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(chore): bring in changes from read_backed pr
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@ilan-gold
ilan-gold committed Jul 30, 2023
1 parent 81b7d58 commit 79c59dd
Showing 1 changed file with 83 additions and 62 deletions.
145 changes: 83 additions & 62 deletions anndata/_core/anndata.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,13 +22,13 @@
from pandas.api.types import infer_dtype, is_string_dtype, is_categorical_dtype
from scipy import sparse
from scipy.sparse import issparse, csr_matrix
from anndata._core.anndata_base import AbstractAnnData

from anndata._warnings import ImplicitModificationWarning
from .raw import Raw
from .index import _normalize_indices, _subset, Index, Index1D, get_vector
from .file_backing import AnnDataFileManager, to_memory
from .access import ElementRef
from .anndata_base import AbstractAnnData
from .aligned_mapping import (
AxisArrays,
AxisArraysView,
Expand Down Expand Up @@ -285,7 +285,7 @@ def __init__(
vidx: Index1D = None,
):
if asview:
if not issubclass(type(X), AnnData):
if not issubclass(type(X), AbstractAnnData):
raise ValueError("`X` has to be an AnnData object.")
self._init_as_view(X, oidx, vidx)
else:
Expand Down Expand Up @@ -364,44 +364,74 @@ def _init_as_view(self, adata_ref: "AnnData", oidx: Index, vidx: Index):
else:
self._raw = None

def _reformat_axes_args_from_X(
self, X, obs, var, uns, obsm, varm, obsp, varp, layers, raw
def _init_as_actual(
self,
X=None,
obs=None,
var=None,
uns=None,
obsm=None,
varm=None,
varp=None,
obsp=None,
raw=None,
layers=None,
dtype=None,
shape=None,
filename=None,
filemode=None,
):
# view attributes
self._is_view = False
self._adata_ref = None
self._oidx = None
self._vidx = None

# ----------------------------------------------------------------------
# various ways of initializing the data
# ----------------------------------------------------------------------

# If X is a data frame, we store its indices for verification
x_indices = []
# init from AnnData
if isinstance(X, AnnData):
if any((obs, var, uns, obsm, varm, obsp, varp)):
raise ValueError(
"If `X` is a dict no further arguments must be provided."

# init from file
if filename is not None:
self.file = AnnDataFileManager(self, filename, filemode)
else:
self.file = AnnDataFileManager(self, None)

# init from AnnData
if issubclass(type(X), AbstractAnnData):
if any((obs, var, uns, obsm, varm, obsp, varp)):
raise ValueError(
"If `X` is a dict no further arguments must be provided."
)
X, obs, var, uns, obsm, varm, obsp, varp, layers, raw = (
X._X,
X.obs,
X.var,
X.uns,
X.obsm,
X.varm,
X.obsp,
X.varp,
X.layers,
X.raw,
)
X, obs, var, uns, obsm, varm, obsp, varp, layers, raw = (
X._X,
X.obs,
X.var,
X.uns,
X.obsm,
X.varm,
X.obsp,
X.varp,
X.layers,
X.raw,
)

# init from DataFrame
elif isinstance(X, pd.DataFrame):
# to verify index matching, we wait until obs and var are DataFrames
if obs is None:
obs = pd.DataFrame(index=X.index)
elif not isinstance(X.index, pd.RangeIndex):
x_indices.append(("obs", "index", X.index))
if var is None:
var = pd.DataFrame(index=X.columns)
elif not isinstance(X.columns, pd.RangeIndex):
x_indices.append(("var", "columns", X.columns))
X = ensure_df_homogeneous(X, "X")
return (X, obs, var, uns, obsm, varm, obsp, varp, layers, raw, x_indices)

def _assign_X(self, X, shape, dtype):
# init from DataFrame
elif isinstance(X, pd.DataFrame):
# to verify index matching, we wait until obs and var are DataFrames
if obs is None:
obs = pd.DataFrame(index=X.index)
elif not isinstance(X.index, pd.RangeIndex):
x_indices.append(("obs", "index", X.index))
if var is None:
var = pd.DataFrame(index=X.columns)
elif not isinstance(X.columns, pd.RangeIndex):
x_indices.append(("var", "columns", X.columns))
X = ensure_df_homogeneous(X, "X")

# ----------------------------------------------------------------------
# actually process the data
# ----------------------------------------------------------------------
Expand Down Expand Up @@ -436,18 +466,9 @@ def _assign_X(self, X, shape, dtype):
X = np.array(X, dtype, copy=False)
# data matrix and shape
self._X = X
else:
self._X = None

def _initialize_indices(self, shape, obs, var):
# ----------------------------------------------------------------------
# actually process the data
# ----------------------------------------------------------------------

# check data type of X
if self._X is not None:
self._n_obs, self._n_vars = self._X.shape
else:
self._X = None
self._n_obs = len([] if obs is None else obs)
self._n_vars = len([] if var is None else var)
# check consistency with shape
Expand All @@ -463,38 +484,34 @@ def _initialize_indices(self, shape, obs, var):
if self._n_vars != shape[1]:
raise ValueError("`shape` is inconsistent with `var`")

# annotations
def _assign_obs(self, obs):
# annotations
self._obs = _gen_dataframe(obs, self._n_obs, ["obs_names", "row_names"])

def _assign_var(self, var):
self._var = _gen_dataframe(var, self._n_vars, ["var_names", "col_names"])

# unstructured annotations
def _assign_uns(self, uns):
# now we can verify if indices match!
for attr_name, x_name, idx in x_indices:
attr = getattr(self, attr_name)
if isinstance(attr.index, pd.RangeIndex):
attr.index = idx
elif not idx.equals(attr.index):
raise ValueError(f"Index of {attr_name} must match {x_name} of X.")

# unstructured annotations
self.uns = uns or OrderedDict()

# TODO: Think about consequences of making obsm a group in hdf
def _assign_obsm(self, obsm):
# TODO: Think about consequences of making obsm a group in hdf
self._obsm = AxisArrays(self, 0, vals=convert_to_dict(obsm))

def _assign_varm(self, varm):
self._varm = AxisArrays(self, 1, vals=convert_to_dict(varm))

def _assign_obsp(self, obsp):
self._obsp = PairwiseArrays(self, 0, vals=convert_to_dict(obsp))

def _assign_varp(self, varp):
self._varp = PairwiseArrays(self, 1, vals=convert_to_dict(varp))

def _run_checks(self):
# Backwards compat for connectivities matrices in uns["neighbors"]
_move_adj_mtx({"uns": self._uns, "obsp": self._obsp})

self._check_dimensions()
self._check_uniqueness()

def _cleanup_raw_and_uns(self, raw, uns):
if self.filename:
assert not isinstance(
raw, Raw
Expand All @@ -511,7 +528,6 @@ def _cleanup_raw_and_uns(self, raw, uns):
# clean up old formats
self._clean_up_old_format(uns)

def _assign_layers(self, layers):
# layers
self._layers = Layers(self, layers)

Expand Down Expand Up @@ -572,6 +588,11 @@ def __eq__(self, other):
"instead compare the desired attributes."
)

@property
def shape(self) -> Tuple[int, int]:
"""Shape of data matrix (:attr:`n_obs`, :attr:`n_vars`)."""
return self.n_obs, self.n_vars

@property
def X(self) -> Optional[Union[np.ndarray, sparse.spmatrix, ArrayView]]:
"""Data matrix of shape :attr:`n_obs` × :attr:`n_vars`."""
Expand Down

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