__all__ = ("find_telescopes", "NFollowStacker")
import numpy as np
from .base_stacker import BaseStacker
from .coord_stackers import ra_dec2_alt_az
[docs]
def find_telescopes(min_size=3.0):
"""Finds telescopes larger than min_size, from list of large telescopes based on
http://astro.nineplanets.org/bigeyes.html.
Returns
-------
np.recarray
Array of large telescopes with columns [aperture, name, lat, lon].
"""
# Aperture Name Location http://astro.nineplanets.org/bigeyes.html
telescopes = [
[10.4, "Gran Canarias", "La Palma"],
[10.0, "Keck", "Mauna Kea"],
[10.0, "Keck II", "Mauna Kea"],
[9.2, "SALT", "South African Astronomical Observatory"],
[9.2, "Hobby-Eberly", "Mt. Fowlkes"],
[8.4, "Large Binocular Telescope", "Mt. Graham"],
[8.3, "Subaru", "Mauna Kea"],
[8.2, "Antu", "Cerro Paranal"],
[8.2, "Kueyen", "Cerro Paranal"],
[8.2, "Melipal", "Cerro Paranal"],
[8.2, "Yepun", "Cerro Paranal"],
[8.1, "Gemini North", "Mauna Kea"],
[8.1, "Gemini South", "Cerro Pachon"],
[6.5, "MMT", "Mt. Hopkins"],
[6.5, "Walter Baade", "La Serena"],
[6.5, "Landon Clay", "La Serena"],
[6.0, "Bolshoi Teleskop Azimutalnyi", "Nizhny Arkhyz"],
[6.0, "LZT", "British Columbia"],
[5.0, "Hale", "Palomar Mountain"],
[4.3, "Dicovery Channel", "Lowell Observatory"],
[4.2, "William Herschel", "La Palma"],
[4.2, "SOAR", "Cerro Pachon"],
[4.2, "LAMOST", "Xinglong Station"],
[4.0, "Victor Blanco", "Cerro Tololo"],
[4.0, "Vista", "Cerro Paranal"],
[3.9, "Anglo-Australian", "Coonabarabran"],
[3.8, "Mayall", "Kitt Peak"],
[3.8, "UKIRT", "Mauna Kea"],
[3.6, "360", "Cerro La Silla"],
[3.6, "Canada-France-Hawaii", "Mauna Kea"],
[3.6, "Telescopio Nazionale Galileo", "La Palma"],
[3.5, "MPI-CAHA", "Calar Alto"],
[3.5, "New Technology", "Cerro La Silla"],
[3.5, "ARC", "Apache Point"],
[3.5, "WIYN", "Kitt Peak"],
[3.0, "Shane", "Mount Hamilton"],
[3.0, "NASA IRTF", "Mauna Kea"],
]
scopes = np.zeros(
len(telescopes),
dtype=list(zip(["aperture", "name", "lat", "lon"], [float, (np.str_, 38), float, float])),
)
# name, lat (S negative), lon (W negative)
observatories = [
["Cerro Paranal", -24, 38, -70, 24],
["Nizhny Arkhyz", 43, 39, 41, 26],
["Cerro La Silla", -29, 15, -70, 44],
["Lowell Observatory", 35, 12, -111, 40],
["Apache Point", 32, 47, -105, 49],
["Mount Hamilton", 37, 21, -121, 38],
["South African Astronomical Observatory", -32, 23, 20, 49],
["Cerro Pachon", -30, 20, -70, 59],
["Coonabarabran", -31, 17, 149, 0o4],
["Mt. Fowlkes", 30, 40, -104, 1],
["La Palma", 28, 46, -17, 53],
["Mt. Graham", 32, 42, -109, 53],
["Calar Alto", 37, 13, -2, 33],
["British Columbia", 49, 17, -122, 34],
["Kitt Peak", 31, 57, -111, 37],
["La Serena", -30, 10, -70, 48],
["Palomar Mountain", 33, 21, -116, 52],
["Xinglong Station", 40, 23, 105, 50],
["Mt. Hopkins", 31, 41, -110, 53],
["Cerro Tololo", -30, 10, -70, 49],
["Mauna Kea", 19, 50, -155, 28],
]
# Make a nice little dict to look up the observatory positions
obs = {}
for i, ob in enumerate(observatories):
obs[ob[0]] = [
(np.abs(ob[1]) + ob[2] / 60.0) * (ob[1] / np.abs(ob[1])),
(np.abs(ob[3]) + ob[4] / 60.0) * (ob[3] / np.abs(ob[3])),
]
for i, telescope in enumerate(telescopes):
scopes["aperture"][i] = telescope[0]
scopes["name"][i] = telescope[1]
scopes["lat"][i], scopes["lon"][i] = obs[telescope[2]]
scopes = scopes[np.where(scopes["aperture"] >= min_size)]
return scopes
[docs]
class NFollowStacker(BaseStacker):
"""Add the number of telescopes ('nObservatories') that could follow up any visit
at (any of the) times in timeStep, specifying the minimum telescope size (in meters) and airmass limit.
Parameters
----------
minSize: float, optional
The minimum telescope aperture to use, in meters. Default 3.0.
airmass_limit: float, optional
The maximum airmass allowable at the follow-up observatory. Default 2.5.
time_steps: np.array or list of floats, optional
The timesteps to check for followup opportunities, in hours. Default is np.arange(0.5, 12., 3.0).
mjd_col: str, optional
The exposure MJD column name. Default 'observationStartMJD'.
ra_col: str, optional
The RA column name. Default 'fieldRA'.
dec_col: str, optional
The Dec column name. Default 'fieldDec'.
raDecDeg: bool, optional
Flag whether RA/Dec are in degrees (True) or radians (False).
"""
cols_added = ["nObservatories"]
def __init__(
self,
min_size=3.0,
airmass_limit=2.5,
time_steps=np.arange(0.5, 12.0, 3.0),
mjd_col="observationStartMJD",
ra_col="fieldRA",
dec_col="fieldDec",
degrees=True,
):
self.mjd_col = mjd_col
self.ra_col = ra_col
self.dec_col = dec_col
self.degrees = degrees
self.cols_added_dtypes = [int]
self.cols_req = [self.mjd_col, self.ra_col, self.dec_col]
self.units = ["#"]
self.airmass_limit = airmass_limit
self.time_steps = time_steps
self.telescopes = find_telescopes(min_size=min_size)
def _run(self, sim_data, cols_present=False):
if cols_present:
return sim_data
sim_data["nObservatories"] = 0
if self.degrees:
ra = np.radians(sim_data[self.ra_col])
dec = np.radians(sim_data[self.dec_col])
else:
ra = sim_data[self.ra_col]
dec = sim_data[self.dec_col]
for obs in self.telescopes:
obs_got_it = np.zeros(len(sim_data[self.ra_col]), int)
obs_lon = np.radians(obs["lon"])
obs_lat = np.radians(obs["lat"])
for step in self.time_steps:
alt, az = ra_dec2_alt_az(
ra,
dec,
obs_lon,
obs_lat,
sim_data[self.mjd_col] + step / 24.0,
altonly=True,
)
airmass = 1.0 / (np.cos(np.pi / 2.0 - alt))
followed = np.where((airmass <= self.airmass_limit) & (airmass >= 1.0))
# If the observatory got an observation, save this into obs_got_it.
# obs_got_it will be 1 if ANY of the times got an observation.
obs_got_it[followed] = 1
# If an observatory got an observation, count it in nObservatories.
sim_data["nObservatories"] += obs_got_it
return sim_data