Pass data between MATLAB R2011b and Python (Windows 7)

Pass data between MATLAB R2011b and Python (Windows 7) - python

Hello Friends,
I want to pass data between MATLAB and Python, One way would be to use matlab.engine in Python or Call Python Libraries from MATLAB. But this approach requires MATLAB 2014 Version unlike mine which is MATLAB R2011b.
So I request you to please guide for a different Approach in order to comunicate between Python and MATLAB R2011b Version.
Thanks in advance

Both matlab and python support hdf5 binary file format.
You can read/write hdf5 data files in matlab using hdf5read/hdf5write:
>> hdf5write('./data_from_matlab.h5', '/data', x);
In python you have h5py:
import h5py, numpy as np
with h5py.File('./data_from_matlab.h5', 'r') as R:
x = np.array(R['data'])
The other way around:
import h5py, numpy as np
with h5py.File('./data_from_python.h5', 'w') as W:
W.create_dataset(name='data', data=np.zeros((10,10),dtype='f4'))
And reading in Matlab
>> data = hdf5read('./data_from_python.h5','/data'); % you might need to remove '/' from '/data'...

Depending on what you want to do and your type of data, you could write it to a file and read from it in the other language. You could use numpy.fromfile for that in the python part.

Related

how to implement lstm model for .mat files in bci competition IV 2a dataset? [duplicate]

Is it possible to read binary MATLAB .mat files in Python?
I've seen that SciPy has alleged support for reading .mat files, but I'm unsuccessful with it. I installed SciPy version 0.7.0, and I can't find the loadmat() method.

An import is required, import scipy.io...
import scipy.io
mat = scipy.io.loadmat('file.mat')

Neither scipy.io.savemat, nor scipy.io.loadmat work for MATLAB arrays version 7.3. But the good part is that MATLAB version 7.3 files are hdf5 datasets. So they can be read using a number of tools, including NumPy.
For Python, you will need the h5py extension, which requires HDF5 on your system.
import numpy as np
import h5py
f = h5py.File('somefile.mat','r')
data = f.get('data/variable1')
data = np.array(data) # For converting to a NumPy array

First save the .mat file as:
save('test.mat', '-v7')
After that, in Python, use the usual loadmat function:
import scipy.io as sio
test = sio.loadmat('test.mat')

There is a nice package called mat4py which can easily be installed using
pip install mat4py
It is straightforward to use (from the website):
Load data from a MAT-file
The function loadmat loads all variables stored in the MAT-file into a simple Python data structure, using only Python’s dict and list objects. Numeric and cell arrays are converted to row-ordered nested lists. Arrays are squeezed to eliminate arrays with only one element. The resulting data structure is composed of simple types that are compatible with the JSON format.
Example: Load a MAT-file into a Python data structure:
from mat4py import loadmat
data = loadmat('datafile.mat')
The variable data is a dict with the variables and values contained in the MAT-file.
Save a Python data structure to a MAT-file
Python data can be saved to a MAT-file, with the function savemat. Data has to be structured in the same way as for loadmat, i.e. it should be composed of simple data types, like dict, list, str, int, and float.
Example: Save a Python data structure to a MAT-file:
from mat4py import savemat
savemat('datafile.mat', data)
The parameter data shall be a dict with the variables.

Having MATLAB 2014b or newer installed, the MATLAB engine for Python could be used:
import matlab.engine
eng = matlab.engine.start_matlab()
content = eng.load("example.mat", nargout=1)

Reading the file
import scipy.io
mat = scipy.io.loadmat(file_name)
Inspecting the type of MAT variable
print(type(mat))
#OUTPUT - <class 'dict'>
The keys inside the dictionary are MATLAB variables, and the values are the objects assigned to those variables.

