python - 具有不同内容的子图的全局图例

Question

我通读了以前的解决方案，但无法使它们中的任何一个起作用。我想要为各个次要情节提供一个全局图例。该子图的 ax 对象由预定义类中的预定义函数“get_plot”生成“The_predefined_plotting_class”大致如下:

该函数返回一个 ax 对象，每个 ax 对象都有多个“图”/来自原始“数据文件”的多个列。

在我在此网站上找到的解决方案之一中，我读到可以使用:

创造全局传奇。不幸的是，我不知道如何将各个 ax 对象(或其中的数据)附加到句柄以完成这项工作。每个图包含一些相同的列名称和一些不同的列名称。如果一个条目/名称存在于许多子图中，则只应打印一次。

Solution1

Solution2

Solution3

编辑

我真的很抱歉我不得不使用图片，但无论我做什么，网页端都不让我发布我的代码，即使它在预览窗口中正确显示(屏幕截图来自此窗口)

编辑2

如果我这样做:

lines=[]
labels=[]
for idata, datafile in enumerate(datafiles):

    MYData = The_predefined_plotting_class.from_file(datafile) 

    axis[idata] = The_predefined_plotting_class.get_plot( *kwargs)
    h, l = axis[idata].get_legend_handles_labels()

    lines.append(h)
    labels.append(l)


LINES=[]
LABELS=[]
for i in range(0, nrows):
    LINES+=lines[i]
    LABELS+=labels[i]
plt.legend( LINES, LABELS, loc="upper left", bbox_to_anchor=[0, 1],ncol=3, shadow=True, title="Legend", fancybox=True)
plt.show()

然后它显示所有数据。一些数据具有相同的行和标签处理程序。我现在面临的问题是迭代两个列表并仅删除一个条目，如果在两个列表中元组 (LINES[j];LABELS[j]) = (LINES[i] ;LABELS[i]) 存在两次(且仅此一次)。最好是第一个条目:

编辑3

labels =[]
lines = []
h=["Cat","Mouse","Dog","Cat","Cat","Kangaroo","Dog"]
l=["black","white","brown","white","black","yellow","brown"]


for handle, label in zip(h, l):
    if label not in labels : 

            lines.append(handle)
            labels.append(label)

print "The disired Output is :"            
print '["Cat","Mouse","Dog","Cat","Kangaroo"]'
print '["black","white","brown","white","yellow"]'

print "currently you get:"               

print lines
print labels  

编辑4

我添加了一个“最小”工作示例，其中应包含我的真实数据中发生的所有可能情况。

lines=[]
labels=[]
legend_properties = {'weight':'bold','size':10}  
# Example data
x1 = np.linspace(0.0, 5.0)
x2 = np.linspace(0.0, 2.0)

a = np.cos(2 * np.pi * x1) * np.exp(-x1)
b = np.cos(2 * np.pi * x2)
c = np.cos(5 * np.pi * x1) * np.exp(-x1)
c2 = np.cos(5 * np.pi * x1**2) * np.exp(-x1)
d = np.cos(2 * np.pi * x2 )
d2 = np.cos(2 * np.pi * x2-1 )
e = x1*5
e2 = -x1*5
f = np.exp(x1)-e
f2 = (np.exp(x1)-e)/2

nrows = 4
# Plot

fig, axis = plt.subplots(nrows, sharex=True, sharey=False, figsize=(5, 8))
fig.subplots_adjust(hspace=0.0001)
fig.suptitle("Stacked Plots with global Legend wich contains to little elements",fontsize=14,weight='bold')



axis[0].plot(x1, e, 'k--', label='Label1',color="green")
axis[0].plot(x1, e2, 'k--', label='Label2',color="blue")
axis[0].plot(x1, a, 'k--', label='Label3',color="yellow")
axis[1].plot(x1, c, 'k--', label='Label1',color="green")
axis[1].plot(x1, c2, 'k--', label='Label2',color="blue")
axis[1].plot(x1, a, 'k--', label='Label3',color="grey")
axis[2].plot(x2, d, '*', label='Label1',color="green")
axis[2].plot(x2, d2, 'D', label='Label2',color="green")
axis[3].plot(x1, f, 'H', label='Label1',color="green")
axis[3].plot(x1, f2, 'D', label='Label2',color="green")

for i in range(nrows):
    h, l = axis[i].get_legend_handles_labels()
    for handle, label in zip(h, l):
        if label not in labels:
            lines.append(handle)
            labels.append(label)

# only 3 Legend entrys Label1 , Label2 and Label3 are visible .. Differences in cloors and markers are ignored
plt.legend(handles=lines, labels=labels,bbox_to_anchor=(0., nrows+.02, 1., .102), loc=3,ncol=3, prop=legend_properties,mode="expand", borderaxespad=0.,frameon=False,framealpha=0.0) 

plt.show()

