Other Stuff (Python)

November 20, 2017

The extensive scope of the SpiceyPy system's functionality includes features the average user may not expect or appreciate, features NAIF refers to as “Other Stuff.” This workbook includes a set of lessons to introduce the beginning to moderate user to such features.

The lessons provide a brief description to several related sets of routines, associated reference documents, a programming task designed to teach the use of the routines, and an example solution to the programming problem.

Overview

This workbook contains lessons to demonstrate use of the less celebrated SpiceyPy routines.

1. Kernel Management with the Kernel Subsystem

2. The Kernel Pool

3. Coordinate Conversions

4. Advanced Time Manipulation Routines

5. Error Handling

6. Windows and Cells

7. Utility and Constants Routines

References

This section lists SPICE documents referred to in this lesson.

The following SPICE tutorials serve as references for the discussions in this lesson:

Name              Lesson steps/functions it describes
----------------  -----------------------------------------------
concepts          Concepts of space geometry and time
intro_to_kernels  Using kernels, meta-kernels
time              Time systems, conversions and formats
lsk_and_sclk      LSK and SCLK
derived_quant     "high-level" observation geometry computations
other_functions   Intro to some SPICE "low level" computations
exceptions        built-in mechanism for trapping/handling errors

These tutorials are available from the NAIF ftp server at JPL:

https://naif.jpl.nasa.gov/naif/tutorials.html

Required Readings

Tip

The Required Readings are also available on the NAIF website at:

https://naif.jpl.nasa.gov/pub/naif/misc/toolkit_docs_N0067/C/req/index.html.

The Required Reading documents are provided with the Toolkit and are located under the "cspice/doc" directory in the CSPICE Toolkit installation tree.

Name             Lesson steps/functions that it describes
---------------  -----------------------------------------
cells.req        The SPICE cell data type
error.req        The SPICE error handling system
kernel.req       Loading SPICE kernels
time.req         Time conversion
windows.req      The SPICE window data type

The Permuted Index

Tip

The Permuted Index is also available on the NAIF website at:

https://naif.jpl.nasa.gov/pub/naif/misc/toolkit_docs_N0067/C/info/cspice_idx.html.

Another useful document distributed with the Toolkit is the permuted index. This is located under the "cspice/doc" directory in the C installation tree.

This text document provides a simple mechanism by which users can discover which SpiceyPy functions perform functions of interest, as well as the names of the source files that contain these functions.

SpiceyPy API Documentation

A SpiceyPy function's parameters specification is available using the built-in Python help system.

import spiceypy

For example, the Python help function

import spiceypy

help(spiceypy.str2et)

describes the str2et function's parameters, while the

str2et documentation

describes extensively the str2et functionality.

Kernels Used

The following kernels are used in examples provided in this lesson:

#  FILE NAME    TYPE DESCRIPTION
-- ------------ ---- ------------------------------------------------
1  naif0008.tls LSK  Generic LSK
2  de405s.bsp   SPK  Planet Ephemeris SPK
3  pck00008.tpc PCK  Generic PCK

These SPICE kernels are included in the lesson package available from the NAIF server at JPL:

https://naif.jpl.nasa.gov/pub/naif/misc/toolkit_docs_N0067/Lessons/

SpiceyPy Modules Used

This section provides a complete list of the functions and kernels that are suggested for usage in each of the exercises in this lesson. (You may wish to not look at this list unless/until you "get stuck" while working on your own.)

CHAPTER EXERCISE   FUNCTIONS        NON-VOID         KERNELS
------- ---------  ---------------  ---------------  ----------
   1    kpool      spiceypy.furnsh  spiceypy.ktotal  1-3
                   spiceypy.unload  spiceypy.kdata
                   spiceypy.kclear

   2    kervar     spiceypy.furnsh  spiceypy.gnpool  1-3
                   spiceypy.kclear  spiceypy.dtpool
                                    spiceypy.gdpool
                                    spiceypy.gcpool

   3    coord      spiceypy.furnsh  spiceypy.dpr     1-3
                   spiceypy.kclear  spiceypy.str2et
                                    spiceypy.bodvrd
                                    spiceypy.spkpos
                                    spiceypy.recrad
                                    spiceypy.reclat
                                    spiceypy.recsph
                                    spiceypy.recgeo

   4    xtic       spiceypy.furnsh  spiceypy.str2et  1
                   spiceypy.tsetyr  spiceypy.timout
                   spiceypy.kclear  spiceypy.tpictr
                                    spiceypy.jyear

   5    aderr      spiceypy.furnsh  spiceypy.spkezr  1-3
                   spiceypy.kclear

   6    win        spiceypy.furnsh  spiceypy.str2et  1-3
                   spiceypy.wninsd  spiceypy.wnvald
                   spiceypy.kclear  spiceypy.wnintd
                                    spiceypy.card
                                    spiceypy.wnfetd
                                    spiceypy.et2utc
                                    spiceypy.wnsumd

   7    units                       spiceypy.tkvrsn
                                    spiceypy.convrt

        xconst                      spiceypy.spd
                                    spiceypy.dpr
                                    spiceypy.rpd
                                    spiceypy.clight
                                    spiceypy.j2100
                                    spiceypy.j2000
                                    spiceypy.tyear
                                    spiceypy.halfpi

Use the Python built-in help system on the various functions listed above for the API parameters' description, and refer to the headers of their corresponding CSPICE versions for detailed interface specifications.

NAIF Documentation

The technical complexity of the various SPICE subsystems mandates an extensive, user-friendly documentation set. The set differs somewhat depending on your choice of development language but provides the same information with regards to SPICE operation. The sources for a user needing information concerning SPICE are:

• Required Readings and Users Guides

• Library Source Code Documentation

• API Documentation

• Tutorials

Required Reading and Users Guides

Tip

The Required Readings are also available on the NAIF website at:

https://naif.jpl.nasa.gov/pub/naif/misc/toolkit_docs_N0067/C/req/index.html.

The User Guides are also available on the NAIF website at:

https://naif.jpl.nasa.gov/pub/naif/misc/toolkit_docs_N0067/C/ug/index.html

NAIF Required Reading (*.req) documents introduce the functionality of particular Spice subsystems:

abcorr.req
cells.req
ck.req
daf.req
das.req
dla.req
dsk.req
ek.req
ellipses.req
error.req
frames.req
gf.req
kernel.req
naif_ids.req
pck.req
planes.req
problems.req
rotation.req
scanning.req
sclk.req
sets.req
spc.req
spk.req
symbols.req
time.req
windows.req

NAIF Users Guides (*.ug) describe the proper use of particular Spice command line tools:

brief.ug
chronos.ug
ckbrief.ug
commnt.ug
convert.ug
dskbrief.ug
dskexp.ug
frmdiff.ug
inspekt.ug
mkdsk.ug
mkspk.ug
msopck.ug
simple.ug
spacit.ug
spkdiff.ug
spkmerge.ug
states.ug
subpt.ug
tictoc.ug
tobin.ug
toxfr.ug
version.ug

Library Source Code Documentation

All SPICELIB and CSPICE source files include usage and design information incorporated in a comment block known as the “header.” (Every toolkit includes either the SPICELIB or CSPICE library.)

