Suppose we want to plot the parabola \(y=t^2\) for \(t\) in the interval \([-3,3]\). In our language, this is the two-dimensional curve \[ \pmb{\gamma}(t) = ( t, t^2 ) \,, \quad t \in [-3,3] \,. \] The two Python libraries we use to plot \(\pmb{\gamma}\) are numpy and matplotlib. In short, numpy handles multi-dimensional arrays and matrices, and can perform high-level mathematical functions on them. For any question you may have about numpy, answers can be found in the searchable documentation available here. Instead matplotlib is a plotting library, with documentation here. Python libraries need to be imported every time you want to use them. In our case we will import:
import numpy as np
import matplotlib.pyplot as pltThe above imports numpy and the module pyplot from matplotlib, and renames them to np and plt, respectively. These shorthands are standard in the literature, and they make code much more readable.
The function for plotting 2D graphs is called plot(x,y) and is contained in plt. As the syntax suggests, plot takes as arguments two arrays \[
x=[x_1, \ldots, x_n]\,, \quad y=[y_1,\ldots,y_n]\,.
\] As output it produces a graph which is the linear interpolation of the points \((x_i,y_i)\) in \(\mathbb{R}^2\), that is, consecutive points \((x_i,y_i)\) and \((x_{i+1},y_{i+1})\) are connected by a segment. Using plot, we can graph the curve \(\pmb{\gamma}(t)=(t,t^2)\) like so:
# Code for plotting gamma
import numpy as np
import matplotlib.pyplot as plt
# Generating array t
t = np.array([-3,-2,-1,0,1,2,3])
# Computing array f
f = t**2
# Plotting the curve
plt.plot(t,f)
# Plotting dots
plt.plot(t,f,"ko")
# Showing the plot
plt.show()
Let us comment the above code. The variable t is a numpy array containing the ordered values \[
t = [-3,-2,-1,0,1,2,3]\,.
\tag{5.1}\] This array is then squared entry-by-entry via the operation \(t\ast\!\ast 2\) and saved in the new numpy array f, that is, \[
f = [9,4,1,0,1,4,9] \,.
\] The arrays t and f are then passed to plot(t,f), which produces the above linear interpolation, with t on the x-axis and f on the y-axis. The command plot(t,f,'ko') instead plots a black dot at each point \((t_i,f_i)\). The latter is clearly not needed to obtain a plot, and it was only included to highlight the fact that plot is actually producing a linear interpolation between points. Finally plt.show() displays the figure in the user window1.
Of course one can refine the plot so that it resembles the continuous curve \(\pmb{\gamma}(t)=(t,t^2)\) that we all have in mind. This is achieved by generating a numpy array t with a finer stepsize, invoking the function np.linspace(a,b,n). Such call will return a numpy array which contains n evenly spaced points, starts at a, and ends in b. For example np.linspace(-3,3,7) returns our original array t at 5.1, as shown below
# Displaying output of np.linspace
import numpy as np
# Generates array t by dividing interval
# (-3,3) in 7 parts
t = np.linspace(-3,3, 7)
# Prints array t
print("t =", t)t = [-3. -2. -1. 0. 1. 2. 3.]In order to have a more refined plot of \(\pmb{\gamma}\), we just need to increase \(n\).
# Plotting gamma with finer step-size
import numpy as np
import matplotlib.pyplot as plt
# Generates array t by dividing interval
# (-3,3) in 100 parts
t = np.linspace(-3,3, 100)
# Computes f
f = t**2
# Plotting
plt.plot(t,f)
plt.show()
We now want to plot a parametric curve \(\pmb{\gamma} \colon (a,b) \to \mathbb{R}^2\) with \[
\pmb{\gamma}(t) = (x(t), y(t)) \,.
\] Clearly we need to modify the above code. The variable t will still be a numpy array produced by linspace. We then need to introduce the arrays x and y which ecode the first and second components of \(\pmb{\gamma}\), respectively.
