def as_ansi(obj, props=(), **propkw): #as_ansi_array???
'''
Convert the obj to an array of AnsiStr objects, applying the properties.
Parameters
----------
obj : If input is unsized - return its AnsiStr representation
If input is 0 size - return empty AnsiStr object
Else return array of AnsiStr objects
'''
precision = propkw.pop('precision', 2)
ndmin = propkw.pop('ndmin', 0)
pretty = propkw.pop('pretty', True)
obja = np.array(as_sequence(obj), dtype=object)
#try:
#obja = np.atleast_1d(obj)
#except Exception as err:
#print(err)
#from IPython import embed
#embed()
#print()
#reshape complex dtype arrays to object arrays
if obja.dtype.kind == 'V': #complex dtype as in record array
#check if all the dtypes are the same. If so we can change view
dtypes = next(zip(*obja.dtype.fields.values()))
dtype0 = dtypes[0]
if np.equal(dtypes, dtype0).all():
obja = obja.view(dtype0).reshape(len(obja), -1)
#else:
#view as object array
#obja = obja.astype(object)
if isinstance(props, dict):
propkw.update(props)
props = ()
else:
props = np.atleast_1d(props) #as_sequence( props, return_as=tuple)
#deal with empty arrays #???????
if not len(obja): #???????
return AnsiStr(str(obj))
fun = lambda s: AnsiStr(as_str(s, precision, pretty)).set_property(*props, **propkw)
out = np.vectorize(fun, (AnsiStr,))(obja)
if len(out)==1 and out.ndim==1 and ndmin==0:
out = out[0] #collapse arrays of shape (1,) to item itself if ndmin=0 asked for
return out
def apply_props(obj, props=()):
if isinstance(props, dict):
return as_superstrings(obj, **props)
else:
props = as_sequence(props, return_as=tuple)
return as_superstrings(obj, props)