A header consists of several marked sections:

• Procedure: Routine name and one line expansion of the routine's name.

• Abstract: A tersely worded explanation describing the routine.

• Copyright: An identification of the copyright holder for the routine.

• Required_Reading: A list of SpiceyPy required reading documents relating to the routine.

• Brief_I/O: A table of arguments, identifying each as either input, output, or both, with a very brief description of the variable.

• Detailed_Input & Detailed_Output: An elaboration of the Brief_I/O section providing comprehensive information on argument use.

• Parameters: Description and declaration of any parameters (constants) specific to the routine.

• Exceptions: A list of error conditions the routine detects and signals plus a discussion of any other exceptional conditions the routine may encounter.

• Files: A list of other files needed for the routine to operate.

• Particulars: A discussion of the routine's function (if needed). This section may also include information relating to "how" and "why" the routine performs an operation and to explain functionality of routines that operate by side effects.

• Examples: Descriptions and code snippets concerning usage of the routine.

• Restrictions: Restrictions or warnings concerning use.

• Literature_References: A list of sources required to understand the algorithms or data used in the routine.

• Author_and_Institution: The names and affiliations for authors of the routine.

• Version: A list of edits and the authors of those edits made to the routine since initial delivery to the Spice system.

The source code for SpiceyPy products is stored in 'src' sub-directory of the main SpiceyPy directory:

API Documentation

The SpiceyPy package is documented in "readthedocs" website:

https://spiceypy.readthedocs.io/en/main/index.html

Each API documentation page is in large part copied from the "Abstract" and" Brief_I/O" sections of the corresponding CSPICE function documentation. Each API page includes a link to the API documentation for the CSPICE routine called by the SpiceyPy interface.

This SpiceyPy API documentation (the same information as in the website but without hyperlinks) is also available from the Python built-in help system:

 import spiceypy
 help(spiceypy.str2et) # hit run button to see help

Text kernels

Several workbooks use SPICE text kernels. SPICE identifies a text kernel as an ASCII text file containing the mark-up tags the kernel subsystem requires to identify data assignments in that file, and “name=value” data assignments.

The subsystem uses two tags:

\begintext

and

\begindata

to mark information blocks within the text kernel. The \begintext tag specifies all text following the tag as comment information to be ignored by the subsystem.

Things to know:

1. The \begindata tag marks the start of a data definition block. The subsystem processes all text following this marker as SPICE kernel data assignments until finding a \begintext marker.

2. The kernel subsystem defaults to the \begintext mode until the parser encounters a \begindata tag. Once in \begindata mode the subsystem processes all text as variable assignments until the next \begintext tag.

3. Enter the tags as the only text on a line, i.e.:

   \begintext
   
      ... commentary information on the data assignments ...
   
   \begindata
   
      ... data assignments ...
   

4. CSPICE delivery N0059 added to the CSPICE and Icy text kernel parsers the functionality to read non native text kernels, i.e. a Unix compiled library can read a MS Windows native text kernel, a MS Windows compiled library can read a Unix native text kernel. Mice acquires this capability from CSPICE.

5. With regards to the FORTRAN distribution, as of delivery N0057 the spiceypy.furnsh call includes a line terminator check, signaling an error on any attempt to read non-native text kernels.

Text kernel format scalar assignments.

VAR_NAME_DP  = 1.234
VAR_NAME_INT = 1234
VAR_NAME_STR = 'FORBIN'

Please note the use of a single quote in string assignments.

Vector assignments. Vectors must contain the same type data.

VEC_NAME_DP  = ( 1.234   , 45.678  , 901234.5 )
VEC_NAME_INT = ( 1234    , 456     , 789      )
VEC_NAME_STR = ( 'FORBIN', 'FALKEN', 'ROBUR'  )

also

VEC_NAME_DP  = ( 1.234,
                45.678,
                901234.5 )

VEC_NAME_STR = ( 'FORBIN',
                 'FALKEN',
                 'ROBUR' )

Time assignments.

TIME_VAL = @31-JAN-2003-12:34:56.798
TIME_VEC = ( @01-DEC-2004, @15-MAR-2004 )

The at-sign character '@' indicates a time string. The pool subsystem converts the strings to double precision TDB (a numeric value). Please note, the time strings must not contain embedded blanks. WARNING - a TDB string is not the same as a UTC string.

The above examples depict direct assignments via the '=' operator. The kernel pool also permits incremental assignments via the '+=' operator.

Please refer to the kernels required reading, kernel.req, for additional information.

Lesson 1: Kernel Management with the Kernel Subsystem

Task Statement

Write a program to load a meta kernel, interrogate the SpiceyPy system for the names and types of all loaded kernels, then demonstrate the unload functionality and the resulting effects.

Learning Goals

This lesson demonstrates use of the kernel subsystem to load, unload, and list loaded kernels.

This lesson requires creation of a SPICE meta kernel.

Code Solution

First, create a meta text kernel:

You can use two versions of a meta kernel with code examples (kpool.tm) in this lesson. Either a kernel with explicit path information:

mk = r"""
KPL/MK
\begindata
KERNELS_TO_LOAD = ( 'kernels/lsk/naif0008.tls',
                    'kernels/spk/de405s.bsp',
                    'kernels/pck/pck00008.tpc')
\begintext
"""
with open("kpool.tm", "w") as dst:
    dst.write(mk)
print("Wrote kernel file kpool.tm")

… or a more generic meta kernel using the PATH_VALUES/PATH_SYMBOLS functionality to declare path names as variables:

mk = r"""
KPL/MK
   Define the paths to the kernel directory. Use the PATH_SYMBOLS
   as aliases to the paths.
   The names and contents of the kernels referenced by this
   meta-kernel are as follows:
      File Name        Description
      ---------------  ------------------------------
      naif0008.tls     Generic LSK.
      de405s.bsp       Planet Ephemeris SPK.
      pck00008.tpc     Generic PCK.
\begindata
   PATH_VALUES     = ( 'kernels/lsk',
                       'kernels/spk',
                       'kernels/pck' )

   PATH_SYMBOLS    = ( 'LSK', 'SPK', 'PCK' )

   KERNELS_TO_LOAD = ( '$LSK/naif0008.tls',
                       '$SPK/de405s.bsp',
                       '$PCK/pck00008.tpc' )
\begintext
"""
with open("kpool_generic.tm", "w") as dst:
    dst.write(mk)
print("Wrote kernel file kpool_generic.tm")

Now the solution source code:

#
# Import the CSPICE-Python interface.
#
import spiceypy


# Assign the path name of the meta kernel to META.
def kpool(META="kpool.tm"):
    #
    # Load the meta kernel then use KTOTAL to interrogate the SPICE
    # kernel subsystem.
    #
    spiceypy.furnsh(META)
    count = spiceypy.ktotal("ALL")
    print(f"Kernel count after load:        {count}\n")
    #
    # Loop over the number of files; interrogate the SPICE system
    # with spiceypy.kdata for the kernel names and the type.
    # 'found' returns a boolean indicating whether any kernel files
    # of the specified type were loaded by the kernel subsystem.
    # This example ignores checking 'found' as kernels are known
    # to be loaded.
    #
    for i in range(count):
        [file, type, source, handle] = spiceypy.kdata(i, "ALL")
        print("File   {0}".format(file))
        print("Type   {0}".format(type))
        print("Source {0}\n".format(source))
    #
    # Unload one kernel then check the count.
    #
    spiceypy.unload("kernels/spk/de405s.bsp")
    count = spiceypy.ktotal("ALL")
    #
    # The subsystem should report one less kernel.
    #
    print(f"Kernel count after one unload:  {count}\n")
    #
    # Now unload the meta kernel. This action unloads all
    # files listed in the meta kernel.
    #
    spiceypy.unload(META)
    #
    # Check the count; spiceypy should return a count of zero.
    #
    count = spiceypy.ktotal("ALL")
    print(f"Kernel count after meta unload: {count}")
    #
    # Done. Unload the kernels.
    #
    spiceypy.kclear()


if __name__ == "__main__":
    kpool()
    kpool(META="kpool_generic.tm")

Run the code example locally or by clicking the run button above.

First we see the number of all loaded kernels returned from the spiceypy.ktotal call.

Then the spiceypy.kdata loop returns the name of each loaded kernel, the type of kernel (SPK, CK, TEXT, etc.) and the source of the kernel - the mechanism that loaded the kernel. The source either identifies a meta kernel, or contains an empty string. An empty source string indicates a direct load of the kernel with a spiceypy.furnsh call.

Kernel count after load:        4

File   kpool.tm
Type   META
Source

File   kernels/lsk/naif0008.tls
Type   TEXT
Source kpool.tm

File   kernels/spk/de405s.bsp
Type   SPK
Source kpool.tm

File   kernels/pck/pck00008.tpc
Type   TEXT
Source kpool.tm

Kernel count after one unload:  3
Kernel count after meta unload: 0

this repeats for the kpool_generic.tm file.

Lesson 2: The Kernel Pool

Task Statement

Write a program to retrieve particular string and numeric text kernel variables, both scalars and arrays. Interrogate the kernel pool for assigned variable names.

Learning Goals

The lesson demonstrates the SpiceyPy system's facility to retrieve different types of data (string, numeric, scalar, array) from the kernel pool.

For the code examples, use this generic text kernel (kervar.tm) containing PCK-type data, kernels to load, and example time strings:

mk = r"""
  KPL/MK
     Name the kernels to load. Use path symbols.
     The names and contents of the kernels referenced by this
     meta-kernel are as follows:
        File Name        Description
        ---------------  ------------------------------
        naif0008.tls     Generic LSK.
        de405s.bsp       Planet Ephemeris SPK.
        pck00008.tpc     Generic PCK.
  \begindata
     PATH_VALUES     = ('kernels/spk',
                        'kernels/pck',
                        'kernels/lsk')
     PATH_SYMBOLS    = ('SPK' , 'PCK' , 'LSK' )
     KERNELS_TO_LOAD = ( '$SPK/de405s.bsp',
                         '$PCK/pck00008.tpc',
                         '$LSK/naif0008.tls')
  \begintext
  Ring model data.
  \begindata
     BODY699_RING1_NAME     = 'A Ring'
     BODY699_RING1          = (122170.0 136780.0 0.1 0.1 0.5)
     BODY699_RING1_1_NAME   = 'Encke Gap'
     BODY699_RING1_1        = (133405.0 133730.0 0.0 0.0 0.0)
     BODY699_RING2_NAME     = 'Cassini Division'
     BODY699_RING2          = (117580.0 122170.0 0.0 0.0 0.0)
  \begintext
  The kernel pool recognizes values preceded by '@' as time
  values. When read, the kernel subsystem converts these
  representations into double precision ephemeris time.
  Caution: The kernel subsystem interprets the time strings
  identified by '@' as TDB. The same string passed as input
  to @STR2ET is processed as UTC.
  The three expressions stored in the EXAMPLE_TIMES array represent
  the same epoch.
  \begindata
     EXAMPLE_TIMES       = ( @APRIL-1-2004-12:34:56.789,
                             @4/1/2004-12:34:56.789,
                             @JD2453097.0242684
                            )
  \begintext
"""
with open("kervar.tm", "w") as dst:
    dst.write(mk)
print("Wrote kernel file kervar.tm")

The main references for pool routines are found in the help command, the CSPICE source files or the API documentation for the particular routines.

Code Solution

#
# Import the CSPICE-Python interface.
#
import spiceypy


def kervar():
    #
    # Define the max number of kernel variables
    # of concern for this examples.
    #
    N_ITEMS = 20
    #
    # Load the example kernel containing the kernel variables.
    # The kernels defined in KERNELS_TO_LOAD load into the
    # kernel pool with this call.
    #
    spiceypy.furnsh("kervar.tm")
    #
    # Initialize the start value. This value indicates
    # index of the first element to return if a kernel
    # variable is an array. START = 0 indicates return everything.
    # START = 1 indicates return everything but the first element.
    #
    START = 0
    #
    # Set the template for the variable names to find. Let's
    # look for all variables containing  the string RING.
    # Define this with the wildcard template '*RING*'. Note:
    # the template '*RING' would match any variable name
    # ending with the RING string.
    #
    tmplate = "*RING*"
    #
    # We're ready to interrogate the kernel pool for the
    # variables matching the template. spiceypy.gnpool tells us:
    #
    #  1. Does the kernel pool contain any variables that
    #     match the template (value of found).
    #  2. If so, how many variables?
    #  3. The variable names. (cvals, an array of strings)
    #
    try:
        cvals = spiceypy.gnpool(tmplate, START, N_ITEMS)
        print("Number variables matching template: {0}".format(len(cvals)))
    except spiceypy.SpiceyError:
        print("No kernel variables matched template.")
        return
    #
    # Okay, now we know something about the kernel pool
    # variables of interest to us. Let's find out more...
    #
    for cval in cvals:
        #
        # Use spiceypy.dtpool to return the dimension and type,
        # C (character) or N (numeric), of each pool
        # variable name in the cvals array. We know the
        # kernel data exists.
        #
        dim, type = spiceypy.dtpool(cval)
        print("\n" + cval)
        print(" Number items: {0}   Of type: {1}\n".format(dim, type))
        #
        # Test character equality, 'N' or 'C'.
        #
        if type == "N":
            #
            # If 'type' equals 'N', we found a numeric array.
            # In this case any numeric array will be an array
            # of double precision numbers ('doubles').
            # spiceypy.gdpool retrieves doubles from the
            # kernel pool.
            #
            dvars = spiceypy.gdpool(cval, START, N_ITEMS)
            for dvar in dvars:
                print("  Numeric value: {0:20.6f}".format(dvar))
        elif type == "C":
            #
            # If 'type' equals 'C', we found a string array.
            # spiceypy.gcpool retrieves string values from the
            # kernel pool.
            #
            cvars = spiceypy.gcpool(cval, START, N_ITEMS)
            for cvar in cvars:
                print("  String value: {0}\n".format(cvar))
        else:
            #
            # This block should never execute.
            #
            print("Unknown type. Code error.")
    #
    # Now look at the time variable EXAMPLE_TIMES. Extract this
    # value as an array of doubles.
    #
    dvars = spiceypy.gdpool("EXAMPLE_TIMES", START, N_ITEMS)
    print("EXAMPLE_TIMES")
    for dvar in dvars:
        print("  Time value:    {0:20.6f}".format(dvar))
    #
    # Done. Unload the kernels.
    #
    spiceypy.kclear()


