def plot_image_solved(self):
"""
Superimpose found stars on the lid CCD image for each area of interest.
If coordinates were found, it also shows the stars
from the astrometry.net results.
"""
plt.ioff()
fig = plt.figure()
ax = plt.gca()
lid_image = fits.open(self.filename)[0].data
if type(lid_image) is not np.ndarray:
raise AttributeError([self.filename, ' must be a fit file'])
plt.imshow(lid_image, cmap='gray')
for sky_image, rect in zip(self.sky_images, self.crop_rectangles):
if sky_image.sources_pixel is not None:
for i in range(min(sky_image.sources_pixel.shape[1], 30)):
pixel_x = sky_image.sources_pixel[0, i] + rect.left
pixel_y = sky_image.sources_pixel[1, i] + rect.bottom
circle = Circle((pixel_x, pixel_y),
radius=9,
fill=False,
color='r')
ax.add_artist(circle)
if sky_image.stars_pixel is not None:
for i in range(sky_image.stars_pixel.shape[1]):
pixel_x = sky_image.stars_pixel[0, i] + rect.left
pixel_y = sky_image.stars_pixel[1, i] + rect.bottom
circle = Circle((pixel_x, pixel_y),
radius=11,
fill=False,
color='g')
ax.add_artist(circle)
rect = Rectangle((rect.left, rect.bottom),
width=rect.width(),
height=rect.height(),
fill=False,
color='k',
linestyle='dashdot')
ax.add_artist(rect)
plt.grid(None)
plt.axis('off')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
output_filename = self.filename.replace('.fits', '-solved.png')
plt.savefig(output_filename, bbox_inches='tight', pad_inches=0)
plt.close(fig)
print(output_filename, 'saved.')
def plot_image_treated(self):
"""
Superimpose stars and gamma sources on the lid CCD image.
Internet access is needed for the Vizier requests.
"""
plt.ioff()
fig = plt.figure()
ax = plt.gca()
lid_image = fits.open(self.filename)[0].data
if type(lid_image) is not np.ndarray:
raise AttributeError([self.filename, ' must be a fit file'])
plt.imshow(lid_image, cmap='gray')
image_treated = np.zeros(self.image_shape)
vizier_blue = Vizier(columns=['all'],
column_filters={"Bjmag": "<12"},
row_limit=-1,
catalog=['I/271/out'])
vizier_red = Vizier(columns=['all'],
column_filters={"Rmag": "<12"},
row_limit=-1,
catalog=['I/271/out'])
vizier_green = Vizier(columns=['all'],
column_filters={"Vmag": "<12"},
row_limit=-1,
catalog=['VI/135/out'])
vizier_gamma = Vizier(columns=['all'],
row_limit=-1,
catalog=['J/ApJS/197/34'])
for (sky_image, rect) in zip(self.sky_images, self.crop_rectangles):
image_treated[rect.bottom:rect.top,
rect.left:rect.right] = np.log(1 +
sky_image.image_stars)
crop_origin = np.array([rect.left, rect.bottom])
if sky_image.sources_pixel is not None:
for i in range(min(sky_image.sources_pixel.shape[1], 30)):
pixel_x = sky_image.sources_pixel[0, i] + rect.left
pixel_y = sky_image.sources_pixel[1, i] + rect.bottom
circle = Circle((pixel_x, pixel_y),
radius=20,
fill=False,
color='w')
ax.add_artist(circle)
if sky_image.wcs is not None:
self.center_px = np.array(
(self.image_shape[1], self.image_shape[0])).reshape(1,
2) / 2
self.center_ra_dec = sky_image.wcs.wcs_pix2world(
self.center_px - crop_origin, 1)[0]
print('image center (ra, dec):', self.center_ra_dec)
center_coordinate = SkyCoord(
ra=self.center_ra_dec[0] * u.degree,
dec=self.center_ra_dec[1] * u.degree,
