Update timeseries

Author: canyon289

pymc/tests/test_distributions_timeseries.py

@@ -69,14 +69,16 @@ def check_not_implemented(self):
         with pytest.raises(NotImplementedError):
             self.pymc_rv.eval()
 
+    # TODO: Move this out of this test class, not related, makes more sense together with 
+    # the other logp tests
     def test_grw_inference(self):
         mu, sigma, steps = 2, 1, 10000
         obs = np.concatenate([[0], np.random.normal(mu, sigma, size=steps)]).cumsum()
 
         with pm.Model():
             _mu = pm.Uniform("mu", -10, 10)
             _sigma = pm.Uniform("sigma", 0, 10)
-            # Workaround for bug in `at.diff` when data is constant
+
             obs_data = pm.MutableData("obs_data", obs)
             grw = GaussianRandomWalk("grw", _mu, _sigma, steps=steps, observed=obs_data)
 
@@ -90,19 +92,18 @@ def test_grw_inference(self):
 class TestGRWScipy(td.TestMatchesScipy):
     # TODO: Test LogP for different inits in its own function
 
-    # TODO: Find issue that says GRW wont take vector
+    # TODO: Find issue that says GRW wont take vector 
     def test_grw_logp(self):
         def grw_logp(value, mu, sigma):
             # Relying on fact that init will be normal
-            # Note: This means we're not testing
+            # Note: This means we're not testing 
             stationary_series = np.diff(value)
-            logp = (
-                stats.norm.logpdf(value[0], mu, sigma)
-                + stats.norm.logpdf(stationary_series, mu, sigma).sum(),
-            )
+            logp = stats.norm.logpdf(value[0], mu, sigma) + \
+            stats.norm.logpdf(stationary_series, mu, sigma).sum(),
             return logp
-
+             
         # TODO: Make base class a static method
+        # TODO: Reuse this make this static so it doesnt run all other ones
         self.check_logp(
             pm.GaussianRandomWalk,
             td.Vector(td.R, 10),