def make_acisI_and_back_for(observation, cluster):
rt.dmcopy.punlearn()
rt.dmcopy(infile="{clean_file}[sky=region({mask})]".format(
clean_file=observation.clean, mask=cluster.master_crop_file),
outfile=cluster.temp_acisI_comb,
clobber=True)
rt.dmcopy.punlearn()
rt.dmcopy(
infile="{temp_acisI_combined}[bin sky=4][energy=700:8000]".format(
temp_acisI_combined=cluster.temp_acisI_comb),
outfile=observation.acisI_comb_img,
clobber=True)
rt.dmcopy.punlearn()
rt.dmcopy(infile="{back_file}[sky=region({mask})]".format(
back_file=observation.back, mask=cluster.master_crop_file),
outfile=cluster.temp_backI_comb,
clobber=True)
rt.dmcopy.punlearn()
rt.dmcopy(
infile="{temp_backI_combined}[bin sky=4][energy=700:8000]".format(
temp_backI_combined=cluster.temp_backI_comb),
outfile=observation.backI_comb_img,
clobber=True)
io.delete(cluster.temp_acisI_comb)
io.delete(cluster.temp_backI_comb)
def make_mask_file(observation):
from astropy.io import fits
print("Creating an image mask for {}.".format(observation.id))
original_fits_filename = observation.acisI_comb_img
mask = fits.open(original_fits_filename)
mask[0].data = np.ones_like(mask[0].data)
mask_filename = observation.temp_acis_comb_mask_filename
mask.writeto(mask_filename, overwrite=True)
rt.dmcopy.punlearn()
# infile = "{mask_filename}[sky=region({fov_file})][opt full]".format( # for ACIS-I & ACIS-S
infile = "{mask_filename}[sky=region({fov_file}[ccd_id=0:3])][opt full]".format( # for ACIS-I
mask_filename=mask_filename,
fov_file=observation.fov_file)
outfile = observation.acisI_combined_mask
clobber = True
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
print("Image mask created for {obsid} and saved as {filename}".format(
obsid=observation.id, filename=outfile))
io.delete(observation.temp_acis_comb_mask_filename)
def runpipe5(cluster):
# This portion of the pypeline
combined_dir = cluster.combined_directory
io.make_directory(combined_dir)
while not io.file_exists(cluster.master_crop_file):
print("Master crop file not found")
run_ds9_for_master_crop(cluster)
for observation in cluster.observations:
# clean data
infile = "{}[sky=region({})]".format(observation.clean,
cluster.master_crop_file)
outfile = cluster.temp_acisI_comb
clobber = True
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
infile = "{}[bin sky=4][energy=700:8000]".format(
cluster.temp_acisI_comb)
outfile = observation.acisI_comb_img
clobber = True
print("ObsID: {}\t- Extracting just 0.7keV - 8keV.".format(
observation.id))
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
# background
infile = "{}[sky=region({})]".format(observation.back,
cluster.master_crop_file)
outfile = cluster.temp_backI_comb
clobber = True
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
infile = "{}[bin sky=4][energy=700:8000]".format(
cluster.temp_backI_comb)
outfile = observation.backI_comb_img
clobber = True
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
io.delete(cluster.temp_acisI_comb)
io.delete(cluster.temp_backI_comb)
make_mask_file(observation)
make_cumulative_mask_file(cluster, observation)
create_combined_images(cluster)
make_nosrc_cropped_xray_sb(cluster)
def runpipe5(cluster):
print("runpipe5")
from astropy.io import fits
# This portion of the pypeline
combined_dir = cluster.combined_directory
io.make_directory(combined_dir)
while not io.file_exists(cluster.master_crop_file):
print("Master crop file not found")
run_ds9_for_master_crop(cluster)
# the contents of this for loop should be refactored/replaced with the make_acisI_and_back function
for observation in cluster.observations:
infile = "{}[sky=region({})]".format(observation.clean, cluster.master_crop_file)
outfile = cluster.temp_acisI_comb
clobber = True
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
print("{} shape: {}".format(outfile, fits.open(outfile)[0].shape))
infile = "{}[bin sky=4][energy=700:8000]".format(cluster.temp_acisI_comb)
outfile = observation.acisI_comb_img
clobber = True
print("ObsID: {}\t- Extracting just 0.7keV - 8keV.".format(observation.id))
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
# background
infile = "{}[sky=region({})]".format(observation.back, cluster.master_crop_file)
outfile = cluster.temp_backI_comb
clobber = True
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
infile = "{}[bin sky=4][energy=700:8000]".format(cluster.temp_backI_comb)
outfile = observation.backI_comb_img
clobber = True
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
io.delete(cluster.temp_acisI_comb)
io.delete(cluster.temp_backI_comb)
