Ejemplo n.º 1
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]
Ejemplo n.º 2
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]
Ejemplo n.º 3
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
Ejemplo n.º 4
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