SNNSNMetric¶

class rubin_sim.maf.metrics.SNNSNMetric(metric_name='SNNSNMetric', mjd_col='observationStartMJD', filter_col='filter', m5_col='fiveSigmaDepth', exptime_col='visitExposureTime', night_col='night', obsid_col='observationId', nexp_col='numExposures', vistime_col='visitTime', seeing_col='seeingFwhmEff', note_col='note', season=[-1], coadd_night=True, zmin=0.1, zmax=0.5, z_step=0.03, daymax_step=3.0, verbose=False, ploteffi=False, n_bef=3, n_aft=8, snr_min=1.0, n_phase_min=1, n_phase_max=1, sigma_c=0.04, zlim_coeff=0.95, bands='grizy', add_dust=False, hard_dust_cut=0.25, gamma_name='gamma_WFD.hdf5', **kwargs)¶

Bases: BaseMetric

Measure zlim of type Ia supernovae.

Parameters:

metricNamestr, opt: metric name (default : SNSNRMetric)
mjd_colstr, opt: mjd column name (default : observationStartMJD)
filter_colstr, opt: filter column name (default: filter)
m5_colstr, opt: five-sigma depth column name (default : fiveSigmaDepth)
exptime_colstr, opt: exposure time column name (default : visitExposureTime)
night_colstr, opt: night column name (default : night)
obsid_colstr, opt: observation id column name (default : observationId)
nexp_colstr, opt: number of exposure column name (default : numExposures)
vistime_colstr, opt: visit time column name (default : visitTime)
seeing_colstr, opt: seeing column name (default: seeingFwhmEff)
note_colstr, opt: note column name (default: note)
seasonlist, opt: list of seasons to process (float)(default: -1 = all seasons)
coaddbool, opt: coaddition per night (and per band) (default : True)
zminfloat, opt: min redshift for the study (default: 0.0)
zmaxfloat, opt: max redshift for the study (default: 1.2)
verbosebool, opt: verbose mode (default: False)
n_befint, opt: number of LC points LC before T0 (default:5)
n_aftint, opt: number of LC points after T0 (default: 10)
snr_minfloat, opt: minimal SNR of LC points (default: 5.0)
n_phase_minint, opt: number of LC points with phase<= -5(default:1)
n_phase_maxint, opt: number of LC points with phase>= 20 (default: 1)
zlim_coeff: float, opt: corresponds to the zlim_coeff fraction of SN with z<zlim
bandsstr, opt: bands to consider (default: grizy)
gammaName: `str`, opt: name of the gamma ref file to load (default: gamma_WFD.hdf5)
dustbool, opt: Apply dust extinction to visit depth values (default False)
hard_dust_cutfloat, opt: If set, cut any point on the sky that has an ebv extinction higher than the hard_dust_cut value. Default 0.25

Methods Summary

`calc_daymax`(grp, daymax_step)	Method to estimate T0 (daymax) values for simulation.
`check_dur_z`(dur_z[, nmin])	"
`coadd`(obs)	Method to coadd data per band and per night
`duration_z`(grp[, min_duration])	Method to estimate the season length vs redshift This is necessary to take into account boundary effects when estimating the number of SN that can be detected
`efficiencies`(dfo)	"
`gen_lc`(grp, gen_par_orig, x1, color)	Method to generate light curves from observations
`get_epochs`(nights, flag, flagph)	Method to get the number of epochs
`get_nsn`(effi, durinterp_z, zmin, zmax, zstep)	Method to estimate to total number of SN: NSN = Sum(effi(z)*rate(z))
`get_season_info`(dfa, zseason[, min_duration])	method to get season infos vs z
`get_sum`(lcarr, varname, nvals, flag)	Method to get the sum of variables using broadcasting
`getseason`(obs[, season_gap, mjd_col])	Method to estimate seasons
`metric`(data_slice, zseason[, x1, color, ...])	Method to run the metric
`nsn`(grp[, sn_type])	Method to estimate the metric nsn up to zlim
`nsn_expected_z`(grp)	Method to estimate the expected nsn vs redshift
`nsn_from_rate`(grp)	Method to estimate the expected number of supernovae
`reducen_sn`(metric_val)
`reducezlim`(metric_val)
`run`(data_slice, slice_point)	Run method of the metric
`season_info`(grp, min_duration)	Method to estimate seasonal info (cadence, season length, ...)
`season_length`(seasons, data_slice, zseason)	Method to estimate season lengths vs z
`sigma_s_nparams`(grp)	Method to estimate variances of SN parameters from inversion of the Fisher matrix
`sn_effi`(lc)	Method to transform LCs to supernovae
`step_efficiencies`(lc)	Method to estimate observing efficiencies
`step_lc`(obs, gen_par[, x1, color])	Method to generate lc
`step_nsn`(sn_effis, dur_z)	Method to estimate the number of supernovae from efficiencies
`z_season`(seasons, data_slice)	Fill the z values per season
`z_season_allz`(zseason)
`zlim`(grp[, sn_type])	Method to estimate the metric zcomp
`zlim_or_nsn`(effi[, sntype, zlim])	Method to estimate the redshift limit or the number of sn

Methods Documentation

calc_daymax(grp, daymax_step)¶

Method to estimate T0 (daymax) values for simulation.

Parameters:

grp: group (pandas df sense)

group of data to process with the following cols:: t0_min: T0 min value (per season) t0_max: T0 max value (per season)

daymax_step: float

step for T0 simulation

Returns:

pandas df with daymax, min_rf_phase, max_rf_phase values

check_dur_z(dur_z, nmin=2)¶

” Method to remove seasons with a poor redshift range due to too low season length

Parameters:

dur_z: pandas df: data to process
nmin: int, opt: minimal number of redshift points per season (default: 2)

Returns:

pandas df with seasons having at least nmin points in redshift

coadd(obs)¶

Method to coadd data per band and per night

Parameters:

datapd.DataFrame: pandas df of observations

Returns:

coadded datapd.DataFrame

duration_z(grp, min_duration=60.0)¶

Method to estimate the season length vs redshift This is necessary to take into account boundary effects when estimating the number of SN that can be detected

daymin, daymax = min and max MJD of a season T0_min(z) = daymin-(1+z)*min_rf_phase_qual T0_max(z) = daymax-(1+z)*max_rf_phase_qual season_length(z) = T0_max(z)-T0_min(z)

Parameters:

grp: pandas df group: data to process: season infos
min_duration: float, opt: min season length for a season to be processed (deafult: 60 days)

Returns:

pandas df with season_length, z, T0_min and T0_max cols

efficiencies(dfo)¶

” Method to estimate selection efficiencies

Parameters:

df: pandas df: data to process

gen_lc(grp, gen_par_orig, x1, color)¶

Method to generate light curves from observations

Parameters:

grp: pandas group: observations to process
gen_par_orig: pandas df: simulation parameters
x1: float: SN stretch
color: float: SN color

Returns:

light curves as pandas df

get_epochs(nights, flag, flagph)¶

Method to get the number of epochs

Parameters:

nights: array: night number array
flag: array(bool): flag to apply
flagph: array(bool): flag to apply

Returns:

array with the number of epochs

get_nsn(effi, durinterp_z, zmin, zmax, zstep)¶

Method to estimate to total number of SN: NSN = Sum(effi(z)*rate(z))

Parameters:

effi: 1D interpolator: efficiencies vs z
durinterp_z: 1D interpolator: duration vs z
zmin: float: redshift min
zmax: float: redshift max
zstep: float: redshift step

Returns:

total number of SN up to zmax

get_season_info(dfa, zseason, min_duration=60.0)¶

method to get season infos vs z

Parameters:

dfa: pandas df: dat to process
zseason: pandas df: redshift infos per season
min_duration: float, opt: min season length to be accepted (default: 60 days)

Returns:

pandas df with season length infos

get_sum(lcarr, varname, nvals, flag)¶

Method to get the sum of variables using broadcasting

Parameters:

lcarr: numpy array: data to process
varname: str: col to process in lcarr
nvals: int: dimension for tiling
flag: array(bool): flag to apply

Returns:

array: the sum of the corresponding variable

getseason(obs, season_gap=80.0, mjd_col='observationStartMJD')¶

Method to estimate seasons

Parameters:

obs: `np.ndarray`: array of observations
season_gap: `float`, optional: minimal gap required to define a season (default: 80 days)
mjd_col: `str`, optional: col name for MJD infos (default: observationStartMJD)

