[REF] Fix more f strings

.github/workflows/flake8.yml

@@ -31,7 +31,7 @@ jobs:
 
       - name: "Install Flake8"
         shell: bash {0}
-        run: python -m pip install --upgrade pip flake8 flake8-docstrings
+        run: python -m pip install --upgrade pip flake8 flake8-docstrings flake8-use-fstring
 
       - name: "Run Flake8 on whole file"
         shell: bash {0}

.pre-commit-config.yaml

@@ -121,4 +121,4 @@ repos:
     hooks:
       - id: flake8
         args: [--verbose]
-        additional_dependencies: [flake8-docstrings]
+        additional_dependencies: [flake8-docstrings, flake8-use-fstring]

examples/04_glm_first_level/plot_fiac_analysis.py

@@ -105,8 +105,7 @@ def pad_vector(contrast_, n_columns):
 
 print('Computing contrasts...')
 for index, (contrast_id, contrast_val) in enumerate(contrasts.items()):
-    print('  Contrast % 2i out of %i: %s' % (
-        index + 1, len(contrasts), contrast_id))
+    print(f"  Contrast {index + 1: 2} out of {len(contrasts)}: {contrast_id}")
     # Estimate the contasts. Note that the model implicitly computes a fixed
     # effect across the two sessions
     z_map = fmri_glm.compute_contrast(

examples/04_glm_first_level/plot_localizer_surface_analysis.py

@@ -178,8 +178,8 @@
 from nilearn.glm.contrasts import compute_contrast
 
 for index, (contrast_id, contrast_val) in enumerate(contrasts.items()):
-    print('  Contrast % i out of %i: %s, right hemisphere' %
-          (index + 1, len(contrasts), contrast_id))
+    print(f"  Contrast {index + 1:1} out of {len(contrasts)}: "
+          f"{contrast_id}, right hemisphere")
     # compute contrast-related statistics
     contrast = compute_contrast(labels, estimates, contrast_val,
                                 contrast_type='t')
@@ -211,8 +211,8 @@
 ###############################################################################
 # Finally, we create contrast-specific maps and plot them.
 for index, (contrast_id, contrast_val) in enumerate(contrasts.items()):
-    print('  Contrast % i out of %i: %s, left hemisphere' %
-          (index + 1, len(contrasts), contrast_id))
+    print(f"  Contrast {index + 1:1}% i out of {len(contrasts)}: "
+          f"{contrast_id}, left hemisphere")
     # compute contrasts
     contrast = compute_contrast(labels, estimates, contrast_val,
                                 contrast_type='t')

nilearn/_utils/cache_mixin.py

@@ -48,26 +48,25 @@ def _check_memory(memory, verbose=0):
                 # Maybe the user want to enable expanded user path.
                 error_msg = (
                     "Given cache path parent directory doesn't "
-                    "exists, you gave '{}'. Enabling "
+                    f"exists, you gave '{split_cache_dir[0]}'. Enabling "
                     "nilearn.EXPAND_PATH_WILDCARDS could solve "
-                    "this issue.".format(split_cache_dir[0])
+                    "this issue."
                 )
             elif memory.startswith("~"):
                 # Path built on top of expanded user path doesn't exist.
                 error_msg = (
                     "Given cache path parent directory doesn't "
-                    "exists, you gave '{}' which was expanded "
-                    "as '{}' but doesn't exist either. Use "
-                    "nilearn.EXPAND_PATH_WILDCARDS to deactivate "
-                    "auto expand user path (~) behavior.".format(
-                        split_cache_dir[0], os.path.dirname(memory)
-                    )
+                    f"exists, you gave '{split_cache_dir[0]}' "
+                    "which was expanded as '{os.path.dirname(memory)}' "
+                    "but doesn't exist either. "
+                    "Use nilearn.EXPAND_PATH_WILDCARDS to deactivate "
+                    "auto expand user path (~) behavior."
                 )
             else:
                 # The given cache base path doesn't exist.
                 error_msg = (
                     "Given cache path parent directory doesn't "
-                    "exists, you gave '{}'.".format(split_cache_dir[0])
+                    "exists, you gave '{split_cache_dir[0]}'."
                 )
             raise ValueError(error_msg)
 
