def fitpix(wave,pararr): # define bad pixels cfg.bad_pixels = update_bad_pixels() # this variable stores the indices of bad pixels ll=pararr[0] lz=pararr[3] lv1=pararr[5] lv2=pararr[6] relpix=[] for i in range(len(ll)): vels=jbg.veltrans(lz[i],wave,ll[i]) #w1=ll[i]*(1.+lz[i]+lv1[i]/c) #w2=ll[i]*(1.+lz[i]+lv2[i]/c) #p1=jbg.closest(wave,w1) #p2=jbg.closest(wave,w2) p1=jbg.closest(vels,lv1[i]) p2=jbg.closest(vels,lv2[i]) if ((p1>=10) & (p2<=(len(wave)-1-10))): relpix.extend(range(p1-10,p2+10)) elif (p1<10): relpix.extend(range(0, p2 + 10)) else: relpix.extend(range(p1 - 10, len(wave)-1)) rp = np.unique(np.array(relpix)) clean_rp = np.array([i for i in rp if i not in cfg.bad_pixels]) return clean_rp
def fitpix(wave,pararr,find_bad_pixels=True): if find_bad_pixels: # define bad pixels cfg.bad_pixels = update_bad_pixels() # this variable stores the indices of bad pixels else: cfg.bad_pixels = [] ll=pararr[0] lz=pararr[3] lv1=pararr[5] lv2=pararr[6] relpix=[] for i in range(len(ll)): vels=jbg.veltrans(lz[i],wave,ll[i]) p1=jbg.closest(vels,lv1[i]) p2=jbg.closest(vels,lv2[i]) if ((p1>=10) & (p2<=(len(wave)-1-10))): relpix.extend(range(p1-10,p2+10)) elif (p1<10): relpix.extend(range(0, p2 + 10)) else: relpix.extend(range(p1 - 10, len(wave)-1)) rp = np.unique(np.array(relpix)) clean_rp = np.array([i for i in rp if i not in cfg.bad_pixels]) return clean_rp
def fitpix(wave, pararr, find_bad_pixels=True):
if find_bad_pixels:
# define bad pixels
cfg.bad_pixels = update_bad_pixels(
) # this variable stores the indices of bad pixels
else:
cfg.bad_pixels = []
ll = pararr[0]
lz = pararr[3]
lv1 = pararr[5]
lv2 = pararr[6]
relpix = []
for i in range(len(ll)):
vels = jbg.veltrans(lz[i], wave, ll[i])
p1 = jbg.closest(vels, lv1[i])
p2 = jbg.closest(vels, lv2[i])
if ((p1 >= 10) & (p2 <= (len(wave) - 1 - 10))):
relpix.extend(range(p1 - 10, p2 + 10))
elif (p1 < 10):
relpix.extend(range(0, p2 + 10))
else:
relpix.extend(range(p1 - 10, len(wave) - 1))
rp = np.unique(np.array(relpix))
clean_rp = np.array([i for i in rp if i not in cfg.bad_pixels])
if len(clean_rp) > 1:
# Matt and Kirill trimming off tiny pixel patches
max_index_jump = 4 # this is called buf in makevoigt
min_group_size = 5
last_idx = clean_rp[0]
group_idxs = [last_idx]
really_clean_rp = []
for idx in clean_rp[1:]:
jump = idx - last_idx
if jump <= max_index_jump:
group_idxs.append(idx)
else:
if len(group_idxs) >= min_group_size:
really_clean_rp.extend(group_idxs)
group_idxs = [idx] # start a new group
last_idx = idx
if len(group_idxs) >= min_group_size:
really_clean_rp.extend(group_idxs)
return np.array(really_clean_rp, dtype=int)
else:
return clean_rp
def initplot(self,fig,numchunks=8):
wlen=len(self.spectrum.wavelength)/numchunks
self.spls=[]
if self.wave1==None: waveidx1=0 # Default to plotting entire spectrum for now
else: waveidx1=jbg.closest(self.wave,self.wave1)
