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Using Autoplot in the IDL and MATLAB Environments

Audience: Autoplot users who would like to use the software to read data into IDL and MATLAB, and to use other Autoplot codes from these environments


  1. Introduction
  2. Getting Started
    1. Connecting the Jar File
      1. Connecting to MATLAB
      2. Connecting to IDL
    2. First Read of Data
      1. MATLAB
      2. IDL
    3. QDataSet in this Interface
  3. DataSet Properties
  4. The Rest of the Reader Interface
  5. Access other Autoplot classes
    1. Format datasets from MATLAB/IDL
    2. Static methods in IDL
  6. Problems
  7. See Also
  8. Complete IDL Examples
  9. Complete MATLAB Examples
  10. Previous Documentation

1. Introduction

Both IDL and MATLAB make it extremely easy to use Java code in these environments. Autoplot is able to read data from a variety of input sources using compact URIs to specify data locations, making it useful accessing digital data. For example, it's difficult to read data from an Excel worksheet into IDL, but since Autoplot can read this data, it becomes just as easy to read data from this source as it is a table of ASCII data. Further, IDL can read data from CDF files, but its low-level CDF interface makes Autoplot attractive, because you can access data with just several lines of code. Last, Autoplot can automatically retrieve and manage data from remote sites via FTP and HTTP, so this mechanism can be used in IDL and MATLAB as well.

Autoplot contains a number of codes that are useful, in addition to reading data. For example, Autoplot is able to write data to a number of data formats, and this code is useful in IDL as well.

Last, IDL and MATLAB adapt to Java code in such a way that code looks very similar in both languages. For example, where in IDL you would have:

sc= OBJ_NEW('IDLjavaObject$ScriptContext', 'org.virbo.autoplot.ScriptContext')
x= sc->getCompletions( 'vap+cdfj:')

in MATLAB you would have:

sc= org.virbo.autoplot.ScriptContext
x= sc.getCompletions( 'vap+cdfj:')

For this reason, examples are shown in IDL within this document.

2. Getting Started

First we need to connect the Autoplot code to the environment. This is, not surprisingly, the biggest difference between IDL and MATLAB. In these examples, "Unix>" is used to indicate commands entered into a Unix BASH shell, "MATLAB>" an MATLAB v7.7 session, and "IDL>" an IDL v7.0 session.

2.1. Connecting the Jar File

In either case, you'll need to download the Autoplot "single jar" release that is available along with each release of the software. For example, the development release at has a link to a single jar version here: For MATLAB, you don't need to download the file, but to be consistent with IDL, we will download it to /tmp/autoplot.jar or some location appropriate for your workstation. This 20 megabyte jar file is similar to a .so file from C/Fortran and contains compiled code and also needed resources. Note this is a full Autoplot and can be used from the command line, bypassing the mechanism normally used to launch Autoplot. If this seems large, consider that this contains code to read: NetCDF, OpenDAP, CDF, Excel, and many other forms of data.

I'm using for the demo here, and also at the latest release is found at

Note sometimes you'll see this called a jumbojar, which is the name we use for our software and all the libraries it needs, combined into one jar file.

2.1.1. Connecting to MATLAB

MATLAB is able to add the jar after the session is started, with the command "javaaddpath":

MATLAB> javaaddpath( '/tmp/autoplot.jar' )

Note for MATLAB, this can be a URL, like

Now we can test to see that the jar file is connected:

MATLAB> apds  = org.virbo.idlsupport.APDataSet;
QDataSetBridge v1.8.01
APDataSet v1.3.2

2.1.2. Connecting to IDL

For IDL, the jar file must be connected to the IDL process before starting the session. This must be a fully-qualified path, because it will work inconsistently if it's not. Using bash:

Unix> export CLASSPATH=/tmp/autoplot.jar

And we can test to see that the jar file is connected:

IDL> apds= OBJ_NEW('IDLjavaObject$APDataSet', 'org.virbo.idlsupport.APDataSet')
% QDataSetBridge v1.7.01
% APDataSet v1.2.3

2.2. First Read of Data

I'll show a first read of data in both MATLAB and IDL.

Autoplot can be used to read data into IDL and MATLAB
Autoplot can be used to read data into IDL and MATLAB

2.2.1. MATLAB

MATLAB> apds.setDataSetURI( '' )
MATLAB> apds.doGetDataSet

Note there's a bug where MATLAB is unable to read AbstractPreferences, and you see an error message associated with this. This message can be ignored. Note the default autoplot_data/fscache must always be used.

