Coverage for python/lsst/sims/maf/stackers/m5OptimalStacker.py : 42%

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""" Fit a line to how the sky brightness changes with airmass. """ import lsst.sims.skybrightness as sb import healpy as hp sm = sb.SkyModel(mags=True, moon=False, twilight=False, zodiacal=False) mjd = 57000 nside = 32 airmass_limit = 2.0 dec, ra = hp.pix2ang(nside, np.arange(hp.nside2npix(nside))) dec = np.pi/2 - dec sm.setRaDecMjd(ra, dec, mjd) mags = sm.returnMags() filters = mags.dtype.names filter_slopes = {} for filterName in filters: good = np.where((~np.isnan(mags[filterName])) & (sm.airmass < airmass_limit)) pf = np.polyfit(sm.airmass[good], mags[filterName][good], 1) filter_slopes[filterName] = pf[0] print(filter_slopes)
""" Make a new m5 column as if observations were taken on the meridian. If the moon is up, assume sky brightness stays the same.
Assumes seeing scales as airmass^0.6. Uses linear fits for sky and airmass relation.
Parameters ---------- airmassCol : str ('airmass') Column name for the airmass per pointing. decCol : str ('dec_rad') Column name for the pointing declination. skyBrightCol: str ('filtSkyBrightness') Column name for the sky brighntess per pointing. filterCol : str ('filter') Column name for the filter name. m5Col : str ('fiveSigmaDepth') Colum name for the five sigma limiting depth per pointing. moonAltCol : str ('moonAlt') Column name for the moon altitude per pointing. sunAltCol : str ('sunAltCol') Column name for the sun altitude column. site : str ('LSST') Name of the site.
Returns ------- numpy.array Adds a column to that is approximately what the five-sigma depth would have been if the observation had been taken on the meridian. """
skyBrightCol='skyBrightness', seeingCol='seeingFwhmEff', filterCol='filter', moonAltCol='moonAlt', sunAltCol='sunAlt', site='LSST'):
seeingCol, filterCol, moonAltCol, sunAltCol]
simData, m5col_present = self.m5_stacker._addStackerCols(simData) simData = self.m5_stacker._run(simData, m5col_present) # kAtm values from lsst.sims.operations gen_output.py kAtm = {'u': 0.50, 'g': 0.21, 'r': 0.13, 'i': 0.10, 'z': 0.07, 'y': 0.18} # Linear fits to sky brightness change, no moon, twilight, or zodiacal components # Use generate_sky_slopes to regenerate if needed. skySlopes = {'g': -0.52611780327408397, 'i': -0.67898669252082422, 'r': -0.61378749766766827, 'u': -0.27840980367303503, 'y': -0.69635091524779691, 'z': -0.69652846002009128} min_z_possible = np.abs(np.radians(simData[self.decCol]) - self.site.latitude_rad) min_airmass_possible = 1./np.cos(min_z_possible) for filterName in np.unique(simData[self.filterCol]): deltaSky = skySlopes[filterName]*(simData[self.airmassCol] - min_airmass_possible) deltaSky[np.where((simData[self.moonAltCol] > 0) | (simData[self.sunAltCol] > np.radians(-18.)))] = 0 # Using Approximation that FWHM~X^0.6. So seeing term in m5 of: 0.25 * log (7.0/FWHMeff ) # Goes to 0.15 log(FWHM_min / FWHM_eff) in the difference m5Optimal = (simData[self.m5Col] - 0.5*deltaSky - 0.15*np.log10(min_airmass_possible / simData[self.airmassCol]) - kAtm[filterName]*(min_airmass_possible - simData[self.airmassCol])) good = np.where(simData[self.filterCol] == filterName) simData['m5Optimal'][good] = m5Optimal[good] return simData |