def bsens_cleanest_diff(finaliter_prefix_b3, finaliter_prefix_b6,
cutoutregion, fignum=1,
finaliter_prefix_b3_bsens=None,
finaliter_prefix_b6_bsens=None,
normpars_b3=None,
normpars_b6=None,
basepath='/home/adam/work/alma-imf/reduction/', ):
image_b3 = SpectralCube.read(f'{finaliter_prefix_b3}.image.tt0', format='casa_image').subcube_from_ds9region(cutoutregion)
if finaliter_prefix_b3_bsens is None:
finaliter_prefix_b3_bsens = finaliter_prefix_b3.replace("cleanest","bsens").replace("merged_12M","merged_bsens_12M")
bsens_b3 = SpectralCube.read(f'{finaliter_prefix_b3_bsens}.image.tt0', format='casa_image').subcube_from_ds9region(cutoutregion)
image_b6 = SpectralCube.read(f'{finaliter_prefix_b6}.image.tt0', format='casa_image').subcube_from_ds9region(cutoutregion)
if finaliter_prefix_b6_bsens is None:
finaliter_prefix_b6_bsens = finaliter_prefix_b6.replace("cleanest","bsens").replace("merged_12M","merged_bsens_12M")
bsens_b6 = SpectralCube.read(f'{finaliter_prefix_b6_bsens}.image.tt0', format='casa_image').subcube_from_ds9region(cutoutregion)
# image_b3 = image_b3 * u.beam / image_b3.beam.sr
# image_b6 = image_b6 * u.beam / image_b6.beam.sr
# bsens_b3 = bsens_b3 * u.beam / bsens_b3.beam.sr
# bsens_b6 = bsens_b6 * u.beam / bsens_b6.beam.sr
fieldname = os.path.basename(finaliter_prefix_b6).split("_")[0]
print(fieldname)
diff_b3 = bsens_b3 - image_b3
diff_b6 = bsens_b6 - image_b6
normpars_b3_default = dict(min_percent=0.1, max_percent=99.9, stretch='linear')
normpars_b6_default = dict(min_percent=0.1, max_percent=99.9, stretch='linear')
if normpars_b3 is not None:
normpars_b3_default.update(normpars_b3)
normpars_b3 = normpars_b3_default
if normpars_b6 is not None:
normpars_b6_default.update(normpars_b6)
normpars_b6 = normpars_b6_default
fig = pl.figure(num=fignum, figsize=(6,6))
fig.clf()
ax = pl.subplot(1,1,1,label='B3', projection=diff_b3[0].wcs)
ax.imshow(diff_b3[0].value, norm=simple_norm(diff_b3[0].value, **normpars_b3), cmap='gray')
ax.set_xlabel('Right Ascension')
ax.set_ylabel('Declination')
fig2 = pl.figure(num=fignum+1, figsize=(6,6))
ax2 = pl.subplot(1,1,1,label='B6', projection=diff_b6[0].wcs)
ax2.imshow(diff_b6[0].value, norm=simple_norm(diff_b6[0].value, **normpars_b6), cmap='gray')
ax2.set_xlabel('Right Ascension')
ax2.set_ylabel('Declination')
# ax2.set_yticklabels([])
# ax2.set_ylabel("")
# lat = ax2.coords['dec']
# lat.set_ticklabel_position('r')
# lat.set_axislabel_position('r')
# lat.set_axislabel('Declination')
return fig,fig2
def plot_model(image,model_morph):
ny, nx = image.shape
imy, imx = np.mgrid[0:ny, 0:nx]
sersic_model = models.Sersic2D(
amplitude=model_morph.sersic_amplitude,
r_eff=model_morph.sersic_rhalf,
n=model_morph.sersic_n,
x_0=model_morph.sersic_xc,
y_0=model_morph.sersic_yc,
ellip=model_morph.sersic_ellip,
theta=model_morph.sersic_theta)
fitted_model = sersic_model(imx, imy)
plt.subplot(121)
plt.imshow(image, cmap='rainbow', origin='lower',
norm=simple_norm(image, stretch='log', log_a=10000))
plt.subplot(122)
plt.imshow(fitted_model, cmap='rainbow', origin='lower',
norm=simple_norm(image, stretch='log', log_a=10000))
plt.show()
plt.imshow(image-fitted_model, cmap='rainbow', origin='lower')
plt.colorbar()
plt.show()
plt.hist((image-fitted_model).flatten(),bins=25)
plt.show()
def plot_psf_fit(image, residual_image):
plt.subplot(1, 2, 1)
norm = simple_norm(image, 'log', percent=99.)
plt.imshow(image, norm=norm, origin='lower', cmap='viridis')
# plt.imshow(image, cmap='viridis', aspect=1, interpolation='nearest',origin='lower')
plt.title('Data')
plt.colorbar(orientation='horizontal', fraction=0.046, pad=0.04)
plt.subplot(1, 2, 2)
norm = simple_norm(residual_image, 'log', percent=99.)
plt.imshow(residual_image, norm=norm, origin='lower', cmap='viridis')
# plt.imshow(residual_image, cmap='viridis', aspect=1,interpolation='nearest', origin='lower')
plt.title('Residual')
plt.colorbar(orientation='horizontal', fraction=0.046, pad=0.04)
plt.show()
return
def w51north_plot():
fig = pl.figure(figsize=(9, 9))
ax = pl.subplot(projection=ww)
im = ax.imshow(fh[0].data,
cmap='gray',
norm=visualization.simple_norm(fh[0].data,
stretch='log',
max_percent=100.00))
cb = pl.colorbar(mappable=im)
cb.set_label(f"$S_\\nu$ [{fh[0].header['BUNIT']}]", fontsize=16)
# cb.set_ticks([np.nanmin(fh[0].data), -0.005, 0.00 ,0.005, 0.01, 0.015, 0.020, 0.025])
ax.axis([6750, 7750, 6750, 7750])
radesys = ww.wcs.radesys
_ = ax.set_xlabel(f"Right Ascension {ww.wcs.radesys}")
_ = ax.set_ylabel(f"Declination {ww.wcs.radesys}")
tick_fontsize = 14
fontsize = 16
ra = ax.coords['ra']
ra.set_major_formatter('hh:mm:ss.s')
dec = ax.coords['dec']
ra.set_axislabel(f"RA ({radesys})", fontsize=fontsize)
dec.set_axislabel(f"Dec ({radesys})", fontsize=fontsize, minpad=0.0)
ra.ticklabels.set_fontsize(tick_fontsize)
ra.set_ticklabel(exclude_overlapping=True)
dec.ticklabels.set_fontsize(tick_fontsize)
dec.set_ticklabel(exclude_overlapping=True)
return ax
def plot_sky_with_detected_stars(data, wcs, positions, filename=None):
'''plot line map with detected sources
Parameters
----------
data : 2d array
numpy array that contains the image data
wcs :
wcs information for the projection
positions : array or tuple
(n,2) shaped array with positions. Can also be a tuple of
multiple such arrays.
