MSK's personal Python library, mostly for astronomy work.

Requires: python3 (3.5+ recommended), numpy, scipy, astropy v2.0+, pytz, spiceypy (v1.1+), FORTRAN compiler.

Recommended: matplotlib (v2), pyds9, photutils (for gcentroid).

I hope you find mskpy useful, but use at your own risk. When you encounter errors, feedback would be appreciated.

After installation, the file $HOME/.config/mskpy/mskpy.cfg should be created. If not simply execute python -c 'import mskpy.config'. This file currently contains paths to your SPICE kernels, Cohen mid-IR standards, and IRTF spextool data files. (none are required to use mskpy).

To use the ephem and observing modules, spiceypy is required. At a minimum, three kernels are needed to be present in your kernel directory: - naif.tls : a leap seconds kernel, e.g., naif0012.tls, - pck.tpc : a planetary constants kernel, - planets.bsp : a planetary ephemeris kernel, e.g., DE431. These kernels are available from the NAIF group at JPL:

http://naif.jpl.nasa.gov/pub/naif/generic_kernels/

In general, download the most recent versions for the leap seconds and planetary constants kernels. For the planetary ephemeris kernel, check the NAIF comments and readme files to determine which is the best for your installation.

Copy the kernels to your kernel directory. I recommend keeping the original file names and using symbolic links to match what mskpy requires, i.e., link naif.tls to naif0012.tls. After importing mskpy for the first time, edit the configuration file (see above) to match your kernel installation location.

There are five optional kernels:

• L2.bsp : an ephemeris kernel for the second Lagrange point in the Earth-Sun system, https://naif.jpl.nasa.gov/pub/naif/generic_kernels/spk/lagrange_point/
• spitzer.bsp : an ephemeris kernel for the Spitzer Space Telescope, ftp://naif.jpl.nasa.gov/pub/naif/SIRTF/kernels/spk/
• kepler.bsp : an ephemeris kernel for the Kepler Telescope, https://archive.stsci.edu/pub/k2/spice/
• deepimpact.txt : an ephemeris meta-kernel for Deep Impact Flyby, ftp://naif.jpl.nasa.gov/pub/naif/
• naif-names.txt : your own body to ID code mappings. See "Use of an External Mapping Definition Kernel" in the NAIF ID Integer Codes document.
• tess.txt : an ephemeris meta-kernel for the TESS spacecraft.

Download 2P/Encke SPICE kernel from JPL HORIZONS; save as 'encke.bsp':

>>> from mskpy import getspiceobj, Earth
>>> encke = getspiceobj('encke')
>>> Earth.observe(encke, '2013-11-01').summary()

                             Date: 2013-11-01
                        Time (UT): 00:00:00
                       Julian day: 2456597.50

       Heliocentric distance (AU):    0.618
    Target-Observer distance (AU):    0.600
  Sun-Object-Observer angle (deg):  109.105

  Sun-Observer-Target angle (deg):   36.038
 Moon-Observer-Target angle (deg):   14.920

                          RA (hr):  12:35:04.3
                        Dec (deg): +09:20:03.7

       Projected sun vector (deg):  130.751
  Projected velocity vector (deg):  164.120

Which is pretty close to what JPL/HORIZONS reports (note the 15 arcsec difference in the Equatorial coordinates):

rh:        0.618
Delta:     0.600
phase:     109.116
s_elong:   36.041
l_elong:   14.9
RA:        12:35:04.1
Dec:       09:20:20.0
PsAng-180: 130.755
PsAMV-180: 344.127

