用pcolormesh按数组索引绘制颜色

Question

我想用pcolormesh用jet colormap绘制数据。我想通过将数据分解成间隔并为每个间隔指定一种颜色来控制颜色拉伸。然后我希望最后一个间隔（任何高于“redvalue”的值，通常是30）用红色绘制。你知道吗

在Matlab中，我使用了尽可能多的数据间隔，因为在彩色地图中有元素。最后一个间隔指定了颜色数组中最后一个元素的颜色（“最闪亮”的红色）。你知道吗

range = redvalue - datamin;
colours  = colormap('hsv');
count = max(size(colours));
localrange = range/count;
localmin = datamin-localrange;
localmax = datamin;

% Plot the first n - 1 number of intervals
for j = 1 : count - 1
    localmin = localmin + localrange;
    localmax = localmax + localrange;
    ind = find(data >= localmin & data < localmax)
    p = plot3(x(ind),y(ind),data(ind), '.', 'Color', colours(j,:), 'MarkerSize', 5, 'MarkerFaceColor', colours(j,:));
end

%% Now plot the final colour to points >= 30
ind = find( data >= redvalue ); % make these points red
p = plot3(x(ind),y(ind),data(ind), '.', 'Color', colours(end,:), 'MarkerSize', 5, 'MarkerFaceColor', colours(end,:));

我的密码。我告诉cmap在查找表中只创建7个条目，而不是使用其默认的颜色映射条目数。你知道吗

    rng = redvalue - radmin
    n_colours = 7
    localrng = rng/n_colours
    localmin = radmin - localrng
    localmax = radmin
    cmp = plt.get_cmap('jet', n_colours)
    for index in range(1,n_colours - 1):
        localmin = localmin + localrng
        localmax = localmax + localrng
        row,col = np.where(np.logical_and(rad >= localmin, rad < localmax))
        plt.pcolormesh(x1[row][col],y1[row][col],rad[row][col], cmap = cmp(index), vmin = radmin, vmax = radmax, edgecolors = 'none')

这并不能很好地翻译成Python。我不知道我对数据矩阵元素的索引是否工作正常，因为直接的错误是关于colormap的。我想要的是可能的吗？我可以从cmap对象中获取单个颜色，并将它们一次提供给pcolormesh一种颜色吗？你知道吗

Traceback (most recent call last):
  File "C:\UserData\Documents\Stuff\tensorflow\OpenGDF2_2.py", line 199, in <module>
    PlotRad(data, 30, 50)
  File "C:\UserData\Documents\Stuff\tensorflow\OpenGDF2_2.py", line 165, in PlotRad
    plt.pcolormesh(x1[row][col],y1[row][col],rad[row][col], cmap = cmp(index), vmin = radmin, vmax = radmax, edgecolors = 'none')
  File "C:\Users\keepit20\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib\pyplot.py", line 2773, in pcolormesh
    **({"data": data} if data is not None else {}), **kwargs)
  File "C:\Users\keepit20\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib\__init__.py", line 1810, in inner
    return func(ax, *args, **kwargs)
  File "C:\Users\keepit20\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib\axes\_axes.py", line 6002, in pcolormesh
    collection.set_cmap(cmap)
  File "C:\Users\keepit20\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib\cm.py", line 342, in set_cmap
    cmap = get_cmap(cmap)
  File "C:\Users\keepit20\AppData\Local\Programs\Python\Python36\lib\site-packages\matplotlib\cm.py", line 182, in get_cmap
    % (name, ', '.join(sorted(cmap_d))))
ValueError: Colormap (0.0, 0.16666666666666666, 1.0, 1.0) is not recognized. Possible values are: Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, inferno, inferno_r, jet, jet_r, magma, magma_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, winter, winter_r

感谢JODY KLYMAK的解决方案：使用颜色.边界规范建立离散边界阵列。记住用相同数量的离散图像来获得你的颜色图。你知道吗

    rng = redvalue - radmin
    n_colours = 7
    localrng = rng/n_colours
    localmin = radmin - localrng
    localmax = radmin
    cmp = plt.get_cmap('jet', n_colours)
    bounds = localmax
    bounds = np.asarray(bounds)
    for index in range(1,n_colours - 1):
        localmin = localmin + localrng
        localmax = localmax + localrng
        bounds = np.append(bounds, localmax)

    bounds = np.append(bounds, radmax)
    norm = colors.BoundaryNorm(boundaries=bounds, ncolors = n_colours)
    plt.pcolormesh(x1,y1,rad, norm = norm, cmap = cmp, edgecolors = 'none')

    plt.axis([x1.min(), x1.max(), y1.min(), y1.max()])
    plt.colorbar()
    plt.show()

Answer 1

感谢JODY KLYMAK的解决方案：使用颜色.边界规范建立离散边界阵列。记住用相同数量的离散间隔来获得你的颜色图。你知道吗

rng = redvalue - radmin
n_colours = 7
localrng = rng/n_colours
localmin = radmin - localrng
localmax = radmin
cmp = plt.get_cmap('jet', n_colours)
bounds = localmax
bounds = np.asarray(bounds)
for index in range(1,n_colours - 1):
    localmin = localmin + localrng
    localmax = localmax + localrng
    bounds = np.append(bounds, localmax)

bounds = np.append(bounds, radmax)
norm = colors.BoundaryNorm(boundaries=bounds, ncolors = n_colours)
plt.pcolormesh(x1,y1,rad, norm = norm, cmap = cmp, edgecolors = 'none')

plt.axis([x1.min(), x1.max(), y1.min(), y1.max()])
plt.colorbar()
plt.show()