def EXTRACT():
path = input("-> Enter path to target's exposure time directory: ")
images = glob.glob(path + '/data/*.fits')
psf_data = glob.glob(path + '/psf/*')
if len(psf_data) == 3 * len(images):
sex.sextractor(path)
sex.src_filter(path)
else:
sex.sextractor_psf(path)
psf.psfex(path)
sex.sextractor(path)
sex.src_filter(path)
def PSF(path):
'''Computes PSF model of science images. Uses ``PSFex`` (Bertin).
:param str path: Path of data file tree (contains the **configs**, **data**, **psf**, **residuals**, **sources**, **templates** directories). Use a comma-separated list for mapping to multiple datasets.
:returns: PSF models of each science image are ouputted into the **psf** directory with the *.psf* suffix.
'''
paths = (path.replace(' ','')).split(',')
del path
for path in paths:
ims = sex.sextractor_psf(path)
psfex(path, images=ims)
def TEST():
#get data from LCO public archive and put in target directory under 'TEST' folder
print("-> Getting data from LCO...")
response = requests.get('https://archive-api.lco.global/frames/?' +
'RLEVEL=91&' +
'PROPID='+'standard&'
'OBJECT='+'L113&' +
'FILTER='+'B&'
'start='+'2018-9-14'+'&' +
'end='+'2018-9-15'+'&'
).json()
frames = response['results']
#delete bad images
del_fr = []
for fr in frames:
if fr['id'] != 9602135 and fr['id'] != 9602132:
del_fr.append(fr)
for delete in del_fr:
del frames[frames.index(delete)]
#download data
temp_loc = loc + '/sdi/temp/'
os.mkdir(temp_loc+'test_data')
for frame in frames:
with open(temp_loc + 'test_data/' + frame['filename'], 'wb') as f:
f.write(requests.get(frame['url']).content)
#funpack and move to 'TEST' folder
obtain.process()
obtain.movetar()
old_data_location = obtain.rename()
data_location = old_data_location.replace('L113', 'TEST')
os.rename(old_data_location[:29], data_location[:29])
#align and combine images
test_loc = data_location[:-5]
check_saturation.check_saturate(test_loc + '/data')
ref_image.ref_image(test_loc + '/data')
align_astroalign.align2(test_loc + '/data')
# align_skimage.skimage(test_loc + '/data')
combine_numpy.combine_median(test_loc + '/data')
# align_skimage.skimage_template(test_loc + '/data')
#add three fake stars to reference image
print("\n-> Adding fake stars to test image...")
hdu = fits.getdata(test_loc + '/data/09:14:00.260_A_.fits')
h, w = img_shape = np.shape(hdu)
pos_x = [1500,2000,1200]
pos_y = [1600,1400,2200]
array = np.array([ 0.65343465, 0.50675629, 0.84946314])
fluxes = 2000000.0 + array * 300.0
img = np.zeros(img_shape)
for x, y, f in zip(pos_x, pos_y, fluxes):
img[x, y] = f
img = gaussian_filter(img, sigma=15.0, mode='constant')
final = fits.PrimaryHDU(hdu+img)
final.writeto(test_loc + '/data/09:14:00.260_A_.fits', overwrite=True)
#subtract images using ISIS
subtract_ais.isis_sub_test(test_loc)
#get PSFs then perform SExtractor on residual images
sex.sextractor_psf(test_loc)
psf.psfex(test_loc)
sex.sextractor(test_loc)
#test the results of the test function against known values
# with open(test_loc + '/sources/sources.txt', 'r') as source:
# lines = source.readlines()
# source.close()
#
# with open(os.path.dirname(sex.__file__) + '/test_config/test_sources.txt', 'r') as test_source:
# lines2 = test_source.readlines()
# test_source.close()
res_image_loc = os.path.dirname(sex.__file__) + '/test_config/09:14:00.260_A_residual_.fits'
test_image_data = fits.getdata(res_image_loc)
residual = glob.glob(test_loc + '/residuals/*_A_residual_.fits')
residual_data = fits.getdata(residual[0])
#
# if lines == lines2:
# print("\t-> Sources matched to control")
if test_image_data.all() == residual_data.all():
print("-> Residuals matched to control\n-> TEST SUCCESSFUL!")
# if lines == lines2 and test_image_data.all() == residual_data.all():
# print("\t-> Test successful!")
if test_image_data.all() != residual_data.all():
print("-> Test failure: Results do not match controls")
#display final residual test image
os.system('ds9 %s -scale zscale' % (residual[0]))
array = np.array([ 0.65343465, 0.50675629, 0.84946314])
fluxes = 2000000.0 + array * 300.0
img = np.zeros(img_shape)
for x, y, f in zip(pos_x, pos_y, fluxes):
img[x, y] = f
img = gaussian_filter(img, sigma=15.0, mode='constant')
final = fits.PrimaryHDU(hdu+img)
final.writeto(test_loc + '/data/09:14:00.260_A_.fits', overwrite=True)
#subtract images using ISIS
subtract_ais.isis_sub_test(test_loc)
#get PSFs then perform SExtractor on residual images
sex.sextractor_psf(test_loc)
psf.psfex(test_loc)
sex.sextractor(test_loc)
#test the results of the test function against known values
# with open(test_loc + '/sources/sources.txt', 'r') as source:
# lines = source.readlines()
# source.close()
#
# with open(os.path.dirname(sex.__file__) + '/test_config/test_sources.txt', 'r') as test_source:
# lines2 = test_source.readlines()
# test_source.close()
res_image_loc = os.path.dirname(sex.__file__) + '/test_config/09:14:00.260_A_residual_.fits'
test_image_data = fits.getdata(res_image_loc)
import sex
import psf
import glob
def EXTRACT():
path = input("-> Enter path to target's exposure time directory: ")
images = glob.glob(path + '/data/*.fits')
psf_data = glob.glob(path + '/psf/*')
if len(psf_data) == 3 * len(images):
sex.sextractor(path)
sex.src_filter(path)
else:
sex.sextractor_psf(path)
psf.psfex(path)
sex.sextractor(path)
sex.src_filter(path)
if __name__ == '__main__':
path = input("-> Enter path to target's exposure time directory: ")
images = glob.glob(path + '/data/*.fits')
psf_data = glob.glob(path + '/psf/*')
if len(psf_data) == 3 * len(images):
sex.sextractor(path)
sex.src_filter(path)
else:
sex.sextractor_psf(path)
psf.psfex(path)
sex.sextractor(path)
sex.src_filter(path)