data

41c8a094-0b85-4e3a-89ad-382500cd00d7 39 4 5 decimal

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

data

data 12.487 28.62 10.642 30.378 8.883 31.904 7.21 33.199 5.623 34.262 4.123 35.094 2.708 35.694 1.38 36.062 0.138 36.199 1.017 - 36.104 2.087 - 35.778 3.07 - 35.22 3.967 - 34.431 4.782 - 33.418 5.532 - 32.215 6.235 - 30.859 6.912 - 29.388 7.581 - 27.841 8.261 - 26.255 8.971 - 24.667 9.732 - 23.115 10.561 - 21.638 11.478 - 20.273 12.503 - 19.058 13.653 - 18.031 14.936 - 17.209 16.323 - 16.566 17.785 - 16.066 19.288 - 15.676 20.803 - 15.361 22.297 - 15.088 23.74 - 14.821 25.1 - 14.527 26.346 - 14.172 27.466 - 13.72 28.369 - 13.138 29.085 - 12.392 29.5578 - 11.468 29.867 - 10.387 29.972 - 9.175 29.913 - 7.857 29.713 - 6.46 29.39 - 5.009 28.966 - 3.529 28.462 - 2.047 27.899 - 0.587 27.296 - 0.824 - 26.675 - 2.161 - 26.057 - 3.398 - 25.453 - 4.528 - 24.864 - 5.568 - 24.291 - 6.539 - 23.733 - 7.458 - 23.19 - 8.347 - 22.662 - 9.223 - 22.148 - 10.107 - 21.65 - 11.018 - 21.165 - 11.976 - 20.695 - 13 - 20.24 - 14.11 - 19.798 - 15.324 - 19.365 - 16.639 - 18.933 - 18.031 - 18.49 - 19.471 - 18.028 - 20.933 - 17.536 - 22.388 - 17.005 - 23.81 - 16.426 - 25.172 - 15.788 - 26.446 - 15.081 - 27.604 - 14.297 - 28.621 - 13.424 - 29.467 - 12.455 - 30.117 - 11.388 - 30.572 - 10.233 - 30.854 - 8.998 - 30.987 - 7.691 - 30.993 - 6.321 - 30.898 - 4.896 - 30.725 - 3.425 - 30.496 - 1.917 - 30.237 - 0.38 - 29.97 - 1.177 29.719 - 2.746 29.508 - 4.319 29.359 - 5.886 29.268 - 7.441 29.216 - 8.976 29.182 - 10.482 29.146 - 11.953 29.087 - 13.379 28.985 - 14.755 28.82 - 16.071 28.571 - 17.319 28.217 - 18.493 27.739 - 19.585 27.116 - 20.587 26.33 - 21.508 25.931 - 22.363 24.325 - 23.167 23.158 - 23.935 21.913 - 24.684 20.617 - 25.427 19.294 - 26.181 17.971 - 26.961 16.673 - 27.781 15.424 - 28.658 14.251 - 29.607 13.178 - 30.638 12.226 - 31.731 11.382 - 32.852 10.618 - 33.957 9.905 - 35.043 9.216 - 36.046 8.523 - 36.941 7.798 - 37.696 7.013 - 38.275 6.14 - 38.646 5.151 - 38.774 4.019 - 38.625 2.715 - 38.171 1.215 - 37.403 0.487 36.322 2.391 34.927 4.497 33.219 6.805 31.197 9.315 28.862 12.028 26.214 14.942 23.252 18.058 19.977 21.377 16.389 24.897 1312 mat :

