def mask_out_signal(self, niter=1):
"""
Sets the mask property of the SpectralCube associated with the noise object
to exclude the noise through a (iterative) application of the Chauvenet
rejection criterion (i.e., mask out all points outside of +/- N sigma of zero).
Parameters
----------
niter : number
Number of iterations used in estimating the separate
components of the spatial and spectral noise variations.
Default=1
"""
# This needs reworking but is close.
for count in range(niter):
if self.spatial_norm is not None:
noise = self.scale_cube
snr = self.cube.filled_data[:].value / noise
else:
snr = self.cube.filled_data[:].value / self.scale
# Include negatives in the signal mask or not?
newmask = BooleanArrayMask(
np.abs(snr) < sig_n_outliers(self.cube.size), self.cube.wcs)
self.cube = self.cube.with_mask(newmask)
def test_spectralnorm():
cube = SpectralCube(data, wcs.WCS(h),
mask=BooleanArrayMask(mask, wcs=wcs.WCS(h)))
noiseobj = noise.Noise(cube)
noiseobj.estimate_noise()
expected = np.array([ 1.14898322, 0.71345272, 1.21989125])
assert np.allclose(noiseobj.spectral_norm, expected)
def to_object(self, data_or_subset, attribute=None):
"""
Convert a glue Data object to a SpectralCube object.
Parameters
----------
data_or_subset : `glue.core.data.Data` or `glue.core.subset.Subset`
The data to convert to a SpectralCube object
attribute : `glue.core.component_id.ComponentID`
The attribute to use for the SpectralCube data
"""
if data_or_subset.ndim > 0 and data_or_subset.ndim != 3:
raise ValueError(
'Data object should have 3 dimensions in order to '
'be converted to a SpectralCube object.')
if isinstance(data_or_subset, Subset):
data = data_or_subset.data
subset_state = data_or_subset.subset_state
else:
data = data_or_subset
subset_state = None
if isinstance(data.coords, BaseLowLevelWCS):
wcs = data.coords
else:
raise TypeError(
'data.coords should be an instance of BaseLowLevelWCS.')
if isinstance(attribute, str):
attribute = data.id[attribute]
elif len(data.main_components) == 0:
raise ValueError('Data object has no attributes.')
elif attribute is None:
if len(data.main_components) == 1:
attribute = data.main_components[0]
else:
raise ValueError(
"Data object has more than one attribute, so "
"you will need to specify which one to use as "
"the flux for the spectral cube using the "
"attribute= keyword argument.")
component = data.get_component(attribute)
values = data.get_data(attribute)
if subset_state is None:
mask = None
else:
mask = data.get_mask(subset_state=subset_state)
values = values.copy()
values[~mask] = np.nan
mask = BooleanArrayMask(mask, wcs=wcs)
values = values * u.Unit(component.units)
return SpectralCube(values, mask=mask, wcs=wcs)
def data_to_cube(self):
"""Glue Data -> SpectralCube"""
if self.component_id is None:
raise Exception("component_id was not provided.")
wcs = self.get_glue_wcs()
data_array = self.data[self.component_id]
mask = BooleanArrayMask(mask=self.get_glue_mask(), wcs=wcs)
return SpectralCube(data=data_array, wcs=wcs, mask=mask)
def test_spatialnorm():
cube = SpectralCube(data, wcs.WCS(h),
mask=BooleanArrayMask(mask, wcs=wcs.WCS(h)))
noiseobj = noise.Noise(cube)
noiseobj.estimate_noise()
print noiseobj.spatial_norm
expected = np.array([[ 0.04430196, 0.78314449, 0.07475047, 0.5494684 , 0.05790756],
[ 0.32931213, 0.76450342, 1.33944507, 1.06416389, 0.27999452],
[ 0.65174339, 0.24128143, 0.27692018, 0.0244925 , 0.11167775],
[ 0.60682872, 0.42536813, 0.20018275, 0.78523107, 0.95516435]])
assert np.allclose(noiseobj.spatial_norm, expected)
def smooth_mask(mask, wcs, diam):
mask_np = np.asarray( mask.include() * 1 )
structure = np.ones((diam, diam, diam))
dist = ((np.indices((diam, diam)) - (diam - 1) / 2.)**2).sum(axis=0)**0.5
structure[dist > diam / 2.] = 0
mask_dilation = ndimage.binary_dilation(mask_np, structure=structure)
mask_erosion = ndimage.binary_erosion(mask_np, structure=structure)
mask_dilation_speccube = BooleanArrayMask(mask=mask_erosion, wcs=wcs)
return mask_dilation_speccube
def data_to_cube(self, to_qcube=False):
"""
smooth_cube: Glue Data -> SpectralCube
multi_threading_smooth: Glue Data -> QSpectralCube
"""
if self.component_id is None:
raise Exception("component_id was not provided.")
wcs = self.get_glue_wcs()
data_array = self.data[self.component_id]
mask = BooleanArrayMask(mask=self.get_glue_mask(), wcs=wcs)
if to_qcube:
return QSpectralCube(data=data_array, wcs=wcs, mask=mask)
else:
return SpectralCube(data=data_array, wcs=wcs, mask=mask)
def _compose_cube(self):
"""
Create a :class:`~spectral_cube.SpectralCube` from a Glue data
component.
