cleanup to_array implementation using fixes on master
@@ -557,28 +557,25 @@ cdef class Doc:
"""
cdef int i, j
cdef attr_id_t feature
+ cdef np.ndarray[attr_t, ndim=1] attr_ids
cdef np.ndarray[attr_t, ndim=2] output
- cdef np.ndarray[attr_t, ndim=1] output_1D
# Handle scalar/list inputs of strings/ints for py_attr_ids
- if( type(py_attr_ids) is not list and type(py_attr_ids) is not tuple ):
+ if not hasattr(py_attr_ids, '__iter__'):
py_attr_ids = [py_attr_ids]
- py_attr_ids_input = []
- for py_attr_id in py_attr_ids:
- if( type(py_attr_id) is int ):
- py_attr_ids_input.append(py_attr_id)
- else:
- py_attr_ids_input.append(IDS[py_attr_id.upper()])
+
+ # Allow strings, e.g. 'lemma' or 'LEMMA'
+ py_attr_ids = [(IDS[id_.upper()] if hasattr(id_, 'upper') else id_)
+ for id_ in py_attr_ids]
# Make an array from the attributes --- otherwise our inner loop is Python
# dict iteration.
- cdef np.ndarray[attr_t, ndim=1] attr_ids = numpy.asarray(py_attr_ids_input, dtype=numpy.uint64)
+ attr_ids = numpy.asarray(py_attr_ids, dtype=numpy.uint64)
output = numpy.ndarray(shape=(self.length, len(attr_ids)), dtype=numpy.uint64)
for i in range(self.length):
for j, feature in enumerate(attr_ids):
output[i, j] = get_token_attr(&self.c[i], feature)
- if( len(attr_ids) == 1 ):
- output_1D = output.reshape((self.length))
- return output_1D
- return output
+ # Handle 1d case
+ return output if len(attr_ids) >= 2 else output.reshape((self.length,))
+
def count_by(self, attr_id_t attr_id, exclude=None, PreshCounter counts=None):
"""Count the frequencies of a given attribute. Produces a dict of