MATLAB> apds
data: data[dep0=287] (dimensionless)
dep0: dep0[287] (t1970) (DEPEND_0)
MATLAB> plot( apds.values )

2.2.2. IDL

Here's the code in IDL. Remember, the CLASSPATH variable must be set in the Unix environment as described above.

IDL> apds->setDataSetURI, ''
IDL> apds->doGetDataSet
IDL> print, apds->toString()
data: data[dep0=287] (dimensionless)
dep0: dep0[287] (t1970) (DEPEND_0)
IDL>  plot, apds->values()

This shows a first look at getting data.

2.3. QDataSet in this Interface

This interface is meant to provide access to anything that can be represented within Autoplot and its internal data model, QDataSet.

QDataSet is meant to be a simple, uniform data interface that is adapted to many different syntaxes, including Java, Python, C, IDL and MATLAB. However, it's more of a guide than a specification, and since both IDL and MATLAB are slow when many commands are executed, we provide access to data via arrays rather than individual values as in Java. Also, we provide access to timetags and other independent data through the one object. This should simplify use in the environments. For example, instead of:

apds->property( QDataSet.DEPEND_0 ).values()

we say:

dep0Name= apds->depend(0)
x= apds->values( dep0Name )


x= apds->values( apds->depend(0) )

Another difference is that the apds is mutable, meaning its state can be changed, whereas QDataSets are generally immutable. For example, you can tell the apds what your preferred units are, affecting what is returned by the values() command.

3. DataSet Properties

You can access the dataset properties like so:

dsp= apds->properties( )
print, ( dsp->get('TITLE') )->toString()    ; unfortunately IDL doesn't quite equate Java strings with IDL strings, so you need "toString()"

yp= apds->properties( 'ds_2' )
print, ( yp->get('LABEL') )->toString()


print, ( apds->property( 'ds_2', 'LABEL') )->toString()

4. The Rest of the Reader Interface

What are the X values? They are in some strange unit that the data source chooses. In this example it is "t1970", which is the number of non-leap seconds since 1970-Jan-01T00:00. We can specify what units we want:

apds->setPreferredUnits, 'hours since 2007-01-17T00:00' 
plot( apds->values('dep0'), apds->values() ) 

(Problem: there's an inconsistency here between ooffice calc and what I'm getting. It almost looks like the autoplot xls export shifts to the local time. Use a different data source, like CDF.) Here are some example units strings: seconds since 2010-01-01T00:00, days since 2010-01-01T00:00, Hz, MHz. Note Autoplot's units are not fully developed, and conversions are not always possible.

We also can work with fill data:

apds->setFillValue, -999

This will convert whatever fill is in the dataset to this value. This saves the developer the time of reading what the fill, validmin, and validmax are in the QDataSet.

5. Access other Autoplot classes

Other classes Autoplot uses can be accessed. For example,

sc= OBJ_NEW('IDLjavaObject$ScriptContext', 'org.virbo.autoplot.ScriptContext')
x= sc->getCompletions( 'vap+cdfj:')

lists all the variables in the CDF file.

5.1. Format datasets from MATLAB/IDL

Here's how we can use Autoplot's formatting to export data:

dsu= OBJ_NEW('IDLjavaObject$DataSetUtil', 'org.virbo.dataset.DataSetUtil' )
ds= dsu->asDataSet( randomu( s, 200 ) )    ; adapt IDL array to QDataSet. This is not the Autoplot function randomu, but IDL's.
sc= OBJ_NEW('IDLjavaObject$ScriptContext', 'org.virbo.autoplot.ScriptContext' )
sc->formatDataSet, ds, '/tmp/foo.xls'

The extension is used to control the output format. Note formatting to CDF is still done via the C-based plugin, not the Java plugin, and cannot be used. Note also that error feedback is poor, see We should consider adding a wrapper for often-used functions to simplify this as well.

There's an asynchronous version of the loader, so that multiple things can be loaded at once, and progress feedback is provided. (I need to remind myself how this is done.)

