def testSM(self):
keys = [
'O', 'B', 'A', 'F', 'G', 'K', 'M', 'HeWD_25200_80', 'WD_11000_85',
'WD_3000_85'
]
filterNames = ['u', 'g', 'r', 'i', 'z', 'y']
# Check each type returns the correct format
for key in keys:
result = utils.stellarMags(key)
for fn in filterNames:
self.assertIn(fn, result)
self.assertTrue(
(isinstance(result[fn], float)) |
(isinstance(result[fn], np.float64)),
msg='result is neither a float nor a numpy float64')
# Check the exception gets raised
self.assertRaises(ValueError, utils.stellarMags, 'ack')
# Check the mags get fainter
for st in keys:
mags = utils.stellarMags(st)
mags2 = utils.stellarMags(st, rmag=20.)
for key in mags:
self.assertLess(mags[key], mags2[key])
def __init__(self, metricName='ParallaxCoverageMetric', m5Col='fiveSigmaDepth',
mjdCol='observationStartMJD', filterCol='filter', seeingCol='seeingFwhmGeom',
rmag=20., SedTemplate='flat',
atm_err=0.01, thetaRange=0., snrLimit=5, **kwargs):
cols = ['ra_pi_amp', 'dec_pi_amp', m5Col, mjdCol, filterCol, seeingCol]
units = 'ratio'
super(ParallaxCoverageMetric, self).__init__(cols,
metricName=metricName, units=units,
**kwargs)
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
self.mjdCol = mjdCol
# Demand the range of theta values
self.thetaRange = thetaRange
self.snrLimit = snrLimit
filters = ['u', 'g', 'r', 'i', 'z', 'y']
self.mags = {}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
caption = "Parallax factor coverage for an r=%.2f star (0 is bad, 0.5-1 is good). " % (rmag)
caption += "One expects the parallax factor coverage to vary because stars on the ecliptic "
caption += "can be observed when they have no parallax offset while stars at the pole are always "
caption += "offset by the full parallax offset."""
self.comment = caption
def __init__(self,
metricName='properMotion',
m5Col='fiveSigmaDepth',
mjdCol='observationStartMJD',
units='mas/yr',
filterCol='filter',
seeingCol='seeingFwhmGeom',
rmag=20.,
SedTemplate='flat',
badval=-666,
atm_err=0.01,
normalize=False,
baseline=10.,
**kwargs):
""" Instantiate metric.
m5Col = column name for inidivual visit m5
mjdCol = column name for exposure time dates
filterCol = column name for filter
seeingCol = column name for seeing (assumed FWHM)
rmag = mag of fiducial star in r filter. Other filters are scaled using sedTemplate keyword
sedTemplate = template to use (can be 'flat' or 'O','B','A','F','G','K','M')
atm_err = centroiding error due to atmosphere in arcsec
normalize = Compare to the uncertainty that would result if half
the observations were taken at the start of the survey and half
at the end. A 'perfect' survey will have a value close to unity,
while a poorly scheduled survey will be close to zero.
baseline = The length of the survey used for the normalization (years)
"""
cols = [m5Col, mjdCol, filterCol, seeingCol]
if normalize:
units = 'ratio'
super(ProperMotionMetric, self).__init__(col=cols,
metricName=metricName,
units=units,
badval=badval,
**kwargs)
# set return type
self.mjdCol = mjdCol
self.seeingCol = seeingCol
self.m5Col = m5Col
filters = ['u', 'g', 'r', 'i', 'z', 'y']
self.mags = {}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
self.normalize = normalize
self.baseline = baseline
self.comment = 'Estimated uncertainty of the proper motion fit (assuming no parallax or that parallax is well fit). '
self.comment += 'Uses visits in all bands, and generates approximate astrometric errors using the SNR in each visit. '
if SedTemplate == 'flat':
self.comment += 'Assumes a flat SED. '
if self.normalize:
self.comment += 'This normalized version of the metric represents the estimated uncertainty in the proper '
self.comment += 'motion divided by the minimum uncertainty possible (if all visits were '
self.comment += 'obtained on the first and last days of the survey). Values closer to 1 '
self.comment += 'indicate more optimal scheduling.'
