Pre commits

Notion task: https://www.notion.so/qim-dtu/Pre-commit-pipeline-1728f41887814960b6f5870460995cf4

All developers need to hook the pre-commit into the git commit command. The required packages are pre-commit and ruff. After they have been installed, the pre-commits are hooked onto git by running pre-commit install.

The details on how it works and how to use it are all in described in Notion.

qim3d/viz/_metrics.py

 """Visualization tools"""
 
+import matplotlib
 import matplotlib.figure
-import numpy as np
 import matplotlib.pyplot as plt
-from matplotlib.colors import LinearSegmentedColormap
+import numpy as np
+import torch
 from matplotlib import colormaps
+from matplotlib.colors import LinearSegmentedColormap
+
 from qim3d.utils._logger import log
-import matplotlib
+
 
 
 def plot_metrics(
     *metrics: tuple[dict[str, float]],
-    linestyle: str = "-",
-    batch_linestyle: str = "dotted",
+    linestyle: str = '-',
+    batch_linestyle: str = 'dotted',
     labels: list | None = None,
     figsize: tuple = (16, 6),
-    show: bool = False
+    show: bool = False,
 ):
     """
     Plots the metrics over epochs and batches.
@@ -35,6 +38,7 @@ def plot_metrics(
         train_loss = {'epoch_loss' : [...], 'batch_loss': [...]}
         val_loss = {'epoch_loss' : [...], 'batch_loss': [...]}
         plot_metrics(train_loss,val_loss, labels=['Train','Valid.'])
+
     """
     import seaborn as snb
 
@@ -44,9 +48,9 @@ def plot_metrics(
         raise ValueError("The number of metrics doesn't match the number of labels.")
 
     # plotting parameters
-    snb.set_style("darkgrid")
+    snb.set_style('darkgrid')
     snb.set(font_scale=1.5)
-    plt.rcParams["lines.linewidth"] = 2
+    plt.rcParams['lines.linewidth'] = 2
 
     fig = plt.figure(figsize=figsize)
 
@@ -68,10 +72,10 @@ def plot_metrics(
         plt.legend()
 
     plt.ylabel(metric_name)
-    plt.xlabel("epoch")
+    plt.xlabel('epoch')
 
     # reset plotting parameters
-    snb.set_style("white")
+    snb.set_style('white')
 
     if show:
         plt.show()
@@ -81,14 +85,15 @@ def plot_metrics(
 
 
 def grid_overview(
-    data: list,
+    data: list | torch.utils.data.Dataset,
     num_images: int = 7,
-    cmap_im: str = "gray",
-    cmap_segm: str = "viridis",
+    cmap_im: str = 'gray',
+    cmap_segm: str = 'viridis',
     alpha: float = 0.5,
     show: bool = False,
 ) -> matplotlib.figure.Figure:
-    """Displays an overview grid of images, labels, and masks (if they exist).
+    """
+    Displays an overview grid of images, labels, and masks (if they exist).
 
     Labels are the annotated target segmentations
     Masks are applied to the output and target prior to the loss calculation in case of
@@ -120,6 +125,7 @@ def grid_overview(
         - The number of displayed images is limited to the minimum between `num_images`
             and the length of the data.
         - The grid layout and dimensions vary based on the presence of a mask.
+
     """
     import torch
 
@@ -128,12 +134,12 @@ def grid_overview(
 
     # Check if image data is RGB and inform the user if it's the case
     if len(data[0][0].squeeze().shape) > 2:
-        log.info("Input images are RGB: color map is ignored")
+        log.info('Input images are RGB: color map is ignored')
 
     # Check if dataset have at least specified number of images
     if len(data) < num_images:
         log.warning(
-            "Not enough images in the dataset. Changing num_images=%d to num_images=%d",
+            'Not enough images in the dataset. Changing num_images=%d to num_images=%d',
             num_images,
             len(data),
         )
@@ -142,14 +148,14 @@ def grid_overview(
     # Adapt segmentation cmap so that background is transparent
     colors_segm = colormaps.get_cmap(cmap_segm)(np.linspace(0, 1, 256))
     colors_segm[:128, 3] = 0
-    custom_cmap = LinearSegmentedColormap.from_list("CustomCmap", colors_segm)
+    custom_cmap = LinearSegmentedColormap.from_list('CustomCmap', colors_segm)
 
     # Check if data have the right format
     if not isinstance(data[0], tuple):
-        raise ValueError("Data elements must be tuples")
+        raise ValueError('Data elements must be tuples')
 
     # Define row titles
-    row_titles = ["Input images", "Ground truth segmentation", "Mask"]
+    row_titles = ['Input images', 'Ground truth segmentation', 'Mask']
 
