{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# cdasws Example IDL Jupyter Notebook\n", "This [Jupyter notebook](https://jupyter.org/) demonstates using the [cdasws](/WebServices/REST/CdasIdlLibrary.html) IDL library to access data from [cdaweb](https://cdaweb.gsfc.nasa.gov/) in the [IDL](https://www.l3harrisgeospatial.com/Software-Technology/IDL) programming language.\n", "\n", "**Note:** This notebook is for the IDL version of cdasws. Jupyter notebooks for the Python version of cdasws is available at [python cdasws notebooks](/WebServices/REST/#Jupyter_Notebook_Examples). This notebook contains the following sections:\n", "\n", "1. [Installation](#Installation)\n", "2. [Setup](#Setup)\n", "3. [Get Observatory Groups](#Get-Observatory-Groups)\n", "4. [Get Instrument Types](#Get-Instrument-Types)\n", "5. [Get Datasets](#Get-Datasets)\n", "6. [Get Inventory](#Get-Inventory)\n", "7. [Get Variable Names](#Get-Variable-Names)\n", "8. [Get Data](#Get-Data)\n", "9. [Binning Example](#Binning-Example)\n", "10. [DOI Example](#DOI-Example)\n", "11. [Additional Documentation](#Additional-Documentation)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Installation\n", "The following contains the procedure to install the [cdasws](https://cdaweb.gsfc.nasa.gov/WebServices/REST/CdasIdlLibrary.html) IDL library into your IDL environment. There are different procedures for different versions of IDL." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IDL 8.7.1 and higher\n", "If you have an old version of the SPDF_CDAS package already installed, remove the old version." ] }, { "cell_type": "code", "execution_count": 0, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Package \"SPDF_CDAS\" was removed
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ipm, /remove, 'SPDF_CDAS'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If the lastest version of the SPDF_CDAS package is not already installed, install it as shown below." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Package: SPDF_CDAS, Version: 1.7.44 installed
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ipm, /install, 'https://cdaweb.gsfc.nasa.gov/WebServices/REST/SPDF_CDAS.zip'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "restore, !package_path + '/SPDF_CDAS/spdfcdas.sav'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### IDL 8.4.0 and newer\n", "Download [spdfcdas.sav](https://cdaweb.gsfc.nasa.gov/WebServices/REST/spdfcdas.sav). You will need to restore the package everytime you restart your IDL session. Restore the package as shown below." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ ";restore, getenv('HOME') + '/Downloads' + '/spdfcdas.sav'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup\n", "Create an SpdfCdas object that will be used in the code that follows." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "cdas = obj_new('SpdfCdas')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get Observatory Groups\n", "The following code demonstrates how to get the mission/observatory groups supported by cdaweb." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
ACE\n",
       "AIM\n",
       "AMPTE\n",
       "ARTEMIS
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
...
