### SLA_PXY

Apply Linear Model

ACTION:
Given arrays of expected and measured $\left[\phantom{\rule{0.3em}{0ex}}x,y\phantom{\rule{0.3em}{0ex}}\right]$ coordinates, and a linear model relating them (as produced by sla_FITXY), compute the array of predicted coordinates and the RMS residuals.
CALL:
CALL sla_PXY (NP,XYE,XYM,COEFFS,XYP,XRMS,YRMS,RRMS)
##### GIVEN:
 NP I number of samples XYE D(2,NP) expected $\left[\phantom{\rule{0.3em}{0ex}}x,y\phantom{\rule{0.3em}{0ex}}\right]$ for each sample XYM D(2,NP) measured $\left[\phantom{\rule{0.3em}{0ex}}x,y\phantom{\rule{0.3em}{0ex}}\right]$ for each sample COEFFS D(6) coefficients of model (see below)

##### RETURNED:
 XYP D(2,NP) predicted $\left[\phantom{\rule{0.3em}{0ex}}x,y\phantom{\rule{0.3em}{0ex}}\right]$ for each sample XRMS D RMS in X YRMS D RMS in Y RRMS D total RMS (vector sum of XRMS and YRMS)

NOTES:
(1)
The model is supplied in the array COEFFS. Naming the six elements of COEFFS $a,b,c,d,e$ & $f$, the model transforms measured coordinates $\left[{x}_{m},{y}_{m}\phantom{\rule{0.3em}{0ex}}\right]$ into predicted coordinates $\left[{x}_{p},{y}_{p}\phantom{\rule{0.3em}{0ex}}\right]$ as follows:

${x}_{p}=a+b{x}_{m}+c{y}_{m}$
${y}_{p}=d+e{x}_{m}+f{y}_{m}$

(2)
The residuals are $\left({x}_{p}-{x}_{e}\right)$ and $\left({y}_{p}-{y}_{e}\right)$.
(3)
If NP is less than or equal to zero, no coordinates are transformed, and the RMS residuals are all zero.
(4)