When an extractor is defined (based on the Extractor object) without
specifying time points (the `tps` argument), the program raises a
`TypeError`.

The definition of `tps` when it is not otherwise defined in `.run()` is
based on the assumption that the number of time points is stored as a
scalar in the metadata of the HDF5
file (`self.meta["time_settings/ntimepoints"]`).  This is wrong -- the
number of time points is stored as a one-element numpy array.  The
reason it is stored in this way is down to the behaviour of the log file
parser (https://git.ecdf.ed.ac.uk/swain-lab/aliby/agora/-/blob/master/logfile_parser/logfile_parser.py)
and how it processes tables as defined in the JSON file that specifies
the grammar.

This issue does not occur when the whole pipeline is run because the
scalar element of the numpy array that stores the number of time points
is properly extracted in
https://git.ecdf.ed.ac.uk/swain-lab/aliby/aliby/-/blob/master/aliby/pipeline.py,
line 89.

The alternative way to fix it was to fix the behaviour of the log file
parser.  The 'proper' way is to store the number of time points as
scalars, not an array with one element.  However:
 (a) It is JSON-parsing code that I am not familiar with, and
 (b) There is a risk of breaking parts of the code base that relies on
 other parts of the metadata.

So, this is essentially a quick-and-dirty fix, and it *should* be okay
given that `time_settings/ntimepoints` only appears twice in the
project.  However, if we decide to handle the metadata
differently (unlikely given the fixed nature of our log files, and we've
kind of exhausted all the useful information already), we will be in for
adding `[0]`s at various places.

Fixes issue #20.