def spec_curve_fit(bin_num, map_name = map_column_dens):
# following loop has not good style. One should build in some break statements or error messages
# if files repeatedly appear in loop.
for one_file in files:
if 'NH3_11' in one_file:
file_name_NH3_11 = one_file
if 'NH3_22' in one_file:
file_name_NH3_22 = one_file
if 'NH3_33' in one_file:
file_name_NH3_33 = one_file
y, x, med = binning(bin_width, bin_num)
s11, _, offset_velocity11, sp_av11 = averaging_over_dopplervel(file_name_NH3_11, y, x)
s22, _, offset_velocity22, sp_av22 = averaging_over_dopplervel(file_name_NH3_22, y, x)
s33, _, offset_velocity33, sp_av33 = averaging_over_dopplervel(file_name_NH3_33, y, x)
xarr11 = SpectroscopicAxis(offset_velocity11*u.km/u.s, velocity_convention='radio',
refX=freq_dict['oneone']).as_unit(u.GHz)
xarr22 = SpectroscopicAxis(offset_velocity22*u.km/u.s, velocity_convention='radio',
refX=freq_dict['twotwo']).as_unit(u.GHz)
xarr33 = SpectroscopicAxis(offset_velocity33*u.km/u.s, velocity_convention='radio',
refX=freq_dict['threethree']).as_unit(u.GHz)
sp11 = psk.Spectrum(data=s11, xarr=xarr11, xarrkwargs={'unit':'km/s'},unit='K')
sp22 = psk.Spectrum(data=s22, xarr=xarr22, xarrkwargs={'unit':'km/s'},unit='K')
sp33 = psk.Spectrum(data=s33, xarr=xarr33, xarrkwargs={'unit':'km/s'},unit='K')
# This joins all the spectra together into one object.
allspec = psk.Spectra([sp11,sp22,sp33])
allspec.xarr.as_unit('Hz',velocity_convention='radio')
# This add the cold_ammonia model to the list of things we can use for fitting
allspec.specfit.Registry.add_fitter('cold_ammonia',ammonia.cold_ammonia_model(),6)
# This does the fit. The values of the guess are
# Kinetic Temperature (usually about 15 to 25 K)
# Excitation Temperature (between 2.73 K and the excitation temperature)
# Log Column Density of ammonia
# Line width (~1 km/s)
# Offset velocity (you will usually use 0 instead of 8.5)
# Ortho fraction (leave at 0)
allspec.specfit(fittype='cold_ammonia',guesses=[23,5,13.1,1,0,0])
# You can make a plot here.
fig = plt.figure()
allspec.plotter()
allspec.specfit.plot_fit(lw=1, components=True)
plt.xlim((23.692,23.697))
# plt.xlim((23.72,23.725))
# plt.xlim((23.8692, 23.8708))
# plt.savefig("OrionA:pyspeckit_fit_bin_width=%rthis_bin=%r.ps" %(bin_width, this_bin))
plt.savefig("Map=%r:bin_num=%r_pyspeckit_fit_NH3_11TEST.ps"%(map_name, bin_num))
plt.show()
# returns the values of the fitted parameters: T_K, T_ex, N, sigma, v, F_0
return allspec.specfit.parinfo, allspec.specfit.parinfo.errors
def nh3_spectrum(pars, kms_vals, lines=['oneone', 'twotwo'], snr=5,
snr_line='oneone', seed=None):
# building a spectroscopic axis
xarr_dict = {}
for line in lines:
xarr_dict[line] = pyspeckit.units.SpectroscopicAxis(kms_vals, 'km/s')
xarr_dict[line].refX = ammonia_constants.freq_dict[line] * u.Hz
xarr_dict[line].velocity_convention = 'radio'
# TODO: bug... see if #182 fixes it
#xarr_dict[line].convert_to_unit('GHz')
xarr_dict[line] = xarr_dict[line].as_unit('GHz')
xarr = SpectroscopicAxes([xarr_dict[line] for line in lines])
xarr.refX = ammonia_constants.freq_dict['oneone'] * u.Hz
