Question

需要在一个图中为8个不同的函数绘制CDF。问题是它只提供了7种不同颜色，而8种颜色再次给出了第一种蓝色。如何制作8种不同的颜色？

这是脚本：

locerror_2d=[Scan_Around[1],Triangle_Around[1],M_shape_Around[1],Hilbert_Around[1],Scan_SbS[1],Triangle_SbS[1],M_shape_SbS[1],Hilbert_SbS[1]]


# N = len(locerror_2d[0]) #same for all ( here, I hope so... )
# N1=len(locerror_2d[2])
H_cent,h_cent1 = np.histogram( locerror_2d[0], bins = 10, normed = True ) # Random Walk Centroid
hy_cent = np.cumsum(H_cent)*(h_cent1[1] - h_cent1[0])

H_1st,h_1st = np.histogram( locerror_2d[1], bins = 10, normed = True ) # Random Walk Weighterd
hy_1st = np.cumsum(H_1st)*(h_1st[1] - h_1st[0])

H_2nd,h_2nd = np.histogram( locerror_2d[2], bins = 10, normed = True ) # Circle Walk Centroid
hy_2nd = np.cumsum(H_2nd)*(h_2nd[1] - h_2nd[0])

H_3rd,h_3rd = np.histogram( locerror_2d[3], bins = 10, normed = True ) # Circle Walk Weighterd
hy_3rd = np.cumsum(H_3rd)*(h_3rd[1] - h_3rd[0])

H_mm,h_mm = np.histogram( locerror_2d[4], bins = 10, normed = True ) # G Walk Centroid
hy_mm = np.cumsum(H_mm)*(h_mm[1] - h_mm[0])

H_shr,h_shr = np.histogram( locerror_2d[5], bins = 10, normed = True ) # G Walk Weighterd
hy_shr = np.cumsum(H_shr)*(h_shr[1] - h_shr[0])

H_s,h_s = np.histogram( locerror_2d[6], bins = 10, normed = True ) # G Walk Weighterd
hy_s = np.cumsum(H_s)*(h_s[1] - h_s[0])

H_sh,h_sh = np.histogram( locerror_2d[7], bins = 10, normed = True ) # G Walk Weighterd
hy_sh = np.cumsum(H_sh)*(h_sh[1] - h_sh[0])


plt.hold(True)
ddd_hist_cent, = plt.plot(h_cent1[1:], hy_cent,label="Scan_Around")        # centroid
ddd_hist_1st, = plt.plot(h_1st[1:], hy_1st,label='Triangle_Around')       #Gradient
ddd_circ_cent, = plt.plot(h_2nd[1:], hy_cent,label="M_shape_around")        # centroid
ddd_circ_wei, = plt.plot(h_3rd[1:], hy_1st,label='Hilbert_Around')       #Gradient
ddd_g_cent, = plt.plot(h_mm[1:], hy_cent,label="Scan_SbS")        # centroid
ddd_g_wei, = plt.plot(h_shr[1:], hy_1st,label='Triangle_SbS')       #Gradient
ddd_g_w, = plt.plot(h_s[1:], hy_cent,label='M_shape_SbS')
ddd_g_we, = plt.plot(h_sh[1:], hy_1st,label='Hilbert_SbS')

plt.hold(False)

plt.rc('legend',**{'fontsize':10})
plt.legend(handles=[ddd_hist_cent, ddd_hist_1st, ddd_circ_cent, ddd_circ_wei, ddd_g_cent,ddd_g_wei, ddd_g_w],loc='center left', bbox_to_anchor=(0.75, 0.18)) #no trilateration here
plt.ylabel('Probability')
plt.xlabel('Localization Error, m')
plt.ylim(ymax = 1.1, ymin = 0)
plt.title('Path Planning Algorithms')
plt.grid()
plt.show()

谢谢

Answer 1

我喜欢直接使用此代码

从色彩图中读取颜色

def getColor(c, N, idx):
    import matplotlib as mpl
    cmap = mpl.cm.get_cmap(c)
    norm = mpl.colors.Normalize(vmin=0.0, vmax=N - 1)
    return cmap(norm(idx))

此处，c是色彩映射的名称（有关列表，请参阅https://matplotlib.org/examples/color/colormaps_reference.html），N是您想要的总色彩数量，idx是只是一个会产生特定颜色的索引。

然后在调用plot函数时，只需添加color=getColor(c, N, idx)选项。

Answer 2

确定。我知道了。在情节的最后我只需要显示颜色。

ddd_hist_cent, = plt.plot(h_cent1[1:], hy_cent,label="Scan_Around", c='yellow')

Answer 3

最简单的解决方案：为最后一条曲线添加不同的颜色：

plt.plot(h_sh[1:], hy_1st,label='Hilbert_SbS', color="orange")

Matplotlib 1.5或更低版本的颜色周期有7种不同的颜色，而matplotlib 2.0有10种不同的颜色。因此，更新matplotlib是另一种选择。

一般情况下，您当然可以定义自己的颜色循环，其颜色可以是您想要的颜色。

从色图中构建循环器，如this question：

所示

import matplotlib.pyplot as plt
from cycler import cycler
import numpy as np

N = 8 # number of colors
plt.rcParams["axes.prop_cycle"] = cycler('color', plt.cm.jet(np.linspace(0,1,N)) )

从颜色列表构建循环器：

import matplotlib.pyplot as plt
from cycler import cycler

colors=["aquamarine","crimson","gold","indigo",
        "lime","orange","orchid","sienna"]
plt.rcParams["axes.prop_cycle"] = cycler('color',colors)

CDF，matplotlib - 没有足够的颜色用于情节，python

3 个答案: