from __future__ import annotations
|
|
import copy as cp
|
import itertools
|
from typing import (
|
TYPE_CHECKING,
|
Sequence,
|
cast,
|
)
|
|
import numpy as np
|
|
from pandas._libs import (
|
NaT,
|
internals as libinternals,
|
)
|
from pandas._libs.missing import NA
|
from pandas._typing import (
|
ArrayLike,
|
AxisInt,
|
DtypeObj,
|
Manager,
|
Shape,
|
)
|
from pandas.util._decorators import cache_readonly
|
|
from pandas.core.dtypes.astype import astype_array
|
from pandas.core.dtypes.cast import (
|
ensure_dtype_can_hold_na,
|
find_common_type,
|
)
|
from pandas.core.dtypes.common import (
|
is_1d_only_ea_dtype,
|
is_dtype_equal,
|
is_scalar,
|
needs_i8_conversion,
|
)
|
from pandas.core.dtypes.concat import concat_compat
|
from pandas.core.dtypes.dtypes import (
|
DatetimeTZDtype,
|
ExtensionDtype,
|
)
|
from pandas.core.dtypes.missing import (
|
is_valid_na_for_dtype,
|
isna,
|
isna_all,
|
)
|
|
import pandas.core.algorithms as algos
|
from pandas.core.arrays import (
|
DatetimeArray,
|
ExtensionArray,
|
)
|
from pandas.core.arrays.sparse import SparseDtype
|
from pandas.core.construction import ensure_wrapped_if_datetimelike
|
from pandas.core.internals.array_manager import (
|
ArrayManager,
|
NullArrayProxy,
|
)
|
from pandas.core.internals.blocks import (
|
ensure_block_shape,
|
new_block_2d,
|
)
|
from pandas.core.internals.managers import BlockManager
|
|
if TYPE_CHECKING:
|
from pandas import Index
|
from pandas.core.internals.blocks import Block
|
|
|
def _concatenate_array_managers(
|
mgrs_indexers, axes: list[Index], concat_axis: AxisInt, copy: bool
|
) -> Manager:
|
"""
|
Concatenate array managers into one.
|
|
Parameters
|
----------
|
mgrs_indexers : list of (ArrayManager, {axis: indexer,...}) tuples
|
axes : list of Index
|
concat_axis : int
|
copy : bool
|
|
Returns
|
-------
|
ArrayManager
|
"""
|
# reindex all arrays
|
mgrs = []
|
for mgr, indexers in mgrs_indexers:
|
axis1_made_copy = False
|
for ax, indexer in indexers.items():
|
mgr = mgr.reindex_indexer(
|
axes[ax], indexer, axis=ax, allow_dups=True, use_na_proxy=True
|
)
|
if ax == 1 and indexer is not None:
|
axis1_made_copy = True
|
if copy and concat_axis == 0 and not axis1_made_copy:
|
# for concat_axis 1 we will always get a copy through concat_arrays
|
mgr = mgr.copy()
|
mgrs.append(mgr)
|
|
if concat_axis == 1:
|
# concatting along the rows -> concat the reindexed arrays
|
# TODO(ArrayManager) doesn't yet preserve the correct dtype
|
arrays = [
|
concat_arrays([mgrs[i].arrays[j] for i in range(len(mgrs))])
|
for j in range(len(mgrs[0].arrays))
|
]
|
else:
|
# concatting along the columns -> combine reindexed arrays in a single manager
|
assert concat_axis == 0
|
arrays = list(itertools.chain.from_iterable([mgr.arrays for mgr in mgrs]))
|
|
new_mgr = ArrayManager(arrays, [axes[1], axes[0]], verify_integrity=False)
|
return new_mgr
|
|
|
def concat_arrays(to_concat: list) -> ArrayLike:
|
"""
|
Alternative for concat_compat but specialized for use in the ArrayManager.
|
|
Differences: only deals with 1D arrays (no axis keyword), assumes
|
ensure_wrapped_if_datetimelike and does not skip empty arrays to determine
|
the dtype.
