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])
image_scaled = visualization.scale_image(image, scale="log",
min_cut=cut[0], max_cut=cut[1]) #min_percent=0.5, max_percent=99.5)
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, axisbg='green')
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 generate_tweet():
"""Generate a tweet and jpg.
Returns
-------
(status, jpg) : (str, str)
"""
imageid, fitsurl = select_random_image()
log.info('Opening {0}'.format(fitsurl))
fts = fits.open(fitsurl)
# Create the status message
imgtime = fts[0].header['IMG-TIME']
instrument = fts[0].header['INSTRUME'][8:]
exptime = fts[0].header['EXPTIME']
timestr = Time(imgtime).datetime.strftime('%d %b %Y, %H:%M UT').lstrip("0")
url = 'http://imagearchives.esac.esa.int/picture.php?/{0}'.format(imageid)
status = ('A new #Rosetta image was recently released!\n'
'⌚ {}.\n'
'📷 #{}.\n'
'⌛ {:.2f}s.\n'
'🔗 {}'.format(timestr, instrument, exptime, url))
# Create the cropped and scaled image
image_cropped = entropy_crop(fts[0].data, width=600, height=300)
image_scaled = scale_image(image_cropped, scale='linear',
min_percent=0.05, max_percent=99.95)
# Save the result as an image
image_fn = '/tmp/rosettabot.png'
log.info('Writing {0}'.format(image_fn))
imsave(image_fn, image_scaled, cmap=cm.gray)
return (status, image_fn)
def scale_and_downsample(data, downsample=4, min_percent=20, max_percent=99.5):
scaled_data = scale_image(data,
min_percent=min_percent,
max_percent=max_percent)
if downsample > 1:
scaled_data = block_reduce(scaled_data,
block_size=(downsample, downsample))
return scaled_data
def scale_and_downsample(data, downsample=4,
min_percent=20,
max_percent=99.5):
scaled_data = scale_image(data,
min_percent=min_percent,
max_percent=max_percent)
if downsample > 1:
scaled_data = block_reduce(scaled_data,
block_size=(downsample, downsample))
return scaled_data
def render_fits_image(hdu, scale='asinh', cmap=plt.cm.gray,
xlabel='RA', ylabel='Dec', title=None):
"Use matplotlib and astropy to render a FITS image HDU"
wcs = astropy.wcs.WCS(hdu.header)
fig = plt.figure()
axes = fig.add_subplot(111, projection=wcs)
plt.imshow(viz.scale_image(hdu.data, scale=scale),
cmap=cmap, origin='lower', interpolation='none')
plt.xlabel(xlabel)
plt.ylabel(ylabel)
if title is not None:
axes.set_title(title)
return fig
def scale(self, scale = 'linear', power = 1.0, min_cut = None, max_cut = None):
"""
Name: scale
Description:
Allows for rescaling the image. This will always scale the original
image so calling scale a second time on the image will overwrite
a previous scaling.
Parameters:
scale
power
min_cut
max_cut
"""
self.__image = vis.scale_image(self.__original,
scale = scale,
power = power,
min_cut = min_cut,
max_cut = max_cut)
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
scaled_data = scale_image(x,
min_percent=min_percent,
max_percent=max_percent)
if downsample > 1:
scaled_data = block_reduce(scaled_data,
block_size=(downsample, downsample))
img_buffer = BytesIO()
mimg.imsave(img_buffer, scaled_data, format='png', cmap='gray')
return img_buffer.getvalue()
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."""
