changes per code review

Author: mitzimorris

test/test_generate_quantities.py

@@ -426,7 +426,7 @@ def test_no_xarray(self):
             with self.assertRaises(RuntimeError):
                 bern_gqs.draws_xr()
 
-    def test_bug_455(self):
+    def test_single_row_csv(self):
         stan = os.path.join(DATAFILES_PATH, 'bernoulli.stan')
         bern_model = CmdStanModel(stan_file=stan)
         jdata = os.path.join(DATAFILES_PATH, 'bernoulli.data.json')

test/test_optimize.py

@@ -581,7 +581,7 @@ def test_optimize_bad(self):
                 data=no_data, seed=1239812093, inits=None, algorithm='BFGS'
             )
 
-    def test_bug_455(self):
+    def test_single_row_csv(self):
         stan = os.path.join(DATAFILES_PATH, 'matrix_var.stan')
         model = CmdStanModel(stan_file=stan)
         mle = model.optimize()

test/test_sample.py

@@ -1668,13 +1668,13 @@ def test_no_xarray(self):
             with self.assertRaises(RuntimeError):
                 bern_fit.draws_xr()
 
-    def test_bug_455(self):
+    def test_single_row_csv(self):
         stan = os.path.join(DATAFILES_PATH, 'matrix_var.stan')
-        bug_455_model = CmdStanModel(stan_file=stan)
-        bug_455_fit = bug_455_model.sample(iter_sampling=1, chains=1)
-        z_as_ndarray = bug_455_fit.stan_variable(var="z")
+        model = CmdStanModel(stan_file=stan)
+        fit = model.sample(iter_sampling=1, chains=1)
+        z_as_ndarray = fit.stan_variable(var="z")
         self.assertEqual(z_as_ndarray.shape, (1, 4, 3))  # flattens chains
-        z_as_xr = bug_455_fit.draws_xr(vars="z")
+        z_as_xr = fit.draws_xr(vars="z")
         self.assertEqual(z_as_xr.z.data.shape, (1, 1, 4, 3))  # keeps chains
         for i in range(4):
             for j in range(3):

test/test_variational.py

@@ -253,7 +253,7 @@ def test_variational_eta_fail(self):
             )
         )
 
-    def test_bug_455(self):
+    def test_single_row_csv(self):
         stan = os.path.join(DATAFILES_PATH, 'matrix_var.stan')
         model = CmdStanModel(stan_file=stan)
         vb_fit = model.variational()