Python matplotlib bar3d裁剪问题
我正在尝试使用Matplotlib 1.2.0和Python 2.7.3创建一个三维条形图。我按照中的建议一个接一个地绘制了条形图,但仍然存在渲染问题(即条形图相互重叠) 此外,当我调用代码时,我得到以下信息: /usr/apps/python/lib/python2.7/site packages/mpl_工具包/mplot3d/axes3d.py:1476:RuntimeWarning:divide for n在正常情况下遇到被零除]) /usr/apps/python/lib/python2.7/site packages/mpl_toolkits/mplot3d/axes3d.py:1476:运行时警告:在正常情况下n的除法中遇到无效值]) 我的问题是:Python matplotlib bar3d裁剪问题,python,matplotlib,Python,Matplotlib,我正在尝试使用Matplotlib 1.2.0和Python 2.7.3创建一个三维条形图。我按照中的建议一个接一个地绘制了条形图,但仍然存在渲染问题(即条形图相互重叠) 此外,当我调用代码时,我得到以下信息: /usr/apps/python/lib/python2.7/site packages/mpl_工具包/mplot3d/axes3d.py:1476:RuntimeWarning:divide for n在正常情况下遇到被零除]) /usr/apps/python/lib/python
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.colors as colors
import matplotlib.cm as cmx
# my data
dat = [2.31778665482167e-310, 0.006232785101850947, 0.0285075971030949, 0.0010248181570355695, 0.0048776795767614825, 0.02877090365176044, 0.002459331469834533, 0.0008594610645495889, 0.002919824084878003, 0.000968081117692596, 0.0, 0.0, 0.0319623949119874, 0.00568752311279771, 0.009994801469036968, 0.03248018520506219, 0.006686905726805326, 0.005987863156039365, 0.0072955095915350045, 0.005568911905473998, 0.0, 0.0, 0.0, 0.028483143996551524, 0.031030793902192794, 0.06125216053962635, 0.02935971973938871, 0.028507530280092265, 0.030112963748812088, 0.028293406731749605, 0.0, 0.0, 0.0, 0.0, 0.004510645022825792, 0.028998119822468988, 0.0013993630391143715, 0.0010726572949244424, 0.002288215944285159, 0.0006513973584945584, 0.0, 1.1625e-320, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.03148966953869102, 0.005215047563268979, 0.004491716298086729, 0.006010166308872446, 0.005186976949223524, 0.0, 0.0, 0.0, 0.0, 0.0, 1.107e-320, 0.02983657915729719, 0.028893006725328373, 0.030526067389954753, 0.028629390713739978, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0015217840289869456, 0.002751587509779179, 0.001413669523724954, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0024680339073824705, 0.0008254364860386303, 0.0, 0.0, 0.0, 9.965e-321, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.0, 0.0, 0.002621588539481613, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.41e-321, 1.15348834e-316, 2.3177866547513e-310]
dat = np.reshape(dat,[10,10],order='F')
lx = len(dat[0])
ly = len(dat[:,0])
n = lx*ly
# generate colors
cm = plt.get_cmap('jet')
vv = range(len(dat))
cNorm = colors.Normalize(vmin=0, vmax=vv[-1])
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
colorVals = [scalarMap.to_rgba(i) for i in range(ly)]
# generate plot data
xpos = np.arange(0,lx,1)
ypos = np.arange(0,ly,1)
xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25)
xpos = xpos.flatten()
ypos = ypos.flatten()
zpos = np.zeros(n)
dx = 0.5*np.ones_like(zpos)
dy = dx.copy()
dz = dat.flatten()
cc = np.tile(range(lx), (ly,1))
cc = cc.T.flatten()
# generate plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
opacity = 1
for i in range(n):
ax.bar3d(xpos[i], ypos[i], zpos[i], dx[i], dy[i], dz[i],
color=colorVals[cc[i]], alpha=opacity, zsort='max')
plt.autoscale(enable=True, axis='both', tight=True)
plt.grid()
plt.show(block=False)
这不是您想要的答案,但我认为这可能是matplotlib中的一个bug。我认为同样的问题也存在。根据报告,这个问题被描述为“难以解决” 但对我来说,这似乎并不难解决。您只需找出哪个对象更靠近查看器,并相应地设置z顺序。所以,我认为问题可能只是一个bug 如果我使用matplotlib 3D,只需将“bins=4”更改为“bins=6”或更高的数字,则会得到相同的“axes3d.py:1476:RuntimeWarning:在法线中的除法/for n中遇到无效值”)。此外,我还可以复制错误的酒吧z顺序(看看前面一个高个子跳到他矮个子朋友前面): 条形图的不正确顺序似乎与“除以零”错误有关,因为当我使用较少数量的箱子时,图看起来很好 axis.py中的第1476行是:
shade = np.array([np.dot(n / proj3d.mod(n), [-1, -1, 0.5]) for n in normals])
基本上,我认为它是试图利用每个面的法向量来计算阴影。但是,一个或多个法向量为零,不应该是这样。所以,我认为这只是matplotlib中的一些bug,可能是比我更具有编程技能的人可以修复的
mplot3d常见问题解答是正确的,如果您想要更好的3D引擎,可以使用MayaVI。我曾经
from mayavi import mlab
mlab.barchart(xpos,ypos,dz*100)
要生成数据的绘图,请执行以下操作:
我希望这个问题能尽快解决。我想在不久的将来制作一些类似的三维条形图。此答案是一个快速修复解决方案,允许您在matplotlib中生成具有正确渲染的特定类型的三维条形图。诀窍是A)单独绘制条形图,B)破解zsort算法,强制按与摄像机的“距离”对条形图进行排序。这可以通过覆盖ax.bar3d返回的PolyCollection3D实例的
\u sort\zpos
属性来实现。下面的代码演示了使用从二维高斯分布绘制的数据的解决方案
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from scipy.stats import multivariate_normal
def sph2cart(r, theta, phi):
'''spherical to cartesian transformation.'''
