# idl

Using Autoplot in the IDL Environment

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

# 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.

# 2. Getting Started

First we need to connect the Autoplot code to the environment. In these examples, "Unix>" is used to indicate commands entered into a Unix BASH shell, and "IDL>" an IDL v7.0 (or newer) session.

You can verify the Java version by calling a Java method:

IDL> System= obj_new( 'IDLJavaObject$Static$System', 'java.lang.System' )
IDL> print, System.getProperty( 'java.version' )
1.8.0_131


You will need at least Java version 1.7.0, and 1.8.0_102 or better is needed to access CDAWeb and other https sites. You will need at least IDL 7.0, and soon 8.4 will be required.

## 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 release at http://autoplot.org/jnlp/v2015a_5/ has a link to a single jar version here: http://autoplot.org/jnlp/v2015a_5/autoplot.jar. 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.

We will use http://autoplot.org/jnlp/v2015a_5/autoplot.jar for the demo here, and also the latest release is found at http://autoplot.org/jnlp/latest/autoplot.jar (for IDL 8.4 and newer).

Notes:

• 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.
• Latest versions of Autoplot require Java 7, and older versions of IDL may still use Java 6. IDL 8.4 is okay, 8.1 will not work with v2015a_6 or newer.
• IDL 8.1 allows obj.method() as well as obj->method(), and this form is used here.

### 2.1.1. 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> wget -N http://autoplot.org/jnlp/v2015a_5/autoplot.jar
Unix> export CLASSPATH=autoplot.jar


And start up a new IDL session and we can test to see that the jar file is connected:

IDL> apds= OBJ_NEW('IDLjavaObject$APDataSet', 'org.virbo.idlsupport.APDataSet') % QDataSetBridge v1.8.01 % APDataSet v1.3.2  ## 2.2. First Read of Data Suppose you have been using the Autoplot URI (data address) http://www.autoplot.org/data/swe-np.xls?column=data&depend0=dep0 to read data into Autoplot. Autoplot can be used to read data into IDL and MATLAB Here's the code in IDL. Remember, the CLASSPATH variable must be set in the Unix environment as described above. IDL> apds.setDataSetURI, 'http://www.autoplot.org/data/swe-np.xls?column=data&depend0=dep0' 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. So we say: dep0Name= apds.depend(0) ; This returns the name of the DEPEND_0 property. x= apds.values( dep0Name )  or 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( ) p= dsp.get('NAME') print, p.toString() ; unfortunately IDL doesn't quite equate Java strings with IDL strings, so you need "toString()" yp= apds.properties( 'ds_2' ) p= yp.get('LABEL') print, p.toString()  or ll= apds.property( 'ds_2', 'NAME') print, ll.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() )  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. Last, we can add a filter process string: apds.setFilter, '|slice1(0)'  Presently this must be done before the getDataSet call. Future versions should allow it at any time. ## 4.1. Length and Slice to access non-qubes IDL and Matlab have 3-D arrays, but do not support "ragged" arrays of C and Java, which is also the structure of QDataSets. For example, qds[0,*,*] may have size [50,20], but qds[1,*,*] has size [40,30]. For example, the CASSINI RPWS instrument uses this feature of C and Java (and QDataSet) to represent data from different modes. Autoplot uses the name "qube" to refer to datasets which are N-D arrays like you would see in IDL. To access these ragged arrays, the "slice" method is called instead of the values method. This will extract data from one slice of the dataset (qds[0,*,*]) at a time. (It is guaranteed that slices are qubes.) The lengths method returns the size of each qube after slicing. The following code makes the ragged array into a regular array. It also utilitizes "rank()" which returns the number of indices in the dataset, and isQube which checks for ragged or qube, and lengths which returns all the dimensions. apds= OBJ_NEW('IDLjavaObject$APDataSet', 'org.virbo.idlsupport.APDataSet')
if apds.rank() eq 3 and not apds.isQube() then begin
n = apds.length()
ndim = lonarr(n, 2)
for i = 0, n-1 do ndim[i,*] = apds.lengths(i)

data = dblarr(n, max(ndim[*,0]), max(ndim[*,1]))
for i = 0, n-1 do begin
temp = apds.slice(i)
data[i,0:ndim[i,0]-1,0:ndim[i,1]-1] = temp
endfor
endif else begin
data = apds.values()
endelse

help, data


# 5. Access other Autoplot classes

Other classes Autoplot uses can be accessed. For example,

sc= OBJ_NEW('IDLjavaObject$ScriptContext', 'org.virbo.autoplot.ScriptContext') x= sc.getCompletions( 'vap+cdfj:http://autoplot.org/data/somedata.cdf?')  In Autoplot v2017a and newer: sc= OBJ_NEW('IDLjavaObject$ScriptContext', 'org.autoplot.ScriptContext')
x= sc.getCompletions( 'vap+cdfj:http://autoplot.org/data/somedata.cdf?')


lists all the variables in the CDF file.

