def update_rest_moment0(region_name='L1688', file_extension='DR1_rebase3', v_mean=2.5, sigma_v=0.3):
"""
Update moment calculation for non-NH3 lines. Don't base on NH3 fit; these lines may be
offset in velocity. Right now including velocity ranges by hand.
"""
for line in ['HC5N','HC7N_21_20','HC7N_22_21','C2S','NH3_33']:
file_in ='{0}/{0}_{2}_{1}.fits'.format(region_name,file_extension,line)
file_out='{0}/{0}_{2}_{1}_mom0_QA.fits'.format(region_name,file_extension,line)
file_rms='{0}/{0}_{2}_{1}_rms_QA.fits'.format(region_name,file_extension,line)
# Load pyspeckit cube
pycube = pyspeckit.Cube(file_in)
# Use spectral cube to calculate integrated intensity maps
cube_raw = SpectralCube.read(file_in)
# in km/s not Hz
cube = cube_raw.with_spectral_unit(u.km / u.s,velocity_convention='radio')
vaxis=cube.spectral_axis
dv=np.abs(vaxis[1]-vaxis[0])
vmean = v_mean*u.km/u.s
sigmav = sigma_v*u.km/u.s
total_spc=np.sqrt( (vaxis-vmean)**2)/sigmav < 3
mask3d = np.ones(cube.shape,dtype=bool)
for ii in np.arange(cube.shape[2]):
for jj in np.arange(cube.shape[1]):
mask3d[:,jj,ii] = total_spc
# create masked cube
cube2 = cube.with_mask(mask3d)
cube3 = cube.with_mask(~mask3d)
# calculate moment map
moment_0 = cube2.moment(axis=0)
moment_0.write( file_out, overwrite=True)
rms=cube3.std(axis=0)
rms.write( file_rms, overwrite=True)
def __init__(self, *args):
"""
A collection of Specfit objects mapped to SubCubes
by the mapper method. Includes* methods to fit multiple
guess grids for different models, and the means to
decide between the results of those fits.
*In a distant[citation needed] future.
Input parameters: see ~pyspeckit.Cube
"""
# parent cube, used for attribute propagation
self.supercube = pyspeckit.Cube(*args)
# making a bunch of references to make our life easier
self.cube = self.SuperCube.cube
self.xarr = self.SuperCube.xarr
self.header = self.SuperCube.header
# FIXME: rewrite mapplot to include the mapper/judge methods!
# doesn't work in its current implementation, will need
# to rewire different params and errors, for variable
# number of parameters across different models
self.multiplot = self.SuperCube.mapplot
# MultiCube's own instances:
self.multigrid = {}
self.tesseract = {}
def test_load_cube_from_spectralcube():
sc = make_fake_cube()
pcube = pyspeckit.Cube(cube=sc)
assert pcube.unit == 'K'
assert pcube.xarr.unit == 'km/s'
return pcube
def make_cube(files=(file_nh311_dr1, file_nh322_dr1),
rms_files=(file_rms_nh311_dr1, file_rms_nh322_dr1)):
"""
Opens the cube and calculates all the pre-fitting attributes of interest.
"""
# make sure we're working on arrays (why?)
files = np.atleast_1d([f for f in files])
rms_files = np.atleast_1d([f for f in rms_files])
if files.size > 1:
spc_dict = {f: pyspeckit.Cube(f) for f in files}
rmsmaps = {f: fits.getdata(ef) for f, ef in zip(files, rms_files)}
for f in files:
spc_dict[f].errorcube = np.repeat([rmsmaps[f]],
spc_dict[f].xarr.size,
axis=0)
# now the errorcubes should merge automatically
spc = pyspeckit.CubeStack([spc_dict[f] for f in files])
spc.xarr.refX = spc.cubelist[0].xarr.refX
spc.xarr.refX_unit = spc.cubelist[0].xarr.refX_unit
else:
spc = pyspeckit.Cube(files[0])
rms = fits.getdata(rms_files[0])
# easier to handle everything get_spectrum-related
spc.errorcube = np.repeat([rms], spc.xarr.size, axis=0)
# I don't see a reason why errorcube should be a masked array
if type(spc.errorcube) == np.ma.MaskedArray:
spc.errorcube = np.array(spc.errorcube)
spc.xarr.velocity_convention = 'radio'
spc.xarr.convert_to_unit('km/s')
snr = (spc.cube / spc.errorcube).max(axis=0)
# TODO: fix multinest-pipeline.py and run_multicube.py
#spc.errmap = rms
spc.snrmap = snr
return spc
def velocity(cube, wavelength, velocity_range, multicore, outfile):
'''
cube: str
The cube path and filename, e.g. path_to_cube/cube_filename.fits
wavelength: str
The line one wishes to compute the velocity map for, e.g. Ha, SII6717, or stacked for the stacked version. Default is stacked.
Available lines are SII6717, SII6731, Ha, NII6548, NII6584, Hb, OIII5007, SIII9068, OI6300.
velocity_range: array
The velocity range, e.g. [-200,200]. Default is [-300,300].
multicore: int
The number of cores for the parallelization. Default is 3.
outfile: str
The name of the output file. Default is wavelength+'_velocity.fits'.
'''
cube = spectral_cube.SpectralCube.read(str(cube), hdu=1)
hd = cube.header
## NEED TO IMPLEMENT CONTINUUM SUBTRACTION BASED ON SPECIFIED WAVELENGTH ##
slabs = [
cube.with_spectral_unit(u.km / u.s, 'optical', wl).spectral_slab(
velocity_range[0] * u.km / u.s, velocity_range[1] * u.km / u.s)
for wl in wavelength
]
newcube_shape = (sum(s.shape[0] for s in slabs), ) + slabs[0].shape[1:]
newcube_spaxis = np.concatenate([s.spectral_axis
for s in slabs]).value * u.km / u.s
sortvect = newcube_spaxis.argsort()
sortspaxis = newcube_spaxis[sortvect]
newcube = np.empty(newcube_shape)
ind = 0
for ii, slab in enumerate(slabs):
data = slab.filled_data[:] / slab.sum(axis=0)
newcube[ind:ind + data.shape[0], :, :] = data
ind += data.shape[0]
supercube = newcube[sortvect, :, :]
pxarr = pyspeckit.units.SpectroscopicAxis(sortspaxis.value, unit='km/s')
print pxarr
pcube = pyspeckit.Cube(cube=supercube, xarr=pxarr, header=hd)
pcube.fiteach(fittype='gaussian',
guesses=[1 / np.sqrt(np.pi), 5, 20.0],
errmap=np.ones(supercube.shape[1:]) / 100.,
multicore=3)
ff = fits.PrimaryHDU(data=pcube.parcube[1], header=hd)
ff.writeto(outfile, clobber=True)
def load_model_fit(cube, filename, ncomp):
# currently only loads ammonia multi-component model
pcube = pyspeckit.Cube(cube=cube)
# reigster fitter
linename = 'oneone'
import ammonia_multiv as ammv
fitter = ammv.nh3_multi_v_model_generator(n_comp=ncomp,
linenames=[linename])
pcube.specfit.Registry.add_fitter('nh3_multi_v', fitter, fitter.npars)
pcube.load_model_fit(filename, npars=fitter.npars, fittype='nh3_multi_v')
gc.collect()
return pcube
def get_subregion_pcube(cube303m, cube303, cube321, region):
#scube = cube_merge_high.subcube_from_ds9region(pyregion.ShapeList([region]))
scube303m = cube303m.subcube_from_ds9region(pyregion.ShapeList([region]))
scube303 = cube303.subcube_from_ds9region(pyregion.ShapeList([region]))
scube321 = cube321.subcube_from_ds9region(pyregion.ShapeList([region]))
# TODO: get error map
#pcube = pyspeckit.Cube(cube=scube)
pcube303 = pyspeckit.Cube(cube=scube303)
pcube303.xarr.refX = cube303.wcs.wcs.restfrq
pcube303.xarr.refX_unit = 'Hz'
pcube321 = pyspeckit.Cube(cube=scube321)
pcube321.xarr.refX = cube321.wcs.wcs.restfrq
pcube321.xarr.refX_unit = 'Hz'
pcube = pyspeckit.CubeStack([
pcube303,
pcube321,
])
pcube.specfit.Registry.add_fitter('h2co_simple',
simple_fitter3,
4,
multisingle='multi')
pcube.xarr.refX = cube303m.wcs.wcs.restfrq
pcube.xarr.refX_unit = 'Hz'
