return line(sp, HG)
def line(sp, refX):
sp = sp.copy()
sp.xarr.units = 'angstroms'
sp.xarr.refX = refX
sp.xarr.refX_units = 'angstroms'
sp.xarr.xtype = 'wavelength'
sp.xarr.convert_to_unit('km/s')
return sp
sp2 = pyspeckit.Spectrum('UT121009_DIS.txt',
skiplines=1,
xarrkwargs={
'xtype': 'wavelength',
'unit': 'angstroms'
})
sp2.units = 'erg s$^{-1}$ cm$^{-2}$ $\\AA^{-1}$'
sp2.specfit.register_fitter('mymodel', ModelClass, 18, multisingle='multi')
sp2.specfit.register_fitter('pcygni', PCygni, 22, multisingle='multi')
sp3 = pyspeckit.Spectrum('UT121002_DIS.txt',
skiplines=1,
xarrkwargs={
'xtype': 'wavelength',
'unit': 'angstroms'
})
sp3.units = 'erg s$^{-1}$ cm$^{-2}$ $\\AA^{-1}$'
sp3.specfit.register_fitter('mymodel', ModelClass, 18, multisingle='multi')
sp3.specfit.register_fitter('pcygni', PCygni, 22, multisingle='multi')
sp4 = pyspeckit.Spectrum('UT120830_DIS.txt',
def fitnh3tkin(input_dict, dobaseline=True, baselinekwargs={}, crop=False, guessline='twotwo',
tex=15,tkin=20,column=15.0,fortho=0.66, tau=None, thin=False, quiet=False, doplot=True, fignum=1,
guessfignum=2, smooth=False, scale_keyword=None, rebase=False, npeaks=1, guesses=None,
**kwargs):
"""
Given a dictionary of filenames and lines, fit them together
e.g. {'oneone':'G000.000+00.000_nh3_11.fits'}
"""
spdict = dict([ (linename,pyspeckit.Spectrum(value,
scale_keyword=scale_keyword))
if type(value) is str else (linename,value)
for linename, value in input_dict.iteritems() ])
splist = spdict.values()
for sp in splist: # required for plotting, cropping
sp.xarr.convert_to_unit('km/s')
if crop and len(crop) == 2:
for sp in splist:
sp.crop(*crop)
if dobaseline:
for sp in splist:
sp.baseline(**baselinekwargs)
if smooth and type(smooth) is int:
for sp in splist:
sp.smooth(smooth)
spdict[guessline].specfit(fittype='gaussian', negamp=False, vheight=False,
guesses='moments')
ampguess,vguess,widthguess = spdict[guessline].specfit.modelpars
if widthguess < 0:
raise ValueError("Width guess was < 0. This is impossible.")
print "RMS guess (errspec): ",spdict[guessline].specfit.errspec.mean()
print "RMS guess (residuals): ",spdict[guessline].specfit.residuals.std()
errguess = spdict[guessline].specfit.residuals.std()
if rebase:
# redo baseline subtraction excluding the centroid +/- about 20 km/s
vlow = spdict[guessline].specfit.modelpars[1]-(19.8+spdict[guessline].specfit.modelpars[2]*2.35)
vhigh = spdict[guessline].specfit.modelpars[1]+(19.8+spdict[guessline].specfit.modelpars[2]*2.35)
for sp in splist:
sp.baseline(exclude=[vlow,vhigh], **baselinekwargs)
for sp in splist:
sp.error[:] = errguess
spdict[guessline].plotter(figure=guessfignum)
spdict[guessline].specfit.plot_fit()
spectra = pyspeckit.Spectra(splist)
spectra.specfit.npeaks = npeaks
if tau is not None:
if guesses is None:
guesses = [a for i in xrange(npeaks) for a in
(tkin+random.random()*i, tex, tau+random.random()*i,
widthguess+random.random()*i, vguess+random.random()*i,
fortho)]
spectra.specfit(fittype='ammonia_tau',quiet=quiet,multifit=None,guesses=guesses,
thin=thin, **kwargs)
else:
if guesses is None:
guesses = [a for i in xrange(npeaks) for a in
(tkin+random.random()*i, tex, column+random.random()*i,
widthguess+random.random()*i, vguess+random.random()*i,
fortho)]
spectra.specfit(fittype='ammonia',quiet=quiet,multifit=None,guesses=guesses,
thin=thin, **kwargs)
if doplot:
plot_nh3(spdict,spectra,fignum=fignum)
return spdict,spectra
c = 3E8
fnameT = './hist_figs/histogram_tkin.png'
# creates an empty array, to store tkin values into for histogram
htkin = []
for thisObject in objects:
spect2 = {}
if os.path.exists('./nh3/' + thisObject + '.n11.fits'):
data1 = fits.getdata('./nh3/' + thisObject + '.n11.fits')
A1 = np.arange(len(data1['DATA'].T))
nu1 = data1['CDELT1'] * (A1 - data1['CRPIX1'] + 1) + data1['CRVAL1']
v1 = c * (nu1 / data1['RESTFREQ'] - 1)
spec11 = psk.Spectrum(data=(data1['DATA'].T).squeeze(),
xarr=v1,
xarrkwargs={
'unit': 'm/s',
'refX': data1['RESTFREQ'] / 1E6,
'refX_units': 'MHz',
'xtype': 'VLSR-RAD'
})
spect2['oneone'] = spec11
if os.path.exists('./nh3/' + thisObject + '.n22.fits'):
data2 = fits.getdata('./nh3/' + thisObject + '.n22.fits')
A2 = np.arange(len(data2['DATA'].T))
nu2 = data2['CDELT1'] * (A2 - data2['CRPIX1'] + 1) + data2['CRVAL1']
v2 = c * (nu2 / data2['RESTFREQ'] - 1)
spec22 = psk.Spectrum(data=(data2['DATA'].T).squeeze(),
xarr=v2,
xarrkwargs={
'unit': 'm/s',
'refX': data2['RESTFREQ'] / 1E6,
'refX_units': 'MHz',
flist = [fn] if 'fn' in locals() else glob.glob(
paths.dpath('stacked_spectra/OrionSourceI_*robust0.5.fits'))
for fn in flist:
basefn = os.path.split(fn)[-1]
if 'B7' in basefn and 'lb' not in basefn:
continue
print(fn)
beam = (radio_beam.Beam(0.1 * u.arcsec, 0.08 * u.arcsec) if 'B3' in fn else
radio_beam.Beam(0.043 * u.arcsec, 0.034 * u.arcsec) if 'B6' in fn
