def _get_baselines(self, isotopes, meas_analysis, unpack):
for dbiso in meas_analysis.isotopes:
if not dbiso.molecular_weight:
continue
name = dbiso.molecular_weight.name
if not name in isotopes:
continue
det = dbiso.detector.name
iso = isotopes[name]
kw = dict(dbrecord=dbiso, name=name, detector=det, unpack=unpack)
if dbiso.kind == 'baseline':
result = None
if dbiso.results:
result = dbiso.results[-1]
r = Baseline(dbresult=result, **kw)
fit = self.get_db_fit(meas_analysis, name, 'baseline')
if fit is None:
fit = Fit(fit='average',
error_type='SEM',
filter_outliers=False,
filter_outlier_iterations=0,
filter_outlier_std_devs=0)
r.set_fit(fit)
iso.baseline = r
elif dbiso.kind == 'sniff':
r = Sniff(**kw)
iso.sniff = r
def _get_baselines(self, meas_analysis, dbisos, unpack, selected_histories):
isotopes = self.isotopes
default_fit = self._default_fit_factory('average', 'SEM')
for dbiso in dbisos:
mw = dbiso.molecular_weight
if not mw:
continue
name = mw.name
det = dbiso.detector.name
try:
iso = isotopes['{}{}'.format(name, det)]
except KeyError:
iso = isotopes[name]
# kw = dict(dbrecord=dbiso,
# name=name,
# detector=det,
# unpack=unpack)
kind = dbiso.kind
if kind == 'baseline':
result = None
if selected_histories is None:
# todo: this needs to be fixed to handle data_reduction_tag
try:
result = dbiso.results[-1]
except IndexError:
result = None
# kw['name'] = '{} bs'.format(name)
# r = Baseline(dbresult=result, **kw)
r = Baseline('{} bs'.format(name), det)
if result:
r.value = result.signal_
r.error = result.signal_err
if unpack:
r.unpack_data(dbiso.signal.data)
fit = self.get_db_fit(meas_analysis, name, 'baseline', selected_histories)
if fit is None:
fit = default_fit()
r.set_fit(fit, notify=False)
iso.baseline = r
elif kind == 'sniff' and unpack:
# r = Sniff(**kw)
r = Sniff(name, det)
if unpack:
r.unpack_data(dbiso.signal.data)
iso.sniff = r
def get_baseline(self, attr):
if attr.endswith('bs'):
attr = attr[:-2]
if attr in self.isotopes:
return self.isotopes[attr].baseline
else:
return Baseline()
def _get_baselines(self, meas_analysis, dbisos, unpack,
selected_histories):
isotopes = self.isotopes
default_fit = self._default_fit_factory('average', 'SEM')
for dbiso in dbisos:
mw = dbiso.molecular_weight
if not mw:
continue
name = mw.name
det = dbiso.detector.name
try:
iso = isotopes['{}{}'.format(name, det)]
except KeyError:
iso = isotopes[name]
kw = dict(dbrecord=dbiso, name=name, detector=det, unpack=unpack)
kind = dbiso.kind
if kind == 'baseline':
result = None
if selected_histories is None:
# todo: this needs to be fixed to handle data_reduction_tag
try:
result = dbiso.results[-1]
except IndexError:
result = None
kw['name'] = '{} bs'.format(name)
r = Baseline(dbresult=result, **kw)
fit = self.get_db_fit(meas_analysis, name, 'baseline',
selected_histories)
if fit is None:
fit = default_fit()
r.set_fit(fit, notify=False)
iso.baseline = r
elif kind == 'sniff' and unpack:
r = Sniff(**kw)
iso.sniff = r
def _get_baselines(self, isotopes, meas_analysis, unpack):
for dbiso in meas_analysis.isotopes:
if not dbiso.molecular_weight:
continue
name = dbiso.molecular_weight.name
if not name in isotopes:
continue
det = dbiso.detector.name
iso = isotopes[name]
kw = dict(dbrecord=dbiso,
name=name,
detector=det,
unpack=unpack)
if dbiso.kind == 'baseline':
result = None
if dbiso.results:
result = dbiso.results[-1]
r = Baseline(dbresult=result, **kw)
fit = self.get_db_fit(meas_analysis, name, 'baseline')
if fit is None:
fit = Fit(fit='average',
error_type='SEM',
filter_outliers=False,
filter_outlier_iterations=0,
filter_outlier_std_devs=0)
r.set_fit(fit)
iso.baseline = r
elif dbiso.kind == 'sniff':
r = Sniff(**kw)
iso.sniff = r
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
iso = Isotope(name=key, value=uv, error=ee)
iso.baseline = Baseline(name=key,
reverse_unpack=True,
dbrecord=dbiso.baseline,
unpacker=lambda x: x.PeakTimeBlob,
fit='average_SEM')
iso.blank = Blank(name=key, value=bv, error=be)
self.isotopes[key] = iso
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 get_analysis_import_spec(self, delimiter=None):
pspec = self.new_persistence_spec()
rspec = pspec.run_spec
