Convert numpy ndarray to matlab mlarray in python
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mlarray to ndarray: np.asarray(x._data, dtype=dtype)
but inversely, ndarray to mlarray: matlab.double(x.tolist()), which is extremely slow, is there a more efficient way to do this? Thank u for answering.
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Accepted Answer
Al Danial
on 28 Apr 2022
Looks like you're making the data load unnecessarily complicated. The file actually loads cleanly into a NumPy array:
from scipy.io import loadmat
data = loadmat('data.mat', squeeze_me=True)
x = data['data']
print(x)
produces
array([[0.00000000e+00, 5.90000000e+01, 5.90000000e+01, 2.25296241e+05],
[1.00000000e+00, 6.20000000e+01, 5.81599120e+01, 5.93159561e+04],
[2.00000000e+00, 1.00000000e+02, 9.47518190e+01, 3.22666379e+04],
...,
[2.04500000e+03, 4.00000000e+00, 4.88991300e+00, 3.01840538e+04],
[2.04600000e+03, 2.00000000e+00, 2.26899200e+00, 6.46032757e+04],
[2.04700000e+03, 1.00000000e+00, 1.00000000e+00, 1.18671912e+05]])
Simplify your function load_mat to
def load_mat(pth_mat, key=None):
data = loadmat(pth_mat, squeeze_me=True)
print(data.keys()) if key is None else None
return data[key]
then call it like this
x1 = load_mat('data.mat', 'data')
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More Answers (3)
Al Danial
on 21 Apr 2022
Which version of MATLAB? 2020a and newer (I don't have easy access to older versions) should just be able to do
>> mx = double(x);
without a conversion to a list.
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Christopher Wunder
on 23 Aug 2022
This is not possible for me either.
The function _is_initializer in matlab._internal.mlarray_utils.py checks for the input to be of type collections.abc.Sequence and a numpy.ndarray fails to be of such a type. The only way (without altering the package) is to convert the array beforehand or pass any kind of Sequence to it instead of an array.
Al Danial
on 24 Apr 2022
Now I'm curious what is in your variable x. Can you make a small version of this data, write it to a .mat file, then attach the .mat file?
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