import numpy as np
import matplotlib.pyplot as plt
# Divides time interval (a,b) in n parts
# and saves output to numpy array t
t = np.linspace(a, b, n)
# Computes gamma from given functions x(y) and y(t)
x = x(t)
y = y(t)
# Plots the curve
plt.plot(x,y)
# Shows the plot
plt.show()We use the above code to plot the 2D curve known as the Fermat’s spiral \[ \pmb{\gamma}(t) = ( \sqrt{t} \cos(t) , \sqrt{t} \sin(t) ) \quad \text{ for } \quad t \in [0,50] \,. \tag{5.2}\]
# Plotting Fermat's spiral
import numpy as np
import matplotlib.pyplot as plt
# Divides time interval (0,50) in 500 parts
t = np.linspace(0, 50, 500)
# Computes Fermat's Spiral
x = np.sqrt(t) * np.cos(t)
y = np.sqrt(t) * np.sin(t)
# Plots the Spiral
plt.plot(x,y)
plt.show()Before displaying the output of the above code, a few comments are in order. The array t has size 500, due to the behavior of linspace. You can also fact check this information by printing np.size(t), which is the numpy function that returns the size of an array. We then use the numpy function np.sqrt to compute the square root of the array t. The outcome is still an array with the same size of t, that is, \[
t=[t_1,\ldots,t_n] \quad \implies \quad \sqrt{t} = [\sqrt{t_1}, \ldots, \sqrt{t_n}] \,.
\] Similary, the call np.cos(t) returns the array \[
\cos(t) = [\cos(t_1), \ldots, \cos(t_n)] \,.
\] The two arrays np.sqrt(t) and np.cos(t) are then multiplied, term-by-term, and saved in the array x. The array y is computed similarly. The command plt.plot(x,y) then yields the graph of the Fermat’s spiral:
The above plots can be styled a bit. For example we can give a title to the plot, label the axes, plot the spiral by means of green dots, and add a plot legend, as coded below:
# Adding some style
import numpy as np
import matplotlib.pyplot as plt
# Computing Spiral
t = np.linspace(0, 50, 500)
x = np.sqrt(t) * np.cos(t)
y = np.sqrt(t) * np.sin(t)
# Generating figure
plt.figure(1, figsize = (4,4))
# Plotting the Spiral with some options
plt.plot(x, y, '--', color = 'deeppink', linewidth = 1.5, label = 'Spiral')
# Adding grid
plt.grid(True, color = 'lightgray')
# Adding title
plt.title("Fermat's spiral for t between 0 and 50")
# Adding axes labels
plt.xlabel("x-axis", fontsize = 15)
plt.ylabel("y-axis", fontsize = 15)
# Showing plot legend
plt.legend()
# Show the plot
plt.show()
Let us go over the novel part of the above code:
plt.figure(): This command generates a figure object. If you are planning on plotting just one figure at a time, then this command is optional: a figure object is generated implicitly when calling plt.plot. Otherwise, if working with n figures, you need to generate a figure object with plt.figure(i) for each i between 1 and n. The number i uniquely identifies the i-th figure: whenever you call plt.figure(i), Python knows that the next commands will refer to the i-th figure. In our case we only have one figure, so we have used the identifier 1. The second argument figsize = (a,b) in plt.figure() specifies the size of figure 1 in inches. In this case we generated a figure 4 x 4 inches.plt.plot: This is plotting the arrays x and y, as usual. However we are adding a few aestethic touches: the curve is plotted in dashed style with --, in deep pink color and with a line width of 1.5. Finally this plot is labelled Spiral.plt.grid: This enables a grid in light gray color.plt.title: This gives a title to the figure, displayed on top.plt.xlabel and plt.ylabel: These assign labels to the axes, with font size 15 points.plt.legend(): This plots the legend, with all the labels assigned in the plt.plot call. In this case the only label is Spiral.There are countless plot types and options you can specify in matplotlib, see for example the Matplotlib Gallery. Of course there is no need to remember every single command: a quick Google search can do wonders.
There are several ways of generating evenly spaced arrays in Python. For example the function np.arange(a,b,s) returns an array with values within the half-open interval \([a,b)\), with spacing between values given by s. For example
import numpy as np
t = np.arange(0,1, 0.2)
print("t =",t)t = [0. 0.2 0.4 0.6 0.8]A curve \(\pmb{\gamma}\) in \(\mathbb{R}^2\) can also be defined as the set of points \((x,y) \in \mathbb{R}^2\) satisfying \[ f(x,y)=0 \] for some given \(f \colon \mathbb{R}^2 \to \mathbb{R}\). For example let us plot the curve \(\pmb{\gamma}\) implicitly defined by \[ f(x,y) =( 3 x^2 - y^2 )^2 \ y^2 - (x^2 + y^2 )^4 \] for \(-1 \leq x,y \leq 1\). First, we need a way to generate a grid in \(\mathbb{R}^2\) so that we can evaluate \(f\) on such grid. To illustrate how to do this, let us generate a grid of spacing 1 in the 2D square \([0,4]^2\). The goal is to obtain the 5 x 5 matrix of coordinates \[ A = \left( \begin{matrix} (0,0) & (1,0) & (2,0) & (3,0) & (4,0) \\ (0,1) & (1,1) & (2,1) & (3,1) & (4,1) \\ (0,2) & (1,2) & (2,2) & (2,3) & (2,4) \\ (0,3) & (1,3) & (2,3) & (3,3) & (3,4) \\ (0,4) & (1,4) & (2,4) & (3,4) & (4,4) \\ \end{matrix} \right) \] which corresponds to the grid of points
To achieve this, first generate x and y coordinates using
x = np.linspace(0, 4, 5)
y = np.linspace(0, 4, 5)This generates coordinates \[ x = [0, 1, 2, 3, 4] \,, \quad y = [0, 1, 2, 3, 4] \,. \] We then need to obtain two matrices \(X\) and \(Y\): one for the \(x\) coordinates in \(A\), and one for the \(y\) coordinates in \(A\). This can be achieved with the code
X[0,0] = 0
X[0,1] = 1
X[0,2] = 2
X[0,3] = 3
X[0,4] = 4
X[1,0] = 0
X[1,1] = 1
...
