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PierreRaybautPierreRaybaut
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Add unit tests for Plotter and Workspace APIs, implement Workspace class
- Created unit tests for the Plotter API, covering basic operations, plotting signals and images, and handling missing objects. - Developed unit tests for the Workspace API, including object addition, retrieval, existence checks, renaming, and clearing. - Implemented the Workspace class with Standalone and Live backends, supporting data access, persistence, and synchronization with DataLab. - Added support for saving and loading workspaces in HDF5 format. - Defined project metadata in pyproject.toml for package management and dependencies.
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‎.github/workflows/test.yml‎

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# This workflow installs dependencies, starts DataLab, and runs tests with live mode
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# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
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name: Test with Live DataLab
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on:
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push:
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branches: [ "main", "develop" ]
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pull_request:
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branches: [ "main", "develop" ]
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workflow_dispatch:
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jobs:
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test:
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env:
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DISPLAY: ':99.0'
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runs-on: ubuntu-latest
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strategy:
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fail-fast: false
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matrix:
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python-version: ["3.9", "3.13"]
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steps:
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- uses: actions/checkout@v4
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v5
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install system dependencies
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run: |
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sudo apt-get update
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sudo apt-get install -y \
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libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 \
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libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 \
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libxcb-shape0 libegl1 libgl1-mesa-glx x11-utils
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- name: Start Xvfb
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run: |
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/sbin/start-stop-daemon --start --quiet \
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--pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background \
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--exec /usr/bin/Xvfb -- :99 -screen 0 1920x1200x24 -ac +extension GLX
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- name: Install Python dependencies
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run: |
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python -m pip install --upgrade pip
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python -m pip install ruff pytest pytest-cov
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python -m pip install PyQt5
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- name: Install DataLab and dependencies
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run: |
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if [ "${{ github.ref_name }}" = "develop" ]; then
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# Clone and install development versions
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cd ..
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git clone --depth 1 https://github.com/PlotPyStack/PythonQwt.git
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git clone --depth 1 --branch develop https://github.com/PlotPyStack/guidata.git
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git clone --depth 1 --branch develop https://github.com/PlotPyStack/plotpy.git
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git clone --depth 1 --branch develop https://github.com/DataLab-Platform/sigima.git
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git clone --depth 1 --branch develop https://github.com/DataLab-Platform/DataLab.git
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cd DataLab-Kernel
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pip install -e ../guidata
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pip install -e ../PythonQwt
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pip install -e ../plotpy
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pip install -e ../sigima
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pip install -e ../DataLab
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else
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# Install from PyPI for main branch
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pip install datalab-platform
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fi
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- name: Install DataLab-Kernel
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run: |
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# Install without deps (we already have them)
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pip install -e . --no-deps
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# Install remaining deps from pyproject.toml
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pip install ipykernel jupyter-client numpy h5py matplotlib
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- name: Lint with Ruff
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run: ruff check --output-format=github datalab_kernel
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- name: Run unit tests
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run: pytest datalab_kernel/tests/unit datalab_kernel/tests/contract -v --tb=long
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- name: Run live integration tests
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run: |
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# Run integration tests with DataLab auto-start
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pytest --live --start-datalab datalab_kernel/tests/integration -v --tb=long

‎.vscode/tasks.json‎

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{
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"version": "2.0.0",
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"tasks": [
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{
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"label": "🧽🔦 Ruff",
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"type": "shell",
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"command": "${command:python.interpreterPath}",
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"args": [
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"-m",
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"ruff",
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"format",
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"datalab_kernel",
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";",
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"${command:python.interpreterPath}",
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"-m",
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"ruff",
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"check",
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"--fix",
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"datalab_kernel"
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],
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"options": {
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"cwd": "${workspaceFolder}"
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},
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"group": {
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"kind": "build",
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"isDefault": true
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}
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},
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{
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"label": "🚀 Pytest",
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"type": "shell",
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"command": "${command:python.interpreterPath}",
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"args": [
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"-m",
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"pytest",
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"--live",
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"--start-datalab",
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"datalab_kernel/tests/",
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"-v",
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"--ff"
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],
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"options": {
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"cwd": "${workspaceFolder}"
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},
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"group": {
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"kind": "test",
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"isDefault": true
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}
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},
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{
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"label": "📊 Coverage",
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"type": "shell",
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"command": "${command:python.interpreterPath}",
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"args": [
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"-m",
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"pytest",
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"datalab_kernel/tests/",
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"--cov=datalab_kernel",
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"--cov-report=term-missing",
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"--cov-report=html"
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],
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"options": {
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"cwd": "${workspaceFolder}"
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},
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"group": "build"
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},
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{
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"label": "🔧 Install Kernel",
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"type": "shell",
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"command": "${command:python.interpreterPath}",
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"args": [
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"-m",
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"datalab_kernel",
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"install",
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"--user"
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],
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"options": {
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"cwd": "${workspaceFolder}"
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},
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"group": "build"
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},
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{
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"label": "🗑️ Uninstall Kernel",
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"type": "shell",
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"command": "${command:python.interpreterPath}",
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"args": [
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"-m",
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"datalab_kernel",
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"uninstall",
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"--user"
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],
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"options": {
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"cwd": "${workspaceFolder}"
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},
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"group": "build"
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}
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]
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}