>>> from mskpy import Earth, Moon >>> print(Moon.ephemeris(Earth, ['2013-1-1', '2013-12-31'], num=365)) date ra dec rh delta phase selong ---------------- ----- ------ ----- ----- ----- ------2013-01-01 00:00 09:25 09:46 0.985 0.003 40 140 2013-01-02 00:00 10:13 05:35 0.985 0.003 52 128 2013-01-03 00:00 11:01 01:05 0.984 0.003 63 116 2013-01-04 00:00 11:50 -3:33 0.984 0.003 75 104 2013-01-05 00:00 12:40 -8:07 0.983 0.003 88 92 ... ... ... ... ... ... ... 2013-12-27 00:00 13:19 -9:42 0.983 0.003 107 73 2013-12-28 00:00 14:12 -13:21 0.982 0.003 119 61 2013-12-29 00:00 15:08 -16:23 0.982 0.002 132 48 2013-12-30 00:00 16:08 -18:32 0.981 0.002 145 35 2013-12-31 00:00 17:11 -19:29 0.981 0.002 159 21

Read in a HORIZONS CSV formatted table:

>>> from mskpy.util import horizons_csv >>> eph = horizons_csv('horizons_results.txt') >>> eph.pprint() Date__(UT)__HR:MN col1 col2 R.A._(ICRF/J2000.0) ... S-O-T /r S-T-O col12 ----------------- ---- ---- ------------------- ... ------- --- ------- -----2016-Dec-01 00:00 -- -- 21 15 16.67 ... 66.9358 /T 40.7852 --2016-Dec-02 00:00 -- -- 21 18 15.10 ... 66.6718 /T 40.6632 --2016-Dec-03 00:00 -- -- 21 21 13.20 ... 66.4081 /T 40.5404 --2016-Dec-04 00:00 -- -- 21 24 10.97 ... 66.1445 /T 40.4166 --2016-Dec-05 00:00 -- -- 21 27 08.40 ... 65.8811 /T 40.2919 --2016-Dec-06 00:00 -- -- 21 30 05.49 ... 65.6179 /T 40.1663 --2016-Dec-07 00:00 -- -- 21 33 02.23 ... 65.3549 /T 40.0398 --2016-Dec-08 00:00 -- -- 21 35 58.62 ... 65.092 /T 39.9126 --2016-Dec-09 00:00 -- -- 21 38 54.65 ... 64.8293 /T 39.7845 --2016-Dec-10 00:00 -- -- 21 41 50.33 ... 64.5666 /T 39.6556 --2016-Dec-11 00:00 -- -- 21 44 45.65 ... 64.304 /T 39.526 --2016-Dec-12 00:00 -- -- 21 47 40.61 ... 64.0414 /T 39.3956 --2016-Dec-13 00:00 -- -- 21 50 35.20 ... 63.7789 /T 39.2645 --2016-Dec-14 00:00 -- -- 21 53 29.44 ... 63.5163 /T 39.1326 --2016-Dec-15 00:00 -- -- 21 56 23.32 ... 63.2537 /T 39.0001 --2016-Dec-16 00:00 -- -- 21 59 16.84 ... 62.9909 /T 38.8667 --2016-Dec-17 00:00 -- -- 22 02 10.01 ... 62.7281 /T 38.7327 --2016-Dec-18 00:00 -- -- 22 05 02.84 ... 62.4652 /T 38.5979 --2016-Dec-19 00:00 -- -- 22 07 55.34 ... 62.2022 /T 38.4624 --2016-Dec-20 00:00 -- -- 22 10 47.50 ... 61.9391 /T 38.3262 --2016-Dec-21 00:00 -- -- 22 13 39.33 ... 61.6758 /T 38.1892 --2016-Dec-22 00:00 -- -- 22 16 30.83 ... 61.4124 /T 38.0515 --2016-Dec-23 00:00 -- -- 22 19 22.02 ... 61.1489 /T 37.913 --2016-Dec-24 00:00 -- -- 22 22 12.90 ... 60.8853 /T 37.7738 --2016-Dec-25 00:00 -- -- 22 25 03.47 ... 60.6216 /T 37.6339 --2016-Dec-26 00:00 -- -- 22 27 53.73 ... 60.3578 /T 37.4932 --2016-Dec-27 00:00 -- -- 22 30 43.70 ... 60.0939 /T 37.3518 --2016-Dec-28 00:00 -- -- 22 33 33.36 ... 59.8299 /T 37.2097 --2016-Dec-29 00:00 -- -- 22 36 22.74 ... 59.5658 /T 37.0669 --2016-Dec-30 00:00 -- -- 22 39 11.83 ... 59.3016 /T 36.9235 --2016-Dec-31 00:00 -- -- 22 42 00.64 ... 59.0374 /T 36.7793 --

