def f16x3_to_rgb(pio, start_depth, clip=1, parallel=None, cli_progress=False):
transform = viz.SqrtStretch() + viz.ManualInterval(0, clip)
_float_to_rgb(pio,
start_depth,
ImageMode.F16x3,
transform,
parallel=parallel,
cli_progress=cli_progress)
def plot_norm(self,
stretch='linear',
power=1.0,
asinh_a=0.1,
min_cut=None,
max_cut=None,
min_percent=None,
max_percent=None,
percent=None,
clip=True):
"""Create a matplotlib norm object for plotting.
This is a copy of this function that will be available in Astropy 1.3:
`astropy.visualization.mpl_normalize.simple_norm`
See the parameter description there!
Examples
--------
>>> image = SkyImage()
>>> norm = image.plot_norm(stretch='sqrt', max_percent=99)
>>> image.plot(norm=norm)
"""
import astropy.visualization as v
from astropy.visualization.mpl_normalize import ImageNormalize
if percent is not None:
interval = v.PercentileInterval(percent)
elif min_percent is not None or max_percent is not None:
interval = v.AsymmetricPercentileInterval(min_percent or 0.,
max_percent or 100.)
elif min_cut is not None or max_cut is not None:
interval = v.ManualInterval(min_cut, max_cut)
else:
interval = v.MinMaxInterval()
if stretch == 'linear':
stretch = v.LinearStretch()
elif stretch == 'sqrt':
stretch = v.SqrtStretch()
elif stretch == 'power':
stretch = v.PowerStretch(power)
elif stretch == 'log':
stretch = v.LogStretch()
elif stretch == 'asinh':
stretch = v.AsinhStretch(asinh_a)
else:
raise ValueError('Unknown stretch: {0}.'.format(stretch))
vmin, vmax = interval.get_limits(self.data)
return ImageNormalize(vmin=vmin, vmax=vmax, stretch=stretch, clip=clip)
def to_fig(self,
rowrange,
colrange,
extension=1,
cmap='Greys_r',
cut=None,
dpi=50):
"""Turns a fits file into a cropped and contrast-stretched matplotlib figure."""
fts = fitsio.FITS(self.fits_filename)
if (np.isfinite(fts[extension].read())).sum() == 0:
raise InvalidFrameException()
image = fts[extension].read()[rowrange[0]:rowrange[1],
colrange[0]:colrange[1]]
fts.close()
if cut is None:
cut = np.percentile(image[np.isfinite(image)], [10, 99.5])
transform = visualization.LogStretch() + visualization.ManualInterval(
vmin=cut[0], vmax=cut[1])
image_scaled = transform(image)
px_per_kepler_px = 20
dimensions = [
image.shape[0] * px_per_kepler_px,
image.shape[1] * px_per_kepler_px
]
figsize = [dimensions[1] / dpi, dimensions[0] / dpi]
dpi = 440 / float(figsize[0])
fig = pl.figure(figsize=figsize, dpi=dpi)
ax = fig.add_subplot(1, 1, 1)
ax.matshow(image_scaled,
aspect='auto',
cmap=cmap,
origin='lower',
interpolation='nearest')
ax.set_xticks([])
ax.set_yticks([])
ax.axis('off')
#ax.set_axis_bgcolor('red')
fig.subplots_adjust(left=0.0, right=1.0, top=1.0, bottom=0.0)
fig.canvas.draw()
return fig
def get_im_interval(pmin=10, pmax=99.9, vmin=None, vmax=None):
''' Returns an interval, to feed the ImageNormalize routine from Astropy.
:param pmin: lower-limit percentile
:type pmin: float
:param pmax: upper-limit percentile
:type pmax: float
:param vmin: absolute lower limit
:type vmin: float
:param vmax: absolute upper limit
:type vmax: float
:return: an :class:`astropy.visualization.interval` thingy ...
:rtype: :class:`astropy.visualization.interval`
.. note:: Specifying *both* vmin and vmax will override pmin and pmax.
'''
if vmin is not None and vmax is not None:
return astrovis.ManualInterval(vmin, vmax)
return astrovis.AsymmetricPercentileInterval(pmin, pmax)
def create_figure(self,
frameno=0,
binning=1,
dpi=None,
stretch='log',
vmin=1,
vmax=5000,
cmap='gray',
data_col='FLUX',
annotate=True,
time_format='ut',
show_flags=False,
label=None):
"""Returns a matplotlib Figure object that visualizes a frame.
