def extract_data_under_condition(hdata, select_list):
"""
extract specified data from the hrc hk data set
input: hdata --- hrc hk data processed by extrct_hrchk_data
select_list --- a list contains lists of [<column name>, <condition>]
used to select data
output: hdata --- selected data
"""
#
#--- the list select_list contains lists of [<name>, <condition>]
#
for ent in select_list:
name = ent[0]
mc = re.search(':', ent[1])
#
#-- numerical range case; need start and stop range
#
if mc is not None:
atemp = re.split(':', ent[1])
cstart = int(float(atemp[0]))
cstop = int(float(atemp[1]))
hdata = hcf.select_data_with_condition(hdata, name, '>=', cstart)
hdata = hcf.select_data_with_condition(hdata, name, '<=', cstop)
#
#--- single condition case
#
else:
cond = ent[1]
hdata = hcf.select_data_with_condition(hdata, name, '==', cond)
#
#--- check whether any data left in the datatable
#
try:
test = hdata.field('time')
if len(test) == 0:
return ''
except:
return ''
#
#--- check one more last time whether any data left in the datatable
#
try:
test = hdata.field(0)
except:
test = []
if len(list(test)) > 0:
return hdata
else:
return ''
def find_scint(tstart, tstop):
"""
find integrated EPHIN flux
input: tstart --- starting time in seconds from 1998.1.1
tstop --- stopping time in seconds from 1998.1.1
output; avg --- average of ephin flux
std --- standard deviation of flux
"""
#
#--- extract ephin data
#
hcf.run_arc5gl('retrieve', tstart, tstop, dataset='flight',detector='ephin',\
level=1,filetype='ephrates')
#
#--- create extracted fits file list
#
ldir = exc_dir + 'Temp_dir/'
flist = hcf.get_file_list(ldir, 'lc1.fits.gz')
#
#--- combine all fits files into one
#
hcf.combine_fits_files(flist, 'ztemp.fits', azip=0)
[cols, tbdata] = hcf.read_fits_file('ztemp.fits')
#
#--- limit data to between tstart and tstop
#
tbdata = hcf.select_data_with_condition(tbdata, 'time', ">=", tstart)
tbdata = hcf.select_data_with_condition(tbdata, 'time', "<=", tstop)
#
#--- compute the basic stat
#
[avg, med, std, vmin, vmax, vcnt] = get_basic_stat(tbdata['scint'])
cmd = 'rm ztemp.fits' + '*'
os.system(cmd)
if hcf.check_file_in_dir(ldir):
cmd = 'rm ' + ldir + '*'
os.system(cmd)
return [avg, std]
def get_time_period_engineer_data(time_list, hrc_type, start, stop):
"""
extract time period in which hrc engireer data gives 2pre<a/b>ds=hrc type
input: time_list --- a list of lists of starting and stoppping time
from previous condition
hrc_type --- hrc type either IMG or SPEC
start --- start time of the entire period
stop --- stop time of the entire period
output: rlist --- a list of lists of starting and stoppping time
"""
#
#--- HRC Side changed from side A to side B on Aug 24, 2020.
#
if stop < 714614394:
msid = '2PREADS'
else:
msid = '2PREBDS'
#
#--- extract hrc engineer fits files
#
fits_list = extract_fits_files(time_list,
'hrc',
'0',
'hrc2eng',
subdetector='eng')
#
#--- extract data needed
#
dout = hcf.combine_and_select_data(fits_list, ['time', msid])
try:
chk = float(dout[0][0])
if chk > 1000:
pass
else:
return [[], []]
except:
return [[], []]
#
#--- select time with 2pre<a/b>ds == hrc_type (either hrc i or hrc s coditino)
#
odata = hcf.select_data_with_condition(dout, msid, '==', hrc_type)
#
#--- get lists of start and stop time periods
#
tlist = list(odata.field('TIME'))
rlist = find_time_span(tlist, start, stop)
#
#--- remove indivisual fits files
#
for ent in fits_list:
mcf.rm_file(ent)
return rlist
def find_evt_stat(efits, chip_id=''):
"""
find count rate, pha postion from hrc evt 1 file
input efits --- hrc evt1 fits file name
chip_id --- chip_id for hrc_s_125 and hrc_s_90; default: 0
output: date_obs --- date of observation
tstart --- starting time in seconds from 1998.1.1
tstop --- stopping time in seconds from 1998.1.1
avg --- average of pha
med --- mediam of pha
std --- standard deviation of pha
duration --- duration of the evt1 data
tcnt --- the number of entries
cnt_p_sec --- counts per second
"""
#
#--- read values from header
#
date_obs = hcf.read_header_value(efits, 'date-obs')
tstart = hcf.read_header_value(efits, 'tstart')
tstop = hcf.read_header_value(efits, 'tstop')
#
#--- read data
#
[cols, tbdata] = hcf.read_fits_file(efits)
#
#--- if chip_id is given, select out the data for the chip_id
#
if chip_id != '':
tbdata = hcf.select_data_with_condition(tbdata, 'chip_id', '==',