f = self.file_gen(delimiter)
row = next(f)
rspec.identifier = row[0][:-1]
rspec.aliquot = 1
rspec.step = row[0][-1]
rspec.extract_device = make_ed(row[0])
rspec.irradiation = row[1]
rspec.irradiation_position = get_int(f, 1)
rspec.irradiation_level = 'A'
for attr in ('sample', 'material', 'project'):
setattr(rspec, attr, get_next(f, 1))
for attr in ('j', 'j_err'):
setattr(pspec, attr, get_float(f, 1))
d = get_next(f, 1)
t = get_next(f, 1)
pspec.timestamp = datetime.strptime('{} {}'.format(d, t),
'%m/%d/%Y %H:%M:%S')
abundance_sens = get_float(f)
abundance_sens_err = get_float(f)
air = get_float(f)
disc = 295.5 / air
pspec.discrimination = disc
row = next(f) # MD errpr
row = next(f) # peakhop cycles
n40 = get_int(f)
n39 = get_int(f)
n38 = get_int(f)
n37 = get_int(f)
n36 = get_int(f)
n41 = get_int(f)
n355 = get_int(f)
_spare = next(f)
int40 = next(f)
int39 = next(f)
int38 = next(f)
int37 = next(f)
int36 = next(f)
int41 = next(f)
int355 = next(f)
bk40 = get_ufloat(f)
bk39 = get_ufloat(f)
bk38 = get_ufloat(f)
bk37 = get_ufloat(f)
bk36 = get_ufloat(f)
bk41 = get_ufloat(f)
bk40 += get_ufloat(f)
bk39 += get_ufloat(f)
bk38 += get_ufloat(f)
bk37 += get_ufloat(f)
bk36 += get_ufloat(f)
bk41 += get_ufloat(f)
bk40 += get_ufloat(f)
bk39 += get_ufloat(f)
bk38 += get_ufloat(f)
bk37 += get_ufloat(f)
bk36 += get_ufloat(f)
bk41 += get_ufloat(f)
isotopes = {
'Ar40': self._get_isotope(f, 'Ar40', n40, bk40),
'Ar39': self._get_isotope(f, 'Ar39', n39, bk39),
'Ar38': self._get_isotope(f, 'Ar38', n38, bk38),
'Ar37': self._get_isotope(f, 'Ar37', n37, bk37),
'Ar36': self._get_isotope(f, 'Ar36', n36, bk36),
'Ar41': self._get_isotope(f, 'Ar41', n41, bk41)
}
xs, ys = self._get_baseline(f, n355)
for iso in isotopes.values():
bs = Baseline(iso.name, iso.detector)
bs.set_fit('average')
bs.set_fit_error_type('SEM')
bs.xs = xs
bs.ys = ys
iso.baseline = bs
try:
next(f)
self.warning('Extra data in file')
except StopIteration:
pass
pspec.isotope_group = IsotopeGroup(isotopes=isotopes)
return pspec
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 get_analysis_import_spec(self, delimiter=None):
pspec = self.new_persistence_spec()
rspec = pspec.run_spec
f = self.file_gen(delimiter)
row = next(f)
rspec.identifier = row[0][:-1]
rspec.aliquot = 1
rspec.step = row[0][-1]
rspec.extract_device = make_ed(row[0])
rspec.irradiation = row[1]
rspec.irradiation_position = get_int(f, 1)
rspec.irradiation_level = 'A'
for attr in ('sample', 'material', 'project'):
setattr(rspec, attr, get_next(f, 1))
for attr in ('j', 'j_err'):
setattr(pspec, attr, get_float(f, 1))
d = get_next(f, 1)
t = get_next(f, 1)
pspec.timestamp = datetime.strptime('{} {}'.format(d, t), '%m/%d/%Y %H:%M:%S')
abundance_sens = get_float(f)
abundance_sens_err = get_float(f)
air = get_float(f)
disc = 295.5 / air
pspec.discrimination = disc
row = next(f) # MD errpr
row = next(f) # peakhop cycles
n40 = get_int(f)
n39 = get_int(f)
n38 = get_int(f)
n37 = get_int(f)
n36 = get_int(f)
n41 = get_int(f)
n355 = get_int(f)
_spare = next(f)
int40 = next(f)
int39 = next(f)
int38 = next(f)
int37 = next(f)
int36 = next(f)
int41 = next(f)
int355 = next(f)
bk40 = get_ufloat(f)
bk39 = get_ufloat(f)
bk38 = get_ufloat(f)
bk37 = get_ufloat(f)
bk36 = get_ufloat(f)
bk41 = get_ufloat(f)
bk40 += get_ufloat(f)
bk39 += get_ufloat(f)
bk38 += get_ufloat(f)
bk37 += get_ufloat(f)
bk36 += get_ufloat(f)
bk41 += get_ufloat(f)
bk40 += get_ufloat(f)
bk39 += get_ufloat(f)
bk38 += get_ufloat(f)
bk37 += get_ufloat(f)
bk36 += get_ufloat(f)
bk41 += get_ufloat(f)
isotopes = {'Ar40': self._get_isotope(f, 'Ar40', n40, bk40),
'Ar39': self._get_isotope(f, 'Ar39', n39, bk39),
'Ar38': self._get_isotope(f, 'Ar38', n38, bk38),
'Ar37': self._get_isotope(f, 'Ar37', n37, bk37),
'Ar36': self._get_isotope(f, 'Ar36', n36, bk36),
'Ar41': self._get_isotope(f, 'Ar41', n41, bk41)}
xs, ys = self._get_baseline(f, n355)
for iso in isotopes.values():
bs = Baseline(iso.name, iso.detector)
bs.set_fit('average')
bs.set_fit_error_type('SEM')
bs.xs = xs
bs.ys = ys
iso.baseline = bs
try:
next(f)
self.warning('Extra data in file')
except StopIteration:
pass
pspec.isotope_group = IsotopeGroup(isotopes=isotopes)
return pspec