if self.fitpars!=None:
model=joebvpfit.voigtfunc(self.wave,self.datamodel.fitpars)
sg=jbg.subplotgrid(numchunks)
for i in range(numchunks):
self.spls.append(fig.add_subplot(sg[i][0],sg[i][1],sg[i][2]))
pixs=np.arange(waveidx1+i*wlen,waveidx1+(i+1)*wlen, dtype='int')
self.spls[i].plot(self.wave[pixs],self.normflux[pixs],
linestyle='steps-mid',linewidth=cfg.spec_linewidth)
if self.fitpars!=None:
self.spls[i].plot(self.wave,model,'r')
self.spls[i].set_xlim(self.wave[pixs[0]],self.wave[pixs[-1]])
self.spls[i].set_ylim(cfg.ylim)
self.spls[i].set_xlabel('wavelength', fontsize=cfg.xy_fontsize,
labelpad=cfg.x_labelpad)
self.spls[i].set_ylabel('relative flux', fontsize=cfg.xy_fontsize,
labelpad=cfg.y_labelpad)
self.spls[i].get_xaxis().get_major_formatter().set_scientific(False)
self.spls[i].tick_params(axis='both', which='major',direction='in',
pad=2,length=2)
fig.subplots_adjust(top=0.98,bottom=0.05,left=0.08,right=0.97,
wspace=0.15,hspace=0.24)
self.addmpl(fig)
def initplot(self,fig,numchunks=8):
wlen=len(self.spectrum.wavelength)/numchunks
self.spls=[]
if self.wave1==None: waveidx1=0 # Default to plotting entire spectrum for now
else: waveidx1=jbg.closest(self.wave,self.wave1)
if self.fitpars!=None:
model=joebvpfit.voigtfunc(self.wave,self.datamodel.fitpars)
sg=jbg.subplotgrid(numchunks)
for i in range(numchunks):
self.spls.append(fig.add_subplot(sg[i][0],sg[i][1],sg[i][2]))
pixs=range(waveidx1+i*wlen,waveidx1+(i+1)*wlen)
self.spls[i].plot(self.wave[pixs],self.normflux[pixs],linestyle='steps-mid')
if self.fitpars!=None:
self.spls[i].plot(self.wave,model,'r')
self.spls[i].set_xlim(self.wave[pixs[0]],self.wave[pixs[-1]])
self.spls[i].set_ylim(cfg.ylim)
self.spls[i].set_xlabel('wavelength',labelpad=0)
self.spls[i].set_ylabel('relative flux',labelpad=-5)
self.spls[i].get_xaxis().get_major_formatter().set_scientific(False)
fig.subplots_adjust(top=0.98,bottom=0.05,left=0.08,right=0.97,wspace=0.15,hspace=0.25)
self.addmpl(fig)
def updateplot(self):
if self.wave1==None: waveidx1=0 # Default to plotting entire spectrum for now
else: waveidx1=jbg.closest(self.wave,self.wave1)
wlen=len(self.spectrum.wavelength)/self.numchunks
for i,sp in enumerate(self.spls):
sp.clear()
prange=range(waveidx1+i*wlen,waveidx1+(i+1)*wlen)
if ((len(self.fitpars[0])>0)):
sp.plot(self.wave,self.normflux,linestyle='steps-mid')
if self.pixtog==1:
sp.plot(self.wave[cfg.fitidx], self.normflux[cfg.fitidx], 'gs', markersize=4, mec='green')
model=joebvpfit.voigtfunc(self.wave,self.fitpars)
res=self.normflux-model
sp.plot(self.wave,model,'r')
if self.restog==1:
sp.plot(self.wave,-res,'.',color='black', ms=2)
sp.plot(self.wave,[0]*len(self.wave),color='gray')
### label lines we are trying to fit
if self.labeltog==1:
for j in range(len(self.fitpars[0])):
labelloc=self.fitpars[0][j]*(1.+self.fitpars[3][j])+self.fitpars[4][j]/c*self.fitpars[0][j]*(1.+self.fitpars[3][j])
