Update failing tests

src/hssm/distribution_utils/dist.py

@@ -324,10 +324,11 @@ def rng_fn(
                     + "distribution but did not specify the distribution."
                 )
                 out_shape = sims_out.shape[:-1]
-                if p_outlier.shape[-1] == 1:
-                    p_outlier = np.broadcast_to(p_outlier, out_shape)
-                else:
-                    p_outlier = p_outlier.reshape(out_shape)
+                if not np.isscalar(p_outlier) and len(p_outlier.shape) > 0:
+                    if p_outlier.shape[-1] == 1:
+                        p_outlier = np.broadcast_to(p_outlier, out_shape)
+                    else:
+                        p_outlier = p_outlier.reshape(out_shape)
                 replace = rng.binomial(n=1, p=p_outlier, size=out_shape).astype(bool)
                 replace_n = int(np.sum(replace, axis=None))
                 if replace_n == 0:

src/hssm/plotting/posterior_predictive.py

@@ -380,7 +380,7 @@ def plot_posterior_predictive(
     # Flip the rt values if necessary
     if np.any(plotting_df["response"] == 0):
         plotting_df["response"] = np.where(plotting_df["response"] == 0, -1, 1)
-    if model.n_responses == 2:
+    if model.n_choices == 2:
         plotting_df["rt"] = plotting_df["rt"] * plotting_df["response"]
 
     # Then, plot the posterior predictive distribution against the observed data

src/hssm/plotting/quantile_probability.py

@@ -346,7 +346,7 @@ def plot_quantile_probability(
     # Flip the rt values if necessary
     if np.any(plotting_df["response"] == 0):
         plotting_df["response"] = np.where(plotting_df["response"] == 0, -1, 1)
-    if model.n_responses == 2:
+    if model.n_choices == 2:
         plotting_df["rt"] = plotting_df["rt"] * plotting_df["response"]
 
     # If group is not provided, we are producing a single plot

tests/test_utils.py

@@ -17,10 +17,8 @@
 
 def test_get_alias_dict():
     # Simulate some data:
-    v_true, a_true, z_true, t_true, sv_true = [0.5, 1.5, 0.5, 0.5, 0.3]
-    obs_ddm = simulator(
-        [v_true, a_true, z_true, t_true, sv_true], model="ddm", n_samples=1000
-    )
+    v_true, a_true, z_true, t_true = [0.5, 1.5, 0.5, 0.5]
+    obs_ddm = simulator([v_true, a_true, z_true, t_true], model="ddm", n_samples=1000)
     obs_ddm = np.column_stack([obs_ddm["rts"][:, 0], obs_ddm["choices"][:, 0]])
 
     dataset = pd.DataFrame(obs_ddm, columns=["rt", "response"])