‎Untitled.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "ac5cee0c-62e1-4b17-890e-9aa443e6503a",
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"metadata": {},
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"outputs": [
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{
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"ename": "KeyError",
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"evalue": "\"Object 'i001' not found. Available objects: [(empty)]\"",
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"output_type": "error",
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"traceback": [
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mKeyError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Pre-configured namespace\u001b[39;00m\n\u001b[32m 2\u001b[39m workspace.list()\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m img = \u001b[43mworkspace\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mi001\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 4\u001b[39m plotter.plot(img)\n\u001b[32m 5\u001b[39m workspace.save(\u001b[33m\"\u001b[39m\u001b[33manalysis.h5\u001b[39m\u001b[33m\"\u001b[39m)\n",
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"\u001b[36mFile \u001b[39m\u001b[32mC:\\Dev\\DataLab-Kernel\\datalab_kernel\\workspace.py:569\u001b[39m, in \u001b[36mWorkspace.get\u001b[39m\u001b[34m(self, name)\u001b[39m\n\u001b[32m 557\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mget\u001b[39m(\u001b[38;5;28mself\u001b[39m, name: \u001b[38;5;28mstr\u001b[39m) -> DataObject:\n\u001b[32m 558\u001b[39m \u001b[38;5;250m \u001b[39m\u001b[33;03m\"\"\"Retrieve an object by name.\u001b[39;00m\n\u001b[32m 559\u001b[39m \n\u001b[32m 560\u001b[39m \u001b[33;03m Args:\u001b[39;00m\n\u001b[32m (...)\u001b[39m\u001b[32m 567\u001b[39m \u001b[33;03m KeyError: If object not found\u001b[39;00m\n\u001b[32m 568\u001b[39m \u001b[33;03m \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m569\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_backend\u001b[49m\u001b[43m.\u001b[49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mname\u001b[49m\u001b[43m)\u001b[49m\n",
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"\u001b[36mFile \u001b[39m\u001b[32mC:\\Dev\\DataLab-Kernel\\datalab_kernel\\workspace.py:104\u001b[39m, in \u001b[36mStandaloneBackend.get\u001b[39m\u001b[34m(self, name)\u001b[39m\n\u001b[32m 102\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m._objects:\n\u001b[32m 103\u001b[39m available = \u001b[33m\"\u001b[39m\u001b[33m, \u001b[39m\u001b[33m\"\u001b[39m.join(\u001b[38;5;28mself\u001b[39m._objects.keys()) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m._objects \u001b[38;5;28;01melse\u001b[39;00m \u001b[33m\"\u001b[39m\u001b[33m(empty)\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m--> \u001b[39m\u001b[32m104\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\n\u001b[32m 105\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mObject \u001b[39m\u001b[33m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m not found. Available objects: [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mavailable\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m]\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 106\u001b[39m )\n\u001b[32m 107\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._objects[name]\n",
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"\u001b[31mKeyError\u001b[39m: \"Object 'i001' not found. Available objects: [(empty)]\""
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]
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}
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],
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"source": [
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"# Pre-configured namespace\n",
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"workspace.list()\n",
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"img = workspace.get(\"i001\")\n",
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"plotter.plot(img)\n",
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"workspace.save(\"analysis.h5\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e755eccc-97e8-4e69-8d47-28bf06de9e86",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "DataLab",
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"language": "python",
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"name": "datalab-kernel"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

‎datalab_kernel/__init__.py‎

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# Copyright (c) DataLab Platform Developers, BSD 3-Clause License
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# See LICENSE file for details
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"""
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DataLab Jupyter Kernel
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======================
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A standalone Jupyter kernel providing seamless, reproducible access to DataLab
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workspaces, with optional live synchronization to the DataLab GUI.
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Main components:
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- `workspace`: Data access and persistence API
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- `plotter`: Visualization frontend
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- `sigima`: Scientific processing library (re-exported)
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Example usage::
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# In a Jupyter notebook with datalab-kernel
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img = workspace.get("i042")
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filtered = sigima.proc.image.butterworth(img, cut_off=0.2)
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workspace.add("filtered_i042", filtered)
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plotter.plot("filtered_i042")
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"""
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from __future__ import annotations
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__version__ = "0.1.0"
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__author__ = "DataLab Platform Developers"
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from datalab_kernel.plotter import Plotter
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from datalab_kernel.workspace import Workspace
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# Global instances (initialized when kernel starts)
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workspace: Workspace | None = None
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plotter: Plotter | None = None
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__all__ = [
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"__version__",
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"Workspace",
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"Plotter",
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"workspace",
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"plotter",
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]

‎datalab_kernel/__main__.py‎

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# Copyright (c) DataLab Platform Developers, BSD 3-Clause License
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# See LICENSE file for details
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"""
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DataLab Kernel main module.
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Supports running as:
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python -m datalab_kernel install
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python -m datalab_kernel uninstall
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"""
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from __future__ import annotations
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import sys
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from datalab_kernel.install import main as install_main
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def main() -> None:
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"""Main entry point for the datalab_kernel module."""
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if len(sys.argv) > 1 and sys.argv[1] in ("install", "uninstall"):
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# Handle install/uninstall commands
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install_main()
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else:
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# Launch kernel (default behavior when run by Jupyter)
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from ipykernel.kernelapp import IPKernelApp
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from datalab_kernel.kernel import DataLabKernel
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IPKernelApp.launch_instance(kernel_class=DataLabKernel)
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if __name__ == "__main__":
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main()

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