Plot a horizontal bar plot and the lower triangle of a heatmap aligned at the base of the bars

barplot_heatmap.png

barplot_heatmap.py

#!/usr/bin/env python
"""
barplot_heatmap.py
Kamil Slowikowski
April 4, 2014

This module has a function for creating a horizontal bar plot with an adjacent
heatmap rotated 45 degrees to show the lower triangle of a correlation
matrix comparing all pairs of bars.

References:
    http://stackoverflow.com/questions/12848581/is-there-a-way-to-rotate-a-matplotlib-plot-by-45-degrees
    http://stackoverflow.com/questions/2982929/plotting-results-of-hierarchical-clustering-ontop-of-a-matrix-of-data-in-python
"""


import matplotlib as mp
import numpy as np
import pandas as pd
import pylab as pl
import scipy.cluster.hierarchy as sch
import string


def main():
    mp.rc("font", family="serif")

    nrows = 5
    ncols = 10
    labels = np.random.choice(list(string.ascii_uppercase),
                              ncols, replace=False)
    series = pd.Series(np.random.random(ncols) * 5.0, index=labels)
    matrix = pd.DataFrame(np.random.random((nrows, ncols)), columns=labels)

    outfile = "barplot_heatmap.png"

    barplot(series, matrix, outfile)


def barplot(series, matrix, outfile,
            figsize=(6, 6), fontsize=10, title=None):
    """Create a bar plot and place the lower triangle of a heatmap directly
    adjacent so that the bases of the bars line up with the diagonal of the
    heatmap.

    Parameters
    ----------
    series : pandas.Series
        The bar heights and labels.
    matrix : pandas.DataFrame
        A matrix where each column corresponds to a bar in the bar plot.
    outfile : str
        Full path to the output file.
    figsize : (width, height)
    fontsize : float
    title : str
    """
    # Create a figure.
    fig = pl.figure(figsize=figsize)

    # Axes for the heatmap triangle.
    ax = fig.add_subplot(121, frame_on=False, aspect=2.0)

    # Get the heatmap triangle's axes and the order of the clustered samples.
    cax, order = heatmap_triangle(matrix, ax)

    # Adjust spacing between the heatmap triangle and the barplot.
    fig.subplots_adjust(wspace=-0.12, hspace=0, left=0, right=1)

    # Axes for the barplot.
    ax = fig.add_subplot(122, frame_on=False)

    # Put gridlines beneath the bars.
    ax.set_axisbelow(True)

    # Order the bars by the clustering.
    series = series.ix[order]

    ax = series.plot(ax=ax, kind='barh', title=title, linewidth=0,
                     grid=False, color='grey')

    # Set the font size for the y-axis labels.
    ax.tick_params(axis='y', which='major', labelsize=fontsize)

    # Grid lines.
    ax.grid(b=True, which='major', axis='both', alpha=0.5)

    # Tick marks for the x-axis.
    ax.set_xticks(np.arange(0, round(series.max() + 1)))

    # Put the y-axis marks on the right.
    ax.yaxis.tick_right()
    ax.yaxis.set_label_position('right')

    # Adjust tick length.
    ax.tick_params(length=0, axis='x')
    ax.tick_params(length=0, axis='y')

    # Labels.
    ax.set_xlabel('')
    ax.set_ylabel('')

    # Save.
    fig.savefig(outfile, bbox_inches='tight')


def heatmap_triangle(dataframe, axes):
    """Create a heatmap of the lower triangle of a pairwise correlation
    matrix of all pairs of columns in the given dataframe. The heatmap
    triangle is rotated 45 degrees clockwise and drawn on the given axes.

    Parameters
    ----------
    dataframe : pandas.DataFrame
    axes : matplotlib.axes.Axes
    """
    N = dataframe.shape[1]
    D = dataframe.corr(method='pearson')

    # UPGMA clustering, but other methods are also available.
    Z = sch.linkage(D, method='average')
    R = sch.dendrogram(Z, no_plot=True)
    cluster_order = R['leaves']
    D = D.ix[cluster_order, cluster_order]

    # Get the lower triangle of the matrix. 
    C = np.tril(D)
    # Mask the upper triangle.
    C = np.ma.masked_array(C, C == 0)
    # Set the diagonal to zero.
    for i in range(N):
        C[i, i] = 0

    # Transformation matrix for rotating the heatmap.
    A = np.array([(y, x) for x in range(N, -1, -1) for y in range(N + 1)])
    t = np.array([[0.5, 1], [0.5, -1]])
    A = np.dot(A, t)

    # -1.0 correlation is blue, 0.0 is white, 1.0 is red.
    cmap = pl.cm.RdBu_r
    norm = mp.colors.BoundaryNorm(np.linspace(-1, 1, 14), cmap.N)

    # This MUST be before the call to pl.pcolormesh() to align properly.
    axes.set_xticks([])
    axes.set_yticks([])

    # Plot the correlation heatmap triangle.
    X = A[:, 1].reshape(N + 1, N + 1)
    Y = A[:, 0].reshape(N + 1, N + 1)
    caxes = pl.pcolormesh(X, Y, np.flipud(C), axes=axes, cmap=cmap, norm=norm)

