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ciao.py
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ciao.py
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from enum import IntEnum
from errors import ClusterPyError
import pypeline_io as io
import os
import config
import subprocess
import numpy as np
import acb
import cluster
import sys
import data_operations as do
import time
import multiprocessing as mp
import astropy.io.fits as fits
import spectral
from tqdm import tqdm
try:
from ciao_contrib.cda.data import download_chandra_obsids
import ciao_contrib.logger_wrapper as lw
lw.initialize_logger("download", verbose=1)
from ciao_contrib import runtool as rt
except ImportError:
print("Failed to import CIAO python scripts. Is CIAO running?")
sys.exit(1)
class Stage(IntEnum):
zero = 0
one = 1
two = 2
three = 3
four = 4
five = 5
tmap = 6
def download_obsid(obsid):
# print(f'Downloading {obsid}')
return download_chandra_obsids([obsid])
def download_data(cluster):
io.set_working_directory(cluster.directory)
obsids = [int(obsid) if obsid != '' else None for obsid in cluster.observation_ids]
num_cpus = mp.cpu_count() // 2
if num_cpus > 5:
num_streams = 3
else:
num_streams = num_cpus // 2
with mp.Pool(num_streams) as pool:
results = pool.map(download_obsid, obsids)
_ = [cluster.observation(obsid).set_ccds() for obsid in obsids]
return results
def dates_and_versions_match(acis_filename, background_filename):
acis = {'date': io.get_date_from_filename(acis_filename),
'version': io.get_version_from_filename(acis_filename)}
background = {'date': io.get_version_from_filename(background_filename),
'version': io.get_version_from_filename(background_filename)}
return (acis['date'] == background['date']) and (acis['version'] == background['version'])
def acis_process_events(gainfile, infile, outfile, clobber=False):
rt.acis_process_events.punlearn()
kwargs = {'infile': infile,
'outfile': outfile,
'acaofffile': None,
'stop': None,
'doevtgrade': False,
'apply_cti': True,
'apply_tgain': False,
'calculate_pi': True,
'pix_adj': None,
'gainfile': gainfile,
'clobber': clobber,
'eventdef': '{s:ccd_id,s:node_id,i:expno,s:chip,s:tdet,f:det,f:sky,s:'
'phas,l:pha,l:pha_ro,f:energy,l:pi,s:fltgrade,s:grade,x:status}'}
rt.acis_process_events(**kwargs)
return None
def reprocess(cluster, observation, acis_gain, background_gain, acis_id):
local_path = "{cluster_dir}/{observation}/analysis/".format(cluster_dir=cluster.directory,
observation=observation)
outfile = "{dir}/back_newgain_ccd{acis_id}.fits".format(dir=local_path,
acis_id=acis_id)
infile = "{dir}/back_ccd{acis_id}.fits".format(dir=local_path,
acis_id=acis_id)
gainfile = "{ciao_dir}/CALDB/data/chandra/acis/det_gain/{acis_gain}".format(ciao_dir=config.sys_config.ciao_directory,
acis_gain=acis_gain)
print("Reprocessing {cluster}/{observation}/{acis_id}".format(cluster=cluster.name,
observation=observation,
acis_id=acis_id))
acis_process_events(gainfile=gainfile, infile=infile, outfile=outfile)
return outfile
def ciao_back(cluster, overwrite=False):
print("Running ciao_back on {}.".format(cluster.name))
for observation in cluster.observations:
pcad_file = make_pcad_lis(observation)
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, replace=True)
except OSError:
print("Problem copying background file {}. Do you have the right permissions and a full CALDB?".format(
path_to_background))
raise
try:
rt.dmkeypar.punlearn()
print(f'Running dmkeypar {acis_file} "GAINFILE" echo=True')
acis_gain = rt.dmkeypar(infile=acis_file,
keyword="GAINFILE",
echo=True)
rt.dmkeypar.punlearn()
print(f'Running dmkeypar {local_background_path} "GAINFILE" echo=True')
background_gain = rt.dmkeypar(infile=local_background_path,
keyword="GAINFILE",
echo=True)
except ValueError:
print("Error getting parameter file in CIAO. Please close ClusterPyXT and re-try the stage. If the problem persists, please file a bug report on https://github.com/bcalden/ClusterPyXT with the following error message:")
raise
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_file),
match=match,
random=0,
clobber=True)
back_reproject = outfile
datamode = rt.dmkeypar(infile=observation.level_1_event_filename,
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
def reprocess_cluster_multiobs(cluster: cluster.ClusterObj):
print("Reprocessing {}.".format(cluster.name))
result = chandra_repro_multi(cluster)
print(result)
return
def reprocess_cluster(cluster):
print("Reprocessing {}. This may take a a little while (potentially 10s of minutes)".format(cluster.name))
for observation in cluster.observations:
print("Reprocessing {}/{}".format(cluster.name, observation.id))
result = chandra_repro(observation)
print(result)
return
def ciao_merge_stack(stack_lis_file):
rt.dmmerge.punlearn()
infile = "@{}[subspace -expno]".format(stack_lis_file)
outfile = io.change_extension(stack_lis_file, "fits")
rt.dmmerge(infile=infile,
outfile=outfile,
outBlock="",
columnList="",
clobber=True)
return outfile
def ciao_merge_background(cluster):
for observation in cluster.observations:
print("Merging background files from {}/{}".format(cluster.name, observation.id))
merged = ciao_merge_stack(observation.merged_back_lis)
print("Merged background written to {}".format(merged))
ciao_hiE_sources(observation)
def chandra_repro(observation: cluster.Observation):
rt.chandra_repro.punlearn()
os.chdir(observation.analysis_directory)
output = rt.chandra_repro(indir=observation.directory,
outdir=observation.reprocessing_directory,
set_ardlib=False,
clobber=True,
verbose=1)
return output
def chandra_repro_multi(cluster: cluster.ClusterObj):
rt.chandra_repro.punlearn()
os.chdir(cluster.directory)
obsids = ",".join(cluster.observation_ids)
output = rt.chandra_repro(indir=obsids, outdir="", set_ardlib=False, clobber=True, verbose=1)
return output
def ccd_sort(cluster):
print("Running ccd_sort on {}.".format(cluster.name))
for observation in cluster.observations:
print("Working on {}/{}".format(cluster.name, observation.id))
#analysis_path = observation.analysis_directory
#os.chdir(analysis_path)
evt1_filename = observation.level_1_event_filename
evt2_filename = observation.reprocessed_evt2_filename
detname = rt.dmkeypar(infile=evt1_filename, keyword="DETNAM", echo=True)
print("evt1 : {}\nevt2 : {}\ndetname : {}".format(evt1_filename,
evt2_filename,
detname))
assert not isinstance(detname, type(None)), "detnam keyword not in level 1 event file: {}".format(
observation.level_1_event_filename
)
detnums = [int(x) for x in detname.split('-')[-1]]
io.make_directory(observation.analysis_directory)
for acis_id in detnums:
print("{cluster}/{observation}: Making level 2 event file for ACIS Chip id: {acis_id}".format(
cluster=cluster.name,
observation=observation.id,
acis_id=acis_id))
try:
rt.dmcopy(infile=observation.reprocessed_evt2_for_ccd(acis_id),
outfile=observation.acis_ccd(acis_id),
clobber=True)
except OSError as oserr:
print("Error generating event files for each CCD.")
print("Observation: {}\t CCD: {}".format(observation.id, acis_id))
print("File: {}".format(observation.reprocessed_evt2_for_ccd(acis_id)))
if not io.file_sizes_match(observation.reprocessed_evt2_filename, observation.original_reprocessed_evt2_filename):
print("File sizes don't match for {} and {}.".format(observation.reprocessed_evt2_filename,
observation.original_reprocessed_evt2_filename))
print("These should be the same file. Try manually copying {og} to {new} and retrying.".format(
og=observation.original_reprocessed_evt2_filename,
new=observation.reprocessed_evt2_filename
))
print("Retry last pipeline step. If problem persists, please post an issue to GitHub.")
