argumentab = math.acos(np.dot(coor_a, coor_b)/(np.linalg.norm(coor_a)*np.linalg.norm(coor_b)))
时间: 2024-03-29 12:36:16 浏览: 15
这是一个在 Python 中使用 NumPy 和 math 模块的代码行。假设 coor_a 和 coor_b 都是长度为 3 的 NumPy 数组,表示两个三维向量。
`np.dot(coor_a, coor_b)` 计算两向量点积(内积),即 $coor_a \cdot coor_b$。
`np.linalg.norm(coor_a)` 和 `np.linalg.norm(coor_b)` 分别计算两个向量的 Euclidean 范数,即 $\sqrt{\sum_{i=1}^{3} a_i^2}$ 和 $\sqrt{\sum_{i=1}^{3} b_i^2}$。
`(np.dot(coor_a, coor_b)/(np.linalg.norm(coor_a)*np.linalg.norm(coor_b)))` 计算两向量的夹角余弦值。
`math.acos()` 计算夹角余弦值的反余弦值,即两向量夹角的弧度值。
因此,`argumentab` 的值就是向量 `coor_a` 和 `coor_b` 之间的夹角的弧度值。
相关问题
import numpy as np import matplotlib.pyplot as plt import math y = 4 theta = [] phi = [] for x in np.linspace(-1.5, 1.5, 100): for z in np.linspace(0, 3, 100): s = math.sqrt(x**2 + y**2 + z**2) theta.append(math.acos(z/s)) phi_ = math.atan2((y/s),(x/s)) if phi_ < 0: phi_ = phi_ + 2 * math.pi phi.append(phi_) min_theta = min(theta) min = theta.index(min_theta) min_phi = phi[min] print(min_phi,min_theta) min_x = math.sin(min_theta)/math.cos(min_phi) if min_x in x.index: print('yes') else: print('no')如何修改错误
这段代码中有一些错误,需要进行修改。首先,在代码的第一行中,需要在两个import语句之间添加一个换行符。其次,在第二行中,需要将“4”改为“4.0”,以确保y是一个浮点数。接下来,在第三行之前,需要添加一个空格。然后,在第七行中,需要将“phi_”改为“phi”,以便正确地存储phi的值。在第十行之前,需要添加一个空格。最后,在第十五行中,需要将“x.index”改为“np.linspace(-1.5, 1.5, 100)”以正确地查找x中是否包含min_x。修改后的代码如下:
```
import numpy as np
import matplotlib.pyplot as plt
import math
y = 4.0
theta = []
phi = []
for x in np.linspace(-1.5, 1.5, 100):
for z in np.linspace(0, 3, 100):
s = math.sqrt(x**2 + y**2 + z**2)
theta.append(math.acos(z/s))
phi.append(math.atan2((y/s),(x/s)))
if phi[-1] < 0:
phi[-1] = phi[-1] + 2 * math.pi
min_theta = min(theta)
min_index = theta.index(min_theta)
min_phi = phi[min_index]
print(min_phi, min_theta)
min_x = math.sin(min_theta)/math.cos(min_phi)
if min_x in np.linspace(-1.5, 1.5, 100):
print('yes')
else:
print('no')
```
注意,这个代码仍然不完整,因为它缺少一些必要的注释和变量说明。
将以下代码改为C++代码: import scipy.special as sp import numpy as np import numba from numba import njit,prange import math import trimesh as tri fileName="data/blub.obj" outName='./output/blub_rec.obj' # 参数 # 限制选取球谐基函数的带宽 bw=64 # 极坐标,经度0<=theta<2*pi,纬度0<=phi<pi; # (x,y,z)=r(sin(phi)cos(theta),sin(phi)sin(theta),cos(phi)) def get_angles(x,y,z): r=np.sqrt(x*x+y*y+z*z) x/=r y/=r z/=r phi=np.arccos(z) if phi==0: theta=0 theta=np.arccos(x/np.sin(phi)) if y/np.sin(phi)<0: theta+=math.pi return [theta,phi] if __name__=='__main__': # 载入网格 mesh=tri.load(fileName) # 获得网格顶点(x,y,z)对应的(theta,phi) numV=len(mesh.vertices) angles=np.zeros([numV,2]) for i in range(len(mesh.vertices)): v=mesh.vertices[i] [angles[i,0],angles[i,1]]=get_angles(v[0],v[1],v[2]) # 求解方程:x(theta,phi)=对m,l求和 a^m_lY^m_l(theta,phi) 解出系数a^m_l # 得到每个theta,phi对应的x X,Y,Z=np.zeros([numV,1]),np.zeros([numV,1]),np.zeros([numV,1]) for i in range(len(mesh.vertices)): X[i],Y[i],Z[i]=mesh.vertices[i,0],mesh.vertices[i,1],mesh.vertices[i,2] # 求出Y^m_l(theta,phi)作为矩阵系数 sph_harm_values=np.zeros([numV,(bw+1)*(bw+1)]) for i in range(numV): for l in range(bw): for m in