def fit_skewedgauss(led_esp, led):
model = SkewedGaussianModel()
params = model.make_params(amplitude=1, center=400, sigma=5, gamma=1)
result = model.fit(led_esp[led][:, 1],
params,
x=led_esp[led][:, 0],
nan_policy='propagate')
return result
def SkewedGaussianFit(x, y, plot=True):
from lmfit.models import SkewedGaussianModel
model = SkewedGaussianModel()
params = model.make_params(amplitude=1, center=60, sigma=30, gamma=0)
# adjust parameters to best fit data.
result = model.fit(y, params, x=x)
v = result.values
plt.plot(x, result.best_fit)
return v['center'], v['gamma']
def skewed_gaussian_fit(x, y, title_name): mod = SkewedGaussianModel() params = mod.make_params(amplitude=-2000, center=6562.801, sigma=1, gamma=0) result = mod.fit(y, params, x=x) print(result.fit_report()) #out = mod.fit(y, pars, x=x) plt.figure() plt.plot(x, y) plt.plot(x, result.best_fit) plt.title(title_name) plt.show()
def skewed_gaussian_fit(x, y, title_name):
mod = SkewedGaussianModel()
params = mod.make_params(amplitude=-2000,
center=6562.801,
sigma=1,
gamma=0)
result = mod.fit(y, params, x=x)
print(result.fit_report())
#out = mod.fit(y, pars, x=x)
plt.figure()
plt.plot(x, y)
plt.plot(x, result.best_fit)
plt.title(title_name)
plt.show()
def fit_GC_residual__(x, y, peak, peak_center):
mod = SkewedGaussianModel(prefix='peak_') + LinearModel(prefix='bkg_')
pars = mod.make_params()
pars['peak_amplitude'].value = 1e6
pars['peak_center'].value = peak_center
pars['peak_gamma'].value = 4
pars['peak_sigma'].value = 0.4
pars['bkg_intercept'].value = 1e5 #
pars['bkg_slope'].value = 500
out = mod.fit(y, pars, x=x) #, iter_cb=per_iteration)
if peak == 'H2':
#print('Nfev = ', out.nfev)
print(out.fit_report())
#print(out.pars['peak_amplitude'].value)
return out
def clean(spectra, sigma=2.6):
md = np.median(spectra[1])
n = int(len(spectra[0])*0.8)
offset = (len(spectra[0])-n)/2
absor = md - min(spectra[1][offset:n-offset])
freq, bin = np.histogram(spectra[1], bins=50, range=(md-absor, md+absor))
rebin = [(bin[b+1]+bin[b])/2 for b in range(len(bin)-1)]
gauss = SkewedGaussianModel()
pars = gauss.make_params()
pars['center'].set(value=md, vary=True)
pars['amplitude'].set(vary=True)
pars['sigma'].set(vary=True)
pars['gamma'].set(vary=True)
out = gauss.fit(freq, pars, x=rebin)
var = sigma*out.best_values['sigma']
xrbn = np.linspace(rebin[0], rebin[-1], num=100)
yrbn = list(out.eval(x=xrbn))
mode = xrbn[yrbn.index(max(yrbn))]
xn = np.copy(spectra[0])
yn = np.copy(spectra[1])
ist=0
pts=[]
errflg = False
for i in range(len(xn)):
if (yn[i] > mode+var) and (errflg == False):
cnt = 0
errflg = True
ist = np.copy(i)
cnt += 1
if (yn[i] > mode+var) and (errflg == True):
cnt += 1
if (yn[i] < mode+var) and (errflg == True):
pts = np.linspace(yn[ist-1], yn[i], cnt+2)[1:-1]
for p in range(ist, i):
yn[p] = pts[p-ist]
errflg = False
return np.array([xn, yn])
def clean(spectra, sigma=4.5):
rej = []
for i, w in enumerate(spectra[1]):
if w <= 0:
rej.append(i)
spectra[0] = np.delete(spectra[0], rej)
spectra[1] = np.delete(spectra[1], rej)
md = np.median(spectra[1])
n = int(len(spectra[0]) * 0.8)
offset = (len(spectra[0]) - n) / 2