Part 1 ► rhmsd between two vectors

data rows 131

4 appVersion 0.98.6179.21440 ≡

x data 1 col : y data 2 col : 12 mat

x y rhmsd ε 1015 -^: j 1 x rows range d j el x j el y j el - : d2 j el d j el d j el * ε + (1 -^: 21 line for d2 Mean (1 -^sqrt 31 line :

d x y - vectorize : 16.133 - 19.736 - 23.021 - 25.989 - 28.639 - 30.971 - 32.986 - 34.682 - 36.061 - 37.121 - 37.865 - 38.29 - 38.398 - 131 mat

x y rhmsd 6.1762

Part 2 ► radius/distance of wild shape

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 13 mat for data r3 r4 r5 augment 21 line :

point radius segment 'j' j 40 :

v data j 1 el data j 2 el 21 mat :

d 00 data j 1 el data j 2 el 0032 mat :

u 0021 mat :

point distance in space
from [0,0] origin u v dist u v - (2^(vectorize sum sqrt :

u v dist 31.345

d data . 8 black plot v transpose + 40 red plot 3 1 sys

x data 1 col : y data 2 col : 12 mat

Area Polygon wrt vertices coordinates A 12 / x i el y i 1 + el * x i 1 + el y i el * - (i 1 x rows 1 - sum * abs :

Perimeter  Polygon wrt vertices coordinates P x i 1 + el x i el - (2^y i 1 + el y i el - (2^+ sqrt i 1 x rows 1 - sum :

Center of gravity Xo 13 A * / x i el x i 1 + el + (x i el y i 1 + el * x i 1 + el y i el * - (* i 1 x rows 1 - sum * : Yo 16 A * / y i el y i 1 + el + (x i el y i 1 + el * x i 1 + el y i el * - (* i 1 x rows 1 - sum * : 21 mat

Center of gravity Xo Yo 21 mat 6.759 0.769 - 21 mat

CG Xo Yo augment :

A P Xo Yo 21 mat 13 mat 2841.266 202.871 6.759 0.769 - 21 mat 13 mat

x y axes x y / - :

data d axes CG 4 1 sys RHMSD isolated matrix

RHMSD 19919619419118918618418117917617417116816516215915615315014614313913513112612211711210710195898275686051423216201925292928272018272641485563707783909610210811412012513113614214715215616116617017417818218619019319720020420721021321722022322622923223523824024324624925225419619319118818618318117817617317116816516215915615315014714314013613212812311911410910397918578706253433121182834384041414036342419323043536168768390961031091151211271321381431481531581621671711751791831871901941972012042072112142172202232262292322352382412432462492521931901881851831801781751731701681651621591561541501471441401371331291251201161111051009487817365564635251431384347495051504845403726936424959687583909710311011612212813414014515015515916416817217618018418819119519820220520821121421722022322622923223523824124424624918918718418218017717517217016716516215915615415114814414113813413012612211711310810296908477696051402214324046515456585858575552484035341734505967758391981051111181241301361411471521561611651691731771811851881921951992022052082122152182212242272302322352382412442471861841811791761741721691671641621591561541511481451421381351311271231191151101059993878073655545322628394753576063646565646361575348382034424556677684929910711312012613213814314815315816216617117417818218618919319619920320620921221521822122422723023323623824124418318117817617317116816616416115915615315114814514213913613212912512111611210710296908477696051391916374653586366687071717170686562575044412423475968768593101109116123129135140145150155159163168172175179183186190193197200203206209212215218221224227230233236239241180177175172170168165163160158156153150148145142139136133129126122118114109104999387817365564532283243525863677173757677777675737066615647292844485667788796104112119125131137142147152156160165169173176180184187190194197200203207210213216219222224227230233236239176174172169167164162160157155152150147145142139136133130127123119115111106102969184787062524019173949576368727578808182828180797774706558494640253958708090991071151221281331391441481531571621661701731771811841881911941982012042072102132162192222252272302332361731711681661631611591561541521491471441421391361341311271241211171131081049994888275675847353033445461677276798284858687868685838077736862543813345362718294103111118124130135140145150154159163167170174178181185188191195198201204207210213216219222225228230233170167165162160158155153151149146144141139136134131128125121118114110106101969185797163544220173950596571768083868889909191919088868480767163524447464458758999108115121126132137142146151155159164167171175178182185189192195198201204207210213216219222225228231166164161159157154152150148145143141138136133131128125122119115112108103999488827668605039303144556369757983868991939495959595939290878379736660523014456986961041111161221271331381431471521561601641681721751791821861891921951982012042072102132162192222252281631601581551531511491461441421401371351331301281251221191161131091051019691867973655644252139505966737883869092959698999999999897959390868176695838285071859410010511111712212813413914414815315716116516917217617918318618919219519920220520821121321621922222515915715415215014714514314113913613413213012712512211911611311010610298938883766961524126244254637076818689939598100101102103103103103102100989693898579706158677887919498103110117123129135140145149153157161165169173176179183186189192196199202205208211214216219222155153150148146144141139137135133131129126124122119116113110107103999591858073665746322838495967737984899296981011031041061061071071071061051031029996928883787780848685838592101110118125130136140145150154158162166169173176180183186189193196199202205208211214216219151149147144142140138136133131129127125123121118116113110107104101979288827770625342221741536370778287919598101104106107109110110111111110109108107105102100979391898988837364657792103113120126131136141145150154158162166169173176180183186189193196199202205208211214216147145143140138136134132130128126124122120117115113110107104101979489857973665848373236475866738085909498101104106108110112113114114114114113113111110108106104101999794897858353862839810711512012613113614114615015415816216617017317618018318618919319619920220520821121414314113813613413213012812612412212011811611411210910710410198949086817670625442201740526270778388939710010410710911111311411611711711711811711711611511311211010810610399917752182356809410310911412012513113614114615015415816216617017317618018318618919219619920220520821113913713413213012812512412212011811611411211010810610310198959187837872665849393031455766738086919599103106109112114116117118119120121121121120120119118117115113111108103958264474866829297101106112119125131136141146150154158162166170173176180183186189192195199202205208135132130127125123121119117115114112110108106104102100979491878479746861534223254152616977838893981021051091111141161181201211221231241241241241241231221211201181161121081019281737379868888899510311111912613113714114615015415816216616917317618018318618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mat :

