By the end of this class, you will be able to:
Last week we saw how to plot a function $y = f(x)$ using NumPy arrays and Matplotlib.
We defined a NumPy array of $x$-values, x_values
, and defined the array of $y$-values using f(x_values)
, so the graph could be plotted with plt.plot(x_values, f(x_values))
.
Although slightly counterintuitive to start with, it makes sense to call the array of $x$-values just x
so that the plot command becomes just plt.plot(x, f(x))
.
You will now try plotting some parametrically defined curves. Recall that the points on the unit circle are given parametrically by \[ \left(\cos(\theta), \sin(\theta)\right) \quad\text{for }\theta\in [0,2\pi].\] To get Python to plot this, type in and run the following program:
1 2 3 4 5 6 7 | import numpy as np import matplotlib.pyplot as plt theta = np.linspace(0, 2*np.pi, 100) plt.plot(np.cos(theta), np.sin(theta)) plt.gca().set_aspect("equal") plt.show() |
You should see a circle.
Line 4 generates an array of $100$ values for $\theta$ between $0$ and $2\pi$. Remember that theta
will be an array of values, rather than a single value.
Line 5 provides the plt.plot()
function with the appropriate arrays of $x$-values and $y$-values.
Line 6 makes sure that the scaling on the $x$- and $y$-axes is the same.
Comment out this line with #
and see how the circle gets deformed.
Line 7 displays the current figure.
The circle looks a little cramped inside the axes, so we can tell Python to let the axes to be a little bigger.
Add the following line before plt.show()
.
plt.axis([-1.2, 1.2, -1.2, 1.2])
What are the four numbers doing there? Change them to find out.
Exercise 11.1
for
loopsPlotting can be combined with for
loops to various effects.
For example, we can consider the parametric equation of a curve:
\[ x= \tfrac32 \cos(\theta) - \cos(k\theta),\quad y=\tfrac32 \sin(\theta) - \sin(k\theta), \quad \text{for }\theta\in [0,2\pi],\]
where $k\in\{2,3 ,4, \dots\}$ is a constant.
import numpy as np import matplotlib.pyplot as plt theta = np.linspace(0, 2*np.pi, 200) for k in range(2, 7): plt.plot(1.5*np.cos(theta) - np.cos(k*theta), 1.5*np.sin(theta) - np.sin(k*theta)) plt.gca().set_aspect("equal") plt.axis([-3, 3, -3, 3]) plt.show()
You can plot the graphs all on the same axes if you unindent the last three lines, thus removing them from the for
loop.
You can build up repetitive elements in a picture using loops.
Suppose that you want to draw lots of spokes in a wheel.
This means that you want to draw a line from $(0,0)$ to $(\cos(\phi), \sin(\phi))$ for various values of $\phi$.
Recall that to use plt.plot()
to draw a line from (x0, y0)
to (x1, y1)
, you need to provide $x$-values [x0, x1]
and $y$-values [y0, y1]
.
We will plot $N$ spokes by taking $\phi=2\pi i/N$ for $i=0,1,\dots, N-1$.
import numpy as np import matplotlib.pyplot as plt NO_OF_SPOKES = 17 for i in range(NO_OF_SPOKES): phi = i * 2 * np.pi / NO_OF_SPOKES plt.plot([0, np.cos(phi)], [0, np.sin(phi)], color="red") plt.gca().set_aspect("equal") plt.axis([-1.1, 1.1, -1.1, 1.1]) plt.show()
Exercise 11.2. Draw the outline of a chess board, starting with the following program.
import matplotlib.pyplot as plt plt.plot([0, 0], [0, 8], color="blue") plt.plot([1, 1], [0, 8], color="blue") plt.plot([0, 8], [0, 0], color="blue") plt.plot([0, 8], [1, 1], color="blue") plt.gca().set_aspect("equal") plt.axis([-1, 9, -1, 9]) plt.show()
Make sure you know why it is drawing each of the lines correctly.
