MATPLOTLIB Tutorial Series | Part 3

Note: This article has also featured on geeksforgeeks.com .

In this part, we will discuss subplots and 3-D plotting.

Subplots

Subplots are required when we want to show two or more plots in same figure. We can do it in two ways using two slightly different methods.

Method 1

# importing required modules
import matplotlib.pyplot as plt
import numpy as np

# function to generate coordinates
def create_plot(ptype):
    # setting the x-axis vaues
    x = np.arange(-10, 10, 0.01)

    # setting the y-axis values
    if ptype == 'linear':
        y = x
    elif ptype == 'quadratic':
        y = x**2
    elif ptype == 'cubic':
        y = x**3
    elif ptype == 'quartic':
        y = x**4

    return(x, y)

# setting a style to use
plt.style.use('fivethirtyeight')

# create a figure
fig = plt.figure()

# define subplots and their positions in figure
plt1 = fig.add_subplot(221)
plt2 = fig.add_subplot(222)
plt3 = fig.add_subplot(223)
plt4 = fig.add_subplot(224)

# plotting points on each subplot
x, y = create_plot('linear')
plt1.plot(x, y, color ='r')
plt1.set_title('$y_1 = x$')

x, y = create_plot('quadratic')
plt2.plot(x, y, color ='b')
plt2.set_title('$y_2 = x^2$')

x, y = create_plot('cubic')
plt3.plot(x, y, color ='g')
plt3.set_title('$y_3 = x^3$')

x, y = create_plot('quartic')
plt4.plot(x, y, color ='k')
plt4.set_title('$y_4 = x^4$')

# adjusting space between subplots
fig.subplots_adjust(hspace=.5,wspace=0.5)

# function to show the plot
plt.show()

Output:
sub2

Let us go through this program step by step:

  • plt.style.use('fivethirtyeight')

    The styling of plots can be configured by setting different styles available or setting your own. You can learn more about this feature here

  • fig = plt.figure()

    Figure acts as a top level container for all plot elements. So, we define a figure as fig which will contain all our subplots.

  • plt1 = fig.add_subplot(221)
    plt2 = fig.add_subplot(222)
    plt3 = fig.add_subplot(223)
    plt4 = fig.add_subplot(224)

    Here we use fig.add_subplot method to define subplots and their positions. The function prototype is like this:

    add_subplot(nrows, ncols, plot_number)

    If a subplot is applied to a figure, the figure will be notionally split into ‘nrows’ * ‘ncols’ sub-axes. The parameter ‘plot_number’ identifies the subplot that the function call has to create. ‘plot_number’ can range from 1 to a maximum of ‘nrows’ * ‘ncols’.

    If the values of the three parameters are less than 10, the function subplot can be called with one int parameter, where the hundreds represent ‘nrows’, the tens represent ‘ncols’ and the units represent ‘plot_number’. This means: Instead of subplot(2, 3, 4) we can write subplot(234).

    This figure will make it clear that how positions are specified:
    sub1

  • x, y = create_plot('linear')
    plt1.plot(x, y, color ='r')
    plt1.set_title('$y_1 = x$')

    Next, we plot our points on each subplot. First, we generate x and y axis coordinates using create_plot function by specifying the type of curve we want.
    Then, we plot those points on our subplot using .plot method. Title of subplot is set by using set_title method. Using $ at starting and end of the title text will ensure that ‘_'(underscore) is read as a subscript and ‘^’ is read as a superscript.

  • fig.subplots_adjust(hspace=.5,wspace=0.5)

    This is another utility method which creates space between subplots.

  • plt.show()

    In the end, we call plt.show() method which will show the current figure.

Method 2

# importing required modules
import matplotlib.pyplot as plt
import numpy as np

# function to generate coordinates
def create_plot(ptype):
    # setting the x-axis vaues
    x = np.arange(0, 5, 0.01)

    # setting y-axis values
    if ptype == 'sin':
        # a sine wave
        y = np.sin(2*np.pi*x)
    elif ptype == 'exp':
        # negative exponential function
        y = np.exp(-x)
    elif ptype == 'hybrid':
        # a damped sine wave
        y = (np.sin(2*np.pi*x))*(np.exp(-x))

    return(x, y)

