我希望將一堆點繪制到極坐標圖上。當我將它與模擬資料一起應用時，它可以作業。當我對我的真實資料進行同樣的嘗試時，它會失敗，我不知道為什么。
# First with simulated data
# The angles for each point
phi = np.linspace(0, math.pi*2, 40) # full circle
phi = np.concatenate([phi, phi, phi, phi]) # 4 full circles
# The radii
rho = np.array([0,1,2,3]) # the radii for each circle
rho = np.repeat(rho, 40)
assert phi.shape == rho.shape
# First just plot the points
plt.figure()
ax = plt.subplot(111, projection='polar')
ax.scatter(phi, rho, c=rho)
ax.set_ylim(0,4)
這似乎有效。接下來，我使用以下內容圍繞原點繪制同心輪廓。該Z屬性設定為半徑，即距離原點同樣遠的點應在輪廓內分組。
# Create a meshgrid from the points
X, Y = np.meshgrid(phi, rho)
plt.figure()
ax = plt.subplot(111, projection='polar')
ax.scatter(X, Y, c=Y)
CS = ax.contourf(X,Y, Y, 2, alpha=0.4)
ax.set_ylim(0,4)
這完全符合我的要求：
接下來，我想將其應用于我的真實資料。我的實際資料有數百萬個資料點，但這里只是一個要重現的小樣本。
rho = np.array([0.38818333, 0.73367091, 0.42336148, 1.39013061, 0.31064486,0.34546275, 0.05445943, 0.85551576, 0.55174167, 1.42371249,0.17644804, 1.76221456, 0.64519126, 0.02408941, 1.43986863,0.72718428, 0.4262945 , 0.1355583 , 0.86319986, 0.71212376,0.14891707, 1.01624534, 1.26915981, 1.39384488, 0.09623481,0.92635469, 1.74757901, 0.15811954, 0.22052651, 0.30784166,0.92740352, 1.29621377, 0.29832842, 1.04442307, 1.36185399,0.42979785, 0.94402815, 0.3786981 , 0.75865969, 1.97273479,0.61140136, 0.71452862, 0.25793468, 1.1751275 , 1.53945948,0.64150917, 0.09274101, 0.52548715, 0.7932458 , 0.90292444])
phi = np.array([1.04208195, 4.67055389, 3.32909655, 1.18709268, 0.86036178,5.820191 , 4.30457004, 1.81242968, 0.64295926, 4.85684143,2.73937709, 3.22891963, 0.25822595, 0.69526782, 0.70709764,1.92901075, 3.44538869, 5.38541473, 0.95255568, 4.01519928,0.8503274 , 5.26774545, 4.07787945, 4.51718652, 0.3170884 ,2.1946835 , 3.12550771, 5.67275731, 1.0000195 , 1.82570239,5.62578391, 0.81923255, 2.00131474, 0.48190872, 4.78875363,5.60395833, 2.01674743, 2.13494958, 5.10829845, 0.95324309,1.59531506, 4.99145225, 6.19873491, 3.32802456, 1.15590926,0.52989939, 6.02205398, 3.66013508, 4.16276819, 2.60498467])
assert rho.shape == phi.shape # both are (50,)
plt.figure()
ax = plt.subplot(111, projection='polar')
ax.scatter(phi, rho, c=rho)
ax.set_ylim(0,3)
當然，我的真實資料比上面的模擬圓圈有更多的變化。接下來，我嘗試以相同的方法繪制輪廓。但是，這現在失敗了：
# Create a meshgrid from the points
X, Y = np.meshgrid(phi, rho)
plt.figure(figsize=(10,10)) # a little bigger to see better
ax = plt.subplot(111, projection='polar')
ax.scatter(X, Y, c=Y)
CS = ax.contourf(X,Y, Y, 2, alpha=0.4)
ax.set_ylim(0,4)
uj5u.com熱心網友回復：
這種行為是由于 rho 和 phi 沒有排序。讓我們來看看：
import matplotlib.pyplot as plt
import numpy as np
rho = np.array([0.38818333, 0.73367091, 0.42336148, 1.39013061, 0.31064486,0.34546275, 0.05445943, 0.85551576, 0.55174167, 1.42371249,0.17644804, 1.76221456, 0.64519126, 0.02408941, 1.43986863,0.72718428, 0.4262945 , 0.1355583 , 0.86319986, 0.71212376,0.14891707, 1.01624534, 1.26915981, 1.39384488, 0.09623481,0.92635469, 1.74757901, 0.15811954, 0.22052651, 0.30784166,0.92740352, 1.29621377, 0.29832842, 1.04442307, 1.36185399,0.42979785, 0.94402815, 0.3786981 , 0.75865969, 1.97273479,0.61140136, 0.71452862, 0.25793468, 1.1751275 , 1.53945948,0.64150917, 0.09274101, 0.52548715, 0.7932458 , 0.90292444])
phi = np.array([1.04208195, 4.67055389, 3.32909655, 1.18709268, 0.86036178,5.820191 , 4.30457004, 1.81242968, 0.64295926, 4.85684143,2.73937709, 3.22891963, 0.25822595, 0.69526782, 0.70709764,1.92901075, 3.44538869, 5.38541473, 0.95255568, 4.01519928,0.8503274 , 5.26774545, 4.07787945, 4.51718652, 0.3170884 ,2.1946835 , 3.12550771, 5.67275731, 1.0000195 , 1.82570239,5.62578391, 0.81923255, 2.00131474, 0.48190872, 4.78875363,5.60395833, 2.01674743, 2.13494958, 5.10829845, 0.95324309,1.59531506, 4.99145225, 6.19873491, 3.32802456, 1.15590926,0.52989939, 6.02205398, 3.66013508, 4.16276819, 2.60498467])
plt.figure(figsize=(10,10))
ax = plt.subplot(111, projection='polar')
ax.set_ylim(0,4)
ax.scatter(phi, rho, c=rho)
#phi.sort()
#rho.sort()
X, Y = np.meshgrid(phi, rho)
CS = ax.contourf(X, Y, Y, 2, alpha=0.4)
plt.show()
給出：
如果取消注釋排序行：
但是現在輪廓看起來很奇怪，因為基礎資料并不是感興趣的整個幾何圖形。所以我們可以為填充輪廓創建相同的資料，而不是：
alfa = np.radians(np.linspace(0, 360, 60))
r = np.arange(0, np.max(rho) np.max(rho)/60, np.max(rho)/60)
r, alfa = np.meshgrid(r, alfa)
ax.contourf(alfa, r, r, 2, alpha=0.4)
給予：
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