<p> Genfit Functions ... plotG ... xd(L,H,N) </p>

94dff878-5dfd-43d4-9024-3dfc374810c6 11 6 5 auto

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

vx vy char size color plotG n vx length : plot vx 1 el vy 1 el char size color augment : i 2 n range plot plot vx i el vy i el char size color augment stack : for plot 41 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 21 line 31 line :

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

Applicable parts are: Optimiz "numeric" XY f φ β βNlin X XY 1 col : Y XY 2 col : n XY rows : x β f x β f : x β φ x β φ transpose : 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 + : 151 line :

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 :

1. Passed as wild depending Re-definition

XY 22.5 23.5 24.5 25.5 27.5 28.5 30.5 33.5 36.5 42.5 52.5 77.5 14.5 12.5 10.5 9.5 7.5 6.5 5.5 4.5 3.5 2.5 1.5 0.5 212 mat transpose :

x β f 0.5 β 1 el x - β 3 el / exp * + β 2 el x - β 4 el / exp * + :

Model function

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

x β φ x β f 4 φ :

1. Passed as wild depending Re-definition

X XY 1 col :

Y XY 2 col :

Range & populate the discrete fit

L X 1 el :

H X X length el :

N 199 :

U L H N xd :

β 38.853934 6326.533967 14.209233 3.240193 41 mat :

50.225 19601 13.02 2.7306 41 mat

37.207 9376.926 - 14.149 3.279 41 mat

38.854 6326.534 14.209 3.24 41 mat

38.692332 6311.302311 14.229014 3.23935 41 mat

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

2. Passed as wild double algo + data set

3. Final call: the model function, optimised wrt paramaters .. and the user discrete range and auto-indexed parameters

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

β 38.692332 6311.302311 14.229014 3.23935 41 mat

MyFit U FIT augment :

data X Y . 12 black plotG :

Most interesting: the ExpDecay2 exposes point [8] misaligned. It's this point that makes Thiele like fail around this point.

data MyFit 2 1 sys

jmGiraud:2016_02_14

2. Passed by algo name as defined in program

n 1000 :

In this module, 'spec' is pased as algo name, wild in 'p' => spec(a,b,c) to be specified in-situ. Thus, 'p' is nested on the collected spec(a,b,c) and "Unested" after in the module.

Unform ...  -1 < rnd < 1 n rnd μ n 11 line : 2 μ Random * 1 - 21 line :

data spec pts i 1 data rows range p i el a b c spec : for v p 1 el : Unest 'p' j 2 p rows range v v p j el stack : for data v augment 51 line :

XY n rnd n rnd augment :

X XY 1 col :

Y XY 2 col :

Random fill the circle UV k 1 : i k XY rows range X i el (2^Y i el (2^+ sqrt 1 ≤ u i el X i el : v i el Y i el : 21 line u i el 0 : v i el 0 : 21 line if for k k 1 + : 31 line u v augment 21 line :

"plotG" module is slow: too many calls from greedy
sub-functions col(,,)...stack(,,)...augment(,,)
Its equivalent below is ~ 12 times faster [1000 pts]   
and much more élégant  for publishing. Pts UV 1 col UV 2 col . 5 blue plotG ≡

a b c spec . 8 red 13 mat :

t0 1 time :

pts UV a b c spec pts :

frame 1 - 111 - 1 - 111 - 1 - 125 mat transpose :

frame pts 2 1 sys

-------- compute time below the plot -----------------

1 time t0 - 37 sec *

X i el (2^2 Y i el * (2^+ sqrt

Prolate/Oblate Ellipse, not UnitCircle as well.

