def results():
x, y, e = get_data()
reg = PolynomialRegressor(degree=1, xs=x, ys=y)
mi, ma = min(x), max(x)
pad = (ma - mi) * 0.1
fxs = linspace(mi - pad, ma + pad)
plt.errorbar(
x,
y,
yerr=e,
)
reg.calculate()
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'b')
plt.plot(fxs, u, 'b')
# plt.plot(fxs, reg.predict(fxs), 'b-')
print('Age={}, SD={} SEM={} CI={}'.format(reg.predict(328),
reg.predict_error(328),
reg.predict_error(328, 'SEM'),
reg.predict_error(328, 'CI')))
reg = WeightedPolynomialRegressor(degree=1, xs=x, ys=y, yserr=e)
reg.calculate()
plt.plot(fxs, reg.predict(fxs), 'g')
l, u = reg.calculate_error_envelope(fxs, error_calc='CI')
plt.plot(fxs, l, 'r')
plt.plot(fxs, u, 'r')
print('Weighted fit Age={}, SD={} SEM={} CI={}'.format(
reg.predict(328), reg.predict_error(328),
reg.predict_error(328, 'SEM'), reg.predict_error(328, 'CI')))
plt.show()
def setUp(self):
self.xs = np.linspace(0, 10, 10)
self.ys = np.random.normal(self.xs, 1)
'''
draper and smith p.8
'''
self.xs = [
35.3, 29.7, 30.8, 58.8, 61.4, 71.3, 74.4, 76.7, 70.7, 57.5, 46.4,
28.9, 28.1, 39.1, 46.8, 48.5, 59.3, 70, 70, 74.5, 72.1, 58.1, 44.6,
33.4, 28.6
]
self.ys = [
10.98, 11.13, 12.51, 8.4, 9.27, 8.73, 6.36, 8.50, 7.82, 9.14, 8.24,
12.19, 11.88, 9.57, 10.94, 9.58, 10.09, 8.11, 6.83, 8.88, 7.68,
8.47, 8.86, 10.36, 11.08
]
self.es = np.random.normal(1, 0.5, len(self.xs))
self.slope = -0.0798
self.intercept = 13.623
self.Xk = 28.6
self.ypred_k = 0.3091
xs = self.xs
ys = self.ys
es = self.es
self.wls = WeightedPolynomialRegressor(xs=xs,
ys=ys,
yserr=es,
fit='linear')
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):
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 _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 _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