label = ' {:.1f}_\nz{:.4f}'.format(self.fitpars[0][j], self.fitpars[3][j])
sp.text(labelloc, cfg.label_ypos, label, rotation=90, withdash=True, ha='center', va='bottom', clip_on=True, fontsize=cfg.label_fontsize)
sp.plot(self.wave,self.normsig,linestyle='steps-mid',color='red', lw=0.5)
sp.plot(self.wave,-self.normsig,linestyle='steps-mid',color='red', lw=0.5)
sp.set_ylim(cfg.ylim)
sp.set_xlim(self.wave[prange[0]],self.wave[prange[-1]])
sp.set_xlabel('wavelength (A)', fontsize=cfg.xy_fontsize, labelpad=cfg.x_labelpad)
sp.set_ylabel('normalized flux', fontsize=cfg.xy_fontsize, labelpad=cfg.y_labelpad)
sp.get_xaxis().get_major_formatter().set_scientific(False)
sp.get_xaxis().get_major_formatter().set_useOffset(False)
self.changefig(self.fig)
def updateplot(self):
if self.wave1==None: waveidx1=0 # Default to plotting entire spectrum for now
else: waveidx1=jbg.closest(self.wave,self.wave1)
wlen=len(self.spectrum.wavelength)/self.numchunks
for i,sp in enumerate(self.spls):
sp.clear()
prange=np.arange(waveidx1+i*wlen,waveidx1+(i+1)*wlen,dtype='int')
if ((len(self.fitpars[0])>0)):
sp.plot(self.wave,self.normflux,linestyle='steps-mid')
if self.pixtog==1:
sp.plot(self.wave[cfg.fitidx], self.normflux[cfg.fitidx], 'gs', markersize=4, mec='green')
model=joebvpfit.voigtfunc(self.wave,self.fitpars)
res=self.normflux-model
sp.plot(self.wave,model,'r')
if self.restog==1:
sp.plot(self.wave,-res,'.',color='black', ms=cfg.residual_markersize)
sp.plot(self.wave,[0]*len(self.wave),color='gray')
### label lines we are trying to fit
if self.labeltog==1:
for j in range(len(self.fitpars[0])):
labelloc=self.fitpars[0][j]*(1.+self.fitpars[3][j])+self.fitpars[4][j]/c*self.fitpars[0][j]*(1.+self.fitpars[3][j])
label = ' {:.1f}_\nz{:.4f}'.format(self.fitpars[0][j], self.fitpars[3][j])
sp.text(labelloc, cfg.label_ypos, label, rotation=90, withdash=True, ha='center', va='bottom', clip_on=True, fontsize=cfg.label_fontsize)
sp.plot(self.wave,self.normsig,linestyle='steps-mid',color='red', lw=0.5)
sp.plot(self.wave,-self.normsig,linestyle='steps-mid',color='red', lw=0.5)
sp.set_ylim(cfg.ylim)
sp.set_xlim(self.wave[prange[0]],self.wave[prange[-1]])
sp.set_xlabel('wavelength (A)', fontsize=cfg.xy_fontsize, labelpad=cfg.x_labelpad)
sp.set_ylabel('normalized flux', fontsize=cfg.xy_fontsize, labelpad=cfg.y_labelpad)
sp.get_xaxis().get_major_formatter().set_scientific(False)
sp.get_xaxis().get_major_formatter().set_useOffset(False)
self.changefig(self.fig)
def picksyslines(z, comps):
''' Return all lines at a systemic redshift
'''
zval = comps['z'][closest(comps['z'], z)]
zmatches = np.where(np.abs(comps['z'] - zval) < 0.0001)[0]
return [comps['idxs'][idx] for idx in zmatches]
def writelinepars(fitpars,
fiterrors,
parinfo,
specfile,
outfilename,
linecmts=None):
'''
Write fit parameters out to file.