The same file can be directly read with `astropy`:

>>> import mskpy >>> from astropy.table import Table >>> eph = Table.read('horizons_results.txt', format='horizons.csv')

Two methods:

1. Thermal emission from (24) Themis. If you are not using SPICE, but know rh, delta, and phase:

   >>> import astropy.units as u
   >>> from mskpy.models import NEATM
   >>> geom = dict(rh=2.741 * u.au, delta=3.317 * u.au, phase=15.5 * u.deg)
   >>> themis = NEATM(198 * u.km, 0.067, G=0.19, eta=1.0)
   >>> print(themis.fluxd(geom,  [0.55, 3.0, 10] * u.um, unit=u.Jy))
   [  6.43548331e-42   9.33984255e-05   6.19350889e+00] Jy

2. Thermal emission and/or reflected light from (24) Themis. Download its SPICE kernel from JPL HORIZONS; save as '2000024.bsp':

   >>> import astropy.units as u
   >>> from mskpy import Asteroid, SpiceState, Earth
   >>> themis = Asteroid(SpiceState(2000024), 198 * u.km, 0.067, G=0.19, eta=1.0)
   # Thermal + Reflected
   >>> print(themis.fluxd(Earth, '2013-10-15', [0.55, 3.0, 10] * u.um, unit=u.Jy))
   [ 0.03174409  0.01327644  6.19537937] Jy
   # Thermal only
   >>> print(themis.fluxd(Earth, '2013-10-15', [0.55, 3.0, 10] * u.um, unit=u.Jy, reflected=False))
   [  6.46956946e-42   9.34730285e-05   6.19402381e+00] Jy
   # Reflected only
   >>> print(themis.fluxd(Earth, '2013-10-15', [0.55, 3.0, 10] * u.um, unit=u.Jy, thermal=False))
   [ 0.03174409  0.01318297  0.00135556] Jy

Download 2P/Encke SPICE kernel from JPL HORIZONS; save as 'encke.bsp'. Download Spitzer Space Telescope kernel from JPL NAIF; save as 'spitzer.bsp':

>>> import astropy.units as u
>>> from mskpy import Coma, SpiceState, Spitzer
>>> Afrho1 = 8.9 * u.cm * 2.53**2
>>> encke = Coma(SpiceState('encke'), Afrho1, ef2af=3.5, Tscale=1.1)
>>> print(encke.fluxd(Spitzer, '2004-06-20 18:35', 23.7 * u.um, rap=12.5 * u.arcsec, unit=u.Jy))
[ 0.02589534] Jy

Create a file with your list of targets¹:

Rubin 149 B,          07:24:18h, -00:33:06d
C/2013 R1 (Lovejoy),    7 19 hr,   2 32 deg
SA 101-316,           09h54m52s, -00d18m35s
C/2012 S1 (ISON),     [[1003203]]

Then, execute the following:

>>> import astropy.units as u
>>> from mskpy import observing
>>> targets = observing.file2targets('targets.txt')
>>> telescope = observing.Observer(-110.791667 * u.deg, 32.441667 * u.deg, -7, None)
>>> observing.am_plot(targets, telescope)

Aperture polarimetry from a half-wave plate polarimeter:

>>> import mskpy.polarimetry as pol
>>> # fluxes and uncertainties from position angles: 0, 45, 90, and 135:
>>> I = [1.0, 1.1, 1.0, 1.0]
>>> sig_I = [0.01, 0.01, 0.01, 0.01]
>>> p = pol.HalfWavePlate(I, sig_I)
>>> print 'p = {:.3f} +/- {:.3f} %'.format(p.p, p.sig_p)
>>> print ' at {:.1f} +/- {:.1f} deg'.format(p.theta, p.sig_theta)
p = 0.047 +/- 0.007 %
at 45.0 +/- 4.1 deg

Polarimetry classes can also take arrays for the wave plate positions, including images. There are keywords that allow for instrumental corrections to Q/I, U/I and total polarization.

