Hi David,
I think that you can do what you want in one, rather long line:
hfile.createTable(grp, 'signal', description=np.array(zip(some_func(t, v)),
dtype=[('time', np.float64), ('value', np.float64)]))
Or two nicer lines:
arr = np.array(zip(some_func(t, v)), dtype=[('time', np.float64), ('value',
np.float64)])
hfile.createTable(grp, 'signal', description=arr)
zip() is your friend =). If zip is too slow and you don't want to make
more than one copy, you could try something like this:
temparr = np.array(some_func(t, v)).T
arr = np.view(temparr, dtype=[('time', np.float64), ('value', np.float64)])
This really only works because both columns have the same dtype.
Of course, you can always keep basically what you have and loop through the
column names programmaticly:
for name, col in zip(A.dtype.names, some_func(t, v)):
A[name] = col
I hope this helps!
Be Well
Anthony
On Wed, Aug 7, 2013 at 5:58 PM, David Reed david.ree...@gmail.com wrote:
Hi there,
I have some generic functions that take time series data with 2 numpy
array arguments, time and value, and return 2 numpy arrays of time and
value.
I would like to place these arrays into a Numpy structured array or
directly into a new pytables table with fields, time and value.
Now Ive found I could do this:
t, v = some_func(t, v)
A = np.empty(len(t), dtype=[('time', np.float64), ('value',
np.float64)])
A['time'] = t
A['value'] = v
hfile.createTable(grp, 'signal', description=A)
hfile.flush()
But this seems rather clunky and inefficient. Any suggestions to make
this repackaging a little smoother?
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