diff --git a/dppy/exotic_dpps_core.py b/dppy/exotic_dpps_core.py
index ee0f80f..e4fedb5 100644
--- a/dppy/exotic_dpps_core.py
+++ b/dppy/exotic_dpps_core.py
@@ -118,7 +118,7 @@ def ust_sampler_aldous_broder(list_of_neighbors, root=None,
     visited[n0] = True
     nb_nodes_in_tree = 1
 
-    tree_edges = np.zeros((nb_nodes - 1, 2), dtype=np.int)
+    tree_edges = np.zeros((nb_nodes - 1, 2), dtype=int)
 
     while nb_nodes_in_tree < nb_nodes:
 
diff --git a/dppy/random_matrices.py b/dppy/random_matrices.py
index 0b7db3f..bdd54d4 100644
--- a/dppy/random_matrices.py
+++ b/dppy/random_matrices.py
@@ -334,7 +334,7 @@ def mu_ref_unif_unit_circle_sampler_quindiag(beta=2, size=10,
         raise ValueError('`beta` must be positive integer.\
                          Given: {}'.format(beta))
 
-    alpha = np.zeros(size, dtype=np.complex_)
+    alpha = np.zeros(size, dtype=np.complex128)
 
     # nu = 1 + beta*(N-1, N-2, ..., 0)
     for i, nu in enumerate(1 + beta * np.arange(size - 1, -1, step=-1)):
@@ -343,7 +343,7 @@ def mu_ref_unif_unit_circle_sampler_quindiag(beta=2, size=10,
 
     rho = np.sqrt(1 - np.abs(alpha[:-1])**2)
 
-    xi = np.zeros((size - 1, 2, 2), dtype=np.complex_)  # xi[0,..,N-1]
+    xi = np.zeros((size - 1, 2, 2), dtype=np.complex128)  # xi[0,..,N-1]
     xi[:, 0, 0], xi[:, 0, 1] = alpha[:-1].conj(), rho
     xi[:, 1, 0], xi[:, 1, 1] = rho, -alpha[:-1]
 
@@ -352,14 +352,14 @@ def mu_ref_unif_unit_circle_sampler_quindiag(beta=2, size=10,
     # xi[N-1] = alpha[N-1].conj()
     if size % 2 == 0:  # even
         L = sp.block_diag(xi[::2, :, :],
-                          dtype=np.complex_)
+                          dtype=np.complex128)
         M = sp.block_diag([1.0, *xi[1::2, :, :], alpha[-1].conj()],
-                          dtype=np.complex_)
+                          dtype=np.complex128)
     else:  # odd
         L = sp.block_diag([*xi[::2, :, :], alpha[-1].conj()],
-                          dtype=np.complex_)
+                          dtype=np.complex128)
         M = sp.block_diag([1.0, *xi[1::2, :, :]],
-                          dtype=np.complex_)
+                          dtype=np.complex128)
 
     return la.eigvals(L.dot(M).toarray())
 
diff --git a/tests/test_beta_ensembles_samplers.py b/tests/test_beta_ensembles_samplers.py
index 3a222c6..0fdb2f5 100644
--- a/tests/test_beta_ensembles_samplers.py
+++ b/tests/test_beta_ensembles_samplers.py
@@ -42,7 +42,7 @@ def limiting_distribution_adequation(samples,
         f_th = [quad(limiting_distribution, a, b)[0]
                   for a, b in zip(bins[:-1], bins[1:])]
 
-        _, pval = chisquare(f_obs=f_emp, f_exp=f_th)
+        _, pval = chisquare(f_obs=f_emp, f_exp=f_th, sum_check=False)
 
         msg = 'pval = {}'.format(pval)
 
diff --git a/tests/test_finite_dpps_samplers.py b/tests/test_finite_dpps_samplers.py
index cbd6f90..4b3d1b9 100644
--- a/tests/test_finite_dpps_samplers.py
+++ b/tests/test_finite_dpps_samplers.py
@@ -90,7 +90,9 @@ def doubleton_adequation(dpp, samples, tol=0.05):
                       for doubl in doubletons]
             marginal_emp = np.array(counts) / len(samples)
 
-            _, pval = chisquare(f_obs=marginal_emp, f_exp=marginal_th)
+            _, pval = chisquare(f_obs=marginal_emp,
+                                f_exp=marginal_th,
+                                sum_check=False)
 
             msg = 'pval = {}, emp = {}, th = {}'.format(pval,
                                                         marginal_emp,