else radio_beam.Beam(0.029 * u.arcsec, 0.022 * u.arcsec))
sp_st = pyspeckit.Spectrum(fn)
jytok = beam.jtok(sp_st.xarr.mean())
sp_st.data *= jytok.value
sp_st.unit = u.K
pl.figure(0, figsize=(16, 6)).clf()
sp_st.plotter(figure=pl.figure(0, figsize=(16, 6)),
clear=True,
ymin=-0.0025 * jytok.value,
ymax=0.01 * jytok.value)
for txt in sp_st.plotter.axis.texts:
txt.set_backgroundcolor((1, 1, 1, 0.9))
catmaps = {
def measure_equivalent_width(filename, xmin, xmax, exclude_min, exclude_max,
n):
sp = p.Spectrum(filename)
sp.xarr.units = 'micron'
sp.xarr.xtype = 'wavelength'
sp.plotter(xmin=xmin, xmax=xmax, ymin=0, errstyle='bars', color='grey')
sp.baseline(xmin=xmin,
xmax=xmax,
exclude=[exclude_min, exclude_max],
subtract=False,
highlight_fitregion=False,
selectregion=True,
order=0)
sp.specfit(plot=True,
fittype='voigt',
color='blue',
guesses='moments',
vheight=True)
sp.specfit.EQW(plot=True,
plotcolor='g',
fitted=False,
components=False,
annotate=True,
loc='lower left',
xmin=None,
xmax=None)
sp2 = sp.copy()
EQWs = []
for w in range(n):
sp2.data = sp.data + np.random.randn(sp.data.size) * sp.error
sp2.baseline(xmin=xmin,
xmax=xmax,
exclude=[exclude_min, exclude_max],
subtract=False,
highlight_fitregion=False,
selectregion=True,
order=0)
sp2.specfit(fittype='voigt', guesses=sp.specfit.parinfo.values)
dist = sp2.specfit.EQW(plotcolor='g',
fitted=False,
components=False,
annotate=True,
loc='lower left',
xmin=None,
xmax=None)
EQWs.append(dist)
EQWs = np.array(EQWs)
EQWs = EQWs * 10000
sp.specfit.EQW(plot=True,
plotcolor='g',
fitted=False,
components=False,
annotate=True,
loc='lower left',
xmin=None,
xmax=None)
sp2 = sp.copy()
EQWs = np.zeros(n)
for w in range(n):
sp2.data = sp.data + np.random.randn(sp.data.size) * sp.error
sp2.baseline(xmin=xmin,
xmax=xmax,
exclude=[exclude_min, exclude_max],
subtract=False,
highlight_fitregion=False,
selectregion=True,
order=0)
sp2.specfit(fittype='voigt', guesses=sp.specfit.parinfo.values)
dist = sp2.specfit.EQW(plotcolor='g',
fitted=False,
components=False,
annotate=True,
loc='lower left',
xmin=None,
xmax=None)
# Convert from microns to angstroms
EQWs[w] = dist * 10000.0
plt.figure()
mu, sigma = norm.fit(EQWs)
print(mu, sigma)
n, bins, patches = plt.hist(EQWs,
10,
normed=True,
facecolor='green',
histtype='stepfilled')
y = mlab.normpdf(bins, mu, sigma)
plt.plot(bins, y, 'r--', linewidth=2)
plt.grid(True)
plt.ylabel('Probability')
plt.xlabel('EQW')
plt.show()
num_bins = np.floor(np.log10(n)) * 10
# Light blue histogram for contrast and for R/G colorblind folks
n, bins, patches = plt.hist(EQWs,
num_bins,
normed=True,
facecolor='lightblue',
histtype='stepfilled')
y = mlab.normpdf(bins, mu, sigma)
plt.plot(bins, y, 'r--', linewidth=2)
# Plot the median and 68th percentile values for comparison
perc = np.percentile(EQWs, [16, 50, 84])
ax = plt.gca()
for p_value in perc:
ax.axvline(p_value, linestyle=":", color="k", lw=1.5)
plt.ylabel('Probability')
plt.xlabel('EQW')
plt.show()
import pyspeckit
import pylab as pl
# Load the spectrum & properly identify the units
# The data is from http://adsabs.harvard.edu/abs/1999A%26A...348..600P
sp = pyspeckit.Spectrum('02232+6138.txt')
sp.xarr.units = 'km/s'
sp.xarr.refX = 88.63184666e9
sp.xarr.xtype = 'velocity'
sp.units = '$T_A^*$'
# set the error array based on a signal-free part of the spectrum
sp.error[:] = sp.stats((-35, -25))['std']
# Register the fitter
# The HCN fitter is 'built-in' but is not registered by default; this example
# shows how to register a fitting procedure
# 'multi' indicates that it is possible to fit multiple components and a
# background will not automatically be fit
# 5 is the number of parameters in the model (line center,
# line width, and amplitude for the 0-1, 2-1, and 1-1 lines)
sp.Registry.add_fitter('hcn_varyhf',
pyspeckit.models.hcn.hcn_varyhf_amp_fitter,
5,
multisingle='multi')
# This one is the same, but with fixed relative ampltidue hyperfine components
sp.Registry.add_fitter('hcn_fixedhf',
pyspeckit.models.hcn.hcn_amp,
3,
multisingle='multi')
import latex_info
dv = 15 * u.km / u.s
v = 5.5 * u.km / u.s
dv_linesearch = 2.5 * u.km / u.s
fits = {}
bad_fits = [
'Unknown_8', # only half the line is detected
]
for spw in (0, 1, 2, 3):
sp = pyspeckit.Spectrum(
paths.dpath(
'stacked_spectra/OrionSourceI_B6_spw{0}.fits'.format(spw)), )
for linename, freq in lines.disk_lines.items():
xmin = freq * (1 + (v - dv) / constants.c)
xmax = freq * (1 + (v + dv) / constants.c)
slc = sp.slice(xmin, xmax)
if len(slc) == 0:
continue
guesses = [
slc.data.max(), (freq * (1 + v / constants.c)).to(u.GHz).value,
(2 * u.km / u.s / constants.c * freq).to(u.GHz).value
]
import pyspeckit
if not 'interactive' in globals():
interactive = False
if not 'savedir' in globals():
savedir = ''
# load a FITS-compliant spectrum
spec = pyspeckit.Spectrum('10074-190_HCOp.fits')
