Merge branch 'develop' into documentation

pyerrors/covobs.py

@@ -32,6 +32,8 @@ class Covobs:
                 raise Exception('Covariance matrix has to be a square matrix!')
         else:
             raise Exception('Covariance matrix has to be a 2 dimensional square matrix!')
+        if '|' in name:
+            raise Exception("Covobs name must not contain replica separator '|'.")
         self.name = name
         if grad is None:
             if pos is None:

tests/covobs_test.py

@@ -0,0 +1,69 @@
+import autograd.numpy as np
+import pyerrors as pe
+import pytest
+
+np.random.seed(0)
+
+
+def test_covobs():
+    val = 1.123124
+    cov = .243423
+    name = 'Covariance'
+    co = pe.cov_Obs(val, cov, name)
+    co.gamma_method()
+    assert (co.dvalue == np.sqrt(cov))
+    assert (co.value == val)
+
+    do = 2 * co
+    assert (do.covobs[name].grad[0] == 2)
+
+    do = co * co
+    assert (do.covobs[name].grad[0] == 2 * val)
+    assert np.array_equal(do.covobs[name].cov, co.covobs[name].cov)
+
+    pi = [16.7457, -19.0475]
+    cov = [[3.49591, -6.07560], [-6.07560, 10.5834]]
+
+    cl = pe.cov_Obs(pi, cov, 'rAP')
+    pl = pe.misc.gen_correlated_data(pi, np.asarray(cov), 'rAPpseudo')
+
+    def rAP(p, g0sq):
+        return -0.0010666 * g0sq * (1 + np.exp(p[0] + p[1] / g0sq))
+
+    for g0sq in [1, 1.5, 1.8]:
+        oc = rAP(cl, g0sq)
+        oc.gamma_method()
+        op = rAP(pl, g0sq)
+        op.gamma_method()
+        assert(np.isclose(oc.value, op.value, rtol=1e-14, atol=1e-14))
+
+    assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
+    assert(pe.covariance2(cl[0], cl[1]) == cov[1][0])
+
+    do = cl[0] * cl[1]
+    assert(np.array_equal(do.covobs['rAP'].grad, np.transpose([pi[1], pi[0]]).reshape(2, 1)))
+
+
+def test_covobs_overloading():
+    covobs = pe.cov_Obs([0.5, 0.5], np.array([[0.02, 0.02], [0.02, 0.02]]), 'test')
+    assert (covobs[0] / covobs[1]) == 1
+    assert (covobs[0] - covobs[1]) == 0
+
+    my_obs = pe.pseudo_Obs(2.3, 0.2, 'obs')
+
+    assert (my_obs * covobs[0] / covobs[1]) == my_obs
+
+    covobs = pe.cov_Obs(0.0, 0.3, 'test')
+    assert not covobs.is_zero()
+
+
+def test_covobs_name_collision():
+    covobs = pe.cov_Obs(0.5, 0.002, 'test')
+    my_obs = pe.pseudo_Obs(2.3, 0.2, 'test')
+    with pytest.raises(Exception):
+        summed_obs = my_obs + covobs
+
+
+def test_covobs_replica_separator():
+    with pytest.raises(Exception):
+        covobs = pe.cov_Obs(0.5, 0.002, 'test|r2')

tests/obs_test.py

@@ -542,62 +542,3 @@ def test_import_jackknife():
     my_jacks = my_obs.export_jackknife()
     reconstructed_obs = pe.import_jackknife(my_jacks, 'test')
     assert my_obs == reconstructed_obs
-
-
-def test_covobs():
-    val = 1.123124
-    cov = .243423
-    name = 'Covariance'
-    co = pe.cov_Obs(val, cov, name)
-    co.gamma_method()
-    assert (co.dvalue == np.sqrt(cov))
-    assert (co.value == val)
-
-    do = 2 * co
-    assert (do.covobs[name].grad[0] == 2)
-
-    do = co * co
-    assert (do.covobs[name].grad[0] == 2 * val)
-    assert np.array_equal(do.covobs[name].cov, co.covobs[name].cov)
-
-    pi = [16.7457, -19.0475]
-    cov = [[3.49591, -6.07560], [-6.07560, 10.5834]]
-
-    cl = pe.cov_Obs(pi, cov, 'rAP')
-    pl = pe.misc.gen_correlated_data(pi, np.asarray(cov), 'rAPpseudo')
-
-    def rAP(p, g0sq):
-        return -0.0010666 * g0sq * (1 + np.exp(p[0] + p[1] / g0sq))
-
-    for g0sq in [1, 1.5, 1.8]:
-        oc = rAP(cl, g0sq)
-        oc.gamma_method()
-        op = rAP(pl, g0sq)
-        op.gamma_method()
-        assert(np.isclose(oc.value, op.value, rtol=1e-14, atol=1e-14))
-
-    assert(pe.covariance(cl[0], cl[1]) == cov[0][1])
-    assert(pe.covariance2(cl[0], cl[1]) == cov[1][0])
-
-    do = cl[0] * cl[1]
-    assert(np.array_equal(do.covobs['rAP'].grad, np.transpose([pi[1], pi[0]]).reshape(2, 1)))
-
-
-def test_covobs_overloading():
-    covobs = pe.cov_Obs([0.5, 0.5], np.array([[0.02, 0.02], [0.02, 0.02]]), 'test')
-    assert (covobs[0] / covobs[1]) == 1
-    assert (covobs[0] - covobs[1]) == 0
-
-    my_obs = pe.pseudo_Obs(2.3, 0.2, 'obs')
-
-    assert (my_obs * covobs[0] / covobs[1]) == my_obs
-
-    covobs = pe.cov_Obs(0.0, 0.3, 'test')
-    assert not covobs.is_zero()
-
-
-def test_covobs_name_collision():
-    covobs = pe.cov_Obs(0.5, 0.002, 'test')
-    my_obs = pe.pseudo_Obs(2.3, 0.2, 'test')
-    with pytest.raises(Exception):
-        summed_obs = my_obs + covobs