Genfit Utilities

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Genfit Utilities

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 :

Bar plot unicolor style, bar width user. data dt BarSize v data 2 col : t v bar t dt - 0 t dt - v t dt + v t dt + 042 mat : j 1 data rows range j 1 ≡ B data j 1 el data j 2 el bar : B B data j 1 el data j 2 el bar stack : if 11 line for B 41 line :

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 31 line for data r3 r4 r5 augment 21 line :

xy Original

xy 101102103104105106107108109101010111012101310141015101610171018101910201 0.0022 211 0.0803 221 0.2117 231 0.3798 241 0.5776 251 0.8013 261 1.0482 271 1.3163 281 1.604 291 1.91 301 2.2333 311 2.5731 321 2.9285 331 3.2989 341 3.6838 351 4.0826 361 4.4947 372 mat :

XY 101102103104105106107108109101010111012101310141015101610171018101910201 0.0022 211 0.0803 221 0.2117 231 0.3798 241 0.5776 251 0.8013 261 1.0482 271 1.3163 281 1.604 291 1.91 301 2.2333 311 2.5731 321 2.9285 331 3.2989 341 3.6838 351 4.0826 361 4.4947 372 mat :

XY XY 20 XY rows 12 submatrix : X XY 1 col 191 - : Y XY 2 col : XY X Y augment : n XY rows : 51 line

f 0.5 :

Model function x β f β 1 el β 2 el (f^1 x β 3 el - / (f^* - exp * :

80100020 - 31 mat 139.5053 2326.5758 26.4162 - 31 mat 12 mat

<= ConjugateGradient Vector Fnct x β φ x β f 3 φ :

L 0 : H 190 : N 100 : 13 mat U L H N xd : 21 line

β β 1 el 100100020 - 31 mat : iter 7 : i 1 : i iter ≤ β i 1 + el XY f φ β i el Minimize : i i 1 + : 21 line while β β iter el : 51 line :

β β :

Absolute  residuals δ j 1 XY rows range Δ j el Y j el X j el β f - eval : for X Δ augment 21 line :

bar δ 1 BarSize :

bar 0 2 1 sys

SSD = "Sum Square Differences" SSD Y i el X i el β f - (2^(i 1 n sum : 0.008

Data X Y o 5 black plotG :

FIT i 1 U rows range vy i el U i el β f eval : for U vy augment 21 line :

data xy o 5 black plot :

Data FIT 2 1 sys 191 1 191 1 - 2 2 mat x 191 - β f data 3 1 sys