Creating Annotated Heatmaps

Introduction

In data analysis, we often want to display data that depends on two independent variables as a color-coded image plot known as a heatmap. In this lab, we will use Matplotlib's imshow function to create a heatmap with annotations. We will start with a simple example and expand it to be usable as a universal function.

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Skills Graph

Simple Categorical Heatmap

We will start by defining some data. We need a 2D list or array that defines the data to color code. We then also need two lists or arrays of categories. The heatmap itself is an imshow plot with the labels set to the categories. We will use the text function to label the data itself by creating a Text within each cell showing the value of that cell.

import matplotlib.pyplot as plt
import numpy as np

vegetables = ["cucumber", "tomato", "lettuce", "asparagus", "potato", "wheat", "barley"]
farmers = ["Farmer Joe", "Upland Bros.", "Smith Gardening", "Agrifun", "Organiculture", "BioGoods Ltd.", "Cornylee Corp."]

harvest = np.array([[0.8, 2.4, 2.5, 3.9, 0.0, 4.0, 0.0],
                    [2.4, 0.0, 4.0, 1.0, 2.7, 0.0, 0.0],
                    [1.1, 2.4, 0.8, 4.3, 1.9, 4.4, 0.0],
                    [0.6, 0.0, 0.3, 0.0, 3.1, 0.0, 0.0],
                    [0.7, 1.7, 0.6, 2.6, 2.2, 6.2, 0.0],
                    [1.3, 1.2, 0.0, 0.0, 0.0, 3.2, 5.1],
                    [0.1, 2.0, 0.0, 1.4, 0.0, 1.9, 6.3]])

fig, ax = plt.subplots()
im = ax.imshow(harvest)

## Show all ticks and label them with the respective list entries
ax.set_xticks(np.arange(len(farmers)), labels=farmers)
ax.set_yticks(np.arange(len(vegetables)), labels=vegetables)

## Rotate the tick labels and set their alignment
plt.setp(ax.get_xticklabels(), rotation=45, ha="right", rotation_mode="anchor")

## Loop over data dimensions and create text annotations
for i in range(len(vegetables)):
    for j in range(len(farmers)):
        text = ax.text(j, i, harvest[i, j], ha="center", va="center", color="w")

ax.set_title("Harvest of local farmers (in tons/year)")
fig.tight_layout()
plt.show()

Using the Helper Function Code Style

We will create a function that takes the data and the row and column labels as input and allows arguments that are used to customize the plot. We will turn the surrounding axes spines off and create a grid of white lines to separate the cells. Here, we also want to create a colorbar and position the labels above the heatmap instead of below it. The annotations shall get different colors depending on a threshold for better contrast against the pixel color.

def heatmap(data, row_labels, col_labels, ax=None, cbar_kw=None, cbarlabel="", **kwargs):
    """
    Create a heatmap from a numpy array and two lists of labels.

    Parameters
    ----------
    data
        A 2D numpy array of shape (M, N).
    row_labels
        A list or array of length M with the labels for the rows.
    col_labels
        A list or array of length N with the labels for the columns.
    ax
        A `matplotlib.axes.Axes` instance to which the heatmap is plotted. If not provided, use current axes or create a new one. Optional.
    cbar_kw
        A dictionary with arguments to `matplotlib.Figure.colorbar`. Optional.
    cbarlabel
        The label for the colorbar. Optional.
    **kwargs
        All other arguments are forwarded to `imshow`.
    """

    if ax is None:
        ax = plt.gca()

    if cbar_kw is None:
        cbar_kw = {}

    ## Plot the heatmap
    im = ax.imshow(data, **kwargs)

    ## Create colorbar
    cbar = ax.figure.colorbar(im, ax=ax, **cbar_kw)
    cbar.ax.set_ylabel(cbarlabel, rotation=-90, va="bottom")

    ## Show all ticks and label them with the respective list entries.
    ax.set_xticks(np.arange(data.shape[1]), labels=col_labels)
    ax.set_yticks(np.arange(data.shape[0]), labels=row_labels)

    ## Let the horizontal axes labeling appear on top.
    ax.tick_params(top=True, bottom=False, labeltop=True, labelbottom=False)

    ## Rotate the tick labels and set their alignment.
    plt.setp(ax.get_xticklabels(), rotation=-30, ha="right", rotation_mode="anchor")

    ## Turn spines off and create white grid.
    ax.spines[:].set_visible(False)
    ax.set_xticks(np.arange(data.shape[1]+1)-.5, minor=True)
    ax.set_yticks(np.arange(data.shape[0]+1)-.5, minor=True)
    ax.grid(which="minor", color="w", linestyle='-', linewidth=3)
    ax.tick_params(which="minor", bottom=False, left=False)

    return im, cbar


def annotate_heatmap(im, data=None, valfmt="{x:.2f}", textcolors=("black", "white"), threshold=None, **textkw):
    """
    A function to annotate a heatmap.