range(-l,l+1): sph_harm_values[i,l*(l+1)+m]=sp.sph_harm(m,l,angles[i,0],angles[i,1]) print('系数矩阵维数:{}'.format(sph_harm_values.shape)) # 求解方程组,得到球谐分解系数 a_x=np.linalg.lstsq(sph_harm_values,X,rcond=None)[0] a_y=np.linalg.lstsq(sph_harm_values,Y,rcond=None)[0] a_z=np.linalg.lstsq(sph_harm_values,Z,rcond=None)[0] # 从系数恢复的x,y,z坐标,存为新的点云用于比较 x=np.matmul(sph_harm_values,a_x) y=np.matmul(sph_harm_values,a_y) z=np.matmul(sph_harm_values,a_z) with open(outName,'w') as output: for i in range(len(x)): output.write("v %f %f %f\n"%(x[i,0],y[i,0],z[i,0]))
#include <iostream>
#include <fstream>
#include <cmath>
#include <vector>
#include <Eigen/Dense>
#include <unsupported/Eigen/Splines>
#include <trimesh/trimesh.h>
using namespace Eigen;
std::string fileName = "data/blub.obj";
std::string outName = "./output/blub_rec.obj";
int bw = 64;
std::vector<double> get_angles(double x, double y, double z){
double r = std::sqrt(x*x + y*y + z*z);
double phi = std::acos(z/r);
double theta;
if(phi == 0){
theta = 0;
}else{
theta = std::acos(x/std::sin(phi));
if(y/std::sin(phi) < 0){
theta += M_PI;
}
}
return {theta, phi};
}
int main(){
// Load mesh
trimesh::TriMesh mesh;
mesh.read(fileName);
// Get (theta, phi) for each vertex
int numV = mesh.vertices.size();
std::vector<std::vector<double>> angles(numV, std::vector<double>(2));
for(int i=0; i<numV; ++i){
auto v = mesh.vertices[i];
angles[i] = get_angles(v[0], v[1], v[2]);
}
// Solve equation: x(theta, phi) = sum(a^m_l * Y^m_l(theta, phi)), and get coefficients a^m_l
// Get x, y, z for each (theta, phi)
std::vector<double> X(numV), Y(numV), Z(numV);
for(int i=0; i<numV; ++i){
X[i] = mesh.vertices[i][0];
Y[i] = mesh.vertices[i][1];
Z[i] = mesh.vertices[i][2];
}
// Get sph_harm_values as matrix coefficients
MatrixXd sph_harm_values(numV, (bw+1)*(bw+1));
for(int i=0; i<numV; ++i){
for(int l=0; l<bw; ++l){
for(int m=-l; m<=l; ++m){
sph_harm_values(i, l*(l+1)+m) = std::real(std::pow(-1, m) * boost::math::spherical_harmonic_r(l, m, angles[i][1], angles[i][0]));
}
}
}
// Solve the equation system to get the spherical harmonic decomposition coefficients
VectorXd a_x = sph_harm_values.colPivHouseholderQr().solve(X);
VectorXd a_y = sph_harm_values.colPivHouseholderQr().solve(Y);
VectorXd a_z = sph_harm_values.colPivHouseholderQr().solve(Z);
// Generate new point clouds from the coefficients
std::vector<double> new_X(numV), new_Y(numV), new_Z(numV);
for(int i=0; i<numV; ++i){
for(int l=0; l<bw; ++l){
for(int m=-l; m<=l; ++m){
new_X[i] += std::real(std::pow(-1, m) * a_x(l*(l+1)+m) * boost::math::spherical_harmonic_r(l, m, angles[i][1], angles[i][0]));
new_Y[i] += std::real(std::pow(-1, m) * a_y(l*(l+1)+m) * boost::math::spherical_harmonic_r(l, m, angles[i][1], angles[i][0]));
new_Z[i] += std::real(std::pow(-1, m) * a_z(l*(l+1)+m) * boost::math::spherical_harmonic_r(l, m, angles[i][1], angles[i][0]));
}
}
}
// Write new point clouds to obj file
std::ofstream output(outName);
for(int i=0; i<numV; ++i){
output << "v " << new_X[i] << " " << new_Y[i] << " " << new_Z[i] << std::endl;
}
output.close();
return 0;
}