if __name__ == "__main__":
    kervar()

Run the code example

The program runs and first reports the number of kernel pool variables matching the template, 6.

The program then loops over spiceypy.dtpool 6 times, reporting the name of each pool variable, the number of data items assigned to that variable, and the variable type. Within the spiceypy.dtpool loop, a second loop outputs the contents of the data variable using spiceypy.gcpool or spiceypy.gdpool.

Number variables matching template: 6

BODY699_RING1_1
 Number items: 5   Of type: N

  Numeric value:        133405.000000
  Numeric value:        133730.000000
  Numeric value:             0.000000
  Numeric value:             0.000000
  Numeric value:             0.000000

BODY699_RING1
 Number items: 5   Of type: N

  Numeric value:        122170.000000
  Numeric value:        136780.000000
  Numeric value:             0.100000
  Numeric value:             0.100000
  Numeric value:             0.500000

BODY699_RING2
 Number items: 5   Of type: N

  Numeric value:        117580.000000
  Numeric value:        122170.000000
  Numeric value:             0.000000
  Numeric value:             0.000000
  Numeric value:             0.000000

BODY699_RING1_1_NAME
 Number items: 1   Of type: C

  String value: Encke Gap


BODY699_RING2_NAME
 Number items: 1   Of type: C

  String value: Cassini Division


BODY699_RING1_NAME
 Number items: 1   Of type: C

  String value: A Ring

EXAMPLE_TIMES
  Time value:        134094896.789000
  Time value:        134094896.789000
  Time value:        134094896.789753

Note the final time value differs from the previous values in the final three decimal places despite the intention that all three strings represent the same time. This results from round-off when converting a decimal Julian day representation to the seconds past J2000 ET representation.

Related Routines

• spiceypy.gipool retrieves integer values from the kernel subsystem.

Lesson 3: Coordinate Conversions

Task Statement

Write a program to convert a Cartesian 3-vector representing some location to the other coordinate representations. Use the position of the Moon with respect to Earth in an inertial and non-inertial reference frame as the example vector.

Learning Goals

The SpiceyPy system provides functions to convert coordinate tuples between Cartesian and various non Cartesian coordinate systems including conversion between geodetic and rectangular coordinates.

This lesson presents these coordinate transform routines for rectangular, cylindrical, and spherical systems.

Code Solution

mk = r"""
KPL/MK
\begindata
KERNELS_TO_LOAD = ( 'kernels/lsk/naif0008.tls',
                    'kernels/spk/de405s.bsp',
                    'kernels/pck/pck00008.tpc')
"""
with open("coord.tm", "w") as dst:
    dst.write(mk)
print("Wrote kernel file corrd.tm")

#
# Import the CSPICE-Python interface.
#
import spiceypy


def coord(timstr: str):
    #
    # Define the inertial and non inertial frame names.
    #
    # Initialize variables or set type. All variables
    # used in a PROMPT construct must be initialized
    # as strings.
    #
    INRFRM = "J2000"
    NONFRM = "IAU_EARTH"
    r2d = spiceypy.dpr()
    #
    # Load the needed kernels using a spiceypy.furnsh call on the
    # meta kernel.
    #
    spiceypy.furnsh("coord.tm")
    #
    # Convert the time string to ephemeris time J2000 (ET).
    #
    et = spiceypy.str2et(timstr)
    #
    # Access the kernel pool data for the triaxial radii of the
    # Earth, rad[1][0] holds the equatorial radius, rad[1][2]
    # the polar radius.
    #
    rad = spiceypy.bodvrd("EARTH", "RADII", 3)
    #
    # Calculate the flattening factor for the Earth.
    #
    #          equatorial_radius - polar_radius
    # flat =   ________________________________
    #
    #                equatorial_radius
    #
    flat = (rad[1][0] - rad[1][2]) / rad[1][0]
    #
    # Make the spiceypy.spkpos call to determine the apparent
    # position of the Moon w.r.t. to the Earth at 'et' in the
    # inertial frame.
    #
    [pos, ltime] = spiceypy.spkpos("MOON", et, INRFRM, "LT+S", "EARTH")
    #
    # Show the current frame and time.
    #
    print(" Time : {0}".format(timstr))
    print(" Inertial Frame: {0}\n".format(INRFRM))
    #
    # First convert the position vector
    # X = pos(1), Y = pos(2), Z = pos(3), to RA/DEC.
    #
    [range, ra, dec] = spiceypy.recrad(pos)
    print("   Range/Ra/Dec")
    print("    Range: {0:20.6f}".format(range))
    print("    RA   : {0:20.6f}".format(ra * r2d))
    print("    DEC  : {0:20.6f}".format(dec * r2d))
    #
    # ...latitudinal coordinates...
    #
    [range, lon, lat] = spiceypy.reclat(pos)
    print("   Latitudinal ")
    print("    Rad  : {0:20.6f}".format(range))
    print("    Lon  : {0:20.6f}".format(lon * r2d))
    print("    Lat  : {0:20.6f}".format(lat * r2d))
    #
    # ...spherical coordinates use the colatitude,
    # the angle from the Z axis.
    #
    [range, colat, lon] = spiceypy.recsph(pos)
    print("   Spherical")
    print("    Rad  : {0:20.6f}".format(range))
    print("    Lon  : {0:20.6f}".format(lon * r2d))
    print("    Colat: {0:20.6f}".format(colat * r2d))
    #
    # Make the spiceypy.spkpos call to determine the apparent
    # position of the Moon w.r.t. to the Earth at 'et' in the
    # non-inertial, body fixed, frame.
    #
    [pos, ltime] = spiceypy.spkpos("MOON", et, NONFRM, "LT+S", "EARTH")
    print()
    print("  Non-inertial Frame: {0}".format(NONFRM))
    #
    # ...latitudinal coordinates...
    #
    [range, lon, lat] = spiceypy.reclat(pos)
    print("   Latitudinal ")
    print("    Rad  : {0:20.6f}".format(range))
    print("    Lon  : {0:20.6f}".format(lon * r2d))
    print("    Lat  : {0:20.6f}".format(lat * r2d))
    #
    # ...spherical coordinates use the colatitude,
    # the angle from the Z axis.
    #
    [range, colat, lon] = spiceypy.recsph(pos)
    print("   Spherical")
    print("    Rad  : {0:20.6f}".format(range))
    print("    Lon  : {0:20.6f}".format(lon * r2d))
    print("    Colat: {0:20.6f}".format(colat * r2d))
    #
    # ...finally, convert the position to geodetic coordinates.
    #
    [lon, lat, range] = spiceypy.recgeo(pos, rad[1][0], flat)
    print("   Geodetic")
    print("    Rad  : {0:20.6f}".format(range))
    print("    Lon  : {0:20.6f}".format(lon * r2d))
    print("    Lat  : {0:20.6f}".format(lat * r2d))
    print()
    #
    # Done. Unload the kernels.
    #
    spiceypy.kclear()


# if running locally, uncomment below
# from builtins import input
# if __name__ == '__main__':
#   timstr = input( 'Time of interest: ')
#   coord(timstr)

Run the code example:

coord("Feb 3 2002 TDB")

Input “Feb 3 2002 TDB” to calculate the Moon's position. (the 'TDB' tag indicates a Barycentric Dynamical Time value).

Time of interest: Feb 3 2002 TDB

Examine the Moon position in the J2000 inertial frame, display the time and frame:

Time : Feb 3 2002 TDB
 Inertial Frame: J2000

Convert the Moon Cartesian coordinates to right ascension declination.

Range/Ra/Dec
 Range:        369340.815193
 RA   :           203.643686
 DEC  :            -4.979010

Latitudinal. Note the difference in the expressions for longitude and right ascension though they represent a measure of the same quantity. The RA/DEC system measures RA in the interval [0,2Pi). Latitudinal coordinates measures longitude in the interval (-Pi,Pi].

Latitudinal
 Rad  :        369340.815193
 Lon  :          -156.356314
 Lat  :            -4.979010

Spherical. Note the difference between the expression of latitude in the Latitudinal system and the corresponding Spherical colatitude. The spherical coordinate system uses the colatitude, the angle measure away from the positive Z axis. Latitude is the angle between the position vector and the x-y (equatorial) plane with positive angle defined as toward the positive Z direction

Spherical
 Rad  :        369340.815193
 Lon  :          -156.356314
 Colat:            94.979010

The same position look-up in a body fixed (non-inertial) frame, IAU_EARTH.

Non-inertial Frame: IAU_EARTH

Latitudinal coordinates return the geocentric latitude.

Latitudinal
 Rad  :        369340.815193
 Lon  :            70.986950
 Lat  :            -4.989675

Spherical.

Spherical
 Rad  :        369340.815193
 Lon  :            70.986950
 Colat:            94.989675

Geodetic. The cartographic lat/lon.

Geodetic
 Rad  :        362962.836755
 Lon  :            70.986950
 Lat  :            -4.990249

Related Routines

• spiceypy.latrec, latitudinal to rectangular

• spiceypy.latcyl, latitudinal to cylindrical

• spiceypy.latsph, latitudinal to spherical

• spiceypy.reccyl, rectangular to cylindrical

• spiceypy.sphrec, spherical to rectangular

• spiceypy.sphcyl, spherical to cylindrical

• spiceypy.sphlat, spherical to latitudinal

• spiceypy.cyllat, cylindrical to latitudinal

• spiceypy.cylsph, cylindrical to spherical

• spiceypy.cylrec, cylindrical to rectangular

• spiceypy.georec, geodetic to rectangular

Lesson 4: Advanced Time Manipulation Routines

Task Statement

Demonstrate the advanced functions of the time utilities with regard to formatting of time strings for output. Formatting options include altering calendar representations of the time strings. Convert time-date strings between different SpiceyPy-supported formats.

Learning Goals

Introduce the routines used for advanced manipulation of time strings. Understand the concept of ephemeris time (ET) as used in SpiceyPy.

Code Solution

Caution: Be sure to assign sufficient string lengths for time formats/pictures.

mk = r"""
KPL/MK
\begindata
KERNELS_TO_LOAD = ( 'kernels/lsk/naif0008.tls',
                    'kernels/spk/de405s.bsp',
                    'kernels/pck/pck00008.tpc')
"""
with open("xtic.tm", "w") as dst:
    dst.write(mk)
print("Wrote kernel file corrd.tm")
#
# Import the CSPICE-Python interface.
#
import spiceypy


def xtic():
    #
    # Assign the META variable to the name of the meta-kernel
    # that contains the LSK kernel and create an arbitrary
    # time string.
    #
    CALSTR = "Mar 15, 2003 12:34:56.789 AM PST"
    META = "xtic.tm"
    AMBIGSTR = "Mar 15, 79 12:34:56"
    T_FORMAT1 = "Wkd Mon DD HR:MN:SC PDT YYYY ::UTC-7"
    T_FORMAT2 = "Wkd Mon DD HR:MN ::UTC-7 YR (JULIAND.##### JDUTC)"
    #
    # Load the meta-kernel.
    #
    spiceypy.furnsh(META)
    print(f"Original time string     : f{CALSTR}")
    #
    # Convert the time string to the number of ephemeris
    # seconds past the J2000 epoch. This is the most common
    # internal time representation used by the CSPICE
    # system; CSPICE refers to this as ephemeris time (ET).
    #
    et = spiceypy.str2et(CALSTR)
    print(f"Corresponding ET         : {et:20.6f}\n")
    #
    # Make a picture of an output format. Describe a Unix-like
    # time string then send the picture and the 'et' value through
    # spiceypy.timout to format and convert the ET representation
    # of the time string into the form described in
    # spiceypy.timout. The '::UTC-7' token indicates the time
    # zone for the `timstr' output - PDT. 'PDT' is part of the
    # output, but not a time system token.
    #
    timstr = spiceypy.timout(et, T_FORMAT1)
    print(f"Time in string format 1  : {timstr}")
    timstr = spiceypy.timout(et, T_FORMAT2)
    print(f"Time in string format 2  : {timstr}")
    #
    # Why create a picture by hand when spiceypy can do it for
    # you? Input a string to spiceypy.tpictr with the format of
    # interest. `ok' returns a boolean indicating whether an
    # error occurred while parsing the picture string, if so,
    # an error diagnostic message returns in 'xerror'. In this
    # example the picture string is known as correct.
    #
    pic = "12:34:56.789 P.M. PDT January 1, 2006"
    [pictr, ok, xerror] = spiceypy.tpictr(pic)
    if not bool(ok):
        print(xerror)
        return
    timstr = spiceypy.timout(et, pictr)
    print(f"Time in string format 3  : {timstr}")
    #
    # Two digit year representations often cause problems due to
    # the ambiguity of the century. The routine spiceypy.tsetyr
    # gives the user the ability to set a default range for 2
    # digit year representation. SPICE uses 1969AD as the default
    # start year so the numbers inclusive of 69 to 99 represent
    # years 1969AD to 1999AD, the numbers inclusive of 00 to 68
    # represent years 2000AD to 2068AD.
    #
    # The defined time string 'AMBIGSTR' contains a two-digit
    # year. Since the SPICE base year is 1969, the time subsystem
    # interprets the string as 1979.
    #
    et1 = spiceypy.str2et(AMBIGSTR)
    #
    # Set 1980 as the base year causes SPICE to interpret the
    # time string's "79" as 2079.
    #
    spiceypy.tsetyr(1980)
    et2 = spiceypy.str2et(AMBIGSTR)
    #
    # Calculate the number of years between the two ET
    # representations, ~100.
    #
    years = (et2 - et1) / spiceypy.jyear()
    print(f"Years between evaluations: {years:20.6f}")
    #
    # Reset the default year to 1969.
    #
    spiceypy.tsetyr(1969)
    #
    # Done. Unload the kernels.
    #
    spiceypy.kclear()


if __name__ == "__main__":
    xtic()

Run the code example

Original time string     : Mar 15, 2003 12:34:56.789 AM PST
Corresponding ET         :     100989360.974561

Time in string format 1  : Sat Mar 15 01:34:56 PDT 2003
Time in string format 2  : Sat Mar 15 01:34  03 (2452713.85760 JDUTC)
Time in string format 3  : 01:34:56.789 A.M. PDT March 15, 2003
Years between evaluations:           100.000000

Lesson 5: Error Handling

Task Statement

Write an interactive program to return a state vector based on a user's input. Code the program with the capability to recover from user input mistakes, inform the user of the mistake, then continue to run.

Learning Goals

Learn how to write a program that has the capability to recover from expected SPICE errors.

The SpiceyPy error subsystem differs from CSPICE and SPICELIB packages in that the user cannot alter the state of the error subsystem, rather the user can respond to an error signal using try-except blocks. This block natively receives and processes any SpiceyError exception signaled from SpiceyPy. The user can therefore “catch” an error signal so as to respond in an appropriate manner.

Code Solution

mk = r"""
KPL/MK
\begindata
KERNELS_TO_LOAD = ( 'kernels/lsk/naif0008.tls',
                    'kernels/spk/de405s.bsp',
                    'kernels/pck/pck00008.tpc')
"""
with open("aderr.tm", "w") as dst:
    dst.write(mk)
print("Wrote kernel file aderr.tm")
print("")
#
# Import the CSPICE-Python interface.
#
import spiceypy


# For simplicity, we request only one input.
# The program calculates the state vector from
# Earth to the user specified target 'targ' in the
# J2000 frame, at ephemeris time zero, using
# aberration correction LT+S (light time plus
# stellar aberration).
def aderr(targ: str):
    print(f"Target: {targ}")
    spiceypy.furnsh("aderr.tm")
    try:
        #
        # Perform the state lookup.
        #
        state, ltime = spiceypy.spkezr(targ, 0.0, "J2000", "LT+S", "EARTH")
        #
        # No error, output the state.
        #
        r0, r1, r2, v0, v1, v2 = state
        print(f"R : {r0:20.6f} {r1:20.6f} {r2:20.6f}")
        print(f"V : {v0:20.6f} {v1:20.6f} {v2:20.6f}")
        print(f"LT: {ltime:20.6f}\n")
    except spiceypy.SpiceyError as err:
        #
        # What if spiceypy.spkezr signaled an error?
        # Then spiceypy signals an error to python.
        #
        # Examine the value of 'err' to retrieve the
        # error message.
        #
        print(err)
        print()
    finally:
        #
        # Done. Unload the kernels.
        #
        spiceypy.kclear()


if __name__ == "__main__":
    aderr("Moon")
    aderr("Mars")
    aderr("Pluto barycenter")
    aderr("Puck")

Run the code example

Now run the code with various inputs to observe behavior. Begin the run using known astronomical bodies, e.g. “Moon”, “Mars”, “Pluto barycenter” and “Puck”. Recall the SpiceyPy default units are kilometers, kilometers per second, kilograms, and seconds. The 'R' marker identifies the (X,Y,Z) position of the body in kilometers, the 'V' marker identifies the velocity of the body in kilometers per second, and the 'LT' marker identifies the one-way light time between the bodies at the requested evaluation time.

Target: Moon
R :       -291584.616595       -266693.402359         -76095.64756
V :             0.643439            -0.666066            -0.301310
LT:             1.342311

Target: Mars
R :     234536077.419136    -132584383.595569      -63102685.70619
V :            30.961373            28.932996            13.113031
LT:           923.001080

Target: Pluto barycenter
R :   -1451304742.838526   -4318174144.406321     -918251433.58736
V :            35.079843             3.053138            -0.036762
LT:         15501.258293

Target: Puck

=====================================================================
===========

Toolkit version: N0067

SPICE(SPKINSUFFDATA) --

Insufficient ephemeris data has been loaded to compute the state of 7
15 (PUCK) relative to 0 (SOLAR SYSTEM BARYCENTER) at the ephemeris ep
och 2000 JAN 01 12:00:00.000.

spkezr_c --> SPKEZR --> SPKEZ --> SPKACS --> SPKAPS --> SPKLTC --> SP
KGEO

=====================================================================
===========

Target:

Perplexing. What happened?

The kernel files named in meta.tm did not include ephemeris data for Puck. When the SPK subsystem tried to evaluate Puck's position, the evaluation failed due to lack of data, so an error signaled.

The above error signifies an absence of state information at ephemeris time 2000 JAN 01 12:00:00.000 (the requested time, ephemeris time zero).

Try another look-up, this time for “Casper”

aderr('Casper')

Target: Casper

=====================================================================
===========

Toolkit version: N0067

SPICE(IDCODENOTFOUND) --

The target, 'Casper', is not a recognized name for an ephemeris objec
t. The cause of this problem may be that you need an updated version
of the SPICE Toolkit. Alternatively you may call SPKEZ directly if yo
u know the SPICE ID codes for both 'Casper' and 'EARTH'

spkezr_c --> SPKEZR

=====================================================================
===========

Target:

An easy to understand error. The SPICE system does not contain information on a body named 'Casper.'

Another look-up, this time, “Venus”.

aderr('Venus')

Target: Venus
R :     -80970027.540532    -139655772.573898      -53860125.95820
V :            31.166910           -27.001056           -12.316514
LT:           567.655074

Target:

The look-up succeeded despite two errors in our run. The SpiceyPy system can respond to error conditions (not system errors) in much the same fashion as languages with catch/throw instructions.

