[Scilab-users] xfpoly: would wish that the filling colour could be made transparent.

[Antoine Monmayrant]

On 10/04/2021 23:58, Heinz Nabielek wrote:
> On 10.04.2021, at 08:10, Antoine Monmayrant <antoine.monmayrant at laas.fr> wrote:
>>
>> On 09/04/2021 23:55, Heinz Nabielek wrote:
>>> xfpoly does generally a good job for me, sometimes I would wish that the filling colour could be made transparent.
>> This is a much needed improvement of the scilab graphical stack that currently does not provide any transparency.
>> I think this is not an easy improvement.
>> At the moment, my workaround is to plot everything I need, export as svg and than add the transparency I need in the svg using inkscape or directly editing the svg file with a text editor...
>>> Heinz
>>>
>>> PS 1: Is there a new version of the 2011 BetweenCurves around?
>> Er, no, it was just a dirty hack I needed for my own publications and I think 2011 is the most recent one.
>> I can try to see how to improve it if this can improve scilab...
>
>
>
> I had initiated by log vs lin plot with
>>> plot2d([0 80],[1 100], style=0,logflag = "nl");
> but BetweenCurves starts with its own plot and here I would not know, what to do...

Hello,

There are two options:

(1) If you already have a handle "h0" to a plot and want BetweenCurves 
to use it, you can do:

    [h,epoly,ey1,ey2]=BetweenCurves(x,y1,y2,handle, h0)

(2) you can also do:

    [h,epoly,ey1,ey2]=BetweenCurves(x,y1,y2,handle, h0)

  and then use

    h.children(1).log_flags='nln'

to change from linear to log y axis.

Is this what you have in mind?

Antoine

> Heinz
>
> BTW, how do the French infection rates look like?
I must admit it is not a metric I track.
For the trend in France I go here 
https://coronavirus.politologue.com/coronavirus-france.FR and for a more 
global view there: https://coronavirus.politologue.com/100k-habitants/
>
>>> PS 2: Any suggestion to make my clumsy coding more elegent, is highly welcome
>>>
>>> PS 2: BTW, since the recent lockdown, infection rates are coming down in Austria.....
>>>
>>>
>>> A=[12.628    13.942    17.077    17.054    15.594    14.976    14.796    11.875    13.448    16.504    17.717    19.447    16.099    13.302    13.762    16.032    19.492    22.098    20.425    21.087    20.649    14.268    19.402    22.525    26.862    23.514    27.603    23.851    15.830    21.570    28.682    26.109    29.974    28.727    24.705    21.458    27.087    28.401    33.670    35.119    33.962    28.120    21.301    27.244    37.467    37.715    39.490    37.569    30.480    27.098    38.366    36.951    35.097    43.759    39.299]';
>>> d=(1:length(A))';
>>> up=10^(d/53);
>>> M=[ones(up) up];
>>> aa=M\A;
>>> B=inv(M'*M);
>>> DD=(1:110)';
>>> U=10^(DD/53);
>>> MM=[ones(U) U];
>>> yh = M*aa;     //Fitted values yh to approximate measured y's
>>> e=A-yh;     //Errors or residuals
>>> SSE=e'*e;     //Sum of squared errors
>>> ybar=mean(A); R2=1-SSE/sum((A-ybar)^2);
>>> [m n]=size(M);
>>> MSE = SSE/(m-n-1);     //Mean square error
>>> C=MSE*B  // covariance matrix
>>> sig=sqrt(MSE);
>>> seb=sqrt(diag(C));
>>> [aa seb]
>>> [n pp]=size(M);
>>> CONF=.95; alpha=1-CONF;
>>> ta2 = cdft('T',n-pp,1-alpha/2,alpha/2);     //t-value for alpha/2
>>> yhh= MM*aa;
>>> p=sig*sqrt(diag(1+MM*B*MM'));
>>> N=[yhh+ta2*p  yhh-ta2*p];
>>> polyX = [DD;flipdim(DD,1)];
>>> polyY = [N(:,1);flipdim(N(:,2),1)];
>>> plot2d([0 80],[1 100], style=0,logflag = "nl");
>>> xgrid;
>>> xfpoly(polyX, polyY,6);
>>> plot(DD,MM*aa,'g.-');
>>> plot(d,A,'b.') ;
>>> title('AUSTRIA daily infection rates per 100,000','fontsize',5);
>>> xlabel('days since 1 Feb 2021','fontsize',3);
>>> ylabel('number of infections per day per 100,000','fontsize',3);
>>> legend('data from Johns Hopkins GitHub','95% confidence range','model prediction','AUSTRIA recorded',4);
>>>
>>>
>>>
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