def render_chem(yth2co321=yth2co321, yth2co303=yth2co303, ytsio=ytsio,
outdir='yt_renders_chem3', size=512, scale=1100.,
nframes=60,
north_vector=[1,0,0],
rot_vector=[1,0,0],
movie=True, camera_angle=[-0.6, 0.4, 0.6]):
if not os.path.exists(paths.mpath(outdir)):
os.makedirs(paths.mpath(outdir))
tf1 = yt.ColorTransferFunction([0.1,2], grey_opacity=True)
#tf1.add_gaussian(0.1,0.05, [1,0,0,1])
#tf1.map_to_colormap(0, 0.5, scale=1, colormap='Reds')
tf1.add_gaussian(0.25, 0.01, [1,0,0,1])
tf1.add_step(0.25,0.5,[1,0,0,1])
#tf1.add_step(1,2,[1,1,1,1])
tf1.map_to_colormap(0.5,2,scale=1,colormap=red)
tf2 = yt.ColorTransferFunction([0.1,2], grey_opacity=True)
#tf2.add_gaussian(0.1,0.05, [0,1,0,1])
#tf2.map_to_colormap(0, 0.5, scale=1, colormap='Greens')
tf2.add_gaussian(0.25, 0.01, [0,1,0,1])
tf2.add_step(0.25,0.5,[0,1,0,1])
tf2.map_to_colormap(0.5,2,scale=1,colormap=green)
tf3 = yt.ColorTransferFunction([0.1,2], grey_opacity=True)
#tf3.add_gaussian(0.1,0.05, [0,0,1,1])
#tf3.map_to_colormap(0, 0.5, scale=1, colormap='Blues')
tf3.add_gaussian(0.25, 0.01, [0,0,1,1])
tf3.add_step(0.25,0.5,[0,0,1,1])
tf3.map_to_colormap(0.5,2,scale=1,colormap=blue)
center = yth2co303.dataset.domain_dimensions /2.
camh2co303 = yth2co303.dataset.h.camera(center, camera_angle, scale, size, tf3,
north_vector=north_vector, fields='flux')
camh2co321 = yth2co321.dataset.h.camera(center, camera_angle, scale, size, tf2,
north_vector=north_vector, fields='flux')
camsio = ytsio.dataset.h.camera(center, camera_angle, scale, size, tf1,
north_vector=north_vector, fields='flux')
imh2co303 = camh2co303.snapshot()
imh2co321 = camh2co321.snapshot()
imsio = camsio.snapshot()
pl.figure(1)
pl.clf()
pl.imshow(imh2co303+imh2co321+imsio)
pl.figure(2)
pl.clf()
pl.imshow(imh2co303[:,:,:3]+imh2co321[:,:,:3]+imsio[:,:,:3])
if movie:
images_h2co303 = [imh2co303]
images_h2co321 = [imh2co321]
images_sio = [imsio]
r1 = camh2co303.rotation(2 * np.pi, nframes, rot_vector=rot_vector)
r2 = camh2co321.rotation(2 * np.pi, nframes, rot_vector=rot_vector)
r3 = camsio.rotation(2 * np.pi, nframes, rot_vector=rot_vector)
pb = ProgressBar(nframes * 3)
for (ii,(imh2co303,imh2co321,imsio)) in enumerate(izip(r1, r2, r3)):
images_h2co303.append(imh2co303)
images_h2co321.append(imh2co321)
images_sio.append(imsio)
imh2co303=imh2co303.swapaxes(0,1)
imh2co303.write_png(paths.mpath(os.path.join(outdir,"h2co303_%04i.png" % (ii))),
rescale=False)
pb.update(ii*3)
imsio=imsio.swapaxes(0,1)
imsio.write_png(paths.mpath(os.path.join(outdir,"sio_%04i.png" % (ii))),
rescale=False)
pb.update(ii*3+1)
imh2co321=imh2co321.swapaxes(0,1)
imh2co321.write_png(paths.mpath(os.path.join(outdir,"h2co321_%04i.png" % (ii))),
rescale=False)
pb.update(ii*3+2)
pb.next()
save_images([i1+i2+i3
for i1,i2,i3 in izip(images_h2co303, images_h2co321, images_sio)],
paths.mpath(outdir))
make_movie(paths.mpath(outdir))
return images_h2co303,images_h2co321,images_sio
else:
return imh2co303,imh2co321,imsio
import os
red = pl.mpl.colors.LinearSegmentedColormap('red',
{'red':[(0,1,1),(1,1,1)],
'green':[(0,0,0),(1,1,1)],
'blue':[(0,0,0),(1,1,1)]})
green = pl.mpl.colors.LinearSegmentedColormap('green',
{'red':[(0,0,0),(1,1,1)],
'green':[(0,1,1),(1,1,1)],
'blue':[(0,0,0),(1,1,1)]})
blue = pl.mpl.colors.LinearSegmentedColormap('blue', {'red':[(0,0,0),(1,1,1)],
'green':[(0,0,0),(1,1,1)],
'blue':[(0,1,1),(1,1,1)]})
if 'yt13co' not in locals():
cube13co = spectral_cube.SpectralCube.read(paths.mpath('APEX_13CO_2014_merge.fits'))
yt13co = cube13co.with_mask(cube13co>0.1).to_yt()
mask = cube13co>0.2
if 'cube_h2co303' not in locals():
cube_sio = SpectralCube.read(paths.molpath('APEX_SiO_54_bl.fits'))
# HACK:
mask._wcs = cube_sio.wcs
ytsio = cube_sio.with_mask(mask).to_yt()
cube_h2co303 = SpectralCube.read(paths.molpath('APEX_H2CO_303_202_bl.fits'))
mask._wcs = cube_h2co303.wcs
yth2co303 = cube_h2co303.with_mask(mask).to_yt()
cube_h2co321 = SpectralCube.read(paths.molpath('APEX_H2CO_321_220_bl.fits'))
mask._wcs = cube_h2co321.wcs
yth2co321 = cube_h2co321.with_mask(mask).to_yt()
def render_13co(ytcube=yt13co, outdir='yt_renders_13CO',
size=512, scale=1200., nframes=180,
movie=True,
camera_angle=[0, 0, 1],
north_vector = [1, 0, 0],
rot_vector1=[1,0,0],
rot_vector2=[0.5,0.5,0.0],
rot_vector3=[0.0,0.5,0.5],
):
if not os.path.exists(paths.mpath(outdir)):
os.makedirs(paths.mpath(outdir))
tf = yt.ColorTransferFunction([0,30], grey_opacity=True)
#tf.map_to_colormap(0.1,5,colormap='Reds')
tf.add_gaussian(2, 1, [1.0, 0.8, 0.0, 1.0])
tf.add_gaussian(3, 2, [1.0, 0.5, 0.0, 1.0])
tf.add_gaussian(5, 3, [1.0, 0.0, 0.0, 1.0])
tf.add_gaussian(10, 5, [1.0, 0.0, 0.0, 0.5])
tf.map_to_colormap(10, 30, colormap=red, scale=1)
center = ytcube.dataset.domain_dimensions /2.
