Python Griddata和Contourf会随着步长/级别的增加而产生瑕疵

我使用SciPy Griddata以笛卡尔形式对数据进行插值，然后使用contourf和极坐标投影绘制这些数据。使用轮廓绘制笛卡尔插值数据时，没有伪影。然而，当投影为极性时，工件会随着“级别”的增加而发展

这些瑕疵是在陡峭渐变区域附近形成的多边形或射线。下面的代码用月亮绘制天空的亮度。对于“12”的图形级别，没有问题。工件以“25”的graphlevel发展。我想要的等级是80或更多-这显示了可怕的工件。下面是一个晚上的真实数据示例。这些伪影总是出现。查看带有和的图像

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我将把我的问题和答案留在StackOverflow上。。。因为我确实从Matploblib的开发者那里得到了答案。问题是轮廓。在尝试以极坐标标注投影数据时，循环边界处的多边形重叠和延伸会导致问题。避免这种情况的唯一方法是在边界处添加点。引用开发商的话：

解决方案需要付出大量努力，并且必须针对每个特定问题进行调整，因此离理想状态还有很长的路要走。我们（Matplotlib）应该在这些情况下做得更好。在三角剖分中插入额外的点不是正确的方法，相反，我们应该纠正穿过不连续的线/多边形，以提供一般解决方案

有关详细讨论，请参见

我决定的答案是在笛卡尔空间中插值并渲染结果。然后我用轴和标签格式化一个空的极坐标图，覆盖在顶部。。。继续我的生活

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata


gridsize =150
graphlevels = 200

fig = plt.figure(figsize=(12,10))

ax = fig.add_subplot(111, aspect='equal')
pax = fig.add_subplot(111,projection='polar')
pax.set_facecolor('none')
ax.set_axis_off()
ax.set_xlim([-75,75])
ax.set_ylim([-75,75])

x = [72.90,68.00,59.14,44.38,29.63,63.94,59.68,51.92,38.98,26.03,47.34,44.20,38.46,28.89,19.31,23.40,20.40,15.34,10.28,-0.18,-0.14,-0.09,-0.04,0.02,-25.39,-23.66,-20.57,-15.40,-10.23,-47.56,-44.34,-38.54,-28.89,-19.22,-64.01,-59.68,-51.89,-38.90,-25.90,-72.77,-67.84,-58.98,-44.21,-29.44,-72.75,-67.83,-58.96,-44.18,-29.41,-59.63,-51.82,-38.83,-25.84,-47.42,-44.20,-38.40,-28.76,-19.12,-23.40,-20.32,-15.19,-10.08,0.27,0.25,0.23,0.20,23.92,20.80,15.63,10.46,47.93,44.67,38.86,29.17,19.48,64.40,60.03,52.20,39.18,26.15,73.08,68.12,59.26,44.47,29.68,-4.81]
y = [12.93,12.01,10.38,7.67,4.99,37.03,34.49,29.93,22.33,14.77,56.60,52.75,45.82,34.26,22.72,64.60,56.14,42.02,27.90,73.66,68.67,59.68,44.68,29.68,69.12,64.45,56.00,41.92,27.84,56.26,52.45,45.56,34.08,22.61,36.59,34.11,29.61,22.11,14.62,12.48,11.62,10.04,7.43,4.83,-13.33,-12.31,-10.78,-8.21,-5.58,-34.84,-30.36,-22.87,-15.36,-57.04,-53.20,-46.31,-34.83,-23.34,-65.20,-56.72,-42.62,-28.53,-69.33,-60.31,-45.31,-30.31,-65.09,-56.63,-42.55,-28.47,-56.81,-52.99,-46.13,-34.69,-23.23,-36.99,-34.53,-30.08,-22.66,-15.22,-12.73,-11.93,-10.44,-7.94,-5.40,-1.22,]
skybrightness = [19.26,19.31,19.21,19.65,19.40,19.26,19.23,19.43,19.57,19.52,19.19,19.31,19.33,19.68,19.50,19.29,19.45,19.50,19.23,18.98,19.28,19.46,19.54,19.22,19.03,19.18,19.35,19.37,19.08,18.99,18.98,19.26,19.36,19.08,18.79,18.85,19.13,19.17,19.05,18.51,18.64,18.88,18.92,18.93,18.12,18.34,18.72,18.82,18.74,18.22,18.46,18.76,18.26,18.13,18.24,18.46,18.58,17.30,18.38,18.08,18.24,17.68,18.34,18.46,18.65,18.23,18.70,18.52,18.79,18.83,18.18,18.51,19.01,19.08,19.08,18.99,19.02,19.07,19.20,19.27,19.06,19.01,19.28,19.46,19.30,18.94]

