def _axis_array(self, axis: int) -> tp.Iterator[np.ndarray]:
'''Generator of arrays across an axis
Args:
axis: 0 iterates over columns, 1 iterates over rows
'''
extractor = get_extractor(
self._deepcopy_from_bus,
is_array=True,
memo_active=False,
)
if axis == 1: # iterate over rows
if self._axis == 0: # bus components aligned vertically
for _, component in self._bus.items():
for array in component._blocks.axis_values(axis):
yield extractor(array)
else: # bus components aligned horizontally
raise NotImplementedAxis()
elif axis == 0: # iterate over columns
if self._axis == 1: # bus components aligned horizontally
for _, component in self._bus.items():
for array in component._blocks.axis_values(axis):
yield extractor(array)
else: # bus components aligned horizontally
raise NotImplementedAxis()
else:
raise AxisInvalid(f'no support for axis {axis}')
def _axis_tuple(self, *,
axis: int,
constructor: tp.Optional[tp.Type[tp.NamedTuple]] = None,
) -> tp.Iterator[tp.NamedTuple]:
'''Generator of named tuples across an axis.
Args:
axis: 0 iterates over columns (index axis), 1 iterates over rows (column axis)
'''
if constructor is None:
if axis == 1:
labels = self._columns.values
elif axis == 0:
labels = self._index.values
else:
raise AxisInvalid(f'no support for axis {axis}')
# uses _make method to call with iterable
constructor = get_tuple_constructor(labels) #type: ignore
elif (isinstance(constructor, type) and
issubclass(constructor, tuple) and
hasattr(constructor, '_make')):
constructor = constructor._make #type: ignore
assert constructor is not None
for axis_values in self._axis_array(axis):
yield constructor(axis_values)
def bus_to_hierarchy(
bus: tp.Union[Bus, 'Yarn'],
axis: int,
deepcopy_from_bus: bool,
init_exception_cls: tp.Type[Exception],
) -> tp.Tuple[IndexHierarchy, IndexBase]:
'''
Given a :obj:`Bus` and an axis, derive a :obj:`IndexHierarchy`; also return and validate the :obj:`Index` of the opposite axis.
'''
# NOTE: need to extract just axis labels, not the full Frame; need new Store/Bus loaders just for label data
extractor = get_extractor(deepcopy_from_bus,
is_array=False,
memo_active=False)
def tree_extractor(index: IndexBase) -> tp.Union[IndexBase, TreeNodeT]:
index = extractor(index)
if isinstance(index, IndexHierarchy):
return index.to_tree()
return index
tree: TreeNodeT = {}
opposite: tp.Optional[IndexBase] = None
for label, f in bus.items():
if axis == 0:
tree[label] = tree_extractor(f.index)
if opposite is None:
opposite = extractor(f.columns)
else:
if not opposite.equals(f.columns):
raise init_exception_cls(
'opposite axis must have equivalent indices')
elif axis == 1:
tree[label] = tree_extractor(f.columns)
if opposite is None:
opposite = extractor(f.index)
else:
if not opposite.equals(f.index):
raise init_exception_cls(
'opposite axis must have equivalent indices')
else:
raise AxisInvalid(f'invalid axis {axis}')
# NOTE: we could try to collect index constructors by using the index of the Bus and observing the inidices of the contained Frames, but it is not clear that will be better then using IndexAutoConstructorFactory
return IndexHierarchy.from_tree(
tree, index_constructors=IndexAutoConstructorFactory
), opposite # type: ignore
def values() -> tp.Iterator[Frame]:
nonlocal opposite
for start, end in zip_longest(starts, ends, fillvalue=vector_len):
if axis == 0: # along rows
f = frame.iloc[start:end]
label = label_extractor(f.index) #type: ignore
axis_map_components[label] = f.index
if opposite is None:
opposite = f.columns
elif axis == 1: # along columns
f = frame.iloc[:, start:end]
label = label_extractor(f.columns) #type: ignore
axis_map_components[label] = f.columns
if opposite is None:
opposite = f.index
else:
raise AxisInvalid(f'invalid axis {axis}')
yield f.rename(label)
def apex_to_name(
rows: tp.Sequence[tp.Sequence[tp.Hashable]],
depth_level: tp.Optional[DepthLevelSpecifier],
axis: int, # 0 is by row (for index, 1 is by column (for columns)
axis_depth: int,
) -> NameType:
'''
Utility for translating apex values (the upper left corner created be index/columns) in the appropriate name.