with fits.open(self.fits_filename) as fts:
if (-np.isnan(fts[extension].data)).sum() == 0:
raise InvalidFrameException()
image = fts[extension].data[rowrange[0]:rowrange[1],
colrange[0]:colrange[1]]
if cut is None:
cut = np.percentile(image[-np.isnan(image)], [10, 99.5])
image_scaled = visualization.scale_image(
image, scale="log", min_cut=cut[0],
max_cut=cut[1]) #min_percent=0.5, max_percent=99.5)
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, axisbg='green')
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
lmaxb=base
#lmr=(99.6,99.5,99.9,99.95,99.5,99.93,99.91)
for i in files:
image=[]
for j in range(7):
image_file_B = i+'_'+type[j]+'_B.fits'
image_file_I = i+'_'+type[j]+'_I.fits'
image_file_V = i+'_'+type[j]+'_V.fits'
image_data_B = fits.getdata(image_file_B)
image_data_V = fits.getdata(image_file_V)
image_data_I = fits.getdata(image_file_I)
image_log_r=scale_image(image_data_I,'log',max_cut= lmaxr[j])
image_log_b=scale_image(image_data_B,'log',max_cut= lmaxb[j])
image_log_g=scale_image(image_data_V,'log',max_cut= lmaxg[j])
# image_log_r=scale_image(image_data_I,'log',max_percent= lmr[j])
# image_log_b=scale_image(image_data_B,'log',max_percent= lmr[j])
# image_log_g=scale_image(image_data_V,'log',max_percent= lmr[j])
if j==4:
image.append(image_cut(image_log_r,image_log_g,image_log_b,1.0,0.05,True))
else:
image.append(image_cut(image_log_r,image_log_g,image_log_b,1.0,0.05))
image_seven=image_align7(image[0],image[1],image[2],image[3],
image[4],image[5],image[6])
fig = plt.figure(figsize=(6*3,6*(np.sqrt(3)+1)))
fontsize=20
if image_entropy(top_slice, img_min, img_max) < image_entropy(bottom_slice, img_min, img_max):
top_y = top_y + slice_size
top_slice = None
else:
bottom_y = bottom_y - slice_size
bottom_slice = None
return img[top_y : top_y + height, left_x + width : left_x : -1]
if __name__ == "__main__":
"""Example use"""
from matplotlib.image import imsave
from matplotlib import cm
from astropy.io import fits
from astropy import log
from astropy.visualization import scale_image
fts = fits.open(
"http://imagearchives.esac.esa.int/data_raw/ROSETTA/NAVCAM"
"/RO-C-NAVCAM-2-PRL-MTP007-V1.0/DATA/CAM1"
"/ROS_CAM1_20140922T060854F.FIT"
)
img = fts[0].data
width, height = 512, 256
image_cropped = entropy_crop(img, width, height)
image_scaled = scale_image(image_cropped, scale="linear", min_percent=0.05, max_percent=99.95)
jpg_fn = "test.jpg"
log.info("Writing {0}".format(jpg_fn))
imsave(jpg_fn, image_scaled, cmap=cm.gray)
def overlap_skeletons(image,
big_skel,
norm_skel,
aplpy_plot=True,
save_figure=True,
save_name="skeletons",
output_file_type="png",
rasterized=True,
vmin=None,
vmax=None):
'''
Make a nice aplpy plot of the different skeletons. The original image
should be passed, and it will be expanded to match the dimensions of the
regridded skeleton.
'''
# Load files in
image, hdr = fits.getdata(image, header=True)
image[np.isnan(image)] = 0.0
norm_skel = fits.getdata(norm_skel)
norm_skel = (norm_skel > 0).astype(int)
big_skel = fits.getdata(big_skel)
big_skel = (big_skel > 0).astype(int)
big_skel_hdu = fits.PrimaryHDU(big_skel, header=hdr)
# The original image and the normal skeleton should have the same
# dimensions.
assert image.shape == norm_skel.shape
image = zoom(image,
[i / float(j) for j, i in zip(image.shape, big_skel.shape)])
assert image.shape == big_skel.shape
hdr['NAXIS1'] = image.shape[1]
hdr['NAXIS2'] = image.shape[0]
image_hdu = fits.PrimaryHDU(image, header=hdr)
norm_skel_zoom = \
zoom(norm_skel,
[i/float(j) for j, i in zip(norm_skel.shape, big_skel.shape)],
order=0)
assert norm_skel_zoom.shape == big_skel.shape
norm_skel_hdu = fits.PrimaryHDU(norm_skel_zoom, header=hdr)
if aplpy_plot:
try:
import aplpy
except ImportError:
ImportError("Cannot import aplpy. Do not enable aplpy.")
fig = aplpy.FITSFigure(image_hdu)
fig.show_grayscale(invert=True,
stretch="arcsinh",
vmin=vmin,
vmax=vmax)
fig.tick_labels.set_xformat('hh:mm')
fig.tick_labels.set_yformat('dd:mm')
fig.tick_labels.set_font(size='large',
weight='medium',
stretch='normal',
family='sans-serif',
style='normal',
variant='normal')
fig.axis_labels.set_font(size='large',
weight='medium',
stretch='normal',
family='sans-serif',
style='normal',
variant='normal')
# fig.add_grid()
# NOTE! - rasterization will only work with my fork of aplpy!
# [email protected]:e-koch/aplpy.git on branch 'rasterize_contours'
fig.show_contour(norm_skel_hdu,
colors="red",
linewidths=1.5,
rasterize=True)
fig.show_contour(big_skel_hdu, colors="blue", rasterize=True)
fig.show_colorbar()
fig.colorbar.set_label_properties(size='large',
weight='medium',
stretch='normal',
family='sans-serif',
style='normal',
variant='normal')
fig.colorbar.set_axis_label_text('Surface Brightness (MJy/sr)')
fig.colorbar.set_axis_label_font(size='large',
weight='medium',
stretch='normal',
family='sans-serif',
style='normal',
variant='normal')
if save_figure:
fig.save(save_name + "." + output_file_type)
fig.close()
else:
# Using matplotlib
from astropy.visualization import scale_image
scaled_image = scale_image(image, scale='asinh', asinh_a=0.005)
p.imshow(scaled_image,
interpolation='nearest',
origin='lower',
cmap='binary')
p.contour(norm_skel_zoom, colors='r', linewidths=2)
p.contour(big_skel, colors='b', linewidths=1)
if save_figure:
p.save(save_name + "." + output_file_type, rasterized=rasterized)
p.close()
else:
p.show(block=True)
tp.lightcurve(info)
position = (xnew,ynew)
aperture = CircularAperture(position, r=radius)
bkg_aperture = CircularAnnulus(position, r_in=15., r_out=20.)
