使用轮廓绘制三个平面中3D绘图的投影
我有data的三列目录，我想制作它们的3D图，加上每个轴的投影作为另外两个轴平面中的投影轮廓。到目前为止，我可以使用matplotlib创建3D图，但仍然没有显示数据属性的任何内容。
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
from numpy import *
data=loadtxt('test.cat')
X=data[:,0]
Y=data[:,1]
Z=data[:,2]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X, Y, Z, c='r', marker='.')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
plt.show()
我如何使用colorbar绘制每个平面中数据的投影？
3 个答案:
答案 0 :(得分：5)
确实，显示难以显示的数据。也许沿着一个轴创建一些切片并创建一定数量的2D图是最好的。然而3D图很花哨。我玩了一些数据，得到了一个3D图，就像你做的一样，还有一个单独的投影图。
- 点的颜色取决于缺少轴
- 添加透明度以提供密度
- 两个图的轴保持相同
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
data = np.loadtxt('test.cat', skiprows=1)
X=data[:,0]
Y=data[:,1]
Z=data[:,2]
plt.figure()
ax1 = plt.subplot(111, projection='3d')
ax1.scatter(X, Y, Z, c='b', marker='.', alpha=0.2)
ax1.set_xlabel('X - axis')
ax1.set_ylabel('Y - axis')
ax1.set_zlabel('Z - axis')
plt.figure()
ax2 = plt.subplot(111, projection='3d')
plt.hot()
cx = np.ones_like(X) * ax1.get_xlim3d()[0]
cy = np.ones_like(X) * ax1.get_ylim3d()[1]
cz = np.ones_like(Z) * ax1.get_zlim3d()[0]
ax2.scatter(X, Y, cz, c=Z, marker='.', lw=0, alpha=0.2)
ax2.scatter(X, cy, Z, c=-Y, marker='.', lw=0, alpha=0.2)
ax2.scatter(cx, Y, Z, c=X, marker='.', lw=0, alpha=0.2)
ax2.set_xlim3d(ax1.get_xlim3d())
ax2.set_ylim3d(ax1.get_ylim3d())
ax2.set_zlim3d(ax1.get_zlim3d())
ax2.set_xlabel('X - axis')
ax2.set_ylabel('Y - axis')
ax2.set_zlabel('Z - axis')
答案 1 :(得分：5)
我希望我不会用[非常丑陋的]大炮射击蚊子，我最近为一个学校项目做了Binning_Array课程，我想它可以帮助你：
import numpy as np
import matplotlib.pyplot as plt
import binning_array as ba
from mpl_toolkits.mplot3d import axes3d
data = np.loadtxt('test.cat')
X = data[:,0]
Y = data[:,1]
Z = data[:,2]
n_points = data.shape[0]
X_min = np.round(np.min(data[:,0])-0.5)
X_max = np.round(np.max(data[:,0])+0.5)
Y_min = np.round(np.min(data[:,1])-0.5)
Y_max = np.round(np.max(data[:,1])+0.5)
Z_min = np.round(np.min(data[:,2])-0.5)
Z_max = np.round(np.max(data[:,2])+0.5)
n_min_bins = 25
step = min([(X_max-X_min)/n_min_bins, (Y_max-Y_min)/n_min_bins, (Z_max-Z_min)/n_min_bins])
# Using three Binners
BinnerXY = ba.Binning_Array([[X_min, X_max, step],
[Y_min, Y_max, step]])
BinnerYZ = ba.Binning_Array([[Y_min, Y_max, step],
[Z_min, Z_max, step]])
BinnerXZ = ba.Binning_Array([[X_min, X_max, step],
[Z_min, Z_max, step]])
for point in data:
BinnerXY.add_value([point[0], point[1]])
BinnerXZ.add_value([point[0], point[2]])
BinnerYZ.add_value([point[1], point[2]])
fig = plt.figure()
ax = [fig.add_subplot(221, projection='3d'),
fig.add_subplot(222),
fig.add_subplot(223),
fig.add_subplot(224)]
