__all__ = (
"id2intid",
"intid2id",
"load_spec_files",
"BaseSingleInterp",
"ScatteredStar",
"LowerAtm",
"UpperAtm",
"MergedSpec",
"Airglow",
"TwilightInterp",
"MoonInterp",
"ZodiacalInterp",
)
import glob
import os
import warnings
import healpy as hp
import numpy as np
from rubin_scheduler.data import get_data_dir
from scipy.interpolate import InterpolatedUnivariateSpline, interp1d
from rubin_sim.phot_utils import Bandpass, Sed
from .twilight_func import twilight_func
# Make backwards compatible with healpy
if hasattr(hp, "get_interp_weights"):
get_neighbours = hp.get_interp_weights
elif hasattr(hp, "get_neighbours"):
get_neighbours = hp.get_neighbours
else:
print("Could not find appropriate healpy function for get_interp_weight or get_neighbours")
[docs]
def id2intid(ids):
"""take an array of ids, and convert them to an integer id.
Handy if you want to put things into a sparse array.
"""
uids = np.unique(ids)
order = np.argsort(ids)
oids = ids[order]
uintids = np.arange(np.size(uids), dtype=int)
left = np.searchsorted(oids, uids)
right = np.searchsorted(oids, uids, side="right")
intids = np.empty(ids.size, dtype=int)
for i in range(np.size(left)):
intids[left[i] : right[i]] = uintids[i]
result = intids * 0
result[order] = intids
return result, uids, uintids
[docs]
def intid2id(intids, uintids, uids, dtype=int):
"""convert an int back to an id"""
ids = np.zeros(np.size(intids))
order = np.argsort(intids)
ointids = intids[order]
left = np.searchsorted(ointids, uintids, side="left")
right = np.searchsorted(ointids, uintids, side="right")
for i, (le, ri) in enumerate(zip(left, right)):
ids[le:ri] = uids[i]
result = np.zeros(np.size(intids), dtype=dtype)
result[order] = ids
return result
[docs]
def load_spec_files(filenames, mags=False):
"""Load up the ESO spectra.
The ESO npz files contain the following arrays:
* filter_wave: The central wavelengths of the pre-computed magnitudes
* wave: wavelengths for the spectra
* spec: array of spectra and magnitudes along with the relevant
variable inputs. For example, airglow has dtype =
[('airmass', '<f8'), ('solar_flux', '<f8'), ('spectra',
'<f8', (17001,)),
('mags', '<f8', (6,)]
For each unique airmass and solar_flux value, there is a 17001 elements
spectra and 6 magnitudes.
"""
if len(filenames) == 1:
temp = np.load(filenames[0])
wave = temp["wave"].copy()
# Note old camelCase here. Might need to update if sav
# files regenerated
filter_wave = temp["filterWave"].copy()
if mags:
# don't copy the spectra to save memory space
dt = np.dtype(
[
(key, temp["spec"].dtype[i])
for i, key in enumerate(temp["spec"].dtype.names)
if key != "spectra"
]
)
spec = np.zeros(temp["spec"].size, dtype=dt)
for key in temp["spec"].dtype.names:
if key != "spectra":
spec[key] = temp["spec"][key].copy()
else:
spec = temp["spec"].copy()
else:
temp = np.load(filenames[0])
wave = temp["wave"].copy()
filter_wave = temp["filterWave"].copy()
if mags:
# don't copy the spectra to save memory space
dt = np.dtype(
[
(key, temp["spec"].dtype[i])
for i, key in enumerate(temp["spec"].dtype.names)
if key != "spectra"
]
)
spec = np.zeros(temp["spec"].size, dtype=dt)
for key in temp["spec"].dtype.names:
if key != "spectra":
spec[key] = temp["spec"][key].copy()
else:
spec = temp["spec"].copy()
for filename in filenames[1:]:
temp = np.load(filename)
if mags:
# don't copy the spectra to save memory space
dt = np.dtype(
[
(key, temp["spec"].dtype[i])
for i, key in enumerate(temp["spec"].dtype.names)
if key != "spectra"
]
)
tempspec = np.zeros(temp["spec"].size, dtype=dt)
for key in temp["spec"].dtype.names:
if key != "spectra":
tempspec[key] = temp["spec"][key].copy()
else:
tempspec = temp["spec"]
spec = np.append(spec, tempspec)
return spec, wave, filter_wave
[docs]
class BaseSingleInterp:
"""Base class for interpolating sky components which only depend
on airmass.
