def _detect_outliers(self, xs, ys, outs, degree=2):
xs = array(xs)
ys = array(ys)
mxs = masked_array(xs, mask=outs)
# print 's', sum(mxs), outs
mys = masked_array(ys, mask=outs)
o = OLS(mxs, mys, fitdegree=degree)
coeffs = o.get_coefficients()
n = len(xs) - sum(outs)
# coeff_errs = o.get_coefficient_standard_errors()
# ymean = ys.mean()
yeval = polyval(coeffs, xs)
# calculate detection_tol. use error of fit
devs = abs(ys - yeval)
ssr = sum(devs ** 2)
detection_tol = 2.5 * (ssr / ((n) - (degree))) ** 0.5
for i, xi, ys, di, mi in zip(xrange(len(xs)), xs, ys, devs, outs):
if di > detection_tol:
outs[i] = 1
omit = 'OK' if di <= detection_tol and not mi else 'User omitted'
# print xi, ys, di, detection_tol, omit, mi
return outs
def _detect_outliers(self, xs, ys, outs, degree=2):
xs = array(xs)
ys = array(ys)
mxs = masked_array(xs, mask=outs)
# print 's', sum(mxs), outs
mys = masked_array(ys, mask=outs)
o = OLS(mxs, mys, fitdegree=degree)
coeffs = o.get_coefficients()
n = len(xs) - sum(outs)
# coeff_errs = o.get_coefficient_standard_errors()
# ymean = ys.mean()
yeval = polyval(coeffs, xs)
# calculate detection_tol. use error of fit
devs = abs(ys - yeval)
ssr = sum(devs**2)
detection_tol = 2.5 * (ssr / ((n) - (degree)))**0.5
for i, xi, ys, di, mi in zip(xrange(len(xs)), xs, ys, devs, outs):
if di > detection_tol:
outs[i] = 1
omit = 'OK' if di <= detection_tol and not mi else 'User omitted'
# print xi, ys, di, detection_tol, omit, mi
return outs
def regress(d, degree=2):
# coeffs = polyfit(x, y, degree)
# o = OLS(x, y, fitdegree=degree)
o = OLS(*d, fitdegree=degree)
return [o.get_coefficients()[2], o.get_coefficient_standard_errors()[2]]
def regress(d, degree=2):
# coeffs = polyfit(x, y, degree)
# o = OLS(x, y, fitdegree=degree)
o = OLS(*d, fitdegree=degree)
return [o.get_coefficients()[2], o.get_coefficient_standard_errors()[2]]