# Plot 2D projections on the 3D graph
vmin = np.min([BinnerXZ.bin_min(), BinnerYZ.bin_min(), BinnerXY.bin_min()])
vmax = np.max([BinnerXZ.bin_max(), BinnerYZ.bin_max(), BinnerXY.bin_max()])
levels = np.linspace(vmin,vmax,20)
xs_c = np.arange(*BinnerXZ.limits[0])
zs_c = np.arange(*BinnerXZ.limits[1])
ZS_C, XS_C = np.meshgrid(zs_c,xs_c)
ax[0].contourf(X=XS_C, Y=BinnerXZ.bins, Z=ZS_C,
zdir='y', offset=Y_max,
vmin=vmin, vmax=vmax,
cmap=plt.cm.coolwarm, levels=levels,
alpha=0.5)
xs_c = np.arange(*BinnerXY.limits[0])
ys_c = np.arange(*BinnerXY.limits[1])
YS_C, XS_C = np.meshgrid(ys_c,xs_c)
ax[0].contourf(X=XS_C, Y=YS_C, Z=BinnerXY.bins,
zdir='z', offset=Z_min,
vmin=vmin, vmax=vmax,
cmap=plt.cm.coolwarm, levels=levels,
alpha=0.5)
ys_c = np.arange(*BinnerYZ.limits[0])
zs_c = np.arange(*BinnerYZ.limits[1])
ZS_C, YS_C = np.meshgrid(zs_c, ys_c)
ax[0].contourf(X=BinnerYZ.bins, Y=YS_C, Z=ZS_C,
zdir='x', offset=X_min,
vmin=vmin, vmax=vmax,
cmap=plt.cm.coolwarm, levels=levels,
alpha=0.5)
# Plot scatter of all data
ax[0].scatter(X, Y, Z, c='g', marker='.', alpha=0.2)
ax[0].set_xlabel(r"$x$")
ax[0].set_ylabel(r"$y$")
ax[0].set_zlabel(r"$z$")
max_range = max([X_max-X_min, Y_max-Y_min, Z_max-Z_min]) / 2.
pos = [(X_max+X_min)/2., (Y_max+Y_min)/2., (Z_max+Z_min)/2.]
ax[0].set_xlim(pos[0] - max_range, pos[0] + max_range)
ax[0].set_ylim(pos[1] - max_range, pos[1] + max_range)
ax[0].set_zlim(pos[2] - max_range, pos[2] + max_range)
# Plot 2D histograms
BinnerXZ.plot_2d_slice(fig=fig, ax=ax[1], xlabel=r"$x$", ylabel=r'$z$')
BinnerXY.plot_2d_slice(fig=fig, ax=ax[2], xlabel=r"$x$", ylabel=r'$y$')
BinnerYZ.plot_2d_slice(fig=fig, ax=ax[3], xlabel=r"$y$", ylabel=r'$z$')
plt.show()
您也可以只使用一个Binner，但请注意您将获得飞机相交的工件：
# ...
# Using three Binners
# ...
# Using only one Binner (adds a small error! see comments!)
Binner = ba.Binning_Array([[X_min, X_max, step],
[Y_min, Y_max, step],
[Z_min, Z_max, step]])
for point in data:
Binner.add_value([point[0], point[1], Z_min])
Binner.add_value([point[0], Y_max-step, point[2]])
Binner.add_value([X_min, point[1], point[2]])
fig = plt.figure()
ax = [fig.add_subplot(221, projection='3d'),
fig.add_subplot(222),
fig.add_subplot(223),
fig.add_subplot(224)]
ax[0].scatter(X, Y, Z, c='g', marker='.', alpha=0.2)
Binner.plot_slices(others={0:X_min, 1:Y_max, 2:Z_min}, fig=fig, ax=ax)
plt.show()
binning_array.py是为一个学校项目而制作的，并没有完全抛光，但它足以满足您的需求。
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
class Binning_Array:
def __init__(self, limits=[[-1.,1.,1.],[-1.,1.,1.]]):
"""Create a new binning array.
The amount of given limits determines the dimension of the array,
although only 2 and 3D have been tested.
