Matplotlib离散色条

Question

我试图在matplotlib中为散点图创建一个离散的颜色条

我有我的x,y数据,每个点都有一个整数标记值,我希望用一种独特的颜色表示,例如

plt.scatter(x, y, c=tag)

通常标记将是0到20之间的整数,但确切的范围可能会改变

到目前为止,我刚刚使用了默认设置,例如

plt.colorbar()

它提供了连续的颜色范围.理想情况下,我想要一组n个离散颜色(在这个例子中n = 20).更好的方法是将标签值设为0以产生灰色,1-20为彩色.

我找到了一些'cookbook'脚本,但它们非常复杂,我不认为它们是解决看似简单问题的正确方法

Answer 1

您可以使用BoundaryNorm作为散射的规范化器,轻松创建自定义离散颜色条.古怪的位(在我的方法中)将0显示为灰色.

对于图像,我经常使用cmap.set_bad()并将我的数据转换为numpy蒙面数组.这将更容易使0灰色,但我无法使用散点图或自定义cmap.

作为替代方案,您可以从头开始创建自己的cmap,或者读出现有的cmap并仅覆盖某些特定条目.

import numpy as np
import matplotlib as mpl
import matplotlib.pylab as plt

fig, ax = plt.subplots(1, 1, figsize=(6, 6))  # setup the plot

x = np.random.rand(20)  # define the data
y = np.random.rand(20)  # define the data
tag = np.random.randint(0, 20, 20)
tag[10:12] = 0  # make sure there are some 0 values to show up as grey

cmap = plt.cm.jet  # define the colormap
# extract all colors from the .jet map
cmaplist = [cmap(i) for i in range(cmap.N)]
# force the first color entry to be grey
cmaplist[0] = (.5, .5, .5, 1.0)

# create the new map
cmap = mpl.colors.LinearSegmentedColormap.from_list(
    'Custom cmap', cmaplist, cmap.N)

# define the bins and normalize
bounds = np.linspace(0, 20, 21)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)

# make the scatter
scat = ax.scatter(x, y, c=tag, s=np.random.randint(100, 500, 20),
                  cmap=cmap, norm=norm)

# create a second axes for the colorbar
ax2 = fig.add_axes([0.95, 0.1, 0.03, 0.8])
cb = plt.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm,
    spacing='proportional', ticks=bounds, boundaries=bounds, format='%1i')

ax.set_title('Well defined discrete colors')
ax2.set_ylabel('Very custom cbar [-]', size=12)

我个人认为,有20种不同的颜色,有点难以阅读具体的价值,但这当然取决于你.

Answer 2

你可以按照这个例子:

#!/usr/bin/env python
"""
Use a pcolor or imshow with a custom colormap to make a contour plot.

Since this example was initially written, a proper contour routine was
added to matplotlib - see contour_demo.py and
http://matplotlib.sf.net/matplotlib.pylab.html#-contour.
"""

from pylab import *


delta = 0.01
x = arange(-3.0, 3.0, delta)
y = arange(-3.0, 3.0, delta)
X,Y = meshgrid(x, y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = Z2 - Z1 # difference of Gaussians

cmap = cm.get_cmap('PiYG', 11)    # 11 discrete colors

im = imshow(Z, cmap=cmap, interpolation='bilinear',
            vmax=abs(Z).max(), vmin=-abs(Z).max())
axis('off')
colorbar()

show()

产生以下图像:

Answer 3

要设置高于或低于色彩映射范围的值,您需要使用色彩映射的set_overand和set_under方法.如果要标记特定值,请将其屏蔽(即创建一个屏蔽数组),然后使用该set_bad方法.(请查看基本colormap类的文档:http://matplotlib.org/api/colors_api.html#matplotlib.colors.Colormap)

听起来你想要这样的东西:

import matplotlib.pyplot as plt
import numpy as np

# Generate some data
x, y, z = np.random.random((3, 30))
z = z * 20 + 0.1

# Set some values in z to 0...
z[:5] = 0

cmap = plt.get_cmap('jet', 20)
cmap.set_under('gray')

fig, ax = plt.subplots()
cax = ax.scatter(x, y, c=z, s=100, cmap=cmap, vmin=0.1, vmax=z.max())
fig.colorbar(cax, extend='min')

plt.show()

