signalDatastore
Datastore for collection of signals
Description
Use a signalDatastore object to manage a collection of in-memory
data or signal files, where each individual file fits in memory, but the entire collection
does not necessarily fit.
Creation
Syntax
Description
sds = signalDatastore(data)data.
sds = signalDatastore(location)location. If location contains a mixture of
MAT files and CSV files, then sds contains MAT files.
sds = signalDatastore(___,Name,Value)
Input Arguments
Name-Value Arguments
Properties
Object Functions
read | Read next consecutive signal observation |
readall | Read all signals from datastore |
writeall | Write datastore to files |
preview | Read first signal observation from datastore for preview |
shuffle | Shuffle signals in signal datastore |
subset | Create datastore with subset of signals |
partition | Partition signal datastore and return partitioned portion |
numpartitions | Return estimate for reasonable number of partitions for parallel processing |
reset | Reset datastore to initial state |
progress | Determine how much data has been read |
hasdata | Determine if data is available to read |
transform | Transform datastore |
combine | Combine data from multiple datastores |
isPartitionable | Determine whether datastore is partitionable |
isShuffleable | Determine whether datastore is shuffleable |
Note
isPartitionable
and isShuffleable
return true by default for signalDatastore. You can test
if the output of combine and
transform are
partitionable or shuffleable using the two functions.
Examples
Create a signal datastore to iterate through the elements of an in-memory cell array of signal data. The data consists of a sinusoidally modulated linear chirp, a concave quadratic chirp, and a voltage controlled oscillator. The signals are sampled at 3000 Hz.
fs = 3000;
t = 0:1/fs:3-1/fs;
data = {chirp(t,300,t(end),800).*exp(2j*pi*10*cos(2*pi*2*t)); ...
2*chirp(t,200,t(end),1000,'quadratic',[],'concave'); ...
vco(sin(2*pi*t),[0.1 0.4]*fs,fs)};
sds = signalDatastore(data,'SampleRate',fs);While the datastore has data, read each observation from the signal datastore and plot the short-time Fourier transform.
plotID = 1; while hasdata(sds) [dataOut,info] = read(sds); subplot(3,1,plotID) stft(dataOut,info.SampleRate) plotID = plotID + 1; end
The folder dataset contains signal samples included with Signal Processing Toolbox™. Create a signal datastore that points to the folder and set the name of the sample rate variable.
folder = "dataset"; sds = signalDatastore(folder,SampleRateVariableName="fs");
Read the first file in the datastore and plot the spectrogram.
[data,info] = read(sds);
pspectrum(data,info.SampleRate,"spectrogram")
Specify the folder that contains signal samples included with Signal Processing Toolbox™. The signals are stored in .csv, .dat, and .mat files.
folder = "healthdata";Create a signal datastore that points to the .csv file in the specified folder. Plot the short-time Fourier transform of the signal.
sds = signalDatastore(folder, ... FileExtensions=".csv",SignalVariableNames=["tx" "x"]); data = read(sds); stft(data{2})
Specify the names of four example files included with Signal Processing Toolbox™.
files = ["INR.mat","relatedsig.mat","spots_num.mat","voice.mat"];
Create a signalDatastore object containing the specified files and set the ReadSize property to 2 to read data from two files at a time. Each read returns a cell array where the first cell contains the first variable of the first file read, and the second cell contains the first variable from the second file. While the datastore has data, display the names of the variables read in each read.
sds = signalDatastore(files,ReadSize=2); while hasdata(sds) [data,info] = read(sds); fprintf("Variable Name:\t%s\n",info.SignalVariableNames) end
Variable Name: Date Variable Name: s1 Variable Name: year Variable Name: fs
Create a signal datastore that contains three signals included with Signal Processing Toolbox™.
The
strong.matfile contains three variables:her,himandfs.The
slogan.matfile contains three variables:hotword,phraseandfs.The
Ring.matfile contains two variables:yandFs.
Each file contains multiple variables of different names. The scalar in each file represents a sample rate. Define a custom read function that reads all the variables in the file as a structure and returns the variable in dataOut and information about the variables in infoOut. The SampleRate field of infoOut contains the scalar contained in each file, and dataOut contains the variables read from each file.
function [dataOut,infoOut] = MyCustomRead(filename) fText = importdata(filename); value = struct2cell(fText); dataOut = {}; for i = 1:length(value) if isscalar(value{i}) == 1 infoOut.SampleRate = value{i}; else dataOut{end+1} = value{i}; end end end
files = ["strong.mat","slogan.mat","Ring.mat"]; sds = signalDatastore(files,ReadFcn=@MyCustomRead);
While the datastore has unread files, read from the datastore and compute the short-time Fourier transforms of the signals.
while hasdata(sds) [data,infoOut] = read(sds); fs = infoOut.SampleRate; figure for i = 1:length(data) if length(data)>1 subplot(2,1,i) end stft(data{i},fs) end end
The dataset folder contains example files included with Signal Processing Toolbox™. Each file contains two signals and a random sample rate fs ranging from 3000 to 4000 Hz.
The first signal,
x1, is a convex quadratic chirp.The second signal,
x2, is a chirp with sinusoidally varying frequency content.
folder = "dataset";Create a signal datastore that points to the specified folder, set the names of the signal variables and sample rate, and specify the output data type as single precision. While the datastore has data, read each observation and visualize the spectrogram of each signal.
sds = signalDatastore(folder,SignalVariableNames=["x1";"x2"], ... SampleRateVariableName="fs",OutputDataType="single"); tiledlayout flow while hasdata(sds) [data,info] = read(sds); nexttile pspectrum(data{1},info.SampleRate,"spectrogram",TwoSided=true) nexttile pspectrum(data{2},info.SampleRate,"spectrogram",TwoSided=true) end
