How to make a shape bigger relevant to its position - SVG - matlab
How can I make a shape, specifically a path bigger, relevant to its position. There is something called scale but it doesn't help me because it's based on x and y which makes the shape bigger relevant to the corner. I want the shape to be zoomed in, from where it's placed, from its center.
Edit: I don't have the positions of the shapes. I have the shapes with their d attribute but I don't know how to read the centers.
Any help would be appreciated.
Transformation commands in SVG transform attributes can be concatenated - they are after all only matrices. What you can do is first translate the object to the origin of the coordinate system, then scale it, and then translate it back.
Suppose the center of your element is at (20, 30) and you want to zoom by factor 2.5. This is executed right to left (read it as a matrix multiplication):
transform="translate(20, 30) scale(2.5) translate(-20, -30)"
You can also compute the transformation matrix yourself and write:
transform="matrix(2.5,0,0,2.5,-30,-45)"
The description of these matrices is here.
In theory, with CSS transforms it should be much easier to to this. Unfortunately, browser support is not what it should be.
The following example works in Chrome, but not in Firefox (see this bug for a squabble about what is the right behaviour), IE or Edge. Stand-alone renderers also probably will fail (I falsified with librsvg and Inkscape). The green duck should be half the size of the yellow one and have the same center:
path {
fill: none;
stroke: yellow;
}
#tr {
transform: scale(0.5);
transform-origin: 50% 50%;
transform-box: fill-box;
stroke: green;
}
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</svg>
Related
Having trouble in using nlinfit function in MATLAB
Kindly please help me with the problem as I need to use nlinfit function for fitting unknown parameters but it is showing some error. Although yesterday I was getting some values for parameters to be fitted but now if I am running it is having some issue for the function output to be used in fitted with NaN answer for last iteration only. X data is a concatenated matrix of three columns as independent variable and yk is dependent variable, taua is a matrix of initial guesses of number of parameters to be fitted. function [yk]=activity_coefficientE(taua,x) T=523; alpha12=0.3; alpha13=0.3; alpha21=0.3; alpha23=0.3; alpha31=0.3; alpha32=0.3; alpha18=0.2; alpha81=0.2; alpha28=0.2; alpha82=0.2; alpha38=0.2; alpha83=0.3; alpha19=0.2; alpha91=0.2; alpha29=0.2; alpha92=0.2; alpha39=0.2; alpha93=0.2; alpha110=0.2;alpha101=0.2;alpha210=0.2;alpha102=0.2;alpha310=0.2;alpha103=0.2; alpha113=0.2;alpha131=0.2;alpha213=0.2;alpha132=0.2;alpha313=0.2;alpha133=0.2; alpha114=0.2;alpha141=0.2;alpha214=0.2;alpha142=0.2;alpha314=0.2;alpha143=0.2; alpha115=0.2;alpha151=0.2;alpha215=0.2;alpha152=0.2;alpha315=0.2;alpha153=0.2; alpha117=0.2;alpha171=0.2;alpha217=0.2;alpha172=0.2;alpha317=0.2;alpha173=0.2; alpha118=0.2;alpha181=0.2;alpha218=0.2;alpha182=0.2;alpha318=0.2;alpha183=0.2; alpha810=0.2;alpha915=0.2;alpha1314=0.2;alpha108=0.2;alpha159=0.2;alpha1413=0.2; alpha1718=0.2;alpha1817=0.2; tau12=0; tau13=0; tau21=0; tau23=0; tau31=0; tau32=0; %taua=randi([-5,5],1,112) tau18=taua(1)+taua(57)/T; tau81=taua(2)+taua(58)/T; tau28=taua(3)+taua(59)/T; tau82=taua(4)+taua(60)/T; tau38=taua(5)+taua(61)/T; tau83=taua(6)+taua(62)/T; tau19=taua(7)+taua(63)/T; tau91=taua(8)+taua(64)/T; tau29=taua(9)+taua(65)/T; tau92=taua(10)+taua(66)/T; tau39=taua(11)+taua(67)/T; tau93=taua(12)+taua(68)/T; tau110=taua(13)+taua(69)/T; tau101=taua(14)+taua(70)/T; tau210=taua(15)+taua(71)/T; tau102=taua(16)+taua(72)/T; tau310=taua(17)+taua(73)/T; tau103=taua(18)+taua(74)/T; tau113=taua(19)+taua(75)/T; tau131=taua(20)+taua(76)/T; tau213=taua(21)+taua(77)/T; tau132=taua(22)+taua(78)/T; tau313=taua(23)+taua(79)/T; tau133=taua(24)+taua(80)/T; tau114=taua(25)+taua(81)/T; tau141=taua(26)+taua(82)/T; tau214=taua(27)+taua(83)/T; tau142=taua(28)+taua(84)/T; tau314=taua(29)+taua(85)/T; tau143=taua(30)+taua(86)/T; tau115=taua(31)+taua(87)/T; tau151=taua(32)+taua(88)/T; tau215=taua(33)+taua(89)/T; tau152=taua(34)+taua(90)/T; tau315=taua(35)+taua(91)/T; tau153=taua(36)+taua(92)/T; tau117=taua(37)+taua(93)/T; tau171=taua(38)+taua(94)/T; tau217=taua(39)+taua(95)/T; tau172=taua(40)+taua(96)/T; tau317=taua(41)+taua(97)/T; tau173=taua(42)+taua(98)/T; tau118=taua(43)+taua(99)/T; tau181=taua(44)+taua(100)/T; tau218=taua(45)+taua(101)/T; tau182=taua(46)+taua(102)/T; tau318=taua(47)+taua(103)/T; tau183=taua(48)+taua(104)/T; tau810=taua(49)+taua(105)/T; tau108=taua(50)+taua(106)/T; tau915=taua(51)+taua(107)/T; tau159=taua(52)+taua(108)/T; tau1314=taua(53)+taua(109)/T; tau1413=taua(54)+taua(110)/T; tau1718=taua(55)+taua(111)/T; tau1817=taua(56)+taua(112)/T; G12=exp(-(tau12*alpha12)); G21=exp(-(tau21*alpha21)); G13=exp(-(tau13*alpha13)); G31=exp(-(tau31*alpha31)); G23=exp(-(tau23*alpha23)); G32=exp(-(tau32*alpha32)); G18=exp(-(tau18*alpha18)); G81=exp(-(tau81*alpha81)); G28=exp(-(tau28*alpha28)); G82=exp(-(tau82*alpha82)); G38=exp(-(tau38*alpha83)); G83=exp(-(tau83*alpha83)); G19=exp(-(tau19*alpha19)); G91=exp(-(tau91*alpha91)); G29=exp(-(tau29*alpha29)); G92=exp(-(tau92*alpha92)); G39=exp(-(tau39*alpha39)); G93=exp(-(tau93*alpha93)); G110=exp(-(tau110*alpha110)); G101=exp(-(tau101*alpha101)); G210=exp(-(tau210*alpha210)); G102=exp(-(tau102*alpha102)); G310=exp(-(tau310*alpha310)); G103=exp(-(tau103*alpha103)); G113=exp(-(tau113*alpha113)); G131=exp(-(tau131*alpha131)); G213=exp(-(tau213*alpha213)); G132=exp(-(tau132*alpha132)); G313=exp(-(tau313*alpha313)); G133=exp(-(tau133*alpha133)); G114=exp(-(tau114*alpha114)); G141=exp(-(tau141