MaxNumCompThreads now issues a warning, and will be removed in a future version
Instead, you should first assign the returned structure to a variable, and then dot index the variable: s = getStruct It is no longer valid to access fields of the structure by directly dot indexing the function's return value, as shown here: getStruct.fieldA StructOut = struct('fieldA', 5, 'fieldB', 10) if you have a function, such as the following, that returns a struct array:.In previous releases, the diagonal of R could contain complex and negative elements. Since the QR factorization is not unique, the answer is still correct. In the output of the qr function, R now contains only real, nonnegative diagonal elements. The following functions are now deprecated (but still work): isstr, setstr, str2mat, strread, strvcat, textread. Options -inline and -argcheck of mex command removed In MATLAB version 7.11, these functions return the expected values ( true for isequalwithequalnans, and false for isequal) when used to compare object arrays. Similarly, calling isequal to compare identical arrays of objects that contain NaNs incorrectly returned true. In previous versions of MATLAB, calling isequalwithequalnans to compare identical arrays of objects that contain one or more NaN (Not a Number) values incorrectly returned false. The output can also be a scalar object, as long as the UniformOutput flag is set to false. The arrayfun function now accepts an array of objects as an input. New functions: isrow, iscolumn, and ismatrixĭeprecated functions: bessel, erfcore, intwarning, mmreader, wavplay, wavrecord, wk1finfo, wk1read, wk1writeĬore MATLAB functions do 64-bit integer arithmetic ( int64 and uint64 classes)Ĭhange in the behavior of timeseries objects
# WSZ: smoothing window size needs, which must be odd number, # a: NumPy 1-D array containing the data to be smoothed Thus, we would have an implementation to handle generic window sizes, like so - def smooth(a,WSZ):
Then, simply append the special case treated values for the boundary elems. So, to replicate the same implementation on NumPy/Python, we can use NumPy's 1D convolution for getting sliding windowed summations and divide them by the window length to give us the average results. The first few elements of yy are given by As per the linked docs, those boundary cases are computed with these formulae - yy = smooth(y) smooths the data in the column vector y.
Matlab repmat windows#
MATLAB's smoooth func is basically same as averaging across sliding windows of length 5, except the way it treats the 2 elems at either ends.