如何单独颜色注释和共享 y 轴？
how to individually color annotate and share y axis?
我想分享两个子图的 y-axis（标题在两个图之间 y-axis。
我写了这个：
ax1,ax2 = axs
ax1.scatter(data1[:,0], data1[:,1])
ax2.scatter(data2[:,0], data2[:,1])
ax1.set_xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
ax2.set_xlabel(r'$\Delta q$')
axs.get_shared_y_axes(r'$\Delta \psi_mem$ cAMP-bound wild-type mHCN2 (mV)')
抛出了这个错误：
AttributeError: 'numpy.ndarray' object 没有属性 'get_shared_y_axes'
我还需要根据我指定的颜色 class 为图上的每个点着色，不知何故，对这些点的迭代不起作用，知道吗？
我的完整代码：
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from scipy.stats import t
data1 = np.array([
[22.8, 22.8],
[19.6, 0.3],
[0.3, 3.1],
[8.9, -1.7],
[13.7, 4.8],
[14.7, -0.7],
[1.9, -2.6],
[-1.8, -0.03],
[-3, -5.7],
[-5.9, -1.5],
[-13.4, -3.9],
[-5.7, -21.5],
[-6.8, -7.7],
])
data2 = np.array([
[-2, 22.8],
[-2, 0.3],
[-2, 3.1],
[-1, -1.7],
[-1, 4.8],
[-1, -0.7],
[ 0, -2.6],
[ 0, -0.03],
[ 1, -5.7],
[ 1, -1.5],
[ 1, -3.9],
[ 2, -21.5],
[ 2, -7.7],
])
custom_annotations = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
fig, axs = plt.subplots(1,2, figsize=(17,9))
ax1,ax2 = axs
ax1.scatter(data1[:,0], data1[:,1])
ax2.scatter(data2[:,0], data2[:,1])
ax1.set_xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
ax2.set_xlabel(r'$\Delta q$')
axs.get_shared_y_axes(r'$\Delta \psi_mem$ cAMP-bound wild-type mHCN2 (mV)')
for ax in axs:
ax.axvline(0, c=(.5, .5, .5), ls= '--')
ax.axhline(0, c=(.5, .5, .5), ls= '--')
for i, txt in enumerate(custom_annotations):
ax1.annotate(txt, (data1[i,0], data1[i,1]))
ax2.annotate(txt, (data2[i,0], data2[i,1]))
# Defining custom 'xlim' and 'ylim' values.
custom_xlim = (-3, 3)
custom_ylim = (-25, 25)
# Setting the values for all axes.
plt.setp(axs, xlim=custom_xlim, ylim=custom_ylim)
classes = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
class_colours = ["r", "r", "r", "r", "r", "r", "g", "g", "b", "b", "b", "b", "b"]
recs = []
for i in range(0,len(class_colours)):
recs.append(mpatches.Rectangle((0,0),1,1,fc=class_colours[i]))
plt.legend(recs,classes,loc=1)
plt.show()
编辑：在下面你会看到我已经用我希望包含的东西生成的情节。共享 y-axis 应该是黄色箭头所在的位置。关于“单独的颜色注释，我需要根据用红色箭头突出显示的 colour_class 为图上的每个点着色。
我不太确定我是否完全理解关于颜色的问题，但是您可以设置 c= 关键字参数以将单独的颜色应用于散点（参见下面的代码）。
关于共享 y-axis，可能有几个解决方案：
第一种解决方案：可以在创建figure/axes时使用关键字参数sharey=True:
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from scipy.stats import t
data1 = np.array([
[22.8, 22.8],
[19.6, 0.3],
[0.3, 3.1],
[8.9, -1.7],
[13.7, 4.8],
[14.7, -0.7],
[1.9, -2.6],
[-1.8, -0.03],
[-3, -5.7],
[-5.9, -1.5],
[-13.4, -3.9],
[-5.7, -21.5],
[-6.8, -7.7],
])
data2 = np.array([
[-2, 22.8],
[-2, 0.3],
[-2, 3.1],