raise
#sys.exit(1)
os.chdir(observation.analysis_directory)
if observation.acis_type == 0: # ACIS-I
acis_list = io.get_filename_matching("acis_ccd[0-3].fits")
elif observation.acis_type == 1: # ACIS-S
acis_list = io.get_filename_matching("acis_ccd[4-8].fits")
for i in range(len(acis_list)):
acis_list[i] = io.get_path("{obs_analysis_dir}/{file}".format(obs_analysis_dir=observation.analysis_directory,
file=acis_list[i]))
io.write_contents_to_file("\n".join(acis_list), observation.ccd_merge_list, binary=False)
merge_data_and_backgrounds(cluster, acis_list)
return
def merge_data_and_backgrounds(cluster, acis_list):
# merges the
rt.dmmerge.punlearn()
merged_file = "acisI.fits" # needs to be renamed to reflect ACIS-I & S
rt.dmmerge(infile="@acisI.lis[subspace -expno]",
outfile=merged_file,
clobber=True)
#detname = rt.dmkeypar(infile=io.get_filename_matching("acis*evt1.fits"),
# keyword="DETNAM")
# acisI3 = detname.find("3")
# acisS3 = detname.find("7")
rt.dmlist.punlearn()
rt.dmlist(infile=merged_file,
opt="header")
return None
def actually_merge_observations_from(cluster):
print("Merging observations from {}.".format(cluster.name))
merged_directory = cluster.merged_directory
io.make_directory(merged_directory)
os.chdir(merged_directory)
merged_observations = []
for observation in cluster.observations:
merged_observations.append(observation.ccd_filtered_reprocessed_evt2_filename)
merged_lis = "{}/merged_obs.lis".format(merged_directory)
io.write_contents_to_file("\n".join(merged_observations), merged_lis, binary=False)
outroot = io.get_path("{}/{}/".format(cluster.directory, cluster.name))
infile = "@{infile}".format(infile=merged_lis) # for ACIS-I & ACIS-S
xygrid = "-3000:10000:4,-3000:10000:4"
if len(merged_observations) == 1:
rt.fluximage.punlearn()
rt.fluximage(infile=infile,
outroot=outroot,
xygrid=xygrid,
clobber=True)
print("Only single observation, flux image created.")
elif len(merged_observations) > 1:
rt.merge_obs.punlearn()
rt.merge_obs(infiles=infile,
outroot=outroot,
binsize=4,
#xygrid=xygrid,
clobber=True,
parallel=True,
nproc=12)
def make_point_spread_function_map(observation, ecf=0.3, energy=1.4):
rt.mkpsfmap.punlearn()
rt.mkpsfmap(infile=observation.broad_threshold_image_filename,
outfile=observation.point_spread_function_map_filename,
energy=energy,
ecf=ecf,
clobber=True)
def wav_detect(observation):
rt.wavdetect.punlearn()
rt.wavdetect(infile=observation.broad_threshold_image_filename,
outfile=observation.source_map_filename,
scellfile=observation.source_cell_map_filename,
imagefile=observation.source_image_filename,
defnbkgfile=observation.normalized_background_without_sources_filename,
scales="1.0 2.0 4.0 8.0 16.0",
psffile=observation.point_spread_function_map_filename,
regfile=observation.source_region_filename,
clobber=True)
def merge_source_files(cluster: cluster.ClusterObj):
region_files = [observation.source_region_filename for observation in cluster.observations]
io.merge_region_files(region_files, cluster.sources_file)
def find_sources(cluster: cluster.ClusterObj, ecf=0.1, energy=1.4):
for observation in cluster.observations:
print("Finding sources in {}".format(observation.id))
make_point_spread_function_map(observation, ecf=ecf, energy=energy)
wav_detect(observation)
merge_source_files(cluster)
def ciao_hiE_sources(observation):
# I don't know that this function is actually worthwhile
data = observation.data_filename
#background = observation.back_filename
#cts_min = '625'
# print("Finding optimal binning fators for ACIS images")
#acis_bin = 4
rt.dmcopy.punlearn()
infile = "{infile}".format(infile=data)
outfile = "{}/img_acisI_fullE.fits".format(observation.analysis_directory)
rt.dmcopy(infile=infile, outfile=outfile, clobber=True)
# final part of runpipe1
def merge_observations(cluster):
reprocess_cluster_multiobs(cluster) #multiobs test
ccd_sort(cluster)
ciao_back(cluster)
ciao_merge_background(cluster)
actually_merge_observations_from(cluster)
return
def sources_file_exists(cluster):
return os.path.isfile(cluster.sources_file)