x[4,3] = 3
x[4,4] = 4and similarly for \(Y\). The output would be the two matrices \(X\) and \(Y\) \[ X = \left( \begin{matrix} 0 & 1 & 2 & 3 & 4 \\ 0 & 1 & 2 & 3 & 4 \\ 0 & 1 & 2 & 3 & 4 \\ 0 & 1 & 2 & 3 & 4 \\ \end{matrix} \right) \,, \quad Y = \left( \begin{matrix} 0 & 0 & 0 & 0 & 0 \\ 1 & 1 & 1 & 1 & 1 \\ 2 & 2 & 2 & 2 & 2 \\ 3 & 3 & 3 & 3 & 3 \\ 4 & 4 & 4 & 4 & 4 \\ \end{matrix} \right) \]
If now we plot \(X\) against \(Y\) via the command
plt.plot(X, Y, 'k.')we obtain Figure 5.1. In the above command the style 'k.' represents black dots. This procedure would be impossible with large vectors. Thankfully there is a function in numpy doing exactly what we need: np.meshgrid.
# Demonstrating np.meshgrid
import numpy as np
# Generating x and y coordinates
xlist = np.linspace(0, 4, 5)
ylist = np.linspace(0, 4, 5)
# Generating grid X, Y
X, Y = np.meshgrid(xlist, ylist)
# Printing the matrices X and Y
# np.array2string is only needed to align outputs
print('X =', np.array2string(X, prefix='X= '))
print('\n')
print('Y =', np.array2string(Y, prefix='Y= '))X = [[0. 1. 2. 3. 4.]
[0. 1. 2. 3. 4.]
[0. 1. 2. 3. 4.]
[0. 1. 2. 3. 4.]
[0. 1. 2. 3. 4.]]
Y = [[0. 0. 0. 0. 0.]
[1. 1. 1. 1. 1.]
[2. 2. 2. 2. 2.]
[3. 3. 3. 3. 3.]
[4. 4. 4. 4. 4.]]Now that we have our grid, we can evaluate the function \(f\) on it. This is simply done with the command
Z =((3*(X**2) - Y**2)**2)*(Y**2) - (X**2 + Y**2)**4 This will return the matrix \(Z\) containing the values \(f(x_i,y_i)\) for all \((x_i,y_i)\) in the grid \([X,Y]\). We are now interested in plotting the points in the grid \([X,Y]\) for which \(Z\) is zero. This is achieved with the command
plt.contour(X, Y, Z, [0])Putting the above observations together, we have the code for plotting the curve \(f=0\) for \(-1 \leq x,y \leq 1\).