x = r * np.sin(theta) * np.cos(phi)
y = r * np.sin(theta) * np.sin(phi)
z = r * np.cos(theta)
return x, y, z
def sphview(ax):
'''returns the camera position for 3D axes in spherical coordinates'''
r = np.square(np.max([ax.get_xlim(), ax.get_ylim()], 1)).sum()
theta, phi = np.radians((90-ax.elev, ax.azim))
return r, theta, phi
def ravzip(*itr):
'''flatten and zip arrays'''
return zip(*map(np.ravel, itr))
#Generate data
res = 15
sl = slice(-3, 3, complex(res))
Y, X = np.mgrid[sl, sl]
grid = np.array([X, Y])
(dx,), (dy,) = 0.8*np.diff(X[0,:2]), 0.8*np.diff(Y[:2,0])
#2D Gaussian
mu = (0, 0)
covm = np.array([[ 0.8, 0.3],
[ 0.3, 0.5]])
rv = multivariate_normal(mu, covm)
Zg = rv.pdf(grid.transpose(1,2,0)).T
#generate the figure
fig, (ax1, ax2) = plt.subplots(1,2, subplot_kw=dict(projection='3d'))
#standard bar3d
ax1.set_title('Standard')
ax1.bar3d(X.ravel(), Y.ravel(), np.zeros(X.size), dx, dy, Zg.ravel(), '0.85')
#Fixed bar3d
ax2.set_title('Fixed')
xyz = np.array(sph2cart(*sphview(ax2)), ndmin=3).T #camera position in xyz
zo = np.multiply([X, Y, np.zeros_like(Zg)], xyz).sum(0) #"distance" of bars from camera
bars = np.empty(X.shape, dtype=object)
for i, (x,y,dz,o) in enumerate(ravzip(X, Y, Zg, zo)):
j, k = divmod(i, res)
bars[j, k] = pl = ax2.bar3d(x, y, 0, dx, dy, dz, '0.85')
pl._sort_zpos = o
plt.show()
这将生成下图:
注意:这仅适用于初始视角。如果旋转轴,则必须再次设置所有条的_sort_zpos,并重新绘制画布以修复渲染。我将其解压缩,并将其应用于原始问题,以提供直接答案。我的代码当然可以被清理——特别是getdistance()
中的循环——但它确实解决了出现的问题,并且应该更容易理解。要推测,必须通过调用sphview()
和sph2cart()
来确定与观众的距离,即摄像机距离。然后,必须通过调用getdistances()
来计算所有条到摄像机的距离。此后,应一次绘制一个条形图,关键是,必须根据先前确定的距离明确设置每个条形图的z顺序
如果结果图形在绘图窗口中实时旋转,则可能无法正确更新。但是,预设相机的位置允许打印任意初始视图而不会出错。(很可能有一种回调机制,可以调用它来显式地重新计算条形图的z顺序,但我不知道这种API。)通过将azim
和elev
传递到图,可以预先设置摄像头的位置。添加_subplot()
。通过设置图add_subplot()返回的轴实例的dist
字段,可以更改其距离
以下是应用于原始问题的更新代码生成的图表:
这种方法(例如,与使用Mayavi处理三维图形相反)允许matplotlib外观保留在图形本身以及其装饰(如轴编号、标签和图例)中 我认为的答案有一个不同高度渲染的问题,因为它使用了从条顶到相机位置的欧几里德距离,并从最大z_顺序中减去该值,我认为这不是正确的方法。最后,我采用了来自的z_顺序的相同度量,并将其更新如下:
z_order=np.乘法([xpos,ypos,np.zero_-like(xpos)],摄像机)。求和(0)
及
pl.\u sort\u zpos=z\u order[i]
现在它在我的情况下起作用了 这确实是一个令人烦恼的问题。这里讨论的底部提到了一个类似的问题,即除法为零的错误和条的错误顺序(但没有解决!):感谢您的建议。是否有方法手动设置z顺序并传递到bar3d()?我的理解是,如果用户决定旋转图形,z顺序将发生变化。对吗?感谢您推荐mayavi。我会调查的,伙计,这太棒了。我不知道你为什么得不到足够的选票!谢谢!如果被迫对我多年来从中受益的所有答案进行排名,这可能是最好的
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.colors as colors
import matplotlib.cm as cmx
# from apodemus's Stackoverflow answer,
# https://stackoverflow.com/questions/18602660/matplotlib-bar3d-clipping-problems
def sph2cart(r, theta, phi):
'''spherical to Cartesian transformation.'''
