================ by Jawad Haider

Chpt 4 - Visualization with Matplotlib¶

02 - Simple Line Plots¶

Simple Line Plots
Adjusting the Plot: Line Colors and Styles
Adjusting the Plot: Axes Limits
Labeling Plots

Simple Line Plots¶

Perhaps the simplest of all plots is the visualization of a single function y = f x . Here we will take a first look at creating a simple plot of this type.

%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use("seaborn-whitegrid")
import numpy as np

# all Matplotlib plots can be started by creating a figure and an axes
# their simplist form
fig=plt.figure()
ax=plt.axes()

In Matplotlib, the figure (an instance of the class plt.Figure) can be thought of as a single container that contains all the objects representing axes, graphics, text, and labels. The axes (an instance of the class plt.Axes) is what we see above: a bounding box with ticks and labels, which will eventually contain the plot elements that make up our visualization.

#Once we have created an axes, we can use the ax.plot function to plot some data.
fig=plt.figure()
ax=plt.axes()
x = np.linspace(0,10,100)
ax.plot(x, np.sin(x))

Alternatively, we can use the pylab interface and let the figure and axes be created for us in the background

fig=plt.figure()
ax=plt.axes()
plt.plot(x,np.sin(x))

If we want to create a single figure with multiple lines, we can simply call the plot function multiple times

fig=plt.figure()
ax=plt.axes()
plt.plot(x,np.sin(x))
plt.plot(x,np.cos(x))
# plt.plot(x,np.tan(x))
plt.plot(x,np.log(x))

/tmp/ipykernel_58842/3301612854.py:6: RuntimeWarning: divide by zero encountered in log
  plt.plot(x,np.log(x))

Adjusting the Plot: Line Colors and Styles¶

The first adjustment you might wish to make to a plot is to control the line colors and styles. The plt.plot() function takes additional arguments that can be used to spec‐ ify these. To adjust the color, you can use the color keyword, which accepts a string argument representing virtually any imaginable color. The color can be specified in a variety of ways

plt.plot(x, np.sin(x - 0), color='blue')
# specify color by name
plt.plot(x, np.sin(x - 1), color='g')
# short color code (rgbcmyk)
plt.plot(x, np.sin(x - 2), color='0.75')
# Grayscale between 0 and 1
plt.plot(x, np.sin(x - 3), color='#FFDD44')
# Hex code (RRGGBB from 00 to FF)
plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB tuple, values 0 and 1
plt.plot(x, np.sin(x - 5), color='chartreuse'); # all HTML color names supported

*If no color is specified, Matplotlib will automatically cycle through a set of default colors for multiple lines.

similarly, you can adjust the line style using linestyle keyword*

plt.plot(x, x + 0, linestyle='solid')
plt.plot(x, x + 1, linestyle='dashed')
plt.plot(x, x + 2, linestyle='dashdot')
plt.plot(x, x + 3, linestyle='dotted');
# For short, you can use the following codes:
plt.plot(x, x + 4, linestyle='-') # solid
plt.plot(x, x + 5, linestyle='--') # dashed
plt.plot(x, x + 6, linestyle='-.') # dashdot
plt.plot(x, x + 7, linestyle=':'); # dotted

If you would like to be extremely terse, these linestyle and color codes can be com‐ bined into a single nonkeyword argument to the plt.plot() function

Adjusting the Plot: Axes Limits¶

Matplotlib does a decent job of choosing default axes limits for your plot, but some‐ times it’s nice to have finer control. The most basic way to adjust axis limits is to use the plt.xlim() and plt.ylim() methods

plt.plot(x, np.sin(x))
plt.xlim(-1,11)
plt.ylim(-1.5,1.5)

(-1.5, 1.5)

if for some reason you’d like either axis to be displayed in reverse, you can simply reverse the order of the arguments

plt.plot(x, np.sin(x))

plt.xlim(10,0)
plt.ylim(1.2,-1.2)

(1.2, -1.2)

A useful related method is plt.axis() (note here the potential confusion between axes with an e, and axis with an i). The plt.axis() method allows you to set the x and y limits with a single call, by passing a list that specifies [xmin, xmax, ymin,ymax]

plt.plot(x, np.sin(x))
plt.axis([-1, 11, -1.5, 1.5]);

The plt.axis() method goes even beyond this, allowing you to do things like auto‐ matically tighten the bounds around the current plot

plt.plot(x, np.sin(x))
plt.axis('tight');

It allows even higher-level specifications, such as ensuring an equal aspect ratio so that on your screen, one unit in x is equal to one unit in y

plt.plot(x, np.sin(x))
plt.axis('equal');

Labeling Plots¶

As the last piece of this section, we’ll briefly look at the labeling of plots: titles, axis labels, and simple legends. Titles and axis labels are the simplest such labels—there are methods that can be used to quickly set them

plt.plot(x, np.sin(x))
plt.title('A sine curve')
plt.xlabel('x')
plt.ylabel('sin(x)')

Text(0, 0.5, 'sin(x)')

When multiple lines are being shown within a single axes, it can be useful to create a plot legend that labels each line type. Again, Matplotlib has a built-in way of quickly creating such a legend. It is done via the (you guessed it) plt.legend() method. Though there are several valid ways of using this, I find it easiest to specify the label of each line using the label keyword of the plot function

plt.plot(x, np.sin(x), '-g', label='sin(x)')
plt.plot(x, np.cos(x), ':b', label='cos(x)')
plt.axis('equal')

plt.legend();