filename : Path
if given, a PDF of the plot is saved to filename
'''
apertures = []
if isinstance(positions, tuple) or isinstance(positions, list):
for position in positions:
apertures.append(CircularAperture(position, r=5))
else:
apertures.append(CircularAperture(positions, r=5))
#fig = figure(figsize=(6.974,6.974))
fig = figure(figsize=(3.321, 3.321))
ax = fig.add_subplot(111, projection=wcs)
vmax = np.nanpercentile(data, 95)
norm = simple_norm(data[~np.isnan(data)],
'log',
max_percent=95.,
clip=False)
cmap = plt.cm.Blues_r
cmap.set_bad('w')
plt.imshow(
data,
origin='lower',
cmap=cmap,
#norm=norm,
#interpolation='none',
vmax=vmax)
colors = ['tab:red', 'yellow', 'orange', 'green']
for i, aperture in enumerate(apertures):
aperture.plot(color=colors[i], lw=.8, alpha=1)
ax.set_xlabel(r'RA (J2000)')
ax.set_ylabel(r'Dec (J2000)')
plt.tight_layout()
if filename:
if not isinstance(filename, Path):
filename = Path(filename)
savefig(filename, bbox_inches='tight')
def ndarray_to_png(x, min_percent=20, max_percent=99.5):
shape = np.array(x.shape)
# Reverse order for reasons I do not understand...
shape = shape[::-1]
if len(shape) != 2:
return
width = 600 # pixels
downsample = (shape[0] // width) + 1
if downsample > 1:
x = block_reduce(x,
block_size=(downsample, downsample))
norm = simple_norm(x,
min_percent=min_percent,
max_percent=max_percent,
clip=True)
x = norm(x)
# Replace NaNs with black pixels
x = np.nan_to_num(x)
img_buffer = BytesIO()
mimg.imsave(img_buffer, x, format='png', cmap='gray')
return img_buffer.getvalue()
def show_psf(self):
norm = simple_norm(self.epsf.data, 'log', percent=99.)
plt.figure()
plt.imshow(self.epsf.data, norm=norm, origin='lower', cmap='viridis')
plt.colorbar()
#plt.show()
plt.savefig('plots/' + self.basename + '-psf.png')
def single_cutout(ax,position,image,mask1=None,mask2=None,points=None,label=None,size=6*u.arcsec):
cutout_image = Cutout2D(image.data,position,size=size,wcs=image.wcs)
norm = simple_norm(cutout_image.data,clip=False,stretch='linear',percent=99.5)
ax.imshow(cutout_image.data,origin='lower',norm=norm,cmap=plt.cm.gray_r)
# plot the nebulae catalogue
cutout_mask, _ = reproject_interp(mask1,output_projection=cutout_image.wcs,shape_out=cutout_image.shape,order='nearest-neighbor')
region_ID = np.unique(cutout_mask[~np.isnan(cutout_mask)])
contours = []
for i in region_ID:
blank_mask = np.zeros_like(cutout_mask)
blank_mask[cutout_mask==i] = 1
contours += find_contours(blank_mask, 0.5)
for coords in contours:
ax.plot(coords[:,1],coords[:,0],color='tab:red',lw=1,label='HII-region')
mask = np.zeros((*cutout_mask.shape,4))
mask[~np.isnan(cutout_mask.data),:] = (0.84, 0.15, 0.16,0.1)
ax.imshow(mask,origin='lower')
# plot the association catalogue
if mask2:
cutout_mask, _ = reproject_interp(mask2,output_projection=cutout_image.wcs,shape_out=cutout_image.shape,order='nearest-neighbor')
region_ID = np.unique(cutout_mask[~np.isnan(cutout_mask)])
contours = []
for i in region_ID:
blank_mask = np.zeros_like(cutout_mask)
blank_mask[cutout_mask==i] = 1
contours += find_contours(blank_mask, 0.5)
for coords in contours:
ax.plot(coords[:,1],coords[:,0],color='tab:blue',lw=1,label='association')
mask = np.zeros((*cutout_mask.shape,4))
mask[~np.isnan(cutout_mask.data),:] = (0.12,0.47,0.71,0.1)
ax.imshow(mask,origin='lower')
# mark the position of the clusters within the cutout
if points:
region = RectangleSkyRegion(position,0.9*size,0.9*size)
in_frame = points[region.contains(points['SkyCoord'],cutout_image.wcs)]
for row in in_frame:
x,y = row['SkyCoord'].to_pixel(cutout_image.wcs)
if 5<x<cutout_image.data.shape[0]-5 and 5<y<cutout_image.data.shape[1]-5:
ax.scatter(x,y,marker='o',facecolors='none',s=20,lw=0.4,color='tab:blue',label='cluster')
if label:
t = ax.text(0.07,0.875,label, transform=ax.transAxes,color='black',fontsize=8)
t.set_bbox(dict(facecolor='white', alpha=1, ec='white'))
ax.set_xticks([])
ax.set_yticks([])
return ax
def show_fov_on_spitzer(finaliter_prefix_b3,
finaliter_prefix_b6,
fieldid,
spitzerpath='spitzer_datapath',
spitzer_display_args=dict(stretch='log',
min_percent=1,
max_percent=99.99,
clip=True),
contour_level={
'B3': [0.01],
'B6': [0.01]
}):
image_b3 = SpectralCube.read(f'{finaliter_prefix_b3}.image.tt0.fits',
use_dask=False,
format='fits')
image_b6 = SpectralCube.read(f'{finaliter_prefix_b6}.image.tt0.fits',
use_dask=False,
format='fits')
spitzfn = f'{spitzerpath}/{fieldid}_spitzer_images.fits'
spitz = fits.open(spitzfn)[0]
ww = wcs.WCS(spitz.header)
fig = pl.figure(1, figsize=(10, 10))
fig.clf()
ax = fig.add_subplot(projection=ww.celestial)
spitz_data = np.array(
[simple_norm(x, **spitzer_display_args)(x) for x in spitz.data])
ax.imshow(spitz_data.T.swapaxes(0, 1))
lims = ax.axis()
ax.contour(image_b3.mask.include()[0],
transform=ax.get_transform(image_b3.wcs.celestial),
levels=[0.5],
colors=['orange'])
ax.contour(image_b6.mask.include()[0],
