def handle_certificate_problem(self, error_message):
logger.warning("Problem with CADC certificate")
self.app.get_model().stop_loading_images()
view = self.app.get_view()
view.hide_image_loading_dialog()
view.show_certificate_dialog(self, error_message)
def handle_general_download_error(self, error_message, download_request):
logger.warning("A problem occurred while downloading: %s" % error_message)
logger.error("-" * 60)
logger.error(traceback.format_exc())
logger.error("-" * 60)
self._failed_downloads.append(download_request)
self.app.get_view().show_retry_download_dialog(self, error_message)
def handle_certificate_problem(self, error_message):
logger.warning("Problem with CADC certificate")
self.app.get_model().stop_loading_images()
view = self.app.get_view()
view.hide_image_loading_dialog()
view.show_certificate_dialog(self, error_message)
def handle_general_download_error(self, error_message, download_request):
logger.warning("A problem occurred while downloading: %s" % error_message)
logger.error("-" * 60)
logger.error(traceback.format_exc())
logger.error("-" * 60)
self._failed_downloads.append(download_request)
self.app.get_view().show_retry_download_dialog(self, error_message)
def build_source_reading(self, expnum, ccd, X, Y):
"""
Given the location of a source in the image, create a source reading.
"""
image_uri = storage.dbimages_uri(expnum=expnum,
ccd=None,
version='p',
ext='.fits',
subdir=None)
logger.debug('Trying to access {}'.format(image_uri))
if not storage.exists(image_uri, force=False):
logger.warning('Image not in dbimages? Trying subdir.')
image_uri = storage.dbimages_uri(expnum=expnum,
ccd=ccd,
version='p')
if not storage.exists(image_uri, force=False):
logger.warning("Image doesn't exist in ccd subdir. %s" % image_uri)
return None
slice_rows=config.read("CUTOUTS.SINGLETS.SLICE_ROWS")
slice_cols=config.read("CUTOUTS.SINGLETS.SLICE_COLS")
if X == -9999 or Y == -9999 :
logger.warning("Skipping {} as x/y not resolved.".format(image_uri))
return None
if not (-slice_cols/2. < X < 2048+slice_cols/2. and -slice_rows/2. < Y < 4600+slice_rows/2.0):
logger.warning("Central location ({},{}) off image cutout.".format(X,Y))
return None
mopheader_uri = storage.dbimages_uri(expnum=expnum,
ccd=ccd,
version='p',
ext='.mopheader')
if not storage.exists(mopheader_uri, force=False):
# ELEVATE! we need to know to go off and reprocess/include this image.
logger.critical('Image exists but processing incomplete. Mopheader missing. {}'.format(image_uri))
return None
mopheader = get_mopheader(expnum, ccd)
# Build astrom.Observation
observation = astrom.Observation(expnum=str(expnum),
ftype='p',
ccdnum=str(ccd),
fk="")
observation.rawname = os.path.splitext(os.path.basename(image_uri))[0]+str(ccd).zfill(2)
observation.header = mopheader
return observation
def from_string(cls, comment):
"""
Build an MPC Comment from a string.
"""
values = comment.split()
if len(values) < 8:
logger.warning("non-OSSOS format MPC line read")
return comment
comment = comment.split('%')[-1]
return MPCComment(source_name = values[1],
frame=values[0],
X=values[3],
Y=values[4],
MPCNote=values[2][1:],
magnitude=values[5],
mag_uncertainty=values[6],
plate_uncertainty=values[7],
comment=comment)
def from_source_reference(expnum, ccd, X, Y):
"""
Given the location of a source in the image, create a source reading.