"""
if issubclass(self.data.__class__, Subset):
wcs = self.data.data.coords.wcs
data = self.data.data
mask = self.data.to_mask()
else:
wcs = self.data.coords.wcs
data = self.data
mask = np.ones(data.shape).astype(bool)
mask = BooleanArrayMask(mask=mask, wcs=wcs)
return SpectralCube(data[self.component_id], wcs=wcs, mask=mask)
def measure_dendrogram_properties(dend=None, cube303=cube303,
cube321=cube321, cube13co=cube13co,
cube18co=cube18co, noise_cube=noise_cube,
sncube=sncube,
suffix="",
last_index=None,
plot_some=True,
line='303',
write=True):
assert (cube321.shape == cube303.shape == noise_cube.shape ==
cube13co.shape == cube18co.shape == sncube.shape)
assert sncube.wcs is cube303.wcs is sncube.mask._wcs
metadata = {}
metadata['data_unit'] = u.K
metadata['spatial_scale'] = 7.2 * u.arcsec
metadata['beam_major'] = 30 * u.arcsec
metadata['beam_minor'] = 30 * u.arcsec
metadata['wavelength'] = 218.22219*u.GHz
metadata['velocity_scale'] = u.km/u.s
metadata['wcs'] = cube303.wcs
keys = [
'density_chi2',
'expected_density',
'dmin1sig_chi2',
'dmax1sig_chi2',
'column_chi2',
'expected_column',
'cmin1sig_chi2',
'cmax1sig_chi2',
'temperature_chi2',
'expected_temperature',
'tmin1sig_chi2',
'tmax1sig_chi2',
'eratio321303',
'ratio321303',
'logh2column',
'elogh2column',
'logabundance',
'elogabundance',
]
obs_keys = [
'Stot303',
'Smin303',
'Smax303',
'Stot321',
'Smean303',
'Smean321',
'npix',
'e303',
'e321',
'r321303',
'er321303',
'13cosum',
'c18osum',
'13comean',
'c18omean',
's_ntotal',
'index',
'is_leaf',
'parent',
'root',
'lon',
'lat',
'vcen',
'higaldusttem',
'reff',
'dustmass',
'dustmindens',
'bad',
#'tkin_turb',
]
columns = {k:[] for k in (keys+obs_keys)}
log.debug("Initializing dendrogram temperature fitting loop")
# FORCE wcs to match
# (technically should reproject here)
cube13co._wcs = cube18co._wcs = cube303.wcs
cube13co.mask._wcs = cube18co.mask._wcs = cube303.wcs
if line == '303':
maincube = cube303
elif line == '321':
maincube = cube321
else:
raise ValueError("Unrecognized line: {0}".format(line))
# Prepare an array to hold the fitted temperatures
tcubedata = np.empty(maincube.shape, dtype='float32')
tcubedata[:] = np.nan
tcubeleafdata = np.empty(maincube.shape, dtype='float32')
tcubeleafdata[:] = np.nan
nbad = 0
catalog = ppv_catalog(dend, metadata)
pb = ProgressBar(len(catalog))
for ii,row in enumerate(catalog):
structure = dend[row['_idx']]
assert structure.idx == row['_idx'] == ii
dend_obj_mask = BooleanArrayMask(structure.get_mask(), wcs=cube303.wcs)
dend_inds = structure.indices()
view = (slice(dend_inds[0].min(), dend_inds[0].max()+1),
slice(dend_inds[1].min(), dend_inds[1].max()+1),
slice(dend_inds[2].min(), dend_inds[2].max()+1),)
#view2 = cube303.subcube_slices_from_mask(dend_obj_mask)
submask = dend_obj_mask[view]
#assert np.count_nonzero(submask.include()) == np.count_nonzero(dend_obj_mask.include())
sn = sncube[view].with_mask(submask)
sntot = sn.sum().value
#np.testing.assert_almost_equal(sntot, structure.values().sum(), decimal=0)
c303 = cube303[view].with_mask(submask)
c321 = cube321[view].with_mask(submask)
co13sum = cube13co[view].with_mask(submask).sum().value
co18sum = cube18co[view].with_mask(submask).sum().value
if hasattr(co13sum,'__len__'):
raise TypeError(".sum() applied to an array has yielded a non scalar.")
npix = submask.include().sum()
assert npix == structure.get_npix()
Stot303 = c303.sum().value
if np.isnan(Stot303):
raise ValueError("NaN in cube. This can't happen: the data from "
"which the dendrogram was derived can't have "
"NaN pixels.")
Smax303 = c303.max().value
Smin303 = c303.min().value
Stot321 = c321.sum().value
if npix == 0:
raise ValueError("npix=0. This is impossible.")
Smean303 = Stot303/npix
if Stot303 <= 0 and line=='303':
raise ValueError("The 303 flux is <=0. This isn't possible because "
"the dendrogram was derived from the 303 data with a "
"non-zero threshold.")
elif Stot303 <= 0 and line=='321':
Stot303 = 0
Smean303 = 0
elif Stot321 <= 0 and line=='321':
raise ValueError("The 321 flux is <=0. This isn't possible because "
"the dendrogram was derived from the 321 data with a "
"non-zero threshold.")
if np.isnan(Stot321):
raise ValueError("NaN in 321 line")
Smean321 = Stot321/npix
#error = (noise_cube[view][submask.include()]).sum() / submask.include().sum()**0.5
var = ((noise_cube[dend_obj_mask.include()]**2).sum() / npix**2)
error = var**0.5
if np.isnan(error):
raise ValueError("error is nan: this is impossible by definition.")
if line == '321' and Stot303 == 0:
r321303 = np.nan
er321303 = np.nan
elif Stot321 < 0:
r321303 = error / Smean303
er321303 = (r321303**2 * (var/Smean303**2 + 1))**0.5
else:
r321303 = Stot321 / Stot303
er321303 = (r321303**2 * (var/Smean303**2 + var/Smean321**2))**0.5
for c in columns:
assert len(columns[c]) == ii
columns['index'].append(row['_idx'])
columns['s_ntotal'].append(sntot)
columns['Stot303'].append(Stot303)
columns['Smax303'].append(Smax303)
columns['Smin303'].append(Smin303)
columns['Stot321'].append(Stot321)
columns['Smean303'].append(Smean303)
columns['Smean321'].append(Smean321)
columns['npix'].append(npix)
columns['e303'].append(error)
columns['e321'].append(error)
columns['r321303'].append(r321303)
columns['er321303'].append(er321303)
columns['13cosum'].append(co13sum)
columns['c18osum'].append(co18sum)
columns['13comean'].append(co13sum/npix)
columns['c18omean'].append(co18sum/npix)
columns['is_leaf'].append(structure.is_leaf)
columns['parent'].append(structure.parent.idx if structure.parent else -1)
columns['root'].append(get_root(structure).idx)
s_main = maincube._data[dend_inds]
x,y,z = maincube.world[dend_inds]
lon = ((z.value-(360*(z.value>180)))*s_main).sum()/s_main.sum()
lat = (y*s_main).sum()/s_main.sum()
vel = (x*s_main).sum()/s_main.sum()
columns['lon'].append(lon)
columns['lat'].append(lat.value)
columns['vcen'].append(vel.value)
mask2d = dend_obj_mask.include().max(axis=0)[view[1:]]
logh2column = np.log10(np.nanmean(column_regridded.data[view[1:]][mask2d]) * 1e22)
if np.isnan(logh2column):
log.info("Source #{0} has NaNs".format(ii))
logh2column = 24
elogh2column = elogabundance
columns['higaldusttem'].append(np.nanmean(dusttem_regridded.data[view[1:]][mask2d]))
r_arcsec = row['radius']*u.arcsec
reff = (r_arcsec*(8.5*u.kpc)).to(u.pc, u.dimensionless_angles())
mass = ((10**logh2column*u.cm**-2)*np.pi*reff**2*2.8*constants.m_p).to(u.M_sun)
density = (mass/(4/3.*np.pi*reff**3)/constants.m_p/2.8).to(u.cm**-3)
columns['reff'].append(reff.value)
columns['dustmass'].append(mass.value)
columns['dustmindens'].append(density.value)
mindens = np.log10(density.value)
if mindens < 3:
mindens = 3
if (r321303 < 0 or np.isnan(r321303)) and line != '321':
raise ValueError("Ratio <0: This can't happen any more because "
"if either num/denom is <0, an exception is "
"raised earlier")
#for k in columns:
# if k not in obs_keys:
# columns[k].append(np.nan)
elif (r321303 < 0 or np.isnan(r321303)) and line == '321':
for k in keys:
columns[k].append(np.nan)
else:
# Replace negatives for fitting
if Smean321 <= 0:
Smean321 = error
mf.set_constraints(ratio321303=r321303, eratio321303=er321303,
#ratio321322=ratio2, eratio321322=eratio2,
logh2column=logh2column, elogh2column=elogh2column,
logabundance=logabundance, elogabundance=elogabundance,
taline303=Smean303, etaline303=error,
taline321=Smean321, etaline321=error,
mindens=mindens,
linewidth=10)
row_data = mf.get_parconstraints()
row_data['ratio321303'] = r321303
row_data['eratio321303'] = er321303
for k in row_data:
columns[k].append(row_data[k])
# Exclude bad velocities from cubes
if row['v_cen'] < -80e3 or row['v_cen'] > 180e3:
# Skip: there is no real structure down here
nbad += 1
is_bad = True
else:
is_bad = False
tcubedata[dend_obj_mask.include()] = row_data['expected_temperature']
if structure.is_leaf:
tcubeleafdata[dend_obj_mask.include()] = row_data['expected_temperature']
columns['bad'].append(is_bad)
width = row['v_rms']*u.km/u.s
lengthscale = reff
#REMOVED in favor of despotic version done in dendrograms.py
# we use the analytic version here; the despotic version is
# computed elsewhere (with appropriate gcor factors)
#columns['tkin_turb'].append(heating.tkin_all(10**row_data['density_chi2']*u.cm**-3,
# width,
# lengthscale,
# width/lengthscale,
# columns['higaldusttem'][-1]*u.K,
# crir=0./u.s))
if len(set(len(c) for k,c in columns.items())) != 1:
print("Columns are different lengths. This is not allowed.")
