def find_lines(data, thresh=3):
nside = 8
nleft = 8
nright = 8
index = 8
B = 1.0 / 1200
while index < data.wave.size - 8:
lines = array([data.wave[index]])
results,chisq = line_flux(data,lines,nleft=nleft,nright=nright, \
quiet=True)
results_fw,chisq_fw = line_flux(data,lines,nleft=nleft,nright=nright,\
quiet=True,fixed_width=True)
chisq_test = (chisq <= thresh) & (chisq_fw <= thresh)
SN_test = (results[0,1]/results[0,2] >= 5) & \
(results_fw[0,1]/results_fw[0,2] >= 5)
width_test = (results[0,3] >= lines[0]*B/2) & (results[0,3] <= \
lines[0]*3*B)
flux_test = absv(results[0, 1] - results_fw[0, 1]) / results[0,
1] <= 0.2
#if (chisq_test & flux_test) & (SN_test & width_test):
if (chisq_test & SN_test):
print(" {0:f} {1:e} {2:e} {3:f} {4:f}".format( \
results[0,0],results[0,1],results[0,2],results[0,3],chisq))
results,chisq = line_flux(data,lines,nleft=nleft,nright=nright, \
plotout="{0:6.3f}.pdf".format(results[0,0]),quiet=True)
index = where(absv(data.wave-data.wave[index]-0.05) == \
absv(data.wave-data.wave[index]-0.05).min())[0][0]
nside = 6
else:
index = where(absv(data.wave-data.wave[index]-0.05) == \
absv(data.wave-data.wave[index]-0.05).min())[0][0]
def find_lines(data,thresh=3):
nside = 8
nleft = 8
nright = 8
index = 8
B = 1.0/1200
while index < data.wave.size-8:
lines = array([data.wave[index]])
results,chisq = line_flux(data,lines,nleft=nleft,nright=nright, \
quiet=True)
results_fw,chisq_fw = line_flux(data,lines,nleft=nleft,nright=nright,\
quiet=True,fixed_width=True)
chisq_test = (chisq <= thresh) & (chisq_fw <= thresh)
SN_test = (results[0,1]/results[0,2] >= 5) & \
(results_fw[0,1]/results_fw[0,2] >= 5)
width_test = (results[0,3] >= lines[0]*B/2) & (results[0,3] <= \
lines[0]*3*B)
flux_test = absv(results[0,1]-results_fw[0,1])/results[0,1] <= 0.2
#if (chisq_test & flux_test) & (SN_test & width_test):
if (chisq_test & SN_test):
print(" {0:f} {1:e} {2:e} {3:f} {4:f}".format( \
results[0,0],results[0,1],results[0,2],results[0,3],chisq))
results,chisq = line_flux(data,lines,nleft=nleft,nright=nright, \
plotout="{0:6.3f}.pdf".format(results[0,0]),quiet=True)
index = where(absv(data.wave-data.wave[index]-0.05) == \
absv(data.wave-data.wave[index]-0.05).min())[0][0]
nside = 6
else:
index = where(absv(data.wave-data.wave[index]-0.05) == \
absv(data.wave-data.wave[index]-0.05).min())[0][0]
def line_flux(data,lines,nleft=8,nright=8,plotout=None,quiet=False, \
fixed_width=False,fringing=False):
B = 1.0 / 1500
lines = array(lines)
wave = data.wave
flux = data.flux
unc = data.unc
