Run formatting and linting

Signed-off-by: Boer-41 <m.deboer-41@umcutrecht.nl>

Author: Mathijs de Boer

python/evdplanner/cli/generate/target.py

@@ -235,7 +235,7 @@ def target(
             visible_points=True,
         )
 
-        markups.save(output.parent / f"EVD.mrk.json")
+        markups.save(output.parent / "EVD.mrk.json")
 
     if verbosity > 0:
         left_distance = (left_tp - left_kp).length

python/evdplanner/cli/plot/times.py

@@ -186,7 +186,7 @@ def times(
     logger.info(f"Creating CSV file with timings. {len(df)} entries.")
     df = pd.DataFrame(df)
 
-    logger.info(f"Saving CSV file with timings.")
+    logger.info("Saving CSV file with timings.")
     df.to_csv(output, index=False)
 
     total_times = np.array([x["total"] for x in evd_times])

python/evdplanner/cli/validate/model.py

@@ -2,7 +2,6 @@
 
 import click
 
-from evdplanner.network.transforms.keypoint_flip import flip_keypoints
 
 
 @click.command()

python/evdplanner/network/training/utils.py

@@ -23,7 +23,7 @@
 
 
 def get_loss_fn(
-    loss: nn.Module | Callable[[Tensor, Tensor], Tensor] | str
+    loss: nn.Module | Callable[[Tensor, Tensor], Tensor] | str,
 ) -> nn.Module | Callable[[Tensor, Tensor], Tensor]:
     """
     Get the loss function.
@@ -56,7 +56,7 @@ def get_loss_fn(
 
 
 def get_metric_fn(
-    metric: nn.Module | Callable[[Tensor, Tensor], Tensor] | str
+    metric: nn.Module | Callable[[Tensor, Tensor], Tensor] | str,
 ) -> nn.Module | Callable[[Tensor, Tensor], Tensor]:
     """
     Get the metric function.

python/evdplanner/notebooks/kakarla.ipynb

@@ -12,7 +12,6 @@
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
-    "from matplotlib.ticker import MaxNLocator\n",
     "from scipy.stats import pearsonr\n",
     "\n",
     "\n",
@@ -54,7 +53,7 @@
     "scores = df[df[\"RaterID\"] == \"Majority\"][\"Score\"].value_counts()\n",
     "total = scores.sum()\n",
     "for score, count in scores.items():\n",
-    "    print(f\"{score}: {count} ({count/total:.2%})\")"
+    "    print(f\"{score}: {count} ({count / total:.2%})\")"
    ]
   },
   {
@@ -148,7 +147,7 @@
     "    print(f\"Effect size: {effect_size}, Power: {power:.3f}\")\n",
     "\n",
     "# Plot the power curve\n",
-    "effects, powers = zip(*effects)\n",
+    "effects, powers = zip(*effects, strict=False)\n",
     "plt.plot(effects, powers, marker=\"o\")\n",
     "plt.xlabel(\"Effect size\")\n",
     "plt.ylabel(\"Power\")\n",
@@ -163,7 +162,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import statsmodels.api as sm\n",
     "from statsmodels.miscmodels.ordinal_model import OrderedModel\n",
     "\n",
     "# Ordinal Logistic Regression\n",
@@ -233,7 +231,7 @@
     "print(labels)\n",
     "\n",
     "# Sort by label name\n",
-    "values, labels = zip(*sorted(zip(values, labels), key=lambda x: x[1]))\n",
+    "values, labels = zip(*sorted(zip(values, labels, strict=False), key=lambda x: x[1]), strict=False)\n",
     "\n",
     "plt.figure(figsize=(4.5, 4.5))\n",
     "plt.pie(\n",
@@ -265,9 +263,7 @@
    "source": [
     "majority_df = df.loc[df[\"RaterID\"] == \"Majority\"]\n",
     "majority_df = majority_df.drop(columns=[\"RaterID\"])\n",
-    "majority_df.to_csv(\n",
-    "    Path(r\"S:\\E_ResearchData\\evdplanner\\MajorityVoting.csv\"), index=False\n",
-    ")"
+    "majority_df.to_csv(Path(r\"S:\\E_ResearchData\\evdplanner\\MajorityVoting.csv\"), index=False)"
    ]
   },
   {