Parameters
----------
fitpars : list of lists
Parameters for fit ready for fitter!
fiterrors : array of numpy vectors
Error array for the fitting initialized to '0' for each param
parinfo : array of arrays
Flags to be used in fit
specfile : str
Name of the input file containing the spectrum
outfilename : str
Parameter output filename
linecmts : list of lists, optional
Reliability flags and comments, e.g., from igmguesses
'''
import os
### Set outputs and open files
bigfiletowrite = cfg.largeVPparfile
filetowrite = outfilename
if os.path.isfile(filetowrite):
VPparfile = open(filetowrite, 'wb')
bigparfile = open(bigfiletowrite, 'ab') # Append to the running list
else:
VPparfile = open(filetowrite, 'wb')
bigparfile = open(bigfiletowrite, 'wb')
### Prep header of line parameter file
if linecmts is not None:
header = b'specfile|restwave|zsys|col|sigcol|bval|sigbval|vel|sigvel|nflag|bflag|vflag|vlim1|vlim2|wobs1|wobs2|pix1|pix2|z_comp|trans|rely|comment \n'
else:
header = b'specfile|restwave|zsys|col|sigcol|bval|sigbval|vel|sigvel|nflag|bflag|vflag|vlim1|vlim2|wobs1|wobs2|pix1|pix2|z_comp|trans \n'
VPparfile.write(header)
bigparfile.write(header)
### Grab parameters/info for each line
for i in range(len(fitpars[0])):
zline = fitpars[3][i]
vlim1 = fitpars[5][i]
vlim2 = fitpars[6][i]
restwave = fitpars[0][i]
wobs1 = restwave * (1 + zline + vlim1 / 299792.458)
wobs2 = restwave * (1 + zline + vlim2 / 299792.458)
pix1 = jbg.closest(cfg.wave, wobs1)
pix2 = jbg.closest(cfg.wave, wobs2)
trans = atomicdata.lam2ion(fitpars[0][i])
z_comp = ltu.z_from_dv(fitpars[4][i] * u.km / u.s, zline)
if linecmts is not None:
towrite = jbg.pipedelimrow([
specfile, restwave,
round(zline, 5),
round(fitpars[1][i], 3),
round(fiterrors[1][i], 3),
round(fitpars[2][i], 3),
round(fiterrors[2][i], 3),
round(fitpars[4][i], 3),
round(fiterrors[4][i], 3), parinfo[1][i], parinfo[2][i],
parinfo[4][i], vlim1, vlim2, wobs1, wobs2, pix1, pix2,
round(z_comp, 5), trans, linecmts[0][i], linecmts[1][i]
])
else:
towrite = jbg.pipedelimrow([
specfile, restwave,
round(zline, 5),
round(fitpars[1][i], 3),
round(fiterrors[1][i], 3),
round(fitpars[2][i], 3),
round(fiterrors[2][i], 3),
round(fitpars[4][i], 3),
round(fiterrors[4][i], 3), parinfo[1][i], parinfo[2][i],
parinfo[4][i], vlim1, vlim2, wobs1, wobs2, pix1, pix2,
round(z_comp, 5), trans
])
VPparfile.write(towrite.encode())
bigparfile.write(towrite.encode())
VPparfile.close()
bigparfile.close()
print('Line parameters written to:')
print(filetowrite)
def initlinepars(zs, restwaves, initvals=[], initinfo=[]):
'''
Parameters
----------
zs : numpy vector of floats
Redshifts of lines (this parameter will be fixed during fitting)
restwaves : numpy vector of floats
Rest frame wavelengths of lines to be fitted
initvals : list of numpy vectors, optional
Contains the following (in order): [restwaves,linecol,lineb,zs,linevel,linevlim1,linevlim2]
Will default to values set in cfg.py if not set
initinfo : list of numpy vectors, optional
Contains the flags for fitting (in order): [colflag,bflag,velflag]
Parameters with flags = 0 and 1 will freely value and fixed, respectively
If 2 or more lines have the same flag value for the same parameter, the parameters will
be tied to one another.
Returns
-------
initpars : list of lists
Parameters for fit ready for fitter!
parinfo : array of arrays
Flags to be used in fit
'''
### Set atomic data for each line
lam, fosc, gam = atomicdata.setatomicdata(restwaves)
cfg.lams = lam
cfg.fosc = fosc
cfg.gam = gam
initpars = [[], [], [], [], [], [], []]
defaultcol = cfg.defaultcol
defaultb = cfg.defaultb
if initvals == []:
for i in range(len(restwaves)):
initpars[0].extend([restwaves[i]])
initpars[1].extend([defaultcol])
initpars[2].extend([defaultb])
initpars[3].extend([zs[i]])
initpars[4].extend([0.])
initpars[5].extend([-cfg.defaultvlim])
initpars[6].extend([cfg.defaultvlim])
else:
if len(initvals) == 5:
for i in range(len(restwaves)):
initpars[0].extend([initvals[0][i]])
initpars[1].extend([initvals[1][i]])
initpars[2].extend([initvals[2][i]])
initpars[3].extend([initvals[3][i]])
initpars[4].extend([initvals[4][i]])
initpars[5].extend([-cfg.defaultvlim])
initpars[6].extend([cfg.defaultvlim])
else:
initpars = [[], [], [], [], [], [], []]
for i in range(len(restwaves)):
initpars[0].extend([initvals[0][i]])
initpars[1].extend([initvals[1][i]])
initpars[2].extend([initvals[2][i]])
initpars[3].extend([initvals[3][i]])
initpars[4].extend([initvals[4][i]])
initpars[5].extend([initvals[5][i]])
initpars[6].extend([initvals[6][i]])
#cfg.lowblim alteration was removed from here
parinfo = np.zeros([5, len(restwaves)], dtype=int)
parinfo[0] = parinfo[0] + 1
parinfo[3] = parinfo[3] + 1
if ((initinfo == []) & (initvals == [])):
### Look for multiplet membership of each line
seriesassoc = np.zeros(len(restwaves)) - 99
for i in range(len(restwaves)):
for j in range(len(cfg.multiplets)):
currmult = np.array(cfg.multiplets[j])
if (abs(restwaves[i] -
currmult[jbg.closest(currmult, restwaves[i])]) < 0.01):
seriesassoc[i] = j
uqions = np.unique(seriesassoc).tolist()
if -99 in uqions: uqions.remove(-99)
flagctr = 2
for uqion in uqions:
ionmatch = np.where(seriesassoc == uqion)[0]
uqzs = np.unique(zs[ionmatch])
for uz in uqzs:
matchcrit = (zs == uz) & (seriesassoc == uqion)
rellines = np.where(matchcrit)[0]
uqlams = np.unique(restwaves[rellines])
if len(uqlams) > 1:
complist = []
numcomps = []
for ul in uqlams:
matchcrit2 = matchcrit & (restwaves == ul)
matches = np.where(matchcrit2)[0]
complist.append(matches)
numcomps.append(len(matches))
numcomps = np.array(numcomps)
complist = np.array(complist)
compidxsort = sorted(range(len(numcomps)),
key=lambda x: numcomps[x],
reverse=True)
numcompsort = numcomps[compidxsort]
complistsort = complist[compidxsort]
maxcomps = numcompsort[0]
for compidx in range(maxcomps):
for li in range(len(complistsort)):
if compidx < numcompsort[li]:
parinfo[1][complistsort[li][compidx]] = flagctr
parinfo[2][complistsort[li][compidx]] = flagctr
parinfo[4][complistsort[li][compidx]] = flagctr
else:
continue
flagctr += 1
elif initinfo != []:
parinfo[1] = initinfo[0]
parinfo[2] = initinfo[1]
parinfo[4] = initinfo[2]
elif ((initinfo == []) & (initvals != [])):
### Look for multiplet membership of each line
seriesassoc = np.zeros(len(restwaves)) - 99
for i in range(len(restwaves)):
for j in range(len(cfg.multiplets)):
currmult = np.array(cfg.multiplets[j])
