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import os
from gzip import GzipFile
from io import BytesIO
import numpy as np
from .tools import download_with_progress_bar
from . import get_data_home
DATA_URL = ('https://github.com/astroML/astroML-data/raw/main/datasets/'
'ADR3.dat.gz')
ARCHIVE_FILE = 'moving_objects.npy'
ADR4_dtype = [('moID', 'a6'),
('sdss_run', 'i4'),
('sdss_col', 'i4'),
('sdss_field', 'i4'),
('sdss_obj', 'i4'),
('rowc', 'f4'),
('colc', 'f4'),
('mjd', 'f8'),
('ra', 'f8'),
('dec', 'f8'),
('lambda', 'f8'),
('beta', 'f8'),
('phi', 'f8'),
('vmu', 'f4'),
('vmu_err', 'f4'),
('vnu', 'f4'),
('vnu_err', 'f4'),
('vlambda', 'f4'),
('vbeta', 'f4'),
('mag_u', 'f4'),
('err_u', 'f4'),
('mag_g', 'f4'),
('err_g', 'f4'),
('mag_r', 'f4'),
('err_r', 'f4'),
('mag_i', 'f4'),
('err_i', 'f4'),
('mag_z', 'f4'),
('err_z', 'f4'),
('mag_a', 'f4'),
('err_a', 'f4'),
('mag_V', 'f4'),
('mag_B', 'f4'),
('ast_flag', 'i4'),
('ast_num', 'i8'),
('ast_designation', 'a17'),
('ast_det_count', 'i4'),
('ast_det_total', 'i4'),
('ast_flags', 'i8'),
('ra_comp', 'f8'),
('dec_comp', 'f8'),
('mag_comp', 'f4'),
('r_helio', 'f4'),
('r_geo', 'f4'),
('phase', 'f4'),
('cat_id', 'a15'),
('H', 'f4'),
('G', 'f4'),
('Arc', 'f4'),
('Epoch', 'f8'),
('a', 'f8'),
('e', 'f8'),
('i', 'f8'),
('asc_node', 'f8'),
('arg_peri', 'f8'),
('M', 'f8'),
('PEcat_id', 'a17'),
('aprime', 'f8'),
('eprime', 'f8'),
('sin_iprime', 'f8')]
def fetch_moving_objects(data_home=None, download_if_missing=True,
Parker2008_cuts=False):
"""Loader for SDSS moving objects datasets
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.
Parker2008_cuts : bool (optional)
If true, apply cuts on magnitudes and orbital parameters used in
Parker et al. 2008
Returns
-------
data : recarray, shape = (??,)
record array containing 60 values for each item
Notes
-----
See http://www.astro.washington.edu/users/ivezic/sdssmoc/sdssmoc3.html
Columns 0, 35, 45, and 56 are left out of the fetch: they are string
parameters. Only columns with known orbital parameters are saved.
Examples
--------
>>> from astroML.datasets import fetch_moving_objects
>>> data = fetch_moving_objects() # doctest: +IGNORE_OUTPUT +REMOTE_DATA
>>> # number of objects
>>> print(len(data)) # doctest: +REMOTE_DATA
43424
>>> # first five u-g colors of the dataset
>>> u_g = data['mag_u'] - data['mag_g'] # doctest: +REMOTE_DATA
>>> print(u_g[:5]) # doctest: +REMOTE_DATA
[1.4899998 1.7800007 1.6500015 2.0100002 1.8199997]
"""
data_home = get_data_home(data_home)
archive_file = os.path.join(data_home, ARCHIVE_FILE)
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')
print("downloading moving object catalog from %s to %s"
% (DATA_URL, data_home))
zipped_buf = download_with_progress_bar(DATA_URL, return_buffer=True)
gzf = GzipFile(fileobj=zipped_buf, mode='rb')
print("uncompressing file...")
extracted_buf = BytesIO(gzf.read())
data = np.loadtxt(extracted_buf, dtype=ADR4_dtype)
# Select unique sources with known orbital elements
flag = (data['ast_flag'] == 1) & (data['ast_det_count'] == 1)
data = data[flag]
np.save(archive_file, data)
else:
data = np.load(archive_file)
if Parker2008_cuts:
i_z = data['mag_i'] - data['mag_z']
flag = ((data['aprime'] >= 0.01) & (data['aprime'] <= 100) &
(data['mag_a'] <= 0.4) & (data['mag_a'] >= -0.3) &
(i_z <= 0.6) & (i_z >= -0.8))
data = data[flag]
return data
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