def test_dtype_to_index_cls_b(self) -> None:
t1 = dtype_to_index_cls(True, np.dtype(str))
self.assertEqual(t1, Index)
t2 = dtype_to_index_cls(False, np.dtype(str))
self.assertEqual(t2, IndexGO)
t3 = dtype_to_index_cls(True, np.dtype(float))
self.assertEqual(t3, Index)
t4 = dtype_to_index_cls(False, np.dtype(float))
self.assertEqual(t4, IndexGO)
def to_index(labels: tp.Iterable[tp.Hashable],
*,
default_constructor: tp.Type[IndexBase],
name: NameType = None,
) -> IndexBase:
'''Create and return the ``Index`` based on the array ``dtype``
'''
from static_frame.core.index_datetime import dtype_to_index_cls
if labels.__class__ is not np.ndarray:
# we can assume that this is 1D; returns an immutable array
labels, _ = iterable_to_array_1d(labels)
return dtype_to_index_cls(
static=default_constructor.STATIC,
dtype=labels.dtype)(labels, name=name) #type: ignore
def test_dtype_to_index_cls_a(self) -> None:
t1 = dtype_to_index_cls(True, np.dtype('datetime64[D]'))
self.assertEqual(t1, IndexDate)
t2 = dtype_to_index_cls(False, np.dtype('datetime64[D]'))
self.assertEqual(t2, IndexDateGO)
t3 = dtype_to_index_cls(True, np.dtype('datetime64[s]'))
self.assertEqual(t3, IndexSecond)
t4 = dtype_to_index_cls(False, np.dtype('datetime64[s]'))
self.assertEqual(t4, IndexSecondGO)
t5 = dtype_to_index_cls(True, np.dtype('datetime64[Y]'))
self.assertEqual(t5, IndexYear)
t6 = dtype_to_index_cls(False, np.dtype('datetime64[Y]'))
self.assertEqual(t6, IndexYearGO)
def from_arrays(
self,
blocks: tp.Iterable[np.ndarray],
*,
index: tp.Optional[IndexInitializer] = None,
columns: tp.Optional[IndexInitializer] = None,
name: NameType = None,
axis: int = 0,
) -> None:
'''
Given an iterable of arrays, write out an NPZ or NPY directly, without building up intermediary :obj:`Frame`. If axis 0, the arrays are vertically stacked; if axis 1, they are horizontally stacked. For both axis, if included, indices must be of appropriate length.
Args:
blocks:
*,
index: An array, :obj:`Index`, or :obj:`IndexHierarchy`.
columns: An array, :obj:`Index`, or :obj:`IndexHierarchy`.
name:
axis:
'''
if not self._writeable:
raise UnsupportedOperation('Open with mode "w" to write.')
metadata: tp.Dict[str, tp.Any] = {}
if isinstance(index, IndexBase):
depth_index = index.depth
name_index = index.name
cls_index = index.__class__
ArchiveIndexConverter.index_encode(
metadata=metadata,
archive=self._archive,
index=index,
key_template_values=Label.FILE_TEMPLATE_VALUES_INDEX,
key_types=Label.KEY_TYPES_INDEX,
depth=depth_index,
include=True,
)
elif index is not None:
if index.__class__ is not np.ndarray:
raise RuntimeError(
'index argument must be an Index, IndexHierarchy, or 1D np.ndarray'
)
depth_index = 1
name_index = None
cls_index = dtype_to_index_cls(True, index.dtype) #type: ignore
ArchiveIndexConverter.array_encode(
metadata=metadata,
archive=self._archive,
array=index,
key_template_values=Label.FILE_TEMPLATE_VALUES_INDEX,
)
else:
depth_index = 1
name_index = None
cls_index = Index
if isinstance(columns, IndexBase):
depth_columns = columns.depth
name_columns = columns.name
cls_columns = columns.__class__
ArchiveIndexConverter.index_encode(
metadata=metadata,
archive=self._archive,
index=columns,
key_template_values=Label.FILE_TEMPLATE_VALUES_COLUMNS,
key_types=Label.KEY_TYPES_COLUMNS,
depth=depth_columns,
include=True,
)
elif columns is not None:
if columns.__class__ is not np.ndarray:
raise RuntimeError(
'index argument must be an Index, IndexHierarchy, or 1D np.ndarray'
)
depth_columns = 1 # only support 1D
name_columns = None
cls_columns = dtype_to_index_cls(True,
columns.dtype) #type: ignore
ArchiveIndexConverter.array_encode(
metadata=metadata,
archive=self._archive,
array=columns,
key_template_values=Label.FILE_TEMPLATE_VALUES_COLUMNS,
)
else:
depth_columns = 1 # only support 1D
name_columns = None
cls_columns = Index
metadata[Label.KEY_NAMES] = [
name,
name_index,
name_columns,
]
# do not store Frame class as caller will determine
metadata[Label.KEY_TYPES] = [
cls_index.__name__,
cls_columns.__name__,
]
if axis == 1:
rows = 0
for i, array in enumerate(blocks):
if not rows:
rows = array.shape[0]
else:
if array.shape[0] != rows:
raise RuntimeError('incompatible block shapes')
self._archive.write_array(Label.FILE_TEMPLATE_BLOCKS.format(i),
array)
elif axis == 0:
# for now, just vertically concat and write, though this has a 2X memory requirement
resolved = concat_resolved(blocks, axis=0)
# if this results in an obect array, an exception will be raised
self._archive.write_array(Label.FILE_TEMPLATE_BLOCKS.format(0),
resolved)
i = 0
else:
raise AxisInvalid(f'invalid axis {axis}')
metadata[Label.KEY_DEPTHS] = [
i + 1, # block count
depth_index,
depth_columns
]
self._archive.write_metadata(metadata)
