diff options
Diffstat (limited to 'numpy/lib/recfunctions.py')
| -rw-r--r-- | numpy/lib/recfunctions.py | 41 |
1 files changed, 28 insertions, 13 deletions
diff --git a/numpy/lib/recfunctions.py b/numpy/lib/recfunctions.py index 20e91af5f..fcc0d9a7a 100644 --- a/numpy/lib/recfunctions.py +++ b/numpy/lib/recfunctions.py @@ -17,6 +17,7 @@ from numpy.ma.mrecords import MaskedRecords from numpy.core.overrides import array_function_dispatch from numpy.lib._iotools import _is_string_like from numpy.compat import basestring +from numpy.testing import suppress_warnings if sys.version_info[0] < 3: from future_builtins import zip @@ -872,7 +873,7 @@ def repack_fields(a, align=False, recurse=False): def _get_fields_and_offsets(dt, offset=0): """ - Returns a flat list of (name, dtype, count, offset) tuples of all the + Returns a flat list of (dtype, count, offset) tuples of all the scalar fields in the dtype "dt", including nested fields, in left to right order. """ @@ -883,7 +884,7 @@ def _get_fields_and_offsets(dt, offset=0): count = 1 for size in field[0].shape: count *= size - fields.append((name, field[0], count, field[1] + offset)) + fields.append((field[0], count, field[1] + offset)) else: fields.extend(_get_fields_and_offsets(field[0], field[1] + offset)) return fields @@ -911,7 +912,7 @@ def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'): arr : ndarray Structured array or dtype to convert. Cannot contain object datatype. dtype : dtype, optional - The dtype of the output unstructured array + The dtype of the output unstructured array. copy : bool, optional See copy argument to `ndarray.astype`. If true, always return a copy. If false, and `dtype` requirements are satisfied, a view is returned. @@ -948,8 +949,9 @@ def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'): raise ValueError('arr must be a structured array') fields = _get_fields_and_offsets(arr.dtype) - names, dts, counts, offsets = zip(*fields) - n_fields = len(names) + n_fields = len(fields) + dts, counts, offsets = zip(*fields) + names = ['f{}'.format(n) for n in range(n_fields)] if dtype is None: out_dtype = np.result_type(*[dt.base for dt in dts]) @@ -964,7 +966,9 @@ def structured_to_unstructured(arr, dtype=None, copy=False, casting='unsafe'): 'formats': dts, 'offsets': offsets, 'itemsize': arr.dtype.itemsize}) - arr = arr.view(flattened_fields) + with suppress_warnings() as sup: # until 1.16 (gh-12447) + sup.filter(FutureWarning, "Numpy has detected") + arr = arr.view(flattened_fields) # next cast to a packed format with all fields converted to new dtype packed_fields = np.dtype({'names': names, @@ -1041,13 +1045,13 @@ def unstructured_to_structured(arr, dtype=None, names=None, align=False, names = ['f{}'.format(n) for n in range(n_elem)] out_dtype = np.dtype([(n, arr.dtype) for n in names], align=align) fields = _get_fields_and_offsets(out_dtype) - names, dts, counts, offsets = zip(*fields) + dts, counts, offsets = zip(*fields) else: if names is not None: raise ValueError("don't supply both dtype and names") # sanity check of the input dtype fields = _get_fields_and_offsets(dtype) - names, dts, counts, offsets = zip(*fields) + dts, counts, offsets = zip(*fields) if n_elem != sum(counts): raise ValueError('The length of the last dimension of arr must ' 'be equal to the number of fields in dtype') @@ -1055,6 +1059,8 @@ def unstructured_to_structured(arr, dtype=None, names=None, align=False, if align and not out_dtype.isalignedstruct: raise ValueError("align was True but dtype is not aligned") + names = ['f{}'.format(n) for n in range(len(fields))] + # Use a series of views and casts to convert to a structured array: # first view as a packed structured array of one dtype @@ -1081,7 +1087,9 @@ def apply_along_fields(func, arr): Apply function 'func' as a reduction across fields of a structured array. This is similar to `apply_along_axis`, but treats the fields of a - structured array as an extra axis. + structured array as an extra axis. The fields are all first cast to a + common type following the type-promotion rules from `numpy.result_type` + applied to the field's dtypes. Parameters ---------- @@ -1145,7 +1153,7 @@ def assign_fields_by_name(dst, src, zero_unassigned=True): """ if dst.dtype.names is None: - dst[:] = src + dst[...] = src return for name in dst.dtype.names: @@ -1164,12 +1172,13 @@ def require_fields(array, required_dtype): """ Casts a structured array to a new dtype using assignment by field-name. - This function assigns to from the old to the new array by name, so the + This function assigns from the old to the new array by name, so the value of a field in the output array is the value of the field with the - same name in the source array. + same name in the source array. This has the effect of creating a new + ndarray containing only the fields "required" by the required_dtype. If a field name in the required_dtype does not exist in the - input array, that field is set to 0 in the output array. + input array, that field is created and set to 0 in the output array. Parameters ---------- @@ -1189,6 +1198,12 @@ def require_fields(array, required_dtype): >>> a = np.ones(4, dtype=[('a', 'i4'), ('b', 'f8'), ('c', 'u1')]) >>> require_fields(a, [('b', 'f4'), ('c', 'u1')]) + array([(1., 1), (1., 1), (1., 1), (1., 1)], + dtype=[('b', '<f4'), ('c', 'u1')]) + >>> require_fields(a, [('b', 'f4'), ('newf', 'u1')]) + array([(1., 0), (1., 0), (1., 0), (1., 0)], + dtype=[('b', '<f4'), ('newf', 'u1')]) + """ out = np.empty(array.shape, dtype=required_dtype) assign_fields_by_name(out, array) |