# setting a style to use
plt.style.use('ggplot')

# defining subplots and their positions
plt1 = plt.subplot2grid((11,1), (0,0), rowspan = 3, colspan = 1)
plt2 = plt.subplot2grid((11,1), (4,0), rowspan = 3, colspan = 1)
plt3 = plt.subplot2grid((11,1), (8,0), rowspan = 3, colspan = 1)

# plotting points on each subplot
x, y = create_plot('sin')
plt1.plot(x, y, label = 'sine wave', color ='b')
x, y = create_plot('exp')
plt2.plot(x, y, label = 'negative exponential', color = 'r')
x, y = create_plot('hybrid')
plt3.plot(x, y, label = 'damped sine wave', color = 'g')

# show legends of each subplot
plt1.legend()
plt2.legend()
plt3.legend()

# function to show plot
plt.show()

Output:
sub3

Let us go through important parts of this program as well:

  • plt1 = plt.subplot2grid((11,1), (0,0), rowspan = 3, colspan = 1)
    plt2 = plt.subplot2grid((11,1), (4,0), rowspan = 3, colspan = 1)
    plt3 = plt.subplot2grid((11,1), (8,0), rowspan = 3, colspan = 1)

    subplot2grid is similar to “pyplot.subplot” but uses 0-based indexing and let subplot to occupy multiple cells.
    Let us try to understand the arguments of the subplot2grid method:
    1. argument 1 : geometry of the grid
    2. argument 2: location of the subplot in the grid
    3. argument 3: (rowspan) No. of rows covered by subplot.
    4. argument 4: (colspan) No. of columns covered by subplot.

    This figure will make this concept more clear:
    sub4

    In our example, each subplot spans over 3 rows and 1 column with two empty rows (row no. 4,8) .

  • x, y = create_plot('sin')
    plt1.plot(x, y, label = 'sine wave', color ='b')

    Nothing special in this part as the syntax to plot points on a subplot remains same.

  • plt1.legend()

    This will show the label of the subplot on the figure.

  • plt.show()

    Finally, we call the plt.show() function to show the current plot.

Note: After going through the above two examples, we can infer that one should use subplot() method when the plots are of uniform size where as subplot2grid() method should be preferred when we want more flexibility on position and sizes of our subplots.

3-D plotting

We can easily plot 3-D figures in matplotlib. Now, we discuss some important and commonly used 3-D plots.

  • Plotting points
    from mpl_toolkits.mplot3d import axes3d
    import matplotlib.pyplot as plt
    from matplotlib import style
    import numpy as np
    
    # setting a custom style to use
    style.use('ggplot')
    
    # create a new figure for plotting
    fig = plt.figure()
    
    # create a new subplot on our figure
    # and set projection as 3d
    ax1 = fig.add_subplot(111, projection='3d')
    
    # defining x, y, z co-ordinates
    x = np.random.randint(0, 10, size = 20)
    y = np.random.randint(0, 10, size = 20)
    z = np.random.randint(0, 10, size = 20)
    
    # plotting the points on subplot
    
    # setting labels for the axes
    ax1.set_xlabel('x-axis')
    ax1.set_ylabel('y-axis')
    ax1.set_zlabel('z-axis')
    
    # function to show the plot
    plt.show()

    Output of above program will provide you with a window which can rotate or enlarge the plot. Here is a screenshot: (dark points are nearer than light ones)figure_1-1Let us try to understand some important aspects of this code now.

    from mpl_toolkits.mplot3d import axes3d

    This is the module required to plot on 3-D space.

    ax1 = fig.add_subplot(111, projection='3d')

    Here, we create a subplot on our figure and set projection argument as 3d.

    ax1.scatter(x, y, z, c = 'm', marker = 'o')

    Now we use .scatter() function to plot the points in XYZ plane.

  • Plotting lines

    # importing required modules
    from mpl_toolkits.mplot3d import axes3d
    import matplotlib.pyplot as plt
    from matplotlib import style
    import numpy as np
    
    # setting a custom style to use
    style.use('ggplot')
    
    # create a new figure for plotting
    fig = plt.figure()
    
    # create a new subplot on our figure
    ax1 = fig.add_subplot(111, projection='3d')
    
    # defining x, y, z co-ordinates
    x = np.random.randint(0, 10, size = 5)
    y = np.random.randint(0, 10, size = 5)
    z = np.random.randint(0, 10, size = 5)
    
    # plotting the points on subplot
    ax1.plot_wireframe(x,y,z)
    
    # setting the labels
    ax1.set_xlabel('x-axis')
    ax1.set_ylabel('y-axis')
    ax1.set_zlabel('z-axis')
    
    plt.show() 

    A screenshot of the 3-D plot of above program will look like:
    figure_1The main difference in this program with previous one is:

    ax1.plot_wireframe(x,y,z)

    We used .plot_wireframe() method to plot lines over a given set of 3-D points.

