pax_global_header00006660000000000000000000000064151401411760014512gustar00rootroot0000000000000052 comment=64a20cdbff580b3d2802daf7cb3e703d9219800e DataLab-1.1.0/000077500000000000000000000000001514014117600130015ustar00rootroot00000000000000DataLab-1.1.0/.coveragerc000066400000000000000000000012531514014117600151230ustar00rootroot00000000000000[run] #--- Ignore this warning because the process isolation feature of DataLab #--- causes coverage to report 0% coverage when no computation is performed #--- in the isolated process during the session. disable_warnings = no-data-collected parallel = True concurrency = multiprocessing,thread omit = */datalab/utils/tests.py */datalab/tests/* */guidata/* */plotpy/* */qwt/* */sigima/* #--- Workaround for certain builds of python-opencv package: ./config-3.9.py ./config-3.py ./config.py #--- [report] exclude_also = def __repr__ if __name__ == .__main__.: if TYPE_CHECKING: if DEBUG[\s]*: if self.__debug[\s]*: DataLab-1.1.0/.env.template000066400000000000000000000000141514014117600153770ustar00rootroot00000000000000PYTHONPATH=.DataLab-1.1.0/.github/000077500000000000000000000000001514014117600143415ustar00rootroot00000000000000DataLab-1.1.0/.github/ISSUE_TEMPLATE/000077500000000000000000000000001514014117600165245ustar00rootroot00000000000000DataLab-1.1.0/.github/ISSUE_TEMPLATE/bug_report.md000066400000000000000000000013341514014117600212170ustar00rootroot00000000000000--- name: Bug report about: Create a report to help us improve title: '' labels: bug assignees: '' --- **Describe the bug** A clear and concise description of what the bug is. **To Reproduce** Steps to reproduce the behavior: 1. 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And please confirm that you are willing to share this data set under the terms of DataLab's license._ DataLab-1.1.0/.github/copilot-instructions.md000066400000000000000000000501641514014117600211040ustar00rootroot00000000000000# DataLab AI Coding Agent Instructions This document provides comprehensive guidance for AI coding agents working on the DataLab codebase. It covers architecture patterns, development workflows, and project-specific conventions. ## Project Overview **DataLab** is an open-source platform for scientific and technical data processing with a Qt-based GUI. It processes **signals** (1D curves) and **images** (2D arrays). ### Key Architecture Components 1. **Sigima**: Separate computation library providing all processing algorithms (`sigima.proc`) 2. **DataLab GUI**: Qt application layer built on PlotPyStack (PlotPy + guidata) 3. **Processor Pattern**: Bridge between GUI and computation functions 4. **Action Handler**: Manages menus, toolbars, and GUI actions 5. **Plugin System**: Extensible architecture for third-party features 6. **Macro System**: User-scriptable automation via Python 7. **Remote Control**: XML-RPC API for external applications ### Technology Stack - **Python**: 3.9+ (using `from __future__ import annotations`) - **Core Libraries**: NumPy (=1.26.4), SciPy (=1.10.1), scikit-image, OpenCV - **GUI**: Qt via PlotPy (=2.8.2) and guidata (=3.13.3) - **Computation**: Sigima (=1.0.2) - separate package - **Testing**: pytest with coverage - **Linting/Formatting**: Ruff (preferred), Pylint (with specific disables) - **Internationalization**: gettext (.po files), sphinx-intl for docs - **Documentation**: Sphinx with French translations - **Packaging**: PyInstaller (standalone), WiX (MSI installer) ### Workspace Structure ``` DataLab/ +-- datalab/ # Main application code +-- gui/ # GUI layer +-- processor/ # Processor pattern (signal.py, image.py, base.py) +-- actionhandler.py # Menu/action management +-- main.py # Main window +-- panel/ # Signal/Image panels +-- control/ # Remote control API (proxy.py, remote.py) +-- plugins/ # Plugin system +-- tests/ # pytest test suite +-- locale/ # Translations (.po files) +-- config.py # Configuration management +-- doc/ # Sphinx documentation +-- locale/fr/ # French documentation translations +-- features/ # Feature documentation (signal/, image/) +-- macros/examples/ # Demo macros +-- scripts/ # Build/development scripts +-- run_with_env.py # Environment loader (.env support) +-- .env # Local Python path (PYTHONPATH=.;../guidata;../plotpy;../sigima) +-- pyproject.toml # Project configuration ``` **Related Projects** (sibling directories in multi-root workspace): - `../Sigima/` - Computation library - `../PlotPy/` - Plotting library - `../guidata/` - GUI toolkit - `../PythonQwt/` - Qwt bindings ## Development Workflows ### Running Commands **ALWAYS use `scripts/run_with_env.py` for Python commands** to load environment from `.env`: ```powershell # ? CORRECT - Loads PYTHONPATH from .env python scripts/run_with_env.py python -m pytest # ? WRONG - Misses local development packages python -m pytest ``` ### Testing ```powershell # Run all tests python scripts/run_with_env.py python -m pytest --ff # Run specific test python scripts/run_with_env.py python -m pytest datalab/tests/features/signal/ # Coverage python scripts/run_with_env.py python -m coverage run -m pytest datalab python -m coverage html ``` ### Linting and Formatting **Prefer Ruff** (fast, modern): ```powershell # Format code python scripts/run_with_env.py python -m ruff format # Lint with auto-fix python scripts/run_with_env.py python -m ruff check --fix ``` **Pylint** (with extensive disables for code structure): ```powershell python scripts/run_with_env.py python -m pylint datalab \ --disable=duplicate-code,fixme,too-many-arguments,too-many-branches, \ too-many-instance-attributes,too-many-lines,too-many-locals, \ too-many-public-methods,too-many-statements ``` ### Translations **UI Translations** (gettext): ```powershell # Scan and update .po files python scripts/run_with_env.py python -m guidata.utils.translations scan \ --name datalab --directory . --copyright-holder "DataLab Platform Developers" \ --languages fr # Compile .mo files python scripts/run_with_env.py python -m guidata.utils.translations compile \ --name datalab --directory . ``` **Documentation Translations** (sphinx-intl): ```powershell # Extract translatable strings python scripts/run_with_env.py python -m sphinx build doc build/gettext -b gettext -W # Update French .po files python scripts/run_with_env.py python -m sphinx_intl update -d doc/locale -p build/gettext -l fr # Build localized docs python scripts/run_with_env.py python -m sphinx build doc build/doc -b html -D language=fr ``` ## Core Patterns ### 1. Processor Pattern (GUI ? Computation Bridge) **Location**: `datalab/gui/processor/` **Key Concept**: Processors bridge GUI panels and Sigima computation functions. They define **generic processing types** based on input/output patterns. #### Generic Processing Types | Method | Pattern | Multi-selection | Use Cases | |--------|---------|----------------|-----------| | `compute_1_to_1` | 1 obj ? 1 obj | k ? k | Independent transformations (FFT, normalization) | | `compute_1_to_0` | 1 obj ? metadata | k ? 0 | Analysis producing scalar results (FWHM, centroid) | | `compute_1_to_n` | 1 obj ? n objs | k ? kn | ROI extraction, splitting | | `compute_n_to_1` | n objs ? 1 obj | n ? 1 (or n ? n pairwise) | Averaging, summing, concatenation | | `compute_2_to_1` | 1 obj + 1 operand ? 1 obj | k + 1 ? k (or n + n pairwise) | Binary operations (add, multiply) | **Example: Implementing a New Processing Feature** ```python # 1. Implement computation in Sigima (sigima/proc/signal/processing.py) def my_processing_func(src: SignalObj, param: MyParam) -> SignalObj: """My processing function.""" dst = src.copy() # ... computation logic ... return dst # 2. Register in DataLab processor (datalab/gui/processor/signal.py) def register_processing(self) -> None: self.register_1_to_1( sips.my_processing_func, _("My Processing"), paramclass=MyParam, icon_name="my_icon.svg", ) ``` #### Registration Methods ```python # In SignalProcessor or ImageProcessor class def register_operations(self) -> None: """Register operations (basic math, transformations).""" # 1-to-1: Apply to each selected object independently self.register_1_to_1( sips.normalize, _("Normalize"), paramclass=sigima.params.NormalizeParam, icon_name="normalize.svg", ) # 2-to-1: Binary operation with a second operand self.register_2_to_1( sips.difference, _("Difference"), icon_name="difference.svg", obj2_name=_("signal to subtract"), skip_xarray_compat=False, # Enable X-array compatibility check ) # n-to-1: Aggregate multiple objects self.register_n_to_1( sips.average, _("Average"), icon_name="average.svg", ) ``` ### 2. X-array Compatibility System **Critical Pattern**: For **2-to-1** and **n-to-1** operations on signals, DataLab checks if X arrays match. If not, it **interpolates** the second signal to match the first. **When to Skip**: Use `skip_xarray_compat=True` when operations **intentionally use mismatched X arrays** (e.g., replacing X with Y values from another signal). ```python # ? BAD: Will trigger unwanted interpolation self.register_2_to_1( sips.replace_x_by_other_y, _("Replace X by other signal's Y"), ) # ? GOOD: Skips compatibility check self.register_2_to_1( sips.replace_x_by_other_y, _("Replace X by other signal's Y"), skip_xarray_compat=True, # Operation uses Y values as X coordinates ) ``` **Code Location**: `datalab/gui/processor/base.py` (lines ~1764, 1886) ### 3. Action Handler Pattern (Menu Management) **Location**: `datalab/gui/actionhandler.py` **Purpose**: Manages all GUI actions (menus, toolbars, context menus). Actions point to processors, panels, or direct operations. **Key Classes**: - `SignalActionHandler`: Signal-specific actions - `ImageActionHandler`: Image-specific actions - `SelectCond`: Conditions for enabling/disabling actions **Example: Adding a Menu Action** ```python # In SignalActionHandler or ImageActionHandler def setup_processing_actions(self) -> None: """Setup processing menu actions.""" # Reference registered processor operation act = self.action_for("my_processing_func") # Add to menu self.processing_menu.addAction(act) ``` **Menu Organization**: Menus are organized by function: - `File` ? Import/export, project management - `Edit` ? Copy/paste, delete, metadata editing - `Operation` ? Basic math (add, multiply, etc.) - `Processing` ? Advanced transformations, filters - `Axis transformation` ? Calibration, X-Y mode, replace X - `Analysis` ? Measurements, ROI extraction - `Computing` ? FFT, convolution, fit The complete menu structure is defined in `datalab/gui/actionhandler.py`. A text extract of the menu hiearchy is available in `scripts/datalab_menus.txt` (it is generated with `scripts/print_datalab_menus.py`). ### 4. Plugin System **Location**: `datalab/plugins.py`, `datalab/plugins/` **Key Classes**: - `PluginBase`: Base class for all plugins (uses metaclass `PluginRegistry`) - `PluginInfo`: Plugin metadata (name, version, description, icon) **Example: Creating a Plugin** ```python from datalab.plugins import PluginBase, PluginInfo class MyPlugin(PluginBase): """My custom plugin.""" def __init__(self): super().__init__() self.info = PluginInfo( name="My Plugin", version="1.0.0", description="Does something useful", icon="my_icon.svg", ) def register(self, mainwindow: DLMainWindow) -> None: """Register plugin with main window.""" # Add menu items, actions, etc. pass def unregister(self) -> None: """Unregister plugin.""" pass ``` **Plugin Discovery**: Plugins are loaded from: 1. `datalab/plugins/` (built-in) 2. User-defined paths in `Conf.get_path("plugins")` 3. For frozen apps, from `plugins/` directory next to executable ### 5. Macro System **Location**: `macros/examples/` **Purpose**: User-scriptable automation using Python. Macros use the **Remote Proxy** API to control DataLab. **Example Macro**: ```python from datalab.control.proxy import RemoteProxy import numpy as np proxy = RemoteProxy() # Create signal x = np.linspace(0, 10, 100) y = np.sin(x) proxy.add_signal("My Signal", x, y) # Apply processing proxy.calc("normalize") proxy.calc("moving_average", sigima.params.MovingAverageParam.create(n=5)) ``` **Key API Methods**: - `proxy.add_signal()`, `proxy.add_image()`: Create objects - `proxy.calc()`: Run processor methods - `proxy.get_object()`: Retrieve data - `proxy.call_method()`: Call any public panel or window method **Generic Method Calling**: ```python # Remove objects from current panel proxy.call_method("remove_object", force=True) # Call method on specific panel proxy.call_method("delete_all_objects", panel="signal") # Call main window method panel_name = proxy.call_method("get_current_panel") ``` ### 6. Remote Control API **Location**: `datalab/control/` **Classes**: - `RemoteProxy`: XML-RPC client for remote DataLab control - `LocalProxy`: Direct access for same-process scripting **Calling Processor Methods**: ```python # Without parameters proxy.calc("average") # With parameters p = sigima.params.MovingAverageParam.create(n=30) proxy.calc("moving_average", p) ``` ## Coding Conventions ### Naming - **Functions**: `snake_case` (e.g., `replace_x_by_other_y`) - **Classes**: `PascalCase` (e.g., `SignalProcessor`) - **Constants**: `UPPER_SNAKE_CASE` (e.g., `PARAM_DEFAULTS`) - **Private methods**: `_snake_case` (single underscore prefix) ### UI Text vs Function Names - **Function name**: Technical, precise (e.g., `replace_x_by_other_y`) - **UI text**: User-friendly, explicit (e.g., _("Replace X by other signal's Y")) - **French translation**: Natural phrasing (e.g., "Remplacer X par le Y d'un autre signal") ### Type Annotations **Always use** `from __future__ import annotations` for forward references: ```python from __future__ import annotations def process_signal(src: SignalObj) -> SignalObj: """Process signal.""" pass ``` ### Docstrings Use **Google-style docstrings** with type hints: ```python def compute_feature(obj: SignalObj, param: MyParam) -> SignalObj: """Compute feature on signal. Args: obj: Input signal object param: Processing parameters Returns: Processed signal object """ ``` For continued lines in enumerations (args, returns), indent subsequent lines by 1 space: ```python def compute_feature(obj: SignalObj, param: MyParam) -> SignalObj: """Compute feature on signal. Args: obj: Input signal object param: Processing parameters, with a very long description that continues on the next line. Returns: Processed signal object """ ``` ### Imports **Order**: Standard library ? Third-party ? Local ```python from __future__ import annotations import os from typing import TYPE_CHECKING import numpy as np from guidata.qthelpers import create_action from datalab.config import _ from datalab.gui.processor.base import BaseProcessor if TYPE_CHECKING: from sigima.objects import SignalObj ``` ### Internationalization **Always wrap UI strings** with `_()`: ```python from datalab.config import _ # ? CORRECT menu_title = _("Processing") action_text = _("Replace X by other signal's Y") # ? WRONG menu_title = "Processing" # Not translatable! ``` ## Common Tasks ### Adding a New Signal Processing Feature **Complete workflow**: 1. **Implement computation in Sigima** (`sigima/proc/signal/processing.py`): ```python def my_feature(src: SignalObj, param: MyParam | None = None) -> SignalObj: """My feature.""" dst = src.copy() # ... computation ... return dst ``` 2. **Export from Sigima** (`sigima/proc/signal/__init__.py`): ```python from sigima.proc.signal.processing import my_feature # Import __all__ = [..., "my_feature"] # Export ``` 3. **Register in DataLab processor** (`datalab/gui/processor/signal.py`): ```python def register_processing(self) -> None: self.register_1_to_1( sips.my_feature, _("My Feature"), paramclass=MyParam, icon_name="my_icon.svg", ) ``` 4. **Add menu action** (`datalab/gui/actionhandler.py`): ```python def setup_processing_actions(self) -> None: act = self.action_for("my_feature") self.processing_menu.addAction(act) ``` 5. **Add tests** (`datalab/tests/features/signal/`): ```python def test_my_feature(): obj = SignalObj.create(...) result = sips.my_feature(obj) assert result is not None ``` 6. **Document** (`doc/features/signal/menu_processing.rst`): ````rst My Feature ^^^^^^^^^^ Create a new signal by applying my feature: .. image:: /images/my_feature.png Parameters: - **Parameter 1**: Description ```` 7. **Translate**: ```powershell # UI translation python scripts/run_with_env.py python -m guidata.utils.translations scan ... # Doc translation python scripts/run_with_env.py python -m sphinx_intl update -d doc/locale -p build/gettext -l fr ``` ### Working with X-array Compatibility **Rule of thumb**: - **Most 2-to-1 operations**: Default behavior (auto-interpolation) is correct - **X coordinate manipulation**: Set `skip_xarray_compat=True` **Examples**: - ? `difference` (subtract two signals): Compatible X arrays expected ? `skip_xarray_compat=False` - ? `xy_mode` (swap X and Y): Uses Y as new X ? `skip_xarray_compat=True` - ? `replace_x_by_other_y`: Takes Y from second signal as X ? `skip_xarray_compat=True` ### Debugging Tips 1. **Check processor registration**: Look in `register_operations()` methods 2. **Verify action handler**: Search `actionhandler.py` for `action_for("function_name")` 3. **Test in isolation**: Use pytest with `--ff` flag (fail-fast) 4. **Check translations**: Missing `_()` wrapper causes English-only UI 5. **Verify imports**: Ensure function is in `__all__` export list ## Release Classification **Bug Fix** (patch release: 1.0.x): - Fixes incorrect behavior - Restores expected functionality - Addresses user-reported issues - **Example**: Adding `replace_x_by_other_y` to handle wavelength calibration (was previously impossible) **Feature** (minor release: 1.x.0): - Adds entirely new capability - Extends functionality beyond original scope - Introduces new UI elements or workflows ## VS Code Tasks The workspace includes predefined tasks (`.vscode/tasks.json`). Access via: - `Ctrl+Shift+B` ? "???? Ruff" (format + lint) - Terminal ? "Run Task..." ? "?? Pytest", "?? Compile translations", etc. **Key Tasks**: - `???? Ruff`: Format and lint code - `?? Pytest`: Run tests with `--ff` - `?? Compile translations`: Build .mo files - `?? Scan translations`: Update .po files - `?? Build/open HTML doc`: Generate and open Sphinx docs ## Multi-Root Workspace DataLab development uses a **multi-root workspace** (`.code-workspace` file) with sibling projects: - `DataLab/` - Main GUI application - `Sigima/` - Computation library - `PlotPy/` - Plotting library - `guidata/` - GUI toolkit - `PythonQwt/` - Qwt bindings **When working across projects**: 1. Changes in `Sigima` require importing in `DataLab` 2. Use `.env` file to point to local development versions 3. Test changes in both Sigima unit tests AND DataLab integration tests ## Release Notes Guidelines **Location**: `doc/release_notes/release_MAJOR.MINOR.md` where MINOR is zero-padded to 2 digits (e.g., `release_1.00.md` for v1.0.x, `release_1.01.md` for v1.1.x) **Writing Style**: Focus on **user impact**, not implementation details. **Good release note** (user-focused): - ? "Fixed syntax errors when using f-strings with nested quotes in macros" - ? "Fixed corrupted Unicode characters in macro console output on Windows" - ? "Fixed 'Lock LUT range' setting not persisting after closing Settings dialog" **Bad release note** (implementation-focused): - ? "Removed `code.replace('"', "'")` that broke f-strings" - ? "Changed QTextCodec.codecForLocale() to codecForName(b'UTF-8')" - ? "Added missing `ima_def_keep_lut_range` option in configuration" **Structure**: - **What went wrong**: Describe the symptom users experienced - **When it occurred**: Specify the context/scenario - **What's fixed**: Explain the benefit, not the implementation **Example**: ```markdown **Macro execution:** * Fixed syntax errors when using f-strings with nested quotes in macros (e.g., `f'text {func("arg")}'` now works correctly) * Fixed corrupted Unicode characters in macro console output on Windows - special characters like ?, ??, and ? now display correctly instead of showing garbled text ``` ## Key Files Reference | File | Purpose | |------|---------| | `datalab/gui/processor/signal.py` | Signal processor registration | | `datalab/gui/processor/image.py` | Image processor registration | | `datalab/gui/processor/base.py` | Base processor class (generic methods) | | `datalab/gui/actionhandler.py` | Menu and action management | | `datalab/config.py` | Configuration, `_()` translation function | | `datalab/plugins.py` | Plugin system implementation | | `datalab/control/proxy.py` | Remote control API (RemoteProxy, LocalProxy) | | `sigima/proc/signal/processing.py` | Signal computation functions | | `sigima/proc/image/processing.py` | Image computation functions | | `scripts/run_with_env.py` | Environment loader (loads `.env`) | | `.env` | Local PYTHONPATH for development | | `doc/release_notes/release_MAJOR.MINOR.md` | Release notes (MINOR is zero-padded: release_1.00.md for v1.0.x, release_1.01.md for v1.1.x) | ## Getting Help - **Documentation**: https://datalab-platform.com/ - **Issues**: https://github.com/DataLab-Platform/DataLab/issues - **Contributing**: https://datalab-platform.com/en/contributing/index.html - **Support**: p.raybaut@codra.fr --- **Remember**: Always use `scripts/run_with_env.py` for Python commands, wrap UI strings with `_()`, and consider X-array compatibility when adding 2-to-1 signal operations. DataLab-1.1.0/.github/workflows/000077500000000000000000000000001514014117600163765ustar00rootroot00000000000000DataLab-1.1.0/.github/workflows/test_pyqt5.yml000066400000000000000000000164231514014117600212500ustar00rootroot00000000000000# This workflow will install Python dependencies, run tests and lint with a variety of Python versions # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python # Inspired from https://pytest-qt.readthedocs.io/en/latest/troubleshooting.html#github-actions name: Install and Test on Ubuntu (latest) with PyQt5 on: push: branches: [ "main", "develop", "release" ] pull_request: branches: [ "main", "develop", "release" ] workflow_dispatch: inputs: job_to_run: description: 'Which job to run' required: true type: choice options: - 'all' - 'build' - 'build_latest' default: 'all' schedule: # Only the "build_latest" job runs on schedule (see execution conditions below) - cron: "0 5 * * 1" jobs: build: if: ${{ (github.event_name == 'push' || github.event_name == 'pull_request') || (github.event_name == 'workflow_dispatch' && (github.event.inputs.job_to_run == 'all' || github.event.inputs.job_to_run == 'build')) }} env: DISPLAY: ':99.0' runs-on: ubuntu-latest strategy: fail-fast: false matrix: python-version: ["3.9", "3.13"] steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | sudo apt-get update sudo apt-get install -y libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 x11-utils /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1920x1200x24 -ac +extension GLX python -m pip install --upgrade pip python -m pip install ruff pytest httpx pip install PyQt5 if [ "${{ github.ref_name }}" = "develop" ]; then # Clone and install development versions of key dependencies with editable install cd .. git clone --depth 1 https://github.com/PlotPyStack/PythonQwt.git git clone --depth 1 --branch develop https://github.com/PlotPyStack/guidata.git git clone --depth 1 --branch develop https://github.com/PlotPyStack/plotpy.git git clone --depth 1 --branch develop https://github.com/DataLab-Platform/sigima.git cd DataLab pip install -e ../guidata pip install -e ../PythonQwt pip install -e ../plotpy pip install -e ../sigima # Install tomli for TOML parsing (safe if already present) pip install tomli # Extract dependencies and save to file, then install python -c "import tomli; f=open('pyproject.toml','rb'); data=tomli.load(f); deps=[d for d in data['project']['dependencies'] if not any(p in d for p in ['guidata','PlotPy','Sigima'])]; open('deps.txt','w').write('\n'.join(deps))" pip install -r deps.txt # Install DataLab without dependencies pip install --no-deps . elif [ "${{ github.ref_name }}" = "release" ]; then # Clone dependencies from release branches (with fallback to main/master) cd .. # Try cloning PythonQwt from main or master git clone --depth 1 https://github.com/PlotPyStack/PythonQwt.git || git clone --depth 1 --branch master https://github.com/PlotPyStack/PythonQwt.git # Try cloning guidata from release, fallback to main or master git clone --depth 1 --branch release https://github.com/PlotPyStack/guidata.git || git clone --depth 1 https://github.com/PlotPyStack/guidata.git || git clone --depth 1 --branch master https://github.com/PlotPyStack/guidata.git # Try cloning plotpy from release, fallback to main or master git clone --depth 1 --branch release https://github.com/PlotPyStack/plotpy.git || git clone --depth 1 https://github.com/PlotPyStack/plotpy.git || git clone --depth 1 --branch master https://github.com/PlotPyStack/plotpy.git # Try cloning sigima from release, fallback to main git clone --depth 1 --branch release https://github.com/DataLab-Platform/sigima.git || git clone --depth 1 https://github.com/DataLab-Platform/sigima.git cd DataLab pip install -e ../guidata pip install -e ../PythonQwt pip install -e ../plotpy pip install -e ../sigima # Install tomli for TOML parsing (safe if already present) pip install tomli # Extract dependencies and save to file, then install python -c "import tomli; f=open('pyproject.toml','rb'); data=tomli.load(f); deps=[d for d in data['project']['dependencies'] if not any(p in d for p in ['guidata','PlotPy','Sigima'])]; open('deps.txt','w').write('\n'.join(deps))" pip install -r deps.txt # Install DataLab without dependencies pip install --no-deps . else # Install from PyPI normally for main branch pip install . fi - name: Lint with Ruff run: ruff check --output-format=github datalab - name: Test with pytest run: pytest -v --tb=long build_latest: if: ${{ github.event_name == 'schedule' || (github.event_name == 'workflow_dispatch' && (github.event.inputs.job_to_run == 'all' || github.event.inputs.job_to_run == 'build_latest')) }} env: DISPLAY: ':99.0' runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Set up Python 3.13 uses: actions/setup-python@v5 with: python-version: "3.13" - name: Install dependencies (latest) run: | set -euxo pipefail sudo apt-get update sudo apt-get install -y \ libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 \ libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 x11-utils /sbin/start-stop-daemon --start --quiet \ --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background \ --exec /usr/bin/Xvfb -- :99 -screen 0 1920x1200x24 -ac +extension GLX python -m pip install --upgrade pip python -m pip install ruff pytest httpx python -m pip install PyQt5 # Clone and install Sigima from the same branch as DataLab cd .. git clone --depth 1 --branch ${{ github.ref_name }} https://github.com/DataLab-Platform/sigima.git || git clone --depth 1 https://github.com/DataLab-Platform/sigima.git cd DataLab # Install DataLab itself, but do NOT install its pinned deps python -m pip install -e . --no-deps # Install Sigima from local clone python -m pip install -e ../sigima # Extract dependency names from pyproject.toml (excluding Sigima) and install latest versions python -m pip install -U --upgrade-strategy eager $(python -c "import tomllib, re; print(' '.join(re.sub(r'[\[\]<>=!~,.\s].*$', '', d).strip() for d in tomllib.loads(open('pyproject.toml', 'rb').read().decode())['project']['dependencies'] if 'Sigima' not in d))") - name: Lint with Ruff (latest) run: ruff check --output-format=github datalab - name: Test with pytest (latest) run: pytest -v --tb=longDataLab-1.1.0/.github/workflows/test_pyqt6.yml000066400000000000000000000164431514014117600212530ustar00rootroot00000000000000# This workflow will install Python dependencies, run tests and lint with a variety of Python versions # For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python # Inspired from https://pytest-qt.readthedocs.io/en/latest/troubleshooting.html#github-actions name: Install and Test on Ubuntu (latest) with PyQt6 on: push: branches: [ "main", "develop", "release" ] pull_request: branches: [ "main", "develop", "release" ] workflow_dispatch: inputs: job_to_run: description: 'Which job to run' required: true type: choice options: - 'all' - 'build' - 'build_latest' default: 'all' schedule: # Only the "build_latest" job runs on schedule (see execution conditions below) - cron: "0 5 * * 1" jobs: build: if: ${{ (github.event_name == 'push' || github.event_name == 'pull_request') || (github.event_name == 'workflow_dispatch' && (github.event.inputs.job_to_run == 'all' || github.event.inputs.job_to_run == 'build')) }} env: DISPLAY: ':99.0' runs-on: ubuntu-latest strategy: fail-fast: false matrix: python-version: ["3.9", "3.13"] steps: - uses: actions/checkout@v4 - name: Set up Python ${{ matrix.python-version }} uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install dependencies run: | sudo apt-get update sudo apt-get install -y libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 x11-utils libegl1 /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1920x1200x24 -ac +extension GLX python -m pip install --upgrade pip python -m pip install ruff pytest httpx pip install PyQt6 if [ "${{ github.ref_name }}" = "develop" ]; then # Clone and install development versions of key dependencies with editable install cd .. git clone --depth 1 https://github.com/PlotPyStack/PythonQwt.git git clone --depth 1 --branch develop https://github.com/PlotPyStack/guidata.git git clone --depth 1 --branch develop https://github.com/PlotPyStack/plotpy.git git clone --depth 1 --branch develop https://github.com/DataLab-Platform/sigima.git cd DataLab pip install -e ../guidata pip install -e ../PythonQwt pip install -e ../plotpy pip install -e ../sigima # Install tomli for TOML parsing (safe if already present) pip install tomli # Extract dependencies and save to file, then install python -c "import tomli; f=open('pyproject.toml','rb'); data=tomli.load(f); deps=[d for d in data['project']['dependencies'] if not any(p in d for p in ['guidata','PlotPy','Sigima'])]; open('deps.txt','w').write('\n'.join(deps))" pip install -r deps.txt # Install DataLab without dependencies pip install --no-deps . elif [ "${{ github.ref_name }}" = "release" ]; then # Clone dependencies from release branches (with fallback to main/master) cd .. # Try cloning PythonQwt from main or master git clone --depth 1 https://github.com/PlotPyStack/PythonQwt.git || git clone --depth 1 --branch master https://github.com/PlotPyStack/PythonQwt.git # Try cloning guidata from release, fallback to main or master git clone --depth 1 --branch release https://github.com/PlotPyStack/guidata.git || git clone --depth 1 https://github.com/PlotPyStack/guidata.git || git clone --depth 1 --branch master https://github.com/PlotPyStack/guidata.git # Try cloning plotpy from release, fallback to main or master git clone --depth 1 --branch release https://github.com/PlotPyStack/plotpy.git || git clone --depth 1 https://github.com/PlotPyStack/plotpy.git || git clone --depth 1 --branch master https://github.com/PlotPyStack/plotpy.git # Try cloning sigima from release, fallback to main git clone --depth 1 --branch release https://github.com/DataLab-Platform/sigima.git || git clone --depth 1 https://github.com/DataLab-Platform/sigima.git cd DataLab pip install -e ../guidata pip install -e ../PythonQwt pip install -e ../plotpy pip install -e ../sigima # Install tomli for TOML parsing (safe if already present) pip install tomli # Extract dependencies and save to file, then install python -c "import tomli; f=open('pyproject.toml','rb'); data=tomli.load(f); deps=[d for d in data['project']['dependencies'] if not any(p in d for p in ['guidata','PlotPy','Sigima'])]; open('deps.txt','w').write('\n'.join(deps))" pip install -r deps.txt # Install DataLab without dependencies pip install --no-deps . else # Install from PyPI normally for main branch pip install . fi - name: Lint with Ruff run: ruff check --output-format=github datalab - name: Test with pytest run: pytest -v --tb=long build_latest: if: ${{ github.event_name == 'schedule' || (github.event_name == 'workflow_dispatch' && (github.event.inputs.job_to_run == 'all' || github.event.inputs.job_to_run == 'build_latest')) }} env: DISPLAY: ':99.0' runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 - name: Set up Python 3.13 uses: actions/setup-python@v5 with: python-version: "3.13" - name: Install dependencies (latest) run: | set -euxo pipefail sudo apt-get update sudo apt-get install -y \ libxkbcommon-x11-0 libxcb-icccm4 libxcb-image0 libxcb-keysyms1 \ libxcb-randr0 libxcb-render-util0 libxcb-xinerama0 libxcb-xfixes0 x11-utils libegl1 /sbin/start-stop-daemon --start --quiet \ --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background \ --exec /usr/bin/Xvfb -- :99 -screen 0 1920x1200x24 -ac +extension GLX python -m pip install --upgrade pip python -m pip install ruff pytest httpx python -m pip install PyQt6 # Clone and install Sigima from the same branch as DataLab cd .. git clone --depth 1 --branch ${{ github.ref_name }} https://github.com/DataLab-Platform/sigima.git || git clone --depth 1 https://github.com/DataLab-Platform/sigima.git cd DataLab # Install DataLab itself, but do NOT install its pinned deps python -m pip install -e . --no-deps # Install Sigima from local clone python -m pip install -e ../sigima # Extract dependency names from pyproject.toml (excluding Sigima) and install latest versions python -m pip install -U --upgrade-strategy eager $(python -c "import tomllib, re; print(' '.join(re.sub(r'[\[\]<>=!~,.\s].*$', '', d).strip() for d in tomllib.loads(open('pyproject.toml', 'rb').read().decode())['project']['dependencies'] if 'Sigima' not in d))") - name: Lint with Ruff (latest) run: ruff check --output-format=github datalab - name: Test with pytest (latest) run: pytest -v --tb=longDataLab-1.1.0/.gitignore000066400000000000000000000131361514014117600147750ustar00rootroot00000000000000# ---------------------------- Specific to this project -------------------------------- # DataLab project specific resources/*.png doc.zip releases/ /tutorialnotes*.md doc/changelog.md scenario_*.h5 # Windows specific Thumbs.db # Microsoft Visual Studio *.pyproj *.sln # Sphinx documentation doctmp/ .doctrees/ doc/install_requires.txt doc/extras_require-dev.txt doc/extras_require-doc.txt doc/locale/pot/_sphinx_design_static/ doc/_download/ DataLab_*.pdf # Backup files (e.g. created during merge conflicts) *.bak # WiX files .wix/ wix/DataLab-*.wxs wix/bin/ wix/obj/ wix/*.bmp *.wixpdb *.msi # ------------------ Template `Python.gitignore` from gitignore.io --------------------- # Created by https://www.gitignore.io/api/python ### Python ### # Byte-compiled / optimized / DLL files __pycache__/ *.py[codz] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest # *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py.cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; otherwise, check them in: # .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # UV # Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. #uv.lock # poetry # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. # This is especially recommended for binary packages to ensure reproducibility, and is more # commonly ignored for libraries. # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control #poetry.lock #poetry.toml # pdm # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. # pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python. # https://pdm-project.org/en/latest/usage/project/#working-with-version-control #pdm.lock #pdm.toml .pdm-python .pdm-build/ # pixi # Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control. #pixi.lock # Pixi creates a virtual environment in the .pixi directory, just like venv module creates one # in the .venv directory. It is recommended not to include this directory in version control. .pixi # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .envrc .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ # pytype static type analyzer .pytype/ # Cython debug symbols cython_debug/ # PyCharm # JetBrains specific template is maintained in a separate JetBrains.gitignore that can # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore # and can be added to the global gitignore or merged into this file. For a more nuclear # option (not recommended) you can uncomment the following to ignore the entire idea folder. #.idea/ # Abstra # Abstra is an AI-powered process automation framework. # Ignore directories containing user credentials, local state, and settings. # Learn more at https://abstra.io/docs .abstra/ # Visual Studio Code # Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore # that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore # and can be added to the global gitignore or merged into this file. However, if you prefer, # you could uncomment the following to ignore the entire vscode folder !.vscode/settings.json !.vscode/tasks.json !.vscode/launch.json # Workspace files are ignored because they may contain user-specific settings and # committing them could interfere with branch switching for example. A workspace # template file is provided in the repository. *.code-workspace # Ruff stuff: .ruff_cache/ # PyPI configuration file .pypirc # Cursor # Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to # exclude from AI features like autocomplete and code analysis. Recommended for sensitive data # refer to https://docs.cursor.com/context/ignore-files .cursorignore .cursorindexingignore # Marimo marimo/_static/ marimo/_lsp/ __marimo__/ DataLab-1.1.0/.pre-commit-config.yaml000066400000000000000000000003251514014117600172620ustar00rootroot00000000000000repos: - repo: https://github.com/astral-sh/ruff-pre-commit # Ruff version. rev: v0.12.2 hooks: # Run the linter. - id: ruff-check args: [ --fix ] # Run the formatter. - id: ruff-formatDataLab-1.1.0/.pylintrc000066400000000000000000000010601514014117600146430ustar00rootroot00000000000000[FORMAT] # Essential to be able to compare code side-by-side (`black` default setting) # and best compromise to minimize file size max-line-length=88 [TYPECHECK] ignored-modules=qtpy.QtWidgets,qtpy.QtCore,qtpy.QtGui,cv2,plotpy._scaler,skimage.restoration,skimage.feature [MESSAGES CONTROL] disable=wrong-import-order [DESIGN] max-args=10 # default: 5 max-positional-arguments=11 # default: 5 max-attributes=12 # default: 7 max-branches=20 # default: 12 max-locals=20 # default: 15 min-public-methods=0 # default: 2 max-public-methods=25 # default: 20DataLab-1.1.0/.readthedocs.yaml000066400000000000000000000005301514014117600162260ustar00rootroot00000000000000# Read the Docs configuration file for Sphinx projects # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details version: 2 build: os: ubuntu-22.04 tools: python: "3.11" sphinx: configuration: doc/conf.py formats: - pdf python: install: - method: pip path: . extra_requirements: - doc DataLab-1.1.0/.vscode/000077500000000000000000000000001514014117600143425ustar00rootroot00000000000000DataLab-1.1.0/.vscode/launch.json000066400000000000000000000075741514014117600165240ustar00rootroot00000000000000{ // Use IntelliSense to learn about possible attributes. // Hover to view descriptions of existing attributes. // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387 "version": "0.2.0", "configurations": [ { "name": "Run DataLab", "type": "debugpy", "request": "launch", "program": "${workspaceFolder}/datalab/app.py", "console": "integratedTerminal", "envFile": "${workspaceFolder}/.env", "justMyCode": false, "args": [ // "--reset" // "--h5browser", // "${workspaceFolder}/datalab/data/tests/empty.h5" // "${workspaceFolder}/datalab/data/tests/reordering_test.h5", // "${workspaceFolder}/datalab/data/tests/reordering_test.h5,/DataLab_Sig/g001: /s003: wiener(s002)/xydata", ], "env": { // "DEBUG": "1", // "LANG": "en", // "QT_COLOR_MODE": "light", } }, { "name": "Run current file", "type": "debugpy", "request": "launch", "program": "${file}", "console": "integratedTerminal", "envFile": "${workspaceFolder}/.env", "justMyCode": false, "args": [ // "--h5browser", // "${workspaceFolder}/datalab/data/tests/format_v1.7.h5", // "${workspaceFolder}/datalab/data/tests/format_v1.7.h5,/DataLab_Ima/i002: i002+i004", // "--unattended", // "--screenshot", // "--verbose", // "quiet", // "--delay", // "1000" ], "env": { // "DEBUG": "1", // ☣️ Debug mode will reset .ini settings // "TEST_SEGFAULT_ERROR": "1", "LANG": "en", "QT_COLOR_MODE": "light", } }, { "name": "Run current file (unattended)", "type": "debugpy", "request": "launch", "program": "${file}", "console": "integratedTerminal", "envFile": "${workspaceFolder}/.env", "pythonArgs": [ "-W error::DeprecationWarning", "-W error::RuntimeWarning", ], "justMyCode": false, "args": [ "--unattended", ], "env": { // "DEBUG": "1", // ☣️ Debug mode will reset .ini settings // "QT_QPA_PLATFORM": "offscreen", // The `DATALAB_DATA` environment variable is set here just for checking // that the data path is not added twice to the test data path list: "DATALAB_DATA": "${workspaceFolder}/datalab/data/tests", "LANG": "en", "QT_COLOR_MODE": "light", } }, { "name": "Profile current file", "type": "debugpy", "request": "launch", "module": "cProfile", "console": "integratedTerminal", "envFile": "${workspaceFolder}/.env", "args": [ "-o", "${file}.prof", "${file}" ], }, { "name": "Run H5browser", "type": "debugpy", "request": "launch", "program": "${workspaceFolder}/datalab/tests/features/hdf5/h5browser1_unit.py", "console": "integratedTerminal", "envFile": "${workspaceFolder}/.env", "justMyCode": true, "env": { "DEBUG": "1", // "QT_COLOR_MODE": "light", }, "args": [ // "--unattended", // "--screenshot", ], }, ] }DataLab-1.1.0/.vscode/settings.json000066400000000000000000000015141514014117600170760ustar00rootroot00000000000000{ "[bat]": { "files.encoding": "cp850" }, "[python]": { "editor.defaultFormatter": "charliermarsh.ruff" }, "[restructuredtext]": { "editor.wordWrap": "on" }, "editor.codeActionsOnSave": { "source.organizeImports.ruff": "explicit" }, "editor.formatOnSave": true, "editor.rulers": [ 88 ], "files.exclude": { "**/__pycache__": true, "**/*.pyc": true, "**/*.pyo": true }, "files.trimFinalNewlines": true, "files.trimTrailingWhitespace": true, "python.analysis.autoFormatStrings": true, "python.testing.pytestArgs": [], "python.testing.pytestEnabled": true, "python.testing.pytestPath": "pytest", "python.testing.unittestEnabled": false, "terminal.integrated.tabs.description": "${workspaceFolder}", }DataLab-1.1.0/.vscode/tasks.json000066400000000000000000000737171514014117600164010ustar00rootroot00000000000000{ // See https://go.microsoft.com/fwlink/?LinkId=733558 // for the documentation about the tasks.json format "version": "2.0.0", "tasks": [ { "label": "🧽 Ruff Formatter", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "ruff", "format", ], "options": { "cwd": "${workspaceFolder}", "statusbar": { "hide": true, }, }, "group": { "kind": "build", "isDefault": true, }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", }, { "label": "🔦 Ruff Linter", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "ruff", "check", "--fix", ], "options": { "cwd": "${workspaceFolder}", "statusbar": { "hide": true, }, }, "group": { "kind": "build", "isDefault": true, }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", }, { "label": "🧽🔦 Ruff", "dependsOrder": "sequence", "dependsOn": [ "🧽 Ruff Formatter", "🔦 Ruff Linter", ], "group": { "kind": "build", "isDefault": false, }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", }, { "label": "🔦 Pylint", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "pylint", "datalab", "--disable=duplicate-code", "--disable=fixme", "--disable=too-many-arguments", "--disable=too-many-branches", "--disable=too-many-instance-attributes", "--disable=too-many-lines", "--disable=too-many-locals", "--disable=too-many-public-methods", "--disable=too-many-statements", ], "options": { "cwd": "${workspaceFolder}", }, "group": { "kind": "build", "isDefault": true, }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", }, { "label": "🚀 Pytest", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "pytest", "--ff", ], "options": { "cwd": "${workspaceFolder}", "env": { // "DEBUG": "1", // ☣️ Debug mode will reset .ini settings "UNATTENDED": "1", }, }, "group": { "kind": "build", "isDefault": true, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "dedicated", "showReuseMessage": true, "clear": true, }, "type": "shell", }, { "label": "sphinx-build", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "sphinx", "build", "doc", "build/gettext", "-b", "gettext", "-W", ], "options": { "cwd": "${workspaceFolder}", "statusbar": { "hide": true, }, }, "group": { "kind": "build", "isDefault": false, }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", }, { "label": "sphinx-intl update", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "sphinx_intl", "update", "-d", "doc/locale", "-p", "build/gettext", "-l", "fr", "--no-obsolete", "-w", "0", ], "options": { "cwd": "${workspaceFolder}", "statusbar": { "hide": true, }, }, "group": { "kind": "build", "isDefault": false, }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", "dependsOrder": "sequence", "dependsOn": [ "sphinx-build", ], }, { "label": "cleanup-doc-translations", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "guidata.utils.translations", "cleanup-doc", "--directory", ".", ], "options": { "cwd": "${workspaceFolder}", "statusbar": { "hide": true, }, }, "group": { "kind": "build", "isDefault": false, }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", "dependsOrder": "sequence", "dependsOn": [ "sphinx-intl update", ], }, { "label": "sphinx-intl build", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "sphinx_intl", "build", "-d", "doc/locale", ], "options": { "cwd": "${workspaceFolder}", "statusbar": { "hide": true, }, }, "group": { "kind": "build", "isDefault": false, }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", }, { "label": "🔎 Scan translations", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "guidata.utils.translations", "scan", "--name", "datalab", "--directory", ".", "--copyright-holder", "DataLab Platform Developers", "--languages", "fr", ], "group": { "kind": "build", "isDefault": false, }, "options": { "cwd": "${workspaceFolder}", }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", "dependsOrder": "sequence", "dependsOn": [ "cleanup-doc-translations", ], }, { "label": "📚 Compile translations", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "guidata.utils.translations", "compile", "--name", "datalab", "--directory", ".", ], "group": { "kind": "build", "isDefault": false, }, "options": { "cwd": "${workspaceFolder}", }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", "dependsOrder": "sequence", "dependsOn": [ "sphinx-intl build", ], }, { "label": "🛠️ Generate doc assets", "command": "${command:python.interpreterPath} scripts/run_with_env.py ${command:python.interpreterPath} -m guidata.utils.genreqs all && ${command:python.interpreterPath} scripts/run_with_env.py ${command:python.interpreterPath} doc/update_validation_status.py", "options": { "cwd": "${workspaceFolder}", "statusbar": { "hide": true, }, }, "group": { "kind": "build", "isDefault": false, }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", }, { "label": "🧪 Coverage tests", "type": "shell", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "coverage", "run", "-m", "pytest", "datalab", ], "options": { "cwd": "${workspaceFolder}", "env": { "COVERAGE_PROCESS_START": "${workspaceFolder}/.coveragerc", }, "statusbar": { "hide": true, }, }, "group": { "kind": "test", "isDefault": true, }, "presentation": { "panel": "dedicated", }, "problemMatcher": [], }, { "label": "📊 Coverage full", "type": "shell", "windows": { "command": "${command:python.interpreterPath} -m coverage combine && ${command:python.interpreterPath} -m coverage html && start htmlcov\\index.html", }, "linux": { "command": "${command:python.interpreterPath} -m coverage combine && ${command:python.interpreterPath} -m coverage html && xdg-open htmlcov/index.html", }, "osx": { "command": "${command:python.interpreterPath} -m coverage combine && ${command:python.interpreterPath} -m coverage html && open htmlcov/index.html", }, "options": { "cwd": "${workspaceFolder}", "env": { "COVERAGE_PROCESS_START": "${workspaceFolder}/.coveragerc", }, }, "presentation": { "panel": "dedicated", }, "problemMatcher": [], "dependsOrder": "sequence", "dependsOn": [ "🧪 Coverage tests", ], }, { "label": "Upgrade Sigima/PlotPyStack", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "pip", "install", "--upgrade", "pip", "PythonQwt", "guidata", "PlotPy", "Sigima", ], "options": { "cwd": "${workspaceFolder}", "statusbar": { "hide": true, }, }, "group": { "kind": "build", "isDefault": true, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "shared", "showReuseMessage": true, "clear": false, }, "type": "shell", }, { "label": "🔁 Reinstall guidata/plotpy/sigima dev", "type": "shell", "windows": { "command": ".venv/scripts/pip uninstall -y guidata plotpy sigima; Remove-Item -Recurse -Force .venv/Lib/site-packages/guidata -ErrorAction SilentlyContinue; Remove-Item -Recurse -Force .venv/Lib/site-packages/plotpy -ErrorAction SilentlyContinue; Remove-Item -Recurse -Force .venv/Lib/site-packages/sigima -ErrorAction SilentlyContinue; .venv/scripts/pip install -e ../guidata; .venv/scripts/pip install -e ../plotpy; .venv/scripts/pip install -e ../sigima", }, "linux": { "command": ".venv/bin/pip uninstall -y guidata plotpy sigima && rm -rf .venv/lib/python*/site-packages/guidata && rm -rf .venv/lib/python*/site-packages/plotpy && rm -rf .venv/lib/python*/site-packages/sigima && .venv/bin/pip install -e ../guidata && .venv/bin/pip install -e ../plotpy && .venv/bin/pip install -e ../sigima", }, "osx": { "command": ".venv/bin/pip uninstall -y guidata plotpy sigima && rm -rf .venv/lib/python*/site-packages/guidata && rm -rf .venv/lib/python*/site-packages/plotpy && rm -rf .venv/lib/python*/site-packages/sigima && .venv/bin/pip install -e ../guidata && .venv/bin/pip install -e ../plotpy && .venv/bin/pip install -e ../sigima", }, "options": { "cwd": "${workspaceFolder}", "statusbar": { "hide": true, }, }, "presentation": { "panel": "dedicated", "reveal": "always", }, "problemMatcher": [], }, { "label": "🧹 Clean Up", "type": "shell", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "guidata.utils.cleanup", ], "options": { "cwd": "${workspaceFolder}" }, "group": { "kind": "build", "isDefault": true }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "shared", "showReuseMessage": true, "clear": false, }, }, { "label": "Create executable", "type": "shell", "command": "cmd", "options": { "cwd": "scripts", "env": { "PYTHON": "${command:python.interpreterPath}", // "RELEASE": "1", "UNATTENDED": "1", }, "statusbar": { "hide": true, }, }, "args": [ "/c", "build_exe.bat", ], "problemMatcher": [], "group": { "kind": "build", "isDefault": false, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "dedicated", "showReuseMessage": true, "clear": true, }, }, { "label": "Create installer", "type": "shell", "command": "cmd", "options": { "cwd": "scripts", "env": { "PYTHON": "${command:python.interpreterPath}", "UNATTENDED": "1", }, "statusbar": { "hide": true, }, }, "args": [ "/c", "build_installer.bat", ], "problemMatcher": [], "group": { "kind": "build", "isDefault": false, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "dedicated", "showReuseMessage": true, "clear": true, }, }, { "label": "Build PDF doc", "type": "shell", "command": "cmd", "options": { "cwd": "scripts", "env": { "PYTHON": "${command:python.interpreterPath}", "QT_COLOR_MODE": "light", // "RELEASE": "1", "UNATTENDED": "1", }, "statusbar": { "hide": true, }, }, "args": [ "/c", "build_doc.bat", ], "problemMatcher": [], "group": { "kind": "build", "isDefault": false, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "shared", "showReuseMessage": true, "clear": true, }, "dependsOrder": "sequence", "dependsOn": [ "🛠️ Generate doc assets", ], }, { "label": "HTML doc: quick preview", "type": "shell", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "sphinx", "build", "doc", "${workspaceFolder}/build/doc", "-b", "html", "-W", ], "options": { "cwd": "${workspaceFolder}", "statusbar": { "hide": true, }, }, "problemMatcher": [], "group": { "kind": "build", "isDefault": true, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "shared", "showReuseMessage": true, "clear": true, }, }, { "label": "🌐 Build/open HTML doc", "type": "shell", "windows": { "command": "start build/doc/index.html", }, "linux": { "command": "xdg-open build/doc/index.html", }, "osx": { "command": "open build/doc/index.html", }, "options": { "cwd": "${workspaceFolder}", }, "problemMatcher": [], "dependsOrder": "sequence", "dependsOn": [ "HTML doc: quick preview", ], }, { "label": "GitHub Pages: build", "type": "shell", "command": "cmd", "options": { "cwd": "scripts", "env": { "PYTHON": "${command:python.interpreterPath}", "QT_COLOR_MODE": "light", "RELEASE": "1", "UNATTENDED": "1", }, "statusbar": { "hide": true, }, }, "args": [ "/c", "build_ghpages.bat", ], "problemMatcher": [], "group": { "kind": "build", "isDefault": false, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "shared", "showReuseMessage": true, "clear": true, }, "dependsOrder": "sequence", "dependsOn": [ "🛠️ Generate doc assets", ], }, { "label": "GitHub Pages: preview", "type": "shell", "command": "cmd", "options": { "cwd": "scripts", "env": { "PYTHON": "${command:python.interpreterPath}", "QT_COLOR_MODE": "light", "RELEASE": "1", "UNATTENDED": "1", }, "statusbar": { "hide": true, }, }, "args": [ "/c", "preview_ghpages.bat", ], "problemMatcher": [], "group": { "kind": "build", "isDefault": false, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "shared", "showReuseMessage": true, "clear": true, }, "dependsOrder": "sequence", "dependsOn": [ "GitHub Pages: build", ], }, { "label": "GitHub Pages: upload", "type": "shell", "command": "cmd", "options": { "cwd": "scripts", "env": { "PYTHON": "${command:python.interpreterPath}", "QT_COLOR_MODE": "light", "UNATTENDED": "1", }, "statusbar": { "hide": true, }, }, "args": [ "/c", "upload_ghpages.bat", ], "problemMatcher": [], "group": { "kind": "build", "isDefault": false, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "shared", "showReuseMessage": true, "clear": true, }, "dependsOrder": "sequence", "dependsOn": [ "GitHub Pages: build", ], }, { "label": "Build Python packages", "type": "shell", "command": "cmd", "options": { "cwd": "scripts", "env": { "PYTHON": "${command:python.interpreterPath}", // "RELEASE": "1", "UNATTENDED": "1", }, "statusbar": { "hide": true, }, }, "args": [ "/c", "build_dist.bat", ], "problemMatcher": [], "group": { "kind": "build", "isDefault": false, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "dedicated", "showReuseMessage": true, "clear": true, }, "dependsOrder": "sequence", "dependsOn": [ "🛠️ Generate doc assets", "Build PDF doc", ], }, { "label": "✨ Release", "type": "shell", "command": "cmd", "args": [ "/c", "release.bat", ], "options": { "cwd": "scripts", "env": { "PYTHON": "${command:python.interpreterPath}", // "RELEASE": "1", "UNATTENDED": "1", }, }, "problemMatcher": [], "group": { "kind": "build", "isDefault": true, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "dedicated", "showReuseMessage": true, "clear": true, }, "dependsOrder": "sequence", "dependsOn": [ "🧹 Clean Up", "Upgrade Sigima/PlotPyStack", "📚 Compile translations", "Build Python packages", "Create executable", "Create installer", ], }, { "label": "Run _tests_.bat", "type": "shell", "command": "cmd", "args": [ "/c", "_tests_.bat", ], "options": { "cwd": "scripts", "env": { "UNATTENDED": "1", "RELEASE": "0", "PYTHON": "${command:python.interpreterPath}", }, "statusbar": { "hide": true, }, }, "group": { "kind": "build", "isDefault": false, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "dedicated", "showReuseMessage": true, "clear": true, }, }, { "label": "❔ Untracked files", "type": "shell", "command": "git ls-files --others | Where-Object { $_ -notmatch '^\\.' -and $_ -notmatch '^(build|dist|releases)/' -and $_ -notmatch '.(pyc|mo)$'}", "options": { "cwd": "${workspaceFolder}", }, "group": { "kind": "build", "isDefault": true, }, "presentation": { "echo": true, "reveal": "always", "focus": false, "panel": "dedicated", "showReuseMessage": true, "clear": true, }, }, { "label": "🖼️ Icon Browser", "command": "${command:python.interpreterPath}", "args": [ "scripts/run_with_env.py", "${command:python.interpreterPath}", "-m", "guidata.widgets.iconbrowser", "${workspaceFolder}/datalab/data/icons", ], "options": { "cwd": "${workspaceFolder}", }, "group": { "kind": "build", "isDefault": true, }, "presentation": { "clear": true, "echo": true, "focus": false, "panel": "dedicated", "reveal": "always", "showReuseMessage": true, }, "type": "shell", }, ], }DataLab-1.1.0/CONTRIBUTING.md000066400000000000000000000010121514014117600152240ustar00rootroot00000000000000# Contributing Guidelines DataLab is **your** platform. If you want it to be improved, you **can** contribute to the project, whether you are a developer or not. There are many ways to contribute to DataLab project, depending on how much time you have, your experience with open source projects, and your skills. To get started, please refer to our [contributing documentation](https://datalab-platform.com/en/contributing/index.html) for detailed instructions on how to contribute to the project. Happy contributing! DataLab-1.1.0/DataLab.bat000066400000000000000000000022131514014117600147570ustar00rootroot00000000000000 @echo off setlocal EnableDelayedExpansion REM Store script directory in a variable (outside IF block for batch compatibility) set SCRIPT_DIR=%~dp0 REM Check if a Python executable path is provided as an argument if "%1" == "" ( REM If not, try to use .venv\Scripts\pythonw.exe in the current directory set PYTHON_EXE=!SCRIPT_DIR!.venv\Scripts\pythonw.exe if not exist "!PYTHON_EXE!" ( echo Error: Python executable path must be provided as an argument. echo Usage: DataLab.bat path\to\python.exe exit /b 1 ) ) else ( REM Use the provided Python executable path set PYTHON_EXE=%1 ) REM Validate that the provided Python executable exists if not exist "%PYTHON_EXE%" ( echo Error: The specified Python executable does not exist: %PYTHON_EXE% exit /b 2 ) cd/D %~dp0 set ORIGINAL_PYTHONPATH=%PYTHONPATH% for /F "tokens=*" %%A in (.env) do (set %%A) set PYTHONPATH=%PYTHONPATH%;%ORIGINAL_PYTHONPATH% REM Extract pythonw.exe from the same directory as the provided python.exe for %%a in ("%PYTHON_EXE%") do set "PYTHON_DIR=%%~dpa" start "" "%PYTHON_DIR%pythonw.exe" datalab\start.pyw %2 %3 %4 %5 %6 %7 %8 %9DataLab-1.1.0/DataLab.code-workspace.template000066400000000000000000000004611514014117600207340ustar00rootroot00000000000000{ "folders": [ { "path": "." }, { "path": "../Sigima" }, { "path": "../PlotPy" }, { "path": "../guidata" } ], "settings": { "python.analysis.autoFormatStrings": true } }DataLab-1.1.0/DataLab.desktop000066400000000000000000000003471514014117600156700ustar00rootroot00000000000000[Desktop Entry] Version=1.0 Type=Application Name=datalab GenericName=DataLab Comment=Run DataLab, the Python Signal and image processing software TryExec=datalab Exec=datalab Icon=DataLab.svg Categories=Education;Science;Physics; DataLab-1.1.0/DataLab.spec000066400000000000000000000023501514014117600151450ustar00rootroot00000000000000# -*- mode: python ; coding: utf-8 -*- # Initial command: # pyinstaller -y --clean -n DataLab -i resources\DataLab.ico datalab\start.pyw from PyInstaller.utils.hooks import collect_submodules, collect_data_files, copy_metadata all_hidden_imports = collect_submodules('datalab') datas = collect_data_files('datalab') + [('datalab\\plugins', 'datalab\\plugins')] datas += collect_data_files('guidata') + collect_data_files('plotpy') datas += collect_data_files('sigima') datas += copy_metadata('imageio') a = Analysis( ['datalab\\start.pyw'], pathex=[], binaries=[], datas=datas, hiddenimports=all_hidden_imports, hookspath=[], hooksconfig={}, runtime_hooks=[], excludes=[], noarchive=False, ) pyz = PYZ(a.pure) exe = EXE( pyz, a.scripts, [], exclude_binaries=True, name='DataLab', debug=False, bootloader_ignore_signals=False, strip=False, upx=True, console=False, disable_windowed_traceback=False, argv_emulation=False, target_arch=None, codesign_identity=None, entitlements_file=None, icon=['resources\\DataLab.ico'], ) coll = COLLECT( exe, a.binaries, a.datas, strip=False, upx=True, upx_exclude=[], name='DataLab', ) DataLab-1.1.0/LICENSE000066400000000000000000000030001514014117600137770ustar00rootroot00000000000000BSD 3-Clause License Copyright (c) 2023, DataLab Platform Developers. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. DataLab-1.1.0/MANIFEST.in000066400000000000000000000002401514014117600145330ustar00rootroot00000000000000graft doc graft resources graft macros graft plugins graft datalab/locale include conftest.py include CONTRIBUTING.md include requirements.txt include *.desktopDataLab-1.1.0/README.md000066400000000000000000000072331514014117600142650ustar00rootroot00000000000000![DataLab](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/DataLab-banner.png) [![license](https://img.shields.io/pypi/l/datalab-platform.svg)](./LICENSE) [![pypi version](https://img.shields.io/pypi/v/datalab-platform.svg)](https://pypi.org/project/datalab-platform/) [![PyPI status](https://img.shields.io/pypi/status/datalab-platform.svg)](https://github.com/DataLab-Platform/DataLab) [![PyPI pyversions](https://img.shields.io/pypi/pyversions/datalab-platform.svg)](https://pypi.python.org/pypi/datalab-platform/) DataLab is an **open-source platform for scientific and technical data processing and visualization** with unique features designed to meet industrial requirements. See [DataLab website](https://datalab-platform.com/) for more details. > **Note:** This project (DataLab Platform) should not be confused with the [datalab-org](https://datalab-org.io/) project, which is a separate and unrelated initiative focused on materials science databases and computational tools. ℹ️ Created by [CODRA](https://codra.net/)/[Pierre Raybaut](https://github.com/PierreRaybaut) in 2023, developed and maintained by DataLab Platform Developers. ![DataLab](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/shots/i_blob_detection_flower.png) 🧮 DataLab's processing power comes from the advanced algorithms of the object-oriented signal and image processing library [Sigima](https://github.com/DataLab-Platform/Sigima) 🚀 which is part of the DataLab Platform. ![Sigima](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/logos/Sigima-Power.png) ℹ️ DataLab is powered by [PlotPyStack](https://github.com/PlotPyStack) 🚀 for curve plotting and fast image visualization. ![PlotPyStack](https://raw.githubusercontent.com/PlotPyStack/.github/main/data/plotpy-stack-powered.png) ℹ️ DataLab is built on Python and scientific libraries. ![Python](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/logos/Python.png) ![NumPy](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/logos/NumPy.png) ![SciPy](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/logos/SciPy.png) ![scikit-image](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/logos/scikit-image.png) ![OpenCV](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/logos/OpenCV.png) ![PlotPyStack](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/logos/plotpystack.png) ![Sigima](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/logos/Sigima.png) ---- Try DataLab online, without installing anything, using Binder: [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/DataLab-Platform/DataLab/binder-environments?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252FDataLab-Platform%252FDataLab%26urlpath%3Ddesktop%252F%26branch%3Dbinder-environments) ---- ✨ Add features to DataLab by writing your own [plugin](https://datalab-platform.com/en/features/advanced/plugins.html) (see [plugin examples](https://github.com/DataLab-Platform/DataLab/tree/main/plugins/examples)) or macro (see [macro examples](https://github.com/DataLab-Platform/DataLab/tree/main/macros/examples)) ✨ DataLab may be remotely controlled from a third-party application (such as Jupyter, Spyder or any IDE): * Using the integrated [remote control](https://datalab-platform.com/en/features/advanced/remote.html) feature (this requires to install DataLab as a Python package) * Using the lightweight client integrated in [Sigima](https://github.com/DataLab-Platform/Sigima) (`pip install sigima`) DataLab-1.1.0/babel.cfg000066400000000000000000000001321514014117600145230ustar00rootroot00000000000000# This file is used to configure Babel for the project. [python: **.py] encoding = utf-8 DataLab-1.1.0/datalab/000077500000000000000000000000001514014117600143715ustar00rootroot00000000000000DataLab-1.1.0/datalab/__init__.py000066400000000000000000000022721514014117600165050ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab ======= DataLab is a generic signal and image processing software based on Python scientific libraries (such as NumPy, SciPy or scikit-image) and Qt graphical user interfaces (thanks to `PlotPyStack`_ libraries). .. _PlotPyStack: https://github.com/PlotPyStack """ import multiprocessing import os # Set multiprocessing start method to 'spawn' early to avoid fork-related warnings # on Linux systems when using Qt and multithreading. This must be done before # any multiprocessing.Pool is created. try: multiprocessing.set_start_method("spawn") except RuntimeError: # This exception is raised if the method is already set (this may happen because # this module is imported more than once, e.g. when running tests) pass __version__ = "1.1.0" __docurl__ = __homeurl__ = "https://datalab-platform.com/" __supporturl__ = "https://github.com/DataLab-Platform/DataLab/issues/new/choose" os.environ["DATALAB_VERSION"] = __version__ # Dear (Debian, RPM, ...) package makers, please feel free to customize the # following path to module's data (images) and translations: DATAPATH = LOCALEPATH = "" DataLab-1.1.0/datalab/adapters_metadata/000077500000000000000000000000001514014117600200345ustar00rootroot00000000000000DataLab-1.1.0/datalab/adapters_metadata/__init__.py000066400000000000000000000014201514014117600221420ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Adapters for Sigima's TableResult and GeometryResult, providing features for storing and retrieving those objects as metadata for DataLab's signal and image objects. """ from .base_adapter import BaseResultAdapter from .common import ( ResultData, create_resultdata_dict, have_geometry_results, have_results, resultadapter_to_html, show_resultdata, ) from .geometry_adapter import GeometryAdapter from .table_adapter import TableAdapter __all__ = [ "BaseResultAdapter", "GeometryAdapter", "ResultData", "TableAdapter", "create_resultdata_dict", "have_geometry_results", "have_results", "resultadapter_to_html", "show_resultdata", ] DataLab-1.1.0/datalab/adapters_metadata/base_adapter.py000066400000000000000000000227231514014117600230260ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Base adapter class for result objects, providing common functionality for storing and retrieving result objects as metadata for DataLab's signal and image objects. """ from __future__ import annotations from abc import ABC, abstractmethod from typing import TYPE_CHECKING, Any, ClassVar, Generator import guidata.dataset as gds from sigima.objects import ImageObj, SignalObj if TYPE_CHECKING: import pandas as pd from sigima.objects.scalar import GeometryResult, TableResult class BaseResultAdapter(ABC): """Base adapter for result objects. This base class provides common functionality for working with result objects, including metadata storage/retrieval and various data representations. Args: result: Result object to adapt """ # Class constants for metadata storage - to be overridden by subclasses META_PREFIX: ClassVar[str] = "" META_SUFFIX: ClassVar[str] = "_dict" def __init__(self, result: TableResult | GeometryResult) -> None: self.result = result def set_applicative_attr(self, key: str, value: Any) -> None: """Set an applicative attribute for the result. Args: key: Attribute key value: Attribute value """ self.result.attrs[key] = value def get_applicative_attr(self, key: str, default: Any = None) -> Any: """Get an applicative attribute for the result. Args: key: Attribute key default: Default value to return if key not found Returns: Attribute value, or default if not set. If default is not None, assign it to the attribute if it was not already set. """ if key not in self.result.attrs and default is not None: self.result.attrs[key] = default return self.result.attrs.get(key, default) @property def title(self) -> str: """Get the title. Returns: Title """ return self.result.title @property def name(self) -> str: """Get the result kind name. Returns: The string value of the kind attribute (e.g., "segment", "circle", "statistics"). This is NOT unique - multiple results can share the same kind. """ return self.result.name @property def func_name(self) -> str: """Get the computation function name. Returns: The name of the computation function that produced this result. """ return self.result.func_name @property @abstractmethod def headers(self) -> list[str]: """Get column headers. Returns: List of column headers """ @property @abstractmethod def category(self) -> str: """Get the category. Returns: Category """ def get_roi_data(self, roi_index: int) -> "pd.DataFrame": """Get data for a specific ROI index. Args: roi_index: ROI index to filter by Returns: DataFrame containing only data for the specified ROI """ df = self.to_dataframe() if "roi_index" in df.columns: return df[df["roi_index"] == roi_index].drop(columns=["roi_index"]) return df def get_column_values(self, column_name: str, roi_index: int = None) -> list: """Get values for a specific column, optionally filtered by ROI. Args: column_name: Name of the column to retrieve roi_index: Optional ROI index to filter by Returns: List of values for the specified column """ df = self.to_dataframe() if roi_index is not None and "roi_index" in df.columns: df = df[df["roi_index"] == roi_index] return df[column_name].tolist() def get_unique_roi_indices(self) -> list[int]: """Get unique ROI indices present in the data. Returns: List of unique ROI indices """ df = self.to_dataframe() if "roi_index" in df.columns: return sorted(df["roi_index"].unique().tolist()) return [] if len(df) == 0 else [0] # Default ROI index for result data @property def metadata_key(self) -> str: """Get the metadata key used to store this result. Returns: Metadata key based on the result's title """ return f"{self.META_PREFIX}{self.func_name}{self.META_SUFFIX}" def add_to( self, obj: SignalObj | ImageObj, param: gds.DataSet | None = None ) -> None: """Add result to object metadata. Args: obj: Signal or image object param: Optional parameter dataset associated with this result """ assert self.func_name, "func_name must be set before adding to object metadata" # Store parameter in result attrs (will be serialized with result) if param is not None: self.result.attrs["param_json"] = gds.dataset_to_json(param) obj.metadata[self.metadata_key] = self.result.to_dict() def get_param(self) -> gds.DataSet | None: """Get parameter dataset associated with this result. Returns: Parameter dataset if present, None otherwise """ param_json = self.result.attrs.get("param_json") if param_json is not None: return gds.json_to_dataset(param_json) return None def remove_from(self, obj: SignalObj | ImageObj) -> None: """Remove result from object metadata. Args: obj: Signal or image object """ obj.metadata.pop(self.metadata_key, None) @classmethod def remove_all_from(cls, obj: SignalObj | ImageObj) -> None: """Remove all results of this type from object metadata. Args: obj: Signal or image object """ # Find all results in the object and remove them for adapter in list(cls.iterate_from_obj(obj)): adapter.remove_from(obj) @classmethod def match(cls, key: str, _value: Any) -> bool: """Check if the key matches the pattern for this result type. Args: key: Metadata key _value: Metadata value (unused) Returns: True if the key matches the pattern """ return key.startswith(cls.META_PREFIX) and key.endswith(cls.META_SUFFIX) @classmethod @abstractmethod def from_metadata_entry(cls, obj: SignalObj | ImageObj, key: str): """Create a result adapter from a metadata entry. Args: obj: Object containing the metadata key: Metadata key for the result data Returns: Adapter object Raises: ValueError: Invalid metadata entry """ @classmethod def from_obj( cls, obj: SignalObj | ImageObj, func_name: str ) -> BaseResultAdapter | None: """Create a result adapter from an object's metadata entry. Args: obj: Signal or image object func_name: Function name to look for Returns: Adapter object if found, None otherwise """ for adapter in cls.iterate_from_obj(obj): if adapter.func_name == func_name: return adapter return None @classmethod def iterate_from_obj( cls, obj: SignalObj | ImageObj ) -> Generator["BaseResultAdapter", None, None]: """Iterate over results stored in an object's metadata. Args: obj: Signal or image object Yields: Adapter objects """ for key, value in obj.metadata.items(): if cls.match(key, value): try: yield cls.from_metadata_entry(obj, key) except (ValueError, TypeError): # Skip invalid entries pass def to_dataframe(self, visible_only: bool = False): """Convert the result to a pandas DataFrame. Args: visible_only: If True, include only visible headers based on display preferences. Default is False. Returns: DataFrame with an optional 'roi_index' column. If visible_only is True, only columns with visible headers are included. """ return self.result.to_dataframe(visible_only=visible_only) def to_html( self, obj=None, visible_only: bool = True, transpose_single_row: bool = True, **kwargs, ) -> str: """Convert the result to HTML format. Args: obj: Optional SignalObj or ImageObj for ROI title extraction visible_only: If True, include only visible headers based on display preferences. Default is False. transpose_single_row: If True, transpose the table when there's only one row **kwargs: Additional arguments passed to DataFrame.to_html() Returns: HTML representation of the result """ return self.result.to_html( obj=obj, visible_only=visible_only, transpose_single_row=transpose_single_row, **kwargs, ) def get_visible_headers(self) -> list[str]: """Get list of currently visible headers based on display preferences. Returns: List of header names that should be displayed """ return self.result.get_visible_headers() DataLab-1.1.0/datalab/adapters_metadata/common.py000066400000000000000000000351511514014117600217030ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Common functions for metadata adapters. """ from __future__ import annotations import dataclasses import warnings from typing import TYPE_CHECKING import numpy as np import pandas as pd from guidata.qthelpers import exec_dialog from guidata.widgets.dataframeeditor import DataFrameEditor from sigima.objects import ImageObj, SignalObj from datalab.adapters_metadata.base_adapter import BaseResultAdapter from datalab.adapters_metadata.geometry_adapter import GeometryAdapter from datalab.adapters_metadata.table_adapter import TableAdapter from datalab.config import Conf, _ from datalab.objectmodel import get_short_id if TYPE_CHECKING: from qtpy.QtWidgets import QWidget @dataclasses.dataclass class ResultData: """Result data associated to a shapetype""" # We now store adapted objects from the new architecture results: list[BaseResultAdapter] | None = None ylabels: list[str] | None = None short_ids: list[str] | None = None def __bool__(self) -> bool: """Return True if there are results stored""" return bool(self.results) @property def category(self) -> str: """Return category of results""" if not self.results: raise ValueError("No result available") return self.results[0].category @property def headers(self) -> list[str]: """Return headers of results""" if not self.results: raise ValueError("No result available") # Return the intersection of all headers headers = set(self.results[0].headers) if len(self.results) > 1: for adapter in self.results[1:]: headers.intersection_update(adapter.headers) return list(headers) def __post_init__(self): """Check and initialize fields""" if self.results is None: self.results = [] if self.ylabels is None: self.ylabels = [] if self.short_ids is None: self.short_ids = [] def append(self, adapter: BaseResultAdapter, obj: SignalObj | ImageObj) -> None: """Append a result adapter Args: adapter: Adapter to append obj: Object associated to the adapter """ # Check that the adapter is compatible with existing ones if self.results: if adapter.category != self.results[0].category: raise ValueError("Incompatible adapter category") if len(set(self.headers).intersection(set(adapter.headers))) == 0: raise ValueError("Incompatible adapter headers") self.results.append(adapter) df = adapter.to_dataframe() for i_row_res in range(len(df)): sid = get_short_id(obj) ylabel = f"{adapter.func_name}({sid})" if "roi_index" in df.columns: i_roi = int(df.iloc[i_row_res]["roi_index"]) roititle = "" if i_roi >= 0 and obj.roi is not None: roititle = obj.roi.get_single_roi_title(i_roi) ylabel += f"|{roititle}" self.ylabels.append(ylabel) self.short_ids.append(sid) def have_results(objs: list[SignalObj | ImageObj]) -> bool: """Return True if any object has results Args: objs: List of objects Returns: True if any object has results, False otherwise """ return any( item for obj in objs for item in GeometryAdapter.iterate_from_obj(obj) ) or any(item for obj in objs for item in TableAdapter.iterate_from_obj(obj)) def have_geometry_results(objs: list[SignalObj | ImageObj]) -> bool: """Return True if any object has geometry results Args: objs: List of objects Returns: True if any object has geometry results, False otherwise """ return any(item for obj in objs for item in GeometryAdapter.iterate_from_obj(obj)) def create_resultdata_dict( objs: list[SignalObj | ImageObj], ) -> dict[str, ResultData]: """Return result data dictionary Args: objs: List of objects Returns: Result data dictionary: keys are result categories, values are ResultData """ rdatadict: dict[str, ResultData] = {} for obj in objs: for adapter in list(GeometryAdapter.iterate_from_obj(obj)) + list( TableAdapter.iterate_from_obj(obj) ): rdata = rdatadict.setdefault(adapter.category, ResultData()) rdata.append(adapter, obj) return rdatadict def show_resultdata(parent: QWidget, rdata: ResultData, object_name: str = "") -> None: """Show result data in a DataFrame editor window Args: parent: Parent widget rdata: Result data to show object_name: Optional object name for the dialog """ with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) # Generate dataframes with visible columns only # Use the object-level visible_only parameter for cleaner implementation dfs = [result.to_dataframe(visible_only=True) for result in rdata.results] # Combine all dataframes df = pd.concat(dfs, ignore_index=True) # Add comparison columns if we have multiple results of the same kind if len(dfs) > 1: df = _add_comparison_columns_to_dataframe(df, rdata) # Remove roi_index column for display (not needed in the GUI) if "roi_index" in df.columns: df = df.drop(columns=["roi_index"]) df.set_index(pd.Index(rdata.ylabels), inplace=True) dlg = DataFrameEditor(parent) dlg.setup_and_check( df, _("Results") + f" ({rdata.category})", readonly=True, add_title_suffix=False, ) if object_name: dlg.setObjectName(object_name) dlg.resize(750, 300) exec_dialog(dlg) def _add_comparison_columns_to_dataframe( df: pd.DataFrame, rdata: ResultData ) -> pd.DataFrame: """Add comparison columns to dataframe with ROI-aware grouping. For each original column, adds one comparison column showing the difference between the current row and the corresponding reference row. Args: df: Combined DataFrame with all results rdata: ResultData containing ylabels and short_ids Returns: DataFrame with comparison columns added (one Δ column per original column) """ # Build signal/image groups: # list of (object_id, start_row, end_row) for each signal/image obj_groups = [] current_obj_id = None group_start = 0 for i, obj_id in enumerate(rdata.short_ids): if obj_id != current_obj_id: # New signal group if current_obj_id is not None: # Close previous group obj_groups.append((current_obj_id, group_start, i - 1)) current_obj_id = obj_id group_start = i # Close the last group if current_obj_id is not None: obj_groups.append((current_obj_id, group_start, len(rdata.ylabels) - 1)) # If we only have one signal group, no need for comparisons if len(obj_groups) <= 1: return df # Use the first signal group as reference reference_group = obj_groups[0] _ref_obj_id, reference_start, reference_end = reference_group # Get columns to compare (exclude roi_index) cols_to_compare = [col for col in df.columns if col != "roi_index"] # Create new dataframe with original columns plus one comparison column per # original column result_df = df.copy() # Add comparison columns - one per original column for col in cols_to_compare: comparison_col_name = f"Δ({col})" comparison_values = [] # For each row in the entire dataframe for row_idx in range(len(df)): # Find which group this row belongs to row_group_idx = None for group_idx, (obj_id, start, end) in enumerate(obj_groups): if start <= row_idx <= end: row_group_idx = group_idx break if row_group_idx == 0: # Reference group - no comparison needed comparison_values.append("") elif row_group_idx is not None: # Non-reference group - calculate comparison with corresponding # reference row group_start = obj_groups[row_group_idx][1] ref_row_idx = reference_start + (row_idx - group_start) if ref_row_idx <= reference_end: ref_val = df.iloc[ref_row_idx][col] curr_val = df.iloc[row_idx][col] comparison_values.append( _compute_comparison_value(ref_val, curr_val) ) else: comparison_values.append("") else: # Should not happen, but handle gracefully comparison_values.append("") # Insert comparison column right after the original column col_idx = result_df.columns.get_loc(col) result_df.insert(col_idx + 1, comparison_col_name, comparison_values) return result_df def _compute_comparison_value(ref_val, curr_val) -> str: """Compute a comparison value between reference and current values. Args: ref_val: Reference value curr_val: Current value to compare Returns: String representation of the comparison """ # Handle different data types if pd.isna(ref_val) or pd.isna(curr_val): return "N/A" if isinstance(ref_val, str) or isinstance(curr_val, str): # String comparison return "=" if str(ref_val) == str(curr_val) else "≠" if isinstance(ref_val, (int, float, np.integer, np.floating)) and isinstance( curr_val, (int, float, np.integer, np.floating) ): # Numeric comparison - show difference diff = curr_val - ref_val # For integers, check exact equality; for floats, use small tolerance if isinstance(ref_val, (int, np.integer)) and isinstance( curr_val, (int, np.integer) ): tolerance = 0 else: tolerance = 1e-10 if abs(diff) <= tolerance: return "=" # Format the difference with appropriate sign sign = "+" if diff > 0 else "" return f"{sign}{diff:.4g}" # Default comparison return "=" if ref_val == curr_val else "≠" def resultadapter_to_html( adapter: BaseResultAdapter | list[BaseResultAdapter], obj: SignalObj | ImageObj, visible_only: bool = True, transpose_single_row: bool = True, max_cells: int | None = None, **kwargs, ) -> str: """Convert a result adapter to HTML format Args: adapter: Adapter to convert, or list of adapters to concatenate obj: Object associated to the adapter visible_only: If True, include only visible headers based on display preferences. Default is False. transpose_single_row: If True, transpose the table when there's only one row max_cells: Maximum number of table cells (rows × columns) to display per result. If None, use the configuration value. If a result has more cells, it will be truncated with a notice. **kwargs: Additional arguments passed to DataFrame.to_html() Returns: HTML representation of the adapter """ if not isinstance(adapter, BaseResultAdapter) and not all( isinstance(adp, BaseResultAdapter) for adp in adapter ): raise ValueError( "Adapter must be a BaseResultAdapter " "or a list of BaseResultAdapter instances" ) # Get max_cells from config if not provided if max_cells is None: max_cells = Conf.view.max_cells_in_label.get(100) if isinstance(adapter, BaseResultAdapter): # Get the dataframe FIRST to check truncation needs df = adapter.to_dataframe(visible_only=visible_only) # Remove roi_index column for display calculations display_df = df.drop(columns=["roi_index"]) if "roi_index" in df.columns else df # For merged labels, limit display columns for readability max_display_cols = Conf.view.max_cols_in_label.get(20) num_cols = len(display_df.columns) cols_truncated = num_cols > max_display_cols if cols_truncated: display_df = display_df.iloc[:, :max_display_cols] num_cols = max_display_cols # Calculate number of cells (rows × columns) num_rows = len(df) num_cells = num_rows * num_cols # Check if truncation is needed BEFORE calling to_html() if num_cells > max_cells or cols_truncated: # Calculate how many rows we can display given max_cells max_rows = max(1, max_cells // num_cols) if num_cols > 0 else num_rows # Truncate to max_rows and make a copy to avoid SettingWithCopyWarning df_truncated = display_df.head(max_rows).copy() # Generate HTML directly from truncated DataFrame for performance # This is MUCH faster than calling adapter.to_html() on full data html_kwargs = {"border": 0} html_kwargs.update(kwargs) # Format numeric columns efficiently for col in df_truncated.select_dtypes(include=["number"]).columns: df_truncated[col] = df_truncated[col].map( lambda x: f"{x:.3g}" if pd.notna(x) else x ) text = f'{adapter.result.title}:' text += df_truncated.to_html(**html_kwargs) # Add truncation notice omitted_parts = [] if num_rows > max_rows: omitted_parts.append(_("%d more rows") % (num_rows - max_rows)) if cols_truncated: num_omitted_cols = len(df.columns) - max_display_cols omitted_parts.append(_("%d more columns") % num_omitted_cols) if omitted_parts: omitted_str = _("%s omitted") % ", ".join(omitted_parts) text += f"

... ({omitted_str})

