Analyze TDMS Files Using Tall Tables
This example shows how to access a TDMS datastore using tall tables in MATLAB®.
For this example, you have measurement vibration measurements of a rotating shaft recorded across multiple TDMS files. Your goals is to extract portions of the files using tall tables and visually analyze them.
Set up Datastore and Create Tall Table
Identify the location of the TDMS datastore files. Create a tall table from the TDMS datastore with an applied transform. The tdmsDatastore function can be applied to either a set of multiple files, or a single large file. Here a transform is used to convert the returned datastore type from a cell array to a table.
folder = "RotatingShaftAnalysis"; mapreducer(0); % Sets the global execution environment to be the local MATLAB session. tData = tall( transform(tdmsDatastore(folder), ... @(cellData)table2timetable(cellData{1}, TimeStep=milliseconds(0.1))) );
Identify Data of Interest
Extract Top and Bottom Rows of TDMS Tall Table
You can extract the first or last N rows using head or tail, respectively.
tHead = head(tData, 1000); tTail = tail(tData, 1000);
Extract Specific Data
You can also apply relational operators or colon expressions to filter the data. The ? characters indicate that evaluation is deferred until you call gather.
filteredPulse = tail(tData(tData.Pulse > 2,:), 1000)
filteredPulse =
M×3 tall timetable
Time Pulse Sensor_X Sensor_Y
____ _____ ________ ________
? ? ? ?
? ? ? ?
? ? ? ?
: : : :
: : : :
Preview deferred. Learn more.
tDataInRange = tData(5000:10000,:)
tDataInRange =
5,001×3 tall timetable
Time Pulse Sensor_X Sensor_Y
____ _____ ________ ________
? ? ? ?
? ? ? ?
? ? ? ?
: : : :
: : : :
Preview deferred. Learn more.
Find the Number of Rows in Table
All table operations can be applied to tall table. One such operation is height, to get the total number of rows in the collection.
tRows = height(tData);
Gather Data for Evaluation
Finally, evaluate the computations using gather, which triggers execution of the operations that were delayed earlier. You can use gather to pass all the variables of interest in a single function call.
[tHead, tTail, filteredPulse, tDataInRange, tRows] = gather(tHead, tTail, filteredPulse, tDataInRange, tRows)
Evaluating tall expression using the Local MATLAB Session: - Pass 1 of 2: Completed in 0.25 sec - Pass 2 of 2: Completed in 0.046 sec Evaluation completed in 0.46 sec
tHead=1000×3 timetable
Time Pulse Sensor_X Sensor_Y
__________ __________ ________ ________
0 sec 0.00087333 0.12865 0.5479
0.0001 sec 0.0007626 0.13118 0.55483
0.0002 sec 0.00085302 0.12971 0.53442
0.0003 sec 0.00081109 0.12775 0.5556
0.0004 sec 0.00078684 0.12813 0.55271
0.0005 sec 0.00084647 0.13173 0.54674
0.0006 sec 0.00081109 0.1281 0.52979
0.0007 sec 0.00078488 0.12769 0.52421
0.0008 sec 0.00074556 0.13112 0.5452
0.0009 sec 0.00084647 0.12977 0.55233
0.001 sec 0.00080191 0.13064 0.53133
0.0011 sec 0.00088578 0.1315 0.52998
0.0012 sec 0.0008314 0.13487 0.51265
0.0013 sec 0.00080388 0.13436 0.5269
0.0014 sec 0.00083074 0.13597 0.52844
