MM们
使用 可以轻松计算直方图numpy.histogram2d。可以使用 matplotlib 的pcolormesh.import numpy as np; np.random.seed(42)import matplotlib.pyplot as plt# two input arraysazimut = np.random.rand(3000)*2*np.piradius = np.random.rayleigh(29, size=3000)# define binningrbins = np.linspace(0,radius.max(), 30)abins = np.linspace(0,2*np.pi, 60)#calculate histogramhist, _, _ = np.histogram2d(azimut, radius, bins=(abins, rbins))A, R = np.meshgrid(abins, rbins)# plotfig, ax = plt.subplots(subplot_kw=dict(projection="polar"))pc = ax.pcolormesh(A, R, hist.T, cmap="magma_r")fig.colorbar(pc)plt.show()
呼如林
这似乎是您要查找的内容:https : //physt.readthedocs.io/en/latest/special_histograms.html#Polar-histogramfrom physt import histogram, binnings, specialimport numpy as npimport matplotlib.pyplot as plt# Generate some points in the Cartesian coordinatesnp.random.seed(42)x = np.random.rand(1000)y = np.random.rand(1000)z = np.random.rand(1000)# Create a polar histogram with default parametershist = special.polar_histogram(x, y)ax = hist.plot.polar_map()链接的文档包括更多带有颜色、bin 大小等的示例。编辑:我认为这需要一些调整才能使您的数据形成正确的形状,但我认为此示例说明了库的功能,并且可以根据您的用例进行调整:import randomimport numpy as npimport matplotlib.pyplot as pltfrom physt import special# Generate some points in the Cartesian coordinatesnp.random.seed(42)gen = lambda l, h, s = 3000: np.asarray([random.random() * (h - l) + l for _ in range(s)])X = gen(-100, 100)Y = gen(-1000, 1000)Z = gen(0, 1400)hist = special.polar_histogram(X, Y, weights=Z, radial_bins=40)# ax = hist.plot.polar_map()hist.plot.polar_map(density=True, show_zero=False, cmap="inferno", lw=0.5, figsize=(5, 5))plt.show()