There is a great library for this task called: pymatreader.
Just do as follows:
Install the package: pip install pymatreader
Import the relevant function of this package: from pymatreader import read_mat
Use the function to read the matlab struct: data = read_mat('matlab_struct.mat')
use data.keys() to locate where the data is actually stored.
The keys will usually look like: dict_keys(['__header__', '__version__', '__globals__', 'data_opp']). Where data_opp will be the actual key which stores the data. The name of this key can ofcourse be changed between different files.
Last step - Create your dataframe: my_df = pd.DataFrame(data['data_opp'])
That's it :)

There is also the MATLAB Engine for Python by MathWorks itself. If you have MATLAB, this might be worth considering (I haven't tried it myself but it has a lot more functionality than just reading MATLAB files). However, I don't know if it is allowed to distribute it to other users (it is probably not a problem if those persons have MATLAB. Otherwise, maybe NumPy is the right way to go?).
Also, if you want to do all the basics yourself, MathWorks provides (if the link changes, try to google for matfile_format.pdf or its title MAT-FILE Format) a detailed documentation on the structure of the file format. It's not as complicated as I personally thought, but obviously, this is not the easiest way to go. It also depends on how many features of the .mat-files you want to support.
I've written a "small" (about 700 lines) Python script which can read some basic .mat-files. I'm neither a Python expert nor a beginner and it took me about two days to write it (using the MathWorks documentation linked above). I've learned a lot of new stuff and it was quite fun (most of the time). As I've written the Python script at work, I'm afraid I cannot publish it... But I can give some advice here:
First read the documentation.
Use a hex editor (such as HxD) and look into a reference .mat-file you want to parse.
Try to figure out the meaning of each byte by saving the bytes to a .txt file and annotate each line.
Use classes to save each data element (such as miCOMPRESSED, miMATRIX, mxDOUBLE, or miINT32)
The .mat-files' structure is optimal for saving the data elements in a tree data structure; each node has one class and subnodes

To read mat file to pandas dataFrame with mixed data types
import scipy.io as sio
mat=sio.loadmat('file.mat')# load mat-file
mdata = mat['myVar'] # variable in mat file
ndata = {n: mdata[n][0,0] for n in mdata.dtype.names}
Columns = [n for n, v in ndata.items() if v.size == 1]
d=dict((c, ndata[c][0]) for c in Columns)
df=pd.DataFrame.from_dict(d)
display(df)

Apart from scipy.io.loadmat for v4 (Level 1.0), v6, v7 to 7.2 matfiles and h5py.File for 7.3 format matfiles, there is anther type of matfiles in text data format instead of binary, usually created by Octave, which can't even be read in MATLAB.
Both of scipy.io.loadmat and h5py.File can't load them (tested on scipy 1.5.3 and h5py 3.1.0), and the only solution I found is numpy.loadtxt.
import numpy as np
mat = np.loadtxt('xxx.mat')

Can also use the hdf5storage library. official documentation here for details on matlab version support.
import hdf5storage
label_file = "./LabelTrain.mat"
out = hdf5storage.loadmat(label_file)
print(type(out)) # <class 'dict'>

from os.path import dirname, join as pjoin
import scipy.io as sio
data_dir = pjoin(dirname(sio.__file__), 'matlab', 'tests', 'data')
mat_fname = pjoin(data_dir, 'testdouble_7.4_GLNX86.mat')
mat_contents = sio.loadmat(mat_fname)
You can use above code to read the default saved .mat file in Python.

After struggling with this problem myself and trying other libraries (I have to say mat4py is a good one as well but with a few limitations) I have built this library ("matdata2py") that can handle most variable types and most importantly for me the "string" type. The .mat file needs to be saved in the -V7.3 version. I hope this can be useful for the community.
Installation:
pip install matdata2py
How to use this lib:
import matdata2py as mtp
To load the Matlab data file:
Variables_output = mtp.loadmatfile(file_Name, StructsExportLikeMatlab = True, ExportVar2PyEnv = False)
print(Variables_output.keys()) # with ExportVar2PyEnv = False the variables are as elements of the Variables_output dictionary.
with ExportVar2PyEnv = True you can see each variable separately as python variables with the same name as saved in the Mat file.
Flag descriptions
StructsExportLikeMatlab = True/False structures are exported in dictionary format (False) or dot-based format similar to Matlab (True)
ExportVar2PyEnv = True/False export all variables in a single dictionary (True) or as separate individual variables into the python environment (False)