import ipdb; ipdb.set_trace()
for c in columns:
assert len(columns[c]) == ii+1
if plot_some and not is_bad and (ii-nbad % 100 == 0 or ii-nbad < 50):
try:
log.info("T: [{tmin1sig_chi2:7.2f},{expected_temperature:7.2f},{tmax1sig_chi2:7.2f}]"
" R={ratio321303:8.4f}+/-{eratio321303:8.4f}"
" Smean303={Smean303:8.4f} +/- {e303:8.4f}"
" Stot303={Stot303:8.2e} npix={npix:6d}"
.format(Smean303=Smean303, Stot303=Stot303,
npix=npix, e303=error, **row_data))
pl.figure(1)
pl.clf()
mf.denstemplot()
pl.savefig(fpath("dendrotem/diagnostics/{0}_{1}.png".format(suffix,ii)))
pl.figure(2).clf()
mf.parplot1d_all(levels=[0.68268949213708585])
pl.savefig(fpath("dendrotem/diagnostics/1dplot{0}_{1}.png".format(suffix,ii)))
pl.draw()
pl.show()
except Exception as ex:
print ex
pass
else:
pb.update(ii+1)
if last_index is not None and ii >= last_index:
break
if last_index is not None:
catalog = catalog[:last_index+1]
for k in columns:
if k not in catalog.keys():
catalog.add_column(table.Column(name=k, data=columns[k]))
for mid,lo,hi,letter in (('expected_temperature','tmin1sig_chi2','tmax1sig_chi2','t'),
('expected_density','dmin1sig_chi2','dmax1sig_chi2','d'),
('expected_column','cmin1sig_chi2','cmax1sig_chi2','c')):
catalog.add_column(table.Column(name='elo_'+letter,
data=catalog[mid]-catalog[lo]))
catalog.add_column(table.Column(name='ehi_'+letter,
data=catalog[hi]-catalog[mid]))
if write:
catalog.write(tpath('PPV_H2CO_Temperature{0}.ipac'.format(suffix)), format='ascii.ipac')
# Note that there are overlaps in the catalog, which means that ORDER MATTERS
# in the above loop. I haven't yet checked whether large scale overwrites
# small or vice-versa; it may be that both views of the data are interesting.
tcube = SpectralCube(data=tcubedata, wcs=cube303.wcs,
mask=cube303.mask, meta={'unit':'K'},
header=cube303.header,
)
tcubeleaf = SpectralCube(data=tcubeleafdata, wcs=cube303.wcs,
mask=cube303.mask, meta={'unit':'K'},
header=cube303.header,
)
if write:
log.info("Writing TemperatureCube")
outpath = 'TemperatureCube_DendrogramObjects{0}.fits'
tcube.write(hpath(outpath.format(suffix)),
overwrite=True)
outpath_leaf = 'TemperatureCube_DendrogramObjects{0}_leaves.fits'
tcubeleaf.write(hpath(outpath_leaf.format(suffix)),
overwrite=True)
return catalog, tcube
cube = SpectralCube.read(hpath(ftemplate.format(smooth)))
if weight:
wcube = (SpectralCube.read(
hpath('APEX_H2CO_303_202_smooth_bl.fits'))
if 'smooth' in smooth else SpectralCube.read(
hpath('APEX_H2CO_303_202_bl.fits')))
mcube = (SpectralCube.read(
hpath('APEX_H2CO_303_202_smooth_bl_mask.fits'))
if 'smooth' in smooth else SpectralCube.read(
hpath('APEX_H2CO_303_202_bl_mask.fits')))
if cube.shape != wcube.shape:
log.info("Not weighting {0}".format(fn))
continue
weighted = copy.copy(cube)
weighted._data = wcube._data * cube._data
mask = (wcube.mask & cube.mask & BooleanArrayMask(
mcube._data == 1, mcube.wcs))
weighted = weighted.with_mask(mask)
wcube = wcube.with_mask(mask)
pv1 = weighted.sum(axis=1)
pv2 = wcube.sum(axis=1)
pv = pv1 / pv2
# Hack to prevent unmatched celestial axis error
pv1.wcs.wcs.ctype[0] = 'OFFSET'
pv2.wcs.wcs.ctype[0] = 'OFFSET'
hdu = copy.copy(pv1.hdu)
hdu.data = pv.value
else:
tproj = cube.mean(axis=1)
tproj.wcs.wcs.ctype[0] = 'OFFSET'
hdu = tproj.hdu
width = 7 * u.MHz
print(f'Per-pixel mask width: {width}')
#shellcube=sc(data=shell,wcs=wcs).with_spectral_axis('Hz')
print('Begin masking procedure')
for y in range(spatdims[0]):
print(f'Start Row {y}')
for x in range(spatdims[1]):
dopplershift = mom1[y, x]
#print(f'dopplershift from reference: {dopplershift}')
v_shifted = v0 + dopplershift
z_shifted = v_shifted / c
freq_shifted = restfreq / (1 + z_shifted)
#print(f'shifted frequency: {freq_shifted}')
tempmask = np.logical_and(spectraxis < (freq_shifted + width),
spectraxis > (freq_shifted - width))
shell[:, y, x] = tempmask
shellmaskcube = BooleanArrayMask(mask=shell, wcs=wcs)
maskedcube = datacube.with_mask(shellmaskcube)
print('Saving')
datacube.write('unmaskedspw08-7slab.fits', overwrite=True)
maskedcube.write(f'{width.value}mhzmasked_spw08-7slab.fits', overwrite=True)
shellmaskcube.write(f'{width.value}mhz8-7mask.fits', overwrite=True)
print('Done.')
