如何從散點圖中組合兩個 matplotlib (python) 顏色圖

Question

我正在嘗試將兩個顏色圖圖例合二為一。 顏色值由第三個 (z) 數據定義。 我正在嘗試用兩種配色方案繪制一個圖例顏色圖。

from scipy.optimize import curve_fit
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

df = pd.read_excel('C:\\Users\user1\\PycharmProjects\\untitled\\Python_test.xlsx')
x = df['Vp_dry']
y = df['Vs_dry']
q = df['Vp_wet']
w = df['Vs_wet']
fig, ax = plt.subplots()
popt, pcov = curve_fit(lambda fx, a, b: a * fx ** -b, x, y)
x_linspace = np.linspace(min(x - 100), max(x + 100), 100)
power_y = popt[0]*x_linspace ** -popt[1]

ax1 = plt.scatter(x, y, c=df['Porosity'], cmap=plt.cm.Greys, vmin=2, vmax=df['Porosity'].max(), edgecolors="#B6BBBD")

plt.plot(x_linspace, power_y, color='grey', label='Dry')
popt, pcov = curve_fit(lambda fx, a, b: a * fx ** -b, q, w)
q_linspace = np.linspace(min(q - 100), max(q + 100), 100)
power_w = popt[0]*q_linspace ** -popt[1]
ax2 = plt.scatter(q, w, c=df['Porosity'], cmap=plt.cm.Blues, vmin=2, vmax=df['Porosity'].max(), edgecolors="#3D83C1")
plt.plot(q_linspace, power_w, label='Wet')

cbar = fig.colorbar(ax2)
cbar = fig.colorbar(ax1)
cbar.set_label("Porosity (%)")

plt.xlabel('Vp (m/s)')
plt.ylabel('Vs (m/s)')
plt.grid()
plt.legend()
plt.show()

想要的結果：

Answer 1

您似乎需要一個包含兩個顏色圖的顏色條，其中一個反轉，並將刻度更改為百分比值。

一種方法是手動創建第二個子圖，使用兩個圖像並使其看起來像一個顏色條：

import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np

# first create some dummy data to plot
N = 100
x = np.random.uniform(0, 10, N)
y = np.random.normal(15, 2, N)
q = np.random.uniform(0, 10, N)
w = np.random.normal(10, 2, N)
df_porosity = np.random.uniform(0, 5, N)

fig, (ax, ax2) = plt.subplots(ncols=2, figsize=(6, 4), gridspec_kw={"width_ratios": [1, 0.08]})

plot1 = ax.scatter(x, y, c=df_porosity, cmap=plt.cm.Greys, vmin=2, vmax=df_porosity.max(), edgecolors="#B6BBBD")
plot2 = ax.scatter(q, w, c=df_porosity, cmap=plt.cm.Blues, vmin=2, vmax=df_porosity.max(), edgecolors="#3D83C1")

img_cbar = np.linspace(0, 1, 256).reshape(256, 1)
ax2.imshow(img_cbar, cmap=plt.cm.Blues, extent=[0, 1, 1, 0])  # aspect='auto')
ax2.imshow(img_cbar, cmap=plt.cm.Greys, extent=[0, 1, -1, 0])
ax2.set_ylim(-1, 1)
ax2.set_aspect(10)
ax2.set_ylabel("Porosity (%)")
ax2.yaxis.set_label_position("right")
ax2.set_xticks([])
ax2.yaxis.tick_right()
# optionally show the ticks as percentage, where 1.0 corresponds to 100 %
ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))

plt.tight_layout()
plt.show()