Each limit must be a list of start, stop and step for the
axis it represents (x, y or z)."""
self.limits = np.array(limits)
self._shape = []
for i in xrange(len(self.limits)):
self._shape.append((self.limits[i][1]-self.limits[i][0]) / \
float(self.limits[i][2]))
self._shape = tuple(self._shape)
self.dimensions = len(self._shape)
self.bins = np.zeros(self._shape)
self.outside = 0.
self._normalized = 1.
def __repr__(self):
"""Representation method. <<REVIEW>>"""
return "Binning Array! Hurray!"
def __getitem__(self, index):
"""Direct acess to read self.bins (use something like self[index])
Direct acess to write would be given by __setitem__
"""
return self.bins.__getitem__(index)
def position2index(self,position,axis=0):
"""Convert a given position to an index in axis.
If it is outside, it returns -1.
"""
if self.limits[axis][0] <= position < self.limits[axis][1]:
return int((position - self.limits[axis][0]) / self.limits[axis][2])
else: return -1
def index2position(self, index, axis=0):
"""Convert a given index to a position in axis.
If it is outisde, it returns -1.
"""
if 0 <= index < self._shape[axis]:
return self.limits[axis][0] + self.limits[axis][2] * index
else:
return -1
def add_value(self, position, value=1., verbose=False):
"""Add a given value to a specified position.
If verbose it returns a list with the axies at which the position
is outside the scope of this Binning_Array.
Not very efficient because of that (verbose was for debugging).
"""
indexs = []
outside = False
if verbose:
outs = []
for i in xrange(self.dimensions):
# using self.dimensions serves as a filter
# if position has non valid shape
index = self.position2index(position[i],i)
if index == -1:
if verbose:
outside = True
outs.append(i)
else:
self.outside += value / self._normalized
return None # nothing, as it is not verbose
else:
indexs.append(index)
if outside: # the only way to get here is if verbose is True...
self.outside += value / self._normalized
return outs # so I can just return outs...
else:
self.bins[tuple(indexs)] += value / self._normalized
if verbose:
return outs
def get_value(self, position, verbose=False):
"""Return the value at the specified position.
If verbose it alse returns a list with the axies at which the position
is outside the scope of this Binning_Array.
"""
indexs = []
outside = False
if verbose:
outs = []
for i in xrange(self.dimensions):
index = self.position2index(position[i],i)
if index == -1:
if verbose:
outside = True
outs.append[i]
else:
return self.outside
else:
indexs.append(index)
if outside: # the only way to get here is if verbose is True
return self.outside, outs # so I can just return outs...
else:
if verbose:
return self.bins[tuple(indexs)], outs
else:
return self.bins[tuple(indexs)]
def normalize(self, total=None):
"""Divide the entire array by the sum of its values (and outside).
Any value added after this will be normalized by the same factor.
"""
if total is None:
total = self.n_counts()
self.bins /= total
self.outside /= total
self.normalize *= total
def n_counts(self):
"""Return the number of counts."""
return np.sum(self.bins) + self.outside
def bin_max(self):
"""Return the value of the largest bin."""
return np.max(self.bins)
def bin_min(self):
"""Return the value of the largest bin."""
return np.min(self.bins)
def plot_2d_slice(self, cuts=[0,1], others={},
fig=None, ax=None, show=True, **kwargs):
"""Plot a 2D slice."""
x = min(cuts)
y = max(cuts)
xs = np.arange(self.limits[x][0],
self.limits[x][1] + self.limits[x][2],
self.limits[x][2])
ys = np.arange(self.limits[y][0],
self.limits[y][1] + self.limits[y][2],
self.limits[y][2])
index = []
title = ''
for i in xrange(self.dimensions):
if i in cuts:
appendix = slice(self._shape[i]+1)
else:
appendix = others.get(i,(self.limits[i][0]+
self.limits[i][1]) / 2.)
