added support notebook in ruff

docs/notebooks/demo.ipynb

@@ -7,6 +7,8 @@
    "outputs": [],
    "source": [
     "import random\n",
+    "\n",
+    "\n",
     "random.seed(123)"
    ]
   },
@@ -16,6 +18,7 @@
    "metadata": {},
    "source": [
     "## Models / inferencers\n",
+    "\n",
     "Models & inferencers accept lists of images, and return lists of results (either segmentation or recognition results)\n",
     "\n",
     "I have made a dummy `SegmentationModel` and `RecognitionModel` in `models.py`. These do the same thing as the current inferencers.\n",
@@ -34,7 +37,6 @@
     "    labels: np.ndarray\n",
     "```\n",
     "\n",
-    "\n",
     "(It would be nice to wrap all models in a \"batching\" function, which divides an input list into chunks if it is too long) -> This is a card in DevOps\n"
    ]
   },
@@ -45,7 +47,7 @@
    "source": [
     "## Using the Volume class\n",
     "\n",
-    "To load images, create a `Volume`. The name of this class is not set in stone... It represents what Catrin called a \"batch\", a divison of an archive volume, but I don't want to use \"batch\" because of potential confusion with a model's batch (the number of inputs it operates on simultaneously). \n"
+    "To load images, create a `Volume`. The name of this class is not set in stone... It represents what Catrin called a \"batch\", a divison of an archive volume, but I don't want to use \"batch\" because of potential confusion with a model's batch (the number of inputs it operates on simultaneously).\n"
    ]
   },
   {
@@ -56,7 +58,8 @@
    "source": [
     "from htrflow_core.volume import Volume\n",
     "\n",
-    "images = ['../assets/demo_image.jpg'] * 5 \n",
+    "\n",
+    "images = [\"../assets/demo_image.jpg\"] * 5\n",
     "\n",
     "volume = Volume(images)"
    ]
@@ -66,7 +69,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The `Volume` instance holds a tree. We see the root `node` and its five children, each representing one input image:"
+    "The `Volume` instance holds a tree. We see the root `node` and its five children, each representing one input image:\n"
    ]
   },
   {
@@ -96,7 +99,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The images are available through `volume.images()`. We pass them through a segmentation model:"
+    "The images are available through `volume.images()`. We pass them through a segmentation model:\n"
    ]
   },
   {
@@ -180,6 +183,7 @@
    "source": [
     "from htrflow_core.models.dummy_models import SegmentationModel\n",
     "\n",
+    "\n",
     "model = SegmentationModel()\n",
     "results = model(volume.images())\n",
     "print(results[0])"
@@ -190,7 +194,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The results are a list of `SegmentationResult`. To apply the results to the input images, we pass them back to the volume with its `update` method. It returns the new regions as a list of images."
+    "The results are a list of `SegmentationResult`. To apply the results to the input images, we pass them back to the volume with its `update` method. It returns the new regions as a list of images.\n"
    ]
   },
   {
@@ -207,7 +211,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The volume tree has now grown:"
+    "The volume tree has now grown:\n"
    ]
   },
   {
@@ -252,7 +256,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The new regions can be passed through a segmentation model (such as a line model) again. The `update` method always updates the leaves of the tree."
+    "The new regions can be passed through a segmentation model (such as a line model) again. The `update` method always updates the leaves of the tree.\n"
    ]
   },
   {
@@ -345,7 +349,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When the segmentation is done, the segments can be passed to a text recognition model. The results are passed to the workbench in the same manner as before:"
+    "When the segmentation is done, the segments can be passed to a text recognition model. The results are passed to the workbench in the same manner as before:\n"
    ]
   },
   {
@@ -430,6 +434,7 @@
    "source": [
     "from htrflow_core.models.dummy_models import RecognitionModel\n",
     "\n",
+    "\n",
     "recognition_model = RecognitionModel()\n",
     "results = recognition_model(volume.segments())\n",
     "volume.update(results)\n",
@@ -442,7 +447,8 @@
    "metadata": {},
    "source": [
     "## Accessing nodes\n",
-    "Specific nodes are accessed by tuple indexing. Here we extract the first line of the first region of the first image:"
+    "\n",
+    "Specific nodes are accessed by tuple indexing. Here we extract the first line of the first region of the first image:\n"
    ]
   },
   {
@@ -479,12 +485,12 @@
     "```python\n",
     "\n",
     "class BaseImageNode:\n",
-    "    \n",
+    "\n",
     "    @property\n",
     "    def image(self):\n",
     "        x1, x2, y1, y2 = self.box\n",
     "        return self.parent.image[y1:y2, x1:x2]\n",
-    "    \n",
+    "\n",
     "    ...\n",
     "```\n"
    ]
@@ -564,7 +570,7 @@
    "source": [
     "## Coordinates\n",
     "\n",
-    "All nodes have a `coordinate` attribute. This is the location of the node's top-left corner *relative to the original image*. The base image node's coordinate is thus (0,0):"
+    "All nodes have a `coordinate` attribute. This is the location of the node's top-left corner _relative to the original image_. The base image node's coordinate is thus (0,0):\n"
    ]
   },
   {
@@ -593,7 +599,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For first-level regions `coordinate` is the same as the corner of the segment bounding box."
+    "For first-level regions `coordinate` is the same as the corner of the segment bounding box.\n"
    ]
   },
   {
@@ -614,16 +620,16 @@
     }
    ],
    "source": [
-    "print('Coordinate:', volume[0, 0].coordinate)\n",
-    "print('Bounding box:', volume[0, 0].data['segment'].box, '(x1, x2, y1, y2)')"
+    "print(\"Coordinate:\", volume[0, 0].coordinate)\n",
+    "print(\"Bounding box:\", volume[0, 0].data[\"segment\"].box, \"(x1, x2, y1, y2)\")"
    ]
   },
   {
    "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "But for nested regions the two differ, because `coordinate` is relative to the original image, while the segment bounding box is relative to the parent region."
+    "But for nested regions the two differ, because `coordinate` is relative to the original image, while the segment bounding box is relative to the parent region.\n"
    ]
   },
   {
@@ -644,8 +650,8 @@
     }
    ],
    "source": [
-    "print('Global coordinate:', volume[0, 0, 0].coordinate)\n",
-    "print('Local bounding box:', volume[0, 0, 0].data['segment'].box)"
+    "print(\"Global coordinate:\", volume[0, 0, 0].coordinate)\n",
+    "print(\"Local bounding box:\", volume[0, 0, 0].data[\"segment\"].box)"
    ]
   },
   {

pyproject.toml

@@ -111,6 +111,7 @@ exclude_lines = [
 [tool.ruff]
 line-length = 119
 target-version = "py310"
+extend-include = ["*.ipynb"]
 
 [tool.ruff.lint]
 ignore = ["C901", "E741", "W605"]

src/htrflow_core/serialization.py

@@ -1,10 +1,10 @@
 from __future__ import annotations
 
-from collections import defaultdict
 import datetime
 import json
 import logging
 import os
+from collections import defaultdict
 from typing import TYPE_CHECKING, Iterable, Sequence, Union
 
 import xmlschema