def __init__(self, metricName='ParallaxHADegenMetric',haCol='HA', snrLimit=5.,
m5Col='fiveSigmaDepth', mjdCol='expMJD',
filterCol='filter', seeingCol='FWHMgeom',
rmag=20., SedTemplate='flat', badval=-666,
**kwargs ):
"""
haCol = Hour angle column name
snrLimit = only inlcude observations above the snrLimit
m5Col = column name for inidivual visit m5
mjdCol = column name for exposure time dates
filterCol = column name for filter
seeingCol = column name for seeing (assumed FWHM)
rmag = mag of fiducial star in r filter. Other filters are scaled using sedTemplate keyword
sedTemplate = template to use (can be 'flat' or 'O','B','A','F','G','K','M')
"""
cols = ['ra_pi_amp', 'dec_pi_amp']
self.haCol = haCol
cols.append(haCol)
units = 'Correlation'
self.snrLimit = snrLimit
super(ParallaxHADegenMetric, self).__init__(cols,
metricName=metricName,
units=units, **kwargs)
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
self.mjdCol = mjdCol
filters=['u','g','r','i','z','y']
self.mags={}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
def __init__(self,
metricName='ParallaxDcrDegenMetric',
seeingCol='seeingFwhmGeom',
m5Col='fiveSigmaDepth',
atm_err=0.01,
rmag=20.,
SedTemplate='flat',
filterCol='filter',
tol=0.05,
**kwargs):
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
self.tol = tol
units = 'Correlation'
# just put all the columns that all the stackers will need here?
cols = [
'ra_pi_amp', 'dec_pi_amp', 'ra_dcr_amp', 'dec_dcr_amp', seeingCol,
m5Col
]
super(ParallaxDcrDegenMetric, self).__init__(cols,
metricName=metricName,
units=units,
**kwargs)
self.filters = ['u', 'g', 'r', 'i', 'z', 'y']
self.mags = {}
if SedTemplate == 'flat':
for f in self.filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
def __init__(self,
metricName='DCRprecision',
seeingCol='seeingFwhmGeom',
m5Col='fiveSigmaDepth',
HACol='HA',
PACol='paraAngle',
filterCol='filter',
atm_err=0.01,
SedTemplate='flat',
rmag=20.,
**kwargs):
self.m5Col = m5Col
self.filterCol = filterCol
self.PACol = PACol
self.seeingCol = seeingCol
self.mags = {}
self.filters = ['u', 'g', 'r', 'i', 'z', 'y']
if SedTemplate == 'flat':
for f in self.filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
cols = [
'ra_dcr_amp', 'dec_dcr_amp', seeingCol, m5Col, filterCol,
'zenithDistance', PACol
]
units = 'arcseconds'
self.atm_err = atm_err
super(DcrPrecisionMetric, self).__init__(cols,
metricName=metricName,
units=units,
**kwargs)
def __init__(self,
metricName='parallax',
m5Col='fiveSigmaDepth',
mjdCol='expMJD',
units='mas',
filterCol='filter',
seeingCol='FWHMgeom',
rmag=20.,
SedTemplate='flat',
badval=-666,
atm_err=0.01,
normalize=False,
**kwargs):
""" Instantiate metric.
m5Col = column name for inidivual visit m5
mjdCol = column name for exposure time dates
filterCol = column name for filter
seeingCol = column name for seeing/FWHMgeom
rmag = mag of fiducial star in r filter. Other filters are scaled using sedTemplate keyword
SedTemplate = string of 'flat' or 'O','B','A','F','G','K','M'.
atm_err = centroiding error due to atmosphere in arcsec
normalize = Compare to a survey that has all observations with maximum parallax factor.
An optimally scheduled survey would be expected to have a normalized value close to unity,
and zero for a survey where the parallax can not be measured.
return uncertainty in mas. Or normalized map as a fraction
"""
Cols = [m5Col, mjdCol, filterCol, seeingCol, 'ra_pi_amp', 'dec_pi_amp']
if normalize:
units = 'ratio'
super(ParallaxMetric, self).__init__(Cols,
metricName=metricName,
units=units,
badval=badval,
**kwargs)
# set return type
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
filters = ['u', 'g', 'r', 'i', 'z', 'y']
self.mags = {}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
self.normalize = normalize
self.comment = 'Estimated uncertainty in parallax measurement (assuming no proper motion or that proper motion '
self.comment += 'is well fit). Uses measurements in all bandpasses, and estimates astrometric error based on SNR '
self.comment += 'in each visit. '
if SedTemplate == 'flat':
self.comment += 'Assumes a flat SED. '
if self.normalize:
self.comment += 'This normalized version of the metric displays the estimated uncertainty in the parallax measurement, '
self.comment += 'divided by the minimum parallax uncertainty possible (if all visits were six '
self.comment += 'months apart). Values closer to 1 indicate more optimal scheduling for parallax measurement.'
