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https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_240834

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_240836

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_240838

      
        >> x
       
        x =
       
        Python
       
        ndarray with properties:
       
        T: [1x1 py.numpy.ndarray]
       
        base: [1x1 py.NoneType]
       
        ctypes: [1x1 py.numpy.core.
       
        _
       
        internal.
       
        _
       
        ctypes]
       
        data: [1x1 py.buffer]
       
        dtype: [1x1 py.numpy.dtype]
       
        flags: [1x1 py.numpy.flagsobj]
       
        flat: [1x1 py.numpy.flatiter]
       
        imag: [1x1 py.numpy.ndarray]
       
        itemsize: 8
       
        nbytes: 128
       
        ndim: 2
       
        real: [1x1 py.numpy.ndarray]
       
        shape: [1x1 py.tuple]
       
        size: 16
       
        strides: [1x1 py.tuple]
       
        [[ 0.84501423  0.72129285  0.53197632  0.56672641]
       
        [ 0.353229    0.40170347  0.99848745  0.74374568]
       
        [ 0.15023563  0.72432396  0.05446134  0.00455388]
       
        [ 0.85040029  0.65360657  0.35074251  0.08965406]]
       
        >>

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#answer_485960

Beginning in MATLAB R2018b, Python functions that accept numpy arrays may also accept MATLAB arrays without explicit conversion. When necessary, a numpy array can be created explicitly from a MATLAB array. For example, if you have a supported version of Python that is installed with the numpy library, you can do the following:

>> x = rand(2,2); % MATLAB array

>> y = py.numpy.array(x); % numpy array

y =

Python ndarray:

0.5943 0.8064

0.6133 0.1372

Use details function to view the properties of the Python object.

Use double function to convert to a MATLAB array.

Also beginning in MATLAB R2018b, it is possible to convert numeric numpy arrays returned from Python into MATLAB arrays. For example:

>> y = py.numpy.random.random([int32(2), int32(2)]) % numpy array

y =

Python ndarray:

0.5943 0.8064

0.6133 0.1372

Use details function to view the properties of the Python object.

Use double function to convert to a MATLAB array.

>> x = 2*double(y) % MATLAB array

x =

1.1885 1.6129

1.2266 0.2744

See the MATLAB documentation on Passing Matrices and Multidimensional Arrays for additional Information.

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_1612433

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_1741704

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_2162490

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#answer_157736

Here's another approach. This is alluded to by http://www.mathworks.com/help/matlab/matlab_external/handling-data-returned-from-python.html

      
        data = double(py.array.array(
       
        'd'
       
        ,py.numpy.nditer(x)));
       
        %d is for double, see link below on types
       
        data = reshape(data,[4 4])';
       
        %Could incorporate x.shape here ...

py.array.array is apparently the way Matlab suggests getting data from Python into Matlab (see link above) https://docs.python.org/2/library/array.html

This however requires an iterable over which the array can be constructed, hence the call to nditer()

Once the value is an array, then Matlab has written functionality for casting to a Matlab type, in this case via double.

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_247816

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_283741

Minor modifications to make the code more generalizable, including some you suggested in your comments:

      
        data_row = double(py.array.array(
       
        'd'
       
        , py.numpy.nditer(x, pyargs(
       
        'order'
       
        ,
       
        'F'
       
        ))));
       
        % Add order='F' to get data in column-major order (as in Fortran 'F' and Matlab
       
        data_size = cell2mat(cell(x.shape));
       
        data = reshape(data, data_size);
       
        % No need for transpose, since we're retrieving the data in column major order

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_373119

I love this solution! I had to modify it slightly (I'm not sure if this is a version issue or not), as cell2mat didn't work properly for me as cell() returned a cell array of py.int, not integers... Then I got to addressing the issue of multidimensional arrays, and realised that things were getting more complicated. As a result, I've written two functions - mat2nparray and nparray2mat - that will convert between the two formats:

      
        function
       
        result = mat2nparray( matarray )
       
        %mat2nparray Convert a Matlab array into an nparray
       
        %   Convert an n-dimensional Matlab array into an equivalent nparray
       
        data_size=size(matarray);
       
        if
       
        length(data_size)==1
       
        % 1-D vectors are trivial
       
        result=py.numpy.array(matarray);
       
        elseif
       
        length(data_size)==2
       
        % A transpose operation is required either in Matlab, or in Python due
       
        % to the difference between row major and column major ordering
       
        transpose=matarray';
       
        % Pass the array to Python as a vector, and then reshape to the correct
       
        % size
       
        result=py.numpy.reshape(transpose(:)', data_size);
       
        else
       
        % For an n-dimensional array, transpose the first two dimensions to
       
        % sort the storage ordering issue
       
        transpose=permute(matarray,[2 1 3:length(data_size)]);
       
        % Pass it to python, and then reshape to the python style of matrix
       
        % sizing
       
        result=py.numpy.reshape(transpose(:)', fliplr(size(transpose)));
       
        end
       
        end

and

      
        function
       
        result = nparray2mat( nparray )
       
