Bugfix for python v3.10 and add bolometer observer test

CHANGELOG.md

@@ -5,6 +5,27 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.4.1] - 2026-04-03
+
+### Added
+
+- Add builtin mock bolometer dataset generator `datasets.bolometer_moc`
+- Add bolometer observer notebook (`docs/notebooks/observer/bolometer.ipynb`)
+- Add observer tests and fixtures for loading and visualizing bolometer data
+- Add `plotly` to test dependencies for bolometer visualization testing
+
+### Changed
+
+- Export builtin dataset helpers from `cherab.imas.datasets`
+- Update bolometer visualization output to show ray-through ratio as a percentage
+- Refine documentation notebooks and formatting related to plasma and bolometer workflows
+
+### Fixed
+
+- Fix dataset path examples in fetcher docstrings
+- Fix notebook raw-cell metadata formatting
+- Fix Python 3.10 typing compatibility in bolometer enum `from_value()` methods by replacing `Self`-based annotations, preventing version-specific type errors
+
 ## [0.4.0] - 2026-03-31
 
 ### Added

docs/notebooks/misc/fractional_abundances.ipynb

@@ -73,13 +73,10 @@
    "cell_type": "raw",
    "id": "6",
    "metadata": {
-    "raw_mimetype": "text/restructuredtext",
-    "vscode": {
-     "languageId": "raw"
-    }
+    "raw_mimetype": "text/restructuredtext"
    },
    "source": [
-    "The `.solve_coronal_equilibrium` function computes the fractional abundances by setting the total density to one."
+    "The :obj:`.solve_coronal_equilibrium` function computes the fractional abundances by setting the total density to one."
    ]
   },
   {

docs/notebooks/observer/bolometer.ipynb

@@ -0,0 +1,165 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0",
+   "metadata": {},
+   "source": [
+    "# Bolometer\n",
+    "\n",
+    "Here we demonstrate how to load bolometer observers from a bolometer IDS, and visualize them to check what they look like.\n",
+    "The dataset used in this example is a mock bolometer dataset, which consists of different types of bolometer cameras."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from plotly import io\n",
+    "\n",
+    "from cherab.imas.datasets import bolometer_moc\n",
+    "from cherab.imas.observer.bolometer import load_bolometers, visualize\n",
+    "\n",
+    "io.renderers.default = \"notebook\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2",
+   "metadata": {},
+   "source": [
+    "## Load bolometer dataset\n"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "3",
+   "metadata": {
+    "raw_mimetype": "text/restructuredtext"
+   },
+   "source": [
+    "The list of instances :obj:`~cherab.tools.observers.bolometry.BolometerCamera` are loaded from the ``\"bolometer\"`` IDS."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "path = bolometer_moc()\n",
+    "bolometers = load_bolometers(path, \"r\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for bolo in bolometers:\n",
+    "    print(f\"Bolometer: {bolo.name}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6",
+   "metadata": {},
+   "source": [
+    "## Visualize bolometer observers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7",
+   "metadata": {},
+   "source": [
+    "### Pinhole camera"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = visualize(\n",
+    "    bolometers[0],\n",
+    "    num_rays=100,\n",
+    "    ray_terminate_distance=2.0e-2,\n",
+    "    show=False,\n",
+    ")\n",
+    "fig.update_layout(template=\"plotly_dark\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9",
+   "metadata": {},
+   "source": [
+    "### Collimator cameras"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = visualize(\n",
+    "    bolometers[1],\n",
+    "    num_rays=100,\n",
+    "    ray_from_channel=[0, 3],\n",
+    "    ray_terminate_distance=2.0e-2,\n",
+    "    show=False,\n",
+    ")\n",
+    "fig.update_layout(template=\"plotly_dark\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "11",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig = visualize(\n",
+    "    bolometers[2],\n",
+    "    num_rays=200,\n",
+    "    ray_from_channel=[0, 3],\n",
+    "    ray_terminate_distance=2.0e-2,\n",
+    "    show=False,\n",
+    ")\n",
+    "fig.update_layout(template=\"plotly_dark\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "docs",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/notebooks/plasma/emission.ipynb

@@ -63,10 +63,7 @@
    "cell_type": "raw",
    "id": "4",
    "metadata": {
-    "mime_type": "text/restructuredtext",
-    "vscode": {
-     "languageId": "raw"
-    }
+    "raw_mimetype": "text/restructuredtext"
    },
    "source": [
     "When using OpenADAS data first time, the relevant data files must be downloaded and installed.\n",

docs/source/conf.py

@@ -183,6 +183,9 @@
 
 # -- NBSphinx configuration ---------------------------------------------------
 # nbsphinx_execute = "never"
+nbsphinx_thumbnails = {
+    "notebooks/observer/bolometer": "_static/images/bolometer.png",
+}
 nbsphinx_prolog = r"""
 {% set docname = 'docs/' + env.doc2path(env.docname, base=None)|string %}
 

docs/source/examples.md

@@ -14,6 +14,17 @@ Each notebook is designed to showcase specific functionalities and provide pract
     notebooks/plasma/*
 ```
 
+## Observer
+
+```{eval-rst}
+.. nbgallery::
+    :name: observer-gallery
+    :glob:
+    :reversed:
+
+    notebooks/observer/*
+```
+
 ## Miscellaneous
 
 ```{eval-rst}

pyproject.toml

@@ -40,7 +40,7 @@ dependencies = [
 dynamic = ["version"]
 
 [project.optional-dependencies]
-test = ["pytest", "pytest-cov", "pooch", "rich"]
+test = ["pytest", "pytest-cov", "pooch", "rich", "plotly"]
 
 [project.urls]
 Homepage = "https://github.com/cherab"

src/cherab/imas/datasets/__init__.py

@@ -1,11 +1,11 @@
-r"""Sample Dataset utilities for fetching and processing data.
+r"""Sample Dataset utilities.
 
 Usage of Datasets
 =================
 
 CHERAB-IMAS dataset methods can be simply called as follows: ``'<dataset-name>()'``
-This downloads the dataset files over the network once, and saves the cache,
-before returning the path to the downloaded data file.
+This downloads/creates the dataset files over the network once, and saves the cache,
+before returning the path to the downloaded/created data file.
 
 How dataset retrieval and storage works
 =======================================
@@ -46,7 +46,14 @@
 the internet connectivity.
 """
 
+from ._builtin import bolometer_moc
 from ._fetchers import iter_jintrac, iter_jorek, iter_solps
 from ._utils import clear_cache
 
-__all__ = ["iter_jintrac", "iter_solps", "iter_jorek", "clear_cache"]
+__all__ = [
+    "iter_jintrac",
+    "iter_solps",
+    "iter_jorek",
+    "bolometer_moc",
+    "clear_cache",
+]