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问题描述
当前，每个不均匀间隔的粒子的体积为几百万，每个粒子都有一个属性(对于那些好奇的人来说是潜在的)，我想为其计算局部力(加速度).
I currently have a volume spanned by a few million every unevenly spaced particles and each particle has an attribute (potential, for those who are curious) that I want to calculate the local force (acceleration) for.
np.gradient仅适用于间隔均匀的数据，我在这里查看:第二次渐变是numpy 在需要插值的地方，但是我在Numpy中找不到3D样条线的实现.
np.gradient only works with evenly spaced data and I looked here: Second order gradient in numpy where interpolation is necessary but I could not find a 3D spline implementation in Numpy.
一些将产生代表性数据的代码:
Some code that will produce representative data:
import numpy as np
from scipy.spatial import cKDTree
x = np.random.uniform(-10, 10, 10000)
y = np.random.uniform(-10, 10, 10000)
z = np.random.uniform(-10, 10, 10000)
phi = np.random.uniform(-10**9, 0, 10000)
kdtree = cKDTree(np.c_[x,y,z])
_, index = kdtree.query([0,0,0], 32) #find 32 nearest particles to the origin
#find the gradient at (0,0,0) by considering the 32 nearest particles?
(我的问题与使用
(My problem is very similar to Function to compute 3D gradient with unevenly spaced sample locations but there seemed to have been no solution there, so I thought I'd ask again.)
任何帮助将不胜感激.
Any help would be appreciated.
推荐答案
这是一个Julia实现，可以实现您的要求
Here is a Julia implementation that does what you ask
using NearestNeighbors
n = 3;
k = 32; # for stability use k > n*(n+3)/2
# Take a point near the center of cube
point = 0.5 + rand(n)*1e-3;
data = rand(n, 10^4);
kdtree = KDTree(data);
idxs, dists = knn(kdtree, point, k, true);
# Coords of the k-Nearest Neighbors
X = data[:,idxs];
# Least-squares recipe for coefficients
C = point * ones(1,k); # central node
dX = X - C; # diffs from central node
G = dX' * dX;
F = G .* G;
v = diag(G);
N = pinv(G) * G;
N = eye(N) - N;
a = N * pinv(F*N) * v; # ...these are the coeffs
# Use a temperature distribution of T = 25.4 * r^2
# whose analytical gradient is gradT = 25.4 * 2*x
X2 = X .* X;
C2 = C .* C;
T = 25.4 * n * mean(X2, 1)';
Tc = 25.4 * n * mean(C2, 1)'; # central node
dT = T - Tc; # diffs from central node
y = dX * (a .* dT); # Estimated gradient
g = 2 * 25.4 * point; # Analytical
# print results
@printf "Estimated Grad = %s\n" string(y')
@printf "Analytical Grad = %s\n" string(g')
@printf "Relative Error = %.8f\n" vecnorm(g-y)/vecnorm(g)
此方法的相对误差约为1％.这是几次运行的结果...
This method has about a 1% relative error. Here are the results from a few runs...
Estimated Grad = [25.51670916224472 25.421038632006926 25.6711949674633]
Analytical Grad = [25.41499027802736 25.44913042322385 25.448202594123806]
Relative Error = 0.00559934
Estimated Grad = [25.310574056859014 25.549736360607493 25.368056350800604]
Analytical Grad = [25.43200914200516 25.43243178887198 25.45061497749628]
Relative Error = 0.00426558
更新
我对Python不太了解，但是这里的翻译似乎很有效
Update
I don't know Python very well, but here is a translation that seems to be working
import numpy as np
from scipy.spatial import KDTree
n = 3;
k = 32;
# fill the cube with random points
data = np.random.rand(10000,n)
kdtree = KDTree(data)
# pick a point (at the center of the cube)
point = 0.5 * np.ones((1,n))
# Coords of k-Nearest Neighbors
dists, idxs = kdtree.query(point, k)
idxs = idxs[0]
X = data[idxs,:]
# Calculate coefficients
C = (np.dot(point.T, np.ones((1,k)))).T # central node
dX= X - C # diffs from central node
G = np.dot(dX, dX.T)
F = np.multiply(G, G)
v = np.diag(G);
N = np.dot(np.linalg.pinv(G), G)
N = np.eye(k) - N;
a = np.dot(np.dot(N, np.linalg.pinv(np.dot(F,N))), v) # these are the coeffs
# Temperature distribution is T = 25.4 * r^2
X2 = np.multiply(X, X)
C2 = np.multiply(C, C)
T = 25.4 * n * np.mean(X2, 1).T
Tc = 25.4 * n * np.mean(C2, 1).T # central node
dT = T - Tc; # diffs from central node
# Analytical gradient ==> gradT = 2*25.4* x
g = 2 * 25.4 * point;
print( "g[]: %s" % (g) )
# Estimated gradient
y = np.dot(dX.T, np.multiply(a, dT))
print( "y[]: %s, Relative Error = %.8f" % (y, np.linalg.norm(g-y)/np.linalg.norm(g)) )
更新#2
我想我可以使用格式化的ASCII而不是LaTeX来编写易于理解的内容...