5.2. Static methods in IDL

It's a little non-trivial to use static methods in IDL, but it is possible. Here is an example showing use of the FileSystem and FileStorageModel objects, note the $Static$ part in the OBJ_NEW part:

Unix> export CLASSPATH=/tmp/autoplot.jar

IDL> fs= OBJ_NEW( 'IDLJavaObject$Static$FileSystem', 'org.das2.util.filesystem.FileSystem' ) ; provide access to the create command
IDL> afs= fs.create('') ; create a filesystem object
IDL> fsm= OBJ_NEW( 'IDLjavaObject$Static$FileStorageModel', 'org.das2.fsm.FileStorageModel' )
IDL> afsm= fsm.create(afs,'$Y/$m/$d/rbsp-b_WFR-waveform-magnitude_emfisis-L4_$Y$m$d_v$(v,sep).cdf')
IDL> dru= OBJ_NEW( 'IDLjavaObject$DatumRangeUtil', 'org.das2.datum.DatumRangeUtil' )
IDL> dr= dru.parseTimeRange('2014-02')
IDL> ff= afsm.getFilesFor( dr )
IDL> for i=0,n_elements(ff)-1 do print, ff[i]->toString()

6. Problems

There are some technical issues with all this.


  • The CLASSPATH problem in IDL is nasty, because you might have multiple jar files when autoplot and other libraries are needed. I assume this can be done.
  • It seems clear that you'd want to be able to use Autoplot to verify data, so applot should accept apds as an argument.
  • Filters are not readily accessible in IDL and MATLAB, and it would nice to show how these could be used.

7. See Also

Here is the nightly test which tells all:

IDL command applot sends data to Autoplot for display: developer.applot

This looks promising for use with native Python, using jpype to bridge to Autoplot, so that it can be used to read data. See

8. Complete IDL Examples


Unix> export CLASSPATH=/tmp/autoplot.jar

And here's the first IDL program:

apds= OBJ_NEW('IDLjavaObject$APDataSet', 'org.virbo.idlsupport.APDataSet')
apds->setDataSetURI, ''
apds->setPreferredUnits, 'hours since 2007-01-17T00:00' 
plot, apds->values( apds->depend(0) ), apds->values()

Accessing aggregated data

apds= OBJ_NEW('IDLjavaObject$APDataSet', 'org.virbo.idlsupport.APDataSet')
t= '2011-01-17'
apds->setDataSetURI, '$Y/ac_k0_swe_$Y$m$d_v$v.cdf?Np&timerange=' + t
apds->setPreferredUnits, 'hours since '+t 
plot, apds->values( apds->depend(0) ), apds->values(), xtitle='hours since '+t

Using slice to get at irregular data

apds  = OBJ_NEW('IDLjavaObject$APDataSet', 'org.virbo.idlsupport.APDataSet')
casargs= "-lfdr+ExEw+-mfdr+ExEw+-mfr+13ExEw+-hfr+ABC12EuEvEx+-n+hfr_snd+-n+lp_rswp+-n+bad_data+-n+dpf_zero+-n+mfdr_mfr2+-n+mfr3_hfra+-n+hf1_hfrc+-a+-b+30+-bgday="
tt= "start_time=2010-01-11T11:15:00.000Z&end_time=2010-01-11T21:45:00.000Z"
KEY= '1234567' ; request key from the RPWS group
ds= 'das2_1/cassini/cassiniLrfc'
apds->setDataSetURI, 'vap+das2server:'+ds+'&key='+KEY+'&'+tt+'&'+casargs 
print, apds->toString() 
help, apds->slice( 3 )
plot, apds->slice( 3 ), /ylog

9. Complete MATLAB Examples

Sarah couldn't write .xls files on her Mac. This script allows her to do this with Autoplot:

javaaddpath( '' )
Ops = org.virbo.dsops.Ops;
Util= org.virbo.dataset.DataSetUtil;
SC= org.virbo.autoplot.ScriptContext; 

tt= Ops.labels( {  'experiment_1', 'experiment_2' } );
ll= Ops.labels( { 'ch1','ch2','ch3','ch4','ch5','ch6' } );
ds= rand(2,6);
ds= Util.asDataSet( ds );
ds= tt, ll, ds );
SC.formatDataSet( ds, '/tmp/forSarah.xls?sheet=sh1' );

ds= rand(2,6);
ds= Util.asDataSet( ds );
ds= tt, ll, ds );
SC.formatDataSet( ds, '/tmp/forSarah.xls?sheet=sh2&append=T' );

10. Previous Documentation

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