def photRepeat(matchedCatalog, numRandomShuffles=50, randomSeed=None, **filterargs):
filteredCat = filterMatches(matchedCatalog, **filterargs)
magKey = filteredCat.schema.find('slot_PsfFlux_mag').key
# Require at least nMinPhotRepeat objects to calculate the repeatability:
nMinPhotRepeat = 50
if filteredCat.count > nMinPhotRepeat:
phot_resid_meas = calcPhotRepeat(filteredCat, magKey, numRandomShuffles=50, randomSeed=randomSeed)
return phot_resid_meas
else:
return {'nomeas': np.nan*u.mmag}
def run(self, matchedCatalog, metric_name):
self.log.info(f"Measuring {metric_name}")
D = self.config.annulus_r * u.arcmin
filteredCat = filterMatches(matchedCatalog)
nMinTEx = 50
if filteredCat.count <= nMinTEx:
return Struct(measurement=Measurement(metric_name, np.nan*u.Unit('')))
radius, xip, xip_err = correlation_function_ellipticity_from_matches(filteredCat)
operator = ThresholdSpecification.convert_operator_str(self.config.comparison_operator)
corr, corr_err = select_bin_from_corr(radius, xip, xip_err, radius=D, operator=operator)
return Struct(measurement=Measurement(metric_name, np.abs(corr)*u.Unit('')))
def astromRms(matchedCatalog, mag_bright_cut, mag_faint_cut, annulus_r, width, **filterargs):
filteredCat = filterMatches(matchedCatalog, **filterargs)
magRange = np.array([mag_bright_cut, mag_faint_cut]) * u.mag
D = annulus_r * u.arcmin
width = width * u.arcmin
annulus = D + (width/2)*np.array([-1, +1])
# Require at least 2 measurements to calculate the repeatability:
nMinMeas = 2
if filteredCat.count > nMinMeas:
astrom_resid_rms_meas = calcRmsDistances(
filteredCat,
annulus,
magRange=magRange)
return astrom_resid_rms_meas
else:
return {'nomeas': np.nan*u.marcsec}
def run(self, matchedCatalogMulti, metric_name, in_id, out_id):
self.log.info(f"Measuring {metric_name}")
if self.config.ref_filter not in filter_dict:
raise Exception('Reference filter supplied for AB1 not in dictionary.')
filteredCat = filterMatches(matchedCatalogMulti)
rmsDistancesAll = []
if len(filteredCat) > 0:
filtnum = filter_dict[self.config.ref_filter]
refVisits = set()
for id in filteredCat.ids:
grptmp = filteredCat[id]
filtmch = (grptmp['filt'] == filtnum)
if len(filtmch) > 0:
refVisits.update(set(grptmp[filtmch]['visit']))
refVisits = list(refVisits)
magRange = np.array([self.config.bright_mag_cut, self.config.faint_mag_cut]) * u.mag
for rv in refVisits:
rmsDistances = calcRmsDistancesVsRef(
filteredCat,
rv,
magRange=magRange,
band=filter_dict[out_id['band']])
finiteEntries = np.where(np.isfinite(rmsDistances))[0]
if len(finiteEntries) > 0:
rmsDistancesAll.append(rmsDistances[finiteEntries])
if len(rmsDistancesAll) == 0:
return Struct(measurement=Measurement(metric_name, np.nan*u.marcsec))
else:
rmsDistancesAll = np.concatenate(rmsDistancesAll)
return Struct(measurement=Measurement(metric_name, np.mean(rmsDistancesAll)*u.marcsec))
else:
return Struct(measurement=Measurement(metric_name, np.nan*u.marcsec))
def run(self, matchedCatalog, metric_name):
self.log.info(f"Measuring {metric_name}")
filteredCat = filterMatches(matchedCatalog)
magRange = np.array([self.config.bright_mag_cut, self.config.faint_mag_cut]) * u.mag
D = self.config.annulus_r * u.arcmin
width = self.config.width * u.arcmin
annulus = D + (width/2)*np.array([-1, +1])
rmsDistances = calcRmsDistances(
filteredCat,
annulus,
magRange=magRange)
values, bins = np.histogram(rmsDistances.to(u.marcsec), bins=self.config.bins*u.marcsec)
extras = {'bins': Datum(bins, label='binvalues', description='bins'),
'values': Datum(values*u.count, label='counts', description='icounts in bins')}
if len(rmsDistances) == 0:
return Struct(measurement=Measurement(metric_name, np.nan*u.marcsec, extras=extras))
return Struct(measurement=Measurement(metric_name, np.median(rmsDistances.to(u.marcsec)),
extras=extras))