def _test(self):
p = '/Users/ross/Pychrondata_demo/data/snapshots/scan6/007.jpg'
g = Graph()
g.new_plot()
for scan_i, z, idxs in [
# 1,
# 2,
# 3, 4, 5,
# (6, [20, 30, 40, 50, 60, 70, 80, 90, 100, ],
# [2, 3, 4, 5, 6, 7, 8, 9, 10]
# ),
(6, [10],
[1]
),
# (6, [100, 90, 80, 70, 60, 50, 40, 30, 20],
# [11, 12, 13, 14, 15, 16, 17, 18, 19]
# )
]:
dxs = []
zs = []
root = '/Users/ross/Pychrondata_demo/data/snapshots/scan{}'.format(scan_i)
for zi, idx in zip(z, idxs):
pn = os.path.join(root, '{:03n}.jpg'.format(idx))
d = load_image(pn)
dx = self._calculate_spacing(d)
dxs.append(dx)
zs.append(zi)
g.new_series(zs, dxs, type='scatter')
coeffs = polyfit(zs, dxs, 2)
print 'parabolic intercept {}'.format(coeffs[-1])
xs = linspace(0, max(zs))
ys = polyval(coeffs, xs)
g.new_series(xs, ys)
fitfunc = lambda p, x: p[0] * exp(p[1] * x) + p[2]
lr = LeastSquaresRegressor(fitfunc=fitfunc,
initial_guess=[1, 0.1, 0],
xs=zs,
ys=dxs
)
xs = linspace(0, max(zs))
ys = lr.predict(xs)
print 'exponential intercept {}'.format(lr.predict(0))
g.new_series(xs, ys)
invoke_in_main_thread(g.edit_traits)
def _least_square_regress(self, scatter, r, fit):
fitfunc, errfunc = fit
if r is None or not isinstance(r, LeastSquaresRegressor):
r = LeastSquaresRegressor()
self._set_regressor(scatter, r)
r.trait_set(fitfunc=fitfunc,
errfunc=errfunc,
trait_change_notify=False)
r.calculate()
self._set_excluded(scatter, r)
return r
def _least_square_regress(self, r, x, y, ox, oy, index, fit, fod,
apply_filter):
fitfunc, errfunc = fit
if r is None or not isinstance(r, LeastSquaresRegressor):
r = LeastSquaresRegressor()
r.trait_set(xs=x, ys=y, fitfunc=fitfunc, errfunc=errfunc)
if apply_filter:
r = self._apply_outlier_filter(r, ox, oy, index, fod)
return r
def _least_square_regress(self, scatter, r, fit):
from pychron.core.regression.least_squares_regressor import LeastSquaresRegressor
func, initial_guess = fit
if r is None or not isinstance(r, LeastSquaresRegressor):
r = LeastSquaresRegressor()
self._set_regressor(scatter, r)
r.trait_set(fitfunc=func,
initial_guess=initial_guess,
trait_change_notify=False)
r.calculate()
self._set_excluded(scatter, r)
return r
def _test(self):
p = '/Users/ross/Pychrondata_demo/data/snapshots/scan6/007.jpg'
g = Graph()
g.new_plot()
for scan_i, z, idxs in [
# 1,
# 2,
# 3, 4, 5,
# (6, [20, 30, 40, 50, 60, 70, 80, 90, 100, ],
# [2, 3, 4, 5, 6, 7, 8, 9, 10]
# ),
(6, [10], [1]),
# (6, [100, 90, 80, 70, 60, 50, 40, 30, 20],
# [11, 12, 13, 14, 15, 16, 17, 18, 19]
# )
]:
dxs = []
zs = []
root = '/Users/ross/Pychrondata_demo/data/snapshots/scan{}'.format(
scan_i)
for zi, idx in zip(z, idxs):
pn = os.path.join(root, '{:03n}.jpg'.format(idx))
d = load_image(pn)
dx = self._calculate_spacing(d)
dxs.append(dx)
zs.append(zi)
g.new_series(zs, dxs, type='scatter')
coeffs = polyfit(zs, dxs, 2)
print 'parabolic intercept {}'.format(coeffs[-1])
xs = linspace(0, max(zs))
ys = polyval(coeffs, xs)
g.new_series(xs, ys)
fitfunc = lambda p, x: p[0] * exp(p[1] * x) + p[2]
lr = LeastSquaresRegressor(fitfunc=fitfunc,
initial_guess=[1, 0.1, 0],
xs=zs,
ys=dxs)
xs = linspace(0, max(zs))
ys = lr.predict(xs)
print 'exponential intercept {}'.format(lr.predict(0))
g.new_series(xs, ys)
invoke_in_main_thread(g.edit_traits)
def _least_square_regress(self, r, x, y, ox, oy, index,
fit, fod, apply_filter):
fitfunc, errfunc = fit
if r is None or not isinstance(r, LeastSquaresRegressor):
r = LeastSquaresRegressor()
r.trait_set(xs=x, ys=y,
fitfunc=fitfunc,
errfunc=errfunc)
if apply_filter:
r = self._apply_outlier_filter(r, ox, oy, index, fod)
return r
def _least_square_regress(self, scatter, r, fit):
fitfunc, errfunc = fit
if r is None or not isinstance(r, LeastSquaresRegressor):
r = LeastSquaresRegressor()
self._set_regressor(scatter, r)
r.trait_set(fitfunc=fitfunc,
errfunc=errfunc,
trait_change_notify=False)
r.calculate()
self._set_excluded(scatter, r)
return r
def _least_square_regress(self, scatter, r, fit):
from pychron.core.regression.least_squares_regressor import LeastSquaresRegressor
func, initial_guess = fit
if r is None or not isinstance(r, LeastSquaresRegressor):
r = LeastSquaresRegressor()
self._set_regressor(scatter, r)
r.trait_set(fitfunc=func,
initial_guess=initial_guess,
trait_change_notify=False)
r.calculate()
self._set_excluded(scatter, r)
return r
