated MATLAB engine', UserWarning, stacklevel=2)
             self.eng=matlab.engine.start_matlab()
             self.keep_alive=False
         else:
@@ -100,7 +101,7 @@ def clean(self):
                 try:
                     self.eng.quit()
                 except Exception as e:
-                    print(f"Warning: Failed to quit MATLAB engine cleanly: {e}")
+                    warnings.warn(f"Failed to quit MATLAB engine cleanly: {e}", UserWarning, stacklevel=2)
 
     def __del__(self):
         self.clean()
diff --git a/src/standardized/ETP_SRI_LinearFitting.py b/src/standardized/ETP_SRI_LinearFitting.py
@@ -47,7 +47,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
 
         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')
+            warnings.warn('Bounds from wrapper are not (yet) used in this algorithm', UserWarning, stacklevel=2)
         self.use_bounds = {"f": False, "Dp": False, "D": False, "S0": False}
         self.use_initial_guess = {"f": False, "Dp": False, "D": False, "S0": False}
 
diff --git a/src/standardized/IAR_LU_biexp.py b/src/standardized/IAR_LU_biexp.py
@@ -60,8 +60,13 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             bvec = np.zeros((self.bvalues.size, 3))
             bvec[:,2] = 1
             gtab = gradient_table(self.bvalues, bvecs=bvec, b0_threshold=0)
-            
-            self.IAR_algorithm = IvimModelBiExp(gtab, bounds=self.bounds, initial_guess=self.initial_guess)
+
+            # Convert dict bounds/initial_guess to list-of-lists as expected by IvimModelBiExp
+            bounds_list = [[self.bounds["S0"][0], self.bounds["f"][0], self.bounds["Dp"][0], self.bounds["D"][0]],
+                           [self.bounds["S0"][1], self.bounds["f"][1], self.bounds["Dp"][1], self.bounds["D"][1]]]
+            initial_guess_list = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
+
+            self.IAR_algorithm = IvimModelBiExp(gtab, bounds=bounds_list, initial_guess=initial_guess_list)
         else:
             self.IAR_algorithm = None
         
@@ -104,6 +109,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
 
         return results
 
+
     def ivim_fit_full_volume(self, signals, bvalues, **kwargs):
         """Perform the IVIM fit
 
diff --git a/src/standardized/IAR_LU_modified_topopro.py b/src/standardized/IAR_LU_modified_topopro.py
@@ -1,3 +1,4 @@
+import warnings
 import numpy as np
 from dipy.core.gradients import gradient_table
 from src.wrappers.OsipiBase import OsipiBase
@@ -53,7 +54,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
 
         # Check the inputs
         if self.bounds["Dp"][0] == self.bounds["D"][1]:
-            print('warning, bounds for D* and D are equal, this will likely cause fitting errors. Setting D_upper to 99 percent of D_upper')
+            warnings.warn('Bounds for D* and D are equal, this will likely cause fitting errors. Setting D_upper to 99 percent of D_upper', UserWarning, stacklevel=2)
             self.bounds["D"][1] = self.bounds["D"][1]*0.99
         # Check the inputs
         
diff --git a/src/standardized/IAR_LU_segmented_3step.py b/src/standardized/IAR_LU_segmented_3step.py
@@ -62,14 +62,15 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
             bvec[:,2] = 1
             gtab = gradient_table(self.bvalues, bvecs=bvec, b0_threshold=0)
 
-            # Adapt the bounds to the format needed for the algorithm
-            bounds = [[self.bounds["S0"][0], self.bounds["f"][0], self.bounds["Dp"][0], self.bounds["D"][0]], \
-                        [self.bounds["S0"][1], self.bounds["f"][1], self.bounds["Dp"][1], self.bounds["D"][1]]]
-        
+            # Adapt the bounds to the format needed for the algorithm (list-of-lists)
+            bounds = [[self.bounds["S0"][0], self.bounds["f"][0], self.bounds["Dp"][0], self.bounds["D"][0]],
+                      [self.bounds["S0"][1], self.bounds["f"][1], self.bounds["Dp"][1], self.bounds["D"][1]]]
+
             # Adapt the initial guess to the format needed for the algorithm
             initial_guess = [self.initial_guess["S0"], self.initial_guess["f"], self.initial_guess["Dp"], self.initial_guess["D"]]
-            
-            self.IAR_algorithm = IvimModelSegmented3Step(gtab, bounds=self.bounds, initial_guess=self.initial_guess)
+
+            # Use the converted list-of-lists bounds and initial_guess, NOT the raw dicts
+            self.IAR_algorithm = IvimModelSegmented3Step(gtab, bounds=bounds, initial_guess=initial_guess)
         else:
             self.IAR_algorithm = None
         
