Matlab large files

Accepted Answer: Jakub. Warning: Variable 'a' cannot be saved to a MAT-file whose version is older than 7. To save this variable, use the -v7. And it cannot be saved. Is there a way matlab can save large data to a mat file? Accepted Answer.

Jakub on 14 Apr Vote 1. Cancel Copy to Clipboard. Try to find it here. Try using HDF5 file.. If the work we are doing is embarrassingly parallel, meaning that each file can be analyzed in isolation, then we can loop through the files one at a time.

If Parallel Computing Toolbox is available, we can accelerate the process by using a parfor loop instead of a for loop. When our files cannot be analyzed in isolation, we need to change our approach. The fileDatastore gives access to large collections of files by using a custom file reader function.

For example, if your analysis only needs the variable "b" from your MAT files, you can use a reader function such as:. If your data is stored in more complicated or irregular formats, you can use any arbitrary code in your reader function to return the values in the format you need.

Once we define our reader function, we can create a fileDatastore , which will read one file at a time using our reader function. Note that by default the fileDatastore will return each file's contents as an element in a cell array.

The UniformRead option will instead keep the data's original format and vertically concatenate the data from different files. After creating the datastore we can read a portion of the dataset with the read method or analyze the full out-of-memory dataset with tall arrays. For example, to use Amazon S3 make the following modifications:.

However, note that the fileDatastore automatically makes a local copy of each file it reads, which may result in downloading the entire dataset when it is stored remotely. If this is problematic, consider rewriting your data to another file format so you can use a datastore that does not require local copies.

This is discussed in more detail later in this post. MAT files can individually be too large to load either because they have many small variables or have large variables. Files with many small variables arise when logging many signals from simulations or data loggers, or by adding more variables to a MAT file over time using the save command's -append option. When working with MAT files containing too many small variables to load all at once, one approach is to only load certain variables needed for your analysis as we did in the prior section.

If this reduces the data needed from each individual file such that each call to the read method fits into memory, then we can use the example from the previous section to avoid running out of memory. However, if even after selecting only the necessary variables the data from individual files is still too large to fit into memory then we must try a different approach.

In the prior section we used fileDatastore to read entire MAT files with a custom reader function. The fileDatastore also supports reading only parts of a file at a time. By adding additional logic into our reader function to manage the current state of reading through a large file, we can grab a portion of each variable. Let's assume that in our collection of MAT files each file contains the same number of variables with the same names. Let's also assume all variables within a particular file are column vectors of the same length.

We can then use matfile objects described in more detail below within the following reader function to partially read only a certain number of rows from each variable and concatenate them into a table. The partial reading of fileDatastore lets you parse arbitrarily large files with an arbitrary reader function. If you want even more control over how a datastore processes a data source, consider using custom datastores. You can use the datastore to manage incremental import of the data.

To analyze the data using common MATLAB functions, such as mean and histogram , create a tall array on top of the datastore. For more complex problems, you can write a MapReduce algorithm that defines the chunking and reduction of the data.

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