def PlotXPAbs(starData,
clusterName='NGC-0752',
ionList=kAllIonList,
fileTag='',
labelPlot=True,
labelPoints=False,
showTrendLine=False,
modelAtms=None,
pradks=None,
referenceCorrect=False):
# Make XP vs. Ab for the passed star
# One element per plot.
starName = starData[0]
starParmTuple = tuple(starData[1:])
isGiant = RC.isGiantStar(starParmTuple)
if isGiant:
modelPath = k.GiantModelPath
else:
modelPath = k.DwarfModelPath
if modelAtms == None or pradks == None:
modelFiles = mk.findDataFiles(modelPath)
modelAtms, pradks = mk.LoadModels(modelFiles)
abdict, uncorrLines, unusedMin, unusedMax = \
AB.CalcAbsAndLines(clusterName+' '+starName,
tuple(starData[1:6]), ionList=ionList,
modelAtms=modelAtms, pradks=pradks)
# uncorrLines:
# # {elem.ion:[[Wavelength, Ex.Pot., logGf, eqw, logRW, abund],...]}
if referenceCorrect:
if isGiant: # Obligatory comment on bad Giant corrections, and using
# Solar instead.
correctDict, referenceLines, lineWeights = \
RC.GetSolarCorrections(ionList=ionList,
modelAtms=modelAtms,
pradks=pradks)
else:
correctDict, referenceLines, lineWeights = \
RC.GetDwarfCorrections(ionList=ionList,
modelAtms=modelAtms,
pradks=pradks)
correctionsAvailable = False
if len(correctDict) > 0 and len(referenceLines) > 0:
correctionsAvailable = True
for ion in ionList:
if ion not in uncorrLines.keys():
continue
# Does this element have NLTE corrections available? Note: we do
# this BEFORE Solar corrections, which assumes that the same
# NLTE corrections are already applied to any Solar corrections
# we use.
if ion in NLTEIons:
LTELines = AB.CorrectNLTEAbs(ion, uncorrLines[ion],
tuple(starData[1:6]))
else:
if ion in uncorrLines.keys():
LTELines = uncorrLines[ion]
else:
# Either synthesized lines, or none available
LTELines = np.array([])
# Do we want the "reference corrected" abundances?
if referenceCorrect and correctionsAvailable:
tempAdj,tempAll = \
RC.SortAndFilterLines(LTELines,
ion,
tuple(starData[1:6]),
solarCorrect=referenceCorrect,
solarLines=referenceLines[ion],
solarCorrs=correctDict[ion],
lineWeights=lineWeights[ion])
# correctedLines:
# [[ab, line STR score, wl, "quality"], ...]
# allLines:
# [[ab, line STR score, wl],...]
# We want to use np.arrays, so...
allLines = np.array(tempAll)
correctedLines = np.array(tempAdj)
if len(allLines) == 0 or len(correctedLines) == 0:
correctionsAvailable = False
elif len(allLines) == 1 or len(correctedLines) == 1:
print('Single Line determination:{0}'.format(starData[0]))
print(allLines, correctedLines)
# One plot per ion.
if labelPlot:
plotLabel = 'XP vs Ab for [{2}/H] in {0} {1}.'.\
format(clusterName, starName, el.getIonName(ion))
else:
plotLabel = ''
if referenceCorrect and correctionsAvailable:
tempPoints = []
for line in uncorrLines[ion]:
correctedAbs = [l[0] for l in correctedLines if \
u.in_range(l[2],line[0]-0.05, line[0]+0.05)]
if len(correctedAbs) > 0:
tempPoints.append(
[line[1],
np.mean(correctedAbs), line[3], line[0]])
else:
tempPoints.append([line[1], line[5], line[3], line[0]])
XPAbPoints = np.array(tempPoints)
else:
XPAbPoints = np.array([[line[1],line[5],line[3],line[0]]\
for line in uncorrLines[ion]])
if labelPoints:
# Label the points with the wavelength
pointLabels = ['{0:2.3f}'.format(point[3]) \
for point in XPAbPoints]
else:
pointLabels = None
ps.XPAbPlot(XPAbPoints,
starName,
ion,
fileTag=fileTag + 'XPAb',
plotTitle=plotLabel,
pointLabels=pointLabels,
showTrendLine=showTrendLine)
def GetAbTable(clusterName='NGC-0752',
starDataList=None,
ions=kAllIonList,
filterBlends=False,
referenceCorrect=False,
useDAOSpec=False,
gModelAtms=None,
gPradks=None,
dModelAtms=None,
dPradks=None):
# Returns a dictionary with each star name (key) has a list of elements
# (secondary key), with a list containing [Abundance in [X/H], variance in the
# line measurements, count of lines measured, average quality score]:
# returnDict={starName:{ion:[[X/H],stdev, count, avg.quality],...},...}
if starDataList == None:
starDataList = GetAllStarParms(clusterName=clusterName)