def __init__(self,
metricName='ParallaxCoverageMetric',
m5Col='fiveSigmaDepth',
mjdCol='observationStartMJD',
filterCol='filter',
seeingCol='seeingFwhmGeom',
rmag=20.,
SedTemplate='flat',
badval=-666,
atm_err=0.01,
thetaRange=0.,
snrLimit=5,
**kwargs):
"""
instantiate metric
m5Col = column name for inidivual visit m5
mjdCol = column name for exposure time dates
filterCol = column name for filter
seeingCol = column name for seeing (assumed FWHM)
rmag = mag of fiducial star in r filter. Other filters are scaled using sedTemplate keyword
sedTemplate = template to use (can be 'flat' or 'O','B','A','F','G','K','M')
atm_err = centroiding error due to atmosphere in arcsec
thetaRange = range of parallax offset angles to demand (in radians) default=0 means no range requirement
snrLimit = only include points above the snrLimit (default 5) when computing thetaRange.
"""
cols = ['ra_pi_amp', 'dec_pi_amp', m5Col, mjdCol, filterCol, seeingCol]
units = 'ratio'
super(ParallaxCoverageMetric, self).__init__(cols,
metricName=metricName,
units=units,
**kwargs)
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
self.mjdCol = mjdCol
# Demand the range of theta values
self.thetaRange = thetaRange
self.snrLimit = snrLimit
filters = ['u', 'g', 'r', 'i', 'z', 'y']
self.mags = {}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
def __init__(self, metricName='parallax', m5Col='fiveSigmaDepth',
mjdCol='expMJD', units = 'mas',
filterCol='filter', seeingCol='FWHMgeom', rmag=20.,
SedTemplate='flat', badval=-666,
atm_err=0.01, normalize=False,**kwargs):
""" Instantiate metric.
m5Col = column name for inidivual visit m5
mjdCol = column name for exposure time dates
filterCol = column name for filter
seeingCol = column name for seeing/FWHMgeom
rmag = mag of fiducial star in r filter. Other filters are scaled using sedTemplate keyword
SedTemplate = string of 'flat' or 'O','B','A','F','G','K','M'.
atm_err = centroiding error due to atmosphere in arcsec
normalize = Compare to a survey that has all observations with maximum parallax factor.
An optimally scheduled survey would be expected to have a normalized value close to unity,
and zero for a survey where the parallax can not be measured.
return uncertainty in mas. Or normalized map as a fraction
"""
Cols = [m5Col, mjdCol,filterCol,seeingCol, 'ra_pi_amp', 'dec_pi_amp']
if normalize:
units = 'ratio'
super(ParallaxMetric, self).__init__(Cols, metricName=metricName, units=units,
badval=badval, **kwargs)
# set return type
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
filters=['u','g','r','i','z','y']
self.mags={}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
self.normalize = normalize
self.comment = 'Estimated uncertainty in parallax measurement (assuming no proper motion or that proper motion '
self.comment += 'is well fit). Uses measurements in all bandpasses, and estimates astrometric error based on SNR '
self.comment += 'in each visit. '
if SedTemplate == 'flat':
self.comment += 'Assumes a flat SED. '
if self.normalize:
self.comment += 'This normalized version of the metric displays the estimated uncertainty in the parallax measurement, '
self.comment += 'divided by the minimum parallax uncertainty possible (if all visits were six '
self.comment += 'months apart). Values closer to 1 indicate more optimal scheduling for parallax measurement.'
def __init__(self, metricName='properMotion',
m5Col='fiveSigmaDepth', mjdCol='expMJD', units='mas/yr',
filterCol='filter', seeingCol='FWHMgeom', rmag=20.,
SedTemplate='flat', badval= -666,
atm_err=0.01, normalize=False,
baseline=10., **kwargs):
""" Instantiate metric.
m5Col = column name for inidivual visit m5
mjdCol = column name for exposure time dates
filterCol = column name for filter
seeingCol = column name for seeing (assumed FWHM)
rmag = mag of fiducial star in r filter. Other filters are scaled using sedTemplate keyword
sedTemplate = template to use (can be 'flat' or 'O','B','A','F','G','K','M')
atm_err = centroiding error due to atmosphere in arcsec
normalize = Compare to the uncertainty that would result if half
the observations were taken at the start of the survey and half
at the end. A 'perfect' survey will have a value close to unity,
while a poorly scheduled survey will be close to zero.
baseline = The length of the survey used for the normalization (years)
"""
cols = [m5Col, mjdCol,filterCol,seeingCol]
if normalize:
units = 'ratio'
super(ProperMotionMetric, self).__init__(col=cols, metricName=metricName, units=units,
badval=badval, **kwargs)
# set return type
self.seeingCol = seeingCol
self.m5Col = m5Col
filters=['u','g','r','i','z','y']
self.mags={}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
self.normalize = normalize
self.baseline = baseline
self.comment = 'Estimated uncertainty of the proper motion fit (assuming no parallax or that parallax is well fit). '
self.comment += 'Uses visits in all bands, and generates approximate astrometric errors using the SNR in each visit. '
if SedTemplate == 'flat':
self.comment += 'Assumes a flat SED. '
if self.normalize:
self.comment += 'This normalized version of the metric represents the estimated uncertainty in the proper '
self.comment += 'motion divided by the minimum uncertainty possible (if all visits were '
self.comment += 'obtained on the first and last days of the survey). Values closer to 1 '
self.comment += 'indicate more optimal scheduling.'
def __init__(self, metricName='ParallaxHADegenMetric',haCol='HA', snrLimit=5.,
m5Col='fiveSigmaDepth', mjdCol='expMJD',
filterCol='filter', seeingCol='FWHMgeom',
rmag=20., SedTemplate='flat', \
useSpearmanR=False,\
badval=-666,
**kwargs ):
"""
haCol = Hour angle column name
snrLimit = only inlcude observations above the snrLimit
m5Col = column name for inidivual visit m5
mjdCol = column name for exposure time dates
filterCol = column name for filter
seeingCol = column name for seeing (assumed FWHM)
rmag = mag of fiducial star in r filter. Other filters are scaled using sedTemplate keyword
sedTemplate = template to use (can be 'flat' or 'O','B','A','F','G','K','M')
useSpearmanR = use spearman-r coefficient for correlation? (Default False)
"""
cols = ['ra_pi_amp', 'dec_pi_amp']
self.haCol = haCol
cols.append(haCol)
units = 'Correlation'
self.snrLimit = snrLimit
# 2016-05-01: New instance-level variable - what correlation
# method are we using?
self.correlMethod = pearsonr
if useSpearmanR:
self.correlMethod = spearmanr
super(ParallaxHADegenMetric, self).__init__(cols,
metricName=metricName,
units=units, \
**kwargs)
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
self.mjdCol = mjdCol
filters = ['u', 'g', 'r', 'i', 'z', 'y']
self.mags = {}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
def __init__(self, metricName='ParallaxHADegenMetric',haCol='HA', snrLimit=5.,
m5Col='fiveSigmaDepth', mjdCol='expMJD',
filterCol='filter', seeingCol='FWHMgeom',
rmag=20., SedTemplate='flat', \
useSpearmanR=False,\
badval=-666,
**kwargs ):
"""
haCol = Hour angle column name
snrLimit = only inlcude observations above the snrLimit
m5Col = column name for inidivual visit m5
mjdCol = column name for exposure time dates
filterCol = column name for filter
seeingCol = column name for seeing (assumed FWHM)
rmag = mag of fiducial star in r filter. Other filters are scaled using sedTemplate keyword
sedTemplate = template to use (can be 'flat' or 'O','B','A','F','G','K','M')
useSpearmanR = use spearman-r coefficient for correlation? (Default False)
"""
cols = ['ra_pi_amp', 'dec_pi_amp']
self.haCol = haCol
cols.append(haCol)
units = 'Correlation'
