Query the Fiber Database

import numpy as np
import time

import matplotlib.pyplot as plt

from astropy.table import Table, vstack, hstack
from astropy.coordinates import SkyCoord
import astropy.units as u

from hetdex_api.shot import get_fibers_table
from hetdex_api.survey import FiberIndex

Query a single coordinate:

coord = SkyCoord(ra=182.21303*u.deg, dec=50.915985*u.deg)

# Intiate the FiberIndex class from hetdex_api.survey:
F = FiberIndex('pdr1')

#help(F.query_region)

FiberIndex.Query_region() returns an astropy table of all fibers within the the aperture defined. Default is 3.5 arcsec radius. amp_flag, gal_flag, meteor, throughput flag populate whether the fiber would make it into the current catalog. 1 is good, 0 is removed. ‘flag’ combines the three flags

# This example was observed in multiple observations so there are many associated fibers
fibtab = F.query_region(coord, radius=3.5*u.arcsec)

fibtab.show_in_notebook()

Table length=19

idx	multiframe	ra	dec	fiber_id	healpix	amp	date	datevobs	expnum	fibidx	fibnum	fpx	fpy	ifuid	ifuslot	ifux	ifuy	shotid	specid	field	flag	amp_flag	meteor_flag	gal_flag	shot_flag	throughput_flag
0	multi_047_092_075_RU	182.21234	50.916267	20190407021_2_multi_047_092_075_RU_012	1441630867	RU	20190407	20190407v021	2	11	12	245.993	373.342	075	092	-5.08	24.24	20190407021	047	dex-spring	True	True	True	True	True	True
1	multi_047_092_075_RU	182.21191	50.916584	20190407021_1_multi_047_092_075_RU_011	1441630867	RU	20190407	20190407v021	1	10	11	247.318	374.042	075	092	-2.54	24.24	20190407021	047	dex-spring	True	True	True	True	True	True
2	multi_047_092_075_RU	182.21208	50.915886	20190407021_1_multi_047_092_075_RU_031	1441630867	RU	20190407	20190407v021	1	30	31	246.048	371.832	075	092	-3.81	22.03	20190407021	047	dex-spring	True	True	True	True	True	True
3	multi_047_092_075_RU	182.21185	50.915493	20190407021_3_multi_047_092_075_RU_051	1441630867	RU	20190407	20190407v021	3	50	51	245.993	370.332	075	092	-5.08	19.83	20190407021	047	dex-spring	True	True	True	True	True	True
4	multi_047_092_075_RU	182.2117	50.91619	20190407021_3_multi_047_092_075_RU_031	1441630867	RU	20190407	20190407v021	3	30	31	247.263	372.532	075	092	-3.81	22.03	20190407021	047	dex-spring	True	True	True	True	True	True
5	multi_047_092_075_RU	182.21443	50.91575	20190407021_2_multi_047_092_075_RU_014	1441630868	RU	20190407	20190407v021	2	13	14	240.903	373.342	075	092	-10.17	24.24	20190407021	047	dex-spring	True	True	True	True	True	True
6	multi_047_092_075_RU	182.214	50.916065	20190407021_1_multi_047_092_075_RU_013	1441630868	RU	20190407	20190407v021	1	12	13	242.228	374.042	075	092	-7.63	24.24	20190407021	047	dex-spring	True	True	True	True	True	True
7	multi_047_092_075_RU	182.21257	50.91663	20190407021_3_multi_047_092_075_RU_012	1441630868	RU	20190407	20190407v021	3	11	12	245.993	374.742	075	092	-5.08	24.24	20190407021	047	dex-spring	True	True	True	True	True	True
8	multi_047_092_075_RU	182.21362	50.91637	20190407021_3_multi_047_092_075_RU_013	1441630868	RU	20190407	20190407v021	3	12	13	243.443	374.742	075	092	-7.63	24.24	20190407021	047	dex-spring	True	True	True	True	True	True
9	multi_047_092_075_RU	182.21223	50.915188	20190407021_1_multi_047_092_075_RU_051	1441738261	RU	20190407	20190407v021	1	50	51	244.778	369.632	075	092	-5.08	19.83	20190407021	047	dex-spring	True	True	True	True	True	True
10	multi_047_092_075_RU	182.21355	50.915306	20190407021_2_multi_047_092_075_RU_033	1441738262	RU	20190407	20190407v021	2	32	33	242.173	371.132	075	092	-8.9	22.03	20190407021	047	dex-spring	True	True	True	True	True	True
11	multi_047_092_075_RU	182.21251	50.915565	20190407021_2_multi_047_092_075_RU_032	1441738262	RU	20190407	20190407v021	2	31	32	244.713	371.132	075	092	-6.36	22.03	20190407021	047	dex-spring	True	True	True	True	True	True
12	multi_047_092_075_RU	182.2134	50.916008	20190407021_2_multi_047_092_075_RU_013	1441738262	RU	20190407	20190407v021	2	12	13	243.443	373.342	075	092	-7.63	24.24	20190407021	047	dex-spring	True	True	True	True	True	True
13	multi_047_092_075_RU	182.21295	50.916325	20190407021_1_multi_047_092_075_RU_012	1441738262	RU	20190407	20190407v021	1	11	12	244.778	374.042	075	092	-5.08	24.24	20190407021	047	dex-spring	True	True	True	True	True	True
14	multi_047_092_075_RU	182.21416	50.915363	20190407021_1_multi_047_092_075_RU_033	1441738262	RU	20190407	20190407v021	1	32	33	240.958	371.832	075	092	-8.9	22.03	20190407021	047	dex-spring	True	True	True	True	True	True
15	multi_047_092_075_RU	182.21312	50.915623	20190407021_1_multi_047_092_075_RU_032	1441738262	RU	20190407	20190407v021	1	31	32	243.498	371.832	075	092	-6.36	22.03	20190407021	047	dex-spring	True	True	True	True	True	True
16	multi_047_092_075_RU	182.2129	50.91523	20190407021_3_multi_047_092_075_RU_052	1441738262	RU	20190407	20190407v021	3	51	52	243.443	370.332	075	092	-7.63	19.83	20190407021	047	dex-spring	True	True	True	True	True	True
17	multi_047_092_075_RU	182.21274	50.915928	20190407021_3_multi_047_092_075_RU_032	1441738262	RU	20190407	20190407v021	3	31	32	244.713	372.532	075	092	-6.36	22.03	20190407021	047	dex-spring	True	True	True	True	True	True
18	multi_047_092_075_RU	182.21378	50.91567	20190407021_3_multi_047_092_075_RU_033	1441738262	RU	20190407	20190407v021	3	32	33	242.173	372.532	075	092	-8.9	22.03	20190407021	047	dex-spring	True	True	True	True	True	True

