Merge branch 'develop' into documentation

tests/io_test.py

@@ -89,3 +89,36 @@ def test_json_string_reconstruction():
 
     assert reconstructed_string == json_string
     assert my_obs == reconstructed_obs2
+
+
+def test_json_corr_io():
+    my_list = [pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']) for o in range(8)]
+    rw_list = pe.reweight(pe.Obs([np.random.normal(1.0, 0.1, 100)], ['ens1']), my_list)
+
+    for obs_list in [my_list, rw_list]:
+        for tag in [None, "test"]:
+            obs_list[3].tag = tag
+            for fp in [0, 2]:
+                for bp in [0, 7]:
+                    for corr_tag in [None, 'my_Corr_tag']:
+                        my_corr = pe.Corr(obs_list, padding_front=fp, padding_back=bp)
+                        my_corr.tag = corr_tag
+                        pe.input.json.dump_to_json(my_corr, 'corr')
+                        recover = pe.input.json.load_json('corr')
+                        assert np.all([o.is_zero() for o in [x for x in (my_corr - recover) if x is not None]])
+                        assert my_corr.tag == recover.tag
+                        assert my_corr.reweighted == recover.reweighted
+
+
+def test_json_corr_2d_io():
+    obs_list = [np.array([[pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test'), pe.pseudo_Obs(0.0, 0.1 * i, 'test')], [pe.pseudo_Obs(0.0, 0.1 * i, 'test'), pe.pseudo_Obs(1.0 + i, 0.1 * i, 'test')]]) for i in range(8)]
+
+    for tag in [None, "test"]:
+        obs_list[3][0, 1].tag = tag
+        for padding in [0, 1]:
+            my_corr = pe.Corr(obs_list, padding_front=padding, padding_back=padding)
+            my_corr.tag = tag
+            pe.input.json.dump_to_json(my_corr, 'corr')
+            recover = pe.input.json.load_json('corr')
+            assert np.all([np.all([o.is_zero() for o in q]) for q in [x.ravel() for x in (my_corr - recover) if x is not None]])
+            assert my_corr.tag == recover.tag

tests/obs_test.py

@@ -615,7 +615,7 @@ def test_covariance_symmetry():
     cov_ab = pe.covariance(test_obs1, a)
     cov_ba = pe.covariance(a, test_obs1)
     assert np.abs(cov_ab - cov_ba) <= 10 * np.finfo(np.float64).eps
-    assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
+    assert np.abs(cov_ab) < test_obs1.dvalue * a.dvalue * (1 + 10 * np.finfo(np.float64).eps)
 
 
 def test_empty_obs():