Time series clean up (#6240)

Author: junpenglao

pymc/distributions/timeseries.py

@@ -267,7 +267,7 @@ class GaussianRandomWalk(PredefinedRandomWalk):
         sigma > 0, innovation standard deviation.
     init_dist : Distribution
         Unnamed univariate distribution of the initial value. Unnamed refers to distributions
-         created with the ``.dist()`` API.
+        created with the ``.dist()`` API.
 
         .. warning:: init_dist will be cloned, rendering them independent of the ones passed as input.
 
@@ -547,7 +547,7 @@ def dist(
                     f"Init dist must be a distribution created via the `.dist()` API, "
                     f"got {type(init_dist)}"
                 )
-                check_dist_not_registered(init_dist)
+            check_dist_not_registered(init_dist)
             if init_dist.owner.op.ndim_supp > 1:
                 raise ValueError(
                     "Init distribution must have a scalar or vector support dimension, ",
@@ -617,7 +617,7 @@ def rv_op(cls, rhos, sigma, init_dist, steps, ar_order, constant_term, size=None
             init_dist_size = batch_size
         init_dist = change_dist_size(init_dist, init_dist_size)
 
-        # Create OpFromGraph representing random draws form AR process
+        # Create OpFromGraph representing random draws from AR process
         # Variables with underscore suffix are dummy inputs into the OpFromGraph
         init_ = init_dist.type()
         rhos_ = rhos.type()

pymc/tests/distributions/test_timeseries.py

@@ -693,10 +693,10 @@ def test_moment(self, size, expected):
 
     def test_init_deprecated_arg(self):
         with pytest.warns(FutureWarning, match="init parameter is now called init_dist"):
-            pm.AR.dist(rho=[1, 2, 3], init=Normal.dist(), shape=(10,))
+            AR.dist(rho=[1, 2, 3], init=Normal.dist(), shape=(10,))
 
     def test_change_dist_size(self):
-        base_dist = pm.AR.dist(rho=[0.5, 0.5], init_dist=pm.Normal.dist(size=(2,)), shape=(3, 10))
+        base_dist = AR.dist(rho=[0.5, 0.5], init_dist=pm.Normal.dist(size=(2,)), shape=(3, 10))
 
         new_dist = change_dist_size(base_dist, (4,))
         assert new_dist.eval().shape == (4, 10)
@@ -819,7 +819,7 @@ def test_moment(self, size, expected):
         assert_moment_is_expected(model, expected, check_finite_logp=True)
 
     def test_change_dist_size(self):
-        base_dist = pm.GARCH11.dist(
+        base_dist = GARCH11.dist(
             omega=1.25, alpha_1=0.5, beta_1=0.45, initial_vol=1.0, shape=(3, 10)
         )