x rhmsd x 24 ≥ (x 50 ≤ (* :

N RHMSD rhmsd RHMSD * vectorize - :

R 1.5 N * 0 N * 0 N * 31 mat transpose :

G 0 N * 1 N * 0 N * 31 mat transpose :

B 0 N * 0 N * 1 N * 31 mat transpose :

Yw 1 N * 1 N * 0 N * 31 mat transpose :

Cy 0 N * 1 N * 1 N * 31 mat transpose :

Ma 1 N * 0 N * 1 N * 31 mat transpose :

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 N 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 Yw

Part 3 ► rhmsd between two selected vectors

x y rhmsd ε 1015 -^: j 1 x rows range d j el x j el y j el - : d2 j el d j el d j el * ε + (1 -^: 21 line for d2 Mean (1 -^sqrt 31 line :

j 13 : k 25 : 12 mat

x RHMSD j col : y RHMSD k col : 12 mat

x y rhmsd 8.7911

Part 4 ► rhmsd ... algo/image

uvz0, uvz1, uvz2, uvz3, uvz4

uvz0 12.487 28.62 10.642 30.378 8.883 31.904 7.21 33.199 5.623 34.262 4.123 35.094 2.708 35.694 1.38 36.062 0.138 36.199 1.017 - 36.104 2.087 - 35.778 3.07 - 35.22 3.967 - 34.431 4.782 - 33.418 5.532 - 32.215 6.235 - 30.859 6.912 - 29.388 7.581 - 27.841 8.261 - 26.255 8.971 - 24.667 9.732 - 23.115 10.561 - 21.638 11.478 - 20.273 12.503 - 19.058 13.653 - 18.031 14.936 - 17.209 262 mat :

uvz1 16.323 - 16.566 17.785 - 16.066 19.288 - 15.676 20.803 - 15.361 22.297 - 15.088 23.74 - 14.821 25.1 - 14.527 26.346 - 14.172 27.466 - 13.72 28.369 - 13.138 29.085 - 12.392 29.5578 - 11.468 29.867 - 10.387 29.972 - 9.175 29.913 - 7.857 29.713 - 6.46 29.39 - 5.009 28.966 - 3.529 28.462 - 2.047 27.899 - 0.587 27.296 - 0.824 - 26.675 - 2.161 - 26.057 - 3.398 - 25.453 - 4.528 - 24.864 - 5.568 - 24.291 - 6.539 - 262 mat :

