matlab get_Bni
时间: 2023-10-11 19:05:50 浏览: 39
在MATLAB中,如果您想获取某个变量的Bni值,您需要先确保该变量已经存在并被定义。然后,您可以直接使用该变量的名称后加上`.Bni`来访问其Bni值。例如,假设您的变量名称为`data`,您可以使用以下代码获取其Bni值:
```matlab
bni_value = data.Bni;
```
请注意,这只适用于在MATLAB工作环境中已经定义了具有Bni属性的对象或结构。如果您的情况不适用于此,请提供更多信息,以便我可以提供更具体的帮助。
相关问题
将以下代码转化为matlab代码表示:import xlrd import sympy import numpy as np from scipy import linalg #%% queue = [ 0, 29, 17, 2, 1, 20, 19, 26, 18, 25, 14, 6, 11, 7, 15, 9, 8, 12, 27, 16, 10, 13, 5, 4, 3, 22, 28, 24, 23, 21, 0] def read_data_model(): data = xlrd.open_workbook("/Users/lzs/Downloads/2020szcupc/data/C2.xlsx") table = data.sheet_by_name("Sheet1") rowNum = table.nrows colNum = table.ncols consumes = [] for i in range(1, rowNum): # 忽略DC的消耗 if i == 1: pass else: consumes.append(0 if table.cell_value(i, 3) == '/' else table.cell_value(i, 3)) return consumes #%% 获得矩阵A def get_A_matrix(data): A = np.ones([29,29], dtype = float) diagonal = np.eye(29) for i in range(29): for j in range(29): A[i][j] = data['consumes'][j] / data['r'] A = A - diagonal return A #%% def get_b_maatrix(data): b = np.ones([29,1], dtype=float) for i in range(29): b[i][0] = -data['dst']*data['consumes'][i]/data['velocity']+data['f'] for j in range(29): b[i][0] = b[i][0] + data['f']*data['consumes'][i]/data['r'] return b #%% 数值解 def numerical(data): data['velocity'] = 50 data['dst'] = 11469 data['r'] = 200 data['f'] = 10 A = get_A_matrix(data) b = get_b_maatrix(data) x = linalg.solve(A, b) return x #%% 符号解决方案 def symbolic(data): data['velocity'] = sympy.symbols("v", integer = True) data['dst'] = 12100 data['r'] = sympy.symbols("r", integer = True) data['f'] = sympy.symbols("f", integer = True) # 获取矩阵A并转移到符号矩阵M A = np.ones([29,29], dtype = float).tolist() diagonal = np.eye(29).tolist() for i in range(29): for j in range(29): A[i][j] = data['consumes'][j] / data['r'] - diagonal[i][j] M = sympy.Matrix(A) # 得到矩阵b并转移到符号矩阵b b = np.ones([29,1], dtype=float).tolist() for i in range(29): b[i][0] = -data['dst']*data['consumes'][i]/data['velocity']+data['f'] for j in range(29): b[i][0] = b[i][0] + data['f']*data['consumes'][i]/data['r'] b = sympy.Matrix(b) # LU solver x = M.LUsolve(b) return x #%% 主功能 if name == 'main': data = {} data['consumes'] = read_data_model() options = {"numerical":1, "symbolic":2} option = 1 if option == options['numerical']: x = numerical(data) print(x) elif option == options['symbolic']: x = symbolic(data) print(x) else: print("WARN!!!")
以下是将给定代码转化为 Matlab 代码的结果:
```matlab
%% 导入所需库
import matlab.io.*
import sympy.*
import numpy as np
from scipy import linalg
%% 读取数据模型
function consumes = read_data_model()
data = xlsread('/Users/lzs/Downloads/2020szcupc/data/C2.xlsx');
consumes = [];
for i = 2:size(data,1)
if i == 2
continue % 忽略DC的消耗
end
consumes = [consumes, 0];
if data(i,4) ~= '/'
consumes(end) = data(i,4);
end
end
end
%% 获取矩阵A
function A = get_A_matrix(data)
A = ones(29,29);
diagonal = eye(29);
for i = 1:29
for j = 1:29
A(i,j) = data.consumes(j) / data.r;
end
A(i,i) = A(i,i) - 1;
end
end
%% 获取矩阵b
function b = get_b_maatrix(data)
b = ones(29,1);
for i = 1:29
b(i) = -data.dst*data.consumes(i)/data.velocity + data.f;
for j = 1:29
b(i) = b(i) + data.f*data.consumes(j)/data.r;
end
end
end
%% 数值解
function x = numerical(data)
data.velocity = 50;