return pcube, scube303m
def multiscale_fit_clouddn(center=(643 - 1, 164 - 1)):
clouddn_slice = np.s_[:, center[1] - 16:center[1] + 16,
center[0] - 16:center[0] + 16]
clouddn_cube = cube_merge_high[clouddn_slice]
clouddn_pcube = pyspeckit.Cube(cube=clouddn_cube)
clouddn_pcube.xarr.refX = clouddn_cube.wcs.wcs.restfrq
return multiscale_fit(clouddn_pcube,
16,
16,
savedir='clouddn',
savepre='clouddn',
guesses=[0.1, 19, 8, 0.3, 0.9, 0.2],
offset_scale=0.4)
def multiscale_fit_g1pt2_cool(center=(236, 95)): # G1.23-0.08box
g1pt2_cool_slice = np.s_[:, center[1] - 16:center[1] + 16,
center[0] - 16:center[0] + 16]
g1pt2_cool_cube = cube_merge_high[g1pt2_cool_slice]
g1pt2_cool_pcube = pyspeckit.Cube(cube=g1pt2_cool_cube)
g1pt2_cool_pcube.xarr.refX = g1pt2_cool_cube.wcs.wcs.restfrq
return multiscale_fit(g1pt2_cool_pcube,
16,
16,
savedir='g1.2',
savepre='g1.2_coolspot',
guesses=[0.1, 91, 13.6, 0.3, 0.9, 0.2],
offset_scale=0.4)
def multiscale_fit_g08south_hot(center=(434, 63)):
g08south_slice = np.s_[:, center[1] - 16:center[1] + 16,
center[0] - 16:center[0] + 16]
g08south_cube = cube_merge_high[g08south_slice]
g08south_pcube = pyspeckit.Cube(cube=g08south_cube)
g08south_pcube.xarr.refX = g08south_cube.wcs.wcs.restfrq
multiscale_fit(g08south_pcube,
16,
16,
savedir='g0.8_south',
savepre='g0.8_south_hotspot',
guesses=[0.5, 40.3, 7.9, 0.57, 0.5, 0.3],
offset_scale=0.4)
def multiscale_fit_g08south_cool(center=(426, 36)):
g08south_cool_slice = np.s_[:, center[1] - 16:center[1] + 16,
center[0] - 16:center[0] + 16]
g08south_cool_cube = cube_merge_high[g08south_cool_slice]
g08south_cool_pcube = pyspeckit.Cube(cube=g08south_cool_cube)
g08south_cool_pcube.xarr.refX = g08south_cool_cube.wcs.wcs.restfrq
multiscale_fit(g08south_cool_pcube,
16,
16,
savedir='g0.8_south',
savepre='g0.8_south_coolspot',
guesses=[0.5, 50.3, 10.6, 0.3, 0.9, 0.2] +
[0.3, 96.3, 3.6, 0.3, 0.9, 0.2],
offset_scale=0.4)
def multiscale_fit_g1pt6(center=(53, 143)):
g1pt6_slice = np.s_[:, center[1] - 16:center[1] + 16,
center[0] - 16:center[0] + 16]
g1pt6_cube = cube_merge_high[g1pt6_slice]
g1pt6_pcube = pyspeckit.Cube(cube=g1pt6_cube)
g1pt6_pcube.xarr.refX = g1pt6_cube.wcs.wcs.restfrq
return multiscale_fit(g1pt6_pcube,
16,
16,
savedir='g1.6',
savepre='g1.6_spot',
guesses=[0.1, 157, 8, 0.3, 0.9, 0.2] +
[0.1, 60, 9, 0.3, 0.9, 0.2],
offset_scale=0.4)
def pyspeckit_fit_cube(cube, max_map, centroid_map, width_map, noisemap, lines,
vz):
"""
This is experimental and doesn't really work: the idea here is to fit all
lines in the cube simultaneously.
"""
import pyspeckit
vz = u.Quantity(vz, u.km / u.s)
fcube = cube.with_spectral_unit(u.GHz)
def inrange(x):
return (x < fcube.spectral_extrema[1]
and x > fcube.spectral_extrema[0])
lines_in_cube = {
linename: linedata
for linename, linedata in lines.items()
if inrange(linedata['frequency'] * (1 - vz / constants.c))
}
frequencies = sorted(linedata['frequency']
for linedata in lines_in_cube.values())
line_guesses = [[
max_map.value,
((1 - centroid_map / constants.c) * frq).to(u.GHz).value,
((width_map / constants.c) * frq).to(u.GHz).value
] for frq in frequencies]
line_guesses = np.array([x for y in line_guesses for x in y])
guesses = np.array([max_map.value, centroid_map.value, width_map.value])
#vcube = cube.with_spectral_unit(u.km/u.s, velocity_convention='optical')
pcube = pyspeckit.Cube(cube=fcube)
pcube.mapplot.plane = max_map.value
pcube.fiteach(guesses=guesses,
start_from_point=(150, 150),
errmap=noisemap.value)
# it's barely worth it; cuts off 10% of pixels
f1 = all_lines['H2CO_303_202']*u.GHz
f2 = all_lines['H2CO_321_220']*u.GHz
h2co_cube_merge_high = cube_merge_high.spectral_slab(f1*(1-(150*u.km/u.s/constants.c)),
f2*(1+(100*u.km/u.s/constants.c)))
h2co_noise_cube = noise_spcube.spectral_slab(f1*(1-(150*u.km/u.s/constants.c)),
f2*(1+(100*u.km/u.s/constants.c)))
h2co_cube_merge_high_sm = cube_merge_high_sm.spectral_slab(f1*(1-(150*u.km/u.s/constants.c)),
f2*(1+(100*u.km/u.s/constants.c)))
h2co_noise_cube_sm = noise_spcube_sm.spectral_slab(f1*(1-(150*u.km/u.s/constants.c)),
f2*(1+(100*u.km/u.s/constants.c)))
# Pyspeckit cube made from spectralcube
pcube_merge_high = pyspeckit.Cube(cube=h2co_cube_merge_high._data,
errorcube=h2co_noise_cube._data,
header=h2co_cube_merge_high.header,
xarr=h2co_cube_merge_high.spectral_axis,
)
pcube_merge_high.xarr.refX = 218.22219
pcube_merge_high.xarr.refX_unit = 'GHz'
pcube_merge_high_sm = pyspeckit.Cube(cube=h2co_cube_merge_high_sm._data,
errorcube=h2co_noise_cube_sm._data,
header=h2co_cube_merge_high_sm.header,
xarr=h2co_cube_merge_high_sm.spectral_axis,
)
pcube_merge_high_sm.xarr.refX = 218.22219
pcube_merge_high_sm.xarr.refX_unit = 'GHz'
import os
import astropy.units as u
if not os.path.exists('n2hp_cube.fit'):
import astropy.utils.data as aud
from astropy.io import fits
f = aud.download_file(
'ftp://cdsarc.u-strasbg.fr/pub/cats/J/A%2BA/472/519/fits/opha_n2h.fit')
with fits.open(f) as ff:
ff[0].header['CUNIT3'] = 'm/s'
for kw in ['CTYPE4', 'CRVAL4', 'CDELT4', 'CRPIX4']:
del ff[0].header[kw]
ff.writeto('n2hp_cube.fit')
# Load the spectral cube
spc = pyspeckit.Cube('n2hp_cube.fit')
# 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)
spc.Registry.add_fitter('n2hp_vtau', pyspeckit.models.n2hp.n2hp_vtau_fitter, 4)
# Get a measurement of the error per pixel
errmap = spc.slice(20, 28, unit='km/s').cube.std(axis=0)
# A good way to write a cube fitter is to have it load from disk if the cube
# fit was completed successfully in the past
if os.path.exists('n2hp_fitted_parameters.fits'):
fit_dir = 'fit/'
# primary beam corrected file and in Kelvin units
file_in_K = data_dir + 'pNH2D.fits'
snr_min = 5
file_thick = fit_dir + 'pNH2D_fit_thick_par_snr{0}.fits'.format(snr_min)
file_thin = fit_dir + 'pNH2D_fit_thin_par_snr{0}.fits'.format(snr_min)
rms_file = data_dir + 'pNH2D_rms.fits'
SNR_file = data_dir + 'pNH2D_SNR.fits'
Tpeak_file = data_dir + 'pNH2D_Tpeak.fits'
mask_file = data_dir + 'pNH2D_mask.fits'
# rest-freq used in Harju et al. (2020) for ortho-NH2D
freq_line = 110.153599 * u.GHz
cube = pyspeckit.Cube(file_in_K)
cube.xarr.refX = freq_line
cube.xarr.velocity_convention = 'radio'
cube.xarr.convert_to_unit('km/s')
#
# source dependent parameters
#
# pixel to start fit and to make sample plot of the line fit
xmax = 269
ymax = 240
# velocity range with signal (of the main hf-component) to
# calculate moment maps
vmin = 3.4
vmax = 5.0
# default velocity used as guess in fit
simple_fitter2)
from full_cubes import cube_merge_high
from masked_cubes import (cube303, cube303sm, cube303m, cube321m, cube303msm,
cube321msm, cube321, cube321sm)
from noise import (noise, noise_cube, sm_noise, cube303nm, cube303nmsm,
cube321nm, cube321nmsm)
import pyspeckit
from astrodendro import Dendrogram
