如何在单个散点图中添加颜色注释
how to add the color annotate in a single scatter plot
我需要在单个图中进行颜色注释，如下图（来自两个图的面板）。
所以，我想把下面这张图转过来
对此，根据图上每个点的颜色标注，同时添加图例，如下图
我的完整代码是：
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from matplotlib.pyplot import figure
figure(figsize=(10, 8), dpi=120)
from scipy.stats import t
plt.close('all')
data = 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],
])
custom_annotations = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
plt.scatter(data[:,0], data[:,1], marker='o', c=data[:,1], edgecolors='black', linewidths=1, alpha=0.75)
#plt.colorbar(orientation='horizontal')
plt.xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
plt.ylabel(r'$\Delta \psi$ cAMP-bound wild-type mHCN2 (mV)')
plt.axvline(0, c=(.5, .5, .5), ls= '--')
plt.axhline(0, c=(.5, .5, .5), ls= '--')
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"]
plt.annotate(txt, (data[i,0], data[i,1]))
plt.legend(recs,classes,loc=(1.04,0))
plt.show()
for循环中的某些内容丢失或写错了，希望的情节应该与post中的图像相同，关于点颜色注释和图例，应该是放置在情节之外。
首先，要生成单独的注释，你的问题是没有循环。您需要在 plt.annotate
之前放置 for i, txt in enumerate(classes):
然而，要准确生成您想要的结果，您需要为每个点执行分散命令，并为每个点关联一个标签，如下所示：
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from matplotlib.pyplot import figure
figure(figsize=(10, 8), dpi=120)
from scipy.stats import t
plt.close('all')
data = 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],
])
custom_annotations = ["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"]
for i, point in enumerate(data):
plt.scatter(point[0], point[1], marker='o', label=custom_annotations[i], c=class_colours[i], edgecolors='black', linewidths=1, alpha=0.75)
plt.annotate(custom_annotations[i], (data[i,0], data[i,1]))
plt.xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
plt.ylabel(r'$\Delta \psi$ cAMP-bound wild-type mHCN2 (mV)')
plt.axvline(0, c=(.5, .5, .5), ls= '--')
plt.axhline(0, c=(.5, .5, .5), ls= '--')
plt.legend(ncol=3)
plt.show()
您需要对字体进行一些小的调整，以便一切都适合，也许将长图例分成两列或三列。
不过，对于图例来说，你在图片中显示的内容似乎是多余的。似乎您想指示三组不同的数据。我建议将您的数据分成三种不同的类型（代码中的 classes）并分别执行 scatter 三次并在图例中放入 class 描述。
以下是我的做法：
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from matplotlib.pyplot import figure
plt.close('all')
data = 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],
])
custom_annotations = ["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"]
class_desc = ['description 1', 'description 2', 'description 3']
dataclass = np.array([0,0,0,0,0,0,1,1,2,2,2,2,2,])
colorclass = ['r','g','b']
for datac in range(3):
plt.scatter(data[dataclass==datac][:,0],data[dataclass==datac][:,1], c=colorclass[datac], label=class_desc[datac])
for i, point in enumerate(data):
# plt.scatter(point[0], point[1], marker='o', label=custom_annotations[i], c=class_colours[i], edgecolors='black', linewidths=1, alpha=0.75)
plt.annotate(custom_annotations[i], (data[i,0], data[i,1]))
plt.xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
plt.ylabel(r'$\Delta \psi$ cAMP-bound wild-type mHCN2 (mV)')
plt.axvline(0, c=(.5, .5, .5), ls= '--')
plt.axhline(0, c=(.5, .5, .5), ls= '--')
plt.legend(fontsize=12)
plt.show()
制作这个：
我需要在单个图中进行颜色注释，如下图（来自两个图的面板）。
所以，我想把下面这张图转过来
对此，根据图上每个点的颜色标注，同时添加图例，如下图
我的完整代码是：
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from matplotlib.pyplot import figure
figure(figsize=(10, 8), dpi=120)
from scipy.stats import t
plt.close('all')
data = 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],
])
custom_annotations = ["K464E", "K472E", "R470E", "K464A", "M155E", "K472A", "M155A", "Q539A", "M155R", "D244A", "E247A", "E247R", "D244K"]
plt.scatter(data[:,0], data[:,1], marker='o', c=data[:,1], edgecolors='black', linewidths=1, alpha=0.75)
#plt.colorbar(orientation='horizontal')
plt.xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
plt.ylabel(r'$\Delta \psi$ cAMP-bound wild-type mHCN2 (mV)')
plt.axvline(0, c=(.5, .5, .5), ls= '--')
plt.axhline(0, c=(.5, .5, .5), ls= '--')
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"]
plt.annotate(txt, (data[i,0], data[i,1]))
plt.legend(recs,classes,loc=(1.04,0))
plt.show()
for循环中的某些内容丢失或写错了，希望的情节应该与post中的图像相同，关于点颜色注释和图例，应该是放置在情节之外。
首先，要生成单独的注释，你的问题是没有循环。您需要在 plt.annotate
for i, txt in enumerate(classes):
然而，要准确生成您想要的结果，您需要为每个点执行分散命令，并为每个点关联一个标签，如下所示：
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from matplotlib.pyplot import figure
figure(figsize=(10, 8), dpi=120)
from scipy.stats import t
plt.close('all')
data = 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],
])
custom_annotations = ["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"]
for i, point in enumerate(data):
plt.scatter(point[0], point[1], marker='o', label=custom_annotations[i], c=class_colours[i], edgecolors='black', linewidths=1, alpha=0.75)
plt.annotate(custom_annotations[i], (data[i,0], data[i,1]))
plt.xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
plt.ylabel(r'$\Delta \psi$ cAMP-bound wild-type mHCN2 (mV)')
plt.axvline(0, c=(.5, .5, .5), ls= '--')
plt.axhline(0, c=(.5, .5, .5), ls= '--')
plt.legend(ncol=3)
plt.show()
您需要对字体进行一些小的调整，以便一切都适合，也许将长图例分成两列或三列。
不过，对于图例来说，你在图片中显示的内容似乎是多余的。似乎您想指示三组不同的数据。我建议将您的数据分成三种不同的类型（代码中的 classes）并分别执行 scatter 三次并在图例中放入 class 描述。 以下是我的做法：
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.patches as mpatches
from matplotlib.pyplot import figure
plt.close('all')
data = 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],
])
custom_annotations = ["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"]
class_desc = ['description 1', 'description 2', 'description 3']
dataclass = np.array([0,0,0,0,0,0,1,1,2,2,2,2,2,])
colorclass = ['r','g','b']
for datac in range(3):
plt.scatter(data[dataclass==datac][:,0],data[dataclass==datac][:,1], c=colorclass[datac], label=class_desc[datac])
for i, point in enumerate(data):
# plt.scatter(point[0], point[1], marker='o', label=custom_annotations[i], c=class_colours[i], edgecolors='black', linewidths=1, alpha=0.75)
plt.annotate(custom_annotations[i], (data[i,0], data[i,1]))
plt.xlabel(r'$\Delta V_{0.5}$ Apo wild-type mHCN2 (mV)')
plt.ylabel(r'$\Delta \psi$ cAMP-bound wild-type mHCN2 (mV)')
plt.axvline(0, c=(.5, .5, .5), ls= '--')
plt.axhline(0, c=(.5, .5, .5), ls= '--')
plt.legend(fontsize=12)
plt.show()
制作这个：