# Since we're doing a bunch of lines (uh...), we'll need both giant and
# dwarf references. Giant references are prefixed by a 'g', dwarfs by 'd'.
if gModelAtms == None or gPradks == None:
gModelFiles = mk.findDataFiles(k.GiantModelPath)
gModelAtms, gPradks = mk.LoadModels(gModelFiles)
if dModelAtms == None or dPradks == None:
dModelFiles = mk.findDataFiles(k.DwarfModelPath)
dModelAtms, dPradks = mk.LoadModels(dModelFiles)
# We need iron to do relative abundances:
if 26.0 not in ions:
ions.append(26.0)
# Turns out our giant corrections are not good
# gCorrectDict, gReferenceLines, gLineWeights = \
# GetGiantCorrections(ionList=ions,
# modelAtms=gModelAtms,
# pradks=gPradks)
# So, use the Solar corrections for now
gCorrectDict, gReferenceLines, gLineWeights = \
RC.GetSolarCorrections(ionList=ions,
modelAtms=dModelAtms,
pradks=dPradks)
dCorrectDict, dReferenceLines, dLineWeights = \
RC.GetDwarfCorrections(ionList=ions,
modelAtms=dModelAtms,
pradks=dPradks)
tableDict = {}
for starData in starDataList:
starName = starData[0]
starParms = tuple(starData[1:6])
if RC.isGiantStar(starParms):
modelAtms = gModelAtms
pradks = gPradks
referenceLines = gReferenceLines
referenceDict = gCorrectDict
lineWeights = gLineWeights
else:
modelAtms = dModelAtms
pradks = dPradks
referenceLines = dReferenceLines
referenceDict = dCorrectDict
lineWeights = dLineWeights
tableDict[starName] = GetAbsForStar(starData, ions=ions,\
filterBlends=filterBlends, refCorrect=referenceCorrect,\
refDict=referenceDict, refLines=referenceLines, \
lineWeights=lineWeights, useDAOSpec=useDAOSpec, \
modelAtms=modelAtms, pradks=pradks)
return tableDict
def MakeVPlots(clusterName='NGC-0752',
starDataList=None,
fileTag='',
filterBlends=False,
referenceCorrect=False,
useDAOSpec=False,
ions=[20.0]):
# Function plots V_turb vs. sigma [Ca/H] (sigma is the line-to-line
# standard deviation of the measured Ca lines), for a range of V_turb values.
# Intended to provide a spectroscopic confirmation/adjustment
# for photometrically-determined parameters.
if starDataList is None:
starDataList = STP.GetAllStarParms(clusterName=clusterName)
dModelFiles = mk.findDataFiles(k.DwarfModelPath)
dModelAtms, dPradks = mk.LoadModels(dModelFiles)
dCorrectDict, dReferenceLines, dLineWeights = \
RC.GetDwarfCorrections(ionList=ions,
modelAtms=dModelAtms,
pradks=dPradks)
gModelFiles = mk.findDataFiles(k.GiantModelPath)
gModelAtms, gPradks = mk.LoadModels(gModelFiles)
# Note: We're using dwarf corrections for giant stars...
# because they're better than giant corrections for the
# giant stars. This needs to be fixed. :(
gCorrectDict, gReferenceLines, gLineWeights = \
RC.GetSolarCorrections(ionList=ions,
modelAtms=dModelAtms,
pradks=dPradks)