@@ -100,7 +101,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1
             gtab = gradient_table(bvalues, bvecs=bvec, b0_threshold=0)
-            
+
             self.IAR_algorithm = IvimModelSegmented3Step(gtab, bounds=bounds, initial_guess=initial_guess)
             
         fit_results = self.IAR_algorithm.fit(signals)
@@ -115,4 +116,4 @@ def ivim_fit(self, signals, bvalues, **kwargs):
         results["Dp"] = fit_results.model_params[2]
         results["D"] = fit_results.model_params[3]
         
-        return results
+        return results
diff --git a/src/standardized/IAR_LU_subtracted.py b/src/standardized/IAR_LU_subtracted.py
@@ -98,7 +98,7 @@ def ivim_fit(self, signals, bvalues, **kwargs):
             bvec = np.zeros((bvalues.size, 3))
             bvec[:,2] = 1
             gtab = gradient_table(bvalues, bvecs=bvec, b0_threshold=0)
-            
+
             self.IAR_algorithm = IvimModelSubtracted(gtab, bounds=bounds, initial_guess=initial_guess)
             
         fit_results = self.IAR_algorithm.fit(signals)
diff --git a/src/standardized/IVIM_NEToptim.py b/src/standardized/IVIM_NEToptim.py
@@ -66,7 +66,7 @@ def initialize(self, bounds, initial_guess, fitS0, traindata, SNR, n):
                 SNR = (5, 100)
             self.training_data(self.bvalues,n=n,SNR=SNR)
         self.arg=Arg()
-        print('note that the bounds in the network are soft bounds and implemented by a sigmoid transform. In order for the network to be sensitive over the range, we extend the bounds ny 30%')
+        warnings.warn('Note that the bounds in the network are soft bounds and implemented by a sigmoid transform. In order for the network to be sensitive over the range, we extend the bounds by 30%', UserWarning, stacklevel=2)
         if bounds is not None:
             self.bounds = bounds
         else:
diff --git a/src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py b/src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py
@@ -1,5 +1,6 @@
 from src.wrappers.OsipiBase import OsipiBase
 from src.original.fitting.OGC_AmsterdamUMC.LSQ_fitting import flat_neg_log_prior, fit_bayesian, empirical_neg_log_prior, fit_segmented, fit_bayesian_array, fit_segmented_array
+import warnings
 import numpy as np
 
 class OGC_AmsterdamUMC_Bayesian_biexp(OsipiBase):
@@ -62,10 +63,10 @@ def initialize(self, bounds=None, initial_guess=None, fitS0=True, prior_in=None,
         self.thresholds = thresholds
 
         if prior_in is None:
-            print('using a flat prior between bounds')
+            warnings.warn('Using a flat prior between bounds', UserWarning, stacklevel=2)
             self.neg_log_prior=flat_neg_log_prior([self.bounds["D"][0],self.bounds["D"][1]],[self.bounds["f"][0],self.bounds["f"][1]],[self.bounds["Dp"][0],self.bounds["Dp"][1]],[self.bounds["S0"][0],self.bounds["S0"][1]])
         else:
-            print('warning, bounds are not used, as a prior is used instead')
+            warnings.warn('Bounds are not used, as a prior is used instead', UserWarning, stacklevel=2)
             if len(prior_in) == 4:
                 self.neg_log_prior = empirical_neg_log_prior(prior_in[0], prior_in[1], prior_in[2],prior_in[3])
             else:
diff --git a/src/standardized/OGC_AmsterdamUMC_biexp_segmented.py b/src/standardized/OGC_AmsterdamUMC_biexp_segmented.py
@@ -1,5 +1,6 @@
 from src.wrappers.OsipiBase import OsipiBase
 from src.original.fitting.OGC_AmsterdamUMC.LSQ_fitting import fit_segmented, fit_segmented_array
+import warnings
 import numpy as np
 
 class OGC_AmsterdamUMC_biexp_segmented(OsipiBase):
@@ -53,7 +54,7 @@ def initialize(self, thresholds):
 
         if self.thresholds is None:
             self.thresholds = 150
-            print('warning, no thresholds were defined, so default threshold of 150 was used')
+            warnings.warn('No thresholds were defined, so default threshold of 150 was used', UserWarning, stacklevel=2)
         else:
             self.thresholds = thresholds
 
diff --git a/src/standardized/OJ_GU_bayesMATLAB.py b/src/standardized/OJ_GU_bayesMATLAB.py
@@ -1,4 +1,5 @@
 from src.wrappers.OsipiBase import OsipiBase
+import warnings
 import numpy as np
 import matlab.engine
 