Answer 4

上面的答案是好的,除了他们没有在彩条上放置正确的刻度线.我喜欢在颜色中间有刻度,以便数字 - >颜色映射更清晰.您可以通过更改matshow调用的限制来解决此问题:

import matplotlib.pyplot as plt
import numpy as np

def discrete_matshow(data):
    #get discrete colormap
    cmap = plt.get_cmap('RdBu', np.max(data)-np.min(data)+1)
    # set limits .5 outside true range
    mat = plt.matshow(data,cmap=cmap,vmin = np.min(data)-.5, vmax = np.max(data)+.5)
    #tell the colorbar to tick at integers
    cax = plt.colorbar(mat, ticks=np.arange(np.min(data),np.max(data)+1))

#generate data
a=np.random.randint(1, 9, size=(10, 10))
discrete_matshow(a)

Answer 5

这个主题已经很好地涵盖了，但我想添加一些更具体的内容：我想确保某个值会映射到该颜色（而不是任何颜色）。

这并不复杂，但因为我花了一些时间，它可能会帮助其他人不会像我一样浪费太多时间:)

import matplotlib
from matplotlib.colors import ListedColormap

# Let's design a dummy land use field
A = np.reshape([7,2,13,7,2,2], (2,3))
vals = np.unique(A)

# Let's also design our color mapping: 1s should be plotted in blue, 2s in red, etc...
col_dict={1:"blue",
          2:"red",
          13:"orange",
          7:"green"}

# We create a colormar from our list of colors
cm = ListedColormap([col_dict[x] for x in col_dict.keys()])

# Let's also define the description of each category : 1 (blue) is Sea; 2 (red) is burnt, etc... Order should be respected here ! Or using another dict maybe could help.
labels = np.array(["Sea","City","Sand","Forest"])
len_lab = len(labels)

# prepare normalizer
## Prepare bins for the normalizer
norm_bins = np.sort([*col_dict.keys()]) + 0.5
norm_bins = np.insert(norm_bins, 0, np.min(norm_bins) - 1.0)
print(norm_bins)
## Make normalizer and formatter
norm = matplotlib.colors.BoundaryNorm(norm_bins, len_lab, clip=True)
fmt = matplotlib.ticker.FuncFormatter(lambda x, pos: labels[norm(x)])

# Plot our figure
fig,ax = plt.subplots()
im = ax.imshow(A, cmap=cm, norm=norm)

diff = norm_bins[1:] - norm_bins[:-1]
tickz = norm_bins[:-1] + diff / 2
cb = fig.colorbar(im, format=fmt, ticks=tickz)
fig.savefig("example_landuse.png")
plt.show()

Answer 6

我一直在研究这些想法，这是我的五美分。它避免调用BoundaryNorm以及指定norm作为参数scatter和colorbar。但是，我发现没有办法消除对matplotlib.colors.LinearSegmentedColormap.from_list.

一些背景是 matplotlib 提供所谓的定性颜色图，旨在用于离散数据。Set1例如，有 9 种易于区分的颜色，tab20可以用于 20 种颜色。对于这些地图，可以很自然地使用它们的前 n 种颜色为 n 个类别的散点图着色，如下例所示。该示例还生成一个颜色条，其中包含适当标记的 n 个离散颜色。

import matplotlib, numpy as np, matplotlib.pyplot as plt
n = 5
from_list = matplotlib.colors.LinearSegmentedColormap.from_list
cm = from_list(None, plt.cm.Set1(range(0,n)), n)
x = np.arange(99)
y = x % 11
z = x % n
plt.scatter(x, y, c=z, cmap=cm)
plt.clim(-0.5, n-0.5)
cb = plt.colorbar(ticks=range(0,n), label='Group')
cb.ax.tick_params(length=0)

产生下面的图像。的n在调用Set1指定第一个n该颜色表颜色，最后n在调用from_list 指定构造与地图n的颜色（默认为256）。为了将 设置cm为默认颜色图plt.set_cmap，我发现有必要给它一个名字并注册它，即：

cm = from_list('Set15', plt.cm.Set1(range(0,n)), n)
plt.cm.register_cmap(None, cm)
plt.set_cmap(cm)
...
plt.scatter(x, y, c=z)