def acis_cti_get_data():
"""
extract acis evt1 files which are not processed for CTI observations
Input: none, but read from directory: /data/mta/www/mp_reports/photons/acis/cti/*
Output: <working_dir>/acisf<obsid>*evt1.fits
"""
#
#--- get a new data list
#
obsid_list = find_new_entry()
#
#--- if there is no new data, just exit
#
if len(obsid_list) > 0:
#
#--- create a temporary saving directory
#
mcf.mk_empty_dir(working_dir)
#
#--- extract acis event1 file
#
outdir = working_dir + '/new_entry' #---- new_entry list will be used later
f = open(outdir, 'w')
for obsid in obsid_list:
f.write(obsid)
f.write('\n')
cnt = 0
chk = extract_acis_evt1(obsid)
if chk != 'na':
cnt += 1
cmd = 'mv *' + str(obsid) + '*.fits.gz ' + working_dir
os.system(cmd)
f.close()
if cnt > 0:
cmd = 'gzip -d ' + working_dir + '*.gz'
os.system(cmd)
else:
exit(1)
def test_run_flt_pipe(self):
mcf.mk_empty_dir(temp_comp_area)
cmd = 'cp ./acisf52675_000N001_evt1.fits.gz ' + temp_comp_area
os.system(cmd)
cmd = 'gzip -d ' + temp_comp_area + '/*.gz'
os.system(cmd)
file = temp_comp_area + '/acisf52675_000N001_evt1.fits'
chk = run_flt_pipe(file)
self.assertEquals(chk, 0)
#------------------------------------------------------------
test_list = [
'0', '52675', '0', '2014-06-27T04:21:09', '2014-06-27T08:31:19',
['5.390+-0.449', '6.721+-0.277', '-99999+-00000', '0.533+-0.229'],
['2.695+-0.036', '1.303+-0.033', '1.371+-0.028', '1.191+-0.023'],
['1.303+-0.050', '2.531+-0.024', '1.352+-0.033', '1.372+-0.036']
]
full_list = find_cti_values_from_file()
self.assertEquals(full_list[0], test_list)
def update_detrend_factor_table():
"""
extract information about amp_avg values and update <house_keeping>/amp_avg_list
Input: none
Output: <house_keeping>/amp_avg_list
"""
#
#--- clean up temp_dir so that we can put new fits files
#
mcf.mk_empty_dir(temp_dir)
#
#--- extract stat fits files
#
new_entry = get_new_entry()
#
#--- if files are extracted, compute amvp_avg values for the obsid
#
if len(new_entry) > 0:
processed_list = update_amp_avg_list(new_entry)
#
#--- clean up "keep_entry" and amp_avg_list
#
update_holding_list(new_entry, processed_list)
cleanup_amp_list()
def get_new_value(year, month):
"""
extract aorwspd values from dataseeker
input: year/month
output: [av1, av2, av3, av4, av5, av6] --- six values of aorwspd(1-6)
"""
#
#--- set month long time interval in sec from 1998.1.1
#
year2 = year
month2 = month -1
if month2 < 1:
mont2 = 12
year2 -= 1
ydate = tcnv.findYearDate(year, month, 15)
t_in = str(year) + ':' + str(ydate) + ':00:00:00'
time1 = tcnv.axTimeMTA(t_in)
ydate = tcnv.findYearDate(year2, month2, 15)
t_in = str(year2) + ':' + str(ydate) + ':00:00:00'
time2 = tcnv.axTimeMTA(t_in)
#
#--- set command to call dataseeker
#
f = open('./test', 'w') #-- we need an empty "test" file to run dataseeker
f.close()
mcf.mk_empty_dir('param') #-- make empty param directory
line = 'columns=_aorwspd1_avg,'
line = line + '_aorwspd2_avg,'
line = line + '_aorwspd3_avg,'
line = line + '_aorwspd4_avg,'
line = line + '_aorwspd5_avg,'
line = line + '_aorwspd6_avg'
line = line + ' timestart=' + str(time2)
line = line + ' timestop=' + str(time1)
cmd = 'punlearn dataseeker; dataseeker.pl infile=test outfile=ztemp.fits search_crit=\''
cmd = 'dataseeker.pl infile=test outfile=ztemp.fits search_crit="'
cmd = cmd + line + '" loginFile="' + loginfile + '"'
#
#--- run dataseeker
#
bash("/usr/bin/env PERL5LIB='' " + cmd, env=ascdsenv)
mcf.rm_file(zspace)
mcf.rm_file('./test')
os.system('rm -rf ./param')
#
#--- read fits file
#
try:
dout = pyfits.getdata('./ztemp.fits')
aw1 = dout.field('AORWSPD1_AVG')
aw2 = dout.field('AORWSPD2_AVG')
aw3 = dout.field('AORWSPD3_AVG')
aw4 = dout.field('AORWSPD4_AVG')
aw5 = dout.field('AORWSPD5_AVG')
aw6 = dout.field('AORWSPD6_AVG')
except:
aw1 = aw2 = aw3 = aw4 = aw5 = aw6 = []
mcf.rm_file("./ztemp.fits")
#
#--- create monthly "sum" of the reaction wheel rotations
#--- dataseeker gives 5 min avg of the value; one day is 24 hr x 60 min / 5min = 288.