make_mask_file(observation)
make_cumulative_mask_file(cluster, observation)
create_combined_images(cluster)
make_nosrc_cropped_xray_sb(cluster)
def make_acisI_and_back_for(observation, cluster):
from astropy.io import fits
rt.dmcopy.punlearn()
rt.dmcopy(
infile="{clean_file}[sky=region({mask})]".format(
clean_file=observation.clean, mask=cluster.master_crop_file),
outfile=cluster.temp_acisI_comb,
clobber=True
)
shp = fits.open(cluster.temp_acisI_comb)[0].shape
print(observation.clean)
print("{} shape {}".format(cluster.temp_acisI_comb,
shp))
rt.dmcopy.punlearn()
rt.dmcopy(
infile="{temp_acisI_combined}[bin sky=4][energy=700:8000]".format(
temp_acisI_combined=cluster.temp_acisI_comb),
outfile=observation.acisI_comb_img,
clobber=True
)
shp = fits.open(observation.acisI_comb_img)[0].shape
print("{} shape {}".format(observation.acisI_comb_img,
shp))
rt.dmcopy.punlearn()
rt.dmcopy(
infile="{back_file}[sky=region({mask})]".format(
back_file=observation.back,
mask=cluster.master_crop_file),
outfile=cluster.temp_backI_comb,
clobber=True
)
rt.dmcopy.punlearn()
rt.dmcopy(
infile="{temp_backI_combined}[bin sky=4][energy=700:8000]".format(temp_backI_combined=cluster.temp_backI_comb),
outfile=observation.backI_comb_img,
clobber=True
)
io.delete(cluster.temp_acisI_comb)
io.delete(cluster.temp_backI_comb)
def ciao_back(cluster, overwrite=False):
print("Running ciao_back on {}.".format(cluster.name))
for observation in cluster.observations:
pcad_file = make_pcad_lis(cluster, observation.id)
backI_lis = []
backS_lis = []
analysis_path = observation.analysis_directory
filelist = io.read_contents_of_file(observation.ccd_merge_list).split('\n')
pcad = io.read_contents_of_file(pcad_file)
for acis_file in filelist:
rt.acis_bkgrnd_lookup.punlearn()
print("Finding background for {}".format(acis_file))
path_to_background = rt.acis_bkgrnd_lookup(infile=acis_file)
print("Found background at {}".format(path_to_background))
acis_id = int(acis_file.split('/')[-1].split('.')[-2][-1])
assert isinstance(acis_id, int), "acis_id = {}".format(acis_id)
assert not isinstance(path_to_background, type(None)), "Cannot find background {}".format(acis_file)
local_background_path = io.get_path("{}/back_ccd{}.fits".format(analysis_path, acis_id))
try:
if io.file_exists(local_background_path) and overwrite:
io.delete(local_background_path)
io.copy(path_to_background, local_background_path)
except OSError:
print("Problem copying background file {}. Do you have the right permissions and a full CALDB?".format(
path_to_background))
raise
acis_gain = rt.dmkeypar(infile=acis_file,
keyword="GAINFILE",
echo=True)
background_gain = rt.dmkeypar(infile=local_background_path,
keyword="GAINFILE",
echo=True)
print("{}/{}/acis_ccd{}.fits gain: {}".format(cluster.name, observation.id, acis_id, acis_gain))
print("{}/{}/back_ccd{}.fits gain: {}".format(cluster.name, observation.id, acis_id, background_gain))
if dates_and_versions_match(acis_gain, background_gain):
print("Date/version numbers don't match on the acis data and background. Reprocessing.")
local_background_path = reprocess(cluster, observation.id, acis_gain, background_gain, acis_id)
print("Reprojecting background")
rt.reproject_events.punlearn()
infile = local_background_path
outfile = io.get_path("{local_path}/back_reproj_ccd{acis_id}.fits".format(local_path=analysis_path,
acis_id=acis_id))
match = acis_file
print(
"Running:\n reproject_events(infile={infile}, outfile={outfile}, aspect={pcad}, match={match})".format(
infile=infile, outfile=outfile, pcad=pcad, match=match)
)
rt.reproject_events(infile=infile,
outfile=outfile,
aspect="{pcad_file}".format(pcad_file=pcad),
match=match,
random=0,
clobber=True)
back_reproject = outfile
datamode = rt.dmkeypar(infile=io.get_filename_matching(io.get_path("{}/acis*evt1*.fits".format(analysis_path))),
keyword="DATAMODE")
if datamode == "VFAINT":
print("VFAINT Mode, resetting setting status bits")
rt.dmcopy.punlearn()
rt.dmcopy(infile="{}[status=0]".format(back_reproject),
outfile=outfile,
clobber=True)
if acis_id <= 3:
backI_lis.append(back_reproject)
else:
backS_lis.append(back_reproject)
merged_back_list = backI_lis + backS_lis
print("writing backI.lis and backS.lis")
io.write_contents_to_file("\n".join(backI_lis), io.get_path("{}/backI.lis".format(analysis_path)),
binary=False)
io.write_contents_to_file("\n".join(backS_lis), io.get_path("{}/backS.lis".format(analysis_path)),
binary=False)
io.write_contents_to_file("\n".join(merged_back_list), observation.merged_back_lis, binary=False)
return