readall

Read all data in datastore

Syntax

data = readall(ds)

data = readall(ds,'UseParallel',tf)

Description

data = readall(ds) returns all the data in the datastore specified by ds. If all the data in the datastore does not fit in memory, then readall returns an error.

data = readall(ds,'UseParallel',tf) reads the data in parallel (requires Parallel Computing Toolbox™).

Examples

Read All Data in ImageDatastore

Create an ImageDatastore object containing four images.

imds = imageDatastore({'street1.jpg','street2.jpg','peppers.png','corn.tif'});

Read all the data in the datastore.

T = readall(imds);

Examine the output.

imout = imtile(T);
imshow(imout)

Read All Data in TabularTextDatastore in Parallel

Create a datastore from the sample file airlinesmall.csv, which contains tabular data.

ds = tabularTextDatastore('airlinesmall.csv','TreatAsMissing','NA');

Specify the variables of interest using the SelectedVariableNames property.

ds.SelectedVariableNames = {'DepTime','ArrTime','ActualElapsedTime'};

Read all the data in the datastore in parallel.

T = readall(ds,'UseParallel',true);

readall returns all the data in a table.

View information about the table. Only the selected variables are included in the output.

T.Properties

ans = 
  TableProperties with properties:

             Description: ''
                UserData: []
          DimensionNames: {'Row'  'Variables'}
           VariableNames: {'DepTime'  'ArrTime'  'ActualElapsedTime'}
    VariableDescriptions: {}
           VariableUnits: {}
      VariableContinuity: []
                RowNames: {}
        CustomProperties: No custom properties are set.
      Use addprop and rmprop to modify CustomProperties.

Read All Data in CombinedDatastore

Create a datastore that maintains parity between the pair of images of the underlying datastores. For instance, create two separate image datastores, and then create a combined datastore representing the two underlying datastores.

Create an image datastore imds1 representing a collection of three images.

imds1 = imageDatastore({'street1.jpg','street2.jpg','peppers.png'}); 

Create a second datastore imds2 by transforming the images of imds1 to grayscale and then downsizing the images.

imds2 = transform(imds1,@(x) imresize(rgb2gray(x),0.5));

Create a combined datastore from imds1 and imds2.

imdsCombined = combine(imds1,imds2);

Read all of the data from the combined datastore. The output is a 3-by-2 cell array. The two columns represent all of the read data from the two underlying datastores imds1 and imds2, respectively.

dataOut = readall(imdsCombined)

dataOut=3×2 cell array
    {480x640x3 uint8}    {240x320 uint8}
    {480x640x3 uint8}    {240x320 uint8}
    {384x512x3 uint8}    {192x256 uint8}

Input Arguments

ds — Input datastore

datastore

Input datastore. You can use these datastores as input to the readall method.

• MATLAB^® datastores — Datastores created using MATLAB datastore functions. For example, create a datastore for a collection of images using ImageDatastore. For a complete list of datastores, see Select Datastore for File Format or Application.

• Combined and transformed datastores — Datastores created using the combine and transform functions.

• Custom datastores — Datastores created using the custom datastore framework. See Develop Custom Datastore.

tf — Read in parallel

false (default) | true

Read in parallel, specified as true or false. If you specify true, readall reads all data from the datastore in parallel (requires Parallel Computing Toolbox). Parallel reading may result in improved performance when reading data, especially with remote data.

• Datastore processing can be improved with the UseParallel property and theParallel Computing Toolbox. readall reads supported datastores faster on the local machine by using low overhead computing environments such as thread-based parallel pools. For more information on thread-based parallel pools see parpool (Parallel Computing Toolbox).

• As a result of MATLAB's built-in multithreading, certain datastores (for example, imageDatastore) perform faster on the local machine when not using parallelism based on MATLAB workers. For more information see MATLAB Multicore.

Example: readall(ds,'UseParallel',true)

Output Arguments

data — All data in the datastore

table | cell array

All data in the datastore, returned as a table or a cell array depending on the type of ds.

Type of Datastore	Data type of data	Description
TabularTextDatastore and SpreadsheetDatastore	Table	The SelectedVariableNames property determines the table variables.
ImageDatastore	Cell array	Each element in the cell array contains the image data for one image. The readall function supports all image types supported by the imread function. For more information on the supported image types, see imread.
KeyValueDatastore	Table	The table variable names are Key and Value.
FileDatastore	Cell array	Each element in the cell array contains the data read from one file using the custom read function specified by the ReadFcn property.
TransformedDatastore	Varies	The output is the same as the output returned by the underlying datastore specified by the UnderlyingDatastores property. For example, if the underlying datastore is an image datastore, then data is returned as a cell array where each element in the cell array contains the image data for one image.
CombinedDatastore	Cell array	Each column of the cell array contains the result from calling readall on the corresponding underlying datastore specified by the UnderlyingDatastores property. If the number of subsets of data in the underlying datastores differs, then readall only returns data while all underlying datastores have data. For example, suppose a combined datastore has two underlying datastores, one with m subsets of data and one with n subsets of data, where m > n. The output is a cell array with two columns and n rows.

Extended Capabilities

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, set the 'UseParallel' option to true.

For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).