警告: You've specified spherical coordinates, but your upper longitude value exceeds 360 degrees. Are you sure you have spherical data? > 位置：read_sms_mesh (第 236 行) 函数或变量 'read_obc_nodes' 无法识别。 出错 read_sms_mesh (第 321 行) Mobj.read_obc_nodes = read_obc_nodes;

这个错误信息表明你在读取一个网格文件时使用了球面坐标系，但是你指定的经度值超出了360度的范围，因此程序无法识别你的数据。建议你检查一下你的数据，看看是否使用了正确的坐标系和正确的数值范围。

另外，这个错误信息还提到了一个函数或变量 "read_obc_nodes" 无法识别，可能是因为你没有正确地定义或者导入这个函数或变量。你可以检查一下代码中是否有定义这个函数或变量，并且是否正确导入了相关的库。

相关问题

has no member named ‘maxmemory’ serverLog(LL_WARNING,"WARNING: You specified a maxmemory value that is less than 1MB (current value is %llu bytes). Are you sure this is what you really want?", server.maxmemory);

这个错误提示说明在你的代码中，使用了一个名为'maxmemory'的成员变量，但是该成员变量在相关的对象或结构体中并不存在。

可能的原因是：
1. 你可能在错误的对象或结构体中引用了'maxmemory'成员变量。请确保你正在访问正确的对象或结构体，并检查是否存在名为'maxmemory'的成员变量。
2. 'maxmemory'成员变量可能被命名为其他名称，或者根本不存在。请仔细检查代码中的拼写错误或名称不匹配的情况。
3. 如果你是在引用第三方库或框架的代码，可能该库或框架版本有所差异，导致相应的成员变量不存在或已被更改。请检查文档或更新相关库或框架版本。

请根据上述可能的原因逐一排查，并确保代码中使用的成员变量名正确存在。如有需要，请提供更多相关的代码和上下文信息，以便更准确地定位问题所在。

Write a function with the declaration: smoothed=rectFilt(x,width). The filter should take a vector of noisy data (x) and smooth it by doing a symmetric moving average with a window of the specified width. For example if width=5, then smoothed(n) should equal mean(x(n-2:n+2)). Note that you may have problems around the edges: when n<3 and n>length(x)-2. a. The lengths of x and smoothed should be equal. b. For symmetry to work, make sure that width is odd. If it isn’t, increase it by 1 to make it odd and display a warning, but still do the smoothing. c. Make sure you correct edge effects so that the smoothed function doesn’t deviate from the original at the start or the end. Also make sure you don’t have any horizontal offset between the smoothed function and the original (since we’re using a symmetric moving average, the smoothed values should lie on top of the original data). d. You can do this using a loop and mean (which should be easy but may be slow), or more efficiently by using conv (if you are familiar with convolution). e. Load the mat file called noisyData.mat. It contains a variable x which is a noisy line. Plot the noisy data as well as your smoothed version, like below (a width of 11 was used):

Sure, here's the code for the rectFilt function:

function smoothed = rectFilt(x, width)
%RECTFILT  Smooths a vector of noisy data using a symmetric moving average with a specified window width.
%   smoothed = rectFilt(x, width) returns a vector that is the same size as x, where each element in the vector is the
%   mean of a symmetric window of width 'width' centered around that element in x.

% Check that width is odd
if mod(width, 2) == 0
    warning('Width is even. Increasing by 1 to make it odd.');
    width = width + 1;
end

% Add padding to handle edge effects
x_padded = [repmat(x(1), [1, (width-1)/2]), x, repmat(x(end), [1, (width-1)/2])];

% Compute moving average using convolution
weights = ones([1, width])/width;
smoothed = conv(x_padded, weights, 'valid');

% Make sure the smoothed function doesn't deviate from the original at the start or the end
smoothed(1:(width-1)/2) = x(1:(width-1)/2);
smoothed(end-(width-1)/2+1:end) = x(end-(width-1)/2+1:end);

end

To use this function on the `noisyData.mat` file and plot the results, you can run the following code:

load('noisyData.mat');

% Smooth the data with a width of 11
smoothed = rectFilt(x, 11);

% Plot the original data and the smoothed data
plot(x, 'b');
hold on;
plot(smoothed, 'r');
legend('Original', 'Smoothed');

I hope this helps! Let me know if you have any questions.