正态分布曲线与使用 matplotlib 的子图中的直方图不太吻合
normal distribution curve doesn't fit well over histogram in subplots using matplotlib
我正在使用“plt.subplots(2, 2, sharex=True, sharey=True)”绘制 2*2 子图。每个子图都有两个 Y 轴，并包含直方图上的正态分布曲线。请注意，我在这里特别设置了“sharex=True，sharey=True”，以使所有子图共享相同的 X 轴和 Y 轴。
在运行我的代码之后，一切都很好，除了第二个、第三个和第四个子图，它们的正态分布曲线不太符合直方图（请看这里的图）
我用谷歌搜索但没能解决这个问题。但是，如果我在我的代码中设置“sharex=True, sharey=False”，那么该图看起来是正确的，但所有子图都使用它们自己的 Y 轴，这不是我想要的。请看这里的图
希望 Whosebug 的专家能解决这个问题。非常感谢！
下面是我的代码：
import matplotlib.pyplot as plt
from scipy.stats import norm
def align_yaxis(ax1, v1, ax2, v2):
#adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1
_, y1 = ax1.transData.transform((0, v1))
_, y2 = ax2.transData.transform((0, v2))
inv = ax2.transData.inverted()
_, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2))
miny, maxy = ax2.get_ylim()
ax2.set_ylim(miny+dy, maxy+dy)
def drawSingle(myax, mydf , title, offset):
num_bins = 200
xs = mydf["gap"]
x = np.linspace(-1,1,1000)
mu =np.mean(x)
sigma =np.std(xs)
n, bins, patche = myax.hist(xs, num_bins, alpha=0.8, facecolor='blue', density=False)
myax.set_ylabel('frequency',color="black",fontsize=12, weight = "bold")
myax.set_xlabel('X', fontsize=12, weight = "bold",horizontalalignment='center')
ax_twin = myax.twinx()
y_normcurve = norm.pdf(bins, mu, sigma)
ax_twin.plot(bins, y_normcurve, 'r--')
align_yaxis(myax,0,ax_twin,0)
peakpoint = norm.pdf(mu,loc=mu,scale=sigma)
plt.vlines(mu, 0, peakpoint, 'y', '--', label='example')
ax_twin.set_ylabel("probablility dense",color="black",fontsize=12, weight = "bold")
def drawSubplots(mydf1,mydf2,mydf3,mydf4, pos1,pos2,pos3,pos4, title, filename):
plt.rcParams['figure.figsize'] = (18,15 )
my_x_ticks = np.arange(-0.8, 0.8,0.1)
rows, cols = 2, 2
fig, ax = plt.subplots(2, 2, sharex=True, sharey=True)
drawSingle(ax[0][0], mydf1, "Subplot1", pos1)
drawSingle(ax[0][1], mydf2, "Subplot2", pos2)
drawSingle(ax[1][0], mydf3, "Subplot3", pos3)
drawSingle(ax[1][1], mydf4, "Subplot4", pos4)
plt.text(-1, -1, title, horizontalalignment='center', fontsize=18)
plt.show()
drawSubplots(df1, df2,df3,df4,3.2,3.1,2.7,2.85,"test9", "test9")
这是一个尝试：
- 将左侧 y 轴设置为“频率”（这在当前 bin 宽度的情况下非常无用）并在 4 个子图中共享
- 将右侧的 y 轴设置为“概率密度”；注意所有高斯的顶部是如何在 y=0.02 附近（双轴只能设置在最后，因为共享的 y 轴可以通过以后的子图更新）
- 使直方图和正态曲线对齐
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy.stats import norm
def drawSingle(myax, mydf, title):
num_bins = 200
xs = mydf["gap"]
x = np.linspace(-1, 1, 1000)
mu = np.mean(x)
sigma = np.std(xs)
n, bins, patches = myax.hist(xs, num_bins, alpha=0.8, facecolor='blue', density=False)
myax.set_ylabel('frequency', color="black", fontsize=12, weight="bold")
myax.set_xlabel('X', fontsize=12, weight="bold", horizontalalignment='center')
normalization_factor = len(xs) * (bins[1] - bins[0])
y_normcurve = norm.pdf(x, mu, sigma) * normalization_factor
myax.plot(x, y_normcurve, 'r--')
