"""
Search functionality for the Gemini archive of observations.
For questions, contact ooberdorf@gemini.edu
"""
import os
from datetime import date
from astroquery import log
from astropy import units
from astropy.table import Table, MaskedColumn
from astroquery.gemini.urlhelper import URLHelper
import numpy as np
from ..query import QueryWithLogin
from ..utils.class_or_instance import class_or_instance
from . import conf
__all__ = ['Observations', 'ObservationsClass'] # specifies what to import
__valid_instruments__ = [
'GMOS',
'GMOS-N',
'GMOS-S',
'GNIRS',
'GRACES',
'NIRI',
'NIFS',
'GSAOI',
'F2',
'GPI',
'NICI',
'MICHELLE',
'TRECS',
'BHROS',
'HRWFS',
'OSCIR',
'FLAMINGOS',
'HOKUPAA+QUIRC',
'PHOENIX',
'TEXES',
'ABU',
'CIRPASS'
]
__valid_observation_class__ = [
'science',
'acq',
'progCal',
'dayCal',
'partnerCal',
'acqCal',
]
__valid_observation_types__ = [
'OBJECT',
'BIAS',
'DARK',
'FLAT',
'ARC',
'PINHOLE',
'RONCHI',
'CAL',
'FRINGE',
'MASK'
]
__valid_modes__ = [
'imaging',
'spectroscopy',
'LS',
'MOS',
'IFS'
]
__valid_adaptive_optics__ = [
'NOTAO',
'AO',
'NGS',
'LGS'
]
__valid_raw_reduced__ = [
'RAW',
'PREPARED',
'PROCESSED_BIAS',
'PROCESSED_FLAT',
'PROCESSED_FRINGE',
'PROCESSED_ARC'
]
[docs]
class ObservationsClass(QueryWithLogin):
server = conf.server
url_helper = URLHelper(server)
def __init__(self, *args):
"""
Query class for observations in the Gemini archive.
This class provides query capabilities against the gemini archive. Queries
can be done by cone search, by name, or by a set of criteria.
"""
super().__init__()
def _login(self, username, password):
"""
Login to the Gemini Archive website.
This method will authenticate the session as a particular user. This may give you access
to additional information or access based on your credentials
Parameters
----------
username : str
The username to login as
password : str
The password for the given account
"""
params = dict(username=username, password=password)
r = self._session.request('POST', 'https://archive.gemini.edu/login/', params=params)
if b'<P>Welcome, you are sucessfully logged in' not in r.content:
log.error('Unable to login, please check your credentials')
return False
return True
[docs]
@class_or_instance
def query_region(self, coordinates, *, radius=0.3*units.deg):
"""
search for Gemini observations by target on the sky.
Given a sky position and radius, returns a `~astropy.table.Table` of Gemini observations.
Parameters
----------
coordinates : str or `~astropy.coordinates` object
The target around which to search. It may be specified as a
string or as the appropriate `~astropy.coordinates` object.
radius : str or `~astropy.units.Quantity` object, optional
Default 0.3 degrees.
The string must be parsable by `~astropy.coordinates.Angle`. The
appropriate `~astropy.units.Quantity` object from
`~astropy.units` may also be used. Defaults to 0.3 deg.
Returns
-------
response : `~astropy.table.Table`
"""
return self.query_criteria(coordinates=coordinates, radius=radius)
[docs]
@class_or_instance
def query_object(self, objectname, *, radius=0.3*units.deg):
"""
search for Gemini observations by target on the sky.
Given an object name and optional radius, returns a `~astropy.table.Table` of Gemini observations.
Parameters
----------
objectname : str
The name of an object to search for. This attempts to resolve the object
by name and do a search on that area of the sky. This does not handle
moving targets.
radius : str or `~astropy.units.Quantity` object, optional
Default 0.3 degrees.
The string must be parsable by `~astropy.coordinates.Angle`. The
appropriate `~astropy.units.Quantity` object from
`~astropy.units` may also be used. Defaults to 0.3 deg.
Returns
-------
response : `~astropy.table.Table`
"""
return self.query_criteria(objectname=objectname, radius=radius)
[docs]
@class_or_instance
def query_criteria(self, *rawqueryargs, coordinates=None, radius=None, pi_name=None, program_id=None, utc_date=None,
instrument=None, observation_class=None, observation_type=None, mode=None,
adaptive_optics=None, program_text=None, objectname=None, raw_reduced=None,
orderby=None, **rawquerykwargs):
"""
search a variety of known parameters against the Gemini observations.
Given various criteria, search the Gemini archive for matching observations. Note that
``rawqueryargs`` and ``rawquerykwargs`` will pick up additional positional and key=value
arguments and pass then on to the raw query as is.
