def stars(image, number, max_counts=10000, gain=1, fwhm=4):
"""
Add some stars to the image.
"""
# Most of the code below is a direct copy/paste from
# https://photutils.readthedocs.io/en/stable/_modules/photutils/datasets/make.html#make_100gaussians_image
flux_range = [max_counts / 10, max_counts]
y_max, x_max = image.shape
xmean_range = [0.1 * x_max, 0.9 * x_max]
ymean_range = [0.1 * y_max, 0.9 * y_max]
xstddev_range = [fwhm, fwhm]
ystddev_range = [fwhm, fwhm]
params = dict([('amplitude', flux_range),
('x_mean', xmean_range),
('y_mean', ymean_range),
('x_stddev', xstddev_range),
('y_stddev', ystddev_range),
('theta', [0, 2 * np.pi])])
sources = make_random_gaussians_table(number, params,
seed=12345)
star_im = make_gaussian_sources_image(image.shape, sources)
return star_im
def genSources():
n_sources = 100
param_ranges = [('amplitude', [1000, 10000]), ('x_mean', [0, 500]),
('y_mean', [0, 300]), ('x_stddev', [3, 12]),
('y_stddev', [3, 12]), ('theta', [0, np.pi])]
param_ranges = OrderedDict(param_ranges)
sources = make_random_gaussians_table(n_sources,
param_ranges,
random_state=12345)
return sources
def genStars(fwhm):
stdev = fwhm / 2.355
n_sources = 100
param_ranges = [('amplitude', [1000, 35000]), ('x_mean', [0, 500]),
('y_mean', [0, 300]),
('x_stddev', [stdev - 0.1, stdev + 0.1]),
('y_stddev', [stdev - 0.1, stdev + 0.1]),
('theta', [0, np.pi])]
param_ranges = OrderedDict(param_ranges)
sources = make_random_gaussians_table(n_sources,
param_ranges,
random_state=12345)
return sources
def add_fake_stars(image,
expTime,
number=N_STARS,
max_counts=MAX_COUNTS,
sky_counts=SKY_LEVEL,
gain=GAIN):
"""
Adds fake stars to a dark image from the CCD. Used for testing while not on-telescope
Input:
- image The numpy array from the CCD.
- number The number of stars to add
- max_counts The max counts for the stars to have
- sky_counts Counts to use for adding sky background
- gain CCD gain
- expTime The exposure time of the raw image, to scale star brightness
Output:
- fakeData A numpy array containing the fake star image
"""
# create sky background
sky_im = np.random.poisson(sky_counts * gain, size=image.shape) / gain
#flux_range = [max_counts/10, max_counts] # this the range for brightness, flux or counts
flux_range = [
float(expTime) * (max_counts / 10),
float(expTime) * (max_counts / 1)
]
y_max, x_max = image.shape
xmean_range = [0.1 * x_max,
0.9 * x_max] # this is where on the chip they land
ymean_range = [0.1 * y_max, 0.9 * y_max]
xstddev_range = [
4, 4
] # this is a proxy for gaussian width, FWHM, or focus I think.
ystddev_range = [4, 4]
params = dict([('amplitude', flux_range), ('x_mean', xmean_range),
('y_mean', ymean_range), ('x_stddev', xstddev_range),
('y_stddev', ystddev_range), ('theta', [0, 2 * np.pi])])
randInt = random.randint(11111, 99999)
sources = make_random_gaussians_table(number, params, random_state=randInt)
star_im = make_gaussian_sources_image(image.shape, sources)
fakeData = image + sky_im + star_im
return fakeData
def test_detection(StarFinder_Algorithm, sigma_psf, amplitude, PSF_size=1):
'''create an image with mock sources and try to detect them
Parameters
----------
StarFinder_Algorithm:
Class to detect stars
sigma_psf:
standard deviation of the PSF of the mock sources
amplitude:
amplitude of the mock sources
PSF_size:
The StarFinder_Algorithm need to know the sigma of the sources
they try to detect. This parameter changes the provided size
compared to the sigma of the mock sources.
'''
# create mock data
n_sources = 20
tshape = (256, 256)
param_ranges = OrderedDict([('amplitude', [amplitude, amplitude * 1.2]),
('x_mean', [0, tshape[0]]),
('y_mean', [0, tshape[1]]),
('x_stddev', [sigma_psf, sigma_psf]),
('y_stddev', [sigma_psf, sigma_psf]),
('theta', [0, 0])])
sources = make_random_gaussians_table(n_sources,
param_ranges,
random_state=1234)
image = (make_gaussian_sources_image(tshape, sources) + make_noise_image(
tshape, type='poisson', mean=6., random_state=1) + make_noise_image(
tshape, type='gaussian', mean=0., stddev=2., random_state=34234))
fwhm = gaussian_sigma_to_fwhm * sigma_psf
mean, median, std = sigma_clipped_stats(image, sigma=3.0)
StarFinder = StarFinder_Algorithm(fwhm=fwhm * PSF_size,
threshold=3. * std,
sharplo=0.1,
sharphi=1.0,
roundlo=-.2,
roundhi=.2)
sources_mock = StarFinder(image)
# for consistent table output
for col in sources_mock.colnames:
sources_mock[col].info.format = '%.8g'
string = str(StarFinder_Algorithm).split(
'.')[-1][:-2] + f' sig={sigma_psf} A={amplitude}'
print(f'{string}: {len(sources_mock):} of {n_sources} sources found')
positions = np.transpose(
[sources_mock['xcentroid'], sources_mock['ycentroid']])
apertures = CircularAperture(positions, r=fwhm)
return image, apertures, sources, string