myax.vlines(mu, 0, y_normcurve.max(), 'y', '--', color='lime', label='example')
return normalization_factor
def drawSubplots(mydf1, mydf2, mydf3, mydf4, title):
plt.rcParams['figure.figsize'] = (18, 15)
fig, ax = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
dfs = [mydf1, mydf2, mydf3, mydf4]
norm_factors = [drawSingle(ax_i, df, title)
for ax_i, df, title in zip(ax.ravel(), dfs, ["Subplot1", "Subplot2", "Subplot3", "Subplot4"])]
for ax_i, norm_factor in zip(ax.ravel(), norm_factors):
ax_twin = ax_i.twinx()
ymax = ax_i.get_ylim()[1]
ax_twin.set_ylim(0, ymax / norm_factor)
plt.suptitle(title, fontsize=18)
plt.tight_layout()
plt.show()
df1, df2, df3, df4 = [pd.DataFrame({"gap": np.random.normal(0, 0.2, n)}) for n in [6000, 4000, 1800, 1200]]
drawSubplots(df1, df2, df3, df4, "Title")
非常感谢 JohanC，你太棒了。
根据您的代码，我只是在 drawSubplots 函数中添加了几行代码，以便使每个子图的下限和上限之间的 95% 的高斯曲线区域阴影化。以下是我的尝试。 ax_twin.fill_between 在这里似乎不能正常工作。从图中可以看出，阴影区域超出了高斯曲线enter image description here。我想要的只是遮蔽下限和上限之间的高斯曲线下的区域。如果你不介意，请你检查一下我的错误好吗？非常感谢！
import matplotlib.pyplot as plt
import math
from scipy.stats import norm
def align_yaxis(ax1, v1, ax2, v2):
#adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1
_, y1 = ax1.transData.transform((0, v1))
_, y2 = ax2.transData.transform((0, v2))
inv = ax2.transData.inverted()
_, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2))
miny, maxy = ax2.get_ylim()
ax2.set_ylim(miny+dy, maxy+dy)
def drawSingle(myax, mydf , title):
num_bins = 200
xs = mydf["gap"]
x = np.linspace(-1,1,1000)
mu =np.mean(xs)
sigma =np.std(xs)
n, bins, patches = myax.hist(xs, num_bins, alpha=0.8, facecolor='blue', density=False)
myax.set_ylabel('Frequency', color="black", fontsize=12, weight="bold")
myax.set_xlabel(title, fontsize=12, weight="bold", horizontalalignment='center')
normalization_factor = len(xs) * (bins[1] - bins[0])
y_normcurve = norm.pdf(x, mu, sigma) * normalization_factor
myax.plot(x, y_normcurve, 'r--')
myax.vlines(mu, 0, y_normcurve.max(), 'y', '--', color='lime', label='example')
plt.xlim(-0.8,0.8)
my_x_ticks = np.arange(-0.8, 0.8,0.1)
plt.xticks(my_x_ticks)
return normalization_factor, mu, sigma
def drawSubplots(mydf1,mydf2,mydf3,mydf4, title):
plt.rcParams['figure.figsize'] = (18,15 )
norm_factors = []
mus = []
sigmas = []
my_x_ticks = np.arange(-0.8, 0.8,0.1)
rows, cols = 2, 2
fig, ax = plt.subplots(nrows=rows, ncols=cols, sharex=True, sharey=True)
dfs = [mydf1, mydf2, mydf3, mydf4]
#norm_factors = [drawSingle(ax_i, df, title)
#for ax_i, df, title in zip(ax.ravel(), dfs, ["Subplot1", "Subplot2", "Subplot3", "Subplot4"])]
for ax_i, df, title in zip(ax.ravel(), dfs, ["Subplot1", "Subplot2", "Subplot3", "Subplot4"]):
norm_factor, mu, sigma = drawSingle(ax_i, df, title)
norm_factors.append(norm_factor)
mus.append(mu)
sigmas.append(sigma)
for ax_i, norm_factor, mu, sigma in zip(ax.ravel(), norm_factors, mus, sigmas ):