Parameters
----------
coordinates : str or `~astropy.coordinates` object
The target around which to search. It may be specified as a
string or as the appropriate `~astropy.coordinates` object.
radius : str or `~astropy.units.Quantity` object, optional
Default 0.3 degrees if coordinates are set, else None
The string must be parsable by `~astropy.coordinates.Angle`. The
appropriate `~astropy.units.Quantity` object from
`~astropy.units` may also be used. Defaults to 0.3 deg.
pi_name : str, optional
Default None.
Can be used to search for data by the PI's name.
program_id : str, optional
Default None.
Can be used to match on program ID
utc_date : date or (date,date) tuple, optional
Default None.
Can be used to search for observations on a particular day or range of days (inclusive).
instrument : str, optional
Can be used to search for a particular instrument. Valid values are:
'GMOS',
'GMOS-N',
'GMOS-S',
'GNIRS',
'GRACES',
'NIRI',
'NIFS',
'GSAOI',
'F2',
'GPI',
'NICI',
'MICHELLE',
'TRECS',
'BHROS',
'HRWFS',
'OSCIR',
'FLAMINGOS',
'HOKUPAA+QUIRC',
'PHOENIX',
'TEXES',
'ABU',
'CIRPASS'
observation_class : str, optional
Specifies the class of observations to search for. Valid values are:
'science',
'acq',
'progCal',
'dayCal',
'partnerCal',
'acqCal'
observation_type : str, optional
Search for a particular type of observation. Valid values are:
'OBJECT',
'BIAS',
'DARK',
'FLAT',
'ARC',
'PINHOLE',
'RONCHI',
'CAL',
'FRINGE',
'MASK'
mode : str, optional
The mode of the observation. Valid values are:
'imaging',
'spectroscopy',
'LS',
'MOS',
'IFS'
adaptive_optics : str, optional
Specify the presence of adaptive optics. Valid values are:
'NOTAO',
'AO',
'NGS',
'LGS'
program_text : str, optional
Specify text in the information about the program. This is free form text.
objectname : str, optional
Give the name of the target.
raw_reduced : str, optional
Indicate the raw or reduced status of the observations to search for. Valid values are:
'RAW',
'PREPARED',
'PROCESSED_BIAS',
'PROCESSED_FLAT',
'PROCESSED_FRINGE',
'PROCESSED_ARC'
orderby : str, optional
Indicates how the results should be sorted. Values should be like the ones used
in the archive website when sorting a column. For example, ``data_label_desc`` would
sort by the data label in descending order.
rawqueryargs : list, optional
Additional arguments will be passed down to the raw query. This covers any
additional parameters that would end up as '/parametervalue/' in the URL to the archive
webservice.
rawquerykwargs : dict, optional
Additional key/value arguments will also be passed down to the raw query. This covers
any parameters that would end up as '/key=value/' in the URL to the archive webservice.
Returns
-------
response : `~astropy.table.Table`
Raises
------
ValueError: passed value is not recognized for the given field, see message for details
"""
# Build parameters into raw query
#
# This consists of a set of unnamed arguments, args, and key/value pairs, kwargs
# These will hold the passed freeform parameters plus the explicit criteria
# for our eventual call to the raw query method
args = list()
kwargs = dict()
# Copy the incoming set of free-form arguments
if rawqueryargs:
for arg in rawqueryargs:
args.append(arg)
if rawquerykwargs:
for (k, v) in rawquerykwargs.items():
kwargs[k] = v
# If coordinates is set but we have no radius, set a default
if (coordinates or objectname) and radius is None:
radius = 0.3 * units.deg
# Now consider the canned criteria
if radius is not None:
kwargs["radius"] = radius
if coordinates is not None:
kwargs["coordinates"] = coordinates
if pi_name is not None:
kwargs["PIname"] = pi_name
if program_id is not None:
kwargs["progid"] = program_id.upper()
if utc_date is not None:
if isinstance(utc_date, date):
args.append(utc_date.strftime("YYYYMMdd"))
elif isinstance(utc_date, tuple):
if len(utc_date) != 2:
raise ValueError("utc_date tuple should have two values")
if not isinstance(utc_date[0], date) or not isinstance(utc_date[1], date):
raise ValueError("utc_date tuple should have date values in it")
args.append("{:%Y%m%d}-{:%Y%m%d}".format(*utc_date))
if instrument is not None:
if instrument.upper() not in __valid_instruments__:
raise ValueError("Unrecognized instrument: %s" % instrument)
args.append(instrument)
if observation_class is not None:
if observation_class not in __valid_observation_class__:
raise ValueError("Unrecognized observation class: %s" % observation_class)
args.append(observation_class)
if observation_type is not None:
if observation_type not in __valid_observation_types__:
raise ValueError("Unrecognized observation type: %s" % observation_type)
args.append(observation_type)
if mode is not None:
if mode not in __valid_modes__:
raise ValueError("Unrecognized mode: %s" % mode)
args.append(mode)
if adaptive_optics is not None:
if adaptive_optics not in __valid_adaptive_optics__:
raise ValueError("Unrecognized adaptive optics: %s" % adaptive_optics)
args.append(adaptive_optics)
if program_text is not None:
kwargs["ProgramText"] = program_text
if objectname is not None:
kwargs["object"] = objectname
if raw_reduced is not None:
if raw_reduced not in __valid_raw_reduced__:
raise ValueError("Unrecognized raw/reduced setting: %s" % raw_reduced)
args.append(raw_reduced)
if orderby is not None:
kwargs["orderby"] = orderby
return self.query_raw(*args, **kwargs)
[docs]
@class_or_instance
def query_raw(self, *args, **kwargs):
"""
perform flexible query against Gemini observations
This is a more flexible query method. This method will do special handling for
coordinates and radius if present in kwargs. However, for the remaining arguments
it assumes all of args are useable as query path elements. For kwargs, it assumes
all of the elements can be passed as name=value within the query path to Gemini.