"""
image_uri = storage.dbimages_uri(expnum=expnum,
ccd=None,
version='p',
ext='.fits',
subdir=None)
logger.debug('Trying to access {}'.format(image_uri))
if not storage.exists(image_uri, force=False):
logger.warning('Image not in dbimages? Trying subdir.')
image_uri = storage.dbimages_uri(expnum=expnum,
ccd=ccd,
version='p')
if not storage.exists(image_uri, force=False):
logger.warning("Image doesn't exist in ccd subdir. %s" % image_uri)
return None
if X == -9999 or Y == -9999 :
logger.warning("Skipping {} as x/y not resolved.".format(image_uri))
return None
mopheader_uri = storage.dbimages_uri(expnum=expnum,
ccd=ccd,
version='p',
ext='.mopheader')
if not storage.exists(mopheader_uri, force=False):
# ELEVATE! we need to know to go off and reprocess/include this image.
logger.critical('Image exists but processing incomplete. Mopheader missing. {}'.format(image_uri))
return None
mopheader = storage.get_mopheader(expnum, ccd)
# Build astrom.Observation
observation = Observation(expnum=str(expnum),
ftype='p',
ccdnum=str(ccd),
fk="")
observation.rawname = os.path.splitext(os.path.basename(image_uri))[0]+str(ccd).zfill(2)
observation.header = mopheader
return observation
def from_source_reference(expnum, ccd, x, y):
"""
Given the location of a source in the image, create a source reading.
"""
image_uri = storage.dbimages_uri(expnum=expnum,
ccd=None,
version='p',
ext='.fits',
subdir=None)
logger.debug('Trying to access {}'.format(image_uri))
if not storage.exists(image_uri, force=False):
logger.warning('Image not in dbimages? Trying subdir.')
image_uri = storage.dbimages_uri(expnum=expnum,
ccd=ccd,
version='p')
if not storage.exists(image_uri, force=False):
logger.warning("Image doesn't exist in ccd subdir. %s" % image_uri)
return None
if x == -9999 or y == -9999:
logger.warning(
"Skipping {} as x/y not resolved.".format(image_uri))
return None
mopheader_uri = storage.dbimages_uri(expnum=expnum,
ccd=ccd,
version='p',
ext='.mopheader')
if not storage.exists(mopheader_uri, force=False):
# ELEVATE! we need to know to go off and reprocess/include this image.
logger.critical(
'Image exists but processing incomplete. Mopheader missing. {}'
.format(image_uri))
return None
# Build astrom.Observation
observation = Observation(expnum=str(expnum),
ftype='p',
ccdnum=str(ccd),
fk="")
observation.rawname = os.path.splitext(
os.path.basename(image_uri))[0] + str(ccd).zfill(2)
return observation
def phot(fits_filename, x_in, y_in, aperture=15, sky=20, swidth=10, apcor=0.3,
maxcount=30000.0, exptime=1.0, zmag=None):
"""
Compute the centroids and magnitudes of a bunch sources detected on
CFHT-MEGAPRIME images.
Args:
fits_filename: str
The name of the file containing the image to be processed.
Returns a MOPfiles data structure.
"""
if (not os.path.exists(fits_filename) and
not fits_filename.endswith(".fits")):
# For convenience, see if we just forgot to provide the extension
fits_filename += ".fits"
try:
input_hdulist = fits.open(fits_filename)
except Exception as err:
logger.debug(str(err))
raise TaskError("Failed to open input image: %s" % err.message)
## get the filter for this image
filter = input_hdulist[0].header.get('FILTER', 'DEFAULT')
### Some nominal CFHT zeropoints that might be useful
zeropoints = {"I": 25.77,
"R": 26.07,
"V": 26.07,
"B": 25.92,
"DEFAULT": 26.0,
"g.MP9401": 26.4
}
photzp = input_hdulist[0].header.get('PHOTZP', zeropoints.get(filter, zeropoints["DEFAULT"]))
if zmag is None:
zmag = input_hdulist[0].header.get('PHOTZP', zeropoints[filter])
### check for magic 'zeropoint.used' files
zpu_file = "zeropoint.used"
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
else:
zpu_file = "%s.zeropoint.used" % ( fits_filename[0:-5])
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
if zmag != photzp:
logger.warning("ZEROPOINT {} used in DAOPHOT doesn't match PHOTZP {} in header".format(zmag, photzp))
### setup IRAF to do the magnitude/centroid measurements
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
iraf.photpars.apertures = aperture
iraf.photpars.zmag = zmag
iraf.datapars.datamin = 0
iraf.datapars.datamax = maxcount
iraf.datapars.exposur = ""
iraf.datapars.itime = exptime
iraf.fitskypars.annulus = sky
iraf.fitskypars.dannulus = swidth
iraf.fitskypars.salgorithm = "mode"
iraf.fitskypars.sloclip = 5.0
iraf.fitskypars.shiclip = 5.0
iraf.centerpars.calgori = "centroid"
iraf.centerpars.cbox = 5.