    Parameters
    ----------
    im
        The AxesImage to be labeled.
    data
        Data used to annotate. If None, the image's data is used. Optional.
    valfmt
        The format of the annotations inside the heatmap. This should either use the string format method, e.g., "$ {x:.2f}", or be a `matplotlib.ticker.Formatter`. Optional.
    textcolors
        A pair of colors. The first is used for values below a threshold, the second for those above. Optional.
    threshold
        Value in data units according to which the colors from textcolors are applied. If None (the default), uses the middle of the colormap as separation. Optional.
    **kwargs
        All other arguments are forwarded to each call to `text` used to create the text labels.
    """

    if not isinstance(data, (list, np.ndarray)):
        data = im.get_array()

    ## Normalize the threshold to the images color range.
    if threshold is not None:
        threshold = im.norm(threshold)
    else:
        threshold = im.norm(data.max())/2.

    ## Set default alignment to center, but allow it to be overwritten by textkw.
    kw = dict(horizontalalignment="center", verticalalignment="center")
    kw.update(textkw)

    ## Get the formatter in case a string is supplied
    if isinstance(valfmt, str):
        valfmt = matplotlib.ticker.StrMethodFormatter(valfmt)

    ## Loop over the data and create a `Text` for each "pixel".
    ## Change the text's color depending on the data.
    texts = []
    for i in range(data.shape[0]):
        for j in range(data.shape[1]):
            kw.update(color=textcolors[int(im.norm(data[i, j]) > threshold)])
            text = im.axes.text(j, i, valfmt(data[i, j], None), **kw)
            texts.append(text)

    return texts

Applying the Function

Now that we have the functions, we can use them to create a heatmap with annotations. We create a new set of data, give further arguments to imshow, use an integer format on the annotations, and provide some colors. We also hide the diagonal elements (which are all 1) by using a matplotlib.ticker.FuncFormatter.

data = np.random.randint(2, 100, size=(7, 7))
y = [f"Book {i}" for i in range(1, 8)]
x = [f"Store {i}" for i in list("ABCDEFG")]

fig, ax = plt.subplots()
im, _ = heatmap(data, y, x, ax=ax, vmin=0, cmap="magma_r", cbarlabel="weekly sold copies")
annotate_heatmap(im, valfmt="{x:d}", size=7, threshold=20, textcolors=("red", "white"))

def func(x, pos):
    return f"{x:.2f}".replace("0.", ".").replace("1.00", "")

annotate_heatmap(im, valfmt=matplotlib.ticker.FuncFormatter(func), size=7)

More Complex Heatmap Examples

In the following, we show the versatility of the previously created functions by applying them in different cases and using different arguments.

np.random.seed(19680801)

fig, ((ax, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8, 6))

## Replicate the above example with a different font size and colormap.

im, _ = heatmap(harvest, vegetables, farmers, ax=ax, cmap="Wistia", cbarlabel="harvest [t/year]")
annotate_heatmap(im, valfmt="{x:.1f}", size=7)

## Sometimes even the data itself is categorical. Here we use a `matplotlib.colors.BoundaryNorm` to get the data into classes and use this to colorize the plot, but also to obtain the class labels from an array of classes.

data = np.random.randn(6, 6)
y = [f"Prod. {i}" for i in range(10, 70, 10)]
x = [f"Cycle {i}" for i in range(1, 7)]

qrates = list("ABCDEFG")
norm = matplotlib.colors.BoundaryNorm(np.linspace(-3.5, 3.5, 8), 7)
fmt = matplotlib.ticker.FuncFormatter(lambda x, pos: qrates[::-1][norm(x)])

im, _ = heatmap(data, y, x, ax=ax3, cmap=mpl.colormaps["PiYG"].resampled(7), norm=norm, cbar_kw=dict(ticks=np.arange(-3, 4), format=fmt), cbarlabel="Quality Rating")
annotate_heatmap(im, valfmt=fmt, size=9, fontweight="bold", threshold=-1, textcolors=("red", "black"))

## We can nicely plot a correlation matrix. Since this is bound by -1 and 1, we use those as vmin and vmax.

corr_matrix = np.corrcoef(harvest)
im, _ = heatmap(corr_matrix, vegetables, vegetables, ax=ax4, cmap="PuOr", vmin=-1, vmax=1, cbarlabel="correlation coeff.")
annotate_heatmap(im, valfmt=matplotlib.ticker.FuncFormatter(func), size=7)

plt.tight_layout()
plt.show()

Summary

In this lab, we learned how to create annotated heatmaps in Python using Matplotlib's imshow function. We started by creating a simple categorical heatmap and then expanded it to be a reusable function. Finally, we explored some more complex heatmap examples using different arguments.