# The units are originally frequency (check this by printing spec.xarr.units).
# I want to know the velocity. Convert!
# Note that this only works because the reference frequency is set in the header
# this is no longer necessary! #spec.xarr.frequency_to_velocity()
# Default conversion is to m/s, but we traditionally work in km/s
spec.xarr.convert_to_unit('km/s')
# plot it up!
spec.plotter()
# compute statistics
stats = spec.stats()
# set the errors
spec.error[:] = stats['std']
# Subtract a baseline (the data is only 'mostly' reduced)
spec.baseline()
# Fit a gaussian. We know it will be an emission line, so we force a positive guess
# nsigcut_moments tells the moment analysis tool to only use high-significance
# data points to estimate the width of the line (it's tricky)
spec.specfit(negamp=False, nsigcut_moments=2)
# Note that the errors on the fits are larger than the fitted parameters.
SIIb = 6732.68
# Offsets between SII lines in each complex
SIIb_off = SIIb - SIIa
OIIIb_off = OIIIb - OIIIa
NIIa_off_Ha = NIIa - Halpha
NIIb_off_Ha = NIIb - Halpha
SIIa_off_Ha = SIIa - Halpha
SIIb_off_Ha = SIIb - Halpha
# Guesses
narrow = 5.
broad = 30.
# Initialize spectrum object
spec = pyspeckit.Spectrum('sample_sdss.txt')
spec.unit = 'erg s^{-1} cm^{-2} \\AA^{-1}'
spec.xarr.set_unit = u.dimensionless_unscaled
# H-alpha
spec.specfit.selectregion(xmin=Halpha - 5, xmax=Halpha + 5)
ampHa = np.max(spec.data[spec.specfit.xmin:spec.specfit.xmax])
# SII
spec.specfit.selectregion(xmin=SIIa - 20, xmax=SIIb + 20)
smallamp = np.max(spec.data[spec.specfit.xmin:spec.specfit.xmax])
spec.specfit(guesses=[smallamp, SIIa, narrow, smallamp, SIIb, narrow],
tied=['', '', 'p[-1]', '', 'p[1] + {0}'.format(SIIb_off), ''],
negamp=False,
quiet=True,
refX=freq_dict['twotwo']).as_unit(u.GHz)
xarr33 = SpectroscopicAxis(np.linspace(-50, 50, 100) * u.km / u.s,
velocity_convention='radio',
refX=freq_dict['threethree']).as_unit(u.GHz)
# Merge the three X-axes into a single axis
xarr = SpectroscopicAxes([xarr11, xarr22, xarr33])
# Compute a synthetic model that is made of two temperature components with
# identical velocities
synthspec = (
ammonia.ammonia(xarr, tkin=20, ntot=15, fortho=0.5, xoff_v=0.0,
width=1.0) +
ammonia.ammonia(xarr, tkin=50, ntot=14, fortho=0.5, xoff_v=0.0, width=1.0))
# Create the Spectrum object
spectrum = pyspeckit.Spectrum(xarr=xarr, data=synthspec)
# Step 2. You have a spectrum.
# plot it
spectrum.plotter()
# Use the multi-tex/multi-tau model generator to build up a model function
# You can use any set of oneone, twotwo, ..., eighteight (no 9-9 or higher)
# This sets the number of parameters to be fit: 2+2*(n_transitions)
fitter = ammonia_hf.nh3_vtau_multimodel_generator(
['oneone', 'twotwo', 'threethree'])
# Register the fitter - i.e., tell pyspeckit where it is and how to use it
spectrum.specfit.Registry.add_fitter('nh3_vtau_123', fitter, fitter.npars)
# These are the parameter names, approximately:
# parnames=['center','width','Tex11','tau11','Tex22','tau22','Tex33','tau33'],
import pyspeckit
# Load the spectrum
sp = pyspeckit.Spectrum('n2hp_opha_example.fits')
# Register the fitter
# The N2H+ fitter is 'built-in' but is not registered by default; this example
# shows how to register a fitting procedure
# 'multi' indicates that it is possible to fit multiple components and a
# background will not automatically be fit 4 is the number of parameters in the
# model (excitation temperature, optical depth, line center, and line width)
sp.Registry.add_fitter('n2hp_vtau', pyspeckit.models.n2hp.n2hp_vtau_fitter, 4)
sp.xarr.velocity_convention = 'radio'
# Run the fitter
sp.specfit(fittype='n2hp_vtau', multifit=None, guesses=[15, 2, 4, 0.2])
# Plot the results
sp.plotter()
# Re-run the fitter (to get proper error bars) and show the individual fit components
sp.specfit(fittype='n2hp_vtau',
multifit=None,
guesses=[15, 2, 4, 0.2],
show_hyperfine_components=True)
# Save the figure (this step is just so that an image can be included on the web page)
sp.plotter.savefig('n2hp_ophA_fit.png')
def splat_1d(filename=None,
vmin=None,
vmax=None,
button=None,
dobaseline=False,
exclude=None,
smooth=None,
order=1,
savepre=None,
vcrop=True,
vconv=None,
vpars=None,
spec=None,
xtora=None,
ytodec=None,
specname=None,
quiet=True,
specnum=0,
errspecnum=None,
wcstype='',
offset=0.0,
continuum=0.0,
annotatebaseline=False,
plotspectrum=True,
smoothto=None,
xunits=None,
units=None,
conversion_factor=None,
smoothtype='gaussian',
convmode='same',
maskspecnum=None,
fignum=1,
axis=None,
autorefresh=False,
title=None,
color=None,
label=None,
clear=False,
negamp=None,
plotscale=1.0,
voff=0.0,
**kwargs):
sp = pyspeckit.Spectrum(filename,
specnum=specnum,
errspecnum=errspecnum,
wcstype=wcstype,
**kwargs)
sp.plotter.xmin = vmin
sp.plotter.xmax = vmax
sp.plotter.offset = offset
if type(continuum) is str:
sp.plotter.offset += sp.header.get(continuum)
else:
sp.plotter.offset += continuum
if vcrop and vmin and vmax:
sp.crop(vmin, vmax)
if button != None:
print "button keyword doesn't do anything"
if maskspecnum:
print "maskspecnum doesn't do anything [not implemented]"
if specname:
sp.plotter.title = specname
if title:
sp.plotter.title = title
if smoothto:
smooth = abs(smoothto / sp.xarr.cdelt())
if smooth:
sp.smooth(smooth, smoothtype=smoothtype, convmode=convmode)
if xunits:
sp.xarr.convert_to_unit(xunits)
if units:
sp.units = units
if dobaseline:
sp.baseline(order=order, exclude=exclude, annotate=annotatebaseline)
if plotspectrum:
if color is None:
sp.plotter(figure=fignum,
axis=axis,
autorefresh=autorefresh,
clear=clear,
silent=quiet,
reset_ylimits=True,
title=title,
plotscale=plotscale,
xoffset=voff,
label=label)
else:
sp.plotter(figure=fignum,
axis=axis,
autorefresh=autorefresh,
color=color,
clear=clear,
silent=quiet,
reset_ylimits=True,
title=title,
plotscale=plotscale,
xoffset=voff,
label=label)
if savepre is not None:
glon, glat = sp.header.get("GLON"), sp.header.get("GLAT")
if glon is None or glat is None:
raise ValueError(
"Header doesn't contain glon/glat. Default savepre will not work."