# close the FiberIndex class (and associated open h5 files) when done
F.close()

Use get_fibers_table to access fiber spectra

Please use the get_fibers_table funtion to extract single fiber spectra. Calibration updates and adjustments are accessed through this function. For example, the raw h5 files are in native 2AA binning and do not have the white dwarf calibration correction applied. You may access those arrays by setting the option rawh5=True. Note that get_fibers_table offers the ability to retrieve fibers within a specified aperture (provide coords + radius option), a specified amp (multiframe option), a specified ifuslot. Please see help(get_fibers_table). The fiber_flux_offset is applicable to stacking analyses.

Note that when combining multiple fibers to perform a spectra extraction you will need to correctly apply the atmospheric diffraction correction. The API has several functions to do this extraction for you.

To extract a PSF-weighted spectrum please see get_spectra.ipynb notebook.
To generate line flux maps please see make_narrowband_image.ipynb.
To make a data cube and display it please see make_data_cube.ipynb.

from hetdex_api.shot import get_fibers_table

wave = np.linspace(3470, 5540, 1036)

shotlist = list(np.unique( fibtab['shotid']))

print('Fiber coverage is available in the following shotids: {}'.format(shotlist))

Fiber coverage is available in the following shotids: [20190407021]

# example of grabbing all fibers in single shot:
spec_tab = get_fibers_table(shotlist[0], coord)

## More info on get_fibers_table

help( get_fibers_table)

Help on function get_fibers_table in module hetdex_api.shot:

get_fibers_table(shot, coords=None, ifuslot=None, multiframe=None, expnum=None, radius=<Quantity 3.5 arcsec>, survey='hdr4', astropy=True, verbose=False, rawh5=False, F=None, fiber_flux_offset=None, add_rescor=False, add_mask=False, mask_options=None, mask_version=None, ignore_mask=None, mask_in_place=False, mask_value=nan)
    Returns fiber specta for a given shot.
    
