我的图的缩放比例使曲线在不同时看起来相同。如何塑造它们以便可以看出差异？

Question

我并排放置了两个地块。但是，我注意到这些地块的形状已被调整为相同的大小，这导致分布曲线看起来相同，而我知道它们并非如此。 Cobalt曲线应该比Rhodium曲线更短更胖。

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

mu = Mean_Sd(rhodium_data, "Mean all Angles")[2]
sigma = Mean_Sd(rhodium_data, "Mean all Angles")[3] 
x = mu + sigma * np.random.randn(437)

num_bins = 50

n, bins, patches = ax.hist(x, num_bins, density=1) # creates histogram

# line of best fit
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins - mu))**2))

#Creating the plot graphic
ax1.plot(bins, y, '-')
ax1.tick_params(top=True, right=True)
ax1.tick_params(direction='in', length=6, width=1, colors='0')
ax1.grid()
ax1.set_xlabel("Mean of the Four Angles")
ax1.set_ylabel("Probability density")
ax1.set_title(r"Rhodium Distribution")

#####-----------------------------------------------------------------------------------####

mu = Mean_Sd(cobalt_data, "Mean all Angles")[2]
sigma = Mean_Sd(cobalt_data, "Mean all Angles")[3] 
x = mu + sigma * np.random.randn(437)

num_bins = 50

n, bins, patches = ax.hist(x, num_bins, density=1) # creates histogram

# line of best fit
y = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins - mu))**2))

#Creating the plot graphic
ax2.plot(bins, y, '-')
ax2.tick_params(top=True, right=True)
ax2.tick_params(direction='in', length=6, width=1, colors='0')
ax2.grid()
ax2.set_xlabel("Mean of the Four Angles")
ax2.set_ylabel("Probability density")
ax2.set_title(r"Cobalt Distribution")

####----------------------------------------------------------------------------------####

fig.tight_layout()
plt.show()

这是我的代码。我在 Jupyter Notebooks 上使用 Python 3。

编辑

'Mean all Angles' 来自 'Cobalt Data' 的平均值是 105.1 度。 'Mean all Angles' 与列 'Cobalt Data' 的标准偏差为 7.866 度。

'Rhodium Data' 的 'Mean all Angles' 的平均值为 90.19 度。 'Mean all Angles' 与列 'Rhodium Data' 的标准偏差为 1.35 度。

mu 是平均值，sigma 是标准差。

铑：mu = 90.19。西格玛 = 1.35 钴：μ＝105.1。西格玛 = 7.866

Answer 1

正如您所指出的，两个分布之间的范围差异很大。您可以尝试设置 ax1.set_xlim、ax1.set_ylim、ax2.set_xlim、ax2.set_ylim，但在我看来，至少有一个子图最终会变得难以辨认。

如果将两个子图合二为一呢？

import matplotlib.pyplot as plt
import numpy as np

fig, ax = plt.subplots(1)

mu = 105.1
sigma = 7.866
x1 = mu + sigma * np.random.randn(437)

num_bins = 50

n, bins1, patches = ax.hist(x1, num_bins, density=1, color="tab:blue", alpha=0.4) # creates histogram

# line of best fit
y1 = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins1 - mu))**2))

#####-----------------------------------------------------------------------------------####

mu = 90.19
sigma = 1.35
x2 = mu + sigma * np.random.randn(437)

num_bins = 50

n, bins2, patches = ax.hist(x2, num_bins, density=1, color="tab:orange", alpha=0.4) # creates histogram

# line of best fit
y2 = ((1 / (np.sqrt(2 * np.pi) * sigma)) *
np.exp(-0.5 * (1 / sigma * (bins2 - mu))**2))

#Creating the plot graphic
ax.plot(bins1, y1, '-', label="Rhodium Distribution", color="tab:blue")
ax.plot(bins2, y2, '-', label="Cobalt Distribution", color="tab:orange")
ax.set_xlabel("Mean of the Four Angles")
ax.grid()
ax.set_ylabel("Probability density")
ax.tick_params(top=True, right=True)
ax.tick_params(direction='in', length=6, width=1, colors='0')
ax.legend()
ax.grid(which='major', axis='x', linewidth=0.75, linestyle='-', color='0.85')
ax.grid(which='minor', axis='x', linewidth=0.25, linestyle='--', color='0.80')
ax.grid(which='major', axis='y', linewidth=0.75, linestyle='-', color='0.85')
ax.grid(which='minor', axis='y', linewidth=0.25, linestyle='--', color='0.80')
ax.minorticks_on()

####----------------------------------------------------------------------------------####

fig.tight_layout()
plt.show()

我的图的缩放比例使曲线在不同时看起来相同。如何塑造它们以便可以看出差异？

The scaling of my plots make the curves appear the same when they are not. How to shape them so that the difference can be seen?

python

gaussian

matplotlib

scale