在子图直方图上绘制范数曲线

Question

I would like to plot multiple subplots containing histograms. Additionally, I would like to plot a curve showing the normal distribution for each subplot. While I found different answers on this forum on how to plot a normal curve over a single plot (histogram), I am struggling to achieve the same with subplots. I have tried the following:

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()  

Now my problem is that the normal curve is always optimal for the second plot but not the first. This is because of xmin and xmax, I however don't know how to fit these two commands invdividually in subplots. Does anyone have any experience with this? I have been trying all afternoon

Any help is highly appreciated, thanks in advance!

Answer 1

You can use axes instead of a tuple. Then you can set each axis individually using sca . See below if that's what you needed.

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()