*alpha141)); G214=exp(-(tau214*alpha214)); G142=exp(-(tau142*alpha142)); G314=exp(-(tau314*alpha314)); G143=exp(-(tau143*alpha143)); G115=exp(-(tau115*alpha115)); G151=exp(-(tau151*alpha151)); G215=exp(-(tau215*alpha215)); G152=exp(-(tau152*alpha152)); G315=exp(-(tau315*alpha315)); G153=exp(-(tau153*alpha153)); G117=exp(-(tau117*alpha117)); G171=exp(-(tau171*alpha171)); G217=exp(-(tau217*alpha217)); G172=exp(-(tau172*alpha172)); G317=exp(-(tau317*alpha317)); G173=exp(-(tau173*alpha173)); G118=exp(-(tau118*alpha118)); G181=exp(-(tau181*alpha181)); G218=exp(-(tau218*alpha218)); G182=exp(-(tau182*alpha182)); G318=exp(-(tau318*alpha318)); G183=exp(-(tau183*alpha183)); G810=exp(-(tau810*alpha810)); G108=exp(-(tau108*alpha108)); G915=exp(-(tau915*alpha915)); G159=exp(-(tau159*alpha159)); G1314=exp(-(tau1314*alpha1314)); G1413=exp(-(tau1413*alpha1413)); G1718=exp(-(tau1718*alpha1718)); G1817=exp(-(tau1817*alpha1817)); %calculating mole fractions of ionic species x1=x(:,1); x2=x(:,2); x3=x(:,3); %x1=[0.1577 0.1492 0.1462 0.1366 0.1299 0.1180 0.0863 0.0761 0.0550 ]; %x2=[0.8278 0.7945 0.7678 0.7450 0.6979 0.6309 0.4611 0.4114 0.2952 ]; %x3=[0.0145 0.0563 0.0860 0.1184 0.1722 0.2511 0.4526 0.5125 0.6498 ]; A=[0.0674243 0.0773881 0.0843400 0.0865343 0.0899223 0.0882858 0.0715087 0.0643867 0.0483658]; B=[0.0141081 0.0479814 0.0643151 0.0737477 0.0820756 0.0838701 0.0701576 0.0634457 0.0479639]; C=[0.0565665 0.0450072 0.0387724 0.0313828 0.02506094 0.0186280 0.0092734 0.0073438 0.0041595 ]; D=[0.0336447 0.0267694 0.0230611 0.0186659 0.0149058 0.0110795 0.0055157 0.0043679 0.0024739 ]; E=[0.0008148 0.0008756 0.00087131 0.0008794 0.0008711 0.0008441 0.0007384 0.0006997 0.0005980 ]; N=length(A); x1n=zeros(N,1);x2n=zeros(N,1);x3n=zeros(N,1); X1=zeros(N,1);X2=zeros(N,1);X3=zeros(N,1);X4=zeros(N,1);X5=zeros(N,1);X6=zeros(N,1);X7=zeros(N,1); X12=zeros(N,1);X16=zeros(N,1); for i=1:N x1n(i)=(x1(i)-A(i)-D(i)-2*E(i)-C(i)+3*B(i)) x2n(i)=(x2(i)-A(i)-C(i)-D(i)) x3n(i)=(x3(i)-B(i)) X1(i)=(x1n(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))) X2(i)=(x2n(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))) X3(i)=(x3n(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))) X4(i)=(A(i)+D(i)+E(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))) X5(i)=(C(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))) X6(i)=(A(i)-B(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))) X7(i)=(B(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))) X12(i)=(E(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))) X16(i)=(C(i)+D(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))) end yc4=X4./(X4+X5); yc5=X5./(X4+X5); yc6=X6./(X6+X7+X12+X16); yc7=X7./(X6+X7+X12+X16); yc12=X12./(X6+X7+X12+X16); yc16=X16./(X6+X7+X12+X16); alpha14=yc6.*alpha18+yc7.*alpha19+yc12.*alpha113+yc16.*alpha117; %alpha41=alpha14; alpha24=yc6.*alpha28+yc7.*alpha29+yc12.*alpha213+yc16.*alpha217; %alpha42=alpha24; alpha34=yc6.*alpha38+yc7.*alpha39+yc12.*alpha313+yc16.*alpha317; %alpha43=alpha34; alpha15=yc6.*alpha110+yc7.*alpha115+yc12.*alpha114+yc16.*alpha118; %alpha51=alpha15; alpha25=yc6.*alpha210+yc7.*alpha215+yc12.*alpha214+yc16.*alpha218; %alpha52=alpha25; alpha35=yc6.*alpha310+yc7.*alpha315+yc12.*alpha314+yc16.*alpha318; %alpha53=alpha35; alpha16=yc4.*alpha81+yc5.*alpha101; %alpha61=alpha16; alpha26=yc4.*alpha82+yc5.*alpha102; %alpha62=alpha26; alpha36=yc4.*alpha83+yc5.*alpha103; %alpha63=alpha36; alpha17=yc4.*alpha91+yc5.*alpha151; %alpha71=alpha17; alpha27=yc4.*alpha92+yc5.*alpha152; %alpha72=alpha27; alpha37=yc4.*alpha93+yc5.*alpha153; %alpha73=alpha37; alpha112=yc4.*alpha131+yc5.*alpha141; %alpha121=alpha112; alpha212=yc4.*alpha132+yc5.*alpha142; %alpha122=alpha212; alpha312=yc4.*alpha133+yc5.*alpha143; %alpha123=alpha312; alpha116=yc4.*alpha171+yc5.*alpha181; %alpha161=alpha116; alpha216=yc4.*alpha172+yc5.*alpha182; %alpha162=alpha216; alpha316=yc4.*alpha173+yc5.*alpha183; %alpha163=alpha316; alpha46=yc5.*alpha810; %alpha64=alpha46; alpha47=yc5.*alpha915; %alpha74=alpha47; alpha412=yc5.*alpha1314; %alpha124=alpha412; alpha416=yc5.*alpha1718; %alpha164=alpha416; alpha56=yc4.*alpha108; %alpha65=alpha56; alpha57=yc4.*alpha159; %alpha75=alpha57; alpha512=yc4.*alpha1413; %alpha125=alpha512; alpha516=yc4.*alpha1817; %alpha165=alpha516; G14=yc6.*G18+yc7.*G19+yc12.*G113+yc16.*G117; %G41=G14; G24=yc6.*G28+yc7.*G29+yc12.*G213+yc16.*G217; %G42=G24; G34=yc6.*G38+yc7.*G39+yc12.*G313+yc16.*G317; %G43=G34; G15=yc6.*G110+yc7.*G115+yc12.*G114+yc16.*G118; %G51=G15; G25=yc6.*G210+yc7.*G215+yc12.*G214+yc16.*G218; %G52=G25; G35=yc6.*G310+yc7.*G315+yc12.*G314+yc16.*G318; %G53=G35; G16=yc4.*G81+yc5.*G101; %G61=G16; G26=yc4.*G82+yc5.*G102; %G62=G26; G36=yc4.*G83+yc5.*G103; %G63=G36; G17=yc4.*G91+yc5.*G151; %G71=G17; G27=yc4.*G92+yc5.*G152; %G72=G27; G37=yc4.*G93+yc5.*G153; %G73=G37; G112=yc4.*G131+yc5.*G141; %G121=G112; G212=yc4.*G132+yc5.*G142; %G122=G212; G312=yc4.*G133+yc5.*G143; %G123=G312; G116=yc4.*G171+yc5.*G181; %G161=G116; G216=yc4.*G172+yc5.*G182; %G162=G216; G316=yc4.*G173+yc5.*G183; %G163=G316; G46=yc5.*G810; %G64=G46; G47=yc5.*G915; %G74=G47; G412=yc5.*G1314; %G124=G412; G416=yc5.*G1718; %G164=G416; G56=yc4.*G108; %G65=G56; G57=yc4.*G159; %G75=G57; G512=yc4.*G1413; %G125=G512; G516=yc4.