def _make(self, ep):
project = 'Minna Bluff'
db = self.db
with db.session_ctx():
prj = db.get_project(project)
Ar40, Ar39, Ar38, Ar37, Ar36 = [], [], [], [], []
for dev in (('Eurotherm', 'Furnace'), ('CO2')):
for si in prj.samples:
for li in si.labnumbers:
self.debug('blanks for {},{}'.format(
si.name, li.identifier))
for ai in li.analyses:
if ai.extraction.extraction_device.name in dev:
bs = self._extract_blanks(ai)
if bs is not None:
r = make_runid(li.identifier, ai.aliquot,
ai.step)
# self.debug('blanks for {} {}'.format(r,bs))
Ar40.append(bs[0])
Ar39.append(bs[1])
Ar38.append(bs[2])
Ar37.append(bs[3])
Ar36.append(bs[4])
reg = WeightedMeanRegressor()
print 'blanks for {}'.format(dev)
for iso in (Ar40, Ar39, Ar38, Ar37, Ar36):
ys, es = zip(*iso)
reg.trait_set(ys=ys, yserr=es)
print reg.predict()
def _make(self, ep):
project = "Minna Bluff"
db = self.db
with db.session_ctx():
prj = db.get_project(project)
Ar40, Ar39, Ar38, Ar37, Ar36 = [], [], [], [], []
for dev in (("Eurotherm", "Furnace"), ("CO2")):
for si in prj.samples:
for li in si.labnumbers:
self.debug("blanks for {},{}".format(si.name, li.identifier))
for ai in li.analyses:
if ai.extraction.extraction_device.name in dev:
bs = self._extract_blanks(ai)
if bs is not None:
r = make_runid(li.identifier, ai.aliquot, ai.step)
# self.debug('blanks for {} {}'.format(r,bs))
Ar40.append(bs[0])
Ar39.append(bs[1])
Ar38.append(bs[2])
Ar37.append(bs[3])
Ar36.append(bs[4])
reg = WeightedMeanRegressor()
print "blanks for {}".format(dev)
for iso in (Ar40, Ar39, Ar38, Ar37, Ar36):
ys, es = zip(*iso)
reg.trait_set(ys=ys, yserr=es)
print reg.predict()
def _mean_regress(self, scatter, r, fit):
if hasattr(scatter, 'yerror'):
if r is None or not isinstance(r, WeightedMeanRegressor):
r = WeightedMeanRegressor()
else:
if r is None or not isinstance(r, MeanRegressor):
r = MeanRegressor()
self._set_regressor(scatter, r)
r.trait_set(fit=fit, trait_change_notify=False)
r.calculate()
self._set_excluded(scatter, r)
return r
def _mean_regress(self, scatter, r, fit):
if hasattr(scatter, 'yerror'):
if r is None or not isinstance(r, WeightedMeanRegressor):
r = WeightedMeanRegressor()
else:
if r is None or not isinstance(r, MeanRegressor):
r = MeanRegressor()
self._set_regressor(scatter, r)
r.trait_set(fit=fit, trait_change_notify=False)
r.calculate()
self._set_excluded(scatter, r)
return r