xarr.velocity_convention = 'radio'
xarr.convert_to_unit('km/s')
# making synthetic spectra
sp = pyspeckit.Spectrum(data=np.zeros_like(xarr.value), xarr=xarr)
sp.Registry.add_fitter('cold_ammonia', ammonia.cold_ammonia_model(), 6)
sp.specfit.fitter = sp.specfit.Registry.multifitters['cold_ammonia']
sp.specfit.fittype = 'cold_ammonia'
signal = sp.specfit.get_full_model(pars=pars)
sp.data = signal
line_mask = xarr_dict[snr_line].min(), xarr_dict[snr_line].max() # GHz
std_noise = sp.slice(*line_mask).data.max() / snr
np.random.seed(seed=0)
try:
noise = np.random.normal(0, std_noise, sp.xarr.size)
except ValueError: # ValueError("scale <= 0")
noise = 0
sp.data += noise
sp.error = np.zeros_like(sp.data) + std_noise
return sp
def update_NH3_moment0(region_name='L1688', file_extension='_DR1', threshold=None, 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
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', 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}_base{1}.fits'.format(region_name,file_extension,line_i)
file_out='{0}/{0}_NH3_{2}_base{1}_mom0_QA.fits'.format(region_name,file_extension,line_i)
file_rms='{0}/{0}_NH3_{2}_base{1}_rms_QA.fits'.format(region_name,file_extension,line_i)
file_rms_mom='{0}/{0}_NH3_{2}_base{1}_mom0_sigma_QA.fits'.format(region_name,file_extension,line_i)
file_temp='{0}/{0}_NH3_{2}_base{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')
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.mean(sigma_map[vmap != 0]) + 0.15)*u.km/u.s
else:
sigma_v=( np.mean(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)
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]):
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)
mom_0_rms=rms * dv * np.sqrt(n_chan)
mom_0_rms.write( file_rms_mom, overwrite=True)
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)
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 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)
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)
line = 'NH3_11'
OneOneFile = 'nh3_data/{0}_NH3_11_{1}_trim.fits'.format(region, file_extension)
TwoTwoFile = 'nh3_data/{0}_NH3_22_{1}_trim.fits'.format(region, file_extension)
FitFile = 'propertyMaps/{0}_parameter_maps_{1}_flag.fits'.format(
region, file_extension)
cube11 = pyspeckit.Cube(OneOneFile)
cube11.unit = 'K'
cube22 = pyspeckit.Cube(TwoTwoFile)
cube22.unit = 'K'
cubes = pyspeckit.CubeStack([cube11, cube22])
cubes.unit = 'K'
if not 'cold_ammonia' in cubes.specfit.Registry.multifitters:
cubes.specfit.Registry.add_fitter('cold_ammonia',
ammonia.cold_ammonia_model(), 6)
#cubes.load_model_fit(FitFile,6,npeaks=1,fittype='cold_ammonia')
#cubes.specfit.parinfo[5]['fixed'] = True
pix_coords = [54, 184]
sp = cubes.get_spectrum(pix_coords[0], pix_coords[1])
F = False
T = True
sp.specfit(fittype='cold_ammonia',
guesses=[10, 3, 14, 0.3, 3.33, 0],
fixed=[F, F, F, F, F, T])
#sp.specfit(fittype='cold_ammonia',guesses=[8,3,14,0.24,3.33,0],fixed=[F,F,F,F,F,T])
# Set up plot parameters
def spec_curve_fit(bin_num, map_name=map_column_dens):
