def plotSequence(*args, **kwargs):
'''
:Purpose: Compares a spectrum to a sequence of standards a batch of spectra into a 2x2 set of PDF files, with options of overplotting comparison spectra, including best-match spectral standards.
:param input: A single or list of Spectrum objects or filenames, or the glob search string for a set of files (e.g., '/Data/myspectra/*.fits').
:type input: required
:param type_range: Number of subtypes to consider above and below best-fit spectral type
:type type_range: optional, default = 2
:param spt: Default spectral type for source; this input skips classifyByStandard
:type spt: optional, default = None
:param output: Filename for output; full path should be include if not saving to current directory. If blank, plot is shown on screen
:type output: optional, default = None (screen display)
Relevant parameters for plotSpectrum may also be passed
:Example:
>>> import glob, splat
>>> files = glob.glob('/home/mydata/*.fits')
>>> sp = splat.plotBatch(files,classify=True,output='comparison.pdf')
>>> sp = splat.plotBatch('/home/mydata/*.fits',classify=True,output='comparison.pdf')
>>> sp = splat.plotBatch([splat.Spectrum(file=f) for f in files],classify=True,output='comparison.pdf')
All three of these commands produce the same result
'''
# check inputs
if len(args) == 0:
raise ValueError('\nNeed to provide a spectrum object or filename for plotStandardSequence')
parameters = ['type_range']
splat.checkKeys(kwargs,parameters,forcekey=False)
type_range =kwargs.get('type_range',2)
kwargs['stack']=kwargs.get('stack',0.5)
# kwargs['legendLocation']=kwargs.get('legendLocation','outside')
kwargs['telluric']=kwargs.get('telluric',True)
kwargs['method']=kwargs.get('method','kirkpatrick')
# kwargs['color']=kwargs.get('color','r')
kwargs['colorComparison']=kwargs.get('colorComparison','k')
kwargs['colorScheme']=kwargs.get('colorScheme','winter')
kwargs['figsize']=kwargs.get('figsize',[10,10*type_range*kwargs['stack']])
kwargs['fontscale']=kwargs.get('fontscale',1.5)
# process input
if isinstance(args[0],str):
if len(glob.glob(args[0])) == 0:
raise ValueError('\nCannot find input file {} - make sure full path is included'.format(args[0]))
try:
sp = splat.Spectrum(file = args[0])
except:
raise ValueError('\nCould not read in file {} - make sure the file is correctly formatted'.format(args[0]))
elif isinstance(args[0],splat.Spectrum):
sp = copy.deepcopy(args[0])
else:
raise ValueError('\nInput should be a Spectrum object or filename')
sp.normalize()
# classify by comparison to standards
spt = kwargs.get('spt',splat.classifyByStandard(sp,**kwargs)[0])
if not isinstance(spt,str):
spt = splat.typeToNum(spt)
# produce range of standards for plot
stdnum = numpy.arange(numpy.floor(splat.typeToNum(spt)-type_range),numpy.ceil(splat.typeToNum(spt)+type_range)+1)
if numpy.max(stdnum) > 39:
stdnum-=(numpy.max(stdnum)-39)
if numpy.min(stdnum) < 10:
stdnum+=(10-numpy.min(stdnum))
stdspt = [splat.typeToNum(i) for i in stdnum]
stds = [splat.SPEX_STDS[s] for s in stdspt]
stdlabels = ['{} Standard'.format(s) for s in stdspt]
plotlist = []
labels = []
colors = []
for i,stdsp in enumerate(stds):
plotlist.append([stdsp,sp])
labels.extend([])
fig = splat.plotSpectrum(stds,comparison=sp,labels=stdlabels,**kwargs)
return fig
def plotSequence(*args, **kwargs):
'''
:Purpose: Compares a spectrum to a sequence of standards a batch of spectra into a 2x2 set of PDF files, with options of overplotting comparison spectra, including best-match spectral standards.
:param input: A single or list of Spectrum objects or filenames, or the glob search string for a set of files (e.g., '/Data/myspectra/*.fits').