def remove_sources_from_observation(observation):
# print("removing sources from {}".format(observation.id))
# remove sources from foreground and background
fore_or_back = [observation.data_filename, observation.back_filename]
for i, type_of_obs in enumerate(fore_or_back):
infile = "{type_of_obs}[exclude sky=region({sources})]".format(
type_of_obs=type_of_obs,
#sources=observation.cluster.sources_file
sources=observation.source_region_filename
)
outfile = [observation.acis_nosrc_filename, observation.background_nosrc_filename][i]
clobber = True
# print("infile: {}".format(infile))
# print("outfile: {}".format(outfile))
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber, verbose=0)
if type_of_obs is observation.background_nosrc_filename:
# print("Copying background to {}".format(observation.back))
io.copy(outfile, observation.back)
def remove_sources(observation):
if sources_file_exists(observation.cluster):
remove_sources_from_observation(observation)
def generate_light_curve(observation):
# filter out high energy background flares
obsid_analysis_dir = observation.analysis_directory
data = observation.acis_nosrc_filename
background = observation.background_nosrc_filename
infile = "{}[energy=9000:12000]".format(data)
outfile = "{}/acisI_hiE.fits".format(obsid_analysis_dir)
clobber = True
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
data_hiE = outfile
infile = "{}[bin sky=8]".format(data_hiE)
outfile = "{}/img_acisI_hiE.fits".format(obsid_analysis_dir)
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
backbin = 259.28
echo = True
tstart = rt.dmkeypar(infile=data_hiE, keyword="TSTART", echo=echo)
tstop = rt.dmkeypar(infile=data_hiE, keyword="TSTOP", echo=echo)
# print("Creating a lightcurve from the high energy events list with dmextract")
rt.dmextract.punlearn()
infile = "{}[bin time={}:{}:{}]".format(data_hiE, tstart, tstop, backbin)
outfile = "{}/acisI_lcurve_hiE.lc".format(obsid_analysis_dir)
# print('Running dmextract infile={} outfile={} opt=ltc1 clobber=True'.format(infile, outfile))
rt.dmextract(infile=infile,
outfile=outfile,
opt='ltc1', clobber=True)
lcurve_hiE = outfile
# print("cleaning the lightcurve for {}, press enter to continue.".format(observation.id))
rt.deflare.punlearn()
outfile = "{}/acisI_gti_hiE.gti".format(obsid_analysis_dir)
method = "clean"
save = "{}/acisI_lcurve_hiE".format(obsid_analysis_dir)
rt.deflare(infile=lcurve_hiE, outfile=outfile, method=method, save=save)
gti_hiE = outfile
# print("Filtering the event list using GTI info from high energy flares.")
infile = "{}[@{}]".format(data, gti_hiE)
outfile = "{}/acisI_nosrc_hiEfilter.fits".format(obsid_analysis_dir)
# print("running: dmcopy infile={} outfile={} clobber={}".format(infile, outfile, clobber))
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
data_nosrc_hiEfilter = outfile
infile = "{}[bin sky=8]".format(data_nosrc_hiEfilter)
outfile = "{}/img_acisI_nosrc_fullE.fits".format(obsid_analysis_dir)
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber, verbose=0)
def lightcurve_with_exclusion_for(observation):
data_nosrc_hiEfilter = "{}/acisI_nosrc_hiEfilter.fits".format(observation.analysis_directory)
# print("Creating the image with sources removed")
data = observation.acis_nosrc_filename
image_nosrc = "{}/img_acisI_nosrc_fullE.fits".format(observation.analysis_directory)
if io.file_exists(observation.exclude_file):
# print("Removing sources from event file to be used in lightcurve")
infile = "{}[exclude sky=region({})]".format(data_nosrc_hiEfilter, observation.exclude)
outfile = "{}/acisI_lcurve.fits".format(observation.analysis_directory)
clobber = True
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
data_lcurve = "{}/acisI_lcurve.fits".format(observation.analysis_directory)
else:
yes_or_no = io.check_yes_no(
"Are there sources to be excluded from observation {} while making the lightcurve? ".format(observation.id))
if yes_or_no: # yes_or_no == True
print("Create the a region file with the region to be excluded and save it as {}".format(observation.exclude_file))
else:
data_lcurve = data_nosrc_hiEfilter
backbin = 259.28
echo = True
tstart = rt.dmkeypar(infile=data_nosrc_hiEfilter, keyword="TSTART", echo=echo)
tstop = rt.dmkeypar(infile=data_nosrc_hiEfilter, keyword="TSTOP", echo=echo)
infile = "{}[bin time={}:{}:{}]".format(data_lcurve, tstart, tstop, backbin)
outfile = "{}/acisI_lcurve.lc".format(observation.analysis_directory)
opt = "ltc1"
rt.dmextract.punlearn()
rt.dmextract(infile=infile, outfile=outfile, opt=opt, clobber=clobber)
lcurve = outfile
rt.deflare.punlearn()
infile = lcurve
outfile = "{}/acisI_gti.gti".format(observation.analysis_directory)
method = "clean"
save = "{}/acisI_lcurve".format(observation.analysis_directory)
rt.deflare(infile=infile, outfile=outfile, method=method, save=save)
gti = outfile
infile = "{}[@{}]".format(data_nosrc_hiEfilter, gti)
outfile = observation.clean
clobber = True
rt.dmcopy.punlearn()
rt.dmcopy(infile=infile, outfile=outfile, clobber=clobber)
data_clean = outfile
# def lightcurves_with_exclusion(cluster:cluster.ClusterObj, args):
# num_obs = len(cluster.observations)
# num_runs = (num_obs // args.num_cpus) + 1
# obs_lists = np.array_split(cluster.observation_ids, num_runs)
# for obs_list in tqdm(obs_lists):
# processes = [mp.Process(target=lightcurve_with_exclusion_for, args=(cluster.observation(obsid),)) for obsid in obs_list]
# for process in processes:
# process.start()
# for process in processes:
# process.join()
def lightcurves_with_exclusion(cluster:cluster.ClusterObj, args):
for observation in tqdm(cluster.observations, desc='Finishing light curves', unit='observation', total=len(cluster.observations)):
lightcurve_with_exclusion_for(observation)
def sources_and_light_curves(cluster):
print("Source removal:")
for observation in cluster.observations:
print("Removing sources for {obsid}".format(obsid=observation.id))
remove_sources(observation)
print("Light curves:")
for observation in cluster.observations:
print("Generating light curves for {obsid}".format(obsid=observation.id))
generate_light_curve(observation)
def remove_sources_in_parallel(cluster, args):
# Remove point sources for each observation in parallel.
try:
with mp.Pool(args.num_cpus) as pool:
_ = list(tqdm(pool.imap(remove_sources, cluster.observations),
total=len(cluster.observations),
desc='Removing point sources',
unit='observations')
)
except:
print(f"Error in removing sources in parallel. CPU count:{args.num_cpus}")
print(f"Trying again with single core.")
for observation in tqdm(cluster.observations,
total=len(cluster.observations),
desc='Removing point sources',
unit='observation'):
remove_sources(observation)
def generate_light_curves(cluster, args):
# Generate light curves for each obsertion. Each observation gets its own process
for observation in tqdm(cluster.observations, total=len(cluster.observations), desc='Generating light curves', unit='observation'):
generate_light_curve(observation)
## For Parallel operation below - sometimes crashes (50/50)
# num_obs = len(cluster.observations)
# num_runs = (num_obs // args.num_cpus) + 1
# obs_lists = np.array_split(cluster.observation_ids, num_runs)
# for obs_list in tqdm(obs_lists, desc='Generating light curves in batches', unit='batch'):
# processes = [mp.Process(target=generate_light_curve, args=(cluster.observation(obsid), )) for obsid in obs_list]
# for process in processes:
# process.start()
# for process in processes:
# process.join()
def create_global_response_file_for(observation: cluster.Observation):
#min_counts = 525
obs_analysis_dir = observation.analysis_directory
global_response_dir = observation.global_response_directory
io.make_directory(global_response_dir)
bad_pixel_file = observation.reprocessed_bad_pixel_filename
clean = observation.clean
back = observation.back
rt.ardlib.punlearn()
rt.acis_set_ardlib(badpixfile=bad_pixel_file)
mask_file = observation.mask_file
make_pcad_lis(observation)
infile = "{}[sky=region({})]".format(clean, observation.response_file_region_covering_ccds)
outroot = "{}/acisI_region_0".format(global_response_dir)
weight = True