# Plotting f=0
import numpy as np
import matplotlib.pyplot as plt
# Generates coordinates and grid
xlist = np.linspace(-1, 1, 5000)
ylist = np.linspace(-1, 1, 5000)
X, Y = np.meshgrid(xlist, ylist)
# Computes f
Z =((3*(X**2) - Y**2)**2)*(Y**2) - (X**2 + Y**2)**4
# Creates figure object
plt.figure(figsize = (4,4))
# Plots level set Z = 0
plt.contour(X, Y, Z, [0])
# Set axes labels
plt.xlabel("x-axis", fontsize = 15)
plt.ylabel("y-axis", fontsize = 15)
# Shows plot
plt.show()
Plotting in 3D with matplotlib requires the mplot3d toolkit, see here for documentation. Therefore our first lines will always be
# Packages for 3D plots
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3dWe can now generate empty 3D axes
# Generates and plots empty 3D axes
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
# Creates figure object
fig = plt.figure(figsize = (4,4))
# Creates 3D axes object
ax = plt.axes(projection = '3d')
# Shows the plot
plt.show()
In the above code fig is a figure object, while ax is an axes object. In practice, the figure object contains the axes objects, and the actual plot information will be contained in axes. If you want multiple plots in the figure container, you should use the command
ax = fig.add_subplot(nrows = m, ncols = n, pos = k)This generates an axes object ax in position k with respect to a m x n grid of plots in the container figure. For example we can create a 3 x 2 grid of empty 3D axes as follows
# Generates 3 x 2 empty 3D axes
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
# Creates container figure object
fig = plt.figure(figsize = (6,8))
# Creates 6 empty 3D axes objects
ax1 = fig.add_subplot(3, 2, 1, projection = '3d')
ax2 = fig.add_subplot(3, 2, 2, projection = '3d')
ax3 = fig.add_subplot(3, 2, 3, projection = '3d')
ax4 = fig.add_subplot(3, 2, 4, projection = '3d')
ax5 = fig.add_subplot(3, 2, 5, projection = '3d')
ax6 = fig.add_subplot(3, 2, 6, projection = '3d')
# Shows the plot
plt.show()
We are now ready to plot a 3D parametric curve \(\pmb{\gamma} \colon (a,b) \to \mathbb{R}^3\) of the form \[ \pmb{\gamma}(t) = (x(t), y(t), z(t)) \] with the code
# Code to plot 3D curve
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
# Generates figure and 3D axes
fig = plt.figure(figsize = (size1,size2))
ax = plt.axes(projection = '3d')
# Plots grid
ax.grid(True)
# Divides time interval (a,b)
# into n parts and saves them in array t
t = np.linspace(a, b, n)
# Computes the curve gamma on array t
# for given functions x(t), y(t), z(t)
x = x(t)
y = y(t)
z = z(t)
# Plots gamma
ax.plot3D(x, y, z)
# Setting title for plot
ax.set_title('3D Plot of gamma')
# Setting axes labels
ax.set_xlabel('x', labelpad = 'p')
ax.set_ylabel('y', labelpad = 'p')
ax.set_zlabel('z', labelpad = 'p')
# Shows the plot
plt.show()For example we can use the above code to plot the Helix \[ x(t) = \cos(t) \,, \quad y(t) = \sin(t) \,, \quad z(t) = t \tag{5.3}\] for \(t \in [0,6\pi]\).
# Plotting 3D Helix
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
# Generates figure and 3D axes
fig = plt.figure(figsize = (4,4))
ax = plt.axes(projection = '3d')
# Plots grid
ax.grid(True)
# Divides time interval (0,6pi) in 100 parts
t = np.linspace(0, 6*np.pi, 100)
# Computes Helix
x = np.cos(t)
y = np.sin(t)
z = t
# Plots Helix - We added some styling
ax.plot3D(x, y, z, color = "deeppink", linewidth = 2)
# Setting title for plot
ax.set_title('3D Plot of Helix')
# Setting axes labels
ax.set_xlabel('x', labelpad = 20)
ax.set_ylabel('y', labelpad = 20)
ax.set_zlabel('z', labelpad = 20)
# Shows the plot
plt.show()
We can also change the viewing angle for a 3D plot store in ax. This is done via
ax.view_init(elev = e, azim = a)which displays the 3D axes with an elevation angle elev of e degrees and an azimuthal angle azim of a degrees. In other words, the 3D plot will be rotated by e degrees above the xy-plane and by a degrees around the z-axis. For example, let us plot the helix with 2 viewing angles. Note that we generate 2 sets of axes with the add_subplot command discussed above.
# Plotting 3D Helix
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
# Generates figure object
fig = plt.figure(figsize = (4,4))
# Generates 2 sets of 3D axes
ax1 = fig.add_subplot(1, 2, 1, projection = '3d')
ax2 = fig.add_subplot(1, 2, 2, projection = '3d')
# We will not show a grid this time
ax1.grid(False)
ax2.grid(False)
# Divides time interval (0,6pi) in 100 parts
t = np.linspace(0, 6*np.pi, 100)
# Computes Helix
x = np.cos(t)
y = np.sin(t)
z = t
# Plots Helix on both axes
ax1.plot3D(x, y, z, color = "deeppink", linewidth = 1.5)
ax2.plot3D(x, y, z, color = "deeppink", linewidth = 1.5)
# Setting title for plots
ax1.set_title('Helix from above')
ax2.set_title('Helix from side')
# Changing viewing angle of ax1
# View from above has elev = 90 and azim = 0
ax1.view_init(elev = 90, azim = 0)
# Changing viewing angle of ax2
# View from side has elev = 0 and azim = 0
ax2.view_init(elev = 0, azim = 0)
# Shows the plot
plt.show()
Matplotlib produces beautiful static plots; however it lacks built in interactivity. For this reason I would also like to show you how to plot curves with Plotly, a very popular Python graphic library which has built in interactivity. Documentation for Plotly and lots of examples can be found here.