from astropy.utils.console import ProgressBar
import paths
import pylab as pl
brick_slice = np.s_[:, 131:154, 710:740]
brick_cube = cube_merge_high[brick_slice]
brick_pcube = pyspeckit.Cube(cube=brick_cube)
brick_pcube.xarr.refX = brick_cube.wcs.wcs.restfrq
brick_pcube.specfit.Registry.add_fitter('h2co_simple',
simple_fitter2,
6,
multisingle='multi')
b303m = cube303m[brick_slice]
m1 = b303m.moment1(axis=0).to(u.km / u.s)
# For radial multiscale fitting, see multiscale_fit.py
def do_pyspeck_fits_1comp():
#guesses_simple = [1,25,5,0.5,0.7,1]
def hmm1_cubefit(vmin=3.4,
vmax=5.0,
tk_ave=10.,
do_plot=False,
snr_min=5.0,
multicore=1,
do_thin=False):
"""
Fit NH3(1,1) and (2,2) cubes for H-MM1.
It fits all pixels with SNR larger than requested.
Initial guess is based on moment maps and neighboring pixels.
The fitting can be done in parallel mode using several cores,
however, this is dangerous for large regions, where using a
good initial guess is important.
It stores the result in a FITS cube.
TODO:
-convert FITS cube into several FITS files
-Improve initial guess
Parameters
----------
vmin : numpy.float
Minimum centroid velocity to plot, in km/s.
vmax : numpy.float
Maximum centroid velocity to plot, in km/s.
tk_ave : numpy.float
Mean kinetic temperature of the region, in K.
do_plot : bool
If True, then a map of the region to map is shown.
snr_min : numpy.float
Minimum signal to noise ratio of the spectrum to be fitted.
multicore : int
Numbers of cores to use for parallel processing.
"""
cube11sc = SpectralCube.read(OneOneFile)
cube22sc = SpectralCube.read(TwoTwoFile)
cube11_v = cube11sc.with_spectral_unit(u.km / u.s,
velocity_convention='radio',
rest_value=freq11)
cube22_v = cube22sc.with_spectral_unit(u.km / u.s,
velocity_convention='radio',
rest_value=freq22)
from pyspeckit.spectrum.units import SpectroscopicAxis
spec11 = SpectroscopicAxis(cube11_v.spectral_axis,
refX=freq11,
velocity_convention='radio')
spec22 = SpectroscopicAxis(cube22_v.spectral_axis,
refX=freq22,
velocity_convention='radio')
errmap11 = fits.getdata(RMSFile_11)
errmap22 = fits.getdata(RMSFile_22)
errmap_K = errmap11 #[errmap11, errmap22]
Tpeak11 = fits.getdata(OneOnePeak)
moment1 = fits.getdata(OneOneMom1)
moment2 = (fits.getdata(OneOneMom2))**0.5
snr = cube11sc.filled_data[:].value / errmap11
peaksnr = Tpeak11 / errmap11
planemask = (peaksnr > snr_min) # *(errmap11 < 0.15)
planemask = remove_small_objects(planemask, min_size=40)
planemask = opening(planemask, disk(1))
#planemask = (peaksnr>20) * (errmap11 < 0.2)
mask = (snr > 3) * planemask
maskcube = cube11sc.with_mask(mask.astype(bool))
maskcube = maskcube.with_spectral_unit(u.km / u.s,
velocity_convention='radio')
slab = maskcube.spectral_slab(vmax * u.km / u.s, vmin * u.km / u.s)
w11 = slab.moment(order=0, axis=0).value
peakloc = np.nanargmax(w11)
ymax, xmax = np.unravel_index(peakloc, w11.shape)
moment2[np.isnan(moment2)] = 0.2
moment2[moment2 < 0.2] = 0.2
## Load FITS files
cube11 = pyspeckit.Cube(OneOneFile, maskmap=planemask)
cube22 = pyspeckit.Cube(TwoTwoFile, maskmap=planemask)
# Stack files
cubes = pyspeckit.CubeStack([cube11, cube22], maskmap=planemask)
cubes.unit = "K"
# Define initial guess
guesses = np.zeros((6, ) + cubes.cube.shape[1:])
moment1[moment1 < vmin] = vmin + 0.2
moment1[moment1 > vmax] = vmax - 0.2
guesses[0, :, :] = tk_ave # Kinetic temperature
guesses[1, :, :] = 7 # Excitation Temp
guesses[2, :, :] = 14.5 # log(column)
guesses[
3, :, :] = moment2 # Line width / 5 (the NH3 moment overestimates linewidth)
guesses[4, :, :] = moment1 # Line centroid
guesses[5, :, :] = 0.5 # F(ortho) - ortho NH3 fraction (fixed)
if do_plot:
import matplotlib.pyplot as plt
plt.imshow(w11 * planemask, origin='lower')
plt.show()
print('start fit')
cubes.specfit.Registry.add_fitter('cold_ammonia',
ammonia.cold_ammonia_model(), 6)
if do_thin:
file_out = "{0}H-MM1_cold_parameter_maps_snr{1}_thin_v1.fits".format(
fit_dir, snr_min)
else:
file_out = "{0}H-MM1_cold_parameter_maps_snr{1}_thick_v1.fits".format(
fit_dir, snr_min)
cubes.fiteach(fittype='cold_ammonia',
guesses=guesses,
integral=False,
verbose_level=3,
fixed=[do_thin, False, False, False, False, True],
signal_cut=2,
limitedmax=[True, False, False, False, True, True],
maxpars=[20, 15, 20, 0.4, vmax, 1],
limitedmin=[True, True, True, True, True, True],
minpars=[5, 2.8, 12.0, 0.05, vmin, 0],
start_from_point=(xmax, ymax),
use_neighbor_as_guess=True,
position_order=1 / peaksnr,
errmap=errmap_K,
multicore=multicore)
# Store fits into FITS cube
fitcubefile = fits.PrimaryHDU(data=np.concatenate(
[cubes.parcube, cubes.errcube]),
header=cubes.header)
fitcubefile.header.set('PLANE1', 'TKIN')
fitcubefile.header.set('PLANE2', 'TEX')
fitcubefile.header.set('PLANE3', 'COLUMN')
fitcubefile.header.set('PLANE4', 'SIGMA')
fitcubefile.header.set('PLANE5', 'VELOCITY')
fitcubefile.header.set('PLANE6', 'FORTHO')
fitcubefile.header.set('PLANE7', 'eTKIN')
fitcubefile.header.set('PLANE8', 'eTEX')
fitcubefile.header.set('PLANE9', 'eCOLUMN')
fitcubefile.header.set('PLANE10', 'eSIGMA')
fitcubefile.header.set('PLANE11', 'eVELOCITY')
fitcubefile.header.set('PLANE12', 'eFORTHO')
fitcubefile.header.set('CDELT3', 1)
fitcubefile.header.set('CTYPE3', 'FITPAR')
fitcubefile.header.set('CRVAL3', 0)
fitcubefile.header.set('CRPIX3', 1)
fitcubefile.writeto(file_out, overwrite=True)
def cube_fit(cubefilename,
outfilename,
errfilename=None,
scale_keyword=None,
vheight=False,
verbose=False,
signal_cut=3,
verbose_level=2,
clobber=True,
**kwargs):
"""
Light-weight wrapper for cube fitting
Takes a cube and error map (error will be computed naively if not given)
and computes moments then fits for each spectrum in the cube. It then
saves the fitted parameters to a reasonably descriptive output file whose
header will look like ::
PLANE1 = 'amplitude'
PLANE2 = 'velocity'
PLANE3 = 'sigma'
PLANE4 = 'err_amplitude'
PLANE5 = 'err_velocity'
PLANE6 = 'err_sigma'
PLANE7 = 'integral'
PLANE8 = 'integral_error'
CDELT3 = 1
CTYPE3 = 'FITPAR'
CRVAL3 = 0
CRPIX3 = 1
Parameters
----------
errfilename: [ None | string name of .fits file ]
A two-dimensional error map to use for computing signal-to-noise cuts
scale_keyword: [ None | Char ]
Keyword to pass to the data cube loader - multiplies cube by the number
indexed by this header kwarg if it exists. e.g., if your cube is in
T_A units and you want T_A*
vheight: [ bool ]
Is there a background to be fit? Used in moment computation
verbose: [ bool ]
verbose_level: [ int ]
How loud will the fitting procedure be? Passed to momenteach and fiteach
signal_cut: [ float ]
Signal-to-Noise ratio minimum. Spectra with a peak below this S/N ratio
will not be fit and will be left blank in the output fit parameter cube
clobber: [ bool ]
Overwrite parameter .fits cube if it exists?