[-1, -1.7],
[-1, 4.8],
[-1, -0.7],
[ 0, -2.6],
[ 0, -0.03],
[ 1, -5.7],
[ 1, -1.5],
[ 1, -3.9],
[ 2, -21.5],
[ 2, -7.7],
])
custom_annotations = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
classes = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
class_colours = ["r", "r", "r", "r", "r", "r", "g", "g", "b", "b", "b", "b", "b"]
fig, axs = plt.subplots(1,2, figsize=(17,9), sharey=True)
ax1,ax2 = axs
ax1.scatter(data1[:,0], data1[:,1])
ax2.scatter(data2[:,0], data2[:,1], c=class_colours)
ax1.set_xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
ax2.set_xlabel(r'$\Delta q$')
ax1.set_ylabel(r'$\Delta \psi_mem$ cAMP-bound wild-type mHCN2 (mV)')
for ax in axs:
ax.axvline(0, c=(.5, .5, .5), ls= '--')
ax.axhline(0, c=(.5, .5, .5), ls= '--')
for i, txt in enumerate(custom_annotations):
ax1.annotate(txt, (data1[i,0], data1[i,1]))
ax2.annotate(txt, (data2[i,0], data2[i,1]))
# Defining custom 'xlim' and 'ylim' values.
custom_xlim = (-3, 3)
custom_ylim = (-25, 25)
# Setting the values for all axes.
plt.setp(axs, xlim=custom_xlim, ylim=custom_ylim)
recs = []
for i in range(0,len(class_colours)):
recs.append(mpatches.Rectangle((0,0),1,1,fc=class_colours[i]))
plt.legend(recs,classes,loc=1)
plt.show()
第二种解决方案：不使用sharey=True，你只需将第一个子图的y-axis向右移动：
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from scipy.stats import t
data1 = np.array([
[22.8, 22.8],
[19.6, 0.3],
[0.3, 3.1],
[8.9, -1.7],
[13.7, 4.8],
[14.7, -0.7],
[1.9, -2.6],
[-1.8, -0.03],
[-3, -5.7],
[-5.9, -1.5],
[-13.4, -3.9],
[-5.7, -21.5],
[-6.8, -7.7],
])
data2 = np.array([
[-2, 22.8],
[-2, 0.3],
[-2, 3.1],
[-1, -1.7],
[-1, 4.8],
[-1, -0.7],
[ 0, -2.6],
[ 0, -0.03],
[ 1, -5.7],
[ 1, -1.5],
[ 1, -3.9],
[ 2, -21.5],
[ 2, -7.7],
])
custom_annotations = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
classes = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
class_colours = ["r", "r", "r", "r", "r", "r", "g", "g", "b", "b", "b", "b", "b"]
fig, axs = plt.subplots(1,2, figsize=(17,9))
ax1,ax2 = axs
ax1.scatter(data1[:,0], data1[:,1])
ax2.scatter(data2[:,0], data2[:,1], c=class_colours)
ax1.set_xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
ax1.yaxis.tick_right()
ax2.set_xlabel(r'$\Delta q$')
ax2.set_ylabel(r'$\Delta \psi_mem$ cAMP-bound wild-type mHCN2 (mV)')
for ax in axs:
ax.axvline(0, c=(.5, .5, .5), ls= '--')
ax.axhline(0, c=(.5, .5, .5), ls= '--')
for i, txt in enumerate(custom_annotations):
ax1.annotate(txt, (data1[i,0], data1[i,1]))
ax2.annotate(txt, (data2[i,0], data2[i,1]))
# Defining custom 'xlim' and 'ylim' values.
custom_xlim = (-3, 3)
custom_ylim = (-25, 25)