        %nparray2mat Convert an nparray from numpy to a Matlab array
       
        %   Convert an n-dimensional nparray into an equivalent Matlab array
       
        data_size = cellfun(@int64,cell(nparray.shape));
       
        if
       
        length(data_size)==1
       
        % This is a simple operation
       
        result=double(py.array.array(
       
        'd'
       
        , py.numpy.nditer(nparray)));
       
        elseif
       
        length(data_size)==2
       
        % order='F' is used to get data in column-major order (as in Fortran
       
        % 'F' and Matlab)
       
        result=reshape(double(py.array.array(
       
        'd'
       
        ,
       
        ...
       
        py.numpy.nditer(nparray, pyargs(
       
        'order'
       
        ,
       
        'F'
       
        )))),
       
        ...
       
        data_size);
       
        else
       
        % For multidimensional arrays more manipulation is required
       
        % First recover in python order (C contiguous order)
       
        result=double(py.array.array(
       
        'd'
       
        ,
       
        ...
       
        py.numpy.nditer(nparray, pyargs(
       
        'order'
       
        ,
       
        'C'
       
        ))));
       
        % Switch the order of the dimensions (as Python views this in the
       
        % opposite order to Matlab) and reshape to the corresponding C-like
       
        % array
       
        result=reshape(result,fliplr(data_size));
       
        % Now transpose rows and columns of the 2D sub-arrays to arrive at the
       
        % correct Matlab structuring
       
        result=permute(result,[2 1 3:length(data_size)]);
       
        end
       
        end

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_437274

Hey thanks for all these beautiful functions. I had to modify David's solution for multi-dimensional arrays. Here is the new code, embedded in a matlab class using static functions, and some test code:

      
        classdef
       
        matpy
       
        %MATPY Summary of this class goes here
       
        %   Detailed explanation goes here
       
        properties
       
        end
       
        methods
       
        (Static)
       
        function
       
        result = mat2nparray( matarray )
       
        %mat2nparray Convert a Matlab array into an nparray
       
        %   Convert an n-dimensional Matlab array into an equivalent nparray
       
        data_size=size(matarray);
       
        if
       
        length(data_size)==1
       
        % 1-D vectors are trivial
       
        result=py.numpy.array(matarray);
       
        elseif
       
        length(data_size)==2
       
        % A transpose operation is required either in Matlab, or in Python due
       
        % to the difference between row major and column major ordering
       
        transpose=matarray';
       
        % Pass the array to Python as a vector, and then reshape to the correct
       
        % size
       
        result=py.numpy.reshape(transpose(:)', int32(data_size));
       
        else
       
        % For an n-dimensional array, transpose the first two dimensions to
       
        % sort the storage ordering issue
       
        transpose=permute(matarray,[length(data_size):-1:1]);
       
        % Pass it to python, and then reshape to the python style of matrix
       
        % sizing
       
        result=py.numpy.reshape(transpose(:)', int32(fliplr(size(transpose))));
       
        end
       
        end
       
        function
       
        result = nparray2mat( nparray )
       
        %nparray2mat Convert an nparray from numpy to a Matlab array
       
        %   Convert an n-dimensional nparray into an equivalent Matlab array
       
        data_size = cellfun(@int64,cell(nparray.shape));
       
        if
       
        length(data_size)==1
       
        % This is a simple operation
       
        result=double(py.array.array(
       
        'd'
       
        , py.numpy.nditer(nparray)));
       
        elseif
       
        length(data_size)==2
       
        % order='F' is used to get data in column-major order (as in Fortran
       
        % 'F' and Matlab)
       
        result=reshape(double(py.array.array(
       
        'd'
       
        ,
       
        ...
       
        py.numpy.nditer(nparray, pyargs(
       
        'order'
       
        ,
       
        'F'
       
        )))),
       
        ...
       
        data_size);
       
        else
       
        % For multidimensional arrays more manipulation is required
       
        % First recover in python order (C contiguous order)
       
        result=double(py.array.array(
       
        'd'
       
        ,
       
        ...
       
        py.numpy.nditer(nparray, pyargs(
       
        'order'
       
        ,
       
        'C'
       
        ))));
       
        % Switch the order of the dimensions (as Python views this in the
       
        % opposite order to Matlab) and reshape to the corresponding C-like
       
        % array
       
        result=reshape(result,fliplr(data_size));
       
        % Now transpose rows and columns of the 2D sub-arrays to arrive at the
       
        % correct Matlab structuring
       
        result=permute(result,[length(data_size):-1:1]);
       
        end
       
        end
       
        function
       
        test()
       
        A = 1:5;
       
        Anp = matpy.mat2nparray(A);
       
        sa = size(A);
       
        sAnp = cellfun( @(x) double(x), cell(Anp.shape));
       
        assert (all(sAnp == sa));
       
        for
       
        i1=1:size(A,1)
       
        for
       
        i2=1:size(A,2)
       
        assert(A(i1,i2) == Anp.item(int32(i1-1), int32(i2-1)));
       
        end
       
        end
       
        Anpm = matpy.nparray2mat(Anp);
       
        assert(all(A == Anpm));
       