#maskedcube.to_ds9('7f000001:41898')
#condition=restfreq+(0.77*u.km/u.s)#and minus
# dendro_temperature but *much* faster
for sm,cubeA,cubeB,objects in zip(("","_smooth",),
(cube303,cube303sm,),
(cube321,cube321sm,),
(dend,dendsm,),):
# reset data
tcubedata = np.empty(cubeA.shape)
tcubedata[:] = np.nan
rcubedata = np.empty(cubeA.shape)
rcubedata[:] = np.nan
pb = ProgressBar(len(objects))
for ii,structure in enumerate(objects):
dend_obj_mask = BooleanArrayMask(structure.get_mask(), wcs=cubeA.wcs)
view = cubeA.subcube_slices_from_mask(dend_obj_mask)
submask = dend_obj_mask[view]
assert submask.include().sum() == dend_obj_mask.include().sum()
c303 = cubeA[view].with_mask(submask)
c321 = cubeB[view].with_mask(submask)
npix = submask.include().sum()
Stot303 = c303.sum().value
Stot321 = c321.sum().value
if npix == 0:
raise ValueError("npix=0. This is impossible.")
Smean303 = Stot303/npix
Smean321 = Stot321/npix
try:
logabundance=logabundance,
elogabundance=elogabundance,
taline303=ta303.value,
etaline303=err,
taline321=ta321.value,
etaline321=err,
linewidth=linewidth)
row_data = mf.get_parconstraints()
tcube[z, y, x] = row_data['temperature_chi2']
row_data['ratio303321'] = rat
row_data['eratio303321'] = erat
if ii % 100 == 0 or ii < 50:
log.info(
"T: [{tmin1sig_chi2:7.2f},{temperature_chi2:7.2f},{tmax1sig_chi2:7.2f}] R={ratio303321:6.2f}+/-{eratio303321:6.2f}"
.format(**row_data))
else:
pb.update(ii)
tcube.flush()
else:
pb.update(ii)
tcube[tcube == 0] = np.nan
tCube = SpectralCube(tcube,
cube303.wcs,
mask=BooleanArrayMask(np.isfinite(tcube),
wcs=cube303.wcs))
tCube.write(hpath('chi2_temperature_cube.fits'), overwrite=True)
print()
def test_scalegen():
cube = SpectralCube(data, wcs.WCS(h),
mask=BooleanArrayMask(mask, wcs=wcs.WCS(h)))
noiseobj = noise.Noise(cube)
assert np.isclose(noiseobj.scale, 1.1382529312849043)
from pvextractor import extract_pv_slice
from pvextractor.geometry import Path
from spectral_cube import SpectralCube, BooleanArrayMask
from astropy.io import fits
from astropy.wcs import WCS
import numpy as np
import matplotlib.pyplot as p
# Start with the full COMPLETE Perseus cube
# Missing end card in the header, so we have to manually open it
hdu = fits.open(
"/srv/astro/erickoch/enzo_sims/complete/PerA_13coFCRAO_F_xyv.fits",
ignore_missing_end=True)
mask = BooleanArrayMask(mask=np.isfinite(hdu[0].data), wcs=WCS(hdu[0].header))
sc = SpectralCube(data=hdu[0].data, wcs=WCS(hdu[0].header), mask=mask)
# Select the region of interest
sc_small = sc[120:300, 156:346, 677:958]
# Ends for the PV slice
# ends = [(107, 74), (151, 76), (220, 54)]
ends = [(105, 70), (145, 80), (227, 28), (238, 1)]
xy = Path(ends, width=10)
pv = extract_pv_slice(sc_small, xy)
p.subplot(121)
return mask_array
cube303 = SpectralCube.read(
hpath('APEX_H2CO_303_202_bl.fits')).with_spectral_unit(
u.km / u.s, velocity_convention='radio')
cube321 = SpectralCube.read(
hpath('APEX_H2CO_321_220_bl.fits')).with_spectral_unit(
u.km / u.s, velocity_convention='radio')
maskarr = mask_out_region(
fits.getdata(hpath('APEX_H2CO_303_202_bl_mask.fits')).astype('bool'),
cube303)
mask = (maskarr & cube303.mask.include(cube303, cube303.wcs)
& cube321.mask.include(cube321, cube321.wcs))
bmask = BooleanArrayMask(mask, cube303.wcs)
cube303m = cube303.with_mask(bmask)
cube321m = cube321.with_mask(bmask)
cube303sm = SpectralCube.read(
hpath('APEX_H2CO_303_202_smooth_bl.fits')).with_spectral_unit(
u.km / u.s, velocity_convention='radio')
cube321sm = SpectralCube.read(
hpath('APEX_H2CO_321_220_smooth_bl.fits')).with_spectral_unit(
u.km / u.s, velocity_convention='radio')
smmaskarr = mask_out_region(
fits.getdata(
hpath('APEX_H2CO_303_202_smooth_bl_mask.fits')).astype('bool'),
cube303sm)
masksm = (smmaskarr & cube303sm.mask.include(cube303sm, cube303sm.wcs)
& cube321sm.mask.include(cube321sm, cube321sm.wcs))
# ---------------- #
# Build the masks. #
# ---------------- #
# The masked data and their corresponding masks are returned as dictionaries.
# Mask where NaNs are TRUE
trueMask = np.ma.masked_where(data==0,data)
# Mask where NaNs are FALSE
falseMask = np.ma.masked_where(data!=0,data)
# Mask the profile range. NaNs are TRUE
profileMask = maskProfile(data=trueMask,xarr=vels,
minProfIdx=minProfIdx,maxProfIdx=maxProfIdx)
# Build mask with a WCS that is compatible with SpectralCube.
# NaNs are FALSE.
cubeMask = BooleanArrayMask(np.ma.getmask(falseMask),pacsWcs)
if nFluxes > 350:
mid = 200
else:
mid = 100
# Make an array of coordinates for the valid spaxels
validPixels = findValidPixels((nCols,nRows),np.ma.getmask(trueMask[mid,:,:]))
# --------------------------------------------------- #
# Find the (col,row) min/max limits of valid spaxels. #
# Min/max will be used to crop the data and the WCS. #
# --------------------------------------------------- #
def load_casa_image(filename,
skipdata=False,
skipvalid=False,
skipcs=False,
**kwargs):
"""
Load a cube (into memory?) from a CASA image. By default it will transpose
the cube into a 'python' order and drop degenerate axes. These options can
be suppressed. The object holds the coordsys object from the image in
memory.