uvz2 23.733 - 7.458 - 23.19 - 8.347 - 22.662 - 9.223 - 22.148 - 10.107 - 21.65 - 11.018 - 21.165 - 11.976 - 20.695 - 13 - 20.24 - 14.11 - 19.798 - 15.324 - 19.365 - 16.639 - 18.933 - 18.031 - 18.49 - 19.471 - 18.028 - 20.933 - 17.536 - 22.388 - 17.005 - 23.81 - 16.426 - 25.172 - 15.788 - 26.446 - 15.081 - 27.604 - 14.297 - 28.621 - 13.424 - 29.467 - 12.455 - 30.117 - 11.388 - 30.572 - 10.233 - 30.854 - 8.998 - 30.987 - 7.691 - 30.993 - 6.321 - 30.898 - 262 mat :

uvz3 4.896 - 30.725 - 3.425 - 30.496 - 1.917 - 30.237 - 0.38 - 29.97 - 1.177 29.719 - 2.746 29.508 - 4.319 29.359 - 5.886 29.268 - 7.441 29.216 - 8.976 29.182 - 10.482 29.146 - 11.953 29.087 - 13.379 28.985 - 14.755 28.82 - 16.071 28.571 - 17.319 28.217 - 18.493 27.739 - 19.585 27.116 - 20.587 26.33 - 21.508 25.931 - 22.363 24.325 - 23.167 23.158 - 23.935 21.913 - 24.684 20.617 - 25.427 19.294 - 26.181 17.971 - 262 mat :

uvz4 26.961 16.673 - 27.781 15.424 - 28.658 14.251 - 29.607 13.178 - 30.638 12.226 - 31.731 11.382 - 32.852 10.618 - 33.957 9.905 - 35.043 9.216 - 36.046 8.523 - 36.941 7.798 - 37.696 7.013 - 38.275 6.14 - 38.646 5.151 - 38.774 4.019 - 38.625 2.715 - 38.171 1.215 - 37.403 0.487 36.322 2.391 34.927 4.497 33.219 6.805 31.197 9.315 28.862 12.028 26.214 14.942 23.252 18.058 19.977 21.377 16.389 24.897 272 mat :

data uvz0 uvz1 uvz2 uvz3 uvz4 stack :

data n Decimate collect every 'n' pairs range 1 data rows n range : k 1 : i range u k el data i 1 el : v k el data i 2 el : k k 1 + : 31 line for u v augment 31 line 31 line :

data data 3 Decimate :

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 13 mat for data r3 r4 r5 augment 21 line :

E data . 10 red plot :

n data rows : 66

x ρ uscale range x Max x Min - : middle x Max x Min + : 2 x * middle - 1 ρ / range * / 31 line :

ρ 1 : border 1 : 12 mat X ρ data 1 col (* : Y ρ data 2 col (* : 31 line

data X ρ uscale Y ρ uscale augment :

index a b scale 2 index 1 - (* a / 1 - (b * :

k 1 n 1 + (range : z k n border scale eval : ε 1015 -^: 31 line

Root Harmonic Mean Square Distance [RHMSD] calculated @ grid points rhmsd i 1 n range j 1 n range rhsmd i j el 1 n / data k 1 el z i el - (2^data k 2 el z j el - (2^+ ε + (1 -^k 1 n sum * (0.5 -^eval : for for rhsmd 21 line :

t0 1 time :

rhmsd rhmsd eval :

rhmsd Min rhmsd Max 21 mat 0.032 1.405 21 mat

1 time t0 - min 9

M Low High Scale High Low - (M M Min - M Max M Min - / (* Low + :

W rhmsd 0255 Scale :

size inner zone

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 W

inner exclusive x c 25 x ≤ (x 64 ≤ (* :

f W c W * vectorize eval :

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 f -

NOT decimated ...40 min...