scipy will work perfectly to load the .mat files.
And we can use the get() function to convert it to a numpy array.
mat = scipy.io.loadmat('point05m_matrix.mat')
x = mat.get("matrix")
print(type(x))
print(len(x))
plt.imshow(x, extent=[0,60,0,55], aspect='auto')
plt.show()

To Upload and Read mat files in python
Install mat4py in python.On successful installation we get:
Successfully installed mat4py-0.5.0.
Importing loadmat from mat4py.
Save file actual location inside a variable.
Load mat file format to a data value using python
pip install mat4py
from mat4py import loadmat
boston = r"E:\Downloads\boston.mat"
data = loadmat(boston, meta=False)

How to write multiple matrix into excel in python

I have a .mat file that I opened it in python. It's a (66365) matrix. I want to write this to a Excel file. How can I di it?
I open it by following code:
import scipy.io
mat= scipy.io.loadmat('2003.mat')

You can either use the builtin csv library:
https://docs.python.org/3.4/library/csv.html
or use NumPy instead using this method:
Dump a NumPy array into a csv file
I generally recommend NumPy, but I don't think there is a significant difference here.

Creating a .mat file of v7.3 in python

I need to perform multiplication involving 60000X70000 matrix either in python or matlab. I have a 16GB RAM and am able to load each row of the matrix easily (which is what I require). I am able to create the matrix as a whole in python but not in matlab.
Is there anyway I can save the array as .mat file of v7.3 using h5py or scipy so that I can load each row separately?

For MATLAB v7.3 you can use hdf5storage which requires h5py, download the file here, extract, then type: python setup.py install from a command prompt.
https://pypi.python.org/pypi/hdf5storage
import h5py
import hdf5storage
import numpy as np
matfiledata = {} # make a dictionary to store the MAT data in
matfiledata[u'variable1'] = np.zeros(100) # *** u prefix for variable name = unicode format, no issues thru Python 3.5; advise keeping u prefix indicator format based on feedback despite docs ***
matfiledata[u'variable2'] = np.ones(300)
hdf5storage.write(matfiledata, '.', 'example.mat', matlab_compatible=True)
If MATLAB can't load the whole thing at once, I think you'll have to save it in different variables matfiledata[u'chunk1'] matfiledata[u'chunk2'] matfiledata[u'chunk3'] etc.
Then in MATLAB if you save each chunk as a variable
load(filename,'chunk1')
do stuff...
clear chunk1
load(filename,'chunk2')
do stuff...
clear chunk2
etc.
The hdf5storage.savemat has a parameter to allow the file to be read into Python correctly in the future so worth checking out, and follows the scipy.io.loadmat format... although you can do something like this if saving data from MATLAB to make it easy to import back into Python:
MATLAB
save('example.mat','-v7.3')
Python
matdata = hdf5storage.loadmat('example.mat')
That will load back into Python as a dictionary which you can then convert into whatever datatypes you need.

How to load *.mat file and convert it to numpy array? [duplicate]

Is it possible to read binary MATLAB .mat files in Python?
I've seen that SciPy has alleged support for reading .mat files, but I'm unsuccessful with it. I installed SciPy version 0.7.0, and I can't find the loadmat() method.

