文章目錄

• 寫在篇前
• 三維繪圖函數
• • • LinePlot
    • ScatterPlot
    • WireframePlot
    • SurfacePlot
    • ContourPlot
    • FilledContourPlot
    • PolygonPlot
    • BarPlot
    • Text
• 寫在篇后

寫在篇前

??matplotlib也支持三維作圖，但是相對于matlab來講，感覺功能更弱。當然話說回來，三維作圖用的場景相對也更少，所以呢，有一定的知識儲備就夠了。matplotlib繪制三維圖形依賴于mpl_toolkits.mplot3d，用法也比較簡單，只需要一個關鍵字參數projection='3d'就可以創建三維Axes。

import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3Dfig = plt.figure() ax = fig.add_subplot(111, projection='3d')

?你可能會看到有的教程寫的是ax = Axes3D(fig)，這是version1.0.0之前的寫法

三維繪圖函數

LinePlot

• Axes3D.``plot(xs, ys, *args, zdir=‘z’, **kwargs)

  ??其他參數向下傳遞給plot函數

ArgumentDescription

xs, ys	x、y 坐標
zs	z 坐標，可以是一個標量或一個x*y維矩陣
zdir	當繪制二維圖像時的z軸方向

from mpl_toolkits.mplot3d import Axes3Dimport numpy as np import matplotlib.pyplot as pltplt.rcParams['legend.fontsize'] = 10 fig = plt.figure() ax = fig.gca(projection='3d') # get current axes# Prepare arrays x, y, z theta = np.linspace(-4 * np.pi, 4 * np.pi, 100) z = np.linspace(-2, 2, 100) r = z**2 + 1 x = r * np.sin(theta) y = r * np.cos(theta)ax.plot(x, y, z, label='parametric curve') ax.legend() # legend content dertermined by label aboveplt.show()

ScatterPlot

• Axes3D.``scatter(xs, ys, zs=0, zdir=‘z’, s=20, c=None, depthshade=True, *args, **kwargs)

  返回Patch3DCollection,

  ??其他參數向下傳遞給plot函數

  ArgumentDescription

  xs, ys	x,y坐標點
  zs	z 坐標，可以是一個標量或一個x*y維矩陣，默認是0.
  zdir	當繪制二維圖像時的z軸方向
  s	size,即散點大小
  c	顏色映射，其取值可以是非常多類型，有時間專門寫一篇講解
  depthshade	是否渲染景深（或則就說陰影吧），默認是True.

from mpl_toolkits.mplot3d import Axes3Dimport matplotlib.pyplot as plt import numpy as np# Fixing random state for reproducibility np.random.seed(19680801)def randrange(n, vmin, vmax):'''Helper function to make an array of random numbers having shape (n, )with each number distributed Uniform(vmin, vmax).'''return (vmax - vmin)*np.random.rand(n) + vminfig = plt.figure() ax = fig.add_subplot(111, projection='3d')n = 100# For each set of style and range settings, plot n random points in the box # defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh]. for c, m, zlow, zhigh in [('r', 'o', -50, -25), ('b', '^', -30, -5)]:xs = randrange(n, 23, 32)ys = randrange(n, 0, 100)zs = randrange(n, zlow, zhigh)ax.scatter(xs, ys, zs, c=c, marker=m)ax.set_xlabel('X Label') ax.set_ylabel('Y Label') ax.set_zlabel('Z Label')plt.show()

WireframePlot

• Axes3D.``plot_wireframe(X, Y, Z, *args, **kwargs)

  ArgumentDescription

  X, Y,Z	坐標點
  rcount,ccount	采樣數，越大采樣越多，默認50
  rstride,cstride	采樣步長，越小采樣越多
  **kwargs	其他參數向下傳入Line3DCollection

  from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as pltfig = plt.figure() ax = fig.add_subplot(111, projection='3d')# Grab some test data. X, Y, Z = axes3d.get_test_data(0.05)# Plot a basic wireframe. ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)plt.show()