xgrid = np.linspace(min(x), max(x),gridsize)
ygrid = np.linspace(min(y), max(y),gridsize)

xgrid, ygrid = np.meshgrid(xgrid, ygrid, indexing='ij')

nsb_grid = griddata((x,y),skybrightness,(xgrid, ygrid), method='linear')

plt.rc('ytick', labelsize=16) #colorbar font

colors = plt.cm.get_cmap('RdYlBu')
levels,steps = np.linspace(min(skybrightness), max(skybrightness)+0.3,graphlevels, retstep=True)
ticks = np.linspace(min(skybrightness), max(skybrightness)+0.3,12)

cax = ax.contourf(xgrid, ygrid, nsb_grid, levels=levels, cmap=colors)

cbar = plt.colorbar(cax, fraction=0.046, pad=0.04, ticks=ticks)
cbar.set_label(r'mag/arcsec$^2$')
pax.set_theta_zero_location('N')
pax.set_theta_direction(-1)
pax.set_rmax(75)
pax.set_yticks(range(10, 80, 20))
pax.set_xticklabels([r'N', r'NE', r'E', r'SE', r'S', r'SW', r'W', r'NW'])
pax.grid(alpha=0.3)

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工件是否也会出现在笛卡尔（非极性）图中？Contourf也进行插值。A将有助于调试。不，工件不会出现在笛卡尔图中。contourf的任何一种方法似乎都不能奏效。我想这的确是一个可以复制的小例子！：）非常好的MCVE！（你仍然可以删除一些样式化的东西，但这足够简洁，非常有用。）我不确定发生了什么，但其他人现在可以帮助你了。谢谢你，安德拉斯。要创建笛卡尔结果，请关闭ax.set极轴样式，并使用cax=ax.tourtf（xgrid、ygrid、nsb_grid、levels=levels、cmap=colors）。无论轮廓级别如何，都没有任何瑕疵。这就是为什么我把极地投影作为一个问题来关注。哦，是的，我明白了。我的意思是我不知道问题来自哪里。我试着从插值数据中删除NaN（似乎没有帮助），我试着直接插值极坐标值（我无法在十分钟内完成这一切）。我从来没有打算让你删除这个问题；留下这个答案是理想的解决方案。很好地解决了这个问题。我还想在github上发布一个讨论的链接，假设你是从那里得到答案的。哦..我想了一下-但认为“外部”链接是不受欢迎的。我会在上面加上…当它们是帖子的内容时，它们是不受欢迎的。“我的问题就在那里”、“你的问题的解决方案就在那里”等等。但在这种情况下，这只是添加了一些上下文，github链接也非常稳定：）

import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata


gridsize =150
graphlevels = 200

fig = plt.figure(figsize=(12,10))

ax = fig.add_subplot(111, aspect='equal')
pax = fig.add_subplot(111,projection='polar')
pax.set_facecolor('none')
ax.set_axis_off()
ax.set_xlim([-75,75])
ax.set_ylim([-75,75])

