Satisfy ruff check

src/muon/__init__.py

@@ -12,19 +12,31 @@
 
 
 from mudata import MuData
-from mudata._core.io import *
+from mudata._core.io import (
+    read,
+    read_anndata,
+    read_h5ad,
+    read_h5mu,
+    read_zarr,
+    write_anndata,
+    write_h5ad,
+    write_h5mu,
+    write_zarr,
+)
 
+from . import _atac as atac
+from . import _prot as prot
+from . import _rna as rna
+from ._core import plot as pl
 from ._core import preproc as pp
 from ._core import tools as tl
-from ._core import plot as pl
 from ._core import utils
-from ._core.io import *
 from ._core.config import set_options
-
-from . import atac
-from . import prot
+from ._core.io import read_10x_h5, read_10x_mtx
 
 __all__ = [
+    "__version__",
+
     "MuData",
     "pp",
     "tl",
@@ -49,4 +61,5 @@
     "set_options",
     "atac",
     "prot",
-]
+    "rna",
+]

src/muon/_atac/__init__.py

@@ -1,4 +1,6 @@
+from . import plot as pl
 from . import preproc as pp
 from . import tools as tl
-from . import plot as pl
-from .io import *
+from .io import read_10x_h5, read_10x_mtx, read_snap
+
+__all__ = ["pp", "tl", "pl", "read_10x_h5", "read_10x_mtx", "read_snap"]

src/muon/_atac/io.py

@@ -1,5 +1,4 @@
 from os import PathLike
-from typing import Optional
 from warnings import warn
 
 import numpy as np
@@ -22,7 +21,7 @@ def read_10x_mtx(filename: PathLike, atac_only: bool = True, *args, **kwargs) ->
     return adata
 
 
-def read_snap(filename: PathLike, matrix: str, bin_size: Optional[int] = None):
+def read_snap(filename: PathLike, matrix: str, bin_size: int | None = None):
     """
     Read a matrix from a .snap file.
 
@@ -47,8 +46,8 @@ def read_snap(filename: PathLike, matrix: str, bin_size: Optional[int] = None):
             "SnapTools library is not available. Install SnapTools from PyPI (`pip install snaptools`) or from GitHub (`pip install git+https://github.com/r3fang/SnapTools`)"
         )
 
-    from scipy.sparse import csr_matrix
     import h5py
+    from scipy.sparse import csr_matrix
 
     # Allow both PM and pm
     matrix = matrix.lower()

src/muon/_atac/plot.py

@@ -1,28 +1,27 @@
-from collections import defaultdict
-from typing import Union, Optional, List, Iterable, Mapping, Sequence
 import warnings
+from collections import defaultdict
+from collections.abc import Iterable, Mapping, Sequence
 
+import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
-from scipy.sparse import issparse
 import scanpy as sc
+import seaborn as sns
 from anndata import AnnData
-
-import matplotlib.pyplot as plt
 from matplotlib.axes import Axes
-import seaborn as sns
-
 from mudata import MuData
+from scipy.sparse import issparse
+
 from . import tools
 
 
 def _average_peaks(
     adata: AnnData,
-    keys: List[str],
-    average: Optional[str],
+    keys: list[str],
+    average: str | None,
     func: str,
     use_raw: bool,
-    layer: Optional[str],
+    layer: str | None,
 ):
     # Define the function to be used for aggregation
     if average:
@@ -131,13 +130,13 @@ def _average_peaks(
 
 
 def embedding(
-    data: Union[AnnData, MuData],
+    data: AnnData | MuData,
     basis: str,
-    color: Optional[Union[str, List[str]]] = None,
-    average: Optional[str] = "total",
-    func: Optional[str] = "mean",
+    color: str | list[str] | None = None,
+    average: str | None = "total",
+    func: str | None = "mean",
     use_raw: bool = True,
-    layer: Optional[str] = None,
+    layer: str | None = None,
     **kwargs,
 ):
     """
@@ -176,7 +175,7 @@ def embedding(
         return sc.pl.embedding(adata, basis=basis, use_raw=use_raw, layer=layer, **kwargs)
 
 
-def pca(data: Union[AnnData, MuData], **kwargs) -> Union[Axes, List[Axes], None]:
+def pca(data: AnnData | MuData, **kwargs) -> Axes | list[Axes] | None:
     """
     Scatter plot for principle components
 
@@ -185,7 +184,7 @@ def pca(data: Union[AnnData, MuData], **kwargs) -> Union[Axes, List[Axes], None]
     return embedding(data, basis="pca", **kwargs)
 
 
-def lsi(data: Union[AnnData, MuData], **kwargs) -> Union[Axes, List[Axes], None]:
+def lsi(data: AnnData | MuData, **kwargs) -> Axes | list[Axes] | None:
     """
     Scatter plot for latent semantic indexing components
 