Parameters
----------
comp_name : `str`, optional
Component name.
sorted_order : `list` [`str`], optional
Order of the dimensions in the input .npz files.
mags : `bool`, optional
Return magnitudes (only) rather than the full spectrum.
"""
def __init__(self, comp_name=None, sorted_order=["airmass", "nightTimes"], mags=False):
self.mags = mags
data_dir = os.path.join(get_data_dir(), "skybrightness", "ESO_Spectra/" + comp_name)
filenames = sorted(glob.glob(data_dir + "/*.npz"))
self.spec, self.wave, self.filter_wave = load_spec_files(filenames, mags=self.mags)
# Take the log of the spectra in case we want to interp in log space.
if not mags:
self.log_spec = np.zeros(self.spec["spectra"].shape, dtype=float)
good = np.where(self.spec["spectra"] != 0)
self.log_spec[good] = np.log10(self.spec["spectra"][good])
self.spec_size = self.spec["spectra"][0].size
else:
self.spec_size = 0
# What order are the dimensions sorted by (from how
# the .npz was packaged)
self.sorted_order = sorted_order
self.dim_dict = {}
self.dim_sizes = {}
for dt in self.sorted_order:
self.dim_dict[dt] = np.unique(self.spec[dt])
self.dim_sizes[dt] = np.size(np.unique(self.spec[dt]))
# Set up and save the dict to order the filters once.
self.filter_name_dict = {"u": 0, "g": 1, "r": 2, "i": 3, "z": 4, "y": 5}
[docs]
def __call__(self, interp_points, filter_names=["u", "g", "r", "i", "z", "y"]):
"""At `interp_points (e.g. airmass), return values."""
if self.mags:
return self.interp_mag(interp_points, filter_names=filter_names)
else:
return self.interp_spec(interp_points)
def _indx_and_weights(self, points, grid):
"""For given 1-D points, find the grid points on
either side and return the weights assume grid is sorted.
Parameters
----------
points : `np.ndarray`, (N,)
The points on the grid to query.
grid : `np.ndarray`, (N,)
The grid on which to locate `points`.
Returns
-------
indx_r, indx_l : `np.ndarray`, `np.ndarray`
The grid indexes for each of the 1-d points
w_r, w_l : `np.ndarray`, `np.ndarray`
The weights for each of these grid points.
"""
order = np.argsort(points)
indx_r = np.empty(points.size, dtype=int)
indx_r[order] = np.searchsorted(grid, points[order])
indx_l = indx_r - 1
# If points off the grid were requested, just use the edge grid point
off_grid = np.where(indx_r == grid.size)
indx_r[off_grid] = grid.size - 1
full_range = grid[indx_r] - grid[indx_l]
w_l = np.zeros(full_range.size, dtype=float)
w_r = np.ones(full_range.size, dtype=float)
good = np.where(full_range != 0)
w_l[good] = (grid[indx_r][good] - points[good]) / full_range[good]
w_r[good] = (points[good] - grid[indx_l[good]]) / full_range[good]
return indx_r, indx_l, w_r, w_l
def _weighting(self, interp_points, values):
"""
given a list/array of airmass values, return a
dict with the interpolated spectrum at each airmass
and the wavelength array.