correct_psf = False
pcad = "@{}/pcad_asol1.lis".format(obs_analysis_dir)
combine = False
bkg_file = ""
bkg_resp = False
group_type = "NUM_CTS"
binspec = 1
clobber = True
rt.specextract.punlearn()
start_time = time.time()
# print("Running specextract on {}".format(observation.id))
# print("Size of region0: {}".format(observation.acisI_region_0_size))
rt.specextract(infile=infile, outroot=outroot, weight=weight, correctpsf=correct_psf,
asp=pcad, combine=combine, mskfile=mask_file, bkgfile=bkg_file, bkgresp=bkg_resp,
badpixfile=bad_pixel_file, grouptype=group_type, binspec=binspec, clobber=clobber)
elapsed = time.time() - start_time
# print("Elapsed time: {:0.2f} sec(s)".format(elapsed))
infile = "{}[sky=region({})][bin pi]".format(back, observation.response_file_region_covering_ccds)
outfile = "{}/acisI_back_region_0.pi".format(global_response_dir)
clobber = True
rt.dmextract.punlearn()
# print("Running dmextract")
#print("Running: dmextract infile={}, outfile={}, clobber={}".format(infile, outfile, clobber))
start_time = time.time()
rt.dmextract(infile=infile, outfile=outfile, clobber=clobber)
elapsed = time.time() - start_time
# print("Elapsed time: {:0.2f} sec(s)".format(elapsed))
rt.dmhedit.punlearn()
infile = "{}/acisI_region_0.pi".format(global_response_dir)
filelist = ""
operation = "add"
key = "BACKFILE"
value = outfile
rt.dmhedit(infile=infile, filelist=filelist, operation=operation, key=key, value=value)
aux_response_file = '{global_response_directory}/acisI_region_0.arf'.format(
global_response_directory=observation.global_response_directory)
redist_matrix_file = '{global_response_directory}/acisI_region_0.rmf'.format(
global_response_directory=observation.global_response_directory)
io.copy(aux_response_file, observation.aux_response_file)
io.copy(redist_matrix_file, observation.redistribution_matrix_file)
def make_pcad_lis(observation: cluster.Observation):
search_str = "{}/*asol1.fits".format(observation.reprocessing_directory)
pcad_files = io.get_filename_matching(search_str)
pcad_list_string = "\n".join(pcad_files)
pcad_filename = "{}/pcad_asol1.lis".format(observation.analysis_directory)
io.write_contents_to_file(pcad_list_string, pcad_filename, binary=False)
return pcad_filename
def do_function_on_observations_in_parallel(cluster: cluster.ClusterObj,
function=None,
num_cpus=1):
observation_lists = cluster.parallel_observation_lists(num_cpus)
num_observations = len(cluster.observations)
start_time = time.time()
for observation_list in observation_lists:
print("Working on {} of {} observations.".format(len(observation_list), num_observations))
processes = [mp.Process(target=function, args=(observation,)) for observation in observation_list]
for process in processes:
process.start()
for process in processes:
process.join()
end_time = time.time()
elapsed_time = end_time - start_time
print("Elapsed time: {:2f} seconds".format(elapsed_time))
def make_response_files_in_parallel(cluster: cluster.ClusterObj, args):
with mp.Pool(args.num_cpus) as pool:
_ = list(tqdm(pool.imap(create_global_response_file_for, cluster.observations), desc='Creating global response files', total=len(cluster.observations), unit='observation'))
def make_response_files(cluster):
for observation in cluster.observations:
region_file = observation.response_file_region_covering_ccds
print("Checking for response region file (region file covering at least a piece of all ACIS-I CCDs) for {}".format(observation.id))
if (not io.file_exists(region_file)) or (io.file_size(region_file) == 0):
print("Region file {} does not exist.".format(region_file))
print("When DS9 opens, draw a small circle that covers a piece of each ACIS-I chip (~20 pixels) and save it as:\n" \
"{}".format(region_file))
print("Opening SAO DS9")
io.write_contents_to_file("", region_file, False)
ds9_arguments = "ds9 -regions system physical -regions shape circle -regions format ciao -zoom 0.5 " \
"-bin factor 4 {clean_obs}".format(clean_obs=observation.clean)
subprocess.run([ds9_arguments], shell=True)
print('Creating global response file.')
