feat: When initializing 1d correlators it is now checked whether all obs

are defined on the same ensembles.

pyerrors/correlators.py

@@ -4,7 +4,7 @@ import autograd.numpy as anp
 import matplotlib.pyplot as plt
 import scipy.linalg
 from .obs import Obs, reweight, correlate, CObs
-from .misc import dump_object
+from .misc import dump_object, _assert_equal_properties
 from .fits import least_squares
 from .linalg import eigh, inv, cholesky
 from .roots import find_root
@@ -42,10 +42,8 @@ class Corr:
         if not isinstance(data_input, list):
             raise TypeError('Corr__init__ expects a list of timeslices.')
 
-        # data_input can have multiple shapes. The simplest one is a list of Obs.
-        # We check, if this is the case
         if all([(isinstance(item, Obs) or isinstance(item, CObs)) for item in data_input]):
-
+            _assert_equal_properties(data_input)
             self.content = [np.asarray([item]) for item in data_input]
             self.N = 1
 

pyerrors/misc.py

@@ -85,4 +85,3 @@ def _assert_equal_properties(ol, otype=Obs):
             raise Exception("All Obs in list have to be defined on the same set of configs.")
         if not ol[0].idl == o.idl:
             raise Exception("All Obs in list have to be defined on the same set of configurations.")
-

tests/correlators_test.py

@@ -120,6 +120,24 @@ def test_padded_correlator():
     [o for o in my_corr]
 
 
+def test_corr_exceptions():
+    obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
+    obs_b= pe.Obs([np.random.normal(0.1, 0.1, 99)], ['test'])
+    with pytest.raises(Exception):
+        pe.Corr([obs_a, obs_b])
+
+    obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
+    obs_b= pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'], idl=[range(1, 200, 2)])
+    with pytest.raises(Exception):
+        pe.Corr([obs_a, obs_b])
+
+    obs_a = pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test'])
+    obs_b= pe.Obs([np.random.normal(0.1, 0.1, 100)], ['test2'])
+    with pytest.raises(Exception):
+        pe.Corr([obs_a, obs_b])
+
+
+
 def test_utility():
     corr_content = []
     for t in range(8):