λ rhmsd i 1 n range j 1 n range rhsmd i j el rhmsd i j el λ Max eval : for for rhsmd 21 line :

λ 0.15 :

Ω λ rhmsd eval :

q Ω 0255 Scale :

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 q -

'0'  Esplina  is the  
black within white 
top right corner 2 q Min 0

RHMSD ... Root Harmonic Mean Square Distance

creates an artificial 'z' vector from an 'X,Y' data set.

Useful for software capable of plotting contour from

matrix, for instance ► Mathcad 11.

]]>

Part 5 ► ESPLINA polygon[P,A,CG,radii]

Original Esplina

uvz0 12.487 28.62 10.642 30.378 8.883 31.904 7.21 33.199 5.623 34.262 4.123 35.094 2.708 35.694 1.38 36.062 0.138 36.199 1.017 - 36.104 2.087 - 35.778 3.07 - 35.22 3.967 - 34.431 4.782 - 33.418 5.532 - 32.215 6.235 - 30.859 6.912 - 29.388 7.581 - 27.841 8.261 - 26.255 8.971 - 24.667 9.732 - 23.115 10.561 - 21.638 11.478 - 20.273 12.503 - 19.058 13.653 - 18.031 14.936 - 17.209 262 mat :

uvz1 16.323 - 16.566 17.785 - 16.066 19.288 - 15.676 20.803 - 15.361 22.297 - 15.088 23.74 - 14.821 25.1 - 14.527 26.346 - 14.172 27.466 - 13.72 28.369 - 13.138 29.085 - 12.392 29.5578 - 11.468 29.867 - 10.387 29.972 - 9.175 29.913 - 7.857 29.713 - 6.46 29.39 - 5.009 28.966 - 3.529 28.462 - 2.047 27.899 - 0.587 27.296 - 0.824 - 26.675 - 2.161 - 26.057 - 3.398 - 25.453 - 4.528 - 24.864 - 5.568 - 24.291 - 6.539 - 262 mat :

uvz2 23.733 - 7.458 - 23.19 - 8.347 - 22.662 - 9.223 - 22.148 - 10.107 - 21.65 - 11.018 - 21.165 - 11.976 - 20.695 - 13 - 20.24 - 14.11 - 19.798 - 15.324 - 19.365 - 16.639 - 18.933 - 18.031 - 18.49 - 19.471 - 18.028 - 20.933 - 17.536 - 22.388 - 17.005 - 23.81 - 16.426 - 25.172 - 15.788 - 26.446 - 15.081 - 27.604 - 14.297 - 28.621 - 13.424 - 29.467 - 12.455 - 30.117 - 11.388 - 30.572 - 10.233 - 30.854 - 8.998 - 30.987 - 7.691 - 30.993 - 6.321 - 30.898 - 262 mat :

uvz3 4.896 - 30.725 - 3.425 - 30.496 - 1.917 - 30.237 - 0.38 - 29.97 - 1.177 29.719 - 2.746 29.508 - 4.319 29.359 - 5.886 29.268 - 7.441 29.216 - 8.976 29.182 - 10.482 29.146 - 11.953 29.087 - 13.379 28.985 - 14.755 28.82 - 16.071 28.571 - 17.319 28.217 - 18.493 27.739 - 19.585 27.116 - 20.587 26.33 - 21.508 25.931 - 22.363 24.325 - 23.167 23.158 - 23.935 21.913 - 24.684 20.617 - 25.427 19.294 - 26.181 17.971 - 262 mat :

uvz4 26.961 16.673 - 27.781 15.424 - 28.658 14.251 - 29.607 13.178 - 30.638 12.226 - 31.731 11.382 - 32.852 10.618 - 33.957 9.905 - 35.043 9.216 - 36.046 8.523 - 36.941 7.798 - 37.696 7.013 - 38.275 6.14 - 38.646 5.151 - 38.774 4.019 - 38.625 2.715 - 38.171 1.215 - 37.403 0.487 36.322 2.391 34.927 4.497 33.219 6.805 31.197 9.315 28.862 12.028 26.214 14.942 23.252 18.058 19.977 21.377 16.389 24.897 272 mat :