"""
try:
from taskinit import ia
except ImportError:
raise ImportError(
"Could not import CASA (casac) and therefore cannot read CASA .image files"
)
# use the ia tool to get the file contents
ia.open(filename)
# read in the data
if not skipdata:
data = ia.getchunk()
# CASA stores validity of data as a mask
if not skipvalid:
valid = ia.getchunk(getmask=True)
# transpose is dealt with within the cube object
# read in coordinate system object
casa_cs = ia.coordsys()
wcs = wcs_casa2astropy(casa_cs)
# don't need this yet
# stokes = get_casa_axis(temp_cs, wanttype="Stokes", skipdeg=False,)
# if stokes == None:
# order = np.arange(self.data.ndim)
# else:
# order = []
# for ax in np.arange(self.data.ndim+1):
# if ax == stokes:
# continue
# order.append(ax)
# self.casa_cs = ia.coordsys(order)
# This should work, but coordsys.reorder() has a bug
# on the error checking. JIRA filed. Until then the
# axes will be reversed from the original.
# if transpose == True:
# new_order = np.arange(self.data.ndim)
# new_order = new_order[-1*np.arange(self.data.ndim)-1]
# print new_order
# self.casa_cs.reorder(new_order)
# close the ia tool
ia.close()
meta = {'filename': filename}
mask = BooleanArrayMask(wcs, np.logical_not(valid))
cube = SpectralCube(data, wcs, mask, meta=meta)
return cube
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,
strides=(0, ) + noisefinite.strides), cube303.wcs))
cube303nmsm = cube303sm.with_mask(
BooleanArrayMask(
as_strided(sm_noisefinite,
shape=cube303sm.shape,
strides=(0, ) + sm_noisefinite.strides), cube303sm.wcs))
cube321nm = cube321.with_mask(
BooleanArrayMask(
as_strided(noisefinite,
shape=cube321.shape,
strides=(0, ) + noisefinite.strides), cube321.wcs))
cube321nmsm = cube321sm.with_mask(
BooleanArrayMask(
as_strided(sm_noisefinite,
im2 = fits.getdata(hpath('H2CO_321220_to_303202_bl_integ_temperature_dens1e4.fits'))
im3 = fits.getdata(hpath('H2CO_321220_to_303202_bl_integ_weighted_temperature_dens1e4_masked.fits'))
tcube = tcube_direct
tcube_mean = tcube.mean(axis=0)
wcube = SpectralCube.read(hpath('APEX_H2CO_303_202_bl.fits'))
mcube = SpectralCube.read(hpath('APEX_H2CO_303_202_bl_mask.fits'))
if tcube.shape != wcube.shape:
raise
weighted = copy.copy(tcube)
weighted._data = wcube._data * tcube._data
mask = (wcube.mask & tcube.mask &
BooleanArrayMask(weighted.filled_data[...] != 0, weighted.wcs) &
BooleanArrayMask(wcube.filled_data[...] != 0, wcube.wcs) &
BooleanArrayMask(mcube._data==1, mcube.wcs)
)
weighted = weighted.with_mask(mask)
wcube = wcube.with_mask(mask)
tcube_wmean = tcube.mean(axis=0)
pl.figure(14, figsize=(12,20)).clf()
pl.subplot(5,1,1).imshow(im1)
pl.subplot(5,1,2).imshow(im2)
pl.subplot(5,1,3).imshow(im3)
pl.subplot(5,1,4).imshow(tcube_mean.value)
pl.subplot(5,1,5).imshow(tcube_wmean.value)
# .with_spectral_unit(u.km/u.s,
# velocity_convention='radio',
# rest_value=restfreqs[mol])
# for mol in molecules}
cubes = {
mol: SpectralCube.read(
'feathered/Feathered_{0}.fits'.format(mol)).with_spectral_unit(
u.km / u.s, velocity_convention='radio',
rest_value=restfreqs[mol]).spectral_slab(-20 * u.km / u.s,
137 * u.km / u.s)
for mol in molecules
}
hc3nmean = cubes['HC3N'].mean(axis=(1, 2))
bad = np.abs(hc3nmean) > 1
mhc3n = cubes['HC3N'].with_mask(
BooleanArrayMask(~bad[:, None, None], cubes['HC3N'].wcs))
cubes['HC3N'] = mhc3n
for cube in cubes.values():
cube._data -= cube.mean(axis=0).value
ytcubes = {mol: cubes[mol][::2, ::2, ::2].to_yt() for mol in molecules}
for ytc in ytcubes.values():
ytc.dataset.periodicity = (True, ) * 3
try:
os.mkdir("IsoSurfs")
except:
pass
#for ii,(level,transparency) in enumerate(zip((2, 3, 4), (0.1,0.3,0.6))):
molecules = ('HNC','HC3N','HCOp','HCN',)
molecules = ('HC3N',)
colors = {'HNC': red,
'HCN': green,
'HC3N': brown,
'HCOp': blue,
}
cubes = {mol: SpectralCube.read('Feathered_{0}.fits'.format(mol))
.with_spectral_unit(u.km/u.s,
velocity_convention='radio')
for mol in molecules}
hc3nmean = cubes['HC3N'].mean(axis=(1,2))
bad = np.abs(hc3nmean)>1
mh3cn = cubes['HC3N'].with_mask(BooleanArrayMask(bad[:,None,None],
cubes['HC3N'].wcs))
cubes['HC3N'] = mh3cn
ytcubes = {mol: cubes[mol].to_yt() for mol in molecules}
try:
os.mkdir("ChemMovie")
except:
pass
def ramp(vals, minval, maxval):
v = (vals)**0.25
return (v - v.min())/(v.max() - v.min())
nframes = 60
size = 512
print(fn, cube)
m0 = cube.moment0(axis=0)
max = cube.max(axis=0)
m0.hdu.writeto('moment0/{0}_moment0.fits'.format(pfx), clobber=True)
max.hdu.writeto('max/{0}_max.fits'.format(pfx), clobber=True)
std = cube.std(axis=0)
mask = cube > std
mcube = cube.with_mask(mask)
m0mask = mcube.moment0(axis=0)
m1mask = mcube.moment1(axis=0)
m0mask.hdu.writeto('moment0/{0}_moment0_mask.fits'.format(pfx),
clobber=True)
m1mask.hdu.writeto('moment1/{0}_moment1_mask.fits'.format(pfx),
clobber=True)
if False:
rendermask_arr = std.value < np.percentile(std.ravel(), 75)
rendermask = BooleanArrayMask(rendermask_arr, mcube.wcs, mcube.shape)
rcube = mcube.with_mask(rendermask).minimal_subcube()
print("Minimal rendering cube: ", fn, rcube)
ytc = rcube.to_yt()
ytc.quick_isocontour(
level=3 * np.nanmean(std.value),
title='Sgr B2 12mTC {0}'.format(pfx),
description='ALMA observations of Sgr B2. File {0}'.format(pfx),
filename=pfx + ".ply")
del mask
del cube
def to_object(self, data_or_subset, attribute=None):
"""
Convert a glue Data object to a SpectralCube object.
