def make_glim(msid):
"""
create limit list in glim format
input: msid --- msid
output: glim --- a list of list of [<start time> <stop time> <lower yellow> <upper yellow>
<lower red> <upper red>]
time is in seconds from 1998.1.1
"""
msid = msid.lower()
[limit_dict, cnd_dict] = rlt.get_limit_table()
out = limit_dict[msid]
glim = []
for ent in out:
try:
lim_list = ent[3]['none']
except:
continue
temp = [
ent[0], ent[1], lim_list[0], lim_list[1], lim_list[2], lim_list[3]
]
glim.append(temp)
if len(glim) == 0:
glim = [[0, 3218831995, -9e6, 9e6, -9e6, 9e6]]
return glim
def run_data_update(mtype, catg_dict):
"""
extract data for the specified limit category type
input: mtype --- limit state type; m: multi state/n: no state
catg_dict --- a dictionary of msid <---> cateogry
output: updated data fits files
"""
[lim_dict, cnd_dict] = rlt.get_limit_table()
# if mtype == 'm':
# ifile = limit_dir + 'Limit_data/multi_switch_limit'
# else:
# ifile = limit_dir + 'Limit_data/trend_limit'
ifile = limit_dir + 'Limit_data/op_limits_new.db'
#
#--- first find which msids are in that category, and extract data
#
data = mcf.read_data_file(ifile)
for ent in data:
if ent[0] == '#':
continue
atemp = re.split('\s+', ent)
msid = atemp[0]
catg = catg_dict[msid]
#
#--- just in a case the data category directory does not exist
#
cmd = 'mkdir -p ' + data_dir + atemp[1]
os.system(cmd)
print("MSID: " + catg + '/' + msid)
#
#--- three different data length
#
for dtype in ['week', 'short', 'long']:
#
#--- set data period
#
[dfile, start,
stop] = find_data_collection_period(msid, catg, dtype)
#
#--- extract new data part; saved as a local fits file
#
alimit = lim_dict[msid]
cnd_msid = cnd_dict[msid]
out = extract_data_from_ska(msid, start, stop, dtype, alimit,
cnd_msid)
#
#--- update the main fits file, either move the local file or append the new part
#
if out == True:
update_data_file(dfile, msid, dtype)
def set_warning_area(msid, xmin, xmax, ymin, ymax, byear):
"""
create warning area for plotting
input: msid --- msid
xmin --- min x
xmax --- max x
ymin --- min y
ymax --- max y
byear --- the base year
output: t_save --- a list of starting and stopping times in ydate
bt_lim --- a list of bottom; usually 0, but can be ymin
lr_lim --- a list of lower red limit
ly_lim --- a list of lower yellow limit
uy_lim --- a list of upper yellow limit
ur_lim --- a list of upper red limit
tp_lim --- a list of top: usually 9e10, but can be ymax
"""
msid = msid.lower()
[limit_dict, cnd_dict] = rlt.get_limit_table()
bval = 0.
if bval > ymin:
bval = ymin
tval = 9e9
if tval < ymax:
tval = ymax
try:
out = limit_dict[msid]
cnd_msid = cnd_dict[msid]
t_save = []
bt_lim = []
lr_lim = []
ly_lim = []
uy_lim = []
ur_lim = []
tp_lim = []
chk = 0
dlen = len(out)
for ent in out:
try:
lim_list = ent[3]['none']
except:
continue
#
x1 = chandratime_to_yday(ent[0], byear)
x2 = chandratime_to_yday(ent[1] - 1.0, byear)
if x2 < xmin:
continue
if x1 < xmin:
x1 = xmin
if x1 < xmax and x2 >= xmax:
x2 = xmax
chk = 1
t_save.append(x1)
t_save.append(x2)
for k in range(0, 2):
bt_lim.append(bval)
ly_lim.append(lim_list[0])
uy_lim.append(lim_list[1])
lr_lim.append(lim_list[2])
ur_lim.append(lim_list[3])
tp_lim.append(tval)
if chk == 1:
break
except:
t_save = [xmin, xmax]
bt_lim = [-9e10, -9e10]
lr_lim = [-9e10, -9e10]
ly_lim = [-9e10, -9e10]
uy_lim = [9e10, 9e10]
ur_lim = [9e10, 9e10]
tp_lim = [9e10, 9e10]
return [t_save, bt_lim, lr_lim, ly_lim, uy_lim, ur_lim, tp_lim]
def run_for_msid_list(msid_list, dtype):
"""
extract data from ska database for a given msid_list
input: misd_list --- the file name of the msid_list
dtype --- data type , week, short, or long
output: updated data fits files
"""
[lim_dict, cnd_dict] = rlt.get_limit_table()
ifile = house_keeping + msid_list
data = mcf.read_data_file(ifile)
for ent in data:
if ent[0] == '#':
continue
elif ent.strip() == '':
continue
atemp = re.split('\s+', ent)
msid = atemp[0].strip()
catg = atemp[1].strip()
print("MSID: " + catg + '/' + msid)
#
#--- just in a case the data category directory does not exist
#
cmd = 'mkdir -p ' + data_dir + atemp[1]
os.system(cmd)
#
#--- set data period
#
[dfile, start, stop] = find_data_collection_period(msid, catg, dtype)
#
#--- extract new data part; saved as a local fits file
#
try:
alimit = lim_dict[msid]
cnd_msid = cnd_dict[msid]
#
#--- if the collection time is larger than a month, extract data for 30 day chunk
#
diff = stop - start
if diff > a_month:
mcnt = int(diff / a_month)
for m in range(0, mcnt):
mstart = start + a_month * m
mstop = mstart + a_month
lstart = "%4.2f" % mcf.chandratime_to_fraq_year(mstart)
lstop = "%4.2f" % mcf.chandratime_to_fraq_year(mstop)
print("Computing: " + str(lstart) + '<-->' + str(lstop))
#
#--- extract data and make a local fits file
#
out = extract_data_from_ska(msid, mstart, mstop, dtype,
alimit, cnd_msid)
#
#--- update the main fits file, either move the local file or append the new part
#
if out == True:
update_data_file(dfile, msid, dtype)
out = extract_data_from_ska(msid, mstop, stop, dtype, alimit,
cnd_msid)
if out == True:
update_data_file(dfile, msid, dtype)
#
#--- the data collection period is < 30 days
#
else:
out = extract_data_from_ska(msid, start, stop, dtype, alimit,
cnd_msid)
if out == True:
update_data_file(dfile, msid, dtype)
except:
#print(msid + ' is not in glimmon database')
print(msid + ' is not in ska fetch database')
continue
catg_dict[atemp[0]] = atemp[1]
return catg_dict
#--------------------------------------------------------------------------------
if __name__ == "__main__":
if len(sys.argv) > 4:
msid = sys.argv[1].strip() #--- msid
start = float(sys.argv[3]) #--- start time in seconds from 1998.1.1
stop = float(sys.argv[4]) #--- stop time in seconds from 1998.1.1
dtype = sys.argv[3].strip() #--- week, short, long
[lim_dict, cnd_dict] = rlt.get_limit_table()
alimit = lim_dict[msid]
cnd_msid = cnd_dict[msid]
extract_data_from_ska(msid, start, stop, dtype, alimit, cnd_msid)
elif len(sys.argv) == 3:
msid_list = sys.argv[1].strip(
) #--- name of a file contain a msid list
dtype = sys.argv[2].strip() #--- week, short, long
run_for_msid_list(msid_list, dtype)
else:
run_glimmon_trend_data_update()