Say we want to plot the 2D curve \(\pmb{\gamma} \colon (a,b) \to \mathbb{R}^2\) parametrized by \[
\pmb{\gamma}(t) = ( x(t) , y(t) ) \,.
\] The Plotly module needed is called graph_objects, usually imported as go. The function for line plots is called Scatter. For documentation and examples see link. The code for plotting \(\pmb{\gamma}\) is as follows.
# Plotting gamma 2D
# Import libraries
import numpy as np
import plotly.graph_objects as go
# Compute times grid by dividing (a,b) in
# n equal parts
t = np.linspace(a, b, n)
# Compute the parametric curve gamma
# for given functions x(t) and y(t)
x = x(t)
y = y(t)
# Create empty figure object and saves
# it in the variable "fig"
fig = go.Figure()
# Create the line plot object
data = go.Scatter(x = x, y = y, mode = 'lines', name = 'gamma')
# Add "data" plot to the figure "fig"
fig.add_trace(data)
# Display the figure
fig.show()Some comments about the functions called above:
go.Figure: generates an empty Plotly figurego.Scatter: generates the actual plot. By default a scatter plot is produced. To obtain linear interpolation of the points, set mode = 'lines'. You can also label the plot with name = "string"add_trace: adds a plot to a figureshow: displays a figureAs an example, let us plot the Fermat’s Spiral defined at 5.2. Compared to the above code, we also add a bit of styling.
# Plotting Fermat's Spiral
# Import libraries
import numpy as np
import plotly.graph_objects as go
# Compute times grid by dividing (0,50) in
# 500 equal parts
t = np.linspace(0, 50, 500)
# Computes Fermat's Spiral
x = np.sqrt(t) * np.cos(t)
y = np.sqrt(t) * np.sin(t)
# Create empty figure object and saves
# it in the variable "fig"
fig = go.Figure()
# Create the line plot object
data = go.Scatter(x = x, y = y, mode = 'lines', name = 'gamma')
# Add "data" plot to the figure "fig"
fig.add_trace(data)
# Here we start with the styling options
# First we set a figure title
fig.update_layout(title_text = "Plotting Fermat's Spiral with Plotly")
# Adjust figure size
fig.update_layout(autosize = False, width = 600, height = 600)
# Change background canvas color
fig.update_layout(paper_bgcolor = "snow")
# Axes styling: adding title and ticks positions
fig.update_layout(
xaxis=dict(
title_text="X-axis Title",
titlefont=dict(size=20),
tickvals=[-6,-4,-2,0,2,4,6],
),
yaxis=dict(
title_text="Y-axis Title",
titlefont=dict(size=20),
tickvals=[-6,-4,-2,0,2,4,6],
)
)
# Display the figure
fig.show()As you can examine by moving the mouse pointer, the above plot is interactive. Note that the style customizations could be listed in a single call of the function update_layout. There are also pretty buit-in themes available, see here. The layout can be specified with the command
fig.update_layout(template = template_name)where template_name can be "plotly", "plotly_white", "plotly_dark", "ggplot2", "seaborn", "simple_white“.
We now want to plot a 3D curve \(\pmb{\gamma} \colon (a,b) \to \mathbb{R}^3\) parametrized by \[
\pmb{\gamma}(t) = ( x(t) , y(t) , z(t)) \,.
\] Again we use the Plotly module graph_objects, imported as go. The function for 3D line plots is called Scatter3d, and documentation and examples can be found at link. The code for plotting \(\pmb{\gamma}\) is as follows.