absor = md - min(spectra[1][offset:n - offset])
freq, bin = np.histogram(spectra[1],
bins=30,
range=(md - absor, md + absor))
rebin = [(bin[b + 1] + bin[b]) / 2 for b in range(len(bin) - 1)]
gauss = SkewedGaussianModel()
pars = gauss.make_params()
pars['center'].set(vary=True)
pars['amplitude'].set(vary=True)
pars['sigma'].set(vary=True)
pars['gamma'].set(vary=True)
out = gauss.fit(freq, pars, x=rebin)
var = sigma * out.best_values['sigma']
xrbn = np.linspace(rebin[0], rebin[-1], num=100)
yrbn = list(out.eval(x=xrbn))
mode = xrbn[yrbn.index(max(yrbn))]
xn = np.copy(spectra[0])
yn = np.copy(spectra[1])
var = mode
for i, w in enumerate(yn):
if w > mode + var:
rej.append(i)
xn = np.delete(xn, rej)
yn = np.delete(yn, rej)
return np.array([xn, yn])
def fit(spectra, obj, sigma=2.0, ord=4, iter=4):
poly = PolynomialModel(3)
pars = poly.make_params()
for p in range(4):
label = 'c'+str(p)
pars[label].set(value=1., vary=True)
wkcopy = np.copy(spectra[1])
truesp = [i for i in wkcopy if i > 5]
truex = [spectra[0][i] for i in range(len(spectra[1])) if spectra[1][i] > 5]
outcont = poly.fit(truesp, pars, x=truex)
firstcont = outcont.eval(x=spectra[0])
xn = np.copy(spectra[0])
yn = np.copy(spectra[1])/firstcont
pl1=plt.subplot((iter+1)*100+11)
pl1.plot(xn, spectra[1], 'k-', linewidth=0.3)
pl1.plot(xn, firstcont, 'r-', linewidth=0.6)
pl1.set_ylim([0, np.mean(firstcont)*1.5])
for i in range(iter):
i_=np.copy(i)
niter=str(i_+1)
sigma = sigma-i*0.21*sigma
md = np.median(yn)
n = len([i for i in yn if i > 0.1])
offset = (len(xn)-n)/2
absor = md - min(yn[offset:n-offset])
freq, bin = np.histogram(yn, bins=50, range=(md-absor, md+absor))
rebin = [(bin[b+1]+bin[b])/2 for b in range(len(bin)-1)]
gauss = SkewedGaussianModel()
pars = gauss.make_params()
pars['center'].set(value=md, vary=True)
pars['amplitude'].set(vary=True)
pars['sigma'].set(vary=True)
pars['gamma'].set(vary=True)
out = gauss.fit(freq, pars, x=rebin)
var = sigma*out.best_values['sigma']
xrbn = np.linspace(rebin[0], rebin[-1], num=100)
yrbn = list(out.eval(x=xrbn))
mode = xrbn[yrbn.index(max(yrbn))]
ync = np.copy(spectra[1])
xnc = np.copy(spectra[0])
mask = []
for j in range(len(yn)):
if (yn[j] > mode+var/2) or (yn[j] < mode-var/2):
mask.append(False)
else:
mask.append(True)
mask = np.array(mask)
ync = ync[mask]
xnc = xnc[mask]
poly2 = PolynomialModel(ord)
pars2 = poly2.make_params()
for p in range(ord+1):
label = 'c'+str(p)
pars2[label].set(value=1., vary=True)
outcont2 = poly2.fit(ync, pars2, x=xnc)
contf = outcont2.eval(x=xn)
yn = spectra[1]/contf
err = spectra[2]/contf
pln=plt.subplot(int((iter+1)*100+10+(i_+2)))
pln.plot(xn, yn*(np.mean(contf)*0.8), 'k-', linewidth=0.3)
pln.plot(xnc, ync, 'r-', linewidth=0.3)
pln.plot(xn, contf, 'b-', linewidth=0.6)
pln.set_ylim([0, np.mean(contf)*1.2])
plt.savefig(obj[0]+'_fit.png', dpi=300)
plt.clf()
return np.array([xn, yn, err])
def fit(spectra, sigma=4.0, ptreg=8., ord=4, iter=2):
rej = []
for i, w in enumerate(spectra[1]):
if w <= 0:
rej.append(i)
spectra[0] = np.delete(spectra[0], rej)
spectra[1] = np.delete(spectra[1], rej)