ax_twin = ax_i.twinx()
xmax = ax_i.get_xlim()[1]
ax_twin.set_ylim(0, xmax / norm_factor)
ax_twin.set_ylabel("probablility dense",color="black",fontsize=12, weight = "bold")
CI_95_lower = mu - (1.96*sigma)
CI_95_upper = mu + (1.96*sigma)
px_shaded = np.arange(CI_95_lower,CI_95_upper,0.1)
ax_twin.fill_between(px_shaded,norm.pdf(px_shaded,loc=mu,scale=sigma) * norm_factor,alpha=0.75, color='pink')
area_shaded_95_CI = norm.cdf(x=CI_95_upper, loc=mu, scale=sigma)-norm.cdf(x=CI_95_lower, loc=mu, scale=sigma)
ax_twin.text(-0.06,0.01,str(round(area_shaded_95_CI*100,1))+"%", fontsize=20)
ax_twin.annotate(s=f'lower bound= {CI_95_lower:.3f}',xy=(CI_95_lower,norm.pdf(CI_95_lower,loc=mu,scale=sigma)),xytext=(-0.75,0.01),weight='bold',color='blue',\
arrowprops=dict(arrowstyle='-|>',connectionstyle='arc3',color='green'),\
fontsize=12
)
ax_twin.annotate(s=f'upper bound= {CI_95_upper:.3f}',xy=(CI_95_upper,norm.pdf(CI_95_upper,loc=mu,scale=sigma)),xytext=(0.28,0.01),weight='bold',color='blue',\
arrowprops=dict(arrowstyle='-|>',connectionstyle='arc3',color='green'),\
fontsize=12
)
ax_twin.text(0.05, 0.03, r"$\mu=" + f'{mu:.6f}' + ", \sigma=" + f'{sigma:.6f}' + "$" + ", confidence interval=95%" ,
horizontalalignment='center', fontsize=15)
plt.suptitle(title, fontsize=18)
plt.tight_layout()
plt.show()
df1, df2, df3, df4 = [pd.DataFrame({"gap": np.random.normal(0, 0.2, n)}) for n in [6000, 4000, 1800, 1200]]
drawSubplots(df1, df2, df3, df4, "Title")
我正在使用“plt.subplots(2, 2, sharex=True, sharey=True)”绘制 2*2 子图。每个子图都有两个 Y 轴，并包含直方图上的正态分布曲线。请注意，我在这里特别设置了“sharex=True，sharey=True”，以使所有子图共享相同的 X 轴和 Y 轴。
在运行我的代码之后，一切都很好，除了第二个、第三个和第四个子图，它们的正态分布曲线不太符合直方图（请看这里的图）
我用谷歌搜索但没能解决这个问题。但是，如果我在我的代码中设置“sharex=True, sharey=False”，那么该图看起来是正确的，但所有子图都使用它们自己的 Y 轴，这不是我想要的。请看这里的图
希望 Whosebug 的专家能解决这个问题。非常感谢！
下面是我的代码：
import matplotlib.pyplot as plt
from scipy.stats import norm
def align_yaxis(ax1, v1, ax2, v2):
#adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1
_, y1 = ax1.transData.transform((0, v1))
_, y2 = ax2.transData.transform((0, v2))
inv = ax2.transData.inverted()
_, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2))
miny, maxy = ax2.get_ylim()
ax2.set_ylim(miny+dy, maxy+dy)
def drawSingle(myax, mydf , title, offset):
num_bins = 200
xs = mydf["gap"]
x = np.linspace(-1,1,1000)
mu =np.mean(x)
sigma =np.std(xs)
n, bins, patche = myax.hist(xs, num_bins, alpha=0.8, facecolor='blue', density=False)
myax.set_ylabel('frequency',color="black",fontsize=12, weight = "bold")
myax.set_xlabel('X', fontsize=12, weight = "bold",horizontalalignment='center')
ax_twin = myax.twinx()
y_normcurve = norm.pdf(bins, mu, sigma)
ax_twin.plot(bins, y_normcurve, 'r--')
align_yaxis(myax,0,ax_twin,0)
peakpoint = norm.pdf(mu,loc=mu,scale=sigma)
plt.vlines(mu, 0, peakpoint, 'y', '--', label='example')
