def __init__(self,
metricName='plasticc_transient',
mjdCol='observationStartMJD',
m5Col='fiveSigmaDepth',
filterCol='filter',
color_gap=0.5,
pre_slope_range=0.3,
days_around_peak=200,
r_mag_limit=28,
nbins=10,
nsamples=5,
maps=['DustMap'],
apply_dust=True,
units='fraction',
**kwargs):
self.mjdCol = mjdCol
self.m5Col = m5Col
self.filterCol = filterCol
self.color_gap = color_gap
self.pre_slope_range = pre_slope_range
self.days_around_peak = days_around_peak
self.rmag_limit = r_mag_limit
self.nbins = nbins
self.nsamples = nsamples
self.apply_dust = apply_dust
super(Plasticc_metric,
self).__init__(col=[self.mjdCol, self.m5Col, self.filterCol],
metricName=metricName,
maps=maps,
units=units,
**kwargs)
dust_properties = Dust_values()
self.Ax1 = dust_properties.Ax1
def __init__(self,
metricName='KNePopMetric',
mjdCol='observationStartMJD',
m5Col='fiveSigmaDepth',
filterCol='filter',
nightCol='night',
ptsNeeded=2,
file_list=None,
mjd0=59853.5,
outputLc=False,
**kwargs):
maps = ['DustMap']
self.mjdCol = mjdCol
self.m5Col = m5Col
self.filterCol = filterCol
self.nightCol = nightCol
self.ptsNeeded = ptsNeeded
# Boolean variable, if True the light curve will be exported
self.outputLc = outputLc
self.lightcurves = KN_lc(file_list=file_list)
self.mjd0 = mjd0
dust_properties = Dust_values()
self.Ax1 = dust_properties.Ax1
cols = [self.mjdCol, self.m5Col, self.filterCol, self.nightCol]
super(KNePopMetric, self).__init__(col=cols,
units='Detected, 0 or 1',
metricName=metricName,
maps=maps,
**kwargs)
def __init__(self,
metricName='TDEsPopMetric',
mjdCol='observationStartMJD',
m5Col='fiveSigmaDepth',
filterCol='filter',
nightCol='night',
ptsNeeded=2,
file_list=None,
mjd0=59853.5,
**kwargs):
maps = ['DustMap']
self.mjdCol = mjdCol
self.m5Col = m5Col
self.filterCol = filterCol
self.nightCol = nightCol
self.ptsNeeded = ptsNeeded
self.lightcurves = Tde_lc(file_list=file_list)
self.mjd0 = mjd0
dust_properties = Dust_values()
self.Ax1 = dust_properties.Ax1
cols = [self.mjdCol, self.m5Col, self.filterCol, self.nightCol]
super(TdePopMetric, self).__init__(col=cols,
units='Detected, 0 or 1',
metricName=metricName,
maps=maps,
**kwargs)
def __init__(self,
seeingCol='seeingFwhmGeom',
metricName='SizePrecision',
fwhm_object=3.,
mu_0_object={
'g': 19.,
'r': 19.,
'i': 19.
},
stellar_density_limit=None,
filterCol='filter',
stellar_ref_peak={
'g': 5000.,
'r': 5000.,
'i': 5000.
},
m5Col='fiveSigmaDepth',
exptimeCol='visitExposureTime',
skyCol='skyBrightness',
airmassCol='airmass',
maps=['StellarDensityMap', 'DustMap'],
return_weights=False,
dust_properties=None,
pixscale=0.2,
phot_parameters=None,
**kwargs):
self.seeingCol = seeingCol
self.m5Col = m5Col
self.fwhm_object = fwhm_object
self.mu_0_object = mu_0_object
self.filterCol = filterCol
self.exptimeCol = exptimeCol
self.return_weights = return_weights
self.stellar_ref_peak = stellar_ref_peak
self.skyCol = skyCol
self.airmassCol = airmassCol
self.cols = [
seeingCol, m5Col, filterCol, exptimeCol, skyCol, airmassCol
]
self.maps = maps
units = 'SNR'
self.pixscale = pixscale
super().__init__(col=self.cols,
maps=self.maps,
units=units,
metricName=metricName,
**kwargs)
if dust_properties is None:
dust_properties = Dust_values()
self.Ax1 = dust_properties.Ax1
else:
dust_properties = dust_properties
self.Ax1 = dust_properties.Ax1
if phot_parameters is None:
self.phot_parameters = SysEngVals()
def __init__(self, m5Col='fiveSigmaDepth', metricName='ExgalM5', units='mag',
filterCol='filter', **kwargs):