@@ -159,18 +158,18 @@ def cache(
             raise TypeError(
                 "'memory' argument must be a string or a "
                 "joblib.Memory object. "
-                "%s %s was given." % (memory, type(memory))
+                f"{memory} {type(memory)} was given."
             )
         if (
             memory.location is None
             and memory_level is not None
             and memory_level > 1
         ):
             warnings.warn(
-                "Caching has been enabled (memory_level = %d) "
+                f"Caching has been enabled (memory_level = {memory_level}) "
                 "but no Memory object or path has been provided"
                 " (parameter memory). Caching deactivated for "
-                "function %s." % (memory_level, func.__name__),
+                f"function {func.__name__}.",
                 stacklevel=2,
             )
     else:

nilearn/_utils/exceptions.py

@@ -52,19 +52,16 @@ def increment_stack_counter(self):
     @property
     def message(self):
         """Format error message."""
+        expected_dim = self.required_dimension + self.stack_counter
+        total_file_dim = f" ({self.file_dimension + self.stack_counter}D)"
         return (
             "Input data has incompatible dimensionality: "
-            "Expected dimension is {}D and you provided a "
-            "{}{}D image{}{}. "
+            f"Expected dimension is {expected_dim}D and you provided a "
+            f"{'list of ' * self.stack_counter}{self.file_dimension}D "
+            f"image{'s' * (self.stack_counter > 0)}"
+            f"{total_file_dim * (self.stack_counter > 0)}. "
             "See https://nilearn.github.io/stable/manipulating_images/"
-            "input_output.html.".format(
-                self.required_dimension + self.stack_counter,
-                "list of " * self.stack_counter,
-                self.file_dimension,
-                "s" * (self.stack_counter != 0),
-                (" (%iD)" % (self.file_dimension + self.stack_counter))
-                * (self.stack_counter > 0),
-            )
+            "input_output.html."
         )
 
     def __str__(self):

nilearn/_utils/logger.py

@@ -106,6 +106,6 @@ def _compose_err_msg(msg, **kwargs):
     updated_msg = msg
     for k, v in sorted(kwargs.items()):
         if isinstance(v, str):  # print only str-like arguments
-            updated_msg += "\n" + k + ": " + v
+            updated_msg += f"\n{k}: {v}"
 
     return updated_msg

nilearn/_utils/niimg.py

@@ -226,14 +226,16 @@ def _repr_niimgs(niimgs, shorten=True):
     # Collection case
     if isinstance(niimgs, collections.abc.Iterable):
         if shorten and len(niimgs) > list_max_display:
-            return "[%s]" % ",\n         ...\n ".join(
+            tmp = ",\n         ...\n ".join(
                 _repr_niimgs(niimg, shorten=shorten)
                 for niimg in [niimgs[0], niimgs[-1]]
             )
+            return f"[{tmp}]"
         elif len(niimgs) > list_max_display:
-            return "[%s]" % ",\n ".join(
+            tmp = ",\n ".join(
                 _repr_niimgs(niimg, shorten=shorten) for niimg in niimgs
             )
+            return f"[{tmp}]"
         else:
             tmp = [_repr_niimgs(niimg, shorten=shorten) for niimg in niimgs]
             return f"[{', '.join(tmp)}]"

nilearn/_utils/niimg_conversions.py

@@ -51,7 +51,7 @@
     """
     raise_error = kwargs.pop("raise_error", False)
     for i, arg in enumerate(args):
-        kwargs["img_#%i" % i] = arg
+        kwargs[f"img_#{i}"] = arg
     errors = []
     for (a_name, a_img), (b_name, b_img) in itertools.combinations(
         kwargs.items(), 2
@@ -64,7 +64,10 @@
         raise ValueError(
             "Following field of view errors were detected:\n"
             + "\n".join(
-                ["- %s and %s do not have the same %s" % e for e in errors]
+                [
+                    f"- {e[0]} and {e[1]} do not have the same {e[2]}"
+                    for e in errors
+                ]
             )
         )
     return len(errors) == 0
@@ -195,7 +198,7 @@
                 img_name = f" ({niimg}) "
 
             exc.args = (
-                "Error encountered while loading image #%d%s" % (i, img_name),
+                f"Error encountered while loading image #{i}{img_name}",
             ) + exc.args
             raise
 