An import is required, import scipy.io...
import scipy.io
mat = scipy.io.loadmat('file.mat')

Neither scipy.io.savemat, nor scipy.io.loadmat work for MATLAB arrays version 7.3. But the good part is that MATLAB version 7.3 files are hdf5 datasets. So they can be read using a number of tools, including NumPy.
For Python, you will need the h5py extension, which requires HDF5 on your system.
import numpy as np
import h5py
f = h5py.File('somefile.mat','r')
data = f.get('data/variable1')
data = np.array(data) # For converting to a NumPy array

First save the .mat file as:
save('test.mat', '-v7')
After that, in Python, use the usual loadmat function:
import scipy.io as sio
test = sio.loadmat('test.mat')

There is a nice package called mat4py which can easily be installed using
pip install mat4py
It is straightforward to use (from the website):
Load data from a MAT-file
The function loadmat loads all variables stored in the MAT-file into a simple Python data structure, using only Python’s dict and list objects. Numeric and cell arrays are converted to row-ordered nested lists. Arrays are squeezed to eliminate arrays with only one element. The resulting data structure is composed of simple types that are compatible with the JSON format.
Example: Load a MAT-file into a Python data structure:
from mat4py import loadmat
data = loadmat('datafile.mat')
The variable data is a dict with the variables and values contained in the MAT-file.
Save a Python data structure to a MAT-file
Python data can be saved to a MAT-file, with the function savemat. Data has to be structured in the same way as for loadmat, i.e. it should be composed of simple data types, like dict, list, str, int, and float.
Example: Save a Python data structure to a MAT-file:
from mat4py import savemat
savemat('datafile.mat', data)
The parameter data shall be a dict with the variables.

Having MATLAB 2014b or newer installed, the MATLAB engine for Python could be used:
import matlab.engine
eng = matlab.engine.start_matlab()
content = eng.load("example.mat", nargout=1)

Reading the file
import scipy.io
mat = scipy.io.loadmat(file_name)
Inspecting the type of MAT variable
print(type(mat))
#OUTPUT - <class 'dict'>
The keys inside the dictionary are MATLAB variables, and the values are the objects assigned to those variables.

There is a great library for this task called: pymatreader.
Just do as follows:
Install the package: pip install pymatreader
Import the relevant function of this package: from pymatreader import read_mat
Use the function to read the matlab struct: data = read_mat('matlab_struct.mat')
use data.keys() to locate where the data is actually stored.
The keys will usually look like: dict_keys(['__header__', '__version__', '__globals__', 'data_opp']). Where data_opp will be the actual key which stores the data. The name of this key can ofcourse be changed between different files.
Last step - Create your dataframe: my_df = pd.DataFrame(data['data_opp'])
That's it :)

To read mat file to pandas dataFrame with mixed data types
import scipy.io as sio
mat=sio.loadmat('file.mat')# load mat-file
mdata = mat['myVar'] # variable in mat file
ndata = {n: mdata[n][0,0] for n in mdata.dtype.names}
Columns = [n for n, v in ndata.items() if v.size == 1]
d=dict((c, ndata[c][0]) for c in Columns)
df=pd.DataFrame.from_dict(d)
display(df)

Apart from scipy.io.loadmat for v4 (Level 1.0), v6, v7 to 7.2 matfiles and h5py.File for 7.3 format matfiles, there is anther type of matfiles in text data format instead of binary, usually created by Octave, which can't even be read in MATLAB.
Both of scipy.io.loadmat and h5py.File can't load them (tested on scipy 1.5.3 and h5py 3.1.0), and the only solution I found is numpy.loadtxt.
import numpy as np
mat = np.loadtxt('xxx.mat')

Can also use the hdf5storage library. official documentation here for details on matlab version support.
import hdf5storage
label_file = "./LabelTrain.mat"
out = hdf5storage.loadmat(label_file)
print(type(out)) # <class 'dict'>

from os.path import dirname, join as pjoin
import scipy.io as sio
data_dir = pjoin(dirname(sio.__file__), 'matlab', 'tests', 'data')
mat_fname = pjoin(data_dir, 'testdouble_7.4_GLNX86.mat')
mat_contents = sio.loadmat(mat_fname)
You can use above code to read the default saved .mat file in Python.