title += '%d:%.4e\t' % (i,appendix)
appendix = self.position2index(appendix,i)
index.append(appendix)
index = tuple(index)
if fig is None:
fig, ax = plt.subplots(1,1)
YS,XS = np.meshgrid(ys, xs)
graph = ax.pcolormesh (XS, YS, self.bins[index], cmap=plt.cm.coolwarm)
fig.colorbar(graph, ax=ax)
ax.axis('equal')
ax.set_xlim(self.limits[x][0], self.limits[x][1])
ax.set_ylim(self.limits[y][0], self.limits[y][1])
if 'xticks' in kwargs:
ax.set_xticks(kwargs['xticks'])
if 'yticks' in kwargs.keys():
ax.set_yticks(kwargs['yticks'])
if 'xlabel' in kwargs:
ax.set_xlabel(kwargs['xlabel'])
if 'ylabel' in kwargs:
ax.set_ylabel(kwargs['ylabel'])
if 'xlim' in kwargs:
ax.set_xlim(*kwargs['xlim'])
if 'ylim' in kwargs:
ax.set_ylim(*kwargs['ylim'])
if show:
fig.tight_layout()
fig.show()
def plot_slices(self, others={}, fig=None, ax=None,
show=True, projections=True):
index = []
pos = []
title = ''
for i in xrange(self.dimensions):
temp = others.get(i,(self.limits[i][0]+self.limits[i][1])/2.)
title += '%d:%.4e\t' % (i,temp)
pos.append(temp)
index.append(self.position2index(temp,i))
if self.dimensions == 3:
if fig is None:
fig = plt.figure()
if projections:
ax = [fig.add_subplot(221, projection='3d'),
fig.add_subplot(222),
fig.add_subplot(223),
fig.add_subplot(224)]
else:
ax = fig.add_subplot(111, projection='3d')
if projections:
xs = np.arange(self.limits[0][0],
self.limits[0][1] + self.limits[0][2],
self.limits[0][2])
ys = np.arange(self.limits[1][0],
self.limits[1][1] + self.limits[1][2],
self.limits[1][2])
zs = np.arange(self.limits[2][0],
self.limits[2][1] + self.limits[2][2],
self.limits[2][2])
xs_c = np.arange(*self.limits[0])
ys_c = np.arange(*self.limits[1])
zs_c = np.arange(*self.limits[2])
vmin = np.min(self.bins)
vmax = np.max(self.bins)
levels = np.linspace(vmin,vmax,20)
#graph 0 (3D)
ax[0].set_xlabel(r"$x$")
ax[0].set_ylabel(r"$y$")
ax[0].set_zlabel(r"$z$")
#ax[0].axis('equal') #not supported in 3D:
#http://stackoverflow.com/questions/13685386/\
#matplotlib-equal-unit-length-with-equal-aspect-ratio-z-axis-is-not-equal-to
max_range = max([xs[-1]-xs[0],ys[-1]-ys[0],zs[-1]-zs[0]]) / 2.
