5 Best Ways to Hide Matplotlib Lines Line2D in IPython Notebook

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πŸ’‘ Problem Formulation: When working with matplotlib in an IPython Notebook, sometimes it’s necessary to hide specific lines after they’ve been plotted, for clearer visualization or to focus on other parts of the plot. For instance, you might create a plot with multiple Line2D objects but later decide to hide one of the lines. This article illustrates how to programmatically hide such lines.

Method 1: Using set_visible() Method

This method involves leveraging the set_visible() attribute of the Line2D object. By passing a False argument to this method, the specific line you wish to hide will no longer appear in the plot. This is a straightforward and common approach to toggle the visibility of plot elements in Matplotlib.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

# Sample data
x = np.linspace(0, 10, 100)
y = np.sin(x)

fig, ax = plt.subplots()
line1, = ax.plot(x, y, label='sin(x)')

# Hide the line
line1.set_visible(False)

plt.legend()
plt.show()

The output would be a plot without the sine wave visible.

In this code snippet, we create a figure and plot a line with ax.plot(). The set_visible(False) method is then used on the line object to hide it before rendering the plot with plt.show(). The line is hidden in the plot, but still exists and could be made visible again by setting it to True.

Method 2: Using remove() Method

The remove() method directly removes the Line2D object from the axes. It’s a more permanent solution that not only hides the line but also disassociates it completely from the axes. This method is a good choice when you are sure that you won’t need the line anymore.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

# Sample data
x = np.linspace(0, 10, 100)
y = np.cos(x)

fig, ax = plt.subplots()
line2, = ax.plot(x, y, label='cos(x)')

# Remove the line
line2.remove()

plt.legend()
plt.show()

The output would be a plot with no cosine wave.

After plotting the cosine wave with ax.plot(), we use the remove() method on the line object to completely remove it. The plot is then displayed without the line. The removed line cannot be restored through the visibility toggle since it’s no longer associated with the axes.

Method 3: Modifying Line Visibility in Legends

If you need to toggle the visibility of lines in the legend interactively, you can attach an event handler to the legend that hides or shows the line when its corresponding label in the legend is clicked. This method enhances user experience by incorporating interactivity directly into the plot.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

# Sample data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

fig, ax = plt.subplots()
line1, = ax.plot(x, y1, label='sin(x)')
line2, = ax.plot(x, y2, label='cos(x)')

# Interactive legend toggle
leg = plt.legend()

def on_legend_click(event):
    line = event.artist
    line.set_visible(not line.get_visible())
    plt.draw()

leg.get_lines()[0].set_picker(5)  # 5 pts tolerance
leg.get_lines()[1].set_picker(5)

fig.canvas.mpl_connect('pick_event', on_legend_click)

plt.show()

The output is an interactive plot where clicking legend items toggles the visibility of their corresponding lines.

This snippet sets up an event handler function on_legend_click(), which toggles the visibility of lines when their respective legend labels are clicked. The .set_picker() method is used to enable picking on the legend lines and mpl_connect() binds the click event to the handler function.

Method 4: Using Alpha Property

Another method for hiding lines is to set the alpha property (which controls the transparency of the line) to zero. This doesn’t remove the line from the axes, but makes it fully transparent, thus effectively hiding it from view.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

# Sample data
x = np.linspace(0, 10, 100)
y = np.sin(x)

fig, ax = plt.subplots()
line, = ax.plot(x, y, label='sin(x)', alpha=1.0)

# Hide line by making it fully transparent
line.set_alpha(0.0)

plt.legend()
plt.show()

The result would be a plot with the sine wave made invisible through transparency.

In the code, set_alpha(0.0) is used to change the alpha property of the line to zero after it has been plotted. This way, the line is made fully transparent. It can be made visible again by setting a non-zero alpha value.

Bonus One-Liner Method 5: Using List Comprehension

In a scenario where multiple lines need to be hidden, a one-liner using list comprehension can be very efficient. This approach utilizes the previously mentioned set_visible() method in combination with list comprehension to hide all lines in a single elegant expression.

Here’s an example:

import matplotlib.pyplot as plt
import numpy as np

# Sample data
x = np.linspace(0, 10, 100)
y1 = np.sin(x)
y2 = np.cos(x)

fig, ax = plt.subplots()
lines = ax.plot(x, y1, x, y2)

# Hide all lines using list comprehension
[line.set_visible(False) for line in lines]

plt.legend()
plt.show()

No lines would be visible in the resulting plot.

The one-liner uses a list comprehension to iterate over all the lines contained in the lines variable, executing the set_visible(False) method on each one. This conveniently hides all plotted lines at once.

Summary/Discussion

  • Method 1: set_visible() Method. Flexibility to toggle visibility without permanent removal. Can manage individual lines easily. Does not reduce clutter from the axes object if many lines are toggled off.
  • Method 2: remove() Method. Permanently removes the line, which cleans up the axes instance. Not suitable for scenarios where the line might need to be shown again.
  • Method 3: Modifying Line Visibility in Legends. Adds interactive functionality to the plot. Enhances user experience. Requires more code and the setup of event handling.
  • Method 4: Using Alpha Property. Offers a simple way to hide lines by changing transparency. Similar to set_visible() but might be more intuitive for users thinking in terms of opacity.
  • Bonus Method 5: List Comprehension. Efficient for managing the visibility of multiple lines simultaneously. Extremely concise. Assumes that all lines need to be treated the same way, which might not always be the case.