*G1817; %G165=G516; tau14=-log(G14)./alpha14; %tau41=tau14; tau24=-log(G24)./alpha24; %tau42=tau24; tau34=-log(G34)./alpha34; %tau43=tau34; tau15=-log(G15)./alpha15; %tau51=tau15; tau25=-log(G25)./alpha25; %tau52=tau25; tau35=-log(G35)./alpha35; %tau53=tau35; tau16=-log(G16)./alpha16; %tau61=tau16; tau26=-log(G26)./alpha26; %tau62=tau26; tau36=-log(G36)./alpha36; %tau63=tau36; tau17=-log(G17)./alpha17; %tau71=tau17; tau27=-log(G27)./alpha27; %tau72=tau27; tau37=-log(G37)./alpha37; %tau73=tau37; tau112=-log(G112)./alpha112; %tau121=tau112; tau212=-log(G212)./alpha212; %tau122=tau212; tau312=-log(G312)./alpha312; %tau123=tau312; tau116=-log(G116)./alpha116; %tau161=tau116; tau216=-log(G216)./alpha216; %tau162=tau216; tau316=-log(G316)./alpha316; %tau163=tau316; tau46=-log(G46)./alpha46; %tau64=tau46; tau47=-log(G47)./alpha47; %tau74=tau47; tau412=-log(G412)./alpha412; %tau124=tau412; tau416=-log(G416)./alpha416; %tau164=tau416; tau56=-log(G56)./alpha56; %tau65=tau56; tau57=-log(G57)./alpha57; %tau75=tau57; tau512=-log(G512)./alpha512; %tau125=tau512; tau516=-log(G516)./alpha516; %tau165=tau516; ln_y1_1=G12.*X2.*tau12+ G31.*X3.*tau13+ G14.*X4.*tau14+G15.*X5.*tau15+G16.*X6.*tau16+G17.*X7.*tau17+G112.*X12.*tau112+G116.*X16.*tau116; ln_y1_2=G12.*X2+ G13.*X3+ G14.*X4+G15.*X5+G16.*X6+G17.*X7+G112.*X12+G116.*X16; ln_y2_1=G21.*X1.*tau12+ G32.*X3.*tau32+ G24.*X4.*tau24+G25.*X5.*tau25+G26.*X6.*tau26+G27.*X7.*tau27+G212.*X12.*tau212+G216.*X16.*tau216; ln_y2_2=G12.*X1+ G23.*X3+G24.*X4+G25.*X5+G26.*X6+G27.*X7+G212.*X12+G216.*X16; ln_y3_1=G13.*X1.*tau13+ G23.*X3.*tau23+ G34.*X4.*tau34+G35.*X5.*tau35+G36.*X6.*tau36+G37.*X7.*tau37+G312.*X12.*tau312+G316.*X16.*tau316; ln_y3_2=G13.*X1+ G23.*X3+ G34.*X4+G35.*X5+G36.*X6+G37.*X7+G312.*X12+G316.*X16; ln_y4_1=G14.*X1.*tau14+G24.*X2.*tau24+G34.*X3.*tau34+G46.*X6.*tau46+G47.*X7.*tau47+G412.*X12.*tau412+G416.*X16.*tau416; ln_y4_2=G14.*X1+G24.*X2+G34.*X3+G46.*X6+G47.*X7+G412.*X12+G416.*X16; ln_y5_1=G15.*X1.*tau15+G25.*X2.*tau25+G35.*X3.*tau35+G56.*X6.*tau56+G57.*X7.*tau57+G512.*X12.*tau512+G516.*X16.*tau516; ln_y5_2=G15.*X1+G25.*X2+G35.*X3+G56.*X6+G57.*X7+G512.*X12+G516.*X16; ln_y6_1=G16.*X1.*tau16+G26.*X2.*tau26+G36.*X3.*tau36+G46.*X4.*tau46+G56.*X5.*tau56; ln_y6_2=G16.*X1+G26.*X2+G36.*X3+G46.*X4+G56.*X5; ln_y7_1=G17.*X1.*tau17+G27.*X2.*tau27+G37.*X3.*tau37+G47.*X4.*tau47+G57.*X5.*tau57; ln_y7_2=G17.*X1+G27.*X2+G37.*X3+G47.*X4+G57.*X5; ln_y12_1=G112.*X1.*tau112+G212.*X2.*tau212+G312.*X3.*tau312+G412.*X4.*tau412+G512.*X5.*tau512; ln_y12_2=G112.*X1+G212.*X2+G312.*X3+G412.*X4+G512.*X5; ln_y16_1=G116.*X1.*tau116+G216.*X2.*tau216+G316.*X3.*tau316+G416.*X4.*tau416+G516.*X5.*tau516; ln_y16_2=G116.*X1+G216.*X2+G316.*X3+G416.*X4+G516.*X5; ln_y1_3=(((X2.*G12)./ln_y2_2).*(tau12-(ln_y2_1)./(ln_y2_2)))+(((X3.*G13)./ln_y3_2).*(tau13-(ln_y3_1)./(ln_y3_2))); ln_y1_4=(((X6.*G16)./ln_y6_2).*(tau16- (ln_y6_1./ln_y6_2))) + (((X7.*G17)./ln_y7_2).*(tau17- (ln_y7_1./ln_y7_2)))+(((X12.*G12)./ln_y12_2).*(tau112- (ln_y12_1./ln_y12_2)))+(((X16.*G16)./ln_y16_2).*(tau116- (ln_y16_1./ln_y16_2))); ln_y1_5=(((X4.*G14)./ln_y4_2).*(tau14- (ln_y4_1./ln_y4_2))) + (((X5.*G15)./ln_y5_2).*(tau15- (ln_y5_1./ln_y5_2))); yk=exp((ln_y1_1./ln_y1_2) + ln_y1_3 + ln_y1_4+ ln_y1_5) % activity coefficient for H2O end ........................................ Another function where above function to be called..... % calling the function act_coeff to estimate the binary interaction parameters for i=1:112 filename = 'EagelsDATA.xlsx'; %reading VLE data from excel file Data = xlsread(filename); x(:,1) = Data([10:15 17:19],16); x(:,2) = Data([10:15 17:19],1); x(:,3)= Data([10:15 17:19],2); taua=(randi([-5,5],1,112)); yk=[0.0606 (values calculated from above function and will be used for fitting) 0.4327 0.6545 0.9417 1.2570 1.6881 1.9108 1.7777 1.3821] % taua =[ -2 3 4 -3 -4 1 4 -2 4 -4 -1 4 5 -3 3 2 -5 3 -4 % 1 4 1 5 -1 -1 -3 2 -3 4 3 4 2 5 4 -2 4 3 -1 % 1 0 -5 -5 -5 -3 4 2 1 4 0 2 -3 -4 5 0 -3 2 5 % 1 0 5 1 -3 5 4 1 5 2 3 2 0 -5 -4 -2 1 -2 5 %-5 5 -2 -2 4 1 -1 3 -1 1 5 -1 0 -1 4 5 5 1 4 % 1 0 4 -4 4 0 -1 -2 -5 -3 -4 -5 % -5 0 -2 0 -5] (random values for which yk was calculted from the command taua= randi([-5,5],1,112)) try % try-catch used to continue the loop without stopping on encountering an error [taua1]= nlinfit(x,yk,#activity_coefficientE,taua) catch exception continue end end I am not able to attach excel sheet here so data from excel sheet is as: x =[0.1577 0.1492 0.1462 0.1366 0.1299 0.1180 0.0863 0.0761 0.0550; column 1 0.8278 0.7945 0.7678 0.7450 0.6979 0.6309 0.4611 0.4114 0.2952 ; column 2 0.0145 0.0563 0.0860 0.1184 0.1722 0.2511 0.4526 0.5125 0.6498 ]; column 3
I found 3 major problems with what you did. Problem #1 - errors The reason you get the error is because your function "activity_coefficientE" can sometimes return NaN or inf values. My suggestion is to look for these values and set the value of "yk" to a large value so that the optimizer in "nlinfit" will stay away from coefficients that produce infinite or NaN values. This is the code at the bottom of the function so that you avoid crashes: if any(~isfinite(yk)) yk = 10 * ones(size(yk)); end Problem #2 - random initial guesses The trouble with using random numbers for your initial conditions is that every time you run it you get a different answer, so sometimes it works and sometimes it doesn't. If you set the random number generator seed, you can get the same random numbers each time you run the script. If you change you seed, you can get a different set of random numbers. I shortened your main script to this, where I try 100 different random seeds (and store the results of each attempt) to see what answers result: for i=1:100 rng(i) taua = randi([-5,5],1,112); taua1(i, :) = nlinfit(x,yk,#activity_coefficientE,taua); end Each row of "taua1" is a set of 111 coefficients. Problem #3 - Trying to fit 9 points with 112 coefficients Every time nlinfit is called, you get this warning: Warning: Rank deficient because you have more coefficients (112) that you are asking nlinfit to find than data points you are fitting (9). It's like trying to find the 2nd order equation that best fits 2 points, there are an infinite number of solutions. When curve fitting you should have