`kwargs` are passed to :class:`pyspeckit.Spectrum.specfit`
"""
# Load the spectrum
sp = pyspeckit.Cube(cubefilename, scale_keyword=scale_keyword)
if os.path.exists(errfilename):
errmap = pyfits.getdata(errfilename)
else:
# very simple error calculation... biased and bad, needs improvement
# try using something from the cubes package
errmap = sp.cube.std(axis=0)
# Run the fitter
sp.mapplot()
# Compute the moments at each position to come up with reasonable guesses.
# This speeds up the process enormously, but can easily mess up the fits if
# there are bad pixels
sp.momenteach(vheight=vheight, verbose=verbose)
sp.fiteach(errmap=errmap,
multifit=None,
verbose_level=verbose_level,
signal_cut=signal_cut,
usemomentcube=True,
blank_value=np.nan,
verbose=verbose,
**kwargs)
# steal the header from the error map
f = pyfits.open(cubefilename)
# start replacing components of the pyfits object
f[0].data = np.concatenate([sp.parcube, sp.errcube, sp.integralmap])
f[0].header['PLANE1'] = 'amplitude'
f[0].header['PLANE2'] = 'velocity'
f[0].header['PLANE3'] = 'sigma'
f[0].header['PLANE4'] = 'err_amplitude'
f[0].header['PLANE5'] = 'err_velocity'
f[0].header['PLANE6'] = 'err_sigma'
f[0].header['PLANE7'] = 'integral'
f[0].header['PLANE8'] = 'integral_error'
f[0].header['CDELT3'] = 1
f[0].header['CTYPE3'] = 'FITPAR'
f[0].header['CRVAL3'] = 0
f[0].header['CRPIX3'] = 1
# save your work
f.writeto(outfilename, clobber=clobber)
return sp
import astropy
import pyspeckit
try:
from astropy.io import fits as pyfits
except ImportError:
import pyfits
import numpy as np
# Load the spectrum
sp = pyspeckit.Cube('region5_hcn_crop.fits')
errmap = pyfits.getdata('region5.hcn.errmap.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('hcn_amp', pyspeckit.models.hcn.hcn_amp, 3)
# Run the fitter
sp.mapplot()
# use an individual spectrum selected semi-arbitrarily from the map to get an estimate of the error
# this method has been rendered obsolete - use the error map instead
#s = sp.get_spectrum(20,20)
#s.plotter()
#s.Registry = sp.Registry
#s.specfit.Registry = sp.Registry
#s.specfit(fittype='hcn_amp',guesses=[2.5,-5.6,1.5],show_components=True,debug=True,quiet=False)
#s.specfit(fittype='hcn_amp',guesses=[2.5,-5.6,1.5],show_components=True,debug=True,quiet=False)
#sp.error = s.specfit.errspec
# set up CASA-like shortcuts
F=False; T=True
# Some optional parameters for the script
# (if False, it will try to load an already-stored version
# of the file)
fitcube = True
# Mask out low S/N pixels (to speed things up)
mask = pyfits.getdata('hotclump_11_mask.fits')
mask = np.isfinite(mask) * (mask > 0)
# Load the data using a mask
# Then calibrate the data (the data we're loading in this case are in Janskys,
# but we want surface brightness in Kelvin for the fitting process)
cube11 = pyspeckit.Cube('hotclump_11.cube_r0.5.image.fits', maskmap=mask)
cube11.cube *= (13.6 * (300.0 /
(pyspeckit.spectrum.models.ammonia.freq_dict['oneone']/1e9))**2 *
1./cube11.header.get('BMAJ')/3600. * 1./cube11.header.get('BMIN')/3600. )
cube11.unit = "K"
cube22 = pyspeckit.Cube('hotclump_22.cube_r0.5_contsub.image.fits', maskmap=mask)
cube22.cube *= (13.6 * (300.0 /
(pyspeckit.spectrum.models.ammonia.freq_dict['twotwo']/1e9))**2 *
1./cube22.header.get('BMAJ')/3600. * 1./cube22.header.get('BMIN')/3600. )
cube22.unit = "K"
cube44 = pyspeckit.Cube('hotclump_44.cube_r0.5_contsub.image.fits', maskmap=mask)
cube44.cube *= (13.6 * (300.0 /
(pyspeckit.spectrum.models.ammonia.freq_dict['fourfour']/1e9))**2 *
1./cube44.header.get('BMAJ')/3600. * 1./cube44.header.get('BMIN')/3600. )
cube44.unit = "K"
def cubefit_gen(cube,
ncomp=2,
paraname=None,
modname=None,
chisqname=None,
guesses=None,
errmap11name=None,
multicore=None,
mask_function=None,
snr_min=3.0,
linename="oneone",
momedgetrim=True,
saveguess=False,
**kwargs):
'''
Perform n velocity component fit on the GAS ammonia 1-1 data.