After struggling with this problem myself and trying other libraries (I have to say mat4py is a good one as well but with a few limitations) I have built this library ("matdata2py") that can handle most variable types and most importantly for me the "string" type. The .mat file needs to be saved in the -V7.3 version. I hope this can be useful for the community.
Installation:
pip install matdata2py
How to use this lib:
import matdata2py as mtp
To load the Matlab data file:
Variables_output = mtp.loadmatfile(file_Name, StructsExportLikeMatlab = True, ExportVar2PyEnv = False)
print(Variables_output.keys()) # with ExportVar2PyEnv = False the variables are as elements of the Variables_output dictionary.
with ExportVar2PyEnv = True you can see each variable separately as python variables with the same name as saved in the Mat file.
Flag descriptions
StructsExportLikeMatlab = True/False structures are exported in dictionary format (False) or dot-based format similar to Matlab (True)
ExportVar2PyEnv = True/False export all variables in a single dictionary (True) or as separate individual variables into the python environment (False)

scipy will work perfectly to load the .mat files.
And we can use the get() function to convert it to a numpy array.
mat = scipy.io.loadmat('point05m_matrix.mat')
x = mat.get("matrix")
print(type(x))
print(len(x))
plt.imshow(x, extent=[0,60,0,55], aspect='auto')
plt.show()

To Upload and Read mat files in python
Install mat4py in python.On successful installation we get:
Successfully installed mat4py-0.5.0.
Importing loadmat from mat4py.
Save file actual location inside a variable.
Load mat file format to a data value using python
pip install mat4py
from mat4py import loadmat
boston = r"E:\Downloads\boston.mat"
data = loadmat(boston, meta=False)

Read .mat files in Python

Is it possible to read binary MATLAB .mat files in Python?
I've seen that SciPy has alleged support for reading .mat files, but I'm unsuccessful with it. I installed SciPy version 0.7.0, and I can't find the loadmat() method.

An import is required, import scipy.io...
import scipy.io
mat = scipy.io.loadmat('file.mat')

Neither scipy.io.savemat, nor scipy.io.loadmat work for MATLAB arrays version 7.3. But the good part is that MATLAB version 7.3 files are hdf5 datasets. So they can be read using a number of tools, including NumPy.
For Python, you will need the h5py extension, which requires HDF5 on your system.
import numpy as np
import h5py
f = h5py.File('somefile.mat','r')
data = f.get('data/variable1')
data = np.array(data) # For converting to a NumPy array

First save the .mat file as:
save('test.mat', '-v7')
After that, in Python, use the usual loadmat function:
import scipy.io as sio
test = sio.loadmat('test.mat')

There is a nice package called mat4py which can easily be installed using
pip install mat4py
It is straightforward to use (from the website):
Load data from a MAT-file
The function loadmat loads all variables stored in the MAT-file into a simple Python data structure, using only Python’s dict and list objects. Numeric and cell arrays are converted to row-ordered nested lists. Arrays are squeezed to eliminate arrays with only one element. The resulting data structure is composed of simple types that are compatible with the JSON format.
Example: Load a MAT-file into a Python data structure:
from mat4py import loadmat
data = loadmat('datafile.mat')
The variable data is a dict with the variables and values contained in the MAT-file.
Save a Python data structure to a MAT-file
Python data can be saved to a MAT-file, with the function savemat. Data has to be structured in the same way as for loadmat, i.e. it should be composed of simple data types, like dict, list, str, int, and float.
Example: Save a Python data structure to a MAT-file:
from mat4py import savemat
savemat('datafile.mat', data)
The parameter data shall be a dict with the variables.

Having MATLAB 2014b or newer installed, the MATLAB engine for Python could be used:
import matlab.engine
eng = matlab.engine.start_matlab()
content = eng.load("example.mat", nargout=1)

Reading the file
import scipy.io
mat = scipy.io.loadmat(file_name)
Inspecting the type of MAT variable
print(type(mat))
#OUTPUT - <class 'dict'>
The keys inside the dictionary are MATLAB variables, and the values are the objects assigned to those variables.

