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# scipy.io.whosmat

scipy.io.whosmat(file_name, appendmat=True, **kwargs)

  List variables inside a MATLAB file.

  Parameters:
    file_name str

      Name of the mat file (do not need .mat extension if appendmat==True) Can also pass open file-like object.

    appendmat bool, optional

      True to append the .mat extension to the end of the given filename, if not already present. Default is True.

    byte_order str or None, optional

      None by default, implying byte order guessed from mat file. Otherwise can be one of (‘native’, ‘=’, ‘little’, ‘<’, ‘BIG’, ‘>’).

    mat_dtype bool, optional

      If True, return arrays in same dtype as would be loaded into MATLAB (instead of the dtype with which they are saved).

    squeeze_me bool, optional

      Whether to squeeze unit matrix dimensions or not.

    chars_as_strings bool, optional

      Whether to convert char arrays to string arrays.

    matlab_compatible bool, optional

      Returns matrices as would be loaded by MATLAB (implies squeeze_me=False, chars_as_strings=False, mat_dtype=True, struct_as_record=True).

    struct_as_record bool, optional

      Whether to load MATLAB structs as NumPy record arrays, or as old-style NumPy arrays with dtype=object. Setting this flag to False replicates the behavior of SciPy version 0.7.x (returning numpy object arrays). The default setting is True, because it allows easier round-trip load and save of MATLAB files.

  Returns:
    variables list of tuples

      A list of tuples, where each tuple holds the matrix name (a string), its shape (tuple of ints), and its data class (a string). Possible data classes are: int8, uint8, int16, uint16, int32, uint32, int64, uint64, single, double, cell, struct, object, char, sparse, function, opaque, logical, unknown.

  Notes

  v4 (Level 1.0), v6 and v7 to 7.2 matfiles are supported.

  You will need an HDF5 python library to read matlab 7.3 format mat files (e.g. h5py). Because SciPy does not supply one, we do not implement the HDF5 / 7.3 interface here.

  New in version 0.12.0.

  Examples

      >>> from io import BytesIO
      >>> import numpy as np
      >>> from scipy.io import savemat, whosmat

  Create some arrays, and use `savemat` to write them to a `BytesIO` instance.

      >>> a = np.array([[10, 20, 30], [11, 21, 31]], dtype=np.int32)
      >>> b = np.geomspace(1, 10, 5)
      >>> f = BytesIO()
      >>> savemat(f, {'a': a, 'b': b})

  Use `whosmat` to inspect `f`. Each tuple in the output list gives the name, shape and data type of the array in `f`.

      >>> whosmat(f)
      [('a', (2, 3), 'int32'), ('b', (1, 5), 'double')]