# Setting the values for all axes.
plt.setp(axs, xlim=custom_xlim, ylim=custom_ylim)
recs = []
for i in range(0,len(class_colours)):
recs.append(mpatches.Rectangle((0,0),1,1,fc=class_colours[i]))
plt.legend(recs,classes,loc=1)
plt.show()
我想分享两个子图的 y-axis（标题在两个图之间 y-axis。
我写了这个：
ax1,ax2 = axs
ax1.scatter(data1[:,0], data1[:,1])
ax2.scatter(data2[:,0], data2[:,1])
ax1.set_xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
ax2.set_xlabel(r'$\Delta q$')
axs.get_shared_y_axes(r'$\Delta \psi_mem$ cAMP-bound wild-type mHCN2 (mV)')
抛出了这个错误：
AttributeError: 'numpy.ndarray' object 没有属性 'get_shared_y_axes'
我还需要根据我指定的颜色 class 为图上的每个点着色，不知何故，对这些点的迭代不起作用，知道吗？
我的完整代码：
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from scipy.stats import t
data1 = np.array([
[22.8, 22.8],
[19.6, 0.3],
[0.3, 3.1],
[8.9, -1.7],
[13.7, 4.8],
[14.7, -0.7],
[1.9, -2.6],
[-1.8, -0.03],
[-3, -5.7],
[-5.9, -1.5],
[-13.4, -3.9],
[-5.7, -21.5],
[-6.8, -7.7],
])
data2 = np.array([
[-2, 22.8],
[-2, 0.3],
[-2, 3.1],
[-1, -1.7],
[-1, 4.8],
[-1, -0.7],
[ 0, -2.6],
[ 0, -0.03],
[ 1, -5.7],
[ 1, -1.5],
[ 1, -3.9],
[ 2, -21.5],
[ 2, -7.7],
])
custom_annotations = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
fig, axs = plt.subplots(1,2, figsize=(17,9))
ax1,ax2 = axs
ax1.scatter(data1[:,0], data1[:,1])
ax2.scatter(data2[:,0], data2[:,1])
ax1.set_xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
ax2.set_xlabel(r'$\Delta q$')
axs.get_shared_y_axes(r'$\Delta \psi_mem$ cAMP-bound wild-type mHCN2 (mV)')
for ax in axs:
ax.axvline(0, c=(.5, .5, .5), ls= '--')
ax.axhline(0, c=(.5, .5, .5), ls= '--')
for i, txt in enumerate(custom_annotations):
ax1.annotate(txt, (data1[i,0], data1[i,1]))
ax2.annotate(txt, (data2[i,0], data2[i,1]))
# Defining custom 'xlim' and 'ylim' values.
custom_xlim = (-3, 3)
custom_ylim = (-25, 25)
# Setting the values for all axes.
plt.setp(axs, xlim=custom_xlim, ylim=custom_ylim)
classes = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
class_colours = ["r", "r", "r", "r", "r", "r", "g", "g", "b", "b", "b", "b", "b"]
recs = []
for i in range(0,len(class_colours)):
recs.append(mpatches.Rectangle((0,0),1,1,fc=class_colours[i]))
plt.legend(recs,classes,loc=1)
plt.show()
编辑：在下面你会看到我已经用我希望包含的东西生成的情节。共享 y-axis 应该是黄色箭头所在的位置。关于“单独的颜色注释，我需要根据用红色箭头突出显示的 colour_class 为图上的每个点着色。
我不太确定我是否完全理解关于颜色的问题，但是您可以设置 c= 关键字参数以将单独的颜色应用于散点（参见下面的代码）。
关于共享 y-axis，可能有几个解决方案：
第一种解决方案：可以在创建figure/axes时使用关键字参数sharey=True:
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from scipy.stats import t
data1 = np.array([
[22.8, 22.8],
[19.6, 0.3],
[0.3, 3.1],
[8.9, -1.7],
[13.7, 4.8],
[14.7, -0.7],
[1.9, -2.6],
[-1.8, -0.03],
[-3, -5.7],
[-5.9, -1.5],
[-13.4, -3.9],
[-5.7, -21.5],
[-6.8, -7.7],
])
data2 = np.array([
[-2, 22.8],
[-2, 0.3],
[-2, 3.1],
[-1, -1.7],
[-1, 4.8],
[-1, -0.7],
