Add SIM checks (#3258)

pyproject.toml

@@ -237,6 +237,7 @@ select = [
     "PLR0917", # Ban APIs with too many positional parameters
     "FBT",     # No positional boolean parameters
     "PT",      # Pytest style
+    "SIM",     # Simplify control flow
 ]
 ignore = [
     # line too long -> we accept long comment lines; black gets rid of long code lines

src/scanpy/_settings.py

@@ -371,7 +371,7 @@ def logpath(self) -> Path | None:
     def logpath(self, logpath: Path | str | None):
         _type_check(logpath, "logfile", (str, Path))
         # set via “file object” branch of logfile.setter
-        self.logfile = Path(logpath).open("a")
+        self.logfile = Path(logpath).open("a")  # noqa: SIM115
         self._logpath = Path(logpath)
 
     @property
@@ -519,7 +519,7 @@ def __str__(self) -> str:
         return "\n".join(
             f"{k} = {v!r}"
             for k, v in inspect.getmembers(self)
-            if not k.startswith("_") and not k == "getdoc"
+            if not k.startswith("_") and k != "getdoc"
         )
 
 

src/scanpy/_utils/__init__.py

@@ -13,7 +13,7 @@
 import sys
 import warnings
 from collections import namedtuple
-from contextlib import contextmanager
+from contextlib import contextmanager, suppress
 from enum import Enum
 from functools import partial, singledispatch, wraps
 from operator import mul, truediv
@@ -281,10 +281,8 @@ def get_igraph_from_adjacency(adjacency, directed=None):
     g = ig.Graph(directed=directed)
     g.add_vertices(adjacency.shape[0])  # this adds adjacency.shape[0] vertices
     g.add_edges(list(zip(sources, targets)))
-    try:
+    with suppress(KeyError):
         g.es["weight"] = weights
-    except KeyError:
-        pass
     if g.vcount() != adjacency.shape[0]:
         logg.warning(
             f"The constructed graph has only {g.vcount()} nodes. "
@@ -613,11 +611,10 @@ def _(
     out: sparse.csr_matrix | sparse.csc_matrix | None = None,
 ) -> sparse.csr_matrix | sparse.csc_matrix:
     check_op(op)
-    if out is not None:
-        if X.data is not out.data:
-            raise ValueError(
-                "`out` argument provided but not equal to X.  This behavior is not supported for sparse matrix scaling."
-            )
+    if out is not None and X.data is not out.data:
+        raise ValueError(
+            "`out` argument provided but not equal to X.  This behavior is not supported for sparse matrix scaling."
+        )
     if not allow_divide_by_zero and op is truediv:
         scaling_array = scaling_array.copy() + (scaling_array == 0)
 
@@ -684,7 +681,7 @@ def _(
     column_scale = axis == 1
 
     if isinstance(scaling_array, DaskArray):
-        if (row_scale and not X.chunksize[0] == scaling_array.chunksize[0]) or (
+        if (row_scale and X.chunksize[0] != scaling_array.chunksize[0]) or (
             column_scale
             and (
                 (

src/scanpy/_utils/compute/is_constant.py

@@ -121,10 +121,7 @@ def _is_constant_csr_rows(
     for i in range(n):
         start = indptr[i]
         stop = indptr[i + 1]
-        if stop - start == shape[1]:
-            val = data[start]
-        else:
-            val = 0
+        val = data[start] if stop - start == shape[1] else 0
         for j in range(start, stop):
             if data[j] != val:
                 result[i] = False

src/scanpy/datasets/_datasets.py

@@ -219,7 +219,7 @@ def moignard15() -> AnnData:
     }
     # annotate each observation/cell
     adata.obs["exp_groups"] = [
-        next(gname for gname in groups.keys() if sname.startswith(gname))
+        next(gname for gname in groups if sname.startswith(gname))
         for sname in adata.obs_names
     ]
     # fix the order and colors of names in "groups"

src/scanpy/datasets/_ebi_expression_atlas.py

@@ -65,10 +65,7 @@ def read_mtx_from_stream(stream: BinaryIO) -> sparse.csr_matrix:
     n, m, _ = (int(x) for x in curline[:-1].split(b" "))
 
     max_int32 = np.iinfo(np.int32).max
-    if n > max_int32 or m > max_int32:
-        coord_dtype = np.int64
-    else:
-        coord_dtype = np.int32
+    coord_dtype = np.int64 if n > max_int32 or m > max_int32 else np.int32
 
     data = pd.read_csv(
         stream,

src/scanpy/external/pl.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import contextlib
 from typing import TYPE_CHECKING
 
 import matplotlib.pyplot as plt
@@ -214,10 +215,8 @@ def sam(
         return axes
 
     if isinstance(c, str):
-        try:
+        with contextlib.suppress(KeyError):
             c = np.array(list(adata.obs[c]))
-        except KeyError:
-            pass
 
     if isinstance(c[0], (str, np.str_)) and isinstance(c, (np.ndarray, list)):
         import samalg.utilities as ut

src/scanpy/get/get.py

@@ -121,10 +121,7 @@ def _check_indices(
     use_raw: bool = False,
 ) -> tuple[list[str], list[str], list[str]]:
     """Common logic for checking indices for obs_df and var_df."""
-    if use_raw:
-        alt_repr = "adata.raw"
-    else:
-        alt_repr = "adata"
+    alt_repr = "adata.raw" if use_raw else "adata"
 
     alt_dim = ("obs", "var")[dim == "obs"]
 