more data points than coefficients to make sure you're not fitting noise. You need more data points in "yk" and "x" and/or fewer coefficients to fit. I've done a lot of curve fitting and I've never seen an equation with 112 coefficients, so I am thinking that you are not solving the problem correctly. Perhaps the 112 coefficients aren't really independent or there are 112 data points and 9 coefficients that you want to find. For completeness, here is my edited version of the activity_coefficientE.m function that I created to work on this solution. In general, I never see Matlab code with this many variables with similar names. Much of this code could be greatly simplified by using vector operations. Most of my changes involve formatting, adding semicolons, and the checks for non-finite values at the end. function yk=activity_coefficientE(taua,x) T=523; alpha12=0.3; alpha13=0.3; alpha21=0.3; alpha23=0.3; alpha31=0.3; alpha32=0.3; alpha18=0.2; alpha81=0.2; alpha28=0.2; alpha82=0.2; alpha38=0.2; alpha83=0.3; alpha19=0.2; alpha91=0.2; alpha29=0.2; alpha92=0.2; alpha39=0.2; alpha93=0.2; alpha110=0.2;alpha101=0.2;alpha210=0.2;alpha102=0.2;alpha310=0.2;alpha103=0.2; alpha113=0.2;alpha131=0.2;alpha213=0.2;alpha132=0.2;alpha313=0.2;alpha133=0.2; alpha114=0.2;alpha141=0.2;alpha214=0.2;alpha142=0.2;alpha314=0.2;alpha143=0.2; alpha115=0.2;alpha151=0.2;alpha215=0.2;alpha152=0.2;alpha315=0.2;alpha153=0.2; alpha117=0.2;alpha171=0.2;alpha217=0.2;alpha172=0.2;alpha317=0.2;alpha173=0.2; alpha118=0.2;alpha181=0.2;alpha218=0.2;alpha182=0.2;alpha318=0.2;alpha183=0.2; alpha810=0.2;alpha915=0.2;alpha1314=0.2;alpha108=0.2;alpha159=0.2;alpha1413=0.2; alpha1718=0.2;alpha1817=0.2; tau12=0; tau13=0; tau21=0; tau23=0; tau31=0; tau32=0; tau18=taua(1)+taua(57)/T; tau81=taua(2)+taua(58)/T; tau28=taua(3)+taua(59)/T; tau82=taua(4)+taua(60)/T; tau38=taua(5)+taua(61)/T; tau83=taua(6)+taua(62)/T; tau19=taua(7)+taua(63)/T; tau91=taua(8)+taua(64)/T; tau29=taua(9)+taua(65)/T; tau92=taua(10)+taua(66)/T; tau39=taua(11)+taua(67)/T; tau93=taua(12)+taua(68)/T; tau110=taua(13)+taua(69)/T; tau101=taua(14)+taua(70)/T; tau210=taua(15)+taua(71)/T; tau102=taua(16)+taua(72)/T; tau310=taua(17)+taua(73)/T; tau103=taua(18)+taua(74)/T; tau113=taua(19)+taua(75)/T; tau131=taua(20)+taua(76)/T; tau213=taua(21)+taua(77)/T; tau132=taua(22)+taua(78)/T; tau313=taua(23)+taua(79)/T; tau133=taua(24)+taua(80)/T; tau114=taua(25)+taua(81)/T; tau141=taua(26)+taua(82)/T; tau214=taua(27)+taua(83)/T; tau142=taua(28)+taua(84)/T; tau314=taua(29)+taua(85)/T; tau143=taua(30)+taua(86)/T; tau115=taua(31)+taua(87)/T; tau151=taua(32)+taua(88)/T; tau215=taua(33)+taua(89)/T; tau152=taua(34)+taua(90)/T; tau315=taua(35)+taua(91)/T; tau153=taua(36)+taua(92)/T; tau117=taua(37)+taua(93)/T; tau171=taua(38)+taua(94)/T; tau217=taua(39)+taua(95)/T; tau172=taua(40)+taua(96)/T; tau317=taua(41)+taua(97)/T; tau173=taua(42)+taua(98)/T; tau118=taua(43)+taua(99)/T; tau181=taua(44)+taua(100)/T; tau218=taua(45)+taua(101)/T; tau182=taua(46)+taua(102)/T; tau318=taua(47)+taua(103)/T; tau183=taua(48)+taua(104)/T; tau810=taua(49)+taua(105)/T; tau108=taua(50)+taua(106)/T; tau915=taua(51)+taua(107)/T; tau159=taua(52)+taua(108)/T; tau1314=taua(53)+taua(109)/T; tau1413=taua(54)+taua(110)/T; tau1718=taua(55)+taua(111)/T; tau1817=taua(56)+taua(112)/T; G12=exp(-(tau12*alpha12)); G21=exp(-(tau21*alpha21)); G13=exp(-(tau13*alpha13)); G31=exp(-(tau31*alpha31)); G23=exp(-(tau23*alpha23)); G32=exp(-(tau32*alpha32)); G18=exp(-(tau18*alpha18)); G81=exp(-(tau81*alpha81)); G28=exp(-(tau28*alpha28)); G82=exp(-(tau82*alpha82)); G38=exp(-(tau38*alpha83)); G83=exp(-(tau83*alpha83)); G19=exp(-(tau19*alpha19)); G91=exp(-(tau91*alpha91)); G29=exp(-(tau29*alpha29)); G92=exp(-(tau92*alpha92)); G39=exp(-(tau39*alpha39)); G93=exp(-(tau93*alpha93)); G110=exp(-(tau110*alpha110)); G101=exp(-(tau101*alpha101)); G210=exp(-(tau210*alpha210)); G102=exp(-(tau102*alpha102)); G310=exp(-(tau310*alpha310)); G103=exp(-(tau103*alpha103)); G113=exp(-(tau113*alpha113)); G131=exp(-(tau131*alpha131)); G213=exp(-(tau213*alpha213)); G132=exp(-(tau132*alpha132)); G313=exp(-(tau313*alpha313)); G133=exp(-(tau133*alpha133)); G114=exp(-(tau114*alpha114)); G141=exp(-(tau141*alpha141)); G214=exp(-(tau214*alpha214)); G142=exp(-(tau142*alpha142)); G314=exp(-(tau314*alpha314)); G143=exp(-(tau143*alpha143)); G115=exp(-(tau115*alpha115)); G151=exp(-(tau151*alpha151)); G215=exp(-(tau215*alpha215)); G152=exp(-(tau152*alpha152)); G315=exp(-(tau315*alpha315)); G153=exp(-(tau153*alpha153)); G117=exp(-(tau117*alpha117)); G171=exp(-(tau171*alpha171)); G217=exp(-(tau217*alpha217)); G172=exp(-(tau172*alpha172)); G317=exp(-(tau317*alpha317)); G173=exp(-(tau173*alpha173)); G118=exp(-(tau118*alpha118)); G181=exp(-(tau181*alpha181)); G218=exp(-(tau218*alpha218)); G182=exp(-(tau182*alpha182)); G318=exp(-(tau318*alpha318)); G183=exp(-(tau183*alpha183)); G810=exp(-(tau810*alpha810)); G108=exp(-(tau108*alpha108)); G915=exp(-(tau915*alpha915)); G159=exp(-(tau159*alpha159)); G1314=exp(-(tau1314*alpha1314)); G1413=exp(-(tau1413*alpha1413)); G1718=exp(-(tau1718*alpha1718)); G1817=exp(-(tau1817*alpha1817)); %calculating mole fractions of ionic species x1=x(:,1); x2=x(:,2); x3=x(:,3); A=[0.0674243 0.0773881 0.0843400 0.0865343 0.0899223 0.0882858 0.0715087 0.0643867 0.0483658]; B=[0.0141081 0.0479814 0.0643151 0.0737477 0.0820756 0.0838701 0.0701576 0.0634457 0.0479639]; C=[0.0565665 0.0450072 0.0387724 0.0313828 0.02506094 0.0186280 0.0092734 0.0073438 0.0041595 ]; D=[0.0336447 0.0267694 0.0230611 0.0186659 0.0149058 0.0110795 0.0055157 0.0043679 0.0024739 ]; E=[0.0008148 0.0008756 0.00087131 0.0008794 0.0008711 0.0008441 0.0007384 0.0006997 0.0005980 ]; N=length(A); x1n=zeros(N,1);x2n=zeros(N,1);x3n=zeros(N,1); X1=zeros(N,1);X2=zeros(N,1);X3=zeros(N,1);X4=zeros(N,1);X5=zeros(N,1);X6=zeros(N,1);X7=zeros(N,1); X12=zeros(N,1);X16=zeros(N,1); for i=1:N x1n(i)=(x1(i)-A(i)-D(i)-2*E(i)-C(i)+3*B(i)); x2n(i)=(x2(i)-A(i)-C(i)-D(i)); x3n(i)=(x3(i)-B(i)); X1(i)=(x1n(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))); X2(i)=(x2n(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))); X3(i)=(x3n(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))); X4(i)=(A(i)+D(i)+E(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))); X5(i)=(C(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))); X6(i)=(A(i)-B(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))); X7(i)=(B(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))); X12(i)=(E(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))); X16(i)=(C(i)+D(i)/(x1n(i)+x2n(i)+x3n(i)+2*A(i)+4*B(i)+2*C(i)+2*D(i)+2*E(i))); end yc4=X4./(X4+X5); yc5=X5./(X4+X5); yc6=X6./(X6+X7+X12+X16); yc7=X7./(X6+X7+X12+X16); yc12=X12./(X6+X7+X12+X16); yc16=X16./(X6+X7+X12+X16); alpha14=yc6.*alpha18+yc7.*alpha19+yc12.*alpha113+yc16.*alpha117; alpha24=yc6.*alpha28+yc7.*alpha29+yc12.*alpha213+yc16.*alpha217; alpha34=yc6.*alpha38+yc7.*alpha39+yc12.*alpha313+yc16.*alpha317; alpha15=yc6.*alpha110+yc7.*alpha115+yc12.*alpha114+yc16.*alpha118; alpha25=yc6.*alpha210+yc7.*alpha215+yc12.*alpha214+yc16.*alpha218; alpha35=yc6.*alpha310+yc7.*alpha315+yc12.*alpha314+yc16.*alpha318; alpha16=yc4.*alpha81+yc5.*alpha101; alpha26=yc4.*alpha82+yc5.*alpha102; alpha36=yc4.*alpha83+yc5.*alpha103; alpha17=yc4.*alpha91+yc5.*alpha151; alpha27=yc4.*alpha92+yc5.*alpha152; alpha37=yc4.*alpha93+yc5.*alpha153; alpha112=yc4.*alpha131+yc5.*alpha141; alpha212=yc4.*alpha132+yc5.*alpha142; alpha312=yc4.*alpha133+yc5.*alpha143; alpha116=yc4.*alpha171+yc5.*alpha181; alpha216=yc4.*alpha172+yc5.*alpha182; alpha316=yc4.*alpha173+yc5.*alpha183; alpha46=yc5.*alpha810; alpha47=yc5.*alpha915; alpha412=yc5.*alpha1314; alpha416=yc5.*alpha1718; alpha56=yc4.*alpha108; alpha57=yc4.*alpha159; alpha512=yc4.*alpha1413; alpha516=yc4.*alpha1817; G14=yc6.*G18+yc7.*G19+yc12.*G113+yc16.*G117; G24=yc6.*G28+yc7.*G29+yc12.*G213+yc16.*G217; G34=yc6.*G38+yc7.*G39+yc12.*G313+yc16.*G317; G15=yc6.*G110+yc7.*G115+yc12.*G114+yc16.*G118; G25=yc6.*G210+yc7.*G215+yc12.*G214+yc16.*G218; G35=yc6.*G310+yc7.*G315+yc12.*G314+yc16.*G318; G16=yc4.*G81+yc5.*G101; G26=yc4.*G82+yc5.*G102; G36=yc4.*G83+yc5.*G103; G17=yc4.*G91+yc5.*G151; G27=yc4.*G92+yc5.*G152; G37=yc4.*G93+yc5.*G153; G112=yc4.*G131+yc5.*G141; G212=yc4.*G132+yc5.*G142; G312=yc4.*G133+yc5.*G143; G116=yc4.*G171+yc5.*G181; G216=yc4.*G172+yc5.*G182; G316=yc4.*G173+yc5.*G183; G46=yc5.*G810; G47=yc5.*G915; G412=yc5.*G1314; G416=yc5.*G1718; G56=yc4.*G108; G57=yc4.*G159; G512=yc4.*G1413; G516=yc4.*G1817; tau14=-log(G14)./alpha14; tau24=-log(G24)./alpha24; tau34=-log(G34)./alpha34; tau15=-log(G15)./alpha15; tau25=-log(G25)./alpha25; tau35=-log(G35)./alpha35; tau16=-log(G16)./alpha16; tau26=-log(G26)./alpha26; tau36=-log(G36)./alpha36; tau17=-log(G17)./alpha17; tau27=-log(G27)./alpha27; tau37=-log(G37)./alpha37; tau112=-log(G112)./alpha112; tau212=-log(G212)./alpha212; tau312=-log(G312)./alpha312; tau116=-log(G116)./alpha116; tau216=-log(G216)./alpha216; tau316=-log(G316)./alpha316; tau46=-log(G46)./alpha46; tau47=-log(G47)./alpha47; tau412=-log(G412)./alpha412; tau416=-log(G416)./alpha416; tau56=-log(G56)./alpha56; tau57=-log(G57)./alpha57; tau512=-log(G512)./alpha512; tau516=-log(G516)./alpha516; ln_y1_1=G12.*X2.*tau12+ G31.*X3.*tau13+ G14.*X4.*tau14+G15.*X5.*tau15+G16.*X6.*tau16+G17.*X7.*tau17+G112.*X12.*tau112+G116.*X16.*tau116; ln_y1_2=G12.*X2+ G13.*X3+ G14.*X4+G15.*X5+G16.*X6+G17.*X7+G112.*X12+G116.*X16; ln_y2_1=G21.*X1.*tau12+ G32.*X3.*tau32+ G24.*X4.*tau24+G25.*X5.*tau25+G26.*X6.*tau26+G27.*X7.*tau27+G212.*X12.*tau212+G216.*X16.*tau216; ln_y2_2=G12.*X1+ G23.*X3+G24.*X4+G25.*X5+G26.*X6+G27.*X7+G212.*X12+G216.*X16; ln_y3_1=G13.*X1.*tau13+ G23.*X3.*tau23+ G34.*X4.*tau34+G35.*X5.*tau35+G36.*X6.*tau36+G37.*X7.*tau37+G312.*X12.*tau312+G316.*X16.*tau316; ln_y3_2=G13.*X1+ G23.*X3+ G34.*X4+G35.*X5+G36.*X6+G37.*X7+G312.*X12+G316.*X16; ln_y4_1=G14.*X1.*tau14+G24.*X2.*tau24+G34.*X3.*tau34+G46.*X6.*tau46+G47.*X7.*tau47+G412.*X12.*tau412+G416.*X16.*tau416; ln_y4_2=G14.*X1+G24.*X2+G34.*X3+G46.*X6+G47.*X7+G412.*X12+G416.*X16; ln_y5_1=G15.*X1.*tau15+G25.*X2.*tau25+G35.*X3.*tau35+G56.*X6.*tau56+G57.*X7.*tau57+G512.*X12.*tau512+G516.*X16.*tau516; ln_y5_2=G15.*X1+G25.*X2+G35.*X3+G56.*X6+G57.*X7+G512.*X12+G516.*X16; ln_y6_1=G16.*X1.*tau16+G26.*X2.*tau26+G36.*X3.*tau36+G46.*X4.*tau46+G56.*X5.*tau56; ln_y6_2=G16.*X1+G26.*X2+G36.*X3+G46.*X4+G56.*X5; ln_y7_1=G17.*X1.*tau17+G27.*X2.*tau27+G37.*X3.*tau37+G47.*X4.*tau47+G57.*X5.*tau57; ln_y7_2=G17.*X1+G27.*X2+G37.*X3+G47.*X4+G57.*X5; ln_y12_1=G112.*X1.*tau112+G212.*X2.*tau212+G312.*X3.*tau312+G412.*X4.*tau412+G512.*X5.*tau512; ln_y12_2=G112.*X1+G212.*X2+G312.*X3+G412.*X4+G512.*X5; ln_y16_1=G116.*X1.*tau116+G216.*X2.*tau216+G316.*X3.*tau316+G416.*X4.*tau416+G516.*X5.*tau516; ln_y16_2=G116.*X1+G216.*X2+G316.*X3+G416.*X4+G516.*X5; ln_y1_3=(((X2.*G12)./ln_y2_2).*(tau12-(ln_y2_1)./(ln_y2_2)))+(((X3.*G13)./ln_y3_2).*(tau13-(ln_y3_1)./(ln_y3_2))); ln_y1_4=(((X6.*G16)./ln_y6_2).*(tau16- (ln_y6_1./ln_y6_2))) + (((X7.*G17)./ln_y7_2).*(tau17- (ln_y7_1./ln_y7_2)))+(((X12.*G12)./ln_y12_2).*(tau112- (ln_y12_1./ln_y12_2)))+(((X16.*G16)./ln_y16_2).*(tau116- (ln_y16_1./ln_y16_2))); ln_y1_5=(((X4.*G14)./ln_y4_2).*(tau14- (ln_y4_1./ln_y4_2))) + (((X5.*G15)./ln_y5_2).*(tau15- (ln_y5_1./ln_y5_2))); yk=exp((ln_y1_1./ln_y1_2) + ln_y1_3 + ln_y1_4+ ln_y1_5)'; % activity coefficient for H2O if any(~isfinite(yk)) yk = 10 * ones(size(yk)); end
"Heap exhausted, game over" message in wxMaxima - Does ccl will work for me?
