cdasws Example IDL Jupyter Notebook

This Jupyter notebook demonstates using the cdasws IDL library to access data from cdaweb in the IDL programming language.

Note: This notebook is for the IDL version of cdasws. Jupyter notebooks for the Python version of cdasws is available at python cdasws notebooks. This notebook contains the following sections:

1. Installation
2. Setup
3. Get Observatory Groups
4. Get Instrument Types
5. Get Datasets
6. Get Inventory
7. Get Variable Names
8. Get Data
9. Binning Example
10. DOI Example
11. Additional Documentation

Installation

The following contains the procedure to install the cdasws IDL library into your IDL environment. There are different procedures for different versions of IDL.

IDL 8.7.1 and higher

If you have an old version of the SPDF_CDAS package already installed, remove the old version.

ipm, /remove, 'SPDF_CDAS'

Package "SPDF_CDAS" not found

If the lastest version of the SPDF_CDAS package is not already installed, install it as shown below.

ipm, /install, 'https://cdaweb.gsfc.nasa.gov/WebServices/REST/SPDF_CDAS.zip'

Package: SPDF_CDAS, Version: 1.7.45 installed

You only need to install a particular version of the package once. You will need to restore the package everytime you restart your IDL session. Restore the package as shown below.

restore, !package_path + '/SPDF_CDAS/spdfcdas.sav'

IDL 8.4.0 and newer

Download spdfcdas.sav. You will need to restore the package everytime you restart your IDL session. Restore the package as shown below.

;restore, getenv('HOME') + '/Downloads' + '/spdfcdas.sav'

Setup

Create an SpdfCdas object that will be used in the code that follows.

cdas = obj_new('SpdfCdas')

Get Observatory Groups

The following code demonstrates how to get the mission/observatory groups supported by cdaweb.

groups = cdas.getObservatoryGroups()
foreach group, groups[0:3] do print, group.getName()
print, '...'

ACE
AIM
AMPTE
ARTEMIS

...

Get Intrument Types

The following code demonstrates how to get the intrument types supported by cdaweb.

instrTypes = cdas.getInstrumentTypes()
foreach instrType, instrTypes do print, instrType.getName()

Activity Indices
Electric Fields (space)
Electron Precipitation Bremsstrahlung
Energetic Particle Detector
Engineering
Ephemeris/Attitude/Ancillary
Gamma and X-Rays
Ground-Based HF-Radars
Ground-Based Imagers
Ground-Based Magnetometers, Riometers, Sounders
Ground-Based VLF/ELF/ULF, Photometers
Housekeeping
Imagers (space)
Imaging and Remote Sensing (ITM)
Imaging and Remote Sensing (ITM/Earth)
Imaging and Remote Sensing (Magnetosphere/Earth)
Imaging and Remote Sensing (Sun)
Magnetic Fields (Balloon)
Magnetic Fields (space)
Particles (space)
Plasma and Solar Wind
Pressure gauge (space)
Radio and Plasma Waves (space)
Spacecraft Potential Control
UV Imaging Spectrograph (Space)

Get Datasets

The following code demonstrates how to find the datasets for a specific observatory group and instrument type.

datasets = cdas.getDatasets(observatoryGroups=[groups[0].getName()], instrumentTypes=[instrTypes[18].getName()])
datasets[-1].print

AC_H4_SWI: ACE/SWICS 1.1 Solar Wind 1-Day Level 2 Data - G. Gloeckler (University of Maryland)
TimeInterval: 1998-02-04T00:09:16.000Z to 2011-08-21T02:28:39.000Z

Get Inventory

The following code demonstrates getting the available data inventory.

inventory = cdas.getInventory(datasets[-1].getId())
foreach interval, inventory.getTimeIntervals() do interval.print

TimeInterval: 1998-02-04T00:09:16.000Z to 1998-12-31T15:33:51.000Z
TimeInterval: 1999-01-01T15:27:59.000Z to 1999-12-31T17:28:07.000Z
TimeInterval: 2000-01-01T17:29:00.000Z to 2000-12-31T07:24:47.000Z
TimeInterval: 2001-01-01T07:25:00.000Z to 2001-12-31T01:14:09.000Z
TimeInterval: 2002-01-01T01:00:00.000Z to 2002-12-31T11:06:38.000Z
TimeInterval: 2003-01-01T10:55:31.000Z to 2003-12-31T01:27:21.000Z
TimeInterval: 2004-01-01T01:25:18.000Z to 2004-12-31T16:44:43.000Z
TimeInterval: 2005-01-01T16:45:06.000Z to 2005-12-31T23:32:52.000Z
TimeInterval: 2006-01-01T23:34:24.000Z to 2006-12-31T04:39:44.000Z
TimeInterval: 2007-01-01T04:40:36.000Z to 2007-12-31T13:31:42.000Z
TimeInterval: 2008-01-01T13:33:36.000Z to 2008-12-31T23:11:12.000Z
TimeInterval: 2009-01-01T23:11:36.000Z to 2009-12-31T12:23:47.000Z
TimeInterval: 2010-01-01T12:25:05.000Z to 2010-12-31T21:12:36.000Z
TimeInterval: 2011-01-01T21:14:23.000Z to 2011-08-21T02:28:39.000Z

Get Variable Names

The following code demonstrates how to a dataset's variable names.