[ 0, -2.6],
[ 0, -0.03],
[ 1, -5.7],
[ 1, -1.5],
[ 1, -3.9],
[ 2, -21.5],
[ 2, -7.7],
])
custom_annotations = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
classes = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
class_colours = ["r", "r", "r", "r", "r", "r", "g", "g", "b", "b", "b", "b", "b"]
fig, axs = plt.subplots(1,2, figsize=(17,9), sharey=True)
ax1,ax2 = axs
ax1.scatter(data1[:,0], data1[:,1])
ax2.scatter(data2[:,0], data2[:,1], c=class_colours)
ax1.set_xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
ax2.set_xlabel(r'$\Delta q$')
ax1.set_ylabel(r'$\Delta \psi_mem$ cAMP-bound wild-type mHCN2 (mV)')
for ax in axs:
ax.axvline(0, c=(.5, .5, .5), ls= '--')
ax.axhline(0, c=(.5, .5, .5), ls= '--')
for i, txt in enumerate(custom_annotations):
ax1.annotate(txt, (data1[i,0], data1[i,1]))
ax2.annotate(txt, (data2[i,0], data2[i,1]))
# Defining custom 'xlim' and 'ylim' values.
custom_xlim = (-3, 3)
custom_ylim = (-25, 25)
# Setting the values for all axes.
plt.setp(axs, xlim=custom_xlim, ylim=custom_ylim)
recs = []
for i in range(0,len(class_colours)):
recs.append(mpatches.Rectangle((0,0),1,1,fc=class_colours[i]))
plt.legend(recs,classes,loc=1)
plt.show()
第二种解决方案：不使用sharey=True，你只需将第一个子图的y-axis向右移动：
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from scipy.stats import t
data1 = np.array([
[22.8, 22.8],
[19.6, 0.3],
[0.3, 3.1],
[8.9, -1.7],
[13.7, 4.8],
[14.7, -0.7],
[1.9, -2.6],
[-1.8, -0.03],
[-3, -5.7],
[-5.9, -1.5],
[-13.4, -3.9],
[-5.7, -21.5],
[-6.8, -7.7],
])
data2 = np.array([
[-2, 22.8],
[-2, 0.3],
[-2, 3.1],
[-1, -1.7],
[-1, 4.8],
[-1, -0.7],
[ 0, -2.6],
[ 0, -0.03],
[ 1, -5.7],
[ 1, -1.5],
[ 1, -3.9],
[ 2, -21.5],
[ 2, -7.7],
])
custom_annotations = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
classes = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
class_colours = ["r", "r", "r", "r", "r", "r", "g", "g", "b", "b", "b", "b", "b"]
fig, axs = plt.subplots(1,2, figsize=(17,9))
ax1,ax2 = axs
ax1.scatter(data1[:,0], data1[:,1])
ax2.scatter(data2[:,0], data2[:,1], c=class_colours)
ax1.set_xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
ax1.yaxis.tick_right()
ax2.set_xlabel(r'$\Delta q$')
ax2.set_ylabel(r'$\Delta \psi_mem$ cAMP-bound wild-type mHCN2 (mV)')
for ax in axs:
ax.axvline(0, c=(.5, .5, .5), ls= '--')
ax.axhline(0, c=(.5, .5, .5), ls= '--')
for i, txt in enumerate(custom_annotations):
ax1.annotate(txt, (data1[i,0], data1[i,1]))
ax2.annotate(txt, (data2[i,0], data2[i,1]))
# Defining custom 'xlim' and 'ylim' values.
custom_xlim = (-3, 3)
custom_ylim = (-25, 25)
# Setting the values for all axes.
plt.setp(axs, xlim=custom_xlim, ylim=custom_ylim)
recs = []
for i in range(0,len(class_colours)):
recs.append(mpatches.Rectangle((0,0),1,1,fc=class_colours[i]))
plt.legend(recs,classes,loc=1)
plt.show()