# prepare first kick
poly = PolynomialModel(3)
pars = poly.make_params()
for p in range(4):
label = 'c' + str(p)
pars[label].set(value=1., vary=True)
wkcopy = np.copy(spectra[1])
truesp = [i for i in wkcopy if i >= 0]
truex = [
spectra[0][i] for i in range(len(spectra[1])) if spectra[1][i] >= 0
]
outcont = poly.fit(truesp, pars, x=truex)
firstcont = outcont.eval(x=spectra[0])
xn = np.copy(spectra[0])
yn = np.copy(spectra[1]) / firstcont
# start cont. cleaning iterations
for i in range(iter):
i_ = np.copy(i)
niter = str(i_ + 1)
sigma = sigma - i * 0.21 * sigma
md = np.median(yn)
n = len([i for i in yn if i > 0.1])
offset = (len(xn) - n) / 2
absor = md - min(yn[offset:n - offset])
freq, bin = np.histogram(yn, bins=50, range=(md - absor, md + absor))
rebin = [(bin[b + 1] + bin[b]) / 2 for b in range(len(bin) - 1)]
gauss = SkewedGaussianModel()
pars = gauss.make_params()
pars['center'].set(vary=True)
pars['amplitude'].set(vary=True)
pars['sigma'].set(vary=True)
pars['gamma'].set(vary=True)
out = gauss.fit(freq, pars, x=rebin)
var = sigma * out.best_values['sigma']
xrbn = np.linspace(rebin[0], rebin[-1], num=100)
yrbn = list(out.eval(x=xrbn))
mode = xrbn[yrbn.index(max(yrbn))]
# clean cont.
ync = np.copy(spectra[1])
xnc = np.copy(spectra[0])
mask = []
for j in range(len(yn)):
if (yn[j] > mode + var / 2) or (yn[j] < mode - var / 2):
mask.append(False)
else:
mask.append(True)
mask = np.array(mask)
ync = ync[mask]
xnc = xnc[mask]
# re-fitting
poly2 = PolynomialModel(ord)
pars2 = poly2.make_params()
for p in range(ord + 1):
label = 'c' + str(p)
pars2[label].set(value=1., vary=True)
try:
outcont2 = poly2.fit(ync, pars2, x=xnc)
except:
plt.plot(xn, yn, 'k-')
plt.plot([xn[0], xn[-1]], [mode, mode], 'b-')
plt.plot([xn[0], xn[-1]], [mode + var / 2, mode + var / 2], 'r-')
plt.plot([xn[0], xn[-1]], [mode - var / 2, mode - var / 2], 'r-')
plt.show()
contf = outcont2.eval(x=xn)
yn = spectra[1] / contf
clspec = [xnc, ync]
# start slicing
firstv = clspec[0][0]
wavrange = clspec[0][-1] - firstv
sliceno = wavrange / ptreg
slisize = wavrange / sliceno
points = [[], []]
# continuum point definition
for s in range(int(sliceno)):
i = bissec(clspec[0], firstv + s * slisize)
f = bissec(clspec[0], firstv + (s + 1) * slisize)
slc = [clspec[0][i:f], clspec[1][i:f]]
if len(slc[1]) > 2.:
md = np.median(slc[1])
absor = min(slc[1])
high = max(slc[1])
freq, bin = np.histogram(slc[1], bins=20, range=(absor, high))
rebin = [(bin[b + 1] + bin[b]) / 2 for b in range(len(bin) - 1)]
fmode = rebin[list(freq).index(max(freq))]
fsigma = rebin[-1] - rebin[0]
gauss = GaussianModel()
pars = gauss.make_params()
pars['center'].set(value=fmode, vary=True)
pars['amplitude'].set(value=max(freq), vary=True)
pars['sigma'].set(value=fsigma, vary=True)
out = gauss.fit(freq, pars, x=rebin)
xrbn = np.linspace(rebin[0], rebin[-1], num=100)
yrbn = list(out.eval(x=xrbn))
mode = xrbn[yrbn.index(max(yrbn))]
xp = slc[0][len(slc[0]) / 2]
points[0].append(xp)
points[1].append(mode)
spline = splrep(points[0], points[1], k=3)
contx = splev(clspec[0], spline)
continuum = splev(spectra[0], spline)
return [spectra[0], spectra[1] / continuum]