cjgunase · January 2, 2018 16:41
diff --git a/barplot_heatmap.png b/barplot_heatmap.png
diff --git a/barplot_heatmap.py b/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()
	#!/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()