编辑5

这是相关脚本中生成实际绘图的部分。 “列”仅包含要绘制的实际数据的名称。

    # add plots
    ic = 0
    for col in columns:
        if col == "envelope":
            ax.plot(self.data.index, self.data.envelope,
                    linewidth=LINEWIDTH_envelope, c=last_color, label="")
        elif col == "Exp":
            ax.plot(self.data.index, self.data.Exp, c=first_color, linestyle="",
                    label="Exp", marker="o", markersize=MARKERSIZE )
        else:
            color = used_colors[ic % len(used_colors)]
            if fill and "BG" in self.data.columns:
                ax.fill_between(self.data.index, self.data.BG,
                                self.data[col], label=col, alpha=ALPHA,
                                color=color)
            else:
                ax.plot(self.data.index, self.data[col], linewidth=LINEWIDTH,
                        c=color, label=col)
            ic += 1

编辑6

我试图根据我在这里提出的想法找到解决方案:

Iteration though lists

不幸的是，适用于两个包含字符串的列表的方法似乎不适用于艺术家处理。

import matplotlib.pyplot as plt
import numpy as np
LI=[]
lines=[]
labels=[]
legend_properties = {'weight':'bold','size':10}  
# Example data
x1 = np.linspace(0.0, 5.0)
x2 = np.linspace(0.0, 2.0)

a = np.cos(2 * np.pi * x1) * np.exp(-x1)
b = np.cos(2 * np.pi * x2)
c = np.cos(5 * np.pi * x1) * np.exp(-x1)
c2 = np.cos(5 * np.pi * x1**2) * np.exp(-x1)
d = np.cos(2 * np.pi * x2 )
d2 = np.cos(2 * np.pi * x2-1 )
e = x1*5
e2 = -x1*5
f = np.exp(x1)-e
f2 = (np.exp(x1)-e)/2

nrows = 4
# Plot

fig, axis = plt.subplots(nrows, sharex=True, sharey=False, figsize=(5, 8))
fig.subplots_adjust(hspace=0.0001)
#fig.suptitle("Stacked Plots with global Legend wich contains to little elements",fontsize=14,weight='bold')



axis[0].plot(x1, e, 'k--', label='Label1',color="green")
axis[0].plot(x1, e2, 'k--', label='Label2',color="blue")
axis[0].plot(x1, a, 'k--', label='Label3',color="yellow")
axis[1].plot(x1, c, 'k--', label='Label1',color="green")
axis[1].plot(x1, c2, 'k--', label='Label2',color="blue")
axis[1].plot(x1, a, 'k--', label='Label3',color="grey")
axis[2].plot(x2, d, '*', label='Label1',color="green")
axis[2].plot(x2, d2, 'D', label='Label2',color="green")
axis[3].plot(x1, f, 'H', label='Label1',color="green")
axis[3].plot(x1, f2, 'D', label='Label2',color="green")

for i in range(nrows):
    print i
    h, l = axis[i].get_legend_handles_labels()
    for hl in zip(h,l):

        if hl not in LI:
            LI.append(hl)
            lines.append(LI[-1][0])
            labels.append(LI[-1][1])

print LI            
















# only 3 Legend entrys Label1 , Label2 and Label3 are visible .. Differences in cloors and markers are ignored
plt.legend(handles=lines, labels=labels,bbox_to_anchor=(0., nrows+.02, 1., .102), loc=3,ncol=3, prop=legend_properties,mode="expand", borderaxespad=0.,frameon=False,framealpha=0.0) 


plt.show()

我认为问题在于仅比较内存地址的字符串

if hl not in LI:

不是“h”的实际内容？

解决方案基于ImportanceOfBeingErnest在相关帖子Link7中给出的解释:

import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
import math
import matplotlib.collections

def is_inlist(handle, handles):
    for h in handles:
        if isinstance(handle, matplotlib.collections.PolyCollection) and isinstance(h, matplotlib.collections.PolyCollection): 
            if np.all(h.get_facecolor() == handle.get_facecolor()) and \
                np.all(h.get_linestyle() == handle.get_linestyle()) and \
                np.all(h.get_alpha() == handle.get_alpha()):
                return True
        if isinstance(handle, matplotlib.lines.Line2D) and isinstance(h, matplotlib.lines.Line2D):
            if h.get_color() == handle.get_color() and \
                h.get_linestyle() == handle.get_linestyle() and \
                h.get_marker() == handle.get_marker():
                return True        


    return False


lines=[]
labels=[]
legend_properties = {'weight':'bold','size':10}  
# Example data