" return text # No truncation needed, use the standard adapter.to_html() method return adapter.to_html( obj=obj, visible_only=visible_only, transpose_single_row=transpose_single_row, **kwargs, ) # For lists of adapters, recursively process each one return "
".join( [ resultadapter_to_html( res, obj, visible_only, transpose_single_row, max_cells ) for res in adapter ] ) DataLab-1.1.0/datalab/adapters_metadata/geometry_adapter.py000066400000000000000000000054201514014117600237420ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Adapter for Sigima's GeometryResult, providing features for storing and retrieving those objects as metadata for DataLab's signal and image objects. """ from __future__ import annotations from typing import ClassVar from sigima.objects import GeometryResult, ImageObj, SignalObj from datalab.adapters_metadata.base_adapter import BaseResultAdapter class GeometryAdapter(BaseResultAdapter): """Adapter for GeometryResult objects. This adapter provides a unified interface for working with GeometryResult objects, including metadata storage/retrieval and various data representations. Args: geometry: GeometryResult object to adapt """ # Class constants for metadata storage META_PREFIX: ClassVar[str] = "Geometry_" def __init__(self, geometry: GeometryResult) -> None: super().__init__(geometry) @classmethod def from_geometry_result(cls, geometry: GeometryResult) -> GeometryAdapter: """Create GeometryAdapter from GeometryResult. Args: geometry: GeometryResult object Returns: GeometryAdapter instance """ return cls(geometry) @property def headers(self) -> list[str]: """Get column headers for the coordinates. Returns: List of column headers """ # Get headers directly from the DataFrame df = self.result.to_dataframe() # Return coordinate columns (exclude 'roi_index' if present) return [col for col in df.columns if col != "roi_index"] @property def category(self) -> str: """Get the category. Returns: Category """ return f"shape_{self.result.kind.value}" @staticmethod def add_geometry_result_to_obj( geometry: GeometryResult, obj: SignalObj | ImageObj ) -> None: """Static method to add GeometryResult to object. Args: geometry: GeometryResult object obj: Signal or image object """ # Create adapter and add to object adapter = GeometryAdapter.from_geometry_result(geometry) adapter.add_to(obj) @classmethod def from_metadata_entry(cls, obj: SignalObj | ImageObj, key: str): """Create a geometry result adapter from a metadata entry. Args: obj: Object containing the metadata key: Metadata key for the array data Returns: GeometryAdapter object Raises: ValueError: Invalid metadata entry """ # Load the geometry data from the dictionary geometry_dict = obj.metadata[key] geometry = GeometryResult.from_dict(geometry_dict) return cls(geometry) DataLab-1.1.0/datalab/adapters_metadata/table_adapter.py000066400000000000000000000045441514014117600232040ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Adapter for Sigima's TableResult, providing features for storing and retrieving those objects as metadata for DataLab's signal and image objects. """ from __future__ import annotations from typing import ClassVar, Union from sigima.objects import ImageObj, SignalObj from sigima.objects.scalar import NO_ROI, TableResult from datalab.adapters_metadata.base_adapter import BaseResultAdapter class TableAdapter(BaseResultAdapter): """Adapter for TableResult objects. This adapter provides a unified interface for working with TableResult objects, including metadata storage/retrieval and various data representations. Args: table: TableResult object to adapt """ # Class constants for metadata storage META_PREFIX: ClassVar[str] = "Table_" def __init__(self, table: TableResult) -> None: super().__init__(table) @property def headers(self) -> list[str]: """Get the column headers. Returns: Column headers """ return list(self.result.headers) @property def category(self) -> str: """Get the category. Returns: Category (uses the title for backward compatibility) """ return self.result.title def get_unique_roi_indices(self) -> list[int]: """Get unique ROI indices present in the data. Returns: List of unique ROI indices """ df = self.to_dataframe() if "roi_index" in df.columns: return sorted(df["roi_index"].unique().tolist()) return [NO_ROI] if len(df) > 0 else [] @classmethod def from_metadata_entry(cls, obj: Union[SignalObj, ImageObj], key: str): """Create a table result adapter from a metadata entry. Args: obj: Object containing the metadata key: Metadata key for the table data Returns: TableAdapter object Raises: ValueError: Invalid metadata entry """ if not cls.match(key, obj.metadata[key]): raise ValueError(f"Invalid metadata key for table result: {key}") # Parse the metadata entry as a TableResult dictionary table_dict = obj.metadata[key] table = TableResult.from_dict(table_dict) return cls(table) DataLab-1.1.0/datalab/adapters_plotpy/000077500000000000000000000000001514014117600176035ustar00rootroot00000000000000DataLab-1.1.0/datalab/adapters_plotpy/__init__.py000066400000000000000000000026111514014117600217140ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Adapters for PlotPy =================== The :mod:`datalab.adapters_plotpy` package provides adapters for PlotPy to integrate with DataLab's data model and GUI. """ __all__ = [ "CURVESTYLES", "CircularROIPlotPyAdapter", "GeometryPlotPyAdapter", "ImageObjPlotPyAdapter", "PolygonalROIPlotPyAdapter", "RectangularROIPlotPyAdapter", "SegmentROIPlotPyAdapter", "SignalObjPlotPyAdapter", "SignalROIPlotPyAdapter", "TablePlotPyAdapter", "TypePlotItem", "TypeROIItem", "configure_roi_item", "create_adapter_from_object", "items_to_json", "json_to_items", "plotitem_to_singleroi", "singleroi_to_plotitem", ] from .base import items_to_json, json_to_items from .converters import ( create_adapter_from_object, plotitem_to_singleroi, singleroi_to_plotitem, ) from .objects.base import TypePlotItem from .objects.image import ( ImageObjPlotPyAdapter, ) from .objects.scalar import ( GeometryPlotPyAdapter, TablePlotPyAdapter, ) from .objects.signal import CURVESTYLES, SignalObjPlotPyAdapter from .roi.base import TypeROIItem, configure_roi_item from .roi.image import ( CircularROIPlotPyAdapter, PolygonalROIPlotPyAdapter, RectangularROIPlotPyAdapter, ) from .roi.signal import SegmentROIPlotPyAdapter, SignalROIPlotPyAdapter DataLab-1.1.0/datalab/adapters_plotpy/annotations.py000066400000000000000000000074251514014117600225220ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Annotation Adapter for PlotPy Integration ----------------------------------------- This module bridges Sigima's format-agnostic annotation storage with PlotPy's plot item system. It handles bidirectional conversion between: - Sigima: list[dict] (JSON-serializable) - PlotPy: list[AnnotatedShape] (plot items) """ from __future__ import annotations import json from typing import TYPE_CHECKING from guidata.io import JSONReader, JSONWriter from plotpy.io import load_items, save_items if TYPE_CHECKING: from plotpy.items import AnnotatedShape from sigima.objects.base import BaseObj class PlotPyAnnotationAdapter: """Adapter for converting between Sigima annotations and PlotPy items. This class provides the bridge between Sigima's generic annotation storage (list of dicts) and PlotPy's specific plot item format. Example: >>> from sigima.objects.signal.creation import create_signal >>> obj = create_signal("Test") >>> adapter = PlotPyAnnotationAdapter(obj) >>> >>> # Add PlotPy items >>> from plotpy.items import AnnotatedRectangle >>> rect = AnnotatedRectangle(0, 0, 10, 10) >>> adapter.add_items([rect]) >>> >>> # Retrieve as PlotPy items >>> items = adapter.get_items() >>> len(items) 1 """ def __init__(self, obj: BaseObj): """Initialize adapter with an object. Args: obj: Signal or image object with annotation support """ self.obj = obj def get_items(self) -> list[AnnotatedShape]: """Get annotations as PlotPy items. Returns: List of PlotPy annotation items Notes: This method deserializes the JSON data stored in the object using PlotPy's load_items() function. """ annotations = self.obj.get_annotations() if not annotations: return [] items = [] for ann_dict in annotations: # Each annotation dict should contain PlotPy's JSON serialization if "plotpy_json" in ann_dict: try: json_str = ann_dict["plotpy_json"] for item in load_items(JSONReader(json_str)): items.append(item) except (json.JSONDecodeError, ValueError, KeyError): # Skip invalid items continue return items def set_items(self, items: list[AnnotatedShape]) -> None: """Set annotations from PlotPy items. Args: items: List of PlotPy annotation items Notes: This method serializes PlotPy items to JSON using PlotPy's save_items() function and stores them in the Sigima format. """ if not items: self.obj.clear_annotations() return # Convert PlotPy items to our annotation format annotations = [] for item in items: writer = JSONWriter(None) save_items(writer, [item]) ann_dict = { "type": "plotpy_item", "item_class": item.__class__.__name__, "plotpy_json": writer.get_json(), } annotations.append(ann_dict) self.obj.set_annotations(annotations) def add_items(self, items: list[AnnotatedShape]) -> None: """Add PlotPy items to existing annotations. Args: items: List of PlotPy annotation items to add """ current_items = self.get_items() current_items.extend(items) self.set_items(current_items) def clear(self) -> None: """Clear all annotations.""" self.obj.clear_annotations() DataLab-1.1.0/datalab/adapters_plotpy/base.py000066400000000000000000000053541514014117600210760ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ PlotPy Adapter Base Module -------------------------- """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import json from typing import TYPE_CHECKING from guidata.io import JSONReader, JSONWriter from plotpy.io import load_items, save_items from plotpy.items import ( AbstractLabelItem, AnnotatedSegment, AnnotatedShape, ) if TYPE_CHECKING: from plotpy.items import AbstractShape from plotpy.styles import AnnotationParam def config_annotated_shape( item: AnnotatedShape, fmt: str, lbl: bool, section: str | None = None, option: str | None = None, show_computations: bool | None = None, ): """Configurate annotated shape Args: item: Annotated shape item fmt: Format string lbl: Show label section: Shape style section (e.g. "plot") option: Shape style option (e.g. "shape/drag") show_computations: Show computations """ param: AnnotationParam = item.annotationparam param.format = fmt param.show_label = lbl if show_computations is not None: param.show_computations = show_computations if isinstance(item, AnnotatedSegment): item.label.labelparam.anchor = "T" item.label.labelparam.update_item(item.label) param.update_item(item) if section is not None and option is not None: item.set_style(section, option) # TODO: [P3] Move this function as a method of plot items in PlotPy def set_plot_item_editable( item: AbstractShape | AbstractLabelItem | AnnotatedShape, state ): """Set plot item editable state Args: item: Plot item state: State """ item.set_movable(state) item.set_resizable(state and not isinstance(item, AbstractLabelItem)) item.set_rotatable(state and not isinstance(item, AbstractLabelItem)) item.set_readonly(not state) item.set_selectable(state) def items_to_json(items: list) -> str | None: """Convert plot items to JSON string Args: items: list of plot items Returns: JSON string or None if items is empty """ if items: writer = JSONWriter(None) save_items(writer, items) return writer.get_json(indent=4) return None def json_to_items(json_str: str | None) -> list: """Convert JSON string to plot items Args: json_str: JSON string or None Returns: List of plot items """ items = [] if json_str: try: for item in load_items(JSONReader(json_str)): items.append(item) except json.decoder.JSONDecodeError: pass return items DataLab-1.1.0/datalab/adapters_plotpy/converters.py000066400000000000000000000050421514014117600223500ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ PlotPy Adapter Converters ------------------------- """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from typing import TYPE_CHECKING from plotpy.items import ( AnnotatedCircle, AnnotatedPolygon, AnnotatedRectangle, AnnotatedXRange, ) from sigima.objects import ( CircularROI, PolygonalROI, RectangularROI, SegmentROI, ) from datalab.adapters_plotpy.factories import create_adapter_from_object from datalab.adapters_plotpy.roi.image import ( PolygonalROIPlotPyAdapter, RectangularROIPlotPyAdapter, ) if TYPE_CHECKING: from sigima.objects import ImageObj, SignalObj def plotitem_to_singleroi( plot_item: AnnotatedXRange | AnnotatedRectangle | AnnotatedCircle | AnnotatedPolygon, obj: SignalObj | ImageObj | None = None, ) -> SegmentROI | RectangularROI | CircularROI | PolygonalROI: """Create a single ROI from the given PlotPy item to integrate with DataLab Args: plot_item: The PlotPy item for which to create a single ROI obj: Optional signal or image object for coordinate rounding Returns: A single ROI instance """ # pylint: disable=import-outside-toplevel from datalab.adapters_plotpy.roi.image import ( CircularROIPlotPyAdapter, ) from datalab.adapters_plotpy.roi.signal import ( SegmentROIPlotPyAdapter, ) if isinstance(plot_item, AnnotatedXRange): adapter = SegmentROIPlotPyAdapter elif isinstance(plot_item, AnnotatedRectangle): adapter = RectangularROIPlotPyAdapter elif isinstance(plot_item, AnnotatedCircle): adapter = CircularROIPlotPyAdapter elif isinstance(plot_item, AnnotatedPolygon): adapter = PolygonalROIPlotPyAdapter else: raise TypeError(f"Unsupported PlotPy item type: {type(plot_item)}") return adapter.from_plot_item(plot_item, obj) def singleroi_to_plotitem( roi: SegmentROI | RectangularROI | CircularROI | PolygonalROI, obj: SignalObj | ImageObj, ) -> AnnotatedXRange | AnnotatedRectangle | AnnotatedCircle | AnnotatedPolygon: """Create a PlotPy item from the given single ROI to integrate with DataLab Args: roi: The single ROI for which to create a PlotPy item obj: The object (signal or image) associated with the ROI Returns: A PlotPy item instance """ adapter = create_adapter_from_object(roi) return adapter.to_plot_item(obj) DataLab-1.1.0/datalab/adapters_plotpy/coordutils.py000066400000000000000000000131421514014117600223450ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ ROI Coordinate Utilities ========================= This module provides utility functions for rounding ROI coordinates to appropriate precision based on the sampling characteristics of signals and images. These functions are used when converting interactive PlotPy shapes to ROI objects to ensure coordinates are displayed with reasonable precision. """ from __future__ import annotations import numpy as np from sigima.objects import ImageObj, ROI1DParam, ROI2DParam, SignalObj def round_signal_coords( obj: SignalObj, coords: list[float], precision_factor: float = 0.1 ) -> list[float]: """Round signal coordinates to appropriate precision based on sampling period. Rounds to a fraction of the median sampling period to avoid excessive decimal places while maintaining reasonable precision. Args: obj: signal object coords: coordinates to round precision_factor: fraction of sampling period to use as rounding precision. Default is 0.1 (1/10th of sampling period). Returns: Rounded coordinates """ if len(obj.x) < 2: # Cannot compute sampling period, return coords as-is return coords # Compute median sampling period sampling_period = float(np.median(np.diff(obj.x))) if sampling_period == 0: # Avoid division by zero for constant x arrays return coords # Round to specified fraction of sampling period precision = sampling_period * precision_factor # Determine number of decimal places if precision > 0: decimals = max(0, int(-np.floor(np.log10(precision)))) return [round(c, decimals) for c in coords] return coords def round_image_coords( obj: ImageObj, coords: list[float], precision_factor: float = 0.1 ) -> list[float]: """Round image coordinates to appropriate precision based on pixel spacing. Rounds to a fraction of the pixel spacing to avoid excessive decimal places while maintaining reasonable precision. Uses separate precision for X and Y. Args: obj: image object coords: flat list of coordinates [x0, y0, x1, y1, ...] to round precision_factor: fraction of pixel spacing to use as rounding precision. Default is 0.1 (1/10th of pixel spacing). Returns: Rounded coordinates Raises: ValueError: if coords does not contain an even number of elements """ if len(coords) % 2 != 0: raise ValueError("coords must contain an even number of elements (x, y pairs).") if len(coords) == 0: return coords rounded = list(coords) if obj.is_uniform_coords: # Use dx, dy for uniform coordinates precision_x = abs(obj.dx) * precision_factor precision_y = abs(obj.dy) * precision_factor else: # Compute average spacing for non-uniform coordinates if len(obj.xcoords) > 1: avg_dx = float(np.mean(np.abs(np.diff(obj.xcoords)))) precision_x = avg_dx * precision_factor else: precision_x = 0 if len(obj.ycoords) > 1: avg_dy = float(np.mean(np.abs(np.diff(obj.ycoords)))) precision_y = avg_dy * precision_factor else: precision_y = 0 # Round X coordinates (even indices) if precision_x > 0: decimals_x = max(0, int(-np.floor(np.log10(precision_x)))) for i in range(0, len(rounded), 2): rounded[i] = round(rounded[i], decimals_x) # Round Y coordinates (odd indices) if precision_y > 0: decimals_y = max(0, int(-np.floor(np.log10(precision_y)))) for i in range(1, len(rounded), 2): rounded[i] = round(rounded[i], decimals_y) return rounded def round_signal_roi_param( obj: SignalObj, param: ROI1DParam, precision_factor: float = 0.1 ) -> None: """Round signal ROI parameter coordinates in-place. Args: obj: signal object param: ROI parameter to round (modified in-place) precision_factor: fraction of sampling period to use as rounding precision """ coords = round_signal_coords(obj, [param.xmin, param.xmax], precision_factor) param.xmin, param.xmax = coords def round_image_roi_param( obj: ImageObj, param: ROI2DParam, precision_factor: float = 0.1 ) -> None: """Round image ROI parameter coordinates in-place. Args: obj: image object param: ROI parameter to round (modified in-place) precision_factor: fraction of pixel spacing to use as rounding precision """ if param.geometry == "rectangle": # Round x0, y0, dx, dy x0, y0, x1, y1 = param.x0, param.y0, param.x0 + param.dx, param.y0 + param.dy coords = round_image_coords(obj, [x0, y0, x1, y1], precision_factor) param.x0, param.y0 = coords[0], coords[1] # Round dx and dy to avoid floating-point errors in subtraction dx_dy_rounded = round_image_coords( obj, [coords[2] - coords[0], coords[3] - coords[1]], precision_factor ) param.dx = dx_dy_rounded[0] param.dy = dx_dy_rounded[1] elif param.geometry == "circle": # Round xc, yc, r coords = round_image_coords(obj, [param.xc, param.yc], precision_factor) param.xc, param.yc = coords # Round radius using X precision r_rounded = round_image_coords(obj, [param.r, 0], precision_factor)[0] param.r = r_rounded elif param.geometry == "polygon": # Round polygon points rounded = round_image_coords(obj, param.points.tolist(), precision_factor) param.points = np.array(rounded) DataLab-1.1.0/datalab/adapters_plotpy/factories.py000066400000000000000000000055321514014117600221410ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ PlotPy Adapter Factories ------------------------ """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations def create_adapter_from_object(object_to_adapt): """Create an adapter for the given object to integrate with PlotPy Args: object_to_adapt: The object to adapt (signal, image, ROI, or scalar result) Returns: An adapter instance """ # pylint: disable=import-outside-toplevel # Import adapters as needed to avoid circular imports from sigima.objects import ( CircularROI, ImageObj, ImageROI, PolygonalROI, RectangularROI, SegmentROI, SignalObj, SignalROI, ) from datalab.adapters_metadata import GeometryAdapter, TableAdapter if isinstance(object_to_adapt, GeometryAdapter): from datalab.adapters_plotpy.objects.scalar import GeometryPlotPyAdapter adapter = GeometryPlotPyAdapter(object_to_adapt) elif isinstance(object_to_adapt, TableAdapter): from datalab.adapters_plotpy.objects.scalar import TablePlotPyAdapter adapter = TablePlotPyAdapter(object_to_adapt) elif isinstance(object_to_adapt, SignalObj): from datalab.adapters_plotpy.objects.signal import SignalObjPlotPyAdapter adapter = SignalObjPlotPyAdapter(object_to_adapt) elif isinstance(object_to_adapt, SignalROI): from datalab.adapters_plotpy.roi.signal import SignalROIPlotPyAdapter adapter = SignalROIPlotPyAdapter(object_to_adapt) elif isinstance(object_to_adapt, SegmentROI): from datalab.adapters_plotpy.roi.signal import SegmentROIPlotPyAdapter adapter = SegmentROIPlotPyAdapter(object_to_adapt) elif isinstance(object_to_adapt, ImageObj): from datalab.adapters_plotpy.objects.image import ImageObjPlotPyAdapter adapter = ImageObjPlotPyAdapter(object_to_adapt) elif isinstance(object_to_adapt, RectangularROI): from datalab.adapters_plotpy.roi.image import RectangularROIPlotPyAdapter adapter = RectangularROIPlotPyAdapter(object_to_adapt) elif isinstance(object_to_adapt, CircularROI): from datalab.adapters_plotpy.roi.image import CircularROIPlotPyAdapter adapter = CircularROIPlotPyAdapter(object_to_adapt) elif isinstance(object_to_adapt, PolygonalROI): from datalab.adapters_plotpy.roi.image import PolygonalROIPlotPyAdapter adapter = PolygonalROIPlotPyAdapter(object_to_adapt) elif isinstance(object_to_adapt, ImageROI): from datalab.adapters_plotpy.roi.image import ImageROIPlotPyAdapter adapter = ImageROIPlotPyAdapter(object_to_adapt) else: raise TypeError(f"Unsupported object type: {type(object_to_adapt)}") return adapter DataLab-1.1.0/datalab/adapters_plotpy/objects/000077500000000000000000000000001514014117600212345ustar00rootroot00000000000000DataLab-1.1.0/datalab/adapters_plotpy/objects/__init__.py000066400000000000000000000000001514014117600233330ustar00rootroot00000000000000DataLab-1.1.0/datalab/adapters_plotpy/objects/base.py000066400000000000000000000150751514014117600225300ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ PlotPy Adapter Base Object Module --------------------------------- """ from __future__ import annotations import abc from typing import ( TYPE_CHECKING, Any, Generic, TypeVar, ) from guidata.dataset import update_dataset from plotpy.items import ( AnnotatedShape, ) from sigima.objects.base import ( ROI_KEY, TypeObj, ) from datalab.adapters_metadata import GeometryAdapter from datalab.adapters_plotpy.annotations import PlotPyAnnotationAdapter from datalab.adapters_plotpy.base import ( config_annotated_shape, set_plot_item_editable, ) from datalab.adapters_plotpy.objects.scalar import GeometryPlotPyAdapter from datalab.config import Conf if TYPE_CHECKING: from plotpy.items import CurveItem, MaskedXYImageItem TypePlotItem = TypeVar("TypePlotItem", bound="CurveItem | MaskedXYImageItem") class BaseObjPlotPyAdapter(Generic[TypeObj, TypePlotItem]): """Object (signal/image) plot item adapter class""" DEFAULT_FMT = "s" # This is overriden in children classes CONF_FMT = Conf.view.sig_format # This is overriden in children classes def __init__(self, obj: TypeObj) -> None: """Initialize the adapter with the object. Args: obj: object (signal/image) """ self.obj = obj self.__default_options = { "format": "%" + self.CONF_FMT.get(self.DEFAULT_FMT), "showlabel": Conf.view.show_label.get(False), } self.annotation_adapter = PlotPyAnnotationAdapter(obj) def get_obj_option(self, name: str) -> Any: """Get object option value. Args: name: option name Returns: Option value """ default = self.__default_options[name] return self.obj.get_metadata_option(name, default) @abc.abstractmethod def make_item(self, update_from: TypePlotItem | None = None) -> TypePlotItem: """Make plot item from data. Args: update_from: update Returns: Plot item """ @abc.abstractmethod def update_item(self, item: TypePlotItem, data_changed: bool = True) -> None: """Update plot item from data. Args: item: plot item data_changed: if True, data has changed """ def add_annotations_from_items(self, items: list) -> None: """Add object annotations (annotation plot items). Args: items: annotation plot items """ # Use the new annotation adapter self.annotation_adapter.add_items(items) def set_annotations_from_items(self, items: list) -> None: """Set object annotations (annotation plot items), replacing any existing ones. Args: items: annotation plot items """ # Use the new annotation adapter self.annotation_adapter.set_items(items) @abc.abstractmethod def add_label_with_title(self, title: str | None = None) -> None: """Add label with title annotation Args: title: title (if None, use object title) """ def iterate_shape_items(self, editable: bool = False): """Iterate over shape items encoded in metadata (if any). Args: editable: if True, annotations are editable Yields: Plot item """ fmt = self.get_obj_option("format") lbl = self.get_obj_option("showlabel") for key, _value in self.obj.metadata.items(): if key == ROI_KEY: roi = self.obj.roi if roi is not None: # Delayed import to avoid circular dependency # pylint: disable=import-outside-toplevel from datalab.adapters_plotpy.roi.factory import create_roi_adapter adapter = create_roi_adapter(roi) yield from adapter.iterate_roi_items( self.obj, fmt=fmt, lbl=lbl, editable=False ) # Process geometry results from metadata (using GeometryAdapter) elif GeometryAdapter.match(key, _value): try: geomadapter = GeometryAdapter.from_metadata_entry(self.obj, key) plot_adapter = GeometryPlotPyAdapter(geomadapter) yield from plot_adapter.iterate_shape_items( fmt, lbl, self.obj.PREFIX ) except (ValueError, TypeError): # Skip invalid entries pass # Use the new annotation adapter to get items if self.obj.has_annotations(): for item in self.annotation_adapter.get_items(): if isinstance(item, AnnotatedShape): config_annotated_shape(item, fmt, lbl) set_plot_item_editable(item, editable) yield item def update_plot_item_parameters(self, item: TypePlotItem) -> None: """Update plot item parameters from object data/metadata Takes into account a subset of plot item parameters. Those parameters may have been overriden by object metadata entries or other object data. The goal is to update the plot item accordingly. This is *almost* the inverse operation of `update_metadata_from_plot_item`. Args: item: plot item """ def_dict = Conf.view.get_def_dict(self.__class__.__name__[:3].lower()) self.obj.set_metadata_options_defaults(def_dict, overwrite=False) # Subclasses have to override this method to update plot item parameters, # then call this implementation of the method to update plot item. update_dataset(item.param, self.obj.get_metadata_options()) item.param.update_item(item) if item.selected: item.select() def update_metadata_from_plot_item(self, item: TypePlotItem) -> None: """Update metadata from plot item. Takes into account a subset of plot item parameters. Those parameters may have been modified by the user through the plot item GUI. The goal is to update the metadata accordingly. This is *almost* the inverse operation of `update_plot_item_parameters`. Args: item: plot item """ def_dict = Conf.view.get_def_dict(self.__class__.__name__[:3].lower()) for key in def_dict: if hasattr(item.param, key): # In case the PlotPy version is not up-to-date self.obj.set_metadata_option(key, getattr(item.param, key)) DataLab-1.1.0/datalab/adapters_plotpy/objects/image.py000066400000000000000000000124671514014117600227020ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ PlotPy Adapter Image Module --------------------------- """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import numpy as np from guidata.dataset import update_dataset from plotpy.builder import make from plotpy.items import MaskedXYImageItem from sigima.objects import ImageObj from datalab.adapters_plotpy.objects.base import ( BaseObjPlotPyAdapter, ) from datalab.config import Conf def get_obj_coords(obj: ImageObj) -> tuple[np.ndarray, np.ndarray]: """Get object coordinates Args: obj: image object Returns: x and y coordinates (pixel centers, not edges) """ if obj.is_uniform_coords: shape = obj.data.shape # Generate coordinates for pixel centers, not edges # For N pixels: centers are at x0, x0+dx, x0+2*dx, ..., x0+(N-1)*dx xcoords = np.linspace(obj.x0, obj.x0 + obj.dx * (shape[1] - 1), shape[1]) ycoords = np.linspace(obj.y0, obj.y0 + obj.dy * (shape[0] - 1), shape[0]) else: xcoords, ycoords = obj.xcoords, obj.ycoords return xcoords, ycoords class ImageObjPlotPyAdapter(BaseObjPlotPyAdapter[ImageObj, MaskedXYImageItem]): """Image object plot item adapter class""" CONF_FMT = Conf.view.ima_format DEFAULT_FMT = ".1f" def update_plot_item_parameters(self, item: MaskedXYImageItem) -> None: """Update plot item parameters from object data/metadata Takes into account a subset of plot item parameters. Those parameters may have been overriden by object metadata entries or other object data. The goal is to update the plot item accordingly. This is *almost* the inverse operation of `update_metadata_from_plot_item`. Args: item: plot item """ o = self.obj for axis in ("x", "y", "z"): unit = getattr(o, axis + "unit") fmt = r"%.1f" if unit: fmt = r"%.1f (" + unit + ")" setattr(item.param, axis + "format", fmt) item.set_xy(*get_obj_coords(o)) zmin, zmax = item.get_lut_range() if o.zscalemin is not None or o.zscalemax is not None: zmin = zmin if o.zscalemin is None else o.zscalemin zmax = zmax if o.zscalemax is None else o.zscalemax item.set_lut_range([zmin, zmax]) super().update_plot_item_parameters(item) def update_metadata_from_plot_item(self, item: MaskedXYImageItem) -> None: """Update metadata from plot item. Takes into account a subset of plot item parameters. Those parameters may have been modified by the user through the plot item GUI. The goal is to update the metadata accordingly. This is *almost* the inverse operation of `update_plot_item_parameters`. Args: item: plot item """ super().update_metadata_from_plot_item(item) o = self.obj # Updating the LUT range: o.zscalemin, o.zscalemax = item.get_lut_range() def __viewable_data(self) -> np.ndarray: """Return viewable data""" data = self.obj.data.real if np.any(np.isnan(data)): data = np.nan_to_num(data, posinf=0, neginf=0) return data def make_item( self, update_from: MaskedXYImageItem | None = None ) -> MaskedXYImageItem: """Make plot item from data. Args: update_from: update from plot item Returns: Plot item """ data = self.__viewable_data() item = make.maskedxyimage( *get_obj_coords(self.obj), data, self.obj.maskdata, title=self.obj.title, colormap="viridis", eliminate_outliers=Conf.view.ima_eliminate_outliers.get(), interpolation="nearest", show_mask=True, ) if update_from is None: self.update_plot_item_parameters(item) else: update_dataset(item.param, update_from.param) item.param.update_item(item) return item def update_item(self, item: MaskedXYImageItem, data_changed: bool = True) -> None: """Update plot item from data. Args: item: plot item data_changed: if True, data has changed """ if data_changed: # When data changes, let set_data() auto-calculate the LUT range from the # new data (by not passing lut_range parameter). The subsequent call to # update_plot_item_parameters() will override it if zscalemin/zscalemax # are explicitly set in the object's metadata. item.set_data(self.__viewable_data()) item.set_mask(self.obj.maskdata) item.param.label = self.obj.title self.update_plot_item_parameters(item) item.plot().update_colormap_axis(item) def add_label_with_title(self, title: str | None = None) -> None: """Add label with title annotation Args: title: title (if None, use image title) """ title = self.obj.title if title is None else title if title: label = make.label(title, (self.obj.x0, self.obj.y0), (10, 10), "TL") self.add_annotations_from_items([label]) DataLab-1.1.0/datalab/adapters_plotpy/objects/scalar.py000066400000000000000000000473021514014117600230610ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ PlotPy Adapter Scalar Module ---------------------------- This module contains adapters for scalar results (GeometryResult, TableResult) to avoid circular imports with the base and factories modules. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from collections.abc import Iterable from typing import TYPE_CHECKING, Literal import guidata.dataset as gds import numpy as np from guidata.configtools import get_font from plotpy.builder import make from plotpy.items import ( AnnotatedCircle, AnnotatedEllipse, AnnotatedPoint, AnnotatedRectangle, AnnotatedSegment, AnnotatedShape, LabelItem, Marker, PolygonShape, ) from sigima.objects.base import BaseObj from sigima.objects.scalar import KindShape from sigima.objects.signal import SignalObj from sigima.tools import coordinates from sigima.tools.signal import pulse from datalab.adapters_metadata import ( GeometryAdapter, TableAdapter, resultadapter_to_html, ) from datalab.adapters_plotpy.base import ( config_annotated_shape, items_to_json, json_to_items, set_plot_item_editable, ) from datalab.config import PLOTPY_CONF, Conf, _ if TYPE_CHECKING: from plotpy.styles import ShapeParam class ResultPlotPyAdapter: """Adapter for converting `sigima` table or geometry adapters to PlotPy Args: result: Table or geometry adapter """ def __init__(self, result_adapter: TableAdapter | GeometryAdapter) -> None: self.result_adapter = result_adapter def get_other_items(self, obj: BaseObj) -> list: # pylint: disable=unused-argument """Return other items associated to this result (excluding label item) Those items are not serialized to JSON. Args: obj: object (signal/image) Returns: List of other items """ return [] class GeometryPlotPyAdapter(ResultPlotPyAdapter): """Adapter for converting `sigima` geometry adapters to PlotPy Args: result: Geometry adapter Raises: AssertionError: invalid argument """ def __init__(self, result_adapter: GeometryAdapter) -> None: assert isinstance(result_adapter, GeometryAdapter) super().__init__(result_adapter) def iterate_shape_items( self, fmt: str, lbl: bool, prefix: Literal["s", "i"] ) -> Iterable: """Iterate over metadata shape plot items. Args: fmt: numeric format (e.g. "%.3f") lbl: if True, show shape labels prefix: "s" for signal, "i" for image Yields: Plot item """ max_shapes = Conf.view.max_shapes_to_draw.get(200) total_coords = len(self.result_adapter.result.coords) # Yield shapes up to the maximum limit for idx, coords in enumerate(self.result_adapter.result.coords): if idx >= max_shapes: break yield self.create_shape_item(coords, fmt, lbl, prefix) # If shapes were truncated, create a warning label if total_coords > max_shapes: warning_text = "⚠ " + _("Only %d out of %d shapes are displayed") % ( max_shapes, total_coords, ) warning_label = make.label(warning_text, "BR", (0, 0), "BR") warning_label.labelparam.font.bold = True warning_label.labelparam.font.size = 10 warning_label.labelparam.bgalpha = 0.8 warning_label.labelparam.bgcolor = "#ff9800" # Orange background warning_label.labelparam.textcolor = "#000000" # Black text warning_label.labelparam.update_item(warning_label) warning_label.set_readonly(True) yield warning_label def create_shape_item( self, coords: np.ndarray, fmt: str, lbl: bool, prefix: Literal["s", "i"] ) -> ( AnnotatedPoint | Marker | AnnotatedRectangle | AnnotatedCircle | AnnotatedSegment | AnnotatedEllipse | PolygonShape | None ): """Create individual shape item from coordinates Args: coords: coordinate array fmt: numeric format (e.g. "%.3f") lbl: if True, show shape labels prefix: "s" for signal, "i" for image Returns: Plot item """ if self.result_adapter.result.kind == KindShape.POINT: assert len(coords) == 2, "Coordinates must be a 2-element array" x0, y0 = coords item = AnnotatedPoint(x0, y0) sparam: ShapeParam = item.shape.shapeparam sparam.symbol.marker = "Ellipse" sparam.symbol.size = 6 sparam.sel_symbol.marker = "Ellipse" sparam.sel_symbol.size = 6 aparam = item.annotationparam aparam.title = self.result_adapter.title sparam.update_item(item.shape) aparam.update_item(item) elif self.result_adapter.result.kind == KindShape.MARKER: assert len(coords) == 2, "Coordinates must be a 2-element array" x0, y0 = coords item = self.__make_marker_item(x0, y0, fmt) elif self.result_adapter.result.kind == KindShape.RECTANGLE: assert len(coords) == 4, "Coordinates must be a 4-element array" x0, y0, dx, dy = coords item = make.annotated_rectangle( x0, y0, x0 + dx, y0 + dy, title=self.result_adapter.title ) elif self.result_adapter.result.kind == KindShape.CIRCLE: assert len(coords) == 3, "Coordinates must be a 3-element array" xc, yc, r = coords x0, y0, x1, y1 = coordinates.circle_to_diameter(xc, yc, r) item = make.annotated_circle( x0, y0, x1, y1, title=self.result_adapter.title ) elif self.result_adapter.result.kind == KindShape.SEGMENT: assert len(coords) == 4, "Coordinates must be a 4-element array" x0, y0, x1, y1 = coords item = make.annotated_segment( x0, y0, x1, y1, title=self.result_adapter.title ) elif self.result_adapter.result.kind == KindShape.ELLIPSE: assert len(coords) == 5, "Coordinates must be a 5-element array" xc, yc, a, b, t = coords coords = coordinates.ellipse_to_diameters(xc, yc, a, b, t) x0, y0, x1, y1, x2, y2, x3, y3 = coords item = make.annotated_ellipse( x0, y0, x1, y1, x2, y2, x3, y3, title=self.result_adapter.title ) elif self.result_adapter.result.kind == KindShape.POLYGON: assert len(coords) >= 6, "Coordinates must be at least 6-element array" assert len(coords) % 2 == 0, "Coordinates must be even-length array" x, y = coords[::2], coords[1::2] # Filter out NaN coordinates to avoid performance issues # Polygons are padded with NaNs to create regular arrays, but we only # need to draw the valid coordinates valid_mask = ~(np.isnan(x) | np.isnan(y)) x, y = x[valid_mask], y[valid_mask] item = make.polygon(x, y, title=self.result_adapter.title, closed=False) else: raise NotImplementedError( f"Unsupported shape kind: {self.result_adapter.result.kind}" ) if isinstance(item, AnnotatedShape): config_annotated_shape(item, fmt, lbl, "results", f"{prefix}/annotation") # Apply settings for annotated shapes (except AnnotatedPoint) if not isinstance(item, AnnotatedPoint): if prefix == "s": config_param = Conf.view.sig_shape_param.get() else: config_param = Conf.view.ima_shape_param.get() shape_param: ShapeParam = item.shape.shapeparam gds.update_dataset(shape_param, config_param) shape_param.update_item(item.shape) if isinstance(item, Marker): item.set_style("results", f"{prefix}/marker") # Apply cursor/marker settings from config if prefix == "s": config_param = Conf.view.sig_marker_param.get() else: config_param = Conf.view.ima_marker_param.get() param = item.markerparam gds.update_dataset(param, config_param) param.update_item(item) set_plot_item_editable(item, False) return item def __make_marker_item(self, x0: float, y0: float, fmt: str) -> Marker: """Make marker item Args: x0: x coordinate y0: y coordinate fmt: numeric format (e.g. '%.3f') """ if np.isnan(x0): mstyle = "-" def label(x, y): # pylint: disable=unused-argument return (self.result_adapter.title + ": " + fmt) % y elif np.isnan(y0): mstyle = "|" def label(x, y): # pylint: disable=unused-argument return (self.result_adapter.title + ": " + fmt) % x else: mstyle = "+" txt = self.result_adapter.title + ": (" + fmt + ", " + fmt + ")" def label(x, y): return txt % (x, y) marker = make.marker(position=(x0, y0), markerstyle=mstyle, label_cb=label) return marker def create_pulse_segment( x0: float, y0: float, x1: float, y1: float, label: str ) -> AnnotatedSegment: """Create a signal segment item for pulse visualization. Args: x0: X-coordinate of the start point y0: Y-coordinate of the start point x1: X-coordinate of the end point y1: Y-coordinate of the end point label: Label for the segment Returns: Annotated segment item styled for pulse visualization """ item = make.annotated_segment(x0, y0, x1, y1, label, show_computations=False) # Configure label appearance similar to Sigima's vistools item.label.labelparam.bgalpha = 0.5 item.label.labelparam.anchor = "T" item.label.labelparam.yc = 10 item.label.labelparam.update_item(item.label) # Configure segment appearance param = item.shape.shapeparam param.line.color = "#33ff00" # Green color for baselines/plateaus param.line.width = 5 param.symbol.facecolor = "#26be00" param.symbol.edgecolor = "#33ff00" param.symbol.marker = "Ellipse" param.symbol.size = 11 param.update_item(item.shape) # Make non-interactive item.set_movable(False) item.set_resizable(False) item.set_selectable(False) return item def create_pulse_crossing_marker( orientation: Literal["h", "v"], position: float, label: str ) -> Marker: """Create a crossing marker for pulse visualization. Args: orientation: 'h' for horizontal, 'v' for vertical cursor position: Position of the cursor along the relevant axis label: Label for the cursor Returns: Marker item styled for crossing visualization """ if orientation == "h": cursor = make.hcursor(position, label=label) elif orientation == "v": cursor = make.vcursor(position, label=label) else: raise ValueError("Orientation must be 'h' or 'v'") # Configure appearance similar to Sigima's vistools cursor.set_movable(False) cursor.set_selectable(False) cursor.markerparam.line.color = "#a7ff33" # Light green cursor.markerparam.line.width = 3 cursor.markerparam.symbol.marker = "NoSymbol" cursor.markerparam.text.textcolor = "#ffffff" cursor.markerparam.text.background_color = "#000000" cursor.markerparam.text.background_alpha = 0.5 cursor.markerparam.text.font.bold = True cursor.markerparam.update_item(cursor) return cursor def are_values_valid(values: list[float | None]) -> bool: """Check if all values are valid (not None or nan) Args: values: list of values Returns: True if all values are valid, False otherwise """ for v in values: if v is None or (isinstance(v, float) and np.isnan(v)): return False return True class TablePlotPyAdapter(ResultPlotPyAdapter): """Adapter for converting `sigima` table adapters to PlotPy Args: result: Table adapter Raises: AssertionError: invalid argument """ def __init__(self, result_adapter: TableAdapter) -> None: assert isinstance(result_adapter, TableAdapter) super().__init__(result_adapter) def get_other_items(self, obj: BaseObj) -> list: """Return other items associated to this result (excluding label item) Those items are not serialized to JSON. Args: obj: object (signal/image) Returns: List of other items """ items = [] if self.result_adapter.result.is_pulse_features(): pulse_items = self.create_pulse_visualization_items(obj) items.extend(pulse_items) return items def create_pulse_visualization_items( self, obj: SignalObj ) -> list[AnnotatedSegment | Marker]: """Create pulse visualization items from table data. Args: obj: Signal object containing the pulse data Returns: List of PlotPy items for pulse visualization """ items = [] df = self.result_adapter.to_dataframe() for _index, row in df.iterrows(): # Start baseline xs0, xs1 = row["xstartmin"], row["xstartmax"] ys = pulse.get_range_mean_y(obj.x, obj.y, (xs0, xs1)) if are_values_valid([xs0, xs1, ys]): items.append(create_pulse_segment(xs0, ys, xs1, ys, "Start baseline")) # End baseline xe0, xe1 = row["xendmin"], row["xendmax"] ye = pulse.get_range_mean_y(obj.x, obj.y, (xe0, xe1)) if are_values_valid([xe0, xe1, ye]): items.append(create_pulse_segment(xe0, ye, xe1, ye, "End baseline")) if "xplateaumin" in row and "xplateaumax" in row: xp0, xp1 = row["xplateaumin"], row["xplateaumax"] yp = pulse.get_range_mean_y(obj.x, obj.y, (xp0, xp1)) if are_values_valid([xp0, xp1, yp]): items.append(create_pulse_segment(xp0, yp, xp1, yp, "Plateau")) for metric in ("x0", "x50", "x100"): if metric in row: x = row[metric] metric_str = metric.replace("x", "x|") + "%" if are_values_valid([x]): items.append(create_pulse_crossing_marker("v", x, metric_str)) return items class MergedResultPlotPyAdapter: """Adapter for merging multiple result adapters into a single label. This adapter manages a merged label that displays all results for a given object. Instead of creating individual labels for each result (which causes overlapping), it creates a single label with all results concatenated as HTML. Args: result_adapters: List of result adapters (GeometryAdapter or TableAdapter) obj: Signal or image object associated with the results """ def __init__( self, result_adapters: list[GeometryAdapter | TableAdapter], obj: BaseObj, ) -> None: self.result_adapters = result_adapters self.obj = obj self._cached_label: LabelItem | None = None def get_cached_label(self) -> LabelItem | None: """Get the cached merged label item, if it exists. Returns: Merged label item, or None if not cached """ return self._cached_label def invalidate_cached_label(self) -> None: """Invalidate the cached merged label item.""" self._cached_label = None @property def item_json(self) -> str | None: """JSON representation of the merged label item. The position is stored in all result adapters so they stay in sync. """ if self.result_adapters: return self.result_adapters[0].get_applicative_attr("item_json") return None @item_json.setter def item_json(self, value: str | None) -> None: """Set JSON representation of the merged label item. The position is stored in all result adapters to keep them synchronized. """ for result_adapter in self.result_adapters: result_adapter.set_applicative_attr("item_json", value) def create_merged_label(self) -> LabelItem | None: """Create a single merged label from all result adapters. Returns: Merged label item, or None if no results """ if not self.result_adapters: return None # Create the label with merged content merged_html = resultadapter_to_html(self.result_adapters, self.obj) item = make.label(merged_html, "TL", (0, 0), "TL", title="Results") font = get_font(PLOTPY_CONF, "results", "label/font") item.set_style("results", "label") item.labelparam.font.update_param(font) item.labelparam.update_item(item) # Make label read-only (user cannot delete it to remove individual results) item.set_readonly(True) self._cached_label = item return item def get_merged_label(self) -> LabelItem | None: """Get the merged label, creating it if necessary or updating if it exists. Returns: Merged label item, or None if no results """ if not self.result_adapters: self._cached_label = None return None # Try to restore existing label from stored JSON position if self.item_json and self._cached_label is None: stored_item = json_to_items(self.item_json)[0] if isinstance(stored_item, LabelItem): # Update the stored item with current merged content merged_html = resultadapter_to_html(self.result_adapters, self.obj) stored_item.set_text(merged_html) stored_item.set_readonly(True) self._cached_label = stored_item return stored_item # Update existing cached label if present if self._cached_label is not None: merged_html = resultadapter_to_html(self.result_adapters, self.obj) self._cached_label.set_text(merged_html) return self._cached_label # Create new label return self.create_merged_label() def update_obj_metadata_from_item(self, item: LabelItem) -> None: """Update all result adapters' metadata with the label item position. Args: item: Merged label item (after user moved it) """ if item is not None: self.item_json = items_to_json([item]) # Update all result adapters in the object's metadata for result_adapter in self.result_adapters: result_adapter.add_to(self.obj) def get_other_items(self) -> list: """Return other items from all result adapters (e.g., geometric shapes). Returns: List of all other items from all result adapters """ items = [] for result_adapter in self.result_adapters: if isinstance(result_adapter, GeometryAdapter): plotpy_adapter = GeometryPlotPyAdapter(result_adapter) elif isinstance(result_adapter, TableAdapter): plotpy_adapter = TablePlotPyAdapter(result_adapter) else: raise NotImplementedError( f"Unsupported result adapter type: {type(result_adapter)}" ) items.extend(plotpy_adapter.get_other_items(self.obj)) return items DataLab-1.1.0/datalab/adapters_plotpy/objects/signal.py000066400000000000000000000223401514014117600230640ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ PlotPy Adapter Signal Module ---------------------------- """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from contextlib import contextmanager from typing import Generator import numpy as np from guidata.dataset import restore_dataset, update_dataset from plotpy.builder import make from plotpy.items import CurveItem from sigima.objects import SignalObj from datalab.adapters_plotpy.objects.base import ( BaseObjPlotPyAdapter, ) from datalab.config import Conf class CurveStyles: """Object to manage curve styles""" #: Curve colors COLORS = ( "#1f77b4", # muted blue "#ff7f0e", # safety orange "#2ca02c", # cooked asparagus green "#d62728", # brick red "#9467bd", # muted purple "#8c564b", # chestnut brown "#e377c2", # raspberry yogurt pink "#7f7f7f", # gray "#bcbd22", # curry yellow-green "#17becf", # blue-teal ) #: Curve line styles LINESTYLES = ("SolidLine", "DashLine", "DashDotLine", "DashDotDotLine") def __init__(self) -> None: self.__suspend = False self.curve_style = self.style_generator() @staticmethod def style_generator() -> Generator[tuple[str, str], None, None]: """Cycling through curve styles""" while True: for linestyle in CurveStyles.LINESTYLES: for color in CurveStyles.COLORS: yield (color, linestyle) def apply_style(self, item: CurveItem) -> None: """Apply style to curve Args: item: curve item """ if self.__suspend: # Suspend mode: always apply the first style color, linestyle = CurveStyles.COLORS[0], CurveStyles.LINESTYLES[0] else: color, linestyle = next(self.curve_style) item.param.line.color = color item.param.line.style = linestyle item.param.symbol.marker = "NoSymbol" # Note: line width is set separately via apply_line_width() # to ensure it's always recalculated based on current data size and settings def reset_styles(self) -> None: """Reset styles""" self.curve_style = self.style_generator() @contextmanager def alternative( self, other_style_generator: Generator[tuple[str, str], None, None] ) -> Generator[None, None, None]: """Use an alternative style generator""" old_style_generator = self.curve_style self.curve_style = other_style_generator yield self.curve_style = old_style_generator @contextmanager def suspend(self) -> Generator[None, None, None]: """Suspend style generator""" self.__suspend = True yield self.__suspend = False CURVESTYLES = CurveStyles() # This is the unique instance of the CurveStyles class def apply_line_width(item: CurveItem) -> None: """Apply line width to curve item with smart clamping for large datasets Args: item: curve item """ # Get data size data_size = item.get_data()[0].size # Get configured line width line_width = Conf.view.sig_linewidth.get() # For large datasets, clamp linewidth to 1.0 for performance # (thick lines cause ~10x rendering slowdown due to Qt raster engine) threshold = Conf.view.sig_linewidth_perfs_threshold.get() if data_size > threshold and line_width > 1.0: line_width = 1.0 # Apply the line width item.param.line.width = line_width def apply_downsampling(item: CurveItem, do_not_update: bool = False) -> None: """Apply downsampling to curve item Args: item: curve item do_not_update: if True, do not update the item even if the downsampling parameters have changed """ old_use_dsamp = item.param.use_dsamp item.param.use_dsamp = False if Conf.view.sig_autodownsampling.get(): nbpoints = item.get_data()[0].size maxpoints = Conf.view.sig_autodownsampling_maxpoints.get() if nbpoints > 5 * maxpoints: item.param.use_dsamp = True item.param.dsamp_factor = nbpoints // maxpoints if not do_not_update and old_use_dsamp != item.param.use_dsamp: item.update_data() class SignalObjPlotPyAdapter(BaseObjPlotPyAdapter[SignalObj, CurveItem]): """Signal object plot item adapter class""" CONF_FMT = Conf.view.sig_format DEFAULT_FMT = "g" def update_plot_item_parameters(self, item: CurveItem) -> None: """Update plot item parameters from object data/metadata Takes into account a subset of plot item parameters. Those parameters may have been overriden by object metadata entries or other object data. The goal is to update the plot item accordingly. This is *almost* the inverse operation of `update_metadata_from_plot_item`. Args: item: plot item """ update_dataset(item.param.line, self.obj.metadata) update_dataset(item.param.symbol, self.obj.metadata) super().update_plot_item_parameters(item) def update_metadata_from_plot_item(self, item: CurveItem) -> None: """Update metadata from plot item. Takes into account a subset of plot item parameters. Those parameters may have been modified by the user through the plot item GUI. The goal is to update the metadata accordingly. This is *almost* the inverse operation of `update_plot_item_parameters`. Args: item: plot item """ super().update_metadata_from_plot_item(item) restore_dataset(item.param.line, self.obj.metadata) restore_dataset(item.param.symbol, self.obj.metadata) def make_item(self, update_from: CurveItem | None = None) -> CurveItem: """Make plot item from data. Args: update_from: plot item to update from Returns: Plot item """ o = self.obj if len(o.xydata) in (2, 4): assert isinstance(o.xydata, np.ndarray) if len(o.xydata) == 2: # x, y signal x, y = o.xydata item = make.mcurve(x.real, y.real, label=o.title) else: # x, y, dx, dy error bar signal x, y, dx, dy = o.xydata if o.dx is None and o.dy is None: # x, y signal with no error item = make.mcurve(x.real, y.real, label=o.title) elif o.dx is None: # x, y, dy error bar signal with y error item = make.merror(x.real, y.real, dy.real, label=o.title) else: # x, y, dx, dy error bar signal with x error dy = np.zeros_like(y) if dy is None else dy item = make.merror(x.real, y.real, dx.real, dy.real, label=o.title) # Apply style (without linewidth, will be set separately) CURVESTYLES.apply_style(item) apply_downsampling(item, do_not_update=True) # Apply linewidth with smart clamping based on actual data size apply_line_width(item) else: raise RuntimeError("data not supported") if update_from is None: self.update_plot_item_parameters(item) else: update_dataset(item.param, update_from.param) item.update_params() return item def update_item(self, item: CurveItem, data_changed: bool = True) -> None: """Update plot item from data. Args: item: plot item data_changed: if True, data has changed """ o = self.obj if data_changed: assert isinstance(o.xydata, np.ndarray) if len(o.xydata) == 2: # x, y signal x, y = o.xydata assert isinstance(x, np.ndarray) and isinstance(y, np.ndarray) item.set_data(x.real, y.real) elif len(o.xydata) == 3: # x, y, dy error bar signal x, y, dy = o.xydata assert ( isinstance(x, np.ndarray) and isinstance(y, np.ndarray) and isinstance(dy, np.ndarray) ) item.set_data(x.real, y.real, dy=dy.real) elif len(o.xydata) == 4: # x, y, dx, dy error bar signal x, y, dx, dy = o.xydata assert ( isinstance(x, np.ndarray) and isinstance(y, np.ndarray) and isinstance(dx, np.ndarray) and isinstance(dy, np.ndarray) ) item.set_data(x.real, y.real, dx.real, dy.real) item.param.label = o.title apply_downsampling(item) # Reapply linewidth with smart clamping (data size may have changed) apply_line_width(item) self.update_plot_item_parameters(item) def add_label_with_title(self, title: str | None = None) -> None: """Add label with title annotation Args: title: title (if None, use signal title) """ title = self.obj.title if title is None else title if title: label = make.label(title, "TL", (0, 0), "TL") self.add_annotations_from_items([label]) DataLab-1.1.0/datalab/adapters_plotpy/roi/000077500000000000000000000000001514014117600203745ustar00rootroot00000000000000DataLab-1.1.0/datalab/adapters_plotpy/roi/__init__.py000066400000000000000000000000001514014117600224730ustar00rootroot00000000000000DataLab-1.1.0/datalab/adapters_plotpy/roi/base.py000066400000000000000000000074061514014117600216670ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ PlotPy Adapter Base ROI Module ------------------------------ """ import abc from typing import TYPE_CHECKING, Generic, Iterator, Literal, TypeVar from plotpy.items import AnnotatedShape from sigima.objects.base import ( BaseROI, TypeObj, TypeROI, TypeROIParam, TypeSingleROI, get_generic_roi_title, ) from datalab.adapters_plotpy.base import config_annotated_shape if TYPE_CHECKING: from plotpy.items import ( AnnotatedCircle, AnnotatedPolygon, AnnotatedRectangle, AnnotatedXRange, ) TypeROIItem = TypeVar( "TypeROIItem", bound="AnnotatedXRange | AnnotatedPolygon | AnnotatedRectangle | AnnotatedCircle", ) class BaseSingleROIPlotPyAdapter(Generic[TypeSingleROI, TypeROIItem], abc.ABC): """Base class for single ROI plot item adapter Args: single_roi: single ROI object """ def __init__(self, single_roi: TypeSingleROI) -> None: self.single_roi = single_roi @abc.abstractmethod def to_plot_item(self, obj: TypeObj) -> TypeROIItem: """Make ROI plot item from ROI. Args: obj: object (signal/image), for physical-indices coordinates conversion Returns: Plot item """ @classmethod @abc.abstractmethod def from_plot_item(cls, item: TypeROIItem) -> TypeSingleROI: """Create single ROI from plot item Args: item: plot item Returns: Single ROI """ def configure_roi_item( item: TypeROIItem, fmt: str, lbl: bool, editable: bool, option: Literal["s", "i"], ): """Configure ROI plot item. Args: item: plot item fmt: numeric format (e.g. "%.3f") lbl: if True, show shape labels editable: if True, make shape editable option: shape style option ("s" for signal, "i" for image) Returns: Plot item """ option += "/" + ("editable" if editable else "readonly") if not editable: if isinstance(item, AnnotatedShape): config_annotated_shape( item, fmt, lbl, "roi", option, show_computations=editable ) item.set_movable(False) item.set_resizable(False) item.set_readonly(True) item.set_style("roi", option) return item class BaseROIPlotPyAdapter(Generic[TypeROI], abc.ABC): """ROI plot item adapter class Args: roi: ROI object """ def __init__(self, roi: BaseROI[TypeObj, TypeSingleROI, TypeROIParam]) -> None: self.roi = roi @abc.abstractmethod def to_plot_item(self, single_roi: TypeSingleROI, obj: TypeObj) -> TypeROIItem: """Make ROI plot item from single ROI Args: single_roi: single ROI object obj: object (signal/image), for physical-indices coordinates conversion Returns: Plot item """ def iterate_roi_items( self, obj: TypeObj, fmt: str, lbl: bool, editable: bool = True ) -> Iterator[TypeROIItem]: """Iterate over ROI plot items associated to each single ROI composing the object. Args: obj: object (signal/image), for physical-indices coordinates conversion fmt: format string lbl: if True, add label editable: if True, ROI is editable Yields: Plot item """ for index, single_roi in enumerate(self.roi.single_rois): roi_item = self.to_plot_item(single_roi, obj) item = configure_roi_item( roi_item, fmt, lbl, editable, option=self.roi.PREFIX ) item.setTitle(single_roi.title or get_generic_roi_title(index)) yield item DataLab-1.1.0/datalab/adapters_plotpy/roi/factory.py000066400000000000000000000053771514014117600224310ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ ROI Adapter Factory ------------------- Factory functions for creating ROI adapters without circular imports. """ from __future__ import annotations from typing import TYPE_CHECKING if TYPE_CHECKING: from sigima.objects.base import TypeObj def create_roi_adapter(roi): """Create ROI adapter from ROI object Args: roi: ROI object Returns: ROI adapter instance """ # pylint: disable=import-outside-toplevel from sigima.objects import ( CircularROI, ImageROI, PolygonalROI, RectangularROI, SegmentROI, SignalROI, ) if isinstance(roi, SignalROI): from datalab.adapters_plotpy.roi.signal import SignalROIPlotPyAdapter return SignalROIPlotPyAdapter(roi) if isinstance(roi, RectangularROI): from datalab.adapters_plotpy.roi.image import RectangularROIPlotPyAdapter return RectangularROIPlotPyAdapter(roi) if isinstance(roi, CircularROI): from datalab.adapters_plotpy.roi.image import CircularROIPlotPyAdapter return CircularROIPlotPyAdapter(roi) if isinstance(roi, PolygonalROI): from datalab.adapters_plotpy.roi.image import PolygonalROIPlotPyAdapter return PolygonalROIPlotPyAdapter(roi) if isinstance(roi, ImageROI): from datalab.adapters_plotpy.roi.image import ImageROIPlotPyAdapter return ImageROIPlotPyAdapter(roi) if isinstance(roi, SegmentROI): from datalab.adapters_plotpy.roi.signal import SegmentROIPlotPyAdapter return SegmentROIPlotPyAdapter(roi) raise TypeError(f"Unsupported ROI type: {type(roi)}") def create_single_roi_plot_item(single_roi, obj: TypeObj): """Create plot item from single ROI Args: single_roi: single ROI object obj: object (signal/image), for physical-indices coordinates conversion Returns: Plot item """ # pylint: disable=import-outside-toplevel from sigima.objects import ( CircularROI, PolygonalROI, RectangularROI, ) if isinstance(single_roi, RectangularROI): from datalab.adapters_plotpy.roi.image import RectangularROIPlotPyAdapter return RectangularROIPlotPyAdapter(single_roi).to_plot_item(obj) if isinstance(single_roi, CircularROI): from datalab.adapters_plotpy.roi.image import CircularROIPlotPyAdapter return CircularROIPlotPyAdapter(single_roi).to_plot_item(obj) if isinstance(single_roi, PolygonalROI): from datalab.adapters_plotpy.roi.image import PolygonalROIPlotPyAdapter return PolygonalROIPlotPyAdapter(single_roi).to_plot_item(obj) raise TypeError(f"Unsupported ROI type: {type(single_roi)}") DataLab-1.1.0/datalab/adapters_plotpy/roi/image.py000066400000000000000000000175611514014117600220420ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ PlotPy Adapter Image ROI Module ------------------------------- """ from __future__ import annotations import numpy as np from plotpy.builder import make from plotpy.items import AnnotatedCircle, AnnotatedPolygon, AnnotatedRectangle from sigima.objects import CircularROI, ImageObj, ImageROI, PolygonalROI, RectangularROI from sigima.tools import coordinates from datalab.adapters_plotpy.coordutils import round_image_coords from datalab.adapters_plotpy.roi.base import ( BaseROIPlotPyAdapter, BaseSingleROIPlotPyAdapter, ) def _vs(var: str, sub: str = "") -> str: """Return variable name with subscript""" txt = f"{var}" if sub: txt += f"{sub}" return txt class PolygonalROIPlotPyAdapter( BaseSingleROIPlotPyAdapter[PolygonalROI, AnnotatedPolygon] ): """Polygonal ROI plot item adapter Args: single_roi: single ROI object """ def to_plot_item(self, obj: ImageObj) -> AnnotatedPolygon: """Make and return the annnotated polygon associated to ROI Args: obj: object (image), for physical-indices coordinates conversion """ def info_callback(item: AnnotatedPolygon) -> str: """Return info string for circular ROI""" xc, yc = item.get_center() if self.single_roi.indices: xc, yc = obj.physical_to_indices([xc, yc]) return "
".join( [ f"({_vs('x', 'c')}, {_vs('y', 'c')}) = ({xc:g}, {yc:g})", ] ) coords = np.array(self.single_roi.get_physical_coords(obj)) points = coords.reshape(-1, 2) item = AnnotatedPolygon(points) item.set_info_callback(info_callback) item.annotationparam.title = self.single_roi.title item.annotationparam.update_item(item) item.set_style("plot", "shape/drag") return item @classmethod def from_plot_item( cls, item: AnnotatedPolygon, obj: ImageObj | None = None ) -> PolygonalROI: """Create ROI from plot item Args: item: plot item obj: image object for coordinate rounding (optional) """ coords = item.get_points().flatten().tolist() # Round coordinates to appropriate precision if obj is not None: coords = round_image_coords(obj, coords) title = str(item.title().text()) return PolygonalROI(coords, False, title) class RectangularROIPlotPyAdapter( BaseSingleROIPlotPyAdapter[RectangularROI, AnnotatedRectangle] ): """Rectangular ROI plot item adapter Args: single_roi: single ROI object """ def to_plot_item(self, obj: ImageObj) -> AnnotatedRectangle: """Make and return the annnotated rectangle associated to ROI Args: obj: object (image), for physical-indices coordinates conversion """ def info_callback(item: AnnotatedRectangle) -> str: """Return info string for rectangular ROI""" x0, y0, x1, y1 = item.get_rect() if self.single_roi.indices: x0, y0, x1, y1 = obj.physical_to_indices([x0, y0, x1, y1]) x0, y0, dx, dy = self.single_roi.rect_to_coords(x0, y0, x1, y1) return "
".join( [ f"({_vs('x', '0')}, {_vs('y', '0')}) = ({x0:g}, {y0:g})", f"{_vs('Δx')} × {_vs('Δy')} = {dx:g} × {dy:g}", ] ) x0, y0, dx, dy = self.single_roi.get_physical_coords(obj) x1, y1 = x0 + dx, y0 + dy item: AnnotatedRectangle = make.annotated_rectangle( x0, y0, x1, y1, title=self.single_roi.title ) item.set_info_callback(info_callback) param = item.label.labelparam param.anchor = "BL" param.xc, param.yc = 5, -5 param.update_item(item.label) return item @classmethod def from_plot_item( cls, item: AnnotatedRectangle, obj: ImageObj | None = None ) -> RectangularROI: """Create ROI from plot item Args: item: plot item obj: image object for coordinate rounding (optional) """ rect = item.get_rect() coords = RectangularROI.rect_to_coords(*rect) # Round coordinates to appropriate precision if obj is not None: coords = round_image_coords(obj, coords) title = str(item.title().text()) return RectangularROI(coords, False, title) class CircularROIPlotPyAdapter( BaseSingleROIPlotPyAdapter[CircularROI, AnnotatedCircle] ): """Circular ROI plot item adapter Args: single_roi: single ROI object """ def to_plot_item(self, obj: ImageObj) -> AnnotatedCircle: """Make and return the annnotated circle associated to ROI Args: obj: object (image), for physical-indices coordinates conversion """ def info_callback(item: AnnotatedCircle) -> str: """Return info string for circular ROI""" x0, y0, x1, y1 = item.get_rect() if self.single_roi.indices: x0, y0, x1, y1 = obj.physical_to_indices([x0, y0, x1, y1]) xc, yc, r = self.single_roi.rect_to_coords(x0, y0, x1, y1) return "
".join( [ f"({_vs('x', 'c')}, {_vs('y', 'c')}) = ({xc:g}, {yc:g})", f"{_vs('r')} = {r:g}", ] ) xc, yc, r = self.single_roi.get_physical_coords(obj) x0, y0, x1, y1 = coordinates.circle_to_diameter(xc, yc, r) item = AnnotatedCircle(x0, y0, x1, y1) item.set_info_callback(info_callback) item.annotationparam.title = self.single_roi.title item.annotationparam.update_item(item) item.set_style("plot", "shape/drag") return item @classmethod def from_plot_item( cls, item: AnnotatedCircle, obj: ImageObj | None = None ) -> CircularROI: """Create ROI from plot item Args: item: plot item obj: image object for coordinate rounding (optional) """ rect = item.get_rect() coords = CircularROI.rect_to_coords(*rect) # Round coordinates to appropriate precision # For circular ROI: [xc, yc, r] - round center (xc, yc) as pair, then radius if obj is not None: xc, yc, r = coords # Round center coordinates xc_rounded, yc_rounded = round_image_coords(obj, [xc, yc]) # Round radius using average of X and Y precision # For radius, we use the X precision (could also average X and Y) r_rounded = round_image_coords(obj, [r, 0])[0] coords = [xc_rounded, yc_rounded, r_rounded] title = str(item.title().text()) return CircularROI(coords, False, title) class ImageROIPlotPyAdapter(BaseROIPlotPyAdapter[ImageROI]): """Image ROI plot item adapter class Args: roi: ROI object """ def to_plot_item( self, single_roi: PolygonalROI | RectangularROI | CircularROI, obj: ImageObj, ) -> AnnotatedCircle | AnnotatedRectangle | AnnotatedPolygon: """Make ROI plot item from single ROI Args: single_roi: single ROI object obj: object (signal/image), for physical-indices coordinates conversion Returns: Plot item """ if isinstance(single_roi, PolygonalROI): return PolygonalROIPlotPyAdapter(single_roi).to_plot_item(obj) if isinstance(single_roi, RectangularROI): return RectangularROIPlotPyAdapter(single_roi).to_plot_item(obj) if isinstance(single_roi, CircularROI): return CircularROIPlotPyAdapter(single_roi).to_plot_item(obj) raise TypeError(f"Invalid ROI type {type(single_roi)}") DataLab-1.1.0/datalab/adapters_plotpy/roi/signal.py000066400000000000000000000044701514014117600222300ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ PlotPy Adapter Signal ROI Module -------------------------------- """ from __future__ import annotations from plotpy.builder import make from plotpy.items import AnnotatedXRange from sigima.objects import SegmentROI, SignalObj, SignalROI from datalab.adapters_plotpy.coordutils import round_signal_coords from datalab.adapters_plotpy.roi.base import ( BaseROIPlotPyAdapter, BaseSingleROIPlotPyAdapter, ) class SegmentROIPlotPyAdapter(BaseSingleROIPlotPyAdapter[SegmentROI, AnnotatedXRange]): """Segment ROI plot item adapter Args: coords: ROI coordinates (xmin, xmax) title: ROI title """ def to_plot_item(self, obj: SignalObj) -> AnnotatedXRange: """Make and return the annnotated segment associated with the ROI Args: obj: object (signal), for physical-indices coordinates conversion """ xmin, xmax = self.single_roi.get_physical_coords(obj) item = make.annotated_xrange(xmin, xmax, title=self.single_roi.title) return item @classmethod def from_plot_item( cls, item: AnnotatedXRange, obj: SignalObj | None = None ) -> SegmentROI: """Create ROI from plot item Args: item: plot item obj: signal object for coordinate rounding (optional) Returns: ROI """ if not isinstance(item, AnnotatedXRange): raise TypeError("Invalid plot item type") coords = sorted(item.get_range()) # Round coordinates to appropriate precision if obj is not None: coords = round_signal_coords(obj, coords) title = str(item.title().text()) return SegmentROI(coords, False, title) class SignalROIPlotPyAdapter(BaseROIPlotPyAdapter[SignalROI]): """Signal ROI plot item adapter class Args: roi: ROI object """ def to_plot_item(self, single_roi: SegmentROI, obj: SignalObj) -> AnnotatedXRange: """Make ROI plot item from single ROI Args: single_roi: single ROI object obj: object (signal/image), for physical-indices coordinates conversion Returns: Plot item """ return SegmentROIPlotPyAdapter(single_roi).to_plot_item(obj) DataLab-1.1.0/datalab/app.py000066400000000000000000000055321514014117600155300ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab launcher module """ from __future__ import annotations from typing import TYPE_CHECKING from guidata.configtools import get_image_file_path from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from datalab.config import Conf from datalab.env import execenv from datalab.gui.main import DLMainWindow from datalab.utils.qthelpers import datalab_app_context if TYPE_CHECKING: from sigima.objects import ImageObj, SignalObj def create( splash: bool = True, console: bool | None = None, objects: list[ImageObj | SignalObj] | None = None, h5files: list[str] | None = None, size: tuple[int, int] | None = None, ) -> DLMainWindow: """Create DataLab application and return mainwindow instance Args: splash: if True, show splash screen console: if True, show console objects: list of objects to add to the mainwindow h5files: list of h5files to open size: mainwindow size (width, height) Returns: Main window instance """ if splash: # Showing splash screen pixmap = QG.QPixmap(get_image_file_path("DataLab-Splash.png")) splashscreen = QW.QSplashScreen(pixmap, QC.Qt.WindowStaysOnTopHint) splashscreen.show() window = DLMainWindow(console=console) if size is not None: width, height = size window.resize(width, height) if splash: splashscreen.finish(window) if Conf.main.window_maximized.get(None): window.showMaximized() else: window.showNormal() if h5files is not None: window.open_h5_files(h5files, import_all=True) if objects is not None: for obj in objects: window.add_object(obj) if execenv.h5browser_file is not None: window.import_h5_file(execenv.h5browser_file) return window def run( console: bool | None = None, objects: list[ImageObj | SignalObj] | None = None, h5files: list[str] | None = None, size: tuple[int, int] | None = None, ) -> None: """Run the DataLab application Note: this function is an entry point in `setup.py` and therefore may not be moved without modifying the package setup script. Args: console: if True, show console objects: list of objects to add to the mainwindow h5files: list of h5files to open size: mainwindow size (width, height) """ if execenv.h5files: h5files = ([] if h5files is None else h5files) + execenv.h5files with datalab_app_context(exec_loop=True): window = create( splash=True, console=console, objects=objects, h5files=h5files, size=size ) QW.QApplication.processEvents() window.execute_post_show_actions() if __name__ == "__main__": run() DataLab-1.1.0/datalab/config.py000066400000000000000000001076151514014117600162220ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab configuration module ---------------------------- This module handles `DataLab` configuration (options, images and icons). """ from __future__ import annotations import os import os.path as osp import sys from typing import Literal from guidata import configtools from plotpy.config import CONF as PLOTPY_CONF from plotpy.config import MAIN_BG_COLOR, MAIN_FG_COLOR from plotpy.constants import LUTAlpha from plotpy.styles import MarkerParam, ShapeParam from sigima.config import options as sigima_options from sigima.proc.title_formatting import ( PlaceholderTitleFormatter, set_default_title_formatter, ) from datalab import __version__ from datalab.utils import conf # Configure Sigima to use DataLab-compatible placeholder title formatting set_default_title_formatter(PlaceholderTitleFormatter()) CONF_VERSION = "1.0.0" APP_NAME = "DataLab" MOD_NAME = "datalab" def get_config_app_name() -> str: """Get configuration application name with major version suffix. This function returns the application name used for configuration storage. Starting from v1.0, the major version is appended to allow different major versions to coexist on the same machine without interfering with each other. Returns: str: Configuration application name (e.g., "DataLab" for v0.x, "DataLab_v1" for v1.x) Examples: - v0.20.x → "DataLab" (configuration stored in ~/.DataLab) - v1.0.x → "DataLab_v1" (configuration stored in ~/.DataLab_v1) - v2.0.x → "DataLab_v2" (configuration stored in ~/.DataLab_v2) """ major_version = __version__.split(".", maxsplit=1)[0] # Keep v0.x configuration folder unchanged for backward compatibility if major_version == "0": return APP_NAME return f"{APP_NAME}_v{major_version}" _ = configtools.get_translation(MOD_NAME) APP_DESC = _("""DataLab is a generic signal and image processing platform""") APP_PATH = osp.dirname(__file__) DEBUG = os.environ.get("DEBUG", "").lower() in ("1", "true") if DEBUG: print("*** DEBUG mode *** [Reset configuration file, do not redirect std I/O]") TEST_SEGFAULT_ERROR = len(os.environ.get("TEST_SEGFAULT_ERROR", "")) > 0 if TEST_SEGFAULT_ERROR: print('*** TEST_SEGFAULT_ERROR mode *** [Enabling test action in "?" menu]') DATETIME_FORMAT = "%d/%m/%Y - %H:%M:%S" configtools.add_image_module_path(MOD_NAME, osp.join("data", "logo")) configtools.add_image_module_path(MOD_NAME, osp.join("data", "icons")) DATAPATH = configtools.get_module_data_path(MOD_NAME, "data") SHOTPATH = osp.join( configtools.get_module_data_path(MOD_NAME), os.pardir, "doc", "images", "shots" ) OTHER_PLUGINS_PATHLIST = [configtools.get_module_data_path(MOD_NAME, "plugins")] def is_frozen(module_name: str) -> bool: """Test if module has been frozen (py2exe/cx_Freeze/pyinstaller) Args: module_name (str): module name Returns: bool: True if module has been frozen (py2exe/cx_Freeze/pyinstaller) """ datapath = configtools.get_module_path(module_name) parentdir = osp.normpath(osp.join(datapath, osp.pardir)) return not osp.isfile(__file__) or osp.isfile(parentdir) # library.zip IS_FROZEN = is_frozen(MOD_NAME) if IS_FROZEN: OTHER_PLUGINS_PATHLIST.append(osp.join(osp.dirname(sys.executable), "plugins")) try: os.mkdir(OTHER_PLUGINS_PATHLIST[-1]) except OSError: pass def get_mod_source_dir() -> str | None: """Return module source directory Returns: str | None: module source directory, or None if not found """ if IS_FROZEN: devdir = osp.abspath(osp.join(sys.prefix, os.pardir, os.pardir)) else: devdir = osp.abspath(osp.join(osp.dirname(__file__), os.pardir)) if osp.isfile(osp.join(devdir, MOD_NAME, "__init__.py")): return devdir # Unhandled case (this should not happen, but just in case): return None class MainSection(conf.Section, metaclass=conf.SectionMeta): """Class defining the main configuration section structure. Each class attribute is an option (metaclass is automatically affecting option names in .INI file based on class attribute names).""" color_mode = conf.EnumOption(["auto", "dark", "light"], default="auto") process_isolation_enabled = conf.Option() rpc_server_enabled = conf.Option() rpc_server_port = conf.Option() webapi_localhost_no_token = conf.Option() # Allow localhost without token traceback_log_path = conf.ConfigPathOption() traceback_log_available = conf.Option() faulthandler_enabled = conf.Option() faulthandler_log_path = conf.ConfigPathOption() faulthandler_log_available = conf.Option() window_maximized = conf.Option() window_position = conf.Option() window_size = conf.Option() window_state = conf.Option() base_dir = conf.WorkingDirOption() available_memory_threshold = conf.Option() current_tab = conf.Option() plugins_enabled = conf.Option() plugins_path = conf.Option() tour_enabled = conf.Option() v020_plugins_warning_ignore = conf.Option() # True: do not warn, False: warn class ConsoleSection(conf.Section, metaclass=conf.SectionMeta): """Classs defining the console configuration section structure. Each class attribute is an option (metaclass is automatically affecting option names in .INI file based on class attribute names).""" console_enabled = conf.Option() show_console_on_error = conf.Option() max_line_count = conf.Option() external_editor_path = conf.Option() external_editor_args = conf.Option() class IOSection(conf.Section, metaclass=conf.SectionMeta): """Class defining the I/O configuration section structure. Each class attribute is an option (metaclass is automatically affecting option names in .INI file based on class attribute names).""" # HDF5 file format options # ------------------------ # When opening an HDF5 file, ask user for confirmation if the current workspace # has to be cleared before loading the file: h5_clear_workspace = conf.Option() # True: clear workspace, False: do not clear h5_clear_workspace_ask = conf.Option() # True: ask user, False: do not ask # Signal or image title when importing from HDF5 file: # - True: use HDF5 full dataset path in signal or image title # - False: use HDF5 dataset name in signal or image title h5_fullpath_in_title = conf.Option() # Signal or image title when importing from HDF5 file: # - True: add HDF5 file name in signal or image title # - False: do not add HDF5 file name in signal or image title h5_fname_in_title = conf.Option() # ImageIO supported file formats: imageio_formats = conf.Option() # Dialog settings persistence (JSON-serialized datasets): save_to_directory_settings = conf.DataSetOption() add_metadata_settings = conf.DataSetOption() class ProcSection(conf.Section, metaclass=conf.SectionMeta): """Class defining the Processing configuration section structure. Each class attribute is an option (metaclass is automatically affecting option names in .INI file based on class attribute names).""" # Operation mode: # - "single": single operand mode # - "pairwise": pairwise operation mode operation_mode = conf.EnumOption(["single", "pairwise"], default="single") # ROI extraction strategy: # - True: extract all ROIs in a single signal or image # - False: extract each ROI in a separate signal or image extract_roi_singleobj = conf.Option() # Keep analysis results after processing: # - True: keep analysis results (dangerous because results may not be valid anymore) # - False: do not keep analysis results (default) keep_results = conf.Option() # Show systematically result dialog after processing: show_result_dialog = conf.Option() # Use xmin and xmax bounds from current signal when creating a new signal: use_signal_bounds = conf.Option() # Use dimensions from current image when creating a new image: use_image_dims = conf.Option() # FFT shift enabled state for signal/image processing: # - True: FFT shift is enabled (default) # - False: FFT shift is disabled fft_shift_enabled = conf.Option() # Auto-normalize convolution kernel for signal/image processing: # - True: automatically normalize kernel (default) # - False: do not normalize kernel auto_normalize_kernel = conf.Option() # Ignore warnings during computation: # - True: ignore warnings # - False: do not ignore warnings ignore_warnings = conf.Option() # X-array compatibility behavior for multi-signal computations: # - "ask": ask user for confirmation when x-arrays are incompatible (default) # - "interpolate": automatically interpolate when x-arrays are incompatible xarray_compat_behavior = conf.EnumOption(["ask", "interpolate"], default="ask") # History and analysis tabs font small_mono_font = conf.FontOption() class ViewSection(conf.Section, metaclass=conf.SectionMeta): """Class defining the view configuration section structure. Each class attribute is an option (metaclass is automatically affecting option names in .INI file based on class attribute names).""" # Toolbar position: # - "top": top # - "bottom": bottom # - "left": left # - "right": right plot_toolbar_position = conf.Option() # Ignore information message when inserting object title as annotation label: ignore_title_insertion_msg = conf.Option() # String formatting for shape legends sig_format = conf.Option() ima_format = conf.Option() show_label = conf.Option() auto_refresh = conf.Option() show_first_only = conf.Option() # Show only first selected item show_contrast = conf.Option() sig_linewidth = conf.Option() sig_linewidth_perfs_threshold = conf.Option() sig_antialiasing = conf.Option() sig_autodownsampling = conf.Option() sig_autodownsampling_maxpoints = conf.Option() # Autoscale margin settings for plots (percentage values) sig_autoscale_margin_percent = conf.Option() ima_autoscale_margin_percent = conf.Option() # If True, lock aspect ratio of images to 1:1 (ignore physical pixel size) ima_aspect_ratio_1_1 = conf.Option() # Default visualization settings at item creation # (e.g. see adapter's `make_item` methods in datalab/adapters_plotpy/*.py) ima_eliminate_outliers = conf.Option() # Default visualization settings, persisted in object metadata # (e.g. see `BaseDataPanel.update_metadata_view_settings`) sig_def_shade = conf.Option() sig_def_curvestyle = conf.Option() sig_def_baseline = conf.Option() # ⚠️ Do not add "sig_def_use_dsamp" and "sig_def_dsamp_factor" options here # because it would not be compatible with the auto-downsampling feature. # Default visualization settings, persisted in object metadata # (e.g. see `BaseDataPanel.update_metadata_view_settings`) ima_def_colormap = conf.Option() ima_def_invert_colormap = conf.Option() ima_def_interpolation = conf.Option() ima_def_alpha = conf.Option() ima_def_alpha_function = conf.Option() ima_def_keep_lut_range = conf.Option() # Annotated shape and marker visualization settings for signals sig_shape_param = conf.DataSetOption() sig_marker_param = conf.DataSetOption() # Annotated shape and marker visualization settings for images ima_shape_param = conf.DataSetOption() ima_marker_param = conf.DataSetOption() # Datetime axis format strings for different time units # Format strings use Python's strftime format codes sig_datetime_format_s = conf.Option() # Format for s, min, h sig_datetime_format_ms = conf.Option() # Format for ms, us, ns # Maximum number of geometry shapes to draw on plot # Even if more results are stored, only the first N shapes are drawn max_shapes_to_draw = conf.Option() # Maximum number of table cells (rows × columns) to display in merged result # label on plot. If exceeded, rows are truncated to stay within this limit. # This prevents slowdown with results that have many columns (e.g., polygons) max_cells_in_label = conf.Option() # Maximum number of columns to display in merged result label # If exceeded, only the first N columns are shown. This ensures readability # for results with many columns (e.g., polygon coordinates: x0, y0, x1, y1...) max_cols_in_label = conf.Option() # Show merged result label on plot by default show_result_label = conf.Option() @classmethod def get_def_dict(cls, category: Literal["ima", "sig"]) -> dict: """Get default visualization settings as a dictionary Args: category: category ("ima" or "sig", respectively for image and signal) Returns: Default visualization settings as a dictionary """ assert category in ("ima", "sig") prefix = f"{category}_def_" def_dict = {} for attrname in dir(cls): if attrname.startswith(prefix): name = attrname[len(prefix) :] opt = getattr(cls, attrname) defval = opt.get(None) if defval is not None: def_dict[name] = defval return def_dict @classmethod def set_def_dict(cls, category: Literal["ima", "sig"], def_dict: dict) -> None: """Set default visualization settings from a dictionary Args: category: category ("ima" or "sig", respectively for image and signal) def_dict: default visualization settings as a dictionary """ assert category in ("ima", "sig") prefix = f"{category}_def_" for attrname in dir(cls): if attrname.startswith(prefix): name = attrname[len(prefix) :] opt = getattr(cls, attrname) if name in def_dict: opt.set(def_dict[name]) # Usage (example): Conf.console.console_enabled.get(True) class Conf(conf.Configuration, metaclass=conf.ConfMeta): """Class defining DataLab configuration structure. Each class attribute is a section (metaclass is automatically affecting section names in .INI file based on class attribute names).""" main = MainSection() console = ConsoleSection() view = ViewSection() proc = ProcSection() io = IOSection() def get_old_log_fname(fname): """Return old log fname from current log fname""" return osp.splitext(fname)[0] + ".1.log" def initialize(): """Initialize application configuration""" config_app_name = get_config_app_name() Conf.initialize(config_app_name, CONF_VERSION, load=not DEBUG) # Set default values: # ------------------- # (do not use "set" method here to avoid overwriting user settings in .INI file) # Setting here the default values only for the most critical options. The other # options default values are set when used in the application code. # # Main section Conf.main.color_mode.get("auto") Conf.main.process_isolation_enabled.get(True) Conf.main.rpc_server_enabled.get(True) Conf.main.webapi_localhost_no_token.get( True ) # Enabled by default (Web API is off by default) Conf.main.traceback_log_path.get(f".{APP_NAME}_traceback.log") Conf.main.faulthandler_log_path.get(f".{APP_NAME}_faulthandler.log") Conf.main.available_memory_threshold.get(500) Conf.main.plugins_enabled.get(True) Conf.main.plugins_path.get(Conf.get_path("plugins")) Conf.main.tour_enabled.get(True) Conf.main.v020_plugins_warning_ignore.get(False) # Console section Conf.console.console_enabled.get(True) Conf.console.show_console_on_error.get(False) Conf.console.external_editor_path.get("code") Conf.console.external_editor_args.get("-g {path}:{line_number}") # IO section Conf.io.h5_clear_workspace.get(True) # Default to avoid objects UUID reset Conf.io.h5_clear_workspace_ask.get(True) Conf.io.h5_fullpath_in_title.get(False) Conf.io.h5_fname_in_title.get(True) iofmts = Conf.io.imageio_formats.get(()) if len(iofmts) > 0: sigima_options.imageio_formats.set(iofmts) # Sync with sigima config # Proc section Conf.proc.operation_mode.get("single") Conf.proc.use_signal_bounds.get(False) Conf.proc.use_image_dims.get(True) Conf.proc.fft_shift_enabled.get(True) sigima_options.fft_shift_enabled.set(True) # Sync with sigima config Conf.proc.auto_normalize_kernel.get(False) sigima_options.auto_normalize_kernel.set(False) # Sync with sigima config Conf.proc.extract_roi_singleobj.get(False) Conf.proc.keep_results.get(False) Conf.proc.show_result_dialog.get(True) Conf.proc.ignore_warnings.get(False) Conf.proc.xarray_compat_behavior.get("ask") Conf.proc.small_mono_font.get((configtools.MONOSPACE, 8, False)) # View section tb_pos = Conf.view.plot_toolbar_position.get("left") assert tb_pos in ("top", "bottom", "left", "right") Conf.view.ignore_title_insertion_msg.get(False) Conf.view.sig_linewidth.get(1.0) Conf.view.sig_linewidth_perfs_threshold.get(1000) Conf.view.sig_autodownsampling.get(True) Conf.view.sig_autodownsampling_maxpoints.get(100000) Conf.view.sig_autoscale_margin_percent.get(2.0) Conf.view.ima_autoscale_margin_percent.get(1.0) Conf.view.ima_aspect_ratio_1_1.get(False) Conf.view.ima_eliminate_outliers.get(0.1) Conf.view.sig_def_shade.get(0.0) Conf.view.sig_def_curvestyle.get("Lines") Conf.view.sig_def_baseline.get(0.0) Conf.view.ima_def_colormap.get("viridis") Conf.view.ima_def_invert_colormap.get(False) Conf.view.ima_def_interpolation.get(5) Conf.view.ima_def_alpha.get(1.0) Conf.view.ima_def_alpha_function.get(LUTAlpha.NONE.value) Conf.view.ima_def_keep_lut_range.get(False) # Datetime format strings: % must be escaped as %% for ConfigParser Conf.view.sig_datetime_format_s.get("%%H:%%M:%%S") Conf.view.sig_datetime_format_ms.get("%%H:%%M:%%S.%%f") Conf.view.max_shapes_to_draw.get(1000) Conf.view.max_cells_in_label.get(100) Conf.view.max_cols_in_label.get(15) Conf.view.show_result_label.get(True) # Initialize PlotPy configuration with versioned app name PLOTPY_CONF.set_application( osp.join(config_app_name, "plotpy"), CONF_VERSION, load=False ) def reset(): """Reset application configuration""" Conf.reset() initialize() initialize() ROI_LINE_COLOR = "#5555ff" ROI_SEL_LINE_COLOR = "#9393ff" MARKER_LINE_COLOR = "#A11818" MARKER_TEXT_COLOR = "#440909" PLOTPY_DEFAULTS = { "plot": { # # XXX: If needed in the future, add here the default settings for PlotPy: # that will override the PlotPy settings. # That is the right way to customize the PlotPy settings for shapes and # annotations when they are added using tools from the DataLab application # (see `BaseDataPanel.ANNOTATION_TOOLS`). # For example, for shapes: # "shape/drag/line/color": "#00ffff", # # Overriding default plot settings from PlotPy "title/font/size": 11, "title/font/bold": False, "selected_curve_symbol/marker": "Ellipse", "selected_curve_symbol/edgecolor": "#a0a0a4", "selected_curve_symbol/facecolor": MAIN_FG_COLOR, "selected_curve_symbol/alpha": 0.3, "selected_curve_symbol/size": 5, "marker/curve/text/textcolor": "black", # Cross marker style (shown when pressing Alt key on plot) "marker/cross/symbol/marker": "Cross", "marker/cross/symbol/edgecolor": MAIN_FG_COLOR, "marker/cross/symbol/facecolor": "#ff0000", "marker/cross/symbol/alpha": 1.0, "marker/cross/symbol/size": 8, "marker/cross/text/font/family": "default", "marker/cross/text/font/size": 8, "marker/cross/text/font/bold": False, "marker/cross/text/font/italic": False, "marker/cross/text/textcolor": "#000000", "marker/cross/text/background_color": "#ffffff", "marker/cross/text/background_alpha": 0.7, "marker/cross/line/style": "DashLine", "marker/cross/line/color": MARKER_LINE_COLOR, "marker/cross/line/width": 1.0, "marker/cross/markerstyle": "Cross", "marker/cross/spacing": 7, # Cursor line and symbol style "marker/cursor/line/style": "SolidLine", "marker/cursor/line/color": MARKER_LINE_COLOR, "marker/cursor/line/width": 1.0, "marker/cursor/symbol/marker": "NoSymbol", "marker/cursor/symbol/size": 11, "marker/cursor/symbol/edgecolor": MAIN_BG_COLOR, "marker/cursor/symbol/facecolor": "#ff9393", "marker/cursor/symbol/alpha": 1.0, "marker/cursor/sel_line/style": "SolidLine", "marker/cursor/sel_line/color": MARKER_LINE_COLOR, "marker/cursor/sel_line/width": 2.0, "marker/cursor/sel_symbol/marker": "NoSymbol", "marker/cursor/sel_symbol/size": 11, "marker/cursor/sel_symbol/edgecolor": MAIN_BG_COLOR, "marker/cursor/sel_symbol/facecolor": MARKER_LINE_COLOR, "marker/cursor/sel_symbol/alpha": 0.8, "marker/cursor/text/font/size": 9, "marker/cursor/text/font/family": "default", "marker/cursor/text/font/bold": False, "marker/cursor/text/font/italic": False, "marker/cursor/text/textcolor": MARKER_TEXT_COLOR, "marker/cursor/text/background_color": "#ffffff", "marker/cursor/text/background_alpha": 0.7, "marker/cursor/sel_text/font/size": 9, "marker/cursor/sel_text/font/family": "default", "marker/cursor/sel_text/font/bold": False, "marker/cursor/sel_text/font/italic": False, "marker/cursor/sel_text/textcolor": MARKER_TEXT_COLOR, "marker/cursor/sel_text/background_color": "#ffffff", "marker/cursor/sel_text/background_alpha": 0.7, # Default annotation text style for segments: "shape/segment/line/style": "SolidLine", "shape/segment/line/color": "#00ff55", "shape/segment/line/width": 1.0, "shape/segment/sel_line/style": "SolidLine", "shape/segment/sel_line/color": "#00ff55", "shape/segment/sel_line/width": 2.0, "shape/segment/fill/style": "NoBrush", "shape/segment/sel_fill/style": "NoBrush", "shape/segment/symbol/marker": "XCross", "shape/segment/symbol/size": 9, "shape/segment/symbol/edgecolor": "#00ff55", "shape/segment/symbol/facecolor": "#00ff55", "shape/segment/symbol/alpha": 1.0, "shape/segment/sel_symbol/marker": "XCross", "shape/segment/sel_symbol/size": 12, "shape/segment/sel_symbol/edgecolor": "#00ff55", "shape/segment/sel_symbol/facecolor": "#00ff55", "shape/segment/sel_symbol/alpha": 0.7, # Default style for drag shapes: (global annotations style) "shape/drag/line/style": "SolidLine", "shape/drag/line/color": "#00ff55", "shape/drag/line/width": 1.0, "shape/drag/fill/style": "SolidPattern", "shape/drag/fill/color": MAIN_BG_COLOR, "shape/drag/fill/alpha": 0.1, "shape/drag/symbol/marker": "Rect", "shape/drag/symbol/size": 3, "shape/drag/symbol/edgecolor": "#00ff55", "shape/drag/symbol/facecolor": "#00ff55", "shape/drag/symbol/alpha": 1.0, "shape/drag/sel_line/style": "SolidLine", "shape/drag/sel_line/color": "#00ff55", "shape/drag/sel_line/width": 2.0, "shape/drag/sel_fill/style": "SolidPattern", "shape/drag/sel_fill/color": MAIN_BG_COLOR, "shape/drag/sel_fill/alpha": 0.1, "shape/drag/sel_symbol/marker": "Rect", "shape/drag/sel_symbol/size": 7, "shape/drag/sel_symbol/edgecolor": "#00ff55", "shape/drag/sel_symbol/facecolor": "#00ff00", "shape/drag/sel_symbol/alpha": 0.7, }, "results": { # Annotated shape style for result shapes: # Signals: "s/annotation/line/style": "SolidLine", "s/annotation/line/color": "#00aa00", "s/annotation/line/width": 2, "s/annotation/fill/style": "NoBrush", "s/annotation/fill/color": MAIN_BG_COLOR, "s/annotation/fill/alpha": 0.1, "s/annotation/symbol/marker": "XCross", "s/annotation/symbol/size": 7, "s/annotation/symbol/edgecolor": "#00aa00", "s/annotation/symbol/facecolor": "#00aa00", "s/annotation/symbol/alpha": 1.0, "s/annotation/sel_line/style": "DashLine", "s/annotation/sel_line/color": "#00ff00", "s/annotation/sel_line/width": 1, "s/annotation/sel_fill/style": "SolidPattern", "s/annotation/sel_fill/color": MAIN_BG_COLOR, "s/annotation/sel_fill/alpha": 0.1, "s/annotation/sel_symbol/marker": "Rect", "s/annotation/sel_symbol/size": 9, "s/annotation/sel_symbol/edgecolor": "#00aa00", "s/annotation/sel_symbol/facecolor": "#00ff00", "s/annotation/sel_symbol/alpha": 0.7, # Images: "i/annotation/line/style": "SolidLine", "i/annotation/line/color": "#ffff00", "i/annotation/line/width": 2, "i/annotation/fill/style": "SolidPattern", "i/annotation/fill/color": MAIN_BG_COLOR, "i/annotation/fill/alpha": 0.1, "i/annotation/symbol/marker": "Rect", "i/annotation/symbol/size": 3, "i/annotation/symbol/edgecolor": "#ffff00", "i/annotation/symbol/facecolor": "#ffff00", "i/annotation/symbol/alpha": 1.0, "i/annotation/sel_line/style": "SolidLine", "i/annotation/sel_line/color": "#00ff00", "i/annotation/sel_line/width": 2, "i/annotation/sel_fill/style": "SolidPattern", "i/annotation/sel_fill/color": MAIN_BG_COLOR, "i/annotation/sel_fill/alpha": 0.1, "i/annotation/sel_symbol/marker": "Rect", "i/annotation/sel_symbol/size": 9, "i/annotation/sel_symbol/edgecolor": "#00aa00", "i/annotation/sel_symbol/facecolor": "#00ff00", "i/annotation/sel_symbol/alpha": 0.7, # Marker styles for results: # Signals: "s/marker/cursor/line/style": "DashLine", "s/marker/cursor/line/color": MARKER_LINE_COLOR, "s/marker/cursor/line/width": 1.0, "s/marker/cursor/symbol/marker": "Ellipse", "s/marker/cursor/symbol/size": 11, "s/marker/cursor/symbol/edgecolor": MAIN_BG_COLOR, "s/marker/cursor/symbol/facecolor": MARKER_LINE_COLOR, "s/marker/cursor/symbol/alpha": 0.7, "s/marker/cursor/sel_line/style": "DashLine", "s/marker/cursor/sel_line/color": MARKER_LINE_COLOR, "s/marker/cursor/sel_line/width": 2.0, "s/marker/cursor/sel_symbol/marker": "Ellipse", "s/marker/cursor/sel_symbol/size": 11, "s/marker/cursor/sel_symbol/edgecolor": MARKER_LINE_COLOR, "s/marker/cursor/sel_symbol/facecolor": MARKER_LINE_COLOR, "s/marker/cursor/sel_symbol/alpha": 0.7, "s/marker/cursor/text/font/size": 9, "s/marker/cursor/text/font/family": "default", "s/marker/cursor/text/font/bold": False, "s/marker/cursor/text/font/italic": False, "s/marker/cursor/text/textcolor": MARKER_TEXT_COLOR, "s/marker/cursor/text/background_color": "#ffffff", "s/marker/cursor/text/background_alpha": 0.7, "s/marker/cursor/sel_text/font/size": 9, "s/marker/cursor/sel_text/font/family": "default", "s/marker/cursor/sel_text/font/bold": False, "s/marker/cursor/sel_text/font/italic": False, "s/marker/cursor/sel_text/textcolor": MARKER_TEXT_COLOR, "s/marker/cursor/sel_text/background_color": "#ffffff", "s/marker/cursor/sel_text/background_alpha": 0.7, "s/marker/cursor/markerstyle": "Cross", # Images: "i/marker/cursor/line/style": "DashLine", "i/marker/cursor/line/color": MARKER_LINE_COLOR, "i/marker/cursor/line/width": 1.0, "i/marker/cursor/symbol/marker": "Diamond", "i/marker/cursor/symbol/size": 11, "i/marker/cursor/symbol/edgecolor": MARKER_LINE_COLOR, "i/marker/cursor/symbol/facecolor": MARKER_LINE_COLOR, "i/marker/cursor/symbol/alpha": 0.7, "i/marker/cursor/sel_line/style": "DashLine", "i/marker/cursor/sel_line/color": MARKER_LINE_COLOR, "i/marker/cursor/sel_line/width": 2.0, "i/marker/cursor/sel_symbol/marker": "Diamond", "i/marker/cursor/sel_symbol/size": 11, "i/marker/cursor/sel_symbol/edgecolor": MARKER_LINE_COLOR, "i/marker/cursor/sel_symbol/facecolor": MARKER_LINE_COLOR, "i/marker/cursor/sel_symbol/alpha": 0.7, "i/marker/cursor/text/font/size": 9, "i/marker/cursor/text/font/family": "default", "i/marker/cursor/text/font/bold": False, "i/marker/cursor/text/font/italic": False, "i/marker/cursor/text/textcolor": MARKER_TEXT_COLOR, "i/marker/cursor/text/background_color": "#ffffff", "i/marker/cursor/text/background_alpha": 0.7, "i/marker/cursor/sel_text/font/size": 9, "i/marker/cursor/sel_text/font/family": "default", "i/marker/cursor/sel_text/font/bold": False, "i/marker/cursor/sel_text/font/italic": False, "i/marker/cursor/sel_text/textcolor": MARKER_TEXT_COLOR, "i/marker/cursor/sel_text/background_color": "#ffffff", "i/marker/cursor/sel_text/background_alpha": 0.7, "i/marker/cursor/markerstyle": "Cross", # Style for labels: "label/symbol/marker": "NoSymbol", "label/symbol/size": 0, "label/symbol/edgecolor": MAIN_BG_COLOR, "label/symbol/facecolor": MAIN_BG_COLOR, "label/border/style": "SolidLine", "label/border/color": "#cbcbcb", "label/border/width": 1, "label/font/size": 8, "label/font/family/nt": ["Cascadia Code", "Consolas", "Courier New"], "label/font/family/posix": "Bitstream Vera Sans Mono", "label/font/family/mac": "Monaco", "label/font/bold": False, "label/font/italic": False, "label/color": MAIN_FG_COLOR, "label/bgcolor": MAIN_BG_COLOR, "label/bgalpha": 0.8, "label/anchor": "TL", "label/xc": 10, "label/yc": 10, "label/abspos": True, "label/absg": "TL", "label/xg": 0.0, "label/yg": 0.0, }, "roi": { # Shape style for ROI # Signals: # - Editable ROI (ROI editor): "s/editable/fill": "#ffff00", "s/editable/shade": 0.10, "s/editable/line/style": "SolidLine", "s/editable/line/color": "#ffff00", "s/editable/line/width": 1, "s/editable/fill/style": "SolidPattern", "s/editable/fill/color": MAIN_BG_COLOR, "s/editable/fill/alpha": 0.1, "s/editable/symbol/marker": "Rect", "s/editable/symbol/size": 3, "s/editable/symbol/edgecolor": "#ffff00", "s/editable/symbol/facecolor": "#ffff00", "s/editable/symbol/alpha": 1.0, "s/editable/sel_line/style": "SolidLine", "s/editable/sel_line/color": "#00ff00", "s/editable/sel_line/width": 1, "s/editable/sel_fill/style": "SolidPattern", "s/editable/sel_fill/color": MAIN_BG_COLOR, "s/editable/sel_fill/alpha": 0.1, "s/editable/sel_symbol/marker": "Rect", "s/editable/sel_symbol/size": 9, "s/editable/sel_symbol/edgecolor": "#00aa00", "s/editable/sel_symbol/facecolor": "#00ff00", "s/editable/sel_symbol/alpha": 0.7, # - Readonly ROI (plot): "s/readonly/line/style": "SolidLine", "s/readonly/line/color": ROI_LINE_COLOR, "s/readonly/line/width": 1, "s/readonly/sel_line/style": "SolidLine", "s/readonly/sel_line/color": ROI_SEL_LINE_COLOR, "s/readonly/sel_line/width": 2, "s/readonly/fill": ROI_LINE_COLOR, "s/readonly/shade": 0.10, "s/readonly/symbol/marker": "Ellipse", "s/readonly/symbol/size": 7, "s/readonly/symbol/edgecolor": MAIN_BG_COLOR, "s/readonly/symbol/facecolor": ROI_LINE_COLOR, "s/readonly/symbol/alpha": 1.0, "s/readonly/sel_symbol/marker": "Ellipse", "s/readonly/sel_symbol/size": 9, "s/readonly/sel_symbol/edgecolor": MAIN_BG_COLOR, "s/readonly/sel_symbol/facecolor": ROI_SEL_LINE_COLOR, "s/readonly/sel_symbol/alpha": 0.9, "s/readonly/multi/color": "#806060", # Images: # - Editable ROI (ROI editor): "i/editable/line/style": "SolidLine", "i/editable/line/color": "#ffff00", "i/editable/line/width": 1, "i/editable/fill/style": "SolidPattern", "i/editable/fill/color": MAIN_BG_COLOR, "i/editable/fill/alpha": 0.1, "i/editable/symbol/marker": "Rect", "i/editable/symbol/size": 3, "i/editable/symbol/edgecolor": "#ffff00", "i/editable/symbol/facecolor": "#ffff00", "i/editable/symbol/alpha": 1.0, "i/editable/sel_line/style": "SolidLine", "i/editable/sel_line/color": "#00ff00", "i/editable/sel_line/width": 1, "i/editable/sel_fill/style": "SolidPattern", "i/editable/sel_fill/color": MAIN_BG_COLOR, "i/editable/sel_fill/alpha": 0.1, "i/editable/sel_symbol/marker": "Rect", "i/editable/sel_symbol/size": 9, "i/editable/sel_symbol/edgecolor": "#00aa00", "i/editable/sel_symbol/facecolor": "#00ff00", "i/editable/sel_symbol/alpha": 0.7, # - Readonly ROI (plot): "i/readonly/line/style": "DotLine", "i/readonly/line/color": ROI_LINE_COLOR, "i/readonly/line/width": 1, "i/readonly/fill/style": "SolidPattern", "i/readonly/fill/color": MAIN_BG_COLOR, "i/readonly/fill/alpha": 0.1, "i/readonly/symbol/marker": "NoSymbol", "i/readonly/symbol/size": 5, "i/readonly/symbol/edgecolor": ROI_LINE_COLOR, "i/readonly/symbol/facecolor": ROI_LINE_COLOR, "i/readonly/symbol/alpha": 0.6, "i/readonly/sel_line/style": "DotLine", "i/readonly/sel_line/color": "#0000ff", "i/readonly/sel_line/width": 1, "i/readonly/sel_fill/style": "SolidPattern", "i/readonly/sel_fill/color": MAIN_BG_COLOR, "i/readonly/sel_fill/alpha": 0.1, "i/readonly/sel_symbol/marker": "Rect", "i/readonly/sel_symbol/size": 8, "i/readonly/sel_symbol/edgecolor": "#0000aa", "i/readonly/sel_symbol/facecolor": "#0000ff", "i/readonly/sel_symbol/alpha": 0.7, }, } # PlotPy configuration will be initialized in initialize() function PLOTPY_CONF.update_defaults(PLOTPY_DEFAULTS) class DataLabShapeParam(ShapeParam): """ShapeParam subclass with internal items hidden from settings dialog""" def __init__(self): super().__init__() # Hide internal items that should not appear in settings dialog for item in self._items: if item._name in ("label", "readonly", "private"): item.set_prop("display", hide=True) def initialize_default_plotpy_instances(): """Initialize default PlotPy instances for DataLab configuration options""" # Initialize default instances for DataSetOptions now that PLOTPY_DEFAULTS exists _sig_shapeparam = DataLabShapeParam() _sig_shapeparam.read_config(PLOTPY_CONF, "results", "s/annotation") Conf.view.sig_shape_param.set_default_instance(_sig_shapeparam) Conf.view.sig_shape_param.get() _sig_markerparam = MarkerParam() _sig_markerparam.read_config(PLOTPY_CONF, "results", "s/marker/cursor") Conf.view.sig_marker_param.set_default_instance(_sig_markerparam) Conf.view.sig_marker_param.get() _ima_shapeparam = DataLabShapeParam() _ima_shapeparam.read_config(PLOTPY_CONF, "results", "i/annotation") Conf.view.ima_shape_param.set_default_instance(_ima_shapeparam) Conf.view.ima_shape_param.get() _ima_markerparam = MarkerParam() _ima_markerparam.read_config(PLOTPY_CONF, "results", "i/marker/cursor") Conf.view.ima_marker_param.set_default_instance(_ima_markerparam) Conf.view.ima_marker_param.get() initialize_default_plotpy_instances() DataLab-1.1.0/datalab/control/000077500000000000000000000000001514014117600160515ustar00rootroot00000000000000DataLab-1.1.0/datalab/control/__init__.py000066400000000000000000000000001514014117600201500ustar00rootroot00000000000000DataLab-1.1.0/datalab/control/baseproxy.py000066400000000000000000000763421514014117600204530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab base proxy module ------------------------- """ # How to add a new method to the proxy: # ------------------------------------- # # 1. Add the method to the AbstractDLControl class, as an abstract method # # 2a. If the method requires any data conversion to get through the XML-RPC layer, # implement the method in both LocalProxy and RemoteClient classes # # 2b. If the method does not require any data conversion, implement the method # directly in the BaseProxy class, so that it is available to both LocalProxy # and RemoteClient classes without any code duplication # # 3. Implement the method in the DLMainWindow class # # 4. Implement the method in the RemoteServer class (it will be automatically # registered as an XML-RPC method, like all methods of AbstractDLControl) from __future__ import annotations import abc from contextlib import contextmanager from typing import TYPE_CHECKING, Generator import guidata.dataset as gds import numpy as np from sigima import ImageObj, SignalObj if TYPE_CHECKING: from collections.abc import Iterator from datalab.control.remote import ServerProxy from datalab.gui.main import DLMainWindow class AbstractDLControl(abc.ABC): """Abstract base class for controlling DataLab (main window or remote server)""" def __len__(self) -> int: """Return number of objects""" return len(self.get_object_uuids()) def __getitem__(self, nb_id_title: int | str | None = None) -> SignalObj | ImageObj: """Return object""" return self.get_object(nb_id_title) def __iter__(self) -> Iterator[SignalObj | ImageObj]: """Iterate over objects""" uuids = self.get_object_uuids() for uuid in uuids: yield self.get_object(uuid) def __str__(self) -> str: """Return object string representation""" return super().__repr__() def __repr__(self) -> str: """Return object representation""" titles = self.get_object_titles() uuids = self.get_object_uuids() text = f"{str(self)} (DataLab, {len(titles)} items):\n" for uuid, title in zip(uuids, titles): text += f" {uuid}: {title}\n" return text def __bool__(self) -> bool: """Return True if model is not empty""" return bool(self.get_object_uuids()) def __contains__(self, id_title: str) -> bool: """Return True if object (UUID or title) is in model""" return id_title in (self.get_object_titles() + self.get_object_uuids()) @classmethod def get_public_methods(cls) -> list[str]: """Return all public methods of the class, except itself. Returns: List of public methods """ return [ method for method in dir(cls) if not method.startswith(("_", "context_")) and method != "get_public_methods" ] @abc.abstractmethod def get_version(self) -> str: """Return DataLab public version. Returns: DataLab version """ @abc.abstractmethod def close_application(self) -> None: """Close DataLab application""" @abc.abstractmethod def raise_window(self) -> None: """Raise DataLab window""" @abc.abstractmethod def get_current_panel(self) -> str: """Return current panel name. Returns: Panel name (valid values: "signal", "image", "macro")) """ @abc.abstractmethod def set_current_panel(self, panel: str) -> None: """Switch to panel. Args: panel: Panel name (valid values: "signal", "image", "macro")) """ @abc.abstractmethod def reset_all(self) -> None: """Reset all application data""" @abc.abstractmethod def remove_object(self, force: bool = False) -> None: """Remove current object from current panel. Args: force: if True, remove object without confirmation. Defaults to False. """ @abc.abstractmethod def toggle_auto_refresh(self, state: bool) -> None: """Toggle auto refresh state. Args: state: Auto refresh state """ # Returns a context manager to temporarily disable autorefresh @contextmanager def context_no_refresh(self) -> Generator[None, None, None]: """Return a context manager to temporarily disable auto refresh. Returns: Context manager Example: >>> with proxy.context_no_refresh(): ... proxy.add_image("image1", data1) ... proxy.calc("fft") ... proxy.calc("wiener") ... proxy.calc("ifft") ... # Auto refresh is disabled during the above operations """ self.toggle_auto_refresh(False) try: yield finally: self.toggle_auto_refresh(True) @abc.abstractmethod def toggle_show_titles(self, state: bool) -> None: """Toggle show titles state. Args: state: Show titles state """ @abc.abstractmethod def save_to_h5_file(self, filename: str) -> None: """Save to a DataLab HDF5 file. Args: filename: HDF5 file name """ @abc.abstractmethod def open_h5_files( self, h5files: list[str] | None = None, import_all: bool | None = None, reset_all: bool | None = None, ) -> None: """Open a DataLab HDF5 file or import from any other HDF5 file. Args: h5files: List of HDF5 files to open. Defaults to None. import_all: Import all objects from HDF5 files. Defaults to None. reset_all: Reset all application data. Defaults to None. """ @abc.abstractmethod def import_h5_file(self, filename: str, reset_all: bool | None = None) -> None: """Open DataLab HDF5 browser to Import HDF5 file. Args: filename: HDF5 file name reset_all: Reset all application data. Defaults to None. """ @abc.abstractmethod def load_h5_workspace(self, h5files: list[str], reset_all: bool = False) -> None: """Load native DataLab HDF5 workspace files without any GUI elements. This method can be safely called from scripts (e.g., internal console, macros) as it does not create any Qt widgets, dialogs, or progress bars. .. warning:: This method only supports native DataLab HDF5 files. For importing arbitrary HDF5 files (non-native), use :meth:`open_h5_files` or :meth:`import_h5_file` instead. Args: h5files: List of native DataLab HDF5 filenames reset_all: Reset all application data before importing. Defaults to False. Raises: ValueError: If a file is not a valid native DataLab HDF5 file """ @abc.abstractmethod def save_h5_workspace(self, filename: str) -> None: """Save current workspace to a native DataLab HDF5 file without GUI elements. This method can be safely called from scripts (e.g., internal console, macros) as it does not create any Qt widgets, dialogs, or progress bars. Args: filename: HDF5 filename to save to Raises: IOError: If file cannot be saved """ @abc.abstractmethod def load_from_files(self, filenames: list[str]) -> None: """Open objects from files in current panel (signals/images). Args: filenames: list of file names """ @abc.abstractmethod def load_from_directory(self, path: str) -> None: """Open objects from directory in current panel (signals/images). Args: path: directory path """ @abc.abstractmethod def add_signal( self, title: str, xdata: np.ndarray, ydata: np.ndarray, xunit: str = "", yunit: str = "", xlabel: str = "", ylabel: str = "", group_id: str = "", set_current: bool = True, ) -> bool: # pylint: disable=too-many-arguments """Add signal data to DataLab. Args: title: Signal title xdata: X data ydata: Y data xunit: X unit. Defaults to "" yunit: Y unit. Defaults to "" xlabel: X label. Defaults to "" ylabel: Y label. Defaults to "" group_id: group id in which to add the signal. Defaults to "" set_current: if True, set the added signal as current Returns: True if signal was added successfully, False otherwise Raises: ValueError: Invalid xdata dtype ValueError: Invalid ydata dtype """ @abc.abstractmethod def add_image( self, title: str, data: np.ndarray, xunit: str = "", yunit: str = "", zunit: str = "", xlabel: str = "", ylabel: str = "", zlabel: str = "", group_id: str = "", set_current: bool = True, ) -> bool: # pylint: disable=too-many-arguments """Add image data to DataLab. Args: title: Image title data: Image data xunit: X unit. Defaults to "" yunit: Y unit. Defaults to "" zunit: Z unit. Defaults to "" xlabel: X label. Defaults to "" ylabel: Y label. Defaults to "" zlabel: Z label. Defaults to "" group_id: group id in which to add the image. Defaults to "" set_current: if True, set the added image as current Returns: True if image was added successfully, False otherwise Raises: ValueError: Invalid data dtype """ @abc.abstractmethod def add_object( self, obj: SignalObj | ImageObj, group_id: str = "", set_current: bool = True ) -> None: """Add object to DataLab. Args: obj: Signal or image object group_id: group id in which to add the object. Defaults to "" set_current: if True, set the added object as current Returns: True if object was added successfully, False otherwise """ @abc.abstractmethod def add_group( self, title: str, panel: str | None = None, select: bool = False ) -> None: """Add group to DataLab. Args: title: Group title panel: Panel name (valid values: "signal", "image"). Defaults to None. select: Select the group after creation. Defaults to False. """ @abc.abstractmethod def get_sel_object_uuids(self, include_groups: bool = False) -> list[str]: """Return selected objects uuids. Args: include_groups: If True, also return objects from selected groups. Returns: List of selected objects uuids. """ @abc.abstractmethod def select_objects( self, selection: list[int | str], panel: str | None = None, ) -> None: """Select objects in current panel. Args: selection: List of object numbers (1 to N) or uuids to select panel: panel name (valid values: "signal", "image"). If None, current panel is used. Defaults to None. """ @abc.abstractmethod def select_groups( self, selection: list[int | str] | None = None, panel: str | None = None ) -> None: """Select groups in current panel. Args: selection: List of group numbers (1 to N), or list of group uuids, or None to select all groups. Defaults to None. panel: panel name (valid values: "signal", "image"). If None, current panel is used. Defaults to None. """ @abc.abstractmethod def delete_metadata( self, refresh_plot: bool = True, keep_roi: bool = False ) -> None: """Delete metadata of selected objects Args: refresh_plot: Refresh plot. Defaults to True. keep_roi: Keep ROI. Defaults to False. """ @abc.abstractmethod def get_group_titles_with_object_info( self, ) -> tuple[list[str], list[list[str]], list[list[str]]]: """Return groups titles and lists of inner objects uuids and titles. Returns: Groups titles, lists of inner objects uuids and titles """ @abc.abstractmethod def get_object_titles(self, panel: str | None = None) -> list[str]: """Get object (signal/image) list for current panel. Objects are sorted by group number and object index in group. Args: panel: panel name (valid values: "signal", "image", "macro"). If None, current data panel is used (i.e. signal or image panel). Returns: List of object titles Raises: ValueError: if panel not found """ @abc.abstractmethod def get_object( self, nb_id_title: int | str | None = None, panel: str | None = None ) -> SignalObj | ImageObj: """Get object (signal/image) from index. Args: nb_id_title: Object number, or object id, or object title. Defaults to None (current object). panel: Panel name. Defaults to None (current panel). Returns: Object Raises: KeyError: if object not found """ @abc.abstractmethod def get_object_uuids( self, panel: str | None = None, group: int | str | None = None ) -> list[str]: """Get object (signal/image) uuid list for current panel. Objects are sorted by group number and object index in group. Args: panel: panel name (valid values: "signal", "image"). If None, current panel is used. group: Group number, or group id, or group title. Defaults to None (all groups). Returns: List of object uuids Raises: ValueError: if panel not found """ @abc.abstractmethod def get_object_shapes( self, nb_id_title: int | str | None = None, panel: str | None = None, ) -> list: """Get plot item shapes associated to object (signal/image). Args: nb_id_title: Object number, or object id, or object title. Defaults to None (current object). panel: Panel name. Defaults to None (current panel). Returns: List of plot item shapes """ @abc.abstractmethod def add_annotations_from_items( self, items: list, refresh_plot: bool = True, panel: str | None = None ) -> None: """Add object annotations (annotation plot items). Args: items: annotation plot items refresh_plot: refresh plot. Defaults to True. panel: panel name (valid values: "signal", "image"). If None, current panel is used. """ @abc.abstractmethod def add_label_with_title( self, title: str | None = None, panel: str | None = None ) -> None: """Add a label with object title on the associated plot Args: title: Label title. Defaults to None. If None, the title is the object title. panel: panel name (valid values: "signal", "image"). If None, current panel is used. """ @abc.abstractmethod def run_macro(self, number_or_title: int | str | None = None) -> None: """Run macro. Args: number: Number of the macro (starting at 1). Defaults to None (run current macro, or does nothing if there is no macro). """ @abc.abstractmethod def stop_macro(self, number_or_title: int | str | None = None) -> None: """Stop macro. Args: number: Number of the macro (starting at 1). Defaults to None (stop current macro, or does nothing if there is no macro). """ @abc.abstractmethod def import_macro_from_file(self, filename: str) -> None: """Import macro from file Args: filename: Filename. """ @abc.abstractmethod def calc(self, name: str, param: gds.DataSet | None = None) -> gds.DataSet: """Call computation feature ``name`` .. note:: This calls either the processor's ``compute_`` method (if it exists), or the processor's ```` computation feature (if it is registered, using the ``run_feature`` method). It looks for the function in all panels, starting with the current one. Args: name: Compute function name param: Compute function parameter. Defaults to None. Raises: ValueError: unknown function """ # ========================================================================= # Web API control methods # ========================================================================= @abc.abstractmethod def start_webapi_server( self, host: str | None = None, port: int | None = None, ) -> dict: """Start the Web API server. Args: host: Host address to bind to. Defaults to "127.0.0.1". port: Port number. Defaults to auto-detect available port. Returns: Dictionary with "url" and "token" keys. Raises: RuntimeError: If Web API deps not installed or server already running. """ @abc.abstractmethod def stop_webapi_server(self) -> None: """Stop the Web API server.""" @abc.abstractmethod def get_webapi_status(self) -> dict: """Get Web API server status. Returns: Dictionary with "running", "url", and "token" keys. """ @abc.abstractmethod def call_method( self, method_name: str, *args, panel: str | None = None, **kwargs, ): """Call a public method on a panel or main window. This generic method allows calling any public method that is not explicitly exposed in the proxy API. The method resolution follows this order: 1. If panel is specified: call method on that specific panel 2. If panel is None: a. Try to call method on main window (DLMainWindow) b. If not found, try to call method on current panel (BaseDataPanel) This makes it convenient to call panel methods without specifying the panel parameter when working on the current panel. Args: method_name: Name of the method to call *args: Positional arguments to pass to the method panel: Panel name ("signal", "image", or None for auto-detection). Defaults to None. **kwargs: Keyword arguments to pass to the method Returns: The return value of the called method Raises: AttributeError: If the method does not exist or is not public ValueError: If the panel name is invalid Examples: >>> # Call remove_object on current panel (auto-detected) >>> proxy.call_method("remove_object", force=True) >>> # Call a signal panel method specifically >>> proxy.call_method("delete_all_objects", panel="signal") >>> # Call main window method >>> proxy.call_method("raise_window") """ class BaseProxy(AbstractDLControl, metaclass=abc.ABCMeta): """Common base class for DataLab proxies Args: datalab: DLMainWindow instance or ServerProxy instance. If None, then the proxy implementation will have to set it later (e.g. see RemoteClient). """ def __init__(self, datalab: DLMainWindow | ServerProxy | None = None) -> None: self._datalab = datalab def get_version(self) -> str: """Return DataLab public version. Returns: DataLab version """ return self._datalab.get_version() def close_application(self) -> None: """Close DataLab application""" self._datalab.close_application() def raise_window(self) -> None: """Raise DataLab window""" self._datalab.raise_window() def get_current_panel(self) -> str: """Return current panel name. Returns: Panel name (valid values: "signal", "image", "macro")) """ return self._datalab.get_current_panel() def set_current_panel(self, panel: str) -> None: """Switch to panel. Args: panel: Panel name (valid values: "signal", "image", "macro")) """ self._datalab.set_current_panel(panel) def reset_all(self) -> None: """Reset all application data""" self._datalab.reset_all() def remove_object(self, force: bool = False) -> None: """Remove current object from current panel. Args: force: if True, remove object without confirmation. Defaults to False. """ self._datalab.remove_object(force) def toggle_auto_refresh(self, state: bool) -> None: """Toggle auto refresh state. Args: state: Auto refresh state """ self._datalab.toggle_auto_refresh(state) def toggle_show_titles(self, state: bool) -> None: """Toggle show titles state. Args: state: Show titles state """ self._datalab.toggle_show_titles(state) def save_to_h5_file(self, filename: str) -> None: """Save to a DataLab HDF5 file. Args: filename: HDF5 file name """ self._datalab.save_to_h5_file(filename) def open_h5_files( self, h5files: list[str] | None = None, import_all: bool | None = None, reset_all: bool | None = None, ) -> None: """Open a DataLab HDF5 file or import from any other HDF5 file. Args: h5files: List of HDF5 files to open. Defaults to None. import_all: Import all objects from HDF5 files. Defaults to None. reset_all: Reset all application data. Defaults to None. """ self._datalab.open_h5_files(h5files, import_all, reset_all) def import_h5_file(self, filename: str, reset_all: bool | None = None) -> None: """Open DataLab HDF5 browser to Import HDF5 file. Args: filename: HDF5 file name reset_all: Reset all application data. Defaults to None. """ self._datalab.import_h5_file(filename, reset_all) def load_h5_workspace(self, h5files: list[str], reset_all: bool = False) -> None: """Load native DataLab HDF5 workspace files without any GUI elements. This method can be safely called from scripts (e.g., internal console, macros) as it does not create any Qt widgets, dialogs, or progress bars. .. warning:: This method only supports native DataLab HDF5 files. For importing arbitrary HDF5 files (non-native), use :meth:`open_h5_files` or :meth:`import_h5_file` instead. Args: h5files: List of native DataLab HDF5 filenames reset_all: Reset all application data before importing. Defaults to False. Raises: ValueError: If a file is not a valid native DataLab HDF5 file """ self._datalab.load_h5_workspace(h5files, reset_all) def save_h5_workspace(self, filename: str) -> None: """Save current workspace to a native DataLab HDF5 file without GUI elements. This method can be safely called from scripts (e.g., internal console, macros) as it does not create any Qt widgets, dialogs, or progress bars. Args: filename: HDF5 filename to save to Raises: IOError: If file cannot be saved """ self._datalab.save_h5_workspace(filename) def load_from_files(self, filenames: list[str]) -> None: """Open objects from files in current panel (signals/images). Args: filenames: list of file names """ self._datalab.load_from_files(filenames) def load_from_directory(self, path: str) -> None: """Open objects from directory in current panel (signals/images). Args: path: directory path """ self._datalab.load_from_directory(path) def get_sel_object_uuids(self, include_groups: bool = False) -> list[str]: """Return selected objects uuids. Args: include_groups: If True, also return objects from selected groups. Returns: List of selected objects uuids. """ return self._datalab.get_sel_object_uuids(include_groups) def add_group( self, title: str, panel: str | None = None, select: bool = False ) -> None: """Add group to DataLab. Args: title: Group title panel: Panel name (valid values: "signal", "image"). Defaults to None. select: Select the group after creation. Defaults to False. """ self._datalab.add_group(title, panel, select) def select_objects( self, selection: list[int | str], panel: str | None = None, ) -> None: """Select objects in current panel. Args: selection: List of object numbers (1 to N) or uuids to select panel: panel name (valid values: "signal", "image"). If None, current panel is used. Defaults to None. """ self._datalab.select_objects(selection, panel) def select_groups( self, selection: list[int | str] | None = None, panel: str | None = None ) -> None: """Select groups in current panel. Args: selection: List of group numbers (1 to N), or list of group uuids, or None to select all groups. Defaults to None. panel: panel name (valid values: "signal", "image"). If None, current panel is used. Defaults to None. """ self._datalab.select_groups(selection, panel) def delete_metadata( self, refresh_plot: bool = True, keep_roi: bool = False ) -> None: """Delete metadata of selected objects Args: refresh_plot: Refresh plot. Defaults to True. keep_roi: Keep ROI. Defaults to False. """ self._datalab.delete_metadata(refresh_plot, keep_roi) def get_group_titles_with_object_info( self, ) -> tuple[list[str], list[list[str]], list[list[str]]]: """Return groups titles and lists of inner objects uuids and titles. Returns: Tuple: groups titles, lists of inner objects uuids and titles """ return self._datalab.get_group_titles_with_object_info() def get_object_titles(self, panel: str | None = None) -> list[str]: """Get object (signal/image) list for current panel. Objects are sorted by group number and object index in group. Args: panel: panel name (valid values: "signal", "image", "macro"). If None, current data panel is used (i.e. signal or image panel). Returns: List of object titles Raises: ValueError: if panel not found """ return self._datalab.get_object_titles(panel) def get_object_uuids( self, panel: str | None = None, group: int | str | None = None ) -> list[str]: """Get object (signal/image) uuid list for current panel. Objects are sorted by group number and object index in group. Args: panel: panel name (valid values: "signal", "image"). If None, current panel is used. group: Group number, or group id, or group title. Defaults to None (all groups). Returns: List of object uuids Raises: ValueError: if panel not found """ return self._datalab.get_object_uuids(panel, group) def add_label_with_title( self, title: str | None = None, panel: str | None = None ) -> None: """Add a label with object title on the associated plot Args: title: Label title. Defaults to None. If None, the title is the object title. panel: panel name (valid values: "signal", "image"). If None, current panel is used. """ self._datalab.add_label_with_title(title, panel) def run_macro(self, number_or_title: int | str | None = None) -> None: """Run macro. Args: number_or_title: Macro number, or macro title. Defaults to None (current macro). Raises: ValueError: if macro not found """ self._datalab.run_macro(number_or_title) def stop_macro(self, number_or_title: int | str | None = None) -> None: """Stop macro. Args: number_or_title: Macro number, or macro title. Defaults to None (current macro). Raises: ValueError: if macro not found """ self._datalab.stop_macro(number_or_title) def import_macro_from_file(self, filename: str) -> None: """Import macro from file Args: filename: Filename. """ return self._datalab.import_macro_from_file(filename) def call_method( self, method_name: str, *args, panel: str | None = None, **kwargs, ): """Call a public method on a panel or main window. Method resolution order when panel is None: 1. Try main window (DLMainWindow) 2. If not found, try current panel (BaseDataPanel) Args: method_name: Name of the method to call *args: Positional arguments to pass to the method panel: Panel name ("signal", "image", or None for auto-detection). Defaults to None. **kwargs: Keyword arguments to pass to the method Returns: The return value of the called method Raises: AttributeError: If the method does not exist or is not public ValueError: If the panel name is invalid """ return self._datalab.call_method(method_name, *args, panel=panel, **kwargs) # ========================================================================= # Web API control methods # ========================================================================= def start_webapi_server( self, host: str | None = None, port: int | None = None, ) -> dict: """Start the Web API server. Args: host: Host address to bind to. Defaults to "127.0.0.1". port: Port number. Defaults to auto-detect available port. Returns: Dictionary with "url" and "token" keys. Raises: RuntimeError: If Web API dependencies not installed or server already running. """ return self._datalab.start_webapi_server(host, port) def stop_webapi_server(self) -> None: """Stop the Web API server.""" return self._datalab.stop_webapi_server() def get_webapi_status(self) -> dict: """Get Web API server status. Returns: Dictionary with "running", "url", and "token" keys. """ return self._datalab.get_webapi_status() DataLab-1.1.0/datalab/control/proxy.py000066400000000000000000000271511514014117600176120ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Proxy objects (:mod:`datalab.control.proxy`) -------------------------------------------- The :mod:`datalab.control.proxy` module provides a way to access DataLab features from a proxy class. Remote proxy ^^^^^^^^^^^^ The remote proxy is used when DataLab is started from a different process than the proxy. In this case, the proxy connects to DataLab XML-RPC server. .. autoclass:: RemoteProxy :members: :inherited-members: Local proxy ^^^^^^^^^^^ The local proxy is used when DataLab is started from the same process as the proxy. In this case, the proxy is directly connected to DataLab main window instance. The typical use case is high-level scripting. .. autoclass:: LocalProxy :members: :inherited-members: Proxy context manager ^^^^^^^^^^^^^^^^^^^^^ The proxy context manager is a convenient way to handle proxy creation and destruction. It is used as follows: .. code-block:: python with proxy_context("local") as proxy: proxy.add_signal(...) The proxy type can be "local" or "remote". For remote proxy, the port can be specified as "remote:port". .. note:: The proxy context manager allows to use the proxy in various contexts (Python script, Jupyter notebook, etc.). It also allows to switch seamlessly between local and remote proxy, keeping the same code inside the context. .. autofunction:: proxy_context Calling processor methods using proxy objects ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ All the proxy objects provide access to the DataLab computing methods exposed by the processor classes: - :class:`datalab.gui.processor.signal.SignalProcessor` - :class:`datalab.gui.processor.image.ImageProcessor` To run a computation feature associated to a processor, you can use the :meth:`calc` method of the proxy object: .. code-block:: python # Call a method without parameter proxy.calc("average") # Call a method with parameters p = sigima.params.MovingAverageParam.create(n=30) proxy.calc("moving_average", p) """ from __future__ import annotations from collections.abc import Generator from contextlib import contextmanager import guidata.dataset as gds import numpy as np from sigima import ImageObj, SignalObj from datalab.control.baseproxy import BaseProxy from datalab.control.remote import RemoteClient from datalab.utils import qthelpers as qth class RemoteProxy(RemoteClient): """DataLab remote proxy class. This class provides access to DataLab features from a proxy class. This is the remote version of proxy, which is used when DataLab is started from a different process than the proxy. Args: autoconnect: Automatically connect to DataLab XML-RPC server. Raises: ConnectionRefusedError: Unable to connect to DataLab ValueError: Invalid timeout (must be >= 0.0) ValueError: Invalid number of retries (must be >= 1) Examples: Here is a simple example of how to use RemoteProxy in a Python script or in a Jupyter notebook: >>> from datalab.control.proxy import RemoteProxy >>> proxy = RemoteProxy() Connecting to DataLab XML-RPC server...OK (port: 28867) >>> proxy.get_version() '1.0.0' >>> proxy.add_signal("toto", np.array([1., 2., 3.]), np.array([4., 5., -1.])) True >>> proxy.get_object_titles() ['toto'] >>> proxy["toto"] # from title >>> proxy[1] # from number >>> proxy[1].data array([1., 2., 3.]) >>> proxy.set_current_panel("image") """ def __init__(self, autoconnect: bool = True) -> None: super().__init__() if autoconnect: self.connect() class LocalProxy(BaseProxy): """DataLab local proxy class. This class provides access to DataLab features from a proxy class. This is the local version of proxy, which is used when DataLab is started from the same process as the proxy. Args: datalab (DLMainWindow): DLMainWindow instance. """ def add_signal( self, title: str, xdata: np.ndarray, ydata: np.ndarray, xunit: str = "", yunit: str = "", xlabel: str = "", ylabel: str = "", group_id: str = "", set_current: bool = True, ) -> bool: # pylint: disable=too-many-arguments """Add signal data to DataLab. Args: title: Signal title xdata: X data ydata: Y data xunit: X unit. Defaults to "" yunit: Y unit. Defaults to "" xlabel: X label. Defaults to "" ylabel: Y label. Defaults to "" group_id: group id in which to add the signal. Defaults to "" set_current: if True, set the added signal as current Returns: True if signal was added successfully, False otherwise Raises: ValueError: Invalid xdata dtype ValueError: Invalid ydata dtype """ return self._datalab.add_signal( title, xdata, ydata, xunit, yunit, xlabel, ylabel, group_id, set_current ) def add_image( self, title: str, data: np.ndarray, xunit: str = "", yunit: str = "", zunit: str = "", xlabel: str = "", ylabel: str = "", zlabel: str = "", group_id: str = "", set_current: bool = True, ) -> bool: # pylint: disable=too-many-arguments """Add image data to DataLab. Args: title: Image title data: Image data xunit: X unit. Defaults to "" yunit: Y unit. Defaults to "" zunit: Z unit. Defaults to "" xlabel: X label. Defaults to "" ylabel: Y label. Defaults to "" zlabel: Z label. Defaults to "" group_id: group id in which to add the image. Defaults to "" set_current: if True, set the added image as current Returns: True if image was added successfully, False otherwise Raises: ValueError: Invalid data dtype """ return self._datalab.add_image( title, data, xunit, yunit, zunit, xlabel, ylabel, zlabel, group_id, set_current, ) def add_object( self, obj: SignalObj | ImageObj, group_id: str = "", set_current: bool = True ) -> None: """Add object to DataLab. Args: obj: Signal or image object group_id: group id in which to add the object. Defaults to "" set_current: if True, set the added object as current """ self._datalab.add_object(obj, group_id, set_current) def calc(self, name: str, param: gds.DataSet | None = None) -> None: """Call computation feature ``name`` .. note:: This calls either the processor's ``compute_`` method (if it exists), or the processor's ```` computation feature (if it is registered, using the ``run_feature`` method). It looks for the function in all panels, starting with the current one. Args: name: Compute function name param: Compute function parameter. Defaults to None Raises: ValueError: unknown function """ return self._datalab.calc(name, param) def get_object( self, nb_id_title: int | str | None = None, panel: str | None = None ) -> SignalObj | ImageObj: """Get object (signal/image) from index. Args: nb_id_title: Object number, or object id, or object title. Defaults to None (current object) panel: Panel name. Defaults to None (current panel) Returns: Object Raises: KeyError: if object not found """ return self._datalab.get_object(nb_id_title, panel) def get_object_shapes( self, nb_id_title: int | str | None = None, panel: str | None = None, ) -> list: """Get plot item shapes associated to object (signal/image). Args: nb_id_title: Object number, or object id, or object title. Defaults to None (current object) panel: Panel name. Defaults to None (current panel) Returns: List of plot item shapes """ return self._datalab.get_object_shapes(nb_id_title, panel) def add_annotations_from_items( self, items: list, refresh_plot: bool = True, panel: str | None = None ) -> None: """Add object annotations (annotation plot items). Args: itemsTrue: annotation plot items refresh_plotTrue: refresh plot. Defaults to True panel: panel name (valid values: "signal", "image"). If None, current panel is used """ self._datalab.add_annotations_from_items(items, refresh_plot, panel) def load_h5_workspace( self, h5files: list[str] | str, reset_all: bool = False ) -> None: """Load HDF5 workspace files without showing file dialog. This method loads one or more DataLab native HDF5 files directly, bypassing the file dialog. It is safe to call from the internal console or any context where Qt dialogs would cause threading issues. Args: h5files: Path(s) to HDF5 file(s). Can be a single path string or a list of paths reset_all: If True, reset workspace before loading. Defaults to False. Raises: ValueError: if file is not a DataLab native HDF5 file """ if isinstance(h5files, str): h5files = [h5files] self._datalab.load_h5_workspace(h5files, reset_all) def save_h5_workspace(self, filename: str) -> None: """Save workspace to HDF5 file without showing file dialog. This method saves the current workspace to a DataLab native HDF5 file directly, bypassing the file dialog. It is safe to call from the internal console or any context where Qt dialogs would cause threading issues. Args: filename: Path to the output HDF5 file """ self._datalab.save_h5_workspace(filename) @contextmanager def proxy_context(what: str) -> Generator[LocalProxy | RemoteProxy, None, None]: """Context manager handling DL proxy creation and destruction. Args: what: proxy type ("local" or "remote") For remote proxy, the port can be specified as "remote:port" Yields: proxy LocalProxy if what == "local" RemoteProxy if what == "remote" or "remote:port" Example: with proxy_context("local") as proxy: proxy.add_signal(...) """ assert what == "local" or what.startswith("remote"), "Invalid proxy type" port = None if ":" in what: port = int(what.split(":")[1].strip()) if what == "local": # pylint: disable=import-outside-toplevel, cyclic-import from datalab.gui.main import DLMainWindow with qth.datalab_app_context(exec_loop=True): try: win = DLMainWindow() proxy = LocalProxy(win) win.show() yield proxy finally: pass else: try: proxy = RemoteProxy(autoconnect=False) proxy.connect(port) yield proxy finally: proxy.disconnect() DataLab-1.1.0/datalab/control/remote.py000066400000000000000000001216221514014117600177220ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab remote control ---------------------- This module provides utilities to control DataLab from a Python script (e.g. with Spyder) or from a Jupyter notebook. The :class:`RemoteClient` class provides the main interface to DataLab XML-RPC server. """ from __future__ import annotations import functools import sys import threading import time import warnings from collections.abc import Callable from typing import TYPE_CHECKING from xmlrpc.client import Binary, ServerProxy from xmlrpc.server import SimpleXMLRPCServer import guidata.dataset as gds import numpy as np from packaging.version import Version from qtpy import QtCore as QC from sigima.client import utils from sigima.objects import ImageObj, SignalObj, create_image, create_signal import datalab from datalab.adapters_plotpy import items_to_json, json_to_items from datalab.config import Conf, initialize from datalab.control.baseproxy import AbstractDLControl, BaseProxy from datalab.env import execenv if TYPE_CHECKING: from datalab.gui.main import DLMainWindow # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code def remote_call(func: Callable) -> object: """Decorator for method calling DataLab main window remotely""" @functools.wraps(func) def method_wrapper(*args, **kwargs): """Decorator wrapper function""" self: RemoteServer = args[0] # extracting 'self' from method arguments self.is_ready = False output = func(*args, **kwargs) while not self.is_ready: QC.QCoreApplication.processEvents() time.sleep(0.05) # Check if an exception was raised and stored by the slot if self.exception is not None: exc = self.exception self.exception = None # Clear the exception raise exc # For methods that use signal/slot pattern (like call_method), # they return self.result which is set by the slot. The function # itself returns this value, but it's set asynchronously, so we # need to return the value AFTER waiting, not the initial return value. # Since we wait above, self.result should now be set by the slot. # If the function returned self.result, use the updated value. if output is None: # Return self.result which should be set by now return self.result return output return method_wrapper # Note: RemoteServer can't inherit from AbstractDLControl because it is a QThread # and most of the methods are not returning expected data types class RemoteServer(QC.QThread): """XML-RPC server QThread""" SIG_SERVER_PORT = QC.Signal(int) SIG_CLOSE_APP = QC.Signal() SIG_RAISE_WINDOW = QC.Signal() SIG_ADD_OBJECT = QC.Signal(object, str, bool) SIG_ADD_GROUP = QC.Signal(str, str, bool) SIG_LOAD_FROM_FILES = QC.Signal(list) SIG_LOAD_FROM_DIRECTORY = QC.Signal(str) SIG_SELECT_OBJECTS = QC.Signal(list, str) SIG_SELECT_GROUPS = QC.Signal(list, str) SIG_SELECT_ALL_GROUPS = QC.Signal(str) SIG_DELETE_METADATA = QC.Signal(bool, bool) SIG_SWITCH_TO_PANEL = QC.Signal(str) SIG_TOGGLE_AUTO_REFRESH = QC.Signal(bool) SIG_TOGGLE_SHOW_TITLES = QC.Signal(bool) SIG_RESET_ALL = QC.Signal() SIG_REMOVE_OBJECT = QC.Signal(bool) SIG_SAVE_TO_H5 = QC.Signal(str) SIG_OPEN_H5 = QC.Signal(list, bool, bool) SIG_IMPORT_H5 = QC.Signal(str, bool) SIG_LOAD_H5_WORKSPACE = QC.Signal(list, bool) SIG_SAVE_H5_WORKSPACE = QC.Signal(str) SIG_CALC = QC.Signal(str, object) SIG_RUN_MACRO = QC.Signal(str) SIG_STOP_MACRO = QC.Signal(str) SIG_IMPORT_MACRO_FROM_FILE = QC.Signal(str) SIG_CALL_METHOD = QC.Signal(str, list, object, dict) def __init__(self, win: DLMainWindow) -> None: QC.QThread.__init__(self) self.port: int = None self.is_ready = True self.server: SimpleXMLRPCServer | None = None self.win = win self.result = None self.exception = None win.SIG_READY.connect(self.datalab_is_ready) win.SIG_CLOSING.connect(self.shutdown_server) self.SIG_CLOSE_APP.connect(win.close) self.SIG_RAISE_WINDOW.connect(win.raise_window) self.SIG_ADD_OBJECT.connect(win.add_object) self.SIG_ADD_GROUP.connect(win.add_group) self.SIG_LOAD_FROM_FILES.connect(win.load_from_files) self.SIG_LOAD_FROM_DIRECTORY.connect(win.load_from_directory) self.SIG_SELECT_OBJECTS.connect(win.select_objects) self.SIG_SELECT_GROUPS.connect(win.select_groups) self.SIG_SELECT_ALL_GROUPS.connect(lambda panel: win.select_groups(None, panel)) self.SIG_DELETE_METADATA.connect(win.delete_metadata) self.SIG_SWITCH_TO_PANEL.connect(win.set_current_panel) self.SIG_TOGGLE_AUTO_REFRESH.connect(win.toggle_auto_refresh) self.SIG_TOGGLE_SHOW_TITLES.connect(win.toggle_show_titles) self.SIG_RESET_ALL.connect(win.reset_all) self.SIG_REMOVE_OBJECT.connect(win.remove_object) self.SIG_SAVE_TO_H5.connect(win.save_to_h5_file) self.SIG_OPEN_H5.connect(win.open_h5_files) self.SIG_IMPORT_H5.connect(win.import_h5_file) self.SIG_LOAD_H5_WORKSPACE.connect(win.load_h5_workspace) self.SIG_SAVE_H5_WORKSPACE.connect(win.save_h5_workspace) self.SIG_CALC.connect(win.calc) self.SIG_RUN_MACRO.connect(win.run_macro) self.SIG_STOP_MACRO.connect(win.stop_macro) self.SIG_IMPORT_MACRO_FROM_FILE.connect(win.import_macro_from_file) self.SIG_CALL_METHOD.connect(win.call_method_slot) def serve(self) -> None: """Start server and serve forever""" with SimpleXMLRPCServer( ("127.0.0.1", 0), logRequests=False, allow_none=True ) as server: self.server = server server.register_introspection_functions() self.register_functions(server) self.port = server.server_address[1] self.notify_port(self.port) with execenv.context(xmlrpcport=self.port): server.serve_forever() def shutdown_server(self) -> None: """Shutdown server""" if self.server is not None: self.server.shutdown() self.server = None def notify_port(self, port: int) -> None: """Notify automatically attributed port. This method is called after the server port has been automatically attributed. It notifies the port number to the main window. Args: port: Server port number """ self.SIG_SERVER_PORT.emit(port) @classmethod def check_remote_functions(cls) -> None: """Check if all AbstractDLControl methods are implemented in RemoteServer""" mlist = [] for method in AbstractDLControl.get_public_methods(): if not hasattr(cls, method): mlist.append(method) if mlist: raise RuntimeError(f"{cls} is missing some methods: {','.join(mlist)}") def register_functions(self, server: SimpleXMLRPCServer) -> None: """Register functions""" for name in AbstractDLControl.get_public_methods(): server.register_function(getattr(self, name)) def run(self) -> None: """Thread execution method""" if "coverage" in sys.modules: # The following is required to make coverage work with threading # pylint: disable=protected-access sys.settrace(threading._trace_hook) self.serve() def datalab_is_ready(self) -> None: """Called when DataLab is ready to process new requests""" self.is_ready = True @staticmethod def get_version() -> str: """Return DataLab public version""" return datalab.__version__ def close_application(self) -> None: """Close DataLab application""" self.SIG_CLOSE_APP.emit() def raise_window(self) -> None: """Raise DataLab window""" self.SIG_RAISE_WINDOW.emit() @remote_call def get_current_panel(self) -> str: """Return current panel name. Returns: Panel name (valid values: 'signal', 'image', 'macro') """ return self.win.get_current_panel() @remote_call def set_current_panel(self, panel: str) -> None: """Switch to panel. Args: panel: Panel name (valid values: 'signal', 'image', 'macro') """ self.SIG_SWITCH_TO_PANEL.emit(panel) @remote_call def toggle_auto_refresh(self, state: bool) -> None: """Toggle auto refresh state. Args: state: True to enable auto refresh, False to disable it """ self.SIG_TOGGLE_AUTO_REFRESH.emit(state) @remote_call def toggle_show_titles(self, state: bool) -> None: """Toggle show titles state. Args: state: True to enable show titles, False to disable it """ self.SIG_TOGGLE_SHOW_TITLES.emit(state) @remote_call def reset_all(self) -> None: """Reset all application data""" self.SIG_RESET_ALL.emit() @remote_call def remove_object(self, force: bool = False) -> None: """Remove current object from current panel. Args: force: if True, remove object without confirmation. Defaults to False. """ self.SIG_REMOVE_OBJECT.emit(force) @remote_call def save_to_h5_file(self, filename: str) -> None: """Save to a DataLab HDF5 file. Args: filename: HDF5 file name (with extension .h5) """ self.SIG_SAVE_TO_H5.emit(filename) @remote_call def open_h5_files( self, h5files: list[str] | None = None, import_all: bool | None = None, reset_all: bool | None = None, ) -> None: """Open a DataLab HDF5 file or import from any other HDF5 file. Args: h5files: HDF5 file names. Defaults to None. import_all: Import all objects from HDF5 file. Defaults to None. reset_all: Reset all application data. Defaults to None. """ import_all = True if import_all is None else import_all reset_all = False if reset_all is None else reset_all self.SIG_OPEN_H5.emit(h5files, import_all, reset_all) @remote_call def import_h5_file(self, filename: str, reset_all: bool | None = None) -> None: """Open DataLab HDF5 browser to Import HDF5 file. Args: filename: HDF5 file name reset_all: Reset all application data. Defaults to None. """ reset_all = False if reset_all is None else reset_all self.SIG_IMPORT_H5.emit(filename, reset_all) @remote_call def load_h5_workspace(self, h5files: list[str], reset_all: bool = False) -> None: """Load native DataLab HDF5 workspace files without any GUI elements. This method can be safely called from scripts as it does not create any Qt widgets, dialogs, or progress bars. Args: h5files: List of native DataLab HDF5 filenames reset_all: Reset all application data before importing. Defaults to False. Raises: ValueError: If a file is not a valid native DataLab HDF5 file """ self.SIG_LOAD_H5_WORKSPACE.emit(h5files, reset_all) @remote_call def save_h5_workspace(self, filename: str) -> None: """Save current workspace to a native DataLab HDF5 file without GUI elements. This method can be safely called from scripts as it does not create any Qt widgets, dialogs, or progress bars. Args: filename: HDF5 filename to save to Raises: IOError: If file cannot be saved """ self.SIG_SAVE_H5_WORKSPACE.emit(filename) @remote_call def load_from_files(self, filenames: list[str]) -> None: """Open objects from files in current panel (signals/images). Args: filenames: list of file names """ self.SIG_LOAD_FROM_FILES.emit(filenames) @remote_call def load_from_directory(self, path: str) -> None: """Open objects from directory in current panel (signals/images). Args: path: directory path """ self.SIG_LOAD_FROM_DIRECTORY.emit(path) @remote_call def add_signal( self, title: str, xbinary: Binary, ybinary: Binary, xunit: str = "", yunit: str = "", xlabel: str = "", ylabel: str = "", group_id: str = "", set_current: bool = True, ) -> bool: # pylint: disable=too-many-arguments """Add signal data to DataLab. Args: title: Signal title xbinary: X data ybinary: Y data xunit: X unit. Defaults to "" yunit: Y unit. Defaults to "" xlabel: X label. Defaults to "" ylabel: Y label. Defaults to "" group_id: group id in which to add the signal. Defaults to "" set_current: if True, set the added signal as current Returns: True if successful """ xdata = utils.rpcbinary_to_array(xbinary) ydata = utils.rpcbinary_to_array(ybinary) signal = create_signal(title, xdata, ydata) signal.xunit = xunit or "" # In case xunit is None signal.yunit = yunit or "" # In case yunit is None signal.xlabel = xlabel or "" # In case xlabel is None signal.ylabel = ylabel or "" # In case ylabel is None self.SIG_ADD_OBJECT.emit(signal, group_id, set_current) return True @remote_call def add_image( self, title: str, zbinary: Binary, xunit: str = "", yunit: str = "", zunit: str = "", xlabel: str = "", ylabel: str = "", zlabel: str = "", group_id: str = "", set_current: bool = True, ) -> bool: # pylint: disable=too-many-arguments """Add image data to DataLab. Args: title: Image title zbinary: Z data xunit: X unit. Defaults to "" yunit: Y unit. Defaults to "" zunit: Z unit. Defaults to "" xlabel: X label. Defaults to "" ylabel: Y label. Defaults to "" zlabel: Z label. Defaults to "" group_id: group id in which to add the image. Defaults to "" set_current: if True, set the added image as current Returns: True if successful """ data = utils.rpcbinary_to_array(zbinary) image = create_image(title, data) image.xunit = xunit or "" # In case xunit is None image.yunit = yunit or "" # In case yunit is None image.zunit = zunit or "" # In case zunit is None image.xlabel = xlabel or "" # In case xlabel is None image.ylabel = ylabel or "" # In case ylabel is None image.zlabel = zlabel or "" # In case zlabel is None self.SIG_ADD_OBJECT.emit(image, group_id, set_current) return True @remote_call def add_object( self, obj_data: list[str], group_id: str = "", set_current: bool = True ) -> bool: """Add object to DataLab. Args: obj_data: Object data group_id: Group id in which to add the object. Defaults to "" set_current: if True, set the added object as current Returns: True if successful """ obj = utils.rpcjson_to_dataset(obj_data) self.SIG_ADD_OBJECT.emit(obj, group_id, set_current) return True @remote_call def get_sel_object_uuids(self, include_groups: bool = False) -> list[str]: """Return selected objects uuids. Args: include_groups: If True, also return objects from selected groups. Returns: List of selected objects uuids. """ return self.win.get_sel_object_uuids(include_groups) @remote_call def add_group( self, title: str, panel: str | None = None, select: bool = False ) -> None: """Add group to DataLab. Args: title: Group title panel: Panel name (valid values: "signal", "image"). Defaults to None. select: Select the group after creation. Defaults to False. """ self.SIG_ADD_GROUP.emit(title, panel, select) @remote_call def select_objects( self, selection: list[int | str], panel: str | None = None, ) -> None: """Select objects in current panel. Args: selection: List of object numbers (1 to N) or uuids to select panel: panel name (valid values: "signal", "image"). If None, current panel is used. Defaults to None. """ self.SIG_SELECT_OBJECTS.emit(selection, panel) @remote_call def select_groups( self, selection: list[int | str] | None = None, panel: str | None = None ) -> None: """Select groups in current panel. Args: selection: List of group numbers (1 to N), or list of group uuids, or None to select all groups. Defaults to None. panel: panel name (valid values: "signal", "image"). If None, current panel is used. Defaults to None. """ if selection is None: self.SIG_SELECT_ALL_GROUPS.emit(panel) else: self.SIG_SELECT_GROUPS.emit(selection, panel) @remote_call def delete_metadata( self, refresh_plot: bool = True, keep_roi: bool = False ) -> None: """Delete metadata of selected objects Args: refresh_plot: Refresh plot. Defaults to True. keep_roi: Keep ROI. Defaults to False. """ self.SIG_DELETE_METADATA.emit(refresh_plot, keep_roi) @remote_call def calc(self, name: str, param_data: list[str] | None = None) -> bool: """Call computation feature ``name`` .. note:: This calls either the processor's ``compute_`` method (if it exists), or the processor's ```` computation feature (if it is registered, using the ``run_feature`` method). It looks for the function in all panels, starting with the current one. Args: name: Compute function name param_data: Compute function parameters. Defaults to None. Returns: True if successful, False otherwise """ if param_data is None: param = None else: param = utils.rpcjson_to_dataset(param_data) self.SIG_CALC.emit(name, param) return True @remote_call def get_group_titles_with_object_info( self, ) -> tuple[list[str], list[list[str]], list[list[str]]]: """Return groups titles and lists of inner objects uuids and titles. Returns: Groups titles, lists of inner objects uuids and titles """ return self.win.get_group_titles_with_object_info() @remote_call def get_object_titles(self, panel: str | None = None) -> list[str]: """Get object (signal/image) list for current panel. Objects are sorted by group number and object index in group. Args: panel: panel name (valid values: "signal", "image", "macro"). If None, current data panel is used (i.e. signal or image panel). Returns: List of object titles """ return self.win.get_object_titles(panel) @remote_call def get_object( self, nb_id_title: int | str | None = None, panel: str | None = None, ) -> list[str]: """Get object (signal/image) from index. Args: nb_id_title: Object number, or object id, or object title. Defaults to None (current object). panel: Panel name. Defaults to None (current panel). Returns: Object Raises: KeyError: if object not found """ obj = self.win.get_object(nb_id_title, panel) if obj is None: return None return utils.dataset_to_rpcjson(obj) @remote_call def get_object_uuids( self, panel: str | None = None, group: int | str | None = None ) -> list[str]: """Get object (signal/image) uuid list for current panel. Objects are sorted by group number and object index in group. Args: panel: panel name (valid values: "signal", "image"). If None, current panel is used. group: Group number, or group id, or group title. Defaults to None (all groups). Returns: Object uuids """ return self.win.get_object_uuids(panel, group) @remote_call def get_object_shapes( self, nb_id_title: int | str | None = None, panel: str | None = None, ) -> list: """Get plot item shapes associated to object (signal/image). Args: nb_id_title: Object number, or object id, or object title. Defaults to None (current object). panel: Panel name. Defaults to None (current panel). Returns: List of plot item shapes """ items = self.win.get_object_shapes(nb_id_title, panel) return items_to_json(items) @remote_call def add_annotations_from_items( self, items_json: str, refresh_plot: bool = True, panel: str | None = None ) -> None: """Add object annotations (annotation plot items). Args: items_json: JSON string of annotation items refresh_plot: refresh plot. Defaults to True. panel: panel name (valid values: "signal", "image"). If None, current panel is used. """ items = json_to_items(items_json) if items: self.win.add_annotations_from_items(items, refresh_plot, panel) @remote_call def add_label_with_title( self, title: str | None = None, panel: str | None = None ) -> None: """Add a label with object title on the associated plot Args: title: Label title. Defaults to None. If None, the title is the object title. panel: panel name (valid values: "signal", "image"). If None, current panel is used. """ self.win.add_label_with_title(title, panel) @remote_call def run_macro(self, number_or_title: int | str | None = None) -> None: """Run macro. Args: number: Number of the macro (starting at 1). Defaults to None (run current macro, or does nothing if there is no macro). """ self.SIG_RUN_MACRO.emit(number_or_title) @remote_call def stop_macro(self, number_or_title: int | str | None = None) -> None: """Stop macro. Args: number: Number of the macro (starting at 1). Defaults to None (stop current macro, or does nothing if there is no macro). """ self.SIG_STOP_MACRO.emit(number_or_title) @remote_call def import_macro_from_file(self, filename: str) -> None: """Import macro from file Args: filename: Filename. """ self.SIG_IMPORT_MACRO_FROM_FILE.emit(filename) @remote_call def call_method( self, method_name: str, call_params: dict, ): """Call a public method on a panel or main window. Method resolution order when panel is None: 1. Try main window (DLMainWindow) 2. If not found, try current panel (BaseDataPanel) Args: method_name: Name of the method to call call_params: Dictionary with keys 'args' (list), 'panel' (str|None), 'kwargs' (dict). Defaults to empty for missing keys. Returns: The return value of the called method Raises: AttributeError: If the method does not exist or is not public ValueError: If the panel name is invalid """ args = call_params.get("args", []) panel = call_params.get("panel") kwargs = call_params.get("kwargs", {}) self.result = None # Initialize result self.SIG_CALL_METHOD.emit(method_name, args, panel, kwargs) # The decorator waits for is_ready, then this returns self.result # which was set by call_method_slot return self.result # ========================================================================= # Web API control methods # ========================================================================= @remote_call def start_webapi_server( self, host: str | None = None, port: int | None = None, ) -> dict: """Start the Web API server. Args: host: Host address to bind to. Defaults to "127.0.0.1". port: Port number. Defaults to auto-detect available port. Returns: Dictionary with "url" and "token" keys. Raises: RuntimeError: If Web API dependencies not installed or server already running. """ # Delegate to main window method which is @remote_controlled # This ensures SIG_READY is emitted for the @remote_call decorator return self.win.start_webapi_server(host, port) @remote_call def stop_webapi_server(self) -> None: """Stop the Web API server.""" # Delegate to main window method which is @remote_controlled return self.win.stop_webapi_server() @remote_call def get_webapi_status(self) -> dict: """Get Web API server status. Returns: Dictionary with "running", "url", and "token" keys. """ # Delegate to main window method which is @remote_controlled return self.win.get_webapi_status() RemoteServer.check_remote_functions() # === Python 2.7 client side: # # # See doc/remotecontrol_py27.py for an almost complete Python 2.7 # # implementation of RemoteClient class # # import io # from xmlrpclib import ServerProxy, Binary # import numpy as np # def array_to_binary(data): # """Convert NumPy array to XML-RPC Binary object, with shape and dtype""" # dbytes = io.BytesIO() # np.save(dbytes, data, allow_pickle=False) # return Binary(dbytes.getvalue()) # s = ServerProxy("http://127.0.0.1:8000") # data = np.array([[3, 4, 5], [7, 8, 0]], dtype=np.uint16) # s.add_image("toto", array_to_binary(data)) def get_datalab_xmlrpc_port() -> str: """Return DataLab current XML-RPC port Returns: XML-RPC port Raises: ConnectionRefusedError: DataLab has not yet been executed """ # The following is valid only when using Python 3.9+ with DataLab # installed on the client side. In any other situation, please use the # ``get_datalab_xmlrpc_port`` function from doc/remotecontrol_py27.py. initialize() try: return Conf.main.rpc_server_port.get() except RuntimeError as exc: raise ConnectionRefusedError("DataLab has not yet been executed") from exc class RemoteClient(BaseProxy): """Object representing a proxy/client to DataLab XML-RPC server. This object is used to call DataLab functions from a Python script. Examples: Here is a simple example of how to use RemoteClient in a Python script or in a Jupyter notebook: >>> from datalab.remote import RemoteClient >>> proxy = RemoteClient() >>> proxy.connect() Connecting to DataLab XML-RPC server...OK (port: 28867) >>> proxy.get_version() '1.0.0' >>> proxy.add_signal("toto", np.array([1., 2., 3.]), np.array([4., 5., -1.])) True >>> proxy.get_object_titles() ['toto'] >>> proxy["toto"] >>> proxy[1] >>> proxy[1].data array([1., 2., 3.]) """ def __init__(self) -> None: super().__init__() self.port: str | None = None self._datalab: ServerProxy def set_port(self, port: str | None = None) -> None: """Set XML-RPC port to connect to. Args: port: XML-RPC port to connect to. If None, the port is automatically retrieved from DataLab configuration. """ execenv.print(f"Setting XML-RPC port... [input:{port}] ", end="") port_str = "" if port is None: port = execenv.xmlrpcport port_str = f"→[execenv.xmlrpcport:{port}] " if port is None: port = get_datalab_xmlrpc_port() port_str = f"→[Conf.main.rpc_server_port:{port}] " execenv.print(port_str, end="") self.port = port if port is None: execenv.print("KO") raise ConnectionRefusedError("DataLab XML-RPC port is not set") execenv.print("OK") def __connect_to_server(self) -> None: """Connect to DataLab XML-RPC server. Args: port: XML-RPC port to connect to. Raises: ConnectionRefusedError: DataLab is currently not running """ self._datalab = ServerProxy(f"http://127.0.0.1:{self.port}", allow_none=True) try: version = self.get_version() except ConnectionRefusedError as exc: raise ConnectionRefusedError("DataLab is currently not running") from exc # If DataLab version is not compatible with this client, show a warning server_ver = Version(version) client_ver = Version(datalab.__version__) if server_ver < client_ver: warnings.warn( f"DataLab server version ({server_ver}) may not be fully compatible " f"with this DataLab client version ({client_ver}).\n" f"Please upgrade the server to {client_ver} or higher." ) def connect( self, port: str | None = None, timeout: float | None = None, retries: int | None = None, ) -> None: """Try to connect to DataLab XML-RPC server. Args: port: XML-RPC port to connect to. If not specified, the port is automatically retrieved from DataLab configuration. timeout: Maximum time to wait for connection in seconds. Defaults to 5.0. This is the total maximum wait time, not per retry. retries: Number of retries. Defaults to 10. This parameter is deprecated and will be removed in a future version (kept for backward compatibility). Raises: ConnectionRefusedError: Unable to connect to DataLab ValueError: Invalid timeout (must be >= 0.0) ValueError: Invalid number of retries (must be >= 1) """ timeout = 5.0 if timeout is None else timeout retries = 10 if retries is None else retries # Kept for backward compatibility if timeout < 0.0: raise ValueError("timeout must be >= 0.0") if retries < 1: raise ValueError("retries must be >= 1") execenv.print(f"Connecting to DataLab XML-RPC server... [port:{port}] ", end="") # Use exponential backoff for more efficient retrying start_time = time.time() poll_interval = 0.1 # Start with 100ms max_poll_interval = 1.0 # Cap at 1 second while True: try: # Try to set the port - this may fail if DataLab hasn't written its # config file yet, so we retry it in the loop self.set_port(port) self.__connect_to_server() elapsed = time.time() - start_time execenv.print(f"OK (port: {self.port}, connected in {elapsed:.1f}s)") return except (ConnectionRefusedError, OSError) as exc: # Catch both ConnectionRefusedError and OSError (includes socket errors) elapsed = time.time() - start_time if elapsed >= timeout: execenv.print("KO") raise ConnectionRefusedError( f"Unable to connect to DataLab after {elapsed:.1f}s" ) from exc # Wait before next retry with exponential backoff time.sleep(poll_interval) poll_interval = min(poll_interval * 1.5, max_poll_interval) def disconnect(self) -> None: """Disconnect from DataLab XML-RPC server.""" # This is not mandatory with XML-RPC, but if we change protocol in the # future, it may be useful to have a disconnect method. self._datalab = None def is_connected(self) -> bool: """Return True if connected to DataLab XML-RPC server.""" if self._datalab is not None: try: self.get_version() return True except ConnectionRefusedError: self._datalab = None return False def get_method_list(self) -> list[str]: """Return list of available methods.""" return self._datalab.system.listMethods() # === Following methods should match the register functions in XML-RPC server def add_signal( self, title: str, xdata: np.ndarray, ydata: np.ndarray, xunit: str = "", yunit: str = "", xlabel: str = "", ylabel: str = "", group_id: str = "", set_current: bool = True, ) -> bool: # pylint: disable=too-many-arguments """Add signal data to DataLab. Args: title: Signal title xdata: X data ydata: Y data xunit: X unit. Defaults to "" yunit: Y unit. Defaults to "" xlabel: X label. Defaults to "" ylabel: Y label. Defaults to "" group_id: group id in which to add the signal. Defaults to "" set_current: if True, set the added signal as current Returns: True if signal was added successfully, False otherwise Raises: ValueError: Invalid xdata dtype ValueError: Invalid ydata dtype """ obj = SignalObj() obj.set_xydata(xdata, ydata) obj.check_data() xbinary = utils.array_to_rpcbinary(xdata) ybinary = utils.array_to_rpcbinary(ydata) return self._datalab.add_signal( title, xbinary, ybinary, xunit, yunit, xlabel, ylabel, group_id, set_current ) def add_image( self, title: str, data: np.ndarray, xunit: str = "", yunit: str = "", zunit: str = "", xlabel: str = "", ylabel: str = "", zlabel: str = "", group_id: str = "", set_current: bool = True, ) -> bool: # pylint: disable=too-many-arguments """Add image data to DataLab. Args: title: Image title data: Image data xunit: X unit. Defaults to "" yunit: Y unit. Defaults to "" zunit: Z unit. Defaults to "" xlabel: X label. Defaults to "" ylabel: Y label. Defaults to "" zlabel: Z label. Defaults to "" group_id: group id in which to add the image. Defaults to "" set_current: if True, set the added image as current Returns: True if image was added successfully, False otherwise Raises: ValueError: Invalid data dtype """ obj = ImageObj() obj.data = data obj.check_data() zbinary = utils.array_to_rpcbinary(data) return self._datalab.add_image( title, zbinary, xunit, yunit, zunit, xlabel, ylabel, zlabel, group_id, set_current, ) def add_object( self, obj: SignalObj | ImageObj, group_id: str = "", set_current: bool = True, ) -> None: """Add object to DataLab. Args: obj: Signal or image object group_id: group id in which to add the object. Defaults to "" set_current: if True, set the added object as current """ obj_data = utils.dataset_to_rpcjson(obj) self._datalab.add_object(obj_data, group_id, set_current) def calc(self, name: str, param: gds.DataSet | None = None) -> None: """Call computation feature ``name`` .. note:: This calls either the processor's ``compute_`` method (if it exists), or the processor's ```` computation feature (if it is registered, using the ``run_feature`` method). It looks for the function in all panels, starting with the current one. Args: name: Compute function name param: Compute function parameter. Defaults to None. Raises: ValueError: unknown function """ if param is None: return self._datalab.calc(name) return self._datalab.calc(name, utils.dataset_to_rpcjson(param)) def get_object( self, nb_id_title: int | str | None = None, panel: str | None = None, ) -> SignalObj | ImageObj: """Get object (signal/image) from index. Args: nb_id_title: Object number, or object id, or object title. Defaults to None (current object). panel: Panel name. Defaults to None (current panel). Returns: Object Raises: KeyError: if object not found """ param_data = self._datalab.get_object(nb_id_title, panel) if param_data is None: return None return utils.rpcjson_to_dataset(param_data) def get_object_shapes( self, nb_id_title: int | str | None = None, panel: str | None = None, ) -> list: """Get plot item shapes associated to object (signal/image). Args: nb_id_title: Object number, or object id, or object title. Defaults to None (current object). panel: Panel name. Defaults to None (current panel). Returns: List of plot item shapes """ items_json = self._datalab.get_object_shapes(nb_id_title, panel) return json_to_items(items_json) def add_annotations_from_items( self, items: list, refresh_plot: bool = True, panel: str | None = None ) -> None: """Add object annotations (annotation plot items). Args: items: annotation plot items refresh_plot: refresh plot. Defaults to True. panel: panel name (valid values: "signal", "image"). If None, current panel is used. """ items_json = items_to_json(items) if items_json is not None: self._datalab.add_annotations_from_items(items_json, refresh_plot, panel) def call_method( self, method_name: str, *args, panel: str | None = None, **kwargs, ): """Call a public method on a panel or main window. Method resolution order when panel is None: 1. Try main window (DLMainWindow) 2. If not found, try current panel (BaseDataPanel) Args: method_name: Name of the method to call *args: Positional arguments to pass to the method panel: Panel name ("signal", "image", or None for auto-detection). Defaults to None. **kwargs: Keyword arguments to pass to the method Returns: The return value of the called method Raises: AttributeError: If the method does not exist or is not public ValueError: If the panel name is invalid """ # Convert args/kwargs to single dict for XML-RPC serialization # This avoids XML-RPC signature mismatch issues with default parameters call_params = { "args": list(args) if args else [], "panel": panel, "kwargs": dict(kwargs) if kwargs else {}, } return self._datalab.call_method(method_name, call_params) # === WebAPI Server Control Methods === def start_webapi_server( self, host: str = "127.0.0.1", port: int = 8080 ) -> dict[str, str | int]: """Start the WebAPI server. Args: host: Host address to bind to. Defaults to "127.0.0.1". port: Port number. Defaults to 8080. Returns: Dictionary with server info including 'url' and 'token'. """ return self._datalab.start_webapi_server(host, port) def stop_webapi_server(self) -> bool: """Stop the WebAPI server. 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NځARwN 惎KCFF)1KQʃF3HEށ94ȤKQQ)$ғN>J;IP3_jL :wIEQ~{ 8=MG=(htʌP94=ip"1 NxFw'4\qFEt\P9PG4bq(Q*> )6ڌm &r2)~ ? 9 @=zRQ4;Qp(&Z0GւF>j:cIqN1P1yւ)'Ҏw;4ccԤv֓?*0)p >>JZ()4wQRpGJ_OJ9^)J\{ъ\By)z-юh=9#/p(ڃ:KK):PGzAvsy3vhRғ Optional[str]: # """ # Get the value of the attribute. # # Args: # instance: The instance of the class. # owner: The class that owns the attribute. # # Returns: # The value of the attribute. # """ # if instance is None: # return self # value = os.environ.get(self.name) # if value is None: # return self.default # return self._envvar_to_value(value) # # def __set__(self, instance: object, value: Optional[str]) -> None: # """ # Set the value of the attribute. # # Args: # instance: The instance of the class. # value: The value to be set. # """ # if value is not None: # os.environ[self.name] = self._value_to_envvar(value) # elif self.name in os.environ: # os.environ.pop(self.name) # # def __delete__(self, instance: object) -> None: # """ # Delete the attribute and remove the associated environment variable. # # Args: # instance: The instance of the class. # """ # os.environ.pop(self.name, None) # # def add_argument(self, parser: argparse.ArgumentParser) -> None: # """ # Add the command-line argument to the given ArgumentParser. # # Args: # parser: The ArgumentParser to add the argument to. # """ # parser.add_argument( # f"--{self.argname}", # default=self.default, # help=f"{self.argname} (environment variable: {self.name})", # ) # # def _envvar_to_value(self, value: str) -> str: # """ # Convert the environment variable value to the attribute value. # This method must be implemented by subclasses. # """ # return value # # def _value_to_envvar(self, value: str) -> str: # """ # Convert the attribute value to the environment variable value. # This method must be implemented by subclasses. # """ # return value # class DLExecEnv: """Object representing DataLab test environment""" UNATTENDED_ARG = "unattended" ACCEPT_DIALOGS_ARG = "accept_dialogs" VERBOSE_ARG = "verbose" SCREENSHOT_ARG = "screenshot" SCREENSHOT_PATH_ARG = "screenshot_path" DELAY_ARG = "delay" XMLRPCPORT_ARG = "xmlrpcport" DO_NOT_QUIT_ENV = "DATALAB_DO_NOT_QUIT" UNATTENDED_ENV = GuiDataExecEnv.UNATTENDED_ENV ACCEPT_DIALOGS_ENV = GuiDataExecEnv.ACCEPT_DIALOGS_ENV VERBOSE_ENV = GuiDataExecEnv.VERBOSE_ENV SCREENSHOT_ENV = GuiDataExecEnv.SCREENSHOT_ENV SCREENSHOT_PATH_ENV = GuiDataExecEnv.SCREENSHOT_PATH_ENV DELAY_ENV = GuiDataExecEnv.DELAY_ENV XMLRPCPORT_ENV = "DATALAB_XMLRPCPORT" CATCHER_TEST_ENV = "DATALAB_CATCHER_TEST" def __init__(self): self.h5files = None self.h5browser_file = None self.demo_mode = False # Check if "pytest" is in the command line arguments: if "pytest" not in sys.argv[0]: # Do not parse command line arguments when running tests with pytest # (otherwise, pytest arguments are parsed as DataLab arguments) self.parse_args() if self.unattended: # Do not run this code in production # Check that calling `to_dict` do not raise any exception self.to_dict() def iterate_over_attrs_envvars(self) -> Generator[tuple[str, str], None, None]: """Iterate over DataLab environment variables Yields: A tuple (attribute name, environment variable name) """ for name in dir(self): if name.endswith("_ENV"): envvar: str = getattr(self, name) attrname = "_".join(name.split("_")[:-1]).lower() yield attrname, envvar def to_dict(self): """Return a dictionary representation of the object""" # The list of properties match the list of environment variable attribute names, # modulo the "_ENV" suffix: props = [attrname for attrname, _envvar in self.iterate_over_attrs_envvars()] # Check that all properties are defined in the class and that they are # really properties: for prop in props: assert hasattr(self, prop), ( f"Property {prop} is not defined in class {self.__class__.__name__}" ) assert isinstance(getattr(self.__class__, prop), property), ( f"Attribute {prop} is not a property in class {self.__class__.__name__}" ) # Add complementary properties: props += [ "h5files", "h5browser_file", "demo_mode", ] # Return a dictionary with the properties as keys and their values as values: return {p: getattr(self, p) for p in props} def __str__(self): """Return a string representation of the object""" return pprint.pformat(self.to_dict()) def enable_demo_mode(self, delay: int): """Enable demo mode Args: delay: Delay (ms) before quitting application in unattended mode """ self.demo_mode = True self.unattended = True self.delay = delay def disable_demo_mode(self): """Disable demo mode""" self.demo_mode = False self.unattended = False self.delay = 0 @staticmethod def __get_mode(env): """Get mode value""" env_val = os.environ.get(env) if env_val is None: return False return env_val.lower() in ("1", "true", "yes", "on", "enable", "enabled") @staticmethod def __set_mode(env, value): """Set mode value""" if env in os.environ: os.environ.pop(env) if value: os.environ[env] = "1" @property def do_not_quit(self): """Keep QApplication running (and widgets opened) after test execution, even in unattended mode (e.g. useful for testing the remote client API: we need to run DataLab in unattended mode [to avoid any user interaction during the test] but we also need to keep the QApplication running to be able to send commands to the remote client API). """ return self.__get_mode(self.DO_NOT_QUIT_ENV) @do_not_quit.setter def do_not_quit(self, value): """Set do_not_quit value""" self.__set_mode(self.DO_NOT_QUIT_ENV, value) @property def unattended(self): """Get unattended value""" return self.__get_mode(self.UNATTENDED_ENV) @unattended.setter def unattended(self, value): """Set unattended value""" self.__set_mode(self.UNATTENDED_ENV, value) @property def accept_dialogs(self): """Whether to accept dialogs in unattended mode""" return self.__get_mode(self.ACCEPT_DIALOGS_ENV) @accept_dialogs.setter def accept_dialogs(self, value): """Set whether to accept dialogs in unattended mode""" self.__set_mode(self.ACCEPT_DIALOGS_ENV, value) @property def catcher_test(self): """Get catcher_test value""" return self.__get_mode(self.CATCHER_TEST_ENV) @catcher_test.setter def catcher_test(self, value): """Set catcher_test value""" self.__set_mode(self.CATCHER_TEST_ENV, value) @property def screenshot(self): """Get screenshot value""" return self.__get_mode(self.SCREENSHOT_ENV) @screenshot.setter def screenshot(self, value): """Set screenshot value""" self.__set_mode(self.SCREENSHOT_ENV, value) @property def screenshot_path(self): """Get screenshot path""" return os.environ.get(self.SCREENSHOT_PATH_ENV, "") @screenshot_path.setter def screenshot_path(self, value): """Set screenshot path""" if value: os.environ[self.SCREENSHOT_PATH_ENV] = str(value) elif self.SCREENSHOT_PATH_ENV in os.environ: os.environ.pop(self.SCREENSHOT_PATH_ENV) @property def verbose(self): """Get verbosity level""" env_val = os.environ.get(self.VERBOSE_ENV) if env_val in (None, ""): return VerbosityLevels.NORMAL.value return env_val.lower() @verbose.setter def verbose(self, value): """Set verbosity level""" os.environ[self.VERBOSE_ENV] = value @property def delay(self): """Delay (ms) before quitting application in unattended mode""" try: return int(os.environ.get(self.DELAY_ENV)) except (TypeError, ValueError): return 0 @delay.setter def delay(self, value: int): """Set delay (ms) before quitting application in unattended mode""" os.environ[self.DELAY_ENV] = str(value) @property def xmlrpcport(self): """XML-RPC port number""" try: return int(os.environ.get(self.XMLRPCPORT_ENV)) except (TypeError, ValueError): return None @xmlrpcport.setter def xmlrpcport(self, value: int): """Set XML-RPC port number""" os.environ[self.XMLRPCPORT_ENV] = str(value) def parse_args(self): """Parse command line arguments""" # WARNING # Do not add an option '-c' to avoid any conflict with macro command # execution mecanism used with DataLab standalone version (see start.pyw) parser = argparse.ArgumentParser(description="Run DataLab") parser.add_argument( "h5", nargs="?", type=str, help="HDF5 file names (separated by ';'), " "optionally with dataset name (separated by ',')", ) parser.add_argument( "-b", "--h5browser", required=False, type=str, metavar="path", help="path to open with HDF5 browser", ) parser.add_argument( "-v", "--version", action="store_true", help="show DataLab version" ) parser.add_argument( "--reset", action="store_true", help="reset DataLab configuration" ) parser.add_argument( "--" + self.UNATTENDED_ARG, action="store_true", help="non-interactive mode", default=None, ) parser.add_argument( "--" + self.ACCEPT_DIALOGS_ARG, action="store_true", help="accept dialogs in unattended mode", default=None, ) parser.add_argument( "--" + self.SCREENSHOT_ARG, action="store_true", help="automatic screenshots", default=None, ) parser.add_argument( "--" + self.SCREENSHOT_PATH_ARG, type=str, help="path to save screenshots", default=None, ) parser.add_argument( "--" + self.DELAY_ARG, type=int, default=None, help="delay (ms) before quitting application in unattended mode", ) parser.add_argument( "--" + self.XMLRPCPORT_ARG, type=int, default=None, help="XML-RPC port number", ) parser.add_argument( "--" + self.VERBOSE_ARG, choices=[lvl.value for lvl in VerbosityLevels], required=False, default=None, help="verbosity level: for debugging/testing purpose", ) args, _unknown = parser.parse_known_args() if args.h5: self.h5files = args.h5.split(";") if args.h5browser: self.h5browser_file = args.h5browser if args.version: version = os.environ["DATALAB_VERSION"] print(f"DataLab {version} on {platform.system()}") sys.exit() if args.reset: # Remove ".DataLab" configuration directory # pylint: disable=import-outside-toplevel from datalab.config import Conf print("Resetting DataLab configuration...", end=" ") try: Conf.reset() except Exception: # pylint: disable=broad-except print("Failed.") traceback.print_exc() finally: print("Done.") sys.exit() self.set_env_from_args(args) def set_env_from_args(self, args): """Set appropriate environment variables""" for argname in ( self.UNATTENDED_ARG, self.ACCEPT_DIALOGS_ARG, self.SCREENSHOT_ARG, self.SCREENSHOT_PATH_ARG, self.VERBOSE_ARG, self.DELAY_ARG, self.XMLRPCPORT_ARG, ): argvalue = getattr(args, argname) if argvalue is not None: setattr(self, argname, argvalue) def log(self, source: Any, *objects: Any) -> None: """Log text on screen Args: source: object from which the log is issued *objects: objects to log """ if DEBUG or self.verbose == VerbosityLevels.DEBUG.value: print(str(source) + ":", *objects) # TODO: [P4] Eventually, log in a file (optionally) def print(self, *objects, sep=" ", end="\n", file=sys.stdout, flush=False): """Print in file, depending on verbosity level""" if self.verbose != VerbosityLevels.QUIET.value or DEBUG: print(*objects, sep=sep, end=end, file=file, flush=flush) def pprint( self, obj, stream=None, indent=1, width=80, depth=None, compact=False, sort_dicts=True, ): """Pretty-print in stream, depending on verbosity level""" if self.verbose != VerbosityLevels.QUIET.value or DEBUG: pprint.pprint( obj, stream=stream, indent=indent, width=width, depth=depth, compact=compact, sort_dicts=sort_dicts, ) @contextmanager def context( self, unattended=None, accept_dialogs=None, screenshot=None, delay=None, verbose=None, xmlrpcport=None, catcher_test=None, ) -> Generator[None, None, None]: """Return a context manager that sets some execenv properties at enter, and restores them at exit. This is useful to run some code in a controlled environment, for example to accept dialogs in unattended mode, and restore the previous value at exit. Args: unattended: whether to run in unattended mode accept_dialogs: whether to accept dialogs in unattended mode screenshot: whether to take screenshots delay: delay (ms) before quitting application in unattended mode verbose: verbosity level xmlrpcport: XML-RPC port number catcher_test: whether to run catcher test .. note:: If a passed value is None, the corresponding property is not changed. """ old_values = self.to_dict() new_values = { "unattended": unattended, "accept_dialogs": accept_dialogs, "screenshot": screenshot, "delay": delay, "verbose": verbose, "xmlrpcport": xmlrpcport, "catcher_test": catcher_test, } for key, value in new_values.items(): if value is not None: setattr(self, key, value) try: yield finally: for key, value in old_values.items(): setattr(self, key, value) execenv = DLExecEnv() DataLab-1.1.0/datalab/gui/000077500000000000000000000000001514014117600151555ustar00rootroot00000000000000DataLab-1.1.0/datalab/gui/__init__.py000066400000000000000000000054221514014117600172710ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ GUI === The :mod:`datalab.gui` package contains functionnalities related to the graphical user interface (GUI) of the DataLab project. Those features are mostly specific to DataLab and are not intended to be used independently. The purpose of this section of the documentation is to provide an overview of the DataLab GUI architecture and to describe the main features of the modules contained in this package. It is not intended to provide a detailed description of the GUI features, but rather to provide a starting point for the reader who wants to understand the DataLab internal architecture. DataLab's main window is composed of several parts, each of them being handled by a specific module of this package: - **Signal and image panels**: those panels are used to display signals and images and to provide a set of tools to manipulate them. Each data panel relies on a set of modules to handle the GUI features (:mod:`datalab.gui.actionhandler` and :mod:`datalab.gui.objectview`), the data model (:mod:`datalab.gui.objectmodel`), the data visualization (:mod:`datalab.gui.plothandler`), and the data processing (:mod:`datalab.gui.processor`). - **Macro panel**: this panel is used to display and run macros. It relies on the :mod:`datalab.gui.macroeditor` module to handle the macro edition and execution. - **Specialized widgets**: those widgets are used to handle specific features such as ROI edition (:mod:`datalab.gui.roieditor`), Intensity profile edition (:mod:`datalab.gui.profiledialog`), etc. .. list-table:: :header-rows: 1 :align: left * - Submodule - Purpose * - :mod:`datalab.gui.main` - DataLab main window and application * - :mod:`datalab.gui.panel` - Signal, image and macro panels * - :mod:`datalab.gui.actionhandler` - Application actions (menus, toolbars, context menu) * - :mod:`datalab.gui.objectview` - Widgets to display object (signal/image) trees * - :mod:`datalab.gui.plothandler` - `PlotPy` plot items for representing signals and images * - :mod:`datalab.gui.roieditor` - ROI editor * - :mod:`datalab.gui.processor` - Processor * - :mod:`datalab.gui.docks` - Dock widgets * - :mod:`datalab.gui.h5io` - HDF5 input/output """ import abc from guidata.io import BaseIOHandler class ObjItf(abc.ABC): """Interface for objects handled by panels""" @property @abc.abstractmethod def title(self) -> str: """Object title""" @abc.abstractmethod def serialize(self, writer: BaseIOHandler): """Serialize this object""" @abc.abstractmethod def deserialize(self, reader: BaseIOHandler): """Deserialize this object""" DataLab-1.1.0/datalab/gui/actionhandler.py000066400000000000000000002141471514014117600203530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Action handler ============== The :mod:`datalab.gui.actionhandler` module handles all application actions (menus, toolbars, context menu). These actions point to DataLab panels, processors, objecthandler, ... Utility classes --------------- .. autoclass:: SelectCond :members: .. autoclass:: ActionCategory :members: Handler classes --------------- .. autoclass:: SignalActionHandler :members: :inherited-members: .. autoclass:: ImageActionHandler :members: :inherited-members: """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import abc import enum from collections.abc import Callable, Generator from contextlib import contextmanager from functools import partial from typing import TYPE_CHECKING import sigima.objects as sio from guidata.configtools import get_icon from guidata.qthelpers import add_actions, create_action from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from datalab.adapters_metadata import GeometryAdapter, TableAdapter, have_results from datalab.config import Conf, _ from datalab.gui import newobject from datalab.gui.processor.base import ( clear_analysis_parameters, extract_analysis_parameters, ) from datalab.widgets import fitdialog if TYPE_CHECKING: from sigima.objects import ImageObj, SignalObj from datalab.gui.panel.image import ImagePanel from datalab.gui.panel.signal import SignalPanel from datalab.objectmodel import ObjectGroup class SelectCond: """Signal or image select conditions""" @staticmethod def __compat_groups(selected_groups: list[ObjectGroup], min_len: int = 1) -> bool: """Check if groups are compatible""" return ( len(selected_groups) >= min_len and all(len(group) == len(selected_groups[0]) for group in selected_groups) and all(len(group) > 0 for group in selected_groups) ) @staticmethod # pylint: disable=unused-argument def always( selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """Always true""" return True @staticmethod def exactly_one( selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """Exactly one signal or image is selected""" return len(selected_groups) == 0 and len(selected_objects) == 1 @staticmethod # pylint: disable=unused-argument def exactly_one_group( selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """Exactly one group is selected""" return len(selected_groups) == 1 @staticmethod def exactly_one_group_or_one_object( selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """Exactly one group or one signal or image is selected""" return len(selected_groups) == 1 or len(selected_objects) == 1 @staticmethod # pylint: disable=unused-argument def at_least_one_group_or_one_object( sel_groups: list[ObjectGroup], sel_objects: list[SignalObj | ImageObj], ) -> bool: """At least one group or one signal or image is selected""" return len(sel_objects) >= 1 or len(sel_groups) >= 1 @staticmethod # pylint: disable=unused-argument def at_least_one( sel_groups: list[ObjectGroup], sel_objects: list[SignalObj | ImageObj], ) -> bool: """At least one signal or image is selected""" return len(sel_objects) >= 1 or SelectCond.__compat_groups(sel_groups, 1) @staticmethod def at_least_two( sel_groups: list[ObjectGroup], sel_objects: list[SignalObj | ImageObj], ) -> bool: """At least two signals or images are selected""" return len(sel_objects) >= 2 or SelectCond.__compat_groups(sel_groups, 2) @staticmethod # pylint: disable=unused-argument def with_roi( selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """At least one signal or image has a ROI""" return any(obj.roi is not None for obj in selected_objects) @staticmethod # pylint: disable=unused-argument def exactly_one_with_roi( selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """Exactly one signal or image has a ROI""" return ( len(selected_groups) == 0 and len(selected_objects) == 1 and selected_objects[0].roi is not None ) @staticmethod # pylint: disable=unused-argument def exactly_one_with_annotations( selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """Exactly one signal or image has annotations""" return ( len(selected_groups) == 0 and len(selected_objects) == 1 and selected_objects[0].has_annotations() ) @staticmethod # pylint: disable=unused-argument def with_annotations( selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """At least one signal or image has annotations""" return any(obj.has_annotations() for obj in selected_objects) @staticmethod # pylint: disable=unused-argument def with_results( selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """At least one signal or image has results""" return have_results(selected_objects) class ActionCategory(enum.Enum): """Action categories""" FILE = enum.auto() CREATE = enum.auto() EDIT = enum.auto() VIEW = enum.auto() ROI = enum.auto() OPERATION = enum.auto() PROCESSING = enum.auto() ANALYSIS = enum.auto() CONTEXT_MENU = enum.auto() PANEL_TOOLBAR = enum.auto() VIEW_TOOLBAR = enum.auto() SUBMENU = enum.auto() # temporary PLUGINS = enum.auto() # for plugins actions class BaseActionHandler(metaclass=abc.ABCMeta): """Object handling panel GUI interactions: actions, menus, ... Args: panel: Panel to handle panel_toolbar: Panel toolbar (actions related to the panel objects management) view_toolbar: View toolbar (actions related to the panel view, i.e. plot) """ OBJECT_STR = "" # e.g. "signal" OBJECT_STR_PLURAL = "" # e.g. "signals" def __init__( self, panel: SignalPanel | ImagePanel, panel_toolbar: QW.QToolBar, view_toolbar: QW.QToolBar, ): self.panel = panel self.panel_toolbar = panel_toolbar self.view_toolbar = view_toolbar self.feature_actions = {} self.operation_end_actions = None self.__category_in_progress: ActionCategory = None self.__submenu_in_progress = False self.__submenu_stack: list[dict[str, any]] = [] # Stack for nested submenus self.__actions: dict[Callable, list[QW.QAction]] = {} self.__submenus: dict[str, QW.QMenu] = {} # Store reference to ROI remove submenu self.roi_remove_submenu: QW.QMenu | None = None # Store reference to results delete submenu self.results_delete_submenu: QW.QMenu | None = None # Store reference to metadata and annotations submenus (for screenshots) self.metadata_submenu: QW.QMenu | None = None self.annotations_submenu: QW.QMenu | None = None # Store reference to show label action (for settings dialog) self.show_label_action: QW.QAction | None = None @property def object_suffix(self) -> str: """Object suffix (e.g. "sig" for signal, "ima" for image)""" return self.__class__.__name__[:3].lower() def populate_roi_remove_submenu(self) -> None: """Populate the ROI Remove submenu dynamically based on current selection""" submenu = self.roi_remove_submenu if submenu is None: return # Clear existing actions submenu.clear() # Get current selected object selected_objects = self.panel.objview.get_sel_objects() if not selected_objects: return obj = selected_objects[0] if obj.roi is None or obj.roi.is_empty(): return # Add individual ROI removal actions for i in range(len(obj.roi.single_rois)): roi_title = obj.roi.get_single_roi_title(i) action = QW.QAction(roi_title, submenu) # Use partial to avoid lambda closure issues action.triggered.connect(partial(self._remove_single_roi_by_index, i)) submenu.addAction(action) # Add separator and "Remove all" action if len(obj.roi.single_rois) > 0: submenu.addSeparator() remove_all_action = QW.QAction(_("Remove all"), submenu) remove_all_action.triggered.connect( self.panel.processor.delete_regions_of_interest ) submenu.addAction(remove_all_action) def _remove_single_roi_by_index(self, roi_index: int) -> None: """Helper method to remove a single ROI by index""" self.panel.processor.delete_single_roi(roi_index) def has_metadata_in_clipboard( self, selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """Check if metadata clipboard is not empty""" # pylint: disable=unused-argument return bool(self.panel.metadata_clipboard) def has_annotations_in_clipboard( self, selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> bool: """Check if annotations clipboard is not empty""" # pylint: disable=unused-argument return bool(self.panel.annotations_clipboard) def populate_results_delete_submenu(self) -> None: """Populate the Results Delete submenu dynamically based on current selection""" submenu = self.results_delete_submenu if submenu is None: return # Clear existing actions submenu.clear() # Get current selected objects (including groups) objs = self.panel.objview.get_sel_objects(include_groups=True) if not objs: submenu.setEnabled(False) return # Collect all results with their metadata keys and titles result_items = [] # List of (metadata_key, title, obj, adapter) for obj in objs: # Get all adapters for this object for adapter in list(GeometryAdapter.iterate_from_obj(obj)) + list( TableAdapter.iterate_from_obj(obj) ): metadata_key = adapter.metadata_key title = adapter.title result_items.append((metadata_key, title, obj, adapter)) if not result_items: submenu.setEnabled(False) return # Enable submenu since we have results submenu.setEnabled(True) # Add individual result deletion actions for metadata_key, title, obj, adapter in result_items: action = QW.QAction(title, submenu) # Use partial to avoid lambda closure issues action.triggered.connect(partial(self._delete_single_result, obj, adapter)) submenu.addAction(action) # Add separator and "Delete all results..." action if result_items: submenu.addSeparator() delete_all_action = QW.QAction(_("Delete all results") + "...", submenu) delete_all_action.triggered.connect(self.panel.delete_results) submenu.addAction(delete_all_action) def _delete_single_result( self, obj: SignalObj | ImageObj, adapter: GeometryAdapter | TableAdapter ) -> None: """Helper method to delete a single result Args: obj: Object containing the result adapter: Adapter for the result to delete """ # Check if this result matches the stored analysis parameters # If so, clear them to prevent auto-recompute from attempting to # recompute the deleted analysis when ROI changes analysis_params = extract_analysis_parameters(obj) if ( analysis_params is not None and analysis_params.func_name == adapter.func_name ): clear_analysis_parameters(obj) adapter.remove_from(obj) # Update properties panel to reflect the removal if obj is self.panel.objview.get_current_object(): self.panel.objprop.update_properties_from(obj) # Update action states to reflect the removal selected_groups = self.panel.objview.get_sel_groups() selected_objects = self.panel.objview.get_sel_objects(include_groups=True) self.selected_objects_changed(selected_groups, selected_objects) # Refresh the plot to update the display # Use the same refresh pattern as delete_results() method self.panel.refresh_plot("selected", True, False) @contextmanager def new_category(self, category: ActionCategory) -> Generator[None, None, None]: """Context manager for creating a new menu. Args: category: Action category Yields: None """ self.__category_in_progress = category try: yield finally: self.__category_in_progress = None @contextmanager def new_menu( self, title: str, icon_name: str | None = None, store_ref: str | None = None, ) -> Generator[None, None, None]: """Context manager for creating a new menu. Args: title: Menu title icon_name: Menu icon name. Defaults to None. store_ref: Optional attribute name to store menu reference. Defaults to None. Yields: None """ # Create a unique key for this submenu level parent_key = "" if self.__submenu_stack: parent_key = self.__submenu_stack[-1]["key"] + "/" elif self.__category_in_progress: parent_key = self.__category_in_progress.name + "/" key = parent_key + title is_new = key not in self.__submenus if is_new: self.__submenus[key] = menu = QW.QMenu(title) if icon_name: menu.setIcon(get_icon(icon_name)) # Store reference to menu if requested if store_ref is not None: setattr(self, store_ref, menu) else: menu = self.__submenus[key] # Save current submenu state and push new submenu onto stack submenu_state = { "key": key, "menu": menu, "is_new": is_new, "actions": [], # Actions for this submenu level } self.__submenu_stack.append(submenu_state) self.__submenu_in_progress = True try: yield finally: # Pop the current submenu from stack current_submenu = self.__submenu_stack.pop() # Get actions for this specific submenu level submenu_actions = current_submenu.get("actions", []) # Also get any actions that were added to the generic SUBMENU category # while this submenu was the active one generic_submenu_actions = self.feature_actions.pop( ActionCategory.SUBMENU, [] ) submenu_actions.extend(generic_submenu_actions) add_actions(current_submenu["menu"], submenu_actions) # Update submenu in progress status BEFORE adding to parent self.__submenu_in_progress = len(self.__submenu_stack) > 0 if current_submenu["is_new"]: # Add this submenu to its parent (either category or parent submenu) if self.__submenu_stack: # We're in a nested submenu, add to parent submenu's actions parent_submenu = self.__submenu_stack[-1] parent_submenu["actions"].append(current_submenu["menu"]) else: # We're at the top level, add to category actions # Force using the current category, not SUBMENU self.add_to_action_list( current_submenu["menu"], category=self.__category_in_progress ) # pylint: disable=too-many-positional-arguments def new_action( self, title: str, position: int | None = None, separator: bool = False, triggered: Callable | None = None, toggled: Callable | None = None, shortcut: QW.QShortcut | None = None, icon_name: str | None = None, tip: str | None = None, select_condition: Callable | str | None = None, context_menu_pos: int | None = None, context_menu_sep: bool = False, toolbar_pos: int | None = None, toolbar_sep: bool = False, toolbar_category: ActionCategory | None = None, ) -> QW.QAction: """Create new action and add it to list of actions. Args: title: action title position: add action to menu at this position. Defaults to None. separator: add separator before action in menu (or after if pos is positive). Defaults to False. triggered: triggered callback. Defaults to None. toggled: toggled callback. Defaults to None. shortcut: shortcut. Defaults to None. icon_name: icon name. Defaults to None. tip: tooltip. Defaults to None. select_condition: selection condition. Defaults to None. If str, must be the name of a method of SelectCond, i.e. one of "always", "exactly_one", "exactly_one_group", "at_least_one_group_or_one_object", "at_least_one", "at_least_two", "with_roi". context_menu_pos: add action to context menu at this position. Defaults to None. context_menu_sep: add separator before action in context menu (or after if context_menu_pos is positive). Defaults to False. toolbar_pos: add action to toolbar at this position. Defaults to None. toolbar_sep: add separator before action in toolbar (or after if toolbar_pos is positive). Defaults to False. toolbar_category: toolbar category. Defaults to None. If toolbar_pos is not None, this specifies the category of the toolbar. If None, defaults to ActionCategory.VIEW_TOOLBAR if the current category is ActionCategory.VIEW, else to ActionCategory.PANEL_TOOLBAR. Returns: New action """ if isinstance(select_condition, str): assert select_condition in SelectCond.__dict__ select_condition = getattr(SelectCond, select_condition) action = create_action( parent=self.panel, title=title, triggered=triggered, toggled=toggled, shortcut=shortcut, icon=get_icon(icon_name) if icon_name else None, tip=tip, context=QC.Qt.WidgetWithChildrenShortcut, # [1] ) self.panel.addAction(action) # [1] # [1] This is needed to make actions work with shortcuts for active panel, # because some of the shortcuts are using the same keybindings for both panels. # (Fixes #10) self.add_action(action, select_condition) self.add_to_action_list(action, None, position, separator) if context_menu_pos is not None: self.add_to_action_list( action, ActionCategory.CONTEXT_MENU, context_menu_pos, context_menu_sep ) if toolbar_pos is not None: if toolbar_category is None: if self.__category_in_progress is ActionCategory.VIEW: toolbar_category = ActionCategory.VIEW_TOOLBAR else: toolbar_category = ActionCategory.PANEL_TOOLBAR self.add_to_action_list(action, toolbar_category, toolbar_pos, toolbar_sep) return action def action_for( self, function_or_name: Callable | str, position: int | None = None, separator: bool = False, context_menu_pos: int | None = None, context_menu_sep: bool = False, toolbar_pos: int | None = None, toolbar_sep: bool = False, toolbar_category: ActionCategory | None = None, ) -> QW.QAction: """Create action for a feature. Args: function_or_name: function or name of the feature position: add action to menu at this position. Defaults to None. separator: add separator before action in menu context_menu_pos: add action to context menu at this position. context_menu_pos: add action to context menu at this position. Defaults to None. context_menu_sep: add separator before action in context menu (or after if context_menu_pos is positive). Defaults to False. toolbar_pos: add action to toolbar at this position. Defaults to None. toolbar_sep: add separator before action in toolbar (or after if toolbar_pos is positive). Defaults to False. toolbar_category: toolbar category. Defaults to None. If toolbar_pos is not None, this specifies the category of the toolbar. If None, defaults to ActionCategory.VIEW_TOOLBAR if the current category is ActionCategory.VIEW, else to ActionCategory.PANEL_TOOLBAR. Returns: New action """ feature = self.panel.processor.get_feature(function_or_name) if feature.pattern == "n_to_1": condition = SelectCond.at_least_two else: condition = SelectCond.at_least_one return self.new_action( feature.action_title, position=position, separator=separator, triggered=lambda: self.panel.processor.run_feature(feature.function), select_condition=condition, icon_name=feature.icon_name, tip=feature.comment, context_menu_pos=context_menu_pos, context_menu_sep=context_menu_sep, toolbar_pos=toolbar_pos, toolbar_sep=toolbar_sep, toolbar_category=toolbar_category, ) def add_to_action_list( self, action: QW.QAction, category: ActionCategory | None = None, pos: int | None = None, sep: bool = False, ) -> None: """Add action to list of actions. Args: action: action to add category: action category. Defaults to None. If None, action is added to the current category. pos: add action to menu at this position. Defaults to None. If None, action is added at the end of the list. sep: add separator before action in menu (or after if pos is positive). Defaults to False. """ if category is None: if self.__submenu_in_progress and self.__submenu_stack: # Add directly to the current submenu's action list current_submenu = self.__submenu_stack[-1] actionlist = current_submenu["actions"] if pos is None: pos = -1 add_separator_after = pos >= 0 if pos < 0: pos = len(actionlist) + pos + 1 actionlist.insert(pos, action) if sep: if add_separator_after: pos += 1 actionlist.insert(pos, None) return if self.__category_in_progress is not None: category = self.__category_in_progress else: raise ValueError("No category specified") if pos is None: pos = -1 actionlist = self.feature_actions.setdefault(category, []) add_separator_after = pos >= 0 if pos < 0: pos = len(actionlist) + pos + 1 actionlist.insert(pos, action) if sep: if add_separator_after: pos += 1 actionlist.insert(pos, None) def add_action( self, action: QW.QAction, select_condition: Callable | None = None ) -> None: """Add action to list of actions. Args: action: action to add select_condition: condition to enable action. Defaults to None. If None, action is enabled if at least one object is selected. """ if select_condition is None: select_condition = SelectCond.at_least_one self.__actions.setdefault(select_condition, []).append(action) def selected_objects_changed( self, selected_groups: list[ObjectGroup], selected_objects: list[SignalObj | ImageObj], ) -> None: """Update actions based on selected objects. Args: selected_groups: selected groups selected_objects: selected objects """ for cond, actlist in self.__actions.items(): if cond is not None: for act in actlist: act.setEnabled(cond(selected_groups, selected_objects)) def create_all_actions(self): """Create all actions""" self.create_first_actions() self.create_last_actions() # Connect ROI remove submenu signal after all actions are created if self.roi_remove_submenu is not None: self.roi_remove_submenu.aboutToShow.connect( self.populate_roi_remove_submenu ) # Add the submenu to the action management system with ROI condition self.add_action(self.roi_remove_submenu, SelectCond.with_roi) # Connect results delete submenu signal after all actions are created if self.results_delete_submenu is not None: self.results_delete_submenu.aboutToShow.connect( self.populate_results_delete_submenu ) # Add the submenu to the action management system self.add_action( self.results_delete_submenu, SelectCond.at_least_one_group_or_one_object, ) add_actions( self.panel_toolbar, self.feature_actions.pop(ActionCategory.PANEL_TOOLBAR) ) # For the view toolbar, we add the actions to the beginning of the toolbar: before = self.view_toolbar.actions()[0] for action in self.feature_actions.pop(ActionCategory.VIEW_TOOLBAR): if action is None: self.view_toolbar.insertSeparator(before) else: self.view_toolbar.insertAction(before, action) self.view_toolbar.insertSeparator(before) def create_first_actions(self): """Create actions that are added to the menus in the first place""" # MARK: FILE with self.new_category(ActionCategory.FILE): self.new_action( _("Open %s...") % self.OBJECT_STR, # Icon name is 'fileopen_sig.svg' or 'fileopen_ima.svg': icon_name=f"fileopen_{self.object_suffix}.svg", tip=_("Open one or more %s files") % self.OBJECT_STR, triggered=self.panel.load_from_files, shortcut=QG.QKeySequence(QG.QKeySequence.Open), select_condition=SelectCond.always, toolbar_pos=-1, ) self.new_action( _("Open from directory..."), icon_name="fileopen_directory.svg", tip=_("Open all %s files from directory") % self.OBJECT_STR, triggered=self.panel.load_from_directory, select_condition=SelectCond.always, toolbar_pos=-1, ) self.new_action( _("Save %s...") % self.OBJECT_STR, # Icon name is 'filesave_sig.svg' or 'filesave_ima.svg' icon_name=f"filesave_{self.object_suffix}.svg", tip=_("Save selected %s") % self.OBJECT_STR_PLURAL, triggered=self.panel.save_to_files, shortcut=QG.QKeySequence(QG.QKeySequence.Save), select_condition=SelectCond.at_least_one, context_menu_pos=-1, toolbar_pos=-1, ) self.new_action( _("Save to directory..."), icon_name="save_to_directory.svg", tip=_("Save selected %s using a filename pattern") % self.OBJECT_STR_PLURAL, triggered=self.panel.save_to_directory, select_condition=SelectCond.at_least_two, context_menu_pos=-1, toolbar_pos=-1, ) self.new_action( _("Import text file..."), icon_name="import_text.svg", triggered=self.panel.exec_import_wizard, select_condition=SelectCond.always, ) # MARK: EDIT with self.new_category(ActionCategory.EDIT): self.new_action( _("Recompute"), icon_name="recompute.svg", shortcut="Ctrl+R", tip=_("Recompute selected %s with its processing parameters") % self.OBJECT_STR, triggered=self.panel.recompute_processing, select_condition=SelectCond.at_least_one_group_or_one_object, context_menu_pos=-1, toolbar_pos=-1, ) self.new_action( _("Select source objects"), icon_name="goto_source.svg", tip=_("Select source object(s) used to create the selected %s") % self.OBJECT_STR, triggered=self.panel.select_source_objects, select_condition=SelectCond.exactly_one, context_menu_pos=-1, toolbar_pos=-1, ) self.new_action( _("Rename"), icon_name="rename.svg", shortcut="F2", tip=_("Edit title of selected %s or group") % self.OBJECT_STR, triggered=self.panel.rename_selected_object_or_group, select_condition=SelectCond.exactly_one_group_or_one_object, context_menu_pos=-1, separator=True, ) self.new_action( _("New group..."), icon_name="new_group.svg", tip=_("Create a new group"), triggered=self.panel.new_group, select_condition=SelectCond.always, context_menu_pos=-1, ) self.new_action( _("Move up"), icon_name="move_up.svg", tip=_("Move up selection (groups or objects)"), triggered=self.panel.objview.move_up, select_condition=SelectCond.at_least_one_group_or_one_object, context_menu_pos=-1, toolbar_pos=-1, ) self.new_action( _("Move down"), icon_name="move_down.svg", tip=_("Move down selection (groups or objects)"), triggered=self.panel.objview.move_down, select_condition=SelectCond.at_least_one_group_or_one_object, context_menu_pos=-1, toolbar_pos=-1, ) self.new_action( _("Duplicate"), icon_name="duplicate.svg", tip=_("Duplicate selected %s") % self.OBJECT_STR, separator=True, triggered=self.panel.duplicate_object, shortcut=QG.QKeySequence(QG.QKeySequence.Copy), select_condition=SelectCond.at_least_one_group_or_one_object, context_menu_pos=-1, toolbar_pos=-1, ) self.new_action( _("Remove"), icon_name="delete.svg", tip=_("Remove selected %s") % self.OBJECT_STR, triggered=self.panel.remove_object, shortcut=QG.QKeySequence(QG.QKeySequence.Delete), select_condition=SelectCond.at_least_one_group_or_one_object, context_menu_pos=-1, toolbar_pos=-1, ) self.new_action( _("Delete all"), select_condition=SelectCond.always, shortcut="Shift+Ctrl+Suppr", tip=_("Delete all groups and objects"), icon_name="delete_all.svg", triggered=self.panel.delete_all_objects, toolbar_pos=-1, ) with self.new_menu( _("Metadata"), icon_name="metadata.svg", store_ref="metadata_submenu" ): self.new_action( _("Copy metadata"), icon_name="metadata_copy.svg", tip=_("Copy metadata from selected %s") % self.OBJECT_STR, triggered=self.panel.copy_metadata, select_condition=SelectCond.exactly_one, toolbar_pos=-1, ) self.new_action( _("Paste metadata"), icon_name="metadata_paste.svg", tip=_("Paste metadata into selected %s") % self.OBJECT_STR, triggered=self.panel.paste_metadata, select_condition=self.has_metadata_in_clipboard, toolbar_pos=-1, ) self.new_action( _("Add metadata") + "...", separator=True, icon_name="metadata_add.svg", tip=_("Add a metadata item to selected %s") % self.OBJECT_STR, triggered=self.panel.add_metadata, select_condition=SelectCond.at_least_one, toolbar_pos=-1, ) self.new_action( _("Import metadata") + "...", icon_name="metadata_import.svg", tip=_("Import metadata into %s") % self.OBJECT_STR, triggered=self.panel.import_metadata_from_file, select_condition=SelectCond.exactly_one, toolbar_pos=-1, ) self.new_action( _("Export metadata") + "...", icon_name="metadata_export.svg", tip=_("Export selected %s metadata") % self.OBJECT_STR, triggered=self.panel.export_metadata_from_file, select_condition=SelectCond.exactly_one, toolbar_pos=-1, ) self.new_action( _("Delete object metadata"), separator=True, icon_name="metadata_delete.svg", tip=_("Delete all that is contained in object metadata"), triggered=self.panel.delete_metadata, toolbar_pos=-1, ) with self.new_menu( _("Annotations"), icon_name="annotations.svg", store_ref="annotations_submenu", ): self.new_action( _("Copy annotations"), icon_name="annotations_copy.svg", tip=_("Copy annotations from selected %s") % self.OBJECT_STR, triggered=self.panel.copy_annotations, select_condition=SelectCond.exactly_one_with_annotations, ) self.new_action( _("Paste annotations"), icon_name="annotations_paste.svg", tip=_("Paste annotations into selected %s") % self.OBJECT_STR, triggered=self.panel.paste_annotations, select_condition=self.has_annotations_in_clipboard, ) self.new_action( _("Edit annotations") + "...", separator=True, icon_name="annotations_edit.svg", tip=_("Edit annotations of selected %s") % self.OBJECT_STR, triggered=lambda: self.panel.open_separate_view( edit_annotations=True ), select_condition=SelectCond.exactly_one, ) self.new_action( _("Import annotations") + "...", icon_name="annotations_import.svg", tip=_("Import annotations into %s") % self.OBJECT_STR, triggered=self.panel.import_annotations_from_file, select_condition=SelectCond.exactly_one, ) self.new_action( _("Export annotations") + "...", icon_name="annotations_export.svg", tip=_("Export selected %s annotations") % self.OBJECT_STR, triggered=self.panel.export_annotations_from_file, select_condition=SelectCond.exactly_one_with_annotations, ) self.new_action( _("Delete annotations"), separator=True, icon_name="annotations_delete.svg", tip=_("Delete all annotations from selected %s") % self.OBJECT_STR, triggered=self.panel.delete_annotations, select_condition=SelectCond.with_annotations, ) self.new_action( _("Insert object title as annotation label"), separator=True, triggered=lambda: self.panel.add_label_with_title(ignore_msg=False), tip=_( "Add the selected object's title as a label to the plot annotations" ), ) self.new_action( _("Copy titles to clipboard"), icon_name="copy_titles.svg", tip=_("Copy titles of selected objects to clipboard"), triggered=self.panel.copy_titles_to_clipboard, ) # MARK: ROI with self.new_category(ActionCategory.ROI): self.new_action( _("Edit graphically") + "...", triggered=self.panel.processor.edit_roi_graphically, icon_name="roi.svg", context_menu_pos=-1, context_menu_sep=True, toolbar_pos=-1, toolbar_category=ActionCategory.VIEW_TOOLBAR, tip=_("Edit regions of interest graphically"), ) self.new_action( _("Edit numerically") + "...", triggered=self.panel.processor.edit_roi_numerically, select_condition=SelectCond.exactly_one_with_roi, tip=_("Edit regions of interest numerically"), ) # MARK: VIEW with self.new_category(ActionCategory.VIEW): self.new_action( _("View in a new window") + "...", icon_name="new_window.svg", tip=_("View selected %s in a new window") % self.OBJECT_STR, triggered=self.panel.open_separate_view, context_menu_pos=0, context_menu_sep=True, toolbar_pos=0, ) # MARK: OPERATION with self.new_category(ActionCategory.OPERATION): self.action_for("arithmetic") with self.new_menu(_("Constant Operations"), icon_name="constant.svg"): self.action_for("addition_constant") self.action_for("difference_constant") self.action_for("product_constant") self.action_for("division_constant") self.action_for("addition") self.action_for("difference") self.action_for("product") self.action_for("division") self.action_for("inverse", separator=True) self.action_for("exp") self.action_for("log10") # MARK: PROCESSING with self.new_category(ActionCategory.PROCESSING): with self.new_menu( _("Axis transformation"), icon_name="axis_transform.svg" ): self.action_for("calibration") with self.new_menu(_("Level adjustment"), icon_name="level_adjustment.svg"): self.action_for("normalize") self.action_for("clip") self.new_action( _("Offset correction"), triggered=self.panel.processor.compute_offset_correction, icon_name="offset_correction.svg", tip=_("Evaluate and subtract the offset value from the data"), ) with self.new_menu(_("Noise addition"), icon_name="noise_addition.svg"): self.action_for("add_gaussian_noise") self.action_for("add_poisson_noise") self.action_for("add_uniform_noise") with self.new_menu(_("Noise reduction"), icon_name="noise_reduction.svg"): self.action_for("gaussian_filter") self.action_for("moving_average") self.action_for("moving_median") self.action_for("wiener") with self.new_menu(_("Fourier analysis"), icon_name="fourier.svg"): self.action_for("zero_padding") self.action_for("fft") self.action_for("ifft") self.action_for("magnitude_spectrum") self.action_for("phase_spectrum") self.action_for("psd") # MARK: ANALYSIS with self.new_category(ActionCategory.ANALYSIS): self.action_for("stats", context_menu_pos=-1, context_menu_sep=True) self.action_for("histogram", context_menu_pos=-1) def create_last_actions(self): """Create actions that are added to the menus in the end""" # MARK: ROI with self.new_category(ActionCategory.ROI): self.new_action( _("Extract") + "...", triggered=self.panel.processor.compute_roi_extraction, # Icon name is 'roi_sig.svg' or 'roi_ima.svg': icon_name=f"roi_{self.object_suffix}.svg", separator=True, ) self.new_action( _("Copy"), separator=True, icon_name="roi_copy.svg", tip=_("Copy regions of interest from selected %s") % self.OBJECT_STR, triggered=self.panel.copy_roi, select_condition=SelectCond.exactly_one_with_roi, toolbar_pos=-1, ) self.new_action( _("Paste"), icon_name="roi_paste.svg", tip=_("Paste regions of interest into selected %s") % self.OBJECT_STR, triggered=self.panel.paste_roi, toolbar_pos=-1, ) self.new_action( _("Import") + "...", icon_name="roi_import.svg", tip=_("Import regions of interest into %s") % self.OBJECT_STR, triggered=self.panel.import_roi_from_file, select_condition=SelectCond.exactly_one, toolbar_pos=-1, ) self.new_action( _("Export") + "...", icon_name="roi_export.svg", tip=_("Export selected %s regions of interest") % self.OBJECT_STR, triggered=self.panel.export_roi_to_file, select_condition=SelectCond.exactly_one_with_roi, toolbar_pos=-1, ) # Create dynamic "Remove" submenu with self.new_menu(_("Remove"), icon_name="roi_delete.svg"): # Store reference to the submenu for dynamic population if self.__submenu_stack: current_submenu = self.__submenu_stack[-1] self.roi_remove_submenu = current_submenu["menu"] # MARK: OPERATION with self.new_category(ActionCategory.OPERATION): self.action_for("absolute", separator=True) self.action_for("phase") self.action_for("complex_from_magnitude_phase") self.action_for("real", separator=True) self.action_for("imag") self.action_for("complex_from_real_imag") self.action_for("astype", separator=True) self.action_for("average", separator=True) self.action_for("standard_deviation") self.action_for("quadratic_difference") self.action_for("convolution", separator=True) self.action_for("deconvolution") # MARK: ANALYSIS with self.new_category(ActionCategory.ANALYSIS): self.new_action( _("Show results") + "...", triggered=self.panel.show_results, icon_name="show_results.svg", separator=True, select_condition=SelectCond.at_least_one_group_or_one_object, ) self.show_label_action = self.new_action( _("Results label"), toggled=self.panel.toggle_result_label_visibility, tip=_("Show or hide the merged result label on the plot"), select_condition=SelectCond.with_results, ) self.show_label_action.setCheckable(True) self.show_label_action.setChecked(Conf.view.show_result_label.get()) self.new_action( _("Plot results") + "...", triggered=self.panel.plot_results, icon_name="plot_results.svg", select_condition=SelectCond.at_least_one_group_or_one_object, ) # Create dynamic "Delete results" submenu with self.new_menu(_("Delete results"), icon_name="delete_results.svg"): # Store reference to the submenu for dynamic population if self.__submenu_stack: current_submenu = self.__submenu_stack[-1] self.results_delete_submenu = current_submenu["menu"] # MARK: VIEW with self.new_category(ActionCategory.VIEW): self.new_action( _("Edit annotations") + "...", icon_name="annotations.svg", tip=_("Edit annotations of selected %s") % self.OBJECT_STR, triggered=lambda: self.panel.open_separate_view(edit_annotations=True), context_menu_pos=1, toolbar_pos=-1, ) main = self.panel.mainwindow for cat in (ActionCategory.VIEW, ActionCategory.VIEW_TOOLBAR): for act in (main.autorefresh_action, main.showfirstonly_action): self.add_to_action_list(act, cat, -1) self.new_action( _("Refresh manually"), icon_name="refresh-manual.svg", tip=_("Refresh plot, even if auto-refresh is enabled"), shortcut=QG.QKeySequence(QG.QKeySequence.Refresh), triggered=self.panel.manual_refresh, select_condition=SelectCond.always, toolbar_pos=-1, ) for cat in (ActionCategory.VIEW, ActionCategory.VIEW_TOOLBAR): self.add_to_action_list(main.showlabel_action, cat, -1) class SignalActionHandler(BaseActionHandler): """Object handling signal panel GUI interactions: actions, menus, ...""" OBJECT_STR = _("signal") OBJECT_STR_PLURAL = _("signals") def create_first_actions(self): """Create actions that are added to the menus in the first place""" super().create_first_actions() # MARK: CREATE with self.new_category(ActionCategory.CREATE): for label, pclass, icon_name, separator in ( (_("Zero"), sio.ZeroParam, "1d-zero.svg", False), ( _("Normal distribution"), sio.NormalDistribution1DParam, "1d-normal.svg", False, ), ( _("Poisson distribution"), sio.PoissonDistribution1DParam, "1d-poisson.svg", False, ), ( _("Uniform distribution"), sio.UniformDistribution1DParam, "1d-uniform.svg", False, ), (_("Gaussian"), sio.GaussParam, "gaussian.svg", True), (_("Lorentzian"), sio.LorentzParam, "lorentzian.svg", False), (_("Voigt"), sio.VoigtParam, "voigt.svg", False), (_("Blackbody (Planck's law)"), sio.PlanckParam, "planck.svg", False), (_("Sine"), sio.SineParam, "sine.svg", True), (_("Cosine"), sio.CosineParam, "cosine.svg", False), (_("Sawtooth"), sio.SawtoothParam, "sawtooth.svg", False), (_("Triangle"), sio.TriangleParam, "triangle.svg", False), (_("Square"), sio.SquareParam, "square.svg", False), (_("Cardinal sine"), sio.SincParam, "sinc.svg", False), (_("Linear chirp"), sio.LinearChirpParam, "linear_chirp.svg", False), (_("Step"), sio.StepParam, "step.svg", True), (_("Exponential"), sio.ExponentialParam, "exponential.svg", False), (_("Logistic"), sio.LogisticParam, "logistic.svg", False), (_("Pulse"), sio.PulseParam, "pulse.svg", False), (_("Step pulse"), sio.StepPulseParam, "step_pulse.svg", False), (_("Square pulse"), sio.SquarePulseParam, "square_pulse.svg", False), (_("Polynomial"), sio.PolyParam, "polynomial.svg", True), (_("Custom"), newobject.CustomSignalParam, None, False), ): self.new_action( label, tip=_("Create new %s") % label, triggered=lambda pclass=pclass: self.panel.new_object(pclass()), icon_name=icon_name, select_condition=SelectCond.always, separator=separator, ) # MARK: OPERATION with self.new_category(ActionCategory.OPERATION): self.action_for("power", separator=True) self.action_for("sqrt") def cra_fit(title, fitdlgfunc, tip: str | None = None): """Create curve fitting action""" return self.new_action( title, triggered=lambda: self.panel.processor.compute_fit(title, fitdlgfunc), icon_name=fitdlgfunc.__name__ + ".svg", tip=tip, ) # MARK: PROCESSING with self.new_category(ActionCategory.PROCESSING): with self.new_menu(_("Axis transformation")): self.action_for("transpose") self.action_for("reverse_x") self.action_for("replace_x_by_other_y") self.action_for("xy_mode") self.action_for("to_cartesian", separator=True) self.action_for("to_polar") with self.new_menu(_("Frequency filters"), icon_name="highpass.svg"): self.action_for("lowpass") self.action_for("highpass") self.action_for("bandpass") self.action_for("bandstop") with self.new_menu(_("Fitting"), icon_name="exponential_fit.svg"): with self.new_menu( _("Interactive fitting"), icon_name="interactive_fit.svg" ): cra_fit(_("Linear fit"), fitdialog.linear_fit) self.new_action( _("Polynomial fit"), triggered=self.panel.processor.compute_polyfit, icon_name="polynomial_fit.svg", ) cra_fit(_("Gaussian fit"), fitdialog.gaussian_fit) cra_fit(_("Lorentzian fit"), fitdialog.lorentzian_fit) cra_fit(_("Voigt fit"), fitdialog.voigt_fit) self.new_action( _("Multi-Gaussian fit"), triggered=self.panel.processor.compute_multigaussianfit, icon_name="multigaussian_fit.svg", ) self.new_action( _("Multi-Lorentzian fit"), triggered=self.panel.processor.compute_multilorentzianfit, icon_name="multilorentzian_fit.svg", ) cra_fit( _("Planckian fit"), fitdialog.planckian_fit, tip=_("Planckian (blackbody radiation) fitting"), ) cra_fit( _("Two half-Gaussian fit"), fitdialog.twohalfgaussian_fit, tip=_("Asymmetric peak fitting with two half-Gaussians"), ) cra_fit( _("Piecewise exponential (raise-decay) fit"), fitdialog.piecewiseexponential_fit, tip=_( "Piecewise exponential fitting with raise and decay " "components" ), ) cra_fit(_("Exponential fit"), fitdialog.exponential_fit) cra_fit(_("Sinusoidal fit"), fitdialog.sinusoidal_fit) cra_fit( _("CDF fit"), fitdialog.cdf_fit, tip=_( "Cumulative distribution function fit, " "related to Error function (erf)" ), ) separator_needed = True for fit_name in ( "linear_fit", "polynomial_fit", "gaussian_fit", "lorentzian_fit", "voigt_fit", "planckian_fit", "twohalfgaussian_fit", "piecewiseexponential_fit", "exponential_fit", "sinusoidal_fit", "cdf_fit", "sigmoid_fit", ): self.action_for(fit_name, separator=separator_needed) separator_needed = False self.action_for("evaluate_fit", separator=True) self.action_for("derivative", separator=True) self.action_for("integral") self.action_for("apply_window", separator=True) self.action_for("detrending") self.action_for("interpolate") self.action_for("resampling") with self.new_menu(_("Stability analysis"), icon_name="stability.svg"): self.action_for("allan_variance") self.action_for("allan_deviation") self.action_for("modified_allan_variance") self.action_for("hadamard_variance") self.action_for("total_variance") self.action_for("time_deviation") self.new_action( _("All stability features") + "...", triggered=self.panel.processor.compute_all_stability, separator=True, tip=_("Compute all stability features"), ) # MARK: ANALYSIS with self.new_category(ActionCategory.ANALYSIS): self.action_for("fwhm") self.action_for("fw1e2") self.new_action( _("Full width at y=..."), triggered=self.panel.processor.compute_full_width_at_y, tip=_("Compute the full width at a given y value"), ) self.action_for("x_at_minmax") self.new_action( _("First abscissa at y=..."), triggered=self.panel.processor.compute_x_at_y, tip=_( "Compute the first abscissa at a given y value " "(linear interpolation)" ), ) self.new_action( _("Ordinate at x=..."), triggered=self.panel.processor.compute_y_at_x, tip=_("Compute the ordinate at a given x value (linear interpolation)"), ) self.action_for("extract_pulse_features") self.new_action( _("Peak detection"), separator=True, triggered=self.panel.processor.compute_peak_detection, icon_name="peak_detect.svg", ) self.action_for("sampling_rate_period", separator=True) self.action_for("dynamic_parameters", context_menu_pos=-1) self.action_for("bandwidth_3db", context_menu_pos=-1) self.action_for("contrast") def create_last_actions(self): """Create actions that are added to the menus in the end""" super().create_last_actions() with self.new_category(ActionCategory.OPERATION): self.action_for("signals_to_image", separator=True) with self.new_category(ActionCategory.VIEW): antialiasing_action = self.new_action( _("Curve anti-aliasing"), icon_name="curve_antialiasing.svg", toggled=self.panel.toggle_anti_aliasing, tip=_("Toggle curve anti-aliasing on/off (may slow down plotting)"), toolbar_pos=-1, ) antialiasing_action.setChecked(Conf.view.sig_antialiasing.get(True)) self.new_action( _("Reset curve styles"), select_condition=SelectCond.always, icon_name="reset_curve_styles.svg", triggered=self.panel.reset_curve_styles, tip=_( "Curve styles are looped over a list of predefined styles.\n" "This action resets the list to its initial state." ), toolbar_pos=-1, ) class ImageActionHandler(BaseActionHandler): """Object handling image panel GUI interactions: actions, menus, ...""" OBJECT_STR = _("image") OBJECT_STR_PLURAL = _("images") def create_first_actions(self): """Create actions that are added to the menus in the first place""" # MARK: PROCESSING (1/2) with self.new_category(ActionCategory.PROCESSING): with self.new_menu(_("Geometry"), icon_name="rotate_right.svg"): self.action_for("fliph", context_menu_pos=-1, context_menu_sep=True) self.action_for("transpose", context_menu_pos=-1) self.action_for("flipv", context_menu_pos=-1) self.action_for("rotate270", context_menu_pos=-1) self.action_for("rotate90", context_menu_pos=-1) self.action_for("rotate") self.new_action( _("Distribute on a grid..."), triggered=self.panel.processor.distribute_on_grid, icon_name="distribute_on_grid.svg", select_condition=SelectCond.at_least_two, separator=True, ) self.new_action( _("Reset image positions"), triggered=self.panel.processor.reset_positions, icon_name="reset_positions.svg", select_condition=SelectCond.at_least_two, ) with self.new_menu( _("Axis transformation"), icon_name="axis_transform.svg" ): self.action_for("set_uniform_coords") super().create_first_actions() # MARK: CREATE with self.new_category(ActionCategory.CREATE): for label, pclass, icon_name, separator in ( (_("Zero"), sio.Zero2DParam, "2d-zero.svg", False), ( _("Normal distribution"), sio.NormalDistribution2DParam, "2d-normal.svg", False, ), ( _("Poisson distribution"), sio.PoissonDistribution2DParam, "2d-poisson.svg", False, ), ( _("Uniform distribution"), sio.UniformDistribution2DParam, "2d-uniform.svg", False, ), (_("Gaussian"), sio.Gauss2DParam, "2d-gaussian.svg", True), (_("2D sinc"), sio.Sinc2DParam, "2d-sinc.svg", False), (_("Ring pattern"), sio.Ring2DParam, "ring.svg", True), (_("Ramp"), sio.Ramp2DParam, "2d-ramp.svg", False), (_("Checkerboard"), sio.Checkerboard2DParam, "checkerboard.svg", False), ( _("Sinusoidal grating"), sio.SinusoidalGrating2DParam, "grating.svg", False, ), (_("Siemens star"), sio.SiemensStar2DParam, "siemens.svg", False), ): self.new_action( label, tip=_("Create new %s") % label, triggered=lambda pclass=pclass: self.panel.new_object(pclass()), icon_name=icon_name, select_condition=SelectCond.always, separator=separator, ) # MARK: ROI with self.new_category(ActionCategory.ROI): self.new_action( _("Create ROI grid") + "...", triggered=self.panel.processor.create_roi_grid, icon_name="roi_grid.svg", tip=_("Create a grid of regions of interest"), ) # MARK: OPERATION with self.new_category(ActionCategory.OPERATION): self.action_for("log10_z_plus_n") # MARK: PROCESSING (2/2) with self.new_category(ActionCategory.PROCESSING): with self.new_menu(_("Frequency filters"), icon_name="noise_reduction.svg"): self.action_for("butterworth") self.action_for("gaussian_freq_filter") with self.new_menu(_("Thresholding"), icon_name="thresholding.svg"): self.action_for("threshold") self.action_for("threshold_isodata") self.action_for("threshold_li") self.action_for("threshold_mean") self.action_for("threshold_minimum") self.action_for("threshold_otsu") self.action_for("threshold_triangle") self.action_for("threshold_yen") self.new_action( _("All thresholding methods") + "...", triggered=self.panel.processor.compute_all_threshold, separator=True, tip=_("Apply all thresholding methods"), ) with self.new_menu(_("Exposure"), icon_name="exposure.svg"): self.action_for("adjust_gamma") self.action_for("adjust_log") self.action_for("adjust_sigmoid") self.action_for("equalize_hist") self.action_for("equalize_adapthist") self.action_for("rescale_intensity") with self.new_menu(_("Restoration"), icon_name="noise_reduction.svg"): self.action_for("denoise_tv") self.action_for("denoise_bilateral") self.action_for("denoise_wavelet") self.action_for("denoise_tophat") self.new_action( _("All denoising methods") + "...", triggered=self.panel.processor.compute_all_denoise, separator=True, tip=_("Apply all denoising methods"), ) with self.new_menu(_("Morphology"), icon_name="morphology.svg"): self.action_for("white_tophat") self.action_for("black_tophat") self.action_for("erosion") self.action_for("dilation") self.action_for("opening") self.action_for("closing") self.new_action( _("All morphological operations") + "...", triggered=self.panel.processor.compute_all_morphology, separator=True, tip=_("Apply all morphological operations"), ) with self.new_menu(_("Edge detection"), icon_name="edge_detection.svg"): self.action_for("canny") self.action_for("farid", separator=True) self.action_for("farid_h") self.action_for("farid_v") self.action_for("laplace", separator=True) self.action_for("prewitt", separator=True) self.action_for("prewitt_h") self.action_for("prewitt_v") self.action_for("roberts", separator=True) self.action_for("scharr", separator=True) self.action_for("scharr_h") self.action_for("scharr_v") self.action_for("sobel", separator=True) self.action_for("sobel_h") self.action_for("sobel_v") self.new_action( _("All edge detection filters..."), triggered=self.panel.processor.compute_all_edges, separator=True, tip=_("Compute all edge detection filters"), ) self.new_action( _("Erase area") + "...", triggered=self.panel.processor.compute_erase, icon_name="erase.svg", separator=True, tip=_("Erase area in the image as defined by a region of interest"), ) # MARK: ANALYSIS with self.new_category(ActionCategory.ANALYSIS): with self.new_menu(_("Intensity profiles"), icon_name="profile.svg"): self.new_action( _("Line profile..."), triggered=self.panel.processor.compute_line_profile, icon_name="profile.svg", tip=_("Extract horizontal or vertical profile"), context_menu_pos=-1, context_menu_sep=True, ) self.new_action( _("Segment profile..."), triggered=self.panel.processor.compute_segment_profile, icon_name="profile_segment.svg", tip=_("Extract profile along a segment"), context_menu_pos=-1, ) self.new_action( _("Average profile..."), triggered=self.panel.processor.compute_average_profile, icon_name="profile_average.svg", tip=_("Extract average horizontal or vertical profile"), context_menu_pos=-1, ) self.new_action( _("Radial profile extraction..."), triggered=self.panel.processor.compute_radial_profile, icon_name="profile_radial.svg", tip=_("Radial profile extraction around image centroid"), ) self.action_for("horizontal_projection", separator=True) self.action_for("vertical_projection") self.action_for("centroid", separator=True) self.action_for("enclosing_circle") self.new_action( _("2D peak detection"), separator=True, triggered=self.panel.processor.compute_peak_detection, tip=_("Compute automatic 2D peak detection"), ) self.action_for("contour_shape") self.action_for("hough_circle_peaks") with self.new_menu(_("Blob detection")): self.action_for("blob_dog") self.action_for("blob_doh") self.action_for("blob_log") self.action_for("blob_opencv") def create_last_actions(self): """Create actions that are added to the menus in the end""" # MARK: PROCESSING with self.new_category(ActionCategory.PROCESSING): self.new_action( _("Resize") + "...", triggered=self.panel.processor.compute_resize, icon_name="resize.svg", separator=True, ) self.new_action( _("Pixel binning") + "...", triggered=self.panel.processor.compute_binning, icon_name="binning.svg", ) self.action_for("resampling") # MARK: VIEW with self.new_category(ActionCategory.VIEW): self.new_action( _("View images side-by-side") + "...", icon_name="new_window.svg", tip=_("View selected images side-by-side in a new window"), triggered=self.panel.view_images_side_by_side, select_condition=SelectCond.at_least_two, context_menu_pos=-1, ) super().create_last_actions() # MARK: OPERATION with self.new_category(ActionCategory.OPERATION): self.action_for("flatfield", separator=True) # MARK: VIEW with self.new_category(ActionCategory.VIEW): showcontrast_action = self.new_action( _("Show contrast panel"), icon_name="contrast.png", tip=_("Show or hide contrast adjustment panel"), select_condition=SelectCond.always, toggled=self.panel.toggle_show_contrast, toolbar_pos=-1, ) showcontrast_action.setChecked(Conf.view.show_contrast.get(True)) DataLab-1.1.0/datalab/gui/docks.py000066400000000000000000000407271514014117600166440ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Docks ===== The :mod:`datalab.gui.docks` module provides the dockable widgets for the DataLab main window. Plot widget ----------- .. autoclass:: DataLabPlotWidget Dockable plot widget -------------------- .. autoclass:: DockablePlotWidget """ from __future__ import annotations import warnings from typing import TYPE_CHECKING import numpy as np import scipy.integrate as spt from guidata.configtools import get_icon, get_image_file_path from guidata.qthelpers import create_action, is_dark_theme from guidata.widgets.dockable import DockableWidget from plotpy.constants import PlotType from plotpy.items import CurveItem from plotpy.panels import XCrossSection, YCrossSection from plotpy.plot import PlotOptions, PlotWidget from plotpy.tools import ( BasePlotMenuTool, CurveStatsTool, DeleteItemTool, DisplayCoordsTool, DoAutoscaleTool, EditItemDataTool, ExportItemDataTool, ImageStatsTool, ItemCenterTool, RectangularSelectionTool, RectZoomTool, SelectTool, YRangeCursorTool, ) from plotpy.tools.image import get_stats as get_image_stats from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from qtpy.QtWidgets import QApplication, QMainWindow from sigima.objects import create_signal from sigima.tools.signal import pulse from skimage import measure from datalab.config import APP_NAME, Conf, _ if TYPE_CHECKING: from plotpy.items.image.base import BaseImageItem from plotpy.plot import BasePlot from plotpy.styles import BaseImageParam class CurveStatsToolFunctions: """Statistical functions for `CurveStatsTool` and `YRangeCursorTool`""" @classmethod def set_labelfuncs(cls, statstool: CurveStatsTool) -> None: """Set label functions for the statistics tool""" if isinstance(statstool, CurveStatsTool): labelfuncs = ( ("%g < x < %g", lambda *args: cls.nan_min_max(args[0])), ("%g < y < %g", lambda *args: cls.nan_min_max(args[1])), ("∆x=%g", lambda *args: cls.nan_delta(args[0])), ("∆y=%g", lambda *args: cls.nan_delta(args[1])), ("<y>=%g", lambda *args: cls.nan_mean(args[1])), ("σ(y)=%g", lambda *args: cls.nan_std(args[1])), ("∑(y)=%g", lambda *args: spt.trapezoid(args[1])), ("∫ydx=%g
", lambda *args: spt.trapezoid(args[1], args[0])), ("FWHM=%s", cls.fwhm_info), ("∆xRISE 10-90=%s", cls.rise_time_info), ( "∆xRISE 20-80=%s", lambda x, y: cls.rise_time_info(x, y, 0.2, 0.8), ), ("∆xFALL 90-10=%s", cls.fall_time_info), ( "∆xFALL 80-20=%s", lambda x, y: cls.fall_time_info(x, y, 0.8, 0.2), ), ) else: # YRangeCursorTool labelfuncs = ( ("%g < y < %g", lambda ymin, ymax: (ymin, ymax)), ("∆y=%g", lambda ymin, ymax: ymax - ymin), ) statstool.set_labelfuncs(labelfuncs) @staticmethod def nan_min_max(arr: np.ndarray) -> tuple[float, float]: """Return min/max tuple""" with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) min_val = np.nanmin(arr) max_val = np.nanmax(arr) return (min_val, max_val) @staticmethod def nan_delta(arr: np.ndarray) -> float: """Return delta value, ignoring NaNs""" with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) delta_val = np.nanmax(arr) - np.nanmin(arr) return delta_val @staticmethod def nan_mean(arr: np.ndarray) -> float: """Return mean value, ignoring NaNs""" with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) mean_val = np.nanmean(arr) return mean_val @staticmethod def nan_std(arr: np.ndarray) -> float: """Return standard deviation, ignoring NaNs""" with warnings.catch_warnings(): warnings.simplefilter("ignore", RuntimeWarning) std_val = np.nanstd(arr) return std_val @staticmethod def fwhm_info(x, y): """Return FWHM information string""" try: with warnings.catch_warnings(record=True) as w: x0, _y0, x1, _y1 = pulse.fwhm(x, y, "zero-crossing") wstr = " ⚠️" if w else "" except (ValueError, ZeroDivisionError, pulse.InvalidSignalError): return "🛑" return f"{x1 - x0:g}{wstr}" @staticmethod def rise_time_info(x, y, start_ratio=0.1, end_ratio=0.9): """Return rise time information string""" try: with warnings.catch_warnings(record=True) as w: dt = pulse.get_rise_time(x, y, start_ratio, end_ratio) wstr = " ⚠️" if w else "" if dt is None: return "🛑" except (ValueError, ZeroDivisionError, pulse.InvalidSignalError): return "🛑" return f"{dt:g}{wstr}" @staticmethod def fall_time_info(x, y, start_ratio=0.9, end_ratio=0.1): """Return fall time information string""" try: with warnings.catch_warnings(record=True) as w: dt = pulse.get_fall_time(x, y, start_ratio, end_ratio) wstr = " ⚠️" if w else "" if dt is None: return "🛑" except (ValueError, ZeroDivisionError, pulse.InvalidSignalError): return "🛑" return f"{dt:g}{wstr}" def get_more_image_stats( item: BaseImageItem, x0: float, y0: float, x1: float, y1: float, ) -> str: """Return formatted string with stats on image rectangular area (output should be compatible with AnnotatedShape.get_info) Args: item: image item x0: X0 y0: Y0 x1: X1 y1: Y1 """ with warnings.catch_warnings(): warnings.simplefilter("ignore", UserWarning) info = get_image_stats(item, x0, y0, x1, y1) ix0, iy0, ix1, iy1 = item.get_closest_index_rect(x0, y0, x1, y1) data = item.data[iy0:iy1, ix0:ix1] p: BaseImageParam = item.param xunit, yunit, zunit = p.get_units() integral = np.nansum(data) integral_fmt = r"%.3e " + zunit info += f"
∑ = {integral_fmt % integral}" if xunit == yunit: surfacefmt = p.xformat.split()[0] + " " + xunit if xunit != "": surfacefmt = surfacefmt + "²" surface = abs((x1 - x0) * (y1 - y0)) info += f"
A = {surfacefmt % surface}" if xunit is not None and zunit is not None: if surface != 0: density = integral / surface densityfmt = r"%.3e" if xunit and zunit: densityfmt += " " + zunit + "/" + xunit + "²" info = info + f"
ρ = {densityfmt % density}" # Convert data (ndarray) to a simple array to compute centroid with the new # einsum optimisation introduce in numpy 2.4.0 and scikit-image 0.26.0 c_i, c_j = measure.centroid(np.array(data)) c_x, c_y = item.get_plot_coordinates(c_j + ix0, c_i + iy0) info += "
" + "
".join( [ "C|x = " + p.xformat % c_x, "C|y = " + p.yformat % c_y, ] ) return info def profile_to_signal(plot: BasePlot, panel: XCrossSection | YCrossSection) -> None: """Send cross section curve to DataLab's signal list Args: panel: Cross section panel """ win = None for win in QApplication.topLevelWidgets(): if isinstance(win, QMainWindow): break if win is None or win.objectName() != APP_NAME: # pylint: disable=import-outside-toplevel # pylint: disable=cyclic-import from datalab.gui import main # Note : this is the only way to retrieve the DataLab main window instance # when the CrossSectionItem object is embedded into an image widget # parented to another main window. win = main.DLMainWindow.get_instance() assert win is not None # Should never happen for item in panel.cs_plot.get_items(): if not isinstance(item, CurveItem): continue x, y, _dx, _dy = item.get_data() if x is None or y is None or x.size == 0 or y.size == 0: continue signal = create_signal(item.param.label) if isinstance(panel, YCrossSection): signal.set_xydata(y, x) xaxis_name = "left" xunit = plot.get_axis_unit("bottom") if xunit: signal.title += " " + xunit else: signal.set_xydata(x, y) xaxis_name = "bottom" yunit = plot.get_axis_unit("left") if yunit: signal.title += " " + yunit signal.ylabel = plot.get_axis_title("right") signal.yunit = plot.get_axis_unit("right") signal.xlabel = plot.get_axis_title(xaxis_name) signal.xunit = plot.get_axis_unit(xaxis_name) win.signalpanel.add_object(signal) # Show DataLab main window on top, if not already visible win.show() win.raise_() class DataLabPlotWidget(PlotWidget): """DataLab PlotWidget This class is a subclass of `plotpy.plot.PlotWidget` that provides a customized widget for DataLab, with a specific set of tools and a customized appearance. Args: plot_type: Plot type """ def __init__(self, plot_type: PlotType) -> None: # Get autoscale margin from configuration based on plot type if plot_type == PlotType.CURVE: autoscale_margin = Conf.view.sig_autoscale_margin_percent.get() elif plot_type == PlotType.IMAGE: autoscale_margin = Conf.view.ima_autoscale_margin_percent.get() else: # For AUTO or MANUAL types, use signal margin as default autoscale_margin = Conf.view.sig_autoscale_margin_percent.get() super().__init__( options=PlotOptions( type=plot_type, show_axes_tab=False, autoscale_margin_percent=autoscale_margin, ), toolbar=True, ) def __register_standard_tools(self) -> None: """Register standard tools The only differences with the `manager.register_standard_tools` method are the following: 1. We don't register the `BasePlotMenuTool, "axes"` tool, because it is not compatible with DataLab's approach to axes management. 2. We don't register the `ItemListPanelTool` tool (this intends to prevent the user from accessing the item list panel, and thus, the parameters of all the items - some of them are read-only and should not be modified, like the annotations for example). """ mgr = self.manager select_tool = mgr.add_tool(SelectTool) mgr.set_default_tool(select_tool) mgr.add_tool(RectangularSelectionTool, intersect=False) mgr.add_tool(RectZoomTool) mgr.add_tool(DoAutoscaleTool) mgr.add_tool(BasePlotMenuTool, "item") mgr.add_tool(ExportItemDataTool) mgr.add_tool(EditItemDataTool) mgr.add_tool(ItemCenterTool) mgr.add_tool(DeleteItemTool) mgr.add_separator_tool() mgr.add_tool(BasePlotMenuTool, "grid") mgr.add_tool(DisplayCoordsTool) def __register_other_tools(self) -> None: """Register other tools""" mgr = self.manager mgr.add_separator_tool() if self.options.type == PlotType.CURVE: mgr.register_curve_tools() xstatstool = mgr.get_tool(CurveStatsTool) CurveStatsToolFunctions.set_labelfuncs(xstatstool) ystatstool = mgr.get_tool(YRangeCursorTool) CurveStatsToolFunctions.set_labelfuncs(ystatstool) else: mgr.register_image_tools() # Customizing the ImageStatsTool statstool = mgr.get_tool(ImageStatsTool) statstool.set_stats_func(get_more_image_stats, replace=True) # Customizing the X and Y cross section panels plot = mgr.get_plot() for panel in (mgr.get_xcs_panel(), mgr.get_ycs_panel()): to_signal_action = create_action( panel, _("Process signal"), icon=get_icon("to_signal.svg"), triggered=lambda panel=panel: profile_to_signal(plot, panel), ) tb = panel.toolbar tb.insertSeparator(tb.actions()[0]) tb.insertAction(tb.actions()[0], to_signal_action) mgr.add_separator_tool() mgr.register_other_tools() mgr.add_separator_tool() mgr.update_tools_status() mgr.get_default_tool().activate() def register_tools(self) -> None: """Register the plotting tools according to the plot type""" self.__register_standard_tools() self.__register_other_tools() class DockablePlotWidget(DockableWidget): """Docked plotting widget Args: parent: Parent widget plot_type: Plot type """ LOCATION = QC.Qt.RightDockWidgetArea def __init__( self, parent: QW.QWidget, plot_type: PlotType, ) -> None: super().__init__(parent) self.plotwidget = DataLabPlotWidget(plot_type) self.toolbar = self.plotwidget.get_toolbar() self.watermark = QW.QLabel() original_image = QG.QPixmap(get_image_file_path("DataLab-watermark.png")) self.watermark.setPixmap(original_image) self.setup_layout() self.setup_plotwidget() def __get_toolbar_row_col(self) -> tuple[int, int]: """Return toolbar row and column""" tb_pos = Conf.view.plot_toolbar_position.get() tb_col, tb_row = 1, 1 if tb_pos in ("left", "right"): self.toolbar.setOrientation(QC.Qt.Vertical) tb_col = 0 if tb_pos == "left" else 2 else: self.toolbar.setOrientation(QC.Qt.Horizontal) tb_row = 0 if tb_pos == "top" else 2 return tb_row, tb_col def setup_layout(self) -> None: """Setup layout""" tb_row, tb_col = self.__get_toolbar_row_col() layout = QW.QGridLayout() layout.addWidget(self.toolbar, tb_row, tb_col) layout.addWidget(self.plotwidget, 1, 1) layout.addWidget(self.watermark, 1, 1, QC.Qt.AlignCenter) self.setLayout(layout) def update_toolbar_position(self) -> None: """Update toolbar position""" tb_row, tb_col = self.__get_toolbar_row_col() layout = self.layout() layout.removeWidget(self.toolbar) layout.addWidget(self.toolbar, tb_row, tb_col) def setup_plotwidget(self) -> None: """Setup plotting widget""" title = self.toolbar.windowTitle() self.plotwidget.get_manager().add_toolbar(self.toolbar, title) # Customizing widget appearances self.update_color_mode() plot = self.plotwidget.get_plot() canvas = plot.canvas() canvas.setFrameStyle(canvas.Plain | canvas.NoFrame) plot.SIG_ITEMS_CHANGED.connect(self.update_watermark) def update_color_mode(self) -> None: """Update plot widget styles according to application color mode""" if is_dark_theme(): palette = QApplication.instance().palette() else: palette = QG.QPalette(QC.Qt.white) for widget in (self.plotwidget, self.plotwidget.get_plot(), self): widget.setBackgroundRole(QG.QPalette.Window) widget.setAutoFillBackground(True) widget.setPalette(palette) def get_plot(self) -> BasePlot: """Return plot instance""" return self.plotwidget.get_plot() def update_watermark(self, plot: BasePlot) -> None: """Update watermark visibility""" items = plot.get_items() if self.plotwidget.options.type == PlotType.IMAGE: enabled = len(items) <= 1 else: enabled = len(items) <= 2 self.watermark.setVisible(enabled) # ------DockableWidget API def visibility_changed(self, enable: bool) -> None: """DockWidget visibility has changed""" DockableWidget.visibility_changed(self, enable) self.toolbar.setVisible(enable) DataLab-1.1.0/datalab/gui/h5io.py000066400000000000000000000146071514014117600164030ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ HDF5 I/O ======== The :mod:`datalab.gui.h5io` module provides the HDF5 file open/save into/from DataLab data model/main window. .. autoclass:: H5InputOutput """ from __future__ import annotations import os.path as osp from typing import TYPE_CHECKING from guidata.qthelpers import exec_dialog from qtpy import QtWidgets as QW from sigima.objects import SignalObj from datalab.config import _ from datalab.env import execenv from datalab.h5 import H5Importer from datalab.h5.native import NativeH5Reader, NativeH5Writer from datalab.utils.qthelpers import create_progress_bar, qt_try_loadsave_file from datalab.widgets.h5browser import H5BrowserDialog if TYPE_CHECKING: from datalab.gui.main import DLMainWindow from datalab.h5.common import BaseNode class H5InputOutput: """Object handling HDF5 file open/save into/from DataLab data model/main window Args: mainwindow: Main window """ def __init__(self, mainwindow: DLMainWindow) -> None: self.mainwindow = mainwindow self.uint32_wng: bool = None @staticmethod def __progbartitle(fname: str) -> str: """Return progress bar title""" return _("Loading data from %s...") % osp.basename(fname) def save_file(self, filename: str) -> None: """Save all signals and images from DataLab model into a HDF5 file""" writer = NativeH5Writer(filename) for panel in self.mainwindow.panels: panel.serialize_to_hdf5(writer) writer.close() def open_file_headless(self, filename: str, reset_all: bool) -> bool: """Open native DataLab HDF5 file without any GUI elements. This method can be safely called from any thread (e.g., the console thread) as it does not create any Qt widgets or dialogs. Args: filename: HDF5 filename reset_all: Reset all application data before importing Returns: True if file was successfully opened as a native DataLab file, False if the file format is not compatible (KeyError was raised) """ try: reader = NativeH5Reader(filename) if reset_all: self.mainwindow.reset_all() for panel in self.mainwindow.panels: panel.deserialize_from_hdf5(reader, reset_all) reader.close() return True except KeyError: return False def open_file(self, filename: str, import_all: bool, reset_all: bool) -> None: """Open HDF5 file""" progress = None try: reader = NativeH5Reader(filename) if reset_all: self.mainwindow.reset_all() with create_progress_bar( self.mainwindow, self.__progbartitle(filename), 2 ) as progress: for idx, panel in enumerate(self.mainwindow.panels): progress.setValue(idx + 1) QW.QApplication.processEvents() panel.deserialize_from_hdf5(reader, reset_all) if progress.wasCanceled(): break reader.close() except KeyError: if progress is not None: # KeyError was encoutered when deserializing datasets (DataLab data # model is not compatible with this version) progress.close() self.import_files([filename], import_all, reset_all) def __add_object_from_node(self, node: BaseNode) -> None: """Add DataLab object from h5 node""" obj = node.get_native_object() if obj is None: return self.uint32_wng = self.uint32_wng or node.uint32_wng if isinstance(obj, SignalObj): self.mainwindow.signalpanel.add_object(obj) else: self.mainwindow.imagepanel.add_object(obj) def __eventually_show_warnings(self) -> None: """Eventually show warnings after everything is imported""" if self.uint32_wng: QW.QMessageBox.warning( self.mainwindow, _("Warning"), _("Clipping uint32 data to int32.") ) def import_files( self, filenames: list[str], import_all: bool, reset_all: bool ) -> None: """Import HDF5 files""" h5browser = H5BrowserDialog(self.mainwindow) for filename in filenames: with qt_try_loadsave_file(self.mainwindow, filename, "load"): h5browser.open_file(filename) if h5browser.is_empty(): h5browser.cleanup() if not execenv.unattended: QW.QMessageBox.warning( self.mainwindow, _("Warning"), _("No supported data available in HDF5 file(s)."), ) return if execenv.unattended: # Unattended mode: import all datasets (for testing) import_all = True if import_all or exec_dialog(h5browser): if import_all: nodes = h5browser.get_all_nodes() else: nodes = h5browser.get_nodes() if nodes is not None: if reset_all: self.mainwindow.reset_all() with qt_try_loadsave_file(self.mainwindow, "*.h5", "load"): with create_progress_bar(self.mainwindow, "", len(nodes)) as prog: self.uint32_wng = False for idx, node in enumerate(nodes): prog.setLabelText(self.__progbartitle(node.h5file.filename)) prog.setValue(idx + 1) QW.QApplication.processEvents() if prog.wasCanceled(): break self.__add_object_from_node(node) self.__eventually_show_warnings() h5browser.cleanup() def import_dataset_from_file(self, filename: str, dsetname: str) -> None: """Import dataset from HDF5 file""" h5importer = H5Importer(filename) try: node = h5importer.get(dsetname) self.uint32_wng = False self.__add_object_from_node(node) self.__eventually_show_warnings() except KeyError as exc: raise KeyError(f"Dataset not found: {dsetname}") from exc h5importer.close() DataLab-1.1.0/datalab/gui/macroeditor.py000066400000000000000000000241041514014117600200400ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Macro editor ============ The :mod:`datalab.gui.macroeditor` module provides the macro editor widget for DataLab. .. autoclass:: Macro """ from __future__ import annotations import abc import os import os.path as osp import re import sys import time from guidata.io import BaseIOHandler from guidata.utils.misc import to_string from guidata.widgets.codeeditor import CodeEditor from guidata.widgets.console.shell import PythonShellWidget from qtpy import QtCore as QC from qtpy import QtWidgets as QW import datalab from datalab.config import _ from datalab.env import execenv from datalab.gui import ObjItf UNTITLED_NB = 0 class MacroMeta(type(QC.QObject), abc.ABCMeta): """Mixed metaclass to avoid conflicts""" class Macro(QC.QObject, ObjItf, metaclass=MacroMeta): """Object representing a macro: editor, path, open/save actions, etc. Args: console: Python shell widget name: Macro name. Defaults to None. """ PREFIX = "m" STARTED = QC.Signal() FINISHED = QC.Signal() MODIFIED = QC.Signal() FILE_HEADER = os.linesep.join( [ "# -*- coding: utf-8 -*-", "", '''""" DataLab Macro: "%s" ------------- This file is a DataLab macro. It can be executed from DataLab's Macro Panel, or from any Python environment, provided that the ``datalab`` package is installed. Please do not modify this file header. It is used to identify the file as a DataLab macro, and to store the macro's title. """''', "", "", ] ) MACRO_TITLE = _("Macro simple example") MACRO_SAMPLE = f"""# {MACRO_TITLE} import numpy as np from datalab.control.proxy import RemoteProxy proxy = RemoteProxy() z = np.random.rand(20, 20) proxy.add_image("toto", z) proxy.calc("fft") print("All done!") """ def __init__(self, console: PythonShellWidget, title: str | None = None) -> None: super().__init__() self.console = console self.title = self.get_untitled_title() if title is None else title self.editor = CodeEditor(language="python") self.editor.setLineWrapMode(QW.QPlainTextEdit.NoWrap) self.set_code(self.MACRO_SAMPLE) self.editor.modificationChanged.connect(self.modification_changed) self.process = None self.__last_exit_code = None @property def title(self) -> str: """Return object title""" return self.objectName() @title.setter def title(self, title: str) -> None: """Set object title""" self.setObjectName(title) def get_code(self) -> str: """Return code to be executed""" text = self.editor.toPlainText() return os.linesep.join(text.splitlines(False)) def set_code(self, code: str) -> None: """Set code to be executed Args: code: Code to be executed """ self.editor.setPlainText(code) def serialize(self, writer: BaseIOHandler) -> None: """Serialize this macro Args: writer: Writer """ with writer.group("title"): writer.write(self.title) with writer.group("contents"): writer.write(self.get_code()) def deserialize(self, reader: BaseIOHandler) -> None: """Deserialize this macro Args: reader: Reader """ with reader.group("title"): self.title = reader.read_any() with reader.group("contents"): self.set_code(reader.read_any()) def to_file(self, filename: str) -> None: """Save macro to file Args: filename: File name """ code = self.FILE_HEADER % self.title + self.get_code() with open(filename, "wb") as fdesc: fdesc.write(code.encode("utf-8")) def from_file(self, filename: str) -> None: """Load macro from file Args: filename: File name """ with open(filename, "rb") as fdesc: code = to_string(fdesc.read()).strip() # Retrieve title from header: lines = code.splitlines() for line in lines: # Match a line exactly like 'DataLab Macro: "Macro title"': if re.match(r"DataLab Macro: \".*\"", line): self.title = line.split('"')[1] break else: self.title = osp.basename(filename) # Remove header: header = (self.FILE_HEADER % self.title).strip() if code.startswith(header): code = code[len(header) :].strip() # Set code: self.set_code(code) @staticmethod def get_untitled_title() -> str: """Increment untitled number and return untitled macro title Returns: Untitled macro title """ global UNTITLED_NB # pylint: disable=global-statement UNTITLED_NB += 1 return f"macro_{UNTITLED_NB:02d}" @staticmethod def set_untitled_number(number: int) -> None: """Set the untitled number counter This is useful when loading macros from HDF5 or files to ensure that the next untitled macro has a unique name. Args: number: New untitled number """ global UNTITLED_NB # pylint: disable=global-statement UNTITLED_NB = number def modification_changed(self, state: bool) -> None: """Method called when macro's editor modification state changed Args: state: Modification state """ if state: self.MODIFIED.emit() @staticmethod def transcode(bytearr: QC.QByteArray) -> str: """Transcode bytes to locale str Args: bytearr: Byte array Returns: Locale str """ # Python 3 outputs UTF-8 by default, so we need to decode as UTF-8 # instead of using the system locale codec (which might be cp1252 on Windows) utf8_codec = QC.QTextCodec.codecForName(b"UTF-8") return utf8_codec.toUnicode(bytearr.data()) def get_stdout(self) -> str: """Return standard output str Returns: Standard output str """ self.process.setReadChannel(QC.QProcess.StandardOutput) bytearr = QC.QByteArray() while self.process.bytesAvailable(): bytearr += self.process.readAllStandardOutput() return self.transcode(bytearr) def get_stderr(self) -> str: """Return standard error str Returns: Standard error str """ self.process.setReadChannel(QC.QProcess.StandardError) bytearr = QC.QByteArray() while self.process.bytesAvailable(): bytearr += self.process.readAllStandardError() return self.transcode(bytearr) def write_output(self) -> None: """Write text as standard output""" self.console.write(self.get_stdout()) def write_error(self) -> None: """Write text as standard error""" self.console.write_error(self.get_stderr()) def print(self, text, error=False, eol_before=True) -> None: """Print text in console, with line separator Args: text: Text to be printed error: Print as error. Defaults to False. """ msg = f"---({time.ctime()})---[{text}]{os.linesep}" if eol_before: msg = os.linesep + msg self.console.write(msg, error=error, prompt=not error) def run(self) -> None: """Run macro""" self.process = QC.QProcess() code = self.get_code() datalab_path = osp.abspath(osp.join(osp.dirname(datalab.__file__), os.pardir)) # Reconfigure stdout/stderr to use UTF-8 encoding to avoid UnicodeEncodeError # on Windows with locales that don't support all Unicode characters # (e.g., cp1252) code = ( f"import sys; sys.path.append(r'{datalab_path}'); " f"sys.stdout.reconfigure(encoding='utf-8'); " f"sys.stderr.reconfigure(encoding='utf-8'){os.linesep}{code}" ) env = QC.QProcessEnvironment() env.insert(execenv.XMLRPCPORT_ENV, str(execenv.xmlrpcport)) sysenv = env.systemEnvironment() for key in sysenv.keys(): env.insert(key, sysenv.value(key)) self.process.readyReadStandardOutput.connect(self.write_output) self.process.readyReadStandardError.connect(self.write_error) self.process.finished.connect(self.finished) self.process.setProcessEnvironment(env) args = ["-c", code] self.process.start(sys.executable, args) running = self.process.waitForStarted(3000) if not running: self.print(_("# ==> Unable to run '%s' macro") % self.title, error=True) QW.QMessageBox.critical( self, _("Error"), _("Macro Python interpreter failed to start!") ) else: self.print(_("# ==> Running '%s' macro...") % self.title) self.STARTED.emit() def is_running(self) -> bool: """Is macro running? Returns: True if macro is running """ if self.process is not None: return self.process.state() == QC.QProcess.Running return False def kill(self) -> None: """Kill process associated to macro""" if self.process is not None: self.print(_("Terminating '%s' macro") % self.title, error=True) self.process.kill() # pylint: disable=unused-argument def finished(self, exit_code: int, exit_status: QC.QProcess.ExitStatus) -> None: """Process has finished Args: exit_code: Exit code exit_status: Exit status """ self.__last_exit_code = exit_code self.print(_("# <== '%s' macro has finished") % self.title, eol_before=False) self.FINISHED.emit() self.process = None def get_exit_code(self) -> int | None: """Return last exit code of the macro process Returns: Last exit code or None if process has not finished yet """ return self.__last_exit_code DataLab-1.1.0/datalab/gui/main.py000066400000000000000000002611721514014117600164640ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Main window =========== The :mod:`datalab.gui.main` module provides the main window of the DataLab project. .. autoclass:: DLMainWindow """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import abc import base64 import functools import os import os.path as osp import sys import time import webbrowser from typing import TYPE_CHECKING import guidata.dataset as gds import numpy as np import scipy.ndimage as spi import scipy.signal as sps from guidata import qthelpers as guidata_qth from guidata.configtools import get_icon from guidata.qthelpers import add_actions, create_action from guidata.widgets.console import DockableConsole from plotpy import config as plotpy_config from plotpy.builder import make from plotpy.constants import PlotType from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from qtpy.compat import getopenfilenames, getsavefilename from sigima.config import options as sigima_options from sigima.objects import ImageObj, SignalObj, create_image, create_signal import datalab from datalab import __docurl__, __homeurl__, __supporturl__, env from datalab.adapters_metadata.common import have_geometry_results from datalab.adapters_plotpy import create_adapter_from_object from datalab.config import ( APP_DESC, APP_NAME, DATAPATH, DEBUG, TEST_SEGFAULT_ERROR, Conf, _, ) from datalab.control.baseproxy import AbstractDLControl from datalab.control.remote import RemoteServer from datalab.env import execenv from datalab.gui.actionhandler import ActionCategory from datalab.gui.docks import DockablePlotWidget from datalab.gui.h5io import H5InputOutput from datalab.gui.panel import base, image, macro, signal from datalab.gui.settings import edit_settings from datalab.objectmodel import ObjectGroup from datalab.plugins import PluginRegistry, discover_plugins, discover_v020_plugins from datalab.utils import qthelpers as qth from datalab.utils.qthelpers import ( add_corner_menu, bring_to_front, configure_menu_about_to_show, ) from datalab.webapi import WEBAPI_AVAILABLE, get_webapi_controller from datalab.webapi.actions import WebApiActions from datalab.widgets import instconfviewer, logviewer, status from datalab.widgets.warningerror import go_to_error if TYPE_CHECKING: from typing import Literal from datalab.gui.panel.base import AbstractPanel, BaseDataPanel from datalab.gui.panel.image import ImagePanel from datalab.gui.panel.macro import MacroPanel from datalab.gui.panel.signal import SignalPanel from datalab.plugins import PluginBase def remote_controlled(func): """Decorator for remote-controlled methods""" @functools.wraps(func) def method_wrapper(*args, **kwargs): """Decorator wrapper function""" win = args[0] # extracting 'self' from method arguments already_busy = not win.ready_flag win.ready_flag = False try: output = func(*args, **kwargs) finally: if not already_busy: win.SIG_READY.emit() win.ready_flag = True QW.QApplication.processEvents() return output return method_wrapper class DLMainWindowMeta(type(QW.QMainWindow), abc.ABCMeta): """Mixed metaclass to avoid conflicts""" class DLMainWindow(QW.QMainWindow, AbstractDLControl, metaclass=DLMainWindowMeta): """DataLab main window Args: console: enable internal console hide_on_close: True to hide window on close """ __instance = None SIG_READY = QC.Signal() SIG_SEND_OBJECT = QC.Signal(object) SIG_SEND_OBJECTLIST = QC.Signal(object) SIG_CLOSING = QC.Signal() @staticmethod def get_instance(console=None, hide_on_close=False): """Return singleton instance""" if DLMainWindow.__instance is None: return DLMainWindow(console, hide_on_close) return DLMainWindow.__instance def __init__(self, console=None, hide_on_close=False): """Initialize main window""" DLMainWindow.__instance = self super().__init__() self.setObjectName(APP_NAME) self.setWindowIcon(get_icon("DataLab.svg")) execenv.log(self, "Starting initialization") self.ready_flag = True self.hide_on_close = hide_on_close self.__old_size: tuple[int, int] | None = None self.__memory_warning = False self.memorystatus: status.MemoryStatus | None = None self.webapistatus: status.WebAPIStatus | None = None self.consolestatus: status.ConsoleStatus | None = None self.console: DockableConsole | None = None self.macropanel: MacroPanel | None = None self.main_toolbar: QW.QToolBar | None = None self.signalpanel_toolbar: QW.QToolBar | None = None self.imagepanel_toolbar: QW.QToolBar | None = None self.signalpanel: SignalPanel | None = None self.imagepanel: ImagePanel | None = None self.signalview: DockablePlotWidget | None = None self.imageview: DockablePlotWidget | None = None self.tabwidget: QW.QTabWidget | None = None self.tabmenu: QW.QMenu | None = None self.docks: dict[AbstractPanel | DockableConsole, QW.QDockWidget] | None = None self.h5inputoutput = H5InputOutput(self) self.webapi_actions: WebApiActions | None = None self.openh5_action: QW.QAction | None = None self.saveh5_action: QW.QAction | None = None self.browseh5_action: QW.QAction | None = None self.settings_action: QW.QAction | None = None self.quit_action: QW.QAction | None = None self.autorefresh_action: QW.QAction | None = None self.showfirstonly_action: QW.QAction | None = None self.showlabel_action: QW.QAction | None = None self.file_menu: QW.QMenu | None = None self.create_menu: QW.QMenu | None = None self.edit_menu: QW.QMenu | None = None self.roi_menu: QW.QMenu | None = None self.operation_menu: QW.QMenu | None = None self.processing_menu: QW.QMenu | None = None self.analysis_menu: QW.QMenu | None = None self.plugins_menu: QW.QMenu | None = None self.view_menu: QW.QMenu | None = None self.help_menu: QW.QMenu | None = None self.__update_color_mode(startup=True) self.__is_modified = False self.set_modified(False) # Starting XML-RPC server thread self.remote_server = RemoteServer(self) if Conf.main.rpc_server_enabled.get(): self.remote_server.SIG_SERVER_PORT.connect(self.xmlrpc_server_started) self.remote_server.start() # Setup actions and menus if console is None: console = Conf.console.console_enabled.get() self.setup(console) self.__restore_pos_and_size() execenv.log(self, "Initialization done") # ------API related to XML-RPC remote control @staticmethod def xmlrpc_server_started(port): """XML-RPC server has started, writing comm port in configuration file""" Conf.main.rpc_server_port.set(port) def __get_current_basedatapanel(self) -> BaseDataPanel: """Return the current BaseDataPanel, or the signal panel if macro panel is active Returns: BaseDataPanel: current panel """ panel = self.tabwidget.currentWidget() if not isinstance(panel, base.BaseDataPanel): panel = self.signalpanel return panel def __get_datapanel( self, panel: Literal["signal", "image"] | None ) -> BaseDataPanel: """Return a specific BaseDataPanel. Args: panel: panel name. If None, current panel is used. Returns: Panel widget Raises: ValueError: if panel is unknown """ if not panel: return self.__get_current_basedatapanel() if panel == "signal": return self.signalpanel if panel == "image": return self.imagepanel raise ValueError(f"Unknown panel: {panel}") @remote_controlled def get_group_titles_with_object_info( self, ) -> tuple[list[str], list[list[str]], list[list[str]]]: """Return groups titles and lists of inner objects uuids and titles. Returns: Groups titles, lists of inner objects uuids and titles """ panel = self.__get_current_basedatapanel() return panel.objmodel.get_group_titles_with_object_info() @remote_controlled def get_object_titles( self, panel: Literal["signal", "image", "macro"] | None = None ) -> list[str]: """Get object (signal/image) list for current panel. Objects are sorted by group number and object index in group. Args: panel: panel name. If None, current data panel is used (i.e. signal or image panel). Returns: List of object titles Raises: ValueError: if panel is unknown """ if not panel or panel in ("signal", "image"): return self.__get_datapanel(panel).objmodel.get_object_titles() if panel == "macro": return self.macropanel.get_macro_titles() raise ValueError(f"Unknown panel: {panel}") @remote_controlled def get_object( self, nb_id_title: int | str | None = None, panel: Literal["signal", "image"] | None = None, ) -> SignalObj | ImageObj: """Get object (signal/image) from index. Args: nb_id_title: Object number, or object id, or object title. Defaults to None (current object). panel: Panel name. Defaults to None (current panel). Returns: Object Raises: KeyError: if object not found TypeError: if index_id_title type is invalid """ panelw = self.__get_datapanel(panel) if nb_id_title is None: return panelw.objview.get_current_object() if isinstance(nb_id_title, int): return panelw.objmodel.get_object_from_number(nb_id_title) if isinstance(nb_id_title, str): try: return panelw.objmodel[nb_id_title] except KeyError: try: return panelw.objmodel.get_object_from_title(nb_id_title) except KeyError as exc: raise KeyError( f"Invalid object index, id or title: {nb_id_title}" ) from exc raise TypeError(f"Invalid index_id_title type: {type(nb_id_title)}") def find_object_by_uuid( self, uuid: str ) -> SignalObj | ImageObj | ObjectGroup | None: """Find an object by UUID, searching across all panels. This method searches for an object in both signal and image panels, making it suitable for cross-panel operations (e.g., radial profile that takes an ImageObj and produces a SignalObj). Difference from get_object(): - get_object() requires specifying a panel and accepts number/id/title - find_object_by_uuid() searches all panels automatically using only UUID Args: uuid: UUID of the object to find Returns: The object if found in any panel, None otherwise """ for panel in (self.signalpanel, self.imagepanel): if panel is not None: try: return panel.objmodel[uuid] except KeyError: continue return None @remote_controlled def get_object_uuids( self, panel: Literal["signal", "image"] | None = None, group: int | str | None = None, ) -> list[str]: """Get object (signal/image) uuid list for current panel. Objects are sorted by group number and object index in group. Args: panel: panel name. If None, current panel is used. group: Group number, or group id, or group title. Defaults to None (all groups). Returns: List of object uuids Raises: ValueError: if panel is unknown """ objmodel = self.__get_datapanel(panel).objmodel if group is None: return objmodel.get_object_ids() if isinstance(group, int): grp = objmodel.get_group_from_number(group) else: try: grp = objmodel.get_group(group) except KeyError: grp = objmodel.get_group_from_title(group) if grp is None: raise KeyError(f"Invalid group index, id or title: {group}") return grp.get_object_ids() @remote_controlled def get_sel_object_uuids(self, include_groups: bool = False) -> list[str]: """Return selected objects uuids. Args: include_groups: If True, also return objects from selected groups. Returns: List of selected objects uuids. """ panel = self.__get_current_basedatapanel() return panel.objview.get_sel_object_uuids(include_groups) @remote_controlled def add_group( self, title: str, panel: Literal["signal", "image"] | None = None, select: bool = False, ) -> None: """Add group to DataLab. Args: title: Group title panel: Panel name. Defaults to None. select: Select the group after creation. Defaults to False. """ self.__get_datapanel(panel).add_group(title, select) @remote_controlled def select_objects( self, selection: list[int | str], panel: Literal["signal", "image"] | None = None, ) -> None: """Select objects in current panel. Args: selection: List of object numbers (1 to N) or uuids to select panel: panel name. If None, current panel is used. Defaults to None. """ panel = self.__get_datapanel(panel) panel.objview.select_objects(selection) @remote_controlled def select_groups( self, selection: list[int | str] | None = None, panel: Literal["signal", "image"] | None = None, ) -> None: """Select groups in current panel. Args: selection: List of group numbers (1 to N), or list of group uuids, or None to select all groups. Defaults to None. panel: panel name. If None, current panel is used. Defaults to None. """ panel = self.__get_datapanel(panel) panel.objview.select_groups(selection) @remote_controlled def delete_metadata( self, refresh_plot: bool = True, keep_roi: bool = False ) -> None: """Delete metadata of selected objects Args: refresh_plot: Refresh plot. Defaults to True. keep_roi: Keep ROI. Defaults to False. """ panel = self.__get_current_basedatapanel() panel.delete_metadata(refresh_plot, keep_roi) @remote_controlled def call_method( self, method_name: str, *args, panel: Literal["signal", "image"] | None = None, **kwargs, ): """Call a public method on a panel or main window. This generic method allows calling any public method that is not explicitly exposed in the proxy API. The method resolution follows this order: 1. If panel is specified: call method on that specific panel 2. If panel is None: a. Try to call method on main window (DLMainWindow) b. If not found, try to call method on current panel (BaseDataPanel) This makes it convenient to call panel methods without specifying the panel parameter when working on the current panel. Args: method_name: Name of the method to call *args: Positional arguments to pass to the method panel: Panel name ("signal", "image", or None for auto-detection). Defaults to None. **kwargs: Keyword arguments to pass to the method Returns: The return value of the called method Raises: AttributeError: If the method does not exist or is not public ValueError: If the panel name is invalid Examples: >>> # Call remove_object on current panel (auto-detected) >>> win.call_method("remove_object", force=True) >>> # Call a signal panel method specifically >>> win.call_method("delete_all_objects", panel="signal") >>> # Call main window method >>> win.call_method("get_current_panel") """ # Security check: only allow public methods (not starting with _) if method_name.startswith("_"): raise AttributeError( f"Cannot call private method '{method_name}' through proxy" ) # If panel is specified, use that panel directly if panel is not None: target = self.__get_datapanel(panel) if not hasattr(target, method_name): raise AttributeError( f"Method '{method_name}' does not exist on {panel} panel" ) method = getattr(target, method_name) if not callable(method): raise AttributeError(f"'{method_name}' is not a callable method") return method(*args, **kwargs) # Panel is None: try main window first, then current panel # Try main window first if hasattr(self, method_name): method = getattr(self, method_name) if callable(method): return method(*args, **kwargs) # Method not found on main window, try current panel current_panel = self.__get_current_basedatapanel() if hasattr(current_panel, method_name): method = getattr(current_panel, method_name) if callable(method): return method(*args, **kwargs) # Method not found anywhere raise AttributeError( f"Method '{method_name}' does not exist on main window or current panel" ) @remote_controlled def call_method_slot( self, method_name: str, args: list, panel: Literal["signal", "image"] | None, kwargs: dict, ) -> None: """Slot to call a method from RemoteServer thread in GUI thread. This slot receives signals from RemoteServer and executes the method in the GUI thread, avoiding thread-safety issues with Qt widgets and dialogs. Args: method_name: Name of the method to call args: Positional arguments as a list panel: Panel name or None for auto-detection kwargs: Keyword arguments as a dict """ # Call the method and store result in RemoteServer try: result = self.call_method(method_name, *args, panel=panel, **kwargs) # Store result in RemoteServer for retrieval by XML-RPC thread self.remote_server.result = result self.remote_server.exception = None # Clear any previous exception except Exception as exc: # pylint: disable=broad-except # Store exception for re-raising in XML-RPC thread self.remote_server.result = None self.remote_server.exception = exc @remote_controlled def get_object_shapes( self, nb_id_title: int | str | None = None, panel: Literal["signal", "image"] | None = None, ) -> list: """Get plot item shapes associated to object (signal/image). Args: nb_id_title: Object number, or object id, or object title. Defaults to None (current object). panel: Panel name. Defaults to None (current panel). Returns: List of plot item shapes """ obj = self.get_object(nb_id_title, panel) return list(create_adapter_from_object(obj).iterate_shape_items(editable=False)) @remote_controlled def add_annotations_from_items( self, items: list, refresh_plot: bool = True, panel: Literal["signal", "image"] | None = None, ) -> None: """Add object annotations (annotation plot items). Args: items: annotation plot items refresh_plot: refresh plot. Defaults to True. panel: panel name. If None, current panel is used. """ panel = self.__get_datapanel(panel) panel.add_annotations_from_items(items, refresh_plot) @remote_controlled def add_label_with_title( self, title: str | None = None, panel: Literal["signal", "image"] | None = None ) -> None: """Add a label with object title on the associated plot Args: title: Label title. Defaults to None. If None, the title is the object title. panel: panel name. If None, current panel is used. """ self.__get_datapanel(panel).add_label_with_title(title) @remote_controlled def run_macro(self, number_or_title: int | str | None = None) -> None: """Run macro. Args: number: Number of the macro (starting at 1). Defaults to None (run current macro, or does nothing if there is no macro). """ self.macropanel.run_macro(number_or_title) @remote_controlled def stop_macro(self, number_or_title: int | str | None = None) -> None: """Stop macro. Args: number: Number of the macro (starting at 1). Defaults to None (stop current macro, or does nothing if there is no macro). """ self.macropanel.stop_macro(number_or_title) @remote_controlled def import_macro_from_file(self, filename: str) -> None: """Import macro from file Args: filename: Filename. """ self.macropanel.import_macro_from_file(filename) # ------WebAPI control @remote_controlled def start_webapi_server( self, host: str | None = None, port: int | None = None, ) -> dict: """Start the Web API server. Args: host: Host address to bind to. Defaults to "127.0.0.1". port: Port number. Defaults to auto-detect available port. Returns: Dictionary with "url" and "token" keys. Raises: RuntimeError: If Web API deps not installed or server already running. """ if not WEBAPI_AVAILABLE: raise RuntimeError( "Web API dependencies not installed. " "Install with: pip install datalab-platform[webapi]" ) controller = get_webapi_controller() controller.set_main_window(self) url, token = controller.start(host=host, port=port) return {"url": url, "token": token} @remote_controlled def stop_webapi_server(self) -> None: """Stop the Web API server.""" if not WEBAPI_AVAILABLE: return controller = get_webapi_controller() controller.stop() @remote_controlled def get_webapi_status(self) -> dict: """Get Web API server status. Returns: Dictionary with "running", "url", and "token" keys. """ if not WEBAPI_AVAILABLE: return {"running": False, "url": None, "token": None, "available": False} controller = get_webapi_controller() info = controller.get_connection_info() info["available"] = True return info # ------Misc. @property def panels(self) -> tuple[AbstractPanel, ...]: """Return the tuple of implemented panels (signal, image) Returns: Tuple of panels """ return (self.signalpanel, self.imagepanel, self.macropanel) def __set_low_memory_state(self, state: bool) -> None: """Set memory warning state""" self.__memory_warning = state def __show_webapi_info(self) -> None: """Show Web API connection info when status widget is clicked.""" if self.webapi_actions is not None: self.webapi_actions.show_connection_info() def __start_webapi_server(self) -> None: """Start Web API server when status widget is clicked.""" if self.webapi_actions is not None: self.webapi_actions.start_server_from_status_widget() def confirm_memory_state(self) -> bool: # pragma: no cover """Check memory warning state and eventually show a warning dialog Returns: True if memory state is ok """ if not env.execenv.unattended and self.__memory_warning: threshold = Conf.main.available_memory_threshold.get() answer = QW.QMessageBox.critical( self, _("Warning"), _("Available memory is below %d MB.