# following loop has not good style. One should build in some break statements or error messages
# if files repeatedly appear in loop.
for one_file in files:
if 'NH3_11' in one_file:
file_name_NH3_11 = one_file
if 'NH3_22' in one_file:
file_name_NH3_22 = one_file
if 'NH3_33' in one_file:
file_name_NH3_33 = one_file
y, x, med = binning(bin_width, bin_num)
s11, _, offset_velocity11, sp_av11 = averaging_over_dopplervel(
file_name_NH3_11, y, x)
s22, _, offset_velocity22, sp_av22 = averaging_over_dopplervel(
file_name_NH3_22, y, x)
s33, _, offset_velocity33, sp_av33 = averaging_over_dopplervel(
file_name_NH3_33, y, x)
xarr11 = SpectroscopicAxis(offset_velocity11 * u.km / u.s,
velocity_convention='radio',
refX=freq_dict['oneone']).as_unit(u.GHz)
xarr22 = SpectroscopicAxis(offset_velocity22 * u.km / u.s,
velocity_convention='radio',
refX=freq_dict['twotwo']).as_unit(u.GHz)
xarr33 = SpectroscopicAxis(offset_velocity33 * u.km / u.s,
velocity_convention='radio',
refX=freq_dict['threethree']).as_unit(u.GHz)
sp11 = psk.Spectrum(data=s11,
xarr=xarr11,
xarrkwargs={'unit': 'km/s'},
unit='K')
sp22 = psk.Spectrum(data=s22,
xarr=xarr22,
xarrkwargs={'unit': 'km/s'},
unit='K')
sp33 = psk.Spectrum(data=s33,
xarr=xarr33,
xarrkwargs={'unit': 'km/s'},
unit='K')
# This joins all the spectra together into one object.
allspec = psk.Spectra([sp11, sp22, sp33])
allspec.xarr.as_unit('Hz', velocity_convention='radio')
# This add the cold_ammonia model to the list of things we can use for fitting
allspec.specfit.Registry.add_fitter('cold_ammonia',
ammonia.cold_ammonia_model(), 6)
# This does the fit. The values of the guess are
# Kinetic Temperature (usually about 15 to 25 K)
# Excitation Temperature (between 2.73 K and the excitation temperature)
# Log Column Density of ammonia
# Line width (~1 km/s)
# Offset velocity (you will usually use 0 instead of 8.5)
# Ortho fraction (leave at 0)
allspec.specfit(fittype='cold_ammonia', guesses=[23, 5, 13.1, 1, 0, 0])
# You can make a plot here.
fig = plt.figure()
allspec.plotter()
allspec.specfit.plot_fit(lw=1, components=True)
plt.xlim((23.692, 23.697))
# plt.xlim((23.72,23.725))
# plt.xlim((23.8692, 23.8708))
# plt.savefig("OrionA:pyspeckit_fit_bin_width=%rthis_bin=%r.ps" %(bin_width, this_bin))
plt.savefig("Map=%r:bin_num=%r_pyspeckit_fit_NH3_11TEST.ps" %
(map_name, bin_num))
plt.show()
# returns the values of the fitted parameters: T_K, T_ex, N, sigma, v, F_0
return allspec.specfit.parinfo, allspec.specfit.parinfo.errors
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):
"""
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)
OneOneIntegrated = '{0}/{0}_NH3_11_base{1}_mom0.fits'.format(region,root)
OneOneFile = '{0}/{0}_NH3_11_base{1}.fits'.format(region,root)
RMSFile = '{0}/{0}_NH3_11_base{1}_rms.fits'.format(region,root)
TwoTwoFile = '{0}/{0}_NH3_22_base{1}.fits'.format(region,root)
ThreeThreeFile = '{0}/{0}_NH3_33_base{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.update('PLANE1','TKIN')
fitcubefile.header.update('PLANE2','TEX')
fitcubefile.header.update('PLANE3','COLUMN')
fitcubefile.header.update('PLANE4','SIGMA')
fitcubefile.header.update('PLANE5','VELOCITY')
fitcubefile.header.update('PLANE6','FORTHO')
fitcubefile.header.update('PLANE7','eTKIN')
fitcubefile.header.update('PLANE8','eTEX')
fitcubefile.header.update('PLANE9','eCOLUMN')
fitcubefile.header.update('PLANE10','eSIGMA')
fitcubefile.header.update('PLANE11','eVELOCITY')
fitcubefile.header.update('PLANE12','eFORTHO')
fitcubefile.header.update('CDELT3',1)
fitcubefile.header.update('CTYPE3','FITPAR')
fitcubefile.header.update('CRVAL3',0)
fitcubefile.header.update('CRPIX3',1)
fitcubefile.writeto("{0}/{0}_parameter_maps_{1}.fits".format(region,root),clobber=True)