#### 5.1. Accessing Aggregation in IDL

agg= OBJ_NEW('IDLjavaObject$Agg', 'org.autoplot.aggregator.AggregatingDataSourceFactory' ) fsm= agg.getFileStorageModel('http://autoplot.org/data/agg/efi/$Y/po_k0_efi_$Y$m$d_v$v.cdf')
ops= OBJ_NEW('IDLJavaObject$Static$Ops', 'org.das2.qds.ops.Ops' )   ; note static is explained below
ff= fsm.getBestNamesFor( ops.datumRange('1999-12-30/2000-01-04') )   ; wait for 2018-07-18 release
print, fsm.getRoot() + ff[0]


#### 5.2. 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'


In Autoplot v2017a:

dsu= OBJ_NEW('IDLjavaObject$DataSetUtil', 'org.das2.qds.DataSetUtil' ) ds= dsu.asDataSet( randomu( s, 200 ) ) ; adapt IDL array to QDataSet. This is not the Autoplot function randomu, but IDL's. sc= OBJ_NEW('IDLjavaObject$ScriptContext', 'org.autplot.ScriptContext' )
sc.formatDataSet, ds, '/tmp/foo.xls'


The extension is used to control the output format. Note also that error feedback is poor, see https://sourceforge.net/tracker/?func=detail&aid=3430015&group_id=199733&atid=970682. 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. (TODO: document this.)

## 5.3. 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> wget -N http://autoplot.org/jnlp/v2015a_5/autoplot.jar
Unix> export CLASSPATH=autoplot.jar

IDL> fs= OBJ_NEW( 'IDLJavaObject$Static$FileSystem', 'org.das2.util.filesystem.FileSystem' ) ; provide access to the create command
IDL> afs= fs.create('http://emfisis.physics.uiowa.edu/Flight/RBSP-B/L4/') ; 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()


And to echo the Java version:

IDL> System= obj_new( 'IDLJavaObject$Static$System', 'java.lang.System' )
IDL> print, System.getProperty( 'java.version' )
1.8.0_131


Sometimes we don't want an XWindows dependence caused by login prompt windows. Set headless property in this case.

IDL> System= obj_new( 'IDLJavaObject$Static$System', 'java.lang.System' )
IDL> apds= OBJ_NEW('IDLjavaObject$APDataSet', 'org.virbo.idlsupport.APDataSet') IDL> apds.loadDataSet, 'vap+das2Server:http://jupiter.physics.uiowa.edu/das/server?dataset=Juno/FGM/MagComponents&start_time=2017-02-01T00:00:00.000Z&end_time=2017-02-04T00:00:00.000Z'  # 6. Problems Also: • The CLASSPATH problem in IDL is nasty, because you might have multiple jar files when autoplot and other libraries are needed. I assume this can be done. • It seems clear that you'd want to be able to use Autoplot to verify data, so applot should accept apds as an argument. New 2017-March: # 7. See Also Here is the nightly test which tells all: http://jfaden.net/jenkins/job/autoplot-test024/ IDL command applot sends data to Autoplot for display: developer.applot This looks promising for use with native Python, using jpype to bridge to Autoplot, so that it can be used to read data. See https://sourceforge.net/p/autoplot/feature-requests/365/ # 8. Complete IDL Examples Remember, Unix> export CLASSPATH=/tmp/autoplot.jar  And here's the first IDL program: apds= OBJ_NEW('IDLjavaObject$APDataSet', 'org.virbo.idlsupport.APDataSet')
apds.setDataSetURI, 'http://www.autoplot.org/data/swe-np.xls?column=data&depend0=dep0'
apds.doGetDataSet
apds.setPreferredUnits, 'hours since 2007-01-17T00:00'
plot, apds.values( apds.depend(0) ), apds.values()


Accessing aggregated data

apds= OBJ_NEW('IDLjavaObject$APDataSet', 'org.virbo.idlsupport.APDataSet') t= '2011-01-17' apds.setDataSetURI, 'http://cdaweb.sci.gsfc.nasa.gov/istp_public/data/ace/swepam/level_2_cdaweb/swe_k0/$Y/ac_k0_swe_$Y$m$d_v$v.cdf?Np&timerange=' + t
apds.doGetDataSet
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')
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:http://www-pw.physics.uiowa.edu/das/das2Server?dataset='+ds+'&key='+KEY+'&'+tt+'&'+casargs
apds.doGetDataSet
print, apds.toString()
help, apds.slice( 3 )
plot, apds.slice( 3 ), /ylog