class GaussianHeightRater(base.BaseRater):
short_name = "gaussheight"
long_name = "Gaussian Height Rating"
description = "Compute the height of the best-fit Gaussian divided " \
"by the RMS of the post-fit residuals. The function " \
"being fit is not actually a Gaussian, it's a von Mises " \
"distribution (exp(k*cos(theta)))"
version = 6
rat_cls = gaussian.GaussianProfileClass()
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is the
amplitude of the von Mises function fit to the candidate's
profile divided by the RMS of the post-fit residuals.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
nbins = len(cand.profile)
resids = cand.profile-cand.gaussfit.histogram(nbins)
rms = resids.std()
return cand.gaussfit.amplitude(nbins)/rms
class GaussianSignificanceRater(base.BaseRater):
short_name = "gausssig"
long_name = "Gaussian Significance Rating"
description = "Compute the significance of the best-fit Gaussian. " \
"The function being fit is not actually a Gaussian, " \
"it's a von Mises distribution (exp(k*cos(theta)))"
version = 2
rat_cls = gaussian.GaussianProfileClass()
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is the
amplitude of the von Mises function fit to the candidate's
profile divided by the RMS of the post-fit residuals.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
nbins = len(cand.profile)
std = np.sqrt(np.mean(np.var(cand.pfd.profs,axis=-1))) * \
np.sqrt(cand.pfd.nsub*cand.pfd.npart)
return cand.gaussfit.amplitude(nbins) / \
(std/np.sqrt(max(cand.gaussfit.fwhm()/(1.0/nbins), 1)))
class GaussianWidthRater(base.BaseRater):
short_name = "gausswidth"
long_name = "Gaussian Width Rating"
description = "Compute the full width at half maxiumum of the best-fit " \
"Gaussian. The function being fit is not actually a " \
"Gaussian, it's a von Mises distribution " \
"(exp(k*cos(theta)))"
version = 5
rat_cls = gaussian.GaussianProfileClass()
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is the
FWHM of the von Mises function fit to the candidate's profile.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
return cand.gaussfit.fwhm()
class GaussianPhaseRater(base.BaseRater):
short_name = "gaussphase"
long_name = "Gaussian Phase Rating"
description = "Compute the phase of the best-fit Gaussian. The function " \
"being fit is not actually a Gaussian, it's a von Mises " \
"distribution (exp(k*cos(theta)))"
version = 5
rat_cls = gaussian.GaussianProfileClass()
def _compute_rating(self, cand):
"""Return a rating for the candidate. The rating value is the
phase of the von Mises function fit to the candidate's
profile.
Input:
cand: A Candidate object to rate.
Output:
value: The rating value.
"""
return cand.gaussfit.mu
xs = np.linspace(0.0, 1.0, n, endpoint=False)
G = gauss._compute_data(cand)
print "k: %g, log(k): %g" % (G.k, np.log10(G.k))
test_ks = np.logspace(np.log10(G.k)-2, np.log10(G.k)+1, 1e3)
#test_ks = np.exp(np.linspace(np.log(1e-1),np.log(1e3),1e3))
plt.figure(1)
resids = [gauss._rms_residual(k,data) for k in test_ks]
plt.loglog(test_ks,resids,color="green", label="_nolabel_")
#plt.axvline(true_k,color="red", label="true k")
best_k = test_ks[np.argmin(resids)]
plt.axvline(best_k,color="green", label="best k")
plt.axvline(G.k,color="cyan", label="k from fit")
plt.ylabel("RMS of residuals")
plt.xlabel("Value of k used (i.e. held fixed) when fitting")
plt.legend(loc="best")
plt.figure(2)
mue, ae, be = gauss._fit_all_but_k(best_k, data)
#plt.plot(xs, true_prof, color="red", label="true")
plt.plot(xs, data, color="black", label="data")
plt.plot(xs, (ae*utils.vonmises_histogram(best_k,mue,n)+be), color="green", label="exhaustive best fit")
plt.plot(xs, G.histogram(n), color="cyan", label="best fit")
plt.legend(loc="best")
plt.show()
if __name__=='__main__':
gauss = gaussian.GaussianProfileClass()
main()