transform=ax.get_transform(image_b6.wcs.celestial),
levels=[0.5],
colors=['cyan'])
ax.contour(image_b3[0].value,
transform=ax.get_transform(image_b3.wcs.celestial),
levels=contour_level['B3'],
colors=['wheat'],
linewidths=[0.5])
ax.contour(image_b6[0].value,
transform=ax.get_transform(image_b6.wcs.celestial),
levels=contour_level['B6'],
colors=['lightblue'],
linewidths=[0.5])
ax.axis(lims)
ax.set_xlabel('Galactic Longitude')
ax.set_ylabel('Galactic Latitude')
#pl.figure(2).gca().imshow(image_b6.mask.include()[0])
return fig
def showimage(imagename, title=None, stretch='asinh', mask=None, **kwargs):
if isinstance(imagename, str):
ia.open(imagename)
data = ia.getchunk().squeeze()
ia.close()
else:
data = imagename
if 'percentiles' in kwargs:
min_percent, max_percent = kwargs.pop('percentiles')
kwargs['min_percent'] = min_percent
kwargs['max_percent'] = max_percent
if mask is not None:
data = data.squeeze().T * mask.T,
else:
data = data.squeeze().T
im = pl.imshow(
data,
norm=simple_norm(data, stretch=stretch, **kwargs),
origin='lower',
interpolation='none',
)
if title is not None:
pl.title(title)
pl.gca().set_xticklabels([])
pl.gca().set_yticklabels([])
pl.colorbar()
return im
def OldPlotCube(cube, tdisplay, timebin):
"""
Plot the image of the cube at tdisplay, as well as the cube projected lightcurve, with a dshed line at tdisplay
"""
Tsize = np.shape(cube)[2]
print("No elements =", Tsize)
time = np.arange(0, Tsize) * timebin # timebin is real time of exposure
im_t = cube[:, :, tdisplay].astype(float) # Create image at tdisplay
LC = np.sum(cube, axis=(0, 1)) # Project the curbe on the time dimension
fig = plt.figure(figsize=(10, 5))
# Image at tdisplay from the cube
left, bottom, width, height = 0.05, 0., 0.45, 1
ax1 = fig.add_axes([left, bottom, width, height])
ax1.grid(False)
stretch = 'sqrt'
p1 = ax1.imshow(im_t, norm=simple_norm(im_t, stretch), vmax=np.max(cube))
cbar = plt.colorbar(p1, fraction=0.045, ax=ax1)
# Lightcurve from the cube
left, bottom, width, height = 0.65, 0.13, 0.5, 0.8
ax2 = fig.add_axes([left, bottom, width, height])
ax2.axvline(tdisplay * timebin, color='blue', ls=':')
ax2.set_xlabel('Time (sec)', fontsize=15)
ax2.set_ylabel('Counts', fontsize=15)
ax2.plot(time, LC)
plt.show()
return
def _data_stretch(image, vmin=None, vmax=None, pmin=0.25, pmax=99.75,
stretch='linear', vmid=None, exponent=2):
if vmin is None or vmax is None:
interval = AsymmetricPercentileInterval(pmin, pmax, n_samples=10000)
try:
vmin_auto, vmax_auto = interval.get_limits(image)
except IndexError: # no valid values
vmin_auto = vmax_auto = 0
if vmin is None:
log.info("vmin = %10.3e (auto)" % vmin_auto)
vmin = vmin_auto
else:
log.info("vmin = %10.3e" % vmin)
if vmax is None:
log.info("vmax = %10.3e (auto)" % vmax_auto)
vmax = vmax_auto
else:
log.info("vmax = %10.3e" % vmax)
if stretch == 'arcsinh':
stretch = 'asinh'
normalizer = simple_norm(image, stretch=stretch, power=exponent,
asinh_a=vmid, min_cut=vmin, max_cut=vmax, clip=False)
data = normalizer(image, clip=True).filled(0)
data = np.nan_to_num(data)
data = np.clip(data * 255., 0., 255.)
return data.astype(np.uint8)
def norm_imshow(ax,
data,
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,
log_a=1000,
**kwargs):
""" Do normalization and do imshow
"""
norm = simple_norm(data,
stretch=stretch,
power=power,
asinh_a=asinh_a,
min_cut=min_cut,
max_cut=max_cut,
min_percent=min_percent,
max_percent=max_percent,
percent=percent,
clip=clip,
log_a=log_a)
im = ax.imshow(data, norm=norm, origin='lower', **kwargs)
return im
def run_test_dectecion():
nrows = 2
ncols = 4
fig, ax = plt.subplots(nrows=nrows,
ncols=ncols,
figsize=(20, 10),
squeeze=True)
ax = ax.ravel()
amplitude_lst = [40, 200]
sigma_lst = [2., 4.]
StarFinder_lst = [DAOStarFinder, IRAFStarFinder]
settings = []
for f in StarFinder_lst:
for s in sigma_lst:
for a in amplitude_lst:
settings.append((f, s, a))
for i in range(nrows * ncols):
f, s, a = settings[i]
img, ap, sc, string = test_detection(f, s, a, PSF_size=1)
norm = simple_norm(img, 'log', percent=99.)
ax[i].imshow(img, norm=norm, origin='lower', cmap='viridis')
ap.plot(color='red', lw=1., alpha=0.9, ax=ax[i])
ax[i].scatter(sc['x_mean'], sc['y_mean'], color='red', s=1)
ax[i].set_title(string)
plt.show()
def _data_stretch(image, vmin=None, vmax=None, pmin=0.25, pmax=99.75,
stretch='linear', vmid=None, exponent=2):
if vmin is None or vmax is None:
interval = AsymmetricPercentileInterval(pmin, pmax, n_samples=10000)
try:
vmin_auto, vmax_auto = interval.get_limits(image)
except IndexError: # no valid values
vmin_auto = vmax_auto = 0
if vmin is None:
vmin = vmin_auto
if vmax is None:
vmax = vmax_auto
normalizer = simple_norm(image, stretch=stretch, power=exponent,
asinh_a=vmid, min_cut=vmin, max_cut=vmax, clip=False)
data = normalizer(image, clip=True).filled(0)
data = np.nan_to_num(data)
data = np.clip(data * 255., 0., 255.)
