1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
|
import os
import numpy as np
from astropy.table import Table
from . import get_data_home
DATA_URL = ("https://github.com/astroML/astroML-data/raw/main/datasets/"
"sgSDSSimagingSample.fit.gz")
def fetch_imaging_sample(data_home=None, download_if_missing=True):
"""Loader for SDSS Imaging sample data
Parameters
----------
data_home : optional, default=None
Specify another download and cache folder for the datasets. By default
all astroML data is stored in '~/astroML_data'.
download_if_missing : optional, default=True
If False, raise a IOError if the data is not locally available
instead of trying to download the data from the source site.
Returns
-------
data : recarray, shape = (330753,)
record array containing imaging data
Examples
--------
>>> from astroML.datasets import fetch_imaging_sample
>>> data = fetch_imaging_sample() # doctest: +IGNORE_OUTPUT +REMOTE_DATA
>>> # number of objects in dataset
>>> data.shape # doctest: +REMOTE_DATA
(330753,)
>>> # names of the first five columns
>>> print(data.dtype.names[:5]) # doctest: +REMOTE_DATA
('ra', 'dec', 'run', 'rExtSFD', 'uRaw')
>>> print(data['ra'][:2]) # doctest: +REMOTE_DATA
[0.358174 0.358382]
>>> print(data['dec'][:2]) # doctest: +REMOTE_DATA
[-0.508718 -0.551157]
Notes
-----
This data was selected from the SDSS database using the following SQL
query::
SELECT
round(p.ra,6) as ra, round(p.dec,6) as dec,
p.run, --- comments are preceded by ---
round(p.extinction_r,3) as rExtSFD, --- r band extinction from SFD
round(p.modelMag_u,3) as uRaw, --- ISM-uncorrected model mags
round(p.modelMag_g,3) as gRaw, --- rounding up model magnitudes
round(p.modelMag_r,3) as rRaw,
round(p.modelMag_i,3) as iRaw,
round(p.modelMag_z,3) as zRaw,
round(p.modelMagErr_u,3) as uErr, --- errors are important!
round(p.modelMagErr_g,3) as gErr,
round(p.modelMagErr_r,3) as rErr,
round(p.modelMagErr_i,3) as iErr,
round(p.modelMagErr_z,3) as zErr,
round(p.psfMag_u,3) as psfRaw, --- psf magnitudes
round(p.psfMag_g,3) as psfRaw,
round(p.psfMag_r,3) as psfRaw,
round(p.psfMag_i,3) as psfRaw,
round(p.psfMag_z,3) as psfRaw,
round(p.psfMagErr_u,3) as psfuErr,
round(p.psfMagErr_g,3) as psfgErr,
round(p.psfMagErr_r,3) as psfrErr,
round(p.psfMagErr_i,3) as psfiErr,
round(p.psfMagErr_z,3) as psfzErr,
p.type, --- tells if a source is resolved or not
(case when (p.flags & '16') = 0 then 1 else 0 end) as ISOLATED
INTO mydb.SDSSimagingSample
FROM PhotoTag p
WHERE
--- 10x2 sq.deg.
p.ra > 0.0 and p.ra < 10.0 and p.dec > -1 and p.dec < 1
--- resolved and unresolved sources
and (p.type = 3 OR p.type = 6) and
--- '4295229440' is magic code for no
--- DEBLENDED_AS_MOVING or SATURATED objects
(p.flags & '4295229440') = 0 and
--- PRIMARY objects only, which implies
--- !BRIGHT && (!BLENDED || NODEBLEND || nchild == 0)]
p.mode = 1 and
--- adopted faint limit (same as about SDSS limit)
p.modelMag_r < 22.5
--- the end of query
"""
data_home = get_data_home(data_home)
archive_file = os.path.join(data_home, os.path.basename(DATA_URL))
if not os.path.exists(archive_file):
if not download_if_missing:
raise IOError('data not present on disk. '
'set download_if_missing=True to download')
data = Table.read(DATA_URL)
data.write(archive_file)
else:
data = Table.read(archive_file)
return np.asarray(data)
|