Parameters
----------
data_or_subset : `glue.core.data.Data` or `glue.core.subset.Subset`
The data to convert to a SpectralCube object
attribute : `glue.core.component_id.ComponentID`
The attribute to use for the SpectralCube data
"""
if data_or_subset.ndim > 0 and data_or_subset.ndim != 3 and data_or_subset.ndim != 4:
raise ValueError('Data object should have 3 or 4 dimensions in order to '
'be converted to a SpectralCube object.')
if isinstance(data_or_subset, Subset):
data = data_or_subset.data
subset_state = data_or_subset.subset_state
else:
data = data_or_subset
subset_state = None
if isinstance(data.coords, BaseLowLevelWCS):
wcs = data.coords
else:
raise TypeError('data.coords should be an instance of BaseLowLevelWCS.')
if isinstance(attribute, str):
attribute = data.id[attribute]
elif len(data.main_components) == 0:
raise ValueError('Data object has no attributes.')
elif attribute is None:
if len(data.main_components) == 1:
attribute = data.main_components[0]
else:
raise ValueError("Data object has more than one attribute, so "
"you will need to specify which one to use as "
"the flux for the spectral cube using the "
"attribute= keyword argument.")
component = data.get_component(attribute)
values = data.get_data(attribute)
if subset_state is None:
mask = None
else:
mask = data.get_mask(subset_state=subset_state)
values = values.copy()
mask = BooleanArrayMask(mask, wcs=wcs)
values = values * u.Unit(component.units)
# Drop Stokes axis if there is one for FITS WCS
if isinstance(wcs, WCS) and wcs.sub([WCSSUB_STOKES]).naxis > 0:
types = [axistype['coordinate_type'] for axistype in wcs.get_axis_types()]
# For now we only ever get the first stokes element. We could in
# principle allow this to be customized, or return a StokesSpectralCube
slc = tuple([0 if tp == 'stokes' else slice(None) for tp in types])
subkeep = tuple([index + 1 for index, tp in enumerate(types) if tp != 'stokes'])
values = values[slc[::-1]]
wcs = wcs.sub(subkeep)
return SpectralCube(values, mask=mask, wcs=wcs, meta=data.meta)
),
(
dend,
dendsm,
),
):
# reset data
tcubedata = np.empty(cubeA.shape)
tcubedata[:] = np.nan
rcubedata = np.empty(cubeA.shape)
rcubedata[:] = np.nan
pb = ProgressBar(len(objects))
for ii, structure in enumerate(objects):
dend_obj_mask = BooleanArrayMask(structure.get_mask(), wcs=cubeA.wcs)
view = cubeA.subcube_slices_from_mask(dend_obj_mask)
submask = dend_obj_mask[view]
assert submask.include().sum() == dend_obj_mask.include().sum()
c303 = cubeA[view].with_mask(submask)
c321 = cubeB[view].with_mask(submask)
npix = submask.include().sum()
Stot303 = c303.sum().value
Stot321 = c321.sum().value
if npix == 0:
raise ValueError("npix=0. This is impossible.")
Smean303 = Stot303 / npix
Smean321 = Stot321 / npix
try:
def measure_dendrogram_properties(dend=None,
cube303=cube303,
cube321=cube321,
cube13co=cube13co,
cube18co=cube18co,
noise_cube=noise_cube,
sncube=sncube,
suffix="",
last_index=None,
plot_some=True,
line='303',
write=True):
assert (cube321.shape == cube303.shape == noise_cube.shape ==
cube13co.shape == cube18co.shape == sncube.shape)
assert sncube.wcs is cube303.wcs is sncube.mask._wcs
metadata = {}
metadata['data_unit'] = u.K
metadata['spatial_scale'] = 7.2 * u.arcsec
metadata['beam_major'] = 30 * u.arcsec
metadata['beam_minor'] = 30 * u.arcsec
metadata['wavelength'] = 218.22219 * u.GHz
metadata['velocity_scale'] = u.km / u.s
metadata['wcs'] = cube303.wcs
keys = [
'density_chi2',
'expected_density',
'dmin1sig_chi2',
'dmax1sig_chi2',
'column_chi2',
'expected_column',
'cmin1sig_chi2',
'cmax1sig_chi2',
'temperature_chi2',
'expected_temperature',
'tmin1sig_chi2',
'tmax1sig_chi2',
'eratio321303',
'ratio321303',
'logh2column',
'elogh2column',
'logabundance',
'elogabundance',
]
obs_keys = [
'Stot303',
'Smin303',
'Smax303',
'Stot321',
'Smean303',
'Smean321',
'npix',
'e303',
'e321',
'r321303',
'er321303',
'13cosum',
'c18osum',
'13comean',
'c18omean',
's_ntotal',
'index',
'is_leaf',
'parent',
'root',
'lon',
'lat',
'vcen',
'higaldusttem',
'reff',
'dustmass',
'dustmindens',
'bad',
#'tkin_turb',
]
columns = {k: [] for k in (keys + obs_keys)}
log.debug("Initializing dendrogram temperature fitting loop")
# FORCE wcs to match
# (technically should reproject here)
cube13co._wcs = cube18co._wcs = cube303.wcs
cube13co.mask._wcs = cube18co.mask._wcs = cube303.wcs
if line == '303':
maincube = cube303
elif line == '321':
maincube = cube321
else:
raise ValueError("Unrecognized line: {0}".format(line))
# Prepare an array to hold the fitted temperatures
tcubedata = np.empty(maincube.shape, dtype='float32')
tcubedata[:] = np.nan
tcubeleafdata = np.empty(maincube.shape, dtype='float32')
tcubeleafdata[:] = np.nan
nbad = 0
catalog = ppv_catalog(dend, metadata)
pb = ProgressBar(len(catalog))
for ii, row in enumerate(catalog):
structure = dend[row['_idx']]
assert structure.idx == row['_idx'] == ii
dend_obj_mask = BooleanArrayMask(structure.get_mask(), wcs=cube303.wcs)
dend_inds = structure.indices()
view = (
slice(dend_inds[0].min(), dend_inds[0].max() + 1),
slice(dend_inds[1].min(), dend_inds[1].max() + 1),
slice(dend_inds[2].min(), dend_inds[2].max() + 1),
)
#view2 = cube303.subcube_slices_from_mask(dend_obj_mask)
submask = dend_obj_mask[view]
#assert np.count_nonzero(submask.include()) == np.count_nonzero(dend_obj_mask.include())
sn = sncube[view].with_mask(submask)
sntot = sn.sum().value
#np.testing.assert_almost_equal(sntot, structure.values().sum(), decimal=0)
c303 = cube303[view].with_mask(submask)
c321 = cube321[view].with_mask(submask)
co13sum = cube13co[view].with_mask(submask).sum().value
co18sum = cube18co[view].with_mask(submask).sum().value
if hasattr(co13sum, '__len__'):
raise TypeError(
".sum() applied to an array has yielded a non scalar.")
npix = submask.include().sum()
assert npix == structure.get_npix()
Stot303 = c303.sum().value
if np.isnan(Stot303):
raise ValueError("NaN in cube. This can't happen: the data from "
"which the dendrogram was derived can't have "
"NaN pixels.")
