.. _speed:

*********************
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 `~astroplan.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``:

.. code-block:: python

    >>> 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  # doctest: +SKIP
    '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:

.. code-block:: shell

    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:

.. code-block:: python

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

And timing the above snippet, we find:

.. code-block:: shell

    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
`~astroplan.Observer.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.