# Plotting gamma 3D
# Import libraries
import numpy as np
import plotly.graph_objects as go
# Compute times grid by dividing (a,b) in
# n equal parts
t = np.linspace(a, b, n)
# Compute the parametric curve gamma
# for given functions x(t), y(t), z(t)
x = x(t)
y = y(t)
z = z(t)
# Create empty figure object and saves
# it in the variable "fig"
fig = go.Figure()
# Create the line plot object
data = go.Scatter3d(x = x, y = y, z = z, mode = 'lines', name = 'gamma')
# Add "data" plot to the figure "fig"
fig.add_trace(data)
# Display the figure
fig.show()The functions go.Figure, add_trace and show appearing above are described in the previous Section. The new addition is go.Scatter3d, which generates a 3D scatter plot of the points stored in the array [x,y,z]. Setting mode = 'lines' results in a linear interpolation of such points. As before, the curve can be labeled by setting name = "string".
As an example, we plot the 3D Helix defined at 5.3. We also add some styling. We can also use the same pre-defined templates descirbed for go.Scatter in the previous section, see here for official documentation.
# Plotting 3D Helix
# Import libraries
import numpy as np
import plotly.graph_objects as go
# Divides time interval (0,6pi) in 100 parts
t = np.linspace(0, 6*np.pi, 100)
# Computes Helix
x = np.cos(t)
y = np.sin(t)
z = t
# Create empty figure object and saves
# it in the variable "fig"
fig = go.Figure()
# Create the line plot object
# We add options for the line width and color
data = go.Scatter3d(
x = x, y = y, z = z,
mode = 'lines', name = 'gamma',
line = dict(width = 10, color = "darkblue")
)
# Add "data" plot to the figure "fig"
fig.add_trace(data)
# Here we start with the styling options
# First we set a figure title
fig.update_layout(title_text = "Plotting 3D Helix with Plotly")
# Adjust figure size
fig.update_layout(
autosize = False,
width = 600,
height = 600
)
# Set pre-defined template
fig.update_layout(template = "seaborn")
# Options for curve line style
# Display the figure
fig.show()The above plot is interactive: you can pan arond by dragging the pointer. Once again, the style customizations could be listed in a single call of the function update_layout.
I will take for granted all the commands explained in Section 5.1. Suppose we want to plot a surface \(S\) which is defined by the parametric equations \[
x = x(u,v) \,, \quad
y = y(u,v) \,, \quad
z = z(u,v)
\] for \(u \in (a,b)\) and \(v \in (c,d)\). This can be done via the function called plot_surface contained in the mplot3d Toolkit. This function works as follows: first we generate a mesh-grid \([U,V]\) from the coordinates \((u,v)\) via the command
[U, V] = np.meshgrid(u, v)Then we compute the parametric surface on the mesh
x = x (U, V)
y = y (U, V)
z = z (U, V)Finally we can plot the surface with the command
plt.plot_surface(x, y, z)The complete code looks as follows.
# Plotting surface S
# Importing numpy, matplotlib and mplot3d
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
# Generates figure object of size m x n
fig = plt.figure(figsize = (m,n))
# Generates 3D axes
ax = plt.axes(projection = '3d')
# Shows axes grid
ax.grid(True)
# Generates coordinates u and v
# by dividing the interval (a,b) in n parts
# and the interval (c,d) in m parts
u = np.linspace(a, b, m)
v = np.linspace(c, d, n)
# Generates grid [U,V] from the coordinates u, v
U, V = np.meshgrid(u, v)
# Computes S given the functions x, y, z
# on the grid [U,V]
x = x(U,V)
y = y(U,V)
z = z(U,V)
# Plots the surface S
ax.plot_surface(x, y, z)
# Setting plot title
ax.set_title('The surface S')
# Setting axes labels
ax.set_xlabel('x', labelpad=10)
ax.set_ylabel('y', labelpad=10)
ax.set_zlabel('z', labelpad=10)
# Setting viewing angle
ax.view_init(elev = e, azim = a)
# Showing the plot
plt.show()For example let us plot a cone described parametrically by: \[ x = u \cos(v) \,, \quad y = u \sin(v) \,, \quad z = u \] for \(u \in (0,1)\) and \(v \in (0,2\pi)\). We adapt the above code:
# Plotting a cone
# Importing numpy, matplotlib and mplot3d
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
# Generates figure object of size 4 x 4
fig = plt.figure(figsize = (4,4))
# Generates 3D axes
ax = plt.axes(projection = '3d')
# Shows axes grid
ax.grid(True)
# Generates coordinates u and v by dividing
# the intervals (0,1) and (0,2pi) in 100 parts
u = np.linspace(0, 1, 100)
v = np.linspace(0, 2*np.pi, 100)
# Generates grid [U,V] from the coordinates u, v
U, V = np.meshgrid(u, v)
# Computes the surface on grid [U,V]
x = U * np.cos(V)
y = U * np.sin(V)
z = U
# Plots the cone
ax.plot_surface(x, y, z)
