🐛 Update varname param to var_names for arviz dep

Author: CloudChaoszero

examples/case_studies/disaster_model.py

@@ -152,4 +152,4 @@
     start = {"early_mean": 2.0, "late_mean": 3.0}
 
     tr = pm.sample(1000, tune=500, start=start)
-    pm.plot_trace(tr)
+    az.plot_trace(tr)

examples/pymc3_howto/LKJ_correlation.py

@@ -54,7 +54,7 @@ def run(n=1000):
     with model:
         trace = pm.sample(n)
     az.plot_trace(
-        trace, varnames=["mu", "r"], lines={"mu": mu_r, "r": corr_r[np.triu_indices(n_var, k=1)]}
+        trace, var_names=["mu", "r"], lines={"mu": mu_r, "r": corr_r[np.triu_indices(n_var, k=1)]}
     )
 
 

examples/pymc3_howto/rankdata_ordered.py

@@ -46,14 +46,14 @@ def run(n=1500):
     with m:
         trace = pm.sample(n)
 
-    az.plot_trace(trace, varnames=["mu_hat"])
+    az.plot_trace(trace, var_names=["mu_hat"])
 
     print("Example observed data: ")
     print(y[:30, :].T)
     print("The true ranking is: ")
     print(yreal.flatten())
     print("The Latent mean is: ")
-    latentmu = np.hstack(([0], az.summary(trace, varnames=["mu_hat"])["mean"].values))
+    latentmu = np.hstack(([0], az.summary(trace, var_names=["mu_hat"])["mean"].values))
     print(np.round(latentmu, 2))
     print("The estimated ranking is: ")
     print(np.argsort(latentmu))