nlines = lines.size
ind = arange(nlines)
left = where(absv(wave - lines[0]) == absv(wave - lines[0]).min())
right = where(absv(wave-lines[lines.size-1]) == absv(wave- \
lines[lines.size-1]).min())
prange = arange(right[0] - left[0] + nleft + nright + 1) - nleft + left[0]
wave_fit = wave[prange]
flux_fit = flux[prange]
unc_fit = unc[prange]
flux_in = ones(lines.size)
#sigma_in = lines*B
fwhm_in = lines / (888.488 - 9.553 * lines)
sigma_in = fwhm_in / (2 * sqrt(2 * log(2)))
omega_in = array([2 * pi / (4 * sigma_in[0])])
amp_in = array([0.1])
phi_in = array([pi / 2])
slope = array([(flux_fit[flux_fit.size-1]-flux_fit[0])/(wave_fit.max()- \
wave_fit.min())])
yint = array([flux_fit[0] - slope[0] * wave_fit.min()])
if fringing:
A = concatenate((flux_in,lines,sigma_in,yint,slope, \
amp_in,omega_in,phi_in))
else:
A = concatenate((flux_in, lines, sigma_in, yint, slope))
parinfo = []
for i in arange(A.size):
parinfo.append({"limited":[False,False], "limits":[0.0,0.0], \
"mpside":2, "fixed":False})
for i in arange(lines.size):
parinfo[i]["limited"] = [True, False]
parinfo[i]["limits"] = [0.001, 0.0]
parinfo[lines.size + i]["limited"] = [True, True]
parinfo[lines.size +
i]["limits"] = [lines[i] - 0.025, lines[i] + 0.025]
parinfo[2 * lines.size + i]["limited"] = [True, False]
parinfo[2 * lines.size + i]["limits"] = [1.0e-3, 0.0]
if fixed_width:
parinfo[2 * lines.size + i]["fixed"] = True
if fringing:
parinfo[-1]["limited"] = [True, True]
parinfo[-1]["limits"] = [0.0, 2 * pi]
fa = {"x": wave_fit, "y": flux_fit, "err": unc_fit, "fringing": fringing}
mfit = mpfit(gauss, xall=A, functkw=fa, parinfo=parinfo, quiet=1)
A = mfit.params
fit = gauss(A,x=wave_fit,y=flux_fit,err=unc_fit,fringing=fringing)[1]* \
unc_fit*(-1)+flux_fit
chisq = ((fit - flux_fit)**2 / unc_fit**2).sum() / (wave_fit.size - A.size)
# Calculate the flux and uncertainty.
F = sqrt(2*pi)*A[ind]*Jy*(c*A[ind+2*nlines]*1.0e-4)/(A[ind+nlines]* \
1.0e-4)**2/1.0e7
deltaF = ones(nlines)
for i in arange(nlines):
deltaF[i] = sqrt(((unc_fit*Jy*c*B/(wave_fit*1.0e-4)*exp(-1.0*( \
wave_fit-A[i+nlines])**2/(2*A[i+2*nlines]**2)))**2).sum())/1.0e7
# Output the results.
Results = concatenate((array(mat(A[ind+nlines]).T),array(mat(F).T), \
array(mat(deltaF).T),array(mat(A[ind+2*nlines]*2*sqrt(2*log(2))).T)), \
axis=1)
if quiet == False:
print("")
for i in arange(lines.size):
print(" {0:>6.3f} {1:>9.3e} {2:>9.3e} {3:>6.4f}".format( \
Results[i,0],Results[i,1],Results[i,2],Results[i,3]))
print("")
print("Reduced chi-squared of the fit: ", chisq)
print("")