iraf.centerpars.cthreshold = 0.
iraf.centerpars.maxshift = 2.
iraf.centerpars.clean = 'no'
iraf.phot.update = 'no'
iraf.phot.verbose = 'no'
iraf.phot.verify = 'no'
iraf.phot.interactive = 'no'
# Used for passing the input coordinates
coofile = tempfile.NamedTemporaryFile(suffix=".coo", delete=False)
coofile.write("%f %f \n" % (x_in, y_in))
# Used for receiving the results of the task
# mag_fd, mag_path = tempfile.mkstemp(suffix=".mag")
magfile = tempfile.NamedTemporaryFile(suffix=".mag", delete=False)
# Close the temp files before sending to IRAF due to docstring:
# "Whether the name can be used to open the file a second time, while
# the named temporary file is still open, varies across platforms"
coofile.close()
magfile.close()
os.remove(magfile.name)
iraf.phot(fits_filename, coofile.name, magfile.name)
# TODO: Move this filtering downstream to the user.
phot_filter = "PIER==0 && CIER==0 && SIER==0"
pdump_out = iraf.pdump(magfile.name, "XCENTER,YCENTER,MAG,MERR,ID,XSHIFT,YSHIFT,LID",
phot_filter, header='no', parameters='yes',
Stdout=1)
if not len(pdump_out) > 0:
mag_content = open(magfile.name).read()
raise TaskError("photometry failed. {}".format(mag_content))
os.remove(coofile.name)
os.remove(magfile.name)
### setup the mop output file structure
hdu = {}
hdu['header'] = {'image': input_hdulist,
'aper': aperture,
's_aper': sky,
'd_s_aper': swidth,
'aper_cor': apcor,
'zeropoint': zmag}
hdu['order'] = ['X', 'Y', 'MAG', 'MERR', 'ID', 'XSHIFT', 'YSHIFT', 'LID']
hdu['format'] = {'X': '%10.2f',
'Y': '%10.2f',
'MAG': '%10.2f',
'MERR': '%10.2f',
'ID': '%8d',
'XSHIFT': '%10.2f',
'YSHIFT': '%10.2f',
'LID': '%8d'}
hdu['data'] = {}
for col in hdu['order']:
hdu['data'][col] = []
for line in pdump_out:
values = line.split()
for col in hdu['order']:
if re.match('\%.*f', hdu['format'][col]):
if col == 'MAG':
values[0] = float(values[0]) - float(apcor)
hdu['data'][col].append(float(values.pop(0)))
elif re.match('\%.*d', hdu['format'][col]):
hdu['data'][col].append(int(values.pop(0)))
else:
hdu['data'][col].append(values.pop(0))
# Clean up temporary files generated by IRAF
os.remove("datistabe.par")
os.remove("datpdump.par")
return hdu
def parse(self, ssos_result_filename_or_lines):
"""
given the result table create 'source' objects.
:type ssos_result_table: Table
:param ssos_result_table:
"""
table_reader = ascii.get_reader(Reader=ascii.Basic)
table_reader.inconsistent_handler = self._skip_missing_data
table_reader.header.splitter.delimiter = '\t'
table_reader.data.splitter.delimiter = '\t'
table = table_reader.read(ssos_result_filename_or_lines)
sources = []
observations = []
source_readings = []
ref_pvwcs = None
downloader = Downloader()
warnings.filterwarnings('ignore')
for row in table:
# check if a dbimages object exists
ccd = int(row['Ext']) - 1
expnum = row['Image'].rstrip('p')
# ADDING THIS TEMPORARILY TO GET THE NON-OSSOS DATA OUT OF THE WAY WHILE DEBUGGING
if (row['Telescope_Insturment'] != 'CFHT/MegaCam') or (row['Filter'] != 'r.MP9601'):
continue
# it's fine for OSSOS, go get the image
image_uri = storage.dbimages_uri(expnum=expnum,
ccd=None,
version='p',
ext='.fits',
subdir=None)
logger.info('Trying to access %s\n%s' % (row.data, image_uri))
if not storage.exists(image_uri, force=False):
logger.warning('Image not in dbimages? Trying subdir.')