@@ -288,10 +285,7 @@ def obs_df(
         var = adata.raw.var
     else:
         var = adata.var
-    if gene_symbols is not None:
-        alias_index = pd.Index(var[gene_symbols])
-    else:
-        alias_index = None
+    alias_index = pd.Index(var[gene_symbols]) if gene_symbols is not None else None
 
     obs_cols, var_idx_keys, var_symbols = _check_indices(
         adata.obs,

src/scanpy/neighbors/__init__.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import contextlib
 from collections.abc import Mapping
 from textwrap import indent
 from types import MappingProxyType
@@ -469,10 +470,7 @@ def transitions(self) -> np.ndarray | csr_matrix:
         -----
         This has not been tested, in contrast to `transitions_sym`.
         """
-        if issparse(self.Z):
-            Zinv = self.Z.power(-1)
-        else:
-            Zinv = np.diag(1.0 / np.diag(self.Z))
+        Zinv = self.Z.power(-1) if issparse(self.Z) else np.diag(1.0 / np.diag(self.Z))
         return self.Z @ self.transitions_sym @ Zinv
 
     @property
@@ -591,10 +589,9 @@ def compute_neighbors(
 
             if isinstance(index, NNDescent):
                 # very cautious here
-                try:
+                # TODO catch the correct exception
+                with contextlib.suppress(Exception):
                     self._rp_forest = _make_forest_dict(index)
-                except Exception:  # TODO catch the correct exception
-                    pass
         start_connect = logg.debug("computed neighbors", time=start_neighbors)
 
         if method == "umap":

src/scanpy/plotting/_anndata.py

@@ -157,15 +157,15 @@ def scatter(
     if x is None or y is None:
         raise ValueError("Either provide a `basis` or `x` and `y`.")
     if (
-        (x in adata.obs.keys() or x in var_index)
-        and (y in adata.obs.keys() or y in var_index)
-        and (color is None or color in adata.obs.keys() or color in var_index)
+        (x in adata.obs.columns or x in var_index)
+        and (y in adata.obs.columns or y in var_index)
+        and (color is None or color in adata.obs.columns or color in var_index)
     ):
         return _scatter_obs(**args)
     if (
-        (x in adata.var.keys() or x in adata.obs.index)
-        and (y in adata.var.keys() or y in adata.obs.index)
-        and (color is None or color in adata.var.keys() or color in adata.obs.index)
+        (x in adata.var.columns or x in adata.obs.index)
+        and (y in adata.var.columns or y in adata.obs.index)
+        and (color is None or color in adata.var.columns or color in adata.obs.index)
     ):
         adata_T = adata.T
         axs = _scatter_obs(
@@ -217,14 +217,12 @@ def _scatter_obs(
     use_raw = _check_use_raw(adata, use_raw)
 
     # Process layers
-    if layers in ["X", None] or (
-        isinstance(layers, str) and layers in adata.layers.keys()
-    ):
+    if layers in ["X", None] or (isinstance(layers, str) and layers in adata.layers):
         layers = (layers, layers, layers)
     elif isinstance(layers, Collection) and len(layers) == 3:
         layers = tuple(layers)
         for layer in layers:
-            if layer not in adata.layers.keys() and layer not in ["X", None]:
+            if layer not in adata.layers and layer not in ["X", None]:
                 raise ValueError(
                     "`layers` should have elements that are "
                     "either None or in adata.layers.keys()."
@@ -256,7 +254,7 @@ def _scatter_obs(
     )
     if title is not None and isinstance(title, str):
         title = [title]
-    highlights = adata.uns["highlights"] if "highlights" in adata.uns else []
+    highlights = adata.uns.get("highlights", [])
     if basis is not None:
         try:
             # ignore the '0th' diffusion component
@@ -292,19 +290,14 @@ def _scatter_obs(
         n = Y.shape[0]
         size = 120000 / n
 
-    if legend_loc.startswith("on data") and legend_fontsize is None:
-        legend_fontsize = rcParams["legend.fontsize"]
-    elif legend_fontsize is None:
+    if legend_fontsize is None:
         legend_fontsize = rcParams["legend.fontsize"]
 
     palette_was_none = False
     if palette is None:
         palette_was_none = True
     if isinstance(palette, Sequence) and not isinstance(palette, str):
-        if not is_color_like(palette[0]):
-            palettes = palette
-        else:
-            palettes = [palette]
+        palettes = palette if not is_color_like(palette[0]) else [palette]
     else:
         palettes = [palette for _ in range(len(keys))]
     palettes = [_utils.default_palette(palette) for palette in palettes]
@@ -328,7 +321,7 @@ def _scatter_obs(
     else:
         component_name = None
     axis_labels = (x, y) if component_name is None else None
-    show_ticks = True if component_name is None else False
+    show_ticks = component_name is None
 