self.snrLimit = snrLimit
# 2016-05-01: New instance-level variable - what correlation
# method are we using?
self.correlMethod = pearsonr
if useSpearmanR:
self.correlMethod = spearmanr
super(ParallaxHADegenMetric, self).__init__(cols,
metricName=metricName,
units=units, \
**kwargs)
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
self.mjdCol = mjdCol
filters=['u','g','r','i','z','y']
self.mags={}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
def __init__(self,
metricName='ParallaxHADegenMetric',
haCol='HA',
snrLimit=5.,
m5Col='fiveSigmaDepth',
mjdCol='expMJD',
filterCol='filter',
seeingCol='FWHMgeom',
rmag=20.,
SedTemplate='flat',
badval=-666,
**kwargs):
"""
haCol = Hour angle column name
snrLimit = only inlcude observations above the snrLimit
m5Col = column name for inidivual visit m5
mjdCol = column name for exposure time dates
filterCol = column name for filter
seeingCol = column name for seeing (assumed FWHM)
rmag = mag of fiducial star in r filter. Other filters are scaled using sedTemplate keyword
sedTemplate = template to use (can be 'flat' or 'O','B','A','F','G','K','M')
"""
cols = ['ra_pi_amp', 'dec_pi_amp']
self.haCol = haCol
cols.append(haCol)
units = 'Correlation'
self.snrLimit = snrLimit
super(ParallaxHADegenMetric, self).__init__(cols,
metricName=metricName,
units=units,
**kwargs)
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
self.mjdCol = mjdCol
filters = ['u', 'g', 'r', 'i', 'z', 'y']
self.mags = {}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
def run(self, dataSlice, slicePoint=None):
result = 0
n_pts = np.size(dataSlice[self.mjdCol])
n_filt = np.size(np.unique(dataSlice[self.filterCol]))
# If we had a correct model with phase, amplitude, period, mean_mags, then chi_squared/DoF would be ~1 with 3+n_filt free parameters.
# The mean is one free parameter
p1 = n_filt
p2 = 3. + n_filt
chi_sq_2 = 1. * (n_pts - p2)
u_filters = np.unique(dataSlice[self.filterCol])
if n_pts > p2:
for period, starMag, amplitude in zip(self.periods, self.starMags,
self.amplitudes):
chi_sq_1 = 0
mags = utils.stellarMags(self.SedTemplate, rmag=starMag)
for filtername in u_filters:
in_filt = np.where(
dataSlice[self.filterCol] == filtername)[0]
lc = amplitude * np.sin(
dataSlice[self.mjdCol][in_filt] *
(np.pi * 2) / period) + mags[filtername]
snr = m52snr(lc, dataSlice[self.m5Col][in_filt])
delta_m = 2.5 * np.log10(1. + 1. / snr)
weights = 1. / (delta_m**2)
weighted_mean = np.sum(weights * lc) / np.sum(weights)
chi_sq_1 += np.sum(((lc - weighted_mean)**2 / delta_m**2))
# Yes, I'm fitting magnitudes rather than flux. At least I feel kinda bad about it.
# F-test for nested models Regression problems: https://en.wikipedia.org/wiki/F-test
f_numerator = (chi_sq_1 - chi_sq_2) / (p2 - p1)
f_denom = 1. # This is just reduced chi-squared for the more complicated model, so should be 1.
f_val = f_numerator / f_denom
# Has DoF (p2-p1, n-p2)
# https://stackoverflow.com/questions/21494141/how-do-i-do-a-f-test-in-python/21503346
p_value = scipy.stats.f.sf(f_val, p2 - p1, n_pts - p2)
if np.isfinite(p_value):
if p_value < self.sig_level:
result += 1
return result
def __init__(self, metricName='ParallaxDcrDegenMetric', seeingCol='seeingFwhmGeom',
m5Col='fiveSigmaDepth', atm_err=0.01, rmag=20., SedTemplate='flat',
filterCol='filter', tol=0.05, **kwargs):
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
self.tol = tol
units = 'Correlation'