# For now, CaI appears to work best for all stars.
# VI, Ti I/II, Fe I/II, Cr I/II also work, but not as well - (See Reddy, et al. 2012).
# ions = [20.0]
abDict = {}
colorArray=['r','orange','gold','darkgreen','navy',\
'm','saddlebrown','skyblue','hotpink']
for starParms in starDataList:
if RC.isGiantStar(starParms[1:]):
modelAtms = gModelAtms
pradks = gPradks
refLines = gReferenceLines
refDict = gCorrectDict
lineWeights = gLineWeights
else:
modelAtms = dModelAtms
pradks = dPradks
refLines = dReferenceLines
refDict = dCorrectDict
lineWeights = dLineWeights
starPoints = {}
for v in np.linspace(0.5, 3.0, 26):
parmTuple = (starParms[0], starParms[1], starParms[2], v, \
starParms[4], starParms[5], starParms[6])
abDict[v] = STP.GetAbsForStar(parmTuple, ions=ions,\
filterBlends=filterBlends, refCorrect=referenceCorrect,\
refDict=refDict, refLines=refLines, lineWeights=lineWeights,\
useDAOSpec=useDAOSpec, modelAtms=modelAtms, pradks=pradks)
for ion in abDict[v].keys():
if ion not in starPoints.keys():
starPoints[ion] = {}
if abDict[v][ion][0] == 0.:
continue
starPoints[ion][v] = abDict[v][ion][1]
colorIdx = 0
ymaxes = []
for ion in starPoints.keys():
Xs = sorted(np.array(list(starPoints[ion].keys())))
if len(Xs) == 0 or ion in STP.SynthAbsDict.keys():
continue
Ys = np.array([starPoints[ion][x] for x in Xs])
if len(Xs) < 2 or len(Ys) < 2:
continue
Ys = Ys / min(Ys)
ymaxes.append(max(Ys))
try:
minV = Xs[np.where(Ys == min(Ys))[0][0]]
except IndexError:
continue
pyplot.plot(Xs,
Ys,
label=r'$({1}) V_{{turb}}={0:3.1f}km/sec$'.format(
minV, el.getIonName(ion)),
color=colorArray[colorIdx])
pyplot.scatter(Xs, Ys, color=colorArray[colorIdx])
pyplot.axvline(minV, linestyle=':', color=colorArray[colorIdx])
colorIdx += 1
if colorIdx == len(colorArray): colorIdx = 0
ax = pyplot.gca()
ax.set_xlabel(r'$V_{{turb}} km/s$')
ax.set_ylabel('Normalized [X/H] variance')
ax.set_ylim((0., min(5.0, max(ymaxes))))
pyplot.legend(fontsize=8)
pyplot.savefig(k.ParmPlotDir + 'Vturb/' + starParms[0] + fileTag +
'.png')
pyplot.close()
def PlotAbs(clusterName,
starData,
ions,
fileTag='',
plotTitle='',
filterBlends=False,
referenceCorrect=False,
labelPoints=False,
useDAOSpec=False,
modelAtms=None,
pradks=None):
pointLabels = None
# Lookup the abundance(s) for the passed star, and plot them
# (into a .png file). One element per plot.
starName = starData[0]
starParmTuple = tuple(starData[1:6])
isGiant = RC.isGiantStar(starParmTuple)
if isGiant:
modelPath = k.GiantModelPath
else:
modelPath = k.DwarfModelPath
if modelAtms == None or pradks == None:
modelFiles = mk.findDataFiles(modelPath)
modelAtms, pradks = mk.LoadModels(modelFiles)
# uncorrLines format:
# {elem.ion:[[Wavelength, Ex.Pot., logGf, eqw, logRW, abund],...]}
# abDict format:
# {elem.ion:[abundance mean, ab. std.dev., # lines]}
abdict, uncorrLines, unusedMin, unusedMax = \
AB.CalcAbsAndLines(clusterName+' '+starName,
starParmTuple,
ionList=ions,
modelAtms=modelAtms,
pradks=pradks,
useDAOlines=useDAOSpec)
if isGiant:
# Obtain the reference corrections for a giant star - Note: this is
# badly broken! So, we're just going to use the Solar corrections for
# now:
correctDict, referenceLines, lineWeights = \
RC.GetDwarfCorrections(ionList=ions,
modelAtms=modelAtms,
pradks=pradks,
useDAOSpec=useDAOSpec)
# correctDict, referenceLines, lineWeights = \
# GetGiantCorrections(ionList=ions,
# modelAtms=modelAtms,
# pradks=pradks)
else:
# ...or for a dwarf star.
correctDict, referenceLines, lineWeights = \
RC.GetDwarfCorrections(ionList=ions,
modelAtms=modelAtms,
pradks=pradks,
useDAOSpec=useDAOSpec)
for ion in ions:
# We'll make one plot per ion.
pointLabels = []
redData = greenData = blueData = []
if ion not in uncorrLines.keys():
print('No {0:2.1f} lines for {1}.'.format(ion, starName))
continue
if (referenceCorrect or filterBlends) and \
ion in referenceLines.keys() and ion in correctDict.keys() and \
ion in lineWeights.keys():
adjustedLines, dataPoints = \
RC.SortAndFilterLines(uncorrLines[ion],
ion,
starParmTuple,
filterBlends=filterBlends,
solarCorrect=referenceCorrect,
solarLines=referenceLines[ion],
solarCorrs=correctDict[ion],
lineWeights=lineWeights[ion])
tempData = []
for line in uncorrLines[ion]:
corrLine = u.find(lambda l: l[0] == line[0], dataPoints)
if corrLine is not None:
tempData.append(corrLine)
else:
tempData.append([line[5], el.STR(line[2], line[1], \
starParmTuple[0]),line[0]])
redData = np.array(tempData)
else:
dataPoints = np.array([[line[5],
el.STR(line[2],
line[1],
starParmTuple[0]),
line[0]] \
for line in uncorrLines[ion]])
if labelPoints:
pointLabels = ['{0:4.3f}'.format(line[2]) for line in dataPoints]
pointLabels.extend(['{0:4.3f}'.\
format(line[2]) for line in redData])
pointLabels.extend(['{0:4.3f}'.\
format(line[2]) for line in greenData])
pointLabels.extend(['{0:4.3f}'.\
format(line[2]) for line in blueData])
loSlope, loIntercept, rv, pv, err = \
ps.GetDetectionLimit(starParmTuple,
ion,
modelAtms=modelAtms,
pradks=pradks)
hiSlope, hiIntercept, rv, pv, err = \
ps.GetCoGLimit(starParmTuple,
ion,
modelAtms=modelAtms,
pradks=pradks)
ps.AbSTRPlot(starName,
ion,
dataPoints,
redSet=redData,
greenSet=greenData,
blueSet=blueData,
lowLimit=(loSlope, loIntercept),
hiLimit=(hiSlope, hiIntercept),
fileTag=fileTag,
plotTitle=plotTitle,
labelPoints=pointLabels)
def MakeTeffPlots(clusterName='NGC-0752',
starDataList=None,
fileTag='',
referenceCorrect=False):