     # generate the colors
     color_ids: list[np.ndarray | ColorLike] = []
@@ -364,9 +357,8 @@ def _scatter_obs(
             categoricals.append(ikey)
         color_ids.append(c)
 
-    if right_margin is None and len(categoricals) > 0:
-        if legend_loc == "right margin":
-            right_margin = 0.5
+    if right_margin is None and len(categoricals) > 0 and legend_loc == "right margin":
+        right_margin = 0.5
     if title is None and keys[0] is not None:
         title = [
             key.replace("_", " ") if not is_color_like(key) else "" for key in keys
@@ -488,10 +480,7 @@ def add_centroid(centroids, name, Y, mask):
 
             all_pos = np.zeros((len(adata.obs[key].cat.categories), 2))
             for iname, name in enumerate(adata.obs[key].cat.categories):
-                if name in centroids:
-                    all_pos[iname] = centroids[name]
-                else:
-                    all_pos[iname] = [np.nan, np.nan]
+                all_pos[iname] = centroids.get(name, [np.nan, np.nan])
             if legend_loc == "on data export":
                 filename = settings.writedir / "pos.csv"
                 logg.warning(f"exporting label positions to {filename}")
@@ -1245,10 +1234,7 @@ def heatmap(
         groupby_width = 0.2 if categorical else 0
         if figsize is None:
             height = 6
-            if show_gene_labels:
-                heatmap_width = len(var_names) * 0.3
-            else:
-                heatmap_width = 8
+            heatmap_width = len(var_names) * 0.3 if show_gene_labels else 8
             width = heatmap_width + dendro_width + groupby_width
         else:
             width, height = figsize
@@ -1352,10 +1338,7 @@ def heatmap(
         dendro_height = 0.8 if dendrogram else 0
         groupby_height = 0.13 if categorical else 0
         if figsize is None:
-            if show_gene_labels:
-                heatmap_height = len(var_names) * 0.18
-            else:
-                heatmap_height = 4
+            heatmap_height = len(var_names) * 0.18 if show_gene_labels else 4
             width = 10
             height = heatmap_height + dendro_height + groupby_height
         else:
@@ -1440,10 +1423,7 @@ def heatmap(
             for idx, (label, pos) in enumerate(
                 zip(var_group_labels, var_group_positions)
             ):
-                if var_groups_subset_of_groupby:
-                    label_code = label2code[label]
-                else:
-                    label_code = idx
+                label_code = label2code[label] if var_groups_subset_of_groupby else idx
                 arr += [label_code] * (pos[1] + 1 - pos[0])
             gene_groups_ax.imshow(
                 np.array([arr]).T, aspect="auto", cmap=groupby_cmap, norm=norm
@@ -1892,10 +1872,7 @@ def correlation_matrix(
         labels = adata.obs[groupby].cat.categories
     num_rows = corr_matrix.shape[0]
     colorbar_height = 0.2
-    if dendrogram:
-        dendrogram_width = 1.8
-    else:
-        dendrogram_width = 0
+    dendrogram_width = 1.8 if dendrogram else 0
     if figsize is None:
         corr_matrix_height = num_rows * 0.6
         height = corr_matrix_height + colorbar_height
@@ -2052,7 +2029,7 @@ def _prepare_dataframe(
                     "groupby has to be a valid observation. "
                     f"Given {group}, is not in observations: {adata.obs_keys()}" + msg
                 )
-            if group in adata.obs.keys() and group == adata.obs.index.name:
+            if group in adata.obs.columns and group == adata.obs.index.name:
                 raise ValueError(
                     f"Given group {group} is both and index and a column level, "
                     "which is ambiguous."
@@ -2171,10 +2148,7 @@ def _plot_gene_groups_brackets(
     if orientation == "top":
         # rotate labels if any of them is longer than 4 characters
         if rotation is None and group_labels:
-            if max([len(x) for x in group_labels]) > 4:
-                rotation = 90
-            else:
-                rotation = 0
+            rotation = 90 if max([len(x) for x in group_labels]) > 4 else 0
         for idx in range(len(left)):
             verts.append((left[idx], 0))  # lower-left
             verts.append((left[idx], 0.6))  # upper-left
@@ -2600,11 +2574,8 @@ def _plot_categories_as_colorblocks(
         )
         if len(labels) > 1:
             groupby_ax.set_xticks(ticks)
-            if max([len(str(x)) for x in labels]) < 3:
-                # if the labels are small do not rotate them
-                rotation = 0
-            else:
-                rotation = 90
+            # if the labels are small do not rotate them
+            rotation = 0 if max(len(str(x)) for x in labels) < 3 else 90
             groupby_ax.set_xticklabels(labels, rotation=rotation)
 
         # remove x ticks