'''
if depth_level is None:
return None
if axis == 0:
if isinstance(depth_level, INT_TYPES):
row = rows[depth_level]
if axis_depth == 1: # return a single label
return row[0] if row[0] != '' else None
else:
return tuple(row)
else: # its a list selection
targets = [rows[level] for level in depth_level]
# combine into tuples
if axis_depth == 1:
return next(zip(*targets))
else:
return tuple(zip(*targets))
elif axis == 1:
if isinstance(depth_level, INT_TYPES):
# depth_level refers to position in inner row
row = [r[depth_level] for r in rows]
if axis_depth == 1: # return a single label
return row[0] if row[0] != '' else None
else:
return tuple(row)
else: # its a list selection
targets = (tuple(row[level] for level in depth_level)
for row in rows) #type: ignore
# combine into tuples
if axis_depth == 1:
return next(targets) #type: ignore
else:
return tuple(targets)
raise AxisInvalid(f'invalid axis: {axis}')
def from_frames(
self,
frames: tp.Iterable['Frame'],
*,
include_index: bool = True,
include_columns: bool = True,
axis: int = 0,
union: bool = True,
name: NameType = None,
fill_value: object = np.nan,
) -> None:
'''Given an iterable of Frames, write out an NPZ or NPY directly, without building up an intermediary Frame. If axis 0, the Frames must be block compatible; if axis 1, the Frames must have the same number of rows. For both axis, if included, concatenated indices must be unique or aligned.
Args:
frames:
*
include_index:
include_columns:
axis:
union:
name:
fill_value:
'''
if not self._writeable:
raise UnsupportedOperation('Open with mode "w" to write.')
from static_frame.core.type_blocks import TypeBlocks
from static_frame.core.frame import Frame
frames = [
f if isinstance(f, Frame) else f.to_frame(axis) for f in frames
] # type: ignore
# NOTE: based on Frame.from_concat
if axis == 1: # stacks columns (extends rows horizontally)
if include_columns:
try:
columns = index_many_concat(
(f._columns for f in frames),
Index,
)
except ErrorInitIndexNonUnique:
raise RuntimeError(
'Column names after horizontal concatenation are not unique; set include_columns to None to ignore.'
)
else:
columns = None
if include_index:
index = index_many_set(
(f._index for f in frames),
Index,
union=union,
)
else:
raise RuntimeError(
'Must include index for horizontal alignment.')
def blocks() -> tp.Iterator[np.ndarray]:
for f in frames:
if len(f.index) != len(index) or (f.index != index).any():
f = f.reindex(index=index, fill_value=fill_value)
for block in f._blocks._blocks:
yield block
elif axis == 0: # stacks rows (extends columns vertically)
if include_index:
try:
index = index_many_concat((f._index for f in frames),
Index)
except ErrorInitIndexNonUnique:
raise RuntimeError(
'Index names after vertical concatenation are not unique; set include_index to None to ignore'
)
else:
index = None
if include_columns:
columns = index_many_set(
(f._columns for f in frames),
Index,
union=union,
)
else:
raise RuntimeError(
'Must include columns for vertical alignment.')
def blocks() -> tp.Iterator[np.ndarray]:
type_blocks = []
previous_f: tp.Optional[Frame] = None
block_compatible = True
reblock_compatible = True
for f in frames:
if len(f.columns) != len(columns) or (f.columns !=
columns).any():
f = f.reindex(columns=columns, fill_value=fill_value)
type_blocks.append(f._blocks)
# column size is all the same by this point
if previous_f is not None: # after the first
if block_compatible:
block_compatible &= f._blocks.block_compatible(
previous_f._blocks,
axis=1) # only compare columns
if reblock_compatible:
reblock_compatible &= f._blocks.reblock_compatible(
previous_f._blocks)
previous_f = f
yield from TypeBlocks.vstack_blocks_to_blocks(
type_blocks=type_blocks,
block_compatible=block_compatible,
reblock_compatible=reblock_compatible,
)
else:
raise AxisInvalid(f'no support for {axis}')
self.from_arrays(
blocks=blocks(),
index=index,
columns=columns,
name=name,
axis=1, # blocks are normalized for horizontal concat
)
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)