if (abs(restwaves[i] -
currmult[jbg.closest(currmult, restwaves[i])]) < 0.01):
seriesassoc[i] = j
### Fix measurements that are imported
for i in range(len(restwaves)):
if ((initpars[1][i] != defaultcol) & (initpars[2][i] != defaultb)):
parinfo[1][i] = 1
parinfo[2][i] = 1
parinfo[4][i] = 1
uqions = np.unique(seriesassoc).tolist()
if -99 in uqions: uqions.remove(-99)
flagctr = 2
for uqion in uqions:
ionmatch = np.where(seriesassoc == uqion)[0]
uqzs = np.unique(zs[ionmatch])
for uz in uqzs:
matchcrit = (zs == uz) & (seriesassoc == uqion)
rellines = np.where(matchcrit)[0]
uqlams = np.unique(restwaves[rellines])
if len(uqlams) > 1:
complist = []
numcomps = []
for ul in uqlams:
matchcrit2 = matchcrit & (restwaves == ul)
matches = np.where(matchcrit2)[0]
complist.append(matches.tolist())
numcomps.append(len(matches))
numcomps = np.array(numcomps)
complist = np.array(complist)
compidxsort = sorted(range(len(numcomps)),
key=lambda x: numcomps[x])
complistsort = complist[compidxsort]
complistsort = complistsort.tolist()
for i in range(len(complistsort) - 1):
for idx in complistsort[i]:
parinfo[1][idx] = flagctr
parinfo[2][idx] = flagctr
parinfo[4][idx] = flagctr
for j in range(i + 1, len(complistsort)):
idxidx = jbg.closest(
initvals[4][complistsort[j]],
initvals[4][idx])
clidx = complistsort[j][idxidx]
parinfo[1][clidx] = flagctr
parinfo[2][clidx] = flagctr
parinfo[4][clidx] = flagctr
complistsort[j].remove(clidx)
flagctr += 1
return initpars, parinfo
def writelinepars(fitpars,fiterrors,parinfo, specfile, outfilename, linecmts=None):
'''
Write fit parameters out to file.
Parameters
----------
fitpars : list of lists
Parameters for fit ready for fitter!
fiterrors : array of numpy vectors
Error array for the fitting initialized to '0' for each param
parinfo : array of arrays
Flags to be used in fit
specfile : str
Name of the input file containing the spectrum
outfilename : str
Parameter output filename
linecmts : list of lists, optional
Reliability flags and comments, e.g., from igmguesses
'''
import os
### Set outputs and open files
bigfiletowrite = cfg.largeVPparfile
filetowrite = outfilename
if os.path.isfile(filetowrite):
VPparfile = open(filetowrite, 'wb')
bigparfile = open(bigfiletowrite, 'ab') # Append to the running list
else:
VPparfile = open(filetowrite, 'wb')
bigparfile = open(bigfiletowrite, 'wb')
### Prep header of line parameter file
if linecmts is not None:
header = 'specfile|restwave|zsys|col|sigcol|bval|sigbval|vel|sigvel|nflag|bflag|vflag|vlim1|vlim2|wobs1|wobs2|pix1|pix2|z_comp|trans|rely|comment \n'
else:
header = 'specfile|restwave|zsys|col|sigcol|bval|sigbval|vel|sigvel|nflag|bflag|vflag|vlim1|vlim2|wobs1|wobs2|pix1|pix2|z_comp|trans \n'
VPparfile.write(header)
bigparfile.write(header)
### Grab parameters/info for each line
for i in range(len(fitpars[0])):
zline = fitpars[3][i]
vlim1 = fitpars[5][i]
vlim2 = fitpars[6][i]
restwave = fitpars[0][i]
wobs1 = restwave * (1 + zline + vlim1 / 299792.458)
wobs2 = restwave * (1 + zline + vlim2 / 299792.458)
pix1 = jbg.closest(cfg.wave, wobs1)
pix2 = jbg.closest(cfg.wave, wobs2)
trans = atomicdata.lam2ion(fitpars[0][i])
