def _poly_regress(self, r, x, y, ox, oy, index, fit, fod, apply_filter,
scatter, selection):
if hasattr(scatter, 'yerror'):
es = scatter.yerror.get_data()
if selection:
es = delete(es, selection, 0)
if r is None or not isinstance(r, WeightedPolynomialRegressor):
r = WeightedPolynomialRegressor(yserr=es)
else:
if r is None or not isinstance(r, PolynomialRegressor):
r = PolynomialRegressor()
r.trait_set(xs=x, ys=y, degree=fit)
if apply_filter:
r = self._apply_outlier_filter(r, ox, oy, index, fod)
return r
def _poly_regress(self, r, x, y, ox, oy, index,
fit, fod, apply_filter, scatter, selection):
if hasattr(scatter, 'yerror'):
es = scatter.yerror.get_data()
if selection:
es = delete(es, selection, 0)
if r is None or not isinstance(r, WeightedPolynomialRegressor):
r = WeightedPolynomialRegressor(yserr=es)
else:
if r is None or not isinstance(r, PolynomialRegressor):
r = PolynomialRegressor()
r.trait_set(xs=x, ys=y, degree=fit)
if apply_filter:
r = self._apply_outlier_filter(r, ox, oy, index, fod)
return r
def _poly_regress(self, scatter, r, fit):
if hasattr(scatter, 'yerror'):
if r is None or not isinstance(r, WeightedPolynomialRegressor):
r = WeightedPolynomialRegressor()
else:
if r is None or not isinstance(r, PolynomialRegressor):
r = PolynomialRegressor()
self._set_regressor(scatter, r)
r.trait_set(degree=fit)
r.set_truncate(scatter.truncate)
r.calculate()
if r.ys.shape[0] < fit + 1:
return
self._set_excluded(scatter, r)
return r
def _poly_regress(self, scatter, r, fit):
if hasattr(scatter, 'yerror'):
if r is None or not isinstance(r, WeightedPolynomialRegressor):
r = WeightedPolynomialRegressor()
else:
if r is None or not isinstance(r, PolynomialRegressor):
r = PolynomialRegressor()
self._set_regressor(scatter, r)
r.trait_set(degree=fit)
r.set_truncate(scatter.truncate)
r.calculate()
if r.ys.shape[0] < fit + 1:
return
self._set_excluded(scatter, r)
return r
def _get_regressor(self, fit, error_type, xs, ys, es):
fit=fit.lower()
if fit in ('linear','parabolic','cubic'):
reg = WeightedPolynomialRegressor(fit=fit)
elif fit in ['preceding', 'bracketing interpolate', 'bracketing average']:
reg = InterpolationRegressor(kind=fit)
elif fit =='weighted mean':
reg = WeightedMeanRegressor()
else:
print 'fit {} not valid'.format(fit)
raise NotImplementedError
mi= min(xs)
xs=[xi-mi for xi in xs]
reg.trait_set(error_type=error_type,
xs=xs, ys=ys, yserr=es)
reg.calculate()
return reg
def _poly_regress(self, scatter, r, fit):
from pychron.core.regression.ols_regressor import PolynomialRegressor
from pychron.core.regression.wls_regressor import WeightedPolynomialRegressor
if hasattr(scatter, 'yerror') and any(scatter.yerror.get_data()):
if r is None or not isinstance(r, WeightedPolynomialRegressor):
r = WeightedPolynomialRegressor()
else:
if r is None or not isinstance(r, PolynomialRegressor):
r = PolynomialRegressor()
self._set_regressor(scatter, r)
r.trait_set(degree=fit)
r.set_truncate(scatter.truncate)
if r.ys.shape[0] < fit + 1:
return
r.calculate()
self._set_excluded(scatter, r)
return r
def _get_regressor(self, fit, error_type, xs, ys, es):
fit = fit.lower()
if fit in ('linear', 'parabolic', 'cubic'):
reg = WeightedPolynomialRegressor(fit=fit)
elif fit in [
'preceding', 'bracketing interpolate', 'bracketing average'
]:
reg = InterpolationRegressor(kind=fit)
elif fit == 'weighted mean':
reg = WeightedMeanRegressor()
else:
print 'fit {} not valid'.format(fit)
raise NotImplementedError
mi = min(xs)
xs = [xi - mi for xi in xs]
reg.trait_set(error_type=error_type, xs=xs, ys=ys, yserr=es)
reg.calculate()
return reg