Do you want to continue?") % threshold, QW.QMessageBox.Yes | QW.QMessageBox.No, ) return answer == QW.QMessageBox.Yes return True def check_stable_release(self) -> None: # pragma: no cover """Check if this is a stable release""" if datalab.__version__.replace(".", "").isdigit(): # This is a stable release return if "b" in datalab.__version__: # This is a beta release rel = _( "This software is in the beta stage of its release cycle. " "The focus of beta testing is providing a feature complete " "software for users interested in trying new features before " "the final release. However, beta software may not behave as " "expected and will probably have more bugs or performance issues " "than completed software." ) else: # This is an alpha release rel = _( "This software is in the alpha stage of its release cycle. " "The focus of alpha testing is providing an incomplete software " "for early testing of specific features by users. " "Please note that alpha software was not thoroughly tested " "by the developer before it is released." ) txtlist = [ f"{APP_NAME} v{datalab.__version__}:", "", _("This is not a stable release."), "", rel, ] if not env.execenv.unattended: QW.QMessageBox.warning( self, APP_NAME, "
".join(txtlist), QW.QMessageBox.Ok ) def check_for_previous_crash(self) -> None: # pragma: no cover """Check for previous crash""" if execenv.unattended and not execenv.do_not_quit: # Showing the log viewer for testing purpose (unattended mode) but only # if option 'do_not_quit' is not set, to avoid blocking the test suite self.__show_logviewer() elif execenv.do_not_quit: # If 'do_not_quit' is set, we do not show any message box to avoid blocking # the test suite return elif Conf.main.faulthandler_log_available.get( False ) or Conf.main.traceback_log_available.get(False): txt = "
".join( [ logviewer.get_log_prompt_message(), "", _("Do you want to see available log files?"), ] ) btns = QW.QMessageBox.StandardButton.Yes | QW.QMessageBox.StandardButton.No choice = QW.QMessageBox.warning(self, APP_NAME, txt, btns) if choice == QW.QMessageBox.StandardButton.Yes: self.__show_logviewer() def check_for_v020_plugins(self) -> None: # pragma: no cover """Check for v0.20 plugins and warn user if any are found""" if Conf.main.v020_plugins_warning_ignore.get(False): return v020_plugins = discover_v020_plugins() if execenv.unattended or not v020_plugins: return # Build plugin list with clickable directory paths plugin_items = [] for name, directory_path in v020_plugins: if directory_path: # Create clickable file:// link to directory dir_url = QC.QUrl.fromLocalFile(directory_path).toString() plugin_items.append( f'