# x_mean = (xs[-1] + xs[0])/2.
# y_mean = (ys[-1] + ys[0])/2.
# z_mean = (zs[-1] +zs[0])/2.
ax[0].set_xlim(pos[0] - max_range, pos[0] + max_range)
ax[0].set_ylim(pos[1] - max_range, pos[1] + max_range)
ax[0].set_zlim(pos[2] - max_range, pos[2] + max_range)
# to understand holes in contour plot:
#http://stackoverflow.com/questions/18897950/\
#matplotlib-pyplot-contourf-function-introduces-holes-or-gaps-when-plotting-regul
# graph 1 (2D)
ZS, XS = np.meshgrid(zs,xs)
ZS_C, XS_C = np.meshgrid(zs_c,xs_c)
ax[1].pcolormesh(XS, ZS, self.bins[:,index[1],:],
vmin=vmin, vmax=vmax,
cmap=plt.cm.coolwarm)
ax[0].contourf(X=XS_C, Y=self.bins[:,index[1],:], Z=ZS_C,
zdir='y', offset=pos[1],
vmin=vmin, vmax=vmax,
cmap=plt.cm.coolwarm, levels=levels,
alpha=0.5)
ax[1].set_xlabel(r"$x$")
ax[1].set_ylabel(r"$z$")
ax[1].set_xlim(xs[0],xs[-1])
ax[1].set_ylim(zs[0],zs[-1])
ax[1].axis('equal')
# graph 2 (2D)
YS, XS = np.meshgrid(ys,xs)
YS_C, XS_C = np.meshgrid(ys_c,xs_c)
ax[2].pcolormesh(XS, YS, self.bins[:,:,index[2]],
vmin=vmin, vmax=vmax,
cmap=plt.cm.coolwarm)
ax[0].contourf(X=XS_C, Y=YS_C, Z=self.bins[:,:,index[2]],
zdir='z', offset=pos[2],
vmin=vmin, vmax=vmax,
cmap=plt.cm.coolwarm, levels=levels,
alpha=0.5)
ax[2].set_xlabel(r"$x$")
ax[2].set_ylabel(r"$y$")
ax[2].set_xlim(xs[0],xs[-1])
ax[2].set_ylim(ys[0],ys[-1])
ax[2].axis('equal')
# graph 3 (2D)
ZS, YS = np.meshgrid(zs, ys)
ZS_C, YS_C = np.meshgrid(zs_c, ys_c)
ax[3].pcolormesh(YS, ZS, self.bins[index[0],:,:],
vmin=vmin, vmax=vmax,
cmap=plt.cm.coolwarm)
ax[0].contourf(X=self.bins[index[0],:,:], Y=YS_C, Z=ZS_C,
zdir='x', offset=pos[0],
vmin=vmin, vmax=vmax,
cmap=plt.cm.coolwarm, levels=levels,
alpha=0.5)
ax[3].set_xlabel(r"$y$")
ax[3].set_ylabel(r"$z$")
ax[3].set_xlim(ys[0],ys[-1])
ax[3].set_ylim(zs[0],zs[-1])
ax[3].axis('equal')
else:
# update to draw a given slice, use it to plot eaxh axes above!
ax.plot(self.XS,self.YS,self.ZS)
ax.set_zlabel(r"$z$")
ax.set_xlabel(r"$x$")
ax.set_ylabel(r"$y$")
ax.axis('equal')
else:
if fig is None:
fig, ax = plt.subplots(1)
xs = np.arange(self.limits[0][0],
self.limits[0][1] + self.limits[0][2],
self.limits[0][2])
ys = np.arange(self.limits[1][0],
self.limits[1][1] + self.limits[1][2],
self.limits[1][2],)
YS, XS = np.meshgrid(ys, xs)
graph = ax.pcolormesh(XS, YS, self.bins, cmap=plt.cm.coolwarm)
fig.colorbar(graph)
ax.set_xlim(self.limits[0][0], self.limits[0][1])
ax.set_ylim(self.limits[1][0], self.limits[1][1])
ax.set_title('Energy Distribution')
ax.set_xlabel(r"$x$")
ax.set_ylabel(r"$y$")
ax.axis('equal')
if show:
fig.tight_layout()
fig.show()
return fig, ax
如果代码上的任何内容有误或有问题请说出来，我会编辑上面的内容（学校项目已经评分，所以你不会做我的作业）。
此外，如果有任何不明确的地方请说明，我会根据需要添加评论或解释。
答案 2 :(得分：4)
根据您要执行的操作，您需要使用zdir和contour函数的contourf参数。这是一个例子：
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
fig = plt.figure()
ax = Axes3D(fig)
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X ** 2 + Y ** 2)
Z = np.sin(R)
ax.contourf(X, Y, Z, zdir='x', offset=-4, cmap=plt.cm.hot)
ax.contour(X, Y, Z, zdir='x', offset=-4, colors='k')
ax.contourf(X, Y, Z, zdir='y', offset=4, cmap=plt.cm.hot)
ax.contour(X, Y, Z, zdir='y', offset=4, colors='k')
ax.contourf(X, Y, Z, zdir='z', offset=-1, cmap=plt.cm.hot)
ax.contour(X, Y, Z, zdir='z', offset=-1, colors='k')
plt.show()
结果：
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