Davrot at 16:59, 17 October 2025

2025-10-17T16:59:01Z

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	~~== The goal ==~~
	We want to read and write Matlab files under Python.		We want to read and write Matlab files under Python.

Davrot: Created page with "== The goal == We want to read and write Matlab files under Python. Questions to [mailto:davrot@uni-bremen.de David Rotermund] Reminder: Learning Python as Matlab user Please read [https://numpy.org/doc/stable/user/numpy-for-matlab-users.html NumPy for MATLAB users] == Mat files under Python == === MATLAB < 7.3 format mat files === This is a job for [https://docs.scipy.org/doc/scipy/reference/io.html#module-scipy.io scipy.io] {| class="wikitable" |[https://docs...."

2025-10-17T13:57:45Z

Created page with "== The goal == We want to read and write Matlab files under Python. Questions to [mailto:davrot@uni-bremen.de David Rotermund] Reminder: Learning Python as Matlab user Please read [https://numpy.org/doc/stable/user/numpy-for-matlab-users.html NumPy for MATLAB users] == Mat files under Python == === MATLAB < 7.3 format mat files === This is a job for [https://docs.scipy.org/doc/scipy/reference/io.html#module-scipy.io scipy.io] {| class="wikitable" |[https://docs...."

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== The goal ==
We want to read and write Matlab files under Python.

Questions to [mailto:davrot@uni-bremen.de David Rotermund]

Reminder: Learning Python as Matlab user Please read [https://numpy.org/doc/stable/user/numpy-for-matlab-users.html NumPy for MATLAB users]

== Mat files under Python ==

=== MATLAB < 7.3 format mat files ===
This is a job for [https://docs.scipy.org/doc/scipy/reference/io.html#module-scipy.io scipy.io]
{| class="wikitable"
|[https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html#scipy.io.loadmat loadmat](file_name[, mdict, appendmat])
|Load MATLAB file.
|-
|[https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.savemat.html#scipy.io.savemat savemat](file_name, mdict[, appendmat, …])
|Save a dictionary of names and arrays into a MATLAB-style .mat file.
|-
|[https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.whosmat.html#scipy.io.whosmat whosmat](file_name[, appendmat])
|List variables inside a MATLAB file.
|}

==== Read ====
Under Matlab we create a test file<syntaxhighlight lang="matlab">>> A = rand(10,100);
>> save -v7 Test_1.mat A</syntaxhighlight>Under Python we look into the file for information:<syntaxhighlight lang="python">import scipy.io as sio

Info = sio.whosmat("Test_1.mat")
print(Info) # --> [('A', (10, 100), 'double')]</syntaxhighlight>And we can read the data: <syntaxhighlight lang="python">import numpy as np
import scipy.io as sio

mat_data = sio.loadmat("Test_1.mat")

print(
mat_data.keys()
) # -> dict_keys(['__header__', '__version__', '__globals__', 'A'])

a = mat_data["A"]
print(type(a)) # --> <class 'numpy.ndarray'>
print(a.dtype) # --> float64
print(a.shape) # --> (10, 100)</syntaxhighlight>

==== Write ====
Under Python we generate a .mat file:<syntaxhighlight lang="python">import numpy as np
import scipy.io as sio

myrng = np.random.default_rng()

A: np.ndarray = myrng.random((10, 100), dtype=np.float64)
B: str = "Hellp world!"
mdic = {"A": A, "ImportantMessage": B}
sio.savemat("Test_2.mat", mdic)</syntaxhighlight>And read it in under Matlab:<syntaxhighlight lang="matlab">>> load Test_2.mat
>> whos
Name Size Bytes Class Attributes

A 10x100 8000 double
ImportantMessage 1x12 24 char

>> ImportantMessage

ImportantMessage =

'Hellp world!'</syntaxhighlight>

=== MATLAB == 7.3 format mat files (for very big files) ===

==== Read ====
Under Matlab we create another test file<syntaxhighlight lang="matlab">>> A = rand(10,100);
>> B = rand(5, 10,100);
>> save -v7.3 Test_3.mat A B</syntaxhighlight>And we can read the data under Python. But be aware that the matrix is in reversed order now. 10x100 -> 100x10 and 5x10x100 -> 100x10x5<syntaxhighlight lang="python">import numpy as np
import h5py

file_handle = h5py.File("Test_3.mat", "r")

print(file_handle.keys()) # --> <KeysViewHDF5 ['A']>

hdf5_a = file_handle["A"]
print(hdf5_a) # --> <HDF5 dataset "A": shape (100, 10), type "<f8">
a = np.array(hdf5_a)
print(type(a)) # --> <class 'numpy.ndarray'>
print(a.dtype) # --> float64
print(a.shape) # --> (100, 10)

hdf5_b = file_handle["B"]
print(hdf5_b) # --> <HDF5 dataset "B": shape (100, 10), type "<f8">
b = np.array(hdf5_b)
print(type(b)) # --> <class 'numpy.ndarray'>
print(b.dtype) # --> float64
print(b.shape) # --> (100, 10, 5)

file_handle.close() # optional</syntaxhighlight>Note: If you – in a real world example – see a HDF5 group (e.g. <HDF5 group “/#refs#” (16 members)> ) instead of a HDF5 dataset then the variable is a container again (very similar to file_handle in the example). It will have keys and you can go down the tree until you find the HDF5 datasets. Especially in the case of Matlab structures you might need to go deeper.

==== Write ====
Under Python we generate a .hd5 file:<syntaxhighlight lang="python">import numpy as np
import h5py

myrng = np.random.default_rng()

a: np.ndarray = myrng.random((10, 100), dtype=np.float64)
b: np.ndarray = myrng.random((2, 20, 200), dtype=np.float64)

file_handle = h5py.File("Test_4.hd5", "w")

dataset_a = file_handle.create_dataset("A", data=a)
dataset_b = file_handle.create_dataset("B", data=b)
file_handle.close() # optional</syntaxhighlight>Under Matlab we now can extract information about the hd5 file:<syntaxhighlight lang="matlab">>> info = h5info('Test_4.hd5');
>> length(info.Datasets)

ans =

2

>> info.Datasets(1)

ans =

struct with fields:

Name: 'A'
Datatype: [1x1 struct]
Dataspace: [1x1 struct]
ChunkSize: []
FillValue: 0
Filters: []
Attributes: []

>> info.Datasets(2)

ans =

struct with fields:

Name: 'B'
Datatype: [1x1 struct]
Dataspace: [1x1 struct]
ChunkSize: []
FillValue: 0
Filters: []
Attributes: []</syntaxhighlight>And now we read it in under Matlab:<syntaxhighlight lang="matlab">>> h5disp('Test_4.hd5')
HDF5 Test_4.hd5
Group '/'
Dataset 'A'
Size: 100x10
MaxSize: 100x10
Datatype: H5T_IEEE_F64LE (double)
ChunkSize: []
Filters: none
FillValue: 0.000000
Dataset 'B'
Size: 200x20x2
MaxSize: 200x20x2
Datatype: H5T_IEEE_F64LE (double)
ChunkSize: []
Filters: none
FillValue: 0.000000</syntaxhighlight>Now we know that the databases are at ‘/A’ and ‘/B’. With information we can read the matrices. But be aware that the matrices are in reversed order!<syntaxhighlight lang="matlab">>> a = h5read('Test_4.hd5','/A');
>> b = h5read('Test_4.hd5','/B');
>> whos
Name Size Bytes Class Attributes

a 100x10 8000 double
b 200x20x2 64000 double</syntaxhighlight>

Dealing with Matlab files - Revision history

Davrot at 16:59, 17 October 2025