z_comp = ltu.z_from_dv(fitpars[4][i]*u.km/u.s, zline)
if linecmts is not None:
towrite = jbg.pipedelimrow(
[specfile, restwave, round(zline, 5), round(fitpars[1][i], 3), round(fiterrors[1][i], 3),
round(fitpars[2][i], 3), round(fiterrors[2][i], 3), round(fitpars[4][i], 3), round(fiterrors[4][i], 3),
parinfo[1][i], parinfo[2][i], parinfo[4][i], vlim1, vlim2, wobs1, wobs2, pix1, pix2,round(z_comp, 5), trans,
linecmts[0][i],linecmts[1][i]])
else:
towrite = jbg.pipedelimrow(
[specfile, restwave, round(zline, 5), round(fitpars[1][i], 3), round(fiterrors[1][i], 3),
round(fitpars[2][i], 3), round(fiterrors[2][i], 3), round(fitpars[4][i], 3), round(fiterrors[4][i], 3),
parinfo[1][i], parinfo[2][i], parinfo[4][i], vlim1, vlim2, wobs1, wobs2, pix1, pix2, round(z_comp, 5),trans])
VPparfile.write(towrite)
bigparfile.write(towrite)
VPparfile.close()
bigparfile.close()
print('Line parameters written to:')
print(filetowrite)
def initlinepars(zs,restwaves,initvals=[],initinfo=[]): ''' Parameters ---------- zs : numpy vector of floats Redshifts of lines (this parameter will be fixed during fitting) restwaves : numpy vector of floats Rest frame wavelengths of lines to be fitted initvals : list of numpy vectors, optional Contains the following (in order): [restwaves,linecol,lineb,zs,linevel,linevlim1,linevlim2] Will default to values set in cfg.py if not set initinfo : list of numpy vectors, optional Contains the flags for fitting (in order): [colflag,bflag,velflag] Parameters with flags = 0 and 1 will freely value and fixed, respectively If 2 or more lines have the same flag value for the same parameter, the parameters will be tied to one another. Returns ------- initpars : list of lists Parameters for fit ready for fitter! parinfo : array of arrays Flags to be used in fit ''' ### Set atomic data for each line lam,fosc,gam=atomicdata.setatomicdata(restwaves) cfg.lams=lam ; cfg.fosc=fosc ; cfg.gam=gam initpars=[[],[],[],[],[],[],[]] defaultcol=cfg.defaultcol defaultb=cfg.defaultb if initvals==[]: for i in range(len(restwaves)): initpars[0].extend([restwaves[i]]) initpars[1].extend([defaultcol]) initpars[2].extend([defaultb]) initpars[3].extend([zs[i]]) initpars[4].extend([0.]) initpars[5].extend([-cfg.defaultvlim]) initpars[6].extend([cfg.defaultvlim]) else: if len(initvals)==5: for i in range(len(restwaves)): initpars[0].extend([initvals[0][i]]) initpars[1].extend([initvals[1][i]]) initpars[2].extend([initvals[2][i]]) initpars[3].extend([initvals[3][i]]) initpars[4].extend([initvals[4][i]]) initpars[5].extend([-cfg.defaultvlim]) initpars[6].extend([cfg.defaultvlim]) else: initpars=[[],[],[],[],[],[],[]] for i in range(len(restwaves)): initpars[0].extend([initvals[0][i]]) initpars[1].extend([initvals[1][i]]) initpars[2].extend([initvals[2][i]]) initpars[3].extend([initvals[3][i]]) initpars[4].extend([initvals[4][i]]) initpars[5].extend([initvals[5][i]]) initpars[6].extend([initvals[6][i]]) ### If hard limits on Doppler b-value are smaller or greater than cfg.lowblim or cfg.upperblim, ### modify those limits maxb=np.max(initpars[2][:]) minb=np.min(initpars[2][:]) if maxb>cfg.upperblim: cfg.upperblim= maxb + 10. if minb<cfg.lowblim: cfg.lowblim= minb - 2. parinfo=np.zeros([5,len(restwaves)],dtype=int) parinfo[0]=parinfo[0]+1 parinfo[3]=parinfo[3]+1 if ((initinfo==[])&(initvals==[])): ### Look for multiplet membership of each line seriesassoc = np.zeros(len(restwaves)) - 99 for i in range(len(restwaves)): for j in range(len(cfg.multiplets)): currmult = np.array(cfg.multiplets[j]) if (abs(restwaves[i] - currmult[jbg.closest(currmult, restwaves[i])]) < 0.01): seriesassoc[i] = j uqions=np.unique(seriesassoc).tolist() if -99 in uqions: uqions.remove(-99) flagctr=2 for uqion in uqions: ionmatch=np.where(seriesassoc == uqion)[0] uqzs=np.unique(zs[ionmatch]) for uz in uqzs: matchcrit=(zs==uz)&(seriesassoc==uqion) rellines=np.where(matchcrit)[0] uqlams=np.unique(restwaves[rellines]) if len(uqlams)>1: complist=[] numcomps=[] for ul in uqlams: matchcrit2=matchcrit&(restwaves==ul) matches=np.where(matchcrit2)[0] complist.append(matches) numcomps.append(len(matches)) numcomps=np.array(numcomps) complist=np.array(complist) compidxsort=sorted(range(len(numcomps)),key = lambda x: numcomps[x],reverse=True) numcompsort=numcomps[compidxsort] complistsort=complist[compidxsort] maxcomps=numcompsort[0] for compidx in range(maxcomps): for li in range(len(complistsort)): if compidx<numcompsort[li]: parinfo[1][complistsort[li][compidx]]=flagctr parinfo[2][complistsort[li][compidx]]=flagctr parinfo[4][complistsort[li][compidx]]=flagctr else: continue flagctr+=1 elif initinfo!=[]: parinfo[1]=initinfo[0] parinfo[2]=initinfo[1] parinfo[4]=initinfo[2] elif ((initinfo==[])&(initvals!=[])): ### Look for multiplet membership of each line seriesassoc = np.zeros(len(restwaves)) - 99 for i in range(len(restwaves)): for j in range(len(cfg.multiplets)): currmult = np.array(cfg.multiplets[j]) if (abs(restwaves[i] - currmult[jbg.closest(currmult, restwaves[i])]) < 0.01): seriesassoc[i] = j ### Fix measurements that are imported for i in range(len(restwaves)): if ((initpars[1][i]!=defaultcol)&(initpars[2][i]!=defaultb)): parinfo[1][i]=1 ; parinfo[2][i]=1 ; parinfo[4][i]=1 uqions=np.unique(seriesassoc).tolist() if -99 in uqions: uqions.remove(-99) flagctr=2 for uqion in uqions: ionmatch=np.where(seriesassoc == uqion)[0] uqzs=np.unique(zs[ionmatch]) for uz in uqzs: matchcrit=(zs==uz)&(seriesassoc==uqion) rellines=np.where(matchcrit)[0] uqlams=np.unique(restwaves[rellines]) if len(uqlams)>1: complist=[] numcomps=[] for ul in uqlams: matchcrit2=matchcrit&(restwaves==ul) matches=np.where(matchcrit2)[0] complist.append(matches.tolist()) numcomps.append(len(matches)) numcomps=np.array(numcomps) complist=np.array(complist) compidxsort=sorted(range(len(numcomps)),key = lambda x: numcomps[x]) complistsort=complist[compidxsort] complistsort=complistsort.tolist() for i in range(len(complistsort)-1): for idx in complistsort[i]: parinfo[1][idx]=flagctr parinfo[2][idx]=flagctr parinfo[4][idx]=flagctr for j in range(i+1,len(complistsort)): idxidx=jbg.closest(initvals[4][complistsort[j]],initvals[4][idx]) clidx=complistsort[j][idxidx] parinfo[1][clidx]=flagctr parinfo[2][clidx]=flagctr parinfo[4][clidx]=flagctr complistsort[j].remove(clidx) flagctr+=1 return initpars,parinfo