image_uri = storage.dbimages_uri(expnum=expnum,
ccd=ccd,
version='p')
if not storage.exists(image_uri, force=False):
logger.warning("Image doesn't exist in ccd subdir. %s" % image_uri)
continue
if row['X'] == -9999 or row['Y'] == -9999 :
logger.warning("Skipping %s as x/y not resolved." % ( row['Image']))
continue
mopheader_uri = storage.dbimages_uri(expnum=expnum,
ccd=ccd,
version='p',
ext='.mopheader')
if not mopheader_uri in mopheaders:
if not storage.exists(mopheader_uri, force=False):
logger.warning('mopheader missing, but images exists')
continue
# raise flag if no MOPHEADER
mopheader_fpt = cStringIO.StringIO(storage.open_vos_or_local(mopheader_uri).read())
mopheader = fits.open(mopheader_fpt)
mopheaders[mopheader_uri] = mopheader
mopheader = mopheaders[mopheader_uri]
# Build astrom.Observation
observation = astrom.Observation(expnum=str(expnum),
ftype='p',
ccdnum=str(ccd),
fk="")
observation.rawname = os.path.splitext(os.path.basename(image_uri))[0]+str(ccd).zfill(2)
observation.header = mopheader[0].header
MJD_OBS_CENTER = mpc.Time(observation.header['MJD-OBSC'],
format='mjd',
scale='utc', precision=5 ).replicate(format='mpc')
observation.header['MJD_OBS_CENTER'] = str(MJD_OBS_CENTER)
observation.header['MAXCOUNT'] = MAXCOUNT
observation.header['SCALE'] = observation.header['PIXSCALE']
#observation.header['CHIP'] = str(observation.header['CHIPNUM']).zfill(2)
observation.header['NAX1'] = observation.header['NAXIS1']
observation.header['NAX2'] = observation.header['NAXIS2']
observation.header['MOPversion'] = observation.header['MOP_VER']
observation.header['FWHM'] = 4
# a download pixel 1,1 of this data to due offsets with.
x_cen = int(min(max(1,row['X']),observation.header['NAX1']))
y_cen = int(min(max(1,row['Y']),observation.header['NAX2']))
if image_uri not in astheaders:
hdulist = downloader.download_hdulist(
uri=image_uri,
view='cutout',
cutout='[{}][{}:{},{}:{}]'.format(ccd+1, x_cen, x_cen, y_cen, y_cen))
astheaders[image_uri] = hdulist
hdulist = astheaders[image_uri]
pvwcs = wcs.WCS(hdulist[0].header)
(ra,dec) = pvwcs.xy2sky(x_cen, y_cen)
if ref_pvwcs is None:
ref_pvwcs = pvwcs
xref = row['X']
yref = row['Y']
(x0, y0) = ref_pvwcs.sky2xy(ra,dec)
x0 += row['X'] - x_cen
y0 += row['Y'] - y_cen
# Build astrom.SourceReading
observations.append(observation)
from_input_file = observation.rawname in self.input_rawnames
null_observation = observation.rawname in self.null_observations
print observation.rawname, observation.header['MJD_OBS_CENTER'], null_observation, from_input_file
source_reading = astrom.SourceReading(x=row['X'], y=row['Y'],
xref=xref, yref=yref,
x0=x0, y0=y0,
ra=row['Object_RA'], dec=row['Object_Dec'],
obs=observation,
ssos=True,
from_input_file=from_input_file,
null_observation=null_observation)
#if observation.rawname in self.input_rawnames:
# source_readings.insert(0, source_reading)
#else:
source_readings.append(source_reading)
# build our array of SourceReading objects
sources.append(source_readings)
warnings.filterwarnings('once')
return SSOSData(observations, sources, self.provisional_name)
def match_planted(cand_filename, measures):
"""
"""