x = [72.90,68.00,59.14,44.38,29.63,63.94,59.68,51.92,38.98,26.03,47.34,44.20,38.46,28.89,19.31,23.40,20.40,15.34,10.28,-0.18,-0.14,-0.09,-0.04,0.02,-25.39,-23.66,-20.57,-15.40,-10.23,-47.56,-44.34,-38.54,-28.89,-19.22,-64.01,-59.68,-51.89,-38.90,-25.90,-72.77,-67.84,-58.98,-44.21,-29.44,-72.75,-67.83,-58.96,-44.18,-29.41,-59.63,-51.82,-38.83,-25.84,-47.42,-44.20,-38.40,-28.76,-19.12,-23.40,-20.32,-15.19,-10.08,0.27,0.25,0.23,0.20,23.92,20.80,15.63,10.46,47.93,44.67,38.86,29.17,19.48,64.40,60.03,52.20,39.18,26.15,73.08,68.12,59.26,44.47,29.68,-4.81]
y = [12.93,12.01,10.38,7.67,4.99,37.03,34.49,29.93,22.33,14.77,56.60,52.75,45.82,34.26,22.72,64.60,56.14,42.02,27.90,73.66,68.67,59.68,44.68,29.68,69.12,64.45,56.00,41.92,27.84,56.26,52.45,45.56,34.08,22.61,36.59,34.11,29.61,22.11,14.62,12.48,11.62,10.04,7.43,4.83,-13.33,-12.31,-10.78,-8.21,-5.58,-34.84,-30.36,-22.87,-15.36,-57.04,-53.20,-46.31,-34.83,-23.34,-65.20,-56.72,-42.62,-28.53,-69.33,-60.31,-45.31,-30.31,-65.09,-56.63,-42.55,-28.47,-56.81,-52.99,-46.13,-34.69,-23.23,-36.99,-34.53,-30.08,-22.66,-15.22,-12.73,-11.93,-10.44,-7.94,-5.40,-1.22,]
skybrightness = [19.26,19.31,19.21,19.65,19.40,19.26,19.23,19.43,19.57,19.52,19.19,19.31,19.33,19.68,19.50,19.29,19.45,19.50,19.23,18.98,19.28,19.46,19.54,19.22,19.03,19.18,19.35,19.37,19.08,18.99,18.98,19.26,19.36,19.08,18.79,18.85,19.13,19.17,19.05,18.51,18.64,18.88,18.92,18.93,18.12,18.34,18.72,18.82,18.74,18.22,18.46,18.76,18.26,18.13,18.24,18.46,18.58,17.30,18.38,18.08,18.24,17.68,18.34,18.46,18.65,18.23,18.70,18.52,18.79,18.83,18.18,18.51,19.01,19.08,19.08,18.99,19.02,19.07,19.20,19.27,19.06,19.01,19.28,19.46,19.30,18.94]

xgrid = np.linspace(min(x), max(x),gridsize)
ygrid = np.linspace(min(y), max(y),gridsize)

xgrid, ygrid = np.meshgrid(xgrid, ygrid, indexing='ij')

nsb_grid = griddata((x,y),skybrightness,(xgrid, ygrid), method='linear')

plt.rc('ytick', labelsize=16) #colorbar font

colors = plt.cm.get_cmap('RdYlBu')
levels,steps = np.linspace(min(skybrightness), max(skybrightness)+0.3,graphlevels, retstep=True)
ticks = np.linspace(min(skybrightness), max(skybrightness)+0.3,12)

cax = ax.contourf(xgrid, ygrid, nsb_grid, levels=levels, cmap=colors)

cbar = plt.colorbar(cax, fraction=0.046, pad=0.04, ticks=ticks)
cbar.set_label(r'mag/arcsec$^2$')
pax.set_theta_zero_location('N')
pax.set_theta_direction(-1)
pax.set_rmax(75)
pax.set_yticks(range(10, 80, 20))
pax.set_xticklabels([r'N', r'NE', r'E', r'SE', r'S', r'SW', r'W', r'NW'])
pax.grid(alpha=0.3)