Input interp_points should be sorted
"""
results = np.zeros((interp_points.size, np.size(values[0])), dtype=float)
in_range = np.where(
(interp_points["airmass"] <= np.max(self.dim_dict["airmass"]))
& (interp_points["airmass"] >= np.min(self.dim_dict["airmass"]))
)
indx_r, indx_l, w_r, w_l = self._indx_and_weights(
interp_points["airmass"][in_range], self.dim_dict["airmass"]
)
nextra = 1
# XXX--should I use the log spectra?
# Make a check and switch back and forth?
results[in_range] = (
w_r[:, np.newaxis] * values[indx_r * nextra] + w_l[:, np.newaxis] * values[indx_l * nextra]
)
return results
def interp_spec(self, interp_points):
result = self._weighting(interp_points, self.log_spec)
mask = np.where(result == 0.0)
result = 10.0**result
result[mask] = 0.0
return {"spec": result, "wave": self.wave}
def interp_mag(self, interp_points, filter_names=["u", "g", "r", "i", "z", "y"]):
filterindx = [self.filter_name_dict[key] for key in filter_names]
result = self._weighting(interp_points, self.spec["mags"][:, filterindx])
mask = np.where(result == 0.0)
result = 10.0 ** (-0.4 * (result - np.log10(3631.0)))
result[mask] = 0.0
return {"spec": result, "wave": self.filter_wave}
[docs]
class ScatteredStar(BaseSingleInterp):
"""Interpolate the spectra caused by scattered starlight."""
def __init__(self, comp_name="ScatteredStarLight", mags=False):
super(ScatteredStar, self).__init__(comp_name=comp_name, mags=mags)
[docs]
class LowerAtm(BaseSingleInterp):
"""Interpolate the spectra caused by the lower atmosphere."""
def __init__(self, comp_name="LowerAtm", mags=False):
super(LowerAtm, self).__init__(comp_name=comp_name, mags=mags)
[docs]
class UpperAtm(BaseSingleInterp):
"""Interpolate the spectra caused by the upper atmosphere."""
def __init__(self, comp_name="UpperAtm", mags=False):
super(UpperAtm, self).__init__(comp_name=comp_name, mags=mags)
[docs]
class MergedSpec(BaseSingleInterp):
"""Interpolate the combined spectra caused by the sum of the scattered
starlight, air glow, upper and lower atmosphere.
"""
def __init__(self, comp_name="MergedSpec", mags=False):
super(MergedSpec, self).__init__(comp_name=comp_name, mags=mags)
[docs]
class Airglow(BaseSingleInterp):
"""Interpolate the spectra caused by airglow."""
def __init__(self, comp_name="Airglow", sorted_order=["airmass", "solarFlux"], mags=False):
super(Airglow, self).__init__(comp_name=comp_name, mags=mags, sorted_order=sorted_order)
self.n_solar_flux = np.size(self.dim_dict["solarFlux"])
def _weighting(self, interp_points, values):
results = np.zeros((interp_points.size, np.size(values[0])), dtype=float)
# Only interpolate point that lie in the model grid
in_range = np.where(
(interp_points["airmass"] <= np.max(self.dim_dict["airmass"]))
& (interp_points["airmass"] >= np.min(self.dim_dict["airmass"]))
& (interp_points["solar_flux"] >= np.min(self.dim_dict["solarFlux"]))
& (interp_points["solar_flux"] <= np.max(self.dim_dict["solarFlux"]))
)
use_points = interp_points[in_range]
am_right_index, am_left_index, am_right_w, am_left_w = self._indx_and_weights(
use_points["airmass"], self.dim_dict["airmass"]
)
sf_right_index, sf_left_index, sf_right_w, sf_left_w = self._indx_and_weights(
use_points["solar_flux"], self.dim_dict["solarFlux"]
)
for am_index, amW in zip([am_right_index, am_left_index], [am_right_w, am_left_w]):
for sf_index, sfW in zip([sf_right_index, sf_left_index], [sf_right_w, sf_left_w]):
results[in_range] += (
amW[:, np.newaxis] * sfW[:, np.newaxis] * values[am_index * self.n_solar_flux + sf_index]
)
return results
[docs]
class TwilightInterp:
"""Use the Solar Spectrum to provide an interpolated spectra or magnitudes
for the twilight sky.
Parameters
----------
mags : `bool`
If True, only return the LSST filter magnitudes,
otherwise return the full spectrum
dark_sky_mags : `dict`
Dict of the zenith dark sky values to be assumed.
The twilight fits are done relative to the dark sky level.
fit_results : `dict`
Dict of twilight parameters based on twilight_func.
Keys should be filter names.