# Load the planted objects associated with this candidate file.
try:
cands = astrom.parse(cand_filename)
except:
sys.stderr("Failed while reading {}".format(cand_filename))
return
matches_fptr = open(os.path.basename(cand_filename)+".eff", 'w')
objects_planted_uri = cands.observations[0].get_object_planted_uri()
planted_object_file = Planted_object_file(objects_planted_uri)
planted_objects = planted_object_file.planted_objects
matched = {}
false_positive_sources = []
false_negative_sources = []
confused_measure = {}
for idx in range(len(planted_objects)):
planted_object = planted_objects[idx]
# look for a matching .cand entry
cand_dist = None
cand_source = None
for source in cands.get_sources():
obs = source.get_readings()
dist = math.sqrt((obs[0].x-planted_object.x)**2 +
(obs[0].y - planted_object.y)**2)
if cand_dist is None or cand_dist > dist:
cand_dist = dist
cand_source = source
# look for a matching .mpc entry
measure_dist = None
measure_source = None
for provisional in measures:
x = float(measures[provisional][0].comment.X)
y = float(measures[provisional][0].comment.Y)
dist = math.sqrt( (x - planted_object.x)**2 + (y-planted_object.y)**2)
if measure_dist is None or measure_dist > dist:
measure_dist = dist
measure_source = measures[provisional]
if measure_dist < 6.0:
# this gets 'unset' if we match this measure with a cand, in the next step.
confused_measure[provisional] = planted_object
planted_object.confused += 1
# determine if planted_object was found
if cand_dist < 6.0:
# In candidate list
if measure_dist is not None and measure_dist < 6.0:
# accepted.
planted_object.recovered = measure_source
planted_object.false_negative = None
planted_object.confused -= 1
del(confused_measure[measure_source[0].provisional_name])
matched[measure_source[0].provisional_name] = True
else:
# rejected.
planted_object.false_negative = cand_source
false_negative_sources.append(cand_source)
matches_fptr.write( ("## F: found\n"
"## M: multiple matches\n"
"## C: other match of confused multiple match\n"
"## N: not found\n"
"## P: false Positive \n") )
matches_fptr.write("{} {} {:>8s} {:>8s} {:>8s} {:>8s} {:>8s} {:>8s} {:>8s} {:>8s} {:>8s} {:>8s}\n".format(
"Key",planted_object_file.header,"x_dao","y_dao", "rate_mes", "ang_mes", "mag1_dao","merr1_dao",
"mag2_dao","merr2_dao", "mag3_dao","merr3_dao" ))
for planted_object in planted_objects:
if planted_object.recovered is not None:
confused = "F"
if planted_object.confused > 1 :
confused = "M"
measure = planted_object.recovered
start_jd = measure[0].date.jd
x = float(measure[0].comment.X)
x3 = float(measure[2].comment.X)
y = float(measure[0].comment.Y)
y3 = float(measure[2].comment.Y)
end_jd = measure[2].date.jd
rate = math.sqrt((x3-x)**2 + (y3-y)**2)/(
24*(end_jd - start_jd)
)
angle = math.degrees(math.atan2(y3 - y,x3 - x))
matches_fptr.write("{:3s} {} {:8.2f} {:8.2f} {:8.2f} {:8.2f} ".format(
confused, str(planted_object), x, y, rate, angle))
# record the three discovery magnitudes
for idx in range(3):
try:
mag = float(measure[idx].comment.mag)
merr = float(measure[idx].comment.mag_uncertainty)
except Exception as e:
mag = -1.0
merr = -1.0
logger.warning(str(e))
matches_fptr.write("{:8.2f} {:8.2f} ".format(mag,merr))
matches_fptr.write("\n")
elif planted_object.false_negative is not None:
source = planted_object.false_negative
reading = source.get_reading(0)
third = source.get_reading(2)
cutout = image_slice_downloader.download_cutout(reading, needs_apcor=True)