cam = ytcube.dataset.h.camera(center, camera_angle, scale, size, tf,
north_vector=north_vector, fields='flux')
im = cam.snapshot()
#images = [im]
if movie:
pb = ProgressBar(nframes*2+30)
for ii,im in enumerate(cam.rotation(2 * np.pi, nframes/3, rot_vector=rot_vector1)):
#images.append(im)
im.write_png(paths.mpath(os.path.join(outdir,"%04i.png" % (ii))),
rescale=False)
pb.update(ii)
for jj,im in enumerate(cam.rotation(2 * np.pi, nframes/3, rot_vector=rot_vector2)):
#images.append(im)
im.write_png(paths.mpath(os.path.join(outdir,"%04i.png" %
(ii+jj))), rescale=False)
pb.update(ii+jj)
for kk,im in enumerate(cam.rotation(2 * np.pi, nframes/3, rot_vector=rot_vector3)):
#images.append(im)
im.write_png(paths.mpath(os.path.join(outdir,"%04i.png" %
(ii+jj+kk))), rescale=False)
pb.update(ii+jj+kk)
TheBrick = ytcube.world2yt([0.253, 0.016, 35])
for LL, snapshot in enumerate(cam.move_to(TheBrick, 15)):
#images.append(snapshot)
snapshot.write_png(paths.mpath(os.path.join(outdir,'%04i.png' %
(ii+jj+kk+LL))),
rescale=False)
pb.update(ii+jj+kk+LL)
for mm, snapshot in enumerate(cam.zoomin(5, 15)):
#images.append(snapshot)
snapshot.write_png(paths.mpath(os.path.join(outdir,'%04i.png' %
(ii+jj+kk+LL+mm))),
rescale=False)
pb.update(ii+jj+kk+LL+mm)
for nn,im in enumerate(cam.rotation(2*np.pi, nframes/3, rot_vector=rot_vector1)):
#images.append(im)
im.write_png(paths.mpath(os.path.join(outdir,"%04i.png" % (ii+jj+kk+LL+mm+nn))),
rescale=False)
pb.update(ii+jj+kk+LL+mm+nn)
for oo,im in enumerate(cam.rotation(2 * np.pi, nframes/3, rot_vector=rot_vector2)):
#images.append(im)
im.write_png(paths.mpath(os.path.join(outdir,"%04i.png" %
(ii+jj+kk+LL+mm+nn+oo))), rescale=False)
pb.update(ii+jj+kk+LL+mm+nn+oo)
for pp,im in enumerate(cam.rotation(2 * np.pi, nframes/3, rot_vector=rot_vector3)):
#images.append(im)
im.write_png(paths.mpath(os.path.join(outdir,"%04i.png" %
(ii+jj+kk+LL+mm+nn+oo+pp))), rescale=False)
pb.update(ii+jj+kk+LL+mm+nn+oo+pp)
#save_images(images, paths.mpath(outdir))
pipe = make_movie(paths.mpath(outdir))
return images
else:
return im
from astropy.io import fits
from matplotlib import cm
import aplpy
import paths
# create "coverage" mask map
covgf = fits.open(paths.mpath('APEX_H2CO_merge_high_nhits.fits'))
covgf[0].data = (covgf[0].data > 0.05).astype('int')
covgf.writeto(paths.mpath('coverage_bool.fits'),clobber=True)
F = aplpy.FITSFigure('/Users/adam/Dropbox/SMA_CMZ_FITS_files/gcmosaic_column_conv25.fits',convention='calabretta')
F.show_colorscale(cmap=cm.hot_r,vmax=100,vmin=0, vmid=-2.5, stretch='log')
F.recenter(0.6,-0.1,height=0.5,width=2.6)
F.show_contour(paths.mpath('coverage_bool.fits'),levels=[0.5], colors=['b'], convention='calabretta')
F.set_tick_labels_xformat('d.dd')
F.set_tick_labels_yformat('d.dd')
F.save(paths.fpath('coverage_on_herschel.png'),dpi=150)
F.save(paths.fpath('coverage_on_herschel.pdf'),dpi=150)
F.remove_layer('contour_set_1')
F.show_contour(paths.molpath('APEX_H2CO_303_202_smooth_bl_mask_integ.fits'),
levels=[5,10,20,50], colors=['b'], convention='calabretta')
F.save(paths.fpath('H2CO_on_herschel.png'),dpi=150)
F.save(paths.fpath('H2CO_on_herschel.pdf'),dpi=150)
F.remove_layer('contour_set_2')
F.show_contour(paths.molpath('APEX_SiO_54_smooth_mask_integ.fits'),
levels=[2,5,10], colors=['g'], convention='calabretta')
F.save(paths.fpath('SiO_on_herschel.png'),dpi=150)
F.save(paths.fpath('SiO_on_herschel.pdf'),dpi=150)
F.remove_layer('contour_set_3')
F.show_contour(paths.molpath('APEX_13CO_smooth_mask_integ.fits'),
from astropy.io import fits
from matplotlib import cm
import aplpy
import paths
# create "coverage" mask map
covgf = fits.open(paths.mpath('APEX_H2CO_merge_high_nhits.fits'))
covgf[0].data = (covgf[0].data > 0.05).astype('int')
covgf.writeto(paths.mpath('coverage_bool.fits'), clobber=True)
F = aplpy.FITSFigure(
'/Users/adam/Dropbox/SMA_CMZ_FITS_files/gcmosaic_column_conv25.fits',
convention='calabretta')
F.show_colorscale(cmap=cm.hot_r, vmax=100, vmin=0, vmid=-2.5, stretch='log')
F.recenter(0.6, -0.1, height=0.5, width=2.6)
F.show_contour(paths.mpath('coverage_bool.fits'),
levels=[0.5],
colors=['b'],
convention='calabretta')
F.set_tick_labels_xformat('d.dd')
F.set_tick_labels_yformat('d.dd')
F.save(paths.fpath('coverage_on_herschel.png'), dpi=150)
F.save(paths.fpath('coverage_on_herschel.pdf'), dpi=150)
F.remove_layer('contour_set_1')
F.show_contour(paths.molpath('APEX_H2CO_303_202_smooth_bl_mask_integ.fits'),
levels=[5, 10, 20, 50],
colors=['b'],
convention='calabretta')
F.save(paths.fpath('H2CO_on_herschel.png'), dpi=150)
F.save(paths.fpath('H2CO_on_herschel.pdf'), dpi=150)