#
sum1 = sum2 = sum3 = sum4 = sum5 = sum6 = 0.0
for i in range(0, len(aw1)):
sum1 += abs(aw1[i])
sum2 += abs(aw2[i])
sum3 += abs(aw3[i])
sum4 += abs(aw4[i])
sum5 += abs(aw5[i])
sum6 += abs(aw6[i])
av1 = sum1 / 288
av2 = sum2 / 288
av3 = sum3 / 288
av4 = sum4 / 288
av5 = sum5 / 288
av6 = sum6 / 288
return [av1, av2, av3, av4, av5, av6]
def use_arc5gl_acis_hist(year, month, end_year, end_month):
'''
using arc5gl, extreact acis hist data
input: year, month, end_year, end_month (the last two are in sec from 1.1.1998)
output: acis hist data in fits files saved in exc_dir/Temp_dir
'''
#
#--- prep for output files
#
tdir = exc_dir + 'Temp_dir'
mcf.mk_empty_dir(tdir)
#
#--- write a command file
#
with open(zspace, 'w') as f:
f.write("operation=retrieve\n")
f.write("dataset=flight\n")
f.write("detector=acis\n")
f.write("level=0\n")
f.write("filetype=histogr\n")
lmon = str(month)
if month < 10:
lmon = '0' + lmon
lyear = str(year)
line = 'tstart=' + lyear + '-' + lmon + '-01T00:00:00\n'
f.write(line)
lmon = str(int(end_month))
if end_month < 10:
lmon = '0' + lmon
lyear = str(end_year)
line = 'tstop=' + lyear + '-' + lmon + '-01T00:00:00\n'
f.write(line)
f.write("go\n")
#
#--- run arc5gl
#
try:
cmd = 'cd ' + exc_dir + '; /proj/sot/ska/bin/arc5gl -user isobe -script ' + zspace
os.system(cmd)
except:
cmd1 = "/usr/bin/env PERL5LIB= "
cmd2 = ' cd ' + exc_dir + '; /proj/axaf/simul/bin/arc5gl -user isobe -script ' + zspace
try:
os.system(cmd2)
except:
cmd = cmd1 + cmd2
bash(cmd, env=ascdsenv)
mcf.rm_files(zspace)
#
#--- check whether the files are extracted. if not just exit.
#
cmd = 'ls ' + exc_dir + '* > ' + zspace
os.system(cmd)
chk = open(zspace, 'r').read()
mcf.rm_files(zspace)
m = re.search('fits.gz', chk)
if m is None:
exit(1)
if mcf.check_file_with_name(exc_dir, 'fits') == False:
exit(1)
cmd = 'mv ' + exc_dir + '*fits.gz ' + exc_dir + 'Temp_dir/.'
os.system(cmd)
cmd = 'gzip -d ' + exc_dir + 'Temp_dir/*.gz'
os.system(cmd)
#
#--- make a list of extracted fits files
#
cmd = 'ls ' + exc_dir + 'Temp_dir/*fits > ' + zspace
os.system(cmd)
data = mcf.read_data_file(zspace, remove=1)
return data
def clean_table():
"""
sort and clean the table data in <data_dir>/Results
Input: none but read from <data_dir>/Results/<elm>_ccd<ccd#>
Output: cleaned up <data_dir>/Results/<elm>_ccd<ccd#>
"""
#
#--- make a backup
#
atime = tcnv.currentTime(format='UTC')
tyear = str(atime[0])
syear = tyear[2] + tyear[3]
tmon = atime[1]
smon = str(tmon)
if int(tmon) < 10:
smon = '0' + smon
tday = atime[2]
sday = str(tday)
if int(tday) < 10:
sday = '0' + sday
cout = data_dir + '/Results/Save_' + smon + sday + syear
mcf.mk_empty_dir(cout)
cmd = 'cp -f ' + data_dir + '/Results/*_ccd* ' + cout + '/.'