@@ -49,7 +50,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.use_bounds = {"f" : True, "D" : True, "Dp" : True, "S0" : True}
 
         if eng is None:
-            print('initiating matlab; this may take some time. For repeated testing one could use the optional input eng as an already initiated matlab engine')
+            warnings.warn('Initiating MATLAB; this may take some time. For repeated testing one could use the optional input eng as an already initiated MATLAB engine', UserWarning, stacklevel=2)
             self.eng=matlab.engine.start_matlab()
             self.keep_alive=False
         else:
@@ -101,7 +102,7 @@ def clean(self):
                 try:
                     self.eng.quit()
                 except Exception as e:
-                    print(f"Warning: Failed to quit MATLAB engine cleanly: {e}")
+                    warnings.warn(f"Failed to quit MATLAB engine cleanly: {e}", UserWarning, stacklevel=2)
 
     def __del__(self):
         self.clean()
diff --git a/src/standardized/OJ_GU_segMATLAB.py b/src/standardized/OJ_GU_segMATLAB.py
@@ -1,4 +1,5 @@
 from src.wrappers.OsipiBase import OsipiBase
+import warnings
 import numpy as np
 import matlab.engine
 
@@ -47,7 +48,7 @@ def __init__(self, bvalues=None, thresholds=200, bounds=None, initial_guess=None
         self.use_bounds = {"f" : True, "D" : True, "Dp" : True, "S0" : True}
         self.use_initial_guess = {"f" : False, "D" : False, "Dp" : False, "S0" : False}
         if eng is None:
-            print('initiating matlab; this may take some time. For repeated testing one could use the optional input eng as an already initiated matlab engine')
+            warnings.warn('Initiating MATLAB; this may take some time. For repeated testing one could use the optional input eng as an already initiated MATLAB engine', UserWarning, stacklevel=2)
             self.eng=matlab.engine.start_matlab()
             self.keep_alive=False
         else:
@@ -95,7 +96,7 @@ def clean(self):
                 try:
                     self.eng.quit()
                 except Exception as e:
-                    print(f"Warning: Failed to quit MATLAB engine cleanly: {e}")
+                    warnings.warn(f"Failed to quit MATLAB engine cleanly: {e}", UserWarning, stacklevel=2)
 
     def __del__(self):
         self.clean()
diff --git a/src/standardized/PV_MUMC_biexp.py b/src/standardized/PV_MUMC_biexp.py
@@ -43,7 +43,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
         self.use_bounds = {"f" : True, "D" : True, "Dp" : True, "S0" : True}
         self.use_initial_guess = {"f" : False, "D" : False, "Dp" : False, "S0" : False}
         
-    
+
     def ivim_fit(self, signals, bvalues=None):
         """Perform the IVIM fit
 
@@ -52,30 +52,51 @@ def ivim_fit(self, signals, bvalues=None):
             bvalues (array-like, optional): b-values for the signals. If None, self.bvalues will be used. Default is None.
 