F.remove_layer('contour_set_2')
def render_chem(yth2co321=yth2co321,
yth2co303=yth2co303,
ytsio=ytsio,
outdir='yt_renders_chem3',
size=512,
scale=1100.,
nframes=60,
north_vector=[1, 0, 0],
rot_vector=[1, 0, 0],
movie=True,
camera_angle=[-0.6, 0.4, 0.6]):
if not os.path.exists(paths.mpath(outdir)):
os.makedirs(paths.mpath(outdir))
tf1 = yt.ColorTransferFunction([0.1, 2], grey_opacity=True)
#tf1.add_gaussian(0.1,0.05, [1,0,0,1])
#tf1.map_to_colormap(0, 0.5, scale=1, colormap='Reds')
tf1.add_gaussian(0.25, 0.01, [1, 0, 0, 1])
tf1.add_step(0.25, 0.5, [1, 0, 0, 1])
#tf1.add_step(1,2,[1,1,1,1])
tf1.map_to_colormap(0.5, 2, scale=1, colormap=red)
tf2 = yt.ColorTransferFunction([0.1, 2], grey_opacity=True)
#tf2.add_gaussian(0.1,0.05, [0,1,0,1])
#tf2.map_to_colormap(0, 0.5, scale=1, colormap='Greens')
tf2.add_gaussian(0.25, 0.01, [0, 1, 0, 1])
tf2.add_step(0.25, 0.5, [0, 1, 0, 1])
tf2.map_to_colormap(0.5, 2, scale=1, colormap=green)
tf3 = yt.ColorTransferFunction([0.1, 2], grey_opacity=True)
#tf3.add_gaussian(0.1,0.05, [0,0,1,1])
#tf3.map_to_colormap(0, 0.5, scale=1, colormap='Blues')
tf3.add_gaussian(0.25, 0.01, [0, 0, 1, 1])
tf3.add_step(0.25, 0.5, [0, 0, 1, 1])
tf3.map_to_colormap(0.5, 2, scale=1, colormap=blue)
center = yth2co303.dataset.domain_dimensions / 2.
camh2co303 = yth2co303.dataset.h.camera(center,
camera_angle,
scale,
size,
tf3,
north_vector=north_vector,
fields='flux')
camh2co321 = yth2co321.dataset.h.camera(center,
camera_angle,
scale,
size,
tf2,
north_vector=north_vector,
fields='flux')
camsio = ytsio.dataset.h.camera(center,
camera_angle,
scale,
size,
tf1,
north_vector=north_vector,
fields='flux')
imh2co303 = camh2co303.snapshot()
imh2co321 = camh2co321.snapshot()
imsio = camsio.snapshot()
pl.figure(1)
pl.clf()
pl.imshow(imh2co303 + imh2co321 + imsio)
pl.figure(2)
pl.clf()
pl.imshow(imh2co303[:, :, :3] + imh2co321[:, :, :3] + imsio[:, :, :3])
if movie:
images_h2co303 = [imh2co303]
images_h2co321 = [imh2co321]
images_sio = [imsio]
r1 = camh2co303.rotation(2 * np.pi, nframes, rot_vector=rot_vector)
r2 = camh2co321.rotation(2 * np.pi, nframes, rot_vector=rot_vector)
r3 = camsio.rotation(2 * np.pi, nframes, rot_vector=rot_vector)
pb = ProgressBar(nframes * 3)
for (ii, (imh2co303, imh2co321, imsio)) in enumerate(izip(r1, r2, r3)):
images_h2co303.append(imh2co303)
images_h2co321.append(imh2co321)
images_sio.append(imsio)
imh2co303 = imh2co303.swapaxes(0, 1)
imh2co303.write_png(paths.mpath(
os.path.join(outdir, "h2co303_%04i.png" % (ii))),
rescale=False)
pb.update(ii * 3)
imsio = imsio.swapaxes(0, 1)
imsio.write_png(paths.mpath(
os.path.join(outdir, "sio_%04i.png" % (ii))),
rescale=False)
pb.update(ii * 3 + 1)
imh2co321 = imh2co321.swapaxes(0, 1)
imh2co321.write_png(paths.mpath(
os.path.join(outdir, "h2co321_%04i.png" % (ii))),
rescale=False)
pb.update(ii * 3 + 2)
pb.next()
save_images([
i1 + i2 + i3
for i1, i2, i3 in izip(images_h2co303, images_h2co321, images_sio)
], paths.mpath(outdir))
make_movie(paths.mpath(outdir))
return images_h2co303, images_h2co321, images_sio
else:
return imh2co303, imh2co321, imsio
from make_apex_cubes import all_lines
from spectral_cube import SpectralCube,BooleanArrayMask
import pyspeckit
from astropy.io import fits
from numpy.lib.stride_tricks import as_strided
from paths import mpath
import numpy as np
import time
from astropy import log
from astropy import constants
from astropy import units as u
cube_merge_high = SpectralCube.read(mpath('APEX_H2CO_merge_high_plait_all.fits'))
noise = fits.getdata(mpath('APEX_H2CO_merge_high_plait_all_noise.fits'))
nhits = fits.getdata(mpath('APEX_H2CO_merge_high_nhits.fits'))
noise[nhits<5] = np.nan
noise_cube = as_strided(noise, shape=cube_merge_high.shape,
strides=(0,)+noise.strides)
noise_spcube = SpectralCube(data=noise_cube, wcs=cube_merge_high.wcs)
cube_merge_high_sm = SpectralCube.read(mpath('APEX_H2CO_merge_high_plait_all_smooth.fits'))
noise_sm = fits.getdata(mpath('APEX_H2CO_merge_high_plait_all_smooth_noise.fits'))
noise_cube_sm = as_strided(noise_sm, shape=cube_merge_high_sm.shape,
strides=(0,)+noise_sm.strides)
noise_spcube_sm = SpectralCube(data=noise_cube_sm, wcs=cube_merge_high_sm.wcs)
# Create a cutout of the cube covering the H2CO lines
# 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)),
and all_lines[line]*u.GHz < fullcube.spectral_axis.max()/1.0005
}
spectra = {line: pyspeckit.Spectrum(data=cube.apply_numpy_function(np.nanmean, axis=(1,2)),
xarr=pyspeckit.units.SpectroscopicAxis(cube.spectral_axis.value,
unit=str(cube.spectral_axis.unit),
refX=cube.wcs.wcs.restfrq,
refX_units='Hz'),