return data.astype(np.uint8)
def create_example_frame(self, plot=True, figsize=(13, 13)):
fits_files = list(self.datadir.glob('MCT*fits'))
assert len(fits_files) > 0, 'No example frame fits-files found.'
f = fits_files[0]
f1 = pf.open(f)
f2 = pf.open(f.with_suffix('.wcs'))
h1 = f1[0].header.copy()
h1.append(('COMMENT', '*********************'))
h1.append(('COMMENT', ' WCS '))
h1.append(('COMMENT', '*********************'))
h1.extend(f2[0].header, unique=True, bottom=True)
f1[0].header = h1
filter = h1['filter']
f1.writeto(self._dres.joinpath(
f'{self.ticname}_20{self.date}_MuSCAT2_{filter}_frame.fits'),
overwrite=True)
if plot:
wcs = WCS(f1[0].header)
data = f1[0].data.astype('d')
norm = simple_norm(data, stretch='log')
fig = figure(figsize=figsize, constrained_layout=True)
ax = fig.add_subplot(111, projection=wcs)
ax.imshow(data, origin='image', norm=norm, cmap=cm.gray_r)
apts = SkyCircularAperture(
self.phs[0].centroids_sky,
float(self.phs[0]._flux.aperture[self.lpf.aid]) * u.pixel)
apts.to_pixel(wcs).plot(color='w')
def _plot_interactive(index, stretch, ax=None, fig=None):
if self.geom.is_image:
raise TypeError('Use .plot() for 2D Maps')
if fig is None:
fig = plt.gcf()
if ax is None:
ax = fig.add_subplot(1, 1, 1, projection=self.geom.wcs)
axes = self.geom.get_axis_by_name(self.geom.axes_names[0])
data = self.get_image_by_idx([index]).data
norm = simple_norm(data[np.isfinite(data)], stretch)
caxes = ax.imshow(data, norm=norm, **kwargs)
fig.colorbar(caxes, ax=ax)
ax.set_title('{:.2f}-{:.2f} {} '.format(
axes.edges[index],
axes.edges[index + 1],
self.geom.axes[0].unit.name,
))
try:
ax.coords['glon'].set_axislabel('Galactic Longitude')
ax.coords['glat'].set_axislabel('Galactic Latitude')
except KeyError:
ax.coords['ra'].set_axislabel('Right Ascension')
ax.coords['dec'].set_axislabel('Declination')
except AttributeError:
log.info(
"Can't set coordinate axes. No WCS information available.")
plt.show()
def scale_and_downsample(data, downsample=4, min_percent=20, max_percent=99.5):
norm = simple_norm(data, min_percent=min_percent, max_percent=max_percent)
scaled_data = norm(data)
if downsample > 1:
scaled_data = block_reduce(scaled_data,
block_size=(downsample, downsample))
return scaled_data
def plot_epsf(epsf):
norm = simple_norm(epsf.data, 'log', percent=99.)
plt.figure()
plt.imshow(epsf.data, norm=norm, origin='lowerleft', cmap='viridis')
plt.colorbar()
plt.show()
return
def plot_sky_with_detected_stars(data, wcs, positions, filename=None):
'''plot line map with detected sources
Parameters
----------
data : 2d array
numpy array that contains the image data
wcs :
wcs information for the projection
positions : array or tuple
(n,2) shaped array with positions. Can also be a tuple of
multiple such arrays.
filename : Path
if given, a PDF of the plot is saved to filename
'''
apertures = []
if isinstance(positions, tuple) or isinstance(positions, list):
for position in positions:
apertures.append(CircularAperture(position, r=8))
else:
apertures.append(CircularAperture(positions, r=8))
fig = figure(figsize=(6.974, 6.974 / 2))
ax = fig.add_subplot(111, projection=wcs)
norm = simple_norm(data,
'asinh',
clip=False,
min_percent=1,
max_percent=99.5)
cmap = plt.cm.hot
cmap.set_bad('w')
plt.imshow(
data,
origin='lower',
cmap=cmap,
norm=norm,
#interpolation='none',
#vmax=vmax
)
for aperture in apertures:
aperture.plot(color='blue', lw=.5, alpha=1)
ax.set_xlabel('RA')
ax.set_ylabel('Dec')
if filename:
if not isinstance(filename, Path):
filename = Path(filename)
plt.savefig(filename, dpi=600)
def plot_spatial(image,
plotfname='spatial.pdf',
coords1='None',
coords2='None',
stars='None',
mags='False',
annotate='no',
interactive=False):
fig, axes = plt.subplots(figsize=(14, 14))
norm = simple_norm(image, 'sqrt', percent=90.)
