probem with FaceColor of bar
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mohamed gryaa
el 30 de Sept. de 2019
Comentada: darova
el 4 de Oct. de 2019
R=[0.779810425627480 0.000659297275251825 0.688360393160301 0.184532497610895 0.0905962113611394 0.0626722269195076 0.115760244765211 9.82482188727207e-06]
R2=[0.780896183843548 0.783089437325240 0.824384828931732 0.783480655246833 0.782324163937975 0.843400789787716 0.780259820660027 0.692761472581375 0.186630591612089 0.112331622540645 0.0684118151132265 0.124754260377728 0.000750940658416743 0.786209417062528 0.696211586340910 0.704468853008563 0.793533655775326 0.689231222654964 0.247394539787509 0.259526400351099 0.187357565089202 0.306685869655797 0.221985207424738 0.213936150337528 0.0911810803863357 0.226392210021089 0.0628097955366125 0.177330398333869]
R3=[0.783714743066040 0.826211863604426 0.783570491492865 0.784235776628874 0.846564931503649 0.781795435841793 0.824795480225099 0.793293977502063 0.807394055917125 0.843543223850931 0.784225204506264 0.828604905652616 0.830131797565598 0.845605618105580 0.866367812401167 0.784125887674771 0.844962871032082 0.783775317173255 0.857006696649914 0.782646132825257 0.886525345744327 0.792443729599900 0.707602204791307 0.706291944439437 0.795689075333992 0.692960273475268 0.269367754096017 0.269897642276868 0.187804460375981 0.338233654026156 0.308011853235251 0.214628124374582 0.117141128901629 0.229057283333948 0.0684491039966618 0.183900696434232 0.789982210441524 0.793671406033401 0.807345796487103 0.835447142202684 0.705376496011716 0.804011717368906 0.696706432568307 0.795703083661133 0.705025556256066 0.832447316043765 0.383708291936295 0.256938376298548 0.368132130189507 0.275653288847231 0.375594581589765 0.328508421589727 0.426445219789627 0.222082122851731 0.295080637436694 0.303500075279901]
R4=[0.826996774935897 0.794557334602349 0.809619574536623 0.846642577477218 0.785322675618129 0.828939025798648 0.833675796861419 0.847202474959195 0.881468153988862 0.784703824104669 0.851106275596922 0.784004921796498 0.858781120350036 0.785070083916637 0.888559577339896 0.828833804725073 0.834955475482176 0.846195401779085 0.867140402842741 0.813983848871127 0.845075441261779 0.794705445368061 0.872508246817512 0.810962400206204 0.886525524768893 0.831303117637229 0.847642143194423 0.868934285031242 0.858409734520770 0.872566944267203 0.906757870765131 0.857428757371442 0.784387451549559 0.886620441089954 0.905697241656523 0.801922541124688 0.797636506176264 0.807358981738157 0.862781849694327 0.710334094086818 0.804080272956492 0.707686352900283 0.798434388040983 0.706487674364295 0.833750942309195 0.471755923663536 0.270095621013156 0.456472753439997 0.277630257351045 0.430498750484947 0.343336309036551 0.454955851249911 0.317945379843094 0.298347039374467 0.304574520635417 0.794166474258310 0.814390776564570 0.840239456373557 0.810347726332535 0.841150193698107 0.877516625338572 0.804070982436114 0.705841896439888 0.849259411824625 0.832907343342206 0.437358816488236 0.495244980869643 0.405878538783720 0.424820970524598 0.547495448988831]
R5=[0.828991929369686 0.838411193709899 0.847498079098537 0.884286848091013 0.813984689455561 0.852689268734507 0.795461615160834 0.873675836205425 0.815033194461026 0.888567443766334 0.833676221089556 0.851532466108260 0.881503316538568 0.859249568891160 0.893364109834186 0.907586430555856 0.858785564028645 0.785320228500511 0.889516792068951 0.906442600699576 0.838035644265987 0.847642955904863 0.868937970747267 0.873278896824283 0.876253552968026 0.908008036718245 0.872689586925684 0.817720580546422 0.886663386046064 0.937203366812540 0.858609315682532 0.872839144872220 0.907099734481859 0.924204051669980 0.910846971035114 0.802163827732857 0.815595411648910 0.884996077524746 0.810350740286317 0.864252213958193 0.883287964927457 0.804103360669447 0.710335861966019 0.849504374181575 0.834436602026480 0.484612221304780 0.704965265425382 0.460974241930084 0.447415007134505 0.598056626502316 0.814518098488400 0.842523239334591 0.888286770499680 0.878177732084617 0.852719060026541 0.604403396129531]