# just put all the columns that all the stackers will need here?
cols = ['ra_pi_amp', 'dec_pi_amp', 'ra_dcr_amp', 'dec_dcr_amp',
seeingCol, m5Col]
super(ParallaxDcrDegenMetric, self).__init__(cols, metricName=metricName, units=units,
**kwargs)
self.filters = ['u', 'g', 'r', 'i', 'z', 'y']
self.mags = {}
if SedTemplate == 'flat':
for f in self.filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
def __init__(self, metricName='properMotion',
m5Col='fiveSigmaDepth', mjdCol='observationStartMJD',
filterCol='filter', seeingCol='seeingFwhmGeom', rmag=20.,
SedTemplate='flat', badval= -666,
atm_err=0.01, normalize=False,
baseline=10., **kwargs):
cols = [m5Col, mjdCol, filterCol, seeingCol]
if normalize:
units = 'ratio'
else:
units = 'mas/yr'
super(ProperMotionMetric, self).__init__(col=cols, metricName=metricName, units=units,
badval=badval, **kwargs)
# set return type
self.mjdCol = mjdCol
self.seeingCol = seeingCol
self.m5Col = m5Col
filters = ['u', 'g', 'r', 'i', 'z', 'y']
self.mags = {}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
self.normalize = normalize
self.baseline = baseline
self.comment = 'Estimated uncertainty of the proper motion fit ' \
'(assuming no parallax or that parallax is well fit). '
self.comment += 'Uses visits in all bands, and generates approximate ' \
'astrometric errors using the SNR in each visit. '
if SedTemplate == 'flat':
self.comment += 'Assumes a flat SED. '
if self.normalize:
self.comment += 'This normalized version of the metric represents ' \
'the estimated uncertainty in the proper '
self.comment += 'motion divided by the minimum uncertainty possible ' \
'(if all visits were '
self.comment += 'obtained on the first and last days of the survey). '
self.comment += 'Values closer to 1 indicate more optimal scheduling.'
def __init__(self, metricName='ParallaxCoverageMetric', m5Col='fiveSigmaDepth',
mjdCol='expMJD', filterCol='filter', seeingCol='FWHMgeom',
rmag=20., SedTemplate='flat', badval=-666,
atm_err=0.01, thetaRange=0., snrLimit=5, **kwargs):
"""
instantiate metric
m5Col = column name for inidivual visit m5
mjdCol = column name for exposure time dates
filterCol = column name for filter
seeingCol = column name for seeing (assumed FWHM)
rmag = mag of fiducial star in r filter. Other filters are scaled using sedTemplate keyword
sedTemplate = template to use (can be 'flat' or 'O','B','A','F','G','K','M')
atm_err = centroiding error due to atmosphere in arcsec
thetaRange = range of parallax offset angles to demand (in radians) default=0 means no range requirement
snrLimit = only include points above the snrLimit (default 5) when computing thetaRange.
"""
cols = ['ra_pi_amp', 'dec_pi_amp', m5Col, mjdCol, filterCol, seeingCol]
units = 'ratio'
super(ParallaxCoverageMetric, self).__init__(cols,
metricName=metricName, units=units,
**kwargs)
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
self.mjdCol = mjdCol
# Demand the range of theta values
self.thetaRange = thetaRange
self.snrLimit = snrLimit
filters=['u','g','r','i','z','y']
self.mags={}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
def __init__(self, metricName='parallax', m5Col='fiveSigmaDepth',
filterCol='filter', seeingCol='seeingFwhmGeom', rmag=20.,
SedTemplate='flat', badval=-666,
atm_err=0.01, normalize=False, **kwargs):
Cols = [m5Col, filterCol, seeingCol, 'ra_pi_amp', 'dec_pi_amp']
if normalize:
units = 'ratio'
else:
units = 'mas'
super(ParallaxMetric, self).__init__(Cols, metricName=metricName, units=units,
badval=badval, **kwargs)
# set return type
self.m5Col = m5Col
self.seeingCol = seeingCol
self.filterCol = filterCol
filters = ['u', 'g', 'r', 'i', 'z', 'y']
self.mags = {}
if SedTemplate == 'flat':
for f in filters:
self.mags[f] = rmag
else:
self.mags = utils.stellarMags(SedTemplate, rmag=rmag)
self.atm_err = atm_err
self.normalize = normalize
self.comment = 'Estimated uncertainty in parallax measurement ' \
'(assuming no proper motion or that proper motion '
self.comment += 'is well fit). Uses measurements in all bandpasses, ' \
'and estimates astrometric error based on SNR '
self.comment += 'in each visit. '
if SedTemplate == 'flat':
self.comment += 'Assumes a flat SED. '
if self.normalize:
self.comment += 'This normalized version of the metric displays the ' \
'estimated uncertainty in the parallax measurement, '
self.comment += 'divided by the minimum parallax uncertainty possible ' \
'(if all visits were six '
self.comment += 'months apart). Values closer to 1 indicate more optimal ' \
'scheduling for parallax measurement.'