|
In addition ensures that all NullArrayProxies get replaced with actual
|
arrays.
|
|
Parameters
|
----------
|
to_concat : list of arrays
|
|
Returns
|
-------
|
np.ndarray or ExtensionArray
|
"""
|
# ignore the all-NA proxies to determine the resulting dtype
|
to_concat_no_proxy = [x for x in to_concat if not isinstance(x, NullArrayProxy)]
|
|
dtypes = {x.dtype for x in to_concat_no_proxy}
|
single_dtype = len(dtypes) == 1
|
|
if single_dtype:
|
target_dtype = to_concat_no_proxy[0].dtype
|
elif all(x.kind in ["i", "u", "b"] and isinstance(x, np.dtype) for x in dtypes):
|
# GH#42092
|
target_dtype = np.find_common_type(list(dtypes), [])
|
else:
|
target_dtype = find_common_type([arr.dtype for arr in to_concat_no_proxy])
|
|
to_concat = [
|
arr.to_array(target_dtype)
|
if isinstance(arr, NullArrayProxy)
|
else astype_array(arr, target_dtype, copy=False)
|
for arr in to_concat
|
]
|
|
if isinstance(to_concat[0], ExtensionArray):
|
cls = type(to_concat[0])
|
return cls._concat_same_type(to_concat)
|
|
result = np.concatenate(to_concat)
|
|
# TODO decide on exact behaviour (we shouldn't do this only for empty result)
|
# see https://github.com/pandas-dev/pandas/issues/39817
|
if len(result) == 0:
|
# all empties -> check for bool to not coerce to float
|
kinds = {obj.dtype.kind for obj in to_concat_no_proxy}
|
if len(kinds) != 1:
|
if "b" in kinds:
|
result = result.astype(object)
|
return result
|
|
|
def concatenate_managers(
|
mgrs_indexers, axes: list[Index], concat_axis: AxisInt, copy: bool
|
) -> Manager:
|
"""
|
Concatenate block managers into one.
|
|
Parameters
|
----------
|
mgrs_indexers : list of (BlockManager, {axis: indexer,...}) tuples
|
axes : list of Index
|
concat_axis : int
|
copy : bool
|
|
Returns
|
-------
|
BlockManager
|
"""
|
# TODO(ArrayManager) this assumes that all managers are of the same type
|
if isinstance(mgrs_indexers[0][0], ArrayManager):
|
return _concatenate_array_managers(mgrs_indexers, axes, concat_axis, copy)
|
|
# Assertions disabled for performance
|
# for tup in mgrs_indexers:
|
# # caller is responsible for ensuring this
|
# indexers = tup[1]
|
# assert concat_axis not in indexers
|
|
if concat_axis == 0:
|
return _concat_managers_axis0(mgrs_indexers, axes, copy)
|
|
mgrs_indexers = _maybe_reindex_columns_na_proxy(axes, mgrs_indexers)
|
|
concat_plans = [
|
_get_mgr_concatenation_plan(mgr, indexers) for mgr, indexers in mgrs_indexers
|
]
|
concat_plan = _combine_concat_plans(concat_plans)
|
blocks = []
|
|
for placement, join_units in concat_plan:
|
unit = join_units[0]
|
blk = unit.block
|
|
if len(join_units) == 1 and not join_units[0].indexers:
|
values = blk.values
|
if copy:
|
values = values.copy()
|
else:
|
values = values.view()
|
fastpath = True
|
elif _is_uniform_join_units(join_units):
|
vals = [ju.block.values for ju in join_units]
|
|
if not blk.is_extension:
|
# _is_uniform_join_units ensures a single dtype, so
|
# we can use np.concatenate, which is more performant
|
# than concat_compat
|
values = np.concatenate(vals, axis=1)
|
else:
|
# TODO(EA2D): special-casing not needed with 2D EAs
|
values = concat_compat(vals, axis=1)
|
values = ensure_block_shape(values, ndim=2)
|
|
values = ensure_wrapped_if_datetimelike(values)
|
|
fastpath = blk.values.dtype == values.dtype
|
else:
|
values = _concatenate_join_units(join_units, copy=copy)
|
fastpath = False
|
|
if fastpath:
|
b = blk.make_block_same_class(values, placement=placement)
|
else:
|
b = new_block_2d(values, placement=placement)
|
|
blocks.append(b)
|
|
return BlockManager(tuple(blocks), axes)
|
|
|
def _concat_managers_axis0(
|
mgrs_indexers, axes: list[Index], copy: bool
|
) -> BlockManager:
|
"""
|
concat_managers specialized to concat_axis=0, with reindexing already
|
having been done in _maybe_reindex_columns_na_proxy.