@@ -504,7 +507,7 @@
     if ndim not in [3, 4]:
         raise TypeError(
             "Concatenated images must be 3D or 4D. You gave a "
-            "list of %dD images" % ndim
+            f"list of {ndim}D images"
         )
 
     lengths = [first_niimg.shape[-1] if ndim == 4 else 1]
@@ -537,9 +540,9 @@
     ):
         if verbose > 0:
             if isinstance(niimg, str):
-                nii_str = "image " + niimg
+                nii_str = f"image {niimg}"
             else:
-                nii_str = "image #" + str(index)
+                nii_str = f"image #{index}"
             print(f"Concatenating {index + 1}: {nii_str}")
 
         data[..., cur_4d_index : cur_4d_index + size] = _get_data(niimg)

nilearn/_utils/testing.py

@@ -93,10 +93,9 @@ def assert_memory_less_than(
 
     if mem_used > memory_limit * (1 + tolerance):
         raise ValueError(
-            "Memory consumption measured ({:.2f} MiB) is "
-            "greater than required memory limit ({} MiB) within "
-            "accepted tolerance ({:.2f}%)."
-            "".format(mem_used, memory_limit, tolerance * 100)
+            f"Memory consumption measured ({mem_used:.2f} MiB) is "
+            f"greater than required memory limit ({memory_limit} MiB) within "
+            f"accepted tolerance ({tolerance * 100:.2f}%)."
         )
 
     # We are confident in memory_profiler measures above 100MiB.
@@ -185,7 +184,7 @@ def write_tmp_imgs(*imgs, **kwargs):
                     del img
 
                 if use_wildcards:
-                    yield prefix + "*" + suffix
+                    yield f"{prefix}*{suffix}"
                 else:
                     if len(imgs) == 1:
                         yield filenames[0]

nilearn/_utils/tests/test_exceptions.py

@@ -0,0 +1,10 @@
+import re
+
+from nilearn._utils.exceptions import DimensionError
+
+
+def test_dimension_error_message():
+    error = DimensionError(file_dimension=3, required_dimension=5)
+    error.increment_stack_counter()
+    error.increment_stack_counter()
+    assert re.match("^.*7D.*list of list of 3D images.*5D.*$", error.message)

nilearn/datasets/atlas.py

@@ -786,10 +786,10 @@
         for idx, name in enumerate(names):
             if name.endswith("L"):
                 names[idx] = re.sub(r" L$", "", name)
-                names[idx] = "Left " + name
+                names[idx] = f"Left {name}"
             if name.endswith("R"):
                 names[idx] = re.sub(r" R$", "", name)
-                names[idx] = "Right " + name
+                names[idx] = f"Right {name}"
 
     new_label = 0
     new_atlas = atlas_data.copy()
@@ -808,10 +808,10 @@
             new_names.append(name)
             continue
         new_atlas[left_atlas == label] = new_label
-        new_names.append("Left " + name)
+        new_names.append(f"Left {name}")
         new_label += 1
         new_atlas[right_atlas == label] = new_label
-        new_names.append("Right " + name)
+        new_names.append(f"Right {name}")
     return new_atlas, new_names
 
 
@@ -1267,7 +1267,7 @@
             f"Please choose one among {versions}."
         )
 
-    dataset_name = "aal_" + version
+    dataset_name = f"aal_{version}"
     opts = {"uncompress": True}
 
     if url is None:
@@ -1428,7 +1428,7 @@
         dataset_name, data_dir=data_dir, verbose=verbose
     )
 
-    folder_name = "template_cambridge_basc_multiscale_nii_" + version
+    folder_name = f"template_cambridge_basc_multiscale_nii_{version}"
     fdescr = _get_dataset_descr(dataset_name)
 
     if resolution:

nilearn/datasets/func.py

@@ -1151,7 +1151,7 @@ def fetch_abide_pcp(
     # Fetch the phenotypic file and load it
     csv = "Phenotypic_V1_0b_preprocessed1.csv"
     path_csv = _fetch_files(
-        data_dir, [(csv, url + "/" + csv, {})], verbose=verbose
+        data_dir, [(csv, f"{url}/{csv}", {})], verbose=verbose
     )[0]
 
     # Note: the phenotypic file contains string that contains comma which mess
@@ -1160,7 +1160,7 @@ def fetch_abide_pcp(
     # done simply with pandas but we don't want such dependency ATM
     # pheno = pandas.read_csv(path_csv).to_records()
     with open(path_csv) as pheno_f:
-        pheno = ["i" + pheno_f.readline()]
+        pheno = [f"i{pheno_f.readline()}"]
 