    Parameters
    ----------
    shot
        either shotid or datevobs
    coords
        astropy coordinate object
    radius
        an astropy quantity object
    astropy
        flag to make it an astropy table. Deprecated. Output is always an astropy table
    survey
        data release you want to access
    rawh5: bool
        if True, this will simply return the fibers from the specified shoth5
        file. If False (the default), any relevent correcctions
        are applied.
    verbose
        print out warnings. Default is False
    F   Fibers class object
        a pre-intiated fibers class object. This is used to limit I/O.
    fiber_flux_offset: 1036 array
        array of values in units of 10**-17 ergs/s/cm2/AA to add
        to each fiber spectrum used in the extraction. Defaults
        to None
    add_rescor
        option to add calfib_ffsky_rescor column generated by Maja Lujan Niemeyer.
        Defaults to False for now
    add_mask
        option to add mask column. Defaults to False for now.
    mask_options
        string array options to select to mask. Default None will select all flags.
        Set this to 'BITMASK' to return the full bitmask array
        Options are 'MAIN', 'FTF', 'CHI2FIB', 'BADPIX', 'BADAMP', 'LARGEGAL', 'METEOR',
        'BADSHOT', 'THROUGHPUT', 'BADFIB', 'SAT'
    ignore_mask
        Option to provide a list of flag names that should be ingored in the mask model
    mask_in_place
        Set to True to apply mask to calfib, calfibe, calfib_ffsky and calfib_ffsky_rescor
    mask_value
        value to fill masked values. Default is np.nan
    
    Returns
    -------
    A table of fibers within the defined aperture. Will be an astropy table
    object if astropy=True is set