# Setting plot title
ax.set_title('Plot of a cone')
# Setting axes labels
ax.set_xlabel('x', labelpad=10)
ax.set_ylabel('y', labelpad=10)
ax.set_zlabel('z', labelpad=10)
# Setting viewing angle
ax.view_init(elev = 25, azim = 45)
# Showing the plot
plt.show()
As discussed in Section 5.1, we can have multiple plots in the same figure. For example let us plot the torus viewed from 2 angles. The parametric equations are: \[ \begin{aligned} x & = (R + r \cos(u)) \cos(v) \\ y & = (R + r \cos(u)) \sin(v) \\ z & = r \sin(u) \end{aligned} \] for \(u, v \in (0,2\pi)\) and with
# Plotting torus seen from 2 angles
# Importing numpy, matplotlib and mplot3d
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
# Generates figure object of size 9 x 5
fig = plt.figure(figsize = (9,5))
# Generates 2 sets of 3D axes
ax1 = fig.add_subplot(1, 2, 1, projection = '3d')
ax2 = fig.add_subplot(1, 2, 2, projection = '3d')
# Shows axes grid
ax1.grid(True)
ax2.grid(True)
# Generates coordinates u and v by dividing
# the interval (0,2pi) in 100 parts
u = np.linspace(0, 2*np.pi, 100)
v = np.linspace(0, 2*np.pi, 100)
# Generates grid [U,V] from the coordinates u, v
U, V = np.meshgrid(u, v)
# Computes the torus on grid [U,V]
# with radii r = 1 and R = 2
R = 2
r = 1
x = (R + r * np.cos(U)) * np.cos(V)
y = (R + r * np.cos(U)) * np.sin(V)
z = r * np.sin(U)
# Plots the torus on both axes
ax1.plot_surface(x, y, z, rstride = 5, cstride = 5, color = 'dimgray', edgecolors = 'snow')
ax2.plot_surface(x, y, z, rstride = 5, cstride = 5, color = 'dimgray', edgecolors = 'snow')
# Setting plot titles
ax1.set_title('Torus')
ax2.set_title('Torus from above')
# Setting range for z axis in ax1
ax1.set_zlim(-3,3)
# Setting viewing angles
ax1.view_init(elev = 35, azim = 45)
ax2.view_init(elev = 90, azim = 0)
# Showing the plot
plt.show()
Notice that we have added some customization to the plot_surface command. Namely, we have set the color of the figure with color = 'dimgray' and of the edges with edgecolors = 'snow'. Moreover the commands rstride and cstride set the number of wires you see in the plot. More precisely, they set by how much the data in the mesh \([U,V]\) is downsampled in each direction, where rstride sets the row direction, and cstride sets the column direction. On the torus this is a bit difficult to visualize, due to the fact that \([U,V]\) represents angular coordinates. To appreciate the effect, we can plot for example the paraboiloid \[
\begin{aligned}
x & = u \\
y & = v \\
z & = - u^2 - v^2
\end{aligned}
\] for \(u,v \in [-1,1]\).
# Showing the effect of rstride and cstride
# Importing numpy, matplotlib and mplot3d
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
# Generates figure object of size 6 x 6
fig = plt.figure(figsize = (6,6))
# Generates 2 sets of 3D axes
ax1 = fig.add_subplot(2, 2, 1, projection = '3d')
ax2 = fig.add_subplot(2, 2, 2, projection = '3d')
ax3 = fig.add_subplot(2, 2, 3, projection = '3d')
ax4 = fig.add_subplot(2, 2, 4, projection = '3d')
# Generates coordinates u and v by dividing
# the interval (-1,1) in 100 parts
u = np.linspace(-1, 1, 100)
v = np.linspace(-1, 1, 100)
# Generates grid [U,V] from the coordinates u, v
U, V = np.meshgrid(u, v)
# Computes the paraboloid on grid [U,V]
x = U
y = V
z = - U**2 - V**2
# Plots the paraboloid on the 4 axes
# but with different stride settings
ax1.plot_surface(x, y, z, rstride = 5, cstride = 5, color = 'dimgray', edgecolors = 'snow')
ax2.plot_surface(x, y, z, rstride = 5, cstride = 20, color = 'dimgray', edgecolors = 'snow')
ax3.plot_surface(x, y, z, rstride = 20, cstride = 5, color = 'dimgray', edgecolors = 'snow')
ax4.plot_surface(x, y, z, rstride = 10, cstride = 10, color = 'dimgray', edgecolors = 'snow')
# Setting plot titles
ax1.set_title('rstride = 5, cstride = 5')
ax2.set_title('rstride = 5, cstride = 20')
ax3.set_title('rstride = 20, cstride = 5')
ax4.set_title('rstride = 10, cstride = 10')
# We do not plot axes, to get cleaner pictures
ax1.axis('off')
ax2.axis('off')
ax3.axis('off')
ax4.axis('off')
# Showing the plot
plt.show()
In this case our mesh is 100 x 100, since u and v both have 100 components. Therefore setting rstride and cstride to 5 implies that each row and column of the mesh is sampled one time every 5 elements, for a total of \[
100/5 = 20
\] samples in each direction. This is why in the first picture you see a 20 x 20 grid. If instead one sets rstride and cstride to 10, then each row and column of the mesh is sampled one time every 10 elements, for a total of \[
100/10 = 10
\] samples in each direction. This is why in the fourth figure you see a 10x10 grid.