ax_twin.set_ylabel("probablility dense",color="black",fontsize=12, weight = "bold")
def drawSubplots(mydf1,mydf2,mydf3,mydf4, pos1,pos2,pos3,pos4, title, filename):
plt.rcParams['figure.figsize'] = (18,15 )
my_x_ticks = np.arange(-0.8, 0.8,0.1)
rows, cols = 2, 2
fig, ax = plt.subplots(2, 2, sharex=True, sharey=True)
drawSingle(ax[0][0], mydf1, "Subplot1", pos1)
drawSingle(ax[0][1], mydf2, "Subplot2", pos2)
drawSingle(ax[1][0], mydf3, "Subplot3", pos3)
drawSingle(ax[1][1], mydf4, "Subplot4", pos4)
plt.text(-1, -1, title, horizontalalignment='center', fontsize=18)
plt.show()
drawSubplots(df1, df2,df3,df4,3.2,3.1,2.7,2.85,"test9", "test9")
这是一个尝试：
- 将左侧 y 轴设置为“频率”（这在当前 bin 宽度的情况下非常无用）并在 4 个子图中共享
- 将右侧的 y 轴设置为“概率密度”；注意所有高斯的顶部是如何在 y=0.02 附近（双轴只能设置在最后，因为共享的 y 轴可以通过以后的子图更新）
- 使直方图和正态曲线对齐
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from scipy.stats import norm
def drawSingle(myax, mydf, title):
num_bins = 200
xs = mydf["gap"]
x = np.linspace(-1, 1, 1000)
mu = np.mean(x)
sigma = np.std(xs)
n, bins, patches = myax.hist(xs, num_bins, alpha=0.8, facecolor='blue', density=False)
myax.set_ylabel('frequency', color="black", fontsize=12, weight="bold")
myax.set_xlabel('X', fontsize=12, weight="bold", horizontalalignment='center')
normalization_factor = len(xs) * (bins[1] - bins[0])
y_normcurve = norm.pdf(x, mu, sigma) * normalization_factor
myax.plot(x, y_normcurve, 'r--')
myax.vlines(mu, 0, y_normcurve.max(), 'y', '--', color='lime', label='example')
return normalization_factor
def drawSubplots(mydf1, mydf2, mydf3, mydf4, title):
plt.rcParams['figure.figsize'] = (18, 15)
fig, ax = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
dfs = [mydf1, mydf2, mydf3, mydf4]
norm_factors = [drawSingle(ax_i, df, title)
for ax_i, df, title in zip(ax.ravel(), dfs, ["Subplot1", "Subplot2", "Subplot3", "Subplot4"])]
for ax_i, norm_factor in zip(ax.ravel(), norm_factors):
ax_twin = ax_i.twinx()
ymax = ax_i.get_ylim()[1]
ax_twin.set_ylim(0, ymax / norm_factor)
plt.suptitle(title, fontsize=18)
plt.tight_layout()
plt.show()
df1, df2, df3, df4 = [pd.DataFrame({"gap": np.random.normal(0, 0.2, n)}) for n in [6000, 4000, 1800, 1200]]
drawSubplots(df1, df2, df3, df4, "Title")
非常感谢 JohanC，你太棒了。
根据您的代码，我只是在 drawSubplots 函数中添加了几行代码，以便使每个子图的下限和上限之间的 95% 的高斯曲线区域阴影化。以下是我的尝试。 ax_twin.fill_between 在这里似乎不能正常工作。从图中可以看出，阴影区域超出了高斯曲线enter image description here。我想要的只是遮蔽下限和上限之间的高斯曲线下的区域。如果你不介意，请你检查一下我的错误好吗？非常感谢！
import matplotlib.pyplot as plt
import math
from scipy.stats import norm
def align_yaxis(ax1, v1, ax2, v2):
#adjust ax2 ylimit so that v2 in ax2 is aligned to v1 in ax1
_, y1 = ax1.transData.transform((0, v1))
_, y2 = ax2.transData.transform((0, v2))
inv = ax2.transData.inverted()
_, dy = inv.transform((0, 0)) - inv.transform((0, y1-y2))
miny, maxy = ax2.get_ylim()
ax2.set_ylim(miny+dy, maxy+dy)
def drawSingle(myax, mydf , title):
num_bins = 200
