def __resid(pars,
ncoefs=1,
knots=None,
order=3,
irbkg=1,
nfft=2048,
kraw=None,
mu=None,
kout=None,
ftwin=1,
kweight=1,
chi_std=None,
nclamp=0,
clamp_lo=1,
clamp_hi=1,
**kws):
# coefs = [getattr(pars, FMT_COEF % i) for i in range(ncoefs)]
coefs = [pars[FMT_COEF % i].value for i in range(ncoefs)]
bkg, chi = spline_eval(kraw, mu, knots, coefs, order, kout)
if chi_std is not None:
chi = chi - chi_std
out = realimag(xftf_fast(chi * ftwin, nfft=nfft)[:irbkg])
if nclamp == 0:
return out
# spline clamps:
scale = (1.0 + 100 * (out * out).sum()) / (len(out) * nclamp)
scaled_chik = scale * chi * kout**kweight
return np.concatenate((out, abs(clamp_lo) * scaled_chik[:nclamp],
abs(clamp_hi) * scaled_chik[-nclamp:]))
def fftf(self, chi, kweight=None):
""" forward FT -- meant to be used internally.
chi must be on self.k_ grid"""
self.make_karrays()
if self.kwin is None:
self.kwin = ftwindow(self.k_, xmin=self.kmin, xmax=self.kmax,
dx=self.dk, dx2=self.dk2, window=self.window)
if kweight is None:
kweight = self.get_kweight()
cx = chi * self.kwin[:len(chi)] * self.k_[:len(chi)]**kweight
return xftf_fast(cx, kstep=self.kstep, nfft=self.nfft)
def fftf(self, chi, kweight=None):
""" forward FT -- meant to be used internally.
chi must be on self.k_ grid"""
if self.kstep != self.__kstep or self.nfft != self.__nfft:
self.make_karrays()
if self.kwin is None:
self.kwin = ftwindow(self.k_, xmin=self.kmin, xmax=self.kmax,
dx=self.dk, dx2=self.dk2, window=self.window)
if kweight is None:
kweight = self.get_kweight()
cx = chi * self.kwin[:len(chi)] * self.k_[:len(chi)]**kweight
return xftf_fast(cx, kstep=self.kstep, nfft=self.nfft)
def __resid(pars, ncoefs=1, knots=None, order=3, irbkg=1, nfft=2048,
kraw=None, mu=None, kout=None, ftwin=1, kweight=1, chi_std=None,
nclamp=0, clamp_lo=1, clamp_hi=1, **kws):
coefs = [getattr(pars, FMT_COEF % i) for i in range(ncoefs)]
bkg, chi = spline_eval(kraw, mu, knots, coefs, order, kout)
if chi_std is not None:
chi = chi - chi_std
out = realimag(xftf_fast(chi*ftwin, nfft=nfft)[:irbkg])
if nclamp == 0:
return out
# spline clamps:
scale = (1.0 + 100*(out*out).sum())/(len(out)*nclamp)
scaled_chik = scale * chi * kout**kweight
return np.concatenate((out,
abs(clamp_lo)*scaled_chik[:nclamp],
abs(clamp_hi)*scaled_chik[-nclamp:]))