os.system(cmd)
#
#--- now clean up the data
#
cmd = 'ls ' + data_dir + '/Results/*_ccd* > ' + zspace
os.system(cmd)
f = open(zspace, 'r')
flist = [line.strip() for line in f.readlines()]
f.close()
mcf.rm_file(zspace)
for file in flist:
f = open(file, 'r')
data = [line.strip() for line in f.readlines()]
f.close()
data.sort()
prev = data[0]
cleaned = [prev]
for ent in data:
if ent == prev:
prev = ent
else:
cleaned.append(ent)
prev = ent
fo = open(file, 'w')
for ent in cleaned:
line = ent + '\n'
fo.write(line)
fo.close()
def get_new_data(obsid_list, start_list, stop_list):
"""
extract fits for the obsid, then extrat data needed
input: obsid_list --- a list of obsids
start_list --- a list of start time of the observation
stop_list --- a list of stop time of the observation
output: data_set --- a list of data sets
"""
#
#--- find middle of the time. it will be used as a time stamp for this data set
#
tstart = start_list[0]
tstop = stop_list[len(stop_list) - 1]
mtime = int(tstart + 0.5 * (tstop - tstart))
#
#--- extract acis event1 file and combined all of them
#
mcf.mk_empty_dir(working_dir)
pobsid = []
fits_list = []
data_set = []
for i in range(0, len(obsid_list)):
obsid = obsid_list[i]
start = start_list[i]
stop = stop_list[i]
#
#--- check whether this data is already extreacted. if so, don't re-extrad the data
#
chk = 0
for comp in pobsid:
if obsid == comp:
chk = 1
break
if chk == 0:
fits = extract_acis_evt1(obsid)
pobsid.append(obsid)
if fits != 'na':
fits_list.append(fits)
else:
fits = 'na'
for ent in fits_list:
m1 = re.search(obsid, ent)
if m1 is not None:
fits = ent
break
if fits == 'na':
continue
#
#--- "Table.read" opens fits file and read fits table data
#
rfits = working_dir + fits
tdata = Table.read(rfits, hdu=1)
tdiff = stop - start
#
#--- extract specified time range, pha range, and chipy
#
mask = (tdata.field('time') >= start) & (tdata.field('time') <= stop) \
& (tdata.field('pha') <= 4000) & (tdata.field('chipy') <= 20)
tdata = tdata[mask]
if len(tdata) < 1000:
continue
tdata = tdata[cselect]
#
#--- grade
#
mask = (tdata.field('grade') <= 6) & (tdata.field('grade') != 1) \
& (tdata.field('grade') != 5)
tdata = tdata[mask]
data_set.append(tdata)
return data_set
def get_new_data(obsid_list, start_list, stop_list):
"""
extract fits for the obsid, then extrat data needed
input: obsid_list --- a list of obsids
start_list --- a list of start time of the observation
stop_list --- a list of stop time of the observation
output: data_set --- a list of data sets
"""
data_set = []
#
#--- find middle of the time. it will be used as a time stamp for this data set
#
tstart = start_list[0]
tstop = stop_list[len(stop_list)-1]
mtime = int (tstart + 0.5 * (tstop - tstart))
#
#--- extract acis event1 file and combined all of them
#
mcf.mk_empty_dir(working_dir)
pobsid = []
fits_list = []
for i in range(0, len(obsid_list)):
obsid = obsid_list[i]
start = start_list[i]
stop = stop_list[i]
#
#--- check whether this data is already extreacted. if so, don't re-extrad the data
#
chk = 0
for comp in pobsid:
if obsid == comp:
chk = 1
break
if chk == 0:
fits = extract_acis_evt1(obsid)
pobsid.append(obsid)
if fits != 'na':
fits_list.append(fits)
else:
fits = 'na'
for ent in fits_list:
m1 = re.search(obsid, ent)
if m1 is not None:
fits = ent
break
if fits == 'na':
continue
#
#--- "Table.read" opens fits file and read fits table data
#
rfits = working_dir + fits
tdata = Table.read(rfits, hdu=1)
tdiff = stop - start
#
#--- extract specified time range, pha range, and chipy
#
mask = (tdata.field('time') >= start) & (tdata.field('time') <= stop) & (tdata.field('pha') <= 4000) & (tdata.field('chipy') <= 20)
tdata = tdata[mask]
if len(tdata) < 1000:
continue
tdata = tdata[cselect]
#
#--- grade
#
mask = (tdata.field('grade') <= 6) & (tdata.field('grade') != 1) & (tdata.field('grade') != 5)
tdata = tdata[mask]
data_set.append(tdata)
return data_set
