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notebooks/examples/densenet.ipynb

@@ -332,7 +332,6 @@
     "import os\n",
     "from tempfile import TemporaryDirectory\n",
     "\n",
-    "import pandas as pd\n",
     "import requests\n",
     "\n",
     "url = \"https://s3.embl.de/spatialdata/spatialdata-sandbox/generated_data/xenium_visium_integration/xenium_rep1_celltype_major.csv\"\n",
@@ -363,9 +362,7 @@
    ],
    "source": [
     "xenium_sdata[\"table\"].obs = pd.merge(xenium_sdata[\"table\"].obs, df, on=\"cell_id\")\n",
-    "xenium_sdata[\"table\"].obs[\"celltype_major\"] = (\n",
-    "    xenium_sdata[\"table\"].obs[\"celltype_major\"].astype(\"category\")\n",
-    ")"
+    "xenium_sdata[\"table\"].obs[\"celltype_major\"] = xenium_sdata[\"table\"].obs[\"celltype_major\"].astype(\"category\")"
    ]
   },
   {
@@ -936,9 +933,7 @@
    "outputs": [],
    "source": [
     "class TilesDataModule(LightningDataModule):\n",
-    "    def __init__(\n",
-    "        self, batch_size: int, num_workers: int, dataset: torch.utils.data.Dataset\n",
-    "    ):\n",
+    "    def __init__(self, batch_size: int, num_workers: int, dataset: torch.utils.data.Dataset):\n",
     "        super().__init__()\n",
     "\n",
     "        self.batch_size = batch_size\n",
@@ -1015,25 +1010,19 @@
     "        self.loss_function = CrossEntropyLoss()\n",
     "\n",
     "        # make the model\n",
-    "        self.model = DenseNet121(\n",
-    "            spatial_dims=2, in_channels=in_channels, out_channels=num_classes\n",
-    "        )\n",
+    "        self.model = DenseNet121(spatial_dims=2, in_channels=in_channels, out_channels=num_classes)\n",
     "\n",
     "    def forward(self, x) -> torch.Tensor:\n",
     "        return self.model(x)\n",
     "\n",
-    "    def _compute_loss_from_batch(\n",
-    "        self, batch: Dict[str, torch.Tensor], batch_idx: int\n",
-    "    ) -> float:\n",
+    "    def _compute_loss_from_batch(self, batch: Dict[str, torch.Tensor], batch_idx: int) -> float:\n",
     "        inputs = batch[0]\n",
     "        labels = batch[1]\n",
     "\n",
     "        outputs = self.model(inputs)\n",
     "        return self.loss_function(outputs, labels)\n",
     "\n",
-    "    def training_step(\n",
-    "        self, batch: Dict[str, torch.Tensor], batch_idx: int\n",
-    "    ) -> Dict[str, float]:\n",
+    "    def training_step(self, batch: Dict[str, torch.Tensor], batch_idx: int) -> Dict[str, float]:\n",
     "        # compute the loss\n",
     "        loss = self._compute_loss_from_batch(batch=batch, batch_idx=batch_idx)\n",
     "\n",
@@ -1124,9 +1113,7 @@
     "print(f\"Using {BATCH_SIZE} batch size.\")\n",
     "print(f\"Using {NUM_WORKERS} workers.\")\n",
     "\n",
-    "tiles_data_module = TilesDataModule(\n",
-    "    batch_size=BATCH_SIZE, num_workers=NUM_WORKERS, dataset=dataset\n",
-    ")\n",
+    "tiles_data_module = TilesDataModule(batch_size=BATCH_SIZE, num_workers=NUM_WORKERS, dataset=dataset)\n",
     "\n",
     "tiles_data_module.setup()\n",
     "train_dl = tiles_data_module.train_dataloader()\n",
@@ -1870,9 +1857,7 @@
    "source": [
     "small_dataset = ImageTilesDataset(\n",
     "    sdata=small_sdata,\n",
-    "    regions_to_images={\n",
-    "        \"cell_boundaries\": \"CytAssist_FFPE_Human_Breast_Cancer_full_image\"\n",
-    "    },\n",
+    "    regions_to_images={\"cell_boundaries\": \"CytAssist_FFPE_Human_Breast_Cancer_full_image\"},\n",
     "    regions_to_coordinate_systems={\"cell_boundaries\": \"aligned\"},\n",
     "    tile_dim_in_units=100,\n",
     "    rasterize=True,\n",
@@ -1925,17 +1910,13 @@
     "    region, instance_id = small_dataset.dataset_index.iloc[i][[\"region\", \"instance_id\"]]\n",
     "    shapes = small_sdata[region]\n",
     "    transformations = get_transformation(shapes, get_all=True)\n",
-    "    tile = ShapesModel.parse(\n",
-    "        GeoDataFrame(geometry=shapes.loc[instance_id]), transformations=transformations\n",
-    "    )\n",
+    "    tile = ShapesModel.parse(GeoDataFrame(geometry=shapes.loc[instance_id]), transformations=transformations)\n",
     "    # BUG: we need to explicitly remove the coordinate system global if we want to combine\n",
     "    # images and shapes plots into a single subplot\n",
     "    # https://github.com/scverse/spatialdata-plot/issues/176\n",
     "    sdata_tile[\"cell_boundaries\"] = tile\n",
     "    if \"global\" in get_transformation(sdata_tile[\"cell_boundaries\"], get_all=True):\n",
-    "        sd.transformations.remove_transformation(\n",
-    "            sdata_tile[\"cell_boundaries\"], \"global\"\n",
-    "        )\n",
+    "        sd.transformations.remove_transformation(sdata_tile[\"cell_boundaries\"], \"global\")\n",
     "    sdata_tile.pl.render_images().pl.render_shapes(\n",
     "        # outline_color='predicted_celltype_major',  # not yet supported: https://github.com/scverse/spatialdata-plot/issues/137\n",
     "        outline_width=3.0,\n",

