[英]How to convert arrays of x,y,z coordinates to 3D path in numpy
給定三個X,Y和Z坐標的1D陣列,如何使用numpy轉換為3D網格路徑?
我設法使用numpy(即沒有for循環)為2D做這個:
import numpy
def path_2d_numpy(x, y):
m1, m2 = numpy.meshgrid(x, y)
m1[1::2] = m1[1::2,::-1]
r = numpy.append(m1, m2)
r.shape = 2,-1
return r.T
from matplotlib import lines
from matplotlib import pyplot
def plot_path_2d(path):
x, y = path.T
pyplot.plot(x, y, '-ro', lw=3)
pyplot.show()
x = numpy.linspace(4, 1, 4)
y = numpy.linspace(1, 5, 5)
path = path_2d_numpy(x, y)
plot_path_2d(path)
哪個輸出:
...但是無法為3D做到這一點。 顯示純python解決方案(即沒有numpy):
import numpy
def path_3d(x, y, z):
nb_points =len(x)*len(y)*len(z)
path = numpy.empty((nb_points, 3))
xord, yord, i = True, True, 0
for zi in z:
for yi in y[::1 if yord else -1]:
for xi in x[::1 if xord else -1]:
path[i] = xi, yi, zi
i += 1
xord = not xord
yord = not yord
return path
from matplotlib import pyplot
from mpl_toolkits.mplot3d import Axes3D
def plot_path_3d(path):
fig = pyplot.figure()
ax = fig.gca(projection='3d')
xx, yy, zz = path.T
ax.plot(xx, yy, zz, '-bo', lw=3)
pyplot.show()
x = numpy.linspace(4, 1, 4)
y = numpy.linspace(1, 5, 5)
z = numpy.linspace(-3, 0, 3)
path = path_3d(x, y, z)
plot_path_3d(path)
哪個輸出:
Essencialy,我所尋找的是一個numpy的實施為我做了path_2d_numpy path_3d的。
我需要這個,因為我正在處理的實際數組非常大。 沒有numpy這樣做太慢了。
這看起來怎么樣?
import numpy as np
def path_3d_numpy(x, y, z):
coords = np.stack(np.meshgrid(x, y, z), axis=-1) # shape = (nx, ny, nz, 3)
coords[1::2,:,:] = coords[1::2,::-1,:]
coords[:,1::2,:] = coords[:,1::2,::-1]
return coords.reshape(-1, 3) # flatten out the other axes
不按照與你的順序相同的順序迭代點,但你可以簡單地通過交換一些索引來解決這個問題
同樣,你的2d案例也可以寫成
def path_2d_numpy(x, y):
coords = np.stack(np.meshgrid(x, y), axis=-1)
coords[1::2] = coords[1::2,::-1]
return coords.reshape(-1, 2)
對於某些真正的矯枉過正,您可以將其擴展為N維:
def path_nd(*args):
coords = np.stack(np.meshgrid(*args), axis=-1)
N = len(args)
axes = np.arange(N)
for i in range(N-1):
# the last axis isn't part of our mesh, so don't roll it
rolled_axes = tuple(np.roll(axes, -i)) + (N,)
rolled_view = np.transpose(coords, rolled_axes)
rolled_view[1::2,:] = rolled_view[1::2,::-1]
return coords.reshape(-1, N)
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