diff --git a/nifty5/library/correlated_fields.py b/nifty5/library/correlated_fields.py
index e74b60bd764aa321d59efc74cd7d74c8d7529898..6ee76fde814ae749ca1d10fae5645bc4556d162d 100644
--- a/nifty5/library/correlated_fields.py
+++ b/nifty5/library/correlated_fields.py
@@ -42,8 +42,9 @@ def _lognormal_moments(mean, sig):
logmean = np.log(mean) - logsig**2/2
return logmean, logsig
+
class _lognormal_moment_matching(Operator):
- def __init__(self,mean, sig, key):
+ def __init__(self, mean, sig, key):
key = str(key)
logmean, logsig = _lognormal_moments(mean, sig)
self._mean = mean
@@ -61,6 +62,7 @@ class _lognormal_moment_matching(Operator):
def std(self):
return self._sig
+
def _normal(mean, sig, key):
return Adder(Field.scalar(mean)) @ (
sig*ducktape(DomainTuple.scalar_domain(), None, key))
@@ -194,17 +196,17 @@ class _Amplitude(Operator):
expander = VdotOperator(sc).adjoint
sigmasq = expander @ flexibility
- dist = np.zeros(twolog.domain.shape)
- dist[0] += 1.
- dist = from_global_data(twolog.domain, dist)
- scale = VdotOperator(dist).adjoint @ asperity
+ dist = np.zeros(twolog.domain.shape, dtype=np.float64)
+ dist[0] += 1
+ scale = VdotOperator(from_global_data(twolog.domain,
+ dist)).adjoint @ asperity
shift = np.ones(scale.target.shape)
shift[0] = dt**2/12.
shift = from_global_data(scale.target, shift)
scale = sigmasq*(Adder(shift) @ scale).sqrt()
-
smooth = twolog @ (scale*ducktape(scale.target, None, key))
+
tg = smooth.target
noslope = _SlopeRemover(tg) @ smooth
_t = VdotOperator(from_global_data(tg, _logkl(tg))).adjoint
@@ -240,7 +242,7 @@ class CorrelatedFieldMaker:
loglogavgslope_mean,
loglogavgslope_stddev,
prefix='',
- index = None):
+ index=None):
fluctuations_mean = float(fluctuations_mean)
fluctuations_stddev = float(fluctuations_stddev)
flexibility_mean = float(flexibility_mean)
@@ -332,18 +334,18 @@ class CorrelatedFieldMaker:
@property
def total_fluctuation(self):
if len(self._a) == 0:
- raise(NotImplementedError)
+ raise NotImplementedError
if len(self._a) == 1:
return self._a[0].fluctuation_amplitude
q = 1.
for a in self._a:
fl = a.fluctuation_amplitude
- q = q * (Adder(full(fl.target,1.)) @ fl**2)
- return (Adder(full(q.target,-1.)) @ q).sqrt()
+ q = q*(Adder(full(fl.target, 1.)) @ fl**2)
+ return (Adder(full(q.target, -1.)) @ q).sqrt()
- def slice_fluctuation(self,space):
+ def slice_fluctuation(self, space):
if len(self._a) == 0:
- raise(NotImplementedError)
+ raise NotImplementedError
assert space < len(self._a)
if len(self._a) == 1:
return self._a[0].fluctuation_amplitude
@@ -351,49 +353,49 @@ class CorrelatedFieldMaker:
for j in range(len(self._a)):
fl = self._a[j].fluctuation_amplitude
if j == space:
- q = q * fl**2
+ q = q*fl**2
else:
- q = q * (Adder(full(fl.target,1.)) @ fl**2)
+ q = q*(Adder(full(fl.target, 1.)) @ fl**2)
return q.sqrt()
-
- def average_fluctuation(self,space):
+
+ def average_fluctuation(self, space):
if len(self._a) == 0:
- raise(NotImplementedError)
+ raise NotImplementedError
assert space < len(self._a)
if len(self._a) == 1:
return self._a[0].fluctuation_amplitude
return self._a[space].fluctuation_amplitude
- def offset_amplitude_realized(self,samples):
+ def offset_amplitude_realized(self, samples):
res = 0.
for s in samples:
res += s.mean()**2
return np.sqrt(res/len(samples))
-
- def total_fluctuation_realized(self,samples):
+
+ def total_fluctuation_realized(self, samples):
res = 0.
for s in samples:
- res = res + (s-s.mean())**2
+ res = res + (s - s.mean())**2
res = res/len(samples)
return np.sqrt(res.mean())
-
- def average_fluctuation_realized(self,samples,space):
+
+ def average_fluctuation_realized(self, samples, space):
ldom = len(samples[0].domain)
assert space < ldom
if ldom == 1:
return self.total_fluctuation_realized(samples)
- spaces=()
+ spaces = ()
for i in range(ldom):
if i != space:
spaces += (i,)
res = 0.
for s in samples:
r = s.mean(spaces)
- res = res + (r-r.mean())**2
+ res = res + (r - r.mean())**2
res = res/len(samples)
return np.sqrt(res.mean())
-
- def slice_fluctuation_realized(self,samples,space):
+
+ def slice_fluctuation_realized(self, samples, space):
ldom = len(samples[0].domain)
assert space < ldom
if ldom == 1:
@@ -405,27 +407,23 @@ class CorrelatedFieldMaker:
res2 = res2 + s.mean(space)**2
res1 = res1/len(samples)
res2 = res2/len(samples)
- res = res1.mean() - res2.mean()
+ res = res1.mean() - res2.mean()
return np.sqrt(res)
- def stats(self,op,samples):
+ def stats(self, op, samples):
sc = StatCalculator()
for s in samples:
sc.add(op(s.extract(op.domain)))
return sc.mean.to_global_data(), sc.var.sqrt().to_global_data()
-
- def moment_slice_to_average(self,
- fluctuations_slice_mean,
- nsamples = 1000):
+
+ def moment_slice_to_average(self, fluctuations_slice_mean, nsamples=1000):
fluctuations_slice_mean = float(fluctuations_slice_mean)
assert fluctuations_slice_mean > 0
scm = 1.
for a in self._a:
m, std = a.fluctuation_amplitude.mean, a.fluctuation_amplitude.std
- mu, sig = _lognormal_moments(m,std)
- flm = np.exp(mu + sig * np.random.normal(size=nsamples))
+ mu, sig = _lognormal_moments(m, std)
+ flm = np.exp(mu + sig*np.random.normal(size=nsamples))
scm *= flm**2 + 1.
scm = np.mean(np.sqrt(scm))
- return fluctuations_slice_mean / scm
-
-
\ No newline at end of file
+ return fluctuations_slice_mean/scm