everyone, I'm trying to do some calculations and plot the results, but it seems that these are too heavy for Maxima. When I try to calculate N1 and N2 the program crashes when parameter j is too high or when I try to plot them, the program displays the following error message: "Heap exhausted, game over." What should I do? I've seen some people saying to try to compile Maxima with ccl, but I don't know how to do it or if it will work. I usually receive error messages like: Message from maxima's stderr stream: Heap exhausted during garbage collection: 0 bytes available, 16 requested. Gen Boxed Unboxed LgBox LgUnbox Pin Alloc Waste Trig WP GCs Mem-age 0 0 0 0 0 0 0 0 20971520 0 0 0,0000 1 0 0 0 0 0 0 0 20971520 0 0 0,0000 2 0 0 0 0 0 0 0 20971520 0 0 0,0000 3 16417 2 0 0 43 1075328496 707088 293986768 16419 1 0,8032 4 13432 21 0 1141 70 955593760 838624 2000000 14594 0 0,2673 5 0 0 0 0 0 0 0 2000000 0 0 0,0000 6 741 184 34 28 0 63259792 1424240 2000000 987 0 0,0000 7 0 0 0 0 0 0 0 2000000 0 0 0,0000 Total bytes allocated = 2094182048 Dynamic-space-size bytes = 2097152000 GC control variables: *GC-INHIBIT* = true *GC-PENDING* = true *STOP-FOR-GC-PENDING* = false fatal error encountered in SBCL pid 13884(tid 0000000001236360): Heap exhausted, game over. Here goes the code: enter code here a: 80$; b: 6*a$; h1: 80$; t: 2$; j: 5$; carga: 250$; sig: -carga/2$; n: 2*q*%pi/b$; m: i*%pi/a$; i: 2*p-1$; i1: 2*p1-1$; /*i1: p1$;*/ Φ: a/b$; τ: cosh(x) - (x/sinh(x))$; σ: sinh(x) - (x/cosh(x))$; Ψ: sinh(x)/τ$; Χ: cosh(x)/σ$; Λ0: 1/(((i/2)^2+Φ^2*q^2)^2)$; Λ1: sum((((i/2)^3*subst([x=(i*%pi/(2*Φ))],Ψ))/(((i/2)^2+Φ^2*q1^2)^2))*Λ0, p, 1, j)$; Λ2: sum(((q1^3*subst([x=(q1*%pi*Φ)],Χ))/(((i/2)^2+Φ^2*q1^2)^2))*Λ1, q1, 1, j)$; Λ3: sum((((i/2)^3*subst([x=(i*%pi/(2*Φ))],Ψ))/(((i/2)^2+Φ^2*q1^2)^2))*Λ2, p, 1, j)$; Λ4: sum(((q1^3*subst([x=(q1*%pi*Φ)],Χ))/(((i/2)^2+Φ^2*q1^2)^2))*Λ3, q1, 1, j)$; Λ5: sum((((i/2)^3*subst([x=(i*%pi/(2*Φ))],Ψ))/(((i/2)^2+Φ^2*q1^2)^2))*Λ4, p, 1, j)$; Ζ0: sum(((q^3*subst([x=(q*%pi*Φ)],Χ))/(((i1/2)^2+Φ^2*q^2)^2))*Λ0, q, 1, j)$; Ζ2: sum(((q^3*subst([x=(q*%pi*Φ)],Χ))/(((i1/2)^2+Φ^2*q^2)^2))*Λ2, q, 1, j)$; Ζ4: sum(((q^3*subst([x=(q*%pi*Φ)],Χ))/(((i1/2)^2+Φ^2*q^2)^2))*Λ4, q, 1, j)$; E: 200000$; ν: 0.3$; λ: (ν*E)/((1+ν)*(1-2*ν))$; μ: E/(2*(1+ν))$; a0: float(1/(b/2)*integrate(0, y, -(b/2), -h1/2)+1/b*integrate(sig, y, -h1/2, h1/2)+1/(b/2)*integrate(0, y, h1/2, (b/2)))$; aq: float(1/(b/2)*integrate(0*cos(q*y*%pi/(b/2)), y, -(b/2), - h1/2)+1/(b/2)*integrate(sig*cos(q*y*%pi/(b/2)), y, -h1/2, h1/2)+1/(b/2)*integrate(0*cos(q*y*%pi/(b/2)), y, h1/2, (b/2)))$; aq1: float(1/(b/2)*integrate(0*cos(q1*y*%pi/(b/2)), y, -(b/2), - h1/2)+1/(b/2)*integrate(sig*cos(q1*y*%pi/(b/2)), y, -h1/2, h1/2)+1/(b/2)*integrate(0*cos(q1*y*%pi/(b/2)), y, h1/2, (b/2)))$; Bq: aq/((λ+μ)*subst([x=q*%pi*Φ],σ))+((16*Φ^4*q^2*(-1)^q)/((λ+μ)*%pi^2*subst([x=q*%pi*Φ],σ)))*sum(q1*aq1*(-1) ^q1*subst([x=q1*%pi*Φ],Χ)*(Λ1+(16*Φ^4/(%pi^2))*Λ3+((16*Φ^4/(%pi^2))^2)*Λ5), q1, 1, j)+(8*λ*Φ^3*q^2*(-1)^q*a0)/((λ+μ)*(λ+2*μ)*(%pi^3)*subst([x=q*%pi*Φ],σ))*sum(subst([x=i*%pi/(2*Φ)],Ψ)/(i/ 2)*(Λ0+(16*Φ^4/(%pi^2))*Λ2+((16*Φ^4/(%pi^2))^2)*Λ4), p, 1, j)$; βp: -(2*λ*a0*(-1)^((i-1)/2))/((λ+μ)*(λ+2*μ)*(i/2)^2*%pi^2*subst([x=i*%pi/(2*Φ)],τ))-((32*λ*Φ^4*(i/2)^2*a0*(-1)^((i-1)/2))/((λ+μ)*(λ+2*μ)*%pi^2*subst([x=i*%pi/(2*Φ)],τ)))*sum(((subst([x=i1*%pi/(2*Φ)],Ψ))/(i1/2))*(Ζ0+Ζ2*((16*Φ^4)/%pi^2)+Ζ4*(((16*Φ^4)/%pi^2)^2)),p1,1,j)-((4*Φ*(i/2)^2*(-1)^((i-1)/2))/((λ+μ)*%pi*subst([x=i*%pi/(2*Φ)],τ)))*sum(q*aq*(-1)^q*subst([x=q*%pi*Φ],Χ)*(Λ0+Λ2*(16*Φ^4/%pi^2)+Λ4*(16*Φ^4/%pi^2)^2),q,1,j)$; N1: (2*a0/a)*x+(λ+μ)*sum(Bq*((1+((n*a*sinh(n*a/2))/(2*cosh(n*a/2))))*sinh(n*x)-n*x*cosh(n*x))*cos(n*y),q,1,j)+(λ+μ)*sum(βp*((1-((m*b*cosh(m*b/2))/(2*sinh(m*b/2))))*cosh(m*y)+m*y*sinh(m*y))*sin(m*x),p,1,j)$; N2: ((2*λ*a0)/(a*(λ+2*μ)))*x+(λ+μ)*sum(Bq*((1-((n*a*sinh(n*a/2))/(2*cosh(n*a/2))))*sinh(n*x)+n*x*cosh(n*x))*cos(n*y),q,1,j)+(λ+μ)*sum(βp*((1+((m*b*cosh(m*b/2))/(2*sinh(m*b/2))))*cosh(m*y)-m*y*sinh(m*y))*sin(m*x),p,1,j); wxplot3d(N1, [x,-a/2,a/2], [y,-b/2,b/2])$; wxplot3d(N2, [x,-a/2,a/2], [y,-b/2,b/2])$;
This is not a complete answer, since I don't know how this should work with wxMaxima: I would suggest that you ask the developers. However it's too long for a comment and I think might be useful to people, and it does answer the question of how you solve the heap-size limit for Maxima itself when using SBCL, at least when run on Linux or some other platform with a command-line. As a note, I suspect that the underlying problem is not the heap size, but that the calculation is blowing up in some horrible way: the best fix is probably to understand what's blowing up and fix that. See Robert Dodier's answer, which is probably going to be a lot more helpful. However, if heap size is the problem, this is how you deal with it for Maxima. The trick is that you can tell SBCL what the heap limit should be by passing it the --dynamic-space-size <MB> argument, and you can pas arguments through the maxima wrapper to do this. Here is a transcript of Maxima, being run on Linux, with SBCL as a back end (this is a version built from source: the packaged version will I assume be the same): $ maxima Maxima 5.43.2 http://maxima.sourceforge.net using Lisp SBCL 2.0.0 Distributed under the GNU Public License. See the file COPYING. Dedicated to the memory of William Schelter. The function bug_report() provides bug reporting information. (%i1) :lisp (sb-ext:dynamic-space-size) 1073741824 So, on this system the defaule heap limit is 1GB (this is SBCL's default limit on the platform). Now we can pass the -X <lisp options> aka --lisp-options=<lisp options> option to the maxima wrapper to pass the appropriate option through to sbcl: $ maxima -X '--dynamic-space-size 2000' Lisp options: (--dynamic-space-size 2000) Maxima 5.43.2 http://maxima.sourceforge.net using Lisp SBCL 2.0.0 Distributed under the GNU Public License. See the file COPYING. Dedicated to the memory of William Schelter. The function bug_report() provides bug reporting information. (%i1) :lisp (sb-ext:dynamic-space-size) 2097152000 As you can see this has doubled the heap size. If someone knows the answer for wxMaxima then please do add an edit to this answer: I can't experiment it because all my Linux VMs are headless.
