Matplotlib - Findobj Demo

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In Matplotlib, the findobj is a method used for locating and manipulating graphical objects within a figure. Its primary purpose is to provide a flexible and convenient way to search for specific artist instances and modify their properties within a plot.

This method is available in both the interfaces such as pyplot.findobj() and axes.Axes.findobj() (pyplot and Object-oriented interfaces). All Artist objects in Matplotlib inherit this method, allowing for recursive searches within their hierarchy. The findobj method takes a matching criterion and optionally includes the object itself in the search.

In this tutorial, we will explore the fundamental concepts behind the findobj method in Matplotlib.

Finding object

The findobj() method can be used to locate specific types of objects within a plot. For example, users can search for Line2D instances to identify plotted lines within the current Axes.

Example

The following example creates two sets of line plots using the plot() method then you can use the findobj() method to find out the Line2D instance corresponding to the sinusoidal line.

import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
import numpy as np

# Generate data
t = np.arange(0, 4, 0.1)
F1 = np.sin(2 * t)
F2 = np.exp(-t*4)

# Plot two lines
fig, ax = plt.subplots(figsize=(7, 4))
line1, = ax.plot(t, F1, 'green', label='Sinusoidal')	
line2, = ax.plot(t, F2, 'b--', label='Exponential Decay')

# Find the Line object corresponding to the sinusoidal line
line_handle = plt.gca().findobj(Line2D)[0]

# Display the results
print("Found Line2D instance:", line_handle)

# Display the modified plot
plt.legend()
plt.show()

Output

On executing the above code we will get the following output −

Found Line2D instance: Line2D(Sinusoidal)

Modifying Objects

Once an object is located/identified using the findobj() method, users can modify its properties to achieve the desired visualization. This includes changing attributes such as color, linestyle, text content, and more.

Example 1

Here is an example that creates a plot with three Text objects containing different text content. The Axes.findobj() method is used to locate a Text object with specific text content ('tutorialspoint'). Once found, the text content is modified to 'Tutorialspoint :)' and its color is changed to green.

import matplotlib.pyplot as plt
from matplotlib.text import Text

# Create a plot with a text object
fig, ax = plt.subplots()
ax.text(.4, .5, s='tutorialspoint')
ax.text(.1, .1, s='Python')
ax.text(.8, .8, s='Matplotlib')

# Find the Text object with the text 'tutorialspoint'
text_handle = ax.findobj(
   lambda artist: isinstance(artist, Text) and artist.get_text() == 'tutorialspoint')[0]

# Modify the text
text_handle.set_text('Tutorialspoint :)')
text_handle.set_color('green')
plt.show()

Output

On executing the above code we will get the following output −

Users can also define custom matching functions to filter objects based on specific criteria. This allows for more complex searches and enables advanced manipulation of graphical objects.

Example 2

Here is another example that demonstrates how to use of a custom matching function to modify objects using the Figure.findobj() method.

import matplotlib.pyplot as plt
import matplotlib.text as text
import numpy as np

# Generate data 
t = np.arange(0, 4, 0.1)
F1 = np.sin(2 * t)
F2 = np.exp(-t * 4)

# Plot two lines
fig, ax = plt.subplots(figsize=(7, 4))
line1, = ax.plot(t, F1, 'red', label='Sinusoidal')
line2, = ax.plot(t, F2, 'k--', label='Exponential Decay')

plt.grid(False)
plt.xlabel('x label')
plt.ylabel('y label')
plt.title('Modifying the Objects')

# Define a custom function for matching
def myfunc(x):
   return hasattr(x, 'set_color') and not hasattr(x, 'set_facecolor')

# Modify objects based on the custom function
for o in fig.findobj(myfunc):
   o.set_color('green')

# Display the modified plot
plt.show()

Output

On executing the above code we will get the following output −