mtopic.pl.corr_heatmap

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mtopic.pl.corr_heatmap#

mtopic.pl.corr_heatmap(arr1, arr2, label1=None, label2=None, cmap='bwr', fontsize=8, figsize=(8, 6), transparent=False, save=None)#

Visualize the correlation matrix between two sets of features as a heatmap.

This function computes and plots a correlation heatmap to visualize the relationships between two sets of features. Each set is represented as a pandas.DataFrame, with columns as features. The color intensity in the heatmap indicates the strength and direction of the correlation.

Parameters:

  • arr1 (pandas.DataFrame) – A pandas DataFrame representing the first set of features. Each column corresponds to a feature.

  • arr2 (pandas.DataFrame) – A pandas DataFrame representing the second set of features. Each column corresponds to a feature.

  • label1 (str, optional) – Label for the y-axis, representing the features from arr1. If None, no label is set. Default is None.

  • label2 (str, optional) – Label for the x-axis, representing the features from arr2. If None, no label is set. Default is None.

  • cmap (str, optional) – Colormap for the heatmap. Default is ‘bwr’ (blue-white-red colormap).

  • fontsize (int, optional) – Font size for axis labels and colorbar ticks. Default is 8.

  • figsize (tuple, optional) – Tuple specifying the figure size (width, height) in inches. Default is (8, 6).

  • transparent (bool, optional) – If True, saves the figure with a transparent background. Default is False.

  • save (str, optional) – File path to save the figure. If None, the figure is displayed but not saved. Default is None.

Returns:

None

Example:
import pandas as pd
import mtopic

# Create example datasets
data1 = pd.DataFrame({'feature1': [1, 2, 3], 'feature2': [4, 5, 6]})
data2 = pd.DataFrame({'feature3': [7, 8, 9], 'feature4': [10, 11, 12]})

# Plot correlation heatmap
mtopic.pl.corr_heatmap(
    data1,
    data2,
    label1='Set 1',
    label2='Set 2',
    cmap='coolwarm',
    fontsize=10,
    save='correlation_heatmap.png'
)
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