From 331d7cb4716ec2dc4601b50d4aaf24634831d6dc Mon Sep 17 00:00:00 2001
From: Fabian Joswig <fabian.joswig@ed.ac.uk>
Date: Mon, 15 Nov 2021 14:18:26 +0000
Subject: [PATCH] test_jackknife added

---
 pyerrors/obs.py   |  2 +-
 tests/obs_test.py | 15 +++++++++++++++
 2 files changed, 16 insertions(+), 1 deletion(-)

diff --git a/pyerrors/obs.py b/pyerrors/obs.py
index d73f92e3..2204a429 100644
--- a/pyerrors/obs.py
+++ b/pyerrors/obs.py
@@ -562,7 +562,7 @@ class Obs:
 
         Returns
         -------
-        np.ndarray
+        numpy.ndarray
             Returns a numpy array of length N + 1 where N is the number of samples
             for the given ensemble and replicum. The zeroth entry of the array contains
             the mean value of the Obs, entries 1 to N contain the N jackknife samples
diff --git a/tests/obs_test.py b/tests/obs_test.py
index f0c63ac7..7ce035ab 100644
--- a/tests/obs_test.py
+++ b/tests/obs_test.py
@@ -504,3 +504,18 @@ def test_covariance2_symmetry():
     cov_ba = pe.covariance2(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)
+
+
+def test_jackknife():
+    full_data = np.random.normal(1.1, 0.87, 5487)
+
+    my_obs = pe.Obs([full_data], ['test'])
+
+    n = full_data.size
+    mean = np.mean(full_data)
+    tmp_jacks = np.zeros(n + 1)
+    tmp_jacks[0] = mean
+    for i in range(n):
+        tmp_jacks[i + 1] = (n * mean - full_data[i]) / (n - 1)
+
+    assert np.allclose(tmp_jacks, my_obs.export_jackknife())