data.dst = 11469;
data.r = 200;
data.f = 10;
A = get_A_matrix(data);
b = get_b_maatrix(data);
x = linalg.solve(A, b);
end
%% 符号解决方案
function x = symbolic(data)
syms v r f
data.velocity = v;
data.dst = 12100;
data.r = r;
data.f = f;
% 获取矩阵A并转移到符号矩阵M
A = ones(29,29);
diagonal = eye(29);
for i = 1:29
for j = 1:29
A(i,j) = data.consumes(j) / data.r;
end
A(i,i) = A(i,i) - 1;
end
M = sym(A);
% 得到矩阵b并转移到符号矩阵b
b = ones(29,1);
for i = 1:29
b(i) = -data.dst*data.consumes(i)/data.velocity + data.f;
for j = 1:29
b(i) = b(i) + data.f*data.consumes(j)/data.r;
end
end
b = sym(b);
% LU求解
x = M\LUsolve(b);
end
%% 主功能
data = struct();
data.consumes = read_data_model();
option = 1;
if option == 1
x = numerical(data);
disp(x);
elseif option == 2
x = symbolic(data);
disp(x);
else
disp('WARN!!!');
end
```
需要注意的是,Matlab 中没有 `xlrd` 库,需要使用 `xlsread` 函数来读取 Excel 文件。另外,Matlab 中的 `eye` 函数可以直接得到单位矩阵,无需转化为列表再使用 `diag` 函数。
暗通道去雾matlab代码
以下是一份 MATLAB 实现的暗通道去雾代码,仅供参考:
```matlab
function [J, A] = dehaze(I, w_size, lambda)
% 对输入图像进行预处理
I = im2double(I);
dark = min(I, [], 3); % 获取暗通道图像
A = get_atmosphere(I, dark); % 获取大气光强度
trans = get_transmission(I, A, w_size, lambda); % 获取透射率
J = get_scene_radiance(I, trans, A); % 获取场景辐射度
end
% 获取暗通道图像
function dark = get_dark_channel(I)
[m, n, ~] = size(I);
padded = padarray(I, [0, (m-n)/2], 'symmetric', 'both');
dark = min(padded,[],3);
end
% 获取大气光强度
function A = get_atmosphere(I, dark)
[~, idx] = maxk(dark(:), round(numel(dark)*0.001)));
A = max(I(idx));
end
% 获取透射率
function trans = get_transmission(I, A, w_size, lambda)
beta = 1; % 参数 beta
t = 1 - beta*get_dark_channel(I)./A; % 估计透射率
trans = guidedfilter(rgb2gray(I), t, w_size, lambda); % 使用引导滤波器平滑透射率
end
% 获取场景辐射度
function J = get_scene_radiance(I, trans, A)
J = zeros(size(I));
for i = 1:3
J(:, :, i) = (I(:, :, i) - A(i))./max(trans, 0.1) + A(i);
end
end
% 引导滤波器
function q = guidedfilter(I, p, w_size, lambda)
mean_I = boxfilter(I, w_size)./boxfilter(ones(size(I)), w_size); % I 的均值
mean_p = boxfilter(p, w_size)./boxfilter(ones(size(p)), w_size); % p 的均值
mean_Ip = boxfilter(I.*p, w_size)./boxfilter(ones(size(I)), w_size); % I 和 p 的乘积的均值
mean_II = boxfilter(I.*I, w_size)./boxfilter(ones(size(I)), w_size); % I 的平方的均值
var_I = mean_II - mean_I.*mean_I; % I 的方差
cov_Ip = mean_Ip - mean_I.*mean_p; % I 和 p 的协方差
a = cov_Ip./(var_I + lambda); % 引导滤波器的参数 a
b = mean_p - a.*mean_I; % 引导滤波器的参数 b
mean_a = boxfilter(a, w_size)./boxfilter(ones(size(a)), w_size); % a 的均值
mean_b = boxfilter(b, w_size)./boxfilter(ones(size(b)), w_size); % b 的均值
q = mean_a.*I + mean_b; % 引导滤波器的输出 q
end
% 方框滤波器
function imDst = boxfilter(imSrc, r)
[hei, wid] = size(imSrc);
imDst = zeros(size(imSrc));
cum = cumsum(imSrc, 1);
imDst(1:r+1, :) = cum(r+1:2*r+1, :);
imDst(r+2:hei-r, :) = cum(2*r+2:hei, :) - cum(1:hei-2*r-1, :);
imDst(hei-r+1:hei, :) = repmat(cum(hei, :), [r, 1]) - cum(hei-2*r:hei-r-1, :);
cum = cumsum(imDst, 2);
imDst(:, 1:r+1) = cum(:, r+1:2*r+1);
imDst(:, r+2:wid-r) = cum(:, 2*r+2:wid) - cum(:, 1:wid-2*r-1);
imDst(:, wid-r+1:wid) = repmat(cum(:, wid), [1, r]) - cum(:, wid-2*r:wid-r-1);
end
```
其中,`dehaze` 函数是主函数,输入需要去雾的图像 `I`、窗口大小 `w_size` 和参数 `lambda`,输出去雾后的图像 `J` 和大气光强度 `A`。其他函数分别实现了暗通道图像的计算、大气光强度的估算、透射率的计算、场景辐射度的计算以及引导滤波器和方框滤波器的实现。
需要注意的是,此实现仅供参考,具体的参数选择还需要根据具体情况进行调整。