plot_streamplot
流图流图或流线图用于显示2D矢量场。此示例显示了 streamplot() 函数的一些功能: 沿着流线改变颜色。 改变流线的密度。 沿流线改变线宽。 控制流线的起点。 流线跳过蒙面区域和NaN值。 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859import numpy as npimport matplotlib.pyplot as pltimport matplotlib.gridspec as gridspecw = 3Y, X = np.mgrid[-w:w:100j, -w:w:100j]U = -1 - X**2 + YV = 1 + X - Y**2speed = np.sqrt(U*U + V*V)fig = plt.figure(figsize=(7, 9))gs = gridspec.GridSpec(nrows=3, ncols=2, height_ratios=[1, 1, 2])# ...
quiver_demo
演示高级箭图和箭袋功能为箭袋展示一些更高级的选项。有关简单示例,请参阅 Quiver Simple Demo。 已知问题:自动缩放图未考虑箭头,因此边界上的那些通常不在图中。以完全一般的方式解决这个问题并不容易。解决方法是手动展开Axes对象。 1234567891011import matplotlib.pyplot as pltimport numpy as npX, Y = np.meshgrid(np.arange(0, 2 * np.pi, .2), np.arange(0, 2 * np.pi, .2))U = np.cos(X)V = np.sin(Y)fig1, ax1 = plt.subplots()ax1.set_title('Arrows scale with plot width, not view')Q = ax1.quiver(X, Y, U, V, units='width')qk = ax1.quiverkey(Q, 0.9, 0.9, 2, r'$2 \frac{m}{...
quadmesh_demo
QuadMesh 演示pcolormesh 使用QuadMesh,一种更快的 pcolor 泛化,但有一些限制。 此演示说明了带有掩码数据的quadmesh中的误差。 12345678910111213141516171819202122232425262728293031323334import copyfrom matplotlib import cm, pyplot as pltimport numpy as npn = 12x = np.linspace(-1.5, 1.5, n)y = np.linspace(-1.5, 1.5, n * 2)X, Y = np.meshgrid(x, y)Qx = np.cos(Y) - np.cos(X)Qz = np.sin(Y) + np.sin(X)Z = np.sqrt(X**2 + Y**2) / 5Z = (Z - Z.min()) / (Z.max() - Z.min())# The color array can include masked values.Zm = np.ma.masked_where(np.abs...
quiver_simple_demo
箭图简单演示带quiverkey的箭袋图的简单示例。 有关更高级的选项,请参阅演示高级箭袋和quiverkey功能。 12345678910111213import matplotlib.pyplot as pltimport numpy as npX = np.arange(-10, 10, 1)Y = np.arange(-10, 10, 1)U, V = np.meshgrid(X, Y)fig, ax = plt.subplots()q = ax.quiver(X, Y, U, V)ax.quiverkey(q, X=0.3, Y=1.1, U=10, label='Quiver key, length = 10', labelpos='E')plt.show() 参考此示例中显示了以下函数和方法的用法: 12345import matplotlibmatplotlib.axes.Axes.quivermatplotlib.pyplot.quivermatplotlib.axes.Axes.quiverk...
pcolormesh_levels
pcolormesh演示如何组合Normalization和Colormap实例以在pcolor(),pcolormesh()和imshow()类型图中绘制“级别”,其方式与contour / contourf的levels关键字参数类似。 1234567891011121314151617181920212223242526272829303132333435363738394041424344454647import matplotlibimport matplotlib.pyplot as pltfrom matplotlib.colors import BoundaryNormfrom matplotlib.ticker import MaxNLocatorimport numpy as np# make these smaller to increase the resolutiondx, dy = 0.05, 0.05# generate 2 2d grids for the x & y boundsy, x = np.mgrid[slice(1, ...
spy_demos
Spy 演示绘制数组的稀疏模式。 1234567891011121314151617181920import matplotlib.pyplot as pltimport numpy as npfig, axs = plt.subplots(2, 2)ax1 = axs[0, 0]ax2 = axs[0, 1]ax3 = axs[1, 0]ax4 = axs[1, 1]x = np.random.randn(20, 20)x[5, :] = 0.x[:, 12] = 0.ax1.spy(x, markersize=5)ax2.spy(x, precision=0.1, markersize=5)ax3.spy(x)ax4.spy(x, precision=0.1)plt.show() 参考此示例中显示了以下函数,方法和类的使用: 123import matplotlibmatplotlib.axes.Axes.spymatplotlib.pyplot.spy 下载这个示例 下载python源码: spy_demos.py 下载Jupyter notebook: spy_de...
shading_example
着色示例显示如何制作阴影浮雕图的示例,如Mathematica (http://reference.wolfram.com/mathematica/ref/ReliefPlot.html)或通用映射工具(https://gmt.soest.hawaii.edu/) 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152import numpy as npimport matplotlib.pyplot as pltfrom matplotlib.colors import LightSourcefrom matplotlib.cbook import get_sample_datadef main(): # Test data x, y = np.mgrid[-5:5:0.05, -5:5:0.05] z = 5 * (np.sqrt(x**2 + y**2) + np.sin(x**2 + y**2)) filename...
specgram_demo
频谱图演示频谱图的演示 (specgram())。 12345678910111213141516171819202122232425262728293031import matplotlib.pyplot as pltimport numpy as np# Fixing random state for reproducibilitynp.random.seed(19680801)dt = 0.0005t = np.arange(0.0, 20.0, dt)s1 = np.sin(2 * np.pi * 100 * t)s2 = 2 * np.sin(2 * np.pi * 400 * t)# create a transient "chirp"mask = np.where(np.logical_and(t > 10, t < 12), 1.0, 0.0)s2 = s2 * mask# add some noise into the mixnse = 0.01 * np.random.random(size=len(t))x = s1 + s2...
tricontour_smooth_delaunay
Tricontour 德洛内三角演示一组随机点的高分辨率三视图;matplotlib.tri.TriAnalyzer用于提高绘图质量。 该演示的初始数据点和三角形网格如下: 在[-1, 1] x [-1, 1] 正方形内实例化一组随机点。 然后计算这些点的Delaunay三角剖分,其中一个随机三角形子集由用户隐藏(基于init_mASK_frac参数)。这将模拟无效数据。 为获得这类数据集的高分辨率轮廓而提出的通用程序如下: 使用matplotlib.tri.TriAnalyzer计算扩展掩码,该掩码将从三角剖分的边框中排除形状不佳(平坦)的三角形。将掩码应用于三角剖分(使用SET_MASK)。 使用matplotlib.tri.UniformTriRefiner对数据进行细化和插值。 用tricontour绘制精确的数据。 12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667...
tricontour_demo
Tricontour 演示非结构化三角形网格的等高线图。 123import matplotlib.pyplot as pltimport matplotlib.tri as triimport numpy as np 在不指定三角形的情况下创建三角剖分会导致点的Delaunay三角剖分。 123456789101112131415161718192021# First create the x and y coordinates of the points.n_angles = 48n_radii = 8min_radius = 0.25radii = np.linspace(min_radius, 0.95, n_radii)angles = np.linspace(0, 2 * np.pi, n_angles, endpoint=False)angles = np.repeat(angles[..., np.newaxis], n_radii, axis=1)angles[:, 1::2] += np.pi / n_anglesx = (radii * np.cos(an...