spec_tab

Table length=19

multiframe	ra	dec	fiber_id	amp	calfib	calfib_counts	calfib_ffsky	calfibe	calfibe_counts	chi2	contid	error1D	expnum	fiber_to_fiber	fibidx	fibnum	fpx	fpy	ifuid	ifuslot	ifux	ifuy	obsind	rms	sky_spectrum	sky_subtracted	specid	spectrum	trace	wavelength
	deg	deg			1e-17 erg / (Angstrom s cm2)		1e-17 erg / (Angstrom s cm2)	1e-17 erg / (Angstrom s cm2)																						Angstrom
bytes20	float32	float32	bytes38	bytes2	float32[1036]	float32[1036]	float32[1036]	float32[1036]	float32[1036]	float32[1032]	bytes8	float32[1032]	int32	float32[1032]	int32	int32	float32	float32	bytes3	bytes3	float32	float32	int32	float32[1032]	float32[1032]	float32[1032]	bytes3	float32[1032]	float32[1032]	float32[1032]
multi_047_092_075_RU	182.21191	50.916584	20190407021_1_multi_047_092_075_RU_011	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.81887656 .. 1.0159861	S/N 0052	13.751459 .. 13.1903715	1	0.84478176 .. 0.90389407	10	11	247.318	374.042	075	092	-2.54	24.24	1	4.917628 .. 5.1587663	121.020706 .. 107.61618	30.544086 .. 28.88933	047	153.01169 .. 137.51656	106.26733 .. 103.973785	3481.7861 .. 5496.499
multi_047_092_075_RU	182.21295	50.916325	20190407021_1_multi_047_092_075_RU_012	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.81769365 .. 0.498122	S/N 0052	14.02096 .. 13.520929	1	0.8829924 .. 0.9350255	11	12	244.778	374.042	075	092	-5.08	24.24	1	4.580227 .. 3.7110221	126.494644 .. 118.53482	14.5966 .. 10.112312	047	142.3144 .. 129.49612	115.1998 .. 112.98161	3481.9663 .. 5497.051
multi_047_092_075_RU	182.214	50.916065	20190407021_1_multi_047_092_075_RU_013	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.7905547 .. 0.49956927	S/N 0052	13.9085455 .. 13.654607	1	0.86432797 .. 0.9170046	12	13	242.228	374.042	075	092	-7.63	24.24	1	4.583771 .. 3.3264768	123.82084 .. 119.40464	1.4037967 .. 12.229787	047	126.22407 .. 132.32137	124.146416 .. 122.011795	3482.1555 .. 5497.596
multi_047_092_075_RU	182.21208	50.915886	20190407021_1_multi_047_092_075_RU_031	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	1.5663015 .. 0.5004975	S/N 0052	13.815502 .. 13.547228	1	0.95131093 .. 0.9643706	30	31	246.048	371.832	075	092	-3.81	22.03	1	5.3305473 .. 3.4207976	122.74672 .. 121.12527	-15.62719 .. -8.274664	047	106.55284 .. 112.40317	285.2453 .. 283.4364	3485.471 .. 5506.621
multi_047_092_075_RU	182.21312	50.915623	20190407021_1_multi_047_092_075_RU_032	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.8067379 .. 0.8620199	S/N 0052	13.71839 .. 13.551976	1	0.9418365 .. 0.9783994	31	32	243.498	371.832	075	092	-6.36	22.03	1	4.0777164 .. 5.0786114	119.15543 .. 120.26886	3.1750033 .. 30.28142	047	121.76375 .. 150.10284	294.14648 .. 292.21472	3485.6223 .. 5507.1123
multi_047_092_075_RU	182.21416	50.915363	20190407021_1_multi_047_092_075_RU_033	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.2944099 .. 0.71716964	S/N 0052	13.55041 .. 13.734056	1	0.92882615 .. 0.9726452	32	33	240.958	371.832	075	092	-8.9	22.03	1	2.6529367 .. 3.892724	115.169556 .. 123.14478	13.93148 .. -2.8704581	047	128.53435 .. 119.82689	303.10278 .. 301.30438	3485.7737 .. 5507.603
multi_047_092_075_RU	182.21223	50.915188	20190407021_1_multi_047_092_075_RU_051	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	1.3008631 .. 1.2362224	S/N 0052	13.153173 .. 13.96853	1	0.9987586 .. 0.97060466	50	51	244.778	369.632	075	092	-5.08	19.83	1	5.777974 .. 5.8537173	106.05574 .. 131.27971	-10.307098 .. 6.966204	047	95.18195 .. 137.79848	471.64044 .. 470.1062	3488.444 .. 5514.905
multi_047_092_075_RU	182.21257	50.91663	20190407021_3_multi_047_092_075_RU_012	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.20949575 .. 0.53787726	S/N 0052	14.16332 .. 13.867719	3	0.8829924 .. 0.9350255	11	12	245.993	374.742	075	092	-5.08	24.24	1	2.2965612 .. 3.5165098	130.89207 .. 128.0368	-1.5701933 .. -12.017702	047	129.58072 .. 116.31113	115.33434 .. 113.00158	3481.723 .. 5497.0137
multi_047_092_075_RU	182.21362	50.91637	20190407021_3_multi_047_092_075_RU_013	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.4315053 .. 1.1913637	S/N 0052	14.045922 .. 13.618023	3	0.86432797 .. 0.9170046	12	13	243.443	374.742	075	092	-7.63	24.24	1	4.2970266 .. 5.1780276	128.12532 .. 118.37282	-6.4042315 .. -15.828482	047	121.85992 .. 102.71937	124.301025 .. 122.12493	3481.9294 .. 5497.556
