Commit 02558ec9 by Philipp Arras

### Mostly formatting

parent 4e97ffbe
 ... @@ -42,8 +42,9 @@ def _lognormal_moments(mean, sig): ... @@ -42,8 +42,9 @@ def _lognormal_moments(mean, sig): logmean = np.log(mean) - logsig**2/2 logmean = np.log(mean) - logsig**2/2 return logmean, logsig return logmean, logsig class _lognormal_moment_matching(Operator): class _lognormal_moment_matching(Operator): def __init__(self,mean, sig, key): def __init__(self, mean, sig, key): key = str(key) key = str(key) logmean, logsig = _lognormal_moments(mean, sig) logmean, logsig = _lognormal_moments(mean, sig) self._mean = mean self._mean = mean ... @@ -61,6 +62,7 @@ class _lognormal_moment_matching(Operator): ... @@ -61,6 +62,7 @@ class _lognormal_moment_matching(Operator): def std(self): def std(self): return self._sig return self._sig def _normal(mean, sig, key): def _normal(mean, sig, key): return Adder(Field.scalar(mean)) @ ( return Adder(Field.scalar(mean)) @ ( sig*ducktape(DomainTuple.scalar_domain(), None, key)) sig*ducktape(DomainTuple.scalar_domain(), None, key)) ... @@ -194,17 +196,17 @@ class _Amplitude(Operator): ... @@ -194,17 +196,17 @@ class _Amplitude(Operator): expander = VdotOperator(sc).adjoint expander = VdotOperator(sc).adjoint sigmasq = expander @ flexibility sigmasq = expander @ flexibility dist = np.zeros(twolog.domain.shape) dist = np.zeros(twolog.domain.shape, dtype=np.float64) dist[0] += 1. dist[0] += 1 dist = from_global_data(twolog.domain, dist) scale = VdotOperator(from_global_data(twolog.domain, scale = VdotOperator(dist).adjoint @ asperity dist)).adjoint @ asperity shift = np.ones(scale.target.shape) shift = np.ones(scale.target.shape) shift[0] = dt**2/12. shift[0] = dt**2/12. shift = from_global_data(scale.target, shift) shift = from_global_data(scale.target, shift) scale = sigmasq*(Adder(shift) @ scale).sqrt() scale = sigmasq*(Adder(shift) @ scale).sqrt() smooth = twolog @ (scale*ducktape(scale.target, None, key)) smooth = twolog @ (scale*ducktape(scale.target, None, key)) tg = smooth.target tg = smooth.target noslope = _SlopeRemover(tg) @ smooth noslope = _SlopeRemover(tg) @ smooth _t = VdotOperator(from_global_data(tg, _logkl(tg))).adjoint _t = VdotOperator(from_global_data(tg, _logkl(tg))).adjoint ... @@ -240,7 +242,7 @@ class CorrelatedFieldMaker: ... @@ -240,7 +242,7 @@ class CorrelatedFieldMaker: loglogavgslope_mean, loglogavgslope_mean, loglogavgslope_stddev, loglogavgslope_stddev, prefix='', prefix='', index = None): index=None): fluctuations_mean = float(fluctuations_mean) fluctuations_mean = float(fluctuations_mean) fluctuations_stddev = float(fluctuations_stddev) fluctuations_stddev = float(fluctuations_stddev) flexibility_mean = float(flexibility_mean) flexibility_mean = float(flexibility_mean) ... @@ -332,18 +334,18 @@ class CorrelatedFieldMaker: ... @@ -332,18 +334,18 @@ class CorrelatedFieldMaker: @property @property def total_fluctuation(self): def total_fluctuation(self): if len(self._a) == 0: if len(self._a) == 0: raise(NotImplementedError) raise NotImplementedError if len(self._a) == 1: if len(self._a) == 1: return self._a[0].fluctuation_amplitude return self._a[0].fluctuation_amplitude q = 1. q = 1. for a in self._a: for a in self._a: fl = a.fluctuation_amplitude fl = a.fluctuation_amplitude q = q * (Adder(full(fl.target,1.)) @ fl**2) q = q*(Adder(full(fl.target, 1.)) @ fl**2) return (Adder(full(q.target,-1.)) @ q).sqrt() return (Adder(full(q.target, -1.)) @ q).sqrt() def slice_fluctuation(self,space): def slice_fluctuation(self, space): if len(self._a) == 0: if len(self._a) == 0: raise(NotImplementedError) raise NotImplementedError assert space < len(self._a) assert space < len(self._a) if len(self._a) == 1: if len(self._a) == 1: return self._a[0].fluctuation_amplitude return self._a[0].fluctuation_amplitude ... @@ -351,49 +353,49 @@ class CorrelatedFieldMaker: ... @@ -351,49 +353,49 @@ class CorrelatedFieldMaker: for