read - Read data in datastore - MATLAB (original) (raw)

Syntax

Description

[data](#budsyj6-1-data) = read([ds](#budsyj6-1-ds)) returns data from a datastore. Subsequent calls to the read function continue reading from the endpoint of the previous call.

example

[[data](#budsyj6-1-data),[info](#budsyj6-1-info)] = read([ds](#budsyj6-1-ds)) also returns information about the extracted data in info, including metadata.

Examples

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Read Data in TabularTextDatastore

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

ds = tabularTextDatastore("airlinesmall_subset.csv","TreatAsMissing","NA","MissingValue",0);

Modify the SelectedVariableNames property to specify the variables of interest.

ds.SelectedVariableNames = ["DepTime","ArrTime","ActualElapsedTime"];

While there is data available to be read from the datastore, read one block of data at a time and analyze the data. In this example, sum the actual elapsed time.

sumElapsedTime = 0; while hasdata(ds) T = read(ds); sumElapsedTime = sumElapsedTime + sum(T.ActualElapsedTime); end

View the sum of the actual elapsed time.

Read Data in KeyValueDatastore

Create a datastore from the sample file, mapredout.mat, which is the output file of the mapreduce function.

ds = datastore('mapredout.mat');

Read a subset of data in the datastore.

T=1×2 table Key Value
______ _________

{'AA'}    {[14930]}

Change the number of key-value pairs to read at a time, by changing the ReadSize property of the datastore.

Read the next five key-value pairs in the datastore.

T=5×2 table Key Value
______ _________

{'AS'}    {[ 2910]}
{'CO'}    {[ 8138]}
{'DL'}    {[16578]}
{'EA'}    {[  920]}
{'HP'}    {[ 3660]}

Read 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 that reads corresponding images from the two image datastores.

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

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

Create a second datastore imds2 containing a mask of the bright regions of the three images. To create this datastore, first transform the images of imds1 to grayscale. Then convert each image to a binary mask by performing thresholding. In this example, the thresholding operation maps pixels with a value above the threshold (250) to white and all other pixels to black.

imds2 = transform(imds1,@(x) im2gray(x)>250);

Create a combined datastore from imds1 and imds2.

imdsCombined = combine(imds1,imds2);

Read the first subset of data from the combined datastore. The output is a 1-by-2 cell array. The two columns represent the first subset of data read from the two underlying datastores imds1 and imds2, respectively.

dataOut = read(imdsCombined)

dataOut=1×2 cell array {384x512x3 uint8} {384x512 logical}

Display the read data from the combined datastore as a pair of tiled images.

tile = imtile(dataOut); imshow(tile)

Read from the combined datastore again. This call to the read function continues reading from the endpoint of the previous call.

dataOut = read(imdsCombined)

dataOut=1×2 cell array {480x640x3 uint8} {480x640 logical}

Display the read data.

tile = imtile(dataOut); imshow(tile)

Output Arguments

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data — Output data

table | timetable | array

Output data, returned as a table, timetable, or array depending on the type of the input ds. The data output can be empty if the amount of data read in a particular call to theread function in combination with the import configuration returns no values.

info — Information about read data

structure array | cell array

Information about read data, returned as a structure array or a cell array of structure arrays.

• For MATLAB datastores andTransformedDatastore,info is a structure array that has fields with information about the datastore.
• For CombinedDatastore,info is a cell array of structure arrays. Each element of the cell array contains a structure with the relevant fields of the corresponding underlying datastore.
• For SequentialDatastore, the data type and format of info are the same as the current underlying datastore.

Information in the structure array depends on the type of the input datastore. The structure array can contain the following fields.

Extended Capabilities

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 read only with the following datastores:
  • ImageDatastore objects
  • CombinedDatastore, SequentialDatastore, orTransformedDatastore objects you create fromImageDatastore objects by usingcombine or transform
    You can use read 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).
• read 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