multi_047_092_075_RU	182.2117	50.91619	20190407021_3_multi_047_092_075_RU_031	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.5849641 .. 0.61896884	S/N 0052	13.649189 .. 13.631497	3	0.95131093 .. 0.9643706	30	31	247.263	372.532	075	092	-3.81	22.03	1	3.4145186 .. 3.6732588	119.1036 .. 123.49708	-5.5495973 .. -4.9322453	047	112.85276 .. 117.92091	285.41534 .. 283.4761	3485.4927 .. 5506.548
multi_047_092_075_RU	182.21274	50.915928	20190407021_3_multi_047_092_075_RU_032	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	1.060439 .. 0.31985188	S/N 0052	13.638253 .. 13.915896	3	0.9418365 .. 0.9783994	31	32	244.713	372.532	075	092	-6.36	22.03	1	5.330388 .. 2.8250048	116.874504 .. 130.39606	19.553514 .. -6.547449	047	135.72678 .. 123.20469	294.10834 .. 292.2741	3485.656 .. 5507.0376
multi_047_092_075_RU	182.21378	50.91567	20190407021_3_multi_047_092_075_RU_033	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	1.0766742 .. 1.3291706	S/N 0052	13.486258 .. 13.960976	3	0.92882615 .. 0.9726452	32	33	242.173	372.532	075	092	-8.9	22.03	1	5.7316694 .. 5.304574	113.32992 .. 129.21686	23.190355 .. -0.31247133	047	135.81903 .. 128.26047	303.04004 .. 301.1845	3485.8196 .. 5507.527
multi_047_092_075_RU	182.21185	50.915493	20190407021_3_multi_047_092_075_RU_051	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.8586132 .. 0.60621494	S/N 0052	13.310113 .. 14.406815	3	0.9987586 .. 0.97060466	50	51	245.993	370.332	075	092	-5.08	19.83	1	3.8591292 .. 4.1524324	111.90196 .. 143.66246	-39.244324 .. -7.660784	047	71.9564 .. 135.35776	471.71622 .. 470.0958	3488.783 .. 5514.8423
multi_047_092_075_RU	182.2129	50.91523	20190407021_3_multi_047_092_075_RU_052	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	1.6403743 .. 1.1830976	S/N 0052	13.228006 .. 14.473989	3	1.0044304 .. 0.98430204	51	52	243.443	370.332	075	092	-7.63	19.83	1	5.9770164 .. 5.2285957	110.2344 .. 146.02223	15.220559 .. -14.7852955	047	124.753716 .. 130.59302	480.45145 .. 478.88885	3488.971 .. 5515.2
multi_047_092_075_RU	182.21234	50.916267	20190407021_2_multi_047_092_075_RU_012	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.72925425 .. 0.67011124	S/N 0052	14.313326 .. 13.745491	2	0.8829924 .. 0.9350255	11	12	245.993	373.342	075	092	-5.08	24.24	1	4.1634545 .. 4.437146	134.7299 .. 124.65459	-9.59449 .. 6.9940414	047	126.62801 .. 131.41603	115.22805 .. 113.01136	3481.9744 .. 5496.923
multi_047_092_075_RU	182.2134	50.916008	20190407021_2_multi_047_092_075_RU_013	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.50302494 .. 1.5292405	S/N 0052	14.191607 .. 14.002187	2	0.86432797 .. 0.9170046	12	13	243.443	373.342	075	092	-7.63	24.24	1	3.3806987 .. 6.3608403	131.88202 .. 128.8987	-3.8954377 .. -11.337533	047	129.18425 .. 117.259575	124.221275 .. 121.997894	3482.1697 .. 5497.469
multi_047_092_075_RU	182.21443	50.91575	20190407021_2_multi_047_092_075_RU_014	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	0.1330721 .. 0.26586938	S/N 0052	14.200979 .. 14.003999	2	0.8750727 .. 0.9212176	13	14	240.903	373.342	075	092	-10.17	24.24	1	2.2647963 .. 2.5130594	133.5215 .. 129.0154	-0.30778193 .. 7.565406	047	134.11646 .. 136.21022	133.1792 .. 130.95549	3482.3875 .. 5498.018
multi_047_092_075_RU	182.21251	50.915565	20190407021_2_multi_047_092_075_RU_032	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	1.3396385 .. 0.6383396	S/N 0052	13.882472 .. 13.91885	2	0.9418365 .. 0.9783994	31	32	244.713	371.132	075	092	-6.36	22.03	1	6.5420685 .. 3.6243832	123.92002 .. 131.79446	-1.133471 .. -33.757107	047	121.9197 .. 97.25282	294.2214 .. 292.19507	3485.7527 .. 5507.031
multi_047_092_075_RU	182.21355	50.915306	20190407021_2_multi_047_092_075_RU_033	RU	0.0 .. 0.0	0.0 .. 0.0	-0.0 .. 0.0	0.0 .. 0.0	0.0 .. 0.0	1.1945068 .. 0.57079405	S/N 0052	13.783499 .. 14.049459	2	0.92882615 .. 0.9726452	32	33	242.173	371.132	075	092	-8.9	22.03	1	4.839354 .. 3.1153224	121.479805 .. 131.79068	-17.410967 .. -39.62772	047	103.20199 .. 91.378426	303.10196 .. 301.29382	3485.9094 .. 5507.525

%matplotlib inline

# Here is every fiber in the 3.5 arcsec aperture
plt.figure(figsize=(10,5))
for row in spec_tab:
    plt.plot(wave, row['calfib'], label='FiberID is {}'.format(row['fiber_id']))
    plt.xlim(3858-25, 3858+25)
#plt.legend()

../_images/8dce4dde2d6e3fcfa116c98ccc58fbc172f43e3ff1ad372c54139c6d0fde934a.png