axes.imshow(image, aspect=1, origin='lower', cmap='Greys', norm=norm)
if coords1 != 'None':
coords1_x, coords1_y = coords1[0], coords1[1]
for i in range(len(coords1_x)):
e = Circle(xy=(coords1_x[i], coords1_y[i]), radius=4)
e.set_facecolor('none')
e.set_edgecolor('deeppink')
axes.add_artist(e)
if coords2 != 'None':
coords2_x, coords2_y = coords2[0], coords2[1]
for j in range(len(coords2_x)):
e = Circle(xy=(coords2_x[j], coords2_y[j]), radius=3)
e.set_facecolor('none')
e.set_edgecolor('blue')
axes.add_artist(e)
if ((stars != 'None') & (mags == 'False')):
for star in stars:
e = Circle(xy=(star.xcoord, star.ycoord), radius=3)
e.set_facecolor('none')
e.set_edgecolor('deeppink')
if annotate == 'yes':
axes.annotate(str(star.star_id), (star.xcoord, star.ycoord),
(5, 5),
textcoords='offset points',
color='deeppink')
axes.add_artist(e)
if ((stars != 'None') & (mags == 'True')):
for star in stars:
e = Circle(xy=(star.xcoord, star.ycoord),
radius=(18 - star.ir_mag) * 2)
e.set_facecolor('none')
e.set_edgecolor('C1')
axes.annotate(str(star.star_id), (star.xcoord, star.ycoord),
(5, 5),
textcoords='offset points',
color='C1')
axes.add_artist(e)
axes.set_xlabel('x coordinate [px]')
axes.set_ylabel('y coordinate [px]')
fig.savefig(plotfname, bbox_inches='tight')
if interactive:
plt.show()
def update_image(self, ax, img, gamma):
try:
R = self.img[:,:,0]; G = self.img[:,:,1]; B = self.img[:,:,2]
Rg = np.clip(np.power(R, gamma), 0, 1)
Gg = np.clip(np.power(G, gamma), 0, 1)
Bg = np.clip(np.power(B, gamma), 0, 1)
img_gamma = np.min([Rg, Gg, Bg], axis=0)
img.set_data(img_gamma)
except IndexError:
img.norm = simple_norm(self.img, 'sqrt')
def gshow(data, ax=None):
if (ax == None):
f, ax = plt.subtplos(1, 1)
norm = simple_norm(data, 'sqrt', percent=99.9)
cs = ax.imshow(data, norm=norm, cmap='gist_gray_r')
if (ax == None):
f.colorbar(cs)
return ax
def scale_and_downsample(data, downsample=4, min_percent=20, max_percent=99.5):
scaled_data = data
if downsample > 1:
scaled_data = block_reduce(scaled_data,
block_size=(downsample, downsample))
norm = simple_norm(scaled_data,
min_percent=min_percent,
max_percent=max_percent,
clip=True)
return norm(scaled_data)
def get_wise_image_old(self):
'''
GOAL: Get the unWISE image from the unWISE catalog
INPUT: nsaid used to grab unwise image information
OUTPUT: Name of file to retrieve from
'''
baseurl = 'http://unwise.me/cutout_fits?version=allwise'
imsize = self.radius * 2
imurl = baseurl + '&ra=%.5f&dec=%.5f&size=%s&bands=%s' % (
self.ra, self.dec, imsize, self.band)
wisetar = wget.download(imurl)
tartemp = tarfile.open(wisetar, mode='r:gz') #mode='r:gz'
wnames = tartemp.getnames()
self.wnames = wnames
print(wnames)
# check for multiple pointings - means galaxy is split between images
self.multiframe = False
if len(wnames) > 4:
self.multiframe = True
os.remove(wisetar)
return
wmembers = tartemp.getmembers()
tartemp.extractall()
for fname in wnames:
t = fname.split('-')
self.rename = self.galname + '-' + t[0] + '-' + t[2] + '-' + t[
3] + '-' + t[4]
#print self.rename
if os.path.exists(
self.rename
): # this should only occur if multiple images are returned from wise
os.remove(self.rename)
os.rename(fname, self.rename)
if self.rename.find('.gz') > -1:
os.system('gunzip ' + self.rename)
if fname.find('img') > -1:
self.inputimage = self.rename
os.remove(wisetar)
print('self.rename = ', self.rename)
##### DISPLAY IMAGE
im = fits.getdata(self.rename)
norm = simple_norm(im, stretch='asinh', percent=99)
plt.imshow(im, norm=norm)
plt.show()
def extract_radio_properties(hdu, survey_name, rms=None, savedir=False):
fig = plt.figure(figsize=(18, 18))
imdata, w = hdu[0].data, wcs.WCS(hdu[0].header, naxis=2)
norm = simple_norm(imdata, percent=99.5)
ax0 = fig.add_axes([0.1, 0.1, 0.85, 0.85], projection=w)
ax0.imshow(imdata, cmap='inferno', norm=norm)
if rms:
ax0.contour(imdata, levels=[3, 5] * rms, colors='white', lw=1)
polypick = PolyPick(ax0)
if savedir == True:
for ext in ['.pdf', '.png']:
fig.savefig('{0}/{1}_LLS_plot{2}'.format(savedir, survey_name,
ext))
plt.show()
if len(polypick.las.x):
LAS_points = Coords()
LAS_points.x, LAS_points.y = w.wcs_pix2world(polypick.las.x,
polypick.las.y, 0)
las = (compute_angular_separation(LAS_points.x[0], LAS_points.y[0],
LAS_points.x[1],
LAS_points.y[1])).arcsecond
las = float('%.4g' % las)
print('Measured a largest angular size of {0}'
' from {1}'.format(las, survey_name))
else:
print('No angular size measured.')
las = np.nan
if len(polypick.ucore.x):
core_coords = Coords()
core_coords.x, core_coords.y = w.wcs_pix2world(
(polypick.ucore.x)[len(polypick.ucore.x) - 1],
(polypick.ucore.y)[len(polypick.ucore.x) - 1], 0)
ra_core, dec_core = core_coords.x, core_coords.y
core_type = 'unresolved'
print(
'Unresolved {0} radio core measured at RA = {1}, Dec = {2}'.format(
las, ra_core, dec_core))
elif len(polypick.ecore.x):
core_coords = Coords()
core_coords.x, core_coords.y = w.wcs_pix2world(
(polypick.ecore.x)[len(polypick.ecore.x) - 1],
(polypick.ecore.y)[len(polypick.ecore.x) - 1], 0)
ra_core, dec_core = core_coords.x, core_coords.y
core_type = 'resolved'
print('Resolved {0} radio core measured at RA = {1}, Dec = {2}'.format(
las, ra_core, dec_core))
else:
print('No radio core measured.')