|
"""
|
had_reindexers = {
|
i: len(mgrs_indexers[i][1]) > 0 for i in range(len(mgrs_indexers))
|
}
|
mgrs_indexers = _maybe_reindex_columns_na_proxy(axes, mgrs_indexers)
|
|
mgrs = [x[0] for x in mgrs_indexers]
|
|
offset = 0
|
blocks = []
|
for i, mgr in enumerate(mgrs):
|
# If we already reindexed, then we definitely don't need another copy
|
made_copy = had_reindexers[i]
|
|
for blk in mgr.blocks:
|
if made_copy:
|
nb = blk.copy(deep=False)
|
elif copy:
|
nb = blk.copy()
|
else:
|
# by slicing instead of copy(deep=False), we get a new array
|
# object, see test_concat_copy
|
nb = blk.getitem_block(slice(None))
|
nb._mgr_locs = nb._mgr_locs.add(offset)
|
blocks.append(nb)
|
|
offset += len(mgr.items)
|
|
result = BlockManager(tuple(blocks), axes)
|
return result
|
|
|
def _maybe_reindex_columns_na_proxy(
|
axes: list[Index], mgrs_indexers: list[tuple[BlockManager, dict[int, np.ndarray]]]
|
) -> list[tuple[BlockManager, dict[int, np.ndarray]]]:
|
"""
|
Reindex along columns so that all of the BlockManagers being concatenated
|
have matching columns.
|
|
Columns added in this reindexing have dtype=np.void, indicating they
|
should be ignored when choosing a column's final dtype.
|
"""
|
new_mgrs_indexers: list[tuple[BlockManager, dict[int, np.ndarray]]] = []
|
|
for mgr, indexers in mgrs_indexers:
|
# For axis=0 (i.e. columns) we use_na_proxy and only_slice, so this
|
# is a cheap reindexing.
|
for i, indexer in indexers.items():
|
mgr = mgr.reindex_indexer(
|
axes[i],
|
indexers[i],
|
axis=i,
|
copy=False,
|
only_slice=True, # only relevant for i==0
|
allow_dups=True,
|
use_na_proxy=True, # only relevant for i==0
|
)
|
new_mgrs_indexers.append((mgr, {}))
|
return new_mgrs_indexers
|
|
|
def _get_mgr_concatenation_plan(mgr: BlockManager, indexers: dict[int, np.ndarray]):
|
"""
|
Construct concatenation plan for given block manager and indexers.
|
|
Parameters
|
----------
|
mgr : BlockManager
|
indexers : dict of {axis: indexer}
|
|
Returns
|
-------
|
plan : list of (BlockPlacement, JoinUnit) tuples
|
|
"""