# Function plots Teff vs. slope of (XP vs. [Fe/H]) for a range of temperatures.
# Intended to provide a spectroscopic confirmation/adjustment
# for photometrically-determined parameters. We assume that at the "correct"
# temperature, the slope of XP vs. [Fe/H] would be zero.
if starDataList is None:
starDataList = STP.GetAllStarParms(clusterName=clusterName)
dModelFiles = mk.findDataFiles(k.DwarfModelPath)
dModelAtms, dPradks = mk.LoadModels(dModelFiles)
gModelFiles = mk.findDataFiles(k.GiantModelPath)
gModelAtms, gPradks = mk.LoadModels(gModelFiles)
# Map Fe I Ab vs. Ex pot.
ions = [26.0]
abDict = {}
dTeffRange = np.linspace(5000, 7000, 81)
gTeffRange = np.linspace(4000, 6000, 81)
for star in starDataList:
starName = star[0]
isGiant = RC.isGiantStar(star[1:])
if isGiant:
modelAtms = gModelAtms
pradks = gPradks
tRange = gTeffRange
if referenceCorrect:
correctDict, referenceLines, lineWeights = \
RC.GetGiantCorrections(ionList=ions,
modelAtms=modelAtms,
pradks=pradks)
else:
modelAtms = dModelAtms
pradks = dPradks
tRange = dTeffRange
if referenceCorrect:
correctDict, referenceLines, lineWeights = \
RC.GetDwarfCorrections(ionList=ions,
modelAtms=modelAtms,
pradks=pradks)
logG = star[2]
vTurb = star[3]
met = star[4]
slopes = []
for Teff in tRange:
unusedDict, uncorrLines, unusedMin, unusedMax = \
AB.CalcAbsAndLines(clusterName+' '+starName,
tuple([Teff, star[2], star[3], star[4], star[5]]),\
ionList=ions, modelAtms=modelAtms, pradks=pradks)
XPs = []
Abs = []
if referenceCorrect:
adjLines, allLines = RC.SortAndFilterLines(
uncorrLines[26.0],
26.0,
tuple([Teff, star[2], star[3], star[4], star[5]]),
solarCorrect=True,
solarLines=referenceLines[26.0],
solarCorrs=correctDict[26.0],
lineWeights=lineWeights[26.0])
if len(adjLines) > 10:
# Assume 10 Fe I lines needed for nice plots
for line in adjLines:
# XP isn't returned, so have to do a lookup into the
# original list.
XPs.append(
u.find(lambda l: l[0] == line[2],
uncorrLines[26.0])[1])
Abs.append(line[0])
if len(XPs) == 0 or len(Abs) == 0:
# Either no corrections, or corrections failed
XPs = [line[1] for line in uncorrLines[26.0]]
Abs = [line[5] for line in uncorrLines[26.0]]
slope, intercept, rVal, pVal, err = sp.stats.linregress(XPs, Abs)
slopes.append(slope)
# We have to flip because interp expects the xvalues to be
# monotomically increasing
interpTs = np.interp([-0.02, 0., 0.02], slopes[::-1], tRange[::-1])
if interpTs[0] > interpTs[1]:
minusR = interpTs[1] - interpTs[2]
plusR = interpTs[0] - interpTs[1]
else:
minusR = interpTs[1] - interpTs[0]
plusR = interpTs[2] - interpTs[1]
fig = pyplot.figure()
TeffLabel = r'$T_{{eff}} = {0:4.0f}^{{+{1:4.0f}}}_{{-{2:4.0f}}}$)'.\
format(interpTs[1],minusR,plusR)
pyplot.scatter(tRange, slopes, label=TeffLabel)
pyplot.axhline(0.0, linestyle=':')
pyplot.legend()
pyplot.savefig(k.ParmPlotDir + 'Teff/' + star[0] + fileTag +
'_FeSlope.png')
pyplot.close()