start_jd = mpc.Time(reading.obs.header['MJD_OBS_CENTER'],format='mpc', scale='utc').jd
end_jd = mpc.Time(third.obs.header['MJD_OBS_CENTER'], format='mpc', scale='utc').jd
rate = math.sqrt((third.x - reading.x)**2 + (third.y - reading.y)**2)/(
24*(end_jd - start_jd) )
angle = math.degrees(math.atan2(third.y - reading.y,third.x - reading.x))
matches_fptr.write("{:3s} {} {:8.2f} {:8.2f} {:8.2f} {:8.2f} ".format(
"N", str(planted_object), reading.x, reading.y, rate, angle))
for idx in range(3):
try:
(x, y, mag, merr) = cutout.get_observed_magnitude()
except TaskError as e:
logger.warning(str(e))
mag = -1.0
merr = -1.0
matches_fptr.write("{:8.2f} {:8.2f} ".format(mag,merr))
matches_fptr.write("\n")
else:
matches_fptr.write("{:3s} {}".format("X",str(planted_object)))
matches_fptr.write(10*" {:8.2f}".format(0.0))
matches_fptr.write("\n")
matches_fptr.flush()
for provisional in measures:
if matched.get(provisional,False):
continue
# this source is a false positive
measure = measures[provisional]
start_jd = measure[0].date.jd
x = float(measure[0].comment.X)
x3 = float(measure[2].comment.X)
y = float(measure[0].comment.Y)
y3 = float(measure[2].comment.Y)
end_jd = measure[2].date.jd
if provisional in confused_measure:
confused = "C"
planted_object = confused_measure[provisional]
else:
confused = "P"
planted_object = " {:4d}".format(-1)+6*" {:8.2f}".format(0)
# look for the matching cand entry
cand_dist = None
cand_source = None
for source in cands.get_sources():
obs = source.get_readings()
dist = math.sqrt((obs[0].x - x)**2 +
(obs[0].y - y)**2)
if cand_dist is None or cand_dist > dist:
cand_dist = dist
cand_source = source
false_positive_sources.append(cand_source)
rate = math.sqrt((x3-x)**2 + (y3-y)**2)/(
24*(end_jd - start_jd))
angle = math.degrees(math.atan2(y3 - y,x3 - x))
# record the three discovery magnitudes
matches_fptr.write("{:3s} {} {:8.2f} {:8.2f} {:8.2f} {:8.2f} ".format(
confused, planted_object, x, y, rate, angle))
for idx in range(3):
try:
mag = float(measure[idx].comment.mag)
merr = float(measure[idx].comment.mag_uncertainty)
except Exception as e:
mag = -1.0
merr = -1.0
logger.warning(str(e))
matches_fptr.write("{:8.2f} {:8.2f} ".format(mag,merr))
matches_fptr.write("\n")
matches_fptr.close()
## write out the false_positives and false_negatives
if not os.access('false_positives',os.R_OK):
os.mkdir('false_positives')
if not os.access('false_negatives', os.R_OK):
os.mkdir('false_negatives')
if len(false_positive_sources) > 0 :
wh = open('false_positives/'+os.path.basename(cand_filename),'w+')
writer = astrom.StreamingAstromWriter(wh,cands.sys_header)
#writer.write_headers(cands.observations)
for source in false_positive_sources:
writer.write_source(source)
writer.flush()
writer.close()
if len(false_negative_sources) > 0 :
wh = open('false_negatives/'+os.path.basename(cand_filename),'w+')
writer = astrom.StreamingAstromWriter(wh,cands.sys_header)
#writer.write_headers(cands.observations)
for source in false_negative_sources:
writer.write_source(source)
writer.flush()
writer.close()
return matches_fptr.name
def match_planted(astrom_filename, match_filename, false_positive_filename):
"""
Using the astrom_filename as input get the Object.planted file from VOSpace and match
planted sources with found sources.
The Object.planted list is pulled from VOSpace based on the standard file-layout and name of the
first exposure as read from the .astrom file.