|
assert len(indexers) == 0
|
|
# Calculate post-reindex shape, save for item axis which will be separate
|
# for each block anyway.
|
mgr_shape_list = list(mgr.shape)
|
for ax, indexer in indexers.items():
|
mgr_shape_list[ax] = len(indexer)
|
mgr_shape = tuple(mgr_shape_list)
|
|
assert 0 not in indexers
|
|
if mgr.is_single_block:
|
blk = mgr.blocks[0]
|
return [(blk.mgr_locs, JoinUnit(blk, mgr_shape, indexers))]
|
|
blknos = mgr.blknos
|
blklocs = mgr.blklocs
|
|
plan = []
|
for blkno, placements in libinternals.get_blkno_placements(blknos, group=False):
|
assert placements.is_slice_like
|
assert blkno != -1
|
|
join_unit_indexers = indexers.copy()
|
|
shape_list = list(mgr_shape)
|
shape_list[0] = len(placements)
|
shape = tuple(shape_list)
|
|
blk = mgr.blocks[blkno]
|
ax0_blk_indexer = blklocs[placements.indexer]
|
|
unit_no_ax0_reindexing = (
|
len(placements) == len(blk.mgr_locs)
|
and
|
# Fastpath detection of join unit not
|
# needing to reindex its block: no ax0
|
# reindexing took place and block
|
# placement was sequential before.
|
(
|
(blk.mgr_locs.is_slice_like and blk.mgr_locs.as_slice.step == 1)
|
or
|
# Slow-ish detection: all indexer locs
|
# are sequential (and length match is
|
# checked above).
|
(np.diff(ax0_blk_indexer) == 1).all()
|
)
|
)
|
|
# Omit indexer if no item reindexing is required.
|
if unit_no_ax0_reindexing:
|
join_unit_indexers.pop(0, None)
|
else:
|
join_unit_indexers[0] = ax0_blk_indexer
|
|
unit = JoinUnit(blk, shape, join_unit_indexers)
|
|
plan.append((placements, unit))
|
|
return plan
|
|
|
class JoinUnit:
|
def __init__(self, block: Block, shape: Shape, indexers=None) -> None:
|
# Passing shape explicitly is required for cases when block is None.
|
# Note: block is None implies indexers is None, but not vice-versa
|
if indexers is None:
|
indexers = {}
|
self.block = block
|
self.indexers = indexers
|
self.shape = shape
|
|
def __repr__(self) -> str:
|
return f"{type(self).__name__}({repr(self.block)}, {self.indexers})"
|
|
@cache_readonly
|
def needs_filling(self) -> bool:
|
for indexer in self.indexers.values():
|
# FIXME: cache results of indexer == -1 checks.
|
if (indexer == -1).any():
|
return True
|
|
return False
|
|
@cache_readonly
|
def dtype(self) -> DtypeObj:
|
blk = self.block
|
if blk.values.dtype.kind == "V":
|
raise AssertionError("Block is None, no dtype")
|
|
if not self.needs_filling:
|
return blk.dtype
|
return ensure_dtype_can_hold_na(blk.dtype)
|
|
def _is_valid_na_for(self, dtype: DtypeObj) -> bool:
|
"""
|
Check that we are all-NA of a type/dtype that is compatible with this dtype.
|
Augments `self.is_na` with an additional check of the type of NA values.
|
"""
|
if not self.is_na:
|
return False
|
if self.block.dtype.kind == "V":
|
return True
|
|
if self.dtype == object:
|
values = self.block.values
|
return all(is_valid_na_for_dtype(x, dtype) for x in values.ravel(order="K"))
|
|
na_value = self.block.fill_value
|
if na_value is NaT and not is_dtype_equal(self.dtype, dtype):
|
# e.g. we are dt64 and other is td64
|
# fill_values match but we should not cast self.block.values to dtype
|
# TODO: this will need updating if we ever have non-nano dt64/td64
|
return False
|
|
if na_value is NA and needs_i8_conversion(dtype):