R6=[0.839071360465035 0.852704784532168 0.887822544505265 0.876021378692969 0.896575199509569 0.909363801082844 0.875080432727216 0.818093523631484 0.890239643298577 0.937369178243077 0.859250167086107 0.903297054324622 0.907629634392680 0.926273966668406 0.911091067394923 0.873423351158586 0.877473297910146 0.908008113026594 0.941060841208969 0.938200096744872 0.927636099224910 0.815597023464531 0.891134879647912 0.903795572804284 0.883537686178393 0.854841118650416 0.733815636601844 0.889727749438983]
R7=[0.878642031246962 0.903669866631326 0.910417834781449 0.941547570214469 0.938233123072641 0.937500290384948 0.942085824188201 0.914574284340743]
R8=[0.945329036585856]
R_all = [R R2 R3 R4 R5 R6 R7 R8];
label_all = [label,label2,label3,label4,label5,label6,label7,label8];
[R_all_sort,ind_sort] = sort(R_all,'descend');
label_all_sort = label_all(ind_sort);
threshold = 0.95;
logical_index = R_all_sort>=threshold;
N_true = length(find(logical_index));
figure
hall=bar(R_all_sort(logical_index));
hold on
grid on
xlabel('metriche lavatrice');
ylabel('R^2 lavatrice ');
ax=gca;
ax.XTick = 1:N_true;
ax.XTickLabels = label_all_sort(logical_index);
ax.XTickLabelRotation = 90;
legend({'soggettività:lavatrice' });
ylim([threshold 1]);
hi i have a problem, i need to have differet color when i plot the figure bar (Facecolor) like this:
R = red
R2= green
R3= white
R4 =cyan
R5=blue
R6=yellow
R7=black
R8= magenta
1 comentario
dpb
el 30 de Sept. de 2019
threshold = 0.95;
logical_index = R_all_sort>=threshold;
...
>> sum(logical_index)
ans =
0
>> max(R_all)
ans =
0.9453
>>
There are no elements above the threshold so nothing will show up on the plot.
Also, you've mixed all elements up in combining into one long vector and then sorted that vector so there's no identification from which element any particular value came.
Would need to define corollary array of group number to carry along.
Respuesta aceptada
darova
el 30 de Sept. de 2019
One way:
R=[0.779810425627480 0.000659297275251825 0.688360393160301 0.184532497610895 0.0905962113611394 0.0626722269195076 0.115760244765211 9.82482188727207e-06];
R2=[0.780896183843548 0.783089437325240 0.824384828931732 0.783480655246833 0.782324163937975 0.843400789787716 0.780259820660027 0.692761472581375 0.186630591612089 0.112331622540645 0.0684118151132265 0.124754260377728 0.000750940658416743 0.786209417062528 0.696211586340910 0.704468853008563 0.793533655775326 0.689231222654964 0.247394539787509 0.259526400351099 0.187357565089202 0.306685869655797 0.221985207424738 0.213936150337528 0.0911810803863357 0.226392210021089 0.0628097955366125 0.177330398333869];
R3=[0.783714743066040 0.826211863604426 0.783570491492865 0.784235776628874 0.846564931503649 0.781795435841793 0.824795480225099 0.793293977502063 0.807394055917125 0.843543223850931 0.784225204506264 0.828604905652616 0.830131797565598 0.845605618105580 0.866367812401167 0.784125887674771 0.844962871032082 0.783775317173255 0.857006696649914 0.782646132825257 0.886525345744327 0.792443729599900 0.707602204791307 0.706291944439437 0.795689075333992 0.692960273475268 0.269367754096017 0.269897642276868 0.187804460375981 0.338233654026156 0.308011853235251 0.214628124374582 0.117141128901629 0.229057283333948 0.0684491039966618 0.183900696434232 0.789982210441524 0.793671406033401 0.807345796487103 0.835447142202684 0.705376496011716 0.804011717368906 0.696706432568307 0.795703083661133 0.705025556256066 0.832447316043765 0.383708291936295 0.256938376298548 0.368132130189507 0.275653288847231 0.375594581589765 0.328508421589727 0.426445219789627 0.222082122851731 0.295080637436694 0.303500075279901];
