Colormap in PyComplexHeatmap

Default matplotlib colormaps

Build-in colormaps (Not included in matplotlib default cmaps)

In addition to the above matplotlib colormaps, PyComplexHeatmap also provide colormaps from package palettable (run pip install palettable before using palettable colormap). Users can use all those build-in colormaps after import PyComplexHeatmap

import os,sys
%matplotlib inline
import matplotlib
import matplotlib.pylab as plt
sys.path.append(os.path.expanduser("~/Projects/Github/PyComplexHeatmap"))
import PyComplexHeatmap
print(PyComplexHeatmap.__version__)
from PyComplexHeatmap.colors import get_palettable_colors

1.7.7.dev0+gddb18da.d20240909

df=get_palettable_colors()
df

	category	prefix	name	type	number	max_n	colors	hex_colors	mpl_colors	mpl_colormap	Name
1094	sequential	Acton	Acton_20	sequential	20	20	[(46, 33, 77), (59, 45, 88), (74, 58, 101), (8...	[#2E214D, #3B2D58, #4A3A65, #594672, #6A527E, ...	[(0.1803921568627451, 0.12941176470588237, 0.3...	<matplotlib.colors.LinearSegmentedColormap obj...	Acton
1095	sequential	Acton	Acton_20_r	sequential	20	20	[(230, 230, 240), (225, 216, 231), (219, 202, ...	[#E6E6F0, #E1D8E7, #DBCADD, #D7BDD4, #D5AFCB, ...	[(0.9019607843137255, 0.9019607843137255, 0.94...	<matplotlib.colors.LinearSegmentedColormap obj...	Acton_r
272	sequential	Algae	Algae_20	sequential	20	20	[[215, 249, 208], [199, 237, 189], [181, 225, ...	[#D7F9D0, #C7EDBD, #B5E1A9, #A4D698, #92CB86, ...	[(0.8431372549019608, 0.9764705882352941, 0.81...	<matplotlib.colors.LinearSegmentedColormap obj...	Algae
273	sequential	Algae	Algae_20_r	sequential	20	20	[[18, 36, 20], [22, 47, 29], [24, 59, 38], [26...	[#122414, #162F1D, #183B26, #1A472E, #1A5336, ...	[(0.07058823529411765, 0.1411764705882353, 0.0...	<matplotlib.colors.LinearSegmentedColormap obj...	Algae_r
308	sequential	Amp	Amp_20	sequential	20	20	[[241, 237, 236], [234, 221, 218], [228, 205, ...	[#F1EDEC, #EADDDA, #E4CDC6, #DFBEB3, #DBAE9F, ...	[(0.9450980392156862, 0.9294117647058824, 0.92...	<matplotlib.colors.LinearSegmentedColormap obj...	Amp
...	...	...	...	...	...	...	...	...	...	...	...
2719	qualitative	Vivid	Vivid_10_r	qualitative	10	10	[[237, 100, 90], [118, 78, 159], [47, 138, 196...	[#ED645A, #764E9F, #2F8AC4, #DAA51B, #24796C, ...	[(0.9294117647058824, 0.39215686274509803, 0.3...	<matplotlib.colors.LinearSegmentedColormap obj...	Vivid_r
238	sequential	agGrnYl	agGrnYl_7	sequential	7	7	[[36, 86, 104], [15, 114, 121], [13, 143, 129]...	[#245668, #0F7279, #0D8F81, #39AB7E, #6EC574, ...	[(0.1411764705882353, 0.33725490196078434, 0.4...	<matplotlib.colors.LinearSegmentedColormap obj...	agGrnYl
239	sequential	agGrnYl	agGrnYl_7_r	sequential	7	7	[[237, 239, 93], [169, 220, 103], [110, 197, 1...	[#EDEF5D, #A9DC67, #6EC574, #39AB7E, #0D8F81, ...	[(0.9294117647058824, 0.9372549019607843, 0.36...	<matplotlib.colors.LinearSegmentedColormap obj...	agGrnYl_r
250	sequential	agSunset	agSunset_7	sequential	7	7	[[75, 41, 145], [135, 44, 162], [192, 54, 157]...	[#4B2991, #872CA2, #C0369D, #EA4F88, #FA7876, ...	[(0.29411764705882354, 0.1607843137254902, 0.5...	<matplotlib.colors.LinearSegmentedColormap obj...	agSunset
251	sequential	agSunset	agSunset_7_r	sequential	7	7	[[237, 217, 163], [246, 169, 122], [250, 120, ...	[#EDD9A3, #F6A97A, #FA7876, #EA4F88, #C0369D, ...	[(0.9294117647058824, 0.8509803921568627, 0.63...	<matplotlib.colors.LinearSegmentedColormap obj...	agSunset_r