:param astrom_filename: name of the fk*reals.astrom file to check against Object.planted
:param match_filename: a file that will contain a list of all planted sources and the matched found source
:param false_positive_filename: .astrom format output containing input objects that had no match in planted
"""
image_slice_downloader = ImageCutoutDownloader(slice_rows=100, slice_cols=100)
fk_candidate_observations = astrom.parse(astrom_filename)
matches_fptr = storage.open_vos_or_local(match_filename,'w')
objects_planted_uri = fk_candidate_observations.observations[0].get_object_planted_uri()
objects_planted = image_slice_downloader.download_raw(objects_planted_uri, view='data').split('\n')
planted_objects = []
for line in objects_planted[1:]:
if len(line) == 0 or line[0] == '#':
continue
planted_objects.append(PlantedObject(line))
false_positives_stream_writer = None
matches_fptr.write("#{}\n".format(fk_candidate_observations.observations[0].rawname))
matches_fptr.write("{:1s}{} {:>8s} {:>8s} {:>8s} {:>8s} {:>8s} {:>8s} {:>8s}\n".format(
"",objects_planted[0],"x_dao","y_dao","mag_dao","merr_dao", "rate_mes", "ang_mes", "dr_pixels" ))
found_idxs = []
for source in fk_candidate_observations.get_sources():
reading = source.get_reading(0)
third = source.get_reading(2)
cutout = image_slice_downloader.download_cutout(reading, needs_apcor=True)
try:
(x, y, mag, merr) = cutout.get_observed_magnitude()
except TaskError as e:
logger.warning(str(e))
mag = 0.0
merr = -1.0
matched = None
for idx in range(len(planted_objects)):
planted_object = planted_objects[idx]
dist = math.sqrt((reading.x-planted_object.x)**2 + (reading.y - planted_object.y)**2)
if matched is None or dist < matched:
matched = dist
matched_object_idx = idx
start_jd = Time(reading.obs.header['MJD_OBS_CENTER'],format='mpc', scale='utc').jd
end_jd = Time(third.obs.header['MJD_OBS_CENTER'], format='mpc', scale='utc').jd
exptime = float(reading.obs.header['EXPTIME'])
rate = math.sqrt((third.x - reading.x)**2 + (third.y - reading.y)**2)/(
24*(end_jd - start_jd) )
angle = math.degrees(math.atan2(third.y - reading.y,third.x - reading.x))
if matched > 3*rate*exptime/3600.0 and False :
# this is a false positive (candidate not near artificial source)
# create a .astrom style line for feeding to validate for checking later
if false_positives_ftpr is None or false_positives_stream_writer is None:
# create false positive file for storing results
false_positives_ftpr = open(false_positive_filename,'w+')
false_positives_stream_writer = StreamingAstromWriter(
false_positives_ftpr,fk_candidate_observations.sys_header)
false_positives_stream_writer.write_source(source)
false_positives_ftpr.flush()
continue
elif matched_object_idx in found_idxs:
repeat = '#'
else:
repeat = ' '
found_idxs.append(matched_object_idx)
mags = []
merrs = []
for this_reading in source.get_readings()[1:]:
cutout = image_slice_downloader.download_cutout(this_reading, needs_apcor=True)
try:
(this_x, this_y, this_mag, this_merr) = cutout.get_observed_magnitude()
except TaskError as e:
logger.warning(str(e))
this_mag = 0.0
this_merr = -1.0
mags.append(this_mag)
merrs.append(this_merr)
matches_fptr.write("{:1s}{} {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} ".format(
repeat,
str(planted_objects[matched_object_idx]), reading.x, reading.y, mag, merr, rate, angle, matched))
for idx in range(len(mags)):
matches_fptr.write("{:8.2f} {:8.2f}".format(mags[idx], merrs[idx]))
matches_fptr.write("\n")
# close the false_positives
if false_positives_ftpr is not None:
false_positives_ftpr.close()
# record the unmatched Object.planted entries, for use in efficiency computing
for idx in range(len(planted_objects)):
if idx not in found_idxs:
planted_object = planted_objects[idx]
matches_fptr.write("{:1s}{} {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f} {:8.2f}\n".format("",str(planted_object),
0, 0, 0, 0, 0, 0, 0))
matches_fptr.close()
def phot(fits_filename,
x_in,
y_in,
aperture=15,
sky=20,
swidth=10,
apcor=0.3,
maxcount=30000.0,
exptime=1.0,
zmag=None):
"""
Compute the centroids and magnitudes of a bunch sources detected on
CFHT-MEGAPRIME images.
