From a6bbaa1eb85cc7d9d1d03dfada3e7f20799f49d4 Mon Sep 17 00:00:00 2001
From: Martin Reinecke <martin@mpa-garching.mpg.de>
Date: Fri, 19 Jun 2020 12:43:42 +0200
Subject: [PATCH] cosmetics
---
nifty6/operators/energy_operators.py | 11 +----------
1 file changed, 1 insertion(+), 10 deletions(-)
diff --git a/nifty6/operators/energy_operators.py b/nifty6/operators/energy_operators.py
index 627c09eaa..21ece3648 100644
--- a/nifty6/operators/energy_operators.py
+++ b/nifty6/operators/energy_operators.py
@@ -167,15 +167,6 @@ class VariableCovarianceGaussianEnergy(EnergyOperator):
return res.add_metric(SamplingDtypeSetter(met, self._sampling_dtype))
-def _build_MultiScalingOperator(domain, scales):
- op = None
- for k, dom in domain.items():
- o = ScalingOperator(dom, scales[k])
- FA = FieldAdapter(dom, k)
- o = FA.adjoint @ o @ FA
- op = o if op is None else op + o
- return op
-
class GaussianEnergy(EnergyOperator):
"""Computes a negative-log Gaussian.
@@ -197,7 +188,7 @@ class GaussianEnergy(EnergyOperator):
Operator domain. By default it is inferred from `mean` or
`covariance` if specified
sampling_dtype : type
- Here one can specify whether the distribution is a compelx Gaussian or
+ Here one can specify whether the distribution is a complex Gaussian or
not. Note that for a complex Gaussian the inverse_covariance is
.. math ::
(<ff^dagger>)^{-1}_P(f)/2,
--
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