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_lazy_arrays.py
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_lazy_arrays.py
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from typing import Tuple
from anndata._core.index import Index, _subset
from anndata._core.views import as_view
from anndata._io.h5ad import read_dataset
from anndata.compat import ZarrArray
import pandas as pd
import numpy as np
from xarray.core.indexing import (
ExplicitlyIndexedNDArrayMixin,
BasicIndexer,
OuterIndexer,
)
import xarray as xr
class MaskedArrayMixIn(ExplicitlyIndexedNDArrayMixin):
def __eq__(self, __o) -> np.ndarray:
return self[...] == __o
def __ne__(self, __o) -> np.ndarray:
return ~(self == __o)
@property
def shape(self) -> Tuple[int, ...]:
"""Shape of this array
Returns:
Tuple[int, ...]: A shape that looks like a 1-d shape i.e., (#, )
"""
return self.values.shape
class LazyCategoricalArray(MaskedArrayMixIn):
__slots__ = (
"values",
"attrs",
"_categories",
"_categories_cache",
"group",
"_drop_unused_cats",
)
def __init__(self, codes, categories, attrs, _drop_unused_cats, *args, **kwargs):
"""Class for lazily reading categorical data from formatted zarr group. Used as base for `LazilyIndexedArray`.
Args:
codes (Union[zarr.Array, h5py.Dataset]): values (integers) of the array, one for each element
categories (Union[zarr.Array, h5py.Dataset]): mappings from values to strings
attrs (Union[zarr.Array, h5py.Dataset]): attrs containing boolean "ordered"
_drop_unused_cats (bool): Whether or not to drop unused categories.
"""
self.values = codes
self._categories = categories
self._categories_cache = None
self.attrs = dict(attrs)
self._drop_unused_cats = _drop_unused_cats # obsm/varm do not drop, but obs and var do. TODO: Should fix in normal AnnData?
@property
def categories(self): # __slots__ and cached_property are incompatible
if self._categories_cache is None:
if isinstance(self._categories, ZarrArray):
self._categories_cache = self._categories[...]
else:
self._categories_cache = read_dataset(self._categories)
return self._categories_cache
@property
def dtype(self) -> pd.CategoricalDtype:
return pd.CategoricalDtype(self.categories, self.ordered)
@property
def ordered(self):
return bool(self.attrs["ordered"])
def __getitem__(self, selection) -> pd.Categorical:
idx = selection
if isinstance(selection, BasicIndexer) or isinstance(selection, OuterIndexer):
idx = selection.tuple[0] # need to better understand this
if isinstance(self.values, ZarrArray):
codes = self.values.oindex[idx]
else:
codes = self.values[idx]
if codes.shape == (): # handle 0d case
codes = np.array([codes])
res = pd.Categorical.from_codes(
codes=codes,
categories=self.categories,
ordered=self.ordered,
)
if self._drop_unused_cats:
return res.remove_unused_categories()
return res
def __repr__(self) -> str:
return f"LazyCategoricalArray(codes=..., categories={self.categories}, ordered={self.ordered})"
def copy(self) -> "LazyCategoricalArray":
"""Returns a copy of this array which can then be safely edited
Returns:
LazyCategoricalArray: copied LazyCategoricalArray
"""
arr = LazyCategoricalArray(
self.values, self._categories, self.attrs
) # self.categories reads in data
return arr
class LazyMaskedArray(MaskedArrayMixIn):
__slots__ = ("mask", "values", "_dtype_str")
def __init__(self, values, mask, dtype_str, *args, **kwargs):
"""Class for lazily reading categorical data from formatted zarr group. Used as base for `LazilyIndexedArray`.
Args:
values (Union[zarr.Array, h5py.Dataset]): Integer/Boolean array of values
mask (Union[zarr.Array, h5py.Dataset]): mask indicating which values are non-null
dtype_str (Nullable): one of `nullable-integer` or `nullable-boolean`
"""
self.values = values
self.mask = mask
self._dtype_str = dtype_str
@property
def dtype(self) -> pd.CategoricalDtype:
if self.mask is not None:
if self._dtype_str == "nullable-integer":
return pd.arrays.IntegerArray
elif self._dtype_str == "nullable-boolean":
return pd.arrays.BooleanArray
return pd.array
def __getitem__(self, selection) -> pd.Categorical:
idx = selection
if isinstance(selection, BasicIndexer) or isinstance(selection, OuterIndexer):
idx = selection.tuple[0] # need to understand this better
if type(idx) == int:
idx = slice(idx, idx + 1)
values = np.array(self.values[idx])
if self.mask is not None:
mask = np.array(self.mask[idx])
if self._dtype_str == "nullable-integer":
return pd.arrays.IntegerArray(values, mask=mask)
elif self._dtype_str == "nullable-boolean":
return pd.arrays.BooleanArray(values, mask=mask)
return pd.array(values)
def __repr__(self) -> str:
if self._dtype_str == "nullable-integer":
return "LazyNullableIntegerArray"
elif self._dtype_str == "nullable-boolean":
return "LazyNullableBooleanArray"
def copy(self) -> "LazyMaskedArray":
"""Returns a copy of this array which can then be safely edited
Returns:
LazyMaskedArray: copied LazyMaskedArray
"""
arr = LazyMaskedArray(self.values, self.mask, self._dtype_str)
return arr
@_subset.register(xr.DataArray)
def _subset_masked(a: xr.DataArray, subset_idx: Index):
return a[subset_idx]
@as_view.register(xr.DataArray)
def _view_pd_boolean_array(a: xr.DataArray, view_args):
return a