Parameters
----------
frameno : int
Image number in the target pixel file.
binning : int
Number of frames around `frameno` to co-add. (default: 1).
dpi : float, optional [dots per inch]
Resolution of the output in dots per Kepler CCD pixel.
By default the dpi is chosen such that the image is 440px wide.
vmin : float, optional
Minimum cut level (default: 1).
vmax : float, optional
Maximum cut level (default: 5000).
cmap : str, optional
The matplotlib color map name. The default is 'gray',
can also be e.g. 'gist_heat'.
raw : boolean, optional
If `True`, show the raw pixel counts rather than
the calibrated flux. Default: `False`.
annotate : boolean, optional
Annotate the Figure with a timestamp and target name?
(Default: `True`.)
show_flags : boolean, optional
Show the quality flags?
(Default: `False`.)
label : str
Label text to show in the bottom left corner of the movie.
Returns
-------
image : array
An array of unisgned integers of shape (x, y, 3),
representing an RBG colour image x px wide and y px high.
"""
# Get the flux data to visualize
flx = self.flux_binned(frameno=frameno,
binning=binning,
data_col=data_col)
# Determine the figsize and dpi
shape = list(flx.shape)
shape = [shape[1], shape[0]]
if dpi is None:
# Twitter timeline requires dimensions between 440x220 and 1024x512
# so we make 440 the default
dpi = 440 / float(shape[0])
# libx264 require the height to be divisible by 2, we ensure this here:
shape[0] -= ((shape[0] * dpi) % 2) / dpi
# Create the figureand display the flux image using matshow
fig = pl.figure(figsize=shape, dpi=dpi)
# Display the image using matshow
ax = fig.add_subplot(1, 1, 1)
if self.verbose:
print('{} vmin/vmax = {}/{} (median={})'.format(
data_col, vmin, vmax, np.nanmedian(flx)))
if stretch == 'linear':
stretch_fn = visualization.LinearStretch()
elif stretch == 'sqrt':
stretch_fn = visualization.SqrtStretch()
elif stretch == 'power':
stretch_fn = visualization.PowerStretch(1.0)
elif stretch == 'log':
stretch_fn = visualization.LogStretch()
elif stretch == 'asinh':
stretch_fn = visualization.AsinhStretch(0.1)
else:
raise ValueError('Unknown stretch: {0}.'.format(stretch))
transform = (stretch_fn +
visualization.ManualInterval(vmin=vmin, vmax=vmax))
flx_transform = 255 * transform(flx)
# Make sure to remove all NaNs!
flx_transform[~np.isfinite(flx_transform)] = 0
ax.imshow(flx_transform.astype(int),
aspect='auto',
origin='lower',
interpolation='nearest',
cmap=cmap,
norm=NoNorm())
if annotate: # Annotate the frame with a timestamp and target name?
fontsize = 3. * shape[0]
margin = 0.03
# Print target name in lower left corner
if label is None:
label = self.objectname
txt = ax.text(margin,
margin,
label,
family="monospace",
fontsize=fontsize,
color='white',
transform=ax.transAxes)
txt.set_path_effects([
path_effects.Stroke(linewidth=fontsize / 6.,
foreground='black'),
path_effects.Normal()
])
# Print a timestring in the lower right corner
txt2 = ax.text(1 - margin,
margin,
self.timestamp(frameno, time_format=time_format),
family="monospace",
fontsize=fontsize,
color='white',
ha='right',
transform=ax.transAxes)
txt2.set_path_effects([
path_effects.Stroke(linewidth=fontsize / 6.,
foreground='black'),
path_effects.Normal()
])
# Print quality flags in upper right corner
if show_flags:
flags = self.quality_flags(frameno)
if len(flags) > 0:
txt3 = ax.text(margin,
1 - margin,
'\n'.join(flags),
family="monospace",
fontsize=fontsize * 1.3,
color='white',
ha='left',
va='top',
transform=ax.transAxes,
linespacing=1.5,
backgroundcolor='red')
txt3.set_path_effects([
path_effects.Stroke(linewidth=fontsize / 6.,