(This should be the function to call for all future codes if it has been proven to be reliable)
# note: the method can probably be renamed to cubefit()
Parameters
----------
cube : str
The file name of the ammonia 1-1 cube or a SpectralCube object
ncomp : int
The number of components one wish to fit. Default is 2
paraname: str
The output file name of the
Returns
-------
pcube : 'pyspeckit.cubes.SpectralCube.Cube'
Pyspeckit cube object containing both the fit and the original data cube
'''
if hasattr(cube, 'spectral_axis'):
pcube = pyspeckit.Cube(cube=cube)
else:
cubename = cube
cube = SpectralCube.read(cubename)
pcube = pyspeckit.Cube(filename=cubename)
pcube.unit = "K"
# the following check on rest-frequency may not be necessarily for GAS, but better be safe than sorry
# note: this assume the data cube has the right units
if cube._wcs.wcs.restfrq == np.nan:
# Specify the rest frequency not present
cube = cube.with_spectral_unit(u.Hz,
rest_value=freq_dict[linename] * u.Hz)
cube = cube.with_spectral_unit(u.km / u.s, velocity_convention='radio')
if pcube.wcs.wcs.restfrq == np.nan:
# Specify the rest frequency not present
pcube.xarr.refX = freq_dict[linename] * u.Hz
pcube.xarr.velocity_convention = 'radio'
# always register the fitter just in case different lines are used
fitter = ammv.nh3_multi_v_model_generator(n_comp=ncomp,
linenames=[linename])
pcube.specfit.Registry.add_fitter('nh3_multi_v', fitter, fitter.npars)
print "number of parameters is {0}".format(fitter.npars)
print "the line to fit is {0}".format(linename)
# Specify a width for the expected velocity range in the data
#v_peak_hwidth = 3.0 # km/s (should be sufficient for GAS Orion, but may not be enough for KEYSTONE)
v_peak_hwidth = 4.0 # km/s (should be sufficient for GAS Orion, but may not be enough for KEYSTONE)
if errmap11name is not None:
errmap11 = fits.getdata(errmap11name)
else:
# a quick way to estimate RMS as long as the noise dominates the spectrum by channels
mask_finite = np.isfinite(cube._data)
errmap11 = mad_std(cube._data[mask_finite], axis=0)
print "median rms: {0}".format(np.nanmedian(errmap11))
snr = cube.filled_data[:].value / errmap11
peaksnr = np.nanmax(snr, axis=0)
#the snr map will inetiabley be noisy, so a little smoothing
kernel = Gaussian2DKernel(1)
peaksnr = convolve(peaksnr, kernel)
# trim the edges by 3 pixels to guess the location of the peak emission
footprint_mask = np.any(np.isfinite(cube._data), axis=0)
if np.logical_and(footprint_mask.size > 1000, momedgetrim):
print "triming the edges to make moment maps"
footprint_mask = binary_erosion(footprint_mask, disk(3))
# the following function is copied directly from GAS
def default_masking(snr, snr_min=5.0):
planemask = (snr > snr_min)
if planemask.size > 100:
planemask = remove_small_objects(planemask, min_size=40)
planemask = opening(planemask, disk(1))
return (planemask)
if 'maskmap' in kwargs:
planemask = kwargs['maskmap']
elif mask_function is None:
planemask = default_masking(peaksnr, snr_min=snr_min)
else:
planemask = mask_function(peaksnr, snr_min=snr_min)
print "planemask size: {0}, shape: {1}".format(planemask[planemask].size,
planemask.shape)
# masking
mask = np.isfinite(cube._data) * planemask * footprint_mask
print "mask size: {0}, shape: {1}".format(mask[mask].size, mask.shape)
maskcube = cube.with_mask(mask.astype(bool))
maskcube = maskcube.with_spectral_unit(u.km / u.s,
velocity_convention='radio')
if guesses is not None:
v_guess = guesses[::4]
v_guess[v_guess == 0] = np.nan
else:
v_guess = np.nan
if np.isfinite(v_guess).sum() > 0:
v_guess = v_guess[np.isfinite(v_guess)]
v_median = np.median(v_guess)
print "The median of the user provided velocities is: {0}".format(
v_median)
m0, m1, m2 = main_hf_moments(maskcube,
window_hwidth=v_peak_hwidth,
v_atpeak=v_median)
else:
m0, m1, m2 = main_hf_moments(maskcube, window_hwidth=v_peak_hwidth)
v_median = np.median(m1[np.isfinite(m1)])
print "median velocity: {0}".format(v_median)
if False:
# save the moment maps for diagnostic purposes
hdr_new = copy.deepcopy(pcube.header)
hdr_new['CDELT3'] = 1
hdr_new['CTYPE3'] = 'FITPAR'
hdr_new['CRVAL3'] = 0
hdr_new['CRPIX3'] = 1
savename = "{0}_moments.fits".format(
os.path.splitext(paraname)[0], "parameter_maps")
fitcubefile = fits.PrimaryHDU(data=np.array([m0, m1, m2]),
header=hdr_new)
fitcubefile.writeto(savename, overwrite=True)
# remove the nana values to allow np.nanargmax(m0) to operate smoothly
m0[np.isnan(
m0
)] = 0.0 # I'm not sure if this is a good way to get around the sum vs nansum issue
# define acceptable v range based on the provided or determined median velocity
vmax = v_median + v_peak_hwidth
vmin = v_median - v_peak_hwidth
# find the location of the peak signal (to determine the first pixel to fit if nearest neighbour method is used)
peakloc = np.nanargmax(m0)
ymax, xmax = np.unravel_index(peakloc, m0.shape)
# set the fit parameter limits (consistent with GAS DR1)
Texmin = 3.0 # K; a more reasonable lower limit (5 K T_kin, 1e3 cm^-3 density, 1e13 cm^-2 column, 3km/s sigma)
Texmax = 40 # K; DR1 T_k for Orion A is < 35 K. T_k = 40 at 1e5 cm^-3, 1e15 cm^-2, and 0.1 km/s yields Tex = 37K
sigmin = 0.07 # km/s
sigmax = 2.5 # km/s; for Larson's law, a 10pc cloud has sigma = 2.6 km/s
taumax = 100.0 # a reasonable upper limit for GAS data. At 10K and 1e5 cm^-3 & 3e15 cm^-2 -> 70
taumin = 0.2 # note: at 1e3 cm^-3, 1e13 cm^-2, 1 km/s linewidth, 40 K -> 0.15
eps = 0.001 # a small perturbation that can be used in guesses
# get the guesses based on moment maps
# tex and tau guesses are chosen to reflect low density, diffusive gas that are likley to have low SNR
gg = moment_guesses(m1, m2, ncomp, sigmin=sigmin, moment0=m0)
if guesses is None:
guesses = gg
else:
# fill in the blanks with moment guesses
guesses[guesses == 0] = np.nan
gmask = np.isfinite(guesses)
guesses[~gmask] = gg[~gmask]
# fill in the failed sigma guesses with moment guesses
gmask = guesses[1::4] < sigmin
guesses[1::4][gmask] = gg[1::4][gmask]
print "user provided guesses accepted"
# The guesses should be fine in the first case, but just in case, make sure the guesses are confined within the
# appropriate limits
guesses[::4][guesses[::4] > vmax] = vmax
guesses[::4][guesses[::4] < vmin] = vmin
guesses[1::4][guesses[1::4] > sigmax] = sigmax
guesses[1::4][guesses[1::4] < sigmin] = sigmin + eps
guesses[2::4][guesses[2::4] > Texmax] = Texmax
guesses[2::4][guesses[2::4] < Texmin] = Texmin
guesses[3::4][guesses[3::4] > taumax] = taumax
guesses[3::4][guesses[3::4] < taumin] = taumin
if saveguess:
# save the guesses for diagnostic purposes
hdr_new = copy.deepcopy(pcube.header)
hdr_new['CDELT3'] = 1
hdr_new['CTYPE3'] = 'FITPAR'
hdr_new['CRVAL3'] = 0
hdr_new['CRPIX3'] = 1
savedir = "{0}/{1}".format(path.dirname(paraname), "guesses")
try:
os.makedirs(savedir)
except OSError as e:
if e.errno != errno.EEXIST:
raise
savename = "{0}_guesses.fits".format(
path.splitext(paraname)[0], "parameter_maps")
savename = "{0}/{1}".format(savedir, path.basename(savename))
fitcubefile = fits.PrimaryHDU(data=guesses, header=hdr_new)
fitcubefile.writeto(savename, overwrite=True)
# set some of the fiteach() inputs to that used in GAS DR1 reduction
if not 'integral' in kwargs:
kwargs['integral'] = False
if not 'verbose_level' in kwargs:
kwargs['verbose_level'] = 3
if not 'signal_cut' in kwargs:
kwargs['signal_cut'] = 2
# Now fit the cube. (Note: the function inputs are consistent with GAS DR1 whenever possible)
print('start fit')
# use SNR masking if not provided
if not 'maskmap' in kwargs:
print "mask mask!"