ra_core, dec_core, core_type = np.nan, np.nan, 0
return (las, ra_core, dec_core, core_type)
def plot(self,line):
'''plot a single emission line'''
if not hasattr(self,line):
raise AttributeError(f'Object has no map {line}')
data = getattr(self,line)
fig = plt.figure(figsize=(two_column,two_column/1.618))
ax = fig.add_subplot(projection=self.wcs)
norm = simple_norm(data,clip=False,percent=99)
ax.imshow(data,norm=norm)
plt.show()
def scale_and_downsample(data, downsample=4,
min_percent=20,
max_percent=99.5):
scaled_data = data
if downsample > 1:
scaled_data = block_reduce(scaled_data,
block_size=(downsample, downsample))
norm = simple_norm(scaled_data,
min_percent=min_percent,
max_percent=max_percent)
return norm(scaled_data)
def make_anim_single(imname, suffix, nselfcaliter=7, stretch='asinh', min_percent=1, max_percent=99.00):
# base imname: W51-E_B6_uid___A001_X1296_X213_continuum_merged_12M_robust0
fig, ax = pl.subplots(ncols=1, figsize=(8,8))
fig.set_tight_layout(True)
dpi = fig.get_dpi()
size_inches = fig.get_size_inches()
print(f"Figure size={size_inches} dpi={dpi}")
ax.set_xticklabels([])
ax.set_yticklabels([])
cube_before = SpectralCube.read(f'{imname}_preselfcal.{suffix}.tt0', format='casa_image')
data = cube_before[0].value
norm = visualization.simple_norm(data=data, stretch=stretch,
min_percent=min_percent,
max_percent=max_percent)
im1 = ax.imshow(data, norm=norm)
title = pl.title("Before selfcal")
def update(ii):
try:
if os.path.exists(f'{imname}_selfcal{ii}_finaliter.{suffix}.tt0'):
cube = SpectralCube.read(f'{imname}_selfcal{ii}_finaliter.{suffix}.tt0', format='casa_image')
im1.set_data(cube[0].value)
title.set_text(f"Selfcal iteration {ii} (final clean)")
return im1, ax
if ii == 0:
im1.set_data(data)
title.set_text(f"Before selfcal")
return im1, ax
else:
cube = SpectralCube.read(f'{imname}_selfcal{ii}.{suffix}.tt0', format='casa_image')
im1.set_data(cube[0].value)
title.set_text(f"Selfcal iteration {ii}")
return im1, ax
except Exception as ex:
print(ex)
anim = FuncAnimation(fig, update, frames=range(0, nselfcaliter), interval=400)
anim.save(f'{imname}_{suffix}_selfcal_anim.gif', dpi=dpi, writer='imagemagick')
return anim
def norm_imshow(ax,
data,
origin="lower",
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,
log_a=1000,
invalid=-1.0,
zscale=False,
vmin=None,
vmax=None,
**kwargs):
"""Do normalization and do imshow"""
if vmin is not None and min_cut is not None:
warn("vmin will override min_cut.")
if vmax is not None and max_cut is not None:
warn("vmax will override max_cut.")
if zscale:
zs = ImageNormalize(data, interval=ZScaleInterval())
min_cut = vmin = zs.vmin
max_cut = vmax = zs.vmax
if vmin is not None or vmax is not None:
im = ax.imshow(data, origin=origin, vmin=vmin, vmax=vmax, **kwargs)
else:
im = ax.imshow(data,
origin=origin,
norm=simple_norm(data=data,
stretch=stretch,
power=power,
asinh_a=asinh_a,
min_cut=min_cut,
max_cut=max_cut,
min_percent=min_percent,
max_percent=max_percent,
percent=percent,
clip=clip,
log_a=log_a,
invalid=invalid),
**kwargs)
return im
def sample_cutouts(data,peaks_tbl,wcs,nrows=10,ncols=10,filename=None):
'''create cutouts of the detected sources and plot them
'''
# exclude stars that are too close to the border
size = 16
hsize = (size - 1) / 2
x = peaks_tbl['x']
y = peaks_tbl['y']
mask = ((x > hsize) & (x < (data.shape[1] -1 - hsize)) &
(y > hsize) & (y < (data.shape[0] -1 - hsize)))
stars_tbl = Table()
stars_tbl['x'] = x[mask]
stars_tbl['y'] = y[mask]
# extract_stars does not include wcs information
#nddata = NDData(data=data,wcs=self.wcs)
#stars = extract_stars(nddata, stars_tbl, size=size)
# defien the size of the cutout region
size = u.Quantity((size, size), u.pixel)
stars = []
for row in stars_tbl:
stars.append(Cutout2D(data, (row['x'],row['y']), size,wcs=wcs))
#fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=(20, 20),squeeze=True)
#ax = ax.ravel()
fig = figure(figsize=(100,100))
# get an index idx from 0 to nrows*ncols and a random index i from 0 to len(stars)
for idx,i in enumerate(random.sample(range(len(stars)), nrows*ncols)):
ax = fig.add_subplot(nrows,ncols,idx+1,projection=stars[i].wcs)
if np.any(np.isnan(stars[i].data)):
print('this should not be')
norm = simple_norm(stars[i].data, 'log', percent=99.)
ax.imshow(stars[i].data, norm=norm, origin='lower', cmap='Blues_r')
if filename:
if not isinstance(filename,Path):
filename = Path(filename)
plt.savefig(filename)
def PlotCube(cube, time, tdisplay):
"""
Plot the image of the cube at tdisplay, as well as the cube projected lightcurve, with a dshed line at tdisplay
"""
#Tsize=np.shape(cube)[-1]
#print("No elements =",Tsize)
#time = np.arange(0,Tsize)*timebin # timebin is real time of exposure
im_t = cube[:, :, np.where(time == tdisplay)[0][0]].astype(
float) # Create image at tdisplay
LC = CubeLC(cube)
fig = plt.figure(figsize=(10, 5))
# Image at tdisplay from the cube
left, bottom, width, height = 0.05, 0., 0.45, 1
ax1 = fig.add_axes([left, bottom, width, height])
ax1.grid(False)
stretch = 'sqrt'
if np.isnan(cube).any():
new_im_t = np.ma.array(im_t,
mask=np.isnan(im_t)) # Array with masked values
cmap = plt.cm.jet
cmap.set_bad('white', 1.)
p1 = ax1.imshow(new_im_t,
cmap=cmap,
vmax=np.max(cube[~np.isnan(cube)]))
else:
p1 = ax1.imshow(im_t,
norm=simple_norm(im_t, stretch),
vmax=np.max(cube))
cbar = plt.colorbar(p1, fraction=0.045, ax=ax1)
ax1.set_xlabel('x (px)')
ax1.set_ylabel('y (px)')
# Lightcurve from the cube
left, bottom, width, height = 0.65, 0.13, 0.5, 0.8
ax2 = fig.add_axes([left, bottom, width, height])
ax2.axvline(tdisplay, color='blue', ls=':')
ax2.set_xlabel('Time (sec)', fontsize=15)
ax2.set_ylabel('Counts', fontsize=15)
ax2.plot(time, LC)
plt.show()
return
def ndarray_to_png(x, min_percent=20, max_percent=99.5):
shape = np.array(x.shape)
# Reverse order for reasons I do not understand...
shape = shape[::-1]
if len(shape) != 2:
return
width = 600 # pixels
downsample = (shape[0] // width) + 1
if downsample > 1:
x = block_reduce(x,
block_size=(downsample, downsample))
norm = simple_norm(x,
min_percent=min_percent,
max_percent=max_percent)
img_buffer = BytesIO()
mimg.imsave(img_buffer, norm(x), format='png', cmap='gray')
return img_buffer.getvalue()
def plot(self, ax=None, fig=None, add_cbar=False, stretch="linear", **kwargs):
"""
Plot image on matplotlib WCS axes.