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "groups = cdas.getObservatoryGroups()\n", "foreach group, groups[0:3] do print, group.getName()\n", "print, '...'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get Intrument Types\n", "The following code demonstrates how to get the intrument types supported by cdaweb." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Activity Indices\n",
       "Electric Fields (space)\n",
       "Electron Precipitation Bremsstrahlung\n",
       "Energetic Particle Detector\n",
       "Engineering\n",
       "Ephemeris/Attitude/Ancillary\n",
       "Gamma and X-Rays\n",
       "Ground-Based HF-Radars\n",
       "Ground-Based Imagers\n",
       "Ground-Based Magnetometers, Riometers, Sounders\n",
       "Ground-Based VLF/ELF/ULF, Photometers\n",
       "Housekeeping\n",
       "Imagers (space)\n",
       "Imaging and Remote Sensing (ITM)\n",
       "Imaging and Remote Sensing (ITM/Earth)\n",
       "Imaging and Remote Sensing (Magnetosphere/Earth)\n",
       "Imaging and Remote Sensing (Sun)\n",
       "Magnetic Fields (Balloon)\n",
       "Magnetic Fields (space)\n",
       "Particles (space)\n",
       "Plasma and Solar Wind\n",
       "Pressure gauge (space)\n",
       "Radio and Plasma Waves (space)\n",
       "Spacecraft Potential Control\n",
       "UV Imaging Spectrograph (Space)
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "instrTypes = cdas.getInstrumentTypes()\n", "foreach instrType, instrTypes do print, instrType.getName()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get Datasets\n", "The following code demonstrates how to find the datasets for a specific observatory group and instrument type." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
AC_H0_MFI: H0 - ACE Magnetic Field 16-Second Level 2 Data - N. Ness (Bartol Research Institute)\n",
       "TimeInterval: 1997-09-02T00:00:12.000Z to 2023-01-16T23:59:55.000Z
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "datasets = cdas.getDatasets(observatoryGroups=[groups[0].getName()], instrumentTypes=[instrTypes[18].getName()])\n", "datasets[-1].print" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get Inventory\n", "The following code demonstrates getting the available data inventory." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
TimeInterval: 1997-09-02T00:00:12.000Z to 2023-01-16T23:59:55.000Z
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "inventory = cdas.getInventory(datasets[-1].getId())\n", "foreach interval, inventory.getTimeIntervals() do interval.print" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get Variable Names\n", "The following code demonstrates how to a dataset's variable names." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Magnitude BGSEc BGSM dBrms SC_pos_GSE SC_pos_GSM
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "names = cdas.getVariableNames(datasets[-1].getId())\n", "print, names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Get Data\n", "The following code demonstrates how to access magnetic field measurements from the [ACE mission dataset](https://cdaweb.gsfc.nasa.gov/misc/NotesA.html#AC_H1_MFI)." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "d = spdfgetdata('AC_H2_MFI', ['Magnitude' , 'BGSEc'], ['2009-06-01T00:00:00.000Z', '2009-06-03T00:00:00.000Z'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the standard IDL PLOT procedure to display the data." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": "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" }, "metadata": { "image/png": { "height": "528", "width": "960" } }, "output_type": "display_data" } ], "source": [ "plot, d.magnitude.dat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Print the values." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
      3.52700      3.40500      2.88200      2.73000      3.54800      3.94800      3.73000      3.70300      3.67200      3.61600\n",
       "      3.58300      3.37000      3.51300      3.77700      3.21800      3.01400      3.19700      3.49900      3.10900      3.03900\n",
       "      2.99700      3.09500      3.15200      3.26500      3.25000      3.07000      2.75000      2.92400      2.82200      2.83700\n",
       "      3.02900      3.54900      3.67500      3.76300      4.02500      4.13600      3.85400      3.85700      3.68800      3.58300\n",
       "      3.85200      2.63600      3.01900      3.25700      3.09100      2.61900      1.92300      1.81800      1.99100
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "print, d.magnitude.dat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the [cdawlib plotmaster function](https://spdf.gsfc.nasa.gov/CDAWlib.html#plotmaster) to plot the data." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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" }, "metadata": { "image/png": { "height": "540", "width": "768" } }, "output_type": "display_data" } ], "source": [ "status=plotmaster(d,/auto, xsize=768)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Binning Example\n", "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](/misc/NotesA.html#AC_H0_SWE) and [AC_H2_SWE](/misc/NotesA.html#AC_H2_SWE). For more information on binning, see [binning in cdaweb](/CDAWeb_Binning_readme.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Display Original Data\n", "Get and gets and displays the original, unbinned data." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13\n",
