Merge branch 'develop' into feature/rwf

pyerrors/correlators.py

@@ -39,7 +39,7 @@ class Corr:
             region indentified for this correlator.
         """
 
-        if isinstance(data_input, np.ndarray):  # Input is an array of Corrs
+        if isinstance(data_input, np.ndarray):
 
             # This only works, if the array fulfills the conditions below
             if not len(data_input.shape) == 2 and data_input.shape[0] == data_input.shape[1]:
@@ -95,7 +95,6 @@ class Corr:
         # An undefined timeslice is represented by the None object
         self.content = [None] * padding[0] + self.content + [None] * padding[1]
         self.T = len(self.content)
-
         self.prange = prange
 
         self.gamma_method()
@@ -160,9 +159,6 @@ class Corr:
                 raise Exception("Vectors are of wrong shape!")
             if normalize:
                 vector_l, vector_r = vector_l / np.sqrt((vector_l @ vector_l)), vector_r / np.sqrt(vector_r @ vector_r)
-            # if (not (0.95 < vector_r @ vector_r < 1.05)) or (not (0.95 < vector_l @ vector_l < 1.05)):
-                # print("Vectors are normalized before projection!")
-
             newcontent = [None if (item is None) else np.asarray([vector_l.T @ item @ vector_r]) for item in self.content]
 
         else:

pyerrors/misc.py

@@ -35,6 +35,38 @@ def load_object(path):
         return pickle.load(file)
 
 
+def pseudo_Obs(value, dvalue, name, samples=1000):
+    """Generate an Obs object with given value, dvalue and name for test purposes
+
+    Parameters
+    ----------
+    value : float
+        central value of the Obs to be generated.
+    dvalue : float
+        error of the Obs to be generated.
+    name : str
+        name of the ensemble for which the Obs is to be generated.
+    samples: int
+        number of samples for the Obs (default 1000).
+    """
+    if dvalue <= 0.0:
+        return Obs([np.zeros(samples) + value], [name])
+    else:
+        for _ in range(100):
+            deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)]
+            deltas -= np.mean(deltas)
+            deltas *= dvalue / np.sqrt((np.var(deltas) / samples)) / np.sqrt(1 + 3 / samples)
+            deltas += value
+            res = Obs(deltas, [name])
+            res.gamma_method(S=2, tau_exp=0)
+            if abs(res.dvalue - dvalue) < 1e-10 * dvalue:
+                break
+
+        res._value = float(value)
+
+        return res
+
+
 def gen_correlated_data(means, cov, name, tau=0.5, samples=1000):
     """ Generate observables with given covariance and autocorrelation times.
 

pyerrors/obs.py

@@ -1301,7 +1301,7 @@ def correlate(obs_a, obs_b):
 
     Keep in mind to only correlate primary observables which have not been reweighted
     yet. The reweighting has to be applied after correlating the observables.
-    Currently only works if ensembles are identical. This is not really necessary.
+    Currently only works if ensembles are identical (this is not strictly necessary).
     """
 
     if sorted(obs_a.names) != sorted(obs_b.names):
@@ -1461,38 +1461,6 @@ def covariance(obs1, obs2, correlation=False, **kwargs):
     return dvalue
 
 
-def pseudo_Obs(value, dvalue, name, samples=1000):
-    """Generate a pseudo Obs with given value, dvalue and name
-
-    Parameters
-    ----------
-    value : float
-        central value of the Obs to be generated.
-    dvalue : float
-        error of the Obs to be generated.
-    name : str
-        name of the ensemble for which the Obs is to be generated.
-    samples: int
-        number of samples for the Obs (default 1000).
-    """
-    if dvalue <= 0.0:
-        return Obs([np.zeros(samples) + value], [name])
-    else:
-        for _ in range(100):
-            deltas = [np.random.normal(0.0, dvalue * np.sqrt(samples), samples)]
-            deltas -= np.mean(deltas)
-            deltas *= dvalue / np.sqrt((np.var(deltas) / samples)) / np.sqrt(1 + 3 / samples)
-            deltas += value
-            res = Obs(deltas, [name])
-            res.gamma_method(S=2, tau_exp=0)
-            if abs(res.dvalue - dvalue) < 1e-10 * dvalue:
-                break
-
-        res._value = float(value)
-
-        return res
-
-
 def import_jackknife(jacks, name, idl=None):
     """Imports jackknife samples and returns an Obs
 

tests/benchmark_test.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pyerrors as pe
 import pytest
-import time
 
 np.random.seed(0)
 

tests/obs_test.py

@@ -1,7 +1,5 @@
 import autograd.numpy as np
 import os
-import random
-import string
 import copy
 import pyerrors as pe
 import pytest
@@ -142,7 +140,7 @@ def test_overloading_vectorization():
     assert [o.value for o in b / a] == [o.value for o in [b / p for p in a]]
 
 
-def test_gamma_method():
+def test_gamma_method_standard_data():
     for data in [np.tile([1, -1], 1000),
                  np.random.rand(100001),
                  np.zeros(1195),
@@ -285,7 +283,7 @@ def test_covariance_symmetry():
     assert np.abs(cov_ab) < test_obs1.dvalue * test_obs2.dvalue * (1 + 10 * np.finfo(np.float64).eps)
 
 
-def test_gamma_method():
+def test_gamma_method_uncorrelated():
     # Construct pseudo Obs with random shape
     value = np.random.normal(5, 10)
     dvalue = np.abs(np.random.normal(0, 1))

tests/sfcf_in_test.py

@@ -1,14 +1,14 @@
-import os,sys,inspect
+import os
+import sys
+import inspect
+import pyerrors as pe
+import pyerrors.input.sfcf as sfin
+import shutil
+
 current_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parent_dir = os.path.dirname(current_dir)
 sys.path.insert(0, parent_dir)
 
-import pyerrors as pe
-import pyerrors.input.openQCD as qcdin
-import pyerrors.input.sfcf as sfin
-import shutil
-
-from time import sleep
 
 def build_test_environment(env_type, cfgs, reps):
     if env_type == "o":
@@ -26,7 +26,6 @@ def build_test_environment(env_type, cfgs, reps):
 
 
 
-
 def clean_test_environment(env_type, cfgs, reps):
     if env_type == "o":
         for i in range(1,reps):