     # Make new list such that possible augmentations remain identical for all three rows
     plot_data = [data[idx] for idx in range(num_images)]
@@ -169,10 +175,10 @@ def grid_overview(
             if row in [1, 2]:  # Ground truth segmentation and mask
                 ax.imshow(plot_data[col][0].squeeze(), cmap=cmap_im)
                 ax.imshow(plot_data[col][row].squeeze(), cmap=custom_cmap, alpha=alpha)
-                ax.axis("off")
+                ax.axis('off')
             else:
                 ax.imshow(plot_data[col][row].squeeze(), cmap=cmap_im)
-                ax.axis("off")
+                ax.axis('off')
 
     if show:
         plt.show()
@@ -184,12 +190,13 @@ def grid_overview(
 def grid_pred(
     in_targ_preds: tuple[np.ndarray, np.ndarray, np.ndarray],
     num_images: int = 7,
-    cmap_im: str = "gray",
-    cmap_segm: str = "viridis",
+    cmap_im: str = 'gray',
+    cmap_segm: str = 'viridis',
     alpha: float = 0.5,
     show: bool = False,
 ) -> matplotlib.figure.Figure:
-    """Displays a grid of input images, predicted segmentations, ground truth segmentations, and their comparison.
+    """
+    Displays a grid of input images, predicted segmentations, ground truth segmentations, and their comparison.
 
     Displays a grid of subplots representing different aspects of the input images and segmentations.
     The grid includes the following rows:
@@ -221,25 +228,26 @@ def grid_pred(
         model = MySegmentationModel()
         in_targ_preds = qim3d.ml.inference(dataset,model)
         qim3d.viz.grid_pred(in_targ_preds, cmap_im='viridis', alpha=0.5)
+
     """
     import torch
 
     # Check if dataset have at least specified number of images
     if len(in_targ_preds[0]) < num_images:
         log.warning(
-            "Not enough images in the dataset. Changing num_images=%d to num_images=%d",
+            'Not enough images in the dataset. Changing num_images=%d to num_images=%d',
             num_images,
             len(in_targ_preds[0]),
         )
         num_images = len(in_targ_preds[0])
 
     # Take only the number of images from in_targ_preds
-    inputs, targets, preds = [items[:num_images] for items in in_targ_preds]
+    inputs, targets, preds = (items[:num_images] for items in in_targ_preds)
 
     # Adapt segmentation cmap so that background is transparent
     colors_segm = colormaps.get_cmap(cmap_segm)(np.linspace(0, 1, 256))
     colors_segm[:128, 3] = 0
-    custom_cmap = LinearSegmentedColormap.from_list("CustomCmap", colors_segm)
+    custom_cmap = LinearSegmentedColormap.from_list('CustomCmap', colors_segm)
 
     N = num_images
     H = inputs[0].shape[-2]
@@ -251,10 +259,10 @@ def grid_pred(
     comp_rgb[:, 3, :, :] = targets.logical_or(preds)
 
     row_titles = [
-        "Input images",
-        "Predicted segmentation",
-        "Ground truth segmentation",
-        "True vs. predicted segmentation",
+        'Input images',
+        'Predicted segmentation',
+        'Ground truth segmentation',
+        'True vs. predicted segmentation',
     ]
 
     fig = plt.figure(figsize=(2 * num_images, 10), constrained_layout=True)
@@ -269,20 +277,20 @@ def grid_pred(
         for col, ax in enumerate(np.atleast_1d(axs)):
             if row == 0:
                 ax.imshow(inputs[col], cmap=cmap_im)
-                ax.axis("off")
+                ax.axis('off')
 
             elif row == 1:  # Predicted segmentation
                 ax.imshow(inputs[col], cmap=cmap_im)
                 ax.imshow(preds[col], cmap=custom_cmap, alpha=alpha)
-                ax.axis("off")
+                ax.axis('off')
             elif row == 2:  # Ground truth segmentation
                 ax.imshow(inputs[col], cmap=cmap_im)
                 ax.imshow(targets[col], cmap=custom_cmap, alpha=alpha)
-                ax.axis("off")
+                ax.axis('off')
             else:
                 ax.imshow(inputs[col], cmap=cmap_im)
                 ax.imshow(comp_rgb[col].permute(1, 2, 0), alpha=alpha)
-                ax.axis("off")
+                ax.axis('off')
 
     if show:
         plt.show()
@@ -315,8 +323,8 @@ def vol_masked(
 
     """
 
-    background = (vol.astype("float") + viz_delta) * (1 - vol_mask) * -1
-    foreground = (vol.astype("float") + viz_delta) * vol_mask
+    background = (vol.astype('float') + viz_delta) * (1 - vol_mask) * -1
+    foreground = (vol.astype('float') + viz_delta) * vol_mask
     vol_masked_result = background + foreground
 
     return vol_masked_result