       "   6.3053770e+13   6.3053770e+13   6.3053770e+13
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 -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\n",
       " -1.00000e+31
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
   6.3053770e+13   6.3053773e+13   6.3053777e+13   6.3053780e+13   6.3053784e+13   6.3053788e+13   6.3053791e+13   6.3053795e+13\n",
       "   6.3053798e+13   6.3053802e+13   6.3053806e+13
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
 -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\n",
       " -1.00000e+31
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset0 = 'AC_H0_SWE'\n", "variables = ['Np']\n", "time = ['1998-02-04T00:00:00Z', '1998-02-06T00:00:00Z']\n", "data0 = spdfgetdata(dataset0, variables, time)\n", "print, data0.epoch.dat[0:10]\n", "print, data0.np.dat[0:10]\n", "dataset1 = 'AC_H2_SWE'\n", "data1 = spdfgetdata(dataset1, variables, time)\n", "print, data1.epoch.dat[0:10]\n", "print, data1.np.dat[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bin Data\n", "The following code gets data after it has been binned with 60 second time intervals and any missing values created by interpolation." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13   6.3053770e+13\n",
       "   6.3053770e+13   6.3053770e+13   6.3053770e+13
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
      16.3343      16.3343      16.3343      16.3343      16.3343      16.3343      16.3343      16.3343      16.3343      16.3343\n",
       "      16.3343
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
   6.3053770e+13   6.3053773e+13   6.3053777e+13   6.3053780e+13   6.3053784e+13   6.3053788e+13   6.3053791e+13   6.3053795e+13\n",
       "   6.3053798e+13   6.3053802e+13   6.3053806e+13
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
      16.6551      16.6551      16.6551      16.6551      16.6551      16.6551      16.6551      16.6551      16.6551      16.6551\n",
       "      16.6551
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data0 = spdfgetdata(dataset0, variables, time, $\n", " binInterval=60.0D, binInterpolateMissingValues=1, $\n", " binSigmaMultiplier=4)\n", "print, data0.epoch.dat[0:10]\n", "print, data0.np.dat[0:10]\n", "data1 = spdfgetdata(dataset1, variables, time, $\n", " binInterval=60.0D, binInterpolateMissingValues=1, $\n", " binSigmaMultiplier=4)\n", "print, data1.epoch.dat[0:10]\n", "print, data1.np.dat[0:10]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Compare Data\n", "The following code compares the binned data from the two datasets by plotting the values." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "image/png": 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}, "metadata": { "image/png": { "height": "540", "width": "768" } }, "output_type": "display_data" } ], "source": [ "plot, data0.np.dat" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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}, "metadata": { "image/png": { "height": "540", "width": "768" } }, "output_type": "display_data" } ], "source": [ "plot, data1.np.dat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DOI Example\n", "The following code gets data from a dataset using the dataset's [Digital Object Identifier](https://www.doi.org/) and displays the dataset's values." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
ISEE2_60SEC_MFI, DOI: 10.21978/p8t923, spase://NASA/NumericalData/ISEE2/MAG/PT1M
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
BX BY BZ BTOT
" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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" }, "metadata": { "image/png": { "height": "540", "width": "768" } }, "output_type": "display_data" } ], "source": [ "ds = cdas.getDatasets(idPattern='ISEE2_60SEC_MFI')\n", "print, ds[0].getId(), ', DOI: ', ds[0].getDoi(), ', ', ds[0].getResourceId()\n", "iseeVars = cdas.getVariableNames(ds[0].getId())\n", "print, iseeVars[0:3]\n", "iseeInventory = cdas.getInventory(ds[0].getId())\n", "iseeIntervals = iseeInventory.getTimeIntervals()\n", "iseeStop = iseeIntervals[-1].getStop()\n", "iseeStart = iseeStop - 1.0\n", "iseeData = spdfgetdata(ds[0].getDoi(), iseeVars[0:3], [iseeStart, iseeStop])\n", "status = plotmaster(iseeData, /auto, xsize=768)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Additional Documentation\n", "View the [cdasws API](https://cdaweb.gsfc.nasa.gov/WebServices/REST/idl/api/) for additional functions." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "IDL", "language": "IDL", "name": "idl" }, "language_info": { "codemirror_mode": "idl", "file_extension": ".pro", "mimetype": "text/x-idl", "name": "idl" } }, "nbformat": 4, "nbformat_minor": 4 }