我有一組測量點，我想在它們之間進行插值，為此我使用了 SciPy 的 griddata()：
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.interpolate import griddata
data = pd.DataFrame({
'time': [0, 1, 2, 3, 4, 0.1, 0.9, 2, 3.05, 4, 0, 1, 2.2, 3, 3.95],
'force': [1, 2, 4, 9, 16, 0, 0, 0, 0, 0, -1, -2, -4, -9, -16]
})
Times, Forces = np.meshgrid(
np.linspace(0, 4, 100),
np.linspace(-16, 16, 100)
)
data['work'] = data['time'] * data['force']
interpolation = griddata(
(data['time'], data['force']),
data['work'],
(Times, Forces),
method= 'linear'
)
fig, ax = plt.subplots()
contour = ax.contourf(
Times, Forces, interpolation
)
ax.scatter(data['time'], data['force'])
fig.show()
我的問題是，我的測量點已經遵循物理可能性的邊界，但插值仍然會為每個可跨越區域進行插值，包括那些無法測量的區域。
如何將插值或至少插值圖限制在外部點的形狀內？不幸的是，“時間”測量有很小的偏差。
任何建議將不勝感激！
uj5u.com熱心網友回復：
首先，您需要獲得所有點的凹多邊形。其次，使用多邊形裁剪輪廓填充。雖然這可能有點復雜，但一些有用的包可以幫助這些任務。
下面是代碼。
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from scipy.interpolate import griddata
import matplotlib.path as mpath
import alphashape
from descartes import PolygonPatch
Path = mpath.Path
data = pd.DataFrame({
'time': [0, 1, 2, 3, 4, 0.1, 0.9, 2, 3.05, 4, 0, 1, 2.2, 3, 3.95],
'force': [1, 2, 4, 9, 16, 0, 0, 0, 0, 0, -1, -2, -4, -9, -16]
})
Times, Forces = np.meshgrid(
np.linspace(0, 4, 100),
np.linspace(-16, 16, 100)
)
data['work'] = data['time'] * data['force']
interpolation = griddata(
(data['time'], data['force']),
data['work'],
(Times, Forces),
method= 'linear')
fig, ax = plt.subplots()
plt.xlim(-.3,4.3)
plt.ylim(-18,18)
#contour = ax.contourf( Times, Forces, interpolation)
contour = ax.tricontourf( data['time'], data['force'], data['work'])
ax.scatter(data['time'], data['force'])
x = data['time']
y = data['force']
points = np.vstack([x, y]).T
alpha = 0.95 * alphashape.optimizealpha(points)
hull = alphashape.alphashape(points, alpha)
hull_pts = hull.exterior.coords.xy
ax.scatter(hull_pts[0], hull_pts[1], color='red')
ax.add_patch(PolygonPatch(hull, fill=False, color='red'))
plt.savefig("clip_before.png")
#make clip path
vertices = []
codes = []
xpts,ypts = hull_pts
## convert polygon to path for cliping contour fill
for ix,iy in zip(xpts,ypts):
vertices.append((ix,iy))
codes = [Path.MOVETO]
codes = [Path.LINETO] * (len(xpts) -2)
codes = [Path.CLOSEPOLY]
clip = Path(vertices, codes)
for collection in contour.collections:
collection.set_clip_path(clip,transform=ax.transData)
plt.savefig("clip_after.png")
plt.show()
這是輸出數字。
剪輯前。
剪輯后。
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標籤：Python matplotlib scipy 插值
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