stitch  Overlapping reflectivity curve stitching¶
poisson_average 
Compute the poisson average of y/dy using a set of data points. 
stitch 
Stitch together multiple measurements into one. 
Data stitching.
Join together datasets yielding unique sorted x.

refl1d.stitch.
poisson_average
(xdxydyw)[source]¶ Compute the poisson average of y/dy using a set of data points.
The returned x, dx is the weighted average of the inputs:
x = sum(x*I)/sum(I) dx = sum(dx*I)/sum(I)
The returned y, dy use Poisson averaging:
w = sum(y/dy^2) y = sum((y/dy)^2)/w dy = sqrt(y/w)
The above formula gives the expected result for combining two measurements, assuming there is no uncertainty in the monitor.
measure N counts during M monitors rate: r = N/M rate uncertainty: dr = sqrt(N)/M weighted rate: r/dr^2 = (N/M) / (N/M^2) = M weighted rate squared: r^2/dr^2 = (N^2/M^2) / (N/M^2) = N for two measurements Na, Nb w = ra/dra^2 + rb/drb^2 = Ma + Mb y = ((ra/dra)^2 + (rb/drb)^2)/w = (Na + Nb)/(Ma + Mb) dy = sqrt(y/w) = sqrt( (Na + Nb)/ w^2 ) = sqrt(Na+Nb)/(Ma + Mb)
This formula isn’t strictly correct when applied to values which have been scaled, for example to account for an attenuator in the counting system.

refl1d.stitch.
stitch
(data, same_x=0.001, same_dx=0.001)[source]¶ Stitch together multiple measurements into one.
data a list of datasets with x, dx, y, dy attributes same_x minimum point separation (default is 0.001). same_dx minimum change in resolution that may be averaged (default is 0.001).
WARNING: the returned x values may be data dependent, with two measured sets having different x after stitching, even though the measurement conditions are identical!!
Either add an intensity weight to the datasets:
probe.I = slitscan
or use interpolation if you need to align two stitched scans:
import numpy as np x1, dx1, y1, dy1 = stitch([a1, b1, c1, d1]) x2, dx2, y2, dy2 = stitch([a2, b2, c2, d2]) x2[0], x2[1] = x1[0], x1[1] # Force matching end points y2 = np.interp(x1, x2, y2) dy2 = np.interp(x1, x2, dy2) x2 = x1
WARNING: the returned dx value underestimates the true x, depending on the relative weights of the averaged data points.