properly seed rng in tests

HERA-Team · Oct 16, 2024 · 6f3ee58 · 6f3ee58
1 parent 2fd0e2b
commit 6f3ee58
Showing 1 changed file with 8 additions and 4 deletions.
diff --git a/hera_cal/tests/test_redcal.py b/hera_cal/tests/test_redcal.py
@@ -306,7 +306,8 @@ def test_init(self):
         antpos = linear_array(NANTS)
         reds = om.get_reds(antpos, pols=['ee'], pol_mode='1pol')
         info = om.RedundantCalibrator(reds)
-        gains, true_vis, d = sim_red_data(reds, gain_scatter=.05)
+        rng = np.random.default_rng(21)
+        gains, true_vis, d = sim_red_data(reds, gain_scatter=.05, rng=rng)
         w = dict([(k, 1.) for k in d.keys()])
         meta, sol = info.logcal(d)
 
@@ -729,7 +730,8 @@ def test_omnical(self):
         antpos = linear_array(NANTS)
         reds = om.get_reds(antpos, pols=['xx'], pol_mode='1pol')
         info = om.RedundantCalibrator(reds)
-        gains, true_vis, d = sim_red_data(reds, gain_scatter=.0099999)
+        rng = np.random.default_rng(21)
+        gains, true_vis, d = sim_red_data(reds, gain_scatter=.0099999, rng=rng)
         w = dict([(k, 1.) for k in d.keys()])
         sol0 = dict([(k, np.ones_like(v)) for k, v in gains.items()])
         sol0.update(info.compute_ubls(d, sol0))
@@ -761,7 +763,8 @@ def test_omnical64(self):
         antpos = linear_array(NANTS)
         reds = om.get_reds(antpos, pols=['xx'], pol_mode='1pol')
         info = om.RedundantCalibrator(reds)
-        gains, true_vis, d = sim_red_data(reds, shape=(2, 1), gain_scatter=.0099999)
+        rng = np.random.default_rng(21)
+        gains, true_vis, d = sim_red_data(reds, shape=(2, 1), gain_scatter=.0099999, rng=rng)
         w = dict([(k, 1.) for k in d.keys()])
         sol0 = dict([(k, np.ones_like(v)) for k, v in gains.items()])
         sol0.update(info.compute_ubls(d, sol0))
@@ -792,7 +795,8 @@ def test_omnical128(self):
         antpos = linear_array(NANTS)
         reds = om.get_reds(antpos, pols=['xx'], pol_mode='1pol')
         info = om.RedundantCalibrator(reds)
-        gains, true_vis, d = sim_red_data(reds, shape=(2, 1), gain_scatter=.0099999)
+        rng = np.random.default_rng(21)
+        gains, true_vis, d = sim_red_data(reds, shape=(2, 1), gain_scatter=.0099999, rng=rng)
         w = dict([(k, 1.) for k in d.keys()])
         sol0 = dict([(k, np.ones_like(v)) for k, v in gains.items()])
         sol0.update(info.compute_ubls(d, sol0))