        A = reshape(1:6, [2,3]);
       
        Anp = matpy.mat2nparray(A);
       
        sa = size(A);
       
        sAnp = cellfun( @(x) double(x), cell(Anp.shape));
       
        assert (all(sAnp == sa));
       
        for
       
        i1=1:size(A,1)
       
        for
       
        i2=1:size(A,2)
       
        assert(A(i1,i2) == Anp.item(int32(i1-1), int32(i2-1)));
       
        end
       
        end
       
        Anpm = matpy.nparray2mat(Anp);
       
        assert(all(all(A == Anpm)));
       
        A = reshape(1:(2*3*4), [2,3,4]);
       
        Anp = matpy.mat2nparray(A);
       
        sa = size(A);
       
        sAnp = cellfun( @(x) double(x), cell(Anp.shape));
       
        assert (all(sAnp == sa));
       
        for
       
        i1=1:size(A,1)
       
        for
       
        i2=1:size(A,2)
       
        for
       
        i3=1:size(A,3)
       
        display(sprintf(
       
        '%d %d %d -> %f %f'
       
        , i1,i2,i3, A(i1,i2,i3), Anp.item(int32(i1-1), int32(i2-1), int32(i3-1))));
       
        assert (A(i1,i2,i3) == Anp.item(int32(i1-1), int32(i2-1), int32(i3-1)))
       
        end
       
        end
       
        end
       
        Anpm = matpy.nparray2mat(Anp);
       
        assert(all(all(all(A == Anpm))));
       
        end
       
        end
       
        end

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_454374

Hi Christoph,

Thanks for the code! However, it doesn't support the conversion of scalar arrays:

      
        Error
       
        using reshape
       
        Size
       
        vector must have at least two elements.
       
        Error
       
        in matpy.nparray2mat (line 55)
       
        result=reshape(result,fliplr(data_size));

This can be solved by modifying the first `if` statement in nparray2mat to:

      
        if
       
        any(numel(data_size) == [0,1])

Cheers!

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_457649

To build further upon y'all's work, I've made a really rough prototype, using `matpy`, to get `numpy`-friendly proxy that allows more natural referencing, indexing / slicing, etc., via `subsref` and `subsassgn`:

Example code:

      
        pyA = py.numpy.eye(3);
       
        mlA = NumPyProxy(pyA);
       
        mlA(:)
       
        double(mlA(:))
       
        mlA(3, 2)
       
        sub = mlA([2, 1], 3)
       
        double(sub)
       
        % 2017-05-30T01:09-04:00 - Presently a bug, wrong order
       
        sub2 = mlA([1, 2], 3)
       
        double(sub2)
       
        mlA([2, 1], 3) = 100 * [1, 2]
       
        mlA(:) = 5

Note that PyProxy also permits casting of >1-D arrays, using `subsref` tricks, whereas MATLAB R2016b presently does not permit passing 2-D matrices (at least, as far as I've tried using other people's examples).

This is an ongoing effort to test some Drake functionality: GitHub Issue

This has only been tested / tinkered with in R2016b, and still needs refinement / robustification.

I will try to see if hacks like this aren't needed in future versions of MATLAB.

Thanks a ton for taking the time to post y'all's stuff!

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#answer_283271

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_637254

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_656215

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#answer_153916

Here's a very ugly solution.

      
        temp = cellfun(@cell,cell(x.tolist),
       
        'un'
       
        ,0);
       
        data = cell2mat(vertcat(temp {:}));

A slightly cleaner solution:

      
        data = typecast(uint8(char(x.flat.base.data)),
       
        'double'
       
        );
       
        data = reshape(data,[4 4])';
       
        %Could incorporate x.shape here ...

Then of course:

plot(data)

Sorry I don't have time to explain how you would know that you need to do these things. I have to run!

Jim

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_241064

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_241177

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_241232

Jim, it's a bit complicated. For example, if I run:

      
        for
       
        jj = 1:10
       
        x = py.numpy.random.random([10,10]);
       
        try
       
        data = typecast(uint8(char(x.flat.base.data)),
       
        'double'
       
        );
       
        disp(numel(data))
       
        catch
       
        me
       
        warning(me.message)
       
        end
       
        end

I should get 100 doubles, but I end up with something very different:

      
        Warning: The first
       
        input must contain a multiple of 8 elements to convert from uint8 (8 bits) to double (64 bits).
       
        29.00
       
        8.00
       
        7.00
       
        10.00
       
        Warning: The first
       
        input must contain a multiple of 8 elements to convert from uint8 (8 bits) to double (64 bits).
       
        24.00
       
        20.00
       
        Warning: The first
       
        input must contain a multiple of 8 elements to convert from uint8 (8 bits) to double (64 bits).
       
        56.00

Do you have any idea why the typecasting is broken? I would guess that it's something to do with the conversion from the flat.base to char.

Peter.

https://se.mathworks.com/matlabcentral/answers/157347-convert-python-numpy-array-to-double#comment_241763

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