kwargs['maskmap'] = planemask * footprint_mask
if np.sum(kwargs['maskmap']) < 1:
print("[WARNING]: maskmap has no pixel, no fitting will be performed")
return pcube
elif np.sum(np.isfinite(guesses)) < 1:
print("[WARNING]: guesses has no pixel, no fitting will be performed")
return pcube
pcube.fiteach(fittype='nh3_multi_v',
guesses=guesses,
start_from_point=(xmax, ymax),
use_neighbor_as_guess=False,
limitedmax=[True, True, True, True] * ncomp,
maxpars=[vmax, sigmax, Texmax, taumax] * ncomp,
limitedmin=[True, True, True, True] * ncomp,
minpars=[vmin, sigmin, Texmin, taumin] * ncomp,
multicore=multicore,
**kwargs)
if paraname != None:
save_pcube(pcube, paraname, ncomp=ncomp)
if modname != None:
model = SpectralCube(pcube.get_modelcube(),
pcube.wcs,
header=cube.header)
model.write(modname, overwrite=True)
if chisqname != None:
chisq = get_chisq(cube, pcube.get_modelcube(), expand=20)
chisqfile = fits.PrimaryHDU(data=chisq,
header=cube.wcs.celestial.to_header())
chisqfile.writeto(chisqname, overwrite=True)
return pcube
def get_multiV_models(paraname,
refcubename,
n_comp=2,
savename=None,
snrname=None,
rms=0.15,
rmspath=None,
linename="oneone"):
'''
Creates a fits file containing the model cubes of individual components stacked into a hypercube
:param paraname:
:param refcubename:
:param n_comp:
:param savename:
:param snrname:
:param rms:
:param rmspath:
:return:
'''
para, hdr = fits.getdata(paraname, header=True)
pcube = pyspeckit.Cube(refcubename)
xarr = pcube.xarr
cubes = [pcube.cube.copy() for i in np.arange(n_comp)]
cubes = np.array(cubes)
cubes[:] = np.nan
# remove the error components
n_para = n_comp * 4
para = para[:n_para]
assert para.shape[0] == n_para
yy, xx = np.indices(para.shape[1:])
nanvals = np.any(~np.isfinite(para), axis=0)
isvalid = np.any(para, axis=0) & ~nanvals
valid_pixels = zip(xx[isvalid], yy[isvalid])
def model_a_pixel(xy):
x, y = int(xy[0]), int(xy[1])
models = [
ammonia._ammonia_spectrum(xarr.as_unit('GHz'),
tex=tex,
tau_dict={linename: tau},
width=width,
xoff_v=vel,
fortho=0.0,
line_names=[linename])
for vel, width, tex, tau in zip(para[::4, y, x], para[1::4, y, x],
para[2::4, y, x], para[3::4, y, x])
]
cubes[:, :, y, x] = models
for xy in ProgressBar(list(valid_pixels)):
print int(xy[0]), int(xy[1])
model_a_pixel(xy)
if savename != None:
f_name, f_extension = path.splitext(savename)
for i, data in enumerate(cubes):
fname = "{0}_v{1}_{2}".format(f_name, i, f_extension)
model = SpectralCube(data, pcube.wcs, header=pcube.header)
model.write(fname, overwrite=True)
if snrname != None:
# calculate the peak temperature
Tpeak = np.array([np.nanmax(cube, axis=0) for cube in cubes])
if rmspath is not None:
rmsdata = fits.getdata(rmspath)
if rmsdata.shape == Tpeak[0].shape:
rms = rmsdata
else:
print "[WARNING]: The shape of the rms map ({0}) does not match the shape of the emission map {1}." \
" An uniform rms value of: {2} has been adopted instead".format(rmsdata.shape, Tpeak[0].shape, rms)
snr = Tpeak / rms
snrfile = fits.PrimaryHDU(data=snr, header=pcube.header)
for i in np.arange(n_comp * 8) + 1:
key = 'PLANE{0}'.format(i)
if key in hdr:
hdr.remove(key)
snrfile.header.set('CDELT3', 1)
snrfile.header.set('CTYPE3', 'FITPAR')
snrfile.header.set('PLANE1', 'SNR_0')
snrfile.header.set('PLANE2', 'SNR_1')
snrfile.header.set('NAXIS3', n_comp * 8)
snrfile.writeto(snrname, overwrite=True)
return cubes
import pyspeckit
from spectral_cube import SpectralCube
from astropy import units as u
import pylab as pl
cube11 = SpectralCube.read(filename='unpb_mosaic_11_trim.fits', allow_huge_operations=True)
cube11.allow_huge_operations=True
cube11 = cube11.to(u.K)
cube11 = cube11.with_spectral_unit(u.km/u.s, velocity_convention='radio')
n11cube = pyspeckit.Cube(cube = cube11)
cube22 = SpectralCube.read('unpb_mosaic_22_trim.fits', allow_huge_operations=True)
cube22.allow_huge_operations=True
cube22 = cube22.to(u.K)
cube22 = cube22.with_spectral_unit(u.km/u.s, velocity_convention='radio')
n22cube = pyspeckit.Cube(cube = cube22)
cube44 = SpectralCube.read('unpb_mosaic_44_trim.fits', allow_huge_operations=True)
cube44.allow_huge_operations=True
cube44 = cube44.to(u.K)
cube44 = cube44.with_spectral_unit(u.km/u.s, velocity_convention='radio')
n44cube = pyspeckit.Cube(cube = cube44)
cube55 = SpectralCube.read('unpb_mosaic_55_trim.fits', allow_huge_operations=True)
cube55.allow_huge_operations=True
cube55 = cube55.to(u.K)
cube55 = cube55.with_spectral_unit(u.km/u.s, velocity_convention='radio')
def update_rest_moment0_2(region_name='L1688', file_extension='base_all_rebase3', threshold=0.0125, save_masked=False):
"""
Function to update moment calculation based on centroid velocity from line fit.
For a given line cube, we check which channels have flux in the model cube,
and then use those channels as the appropiate channels for integration.
Based on code provided by Vlas Sokolov
Parameters
----------
region : str
Name of region to re-calculate moment map
file_extension : str
filename extension
threshold : float
minimum threshold in model cube used to identify channels with emission
Default is down to the machine precision, a better result could be
obtained with 0.0125
save_masked : Boolean
Keyword to store the masked cube used in the integrated intensity calculation.
This is useful to
Usage:
import GAS
GAS.PropertyMaps.update_rest_moment0_2(region_name='NGC1333', file_extension='DR1_rebase3', threshold=0.0125, save_masked=True)
"""
# Add NH3 (3,3) to this list once change Gaussian fits to fit this line as well
for line in ['HC5N','HC7N_21_20','HC7N_22_21','C2S']:
fit_file='{0}/{0}_{1}_{2}_param_cube.fits'.format(region_name,line,file_extension)
fit_model_file='{0}/{0}_{1}_{2}_gauss_cube.fits'.format(region_name,line,file_extension)
file_in ='{0}/{0}_{1}_{2}.fits'.format(region_name,line,file_extension)
file_out='{0}/{0}_{1}_{2}_mom0_QA.fits'.format(region_name,line,file_extension)
file_rms='{0}/{0}_{1}_{2}_rms_QA.fits'.format(region_name,line,file_extension)
file_rms_mom='{0}/{0}_{1}_{2}_mom0_sigma_QA.fits'.format(region_name,line,file_extension)
file_temp='{0}/{0}_{1}_{2}_masked_temp.fits'.format(region_name,line,file_extension)
# Load pyspeckit cube
# Might be able to just load the Gaussian cube here..
pycube = pyspeckit.Cube(file_in)
pycube.load_model_fit( fit_file, npars=3, npeaks=1, fittype='gaussian')
# If threshold is not defined, then use the machine accuracy
if threshold == None:
threshold=np.finfo(pycube.data.dtype).eps
# Get model cube from pyspeckit. Is completely zero for Gaussian fits. Why?
#modelcube = pycube.get_modelcube()
# Using output model file from the Gaussian fitter instead
model = pyspeckit.Cube(fit_model_file)
modelcube = model.cube
# Use spectral cube to calculate integrated intensity maps
cube_raw = SpectralCube.read(file_in)
# in km/s not Hz
cube = cube_raw.with_spectral_unit(u.km / u.s,velocity_convention='radio')
vaxis=cube.spectral_axis
dv=np.abs(vaxis[1]-vaxis[0])
# define mask
mask3d = modelcube > threshold
# What to do with pixels without signal
# Calculate mean velocity and velocity dispersion
vmap=pycube.parcube[1,:,:]
sigma_map=pycube.parcube[2,:,:]
vmean=np.nanmean(vmap[vmap != 0])*u.km/u.s
sigma_v=( np.nanmedian(sigma_map[vmap != 0]))*u.km/u.s
total_spc=np.sqrt( (vaxis-vmean)**2)/sigma_v < 3.0
#
im_mask=np.sum(mask3d, axis=0)
# Here checking for bad fits. Places where parameter uncertainties are zero or unreasonably low
# Probably a more elegant way to do this!
# For Gaussian fits: parameters are [amp, vlsr, sigma]
for ii in np.arange( im_mask.shape[1]):
for jj in np.arange( im_mask.shape[0]):
if ((im_mask[jj,ii] == 0) or (pycube.parcube[2,jj,ii] < 3*pycube.errcube[2,jj,ii]) or
(pycube.errcube[1,jj,ii] == 0) or (pycube.errcube[1,jj,ii] > 0.2)):
mask3d[:,jj,ii] = total_spc
n_chan=np.sum(mask3d, axis=0)
# create masked cube
cube2 = cube.with_mask(mask3d)
cube3 = cube.with_mask(~mask3d)
#
if save_masked:
cube2.write( file_temp, overwrite=True)
# calculate moment map
moment_0 = cube2.moment(axis=0)
moment_0.write( file_out, overwrite=True)
rms=cube3.std(axis=0)
rms.write( file_rms, overwrite=True)
F = False
T = True
# Some optional parameters for the script
# (if False, it will try to load an already-stored version
# of the file)
fitcube = True
# Mask out low S/N pixels (to speed things up)
mask = pyfits.getdata('badpix_mask.fits')
mask = np.isfinite(mask) * (mask > 0)
# Load the data using a mask
# Then calibrate the data (the data we're loading in this case are in Janskys,
# but we want surface brightness in Kelvin for the fitting process)
cube11 = pyspeckit.Cube('unpb_mosaic_11.fits', maskmap=mask)
cube11.cube *= (
13.6 * (300.0 /
(pyspeckit.spectrum.models.ammonia.freq_dict['oneone'] / 1e9))**2 *
1. / cube11.header.get('BMAJ') / 3600. * 1. / cube11.header.get('BMIN') /
3600.)
cube11.unit = "K"
cube22 = pyspeckit.Cube('unpb_mosaic_22.fits', maskmap=mask)
cube22.cube *= (
13.6 * (300.0 /
(pyspeckit.spectrum.models.ammonia.freq_dict['twotwo'] / 1e9))**2 *
1. / cube22.header.get('BMAJ') / 3600. * 1. / cube22.header.get('BMIN') /
3600.)
cube22.unit = "K"
cube44 = pyspeckit.Cube('unpb_mosaic_44.fits', maskmap=mask)
cube44.cube *= (
def update_NH3_moment0(region_name='L1688', file_extension='DR1_rebase3', threshold=0.0125, trim_edge=True, save_masked=False):
"""
Function to update moment calculation based on centroid velocity from line fit.
For a given NH3(1,1) cube, we check which channels have flux in the model cube,
and then use those channels as the appropiate channels for integration.
Based on code provided by Vlas Sokolov
Parameters
----------
region : str
Name of region to re-calculate moment map
file_extension : str
filename extension
threshold : float
minimum threshold in model cube used to identify channels with emission
Default is down to the machine precision, a better result could be
obtained with 0.0125
trim_edge : Boolean
Keyword to trim noisy edges of output maps using disk erode
save_masked : Boolean
Keyword to store the masked cube used in the integrated intensity calculation.
This is useful to
Usage:
import GAS
GAS.PropertyMaps.update_NH3_moment0(region_name='NGC1333', file_extension='DR1_rebase3', threshold=0.0125, save_masked=True)
"""
fit_file='{0}/{0}_parameter_maps_{1}.fits'.format(region_name,file_extension)
for line_i in ['11','22']:
file_in ='{0}/{0}_NH3_{2}_{1}.fits'.format(region_name,file_extension,line_i)
file_out='{0}/{0}_NH3_{2}_{1}_mom0_QA.fits'.format(region_name,file_extension,line_i)
file_rms='{0}/{0}_NH3_{2}_{1}_rms_QA.fits'.format(region_name,file_extension,line_i)
file_rms_mom='{0}/{0}_NH3_{2}_{1}_mom0_sigma_QA.fits'.format(region_name,file_extension,line_i)
file_temp='{0}/{0}_NH3_{2}_{1}_masked_temp.fits'.format(region_name,file_extension,line_i)
# Load pyspeckit cube
pycube = pyspeckit.Cube(file_in)
if 'FITTYPE' in fits.getheader(fit_file):
# 'FITTYPE' is not present in old versions of the parameter files
pycube.load_model_fit( fit_file, npars=6, npeaks=1)
else:
if not 'cold_ammonia' in pycube.specfit.Registry.multifitters:
pycube.specfit.Registry.add_fitter('cold_ammonia',ammonia.cold_ammonia_model(),6)
pycube.load_model_fit( fit_file, npars=6, npeaks=1, fittype='cold_ammonia')
# If threshold is not defined, then use the machine accuracy
if threshold == None:
threshold=np.finfo(pycube.data.dtype).eps
# Get model cube from pyspeckit, this take some time
modelcube = pycube.get_modelcube()
# Use spectral cube to calculate integrated intensity maps
cube_raw = SpectralCube.read(file_in)
# in km/s not Hz
cube = cube_raw.with_spectral_unit(u.km / u.s,velocity_convention='radio')
if trim_edge:
cube = trim_edge_spectral_cube(cube)
vaxis=cube.spectral_axis
dv=np.abs(vaxis[1]-vaxis[0])
# define mask
mask3d = modelcube > threshold
# What to do with pixels without signal
# Calculate mean velocity and velocity dispersion
vmap=pycube.parcube[4,:,:]
sigma_map=pycube.parcube[3,:,:]
vmean=np.mean(vmap[vmap != 0])*u.km/u.s
if line_i == '11':
sigma_v=( np.median(sigma_map[vmap != 0]) + 0.15)*u.km/u.s
else:
sigma_v=( np.median(sigma_map[vmap != 0]))*u.km/u.s
total_spc=np.sqrt( (vaxis-vmean)**2)/sigma_v < 3.0
#
im_mask=np.sum(mask3d, axis=0)
# Here checking for bad fits. Places where parameter uncertainties are zero or unreasonably low
# Probably a more elegant way to do this!
for ii in np.arange( im_mask.shape[1]):
for jj in np.arange( im_mask.shape[0]):
if ((im_mask[jj,ii] == 0) or (pycube.parcube[3,jj,ii] < 3*pycube.errcube[3,jj,ii]) or
(pycube.errcube[4,jj,ii] == 0) or (pycube.errcube[1,jj,ii] < 0.01) or
(pycube.parcube[0,jj,ii] == 5)):
mask3d[:,jj,ii] = total_spc
n_chan=np.sum(mask3d, axis=0)
# create masked cube
cube2 = cube.with_mask(mask3d)
cube3 = cube.with_mask(~mask3d)
#
if save_masked:
cube2.write( file_temp, overwrite=True)
# calculate moment map
moment_0 = cube2.moment(axis=0)
moment_0.write( file_out, overwrite=True)
rms=cube3.std(axis=0)
rms.write( file_rms, overwrite=True)
import os
import astropy.units as u
if not os.path.exists('n2hp_cube.fit'):
import astropy.utils.data as aud
from astropy.io import fits
f = aud.download_file(
'ftp://cdsarc.u-strasbg.fr/pub/cats/J/A%2BA/472/519/fits/opha_n2h.fit')
with fits.open(f) as ff:
ff[0].header['CUNIT3'] = 'm/s'
for kw in ['CTYPE4', 'CRVAL4', 'CDELT4', 'CRPIX4']:
del ff[0].header[kw]
ff.writeto('n2hp_cube.fit')
# Load the spectral cube cropped in the middle for efficiency
spc = pyspeckit.Cube('n2hp_cube.fit')[:, 25:28, 12:15]