"""
def __init__(self, mags=False, dark_sky_mags=None, fit_results=None):
if dark_sky_mags is None:
dark_sky_mags = {
"u": 22.8,
"g": 22.3,
"r": 21.2,
"i": 20.3,
"z": 19.3,
"y": 18.0,
"B": 22.35,
"G": 21.71,
"R": 21.3,
}
self.mags = mags
data_dir = os.path.join(get_data_dir(), "skybrightness")
solar_saved = np.load(os.path.join(data_dir, "solarSpec/solarSpec.npz"))
self.solar_spec = Sed(wavelen=solar_saved["wave"], flambda=solar_saved["spec"])
solar_saved.close()
canon_filters = {}
fnames = ["blue_canon.csv", "green_canon.csv", "red_canon.csv"]
# Filter names, from bluest to reddest.
self.filter_names = ["B", "G", "R"]
# Supress warning that Canon filters are low sampling
warnings.filterwarnings("ignore", message="There is an area of")
warnings.filterwarnings("ignore", message="Wavelength sampling of")
for fname, filter_name in zip(fnames, self.filter_names):
bpdata = np.genfromtxt(
os.path.join(data_dir, "Canon/", fname),
delimiter=", ",
dtype=list(zip(["wave", "through"], [float] * 2)),
)
bp_temp = Bandpass()
bp_temp.set_bandpass(bpdata["wave"], bpdata["through"])
bp_temp.resample_bandpass(
wavelen_min=self.solar_spec.wavelen.min(),
wavelen_max=self.solar_spec.wavelen.max(),
wavelen_step=self.solar_spec.wavelen[1] - self.solar_spec.wavelen[0],
)
# Force wavelengths to be identical so
# it doesn't try to resample again later
bp_temp.wavelen = self.solar_spec.wavelen
canon_filters[filter_name] = bp_temp
# Tack on the LSST filters
through_path = os.path.join(get_data_dir(), "throughputs", "baseline")
lsst_keys = ["u", "g", "r", "i", "z", "y"]
for key in lsst_keys:
bp = np.loadtxt(
os.path.join(through_path, "total_" + key + ".dat"),
dtype=list(zip(["wave", "trans"], [float] * 2)),
)
temp_b = Bandpass()
temp_b.set_bandpass(bp["wave"], bp["trans"])
canon_filters[key] = temp_b
self.filter_names.append(key)
# MAGIC NUMBERS from fitting the all-sky camera:
# Code to generate values in
# sims_skybrightness/examples/fitTwiSlopesSimul.py
# Which in turn uses twilight maps from
# sims_skybrightness/examples/buildTwilMaps.py
# values are of the form:
# 0: ratio of f^z_12 to f_dark^z
# 1: slope of curve wrt sun alt
# 2: airmass term (10^(arg[2]*(X-1)))
# 3: azimuth term.
# 4: zenith dark sky flux (erg/s/cm^2)
# For z and y, just assuming the shape parameter
# fits are similar to the other bands.
# Looks like the diode is not sensitive enough to detect faint sky.
# Using the Patat et al 2006 I-band values for z and
# modeified a little for y as a temp fix.
if fit_results is None:
self.fit_results = {
"B": [
7.56765633e00,
2.29798055e01,
2.86879956e-01,
3.01162143e-01,
2.58462036e-04,
],
"G": [
2.38561156e00,
2.29310648e01,
2.97733083e-01,
3.16403197e-01,
7.29660095e-04,
],
"R": [
1.75498017e00,
2.22011802e01,
2.98619033e-01,
3.28880254e-01,
3.24411056e-04,
],
"z": [2.29, 24.08, 0.3, 0.3, -666],
"y": [2.0, 24.08, 0.3, 0.3, -666],
}
# XXX-completely arbitrary fudge factor to make things
# brighter in the blue
# Just copy the blue and say it's brighter.
self.fit_results["u"] = [
16.0,
2.29622121e01,
2.85862729e-01,
2.99902574e-01,
2.32325117e-04,
]
else:
self.fit_results = fit_results
# Take out any filters that don't have fit results
self.filter_names = [key for key in self.filter_names if key in self.fit_results]
self.eff_wave = []
self.solar_mag = []
for filter_name in self.filter_names:
self.eff_wave.append(canon_filters[filter_name].calc_eff_wavelen()[0])
self.solar_mag.append(self.solar_spec.calc_mag(canon_filters[filter_name]))
order = np.argsort(self.eff_wave)
self.filter_names = np.array(self.filter_names)[order]
self.eff_wave = np.array(self.eff_wave)[order]
self.solar_mag = np.array(self.solar_mag)[order]
# update the fit results to be zeropointed properly
for key in self.fit_results:
f0 = 10.0 ** (-0.4 * (dark_sky_mags[key] - np.log10(3631.0)))
self.fit_results[key][-1] = f0
self.solar_wave = self.solar_spec.wavelen
self.solar_flux = self.solar_spec.flambda
# This one isn't as bad as the model grids, maybe we could get
# away with computing the magnitudes in the __call__ each time.
if mags:
# Load up the LSST filters and convert the
# solarSpec.flambda and solarSpec.wavelen to fluxes
self.lsst_filter_names = ["u", "g", "r", "i", "z", "y"]
self.lsst_equations = np.zeros(
(np.size(self.lsst_filter_names), np.size(self.fit_results["B"])),
dtype=float,
)
self.lsst_eff_wave = []
fits = np.empty((np.size(self.eff_wave), np.size(self.fit_results["B"])), dtype=float)
for i, fn in enumerate(self.filter_names):
fits[i, :] = self.fit_results[fn]
through_path = os.path.join(get_data_dir(), "throughputs", "baseline")
for filtername in self.lsst_filter_names:
bp = np.loadtxt(
os.path.join(through_path, "total_" + filtername + ".dat"),
dtype=list(zip(["wave", "trans"], [float] * 2)),
)
temp_b = Bandpass()
temp_b.set_bandpass(bp["wave"], bp["trans"])
self.lsst_eff_wave.append(temp_b.calc_eff_wavelen()[0])
# Loop through the parameters and interpolate to new
# eff wavelengths
for i in np.arange(self.lsst_equations[0, :].size):
interp = InterpolatedUnivariateSpline(self.eff_wave, fits[:, i])
self.lsst_equations[:, i] = interp(self.lsst_eff_wave)
# Set the dark sky flux
for i, filter_name in enumerate(self.lsst_filter_names):
self.lsst_equations[i, -1] = 10.0 ** (-0.4 * (dark_sky_mags[filter_name] - np.log10(3631.0)))
self.filter_name_dict = {"u": 0, "g": 1, "r": 2, "i": 3, "z": 4, "y": 5}
[docs]
def print_fits_used(self):
"""Print out the fit parameters being used"""
print(
r"\\tablehead{\colhead{Filter} & \colhead{$r_{12/z}$} & "
r"\colhead{$a$ (1/radians)} & \colhead{$b$ (1/airmass)} & "
r"\colhead{$c$ (az term/airmass)} & "
r"\colhead{$f_z_dark$ (erg/s/cm$^2$)$\\times 10^8$} & "
r"\colhead{m$_z_dark$}}"
)
for key in self.fit_results:
numbers = ""
for num in self.fit_results[key]:
if num > 0.001:
numbers += " & %.2f" % num
else:
numbers += " & %.2f" % (num * 1e8)
print(
key,
numbers,
" & ",
"%.2f" % (-2.5 * np.log10(self.fit_results[key][-1]) + np.log10(3631.0)),
)
def __call__(self, intep_points, filter_names=["u", "g", "r", "i", "z", "y"]):
if self.mags:
return self.interp_mag(intep_points, filter_names=filter_names)
else:
return self.interp_spec(intep_points)
[docs]
def interp_mag(
self,
interp_points,
max_am=3.0,
limits=(np.radians(15.0), np.radians(-20.0)),
filter_names=["u", "g", "r", "i", "z", "y"],
):
"""
Parameters
----------
interp_points : `np.ndarray`, (N, 3)
Interpolation points. Should contain sunAlt, airmass and azRelSun.
max_am : `float`, optional
Maximum airmass to calculate twilight sky to.
limits : `np.ndarray`, (N,), optional
Sun altitude limits
Returns
-------
spectra, wavelength : `np.ndarray`, (N, 3), `np.ndarray`, (M,)
Note
----
Originally fit the twilight with a cutoff of sun altitude of
-11 degrees. I think it can be safely extrapolated farther,
but be warned you may be entering a regime where it breaks down.