         Returns:
-            _type_: _description_
+            dict: Fitted IVIM parameters f, Dp (D*), and D.
         """
-        if self.bounds is None:
-            self.bounds = ([0.9, 0.0001, 0.0, 0.0025], [1.1, 0.003, 1, 0.2])
+        # --- bvalues resolution ---
+        # Edge case: bvalues not passed here → fall back to the ones set at __init__ time
+        if bvalues is None:
+            if self.bvalues is None:
+                raise ValueError(
+                    "PV_MUMC_biexp: bvalues must be provided either at initialization or at fit time."
+                )
+            bvalues = self.bvalues
         else:
-            bounds = ([self.bounds["S0"][0], self.bounds["D"][0], self.bounds["f"][0], self.bounds["Dp"][0]],
-                    [self.bounds["S0"][1], self.bounds["D"][1], self.bounds["f"][1], self.bounds["Dp"][1]])
-        
+            bvalues = np.asarray(bvalues)
+
+        # --- Bounds resolution ---
+        # self.bounds is always a dict (OsipiBase force_default_settings=True).
+        # The underlying fit_least_squares expects: ([S0min, Dmin, fmin, Dpmin], [S0max, Dmax, fmax, Dpmax])
+        if isinstance(self.bounds, dict):
+            bounds = (
+                [self.bounds["S0"][0], self.bounds["D"][0], self.bounds["f"][0], self.bounds["Dp"][0]],
+                [self.bounds["S0"][1], self.bounds["D"][1], self.bounds["f"][1], self.bounds["Dp"][1]],
+            )
+        else:
+            # Fallback: already in list/tuple form (legacy)
+            bounds = self.bounds
+
         if self.thresholds is None:
             self.thresholds = 200
 
-        DEFAULT_PARAMS = [0.003,0.1,0.05]
+        # Default fallback parameters (D, f, Dp) used if the optimizer fails
+        DEFAULT_PARAMS = [0, 0, 0]
 
         try:
             fit_results = self.PV_algorithm(bvalues, signals, bounds=bounds, cutoff=self.thresholds)
         except RuntimeError as e:
-            if "maximum number of function evaluations" in str(e):
-                fit_results = DEFAULT_PARAMS
-            else:
-                raise
+            # curve_fit raises RuntimeError both for max-evaluations exceeded and other failures
+            print(f"PV_MUMC_biexp: optimizer failed ({e}). Returning default parameters.")
+            fit_results = DEFAULT_PARAMS
+        except Exception as e:
+            # Catch any other unexpected error (e.g. all-zero signal, NaNs in input)
+            print(f"PV_MUMC_biexp: unexpected error during fit ({type(e).__name__}: {e}). Returning default parameters.")
+            fit_results = DEFAULT_PARAMS
 
-        results = {} 
+        results = {}
         results["f"] = fit_results[1]
         results["Dp"] = fit_results[2]
         results["D"] = fit_results[0]
-        
+
         return results
diff --git a/src/standardized/PvH_KB_NKI_IVIMfit.py b/src/standardized/PvH_KB_NKI_IVIMfit.py
@@ -47,7 +47,7 @@ def __init__(self, bvalues=None, thresholds=None,bounds=None,initial_guess=None)
         super(PvH_KB_NKI_IVIMfit, self).__init__(bvalues=bvalues, thresholds=thresholds,bounds=bounds,initial_guess=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')
+            warnings.warn('Bounds from wrapper are not (yet) used in this algorithm', UserWarning, stacklevel=2)
         self.use_bounds = {"f" : False, "D" : False, "Dp" : False, "S0" : False}
         self.use_initial_guess = {"f" : False, "D" : False, "Dp" : False, "S0" : False}
 
diff --git a/src/standardized/TCML_TechnionIIT_SLS.py b/src/standardized/TCML_TechnionIIT_SLS.py
@@ -55,7 +55,7 @@ def initialize(self, bounds, fitS0,thresholds):
         self.fitS0=fitS0
         if thresholds is None:
             self.thresholds = 150
-            print('warning, no thresholds were defined, so default bounds are used of  150')
+            warnings.warn('No thresholds were defined, so default threshold of 150 is used', UserWarning, stacklevel=2)
         else:
             self.thresholds = thresholds
 
diff --git a/src/standardized/TCML_TechnionIIT_lsq_sls_BOBYQA.py b/src/standardized/TCML_TechnionIIT_lsq_sls_BOBYQA.py
@@ -1,5 +1,6 @@
 from src.wrappers.OsipiBase import OsipiBase
 from super_ivim_dc.source.Classsic_ivim_fit import IVIM_fit_sls_BOBYQA
+import warnings
 import numpy as np
 
 class TCML_TechnionIIT_lsq_sls_BOBYQA(OsipiBase):
@@ -50,7 +51,7 @@ def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=Non
     def initialize(self, bounds, fitS0, thresholds):
         if self.thresholds is None:
             self.thresholds = 200
-            print('warning, no thresholds were defined, so default bounds are used of  200')
+            warnings.warn('No thresholds were defined, so default threshold of 200 is used', UserWarning, stacklevel=2)
         else:
             self.thresholds = thresholds
         self.fitS0=fitS0
diff --git a/src/standardized/TCML_TechnionIIT_lsq_sls_lm.py b/src/standardized/TCML_TechnionIIT_lsq_sls_lm.py
@@ -54,7 +54,7 @@ def initialize(self, bounds, fitS0,thresholds):
 