Update #2
I think I can write something comprehensible using formatted ASCII instead of LaTeX...
`Given a set of M vectors in n-dimensions (call them b_k), find a set of
`coeffs (call them a_k) which yields the best estimate of the identity
`matrix and the zero vector
`
` M
` (1) min ||E - I||, where E = sum a_k b_k b_k
` a_k k=1
`
` M
` (2) min ||z - 0||, where z = sum a_k b_k
` a_k k=1
`
`
`Note that the basis vectors {b_k} are not required
`to be normalized, orthogonal, or even linearly independent.
`
`First, define the following quantities:
`
` B ==> matrix whose columns are the b_k
` G = B'.B ==> transpose of B times B
` F = G @ G ==> @ represents the hadamard product
` v = diag(G) ==> vector composed of diag elements of G
`
`The above minimizations are equivalent to this linearly constrained problem
`
` Solve F.a = v
` s.t. G.a = 0
`
`Let {X} denote the Moore-Penrose inverse of X.
`Then the solution of the linear problem can be written:
`
` N = I - {G}.G ==> projector into nullspace of G
` a = N . {F.N} . v
`
`The utility of these coeffs is that they allow you to write
`very simple expressions for the derivatives of a tensor field.
`
`
`Let D be the del (or nabla) operator
`and d be the difference operator wrt the central (aka 0th) node,
`so that, for any scalar/vector/tensor quantity Y, we have:
` dY = Y - Y_0
`
`Let x_k be the position vector of the kth node.
`And for our basis vectors, take
` b_k = dx_k = x_k - x_0.
`
`Assume that each node has a field value associated with it
` (e.g. temperature), and assume a quadratic model [about x = x_0]
` for the field [g=gradient, H=hessian, ":" is the double-dot product]
`
` Y = Y_0 + (x-x_0).g + (x-x_0)(x-x_0):H/2
` dY = dx.g + dxdx:H/2
` D2Y = I:H ==> Laplacian of Y
`
`
`Evaluate the model at the kth node
`
` dY_k = dx_k.g + dx_k dx_k:H/2
`
`Multiply by a_k and sum
`
` M M M
` sum a_k dY_k = sum a_k dx_k.g + sum a_k dx_k dx_k:H/2
` k=1 k=1 k=1
`
` = 0.g + I:H/2
` = D2Y / 2
`
`Thus, we have a second order estimate of the Laplacian
`
` M
` Lap(Y_0) = sum 2 a_k dY_k
` k=1
`
`
`Now play the same game with a linear model
` dY_k = dx_k.g
`
`But this time multiply by (a_k dx_k) and sum
`
` M M
` sum a_k dx_k dY_k = sum a_k dx_k dx_k.g
` k=1 k=1
`
` = I.g
` = g
`
`
`In general, the derivatives at the central node can be estimated as
`
` M
` D#Y = sum a_k dx_k#dY_k
` k=1
`
` M
` D2Y = sum 2 a_k dY_k
` k=1
`
` where
` # stands for the {dot, cross, or tensor} product
` yielding the {div, curl, or grad} of Y
` and
` D2Y stands for the Laplacian of Y
` D2Y = D.DY = Lap(Y)
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