chip_id)
#
#--- find stat of pha
#
[avg, med, std, vmin, vmax, vcnt] = get_basic_stat(tbdata['pha'])
#
#--- find time related quantities
#
time = tbdata['time']
[tavg, tmed, tstd, tmin, tmax, tcnt] = get_basic_stat(tbdata['time'])
#
#--- compute duration etc
#
duration = tmax - tmin
if duration > 0:
cnt_p_sec = tcnt / duration
else:
cnt_p_sec = 0
return [date_obs, tstart, tstop, avg, med, std, duration, tcnt, cnt_p_sec]
def create_data_period(start, stop, dlist, colname='', lgc='', val=''):
"""
create a pair of time lists which indicate when tscpos is in positive side
input: start --- starting time in mm/dd/yy,hh:mm:ss or seconds from 1998.1.1
stop --- stopping time in mm/dd/yy,hh:mm:ss or seconds from 1998.1.1
dlist --- a list of fits file names
colname --- if given, the following two must be given. default: ''
lgc --- logical indicator such as "<=", "==". not effective unless colname is given
val --- value of the condition. not effective unless colname is given
output: start_list --- a list of starting time in format of seconds from 1998.1.1
stop_list --- a list of stopping tine in format of seconds from 1998.1.1
"""
#
#--- check time format
#
if (not isinstance(start, float)) and (not isinstance(start, int)):
start = hcf.convertto1998sec(start)
stop = hcf.convertto1998sec(stop)
time_list = []
chk = 0
for fits in dlist:
#
#--- extract time and tscpos information from fits file
#
[cols_in, hdata] = hcf.read_fits_file(fits)
#
#--- if an extra condition is given, run the next
#
if colname != '':
hdata = hcf.select_data_with_condition(hdata, colname, lgc, val)
else:
pass
tlist = list(hdata.field('TIME'))
if chk == 0:
time_list = tlist
chk = 1
else:
time_list = time_list + tlist
#
#--- create two lists: starting time and ending time
#
time_list = sort_and_clean(time_list)
t_lists = find_time_span(time_list, start, stop)
return t_lists
def get_time_period_engineer_data(time_list, hrc_type, start, stop):
"""
extract time period in which hrc engireer data gives 2preads=hrc type
input: time_list --- a list of lists of starting and stoppping time
from previous condition
hrc_type --- hrc type either IMG or SPEC
start --- start time of the entire period
stop --- stop time of the entire period
output: rlist --- a list of lists of starting and stoppping time
"""
#
#--- extract hrc engineer fits files
#
fits_list = extract_fits_files(time_list, 'hrc', '0', 'hrc2eng', subdetector='eng')
#
#--- extract data needed
#
dout = hcf.combine_and_select_data(fits_list, ['time','2PREADS'])
if dout == NULL:
return [[], []]
#
#--- select time with 2preads == hrc_type (either hrc i or hrc s coditino)
#
odata = hcf.select_data_with_condition(dout ,'2PREADS', '==', hrc_type)
#
#--- get lists of start and stop time periods
#
tlist = list(odata.field('TIME'))
rlist = find_time_span(tlist, start, stop)
#
#--- remove indivisual fits files
#
for ent in fits_list:
mcf.rm_file(ent)
return rlist
def find_antico_rate(ss_file, tstart, tstop):
"""
find a ratio of valid/total of anti-co data
input: ss_file --- science fits file name
tstart --- starting time in seconds from 1998.1.1
tstop --- stopping tine in seconds from 1998.1.1
output: tstat --- statistics of tlevart column
vstat --- statistics of vlevart column
sstat --- statistics of shevart column
astat --- statistics of antico values
see get_basic_stat for output (a list of statistics)
"""
[cols, tbdata] = hcf.read_fits_file(ss_file)
tbdata = hcf.select_data_with_condition(tbdata, 'time', ">=", tstart)
tbdata = hcf.select_data_with_condition(tbdata, 'time', "<=", tstop)
#
#--- remove outlyers; the first several entries are not reliable
#
tbdata = hcf.select_data_with_condition(tbdata, 'tlevart', ">", 0)
tbdata = hcf.select_data_with_condition(tbdata, 'tlevart', "<", 500)
tbdata = hcf.select_data_with_condition(tbdata, 'vlevart', ">", 0)
tbdata = hcf.select_data_with_condition(tbdata, 'vlevart', "<", 300)
tlevart = tbdata['tlevart']
vlevart = tbdata['vlevart']
shevart = tbdata['shevart']
#
#--- compute antico values
#
antico = vlevart / tlevart #--- array / array gives array
#
#--- compute basic statistics for each column
#
tstat = get_basic_stat(tlevart)
vstat = get_basic_stat(vlevart)
sstat = get_basic_stat(shevart)
astat = get_basic_stat(antico)
#
#--- output are in a list: [avg, med, std, min, max, cnt]
#
return [tstat, vstat, sstat, astat]