def add_object_info(directory='.', object_list=None,
match_radius=20.0, new_file_ext='new',
overwrite=False, detailed_history=True):
"""
Automagically add object information to FITS files.
`directory` is the directory containing the FITS files to be fixed
up and an `object_list`. Set `object_list` to `None` to use
default object list name.
`match_radius` is the maximum distance, in arcmin, between the
RA/Dec of the image and a particular object for the image to be
considered an image of that object.
"""
from astro_object import AstroObject
import numpy as np
from fitskeyword import FITSKeyword
images = ImageFileCollection(directory,
keywords=['imagetyp', 'RA',
'Dec', 'object'])
summary = images.summary_info
print summary['file']
try:
observer, object_names = read_object_list(directory)
except IOError:
print 'No object list in directory %s, skipping.' % directory
return
# ra_dec_obj = {'er ori':(93.190,12.382), 'm101':(210.826,54.335), 'ey uma':(135.575,49.810)}
object_names = np.array(object_names)
ra_dec = []
for object_name in object_names:
obj = AstroObject(object_name)
ra_dec.append(obj.ra_dec)
object_ra_dec = np.array(ra_dec)
for header in images.headers(save_with_name=new_file_ext,
clobber=overwrite,
object='', RA='*', Dec='*'):
image_ra_dec = coords.coordsys.FK5Coordinates(header['ra'],
header['dec'])
distance = [(ra_dec - image_ra_dec).arcmin for ra_dec in object_ra_dec]
distance = np.array(distance)
matches = (distance < match_radius)
if matches.sum() > 1:
raise RuntimeError("More than one object match for image")
if not matches.any():
raise RuntimeWarning("No object foundn for image")
continue
object_name = (object_names[matches])[0]
obj_keyword = FITSKeyword('object',value=object_name)
obj_keyword.addToHeader(header, history=True)
def patch_headers(dir='.', new_file_ext='new',
overwrite=False, detailed_history=True):
"""
Add minimal information to Feder FITS headers.
`dir` is the directory containing the files to be patched.
`new_file_ext` is the name added to the FITS files with updated
header information. It is added to the base name of the input
file, between the old file name and the `.fit` or `.fits` extension.
`overwrite` should be set to `True` to replace the original files.
detailed_history : bool
If `True`, write name and value of each keyword changed to
output FITS files. If `False`, write only a list of which
keywords changed.
"""
images = ImageFileCollection(location=dir, keywords=['imagetyp'])
latitude.value = sexagesimal_string(feder.latitude.dms)
longitude.value = sexagesimal_string(feder.longitude.dms)
obs_altitude.value = feder.altitude
for header in images.headers(save_with_name=new_file_ext,
clobber=overwrite,
do_not_scale_image_data=True):
run_time = datetime.now()
header.add_history(history(patch_headers, mode='begin',
time=run_time))
header.add_history('patch_headers.py modified this file on %s'
% run_time)
add_time_info(header, history=detailed_history)
if not detailed_history:
header.add_history('patch_headers.py updated keywords %s' %
keyword_names_as_string(keywords_for_all_files))
if header['imagetyp'] == 'LIGHT':
try:
add_object_pos_airmass(header,
history=detailed_history)
if not detailed_history:
header.add_history('patch_headers.py updated keywords %s' %
keyword_names_as_string(keywords_for_light_files))
except ValueError:
print 'Skipping file %s' % image
continue
header.add_history(history(patch_headers, mode='end',
time=run_time))
def add_overscan(dir='.', new_file_ext='new',
overwrite=False, detailed_history=True):
"""
Add overscan information to Feder FITS headers.
`dir` is the directory containing the files to be patched.
`new_file_ext` is the name added to the FITS files with updated
header information. It is added to the base name of the input
file, between the old file name and the `.fit` or `.fits` extension.
`overwrite` should be set to `True` to replace the original files.
detailed_history : bool
If `True`, write name and value of each keyword changed to
output FITS files. If `False`, write only a list of which
keywords changed.
"""
feder_info = Feder()
images = ImageFileCollection(location=dir, keywords=['imagetyp', 'instrume'])
for header in images.headers(save_with_name=new_file_ext,
clobber=overwrite,
do_not_scale_image_data=True):
image_dim = [header['naxis1'], header['naxis2']]
instrument = feder_info.instrument[header['instrume']]
run_time = datetime.now()
header.add_history(history(add_overscan, mode='begin',
time=run_time))
overscan_present.value = instrument.has_overscan(image_dim)
overscan_present.addToHeader(header, history=detailed_history)
modified_keywords = [overscan_present]
if overscan_present.value:
overscan_axis.value = instrument.overscan_axis
overscan_start.value = instrument.overscan_start
overscan_axis.addToHeader(header,
history=detailed_history)
overscan_start.addToHeader(header,
history=detailed_history)
modified_keywords.extend([overscan_axis, overscan_start])
if not detailed_history:
header.add_history('add_overscan updated keywords %s' %
keyword_names_as_string(modified_keywords))
header.add_history(history(add_overscan, mode='end',
time=run_time))