foreground='black'),
path_effects.Normal()
])
ax.set_xticks([])
ax.set_yticks([])
ax.axis('off')
fig.subplots_adjust(left=0.0, right=1.0, top=1.0, bottom=0.0)
fig.canvas.draw()
return fig
neb_subtracted[z, :, :] = neb_subtracted[z, :, :] - neb_spect[z]
if not os.path.exists(os.path.join(data_path, 'HH305E_nebsub.fits')):
hdr = hdul[0].header
now = dt.utcnow().strftime('%Y/%m/%d %H:%M:%S UT')
hdr.set('HISTORY', f'Background subtracted {now}')
hdu = fits.PrimaryHDU(data=neb_subtracted, header=hdr)
hdu.writeto(os.path.join(data_path, 'HH305E_nebsub.fits'))
##-------------------------------------------------------------------------
## Plot mask of low H-beta emission
plt.figure(figsize=(8, 8))
plt.subplot(1, 2, 1)
plt.title('Sum of H-beta Bins')
norm = v.ImageNormalize(image,
interval=v.ManualInterval(vmin=image.min() - 5,
vmax=image.max() + 10),
stretch=v.LogStretch(10))
im = plt.imshow(image, origin='lower', norm=norm, cmap='Greys')
plt.colorbar(im)
plt.subplot(1, 2, 2)
plt.title('Nebular Emission Mask')
mimage = np.ma.MaskedArray(image)
mimage.mask = ~nmask
mimagef = np.ma.filled(mimage, fill_value=0)
norm = v.ImageNormalize(mimagef,
interval=v.ManualInterval(
vmin=image.min() - 5,
vmax=np.percentile(image, mask_pcnt) + 5),
stretch=v.LinearStretch())
im = plt.imshow(mimagef, origin='lower', norm=norm, cmap='Greys')
def set_normalization(self,
stretch=None,
interval=None,
stretchkwargs={},
intervalkwargs={},
perm_linear=None):
if stretch is None:
if self.stretch is None:
stretch = 'linear'
else:
stretch = self.stretch
if isinstance(stretch, str):
print(stretch,
' '.join([f'{k}={v}' for k, v in stretchkwargs.items()]))
if self.data is None: #can not calculate objects yet
self.stretch_kwargs = stretchkwargs
else:
kwargs = self.prepare_kwargs(
self.stretch_kws_defaults[stretch], self.stretch_kwargs,
stretchkwargs)
if perm_linear is not None:
perm_linear_kwargs = self.prepare_kwargs(
self.stretch_kws_defaults['linear'], perm_linear)
print(
'linear', ' '.join([
f'{k}={v}' for k, v in perm_linear_kwargs.items()
]))
if stretch == 'asinh': # arg: a=0.1
stretch = vis.CompositeStretch(
vis.LinearStretch(**perm_linear_kwargs),
vis.AsinhStretch(**kwargs))
elif stretch == 'contrastbias': # args: contrast, bias
stretch = vis.CompositeStretch(
vis.LinearStretch(**perm_linear_kwargs),
vis.ContrastBiasStretch(**kwargs))
elif stretch == 'histogram':
stretch = vis.CompositeStretch(
vis.HistEqStretch(self.data, **kwargs),
vis.LinearStretch(**perm_linear_kwargs))
elif stretch == 'log': # args: a=1000.0
stretch = vis.CompositeStretch(
vis.LogStretch(**kwargs),
vis.LinearStretch(**perm_linear_kwargs))
elif stretch == 'powerdist': # args: a=1000.0
stretch = vis.CompositeStretch(
vis.LinearStretch(**perm_linear_kwargs),
vis.PowerDistStretch(**kwargs))
elif stretch == 'power': # args: a
stretch = vis.CompositeStretch(
vis.PowerStretch(**kwargs),
vis.LinearStretch(**perm_linear_kwargs))
elif stretch == 'sinh': # args: a=0.33
stretch = vis.CompositeStretch(
vis.LinearStretch(**perm_linear_kwargs),
vis.SinhStretch(**kwargs))
elif stretch == 'sqrt':
stretch = vis.CompositeStretch(
vis.SqrtStretch(),
vis.LinearStretch(**perm_linear_kwargs))
elif stretch == 'square':
stretch = vis.CompositeStretch(
vis.LinearStretch(**perm_linear_kwargs),
vis.SquaredStretch())
else:
raise ValueError('Unknown stretch:' + stretch)
else:
if stretch == 'linear': # args: slope=1, intercept=0
stretch = vis.LinearStretch(**kwargs)
else:
raise ValueError('Unknown stretch:' + stretch)
self.stretch = stretch
if interval is None:
if self.interval is None:
interval = 'zscale'
else:
interval = self.interval
if isinstance(interval, str):
print(interval,
' '.join([f'{k}={v}' for k, v in intervalkwargs.items()]))
kwargs = self.prepare_kwargs(self.interval_kws_defaults[interval],
self.interval_kwargs, intervalkwargs)
if self.data is None:
self.interval_kwargs = intervalkwargs
else:
if interval == 'minmax':
interval = vis.MinMaxInterval()
elif interval == 'manual': # args: vmin, vmax
interval = vis.ManualInterval(**kwargs)
elif interval == 'percentile': # args: percentile, n_samples
interval = vis.PercentileInterval(**kwargs)
elif interval == 'asymetric': # args: lower_percentile, upper_percentile, n_samples
interval = vis.AsymmetricPercentileInterval(**kwargs)
elif interval == 'zscale': # args: nsamples=1000, contrast=0.25, max_reject=0.5, min_npixels=5, krej=2.5, max_iterations=5
interval = vis.ZScaleInterval(**kwargs)
else:
raise ValueError('Unknown interval:' + interval)
self.interval = interval
if self.img is not None:
self.img.set_norm(
vis.ImageNormalize(self.data,
interval=self.interval,
stretch=self.stretch,
clip=True))
import astropy.visualization as vis
from astropy.wcs import utils as wcsutils
import pylab as pl
import pyspeckit
import paths
from astropy import modeling
from astropy import stats
cube = SpectralCube.read(
'/Users/adam/work/w51/alma/FITS/longbaseline/velo_cutouts/w51e2e_csv0_j2-1_r0.5_medsub.fits'
)
cs21cube = subcube = cube.spectral_slab(16 * u.km / u.s, 87 * u.km / u.s)[::-1]
norm = vis.ImageNormalize(
subcube,
interval=vis.ManualInterval(-0.002, 0.010),
stretch=vis.AsinhStretch(),
)
pl.rcParams['font.size'] = 12
szinch = 18
fig = pl.figure(1, figsize=(szinch, szinch))
pl.pause(0.1)
for ii in range(5):
fig.set_size_inches(szinch, szinch)
pl.pause(0.1)
try:
assert np.all(fig.get_size_inches() == np.array([szinch, szinch]))
break
except AssertionError:
def create_figure(self,
output_filename,
survey,
stretch='log',
vmin=1,
vmax=None,
min_percent=1,
max_percent=95,
cmap='gray',
contour_color='red',
data_col='FLUX'):
"""Returns a matplotlib Figure object that visualizes a frame.
Parameters
----------
vmin : float, optional
Minimum cut level (default: 0).
vmax : float, optional
Maximum cut level (default: 5000).
cmap : str, optional
The matplotlib color map name. The default is 'gray',
can also be e.g. 'gist_heat'.
raw : boolean, optional
If `True`, show the raw pixel counts rather than
the calibrated flux. Default: `False`.
Returns
-------
image : array
An array of unisgned integers of shape (x, y, 3),
representing an RBG colour image x px wide and y px high.
"""
# Get the flux data to visualize
# Update to use TPF
flx = self.TPF.flux_binned()
# print(np.shape(flx))
# calculate cut_levels
if vmax is None:
vmin, vmax = self.cut_levels(min_percent, max_percent, data_col)
# Determine the figsize
shape = list(flx.shape)
# print(shape)
# Create the figure and display the flux image using matshow
fig = plt.figure(figsize=shape)
# Display the image using matshow
# Update to generate axes using WCS axes instead of plain axes
ax = plt.subplot(projection=self.TPF.wcs)
ax.set_xlabel('RA')
ax.set_ylabel('Dec')
if self.verbose:
print('{} vmin/vmax = {}/{} (median={})'.format(
data_col, vmin, vmax, np.nanmedian(flx)))
if stretch == 'linear':
stretch_fn = visualization.LinearStretch()
elif stretch == 'sqrt':
stretch_fn = visualization.SqrtStretch()
elif stretch == 'power':
stretch_fn = visualization.PowerStretch(1.0)
elif stretch == 'log':
stretch_fn = visualization.LogStretch()
elif stretch == 'asinh':
stretch_fn = visualization.AsinhStretch(0.1)