    # Remove the ticks and reset the x limit.
    axes.set_xlim(right=0)

    # Add a colorbar below the heatmap triangle.
    cb = pl.colorbar(caxes, ax=axes, orientation='horizontal', shrink=0.5825,
                     fraction=0.05, pad=-0.035, ticks=np.linspace(-1, 1, 5),
                     use_gridspec=True)
    cb.set_label("$\mathrm{Pearson's}\ r$")

    return caxes, D.index


if __name__ == '__main__':
    main()

@MdUmar-tech Sorry, but I don't understand your goals. Please consider creating a new post on Stackoverflow, where hundreds of people will see it, and someone might have time to help you. First, I would recommend that you try to make figures by yourself. Then, once you have something that is similar to your desired output, share with us:

• your code
• the complete output produced by your code
• an example of your desired output (it's OK to make this in an image editor, or to share a figure that you saw somewhere)

If you can give an example of a figure that you like, then there's a better chance that someone might be able to help you.

@slowkow Thanks for this function! A snippet of a shorter adaptation of the function, with some simple rotations is below. I hope this is useful for additional use cases.

If possible, I have a question regarding the usage of white space: As the single rotation case with t values [[.5, 1], [.5, -1]] expands the triangle and covers more white space by cutting it in half (last image/panel, and also your example above), I was wondering if you can list other rotations that would allow this for other orientations. In general, this would be very useful to cover some of the panels below.

import matplotlib.pyplot as plt
import numpy as np

def heatmap_triangle(C, axes, rotation=None, show_cbar=False):
    N = len(C)
    # Transformation matrix for rotating the heatmap.
    A = np.array([(y, x) for x in range(N, -1, -1) for y in range(N + 1)])
    a = .5
    b = 1

    if rotation is not None:
        t = np.array(rotation)
        A = np.dot(A, t)

    cmap = plt.cm.coolwarm
    axes.set_xticks([])
    axes.set_yticks([])

    X = A[:, 1].reshape(N + 1, N + 1)
    Y = A[:, 0].reshape(N + 1, N + 1)
    caxes = plt.pcolormesh(X, Y, np.flipud(C), axes=axes, cmap=cmap)
    # Remove the ticks and reset the x limit.
    axes.set_xlim(right=0)
    # Add a colorbar below the heatmap triangle.
    if show_cbar:
        cb = plt.colorbar(caxes, ax=axes, orientation='horizontal', shrink=0.2825,
                         fraction=0.08, pad=-4.535, ticks=np.linspace(-1, 1, 5),
                         use_gridspec=True,)
        cb.set_label("weight")

np.random.seed(500)
X = np.random.rand(10, 10, )
X = np.array(X)
X = np.tril(X)
X = np.ma.masked_array(X, X == 0)
r1 = [[-.5, -.25], [.5, -.25]]
# 315 degrees (expanded)
r2 = [[.5, 1], [.5, -1]]
# 45 degrees (alternative)
r3 = [[.5, -.25], [-.5, -.25]]

fig = plt.figure(figsize=[3, 3])
plt.subplot()
sns.heatmap(X, cmap=plt.cm.coolwarm, mask=C == 0)
fig = plt.figure(figsize=[20, 6])

n_rotations = 6
i = 0

# 45 degrees
ax = plt.subplot(1, n_rotations, i + 1, frameon=False)
x = np.flip(np.flip(X, axis=1), axis=0)
heatmap_triangle(x, ax, r1)

# 45 degrees (alternate rotation)
ax = plt.subplot(1, n_rotations, i + 2, frameon=False)
x = np.rot90(np.fliplr(X))
x = np.rot90(x)
x = np.rot90(x)
heatmap_triangle(x, ax, r3)

# 135 degrees
ax = plt.subplot(1, n_rotations, i + 3, frameon=False)
x = np.flip(np.flip(X, axis=1), axis=0)
x = np.rot90(x)
heatmap_triangle(x, ax, r1)

# 225 degrees
ax = plt.subplot(1, n_rotations, i + 4, frameon=False)
x = np.rot90(X)
x = np.rot90(x)
x = np.rot90(x)
x = np.rot90(x)
x = np.ma.masked_array(x, x == 0)
heatmap_triangle(x, ax, r1)

plt.show()

fig = plt.figure(figsize=[10, 3])
# 315 degrees
ax = plt.subplot(1, 2, 1, frameon=False)
x = np.rot90(X)
x = np.rot90(x)
x = np.rot90(x)
x = np.rot90(x)
x = np.rot90(x)
x = np.ma.masked_array(x, x == 0)
heatmap_triangle(x, ax, r1)

# 315 degrees (expanded)
ax = plt.subplot(1, 2, 2, frameon=False)
x = np.rot90(np.fliplr(X))
x = np.rot90(x)
x = np.rot90(x)
heatmap_triangle(x, ax, r2)

plt.show()