xs = mydf["gap"]
x = np.linspace(-1,1,1000)
mu =np.mean(xs)
sigma =np.std(xs)
n, bins, patches = myax.hist(xs, num_bins, alpha=0.8, facecolor='blue', density=False)
myax.set_ylabel('Frequency', color="black", fontsize=12, weight="bold")
myax.set_xlabel(title, fontsize=12, weight="bold", horizontalalignment='center')
normalization_factor = len(xs) * (bins[1] - bins[0])
y_normcurve = norm.pdf(x, mu, sigma) * normalization_factor
myax.plot(x, y_normcurve, 'r--')
myax.vlines(mu, 0, y_normcurve.max(), 'y', '--', color='lime', label='example')
plt.xlim(-0.8,0.8)
my_x_ticks = np.arange(-0.8, 0.8,0.1)
plt.xticks(my_x_ticks)
return normalization_factor, mu, sigma
def drawSubplots(mydf1,mydf2,mydf3,mydf4, title):
plt.rcParams['figure.figsize'] = (18,15 )
norm_factors = []
mus = []
sigmas = []
my_x_ticks = np.arange(-0.8, 0.8,0.1)
rows, cols = 2, 2
fig, ax = plt.subplots(nrows=rows, ncols=cols, sharex=True, sharey=True)
dfs = [mydf1, mydf2, mydf3, mydf4]
#norm_factors = [drawSingle(ax_i, df, title)
#for ax_i, df, title in zip(ax.ravel(), dfs, ["Subplot1", "Subplot2", "Subplot3", "Subplot4"])]
for ax_i, df, title in zip(ax.ravel(), dfs, ["Subplot1", "Subplot2", "Subplot3", "Subplot4"]):
norm_factor, mu, sigma = drawSingle(ax_i, df, title)
norm_factors.append(norm_factor)
mus.append(mu)
sigmas.append(sigma)
for ax_i, norm_factor, mu, sigma in zip(ax.ravel(), norm_factors, mus, sigmas ):
ax_twin = ax_i.twinx()
xmax = ax_i.get_xlim()[1]
ax_twin.set_ylim(0, xmax / norm_factor)
ax_twin.set_ylabel("probablility dense",color="black",fontsize=12, weight = "bold")
CI_95_lower = mu - (1.96*sigma)
CI_95_upper = mu + (1.96*sigma)
px_shaded = np.arange(CI_95_lower,CI_95_upper,0.1)
ax_twin.fill_between(px_shaded,norm.pdf(px_shaded,loc=mu,scale=sigma) * norm_factor,alpha=0.75, color='pink')
area_shaded_95_CI = norm.cdf(x=CI_95_upper, loc=mu, scale=sigma)-norm.cdf(x=CI_95_lower, loc=mu, scale=sigma)
ax_twin.text(-0.06,0.01,str(round(area_shaded_95_CI*100,1))+"%", fontsize=20)
ax_twin.annotate(s=f'lower bound= {CI_95_lower:.3f}',xy=(CI_95_lower,norm.pdf(CI_95_lower,loc=mu,scale=sigma)),xytext=(-0.75,0.01),weight='bold',color='blue',\
arrowprops=dict(arrowstyle='-|>',connectionstyle='arc3',color='green'),\
fontsize=12
)
ax_twin.annotate(s=f'upper bound= {CI_95_upper:.3f}',xy=(CI_95_upper,norm.pdf(CI_95_upper,loc=mu,scale=sigma)),xytext=(0.28,0.01),weight='bold',color='blue',\
arrowprops=dict(arrowstyle='-|>',connectionstyle='arc3',color='green'),\
fontsize=12
)
ax_twin.text(0.05, 0.03, r"$\mu=" + f'{mu:.6f}' + ", \sigma=" + f'{sigma:.6f}' + "$" + ", confidence interval=95%" ,
horizontalalignment='center', fontsize=15)
plt.suptitle(title, fontsize=18)
plt.tight_layout()
plt.show()
df1, df2, df3, df4 = [pd.DataFrame({"gap": np.random.normal(0, 0.2, n)}) for n in [6000, 4000, 1800, 1200]]
drawSubplots(df1, df2, df3, df4, "Title")