# Set the name for the dust map to use. This is gathered into the MetricBundle.
maps = ['DustMap']
self.m5Col = m5Col
self.filterCol = filterCol
super().__init__(col=[self.m5Col, self.filterCol], maps=maps, metricName=metricName, units=units, **kwargs)
# Set the default wavelength limits for the lsst filters. These are approximately correct.
dust_properties = Dust_values()
self.Ax1 = dust_properties.Ax1
# We will call Coaddm5Metric to calculate the coadded depth. Set it up here.
self.Coaddm5Metric = Coaddm5Metric(m5Col=m5Col)
def __init__(self,
mjdCol='observationStartMJD',
nightCol='night',
filterCol='filter',
m5Col='fiveSigmaDepth',
magCuts=None,
metricName='TDC',
cadNorm=3.,
seaNorm=4.,
campNorm=5.,
badval=-999,
**kwargs):
# Save the normalization values.
self.cadNorm = cadNorm
self.seaNorm = seaNorm
self.campNorm = campNorm
self.mjdCol = mjdCol
self.m5Col = m5Col
self.nightCol = nightCol
self.filterCol = filterCol
if magCuts is None:
self.magCuts = {
'u': 22.7,
'g': 24.1,
'r': 23.7,
'i': 23.1,
'z': 22.2,
'y': 21.4
}
else:
self.magCuts = magCuts
if not isinstance(self.magCuts, dict):
raise Exception('magCuts should be a dictionary')
# Set up dust map requirement
maps = ['DustMap']
# Set the default wavelength limits for the lsst filters. These are approximately correct.
dust_properties = Dust_values()
self.Ax1 = dust_properties.Ax1
super().__init__(
col=[self.mjdCol, self.m5Col, self.nightCol, self.filterCol],
badval=badval,
maps=maps,
metricName=metricName,
units='%s' % ('%'),
**kwargs)
def __init__(self, metricName='SNSLMetric',
mjdCol='observationStartMJD', RaCol='fieldRA', DecCol='fieldDec',
filterCol='filter', exptimeCol='visitExposureTime',
nightCol='night', obsidCol='observationId', nexpCol='numExposures',
vistimeCol='visitTime', m5Col='fiveSigmaDepth', season=[-1], night_collapse=False,
nfilters_min=4, min_season_obs=5,
m5mins={'u': 22.7, 'g': 24.1, 'r': 23.7, 'i': 23.1, 'z': 22.2, 'y': 21.4},
maps=['DustMap'], **kwargs):
"""
Strongly Lensed SN metric
The number of is given by:
N (lensed SNe Ia with well measured time delay) = 45.7 * survey_area /
(20000 deg^2) * cumulative_season_length / (2.5 years) / (2.15 *
exp(0.37 * gap_median_all_filter))
where:
survey_area: survey area (in deg2)
cumulative_season_length: cumulative season length (in years)
gap_median_all_filter: median gap (all filters)
Parameters
--------------
metricName : str, opt
metric name
Default : SNCadenceMetric
mjdCol : str, opt
mjd column name
Default : observationStartMJD,
RaCol : str,opt
Right Ascension column name
Default : fieldRa
DecCol : str,opt
Declinaison column name
Default : fieldDec
filterCol : str,opt
filter column name
Default: filter
exptimeCol : str,opt
exposure time column name
Default : visitExposureTime
nightCol : str,opt
night column name
Default : night
obsidCol : str,opt
observation id column name
Default : observationId
nexpCol : str,opt
number of exposure column name
Default : numExposures
vistimeCol : str,opt
visit time column name
Default : visitTime
season: int (list) or -1, opt
season to process (default: -1: all seasons)
nfilters_min : int (5)
The number of filters to demand in a season
"""
self.mjdCol = mjdCol
self.filterCol = filterCol
self.RaCol = RaCol
self.DecCol = DecCol
self.exptimeCol = exptimeCol
self.nightCol = nightCol
self.obsidCol = obsidCol
self.nexpCol = nexpCol
self.vistimeCol = vistimeCol
self.seasonCol = 'season'
self.m5Col = m5Col
self.maps= maps
cols = [self.nightCol, self.filterCol, self.mjdCol, self.obsidCol,
self.nexpCol, self.vistimeCol, self.exptimeCol, self.m5Col]
super(SNSLMetric, self).__init__(
col=cols, metricName=metricName, maps=self.maps, units='N SL', **kwargs)
self.badVal = 0
self.season = season
self.bands = 'ugrizy'
self.night_collapse = night_collapse
self.m5mins = m5mins
self.min_season_obs = min_season_obs
self.nfilters_min = nfilters_min
self.phot_properties = Dust_values()