"""
npts = len(filter_names)
result = np.zeros((np.size(interp_points), npts), dtype=float)
out_of_range = np.where(interp_points["sunAlt"] > np.radians(-11))[0]
if np.size(out_of_range) > 0:
warnings.warn("Extrapolating twilight beyond a sun altitude of -11 degrees")
good = np.where(
(interp_points["sunAlt"] >= np.min(limits))
& (interp_points["sunAlt"] <= np.max(limits))
& (interp_points["airmass"] <= max_am)
& (interp_points["airmass"] >= 1.0)
)[0]
for i, filterName in enumerate(filter_names):
out_of_range = np.where(interp_points["sunAlt"] > np.max(limits))[0]
if np.size(out_of_range) > 0:
result[:, i] = np.nan
else:
result[good, i] = twilight_func(
interp_points[good], *self.lsst_equations[self.filter_name_dict[filterName], :].tolist()
)
return {"spec": result, "wave": self.lsst_eff_wave}
[docs]
def interp_spec(self, interp_points, max_am=3.0, limits=(np.radians(15.0), np.radians(-20.0))):
"""
Parameters
----------
interp_points : `np.ndarray`, (N, 3)
Interpolation points. Should contain sunAlt, airmass and azRelSun.
max_am : `float`, optional
Maximum airmass to calculate twilight sky to.
limits : `np.ndarray`, (N,), optional
Sun altitude limits
Returns
-------
spectra, wavelength : `np.ndarray`, (N, 3), `np.ndarray`, (M,)
Note
----
Originally fit the twilight with a cutoff of sun altitude of
-11 degrees. I think it can be safely extrapolated farther,
but be warned you may be entering a regime where it breaks down.
"""
npts = np.size(self.solar_wave)
result = np.zeros((np.size(interp_points), npts), dtype=float)
out_of_range = np.where(interp_points["sunAlt"] > np.radians(-11))[0]
if np.size(out_of_range) > 0:
warnings.warn("Extrapolating twilight beyond a sun altitude of -11 degrees")
good = np.where(
(interp_points["sunAlt"] >= np.min(limits))
& (interp_points["sunAlt"] <= np.max(limits))
& (interp_points["airmass"] <= max_am)
& (interp_points["airmass"] >= 1.0)
)[0]
# Compute the expected flux in each of the filters that
# we have fits for
fluxes = []
for filter_name in self.filter_names:
out_of_range = np.where(interp_points["sunAlt"] > np.max(limits))[0]
if np.size(out_of_range) > 0:
fluxes.append(np.nan)
else:
fluxes.append(twilight_func(interp_points[good], *self.fit_results[filter_name]))
fluxes = np.array(fluxes)
# ratio of model flux to raw solar flux:
yvals = fluxes.T / (10.0 ** (-0.4 * (self.solar_mag - np.log10(3631.0))))
# Find wavelengths bluer than cutoff
blue_region = np.where(self.solar_wave < np.min(self.eff_wave))
for i, yval in enumerate(yvals):
interp_f = interp1d(self.eff_wave, yval, bounds_error=False, fill_value=yval[-1])
ratio = interp_f(self.solar_wave)
interp_blue = InterpolatedUnivariateSpline(self.eff_wave, yval, k=1)
ratio[blue_region] = interp_blue(self.solar_wave[blue_region])
result[good[i]] = self.solar_flux * ratio
return {"spec": result, "wave": self.solar_wave}
[docs]
class MoonInterp(BaseSingleInterp):
"""
Read in the saved Lunar spectra and interpolate.
"""
def __init__(
self,
comp_name="Moon",
sorted_order=["moonSunSep", "moonAltitude", "hpid"],
mags=False,
):
super(MoonInterp, self).__init__(comp_name=comp_name, sorted_order=sorted_order, mags=mags)
# Magic number from when the templates were generated
self.nside = 4
def _weighting(self, interp_points, values):
"""
Weighting for the scattered moonlight.