Also not a complete answer here, but some more notes and pointers which I hope will help. To make the problem easier for Maxima to digest, use only exact numbers (integers and ratios), and avoid float and numer. (Plotting functions will apply float and numer automatically.) I changed 0.3 to 3/10 and cut out the calls to float. Also, try setting j to a smaller number (I tried j equal to 1) to try to work all the way through the problem before increasing it to 5 again. Also, replace all sum and integrate with 'sum and 'integrate (i.e. noun expressions instead of verb expressions). Take a look at the summands and integrands to see if they look right. You can evaluate the sums and/or integrals or both via ev(expr, sum) or ev(expr, integrate) or ev(expr, nouns) to evaluate 'sum, 'integrate, or all noun expressions, respectively. With j equal to 1, I get the following expression for N1: (2500000*((-(13*cosh(%pi/6) *((8503056*cosh(%pi/6)^2*sinh(3*%pi)^2) /(9765625*%pi^4 *(sinh(%pi/6)-%pi/(6*cosh(%pi/6)))^2 *(cosh(3*%pi)-(3*%pi)/sinh(3*%pi))^2) +(52488*cosh(%pi/6)*sinh(3*%pi)) /(15625*%pi^2*(sinh(%pi/6)-%pi/(6*cosh(%pi/6))) *(cosh(3*%pi)-(3*%pi)/sinh(3*%pi))) +324/25)) /(120000*%pi^2*(sinh(%pi/6)-%pi/(6*cosh(%pi/6))) *(cosh(3*%pi)-(3*%pi)/sinh(3*%pi)))) +(13*sinh(3*%pi) *((2754990144*cosh(%pi/6)^3*sinh(3*%pi)^2) /(244140625*%pi^4 *(sinh(%pi/6)-%pi/(6*cosh(%pi/6)))^3 *(cosh(3*%pi)-(3*%pi)/sinh(3*%pi))^2) +(17006112*cosh(%pi/6)^2*sinh(3*%pi)) /(390625*%pi^2 *(sinh(%pi/6)-%pi/(6*cosh(%pi/6)))^2 *(cosh(3*%pi)-(3*%pi)/sinh(3*%pi))) +(104976*cosh(%pi/6)) /(625*(sinh(%pi/6)-%pi/(6*cosh(%pi/6)))))) /(22680000*%pi^2*(cosh(3*%pi)-(3*%pi)/sinh(3*%pi))^2) +13/(35000*%pi^2*(cosh(3*%pi)-(3*%pi)/sinh(3*%pi)))) *sin((%pi*(2*p-1)*x)/80) *((%pi*(2*p-1)*y*sinh((%pi*(2*p-1)*y)/80))/80 +(1-(3*%pi*(2*p-1)*cosh(3*%pi*(2*p-1))) /sinh(3*%pi*(2*p-1))) *cosh((%pi*(2*p-1)*y)/80))) /13 +(2500000*((-(13*cosh(%pi/6) *((344373768*cosh(%pi/6)^2*sinh(3*%pi)^3) /(244140625*%pi^4 *(sinh(%pi/6)-%pi/(6*cosh(%pi/6))) ^2 *(cosh(3*%pi)-(3*%pi)/sinh(3*%pi)) ^3) +(2125764*cosh(%pi/6)*sinh(3*%pi)^2) /(390625*%pi^2 *(sinh(%pi/6)-%pi/(6*cosh(%pi/6))) *(cosh(3*%pi)-(3*%pi)/sinh(3*%pi))^2) +(13122*sinh(3*%pi)) /(625*(cosh(3*%pi)-(3*%pi)/sinh(3*%pi))))) /(1620000*%pi^3*(sinh(%pi/6)-%pi/(6*cosh(%pi/6)))^2)) +(13*sinh(3*%pi) *((8503056*cosh(%pi/6)^2*sinh(3*%pi)^2) /(9765625*%pi^4 *(sinh(%pi/6)-%pi/(6*cosh(%pi/6)))^2 *(cosh(3*%pi)-(3*%pi)/sinh(3*%pi))^2) +(52488*cosh(%pi/6)*sinh(3*%pi)) /(15625*%pi^2*(sinh(%pi/6)-%pi/(6*cosh(%pi/6))) *(cosh(3*%pi)-(3*%pi)/sinh(3*%pi))) +324/25)) /(3780000*%pi^3*(sinh(%pi/6)-%pi/(6*cosh(%pi/6))) *(cosh(3*%pi)-(3*%pi)/sinh(3*%pi))) -13/(20000*%pi*(sinh(%pi/6)-%pi/(6*cosh(%pi/6))))) *(((%pi*sinh(%pi/6))/(6*cosh(%pi/6))+1) *sinh((%pi*x)/240) -(%pi*x*cosh((%pi*x)/240))/240)*cos((%pi*y)/240)) /13-(25*x)/48$ Now in order to plot that, it should be a function of x and y only. However listofvars reports that it contains x, y, and p. Hmm. I see that βp has a summation over p1 but it contains Ζ0, which contains Λ0, which contains p. Is the summation over p1 supposed to be over p? Is the summand supposed to contain p1 instead of p? Likewise it appears that N2, after evaluating the sums and integrals with j equal to 1, contains p. Maybe you need to rework the formulas somewhat? I don't know what the correct form might be.
How to Have Vector Inequality in Matlab's Assert?
Code NA = 1.33; naValues = NA+pi/180:pi/180:(NA+pi/2); assert( (naValues > NA), 'naValues not bigger than NA'); where I also tried unsuccessfully floor(sum(naValues > NA))/90 in the place of the inequality that is trying assert( [logical], msg). Output Error using assert The condition input argument must be a scalar logical. How can you use a vector inequality in Matlab's assert?
Use all or any , but in general I would columnize the value you pass. This way it will always be robust to matrix and N-d arrays in addition to vectors. Observe: >> matrixValues = reshape(naValues,10,9) matrixValues = Columns 1 through 7 1.34745329251994 1.52198621771938 1.69651914291881 1.87105206811824 2.04558499331768 2.22011791851711 2.39465084371654 1.36490658503989 1.53943951023932 1.71397243543875 1.88850536063819 2.06303828583762 2.23757121103705 2.41210413623648 1.38235987755983 1.55689280275926 1.7314257279587 1.90595865315813 2.08049157835756 2.25502450355699 2.42955742875643 1.39981317007977 1.57434609527921 1.74887902047864 1.92341194567807 2.09794487087751 2.27247779607694 2.44701072127637 1.41726646259972 1.59179938779915 1.76633231299858 1.94086523819802 2.11539816339745 2.28993108859688 2.46446401379631 1.43471975511966 1.60925268031909 1.78378560551853 1.95831853071796 2.13285145591739 2.30738438111682 2.48191730631626 1.4521730476396 1.62670597283904 1.80123889803847 1.9757718232379 2.15030474843733 2.32483767363677 2.4993705988362 1.46962634015955 1.64415926535898 1.81869219055841 1.99322511575785 2.16775804095728 2.34229096615671 2.51682389135614 1.48707963267949 1.66161255787892 1.83614548307836 2.01067840827779 2.18521133347722 2.35974425867665 2.53427718387609 1.50453292519943 1.67906585039887 1.8535987755983 2.02813170079773 2.20266462599716 2.3771975511966 2.55173047639603 Columns 8 through 9 2.56918376891597 2.74371669411541 2.58663706143592 2.76116998663535 2.60409035395586 2.77862327915529 2.6215436464758 2.79607657167524 2.63899693899575 2.81352986419518 2.65645023151569 2.83098315671512 2.67390352403563 2.84843644923507 2.69135681655558 2.86588974175501 2.70881010907552 2.88334303427495 2.72626340159546 2.9007963267949 >> all(matrixValues) ans = 1 1 1 1 1 1 1 1 1 >> all(matrixValues(:)) ans = 1 >>
If you want all the naValues to be bigger than NA then you can make you vector comparison a scalar (required by assert) using the all function like this: assert(all(naValues > NA)), 'naValues not bigger than NA');
Kendo Chart control won't render correct SVG in IE 10
I am testing out creating a SPA using the Kendo UI Pro suite. I have an issue currently with the Kendo UI Chart (in Donut mode). The chart's SVG does not properly render in IE 10. This issue does not appear to happen in IE 11 or Firefox.
JSFiddle Example: http://jsfiddle.net/m9vq7gu4/
In the View's Show event, I call to an ajax API and get the data:
$.ajax({
url: '/echo/json/',
data: {
json: JSON.stringify({
"TotalHours": Math.random() * 714,
"Budget": 714.6
}),
delay: 2
},
type: 'POST',
success: function (response) {
console.log(response);
var data = [{
value: response.TotalHours,
color: "red"
}, {
value: response.Budget - response.TotalHours,
color: "transparent"
}];
loadRadial(id, data);
}
});
I then kendoChart the div and refresh it.
