# Speeding up astroplan#

Some users of astroplan may find it useful to trade-off a bit of precision in the rise/set/transit times of targets in exchange for computational efficiency. In this short tutorial, we show you how to speed up astroplan in exchange for a bit of time precision, which is especially useful when planning many observations over a long period of time.

## Rise/set/transit times#

The rise, set, and transit time methods on the Observer object take an optional keyword argument called n_grid_points as of astroplan version 0.6 (in earlier versions of astroplan, n_grid_points is fixed to 150). To understand n_grid_points you first need to know how target rise/set/transit times are computed in astroplan.

Astroplan computes rise/set times relative to a given reference time by computing the altitude of the target on a grid which spans a period of 24 hours before/after the reference time. The grid is then searched for horizon-crossings, and astroplan interpolates between the two nearest-to-zero altitudes to approximate the target rise/set times.

The n_grid_points keyword argument dictates the number of grid points on which to compute the target altitude. The larger the n_grid_points, the more precise the rise/set/transit time will be, but the operation also becomes more computationally expensive. As a general rule of thumb, if you choose n_grid_points=150 your rise/set time precisions will be precise to better than one minute; this is the default if you don’t specify n_grid_points. If you choose n_grid_points=10 you’ll get significantly faster rise/set time computations, but your precision degrades to better than five minutes.

## Examples#

Let’s see some simple examples. We can compute a very accurate rise time for Sirius over Apache Point Observatory, by specifying n_grid_points=1000:

>>> from astroplan import Observer, FixedTarget
>>> from astropy.time import Time

>>> time = Time('2019-01-01 00:00')
>>> sirius = FixedTarget.from_name('Sirius')
>>> apo = Observer.at_site('APO')

>>> rise_time_accurate = apo.target_rise_time(time, sirius, n_grid_points=1000)
>>> rise_time_accurate.iso
'2019-01-01 01:52:13.393'

That’s the rise time computed on a grid of 1000 altitudes in a 24 hour period, so it should be very accurate, but we can run the timeit function on the above code snippet to see how slow this is:

290 ms ± 4.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

Now let’s compute a lower precision, but much faster rise time, using N=10 this time:

>>> rise_time_fast = apo.target_rise_time(time, sirius, n_grid_points=10)
>>> rise_time_fast.iso
'2019-01-01 01:54:09.946'

And timing the above snippet, we find:

27.3 ms ± 709 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)

You can see that the rise time returned by target_rise_time with n_grid_points=10 is only two minutes different from the prediction with n_grid_points=1000, so it looks like we haven’t lost much precision despite the drastically different number of grid points and an order-of-magnitude speedup.