[英]Plotting concave shape (lens focus) using mplot3d
我目前正在嘗試使用matplotlib(特別是mplot3d工具箱)來可視化鏡頭的焦點形狀。 我從將橢圓擬合到一組在不同焦距下的顯微鏡圖像中獲得了數據,這些焦距分別是主要的major
半徑和次要的minor
半徑,以及所述橢圓的旋轉角度ang
。 由此,我生成了x
, y
和z
數組,其中包含像這樣的橢圓的坐標。
i = 100
omega = np.linspace(0, 2 * np.pi, i, endpoint=True)
x = [major * np.cos(omega) * np.cos(np.deg2rad(ang + 90)) - minor * np.sin(omega) * np.sin(np.deg2rad(ang + 90)) for major, minor, ang in zip(maj_avg, min_avg, ang_avg)]
y = [major * np.cos(omega) * np.sin(np.deg2rad(ang + 90)) + minor * np.sin(omega) * np.cos(np.deg2rad(ang + 90)) for major, minor, ang in zip(maj_avg, min_avg, ang_avg)]
z = [np.full(i, zi) for zi in zs]
如果現在在3D空間中繪制單個橢圓,則所有操作均按預期進行。
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111, projection='3d')
for x_arr, y_arr, z_arr in zip(x, y, z):
ax.plot(x_arr, y_arr, z_arr)
plt.show()
我要嘗試的是從此數據集中生成一個表面圖,該表面圖顯示了鏡頭的焦距形狀。 到現在plot_surface
,我像這樣嘗試了plot_surface
和meshgrid
/ griddata
:
xi = np.arange(-300, 300, 0.1)
yi = np.arange(-300, 300, 0.1)
xgrid, ygrid = np.meshgrid(xi, yi)
zgrid = griddata(np.ravel(x), np.ravel(y), np.ravel(z), xi, yi, interp='linear')
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111, projection='3d')
surf = ax.plot_surface(xgrid, ygrid, zgrid)
plt.show()
而且plot_trisurf
給出的結果也不盡如人意:
triang = mtri.Triangulation(np.ravel(x), np.ravel(y))
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111, projection='3d')
ax.plot_trisurf(triang, np.ravel(z), cmap=plt.cm.CMRmap)
plt.show()
有人可以建議一種在曲面圖中正確顯示我的數據集的高z區域的方法嗎?
問題是您正在嘗試對網格上的參數曲線進行插值。 由於繪制的形狀是非雙射,非雙射的,因此您會感到一團糟。
可以嘗試直接繪制點,而不是嘗試對這些點進行插值。
X = np.array(x)
Y = np.array(y)
Z = np.array(z)
ax.plot_surface(X,Y,Z, cmap="RdYlBu")
plt.show()
復制的完整示例:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np
maj_avg = 50*(np.linspace(0,1,20)-0.6)**2+50
min_avg = 60*(np.linspace(0,1,20)-0.7)**2+60
ang_avg = np.linspace(0,90,20)
zs = np.arange(0,40,2)
i = 100
omega = np.linspace(0, 2 * np.pi, i, endpoint=True)
x = [major * np.cos(omega) * np.cos(np.deg2rad(ang + 90)) \
- minor * np.sin(omega) * np.sin(np.deg2rad(ang + 90)) \
for major, minor, ang in zip(maj_avg, min_avg, ang_avg)]
y = [major * np.cos(omega) * np.sin(np.deg2rad(ang + 90)) \
+ minor * np.sin(omega) * np.cos(np.deg2rad(ang + 90)) \
for major, minor, ang in zip(maj_avg, min_avg, ang_avg)]
z = [np.full(i, zi) for zi in zs]
fig = plt.figure(figsize=(16, 12))
ax = fig.add_subplot(111, projection='3d')
#for x_arr, y_arr, z_arr in zip(x, y, z):
# ax.plot(x_arr, y_arr, z_arr)
X = np.array(x)
Y = np.array(y)
Z = np.array(z)
ax.plot_surface(X,Y,Z, cmap="RdYlBu")
plt.show()
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