Parameters
----------
ax : `~astropy.visualization.wcsaxes.WCSAxes`, optional
WCS axis object to plot on.
fig : `~matplotlib.figure.Figure`
Figure object.
add_cbar : bool
Add color bar?
stretch : str
Passed to `astropy.visualization.simple_norm`.
**kwargs : dict
Keyword arguments passed to `~matplotlib.pyplot.imshow`.
Returns
-------
fig : `~matplotlib.figure.Figure`
Figure object.
ax : `~astropy.visualization.wcsaxes.WCSAxes`
WCS axis object
cbar : `~matplotlib.colorbar.Colorbar` or None
Colorbar object.
"""
import matplotlib.pyplot as plt
from astropy.visualization import simple_norm
from astropy.visualization.wcsaxes.frame import EllipticalFrame
if not self.geom.is_image:
raise TypeError("Use .plot_interactive() for Map dimension > 2")
if fig is None:
fig = plt.gcf()
if ax is None:
if self.geom.is_allsky:
ax = fig.add_subplot(
1, 1, 1, projection=self.geom.wcs, frame_class=EllipticalFrame
)
else:
ax = fig.add_subplot(1, 1, 1, projection=self.geom.wcs)
data = self.data.astype(float)
kwargs.setdefault("interpolation", "nearest")
kwargs.setdefault("origin", "lower")
kwargs.setdefault("cmap", "afmhot")
norm = simple_norm(data[np.isfinite(data)], stretch)
kwargs.setdefault("norm", norm)
caxes = ax.imshow(data, **kwargs)
cbar = fig.colorbar(caxes, ax=ax, label=str(self.unit)) if add_cbar else None
if self.geom.is_allsky:
ax = self._plot_format_allsky(ax)
else:
ax = self._plot_format(ax)
# without this the axis limits are changed when calling scatter
ax.autoscale(enable=False)
return fig, ax, cbar
def main(argv=None):
"""Process command line"""
import argparse
import json
import matplotlib.pyplot as plt
import astropy.io.fits as fits
from astropy.wcs import WCS
from astropy.visualization import simple_norm
import matplotlib.transforms as mtransforms
import megaradrp.datamodel as dm
from megaradrp.processing.wcs import update_wcs_from_ipa, compute_pa_from_ipa
try:
from megaradrp.processing.cube import create_cube_from_rss
has_contours = True
except ImportError:
create_cube_from_rss = None
has_contours = False
parser = argparse.ArgumentParser(description='Display MEGARA RSS images')
parser.add_argument('--wcs-grid', action='store_true',
help='Display WCS grid')
parser.add_argument('--wcs-pa-from-header', action='store_true',
help="Use PA angle from PC keys", dest='pa_from_header')
parser.add_argument('--average-region', nargs=2, default=[1000, 3000],
type=int, help='Region of the RSS averaged on display')
parser.add_argument('--extname', '-e', default='PRIMARY',
help='Name of the extension used')
parser.add_argument('--column', '-c', type=int,
help='Column of the RSS on display')
parser.add_argument('--continuum-region', nargs=2,
type=int,
help='Region of the RSS used for continuum subtraction')
parser.add_argument('--coordinate-type', choices=['pixel', 'wcs'],
default='pixel',
help='Types of coordinates used')
parser.add_argument('--colormap', type=plt.get_cmap,
help='Name of a valid matplotlib colormap')
parser.add_argument('--plot-sky', action='store_true',
help='Plot SKY bundles')
parser.add_argument('--plot-nominal-config', action='store_true',
help='Plot nominal configuration, do not use the header')
parser.add_argument('--hide-values', action='store_true',
help='Do not show values out of range')
parser.add_argument('--title', help='Title of the plot')
parser.add_argument('--label', help='Legend of the colorbar')
parser.add_argument('--hex-size', type=float,
help='Size of the hexagons (default is {})'.format(SCALE),
default=SCALE)
parser.add_argument('--hex-rel-size', type=float,
help='Scale the size of hexagons by a factor',
default=1.0)
parser.add_argument('--min-cut', type=float,
help='Inferior cut level')
parser.add_argument('--max-cut', type=float,
help='Superior cut level')
parser.add_argument('--percent', type=float,
help='Compute cuts using percentiles')
parser.add_argument('--stretch',
choices=['linear', 'sqrt', 'power', 'log', 'asinh'],
default='linear',
help='Name of the strech method used for display'
)
if has_contours:
parser.set_defaults(has_contours=True)
group_c = parser.add_argument_group('contouring')
group_c.add_argument('--contour-pixel-size', type=float, default=0.4,
help="Pixel size in arc seconds for image reconstruction")
group_c.add_argument('--contour-levels',
help="Contour levels")
group_c.add_argument('--contour', action='store_true',
help="Draw contours")
group_c.add_argument('--contour-image',
help="Image for computing contours")
group_c.add_argument('--contour-image-column', type=int,
help='Column of image used for contouring')
group_c.add_argument('--contour-image-save',
help='Save image used for contouring')
group_c.add_argument('--contour-image-region', nargs=2, default=[1000, 3000],
type=int, help='Region of the image used for contouring')
group_c.add_argument('--contour-is-density', action='store_true',
help='The data is a magnitude that does not require scaling')
else:
parser.set_defaults(has_contours=False)
parser.add_argument('rss', metavar='RSS', nargs='+',
help='RSS images to process')
args = parser.parse_args(args=argv)
for fname in args.rss:
with fits.open(fname) as img:
datamodel = dm.MegaraDataModel()
if args.plot_nominal_config:
insmode = img['FIBERS'].header['INSMODE']
fiberconf = datamodel.get_fiberconf_default(insmode)
else:
fiberconf = datamodel.get_fiberconf(img)
plot_mask = np.ones((fiberconf.nfibers,), dtype=np.bool)
if not args.plot_sky:
skyfibers = fiberconf.sky_fibers()
skyfibers.sort()
skyfibers_idx = [(fibid - 1) for fibid in skyfibers]
