def crop_masters(path=cmpath):
mastercollection=ImageFileCollection('Master_Files')
for image, imname in mastercollection.ccds(imtype='trimmed bias',return_fname=True):
trimage=ccdp.trim_image(image,fits_section=str(sciwin))
trimage.meta['trimwind']=(str(sciwin),'readout window')
trimage.meta['imtype'] = ('mbias', 'windowed master bias')
trimage.write(path+imname,overwrite=True)
for image, imname in mastercollection.ccds(imtype='subflat',return_fname=True):
trimage=ccdp.trim_image(image,fits_section=str(sciwin))
trimage.meta['trimwind']=(str(sciwin),'readout window')
trimage.meta['imtype'] = ('mflat', 'windowed master flat')
trimage.write(path+imname,overwrite=True)
def sub_bias(refresh='2', bias='2'):
tflatcollection = ImageFileCollection('Trimmed_Flat')
if bias == '1':
biaspath = 'Master_Files/mbias_median.fits'
dest = 'Trimmed_Flat/subflatsmed/'
elif bias == '2':
biaspath = 'Master_Files/mbias.fits'
dest = 'Trimmed_Flat/subflatssig/'
if refresh == '1':
subflatpathlist = []
mbias = CCDData.read(biaspath, unit='adu')
for ccdf, flatn in tflatcollection.ccds(imtype='trimmed flat',
return_fname=True):
subflat = ccdp.subtract_bias(ccdf, mbias, add_keyword='subbias')
subflat.meta['imtype'] = ('subflat', 'bias subtracted flat')
subflat.write(dest + flatn[0:8] + '_subbias.fits', overwrite=True)
subflatpathlist.append(dest + flatn[0:8] + '_subbias.fits')
else:
try:
subflatcollection = ImageFileCollection(dest)
subflatpathlist = subflatcollection.files_filtered(
imtype='subflat', include_path=True)
print('found', len(subflatpathlist), 'subflats')
except:
print('can\'t locate subflats, create or check directory')
sys.exit()
return tflatcollection, subflatpathlist
def trim_flat(refresh='2'):
flatcollection = ImageFileCollection('HD115709/flat_SII', ext=4)
flag = 0
tflatpathlist = []
if refresh == '1':
for ccdf, flatn in flatcollection.ccds(return_fname=True,
ccd_kwargs={'unit': 'adu'}):
if flag == 0:
print('all flats will be trimmed to :', ccdf.meta['trimsec'])
flag = 1
print('trimming', flatn)
tflat = ccdp.trim_image(ccdf,
fits_section=str(ccdf.meta['trimsec']))
tflat.meta['imtype'] = ('trimmed flat', 'type of image')
tflat.meta['taxis1'] = (2048, 'dimension1')
tflat.meta['taxis2'] = (4096, 'dimension2')
tflat.write('Trimmed_Flat/' + flatn[0:8] + '_trim.fits',
overwrite=True)
tflatpathlist.append('Trimmed_Flat/' + flatn[0:8] + '_trim.fits')
print('created', len(tflatpathlist), 'trimmed flats')
elif refresh == '2':
try:
tflatcollection = ImageFileCollection('Trimmed_Flat')
tflatpathlist = tflatcollection.files_filtered(
imtype='trimmed flat', include_path=True)
print('found', len(tflatpathlist), 'trimmed flats')
except:
print('can\'t locate trimmed flats, create or check directory')
sys.exit(0)
return flatcollection, tflatpathlist
def trim_bias(refresh='2'):
biascollection = ImageFileCollection('HD115709/bias', ext=4)
flag = 0
if refresh == '1':
tbiaspathlist = []
for ccdb, biasn in biascollection.ccds(return_fname=True,
ccd_kwargs={'unit': 'adu'}):
if flag == 0:
print('all biases will be trimmed to :', ccdb.meta['trimsec'])
flag = 1
print('trimming', biasn)
tbias = ccdp.trim_image(ccdb,