names = cdas.getVariableNames(datasets[-1].getId())
print, names

nHe2 nHe2_err He_qual C6to4 C6to4_err C6to4_qual C6to5 C6to5_err C6to5_qual O7to6 O7to6_err O7to6_qual avqC avqC_err avqC_qual avqO
avqO_err avqO_qual avqMg avqMg_err avqMg_qual avqSi avqSi_err avqSi_qual avqFe avqFe_err avqFe_qual HetoO HetoO_err HetoO_qual CtoO
CtoO_err CtoO_qual NtoO NtoO_err NtoO_qual NetoO NetoO_err NetoO_qual MgtoO MgtoO_err MgtoO_qual SitoO SitoO_err SitoO_qual StoO
StoO_err StoO_qual FetoO FetoO_err FetoO_qual

Get Data

The following code demonstrates how to access magnetic field measurements from the ACE mission dataset.

d = spdfgetdata('AC_H2_MFI', ['Magnitude' , 'BGSEc'], ['2009-06-01T00:00:00.000Z', '2009-06-03T00:00:00.000Z'])

Use the standard IDL PLOT procedure to display the data.

plot, d.magnitude.dat

Print the values.

print, d.magnitude.dat

      3.52700      3.40500      2.88200      2.73000      3.54800      3.94800      3.73000      3.70300      3.67200      3.61600
      3.58300      3.37000      3.51300      3.77700      3.21800      3.01400      3.19700      3.49900      3.10900      3.03900
      2.99700      3.09500      3.15200      3.26500      3.25000      3.07000      2.75000      2.92400      2.82200      2.83700
      3.02900      3.54900      3.67500      3.76300      4.02500      4.13600      3.85400      3.85700      3.68800      3.58300
      3.85200      2.63600      3.01900      3.25700      3.09100      2.61900      1.92300      1.81800      1.99100

Use the cdawlib plotmaster function to plot the data.

status=plotmaster(d,/auto, xsize=768)

Binning Example

For analysis, it is often useful to place two datasets that have different timestamps on the same time grid (with optional spike removal). The following demonstrates doing this with cdasws and the datasets AC_H0_SWE and AC_H2_SWE. For more information on binning, see binning in cdaweb.

Display Original Data

Get and gets and displays the original, unbinned data.

dataset0 = 'AC_H0_SWE'
variables = ['Np']
time = ['1998-02-04T00:00:00Z', '1998-02-06T00:00:00Z']
data0 = spdfgetdata(dataset0, variables, time)
print, data0.epoch.dat[0:10]
print, data0.np.dat[0:10]
dataset1 = 'AC_H2_SWE'
data1 = spdfgetdata(dataset1, variables, time)
print, data1.epoch.dat[0:10]
print, data1.np.dat[0:10]

   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13
   6.3053770e+13   6.3053770e+13   6.3053770e+13

 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31
 -1.00000e+31

   6.3053770e+13   6.3053773e+13   6.3053777e+13   6.3053780e+13   6.3053784e+13   6.3053788e+13   6.3053791e+13   6.3053795e+13
   6.3053798e+13   6.3053802e+13   6.3053806e+13

 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31 -1.00000e+31
 -1.00000e+31

Bin Data

The following code gets data after it has been binned with 60 second time intervals and any missing values created by interpolation.

data0 = spdfgetdata(dataset0, variables, time, $
                binInterval=60.0D, binInterpolateMissingValues=1, $
                binSigmaMultiplier=4)
print, data0.epoch.dat[0:10]
print, data0.np.dat[0:10]
data1 = spdfgetdata(dataset1, variables, time, $
                binInterval=60.0D, binInterpolateMissingValues=1, $
                binSigmaMultiplier=4)
print, data1.epoch.dat[0:10]
print, data1.np.dat[0:10]

   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13
   6.3053770e+13   6.3053770e+13   6.3053770e+13

      16.3343      16.3343      16.3343      16.3343      16.3343      16.3343      16.3343      16.3343      16.3343      16.3343
      16.3343

   6.3053770e+13   6.3053773e+13   6.3053777e+13   6.3053780e+13   6.3053784e+13   6.3053788e+13   6.3053791e+13   6.3053795e+13
   6.3053798e+13   6.3053802e+13   6.3053806e+13

      16.6551      16.6551      16.6551      16.6551      16.6551      16.6551      16.6551      16.6551      16.6551      16.6551
      16.6551

Compare Data

The following code compares the binned data from the two datasets by plotting the values.

plot, data0.np.dat

plot, data1.np.dat

DOI Example

The following code gets data from a dataset using the dataset's Digital Object Identifier and displays the dataset's values.

ds = cdas.getDatasets(idPattern='ISEE2_60SEC_MFI')
print, ds[0].getId(), ', DOI: ', ds[0].getDoi(), ', ', ds[0].getResourceId()
iseeVars = cdas.getVariableNames(ds[0].getId())
print, iseeVars[0:3]
iseeInventory = cdas.getInventory(ds[0].getId())
iseeIntervals = iseeInventory.getTimeIntervals()
iseeStop = iseeIntervals[-1].getStop()
iseeStart = iseeStop - 1.0
iseeData = spdfgetdata(ds[0].getDoi(), iseeVars[0:3], [iseeStart, iseeStop])
status = plotmaster(iseeData, /auto, xsize=768)

ISEE2_60SEC_MFI, DOI: 10.21978/p8t923, spase://NASA/NumericalData/ISEE2/MAG/PT1M

BX BY BZ BTOT

Additional Documentation

View the cdasws API for additional functions.