Matplotlib 在 3d 中绘制脉冲传播 [英] Matplotlib plot pulse propagation in 3d

问题描述

我想在每一步以这种方式绘制脉冲传播，它绘制脉冲形状.换句话说，对于 y 的每个值，我想要一系列 x-z 图.像这样(没有颜色):

I'd like to plot pulse propagation in such a way at each step, it plots the pulse shape. In other words, I want a serie of x-z plots, for each values of y. Something like this (without color):

我如何使用 matplotlib(或 Mayavi)来做到这一点?这是我到目前为止所做的:

How can I do this using matplotlib (or Mayavi)? Here is what I did so far:

def drawPropagation(beta2, C, z):
    """ beta2 in ps / km
        C is chirp
        z is an array of z positions """
    T = numpy.linspace(-10, 10, 100)
    sx = T.size
    sy = z.size

    T = numpy.tile(T, (sy, 1))
    z = numpy.tile(z, (sx, 1)).T

    U = 1 / numpy.sqrt(1 - 1j*beta2*z * (1 + 1j * C)) * numpy.exp(- 0.5 * (1 + 1j * C) * T * T / (1 - 1j*beta2*z*(1 + 1j*C)))

    fig = pyplot.figure()
    ax = fig.add_subplot(1,1,1, projection='3d')
    surf = ax.plot_wireframe(T, z, abs(U))

推荐答案

更改为:

ax.plot_wireframe(T, z, abs(U), cstride=1000)

并调用:

drawPropagation(1.0, 1.0, numpy.linspace(-2, 2, 10))

将创建以下图表:

如果你需要用白色填充曲线:

If you need the curve been filled with white color:

import numpy
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import pyplot
from matplotlib.collections import PolyCollection

def drawPropagation(beta2, C, z):
    """ beta2 in ps / km
        C is chirp
        z is an array of z positions """
    T = numpy.linspace(-10, 10, 100)
    sx = T.size
    sy = z.size

    T = numpy.tile(T, (sy, 1))
    z = numpy.tile(z, (sx, 1)).T

    U = 1 / numpy.sqrt(1 - 1j*beta2*z * (1 + 1j * C)) * numpy.exp(- 0.5 * (1 + 1j * C) * T * T / (1 - 1j*beta2*z*(1 + 1j*C)))

    fig = pyplot.figure()
    ax = fig.add_subplot(1,1,1, projection='3d')
    U = numpy.abs(U)

    verts = []
    for i in xrange(T.shape[0]):
        verts.append(zip(T[i, :], U[i, :]))

    poly = PolyCollection(verts, facecolors=(1,1,1,1), edgecolors=(0,0,1,1))
    ax.add_collection3d(poly, zs=z[:, 0], zdir='y')
    ax.set_xlim3d(numpy.min(T), numpy.max(T))
    ax.set_ylim3d(numpy.min(z), numpy.max(z))
    ax.set_zlim3d(numpy.min(U), numpy.max(U))

drawPropagation(1.0, 1.0, numpy.linspace(-2, 2, 10))
pyplot.show()

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