         # This regexp replaces commas between double quotes
         pheno.extend(
@@ -1202,9 +1202,9 @@ def fetch_abide_pcp(
         for file_id in file_ids:
             file_ = [
                 (
-                    file_id + "_" + derivative + ext,
+                    f"{file_id}_{derivative}{ext}",
                     "/".join(
-                        [url, derivative, file_id + "_" + derivative + ext]
+                        [url, derivative, f"{file_id}_{derivative}{ext}"]
                     ),
                     {},
                 )

nilearn/datasets/neurovault.py

@@ -675,8 +675,9 @@ def __eq__(self, other):
         )
 
     def __repr__(self):
-        return "{}(pattern={!r}, flags={})".format(
-            self.__class__.__name__, self.pattern_, self.flags_
+        return (
+            f"{self.__class__.__name__}(pattern={self.pattern_!r}, "
+            f"flags={self.flags_})"
         )
 
 
@@ -1342,7 +1343,7 @@ def _add_absolute_paths(root_dir, metadata, force=True):
     for name, value in metadata.items():
         match = re.match(r"(.*)relative_path(.*)", name)
         if match is not None:
-            abs_name = "{}absolute_path{}".format(*match.groups())
+            abs_name = f"{match.groups()[0]}absolute_path{match.groups()[1]}"
             absolute_paths[abs_name] = os.path.join(root_dir, value)
     if not absolute_paths:
         return metadata

nilearn/datasets/tests/test_atlas.py

@@ -155,7 +155,7 @@ def test_fetch_atlas_source(tmp_path, request_mocker):
 
 
 def _write_sample_atlas_metadata(ho_dir, filename, is_symm):
-    with open(os.path.join(ho_dir, filename + ".xml"), "w") as dm:
+    with open(os.path.join(ho_dir, f"{filename}.xml"), "w") as dm:
         if not is_symm:
             dm.write(
                 "<?xml version='1.0' encoding='us-ascii'?>\n"
@@ -397,7 +397,7 @@ def _destrieux_data():
     atlas = np.random.randint(0, 10, (10, 10, 10), dtype="int32")
     atlas_img = nibabel.Nifti1Image(atlas, np.eye(4))
     labels = "\n".join([f"{idx},label {idx}" for idx in range(10)])
-    labels = "index,name\n" + labels
+    labels = f"index,name\n{labels}"
     for lat in ["_lateralized", ""]:
         lat_data = {
             f"destrieux2009_rois_labels{lat}.csv": labels,
@@ -631,8 +631,7 @@ def test_fetch_atlas_basc_multiscale_2015(tmp_path, request_mocker):
     dataset_name = "basc_multiscale_2015"
     name_sym = "template_cambridge_basc_multiscale_nii_sym"
     basenames_sym = [
-        "template_cambridge_basc_multiscale_sym_" + key + ".nii.gz"
-        for key in keys
+        f"template_cambridge_basc_multiscale_sym_{key}.nii.gz" for key in keys
     ]
     for key, basename_sym in zip(keys, basenames_sym):
         assert data_sym[key] == str(
@@ -641,8 +640,7 @@ def test_fetch_atlas_basc_multiscale_2015(tmp_path, request_mocker):
 
     name_asym = "template_cambridge_basc_multiscale_nii_asym"
     basenames_asym = [
-        "template_cambridge_basc_multiscale_asym_" + key + ".nii.gz"
-        for key in keys
+        f"template_cambridge_basc_multiscale_asym_{key}.nii.gz" for key in keys
     ]
     for key, basename_asym in zip(keys, basenames_asym):
         assert data_asym[key] == str(
@@ -742,9 +740,9 @@ def test_fetch_atlas_talairach(tmp_path, request_mocker):
 
 
 def test_fetch_atlas_pauli_2017(tmp_path, request_mocker):
-    labels = pd.DataFrame(
-        {"label": list(map("label_{}".format, range(16)))}
-    ).to_csv(sep="\t", header=False)
+    labels = pd.DataFrame({"label": [f"label_{i}" for i in range(16)]}).to_csv(
+        sep="\t", header=False
+    )
     det_atlas = data_gen.generate_labeled_regions((7, 6, 5), 16)
     prob_atlas, _ = data_gen.generate_maps((7, 6, 5), 16)
     request_mocker.url_mapping["*osf.io/6qrcb/*"] = labels

nilearn/datasets/tests/test_struct.py

@@ -98,7 +98,7 @@ def _make_oasis_data(dartel=True):
     n_subjects = 457
     prefix = "mwrc" if dartel else "mwc"
     ids = pd.DataFrame(
-        {"ID": list(map("OAS1_{:04}".format, range(n_subjects)))}
+        {"ID": [f"OAS1_{i:04}" for i in range(n_subjects)]}
     ).to_csv(index=False, sep="\t")
     data = {"oasis_cross-sectional.csv": ids, "data_usage_agreement.txt": ""}
     path_pattern = str(