notebooks/examples/napari_rois.ipynb

@@ -1118,9 +1118,7 @@
     "\n",
     "for shape in [\"lasso\", \"my_shapes\", \"my_shapes_2\"]:\n",
     "    for polygon in visium_sdata[shape].geometry:\n",
-    "        table = polygon_query(\n",
-    "            visium_sdata, polygon=polygon, target_coordinate_system=\"aligned\"\n",
-    "        )[\"table\"]\n",
+    "        table = polygon_query(visium_sdata, polygon=polygon, target_coordinate_system=\"aligned\")[\"table\"]\n",
     "        filtered_tables_unmerged[shape].append(table)\n",
     "    filtered_tables[shape] = ad.concat(filtered_tables_unmerged[shape])"
    ]
@@ -1277,9 +1275,7 @@
     "categories = [\"unassigned\"] + list(filtered_tables.keys())\n",
     "n = len(visium_sdata[\"table\"])\n",
     "\n",
-    "visium_sdata[\"table\"].obs[\"annotation\"] = pd.Categorical(\n",
-    "    [\"unassigned\" for _ in range(n)], categories=categories\n",
-    ")\n",
+    "visium_sdata[\"table\"].obs[\"annotation\"] = pd.Categorical([\"unassigned\" for _ in range(n)], categories=categories)\n",
     "\n",
     "for shape, subtable in filtered_tables.items():\n",
     "    in_shape = subtable.obs.index\n",
@@ -1364,9 +1360,8 @@
     "plt.figure(figsize=(12, 7))\n",
     "ax = plt.gca()\n",
     "(\n",
-    "    visium_sdata.pl.render_images(\n",
-    "        \"CytAssist_FFPE_Human_Breast_Cancer_full_image\"\n",
-    "    ).pl.render_shapes(\"CytAssist_FFPE_Human_Breast_Cancer\", color=\"annotation\")\n",
+    "    visium_sdata.pl.render_images(\"CytAssist_FFPE_Human_Breast_Cancer_full_image\")\n",
+    "    .pl.render_shapes(\"CytAssist_FFPE_Human_Breast_Cancer\", color=\"annotation\")\n",
     "    # .pl.render_shapes(\"lasso\", color=\"#00000040\")\n",
     "    # .pl.render_shapes(\"my_shapes\", color=\"#00000040\")\n",
     "    # .pl.render_shapes(\"my_shapes_2\", color=\"#00000040\")\n",