x = r * np.sin(theta) * np.cos(phi)
y = r * np.sin(theta) * np.sin(phi)
z = r * np.cos(theta)
return x, y, z
def sphview(ax):
'''returns the camera position for 3D axes in spherical coordinates'''
r = np.square(np.max([ax.get_xlim(), ax.get_ylim()], 1)).sum()
theta, phi = np.radians((90-ax.elev, ax.azim))
return r, theta, phi
#
# end of apodemus's code
def getDistances(view):
distances = []
a = np.array((xpos, ypos, dz))
for i in range(len(xpos)):
distance = (a[0, i] - view[0])**2 + (a[1, i] - view[1])**2 + (a[2, i] - view[2])**2
distances.append(np.sqrt(distance))
return distances
# ================================================================
# my data
dat = [2.31778665482167e-310, 0.006232785101850947, 0.0285075971030949, 0.0010248181570355695, 0.0048776795767614825, 0.02877090365176044, 0.002459331469834533, 0.0008594610645495889, 0.002919824084878003, 0.000968081117692596, 0.0, 0.0, 0.0319623949119874, 0.00568752311279771, 0.009994801469036968, 0.03248018520506219, 0.006686905726805326, 0.005987863156039365, 0.0072955095915350045, 0.005568911905473998, 0.0, 0.0, 0.0, 0.028483143996551524, 0.031030793902192794, 0.06125216053962635, 0.02935971973938871, 0.028507530280092265, 0.030112963748812088, 0.028293406731749605, 0.0, 0.0, 0.0, 0.0, 0.004510645022825792, 0.028998119822468988, 0.0013993630391143715, 0.0010726572949244424, 0.002288215944285159, 0.0006513973584945584, 0.0, 1.1625e-320, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.03148966953869102, 0.005215047563268979, 0.004491716298086729, 0.006010166308872446, 0.005186976949223524, 0.0, 0.0, 0.0, 0.0, 0.0, 1.107e-320, 0.02983657915729719, 0.028893006725328373, 0.030526067389954753, 0.028629390713739978, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0015217840289869456, 0.002751587509779179, 0.001413669523724954, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0024680339073824705, 0.0008254364860386303, 0.0, 0.0, 0.0, 9.965e-321, 1.15348834e-316, 2.3177866547513e-310, 0.0, 0.0, 0.0, 0.002621588539481613, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 9.41e-321, 1.15348834e-316, 2.3177866547513e-310]
dat = np.reshape(dat,[10,10],order='F')
lx = len(dat[0])
ly = len(dat[:,0])
n = lx*ly
# generate colors
cm = plt.get_cmap('jet')
vv = range(len(dat))
cNorm = colors.Normalize(vmin=0, vmax=vv[-1])
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
colorVals = [scalarMap.to_rgba(i) for i in range(ly)]
# generate plot data
xpos = np.arange(0,lx,1)
ypos = np.arange(0,ly,1)
xpos, ypos = np.meshgrid(xpos+0.25, ypos+0.25)
xpos = xpos.flatten()
ypos = ypos.flatten()
zpos = np.zeros(n)
dx = 0.5*np.ones_like(zpos)
dy = dx.copy()
dz = dat.flatten()
cc = np.tile(range(lx), (ly,1))
cc = cc.T.flatten()
# generate plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
opacity = 1
# Get the camera's location in Cartesian coordinates.
x1, y1, z1 = sph2cart(*sphview(ax))
camera = np.array((x1,y1,0))
# Calculate the distance of each bar from the camera.
z_order = getDistances(camera)
max = max(z_order)
for i in range(n):
pl = ax.bar3d(xpos[i], ypos[i], zpos[i], dx[i], dy[i], dz[i],
color=colorVals[cc[i]], alpha=opacity, zsort='max')
# The z-order must be set explicitly.
#
# z-order values are somewhat backwards in magnitude, in that the largest
# value is closest to the camera - unlike, in say, a coordinate system.
# Therefore, subtracting the maximum distance from the calculated distance
# inverts the z-order to the proper form.
pl._sort_zpos = max - z_order[i]
plt.autoscale(enable=True, axis='both', tight=True)
plt.grid()
plt.show()