0.0015 sec 0.00083336 0.13029 0.53499
⋮
tTail=1000×3 timetable
Time Pulse Sensor_X Sensor_Y
__________ __________ ________ ________
1.9 sec -0.0015896 0.1368 0.5503
1.9001 sec -0.0016119 0.13764 0.5842
1.9002 sec -0.0016119 0.14088 0.55338
1.9003 sec -0.0016571 0.13696 0.55896
1.9004 sec -0.0016938 0.13838 0.56551
1.9005 sec -0.0016394 0.13716 0.5894
1.9006 sec -0.0016401 0.13857 0.55954
1.9007 sec -0.0017646 0.13809 0.56879
1.9008 sec -0.0016237 0.13918 0.5711
1.9009 sec -0.0016938 0.13934 0.53142
1.901 sec -0.0016027 0.13822 0.55607
1.9011 sec -0.0016217 0.13854 0.56821
1.9012 sec -0.0016951 0.14184 0.57283
1.9013 sec -0.0016283 0.13783 0.55858
1.9014 sec -0.001743 0.1361 0.55627
1.9015 sec -0.0017508 0.13786 0.54201
⋮
filteredPulse=27×3 timetable
Time Pulse Sensor_X Sensor_Y
__________ ______ ________ ________
0.1985 sec 2.7111 0.17406 0.57833
0.6452 sec 3.3274 0.12907 0.53788
1.0919 sec 3.3197 0.11504 0.53865
1.5386 sec 2.7469 0.14389 0.51669
1.9853 sec 2.8597 0.075178 0.55406
0.2596 sec 3.3864 0.20214 0.52591
0.5694 sec 2.182 0.17233 0.5367
0.5695 sec 2.5165 0.17727 0.60874
0.8793 sec 3.4009 0.13664 0.5315
1.1892 sec 2.2825 0.12525 0.55307
1.1893 sec 2.4268 0.097772 0.60046
1.4991 sec 3.4114 0.14396 0.54325
1.8089 sec 2.3788 0.11822 0.55134
1.809 sec 2.3314 0.081532 0.58023
0.0948 sec 3.2981 0.090041 0.57322
0.3035 sec 2.3475 0.14608 0.56763
⋮
tDataInRange=5001×3 timetable
Time Pulse Sensor_X Sensor_Y
__________ ___________ ________ ________
0.4999 sec -0.00076734 0.13439 0.53519
0.5 sec -0.00077651 0.13228 0.53557
0.5001 sec -0.00081844 0.13125 0.54597
0.5002 sec -0.00086627 0.12756 0.53557
0.5003 sec -0.00079617 0.12939 0.54231
0.5004 sec -0.00080272 0.12887 0.55175
0.5005 sec -0.00075685 0.12964 0.55849
0.5006 sec -0.00085841 0.12788 0.57198
0.5007 sec -0.00084007 0.12406 0.54289
0.5008 sec -0.00092131 0.12685 0.55792
0.5009 sec -0.00078765 0.12656 0.54963
0.501 sec -0.0007942 0.12801 0.56235
0.5011 sec -0.00076472 0.12984 0.54732
0.5012 sec -0.00074113 0.12968 0.57872
0.5013 sec -0.00083744 0.13102 0.58065
0.5014 sec -0.00078175 0.12935 0.58334
⋮
tRows = 60000
Plot Data
Use a plot to visually analyze the filtered data.
plot(tDataInRange.Time, tDataInRange.Pulse); hold on plot(tDataInRange.Time, tDataInRange.Sensor_X); hold on plot(tDataInRange.Time, tDataInRange.Sensor_Y); legend('Pulse','Sensor X', 'Sensor Y'); xlabel('Time (s)') ylabel('Pulse, Sensor_X, Sensor_Y');
Summarize Tall Table Contents
You can call the summary function to trigger immediate evaluation of all the table variables.
summary(tData);
Evaluating tall expression using the Local MATLAB Session:
- Pass 1 of 1: Completed in 0.1 sec
Evaluation completed in 0.13 sec
RowTimes:
Time: 60,000×1 duration
Values:
Min 0 sec
Max 1.9999 sec
Variables:
Pulse: 60,000×1 double
Values:
Min -0.69755
Max 3.4114
Sensor_X: 60,000×1 double
Values:
Min -0.212
Max 0.50583
Sensor_Y: 60,000×1 double
Values:
Min 0.18122
Max 0.94865
To learn more about tall tables, see Tall Arrays. Note: tdmsDatastore is not enabled to work with Parallel Computing Toolbox.
