Merge branch 'develop' into feature/dobs

tests/fits_test.py

@@ -157,6 +157,28 @@ def test_correlated_fit():
             assert(diff.is_zero_within_error(sigma=5))
 
 
+def test_fit_corr_independent():
+    dim = 50
+    x = np.arange(dim)
+    y = 0.84 * np.exp(-0.12 * x) + np.random.normal(0.0, 0.1, dim)
+    yerr = [0.1] * dim
+
+    oy = []
+    for i, item in enumerate(x):
+        oy.append(pe.pseudo_Obs(y[i], yerr[i], str(i)))
+
+    def func(a, x):
+        y = a[0] * anp.exp(-a[1] * x)
+        return y
+
+    out = pe.least_squares(x, oy, func)
+    out_corr = pe.least_squares(x, oy, func, correlated_fit=True)
+
+    assert np.isclose(out.chisquare, out_corr.chisquare)
+    assert (out[0] - out_corr[0]).is_zero(atol=1e-5)
+    assert (out[1] - out_corr[1]).is_zero(atol=1e-5)
+
+
 def test_total_least_squares():
     dim = 10 + int(30 * np.random.rand())
     x = np.arange(dim) + np.random.normal(0.0, 0.15, dim)

tests/obs_test.py

@@ -510,6 +510,10 @@ def test_correlate():
         pe.correlate(r_obs, r_obs)
 
 
+def test_merge_idx():
+    assert pe.obs._merge_idx([range(10, 1010, 10), range(10, 1010, 50)]) == range(10, 1010, 10)
+    assert pe.obs._merge_idx([range(500, 6050, 50), range(500, 6250, 250)]) == range(500, 6250, 50)
+
 
 def test_irregular_error_propagation():
     obs_list = [pe.Obs([np.random.rand(100)], ['t']),
@@ -713,10 +717,21 @@ def test_covariance_rank_deficient():
     with pytest.warns(RuntimeWarning):
         pe.covariance(obs)
 
+def test_covariance_idl():
+    range1 = range(10, 1010, 10)
+    range2 = range(10, 1010, 50)
+
+    obs1 = pe.Obs([np.random.normal(1.0, 0.1, len(range1))], ["ens"], idl=[range1])
+    obs2 = pe.Obs([np.random.normal(1.0, 0.1, len(range2))], ["ens"], idl=[range2])
+    obs1.gamma_method()
+    obs2.gamma_method()
+
+    pe.covariance([obs1, obs2])
+
 
 def test_empty_obs():
     o = pe.Obs([np.random.rand(100)], ['test'])
-    q = o + pe.Obs([], [])
+    q = o + pe.Obs([], [], means=[])
     assert q == o
 
 
@@ -769,6 +784,7 @@ def test_merge_idx():
     for i in range(1, len(new_idx)):
         assert(new_idx[i - 1] < new_idx[i])
 
+
 def test_cobs_array():
     cobs = pe.Obs([np.random.normal(1.0, 0.1, 100)], ['t']) * (1 + 2j)
     np.identity(4) + cobs
@@ -779,4 +795,3 @@ def test_cobs_array():
     cobs * np.identity(4)
     np.identity(4) / cobs
     cobs / np.ones((4, 4))
-