Merge branch 'develop' into documentation

pyerrors/__init__.py

@@ -1,17 +1,14 @@
 r'''
 # What is pyerrors?
 `pyerrors` is a python package for error computation and propagation of Markov chain Monte Carlo data.
-It is based on the **gamma method** [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). Some of its features are:
-- **automatic differentiation** as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289) (partly based on the [autograd](https://github.com/HIPS/autograd) package)
-- **treatment of slow modes** in the simulation as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228)
-- coherent **error propagation** for data from **different Markov chains**
-- **non-linear fits with x- and y-errors** and exact linear error propagation based on automatic differentiation as introduced in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289)
-- **real and complex matrix operations** and their error propagation based on automatic differentiation (Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...)
+It is based on the gamma method [arXiv:hep-lat/0306017](https://arxiv.org/abs/hep-lat/0306017). Some of its features are:
+- automatic differentiation for exact liner error propagation as suggested in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289) (partly based on the [autograd](https://github.com/HIPS/autograd) package).
+- treatment of slow modes in the simulation as suggested in [arXiv:1009.5228](https://arxiv.org/abs/1009.5228).
+- coherent error propagation for data from different Markov chains.
+- non-linear fits with x- and y-errors and exact linear error propagation based on automatic differentiation as introduced in [arXiv:1809.01289](https://arxiv.org/abs/1809.01289).
+- real and complex matrix operations and their error propagation based on automatic differentiation (Matrix inverse, Cholesky decomposition, calculation of eigenvalues and eigenvectors, singular value decomposition...).
 
-There exist similar publicly available implementations of gamma method error analysis suites in
-- [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors)
-- [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl)
-- [Python](https://github.com/mbruno46/pyobs)
+There exist similar publicly available implementations of gamma method error analysis suites in [Fortran](https://gitlab.ift.uam-csic.es/alberto/aderrors), [Julia](https://gitlab.ift.uam-csic.es/alberto/aderrors.jl) and [Python](https://github.com/mbruno46/pyobs).
 
 ## Basic example
 

tests/obs_test.py

@@ -586,6 +586,12 @@ def test_covariance2_symmetry():
     assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
 
 
+def test_empty_obs():
+    o = pe.Obs([np.random.rand(100)], ['test'])
+    q = o + pe.Obs([], [])
+    assert q == o
+
+
 def test_jackknife():
     full_data = np.random.normal(1.1, 0.87, 5487)