如何在 matplotlib 中調整散點圖的大小並向其中添加 reg 行？

Question

我有一個數組中的數據，想調整大小，即增加生成的圖的高度並繪制回歸線，我嘗試了兩次調整大小，但要么產生了“歸因錯誤”，要么在沒有數據的情況下調整了圖的大小被印在上面。

這是我的代碼：

import matplotlib.pyplot as plt
import numpy as np


plt.close('all')



data = np.array([
    [22.8, 13.2],
    [19.6, 4.9],
    [0.3, -16.5],
    [8.9, 3.9],
    [13.7, 9.5],
    [14.7, -0.4],
    [1.9, 4.8],
    [-1.8, -11.4],
    [-3, -4.6],
    [-5.9, 2.2],
    [-13.4, -6.3],
    [-5.7, -1.7],
    [-6.8, 5],
]) 

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.figure(figsize=(10,10))
    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'$\Delta q$', fontsize=12)
plt.ylabel(r'$\Delta  V_{0.5}$  Apo wild-type mHCN2 (mV)', fontsize=12)

plt.axvline(0, c=(.5, .5, .5), ls= '--')
plt.axhline(0, c=(.5, .5, .5), ls= '--')




# for i, txt in enumerate(custom_annotations):
   # plt.annotate(txt, (data[i,0], data[i,1]))

plt.legend(ncol=3, loc=(1.04,0))
plt.show()

如果您從代碼中刪除plt.figure(figsize=(10,10)) ，它將產生：

我感興趣的尺寸是：

或者

更新：關於回歸線

我試過了

x = np.array[:,0]
y = np.array[:,1]

m, b = np.polyfit(x, y, 1)

plt.plot(x, m*x + b)

它拋出了這個錯誤：

TypeError: 'builtin_function_or_method' object is not subscriptable

Answer 1

您需要添加

#regression line

x = np.array(range(-20,20))
y =  np.array(range(-20,20))
m, b = np.polyfit(x, y, 1)

plt.plot(x, m*x + b)

plt.rcParams["figure.figsize"] = (12, 12) # added code

並在下面更新

fig, ax = plt.subplots()


for i, point in enumerate(data): 
    ax.scatter(point[0], point[1], marker='o', label=custom_annotations[i], c=class_colours[i], edgecolors='black', linewidths=1, alpha=0.75)
    ax.annotate(custom_annotations[i], (data[i,0], data[i,1]),fontsize=30)

新代碼

import matplotlib.pyplot as plt
import numpy as np


plt.close('all')



data = np.array([
    [22.8, 13.2],
    [19.6, 4.9],
    [0.3, -16.5],
    [8.9, 3.9],
    [13.7, 9.5],
    [14.7, -0.4],
    [1.9, 4.8],
    [-1.8, -11.4],
    [-3, -4.6],
    [-5.9, 2.2],
    [-13.4, -6.3],
    [-5.7, -1.7],
    [-6.8, 5],
]) 
plt.rcParams["figure.figsize"] = (12, 12) # added code

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"]

fig, ax = plt.subplots()


for i, point in enumerate(data): 
    ax.scatter(point[0], point[1], marker='o', label=custom_annotations[i], c=class_colours[i], edgecolors='black', linewidths=1, alpha=0.75)
    ax.annotate(custom_annotations[i], (data[i,0], data[i,1]),fontsize=30)



plt.xlabel(r'$\Delta q$', fontsize=12)
plt.ylabel(r'$\Delta  V_{0.5}$  Apo wild-type mHCN2 (mV)', fontsize=12)

plt.axvline(0, c=(.5, .5, .5), ls= '--')
plt.axhline(0, c=(.5, .5, .5), ls= '--')

x = np.array(range(-20,20))
y =  np.array(range(-20,20))
m, b = np.polyfit(x, y, 1)

plt.plot(x, m*x + b)

# for i, txt in enumerate(custom_annotations):
   # plt.annotate(txt, (data[i,0], data[i,1]))

plt.legend(ncol=3, loc=(1.04,0))
plt.show()