else:
raise ValueError('Unknown stretch: {0}.'.format(stretch))
transform = (stretch_fn +
visualization.ManualInterval(vmin=vmin, vmax=vmax))
ax.imshow((255 * transform(flx)).astype(int),
aspect='auto',
origin='lower',
interpolation='nearest',
cmap=cmap,
norm=NoNorm())
ax.set_xticks([])
ax.set_yticks([])
current_ylims = ax.get_ylim()
current_xlims = ax.get_xlim()
pixels, header = surveyquery.getSVImg(self.TPF.position, survey)
levels = np.linspace(np.min(pixels), np.percentile(pixels, 95), 10)
ax.contour(pixels,
transform=ax.get_transform(WCS(header)),
levels=levels,
colors=contour_color)
ax.set_xlim(current_xlims)
ax.set_ylim(current_ylims)
fig.canvas.draw()
plt.savefig(output_filename, bbox_inches='tight', dpi=300)
return fig
img2.data = img2.data * photflam / exptime / 0.0455**2 # get into units of erg/s/cm^2/A/arcsec^2
#Zoom into region of interest
cut_ctr = SkyCoord('12h18m57.5s 47d18m14s')
cut_dims = np.array([4.0, 4.0]) * u.arcmin
cut = Cutout2D(img2.data, cut_ctr, cut_dims, wcs=img.wcs)
#Plot first subplot: raw data gathered by the telescope
plt.subplot(131, projection=img.wcs)
plt.imshow(cut.data, origin='lower', cmap='plasma')
plt.grid(color='yellow', ls='solid')
plt.title('Raw Telescope Data', weight='bold')
plt.ylabel('Declination (J2000)')
#Features of raw data are hard to see, so time to stretch the values
trans = viz.LogStretch() + viz.ManualInterval(0, 5e-19)
cut.data = trans(cut.data)
#Plot second subplot: Enhanced so all bright regions are more visible
plt.subplot(132, projection=img.wcs)
plt.imshow(cut.data, origin='lower', cmap="plasma")
plt.grid(color='yellow', ls='solid')
plt.title('Enhanced', weight='bold')
plt.xlabel('Right Ascension (J2000)')
#Gaussian filter to supress point sources of light, like other stars
def destar(I, sigma, t):
D = np.zeros_like(I)
B = gauss(I, sigma)
M = I - B
wcs_sio54 = wcs.WCS(e2siored[0].header)
#cont3mm_proj,_ = reproject.reproject_interp((cont3mm[0].data.squeeze(),
# wcs.WCS(cont3mm[0].header).celestial),
# e2siored[0].header)
cont1mm_proj, _ = reproject.reproject_interp(
(cont1mm[0].data.squeeze(), wcs.WCS(cont1mm[0].header).celestial),
e2siored[0].header)
fig = pl.figure(1)
fig.set_size_inches(8, 8)
fig.clf()
ax = pl.subplot(projection=wcs_sio54)
pixscale = np.mean(wcs.utils.proj_plane_pixel_scales(wcs_sio54)) * u.deg
contnorm = vis.ManualInterval(-0.0005, 0.007)
siorednorm = vis.ManualInterval(-0.05, 0.15)
siobluenorm = vis.ManualInterval(-0.15, 0.60)
rgbim = np.array([
siorednorm(e2siored[0].data),
contnorm(cont1mm_proj),
siobluenorm(e2sioblue[0].data)
])
ax.imshow(rgbim.T.swapaxes(0, 1), origin='lower', interpolation='none')
#csblue_reproj,_ = reproject.reproject_interp(e2sioj21blue, e2siored[0].header)
ax.contour( #e2CSj21blue[0].data,
cs21max.value,
#transform=ax.get_transform(wcs.WCS(e2CSj21blue[0].header)),
def make_hiidust_plot(
reg,
mgpsfile,
width=1 * u.arcmin,
surveys=['atlasgal'],
figure=None,
regname='GAL_031',
fifth_panel_synchro=False,
alpha=-0.12,
cmap=None,
):
if cmap is None:
cmap = pl.cm.viridis
cmap.set_bad('w')
mgps_fh = fits.open(mgpsfile)[0]
frame = wcs.utils.wcs_to_celestial_frame(wcs.WCS(mgps_fh.header))
coordinate = reg.center
coordname = "{0:06.3f}_{1:06.3f}".format(coordinate.galactic.l.deg,
coordinate.galactic.b.deg)
mgps_cutout = Cutout2D(mgps_fh.data,
coordinate.transform_to(frame.name),
size=width * 2,
wcs=wcs.WCS(mgps_fh.header))
print()
print(reg.meta['text'])
print(
f"Retrieving MAGPIS data for {coordname} ({coordinate.to_string()} {coordinate.frame.name})"
)
# we're treating 'width' as a radius elsewhere, here it's a full width
images = {