header=cube.wcs.to_header())
for line,cube in cubes.iteritems()}
return cubes,spectra
for lh in ('low','high'):
# reject < 217 GHz
cube = SpectralCube.read(mpath(ftemplate.format(lh))).spectral_slab(217*u.GHz, 250*u.GHz)
noisehdu = cube.std(axis=0).hdu
noisehdr = noisehdu.header
noise = noisehdu.data
noiseokmask = np.isfinite(noise)
xarr = pyspeckit.units.SpectroscopicAxis(cube.spectral_axis.value,
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']
print name
if name not in spectra:
import numpy as np
from astropy import units as u
from astropy import constants
from astropy.table import Table
import paths
import pyspeckit
import glob
import matplotlib
matplotlib.use('Qt4Agg')
import os
import pylab as pl
tbl = Table.read(paths.mpath('diffuse_lines_mainonly.ipac'),
format='ascii.ipac')
rfreq = tbl['FreqGHz']
rfreq[rfreq.mask] = tbl['MeasFreqGHz'][rfreq.mask]
freq = rfreq * (1-65*u.km/u.s/constants.c)
for fn in glob.glob(paths.spath("*.fits")):
sp = pyspeckit.Spectrum(fn)
sp.xarr.convert_to_unit('GHz')
sp.plotter(figure=pl.figure(1), clear=True)
sp.plotter.line_ids(tbl['Species'], freq*u.GHz, unit='GHz')
outname = os.path.splitext(os.path.split(fn)[1])[0]
sp.plotter.savefig(paths.sppath("{0}.png".format(outname)))
"""
import paths
import pyspeckit
with open(regpath + 'spectral_ncomp.txt') as f:
pars = eval(f.read())
ftemplate = 'APEX_H2CO_2014_merge_{0}.fits'
# Nice idea, but Splatalogue doesn't know what lines are really there
#line_table = Splatalogue.query_lines(216.9*u.GHz,
# 221*u.GHz, top20='top20',
# energy_max=150, energy_type='eu_k',
# line_lists=['SLAIM'])
#names = np.array(['{0}_{1}'.format(a,b) for a,b in zip(line_table['Species'],
# line_table['Resolved QNs'])])
#lfreq = line_table['Freq-GHz']
for lh in ('low', 'high'):
cube = SpectralCube.read(mpath(ftemplate.format(lh)))
noisehdu = cube.std(axis=0).hdu
noisehdr = noisehdu.header
noise = noisehdu.data
noiseokmask = np.isfinite(noise)
xarr = pyspeckit.units.SpectroscopicAxis(cube.spectral_axis.value,
unit=str(cube.spectral_axis.unit),
refX=cube.wcs.wcs.restfrq,
refX_unit='Hz')
spectra = {}
for region_number, reg in enumerate(regs):
name = reg.attr[1]['text']
print name
if name not in spectra:
from paths import rpath,mpath,opath,fpath
from astropy.io import fits
import pyregion
import numpy as np
from astropy import table
import paths
import matplotlib
matplotlib.rc_file(paths.pcpath('pubfiguresrc'))
noise_img = fits.open(mpath('APEX_H2CO_merge_high_plait_all_noise.fits'))[0]
nhits_img = fits.open(mpath('APEX_H2CO_merge_high_plait_all_nhits.fits'))[0]
map_regions = pyregion.open(rpath('target_fields_8x8.reg'))
nhits = {}
noise = {}
for reg in map_regions:
regs = pyregion.ShapeList([reg])
mask = (regs.get_mask(hdu=noise_img) & np.isfinite(noise_img.data) &
np.isfinite(nhits_img.data))
name = reg.attr[1]['text'].split()[0]
noise[name] = {'mean':noise_img.data[mask].mean(),
'median':np.median(noise_img.data[mask]),
'std':noise_img.data[mask].std()}
nhits[name] = {'mean':nhits_img.data[mask].mean(),
'median':np.median(nhits_img.data[mask]),
'std':nhits_img.data[mask].std()}
and all_lines[line]*u.GHz < fullcube.spectral_axis.max()/1.0005
}
spectra = {line: pyspeckit.Spectrum(data=cube.apply_numpy_function(np.nanmean, axis=(1,2)),
xarr=pyspeckit.units.SpectroscopicAxis(cube.spectral_axis.value,
unit=str(cube.spectral_axis.unit),
refX=cube.wcs.wcs.restfrq,
refX_unit='Hz'),
header=cube.wcs.to_header())
for line,cube in cubes.items()}
return cubes,spectra
for lh in ('low','high'):
# reject < 217 GHz
cube = SpectralCube.read(mpath(ftemplate.format(lh))).spectral_slab(217*u.GHz, 250*u.GHz)
noisehdu = cube.std(axis=0).hdu
noisehdr = noisehdu.header
noise = noisehdu.data
noiseokmask = np.isfinite(noise)
xarr = pyspeckit.units.SpectroscopicAxis(cube.spectral_axis.value,
unit=str(cube.spectral_axis.unit),
refX=cube.wcs.wcs.restfrq,
refX_unit='Hz')
spectra = {}
for region_number,reg in enumerate(regs):
name = reg.attr[1]['text']
print name
if name not in spectra:
def fit_a_spectrum(sp,
radexfit=False,
write=True,
vlimits=(-105, 125),
pars=pars):
sp.plotter.autorefresh = False
sp.plotter(figure=1)
ncomp = pars[sp.specname]['ncomp']
if ncomp == 0:
log.info(
"Skipping {0} - no velocity components detected.".format(ncomp))
return
returns = [ncomp]
velos = pars[sp.specname]['velo']
spname = sp.specname.replace(" ", "_")
width_min = 1
if 'width_max' in pars[sp.specname]:
width_max = pars[sp.specname]['width_max']
elif 'Map' in sp.specname or 'box' in sp.specname:
width_max = 40
else:
width_max = 15
sp.specfit.Registry.add_fitter('h2co_simple',