)
if glat < 0: pm = ""
else: pm = "+"
savename = savepre + "G%07.3f%0s%07.3f_" % (
glon, pm, glat) + sp.header.get('MOLECULE').replace(
' ', '') + sp.header.get('TRANSITI').replace(' ', '')
sp.plotter.savefig(savename + '.png')
sp.fitspec = sp.specfit
sp.spectrum = sp.data
sp.axis = sp.plotter.axis
sp.dv = sp.xarr.cdelt()
sp.vind = sp.xarr
sp.cube = sp.data
sp.save = sp.write
return sp
import pyspeckit
from DISfunctions import *
tsp1 = pyspeckit.Spectrum('UT120831_TSpec.final.txt', skiplines=1)
tsp1.specname = "UT120831"
tsp1.xarr.units = 'angstroms'
tsp1.xarr.xtype = 'wavelength'
tsp1 = tsp1 * 10
tsp1.units = '$10^{-15}$ erg s$^{-1}$ cm$^{-2}$ $\\AA^{-1}$'
tsp1.data.mask[tsp1.data > 0.1] = 0
tsp1.data.mask[(tsp1.xarr > 11100) * (tsp1.xarr < 11300)] = True
tsp2 = pyspeckit.Spectrum('UT121002_TSpec.final.txt', skiplines=1)
tsp2.specname = "UT121002"
tsp2.xarr.units = 'angstroms'
tsp2.xarr.xtype = 'wavelength'
tsp2.data.mask = tsp2.data < 0
tsp2.units = '$10^{-14}$ erg s$^{-1}$ cm$^{-2}$ $\\AA^{-1}$'
tsp2.data.mask[(tsp2.xarr > 11100) * (tsp2.xarr < 11300)] = True
for sp in [tsp1, tsp2]:
print sp.specname
sp.plotter(xmin=12400, xmax=13300)
sp.baseline(xmin=12400,
xmax=13300,
exclude=[12770, 12866],
powerlaw=True,
subtract=False,
reset_selection=True)
yoffset = sp.baseline.basespec[sp.xarr.x_to_pix(12821)] - 0.05
sp.plotter(xmin=12400, xmax=13300, ymin=yoffset - 0.05)
sp.baseline.highlight_fitregion()
SIIb = 6732.68
# Offsets between SII lines in each complex
SIIb_off = SIIb - SIIa
OIIIb_off = OIIIb - OIIIa
NIIa_off_Ha = NIIa - Halpha
NIIb_off_Ha = NIIb - Halpha
SIIa_off_Ha = SIIa - Halpha
SIIb_off_Ha = SIIb - Halpha
# Guesses
narrow = 5.
broad = 30.
# Initialize spectrum object
spec = pyspeckit.Spectrum('../tests/sample_sdss.txt')
spec.units = 'erg s^{-1} cm^{-2} \\AA^{-1}'
spec.xarr.units='angstroms'
# H-alpha
spec.specfit.selectregion(xmin = Halpha - 5, xmax = Halpha + 5)
ampHa = np.max(spec.data[spec.specfit.xmin:spec.specfit.xmax])
# SII
spec.specfit.selectregion(xmin = SIIa - 20, xmax = SIIb + 20)
smallamp = np.max(spec.data[spec.specfit.xmin:spec.specfit.xmax])
spec.specfit(guesses = [smallamp, SIIa, narrow, smallamp, SIIb, narrow],
tied=['', '', 'p[-1]', '', 'p[1] + {0}'.format(SIIb_off), ''],
negamp=False, quiet=True, multifit=True, show_components=True)
import pyspeckit
# Grab a .fits spectrum with a legitimate header
sp = pyspeckit.Spectrum('G031.947+00.076_nh3_11_Tastar.fits')
""" HEADER:
SIMPLE = T / Written by IDL: Tue Aug 31 18:17:01 2010
BITPIX = -64
NAXIS = 1 / number of array dimensions
NAXIS1 = 8192 /Number of positions along axis 1
CDELT1 = -0.077230503
CRPIX1 = 4096.0000
CRVAL1 = 68.365635
CTYPE1 = 'VRAD'
CUNIT1 = 'km/s '
SPECSYS = 'LSRK'
RESTFRQ = 2.3694500e+10
VELOSYS = -43755.930
CDELT1F = 6103.5156
CRPIX1F = 4096.0000
CRVAL1F = 2.3692555e+10
CTYPE1F = 'FREQ'
CUNIT1F = 'Hz'
SPECSYSF= 'LSRK'
RESTFRQF= 2.3694500e+10
VELOSYSF= -43755.930
VDEF = 'RADI-LSR'
SRCVEL = 70.000000
ZSOURCE = 0.00023349487
BUNIT = 'K '
OBJECT = 'G031.947+00.076'
TELESCOP= 'GBT'
from __future__ import print_function
import pyspeckit
