python plotting tutorial - 1

Use matplotlib in Python to create wonderful graphs!

## Copyright statement

The following is the note I took when I was learning scientific computing with Python.

The book I use is ** Scientific computing with Python3** published by

**Packt**and the contents in this article are mostly from this book. The copyright belongs to the author.

I used **Jupyter notebook** to generate this markdown file.

## Preface

Plotting in Python is done in the `pyplot`

part in matplotlib module.

Here we import the module in this way:

```
from matplotlib.pyplot import *
```

Since we are using Jupyter notebook, we start with the magic command **“%matplotlib”**

```
%matplotlib inline # in order to show plot in jupyter notebook
```

## Basic plotting

function: `plot`

.`plot(x,y)`

: a plot of y as a function of x (inputs are **arrays/lists** of equal length)

You can also use `plot(y)`

as it is in fact short for `plot(range(len(y)), y)`

### example 1

The following is a plot drawing with this function. We plot the function $sin(x)$ for $x \in [-2\pi, 2\pi]$ using 200 sample points and set markers every fourth points.

```
from scipy import *
from matplotlib.pyplot import *
# plot sin(x) for some interval
x = linspace(-2*pi, 2*pi, 200)
plot(x, sin(x))
# plot markers for every 4th point
samples = x[::4]
plot(samples, sin(samples),"r*") # mark with red star-shape markers"
title("Function $sin(x)$ with some points marked")
grid()
```

To call a new clean window of plot, use function `figure()`

. To switch from figures, using numbers, for example, `figure(2)`

. Without a number, python will create a new window.

### example 2

To add lebels, we use the function `legend`

.

example:

```
# ---polyfit example---
x = range(5)
y = [1, 2, 1, 3, 5]
p2 = polyfit(x, y, 2)
p4 = polyfit(x, y, 4)
# plot the polynomials and points
xx = linspace(-1, 5, 200)
plot(xx, polyval(p2, xx), label = 'fitting polynomials of degree 2')
plot(xx, polyval(p4, xx), label = 'fitting polynomials of degree 4')
plot(x, y, '*')
# set the axis and legend
axis([-1, 5, 0, 6])
legend(loc = 'upper left', fontsize = 'small')
```

```
<matplotlib.legend.Legend at 0x19d0f9a75c0>
```

### example 3

do scatter plots and logarithmic plots:

```
# create random 2D points
import numpy
x1 = 2 * numpy. random.standard_normal((2,100))
x2 = 0.8 * numpy.random.standard_normal((2,100)) + array([[6], [2]])
plot(x1[0], x1[1], '*')
plot(x2[0], x2[1], 'r*')
title('2D scatter plot')
```

```
Text(0.5,1,'2D scatter plot')
```

the logarithmic plot using `loglog`

:

```
# log both x and y axis
x = linspace(0,10,200)
loglog(x,2*x**2, label = 'quadratic polynomial',
linestyle = '-', linewidth = 3)
loglog(x,4*x**4, label = '4th degree polynomial',
linestyle = '-.', linewidth = 3)
loglog(x,5*exp(x), label = 'exponential function', linewidth = 3)
title('Logarithmic plots')
legend(loc = 'best')
```

```
<matplotlib.legend.Legend at 0x19d0fb20e80>
```

## Formatting

The appearance of figures and plots can be styled and customized to look how you want them to look.

Some important variables are

`linewidth`

, which controls the**thickness**of plot lines;`xlabel, ylabel`

, which set the axis labels;`color`

for plot colors;`transparent`

for transparency.

The following is an example with more keywords:

```
k = 0.2
x = [sin(2*n*k) for n in range(20)]
plot(x, color='green', linestyle='dashed', marker='o',
markerfacecolor='blue', markersize=12, linewidth=6)
```

```
[<matplotlib.lines.Line2D at 0x19d0fc342b0>]
```

You can use `plot(...,'ro-')`

, or the more explicit syntax `plot(..., marker='o', color='r', linestyle='-')`

.

### hist function

To get histgram instead of normal plot, use the function `hist`

.

example:

```
# random vector with normal distribution
sigma, mu = 2, 10
x = sigma * numpy.random.standard_normal(10000) + mu
hist(x, 50, normed = 1)
z = linspace(0, 20, 200)
plot(z, (1/sqrt(2*pi*sigma**2))*exp(-(z-mu)**2/(2*sigma**2)),'g')
# title with LaTeX formatting
title('Histogram with $\mu$ = {}, $\sigma$ = {}'.format(mu,sigma))
```

```
Text(0.5,1,'Histogram with $\\mu$ = 10, $\\sigma$ = 2')
```

- sometimes the string formatting will interfere with our LaTeX bracket. When this happens, we use double brackets in LaTeX, for example use
`x_1`

instead of`x_{1}`

. - the text sometimes will also overlap with the escaping sequences. For example
`\tau`

will be interpreted as`\t`

. In this case we should use**raw string**such as`r'\tau'`

### subplot function

To show several plot in the same window:`subplot(v, h, c)`

**v**: the number of vertical plots.

**h**: the number of horizontal plots.

**c**: the index indicating which position to plot in (counted row-wise)

use `subplot_adjust`

to add extra space to adjust the distance between different subplots.

```
def avg(x):
""" simple running average """
return (roll(x,1) + x + roll(x,-1)) / 3
# sine function with noise
x = linspace(-2*pi, 2*pi,200)
y = sin(x) + 0.4*rand(200)
# make successive subplots
for iteration in range(3):
subplot(3, 1, iteration + 1)
plot(x,y, label = '{:d} average{}'.format(iteration, 's' if iteration > 1 else ''))
yticks([])
legend(loc = 'lower left', frameon = False)
y = avg(y) #apply running average
subplots_adjust(hspace = 0.7)
```