:type input: required
:param type_range: Number of subtypes to consider above and below best-fit spectral type
:type type_range: optional, default = 2
:param spt: Default spectral type for source; this input skips classifyByStandard
:type spt: optional, default = None
:param output: Filename for output; full path should be include if not saving to current directory. If blank, plot is shown on screen
:type output: optional, default = None (screen display)
Relevant parameters for plotSpectrum may also be passed
:Example:
>>> import glob, splat
>>> files = glob.glob('/home/mydata/*.fits')
>>> sp = splat.plotBatch(files,classify=True,output='comparison.pdf')
>>> sp = splat.plotBatch('/home/mydata/*.fits',classify=True,output='comparison.pdf')
>>> sp = splat.plotBatch([splat.Spectrum(file=f) for f in files],classify=True,output='comparison.pdf')
All three of these commands produce the same result
'''
# check inputs
if len(args) == 0:
raise ValueError(
'\nNeed to provide a spectrum object or filename for plotStandardSequence'
)
parameters = ['type_range']
splat.checkKeys(kwargs, parameters, forcekey=False)
type_range = kwargs.get('type_range', 2)
kwargs['stack'] = kwargs.get('stack', 0.5)
# kwargs['legendLocation']=kwargs.get('legendLocation','outside')
kwargs['telluric'] = kwargs.get('telluric', True)
kwargs['method'] = kwargs.get('method', 'kirkpatrick')
# kwargs['color']=kwargs.get('color','r')
kwargs['colorComparison'] = kwargs.get('colorComparison', 'k')
kwargs['colorScheme'] = kwargs.get('colorScheme', 'winter')
kwargs['figsize'] = kwargs.get('figsize',
[10, 10 * type_range * kwargs['stack']])
kwargs['fontscale'] = kwargs.get('fontscale', 1.5)
# process input
if isinstance(args[0], str):
if len(glob.glob(args[0])) == 0:
raise ValueError(
'\nCannot find input file {} - make sure full path is included'
.format(args[0]))
try:
sp = splat.Spectrum(file=args[0])
except:
raise ValueError(
'\nCould not read in file {} - make sure the file is correctly formatted'
.format(args[0]))
elif isinstance(args[0], splat.Spectrum):
sp = copy.deepcopy(args[0])
else:
raise ValueError('\nInput should be a Spectrum object or filename')
sp.normalize()
# classify by comparison to standards
spt = kwargs.get('spt', splat.classifyByStandard(sp, **kwargs)[0])
if not isinstance(spt, str):
spt = splat.typeToNum(spt)
# produce range of standards for plot
stdnum = numpy.arange(numpy.floor(splat.typeToNum(spt) - type_range),
numpy.ceil(splat.typeToNum(spt) + type_range) + 1)
if numpy.max(stdnum) > 39:
stdnum -= (numpy.max(stdnum) - 39)
if numpy.min(stdnum) < 10:
stdnum += (10 - numpy.min(stdnum))
stdspt = [splat.typeToNum(i) for i in stdnum]
stds = [splat.SPEX_STDS[s] for s in stdspt]
stdlabels = ['{} Standard'.format(s) for s in stdspt]
plotlist = []
labels = []
colors = []
for i, stdsp in enumerate(stds):
plotlist.append([stdsp, sp])
labels.extend([])
fig = splat.plotSpectrum(stds, comparison=sp, labels=stdlabels, **kwargs)
return fig
def plotBatch(*args, **kwargs):
'''
:Purpose: Plots a batch of spectra into a 2x2 set of PDF files, with options of overplotting comparison spectra, including best-match spectral standards.
:param input: A single or list of Spectrum objects or filenames, or the glob search string for a set of files (e.g., '/Data/myspectra/*.fits').
:type input: required
:param output: Filename for PDF file output; full path should be include if not saving to current directory
:type output: optional, default = 'spectra_plot.pdf'
:param comparisons: list of Spectrum objects or filenames for comparison spectra. If comparisons list is shorter than source list, then last comparison source will be repeated. If the comparisons list is longer, the list will be truncated.
:type comparisons: optional, default = None
:param classify: Set to True to classify sources based on comparison to MLT spectral standards following the method of `Kirkpatrick et al. (2010) <http://adsabs.harvard.edu/abs/2010ApJS..190..100K>`_. This option normalizes the spectra by default
:type classify: optional, default = False
:param normalize: Set to True to normalize source and (if passed) comparison spectra.