|
# FIXME: kludge; test_append_empty_frame_with_timedelta64ns_nat
|
# e.g. self.dtype == "Int64" and dtype is td64, we dont want
|
# to consider these as matching
|
return False
|
|
# TODO: better to use can_hold_element?
|
return is_valid_na_for_dtype(na_value, dtype)
|
|
@cache_readonly
|
def is_na(self) -> bool:
|
blk = self.block
|
if blk.dtype.kind == "V":
|
return True
|
|
if not blk._can_hold_na:
|
return False
|
|
values = blk.values
|
if values.size == 0:
|
return True
|
if isinstance(values.dtype, SparseDtype):
|
return False
|
|
if values.ndim == 1:
|
# TODO(EA2D): no need for special case with 2D EAs
|
val = values[0]
|
if not is_scalar(val) or not isna(val):
|
# ideally isna_all would do this short-circuiting
|
return False
|
return isna_all(values)
|
else:
|
val = values[0][0]
|
if not is_scalar(val) or not isna(val):
|
# ideally isna_all would do this short-circuiting
|
return False
|
return all(isna_all(row) for row in values)
|
|
def get_reindexed_values(self, empty_dtype: DtypeObj, upcasted_na) -> ArrayLike:
|
values: ArrayLike
|
|
if upcasted_na is None and self.block.dtype.kind != "V":
|
# No upcasting is necessary
|
fill_value = self.block.fill_value
|
values = self.block.values
|
else:
|
fill_value = upcasted_na
|
|
if self._is_valid_na_for(empty_dtype):
|
# note: always holds when self.block.dtype.kind == "V"
|
blk_dtype = self.block.dtype
|
|
if blk_dtype == np.dtype("object"):
|
# we want to avoid filling with np.nan if we are
|
# using None; we already know that we are all
|
# nulls
|
values = self.block.values.ravel(order="K")
|
if len(values) and values[0] is None:
|
fill_value = None
|
|
if isinstance(empty_dtype, DatetimeTZDtype):
|
# NB: exclude e.g. pyarrow[dt64tz] dtypes
|
i8values = np.full(self.shape, fill_value._value)
|
return DatetimeArray(i8values, dtype=empty_dtype)
|
|
elif is_1d_only_ea_dtype(empty_dtype):
|
if is_dtype_equal(blk_dtype, empty_dtype) and self.indexers:
|
# avoid creating new empty array if we already have an array
|
# with correct dtype that can be reindexed
|
pass
|
else:
|
empty_dtype = cast(ExtensionDtype, empty_dtype)
|
cls = empty_dtype.construct_array_type()
|
|
missing_arr = cls._from_sequence([], dtype=empty_dtype)
|
ncols, nrows = self.shape
|
assert ncols == 1, ncols
|
empty_arr = -1 * np.ones((nrows,), dtype=np.intp)
|
return missing_arr.take(
|
empty_arr, allow_fill=True, fill_value=fill_value
|
)
|
elif isinstance(empty_dtype, ExtensionDtype):
|
# TODO: no tests get here, a handful would if we disabled
|
# the dt64tz special-case above (which is faster)
|
cls = empty_dtype.construct_array_type()
|
missing_arr = cls._empty(shape=self.shape, dtype=empty_dtype)
|
missing_arr[:] = fill_value
|
return missing_arr
|
else:
|
# NB: we should never get here with empty_dtype integer or bool;
|
# if we did, the missing_arr.fill would cast to gibberish
|
missing_arr = np.empty(self.shape, dtype=empty_dtype)
|
missing_arr.fill(fill_value)
|
return missing_arr
|
|
if (not self.indexers) and (not self.block._can_consolidate):
|
# preserve these for validation in concat_compat
|
return self.block.values
|
|
if self.block.is_bool:
|
# External code requested filling/upcasting, bool values must
|
# be upcasted to object to avoid being upcasted to numeric.
|
values = self.block.astype(np.dtype("object")).values
|
else:
|
# No dtype upcasting is done here, it will be performed during
|
# concatenation itself.
|
values = self.block.values
|
|
if not self.indexers:
|
# If there's no indexing to be done, we want to signal outside
|
# code that this array must be copied explicitly. This is done
|
# by returning a view and checking `retval.base`.
|
values = values.view()
|
|
else:
|
for ax, indexer in self.indexers.items():
|
values = algos.take_nd(values, indexer, axis=ax)
|
|
return values
|
|
|
def _concatenate_join_units(join_units: list[JoinUnit], copy: bool) -> ArrayLike:
|
"""
|
Concatenate values from several join units along axis=1.
