Changed "accepts_priors" to "supported_priors" for conformance

Author: IvanARashid

src/standardized/OGC_AmsterdamUMC_Bayesian_biexp.py

@@ -26,13 +26,13 @@ class OGC_AmsterdamUMC_Bayesian_biexp(OsipiBase):
     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?
-    accepts_priors = True
 
 
     # Supported inputs in the standardized class
     supported_bounds = True
     supported_initial_guess = True
     supported_thresholds = True
+    supported_priors = True
 
     def __init__(self, bvalues=None, thresholds=None, bounds=None, initial_guess=None, fitS0=True, prior_in=None):
 

tests/IVIMmodels/unit_tests/test_ivim_synthetic.py

@@ -26,7 +26,7 @@ def test_generated(ivim_algorithm, ivim_data, SNR, rtol, atol, fit_count, rician
     Dp = data["Dp"]
     fit = OsipiBase(algorithm=ivim_algorithm)
     # here is a prior
-    if use_prior and hasattr(fit, "accepts_priors") and fit.accepts_priors:
+    if use_prior and hasattr(fit, "supported_priors") and fit.supported_priors:
         prior = [rng.normal(D, D/3, 10), rng.normal(f, f/3, 10), rng.normal(Dp, Dp/3, 10), rng.normal(1, 1/3, 10)]
         # prior = [np.repeat(D, 10)+np.random.normal(0,D/3,np.shape(np.repeat(D, 10))), np.repeat(f, 10)+np.random.normal(0,f/3,np.shape(np.repeat(D, 10))), np.repeat(Dp, 10)+np.random.normal(0,Dp/3,np.shape(np.repeat(D, 10))),np.repeat(np.ones_like(Dp), 10)+np.random.normal(0,1/3,np.shape(np.repeat(D, 10)))]  # D, f, D*
         fit.initialize(prior_in=prior)