You could continue in this way, writing out a total of eighteen (why eighteen?) plt.plot()
commands to draw an eight-by-eight block of squares.
Instead, use just two plt.plot()
commands and either one or two for
loops to
draw an eight-by-eight block of squares.
If you are doing the same thing, such as drawing a circle, in different situations then it makes sense to define and re-use a function for this.
Below, the function draw_circle()
takes two parameters: a list with two elements for the coordinates of the centre, and a number for the radius.
Recall that the parametric form for a circle with centre $(x,y)$ and radius $r$ is $(r\cos(\theta)+x, r\sin(\theta)+y)$.
import numpy as np import matplotlib.pyplot as plt def draw_circle(centre, radius): """Draw a black circle with specified centre and radius.""" theta = np.linspace(0, 2*np.pi, 100) plt.plot(radius*np.cos(theta) + centre[0], radius*np.sin(theta) + centre[1], color="black") draw_circle([0, 0], 5) draw_circle([0, 0], 1) draw_circle([3, 0], 2) draw_circle([-3, 0], 2) draw_circle([0, 3], 2) draw_circle([0, -3], 2) plt.gca().set_aspect("equal") plt.show()
Exercise 11.3. Add the draw_circle()
function to the spoke drawing program above and add a for
loop to draw nine circles to produce this spider's web:
You might wish to save your plots so you can include them in webpages or LaTeX documents.
Replace plt.show()
with the following to save a plot as a png file.
plt.savefig("picture.png")
This is saved in the working directory. Open that directory and view the graph. If using Google Colab, click on the `Files' icon to the left of the window to find where the file has been saved; to save the file to your Google Drive, click on the `Mount Drive' icon and follow the on-screen instructions.
Exercise 11.4. Alter one of your programs so that it saves the picture as a file. Find the file and make sure you can view it.
For different purposes you should use different types of graphic files: for a webpage, save as either a png or an svg file; for a LaTeX document, save as a pdf or eps file.
Python will automatically detect which format to save as based on the extension you give the filename, such as .png or .svg.
To reduce the amount of padding round the graph, use the following variant:
plt.savefig("picture.png", bbox_inches="tight")
To draw points, not lines, the command plt.scatter(x, y, c=color, s=size)
can be used.
This command takes four lists or Numpy arrays as arguments: x
and y
store the coordinates of the points to plot, while colour
and size
are lists whose $i$th entries store the colour and size of the $i$th point.
The command np.random.rand(N)
produces a Numpy array of length N
whose entries are random numbers between $0$ and $1$, so the following code produces two arrays, x
and y
, with entries between $-1$ and $1$.
It colours the $i$th point, $(x_i, y_i)$, red if $(x_i)^{2/3}+(y_i)^{2/3}<1$ and blue otherwise.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | import matplotlib.pyplot as plt import numpy as np N = 20 x = 2*np.random.rand(N) - 1 y = 2*np.random.rand(N) - 1 size = [5] * N colour = ['blue'] * N for i in range(N): if (x[i]**2)**(1/3) + (y[i]**2)**(1/3) < 1: colour[i] = ('red') plt.scatter(x, y, c=colour, s=size) plt.axis([-1.1, 1.1, -1.1, 1.1]) plt.gca().set_aspect("equal") plt.show() |
Run this a couple of times to see if you can spot the pattern that is formed. Now change the number of points from $20$ to $200$ and repeat. Then try for $2000$ and $20000$ points. The outline of the resulting figure is called an astroid curve.
Exercise 11.5. Alter the code to plot blue dots with size $10$ and red with size $1$.
import numpy as np x = np.linspace(0, 1, 5) print(x) print(20*x)
import matplotlib.pyplot as plt plt.plot([0, 0, 1, 1], [1, 0, 0, 1], color='red') plt.axis([-1, 2, -1, 2]) plt.show()