2 X i el * (2^Y i el (2^+ sqrt

X i el (2^Y i el (2^+ sqrt 1 ≠

r 1 ≠ NOT unit circle ≡

3. Passed by complete function definition

Mesh U, V  within the bounds L, H L H N xd N intervals U 0 : dx H L - N / : i 1 N 1 + range U i el L dx i * dx - (+ : for U 21 line 41 line :

L 1 - :

H 1 :

N 64 :

U L H N xd :

V U :

x y f 255 x 2^y 2^+ (- 0.25 / exp * :

Generate the symmetric square matrix x y f U V Sym r 1 : i 1 U rows range c 1 : j 1 V rows range M r c el U i el V j el f eval : c c 1 + : 21 line for r r 1 + : 31 line for M 31 line :

N x y f U V Sym :

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f M Vectorise i 1 M rows range j 1 M cols range V i j el M i j el f eval : for for V 21 line :

Highlight by negative pixelation

xo 127 :

A 2 - :

α 2.29 :

x f A x xo - (α^abs * :

fmap f N Vectorise Re :

iVBORw0KGgoAAAANSUhEUgAAAEEAAABBCAIAAAABlV4SAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAgY0hSTQAAeiYAAICEAAD6AAAAgOgAAHUwAADqYAAAOpgAABdwnLpRPAAADjBJREFUaEPd2mWrVl0Qh/FtoGJid3dhd3diYIuiYiKKHdgYiIWt2N2NYGOLrYidiPpCsRsV9fnJgudD7PNis8/aM/+57lmzZq19n5OoQ4cOvXv3njNnzqlTp8aPH3/u3Lnly5f/+fPn48eP3759S5UqVcWKFYcNG3bmzJlNmzZlyZIle/bsJUqUaNiwYd++fWfPnr179+6mTZt27ty5U6dOdevWTZs2LZ3r16/z+vr166xZs0aPHl2lSpVbt241btw4a9asly9ffvbsWc+ePdu2bVu5cuWMGTN++PDh2rVrZcuWzZs3b44cOQoXLtygQYPhw4cfOHCAPhEGv379ypw5M/0xY8YcP36cLJGTJ08WKlQoTZo00bx58+7du1emTJnVq1fny5fPg3HjxmHq0qXL9OnTHz161K1bN2SfP39OnDgxlR07dlStWvXly5d79uyZNm1a9+7dK1So8P379xUrVgCSkfbt2ydLlgxljx49xC5VqpSreyPGPWXDkj0vvhToUKNJmb4oYokorugYkOBBhQ0hTrSYkeOPbt++7Zf169fL7s2bN1euXDlz5swNGza8fv3a/GTKlOnv378FChQQhqeoCRIkEAlBtmzZtm/fXqdOHVmXuUWLFu3atWvSpEnsc+bMaTBFihRXrlwxk67ujRj3lA1L9rwMUqBDjSZl+qKIJaK47DEgwYMKG0KcaDEjxx8VL17cZzL04MGDnTt3btu2zahKuHHjhl9NvVl+/vz5kydP8ufPL1syMWXKlCNHjsilMHLcp0+fffv2sVy3bl3u3LnLly/P99WrV4MGDeratStcV/dGjHvKhiV7Xnwp0KFGkzJ9UcQSUVzRWSLhiwobQr+ixYwcf7RgwQIf2ifzYP/+/e/evevVq5e6lLOBAweazdSpUyt9y6BYsWJz586VvNq1a48YMYJcixYtBFDWEyZMmDp1qsJQrzVq1OD48+dPBjLasWNHV/dGjHvKhiV7Xnwp0GFAkzJ9UcQSUVzRMXDEgwobQpxoMSPHHzFSYSbI5/PY8lWOavfx48eHDh2SJAV66dIltYjmxIkTcrZ27VpTLJKpl1218eLFC4gWPaB27drJTbVq1UaOHCl5Vr+reyPGPWXDkj0vvhToUKNJmb4oYokorugYkOBBhQ0hTrSYkeOPTKvpU2cy4VOGta8EzfiaNWtMVp48eYoWLWqi1a6lqTTV9+TJk5UHS7M/YMCADBky6EsyJ804SpYsqdUMHTrUYli6dKmreyPGPWXDkj0vvhToUKNJmb4oYokorugYkOBBxRIhTrSYkeOPzKNPbLmoNpPlszJlpy2o3ZQpU4phteknKhtQ0qRJZc5qk4bSpUsfO3YMkIZ4+PBh86uHKIOxY8fqjBcvXtyyZUujRo1c3Rsx7ikblux58aVAhxpNyvRFEUtEcUXHgAQPKmwIcaLFjBx/pOVrW1a9GVdzpswn5mC1mVZzSleDhyKdKlibU6A2EIUheZqDkFq7pLJZuHChrcBcnz9/vlmzZuoYmat7I8Y9ZcOSPS++FOhQo0mZvihisRFXdAxI8KDChhAnWszI8UcJEybkZgblRjCVZ+J8bm6mSTyfO3369O5Z2lMsUDUtfwgkQ17l+OnTp7Vq1Tp48GCuXLlat24tr8j42iVPnz7t6t6IcU/ZsGTPiy8FOtRoUqYvilgiiis6X/d4UGFDiBMtZuQs/61pk2uOLDVpsIDUn+nz6TlfuHDBUtM9pErOli1bpo737t2rvgn169cPgRqFZeNkrxJ0epWwdetWq3bJkiVWs6t7I8Y9ZcOSPS++FOhQo0mZvihiiSiu6BjY40GFDSFOtJiR/1vTVpi9kKlO7Hd9wDJShSZRDoxLpNiqmZsZlE7bu84ti8pD5pw1NEGng5o1a65atcoynT9//o8fPxYvXmyuPXV1b8S4p2xYsuflKQU61GhSpi+KWCKKKzoGJHhQYUOIE61x5Pgjuj6WqiVBVDvTDSwmtWgqZYKQJPnRiS1EvVmllitXzh6pSCpVqsRAStjrlZ8+fapfv77MDR482NSnS5cuSZIkru6NGPeUDUv2vPhSoEONJmX6oogVgjLAgIQ9KmwIcaLFjBx/5BRgrag2+6KKtNI1NT2BnCo0ofJBSyPXHM176IkSJrBcKuX379/LNDidDlz//v0TJUrkLMirXr16tjNX90aMe8qGJXtefCnQoUYz9GhRxBKRl+gYkOBBhQ0hTrSYkeOPnFUcsGx4tnfOyk5v9ul1BstFXZpWWSFq0t+8eaPDqGM1rb41e0UCwv4qpI1Tuxw1alRYqbYhUeXP1X1Y/Z6yYcmeF18KdKjRpExfFLFEFFd0DEjwoMKGkBRazMjxRz6rvqsbOKXYLO2RthhuGpz+YHmpTpPLjbR+ord8+fLFMhU1HHKdhOX46NGjFhxcuaHTvHnzJk2aKG7r1dW9EeOesmHJnhffcBSlRpMyfVHEElFc0TEgwYMKG0I6aDEjxx9pdlaJ0yItZxUTpGdJibbFyMwiUKNuZChsRupYGhSDWlce+jRjx3rSSn/IkCEWKCBZ52vNubo3YtxTNizZ8+JLgQ41mpTD1imWiOLydYOEMSpsCHGixYwcf+RdxHHFj1Vvck2rDV8TsFxMqGanTMOWZKLlSRgdRhV6K1KmXEKZyrdtuEiRIhLsPHf37t3fv397ebDmXN0bMe4pG5bsw8KjQIcaTcr0RRErbJqiY0CCBxU2hFzQBmz8kXbh5cOSp+jkqGE5t1jydk1LR8/WAXQMWtIgl7IlWNgQZde65C5V7rmbfTUa7p1qvKPYGVzdGzHuKZtwz4svBfdh26JMXxSxRBRXdAxI8KDChpA7Wszc8Ud2PrVl63GCd5Ayy/qACbX52ztJK4DQRpWsH5MuZ8bVtGVnrt++fSu7Kh4fda1Q54miSE2DQ+zq3ohxT9mwZM+LLwU61GhSph8ChYYrunEkeFBhQ4gTLWbk+KOHDx+qMKvb1fnJ7Ng7VSRrTcOM24BCGzWJFp/aNfWwwjlUzmycCkYDadOmjSSB3rx5s5KQM5lG6ereiHFP2bBkz4svhXDOpUmZvihihYYrOgYkeFBhQ4jzf2b8kWnyRuuFkBzdsB06S0qPzuBHLdqGQhO0kixB5agAGAjvV0lVA3IMInwnYDN2CpBCcBLv6t6IcU/ZsGTPiy8FOtRoUvarKKFliyt6wGCAKhwgcKLFjBx/5A3dNqTvOhiKp22L5IFGJpLP7TigMYfNSCvUSSxEhSjBhECodUvW8UFK7ty5AxeHMm3ZsqWSlUtX90aMe8qGJXtefCnQoUaTMn1RwtYprugYkOBBhQ0hTrSYkeOPTIrOpVt5u9WDpUdDsA0pQfH0Bxu7MghJ0tH1OOtJeahmxSCXUPQTIzLtVKygr169Slq8Vq1a3b9/39W9EeOesmHJnhdfCnSoGaFMX5Qw1eKKjgEJHlTYEOJEixk5/kiS/PiWwQTpfdoILVXL2vHY6VJd2mvFlgCR9HUFKjEWJReKMsoFovCyzksBeGpnVQw2KVf3Rox7yoYle158KdDxlCZl+qKIJaK4omPghQcVF4Q4uWAO8JFlbo2bNevJ1HjTNWXanN5sv3ToDWHUqAOC7sFedw+9xdIUWGGEr4B81SPfahe0ZLva0ZInT+76/4inbFiGL6/4UqATehpl+qKIFU5fomNAggcVNoQ40WJGzj5y/JASn9srksrz+eys1HmGc5Gjcjhpmkr1ape1SYUer8NYoOobseyCkydlwEvX09E1+LNnz7q6N2LcUzYs2fPiSyG0fJqU6YsiVjgFix7OUXhQYUOIEy1m5Pgji4kPFcdjFqZbX5MtL422cT1YPNPqvOm45rQjqeZa2iwsUfUZxcrAdPOCyMD3ihs3blSvM2bM8P2kq3sjxj1lw5I9L74U6FCjSZmBKGKJyEB0DEjw8MKGECdazMjxR9R9hRjeUO0mthKt0Jx699WPzZcF59yvpZh0EyeqcgzNUb8XwyOLNbRgOQMqGHSPNBx7kKt7I8Y9ZROaJi++HtEJrZkyfVHceySu6BiQ4EGFDSHO8PaPHP+/WvKWpE/pu5aLb4FsN07tNhct2X6pe2iLegUCZ0+93MnH1Nt3w9ugp+bUoiSqYOQbbvXq1R0TChYsKFuu7o0Y95QNS/a8+IY3YWo0KdMXRSwRPRUdAxI8qLAhxIkWM/J/taRnOdBqcDZCe7jXX+cTaXByVIKsbTcmzkf3DhC6R+jrjg92X2nT++UydCF1LzF+BW1LEs/SdHVvxLinbEKn8itfCnSohX0mdDyxRBRXdAxI8HiEDSFOtJiR4//XW82186BvRbVbc0TdN0IkLDtZ0aetM+9iik8+/DiHOsaFQ5vJpWAHCF3I8lX9BOUeug4zceJEV/dGjHvKJnQqXnwphCMjTcohhFgiiis6BiR4UGFDiBMtZoL/eqs/OPAMb0nmxVeLdkSNzCHR+7gPbad0htHOdGv923pSo3JjIVpP1qIANjLS2qgupGCsASVkBnwAzdHVvRHjnrJhyZ4XXwp0qNGkTF8UsUQUV3QMSPCgwoYQZ3jTRI4/FvMQh/UQh74Uh/0hDvt0HM5LcTi3xuH9IQ7vcXF4n47D9xpx+H4pDt/zxeH71jh87x2Hvz/E4e9Acfh7XBz+LhqHv0/H4f8E4vD/GnH4v5k4/P9SHP6PLA7/zxeH/6uMw/+3xuH/jOPw/95x+L9732v4GGZDUVkblrhOpeHaN2x/fg1/cHA0dMJ1UPe+4bXJ25+XWO/ivlKg4Ase31P5us23hr789B2uG9+oG/T3AVf3Rox76oalQV58KdChRpMyfVHECn97Fx0DEjyosPkVJ1rMyCn8B26LAilwVX5JAAAAAElFTkSuQmCC fmap

x q 0255 ≤ (x 255 ≤ (& x f Nop if :

NTSC => 0.299*Red+0.587*Green+0.114*Blue = 1.

NTSC 0.299 fmap * 0.587 fmap * 0.114 fmap * augment :

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 NTSC x q 0 2 1 sys