2 回答
TA贡献1886条经验 获得超2个赞
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 arrays
azimut = np.random.rand(3000)*2*np.pi
radius = np.random.rayleigh(29, size=3000)
# define binning
rbins = np.linspace(0,radius.max(), 30)
abins = np.linspace(0,2*np.pi, 60)
#calculate histogram
hist, _, _ = np.histogram2d(azimut, radius, bins=(abins, rbins))
A, R = np.meshgrid(abins, rbins)
# plot
fig, ax = plt.subplots(subplot_kw=dict(projection="polar"))
pc = ax.pcolormesh(A, R, hist.T, cmap="magma_r")
fig.colorbar(pc)
plt.show()
TA贡献1798条经验 获得超3个赞
这似乎是您要查找的内容:https : //physt.readthedocs.io/en/latest/special_histograms.html#Polar-histogram
from physt import histogram, binnings, special
import numpy as np
import matplotlib.pyplot as plt
# Generate some points in the Cartesian coordinates
np.random.seed(42)
x = np.random.rand(1000)
y = np.random.rand(1000)
z = np.random.rand(1000)
# Create a polar histogram with default parameters
hist = 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 random
import numpy as np
import matplotlib.pyplot as plt
from physt import special
# Generate some points in the Cartesian coordinates
np.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()
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