@@ -194,7 +193,7 @@ def lsi(data: Union[AnnData, MuData], **kwargs) -> Union[Axes, List[Axes], None]
     return embedding(data, basis="lsi", **kwargs)
 
 
-def umap(data: Union[AnnData, MuData], **kwargs) -> Union[Axes, List[Axes], None]:
+def umap(data: AnnData | MuData, **kwargs) -> Axes | list[Axes] | None:
     """
     Scatter plot in UMAP space
 
@@ -203,7 +202,7 @@ def umap(data: Union[AnnData, MuData], **kwargs) -> Union[Axes, List[Axes], None
     return embedding(data, basis="umap", **kwargs)
 
 
-def mofa(mdata: MuData, **kwargs) -> Union[Axes, List[Axes], None]:
+def mofa(mdata: MuData, **kwargs) -> Axes | list[Axes] | None:
     """
     Scatter plot in MOFA factors coordinates
 
@@ -213,13 +212,13 @@ def mofa(mdata: MuData, **kwargs) -> Union[Axes, List[Axes], None]:
 
 
 def dotplot(
-    data: Union[AnnData, MuData],
-    var_names: Union[str, Sequence[str], Mapping[str, Union[str, Sequence[str]]]],
-    groupby: Optional[Union[str]] = None,
-    average: Optional[str] = "total",
-    func: Optional[str] = "mean",
-    use_raw: Optional[Union[bool]] = None,
-    layer: Optional[str] = None,
+    data: AnnData | MuData,
+    var_names: str | Sequence[str] | Mapping[str, str | Sequence[str]],
+    groupby: str | None = None,
+    average: str | None = "total",
+    func: str | None = "mean",
+    use_raw: bool | None = None,
+    layer: str | None = None,
     **kwargs,
 ):
     """
@@ -257,9 +256,9 @@ def dotplot(
 
 def tss_enrichment(
     data: AnnData,
-    color: Optional[Union[str]] = None,
+    color: str | None = None,
     title: str = "TSS Enrichment",
-    ax: Optional[Axes] = None,
+    ax: Axes | None = None,
 ):
     """
     Plot relative enrichment scores around a TSS.
@@ -314,12 +313,12 @@ def _tss_enrichment_single(data: AnnData, ax: Axes, sd: bool = False, *args, **k
 
 
 def fragment_histogram(
-    data: Union[AnnData, MuData],
+    data: AnnData | MuData,
     region: str = "chr1-1-2000000",
-    groupby: Optional[Union[str]] = None,
-    barcodes: Optional[str] = None,
-    show: Optional[bool] = None,
-    save: Union[str, bool, None] = None,
+    groupby: str | None = None,
+    barcodes: str | None = None,
+    show: bool | None = None,
+    save: str | bool | None = None,
 ):
     """
     Plot Histogram of Fragment lengths within specified region.

src/muon/_atac/preproc.py

@@ -1,28 +1,24 @@
-from typing import Union, Optional
 from warnings import warn
 
 import numpy as np
-from scipy.sparse import csr_matrix, dia_matrix, issparse
-
 from anndata import AnnData
 from mudata import MuData
-
 from scanpy._utils import view_to_actual
-
+from scipy.sparse import csr_matrix, dia_matrix, issparse
 
 # Computational methods for preprocessing
 
 
 def tfidf(
-    data: Union[AnnData, MuData],
+    data: AnnData | MuData,
     log_tf: bool = True,
     log_idf: bool = True,
     log_tfidf: bool = False,
-    scale_factor: Union[int, float] = 1e4,
+    scale_factor: int | float = 1e4,
     inplace: bool = True,
     copy: bool = False,
-    from_layer: Optional[str] = None,
-    to_layer: Optional[str] = None,
+    from_layer: str | None = None,
+    to_layer: str | None = None,
 ):
     """
     Transform peak counts with TF-IDF (Term Frequency - Inverse Document Frequency).
@@ -129,7 +125,7 @@ def tfidf(
         return adata
 
 
-def binarize(data: Union[AnnData, MuData]):
+def binarize(data: AnnData | MuData):
     """
     Transform peak counts to the binary matrix (all the non-zero values become 1).
 
@@ -153,7 +149,7 @@ def binarize(data: Union[AnnData, MuData]):
 
 
 def scopen(
-    data: Union[AnnData, MuData],
+    data: AnnData | MuData,
     n_components: int = 30,
     max_iter: int = 500,
     min_rho: float = 0.0,
@@ -182,6 +178,7 @@ def scopen(
 
     try:
         import time
+
         from scopen.MF import non_negative_factorization
     except ImportError:
         raise ImportError(