var dsRadial = new kendo.data.DataSource({
data: data
});
dsRadial.read();
var radial = $("#" + id).kendoChart({
dataSource: dsRadial,
dataBound: function () {
console.log("dataBound");
},
legend: {
visible: false
},
seriesDefaults: {
type: "donut",
holeSize: 60,
size: 20
},
series: [{
field: "value",
colorField: "color"
}],
chartArea: {
background: "transparent"
},
tooltip: {
visible: true
}
}).data("kendoChart");
I show that all events down the View/Layout/Router fire correctly. At first I thought that the chart was not binding properly, but if I look at the source code of the page the SVG is created:
<svg xmlns="http://www.w3.org/2000/svg" style="left: 0px; top: -0.47px; width: 100%; height: 100%; overflow: hidden;" version="1.1"><defs><radialGradient id="5c7e3a1b-06a7-4665-bb46-44db26b308e7" gradientUnits="userSpaceOnUse" cx="409.5" cy="100" r="80"><stop style="stop-color: rgb(255, 255, 255); stop-opacity: 0;" offset="0.75" /><stop style="stop-color: rgb(255, 255, 255); stop-opacity: 0.3;" offset="0.875" /><stop style="stop-color: rgb(255, 255, 255); stop-opacity: 0;" offset="0.9975" /></radialGradient></defs><g><path fill="none" stroke="none" d="M 0 0 L 819 0 L 819 200 L 0 200 Z" /><path fill="none" stroke="none" d="M 5 5 L 814 5 L 814 195 L 5 195 Z" /><g><g /></g><g><g><g transform="matrix(1 0 0 1 0 0)"><path fill="red" stroke="none" d="M 409.5 20 C 428.398 20 447.219 26.963 461.567 39.263 C 475.915 51.563 485.673 69.099 488.56 87.775 C 491.448 106.452 487.443 126.115 477.48 142.174 C 467.517 158.233 451.679 170.556 433.663 176.264 L 427.622 157.198 C 441.134 152.917 453.013 143.675 460.485 131.631 C 467.957 119.587 470.961 104.839 468.795 90.831 C 466.629 76.824 459.311 63.672 448.55 54.447 C 437.789 45.222 423.674 40 409.5 40 Z" /><path fill="url(#5c7e3a1b-06a7-4665-bb46-44db26b308e7)" stroke="none" d="M 409.5 20 C 428.398 20 447.219 26.963 461.567 39.263 C 475.915 51.563 485.673 69.099 488.56 87.775 C 491.448 106.452 487.443 126.115 477.48 142.174 C 467.517 158.233 451.679 170.556 433.663 176.264 L 427.622 157.198 C 441.134 152.917 453.013 143.675 460.485 131.631 C 467.957 119.587 470.961 104.839 468.795 90.831 C 466.629 76.824 459.311 63.672 448.55 54.447 C 437.789 45.222 423.674 40 409.5 40 Z" /></g><g transform="matrix(1 0 0 1 0 0)"><path fill="none" stroke="none" d="M 433.663 176.264 C 416.13 181.819 396.674 181.079 379.614 174.208 C 362.554 167.337 348.016 154.387 339.227 138.231 C 330.437 122.076 327.462 102.835 330.961 84.779 C 334.461 66.723 344.408 49.987 358.597 38.284 C 372.785 26.582 391.108 20 409.5 20 L 409.5 40 C 395.706 40 381.964 44.936 371.322 53.713 C 360.681 62.49 353.22 75.043 350.596 88.584 C 347.972 102.126 350.203 116.557 356.795 128.674 C 363.387 140.79 374.291 150.503 387.086 155.656 C 399.881 160.809 414.473 161.364 427.622 157.198 Z" /><path fill="url(#5c7e3a1b-06a7-4665-bb46-44db26b308e7)" stroke="none" d="M 433.663 176.264 C 416.13 181.819 396.674 181.079 379.614 174.208 C 362.554 167.337 348.016 154.387 339.227 138.231 C 330.437 122.076 327.462 102.835 330.961 84.779 C 334.461 66.723 344.408 49.987 358.597 38.284 C 372.785 26.582 391.108 20 409.5 20 L 409.5 40 C 395.706 40 381.964 44.936 371.322 53.713 C 360.681 62.49 353.22 75.043 350.596 88.584 C 347.972 102.126 350.203 116.557 356.795 128.674 C 363.387 140.79 374.291 150.503 387.086 155.656 C 399.881 160.809 414.473 161.364 427.622 157.198 Z" /></g></g></g></g></svg>
What's really odd is that as soon as I switch the router over to the other page, the SVG displays properly until the data is changed again (you can see this by clicking the Home/Data links on the demo). What could be causing this other than a weird issue between Chart and View/Layout/Router?
This is a known issue with Kendo UI prior to versions 2013.2.1215 (an internal build). It's caused by a glitch in the IE10 SVG implementation. Once a path is scaled to 0 it disappears even if the transformation is cleared.
Insert a row between two known rows in Matlab
I have a set of data shown bellow: flow Rate (L/min) Speed(rpm) 1 1.25 1.5 1.75 2 2.25 2.5 2.77 ... 6 Pressure (Pa) 2000 15251.2 15232 15200 15168 15027.2 14912 14752 0 ... 0 2050 16000 15840 15808 15744 15680 15520 15488 15232 ... 0 2100 16384 16256 16217.6 16192 16128 16064 16032 15872 ... 0 2150 17088 17024 16992 16960 16928 16832 16704 16512 ... 0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4250 61120 60800 60768 60736 60672 60736 60608 60416 ... 56960 At a specific speed (from 2000-4250rpm) and flow rate (from 1-6 L/min) as shown there are different pressures. 1) i want to know how can i insert a new row in between two of these speeds for example if i have a speed of 2030rpm i want to be able to find in between which two values the 2030rpm is and insert a row on matlab demonstration hown below: 2000 15251.2 15232 15200 15168 15027.2 14912 14752 0 ... 0 2030 0 0 0 0 0 0 0 0 2050 16000 15840 15808 15744 15680 15520 15488 15232 ... 0 2) my second problem is how can i interpolate between the two values below (where the zero is and get a value. 15232 0 16000 I really appreciate if any one can answer any of my questions preferably the first one so ic an actually get to the second step lol Thank you so much
newmat = zeros(size(oldmat,1)+1,size(oldmat,2)) newmat(1:x) = oldmat(1:x) newmat(x+2:end) = oldmat(x+1:end) where oldmat, newmat are the old and new versions of your matrix and x+1 the index of the row of 0s inserted into newmat. Then, supposing that you want linear interpolation, something like: newmat(x+1,:) = newmat(x,:)+0.6*(newmat(x+2,:)-newmat(x,:)) I expect I've made some small errors, and this is quite specific to your example, if you have trouble fixing and generalising, update your question or comment.
Assuming the data is stored in a matrix called p, for automatically positioning the new row in correct sequence: Append the new row at the end of p, then: p = sortrows(p) Following up on the comments, we have: newrow = [2130, zeros(1,size(test,2)-1)] p(size(p,1)+1,:) = newrow p = sortrows(p) (if 2130 is the first value of the new row.)
This may help you: % Matrix dimensions nCols = 10; nRows = 8; % Synthetic data matrix = [ linspace(2000,4250,nRows)' , 2000*rand(nRows,nCols-1)]; matrix([2,4],2:end) = zeros(2,nCols-1); % where some rows are zeros (2 and 4 on this example) matrix matrix = 1.0e+03 * 2.0000 1.7810 1.3674 1.4983 0.7329 1.5439 1.5639 0.2246 0.8653 1.5379 2.3214 0 0 0 0 0 0 0 0 0 2.6429 1.4687 1.4454 1.4801 1.3701 0.7765 0.5881 0.5831 0.2195 0.5459 2.9643 0 0 0 0 0 0 0 0 0 3.2857 0.1458 0.2350 1.4699 1.5787 0.4579 1.0617 1.9288 0.3749 1.3466 3.6071 0.1771 1.2814 1.9412 0.7353 1.2839 0.1830 0.8650 0.5324 0.8591 3.9286 1.5967 0.6576 1.7339 0.4121 0.9690 0.8106 1.3895 1.5957 0.9035 4.2500 1.8860 1.3076 0.1725 0.1733 0.3037 0.2097 1.5162 0.9752 1.2197 If you just want to fill the rows whose elements from the second to the last columns are zeros with the average value of the previous and the next rows. for i=2:nRows-1 if ( sum(matrix(i,2:end))==0 ) matrix(i,2:end) = mean( matrix([i-1,i+1],2:end) ); end end matrix matrix = 1.0e+03 * 2.0000 1.7810 1.3674 1.4983 0.7329 1.5439 1.5639 0.2246 0.8653 1.5379 2.3214 1.6248 1.4064 1.4892 1.0515 1.1602 1.0760 0.4039 0.5424 1.0419 2.6429 1.4687 1.4454 1.4801 1.3701 0.7765 0.5881 0.5831 0.2195 0.5459 2.9643 0.8072 0.8402 1.4750 1.4744 0.6172 0.8249 1.2560 0.2972 0.9462 3.2857 0.1458 0.2350 1.4699 1.5787 0.4579 1.0617 1.9288 0.3749 1.3466 3.6071 0.1771 1.2814 1.9412 0.7353 1.2839 0.1830 0.8650 0.5324 0.8591 3.9286 1.5967 0.6576 1.7339 0.4121 0.9690 0.8106 1.3895 1.5957 0.9035 4.2500 1.8860 1.3076 0.1725 0.1733 0.3037 0.2097 1.5162 0.9752 1.2197 This code assumes that: You want to fill rows were only the first column element is non-zero. You want to replace the zeros with the average between previous and next rows values. You only interpolate inner rows. I hope it helps.