if not 'interactive' in globals():
interactive=False
if not 'savedir' in globals():
savedir = ''
filenames = {'oneone':'G032.751-00.071_nh3_11_Tastar.fits',
'twotwo':'G032.751-00.071_nh3_22_Tastar.fits',
'threethree':'G032.751-00.071_nh3_33_Tastar.fits',}
sp1 = pyspeckit.Spectrum('G032.751-00.071_nh3_11_Tastar.fits')
sp1.crop(0,80)
sp1.smooth(4)
sp1.plotter()
sp1.baseline(exclude=[15,62])
sp1.plotter()
sp1.plotter.savefig(savedir+'nh3_11_baselined.png')
sp2 = pyspeckit.Spectrum('G032.751-00.071_nh3_22_Tastar.fits')
sp2.crop(0,80)
sp2.smooth(4)
sp2.plotter()
guesses = sp2.specfit.moments(fittype='gaussian')[1:]
sp2.specfit(fittype='gaussian', guesses=guesses)
sp2.baseline(exclude=[34,42])
sp2.plotter()
sp2.plotter.savefig(savedir+'nh3_22_baselined.png')
sp3 = pyspeckit.Spectrum('G032.751-00.071_nh3_33_Tastar.fits')
sp3.crop(0,80)
sp3.smooth(4)
import numpy as np
import pylab as pl
import pyspeckit
import glob
from astropy import log
fig = pl.figure(figsize=(12, 16))
for fn in glob.glob("*_full_SgrB2_TETC7m_r0_spw0_lines.fits"):
for spw in (0, 1, 2, 3):
fn_ = fn.replace("spw0", "spw{0}".format(spw))
ax = pl.subplot(4, 1, spw + 1)
sp = pyspeckit.Spectrum(fn_)
sp.xarr.convert_to_unit('GHz')
if not any(np.isfinite(sp.data)):
log.warn("{0} is bad".format(fn_))
else:
log.info("{0} is good".format(fn_))
sp.plotter(figure=fig, axis=ax, clear=True)
if spw != 0:
ax.set_title("")
if spw == 3:
sp.plotter.savefig('quicklooks/{0}.png'.format(
fn.replace("_spw0", "")[:-5]))
fake_spec = ammonia.ammonia(xarr, tkin=tk, tex=tx, ntot=nt, width=sig, xoff_v=0,
fortho=0.5)
real_tau = ammonia.ammonia(xarr, tkin=tk, tex=tx, ntot=nt, width=sig, xoff_v=0,
fortho=0.5, return_tau=True)
if fake_spec.max()*3 < noise:
noises.append(fake_spec.max()/3.)
fake_spec += np.random.randn(fake_spec.size) * fake_spec.max()/3.
signoise.append(3)
else:
noises.append(noise)
signoise.append(fake_spec.max()/noise)
fake_spec += np.random.randn(fake_spec.size) * noise
sp = pyspeckit.Spectrum(xarr=xarr, data=fake_spec, err=err,units='K', header=pyfits.Header({'BUNIT':'K'}))
# sp.plotter()
sp.specfit(fittype='ammonia', multifit=True, guesses=[tk,tx,nt,sig,0,0.5],
fixed=[F,F,F,F,F,T],
limitedmax=[T,T,T,T,T,T],
maxpars=[100,100,25,5,20,1],
limitedmin=[T,T,T,T,T,T],
minpars=[2.73,2.73,10,0,-20,0],
use_lmfit=True,
verbose=False)
outs.append(sp.specfit.parinfo.values[:4])
errors.append(sp.specfit.parinfo.errors[:4])
print(" ".join(["%7s" % round(x,1) for x in ins[-1]]),"|",)
refX=freq_dict['twotwo']).as_unit(u.GHz)
xarr33 = SpectroscopicAxis(np.linspace(-50, 50, 100) * u.km / u.s,
velocity_convention='radio',
refX=freq_dict['threethree']).as_unit(u.GHz)
# Merge the three X-axes into a single axis
xarr = SpectroscopicAxes([xarr11, xarr22, xarr33])
# Compute a synthetic model that is made of two temperature components with
# identical velocities
synthspec = (
ammonia.ammonia(xarr, trot=20, ntot=15, fortho=0.5, xoff_v=0.0,
width=1.0) +
ammonia.ammonia(xarr, trot=50, ntot=14, fortho=0.5, xoff_v=0.0, width=1.0))
# Create the Spectrum object
spectrum = pyspeckit.Spectrum(xarr=xarr, data=synthspec, header={})