Lesson 6: Windows, and Cells

Programming task

Given the times of line-of-sight for a vehicle from a ground station and the times for an acceptable Sun-station-vehicle phase angle, write a program to determine the time intervals common to both configurations.

Learning Goals

SpiceyPy implementation of SPICE cells consists of a class that provides an interface to the underlying CSPICE cell structure.

A user should create cells by use of the appropriate SpiceyPy calls. NAIF recommends against manual creation of cells.

A 'window' is a type of cell containing ordered, double precision values describing a collection of zero or more intervals.

We define an interval, 'i', as all double precision values bounded by and including an ordered pair of numbers,

[ a , b ]
   i   i

where

a    <   b
 i   -    i

The intervals within a window are both ordered and disjoint. That is, the beginning of each interval is greater than the end of the previous interval:

b  <  a
 i     i+1

A common use of the windows facility is to calculate the intersection set of a number of time intervals.

Code Solution

mk = r"""
KPL/MK
\begindata
KERNELS_TO_LOAD = ( 'kernels/lsk/naif0008.tls',
                    'kernels/spk/de405s.bsp',
                    'kernels/pck/pck00008.tpc')
"""
with open("win.tm", "w") as dst:
    dst.write(mk)
print("Wrote kernel file win.tm")
print("")

#
# Import the CSPICE-Python interface.
#
import spiceypy


def win():
    MAXSIZ = 8

    #
    # Define a set of time intervals. For the purposes of this
    # tutorial program, define time intervals representing
    # an unobscured line of sight between a ground station
    # and some body.
    #
    los = [
        "Jan 1, 2003 22:15:02",
        "Jan 2, 2003  4:43:29",
        "Jan 4, 2003  9:55:30",
        "Jan 4, 2003 11:26:52",
        "Jan 5, 2003 11:09:17",
        "Jan 5, 2003 13:00:41",
        "Jan 6, 2003 00:08:13",
        "Jan 6, 2003  2:18:01",
    ]

    #
    # A second set of intervals representing the times for which
    # an acceptable phase angle exists between the ground station,
    # the body and the Sun.
    #
    phase = [
        "Jan 2, 2003 00:03:30",
        "Jan 2, 2003 19:00:00",
        "Jan 3, 2003  8:00:00",
        "Jan 3, 2003  9:50:00",
        "Jan 5, 2003 12:00:00",
        "Jan 5, 2003 12:45:00",
        "Jan 6, 2003 00:30:00",
        "Jan 6, 2003 23:00:00",
    ]

    #
    # Load our meta kernel for the leapseconds data.
    #
    spiceypy.furnsh("win.tm")

    #
    # SPICE windows consist of double precision values; convert
    # the string time tags defined in the 'los' and 'phase'
    # arrays to double precision ET. Store the double values
    # in the 'loswin' and 'phswin' windows.
    #
    los_et = spiceypy.str2et(los)
    phs_et = spiceypy.str2et(phase)

    loswin = spiceypy.stypes.SPICEDOUBLE_CELL(MAXSIZ)
    phswin = spiceypy.stypes.SPICEDOUBLE_CELL(MAXSIZ)

    for i in range(0, int(MAXSIZ / 2)):
        spiceypy.wninsd(los_et[2 * i], los_et[2 * i + 1], loswin)
        spiceypy.wninsd(phs_et[2 * i], phs_et[2 * i + 1], phswin)

    spiceypy.wnvald(MAXSIZ, MAXSIZ, loswin)
    spiceypy.wnvald(MAXSIZ, MAXSIZ, phswin)

    #
    # The issue for consideration, at what times do line of
    # sight events coincide with acceptable phase angles?
    # Perform the set operation AND on loswin, phswin,
    # (the intersection of the time intervals)
    # place the results in the window 'sched'.
    #
    sched = spiceypy.wnintd(loswin, phswin)

    print("Number data values in sched : {0:2d}".format(spiceypy.card(sched)))

    #
    # Output the results. The number of intervals in 'sched'
    # is half the number of data points (the cardinality).
    #
    print(" ")
    print("Time intervals meeting defined criterion.")

    for i in range(spiceypy.card(sched) // 2):
        #
        # Extract from the derived 'sched' the values defining the
        # time intervals.
        #
        [left, right] = spiceypy.wnfetd(sched, i)

        #
        # Convert the ET values to UTC for human comprehension.
        #
        utcstr_l = spiceypy.et2utc(left, "C", 3)
        utcstr_r = spiceypy.et2utc(right, "C", 3)
        #
        # Output the UTC string and the corresponding index
        # for the interval.
        #
        print("{0:2d}   {1}   {2}".format(i, utcstr_l, utcstr_r))
    #
    # Summarize the 'sched' window.
    #
    [meas, avg, stddev, small, large] = spiceypy.wnsumd(sched)
    print("\nSummary of sched window\n")
    print("o Total measure of sched    : {0:16.6f}".format(meas))
    print("o Average measure of sched  : {0:16.6f}".format(avg))
    print("o Standard deviation of ")
    print("  the measures in sched     : {0:16.6f}".format(stddev))
    #
    # The values for small and large refer to the indexes of the
    # values in the window ('sched'). The shortest interval is
    #
    #      [ sched.base[ sched.data + small]
    #        sched.base[ sched.data + small +1]  ];
    #
    # the longest is
    #
    #      [ sched.base[ sched.data + large]
    #        sched.base[ sched.data + large +1]  ];
    #
    # Output the interval indexes for the shortest and longest
    # intervals. As Python bases an array index on 0, the interval
    # index is half the array index.
    #
    print("o Index of shortest interval: {0:2d}".format(int(small / 2)))
    print("o Index of longest interval : {0:2d}".format(int(large / 2)))
    #
    # Done. Unload the kernels.
    #
    spiceypy.kclear()


if __name__ == "__main__":
    win()

Run the code example

The output window has the name `sched' (schedule).

Output the amount of data held in `sched' compared to the maximum possible amount.

Number data values in sched :  6

List the time intervals for which a line of sight exists during the time of a proper phase angle.

Time intervals meeting defined criterion.
 0   2003 JAN 02 00:03:30.000   2003 JAN 02 04:43:29.000
 1   2003 JAN 05 12:00:00.000   2003 JAN 05 12:45:00.000
 2   2003 JAN 06 00:30:00.000   2003 JAN 06 02:18:01.000

Finally, an analysis of the `sched' data. The measure of an interval [a,b] (a <= b) equals b-a. Real values output in units of seconds.

Summary of sched window

o Total measure of sched    :     25980.000009
o Average measure of sched  :      8660.000003
o Standard deviation of
  the measures in sched     :      5958.550217
o Index of shortest interval:  1
o Index of longest interval :  0

Related Routines

• spiceypy.wncomd determines the complement of a window with respect to a defined interval.

• spiceypy.wncond contracts a window's intervals.