plot_mask[skyfibers_idx] = False
x = np.empty((fiberconf.nfibers,))
y = np.empty((fiberconf.nfibers,))
# Key is fibid
for _, fiber in sorted(fiberconf.fibers.items()):
idx = fiber.fibid - 1
x[idx] = fiber.x
y[idx] = fiber.y
extname = args.extname
if args.coordinate_type == 'wcs':
# read WCS from extname
wcs_wl = WCS(img[extname].header)
else:
wcs_wl = WCS(naxis=len(img[extname].shape))
rssdata = np.squeeze(img[extname].data)
zval = extract_zval(rssdata,
wcs_wl,
args.coordinate_type,
args.column,
args.average_region,
args.continuum_region
)
fig = plt.figure()
x = x[plot_mask]
y = y[plot_mask]
zval = zval[plot_mask]
projection = None
if args.wcs_grid:
print('display wcs grid')
if not args.pa_from_header:
print('compute PA from IPA')
ipa = img[extname].header['IPA']
pa = compute_pa_from_ipa(ipa)
print('IPA is', ipa, ' PA is', pa)
hdr_fibers = update_wcs_from_ipa(img['FIBERS'].header, pa)
else:
hdr_fibers = img['FIBERS'].header
projection = WCS(hdr_fibers)
# plt.subplots_adjust(hspace=0.5)
ax = fig.add_axes([0.15, 0.1, 0.8, 0.8], projection=projection)
# ax.set_xlim([-6.5, 6.5])
# ax.set_ylim([-6, 6])
if args.wcs_grid:
ax.coords.grid(color='black', alpha=1.0, linestyle='solid')
norm = simple_norm(zval, args.stretch,
min_cut=args.min_cut,
max_cut=args.max_cut,
percent=args.percent
)
if args.hide_values:
zdisp = zval.copy()
if norm.vmin is not None:
zdisp[zval < norm.vmin] = np.nan
if norm.vmax is not None:
zdisp[zval > norm.vmax] = np.nan
else:
zdisp = zval
scale = args.hex_rel_size * args.hex_size
col = hexplot(ax, x, y, zdisp, scale=scale, cmap=args.colormap, norm=norm)
if args.title is not None:
ax.set_title(args.title)
cb = fig.colorbar(col)
if args.label is not None:
cb.set_label(args.label)
if args.has_contours and args.contour:
target_scale_arcsec = args.contour_pixel_size
if args.contour_image is not None:
with fits.open(args.contour_image) as hdu_con:
extname = args.extname
if args.coordinate_type == 'wcs':
# read WCS from extname
wcs_wl_c = WCS(hdu_con[extname].header)
else:
wcs_wl_c = WCS(naxis=len(hdu_con[extname].shape))
rssdata_con = np.squeeze(hdu_con[extname].data)
zval_c = extract_zval(rssdata_con,
wcs_wl_c,
args.coordinate_type,
column=args.contour_image_column,
average_region=args.contour_image_region,
continuum_region=None)
else:
zval_c = zval
# Build synthetic rss... for reconstruction
primary = fits.PrimaryHDU(data=zval_c[:, np.newaxis], header=img[extname].header)
synt = fits.HDUList([primary, img['FIBERS']])
if args.contour_image_save is not None:
synt.writeto(args.contour_image_save)
conserve_flux = not args.contour_is_density
s_cube = create_cube_from_rss(synt, target_scale_arcsec, conserve_flux=conserve_flux)
cube_wcs = WCS(s_cube[0].header).celestial
px, py = cube_wcs.wcs.crpix
interp = np.squeeze(s_cube[0].data)
td = mtransforms.Affine2D().translate(-px, -py).scale(target_scale_arcsec, target_scale_arcsec)
tt_d = td + ax.transData
# im = ax.imshow(interp, alpha=0.9, cmap='jet', transform=tt_d)
# im = ax.imshow(interp, alpha=0.9, cmap='jet', transform=ax.get_transform(cube_wcs))
if args.contour_levels is not None:
levels = json.loads(args.contour_levels)
mm = ax.contour(interp, levels, transform=tt_d)
else:
mm = ax.contour(interp, transform=tt_d)
print('contour levels', mm.levels)
plt.show()
def plot(self, ax=None, fig=None, add_cbar=False, stretch='linear', **kwargs):
"""
Plot image on matplotlib WCS axes.
Parameters
----------
ax : `~astropy.visualization.wcsaxes.WCSAxes`, optional
WCS axis object to plot on.
fig : `~matplotlib.figure.Figure`, optional
Figure
stretch : str, optional
Scaling for image ('linear', 'sqrt', 'log').
Similar to normalize and stretch functions in ds9.
See http://docs.astropy.org/en/stable/visualization/normalization.html
Returns
-------
fig : `~matplotlib.figure.Figure`, optional
Figure
ax : `~astropy.visualization.wcsaxes.WCSAxes`, optional
WCS axis object
cbar : ?
Colorbar object (if ``add_cbar=True`` was set)
Examples
--------
>>> from astropy.visualization import simple_norm
>>> from gammapy.image import SkyImage
>>> filename = '$GAMMAPY_EXTRA/datasets/fermi_2fhl/fermi_2fhl_vela.fits.gz'
>>> image = SkyImage.read(filename, hdu=2)
>>> norm = simple_norm(image, 'sqrt')
>>> plt.imshow(image, norm = norm)
>>> plt.show()
>>> #Equivalent to :
>>> image.plot(stretch='sqrt')
"""
import matplotlib.pyplot as plt
from astropy.visualization import simple_norm
# TODO: make skyimage.data a quantity
try:
data = self.data.value
except AttributeError:
data = self.data
if fig is None:
fig = plt.gcf()
if ax is None:
ax = fig.add_subplot(1, 1, 1, projection=self.wcs)
kwargs['origin'] = kwargs.get('origin', 'lower')
kwargs['cmap'] = kwargs.get('cmap', 'afmhot')
kwargs['interpolation'] = kwargs.get('interpolation', 'None')
norm = simple_norm(data[np.isfinite(data)], stretch)
kwargs.setdefault('norm', norm)
caxes = ax.imshow(data, **kwargs)
if add_cbar:
unit = self.unit or 'A.U.'
label = self.name or 'None'
cbar = fig.colorbar(caxes, ax=ax, label='{} ({})'.format(label.title(), unit))
else:
cbar = None
try:
ax.coords['glon'].set_axislabel('Galactic Longitude')
ax.coords['glat'].set_axislabel('Galactic Latitude')
except KeyError:
ax.coords['ra'].set_axislabel('Right Ascension')
ax.coords['dec'].set_axislabel('Declination')
except AttributeError:
log.info("Can't set coordinate axes. No WCS information available.")
# without this the axis limits are changed when calling scatter
ax.autoscale(enable=False)
return fig, ax, cbar
