Lat, lon was mixed up in evaluation, reduced the sample size for faster examples.

corbass/evaluation.py

@@ -148,8 +148,7 @@ def location(posterior, mu_dips, cov_dips, n_points=10000,
         The dipole location pdf, evaluated at the gridpoints
     """
     # set up grid to evaluate on
-    (ti, tf), (pi, pf) = bounds
-    theta, phi = zip(*equi_sph(n_points, ti, tf, pi, pf))
+    theta, phi = zip(*equi_sph(n_points, bounds=bounds))
     phi = np.asarray(phi)
     theta = np.pi/2. - np.asarray(theta)
     # allocate array for the density
@@ -176,8 +175,8 @@ def location(posterior, mu_dips, cov_dips, n_points=10000,
         dens += posterior[np.unravel_index(it, posterior.shape)] \
             / np.sqrt((2*np.pi)**3 * np.linalg.det(cov_dips[it])) \
             * np.cos(theta) * zk / np.sqrt(zalpha**3) * dummy
-    lat = np.rad2deg(phi)
-    lon = np.rad2deg(theta)
+    lat = np.rad2deg(theta)
+    lon = np.rad2deg(phi)
 
     return lat, lon, dens
 
@@ -286,8 +285,7 @@ def coeffs(posterior, mu_coeffs, cov_coeffs, r_ref, r_at=REARTH):
                                                   cov_coeffs)
 
     gm_weights = posterior / posterior.sum()
-    ens = sample_GM(gm_weights.flatten(), mu_coeffs, cov_coeffs,
-                    n_samps=50000)
+    ens = sample_GM(gm_weights.flatten(), mu_coeffs, cov_coeffs)
 
     err_16, err_84 = np.percentile(ens, (16, 84), axis=1)
     mean = (mu_coeffs.T * gm_weights.flatten()).sum(axis=1)
@@ -338,8 +336,7 @@ def spectrum(posterior, mu_coeffs, cov_coeffs, r_ref, r_at=REARTH):
 
     gm_weights = posterior / posterior.sum()
 
-    ens = sample_GM(gm_weights.flatten(), mu_coeffs, cov_coeffs,
-                    n_samps=50000)
+    ens = sample_GM(gm_weights.flatten(), mu_coeffs, cov_coeffs)
 
     # 1st two moments of mixture
     mean = np.sum(mu_coeffs * gm_weights.flatten()[:, None], axis=0)