75468112-2181-42f4-903a-c714efb1fbde 12 6 9 decimal

&[DATE] &[TIME] - &[FILENAME]

vx vy char size clr plotG n vx length : plot vx 1 el vy 1 el char size clr augment : i 2 n range plot plot vx i el vy i el char size clr augment stack : for plot 41 line :

Error stem plot for QuickPlot unicolor data Error stemX 1 data rows range : i 1 stemX rows range stem i el data i 1 el data i 2 el data i 1 el 022 mat : for i 1 stem rows 1 - range stem i el stem i el stem i 1 + (1 el 1 el 012 mat stack : for V stem 1 el : j 2 stem rows 1 - range V V stem j el stack : for V V data data rows row stack : 71 line :

Applicable parts are: Optimiz "numeric" XY f φ β Minimize X XY 1 col : Y XY 2 col : n XY rows : k x β φ length 1 - : vx-> raw initial vector fit i 1 X length range vx i el X i el β f eval : for M-> Cholesky GradientMatrix j 1 k range i 1 n range M i j el X i el β φ j 1 + el eval : for for CholeskySolver LeastSquares Φ M transpose M * : d M transpose Y vx - (* : Δβ Φ 1 -^eval d * : b β Δβ + : 131 line :

L H N xd U 0 : dx H L - N / : i 1 N 1 + range U i el L dx - (dx i * + : for U 41 line :

Module xd(L,H,N) is a discretiser for use in-situ [user spec's]: range [L,H], density [N].

Vector gradient ... Optimiz Symbolic x β f n φ ct 1 : i 1 n range PD ct el x β f (β ct el diff : stack f(x,β) & PD V 1 el x β f : V i 1 + el PD i el : 21 line ct ct 1 + : 41 line for V 31 line :

XY 0.5 - 107.477 0 101.325 0.5 95.461 1 89.876 1.5 84.559 2 79.501 2.5 74.691 3 70.121 3.5 65.78 4 61.66 4.5 57.752 5 54.048 5.5 50.539 6 47.217 6.5 44.075 7 41.105 7.5 38.299 8 35.651 8.5 33.154 9 30.8 9.5 28.584 10 26.499 10.5 24.54 11 22.699 11.5 20.984 12 19.399 12.5 17.933 13 16.579 13.5 15.327 14 14.17 14.5 13.1 15 12.111 15.5 11.197 16 10.352 16.5 9.571 17 8.849 17.5 8.182 18 7.565 18.5 6.994 19 6.467 19.5 5.979 20 5.529 422 mat :

XY XY 244212 submatrix :

data X Y . 12 black plotG :

x β f β 1 el β 2 el x β 3 el / - exp * + :

Model function

in φ(x,β,f,n) -> set 'n' to last index β # el ≡

x β φ x β f 3 φ :

X XY 1 col : Y XY 2 col : n XY rows : 31 line

β 0.022641 127.895427 6.358736 31 mat :

0.025 100531 mat

re-inject to iterate manually

β length 3

Genfit coeff's of fit β XY f φ β Minimize :

0.02264 127.8954 6.35874 31 mat

FIT range, populate U 1 - 21 0.95 - range : i 1 U rows range FIT i el U i el β f eval : for U FIT augment 41 line :

β 0.022641 127.895437 6.358737 31 mat

data FIT 2 1 sys

Relative residuals Residuals i 1 XY rows range Δ i el 1 Y i el X i el β f / - eval : for X Δ augment 21 line :

Relative residuals

Residuals Error Residuals 2 1 sys

SSD = "Sum Square Differences" β SSD Y i el X i el β f - (2^(i 1 n sum :

β SSD 0.000001742

RMS difference β SSD n / sqrt 0.000303

Lifetime of  decay τ 1 β 2 el / abs : 0

χ X Mean : μ Y Mean : n XY rows : 31 line

χ μ n 31 mat 15.5 12.262474 1931 mat

Pearson’s correlation coefficient corr X i el χ - (Y i el μ - (* (i 1 n sum X i el χ - (2^(i 1 n sum Y i el μ - (2^(i 1 n sum * sqrt / X i el χ - (X i el β f eval μ - (* (i 1 n sum X i el χ - (2^(i 1 n sum X i el β f eval μ - (2^(i 1 n sum * sqrt / / : 1.000001

correlation between [X,Y]

correlation between [X,fitted model]

X i el χ - (Y i el μ - (* (i 1 n sum X i el χ - (2^(i 1 n sum Y i el μ - (2^(i 1 n sum * sqrt / X i el χ - (X i el β f eval μ - (* (i 1 n sum X i el χ - (2^(i 1 n sum X i el β f eval μ - (2^(i 1 n sum * sqrt / 21 mat 0.982381 - 0.98238 - 21 mat