create_global_response_file_for(observation)
def make_mask_file(observation: cluster.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)
# print("{} shape: {}".format(original_fits_filename, mask[0].shape))
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()
# need to check type of observation, S or I, and then generate a new string with the approriate ccd filtering
if observation.acis_type == 0: # ACIS-I
ccd_filter = "0:3"
else:
ccd_filter = "4:9"
#infile = "{mask_filename}[sky=region({fov_file})][opt full]".format( # for ACIS-I & ACIS-S
infile = "{mask_filename}[sky=region({fov_file}[ccd_id={ccd_filter}])][opt full]".format(
mask_filename=mask_filename,
fov_file=observation.fov_file,
ccd_filter=ccd_filter
)
outfile = observation.acisI_combined_mask_file
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 make_cumulative_mask_file(cluster, observation):
cumulative_mask_filename = cluster.combined_mask
current_obs_mask_filename = observation.acisI_combined_mask_file
if not io.file_exists(cumulative_mask_filename):
# print("Cumulative mask file not found. Creating it.")
cumulative_mask = fits.open(current_obs_mask_filename)
cumulative_mask.writeto(cumulative_mask_filename)
else:
current_mask = fits.open(current_obs_mask_filename)
cumulative_mask = fits.open(cumulative_mask_filename)
# print("Cumulative mask {} shape:{}".format(cumulative_mask_filename,
# cumulative_mask[0].shape))
# print("current mask {} shape:{}".format(current_obs_mask_filename,
# current_mask[0].shape))
try:
cumulative_mask[0].data = current_mask[0].data + cumulative_mask[0].data
except ValueError as err:
# print("Shapes don't match, reprojecting image.")
rt.reproject_image(infile=observation.acisI_combined_mask_file,
matchfile=cluster.combined_mask,
outfile=observation.temp_acis_comb_mask_filename)
io.move(observation.temp_acis_comb_mask_filename, observation.acisI_combined_mask_file)
# print("Combining masks")
rt.dmimgcalc(infile=observation.acisI_combined_mask_file,
infile2=cluster.combined_mask,
operation='add',
outfile=cluster.combined_mask,
clobber=True)
#cumulative_mask[0].data += fits.open(observation.temp_acis_comb_mask_filename)[0].data
cumulative_mask[0].data = fits.open(cluster.combined_mask)[0].data
cumulative_mask[0].data[np.where(cumulative_mask[0].data > 1)] = 1
cumulative_mask.writeto(cumulative_mask_filename, overwrite=True)
def reproject(infile=None, matchfile=None, outfile=None, overwrite=False):
rt.reproject_image.punlearn()
rt.reproject_image(infile=infile, matchfile=matchfile, outfile=outfile, clobber=overwrite)
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 run_ds9_for_master_crop(cluster):
print("Need to create a box region containing all parts of the image you want included.")
print("Save this file as {master_crop}.".format(master_crop=cluster.master_crop_file))
ds9_arguments = "ds9 -regions system physical -regions shape circle -regions format ciao -zoom 0.5 " \
"-bin factor 4 {cluster_dir}/{name}_broad_flux.img".format(cluster_dir=cluster.directory,
name=cluster.name)
subprocess.run([ds9_arguments], shell=True)
def stage_4_parallel(cluster: cluster.ClusterObj):
print("Making observation masks.")
do_function_on_observations_in_parallel(cluster, function=make_masks_for)
print("Making the cumulative mask file.")
make_cumulative_mask(cluster)
def make_cumulative_mask(cluster: cluster.ClusterObj):
cumulative_mask_filename = cluster.combined_mask
cumulative_mask = np.zeros(fits.open(cumulative_mask_filename)[0].data.shape)
for observation in cluster.observations:
current_obs_mask_filename = observation.acisI_combined_mask_file
current_mask = observation.acisI_combined_mask
try:
cumulative_mask += current_mask
except ValueError as err:
print("Shapes don't match, reprojecting image.")
rt.reproject_image(infile=observation.acisI_combined_mask_file,
matchfile=cluster.combined_mask,
outfile=observation.temp_acis_comb_mask_filename)
io.move(observation.temp_acis_comb_mask_filename, observation.acisI_combined_mask_file)
current_mask = fits.open(observation.acisI_combined_mask_file)
print("Combining masks")
cumulative_mask += current_mask
# write any val > 1 = 1
if not io.file_exists(cumulative_mask_filename):
print("Cumulative mask file not found. Creating it.")
cumulative_mask = fits.open(current_obs_mask_filename)
cumulative_mask.writeto(cumulative_mask_filename)
else:
current_mask = fits.open(current_obs_mask_filename)
cumulative_mask = fits.open(cumulative_mask_filename)
cumulative_mask[0].data[np.where(cumulative_mask[0].data > 1)] = 1
cumulative_mask.writeto(cumulative_mask_filename, overwrite=True)
def make_energy_filtered_image(observation: cluster.Observation): # Energies in eV