As done in Section 5.1.4, we now see how to use Plotly to generate an interactive 3D plot of a surface. This can be done by means of functions contained in the Plotly module graph_objects, usually imported as go. Specifically, we will use the function go.Surface. The code will look similar to the one used to plot surfaces with matplotlib:
[x,y,z],[x,y,z] to go.Surface to produce the plot.The full code is below.
# Plotting a Torus with Plotly
# Import "numpy" and the "graph_objects" module from Plotly
import numpy as np
import plotly.graph_objects as go
# Generates coordinates u and v by dividing
# the interval (0,2pi) in 100 parts
u = np.linspace(0, 2*np.pi, 100)
v = np.linspace(0, 2*np.pi, 100)
# Generates grid [U,V] from the coordinates u, v
U, V = np.meshgrid(u, v)
# Computes the torus on grid [U,V]
# with radii r = 1 and R = 2
R = 2
r = 1
x = (R + r * np.cos(U)) * np.cos(V)
y = (R + r * np.cos(U)) * np.sin(V)
z = r * np.sin(U)
# Generate and empty figure object with Plotly
# and saves it to the variable called "fig"
fig = go.Figure()
# Plot the torus with go.Surface and store it
# in the variable "data". We also do now show the
# plot scale, and set the color map to "teal"
data = go.Surface(
x = x , y = y, z = z,
showscale = False,
colorscale='teal'
)
# Add the plot stored in "data" to the figure "fig"
# This is done with the command add_trace
fig.add_trace(data)
# Set the title of the figure in "fig"
fig.update_layout(title_text="Plotting a Torus with Plotly")
# Show the figure
fig.show()You can rotate the above image by clicking on it and dragging the cursor. To further customize your plots, you can check out the documentation of go.Surface at this link. For example, note that we have set the colormap to teal: for all the pretty colorscales available in Plotly, see this page.
One could go even fancier and use the tri-surf plots in Plotly. This is done with the function create_trisurf contained in the module figure_factory of Plotly, usually imported as ff. The documentation can be found here. We also need to import the Python library scipy, which we use to generate a Delaunay triangulation for our plot. Let us for example plot the torus.
# Plotting Torus with tri-surf
# Importing libraries
import numpy as np
import plotly.figure_factory as ff
from scipy.spatial import Delaunay
# Generates coordinates u and v by dividing
# the interval (0,2pi) in 100 parts
u = np.linspace(0, 2*np.pi, 20)
v = np.linspace(0, 2*np.pi, 20)
# Generates grid [U,V] from the coordinates u, v
U, V = np.meshgrid(u, v)
# Collapse meshes to 1D array
# This is needed for create_trisurf
U = U.flatten()
V = V.flatten()
# Computes the torus on grid [U,V]
# with radii r = 1 and R = 2
R = 2
r = 1
x = (R + r * np.cos(U)) * np.cos(V)
y = (R + r * np.cos(U)) * np.sin(V)
z = r * np.sin(U)
# Generate Delaunay triangulation
points2D = np.vstack([U,V]).T
tri = Delaunay(points2D)
simplices = tri.simplices
# Plot the Torus
fig = ff.create_trisurf(
x=x, y=y, z=z,
colormap = "Portland",
simplices=simplices,
title="Torus with tri-surf",
aspectratio=dict(x=1, y=1, z=0.3),
show_colorbar = False
)
# Adjust figure size
fig.update_layout(autosize = False, width = 700, height = 700)
# Show the figure
fig.show()Again, the above figure is interactive. Try rotating the torus with the pointer.
The command plt.show() can be omitted if working in Jupyter Notebook, as it is called by default.↩︎