:type normalize: optional, default = False
:param legend: Set to list of legends for plots. The number of legends should equal the number of sources and comparisons (if passed) in an alternating sequence. T
:type legend: optional, default = displays file name for source and object name for comparison (if passed)
Relevant parameters for plotSpectrum may also be passed
:Example:
>>> import glob, splat
>>> files = glob.glob('/home/mydata/*.fits')
>>> sp = splat.plotBatch(files,classify=True,output='comparison.pdf')
>>> sp = splat.plotBatch('/home/mydata/*.fits',classify=True,output='comparison.pdf')
>>> sp = splat.plotBatch([splat.Spectrum(file=f) for f in files],classify=True,output='comparison.pdf')
All three of these commands produce the same result
'''
# keyword check
parameters = ['output','comparisons','classify','normalize','legend']
splat.checkKeys(kwargs,parameters,forcekey=False)
kwargs['layout']=kwargs.get('layout',[2,2])
kwargs['fontscale']=kwargs.get('fontscale',0.7)
# input check
if len(args) == 0:
print('\nNeed to provide a list of spectra or filenames, or a file search string, to use plotBatch')
return
kwargs['output'] = kwargs.get('output','spectra_plot.pdf')
# break out a list
if isinstance(args[0],list):
inputlist = args[0]
else:
inputlist = args
# if filenames, read in each file to a spectrum object
if isinstance(inputlist[0],str):
# try a glob search string
files = glob.glob(inputlist[0])
if len(files) > 1 or (len(files) == 1 and inputlist[0].find('*') != -1):
inputlist = files
# try reading in files into Spectrum object
try:
splist = [splat.Spectrum(file = f) for f in inputlist]
except:
raise ValueError('\nCould not read in list of files - make sure the full path is specified and the files are correctly formatted')
# if filenames, read in each file to a spectrum object
elif isinstance(inputlist[0],splat.Spectrum):
splist = copy.deepcopy(inputlist)
else:
print('\nInput should be list of Spectra objects or filenames')
return
# comparison files are present
compflag = False
complist = []
if kwargs.get('comparisons',False) != False:
comp = kwargs.get('comparisons')
if not isinstance(comp,list):
comp = [comp]
if len(comp) < len(splist):
while len(comp) < len(splist):
comp.append(comp[-1])
if isinstance(comp[0],str):
try:
complist = [splat.Spectrum(file = f) for f in comp]
compflag = True
except:
print('\nCould not read in comparison files: ignoring comparisons')
if isinstance(comp[0],splat.Spectrum):
complist = comp
compflag = True
# set comparison files to be standards for spectral classification
if kwargs.get('classify',False):
complist = []
for sp in splist:
spt = splat.classifyByStandard(sp,method='kirkpatrick')
complist.append(splat.SPEX_STDS[spt[0]])
compflag = True
kwargs['normalize'] = kwargs.get('normalize',True)
# normalize if desired
if kwargs.get('normalize',False):
tmp = [sp.normalize() for sp in splist]
if compflag == True:
tmp = [sp.normalize() for sp in complist]
# prep for plotting
plotlist = []
clist = []
for i,sp in enumerate(splist):
if compflag == True:
plotlist.append([sp,complist[i]])
clist.extend(['k','r'])
else:
plotlist.append([sp])
clist.extend(['k'])
# manage legends
if kwargs.get('legend',False) != False:
legends = kwargs.get('legend')
if not isinstance(legends,list):
legends = [legends]
if len(legends) < (len(splist)+len(complist)):
# guess: just left out the comparison legends
if len(complist) > 0 and len(legends) == len(splist):
legtmp = []
for i,l in enumerate(legends):
legtmp.extend([l,'{}'.format(complist[i].name)])
legends = legtmp
else:
# otherwise: pad the remaining legends with the last legend (pairs)
while len(legends) < (len(splist)+len(complist)):
if compflag:
legends.extend([legends[-2],legends[-1]])
else:
legends.extend([legends[-1]])
if len(legends) > (len(splist)+len(complist)):
legends = legends[0:(len(splist)+len(complist))]
else:
legends = []
for i,sp in enumerate(splist):
if compflag == True:
legends.extend(['{}'.format(os.path.basename(sp.filename)),'{}'.format(complist[i].name)])
else:
legends.extend(['{}'.format(os.path.basename(sp.filename))])
# generate pdf
splat.plotSpectrum(plotlist,multiplot=True,multipage=True,legends=legends,colors=clist,**kwargs)
return
def plotBatch(*args, **kwargs):
'''
:Purpose: Plots a batch of spectra into a 2x2 set of PDF files, with options of overplotting comparison spectra, including best-match spectral standards.
:param input: A single or list of Spectrum objects or filenames, or the glob search string for a set of files (e.g., '/Data/myspectra/*.fits').
:type input: required
:param output: Filename for PDF file output; full path should be include if not saving to current directory
:type output: optional, default = 'spectra_plot.pdf'
:param comparisons: list of Spectrum objects or filenames for comparison spectra. If comparisons list is shorter than source list, then last comparison source will be repeated. If the comparisons list is longer, the list will be truncated.
:type comparisons: optional, default = None
:param classify: Set to True to classify sources based on comparison to MLT spectral standards following the method of `Kirkpatrick et al. (2010) <http://adsabs.harvard.edu/abs/2010ApJS..190..100K>`_. This option normalizes the spectra by default
:type classify: optional, default = False
:param normalize: Set to True to normalize source and (if passed) comparison spectra.
