tilepix.fits
- Summary:
This file maps which DESI tiles overlap which HEALpix pixels (nested nside=64).
- Naming Convention:
tilepix.fits- Regex:
tilepix\.fits- File Type:
FITS, 630 KB
Contents
Number |
EXTNAME |
Type |
Contents |
|---|---|---|---|
IMAGE |
Blank |
||
TILEPIX |
BINTABLE |
table with healpix:tile mapping |
FITS Header Units
HDU0
Empty HDU.
HDU1
EXTNAME = TILEPIX
Table mapping tile petals to HEALPix pixels (nested nside=64).
Required Header Keywords
Required Header Keywords Table
KEY |
Example Value |
Type |
Comment |
|---|---|---|---|
NAXIS1 |
9 |
int |
length of dimension 1 |
NAXIS2 |
70894 |
int |
length of dimension 2 |
HPXNSIDE |
64 |
int |
|
HPXNEST |
T |
bool |
Required Data Table Columns
Name |
Type |
Units |
Description |
|---|---|---|---|
TILEID |
int32 |
DESI Tile ID |
|
SURVEY |
char[7] |
DESI survey (sv1, sv3, main…) |
|
PROGRAM |
char[6] |
DESI program (dark, bright, …) |
|
PETAL_LOC |
int16 |
Petal location 0-9 = spectrograph number |
|
HEALPIX |
int32 |
Nested nside=64 healpix number |
Notes and Examples
Each DESI tile has 10 petals/spectrographs, each of which overlaps multiple healpixels. Similarly, each healpixel could be covered by multiple tile petals. Since many DESI files are split by petal (spectrograph), this map gives the individual petal coverage as well, not just that the tile overlaps the healpixel.
Example:
import numpy as np
from astropy.table import Table
tilepix = Table.read('tilepix.fits')
#- All healpix that cover tile 100 (20 healpix)
np.unique(tilepix['HEALPIX'][tilepix['TILEID']==100])
#- All tiles that cover healpix 11250 (28 tiles)
np.unique(tilepix['TILEID'][tilepix['HEALPIX'] == 11250])
There is also a json version of this file with a dictionary structured as:
tilepix[tileid][petal] -> list of healpix covered by that tile+petal
Due to limitations of the json format, the tileid and petal keys of the
dictionary are strings, not integers.