SurfacePlot

• Axes3D.``plot_surface(X, Y, Z, *args, norm=None, vmin=None, vmax=None, lightsource=None, **kwargs)

  ??這個函數算是比較常用的函數，用于繪制三維表面圖，讓人驚艷的是它的著色效果。

  ArgumentDescription

  X, Y，Z	坐標點
  rcount,ccount,rstride,cstride	同上
  color	定義surface patch的顏色,type:color-like
  cmap	定義surface patch的顏色，只不過是colorMap,type:colormap
  facecolors	指定單個patch的顏色, type:array-like of colors
  norm	colormap的normalization， type:Normalize
  shade	陰影效果，type:boolean
  vmin, vmax	normalization的邊界
  **kwargs	向下傳遞到Poly3DCollection
  antialiased	抗鋸齒，type:boolean

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cmfrom matplotlib.ticker import LinearLocator, FormatStrFormatter import numpy as npfig = plt.figure() ax = fig.gca(projection='3d')# Make data. X = np.arange(-5, 5, 0.25) Y = np.arange(-5, 5, 0.25) X, Y = np.meshgrid(X, Y) R = np.sqrt(X**2 + Y**2) Z = np.sin(R)# Plot the surface. surf = ax.plot_surface(X, Y, Z, cmap=cm.coolwarm,linewidth=0, antialiased=False)# Customize the z axis. ax.set_zlim(-1.01, 1.01) ax.zaxis.set_major_locator(LinearLocator(10)) ax.zaxis.set_major_formatter(FormatStrFormatter('%.02f'))# Add a color bar which maps values to colors. fig.colorbar(surf, shrink=0.5, aspect=5)plt.show()

ContourPlot

• Axes3D.``contour(X, Y, Z, *args, extend3d=False, stride=5, zdir=‘z’, offset=None, **kwargs)

  ArgumentDescription

  X, Y,Z	Data values as numpy.arrays
  extend3d	是否延申到3d空間 (default: False)
  *stride	（extend3d的）采樣步長
  zdir	同上
  offset	繪制輪廓線在zdir垂直的水平面上的投影

  ??其他位置、關鍵字參數向下傳遞到二維contour()函數

from mpl_toolkits.mplot3d import axes3d import matplotlib.pyplot as plt from matplotlib import cmfig = plt.figure() ax = fig.gca(projection='3d') X, Y, Z = axes3d.get_test_data(0.05)# Plot contour curves cset = ax.contour(X, Y, Z, cmap=cm.coolwarm)ax.clabel(cset, fontsize=9, inline=1) # function to label a contourplt.show()

FilledContourPlot

• Axes3D.``contourf(X, Y, Z, *args, zdir=‘z’, offset=None, **kwargs)

ArgumentDescription

X, Y,Z	Data values as numpy.arrays
zdir	同上
offset	繪制輪廓線在zdir垂直的水平面上的投影

??其他位置、關鍵字參數向下傳遞到二維contourf(),例子請參考上面的contour

PolygonPlot

• Axes3D.``add_collection3d(col, zs=0, zdir=‘z’)