# Step 2. You have a spectrum.
# plot it
spectrum.plotter()
# Use the multi-tex/multi-tau model generator to build up a model function
# You can use any set of oneone, twotwo, ..., eighteight (no 9-9 or higher)
# This sets the number of parameters to be fit: 2+2*(n_transitions)
fitter = ammonia_hf.nh3_vtau_multimodel_generator(
['oneone', 'twotwo', 'threethree'])
# Register the fitter - i.e., tell pyspeckit where it is and how to use it
spectrum.specfit.Registry.add_fitter('nh3_vtau_123', fitter, fitter.npars)
# These are the parameter names, approximately:
# parnames=['center','width','Tex11','tau11','Tex22','tau22','Tex33','tau33'],
import pyspeckit
import os
from pyspeckit.spectrum.models import nh2d
import numpy as np
import astropy.units as u
if not os.path.exists('p-nh2d_spec.fits'):
import astropy.utils.data as aud
from astropy.io import fits
f = aud.download_file('https://github.com/pyspeckit/pyspeckit-example-files/raw/master/p-nh2d_spec.fits')
with fits.open(f) as ff:
ff.writeto('p-nh2d_spec.fits')
# Load the spectrum
spec = pyspeckit.Spectrum('p-nh2d_spec.fits')
# Determine rms from line free section and load into cube
rms = np.std(spec.data[10:340])
spec.error[:] = rms
# setup spectral axis
spec.xarr.refX = 110.153594*u.GHz
spec.xarr.velocity_convention = 'radio'
spec.xarr.convert_to_unit('km/s')
# define useful shortcuts for True and False
F=False
T=True
# Setup of matplotlib
import matplotlib.pyplot as plt
plt.ion()
# Add NH2D fitter
spec.Registry.add_fitter('nh2d_vtau', pyspeckit.models.nh2d.nh2d_vtau_fitter,4)
unit=str(cube.spectral_axis.unit),
refX=cube.wcs.wcs.restfrq,
refX_units='Hz')
spectra = {}
for region_number,reg in enumerate(regs):
name = reg.attr[1]['text']
if name not in spectra:
#sp = cube.get_apspec(reg.coord_list,coordsys='galactic',wunit='degree')
shape = pyregion.ShapeList([reg])
#mask = shape.get_mask(header=noisehdr, shape=noise.shape)
scube = cube.subcube_from_ds9region(shape)
data = scube.apply_numpy_function(np.nanmean, axis=(1,2))
#error = ((noise[mask & noiseokmask]**2).sum()**0.5/np.count_nonzero(mask))
sp = pyspeckit.Spectrum(data=data,
error=np.ones(data.size),#*error,
xarr=xarr, header=cube.wcs.to_header())
sp.xarr.convert_to_unit('GHz')
#sp.header['ERROR'] = error
#sp.error[:] = sp.stats((218.5e9,218.65e9))['std']
sp.specname = reg.attr[1]['text']
# Error is already computed above; this is an old hack
#sp.error[:] = sp.stats((218e9,218.1e9))['std']
spectra[name] = sp
sp.unit = "$T_{A}$ [K]"
else:
sp = spectra[name]
sp.plotter.figure = pl.figure(1)
sp.plotter()
#linesel = (lfreq > sp.xarr.as_unit('GHz').min()) & (lfreq < sp.xarr.as_unit('GHz').max())
import pyspeckit
# Rest wavelengths of the lines we are fitting - use as initial guesses
NIIa = 6549.86
NIIb = 6585.27
Halpha = 6564.614
SIIa = 6718.29
SIIb = 6732.68
# Initialize spectrum object and plot region surrounding Halpha-[NII] complex
spec = pyspeckit.Spectrum('sample_sdss.txt', errorcol=2)
spec.plotter(xmin=6450, xmax=6775, ymin=0, ymax=150)
# We fit the [NII] and [SII] doublets, and allow two components for Halpha.
# The widths of all narrow lines are tied to the widths of [SII].
guesses = [
50, NIIa, 5, 100, Halpha, 5, 50, Halpha, 50, 50, NIIb, 5, 20, SIIa, 5, 20,
SIIb, 5
]
tied = [
'', '', 'p[17]', '', '', 'p[17]', '', 'p[4]', '', '3 * p[0]', '', 'p[17]',
'', '', 'p[17]', '', '', ''
]
# Actually do the fit.
spec.specfit(guesses=guesses, tied=tied, annotate=False)
spec.plotter.refresh()
# Let's use the measurements class to derive information about the emission
# lines. The galaxy's redshift and the flux normalization of the spectrum
# must be supplied to convert measured fluxes to line luminosities. If the
import pyspeckit
import pylab as pl
import astropy.units as u
# Load the spectrum & properly identify the units
# The data is from http://adsabs.harvard.edu/abs/1999A%26A...348..600P
sp = pyspeckit.Spectrum('02232_plus_6138.txt')
sp.xarr.set_unit(u.km / u.s)
sp.xarr.refX = 88.63184666e9 * u.Hz
sp.xarr.velocity_convention = 'radio'
sp.xarr.xtype = 'velocity'
sp.unit = '$T_A^*$'
# set the error array based on a signal-free part of the spectrum
sp.error[:] = sp.stats((-35, -25))['std']
# Register the fitter
# The HCN fitter is 'built-in' but is not registered by default; this example
# shows how to register a fitting procedure
# 'multi' indicates that it is possible to fit multiple components and a
# background will not automatically be fit
# 5 is the number of parameters in the model (line center,
# line width, and amplitude for the 0-1, 2-1, and 1-1 lines)
sp.Registry.add_fitter('hcn_varyhf',
pyspeckit.models.hcn.hcn_varyhf_amp_fitter, 5)
# This one is the same, but with fixed relative ampltidue hyperfine components
sp.Registry.add_fitter('hcn_fixedhf', pyspeckit.models.hcn.hcn_amp, 3)
# Plot the results
sp.plotter()
# Run the fixed-ampltiude fitter and show the individual fit components
def line_ids(fn):
results = []
Ulines = []
sp = pyspeckit.Spectrum(fn)