R4=[0.826996774935897 0.794557334602349 0.809619574536623 0.846642577477218 0.785322675618129 0.828939025798648 0.833675796861419 0.847202474959195 0.881468153988862 0.784703824104669 0.851106275596922 0.784004921796498 0.858781120350036 0.785070083916637 0.888559577339896 0.828833804725073 0.834955475482176 0.846195401779085 0.867140402842741 0.813983848871127 0.845075441261779 0.794705445368061 0.872508246817512 0.810962400206204 0.886525524768893 0.831303117637229 0.847642143194423 0.868934285031242 0.858409734520770 0.872566944267203 0.906757870765131 0.857428757371442 0.784387451549559 0.886620441089954 0.905697241656523 0.801922541124688 0.797636506176264 0.807358981738157 0.862781849694327 0.710334094086818 0.804080272956492 0.707686352900283 0.798434388040983 0.706487674364295 0.833750942309195 0.471755923663536 0.270095621013156 0.456472753439997 0.277630257351045 0.430498750484947 0.343336309036551 0.454955851249911 0.317945379843094 0.298347039374467 0.304574520635417 0.794166474258310 0.814390776564570 0.840239456373557 0.810347726332535 0.841150193698107 0.877516625338572 0.804070982436114 0.705841896439888 0.849259411824625 0.832907343342206 0.437358816488236 0.495244980869643 0.405878538783720 0.424820970524598 0.547495448988831];
R5=[0.828991929369686 0.838411193709899 0.847498079098537 0.884286848091013 0.813984689455561 0.852689268734507 0.795461615160834 0.873675836205425 0.815033194461026 0.888567443766334 0.833676221089556 0.851532466108260 0.881503316538568 0.859249568891160 0.893364109834186 0.907586430555856 0.858785564028645 0.785320228500511 0.889516792068951 0.906442600699576 0.838035644265987 0.847642955904863 0.868937970747267 0.873278896824283 0.876253552968026 0.908008036718245 0.872689586925684 0.817720580546422 0.886663386046064 0.937203366812540 0.858609315682532 0.872839144872220 0.907099734481859 0.924204051669980 0.910846971035114 0.802163827732857 0.815595411648910 0.884996077524746 0.810350740286317 0.864252213958193 0.883287964927457 0.804103360669447 0.710335861966019 0.849504374181575 0.834436602026480 0.484612221304780 0.704965265425382 0.460974241930084 0.447415007134505 0.598056626502316 0.814518098488400 0.842523239334591 0.888286770499680 0.878177732084617 0.852719060026541 0.604403396129531];
R6=[0.839071360465035 0.852704784532168 0.887822544505265 0.876021378692969 0.896575199509569 0.909363801082844 0.875080432727216 0.818093523631484 0.890239643298577 0.937369178243077 0.859250167086107 0.903297054324622 0.907629634392680 0.926273966668406 0.911091067394923 0.873423351158586 0.877473297910146 0.908008113026594 0.941060841208969 0.938200096744872 0.927636099224910 0.815597023464531 0.891134879647912 0.903795572804284 0.883537686178393 0.854841118650416 0.733815636601844 0.889727749438983];
R7=[0.878642031246962 0.903669866631326 0.910417834781449 0.941547570214469 0.938233123072641 0.937500290384948 0.942085824188201 0.914574284340743];
R8=[0.945329036585856];
R_all = [R R2 R3 R4 R5 R6 R7 R8];
color_ind = [R*0+1 R2*0+2 R3*0+3 R4*0+4 R5*0+5 R6*0+6 R7*0+7 R8*0+8];
cm = 'rgwcbykm'; % reg green white ...
[R_all_sort,ind_sort] = sort(R_all,'descend');
color_sort = color_ind(ind_sort);
threshold = 0.95;
ind1 = find( R_all_sort<=threshold );
cla
hold on
for i = ind1
h = bar(ind1(i),R_all_sort(i));
set(h,'EdgeColor','none','FaceColor',cm(color_sort(i)))
end
hold off
But works slow. Any idea of how speed it up?
6 comentarios
Más respuestas (1)
dpb
el 30 de Sept. de 2019
Editada: dpb
el 30 de Sept. de 2019
Carrying on from the above after defining data...
clrs=[[1 0 0];[0 1 0];[1 1 1];[0 1 1];[0 0 1];[1 1 0];[0 0 0];[1 0 1]]; % rgb for named colors
R1=R; % just for symmetry in naming
G=[1+R1*0 2+R2*0 3+R3*0 4+R4*0 5+R5*0 6+R6*0 7+R7*0 8+R8*0];
threshold=0.925; % 0.95 > max() --> no elements selected
ix=(R_all>=threshold);
R=R_all(ix);
[~,isort]=sort(R,'descend');
hBar=bar(R(isort));
hBar.FaceColor='flat';
hBar.CData=clrs(isort(G),:);
returns
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