python/evdplanner/notebooks/method_stability.ipynb

@@ -21,9 +21,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from itertools import product\n",
     "from pathlib import Path\n",
-    "from typing import Any\n",
     "from random import choice\n",
     "\n",
     "import numpy as np\n",
@@ -36,7 +34,7 @@
     "from evdplanner.generation import measure_kocher\n",
     "from evdplanner.geometry import Mesh\n",
     "from evdplanner.linalg import Vec3\n",
-    "from evdplanner.markups import DisplaySettings, MarkupManager\n",
+    "from evdplanner.markups import MarkupManager\n",
     "from evdplanner.rendering import find_target, Ray, IntersectionSort\n",
     "\n",
     "skin_mesh_file = \"mesh_skin.stl\"\n",
@@ -51,7 +49,7 @@
     "\n",
     "scores_file = Path(r\"S:\\E_ResearchData\\evdplanner\\MajorityVoting.csv\")\n",
     "\n",
-    "set_verbosity(0)\n"
+    "set_verbosity(0)"
    ]
   },
   {
@@ -110,7 +108,7 @@
     "    except:\n",
     "        print(f\"Skin Mesh or landmarks not found for patient {patient.name}.\")\n",
     "        continue\n",
-    "    \n",
+    "\n",
     "    gt_nasion = Vec3(*gt_landmarks.find_fiducial(\"Nasion\").position)\n",
     "    gt_left_ear = Vec3(*gt_landmarks.find_fiducial(\"Pre-Auricle Left\").position)\n",
     "    gt_right_ear = Vec3(*gt_landmarks.find_fiducial(\"Pre-Auricle Right\").position)\n",
@@ -125,14 +123,14 @@
     "\n",
     "    print(\"Finding GT target points...\")\n",
     "    gt_left_tp, _ = find_target(\n",
-    "            ventricles_mesh,\n",
-    "            gt_left_kp,\n",
-    "            check_radially=check_radially,\n",
-    "            radius=radius,\n",
-    "            objective_distance_weight=objective_distance_weight,\n",
-    "            thickness_threshold=thickness_threshold,\n",
-    "            depth_threshold=depth_threshold,\n",
-    "        )\n",
+    "        ventricles_mesh,\n",
+    "        gt_left_kp,\n",
+    "        check_radially=check_radially,\n",
+    "        radius=radius,\n",
+    "        objective_distance_weight=objective_distance_weight,\n",
+    "        thickness_threshold=thickness_threshold,\n",
+    "        depth_threshold=depth_threshold,\n",
+    "    )\n",
     "    gt_right_tp, _ = find_target(\n",
     "        ventricles_mesh,\n",
     "        gt_right_kp,\n",
@@ -154,21 +152,21 @@
     "            \"Patient\": patient.name,\n",
     "            \"Side\": \"Right\",\n",
     "        }\n",
-    "        \n",
+    "\n",
     "        wiggle_nasion = gt_nasion + Vec3(\n",
-    "            np.random.uniform(-max_error, max_error), \n",
-    "            np.random.uniform(-max_error, max_error), \n",
-    "            np.random.uniform(-max_error, max_error)\n",
+    "            np.random.uniform(-max_error, max_error),\n",
+    "            np.random.uniform(-max_error, max_error),\n",
+    "            np.random.uniform(-max_error, max_error),\n",
     "        )\n",
     "        wiggle_left_ear = gt_left_ear + Vec3(\n",
     "            np.random.uniform(-max_error, max_error),\n",
     "            np.random.uniform(-max_error, max_error),\n",
-    "            np.random.uniform(-max_error, max_error)\n",
+    "            np.random.uniform(-max_error, max_error),\n",
     "        )\n",
     "        wiggle_right_ear = gt_right_ear + Vec3(\n",
     "            np.random.uniform(-max_error, max_error),\n",
     "            np.random.uniform(-max_error, max_error),\n",
-    "            np.random.uniform(-max_error, max_error)\n",
+    "            np.random.uniform(-max_error, max_error),\n",
     "        )\n",
     "\n",
     "        # Project the wiggled points to the skin mesh\n",
@@ -188,7 +186,7 @@
     "        if wiggle_nasion is None or wiggle_left_ear is None or wiggle_right_ear is None:\n",
     "            print(f\"Intersection failed for patient {patient.name}.\")\n",
     "            continue\n",
-    "        \n",
+    "\n",
     "        wiggle_nasion = wiggle_nasion.position\n",
     "        wiggle_left_ear = wiggle_left_ear.position\n",
     "        wiggle_right_ear = wiggle_right_ear.position\n",
@@ -319,7 +317,7 @@
     "    constrained_layout=True,\n",
     ")\n",
     "\n",
-    "for ax, (x, y) in zip(axs, pairs):\n",
+    "for ax, (x, y) in zip(axs, pairs, strict=False):\n",
     "    # Calculate Spearman correlation coefficient\n",
     "    res = spearmanr(df[x], df[y])\n",
     "    corr = res.statistic\n",
@@ -336,7 +334,7 @@
     "    )\n",
     "    p.set_title(f\"{y} vs {x}\")\n",
     "    p.set_xlabel(x)\n",
-    "    p.set_ylabel(y)\n"
+    "    p.set_ylabel(y)"
    ]
   },
   {
@@ -378,7 +376,7 @@
     "            corr = f\"+{corr}\"\n",
     "        else:\n",
     "            corr = f\"{corr}\"\n",
-    "        \n",
+    "\n",
     "        if p_values.iloc[i, j] < 0.001:\n",
     "            p_val = r\"$^{*}$\"\n",
     "        elif p_values.iloc[i, j] < 0.01:\n",
@@ -443,7 +441,9 @@
     "    median = df[col].median()\n",
     "    low_ci = df[col].quantile(0.025)\n",
     "    high_ci = df[col].quantile(0.975)\n",
-    "    print(f\"{col}: {mean:.2f} ± {std:.2f} (median: {median:.2f}, 95% CI: [{low_ci:.2f}, {high_ci:.2f}])\")"
+    "    print(\n",
+    "        f\"{col}: {mean:.2f} ± {std:.2f} (median: {median:.2f}, 95% CI: [{low_ci:.2f}, {high_ci:.2f}])\"\n",
+    "    )"
    ]
   },
   {

