Merge pull request #1162 from DimitriPapadopoulos/refurb

STY: Apply a few refurb suggestions

mriqc/bin/dfcheck.py

@@ -38,20 +38,19 @@ def read_iqms(feat_file):
     feat_file = Path(feat_file)
 
     if feat_file.suffix == ".csv":
-        bids_comps = list(BIDS_COMP.keys())
         x_df = pd.read_csv(
-            feat_file, index_col=False, dtype={col: str for col in bids_comps}
+            feat_file, index_col=False, dtype={col: str for col in BIDS_COMP}
         )
         # Find present bids bits and sort by them
-        bids_comps_present = list(set(x_df.columns.ravel().tolist()) & set(bids_comps))
-        bids_comps_present = [bit for bit in bids_comps if bit in bids_comps_present]
+        bids_comps_present = list(set(x_df.columns.ravel().tolist()) & set(BIDS_COMP))
+        bids_comps_present = [bit for bit in BIDS_COMP if bit in bids_comps_present]
         x_df = x_df.sort_values(by=bids_comps_present)
         # Remove sub- prefix in subject_id
         x_df.subject_id = x_df.subject_id.str.lstrip("sub-")
 
         # Remove columns that are not IQMs
         feat_names = list(x_df._get_numeric_data().columns.ravel())
-        for col in bids_comps:
+        for col in BIDS_COMP:
             try:
                 feat_names.remove(col)
             except ValueError:

mriqc/interfaces/anatomical.py

@@ -156,7 +156,7 @@ def _run_interface(self, runtime):  # pylint: disable=R0914,E1101
         # SNR
         snrvals = []
         self._results["snr"] = {}
-        for tlabel in ["csf", "wm", "gm"]:
+        for tlabel in ("csf", "wm", "gm"):
             snrvals.append(
                 snr(
                     stats[tlabel]["median"],

mriqc/interfaces/bids.py

@@ -161,7 +161,7 @@ def _run_interface(self, runtime):
 
         # Fill in the "bids_meta" key
         id_dict = {}
-        for comp in list(BIDS_COMP.keys()):
+        for comp in BIDS_COMP:
             comp_val = getattr(self.inputs, comp, None)
             if isdefined(comp_val) and comp_val is not None:
                 id_dict[comp] = comp_val

mriqc/qc/functional.py

@@ -242,14 +242,14 @@ def gsr(epi_data, mask, direction="y", ref_file=None, out_file=None):
 
     """
     direction = direction.lower()
-    if direction[-1] not in ["x", "y", "all"]:
+    if direction[-1] not in ("x", "y", "all"):
         raise Exception(
             f"Unknown direction {direction}, should be one of x, -x, y, -y, all"
         )
 
     if direction == "all":
         result = []
-        for newdir in ["x", "y"]:
+        for newdir in ("x", "y"):
             ofile = None
             if out_file is not None:
                 fname, ext = op.splitext(ofile)

mriqc/reports/group.py

@@ -166,7 +166,7 @@ def gen_html(csv_file, mod, csv_failed=None, out_file=None):
             (["efc"], None),
             (["fber"], None),
             (["fwhm", "fwhm_x", "fwhm_y", "fwhm_z"], "mm"),
-            (["gsr_%s" % a for a in ["x", "y"]], None),
+            (["gsr_%s" % a for a in ("x", "y")], None),
             (["snr"], None),
             (["dvars_std", "dvars_vstd"], None),
             (["dvars_nstd"], None),
@@ -203,12 +203,11 @@ def gen_html(csv_file, mod, csv_failed=None, out_file=None):
     }
 
     if csv_file.suffix == ".csv":
-        def_comps = list(BIDS_COMP.keys())
         dataframe = pd.read_csv(
-            csv_file, index_col=False, dtype={comp: object for comp in def_comps}
+            csv_file, index_col=False, dtype={comp: object for comp in BIDS_COMP}
         )
 
-        id_labels = list(set(def_comps) & set(dataframe.columns.ravel().tolist()))
+        id_labels = list(set(BIDS_COMP) & set(dataframe.columns.ravel().tolist()))
         dataframe["label"] = dataframe[id_labels].apply(
             _format_labels, args=(id_labels,), axis=1
         )
@@ -291,9 +290,9 @@ def gen_html(csv_file, mod, csv_failed=None, out_file=None):
 
 def _format_labels(row, id_labels):
     """format participant labels"""
-    crow = []
-
-    for col_id, prefix in list(BIDS_COMP.items()):
-        if col_id in id_labels:
-            crow.append(f"{prefix}-{row[[col_id]].values[0]}")
+    crow = (
+        f"{prefix}-{row[[col_id]].values[0]}"
+        for col_id, prefix in list(BIDS_COMP.items())
+        if col_id in id_labels
+    )
     return "_".join(crow)

mriqc/utils/misc.py

@@ -83,7 +83,7 @@ def reorder_csv(csv_file, out_file=None):
         # The column does not exist
         pass
 
-    for col in ["scan", "session", "subject"]:
+    for col in ("scan", "session", "subject"):
         cols.remove(col)
         cols.insert(0, col)
 
@@ -144,7 +144,7 @@ def generate_pred(derivatives_dir, output_dir, mod):
     if not jsonfiles:
         return None
 
-    headers = list(BIDS_COMP.keys()) + ["mriqc_pred"]
+    headers = list(BIDS_COMP) + ["mriqc_pred"]
     predictions = {k: [] for k in headers}
 
     for jsonfile in jsonfiles:
@@ -157,12 +157,12 @@ def generate_pred(derivatives_dir, output_dir, mod):
         for k in headers:
             predictions[k].append(data.pop(k, None))
 
-    dataframe = pd.DataFrame(predictions).sort_values(by=list(BIDS_COMP.keys()))
+    dataframe = pd.DataFrame(predictions).sort_values(by=list(BIDS_COMP))
 
     # Drop empty columns
     dataframe.dropna(axis="columns", how="all", inplace=True)
 
-    bdits_cols = list(set(BIDS_COMP.keys()) & set(dataframe.columns.ravel()))
+    bdits_cols = list(set(BIDS_COMP) & set(dataframe.columns.ravel()))
 
     # Drop duplicates
     dataframe.drop_duplicates(bdits_cols, keep="last", inplace=True)
@@ -208,8 +208,7 @@ def generate_tsv(output_dir, mod):
 
 
 def _read_and_save(in_file):
-    data = json.loads(Path(in_file).read_text())
-    return data if data else None
+    return json.loads(Path(in_file).read_text()) or None
 
 
 def _flatten(in_dict, parent_key="", sep="_"):

mriqc/workflows/functional/output.py

@@ -384,7 +384,7 @@ def spikes_mask(in_file, in_mask=None, out_file=None):
     else:
         new_mask_3d = np.zeros(in_4d_nii.shape[:3]) == 1
 
-    if orientation[0] in ["L", "R"]:
+    if orientation[0] in ("L", "R"):
         new_mask_3d[0:2, :, :] = True
         new_mask_3d[-3:-1, :, :] = True
     else: