p_1_clg_Norm[i*3:(i+1)*3] = clf.weights_[i], clf.means_[i], sqrt(clf.covars_[i])
p_1_clg = Rescale_GaussianParameters(p_1_clg_Norm, A_Values, mu_Values, Number_Gaussians)
Wave_Gaussian_clg, Flux_Gaussian_clg, zerolev_Gaussian_clg = ResampleGaussian(p_1_clg, Wave[Ind_left:Ind_right], Continuum[Ind_left:Ind_right])
#---------Kmpfit method no normalized----------
#Guessing components
ncomps = 0
Q = 1
cutamp = 0.01 * max(y)
cutsig = 2.0
print 'puttinig in'
print x, y, continuum_error_norm, cutamp, cutsig, Number_Gaussians
while ncomps != 2 and Q < 8:
comps = gauest(x, y, continuum_error_norm, cutamp, cutsig, q=Q, ncomp=Number_Gaussians)
ncomps = len(comps)
Q += 1
print 'the comps are', comps
p_0_kmpfit_Norm = []
for c in p_0:
p_0_kmpfit_Norm.append(round(c, 3))
fitobj = kmpfit.Fitter(residuals=my_residuals, data=(x, y, continuum_error_norm, Number_Gaussians))
Dictionary_Conditions = [0] * (Number_Gaussians*3)
for i in range(Number_Gaussians):
A_i, mu_i, sigma_i = p_0_kmpfit_Norm[i*3:(i+1)*3]
p_1_clg, Wave[Ind_left:Ind_right], Continuum[Ind_left:Ind_right])
#---------Kmpfit method no normalized----------
#Guessing components
ncomps = 0
Q = 1
cutamp = 0.01 * max(y)
cutsig = 2.0
print 'puttinig in'
print x, y, continuum_error_norm, cutamp, cutsig, Number_Gaussians
while ncomps != 2 and Q < 8:
comps = gauest(x,
y,
continuum_error_norm,
cutamp,
cutsig,
q=Q,
ncomp=Number_Gaussians)
ncomps = len(comps)
Q += 1
print 'the comps are', comps
p_0_kmpfit_Norm = []
for c in p_0:
p_0_kmpfit_Norm.append(round(c, 3))
fitobj = kmpfit.Fitter(residuals=my_residuals,
data=(x, y, continuum_error_norm, Number_Gaussians))
x = numpy.linspace(-5, 10, N)
truepars1 = [10.0, 5.0, 1.0, 3.0, -1.0, 1.5, 0.0]
#p0 = [9, 4.5, 0.8, 0]
y = my_model(truepars1, x, 2) + 0.3*numpy.random.randn(len(x))
err = 0.3*numpy.random.randn(N)
cutamp = 0.1*y.max()
cutsig = 5.0
rms = 0.3
# We use gauest to get the initial estimates
# Gauest returns a list with up to ncomp tuples of which each tuple contains the amplitude,
# the centre and the dispersion of the gaussian, in that order.
ncomps = 0
Q = 1
while ncomps != 2 and Q < 8:
comps = gauest(x, y, rms, cutamp, cutsig, q=Q, ncomp=2)
ncomps = len(comps)
Q += 1
if ncomps != 2:
raise Exception("Cannot estimate two components")
print("Gauest with cutamp, cutsig, rms", cutamp, cutsig, rms)
print("Number of components found:", ncomps)
print("Value of Q for which 2 comps. were found:", Q-1)
p0 = []
for c in comps:
p0 += c
p0.append(0.0) # Zero level
print("Initial estimates p0=", p0)
xse = xs
yse = ys
# thresholds for filters
cutamp = 0.1*yse.max()
cutsig = 5.0
rms = 0.3
# Gauest returns a list with up to ncomp tuples
# of which each tuple contains the amplitude,
# the centre and the dispersion of the gaussian, in that order.
ncomps = 0
Q = 6
while ncomps != 2 and Q < 15:
comps = gauest(yse, rms, cutamp, cutsig, q=Q, ncomp=2)
ncomps = len(comps)
Q += 1
if ncomps == 2:
d = xse[1] - xse[0]
p0 = []
for comp in comps: # Scale to real x range
p0.append(comp[0])
p0.append(xse[0] + comp[1]*d),
p0.append(comp[2]*d)
print("Gauest with cutamp, cutsig, rms", cutamp, cutsig, rms)
print("Number of components found:", ncomps)
print("Value of Q for which 2 comps. were found:", Q-1)
print("Found ampl, center, dispersion:", p0)
xse = xs
yse = ys
# thresholds for filters
cutamp = 0.1 * yse.max()
cutsig = 5.0
rms = 0.3
# Gauest returns a list with up to ncomp tuples
# of which each tuple contains the amplitude,
# the centre and the dispersion of the gaussian, in that order.
ncomps = 0
Q = 6
while ncomps != 2 and Q < 15:
comps = gauest(yse, rms, cutamp, cutsig, q=Q, ncomp=2)
ncomps = len(comps)
Q += 1
if ncomps == 2:
d = xse[1] - xse[0]
p0 = []
for comp in comps: # Scale to real x range
p0.append(comp[0])
p0.append(xse[0] + comp[1] * d),
p0.append(comp[2] * d)
print("Gauest with cutamp, cutsig, rms", cutamp, cutsig, rms)
print("Number of components found:", ncomps)
print("Value of Q for which 2 comps. were found:", Q - 1)
print("Found ampl, center, dispersion:", p0)
