使用regression来估计双等维轴之间的最佳拟合关系（例如，两个y轴）

→显示 * 对齐的 * 二元数据，相对于共享的相同自变量（x）

例如，下面我根据您提供的样本数据生成了一个粗略的外推扩展-其中y维中的两个单调增加的变量在x维中共享相同的值，但在x维中共享不同的尺度和变化率，因此需要进行估计（即回归分析）以最佳地划分和分配它们的tick值，以确保两个数据的对齐。𝒙

import math
import random

import numpy as np
import pandas as pd

import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt

# Sample data
num_points = 37

linear_values = np.linspace(4200.0, 4500.0, num_points)

# Some noise is introduced to complicate the linear spacing 
noise = np.random.uniform(-2, 2, num_points)
secondary_values = linear_values + noise

# Ensure the secondary y-axis values always have a positive rate of change
for i in range(1, len(secondary_values)):
    if secondary_values[i] <= secondary_values[i - 1]:
        secondary_values[i] = secondary_values[i - 1] + np.abs(noise[i])

data = {
    "DEPTH": np.arange(4398.1, 4398.1 + (num_points * 0.1), 0.1),
    "TVD_SCS_interp": secondary_values,
    "Gamma_ray": random.choices(range(130, 170), k=num_points),
}
df = pd.DataFrame(data)

DEPTH = df.DEPTH
TVD_SCS_interp = df.TVD_SCS_interp
GAMMA_RAY = df.Gamma_ray

# Estimate the relationship between DEPTH and TVD_SCS_interp
# using polynomial regression (least squares fit)
slope, intercept = np.polyfit(DEPTH, TVD_SCS_interp, 1)

### Plotting
fig = plt.figure(figsize=(6, 8), dpi=150)
gs = gridspec.GridSpec(1, 1, width_ratios=[0.4])

ax1 = plt.subplot(gs[0])
ax2 = ax1.twinx()

ax1.plot(
    GAMMA_RAY,
    DEPTH,
    color="b",
    marker="o",
    markersize=6,
    linestyle="-",
    linewidth=3,
    alpha=0.5,
    label="DEPTH",
)
ax1.set_ylabel("DEPTH", color="b")
depth_max = math.ceil(max(DEPTH))
ax1.set_ylim(4398, depth_max)

# Set limits for the secondary y-axis based on the relationship
# & adjust tick locations for the secondary y-axis based on the primary y-axis
ax2.set_ylim(4398 * slope + intercept, depth_max * slope + intercept)
ax2_ticks = [y * slope + intercept for y in ax1.get_yticks()]
ax2.set_yticks(ax2_ticks)

ax2.plot(
    GAMMA_RAY,
    TVD_SCS_interp,
    color="r",
    marker="o",
    markersize=2,
    linestyle="--",
    label="TVD_SCS_interp",
)
ax2.set_ylabel("DEPTH_TRU", color="r")

ax1.set_xlabel("GAMMA_RAY")
ax1.set_title("Two Y-Axes Plot")

ax1.legend(loc="upper left")
ax2.legend(loc="upper right")

plt.grid(True, which='major', color='k', linestyle='--', linewidth=0.6, alpha=0.4)
plt.grid(True, which='minor', color='k', linestyle='--', linewidth=0.5, alpha=0.2)
plt.minorticks_on()

plt.tight_layout()
plt.show()

结果：