Plot scatter plot as shade
显示 更早的评论
Hi,
I have the attached data (conc.mat) where each row corresponds to y axis value for a given x value(t=[0;30;60;120;300;600;900]). I am doing uncertainty analysis here. I also have the actual data set (single y value for single x value), which should construct a firm plot in the same graph. So, in summary, I should have a firm line from the actual data set which I have not shared here, but also I should be able to generate a shaded area from the attached data. The final product should be something like the attached file (Capture.JPG). Please suggest some solutions. Thanks.
采纳的回答
Star Strider
2021-10-22
Try this —
LD = load('conc.mat');
conc = LD.conc;
t=[0;30;60;120;300;600;900];
figure
plot(t, conc)
hold on
shadeWidth = 0.01;
patch([t; flipud(t)], [min(conc,[],2)-shadeWidth; flipud(max(conc,[],2))+shadeWidth], [1 1 1]*0.5, 'FaceAlpha',0.25, 'EdgeColor','none')
hold off
Experiment to get different results.
.
12 个评论
Shahriar Mahmud
2021-10-22
Star Strider
2021-10-22
My pleasure!
That is straightforward — just don’t plot them (the posted example had the centre trace, so I thought it was to be included) —
LD = load('conc.mat');
conc = LD.conc;
t=[0;30;60;120;300;600;900];
figure
% plot(t, conc)
hold on
shadeWidth = 0.01;
patch([t; flipud(t)], [min(conc,[],2)-shadeWidth; flipud(max(conc,[],2))+shadeWidth], [1 1 1]*0.5, 'FaceAlpha',0.25, 'EdgeColor','none')
hold off
As for the colour, choose whatever colour that can be defined. See C and FaceColor for some of them. Search the documentation for other options.
.
Shahriar Mahmud
2021-10-27
I do not understand.
The data in the file are plotted correctly, and the shaded portion extends from a given distance below the lowest data to the same distance above the highest data.
To plot it around the mean instead —
patch([t; flipud(t)], [mean(conc,2)-shadeWidth; flipud(mean(conc,2))+shadeWidth], [1 1 1]*0.5, 'FaceAlpha',0.25, 'EdgeColor','none')
or the median —
patch([t; flipud(t)], [median(conc,2)-shadeWidth; flipud(median(conc,2))+shadeWidth], [1 1 1]*0.5, 'FaceAlpha',0.25, 'EdgeColor','none')
Those vectors are directly derived from the data, and the ‘shadeWidth’ is constant for the entire line. It may not appear to be constant because ‘shadeWidth’ is plotted in the ‘y’ direction, and that will appear different in the first part of the curve where the slope is greatest than in the last part of the curve where the slope is least. It is neverhteless the same throughout the curve.
Consider a sine curve —
x = linspace(-pi, pi);
s = sin(x);
figure
plot(x,s,'-r')
hold on
patch([x flip(x)], [s-0.1 flip(s)+0.1], 'b', 'FaceAlpha',0.25)
hold off
grid
xlim([min(x) max(x)])
The difference between the curves is the same throughout, however the shading appears to be thicker at the extreme ‘y’ values than it does near 0.
.
Shahriar Mahmud
2021-11-8
编辑：Shahriar Mahmud
2021-11-8
Sure!
x1 = 0:7;
y1 = 1-exp(-0.5*x1) + 0.04 + randn(size(x1))*0.035;
s1 = 0.04*[-1;1] + y1;
x2 = 0:9;
y2 = 1-exp(-0.2*x2) + 0.08 + randn(size(x2))*0.035;
s2 = 0.08*[-1;1] + y2;
figure
patch([x1 flip(x1)], [s1(1,:) flip(s1(2,:))], 'r', 'FaceAlpha',0.2, 'EdgeColor','none')
hold on
hp1 = plot(x1, y1, '-r', 'LineWidth',2, 'DisplayName','Function #1');
patch([x2 flip(x2)], [s2(1,:) flip(s2(2,:))], 'b', 'FaceAlpha',0.2, 'EdgeColor','none')
hp2 = plot(x2, y2, '-b', 'LineWidth',2, 'DisplayName','Function #2');
hold off
grid
legend([hp1; hp2], 'Location','best')
xlim([0 10])
Use the data available in place of the data I created for the plot. This does not automatically assign the respective colours (although it could be changed to do that if required), so that must be hard-coded.
.
Shahriar Mahmud
2021-11-17
Star Strider
2021-11-17
It depends on the data, the noise spectrum, and the desired result. The interp1 function is an option in some instances, however if the data are noisy, the smoothdata function (versions >= R2017a) may be a better choice.
Shahriar Mahmud
2021-11-17
Star Strider
2021-11-17
As always, my pleasure!
There is not much noise, so smoothdata is likely not effective.
To create more points (that would appear to be desired), my favourite approach, assuming that ‘x’ is the independent variable and ‘y’ is the dependent variable, is:
N = 250;
xq = linspace(min(x), max(x), N);
yq = interp1(x, y, xq, 'pchip');
figure
plot(xq, yq)
grid
The ‘pchip’ interpolation method creates a smooth, nonlinear interpolation, without all the wild extremes that a spline interpolation would produce. It is the one I usually use for such problems. Explore other options as well, to get the desired result.
Using the interpolated values should not require changing the rest of the original code.
.
Shahriar Mahmud
2021-11-17
Star Strider
2021-11-17
As always, my pleasure!
.
更多回答（0 个）
类别
在 帮助中心 和 File Exchange 中查找有关 Surface and Mesh Plots 的更多信息
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!
Translated by 