# this is a bit hackier than I like
# we'll do all our measurements in Kelvin!
beams = Beams.from_fits_bintable(fits.open(fn)[1])
factors = jtok_factors(beams, sp.xarr.to(u.GHz))
sp.data = sp.data * factors
sp.unit = u.K
# want km/s - reference will be ~middle of SPW
sp.xarr.convert_to_unit(u.km / u.s)
med = np.nanmedian(sp.data)
mad = stats.mad_std(sp.data - med)
detections = (sp.data - med) > 5 * mad
labels, ct = label(detections)
for labelid in range(1, ct + 1):
ssp = sp[labels == labelid]
try:
ssp.xarr.convert_to_unit(u.GHz)
ssp.specfit()
ssp.specfit.parinfo
frq = ssp.specfit.parinfo['SHIFT0'].value * ssp.xarr.unit
except Exception as ex:
print(ex)
frq = ssp.xarr.to(u.GHz).mean()
sq = Splatalogue.query_lines(
frq * (1 + 0 / 3e5),
frq * (1 + 75 / 3e5), # 30/3e5 original lower bound
only_astronomically_observed=True)
if len(sq) > 0:
tbl = utils.minimize_table(sq)
try:
total_intensity = ssp.data.sum() * np.abs(
ssp.xarr.to(u.km / u.s).cdelt())
except ValueError:
total_intensity = ssp.data.sum() * np.abs(
sp.xarr.to(u.km / u.s).cdelt())
peak_intensity = ssp.data.max()
tbl.add_column(Column(data=total_intensity, name='TotalIntensity'))
tbl.add_column(Column(data=peak_intensity, name='PeakIntensity'))
tbl.add_column(Column(data=mad, name='RMS'))
tbl.add_column(
Column(data=u.Quantity((-(frq.to(u.GHz).value - tbl['Freq']) /
tbl['Freq'] * constants.c),
u.km / u.s),
name='Velocity'))
# print(tbl.pprint(max_width=200))
results.append(tbl)
else:
log.warning(f"Frequency {frq.to(u.GHz)} had no hits")
Ulines.append(frq)
try:
match_table = table.vstack(results)
except ValueError:
pass
else:
# match_table.remove_column('QNs')
match_table = table.unique(match_table, keys='Species')
match_table.sort('Freq')
print(match_table.pprint(max_width=200))
print(match_table['Species', 'Freq', 'Velocity'])
# match_table.write(f"line_fit_table_{suffix}.ipac", format='ascii.ipac', overwrite=True)
return match_table
def measure_equivalent_width(filename, xmin, xmax, exclude_min, exclude_max,
n):
"""
Calculate the equivalent width of an absorption or emission line for a given spectrum using PySpecKit. By: Munazza Alam
=======
Args:
=======
xmin,xmax - the specified interval of the spectrum to plot
excludemin, excludemax - the specified interval (in wavelength space) of the absorption feature
n - the number of Monte Carlo iterations
=======
Returns:
=======
- the mean and standard deviation of the equivalent width measured n times
- the spectrum plotted with the Voigt profile line fit (blue), the pseudo-continuum (yellow),
and the approximated rectangle (green)
- a histogram of the EqW distribution
"""
sp = p.Spectrum(filename)
sp.xarr.units = 'micron'
sp.xarr.xtype = 'wavelength'
sp.plotter(xmin=xmin, xmax=xmax, ymin=0, errstyle='bars', color='grey')
sp.baseline(xmin=xmin,
xmax=xmax,
exclude=[exclude_min, exclude_max],
subtract=False,
highlight_fitregion=False,
selectregion=True,
order=0)
sp.specfit(plot=True,
fittype='voigt',
color='blue',
guesses='moments',
vheight=True)
sp.specfit.EQW(plot=True,
plotcolor='g',
fitted=False,
components=False,
annotate=True,
loc='lower left',
xmin=None,
xmax=None)
sp2 = sp.copy()
EQWs = []
for w in range(n):
sp2.data = sp.data + np.random.randn(sp.data.size) * sp.error
sp2.baseline(xmin=xmin,
xmax=xmax,
exclude=[exclude_min, exclude_max],
subtract=False,
highlight_fitregion=False,
selectregion=True,
order=0)
sp2.specfit(fittype='voigt', guesses=sp.specfit.parinfo.values)
dist = sp2.specfit.EQW(plotcolor='g',
fitted=False,
components=False,
annotate=True,
loc='lower left',
xmin=None,
xmax=None)
EQWs.append(dist)
EQWs = np.array(EQWs)
EQWs = EQWs * 10000
sp.specfit.EQW(plot=True,
plotcolor='g',
fitted=False,
components=False,
annotate=True,
loc='lower left',
xmin=None,
xmax=None)
sp2 = sp.copy()
EQWs = np.zeros(n)
for w in range(n):
sp2.data = sp.data + np.random.randn(sp.data.size) * sp.error
sp2.baseline(xmin=xmin,
xmax=xmax,
exclude=[exclude_min, exclude_max],
subtract=False,
highlight_fitregion=False,
selectregion=True,
order=0)
sp2.specfit(fittype='voigt', guesses=sp.specfit.parinfo.values)
dist = sp2.specfit.EQW(plotcolor='g',
fitted=False,
components=False,
annotate=True,
loc='lower left',
xmin=None,
xmax=None)
# Convert from microns to angstroms
EQWs[w] = dist * 10000.0
plt.figure()
mu, sigma = norm.fit(EQWs)
print mu, sigma
n, bins, patches = plt.hist(EQWs,
10,
normed=True,
facecolor='green',
histtype='stepfilled')
y = mlab.normpdf(bins, mu, sigma)
plt.plot(bins, y, 'r--', linewidth=2)
plt.grid(True)
plt.ylabel('Probability')
plt.xlabel('EQW')
plt.show()
num_bins = np.floor(np.log10(n)) * 10
# Light blue histogram for contrast and for R/G colorblind folks
n, bins, patches = plt.hist(EQWs,
num_bins,
normed=True,
facecolor='lightblue',
histtype='stepfilled')
y = mlab.normpdf(bins, mu, sigma)
plt.plot(bins, y, 'r--', linewidth=2)
# Plot the median and 68th percentile values for comparison
perc = np.percentile(EQWs, [16, 50, 84])
ax = plt.gca()
for p_value in perc:
ax.axvline(p_value, linestyle=":", color="k", lw=1.5)
plt.ylabel('Probability')
plt.xlabel('EQW')
plt.show()
def fitrow(n):
global guesses, sp
Y = z[239 * n:239 * (n + 1)]
X = x[239 * n:239 * (n + 1)]
xarr = pyspeckit.spectrum.units.SpectroscopicAxis(X)
yarr = pyspeckit.spectrum.units.SpectroscopicAxis(Y)
sp = pyspeckit.Spectrum(xarr=xarr, data=yarr)
sp.specfit.Registry.add_fitter('lorentzian',
lorentzian_fitter(multisingle='multi'),
3,
multisingle='multi',
key='L')
## sp.Registry.add_fitter ('lorentzian',lorentzian_fitter(multisingle='multi'),3,multisingle='multi',key='L')
sp.plotter()
## sp.smooth(2)
guesses, tied = findpks(Y, X, 1e5)
if len(guesses) == 0:
return [0., 1., 1.]