fits_section=str(ccdb.meta['trimsec']))
tbias.meta['imtype'] = ('trimmed bias', 'type of image')
tbias.meta['taxis1'] = (2048, 'dimension1')
tbias.meta['taxis2'] = (4096, 'dimension2')
tbias.write('Trimmed_Bias/' + biasn[0:8] + '_trim.fits',
overwrite=True)
tbiaspathlist.append('Trimmed_Bias/' + biasn[0:8] + '_trim.fits')
print('created', len(tbiaspathlist), 'trimmed biases')
else:
try:
tbiascollection = ImageFileCollection('Trimmed_Bias')
tbiaspathlist = tbiascollection.files_filtered(
imtype='trimmed bias', include_path=True)
print('found', len(tbiaspathlist), 'trimmed bias')
except:
print('can\'t locate trimmed biases, create or check directory')
sys.exit()
return biascollection, tbiaspathlist
def t120_mkoffset(offset_dir=t120.t120_ofst_dir,
master_file_name=t120.t120_master_name):
master_file = offset_dir + master_file_name
listimg = ImageFileCollection(
offset_dir) #,glob_include='*.fit',glob_exclude='*.fits')
listccd = []
for ccd, file_name in listimg.ccds(ccd_kwargs={'unit': 'adu'},
return_fname=True):
t120.log.info('now considering file ' + file_name)
listccd.append(ccd)
combiner = Combiner(listccd)
t120.log.info('now making the combination')
master_offset = combiner.median_combine()
fits_ccddata_writer(master_offset, master_file)
t120.log.info('Result saved in ' + master_file)
return master_file
def trim_bias(refresh=False):
biascollection = ImageFileCollection('HD115709/bias', ext=4)
flag = 0
tbiaspathlist=[]
if refresh == True:
for ccdb, biasn in biascollection.ccds(return_fname=True, ccd_kwargs={'unit': 'adu'}):
print('trimming', biasn)
ccdb.header['imtype'] = ('bias', 'type of image')
if flag == 0:
print('all biases will be trimmed to :', ccdb.meta['trimsec'])
flag = 1
tbias = ccdp.trim_image(ccdb, fits_section=str(ccdb.meta['trimsec']))
tbias.meta['imtype'] = ('trimmed bias', 'type of image')
tbias.meta['taxis1'] = (2048, 'dimension1')
tbias.meta['taxis2'] = (4096, 'dimension2')
tbias.write('Trimmed_Bias/' + biasn[0:8] + '_trim.fits', overwrite=True)
tbiaspathlist.append('Trimmed_Bias/' + biasn[0:8] + '_trim.fits')
return biascollection,tbiaspathlist
listimg = ImageFileCollection(
t120.t120_flat_dir) #,glob_include='*.fit',glob_exclude='*.fits')
list_filters = listimg.values('filter', unique=True)
#for filter_name in listimg.values('filter',unique=True):
for filter_name in list_filters:
t120.log.info('*** filter: ' + filter_name)
my_files = listimg.files_filtered(filter=filter_name)
t120.log.info('my_files=' + my_files)
pouet
for filter_name in listimg.values('filter', unique=True):
t120.log.info('*** filter: ' + filter_name)
listccd = []
for ccd, file_name in listimg.ccds(ccd_kwargs={'unit': 'adu'},
filter=filter_name,
return_fname=True):
t120.log.info('now considering file ' + file_name)
listccd.append(ccd)
"""
t120.log.info('now making the Combiner object')
combiner = Combiner(listccd)
t120.log.info('now making the combination')
master_flat = combiner.average_combine()
master_file = t120.t120_flat_dir+'/master-'+filter_name+'.fits'
fits_ccddata_writer(master_flat,master_file)
t120.log.info('Master flat saved in '+master_file)
"""
import itertools
