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feat: add release notes section and initial content for version 0.2
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‎README.md‎

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## Documentation
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📖 Full documentation is available at:
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👉 <https://datalab-kernel.readthedocs.io/>
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> Want to use DataLab-Kernel inside DataLab with GUI tools?
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> Check out the full platform: [DataLab](https://datalab-platform.com/)
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---
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## Key Features
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- **Single, stable user API**
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## Documentation
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- **User contract and behavior**: see `plans/specification.md`
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- **Vision and architectural principles**: see `plans/architecture.md`
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---
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## Project Status
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DataLab-Kernel is under active design and development.

‎doc/index.rst‎

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api/index
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examples/index
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contributing/index
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release_notes/index
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requirements

‎doc/release_notes/index.rst‎

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Release notes
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=============
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This section contains the release notes for all versions of DataLab-Kernel,
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documenting new features, improvements, bug fixes, and breaking changes.
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.. toctree::
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:maxdepth: 1
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:glob:
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:reversed:
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release_*

‎doc/release_notes/release_0.02.md‎

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# Version 0.2 #
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## DataLab-Kernel Version 0.2.10 (not released yet) ##
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### ✨ New features since version 0.2.9 ###
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* **Multi-object plotting**: `plotter.plot()` now accepts lists of signals or images,
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enabling side-by-side visualization of multiple objects in a single call
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* **Analysis result display**: Geometry and table results now display as separate
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HTML tables below the plot instead of cluttering the figure with on-plot text
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annotations — especially important when working with multiple ROIs
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* **Consistent figure sizing**: Introduced `DEFAULT_PLOT_WIDTH` constant for
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uniform figure dimensions across Plotly and Matplotlib backends
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* **User-defined figure dimensions**: Plotly plots now accept custom `width` and
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`height` keyword arguments for fine-grained control over figure sizing
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* **Advanced plotting showcase notebook**: Added a comprehensive notebook
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demonstrating advanced features: multiple curve styles, colormaps, ROI-based
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analysis, geometry/table results, and interactive Plotly visualization
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### 🛠️ Bug Fixes since version 0.2.9 ###
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* Fixed legend display in Plotly plots — legends are now hidden by default for
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cleaner visualization
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* Fixed default Plotly signal figure dimensions to match Matplotlib's aspect ratio
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* Fixed figure serialization performance in Plotly backend
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* Fixed image rendering edge cases in Matplotlib backend
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### 🔧 Other changes since version 0.2.9 ###
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* Updated documentation with structured "Try it Online" section linking to both
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Quick Start and Advanced Showcase notebooks
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* Improved portability of development scripts by using relative paths
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## DataLab-Kernel Version 0.2.9 (2026-02-08) ##
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### ✨ New features ###
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#### Plotly visualization backend ####
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* Added a new **Plotly** interactive visualization backend as an alternative to
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the existing Matplotlib static backend. Plotly figures provide rich
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interactivity (zoom, pan, hover tooltips) directly in Jupyter notebooks.
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* Install with `pip install datalab-kernel[plotly]`.
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#### Smart backend selection ####
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* When both Plotly and Matplotlib are available, **Plotly is automatically
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preferred** for its richer interactive experience.
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* Switch backends at runtime with `plotter.set_backend("matplotlib")` or
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`plotter.set_backend("plotly")`.
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* Override the default backend via the `DATALAB_PLOTTER_BACKEND` environment
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variable.
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#### Enhanced Matplotlib backend ####
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* Extracted the Matplotlib rendering logic into a dedicated `matplotlib_backend`
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module with improved support for:
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* Error bars
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* Curve styles (Sticks, Steps)
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* Per-object line styling from metadata
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* Log scale and axis bounds
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* Non-uniform image coordinates (`pcolormesh`)
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* Per-object colormaps
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#### Figure height customization ####
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* Enhanced figure height customization for both backends, with updated
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documentation.
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#### MIME bundle support for Plotly ####
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* Plotly figures now produce proper MIME bundles for native Jupyter display
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integration.
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### 🔧 Improvements ###
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#### Documentation ####
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* Removed references to the legacy XML-RPC connection method; documentation now
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emphasizes the **Web API** as the primary (and recommended) connection method.
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* Updated README with visualization backend documentation, usage examples, and
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runtime switching instructions.
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* Updated online demo badge link.
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#### Python version support ####
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* Added Python **3.13** and **3.14** to the supported version classifiers.
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#### Optional dependency ####
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* Added `plotly` optional dependency group in `pyproject.toml`
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(`pip install datalab-kernel[plotly]`).
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#### Dev tooling ####
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* Enhanced wheel building script (`serve_dev_wheels.py`) to support package
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selection via `--packages` argument.
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* New VS Code tasks for interactive plot tests, dev wheel serving, and
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backend-specific testing.
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### 🛠️ Bug Fixes ###
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* Fixed test compatibility with **Python 3.9** by using `ExitStack` instead of
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multiple `mock.patch` context managers.
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* Fixed test module shadowing issue by using `mock.patch.object` instead of
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`mock.patch` with string targets.
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* Fixed various Ruff and Pylint warnings across the codebase.
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* Fixed test workflow branch references for DataLab installation.
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### 🧪 Test Suite ###
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* Added **65 tests** for the Matplotlib backend covering all rendering features,
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backward-compatibility aliases, and edge cases.
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* Added **40 tests** for the Plotly backend covering all rendering features and
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edge cases.
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* Added backend selection and resolution logic tests.
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* Added `--gui` interactive gallery viewers for visual test validation (tkinter
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for Matplotlib, tabbed HTML for Plotly).
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## DataLab-Kernel Versions 0.2.0–0.2.8 ##
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These early development releases established the core architecture of
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DataLab-Kernel, including:
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* Workspace API (`add`, `get`, `remove`, `list`, `save`, `load`)
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* Standalone and Live operating modes with auto-discovery
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* Matplotlib-based plotting (`Plotter` class)
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* Geometry and table result display
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* HDF5 persistence for reproducible analysis
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* `%load_ext datalab_kernel` extension for JupyterLite compatibility
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* Web API-based connection to DataLab (replacing legacy XML-RPC)
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* Comprehensive test suite foundation

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