simple_fitter2,
6,
multisingle='multi')
guesses_simple = [
x for ii in range(ncomp)
for x in (sp.data.max(), velos[ii], 5, 0.5, 1.0, sp.data.max())
]
if not (min(velos) > vlimits[0] and max(velos) < vlimits[1]):
log.warn("A velocity guess {0} is outside limits {1}.".format(
velos, vlimits))
vlimits = (min(velos) - 25, max(velos) + 25)
log.warn("Changing limits to {0}".format(vlimits))
sp.specfit(
fittype='h2co_simple',
multifit=True,
guesses=guesses_simple,
limited=[(True, True)] * 6,
limits=[(0, 20), vlimits, (width_min, width_max), (0, 1), (0.3, 1.1),
(0, 1e5)],
)
sp.baseline(excludefit=True,
subtract=True,
highlight_fitregion=True,
order=1)
sp.plotter(clear=True)
sp.specfit(
fittype='h2co_simple',
multifit=True,
guesses=guesses_simple,
limited=[(True, True)] * 6,
limits=[(0, 20), vlimits, (width_min, width_max), (0, 1), (0.3, 1.1),
(0, 1e5)],
)
returns.append(copy.copy(sp.specfit.parinfo))
err = sp.error.mean()
sp.plotter()
sp.specfit.plot_fit(show_components=True)
sp.specfit.annotate(fontsize=font_sizes[ncomp])
sp.specfit.plotresiduals(axis=sp.plotter.axis,
yoffset=-err * 5,
clear=False,
color='#444444',
label=False)
sp.plotter.axis.set_ylim(sp.plotter.ymin - err * 5, sp.plotter.ymax)
sp.plotter.savefig(
os.path.join(figurepath,
"simple/{0}_fit_4_lines_simple.pdf".format(spname)))
if write:
sp.write(mpath("spectra/{0}_spectrum.fits".format(spname)))
# This will mess things up for the radexfit (maybe in a good way) but *cannot*
# be done after the radexfit
# Set the spectrum to be the fit residuals. The linear baseline has
# already been subtracted from both the data and the residuals
linear_baseline = sp.baseline.basespec
sp.baseline.unsubtract()
fitted_residuals = sp.baseline.spectofit = sp.specfit.residuals
sp.baseline.includemask[:] = True # Select ALL residuals
sp.baseline.fit(spline=True, order=3, spline_sampling=50)
spline_baseline = sp.baseline.basespec
sp.data -= spline_baseline + linear_baseline
sp.baseline.subtracted = True
sp.error[:] = sp.stats((218.5e9, 218.65e9))['std']
sp.specfit(
fittype='h2co_simple',
multifit=True,
guesses=guesses_simple,
limited=[(True, True)] * 6,
limits=[(0, 1e5), vlimits, (width_min, width_max), (0, 1), (0.3, 1.1),
(0, 1e5)],
)
sp.plotter()
sp.plotter.axis.plot(sp.xarr,
spline_baseline - err * 5,
color='orange',
alpha=0.75,
zorder=-1,
linewidth=2)
sp.specfit.plot_fit(show_components=True)
sp.specfit.annotate(fontsize=font_sizes[ncomp])
sp.plotter.axis.plot(sp.xarr,
fitted_residuals - err * 5,
color="#444444",
linewidth=0.5,
drawstyle='steps-mid')
#sp.specfit.plotresiduals(axis=sp.plotter.axis, yoffset=-err*5, clear=False,
# color='#444444', label=False)
sp.plotter.axis.set_ylim(sp.plotter.ymin - err * 5, sp.plotter.ymax)
sp.plotter.savefig(
os.path.join(
figurepath,
"simple/{0}_fit_4_lines_simple_splinebaselined.pdf".format(
spname)))
returns.append(copy.copy(sp.specfit.parinfo))
if write:
sp.write(mpath("spectra/{0}_spectrum_basesplined.fits".format(spname)))
if radexfit:
guesses = [
x for ii in range(ncomp) for x in
(100, 14, 4.5, sp.specfit.parinfo['VELOCITY{0}'.format(ii)].value,
(sp.specfit.parinfo['WIDTH{0}'.format(ii)].value if
(sp.specfit.parinfo['WIDTH{0}'.format(ii)].value < width_max
and sp.specfit.parinfo['WIDTH{0}'.format(ii)].value > width_min
) else 5))
]
sp.specfit.Registry.add_fitter('h2co_mm_radex',
h2co_radex_fitter,
5,
multisingle='multi')
sp.specfit(
fittype='h2co_mm_radex',
multifit=True,
guesses=guesses,
limits=[(10, 300), (11, 15), (3, 5.5), (-105, 125),
(width_min, width_max)] * ncomp,
limited=[(True, True)] * 5 * ncomp,
fixed=[False, False, False, True, True] * ncomp,
quiet=False,
)
sp.plotter.savefig(
os.path.join(figurepath,
"radex/{0}_fit_h2co_mm_radex.pdf".format(spname)))
returns.append(copy.copy(sp.specfit.parinfo))
return returns
def fit_a_spectrum(sp, radexfit=False, write=True, vlimits=(-105, 125), pars=pars):
sp.plotter.autorefresh = False
sp.plotter(figure=1)
ncomp = pars[sp.specname]["ncomp"]
if ncomp == 0:
log.info("Skipping {0} - no velocity components detected.".format(ncomp))
return
returns = [ncomp]
velos = pars[sp.specname]["velo"]
spname = sp.specname.replace(" ", "_")
width_min = 1
if "width_max" in pars[sp.specname]:
width_max = pars[sp.specname]["width_max"]
elif "Map" in sp.specname or "box" in sp.specname:
width_max = 40
else:
width_max = 15
sp.specfit.Registry.add_fitter("h2co_simple", simple_fitter2, 6, multisingle="multi")
guesses_simple = [x for ii in range(ncomp) for x in (sp.data.max(), velos[ii], 5, 0.5, 1.0, sp.data.max())]
if not (min(velos) > vlimits[0] and max(velos) < vlimits[1]):
