在matplotlib中定义一个色彩图的中点

我想设置一个色彩地图的中间点，即我的数据从-5到10，我想零为中间。 我认为做到这一点的方法是规范化和使用规范的子类化，但是我没有find任何示例，而且我不清楚，到底要实现什么。

Answer 1

我知道这个游戏已经晚了，但是我刚刚经历了这个过程，并提出了一个解决scheme，这个解决scheme的可能性比子类归一化要less，但是要简单得多。 我认为在这里分享后代是件好事。

function

import numpy as np import matplotlib import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import AxesGrid def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name='shiftedcmap'): ''' Function to offset the "center" of a colormap. Useful for data with a negative min and positive max and you want the middle of the colormap's dynamic range to be at zero Input ----- cmap : The matplotlib colormap to be altered start : Offset from lowest point in the colormap's range. Defaults to 0.0 (no lower ofset). Should be between 0.0 and `midpoint`. midpoint : The new center of the colormap. Defaults to 0.5 (no shift). Should be between 0.0 and 1.0. In general, this should be 1 - vmax/(vmax + abs(vmin)) For example if your data range from -15.0 to +5.0 and you want the center of the colormap at 0.0, `midpoint` should be set to 1 - 5/(5 + 15)) or 0.75 stop : Offset from highets point in the colormap's range. Defaults to 1.0 (no upper ofset). Should be between `midpoint` and 1.0. ''' cdict = { 'red': [], 'green': [], 'blue': [], 'alpha': [] } # regular index to compute the colors reg_index = np.linspace(start, stop, 257) # shifted index to match the data shift_index = np.hstack([ np.linspace(0.0, midpoint, 128, endpoint=False), np.linspace(midpoint, 1.0, 129, endpoint=True) ]) for ri, si in zip(reg_index, shift_index): r, g, b, a = cmap(ri) cdict['red'].append((si, r, r)) cdict['green'].append((si, g, g)) cdict['blue'].append((si, b, b)) cdict['alpha'].append((si, a, a)) newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict) plt.register_cmap(cmap=newcmap) return newcmap 

一个例子

 biased_data = np.random.random_integers(low=-15, high=5, size=(37,37)) orig_cmap = matplotlib.cm.coolwarm shifted_cmap = shiftedColorMap(orig_cmap, midpoint=0.75, name='shifted') shrunk_cmap = shiftedColorMap(orig_cmap, start=0.15, midpoint=0.75, stop=0.85, name='shrunk') fig = plt.figure(figsize=(6,6)) grid = AxesGrid(fig, 111, nrows_ncols=(2, 2), axes_pad=0.5, label_mode="1", share_all=True, cbar_location="right", cbar_mode="each", cbar_size="7%", cbar_pad="2%") # normal cmap im0 = grid[0].imshow(biased_data, interpolation="none", cmap=orig_cmap) grid.cbar_axes[0].colorbar(im0) grid[0].set_title('Default behavior (hard to see bias)', fontsize=8) im1 = grid[1].imshow(biased_data, interpolation="none", cmap=orig_cmap, vmax=15, vmin=-15) grid.cbar_axes[1].colorbar(im1) grid[1].set_title('Centered zero manually,\nbut lost upper end of dynamic range', fontsize=8) im2 = grid[2].imshow(biased_data, interpolation="none", cmap=shifted_cmap) grid.cbar_axes[2].colorbar(im2) grid[2].set_title('Recentered cmap with function', fontsize=8) im3 = grid[3].imshow(biased_data, interpolation="none", cmap=shrunk_cmap) grid.cbar_axes[3].colorbar(im3) grid[3].set_title('Recentered cmap with function\nand shrunk range', fontsize=8) for ax in grid: ax.set_yticks([]) ax.set_xticks([]) 

例子的结果：

Answer 2

这是一个解决scheme子类Normalize。 使用它

 norm = MidPointNorm(midpoint=3) imshow(X, norm=norm) 

这是class级：

 from numpy import ma from matplotlib import cbook from matplotlib.colors import Normalize class MidPointNorm(Normalize): def __init__(self, midpoint=0, vmin=None, vmax=None, clip=False): Normalize.__init__(self,vmin, vmax, clip) self.midpoint = midpoint def __call__(self, value, clip=None): if clip is None: clip = self.clip result, is_scalar = self.process_value(value) self.autoscale_None(result) vmin, vmax, midpoint = self.vmin, self.vmax, self.midpoint if not (vmin < midpoint < vmax): raise ValueError("midpoint must be between maxvalue and minvalue.") elif vmin == vmax: result.fill(0) # Or should it be all masked? Or 0.5? elif vmin > vmax: raise ValueError("maxvalue must be bigger than minvalue") else: vmin = float(vmin) vmax = float(vmax) if clip: mask = ma.getmask(result) result = ma.array(np.clip(result.filled(vmax), vmin, vmax), mask=mask) # ma division is very slow; we can take a shortcut resdat = result.data #First scale to -1 to 1 range, than to from 0 to 1. resdat -= midpoint resdat[resdat>0] /= abs(vmax - midpoint) resdat[resdat<0] /= abs(vmin - midpoint) resdat /= 2. resdat += 0.5 result = ma.array(resdat, mask=result.mask, copy=False) if is_scalar: result = result[0] return result def inverse(self, value): if not self.scaled(): raise ValueError("Not invertible until scaled") vmin, vmax, midpoint = self.vmin, self.vmax, self.midpoint if cbook.iterable(value): val = ma.asarray(value) val = 2 * (val-0.5) val[val>0] *= abs(vmax - midpoint) val[val<0] *= abs(vmin - midpoint) val += midpoint return val else: val = 2 * (val - 0.5) if val < 0: return val*abs(vmin-midpoint) + midpoint else: return val*abs(vmax-midpoint) + midpoint 

