def login(user):
eso = Eso()
eso.login(user, store_password=True)
return eso
"2013-01-23",
"2013-01-26",
"2013-01-30",
"2013-02-01",
"2013-02-06",
"2013-02-07",
"2013-02-08",
"2013-02-11",
"2013-02-13",
]
destination = join(dirname(__file__), "raw")
print(destination)
dates = [dt.datetime.strptime(d, "%Y-%m-%d") for d in dates]
day = dt.timedelta(days=1)
eso = Eso()
eso.login("awehrhahn")
for d in dates:
filters = {
"instrument": instrument,
"dp_cat": "CALIB",
"stime": str(d - day)[:10],
"etime": str(d + day)[:10],
}
table = eso.query_main(column_filters=filters)
files = table["Dataset ID"]
eso.retrieve_data(files, destination=destination, continuation=True)
def wrangle_eso_for_rv_availability(ra, dec):
"""
Checks via ESO query for available RVs on:
['HARPS', 'ESPRESSO', 'FORS2', 'UVES', 'XSHOOTER']
Possible future expansion: actually get the RVs. (For now, just this is
just used as a flag to let the user know the RVs might exist!)
Returns tuple of:
(nan, nan, provenance)
"""
eso = Eso()
eso.ROW_LIMIT = 9999
coord = SkyCoord(ra=ra, dec=dec, unit=(u.deg, u.deg), frame='icrs')
print('begin ESO search for {}'.format(repr(coord)))
rastr = (str(coord.ra.to_string(u.hour)).replace('h', ' ').replace(
'm', ' ').replace('s', ' '))
decstr = (str(coord.dec.to_string()).replace('d', ' ').replace(
'm', ' ').replace('s', ' '))
# search within 10 arcsec of given position
boxsize = '00 00 10'
res = eso.query_main(column_filters={
'ra': rastr,
'dec': decstr,
'box': boxsize
})
if res is None:
return np.nan, np.nan, np.nan
# limit search to the following instruments, in order of preference
instruments = ['HARPS', 'ESPRESSO', 'FORS2', 'UVES', 'XSHOOTER']
sel = np.zeros((len(res))).astype(bool)
for instrument in instruments:
sel |= (nparr(res['Instrument']) == instrument)
res = res[sel]
# limit returned cateogires
badcategories = ['CALIB']
sel = np.zeros((len(res))).astype(bool)
for badcategory in badcategories:
sel |= (nparr(res['Category']) != badcategory)
res = res[sel]
if len(res) >= 1:
# XSHOOTER doesn't seem to give archival RVs. would need to derive
# from spectra yourself
if np.all(nparr(res['Instrument']) == 'XSHOOTER'):
return np.nan, np.nan, 'XSHOOTER'
# Embargo lasts a year on all ESO observations.
nt = Time.now()
embargo_end = nt.mjd - 365
if np.all(nparr(res['MJD-OBS']) > embargo_end):
return np.nan, np.nan, np.unique(res['Instrument'])[0]
# HARPS gives archival RVs. downloading them can be done... but for
# s6+s7, only a few objects are viable.
if np.all(nparr(res['Instrument']) == 'HARPS'):
print('WARNING: SKIPPING AUTOMATION OF HARPS ARCHIVAL RV GETTING')
return np.nan, np.nan, 'HARPS'
else:
return np.nan, np.nan, np.nan
def grab_calfiles():
workingdir = os.getcwd()
handler = Eso()
### Set your username here!
USERNAME = ""
handler.ROW_LIMIT = 10000
handler.login(USERNAME, store_password=True)
def mkdir_safe(dirname):
'''
Check if directory exists - if it doesn't, make it, if it does, clear it out
'''
if os.path.isdir(dirname) == True:
flist = glob.glob(dirname + "/*")
for f in flist:
os.remove(f)
else:
os.mkdir(dirname)
mkdir_safe("flats")
mkdir_safe("flatdarks")
mkdir_safe("darks")
# Read the first FITS in the folder
filelist = glob.glob("obj/*.fits")
print(os.getcwd())
temphdu = fits.open(filelist[0])
header = temphdu[0].header
print("FITS header loaded")
# Extract relevant query params from science frame
prop_ID = header["HIERARCH ESO OBS PROG ID"]
date = Time(header["DATE-OBS"])
sci_exp = header["EXPTIME"]
# Set start and end time of search - may need to tweak this manually to find the right calfiles.
# Best to be generous with this window since need to find flats, darks, and flat-darks for the pipeline to run.
stime = date
etime = date + 18 * u.hour
win_size = header["HIERARCH ESO DET WINDOW NY"]
sci_wav = header["HIERARCH ESO INS WLEN CWLEN"]
#print(filelist[0], sci_wav, date)
# Query flat frames - check they match
print("Querying ESO Archive")
flat_table = handler.query_instrument("crires",
column_filters={
'stime': stime.value,
'etime': etime.value,
'dp_type': 'FLAT',
'ins_wlen_cwlen': sci_wav
})
flat_header = handler.get_headers(flat_table["DP.ID"])
mask = flat_header["HIERARCH ESO DET WINDOW NY"] != win_size
flat_table = flat_table[~mask]
#### if flat_exp_time not all the same value, choose the highest one
#### Download flat fields
flat_exp_time = np.max(flat_table["EXPTIME"])
flat_files = handler.retrieve_data(flat_table["DP.ID"])
#print(flat_files)
for f in flat_files:
shutil.copy(f, "flats")
#### Grab the dark frames matching the science exposure time
dark_table = handler.query_instrument("crires",
column_filters={
'stime': stime.value,
'etime': etime.value,
'dp_type': 'DARK',
'exptime': sci_exp
})
dark_header = handler.get_headers(dark_table['DP.ID'])
mask = dark_header["HIERARCH ESO DET WINDOW NY"] != win_size
dark_table = dark_table[~mask]
dark_files = handler.retrieve_data(dark_table["DP.ID"])
for d in dark_files:
shutil.copy(d, "darks")
#### Grab darks matched to flat fields
flatdark_table = handler.query_instrument("crires",
column_filters={
'stime': stime.value,
'etime': etime.value,
'dp_type': 'DARK',
'exptime': flat_exp_time
})
flatdark_header = handler.get_headers(flatdark_table["DP.ID"])
mask = flatdark_header["HIERARCH ESO DET WINDOW NY"] != win_size
flatdark_table = flatdark_table[~mask]
flatdark_files = handler.retrieve_data(flatdark_table["DP.ID"])
for d in flatdark_files:
shutil.copy(d, "flatdarks")
print("Unpacking and moving!")
### Unpack all the files -- several possible commands for thisself.
### maximum compatibility use "gzip -d *.Z"
### pigz is a parallel gzip, but also it can't decompress in parallel apparently.
### if you want to use it despite this, "pigz -d *.Z"
### For maximum SPEED you could try "ls *.Z | parallel pigz -d" if you have GNU parallel installed.
os.chdir("flats")
os.system("pigz -d *.Z")
os.chdir("../flatdarks")
os.system("pigz -d *.Z")
os.chdir("../darks")
os.system("pigz -d *.Z")
os.chdir("../")
print("Calibration selection complete!")
def __init__(self, user):
self.user = user
self.eso = Eso()
self.eso.login(self.user) #login in eso
self.eso.ROW_LIMIT = -1 #unlimited number of search results
self.instruments = np.array(['FEROS', 'UVES', 'HARPS', 'ESPRESSO'])