Returns:

obsnp.ndarray: original numpy array with seasonnumber appended

metric(data_slice, zseason, x1=-2.0, color=0.2, zlim=-1, metric='zlim')¶

Method to run the metric

Parameters:

data_slice: array: observations to use for processing
zseason: array: season infos (season length vs z)
x1: float, opt: SN stretch (default: -2.0)
color: float, opt: SN color (default: -0.2)
zlim: float, opt: redshift limit used to estimate NSN (default: -1)
metric: str, opt: metric to estimate [zlim or nsn] (default: zlim)

nsn(grp, sn_type='medium')¶

Method to estimate the metric nsn up to zlim

Parameters:

grp: pandas group
sn_type: str, opt: type of SN to estimate zlim (default: medium)

Returns:

pandas df with the metric as cols

nsn_expected_z(grp)¶

Method to estimate the expected nsn vs redshift

Parameters:

grp: pandas df group: data to process: season infos

Returns:

pandas df with season_length, z, nsn_expected cols

nsn_from_rate(grp)¶

Method to estimate the expected number of supernovae

Parameters:

grp: pandas df: data to process

Returns:

pandas df with z and nsn_expected as cols

reducen_sn(metric_val)¶

reducezlim(metric_val)¶

run(data_slice, slice_point)¶

Run method of the metric

Parameters:

data_slice: `np.array`: Observations to process (scheduler simulations)
slice_point: `bool`, opt: Information about the location on the sky from the slicer

Returns:

metricValnp.recarray: [‘n_sn’, ‘zlim’] at this point on the sky

season_info(grp, min_duration)¶

Method to estimate seasonal info (cadence, season length, …)

Parameters:

grp: pandas df group
min_duration: float: minimal duration for a season to be considered

Returns:

pandas df with the following cols:

Nvisits: number of visits for this group
N_xx: number of visits in xx where xx is defined in self.bandstat

season_length(seasons, data_slice, zseason)¶

Method to estimate season lengths vs z

Parameters:

seasonslist(int): list of seasons to process
data_slice: numpy array: array of observations

Returns:

seasonslist(int): list of seasons to process
dur_zpandas df: season lengths vs z

sigma_s_nparams(grp)¶

Method to estimate variances of SN parameters from inversion of the Fisher matrix

Parameters:

grp: pandas df of flux derivatives wrt SN parameters
Returns
———-
Diagonal elements of the inverted matrix (as pandas df)

sn_effi(lc)¶

Method to transform LCs to supernovae

Parameters:

lc: pandas grp: light curve

Returns:

pandas df of sn efficiencies vs z

step_efficiencies(lc)¶

Method to estimate observing efficiencies

Returns:

pandas df with efficiencies

step_lc(obs, gen_par, x1=-2.0, color=0.2)¶

Method to generate lc

Parameters:

obs: array: observations
gen_par: array: simulation parameters
x1: float, opt: stretch value (default: -2.0)
color: float, opt: color value (default: 0.2)

Returns:

SN light curves (astropy table)

step_nsn(sn_effis, dur_z)¶

Method to estimate the number of supernovae from efficiencies

Parameters:

sn_effis: pandas df: data with efficiencies of observation
dur_z: array: array of season length

Returns:

initial sn_effis appended with a set of infos (duration, nsn)

z_season(seasons, data_slice)¶

Fill the z values per season

Parameters:

seasons: list: seasons to process
data_slice: array: data to process

z_season_allz(zseason)¶

zlim(grp, sn_type='faint')¶

Method to estimate the metric zcomp

Parameters:

grp: pandas group
sn_type: str, opt: type of SN to estimate zlim (default: faint)

Returns:

pandas df with the metric as cols

zlim_or_nsn(effi, sntype='faint', zlim=-1.0)¶

Method to estimate the redshift limit or the number of sn

Parameters:

effi: pandas df: data to process
sntype: str, opt: type of SN to consider for estimation (default: faint)
zlim: float, opt: redshift limit

Returns:

if zlim<0: returns the redshift limit
if zlim>0: returns the number of sn up to zlim

Navigation

SNNSNMetric¶