log.warn("A velocity guess {0} is outside limits {1}.".format(velos, vlimits))
vlimits = (min(velos) - 25, max(velos) + 25)
log.warn("Changing limits to {0}".format(vlimits))
sp.specfit(
fittype="h2co_simple",
multifit=True,
guesses=guesses_simple,
limited=[(True, True)] * 6,
limits=[(0, 20), vlimits, (width_min, width_max), (0, 1), (0.3, 1.1), (0, 1e5)],
)
sp.baseline(excludefit=True, subtract=True, highlight_fitregion=True, order=1)
sp.plotter(clear=True)
sp.specfit(
fittype="h2co_simple",
multifit=True,
guesses=guesses_simple,
limited=[(True, True)] * 6,
limits=[(0, 20), vlimits, (width_min, width_max), (0, 1), (0.3, 1.1), (0, 1e5)],
)
returns.append(copy.copy(sp.specfit.parinfo))
err = sp.error.mean()
sp.plotter()
sp.specfit.plot_fit(show_components=True)
sp.specfit.annotate(fontsize=font_sizes[ncomp])
sp.specfit.plotresiduals(axis=sp.plotter.axis, yoffset=-err * 5, clear=False, color="#444444", label=False)
sp.plotter.axis.set_ylim(sp.plotter.ymin - err * 5, sp.plotter.ymax)
sp.plotter.savefig(os.path.join(figurepath, "simple/{0}_fit_4_lines_simple.pdf".format(spname)))
if write:
sp.write(mpath("spectra/{0}_spectrum.fits".format(spname)))
# This will mess things up for the radexfit (maybe in a good way) but *cannot*
# be done after the radexfit
# Set the spectrum to be the fit residuals. The linear baseline has
# already been subtracted from both the data and the residuals
linear_baseline = sp.baseline.basespec
sp.baseline.unsubtract()
fitted_residuals = sp.baseline.spectofit = sp.specfit.residuals
sp.baseline.includemask[:] = True # Select ALL residuals
sp.baseline.fit(spline=True, order=3, spline_sampling=50)
spline_baseline = sp.baseline.basespec
sp.data -= spline_baseline + linear_baseline
sp.baseline.subtracted = True
sp.error[:] = sp.stats((218.5e9, 218.65e9))["std"]
sp.specfit(
fittype="h2co_simple",
multifit=True,
guesses=guesses_simple,
limited=[(True, True)] * 6,
limits=[(0, 1e5), vlimits, (width_min, width_max), (0, 1), (0.3, 1.1), (0, 1e5)],
)
sp.plotter()
sp.plotter.axis.plot(sp.xarr, spline_baseline - err * 5, color="orange", alpha=0.75, zorder=-1, linewidth=2)
sp.specfit.plot_fit(show_components=True)
sp.specfit.annotate(fontsize=font_sizes[ncomp])
sp.plotter.axis.plot(sp.xarr, fitted_residuals - err * 5, color="#444444", linewidth=0.5, drawstyle="steps-mid")
# sp.specfit.plotresiduals(axis=sp.plotter.axis, yoffset=-err*5, clear=False,
# color='#444444', label=False)
sp.plotter.axis.set_ylim(sp.plotter.ymin - err * 5, sp.plotter.ymax)
sp.plotter.savefig(os.path.join(figurepath, "simple/{0}_fit_4_lines_simple_splinebaselined.pdf".format(spname)))
returns.append(copy.copy(sp.specfit.parinfo))
if write:
sp.write(mpath("spectra/{0}_spectrum_basesplined.fits".format(spname)))
if radexfit:
guesses = [
x
for ii in range(ncomp)
for x in (
100,
14,
4.5,
sp.specfit.parinfo["VELOCITY{0}".format(ii)].value,
(
sp.specfit.parinfo["WIDTH{0}".format(ii)].value
if (
sp.specfit.parinfo["WIDTH{0}".format(ii)].value < width_max
and sp.specfit.parinfo["WIDTH{0}".format(ii)].value > width_min
)
else 5
),
)
]
sp.specfit.Registry.add_fitter("h2co_mm_radex", h2co_radex_fitter, 5, multisingle="multi")
sp.specfit(
fittype="h2co_mm_radex",
multifit=True,
guesses=guesses,
limits=[(10, 300), (11, 15), (3, 5.5), (-105, 125), (width_min, width_max)] * ncomp,
limited=[(True, True)] * 5 * ncomp,
fixed=[False, False, False, True, True] * ncomp,
quiet=False,
)
sp.plotter.savefig(os.path.join(figurepath, "radex/{0}_fit_h2co_mm_radex.pdf".format(spname)))
returns.append(copy.copy(sp.specfit.parinfo))
return returns
green = pl.mpl.colors.LinearSegmentedColormap(
'green', {
'red': [(0, 0, 0), (1, 1, 1)],
'green': [(0, 1, 1), (1, 1, 1)],
'blue': [(0, 0, 0), (1, 1, 1)]
})
blue = pl.mpl.colors.LinearSegmentedColormap(
'blue', {
'red': [(0, 0, 0), (1, 1, 1)],
'green': [(0, 0, 0), (1, 1, 1)],
'blue': [(0, 1, 1), (1, 1, 1)]
})
if 'yt13co' not in locals():
cube13co = spectral_cube.SpectralCube.read(
paths.mpath('APEX_13CO_2014_merge.fits'))
yt13co = cube13co.with_mask(cube13co > 0.1).to_yt()
mask = cube13co > 0.2
if 'cube_h2co303' not in locals():
cube_sio = SpectralCube.read(paths.molpath('APEX_SiO_54_bl.fits'))
# HACK:
mask._wcs = cube_sio.wcs
ytsio = cube_sio.with_mask(mask).to_yt()
cube_h2co303 = SpectralCube.read(
paths.molpath('APEX_H2CO_303_202_bl.fits'))
mask._wcs = cube_h2co303.wcs
yth2co303 = cube_h2co303.with_mask(mask).to_yt()
cube_h2co321 = SpectralCube.read(
paths.molpath('APEX_H2CO_321_220_bl.fits'))
"pv_H2CO_321220_to_303202_bl_integ_masked_weighted_temperature_dens3e4.fits",
