# 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.