If you are only reading in basic arrays and structs, see vikrantt's answer on a similar post. However, if you are working with a Matlab table
, then IMHO the best solution is to avoid the save
option altogether.
I've created a simple helper function to convert a Matlab table
to a
standard hdf5 file, and another helper function in Python to extract the data into a Pandas DataFrame
.
Matlab Helper Function
function table_to_hdf5[T, path, group]
%TABLE_TO_HDF5 Save a Matlab table in an hdf5 file format
%
% TABLE_TO_HDF5[T] Saves the table T to the HDF5 file inputname.h5 at the root ['/']
% group, where inputname is the name of the input argument for T
%
% TABLE_TO_HDF5[T, path] Saves the table T to the HDF5 file specified by path at the
% root ['/'] group.
%
% TABLE_TO_HDF5[T, path, group] Saves the table T to the HDF5 file specified by path
% at the group specified by group.
%
%%%
if nargin < 2
path = [inputname[1],'.h5']; % default file name to input argument
end
if nargin < 3
group = ''; % We will prepend '/' later, so this is effectively root
end
for field = T.Properties.VariableNames
% Prepare to write
field = field{:};
dataset_name = [group '/' field];
data = T.[field];
if ischar[data] || isstring[data]
warning['String columns not supported. Skipping...']
continue
end
% Write the data
h5create[path, dataset_name, size[data]]
h5write[path, dataset_name, data]
end
end
Python Helper Function
import pandas as pd
import h5py
def h5_to_df[path, group = '/']:
"""
Load an hdf5 file into a pandas DataFrame
"""
df = pd.DataFrame[]
with h5py.File[path, 'r'] as f:
data = f[group]
for k,v in data.items[]:
if v.shape[0] > 1: # Multiple column field
for i in range[v.shape[0]]:
k_new = f'{k}_{i}'
df[k_new] = v[i]
else:
df[k] = v[0]
return df
Important Notes
- This will only work on numerical data. If you know how to add string data, please comment.
- This will create the file if it does not already exist.
- This will crash if the data already exists in the file. You'll want to include logic to handle those cases as you deem appropriate.
Project description
mat 7.3
Load MATLAB 7.3 .mat files into Python.
Starting with MATLAB 7.3, .mat
files have been changed to store as custom hdf5
files. This means they cannot be loaded by scipy.io.loadmat
any longer and raise.
NotImplementedError: Please use HDF reader for matlab v7.3 files
Quickstart
This library loads MATLAB 7.3 HDF5 files into a Python dictionary.
import mat73 data_dict = mat73.loadmat['data.mat']
As easy as that!
By enabling use_attrdict=True
you can even access sub-entries of structs
as attributes, just like in MATLAB:
data_dict = mat73.loadmat['data.mat', use_attrdict=True] struct = data_dict['structure'] # assuming a structure was saved in the .mat struct[0].var1 == struct[0]['var1'] # it's the same!
You can also specifiy to only load a specific variable or variable tree, useful to reduce loading times
data_dict = mat73.loadmat['data.mat', only_include='structure'] struct = data_dict['structure'] # now only structure is loaded and nothing else data_dict = mat73.loadmat['data.mat', only_include=['var/subvar/subsubvar', 'tree1/']] tree1 = data_dict['tree1'] # the entire tree has been loaded, so tree1 is a dict with all subvars of tree1 subsubvar = data_dict['var']['subvar']['subsubvar'] # this subvar has been loaded
Installation
To install, run:
pip install mat73
Alternatively for most recent version:
pip install git+//github.com/skjerns/mat7.3
Datatypes
The following MATLAB datatypes can be loaded
logical | np.bool_ |
single | np.float32 |
double | np.float64 |
int8/16/32/64 | np.int8/16/32/64 |
uint8/16/32/64 | np.uint8/16/32/64 |
complex | np.complex128 |
char | str |
struct | list of dicts |
cell | list of lists |
canonical empty | [] |
missing | None |
Other [ie Datetime, ...] | Not supported |
Short-comings
- This library will only load mat 7.3 files. For older versions use
scipy.io.loadmat
- Proprietary MATLAB types [e.g
datetime
,duriation
, etc] are not supported. If someone tells me how to convert them, I'll implement that - For now, you can't save anything back to the .mat. It's a bit more difficult than expected, so it's not on the roadmap for now
- See also hdf5storage, which can indeed be used for saving .mat, but has less features for loading
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