data char size clr plot k 1 data rows range r3 k el char : r4 k el size : r5 k el clr : 13 mat for data r3 r4 r5 augment 21 line :

data uvz0 uvz1 uvz2 uvz3 uvz4 stack :

point radius segment 2 j 12 :

v uvz1 j 1 el uvz1 j 2 el 21 mat :

d 00 uvz1 j 1 el uvz1 j 2 el 22 mat :

u 0021 mat :

uvz0 uvz1 uvz2 uvz3 uvz4 v transpose . 14 black plot d 7 1 sys

point distance origin [0,0] u v dist u v - (2^(vectorize sum sqrt :

u v dist 31.705

d data . 8 black plot v transpose + 40 red plot 3 1 sys

x data 1 col : y data 2 col : 12 mat

Area Polygon wrt vertices coordinates A 12 / x i el y i 1 + el * x i 1 + el y i el * - (i 1 x rows 1 - sum * abs :

Perimeter  Polygon wrt vertices coordinates P x i 1 + el x i el - (2^y i 1 + el y i el - (2^+ sqrt i 1 x rows 1 - sum :

Center of gravity of a wild  polygon [non-geomatric] ... Xo 13 A * / x i el x i 1 + el + (x i el y i 1 + el * x i 1 + el y i el * - (* i 1 x rows 1 - sum * : Yo 16 A * / y i el y i 1 + el + (x i el y i 1 + el * x i 1 + el y i el * - (* i 1 x rows 1 - sum * : 21 mat

A P Xo Yo 21 mat 13 mat 2841.266 202.871 6.759 0.769 - 21 mat 13 mat

CG Xo Yo augment :

x y axes x y / - :

point radius segment 2 j 12 :

v uvz1 j 1 el uvz1 j 2 el 21 mat :

d Xo Yo uvz1 j 1 el uvz1 j 2 el 22 mat :

u Xo Yo 21 mat :

point distance 2D space u v radiusCG u v - (2^(vectorize sum sqrt :

u v radiusCG 38.323

uvz0 uvz1 uvz2 uvz3 uvz4 d axes CG 81 sys

uvz0 uvz1 uvz2 uvz3 uvz4 d axes CG 8 1 sys

Part 6 ► ESPLINA rhmsd isocurve [RemToDo]

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Utilities

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 :

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 51 line :

The contour extractor , pair wise in X direction M ε cntr CONTR count 1 : i 1 M rows range j 1 M cols range u count el i : v count el j : 21 line M i j el cntr - abs ε ≤ count count 1 + : cases for for u v augment 31 line :

data Strip2 nest striped in 'R' u data 1 col : v data 2 col : k 0 : i 1 data rows range u i el v i el * 0 ≠ R k k 1 + : el u i el v i el 12 mat : 11 line Undefined if 11 line for 21 line Un-nest R v R 1 el : j 2 R rows range v v R j el stack : for v 81 line :

data n Decimate collect every 'n' pairs range 1 data rows n range : k 1 : i range u k el data i 1 el : v k el data i 2 el : k k 1 + : 31 line for u v augment 31 line 31 line :

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 :

M Low High Scale High Low - (M M Min - M Max M Min - 106 -^+ / (* Low + :

ε 2 :

level 64 - :

Q data ε level CONTR :

XY Q Strip2 : Export the project ≡

v i 1 XY rows range v i el XY 2 col i el : for v 2 v * v Min - v Max - v Max v Min - / : 21 line :

i 1 XY rows range u i el XY 1 col i el : for u 2 u * u Min - u Max - u Max u Min - / : 21 line

Export u v augment eval :

Export 2 1 sys

Note ... The isocurves system is circular. There is no way to sort ? Manual sorting works well, tedious.