"pv_H2CO_321220_to_303202_bl_integ_weighted_temperature_dens1e4.fits",
"pv_H2CO_321220_to_303202_bl_integ_weighted_temperature_dens1e4_abund1e-10.fits",
"pv_H2CO_321220_to_303202_bl_integ_weighted_temperature_dens1e4_abund1e-8.fits",
"pv_H2CO_321220_to_303202_bl_integ_weighted_temperature_dens1e5.fits",
"pv_H2CO_321220_to_303202_bl_integ_weighted_temperature_dens3e4.fits",
)
]
cubes = [hpath(x)
for x in ("H2CO_321220_to_303202_cube_bl.fits",
"H2CO_321220_to_303202_cube_smooth_bl.fits",
"APEX_H2CO_303_202_bl.fits",
"APEX_H2CO_321_220_bl.fits",
"APEX_H2CO_322_221_bl.fits",
)
] + [mpath(x)
for x in ("APEX_13CO_2014_merge.fits",
"APEX_C18O_2014_merge.fits",
"APEX_H2CO_merge_high_plait_all.fits",
)
] + [molpath(x)
for x in
("APEX_SiO_54_bl.fits",)
]
dendrograms = [hpath(x) for x in
("DendroMask_H2CO303202.hdf5",)
] + [tpath(x) for x in
("fitted_line_parameters_Chi2Constraints.ipac",
"PPV_H2CO_Temperature.ipac",
def render_13co(
ytcube=yt13co,
outdir='yt_renders_13CO',
size=512,
scale=1200.,
nframes=180,
movie=True,
camera_angle=[0, 0, 1],
north_vector=[1, 0, 0],
rot_vector1=[1, 0, 0],
rot_vector2=[0.5, 0.5, 0.0],
rot_vector3=[0.0, 0.5, 0.5],
):
if not os.path.exists(paths.mpath(outdir)):
os.makedirs(paths.mpath(outdir))
tf = yt.ColorTransferFunction([0, 30], grey_opacity=True)
#tf.map_to_colormap(0.1,5,colormap='Reds')
tf.add_gaussian(2, 1, [1.0, 0.8, 0.0, 1.0])
tf.add_gaussian(3, 2, [1.0, 0.5, 0.0, 1.0])
tf.add_gaussian(5, 3, [1.0, 0.0, 0.0, 1.0])
tf.add_gaussian(10, 5, [1.0, 0.0, 0.0, 0.5])
tf.map_to_colormap(10, 30, colormap=red, scale=1)
center = ytcube.dataset.domain_dimensions / 2.
cam = ytcube.dataset.h.camera(center,
camera_angle,
scale,
size,
tf,
north_vector=north_vector,
fields='flux')
im = cam.snapshot()
#images = [im]
if movie:
pb = ProgressBar(nframes * 2 + 30)
for ii, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector1)):
#images.append(im)
im.write_png(paths.mpath(os.path.join(outdir, "%04i.png" % (ii))),
rescale=False)
pb.update(ii)
for jj, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector2)):
#images.append(im)
im.write_png(paths.mpath(
os.path.join(outdir, "%04i.png" % (ii + jj))),
rescale=False)
pb.update(ii + jj)
for kk, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector3)):
#images.append(im)
im.write_png(paths.mpath(
os.path.join(outdir, "%04i.png" % (ii + jj + kk))),
rescale=False)
pb.update(ii + jj + kk)
TheBrick = ytcube.world2yt([0.253, 0.016, 35])
for LL, snapshot in enumerate(cam.move_to(TheBrick, 15)):
#images.append(snapshot)
snapshot.write_png(paths.mpath(
os.path.join(outdir, '%04i.png' % (ii + jj + kk + LL))),
rescale=False)
pb.update(ii + jj + kk + LL)
for mm, snapshot in enumerate(cam.zoomin(5, 15)):
#images.append(snapshot)
snapshot.write_png(paths.mpath(
os.path.join(outdir, '%04i.png' % (ii + jj + kk + LL + mm))),
rescale=False)
pb.update(ii + jj + kk + LL + mm)
for nn, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector1)):
#images.append(im)
im.write_png(paths.mpath(
os.path.join(outdir,
"%04i.png" % (ii + jj + kk + LL + mm + nn))),
rescale=False)
pb.update(ii + jj + kk + LL + mm + nn)
for oo, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector2)):
#images.append(im)
im.write_png(paths.mpath(
os.path.join(outdir,
"%04i.png" % (ii + jj + kk + LL + mm + nn + oo))),
rescale=False)
pb.update(ii + jj + kk + LL + mm + nn + oo)
for pp, im in enumerate(
cam.rotation(2 * np.pi, nframes / 3, rot_vector=rot_vector3)):
#images.append(im)
im.write_png(paths.mpath(
os.path.join(
outdir,
"%04i.png" % (ii + jj + kk + LL + mm + nn + oo + pp))),
rescale=False)
pb.update(ii + jj + kk + LL + mm + nn + oo + pp)
#save_images(images, paths.mpath(outdir))
pipe = make_movie(paths.mpath(outdir))
return images
else:
return im
from masked_cubes import (cube303, cube303sm, cube303m, cube321m, cube303msm,
cube321msm, cube321, cube321sm)
from spectral_cube import SpectralCube, BooleanArrayMask
from astropy.io import fits
from numpy.lib.stride_tricks import as_strided
from paths import mpath
import numpy as np
import time
from astropy import log
t0 = time.time()
noise_fn = mpath('APEX_H2CO_merge_high_plait_all_noise.fits')
noise = fits.getdata(noise_fn)
nhits = fits.getdata(mpath('APEX_H2CO_merge_high_nhits.fits'))
noise[nhits < 20] = np.nan
noise_cube = as_strided(noise,
shape=cube303m.shape,
strides=(0, ) + noise.strides)
noisehdr = fits.getheader(noise_fn)
sm_noise = fits.getdata(
mpath('APEX_H2CO_merge_high_plait_all_smooth_noise.fits'))
sm_noise[nhits < 20] = np.nan
sm_noise_cube = as_strided(sm_noise,
shape=cube303msm.shape,
strides=(0, ) + sm_noise.strides)
