[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

nigsp/io.py

@@ -162,8 +162,7 @@ def check_mtx_dim(fname, data, shape=None):
         raise ValueError(f"{fname} is empty!")
     if data.ndim > 3:
         raise NotImplementedError(
-            "Only matrices up to 3D are supported, but "
-            f"given matrix is {data.ndim}D."
+            f"Only matrices up to 3D are supported, but given matrix is {data.ndim}D."
         )
     if shape is not None:
         if data.ndim > 2:
@@ -176,8 +175,7 @@ def check_mtx_dim(fname, data, shape=None):
             )
         if shape == "square" and data.shape[0] != data.shape[1]:
             raise ValueError(
-                f"Square matrix required, but {fname} matrix has "
-                f"shape {data.shape}."
+                f"Square matrix required, but {fname} matrix has shape {data.shape}."
             )
 
     return data

nigsp/operations/nifti.py

@@ -100,9 +100,9 @@ def apply_mask(data, mask):
             f"with shape {mask.shape}"
         )
     if (data.ndim - mask.ndim) > 1:
-        LGR.warning(f"Returning volume with {data.ndim-mask.ndim+1} dimensions.")
+        LGR.warning(f"Returning volume with {data.ndim - mask.ndim + 1} dimensions.")
     else:
-        LGR.info(f"Returning {data.ndim-mask.ndim+1}D array.")
+        LGR.info(f"Returning {data.ndim - mask.ndim + 1}D array.")
 
     mask = mask != 0
     return data[mask]
@@ -149,8 +149,7 @@ def unmask(data, mask, shape=None, asdata=None):
 
     if shape[: mask.ndim] != mask.shape:
         raise ValueError(
-            f"Cannot unmask data into shape {shape} using mask "
-            f"with shape {mask.shape}"
+            f"Cannot unmask data into shape {shape} using mask with shape {mask.shape}"
         )
     if data.ndim > 1 and (data.shape[0] != mask.sum()):
         raise ValueError(
@@ -216,10 +215,10 @@ def apply_atlas(data, atlas, mask=None):
             f"with shape {data.shape}"
         )
     if (data.ndim - atlas.ndim) > 1:
-        LGR.warning(f"returning volume with {data.ndim-atlas.ndim+1} dimensions.")
+        LGR.warning(f"returning volume with {data.ndim - atlas.ndim + 1} dimensions.")
     else:
         LGR.info(
-            f"Returning {data.ndim-atlas.ndim+1}D array of signal averages "
+            f"Returning {data.ndim - atlas.ndim + 1}D array of signal averages "
             f"in atlas {atlas}."
         )
 
@@ -297,7 +296,9 @@ def unfold_atlas(data, atlas, mask=None):
             f"with {len(labels)} parcels"
         )
 
-    LGR.info(f"Unmasking data into atlas-like volume of {3+data.ndim-1} dimensions.")
+    LGR.info(
+        f"Unmasking data into atlas-like volume of {3 + data.ndim - 1} dimensions."
+    )
     out = np.zeros_like(atlas, dtype="float32")
 
     for ax in range(1, data.ndim):

nigsp/operations/surrogates.py

@@ -299,7 +299,7 @@ def test_significance(
             raise ValueError(
                 "Provided empirical data and surrogate data shapes "
                 f"do not agree, with shapes {data.shape} and "
-                f"{surr.shape[:data.ndim]} (last axis excluded)"
+                f"{surr.shape[: data.ndim]} (last axis excluded)"
             )
         if not (surr[..., -1] == data).all():
             # Check that data was not appended yet.
@@ -332,16 +332,16 @@ def test_significance(
     LGR.info(f"Adopting {method} testing method.")
     # Testing both tails requires to split p
     if method == "frequentist":
-        LGR.info(f"Testing for p={p} two-tails (p={p/2} each tail)")
+        LGR.info(f"Testing for p={p} two-tails (p={p / 2} each tail)")
         p = p / 2
         # If there aren't enough surrogates, send a warning message on the real p
         # Then update p
         if 1 / surr.shape[-1] > p:
             LGR.warning(
                 "The generated surrogates are not enough to test for "
-                f"the selected p ({p*2} two-tails), since at least "
-                f"{ceil(1/p)-1} surrogates are required for the selected "
-                f"p value. Testing for p={1/surr.shape[-1]} two-tails instead."
+                f"the selected p ({p * 2} two-tails), since at least "
+                f"{ceil(1 / p) - 1} surrogates are required for the selected "
+                f"p value. Testing for p={1 / surr.shape[-1]} two-tails instead."
             )
             p = 1 / surr.shape[-1]
 
@@ -355,7 +355,7 @@ def test_significance(
             LGR.warning(
                 "The provided subjects are not enough to test for "
                 f"p={p_bernoulli} one-tail at the group level, since "
-                f"at least {ceil(1/p_bernoulli)} subjects are required."
+                f"at least {ceil(1 / p_bernoulli)} subjects are required."
             )
             p_bernoulli = 1 / surr.shape[1]
         # If there aren't enough surrogates, send a warning message on the real p
@@ -364,13 +364,13 @@ def test_significance(
             LGR.warning(
                 "The generated surrogates are not enough to test for "
                 f"p={p} two-tails at the subject level. "
-                f"{ceil(1/p)-1} surrogates are required for p={p}."
+                f"{ceil(1 / p) - 1} surrogates are required for p={p}."
             )
             p = 1 / surr.shape[-1]
 
         LGR.info(
             f"Testing for p={p_bernoulli} one-tail at the group level and "
-            f"at p={p*2} two-tails (p={p} each tail) at the subject level."
+            f"at p={p * 2} two-tails (p={p} each tail) at the subject level."
         )
     else:
         raise NotImplementedError(

nigsp/operations/timeseries.py

@@ -328,7 +328,7 @@ def median_cutoff_frequency_idx(energy):
     if energy.ndim == 2:
         energy = energy.mean(axis=-1)
     half_tot_auc = _trapezoid_compat(energy, axis=0) / 2
-    LGR.debug(f"Total AUC = {half_tot_auc*2}, targeting half of total AUC")
+    LGR.debug(f"Total AUC = {half_tot_auc * 2}, targeting half of total AUC")
 
     # Compute the AUC from first to one to last frequency,
     # skipping first component because AUC(1)=0.
@@ -395,7 +395,7 @@ def graph_filter(timeseries, eigenvec, freq_idx, keys=["low", "high"]):  # noqa:
                 f"eigenvector matrix of shape {eigenvec.shape}."
             )
 
-    LGR.info(f"Splitting graph into {len(freq_idx)+1} parts")
+    LGR.info(f"Splitting graph into {len(freq_idx) + 1} parts")
 
     # Check that there is the right amount of keys
     if len(keys) > len(freq_idx) + 1:
@@ -413,7 +413,7 @@ def graph_filter(timeseries, eigenvec, freq_idx, keys=["low", "high"]):  # noqa:
         )
 
         for i in range(len(keys), len(freq_idx) + 1):
-            keys = keys + [f"key-{i+1:03d}"]
+            keys = keys + [f"key-{i + 1:03d}"]
 
     # Add 0 and None to freq_idx to have full indexes
     freq_idx = [0] + freq_idx + [None]