"""
result = np.zeros((interp_points.size, np.size(values[0])), dtype=float)
# Check that moonAltitude is in range, otherwise return zero array
if np.max(interp_points["moonAltitude"]) < np.min(self.dim_dict["moonAltitude"]):
return result
# Find the neighboring healpixels
hpids, hweights = get_neighbours(
self.nside, np.pi / 2.0 - interp_points["alt"], interp_points["azRelMoon"]
)
badhp = np.in1d(hpids.ravel(), self.dim_dict["hpid"], invert=True).reshape(hpids.shape)
hweights[badhp] = 0.0
norm = np.sum(hweights, axis=0)
good = np.where(norm != 0.0)[0]
hweights[:, good] = hweights[:, good] / norm[good]
# Find the neighboring moonAltitude points in the grid
right_m_as, left_m_as, ma_right_w, ma_left_w = self._indx_and_weights(
interp_points["moonAltitude"], self.dim_dict["moonAltitude"]
)
# Find the neighboring moonSunSep points in the grid
right_mss, left_mss, mss_right_w, mss_left_w = self._indx_and_weights(
interp_points["moonSunSep"], self.dim_dict["moonSunSep"]
)
nhpid = self.dim_dict["hpid"].size
n_ma = self.dim_dict["moonAltitude"].size
# Convert the hpid to an index.
tmp = intid2id(hpids.ravel(), self.dim_dict["hpid"], np.arange(self.dim_dict["hpid"].size))
hpindx = tmp.reshape(hpids.shape)
# loop though the hweights and the moonAltitude weights
for hpid, hweight in zip(hpindx, hweights):
for maid, maW in zip([right_m_as, left_m_as], [ma_right_w, ma_left_w]):
for mssid, mssW in zip([right_mss, left_mss], [mss_right_w, mss_left_w]):
weight = hweight * maW * mssW
result += weight[:, np.newaxis] * values[mssid * nhpid * n_ma + maid * nhpid + hpid]
return result
[docs]
class ZodiacalInterp(BaseSingleInterp):
"""
Interpolate the zodiacal light based on the airmass
and the healpix ID where the healpixels are in ecliptic
coordinates, with the sun at ecliptic longitude zero
"""
def __init__(self, comp_name="Zodiacal", sorted_order=["airmass", "hpid"], mags=False):
super(ZodiacalInterp, self).__init__(comp_name=comp_name, sorted_order=sorted_order, mags=mags)
self.nside = hp.npix2nside(
np.size(np.where(self.spec["airmass"] == np.unique(self.spec["airmass"])[0])[0])
)
def _weighting(self, interp_points, values):
"""
interp_points is a numpy array where interpolation is desired
values are the model values.
"""
result = np.zeros((interp_points.size, np.size(values[0])), dtype=float)
in_range = np.where(
(interp_points["airmass"] <= np.max(self.dim_dict["airmass"]))
& (interp_points["airmass"] >= np.min(self.dim_dict["airmass"]))
)
use_points = interp_points[in_range]
# Find the neighboring healpixels
hpids, hweights = get_neighbours(
self.nside,
np.pi / 2.0 - use_points["altEclip"],
use_points["azEclipRelSun"],
)
badhp = np.in1d(hpids.ravel(), self.dim_dict["hpid"], invert=True).reshape(hpids.shape)
hweights[badhp] = 0.0
norm = np.sum(hweights, axis=0)
good = np.where(norm != 0.0)[0]
hweights[:, good] = hweights[:, good] / norm[good]
am_right_index, am_left_index, am_right_w, am_left_w = self._indx_and_weights(
use_points["airmass"], self.dim_dict["airmass"]
)
nhpid = self.dim_dict["hpid"].size
# loop though the hweights and the airmass weights
for hpid, hweight in zip(hpids, hweights):
for am_index, amW in zip([am_right_index, am_left_index], [am_right_w, am_left_w]):
weight = hweight * amW
result[in_range] += weight[:, np.newaxis] * values[am_index * nhpid + hpid]
return result