Answer 3

使用vmin和vmax参数imshow （假设您正在处理图像数据）而不是imshow matplotlib.colors.Normalize 。

例如

 import numpy as np import matplotlib.pyplot as plt data = np.random.random((10,10)) # Make the data range from about -5 to 10 data = 10 / 0.75 * (data - 0.25) plt.imshow(data, vmin=-10, vmax=10) plt.colorbar() plt.show() 

Answer 4

不知道你是否还在寻找答案。 对我来说，尝试子类Normalize是不成功的。 所以我专注于手动创build一个新的数据集，刻度和刻度标签，以获得我认为您所要达到的效果。

我在matplotlib中find了具有用于通过'syslog'规则变换线图的类的scale模块，所以我使用它来转换数据。 然后我缩放数据，使其从0到1（ Normalize通常会这样做），但是我将正数与负数进行了不同的缩放。 这是因为你的vmax和vmin可能不是相同的，所以.5 – > 1可能覆盖的正范围大于.5 – > 0，负范围是。 我创build一个例程来计算刻度和标签值比较容易。

以下是代码和示例图。

 import numpy as np import matplotlib.pyplot as plt import matplotlib.mpl as mpl import matplotlib.scale as scale NDATA = 50 VMAX=10 VMIN=-5 LINTHRESH=1e-4 def makeTickLables(vmin,vmax,linthresh): """ make two lists, one for the tick positions, and one for the labels at those positions. The number and placement of positive labels is different from the negative labels. """ nvpos = int(np.log10(vmax))-int(np.log10(linthresh)) nvneg = int(np.log10(np.abs(vmin)))-int(np.log10(linthresh))+1 ticks = [] labels = [] lavmin = (np.log10(np.abs(vmin))) lvmax = (np.log10(np.abs(vmax))) llinthres = int(np.log10(linthresh)) # f(x) = mx+b # f(llinthres) = .5 # f(lavmin) = 0 m = .5/float(llinthres-lavmin) b = (.5-llinthres*m-lavmin*m)/2 for itick in range(nvneg): labels.append(-1*float(pow(10,itick+llinthres))) ticks.append((b+(itick+llinthres)*m)) # add vmin tick labels.append(vmin) ticks.append(b+(lavmin)*m) # f(x) = mx+b # f(llinthres) = .5 # f(lvmax) = 1 m = .5/float(lvmax-llinthres) b = m*(lvmax-2*llinthres) for itick in range(1,nvpos): labels.append(float(pow(10,itick+llinthres))) ticks.append((b+(itick+llinthres)*m)) # add vmax tick labels.append(vmax) ticks.append(b+(lvmax)*m) return ticks,labels data = (VMAX-VMIN)*np.random.random((NDATA,NDATA))+VMIN # define a scaler object that can transform to 'symlog' scaler = scale.SymmetricalLogScale.SymmetricalLogTransform(10,LINTHRESH) datas = scaler.transform(data) # scale datas so that 0 is at .5 # so two seperate scales, one for positive and one for negative data2 = np.where(np.greater(data,0), .75+.25*datas/np.log10(VMAX), .25+.25*(datas)/np.log10(np.abs(VMIN)) ) ticks,labels=makeTickLables(VMIN,VMAX,LINTHRESH) cmap = mpl.cm.jet fig = plt.figure() ax = fig.add_subplot(111) im = ax.imshow(data2,cmap=cmap,vmin=0,vmax=1) cbar = plt.colorbar(im,ticks=ticks) cbar.ax.set_yticklabels(labels) fig.savefig('twoscales.png') 

随意调整脚本顶部的“常数”（例如VMAX ），以确认其performance良好。

Answer 5

如果你不介意vmin，vmax和零之间的比例，这是一个非常基本的线性地图，从蓝色到白色到红色，根据比例z ：

 def colormap(z): """custom colourmap for map plots""" cdict1 = {'red': ((0.0, 0.0, 0.0), (z, 1.0, 1.0), (1.0, 1.0, 1.0)), 'green': ((0.0, 0.0, 0.0), (z, 1.0, 1.0), (1.0, 0.0, 0.0)), 'blue': ((0.0, 1.0, 1.0), (z, 1.0, 1.0), (1.0, 0.0, 0.0)) } return LinearSegmentedColormap('BlueRed1', cdict1) 

cdict格式非常简单：行是渐变中的点，第一个是x值（沿着梯度从0到1的比例），第二个是前一个段的结束值，第三个是下一个段的起始值 – 如果你想要平滑的梯度，后两者总是相同的。 有关更多详细信息， 请参阅文档 。