:type normalize: optional, default = False
:param legend: Set to list of legends for plots. The number of legends should equal the number of sources and comparisons (if passed) in an alternating sequence. T
:type legend: optional, default = displays file name for source and object name for comparison (if passed)
Relevant parameters for plotSpectrum may also be passed
:Example:
>>> import glob, splat
>>> files = glob.glob('/home/mydata/*.fits')
>>> sp = splat.plotBatch(files,classify=True,output='comparison.pdf')
>>> sp = splat.plotBatch('/home/mydata/*.fits',classify=True,output='comparison.pdf')
>>> sp = splat.plotBatch([splat.Spectrum(file=f) for f in files],classify=True,output='comparison.pdf')
All three of these commands produce the same result
'''
# keyword check
parameters = ['output', 'comparisons', 'classify', 'normalize', 'legend']
splat.checkKeys(kwargs, parameters, forcekey=False)
kwargs['layout'] = kwargs.get('layout', [2, 2])
kwargs['fontscale'] = kwargs.get('fontscale', 0.7)
# input check
if len(args) == 0:
print(
'\nNeed to provide a list of spectra or filenames, or a file search string, to use plotBatch'
)
return
kwargs['output'] = kwargs.get('output', 'spectra_plot.pdf')
# break out a list
if isinstance(args[0], list):
inputlist = args[0]
else:
inputlist = args
# if filenames, read in each file to a spectrum object
if isinstance(inputlist[0], str):
# try a glob search string
files = glob.glob(inputlist[0])
if len(files) > 1 or (len(files) == 1
and inputlist[0].find('*') != -1):
inputlist = files
# try reading in files into Spectrum object
try:
splist = [splat.Spectrum(file=f) for f in inputlist]
except:
raise ValueError(
'\nCould not read in list of files - make sure the full path is specified and the files are correctly formatted'
)
# if filenames, read in each file to a spectrum object
elif isinstance(inputlist[0], splat.Spectrum):
splist = copy.deepcopy(inputlist)
else:
print('\nInput should be list of Spectra objects or filenames')
return
# comparison files are present
compflag = False
complist = []
if kwargs.get('comparisons', False) != False:
comp = kwargs.get('comparisons')
if not isinstance(comp, list):
comp = [comp]
if len(comp) < len(splist):
while len(comp) < len(splist):
comp.append(comp[-1])
if isinstance(comp[0], str):
try:
complist = [splat.Spectrum(file=f) for f in comp]
compflag = True
except:
print(
'\nCould not read in comparison files: ignoring comparisons'
)
if isinstance(comp[0], splat.Spectrum):
complist = comp
compflag = True
# set comparison files to be standards for spectral classification
if kwargs.get('classify', False):
complist = []
for sp in splist:
spt = splat.classifyByStandard(sp, method='kirkpatrick')
complist.append(splat.SPEX_STDS[spt[0]])
compflag = True
kwargs['normalize'] = kwargs.get('normalize', True)
# normalize if desired
if kwargs.get('normalize', False):
tmp = [sp.normalize() for sp in splist]
if compflag == True:
tmp = [sp.normalize() for sp in complist]
# prep for plotting
plotlist = []
clist = []
for i, sp in enumerate(splist):
if compflag == True:
plotlist.append([sp, complist[i]])
clist.extend(['k', 'r'])
else:
plotlist.append([sp])
clist.extend(['k'])
# manage legends
if kwargs.get('legend', False) != False:
legends = kwargs.get('legend')
if not isinstance(legends, list):
legends = [legends]
if len(legends) < (len(splist) + len(complist)):
# guess: just left out the comparison legends
if len(complist) > 0 and len(legends) == len(splist):
legtmp = []
for i, l in enumerate(legends):
legtmp.extend([l, '{}'.format(complist[i].name)])
legends = legtmp
else:
# otherwise: pad the remaining legends with the last legend (pairs)
while len(legends) < (len(splist) + len(complist)):
if compflag:
legends.extend([legends[-2], legends[-1]])
else:
legends.extend([legends[-1]])
if len(legends) > (len(splist) + len(complist)):
legends = legends[0:(len(splist) + len(complist))]
else:
legends = []
for i, sp in enumerate(splist):
if compflag == True:
legends.extend([
'{}'.format(os.path.basename(sp.filename)),
'{}'.format(complist[i].name)
])
else:
legends.extend(['{}'.format(os.path.basename(sp.filename))])
# generate pdf
splat.plotSpectrum(plotlist,
multiplot=True,
multipage=True,
legends=legends,
colors=clist,
**kwargs)
return