matplotlib线框剧情/ 3D绘图HOWTO [英] matplotlib wireframe plot / 3d plot howTo
问题描述
我想有一个3D绘图与matplotlib。
I would like to have a 3d plot with matplotlib.
数据如下：我与含Y的每一行的矩阵坐标的三维图。每一行第一个元件是在X坐标的三维图。最后，第二基质中含有较高的每一个点，在X，Y位置。这第二个矩阵因此包含我的Z轴坐标。两个矩阵是与Python数组的数组。我想知道如何转换数据，以便获得：
Data are the following: I have a matrix with each row containing Y coordinates for the 3d plot. Each row first elements are the X coordinates for the 3d plot. Finally, a second matrix contains high for each point, at a X,Y position. This second matrix thus contains my Z coordinates. Both matrices are arrays of arrays with Python. I would like to know how to transform data so as to obtain:
- 对应一个X每1D信号的情节，这（照片在网上提供），如
- 线框剧情为相同的数据，这样的
我写了一个辅助函数的线框工作，
I have written an helper function for a wireframe work,
######## HELPER FOR PLOT 3-D
def plot_3d(name,X,Y,Z):
fig = plt.figure(name)
ax = fig.gca(projection='3d')
X = np.array(X)
Y = np.array(Y)
Z = np.array(Z)
ax.plot_wireframe(X,Y,Z,rstride=10,cstride=10)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
plt.show()
但我不知道如何将数据X，Y，Z，使他们matplotlib功能的配合要求，希望对于x 2D名单，Y，Z。
but I dont know how to transform data X,Y,Z to make them fit requirements for matplotlib function, which want 2D lists for X, Y ,Z.
有关第一张图，我读的帮助，并希望使用2D情节的3D。示例源$ C $ C给出：
For first graph, I read help, and want to use 2d plot in 3d. Example source code gives:
x = np.linspace(0, 1, 100)
y = np.sin(x * 2 * np.pi) / 2 + 0.5
ax.plot(x, y, zs=0, zdir='z', label='zs=0, zdir=z')
其中z是常数坐标。就我而言，x是恒定的坐标。我和适应
where z is the constant coordinate. In my case, x is the constant coordinate. I adapt with
fig = plt.figure('2d profiles')
ax = fig.gca(projection='3d')
for i in range(10):
x = pt ## this is a scalar
y = np.array(y)
z = np.array(z)
ax.plot(xs = x, y, z, xdir='x')
plt.show()
但有警告：非关键字ARG后关键字ARG 。如何解决？
感谢和问候
推荐答案
对于三维向量的意甲的显示，我带着下面的几乎工作的解决方案：
Regarding the display of a serie of vectors in 3D, I came with following 'almost working' solution:
def visualizeSignals(self, imin, imax):
times = self.time[imin:imax]
nrows = (int)((times[(len(times)-1)] - times[0])/self.mod) + 1
fig = plt.figure('2d profiles')
ax = fig.gca(projection='3d')
for i in range(nrows-1):
x = self.mat1[i][0] + self.mod * i
y = np.array(self.mat1T[i])
z = np.array(self.mat2[i])
ax.plot(y, z, zs = x, zdir='z')
plt.show()
对于二维表面或meshgrid情节，我通过使用meshgrid。需要注意的是，你可以通过自己一旦你知道如何meshgrid是建立再现meshgrid。有关meshgrid更多的信息，我指的是这个帖子。
下面是code（因为它指向类成员不能使用它作为这样的，但你可以根据从matplotlib我使用的3D绘图方式建立你的code）
Here is the code (cannot use it as such since it refers to class members, but you can build your code based on 3d plot methods from matplotlib I am using)
def visualize(self, imin, imax, typ_ = "wireframe"):
"""
3d plot signal between imin and imax
. typ_: type of plot, "wireframce", "surface"
"""
times = self.retT[imin:imax]
nrows = (int)((times[(len(times)-1)] - times[0])/self.mod) + 1
self.modulate(imin, imax)
fig = plt.figure('3d view')
ax = fig.gca(projection='3d')
x = []
for i in range(nrows):
x.append(self.matRetT[i][0] + self.mod * i)
y = []
for i in range(len(self.matRetT[0])):
y.append(self.matRetT[0][i])
y = y[:-1]
X,Y = np.meshgrid(x,y)
z = [tuple(self.matGC2D[i]) for i in range(len(self.matGC))] # matGC a matrix
zzip = zip(*z)
for i in range(len(z)):
print len(z[i])
if(typ_ == "wireframe"):
ax.plot_wireframe(X,Y,zzip)
plt.show()
elif(typ_ == "contour"):
cset = ax.contour(X, Y, zzip, zdir='z', offset=0)
plt.show()
elif(typ_ == "surf_contours"):
surf = ax.plot_surface(X, Y, zzip, rstride=1, cstride=1, alpha=0.3)
cset = ax.contour(X, Y, zzip, zdir='z', offset=-40)
cset = ax.contour(X, Y, zzip, zdir='x', offset=-40)
cset = ax.contour(X, Y, zzip, zdir='y', offset=-40)
plt.show()
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