         if thresholds is None:
             self.thresholds = 150
-            print('warning, no thresholds were defined, so default bounds are used of  150')
+            warnings.warn('No thresholds were defined, so default threshold of 150 is used', UserWarning, stacklevel=2)
         else:
             self.thresholds = thresholds
         self.fitS0=fitS0
diff --git a/src/standardized/TCML_TechnionIIT_lsq_sls_trf.py b/src/standardized/TCML_TechnionIIT_lsq_sls_trf.py
@@ -1,5 +1,6 @@
 from src.wrappers.OsipiBase import OsipiBase
 from super_ivim_dc.source.Classsic_ivim_fit import IVIM_fit_sls_trf
+import warnings
 import numpy as np
 
 class TCML_TechnionIIT_lsq_sls_trf(OsipiBase):
@@ -56,7 +57,7 @@ def initialize(self, bounds, fitS0, thresholds):
 
         if thresholds is None:
             self.thresholds = 200
-            print('warning, no thresholds were defined, so default bounds are used of  200')
+            warnings.warn('No thresholds were defined, so default threshold of 200 is used', UserWarning, stacklevel=2)
         else:
             self.thresholds = thresholds
         self.fitS0=fitS0
diff --git a/src/standardized/TF_reference_IVIMfit.py b/src/standardized/TF_reference_IVIMfit.py
@@ -1,5 +1,6 @@
 from src.wrappers.OsipiBase import OsipiBase
 from src.original.fitting.TF_reference.segmented_IVIMfit import segmented_IVIM_fit
+import warnings
 import numpy as np
 
 class TF_reference_IVIMfit(OsipiBase):
@@ -51,7 +52,7 @@ def initialize(self, bounds, thresholds):
         self.use_bounds = {"f" : True, "D" : True, "Dp" : True, "S0" : True}
         if self.thresholds is None:
             self.thresholds = 200
-            print('warning, no thresholds were defined, so default threshold are used of 200')
+            warnings.warn('No thresholds were defined, so default threshold of 200 is used', UserWarning, stacklevel=2)
         else:
             self.thresholds = thresholds
 
@@ -73,7 +74,7 @@ def ivim_fit(self, signals, bvalues=None):
 
         if np.any(signals < 0):
             signals = np.clip(signals,0.01, None)
-            print('warning, negative values in signal: values clipped to 0.01')
+            warnings.warn('Negative values in signal: values clipped to 0.01', UserWarning, stacklevel=2)
 
 
         fit_results = self.TF_reference_algorithm(bvalues,signals,b_cutoff=self.thresholds, bounds=bounds)
diff --git a/src/wrappers/OsipiBase.py b/src/wrappers/OsipiBase.py
diff --git a/src/wrappers/ivim_body_part_defaults.py b/src/wrappers/ivim_body_part_defaults.py
diff --git a/tests/IVIMmodels/unit_tests/algorithms.json b/tests/IVIMmodels/unit_tests/algorithms.json
diff --git a/tests/IVIMmodels/unit_tests/test_body_part_defaults.py b/tests/IVIMmodels/unit_tests/test_body_part_defaults.py
diff --git a/tests/IVIMmodels/unit_tests/test_ivim_fit.py b/tests/IVIMmodels/unit_tests/test_ivim_fit.py

Original file line number	Diff line number	Diff line change
`@@ -19,14 +19,14 @@ def pytest_addoption(parser):`
`19`	`19`	`)`
`20`	`20`	`parser.addoption(`
`21`	`21`	`"--ricianNoise",`
	`22`	`+ action="store_true",`
`22`	`23`	`default=False,`
`23`		`- type=bool,`
`24`	`24`	`help="Use Rician noise, non-rician is gaussian",`
`25`	`25`	`)`
`26`	`26`	`parser.addoption(`
`27`	`27`	`"--usePrior",`
	`28`	`+ action="store_true",`
`28`	`29`	`default=False,`
`29`		`- type=bool,`
`30`	`30`	`help="Use a prior where accepted",`
`31`	`31`	`)`
`32`	`32`	`parser.addoption(`