' ) else: plugin_items.append(f"
  • {name}
    • ") plugin_list = "
      " + "".join(plugin_items) + "
    " txtlist = [ "" + _("DataLab v0.20 plugins detected") + "", "", _("The following plugins are using the old DataLab v0.20 format:"), plugin_list, _( "These plugins will not be loaded in DataLab v1.0 because " "they are not compatible with the new architecture." ), "", _( "To use these plugins with DataLab v1.0, you need to update them. " "Please refer to the migration guide on the DataLab website " ) + '(Migration guide)' + _(" or in the PDF documentation."), "", _("Choosing to ignore this message will prevent it from appearing again."), ] answer = QW.QMessageBox.question( self, APP_NAME, "
    ".join(txtlist), QW.QMessageBox.Ok | QW.QMessageBox.Ignore, ) if answer == QW.QMessageBox.Ignore: Conf.main.v020_plugins_warning_ignore.set(True) def execute_post_show_actions(self) -> None: """Execute post-show actions""" self.check_stable_release() self.check_for_previous_crash() self.check_for_v020_plugins() tour = Conf.main.tour_enabled.get() if tour: Conf.main.tour_enabled.set(False) self.show_tour() # Auto-start WebAPI server if environment variable is set if os.environ.get("DATALAB_WEBAPI_ENABLED") == "1": try: self.start_webapi_server() except Exception as e: # pylint: disable=broad-exception-caught print(f"Warning: Failed to auto-start WebAPI server: {e}") def take_screenshot(self, name: str) -> None: # pragma: no cover """Take main window screenshot""" # For esthetic reasons, we set the central widget width to a lower value: old_width = self.tabwidget.maximumWidth() self.tabwidget.setMaximumWidth(500) # To avoid having screenshot depending on memory status, we set demo mode ON: self.memorystatus.set_demo_mode(True) qth.grab_save_window(self, f"{name}") # Restore previous state: self.memorystatus.set_demo_mode(False) self.tabwidget.setMaximumWidth(old_width) def take_menu_screenshots(self) -> None: # pragma: no cover """Take menu screenshots""" for panel in self.panels: if isinstance(panel, base.BaseDataPanel): self.tabwidget.setCurrentWidget(panel) for name in ( "file", "create", "edit", "roi", "view", "operation", "processing", "analysis", "help", ): menu = getattr(self, f"{name}_menu") menu.popup(self.pos()) qth.grab_save_window(menu, f"{panel.objectName()}_{name}") menu.close() if panel in (self.signalpanel, self.imagepanel): panel: BaseDataPanel # Take screenshots of Edit menu submenus (Metadata and Annotations) for submenu, suffix in ( (panel.acthandler.metadata_submenu, "_edit_metadata"), (panel.acthandler.annotations_submenu, "_edit_annotations"), ): submenu.popup(self.pos()) qth.grab_save_window(submenu, f"{panel.objectName()}{suffix}") submenu.close() # ------GUI setup def __restore_pos_and_size(self) -> None: """Restore main window position and size from configuration""" pos = Conf.main.window_position.get(None) if pos is not None: posx, posy = pos self.move(QC.QPoint(posx, posy)) size = Conf.main.window_size.get(None) if size is None: sgeo = self.screen().availableGeometry() sw, sh = sgeo.width(), sgeo.height() w = max(1200, min(1800, int(sw * 0.8))) h = max(700, min(1100, int(sh * 0.8))) size = (w, h) if pos is None: cx = sgeo.x() + (sw - w) // 2 cy = sgeo.y() + (sh - h) // 2 self.move(QC.QPoint(cx, cy)) width, height = size self.resize(QC.QSize(width, height)) if pos is not None and size is not None: sgeo = self.screen().availableGeometry() out_inf = posx < -int(0.9 * width) or posy < -int(0.9 * height) out_sup = posx > int(0.9 * sgeo.width()) or posy > int(0.9 * sgeo.height()) if len(QW.QApplication.screens()) == 1 and (out_inf or out_sup): # Main window is offscreen posx = min(max(posx, 0), sgeo.width() - width) posy = min(max(posy, 0), sgeo.height() - height) self.move(QC.QPoint(posx, posy)) def __restore_state(self) -> None: """Restore main window state from configuration""" state = Conf.main.window_state.get(None) if state is not None: state = base64.b64decode(state) self.restoreState(QC.QByteArray(state)) for widget in self.children(): if isinstance(widget, QW.QDockWidget): self.restoreDockWidget(widget) def __save_pos_size_and_state(self) -> None: """Save main window position, size and state to configuration""" is_maximized = self.windowState() == QC.Qt.WindowMaximized Conf.main.window_maximized.set(is_maximized) if not is_maximized: size = self.size() Conf.main.window_size.set((size.width(), size.height())) pos = self.pos() Conf.main.window_position.set((pos.x(), pos.y())) # Encoding window state into base64 string to avoid sending binary data # to the configuration file: state = base64.b64encode(self.saveState().data()).decode("ascii") Conf.main.window_state.set(state) def setup(self, console: bool = False) -> None: """Setup main window Args: console: True to setup console """ self.__register_plugins() self.__configure_statusbar(console) self.__setup_global_actions() self.__add_signal_image_panels() self.__create_plugins_actions() self.__setup_central_widget() self.__add_menus() self.__setup_webapi() if console: self.__setup_console() self.__update_actions(update_other_data_panel=True) self.__add_macro_panel() self.__configure_panels() # Now that everything is set up, we can restore the window state: self.__restore_state() def __setup_webapi(self) -> None: """Setup Web API actions.""" self.webapi_actions = WebApiActions(self) # Note: Menu is added in __update_view_menu since view_menu is cleared each show def __register_plugins(self) -> None: """Register plugins""" with qth.try_or_log_error("Discovering plugins"): # Discovering plugins plugin_nb = len(discover_plugins()) execenv.log(self, f"{plugin_nb} plugin(s) found") for plugin_class in PluginRegistry.get_plugin_classes(): with qth.try_or_log_error(f"Instantiating plugin {plugin_class.__name__}"): # Instantiating plugin plugin: PluginBase = plugin_class() with qth.try_or_log_error(f"Registering plugin {plugin.info.name}"): # Registering plugin plugin.register(self) def __create_plugins_actions(self) -> None: """Create plugins actions""" with self.signalpanel.acthandler.new_category(ActionCategory.PLUGINS): with self.imagepanel.acthandler.new_category(ActionCategory.PLUGINS): for plugin in PluginRegistry.get_plugins(): with qth.try_or_log_error(f"Create actions for {plugin.info.name}"): plugin.create_actions() @staticmethod def __unregister_plugins() -> None: """Unregister plugins""" with qth.try_or_log_error("Unregistering plugins"): PluginRegistry.unregister_all_plugins() def __configure_statusbar(self, console: bool) -> None: """Configure status bar Args: console: True if console is enabled """ self.statusBar().showMessage(_("Welcome to %s!") % APP_NAME, 5000) if console: # Console status self.consolestatus = status.ConsoleStatus() self.statusBar().addPermanentWidget(self.consolestatus) # Plugin status pluginstatus = status.PluginStatus() self.statusBar().addPermanentWidget(pluginstatus) # XML-RPC server status xmlrpcstatus = status.XMLRPCStatus() xmlrpcstatus.set_port(self.remote_server.port) self.statusBar().addPermanentWidget(xmlrpcstatus) # Web API server status self.webapistatus = status.WebAPIStatus() self.webapistatus.SIG_SHOW_INFO.connect(self.__show_webapi_info) self.webapistatus.SIG_START_SERVER.connect(self.__start_webapi_server) self.statusBar().addPermanentWidget(self.webapistatus) # Memory status threshold = Conf.main.available_memory_threshold.get() self.memorystatus = status.MemoryStatus(threshold) self.memorystatus.SIG_MEMORY_ALARM.connect(self.__set_low_memory_state) self.statusBar().addPermanentWidget(self.memorystatus) def __add_toolbar( self, title: str, position: Literal["top", "bottom", "left", "right"], name: str ) -> QW.QToolBar: """Add toolbar to main window Args: title: toolbar title position: toolbar position name: toolbar name (Qt object name) """ toolbar = QW.QToolBar(title, self) toolbar.setObjectName(name) area = getattr(QC.Qt, f"{position.capitalize()}ToolBarArea") self.addToolBar(area, toolbar) return toolbar def __setup_global_actions(self) -> None: """Setup global actions""" self.openh5_action = create_action( self, _("Open HDF5 files..."), icon=get_icon("fileopen_h5.svg"), tip=_("Open one or more HDF5 files"), triggered=lambda checked=False: self.open_h5_files(import_all=True), ) self.saveh5_action = create_action( self, _("Save to HDF5 file..."), icon=get_icon("filesave_h5.svg"), tip=_("Save to HDF5 file"), triggered=self.save_to_h5_file, ) self.browseh5_action = create_action( self, _("Browse HDF5 file..."), icon=get_icon("h5browser.svg"), tip=_("Browse an HDF5 file"), triggered=lambda checked=False: self.open_h5_files(import_all=None), ) self.settings_action = create_action( self, _("Settings..."), icon=get_icon("libre-gui-settings.svg"), tip=_("Open settings dialog"), triggered=self.__edit_settings, ) self.main_toolbar = self.__add_toolbar( _("Main Toolbar"), "left", "main_toolbar" ) add_actions( self.main_toolbar, [ self.openh5_action, self.saveh5_action, self.browseh5_action, None, self.settings_action, ], ) # Quit action for "File menu" (added when populating menu on demand) if self.hide_on_close: quit_text = _("Hide window") quit_tip = _("Hide DataLab window") else: quit_text = _("Quit") quit_tip = _("Quit application") if sys.platform != "darwin": # On macOS, the "Quit" action is automatically added to the application menu self.quit_action = create_action( self, quit_text, shortcut=QG.QKeySequence(QG.QKeySequence.Quit), icon=get_icon("libre-gui-close.svg"), tip=quit_tip, triggered=self.close, ) # View menu actions self.autorefresh_action = create_action( self, _("Auto-refresh"), icon=get_icon("refresh-auto.svg"), tip=_("Auto-refresh plot when object is modified, added or removed"), toggled=self.handle_autorefresh_action, ) self.showfirstonly_action = create_action( self, _("Show first object only"), icon=get_icon("show_first.svg"), tip=_("Show only the first selected object (signal or image)"), toggled=self.toggle_show_first_only, ) self.showlabel_action = create_action( self, _("Show graphical object titles"), icon=get_icon("show_titles.svg"), tip=_("Show or hide ROI and other graphical object titles or subtitles"), toggled=self.toggle_show_titles, ) def __add_signal_panel(self) -> None: """Setup signal toolbar, widgets and panel""" self.signalpanel_toolbar = self.__add_toolbar( _("Signal Panel Toolbar"), "left", "signalpanel_toolbar" ) dpw = DockablePlotWidget(self, PlotType.CURVE) self.signalpanel = signal.SignalPanel(self, dpw, self.signalpanel_toolbar) self.signalpanel.SIG_STATUS_MESSAGE.connect(self.statusBar().showMessage) plot = dpw.get_plot() plot.add_item(make.legend("TR")) plot.SIG_ITEM_PARAMETERS_CHANGED.connect( self.signalpanel.plot_item_parameters_changed ) plot.SIG_ITEM_MOVED.connect(self.signalpanel.plot_item_moved) return dpw def __add_image_panel(self) -> None: """Setup image toolbar, widgets and panel""" self.imagepanel_toolbar = self.__add_toolbar( _("Image Panel Toolbar"), "left", "imagepanel_toolbar" ) dpw = DockablePlotWidget(self, PlotType.IMAGE) self.imagepanel = image.ImagePanel(self, dpw, self.imagepanel_toolbar) # ----------------------------------------------------------------------------- # # Before eventually disabling the "peritem" mode by default, wait for the # # plotpy bug to be fixed (peritem mode is not compatible with multiple image # # items): # for cspanel in ( # self.imagepanel.plotwidget.get_xcs_panel(), # self.imagepanel.plotwidget.get_ycs_panel(), # ): # cspanel.peritem_ac.setChecked(False) # ----------------------------------------------------------------------------- self.imagepanel.SIG_STATUS_MESSAGE.connect(self.statusBar().showMessage) plot = dpw.get_plot() plot.SIG_ITEM_PARAMETERS_CHANGED.connect( self.imagepanel.plot_item_parameters_changed ) plot.SIG_ITEM_MOVED.connect(self.imagepanel.plot_item_moved) plot.SIG_LUT_CHANGED.connect(self.imagepanel.plot_lut_changed) return dpw def __update_tab_menu(self) -> None: """Update tab menu""" current_panel: BaseDataPanel = self.tabwidget.currentWidget() add_actions(self.tabmenu, current_panel.get_context_menu().actions()) def __add_signal_image_panels(self) -> None: """Add signal and image panels""" self.tabwidget = QW.QTabWidget() self.tabmenu = add_corner_menu(self.tabwidget) configure_menu_about_to_show(self.tabmenu, self.__update_tab_menu) self.signalview = self.__add_signal_panel() self.imageview = self.__add_image_panel() sdock = self.__add_dockwidget(self.signalview, title=_("Signal View")) idock = self.__add_dockwidget(self.imageview, title=_("Image View")) self.tabifyDockWidget(sdock, idock) self.docks = {self.signalpanel: sdock, self.imagepanel: idock} self.tabwidget.currentChanged.connect(self.__tab_index_changed) self.signalpanel.SIG_OBJECT_ADDED.connect( lambda: self.set_current_panel("signal") ) self.imagepanel.SIG_OBJECT_ADDED.connect( lambda: self.set_current_panel("image") ) for panel in (self.signalpanel, self.imagepanel): panel.setup_panel() def __setup_central_widget(self) -> None: """Setup central widget (main panel)""" self.tabwidget.setMaximumWidth(600) s_idx = self.tabwidget.addTab( self.signalpanel, get_icon("signal.svg"), _("Signal Panel") ) i_idx = self.tabwidget.addTab( self.imagepanel, get_icon("image.svg"), _("Image Panel") ) self.tabwidget.setTabToolTip( s_idx, _("1D Signals: Manage and process one-dimensional data") ) self.tabwidget.setTabToolTip( i_idx, _("2D Images: Manage and process two-dimensional data") ) # Apply enhanced tab bar styling tab_bar = self.tabwidget.tabBar() font = tab_bar.font() font.setPointSize(10) tab_bar.setFont(font) # Use QTimer to ensure tab bar is properly sized first QC.QTimer.singleShot(0, self.__update_tab_icon_size) self.setCentralWidget(self.tabwidget) def __update_tab_icon_size(self) -> None: """Update tab icon size based on tab bar height""" tab_bar = self.tabwidget.tabBar() if tab_bar.height() > 0: # Use approximately 80% of tab height for icon size icon_size = int(tab_bar.height() * 0.8) self.tabwidget.setIconSize(QC.QSize(icon_size, icon_size)) @staticmethod def __get_local_doc_path() -> str | None: """Return local documentation path, if it exists""" locale = QC.QLocale.system().name() for suffix in ("_" + locale[:2], "_en"): path = osp.join(DATAPATH, "doc", f"{APP_NAME}{suffix}.pdf") if osp.isfile(path): return path return None def __add_menus(self) -> None: """Adding menus""" self.file_menu = self.menuBar().addMenu(_("&File")) configure_menu_about_to_show(self.file_menu, self.__update_file_menu) self.create_menu = self.menuBar().addMenu(_("&Create")) self.edit_menu = self.menuBar().addMenu(_("&Edit")) self.roi_menu = self.menuBar().addMenu(_("ROI")) self.operation_menu = self.menuBar().addMenu(_("Operations")) self.processing_menu = self.menuBar().addMenu(_("Processing")) self.analysis_menu = self.menuBar().addMenu(_("Analysis")) self.plugins_menu = self.menuBar().addMenu(_("Plugins")) self.view_menu = self.menuBar().addMenu(_("&View")) configure_menu_about_to_show(self.view_menu, self.__update_view_menu) self.help_menu = self.menuBar().addMenu("?") for menu in ( self.create_menu, self.edit_menu, self.roi_menu, self.operation_menu, self.processing_menu, self.analysis_menu, self.plugins_menu, ): configure_menu_about_to_show(menu, self.__update_generic_menu) help_menu_actions = [ create_action( self, _("Online documentation"), icon=get_icon("libre-gui-help.svg"), triggered=lambda: webbrowser.open(__docurl__), ), ] localdocpath = self.__get_local_doc_path() if localdocpath is not None: help_menu_actions += [ create_action( self, _("PDF documentation"), icon=get_icon("help_pdf.svg"), triggered=lambda: webbrowser.open(localdocpath), ), ] help_menu_actions += [ create_action( self, _("Tour") + "...", icon=get_icon("tour.svg"), triggered=self.show_tour, ), create_action( self, _("Demo") + "...", icon=get_icon("play_demo.svg"), triggered=self.play_demo, ), None, ] if TEST_SEGFAULT_ERROR: help_menu_actions += [ create_action( self, _("Test segfault/Python error"), triggered=self.test_segfault_error, ) ] help_menu_actions += [ create_action( self, _("Log files") + "...", icon=get_icon("logs.svg"), triggered=self.__show_logviewer, ), create_action( self, _("Installation and configuration") + "...", icon=get_icon("libre-toolbox.svg"), triggered=lambda: instconfviewer.exec_datalab_installconfig_dialog( self ), ), None, create_action( self, _("Project home page"), icon=get_icon("libre-gui-globe.svg"), triggered=lambda: webbrowser.open(__homeurl__), ), create_action( self, _("Bug report or feature request"), icon=get_icon("libre-gui-globe.svg"), triggered=lambda: webbrowser.open(__supporturl__), ), create_action( self, _("About..."), icon=get_icon("libre-gui-about.svg"), triggered=self.__about, ), ] add_actions(self.help_menu, help_menu_actions) def __update_console_show_mode(self) -> None: """Update console show mode from configuration option Console show mode is whether the console is shown or not when an error occurs. """ if self.console is not None: state = Conf.console.show_console_on_error.get() cdock = self.docks[self.console] if not state and cdock.isVisible(): cdock.hide() if state: self.console.exception_occurred.connect(self.console.show_console) else: self.console.exception_occurred.disconnect(self.console.show_console) def __setup_console(self) -> None: """Add an internal console""" ns = { "dl": self, "np": np, "sps": sps, "spi": spi, "os": os, "sys": sys, "osp": osp, "time": time, } msg = _( "Welcome to DataLab console!\n" "---------------------------\n" "You can access the main window with the 'dl' variable.\n" "Example:\n" " o = dl.get_object() # returns currently selected object\n" " o = dl[1] # returns object number 1\n" " o = dl['My image'] # returns object which title is 'My image'\n" " o.data # returns object data\n" "Modules imported at startup: " "os, sys, os.path as osp, time, " "numpy as np, scipy.signal as sps, scipy.ndimage as spi" ) self.console = DockableConsole(self, namespace=ns, message=msg, debug=DEBUG) self.console.setMaximumBlockCount(Conf.console.max_line_count.get(5000)) self.console.go_to_error.connect(go_to_error) cdock = self.__add_dockwidget(self.console, _("Console")) self.docks[self.console] = cdock cdock.hide() self.console.interpreter.widget_proxy.sig_new_prompt.connect( lambda txt: self.repopulate_panel_trees() ) self.__update_console_show_mode() self.console.exception_occurred.connect(self.consolestatus.exception_occurred) cdock.visibilityChanged.connect(self.consolestatus.console_visibility_changed) self.consolestatus.SIG_SHOW_CONSOLE.connect(self.console.show_console) def __add_macro_panel(self) -> None: """Add macro panel""" self.macropanel = macro.MacroPanel(self) mdock = self.__add_dockwidget(self.macropanel, _("Macro Panel")) self.docks[self.macropanel] = mdock self.tabifyDockWidget(self.docks[self.imagepanel], mdock) self.docks[self.signalpanel].raise_() def __configure_panels(self) -> None: """Configure panels""" # Connectings signals for panel in self.panels: panel.SIG_OBJECT_ADDED.connect(self.set_modified) panel.SIG_OBJECT_REMOVED.connect(self.set_modified) self.macropanel.SIG_OBJECT_MODIFIED.connect(self.set_modified) # Initializing common panel actions self.autorefresh_action.setChecked(Conf.view.auto_refresh.get(True)) self.showfirstonly_action.setChecked(Conf.view.show_first_only.get(False)) self.showlabel_action.setChecked(Conf.view.show_label.get(False)) # Restoring current tab from last session tab_idx = Conf.main.current_tab.get(None) if tab_idx is not None: self.tabwidget.setCurrentIndex(tab_idx) # Set focus on current panel, so that keyboard shortcuts work (Fixes #10) self.tabwidget.currentWidget().setFocus() def set_process_isolation_enabled(self, state: bool) -> None: """Enable/disable process isolation Args: state: True to enable process isolation """ for processor in (self.imagepanel.processor, self.signalpanel.processor): processor.set_process_isolation_enabled(state) # ------Remote control @remote_controlled def get_current_panel(self) -> str: """Return current panel name Returns: Panel name (valid values: "signal", "image", "macro") """ panel = self.tabwidget.currentWidget() dock = self.docks[panel] if panel is self.signalpanel and dock.isVisible(): return "signal" if panel is self.imagepanel and dock.isVisible(): return "image" return "macro" @remote_controlled def set_current_panel( self, panel: Literal["signal", "image", "macro"] | BaseDataPanel ) -> None: """Switch to panel. Args: panel: panel name or panel instance Raises: ValueError: unknown panel """ if not isinstance(panel, str): if panel not in self.panels: raise ValueError(f"Unknown panel {panel}") panel = ( "signal" if panel is self.signalpanel else "image" if panel is self.imagepanel else "macro" ) if self.get_current_panel() == panel: if panel in ("signal", "image"): # Force tab index changed event to be sure that the dock associated # to the current panel is raised self.__tab_index_changed(self.tabwidget.currentIndex()) return if panel == "signal": self.tabwidget.setCurrentWidget(self.signalpanel) elif panel == "image": self.tabwidget.setCurrentWidget(self.imagepanel) elif panel == "macro": self.docks[self.macropanel].raise_() else: raise ValueError(f"Unknown panel {panel}") @remote_controlled def calc( self, name: str, param: gds.DataSet | None = None, edit: bool = True ) -> None: """Call computation feature ``name`` .. note:: This calls either the processor's ``compute_`` method (if it exists), or the processor's ```` computation feature (if it is registered, using the ``run_feature`` method). It looks for the function in all panels, starting with the current one. Args: name: Compute function name param: Compute function parameter. Defaults to None. edit: Whether to show parameter edit dialog. Defaults to True. Set to False when calling from remote/API to avoid blocking dialogs. Raises: ValueError: unknown function """ panels = [self.tabwidget.currentWidget()] panels.extend(self.panels) for panel in panels: if isinstance(panel, base.BaseDataPanel): name = name.removeprefix("compute_") panel: base.BaseDataPanel # Some computation features are wrapped in a method with a # "compute_" prefix, so we check for this first: func = getattr(panel.processor, f"compute_{name}", None) if func is not None: if param is None: func() else: func(param) return # If the function is not wrapped, we check if it is a # registered feature: try: feature = panel.processor.get_feature(name) panel.processor.run_feature(feature, param, edit=edit) return except ValueError: continue raise ValueError(f"Unknown computation function {name}") # ------GUI refresh def has_objects(self) -> bool: """Return True if sig/ima panels have any object""" return sum(len(panel) for panel in self.panels) > 0 def set_modified(self, state: bool = True) -> None: """Set mainwindow modified state""" state = state and self.has_objects() self.__is_modified = state title = APP_NAME + ("*" if state else "") if not datalab.__version__.replace(".", "").isdigit(): title += f" [{datalab.__version__}]" self.setWindowTitle(title) def is_modified(self) -> bool: """Return True if mainwindow is modified""" return self.__is_modified def __add_dockwidget(self, child, title: str) -> QW.QDockWidget: """Add QDockWidget and toggleViewAction""" dockwidget, location = child.create_dockwidget(title) dockwidget.setObjectName(title) self.addDockWidget(location, dockwidget) return dockwidget def repopulate_panel_trees(self) -> None: """Repopulate all panel trees""" for panel in self.panels: if isinstance(panel, base.BaseDataPanel): panel.objview.populate_tree() def __update_actions(self, update_other_data_panel: bool = False) -> None: """Update selection dependent actions Args: update_other_data_panel: True to update other data panel actions (i.e. if the current panel is the signal panel, also update the image panel actions, and vice-versa) """ is_signal = self.tabwidget.currentWidget() is self.signalpanel panel = self.signalpanel if is_signal else self.imagepanel other_panel = self.imagepanel if is_signal else self.signalpanel if update_other_data_panel: other_panel.selection_changed() panel.selection_changed() self.signalpanel_toolbar.setVisible(is_signal) self.imagepanel_toolbar.setVisible(not is_signal) if self.plugins_menu is not None: plugin_actions = panel.get_category_actions(ActionCategory.PLUGINS) self.plugins_menu.setEnabled(len(plugin_actions) > 0) def __tab_index_changed(self, index: int) -> None: """Switch from signal to image mode, or vice-versa""" dock = self.docks[self.tabwidget.widget(index)] dock.raise_() self.__update_actions() def __update_generic_menu(self, menu: QW.QMenu | None = None) -> None: """Update menu before showing up -- Generic method""" if menu is None: menu = self.sender() menu.clear() panel = self.tabwidget.currentWidget() category = { self.file_menu: ActionCategory.FILE, self.create_menu: ActionCategory.CREATE, self.edit_menu: ActionCategory.EDIT, self.roi_menu: ActionCategory.ROI, self.view_menu: ActionCategory.VIEW, self.operation_menu: ActionCategory.OPERATION, self.processing_menu: ActionCategory.PROCESSING, self.analysis_menu: ActionCategory.ANALYSIS, self.plugins_menu: ActionCategory.PLUGINS, }[menu] actions = panel.get_category_actions(category) add_actions(menu, actions) def __update_file_menu(self) -> None: """Update file menu before showing up""" self.saveh5_action.setEnabled(self.has_objects()) self.__update_generic_menu(self.file_menu) add_actions( self.file_menu, [ None, self.openh5_action, self.saveh5_action, self.browseh5_action, None, self.settings_action, ], ) # Add Web API submenu if self.webapi_actions is not None: self.file_menu.addSeparator() self.webapi_actions.create_menu(self.file_menu) if self.quit_action is not None: add_actions(self.file_menu, [self.quit_action]) def __update_view_menu(self) -> None: """Update view menu before showing up""" self.__update_generic_menu(self.view_menu) add_actions(self.view_menu, [None] + self.createPopupMenu().actions()) @remote_controlled def toggle_show_titles(self, state: bool) -> None: """Toggle show annotations option Args: state: state """ Conf.view.show_label.set(state) for datapanel in (self.signalpanel, self.imagepanel): for obj in datapanel.objmodel: obj.set_metadata_option("showlabel", state) datapanel.refresh_plot("selected", True, False) def handle_autorefresh_action(self, state: bool) -> None: """Handle auto-refresh action from UI (with confirmation dialog) Args: state: desired state """ # If disabling auto-refresh, show confirmation dialog if not state: txtlist = [ "" + _("Disable auto-refresh?") + "", "", _( "When auto-refresh is disabled, the plot view will not " "automatically update when objects are modified, added or removed." ), "", _( "You will need to manually click the refresh button to update " "the view." ), "", _("Are you sure you want to disable auto-refresh?"), ] answer = QW.QMessageBox.question( self, APP_NAME, "
    ".join(txtlist), QW.QMessageBox.Yes | QW.QMessageBox.No, QW.QMessageBox.No, ) if answer == QW.QMessageBox.No: # User cancelled, restore the action's checked state self.autorefresh_action.blockSignals(True) self.autorefresh_action.setChecked(True) self.autorefresh_action.blockSignals(False) return # Apply the change self.toggle_auto_refresh(state) @remote_controlled def toggle_auto_refresh(self, state: bool) -> None: """Toggle auto refresh option Args: state: state """ Conf.view.auto_refresh.set(state) for datapanel in (self.signalpanel, self.imagepanel): datapanel.plothandler.set_auto_refresh(state) @remote_controlled def toggle_show_first_only(self, state: bool) -> None: """Toggle show first only option Args: state: state """ Conf.view.show_first_only.set(state) for datapanel in (self.signalpanel, self.imagepanel): datapanel.plothandler.set_show_first_only(state) # ------Common features @remote_controlled def reset_all(self) -> None: """Reset all application data""" for panel in self.panels: if panel is not None: panel.remove_all_objects() @remote_controlled def remove_object(self, force: bool = False) -> None: """Remove current object from current panel. Args: force: if True, remove object without confirmation. Defaults to False. """ panel = self.__get_current_basedatapanel() panel.remove_object(force) @staticmethod def __check_h5file(filename: str, operation: str) -> str: """Check HDF5 filename""" filename = osp.abspath(osp.normpath(filename)) bname = osp.basename(filename) if operation == "load" and not osp.isfile(filename): raise IOError(f'File not found "{bname}"') Conf.main.base_dir.set(filename) return filename @remote_controlled def save_to_h5_file(self, filename=None) -> None: """Save to a DataLab HDF5 file Args: filename: HDF5 filename. If None, a file dialog is opened. Raises: IOError: if filename is invalid or file cannot be saved. """ if filename is None: basedir = Conf.main.base_dir.get() with qth.save_restore_stds(): filename, _fl = getsavefilename( self, _("Save"), basedir, "HDF5 (*.h5 *.hdf5 *.hdf *.he5);;All files (*)", ) if not filename: return with qth.qt_try_loadsave_file(self, filename, "save"): self.save_h5_workspace(filename) @remote_controlled def open_h5_files( self, h5files: list[str] | None = None, import_all: bool | None = None, reset_all: bool | None = None, ) -> None: """Open a DataLab HDF5 file or import from any other HDF5 file. Args: h5files: HDF5 filenames (optionally with dataset name, separated by ":") import_all: Import all datasets from HDF5 files reset_all: Reset all application data before importing """ if not self.confirm_memory_state(): return if reset_all is None: # When workspace is empty, always preserve UUIDs (reset_all=True) # since there's no risk of conflicts if not self.has_objects(): reset_all = True else: reset_all = Conf.io.h5_clear_workspace.get() if Conf.io.h5_clear_workspace_ask.get(): # Build message with optional note for native workspace import msg = _( "Do you want to clear current workspace " "(signals and images) before importing data from " "HDF5 files?" ) # Only show the UUID conflict note when importing native DataLab # workspace files (import_all=True), not when using HDF5 browser if import_all: msg += "

    " + _( "Note: If you choose No, when importing " "DataLab workspace files, objects with conflicting " "identifiers will have their processing history lost " "(features like 'Show source' and 'Recompute' will not " "work for those objects). Non-conflicting objects will " "preserve their processing history." ) msg += "

    " + _( "Choosing to ignore this message will prevent it " "from being displayed again, and will use the " "current setting (%s)." ) % (_("Yes") if reset_all else _("No")) answer = QW.QMessageBox.question( self, _("Warning"), msg, QW.QMessageBox.Yes | QW.QMessageBox.No | QW.QMessageBox.Ignore, ) if answer == QW.QMessageBox.Yes: reset_all = True elif answer == QW.QMessageBox.No: reset_all = False elif answer == QW.QMessageBox.Ignore: Conf.io.h5_clear_workspace_ask.set(False) if h5files is None: basedir = Conf.main.base_dir.get() with qth.save_restore_stds(): h5files, _fl = getopenfilenames( self, _("Open"), basedir, _("HDF5 files (*.h5 *.hdf5 *.hdf *.he5);;All files (*)"), ) if not h5files: return filenames, dsetnames = [], [] for fname_with_dset in h5files: if "," in fname_with_dset: filename, dsetname = fname_with_dset.split(",") dsetnames.append(dsetname) else: filename = fname_with_dset dsetnames.append(None) filenames.append(filename) if import_all is None and all(dsetname is None for dsetname in dsetnames): self.browse_h5_files(filenames, reset_all) return for filename, dsetname in zip(filenames, dsetnames): if import_all is None and dsetname is None: self.import_h5_file(filename, reset_all) else: with qth.qt_try_loadsave_file(self, filename, "load"): filename = self.__check_h5file(filename, "load") if dsetname is None: self.h5inputoutput.open_file(filename, import_all, reset_all) else: self.h5inputoutput.import_dataset_from_file(filename, dsetname) reset_all = False def browse_h5_files(self, filenames: list[str], reset_all: bool) -> None: """Browse HDF5 files Args: filenames: HDF5 filenames reset_all: Reset all application data before importing """ for filename in filenames: self.__check_h5file(filename, "load") self.h5inputoutput.import_files(filenames, False, reset_all) @remote_controlled def load_h5_workspace(self, h5files: list[str], reset_all: bool = False) -> None: """Load native DataLab HDF5 workspace files without any GUI elements. This method can be safely called from the internal console as it does not create any Qt widgets, dialogs, or progress bars. It is designed for programmatic use when loading DataLab workspace files. .. warning:: This method only supports native DataLab HDF5 files. For importing arbitrary HDF5 files (non-native), use the GUI menu or macros with :class:`datalab.control.proxy.RemoteProxy`. Args: h5files: List of native DataLab HDF5 filenames reset_all: Reset all application data before importing. Defaults to False. Raises: ValueError: If a file is not a valid native DataLab HDF5 file """ for idx, filename in enumerate(h5files): filename = self.__check_h5file(filename, "load") success = self.h5inputoutput.open_file_headless( filename, reset_all=(reset_all and idx == 0) ) if not success: raise ValueError( f"File '{filename}' is not a native DataLab HDF5 file. " f"Use the GUI menu or a macro with RemoteProxy to import " f"arbitrary HDF5 files." ) # Refresh panel trees after loading self.repopulate_panel_trees() @remote_controlled def save_h5_workspace(self, filename: str) -> None: """Save current workspace to a native DataLab HDF5 file without GUI elements. This method can be safely called from the internal console as it does not create any Qt widgets, dialogs, or progress bars. It is designed for programmatic use when saving DataLab workspace files. Args: filename: HDF5 filename to save to Raises: IOError: If file cannot be saved """ filename = self.__check_h5file(filename, "save") self.h5inputoutput.save_file(filename) self.set_modified(False) @remote_controlled def import_h5_file(self, filename: str, reset_all: bool | None = None) -> None: """Import HDF5 file into DataLab Args: filename: HDF5 filename (optionally with dataset name, separated by ":") reset_all: Delete all DataLab signals/images before importing data """ with qth.qt_try_loadsave_file(self, filename, "load"): filename = self.__check_h5file(filename, "load") self.h5inputoutput.import_files([filename], False, reset_all) # This method is intentionally *not* remote controlled # (see TODO regarding RemoteClient.add_object method) # @remote_controlled def add_object( self, obj: SignalObj | ImageObj, group_id: str = "", set_current=True ) -> None: """Add object - signal or image Args: obj: object to add (signal or image) group_id: group ID (optional) set_current: True to set the object as current object """ if self.confirm_memory_state(): if isinstance(obj, SignalObj): self.signalpanel.add_object(obj, group_id, set_current) elif isinstance(obj, ImageObj): self.imagepanel.add_object(obj, group_id, set_current) else: raise TypeError(f"Unsupported object type {type(obj)}") @remote_controlled def load_from_files(self, filenames: list[str]) -> None: """Open objects from files in current panel (signals/images) Args: filenames: list of filenames """ panel = self.__get_current_basedatapanel() panel.load_from_files(filenames) @remote_controlled def load_from_directory(self, path: str) -> None: """Open objects from directory in current panel (signals/images). Args: path: directory path """ panel = self.__get_current_basedatapanel() panel.load_from_directory(path) # ------Other methods related to AbstractDLControl interface def get_version(self) -> str: """Return DataLab public version. Returns: DataLab version """ return datalab.__version__ def close_application(self) -> None: # Implementing AbstractDLControl interface """Close DataLab application""" self.close() def raise_window(self) -> None: # Implementing AbstractDLControl interface """Raise DataLab window""" bring_to_front(self) def add_signal( self, title: str, xdata: np.ndarray, ydata: np.ndarray, xunit: str = "", yunit: str = "", xlabel: str = "", ylabel: str = "", group_id: str = "", set_current: bool = True, ) -> bool: # pylint: disable=too-many-arguments """Add signal data to DataLab. Args: title: Signal title xdata: X data ydata: Y data xunit: X unit. Defaults to "" yunit: Y unit. Defaults to "" xlabel: X label. Defaults to "" ylabel: Y label. Defaults to "" group_id: group id in which to add the signal. Defaults to "" set_current: if True, set the added signal as current Returns: True if signal was added successfully, False otherwise Raises: ValueError: Invalid xdata dtype ValueError: Invalid ydata dtype """ obj = create_signal( title, xdata, ydata, units=(xunit, yunit), labels=(xlabel, ylabel), ) self.add_object(obj, group_id, set_current) return True def add_image( self, title: str, data: np.ndarray, xunit: str = "", yunit: str = "", zunit: str = "", xlabel: str = "", ylabel: str = "", zlabel: str = "", group_id: str = "", set_current: bool = True, ) -> bool: # pylint: disable=too-many-arguments """Add image data to DataLab. Args: title: Image title data: Image data xunit: X unit. Defaults to "" yunit: Y unit. Defaults to "" zunit: Z unit. Defaults to "" xlabel: X label. Defaults to "" ylabel: Y label. Defaults to "" zlabel: Z label. Defaults to "" group_id: group id in which to add the image. Defaults to "" set_current: if True, set the added image as current Returns: True if image was added successfully, False otherwise Raises: ValueError: Invalid data dtype """ obj = create_image( title, data, units=(xunit, yunit, zunit), labels=(xlabel, ylabel, zlabel), ) self.add_object(obj, group_id, set_current) return True # ------? def __about(self) -> None: # pragma: no cover """About dialog box""" self.check_stable_release() if self.remote_server.port is None: xrpcstate = '' + _("not started") + "" else: xrpcstate = _("started (port %s)") % self.remote_server.port xrpcstate = f"{xrpcstate}" if Conf.main.process_isolation_enabled.get(): pistate = "" + _("enabled") + "" else: pistate = "" + _("disabled") + "" adv_conf = "
    ".join( [ "" + _("Advanced configuration:") + "", "• " + _("XML-RPC server:") + " " + xrpcstate, "• " + _("Process isolation:") + " " + pistate, ] ) created_by = _("Created by") dev_by = _("Developed and maintained by %s open-source project team") % APP_NAME cprght = "2023 DataLab Platform Developers" QW.QMessageBox.about( self, _("About") + " " + APP_NAME, f"""{APP_NAME} v{datalab.__version__}
    {APP_DESC}

    {created_by} Pierre Raybaut
    {dev_by}
    Copyright © {cprght}

    {adv_conf}""", ) def __update_color_mode(self, startup: bool = False) -> None: """Update color mode Args: startup: True if method is called during application startup (in that case, color theme is applied only if mode != "auto") """ mode = Conf.main.color_mode.get() if startup and mode == "auto": guidata_qth.win32_fix_title_bar_background(self) return # Prevent Qt from refreshing the window when changing the color mode: self.setUpdatesEnabled(False) plotpy_config.set_plotpy_color_mode(mode) if self.console is not None: self.console.update_color_mode() if self.macropanel is not None: self.macropanel.update_color_mode() if self.docks is not None: for dock in self.docks.values(): widget = dock.widget() if isinstance(widget, DockablePlotWidget): widget.update_color_mode() # Allow Qt to refresh the window: self.setUpdatesEnabled(True) def __edit_settings(self) -> None: """Edit settings""" changed_options = edit_settings(self) sigima_options.fft_shift_enabled.set(Conf.proc.fft_shift_enabled.get()) sigima_options.auto_normalize_kernel.set(Conf.proc.auto_normalize_kernel.get()) refresh_signal_panel = refresh_image_panel = False # Handling changes to shape/marker parameters s_view_result_param = ( "sig_shape_param" in changed_options or "sig_marker_param" in changed_options ) and have_geometry_results(self.signalpanel.objview.get_sel_objects(True)) i_view_result_param = ( "ima_shape_param" in changed_options or "ima_marker_param" in changed_options ) and have_geometry_results(self.imagepanel.objview.get_sel_objects(True)) if (s_view_result_param or i_view_result_param) and ( QW.QMessageBox.question( self, _("Apply settings to existing results?"), _( "Visualization settings for annotated shapes and " "markers have been modified.\n\n" "Do you want to apply these settings to existing results " "in the workspace?" ), QW.QMessageBox.Yes | QW.QMessageBox.No, QW.QMessageBox.No, ) == QW.QMessageBox.Yes ): if s_view_result_param: self.signalpanel.plothandler.refresh_all_shape_items() if i_view_result_param: self.imagepanel.plothandler.refresh_all_shape_items() for option in changed_options: if option in ( "max_shapes_to_draw", "max_cells_in_label", "max_cols_in_label", ): refresh_signal_panel = refresh_image_panel = True if option == "show_result_label": for panel in (self.signalpanel, self.imagepanel): panel.acthandler.show_label_action.setChecked( Conf.view.show_result_label.get() ) if option == "color_mode": self.__update_color_mode() if option == "show_console_on_error": self.__update_console_show_mode() if option == "plot_toolbar_position": for dock in self.docks.values(): widget = dock.widget() if isinstance(widget, DockablePlotWidget): widget.update_toolbar_position() if option.startswith(("sig_autodownsampling", "sig_linewidth")): refresh_signal_panel = True if option == "sig_autoscale_margin_percent": # Update signal plot widget autoscale margin sig_margin = Conf.view.sig_autoscale_margin_percent.get() for dock in self.docks.values(): widget: DockablePlotWidget | QW.QWidget = dock.widget() if isinstance(widget, DockablePlotWidget): plot = widget.get_plot() if ( hasattr(plot, "options") and plot.options.type == PlotType.CURVE ): plot.set_autoscale_margin_percent(sig_margin) if option == "ima_autoscale_margin_percent": # Update image plot widget autoscale margin ima_margin = Conf.view.ima_autoscale_margin_percent.get() for dock in self.docks.values(): widget: DockablePlotWidget | QW.QWidget = dock.widget() if isinstance(widget, DockablePlotWidget): plot = widget.get_plot() if ( hasattr(plot, "options") and plot.options.type == PlotType.IMAGE ): plot.set_autoscale_margin_percent(ima_margin) if option == "ima_defaults" and len(self.imagepanel) > 0: answer = QW.QMessageBox.question( self, _("Visualization settings"), _( "Default visualization settings have changed.