survey: getimg(coordinate, image_size=width * 2, survey=survey)
for survey in surveys
}
images = {x: y for x, y in images.items() if y is not None}
assert len(images) > 0
#images['mgps'] = [mgps_cutout]
# coordinate stuff so images can be reprojected to same frame
ww = mgps_cutout.wcs.celestial
mgps_pixscale = (wcs.utils.proj_plane_pixel_area(ww) * u.deg**2)**0.5
if figure is None:
figure = pl.gcf()
figure.clf()
(survey, img), = images.items()
new_img = img[0].data
if hasattr(img[0], 'header'):
outwcs = wcs.WCS(img[0].header)
else:
outwcs = img[0].wcs
reproj_pixscale = (wcs.utils.proj_plane_pixel_area(outwcs) * u.deg**2)**0.5
agal_bm = tgt_bm = Beam(beam_map[survey])
convbm = tgt_bm.deconvolve(mgps_beam)
mgps_sm = convolution.convolve_fft(mgps_cutout.data,
convbm.as_kernel(mgps_pixscale))
mgps_reproj, _ = reproject.reproject_interp((mgps_sm, mgps_cutout.wcs),
outwcs,
shape_out=img[0].data.shape)
mgpsMjysr = mgps_cutout.data / mgps_beam.sr.value / 1e6
dust_pred = dust_emissivity.blackbody.modified_blackbody(
u.Quantity(
[wlmap[survey].to(u.GHz, u.spectral()),
mustang_central_frequency]),
assumed_temperature,
beta=assumed_dustbeta)
# assumes "surv" is dust
surv_to_mgps = new_img * dust_pred[1] / dust_pred[0]
print(f"{regname} {survey}")
print(f"{survey} to mgps ratio: {dust_pred[1]/dust_pred[0]}")
dusty = surv_to_mgps.value / tgt_bm.sr.value / 1e6
freefree = (mgps_reproj / mgps_beam.sr.value / 1e6 - dusty)
assert not hasattr(freefree, 'unit')
print("Max values: ", img[0].data.max(), mgps_sm.max())
print("More max values: ", np.nanmax(dusty), np.nanmax(freefree),
np.nanmax(mgps_reproj / mgps_beam.sr.value / 1e6))
norm = visualization.ImageNormalize(
freefree,
interval=visualization.ManualInterval(np.nanpercentile(freefree, 0.1),
np.nanpercentile(freefree,
99.9)),
stretch=visualization.LogStretch(),
)
mgpsnorm = visualization.ImageNormalize(
mgps_cutout.data,
interval=visualization.PercentileInterval(99.95),
stretch=visualization.LogStretch(),
)
print(f"interval: {norm.interval.vmin}, {norm.interval.vmax}")
assert not hasattr(norm.vmin, 'unit')
assert not hasattr(norm.vmax, 'unit')
assert not hasattr(mgpsnorm.vmin, 'unit')
assert not hasattr(mgpsnorm.vmax, 'unit')
Magpis.cache_location = '/Volumes/external/mgps/cache/'
ax0 = figure.add_subplot(1, 6, 3, projection=mgps_cutout.wcs)
ax0.imshow(mgpsMjysr,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
ax0.set_title("3 mm")
ax1 = figure.add_subplot(1, 6, 1, projection=outwcs)
ax1.imshow(dusty,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
ax1.set_title("870 $\\mu$m scaled")
ax1.set_ylabel("Galactic Latitude")
ax2 = figure.add_subplot(1, 6, 2, projection=outwcs)
ax2.imshow(freefree,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
ax2.set_title("3 mm Free-Free")
for ax in (ax0, ax1, ax2):
#ax.set_xlabel("Galactic Longitude")
ax.tick_params(direction='in')
ax.tick_params(color='w')
ax0.coords[1].set_axislabel("")
ax0.coords[1].set_ticklabel_visible(False)
ax2.coords[1].set_axislabel("")
ax2.coords[1].set_ticklabel_visible(False)
pl.subplots_adjust(hspace=0, wspace=0)
if 'G01' in regname:
gps20im = fits.open('/Users/adam/work/gc/20cm_0.fits', )
elif 'G49' in regname:
gps20im = fits.open(
'/Users/adam/work/w51/vla_old/W51-LBAND-feathered_ABCD.fits')
#gps20im = fits.open('/Users/adam/work/w51/vla_old/W51-LBAND_Carray.fits')
else:
gps20im = getimg(coordinate, image_size=width * 2, survey='gps20new')
reproj_gps20, _ = reproject.reproject_interp(
(gps20im[0].data.squeeze(), wcs.WCS(gps20im[0].header).celestial),
#mgps_fh.header)
# refactoring to make a smaller cutout would make this faster....
mgps_cutout.wcs,
shape_out=mgps_cutout.data.shape)
gps20cutout = Cutout2D(
reproj_gps20, #gps20im[0].data.squeeze(),
coordinate.transform_to(frame.name),
size=width * 2,
wcs=mgps_cutout.wcs)
#wcs=wcs.WCS(mgps_fh.header))
#wcs.WCS(gps20im[0].header).celestial)
ax3 = figure.add_subplot(1, 6, 5, projection=gps20cutout.wcs)
gps20_bm = Beam.from_fits_header(gps20im[0].header)
print(f"GPS 20 beam: {gps20_bm.__repr__()}")
norm20 = visualization.ImageNormalize(
gps20cutout.data,
interval=visualization.ManualInterval(
np.nanpercentile(gps20cutout.data, 0.5),
np.nanpercentile(gps20cutout.data, 99.9)),
stretch=visualization.LogStretch(),
)
# use 0.12 per Loren's suggestion
freefree_20cm_to_3mm = (90 * u.GHz / (1.4 * u.GHz))**alpha
gps20_Mjysr = gps20cutout.data / gps20_bm.sr.value / 1e6
ax3.imshow((gps20_Mjysr * freefree_20cm_to_3mm).value,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
ax3.set_title("20 cm scaled")
ax3.coords[1].set_axislabel("")
ax3.coords[1].set_ticklabel_visible(False)
ax3.tick_params(direction='in')
ax3.tick_params(color='w')
# Fifth Panel:
# use freefree_proj to get the 20cm-estimated free-free contribution even
# if we're not using it for plotting
# MAGPIS data are high-resolution (comparable to but better than MGPS)
# Zadeh data are low-resolution, 30ish arcsec
# units: Jy/sr
freefree_proj, _ = reproject.reproject_interp(
(freefree, outwcs), gps20cutout.wcs, shape_out=gps20cutout.data.shape)
gps20_pixscale = (wcs.utils.proj_plane_pixel_area(gps20cutout.wcs) *
u.deg**2)**0.5
# depending on which image has higher resolution, convolve one to the other
try:
gps20convbm = tgt_bm.deconvolve(gps20_bm)
gps20_Mjysr_sm = convolution.convolve_fft(
gps20_Mjysr, gps20convbm.as_kernel(gps20_pixscale))
except ValueError:
gps20_Mjysr_sm = gps20_Mjysr
ff_convbm = gps20_bm.deconvolve(tgt_bm)
freefree_proj = convolution.convolve_fft(
freefree_proj, ff_convbm.as_kernel(gps20_pixscale))
if fifth_panel_synchro:
ax4 = figure.add_subplot(1, 6, 5, projection=gps20cutout.wcs)
# use the central frequency corresponding to an approximately flat spectrum (flat -> 89.72)
freefree_3mm_to_20cm = 1 / (90 * u.GHz / (1.4 * u.GHz))**-0.12
#empirical_factor = 3 # freefree was coming out way too high, don't understand why yet
synchro = gps20_Mjysr_sm - freefree_proj * freefree_3mm_to_20cm
synchro[np.isnan(gps20_Mjysr) | (gps20_Mjysr == 0)] = np.nan
synchroish_ratio = gps20_Mjysr_sm / (freefree_proj *
freefree_3mm_to_20cm)
#synchro = synchroish_ratio
normsynchro = visualization.ImageNormalize(
gps20_Mjysr_sm,
interval=visualization.ManualInterval(
np.nanpercentile(gps20_Mjysr_sm, 0.5),
np.nanpercentile(gps20_Mjysr_sm, 99.9)),
stretch=visualization.LogStretch(),
)
ax4.imshow(synchro.value,
origin='lower',
interpolation='none',
norm=normsynchro,
cmap=cmap)
ax4.set_title("Synchrotron")
ax4.tick_params(direction='in')
ax4.tick_params(color='w')
ax4.coords[1].set_axislabel("")
ax4.coords[1].set_ticklabel_visible(False)
pl.tight_layout()
else:
# scale 20cm to match MGPS and subtract it
gps20_pixscale = (wcs.utils.proj_plane_pixel_area(gps20cutout.wcs) *
u.deg**2)**0.5
if gps20_bm.sr < mgps_beam.sr:
# smooth GPS20 to MGPS
gps20convbm = mgps_beam.deconvolve(gps20_bm)
gps20_Mjysr_sm = convolution.convolve_fft(
gps20_Mjysr, gps20convbm.as_kernel(gps20_pixscale))
gps20_Mjysr_sm[~np.isfinite(gps20_Mjysr)] = np.nan