from astropy.stats import sigma_clip, mad_std
import time
import sys
if not os.path.exists('Master_Files/Windowed'):
os.makedirs('Master_Files/Windowed')
print('folder \'Master_Files/Windowed\' created')
if not os.path.exists('Final_Science'):
os.makedirs('Final_Science')
print('folder \'Final_Science\' created')
#read science images and find RTDATSEC
sciencecollection=ImageFileCollection('HD115709/SII',ext=1)
for sci in sciencecollection.ccds(ccd_kwargs={'unit': 'adu'}):
sciwin=sci.meta['RTDATSEC']
break
cmpath='Master_Files/Windowed/'
cspath='Final_Science/'
#crop master biases and master flats
def crop_masters(path=cmpath):
mastercollection=ImageFileCollection('Master_Files')
for image, imname in mastercollection.ccds(imtype='trimmed bias',return_fname=True):
trimage=ccdp.trim_image(image,fits_section=str(sciwin))
trimage.meta['trimwind']=(str(sciwin),'readout window')
trimage.meta['imtype'] = ('mbias', 'windowed master bias')
trimage.write(path+imname,overwrite=True)
for image, imname in mastercollection.ccds(imtype='subflat',return_fname=True):
def t120_makecosmetic(work_dir=t120.t120_data_path,
orig_dir_root=t120.t120_orig_dir,
reduc_dir_root=t120.t120_redu_dir,
offset_dir_root=t120.t120_ofst_dir,
flat_dir_root=t120.t120_flat_dir,
dark_dir_root=t120.t120_dark_dir,
common_dir_root=t120.t120_common_root,
scampahead_file=t120.t120_scamp_ahead,
master_offset_name=t120.t120_master_name):
orig_dir = work_dir + orig_dir_root
reduc_dir = work_dir + reduc_dir_root
offset_dir = work_dir + offset_dir_root
flat_dir = work_dir + flat_dir_root
dark_dir = work_dir + dark_dir_root
master_offset_file = offset_dir + t120.t120_master_name
# now check existence of useful files and directories
for subdir in [
work_dir, work_dir, orig_dir, offset_dir, flat_dir,
t120.t120_common_dir
]:
if not os.path.isdir(subdir):
msg = '*** FATAL ERROR: directory ' + subdir + ' does not exist'
t120.log.error(msg)
raise IOError(msg)
try:
master_offset = fits_ccddata_reader(master_offset_file)
except:
msg = '*** FATAL ERROR while reading ' + master_offset_file
t120.log.error(msg)
raise IOError(msg)
# read scamp ahead file
#scamp_header = read_scamp_ahead(scampahead_file)
# loop over imagess
listremove = []
listimg = ImageFileCollection(
orig_dir + '/') #,glob_include='*-c.fits',glob_exclude='*.fit')
for ccd, fit_file in listimg.ccds(
ccd_kwargs={'unit':
'adu'}, return_fname=True, save_location=reduc_dir
): #,save_with_name='-c',save_location=reduc_dir+'/'):
t120.log.info('now treating file: ' + orig_dir + fit_file)
filter_name = ccd.header['FILTER']
flat_name = flat_dir + '/master-' + filter_name + '.fits'
master_flat = fits_ccddata_reader(flat_name, unit=u.adu)
hdu = fits.open(orig_dir + fit_file)
exp_time = hdu[0].header['EXPTIME']
strexptime = "%3.1f" % exp_time
t120.log.info('exp_time=' + strexptime)
dark_name = dark_dir + t120.t120_master_name.replace(
'.fits', '') + '-' + strexptime + '.fits'
master_dark = fits_ccddata_reader(dark_name, unit=u.adu)
t120.log.info('Flat: ' + flat_name)
t120.log.info('Dark: ' + dark_name)
master_dark.header['EXPOSURE'] = ccd.header[
'EXPOSURE'] # for dark subtraction
master_offset.header['EXPOSURE'] = ccd.header[
