diff --git a/cobaya/likelihoods/base_classes/bao.py b/cobaya/likelihoods/base_classes/bao.py
index 2533406a2..af03508d9 100644
--- a/cobaya/likelihoods/base_classes/bao.py
+++ b/cobaya/likelihoods/base_classes/bao.py
@@ -266,14 +266,13 @@ def initialize(self):
                                   "needs to be specified.")
                 self.data["observable"] = [self.observable_1]
                 x = self.grid_data[:, 0]
-                Nx = x.shape[0]
                 chi2 = np.log(self.grid_data[:, 1])
                 self.interpolator = UnivariateSpline(x, chi2, s=0, ext=2)
             elif self.grid_data.shape[1] == 3:
                 self.use_grid_1d = False
                 self.use_grid_2d = True
                 self.use_grid_3d = False
-                if (not self.observable_1) or (not self.observable_2):
+                if not (self.observable_1 and self.observable_2):
                     raise LoggedError(
                         self.log, "If using grid data, 'observable_1' and 'observable_2'"
                                   "need to be specified.")
@@ -293,10 +292,11 @@ def initialize(self):
                 self.use_grid_1d = False
                 self.use_grid_2d = False
                 self.use_grid_3d = True
-                if (not self.observable_1) or (not self.observable_2) or (not self.observable_3):
+                if not (self.observable_1 and self.observable_2 and self.observable_3):
                     raise LoggedError(
-                        self.log, "If using grid data, 'observable_1', 'observable_2' and 'observable_3'"
-                                  "need to be specified.")
+                        self.log,
+                        "If using grid data, 'observable_1', 'observable_2' "
+                        "and 'observable_3' need to be specified.")
                 self.data["observable"] = [self.observable_1, self.observable_2,
                                            self.observable_3]
 
@@ -390,21 +390,22 @@ def get_requirements(self):
                                               if obs not in theory_reqs])
         if len(obs_used_not_implemented):
             raise LoggedError(
-                self.log, "This likelihood refers to observables '%s' that have not been"
-                          " implemented yet. Did you mean any of %s? "
+                self.log, "This likelihood refers to observables '%s' that "
+                          "have not been  implemented yet. Did you mean any of %s? "
                           "If you didn't, please, open an issue in github.",
                 obs_used_not_implemented, list(theory_reqs))
         requisites = {}
         if self.has_type:
             for observable in self.data["observable"].unique():
                 for req, req_values in theory_reqs[observable].items():
-                    if req not in requisites.keys():
+                    if req not in requisites:
                         requisites[req] = req_values
                     else:
                         if isinstance(req_values, dict):
                             for k, v in req_values.items():
                                 if v is not None:
-                                    requisites[req][k] = np.unique(np.concatenate((requisites[req][k], v)))
+                                    requisites[req][k] = np.unique(
+                                        np.concatenate((requisites[req][k], v)))
         return requisites
 
     def theory_fun(self, z, observable):
@@ -419,8 +420,8 @@ def theory_fun(self, z, observable):
         elif observable == "rs_over_DV":
             return np.cbrt(
                 ((1 + z) * self.provider.get_angular_diameter_distance(z)) ** 2 *
-                Const.c_km_s * z / self.provider.get_Hubble(z, units="km/s/Mpc")) ** (
-                       -1) * self.rs()
+                Const.c_km_s * z / self.provider.get_Hubble(z, units="km/s/Mpc")
+            ) ** (-1) * self.rs()
         # Comoving angular diameter distance, over sound horizon radius
         elif observable == "DM_over_rs":
             return (1 + z) * self.provider.get_angular_diameter_distance(z) / self.rs()