![]() # Supported in R2022a and earlier: must initialize a matlab.uint16 from From the MATLAB Release Notes for R2022b, under the "External Language Interfaces" section: import matlab.engineīuf = numpy.array(, ], dtype='uint16') UPDATE: Furthermore, from MATLAB R2022b on, objects that implement the buffer protocol (such as NumPy objects) can be passed directly into MATLAB functions that are called via Python. Thus, for instance, a matlab.double can be constructed from a NumPy double array. That is, matlab objects can be constructed from objects that implement the buffer protocol. Construction in the opposite direction (which is the subject of the question here) is supported as well. This means that you can pass them into NumPy array constructors. ![]() tested with Matlab R2016b + python 3.5.4 (64bit)įrom MATLAB R2022a on, matlab.double (and matlab.int8, matlab.uint8, etc.) objects implement the buffer protocol. Maybe it helps someone to shorten the time of struggling. sorry for necroposting but I struggled a lot with its and this topic was one of the closest hits. Second trick is to pass the data to matlabs double in an efficient way (via pythons native array.array). It relies on the fact that matlabs double seems be working efficiently on arrays compared to cells/matrices. Shape = py.array.array('d',A.flatten('F').tolist()) Mod = py.importlib.import_module('dump_reader') Not sure if it counts as "simpler" but I found a solution to move data from a numpy arrray created in a python script which is called by matlab quite fast:ĭump_reader.py (python source): import numpy So the speed gain is marginal with this approach. Unfortunately, matlab is not using the _data attribute efficiently internally, and is iterating over it one item at a time rather than using the python memoryview protocol :(. It seems that any indexable object can be placed in the.Matlab seems to only look at type(x)._name_ and type(x)._module_ to determine if it understands the type.The observations necessary to get here were: _define_wrapper(matlab.logical, np.bool_) _define_wrapper(matlab.uint64, np.uint64) _define_wrapper(matlab.uint32, np.uint32) _define_wrapper(matlab.uint16, np.uint16) _define_wrapper(matlab.single, np.single) _define_wrapper(matlab.double, np.double) # this tricks matlab into accepting our new type T = type(matlab_type._name_, (matlab_type,), dict( Self._strides = _get_strides(self._size)ĭef _define_wrapper(matlab_type, numpy_type): Self._is_complex = np.issubdtype(arr.dtype, np.complexfloating) # Importing it under a different name would be a bad idea.įrom _internal.mlarray_utils import _get_strides, _get_mlsizeĪssert arr.dtype = type(self)._numpy_type # mangles the path making it appear as `_internal`. # This is actually `matlab._internal`, but matlab/_init_.py Here's a solution that avoids iterating in python, or using file IO - at the expense of relying on (ugly) matlab internals: import matlab
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