# perform the photometry; the default method is 'exact'
phot = aperture_photometry(image, aperture)
bkg = aperture_photometry(image, bkg_aperture)
# calculate the mean background level (per pixel) in the annuli
bkg_mean = bkg['aperture_sum'] / bkg_aperture.area()
bkg_mean
bkg_sum = bkg_mean * aperture.area()
# plot the apertures
plt.imshow(scale_image(image, scale='sqrt', percent=98.),
origin='lower',cmap='gray')
aperture.plot(color='blue')
bkg_aperture.plot(color='cyan', hatch='//', alpha=0.8)
plt.xlim(xnew-100,xnew+100)
plt.ylim(ynew-100,ynew+100)
phot['bkg_sum'] = bkg_sum
def overlap_skeletons(image, big_skel, norm_skel, aplpy_plot=True,
save_figure=True, save_name="skeletons",
output_file_type="png", rasterized=True,
vmin=None, vmax=None):
'''
Make a nice aplpy plot of the different skeletons. The original image
should be passed, and it will be expanded to match the dimensions of the
regridded skeleton.
'''
# Load files in
image, hdr = fits.getdata(image, header=True)
image[np.isnan(image)] = 0.0
norm_skel = fits.getdata(norm_skel)
norm_skel = (norm_skel > 0).astype(int)
big_skel = fits.getdata(big_skel)
big_skel = (big_skel > 0).astype(int)
big_skel_hdu = fits.PrimaryHDU(big_skel, header=hdr)
# The original image and the normal skeleton should have the same
# dimensions.
assert image.shape == norm_skel.shape
image = zoom(image,
[i/float(j) for j, i in zip(image.shape, big_skel.shape)])
assert image.shape == big_skel.shape
hdr['NAXIS1'] = image.shape[1]
hdr['NAXIS2'] = image.shape[0]
image_hdu = fits.PrimaryHDU(image, header=hdr)
norm_skel_zoom = \
zoom(norm_skel,
[i/float(j) for j, i in zip(norm_skel.shape, big_skel.shape)],
order=0)
assert norm_skel_zoom.shape == big_skel.shape
norm_skel_hdu = fits.PrimaryHDU(norm_skel_zoom, header=hdr)
if aplpy_plot:
try:
import aplpy
except ImportError:
ImportError("Cannot import aplpy. Do not enable aplpy.")
fig = aplpy.FITSFigure(image_hdu)
fig.show_grayscale(invert=True, stretch="arcsinh", vmin=vmin,
vmax=vmax)
fig.tick_labels.set_xformat('hh:mm')
fig.tick_labels.set_yformat('dd:mm')
fig.tick_labels.set_font(size='large', weight='medium',
stretch='normal', family='sans-serif',
style='normal', variant='normal')
fig.axis_labels.set_font(size='large', weight='medium',
stretch='normal', family='sans-serif',
style='normal', variant='normal')
# fig.add_grid()
# NOTE! - rasterization will only work with my fork of aplpy!
# [email protected]:e-koch/aplpy.git on branch 'rasterize_contours'
fig.show_contour(norm_skel_hdu, colors="red", linewidths=1.5,
rasterize=True)
fig.show_contour(big_skel_hdu, colors="blue", rasterize=True)
fig.show_colorbar()
fig.colorbar.set_label_properties(size='large', weight='medium',
stretch='normal',
family='sans-serif',
style='normal', variant='normal')
fig.colorbar.set_axis_label_text('Surface Brightness (MJy/sr)')
fig.colorbar.set_axis_label_font(size='large', weight='medium',
stretch='normal',
family='sans-serif',
style='normal', variant='normal')
if save_figure:
fig.save(save_name+"."+output_file_type)
fig.close()
else:
# Using matplotlib
from astropy.visualization import scale_image
scaled_image = scale_image(image, scale='asinh', asinh_a=0.005)
p.imshow(scaled_image, interpolation='nearest', origin='lower',
cmap='binary')
p.contour(norm_skel_zoom, colors='r', linewidths=2)
p.contour(big_skel, colors='b', linewidths=1)
if save_figure:
p.save(save_name+"."+output_file_type, rasterized=rasterized)
p.close()
else:
p.show(block=True)