    " "Do you want to update all active %s objects?" ) % _("image"), QW.QMessageBox.Yes | QW.QMessageBox.No, ) if answer == QW.QMessageBox.Yes: self.imagepanel.update_metadata_view_settings() if option == "ima_aspect_ratio_1_1": refresh_image_panel = True if refresh_signal_panel: self.signalpanel.manual_refresh() if refresh_image_panel: self.imagepanel.manual_refresh() def __show_logviewer(self) -> None: """Show error logs""" logviewer.exec_datalab_logviewer_dialog(self) def play_demo(self) -> None: """Play demo""" # pylint: disable=import-outside-toplevel # pylint: disable=cyclic-import from datalab.tests.scenarios import demo demo.play_demo(self) def show_tour(self) -> None: """Show tour""" # pylint: disable=import-outside-toplevel # pylint: disable=cyclic-import from datalab.gui import tour tour.start(self) @staticmethod def test_segfault_error() -> None: """Generate errors (both fault and traceback)""" import ctypes # pylint: disable=import-outside-toplevel ctypes.string_at(0) raise RuntimeError("!!! Testing RuntimeError !!!") def show(self) -> None: """Reimplement QMainWindow method""" super().show() if self.__old_size is not None: self.resize(self.__old_size) # ------Close window def close_properly(self) -> bool: """Close properly Returns: True if closed properly, False otherwise """ if not env.execenv.unattended and self.is_modified(): answer = QW.QMessageBox.warning( self, _("Quit"), _( "Do you want to save all signals and images " "to an HDF5 file before quitting DataLab?" ), QW.QMessageBox.Yes | QW.QMessageBox.No | QW.QMessageBox.Cancel, ) if answer == QW.QMessageBox.Yes: self.save_to_h5_file() if self.is_modified(): return False elif answer == QW.QMessageBox.Cancel: return False self.hide() # Avoid showing individual widgets closing one after the other for panel in self.panels: if panel is not None: panel.close() if self.console is not None: try: self.console.close() except RuntimeError: # TODO: [P3] Investigate further why the following error occurs when # restarting the mainwindow (this is *not* a production case): # "RuntimeError: wrapped C/C++ object of type DockableConsole # has been deleted". # Another solution to avoid this error would be to really restart # the application (run each unit test in a separate process), but # it would represent too much effort for an error occuring in test # configurations only. pass if self.webapi_actions is not None: self.webapi_actions.cleanup() self.reset_all() self.__save_pos_size_and_state() self.__unregister_plugins() # Saving current tab for next session Conf.main.current_tab.set(self.tabwidget.currentIndex()) execenv.log(self, "closed properly") return True def closeEvent(self, event: QG.QCloseEvent) -> None: """Reimplement QMainWindow method""" if self.hide_on_close: self.__old_size = self.size() self.hide() else: if self.close_properly(): self.SIG_CLOSING.emit() event.accept() else: event.ignore() DataLab-1.1.0/datalab/gui/newobject.py000066400000000000000000000163511514014117600175150ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ New object creation GUI ======================= This module provides a graphical user interface (GUI) for creating new objects in the DataLab environment. It allows users to create new signals and images interactively. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import guidata.dataset as gds import numpy as np from guidata.qthelpers import exec_dialog from plotpy.builder import make from plotpy.plot import PlotDialog from plotpy.tools import EditPointTool from qtpy import QtWidgets as QW from sigima.objects import CustomSignalParam as OrigCustomSignalParam from sigima.objects import ( Gauss2DParam, ImageDatatypes, ImageObj, NewImageParam, NewSignalParam, SignalObj, create_signal, ) from sigima.objects import create_image_from_param as create_image_headless from sigima.objects import create_signal_from_param as create_signal_headless from sigima.objects.base import BaseProcParam from sigima.objects.signal import DEFAULT_TITLE as SIGNAL_DEFAULT_TITLE from datalab.config import _ CREATION_PARAMETERS_OPTION = "creation_param_json" def insert_creation_parameters(obj: SignalObj | ImageObj, param: gds.DataSet) -> None: """Insert creation parameters into object metadata. Args: param: creation parameters """ obj.set_metadata_option(CREATION_PARAMETERS_OPTION, gds.dataset_to_json(param)) def extract_creation_parameters(obj: SignalObj | ImageObj) -> gds.DataSet | None: """Extract creation parameters from object metadata. Returns: Creation parameters or None if not found """ try: param_json = obj.get_metadata_option(CREATION_PARAMETERS_OPTION) except ValueError: return None return gds.json_to_dataset(param_json) class CustomSignalParam(OrigCustomSignalParam): """Parameters for custom signal (e.g. manually defined experimental data)""" def edit_curve(self, *args) -> None: # pylint: disable=unused-argument """Edit custom curve""" win: PlotDialog = make.dialog( wintitle=_("Select one point then press OK to accept"), edit=True, type="curve", ) edit_tool = win.manager.add_tool( EditPointTool, title=_("Edit curve interactively") ) edit_tool.activate() plot = win.manager.get_plot() x, y = self.xyarray[:, 0], self.xyarray[:, 1] curve = make.mcurve(x, y, "-+") plot.add_item(curve) plot.set_active_item(curve) insert_btn = QW.QPushButton(_("Insert point"), win) insert_btn.clicked.connect(edit_tool.trigger_insert_point_at_selection) win.button_layout.insertWidget(0, insert_btn) exec_dialog(win) new_x, new_y = curve.get_data() self.xmax = new_x.max() self.xmin = new_x.min() self.size = new_x.size self.xyarray = np.vstack((new_x, new_y)).T btn_curve_edit = gds.ButtonItem( "Edit curve", callback=edit_curve, icon="signal.svg" ) def create_signal_gui( param: NewSignalParam | None = None, edit: bool = False, parent: QW.QWidget | None = None, ) -> SignalObj | None: """Create a new Signal object from GUI Args: param: base signal parameters (NewSignalParam) edit: Open a dialog box to edit parameters (default: False) parent: parent widget Returns: Signal object or None if canceled Raises: ValueError: if base_param is None and edit is False """ if param is None: param = NewSignalParam() edit = True # Default to editing if no parameters provided # CustomSignalParam requires edit mode to initialize the xyarray. # Without this, if edit=False (the default in new_object), the setup_array # call would be skipped, leaving xyarray as None, which would cause an # AttributeError when trying to access param.xyarray.T later. if isinstance(param, OrigCustomSignalParam): edit = True if isinstance(param, OrigCustomSignalParam) and edit: p_init = NewSignalParam(_("Custom signal")) p_init.size = 10 # Set smaller default size for initial input if not p_init.edit(parent=parent): return None param.setup_array(size=p_init.size, xmin=p_init.xmin, xmax=p_init.xmax) if edit: if not param.edit(parent=parent): return None if isinstance(param, OrigCustomSignalParam): signal = create_signal(param.title) signal.xydata = param.xyarray.T if signal.title == SIGNAL_DEFAULT_TITLE: signal.title = f"custom(npts={param.size})" return signal try: signal = create_signal_headless(param) except Exception as exc: # pylint: disable=broad-except if parent is not None: QW.QMessageBox.warning(parent, _("Error"), str(exc)) else: raise ValueError(f"Error creating signal: {exc}") from exc signal = None # Insert creation parameters into metadata, only if `param` is an instance of a # class deriving from `NewSignalParam` (not an instance of `NewSignalParam` itself): # pylint: disable=unidiomatic-typecheck if isinstance(param, NewSignalParam) and type(param) is not NewSignalParam: insert_creation_parameters(signal, param) return signal def create_image_gui( param: NewImageParam | None = None, edit: bool = False, parent: QW.QWidget | None = None, ) -> ImageObj | None: """Create a new Image object from GUI Args: param: image parameters edit: Open a dialog box to edit parameters (default: False) parent: parent widget Returns: Image object or None if canceled Raises: ValueError: if base_param is None and edit is False """ if param is None: param = NewImageParam() edit = True # Default to editing if no parameters provided if param.height is None: param.height = 500 if param.width is None: param.width = 500 if param.dtype is None: param.dtype = ImageDatatypes.UINT16 dtype: ImageDatatypes = param.dtype numpy_dtype = dtype.to_numpy_dtype() if isinstance(param, Gauss2DParam): if param.a is None: try: param.a = np.iinfo(numpy_dtype).max / 2.0 except ValueError: param.a = 10.0 elif isinstance(param, BaseProcParam): param.set_from_datatype(numpy_dtype) if edit: if not param.edit(parent=parent): return None try: image = create_image_headless(param) except Exception as exc: # pylint: disable=broad-except if parent is not None: QW.QMessageBox.warning(parent, _("Error"), str(exc)) else: raise ValueError(f"Error creating image: {exc}") from exc return None # Insert creation parameters into metadata, only if `param` is an instance of a # class deriving from `NewImageParam` (not an instance of `NewImageParam` itself): # pylint: disable=unidiomatic-typecheck if isinstance(param, NewImageParam) and type(param) is not NewImageParam: insert_creation_parameters(image, param) return image DataLab-1.1.0/datalab/gui/objectview.py000066400000000000000000000764571514014117600177130ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Object view =========== The :mod:`datalab.gui.objectview` module provides widgets to display object (signal/image) trees. .. note:: This module provides tree widgets to display signals, images and groups. It is important to note that, by design, the user can only select either individual signals/images or groups, but not both at the same time. This is an important design choice, as it allows to simplify the user experience, and to avoid potential confusion between the two types of selection. Simple object tree ------------------ .. autoclass:: SimpleObjectTree Get object dialog ----------------- .. autoclass:: GetObjectsDialog Object view ----------- .. autoclass:: ObjectView """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import os from collections.abc import Iterator from typing import TYPE_CHECKING from guidata.configtools import get_icon from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from sigima.objects import ImageObj, SignalObj from datalab.config import _ from datalab.objectmodel import ObjectGroup, get_short_id, get_uuid from datalab.utils.qthelpers import block_signals if TYPE_CHECKING: from typing import Any from datalab.gui.panel.base import BaseDataPanel from datalab.objectmodel import ObjectModel def metadata_to_html(metadata: dict[str, Any]) -> str: """Convert metadata to human-readable string. Returns: HTML string """ textlines = [] for key, value in metadata.items(): if len(textlines) > 5: textlines.append("[...]") break if not key.startswith("_"): vlines = str(value).splitlines() if vlines: text = f"{key}: {vlines[0][:100]}" if len(vlines) > 1: text += " [...]" textlines.append(text) if textlines: ptit = _("Object metadata") psub = _("(click on Metadata button for more details)") prefix = f"{ptit}: {psub}
    " return f"

    {prefix}{'
    '.join(textlines)}

    " return "" class SimpleObjectTree(QW.QTreeWidget): """Base object handling panel list widget, object (sig/ima) lists""" SIG_ITEM_DOUBLECLICKED = QC.Signal(str) SIG_CONTEXT_MENU = QC.Signal(QC.QPoint) def __init__(self, parent: QW.QWidget, objmodel: ObjectModel) -> None: self.objmodel: ObjectModel = objmodel super().__init__(parent) self.setHeaderHidden(True) self.setColumnCount(1) self.setAlternatingRowColors(True) self.itemDoubleClicked.connect(self.item_double_clicked) self.header().setSectionResizeMode(QW.QHeaderView.Interactive) self.itemChanged.connect(lambda item: self.resizeColumnToContents(0)) def __str__(self) -> str: """Return string representation""" textlist = [] for tl_index in range(self.topLevelItemCount()): tl_item = self.topLevelItem(tl_index) textlist.append(tl_item.text(0)) for index in range(tl_item.childCount()): textlist.append(" " + tl_item.child(index).text(0)) return os.linesep.join(textlist) def initialize_from(self, sobjlist: SimpleObjectTree) -> None: """Init from another SimpleObjectList, without making copies of objects""" self.objmodel = sobjlist.objmodel self.populate_tree() self.set_current_item_id(sobjlist.get_current_item_id()) def iter_items( self, item: QW.QTreeWidgetItem | None = None ) -> Iterator[QW.QTreeWidgetItem]: """Recursively iterate over all items""" if item is None: for index in range(self.topLevelItemCount()): yield from self.iter_items(self.topLevelItem(index)) else: yield item for index in range(item.childCount()): yield from self.iter_items(item.child(index)) def get_item_from_id(self, item_id) -> QW.QTreeWidgetItem: """Return QTreeWidgetItem from id (stored in item's data)""" for item in self.iter_items(): if item.data(0, QC.Qt.UserRole) == item_id: return item return None def get_current_item_id(self, object_only: bool = False) -> str | None: """Return current item id""" item = self.currentItem() if item is not None and (not object_only or item.parent() is not None): return item.data(0, QC.Qt.UserRole) return None def set_current_item_id(self, uuid: str, extend: bool = False) -> None: """Set current item by id""" item = self.get_item_from_id(uuid) if extend: self.setCurrentItem(item, 0, QC.QItemSelectionModel.Select) else: self.setCurrentItem(item) def get_current_group_id(self) -> str: """Return current group ID""" selected_item = self.currentItem() if selected_item is None: return None if selected_item.parent() is None: return selected_item.data(0, QC.Qt.UserRole) return selected_item.parent().data(0, QC.Qt.UserRole) def get_sel_group_items(self) -> list[QW.QTreeWidgetItem]: """Return selected group items""" return [item for item in self.selectedItems() if item.parent() is None] def get_sel_group_uuids(self) -> list[str]: """Return selected group uuids""" return [item.data(0, QC.Qt.UserRole) for item in self.get_sel_group_items()] def get_sel_object_items(self) -> list[QW.QTreeWidgetItem]: """Return selected object items""" return [item for item in self.selectedItems() if item.parent() is not None] def get_sel_object_uuids(self, include_groups: bool = False) -> list[str]: """Return selected objects uuids. Args: include_groups: If True, also return objects from selected groups. Returns: List of selected objects uuids. """ sel_items = self.get_sel_object_items() if not sel_items: cur_item = self.currentItem() if cur_item is not None and cur_item.parent() is not None: sel_items = [cur_item] uuids = [item.data(0, QC.Qt.UserRole) for item in sel_items] if include_groups: for group_id in self.get_sel_group_uuids(): uuids.extend(self.objmodel.get_group_object_ids(group_id)) return uuids def get_sel_objects( self, include_groups: bool = False ) -> list[SignalObj | ImageObj]: """Return selected objects. If include_groups is True, also return objects from selected groups.""" return [self.objmodel[oid] for oid in self.get_sel_object_uuids(include_groups)] def get_sel_groups(self) -> list[ObjectGroup]: """Return selected groups""" return self.objmodel.get_groups(self.get_sel_group_uuids()) @staticmethod def __update_item( item: QW.QTreeWidgetItem, obj: SignalObj | ImageObj | ObjectGroup ) -> None: """Update item""" item.setText(0, f"{get_short_id(obj)}: {obj.title}") if isinstance(obj, (SignalObj, ImageObj)): item.setToolTip(0, metadata_to_html(obj.metadata)) item.setData(0, QC.Qt.UserRole, get_uuid(obj)) def populate_tree(self) -> None: """Populate tree with objects""" uuid = self.get_current_item_id() with block_signals(widget=self): self.clear() for group in self.objmodel.get_groups(): self.add_group_item(group) if uuid is not None: self.set_current_item_id(uuid) def update_tree(self) -> None: """Update tree Note: If an item is not found in the tree, the tree is repopulated to ensure consistency between the tree and the model. This can happen in rare cases when objects are added to the model but the tree was not properly updated. """ for group in self.objmodel.get_groups(): group_item = self.get_item_from_id(get_uuid(group)) if group_item is None: # Group item not found, repopulate tree to fix inconsistency self.populate_tree() return self.__update_item(group_item, group) for obj in group: obj_item = self.get_item_from_id(get_uuid(obj)) if obj_item is None: # Object item not found, repopulate tree to fix inconsistency self.populate_tree() return self.__update_item(obj_item, obj) def __add_to_group_item( self, obj: SignalObj | ImageObj, group_item: QW.QTreeWidgetItem ) -> None: """Add object to group item""" item = QW.QTreeWidgetItem() icon = "signal.svg" if isinstance(obj, SignalObj) else "image.svg" item.setIcon(0, get_icon(icon)) self.__update_item(item, obj) group_item.addChild(item) def add_group_item(self, group: ObjectGroup) -> None: """Add group item""" group_item = QW.QTreeWidgetItem() group_item.setIcon(0, get_icon("group.svg")) self.__update_item(group_item, group) self.addTopLevelItem(group_item) group_item.setExpanded(True) for obj in group: self.__add_to_group_item(obj, group_item) self.resizeColumnToContents(0) def add_object_item( self, obj: SignalObj | ImageObj, group_id: str, set_current: bool = True ) -> None: """Add item""" group_item = self.get_item_from_id(group_id) self.__add_to_group_item(obj, group_item) if set_current: self.set_current_item_id(get_uuid(obj)) self.resizeColumnToContents(0) def update_item(self, uuid: str) -> None: """Update item""" obj_or_group = self.objmodel.get_object_or_group(uuid) item = self.get_item_from_id(uuid) self.__update_item(item, obj_or_group) def remove_item(self, oid: str, refresh: bool = True) -> None: """Remove item""" item = self.get_item_from_id(oid) if item is not None: with block_signals(widget=self, enable=not refresh): if item.parent() is None: # Group item: remove from tree self.takeTopLevelItem(self.indexOfTopLevelItem(item)) else: # Object item: remove from parent item.parent().removeChild(item) def item_double_clicked(self, item: QW.QTreeWidgetItem) -> None: """Item was double-clicked: open a pop-up plot dialog""" if item.parent() is not None: oid = item.data(0, QC.Qt.UserRole) self.SIG_ITEM_DOUBLECLICKED.emit(oid) def contextMenuEvent(self, event: QG.QContextMenuEvent) -> None: # pylint: disable=C0103 """Override Qt method""" self.SIG_CONTEXT_MENU.emit(event.globalPos()) class GetObjectsDialog(QW.QDialog): """Dialog box showing groups and objects (signals or images) to select one, or more. Args: parent: parent widget panel: data panel title: dialog title comment: optional dialog comment nb_objects: number of objects to select (default: 1) minimum_size: minimum size (width, height) """ def __init__( self, parent: QW.QWidget, panel: BaseDataPanel, title: str, comment: str = "", nb_objects: int = 1, minimum_size: tuple[int, int] | None = None, ) -> None: super().__init__(parent) self.__nb_objects = nb_objects self.__selected_objects: list[SignalObj | ImageObj] = [] self.setWindowTitle(title) vlayout = QW.QVBoxLayout() self.setLayout(vlayout) self.tree = SimpleObjectTree(parent, panel.objmodel) self.tree.initialize_from(panel.objview) self.tree.SIG_ITEM_DOUBLECLICKED.connect(lambda oid: self.accept()) self.tree.itemSelectionChanged.connect(self.__item_selection_changed) if nb_objects > 1: self.tree.setSelectionMode(QW.QAbstractItemView.ExtendedSelection) vlayout.addWidget(self.tree) if comment: lbl = QW.QLabel(comment) lbl.setWordWrap(True) vlayout.addSpacing(10) vlayout.addWidget(lbl) bbox = QW.QDialogButtonBox(QW.QDialogButtonBox.Ok | QW.QDialogButtonBox.Cancel) bbox.accepted.connect(self.accept) bbox.rejected.connect(self.reject) self.ok_btn = bbox.button(QW.QDialogButtonBox.Ok) vlayout.addSpacing(10) vlayout.addWidget(bbox) # Update OK button state: self.__item_selection_changed() if minimum_size is not None: self.setMinimumSize(*minimum_size) else: self.setMinimumWidth(400) def __item_selection_changed(self) -> None: """Item selection has changed""" nobj = self.__nb_objects self.__selected_objects = self.tree.get_sel_objects(include_groups=nobj > 1) self.ok_btn.setEnabled(len(self.__selected_objects) == nobj) def get_selected_objects(self) -> list[SignalObj | ImageObj]: """Return selected objects""" return self.__selected_objects class ObjectView(SimpleObjectTree): """Object handling panel list widget, object (sig/ima) lists""" SIG_SELECTION_CHANGED = QC.Signal() SIG_IMPORT_FILES = QC.Signal(list) def __init__(self, parent: BaseDataPanel, objmodel: ObjectModel) -> None: super().__init__(parent, objmodel) self.setSelectionMode(QW.QAbstractItemView.ExtendedSelection) self.setAcceptDrops(True) self.setDragEnabled(True) self.setDragDropMode(QW.QAbstractItemView.InternalMove) self.itemSelectionChanged.connect(self.item_selection_changed) self.__dragged_objects: list[QW.QListWidgetItem] = [] self.__dragged_groups: list[QW.QListWidgetItem] = [] self.__dragged_expanded_states: dict[QW.QListWidgetItem, bool] = {} def paintEvent(self, event): # pylint: disable=C0103 """Reimplement Qt method""" super().paintEvent(event) if len(self.objmodel) > 0: return # Draw empty state message with contextual hints painter = QG.QPainter(self.viewport()) rect = self.rect() # Set font for main message font = painter.font() font.setPointSize(11) painter.setFont(font) # Main message main_msg = _("Drag files here to open") main_rect = QC.QRect(rect) main_rect.setHeight(rect.height() // 2) painter.drawText(main_rect, QC.Qt.AlignCenter, main_msg) # Contextual hint hint_rect = QC.QRect(rect) hint_rect.setTop(rect.height() // 2) font.setPointSize(9) font.setItalic(True) painter.setFont(font) painter.setPen(QG.QColor(128, 128, 128)) # Gray color panel: BaseDataPanel = self.parent() if panel.PANEL_STR_ID == "signal": hint = _( "Working with 2D images?\n" "Switch to the Image Panel using the tab above." ) else: hint = _( "Working with 1D signals?\n" "Switch to the Signal Panel using the tab above." ) painter.drawText(hint_rect, QC.Qt.AlignCenter, hint) # pylint: disable=unused-argument def dragEnterEvent(self, event: QG.QDragEnterEvent) -> None: """Reimplement Qt method""" if event.mimeData().hasUrls(): event.acceptProposedAction() else: super().dragEnterEvent(event) self.__dragged_groups = self.get_sel_group_items() self.__dragged_objects = self.get_sel_object_items() self.__dragged_expanded_states = { item.data(0, QC.Qt.UserRole): item.isExpanded() for item in self.__dragged_groups } def dragLeaveEvent(self, event: QG.QDragLeaveEvent) -> None: """Reimplement Qt method""" super().dragLeaveEvent(event) self.__dragged_groups = [] self.__dragged_objects = [] self.__dragged_expanded_states = {} # pylint: disable=unused-argument def dragMoveEvent(self, event: QG.QDragMoveEvent) -> None: """Reimplement Qt method""" self.setDropIndicatorShown(True) if event.mimeData().hasUrls(): event.setDropAction(QC.Qt.CopyAction) event.accept() else: super().dragMoveEvent(event) self.setDropIndicatorShown(self.__is_drop_allowed(event)) def __is_drop_allowed(self, event: QG.QDropEvent | QG.QDragMoveEvent) -> bool: """Return True if drop is allowed""" # Yes, this method has too many return statements. # But it's still quite readable, so let's focus on other things and just disable # the pylint warning. # # pylint: disable=too-many-return-statements if event.mimeData().hasUrls(): return True drop_pos = self.dropIndicatorPosition() on_item = drop_pos == QW.QAbstractItemView.OnItem above_item = drop_pos == QW.QAbstractItemView.AboveItem below_item = drop_pos == QW.QAbstractItemView.BelowItem on_viewport = drop_pos == QW.QAbstractItemView.OnViewport target_item = self.itemAt(event.pos()) # If moved items are objects, refuse the drop on the viewport if self.__dragged_objects and on_viewport: return False # If drop indicator is on an item, refuse the drop if the target item # is anything but a group if on_item and (target_item is None or target_item.parent() is not None): return False # If drop indicator is on an item, refuse the drop if the moved items # are groups if on_item and self.__dragged_groups: return False # If target item is None, it means that the drop position is # outside of the tree. In this case, we accept the drop and move # the objects to the end of the list. if target_item is None or on_viewport: return True # If moved items are groups, refuse the drop if the target item is # not a group if self.__dragged_groups and target_item.parent() is not None: return False # If moved items are groups, refuse the drop if the target item is # a group but the target position is below the target instead of above if self.__dragged_groups and below_item: return False # If moved items are objects, refuse the drop if the target item is # a group and the drop indicator is anything but on the target item if self.__dragged_objects and target_item.parent() is None and not on_item: return False # If moved items are objects, refuse the drop if the target item is # the first group item and the drop position is above the target item if ( self.__dragged_objects and target_item.parent() is None and self.indexFromItem(target_item).row() == 0 and above_item ): return False return True def get_all_group_uuids(self) -> list[str]: """Return all group uuids, in a list ordered by group position in the tree""" return [ self.topLevelItem(index).data(0, QC.Qt.UserRole) for index in range(self.topLevelItemCount()) ] def get_all_object_uuids(self) -> dict[str, list[str]]: """Return all object uuids, in a dictionary that maps group uuids to the list of object uuids in each group, in the correct order""" return { group_id: [ self.topLevelItem(index).child(idx).data(0, QC.Qt.UserRole) for idx in range(self.topLevelItem(index).childCount()) ] for index, group_id in enumerate(self.get_all_group_uuids()) } def dropEvent(self, event: QG.QDropEvent) -> None: # pylint: disable=C0103 """Reimplement Qt method""" if event.mimeData().hasUrls(): fnames = [url.toLocalFile() for url in event.mimeData().urls()] self.SIG_IMPORT_FILES.emit(fnames) event.setDropAction(QC.Qt.CopyAction) event.accept() else: is_allowed = self.__is_drop_allowed(event) if not is_allowed: event.ignore() else: drop_pos = self.dropIndicatorPosition() on_viewport = drop_pos == QW.QAbstractItemView.OnViewport target_item = self.itemAt(event.pos()) # If target item is None, it means that the drop position is # outside of the tree. In this case, we accept the drop and move # the objects to the end of the list. if target_item is None or on_viewport: # If moved items are groups, move them to the end of the list if self.__dragged_groups: for item in self.__dragged_groups: self.takeTopLevelItem(self.indexOfTopLevelItem(item)) self.addTopLevelItem(item) # If moved items are objects, move them to the last group if self.__dragged_objects: lastgrp_item = self.topLevelItem(self.topLevelItemCount() - 1) for item in self.__dragged_objects: item.parent().removeChild(item) lastgrp_item.addChild(item) event.accept() else: super().dropEvent(event) if event.isAccepted(): # Ok, the drop was accepted, so we need to update the model accordingly # (at this stage, the model has not been updated yet but the tree has # been updated already, e.g. by the super().dropEvent(event) calls). # Thus, we have to loop over all tree items and reproduce the tree # structure in the model, by reordering the groups and objects. # We have two cases to consider (that mutually exclude each other): # 1. Groups are moved: we need to reorder the groups in the model # 2. Objects are moved: we need to reorder the objects in all groups # in the model # Let's start with case 1: if self.__dragged_groups: # First, we need to get the list of all groups in the model # (in the correct order) gids = self.get_all_group_uuids() # Then, we need to reorder the groups in the model self.objmodel.reorder_groups(gids) # Now, let's consider case 2: if self.__dragged_objects: # First, we need to get a dictionary that maps group ids to # the list of objects in each group (in the correct order) oids = self.get_all_object_uuids() # Then, we need to reorder the objects in all groups in the model self.objmodel.reorder_objects(oids) # Finally, we need to update tree self.update_tree() # Restore expanded states of moved groups for item in self.__dragged_groups: item.setExpanded( self.__dragged_expanded_states[item.data(0, QC.Qt.UserRole)] ) # Restore selection, either of groups or objects sel_items = self.__dragged_groups or self.__dragged_objects extend = len(sel_items) > 1 for item in sel_items: if extend: self.setCurrentItem(item, 0, QC.QItemSelectionModel.Select) else: self.setCurrentItem(item) def get_current_object(self) -> SignalObj | ImageObj | None: """Return current object""" oid = self.get_current_item_id(object_only=True) if oid is not None: return self.objmodel[oid] return None def set_current_object(self, obj: SignalObj | ImageObj) -> None: """Set current object""" self.set_current_item_id(get_uuid(obj)) def item_selection_changed(self) -> None: """Refreshing the selection of objects and groups, emitting the SIG_SELECTION_CHANGED signal which triggers the update of the object properties panel, the plot items and the actions of the toolbar and menu bar. This method is called when the user selects or deselects items in the tree. It is also called when the user clicks on an item that was already selected. This method emits the SIG_SELECTION_CHANGED signal. """ # ==> This is a very important design choice <== # When a group is selected, all individual objects are deselected, even if # they belong to other groups. This is intended to simplify the user experience. # In other words, the user may either select groups or individual objects, but # not both at the same time. sel_groups = self.get_sel_group_items() if sel_groups: for item in self.get_sel_object_items(): item.setSelected(False) if self.currentItem().parent() is not None: self.setCurrentItem(sel_groups[0]) self.SIG_SELECTION_CHANGED.emit() def select_objects( self, selection: list[SignalObj | ImageObj | int | str], ) -> None: """Select multiple objects Args: selection: list of objects, object numbers (1 to N) or object uuids """ if all(isinstance(obj, int) for obj in selection): all_uuids = self.objmodel.get_object_ids() uuids = [all_uuids[num - 1] for num in selection] elif all(isinstance(obj, str) for obj in selection): uuids = selection else: assert all(isinstance(obj, (SignalObj, ImageObj)) for obj in selection) uuids = [get_uuid(obj) for obj in selection] for idx, uuid in enumerate(uuids): self.set_current_item_id(uuid, extend=idx > 0) def select_groups( self, groups: list[ObjectGroup | int | str] | None = None ) -> None: """Select multiple groups Args: groups: list of groups, group numbers (1 to N), group names or None (select all groups). Defaults to None. """ if groups is None: groups = self.objmodel.get_groups() elif all(isinstance(group, int) for group in groups): groups = [self.objmodel.get_groups()[grp_num - 1] for grp_num in groups] elif all(isinstance(group, str) for group in groups): groups = self.objmodel.get_groups(groups) assert all(isinstance(group, ObjectGroup) for group in groups) for idx, group in enumerate(groups): self.set_current_item_id(get_uuid(group), extend=idx > 0) def __reorder_model(self) -> None: """Reorder model""" self.objmodel.reorder_groups(self.get_all_group_uuids()) self.objmodel.reorder_objects(self.get_all_object_uuids()) self.update_tree() def move_up(self): """Move selected objects/groups up""" sel_objs = self.get_sel_object_items() sel_groups = self.get_sel_group_items() # Sort selected objects/groups by their position in the tree sel_objs.sort(key=lambda item: self.indexFromItem(item).row()) sel_groups.sort(key=lambda item: self.indexFromItem(item).row()) if not sel_objs and not sel_groups: return if sel_objs: for item in sel_objs: parent = item.parent() idx_item = parent.indexOfChild(item) idx_parent = self.indexOfTopLevelItem(parent) if idx_item > 0: parent.takeChild(idx_item) parent.insertChild(idx_item - 1, item) elif idx_parent > 0: # If the object is the first child of its parent, we check if # there is a group above the parent. If so, we move the object # to the end of the group above. parent.takeChild(idx_item) self.topLevelItem(idx_parent - 1).addChild(item) else: return else: # Store groups expanded state expstates = { item.data(0, QC.Qt.UserRole): item.isExpanded() for item in sel_groups } for item in sel_groups: idx_item = self.indexOfTopLevelItem(item) if idx_item > 0: self.takeTopLevelItem(idx_item) self.insertTopLevelItem(idx_item - 1, item) else: return # Restore groups expanded state for item in sel_groups: item.setExpanded(expstates[item.data(0, QC.Qt.UserRole)]) self.__reorder_model() # Restore selection for item in sel_objs + sel_groups: item.setSelected(True) def move_down(self): """Move selected objects/groups down""" sel_objs = self.get_sel_object_items() sel_groups = self.get_sel_group_items() # Sort selected objects/groups by their position in the tree sel_objs.sort(key=lambda item: self.indexFromItem(item).row(), reverse=True) sel_groups.sort(key=lambda item: self.indexFromItem(item).row(), reverse=True) if not sel_objs and not sel_groups: return if sel_objs: for item in sel_objs: parent = item.parent() idx_item = parent.indexOfChild(item) idx_parent = self.indexOfTopLevelItem(parent) if idx_item < parent.childCount() - 1: parent.takeChild(idx_item) parent.insertChild(idx_item + 1, item) elif idx_parent < self.topLevelItemCount() - 1: # If the object is the last child of its parent, we check if # there is a group below the parent. If so, we move the object # to the beginning of the group below. parent.takeChild(idx_item) self.topLevelItem(idx_parent + 1).insertChild(0, item) else: return else: # Store groups expanded state expstates = { item.data(0, QC.Qt.UserRole): item.isExpanded() for item in sel_groups } for item in sel_groups: idx_item = self.indexOfTopLevelItem(item) if idx_item < self.topLevelItemCount() - 1: self.takeTopLevelItem(idx_item) self.insertTopLevelItem(idx_item + 1, item) else: return # Restore groups expanded state for item in sel_groups: item.setExpanded(expstates[item.data(0, QC.Qt.UserRole)]) self.__reorder_model() # Restore selection for item in sel_objs + sel_groups: item.setSelected(True) DataLab-1.1.0/datalab/gui/panel/000077500000000000000000000000001514014117600162545ustar00rootroot00000000000000DataLab-1.1.0/datalab/gui/panel/__init__.py000066400000000000000000000025741514014117600203750ustar00rootroot00000000000000""" Panel ===== The :mod:`datalab.gui.panel` package provides the **panel objects** for signals and images. Three types of panels are available: - :class:`datalab.gui.panel.signal.SignalPanel`: Signal panel - :class:`datalab.gui.panel.image.ImagePanel`: Image panel - :class:`datalab.gui.panel.macro.MacroPanel`: Macro panel Signal and Image Panels are called **Data Panels** and are used to display and handle signals and images in the main window of DataLab. Data Panels rely on the :class:`datalab.gui.panel.base.ObjectProp` class (managing the object properties) and a set of modules to handle the GUI features: - :mod:`datalab.gui.actionhandler`: Application actions (menus, toolbars, context menu) - :mod:`datalab.gui.objectview`: Widgets to display object (signal/image) trees - :mod:`datalab.gui.plothandler`: `PlotPy` items for representing signals and images - :mod:`datalab.gui.processor`: Processor (computation) - :mod:`datalab.gui.panel.roieditor`: ROI editor The Macro Panel is used to display and run macros. It relies on the :mod:`datalab.gui.macroeditor` module to handle the macro edition and execution. Base features ------------- .. automodule:: datalab.gui.panel.base Signal panel ------------ .. automodule:: datalab.gui.panel.signal Image panel ----------- .. automodule:: datalab.gui.panel.image Macro panel ----------- .. automodule:: datalab.gui.panel.macro """ DataLab-1.1.0/datalab/gui/panel/base.py000066400000000000000000004071711514014117600175520ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ .. Base panel objects (see parent package :mod:`datalab.gui.panel`) """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import abc import glob import os import os.path as osp import re import warnings from dataclasses import dataclass from typing import TYPE_CHECKING, Any, Generator, Generic, Literal, Type import guidata.dataset as gds import guidata.dataset.qtwidgets as gdq import h5py import numpy as np import plotpy.io from guidata.configtools import get_icon from guidata.dataset import restore_dataset, update_dataset from guidata.qthelpers import add_actions, create_action, exec_dialog from plotpy.plot import BasePlot, BasePlotOptions, PlotDialog, SyncPlotDialog from plotpy.tools import ActionTool from qtpy import QtCore as QC # type: ignore[import] from qtpy import QtWidgets as QW from qtpy.compat import ( getexistingdirectory, getopenfilename, getopenfilenames, getsavefilename, ) from sigima.io import ( read_annotations, read_metadata, read_roi, write_annotations, write_metadata, write_roi, ) from sigima.io.base import get_file_extensions from sigima.io.common.basename import format_basenames from sigima.objects import ( ImageObj, NewImageParam, SignalObj, TypeObj, TypeROI, create_image_from_param, create_signal, create_signal_from_param, ) from sigima.objects.base import ROI_KEY from sigima.params import SaveToDirectoryParam from datalab import objectmodel from datalab.adapters_metadata import ( GeometryAdapter, ResultData, TableAdapter, create_resultdata_dict, show_resultdata, ) from datalab.adapters_plotpy import create_adapter_from_object from datalab.config import APP_NAME, Conf, _ from datalab.env import execenv from datalab.gui import actionhandler, objectview from datalab.gui.newobject import ( CREATION_PARAMETERS_OPTION, NewSignalParam, extract_creation_parameters, insert_creation_parameters, ) from datalab.gui.processor.base import ( PROCESSING_PARAMETERS_OPTION, ProcessingParameters, clear_analysis_parameters, extract_processing_parameters, insert_processing_parameters, ) from datalab.gui.roieditor import TypeROIEditor from datalab.objectmodel import ObjectGroup, get_short_id, get_uuid, set_uuid from datalab.utils.qthelpers import ( CallbackWorker, create_progress_bar, qt_long_callback, qt_try_except, qt_try_loadsave_file, save_restore_stds, ) from datalab.widgets.textimport import TextImportWizard if TYPE_CHECKING: from plotpy.items import CurveItem, LabelItem, MaskedXYImageItem from sigima.io.image import ImageIORegistry from sigima.io.signal import SignalIORegistry from datalab.gui import ObjItf from datalab.gui.main import DLMainWindow from datalab.gui.plothandler import ImagePlotHandler, SignalPlotHandler from datalab.gui.processor.image import ImageProcessor from datalab.gui.processor.signal import SignalProcessor from datalab.h5.native import NativeH5Reader, NativeH5Writer # Metadata keys that should not be pasted when copying metadata between objects METADATA_PASTE_EXCLUSIONS = { ROI_KEY, # ROI has dedicated copy/paste operations "__uuid", # Each object must have a unique identifier f"__{PROCESSING_PARAMETERS_OPTION}", # Object-specific processing history f"__{CREATION_PARAMETERS_OPTION}", # Object-specific creation parameters } def is_plot_item_serializable(item: Any) -> bool: """Return True if plot item is serializable""" try: plotpy.io.item_class_from_name(item.__class__.__name__) return True except AssertionError: return False def is_hdf5_file(filename: str, check_content: bool = False) -> bool: """Return True if filename has an HDF5 extension or is an HDF5 file. Args: filename: Path to the file to check check_content: If True, also attempts to open the file to verify it's a valid HDF5 file. If False, only checks the file extension. Returns: True if the file is (likely) an HDF5 file, False otherwise. """ # First, check by extension (fast) has_hdf5_extension = filename.lower().endswith((".h5", ".hdf5", ".hdf", ".he5")) if not check_content: return has_hdf5_extension # If checking content, try to open as HDF5 file if has_hdf5_extension: return True # Trust common HDF5 extensions # For other extensions, attempt to open the file to verify it's HDF5 try: with h5py.File(filename, "r"): return True except (OSError, IOError, ValueError): # Not a valid HDF5 file return False @dataclass class ProcessingReport: """Report of processing operation Args: success: True if processing succeeded obj_uuid: UUID of the processed object message: Optional message (error or info) """ success: bool obj_uuid: str | None = None message: str | None = None class ObjectProp(QW.QWidget): """Object handling panel properties Args: panel: parent data panel objclass: class of the object handled by the panel (SignalObj or ImageObj) """ def __init__(self, panel: BaseDataPanel, objclass: SignalObj | ImageObj) -> None: super().__init__(panel) # Create the tab widget self.tabwidget = QW.QTabWidget(self) self.tabwidget.setTabBarAutoHide(True) self.tabwidget.setTabPosition(QW.QTabWidget.West) self.panel = panel self.objclass = objclass # Object creation tab self.creation_param_editor: gdq.DataSetEditGroupBox | None = None self.current_creation_obj: SignalObj | ImageObj | None = None self.creation_scroll: QW.QScrollArea | None = None # Object processing tab self.processing_param_editor: gdq.DataSetEditGroupBox | None = None self.current_processing_obj: SignalObj | ImageObj | None = None self.processing_scroll: QW.QScrollArea | None = None # Properties tab self.properties = gdq.DataSetEditGroupBox("", objclass) self.properties.SIG_APPLY_BUTTON_CLICKED.connect(panel.properties_changed) self.properties.setEnabled(False) self.__original_values: dict[str, Any] = {} # Create Analysis and History widgets font = Conf.proc.small_mono_font.get_font() self.processing_history = QW.QTextEdit() self.processing_history.setReadOnly(True) self.processing_history.setFont(font) self.analysis_parameters = QW.QTextEdit() self.analysis_parameters.setReadOnly(True) self.analysis_parameters.setFont(font) # Track newly created objects to show Creation tab only once self.newly_created_obj_uuid: str | None = None # Track when analysis results were just computed self.fresh_analysis_obj_uuid: str | None = None # Track when object was just processed (1-to-1) self.fresh_processing_obj_uuid: str | None = None self.tabwidget.addTab( self.processing_history, get_icon("history.svg"), _("History") ) self.tabwidget.addTab( self.analysis_parameters, get_icon("analysis.svg"), _("Analysis parameters") ) self.tabwidget.addTab( self.properties, get_icon("properties.svg"), _("Properties") ) self.processing_history.textChanged.connect(self._update_tab_visibility) self.analysis_parameters.textChanged.connect(self._update_tab_visibility) # Create vertical layout for the container layout = QW.QVBoxLayout(self) layout.setContentsMargins(0, 0, 0, 0) layout.setSpacing(0) # Add tab widget and button area to main layout layout.addWidget(self.tabwidget) # Here we could add another widget or layout if needed (in DataLab v0.20, we # had a permanent button area here, but it was removed to avoid clutter) def _update_tab_visibility(self) -> None: """Update visibility of tabs based on their content.""" for textedit in (self.processing_history, self.analysis_parameters): tab_index = self.tabwidget.indexOf(textedit) if tab_index >= 0: has_content = bool(textedit.toPlainText().strip()) self.tabwidget.setTabVisible(tab_index, has_content) def display_analysis_parameters(self, obj: SignalObj | ImageObj) -> bool: """Set analysis parameter label. Args: obj: Signal or Image object Returns: True if analysis parameters were found and displayed, False otherwise. """ text = "" # Iterate through all result adapters and extract parameter info for adapter_class in (GeometryAdapter, TableAdapter): for adapter in adapter_class.iterate_from_obj(obj): param = adapter.get_param() if param is not None: if text: text += "

    " # Get function name for context func_name = adapter.func_name if func_name: # Add function name as a header for better context param.set_comment( "(" + _("Parameters for function `%s`") % func_name + ")" ) text += param.to_html() self.analysis_parameters.setText(text) return bool(text) def _build_processing_history(self, obj: SignalObj | ImageObj) -> str: """Build processing history as a simple text list. Args: obj: Signal or Image object Returns: Processing history as text """ history_items = [] current_obj = obj max_depth = 20 # Prevent infinite loops # Walk backwards through processing chain, collecting items while current_obj is not None and len(history_items) < max_depth: proc_params = extract_processing_parameters(current_obj) if proc_params is None: # Check for creation parameters creation_params = extract_creation_parameters(current_obj) if creation_params is not None: text = f"{_('Created')}: {creation_params.title}" history_items.append(text) else: history_items.append(_("Original object")) break # Skip 1-to-0 operations (analysis) as they don't transform the object # They just add metadata, so they shouldn't appear in processing history if proc_params.pattern == "1-to-0": # For 1-to-0 operations, there's no processing history to show # (they analyze but don't transform the object) # Check if there's any earlier processing creation_params = extract_creation_parameters(current_obj) if creation_params is not None: text = f"{_('Created')}: {creation_params.title}" history_items.append(text) else: history_items.append(_("Original object")) break # Add current processing step func_name = proc_params.func_name.replace("_", " ").title() history_items.append(func_name) # Try to find source object if proc_params.source_uuid: current_obj = self.panel.mainwindow.find_object_by_uuid( proc_params.source_uuid ) if current_obj is None: history_items.append(_("(source deleted)")) break else: if proc_params.source_uuids: # Multiple sources (n-to-1 or 2-to-1 pattern) history_items.append(_("(multiple sources)")) break if len(history_items) <= 1: return "" # Shows the history tab only when there is some history # Reverse to show from oldest to newest, then add indentation history_items.reverse() history_lines = [] for i, item in enumerate(history_items): indent = " " * i history_lines.append(f"{indent}└─ {item}") return "\n".join(history_lines) def display_processing_history(self, obj: SignalObj | ImageObj) -> bool: """Display processing history. Args: obj: Signal or Image object Returns: True if processing history was found and displayed, False otherwise. """ history_text = self._build_processing_history(obj) self.processing_history.setText(history_text) return bool(history_text) def __update_properties_dataset(self, obj: SignalObj | ImageObj) -> None: """Update properties dataset from signal/image dataset Args: obj: Signal or Image object """ dataset: SignalObj | ImageObj = self.properties.dataset dataset.set_defaults() update_dataset(dataset, obj) self.properties.get() self.properties.apply_button.setEnabled(False) def update_properties_from( self, obj: SignalObj | ImageObj | None = None, force_tab: Literal["creation", "processing", "analysis", None] | None = None, ) -> None: """Update properties panel (properties, creation, processing) from object. Args: obj: Signal or Image object force_tab: Force a specific tab to be current """ self.properties.setDisabled(obj is None) if obj is None: obj = self.objclass() # Update the properties dataset self.__update_properties_dataset(obj) # Store original values to detect which properties have changed # (using `restore_dataset` to convert the dataset to a dictionary) self.__original_values = {} restore_dataset(self.properties.dataset, self.__original_values) # Display analysis parameters and processing history has_analysis_parameters = self.display_analysis_parameters(obj) self.display_processing_history(obj) # Remove only Creation and Processing tabs (dynamic tabs) # Use widget references instead of text labels for reliable identification if self.creation_scroll is not None: index = self.tabwidget.indexOf(self.creation_scroll) if index >= 0: self.tabwidget.removeTab(index) if self.processing_scroll is not None: index = self.tabwidget.indexOf(self.processing_scroll) if index >= 0: self.tabwidget.removeTab(index) # Reset references for dynamic tabs self.creation_param_editor = None self.current_creation_obj = None self.creation_scroll = None self.processing_param_editor = None self.current_processing_obj = None self.processing_scroll = None # Setup Creation and Processing tabs (if applicable) has_creation_tab = False has_processing_tab = False if obj is not None: has_creation_tab = self.setup_creation_tab(obj) has_processing_tab = self.setup_processing_tab(obj) # Processing tab setup # Trigger visibility update for History and Analysis parameters tabs # (will be called via textChanged signals, but we call explicitly # here to ensure initial state is correct) self._update_tab_visibility() # Determine which tab to show based on force_tab parameter: # - If force_tab="creation" and Creation tab exists, show it # - If force_tab="processing" and Processing tab exists, show it # - If force_tab="analysis" and Analysis tab has content, show it # - Otherwise, always show Properties tab (default behavior) if force_tab == "creation" and has_creation_tab: self.tabwidget.setCurrentWidget(self.creation_scroll) elif force_tab == "processing" and has_processing_tab: self.tabwidget.setCurrentWidget(self.processing_scroll) elif force_tab == "analysis" and has_analysis_parameters: self.tabwidget.setCurrentWidget(self.analysis_parameters) else: # Default: always show Properties tab when switching objects self.tabwidget.setCurrentWidget(self.properties) def mark_as_newly_created(self, obj: SignalObj | ImageObj) -> None: """Mark object to show Creation tab on next selection. Args: obj: Object to mark """ self.newly_created_obj_uuid = get_uuid(obj) def mark_as_freshly_processed(self, obj: SignalObj | ImageObj) -> None: """Mark object to show Processing tab on next selection. Args: obj: Object to mark """ self.fresh_processing_obj_uuid = get_uuid(obj) def mark_as_fresh_analysis(self, obj: SignalObj | ImageObj) -> None: """Mark object to show Analysis tab on next selection. Args: obj: Object to mark """ self.fresh_analysis_obj_uuid = get_uuid(obj) def get_changed_properties(self) -> dict[str, Any]: """Get dictionary of properties that have changed from original values. Returns: Dictionary mapping property names to their new values, containing only the properties that were modified by the user. """ dataset = self.properties.dataset changed = {} # Get current values as a dictionary current_values = {} restore_dataset(dataset, current_values) # Compare with original values for key, current_value in current_values.items(): original_value = self.__original_values.get(key) # Check if value has changed if not self._values_equal(current_value, original_value): changed[key] = current_value return changed def update_original_values(self) -> None: """Update the stored original values to the current dataset values. This should be called after applying changes to reset the baseline for detecting future changes. """ dataset = self.properties.dataset self.__original_values = {} restore_dataset(dataset, self.__original_values) @staticmethod def _values_equal(val1: Any, val2: Any) -> bool: """Compare two values, handling special cases like numpy arrays. Args: val1: first value val2: second value Returns: True if values are equal """ # Handle numpy arrays if isinstance(val1, np.ndarray) or isinstance(val2, np.ndarray): if not isinstance(val1, np.ndarray) or not isinstance(val2, np.ndarray): return False return np.array_equal(val1, val2) # Handle regular comparison return val1 == val2 def setup_creation_tab( self, obj: SignalObj | ImageObj, set_current: bool = False ) -> bool: """Setup the Creation tab with parameter editor for interactive object creation. Args: obj: Signal or Image object set_current: If True, set the Creation tab as current after creation Returns: True if Creation tab was set up, False otherwise """ param = extract_creation_parameters(obj) if param is None: return False # Create parameter editor widget using the actual parameter class # (which is a subclass of NewSignalParam or NewImageParam) editor = gdq.DataSetEditGroupBox(_("Creation Parameters"), param.__class__) update_dataset(editor.dataset, param) editor.get() # Connect Apply button to recreation handler editor.SIG_APPLY_BUTTON_CLICKED.connect(self.apply_creation_parameters) editor.set_apply_button_state(False) # Store reference to be able to retrieve it later self.creation_param_editor = editor self.current_creation_obj = obj # Remove existing Creation tab if it exists if self.creation_scroll is not None: index = self.tabwidget.indexOf(self.creation_scroll) if index >= 0: self.tabwidget.removeTab(index) # Set the parameter editor as the scroll area widget # Creation tab is always at index 0 (before all other tabs) self.creation_scroll = QW.QScrollArea() self.creation_scroll.setWidgetResizable(True) self.creation_scroll.setWidget(editor) icon_name = "new_sig.svg" if isinstance(obj, SignalObj) else "new_ima.svg" self.tabwidget.insertTab( 0, self.creation_scroll, get_icon(icon_name), _("Creation") ) # Set as current tab if requested if set_current: self.tabwidget.setCurrentWidget(self.creation_scroll) return True def apply_creation_parameters(self) -> None: """Apply creation parameters: recreate object with updated parameters.""" editor = self.creation_param_editor if editor is None or self.current_creation_obj is None: return if isinstance(self.current_creation_obj, SignalObj): otext = _("Signal was modified in-place.") else: otext = _("Image was modified in-place.") text = f"⚠️ {otext} ⚠️ " text += _( "If computation were performed based on this object, " "they may need to be redone." ) self.panel.SIG_STATUS_MESSAGE.emit(text, 20000) # Recreate object with new parameters # (serialization is done automatically in create_signal/image_from_param) param = editor.dataset try: if isinstance(self.current_creation_obj, SignalObj): new_obj = create_signal_from_param(param) else: # ImageObj new_obj = create_image_from_param(param) except Exception as exc: # pylint: disable=broad-exception-caught if execenv.unattended: raise exc QW.QMessageBox.warning( self, _("Error"), _("Failed to recreate object with new parameters:\n%s") % str(exc), ) return # Update the current object in-place obj_uuid = get_uuid(self.current_creation_obj) self.current_creation_obj.title = new_obj.title if isinstance(self.current_creation_obj, SignalObj): self.current_creation_obj.xydata = new_obj.xydata else: # ImageObj self.current_creation_obj.data = new_obj.data # Invalidate ROI mask cache when image dimensions change # (the mask is computed based on image shape, so it must be recomputed) self.current_creation_obj.invalidate_maskdata_cache() # Update metadata with new creation parameters insert_creation_parameters(self.current_creation_obj, param) # Auto-recompute analysis if the object had analysis parameters # Since the data has changed, any analysis results are now invalid # Use the processor for the current object's type obj_processor = self.__get_processor_associated_to(self.current_creation_obj) obj_processor.auto_recompute_analysis(self.current_creation_obj) # Update the tree view item (to show new title if it changed) self.panel.objview.update_item(obj_uuid) # Refresh only the plot, not the entire panel # (avoid calling selection_changed which would trigger a full refresh # of the Properties tab and could cause recursion issues) self.panel.refresh_plot(obj_uuid, update_items=True, force=True) # Update the Properties tab to reflect the new object properties # (e.g., data type, dimensions, etc.) self.__update_properties_dataset(self.current_creation_obj) # Refresh the Creation tab with the new parameters # Use QTimer to defer this until after the current event is processed # Set the Creation tab as current to keep it visible after refresh QC.QTimer.singleShot( 0, lambda: self.setup_creation_tab( self.current_creation_obj, set_current=True ), ) def setup_processing_tab( self, obj: SignalObj | ImageObj, reset_params: bool = True, set_current: bool = False, ) -> bool: """Setup the Processing tab with parameter editor for re-processing. Args: obj: Signal or Image object reset_params: If True, call update_from_obj() to reset parameters from source object. If False, use parameters as stored in metadata. set_current: If True, set the Processing tab as current after creation Returns: True if Processing tab was set up, False otherwise """ # Extract processing parameters proc_params = extract_processing_parameters(obj) if proc_params is None: return False # Check if the pattern type is 1-to-1 (only interactive pattern) if proc_params.pattern != "1-to-1": return False # Store reference to be able to retrieve it later self.current_processing_obj = obj # Check if object has processing parameter param = proc_params.param if param is None: return False # Skip interactive processing for list of parameters # (e.g., ROI extraction, erase operations) if isinstance(param, list): return False # Eventually call the `update_from_obj` method to properly initialize # the parameter object from the current object state. # Only do this when reset_params is True (initial setup), not when # refreshing after user has modified parameters. if reset_params and hasattr(param, "update_from_obj"): # Warning: the `update_from_obj` method takes the input object as argument, # not the output object (`obj` is the processed object here): # Retrieve the input object from the source UUID if proc_params.source_uuid is not None: source_obj = self.panel.mainwindow.find_object_by_uuid( proc_params.source_uuid ) if source_obj is not None: param.update_from_obj(source_obj) # Create parameter editor widget editor = gdq.DataSetEditGroupBox( _("Processing Parameters"), param.__class__, wordwrap=True ) update_dataset(editor.dataset, param) editor.get() # Connect Apply button to reprocessing handler editor.SIG_APPLY_BUTTON_CLICKED.connect(self.apply_processing_parameters) editor.set_apply_button_state(False) # Store reference to be able to retrieve it later self.processing_param_editor = editor # Remove existing Processing tab if it exists if self.processing_scroll is not None: index = self.tabwidget.indexOf(self.processing_scroll) if index >= 0: self.tabwidget.removeTab(index) # Processing tab comes after Creation tab (if it exists) # Find the correct insertion index: after Creation (index 0) if it exists, # otherwise at index 0 has_creation = ( self.creation_scroll is not None and self.tabwidget.indexOf(self.creation_scroll) >= 0 ) insert_index = 1 if has_creation else 0 # Create new processing scroll area and tab self.processing_scroll = QW.QScrollArea() self.processing_scroll.setWidgetResizable(True) self.processing_scroll.setHorizontalScrollBarPolicy(QC.Qt.ScrollBarAlwaysOff) self.processing_scroll.setSizePolicy( QW.QSizePolicy.Expanding, QW.QSizePolicy.Preferred ) self.processing_scroll.setWidget(editor) self.tabwidget.insertTab( insert_index, self.processing_scroll, get_icon("libre-tech-ram.svg"), _("Processing"), ) # Set as current tab if requested if set_current: self.tabwidget.setCurrentWidget(self.processing_scroll) return True def __get_processor_associated_to( self, obj: SignalObj | ImageObj ) -> SignalProcessor | ImageProcessor: """Get the processor associated to the given object type. Args: obj: Signal or Image object Returns: Processor associated to the object's type """ assert isinstance(obj, (SignalObj, ImageObj)) if isinstance(obj, SignalObj): return self.panel.mainwindow.signalpanel.processor return self.panel.mainwindow.imagepanel.processor def apply_processing_parameters( self, obj: SignalObj | ImageObj | None = None, interactive: bool = True ) -> ProcessingReport: """Apply processing parameters: re-run processing with updated parameters. Args: obj: Signal or Image object to reprocess. If None, uses the current object. interactive: If True, show progress and error messages in the UI. Returns: ProcessingReport with success status, object UUID, and optional message. """ if execenv.unattended: interactive = False report = ProcessingReport(success=False) editor = self.processing_param_editor obj = obj or self.current_processing_obj if obj is None: report.message = _("No processing object available.") return report report.obj_uuid = get_uuid(obj) # Extract processing parameters proc_params = extract_processing_parameters(obj) if proc_params is None: report.message = _("Processing metadata is incomplete.") if interactive: QW.QMessageBox.critical(self, _("Error"), report.message) return report # Check if source object still exists if proc_params.source_uuid is None: report.message = _( "Processing metadata is incomplete (missing source UUID)." ) if interactive: QW.QMessageBox.critical(self, _("Error"), report.message) return report # Find source object source_obj = self.panel.mainwindow.find_object_by_uuid(proc_params.source_uuid) if source_obj is None: report.message = _("Source object no longer exists.") if interactive: QW.QMessageBox.critical( self, _("Error"), report.message + "\n\n" + _( "The object that was used to create this processed object " "has been deleted and cannot be used for reprocessing." ), ) return report # Get updated parameters from editor param = editor.dataset if editor is not None else proc_params.param # For cross-panel computations, we need to use the processor from the panel # that owns the source object (e.g., radial_profile is in ImageProcessor) source_processor = self.__get_processor_associated_to(source_obj) # Recompute using the dedicated method (with multiprocessing support) try: new_obj = source_processor.recompute_1_to_1( proc_params.func_name, source_obj, param ) except Exception as exc: # pylint: disable=broad-exception-caught report.message = _("Failed to reprocess object:\n%s") % str(exc) if interactive: QW.QMessageBox.warning(self, _("Error"), report.message) return report if new_obj is None: # User cancelled the operation report.message = _("Processing was cancelled.") else: report.success = True # Update the current object in-place with data from new object obj.title = new_obj.title if isinstance(obj, SignalObj): obj.xydata = new_obj.xydata else: # ImageObj obj.data = new_obj.data # Invalidate ROI mask cache when image dimensions may have changed # (the mask is computed based on image shape, so it must be recomputed) obj.invalidate_maskdata_cache() # Update metadata with new processing parameters updated_proc_params = ProcessingParameters( func_name=proc_params.func_name, pattern=proc_params.pattern, param=param, source_uuid=proc_params.source_uuid, ) insert_processing_parameters(obj, updated_proc_params) # Auto-recompute analysis if the object had analysis parameters # Since the data has changed, any analysis results are now invalid # Use the processor for the current object's type (not source object's type) obj_processor = self.__get_processor_associated_to(obj) obj_processor.auto_recompute_analysis(obj) # Update the tree view item and refresh plot obj_uuid = get_uuid(obj) self.panel.objview.update_item(obj_uuid) self.panel.refresh_plot(obj_uuid, update_items=True, force=True) # Update the Properties tab to reflect the new object properties # (e.g., data type, dimensions, etc.) self.__update_properties_dataset(obj) # Refresh the Processing tab with the new parameters # Don't reset parameters from source object - keep the user's values # Set the Processing tab as current to keep it visible after refresh QC.QTimer.singleShot( 0, lambda: self.setup_processing_tab( obj, reset_params=False, set_current=True ), ) if isinstance(obj, SignalObj): report.message = _("Signal was reprocessed.") else: report.message = _("Image was reprocessed.") self.panel.SIG_STATUS_MESSAGE.emit("✅ " + report.message, 5000) return report class AbstractPanelMeta(type(QW.QSplitter), abc.ABCMeta): """Mixed metaclass to avoid conflicts""" class AbstractPanel(QW.QSplitter, metaclass=AbstractPanelMeta): """Object defining DataLab panel interface, based on a vertical QSplitter widget A panel handle an object list (objects are signals, images, macros...). Each object must implement ``datalab.gui.ObjItf`` interface """ H5_PREFIX = "" SIG_OBJECT_ADDED = QC.Signal() SIG_OBJECT_REMOVED = QC.Signal() @abc.abstractmethod def __init__(self, parent): super().__init__(QC.Qt.Vertical, parent) self.setObjectName(self.__class__.__name__[0].lower()) # Check if the class implements __len__, __getitem__ and __iter__ for method in ("__len__", "__getitem__", "__iter__"): if not hasattr(self, method): raise NotImplementedError( f"Class {self.__class__.__name__} must implement method {method}" ) # pylint: disable=unused-argument def get_serializable_name(self, obj: ObjItf) -> str: """Return serializable name of object""" title = re.sub("[^-a-zA-Z0-9_.() ]+", "", obj.title.replace("/", "_")) name = f"{get_short_id(obj)}: {title}" return name def serialize_object_to_hdf5(self, obj: ObjItf, writer: NativeH5Writer) -> None: """Serialize object to HDF5 file""" with writer.group(self.get_serializable_name(obj)): obj.serialize(writer) def deserialize_object_from_hdf5( self, reader: NativeH5Reader, name: str, reset_all: bool = False ) -> ObjItf: """Deserialize object from a HDF5 file Args: reader: HDF5 reader name: Object name in HDF5 file reset_all: If True, preserve original UUIDs (workspace reload). If False, regenerate UUIDs (importing objects). """ with reader.group(name): obj = self.create_object() obj.deserialize(reader) # Only regenerate UUIDs when importing objects (reset_all=False). # When reopening a workspace (reset_all=True), preserve original UUIDs # so that processing parameter references (source_uuid, source_uuids) # remain valid and features like "Show source" and "Recompute" work. # When importing, only regenerate UUID if it conflicts with an existing one. if not reset_all and isinstance(obj, (SignalObj, ImageObj, ObjectGroup)): if self.objmodel.has_uuid(get_uuid(obj)): set_uuid(obj) return obj @abc.abstractmethod def serialize_to_hdf5(self, writer: NativeH5Writer) -> None: """Serialize whole panel to a HDF5 file""" @abc.abstractmethod def deserialize_from_hdf5( self, reader: NativeH5Reader, reset_all: bool = False ) -> None: """Deserialize whole panel from a HDF5 file Args: reader: HDF5 reader reset_all: If True, preserve original UUIDs (workspace reload). If False, regenerate UUIDs (importing objects). """ @abc.abstractmethod def create_object(self) -> ObjItf: """Create and return object""" @abc.abstractmethod def add_object(self, obj: ObjItf) -> None: """Add object to panel""" @abc.abstractmethod def remove_all_objects(self): """Remove all objects""" self.SIG_OBJECT_REMOVED.emit() class PasteMetadataParam(gds.DataSet): """Paste metadata parameters""" keep_roi = gds.BoolItem(_("Regions of interest"), default=True) keep_geometry = gds.BoolItem(_("Geometry results"), default=False).set_pos(col=1) keep_tables = gds.BoolItem(_("Table results"), default=False).set_pos(col=1) keep_other = gds.BoolItem(_("Other metadata"), default=True) class NonModalInfoDialog(QW.QMessageBox): """Non-modal information message box with selectable text. This widget displays an information message in a message dialog box, allowing users to select and copy the text content. """ def __init__(self, parent: QW.QWidget, title: str, text: str) -> None: """Create a non-modal information message box with selectable text. Args: parent: The parent widget. title: The title of the message box. text: The text to display in the message box. """ super().__init__(parent) self.setIcon(QW.QMessageBox.Information) self.setWindowTitle(title) if re.search(r"<[a-zA-Z/][^>]*>", text): self.setTextFormat(QC.Qt.RichText) # type: ignore[attr-defined] self.setTextInteractionFlags( QC.Qt.TextBrowserInteraction # type: ignore[attr-defined] ) else: self.setTextFormat(QC.Qt.PlainText) # type: ignore[attr-defined] self.setTextInteractionFlags( QC.Qt.TextSelectableByMouse # type: ignore[attr-defined] | QC.Qt.TextSelectableByKeyboard # type: ignore[attr-defined] ) self.setText(text) self.setStandardButtons(QW.QMessageBox.Close) self.setDefaultButton(QW.QMessageBox.Close) # ! Necessary only on non-Windows platforms self.setWindowFlags(QC.Qt.Window) # type: ignore[attr-defined] self.setModal(False) class SaveToDirectoryGUIParam(gds.DataSet, title=_("Save to directory")): """Save to directory parameters""" def __init__( self, objs: list[TypeObj] | None = None, extensions: list[str] | None = None ) -> None: super().__init__() self.__objs = objs or [] self.__extensions = extensions or [] def on_button_click( self: SaveToDirectoryGUIParam, _item: gds.ButtonItem, _value: None, parent: QW.QWidget, ) -> None: """Help button callback.""" text = "
    ".join( [ """Pattern accepts a Python format string. Standard Python format specifiers apply. Two extra modifiers are supported: 'upper' for uppercase and 'lower' for lowercase.""", "", "Available placeholders:", """
    KeywordDescription
    {title}Title
    {index}1-based index
    {count}Total number of selected objects
    {xlabel}, {xunit}, {ylabel}, {yunit} Axis information for signals
    {metadata[key]}Specific metadata value
    (direct {metadata} use is ignored)
    """, "", "Examples:", """
    PatternDescription
    {index:03d} 3-digit index with leading zeros
    {title:20.20} Title truncated to 20 characters
    {title:20.20upper} Title truncated to 20 characters, upper case
    {title:20.20lower} Title truncated to 20 characters, lower case
    """, ] ) NonModalInfoDialog(parent, "Pattern help", text).show() def get_extension_choices(self, _item=None, _value=None): """Return list of available extensions for choice item.""" return [("." + ext, "." + ext, None) for ext in self.__extensions] def build_filenames(self, objs: list[TypeObj] | None = None) -> list[str]: """Build filenames according to current parameters.""" objs = objs or self.__objs extension = self.extension if self.extension is not None else "" filenames = format_basenames(objs, self.basename + extension) used: set[str] = set() # Ensure all filenames are unique. for i, filename in enumerate(filenames): root, ext = osp.splitext(filename) filepath = osp.join(self.directory, filename) k = 1 while (filename in used) or (not self.overwrite and osp.exists(filepath)): filename = f"{root}_{k}{ext}" filepath = osp.join(self.directory, filename) k += 1 used.add(filename) filenames[i] = filename return filenames def generate_filepath_obj_pairs( self, objs: list[TypeObj] ) -> Generator[tuple[str, TypeObj], None, None]: """Iterate over (filepath, object) pairs to be saved.""" for filename, obj in zip(self.build_filenames(objs), objs): yield osp.join(self.directory, filename), obj def update_preview(self, _item=None, _value=None) -> None: """Update preview.""" try: filenames = self.build_filenames() preview_lines = [] for i, (obj, filename) in enumerate(zip(self.__objs, filenames), start=1): # Try to get short ID if object has been added to panel try: obj_id = get_short_id(obj) except (ValueError, KeyError): # Fallback to simple index for objects not yet in panel obj_id = str(i) preview_lines.append(f"{obj_id}: {filename}") self.preview = "\n".join(preview_lines) except (ValueError, KeyError, TypeError) as exc: # Handle formatting errors gracefully (e.g., incomplete format string) self.preview = f"Invalid pattern:{os.linesep}{exc}" directory = gds.DirectoryItem(_("Directory"), default=Conf.main.base_dir.get()) basename = gds.StringItem( _("Basename pattern"), default="{title}", help=_("Python format string. See description for details."), ).set_prop("display", callback=update_preview) help = gds.ButtonItem(_("Help"), on_button_click, "MessageBoxInformation").set_pos( col=1 ) extension = gds.ChoiceItem(_("Extension"), get_extension_choices).set_prop( "display", callback=update_preview ) overwrite = gds.BoolItem( _("Overwrite"), default=False, help=_("Overwrite existing files") ).set_pos(col=1) preview = gds.TextItem( _("Preview"), default=None, regexp=r"^(?!Invalid).*" ).set_prop("display", readonly=True) class AddMetadataParam( gds.DataSet, title=_("Add metadata"), comment=_( "Add a new metadata item to the selected objects.

    " "The metadata key will be the same for all objects, " "but the value can use a pattern to generate different values.
    " "Click the Help button for details on the pattern syntax.
    " ), ): """Add metadata parameters""" def __init__(self, objs: list[TypeObj] | None = None) -> None: super().__init__() self.__objs = objs or [] def on_help_button_click( self: AddMetadataParam, _item: gds.ButtonItem, _value: None, parent: QW.QWidget, ) -> None: """Help button callback.""" text = "
    ".join( [ """Pattern accepts a Python format string. Standard Python format specifiers apply. Two extra modifiers are supported: 'upper' for uppercase and 'lower' for lowercase.""", "", "Available placeholders:", """
    KeywordDescription
    {title}Title
    {index}1-based index
    {count}Total number of selected objects
    {xlabel}, {xunit}, {ylabel}, {yunit} Axis information for signals
    {metadata[key]}Specific metadata value
    (direct {metadata} use is ignored)
    """, "", "Examples:", """
    PatternDescription
    {index:03d} 3-digit index with leading zeros
    {title:20.20} Title truncated to 20 characters
    {title:20.20upper} Title truncated to 20 characters, upper case
    {title:20.20lower} Title truncated to 20 characters, lower case
    """, ] ) NonModalInfoDialog(parent, "Pattern help", text).show() def get_conversion_choices(self, _item=None, _value=None): """Return list of available conversion choices.""" return [ ("string", _("String"), None), ("float", _("Float"), None), ("int", _("Integer"), None), ("bool", _("Boolean"), None), ] def build_values( self, objs: list[TypeObj] | None = None ) -> list[str | float | int | bool]: """Build values according to current parameters. Raises: ValueError: If a value cannot be converted to the target type. """ objs = objs or self.__objs # Generate values using the pattern raw_values = format_basenames(objs, self.value_pattern) # Convert values according to the selected conversion type converted_values = [] for i, value_str in enumerate(raw_values, start=1): if self.conversion == "string": converted_values.append(value_str) elif self.conversion == "float": try: converted_values.append(float(value_str)) except ValueError as exc: raise ValueError( f"Cannot convert value at index {i} to float: '{value_str}'" ) from exc elif self.conversion == "int": try: converted_values.append(int(value_str)) except ValueError as exc: raise ValueError( f"Cannot convert value at index {i} to integer: '{value_str}'" ) from exc elif self.conversion == "bool": # Convert to boolean: "true", "1", "yes" -> True, others -> False lower_val = value_str.lower() converted_values.append(lower_val in ("true", "1", "yes", "on")) return converted_values def update_preview(self, _item=None, _value=None) -> None: """Update preview.""" try: values = self.build_values() preview_lines = [] for i, (obj, value) in enumerate(zip(self.__objs, values), start=1): # Try to get short ID if object has been added to panel try: obj_id = get_short_id(obj) except (ValueError, KeyError): # Fallback to simple index for objects not yet in panel obj_id = str(i) preview_lines.append(f"{obj_id}: {self.metadata_key} = {value!r}") self.preview = "\n".join(preview_lines) except ValueError as exc: # Handle conversion errors self.preview = f"Invalid conversion:{os.linesep}{exc}" except (KeyError, TypeError) as exc: # Handle formatting errors (e.g., incomplete format string) self.preview = f"Invalid pattern:{os.linesep}{exc}" metadata_key = gds.StringItem( _("Metadata key"), default="custom_key", notempty=True, regexp=r"^[a-zA-Z_][a-zA-Z0-9_]*$", help=_("The key name for the metadata item"), ).set_prop("display", callback=update_preview) value_pattern = gds.StringItem( _("Value pattern"), default="{index}", help=_("Python format string. See description for details."), ).set_prop("display", callback=update_preview) help = gds.ButtonItem( _("Help"), on_help_button_click, "MessageBoxInformation" ).set_pos(col=1) conversion = gds.ChoiceItem( _("Conversion"), get_conversion_choices, default="string" ).set_prop("display", callback=update_preview) preview = gds.TextItem(_("Preview"), default="", regexp=r"^(?!Invalid).*").set_prop( "display", readonly=True ) class BaseDataPanel(AbstractPanel, Generic[TypeObj, TypeROI, TypeROIEditor]): """Object handling the item list, the selected item properties and plot""" PANEL_STR = "" # e.g. "Signal Panel" PANEL_STR_ID = "" # e.g. "signal" PARAMCLASS: TypeObj = None # Replaced in child object ANNOTATION_TOOLS = () MINDIALOGSIZE = (800, 600) MAXDIALOGSIZE = 0.95 # % of DataLab's main window size # Replaced by the right class in child object: IO_REGISTRY: SignalIORegistry | ImageIORegistry | None = None SIG_STATUS_MESSAGE = QC.Signal(str, int) # emitted by "qt_try_except" decorator SIG_REFRESH_PLOT = QC.Signal( str, bool, bool, bool, bool ) # Connected to PlotHandler.refresh_plot @staticmethod @abc.abstractmethod def get_roi_class() -> Type[TypeROI]: """Return ROI class""" @staticmethod @abc.abstractmethod def get_roieditor_class() -> Type[TypeROIEditor]: """Return ROI editor class""" @abc.abstractmethod def __init__(self, parent: QW.QWidget) -> None: super().__init__(parent) self.mainwindow: DLMainWindow = parent self.objprop = ObjectProp(self, self.PARAMCLASS) self.objmodel = objectmodel.ObjectModel(f"g{self.PARAMCLASS.PREFIX}") self.objview = objectview.ObjectView(self, self.objmodel) self.objview.SIG_IMPORT_FILES.connect(self.handle_dropped_files) self.objview.populate_tree() self.plothandler: SignalPlotHandler | ImagePlotHandler = None self.processor: SignalProcessor | ImageProcessor = None self.acthandler: actionhandler.BaseActionHandler = None self.metadata_clipboard = {} self.annotations_clipboard: list[dict[str, Any]] = [] self.__roi_clipboard: TypeROI | None = None self.context_menu = QW.QMenu() self.__separate_views: dict[QW.QDialog, TypeObj] = {} def closeEvent(self, event): """Reimplement QMainWindow method""" self.processor.close() super().closeEvent(event) # ------AbstractPanel interface----------------------------------------------------- def plot_item_parameters_changed( self, item: CurveItem | MaskedXYImageItem | LabelItem ) -> None: """Plot items changed: update metadata of all objects from plot items""" # Find the object corresponding to the plot item obj = self.plothandler.get_obj_from_item(item) if obj is not None: # Unselect the item in the plot so that we update the item parameters # in the right state (fix issue #184): item.unselect() # Ensure that item's parameters are up-to-date: item.param.update_param(item) # Update object metadata from plot item parameters create_adapter_from_object(obj).update_metadata_from_plot_item(item) if obj is self.objview.get_current_object(): self.objprop.update_properties_from(obj) self.plothandler.update_resultproperty_from_plot_item(item) # pylint: disable=unused-argument def plot_item_moved( self, item: LabelItem, x0: float, y0: float, x1: float, y1: float ) -> None: """Plot item moved: update metadata of all objects from plot items Args: item: Plot item x0: new x0 coordinate y0: new y0 coordinate x1: new x1 coordinate y1: new y1 coordinate """ self.plothandler.update_resultproperty_from_plot_item(item) def serialize_object_to_hdf5(self, obj: TypeObj, writer: NativeH5Writer) -> None: """Serialize object to HDF5 file""" # Before serializing, update metadata from plot item parameters, in order to # save the latest visualization settings: try: item = self.plothandler[get_uuid(obj)] create_adapter_from_object(obj).update_metadata_from_plot_item(item) except KeyError: # Plot item has not been created yet (this happens when auto-refresh has # been disabled) pass super().serialize_object_to_hdf5(obj, writer) def serialize_to_hdf5(self, writer: NativeH5Writer) -> None: """Serialize whole panel to a HDF5 file""" with writer.group(self.H5_PREFIX): for group in self.objmodel.get_groups(): with writer.group(self.get_serializable_name(group)): with writer.group("title"): writer.write_str(group.title) for obj in group.get_objects(): self.serialize_object_to_hdf5(obj, writer) def deserialize_from_hdf5( self, reader: NativeH5Reader, reset_all: bool = False ) -> None: """Deserialize whole panel from a HDF5 file Args: reader: HDF5 reader reset_all: If True, preserve original UUIDs (workspace reload). If False, regenerate UUIDs (importing objects). """ with reader.group(self.H5_PREFIX): for name in reader.h5.get(self.H5_PREFIX, []): with reader.group(name): group = self.add_group("") with reader.group("title"): group.title = reader.read_str() for obj_name in reader.h5.get(f"{self.H5_PREFIX}/{name}", []): obj = self.deserialize_object_from_hdf5( reader, obj_name, reset_all ) self.add_object(obj, get_uuid(group), set_current=False) self.selection_changed() def __len__(self) -> int: """Return number of objects""" return len(self.objmodel) def __getitem__(self, nb: int) -> TypeObj: """Return object from its number (1 to N)""" return self.objmodel.get_object_from_number(nb) def __iter__(self): """Iterate over objects""" return iter(self.objmodel) def create_object(self) -> TypeObj: """Create object (signal or image) Returns: SignalObj or ImageObj object """ return self.PARAMCLASS() # pylint: disable=not-callable @qt_try_except() def add_object( self, obj: TypeObj, group_id: str | None = None, set_current: bool = True, ) -> None: """Add object Args: obj: SignalObj or ImageObj object group_id: group id to which the object belongs. If None or empty string, the object is added to the current group. set_current: if True, set the added object as current """ if obj in self.objmodel: # Prevent adding the same object twice raise ValueError( f"Object {hex(id(obj))} already in panel. " f"The same object cannot be added twice: " f"please use a copy of the object." ) if group_id is None or group_id == "": group_id = self.objview.get_current_group_id() if group_id is None: groups = self.objmodel.get_groups() if groups: group_id = get_uuid(groups[0]) else: group_id = get_uuid(self.add_group("")) obj.check_data() self.objmodel.add_object(obj, group_id) # Mark this object as newly created to show Creation tab on first selection # BUT: Don't overwrite if this object is already marked as freshly processed # or has fresh analysis results (those take precedence) obj_uuid = get_uuid(obj) if obj_uuid not in ( self.objprop.fresh_processing_obj_uuid, self.objprop.fresh_analysis_obj_uuid, ): self.objprop.mark_as_newly_created(obj) # Block signals to avoid updating the plot (unnecessary refresh) self.objview.blockSignals(True) self.objview.add_object_item(obj, group_id, set_current=set_current) self.objview.blockSignals(False) # Emit signal to ensure that the data panel is shown in the main window and # that the plot is updated (trigger a refresh of the plot) self.SIG_OBJECT_ADDED.emit() self.objview.update_tree() def remove_all_objects(self) -> None: """Remove all objects""" # iterate over a copy of self.__separate_views dict keys to avoid RuntimeError: # dictionary changed size during iteration for dlg in list(self.__separate_views): dlg.done(QW.QDialog.DialogCode.Rejected) self.objmodel.clear() self.plothandler.clear() self.objview.populate_tree() self.refresh_plot("selected", True, False) super().remove_all_objects() # Update object properties panel to clear creation/processing tabs self.selection_changed() # ---- Signal/Image Panel API ------------------------------------------------------ def setup_panel(self) -> None: """Setup panel""" self.acthandler.create_all_actions() self.processor.SIG_ADD_SHAPE.connect(self.plothandler.add_shapes) self.SIG_REFRESH_PLOT.connect(self.plothandler.refresh_plot) self.objview.SIG_SELECTION_CHANGED.connect(self.selection_changed) self.objview.SIG_ITEM_DOUBLECLICKED.connect( lambda oid: self.open_separate_view([oid]) ) self.objview.SIG_CONTEXT_MENU.connect(self.__popup_contextmenu) self.objprop.properties.SIG_APPLY_BUTTON_CLICKED.connect( self.properties_changed ) self.addWidget(self.objview) self.addWidget(self.objprop) def refresh_plot( self, what: str, update_items: bool = True, force: bool = False, only_visible: bool = True, only_existing: bool = False, ) -> None: """Refresh plot. Args: what: string describing the objects to refresh. Valid values are "selected" (refresh the selected objects), "all" (refresh all objects), "existing" (refresh existing plot items), or an object uuid. update_items: if True, update the items. If False, only show the items (do not update them). Defaults to True. force: if True, force refresh even if auto refresh is disabled. Defaults to False. only_visible: if True, only refresh visible items. Defaults to True. Visible items are the ones that are not hidden by other items or the items except the first one if the option "Show first only" is enabled. This is useful for images, where the last image is the one that is shown. If False, all items are refreshed. only_existing: if True, only refresh existing items. Defaults to False. Existing items are the ones that have already been created and are associated to the object uuid. If False, create new items for the objects that do not have an item yet. Raises: ValueError: if `what` is not a valid value """ if what not in ("selected", "all", "existing") and not isinstance(what, str): raise ValueError(f"Invalid value for 'what': {what}") self.SIG_REFRESH_PLOT.emit( what, update_items, force, only_visible, only_existing ) def manual_refresh(self) -> None: """Manual refresh""" self.refresh_plot("selected", True, True) def get_category_actions( self, category: actionhandler.ActionCategory ) -> list[QW.QAction]: # pragma: no cover """Return actions for category""" return self.acthandler.feature_actions.get(category, []) def get_context_menu(self) -> QW.QMenu: """Update and return context menu""" # Note: For now, this is completely unnecessary to clear context menu everytime, # but implementing it this way could be useful in the future in menu contents # should take into account current object selection self.context_menu.clear() actions = self.get_category_actions(actionhandler.ActionCategory.CONTEXT_MENU) add_actions(self.context_menu, actions) return self.context_menu def __popup_contextmenu(self, position: QC.QPoint) -> None: # pragma: no cover """Popup context menu at position""" menu = self.get_context_menu() menu.popup(position) # ------Creating, adding, removing objects------------------------------------------ def add_group(self, title: str, select: bool = False) -> objectmodel.ObjectGroup: """Add group Args: title: group title select: if True, select the group in the tree view. Defaults to False. Returns: Created group object """ group = self.objmodel.add_group(title) self.objview.add_group_item(group) if select: self.objview.select_groups([group]) return group def __duplicate_individual_obj( self, oid: str, new_group_id: str | None = None, set_current: bool = True ) -> None: """Duplicate individual object""" obj = self.objmodel[oid] if new_group_id is None: new_group_id = self.objmodel.get_object_group_id(obj) self.add_object(obj.copy(), group_id=new_group_id, set_current=set_current) def duplicate_object(self) -> None: """Duplication signal/image object""" if not self.mainwindow.confirm_memory_state(): return # Duplicate individual objects (exclusive with respect to groups) for oid in self.objview.get_sel_object_uuids(): self.__duplicate_individual_obj(oid, set_current=False) # Duplicate groups (exclusive with respect to individual objects) for group in self.objview.get_sel_groups(): new_group = self.add_group(group.title) for oid in self.objmodel.get_group_object_ids(get_uuid(group)): self.__duplicate_individual_obj( oid, get_uuid(new_group), set_current=False ) self.selection_changed(update_items=True) def copy_metadata(self) -> None: """Copy object metadata""" obj = self.objview.get_sel_objects()[0] self.metadata_clipboard = obj.metadata.copy() # Rename geometry results to avoid conflicts when pasting to same object type new_pref = get_short_id(obj) + "_" self._rename_results_in_clipboard(new_pref) # Update action states (e.g., "Paste metadata" should now be enabled) self.selection_changed() def _rename_results_in_clipboard(self, prefix: str) -> None: """Rename geometry and table results in clipboard to avoid conflicts. Args: prefix: Prefix to add to result titles """ for aclass in (GeometryAdapter, TableAdapter): result_keys = [ k for k, v in self.metadata_clipboard.items() if aclass.match(k, v) ] for dict_key in result_keys: try: # Get the result data result_data = self.metadata_clipboard[dict_key] # Update the title in the result data if isinstance(result_data, dict) and "title" in result_data: result_data = result_data.copy() # Don't modify original result_data["title"] = prefix + result_data["title"] # Create new key with updated title new_dict_key = dict_key.replace( aclass.META_PREFIX, aclass.META_PREFIX + prefix, 1 ) # Remove old entry and add new one del self.metadata_clipboard[dict_key] self.metadata_clipboard[new_dict_key] = result_data except (KeyError, ValueError, IndexError, TypeError): # If we can't process this result, leave it as is continue def paste_metadata(self, param: PasteMetadataParam | None = None) -> None: """Paste metadata to selected object(s)""" if param is None: param = PasteMetadataParam( _("Paste metadata"), comment=_( "Select what to keep from the clipboard.