There is a great library for this task called: pymatreader.
Just do as follows:
Install the package: pip install pymatreader
Import the relevant function of this package: from pymatreader import read_mat
Use the function to read the matlab struct: data = read_mat('matlab_struct.mat')
use data.keys() to locate where the data is actually stored.
The keys will usually look like: dict_keys(['__header__', '__version__', '__globals__', 'data_opp']). Where data_opp will be the actual key which stores the data. The name of this key can ofcourse be changed between different files.
Last step - Create your dataframe: my_df = pd.DataFrame(data['data_opp'])
That's it :)

To read mat file to pandas dataFrame with mixed data types
import scipy.io as sio
mat=sio.loadmat('file.mat')# load mat-file
mdata = mat['myVar'] # variable in mat file
ndata = {n: mdata[n][0,0] for n in mdata.dtype.names}
Columns = [n for n, v in ndata.items() if v.size == 1]
d=dict((c, ndata[c][0]) for c in Columns)
df=pd.DataFrame.from_dict(d)
display(df)

Apart from scipy.io.loadmat for v4 (Level 1.0), v6, v7 to 7.2 matfiles and h5py.File for 7.3 format matfiles, there is anther type of matfiles in text data format instead of binary, usually created by Octave, which can't even be read in MATLAB.
Both of scipy.io.loadmat and h5py.File can't load them (tested on scipy 1.5.3 and h5py 3.1.0), and the only solution I found is numpy.loadtxt.
import numpy as np
mat = np.loadtxt('xxx.mat')

Can also use the hdf5storage library. official documentation here for details on matlab version support.
import hdf5storage
label_file = "./LabelTrain.mat"
out = hdf5storage.loadmat(label_file)
print(type(out)) # <class 'dict'>

from os.path import dirname, join as pjoin
import scipy.io as sio
data_dir = pjoin(dirname(sio.__file__), 'matlab', 'tests', 'data')
mat_fname = pjoin(data_dir, 'testdouble_7.4_GLNX86.mat')
mat_contents = sio.loadmat(mat_fname)
You can use above code to read the default saved .mat file in Python.

After struggling with this problem myself and trying other libraries (I have to say mat4py is a good one as well but with a few limitations) I have built this library ("matdata2py") that can handle most variable types and most importantly for me the "string" type. The .mat file needs to be saved in the -V7.3 version. I hope this can be useful for the community.
Installation:
pip install matdata2py
How to use this lib:
import matdata2py as mtp
To load the Matlab data file:
Variables_output = mtp.loadmatfile(file_Name, StructsExportLikeMatlab = True, ExportVar2PyEnv = False)
print(Variables_output.keys()) # with ExportVar2PyEnv = False the variables are as elements of the Variables_output dictionary.
with ExportVar2PyEnv = True you can see each variable separately as python variables with the same name as saved in the Mat file.
Flag descriptions
StructsExportLikeMatlab = True/False structures are exported in dictionary format (False) or dot-based format similar to Matlab (True)
ExportVar2PyEnv = True/False export all variables in a single dictionary (True) or as separate individual variables into the python environment (False)

scipy will work perfectly to load the .mat files.
And we can use the get() function to convert it to a numpy array.
mat = scipy.io.loadmat('point05m_matrix.mat')
x = mat.get("matrix")
print(type(x))
print(len(x))
plt.imshow(x, extent=[0,60,0,55], aspect='auto')
plt.show()

To Upload and Read mat files in python
Install mat4py in python.On successful installation we get:
Successfully installed mat4py-0.5.0.
Importing loadmat from mat4py.
Save file actual location inside a variable.
Load mat file format to a data value using python
pip install mat4py
from mat4py import loadmat
boston = r"E:\Downloads\boston.mat"
data = loadmat(boston, meta=False)

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