issue_comments

Thanks for the confirmation @shoyer !

@shoyer Aww, great. Thanks for pointing this out.

There is an open issue at numpy about this in https://github.com/numpy/numpy/issues/6266

Also, for future reference, locally re-defining np.angle by removing the z = array(z) line from the official function appears to work well enough as a workaround, assuming the input is an xarray, ```py import numpy.core.numeric as _nx

def angle(z, deg=0): """Compute the angle of an xarray

Parameters
----------
z : array_like
    A complex number or sequence of complex numbers.
deg : bool, optional
    Return angle in degrees if True, radians if False (default).

Returns
-------
angle : ndarray or scalar
    The counterclockwise angle from the positive real axis on
    the complex plane, with dtype as numpy.float64.

See: https://github.com/pydata/xarray/issues/553
     https://github.com/numpy/numpy/blob/v1.13.0/numpy/lib/function_base.py#L2072-L2115
"""
if deg:
    fact = 180/pi
else:
    fact = 1.0
if (issubclass(z.dtype.type, _nx.complexfloating)):
    zimag = z.imag
    zreal = z.real
else:
    zimag = 0
    zreal = z
return np.arctan2(zimag, zreal)

```

do you have an application that we could use to drive this?

Other examples where labeled sparse arrays would be useful are, * one-hot encoding that are widely used in machine learning. * tokenizing textual data produces large sparse matrices where the column labels correspond to the vocabulary, while row labels correspond to document ids. Here is a minimal example using scikit-learn, ```py import os.path from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import CountVectorizer

 ds = fetch_20newsgroups()
 vect = CountVectorizer()
 X = vect.fit_transform(ds.data)
 print(X)  # Extracted tokens
 # Returns:
 # <11314x130107 sparse matrix of type '<class 'numpy.int64'>'
 #  with 1787565 stored elements in Compressed Sparse Row format>

 column_labels = vect.get_feature_names()
 print(np.asarray(column_labels))
 # Returns:
 # array(['00', '000', '0000', ..., 'íålittin', 'ñaustin', 'ýé'],   dtype='<U180')

 row_labels = [int(os.path.split(el)[1]) for el in ds.filenames]
 print(np.asarray(row_labels))
 # Returns:
 # array([102994,  51861,  51879, ...,  60695,  38319, 104440])
 ```