An example calibration of HB filter photometry is given in examples/hb-cal.py. It calibrates the following raw photometry of standard stars and produces the calibration coefficients below.

Standard stars:

# standard star photometry
# m : apparent magnitude (Farnham et al. 2000)
# m_inst : instrumental magnitude
# z : zenith angle in degrees
#
filter   m    m_inst m_inst unc   z    airmass
------ ----- ------- ---------- ------ -------
    RC 7.766 -13.877      0.004 59.932   1.989
    RC 7.766 -13.902      0.003 48.062   1.494
    RC 7.448 -14.236      0.001 42.090   1.346
    RC 7.448 -14.211      0.003 42.553   1.356
    BC  7.68 -14.482      0.007 59.565   1.968
    BC  7.68 -14.604      0.006 47.767   1.486
    BC 7.784 -14.518      0.005 42.690   1.359
    BC 7.784 -14.543      0.005 41.977   1.344
    CN 7.619 -13.936      0.008 60.297   2.011
    CN 7.619 -14.137      0.007 48.360   1.503
    CN 7.748 -14.096      0.003 42.210   1.349
    CN 7.748 -14.071      0.006 42.422   1.353
    OH 7.414  -8.746      0.037 60.691   2.036
    OH 7.414  -9.731      0.014 48.685   1.512
    OH 7.536  -9.955      0.009 42.339   1.351
    OH 7.536  -9.942      0.012 42.289   1.351

Results:

filter  N  magzp  magzp unc   Ex  Ex unc  toz  toz unc mean residuals stdev residuals standard error
------ --- ------ --------- ----- ------ ----- ------- -------------- --------------- --------------
    CN   4 22.423     0.018 0.434  0.013    --      --         -0.005           0.012          0.006
    BC   4 22.651     0.019 0.248  0.013    --      --          0.000           0.009          0.004
    RC   4 21.766     0.009 0.063  0.006    --      --         -0.005           0.011          0.006
    OH   4 20.292     0.082    --     -- 0.393   0.021          0.003           0.019          0.009

Some code for migration to Python 3 provided by Miguel de Val-Borro.

This project is Copyright (c) Michael S. P. Kelley and licensed under the terms of the BSD 3-Clause license. This package is based upon the Astropy package template which is licensed under the BSD 3-clause license. See the licenses folder for more information.

We love contributions! mskpy is open source, built on open source, and we'd love to have you hang out in our community.

Imposter syndrome disclaimer: We want your help. No, really.

There may be a little voice inside your head that is telling you that you're not ready to be an open source contributor; that your skills aren't nearly good enough to contribute. What could you possibly offer a project like this one?

We assure you - the little voice in your head is wrong. If you can write code at all, you can contribute code to open source. Contributing to open source projects is a fantastic way to advance one's coding skills. Writing perfect code isn't the measure of a good developer (that would disqualify all of us!); it's trying to create something, making mistakes, and learning from those mistakes. That's how we all improve, and we are happy to help others learn.

Being an open source contributor doesn't just mean writing code, either. You can help out by writing documentation, tests, or even giving feedback about the project (and yes - that includes giving feedback about the contribution process). Some of these contributions may be the most valuable to the project as a whole, because you're coming to the project with fresh eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.

Note: This disclaimer was originally written by Adrienne Lowe for a PyCon talk, and was adapted by mskpy based on its use in the README file for the MetPy project.