Ideally I would like a function which works as follows (for all kinds of numpy functions):
parameter_types('np.random.binomial')
and returns:
{'a: 'int', 'b':'float', 'size':'int'}
I understand that jedi has some support for extracting this information from docstrings, but I cannot make it work. Is it possible to get something like this using jedi?
As found in this answer, your best bet is to install numpydoc and its requirements.
import numpydoc
import numpy as np
doc = numpydoc.docscrape.NumpyDocString(np.random.binomial.__doc__)
wich can then be inspected
In [45]: doc['Parameters']
Out[45]:
[('n',
'int or array_like of ints',
['Parameter of the distribution, >= 0. Floats are also accepted,',
'but they will be truncated to integers.']),
('p',
'float or array_like of floats',
['Parameter of the distribution, >= 0 and <=1.']),
('size',
'int or tuple of ints, optional',
['Output shape. If the given shape is, e.g., ``(m, n, k)``, then',
'``m * n * k`` samples are drawn. If size is ``None`` (default),',
'a single value is returned if ``n`` and ``p`` are both scalars.',
'Otherwise, ``np.broadcast(n, p).size`` samples are drawn.'])]
Note that you'll have to do some postprocessing such as converting the list of tuples to a dictionary.
In [46]: {t[0]: t[1] for t in doc['Parameters']}
Out[46]:
{'n': 'int or array_like of ints',
'p': 'float or array_like of floats',
'size': 'int or tuple of ints, optional'}