I'm concerned that a first-time visitor to the Astronomy Engine
repo on GitHub will get lost. I made it more obvious where to
quickly find the source code needed for a given language.
Now the Python version of Astronomy Engine supports calculating
the Earth/Moon Barycenter (EMB) state vector (position and velocity)
relative to the Earth's center (geocentric) or relative
to the Solar System Barycenter (SSB).
This completes support for this feature across C, C#, JavaScript, and Python.
The BaryState function did not support Pluto before.
Refactored the code so that the internal CalcPluto function
returns both the position and velocity, and its caller
can select from heliocentric or barycentric coordinates.
HelioVector asks for heliocentric coordinates and keeps
only the position vector. BaryState asks for barycentric
coordinates and returns both position and velocity.
I added test data for Pluto generated by JPL Horizons.
It turns out the Pluto system barycenter is the best fit
for TOP2013, presumably because Charon causes Pluto to
wobble quite a bit.
I also generated JPL Horizons test data for the Moon
and the Earth/Moon barycenter, anticipating that I will
support calculating their barycentric state vectors soon.
I had to increase the enforced size limit for minified
JavaScript from 100000 bytes to 120000 bytes.
I guess this is like raising the "debt ceiling".
Fixed a bug in Python unit tests: if "-v" verbose option
was specified, it was printing a summary line for every
single line of input, instead of a single summary after
processing the whole file, as was intended. This is one
of those Python whitespace indentation bugs!
I'm getting much better accuracy sticking with my original
gravity simulator, just with smaller time increments, than
I was with the Runge-Kutta 4 method. The PlutoStateTable
gets a bit larger (51 state vectors instead of 41), but the
accuracy is so much higher.
Removed the Runge-Kutta code because I won't be going back to it.
The Pluto gravity simulator constants now come from
a single source: pluto_gravsim.h. This will allow me
to experiment with the Pluto state table to get a better
compromise between size and accuracy.
Tonight as I was walking outside, I saw a fairly bright
star about half a degree away from the edge of the Moon.
I wondered what it was, so I decided to write a quick
program to find out.
This Python demo program scans the HYG Database
(https://github.com/astronexus/HYG-Database)
to find which bright stars are within a small angular
distance of the Moon, as seen at a given time, latitude, and longitude.
It turns out the star I saw was Nunki (Sigma Sagittarii).
It was handy to do vector subtraction to implement this program,
and it was trivial to do in the Python code's Vector class,
so I went ahead and added that.
There is now a Libration function in all 4 supported languages.
The returned structure contains libration angles in
ecliptic latitude and ecliptic longitude, along with
the Moon's ecliptic position and distance.
Also included is the Moon's apparent angular diameter.
All 4 languages have added a `diam_deg` field to the
structure returned by the Libration function.
It is the apparent angular diameter of the Moon as
seen from the center of the Earth, expressed in degrees.
In JavaScript and Python, throw an exception if provided
an invalid refraction option. Especially in JavaScript,
it was too easy to pass in a value like 'true', which did
not calculate refraction as expected.
Instead of the hack call to Search(), the latitude
solver now uses Newton's Method directly. This
significantly speeds up the code, and is more elegant.
Added more exhaustive testing of VectorObserver.
I found a few cases where the height calculation
was off by more than 5 millimeters.
In the VectorObserver function, require the latitude solver
to keep iterating until the error is less than one billionth
of a degree. Now the height error is always within 1 mm.
I already had the function ObserverVector that converts geographic
coordinates (latitude, longitude, elevation) to an equatorial-of-date
(EQD) vector.
Now I'm in the process of adding the inverse function VectorObserver
that calculates geographic coordinates from an EQD vector.
This commit implements VectorObserver in Python.
The other languages will follow in future commits.
The motivation was from the following request:
https://github.com/cosinekitty/geocalc/issues/1
The goal is to find the near-intersection between two different lines
of sight from two different observers on the Earth's surface.
Added a demo program triangulate.py that solves this problem.
Ported conversion to/from galactic coordinates to Python.
Added unit test for new Python code.
Updated documentation for all 4 supported languages.
Fixed mistakes in JavaScript function documentation.
Started work on a Python demo for finding when the moon
reaches relative longitudes with other solar system bodies
that are multiples of 30 degrees. It is not finished yet,
but getting close.
Added operator overloads for the Python Time class so
that times can be compared against each other.
This makes it easier to sort a list of times, for example.
Before making these changes, I had the following discrepancies
between the calculations made by the different programming
language implementations of Astronomy Engine:
C vs C#: 5.55112e-17, worst line number = 6
C vs JS: 2.78533e-12, worst line number = 196936
C vs PY: 1.52767e-12, worst line number = 159834
Now the results are:
Diffing calculations: C vs C#
ctest(Diff): Maximum numeric difference = 5.55112e-17, worst line number = 5
Diffing calculations: C vs JS
ctest(Diff): Maximum numeric difference = 1.02318e-12, worst line number = 133677
Diffing calculations: C vs PY
ctest(Diff): Maximum numeric difference = 5.68434e-14, worst line number = 49066
Diffing calculations: JS vs PY
ctest(Diff): Maximum numeric difference = 1.02318e-12, worst line number = 133677
Here is how I did this:
1. Use new constants HOUR2RAD, RAD2HOUR that directly convert between radians and sidereal hours.
This reduces tiny roundoff errors in the conversions.
