diff --git a/src/aspire/classification/reddy_chatterji.py b/src/aspire/classification/reddy_chatterji.py
index cd6f26524d..949f162cca 100644
--- a/src/aspire/classification/reddy_chatterji.py
+++ b/src/aspire/classification/reddy_chatterji.py
@@ -39,7 +39,7 @@ def _phase_cross_correlation(img0, img1):
     maxima = np.unravel_index(
         np.argmax(np.abs(cross_correlation)), cross_correlation.shape
     )
-    midpoints = np.array([np.fix(axis_size / 2) for axis_size in shape])
+    midpoints = np.array([np.trunc(axis_size / 2) for axis_size in shape])
 
     shifts = np.array(maxima, dtype=np.float64)
     shifts[shifts > midpoints] -= np.array(shape)[shifts > midpoints]
diff --git a/src/aspire/covariance/covar2d.py b/src/aspire/covariance/covar2d.py
index 1e05cf3ced..cbb826ac47 100644
--- a/src/aspire/covariance/covar2d.py
+++ b/src/aspire/covariance/covar2d.py
@@ -2,7 +2,7 @@
 from time import perf_counter
 
 import numpy as np
-from numpy.linalg import eig, inv
+from numpy.linalg import eigh, inv
 from scipy.linalg import solve, sqrtm
 
 from aspire.basis import Coef, FFBBasis2D
@@ -30,7 +30,7 @@ def shrink_covar(covar, noise_var, gamma, shrinker="frobenius_norm"):
         "soft_threshold",
     ), "Unsupported shrink method"
 
-    lambs, eig_vec = eig(make_symmat(covar))
+    lambs, eig_vec = eigh(make_symmat(covar))
 
     lambda_max = noise_var * (1 + np.sqrt(gamma)) ** 2