minp, maxp, lmin, lmax = limitparams(1, 0.02)
sp.specfit(fittype='gaussian',
multifit=True,
negamp=False,
annotate=False,
limitedmin=lmin,
limitedmax=lmax,
minpars=minp,
maxpars=maxp,
guesses=guesses,
tied=tied,
quiet=True)
guesses = sp.specfit.modelpars
fixed = fixparams([True, True, False])
minp, maxp, lmin, lmax = limitparams(2, 3)
## minp, maxp, lmin, lmax = limitparams(2,0.05)
sp.specfit(fittype='gaussian',
multifit=True,
negamp=False,
annotate=False,
limitedmin=lmin,
limitedmax=lmax,
minpars=minp,
maxpars=maxp,
guesses=guesses,
fixed=fixed,
quiet=True)
guesses = sp.specfit.modelpars
fixed = fixparams([False, True, True])
sp.specfit(fittype='lorentzian',
multifit=True,
negamp=False,
annotate=False,
guesses=guesses,
fixed=fixed,
quiet=True)
guesses = sp.specfit.modelpars
fixed = fixparams([True, True, False])
minp, maxp, lmin, lmax = limitparams(2, 0.2)
sp.specfit(fittype='lorentzian',
multifit=True,
negamp=False,
annotate=False,
limitedmin=lmin,
limitedmax=lmax,
minpars=minp,
maxpars=maxp,
guesses=guesses,
fixed=fixed,
quiet=True)
## guesses=sp.specfit.modelpars
## sp.specfit(fittype='lorentzian',multifit=True,guesses=guesses,annotate=False)
return sp.specfit.modelpars
import pyspeckit import numpy as np from pyspeckit.spectrum.models import voigtfitter # technically, the voigt fitter works as a singlefitter (i.e., you can fit the # background level and the peak simultaneously) # in practice, however, you need to fit the background independently except for # gaussians. I don't know why this is. xarr = pyspeckit.spectrum.units.SpectroscopicAxis(np.linspace(-100,100,500),unit='km/s',refX=1e9,refX_units='Hz') VF = voigtfitter.voigt_fitter() sp1 = pyspeckit.Spectrum(xarr=xarr, data=VF.voigt(xarr,1,0,2.5,2.5) + np.random.randn(xarr.shape[0])/20., error=np.ones(xarr.shape[0])/20.) sp1.plotter() sp1.specfit(fittype='gaussian',composite_fit_color='b',clear=False,annotate=False) sp1.specfit(fittype='lorentzian',composite_fit_color='g',clear=False,annotate=False) sp1.specfit(fittype='voigt',composite_fit_color='r',clear=False,annotate=True) sp1.baseline.annotate() sp2 = pyspeckit.Spectrum(xarr=xarr, data=VF.voigt(xarr,1,0,2.5,5.0) + np.random.randn(xarr.shape[0])/20., error=np.ones(xarr.shape[0])/20.) sp2.plotter() sp2.specfit(fittype='gaussian',composite_fit_color='b',clear=False,annotate=False) sp2.specfit(fittype='lorentzian',composite_fit_color='g',clear=False,annotate=False) sp2.specfit(fittype='voigt',composite_fit_color='r',clear=False,annotate=True) sp2.baseline.annotate() sp3 = pyspeckit.Spectrum(xarr=xarr, data=VF.voigt(xarr,1,0,2.5,5.0) + np.random.randn(xarr.shape[0])/50., error=np.ones(xarr.shape[0])/50.) sp3.plotter() sp3.specfit(fittype='gaussian',composite_fit_color='b',clear=False,annotate=False) sp3.specfit(fittype='lorentzian',composite_fit_color='g',clear=False,annotate=False) sp3.specfit(fittype='voigt',composite_fit_color='r',clear=False,annotate=True)
from __future__ import print_function
"""
Try to identify a pseudocontinuum zone
"""
import pyspeckit
from astropy import units as u
import numpy as np
import scipy.ndimage
import pylab as pl
from astropy import log
#log.setLevel(10)
for spw in range(4):
sp = pyspeckit.Spectrum('e8mm_spw{0}_mean.fits'.format(spw))
sp.plotter(figure=pl.figure(1))
sp.baseline.includemask &= (sp.data > 0.25) & (sp.data < 0.35)
sp.baseline.highlight_fitregion()
sp.baseline(order=1,
subtract=False,
reset=False,
highlight_fitregion=False,
reset_selection=False,
selectregion=False)
sp.baseline(order=1,
subtract=True,
reset=False,
highlight_fitregion=False,
reset_selection=False,
selectregion=False)
trot=trot,
tex=tex,
width=sigma,
xoff_v=center,
ntot=ntot)
# Add noise
stddev = 0.1
noise = np.random.randn(xaxis.size) * stddev
error = stddev * np.ones_like(synth_data)
data = noise + synth_data
# this will give a "blank header" warning, which is fine
sp = pyspeckit.Spectrum(data=data,
error=error,
xarr=xaxis,
xarrkwargs={'unit': 'km/s'},
unit=u.K)
sp.plotter(figure=pl.figure(1), errstyle='fill')
# fit with some vague initial guesses and a fixed ortho/para
# fraction of 1 (it is unconstrained in our data)
sp.specfit(
fittype='ammonia',
#guesses=[20, 15, 15.5, 3, 2, 1],
guesses=[30, 25, 15.5, 3, 2, 0],
fixed=[False, False, False, False, False, True])
# do it again to set the errors neatly
sp.specfit(
unit=u.GHz)
for fn in glob.glob("*merge.fits"):
outf = 'avspec/avg_{0}'.format(fn)
if not os.path.exists(outf):
cube = SpectralCube.read(fn)
cube.allow_huge_operations=True
mad = cube.mad_std(axis=0)
sum = (cube*mad).sum(axis=(1,2))
mean = sum/np.nansum(mad)
mean.write('avspec/weighted_avg_{0}'.format(fn), overwrite=True)
mn = cube.mean(axis=(1,2))
mn.write(outf, overwrite=True)
for fn in glob.glob("*merge.fits"):
spw = pyspeckit.Spectrum('avspec/weighted_avg_{0}'.format(fn))
sp = pyspeckit.Spectrum('avspec/avg_{0}'.format(fn))
sp.plotter()
#spw.plotter(axis=sp.plotter.axis, clear=False, color='b')
sp.plotter.savefig('avspec/avg_{0}'.format(fn.replace(".fits",".png")))
med = np.median(sp.data)
sp.plotter.axis.set_ylim(med-0.1, med+0.2)
sp.plotter.savefig('avspec/avg_yzoom_{0}'.format(fn.replace(".fits",".png")))
plot_kwargs = {'color':'r', 'linestyle':'--'}
annotate_kwargs = {'color': 'r'}
sp.plotter.line_ids(species_names, u.Quantity(frequencies),
velocity_offset=velo, plot_kwargs=plot_kwargs,
annotate_kwargs=annotate_kwargs)