  ???這個函數挺有趣，但是我沒有遇到過這種場景。它可以將三維 collection對象或二維collection對象加入到一個圖形中，包括：

  • PolyCollection

  • LineCollection

  • PatchCollection

  from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused importfrom matplotlib.collections import PolyCollection import matplotlib.pyplot as plt from matplotlib import colors as mcolors import numpy as np# Fixing random state for reproducibility np.random.seed(19680801)def cc(arg):'''Shorthand to convert 'named' colors to rgba format at 60% opacity.'''return mcolors.to_rgba(arg, alpha=0.6)def polygon_under_graph(xlist, ylist):'''Construct the vertex list which defines the polygon filling the space underthe (xlist, ylist) line graph. Assumes the xs are in ascending order.'''return [(xlist[0], 0.), *zip(xlist, ylist), (xlist[-1], 0.)]fig = plt.figure() ax = fig.gca(projection='3d')# Make verts a list, verts[i] will be a list of (x,y) pairs defining polygon i verts = []# Set up the x sequence xs = np.linspace(0., 10., 26)# The ith polygon will appear on the plane y = zs[i] zs = range(4)for i in zs:ys = np.random.rand(len(xs))verts.append(polygon_under_graph(xs, ys))poly = PolyCollection(verts, facecolors=[cc('r'), cc('g'), cc('b'), cc('y')]) ax.add_collection3d(poly, zs=zs, zdir='y')ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z') ax.set_xlim(0, 10) ax.set_ylim(-1, 4) ax.set_zlim(0, 1)plt.show()

BarPlot

• Axes3D.``bar(left, height, zs=0, zdir=‘z’, *args, **kwargs)

  ?其他參數向下傳遞給bar函數，返回Patch3DCollection對象

  ArgumentDescription

  left	條形圖水平坐標
  height	條形的高度
  zs	Z方向
  zdir	同上

  from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused importimport matplotlib.pyplot as plt import numpy as np# Fixing random state for reproducibility np.random.seed(19680801)fig = plt.figure() ax = fig.add_subplot(111, projection='3d')colors = ['r', 'g', 'b', 'y'] yticks = [3, 2, 1, 0] for c, k in zip(colors, yticks):# Generate the random data for the y=k 'layer'.xs = np.arange(20)ys = np.random.rand(20)# You can provide either a single color or an array with the same length as# xs and ys. To demonstrate this, we color the first bar of each set cyan.cs = [c] * len(xs)# Plot the bar graph given by xs and ys on the plane y=k with 80% opacity.ax.bar(xs, ys, zs=k, zdir='y', color=cs, alpha=0.8)ax.set_xlabel('X') ax.set_ylabel('Y') ax.set_zlabel('Z')# On the y axis let's only label the discrete values that we have data for. ax.set_yticks(yticks)plt.show()

Text

• Axes3D.``text(x, y, z, s, zdir=None, **kwargs)

  ??text的內容其實也很繁雜，需要用一篇內容去探討，在三維中很重要的一點是要學會二維、三維文字的添加。

  from mpl_toolkits.mplot3d import Axes3D # noqa: F401 unused importimport matplotlib.pyplot as pltfig = plt.figure() ax = fig.gca(projection='3d')# Demo 1: zdir zdirs = (None, 'x', 'y', 'z', (1, 1, 0), (1, 1, 1)) xs = (1, 4, 4, 9, 4, 1) ys = (2, 5, 8, 10, 1, 2) zs = (10, 3, 8, 9, 1, 8)for zdir, x, y, z in zip(zdirs, xs, ys, zs):label = '(%d, %d, %d), dir=%s' % (x, y, z, zdir)ax.text(x, y, z, label, zdir)# Demo 2: color ax.text(9, 0, 0, "red", color='red')# Demo 3: text2D # Placement 0, 0 would be the bottom left, 1, 1 would be the top right. ax.text2D(0.05, 0.95, "2D Text", transform=ax.transAxes)# Tweaking display region and labels ax.set_xlim(0, 10) ax.set_ylim(0, 10) ax.set_zlim(0, 10) ax.set_xlabel('X axis') ax.set_ylabel('Y axis') ax.set_zlabel('Z axis')plt.show()

寫在篇后

??三維繪圖不是很常用，主要就是scatterPlot以及surfacePlot稍微更常用。關于三維繪圖總結的也有點糙，更多關于matplotlib,可以閱讀參考我寫的同類文章請參考:

• Matplotlib（一）工作流程
• Matplotlib（二）繪圖生命周期
• Matplotlib（三） rcParams 自定義樣式控制
• matplotlib（四）核心模式以及注意事項
• matplotlib（五）排版布局

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