【Python】python matplotlib 圖像配色方案【轉載】

原文鏈接：https://matplotlib.org/examples/color/colormaps_reference.html

Perceptually Uniform Sequential

• ["viridis", "plasma", "inferno", "magma"]
  

---

Sequential

• ["Greys", "Purples", "Blues", "Greens", "Oranges", "Reds", "YlOrBr", "YlOrRd", "OrRd", "PuRd", "RdPu", "BuPu", "GnBu", "PuBu", "YlGnBu", "PuBuGn", "BuGn", "YlGn"]
  

---

Sequential (2)

• ["binary", "gist_yarg", "gist_gray", "gray", "bone", "pink", "spring", "summer", "autumn", "winter", "cool", "Wistia", "hot", "afmhot", "gist_heat", "copper"]
  

---

Diverging

• ["PiYG", "PRGn", "BrBG", "PuOr", "RdGy", "RdBu", "RdYlBu", "RdYlGn", "Spectral", "coolwarm", "bwr", "seismic"]
  

---

Qualitative

• ["Pastel1", "Pastel2", "Paired", "Accent", "Dark2", "Set1", "Set2", "Set3", "tab10", "tab20", "tab20b", "tab20c"]
  

---

Miscellaneous

• ["flag", "prism", "ocean", "gist_earth", "terrain", "gist_stern", "gnuplot", "gnuplot2", "CMRmap", "cubehelix", "brg", "hsv", "gist_rainbow", "rainbow", "jet", "nipy_spectral", "gist_ncar"]
  

---

Soruce code

"""
==================
Colormap reference
==================

Reference for colormaps included with Matplotlib.

This reference example shows all colormaps included with Matplotlib. Note that
any colormap listed here can be reversed by appending "_r" (e.g., "pink_r").
These colormaps are divided into the following categories:

Sequential:
    These colormaps are approximately monochromatic colormaps varying smoothly
    between two color tones---usually from low saturation (e.g. white) to high
    saturation (e.g. a bright blue). Sequential colormaps are ideal for
    representing most scientific data since they show a clear progression from
    low-to-high values.

Diverging:
    These colormaps have a median value (usually light in color) and vary
    smoothly to two different color tones at high and low values. Diverging
    colormaps are ideal when your data has a median value that is significant
    (e.g.  0, such that positive and negative values are represented by
    different colors of the colormap).

Qualitative:
    These colormaps vary rapidly in color. Qualitative colormaps are useful for
    choosing a set of discrete colors. For example::

        color_list = plt.cm.Set3(np.linspace(0, 1, 12))

    gives a list of RGB colors that are good for plotting a series of lines on
    a dark background.

Miscellaneous:
    Colormaps that don"t fit into the categories above.

"""
import numpy as np
import matplotlib.pyplot as plt


# Have colormaps separated into categories:
# http://matplotlib.org/examples/color/colormaps_reference.html
cmaps = [("Perceptually Uniform Sequential", [
            "viridis", "plasma", "inferno", "magma"]),
         ("Sequential", [
            "Greys", "Purples", "Blues", "Greens", "Oranges", "Reds",
            "YlOrBr", "YlOrRd", "OrRd", "PuRd", "RdPu", "BuPu",
            "GnBu", "PuBu", "YlGnBu", "PuBuGn", "BuGn", "YlGn"]),
         ("Sequential (2)", [
            "binary", "gist_yarg", "gist_gray", "gray", "bone", "pink",
            "spring", "summer", "autumn", "winter", "cool", "Wistia",
            "hot", "afmhot", "gist_heat", "copper"]),
         ("Diverging", [
            "PiYG", "PRGn", "BrBG", "PuOr", "RdGy", "RdBu",
            "RdYlBu", "RdYlGn", "Spectral", "coolwarm", "bwr", "seismic"]),
         ("Qualitative", [
            "Pastel1", "Pastel2", "Paired", "Accent",
            "Dark2", "Set1", "Set2", "Set3",
            "tab10", "tab20", "tab20b", "tab20c"]),
         ("Miscellaneous", [
            "flag", "prism", "ocean", "gist_earth", "terrain", "gist_stern",
            "gnuplot", "gnuplot2", "CMRmap", "cubehelix", "brg", "hsv",
            "gist_rainbow", "rainbow", "jet", "nipy_spectral", "gist_ncar"])]


nrows = max(len(cmap_list) for cmap_category, cmap_list in cmaps)
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))


def plot_color_gradients(cmap_category, cmap_list, nrows):
    fig, axes = plt.subplots(nrows=nrows)
    fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
    axes[0].set_title(cmap_category + " colormaps", fontsize=14)

    for ax, name in zip(axes, cmap_list):
        ax.imshow(gradient, aspect="auto", cmap=plt.get_cmap(name))
        pos = list(ax.get_position().bounds)
        x_text = pos[0] - 0.01
        y_text = pos[1] + pos[3]/2.
        fig.text(x_text, y_text, name, va="center", ha="right", fontsize=10)

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


for cmap_category, cmap_list in cmaps:
    plot_color_gradients(cmap_category, cmap_list, nrows)

plt.show()