This method does not do any validation checking or attempt to interperet the
values being passed, aside from coordinates and radius.
This method is most useful when the query_criteria and query_region do not
meet your needs and you can build the appropriate search in the website. When
you see the URL that is generated by the archive, you can translate that into
an equivalent python call with this method. For example, if the URL in the
website is:
https://archive.gemini.edu/searchform/RAW/cols=CTOWEQ/notengineering/GMOS-N/PIname=Hirst/NotFail
You can disregard NotFail, cols=x and notengineering. You would run this query as
query_raw('GMOS-N', PIname='Hirst')
Parameters
----------
args :
The list of parameters to be passed via the query path to the webserver
kwargs :
The dictionary of parameters to be passed by name=value within the query
path to the webserver. The ``orderby`` key value pair has a special
intepretation and is appended as a query parameter like the one used
in the archive website for sorting results.
Returns
-------
response : `~astropy.table.Table`
"""
url = self.url_helper.build_url(*args, **kwargs)
response = self._request(method="GET", url=url, data={}, timeout=180, cache=False)
js = response.json()
return _gemini_json_to_table(js)
[docs]
def get_file(self, filename, *, download_dir='.', timeout=None):
"""
Download the requested file to the current directory
filename : str
Name of the file to download
download_dir : str, optional
Name of the directory to download to
timeout : int, optional
Timeout of the request in milliseconds
"""
url = "https://archive.gemini.edu/file/%s" % filename
local_filepath = os.path.join(download_dir, filename)
self._download_file(url=url, local_filepath=local_filepath, timeout=timeout)
def _gemini_json_to_table(json):
"""
takes a JSON object as returned from the Gemini archive webserver and turns it into an `~astropy.table.Table`
Parameters
----------
json : dict
A JSON object from the Gemini archive webserver
Returns
-------
response : `~astropy.table.Table`
"""
data_table = Table(masked=True)
for key in __keys__:
col_data = np.array([obj.get(key, None) for obj in json])
atype = str
col_mask = np.equal(col_data, None)
data_table.add_column(MaskedColumn(col_data.astype(atype), name=key, mask=col_mask))
return data_table
__keys__ = ["exposure_time",
"detector_roi_setting",
"detector_welldepth_setting",
"telescope",
"mdready",
"requested_bg",
"engineering",
"cass_rotator_pa",
"ut_datetime",
"file_size",
"types",
"requested_wv",
"detector_readspeed_setting",
"size",
"laser_guide_star",
"observation_id",
"science_verification",
"raw_cc",
"filename",
"instrument",
"reduction",
"camera",
"ra",
"detector_binning",
"lastmod",
"wavelength_band",
"data_size",
"mode",
"raw_iq",
"airmass",
"elevation",
"data_label",
"requested_iq",
"object",
"requested_cc",
"program_id",
"file_md5",
"central_wavelength",
"raw_wv",
"compressed",
"filter_name",
"detector_gain_setting",
"path",
"observation_class",
"qa_state",
"observation_type",
"calibration_program",
"md5",
"adaptive_optics",
"name",
"focal_plane_mask",
"data_md5",
"raw_bg",
"disperser",
"wavefront_sensor",
"gcal_lamp",
"detector_readmode_setting",
"phot_standard",
"local_time",
"spectroscopy",
"azimuth",
"release",
"dec"]
Observations = ObservationsClass()