readall - Read all data in datastore - MATLAB (original) (raw)

Read all data in datastore

Syntax

Description

[data](#buefs9d-1-data) = readall([ds](#buefs9d-1-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.

example

[data](#buefs9d-1-data) = readall([ds](#buefs9d-1-ds),UseParallel=[tf](#mw%5F6c0291a2-e71b-4737-8cf3-298101b13d9b)) reads the data in parallel if tf is true (requires Parallel Computing Toolbox™).

example

Examples

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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.

Examine the output.

imout = imtile(T); imshow(imout)

Read All Data in TabularTextDatastore in Parallel

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

ds = tabularTextDatastore("airlinesmall_subset.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.

ans = TableProperties with properties:

         Description: ''
            UserData: []
      DimensionNames: {'Row'  'Variables'}
       VariableNames: {'DepTime'  'ArrTime'  'ActualElapsedTime'}
       VariableTypes: ["double"    "double"    "double"]
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(im2gray(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

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ds — Input datastore

datastore

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

• MATLAB® datastores — Datastores created using MATLABdatastore 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 orfalse. 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 theUseParallel property and the Parallel 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

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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.

Extended Capabilities

Automatic Parallel Support

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

Usage notes and limitations:

• To run in parallel, set the [UseParallel](matlab.io.datastore.readall.html#mw%5F6c0291a2-e71b-4737-8cf3-298101b13d9b) option totrue.

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

Thread-Based Environment

Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

Usage notes and limitations:

• In a thread-based environment, you can use readall only with the following datastores:
  • ImageDatastore objects
  • CombinedDatastore, SequentialDatastore, orTransformedDatastore objects you create fromImageDatastore objects by usingcombine or transform
    You can use readall with other datastores if you have Parallel Computing Toolbox. To do so, run the function using a process-backed parallel pool instead of using backgroundPool or ThreadPool (use eitherProcessPool or ClusterPool).
• readall does not support the following image formats in a thread-based environment:
  • Hierarchical Data Format (HDF)
  • SVS
  • TIFF

For more information, see Run MATLAB Functions in Thread-Based Environment.

Version History

Introduced in R2014b