Smax303 = c303.max().value
Smin303 = c303.min().value
Stot321 = c321.sum().value
if npix == 0:
raise ValueError("npix=0. This is impossible.")
Smean303 = Stot303 / npix
if Stot303 <= 0 and line == '303':
raise ValueError(
"The 303 flux is <=0. This isn't possible because "
"the dendrogram was derived from the 303 data with a "
"non-zero threshold.")
elif Stot303 <= 0 and line == '321':
Stot303 = 0
Smean303 = 0
elif Stot321 <= 0 and line == '321':
raise ValueError(
"The 321 flux is <=0. This isn't possible because "
"the dendrogram was derived from the 321 data with a "
"non-zero threshold.")
if np.isnan(Stot321):
raise ValueError("NaN in 321 line")
Smean321 = Stot321 / npix
#error = (noise_cube[view][submask.include()]).sum() / submask.include().sum()**0.5
var = ((noise_cube[dend_obj_mask.include()]**2).sum() / npix**2)
error = var**0.5
if np.isnan(error):
raise ValueError("error is nan: this is impossible by definition.")
if line == '321' and Stot303 == 0:
r321303 = np.nan
er321303 = np.nan
elif Stot321 < 0:
r321303 = error / Smean303
er321303 = (r321303**2 * (var / Smean303**2 + 1))**0.5
else:
r321303 = Stot321 / Stot303
er321303 = (r321303**2 *
(var / Smean303**2 + var / Smean321**2))**0.5
for c in columns:
assert len(columns[c]) == ii
columns['index'].append(row['_idx'])
columns['s_ntotal'].append(sntot)
columns['Stot303'].append(Stot303)
columns['Smax303'].append(Smax303)
columns['Smin303'].append(Smin303)
columns['Stot321'].append(Stot321)
columns['Smean303'].append(Smean303)
columns['Smean321'].append(Smean321)
columns['npix'].append(npix)
columns['e303'].append(error)
columns['e321'].append(error)
columns['r321303'].append(r321303)
columns['er321303'].append(er321303)
columns['13cosum'].append(co13sum)
columns['c18osum'].append(co18sum)
columns['13comean'].append(co13sum / npix)
columns['c18omean'].append(co18sum / npix)
columns['is_leaf'].append(structure.is_leaf)
columns['parent'].append(
structure.parent.idx if structure.parent else -1)
columns['root'].append(get_root(structure).idx)
s_main = maincube._data[dend_inds]
x, y, z = maincube.world[dend_inds]
lon = ((z.value - (360 *
(z.value > 180))) * s_main).sum() / s_main.sum()
lat = (y * s_main).sum() / s_main.sum()
vel = (x * s_main).sum() / s_main.sum()
columns['lon'].append(lon)
columns['lat'].append(lat.value)
columns['vcen'].append(vel.value)
mask2d = dend_obj_mask.include().max(axis=0)[view[1:]]
logh2column = np.log10(
np.nanmean(column_regridded.data[view[1:]][mask2d]) * 1e22)
if np.isnan(logh2column):
log.info("Source #{0} has NaNs".format(ii))
logh2column = 24
elogh2column = elogabundance
columns['higaldusttem'].append(
np.nanmean(dusttem_regridded.data[view[1:]][mask2d]))
r_arcsec = row['radius'] * u.arcsec
reff = (r_arcsec * (8.5 * u.kpc)).to(u.pc, u.dimensionless_angles())
mass = ((10**logh2column * u.cm**-2) * np.pi * reff**2 * 2.8 *
constants.m_p).to(u.M_sun)
density = (mass / (4 / 3. * np.pi * reff**3) / constants.m_p / 2.8).to(
u.cm**-3)
columns['reff'].append(reff.value)
columns['dustmass'].append(mass.value)
columns['dustmindens'].append(density.value)
mindens = np.log10(density.value)
if mindens < 3:
mindens = 3
if (r321303 < 0 or np.isnan(r321303)) and line != '321':
raise ValueError("Ratio <0: This can't happen any more because "
"if either num/denom is <0, an exception is "
"raised earlier")
#for k in columns:
# if k not in obs_keys:
# columns[k].append(np.nan)
elif (r321303 < 0 or np.isnan(r321303)) and line == '321':
for k in keys:
columns[k].append(np.nan)
else:
# Replace negatives for fitting
if Smean321 <= 0:
Smean321 = error
mf.set_constraints(
ratio321303=r321303,
eratio321303=er321303,
#ratio321322=ratio2, eratio321322=eratio2,
logh2column=logh2column,
elogh2column=elogh2column,
logabundance=logabundance,
elogabundance=elogabundance,
taline303=Smean303,
etaline303=error,
taline321=Smean321,
etaline321=error,
mindens=mindens,
linewidth=10)
row_data = mf.get_parconstraints()
row_data['ratio321303'] = r321303
row_data['eratio321303'] = er321303
for k in row_data:
columns[k].append(row_data[k])
# Exclude bad velocities from cubes
if row['v_cen'] < -80e3 or row['v_cen'] > 180e3:
# Skip: there is no real structure down here
nbad += 1
is_bad = True
else:
is_bad = False
tcubedata[
dend_obj_mask.include()] = row_data['expected_temperature']
if structure.is_leaf:
tcubeleafdata[dend_obj_mask.include(
)] = row_data['expected_temperature']
columns['bad'].append(is_bad)
width = row['v_rms'] * u.km / u.s
lengthscale = reff
#REMOVED in favor of despotic version done in dendrograms.py
# we use the analytic version here; the despotic version is
# computed elsewhere (with appropriate gcor factors)
#columns['tkin_turb'].append(heating.tkin_all(10**row_data['density_chi2']*u.cm**-3,
# width,
# lengthscale,
# width/lengthscale,
# columns['higaldusttem'][-1]*u.K,
# crir=0./u.s))
if len(set(len(c) for k, c in columns.items())) != 1:
print("Columns are different lengths. This is not allowed.")
