Commit 370ccf92 authored by Philipp Haim's avatar Philipp Haim

Cosmetics

parent 919e2439
Pipeline #65440 passed with stages
in 16 minutes and 13 seconds
...@@ -114,9 +114,7 @@ def _total_fluctuation_realized(samples): ...@@ -114,9 +114,7 @@ def _total_fluctuation_realized(samples):
for s in samples: for s in samples:
res = res + (s - co.adjoint(co(s)/size))**2 res = res + (s - co.adjoint(co(s)/size))**2
res = res.mean(spaces)/len(samples) res = res.mean(spaces)/len(samples)
if np.isscalar(res): return np.sqrt(res if np.isscalar(res) else res.val)
return np.sqrt(res)
return np.sqrt(res.val)
class _LognormalMomentMatching(Operator): class _LognormalMomentMatching(Operator):
...@@ -592,9 +590,8 @@ class CorrelatedFieldMaker: ...@@ -592,9 +590,8 @@ class CorrelatedFieldMaker:
res = 0. res = 0.
for s in samples: for s in samples:
res = res + s.mean(spaces)**2 res = res + s.mean(spaces)**2
if np.isscalar(res): res = res/len(samples)
return np.sqrt(res/len(samples)) return np.sqrt(res if np.isscalar(res) else res.val)
return np.sqrt(res.val/len(samples))
@staticmethod @staticmethod
def total_fluctuation_realized(samples): def total_fluctuation_realized(samples):
...@@ -617,9 +614,7 @@ class CorrelatedFieldMaker: ...@@ -617,9 +614,7 @@ class CorrelatedFieldMaker:
res1 = res1/len(samples) res1 = res1/len(samples)
res2 = res2/len(samples) res2 = res2/len(samples)
res = res1.mean(spaces) - res2.mean(spaces[:-1]) res = res1.mean(spaces) - res2.mean(spaces[:-1])
if np.isscalar(res): return np.sqrt(res if np.isscalar(res) else res.val)
return np.sqrt(res)
return np.sqrt(res.val)
@staticmethod @staticmethod
def average_fluctuation_realized(samples, space): def average_fluctuation_realized(samples, space):
...@@ -643,6 +638,4 @@ class CorrelatedFieldMaker: ...@@ -643,6 +638,4 @@ class CorrelatedFieldMaker:
r = s.mean(sub_spaces) r = s.mean(sub_spaces)
res = res + (r - co.adjoint(co(r)/size))**2 res = res + (r - co.adjoint(co(r)/size))**2
res = res.mean(spaces[0])/len(samples) res = res.mean(spaces[0])/len(samples)
if np.isscalar(res): return np.sqrt(res if np.isscalar(res) else res.val)
return np.sqrt(res)
return np.sqrt(res.val)
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