def EXTRACT(path, method='both'):
'''Extracts sources from individual residual frames located in **residuals** directory. Automatically filters out false positives and objects not of interest (see documentation for details). Uses ``SExtractor`` to create the initial sources catalogs, which are outputted to the **sources** directory.
: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.
:param str method: Method of source extraction. Tells **OASIS** whether to extract sources from the individual residuals, the master residual, or both.
* *both* (default): Runs ``SExtractor`` on both residuals and master residual.
* *indiv*: Runs ``SExtractor`` only on residuals.
* *MR*: Runs ``SExtractor`` only on master residual.
:returns: A filtered source catalog for each image specified with the *method* parameter is created and appended to the text file *filtered_sources.txt* located in the **sources** directory. Source extraction statistics and extra info are located in *total_sources.txt*.
'''
paths = (path.replace(' ', '')).split(',')
del path
for path in paths:
if method == 'both':
sex.sextractor(path)
sex.sextractor_MR(path)
elif method == 'indiv':
sex.sextractor(path)
elif method == 'MR':
sex.sextractor_MR(path)
else:
print(
"\n-> Error: Unrecognized method. Please enter either 'both', 'indiv', or 'MR'.\n-> Exiting..."
)
sys.exit()
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 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]))
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')
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)