# Cubes masked with noise cube == OK
# (can I make this lazier?)
with open(regpath+'spectral_ncomp.txt') as f:
pars = eval(f.read())
ftemplate = 'APEX_H2CO_2014_merge_{0}.fits'
# Nice idea, but Splatalogue doesn't know what lines are really there
#line_table = Splatalogue.query_lines(216.9*u.GHz,
# 221*u.GHz, top20='top20',
# energy_max=150, energy_type='eu_k',
# line_lists=['SLAIM'])
#names = np.array(['{0}_{1}'.format(a,b) for a,b in zip(line_table['Species'],
# line_table['Resolved QNs'])])
#lfreq = line_table['Freq-GHz']
for lh in ('low','high'):
cube = SpectralCube.read(mpath(ftemplate.format(lh)))
noisehdu = cube.std(axis=0).hdu
noisehdr = noisehdu.header
noise = noisehdu.data
noiseokmask = np.isfinite(noise)
xarr = pyspeckit.units.SpectroscopicAxis(cube.spectral_axis.value,
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']
print name
if name not in spectra:
from masked_cubes import (cube303, cube303sm, cube303m, cube321m, cube303msm,
cube321msm, cube321, cube321sm)
from spectral_cube import SpectralCube,BooleanArrayMask
from astropy.io import fits
from numpy.lib.stride_tricks import as_strided
from paths import mpath
import numpy as np
import time
from astropy import log
t0 = time.time()
noise_fn = mpath('APEX_H2CO_merge_high_plait_all_noise.fits')
noise = fits.getdata(noise_fn)
nhits = fits.getdata(mpath('APEX_H2CO_merge_high_nhits.fits'))
noise[nhits<20] = np.nan
noise_cube = as_strided(noise, shape=cube303m.shape, strides=(0,)+noise.strides)
noisehdr = fits.getheader(noise_fn)
sm_noise = fits.getdata(mpath('APEX_H2CO_merge_high_plait_all_smooth_noise.fits'))
sm_noise[nhits<20] = np.nan
sm_noise_cube = as_strided(sm_noise, shape=cube303msm.shape,
strides=(0,)+sm_noise.strides)
# Cubes masked with noise cube == OK
# (can I make this lazier?)
noisefinite = np.isfinite(noise) # stride length changes for bools?
sm_noisefinite = np.isfinite(sm_noise)
cube303nm = cube303.with_mask(BooleanArrayMask(as_strided(noisefinite,
shape=cube303.shape,
"pv_H2CO_321220_to_303202_bl_integ_weighted_temperature_dens1e4_abund1e-10.fits",
"pv_H2CO_321220_to_303202_bl_integ_weighted_temperature_dens1e4_abund1e-8.fits",
"pv_H2CO_321220_to_303202_bl_integ_weighted_temperature_dens1e5.fits",
"pv_H2CO_321220_to_303202_bl_integ_weighted_temperature_dens3e4.fits",
)
]
cubes = [
hpath(x) for x in (
"H2CO_321220_to_303202_cube_bl.fits",
"H2CO_321220_to_303202_cube_smooth_bl.fits",
"APEX_H2CO_303_202_bl.fits",
"APEX_H2CO_321_220_bl.fits",
"APEX_H2CO_322_221_bl.fits",
)
] + [
mpath(x) for x in (
"APEX_13CO_2014_merge.fits",
"APEX_C18O_2014_merge.fits",
"APEX_H2CO_merge_high_plait_all.fits",
)
] + [molpath(x) for x in ("APEX_SiO_54_bl.fits", )]
dendrograms = [hpath(x) for x in ("DendroMask_H2CO303202.hdf5", )] + [
tpath(x) for x in (
"fitted_line_parameters_Chi2Constraints.ipac",
"PPV_H2CO_Temperature.ipac",
)
]
if not os.path.isdir('cds'):
os.mkdir('cds')
import numpy as np
from astropy import units as u
from astropy import constants
from astropy.table import Table
import paths
import pyspeckit
import glob
import matplotlib
matplotlib.use('Qt4Agg')
import os
import pylab as pl
tbl = Table.read(paths.mpath('diffuse_lines_mainonly.ipac'),
format='ascii.ipac')
rfreq = tbl['FreqGHz']
rfreq[rfreq.mask] = tbl['MeasFreqGHz'][rfreq.mask]
freq = rfreq * (1 - 65 * u.km / u.s / constants.c)
for fn in glob.glob(paths.spath("*.fits")):
sp = pyspeckit.Spectrum(fn)
sp.xarr.convert_to_unit('GHz')
sp.plotter(figure=pl.figure(1), clear=True)
sp.plotter.line_ids(tbl['Species'], freq * u.GHz, unit='GHz')
outname = os.path.splitext(os.path.split(fn)[1])[0]
sp.plotter.savefig(paths.sppath("{0}.png".format(outname)))
"""
import paths
import pyspeckit
import numpy as np
import glob