### savefig function

To save the fig, we use the function `savefig`

For example:

```
savefig('test.pdf') # save to pdf
savefig('test.svg') # save to svg
savefig('test.pdf', transparent = true) # make the background transparent
savefig('test.pdf', bbox_inches = 'tight') # reduce the surrounding space by setting the bounding box tight
```

## Meshgrid and contour

To generate a grid on the rectangle $[a,b] \times [c,d]$, we use the `meshgrid`

command:

```
n = ... # number of discretization points along the x-axis
m = ... # number of discretization points along the x-axis
X,Y = meshgrid(linspace(a,b,n), linspace(c,d,m))
```

A rectangle discretized by `meshgrid`

can be used to visualize the behavior of an iteration.

But first we use it to plot **level curves** of a function. This is done by the command `contour`

.

We choose Rosenbrock’s **banana function** as an example:

$ f(x,y) = (1-x)^2 + 100(y-x^2)^2$

```
rosenbrockfunction = lambda x,y: (1-x)**2 + 100*(y-x**2)**2
X,Y = meshgrid(linspace(-.5, 2., 100), linspace(-1.5, 4., 100))
Z = rosenbrockfunction(X, Y)
contour(X, Y, Z, logspace(-0.5, 3.5, 20, base = 10), cmap = 'gray')
title('Rosenbrock Function: $f(x,y) = (1-x)^2+100(y-x^2)^2$')
xlabel('x')
ylabel('y')
```

```
Text(0,0.5,'y')
```

the command `contourf`

has the same use of `contour`

but fills the plot with different colors according to different levels. Here we simply change the `contour`

to `contourf`

in the above code to see the output:

```
rosenbrockfunction = lambda x,y: (1-x)**2 + 100*(y-x**2)**2
X,Y = meshgrid(linspace(-.5, 2., 100), linspace(-1.5, 4., 100))
Z = rosenbrockfunction(X, Y)
contourf(X, Y, Z, logspace(-0.5, 3.5, 20, base = 10), cmap = 'gray')
title('Rosenbrock Function: $f(x,y) = (1-x)^2+100(y-x^2)^2$')
xlabel('x')
ylabel('y')
```

```
Text(0,0.5,'y')
```

And we continue with the above example of Rosenbrock Function.

We use **Powell’s method** to find the minimum of Rosenbrock Function.

```
import scipy.optimize as so
rosenbrockfunction = lambda x,y: (1-x)**2+100*(y-x**2)**2
X,Y=meshgrid(linspace(-.5,2.,100),linspace(-1.5,4.,100))
Z=rosenbrockfunction(X,Y)
cs=contour(X,Y,Z,logspace(0,3.5,7,base=10),cmap='gray')
rosen=lambda x: rosenbrockfunction(x[0],x[1])
solution, iterates = so.fmin_powell(rosen,x0=array([0,-0.7]),retall=True)
x,y=zip(*iterates)
plot(x,y,'ko') # plot black bullets
plot(x,y,'k:',linewidth=1) # plot black dotted lines
title("Steps of Powell's method to compute a minimum")
clabel(cs)
```

```
Optimization terminated successfully.
Current function value: 0.000000
Iterations: 16
Function evaluations: 462
<a list of 11 text.Text objects>
```

The iterative method `fmin_powell`

applies Powell’s method to find a minimum.

It is started by a given start value of $x_0$ and reports all iterates when the option `retall=True`

is

given.

After sixteen iterations, the solution $x=0, y=0$ was found. The iterations are depicted

as bullets in the above contour plot.

PS:

- to know about the function
`zip`

, you can check here. `contour`

also creates a contour set object that we assigned to the variable`cs`

, which is then used by the command`clabel`

to annotate the levels of the corresponding function values.

## images and contours

The following function will create a matrix of color values for the Mandelbrot fractal.

(click here to know about it)

Consider a fixed point iteration, that depends on a complex parameter $c$:

$ z_{n+1} = z_n^2 + c, with \space c \in \mathbb{C}$

Whether the sequence is bounded depends on the parameter $c$.

Here we consider the sequence to be bounded if the value in below the bound within `maxit`

times iteration.

```
def mandelbrot(h,w, maxit=20):
X,Y = meshgrid(linspace(-2, 0.8, w), linspace(-1.4, 1.4, h))
c = X + Y*1j
z = c
exceeds = zeros(z.shape, dtype=bool)
for iteration in range(maxit):
z = z**2 + c
exceeded = abs(z) > 4
exceeds_now = exceeded & (logical_not(exceeds))
exceeds[exceeds_now] = True
z[exceeded] = 2 # limit the values to avoid overflow
return exceeds
imshow(mandelbrot(400,400),cmap='gray')
axis('off')
```

```
(-0.5, 399.5, 399.5, -0.5)
```

The command `imshow`

displays the matrix as an image. The selected color map shows the regions where the sequence appeared unbounded in white and others in black.

Here we used `axis('off')`

to turn off the axis as this might be not so useful for images.

This blog is under a CC BY-NC-SA 3.0 Unported License

Link to this article:http://huangweiran.club/2018/01/13/python-plotting-1/