在子图直方图上绘制范数曲线

Question

我想绘制多个包含直方图的子图。此外，我想绘制一条曲线，显示每个子图的正态分布。虽然我在这个论坛上找到了关于如何在单个图（直方图）上绘制正态曲线的不同答案，但我正在努力用子图实现相同的效果。我尝试了以下方法：

from scipy import stats  
import numpy as np  
import matplotlib.pylab as plt

fig, ((ax1, ax2)) = plt.subplots(1,2,figsize=(10,4))

# create some normal random noisy data
data1 = 50*np.random.rand() * np.random.normal(10, 10, 100) + 20
data2=  50*np.random.rand() * np.random.normal(10, 10, 100) + 50

# plot normed histogram
ax1.hist(data1, density=True)

# find minimum and maximum of xticks,
xt = plt.xticks()[0]  
xmin, xmax = min(xt), max(xt)  
lnspc = np.linspace(xmin, xmax, len(data1))

# lets try the normal distribution first
m1, s1 = stats.norm.fit(data1) # get mean and standard deviation  
pdf_1 = stats.norm.pdf(lnspc, m1, s1) # now get theoretical values in our interval  
ax1.plot(lnspc, pdf_1, label="Norm") # plot it

# plot second hist
ax2.hist(data2, density=True)

# find minimum and maximum of xticks
xt = plt.xticks()[0]  
xmin, xmax = min(xt), max(xt)  
lnspc = np.linspace(xmin, xmax, len(data2))

# lets try the normal distribution first
m2, s2 = stats.norm.fit(data2) # get mean and standard deviation  
pdf_2 = stats.norm.pdf(lnspc, m2, s2) # now get theoretical values in our interval  
ax2.plot(lnspc, pdf_2, label="Norm") # plot it
plt.show()  

现在我的问题是，正态曲线对于第二个情节总是最优的，但不是第一个情节。这是因为 xmin 和 xmax，但是我不知道如何将这两个命令单独放入子图中。有人对这个有经验么？我一直在尝试整个下午

任何帮助都非常感谢，在此先感谢！

Answer 1

您可以使用axes 代替元组。然后您可以使用sca 单独设置每个轴。如果这是您需要的，请参见下文。

from scipy import stats  
import numpy as np  
import matplotlib.pylab as plt


# fig, ((ax1, ax2)) = plt.subplots(1,2,figsize=(10,4)) << INSTEAD OF THIS DO:
fig, axes = plt.subplots(nrows = 1, ncols = 2,figsize=(10,4))

# create some normal random noisy data
data1 = 50*np.random.rand() * np.random.normal(10, 10, 100) + 20
data2=  50*np.random.rand() * np.random.normal(10, 10, 100) + 50


plt.sca(axes[0]) #Refer to the first axis
# plot normed histogram
axes[0].hist(data1, density=True)

# find minimum and maximum of xticks,
xt = plt.xticks()[0]  
xmin, xmax = min(xt), max(xt)  
lnspc = np.linspace(xmin, xmax, len(data1))

# lets try the normal distribution first
m1, s1 = stats.norm.fit(data1) # get mean and standard deviation  
pdf_1 = stats.norm.pdf(lnspc, m1, s1) # now get theoretical values in our interval  
axes[0].plot(lnspc, pdf_1, label="Norm") # plot it



plt.sca(axes[1]) #Refer to the second axis
# plot second hist
axes[1].hist(data2, density=True)

# find minimum and maximum of xticks
xt = plt.xticks()[0]  
xmin, xmax = min(xt), max(xt)  
lnspc = np.linspace(xmin, xmax, len(data2))

# lets try the normal distribution first
m2, s2 = stats.norm.fit(data2) # get mean and standard deviation  
pdf_2 = stats.norm.pdf(lnspc, m2, s2) # now get theoretical values in our interval  
axes[1].plot(lnspc, pdf_2, label="Norm") # plot it
plt.show()