Args:
fits_filename: str
The name of the file containing the image to be processed.
Returns a MOPfiles data structure.
"""
if not hasattr(x_in, '__iter__'):
x_in = [
x_in,
]
if not hasattr(y_in, '__iter__'):
y_in = [
y_in,
]
if (not os.path.exists(fits_filename)
and not fits_filename.endswith(".fits")):
# For convenience, see if we just forgot to provide the extension
fits_filename += ".fits"
try:
input_hdulist = fits.open(fits_filename)
except Exception as err:
logger.debug(str(err))
raise TaskError("Failed to open input image: %s" % err.message)
## get the filter for this image
filter = input_hdulist[0].header.get('FILTER', 'DEFAULT')
### Some nominal CFHT zeropoints that might be useful
zeropoints = {
"I": 25.77,
"R": 26.07,
"V": 26.07,
"B": 25.92,
"DEFAULT": 26.0,
"g.MP9401": 26.4
}
if zmag is None:
zmag = input_hdulist[0].header.get('PHOTZP', zeropoints[filter])
### check for magic 'zeropoint.used' files
zpu_file = "zeropoint.used"
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
else:
zpu_file = "%s.zeropoint.used" % (fits_filename[0:-5])
if os.access(zpu_file, os.R_OK):
with open(zpu_file) as zpu_fh:
zmag = float(zpu_fh.read())
photzp = input_hdulist[0].header.get(
'PHOTZP', zeropoints.get(filter, zeropoints["DEFAULT"]))
if zmag != photzp:
logger.warning(
"ZEROPOINT {} sent to DAOPHOT doesn't match PHOTZP {} in header".
format(zmag, photzp))
### setup IRAF to do the magnitude/centroid measurements
iraf.set(uparm="./")
iraf.digiphot()
iraf.apphot()
iraf.daophot(_doprint=0)
iraf.photpars.apertures = aperture
iraf.photpars.zmag = zmag
iraf.datapars.datamin = 0
iraf.datapars.datamax = maxcount
iraf.datapars.exposur = ""
iraf.datapars.itime = exptime
iraf.fitskypars.annulus = sky
iraf.fitskypars.dannulus = swidth
iraf.fitskypars.salgorithm = "mode"
iraf.fitskypars.sloclip = 5.0
iraf.fitskypars.shiclip = 5.0
iraf.centerpars.calgori = "centroid"
iraf.centerpars.cbox = 5.
iraf.centerpars.cthreshold = 0.
iraf.centerpars.maxshift = 2.
iraf.centerpars.clean = 'no'
iraf.phot.update = 'no'
iraf.phot.verbose = 'no'
iraf.phot.verify = 'no'
iraf.phot.interactive = 'no'
# Used for passing the input coordinates
coofile = tempfile.NamedTemporaryFile(suffix=".coo", delete=False)
for i in range(len(x_in)):
coofile.write("%f %f \n" % (x_in[i], y_in[i]))
# Used for receiving the results of the task
# mag_fd, mag_path = tempfile.mkstemp(suffix=".mag")
magfile = tempfile.NamedTemporaryFile(suffix=".mag", delete=False)
# Close the temp files before sending to IRAF due to docstring:
# "Whether the name can be used to open the file a second time, while
# the named temporary file is still open, varies across platforms"
coofile.close()
magfile.close()
os.remove(magfile.name)
iraf.phot(fits_filename, coofile.name, magfile.name)
pdump_out = ascii.read(magfile.name, format='daophot')
if not len(pdump_out) > 0:
mag_content = open(magfile.name).read()
raise TaskError("photometry failed. {}".format(mag_content))
# Clean up temporary files generated by IRAF
os.remove(coofile.name)
os.remove(magfile.name)
return pdump_out