'EXPOSURE'] # for dark subtraction
ccd_corr = ccd_process(ccd,
exposure_key='EXPOSURE',
exposure_unit=u.second,
dark_frame=master_dark,
master_flat=master_flat)
#ccd_corr.header = put_scamp_header(ccd_corr.header,scamp_header) # update header
skycoo = SkyCoord(ccd_corr.header['OBJCTRA'] + ' ' +
ccd_corr.header['OBJCTDEC'],
unit=(u.hourangle, u.deg))
ccd_corr.header['CRVAL1'] = (
skycoo.ra.to('deg').value,
'Reference Right ascencion in decimal deg')
ccd_corr.header['CRVAL2'] = (skycoo.dec.to('deg').value,
'Reference Declination in decimal deg')
out_fit_file = reduc_dir + '/' + os.path.splitext(
fit_file)[0] + '-c.fits'
if os.path.exists(out_fit_file):
os.system('rm ' + out_fit_file)
t120.log.info('File ' + out_fit_file + ' has been removed')
# make primary HDU
hducorrlist = ccd_corr.to_hdu()
ccd_tosave = CCDData(hducorrlist[0].data, unit=u.adu)
ccd_tosave.header = hducorrlist[0].header
fits_ccddata_writer(ccd_tosave, out_fit_file)
t120.log.info('corrected image saved in ' + out_fit_file)
copy_fit_file = reduc_dir + fit_file
t120.log.info('copy_fit_file ' + copy_fit_file)
listremove.append(copy_fit_file)
# remove copy of original fits files
for file2remove in listremove:
t120.log.info('now removing file ' + file2remove)
if os.path.exists(file2remove):
os.system('rm ' + file2remove)
t120.log.info('File ' + file2remove + ' has been removed')
else:
t120.log.info('File ' + file2remove + ' does not exist')
return
def photometry_on_directory(directory_with_images,
object_of_interest,
star_locs,
aperture_rad,
inner_annulus,
outer_annulus,
max_adu,
star_ids,
camera,
bjd_coords=None,
observatory_location=None,
fwhm_by_fit=True):
"""
Perform aperture photometry on a directory of images.
Parameters
----------
directory_with_images : str
Folder containing the images on which to do photometry. Photometry
will only be done on images that contain the ``object_of_interest``.
object_of_interest : str
Name of the object of interest. The only files on which photometry
will be done are those whose header contains the keyword ``OBJECT``
whose value is ``object_of_interest``.
star_locs : tuple of numpy array
The first entry in the tuple should be the right ascension of the
sources, in degrees. The second should be the declination of
the sources, in degrees.
aperture_rad : int
Radius of the aperture to use when performing photometry.
inner_annulus : int
Inner radius of annulus to use in for performing local sky
subtraction.
outer_annulus : int
Outer radius of annulus to use in for performing local sky
subtraction.
max_adu : int
Maximum allowed pixel value before a source is considered
saturated.
star_ids : array-like
Unique identifier for each source in ``star_locs``.
camera : `stellarphot.Camera` object
Camera object which has gain, read noise and dark current set.
gain : float
Gain, in electrons/ADU, of the camera that took the image. The gain
is used in calculating the instrumental magnitude.
read_noise : float
Read noise of the camera in electrons. Used in the CCD equation
to calculate error.
dark_current : float
Dark current, in electron/sec. Used in the CCD equation
to calculate error.