186 rows × 11 columns

import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
cmaps = {}

gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))


def plot_color_gradients(category, cmap_list):
    # Create figure and adjust figure height to number of colormaps
    nrows = len(cmap_list)
    figh = 0.35 + 0.15 + (nrows + (nrows - 1) * 0.1) * 0.22
    fig, axs = plt.subplots(nrows=nrows + 1, figsize=(6.4, figh))
    fig.subplots_adjust(top=1 - 0.35 / figh, bottom=0.15 / figh,
                        left=0.2, right=0.99)
    axs[0].set_title(f'{category} colormaps', fontsize=14)

    for ax, name in zip(axs, cmap_list):
        if isinstance(name,str):
            cmap=mpl.colormaps[name]
            text=name
        else:
            cmap=name
            text=name.name
        ax.imshow(gradient, aspect='auto', cmap=cmap)
        ax.text(-0.01, 0.5, text, va='center', ha='right', fontsize=10,
                transform=ax.transAxes)
        N=cmap.N
        if N < 256:
            ax.text(1.01, 0.5, N, va='center', ha='left', fontsize=10,
                transform=ax.transAxes)

    # Turn off *all* ticks & spines, not just the ones with colormaps.
    for ax in axs:
        ax.set_axis_off()

    # Save colormap list for later.
    cmaps[category] = cmap_list

plot_color_gradients('Sequential',df.loc[(df.category=='sequential') & (df.Name.apply(lambda x:not x.endswith('_r')))].sort_values('max_n').mpl_colormap.tolist())

plot_color_gradients('Diverging',df.loc[(df.category=='diverging') & (df.Name.apply(lambda x:not x.endswith('_r')))].sort_values('max_n').mpl_colormap.tolist()+['parula'])

# mpl.colormaps['parula']

from matplotlib.colors import LinearSegmentedColormap, ListedColormap
df1=df.loc[(df.category=='qualitative') & (df.Name.apply(lambda x:not x.endswith('_r')))].sort_values('max_n')
plot_color_gradients('Qualitative',[ListedColormap(c,name=name) for c,name in zip(
    df1.mpl_colors.tolist(),df1.Name.tolist())]+['nature3_1','nature3_2','nature3_3',
                                                 'nature4_1','nature4_2','nature4_3','nature4_4',
                                                 'nature5_1','nature5_2','nature6_1','nature6_2',
                                                 'random50','random100'])

# mpl.colormaps['random50']

How to use the Build-in cmap?

Just set cmap="custom_cmap" in ClusterMapPlotter or all kinds of annotation

from PyComplexHeatmap import (
    ClusterMapPlotter,HeatmapAnnotation,anno_simple,anno_scatterplot,anno_lineplot,anno_barplot,
    anno_label,anno_boxplot,anno_img,
)
df = pd.DataFrame(['GroupA'] * 5 + ['GroupB'] * 5, columns=['AB'])
df['CD'] = ['C'] * 3 + ['D'] * 3 + ['G'] * 4
df['EF'] = ['E'] * 6 + ['F'] * 2 + ['H'] * 2
df['F'] = np.random.normal(0, 1, 10)
df.index = ['sample' + str(i) for i in range(1, df.shape[0] + 1)]

df_heatmap = pd.DataFrame(np.random.randn(30, 10), columns=['sample' + str(i) for i in range(1, 11)])
df_heatmap.index = ["Fea" + str(i) for i in range(1, df_heatmap.shape[0] + 1)]
df_heatmap.iloc[1, 2] = np.nan

plt.figure(figsize=(3.5, 5))
cm = ClusterMapPlotter(
        data=df_heatmap,
        col_cluster=True,row_cluster=True,
        col_split=df.AB,row_split=2,
        col_split_gap=0.5,row_split_gap=0.8,
        label='values',row_dendrogram=True,
        show_rownames=False,show_colnames=True,
        tree_kws={'row_cmap': 'Set1'},verbose=0,legend_vgap=5,
        cmap='Vik',xticklabels_kws={'labelrotation':-90,'labelcolor':'blue'},
        ylabel="Features",xlabel="Samples")
# plt.savefig("example0.pdf", bbox_inches='tight')
plt.show()

A suffix “_r” could be added to all cmap to reverse the colormap, for example:

plt.figure(figsize=(3.5, 5))
cm = ClusterMapPlotter(
        data=df_heatmap,
        col_cluster=True,row_cluster=True,
        col_split=df.AB,row_split=2,
        col_split_gap=0.5,row_split_gap=0.8,
        label='values',row_dendrogram=True,
        show_rownames=True,show_colnames=True,row_names_side='right',
        tree_kws={'row_cmap': 'Set1'},verbose=0,legend_vgap=5,
        cmap='RedYellowBlue_r',xticklabels_kws={'labelrotation':-90,'labelcolor':'blue'}) #PuOr_r,RdYlGn_r
plt.show()