python/evdplanner/notebooks/plot_colin.ipynb

@@ -10,7 +10,6 @@
     "from pathlib import Path\n",
     "\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
     "\n",
     "normal_map = Path(r\"S:\\E_ResearchData\\evdplanner\\Templates\\colin\\map_skin_normal.png\")\n",
     "keypoint_json = Path(r\"S:\\E_ResearchData\\evdplanner\\Templates\\colin\\projected_skin.kp.json\")"
@@ -62,7 +61,7 @@
    "source": [
     "image = plt.imread(normal_map)\n",
     "\n",
-    "with open(keypoint_json, \"r\") as f:\n",
+    "with open(keypoint_json) as f:\n",
     "    keypoints = json.load(f)\n",
     "\n",
     "y_shape, x_shape = image.shape[:2]\n",

python/evdplanner/notebooks/timing.ipynb

@@ -13,8 +13,7 @@
     "import numpy as np\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
-    "from matplotlib import cm\n",
-    "from tqdm.auto import tqdm"
+    "from matplotlib import cm"
    ]
   },
   {
@@ -36,7 +35,7 @@
     }
    ],
    "source": [
-    "timings = pd.read_csv(Path(\"F:\\evdplanner\\Test\\evd_times.csv\"))\n",
+    "timings = pd.read_csv(Path(r\"F:\\evdplanner\\Test\\evd_times.csv\"))\n",
     "\n",
     "total_times = timings.loc[timings[\"substage\"] == \"total\"]\n",
     "\n",