# Set the velocity convention: in the future, this may be read directly from
# the file, but for now it cannot be.
spc.xarr.refX = 93176265000.0 * u.Hz
spc.xarr.velocity_convention = 'radio'
spc.xarr.convert_to_unit('km/s')
# 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)
spc.Registry.add_fitter('n2hp_vtau', pyspeckit.models.n2hp.n2hp_vtau_fitter, 4)
# Get a measurement of the error per pixel
guesses[0, :, :] = 58
guesses[1, :, :] = 26.9
guesses[2, :, :] = 0.010
guesses[3, :, :] = 1.5
guesses[4, :, :] = 31.4
guesses[5, :, :] = 0.010
guesses[6, :, :] = 1.5
negmask = mn < -0.005
guesses[2, negmask] = -0.1
guesses[5, negmask] = -0.1
#peak = scube.max(axis=0)
#mask = peak>0.005*peak.unit
pcube = pyspeckit.Cube(cube=scube)
parfilename = 'fitted_nh366_hf_emission.fits'
if os.path.exists(parfilename):
pcube.load_model_fit(parfilename, 7, 1, 'hfonly')
else:
pcube.fiteach(
fittype='hfonly',
guesses=guesses,
limits=[(50, 70), (24, 29), (-10, 5), (0, 4), (29, 34), (-10, 5),
(0, 4)],
limitedmin=[True] * 7,
limitedmax=[True] * 7,
integral=False,
errmap=errmap.value,
signal_cut=0,
#maskmap=mask,
def cubefit(region='NGC1333', blorder=1, vmin=5, vmax=15, do_plot=False,
snr_min=5.0, multicore=1, file_extension=None, mask_function = None, gauss_fit=False):
"""
Fit NH3(1,1) and (2,2) cubes for the requested region.
It fits all pixels with SNR larger than requested.
Initial guess is based on moment maps and neighboring pixels.
The fitting can be done in parallel mode using several cores,
however, this is dangerous for large regions, where using a
good initial guess is important.
It stores the result in a FITS cube.
TODO:
-Improve initial guess
Parameters
----------
region : str
Name of region to reduce
blorder : int
order of baseline removed
vmin : numpy.float
Minimum centroid velocity to plot, in km/s.
vmax : numpy.float
Maximum centroid velocity to plot, in km/s.
do_plot : bool
If True, then a map of the region to map is shown.
snr_min : numpy.float
Minimum signal to noise ratio of the spectrum to be fitted.
multicore : int
Numbers of cores to use for parallel processing.
file_extension : str
File extension of the input maps. Default is 'base#' where # is the
blorder parameter above.
mask_function : fun
function to create a custom made mask for analysis. Defaults to using
`default_masking`
"""
if file_extension:
root = file_extension
else:
# root = 'base{0}'.format(blorder)
root = 'all'
OneOneIntegrated = '{0}/{0}_NH3_11_{1}_mom0.fits'.format(region,root)
OneOneFile = '{0}/{0}_NH3_11_{1}.fits'.format(region,root)
RMSFile = '{0}/{0}_NH3_11_{1}_rms.fits'.format(region,root)
TwoTwoFile = '{0}/{0}_NH3_22_{1}.fits'.format(region,root)
ThreeThreeFile = '{0}/{0}_NH3_33_{1}.fits'.format(region,root)
cube11sc = SpectralCube.read(OneOneFile)
cube22sc = SpectralCube.read(TwoTwoFile)
errmap11 = fits.getdata(RMSFile)
rms = np.nanmedian(errmap11)
snr = cube11sc.filled_data[:].value/errmap11
peaksnr = np.max(snr,axis=0)
if mask_function is None:
planemask = default_masking(peaksnr,snr_min = snr_min)
else:
planemask = mask_function(peaksnr,snr_min = snr_min)
#planemask = (peaksnr>20) * (errmap11 < 0.2)
mask = (snr>3)*planemask
maskcube = cube11sc.with_mask(mask.astype(bool))
maskcube = maskcube.with_spectral_unit(u.km/u.s,velocity_convention='radio')
slab = maskcube.spectral_slab( vmax*u.km/u.s, vmin*u.km/u.s)
w11=slab.moment( order=0, axis=0).value
peakloc = np.nanargmax(w11)
ymax,xmax = np.unravel_index(peakloc,w11.shape)
moment1 = slab.moment( order=1, axis=0).value
moment2 = (slab.moment( order=2, axis=0).value)**0.5
moment2[np.isnan(moment2)]=0.2
moment2[moment2<0.2]=0.2
cube11 = pyspeckit.Cube(OneOneFile,maskmap=planemask)
cube11.unit="K"
cube22 = pyspeckit.Cube(TwoTwoFile,maskmap=planemask)
cube22.unit="K"
#cube33 = pyspeckit.Cube(ThreeThreeFile,maskmap=planemask)
#cube33.unit="K" # removed as long as we're not modeling OPR
cubes = pyspeckit.CubeStack([cube11,cube22],maskmap=planemask)
cubes.unit="K"
guesses = np.zeros((6,)+cubes.cube.shape[1:])
moment1[moment1<vmin] = vmin+0.2
moment1[moment1>vmax] = vmax-0.2
guesses[0,:,:] = 12 # Kinetic temperature
guesses[1,:,:] = 3 # Excitation Temp
guesses[2,:,:] = 14.5 # log(column)
guesses[3,:,:] = moment2 # Line width / 5 (the NH3 moment overestimates linewidth)
guesses[4,:,:] = moment1 # Line centroid
guesses[5,:,:] = 0.0 # F(ortho) - ortho NH3 fraction (fixed)
if do_plot:
import matplotlib.pyplot as plt
plt.imshow( w11, origin='lower',interpolation='nearest')
plt.show()
F=False
T=True
if not 'cold_ammonia' in cubes.specfit.Registry.multifitters:
cubes.specfit.Registry.add_fitter('cold_ammonia',ammonia.cold_ammonia_model(),6)
print('start fit')
cubes.fiteach(fittype='cold_ammonia', guesses=guesses,
integral=False, verbose_level=3,
fixed=[F,F,F,F,F,T], signal_cut=2,
limitedmax=[F,F,T,F,T,T],
maxpars=[0,0,17.0,0,vmax,1],
limitedmin=[T,T,T,T,T,T],
minpars=[5,2.8,12.0,0.04,vmin,0],
start_from_point=(xmax,ymax),
use_neighbor_as_guess=True,
position_order = 1/peaksnr,
errmap=errmap11, multicore=multicore)
fitcubefile = fits.PrimaryHDU(data=np.concatenate([cubes.parcube,cubes.errcube]), header=cubes.header)
fitcubefile.header.set('PLANE1','TKIN')
fitcubefile.header.set('PLANE2','TEX')
fitcubefile.header.set('PLANE3','COLUMN')
fitcubefile.header.set('PLANE4','SIGMA')
fitcubefile.header.set('PLANE5','VELOCITY')
fitcubefile.header.set('PLANE6','FORTHO')
fitcubefile.header.set('PLANE7','eTKIN')
fitcubefile.header.set('PLANE8','eTEX')
fitcubefile.header.set('PLANE9','eCOLUMN')
fitcubefile.header.set('PLANE10','eSIGMA')
fitcubefile.header.set('PLANE11','eVELOCITY')
fitcubefile.header.set('PLANE12','eFORTHO')
fitcubefile.header.set('CDELT3',1)
fitcubefile.header.set('CTYPE3','FITPAR')
fitcubefile.header.set('CRVAL3',0)
fitcubefile.header.set('CRPIX3',1)
fitcubefile.writeto("{0}/{0}_parameter_maps_{1}.fits".format(region,root),clobber=True)
if gauss_fit==True:
molecules = ['C2S', 'HC7N_22_21', 'HC7N_21_20', 'HC5N']
for i in molecules:
gauss_fitter(region=region, mol=i, vmin=vmin, vmax=vmax, snr_min=snr_min, multicore=multicore, file_extension=file_extension)