"""
ifc = ImageFileCollection(directory_with_images)
phots = []
missing_stars = []
for a_ccd, fname in ifc.ccds(object=object_of_interest, return_fname=True):
print('on image ', fname)
try:
# Convert RA/Dec to pixel coordinates for this image
pix_coords = a_ccd.wcs.all_world2pix(star_locs[0], star_locs[1], 0)
except AttributeError:
print(' ....SKIPPING THIS IMAGE, NO WCS')
continue
xs, ys = pix_coords
# Remove anything that is too close to the edges/out of frame
padding = 3 * aperture_rad
out_of_bounds = ((xs < padding) | (xs > (a_ccd.shape[1] - padding)) |
(ys < padding) | (ys > (a_ccd.shape[0] - padding)))
in_bounds = ~out_of_bounds
# Find centroids of each region around star that is in_bounds
xs_in = xs[in_bounds]
ys_in = ys[in_bounds]
print(' ...finding centroids')
try:
xcen, ycen = centroid_sources(a_ccd.data,
xs_in,
ys_in,
box_size=2 * aperture_rad + 1)
except NoOverlapError:
print(' ....SKIPPING THIS IMAGE, CENTROID FAILED')
continue
# Calculate offset between centroid in this image and the positions
# based on input RA/Dec. Later we will set the magnitude of those with
# large differences to an invalid value (maybe).
center_diff = np.sqrt((xs_in - xcen)**2 + (ys_in - ycen)**2)
# FWHM is typically 5-6 pixels. The center really shouldn't move
# by more than that.
too_much_shift = center_diff > 6
xcen[too_much_shift] = xs_in[too_much_shift]
ycen[too_much_shift] = ys_in[too_much_shift]
# Set up apertures and annuli based on the centroids in this image.
ap_locs = np.array([xcen, ycen]).T
aps = CircularAperture(ap_locs, r=aperture_rad)
anuls = CircularAnnulus(ap_locs, inner_annulus, outer_annulus)
# Set any clearly bad values to NaN
a_ccd.data[a_ccd.data > max_adu] = np.nan
print(' ...doing photometry')
# Do the photometry...
pho = aperture_photometry(a_ccd.data, (aps, anuls),
mask=a_ccd.mask,
method='center')
# We may have some stars we did not do photometry for because
# those stars were out of bounds.
# Add the ones we missed to the list of missing
missed = star_ids[out_of_bounds]
missing_stars.append(missed)
# Add all the extra goodies to the table
print(' ...adding extra columns')
add_to_photometry_table(pho,
a_ccd,
anuls,
aps,
fname=fname,
star_ids=star_ids[in_bounds],
camera=camera,
bjd_coords=bjd_coords,
observatory_location=observatory_location,
fwhm_by_fit=fwhm_by_fit)
# And add the final table to the list of tables
phots.append(pho)
# ### Combine all of the individual photometry tables into one
all_phot = vstack(phots)
# ### Eliminate any stars that are missing from one or more images
#
# This makes life a little easier later...
uniques = set()
for miss in missing_stars:
uniques.update(set(miss))
actually_bad = sorted([u for u in uniques if u in all_phot['star_id']])
len(uniques), len(actually_bad)
all_phot.add_index('star_id')
if actually_bad:
bad_rows = all_phot.loc_indices[actually_bad]
try:
bad_rows = list(bad_rows)
except TypeError:
bad_rows = [bad_rows]
all_phot.remove_indices('star_id')
all_phot.remove_rows(sorted(bad_rows))
all_phot.remove_indices('star_id')
gain = camera.gain
noise = calculate_noise(gain=camera.gain,
read_noise=camera.read_noise,
dark_current_per_sec=camera.dark_current,
flux=all_phot['aperture_net_flux'],
sky_per_pix=all_phot['sky_per_pix_avg'].value,
aperture_area=all_phot['aperture_area'],
annulus_area=all_phot['annulus_area'],
exposure=all_phot['exposure'].value,
include_digitization=False)
snr = gain * all_phot['aperture_net_flux'] / noise
all_phot['mag_error'] = 1.085736205 / snr
all_phot['noise'] = noise
# AstroImageJ includes a factor of gain in the noise. IMHO it is part of the
# flux but, for convenience, here it is
all_phot['noise-aij'] = noise / gain
all_phot['snr'] = snr
return all_phot