|
"""
|
empty_dtype = _get_empty_dtype(join_units)
|
|
has_none_blocks = any(unit.block.dtype.kind == "V" for unit in join_units)
|
upcasted_na = _dtype_to_na_value(empty_dtype, has_none_blocks)
|
|
to_concat = [
|
ju.get_reindexed_values(empty_dtype=empty_dtype, upcasted_na=upcasted_na)
|
for ju in join_units
|
]
|
|
if len(to_concat) == 1:
|
# Only one block, nothing to concatenate.
|
concat_values = to_concat[0]
|
if copy:
|
if isinstance(concat_values, np.ndarray):
|
# non-reindexed (=not yet copied) arrays are made into a view
|
# in JoinUnit.get_reindexed_values
|
if concat_values.base is not None:
|
concat_values = concat_values.copy()
|
else:
|
concat_values = concat_values.copy()
|
|
elif any(is_1d_only_ea_dtype(t.dtype) for t in to_concat):
|
# TODO(EA2D): special case not needed if all EAs used HybridBlocks
|
|
# error: No overload variant of "__getitem__" of "ExtensionArray" matches
|
# argument type "Tuple[int, slice]"
|
to_concat = [
|
t
|
if is_1d_only_ea_dtype(t.dtype)
|
else t[0, :] # type: ignore[call-overload]
|
for t in to_concat
|
]
|
concat_values = concat_compat(to_concat, axis=0, ea_compat_axis=True)
|
concat_values = ensure_block_shape(concat_values, 2)
|
|
else:
|
concat_values = concat_compat(to_concat, axis=1)
|
|
return concat_values
|
|
|
def _dtype_to_na_value(dtype: DtypeObj, has_none_blocks: bool):
|
"""
|
Find the NA value to go with this dtype.
|
"""
|
if isinstance(dtype, ExtensionDtype):
|
return dtype.na_value
|
elif dtype.kind in ["m", "M"]:
|
return dtype.type("NaT")
|
elif dtype.kind in ["f", "c"]:
|
return dtype.type("NaN")
|
elif dtype.kind == "b":
|
# different from missing.na_value_for_dtype
|
return None
|
elif dtype.kind in ["i", "u"]:
|
if not has_none_blocks:
|
# different from missing.na_value_for_dtype
|
return None
|
return np.nan
|
elif dtype.kind == "O":
|
return np.nan
|
raise NotImplementedError
|
|
|
def _get_empty_dtype(join_units: Sequence[JoinUnit]) -> DtypeObj:
|
"""
|
Return dtype and N/A values to use when concatenating specified units.
|
|
Returned N/A value may be None which means there was no casting involved.
|
|
Returns
|
-------
|
dtype
|
"""
|
if len(join_units) == 1:
|
blk = join_units[0].block
|
return blk.dtype
|
|
if _is_uniform_reindex(join_units):
|
empty_dtype = join_units[0].block.dtype
|
return empty_dtype
|
|
has_none_blocks = any(unit.block.dtype.kind == "V" for unit in join_units)
|
|
dtypes = [unit.dtype for unit in join_units if not unit.is_na]
|
if not len(dtypes):
|
dtypes = [unit.dtype for unit in join_units if unit.block.dtype.kind != "V"]
|
|
dtype = find_common_type(dtypes)
|
if has_none_blocks:
|
dtype = ensure_dtype_can_hold_na(dtype)
|
return dtype
|
|
|
def _is_uniform_join_units(join_units: list[JoinUnit]) -> bool:
|
"""
|
Check if the join units consist of blocks of uniform type that can
|
be concatenated using Block.concat_same_type instead of the generic
|
_concatenate_join_units (which uses `concat_compat`).
