def make_ratio_integ():
"""
This is an alternate implementation to that already included in
make_ratiotem_cubesims
"""
npixflat = bmasksm_rs.include().sum(axis=0)
sum321 = cube321.with_mask(bmasksm_rs).sum(axis=0)
sum303 = cube303.with_mask(bmasksm_rs).sum(axis=0)
integ303321b = sum321 / sum303
error303 = cube303.with_mask(~bmasksm_rs).std(axis=0)
error321 = cube321.with_mask(~bmasksm_rs).std(axis=0)
eintegratio303321 = ((integ303321b.value**2 *
(error303.value**2 /
(sum303.value / npixflat)**2 + error321.value**2 /
(sum321.value / npixflat)**2))**0.5)
ok = (integ303321b.value / eintegratio303321) > 3
integ303321ok = integ303321b.value
integ303321ok[~ok] = np.nan
hdu = cube321[0, :, :].hdu
hdu.data = integ303321ok
hdu.writeto(paths.hpath('H2CO_321220_to_303202_integ_smoothmask.fits'),
clobber=True)
hdu.data = eintegratio303321
hdu.writeto(paths.hpath('H2CO_321220_to_303202_einteg_smoothmask.fits'),
clobber=True)
def make_ratio_max():
npixflat = bmasksm_rs.include().sum(axis=0)
max321 = cube321.with_mask(bmasksm_rs).max(axis=0)
max303 = cube303.with_mask(bmasksm_rs).max(axis=0)
maxratio303321b = max321/max303
error303 = cube303.with_mask(~bmasksm_rs).std(axis=0)
error321 = cube321.with_mask(~bmasksm_rs).std(axis=0)
eintegratio303321 = ((maxratio303321b.value**2 * (error303.value**2/(max303.value)**2 +
error321.value**2/(max321.value)**2))**0.5)
ok = (maxratio303321b.value/eintegratio303321) > 3
maxratio303321ok = maxratio303321b.value
maxratio303321ok[~ok] = np.nan
hdu = cube321[0,:,:].hdu
hdu.data = maxratio303321ok
hdu.writeto(paths.hpath('H2CO_321220_to_303202_maxratio_smoothmask.fits'),clobber=True)
hdu.data = eintegratio303321
hdu.writeto(paths.hpath('H2CO_321220_to_303202_emaxratio_smoothmask.fits'),clobber=True)
def make_ratio_integ():
"""
This is an alternate implementation to that already included in
make_ratiotem_cubesims
"""
npixflat = bmasksm_rs.include().sum(axis=0)
sum321 = cube321.with_mask(bmasksm_rs).sum(axis=0)
sum303 = cube303.with_mask(bmasksm_rs).sum(axis=0)
integ303321b = sum321/sum303
error303 = cube303.with_mask(~bmasksm_rs).std(axis=0)
error321 = cube321.with_mask(~bmasksm_rs).std(axis=0)
eintegratio303321 = ((integ303321b.value**2 * (error303.value**2/(sum303.value/npixflat)**2 +
error321.value**2/(sum321.value/npixflat)**2))**0.5)
ok = (integ303321b.value/eintegratio303321) > 3
integ303321ok = integ303321b.value
integ303321ok[~ok] = np.nan
hdu = cube321[0,:,:].hdu
hdu.data = integ303321ok
hdu.writeto(paths.hpath('H2CO_321220_to_303202_integ_smoothmask.fits'),clobber=True)
hdu.data = eintegratio303321
hdu.writeto(paths.hpath('H2CO_321220_to_303202_einteg_smoothmask.fits'),clobber=True)
def make_ratio_max():
npixflat = bmasksm_rs.include().sum(axis=0)
max321 = cube321.with_mask(bmasksm_rs).max(axis=0)
max303 = cube303.with_mask(bmasksm_rs).max(axis=0)
maxratio303321b = max321 / max303
error303 = cube303.with_mask(~bmasksm_rs).std(axis=0)
error321 = cube321.with_mask(~bmasksm_rs).std(axis=0)
eintegratio303321 = ((maxratio303321b.value**2 *
(error303.value**2 /
(max303.value)**2 + error321.value**2 /
(max321.value)**2))**0.5)
ok = (maxratio303321b.value / eintegratio303321) > 3
maxratio303321ok = maxratio303321b.value
maxratio303321ok[~ok] = np.nan
hdu = cube321[0, :, :].hdu
hdu.data = maxratio303321ok
hdu.writeto(paths.hpath('H2CO_321220_to_303202_maxratio_smoothmask.fits'),
clobber=True)
hdu.data = eintegratio303321
hdu.writeto(paths.hpath('H2CO_321220_to_303202_emaxratio_smoothmask.fits'),
clobber=True)
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,
shape=cube321sm.shape,
strides=(0,)+sm_noisefinite.strides),
cube321sm.wcs))
log.debug("Noise creation took {0:0.1f} seconds".format(time.time()-t0))
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,
shape=cube321sm.shape,
strides=(0, ) + sm_noisefinite.strides), cube321sm.wcs))
log.debug("Noise creation took {0:0.1f} seconds".format(time.time() - t0))