Answer 6

我有类似的问题，但我想要最高的价值是全红，并切断低价值的蓝色，使得它看起来基本上像颜色条的底部被砍掉。 这工作对我来说（包括可选的透明度）：

 def shift_zero_bwr_colormap(z: float, transparent: bool = True): """shifted bwr colormap""" if (z < 0) or (z > 1): raise ValueError('z must be between 0 and 1') cdict1 = {'red': ((0.0, max(-2*z+1, 0), max(-2*z+1, 0)), (z, 1.0, 1.0), (1.0, 1.0, 1.0)), 'green': ((0.0, max(-2*z+1, 0), max(-2*z+1, 0)), (z, 1.0, 1.0), (1.0, max(2*z-1,0), max(2*z-1,0))), 'blue': ((0.0, 1.0, 1.0), (z, 1.0, 1.0), (1.0, max(2*z-1,0), max(2*z-1,0))), } if transparent: cdict1['alpha'] = ((0.0, 1-max(-2*z+1, 0), 1-max(-2*z+1, 0)), (z, 0.0, 0.0), (1.0, 1-max(2*z-1,0), 1-max(2*z-1,0))) return LinearSegmentedColormap('shifted_rwb', cdict1) cmap = shift_zero_bwr_colormap(.3) x = np.arange(0, np.pi, 0.1) y = np.arange(0, 2*np.pi, 0.1) X, Y = np.meshgrid(x, y) Z = np.cos(X) * np.sin(Y) * 5 + 5 plt.plot([0, 10*np.pi], [0, 20*np.pi], color='c', lw=20, zorder=-3) plt.imshow(Z, interpolation='nearest', origin='lower', cmap=cmap) plt.colorbar() 

Answer 7

我使用Paul H的优秀答案，但遇到了一个问题，因为我的一些数据范围从负面到正面，而其他设置范围从0到正面或从负面到0; 在这两种情况下，我都希望0被着色为白色（我使用的色彩地图的中点）。 在现有实现中，如果您的midpoint值等于1或0，则原始映射不会被覆盖。 你可以看到在下面的图片： 第三列看起来是正确的，但是第二列的深蓝色区域和其余列中的深红色区域都应该是白色的（它们的数据值实际上是0）。 使用我的修复程序给我： 我的function与Paul H的function基本相同，在for循环开始时进行了编辑：

  def shiftedColorMap(cmap, min_val, max_val, name): '''Function to offset the "center" of a colormap. Useful for data with a negative min and positive max and you want the middle of the colormap's dynamic range to be at zero. Adapted from https://stackoverflow.com/questions/7404116/defining-the-midpoint-of-a-colormap-in-matplotlib Input ----- cmap : The matplotlib colormap to be altered. start : Offset from lowest point in the colormap's range. Defaults to 0.0 (no lower ofset). Should be between 0.0 and `midpoint`. midpoint : The new center of the colormap. Defaults to 0.5 (no shift). Should be between 0.0 and 1.0. In general, this should be 1 - vmax/(vmax + abs(vmin)) For example if your data range from -15.0 to +5.0 and you want the center of the colormap at 0.0, `midpoint` should be set to 1 - 5/(5 + 15)) or 0.75 stop : Offset from highets point in the colormap's range. Defaults to 1.0 (no upper ofset). Should be between `midpoint` and 1.0.''' epsilon = 0.001 start, stop = 0.0, 1.0 min_val, max_val = min(0.0, min_val), max(0.0, max_val) # Edit #2 midpoint = 1.0 - max_val/(max_val + abs(min_val)) cdict = {'red': [], 'green': [], 'blue': [], 'alpha': []} # regular index to compute the colors reg_index = np.linspace(start, stop, 257) # shifted index to match the data shift_index = np.hstack([np.linspace(0.0, midpoint, 128, endpoint=False), np.linspace(midpoint, 1.0, 129, endpoint=True)]) for ri, si in zip(reg_index, shift_index): if abs(si - midpoint) < epsilon: r, g, b, a = cmap(0.5) # 0.5 = original midpoint. else: r, g, b, a = cmap(ri) cdict['red'].append((si, r, r)) cdict['green'].append((si, g, g)) cdict['blue'].append((si, b, b)) cdict['alpha'].append((si, a, a)) newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict) plt.register_cmap(cmap=newcmap) return newcmap 

编辑：我遇到了一个类似的问题，当我的一些数据范围从一个小的正值到一个较大的正值，其中非常低的价值是红色而不是白色。 我通过在上面的代码中添加行Edit #2来修复它。