2. In VSOP longitude calculations, keep clamping the angular sum to
the range [-2pi, +2pi], to prevent it from accumulating thousands
of radians. This reduces the accumulated error in the final result
before it is fed into trig functions.
The remaining discrepancies are largely because of an "azimuth amplification" effect:
When converting equatorial coordinates to horizontal coordinates, an object near
the zenith (or nadir) has an azimuth that is highly sensitive to the input
equatorial coordinates. A tiny change in right ascension (RA) can cause a much
larger change in azimuth.
I tracked down the RA discrepancy, and it is due to a different behavior
of the atan2 function in C and JavaScript. There are cases where the least
significant decimal digit is off by 1, as if due to a difference of opinion
about rounding policy.
My best thought is to go back and have a more nuanced diffcalc that
applies less strict tests for azimuth values than the other calculated values.
It seems like every other computed quantity is less sensitive, because solar
system bodies tend to stay away from "poles" of other angular coordinate
systems: their ecliptic latitudes and equatorial declinations are usually
reasonably close to zero. Therefore, right ascensions and ecliptic longitudes
are usually insensitive to changes in the cartesian coordinates they
are calculated from.
This change has no effect on client-facing behavior.
It just makes the internal data tables for the array of
constellation appear more compact in C, C#, and Python.
This is what the TypeScript/JavaScript code was already doing.
The demo shows how to correct for light travel
time to render Jupiter's moons as they appear
from the Earth.
Created an addition operator for the Vector
class in the Python code, because it is handy.
Corrected a bug in the string representation
of the Python StateVector class.
Now there are constants for the mean radii of Jupiter's
four major moons available in the C, C#, Python, and JavaScript
versions of Astronomy Engine.
Clarified that these are all mean radii.
Fixed some lingering "//" comments in the C code
(I want to keep ANSI C code as portable as possible.)
To assist software that wants to depict Jupiter and its 4 major moons
as they would appear in a telescope, it is important to know their
physical sizes. I already had constants for Jupiter's equatorial
and polar radii. Here I add constants for the radii of the moons
Io, Europa, Ganymede, and Callisto. They are all nearly spherical,
so a single mean radius value is sufficient.
My pydown.py custom Markdown generator was printing bogus
warnings about unknown symbol types, when it was actually
generating correct documentation for those symbols.
Eliminated the warnings, and improved the output format
for global constant documentation: no more extraneous spaces.
If there really is an undocumented symbol detected, fail the build!
Don't just print a warning that slides up the screen unnoticed.
I am starting the process of implementing calculation
of Jupiter's four largest moons: Io, Europa, Ganymede, Callisto.
This commit just contains constant declarations for the
equatorial, polar, and volumetric mean radii of Jupiter.
The positions of the moons will be related to the center
of Jupiter and be expressed in Jupiter equatorial radius units,
so I felt it would be good to give users a way to convert to
kilometers, which can in turn be converted to AU.
Python docstrings don't work for variables, so I hacked
a special comment format for helping pydown generate Markdown
text for the README.md for the exported constant KM_PER_AU,
or any other constants I may want to expose in the future.
Use a private enumerated type to select which direction
the precession and nutation is to be done:
- from date to J2000
- from J2000 to date
Normalize the order of parameters to be consistent
between precession() and nutation(), and across languages.
Pass in AstroTime instead of a pair of floating point TT
values (one of which had to be 0).
Added TypeScript version of ObserverVector(),
but it has not yet been documented or tested.
Astronomy Engine used to use USNO historical and predictive tables,
along with linear interpolation, to calculate Delta-T values.
The problem with the USNO tables is, they did not work well outside
a few centuries around present day.
Later I replaced with Espenak & Meeus piecewise polynomials
that work over a much larger time span (thousands of years).
I just discovered there were still comments in the code referring
to the USNO models. I updated the ones I could find to reflect
the current truth about how the code works today.
This is technically a breaking change, but only for clients
that use the cartesian coordinates in an ecliptic coordinate
return type. Before now, the coordinates were just separate
floating-point members ex, ey, ez. Now they are a standard
vector type.
The purpose is to allow seamless interfacing with vector
rotation functions, and to be consistent with the equatorial
coordinate types.
Now that equatorial coordinates include both angles
and cartesian coordinates, there is no need for the
VectorFromEquator function. It has been removed
from all four supported languages.
The expression "VectorFromEquator(equ, time)" can be
replaced with "equ.vec" in any calling code.
I'm about to start working on adding a new output
from the Horizon functions. It was a good time to better
document the ideas behind these calculations, before
adding anything new. These are internal comments only
and do not affect generated documentation.
While I was in there, I noticed extra code that was
checking for impossible return values from atan2().
I eliminated these.
I forgot that my build process automatically updates
copyright years when the current year changes.
My Travis CI unit tests verify that there are no local
changes after running all the tests.
That test failed because the update_copyrights.py changed
all the "2019-2020" to "2019-2021".
In all four versions of Astronomy Engine (C, C#, JavaScript, and Python),
starting a search for a full moon near December 19, 2020 would fail.
I added a unit test to all four languages and it failed consistently
across them all.
The root cause: I was too optimistic about how narrow I could make
the window around the approximate moon phase time in the
SearchMoonPhase functions. Finding the exact moon phase time failed
because it was outside this excessively small window around the approximate
time. I increased the window from 1.8 days to 3.0 days.
This should handle all cases with minimal impact on performance.
Now all four of the new unit tests pass.
