Normalize scalar types for prior, likelihood, and posterior in Link

tinyDA/link.py

@@ -1,3 +1,5 @@
+import numpy as np
+
 class Link:
 
     """The Link class holds all relevant information about an MCMC sample, i.e.
@@ -27,22 +29,31 @@ def __init__(self, parameters, prior, model_output, likelihood, qoi=None):
         ----------
         parameters : numpy.ndarray
             The parameters used to generate the sample
-        prior : float
+        prior : float or 1-element array-like
             The prior log-density
         model_output : numpy.ndarray
             The model output
-        likelihood : float
+        likelihood : float or 1-element array-like
             The log-likelihood of the data, given the parameters.
         qoi : optional
             A Quantity of Interest. Default is None
         """
 
         # internalise parameters.
         self.parameters = parameters
-        self.prior = prior
+        self.prior = _to_scalar(prior, "prior")
         self.model_output = model_output
-        self.likelihood = likelihood
+        self.likelihood = _to_scalar(likelihood, "likelihood")
         self.qoi = qoi
 
         # compute the (unnormalised) posterior.
         self.posterior = self.prior + self.likelihood
+
+def _to_scalar(x, name):
+    arr = np.asarray(x)
+    if arr.ndim > 0 and arr.size != 1:
+        raise ValueError(
+            f"{name} must be a scalar or length-1 array, got shape {arr.shape}"
+        )
+    return arr.item()
+