def _sync(self, obj):
for dbiso in obj.isotopes:
r = dbiso.results[-1]
uv = r.Iso
ee = r.IsoEr
bv = r.Bkgd
be = r.BkgdEr
key = dbiso.Label
n = dbiso.NumCnts
iso = Isotope(name=key, value=uv, error=ee, n=n)
det =dbiso.detector
iso.ic_factor=ufloat(det.ICFactor, det.ICFactorEr)
iso.fit = r.fit.Label.lower()
iso.baseline = Baseline(name=key,
reverse_unpack=True,
dbrecord=dbiso.baseline,
unpack=True,
unpacker=lambda x: x.PeakTimeBlob,
error_type='SEM',
fit='average')
iso.baseline.set_filter_outliers_dict()
iso.blank = Blank(name=key, value=bv, error=be)
self.isotopes[key] = iso
def setUp(self):
self.arun = Arun()
xs = linspace(0, 100)
ys = 2 * xs + 4
# ar40 = Isotope(name='Ar40', xs=xs, ys=ys)
ar40 = Isotope('Ar40', 'H1')
ar40.xs = xs
ar40.ys = ys
ar40.fit = 'parabolic'
ar40.baseline.value = 0.25
ar40.blank.value = 0.75
ys = 2 * xs + 1
ar39 = Isotope('Ar39', 'AX')
ar39.fit = 'parabolic'
ar39.xs = xs
ar39.ys = ys
# ar39.baseline.value = 0.25
# ar39.blank.value = 0.75
self.arun.isotope_group.isotopes = {'Ar40': ar40, 'Ar39': ar39}
self.arun.isotope_group.age = 10
def setUp(self):
self.arun = Arun()
xs = linspace(0, 100)
ys = 2 * xs + 4
# ar40 = Isotope(name='Ar40', xs=xs, ys=ys)
ar40 = Isotope('Ar40', 'H1')
ar40.xs = xs
ar40.ys = ys
ar40.fit = 'parabolic'
ar40.baseline.value = 0.25
ar40.blank.value = 0.75
ys = 2 * xs + 1
ar39 = Isotope('Ar39', 'AX')
ar39.fit = 'parabolic'
ar39.xs = xs
ar39.ys = ys
# ar39.baseline.value = 0.25
# ar39.blank.value = 0.75
self.arun.isotope_group.isotopes = {'Ar40': ar40, 'Ar39': ar39}
self.arun.isotope_group.age = 10
def _sync(self, obj):
arar = obj.araranalyses[-1]
if arar:
self.j = ufloat(arar.JVal, arar.JEr)
self.age = arar.Age
self.age_err = arar.ErrAge
self.age_err_wo_j = arar.ErrAgeWOErInJ
self.rad40_percent = ufloat(arar.PctRad, arar.PctRadEr)
for dbiso in obj.isotopes:
r = dbiso.results[-1]
uv = r.Iso
ee = r.IsoEr
bv = r.Bkgd
be = r.BkgdEr
key = dbiso.Label
n = dbiso.NumCnts
iso = Isotope(name=key, value=uv, error=ee, n=n)
det =dbiso.detector
iso.ic_factor=ufloat(det.ICFactor, det.ICFactorEr)
iso.fit = r.fit.Label.lower()
iso.baseline = Baseline(name=key,
reverse_unpack=True,
dbrecord=dbiso.baseline,
unpack=True,
unpacker=lambda x: x.PeakTimeBlob,
error_type='SEM',
fit='average')
iso.baseline.set_filter_outliers_dict()
iso.blank = Blank(name=key, value=bv, error=be)
self.isotopes[key] = iso
def _sync(self, obj):
self.j = ufloat(0, 0)
self.age = 0
self.age_err = 0
self.age_err_wo_j = 0
self.radiogenic_yield = ufloat(0, 0)
self.rad4039 = ufloat(0, 0)
arar = None
if obj.araranalyses:
arar = obj.araranalyses[-1]
if arar:
self.j = ufloat(arar.JVal, arar.JEr)
self.age = arar.Age
self.age_err = arar.ErrAge
self.age_err_wo_j = arar.ErrAgeWOErInJ
self.radiogenic_yield = ufloat(arar.PctRad, arar.PctRadEr)
self.rad4039 = ufloat(arar.Rad4039, arar.Rad4039Er)
self.r3739 = ufloat(arar.R3739Cor, arar.ErR3739Cor)
self.Cl3839 = ufloat(arar.Cl3839, 0)
try:
self.kca = ufloat(arar.CaOverK, arar.CaOverKEr)**-1
except ZeroDivisionError:
self.kca = 0
try:
self.kcl = ufloat(arar.ClOverK, arar.ClOverKEr)**-1
except ZeroDivisionError:
self.kcl = 0
changeable = obj.changeable
fo, fi, fs = 0, 0, 0
if changeable:
self.comment = changeable.Comment
self.tag = STATUS_MAP.get(changeable.StatusLevel)