# Plot the results.
if (plotout != None) or (quiet == False):
plt.errorbar(wave_fit, flux_fit, fmt="b", yerr=unc_fit)
plt.plot(wave_fit, fit, "r")
plt.xlabel("$\lambda$ [$\mu$" + "m]")
plt.ylabel(r"F$_{\nu}$ [Jy]")
if plotout != None:
plt.savefig(plotout)
elif quiet == False:
plt.show()
plt.clf()
return Results, chisq
def line_flux(data,lines,nleft=8,nright=8,plotout=None,quiet=False, \
fixed_width=False,fringing=False):
B = 1.0/1500
lines = array(lines)
wave = data.wave
flux = data.flux
unc = data.unc
nlines = lines.size
ind = arange(nlines)
left = where(absv(wave-lines[0]) == absv(wave-lines[0]).min())
right = where(absv(wave-lines[lines.size-1]) == absv(wave- \
lines[lines.size-1]).min())
prange = arange(right[0]-left[0]+nleft+nright+1)-nleft+left[0]
wave_fit = wave[prange]
flux_fit = flux[prange]
unc_fit = unc[prange]
flux_in = ones(lines.size)
#sigma_in = lines*B
fwhm_in = lines/(888.488-9.553*lines)
sigma_in = fwhm_in/(2*sqrt(2*log(2)))
omega_in = array([2*pi/(4*sigma_in[0])])
amp_in = array([0.1])
phi_in = array([pi/2])
slope = array([(flux_fit[flux_fit.size-1]-flux_fit[0])/(wave_fit.max()- \
wave_fit.min())])
yint = array([flux_fit[0]-slope[0]*wave_fit.min()])
if fringing:
A = concatenate((flux_in,lines,sigma_in,yint,slope, \
amp_in,omega_in,phi_in))
else:
A = concatenate((flux_in,lines,sigma_in,yint,slope))
parinfo = []
for i in arange(A.size):
parinfo.append({"limited":[False,False], "limits":[0.0,0.0], \
"mpside":2, "fixed":False})
for i in arange(lines.size):
parinfo[i]["limited"] = [True,False]
parinfo[i]["limits"] = [0.001,0.0]
parinfo[lines.size+i]["limited"] = [True,True]
parinfo[lines.size+i]["limits"] = [lines[i]-0.025,lines[i]+0.025]
parinfo[2*lines.size+i]["limited"] = [True,False]
parinfo[2*lines.size+i]["limits"] = [1.0e-3,0.0]
if fixed_width:
parinfo[2*lines.size+i]["fixed"] = True
if fringing:
parinfo[-1]["limited"] = [True,True]
parinfo[-1]["limits"] = [0.0,2*pi]
fa = {"x":wave_fit, "y":flux_fit, "err":unc_fit, "fringing":fringing}
mfit = mpfit(gauss,xall=A,functkw=fa,parinfo=parinfo,quiet=1)
A=mfit.params
fit = gauss(A,x=wave_fit,y=flux_fit,err=unc_fit,fringing=fringing)[1]* \
unc_fit*(-1)+flux_fit
chisq = ((fit - flux_fit)**2/unc_fit**2).sum()/(wave_fit.size-A.size)
# Calculate the flux and uncertainty.
F = sqrt(2*pi)*A[ind]*Jy*(c*A[ind+2*nlines]*1.0e-4)/(A[ind+nlines]* \
1.0e-4)**2/1.0e7
deltaF = ones(nlines)
for i in arange(nlines):
deltaF[i] = sqrt(((unc_fit*Jy*c*B/(wave_fit*1.0e-4)*exp(-1.0*( \
wave_fit-A[i+nlines])**2/(2*A[i+2*nlines]**2)))**2).sum())/1.0e7
# Output the results.
Results = concatenate((array(mat(A[ind+nlines]).T),array(mat(F).T), \
array(mat(deltaF).T),array(mat(A[ind+2*nlines]*2*sqrt(2*log(2))).T)), \
axis=1)
if quiet == False:
print("")
for i in arange(lines.size):
print(" {0:>6.3f} {1:>9.3e} {2:>9.3e} {3:>6.4f}".format( \
Results[i,0],Results[i,1],Results[i,2],Results[i,3]))
print("")
print("Reduced chi-squared of the fit: ",chisq)
print("")
# Plot the results.
if (plotout != None) or (quiet == False):
plt.errorbar(wave_fit,flux_fit,fmt="b",yerr=unc_fit)
plt.plot(wave_fit,fit,"r")
plt.xlabel("$\lambda$ [$\mu$"+"m]")
plt.ylabel(r"F$_{\nu}$ [Jy]")
if plotout != None:
plt.savefig(plotout)
elif quiet == False:
plt.show()
plt.clf()
return Results, chisq