gps20_proj = gps20_Mjysr_sm
#gps20_proj,_ = reproject.reproject_interp((gps20_Mjysr_sm, gps20cutout.wcs),
# ww,
# shape_out=mgps_cutout.data.shape)
else:
gps20_proj = gps20_Mjysr
gps20_convbm = gps20_bm.deconvolve(mgps_beam)
mgpsMjysr = convolution.convolve_fft(
mgpsMjysr, gps20_convbm.as_kernel(mgps_pixscale))
ax4 = figure.add_subplot(1, 6, 4, projection=mgps_cutout.wcs)
# use the central frequency corresponding to an approximately flat spectrum (flat -> 89.72)
freefree20 = gps20_proj * freefree_20cm_to_3mm
dust20 = (mgpsMjysr - freefree20).value
dust20[np.isnan(gps20_proj) | (gps20_proj == 0)] = np.nan
normdust20 = visualization.ImageNormalize(
mgpsMjysr,
interval=visualization.ManualInterval(
np.nanpercentile(mgpsMjysr, 0.5),
np.nanpercentile(mgpsMjysr, 99.9)),
stretch=visualization.LogStretch(),
)
# show smoothed 20 cm
ax3.imshow((freefree20).value,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
ax4.imshow(dust20,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
ax4.set_title("3 mm Dust")
ax4.tick_params(direction='in')
ax4.tick_params(color='w')
ax4.coords[1].set_axislabel("")
ax4.coords[1].set_ticklabel_visible(False)
pl.tight_layout()
#elif 'G01' not in regname:
# norm.vmin = np.min([np.nanpercentile(dust20, 0.5), np.nanpercentile(freefree, 0.1)])
if np.abs(np.nanpercentile(dust20, 0.5) -
np.nanpercentile(freefree, 0.1)) < 1e2:
norm.vmin = np.min(
[np.nanpercentile(dust20, 0.5),
np.nanpercentile(freefree, 0.1)])
if 'arches' in reg.meta['text']:
norm.vmin = 0.95 # force 1 to be on-scale
if 'w49b' in reg.meta['text']:
norm.vmin = np.min(
[np.nanpercentile(dust20, 8),
np.nanpercentile(freefree, 0.1)])
norm.vmin = -4
norm.vmax = 11
ax0.imshow(mgpsMjysr,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
ax1.imshow(dusty,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
ax2.imshow(freefree,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
ax3.imshow((gps20_proj * freefree_20cm_to_3mm).value,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
ax4.imshow(dust20,
origin='lower',
interpolation='none',
norm=norm,
cmap=cmap)
print(
f"{reg}: dusty sum: {dusty[dusty>0].sum()} freefreeish sum: {freefree[freefree>0].sum()}"
)
area = mgps_reproj.size * (reproj_pixscale**2).to(u.sr)
mgps_reproj_Mjysr = mgps_reproj / mgps_beam.sr.value / 1e6
# only label the middle axis
for ax in figure.axes:
ax.set_xlabel("Galactic Longitude")
for ax in figure.axes:
ax.set_xlabel(" ")
ax0.set_xlabel("Galactic Longitude")
lastax = ax3
bbox = lastax.get_position()
# this is a painful hack to force the bbox to update
while bbox.height > 0.9:
print(f"bbox_height = {bbox.height}")
pl.pause(0.1)
bbox = lastax.get_position()
cax = figure.add_axes([bbox.x1 + 0.01, bbox.y0, 0.02, bbox.height])
cb = figure.colorbar(mappable=lastax.images[-1], cax=cax)
cb.set_ticks([-3, 0, 10, 50, 100])
if 'w51' in reg.meta['text']:
cb.set_ticks([-10, 0, 20, 200])
if 'w49b' in reg.meta['text']:
cb.set_ticks([-3, 0, 3, 10])
if 'arches' in reg.meta['text']:
cb.set_ticks([0, 1, 5, 10])
cb.set_label('MJy sr$^{-1}$')
return {
'dust': dusty[dusty > 0].sum(),
'dust20': dust20[dust20 > 0].sum(),
'freefree': freefree[freefree > 0].sum(),
'freefree20': freefree20[freefree20 > 0].sum(),
'totalpos': mgps_reproj_Mjysr[mgps_reproj_Mjysr > 0].sum(),
'total': mgps_reproj_Mjysr.sum(),
'totalpos20': mgpsMjysr[mgpsMjysr > 0].sum(),
'total20': mgpsMjysr.sum(),
}