  • Plotting Bars
    # importing required modules
    from mpl_toolkits.mplot3d import axes3d
    import matplotlib.pyplot as plt
    from matplotlib import style
    import numpy as np
    
    # setting a custom style to use
    style.use('ggplot')
    
    # create a new figure for plotting
    fig = plt.figure()
    
    # create a new subplot on our figure
    ax1 = fig.add_subplot(111, projection='3d')
    
    # defining x, y, z co-ordinates for bar position
    x = [1,2,3,4,5,6,7,8,9,10]
    y = [4,3,1,6,5,3,7,5,3,7]
    z = np.zeros(10)
    
    # size of bars
    dx = np.ones(10)              # length along x-axis
    dy = np.ones(10)              # length along y-axs
    dz = [1,3,4,2,6,7,5,5,10,9]   # height of bar
    
    # setting color scheme
    color = []
    for h in dz:
        if h > 5:
            color.append('r')
        else:
            color.append('b')
    
    # plotting the bars
    ax1.bar3d(x, y, z, dx, dy, dz, color = color)
    
    # setting axes labels
    ax1.set_xlabel('x-axis')
    ax1.set_ylabel('y-axis')
    ax1.set_zlabel('z-axis')
    
    plt.show()

    A screenshot of the 3-D environment created is here:
    figure_1-2Let us go through important aspects of this program:

    x = [1,2,3,4,5,6,7,8,9,10]
    y = [4,3,1,6,5,3,7,5,3,7]
    z = np.zeros(10)

    Here, we define the base positions of bars. Setting z = 0 means all bars start from XY plane.

    dx = np.ones(10)              # length along x-axis
    dy = np.ones(10)              # length along y-axs
    dz = [1,3,4,2,6,7,5,5,10,9]   # height of bar

    dx, dy, dz denote the size of bar. Consider he bar as a cuboid, then dx, dy, dz are its expansions along x, y, z axis respectively.

    for h in dz:
        if h > 5:
            color.append('r')
        else:
            color.append('b')

    Here, we set the color for each bar as a list. The color scheme is red for bars with height greater than 5 and blue otherwise.

    ax1.bar3d(x, y, z, dx, dy, dz, color = color)

    Finally, to plot the bars, we use .bar3d() function.

  • Plotting curves

    # importing required modules
    from mpl_toolkits.mplot3d import axes3d
    import matplotlib.pyplot as plt
    from matplotlib import style
    import numpy as np
    
    # setting a custom style to use
    style.use('ggplot')
    
    # create a new figure for plotting
    fig = plt.figure()
    
    # create a new subplot on our figure
    ax1 = fig.add_subplot(111, projection='3d')
    
    # get points for a mesh grid
    u, v = np.mgrid[0:2*np.pi:200j, 0:np.pi:100j]
    
    # setting x, y, z co-ordinates
    x=np.cos(u)*np.sin(v)
    y=np.sin(u)*np.sin(v)
    z=np.cos(v)
    
    # plotting the curve
    ax1.plot_wireframe(x, y, z, rstride = 5, cstride = 5, linewidth = 1)
    
    plt.show()

    Output of this program will look like this:
    figure_1-3
    Here, we plotted a sphere as a mesh grid.
    Let us go through some important parts:

    u, v = np.mgrid[0:2*np.pi:200j, 0:np.pi:100j]

    We use np.mgrid in order to get points so that we can create a mesh.
    You can read more about this here.

    x=np.cos(u)*np.sin(v)
    y=np.sin(u)*np.sin(v)
    z=np.cos(v)

    This is nothing but the parametric equation of a sphere.

    ax1.plot_wireframe(x, y, z, rstride = 5, cstride = 5, linewidth = 1)

    Agan, we use .plot_wireframe() method. Here, rstride and cstride arguments can be used to set how much dense our mesh must be.

So, thats it from me for this series…
Maybe I will write about some more interesting things in future about Matplotlib.
But for now, this is a brief yet concise tutorial series to get started with Matplotlib. 🙂

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