prefs = changeable.preferences_set
if prefs:
fo = prefs.DelOutliersAfterFit == 'true'
fi = int(prefs.NFilterIter)
fs = int(prefs.OutlierSigmaFactor)
self.lambda_k = prefs.Lambda40Kepsilon + prefs.Lambda40KBeta
self.lambda_Ar37 = prefs.LambdaAr37
self.lambda_Ar39 = prefs.LambdaAr39
self.lambda_Cl36 = prefs.LambdaCl36
for dbiso in obj.isotopes:
r = dbiso.results[-1]
uv, ee = self._intercept_value(r)
key = dbiso.Label
n = dbiso.NumCnts
det = dbiso.detector
iso = Isotope(key, det.detector_type.Label)
iso.baseline_corrected = ufloat(uv, ee)
tv, te = 0, 0
if arar:
try:
k = key[2:]
tv, te = getattr(arar, 'Tot{}'.format(k)), getattr(
arar, 'Tot{}Er'.format(k))
except AttributeError:
pass
iso.set_filter_outliers_dict(filter_outliers=fo,
iterations=fi,
std_devs=fs)
iso.total_value = ufloat(tv, te)
# iso.set_uvalue((uv, ee))
iso.n = n
iso.ic_factor = ufloat(det.ICFactor, det.ICFactorEr)
iso.fit = r.fit.Label.lower() if r.fit else ''
iso.baseline = Baseline(key, det.detector_type.Label)
iso.baseline.fit = 'average'
iso.baseline.set_filter_outliers_dict(filter_outliers=fo,
iterations=fi,
std_devs=fs)
iso.baseline.n = dbiso.baseline.NumCnts
# uv = iso.baseline_corrected + iso.baseline.uvalue
# print('asdf',key, uv, iso.baseline_corrected, iso.baseline.uvalue)
# iso.value = nominal_value(uv)
# iso.error = std_dev(uv)
# iso.set_uvalue()
blank = self._blank(r)
if blank:
iso.blank.set_uvalue(blank)
self.isotopes[key] = iso
def _sync(self, obj):
self.j = ufloat(0, 0)
self.age = 0
self.age_err = 0
self.age_err_wo_j = 0
self.rad40_percent = ufloat(0, 0)
self.rad4039 = ufloat(0, 0)
arar = None
if obj.araranalyses:
arar = obj.araranalyses[-1]
if arar:
self.j = ufloat(arar.JVal, arar.JEr)
self.age = arar.Age
self.age_err = arar.ErrAge
self.age_err_wo_j = arar.ErrAgeWOErInJ
self.rad40_percent = ufloat(arar.PctRad, arar.PctRadEr)
self.rad4039 = ufloat(arar.Rad4039, arar.Rad4039Er)
self.r3739 = ufloat(arar.R3739Cor, arar.ErR3739Cor)
self.Cl3839 = ufloat(arar.Cl3839, 0)
self.kca = ufloat(arar.CaOverK, arar.CaOverKEr) ** -1
self.kcl = ufloat(arar.ClOverK, arar.ClOverKEr) ** -1
prefs = obj.changeable.preferences_set
fo, fi, fs = 0, 0, 0
if prefs:
fo = prefs.DelOutliersAfterFit == 'true'
fi = int(prefs.NFilterIter)
fs = int(prefs.OutlierSigmaFactor)
self.lambda_k = prefs.Lambda40Kepsilon + prefs.Lambda40KBeta
self.lambda_Ar37 = prefs.LambdaAr37
self.lambda_Ar39 = prefs.LambdaAr39
self.lambda_Cl36 = prefs.LambdaCl36
for dbiso in obj.isotopes:
r = dbiso.results[-1]
uv, ee = self._intercept_value(r)
key = dbiso.Label
n = dbiso.NumCnts
det = dbiso.detector
iso = Isotope(key, det.detector_type.Label)
iso.baseline_corrected = ufloat(uv, ee)
tv, te = 0, 0
if arar:
try:
k = key[2:]
tv, te = getattr(arar, 'Tot{}'.format(k)), getattr(arar, 'Tot{}Er'.format(k))
except AttributeError:
pass
iso.total_value = ufloat(tv, te)
# iso.set_uvalue((uv, ee))
iso.n = n
iso.ic_factor = ufloat(det.ICFactor, det.ICFactorEr)
iso.fit = r.fit.Label.lower() if r.fit else ''
iso.baseline = Baseline(key, det.detector_type.Label)
iso.baseline.fit = 'average'
iso.baseline.set_filter_outliers_dict(filter_outliers=fo, iterations=fi, std_devs=fs)
iso.baseline.n = dbiso.baseline.NumCnts
blank = self._blank(r)
if blank:
iso.blank.set_uvalue(blank)
self.isotopes[key] = iso