Matplotlib Annotate Font Size

Matplotlib Annotate Font Size

Matplotlib is a powerful visualization library in Python that allows users to create various types of plots. One common feature in matplotlib is annotations, which can be used to add text or arrows to specific data points on a plot. In this article, we will focus on customizing the font size of annotations in matplotlib.

1. Setting the Font Size of Annotations

To set the font size of annotations in matplotlib, you can use the fontsize parameter in the annotate function. Here is an example:

import matplotlib.pyplot as plt

plt.figure()
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.annotate('Example Annotation', (2, 4), fontsize=12)
plt.show()

Output:

In the code above, the fontsize=12 parameter sets the font size of the annotation to 12.

2. Customizing Font Size with Mathtext

Matplotlib also supports mathtext, which allows users to include mathematical expressions in annotations. When using mathtext, you can further customize the font size using LaTeX syntax. Here is an example:

import matplotlib.pyplot as plt

plt.figure()
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.annotate(r'\frac{1}{2}', (2, 4), fontsize=16)
plt.show()

Output:

In the code above, the fontsize=16 parameter sets the font size of the mathtext annotation to 16.

3. Dynamically Setting Font Size

Sometimes, you may want to dynamically set the font size of annotations based on certain conditions. You can achieve this by calculating the font size within the annotate function. Here is an example:

import matplotlib.pyplot as plt

plt.figure()
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
font_size = 14 if 2 + 2 == 4 else 10
plt.annotate('Dynamic Font Size', (2, 4), fontsize=font_size)
plt.show()

Output:

In the code above, the font size is dynamically set based on the condition 2 + 2 == 4.

4. Changing Default Font Size

If you want to change the default font size for all annotations in a plot, you can do so by modifying the rcParams dictionary in matplotlib. Here is an example:

import matplotlib.pyplot as plt

plt.rcParams['annotation.fontsize'] = 14

plt.figure()
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.annotate('Default Font Size', (2, 4))
plt.show()

In the code above, the default font size for annotations is set to 14 using plt.rcParams['annotation.fontsize'].

5. Adjusting Font Size for Multiple Annotations

When adding multiple annotations to a plot, you may want to adjust the font size for each annotation individually. You can achieve this by setting the fontsize parameter for each annotate function call. Here is an example:

import matplotlib.pyplot as plt

plt.figure()
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.annotate('Annotation 1', (2, 4), fontsize=12)
plt.annotate('Annotation 2', (3, 9), fontsize=16)
plt.show()

Output:

In the code above, two annotations are added to the plot with different font sizes.

6. Using Different Font Sizes in Annotations

You can also use different font sizes within a single annotation by using HTML-like tags. Here is an example:

import matplotlib.pyplot as plt

plt.figure()
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.annotate('<font size="2">Small Text</font>', (2, 4))
plt.annotate('<font size="6">Large Text</font>', (3, 9))
plt.show()

Output:

In the code above, the <font size="2"> tag is used to create smaller text, while the <font size="6"> tag is used to create larger text within the annotations.

7. Controlling Font Size with Axes Coordinates

When adding annotations to a plot, you can control the font size using axes coordinates rather than data coordinates. This can be useful for ensuring consistency in font size across different plots. Here is an example:

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
ax.plot([1, 2, 3, 4], [1, 4, 9, 16])
ax.annotate('Axes Coordinates', (0.5, 0.5), xycoords='axes fraction', fontsize=12)
plt.show()

Output:

In the code above, the xycoords='axes fraction' parameter sets the coordinates of the annotation to be relative to the axes, allowing for consistent font size across different plots.

8. Using Text Properties for Font Size

In matplotlib, you can also customize the font size of annotations using text properties. By creating a FontProperties object, you can specify the font size, style, weight, and family for the annotations. Here is an example:

import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties

font = FontProperties()
font.set_size(16)

plt.figure()
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
plt.annotate('Custom Font Size', (2, 4), fontproperties=font)
plt.show()

Output:

In the code above, a FontProperties object is created with a font size of 16 and applied to the annotation.

9. Adjusting Font Size Based on Data

You can also adjust the font size of annotations based on the data being displayed on the plot. For example, you may want to increase the font size for annotations near the maximum value of the data. Here is an example:

import matplotlib.pyplot as plt

data = [1, 4, 9, 16]
max_value = max(data)

plt.figure()
plt.plot([1, 2, 3, 4], data)
for i, value in enumerate(data):
    if value == max_value:
        font_size = 16
    else:
        font_size = 12
    plt.annotate(f'{value}', (i+1, value), fontsize=font_size)
plt.show()

Output:

In the code above, the font size of the annotations is increased for the data point with the maximum value.

10. Changing Font Size for Specific Annotations

If you only want to change the font size for specific annotations, you can do so by accessing the Text object returned by the annotate function and setting the font properties. Here is an example:

import matplotlib.pyplot as plt

plt.figure()
plt.plot([1, 2, 3, 4], [1, 4, 9, 16])
annotation = plt.annotate('Custom Font Size', (2, 4))
annotation.set_fontsize(18)
plt.show()

Output:

In the code above, the font size of a specific annotation is changed after it has been added to the plot.

Conclusion

In conclusion, customizing the font size of annotations in matplotlib allows users to create visually appealing and informative plots. By using the examples provided in this article, you can easily adjust the font size of annotations based on your specific requirements and preferences. Experiment with different font sizes and styles to enhance the readability and aesthetics of your matplotlib plots.