mu = 0
mu2 = 5
variance = 1
variance2 = 2
sigma = math.sqrt(variance)
sigma2 = math.sqrt(variance2)
x = np.linspace(mu-3*variance,mu+3*variance, 100)
x2 = np.linspace(mu2-3*variance2,mu2+3*variance2, 100)

nrows = 4
# Plot

fig, axis = plt.subplots(nrows, sharex=True, sharey=False, figsize=(5, 8))
fig.subplots_adjust(hspace=0.0001)
#fig.suptitle("Stacked Plots with global Legend wich contains to little elements",fontsize=14,weight='bold')


axis[0].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
axis[0].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='orange',alpha=0.5,label="PEAK2", interpolate=True)
axis[0].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
axis[0].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
axis[0].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)

axis[1].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
axis[1].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='purple',alpha=0.5,label="PEAK2", interpolate=True)
axis[1].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
axis[1].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
axis[1].fill_between(x+6.5,0,mlab.normpdf(x, mu, sigma), color='yellow',alpha=0.5,label="PEAK5", interpolate=True)
axis[1].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)

axis[2].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
axis[2].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='orange',alpha=0.5,label="PEAK2", interpolate=True)
axis[2].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='#73d216',alpha=0.5,label="PEAK3", interpolate=True)
axis[2].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
axis[2].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)


axis[3].fill_between(x+6,0,mlab.normpdf(x, mu, sigma), color='green',alpha=0.5,label="PEAK1", interpolate=True)
axis[3].fill_between(x+4,0,mlab.normpdf(x, mu, sigma), color='purple',alpha=0.5,label="PEAK2", interpolate=True)
axis[3].fill_between(x+3,0,mlab.normpdf(x, mu, sigma), color='blue',alpha=0.5,label="PEAK3", interpolate=True)
axis[3].fill_between(x+7,0,mlab.normpdf(x, mu, sigma), color='red',alpha=0.5,label="PEAK4", interpolate=True)
axis[3].fill_between(x+6.5,0,mlab.normpdf(x, mu, sigma), color='#73d216',alpha=0.5,label="PEAK5", interpolate=True)
axis[3].fill_between(x+5.5,0,mlab.normpdf(x, mu, sigma), color='violet',alpha=0.5,label="PEAK6", interpolate=True)
axis[3].plot(x2,2.5*mlab.normpdf(x2, mu2, sigma2),color='black',linestyle="",label="Exp", marker="o", markersize=4)



for i in range(nrows):
    h, l = axis[i].get_legend_handles_labels()
    for hi, li in zip(h,l):
        if not is_inlist(hi, lines):
            lines.append(hi)
            labels.append(li)






# only 3 Legend entrys Label1 , Label2 and Label3 are visible .. Differences in cloors and markers are ignored
plt.legend(handles=lines, labels=labels,bbox_to_anchor=(0., nrows-1+.02, 1., .102), loc=3,ncol=3, prop=legend_properties,mode="expand", borderaxespad=0.,frameon=False,framealpha=0.0) 


plt.show()

这里我的真实数据得到了更好的反射(reflect)，因为我有 matplotlib.collections.PolyCollection) 和 matplotlib.lines.Line2D 对象需要进行比较。

Answer 1

Edit2 看起来很有前途。然后，您可以检查该标签是否已在标签列表中，如果没有，则将其追加。当然我无法测试以下内容，但它至少应该展示这个概念。

lines=[]
labels=[]
for idata, datafile in enumerate(datafiles):

    MYData = The_predefined_plotting_class.from_file(datafile) 

    axis[idata] = The_predefined_plotting_class.get_plot( *kwargs)
    h, l = axis[idata].get_legend_handles_labels()

    for handle, label in zip(h, l):
        if label not in labels:
            lines.append(handle)
            labels.append(label)

plt.legend(handles=lines, labels=labels, loc="upper left", bbox_to_anchor=[0, 1],ncol=3, shadow=True, title="Legend", fancybox=True)
plt.show()

<小时/>

如果您想避免重复句柄，您可以使用使它们看起来相等的属性，并查看句柄列表中是否已存在类似的艺术家。

def is_inlist(handle, handles):
    for h in handles:
        if h.get_color() == handle.get_color() and \
            h.get_linestyle() == handle.get_linestyle() and \
            h.get_marker() == handle.get_marker():
            return True
    return False

lines=[]
labels=[]
for i in range(nrows):
    h, l = axis[i].get_legend_handles_labels()
    for hi, li in zip(h,l):
        if not is_inlist(hi, lines):
            lines.append(hi)
            labels.append(li)

plt.legend(handles=lines, labels=labels)