    " "Result shapes and annotations, if kept, will be merged with " "existing ones. All other metadata will be replaced." ), ) if not param.edit(parent=self.parentWidget()): return metadata = {} if param.keep_roi and ROI_KEY in self.metadata_clipboard: metadata[ROI_KEY] = self.metadata_clipboard[ROI_KEY] if param.keep_geometry: for key, value in self.metadata_clipboard.items(): if GeometryAdapter.match(key, value): metadata[key] = value if param.keep_tables: for key, value in self.metadata_clipboard.items(): if TableAdapter.match(key, value): metadata[key] = value if param.keep_other: for key, value in self.metadata_clipboard.items(): if ( not GeometryAdapter.match(key, value) and not TableAdapter.match(key, value) and key not in METADATA_PASTE_EXCLUSIONS ): metadata[key] = value sel_objects = self.objview.get_sel_objects(include_groups=True) for obj in sorted(sel_objects, key=get_short_id, reverse=True): obj.update_metadata_from(metadata) # We have to do a special refresh in order to force the plot handler to update # all plot items, even the ones that are not visible (otherwise, image masks # would not be updated after pasting the metadata: see issue #123) self.refresh_plot( "selected", update_items=True, only_visible=False, only_existing=True ) def add_metadata(self, param: AddMetadataParam | None = None) -> None: """Add metadata item to selected object(s) Args: param: Add metadata parameters """ sel_objects = self.objview.get_sel_objects(include_groups=True) if not sel_objects: return if param is None: param = AddMetadataParam(sel_objects) # Restore settings from config saved_param = Conf.io.add_metadata_settings.get(default=AddMetadataParam()) update_dataset(param, saved_param) with warnings.catch_warnings(): warnings.simplefilter("ignore", category=gds.DataItemValidationWarning) if not param.edit(parent=self.parentWidget(), wordwrap=False): return # Save settings to config Conf.io.add_metadata_settings.set(param) # Build values for all selected objects values = param.build_values(sel_objects) # Add metadata to each object for obj, value in zip(sel_objects, values): obj.metadata[param.metadata_key] = value # Refresh the plot to update any changes self.refresh_plot( "selected", update_items=True, only_visible=False, only_existing=True ) def copy_roi(self) -> None: """Copy regions of interest""" obj = self.objview.get_sel_objects()[0] self.__roi_clipboard = obj.roi.copy() def paste_roi(self) -> None: """Paste regions of interest""" sel_objects = self.objview.get_sel_objects(include_groups=True) for obj in sel_objects: if obj.roi is None: obj.roi = self.__roi_clipboard.copy() else: obj.roi = obj.roi.combine_with(self.__roi_clipboard) self.selection_changed(update_items=True) self.refresh_plot( "selected", update_items=True, only_visible=False, only_existing=True ) def remove_object(self, force: bool = False) -> None: """Remove signal/image object Args: force: if True, remove object without confirmation. Defaults to False. """ sel_groups = self.objview.get_sel_groups() if sel_groups and not force and not execenv.unattended: answer = QW.QMessageBox.warning( self, _("Delete group(s)"), _("Are you sure you want to delete the selected group(s)?"), QW.QMessageBox.Yes | QW.QMessageBox.No, ) if answer == QW.QMessageBox.No: return sel_objects = self.objview.get_sel_objects(include_groups=True) for obj in sorted(sel_objects, key=get_short_id, reverse=True): dlg_list: list[QW.QDialog] = [] for dlg, obj_i in self.__separate_views.items(): if obj_i is obj: dlg_list.append(dlg) for dlg in dlg_list: dlg.done(QW.QDialog.DialogCode.Rejected) self.plothandler.remove_item(get_uuid(obj)) self.objview.remove_item(get_uuid(obj), refresh=False) self.objmodel.remove_object(obj) for group in sel_groups: self.objview.remove_item(get_uuid(group), refresh=False) self.objmodel.remove_group(group) self.objview.update_tree() self.selection_changed(update_items=True) self.SIG_OBJECT_REMOVED.emit() def delete_all_objects(self) -> None: # pragma: no cover """Confirm before removing all objects""" if len(self) == 0: return if execenv.unattended: raise RuntimeError( "This method should not be executed in unattended mode: " "call remove_all_objects instead." ) answer = QW.QMessageBox.warning( self, _("Delete all"), _("Do you want to delete all objects (%s)?") % self.PANEL_STR, QW.QMessageBox.Yes | QW.QMessageBox.No, ) if answer == QW.QMessageBox.Yes: self.remove_all_objects() def delete_metadata( self, refresh_plot: bool = True, keep_roi: bool | None = None ) -> None: """Delete metadata of selected objects Args: refresh_plot: Refresh plot. Defaults to True. keep_roi: Keep regions of interest, if any. Defaults to None (ask user). """ sel_objs = self.objview.get_sel_objects(include_groups=True) # Check if there are regions of interest first: roi_backup: dict[TypeObj, np.ndarray] = {} if any(obj.roi is not None for obj in sel_objs): if execenv.unattended and keep_roi is None: keep_roi = False elif keep_roi is None: answer = QW.QMessageBox.warning( self, _("Delete metadata"), _( "Some selected objects have regions of interest.
    " "Do you want to delete them as well?" ), QW.QMessageBox.Yes | QW.QMessageBox.No | QW.QMessageBox.Cancel, ) if answer == QW.QMessageBox.Cancel: return keep_roi = answer == QW.QMessageBox.No if keep_roi: for obj in sel_objs: if obj.roi is not None: roi_backup[obj] = obj.roi # Delete metadata: for index, obj in enumerate(sel_objs): obj.reset_metadata_to_defaults() if not keep_roi: obj.mark_roi_as_changed() if obj in roi_backup: obj.roi = roi_backup[obj] if index == 0: self.selection_changed() # When calling object `reset_metadata_to_defaults` method, we removed all # metadata application options, among them the object number which is used # to compute the short ID of the object. # So we have to reset the short IDs of all objects in the model to recalculate # the object numbers: self.objmodel.reset_short_ids() if refresh_plot: # We have to do a special refresh in order to force the plot handler to # update all plot items, even the ones that are not visible (otherwise, # image masks would remained visible after deleting the ROI for example: # see issue #122) self.refresh_plot( "selected", update_items=True, only_visible=False, only_existing=True ) def add_annotations_from_items( self, items: list, refresh_plot: bool = True ) -> None: """Add object annotations (annotation plot items). Args: items: annotation plot items refresh_plot: refresh plot. Defaults to True. """ for obj in self.objview.get_sel_objects(include_groups=True): create_adapter_from_object(obj).add_annotations_from_items(items) if refresh_plot: self.refresh_plot("selected", True, False) def update_metadata_view_settings(self) -> None: """Update metadata view settings""" def_dict = Conf.view.get_def_dict(self.__class__.__name__[:3].lower()) for obj in self.objmodel: obj.set_metadata_options_defaults(def_dict, overwrite=True) self.refresh_plot("all", True, False) def copy_titles_to_clipboard(self) -> None: """Copy object titles to clipboard (for reproducibility)""" QW.QApplication.clipboard().setText(str(self.objview)) def new_group(self) -> None: """Create a new group""" # Open a message box to enter the group name group_name, ok = QW.QInputDialog.getText(self, _("New group"), _("Group name:")) if ok: self.add_group(group_name) def rename_selected_object_or_group(self, new_name: str | None = None) -> None: """Rename selected object or group Args: new_name: new name (default: None, i.e. ask user) """ sel_objects = self.objview.get_sel_objects(include_groups=False) sel_groups = self.objview.get_sel_groups() if (not sel_objects and not sel_groups) or len(sel_objects) + len( sel_groups ) > 1: # Won't happen in the application, but could happen in tests or using the # API directly raise ValueError("Select one object or group to rename") if sel_objects: obj = sel_objects[0] if new_name is None: new_name, ok = QW.QInputDialog.getText( self, _("Rename object"), _("Object name:"), QW.QLineEdit.Normal, obj.title, ) if not ok: return obj.title = new_name self.objview.update_item(get_uuid(obj)) self.objprop.update_properties_from(obj) elif sel_groups: group = sel_groups[0] if new_name is None: new_name, ok = QW.QInputDialog.getText( self, _("Rename group"), _("Group name:"), QW.QLineEdit.Normal, group.title, ) if not ok: return group.title = new_name self.objview.update_item(get_uuid(group)) @abc.abstractmethod def get_newparam_from_current( self, newparam: NewSignalParam | NewImageParam | None = None ) -> NewSignalParam | NewImageParam | None: """Get new object parameters from the current object. Args: newparam: new object parameters. If None, create a new one. Returns: New object parameters """ @abc.abstractmethod def new_object( self, param: NewSignalParam | NewImageParam | None = None, edit: bool = False, add_to_panel: bool = True, ) -> TypeObj | None: """Create a new object (signal/image). Args: param: new object parameters edit: Open a dialog box to edit parameters (default: False). When False, the object is created with default parameters and creation parameters are stored in metadata for interactive editing. add_to_panel: Add object to panel (default: True) Returns: New object """ def set_current_object_title(self, title: str) -> None: """Set current object title""" obj = self.objview.get_current_object() obj.title = title self.objview.update_item(get_uuid(obj)) def __load_from_file( self, filename: str, create_group: bool = True, add_objects: bool = True ) -> list[SignalObj] | list[ImageObj]: """Open objects from file (signal/image), add them to DataLab and return them. Args: filename: file name create_group: if True, create a new group if more than one object is loaded. Defaults to True. If False, all objects are added to the current group. add_objects: if True, add objects to the panel. Defaults to True. Returns: New object or list of new objects """ worker = CallbackWorker(lambda worker: self.IO_REGISTRY.read(filename, worker)) objs = qt_long_callback(self, _("Reading objects from file"), worker, True) group_id = None if len(objs) > 1 and create_group: # Create a new group if more than one object is loaded group_id = get_uuid(self.add_group(osp.basename(filename))) with create_progress_bar( self, _("Adding objects to workspace"), max_=len(objs) - 1 ) as progress: for i_obj, obj in enumerate(objs): progress.setValue(i_obj + 1) if progress.wasCanceled(): break if add_objects: set_uuid(obj) # In case the object UUID was serialized in the file, # we need to reset it to a new UUID to avoid conflicts # (e.g. HDF5 file) self.add_object(obj, group_id=group_id, set_current=obj is objs[-1]) self.selection_changed() return objs def __save_to_file(self, obj: TypeObj, filename: str) -> None: """Save object to file (signal/image). Args: obj: object filename: file name """ self.IO_REGISTRY.write(filename, obj) def load_from_directory(self, directory: str | None = None) -> list[TypeObj]: """Open objects from directory (signals or images, depending on the panel), add them to DataLab and return them. If the directory is not specified, ask the user to select a directory. Args: directory: directory name Returns: list of new objects """ if not self.mainwindow.confirm_memory_state(): return [] if directory is None: # pragma: no cover basedir = Conf.main.base_dir.get() with save_restore_stds(): directory = getexistingdirectory(self, _("Open"), basedir) if not directory: return [] folders = [ path for path in glob.glob(osp.join(directory, "**"), recursive=True) if osp.isdir(path) and len(os.listdir(path)) > 0 ] objs = [] with create_progress_bar( self, _("Scanning directory"), max_=len(folders) - 1 ) as progress: # Iterate over all subfolders in the directory: for i_path, path in enumerate(folders): progress.setValue(i_path + 1) if progress.wasCanceled(): break path = osp.normpath(path) fnames = sorted( [ osp.join(path, fname) for fname in os.listdir(path) if osp.isfile(osp.join(path, fname)) ] ) new_objs = self.load_from_files( fnames, create_group=False, add_objects=False, ignore_errors=True, ) if new_objs: objs += new_objs grp_name = osp.relpath(path, directory) if grp_name == ".": grp_name = osp.basename(path) grp = self.add_group(grp_name) for obj in new_objs: self.add_object(obj, group_id=get_uuid(grp), set_current=False) return objs def load_from_files( self, filenames: list[str] | None = None, create_group: bool = False, add_objects: bool = True, ignore_errors: bool = False, ) -> list[TypeObj]: """Open objects from file (signals/images), add them to DataLab and return them. Args: filenames: File names create_group: if True, create a new group if more than one object is loaded for a single file. Defaults to False: all objects are added to the current group. add_objects: if True, add objects to the panel. Defaults to True. ignore_errors: if True, ignore errors when loading files. Defaults to False. Returns: list of new objects """ if not self.mainwindow.confirm_memory_state(): return [] if filenames is None: # pragma: no cover basedir = Conf.main.base_dir.get() filters = self.IO_REGISTRY.get_read_filters() with save_restore_stds(): filenames, _filt = getopenfilenames(self, _("Open"), basedir, filters) # Sort filenames to ensure consistent alphabetical order across all platforms filenames = sorted(filenames) objs = [] for filename in filenames: with qt_try_loadsave_file(self.parentWidget(), filename, "load"): Conf.main.base_dir.set(filename) try: objs += self.__load_from_file( filename, create_group=create_group, add_objects=add_objects ) except Exception as exc: # pylint: disable=broad-exception-caught if ignore_errors: # Ignore unknown file types pass else: raise exc return objs def save_to_files(self, filenames: list[str] | str | None = None) -> None: """Save selected objects to files (signal/image). Args: filenames: File names """ objs = self.objview.get_sel_objects(include_groups=True) if filenames is None: # pragma: no cover filenames = [None] * len(objs) assert len(filenames) == len(objs), ( "Number of filenames must match number of objects" ) for index, obj in enumerate(objs): filename = filenames[index] if filename is None: basedir = Conf.main.base_dir.get() filters = self.IO_REGISTRY.get_write_filters() with save_restore_stds(): filename, _filt = getsavefilename( self, _("Save as"), basedir, filters ) if filename: with qt_try_loadsave_file(self.parentWidget(), filename, "save"): Conf.main.base_dir.set(filename) self.__save_to_file(obj, filename) def save_to_directory(self, param: SaveToDirectoryParam | None = None) -> None: """Save signals or images to directory using a filename pattern. Opens a dialog to select the output directory, the basename pattern and the extension. Args: param: parameters. """ objs = self.objview.get_sel_objects(include_groups=True) if param is None: extensions = get_file_extensions(self.IO_REGISTRY.get_write_filters()) with warnings.catch_warnings(): warnings.simplefilter("ignore", category=gds.DataItemValidationWarning) guiparam = SaveToDirectoryGUIParam(objs, extensions) # Restore settings from config saved_param = Conf.io.save_to_directory_settings.get( default=SaveToDirectoryParam() ) update_dataset(guiparam, saved_param) # Validate extension: set to first if None or not in available list # Note: extensions list has no dots, but guiparam.extension has dot extensions_with_dot = ["." + ext for ext in extensions] if ( guiparam.extension is None or guiparam.extension not in extensions_with_dot ): guiparam.extension = extensions_with_dot[0] if extensions else "" if not guiparam.edit(parent=self.parentWidget()): return param = SaveToDirectoryParam() update_dataset(param, guiparam) # Save settings to config Conf.io.save_to_directory_settings.set(param) Conf.main.base_dir.set(param.directory) with create_progress_bar(self, _("Saving..."), max_=len(objs)) as progress: for i, (path, obj) in enumerate(param.generate_filepath_obj_pairs(objs)): progress.setValue(i + 1) if progress.wasCanceled(): break with qt_try_loadsave_file(self.parentWidget(), path, "save"): self.__save_to_file(obj, path) def handle_dropped_files(self, filenames: list[str] | None = None) -> None: """Handle dropped files Args: filenames: File names Returns: None """ dirnames = [fname for fname in filenames if osp.isdir(fname)] h5_fnames = [ fname for fname in filenames if is_hdf5_file(fname, check_content=True) ] other_fnames = sorted(list(set(filenames) - set(h5_fnames) - set(dirnames))) if dirnames: for dirname in dirnames: self.load_from_directory(dirname) if h5_fnames: self.mainwindow.open_h5_files(h5_fnames, import_all=True) if other_fnames: self.load_from_files(other_fnames) def exec_import_wizard(self) -> None: """Execute import wizard""" wizard = TextImportWizard(self.PANEL_STR_ID, parent=self.parentWidget()) if exec_dialog(wizard): objs = wizard.get_objs() if objs: with create_progress_bar( self, _("Adding objects to workspace"), max_=len(objs) - 1 ) as progress: for idx, obj in enumerate(objs): progress.setValue(idx) QW.QApplication.processEvents() if progress.wasCanceled(): break self.add_object(obj) def import_metadata_from_file(self, filename: str | None = None) -> None: """Import metadata from file (JSON). Args: filename: File name """ if filename is None: # pragma: no cover basedir = Conf.main.base_dir.get() with save_restore_stds(): filename, _filter = getopenfilename( self, _("Import metadata"), basedir, "*.dlabmeta" ) if filename: with qt_try_loadsave_file(self.parentWidget(), filename, "load"): Conf.main.base_dir.set(filename) obj = self.objview.get_sel_objects(include_groups=True)[0] obj.metadata = read_metadata(filename) self.refresh_plot("selected", True, False) def export_metadata_from_file(self, filename: str | None = None) -> None: """Export metadata to file (JSON). Args: filename: File name """ obj = self.objview.get_sel_objects(include_groups=True)[0] if filename is None: # pragma: no cover basedir = Conf.main.base_dir.get() with save_restore_stds(): filename, _filt = getsavefilename( self, _("Export metadata"), basedir, "*.dlabmeta" ) if filename: with qt_try_loadsave_file(self.parentWidget(), filename, "save"): Conf.main.base_dir.set(filename) write_metadata(filename, obj.metadata) def copy_annotations(self) -> None: """Copy annotations from selected object""" obj = self.objview.get_sel_objects(include_groups=True)[0] self.annotations_clipboard = obj.get_annotations() # Update action states (e.g., "Paste annotations" should now be enabled) self.selection_changed() def paste_annotations(self) -> None: """Paste annotations to selected object(s)""" if not self.annotations_clipboard: return sel_objects = self.objview.get_sel_objects(include_groups=True) for obj in sel_objects: obj.set_annotations(self.annotations_clipboard) self.refresh_plot("selected", True, False) # Update action states (e.g., annotation-related actions should now be enabled) self.selection_changed() def import_annotations_from_file(self, filename: str | None = None) -> None: """Import annotations from file (JSON). Args: filename: File name """ if filename is None: # pragma: no cover basedir = Conf.main.base_dir.get() with save_restore_stds(): filename, _filter = getopenfilename( self, _("Import annotations"), basedir, "*.dlabann" ) if filename: with qt_try_loadsave_file(self.parentWidget(), filename, "load"): Conf.main.base_dir.set(filename) obj = self.objview.get_sel_objects(include_groups=True)[0] annotations = read_annotations(filename) obj.set_annotations(annotations) self.refresh_plot("selected", True, False) # Update action states (annotation-related actions should now be enabled) self.selection_changed() def export_annotations_from_file(self, filename: str | None = None) -> None: """Export annotations to file (JSON). Args: filename: File name """ obj = self.objview.get_sel_objects(include_groups=True)[0] if filename is None: # pragma: no cover basedir = Conf.main.base_dir.get() with save_restore_stds(): filename, _filt = getsavefilename( self, _("Export annotations"), basedir, "*.dlabann" ) if filename: with qt_try_loadsave_file(self.parentWidget(), filename, "save"): Conf.main.base_dir.set(filename) annotations = obj.get_annotations() write_annotations(filename, annotations) def delete_annotations(self) -> None: """Delete all annotations from selected object(s)""" sel_objects = self.objview.get_sel_objects(include_groups=True) for obj in sel_objects: obj.clear_annotations() self.refresh_plot("selected", True, False) # Update action states (annotation-related actions should now be disabled) self.selection_changed() def import_roi_from_file(self, filename: str | None = None) -> None: """Import regions of interest from file (JSON). Args: filename: File name """ if filename is None: # pragma: no cover basedir = Conf.main.base_dir.get() with save_restore_stds(): filename, _filter = getopenfilename( self, _("Import ROI"), basedir, "*.dlabroi" ) if filename: with qt_try_loadsave_file(self.parentWidget(), filename, "load"): Conf.main.base_dir.set(filename) obj = self.objview.get_sel_objects(include_groups=True)[0] roi = read_roi(filename) if obj.roi is None: obj.roi = roi else: obj.roi = obj.roi.combine_with(roi) self.selection_changed(update_items=True) self.refresh_plot("selected", True, False) def export_roi_to_file(self, filename: str | None = None) -> None: """Export regions of interest to file (JSON). Args: filename: File name """ obj = self.objview.get_sel_objects(include_groups=True)[0] assert obj.roi is not None if filename is None: # pragma: no cover basedir = Conf.main.base_dir.get() with save_restore_stds(): filename, _filt = getsavefilename( self, _("Export ROI"), basedir, "*.dlabroi" ) if filename: with qt_try_loadsave_file(self.parentWidget(), filename, "save"): Conf.main.base_dir.set(filename) write_roi(filename, obj.roi) # ------Refreshing GUI-------------------------------------------------------------- def selection_changed(self, update_items: bool = False) -> None: """Object selection changed: update object properties, refresh plot and update object view. Args: update_items: Update plot items (default: False) """ selected_objects = self.objview.get_sel_objects(include_groups=True) selected_groups = self.objview.get_sel_groups() # Determine which tab to show based on object state current_obj = self.objview.get_current_object() force_tab = None if current_obj is not None: obj_uuid = get_uuid(current_obj) # Show Creation tab for newly created objects (only once) if obj_uuid == self.objprop.newly_created_obj_uuid: force_tab = "creation" self.objprop.newly_created_obj_uuid = None # Show Processing tab for freshly processed objects (only once) elif obj_uuid == self.objprop.fresh_processing_obj_uuid: force_tab = "processing" self.objprop.fresh_processing_obj_uuid = None # Show Analysis tab for objects with fresh analysis results elif obj_uuid == self.objprop.fresh_analysis_obj_uuid: force_tab = "analysis" self.objprop.fresh_analysis_obj_uuid = None self.objprop.update_properties_from(current_obj, force_tab=force_tab) self.acthandler.selected_objects_changed(selected_groups, selected_objects) self.refresh_plot("selected", update_items, False) def properties_changed(self) -> None: """The properties 'Apply' button was clicked: update object properties, refresh plot and update object view.""" # Get only the properties that have changed from the original values changed_props = self.objprop.get_changed_properties() # Apply only the changed properties to all selected objects for obj in self.objview.get_sel_objects(include_groups=True): obj.mark_roi_as_changed() # Update only the changed properties instead of all properties update_dataset(obj, changed_props) self.objview.update_item(get_uuid(obj)) # Auto-recompute analysis if the object had analysis parameters # Since properties have changed, any analysis results may now be invalid self.processor.auto_recompute_analysis(obj) # Refresh all selected items, including non-visible ones (only_visible=False) # This ensures that plot items are updated for all selected objects, even if # they are temporarily hidden behind other objects self.refresh_plot( "selected", update_items=True, force=False, only_visible=False ) # Update the stored original values to reflect the new state # This ensures subsequent changes are compared against the current values self.objprop.update_original_values() def recompute_processing(self) -> None: """Recompute/rerun selected objects or group with stored processing parameters. This method handles both single objects and groups. For each object, it checks if it has 1-to-1 processing parameters that can be recomputed. Objects without recomputable parameters are skipped. """ # Get selected objects (handles both individual selection and groups) objects = self.objview.get_sel_objects(include_groups=True) if not objects: return # Filter objects that have recomputable processing parameters recomputable_objects: list[SignalObj | ImageObj] = [] for obj in objects: proc_params = extract_processing_parameters(obj) if proc_params is not None and proc_params.pattern == "1-to-1": recomputable_objects.append(obj) if not recomputable_objects: if not execenv.unattended: QW.QMessageBox.information( self, _("Recompute"), _( "Selected object(s) do not have processing parameters " "that can be recomputed." ), ) return # Recompute each object with create_progress_bar( self, _("Recomputing objects"), max_=len(recomputable_objects) ) as progress: for index, obj in enumerate(recomputable_objects): progress.setValue(index + 1) QW.QApplication.processEvents() if progress.wasCanceled(): break # Temporarily set this object as current to use existing infrastructure self.objview.set_current_object(obj) report = self.objprop.apply_processing_parameters( obj=obj, interactive=False ) if not report.success and not execenv.unattended: failtxt = _("Failed to recompute object") if index == len(recomputable_objects) - 1: QW.QMessageBox.warning( self, _("Recompute"), f"{failtxt} '{obj.title}':\n{report.message}", ) else: conttxt = _("Do you want to continue with the next object?") answer = QW.QMessageBox.warning( self, _("Recompute"), f"{failtxt} '{obj.title}':\n{report.message}\n\n{conttxt}", QW.QMessageBox.Yes | QW.QMessageBox.No, ) if answer == QW.QMessageBox.No: break def select_source_objects(self) -> None: """Select source objects associated with the selected object's processing. This method retrieves the source object UUIDs from the selected object's processing parameters and selects them in the object view. """ # Get the selected object (should be exactly one) objects = self.objview.get_sel_objects(include_groups=False) if len(objects) != 1: return obj = objects[0] # Extract processing parameters proc_params = extract_processing_parameters(obj) if proc_params is None: if not execenv.unattended: QW.QMessageBox.information( self, _("Select source objects"), _("Selected object does not have processing metadata."), ) return # Get source UUIDs source_uuids = [] if proc_params.source_uuid: source_uuids.append(proc_params.source_uuid) if proc_params.source_uuids: source_uuids.extend(proc_params.source_uuids) if not source_uuids: if not execenv.unattended: QW.QMessageBox.information( self, _("Select source objects"), _("Selected object does not have source object references."), ) return # Check if source objects still exist (search across all panels) existing_objs = [] for uuid in source_uuids: obj = self.mainwindow.find_object_by_uuid(uuid) if obj is not None: existing_objs.append(obj) if not existing_objs: if not execenv.unattended: if len(source_uuids) == 1: msg = _("Source object no longer exists.") else: msg = _("Source objects no longer exist.") QW.QMessageBox.warning(self, _("Select source objects"), msg) return # Determine which panel contains the source objects # Source objects are always in the same panel (either all signals or all images) if all(uuid in self.objmodel.get_object_ids() for uuid in source_uuids): source_panel = self elif isinstance(existing_objs[0], SignalObj): source_panel = self.mainwindow.signalpanel else: # ImageObj source_panel = self.mainwindow.imagepanel # Switch to the source panel if needed if source_panel is not self: self.mainwindow.set_current_panel(source_panel) # Select the existing source objects # Note: Since all sources are in the same panel, all UUIDs in existing_objs # are guaranteed to be in source_panel source_panel.objview.clearSelection() for obj in existing_objs: source_panel.objview.set_current_item_id(get_uuid(obj), extend=True) # Show info if some sources were deleted missing_count = len(source_uuids) - len(existing_objs) if missing_count > 0 and not execenv.unattended: if len(existing_objs) == 1: msg = _("Selected a single source object") else: msg = _("Selected %d source objects") % len(existing_objs) msg += " (" if missing_count == 1: msg += _("1 source object no longer exists") else: msg += _("%d source objects no longer exist") % missing_count msg += ")" QW.QMessageBox.warning(self, _("Select source objects"), msg) # ------Plotting data in modal dialogs---------------------------------------------- def add_plot_items_to_dialog(self, dlg: PlotDialog, oids: list[str]) -> None: """Add plot items to dialog Args: dlg: Dialog oids: Object IDs """ objs = self.objmodel.get_objects(oids) plot = dlg.get_plot() with create_progress_bar( self, _("Creating plot items"), max_=len(objs) ) as progress: for index, obj in enumerate(objs): progress.setValue(index + 1) QW.QApplication.processEvents() if progress.wasCanceled(): return None item = create_adapter_from_object(obj).make_item( update_from=self.plothandler[get_uuid(obj)] ) item.set_readonly(True) plot.add_item(item, z=0) plot.set_active_item(item) item.unselect() plot.replot() return dlg def open_separate_view( self, oids: list[str] | None = None, edit_annotations: bool = False ) -> PlotDialog | None: """ Open separate view for visualizing selected objects Args: oids: Object IDs (default: None) edit_annotations: Edit annotations (default: False) Returns: Instance of PlotDialog """ if oids is None: oids = self.objview.get_sel_object_uuids(include_groups=True) obj = self.objmodel[oids[-1]] # last selected object if not all(oid in self.plothandler for oid in oids): # This happens for example when opening an already saved workspace with # multiple images, and if the user tries to view in a new window a group of # images without having selected any object yet. In this case, only the # last image is actually plotted (because if the other have the same size # and position, they are hidden), and the plot item of every other image is # not created yet. So we need to refresh the plot to create the plot item of # those images. self.plothandler.refresh_plot( "selected", update_items=True, force=True, only_visible=False ) # Create a new dialog and add plot items to it dlg = self.create_new_dialog( title=obj.title if len(oids) == 1 else None, edit=True, name=f"{obj.PREFIX}_new_window", ) if dlg is None: return None self.add_plot_items_to_dialog(dlg, oids) mgr = dlg.get_manager() toolbar = QW.QToolBar(_("Annotations"), self) dlg.button_layout.insertWidget(0, toolbar) mgr.add_toolbar(toolbar, id(toolbar)) toolbar.setToolButtonStyle(QC.Qt.ToolButtonTextUnderIcon) for tool in self.ANNOTATION_TOOLS: mgr.add_tool(tool, toolbar_id=id(toolbar)) def toggle_annotations(enabled: bool): """Toggle annotation tools / edit buttons visibility""" for widget in (dlg.button_box, toolbar, mgr.get_itemlist_panel()): if enabled: widget.show() else: widget.hide() edit_ann_action = create_action( dlg, _("Annotations"), toggled=toggle_annotations, icon=get_icon("annotations.svg"), ) mgr.add_tool(ActionTool, edit_ann_action) default_toolbar = mgr.get_default_toolbar() action_btn = default_toolbar.widgetForAction(edit_ann_action) action_btn.setToolButtonStyle(QC.Qt.ToolButtonTextBesideIcon) plot = dlg.get_plot() for item in plot.items: item.set_selectable(False) for item in create_adapter_from_object(obj).iterate_shape_items(editable=True): plot.add_item(item) self.__separate_views[dlg] = obj toggle_annotations(edit_annotations) if len(oids) > 1: # If multiple objects are displayed, show the item list panel # (otherwise, it is hidden by default to lighten the dialog, except # if `edit_annotations` is True): plot.manager.get_itemlist_panel().show() if edit_annotations: edit_ann_action.setChecked(True) dlg.show() dlg.finished.connect(self.__separate_view_finished) return dlg def __separate_view_finished(self, result: int) -> None: """Separate view was closed Args: result: result """ dlg: PlotDialog = self.sender() if result == QW.QDialog.DialogCode.Accepted: rw_items = [] for item in dlg.get_plot().get_items(): if not item.is_readonly() and is_plot_item_serializable(item): rw_items.append(item) obj = self.__separate_views[dlg] # Use the annotation adapter to set annotations in the new format adapter = create_adapter_from_object(obj) adapter.set_annotations_from_items(rw_items) self.selection_changed(update_items=True) self.__separate_views.pop(dlg) dlg.deleteLater() def view_images_side_by_side(self, oids: list[str] | None = None) -> None: """ View selected images side-by-side in a grid layout Args: oids: Object IDs (default: None, uses selected objects) """ if oids is None: oids = self.objview.get_sel_object_uuids(include_groups=True) if len(oids) < 2: return objs = self.objmodel.get_objects(oids) # Compute grid dimensions max_cols = 4 num_cols = min(len(objs), max_cols) num_rows = (len(objs) + num_cols - 1) // num_cols # Create dialog with synchronized plots dlg = SyncPlotDialog(title=_("View images side-by-side"), toolbar=False) dlg.setObjectName(f"{self.PANEL_STR_ID}_side_by_side") # Add each image to the grid for idx, obj in enumerate(objs): row = idx // num_cols col = idx % num_cols # Create plot with title plot = BasePlot(options=BasePlotOptions(title=obj.title, type="image")) # Create plot item from object adapter = create_adapter_from_object(obj) item = adapter.make_item() item.set_readonly(True) plot.add_item(item, z=0) # Add ROI items if available if obj.roi is not None: for roi_item in adapter.iterate_roi_items(editable=False): plot.add_item(roi_item) # Add to synchronized dialog dlg.add_plot(row, col, plot, sync=True) # Finalize and show dialog dlg.finalize_configuration() # Set explicit size for proper display dlg.resize(20 + 440 * num_cols, 20 + 400 * num_rows) exec_dialog(dlg) def get_dialog_size(self) -> tuple[int, int]: """Get dialog size (minimum and maximum)""" # Resize the dialog so that it's at least MINDIALOGSIZE (absolute values), # and at most MAXDIALOGSIZE (% of the main window size): minwidth, minheight = self.MINDIALOGSIZE maxwidth = int(self.mainwindow.width() * self.MAXDIALOGSIZE) maxheight = int(self.mainwindow.height() * self.MAXDIALOGSIZE) size = min(minwidth, maxwidth), min(minheight, maxheight) return size def create_new_dialog( self, edit: bool = False, toolbar: bool = True, title: str | None = None, name: str | None = None, options: dict[str, Any] | None = None, ) -> PlotDialog | None: """Create new pop-up signal/image plot dialog. Args: edit: Edit mode toolbar: Show toolbar title: Dialog title name: Dialog object name options: Plot options Returns: Plot dialog instance """ plot_options = self.plothandler.get_plot_options() if options is not None: plot_options = plot_options.copy(options) # pylint: disable=not-callable dlg = PlotDialog( parent=self.parentWidget(), title=APP_NAME if title is None else f"{title} - {APP_NAME}", options=plot_options, toolbar=toolbar, icon="DataLab.svg", edit=edit, size=self.get_dialog_size(), ) dlg.setObjectName(name) return dlg def get_roi_editor_output( self, mode: Literal["apply", "extract", "define"] = "apply" ) -> tuple[TypeROI, bool] | None: """Get ROI data (array) from specific dialog box. Args: mode: Mode of operation, either "apply" (define ROI, then apply it to selected objects), "extract" (define ROI, then extract data from it), or "define" (define ROI without applying or extracting). Returns: A tuple containing the ROI object and a boolean indicating whether the dialog was accepted or not. """ obj = self.objview.get_sel_objects(include_groups=True)[-1] # Use get() instead of [] to avoid KeyError if the plot item doesn't exist # (can happen when "auto refresh" is disabled or in "show first only" mode # where not all objects have plot items created yet) existing_item = self.plothandler.get(get_uuid(obj)) item = create_adapter_from_object(obj).make_item(update_from=existing_item) roi_editor_class = self.get_roieditor_class() # pylint: disable=not-callable roi_editor = roi_editor_class( parent=self.parentWidget(), obj=obj, mode=mode, item=item, options=self.plothandler.get_plot_options(), size=self.get_dialog_size(), ) if exec_dialog(roi_editor): return roi_editor.get_roieditor_results() return None def get_objects_with_dialog( self, title: str, comment: str = "", nb_objects: int = 1, parent: QW.QWidget | None = None, ) -> TypeObj | None: """Get object with dialog box. Args: title: Dialog title comment: Optional dialog comment nb_objects: Number of objects to select parent: Parent widget Returns: Object(s) (signal(s) or image(s), or None if dialog was canceled) """ parent = self if parent is None else parent dlg = objectview.GetObjectsDialog(parent, self, title, comment, nb_objects) if exec_dialog(dlg): return dlg.get_selected_objects() return None def __show_no_result_warning(self): """Show no result warning""" msg = "
    ".join( [ _("No result currently available for this object."), "", _( "This feature leverages the results of previous analysis " "performed on the selected object(s).

    " "To compute results, select one or more objects and choose " "a feature in the Analysis menu." ), ] ) if not execenv.unattended: QW.QMessageBox.information(self, APP_NAME, msg) def show_results(self) -> None: """Show results""" objs = self.objview.get_sel_objects(include_groups=True) rdatadict = create_resultdata_dict(objs) if rdatadict: for rdata in rdatadict.values(): show_resultdata(self.parentWidget(), rdata, f"{objs[0].PREFIX}_results") else: self.__show_no_result_warning() def toggle_result_label_visibility(self, state: bool) -> None: """Toggle the visibility of the merged result label on the plot. Args: state: True to show the label, False to hide it """ show_label = state # Update the configuration Conf.view.show_result_label.set(show_label) # Synchronize the other panel's action state for panel in (self.mainwindow.signalpanel, self.mainwindow.imagepanel): if panel is not self and panel.acthandler.show_label_action is not None: panel.acthandler.show_label_action.blockSignals(True) panel.acthandler.show_label_action.setChecked(show_label) panel.acthandler.show_label_action.blockSignals(False) # Refresh the plot to show/hide the label self.plothandler.toggle_result_label_visibility(show_label) def __add_result_signal( self, x: np.ndarray | list[float], y: np.ndarray | list[float], title: str, xaxis: str, yaxis: str, group_id: str | None = None, ) -> None: """Add result signal Args: x: X data y: Y data title: Signal title xaxis: X axis label yaxis: Y axis label group_id: UUID of the group to add the signal to. If None, add to current group. """ xdata = np.array(x, dtype=float) ydata = np.array(y, dtype=float) obj = create_signal( title=f"{title}: {yaxis} = f({xaxis})", x=xdata, y=ydata, labels=[xaxis, yaxis], ) self.mainwindow.signalpanel.add_object(obj, group_id=group_id) def __create_plot_result_param_class(self, rdata: ResultData) -> type[gds.DataSet]: """Create PlotResultParam class dynamically based on result data. Args: rdata: Result data Returns: PlotResultParam class """ # Build X and Y choices from result data headers xchoices = (("indices", _("Indices")),) for xlabel in rdata.headers: # If this column data is not numeric, we skip it: if not isinstance( rdata.results[0].get_column_values(xlabel)[0], (int, float, np.number) ): continue xchoices += ((xlabel, xlabel),) ychoices = xchoices[1:] # Determine default plot kind based on result data default_kind = ( "one_curve_per_object" if any(len(result.to_dataframe()) > 1 for result in rdata.results) else "one_curve_per_title" ) class PlotResultParam(gds.DataSet): """Plot results parameters""" kind = gds.ChoiceItem( _("Plot kind"), ( ( "one_curve_per_object", _("One curve per object (or ROI) and per result title"), ), ("one_curve_per_title", _("One curve per result title")), ), default=default_kind, ) xaxis = gds.ChoiceItem(_("X axis"), xchoices, default="indices") yaxis = gds.ChoiceItem(_("Y axis"), ychoices, default=ychoices[0][0]) return PlotResultParam def __plot_result( self, category: str, rdata: ResultData, objs: list[SignalObj | ImageObj], param: gds.DataSet | None = None, result_group_id: str | None = None, ) -> None: """Plot results for a specific category Args: category: Result category rdata: Result data objs: List of objects param: Plot result parameters. If None, show dialog to get parameters. result_group_id: UUID of the group to add result signals to. If None, add to current group. """ # Regrouping ResultShape results by their `title` attribute: grouped_results: dict[str, list[GeometryAdapter | TableAdapter]] = {} for result in rdata.results: grouped_results.setdefault(result.title, []).append(result) # From here, results are already grouped by their `category` attribute, # and then by their `title` attribute. We can now plot them. # # Now, we have two common use cases: # 1. Plotting one curve per object (signal/image) and per `title` # attribute, if each selected object contains a result object # with multiple values (e.g. from a blob detection feature). # 2. Plotting one curve per `title` attribute, if each selected object # contains a result object with a single value (e.g. from a FHWM # feature) - in that case, we select only the first value of each # result object. if param is None: # Create parameter class and show dialog PlotResultParam = self.__create_plot_result_param_class(rdata) comment = ( _( "Plot results obtained from previous analyses.

    " "This plot is based on results associated with '%s' prefix." ) % category ) param = PlotResultParam(_("Plot results"), comment=comment) if not param.edit(parent=self.parentWidget()): return if param.kind == "one_curve_per_title": # One curve per ROI (if any) and per result title # ------------------------------------------------------------------ # Begin by checking if all results have the same number of ROIs: # for simplicity, let's check the number of unique ROI indices. all_roi_indexes = [ result.get_unique_roi_indices() for result in rdata.results ] # Check if all roi_indexes are the same: if len(set(map(tuple, all_roi_indexes))) > 1: if not execenv.unattended: QW.QMessageBox.warning( self, _("Plot results"), _( "All objects associated with results must have the " "same regions of interest (ROIs) to plot results " "together." ), ) return obj = objs[0] # Build a string with source object short IDs (max 3, then use "...") max_ids_to_show = 3 short_ids = [get_short_id(obj) for obj in objs] if len(short_ids) <= max_ids_to_show: source_ids = ", ".join(short_ids) else: # Show first 2, "...", then last one: "s001, s002, ..., s010" source_ids = ( ", ".join(short_ids[: max_ids_to_show - 1]) + ", ..., " + short_ids[-1] ) for i_roi in all_roi_indexes[0]: roi_suffix = f"|ROI{int(i_roi + 1)}" if i_roi >= 0 else "" for title, results in grouped_results.items(): # title x, y = [], [] for index, result in enumerate(results): if param.xaxis == "indices": x.append(index) else: x.append(result.get_column_values(param.xaxis, i_roi)[0]) y.append(result.get_column_values(param.yaxis, i_roi)[0]) if i_roi >= 0: roi_suffix = f"|{obj.roi.get_single_roi_title(int(i_roi))}" self.__add_result_signal( x, y, f"{title} ({source_ids}){roi_suffix}", param.xaxis, param.yaxis, result_group_id, ) else: # One curve per result title, per object and per ROI # ------------------------------------------------------------------ for title, results in grouped_results.items(): # title for index, result in enumerate(results): # object obj = objs[index] roi_indices = result.get_unique_roi_indices() for i_roi in roi_indices: # ROI roi_suffix = "" if i_roi >= 0: roi_suffix = f"|{obj.roi.get_single_roi_title(int(i_roi))}" roi_data = result.get_roi_data(i_roi) if param.xaxis == "indices": x = list(range(len(roi_data))) else: x = roi_data[param.xaxis].values y = roi_data[param.yaxis].values shid = get_short_id(objs[index]) stitle = f"{title} ({shid}){roi_suffix}" self.__add_result_signal( x, y, stitle, param.xaxis, param.yaxis, result_group_id ) def plot_results( self, kind: str | None = None, xaxis: str | None = None, yaxis: str | None = None, ) -> None: """Plot results Args: kind: Plot kind. Either "one_curve_per_object" or "one_curve_per_title". If None, show dialog to get parameters. xaxis: X axis column name. If None, show dialog to get parameters. yaxis: Y axis column name. If None, show dialog to get parameters. """ objs = self.objview.get_sel_objects(include_groups=True) rdatadict = create_resultdata_dict(objs) if rdatadict: # Always use or create a "Results" group for all plot results rgroup_title = _("Results") target_panel = self.mainwindow.signalpanel try: # Check if a "Results" group already exists in the signal panel rgroup = target_panel.objmodel.get_group_from_title(rgroup_title) except KeyError: # Create the group if it doesn't exist rgroup = target_panel.add_group(rgroup_title) result_group_id = get_uuid(rgroup) for category, rdata in rdatadict.items(): param = None if kind is not None and xaxis is not None and yaxis is not None: # Create parameter object programmatically PlotResultParam = self.__create_plot_result_param_class(rdata) param = PlotResultParam() param.kind = kind param.xaxis = xaxis param.yaxis = yaxis self.__plot_result(category, rdata, objs, param, result_group_id) else: self.__show_no_result_warning() def delete_results(self) -> None: """Delete results""" objs = self.objview.get_sel_objects(include_groups=True) rdatadict = create_resultdata_dict(objs) if rdatadict: if execenv.unattended: confirmed = True else: answer = QW.QMessageBox.warning( self, _("Delete results"), _( "Are you sure you want to delete all results " "of the selected object(s)?" ), QW.QMessageBox.Yes | QW.QMessageBox.No, ) confirmed = answer == QW.QMessageBox.Yes if confirmed: objs = self.objview.get_sel_objects(include_groups=True) for obj in objs: # Remove all table and geometry results using adapter methods TableAdapter.remove_all_from(obj) GeometryAdapter.remove_all_from(obj) # Clear analysis parameters to prevent auto-recompute from # attempting to recompute deleted analyses when ROI changes clear_analysis_parameters(obj) if obj is self.objview.get_current_object(): self.objprop.update_properties_from(obj) # Update action states to reflect the removal selected_groups = self.objview.get_sel_groups() self.acthandler.selected_objects_changed(selected_groups, objs) self.refresh_plot("selected", True, False) else: self.__show_no_result_warning() def add_label_with_title( self, title: str | None = None, ignore_msg: bool = True ) -> None: """Add a label with object title on the associated plot Args: title: Label title. Defaults to None. If None, the title is the object title. ignore_msg: If True, do not show the information message. Defaults to True. If False, show a message box to inform the user that the label has been added as an annotation, and that it can be edited or removed using the annotation editing window. """ objs = self.objview.get_sel_objects(include_groups=True) for obj in objs: create_adapter_from_object(obj).add_label_with_title(title=title) if ( not Conf.view.ignore_title_insertion_msg.get(False) and not ignore_msg and not execenv.unattended ): answer = QW.QMessageBox.information( self, _("Annotation added"), _( "The label has been added as an annotation. " "You can edit or remove it using the annotation editing window." "

    " "Choosing to ignore this message will prevent it " "from being displayed again." ), QW.QMessageBox.Ok | QW.QMessageBox.Ignore, ) if answer == QW.QMessageBox.Ignore: Conf.view.ignore_title_insertion_msg.set(True) self.refresh_plot("selected", True, False) DataLab-1.1.0/datalab/gui/panel/image.py000066400000000000000000000126431514014117600177160ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ .. Image panel (see parent package :mod:`datalab.gui.panel`) """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from typing import TYPE_CHECKING, Type from plotpy.tools import ( AnnotatedCircleTool, AnnotatedEllipseTool, AnnotatedPointTool, AnnotatedRectangleTool, AnnotatedSegmentTool, LabelTool, ) from sigima.io.image import ImageIORegistry from sigima.objects import ImageDatatypes, ImageObj, ImageROI, NewImageParam from datalab.config import Conf, _ from datalab.gui import roieditor from datalab.gui.actionhandler import ImageActionHandler from datalab.gui.newobject import create_image_gui from datalab.gui.panel.base import BaseDataPanel from datalab.gui.plothandler import ImagePlotHandler from datalab.gui.processor.image import ImageProcessor if TYPE_CHECKING: from plotpy.plot import BasePlot from qtpy import QtWidgets as QW from datalab.gui.docks import DockablePlotWidget class ImagePanel(BaseDataPanel[ImageObj, ImageROI, roieditor.ImageROIEditor]): """Object handling the item list, the selected item properties and plot, specialized for Image objects""" PANEL_STR = _("Image Panel") PANEL_STR_ID = "image" PARAMCLASS = ImageObj MINDIALOGSIZE = (800, 800) # The following tools are used to create annotations on images. The annotation # items are created using PlotPy's default settings. Those appearance settings # may be modified in the configuration (see `datalab.config`). ANNOTATION_TOOLS = ( AnnotatedCircleTool, AnnotatedSegmentTool, AnnotatedRectangleTool, AnnotatedPointTool, AnnotatedEllipseTool, LabelTool, ) IO_REGISTRY = ImageIORegistry H5_PREFIX = "DataLab_Ima" # pylint: disable=duplicate-code @staticmethod def get_roi_class() -> Type[ImageROI]: """Return ROI class""" return ImageROI @staticmethod def get_roieditor_class() -> Type[roieditor.ImageROIEditor]: """Return ROI editor class""" return roieditor.ImageROIEditor def __init__( self, parent: QW.QWidget, dockableplotwidget: DockablePlotWidget, panel_toolbar: QW.QToolBar, ) -> None: super().__init__(parent) self.plothandler = ImagePlotHandler(self, dockableplotwidget.plotwidget) self.processor = ImageProcessor(self, dockableplotwidget.plotwidget) view_toolbar = dockableplotwidget.toolbar self.acthandler = ImageActionHandler(self, panel_toolbar, view_toolbar) # ------Refreshing GUI-------------------------------------------------------------- def plot_lut_changed(self, plot: BasePlot) -> None: """The LUT of the plot has changed: updating image objects accordingly Args: plot: Plot object """ zmin, zmax = plot.get_axis_limits(plot.colormap_axis) for obj in self.objview.get_sel_objects(): obj.zscalemin, obj.zscalemax = zmin, zmax if obj is self.objview.get_current_object(): self.objprop.update_properties_from(obj) # ------Creating, adding, removing objects------------------------------------------ def get_newparam_from_current( self, newparam: NewImageParam | None = None, title: str | None = None ) -> NewImageParam | None: """Get new object parameters from the current object. Args: newparam (guidata.dataset.DataSet): new object parameters. If None, create a new one. title: new object title. If None, use the current object title, or the default title. Returns: New object parameters """ curobj: ImageObj = self.objview.get_current_object() if newparam is None: newparam = NewImageParam() if title is not None: newparam.title = title if curobj is not None and Conf.proc.use_image_dims.get(True): # Use current image dimensions for new image: newparam.height, newparam.width = curobj.data.shape newparam.dtype = ImageDatatypes.from_numpy_dtype(curobj.data.dtype) return newparam def new_object( self, param: NewImageParam | None = None, edit: bool = False, add_to_panel: bool = True, ) -> ImageObj | None: """Create a new object (image). Args: param (guidata.dataset.DataSet): new object parameters edit (bool): Open a dialog box to edit parameters (default: False). When False, the object is created with default parameters and creation parameters are stored in metadata for interactive editing. add_to_panel (bool): Add the object to the panel (default: True) Returns: New object """ if not self.mainwindow.confirm_memory_state(): return None param = self.get_newparam_from_current(param) image = create_image_gui(param, edit=edit, parent=self.parentWidget()) if image is None: return None if add_to_panel: self.add_object(image) return image def toggle_show_contrast(self, state: bool) -> None: """Toggle show contrast option""" Conf.view.show_contrast.set(state) self.refresh_plot("selected", True, False) DataLab-1.1.0/datalab/gui/panel/macro.py000066400000000000000000000512241514014117600177330ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ .. Macro panel (see parent package :mod:`datalab.gui.panel`) """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import os.path as osp import re from typing import TYPE_CHECKING from guidata.config import CONF from guidata.configtools import get_font, get_icon from guidata.qthelpers import add_actions, create_action from guidata.widgets.console.shell import PythonShellWidget from guidata.widgets.dockable import DockableWidgetMixin from qtpy import QtCore as QC from qtpy import QtWidgets as QW from qtpy.compat import getopenfilename, getsavefilename from datalab.config import Conf, _ from datalab.env import execenv from datalab.gui.macroeditor import Macro from datalab.gui.panel.base import AbstractPanel from datalab.utils.qthelpers import ( create_menu_button, qt_try_loadsave_file, save_restore_stds, ) if TYPE_CHECKING: from guidata.widgets.codeeditor import CodeEditor from datalab.h5.native import NativeH5Reader, NativeH5Writer class MacroTabs(QW.QTabWidget): """Macro tabwidget Args: parent (QWidget): Parent widget """ SIG_CONTEXT_MENU = QC.Signal(QC.QPoint) SIG_RENAME = QC.Signal(int) SIG_REMOVE = QC.Signal(int) def __init__(self, parent=None) -> None: super().__init__(parent) self.setTabsClosable(True) self.setMovable(True) self.tabBarDoubleClicked.connect(self.__rename) self.tabCloseRequested.connect(self.__remove) self.__titles: list[str] = [] def clear(self) -> None: """Override Qt method""" super().clear() self.__titles.clear() def contextMenuEvent(self, event): # pylint: disable=C0103 """Override Qt method""" self.SIG_CONTEXT_MENU.emit(event.globalPos()) def __rename(self, index: int) -> None: """Rename tab Args: index: Index of the tab """ self.SIG_RENAME.emit(index + 1) def __remove(self, index: int) -> None: """Remove tab Args: index: Index of the tab """ self.SIG_REMOVE.emit(index + 1) def __update_tab_titles(self) -> None: """Update tab titles""" for number, title in enumerate(self.__titles, 1): self.setTabText(number - 1, f"{number:02d}: {title}") def add_tab(self, macro: Macro) -> int: """Add tab Args: macro: Macro object Returns: int: Number of the tab (starting at 1) """ self.__titles.append(macro.title) index = self.addTab(macro.editor, "") self.__update_tab_titles() return index + 1 # Numbering starts at 1 def remove_tab(self, number: int) -> None: """Remove tab Args: number: Number of the tab (starting at 1) """ self.removeTab(number - 1) self.__titles.pop(number - 1) self.__update_tab_titles() def get_widget(self, number: int) -> CodeEditor: """Return macro editor widget at number Args: number: Number of the tab (starting at 1) Returns: Macro editor widget """ return self.widget(number - 1) def set_current_number(self, number: int) -> None: """Set current tab number Args: number: Number of the tab (starting at 1) """ self.setCurrentIndex(number - 1) def get_current_number(self) -> int: """Return current tab number Returns: int: Number of the tab (starting at 1) """ return self.currentIndex() + 1 def set_tab_title(self, number: int, name: str) -> None: """Set tab title Args: number: Number of the tab (starting at 1) name: Macro name """ self.__titles[number - 1] = name self.__update_tab_titles() class MacroPanel(AbstractPanel, DockableWidgetMixin): """Macro Panel widget Args: parent (QWidget): Parent widget """ LOCATION = QC.Qt.RightDockWidgetArea PANEL_STR = _("Macro panel") H5_PREFIX = "DataLab_Mac" SIG_OBJECT_MODIFIED = QC.Signal() FILE_FILTERS = f"{_('Python files')} (*.py)" def __init__(self, parent: QW.QMainWindow) -> None: super().__init__(parent) self.setWindowTitle(self.PANEL_STR) self.setWindowIcon(get_icon("libre-gui-cogs.svg")) self.setOrientation(QC.Qt.Vertical) self.context_menu = QW.QMenu() self.tabwidget_tb = QW.QToolBar(self) self.tabwidget_tb.setOrientation(QC.Qt.Vertical) self.console = PythonShellWidget(self, read_only=True) self.console.setMaximumBlockCount(5000) font = get_font(CONF, "console") font.setPointSize(10) self.console.set_font(font) self.console.write(_("-***- Macro Console -***-"), prompt=True) self.tabwidget = MacroTabs(self) self.tabwidget.SIG_RENAME.connect(self.rename_macro) self.tabwidget.SIG_REMOVE.connect(self.remove_macro) self.tabwidget.currentChanged.connect(self.__update_actions) tabwidget_with_tb = QW.QWidget(self) tabwidget_with_tb.setLayout(QW.QHBoxLayout()) tabwidget_with_tb.layout().addWidget(self.tabwidget_tb) tabwidget_with_tb.layout().addWidget(self.tabwidget) # Put console in a groupbox to have a title console_groupbox = QW.QGroupBox(_("Console"), self) console_groupbox.setLayout(QW.QHBoxLayout()) console_groupbox.layout().addWidget(self.console) # Put console groupbox in a frame to have a nice margin console_frame = QW.QFrame(self) console_frame.setLayout(QW.QHBoxLayout()) console_frame.layout().addWidget(console_groupbox) for widget in (tabwidget_with_tb, console_frame): self.addWidget(widget) self.setStretchFactor(0, 2) self.setStretchFactor(1, 1) # Set initial sizes: give more space to editor (70%) than console (30%) # This ensures proper layout on first open total_height = 600 # Default reasonable height self.setSizes([int(total_height * 0.7), int(total_height * 0.3)]) self.run_action = None self.stop_action = None self.obj_actions: list[QW.QAction] = [] # Object-dependent actions self.__macros: list[Macro] = [] self.setup_actions() def update_color_mode(self) -> None: """Update color mode according to the current theme""" self.console.update_color_mode() for macro in self.__macros: macro.editor.update_color_mode() # ------AbstractPanel interface----------------------------------------------------- # pylint: disable=unused-argument def get_serializable_name(self, obj: Macro) -> str: """Return serializable name of object""" title = re.sub("[^-a-zA-Z0-9_.() ]+", "", obj.title.replace("/", "_")) name = f"{obj.PREFIX}{(self.__macros.index(obj) + 1):03d}: {title}" return name def serialize_to_hdf5(self, writer: NativeH5Writer) -> None: """Serialize whole panel to a HDF5 file Args: writer: HDF5 writer """ with writer.group(self.H5_PREFIX): for obj in self.__macros: self.serialize_object_to_hdf5(obj, writer) def deserialize_from_hdf5( self, reader: NativeH5Reader, reset_all: bool = False ) -> None: """Deserialize whole panel from a HDF5 file Args: reader: HDF5 reader reset_all: If True, preserve original UUIDs (workspace reload). If False, regenerate UUIDs (importing objects). """ with reader.group(self.H5_PREFIX): for name in reader.h5.get(self.H5_PREFIX, []): # Contrary to signal or image panels, macros are not stored # in a group but directly in the root of the HDF5 file obj = self.deserialize_object_from_hdf5(reader, name, reset_all) self.add_object(obj) # Update untitled number counter to prevent duplicate names self.update_untitled_counter() def __len__(self) -> int: """Return number of objects""" return len(self.__macros) def __getitem__(self, nb: int) -> Macro: """Return object from its number (1 to N)""" return self.__macros[nb - 1] def __iter__(self): """Iterate over objects""" return iter(self.__macros) def create_object(self) -> Macro: """Create object. Returns: Macro object """ macro = Macro(self.console) macro.objectNameChanged.connect(self.macro_name_changed) macro.STARTED.connect( lambda orig_macro=macro: self.macro_state_changed(orig_macro, True) ) macro.FINISHED.connect( lambda orig_macro=macro: self.macro_state_changed(orig_macro, False) ) macro.MODIFIED.connect(self.macro_contents_changed) return macro def add_object(self, obj: Macro) -> None: """Add object. Args: obj: Macro object """ self.__macros.append(obj) number = self.tabwidget.add_tab(obj) self.SIG_OBJECT_ADDED.emit() self.tabwidget.set_current_number(number) def remove_all_objects(self) -> None: """Remove all objects""" self.tabwidget.clear() self.__macros.clear() super().remove_all_objects() # Reset untitled counter when clearing all macros Macro.set_untitled_number(0) # ---- Macro panel API ------------------------------------------------------------- def setup_actions(self) -> None: """Setup macro menu actions""" self.run_action = create_action( self, _("Run macro"), icon=get_icon("libre-camera-flash-on.svg"), triggered=self.run_macro, shortcut="Ctrl+F5", ) self.stop_action = create_action( self, _("Stop macro"), icon=get_icon("libre-camera-flash-off.svg"), triggered=self.stop_macro, shortcut="Shift+F5", ) self.stop_action.setDisabled(True) add_act = create_action( self, _("New macro"), icon=get_icon("libre-gui-add.svg"), triggered=self.add_macro, ) ren_act = create_action( self, _("Rename macro"), icon=get_icon("libre-gui-pencil.svg"), triggered=self.rename_macro, ) imp_act = create_action( self, _("Import macro from file"), icon=get_icon("fileopen_py.svg"), triggered=self.import_macro_from_file, ) exp_act = create_action( self, _("Export macro to file"), icon=get_icon("filesave_py.svg"), triggered=self.export_macro_to_file, ) rem_act = create_action( self, _("Remove macro"), icon=get_icon("libre-gui-action-delete.svg"), triggered=self.remove_macro, ) self.obj_actions += [ self.run_action, self.stop_action, ren_act, exp_act, rem_act, ] self.tabwidget.SIG_CONTEXT_MENU.connect(self.context_menu.popup) tabwidget_corner = QW.QToolBar(_("Macro editor toolbar"), self) self.context_menu.aboutToShow.connect(self.__update_actions) tabwidget_menu_btn = create_menu_button(self, self.context_menu) tabwidget_corner.addWidget(tabwidget_menu_btn) self.tabwidget.setCornerWidget(tabwidget_corner) main_actions = [ self.run_action, self.stop_action, None, add_act, ren_act, imp_act, exp_act, ] add_actions(tabwidget_corner, [self.run_action, self.stop_action]) add_actions(self.tabwidget_tb, main_actions) add_actions(self.context_menu, main_actions + [None, rem_act]) self.__update_actions() def __update_actions(self) -> None: """Update actions""" not_empty = self.tabwidget.count() > 0 for action in self.obj_actions: action.setEnabled(not_empty) if not_empty: macro = self.get_macro() if macro is not None: macro: Macro self.macro_state_changed(macro, macro.is_running()) def get_macro(self, number_or_title: int | str | None = None) -> Macro | None: """Return macro at number (if number is None, return current macro) Args: number: Number of the macro (starting at 1) or title of the macro. Defaults to None (current macro). Returns: Macro object or None (if not found) """ if number_or_title is None: number_or_title = self.tabwidget.get_current_number() if isinstance(number_or_title, str): return self.get_macro(self.get_number_from_title(number_or_title)) for macro in self.__macros: if self.tabwidget.get_widget(number_or_title) is macro.editor: return macro return None def get_number_from_title(self, title: str) -> int | None: """Return macro number from title Args: title: Title of the macro Returns: Number of the macro (starting at 1) or None (if not found) """ for number in range(1, self.tabwidget.count() + 1): if self.tabwidget.tabText(number - 1).endswith(title): return number return None def get_number_from_macro(self, macro: Macro) -> int | None: """Return macro number from macro object Args: macro: Macro object Returns: Number of the macro (starting at 1) or None (if not found) """ for number in range(1, self.tabwidget.count() + 1): if self.tabwidget.get_widget(number) is macro.editor: return number return None def get_macro_titles(self) -> list[str]: """Return list of macro titles""" return [macro.title for macro in self.__macros] def macro_contents_changed(self) -> None: """One of the macro contents has changed""" self.SIG_OBJECT_MODIFIED.emit() def run_macro(self, number_or_title: int | str | None = None) -> None: """Run current macro Args: number: Number of the macro (starting at 1). Defaults to None (run current macro, or does nothing if there is no macro). """ macro = self.get_macro(number_or_title) if macro is not None: macro: Macro macro.run() def stop_macro(self, number_or_title: int | str | None = None) -> None: """Stop current macro Args: number: Number of the macro (starting at 1). Defaults to None (run current macro, or does nothing if there is no macro). """ macro = self.get_macro(number_or_title) if macro is not None: macro: Macro macro.kill() def macro_state_changed(self, orig_macro: Macro, state: bool) -> None: """Macro state has changed (True: started, False: stopped) Args: orig_macro: Macro object state: State of the macro """ macro = self.get_macro() if macro is orig_macro: self.run_action.setEnabled(not state) self.stop_action.setEnabled(state) def add_macro(self) -> Macro: """Add macro, optionally with name Returns: Macro object """ macro = self.create_object() self.add_object(macro) if not macro.title: self.rename_macro() return macro def macro_name_changed(self, name: str) -> None: """Macro name has been changed Args: name: New name of the macro """ number = self.get_number_from_macro(self.sender()) if number is not None: self.tabwidget.set_tab_title(number, name) def rename_macro(self, number: int | None = None, title: str | None = None) -> None: """Rename macro Args: number: Number of the macro (starting at 1). Defaults to None. title: Title of the macro. Defaults to None. """ macro = self.get_macro(number) assert isinstance(macro, Macro) if title is None: title, valid = QW.QInputDialog.getText( self, _("Rename"), _("New title:"), QW.QLineEdit.Normal, macro.title, ) title = title if valid else None if title: macro.title = title if number is not None: self.tabwidget.set_current_number(number) def export_macro_to_file( self, number_or_title: int | str | None = None, filename: str | None = None ) -> None: """Export macro to file Args: number_or_title: Number of the macro (starting at 1) or title of the macro. Defaults to None. filename: Filename. Defaults to None. Raises: ValueError: If title is not found """ macro = self.get_macro(number_or_title) assert isinstance(macro, Macro) if filename is None: basedir = Conf.main.base_dir.get() with save_restore_stds(): filename, _filt = getsavefilename( self, _("Save as"), basedir, self.FILE_FILTERS ) if filename: with qt_try_loadsave_file(self.parentWidget(), filename, "save"): Conf.main.base_dir.set(filename) macro.title = osp.basename(filename) macro.to_file(filename) def import_macro_from_file(self, filename: str | None = None) -> int: """Import macro from file Args: filename: Filename. Defaults to None. Returns: Number of the macro (starting at 1) """ if filename is None: basedir = Conf.main.base_dir.get() with save_restore_stds(): filename, _filt = getopenfilename( self, _("Open"), basedir, self.FILE_FILTERS ) if filename: with qt_try_loadsave_file(self.parentWidget(), filename, "load"): Conf.main.base_dir.set(filename) macro = self.add_macro() macro.from_file(filename) # Update untitled number counter to prevent duplicate names self.update_untitled_counter() return self.get_number_from_macro(macro) return -1 def update_untitled_counter(self) -> None: """Update the untitled counter based on existing macro titles This scans all macro titles to find the highest "macro_XX" number and updates the global counter to prevent duplicate names. """ max_untitled = 0 for macro in self.__macros: # Match titles like "macro_01", "macro_02", etc. match = re.match(r"macro_(\d+)", macro.title) if match: number = int(match.group(1)) max_untitled = max(max_untitled, number) # Set the counter to the highest found number Macro.set_untitled_number(max_untitled) def remove_macro(self, number_or_title: int | str | None = None) -> None: """Remove macro Args: number_or_title: Number of the macro (starting at 1) or title of the macro. Defaults to None. """ if number_or_title is None: number_or_title = self.tabwidget.get_current_number() if isinstance(number_or_title, str): number_or_title = self.get_number_from_title(number_or_title) txt = "
    ".join( [ _( "When closed, the macro is permanently destroyed, " "unless it has been exported first." ), "", _("Do you want to continue?"), ] ) btns = QW.QMessageBox.StandardButton.Yes | QW.QMessageBox.StandardButton.No if execenv.unattended: choice = QW.QMessageBox.StandardButton.Yes else: choice = QW.QMessageBox.warning(self, self.windowTitle(), txt, btns) if choice == QW.QMessageBox.StandardButton.Yes: self.tabwidget.remove_tab(number_or_title) self.__macros.pop(number_or_title - 1) self.SIG_OBJECT_REMOVED.emit() DataLab-1.1.0/datalab/gui/panel/signal.py000066400000000000000000000127361514014117600201140ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ .. Signal panel (see parent package :mod:`datalab.gui.panel`) """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from typing import TYPE_CHECKING, Type import guidata.dataset as gds from guidata.config import ValidationMode, temporary_validation_mode from plotpy.tools import ( HCursorTool, HRangeTool, LabelTool, RectangleTool, SegmentTool, VCursorTool, XCursorTool, ) from sigima.io.signal import SignalIORegistry from sigima.objects import SignalObj, SignalROI from datalab.adapters_plotpy import CURVESTYLES from datalab.config import Conf, _ from datalab.gui import roieditor from datalab.gui.actionhandler import SignalActionHandler from datalab.gui.newobject import NewSignalParam, create_signal_gui from datalab.gui.panel.base import BaseDataPanel from datalab.gui.plothandler import SignalPlotHandler from datalab.gui.processor.signal import SignalProcessor if TYPE_CHECKING: from qtpy import QtWidgets as QW from datalab.gui.docks import DockablePlotWidget class SignalPanel(BaseDataPanel[SignalObj, SignalROI, roieditor.SignalROIEditor]): """Object handling the item list, the selected item properties and plot, specialized for Signal objects""" PANEL_STR = _("Signal Panel") PANEL_STR_ID = "signal" PARAMCLASS = SignalObj # The following tools are used to create annotations on signals. The annotation # items are created using PlotPy's default settings. Those appearance settings # may be modified in the configuration (see `datalab.config`). ANNOTATION_TOOLS = ( LabelTool, VCursorTool, HCursorTool, XCursorTool, SegmentTool, RectangleTool, HRangeTool, ) IO_REGISTRY = SignalIORegistry H5_PREFIX = "DataLab_Sig" # pylint: disable=duplicate-code @staticmethod def get_roi_class() -> Type[SignalROI]: """Return ROI class""" return SignalROI @staticmethod def get_roieditor_class() -> Type[roieditor.SignalROIEditor]: """Return ROI editor class""" return roieditor.SignalROIEditor def __init__( self, parent: QW.QWidget, dockableplotwidget: DockablePlotWidget, panel_toolbar: QW.QToolBar, ) -> None: super().__init__(parent) self.plothandler = SignalPlotHandler(self, dockableplotwidget.plotwidget) self.processor = SignalProcessor(self, dockableplotwidget.plotwidget) view_toolbar = dockableplotwidget.toolbar self.acthandler = SignalActionHandler(self, panel_toolbar, view_toolbar) # ------Creating, adding, removing objects------------------------------------------ def get_newparam_from_current( self, newparam: NewSignalParam | None = None, title: str | None = None ) -> NewSignalParam | None: """Get new object parameters from the current object. Args: newparam (guidata.dataset.DataSet): new object parameters. If None, create a new one. title: new object title. If None, use the current object title, or the default title. Returns: New object parameters """ curobj: SignalObj = self.objview.get_current_object() if newparam is None: newparam = NewSignalParam() if title is not None: newparam.title = title if curobj is not None and Conf.proc.use_signal_bounds.get(False): # Use current signal bounds for new signal: newparam.size = len(curobj.data) # try to set xmin/xmax from current signal data range with temporary_validation_mode(ValidationMode.STRICT): try: newparam.xmin = curobj.x.min() except gds.DataItemValidationError: pass try: newparam.xmax = curobj.x.max() except gds.DataItemValidationError: pass return newparam def new_object( self, param: NewSignalParam | None = None, edit: bool = False, add_to_panel: bool = True, ) -> SignalObj | None: """Create a new object (signal). Args: param (guidata.dataset.DataSet): new object parameters edit (bool): Open a dialog box to edit parameters (default: False). When False, the object is created with default parameters and creation parameters are stored in metadata for interactive editing. add_to_panel (bool): Add the new object to the panel (default: True) Returns: New object """ if not self.mainwindow.confirm_memory_state(): return None param = self.get_newparam_from_current(param) signal = create_signal_gui(param, edit=edit, parent=self.parentWidget()) if signal is None: return None if add_to_panel: self.add_object(signal) return signal # ------Plotting-------------------------------------------------------------------- def toggle_anti_aliasing(self, state: bool) -> None: """Toggle anti-aliasing on/off Args: state: state of the anti-aliasing """ self.plothandler.toggle_anti_aliasing(state) def reset_curve_styles(self) -> None: """Reset curve styles""" CURVESTYLES.reset_styles() DataLab-1.1.0/datalab/gui/plothandler.py000066400000000000000000001007641514014117600200530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Plot handler ============ The :mod:`datalab.gui.plothandler` module provides plot handlers for signal and image panels, that is, classes handling `PlotPy` plot items for representing signals and images. Signal plot handler ------------------- .. autoclass:: SignalPlotHandler :members: :inherited-members: Image plot handler ------------------ .. autoclass:: ImagePlotHandler :members: :inherited-members: """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import hashlib from collections.abc import Iterator from typing import TYPE_CHECKING, Generic from weakref import WeakKeyDictionary import numpy as np from plotpy.constants import PlotType from plotpy.items import CurveItem, GridItem, LegendBoxItem, MaskedXYImageItem from plotpy.plot import PlotOptions from qtpy import QtWidgets as QW from qwt import QwtScaleDraw from sigima.objects import ImageObj, SignalObj, TypeObj from datalab.adapters_metadata import GeometryAdapter, TableAdapter from datalab.adapters_plotpy import TypePlotItem, create_adapter_from_object from datalab.adapters_plotpy.objects.scalar import MergedResultPlotPyAdapter from datalab.config import Conf, _ from datalab.objectmodel import get_uuid from datalab.utils.qthelpers import block_signals, create_progress_bar if TYPE_CHECKING: from plotpy.items import LabelItem from plotpy.plot import PlotWidget from datalab.gui.panel.base import BaseDataPanel def calc_data_hash(obj: SignalObj | ImageObj) -> str: """Calculate a hash for a SignalObj | ImageObj object's data For signals, this includes both X and Y data to detect axis changes. For images, this includes only the Z data. """ if isinstance(obj, SignalObj): # For signals, hash both X and Y data to detect axis changes # (e.g., when xmin/xmax is modified without changing Y values) return hashlib.sha1(np.ascontiguousarray(obj.xydata)).hexdigest() # For images, hash only the image data return hashlib.sha1(np.ascontiguousarray(obj.data)).hexdigest() class BasePlotHandler(Generic[TypeObj, TypePlotItem]): # type: ignore """Object handling plot items associated to objects (signals/images)""" PLOT_TYPE: PlotType | None = None # Replaced in subclasses def __init__( self, panel: BaseDataPanel, plotwidget: PlotWidget, ) -> None: self.panel = panel self.plotwidget = plotwidget self.plot = plotwidget.get_plot() # Plot items: key = object uuid, value = plot item self.__plotitems: dict[str, TypePlotItem] = {} self.__shapeitems = [] self.__cached_hashes: WeakKeyDictionary[TypeObj, list[int]] = ( WeakKeyDictionary() ) self.__auto_refresh = False self.__show_first_only = False # Mapping from object UUID to merged result adapter # The merged adapter consolidates all result labels for an object self.__merged_result_adapters: dict[str, MergedResultPlotPyAdapter] = {} def __len__(self) -> int: """Return number of items""" return len(self.__plotitems) def __getitem__(self, oid: str) -> TypePlotItem: """Return item associated to object uuid""" try: return self.__plotitems[oid] except KeyError as exc: # Item does not exist: this may happen when "auto refresh" is disabled # (object has been added to model but the corresponding plot item has not # been created yet) if not self.__auto_refresh: self.refresh_plot(oid, True, force=True, only_visible=False) return self.__plotitems[oid] # Item does not exist and auto refresh is enabled: this should not happen raise exc def get(self, key: str, default: TypePlotItem | None = None) -> TypePlotItem | None: """Return item associated to object uuid. If the key is not found, default is returned if given, otherwise None is returned.""" return self.__plotitems.get(key, default) def get_obj_from_item(self, item: TypePlotItem) -> TypeObj | None: """Return object associated to plot item Args: item: plot item Returns: Object associated to plot item """ for obj in self.panel.objmodel: if self.get(get_uuid(obj)) is item: return obj return None def __setitem__(self, oid: str, item: TypePlotItem) -> None: """Set item associated to object uuid""" self.__plotitems[oid] = item def __iter__(self) -> Iterator[TypePlotItem]: """Return an iterator over plothandler values (plot items)""" return iter(self.__plotitems.values()) def remove_item(self, oid: str) -> None: """Remove plot item associated to object uuid""" try: item = self.__plotitems.pop(oid) except KeyError: # Item does not exist: this may happen when "auto refresh" is disabled # (object has been added to model but the corresponding plot item has not # been created yet). # This may also happen after opening a project, then immediately selecting # a group containing more than one object: plot item would have been created # only for the first object in group, and this exception would be raised # for the second one (which does not have a plot item yet). return self.plot.del_item(item) def clear(self) -> None: """Clear plot items""" self.__plotitems = {} self.__merged_result_adapters = {} self.cleanup_dataview() self.remove_all_shape_items() self.plot.replot() def add_shapes(self, oid: str, do_autoscale: bool = False) -> None: """Add geometric shape items associated to computed results and annotations, for the object with the given uuid""" obj = self.panel.objmodel[oid] if obj.metadata: obj_adapter = create_adapter_from_object(obj) items = list(obj_adapter.iterate_shape_items(editable=False)) # Collect all result adapters results = list(TableAdapter.iterate_from_obj(obj)) + list( GeometryAdapter.iterate_from_obj(obj) ) # Create or update merged result adapter if results: merged_adapter = MergedResultPlotPyAdapter(results, obj) self.__merged_result_adapters[oid] = merged_adapter # Get the merged label merged_label = merged_adapter.get_merged_label() if merged_label is not None: # Set initial visibility based on configuration merged_label.setVisible(Conf.view.show_result_label.get()) items.append(merged_label) # Add other items from the merged adapter (e.g., geometric shapes) items.extend(merged_adapter.get_other_items()) else: # No results, remove any existing adapter self.__merged_result_adapters.pop(oid, None) if items: if do_autoscale: self.plot.do_autoscale() # Performance optimization: block `plotpy.plot.BasePlot` signals, add # all items except the last one, unblock signals, then add the last one # (this avoids some unnecessary refresh process by PlotPy) with block_signals(self.plot): with create_progress_bar( self.panel, _("Creating geometric shapes"), max_=len(items) - 1 ) as progress: for i_item, item in enumerate(items[:-1]): progress.setValue(i_item + 1) if progress.wasCanceled(): break self.plot.add_item(item) self.__shapeitems.append(item) # Only process events every 10 items to keep UI responsive # without killing performance if (i_item + 1) % 10 == 0: QW.QApplication.processEvents() self.plot.add_item(items[-1]) self.__shapeitems.append(items[-1]) def update_resultproperty_from_plot_item(self, item: LabelItem) -> None: """Update result properties from merged label plot item. When the merged results label is moved, update the stored position in the merged result adapter. Args: item: Merged results label item """ # Find the merged adapter that owns this label for merged_adapter in self.__merged_result_adapters.values(): if merged_adapter.get_cached_label() is item: # Update the adapter's metadata with the new position merged_adapter.update_obj_metadata_from_item(item) break def remove_all_shape_items(self) -> None: """Remove all geometric shapes associated to result items""" if set(self.__shapeitems).issubset(set(self.plot.items)): self.plot.del_items(self.__shapeitems) self.__shapeitems = [] # Clear cached labels in merged result adapters since they were removed for merged_adapter in self.__merged_result_adapters.values(): merged_adapter.invalidate_cached_label() def refresh_all_shape_items(self) -> None: """Refresh all geometric shapes to apply new style parameters. This method is called when shape/marker visualization settings are changed in the Settings dialog. It removes and recreates all shape items to apply the new parameters. """ # Get all object IDs that have shape items oids_with_shapes = list(self.__merged_result_adapters.keys()) if not oids_with_shapes: return # Remove all existing shape items self.remove_all_shape_items() # Recreate shape items for all objects with the new settings for oid in oids_with_shapes: self.add_shapes(oid, do_autoscale=False) self.plot.replot() def toggle_result_label_visibility(self, show: bool) -> None: """Toggle the visibility of all merged result labels on the plot. Args: show: True to show the labels, False to hide them """ for merged_adapter in self.__merged_result_adapters.values(): label = merged_adapter.get_cached_label() if label is not None: label.setVisible(show) self.plot.replot() def __add_item_to_plot(self, oid: str) -> TypePlotItem: """Make plot item and add it to plot. Args: oid: object uuid Returns: Plot item """ obj = self.panel.objmodel[oid] self.__cached_hashes[obj] = calc_data_hash(obj) item: TypePlotItem = create_adapter_from_object(obj).make_item() item.set_readonly(True) self[oid] = item self.plot.add_item(item) return item def __update_item_on_plot(self, oid: str, just_show: bool = False) -> None: """Update plot item. Args: oid: object uuid just_show: if True, only show the item (do not update it). Defaults to False. """ if not just_show: obj = self.panel.objmodel[oid] cached_hash = self.__cached_hashes.get(obj) new_hash = calc_data_hash(obj) data_changed = cached_hash is None or cached_hash != new_hash self.__cached_hashes[obj] = new_hash adapter = create_adapter_from_object(obj) adapter.update_item(self[oid], data_changed=data_changed) def set_auto_refresh(self, auto_refresh: bool) -> None: """Set auto refresh mode. Args: auto_refresh: if True, refresh plot items automatically """ self.__auto_refresh = auto_refresh if auto_refresh: self.refresh_plot("selected") def set_show_first_only(self, show_first_only: bool) -> None: """Set show first only mode. Args: show_first_only: if True, show only the first selected item """ self.__show_first_only = show_first_only if self.__auto_refresh: self.refresh_plot("selected") def get_existing_oids(self) -> list[str]: """Get existing object uuids. Returns: List of object uuids that have a plot item associated to them. """ return list(self.__plotitems.keys()) def reduce_shown_oids(self, oids: list[str]) -> list[str]: """Reduce the number of shown objects to visible items only. The base implementation is to show only the first selected item if the option "Show first only" is enabled. Args: oids: list of object uuids Returns: Reduced list of object uuids """ if self.__show_first_only: return oids[:1] return oids def refresh_plot( self, what: str, update_items: bool = True, force: bool = False, only_visible: bool = True, only_existing: bool = False, ) -> None: """Refresh plot. Args: what: string describing the objects to refresh. Valid values are "selected" (refresh the selected objects), "all" (refresh all objects), "existing" (refresh existing plot items), or an object uuid. update_items: if True, update the items. If False, only show the items (do not update them). Defaults to True. force: if True, force refresh even if auto refresh is disabled. Defaults to False. only_visible: if True, only refresh visible items. Defaults to True. Visible items are the ones that are not hidden by other items or the items except the first one if the option "Show first only" is enabled. This is useful for images, where the last image is the one that is shown. If False, all items are refreshed. only_existing: if True, only refresh existing items. Defaults to False. Existing items are the ones that have already been created and are associated to the object uuid. If False, create new items for the objects that do not have an item yet. Raises: ValueError: if `what` is not a valid value """ if not self.__auto_refresh and not force: return if what == "selected": # Refresh selected objects oids = self.panel.objview.get_sel_object_uuids(include_groups=True) if len(oids) <= 1: # Cleanup data view when there is 0 or 1 selected object. # This removes stray plot items (like XRangeSelection, DataInfoLabel) # that were created by PlotPy tools but are not managed by DataLab. self.cleanup_dataview() self.remove_all_shape_items() for item in self: if item is not None: item.hide() elif what == "existing": # Refresh existing objects oids = self.get_existing_oids() elif what == "all": # Refresh all objects oids = self.panel.objmodel.get_object_ids() else: # Refresh a single object defined by its uuid oids = [what] try: # Check if this is a valid object uuid self.panel.objmodel.get_objects(oids) except KeyError as exc: raise ValueError(f"Invalid value for `what`: {what}") from exc # Initialize titles and scales dictionaries title_keys = ("title", "xlabel", "ylabel", "zlabel", "xunit", "yunit", "zunit") titles_dict = {} autoscale = False scale_keys = ( "xscalelog", "xscalemin", "xscalemax", "yscalelog", "yscalemin", "yscalemax", ) scales_dict = {} if oids: if what != "existing" and only_visible: # Remove hidden items from the list of objects to refresh oids = self.reduce_shown_oids(oids) with create_progress_bar( self.panel, _("Creating plot items"), max_=len(oids) ) as progress: # Iterate over objects for i_obj, oid in enumerate(oids): progress.setValue(i_obj + 1) if progress.wasCanceled(): break obj = self.panel.objmodel[oid] # Collecting titles information for key in title_keys: title = getattr(obj, key, "") value = titles_dict.get(key) if value is None: titles_dict[key] = title elif value != title: titles_dict[key] = "" # Collecting scales information autoscale = autoscale or obj.autoscale for key in scale_keys: scale = getattr(obj, key, None) if scale is not None: cmp = min if "min" in key else max scales_dict[key] = cmp(scales_dict.get(key, scale), scale) # Update or add item to plot item = self.get(oid) if item is None: if only_existing: continue item = self.__add_item_to_plot(oid) else: self.__update_item_on_plot(oid, just_show=not update_items) if what != "existing" or item.isVisible(): self.plot.set_item_visible(item, True, replot=False) self.plot.set_active_item(item) item.unselect() # Add geometric shapes self.add_shapes(oid, do_autoscale=autoscale) self.plot.replot() else: # No object to refresh: clean up titles for key in title_keys: titles_dict[key] = "" # Set titles tdict = titles_dict tdict["ylabel"] = (tdict["ylabel"], tdict.pop("zlabel")) tdict["yunit"] = (tdict["yunit"], tdict.pop("zunit")) self.plot.set_titles(**titles_dict) # Set scales replot = False for axis_name, axis in (("bottom", "x"), ("left", "y")): axis_id = self.plot.get_axis_id(axis_name) scalelog = scales_dict.get(f"{axis}scalelog") if scalelog is not None: new_scale = "log" if scalelog else "lin" self.plot.set_axis_scale(axis_id, new_scale, autoscale=False) replot = True if autoscale: self.plot.do_autoscale() else: for axis_name, axis in (("bottom", "x"), ("left", "y")): axis_id = self.plot.get_axis_id(axis_name) new_vmin = scales_dict.get(f"{axis}scalemin") new_vmax = scales_dict.get(f"{axis}scalemax") if new_vmin is not None or new_vmax is not None: self.plot.do_autoscale(replot=False, axis_id=axis_id) vmin, vmax = self.plot.get_axis_limits(axis_id) new_vmin = new_vmin if new_vmin is not None else vmin new_vmax = new_vmax if new_vmax is not None else vmax self.plot.set_axis_limits(axis_id, new_vmin, new_vmax) replot = True if replot: self.plot.replot() def cleanup_dataview(self) -> None: """Clean up data view""" # Find items to remove (items that are not in the handler and not special items) # Skip shape items since they're managed separately and checking membership # for many items is expensive shape_items_set = set(self.__shapeitems) items_to_remove = [ item for item in self.plot.items[:] if item not in shape_items_set and item not in self and not isinstance(item, (LegendBoxItem, GridItem)) ] # Delete items one by one with error handling for items already removed for item in items_to_remove: try: self.plot.del_item(item) except ValueError: # Item was already removed (e.g., by detach()) pass def get_plot_options(self) -> PlotOptions: """Return standard signal/image plot options""" return PlotOptions( type=self.PLOT_TYPE, xlabel=self.plot.get_axis_title("bottom"), ylabel=self.plot.get_axis_title("left"), xunit=self.plot.get_axis_unit("bottom"), yunit=self.plot.get_axis_unit("left"), show_axes_tab=False, ) class SignalPlotHandler(BasePlotHandler[SignalObj, CurveItem]): """Object handling signal plot items, plot dialogs, plot options""" PLOT_TYPE = PlotType.CURVE def toggle_anti_aliasing(self, state: bool) -> None: """Toggle anti-aliasing Args: state: if True, enable anti-aliasing """ self.plot.set_antialiasing(state) self.plot.replot() def get_plot_options(self) -> PlotOptions: """Return standard signal/image plot options""" options = super().get_plot_options() options.curve_antialiasing = self.plot.antialiased return options def refresh_plot( self, what: str, update_items: bool = True, force: bool = False, only_visible: bool = True, only_existing: bool = False, ) -> None: """Refresh plot and configure datetime axis if needed. This override adds automatic datetime axis configuration when at least one of the displayed signals has a datetime X-axis. Args: what: string describing the objects to refresh update_items: if True, update the items force: if True, force refresh even if auto refresh is disabled only_visible: if True, only refresh visible items only_existing: if True, only refresh existing items """ # Call parent implementation super().refresh_plot(what, update_items, force, only_visible, only_existing) # Check if any visible signal has datetime X-axis has_datetime = False datetime_format = None for item in self: if item is not None and item.isVisible(): obj = self.get_obj_from_item(item) if obj is not None and obj.is_x_datetime(): has_datetime = True # Get format from signal metadata, or use configured default datetime_format = obj.metadata.get("x_datetime_format") if datetime_format is None: # Use configured format based on time unit unit = obj.xunit if obj.xunit else "s" if unit in ("ns", "us", "ms"): datetime_format = Conf.view.sig_datetime_format_ms.get( "%H:%M:%S.%f" ) else: datetime_format = Conf.view.sig_datetime_format_s.get( "%H:%M:%S" ) break # Configure X-axis for datetime or restore default if has_datetime and datetime_format is not None: self.plot.set_axis_datetime("bottom", format=datetime_format) else: # Restore default scale draw (remove datetime formatting) self.plot.setAxisScaleDraw(self.plot.get_axis_id("bottom"), QwtScaleDraw()) class ImagePlotHandler(BasePlotHandler[ImageObj, MaskedXYImageItem]): """Object handling image plot items, plot dialogs, plot options""" PLOT_TYPE = PlotType.IMAGE def reduce_shown_oids(self, oids: list[str]) -> list[str]: """Reduce the number of shown objects to visible items only. The base implementation is to show only the first selected item if the option "Show first only" is enabled. Args: oids: list of object uuids Returns: Reduced list of object uuids """ oids = super().reduce_shown_oids(oids) # For Image View, we show only the last image (which is the highest z-order # plot item) if more than one image is selected, if last image has no # transparency and if the other images are all completely covered by the last # image. # TODO: [P4] Enhance this algorithm to handle more complex cases # (not sure it's worth it) if len(oids) > 1: # Get objects associated to the oids objs = self.panel.objmodel.get_objects(oids) # First condition is about the image transparency last_obj = objs[-1] alpha_cond = ( last_obj.get_metadata_option("alpha", 1.0) == 1.0 and last_obj.get_metadata_option("alpha_function", 0) == 0 ) if alpha_cond: # Second condition is about the image size and position geom_cond = True for obj in objs[:-1]: # Handle both uniform and non-uniform coordinates if last_obj.is_uniform_coords and obj.is_uniform_coords: # Both have uniform coordinates, use old logic geom_cond = ( geom_cond and last_obj.x0 <= obj.x0 and last_obj.y0 <= obj.y0 and last_obj.x0 + last_obj.width >= obj.x0 + obj.width and last_obj.y0 + last_obj.height >= obj.y0 + obj.height ) else: # At least one has non-uniform coordinates, # use extent comparison last_x0 = ( last_obj.x0 if last_obj.is_uniform_coords else last_obj.xcoords[0] ) last_y0 = ( last_obj.y0 if last_obj.is_uniform_coords else last_obj.ycoords[0] ) last_x1 = ( last_obj.x0 + last_obj.width if last_obj.is_uniform_coords else last_obj.xcoords[-1] ) last_y1 = ( last_obj.y0 + last_obj.height if last_obj.is_uniform_coords else last_obj.ycoords[-1] ) obj_x0 = obj.x0 if obj.is_uniform_coords else obj.xcoords[0] obj_y0 = obj.y0 if obj.is_uniform_coords else obj.ycoords[0] obj_x1 = ( obj.x0 + obj.width if obj.is_uniform_coords else obj.xcoords[-1] ) obj_y1 = ( obj.y0 + obj.height if obj.is_uniform_coords else obj.ycoords[-1] ) geom_cond = ( geom_cond and last_x0 <= obj_x0 and last_y0 <= obj_y0 and last_x1 >= obj_x1 and last_y1 >= obj_y1 ) if not geom_cond: break if geom_cond: oids = oids[-1:] return oids def refresh_plot( self, what: str, update_items: bool = True, force: bool = False, only_visible: bool = True, only_existing: bool = False, ) -> None: """Refresh plot. Args: what: string describing the objects to refresh. Valid values are "selected" (refresh the selected objects), "all" (refresh all objects), "existing" (refresh existing plot items), or an object uuid. update_items: if True, update the items. If False, only show the items (do not update them). Defaults to True. force: if True, force refresh even if auto refresh is disabled. Defaults to False. only_visible: if True, only refresh visible items. Defaults to True. Visible items are the ones that are not hidden by other items or the items except the first one if the option "Show first only" is enabled. This is useful for images, where the last image is the one that is shown. If False, all items are refreshed. only_existing: if True, only refresh existing items. Defaults to False. Existing items are the ones that have already been created and are associated to the object uuid. If False, create new items for the objects that do not have an item yet. Raises: ValueError: if `what` is not a valid value """ super().refresh_plot( what=what, update_items=update_items, force=force, only_visible=only_visible, only_existing=only_existing, ) self.plotwidget.contrast.setVisible(Conf.view.show_contrast.get(True)) plot = self.plotwidget.get_plot() new_aspect_ratio = current_aspect_ratio = plot.get_aspect_ratio() new_lock = current_lock = plot.lock_aspect_ratio if Conf.view.ima_aspect_ratio_1_1.get(): # Lock aspect ratio to 1:1 new_aspect_ratio = 1.0 new_lock = True else: # Use physical pixel size to set aspect ratio # Determine which objects to check based on the 'what' parameter if what == "selected": # Use selected objects to determine aspect ratio oids = self.panel.objview.get_sel_object_uuids(include_groups=True) elif what == "existing": # Use existing objects oids = self.get_existing_oids() elif what == "all": # Use all objects oids = self.panel.objmodel.get_object_ids() else: # Single object by uuid oids = [what] # Reduce to visible items and check aspect ratio for oid in reversed(self.reduce_shown_oids(oids)): if oid in self.get_existing_oids() and self.get(oid).isVisible(): obj: ImageObj = self.panel.objmodel[oid] if obj.is_uniform_coords: new_aspect_ratio = obj.dx / obj.dy new_lock = True else: new_lock = False break if new_aspect_ratio != current_aspect_ratio or new_lock != current_lock: # Update aspect ratio only if it has changed plot.set_aspect_ratio(new_aspect_ratio, lock=new_lock) plot.do_autoscale() def cleanup_dataview(self) -> None: """Clean up data view""" for widget in (self.plotwidget.xcsw, self.plotwidget.ycsw): widget.hide() super().cleanup_dataview() def get_plot_options(self) -> PlotOptions: """Return standard signal/image plot options""" options = super().get_plot_options() options.zlabel = self.plot.get_axis_title("right") options.zunit = self.plot.get_axis_unit("right") # Include aspect ratio configuration so that separate plot dialogs # (e.g. "View in a new window", ROI editors, profile dialogs) use the same # settings as the integrated plot handler: options.aspect_ratio = self.plot.get_aspect_ratio() options.lock_aspect_ratio = self.plot.lock_aspect_ratio return options DataLab-1.1.0/datalab/gui/processor/000077500000000000000000000000001514014117600171745ustar00rootroot00000000000000DataLab-1.1.0/datalab/gui/processor/__init__.py000066400000000000000000000057501514014117600213140ustar00rootroot00000000000000""" Processor ========= The :mod:`datalab.gui.processor` package provides the **processor objects** for signals and images. Processor objects are the bridge between the computation modules (in :mod:`sigima.proc`) and the GUI modules (in :mod:`datalab.gui`). They are used to call the computation functions and to update the GUI from inside the data panel objects. When implementing a processing feature in DataLab, the steps are usually the following: - Add an action in the :mod:`datalab.gui.actionhandler` module to trigger the processing feature from the GUI (e.g. a menu item or a toolbar button). - Implement the computation function in the :mod:`sigima.proc` module (that would eventually call the algorithm from the :mod:`sigima.tools` module). - Implement the processor object method in this package to call the computation function and eventually update the GUI. The processor objects are organized in submodules according to their purpose: - :mod:`datalab.gui.processor.base`: Common processing features - :mod:`datalab.gui.processor.signal`: Signal processing features - :mod:`datalab.gui.processor.image`: Image processing features Generic processing types ------------------------- To support consistent processing workflows, the :class:`BaseProcessor` class defines five generic processing methods, each corresponding to a fundamental input/output pattern. These methods are tightly integrated with the GUI logic: the input objects are taken from the current selection in the active panel (Signal or Image), and the output objects are automatically appended to the same panel. Descriptions: - ``compute_1_to_1``: Applies an independent transformation to each selected object. - ``compute_1_to_0``: Runs an analysis or measurement producing metadata or scalar data. - ``compute_1_to_n``: Produces multiple output objects from a single input (e.g. ROI extraction). - ``compute_n_to_1``: Aggregates multiple objects into one (e.g. sum, average); supports pairwise mode. - ``compute_2_to_1``: Applies a binary operation with a second operand (object or constant); supports pairwise mode. .. list-table:: :header-rows: 1 :align: left * - Method name - Signature - Multi-selection behavior * - ``compute_1_to_1`` - 1 object ➝ 1 object - k ➝ k * - ``compute_1_to_0`` - 1 object ➝ no object - k ➝ 0 * - ``compute_1_to_n`` - 1 object ➝ n objects - k ➝ k·n * - ``compute_n_to_1`` - n objects ➝ 1 object - n ➝ 1
    n ➝ n (pairwise mode) * - ``compute_2_to_1`` - 1 object + 1 operand ➝ 1 object - k + 1 ➝ k
    n + n ➝ n (pairwise mode) These methods are for internal or advanced use (e.g. plugin or macro authors) and will evolve without backward compatibility guarantees. Future developments (such as a visual pipeline editor) may require generalizing this model to support additional sources and destinations beyond the current panel-based selection/output logic. """ # noqa: E501 DataLab-1.1.0/datalab/gui/processor/base.py000066400000000000000000003036601514014117600204700ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ .. Base processor object (see parent package :mod:`datalab.gui.processor`) """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import abc import multiprocessing import time import warnings from dataclasses import asdict, dataclass from enum import Enum, auto from multiprocessing.pool import Pool from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, Optional import guidata.dataset as gds import numpy as np from qtpy import QtCore as QC from qtpy import QtWidgets as QW from sigima.config import options as sigima_options from sigima.enums import Interpolation1DMethod from sigima.objects import ( GeometryResult, ImageObj, SignalObj, TableResult, TypeROI, TypeROIParam, concat_geometries, ) from sigima.proc.decorator import is_computation_function from sigima.tools.signal.interpolation import interpolate from datalab import env from datalab.adapters_metadata import ( GeometryAdapter, ResultData, TableAdapter, show_resultdata, ) from datalab.adapters_plotpy import coordutils from datalab.config import Conf, _ from datalab.gui.processor.catcher import CompOut, wng_err_func from datalab.objectmodel import get_short_id, get_uuid, patch_title_with_ids from datalab.utils.qthelpers import create_progress_bar, qt_try_except from datalab.widgets.warningerror import show_warning_error if TYPE_CHECKING: from multiprocessing.pool import AsyncResult from plotpy.plot import PlotWidget from datalab.gui.panel.image import ImagePanel from datalab.gui.panel.signal import SignalPanel @dataclass class ProcessingParameters: """Processing parameters stored in object metadata. Attributes: func_name: Processing function name pattern: Processing pattern ("1-to-1", "n-to-1", or "2-to-1") param: Processing parameter dataset (optional, for 1-to-1 only) source_uuid: Source object UUID (for 1-to-1 pattern) source_uuids: Source object UUIDs (for n-to-1 and 2-to-1 patterns) """ func_name: str pattern: str param: gds.DataSet | None = None source_uuid: str | None = None source_uuids: list[str] | None = None def set_param_from_json(self, param_json: str | list[str]) -> None: """Set the param attribute from a JSON string or list of JSON strings. Args: param_json: JSON string or list of JSON strings representing the parameters """ try: if isinstance(param_json, list): # Handle list of JSON strings self.param = [gds.json_to_dataset(p) for p in param_json] else: # Handle single JSON string self.param = gds.json_to_dataset(param_json) except Exception: # pylint: disable=broad-except warnings.warn(_("Failed to deserialize processing parameters from JSON.")) self.param = None def to_dict(self) -> dict[str, Any]: """Convert ProcessingParameters to a dictionary. Returns: Dictionary representation of ProcessingParameters, ignoring None values. """ pp_dict = {k: v for k, v in asdict(self).items() if v is not None} param = pp_dict.pop("param", None) if param is not None: if isinstance(param, list): # Handle list of DataSet objects pp_dict["param_json"] = [gds.dataset_to_json(p) for p in param] else: # Handle single DataSet object pp_dict["param_json"] = gds.dataset_to_json(param) return pp_dict @classmethod def from_dict(cls, data: dict[str, Any]) -> ProcessingParameters: """Create ProcessingParameters from a dictionary. Args: data: Dictionary representation of ProcessingParameters """ instance = cls("", "") # Temporary values for key, value in data.items(): if key == "param_json": instance.set_param_from_json(value) else: setattr(instance, key, value) return instance # Metadata options for storing processing parameters (DataLab-specific) PROCESSING_PARAMETERS_OPTION = "processing_parameters" # Transformation history ANALYSIS_PARAMETERS_OPTION = "analysis_parameters" # Analysis operation (1-to-0) def extract_processing_parameters( obj: SignalObj | ImageObj, ) -> ProcessingParameters | None: """Extract processing parameters from object metadata. This extracts transformation history (1-to-1, n-to-1, 2-to-1, 1-to-n operations). For analysis operations (1-to-0), use extract_analysis_parameters instead. Args: obj: Signal or Image object Returns: ProcessingParameters instance if processing metadata exists, None otherwise. """ try: pp_dict = obj.get_metadata_option(PROCESSING_PARAMETERS_OPTION) except ValueError: return None return ProcessingParameters.from_dict(pp_dict) def extract_analysis_parameters( obj: SignalObj | ImageObj, ) -> ProcessingParameters | None: """Extract analysis parameters from object metadata. This extracts analysis operation parameters (1-to-0 pattern only). For transformation history, use extract_processing_parameters instead. Args: obj: Signal or Image object Returns: ProcessingParameters instance if analysis metadata exists, None otherwise. """ try: pp_dict = obj.get_metadata_option(ANALYSIS_PARAMETERS_OPTION) except ValueError: return None return ProcessingParameters.from_dict(pp_dict) def insert_processing_parameters( obj: SignalObj | ImageObj, pp: ProcessingParameters, ) -> None: """Insert processing parameters into object metadata. This stores transformation history (1-to-1, n-to-1, 2-to-1, 1-to-n) or analysis parameters (1-to-0) in separate metadata options to avoid overwriting transformation history when performing analysis operations. Args: obj: Signal or Image object pp: ProcessingParameters instance containing all processing metadata """ if pp.pattern == "1-to-0": # Store analysis parameters separately to preserve transformation history obj.set_metadata_option(ANALYSIS_PARAMETERS_OPTION, pp.to_dict()) else: # Store transformation history obj.set_metadata_option(PROCESSING_PARAMETERS_OPTION, pp.to_dict()) def clear_analysis_parameters(obj: SignalObj | ImageObj) -> None: """Clear analysis parameters from object metadata. This removes the stored analysis parameters (1-to-0 operations) from the object. Should be called when all analysis results are deleted to prevent the auto_recompute_analysis function from attempting to recompute deleted analyses. Args: obj: Signal or Image object """ key = f"__{ANALYSIS_PARAMETERS_OPTION}" if key in obj.metadata: del obj.metadata[key] def run_with_env(func: Callable, args: tuple, env_json: str) -> CompOut: """Wrapper to apply environment config before calling func Args: func: function to call args: function arguments env_json: JSON string with environment configuration Returns: Computation output object containing the result, error message, or warning message. """ sigima_options.set_env(env_json) sigima_options.ensure_loaded_from_env() # recharge depuis l'env return wng_err_func(func, args) # Enable multiprocessing support for Windows, with frozen executable (e.g. PyInstaller) multiprocessing.freeze_support() # Set start method to 'spawn' for Linux (default is 'fork' which is not safe here # because of the use of Qt and multithreading) - for other OS, the default is # 'spawn' anyway try: multiprocessing.set_start_method("spawn") except RuntimeError: # This exception is raised if the method is already set (this may happen because # this module is imported more than once, e.g. when running tests) pass COMPUTATION_TIP = _( "DataLab relies on various libraries to perform the computation. During the " "computation, errors may occur because of the data (e.g. division by zero, " "unexpected data type, etc.) or because of the libraries (e.g. memory error, " "etc.). If you encounter an error, before reporting it, please ensure that " "the computation is correct, by checking the data and the parameters." ) POOL: Pool | None = None class WorkerState(Enum): """Worker states for computation lifecycle.""" IDLE = auto() # Ready to start new computation STARTING = auto() # Computation starting (prevents race conditions) RUNNING = auto() # Computation in progress FINISHED = auto() # Computation completed, result available class WorkerStateMachine: """State machine for managing worker computation lifecycle. This class handles state transitions for worker computations, ensuring valid state flow and preventing invalid operations. """ def __init__(self) -> None: """Initialize the state machine in IDLE state.""" self._current_state = WorkerState.IDLE @property def current_state(self) -> WorkerState: """Get the current state. Returns: Current WorkerState. """ return self._current_state def transition_to(self, target_state: WorkerState) -> None: """Transition to the specified target state. Args: target_state: The state to transition to. Raises: ValueError: If the transition is not valid from the current state. """ # Define valid state transitions valid_transitions = { WorkerState.IDLE: {WorkerState.STARTING}, WorkerState.STARTING: {WorkerState.RUNNING}, WorkerState.RUNNING: {WorkerState.FINISHED}, WorkerState.FINISHED: {WorkerState.IDLE}, } # Allow transitions to the same state (no-op) if target_state == self._current_state: return # Check if transition is valid allowed_targets = valid_transitions.get(self._current_state, set()) if target_state not in allowed_targets: raise ValueError( f"Invalid transition from {self._current_state} to {target_state}. " f"Valid transitions: {allowed_targets}" ) self._current_state = target_state def reset_to_idle(self) -> None: """Reset state to IDLE unconditionally. This is used for restart/cancel operations where we need to force the state back to IDLE regardless of current state. """ self._current_state = WorkerState.IDLE class Worker: """Multiprocessing worker, to run long-running tasks in a separate process""" def __init__(self) -> None: self.asyncresult: AsyncResult = None self.state_machine = WorkerStateMachine() @staticmethod def create_pool() -> None: """Create multiprocessing pool""" global POOL # pylint: disable=global-statement # Create a pool with one process POOL = Pool(processes=1) # pylint: disable=not-callable,consider-using-with @staticmethod def terminate_pool(wait: bool = False) -> None: """Terminate multiprocessing pool. Args: wait: wait for all tasks to finish. Defaults to False. """ global POOL # pylint: disable=global-statement if POOL is not None: if wait: # Close the pool properly (wait for all tasks to finish) POOL.close() else: # Terminate the pool and stop the timer POOL.terminate() POOL.join() POOL = None def restart_pool(self) -> None: """Terminate and recreate the pool""" # Terminate the process and stop the timer Worker.terminate_pool(wait=False) # Recreate the pool for the next computation Worker.create_pool() # Reset worker state after pool restart self.asyncresult = None self.state_machine.reset_to_idle() def run(self, func: Callable, args: tuple[Any]) -> None: """Run computation. Args: func: function to run args: arguments Raises: ValueError: If not in IDLE state or pool is not available. """ # Check if we can start computation if self.state_machine.current_state != WorkerState.IDLE: current_state = self.state_machine.current_state raise ValueError(f"Cannot start computation from {current_state} state") # Transition to starting state self.state_machine.transition_to(WorkerState.STARTING) global POOL # pylint: disable=global-statement,global-variable-not-assigned if POOL is None: raise ValueError("Multiprocessing pool is not available") # Start the computation env_json = sigima_options.get_env() self.asyncresult = POOL.apply_async(run_with_env, (func, args, env_json)) # Transition to running state self.state_machine.transition_to(WorkerState.RUNNING) def restart(self) -> None: """Restart/cancel current computation""" current_state = self.state_machine.current_state if current_state == WorkerState.IDLE: return # Already idle, nothing to restart if current_state == WorkerState.STARTING: # If we're still starting, just go back to idle self.asyncresult = None elif current_state == WorkerState.RUNNING: # Cancel the running computation - use restart_pool for consistency self.restart_pool() return # restart_pool already handles state reset if current_state == WorkerState.FINISHED: # Clean up and go to idle self.asyncresult = None # Let state machine handle the transition to idle self.state_machine.reset_to_idle() def close(self) -> None: """Close worker: close pool properly and wait for all tasks to finish""" # Close multiprocessing Pool properly, but only if no computation is running, # to avoid blocking the GUI at exit (so, when wait=True, we wait for the # task to finish before closing the pool but there is actually no task running, # so the pool is closed immediately but *properly*) Worker.terminate_pool(wait=self.asyncresult is None) def is_computation_finished(self) -> bool: """Return True if computation is finished. Returns: bool: True if computation is finished """ current_state = self.state_machine.current_state if current_state == WorkerState.IDLE: return True # No computation has been started if current_state == WorkerState.STARTING: return False # Computation is starting, not finished yet if current_state == WorkerState.FINISHED: return True # Already finished if current_state == WorkerState.RUNNING: if self.asyncresult is None: return False # Should not happen, but defensive finished = self.asyncresult.ready() if finished: # Transition to finished state self.state_machine.transition_to(WorkerState.FINISHED) return finished raise ValueError(f"Invalid worker state: {current_state}") def get_result(self) -> CompOut: """Return computation result. Returns: CompOut: computation result Raises: ValueError: If not in FINISHED state or no result available. """ # Check if we can get result if self.state_machine.current_state != WorkerState.FINISHED: current_state = self.state_machine.current_state raise ValueError(f"Cannot get result from {current_state} state") if self.asyncresult is None: raise ValueError("No result available") # Get result and clean up (ensure cleanup happens even if exception occurs) try: result = self.asyncresult.get() return result finally: # Always clean up, even if get() raises an exception self.asyncresult = None self.state_machine.transition_to(WorkerState.IDLE) def has_result_available(self) -> bool: """Check if computation finished successfully and result is available. Returns: True if computation completed successfully and result can be retrieved. """ return self.state_machine.current_state == WorkerState.FINISHED def is_pairwise_mode() -> bool: """Return True if operation mode is pairwise. Returns: bool: True if operation mode is pairwise """ state = Conf.proc.operation_mode.get() == "pairwise" return state @dataclass class ComputingFeature: """Computing feature dataclass. Args: pattern: pattern function: function paramclass: parameter class title: title icon_name: icon name comment: comment edit: whether to edit the parameters obj2_name: name of the second object skip_xarray_compat: whether to skip X-array compatibility check for this feature """ pattern: Literal["1_to_1", "1_to_0", "1_to_n", "n_to_1", "2_to_1"] function: Optional[Callable] = None paramclass: Optional[type[gds.DataSet]] = None title: Optional[str] = None icon_name: Optional[str] = None comment: Optional[str] = None edit: Optional[bool] = None obj2_name: Optional[str] = None skip_xarray_compat: Optional[bool] = None def __post_init__(self): """Validate the function after initialization.""" if self.function is not None and not is_computation_function(self.function): raise ValueError( f"'{self.function.__name__}' is not a valid computation function." ) @property def name(self) -> str: """Return the name of the computing feature.""" if self.function is None: raise ValueError( "ComputingFeature must have a 'function' to derive its name." ) return self.function.__name__ @property def action_title(self) -> str: """Return the action title of the computing feature.""" title = self.title if ( self.paramclass is not None and (self.edit is None or self.edit) ) or self.pattern == "1_to_0": title += "..." return title class BaseProcessor(QC.QObject, Generic[TypeROI, TypeROIParam]): """Object handling data processing: operations, processing, analysis. Args: panel: panel plotwidget: plot widget """ SIG_ADD_SHAPE = QC.Signal(str) PARAM_DEFAULTS: dict[str, gds.DataSet] = {} def __init__(self, panel: SignalPanel | ImagePanel, plotwidget: PlotWidget): super().__init__() self.panel = panel self.mainwindow = panel.mainwindow self.plotwidget = plotwidget self.worker: Worker | None = None self.set_process_isolation_enabled(Conf.main.process_isolation_enabled.get()) self.computing_registry: dict[str, ComputingFeature] = {} self.register_computations() def close(self): """Close processor properly""" if self.worker is not None: self.worker.close() self.worker = None def set_process_isolation_enabled(self, enabled: bool) -> None: """Set process isolation enabled. Args: enabled: enabled """ if enabled: if self.worker is None: self.worker = Worker() Worker.create_pool() else: if self.worker is not None: self.worker.terminate_pool() self.worker = None def _is_signal_panel(self) -> bool: """Check if the current panel is a signal panel. Returns: True if processing signals, False if processing images """ return self.panel.PARAMCLASS == SignalObj def _check_signal_xarray_compatibility( self, signals: list[SignalObj], progress: QW.QProgressDialog | None = None ) -> tuple[list[SignalObj], bool] | None: """Check X-array compatibility for multiple signals and handle conflicts. Args: signals: List of signal objects to check progress: Progress dialog (if method is called from a long-running task, we need to handle the progress dialog: the dialog will show up after a short delay on top of the message box if we don't handle it here) Returns: Tuple of (signals, yes_to_all_selected) where signals is the list of signals (potentially with interpolated signals) and yes_to_all_selected is True if user chose "Yes to All". Returns None if user canceled. """ if not self._is_signal_panel() or len(signals) <= 1: return signals, False initial_duration = 0 if progress is not None: initial_duration = progress.minimumDuration() # Set progress dialog minimum duration to a very high value to effectively # hide it if it shows up (we handle the dialog manually here) progress.setMinimumDuration(2000000) QW.QApplication.processEvents() # Get X arrays for comparison x_arrays = [sig.x for sig in signals] # Check if all X arrays are identical x_arrays_identical = True if len(x_arrays) > 1: # Compare sizes first sizes = [len(x) for x in x_arrays] if len(set(sizes)) > 1: x_arrays_identical = False else: # Same sizes - check if xmin and xmax are also the same xmins = [x.min() for x in x_arrays] xmaxs = [x.max() for x in x_arrays] # Use relative tolerance for floating point comparison if not ( np.allclose(xmins, xmins[0], rtol=1e-12) and np.allclose(xmaxs, xmaxs[0], rtol=1e-12) ): x_arrays_identical = False # If X arrays are identical, proceed normally if x_arrays_identical: if initial_duration > 0: # Restore initial progress dialog duration progress.setMinimumDuration(initial_duration) return signals, False # X arrays differ - handle based on configuration behavior = Conf.proc.xarray_compat_behavior.get("ask") yes_to_all_selected = False if behavior == "ask" and not env.execenv.unattended: # Create custom message box with "Yes to All" option msg_box = QW.QMessageBox(self.mainwindow) msg_box.setWindowTitle(_("X-array incompatibility")) msg_box.setText( _( "The selected signals have different X arrays.\n\n" "To perform the computation, signals need to be interpolated " "to match a common X array.\n\n" "Do you want to continue with automatic interpolation?" ) ) msg_box.setIcon(QW.QMessageBox.Icon.Question) # Add custom buttons msg_box.addButton(_("Yes"), QW.QMessageBox.ButtonRole.YesRole) yes_all_button = msg_box.addButton( _("Yes to All"), QW.QMessageBox.ButtonRole.YesRole ) no_button = msg_box.addButton(_("No"), QW.QMessageBox.ButtonRole.NoRole) msg_box.setDefaultButton(no_button) # Execute dialog and get user choice msg_box.exec() clicked_button = msg_box.clickedButton() if clicked_button == no_button: return None if clicked_button == yes_all_button: yes_to_all_selected = True # Perform interpolation to the smallest X array sizes = [len(x) for x in x_arrays] min_size_idx = np.argmin(sizes) target_x = x_arrays[min_size_idx] interpolated_signals = [] for i, sig in enumerate(signals): if i == min_size_idx: # Keep the target signal as-is interpolated_signals.append(sig) else: # Create interpolated copy interpolated_sig = sig.copy( title=f"{sig.title} (interpolated)", all_metadata=True ) x_orig, y_orig = sig.x, sig.y # Interpolate using linear method (safe default) y_new = interpolate( x_orig, y_orig, target_x, Interpolation1DMethod.LINEAR, fill_value=None, ) interpolated_sig.set_xydata(target_x, y_new) interpolated_signals.append(interpolated_sig) signals = interpolated_signals if initial_duration > 0: # Restore initial progress dialog duration progress.setMinimumDuration(initial_duration) return signals, yes_to_all_selected def _add_object_to_appropriate_panel( self, new_obj: SignalObj | ImageObj, group_id: str | None = None, use_group_for_non_native: bool = True, ) -> None: """Add object to the appropriate panel based on its type. For native objects (e.g., SignalObj in Signal panel, ImageObj in Image panel), adds to the current panel. For non-native objects (e.g., ImageObj created in Signal panel), adds to the target panel via mainwindow. Args: new_obj: Object to add group_id: Group ID to add the object to (optional) use_group_for_non_native: If True, use group_id even for non-native objects. If False, non-native objects are added to default group. Set to False when group_id is from the source panel and object goes to a different panel. """ is_new_obj_native = isinstance(new_obj, self.panel.PARAMCLASS) if is_new_obj_native: self.panel.add_object(new_obj, group_id=group_id) else: if use_group_for_non_native: self.panel.mainwindow.add_object(new_obj, group_id=group_id) else: self.panel.mainwindow.add_object(new_obj) def _create_group_for_result( self, new_obj: SignalObj | ImageObj, group_name: str ) -> str: """Create a group in the appropriate panel for the result object. For native objects, creates group in current panel. For non-native objects, creates group in the target panel. Args: new_obj: Result object to determine target panel group_name: Name for the new group Returns: UUID of the created group """ is_new_obj_native = isinstance(new_obj, self.panel.PARAMCLASS) if is_new_obj_native: return get_uuid(self.panel.add_group(group_name)) # Create group in target panel for non-native objects target_panel = ( self.panel.mainwindow.signalpanel if isinstance(new_obj, SignalObj) else self.panel.mainwindow.imagepanel ) return get_uuid(target_panel.add_group(group_name)) @abc.abstractmethod def register_operations(self) -> None: """Register operations.""" @abc.abstractmethod def register_processing(self) -> None: """Register processing functions.""" @abc.abstractmethod def register_analysis(self) -> None: """Register analysis functions.""" def register_computations(self) -> None: """Register computations.""" self.register_operations() self.register_processing() self.register_analysis() # pylint: disable=unused-argument def postprocess_1_to_0_result( self, obj: SignalObj | ImageObj, result: GeometryResult | TableResult ) -> bool: """Post-process results from 1-to-0 operations (hook method). This method is called after a 1-to-0 computation function has been executed and the result has been added to the object's metadata. Subclasses can override this method to perform additional processing on the result. Args: obj: The object that was analyzed result: The analysis result (GeometryResult or TableResult) Returns: True if the object was modified and needs a plot refresh, False otherwise """ # Default implementation does nothing and needs no refresh return False def has_param_defaults(self, paramclass: type[gds.DataSet]) -> bool: """Return True if parameter defaults are available. Args: paramclass: parameter class Returns: bool: True if parameter defaults are available """ return paramclass.__name__ in self.PARAM_DEFAULTS def update_param_defaults(self, param: gds.DataSet) -> None: """Update parameter defaults. Args: param: parameters """ key = param.__class__.__name__ pdefaults = self.PARAM_DEFAULTS.get(key) if pdefaults is not None: gds.update_dataset(param, pdefaults) self.PARAM_DEFAULTS[key] = param def init_param( self, param: gds.DataSet, paramclass: type[gds.DataSet], title: str, comment: str | None = None, ) -> tuple[bool, gds.DataSet]: """Initialize processing parameters. Args: param: parameter paramclass: parameter class title: title comment: comment Returns: Tuple (edit, param) where edit is True if parameters have been edited, False otherwise. """ edit = param is None if edit: param = paramclass(title, comment) self.update_param_defaults(param) if hasattr(param, "update_from_obj"): obj = self.panel.objview.get_sel_objects(include_groups=True)[0] param.update_from_obj(obj) return edit, param def handle_output( self, compout: CompOut, context: str, progress: QW.QProgressDialog ) -> SignalObj | ImageObj | GeometryResult | TableResult | None: """Handle computation output: if error, display error message, if warning, display warning message. Args: compout: computation output context: context (e.g. "Computing: Gaussian filter") progress: progress dialog Returns: Output object: a signal or image object, or a geometry/table result object, or None if error """ if compout.error_msg or compout.warning_msg: mindur = progress.minimumDuration() progress.setMinimumDuration(1000000) if compout.error_msg: show_warning_error( self.panel, "error", context, compout.error_msg, COMPUTATION_TIP ) if compout.warning_msg: show_warning_error(self.panel, "warning", context, compout.warning_msg) progress.setMinimumDuration(mindur) if compout.error_msg: return None result = compout.result return result def _merge_geometry_results_for_n_to_1( self, result_obj: SignalObj | ImageObj, src_obj_list: list[SignalObj | ImageObj] ) -> None: """Merge geometry results from source objects into the result object. This method handles geometry result merging for n_to_1 operations when keep_results is enabled, providing a clean alternative to monkey patching. Args: result_obj: The result object from the computation src_obj_list: The list of source objects used in the computation """ # Only merge if keep_results is enabled and we have multiple source objects if not Conf.proc.keep_results.get() or len(src_obj_list) <= 1: return # Group geometry results by title for merging geometry_by_title = {} # Collect all geometry results from all source objects for src_obj in src_obj_list: for geom_adapter in GeometryAdapter.iterate_from_obj(src_obj): title = geom_adapter.title if title not in geometry_by_title: geometry_by_title[title] = [] geometry_by_title[title].append(geom_adapter.result) # Only proceed if we have geometry results to merge if not geometry_by_title: return # Remove any existing geometry results from the result object result_keys_to_remove = [] for key in result_obj.metadata.keys(): if GeometryAdapter.match(key, result_obj.metadata[key]): result_keys_to_remove.append(key) for key in result_keys_to_remove: result_obj.metadata.pop(key, None) # Merge and add back concatenated geometry results for title, geometries in geometry_by_title.items(): if len(geometries) > 1: # Concatenate multiple geometry results merged_geometry = concat_geometries(title, geometries) adapter = GeometryAdapter(merged_geometry) adapter.add_to(result_obj) elif len(geometries) == 1: # Just one geometry result, add it adapter = GeometryAdapter(geometries[0]) adapter.add_to(result_obj) def _handle_keep_results(self, result_obj: SignalObj | ImageObj) -> None: """Handle keep_results logic by removing all results if keep_results is False. This method implements the logic that was previously in Sigima's dst_1_to_1, dst_n_to_1, and dst_2_to_1 functions, where results were deleted from the destination object when keep_results was False. Args: result_obj: The result object from the computation """ if not Conf.proc.keep_results.get(): # Remove all table and geometry results when keep_results is disabled TableAdapter.remove_all_from(result_obj) GeometryAdapter.remove_all_from(result_obj) def auto_recompute_analysis( self, obj: SignalObj | ImageObj, refresh_plot: bool = True ) -> None: """Automatically recompute analysis (1-to-0) operations after data changes. This method checks if the object has 1-to-0 analysis parameters (analysis operations like statistics, measurements, etc.) and automatically recomputes the analysis to update the results based on the modified data. This should be called after: - ROI modifications (which change the data to be analyzed) - Data transformations via recompute_1_to_1 (which modify data in-place) Note: Should be called explicitly after ROI modifications, not during selection changes, to avoid interfering with the ROI change detection mechanism used by the mask refresh system. Args: obj: The object whose data was modified refresh_plot: Whether to refresh the plot after recomputation """ # Check if object has 1-to-0 analysis parameters (analysis operations) proc_params = extract_analysis_parameters(obj) if proc_params is None or proc_params.pattern != "1-to-0": return # Get the parameter from processing parameters param = proc_params.param # Get the actual function from the function name feature = self.get_feature(proc_params.func_name) # Recompute the analysis operation silently, only for this specific object # (not all selected objects, to avoid O(n²) behavior when called in a loop) with Conf.proc.show_result_dialog.temp(False): self.compute_1_to_0(feature.function, param, edit=False, target_objs=[obj]) # Update the view obj_uuid = get_uuid(obj) self.panel.objview.update_item(obj_uuid) if refresh_plot: self.panel.refresh_plot(obj_uuid, update_items=True, force=True) def __exec_func( self, func: Callable, args: tuple, progress: QW.QProgressDialog, ) -> CompOut | None: """Execute function, eventually in a separate process. Args: func: function to execute args: function arguments progress: progress dialog Returns: Computation output object or None if canceled """ QW.QApplication.processEvents() if not progress.wasCanceled(): if self.worker is None: # No process isolation: run function directly return wng_err_func(func, args) # Process isolation: run function in a separate process self.worker.run(func, args) while not self.worker.is_computation_finished(): QW.QApplication.processEvents() time.sleep(0) # Just yields to other threads - no forced delay if progress.wasCanceled(): # User canceled the operation self.worker.restart() # Cancel computation and reset to idle break # Only get result if computation actually finished (not canceled) if self.worker.has_result_available(): return self.worker.get_result() return None def recompute_1_to_1( self, func_name: str, obj: SignalObj | ImageObj, param: gds.DataSet | None = None, ) -> SignalObj | ImageObj | None: """Recompute a 1-to-1 processing operation without adding result to panel. This method is specifically designed for the interactive re-processing feature where we want to update an existing object in-place. It executes the processing with full multiprocessing support (allowing cancellation) but returns the result without adding it to the panel. Args: func_name: Name of the processing function obj: Source object to process param: Processing parameters (optional) Returns: New processed object (not added to panel), or None if cancelled or error Raises: ValueError: If function is not found in registry """ # Get the function from the registry try: feature = self.get_feature(func_name) except ValueError as exc: raise ValueError(f"Function '{func_name}' not found in registry") from exc func = feature.function # Create progress dialog with short delay so it appears for long computations with create_progress_bar(self.panel, _("Recomputing..."), max_=1) as progress: progress.setValue(0) progress.setLabelText(_("Processing object with updated parameters...")) # Execute with multiprocessing support args = (obj, param) if param is not None else (obj,) comp_out = self.__exec_func(func, args, progress) if comp_out is None: # Cancelled by user return None # Handle the output new_obj = self.handle_output(comp_out, _("Recomputing"), progress) if new_obj is None: return None # Handle keep_results logic if isinstance(new_obj, (SignalObj, ImageObj)): self._handle_keep_results(new_obj) patch_title_with_ids(new_obj, [obj], get_short_id) return new_obj def _compute_1_to_1_subroutine( self, funcs: list[Callable], params: list, title: str ) -> None: """Generic subroutine for 1-to-1 processing. Args: funcs: list of functions to execute params: list of parameters title: title of progress bar """ assert len(funcs) == len(params) objs = self.panel.objview.get_sel_objects(include_groups=True) grps = self.panel.objview.get_sel_groups() n_glob = len(objs) * len(params) new_gids = {} with create_progress_bar(self.panel, title, max_=n_glob) as progress: for i_row, obj in enumerate(objs): for i_param, (param, func) in enumerate(zip(params, funcs)): name = func.__name__ pvalue = (i_row + 1) * (i_param + 1) pvalue = 0 if pvalue == 1 else pvalue i_title = f"{title} ({pvalue}/{n_glob})" progress.setLabelText(i_title) progress.setValue(pvalue) args = (obj,) if param is None else (obj, param) result = self.__exec_func(func, args, progress) if result is None: break new_obj = self.handle_output( result, _("Computing: %s") % i_title, progress ) if new_obj is None: continue assert isinstance(new_obj, (SignalObj, ImageObj)) patch_title_with_ids(new_obj, [obj], get_short_id) # Handle keep_results logic for 1_to_1 operations self._handle_keep_results(new_obj) # Store processing metadata for interactive re-processing pp = ProcessingParameters( func_name=name, pattern="1-to-1", param=param, source_uuid=get_uuid(obj), ) insert_processing_parameters(new_obj, pp) # Mark object as freshly processed to show Processing tab self.panel.objprop.mark_as_freshly_processed(new_obj) new_gid = None if grps: # If groups are selected, then it means that there is no # individual object selected: we work on groups only old_gid = self.panel.objmodel.get_object_group_id(obj) new_gid = new_gids.get(old_gid) if new_gid is None: # Create a new group for each selected group old_g = self.panel.objmodel.get_group(old_gid) new_gid = self._create_group_for_result( new_obj, f"{name}({get_short_id(old_g)})" ) new_gids[old_gid] = new_gid self._add_object_to_appropriate_panel( new_obj, group_id=new_gid, use_group_for_non_native=True ) # Select newly created groups, if any for group_id in new_gids.values(): self.panel.objview.set_current_item_id(group_id, extend=True) def __get_src_grps_gids_objs_nbobj_valid( self, min_group_nb: int ) -> tuple[list, list, dict, int]: """In pairwise mode only: get source groups, group ids, objects, and number of objects. Check if the number of objects is valid. Args: min_group_nb: minimum number of groups (typically, 2 for `n1` functions and 1 for `n1n` functions) Returns: Tuple (source groups, group ids, objects, number of objects, valid) """ # In pairwise mode, we need to create a new object for each pair of objects objs = self.panel.objview.get_sel_objects(include_groups=True) objmodel = self.panel.objmodel src_grps = sorted( {objmodel.get_group_from_object(obj) for obj in objs}, key=objmodel.get_number, ) src_gids = [get_uuid(grp) for grp in src_grps] # [src_objs dictionary] keys: old group id, values: list of old objects src_objs: dict[str, list[SignalObj | ImageObj]] = {} for src_gid in src_gids: src_objs[src_gid] = [ obj for obj in objs if objmodel.get_object_group_id(obj) == src_gid ] nbobj = len(src_objs[src_gids[0]]) valid = len(src_grps) >= min_group_nb if not valid: # In pairwise mode, we need selected objects in at least two groups. if env.execenv.unattended: raise ValueError( "Pairwise mode: objects must be selected in at least two groups" ) QW.QMessageBox.warning( self.mainwindow, _("Warning"), _( "In pairwise mode, you need to select objects " "in at least two groups." ), ) if valid: valid = all(len(src_objs[src_gid]) == nbobj for src_gid in src_gids) if not valid: if env.execenv.unattended: raise ValueError( "Pairwise mode: invalid number of objects in each group" ) QW.QMessageBox.warning( self.mainwindow, _("Warning"), _( "In pairwise mode, you need to select " "the same number of objects in each group." ), ) return src_grps, src_gids, src_objs, nbobj, valid def compute_1_to_1( self, func: Callable, param: gds.DataSet | None = None, paramclass: type[gds.DataSet] | None = None, title: str | None = None, comment: str | None = None, edit: bool | None = None, ) -> None: """Generic processing method: 1 object in → 1 object out. Applies a function independently to each selected object in the active panel. The result of each computation is a new object appended to the same panel. Args: func: Function to execute, that takes either `(dst_obj, src_obj)` or `(dst_obj, src_obj, param)` as arguments, where `dst_obj` is the output object, `src_obj` is the input object, and `param` is an optional parameter set. param: Optional parameter instance. paramclass: Optional parameter class for editing. title: Optional progress bar title. comment: Optional comment for parameter dialog. edit: Whether to open the parameter editor before execution. .. note:: With k selected objects, the method produces k outputs (one per input). .. note:: This method does not support pairwise mode. """ if (edit is None or param is None) and paramclass is not None: old_edit = edit edit, param = self.init_param(param, paramclass, title, comment) if old_edit is not None: edit = old_edit if param is not None: if edit and not param.edit(parent=self.mainwindow): return self._compute_1_to_1_subroutine([func], [param], title) def compute_multiple_1_to_1( self, funcs: list[Callable], params: list[gds.DataSet] | None = None, title: str | None = None, edit: bool | None = None, ) -> None: """Generic processing method: 1 object in → n objects out. Applies multiple functions to each selected object, generating multiple outputs per object. The resulting objects are appended to the active panel. Args: funcs: List of functions to apply. Each function takes either `(dst_obj, src_obj)` or `(dst_obj, src_obj, param)` as arguments, where `dst_obj` is the output object, `src_obj` is the input object, and `param` is an optional parameter set. params: List of parameter instances corresponding to each function. title: Optional progress bar title. edit: Whether to open the parameter editor before execution. .. note:: With k selected objects and n outputs per function, the method produces k × n outputs. .. note:: This method does not support pairwise mode. """ if params is None: params = [None] * len(funcs) else: group = gds.DataSetGroup(params, title=_("Parameters")) if edit and not group.edit(parent=self.mainwindow): return if len(funcs) != len(params): raise ValueError("Number of functions must match number of parameters") self._compute_1_to_1_subroutine(funcs, params, title) def compute_1_to_n( self, func: Callable, params: list[gds.DataSet], title: str | None = None, edit: bool | None = None, ) -> None: """Generic processing method: 1 object in → n objects out. Applies a single function to each selected object, with n different parameters set, thus generating n outputs per object. The resulting objects are appended to the active panel. Args: func: Single function to apply, that takes either `(dst_obj, src_obj)` or `(dst_obj, src_obj, param)` as arguments, where `dst_obj` is the output object, `src_obj` is the input object, and `param` is an optional parameter set. params: List of parameter instances. title: Optional progress bar title. edit: Whether to open the parameter editor before execution. .. note:: With k selected objects and n parameter sets, the method produces k × n outputs. .. note:: This method does not support pairwise mode. """ assert params is not None if edit: group = gds.DataSetGroup(params, title=_("Parameters")) if not group.edit(parent=self.mainwindow): return self._compute_1_to_1_subroutine([func] * len(params), params, title) def compute_1_to_0( self, func: Callable, param: gds.DataSet | None = None, paramclass: type[gds.DataSet] | None = None, title: str | None = None, comment: str | None = None, edit: bool | None = None, target_objs: list[SignalObj | ImageObj] | None = None, ) -> ResultData: """Generic processing method: 1 object in → no object out. Applies a function to each selected object (or specified target objects), returning metadata or measurement results (e.g. peak coordinates, statistical properties) without generating new objects. Results are stored in the object's metadata and returned as a ResultData instance. Args: func: Function to execute, that takes either `(obj)` or `(obj, param)` as arguments, where `obj` is the input object and `param` is an optional parameter set. param: Optional parameter instance. paramclass: Optional parameter class for editing. title: Optional progress bar title. comment: Optional comment for parameter dialog. edit: Whether to open the parameter editor before execution. target_objs: Optional list of specific objects to process. If None, processes all currently selected objects. Returns: ResultData instance containing the results for all processed objects. .. note:: With k selected objects, the method performs k analyses and produces no output objects. .. note:: This method does not support pairwise mode. """ if (edit is None or param is None) and paramclass is not None: edit, param = self.init_param(param, paramclass, title, comment) if param is not None: if edit and not param.edit(parent=self.mainwindow): return None objs = ( target_objs if target_objs is not None else self.panel.objview.get_sel_objects(include_groups=True) ) current_obj = self.panel.objview.get_current_object() title = func.__name__ if title is None else title refresh_needed = False with create_progress_bar(self.panel, title, max_=len(objs)) as progress: rdata = ResultData() for idx, obj in enumerate(objs): pvalue = idx + 1 pvalue = 0 if pvalue == 1 else pvalue progress.setValue(pvalue) args = (obj,) if param is None else (obj, param) # Execute function compout = self.__exec_func(func, args, progress) if compout is None: break result = self.handle_output( compout, _("Computing: %s") % title, progress ) if result is None: continue # Using the adapters: if isinstance(result, GeometryResult): adapter = GeometryAdapter(result) elif isinstance(result, TableResult): adapter = TableAdapter(result) else: # For "compute 1 to 0" functions, the result is either a # GeometryResult or TableResult: raise TypeError("Unsupported result type") # Add result shape to object's metadata # Pass function name for better parameter context in the Analysis tab adapter.add_to(obj, param) # Store processing parameters for auto-recompute on ROI change # This enables automatic recalculation when ROI is modified # Analysis parameters (1-to-0) are stored separately from # transformation history to avoid overwriting the processing chain # when analyzing objects. pp = ProcessingParameters( func_name=func.__name__, pattern="1-to-0", param=param, source_uuid=get_uuid(obj), ) insert_processing_parameters(obj, pp) # Apply processor-specific post-processing on the result refresh_needed |= self.postprocess_1_to_0_result(obj, result) # Append result to result data for later display rdata.append(adapter, obj) if obj is current_obj: # Mark object as having fresh analysis results to show Analysis tab self.panel.objprop.mark_as_fresh_analysis(obj) self.panel.selection_changed(update_items=True) else: self.panel.refresh_plot(get_uuid(obj), True, False) # Refresh plot if post-processing modified any objects (e.g., ROI creation) if refresh_needed: self.panel.refresh_plot("selected", only_visible=False, only_existing=True) if rdata and Conf.proc.show_result_dialog.get(): show_resultdata(self.mainwindow, rdata, f"{objs[0].PREFIX}_results") return rdata def compute_n_to_1( self, func: Callable, param: gds.DataSet | None = None, paramclass: type[gds.DataSet] | None = None, title: str | None = None, comment: str | None = None, edit: bool | None = None, ) -> None: """Generic processing method: n objects in → 1 object out. Aggregates multiple selected objects into a single result using the provided function. In pairwise mode, applies the function to object pairs (grouped by index) and generates one output per pair. Args: func: Function to apply, that takes either `(dst_obj, src_obj_list)` or `(dst_obj, src_obj_list, param)` as arguments, where `dst_obj` is the output object, `src_obj_list` is the input object list, and `param` is an optional parameter set. param: Optional parameter instance. paramclass: Optional parameter class for editing. title: Optional progress bar title. comment: Optional comment for parameter dialog. edit: Whether to open the parameter editor before execution. .. note:: With n selected objects: - in default mode, produces 1 output. - in pairwise mode, produces n outputs (one per pair). """ if (edit is None or param is None) and paramclass is not None: edit, param = self.init_param(param, paramclass, title, comment) if param is not None: if edit and not param.edit(parent=self.mainwindow): return objs = self.panel.objview.get_sel_objects(include_groups=True) objmodel = self.panel.objmodel pairwise = is_pairwise_mode() name = func.__name__ if pairwise: src_grps, src_gids, src_objs, _nbobj, valid = ( self.__get_src_grps_gids_objs_nbobj_valid(min_group_nb=2) ) if not valid: return dst_gname = ( f"{name}({','.join([get_short_id(grp) for grp in src_grps])})|pairwise" ) group_exclusive = len(self.panel.objview.get_sel_groups()) != 0 if not group_exclusive: # This is not a group exclusive selection dst_gname += "[...]" # Delay group creation until after first result to determine target panel dst_gid = None n_pairs = len(src_objs[src_gids[0]]) max_i_pair = min( n_pairs, max(len(src_objs[get_uuid(grp)]) for grp in src_grps) ) # Track "Yes to All" choice for this compute operation auto_interpolate_for_operation = False with create_progress_bar(self.panel, title, max_=n_pairs) as progress: for i_pair, src_obj1 in enumerate(src_objs[src_gids[0]][:max_i_pair]): progress.setValue(i_pair + 1) progress.setLabelText(title) src_objs_pair = [src_obj1] for src_gid in src_gids[1:]: src_obj = src_objs[src_gid][i_pair] src_objs_pair.append(src_obj) # Check signal x-array compatibility for n-to-1 operations if auto_interpolate_for_operation: # "Yes to All" selected, automatically interpolate # by temporarily changing the configuration with Conf.proc.xarray_compat_behavior.temp("interpolate"): result = self._check_signal_xarray_compatibility( src_objs_pair, progress=progress ) else: # Normal compatibility check with dialog result = self._check_signal_xarray_compatibility( src_objs_pair, progress=progress ) if result is None: # User canceled or compatibility check failed return checked_objs, yes_to_all_selected = result if yes_to_all_selected: auto_interpolate_for_operation = True src_objs_pair = checked_objs if param is None: args = (src_objs_pair,) else: args = (src_objs_pair, param) result = self.__exec_func(func, args, progress) if result is None: break new_obj = self.handle_output( result, _("Calculating: %s") % title, progress ) if new_obj is None: break assert isinstance(new_obj, (SignalObj, ImageObj)) patch_title_with_ids(new_obj, src_objs_pair, get_short_id) # Handle keep_results and geometry result merging self._handle_keep_results(new_obj) self._merge_geometry_results_for_n_to_1(new_obj, src_objs_pair) # Store lightweight processing metadata (non-interactive) proc_params = ProcessingParameters( func_name=name, pattern="n-to-1", param=param, source_uuids=[get_uuid(obj) for obj in src_objs_pair], ) insert_processing_parameters(new_obj, proc_params) # Create destination group on first result, in appropriate panel if dst_gid is None: dst_gid = self._create_group_for_result(new_obj, dst_gname) self._add_object_to_appropriate_panel(new_obj, group_id=dst_gid) else: # In single operand mode, we create a single object for all selected objects # [src_objs dictionary] keys: old group id, values: list of old objects src_objs: dict[str, list[SignalObj | ImageObj]] = {} grps = self.panel.objview.get_sel_groups() dst_group_name = None if grps: # (Group exclusive selection) # At least one group is selected: create a new group dst_gname = f"{name}({','.join([get_uuid(grp) for grp in grps])})" # Delay group creation until after first result dst_gid = None dst_group_name = dst_gname # Store name for later use else: # (Object exclusive selection) # No group is selected: use each object's group dst_gid = None for src_obj in objs: src_gid = objmodel.get_object_group_id(src_obj) src_objs.setdefault(src_gid, []).append(src_obj) # Track "Yes to All" choice for this compute operation auto_interpolate_for_operation = False with create_progress_bar(self.panel, title, max_=len(objs)) as progress: progress.setValue(0) progress.setLabelText(title) for src_gid, src_obj_list in src_objs.items(): # Check signal x-array compatibility for n-to-1 operations if auto_interpolate_for_operation: # "Yes to All" selected, automatically interpolate with Conf.proc.xarray_compat_behavior.temp("interpolate"): result = self._check_signal_xarray_compatibility( src_obj_list, progress=progress ) else: # Normal compatibility check with dialog result = self._check_signal_xarray_compatibility( src_obj_list, progress=progress ) if result is None: # User canceled or compatibility check failed return checked_objs, yes_to_all_selected = result if yes_to_all_selected: auto_interpolate_for_operation = True src_obj_list = checked_objs if param is None: args = (src_obj_list,) else: args = (src_obj_list, param) result = self.__exec_func(func, args, progress) if result is None: break new_obj = self.handle_output( result, _("Calculating: %s") % title, progress ) if new_obj is None: break assert isinstance(new_obj, (SignalObj, ImageObj)) group_id = dst_gid if dst_gid is not None else src_gid patch_title_with_ids(new_obj, src_obj_list, get_short_id) # Handle keep_results and geometry result merging self._handle_keep_results(new_obj) self._merge_geometry_results_for_n_to_1(new_obj, src_obj_list) # Store lightweight processing metadata (non-interactive) proc_params = ProcessingParameters( func_name=name, pattern="n-to-1", param=param, source_uuids=[get_uuid(obj) for obj in src_obj_list], ) insert_processing_parameters(new_obj, proc_params) # Create destination group on first result, in appropriate panel use_group_for_non_native = False if dst_gid is None and dst_group_name is not None: dst_gid = self._create_group_for_result(new_obj, dst_group_name) group_id = dst_gid use_group_for_non_native = True self._add_object_to_appropriate_panel( new_obj, group_id=group_id, use_group_for_non_native=use_group_for_non_native, ) # Select newly created group, if any if dst_gid is not None: self.panel.objview.set_current_item_id(dst_gid) def compute_2_to_1( self, obj2: SignalObj | ImageObj | list[SignalObj | ImageObj] | None, obj2_name: str, func: Callable, param: gds.DataSet | None = None, paramclass: type[gds.DataSet] | None = None, title: str | None = None, comment: str | None = None, edit: bool | None = None, skip_xarray_compat: bool | None = None, ) -> None: """Generic processing method: binary operation 1+1 → 1. Applies a binary function between each selected object and a second operand. Supports both single operand mode (same operand for all objects) and pairwise mode (one-to-one matching between two object lists). Args: obj2: Second operand (single object or list for pairwise mode). obj2_name: Display name for the second operand (used in selection dialog). func: Function to apply, that takes either `(dst_obj, src_obj1, src_obj2)` or `(dst_obj, src_obj1, src_obj2, param)` as arguments, where `dst_obj` is the output object, `src_obj1` is the first input object, `src_obj2` is the second input object (operand), and `param` is an optional parameter set. param: Optional parameter instance. paramclass: Optional parameter class for editing. title: Optional progress bar title. comment: Optional comment for parameter dialog. edit: Whether to open the parameter editor before execution. skip_xarray_compat: If True, skip x-array compatibility checks (only for signal panels). .. note:: With k selected objects: - in single operand mode and 1 secondary object: produces k outputs. - in pairwise mode with k secondary objects: produces k outputs (one per pair). """ if (edit is None or param is None) and paramclass is not None: edit, param = self.init_param(param, paramclass, title, comment) if param is not None: if edit and not param.edit(parent=self.mainwindow): return objs = self.panel.objview.get_sel_objects(include_groups=True) objmodel = self.panel.objmodel pairwise = is_pairwise_mode() name = func.__name__ if obj2 is None: objs2 = [] elif isinstance(obj2, list): objs2 = obj2 assert pairwise else: objs2 = [obj2] dlg_title = _("Select %s") % obj2_name if pairwise: group_exclusive = len(self.panel.objview.get_sel_groups()) != 0 src_grps, src_gids, src_objs, nbobj, valid = ( self.__get_src_grps_gids_objs_nbobj_valid(min_group_nb=1) ) if not valid: return if not objs2: objs2 = self.panel.get_objects_with_dialog( dlg_title, _( "Note: operation mode is pairwise: " "%s object(s) expected (i.e. as many as in the first group)" ) % nbobj, nbobj, ) if objs2 is None: return n_pairs = len(src_objs[src_gids[0]]) max_i_pair = min( n_pairs, max(len(src_objs[get_uuid(grp)]) for grp in src_grps) ) grp2_id = objmodel.get_object_group_id(objs2[0]) grp2 = objmodel.get_group(grp2_id) # Initialize pair mapping for potential interpolations pair_maps = {} # Check x-array compatibility for signal processing (pairwise mode) if self._is_signal_panel() and not skip_xarray_compat: # Check compatibility between objects from both groups all_pairs = [] for src_gid in src_gids: for i_pair in range(max_i_pair): src_obj1 = src_objs[src_gid][i_pair] src_obj2 = objs2[i_pair] if isinstance(src_obj1, SignalObj) and isinstance( src_obj2, SignalObj ): all_pairs.append((src_obj1, src_obj2)) # Track "Yes to All" choice for this compute operation auto_interpolate_for_operation = False # Check all pairs for compatibility and create interpolation maps for src_obj1, src_obj2 in all_pairs: if auto_interpolate_for_operation: # "Yes to All" selected, automatically interpolate with Conf.proc.xarray_compat_behavior.temp("interpolate"): result = self._check_signal_xarray_compatibility( [src_obj1, src_obj2] ) else: # Normal compatibility check with dialog result = self._check_signal_xarray_compatibility( [src_obj1, src_obj2] ) if result is None: return # User cancelled or error occurred checked_pair, yes_to_all_selected = result if yes_to_all_selected: auto_interpolate_for_operation = True # Store mapping for this specific pair pair_maps[(src_obj1, src_obj2)] = checked_pair with create_progress_bar(self.panel, title, max_=len(src_gids)) as progress: for i_group, src_gid in enumerate(src_gids): progress.setValue(i_group + 1) progress.setLabelText(title) if group_exclusive: # This is a group exclusive selection src_grp = objmodel.get_group(src_gid) grp_short_ids = [get_uuid(grp) for grp in (src_grp, grp2)] dst_gname = f"{name}({','.join(grp_short_ids)})|pairwise" else: dst_gname = f"{name}[...]" # Delay group creation until after first result dst_gid = None for i_pair in range(max_i_pair): orig_obj1, orig_obj2 = src_objs[src_gid][i_pair], objs2[i_pair] # Use interpolated signals if available, keep original refs actual_obj1, actual_obj2 = orig_obj1, orig_obj2 if (orig_obj1, orig_obj2) in pair_maps: interpolated_pair = pair_maps[(orig_obj1, orig_obj2)] actual_obj1 = interpolated_pair[0] actual_obj2 = interpolated_pair[1] args = [actual_obj1, actual_obj2] if param is not None: args.append(param) result = self.__exec_func(func, tuple(args), progress) if result is None: break new_obj = self.handle_output( result, _("Calculating: %s") % title, progress ) if new_obj is None: continue assert isinstance(new_obj, (SignalObj, ImageObj)) # Use original objects for title generation patch_title_with_ids( new_obj, [orig_obj1, orig_obj2], get_short_id ) # Handle keep_results logic for 2_to_1 operations self._handle_keep_results(new_obj) # Store lightweight processing metadata (non-interactive) proc_params = ProcessingParameters( func_name=name, pattern="2-to-1", param=param, source_uuids=[ get_uuid(orig_obj1), get_uuid(orig_obj2), ], ) insert_processing_parameters(new_obj, proc_params) # Create destination group on first result, in appropriate panel if dst_gid is None: dst_gid = self._create_group_for_result(new_obj, dst_gname) self._add_object_to_appropriate_panel(new_obj, group_id=dst_gid) else: if not objs2: objs2 = self.panel.get_objects_with_dialog( dlg_title, _( "Note: operation mode is single operand: " "1 object expected" ), ) if objs2 is None: return obj2 = objs2[0] # Initialize signal mapping for potential interpolations signal_map = {} # Check x-array compatibility for signal processing (single operand mode) orig_obj2 = obj2 # Keep reference to original obj2 for title generation if ( self._is_signal_panel() and isinstance(obj2, SignalObj) and not skip_xarray_compat ): signal_objs = [obj for obj in objs if isinstance(obj, SignalObj)] if signal_objs: # Check compatibility and get potentially interpolated signals result = self._check_signal_xarray_compatibility( signal_objs + [obj2] ) if result is None: return # User cancelled or error occurred checked_objs, _yes_to_all_selected = result # Note: In single operand mode, "Yes to All" doesn't apply # since there's only one compatibility check # Replace obj2 with the potentially interpolated version obj2 = checked_objs[-1] # obj2 was added last # Create a mapping of original to interpolated signals for orig_obj, checked_obj in zip(signal_objs, checked_objs[:-1]): signal_map[orig_obj] = checked_obj with create_progress_bar(self.panel, title, max_=len(objs)) as progress: for index, obj in enumerate(objs): progress.setValue(index + 1) progress.setLabelText(title) # Use interpolated signal if available actual_obj = obj if ( self._is_signal_panel() and isinstance(obj, SignalObj) and obj in signal_map ): actual_obj = signal_map[obj] args = ( (actual_obj, obj2) if param is None else (actual_obj, obj2, param) ) result = self.__exec_func(func, args, progress) if result is None: break new_obj = self.handle_output( result, _("Calculating: %s") % title, progress ) if new_obj is None: continue assert isinstance(new_obj, (SignalObj, ImageObj)) group_id = objmodel.get_object_group_id(obj) # Use original objects for title generation patch_title_with_ids(new_obj, [obj, orig_obj2], get_short_id) # Handle keep_results logic for 2_to_1 operations self._handle_keep_results(new_obj) # Store lightweight processing metadata (non-interactive) proc_params = ProcessingParameters( func_name=name, pattern="2-to-1", param=param, source_uuids=[ get_uuid(obj), get_uuid(orig_obj2), ], ) insert_processing_parameters(new_obj, proc_params) # group_id is from source panel, don't use for non-native objects self._add_object_to_appropriate_panel( new_obj, group_id=group_id, use_group_for_non_native=False ) def register_1_to_1( self, function: Callable, title: str, paramclass: type[gds.DataSet] | None = None, icon_name: str | None = None, comment: str | None = None, edit: bool | None = None, ) -> ComputingFeature: """Register a 1-to-1 processing function. The `register_1_to_1` method is used to register a function that takes one object as input and produces one object as output. The function is called with the input object and an optional parameter set. The result of the function is returned. Args: function: function to register title: title of the function paramclass: parameter class. Defaults to None. icon_name: icon name. Defaults to None. comment: comment. Defaults to None. edit: whether to open the parameter editor before execution. Returns: Registered feature. """ feature = ComputingFeature( pattern="1_to_1", function=function, title=title, paramclass=paramclass, icon_name=icon_name, comment=comment, edit=edit, ) self.add_feature(feature) return feature def register_1_to_0( self, function: Callable, title: str, paramclass: type[gds.DataSet] | None = None, icon_name: str | None = None, comment: str | None = None, edit: bool | None = None, ) -> ComputingFeature: """Register a 1-to-0 processing function. The function takes one object as input and produces no output. The function is called with the input object and an optional parameter set. The result of the function is returned. Args: function: function to register title: title of the function paramclass: parameter class. Defaults to None. icon_name: icon name. Defaults to None. comment: comment. Defaults to None. edit: whether to open the parameter editor before execution. Returns: Registered feature. """ feature = ComputingFeature( pattern="1_to_0", function=function, title=title, paramclass=paramclass, icon_name=icon_name, comment=comment, edit=edit, ) self.add_feature(feature) return feature def register_1_to_n( self, function: Callable, title: str, icon_name: str | None = None ) -> ComputingFeature: """Register a 1-to-n processing function. The function takes one object as input and produces multiple objects as output. The function is called with the input object and an optional parameter set. The result of the function is returned. Args: function: function to register title: title of the function icon_name: icon name. Defaults to None. Returns: Registered feature. """ feature = ComputingFeature( pattern="1_to_n", function=function, title=title, icon_name=icon_name, ) self.add_feature(feature) return feature def register_n_to_1( self, function: Callable, title: str, paramclass: type[gds.DataSet] | None = None, icon_name: str | None = None, comment: str | None = None, edit: bool | None = None, ) -> ComputingFeature: """Register a n-to-1 processing function. The function takes multiple objects as input and produces one object as output. The function is called with the input objects and an optional parameter set. The result of the function is returned. Args: function: function to register title: title of the function paramclass: parameter class. Defaults to None. icon_name: icon name. Defaults to None. comment: comment. Defaults to None. edit: whether to open the parameter editor before execution. Returns: Registered feature. """ feature = ComputingFeature( pattern="n_to_1", function=function, title=title, paramclass=paramclass, icon_name=icon_name, comment=comment, edit=edit, ) self.add_feature(feature) return feature def register_2_to_1( self, function: Callable, title: str, paramclass: type[gds.DataSet] | None = None, icon_name: str | None = None, comment: str | None = None, edit: bool | None = None, obj2_name: str | None = None, skip_xarray_compat: bool | None = None, ) -> ComputingFeature: """Register a 2-to-1 processing function. The function takes two objects as input and produces one object as output. The function is called with the input objects and an optional parameter set. The result of the function is returned. Args: function: function to register title: title of the function paramclass: parameter class. Defaults to None. icon_name: icon name. Defaults to None. comment: comment. Defaults to None. edit: whether to open the parameter editor before execution. obj2_name: name of the second object. Defaults to None. skip_xarray_compat: whether to skip X-array compatibility check. Defaults to None. Set to True for operations like interpolation where different X-arrays are expected and desired. Returns: Registered feature. """ feature = ComputingFeature( pattern="2_to_1", function=function, title=title, paramclass=paramclass, icon_name=icon_name, comment=comment, edit=edit, obj2_name=obj2_name, skip_xarray_compat=skip_xarray_compat, ) self.add_feature(feature) return feature def add_feature(self, feature: ComputingFeature) -> None: """Add a computing feature to the registry. Args: feature: ComputingFeature instance to add. """ self.computing_registry[feature.function] = feature def get_feature(self, function_or_name: Callable | str) -> ComputingFeature: """Get a computing feature by name or function. Args: function_or_name: Name of the feature or the function itself. Returns: Computing feature instance. """ try: return self.computing_registry[function_or_name] except KeyError as exc: for _func, feature in self.computing_registry.items(): if feature.name == function_or_name: return feature raise ValueError(f"Unknown computing feature: {function_or_name}") from exc @qt_try_except() def run_feature( self, key: str | Callable | ComputingFeature, *args, **kwargs, ) -> ResultData | list[SignalObj | ImageObj] | SignalObj | ImageObj | None: """Run a computing feature that has been previously registered. This method is a generic dispatcher for all compute methods. It uses the central registry to find the appropriate compute method based on the pattern (`1_to_1`, `1_to_0`, `n_to_1`, `2_to_1`, `1_to_n`). It then calls the appropriate compute method with the provided arguments. Depending on the pattern, this method can take different arguments: .. code-block:: python import sigima.proc.signal as sips import sigima.params proc = win.signalpanel.processor # where `win` is DataLab's main window # For patterns `1_to_1`, `1_to_0`, `n_to_1`: proc.run_feature(sips.normalize) param = sigima.params.MovingAverageParam(n=3) proc.run_feature(sips.moving_average, param) proc.run_feature(computation_function, param, edit=False) # For pattern `2_to_1`: proc.run_feature(sips.difference, obj2) param = sigima.params.InterpolationParam(method="cubic") proc.run_feature(sips.interpolation, obj2, param) # For pattern `1_to_n`: params = roi.to_params(obj) proc.run_feature(sips.extract_roi, params=params) Args: key: The key to look up in the registry. It can be a string, a callable, or a ComputingFeature instance. *args: Positional arguments to pass to the compute method. **kwargs: Keyword arguments to pass to the compute method. Returns: The result of the computation or None. """ if not isinstance(key, ComputingFeature): feature = self.get_feature(key) else: feature = key # Some keyword parameters may be overridden edit = kwargs.pop("edit", feature.edit) title = kwargs.pop("title", feature.title) comment = kwargs.pop("comment", feature.comment) pattern = feature.pattern if pattern in {"1_to_1", "1_to_0", "n_to_1"}: compute_method = getattr(self, f"compute_{pattern}") param = kwargs.pop("param", args[0] if args else None) assert isinstance(param, (gds.DataSet, type(None))), ( f"For pattern '{pattern}', 'param' must be a DataSet or None" ) return compute_method( feature.function, param=param, paramclass=feature.paramclass, title=title, comment=comment, edit=edit, ) if pattern == "2_to_1": obj2 = kwargs.pop("obj2", args[0] if args else None) assert isinstance(obj2, (SignalObj, ImageObj, list, type(None))), ( "For pattern '2_to_1', 'obj2' must be a SignalObj, ImageObj, " "list of SignalObj/ImageObj, or None" ) param = kwargs.pop("param", args[1] if args and len(args) > 1 else None) assert isinstance(param, (gds.DataSet, type(None))), ( "For pattern '2_to_1', 'param' must be a DataSet or None" ) return self.compute_2_to_1( obj2, feature.obj2_name or _("Second operand"), feature.function, param=param, paramclass=feature.paramclass, title=title, comment=comment, edit=edit, skip_xarray_compat=feature.skip_xarray_compat, ) if pattern == "1_to_n": params = kwargs.get("params", args[0] if args else []) if not isinstance(params, list) or any( not isinstance(param, gds.DataSet) for param in params ): raise ValueError( "For pattern '1_to_n', 'params' must be " "a list of DataSet or a DataSetGroup" ) return self.compute_1_to_n( feature.function, params=params, title=title, edit=edit, ) raise ValueError(f"Unsupported compute pattern: {pattern}") # ------Data Processing------------------------------------------------------------- @qt_try_except() def compute_roi_extraction(self, roi: TypeROI | None = None) -> None: """Extract Region Of Interest (ROI) from data with: - :py:func:`sigima.proc.image.compute_extract_roi` for single ROI - :py:func:`sigima.proc.image.compute_extract_rois` for multiple ROIs""" # Expected behavior: # ----------------- # * If `roi` is not None or not empty, skip the ROI dialog # * If first selected obj has a ROI, use this ROI as default but open # ROI Editor dialog anyway # * If multiple objs are selected, then apply the first obj ROI to all if roi is None or roi.is_empty(): roi = self.edit_roi_graphically(mode="extract") if roi is None or roi.is_empty(): return obj = self.panel.objview.get_sel_objects(include_groups=True)[0] params = roi.to_params(obj) if Conf.proc.extract_roi_singleobj.get() and len(params) > 1: # Extract multiple ROIs into a single object (remove all the ROIs), # if the "Extract all ROIs into a single image object" # option is checked and if there are more than one ROI self._extract_multiple_roi_in_single_object(params) else: # Extract each ROI into a separate object (keep the ROI in the case of # a circular ROI), if the "Extract all ROIs into a single image object" # option is not checked or if there is only one ROI (See Issue #31) self.run_feature("extract_roi", params=params, edit=False) @abc.abstractmethod @qt_try_except() def _extract_multiple_roi_in_single_object( self, params: list[TypeROIParam] ) -> None: """Extract multiple Regions Of Interest (ROIs) from data in a single object""" # ------Analysis------------------------------------------------------------------- def edit_roi_graphically( self, mode: Literal["apply", "extract", "define"] = "apply" ) -> TypeROI | None: """Define Region Of Interest (ROI). Args: mode: Mode of operation, either "apply" (define ROI, then apply it to selected objects), "extract" (define ROI, then extract data from it), or "define" (define ROI without applying or extracting). Returns: ROI object or None if ROI dialog has been canceled. """ assert mode in ("apply", "extract", "define"), ( f"Invalid mode: {mode}. Must be either 'apply', 'extract' or 'define'." ) # Expected behavior: # ----------------- # * If first selected obj has a ROI, use this ROI as default but open # ROI Editor dialog anyway # * If multiple objs are selected, then apply the first obj ROI to all results = self.panel.get_roi_editor_output(mode=mode) if results is None: return None edited_roi, modified = results objs = self.panel.objview.get_sel_objects(include_groups=True) obj = objs[-1] params = edited_roi.to_params(obj) group = gds.DataSetGroup(params, title=_("Regions of Interest")) if ( env.execenv.unattended # Unattended mode (automated unit tests) or edited_roi.is_empty() # No ROI has been defined or group.edit(parent=self.mainwindow) # ROI dialog has been accepted ): if modified: # If ROI has been modified, save ROI (not in "extract" mode) if edited_roi.is_empty() and mode != "define": # If ROI is empty, remove it from all selected objects # (not in "define" mode because the ROI is just defined and used # in a processing function for example: it's not bound to any # object yet) for obj_i in objs: obj_i.roi = None else: edited_roi = edited_roi.__class__.from_params(obj, params) if mode == "apply": # Apply ROI to all selected objects for obj_i in objs: obj_i.roi = edited_roi self.SIG_ADD_SHAPE.emit(get_uuid(obj)) self.panel.selection_changed(update_items=True) self.panel.refresh_plot( "selected", update_items=True, only_visible=False, only_existing=True, ) # Auto-recompute analysis operations for objects with modified ROIs if mode == "apply": with create_progress_bar( self.panel, _("Recomputing..."), max_=len(objs) ) as progress: for idx, obj_i in enumerate(objs): progress.setValue(idx) self.auto_recompute_analysis(obj_i, refresh_plot=False) self.panel.manual_refresh() return edited_roi def edit_roi_numerically(self) -> TypeROI: """Edit Regions Of Interest (ROIs) numerically. Opens a dialog to edit the parameters of the selected ROIs. If no ROIs are selected, it will prompt the user to select ROIs. Returns: The edited ROI object if the dialog is accepted, otherwise the original ROI. """ obj = self.panel.objview.get_sel_objects()[0] assert obj.roi is not None, _("No ROI selected for editing.") params = obj.roi.to_params(obj) # Round coordinates to appropriate precision before displaying for param in params: if isinstance(obj, SignalObj): coordutils.round_signal_roi_param(obj, param) elif isinstance(obj, ImageObj): coordutils.round_image_roi_param(obj, param) group = gds.DataSetGroup(params, title=_("Regions of Interest")) if group.edit(parent=self.mainwindow): edited_roi = obj.roi.__class__.from_params(obj, params) obj.roi = edited_roi self.SIG_ADD_SHAPE.emit(get_uuid(obj)) self.panel.refresh_plot( "selected", update_items=True, only_visible=False, only_existing=True, ) # Auto-recompute analysis operations after ROI modification self.auto_recompute_analysis(obj) return edited_roi return obj.roi def delete_regions_of_interest(self) -> None: """Delete Regions Of Interest""" if ( env.execenv.unattended or QW.QMessageBox.question( self.mainwindow, _("Remove all ROIs"), _("Are you sure you want to remove all ROIs?"), ) == QW.QMessageBox.Yes ): modified_objs = [] for obj in self.panel.objview.get_sel_objects(): if obj.roi is not None: obj.roi = None modified_objs.append(obj) self.panel.selection_changed(update_items=True) # Auto-recompute analysis operations after ROI deletion for obj in modified_objs: self.auto_recompute_analysis(obj) def delete_single_roi(self, roi_index: int) -> None: """Delete a single ROI by index Args: roi_index: Index of the ROI to remove """ obj = self.panel.objview.get_sel_objects()[0] if obj.roi is not None and 0 <= roi_index < len(obj.roi.single_rois): roi_title = obj.roi.get_single_roi_title(roi_index) if ( env.execenv.unattended or QW.QMessageBox.question( self.mainwindow, _("Remove ROI"), _("Are you sure you want to remove ROI '%s'?") % roi_title, ) == QW.QMessageBox.Yes ): obj.roi.single_rois.pop(roi_index) # If no ROIs left, set roi to None if len(obj.roi.single_rois) == 0: obj.roi = None obj.mark_roi_as_changed() # Auto-recompute analysis operations after ROI modification # (must be done BEFORE selection_changed to avoid stale results) self.auto_recompute_analysis(obj) self.panel.selection_changed(update_items=True) DataLab-1.1.0/datalab/gui/processor/catcher.py000066400000000000000000000050401514014117600211560ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab warning and error catcher utility for processors """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import dataclasses import traceback import warnings from collections.abc import Callable from typing import TYPE_CHECKING, Any from datalab.env import execenv if TYPE_CHECKING: from sigima.objects import ImageObj, SignalObj from sigima.objects.scalar import GeometryResult, TableResult @dataclasses.dataclass class CompOut: """Class for representing computation output Attributes: result: computation result error_msg: error message warning_msg: warning message """ result: SignalObj | ImageObj | GeometryResult | TableResult | None = None error_msg: str | None = None warning_msg: str | None = None def wng_err_func(func: Callable, args: tuple[Any]) -> CompOut: """Wrapper function to catch errors and warnings during computation Args: func: function to call args: function arguments Returns: Computation output object containing the result, error message, or warning message. """ with warnings.catch_warnings(record=True) as wngs: try: result = func(*args) if wngs: wng = wngs[-1] warning_msg = warnings.formatwarning( message=wng.message, category=wng.category, filename=wng.filename, lineno=wng.lineno, line=wng.line, ) return CompOut(result=result, warning_msg=warning_msg) return CompOut(result=result) except Exception: # pylint: disable=broad-except if execenv.unattended and not execenv.catcher_test: # In unattended mode (test cases), we want to raise the exception # because test cases are supposed to work without any error. In real # life, we want to avoid raising the exception because it would stop # the application, and exceptions could be related to non-critical # errors due to external libraries. # When testing the catcher, on the other hand, we don't want to # raise the exception because it would stop the unattended test # execution. raise return CompOut(error_msg=traceback.format_exc()) DataLab-1.1.0/datalab/gui/processor/image.py000066400000000000000000001336741514014117600206460ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ .. Image processor object (see parent package :mod:`datalab.gui.processor`) """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import importlib from typing import Literal import numpy as np import sigima.params import sigima.proc.base as sipb import sigima.proc.image as sipi from guidata.qthelpers import exec_dialog from plotpy.widgets.resizedialog import ResizeDialog from qtpy import QtWidgets as QW from sigima.objects import ( ImageObj, ImageROI, NormalDistributionParam, PoissonDistributionParam, ROI2DParam, UniformDistributionParam, ) from sigima.objects.scalar import GeometryResult, TableResult from sigima.proc.decorator import ComputationMetadata from datalab.adapters_metadata import GeometryAdapter from datalab.config import APP_NAME, _ from datalab.gui.processor.base import BaseProcessor from datalab.gui.profiledialog import ProfileExtractionDialog from datalab.gui.roigrideditor import ImageGridROIEditor from datalab.objectmodel import get_uuid from datalab.utils.qthelpers import create_progress_bar, qt_try_except from datalab.widgets import imagebackground def apply_geometry_transform( obj: ImageObj, operation: Literal[ "translate", "scale", "rotate90", "rotate270", "fliph", "flipv", "transpose" ], **kwargs, ) -> None: """Apply a geometric transformation to all geometry results in an object. This uses the Sigima transformation system for proper geometric operations. For image objects, rotations are performed around the image center to match how the image data is transformed. Args: obj: The object containing geometry results to transform operation: The transformation operation name **kwargs: Optional parameters for the transformation (e.g., angle for rotate) """ assert operation in [ "translate", "scale", "rotate90", "rotate270", "fliph", "flipv", "transpose", ], f"Unknown operation: {operation}" if operation == "translate": if not kwargs or "dx" not in kwargs or "dy" not in kwargs: raise ValueError("translate operation requires 'dx' and 'dy' parameters") dx, dy = kwargs["dx"], kwargs["dy"] elif operation == "scale": if not kwargs or "sx" not in kwargs or "sy" not in kwargs: raise ValueError("scale operation requires 'sx' and 'sy' parameters") sx, sy = kwargs["sx"], kwargs["sy"] for adapter in list(GeometryAdapter.iterate_from_obj(obj)): geom = adapter.result assert geom is not None, "Geometry should not be None" assert len(geom.coords) > 0, "Geometry coordinates should not be empty" if operation == "translate": tr_geom = sipi.transformer.translate(geom, dx, dy) elif operation == "scale": tr_geom = sipi.transformer.scale(geom, sx, sy, (obj.xc, obj.yc)) elif operation == "rotate90": tr_geom = sipi.transformer.rotate(geom, -np.pi / 2, (obj.xc, obj.yc)) elif operation == "rotate270": tr_geom = sipi.transformer.rotate(geom, np.pi / 2, (obj.xc, obj.yc)) elif operation == "fliph": tr_geom = sipi.transformer.fliph(geom, obj.xc) elif operation == "flipv": tr_geom = sipi.transformer.flipv(geom, obj.yc) elif operation == "transpose": tr_geom = sipi.transformer.transpose(geom) else: raise ValueError(f"Unknown operation: {operation}") # Remove the old geometry and add the transformed one adapter.remove_from(obj) tr_adapter = GeometryAdapter(tr_geom) tr_adapter.add_to(obj) class GeometricTransformWrapper: """Pickleable wrapper for geometric transformation functions. This class creates a callable wrapper that can be pickled, unlike nested functions. Instead of storing the function directly, it stores the module path and function name to allow proper pickling. """ def __init__(self, func, operation: str): self.operation = operation # Store function reference for execution self.func = func # Store function module and name for pickling self.__module__ = func.__module__ self.__qualname__ = func.__qualname__ self.__annotations__ = getattr(func, "__annotations__", {}) self.__name__ = getattr(func, "__name__", str(func)) # Copy the __wrapped__ attribute if it exists (for Sigima compatibility) # Note: We don't copy __wrapped__ as it may contain unpickleable references # The wrapper functionality will still work without it # Copy Sigima computation metadata (required for validation) computation_metadata_attr = "__computation_function_metadata" if hasattr(func, computation_metadata_attr): setattr( self, computation_metadata_attr, getattr(func, computation_metadata_attr), ) def __call__(self, src_obj, param=None): """Call the wrapped function and apply geometry transformations.""" # Call the original function if param is not None: dst_obj = self.func(src_obj, param) else: dst_obj = self.func(src_obj) apply_geometry_transform(dst_obj, operation=self.operation) return dst_obj def __getstate__(self): """Custom pickling: exclude the function reference.""" # Build state manually to avoid any problematic attributes state = { "operation": self.operation, "__module__": self.__module__, "__qualname__": self.__qualname__, "__annotations__": self.__annotations__, "__name__": self.__name__, } # Store function information for reconstruction if hasattr(self, "func"): state["_func_module"] = self.func.__module__ state["_func_name"] = self.func.__name__ # Note: We don't copy __wrapped__ as it may contain unpickleable references # Copy computation metadata safely computation_metadata_attr = "__computation_function_metadata" if hasattr(self, computation_metadata_attr): metadata = getattr(self, computation_metadata_attr) # Store as a dict to avoid any pickling issues with the object itself if hasattr(metadata, "__dict__"): state[computation_metadata_attr] = metadata.__dict__.copy() else: state[computation_metadata_attr] = metadata return state def __setstate__(self, state): """Custom unpickling: restore the function reference.""" self.__dict__.update(state) # Restore function from module and name if "_func_module" in state and "_func_name" in state: module = importlib.import_module(state["_func_module"]) self.func = getattr(module, state["_func_name"]) # Reconstruct computation metadata if it was stored as dict computation_metadata_attr = "__computation_function_metadata" if computation_metadata_attr in state: metadata_dict = state[computation_metadata_attr] if isinstance(metadata_dict, dict): metadata = ComputationMetadata(**metadata_dict) setattr(self, computation_metadata_attr, metadata) class ImageProcessor(BaseProcessor[ImageROI, ROI2DParam]): """Object handling image processing: operations, processing, analysis""" # pylint: disable=duplicate-code def _wrap_geometric_transform(self, func, operation: str): """Wrap a geometric transformation function to apply geometry transforms. Args: func: The original Sigima function operation: The operation name for geometry transformation Returns: Pickleable wrapped function that applies geometry transformations """ return GeometricTransformWrapper(func, operation) def postprocess_1_to_0_result( self, obj: ImageObj, result: GeometryResult | TableResult ) -> bool: """Post-process results from 1-to-0 operations for images. For image objects with geometry results, applies detection ROIs if requested in the result metadata (via DetectionROIParam). Args: obj: The image object that was analyzed result: The analysis result (GeometryResult or TableResult) Returns: True if the object was modified and needs refresh, False otherwise """ if isinstance(result, GeometryResult): return sipi.apply_detection_rois(obj, result) return False def register_operations(self) -> None: """Register operations.""" self.register_n_to_1(sipi.addition, _("Sum"), icon_name="sum.svg") self.register_n_to_1(sipi.average, _("Average"), icon_name="average.svg") self.register_n_to_1( sipi.standard_deviation, _("Standard deviation"), icon_name="std.svg", ) self.register_2_to_1( sipi.difference, _("Difference"), icon_name="difference.svg", obj2_name=_("image to subtract"), ) self.register_2_to_1( sipi.quadratic_difference, _("Quadratic Difference"), icon_name="quadratic_difference.svg", obj2_name=_("image to subtract"), ) self.register_n_to_1(sipi.product, _("Product"), icon_name="product.svg") self.register_2_to_1( sipi.division, _("Division"), icon_name="division.svg", obj2_name=_("divider"), ) self.register_1_to_1(sipi.inverse, _("Inverse"), icon_name="inverse.svg") self.register_2_to_1( sipi.arithmetic, _("Arithmetic"), paramclass=sipb.ArithmeticParam, icon_name="arithmetic.svg", obj2_name=_("signal to operate with"), ) self.register_1_to_1( sipi.addition_constant, _("Add constant"), paramclass=sipb.ConstantParam, icon_name="constant_add.svg", ) self.register_1_to_1( sipi.difference_constant, _("Subtract constant"), paramclass=sipb.ConstantParam, icon_name="constant_subtract.svg", ) self.register_1_to_1( sipi.product_constant, _("Multiply by constant"), paramclass=sipb.ConstantParam, icon_name="constant_multiply.svg", ) self.register_1_to_1( sipi.division_constant, _("Divide by constant"), paramclass=sipb.ConstantParam, icon_name="constant_divide.svg", ) self.register_1_to_1(sipi.absolute, _("Absolute value"), icon_name="abs.svg") self.register_1_to_1( sipi.phase, _("Phase"), paramclass=sigima.params.PhaseParam, icon_name="phase.svg", ) self.register_2_to_1( sipi.complex_from_magnitude_phase, _("Combine with phase"), paramclass=sipb.AngleUnitParam, icon_name="complex_from_magnitude_phase.svg", comment=_("Create a complex-valued image from magnitude and phase"), obj2_name=_("phase"), ) self.register_1_to_1(sipi.real, _("Real part"), icon_name="re.svg") self.register_1_to_1(sipi.imag, _("Imaginary part"), icon_name="im.svg") self.register_2_to_1( sipi.complex_from_real_imag, _("Combine with imaginary part"), icon_name="complex_from_real_imag.svg", comment=_("Create a complex-valued image from real and imaginary parts"), obj2_name=_("imaginary part"), ) self.register_1_to_1( sipi.astype, _("Convert data type"), paramclass=sigima.params.DataTypeIParam, icon_name="convert_dtype.svg", ) self.register_1_to_1(sipi.exp, _("Exponential"), icon_name="exp.svg") self.register_1_to_1( sipi.log10, _("Logarithm (base 10)"), icon_name="log10.svg" ) self.register_1_to_1( sipi.log10_z_plus_n, "Log10(z+n)", paramclass=sigima.params.Log10ZPlusNParam, ) self.register_2_to_1( sipi.flatfield, _("Flat-field correction"), sipi.FlatFieldParam, obj2_name=_("flat field image"), ) # Flip or rotation self.register_1_to_1( self._wrap_geometric_transform(sipi.fliph, "fliph"), _("Flip horizontally"), icon_name="flip_horizontally.svg", ) self.register_1_to_1( self._wrap_geometric_transform(sipi.transpose, "transpose"), _("Flip diagonally"), icon_name="swap_x_y.svg", ) self.register_1_to_1( self._wrap_geometric_transform(sipi.flipv, "flipv"), _("Flip vertically"), icon_name="flip_vertically.svg", ) self.register_1_to_1( self._wrap_geometric_transform(sipi.rotate270, "rotate270"), _("Rotate %s right") % "90°", icon_name="rotate_right.svg", ) self.register_1_to_1( self._wrap_geometric_transform(sipi.rotate90, "rotate90"), _("Rotate %s left") % "90°", icon_name="rotate_left.svg", ) self.register_1_to_1(sipi.rotate, _("Rotate by"), sipi.RotateParam) # Intensity profiles self.register_1_to_1( sipi.line_profile, _("Line profile"), sipi.LineProfileParam, icon_name="profile.svg", edit=False, ) self.register_1_to_1( sipi.segment_profile, _("Segment profile"), sipi.SegmentProfileParam, icon_name="profile_segment.svg", edit=False, ) self.register_1_to_1( sipi.average_profile, _("Average profile"), sipi.AverageProfileParam, icon_name="profile_average.svg", edit=False, ) self.register_1_to_1( sipi.radial_profile, _("Radial profile"), sipi.RadialProfileParam, icon_name="profile_radial.svg", ) self.register_2_to_1( sipi.convolution, _("Convolution"), icon_name="convolution.svg", obj2_name=_("kernel to convolve with"), ) self.register_2_to_1( sipi.deconvolution, _("Deconvolution"), icon_name="deconvolution.svg", obj2_name=_("kernel to deconvolve with"), ) def register_processing(self) -> None: """Register processing functions.""" # Axis transformation self.register_1_to_1( sipi.set_uniform_coords, _("Set uniform coordinates"), sipi.UniformCoordsParam, ) self.register_1_to_1( sipi.calibration, _("Polynomial calibration"), sipi.XYZCalibrateParam, comment=_( "Apply polynomial calibration to the X, Y or Z axis:\n" " • x' = a0 + a1*x + a2*x^2 + ...\n" " • y' = a0 + a1*y + a2*y^2 + ...\n" " • z' = a0 + a1*z + a2*z^2 + ..." ), ) self.register_1_to_1( sipi.transpose, _("Swap X/Y axes"), icon_name="swap_x_y.svg", ) # Level adjustment self.register_1_to_1( sipi.normalize, _("Normalize"), paramclass=sipb.NormalizeParam, icon_name="normalize.svg", ) self.register_1_to_1(sipi.clip, _("Clipping"), sipb.ClipParam, "clip.svg") self.register_1_to_1( sipi.offset_correction, _("Offset correction"), ROI2DParam, comment=_("Evaluate and subtract the offset value from the data"), icon_name="offset_correction.svg", ) # Noise addition self.register_1_to_1( sipi.add_gaussian_noise, _("Add Gaussian noise"), NormalDistributionParam ) self.register_1_to_1( sipi.add_poisson_noise, _("Add Poisson noise"), PoissonDistributionParam ) self.register_1_to_1( sipi.add_uniform_noise, _("Add uniform noise"), UniformDistributionParam ) # Noise reduction self.register_1_to_1( sipi.gaussian_filter, _("Gaussian filter"), sipb.GaussianParam, ) self.register_1_to_1( sipi.moving_average, _("Moving average"), sipb.MovingAverageParam, ) self.register_1_to_1( sipi.moving_median, _("Moving median"), sipb.MovingMedianParam, ) self.register_1_to_1(sipi.wiener, _("Wiener filter")) self.register_1_to_1( sipi.erase, _("Erase area") + "...", ROI2DParam, comment=_("Erase area in the image as defined by a region of interest"), icon_name="erase.svg", ) # Fourier analysis self.register_1_to_1( sipi.zero_padding, _("Zero padding"), sipi.ZeroPadding2DParam, comment=_( "Zero padding is used to increase the frequency resolution of the FFT" ), ) self.register_1_to_1( sipi.fft, _("FFT"), sipb.FFTParam, comment=_( "Fast Fourier Transform (FFT) is an estimation of the " "Discrete Fourier Transform (DFT). " "Results are complex numbers, but only the real part is plotted." ), edit=False, ) self.register_1_to_1( sipi.ifft, _("Inverse FFT"), sipb.FFTParam, comment=_( "Inverse Fast Fourier Transform (IFFT) is an estimation of the " "Inverse Discrete Fourier Transform (IDFT). " "Results are complex numbers, but only the real part is plotted." ), edit=False, ) self.register_1_to_1( sipi.magnitude_spectrum, _("Magnitude spectrum"), paramclass=sigima.params.SpectrumParam, comment=_( "Magnitude spectrum is the absolute value of the FFT result. " "It is a measure of the amplitude of the frequency components." ), ) self.register_1_to_1( sipi.phase_spectrum, _("Phase spectrum"), comment=_( "Phase spectrum is the angle of the FFT result. " "It is a measure of the phase of the frequency components." ), ) self.register_1_to_1( sipi.psd, _("Power spectral density"), paramclass=sigima.params.SpectrumParam, comment=_( "Power spectral density (PSD) is the square of the magnitude spectrum. " "It is a measure of the power of the frequency components." ), ) # Frequency filters self.register_1_to_1( sipi.butterworth, _("Butterworth"), sipi.ButterworthParam, ) self.register_1_to_1( sipi.gaussian_freq_filter, _("Gaussian bandpass"), sipi.GaussianFreqFilterParam, ) # Thresholding self.register_1_to_1( sipi.threshold, _("Parametric thresholding"), sipi.ThresholdParam, comment=_( "Parametric thresholding allows to select a thresholding method " "and a threshold value." ), ) self.register_1_to_1(sipi.threshold_isodata, _("ISODATA thresholding")) self.register_1_to_1(sipi.threshold_li, _("Li thresholding")) self.register_1_to_1(sipi.threshold_mean, _("Mean thresholding")) self.register_1_to_1(sipi.threshold_minimum, _("Minimum thresholding")) self.register_1_to_1(sipi.threshold_otsu, _("Otsu thresholding")) self.register_1_to_1(sipi.threshold_triangle, _("Triangle thresholding")) self.register_1_to_1(sipi.threshold_yen, _("Yen thresholding")) # Exposure self.register_1_to_1( sipi.adjust_gamma, _("Gamma correction"), sipi.AdjustGammaParam, ) self.register_1_to_1( sipi.adjust_log, _("Logarithmic correction"), sipi.AdjustLogParam, ) self.register_1_to_1( sipi.adjust_sigmoid, _("Sigmoid correction"), sipi.AdjustSigmoidParam, ) self.register_1_to_1( sipi.equalize_hist, _("Histogram equalization"), sipi.EqualizeHistParam, ) self.register_1_to_1( sipi.equalize_adapthist, _("Adaptive histogram equalization"), sipi.EqualizeAdaptHistParam, ) self.register_1_to_1( sipi.rescale_intensity, _("Intensity rescaling"), sipi.RescaleIntensityParam, ) # Restoration self.register_1_to_1( sipi.denoise_tv, _("Total variation denoising"), sipi.DenoiseTVParam, ) self.register_1_to_1( sipi.denoise_bilateral, _("Bilateral filter denoising"), sipi.DenoiseBilateralParam, ) self.register_1_to_1( sipi.denoise_wavelet, _("Wavelet denoising"), sipi.DenoiseWaveletParam, ) self.register_1_to_1( sipi.denoise_tophat, _("White Top-Hat denoising"), sipi.MorphologyParam, ) # Morphology self.register_1_to_1( sipi.white_tophat, _("White Top-Hat (disk)"), sipi.MorphologyParam, ) self.register_1_to_1( sipi.black_tophat, _("Black Top-Hat (disk)"), sipi.MorphologyParam, ) self.register_1_to_1( sipi.erosion, _("Erosion (disk)"), sipi.MorphologyParam, ) self.register_1_to_1( sipi.dilation, _("Dilation (disk)"), sipi.MorphologyParam, ) self.register_1_to_1( sipi.opening, _("Opening (disk)"), sipi.MorphologyParam, ) self.register_1_to_1( sipi.closing, _("Closing (disk)"), sipi.MorphologyParam, ) # Edge detection self.register_1_to_1(sipi.canny, _("Canny filter"), sipi.CannyParam) self.register_1_to_1(sipi.farid, _("Farid filter")) self.register_1_to_1(sipi.farid_h, _("Farid filter (horizontal)")) self.register_1_to_1(sipi.farid_v, _("Farid filter (vertical)")) self.register_1_to_1(sipi.laplace, _("Laplace filter")) self.register_1_to_1(sipi.prewitt, _("Prewitt filter")) self.register_1_to_1(sipi.prewitt_h, _("Prewitt filter (horizontal)")) self.register_1_to_1(sipi.prewitt_v, _("Prewitt filter (vertical)")) self.register_1_to_1(sipi.roberts, _("Roberts filter")) self.register_1_to_1(sipi.scharr, _("Scharr filter")) self.register_1_to_1(sipi.scharr_h, _("Scharr filter (horizontal)")) self.register_1_to_1(sipi.scharr_v, _("Scharr filter (vertical)")) self.register_1_to_1(sipi.sobel, _("Sobel filter")) self.register_1_to_1(sipi.sobel_h, _("Sobel filter (horizontal)")) self.register_1_to_1(sipi.sobel_v, _("Sobel filter (vertical)")) # Other processing self.register_1_to_n(sipi.extract_roi, "ROI", icon_name="roi.svg") self.register_1_to_1( sipi.resize, _("Resize"), sipi.ResizeParam, icon_name="resize.svg", ) self.register_1_to_1( sipi.binning, _("Pixel binning"), sipi.BinningParam, icon_name="binning.svg", ) self.register_1_to_1( sipi.resampling, _("Resampling"), sipi.Resampling2DParam, icon_name="resampling2d.svg", ) def register_analysis(self) -> None: """Register analysis functions.""" self.register_1_to_0(sipi.stats, _("Statistics"), icon_name="stats.svg") self.register_1_to_1( sipi.horizontal_projection, _("Horizontal projection"), # icon_name="horizontal_projection.svg", comment=_( "Compute the sum of pixel intensities along each column " "(projection on the x-axis)" ), ) self.register_1_to_1( sipi.vertical_projection, # icon_name="vertical_projection.svg", _("Vertical projection"), comment=_( "Compute the sum of pixel intensities along each row " "(projection on the y-axis)" ), ) self.register_1_to_1( sipi.histogram, _("Histogram"), paramclass=sipb.HistogramParam, icon_name="histogram.svg", ) self.register_1_to_0( sipi.centroid, _("Centroid"), comment=_("Compute image centroid"), ) self.register_1_to_0( sipi.enclosing_circle, _("Minimum enclosing circle center"), comment=_("Compute smallest enclosing circle center"), ) self.register_1_to_0( sipi.contour_shape, _("Contour detection"), sipi.ContourShapeParam, comment=_("Compute contour shape fit"), ) self.register_1_to_0( sipi.peak_detection, _("Peak detection"), sipi.Peak2DDetectionParam, comment=_("Detect peaks in the image"), ) self.register_1_to_0( sipi.hough_circle_peaks, _("Circle Hough transform"), sipi.HoughCircleParam, comment=_("Detect circular shapes using circle Hough transform"), ) # Blob detection self.register_1_to_0( sipi.blob_dog, _("Blob detection (DOG)"), sipi.BlobDOGParam, comment=_("Detect blobs using Difference of Gaussian (DOG) method"), ) self.register_1_to_0( sipi.blob_doh, _("Blob detection (DOH)"), sipi.BlobDOHParam, comment=_("Detect blobs using Difference of Gaussian (DOH) method"), ) self.register_1_to_0( sipi.blob_log, _("Blob detection (LOG)"), sipi.BlobLOGParam, comment=_("Detect blobs using Laplacian of Gaussian (LOG) method"), ) self.register_1_to_0( sipi.blob_opencv, _("Blob detection (OpenCV)"), sipi.BlobOpenCVParam, comment=_("Detect blobs using OpenCV SimpleBlobDetector"), ) def create_roi_grid(self) -> None: """Create a grid of regions of interest""" obj0 = self.panel.objview.get_sel_objects(include_groups=True)[0] if any(obj.roi is not None for obj in self.panel.objview.get_sel_objects()): if ( QW.QMessageBox.question( self.mainwindow, _("Warning"), _( "Creating a ROI grid will overwrite any existing ROI.