• spiceypy.wndifd: Calculate the difference between two windows; i.e. every point existing in the first but not the second.

• spiceypy.wnelmd returns TRUE or FALSE if a value exists in a window.

• spiceypy.wnexpd expands the size of the intervals in a window.

• spiceypy.wnextd extracts a window's endpoints.

• spiceypy.wnfild fills gaps between intervals in a window.

• spiceypy.wnfltd filter/removes small intervals from a window.

• spiceypy.wnincd determines if an interval exists within a window.

• spiceypy.wninsd inserts an interval into a window.

• spiceypy.wnreld compares two windows. Comparison operations available, equality '=', inequality '<>', subset '<=' and '>=', proper subset '<' and '>'.

• spiceypy.wnunid calculates the union of two windows.

Lesson 7: Utility and Constants Routines

Task Statement

Write an interactive program to convert values between various units. Demonstrate the flexibility of the unit conversion routine, the string equality function, and show the version ID function.

Learning Goals

SpiceyPy provides several routines to perform commonly needed tasks. Among these:

SpiceyPy also includes a set of functions that return constant values often used in astrodynamics, time calculations, and geometry.

Code Solution

#
# Import the CSPICE-Python interface.
#
import spiceypy

aliases = {
    "meter": "METER",
    "klicks": "KM",
    "kilometers": "KM",
    "kilometer": "KM",
    "secs": "SECONDS",
    "miles": "STATUTE_MILES",
}


def tostan(alias):
    return aliases.get(alias, alias)


def units(funits, fvalue, tunits):
    #
    # Display the Toolkit version string with a spiceypy.tkvrsn
    # call.
    #
    vers = spiceypy.tkvrsn("TOOLKIT")
    print("\nConvert demo program compiled against CSPICE Toolkit " + vers)
    #
    # The user first inputs the name of a unit of measure.
    # Send the name through tostan for de-aliasing.
    #
    print(f"From Units : {funits}")
    funits = tostan(funits)
    #
    # Input a double precision value to express in a new
    # unit format.
    #
    print(f"From Value : {fvalue}")
    #
    # Now the user inputs the name of the output units.
    # Again we send the units name through tostan for
    # de-aliasing.
    #
    print(f"To Units : {tunits}")
    tunits = tostan(tunits)
    tvalue = spiceypy.convrt(fvalue, funits, tunits)
    print("{0:12.5f} {1}".format(tvalue, tunits))


if __name__ == "__main__":
    units("klicks", 3, "miles")
    units("miles", 26.2, "km")

Run the code example

Run a few conversions through the application to ensure it works. The intro banner gives us the Toolkit version against which the application was linked:

Convert demo program compiled against CSPICE Toolkit CSPICE_N0067
From Units : klicks
From Value : 3
To Units   : miles
     1.86411 STATUTE_MILES

Now we know. Three kilometers equals 1.864 miles.

Legend states Pheidippides ran from the Marathon Plain to Athens. The modern marathon race (inspired by this event) spans 26.2 miles. How far in kilometers?

Convert demo program compiled against CSPICE Toolkit CSPICE_N0067
From Units : miles
From Value : 26.2
To Units   : km
    42.16481 km

Task Statement

Write a program to output SpiceyPy constants and use those constants to calculate some rudimentary values.

Code Solution

#
# Import the CSPICE-Python interface.
#
import spiceypy


def xconst():
    #
    # All the function have the same calling sequence:
    #
    #    VALUE = function_name()
    #
    #    some_procedure( function_name() )
    #
    # First a simple example using the seconds per day
    # constant...
    #
    print("Number of (S)econds (P)er (D)ay   : {0:19.12f}".format(spiceypy.spd()))

    #
    # ...then show the value of degrees per radian, 180/Pi...
    #
    print("Number of (D)egrees (P)er (R)adian: {0:19.16f}".format(spiceypy.dpr()))

    #
    # ...and the inverse, radians per degree, Pi/180.
    # It is obvious spiceypy.dpr() equals 1.d/spiceypy.rpd(), or
    # more simply spiceypy.dpr() * spiceypy.rpd() equals 1
    #
    print("Number of (R)adians (P)er (D)egree: {0:19.16f}".format(spiceypy.rpd()))

    #
    # What's the value for the astrophysicist's favorite
    # physical constant (in a vacuum)?
    #
    print("Speed of light in KM per second   : {0:19.12f}".format(spiceypy.clight()))

    #
    # How long (in Julian days) from the J2000 epoch to the
    # J2100 epoch?
    #
    print("Number of days between epochs J2000")
    print("  and J2100     : {0:19.12f}".format(spiceypy.j2100() - spiceypy.j2000()))

    #
    # Redo the calculation returning seconds...
    #
    print("Number of seconds between epochs")
    print(
        "  J2000 and J2100     : {0:19.5f}".format(
            spiceypy.spd() * (spiceypy.j2100() - spiceypy.j2000())
        )
    )

    #
    # ...then tropical years.
    #
    val = (spiceypy.spd() / spiceypy.tyear()) * (spiceypy.j2100() - spiceypy.j2000())
    print("Number of tropical years between")
    print("  epochs J2000 and J2100    : {0:19.12f}".format(val))

    #
    # Finally, how can I convert a radian value to degrees.
    #
    print(
        "Number of degrees in Pi/2 radians of arc: {0:19.16f}".format(
            spiceypy.halfpi() * spiceypy.dpr()
        )
    )

    #
    # and degrees to radians.
    #
    print(
        "Number of radians in 250 degrees of arc : {0:19.16f}".format(
            250.0 * spiceypy.rpd()
        )
    )


if __name__ == "__main__":
    xconst()

Run the code example

Number of (S)econds (P)er (D)ay         :  86400.000000000000
Number of (D)egrees (P)er (R)adian      : 57.2957795130823229
Number of (R)adians (P)er (D)egree      :  0.0174532925199433
Speed of light in KM per second         : 299792.457999999984
Number of days between epochs J2000
  and J2100                             :  36525.000000000000
Number of seconds between epochs
  J2000 and J2100                       :    3155760000.00000
Number of tropical years between
  epochs J2000 and J2100                :    100.002135902909
Number of degrees in Pi/2 radians of arc: 90.0000000000000000
Number of radians in 250 degrees of arc :  4.3633231299858242

Related Routines

• spiceypy.b1900: Julian Date of the epoch Besselian Date 1900.0

• spiceypy.b1950: Julian date of the epoch Besselian Date 1950.0

• spiceypy.j1900: Julian date of 1900 JAN 0.5 this corresponds to calendar date 1899 DEC 31 12:00:00

• spiceypy.j1950: Julian date of 1950 JAN 1.0 this corresponds to calendar date 1950 JAN 01 00:00:00

• spiceypy.twopi: double precision value of 2 * Pi

• spiceypy.pi: double precision value of Pi

• spiceypy.jyear: seconds per Julian year (365.25 Julian days)