Combine Multiple Plots

This example shows how to combine plots in the same axes using the hold function, and how to create multiple axes in a figure using the tiledlayout function. The tiledlayout function is available starting in R2019b. If you are using an earlier release, use the subplot function instead.

Combine Plots in Same Axes

By default, new plots clear existing plots and reset axes properties, such as the title. However, you can use the hold on command to combine multiple plots in the same axes. For example, plot two lines and a scatter plot. Then reset the hold state to off.

x = linspace(0,10,50);
y1 = sin (x);
plot(x,y1)
title('Combine Plots')

hold on

y2 = sin (x / 2);
plot(x,y2)

y3 = 2 * sin (x);
scatter(x,y3) 

hold off

When the hold state is on, new plots do not clear existing plots or reset axes properties, such as the title or axis labels. The plots cycle through colors and line styles based on the ColorOrder and LineStyleOrder properties of the axes. The axes limits and tick values might adjust to accommodate new data.

Display Multiple Axes in a Figure

You can display multiple axes in a single figure by using the tiledlayout function. This function creates a tiled chart layout containing an invisible grid of tiles over the entire figure. Each tile can contain an axes for displaying a plot. After creating a layout, call the nexttile function to place an axes object into the layout. Then call a plotting function to plot into the axes. For example, create two plots in a 2-by-1 layout. Add a title to each plot.

Note: This code uses the tiledlayout function, which is available starting in R2019b. If you are using an earlier release, use the subplot function instead.

x = linspace(0,10,50);
y1 = sin (x);
y2 = rand(50,1);
tiledlayout(2,1) % Requires R2019b or later

% Top plot
nexttile
plot(x,y1)
title('Plot 1')

% Bottom plot
nexttile
scatter(x,y2)
title('Plot 2')

Create Plot Spanning Multiple Rows or Columns

To create a plot that spans multiple rows or columns, specify the span argument when you call nexttile. For example, create a 2-by-2 layout. Plot into the first two tiles. Then create a plot that spans one row and two columns.

x = linspace(0,10,50);
y1 = sin (x);
y2 = rand(50,1);

% Top two plots
tiledlayout(2,2) % Requires R2019b or later
nexttile
plot(x,y1)
nexttile
scatter(x,y2)

% Plot that spans
nexttile([1 2])
y2 = rand(50,1);
plot(x,y2)

Modify Axes Appearance

Modify the axes appearance by setting properties on each of the axes objects. You can get the axes object by calling the nexttile function with an output argument. You also can specify the axes object as the first input argument to a graphics function to ensure that the function targets the correct axes.

For example, create two plots and assign the axes objects to the variables ax1 and ax2. Change the axes font size and x-axis color for the first plot. Add grid lines to the second plot.

x = linspace(0,10,50);
y1 = sin (x);
y2 = rand(50,1);
tiledlayout(2,1) % Requires R2019b or later

% Top plot
ax1 = nexttile;
plot(ax1,x,y1)
title(ax1,'Plot 1')
ax1.FontSize = 14;
ax1.XColor = 'red';

% Bottom plot
ax2 = nexttile;
scatter(ax2,x,y2)
title(ax2,'Plot 2')
grid(ax2,'on')

Control Spacing Around the Tiles

You can control the spacing around the tiles in a layout by specifying the Padding and TileSpacing properties. For example, display four plots in a 2-by-2 layout.

x = linspace(0,30);
y1 = sin (x);
y2 = sin (x / 2);
y3 = sin (x / 3);
y4 = sin (x / 4);

% Create plots
t = tiledlayout(2,2); % Requires R2019b or later
nexttile
plot(x,y1)
nexttile
plot(x,y2)
nexttile
plot(x,y3)
nexttile
plot(x,y4)

Minimize the spacing around the perimeter of the layout and around each tile by setting the Padding and TileSpacing properties to 'none'.

t.Padding = 'none';
t.TileSpacing = 'none';

Display Shared Title and Axis Labels

You can display a shared title and shared axis labels in a layout. Create a 2-by-1 layout t. Then display a line plot and a stem plot. Synchronize the x-axis limits by calling the linkaxes function.

x1 = linspace(0,20,100);
y1 = sin(x1);
x2 = 3:17;
y2 = rand(1,15);

% Create plots.
t = tiledlayout(2,1); % Requires R2019b or later
ax1 = nexttile;
plot(ax1,x1,y1)
ax2 = nexttile;
stem(ax2,x2,y2)

% Link the axes
linkaxes([ax1,ax2],'x');

Add a shared title and shared axis labels by passing t to the titlexlabel, and ylabel functions. Move the plots closer together by removing the x-axis tick labels from the top plot and setting the TileSpacing property of t to 'compact'.

% Add shared title and axis labels
title(t,'My Title')
xlabel(t,'x-values')
ylabel(t,'y-values')

% Move plots closer together
xticklabels(ax1,{})
t.TileSpacing = 'compact';

Matlabsolutions.com provides guaranteed satisfaction with a commitment to complete the work within time. Combined with our meticulous work ethics and extensive domain experience, We are the ideal partner for all your homework/assignment needs. We pledge to provide 24*7 support to dissolve all your academic doubts. We are composed of 300+ esteemed Matlab and other experts who have been empanelled after extensive research and quality check.

Matlabsolutions.com provides undivided attention to each Matlab assignment order with a methodical approach to solution. Our network span is not restricted to US, UK and Australia rather extends to countries like Singapore, Canada and UAE. Our Matlab assignment help services include Image Processing Assignments, Electrical Engineering Assignments, Matlab homework help, Matlab Research Paper help, Matlab Simulink help. Get your work done at the best price in industry.

Machine Learning in MATLAB

Train Classification Models in Classification Learner App

Train Regression Models in Regression Learner App

Distribution Plots

Explore the Random Number Generation UI

Design of Experiments

Machine Learning Models

Logistic regression

Logistic regression create generalized linear regression model - MATLAB fitglm 2

Support Vector Machines for Binary Classification

Support Vector Machines for Binary Classification 2

Support Vector Machines for Binary Classification 3

Support Vector Machines for Binary Classification 4

Support Vector Machines for Binary Classification 5

Assess Neural Network Classifier Performance

Naive Bayes Classification

ClassificationTree class

Discriminant Analysis Classification

Ensemble classifier

ClassificationTree class 2

Train Generalized Additive Model for Binary Classification

Train Generalized Additive Model for Binary Classification 2

Classification Using Nearest Neighbors

Classification Using Nearest Neighbors 2

Classification Using Nearest Neighbors 3

Classification Using Nearest Neighbors 4

Classification Using Nearest Neighbors 5

Linear Regression

Linear Regression 2

Linear Regression 3

Linear Regression 4

Nonlinear Regression

Nonlinear Regression 2

Visualizing Multivariate Data

Generalized Linear Models

Generalized Linear Models 2

RegressionTree class

RegressionTree class 2

Neural networks

Gaussian Process Regression Models

Gaussian Process Regression Models 2

Understanding Support Vector Machine Regression

Understanding Support Vector Machine Regression 2

RegressionEnsemble



matlab assignment help


matlab assignment help