parse_flt_files

grizli.utils.parse_flt_files(files=[], info=None, uniquename=False, use_visit=False, get_footprint=False, translate={'AEGIS-': 'aegis-', 'COSMOS-': 'cosmos-', 'GNGRISM': 'goodsn-', 'GOODS-SOUTH-': 'goodss-', 'UDS-': 'uds-'}, visit_split_shift=1.5, max_dt=1000000000.0, path='../RAW')

Read header information from a list of exposures and parse out groups

based on filter/target/orientation.

Parameters

fileslist, optional

List of exposure filenames. If not specified, will use *flt.fits.

infoNone or Table, optional

Output from get_flt_info.

uniquenamebool, optional

If True, then split everything by program ID and visit name. If False, then just group by targname/filter/pa_v3.

use_visitbool, optional

For parallel observations with targname='ANY', use the filename up to the visit ID as the target name. For example:

>>> flc = 'jbhj64d8q_flc.fits'
>>> visit_targname = flc[:6]
>>> print(visit_targname)
jbhj64

If False, generate a targname for parallel observations based on the pointing coordinates using radec_to_targname. Use this keyword for dithered parallels like 3D-HST / GLASS but set to False for undithered parallels like WISP. Should also generally be used with uniquename=False otherwise generates names that are a bit redundant:

uniquename	Output Targname
True	jbhj45-bhj-45-180.0-F814W
False	jbhj45-180.0-F814W

get_footprintbool, optional

If True, get the visit footprint from FLT WCS.

translatedict, optional

Translation dictionary to modify TARGNAME keywords to some other value. Used like:

>>> targname = 'GOODS-SOUTH-10'
>>> translate = {'GOODS-SOUTH-': 'goodss-'}
>>> for k in translate:
>>>     targname = targname.replace(k, translate[k])
>>> print(targname)
goodss-10

visit_split_shiftfloat, optional

Separation in arcmin beyond which exposures in a group are split into separate visits.

max_dtfloat, optional

Maximum time separation between exposures in a visit, in seconds.

pathstr, optional

PATH to search for flt files if info not provided

Returns

output_listdict

Dictionary split by target/filter/pa_v3. Keys are derived visit product names and values are lists of exposure filenames corresponding to that set. Keys are generated with the formats like:

>>> targname = 'macs1149+2223'
>>> pa_v3 = 32.0
>>> filter = 'f140w'
>>> flt_filename = 'ica521naq_flt.fits'
>>> propstr = flt_filename[1:4]
>>> visit = flt_filename[4:6]
>>> # uniquename = False
>>> print('{0}-{1:05.1f}-{2}'.format(targname, pa_v3, filter))
macs1149.6+2223-032.0-f140w
>>> # uniquename = True
>>> print('{0}-{1:3s}-{2:2s}-{3:05.1f}-{4:s}'.format(targname, propstr, visit, pa_v3, filter))
macs1149.6+2223-ca5-21-032.0-f140w

filter_listdict

Nested dictionary split by filter and then PA_V3. This shouldn’t be used if exposures from completely disjoint pointings are stored in the same working directory.