frame='icrs')
result_blue = vizier_blue.query_region(center_coordinate,
radius=Angle(5, "deg"),
catalog=['I/271/out'
])[0]
blue_stars_ra_dec = np.array(
(result_blue['RA_ICRS_'],
result_blue['DE_ICRS_'])).transpose() # .reshape((-1,2))
blue_stars_mag = result_blue['Bjmag']
blue_stars_px = sky_image.wcs.wcs_world2pix(
blue_stars_ra_dec, 1)
for star_px, star_mag in zip(blue_stars_px, blue_stars_mag):
if -5 < star_mag <= 12:
radius = 18 - star_mag
else:
radius = 5
circle = Circle(
(star_px[0] + rect.left, star_px[1] + rect.right),
radius=radius,
fill=False,
color='b')
ax.add_artist(circle)
result_red = vizier_red.query_region(center_coordinate,
radius=Angle(5, "deg"),
catalog=['I/271/out'])[0]
red_stars_ra_dec = np.array(
(result_red['RA_ICRS_'],
result_red['DE_ICRS_'])).transpose() # .reshape((-1,2))
red_stars_mag = result_red['Rmag']
red_stars_px = sky_image.wcs.wcs_world2pix(red_stars_ra_dec, 1)
for star_px, star_mag in zip(red_stars_px, red_stars_mag):
if -5 < star_mag <= 12:
radius = 18 - star_mag
else:
radius = 5
circle = Circle(
(star_px[0] + rect.left, star_px[1] + rect.right),
radius=radius,
fill=False,
color='r')
ax.add_artist(circle)
result_green = vizier_green.query_region(
center_coordinate,
radius=Angle(5, "deg"),
catalog=['I/271/out'])[0]
green_stars_ra_dec = np.array(
(result_green['RA_ICRS_'],
result_green['DE_ICRS_'])).transpose() # .reshape((-1,2))
green_stars_mag = result_blue['Vmag']
green_stars_px = sky_image.wcs.wcs_world2pix(
green_stars_ra_dec, 1)
for star_px, star_mag in zip(green_stars_px, green_stars_mag):
if -5 < star_mag <= 12:
radius = 18 - star_mag
else:
radius = 5
circle = Circle(
(star_px[0] + rect.left, star_px[1] + rect.right),
radius=radius,
fill=False,
color='g')
ax.add_artist(circle)
result_gamma = vizier_gamma.query_region(
center_coordinate,
radius=Angle(5, "deg"),
catalog=['J/ApJS/197/34'])[0]
gamma_stars_ra_dec = np.array(
(result_gamma['RAJ2000'],
result_gamma['DEJ2000'])).transpose() # .reshape((-1,2))
gamma_stars_name = result_gamma['Name']
gamma_stars_px = sky_image.wcs.wcs_world2pix(
gamma_stars_ra_dec, 1)
for star_px, gamma_star_name in zip(gamma_stars_px,
gamma_stars_name):
circle = Circle(
(star_px[0] + rect.left, star_px[1] + rect.bottom),
radius=20,
fill=False,
color='Y')
ax.add_artist(circle)
ax.text(star_px[0] + rect.left,
star_px[1] + rect.bottom,
gamma_star_name,
color='Y')
crab_nebula_px = sky_image.wcs.wcs_world2pix(
83.640187, 22.044295, 1)
circle = Circle((crab_nebula_px[0] + rect.left,
crab_nebula_px[1] + rect.bottom),
radius=20,
fill=False,
color='Y')
ax.add_artist(circle)
ax.text(crab_nebula_px[0] + rect.left,
crab_nebula_px[1] + rect.right,
'Crab Pulsar',
color='Y')
rect = Rectangle(crop_origin,
width=rect.width(),
height=rect.height(),
fill=False,
color='y',
linestyle='dashdot')
ax.add_artist(rect)
# plt.imshow(image_treated, cmap='gray')
plt.plot(0, 0, 'w+')
plt.grid(None)
plt.axis('off')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
output_filename = self.filename.replace('.fits', '-treated.png')
plt.savefig(output_filename, bbox_inches='tight', pad_inches=0)
plt.close(fig)
print(output_filename, 'saved.')
