def sqrt(data, copy=False, chunked=False, chunk_size=None):
"""Square root the data matrix.
Computes `X = sqrt(X)`.
Parameters
----------
data : :class:`~scanpy.api.AnnData`, `np.ndarray`, `sp.sparse`
The (annotated) data matrix of shape `n_obs` × `n_vars`. Rows correspond
to cells and columns to genes.
copy : `bool`, optional (default: `False`)
If an :class:`~scanpy.api.AnnData` is passed, determines whether a copy
is returned.
Returns
-------
Returns or updates `data`, depending on `copy`.
"""
if isinstance(data, AnnData):
adata = data.copy() if copy else data
if chunked:
for chunk, start, end in adata.chunked_X(chunk_size):
adata.X[start:end] = sqrt(chunk)
else:
adata.X = sqrt(data.X)
return adata if copy else None
X = data # proceed with data matrix
if not issparse(X):
return np.sqrt(X)
else:
return X.sqrt()
def _scale(X, zero_center=True):
# - using sklearn.StandardScaler throws an error related to
# int to long trafo for very large matrices
# - using X.multiply is slower
# the result differs very slightly, why?
if True:
mean, var = _get_mean_var(X)
scale = np.sqrt(var)
if issparse(X):
if zero_center: raise ValueError('Cannot zero-center sparse matrix.')
sparsefuncs.inplace_column_scale(X, 1/scale)
else:
X -= mean
X /= scale
else:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler(with_mean=zero_center, copy=False).partial_fit(X)
# user R convention (unbiased estimator)
scaler.scale_ *= np.sqrt(X.shape[0]/(X.shape[0]-1))
scaler.transform(X)
def sqrt(
data: AnnData,
copy: bool = False,
chunked: bool = False,
chunk_size: Optional[int] = None,
):
"""Square root the data matrix.
Computes :math:`X = \\sqrt(X)`.
Parameters
----------
data
The (annotated) data matrix of shape ``n_obs`` × ``n_vars``.
Rows correspond to cells and columns to genes.
copy
If an :class:`~scanpy.api.AnnData` is passed,
determines whether a copy is returned.
chunked
Process the data matrix in chunks, which will save memory.
Applies only to :class:`~anndata.AnnData`.
chunk_size
``n_obs`` of the chunks to process the data in.
Returns
-------
AnnData, None
Returns or updates `data`, depending on `copy`.
"""
if isinstance(data, AnnData):
adata = data.copy() if copy else data
if chunked:
for chunk, start, end in adata.chunked_X(chunk_size):
adata.X[start:end] = sqrt(chunk)
else:
adata.X = sqrt(data.X)
return adata if copy else None
X = data # proceed with data matrix
if not issparse(X):
return np.sqrt(X)
else:
return X.sqrt()
