5 Best Ways to Manipulate Figures While a Script Is Running in Python Matplotlib

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💡 Problem Formulation: Python’s Matplotlib library is a powerhouse for generating a wide array of figures and graphs. However, one frequent obstacle is the need to alter these visuals on-the-fly while a script is running. For example, you may want to update the data points on a plot in real-time as new data comes in or adjust the aesthetics of a figure based on user input. This article details methods to dynamically manipulate Matplotlib figures without interrupting the script’s execution flow.

Method 1: Using draw() for On-Demand Updating

The draw() function in Matplotlib allows for piecemeal updates to a figure’s display. This is ideal for scenarios where changes to the figure are not continuous, but happen sporadically in response to certain events or conditions.

Here’s an example:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([0, 1], [0, 1])
plt.show(block=False)

# Update the plot with new data
ax.plot([1, 2], [1, 2])
fig.canvas.draw()
plt.pause(0.001)

The output will show an updated plot with the new data points added.

This code snippet creates a simple line plot and displays it without blocking the script’s progression using plt.show(block=False). It then adds another line to the graph and refreshes the display with fig.canvas.draw() and a brief pause to ensure the GUI backend has time to process the events.

Method 2: Using ion() and ioff() for Interactive Mode

Matplotlib’s interactive mode, toggled by the ion() and ioff() functions, is useful for scripts that require the plot to be updatable without blocking other processes. By turning interactive mode on, one can continuously modify the figure without needing to call draw() explicitly.

Here’s an example:

import matplotlib.pyplot as plt
import time

plt.ion()
fig, ax = plt.subplots()
line, = ax.plot([0, 1], [0, 1])

for _ in range(5):
    line.set_ydata([_, _+1])
    fig.canvas.flush_events()
    time.sleep(1)

plt.ioff()

This will iteratively update the figure once every second for five seconds.

The example initializes Matplotlib’s interactive mode and iteratively adjusts the line’s data within a loop. Each iteration calls fig.canvas.flush_events() to process GUI events, thus updating the figure on the screen. Finally, interactive mode is turned off with plt.ioff().

Method 3: Animations Using FuncAnimation

The FuncAnimation class from Matplotlib’s animation module facilitates the creation of animations by repeatedly calling a function that modifies the figure. It is perfect for when you want to show changes in data over time or create complex interactive visualizations.

Here’s an example:

import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

fig, ax = plt.subplots()
x, y = [], []
line, = ax.plot(x, y)

def update(frame):
    x.append(frame)
    y.append(frame ** 2)
    line.set_data(x, y)
    return line,

ani = FuncAnimation(fig, update, frames=range(10), blit=True)
plt.show()

This will display an animated line plot that updates in real-time.

In this code, a line plot is set up initially without any data. The update function is defined to append new data to the lists x and y and update the line’s data accordingly. The FuncAnimation class uses this function to animate the plot, with blit=True optimizing the rendering process by redrawing only parts of the frame that have changed.

Method 4: Real-time Data Visualization with pyplot.pause()

The pyplot.pause() function allows Matplotlib to update its figures while pausing the script for a specified amount of time. This method can be handy for simple real-time data visualization tasks where continuous user input or data processing is not required.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

plt.ion()
fig, ax = plt.subplots()
x = np.linspace(0, 10, 100)

for i in range(10):
    y = np.sin(x - i / 10)
    ax.clear()
    ax.plot(x, y)
    plt.pause(0.1)

plt.ioff()

This will generate a continuously updating sine wave plot.

This example demonstrates real-time plotting by creating a moving sine wave. At each iteration, the previous plot is cleared using ax.clear(), and a new sine wave is plotted with a slight phase shift. The script is briefly paused at each interval to update the display, providing the illusion of animation.

Bonus One-Liner Method 5: Inline Modification with set_data()

Modification of an already rendered plot can be done inline using the set_data() method. This is the most direct method and is often used for simple, fast updates where redrawing the entire figure is not necessary.

Here’s an example:

import matplotlib.pyplot as plt

x = [0, 1, 2]
y = [0, 1, 2]
plt.ion()
line, = plt.plot(x, y)
line.set_data([0, 1, 2, 3], [0, 1, 4, 9])
plt.draw()
plt.pause(0.001)
plt.ioff()

The output shows an updated plot with the new dataset.

This code snippet initially plots a simple line graph. It then uses the line object’s set_data() method to directly change the x and y data shown in the plot. It calls plt.draw() to render the changes, and pauses briefly to ensure the display is updated. Finally, plt.ioff() is called to end interactive mode.

Summary/Discussion

  • Method 1: Using draw(). Strength: Allows for selective updates. Weakness: Not suitable for continuous data streams.
  • Method 2: Using ion() and ioff(). Strength: Suitable for scripts with a mix of real-time and static plotting needs. Weakness: May not be efficient for high-frequency updates.
  • Method 3: Animations Using FuncAnimation. Strength: Powerful for complex animations and updating based on time or frame count. Weakness: Overkill for simple updates.
  • Method 4: Real-time Data Visualization with pyplot.pause(). Strength: Simplicity; useful for quick demonstrations. Weakness: Not a true animation technique; can be blocking.
  • Method 5: Inline Modification with set_data(). Strength: Quick and direct method for updating data. Weakness: Limited to updates where re-rendering the entire plot isn’t needed.