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我是一名气候学家，经常使用“蓝到白到红”的颜色图来绘制温度场的异常。为了使绘图更具可读性，我使用我在 Internet 中“找到”的函数（但我不太了解）将颜色图离散化为一定数量的级别（箱）：

像这样的东西：

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
import matplotlib.colors as cols
from numpy.random import randn

def cmap_discretize(cmap, N):
    colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.)))
    colors_rgba = cmap(colors_i)
    indices = np.linspace(0, 1., N+1)
    cdict = {}
    for ki,key in enumerate(('red','green','blue')):
        cdict[key] = [ (indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1) ]
    # Return colormap object.
    return cols.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)



cmap_disc= cmap_discretize(cm.RdBu_r,12)


fig, ax = plt.subplots()
data = np.clip(randn(250, 250), -1, 1)

cax = ax.pcolor(data, cmap=cmap_disc)
plt.colorbar(cax)

plt.show()

这导致

在此处输入图像描述

现在我想将最中间的两个段（即接近 0 的两个段）设置为白色，因为我不想显示非常小的偏差。

我的目标是最终得到类似的东西：

在此处输入图像描述

我真的很难弄清楚如何相应地修改这些 LinearSegmentedColormap。有人可以帮我吗？

4

2 回答 2

5

您找到的函数构建了一个数据结构 (in cdict)，用于定义带有不执行任何插值的段的LinearSegmentedColormapy1 (即in rowi始终与y0in row相同i+1，这给出了常量或离散的颜色“带” ）。

cdict是一个奇怪的数据结构，一个包含键的字典'red'，'green'和'blue'。这些键中的每一个的值都是一个列表结构，其中包含形式为 的元组(x, y0, y1)。 x是颜色图坐标，它是 0 到 1 之间的某个浮点数。 y0是 的“左侧”的颜色值x，y1是 的“右侧”的颜色值x。颜色在连续值之间的带中线性插值x；如果第一个元组由 给出，(0, A, B)第二个元组由给出，则和之间(X, C, D)的点的颜色将由 给出。t0X(t - 0) / (X - 0) * (C - B) + B

出于您的目的，您的函数运行良好，但需要将颜色图中间附近的“带”替换为白色。您可以尝试以下方法：

def cmap_discretize(cmap, N):
    colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.)))
    colors_rgba = cmap(colors_i)
    indices = np.linspace(0, 1., N+1)
    cdict = {}
    for ki,key in enumerate(('red','green','blue')):
        cdict[key] = [ (indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1) ]
    # "white out" the bands closest to the middle
    num_middle_bands = 2 - (N % 2)
    middle_band_start_idx = (N - num_middle_bands) // 2
    for middle_band_idx in range(middle_band_start_idx,
                                 middle_band_start_idx + num_middle_bands):
        for key in cdict.keys():
            old = cdict[key][middle_band_idx]
            cdict[key][middle_band_idx] = old[:2] + (1.,)
            old = cdict[key][middle_band_idx + 1]
            cdict[key][middle_band_idx + 1] = old[:1] + (1.,) + old[2:]
    # Return colormap object.
    return cols.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)
于 2013-10-05T15:59:31.900 回答
3

让我们从浏览您拥有的代码开始

# get some uniformly sampled data, padded out a bit
colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.)))
# sample the input colormap at our sample points
colors_rgba = cmap(colors_i)
# indices for color map
indices = np.linspace(0, 1., N+1)
# dict to pass to the LinearSegmentedColormap
cdict = {}
# loop over the colors
for ki,key in enumerate(('red','green','blue')):
    # in each color assemble a list that looks like
    #[...,
    # (indices[2], colors_rgba[1,ki], colors_rgba[2,ki]),
    # (indices[3], colors_rgba[2,ki], colors_rgba[3,ki]),
    # ....]
    cdict[key] = [ (indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1) ]
    # The color for a number between [indices[2], indices[3]] are interpolated
    # between colors_rgba[2,ki] and colors_rgba[2,ki] which are the same
    # which is what gives you the discrete blocks.
# Construct and return colormap object.
return cols.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)

所以现在的问题是如何创建一个中间有“双倍”白色带的颜色图。我会更改功能位以使其具有两个颜色图（顶部和底部）

import matplotlib.pyplot as plt
import numpy as np
from matplotlib import cm
import matplotlib.colors as cols
from numpy.random import randn

def cmap_double_discretize(cmap_bottom, cmap_top, N, split=.5):
    """
    Generates a descritized color map using two existing color maps

    Parameters
    ----------
    cmap_bottom : cmap
        The bottom cmap

    cmap_top : cmap
        The top cmap

    N : int
       The number of bins in each color map

    split : float, optional
       Where to join the maps, must be in [0, 1]
    """
    # sanity check
    assert split < 1 and split > 0
    # set up the data structure
    cdict = {lab: [] for lab in ('red','green','blue')}
    # do this in a fancy loop to a) save typing, b) make it easy to
    # retrofit to do arbitrary splits
    for cmap, ends in zip((cmap_bottom, cmap_top), ((0, split), (split, 1))):

        # run over the _whole_ range for each color map
        colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.)))
        # map the color
        colors_rgba = cmap(colors_i)
        # get the values 
        indices = np.linspace(ends[0], ends[1], N+1, endpoint=True)

        for ki,key in enumerate(('red','green','blue')):
            cdict[key].extend((indices[i], colors_rgba[i-1,ki], colors_rgba[i,ki]) for i in xrange(N+1))
            #    print cdict
    # Return colormap object.
    return cols.LinearSegmentedColormap(cmap.name + "_%d"%N, cdict, 1024)

red_cdict = {'red': [(0, 0, 1),
                     (1, 1, 0)],
            'blue': [(0, 0, 0),
                     (1, 1, 0)],
            'green': [(0, 0, 0),
                     (1, 1, 0)]}

blue_cdict = {'blue': [(0, 0, 1),
                       (1, 1, 0),],
            'red': [(0, 0, 1),
                    (1, 0, 0)],
            'green': [(0, 0, 1),
                     (1, 0, 0)]}
red_cmap = cols.LinearSegmentedColormap('red', red_cdict, 1024)
blue_cmap = cols.LinearSegmentedColormap('blue', blue_cdict, 1024)

test_cmap = cmap_double_discretize(red_cmap, blue_cmap, 6)
# these don't actually go to white!
# test_cmap = cmap_double_discretize(cm.get_cmap('Reds_r'), cm.get_cmap('Blues'), 6)


fig, ax = plt.subplots()
data = np.clip(randn(250, 250), -1, 1)

cax = ax.pcolor(data, cmap=test_cmap)
plt.colorbar(cax)

plt.show()

在此处输入图像描述

您可以轻松地对其进行修改以拆分两个以上的颜色图。

于 2013-10-05T16:01:32.890 回答