j in range(len(self._a)): for j in range(len(self._a)): fl = self._a[j].fluctuation_amplitude fl = self._a[j].fluctuation_amplitude if j == space: if j == space: q = q * fl**2 q = q*fl**2 else: else: q = q * (Adder(full(fl.target,1.)) @ fl**2) q = q*(Adder(full(fl.target, 1.)) @ fl**2) return q.sqrt() return q.sqrt() def average_fluctuation(self,space): def average_fluctuation(self, space): if len(self._a) == 0: if len(self._a) == 0: raise(NotImplementedError) raise NotImplementedError assert space < len(self._a) assert space < len(self._a) if len(self._a) == 1: if len(self._a) == 1: return self._a[0].fluctuation_amplitude return self._a[0].fluctuation_amplitude return self._a[space].fluctuation_amplitude return self._a[space].fluctuation_amplitude def offset_amplitude_realized(self,samples): def offset_amplitude_realized(self, samples): res = 0. res = 0. for s in samples: for s in samples: res += s.mean()**2 res += s.mean()**2 return np.sqrt(res/len(samples)) return np.sqrt(res/len(samples)) def total_fluctuation_realized(self,samples): def total_fluctuation_realized(self, samples): res = 0. res = 0. for s in samples: for s in samples: res = res + (s-s.mean())**2 res = res + (s - s.mean())**2 res = res/len(samples) res = res/len(samples) return np.sqrt(res.mean()) return np.sqrt(res.mean()) def average_fluctuation_realized(self,samples,space): def average_fluctuation_realized(self, samples, space): ldom = len(samples[0].domain) ldom = len(samples[0].domain) assert space < ldom assert space < ldom if ldom == 1: if ldom == 1: return self.total_fluctuation_realized(samples) return self.total_fluctuation_realized(samples) spaces=() spaces = () for i in range(ldom): for i in range(ldom): if i != space: if i != space: spaces += (i,) spaces += (i,) res = 0. res = 0. for s in samples: for s in samples: r = s.mean(spaces) r = s.mean(spaces) res = res + (r-r.mean())**2 res = res + (r - r.mean())**2 res = res/len(samples) res = res/len(samples) return np.sqrt(res.mean()) return np.sqrt(res.mean()) def slice_fluctuation_realized(self,samples,space): def slice_fluctuation_realized(self, samples, space): ldom = len(samples[0].domain) ldom = len(samples[0].domain) assert space < ldom assert space < ldom if ldom == 1: if ldom == 1: ... @@ -405,27 +407,23 @@ class CorrelatedFieldMaker: ... @@ -405,27 +407,23 @@ class CorrelatedFieldMaker: res2 = res2 + s.mean(space)**2 res2 = res2 + s.mean(space)**2 res1 = res1/len(samples) res1 = res1/len(samples) res2 = res2/len(samples) res2 = res2/len(samples) res = res1.mean() - res2.mean() res = res1.mean() - res2.mean() return np.sqrt(res) return np.sqrt(res) def stats(self,op,samples): def stats(self, op, samples): sc = StatCalculator() sc = StatCalculator() for s in samples: for s in samples: sc.add(op(s.extract(op.domain))) sc.add(op(s.extract(op.domain))) return sc.mean.to_global_data(), sc.var.sqrt().to_global_data() return sc.mean.to_global_data(), sc.var.sqrt().to_global_data() def moment_slice_to_average(self, def moment_slice_to_average(self, fluctuations_slice_mean, nsamples=1000): fluctuations_slice_mean, nsamples = 1000): fluctuations_slice_mean = float(fluctuations_slice_mean) fluctuations_slice_mean = float(fluctuations_slice_mean) assert fluctuations_slice_mean > 0 assert fluctuations_slice_mean > 0 scm = 1. scm = 1. for a in self._a: for a in self._a: m, std = a.fluctuation_amplitude.mean, a.fluctuation_amplitude.std m, std = a.fluctuation_amplitude.mean, a.fluctuation_amplitude.std mu, sig = _lognormal_moments(m,std) mu, sig = _lognormal_moments(m, std) flm = np.exp(mu + sig * np.random.normal(size=nsamples)) flm = np.exp(mu + sig*np.random.normal(size=nsamples)) scm *= flm**2 + 1. scm *= flm**2 + 1. scm = np.mean(np.sqrt(scm)) scm = np.mean(np.sqrt(scm)) return fluctuations_slice_mean / scm return fluctuations_slice_mean/scm \ No newline at end of file
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