upgrade black version in pre-commit config

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/psf/black
-    rev: 23.12.1
+    rev: 24.3.0
     hooks:
       - id: black
         args: [--line-length=128, --extend-exclude=.ipynb, --verbose]

benchmarks/train_hybrid_cpsc2020/signal_processing/ecg_denoise.py

@@ -14,6 +14,7 @@
 ----------
 to add
 """
+
 from numbers import Real
 from typing import List
 

benchmarks/train_hybrid_cpsc2020/signal_processing/ecg_preproc.py

@@ -14,6 +14,7 @@
 [2] to add
 
 """
+
 import multiprocessing as mp
 import os
 import time

benchmarks/train_mtl_cinc2022/data_reader.py

@@ -1629,9 +1629,11 @@ def load_data(
             channels = [channels]
         assert set(channels).issubset(self._channels), "invalid channels"
         data = {
-            k: torch.from_numpy(data[k].astype(np.float32))
-            if data_format.lower() == "channel_first"
-            else torch.from_numpy(data[k].astype(np.float32).T)
+            k: (
+                torch.from_numpy(data[k].astype(np.float32))
+                if data_format.lower() == "channel_first"
+                else torch.from_numpy(data[k].astype(np.float32).T)
+            )
             for k in channels
             if k in data
         }

benchmarks/train_mtl_cinc2022/trainer.py

@@ -345,9 +345,11 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
             head_preds_classes = [np.array(all_outputs[0].murmur_output.classes)[np.where(row)[0]] for row in head_bin_preds]
             head_labels = all_labels[0]["murmur"][:log_head_num]
             head_labels_classes = [
-                np.array(all_outputs[0].murmur_output.classes)[np.where(row)]
-                if head_labels.ndim == 2
-                else np.array(all_outputs[0].murmur_output.classes)[row]
+                (
+                    np.array(all_outputs[0].murmur_output.classes)[np.where(row)]
+                    if head_labels.ndim == 2
+                    else np.array(all_outputs[0].murmur_output.classes)[row]
+                )
                 for row in head_labels
             ]
             log_head_num = min(log_head_num, len(head_scalar_preds))
@@ -372,9 +374,11 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
                 ]
                 head_labels = all_labels[0]["outcome"][:log_head_num]
                 head_labels_classes = [
-                    np.array(all_outputs[0].outcome_output.classes)[np.where(row)[0]]
-                    if head_labels.ndim == 2
-                    else np.array(all_outputs[0].outcome_output.classes)[row]
+                    (
+                        np.array(all_outputs[0].outcome_output.classes)[np.where(row)[0]]
+                        if head_labels.ndim == 2
+                        else np.array(all_outputs[0].outcome_output.classes)[row]
+                    )
                     for row in head_labels
                 ]
                 log_head_num = min(log_head_num, len(head_scalar_preds))

benchmarks/train_mtl_cinc2022/utils/_transforms.py

@@ -45,6 +45,7 @@ class PitchShift(torch.nn.Module):
         >>> waveform_shift = transform(waveform)  # (channel, time)
 
     """
+
     __constants__ = [
         "sample_rate",
         "n_steps",

legacy/_pantompkins.py

@@ -4,6 +4,7 @@
 source: https://pypi.org/project/wfdb/2.2.1/#files
 
 """
+
 import numpy as np
 import scipy.signal as scisig
 

test/test_pipelines/test_mtl_cinc2022_pipeline.py

@@ -3321,9 +3321,11 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
             head_preds_classes = [np.array(all_outputs[0].murmur_output.classes)[np.where(row)[0]] for row in head_bin_preds]
             head_labels = all_labels[0]["murmur"][:log_head_num]
             head_labels_classes = [
-                np.array(all_outputs[0].murmur_output.classes)[np.where(row)]
-                if head_labels.ndim == 2
-                else np.array(all_outputs[0].murmur_output.classes)[row]
+                (
+                    np.array(all_outputs[0].murmur_output.classes)[np.where(row)]
+                    if head_labels.ndim == 2
+                    else np.array(all_outputs[0].murmur_output.classes)[row]
+                )
                 for row in head_labels
             ]
             log_head_num = min(log_head_num, len(head_scalar_preds))
@@ -3348,9 +3350,11 @@ def evaluate(self, data_loader: DataLoader) -> Dict[str, float]:
                 ]
                 head_labels = all_labels[0]["outcome"][:log_head_num]
                 head_labels_classes = [
-                    np.array(all_outputs[0].outcome_output.classes)[np.where(row)[0]]
-                    if head_labels.ndim == 2
-                    else np.array(all_outputs[0].outcome_output.classes)[row]
+                    (
+                        np.array(all_outputs[0].outcome_output.classes)[np.where(row)[0]]
+                        if head_labels.ndim == 2
+                        else np.array(all_outputs[0].outcome_output.classes)[row]
+                    )
                     for row in head_labels
                 ]
                 log_head_num = min(log_head_num, len(head_scalar_preds))

torch_ecg/model_configs/ecg_unet.py

@@ -39,8 +39,7 @@
 ECG_UNET_VANILLA_CONFIG.up_mode = "nearest"
 ECG_UNET_VANILLA_CONFIG.up_scales = list(repeat(2, ECG_UNET_VANILLA_CONFIG.down_up_block_num))
 ECG_UNET_VANILLA_CONFIG.up_num_filters = [
-    ECG_UNET_VANILLA_CONFIG.init_num_filters * (2**idx)
-    for idx in range(ECG_UNET_VANILLA_CONFIG.down_up_block_num - 1, -1, -1)
+    ECG_UNET_VANILLA_CONFIG.init_num_filters * (2**idx) for idx in range(ECG_UNET_VANILLA_CONFIG.down_up_block_num - 1, -1, -1)
 ]
 ECG_UNET_VANILLA_CONFIG.up_deconv_filter_lengths = list(repeat(_base_filter_length, ECG_UNET_VANILLA_CONFIG.down_up_block_num))
 ECG_UNET_VANILLA_CONFIG.up_conv_filter_lengths = list(repeat(_base_filter_length, ECG_UNET_VANILLA_CONFIG.down_up_block_num))

torch_ecg/utils/__init__.py

@@ -158,6 +158,7 @@
     select_k
 
 """
+
 from . import ecg_arrhythmia_knowledge as EAK
 from ._ecg_plot import ecg_plot
 from .download import http_get