|
|
"""
|
first = join_units[0].block
|
if first.dtype.kind == "V":
|
return False
|
return (
|
# exclude cases where a) ju.block is None or b) we have e.g. Int64+int64
|
all(type(ju.block) is type(first) for ju in join_units)
|
and
|
# e.g. DatetimeLikeBlock can be dt64 or td64, but these are not uniform
|
all(
|
is_dtype_equal(ju.block.dtype, first.dtype)
|
# GH#42092 we only want the dtype_equal check for non-numeric blocks
|
# (for now, may change but that would need a deprecation)
|
or ju.block.dtype.kind in ["b", "i", "u"]
|
for ju in join_units
|
)
|
and
|
# no blocks that would get missing values (can lead to type upcasts)
|
# unless we're an extension dtype.
|
all(not ju.is_na or ju.block.is_extension for ju in join_units)
|
and
|
# no blocks with indexers (as then the dimensions do not fit)
|
all(not ju.indexers for ju in join_units)
|
and
|
# only use this path when there is something to concatenate
|
len(join_units) > 1
|
)
|
|
|
def _is_uniform_reindex(join_units) -> bool:
|
return (
|
# TODO: should this be ju.block._can_hold_na?
|
all(ju.block.is_extension for ju in join_units)
|
and len({ju.block.dtype.name for ju in join_units}) == 1
|
)
|
|
|
def _trim_join_unit(join_unit: JoinUnit, length: int) -> JoinUnit:
|
"""
|
Reduce join_unit's shape along item axis to length.
|
|
Extra items that didn't fit are returned as a separate block.
|
"""
|
if 0 not in join_unit.indexers:
|
extra_indexers = join_unit.indexers
|
|
if join_unit.block is None:
|
extra_block = None
|
else:
|
extra_block = join_unit.block.getitem_block(slice(length, None))
|
join_unit.block = join_unit.block.getitem_block(slice(length))
|
else:
|
extra_block = join_unit.block
|
|
extra_indexers = cp.copy(join_unit.indexers)
|
extra_indexers[0] = extra_indexers[0][length:]
|
join_unit.indexers[0] = join_unit.indexers[0][:length]
|
|
extra_shape = (join_unit.shape[0] - length,) + join_unit.shape[1:]
|
join_unit.shape = (length,) + join_unit.shape[1:]
|
|
return JoinUnit(block=extra_block, indexers=extra_indexers, shape=extra_shape)
|
|
|
def _combine_concat_plans(plans):
|
"""
|
Combine multiple concatenation plans into one.
|
|
existing_plan is updated in-place.
|
|
We only get here with concat_axis == 1.
|
"""
|
if len(plans) == 1:
|
for p in plans[0]:
|
yield p[0], [p[1]]
|
|
else:
|
# singleton list so we can modify it as a side-effect within _next_or_none
|
num_ended = [0]
|
|
def _next_or_none(seq):
|
retval = next(seq, None)
|
if retval is None:
|
num_ended[0] += 1
|
return retval
|
|
plans = list(map(iter, plans))
|
next_items = list(map(_next_or_none, plans))
|
|
while num_ended[0] != len(next_items):
|
if num_ended[0] > 0:
|
raise ValueError("Plan shapes are not aligned")
|
|
placements, units = zip(*next_items)
|
|
lengths = list(map(len, placements))
|
min_len, max_len = min(lengths), max(lengths)
|
|
if min_len == max_len:
|
yield placements[0], units
|
next_items[:] = map(_next_or_none, plans)
|
else:
|
yielded_placement = None
|
yielded_units = [None] * len(next_items)
|
for i, (plc, unit) in enumerate(next_items):
|
yielded_units[i] = unit
|
if len(plc) > min_len:
|
# _trim_join_unit updates unit in place, so only
|
# placement needs to be sliced to skip min_len.
|
next_items[i] = (plc[min_len:], _trim_join_unit(unit, min_len))
|
else:
|
yielded_placement = plc
|
next_items[i] = _next_or_none(plans[i])
|
|
yield yielded_placement, yielded_units
|
