__all__ = ("CoaddStacker",)
import numpy as np
from rubin_sim.maf.stackers import BaseStacker
[docs]
class CoaddStacker(BaseStacker):
"""Stacker to estimate m5 "coadded" per band and par night
Parameters
----------
mjd_col : `str`, optional
Name of the MJD column
ra_col : `str`, optional
Name of the RA column
dec_col : `str`, optional
Name of the Dec column
m5_col : `str`, optional
Name of the m5 column
filter_col : `str`, optional
Name of the filter column
night_col : `str`, optional
Name of the night column
num_exposures_col : `str`, optional
Name of the number of exposures per visit column
visit_time_col : `str`, optional
Name of the total visit time column
visit_exposure_time_col : `str`, optional
Name of the on-sky exposure time column
"""
cols_added = ["coadd"]
def __init__(
self,
mjd_col="observationStartMJD",
ra_col="fieldRA",
dec_col="fieldDec",
m5_col="fiveSigmaDepth",
filter_col="band",
night_col="night",
num_exposures_col="numExposures",
visit_time_col="visitTime",
visit_exposure_time_col="visitExposureTime",
):
self.cols_req = [
mjd_col,
ra_col,
dec_col,
m5_col,
filter_col,
night_col,
num_exposures_col,
visit_time_col,
visit_exposure_time_col,
]
self.ra_col = ra_col
self.dec_col = dec_col
self.night_col = night_col
self.filter_col = filter_col
self.m5_col = m5_col
self.num_exposures_col = num_exposures_col
self.visit_time_col = visit_time_col
self.visit_exposure_time_col = visit_exposure_time_col
self.units = ["int"]
def _run(self, sim_data, cols_present=False):
if cols_present:
# Column already present in data; assume it is correct
# and does not need recalculating.
return sim_data
self.dtype = sim_data.dtype
r = []
for ra, dec, band in np.unique(sim_data[[self.ra_col, self.dec_col, self.filter_col]]):
idx = np.abs(sim_data[self.ra_col] - ra) < 1.0e-5
idx &= np.abs(sim_data[self.dec_col] - dec) < 1.0e-5
idx &= sim_data[self.filter_col] == band
sel = sim_data[idx]
for night in np.unique(sel[self.night_col]):
idxb = sel[self.night_col] == night
r.append(tuple(self.fill(sel[idxb])))
myarray = np.array(r, dtype=self.dtype)
return myarray
[docs]
def fill(self, tab):
"""Estimation of new fields (m5 "coadded" values, ...)
Parameters
---------------
tab : array of (initial) data
Returns
-----------
tuple with modified field values:
- m5Col: "coadded" m5
- numExposuresCol: sum of numExposuresCol
- visitTimeCol: sum of visitTimeCol
- visitExposureTimeCol: sum of visitExposureTimeCol
- all other input fields except band (Ra, Dec, night) : median(field)
"""
r = []
for colname in self.dtype.names:
if colname not in [
self.m5_col,
self.num_exposures_col,
self.visit_time_col,
self.visit_exposure_time_col,
self.filter_col,
]:
if colname == "coadd":
r.append(1)
else:
if tab[colname].dtype == "object":
r.append(tab[colname][0])
else:
r.append(np.median(tab[colname]))
if colname == self.m5_col:
r.append(self.m5_coadd(tab[self.m5_col]))
if colname in [
self.num_exposures_col,
self.visit_time_col,
self.visit_exposure_time_col,
]:
r.append(np.sum(tab[colname]))
if colname == self.filter_col:
r.append(np.unique(tab[self.filter_col])[0])
return r
[docs]
def m5_coadd(self, m5):
"""Estimation of "coadded" m5 values based on:
flux_5sigma = 10**(-0.4*m5)
sigmas = flux_5sigma/5.
sigma_tot = 1./sqrt(np.sum(1/sigmas**2))
flux_tot = 5.*sigma_tot
Parameters
---------------
m5 : set of m5 (five-sigma depths) values
Returns
-----------
"coadded" m5 value
"""
return 1.25 * np.log10(np.sum(10.0 ** (0.8 * m5)))