import ipdb
ipdb.set_trace()
for c in columns:
assert len(columns[c]) == ii + 1
if plot_some and not is_bad and (ii - nbad % 100 == 0
or ii - nbad < 50):
try:
log.info(
"T: [{tmin1sig_chi2:7.2f},{expected_temperature:7.2f},{tmax1sig_chi2:7.2f}]"
" R={ratio321303:8.4f}+/-{eratio321303:8.4f}"
" Smean303={Smean303:8.4f} +/- {e303:8.4f}"
" Stot303={Stot303:8.2e} npix={npix:6d}".format(
Smean303=Smean303,
Stot303=Stot303,
npix=npix,
e303=error,
**row_data))
pl.figure(1)
pl.clf()
mf.denstemplot()
pl.savefig(
fpath("dendrotem/diagnostics/{0}_{1}.png".format(
suffix, ii)))
pl.figure(2).clf()
mf.parplot1d_all(levels=[0.68268949213708585])
pl.savefig(
fpath("dendrotem/diagnostics/1dplot{0}_{1}.png".format(
suffix, ii)))
pl.draw()
pl.show()
except Exception as ex:
print ex
pass
else:
pb.update(ii + 1)
if last_index is not None and ii >= last_index:
break
if last_index is not None:
catalog = catalog[:last_index + 1]
for k in columns:
if k not in catalog.keys():
catalog.add_column(table.Column(name=k, data=columns[k]))
for mid, lo, hi, letter in (('expected_temperature', 'tmin1sig_chi2',
'tmax1sig_chi2', 't'),
('expected_density', 'dmin1sig_chi2',
'dmax1sig_chi2', 'd'),
('expected_column', 'cmin1sig_chi2',
'cmax1sig_chi2', 'c')):
catalog.add_column(
table.Column(name='elo_' + letter,
data=catalog[mid] - catalog[lo]))
catalog.add_column(
table.Column(name='ehi_' + letter,
data=catalog[hi] - catalog[mid]))
if write:
catalog.write(tpath('PPV_H2CO_Temperature{0}.ipac'.format(suffix)),
format='ascii.ipac')
# Note that there are overlaps in the catalog, which means that ORDER MATTERS
# in the above loop. I haven't yet checked whether large scale overwrites
# small or vice-versa; it may be that both views of the data are interesting.
tcube = SpectralCube(
data=tcubedata,
wcs=cube303.wcs,
mask=cube303.mask,
meta={'unit': 'K'},
header=cube303.header,
)
tcubeleaf = SpectralCube(
data=tcubeleafdata,
wcs=cube303.wcs,
mask=cube303.mask,
meta={'unit': 'K'},
header=cube303.header,
)
if write:
log.info("Writing TemperatureCube")
outpath = 'TemperatureCube_DendrogramObjects{0}.fits'
tcube.write(hpath(outpath.format(suffix)), overwrite=True)
outpath_leaf = 'TemperatureCube_DendrogramObjects{0}_leaves.fits'
tcubeleaf.write(hpath(outpath_leaf.format(suffix)), overwrite=True)
return catalog, tcube
# ---------------- #
# Build the masks. #
# ---------------- #
# The masked data and their corresponding masks are returned as dictionaries.
# Mask where NaNs are TRUE
trueMask = maskTrue(data=data)
# Mask where NaNs are FALSE
falseMask = maskFalse(data=data)
# Mask the profile range. NaNs are TRUE
profileMask = maskProfile(data=trueMask['maskedData'],
xarr=vels,
minProfIdx=minProfIdx,
maxProfIdx=maxProfIdx)
# Build mask with a WCS that is compatible with SpectralCube.
# NaNs are FALSE.
cubeMask = BooleanArrayMask(falseMask['mask'], pacsWcs)
# Make an array of coordinates for the valid spaxels
validPixels = findValidPixels((nCols, nRows), trueMask['mask'][0, :, :])
# --------------------------------------------------- #
# Find the (col,row) min/max limits of valid spaxels. #
# Min/max will be used to crop the data and the WCS. #
# --------------------------------------------------- #
wcsMinMax = pixMinMax(validPixels)
# ------------------------------------------------------------- #
# Fit the continuum with a polynomial and subtract from fluxes. #
# ------------------------------------------------------------- #
# The edges of the spectrum and the velocity region
# of the line profile is masked as TRUE. Data which
noise_flat = noise_cube[mask]
var_flat = noise_flat**2
ratio303321 = cube321m.flattened().value / cube303m.flattened().value
eratio303321 = ((ratio303321**2 *
(var_flat / cube303m.flattened().value**2 +
var_flat / cube321m.flattened().value**2))**0.5)
ratioOK = ratio303321 > eratio303321 * 1
#ratioOK = cube303m.mask.include()
data = np.zeros(cube303m.shape, dtype='float32') * np.nan
mask[mask] = ratioOK
data[mask] = ratio303321[ratioOK]
ratiocube_303321 = SpectralCube(data,
mask=BooleanArrayMask(np.isfinite(data),
wcs=cube303m.wcs),
wcs=cube303m.wcs)
noise_flat_sm = sm_noise_cube[masksm]
var_flat_sm = noise_flat_sm**2
ratio303321sm = cube321msm.flattened().value / cube303msm.flattened().value
eratio303321sm = ((ratio303321sm**2 *
(var_flat_sm / cube303msm.flattened().value**2 +
var_flat_sm / cube321msm.flattened().value**2))**0.5)
ratioOKsm = ratio303321sm > eratio303321sm * 1
#ratioOKsm = cube303msm.mask.include()
datasm = np.zeros(cube303msm.shape, dtype='float32') * np.nan
masksm[masksm] = ratioOKsm
datasm[masksm] = ratio303321sm[ratioOKsm]
ratiocube_303321, ratiocubesm_303321),
('dend', 'dendsm', 'direct', 'directsm', 'ratio', 'ratiosm')):
log.info("Starting {0} {1}".format(name, weight))
mask = (cube303sm > 0.2 if 'sm' in name else cube303 > 0.3)
if weight:
wcube = (cube303msm if 'sm' in name else cube303m)
if tcube.shape != wcube.shape:
log.info("Not weighting {0}".format(fn))
continue
weighted = copy.copy(tcube).with_mask(mask)
weighted._data = wcube._data * tcube._data
mask = (wcube.mask & tcube.mask & mask & BooleanArrayMask(
weighted.filled_data[...] != 0, weighted.wcs)
& BooleanArrayMask(wcube.filled_data[...] != 0, wcube.wcs))
wcube = wcube.with_mask(mask)
pv1 = weighted.sum(axis=1)
pv2 = wcube.sum(axis=1)
pv = pv1 / pv2
bmean = pv.value
else:
bmean = tcube.with_mask(mask).mean(axis=1)
ds = 10 if 'sm' in name else 20
tcube_ds = tcube[::ds, :, :] # for the WCS
tcube_ds.data = downsample.downsample_axis(tcube._data, ds, axis=0)
bmeands = downsample.downsample_axis(bmean, ds, axis=0)