    " "Do you want to continue?" ), QW.QMessageBox.Yes | QW.QMessageBox.No, QW.QMessageBox.No, ) == QW.QMessageBox.No ): return options = self.panel.plothandler.get_plot_options() editor = ImageGridROIEditor(parent=self.parent(), obj=obj0, options=options) if exec_dialog(editor): for obj in self.panel.objview.get_sel_objects(): obj.roi = editor.get_roi() self.SIG_ADD_SHAPE.emit(get_uuid(obj0)) self.panel.selection_changed(update_items=True) self.panel.refresh_plot( "selected", update_items=True, only_visible=False, only_existing=True, ) # Now, we ask the user if we shall extract the freshly defined ROI: if ( QW.QMessageBox.question( self.mainwindow, _("Extract ROI"), _("Do you want to extract images from the defined ROI?"), QW.QMessageBox.Yes | QW.QMessageBox.No, QW.QMessageBox.No, ) == QW.QMessageBox.Yes ): self.compute_roi_extraction(editor.get_roi()) @qt_try_except() def compute_resize(self, param: sigima.params.ResizeParam | None = None) -> None: """Resize image with :py:func:`sigima.proc.image.resize`""" obj0 = self.panel.objview.get_sel_objects(include_groups=True)[0] for obj in self.panel.objview.get_sel_objects(): if obj.data.shape != obj0.data.shape: QW.QMessageBox.warning( self.mainwindow, APP_NAME, _("Warning:") + "\n" + _("Selected images do not have the same size"), ) edit, param = self.init_param(param, sipi.ResizeParam, _("Resize")) if edit: original_size = obj0.data.shape dlg = ResizeDialog( self.plotwidget, new_size=original_size, old_size=original_size, text=_("Destination size:"), ) if not exec_dialog(dlg): return param.zoom = dlg.get_zoom() self.run_feature("resize", param, title=_("Resize"), edit=edit) @qt_try_except() def compute_binning(self, param: sigima.params.BinningParam | None = None) -> None: """Binning image with :py:func:`sigima.proc.image.binning`""" edit = param is None obj0 = self.panel.objview.get_sel_objects(include_groups=True)[0] input_dtype_str = str(obj0.data.dtype) title = _("Binning") edit, param = self.init_param(param, sipi.BinningParam, title) if edit: param.dtype_str = input_dtype_str if param.dtype_str is None: param.dtype_str = input_dtype_str self.run_feature("binning", param, title=title, edit=edit) @qt_try_except() def compute_line_profile( self, param: sigima.params.LineProfileParam | None = None ) -> None: """Compute profile along a vertical or horizontal line with :py:func:`sigima.proc.image.line_profile`""" title = _("Profile") add_initial_shape = self.has_param_defaults(sigima.params.LineProfileParam) edit, param = self.init_param(param, sipi.LineProfileParam, title) if edit: options = self.panel.plothandler.get_plot_options() dlg = ProfileExtractionDialog( "line", param, options, self.mainwindow, add_initial_shape ) obj = self.panel.objview.get_sel_objects(include_groups=True)[0] dlg.set_obj(obj) if not exec_dialog(dlg): return self.run_feature("line_profile", param, title=title, edit=False) @qt_try_except() def compute_segment_profile( self, param: sigima.params.SegmentProfileParam | None = None ): """Compute profile along a segment with :py:func:`sigima.proc.image.segment_profile`""" title = _("Profile") add_initial_shape = self.has_param_defaults(sigima.params.SegmentProfileParam) edit, param = self.init_param(param, sipi.SegmentProfileParam, title) if edit: options = self.panel.plothandler.get_plot_options() dlg = ProfileExtractionDialog( "segment", param, options, self.mainwindow, add_initial_shape ) obj = self.panel.objview.get_sel_objects(include_groups=True)[0] dlg.set_obj(obj) if not exec_dialog(dlg): return self.run_feature("segment_profile", param, title=title, edit=False) @qt_try_except() def compute_average_profile( self, param: sigima.params.AverageProfileParam | None = None ) -> None: """Compute average profile with :py:func:`sigima.proc.image.average_profile`""" title = _("Average profile") add_initial_shape = self.has_param_defaults(sigima.params.AverageProfileParam) edit, param = self.init_param(param, sipi.AverageProfileParam, title) if edit: options = self.panel.plothandler.get_plot_options() dlg = ProfileExtractionDialog( "rectangle", param, options, self.mainwindow, add_initial_shape, ) obj = self.panel.objview.get_sel_objects(include_groups=True)[0] dlg.set_obj(obj) if not exec_dialog(dlg): return self.run_feature("average_profile", param, title=title, edit=False) @qt_try_except() def compute_radial_profile( self, param: sigima.params.RadialProfileParam | None = None ) -> None: """Compute radial profile with :py:func:`sigima.proc.image.radial_profile`""" title = _("Radial profile") edit, param = self.init_param(param, sipi.RadialProfileParam, title) if edit: obj = self.panel.objview.get_sel_objects(include_groups=True)[0] param.update_from_obj(obj) self.run_feature("radial_profile", param, title=title, edit=edit) @qt_try_except() def distribute_on_grid(self, param: sigima.params.GridParam | None = None) -> None: """Distribute images on a grid""" title = _("Distribute on grid") edit, param = self.init_param(param, sipi.GridParam, title) if edit and not param.edit(parent=self.mainwindow): return objs = self.panel.objview.get_sel_objects(include_groups=True) g_row, g_col, x0, y0, x0_0, y0_0 = 0, 0, 0.0, 0.0, 0.0, 0.0 dx0, dy0 = 0.0, 0.0 with create_progress_bar(self.panel, title, max_=len(objs)) as progress: for i_row, obj in enumerate(objs): progress.setValue(i_row + 1) QW.QApplication.processEvents() if progress.wasCanceled(): break if i_row == 0: if obj.is_uniform_coords: x0_0, y0_0 = x0, y0 = obj.x0, obj.y0 else: x0_0, y0_0 = x0, y0 = obj.xcoords[0], obj.ycoords[0] else: if obj.is_uniform_coords: dx0, dy0 = x0 - obj.x0, y0 - obj.y0 obj.x0 += dx0 obj.y0 += dy0 else: dx0, dy0 = x0 - obj.xcoords[0], y0 - obj.ycoords[0] obj.xcoords += dx0 obj.ycoords += dy0 apply_geometry_transform(obj, "translate", dx=dx0, dy=dy0) sipi.transformer.transform_roi(obj, "translate", dx=dx0, dy=dy0) # Get image width and height if obj.is_uniform_coords: img_width, img_height = obj.width, obj.height else: img_width = obj.xcoords[-1] - obj.xcoords[0] img_height = obj.ycoords[-1] - obj.ycoords[0] if param.direction == "row": # Distributing images over rows sign = np.sign(param.rows) g_row = (g_row + sign) % param.rows y0 += (img_height + param.rowspac) * sign if g_row == 0: g_col += 1 x0 += img_width + param.colspac y0 = y0_0 else: # Distributing images over columns sign = np.sign(param.cols) g_col = (g_col + sign) % param.cols x0 += (img_width + param.colspac) * sign if g_col == 0: g_row += 1 x0 = x0_0 y0 += img_height + param.rowspac self.panel.refresh_plot("selected", True, False) @qt_try_except() def reset_positions(self) -> None: """Reset image positions""" x0_0, y0_0 = 0.0, 0.0 dx0, dy0 = 0.0, 0.0 objs = self.panel.objview.get_sel_objects(include_groups=True) for i_row, obj in enumerate(objs): if i_row == 0: if obj.is_uniform_coords: x0_0, y0_0 = obj.x0, obj.y0 else: x0_0, y0_0 = obj.xcoords[0], obj.ycoords[0] else: if obj.is_uniform_coords: dx0, dy0 = x0_0 - obj.x0, y0_0 - obj.y0 obj.x0 += dx0 obj.y0 += dy0 else: dx0, dy0 = x0_0 - obj.xcoords[0], y0_0 - obj.ycoords[0] obj.xcoords += dx0 obj.ycoords += dy0 apply_geometry_transform(obj, "translate", dx=dx0, dy=dy0) sipi.transformer.transform_roi(obj, "translate", dx=dx0, dy=dy0) self.panel.refresh_plot("selected", True, False) # ------Image Processing @qt_try_except() def compute_offset_correction(self, param: ROI2DParam | None = None) -> None: """Compute offset correction with :py:func:`sigima.proc.image.offset_correction`""" obj = self.panel.objview.get_sel_objects(include_groups=True)[0] if param is None: options = self.panel.plothandler.get_plot_options() dlg = imagebackground.ImageBackgroundDialog( obj, parent=self.mainwindow, options=options ) if exec_dialog(dlg): x0, y0, x1, y1 = dlg.get_rect_coords() param = ROI2DParam.create( geometry="rectangle", x0=int(x0), y0=int(y0), dx=int(x1 - x0), dy=int(y1 - y0), ) else: return self.run_feature("offset_correction", param) @qt_try_except() def compute_erase(self, roi: ImageROI | None = None) -> None: """Erase area in the image as defined by a region of interest Args: roi: Region of interest to erase """ if roi is None or roi.is_empty(): roi = self.edit_roi_graphically(mode="define") if roi is None or roi.is_empty(): return obj = self.panel.objview.get_sel_objects(include_groups=True)[0] params = roi.to_params(obj) # TODO: This `compute_1_to_1` call is not ideal, as it passes a list of # parameter sets (`params` is a list of `DataSet` objects) instead of a single # parameter set as expected by the method. Currently, the method implementation # is compatible with this call, and it simply passes the second argument through # to the `extract_rois` function. However, this should be rectified in the # future to ensure that the method signature and its usage are consistent. # The question is: should we pass a list of `DataSet` objects or directly a # `ImageROI` object? # (same as `_extract_multiple_roi_in_single_object`) self.compute_1_to_1(sipi.erase, params, title=_("Erase area"), edit=False) @qt_try_except() def compute_all_threshold(self) -> None: """Compute all threshold algorithms using the following functions: - :py:func:`sigima.proc.image.threshold_isodata` - :py:func:`sigima.proc.image.threshold_li` - :py:func:`sigima.proc.image.threshold_mean` - :py:func:`sigima.proc.image.threshold_minimum` - :py:func:`sigima.proc.image.threshold_otsu` - :py:func:`sigima.proc.image.threshold_triangle` - :py:func:`sigima.proc.image.threshold_yen` """ self.compute_multiple_1_to_1( [ sipi.threshold_isodata, sipi.threshold_li, sipi.threshold_mean, sipi.threshold_minimum, sipi.threshold_otsu, sipi.threshold_triangle, sipi.threshold_yen, ], None, "Threshold", edit=False, ) @qt_try_except() def compute_all_denoise(self, params: list | None = None) -> None: """Compute all denoising filters using the following functions: - :py:func:`sigima.proc.image.denoise_tv` - :py:func:`sigima.proc.image.denoise_bilateral` - :py:func:`sigima.proc.image.denoise_wavelet` - :py:func:`sigima.proc.image.denoise_tophat` """ if params is not None: assert len(params) == 4, "Wrong number of parameters (4 expected)" funcs = [ sipi.denoise_tv, sipi.denoise_bilateral, sipi.denoise_wavelet, sipi.denoise_tophat, ] edit = params is None if edit: params = [] for paramclass, title in ( (sipi.DenoiseTVParam, _("Total variation denoising")), (sipi.DenoiseBilateralParam, _("Bilateral filter denoising")), (sipi.DenoiseWaveletParam, _("Wavelet denoising")), (sipi.MorphologyParam, _("Denoise / Top-Hat")), ): param = paramclass(title) self.update_param_defaults(param) params.append(param) self.compute_multiple_1_to_1(funcs, params, "Denoise", edit=edit) @qt_try_except() def compute_all_morphology( self, param: sigima.params.MorphologyParam | None = None ) -> None: """Compute all morphology filters using the following functions: - :py:func:`sigima.proc.image.white_tophat` - :py:func:`sigima.proc.image.black_tophat` - :py:func:`sigima.proc.image.erosion` - :py:func:`sigima.proc.image.dilation` - :py:func:`sigima.proc.image.opening` - :py:func:`sigima.proc.image.closing` """ if param is None: param = sipi.MorphologyParam() if not param.edit(parent=self.mainwindow): return funcs = [ sipi.white_tophat, sipi.black_tophat, sipi.erosion, sipi.dilation, sipi.opening, sipi.closing, ] self.compute_multiple_1_to_1(funcs, [param] * len(funcs), "Morph", edit=False) @qt_try_except() def compute_all_edges(self) -> None: """Compute all edge detection algorithms. This function calls the following edge detection algorithms: - :py:func:`sigima.proc.image.canny` - :py:func:`sigima.proc.image.farid` - :py:func:`sigima.proc.image.farid_h` - :py:func:`sigima.proc.image.farid_v` - :py:func:`sigima.proc.image.laplace` - :py:func:`sigima.proc.image.prewitt` - :py:func:`sigima.proc.image.prewitt_h` - :py:func:`sigima.proc.image.prewitt_v` - :py:func:`sigima.proc.image.roberts` - :py:func:`sigima.proc.image.scharr` - :py:func:`sigima.proc.image.scharr_h` - :py:func:`sigima.proc.image.scharr_v` - :py:func:`sigima.proc.image.sobel` - :py:func:`sigima.proc.image.sobel_h` - :py:func:`sigima.proc.image.sobel_v` """ funcs = [ sipi.canny, sipi.farid, sipi.farid_h, sipi.farid_v, sipi.laplace, sipi.prewitt, sipi.prewitt_h, sipi.prewitt_v, sipi.roberts, sipi.scharr, sipi.scharr_h, sipi.scharr_v, sipi.sobel, sipi.sobel_h, sipi.sobel_v, ] self.compute_multiple_1_to_1(funcs, None, "Edges") @qt_try_except() def _extract_multiple_roi_in_single_object(self, params: list[ROI2DParam]) -> None: """Extract multiple Regions Of Interest (ROIs) from data in a single object""" # TODO: This `compute_1_to_1` call is not ideal, as it passes a list of # parameter sets (`params` is a list of `DataSet` objects) instead of a single # parameter set as expected by the method. Currently, the method implementation # is compatible with this call, and it simply passes the second argument through # to the `extract_rois` function. However, this should be rectified in the # future to ensure that the method signature and its usage are consistent. # The question is: should we pass a list of `DataSet` objects or directly a # `ImageROI` object? # (same as `compute_erase`) self.compute_1_to_1(sipi.extract_rois, params, title=_("Extract ROI")) # ------Image Analysis @qt_try_except() def compute_peak_detection( self, param: sigima.params.Peak2DDetectionParam | None = None ) -> dict[str, GeometryResult]: """Compute 2D peak detection with :py:func:`sigima.proc.image.peak_detection`""" edit, param = self.init_param( param, sipi.Peak2DDetectionParam, _("Peak detection") ) if edit: data = self.panel.objview.get_sel_objects(include_groups=True)[0].data param.size = max(min(data.shape) // 40, 50) # Run peak detection (ROI creation is handled automatically by base class) return self.run_feature("peak_detection", param, edit=edit) DataLab-1.1.0/datalab/gui/processor/signal.py000066400000000000000000000672021514014117600210320ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ .. Signal processor object (see parent package :mod:`datalab.gui.processor`) """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import re from collections.abc import Callable import numpy as np import sigima.params import sigima.proc.base as sigima_base import sigima.proc.signal as sips from guidata.qthelpers import exec_dialog from sigima.objects import ( NormalDistributionParam, PoissonDistributionParam, ROI1DParam, SignalObj, SignalROI, UniformDistributionParam, create_signal, ) from sigima.objects.scalar import GeometryResult, TableResult from datalab.config import _ from datalab.gui.processor.base import BaseProcessor from datalab.utils.qthelpers import qt_try_except from datalab.widgets import ( fitdialog, signalbaseline, signalcursor, signaldeltax, signalpeak, ) class SignalProcessor(BaseProcessor[SignalROI, ROI1DParam]): """Object handling signal processing: operations, processing, analysis""" FIT_FUNCTIONS = ( (_("Linear fit"), sips.linear_fit), (_("Polynomial fit"), sips.polynomial_fit), (_("Gaussian fit"), sips.gaussian_fit), (_("Lorentzian fit"), sips.lorentzian_fit), (_("Voigt fit"), sips.voigt_fit), (_("Planckian fit"), sips.planckian_fit), (_("Two Half-Gaussians fit"), sips.twohalfgaussian_fit), (_("Piecewise exponential fit"), sips.piecewiseexponential_fit), (_("Exponential fit"), sips.exponential_fit), (_("Sinusoidal fit"), sips.sinusoidal_fit), (_("CDF fit"), sips.cdf_fit), (_("Sigmoid fit"), sips.sigmoid_fit), ) # pylint: disable=duplicate-code def register_operations(self) -> None: """Register operations.""" self.register_n_to_1(sips.addition, _("Sum"), icon_name="sum.svg") self.register_n_to_1(sips.average, _("Average"), icon_name="average.svg") self.register_n_to_1( sips.standard_deviation, _("Standard deviation"), icon_name="std.svg", ) self.register_2_to_1( sips.difference, _("Difference"), icon_name="difference.svg", obj2_name=_("signal to subtract"), ) self.register_2_to_1( sips.quadratic_difference, _("Quadratic Difference"), icon_name="quadratic_difference.svg", obj2_name=_("signal to subtract"), ) self.register_n_to_1(sips.product, _("Product"), icon_name="product.svg") self.register_2_to_1( sips.division, _("Division"), icon_name="division.svg", obj2_name=_("divider"), ) self.register_1_to_1(sips.inverse, _("Inverse"), icon_name="inverse.svg") self.register_2_to_1( sips.arithmetic, _("Arithmetic"), paramclass=sigima_base.ArithmeticParam, icon_name="arithmetic.svg", obj2_name=_("signal to operate with"), ) self.register_1_to_1( sips.addition_constant, _("Add constant"), paramclass=sigima_base.ConstantParam, icon_name="constant_add.svg", ) self.register_1_to_1( sips.difference_constant, _("Subtract constant"), paramclass=sigima_base.ConstantParam, icon_name="constant_subtract.svg", ) self.register_1_to_1( sips.product_constant, _("Multiply by constant"), paramclass=sigima_base.ConstantParam, icon_name="constant_multiply.svg", ) self.register_1_to_1( sips.division_constant, _("Divide by constant"), paramclass=sigima_base.ConstantParam, icon_name="constant_divide.svg", ) self.register_1_to_1(sips.absolute, _("Absolute value"), icon_name="abs.svg") self.register_1_to_1( sips.phase, _("Phase"), paramclass=sigima.params.PhaseParam, icon_name="phase.svg", ) self.register_2_to_1( sips.complex_from_magnitude_phase, _("Combine with phase"), paramclass=sigima_base.AngleUnitParam, icon_name="complex_from_magnitude_phase.svg", comment=_("Create a complex-valued signal from magnitude and phase"), obj2_name=_("phase"), ) self.register_1_to_1(sips.real, _("Real part"), icon_name="re.svg") self.register_1_to_1(sips.imag, _("Imaginary part"), icon_name="im.svg") self.register_2_to_1( sips.complex_from_real_imag, _("Combine with imaginary part"), icon_name="complex_from_real_imag.svg", comment=_("Create a complex-valued signal from real and imaginary parts"), obj2_name=_("imaginary part"), ) self.register_1_to_1( sips.astype, _("Convert data type"), paramclass=sigima.params.DataTypeSParam, icon_name="convert_dtype.svg", ) self.register_1_to_1(sips.exp, _("Exponential"), icon_name="exp.svg") self.register_1_to_1( sips.log10, _("Logarithm (base 10)"), icon_name="log10.svg" ) self.register_1_to_1(sips.sqrt, _("Square root"), icon_name="sqrt.svg") self.register_1_to_1( sips.derivative, _("Derivative"), icon_name="derivative.svg" ) self.register_1_to_1(sips.integral, _("Integral"), icon_name="integral.svg") self.register_2_to_1( sips.convolution, _("Convolution"), icon_name="convolution.svg", obj2_name=_("signal to convolve with"), ) self.register_2_to_1( sips.replace_x_by_other_y, _("Replace X by other signal's Y"), comment=_( "Replace X coordinates using Y values from another signal.\n" "Useful for calibration: plot data vs wavelength scale." ), obj2_name=_("signal providing Y values for X axis"), skip_xarray_compat=True, ) self.register_2_to_1( sips.deconvolution, _("Deconvolution"), icon_name="deconvolution.svg", obj2_name=_("signal to deconvolve with"), ) self.register_n_to_1( sips.signals_to_image, _("Assemble signals into image"), paramclass=sigima_base.SignalsToImageParam, icon_name="signals_to_image.svg", comment=_( "Assemble multiple signals into a 2D image.\n" "Signals can be stacked as rows or columns, " "with optional normalization." ), ) def register_processing(self) -> None: """Register processing functions.""" # Axis transformation self.register_1_to_1( sips.calibration, _("Linear calibration"), sips.XYCalibrateParam, comment=_( "Apply linear calibration to the X or Y axis:\n" " • x' = ax + b\n" " • y' = ay + b" ), ) self.register_1_to_1( sips.transpose, _("Swap X/Y axes"), icon_name="swap_x_y.svg" ) self.register_1_to_1( sips.reverse_x, _("Reverse X-axis"), icon_name="reverse_signal_x.svg", ) self.register_1_to_1( sips.to_polar, _("Convert to polar coordinates"), paramclass=sigima.params.AngleUnitParam, ) self.register_1_to_1( sips.to_cartesian, _("Convert to cartesian coordinates"), paramclass=sigima.params.AngleUnitParam, ) # Level adjustment self.register_1_to_1( sips.normalize, _("Normalize"), sigima_base.NormalizeParam, "normalize.svg", ) self.register_1_to_1( sips.clip, _("Clipping"), sigima_base.ClipParam, "clip.svg" ) self.register_1_to_1( sips.offset_correction, _("Offset correction"), icon_name="offset_correction.svg", comment=_("Evaluate and subtract the offset value from the data"), ) # Noise addition self.register_1_to_1( sips.add_gaussian_noise, _("Add Gaussian noise"), NormalDistributionParam ) self.register_1_to_1( sips.add_poisson_noise, _("Add Poisson noise"), PoissonDistributionParam ) self.register_1_to_1( sips.add_uniform_noise, _("Add uniform noise"), UniformDistributionParam ) # Noise reduction self.register_1_to_1( sips.gaussian_filter, _("Gaussian filter"), sigima_base.GaussianParam, ) self.register_1_to_1( sips.moving_average, _("Moving average"), sigima_base.MovingAverageParam, ) self.register_1_to_1( sips.moving_median, _("Moving median"), sigima_base.MovingMedianParam, ) self.register_1_to_1(sips.wiener, _("Wiener filter")) # Fourier analysis self.register_1_to_1( sips.zero_padding, _("Zero padding"), sips.ZeroPadding1DParam, comment=_( "Zero padding is used to increase the frequency resolution of the FFT" ), ) self.register_1_to_1( sips.fft, _("FFT"), sigima_base.FFTParam, comment=_( "Fast Fourier Transform (FFT) is an estimation of the " "Discrete Fourier Transform (DFT). " "Results are complex numbers, but only the real part is plotted." ), edit=False, ) self.register_1_to_1( sips.ifft, _("Inverse FFT"), comment=_( "Inverse Fast Fourier Transform (IFFT) is an estimation of the " "Inverse Discrete Fourier Transform (IDFT). " "Results are complex numbers, but only the real part is plotted." ), edit=False, ) self.register_1_to_1( sips.magnitude_spectrum, _("Magnitude spectrum"), paramclass=sigima.params.SpectrumParam, comment=_( "Magnitude spectrum is the absolute value of the FFT result. " "It is a measure of the amplitude of the frequency components." ), ) self.register_1_to_1( sips.phase_spectrum, _("Phase spectrum"), comment=_( "Phase spectrum is the angle of the FFT result. " "It is a measure of the phase of the frequency components." ), ) self.register_1_to_1( sips.psd, _("Power spectral density"), paramclass=sigima.params.SpectrumParam, comment=_( "Power spectral density (PSD) is the square of the magnitude spectrum. " "It is a measure of the power of the frequency components." ), ) self.register_1_to_1( sips.power, _("Power"), paramclass=sigima.params.PowerParam, icon_name="power.svg", ) self.register_1_to_1( sips.peak_detection, _("Peak detection"), paramclass=sigima.params.PeakDetectionParam, icon_name="peak_detect.svg", ) # Frequency filters self.register_1_to_1( sips.lowpass, _("Low-pass filter"), sigima.params.LowPassFilterParam, "lowpass.svg", ) self.register_1_to_1( sips.highpass, _("High-pass filter"), sigima.params.HighPassFilterParam, "highpass.svg", ) self.register_1_to_1( sips.bandpass, _("Band-pass filter"), sigima.params.BandPassFilterParam, "bandpass.svg", ) self.register_1_to_1( sips.bandstop, _("Band-stop filter"), sigima.params.BandStopFilterParam, "bandstop.svg", ) # Curve fitting for fit_name, fit_func in self.FIT_FUNCTIONS: icon_name = f"{fit_func.__name__}.svg" self.register_1_to_1(fit_func, fit_name, icon_name=icon_name) # Evaluate fit on another signal's x-axis self.register_2_to_1( sips.evaluate_fit, _("Evaluate fit"), obj2_name=_("signal for X values"), comment=_("Evaluate a fitting curve on the x-axis of another signal"), ) # Other processing self.register_1_to_1( sips.apply_window, _("Windowing"), paramclass=sigima.params.WindowingParam, icon_name="windowing.svg", comment=_("Apply a window (apodization) function: Hann, Hamming..."), ) self.register_1_to_1( sips.detrending, _("Detrending"), sips.DetrendingParam, icon_name="detrending.svg", ) self.register_2_to_1( sips.interpolate, _("Interpolation"), paramclass=sigima.params.InterpolationParam, obj2_name=_("signal for X values"), icon_name="interpolation.svg", skip_xarray_compat=True, ) self.register_1_to_1( sips.resampling, _("Resampling"), sips.Resampling1DParam, icon_name="resampling1d.svg", ) # Stability analysis self.register_1_to_1( sips.allan_variance, _("Allan variance"), paramclass=sigima.params.AllanVarianceParam, ) self.register_1_to_1( sips.allan_deviation, _("Allan deviation"), paramclass=sigima.params.AllanVarianceParam, ) self.register_1_to_1( sips.overlapping_allan_variance, _("Overlapping Allan variance"), paramclass=sigima.params.AllanVarianceParam, ) self.register_1_to_1( sips.modified_allan_variance, _("Modified Allan variance"), paramclass=sigima.params.AllanVarianceParam, ) self.register_1_to_1( sips.hadamard_variance, _("Hadamard variance"), paramclass=sigima.params.AllanVarianceParam, ) self.register_1_to_1( sips.modified_allan_variance, _("Modified Allan variance"), paramclass=sigima.params.AllanVarianceParam, ) self.register_1_to_1( sips.hadamard_variance, _("Hadamard variance"), paramclass=sigima.params.AllanVarianceParam, ) self.register_1_to_1( sips.modified_allan_variance, _("Modified Allan variance"), paramclass=sigima.params.AllanVarianceParam, ) self.register_1_to_1( sips.hadamard_variance, _("Hadamard variance"), paramclass=sigima.params.AllanVarianceParam, ) self.register_1_to_1( sips.total_variance, _("Total variance"), paramclass=sigima.params.AllanVarianceParam, ) self.register_1_to_1( sips.time_deviation, _("Time deviation"), paramclass=sigima.params.AllanVarianceParam, ) # Other processing self.register_2_to_1( sips.xy_mode, _("X-Y mode"), obj2_name=_("Y-signal of the X-Y mode"), comment=_("Plot one signal as a fonction of the other one"), ) self.register_1_to_n(sips.extract_roi, "ROI", icon_name="roi.svg") def register_analysis(self) -> None: """Register analysis functions.""" self.register_1_to_0(sips.stats, _("Statistics"), icon_name="stats.svg") self.register_1_to_1( sips.histogram, _("Histogram"), paramclass=sigima.params.HistogramParam, icon_name="histogram.svg", ) self.register_1_to_0( sips.fwhm, _("Full width at half-maximum"), paramclass=sips.FWHMParam, icon_name="fwhm.svg", ) self.register_1_to_0( sips.fw1e2, _("Full width at") + " 1/e²", icon_name="fw1e2.svg", ) self.register_1_to_0( sips.full_width_at_y, _("Full width at y=..."), paramclass=sips.OrdinateParam, comment=_("Compute the full width at a given y value"), ) self.register_1_to_0( sips.x_at_y, _("First abscissa at y=..."), paramclass=sips.OrdinateParam, comment=_( "Compute the first abscissa at a given y value (linear interpolation)" ), ) self.register_1_to_0( sips.y_at_x, _("Ordinate at x=..."), paramclass=sips.AbscissaParam, comment=_("Compute the ordinate at a given x value (linear interpolation)"), ) self.register_1_to_0( sips.extract_pulse_features, _("Extract pulse features"), paramclass=sips.PulseFeaturesParam, comment=_("Extract pulse features (amplitude, rise time, fall time...)"), ) self.register_1_to_0( sips.x_at_minmax, _("Abscissa of the minimum and maximum"), comment=_( "Compute the smallest argument of the minima and the smallest " "argument of the maxima" ), ) self.register_1_to_0( sips.sampling_rate_period, _("Sampling rate and period"), comment=_( "Compute sampling rate and period for a constant sampling signal" ), ) self.register_1_to_0( sips.dynamic_parameters, _("Dynamic parameters"), paramclass=sips.DynamicParam, comment=_("Compute dynamic parameters: ENOB, SNR, SINAD, THD, ..."), ) self.register_1_to_0( sips.bandwidth_3db, _("Bandwidth at -3dB"), comment=_( "Compute bandwidth at -3dB assuming a low-pass filter " "already expressed in dB" ), ) self.register_1_to_0( sips.contrast, _("Contrast"), comment=_( "Compute contrast of a signal, i.e. (max-min)/(max+min), " "e.g. for an image profile" ), ) @qt_try_except() def compute_offset_correction(self, param: ROI1DParam | None = None) -> None: """Compute offset correction with :py:func:`sigima.proc.signal.offset_correction`""" obj = self.panel.objview.get_sel_objects(include_groups=True)[0] if param is None: dlg = signalbaseline.SignalBaselineDialog(obj, parent=self.mainwindow) if exec_dialog(dlg): param = ROI1DParam() param.xmin, param.xmax = dlg.get_x_range() else: return self.run_feature("offset_correction", param) @qt_try_except() def compute_all_stability( self, param: sigima.params.AllanVarianceParam | None = None ) -> None: """Compute all stability analysis features using the following functions: - :py:func:`sigima.proc.signal.allan_variance` - :py:func:`sigima.proc.signal.allan_deviation` - :py:func:`sigima.proc.signal.overlapping_allan_variance` - :py:func:`sigima.proc.signal.modified_allan_variance` - :py:func:`sigima.proc.signal.hadamard_variance` - :py:func:`sigima.proc.signal.total_variance` - :py:func:`sigima.proc.signal.time_deviation` """ if param is None: param = sips.AllanVarianceParam() if not param.edit(parent=self.mainwindow): return funcs = [ sips.allan_variance, sips.allan_deviation, sips.overlapping_allan_variance, sips.modified_allan_variance, sips.hadamard_variance, sips.total_variance, sips.time_deviation, ] self.compute_multiple_1_to_1( funcs, [param] * len(funcs), "Stability", edit=False ) @qt_try_except() def compute_peak_detection( self, param: sigima.params.PeakDetectionParam | None = None ) -> None: """Detect peaks from data with :py:func:`sigima.proc.signal.peak_detection`""" obj = self.panel.objview.get_sel_objects(include_groups=True)[0] edit, param = self.init_param( param, sips.PeakDetectionParam, _("Peak detection") ) if edit: dlg = signalpeak.SignalPeakDetectionDialog(obj, parent=self.mainwindow) if exec_dialog(dlg): param.threshold = int(dlg.get_threshold() * 100) param.min_dist = dlg.get_min_dist() else: return self.run_feature("peak_detection", param) @qt_try_except() def compute_polyfit( self, param: sigima.params.PolynomialFitParam | None = None ) -> None: """Compute polynomial fitting curve""" txt = _("Polynomial fit") edit, param = self.init_param(param, sips.PolynomialFitParam, txt) if not edit or param.edit(self.mainwindow): dlgfunc = fitdialog.polynomial_fit def polynomialfit(x, y, parent=None): """Polynomial fit dialog function""" return dlgfunc(x, y, param.degree, parent=parent) self.compute_fit(txt, polynomialfit) def __row_compute_fit( self, obj: SignalObj, name: str, fitdlgfunc: Callable ) -> None: """Curve fitting computing sub-method""" output = fitdlgfunc(obj.x, obj.y, parent=self.mainwindow) if output is not None: y, params = output params: list[fitdialog.FitParam] pvalues = {} for param in params: if re.match(r"[\S\_]*\d{2}$", param.name): shname = param.name[:-2] value = pvalues.get(shname, np.array([])) pvalues[shname] = np.array(list(value) + [param.value]) else: pvalues[param.name] = param.value # Creating new signal metadata = {fitdlgfunc.__name__: pvalues} signal = create_signal(f"{name}({obj.title})", obj.x, y, metadata=metadata) # Creating new plot item self.panel.add_object(signal) @qt_try_except() def compute_fit(self, title: str, fitdlgfunc: Callable) -> None: """Compute fitting curve using an interactive dialog Args: title: Title of the dialog fitdlgfunc: Fitting dialog function """ for obj in self.panel.objview.get_sel_objects(): self.__row_compute_fit(obj, title, fitdlgfunc) @qt_try_except() def compute_multigaussianfit(self) -> None: """Compute multi-Gaussian fitting curve using an interactive dialog""" fitdlgfunc = fitdialog.multigaussian_fit for obj in self.panel.objview.get_sel_objects(): dlg = signalpeak.SignalPeakDetectionDialog(obj, parent=self.mainwindow) if exec_dialog(dlg): # Computing x, y peaks = dlg.get_peak_indices() def multigaussianfit(x, y, parent=None): """Multi-Gaussian fit dialog function""" # pylint: disable=cell-var-from-loop return fitdlgfunc(x, y, peaks, parent=parent) self.__row_compute_fit(obj, _("Multi-Gaussian fit"), multigaussianfit) @qt_try_except() def compute_multilorentzianfit(self) -> None: """Compute Multi-Lorentzian fitting curve using an interactive dialog""" fitdlgfunc = fitdialog.multilorentzian_fit for obj in self.panel.objview.get_sel_objects(): dlg = signalpeak.SignalPeakDetectionDialog(obj, parent=self.mainwindow) if exec_dialog(dlg): # Computing x, y peaks = dlg.get_peak_indices() def multilorentzianfit(x, y, parent=None): """Multi-Lorentzian fit dialog function""" # pylint: disable=cell-var-from-loop return fitdlgfunc(x, y, peaks, parent=parent) self.__row_compute_fit( obj, _("Multi-Lorentzian fit"), multilorentzianfit ) @qt_try_except() def _extract_multiple_roi_in_single_object(self, params: list[ROI1DParam]) -> None: """Extract multiple Regions Of Interest (ROIs) from data in a single object""" # TODO: This `compute_1_to_1` call is not ideal, as it passes a list of # parameter sets (`params` is a list of `DataSet` objects) instead of a single # parameter set as expected by the method. Currently, the method implementation # is compatible with this call, and it simply passes the second argument through # to the `extract_rois` function. However, this should be rectified in the # future to ensure that the method signature and its usage are consistent. self.compute_1_to_1(sips.extract_rois, params, title=_("Extract ROI")) # ------Signal Analysis @qt_try_except() def compute_full_width_at_y( self, param: sigima.params.OrdinateParam | None = None ) -> dict[str, GeometryResult] | None: """Compute full width at a given y with :py:func:`sigima.proc.signal.full_width_at_y`""" if param is None: obj = self.panel.objview.get_sel_objects(include_groups=True)[0] dlg = signaldeltax.SignalDeltaXDialog(obj, parent=self.mainwindow) if exec_dialog(dlg): param = sips.OrdinateParam(_("Full width at y")) param.y = dlg.get_y_value() else: return None return self.run_feature("full_width_at_y", param) @qt_try_except() def compute_x_at_y( self, param: sips.OrdinateParam | None = None ) -> dict[str, TableResult] | None: """Compute x at y with :py:func:`sigima.proc.signal.x_at_y`.""" if param is None: obj = self.panel.objview.get_sel_objects(include_groups=True)[0] dlg = signalcursor.SignalCursorDialog( obj, cursor_orientation="horizontal", parent=self.mainwindow ) if exec_dialog(dlg): param = sips.OrdinateParam(_("Abscissa at y")) param.y = dlg.get_y_value() else: return None return self.run_feature("x_at_y", param) @qt_try_except() def compute_y_at_x( self, param: sips.AbscissaParam | None = None ) -> dict[str, TableResult] | None: """Compute y at x with :py:func:`sigima.proc.signal.y_at_x`.""" if param is None: obj = self.panel.objview.get_sel_objects(include_groups=True)[0] dlg = signalcursor.SignalCursorDialog( obj, cursor_orientation="vertical", parent=self.mainwindow ) if exec_dialog(dlg): param = sips.AbscissaParam(_("Ordinate at x")) param.x = dlg.get_x_value() else: return None return self.run_feature("y_at_x", param) DataLab-1.1.0/datalab/gui/profiledialog.py000066400000000000000000000302621514014117600203520ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Profile dialog ============== The :mod:`datalab.gui.profiledialog` module provides the profile extraction dialog. .. autoclass:: ProfileExtractionDialog """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from typing import TYPE_CHECKING, Literal import qtpy.QtCore as QC import sigima.params from guidata.configtools import get_icon from plotpy.interfaces import ICurveItemType from plotpy.items import AnnotatedPoint, AnnotatedRectangle, AnnotatedSegment from plotpy.panels.csection.cswidget import LineCrossSection from plotpy.plot import PlotDialog, PlotOptions from plotpy.tools import AverageCrossSectionTool, CrossSectionTool, LineCrossSectionTool from qtpy import QtWidgets as QW from qtpy.QtWidgets import QWidget from datalab.adapters_plotpy import create_adapter_from_object from datalab.config import _ from datalab.utils.qthelpers import resize_widget_to_parent if TYPE_CHECKING: from plotpy.items import CurveItem from plotpy.panels import XCrossSection, YCrossSection from plotpy.plot import BasePlot from sigima.objects import ImageObj class ProfileExtractionDialog(PlotDialog): """Profile extraction dialog Args: mode: "line", "segment" or "rectangle" options: Plot options parent: Parent widget """ def __init__( self, mode: Literal["line", "segment", "rectangle"], param: sigima.params.LineProfileParam | sigima.params.SegmentProfileParam | sigima.params.AverageProfileParam, options: PlotOptions | None = None, parent: QWidget | None = None, add_initial_shape: bool = False, ) -> None: self.__obj: ImageObj | None = None self.__default_curve_color: str | None = None assert mode in ("line", "segment", "rectangle") self.mode = mode self.param = param title = param.get_title() self.__add_initial_shape = add_initial_shape if options is None: options = PlotOptions(show_contrast=True, show_axes_tab=False) options.show_xsection = options.show_ysection = True toolklass = { "line": CrossSectionTool, "segment": LineCrossSectionTool, "rectangle": AverageCrossSectionTool, }[mode] super().__init__(title=title, edit=True, options=options, parent=parent) self.setObjectName("profileextraction") self.setWindowIcon(get_icon("DataLab.svg")) resize_widget_to_parent(self, parent, aspect_ratio=1.0) mgr = self.get_manager() tool: ( CrossSectionTool | LineCrossSectionTool | AverageCrossSectionTool | None ) = mgr.get_tool(toolklass) if tool is None: tool = mgr.add_tool(toolklass) self.cstool = tool self.shape: AnnotatedPoint | AnnotatedSegment | AnnotatedRectangle | None = None self.param_btn = pbtn = QW.QPushButton(_("Edit profile parameters")) pbtn.setIcon(get_icon("edit.svg")) pbtn.clicked.connect(self.edit_values) pbtn.setEnabled(False) self.button_box.button(QW.QDialogButtonBox.Ok).setEnabled(False) tool.SIG_TOOL_JOB_FINISHED.connect(self.__tool_job_finished) self.button_layout.insertWidget(0, pbtn) rbtn = QW.QPushButton(_("Reset selection")) rbtn.setIcon(get_icon("reset.svg")) rbtn.clicked.connect(self.reset_to_initial) self.button_layout.insertWidget(1, rbtn) self.update_cs_panels_state() self.get_plot().SIG_ITEMS_CHANGED.connect(self.update_cs_panels_state) def populate_plot_layout(self): """Populate the plot layout""" super().populate_plot_layout() lcs_panel = LineCrossSection(self) splitter = self.plot_widget.xcsw_splitter splitter.addWidget(lcs_panel) splitter.setSizes([0, 1, 0]) self.manager.add_panel(lcs_panel) # Install event filters on cross-section plots to detect clicks mgr = self.get_manager() xcs_panel = mgr.get_xcs_panel() ycs_panel = mgr.get_ycs_panel() if xcs_panel is not None: xcs_panel.cs_plot.canvas().installEventFilter(self) # Store reference to know which plot was clicked xcs_panel.cs_plot.canvas().setProperty("cs_direction", "horizontal") if ycs_panel is not None: ycs_panel.cs_plot.canvas().installEventFilter(self) ycs_panel.cs_plot.canvas().setProperty("cs_direction", "vertical") def __set_curve_enable_state(self, curve: CurveItem, state: bool) -> None: """Set curve enable state Args: curve: Curve item state: Enable state """ if self.__default_curve_color is None: self.__default_curve_color = curve.param.line.color color = self.__default_curve_color if state else "#666666" curve.param.line.color = color curve.param.update_item(curve) def __get_curve(self, plot: BasePlot) -> CurveItem | None: """Get cross-section curve Args: plot: Plot """ curves = plot.get_items(item_type=ICurveItemType) if curves: return curves[0] return None def __update_xycs_panel_state( self, cs_panel: XCrossSection | YCrossSection, state: bool ) -> None: """Update cross-section panel state Args: cs_panel: Cross-section panel state: Enable state """ cs_panel.setEnabled(state) cs_plot = cs_panel.cs_plot curve = self.__get_curve(cs_plot) if curve: self.__set_curve_enable_state(curve, state) def __on_cs_plot_clicked(self, direction: str) -> None: """Handle click on cross-section plot to switch direction Args: direction: "horizontal" or "vertical" """ # Only handle clicks for line and rectangle modes if self.mode not in ("line", "rectangle"): return # Switch direction if different if self.param.direction != direction: self.param.direction = direction self.update_cs_panels_state() self.get_plot().replot() def eventFilter(self, obj: QC.QObject, event: QC.QEvent) -> bool: """Event filter to detect clicks on cross-section plots Args: obj: Object event: Event Returns: True if event was handled, False otherwise """ # Check if this is a cross-section plot canvas direction = obj.property("cs_direction") if direction: # Handle mouse press to switch direction if event.type() == QC.QEvent.MouseButtonPress: self.__on_cs_plot_clicked(direction) # Consume the event to prevent curve selection return True return super().eventFilter(obj, event) def update_cs_panels_state(self): """Enable or disable X or Y cross-section panels depending on the direction of the desired profile extraction.""" mgr = self.get_manager() lcs_panel = mgr.get_panel(LineCrossSection.PANEL_ID) xcs_panel, ycs_panel = mgr.get_xcs_panel(), mgr.get_ycs_panel() xymode = self.mode in ("line", "rectangle") lcs_panel.setVisible(not xymode) xcs_panel.setVisible(xymode) ycs_panel.setVisible(xymode) if xymode: p = self.param self.__update_xycs_panel_state(xcs_panel, p.direction == "horizontal") self.__update_xycs_panel_state(ycs_panel, p.direction == "vertical") def accept(self) -> None: """Accept""" if self.shape is not None: self.shape_to_param() super().accept() def reset_to_initial(self) -> None: """Reset to initial""" self.param_btn.setEnabled(False) self.button_box.button(QW.QDialogButtonBox.Ok).setEnabled(False) if self.mode == "line": self.param = sigima.params.LineProfileParam() elif self.mode == "segment": self.param = sigima.params.SegmentProfileParam() else: self.param = sigima.params.AverageProfileParam() plot = self.get_plot() if self.shape is not None: plot.del_item(self.shape) self.cstool.activate() self.update_cs_panels_state() self.get_plot().replot() def __tool_job_finished(self): """Tool job finished""" self.param_btn.setEnabled(True) self.button_box.button(QW.QDialogButtonBox.Ok).setEnabled(True) self.shape = self.cstool.get_last_final_shape() assert self.shape is not None self.shape.set_readonly(True) self.shape_to_param() self.update_cs_panels_state() def __physical_to_indices(self, coords: list[float]) -> tuple[int, int]: """Physical to indices Args: coords: Coordinates Returns: Tuple of indices """ assert self.__obj is not None return self.__obj.physical_to_indices(coords, clip=True) def shape_to_param(self) -> None: """Shape to param""" assert self.__obj is not None p2i = self.__physical_to_indices p = self.param if isinstance(self.shape, AnnotatedPoint): assert isinstance(p, sigima.params.LineProfileParam) p.col, p.row = p2i(self.shape.get_pos()) elif isinstance(self.shape, AnnotatedSegment): assert isinstance(p, sigima.params.SegmentProfileParam) p.col1, p.row1, p.col2, p.row2 = p2i(self.shape.get_rect()) if p.col1 > p.col2: p.col1, p.col2 = p.col2, p.col1 p.row1, p.row2 = p.row2, p.row1 else: assert isinstance(p, sigima.params.AverageProfileParam) p.col1, p.row1, p.col2, p.row2 = p2i(self.shape.get_rect()) if p.col1 > p.col2: p.col1, p.col2 = p.col2, p.col1 p.row1, p.row2 = p.row2, p.row1 def param_to_shape(self) -> None: """Param to shape""" assert self.__obj is not None i2p = self.__obj.indices_to_physical p = self.param if isinstance(self.shape, AnnotatedPoint): assert isinstance(p, sigima.params.LineProfileParam) x, y = i2p([p.col, p.row]) self.shape.set_pos(x, y) elif isinstance(self.shape, AnnotatedSegment): assert isinstance(p, sigima.params.SegmentProfileParam) x1, y1, x2, y2 = i2p([p.col1, p.row1, p.col2, p.row2]) self.shape.set_rect(x1, y1, x2, y2) else: assert isinstance(p, sigima.params.AverageProfileParam) x1, y1, x2, y2 = i2p([p.col1, p.row1, p.col2, p.row2]) self.shape.set_rect(x1, y1, x2, y2) def edit_values(self) -> None: """Edit values""" self.shape_to_param() if self.param.edit(parent=self, apply=lambda _param: self.apply_callback()): self.param_to_shape() self.update_cs_panels_state() self.get_plot().replot() def apply_callback(self) -> None: """Apply callback""" self.param_to_shape() self.update_cs_panels_state() self.get_plot().replot() def set_obj(self, obj: ImageObj): """Set object Args: obj: Image object """ self.__obj = obj item = create_adapter_from_object(obj).make_item() item.set_readonly(True) item.set_resizable(False) item.set_rotatable(False) item.set_selectable(False) plot = self.get_plot() plot.add_item(item) plot.set_active_item(item) item.unselect() if self.__add_initial_shape: plot = self.get_plot() self.show() # To be able to convert axes to canvas coordinates p0 = QC.QPointF(0.0, 0.0) self.cstool.add_shape_to_plot(plot, p0, p0) self.shape = self.cstool.get_last_final_shape() self.param_to_shape() self.param_btn.setEnabled(True) self.button_box.button(QW.QDialogButtonBox.Ok).setEnabled(True) else: self.cstool.activate() DataLab-1.1.0/datalab/gui/roieditor.py000066400000000000000000000522131514014117600175320ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ ROI editor ========== The :mod:`datalab.gui.roieditor` module provides the ROI editor widgets for signals and images. Signal ROI editor ----------------- .. autoclass:: SignalROIEditor :members: Image ROI editor ---------------- .. autoclass:: ImageROIEditor :members: """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import abc import re from typing import TYPE_CHECKING, Any, Generic, Literal, TypeVar, Union from guidata.configtools import get_icon from guidata.qthelpers import add_actions, create_action from plotpy.items import ( AnnotatedCircle, AnnotatedPolygon, AnnotatedRectangle, AnnotatedXRange, CurveItem, MaskedXYImageItem, ) from plotpy.plot import PlotDialog, PlotManager, PlotOptions from plotpy.tools import CircleTool, HRangeTool, PolygonTool, RectangleTool, SelectTool from qtpy import QtCore as QC from qtpy import QtWidgets as QW from sigima.objects import ( CircularROI, ImageObj, ImageROI, PolygonalROI, RectangularROI, ROI1DParam, ROI2DParam, SegmentROI, SignalObj, SignalROI, TypeObj, TypeROI, ) from sigima.objects.base import get_generic_roi_title from datalab.adapters_plotpy import ( TypePlotItem, TypeROIItem, configure_roi_item, create_adapter_from_object, plotitem_to_singleroi, ) from datalab.config import Conf, _ from datalab.env import execenv if TYPE_CHECKING: from plotpy.plot import BasePlot from plotpy.tools.base import InteractiveTool def configure_roi_item_in_tool(shape, obj: SignalObj | ImageObj) -> None: """Configure ROI item in tool""" fmt = create_adapter_from_object(obj).get_obj_option("format") configure_roi_item(shape, fmt, lbl=True, editable=True, option=obj.PREFIX) def tool_deselect_items(tool: InteractiveTool) -> None: """Deselect all items in plot associated with the tool""" plot = tool.get_active_plot() plot.select_some_items([]) # Deselect all items def tool_setup_shape( plot: BasePlot, shape: TypeROIItem, obj: SignalObj | ImageObj, ) -> None: """Tool setup shape""" configure_roi_item_in_tool(shape, obj) max_index = -1 for item in plot.get_items(): name = str(item.title().text()) match = re.match(r"ROI(\d+)", name) if match is not None: max_index = max(max_index, int(match.group(1))) shape.setTitle(get_generic_roi_title(max_index + 1)) class ROISegmentTool(HRangeTool): """ROI segment tool""" TITLE = _("Range ROI") ICON = "roi_sig.svg" def __init__(self, manager: PlotManager, obj: SignalObj) -> None: super().__init__(manager, switch_to_default_tool=False, toolbar_id=None) self.roi = SegmentROI([0, 1], False) self.obj = obj def activate(self): """Activate tool""" tool_deselect_items(self) super().activate() def create_shape(self) -> AnnotatedXRange: """Create shape""" shape = create_adapter_from_object(self.roi).to_plot_item(self.obj) tool_setup_shape(self.get_active_plot(), shape, self.obj) return shape class ROIRectangleTool(RectangleTool): """ROI rectangle tool""" TITLE = _("Rectangular ROI") ICON = "roi_new_rectangle.svg" def __init__(self, manager: PlotManager, obj: ImageObj) -> None: super().__init__(manager, switch_to_default_tool=False, toolbar_id=None) self.roi = RectangularROI([0, 0, 1, 1], False) self.obj = obj def activate(self): """Activate tool""" tool_deselect_items(self) super().activate() def create_shape(self) -> tuple[AnnotatedRectangle, int, int]: """Create shape""" item = create_adapter_from_object(self.roi).to_plot_item(self.obj) return item, 0, 2 # Reimplement `RectangularShapeTool` method def setup_shape(self, shape: AnnotatedRectangle) -> None: """Setup shape""" tool_setup_shape(self.get_active_plot(), shape, self.obj) class ROICircleTool(CircleTool): """ROI circle tool""" TITLE = _("Circular ROI") ICON = "roi_new_circle.svg" def __init__(self, manager: PlotManager, obj: ImageObj) -> None: super().__init__(manager, switch_to_default_tool=False, toolbar_id=None) self.roi = CircularROI([0, 0, 1], False) self.obj = obj def activate(self): """Activate tool""" tool_deselect_items(self) super().activate() def create_shape(self) -> tuple[AnnotatedCircle, int, int]: """Create shape""" item = create_adapter_from_object(self.roi).to_plot_item(self.obj) return item, 0, 1 # Reimplement `RectangularShapeTool` method def setup_shape(self, shape: AnnotatedCircle) -> None: """Setup shape""" tool_setup_shape(self.get_active_plot(), shape, self.obj) class ROIPolygonTool(PolygonTool): """ROI polygon tool""" TITLE = _("Polygonal ROI") ICON = "roi_new_polygon.svg" def __init__(self, manager: PlotManager, obj: ImageObj) -> None: super().__init__(manager, switch_to_default_tool=False, toolbar_id=None) self.roi = PolygonalROI([0, 0, 1, 0, 1, 1, 0, 1], False) self.obj = obj def activate(self): """Activate tool""" tool_deselect_items(self) super().activate() def create_shape(self) -> tuple[AnnotatedPolygon, int, int]: """Create shape""" return create_adapter_from_object(self.roi).to_plot_item(self.obj) # Reimplement `RectangularShapeTool` method def setup_shape(self, shape: AnnotatedPolygon) -> None: """Setup shape""" tool_setup_shape(self.get_active_plot(), shape, self.obj) TypeROIEditor = TypeVar("TypeROIEditor", bound="BaseROIEditor") class BaseROIEditorMeta(type(PlotDialog), abc.ABCMeta): """Mixed metaclass to avoid conflicts""" class BaseROIEditor( PlotDialog, Generic[TypeObj, TypeROI, TypePlotItem, TypeROIItem], # type: ignore metaclass=BaseROIEditorMeta, ): """ROI Editor Args: parent: Parent plot dialog obj: Object to edit (:class:`sigima.objects.SignalObj` or :class:`sigima.objects.ImageObj`) mode: Mode of operation, either "apply" (define ROI, then apply it to selected objects), "extract" (define ROI, then extract data from it), or "define" (define ROI without applying or extracting). item: Optional plot item to add to the plot (if None, a new item is created from the object) """ ICON_NAME = None OBJ_NAME = None ROI_ITEM_TYPES = () ADDITIONAL_OPTIONS: dict[str, Any] = {} def __init__( self, parent: QW.QWidget | None, obj: SignalObj | ImageObj, mode: Literal["apply", "extract", "define"] = "apply", item: TypePlotItem | None = None, options: PlotOptions | dict[str, Any] | None = None, size: tuple[int, int] | None = None, ) -> None: assert mode in ("apply", "extract", "define"), ( f"Invalid mode: {mode}. Must be either 'apply', 'extract' or 'define'." ) self.editor_layout: QW.QHBoxLayout | None = None self.remove_all_action: QW.QAction | None = None self.singleobj_btn: QW.QCheckBox | None = None self.obj = obj self.mode = mode if item is None: item = create_adapter_from_object(obj).make_item() self.main_item = item self.__modified: bool | None = None self._tools: list[InteractiveTool] = [] # Create ROI associated with the object if mode == "define": # In "define" mode, we do not want to show the object's ROI to avoid any # confusion with the ROI being defined roi = None else: # In "apply" or "extract" mode, we want to show the object's ROI # if it exists, otherwise create a new empty ROI roi = obj.roi if roi is None: roi = self.get_obj_roi_class()() self.__roi: TypeROI = roi if options is None: options = self.ADDITIONAL_OPTIONS else: options = options.copy(self.ADDITIONAL_OPTIONS) roi_s = _("Regions of interest") super().__init__( parent=parent, toolbar=mode != "define", options=options, title=f"{roi_s} - {obj.title}", icon="DataLab.svg", edit=True, size=size, ) self.setObjectName(f"{obj.PREFIX}_roi_editor") self.accepted.connect(self.dialog_accepted) # Create the ROI items fmt = create_adapter_from_object(obj).get_obj_option("format") roi_adapter = create_adapter_from_object(self.__roi) self.roi_items: list[TypeROIItem] = list( roi_adapter.iterate_roi_items(obj, fmt, True, True) ) self.toolbar = QW.QToolBar(self) self.add_tools_to_plot_dialog() self.setup_editor_layout() self.setup_items() # force update of ROI titles and remove_all_btn state self.items_changed(self.get_plot()) self.get_plot().SIG_ITEMS_CHANGED.connect(self.items_changed) self.get_plot().SIG_ITEM_REMOVED.connect(self.item_removed) self.get_plot().SIG_RANGE_CHANGED.connect( lambda _rng, _min, _max: self.item_moved() ) self.get_plot().SIG_ANNOTATION_CHANGED.connect(lambda _plt: self.item_moved()) # In "extract mode", the dialog box OK button should always been enabled # when at least one ROI is defined, # whereas in non-extract mode (when editing ROIs) the OK button is by default # disabled (until ROI data is modified) if self.mode == "extract": self.modified = len(self.roi_items) > 0 else: self.modified = False def setup_layout(self) -> None: # Reimplement PlotDialog method """Populate the plot layout""" super().setup_layout() self.editor_layout = QW.QHBoxLayout() self.button_layout.insertLayout(0, self.editor_layout) def setup_editor_layout(self) -> None: """Setup editor layout""" self.toolbar.setToolButtonStyle(QC.Qt.ToolButtonTextUnderIcon) add_actions(self.toolbar, self.create_actions()) for action in self.toolbar.actions(): if action.menu() is not None: widget = self.toolbar.widgetForAction(action) widget.setPopupMode(QW.QToolButton.ToolButtonPopupMode.InstantPopup) self.editor_layout.addWidget(self.toolbar) if self.mode == "extract": self.singleobj_btn = QW.QCheckBox( _("Extract all ROIs\ninto a single %s") % self.OBJ_NAME, self, ) self.editor_layout.addWidget(self.singleobj_btn) self.singleobj_btn.setChecked(Conf.proc.extract_roi_singleobj.get()) self.editor_layout.addStretch() def setup_items(self) -> None: """Setup items""" self.main_item.set_selectable(False) self.main_item.set_readonly(True) plot = self.get_plot() plot.add_item(self.main_item) plot.set_active_item(self.main_item) self.main_item.unselect() for roi_item in self.roi_items: plot.add_item(roi_item) plot.set_active_item(roi_item) @abc.abstractmethod def get_obj_roi_class(self) -> type[TypeROI]: """Get object ROI class""" def add_tools_to_plot_dialog(self) -> None: """Add tools to plot dialog""" mgr = self.get_manager() select_tool = mgr.get_tool(SelectTool) add_actions(self.toolbar, [select_tool.action]) @property def modified(self) -> bool: """Return dialog modified state""" return self.__modified @modified.setter def modified(self, value: bool) -> None: """Set dialog modified state""" self.__modified = value if self.mode in ("extract", "define"): # In "extract" mode or in "define" mode, OK button is enabled # when at least one ROI is defined value = value and len(self.roi_items) > 0 self.button_box.button(QW.QDialogButtonBox.Ok).setEnabled(value) def dialog_accepted(self) -> None: """Parent dialog was accepted: updating ROI Editor data""" self.__roi.empty() for roi_item in self.roi_items: self.__roi.add_roi(plotitem_to_singleroi(roi_item, self.obj)) if self.singleobj_btn is not None: Conf.proc.extract_roi_singleobj.set(self.singleobj_btn.isChecked()) def get_roieditor_results(self) -> tuple[TypeROI, bool]: """Get ROI editor results Returns: A tuple containing the ROI data parameters and ROI modified state. ROI modified state is True if the ROI data has been modified within the dialog box. """ return self.__roi, self.modified def create_coordinate_based_roi_actions(self) -> list[QW.QAction]: """Create coordinate-based ROI actions""" return [] def __new_action_menu( self, title: str, icon: str, actions: list[QW.QAction] ) -> QW.QAction: """Create a new action menu""" menu = QW.QMenu(title) for action in actions: menu.addAction(action) action = QW.QAction(get_icon(icon), title, self) action.setMenu(menu) return action def create_actions(self) -> list[QW.QAction]: """Create actions""" g_menu_act = self.__new_action_menu( _("Graphical ROI"), "roi_graphical.svg", [tool.action for tool in self._tools], ) c_menu_act = self.__new_action_menu( _("Coordinate-based ROI"), "roi_coordinate.svg", self.create_coordinate_based_roi_actions(), ) self.remove_all_action = create_action( self, _("Remove all"), icon=get_icon("roi_delete.svg"), triggered=self.remove_all_rois, ) return [g_menu_act, c_menu_act, None, self.remove_all_action] def remove_all_rois(self) -> None: """Remove all ROIs""" if ( execenv.unattended or QW.QMessageBox.question( self, _("Remove all ROIs"), _("Are you sure you want to remove all ROIs?"), ) == QW.QMessageBox.Yes ): self.get_plot().del_items(self.roi_items) def get_roi_items(self) -> list[TypeROIItem]: """Get ROI items""" return self.roi_items def update_roi_items(self) -> None: """Update ROI items""" old_nb_items = len(self.roi_items) self.roi_items = [ item for item in self.get_plot().get_items() if isinstance(item, self.ROI_ITEM_TYPES) ] self.get_plot().select_some_items([]) if old_nb_items != len(self.roi_items): self.modified = True def items_changed(self, _plot: BasePlot) -> None: """Items have changed""" self.update_roi_items() self.remove_all_action.setEnabled(len(self.roi_items) > 0) def item_removed(self, item) -> None: """Item was removed. Since all items are read-only except ROIs... this must be an ROI.""" assert item in self.roi_items self.update_roi_items() self.modified = True def item_moved(self) -> None: """ROI plot item has just been moved""" self.modified = True class SignalROIEditor(BaseROIEditor[SignalObj, SignalROI, CurveItem, AnnotatedXRange]): """Signal ROI Editor Args: parent: Parent plot dialog obj: Object to edit (:class:`sigima.objects.SignalObj` or :class:`sigima.objects.ImageObj`) mode: Mode of operation, either "apply" (define ROI, then apply it to selected objects), "extract" (define ROI, then extract data from it), or "define" (define ROI without applying or extracting). item: Optional plot item to add to the plot (if None, a new item is created from the object) """ ICON_NAME = "roi_sig.svg" OBJ_NAME = _("signal") ROI_ITEM_TYPES = (AnnotatedXRange,) def __init__( self, parent: QW.QWidget | None, obj: SignalObj | ImageObj, mode: Literal["apply", "extract", "define"] = "apply", item: TypePlotItem | None = None, options: PlotOptions | dict[str, Any] | None = None, size: tuple[int, int] | None = None, ) -> None: super().__init__( parent=parent, obj=obj, mode=mode, item=item, options=options, size=size, ) def get_obj_roi_class(self) -> type[SignalROI]: """Get object ROI class""" return SignalROI def add_tools_to_plot_dialog(self) -> None: """Add tools to plot dialog""" super().add_tools_to_plot_dialog() mgr = self.get_manager() segm_tool = mgr.add_tool(ROISegmentTool, self.obj) self._tools.append(segm_tool) segm_tool.activate() def manually_add_roi(self) -> None: """Manually add segment ROI""" param = ROI1DParam() if param.edit(parent=self): segment_roi = param.to_single_roi(self.obj) shape = create_adapter_from_object(segment_roi).to_plot_item(self.obj) configure_roi_item_in_tool(shape, self.obj) self.get_plot().add_item(shape) self.get_plot().set_active_item(shape) def create_coordinate_based_roi_actions(self) -> list[QW.QAction]: """Create coordinate-based ROI actions""" segcoord_act = create_action( self, _("Range ROI"), icon=get_icon("roi_sig.svg"), triggered=self.manually_add_roi, ) return [segcoord_act] class ImageROIEditor( BaseROIEditor[ ImageObj, ImageROI, MaskedXYImageItem, # `Union` is mandatory here for Python 3.9-3.10 compatibility: Union[AnnotatedPolygon, AnnotatedRectangle, AnnotatedCircle], ] ): """Image ROI Editor Args: parent: Parent plot dialog obj: Object to edit (:class:`sigima.objects.SignalObj` or :class:`sigima.objects.ImageObj`) mode: Mode of operation, either "apply" (define ROI, then apply it to selected objects), "extract" (define ROI, then extract data from it), or "define" (define ROI without applying or extracting). item: Optional plot item to add to the plot (if None, a new item is created from the object) """ ICON_NAME = "roi_ima.svg" OBJ_NAME = _("image") ROI_ITEM_TYPES = (AnnotatedRectangle, AnnotatedCircle, AnnotatedPolygon) ADDITIONAL_OPTIONS = {"show_itemlist": True, "show_contrast": False} def get_obj_roi_class(self) -> type[ImageROI]: """Get object ROI class""" return ImageROI def add_tools_to_plot_dialog(self) -> None: """Add tools to plot dialog""" super().add_tools_to_plot_dialog() mgr = self.get_manager() rect_tool = mgr.add_tool(ROIRectangleTool, self.obj) circ_tool = mgr.add_tool(ROICircleTool, self.obj) poly_tool = mgr.add_tool(ROIPolygonTool, self.obj) self._tools.extend([rect_tool, circ_tool, poly_tool]) rect_tool.activate() def manually_add_roi( self, roi_type: Literal["rectangle", "circle", "polygon"] ) -> None: """Manually add image ROI""" assert roi_type in ("rectangle", "circle", "polygon") if roi_type == "polygon": raise NotImplementedError("Manual polygonal ROI creation is not supported") param = ROI2DParam() param.geometry = roi_type if param.edit(parent=self): roi = param.to_single_roi(self.obj) shape = create_adapter_from_object(roi).to_plot_item(self.obj) configure_roi_item_in_tool(shape, self.obj) self.get_plot().add_item(shape) self.get_plot().set_active_item(shape) def create_coordinate_based_roi_actions(self) -> list[QW.QAction]: """Create coordinate-based ROI actions""" rectcoord_act = create_action( self, _("Rectangular ROI"), icon=get_icon("roi_new_rectangle.svg"), triggered=lambda: self.manually_add_roi("rectangle"), ) circcoord_act = create_action( self, _("Circular ROI"), icon=get_icon("roi_new_circle.svg"), triggered=lambda: self.manually_add_roi("circle"), ) return [rectcoord_act, circcoord_act] def setup_items(self) -> None: """Setup items""" super().setup_items() # In "define" mode, we want to show the mask that is associated with the # image's ROI, if it exists, because the ROI that is being defined has nothing # to do with the image's ROI: it is just a temporary ROI that will for example # be used in a processing function, so the user need to visualize the mask that # corresponds to the data on which the processing function will be applied. self.main_item.set_mask_visible(self.mode == "define") DataLab-1.1.0/datalab/gui/roigrideditor.py000066400000000000000000000171401514014117600204000ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ROI Grid editor""" from __future__ import annotations from typing import Any import guidata.dataset as gds import guidata.dataset.qtwidgets as gdq from plotpy.plot import PlotDialog, PlotOptions from qtpy import QtCore as QC from qtpy import QtWidgets as QW from sigima.io import read_roi_grid, write_roi_grid from sigima.objects import ImageObj, ImageROI from sigima.params import ROIGridParam from sigima.proc.image import generate_image_grid_roi from datalab.adapters_plotpy import create_adapter_from_object from datalab.config import _ from datalab.utils.qthelpers import block_signals class DisplayParam(gds.DataSet): """ROI Grid display parameters""" def __init__(self, roi_editor: ImageGridROIEditor | None = None) -> None: super().__init__() self.roi_editor = roi_editor def display_changed(self, item, value) -> None: # pylint: disable=unused-argument """Emit display changed signal.""" assert self.roi_editor is not None, "ROI editor is not set" if self.roi_editor.is_ready(): self.roi_editor.SIG_DISPLAY_CHANGED.emit() show_mask = gds.BoolItem(_("Show mask"), default=True).set_prop( "display", callback=display_changed ) _prop_show_roi = gds.ValueProp(False) show_roi = gds.BoolItem(_("Show ROI"), default=True).set_prop( "display", callback=display_changed, store=_prop_show_roi ) show_names = gds.BoolItem(_("Show names"), default=True).set_prop( "display", callback=display_changed, active=_prop_show_roi ) class ImageGridROIEditor(PlotDialog): """Image Grid ROI Editor. Args: parent: Parent plot dialog obj: Object to edit (:class:`sigima.objects.ImageObj`) item: Optional plot item to add to the plot (if None, a new item is created from the object) """ SIG_GEOMETRY_CHANGED = QC.Signal() SIG_DISPLAY_CHANGED = QC.Signal() ADDITIONAL_OPTIONS = {"show_itemlist": False, "show_contrast": False} IO_FILTERS = _("ROI grid files (*.dlabgrid);;All files (*)") def __init__( self, parent: QW.QWidget | None, obj: ImageObj, gridparam: ROIGridParam | None = None, displayparam: DisplayParam | None = None, options: PlotOptions | dict[str, Any] | None = None, size: tuple[int, int] | None = None, ) -> None: self.editor_layout: QW.QVBoxLayout | None = None self.__roi = ImageROI() self.__is_ready = False self.obj = obj = obj.copy() # Avoid modifying the original object obj.roi = None # Clear the ROI to avoid conflicts with the editor gridparam = gridparam or ROIGridParam() displayparam = displayparam or DisplayParam() self.gridparamwidget = gdq.DataSetEditGroupBox( _("Grid parameters"), ROIGridParam, show_button=False ) gds.update_dataset(self.gridparamwidget.dataset, gridparam) self.displayparamwidget = gdq.DataSetEditGroupBox( _("Display parameters"), DisplayParam, show_button=False, roi_editor=self ) gds.update_dataset(self.displayparamwidget.dataset, displayparam) self.update_obj(update_item=False) if options is None: options = self.ADDITIONAL_OPTIONS else: options = options.copy(self.ADDITIONAL_OPTIONS) roi_s = _("ROI grid") super().__init__( parent=parent, toolbar=False, options=options, title=f"{roi_s} - {obj.title}", icon="DataLab.svg", edit=True, size=size, ) self.gridparamwidget.dataset.on_geometry_changed = ( self.SIG_GEOMETRY_CHANGED.emit ) self.setObjectName("i_grid_roi_editor") self.setup_editor_layout() self.update_items() self.SIG_GEOMETRY_CHANGED.connect(self.update_obj) self.SIG_DISPLAY_CHANGED.connect(self.update_items) self.__is_ready = True def get_roi(self) -> ImageROI: """Get the current ROI""" return self.__roi def is_ready(self) -> bool: """Check if the editor is ready for use""" return self.__is_ready def setup_layout(self) -> None: # Reimplement PlotDialog method """Populate the plot layout""" super().setup_layout() self.editor_layout = QW.QVBoxLayout() self.plot_layout.addLayout(self.editor_layout, 0, 1) self.plot_layout.setColumnStretch(0, 2) self.plot_layout.setColumnStretch(1, 1) def setup_editor_layout(self) -> None: """Setup editor layout""" self.editor_layout.addWidget(self.gridparamwidget) gbox = QW.QGroupBox(_("Import/Export")) glayout = QW.QHBoxLayout() glayout.addWidget( QW.QPushButton(_("Import grid"), clicked=self.import_roi_grid) ) glayout.addWidget( QW.QPushButton(_("Export grid"), clicked=self.export_roi_grid) ) gbox.setLayout(glayout) self.editor_layout.addWidget(gbox) self.editor_layout.addWidget(self.displayparamwidget) self.editor_layout.addStretch() def import_roi_grid(self) -> None: """Import ROI grid from file""" filename, _filter = QW.QFileDialog.getOpenFileName( self, _("Import ROI grid"), "", self.IO_FILTERS ) if filename: try: gp = read_roi_grid(filename) except Exception as exc: # pylint: disable=broad-except QW.QMessageBox.critical( self, _("Error"), _("Failed to import ROI grid: {0}").format(str(exc)), ) return gds.update_dataset(self.gridparamwidget.dataset, gp) with block_signals(widget=self.gridparamwidget, children=True): self.gridparamwidget.get() self.update_items() self.SIG_GEOMETRY_CHANGED.emit() def export_roi_grid(self) -> None: """Export ROI grid to file""" self.gridparamwidget.set() filename, _filter = QW.QFileDialog.getSaveFileName( self, _("Export ROI grid"), "", self.IO_FILTERS ) if filename: try: write_roi_grid(filename, self.gridparamwidget.dataset) except Exception as exc: # pylint: disable=broad-except QW.QMessageBox.critical( self, _("Error"), _("Failed to export ROI grid: {0}").format(str(exc)), ) def update_items(self) -> None: """Setup items""" dp = self.displayparamwidget.dataset obj = self.obj plot = self.get_plot() plot.del_all_items() item = create_adapter_from_object(obj).make_item() item.set_mask_visible(dp.show_mask) item.set_selectable(False) item.set_readonly(True) plot.add_item(item) plot.set_active_item(item) item.unselect() if dp.show_roi: fmt = create_adapter_from_object(obj).get_obj_option("format") roi_adapter = create_adapter_from_object(obj.roi) for ritem in roi_adapter.iterate_roi_items(obj, fmt, dp.show_names, False): plot.add_item(ritem) plot.replot() def update_obj(self, update_item: bool = True) -> None: """Update the object with the current parameters""" roi = generate_image_grid_roi(self.obj, self.gridparamwidget.dataset) self.__roi = self.obj.roi = roi if update_item: self.update_items() DataLab-1.1.0/datalab/gui/settings.py000066400000000000000000000535561514014117600174050ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Settings ======== The :mod:`datalab.gui.settings` module provides the DataLab settings dialog and related classes. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from typing import Any, Generator import guidata.dataset as gds from guidata.dataset import restore_dataset, update_dataset from plotpy.styles import BaseImageParam from qtpy import QtWidgets as QW from datalab.config import Conf, _ class MainSettings(gds.DataSet): """DataLab main settings""" g0 = gds.BeginGroup(_("Settings for main window and general features")) color_mode = gds.ChoiceItem( _("Color mode"), zip(Conf.main.color_mode.values, Conf.main.color_mode.values), help=_("Color mode for the application"), ) process_isolation_enabled = gds.BoolItem( "", _("Process isolation"), help=_( "With process isolation, each computation is run in a separate process," "
    which prevents the application from freezing during long computations." ), ) rpc_server_enabled = gds.BoolItem( "", _("RPC server"), help=_( "RPC server is used to communicate with external applications," "
    like your own scripts (e.g. from Spyder or Jupyter) or other software." ), ) webapi_localhost_no_token = gds.BoolItem( "", _("Web API localhost bypass"), help=_( "When enabled (default), connections from localhost (127.0.0.1) to the " "
    Web API do not require authentication. This simplifies notebook " "
    integration when DataLab-Kernel runs on the same machine." "
    Disable for stricter security if needed." ), ) available_memory_threshold = gds.IntItem( _("Memory threshold"), default=0, unit=_("MB"), help=_( "Memory threshold below which a warning is displayed " "before loading any new data" ), ) plugins_enabled = gds.BoolItem( "", _("Third-party plugins"), help=_("Disable third-party plugins at startup"), ) plugins_path = gds.DirectoryItem( _("Plugins path"), allow_none=True, help=_( "Path to third-party plugins.

    " "DataLab will discover plugins in this path, " "as well as in your PYTHONPATH." ), ) v020_plugins_warning_ignore = gds.BoolItem( _("Ignore compatibility issues warning"), _("DataLab v0.20 plugins"), help=_( "If enabled, DataLab will not warn you about v0.20 plugins that are " "no longer compatible with v1.0." ), ) _g0 = gds.EndGroup("") class ConsoleSettings(gds.DataSet): """DataLab console settings""" g0 = gds.BeginGroup( _("Settings for internal console, used for debugging or advanced users") ) console_enabled = gds.BoolItem("", _("Console enabled")) show_console_on_error = gds.BoolItem( "", _("Show console on error"), help=_( "If enabled, the console will be shown when an error occurs in the " "application. This is useful for debugging purposes as it allows to " "see the error traceback." ), ) external_editor_path = gds.StringItem(_("External editor path"), "") external_editor_args = gds.StringItem(_("External editor arguments"), "") _g0 = gds.EndGroup("") class IOSettings(gds.DataSet): """DataLab I/O settings""" g0 = gds.BeginGroup(_("Settings for I/O operations")) h5_clear_workspace = gds.BoolItem( "", _("Clear workspace before loading HDF5 file"), help=_( "If enabled, the current workspace will be cleared before loading a " "new HDF5 file.\n" "If disabled, when importing DataLab workspace files, only objects with " "conflicting identifiers will have their processing history lost " "(features like 'Show source' and 'Recompute' will not work for those " "objects). Non-conflicting objects will preserve their processing history." ), ) h5_clear_workspace_ask = gds.BoolItem("", _("Ask before clearing workspace")) h5_fullpath_in_title = gds.BoolItem( "", _("HDF5 full path in title"), help=_( "If enabled, the full path of the HDF5 data set will be used as the title " "for the signal/image object.
    " "If disabled, only the name of the data set will be used as the title." ), ) h5_fname_in_title = gds.BoolItem( "", _("HDF5 file name in title"), help=_( "If enabled, the name of the HDF5 file will be used as a suffix in the " "title of the signal/image object." ), ) _g0 = gds.EndGroup("") class ProcSettings(gds.DataSet): """DataLab processing settings""" g0 = gds.BeginGroup(_("Settings for computations")) operation_mode = gds.ChoiceItem( _("Operation mode"), zip(Conf.proc.operation_mode.values, Conf.proc.operation_mode.values), help=_( "Operation mode for computations taking N inputs:" "
    • single: single operand mode
      • " "
    • pairwise: pairwise operation mode
    " "
    Computations taking N inputs are the ones where:" "
    • N(>=2) objects in %s 1 object out
      • " "
    • N(>=1) objects + 1 object in %s N objects out
    " ) % ("→", "→"), ) use_signal_bounds = gds.BoolItem( _("Use current signal bounds"), _("New signals"), help=_( "If enabled, when creating a new signal, the xmin and xmax values will be " "initialized from the current signal's bounds.
    " "If disabled, the default values from the New Signal dialog will be used." ), ) use_image_dims = gds.BoolItem( _("Use current image dimensions"), _("New images"), help=_( "If enabled, when creating a new image, the width and height values will " "be initialized from the current image's dimensions.
    " "If disabled, the default values from the New Image dialog will be used." ), ) fft_shift_enabled = gds.BoolItem( "", _("FFT shift"), help=_( "Enable FFT shift to center the zero-frequency component in the frequency " "spectrum for easier visualization and analysis." ), ) auto_normalize_kernel = gds.BoolItem( _("Auto-normalize convolution/deconvolution kernel"), _("Convolution"), help=_( "If enabled, convolution and deconvolution kernels will be automatically " "normalized for signal and image processing operations to preserve signal " "energy and image intensity." ), ) extract_roi_singleobj = gds.BoolItem( _("Extract multiple ROIs in a single object"), _("ROI extraction"), help=_( "If enabled, multiple ROIs will be extracted into a single object.
    " "If disabled, each ROI will be extracted into a separate object." ), ) ignore_warnings = gds.BoolItem( _("Ignore warnings during computations"), _("Warnings"), help=_("If enabled, warnings during computations will be ignored."), ) xarray_compat_behavior = gds.ChoiceItem( _("X-axis"), zip( Conf.proc.xarray_compat_behavior.values, [_("Ask before interpolation"), _("Interpolate systematically")], ), help=_( "Behavior when X-axis arrays are incompatible in multi-signal computations:" "
    • Ask: display confirmation dialog (default)
      • " "
    • Interpolate: automatically interpolate signals
    " ), ) _g0 = gds.EndGroup("") g1 = gds.BeginGroup(_("Settings for results management")) keep_results = gds.BoolItem( _("Keep results in metadata after computation"), _("Keep results"), help=_( "If enabled, the results of a previous analysis will be kept in object's " "metadata after the computation.
    " "If disabled, the results will be removed from the object's metadata." "

    " "This option is disabled by default because keeping analysis results may " "be confusing as those results could be outdated following the " "computation." ), ) show_result_dialog = gds.BoolItem( _("Show results dialog automatically after processing"), _("Show results"), help=_( "If enabled, the results dialog will be shown automatically after each " "processing operation producing results.
    " "If disabled, the results dialog will not be shown automatically." ), ) _g1 = gds.EndGroup("") class ImageDefaultSettings(BaseImageParam): """Image visualization default settings""" _multiselection = True # Hide label (not clean because it's not the intended use) # pylint:disable=unused-argument def edit_default_image_settings( dataset: gds.DataSet, item: gds.DataItem, value: Any, parent: QW.QWidget ) -> bool: """Edit default image settings Args: dataset: dataset item: Data item value: Value parent: Parent widget Returns: True if the settings were edited """ param = ImageDefaultSettings(_("Default image visualization settings")) ima_def_dict = Conf.view.get_def_dict("ima") update_dataset(param, ima_def_dict) if param.edit(parent=parent): restore_dataset(param, ima_def_dict) Conf.view.set_def_dict("ima", ima_def_dict) return True return False # pylint:disable=unused-argument def edit_dataset_settings( dataset: gds.DataSet, item: gds.DataItem, param: gds.DataSet, parent: QW.QWidget ) -> gds.DataSet: """Edit dataset-like settings Args: dataset: dataset item: Data item param: The DataSet instance from config, or None if not set parent: Parent widget Returns: The modified dataset, or None if cancelled """ assert param is not None, "Should not be None" param.set_title(_("Visualization settings")) param.edit(parent=parent) return param class ViewSettings(gds.DataSet): """DataLab Visualization settings""" _view_tabs = gds.BeginTabGroup("") g0 = gds.BeginGroup(_("Common")) plot_toolbar_position = gds.ImageChoiceItem( _("Plot toolbar position"), ( ("top", _("Top (above plot)"), "libre-gui-arrow-up.svg"), ("bottom", _("Bottom (below plot)"), "libre-gui-arrow-down.svg"), ("left", _("Left (of plot)"), "libre-gui-arrow-left.svg"), ("right", _("Right (of plot)"), "libre-gui-arrow-right.svg"), ), ) ignore_title_insertion_msg = gds.BoolItem( "", _("Ignore title insertion message"), help=_( "Ignore information message when inserting object title as annotation label" ), default=False, ) _g0 = gds.EndGroup("") g1 = gds.BeginGroup(_("Signals")).set_prop("display", icon="signal.svg") _prop_ads = gds.ValueProp(False) sig_linewidth = gds.FloatItem( _("Default line width"), default=1.0, min=0.1, help=_("Default line width for curves representing signals"), ) sig_linewidth_perfs_threshold = gds.IntItem( _("Performance threshold"), default=1000, min=100, help=_( "Number of points threshold for line width limitation.\n" "For signals with more than this number of points, " "line width is automatically limited to 1.0 for performance reasons.\n\n" "That's because the Qt raster engine experiences significant rendering " "slowdowns with thick lines on large datasets." ), ) sig_autodownsampling = gds.BoolItem( "", _("Use auto downsampling"), help=_("Use auto downsampling for large signals"), ).set_prop("display", store=_prop_ads) sig_autodownsampling_maxpoints = gds.IntItem( _("Downsampling max points"), default=10000, min=1000, help=_("Maximum number of points for downsampling"), ).set_prop("display", active=_prop_ads) sig_autoscale_margin_percent = gds.FloatItem( _("Autoscale margin"), unit=_("%"), default=0.2, min=0.0, max=50.0, help=_( "Percentage of margin to add around data when auto-scaling signal " "plots.
    A value of 0.2% adds a small margin for better " "visualization.
    Set to 0% for no margin (exact data bounds)." ), ) sig_datetime_format_s = gds.StringItem( _("DateTime format (s/min/h)"), default="%H:%M:%S", help=_( "Format string for datetime X-axis labels when using standard time " "units (s, min, h).
    Uses Python's strftime format codes " "(e.g., %H:%M:%S for hours:minutes:seconds)." ), ) sig_datetime_format_ms = gds.StringItem( _("DateTime format (ms/μs/ns)"), default="%H:%M:%S.%f", help=_( "Format string for datetime X-axis labels when using sub-second time " "units (ms, μs, ns).
    Uses Python's strftime format codes " "(e.g., %H:%M:%S.%f for hours:minutes:seconds.microseconds)." ), ) sig_shape_param = gds.ButtonItem( _("Results: edit shape styles"), edit_dataset_settings, icon="shapes.svg" ) sig_marker_param = gds.ButtonItem( _("Results: edit marker styles"), edit_dataset_settings, icon="markers.svg" ) _g1 = gds.EndGroup("") g2 = gds.BeginGroup(_("Images")).set_prop("display", icon="image.svg") ima_aspect_ratio_1_1 = gds.BoolItem( "", _("Lock aspect ratio to 1:1"), help=_( "If enabled, the aspect ratio of the image will be locked to 1:1.
    " "If disabled, the aspect ratio will be determined by the physical pixel " "size (this is the default and recommended setting)." ), ) ima_eliminate_outliers = gds.FloatItem( _("Eliminate outliers"), unit=_("%"), default=2.0, min=0.0, max=100.0, help=_( "Eliminate a percentage of the highest and lowest values
    " "of the image histogram - recommanded values are below 1%" ), ) ima_autoscale_margin_percent = gds.FloatItem( _("Autoscale margin"), unit=_("%"), default=0.2, min=0.0, max=50.0, help=_( "Percentage of margin to add around data when auto-scaling image " "plots.
    A value of 0.2% adds a small margin for better " "visualization.
    Set to 0% for no margin (exact data bounds)." ), ) ima_defaults = gds.ButtonItem( _("Default image visualization settings"), edit_default_image_settings, icon="image.svg", default=False, ) ima_shape_param = gds.ButtonItem( _("Results: edit shape styles"), edit_dataset_settings, icon="shapes.svg" ) ima_marker_param = gds.ButtonItem( _("Results: edit marker styles"), edit_dataset_settings, icon="markers.svg" ) _g2 = gds.EndGroup("") g3 = gds.BeginGroup(_("Results")).set_prop("display", icon="show_results.svg") max_shapes_to_draw = gds.IntItem( _("Maximum shapes to draw"), default=1000, min=1, help=_("Maximum number of geometry shapes to draw on plot."), ) max_cells_in_label = gds.IntItem( _("Maximum cells in label"), default=100, min=1, help=_( "Maximum number of table cells (rows × columns) to display " "in merged result label on plot." ), ) max_cols_in_label = gds.IntItem( _("Maximum columns in label"), default=15, min=1, help=_("Maximum number of columns to display in merged result label."), ) show_result_label = gds.BoolItem( _("Show the merged result label by default"), _("Result label"), default=True, help=_( "Show the merged result label on the plot by default. " "Can be toggled per-object using the checkbox in the Properties panel." ), ) _g3 = gds.EndGroup("") _end_view_tabs = gds.EndTabGroup("") # Generator yielding (param, section, option) tuples from configuration dictionary def _iter_conf( paramdict: dict[str, gds.DataSet], ) -> Generator[tuple[gds.DataSet, str, str], None, None]: """Iterate over configuration parameters""" confdict = Conf.to_dict() for section_name, section in confdict.items(): if section_name in paramdict: for option in section: param = paramdict[section_name] if hasattr(param, option): yield param, section_name, option def conf_to_datasets(paramdict: dict[str, gds.DataSet]) -> None: """Convert DataLab configuration to datasets""" for param, section, option in _iter_conf(paramdict): value = getattr(getattr(Conf, section), option).get() # ConfigParser automatically unescapes %% to % when reading, but to be safe # we ensure datetime format strings are properly unescaped for display if option in ("sig_datetime_format_s", "sig_datetime_format_ms"): value = value.replace("%%", "%") setattr(param, option, value) def datasets_to_conf(paramdict: dict[str, gds.DataSet]) -> None: """Convert datasets to DataLab configuration""" for param, section, option in _iter_conf(paramdict): value = getattr(param, option) # Escape % characters for datetime format strings (ConfigParser requirement) if option in ("sig_datetime_format_s", "sig_datetime_format_ms"): value = value.replace("%", "%%") getattr(getattr(Conf, section), option).set(value) RESTART_OPTIONS = ( ("process_isolation_enabled", _("Process isolation enable status")), ("rpc_server_enabled", _("RPC server enable status")), ("console_enabled", _("Console enable status")), ("plugins_enabled", _("Third-party plugins support")), ("plugins_path", _("Third-party plugins path")), ) def get_restart_items_values(paramdict: dict[str, gds.DataSet]) -> list: """Get restart items values""" values = [] for option, _name in RESTART_OPTIONS: for param, _section, _option in _iter_conf(paramdict): if option == _option: values.append(getattr(param, option)) return values def get_all_values(paramdict: dict[str, gds.DataSet]) -> list: """Get all values""" values = [] for param, _section, _option in _iter_conf(paramdict): value = getattr(param, _option) if isinstance(value, gds.DataSet): # For dataset-like options, get a serializable representation value = gds.dataset_to_json(value) values.append(value) return values def get_all_options(paramdict: dict[str, gds.DataSet]) -> list: """Get all options""" options = [] for _param, _section, _option in _iter_conf(paramdict): options.append(_option) return options def create_dataset_dict() -> dict[str, gds.DataSet]: """Create a dictionary of datasets for each settings tab, populate it from Conf.""" paramdict = { "main": MainSettings(_("General"), icon="libre-gui-settings.svg"), "proc": ProcSettings(_("Processing"), icon="libre-tech-ram.svg"), "view": ViewSettings(_("Visualization"), icon="visualization.svg"), "io": IOSettings(_("I/O"), icon="io.svg"), "console": ConsoleSettings(_("Console"), icon="console.svg"), } conf_to_datasets(paramdict) return paramdict def edit_settings(parent: QW.QWidget) -> list[str]: """Edit DataLab settings Args: parent: Parent widget Returns: List of changed option names """ paramdict = create_dataset_dict() before = get_restart_items_values(paramdict) all_values_before = get_all_values(paramdict) params = gds.DataSetGroup(paramdict.values(), title=_("Settings")) changed_options = [] if params.edit(parent=parent): after = get_restart_items_values(paramdict) if before != after: # List the options that were changed changed = [ item[1] for item, value in zip(RESTART_OPTIONS, before) if value != after[RESTART_OPTIONS.index(item)] ] # Show a message box to inform the user that a restart is required QW.QMessageBox.information( parent, _("Restart required"), _( "The following options have been changed:\n\n" "- %s\n\n" "A restart is required for these changes to take effect." ) % "\n- ".join(changed), ) datasets_to_conf(paramdict) all_values_after = get_all_values(paramdict) all_options = get_all_options(paramdict) if all_values_before != all_values_after: changed_options = [ option for option, value in zip(all_options, all_values_before) if value != all_values_after[all_options.index(option)] ] # Check for image default settings changes for vis_defaults in ("ima_defaults",): if getattr(paramdict["view"], vis_defaults): changed_options.append(vis_defaults) return changed_options DataLab-1.1.0/datalab/gui/tour.py000066400000000000000000000752751514014117600165400ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. # pylint: disable=invalid-name # Allows short reference names like x, y, ... """ Tour ==== The :mod:`datalab.gui.tour` module implements a tour of DataLab features. A dialog box is displayed when the user launches DataLab for the first time or when the user clicks on the "Show tour" entry in the "?" menu. The tour user experience is the following: - First, the DataLab main window is maximized, and it is grayed out (the whole window is covered with a gray area with an opacity of 50%). The user can not interact with the DataLab main window while the tour is running. - A first modal dialog box is displayed with a short description of DataLab, and two buttons: "Start" and "Close". If the user clicks on "Start", the tour starts. If the user clicks on "Close", the tour is not started and the dialog box is closed. - The tour is composed of several steps. Each step is a modal dialog box with a short description of a DataLab feature, and three buttons: "Previous", "Next" and "Close". If the user clicks on "Previous", the previous step is displayed. If the user clicks on "Next", the next step is displayed. If the user clicks on "Close", the tour is stopped and the dialog box is closed. - At each step, the dialog box is moved to the center of the DataLab main window (i.e. the center of the screen), and the feature described in the step is highlighted in the DataLab main window. The highlight is a red rectangle around the feature, and the feature is the only part of the DataLab main window that is not grayed out. - The last step of the tour is a modal dialog box with a conclusion message and two buttons: "Show tour again", "Close", and "Show demo". If the user clicks on "Show tour again", the tour starts again. If the user clicks on "Close", the tour is stopped and the dialog box is closed. If the user clicks on "Show demo", the demo is started. .. autoclass:: Tour :members: :inherited-members: """ from __future__ import annotations import abc import enum import os from collections.abc import Callable from typing import TYPE_CHECKING import numpy as np from guidata.configtools import get_icon, get_image_file_path from guidata.qthelpers import is_dark_theme from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from sigima.objects import create_image, create_signal from datalab.config import _ from datalab.env import execenv if TYPE_CHECKING: from datalab.gui.main import DLMainWindow class Cover(QW.QWidget): """ Widget that covers the main window with a gray area with an opacity of 50%. """ def __init__(self, parent: DLMainWindow) -> None: super().__init__(parent) self.setWindowFlags(QC.Qt.Tool | QC.Qt.FramelessWindowHint) self.setAttribute(QC.Qt.WA_TranslucentBackground) self.__opacity_factor = 1.0 # Widgets to be excluded from the grayed out area: self.__excluded_widgets: list[QW.QWidget] | None = None # Path to be grayed out: self.__path: QG.QPainterPath = QG.QPainterPath() def set_opacity_factor(self, opacity_factor: float) -> None: """ Set the opacity factor of the grayed out area. Args: opacity_factor: Opacity factor. """ self.__opacity_factor = opacity_factor def exclude(self, widgets: list[QW.QWidget]) -> None: """ Exclude widgets from the grayed out area. Args: widgets: Widgets to be excluded. """ self.__excluded_widgets = widgets def update_geometry(self) -> None: """ Update the geometry of the widget. """ if os.name == "nt": QW.QApplication.processEvents() self.setGeometry(self.parentWidget().geometry()) self.__path = QG.QPainterPath() # Path is defined as the rectangle of the main window minus the rectangles of # the excluded widgets: self.__path.addRect(QC.QRectF(self.parentWidget().rect())) if self.__excluded_widgets is not None: for widget in self.__excluded_widgets: widget.raise_() widget.show() geometry = widget.frameGeometry() width, height = geometry.width(), geometry.height() point = widget.mapTo(self.parentWidget(), QC.QPoint(0, 0)) x, y = point.x(), point.y() widget_path = QG.QPainterPath() widget_path.addRect(QC.QRectF(x, y, width, height)) self.__path -= QG.QPainterPath(widget_path) self.repaint() def showEvent(self, a0: QG.QShowEvent | None) -> None: """ Event handler for the "show" event. Args: a0: Show event. """ super().showEvent(a0) self.setGeometry(self.parentWidget().geometry()) def paintEvent(self, event: QG.QPaintEvent) -> None: """ Event handler for the "paint" event. Args: event: Paint event. """ super().paintEvent(event) painter = QG.QPainter(self) painter.setOpacity((0.75 if is_dark_theme() else 0.5) * self.__opacity_factor) painter.setBrush(QG.QBrush(QC.Qt.black)) painter.setPen(QC.Qt.NoPen) painter.drawPath(self.__path) class StepResult(enum.Enum): """ Result of a step. """ PREVIOUS = enum.auto() NEXT = enum.auto() CLOSE = enum.auto() DEMO = enum.auto() RESTART = enum.auto() class StepDialog(QW.QDialog): """ Base class for tour dialog boxes. Args: parent: Parent widget. step: Tour step. """ def __init__( self, parent: QW.QWidget, step: TourStep, ) -> None: super().__init__(parent) self.setWindowTitle(step.title) self.setWindowFlags(QC.Qt.Popup) self.setModal(True) self.step = step self.result: StepResult | None = None self.__btnmap: dict[QW.QPushButton, StepResult] = {} self.__btnlayout = QW.QHBoxLayout() self._setup_ui() def __btn(self, text: str, result: StepResult, default: bool = False) -> None: """ Create a button. Args: text: Button text. result: Result of the button. default: Default button. Defaults to False. """ btn = QW.QPushButton(text) btn.setAutoDefault(default) btn.setDefault(default) icon = { "<<": "rewind.svg", "<": "previous.svg", ">": "next.svg", "X": "stop.svg", }.get(text) if icon is not None: btn.setIcon(get_icon(icon)) btn.setText("") btn.setMinimumWidth(100) btn.clicked.connect(lambda: self.button_clicked(btn)) self.__btnmap[btn] = result self.__btnlayout.addWidget(btn) def __btn_spacing(self, spacing: int) -> None: """ Add spacing between buttons. Args: spacing: Spacing. """ self.__btnlayout.addSpacing(spacing) def __create_label( self, text: str, delta_fontsize: int, color: QC.Qt.GlobalColor, bold: bool = False, ) -> QW.QLabel: """ Create a label. Args: text: Label text. delta_fontsize: Delta font size. color: Text color. bold: Bold text. Returns: Label. """ label = QW.QLabel(text) label.setWordWrap(True) label.setAlignment(QC.Qt.AlignCenter) # Change font size for something bigger: font = label.font() font.setPointSize(font.pointSize() + delta_fontsize) label.setFont(font) # Set text color: palette = label.palette() palette.setColor(QG.QPalette.WindowText, color) label.setPalette(palette) # Set bold text: font = label.font() font.setBold(bold) return label def __create_horizontal_line(self) -> QW.QFrame: """ Create a horizontal line. Returns: Horizontal line. """ line = QW.QFrame() line.setFrameShape(QW.QFrame.HLine) line.setFrameShadow(QW.QFrame.Sunken) line.setLineWidth(1) line.setMidLineWidth(0) palette = line.palette() palette.setColor(QG.QPalette.WindowText, QC.Qt.lightGray) line.setPalette(palette) return line def _setup_ui(self) -> None: """ Setup the user interface. """ if self.step.step_type == "introduction": self.__btn_spacing(50) self.__btn(_("Start"), StepResult.NEXT, True) self.__btn(_("Close"), StepResult.CLOSE) self.__btn_spacing(50) elif self.step.step_type == "conclusion": self.__btn("<<", StepResult.RESTART) self.__btn("<", StepResult.PREVIOUS) self.__btn(_("Demo"), StepResult.DEMO) self.__btn("X", StepResult.CLOSE, True) else: self.__btn("<<", StepResult.RESTART) self.__btn("<", StepResult.PREVIOUS) self.__btn(">", StepResult.NEXT, True) self.__btn("X", StepResult.CLOSE) self._layout = QW.QVBoxLayout() if self.step.step_type in ("introduction", "conclusion"): logo = QW.QLabel() logo.setPixmap(QG.QPixmap(get_image_file_path("DataLab-Banner2-100.png"))) logo.setAlignment(QC.Qt.AlignCenter) self._layout.addWidget(logo) title_color = QC.Qt.lightGray if is_dark_theme() else QC.Qt.darkGray title = self.__create_label(self.step.title, 1, title_color, True) self._layout.addWidget(title) self._layout.addWidget(self.__create_horizontal_line()) label_color = QC.Qt.cyan if is_dark_theme() else QC.Qt.darkBlue label_dsize = 3 if self.step.step_type == "regular" else 4 label = self.__create_label(self.step.text, label_dsize, label_color) if self.step.step_type == "regular": label.setAlignment(QC.Qt.AlignLeft) self._layout.addWidget(label) self._layout.addSpacing(5) self._layout.addLayout(self.__btnlayout) if self.step.step_type == "introduction": self._layout.addSpacing(5) help_text = _( "Hit Enter to continue to the next step, or " "Esc to close the tour." ) help_label = self.__create_label(help_text, -1, title_color) help_label.setAlignment(QC.Qt.AlignLeft) self._layout.addWidget(help_label) self.setLayout(self._layout) def button_clicked(self, button: QW.QPushButton) -> None: """ Event handler for the "clicked" event on the buttons. Args: button: Button that was clicked. """ self.result = self.__btnmap[button] if self.result is StepResult.CLOSE: self.reject() else: self.accept() def reject(self) -> None: """ Event handler for the "reject" event. """ self.result = StepResult.CLOSE super().reject() def paintEvent(self, event: QG.QPaintEvent) -> None: """ Event handler for the "paint" event. Args: event: Paint event. """ super().paintEvent(event) painter = QG.QPainter(self) painter.setOpacity(0.5) painter.setBrush(QG.QBrush(QC.Qt.black)) painter.setPen(QC.Qt.NoPen) class TourStep: """ Tour step. Args: tour: Tour. title: Step title. text: Step text. widgets: Widgets to be highlighted. If None, no widget is highlighted, meaning that the step is probably an introduction or a conclusion. setup_callback: Callback function to be called before the step is displayed, which takes a single argument, the `DLMainWindow` instance. teardown_callback: Callback function to be called after the step is displayed, which takes a single argument, the `DLMainWindow` instance. step_type: Step type. Can be "regular", "introduction" or "conclusion". """ def __init__( self, tour: BaseTour, title: str, text: str, widgets: list[QW.QWidget] | None = None, setup_callback: Callable | None = None, teardown_callback: Callable | None = None, step_type: str = "regular", ) -> None: self.tour = tour self.title = title self.text = text self.widgets = widgets self.setup_callback = setup_callback self.teardown_callback = teardown_callback self.step_type = step_type assert step_type in ["regular", "introduction", "conclusion"] def show(self) -> StepResult: """ Show the step. Returns: Result of the step. """ if self.setup_callback is not None: self.setup_callback(self.tour.win) self.update_cover(self.tour.cover) dialog = StepDialog(self.tour.win, self) if not execenv.unattended: dialog.exec() elif self.step_type != "conclusion": dialog.result = StepResult.NEXT dialog.accept() else: dialog.result = StepResult.CLOSE dialog.reject() if self.teardown_callback is not None: self.teardown_callback(self.tour.win) return dialog.result def update_cover(self, cover: Cover) -> None: """ Update the cover widget. Args: cover: Cover widget. """ cover.set_opacity_factor(1.0 if self.step_type == "regular" else 0.7) cover.exclude(self.widgets) cover.update_geometry() class BaseTourMeta(type(QW.QWidget), abc.ABCMeta): """Mixed metaclass to avoid conflicts""" class BaseTour(QW.QWidget, metaclass=BaseTourMeta): """ Base class for the tour of DataLab features. Args: win: DataLab main window. """ def __init__(self, win: DLMainWindow) -> None: super().__init__(win) self.win = win self._steps: list[TourStep] = [] self._current_step = 0 self.cover = Cover(win) self.cover.show() self.__window_geometry: tuple[tuple[int, int], tuple[int, int]] | None = None self.__window_prepared = False self.setup_tour(win) def __resize_window(self, factor: float) -> None: """ Resize the window so that it is centered on the screen and its size is `factor` times the size of the screen. Args: factor: Factor by which the size of the window is multiplied. """ screen = QW.QApplication.primaryScreen() screen_geometry = screen.geometry() width = int(screen_geometry.width() * factor) height = int(screen_geometry.height() * factor) self.win.resize(width, height) self.win.move(screen_geometry.center() - QC.QPoint(width // 2, height // 2)) def __save_window_geometry(self) -> None: """Save the window geometry (size and position).""" self.__window_geometry = self.win.saveGeometry() def __restore_window_geometry(self) -> None: """Restore the window geometry (size and position).""" if self.__window_geometry is not None: self.win.restoreGeometry(self.__window_geometry) @abc.abstractmethod def setup_tour(self, win: DLMainWindow) -> None: """ Setup the tour: add steps to the tour. """ @abc.abstractmethod def cleanup_tour(self, win: DLMainWindow) -> None: """ Cleanup the tour. """ def add_step( self, title: str, text: str, widgets: list[QW.QWidget] | None = None, setup_callback: Callable | None = None, teardown_callback: Callable | None = None, step_type: str | None = None, ) -> None: """ Add a step to the tour. Args: title: Step title. text: Step text. widgets: Widgets to be highlighted. If None, no widget is highlighted, meaning that the step is probably an introduction or a conclusion. setup_callback: Callback function to be called before the step is displayed, which takes a single argument, the `DLMainWindow` instance. teardown_callback: Callback function to be called after the step is displayed, which takes a single argument, the `DLMainWindow` instance. step_type: Step type. Can be "regular", "introduction" or "conclusion". Defaults to None. """ if step_type is None: if self._steps: step_type = "regular" else: step_type = "introduction" step = TourStep( self, title, text, widgets, setup_callback, teardown_callback, step_type ) self._steps.append(step) def show_current_step(self) -> None: """ Show the current step. """ step = self._steps[self._current_step] result = step.show() if result is StepResult.PREVIOUS: self.previous_step() elif result is StepResult.NEXT: self.next_step() elif result is StepResult.CLOSE: self.end() elif result is StepResult.DEMO: self.end() # pylint: disable=import-outside-toplevel from datalab.tests.scenarios import demo demo.play_demo(self.win) elif result is StepResult.RESTART: self.start() else: raise RuntimeError(f"Unknown result: {result}") def start(self) -> None: """ Start the tour. """ self._current_step = 0 self.show_current_step() def end(self) -> None: """ End the tour. """ self.cover.close() self.cleanup_tour(self.win) self.__restore_window_geometry() def next_step(self) -> None: """ Go to the next step. """ if self._current_step == 0 and not self.__window_prepared: self.__save_window_geometry() self.__resize_window(0.7) self.cover.update_geometry() self.__window_prepared = True self._current_step += 1 self.show_current_step() def previous_step(self) -> None: """ Go to the previous step. """ self._current_step -= 1 self.show_current_step() class Tour(BaseTour): """ Tour of DataLab features. Args: win: DataLab main window. """ def prepare_signalpanel(self, win: DLMainWindow) -> None: """ Prepare the signal panel. Args: win: DataLab main window. """ # Create a signal: x = np.linspace(0, 10, 100) y = np.sin(x) sig = create_signal(_("Test signal, for the tour"), x, y) win.add_object(sig) win.set_current_panel("signal") def prepare_imagepanel(self, win: DLMainWindow) -> None: """ Prepare the image panel. Args: win: DataLab main window. """ # Create an image: x = np.linspace(0, 10, 100) y = np.linspace(0, 10, 100) xx, yy = np.meshgrid(x, y) zz = np.sin(xx) * np.cos(yy) img = create_image(_("Test image, for the tour"), zz) win.add_object(img) win.set_current_panel("image") def popup_menu(self, win: DLMainWindow, menu: QW.QMenu) -> None: """ Popup a menu. Args: win: DataLab main window. menu_name: Name of the menu to popup. """ menu.popup(win.mapToGlobal(QC.QPoint(50, 50))) def cleanup_tour(self, win: DLMainWindow) -> None: """ Cleanup the tour. Args: win: DataLab main window. """ if len(win.signalpanel.objmodel) == 1: win.signalpanel.remove_all_objects() else: win.signalpanel.remove_object() if len(win.imagepanel.objmodel) == 1: win.imagepanel.remove_all_objects() else: win.imagepanel.remove_object() def setup_tour(self, win: DLMainWindow) -> None: """ Setup the tour: add steps to the tour. """ self.add_step( _("Welcome to DataLab!"), _("This tour will guide you through the main features of DataLab."), ) self.add_step( _("DataLab main window"), _( "This is the main window of DataLab. It is composed of several parts " "that we will describe in the following steps." ), ) self.add_step( _("DataLab main window"), _( "Menus and toolbars regroup the main actions that can be performed " "in DataLab. Their content is adapted to the current panel, as we " "will see in the following steps." ), [ win.menuBar(), win.main_toolbar, win.signalpanel_toolbar, win.imagepanel_toolbar, win.signalpanel.acthandler.view_toolbar, win.imagepanel.acthandler.view_toolbar, ], ) self.add_step( _("DataLab main window"), _( "The main window is composed of two main panels: the Signal Panel, " "and the Image Panel.
    " "Switching between panels is done using the highlighted tabs." ), [win.tabwidget.tabBar()], ) self.add_step( _("Signal Panel"), _( "The Signal Panel is used to manage 1D signals." "It is composed of the elements shown in the following steps." ), ) self.add_step( _("Signal Panel") + " – " + _("List and properties"), _( "In the highlighted area, signals are listed at the top, and their " "properties may be displayed and edited at the bottom.

    " "Signals are numbered (but may be reorganized) and put together in " "numbered groups." ), [win.tabwidget.tabBar(), win.signalpanel], self.prepare_signalpanel, ) self.add_step( _("Signal Panel") + " – " + _("View"), _( "Signals are plotted in the Signal View.

    " "Curves may be customized using context menus or the vertical " "toolbar on the left (appearance settings are saved in the " "signal metadata)." ), [win.docks[win.signalpanel]], ) self.add_step( _("Signal Panel") + " – " + _("File menu"), _( "The File menu contains actions to " "import and export signals individually (various formats) or " "to save or restore the whole workspace (HDF5 files)." ), [win.menuBar()], lambda win: self.popup_menu(win, win.file_menu), lambda win: win.file_menu.hide(), ) self.add_step( _("Signal Panel") + " – " + _("Create menu"), _( "The Create menu contains actions to create new signals " "from various models (Gaussian, sine, etc.)." ), [win.menuBar()], lambda win: self.popup_menu(win, win.create_menu), lambda win: win.create_menu.hide(), ) self.add_step( _("Signal Panel") + " – " + _("Edit menu"), _( "The Edit menu contains actions to edit signals individually " "or in groups." ), [win.menuBar()], lambda win: self.popup_menu(win, win.edit_menu), lambda win: win.edit_menu.hide(), ) self.add_step( _("Signal Panel") + " – " + _("Operations menu"), _( "The Operations menu is focused on arithmetic operations, " "basic mathematical functions or data type conversions." ), [win.menuBar()], lambda win: self.popup_menu(win, win.operation_menu), lambda win: win.operation_menu.hide(), ) self.add_step( _("Signal Panel") + " – " + _("Processing menu"), _("The Processing menu regroups 1->1 signal processing actions."), [win.menuBar()], lambda win: self.popup_menu(win, win.processing_menu), lambda win: win.processing_menu.hide(), ) self.add_step( _("Signal Panel") + " – " + _("Analysis menu"), _( "The Analysis menu regroups 1->0 signal computing actions " "(that is, actions that do not modify the signals, but compute " "a result, e.g. scalar values), with optional ROI selection." ), [win.menuBar()], lambda win: self.popup_menu(win, win.analysis_menu), lambda win: win.analysis_menu.hide(), ) self.add_step( _("Image Panel"), _( "The Image Panel is used to manage 2D images. It is composed " "of the elements shown in the following steps." ), [], lambda win: win.set_current_panel("image"), ) self.add_step( _("Image Panel") + " – " + _("List and properties"), _( "In the highlighted area, images are listed at the top, and their " "properties may be displayed and edited at the bottom.

    " "Images are numbered (but may be reorganized) and put together in " "numbered groups." ), [win.tabwidget.tabBar(), win.imagepanel], self.prepare_imagepanel, ) self.add_step( _("Image Panel") + " – " + _("View"), _( "Images are shown in the Image View.

    " "The displayed images may be customized using context menus " "or the vertical toolbar on the left (appearance settings " "are saved in the image metadata)." ), [win.docks[win.imagepanel]], ) self.add_step( _("Image Panel") + " – " + _("File menu"), _( "The File menu contains actions to " "import and export images individually (various formats) or " "to save or restore the whole workspace (HDF5 files)." ), [win.menuBar()], lambda win: self.popup_menu(win, win.file_menu), lambda win: win.file_menu.hide(), ) self.add_step( _("Image Panel") + " – " + _("Create menu"), _( "The Create menu contains actions to create new images " "from various models (Gaussian, checkerboard, etc.)." ), [win.menuBar()], lambda win: self.popup_menu(win, win.create_menu), lambda win: win.create_menu.hide(), ) self.add_step( _("Image Panel") + " – " + _("Edit menu"), _( "The Edit menu contains actions to edit images individually " "or in groups." ), [win.menuBar()], lambda win: self.popup_menu(win, win.edit_menu), lambda win: win.edit_menu.hide(), ) self.add_step( _("Image Panel") + " – " + _("Operations menu"), _( "The Operations menu is focused on arithmetic operations, " "data type conversions, pixel binning resize, intensity profiles..." ), [win.menuBar()], lambda win: self.popup_menu(win, win.operation_menu), lambda win: win.operation_menu.hide(), ) self.add_step( _("Image Panel") + " – " + _("Processing menu"), _( "The Processing menu regroups 1->1 image processing actions " "(that is, actions that modify the images)." ), [win.menuBar()], lambda win: self.popup_menu(win, win.processing_menu), lambda win: win.processing_menu.hide(), ) self.add_step( _("Image Panel") + " – " + _("Analysis menu"), _( "The Analysis menu regroups 1->0 image computing actions " "(that is, actions that do not modify the images, but compute " "a result, e.g. circle coordinates), with optional ROI selection." ), [win.menuBar()], lambda win: self.popup_menu(win, win.analysis_menu), lambda win: win.analysis_menu.hide(), ) self.add_step( _("Extensions"), _( "DataLab is designed to be easily extended with new features, " "by using Macros, Plugins or Remote Control." "

    The common point between these mechanisms is that " "they are all relying on Python scripts and the DataLab API." ), ) self.add_step( _("Extensions") + " – " + _("Macros"), _( "The Macro Panel allows to create, edit and run macros.
    " "Macros are saved together with the DataLab workspace (HDF5 file)." ), [win.docks[win.macropanel]], lambda win: win.set_current_panel("macro"), lambda win: win.set_current_panel("signal"), ) self.add_step( _("Extensions") + " – " + _("Plugins"), _( "The Plugins menu regroups features that are not part of " "the core of DataLab, but that are provided as plugins.
    " "(See the documentation for more information about plugins.)" ), [win.menuBar()], lambda win: self.popup_menu(win, win.plugins_menu), lambda win: win.plugins_menu.hide(), ) self.add_step( _("This is the end of the tour!"), _("You can show the tour again, or close this dialog box."), step_type="conclusion", ) def start(win: DLMainWindow) -> None: """ Start the tour of DataLab features. Args: win: DataLab main window. """ tour = Tour(win) tour.start() DataLab-1.1.0/datalab/h5/000077500000000000000000000000001514014117600147055ustar00rootroot00000000000000DataLab-1.1.0/datalab/h5/__init__.py000066400000000000000000000004411514014117600170150ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab HDF5 I/O module """ # pylint: disable=unused-import # Registering dynamic I/O features: from datalab.h5 import generic # noqa: F401 from datalab.h5.common import H5Importer # noqa: F401 DataLab-1.1.0/datalab/h5/common.py000066400000000000000000000233421514014117600165530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab Common tools for exogenous HDF5 format support """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import abc import os.path as osp from collections.abc import Callable import h5py import numpy as np from guidata.utils.misc import to_string from sigima.io.common.converters import convert_array_to_valid_dtype from sigima.objects import ImageObj, SignalObj from datalab.config import Conf def data_to_xy(data: np.ndarray) -> list[np.ndarray]: """Convert 2-D array into a list of 1-D array data (x, y, dx, dy). This is useful for importing data and creating a DataLab signal with it. Args: data (numpy.ndarray): 2-D array of data Returns: list[np.ndarray]: list of 1-D array data (x, y, dx, dy) """ if len(data.ravel()) == len(data): return np.arange(len(data)), data.ravel(), None, None rows, cols = data.shape for colnb in (2, 3, 4): if cols == colnb and rows > colnb: data = data.T break if len(data) == 1: data = data.T if len(data) not in (2, 3, 4): raise ValueError(f"Invalid data: len(data)={len(data)} (expected 2, 3 or 4)") x, y = data[:2] dx, dy = None, None if len(data) == 3: dy = data[2] if len(data) == 4: dx, dy = data[2:] return x, y, dx, dy class BaseNode(metaclass=abc.ABCMeta): """Object representing a HDF5 node""" IS_ARRAY = False def __init__(self, h5file, dname): self.h5file = h5file self.dset = h5file[dname] self.metadata = {} self.__obj = None self.children = [] self.uint32_wng = False @property def id(self): """Return node id""" return self.dset.name @property def name(self): """Return node name, constructed from dataset name""" return to_string(self.dset.name).split("/")[-1] @property def data(self): """Data associated to node, if available""" return None @property def icon_name(self): """Icon name associated to node""" @property def shape_str(self): """Return string representation of node shape, if any""" return "" @property def dtype_str(self): """Return string representation of node data type, if any""" return "" @property def text(self): """Return node textual representation""" return "" @property def description(self): """Return node description""" return "" @classmethod def match(cls, dset): """Return True if h5 dataset match node pattern""" def is_supported(self) -> bool: """Return True if node is associated to supported data""" return False def create_native_object(self): """Create native object, if supported""" return None def get_native_object(self): """Return native object, if supported""" if self.__obj is None: obj = self.create_native_object() # pylint: disable=assignment-from-none if obj is not None: self.__process_metadata(obj) self.__obj = obj return self.__obj def collect_attributes(self): """Collect attributes from node""" for key, value in self.dset.attrs.items(): if isinstance(value, bytes): value = to_string(value) if isinstance(value, (np.ndarray, str, float, int, bool)) or np.isscalar( value ): self.metadata[key] = value def __process_metadata(self, obj): """Process metadata from dataset to obj""" obj.reset_metadata_to_defaults() obj.set_metadata_option("HDF5Path", self.h5file.filename) obj.set_metadata_option("HDF5Dataset", self.id) obj.metadata.update(self.metadata) @property def object_title(self): """Return signal/image object title""" if Conf.io.h5_fullpath_in_title.get(): title = self.id else: title = self.name if Conf.io.h5_fname_in_title.get(): title += f" ({osp.basename(self.h5file.filename)})" return title def set_signal_data(self, obj: SignalObj) -> None: """Set signal data (handles various issues)""" data = self.data if data.dtype not in (float, np.complex128): data = np.array(data, dtype=float) data = convert_array_to_valid_dtype(data, SignalObj.VALID_DTYPES) if len(data.shape) == 1: obj.set_xydata(np.arange(data.size), data) else: x, y, dx, dy = data_to_xy(data) obj.set_xydata(x, y, dx, dy) def set_image_data(self, obj: ImageObj) -> None: """Set image data (handles various issues)""" data = self.data if data.dtype == np.uint32: self.uint32_wng = data.max() > np.iinfo(np.int32).max clipped_data = data.clip(0, np.iinfo(np.int32).max) data = np.array(clipped_data, dtype=np.int32) obj.data = convert_array_to_valid_dtype(data, ImageObj.VALID_DTYPES) class H5Importer: """DataLab HDF5 importer class""" def __init__(self, filename): self.h5file = h5py.File(filename) self.__nodes = {} self.root = RootNode(self.h5file) self.__nodes[self.root.id] = self.root self.root.collect_children(self.__nodes) NODE_FACTORY.run_post_triggers(self) @property def nodes(self): """Return all nodes""" return self.__nodes.values() def get(self, node_id: str): """Return node associated to id""" return self.__nodes[node_id] def get_relative(self, node: BaseNode, relpath: str, ancestor: int = 0): """Return node using relative path to another node""" path = "/" + ( "/".join(node.id.split("/")[:-ancestor]) + "/" + relpath.strip("/") ).strip("/") return self.__nodes[path] def close(self): """Close HDF5 file""" self.__nodes = {} self.h5file.close() class NodeFactory: """Factory for node classes""" def __init__(self): self.__ignored_datasets = [] self.__generic_classes = [] self.__thirdparty_classes = [] self.__post_triggers = {} def add_ignored_datasets(self, names): """Add h5 dataset name to ignore list""" self.__ignored_datasets.extend(names) def add_post_trigger(self, nodecls: BaseNode, callback: Callable): """Add post trigger function, to be called at the end of the collect process. Callbacks take only one argument: H5Importer instance.""" triggers = self.__post_triggers.setdefault(nodecls, []) triggers.append(callback) def register(self, cls, is_generic=False): """Register node class. Generic classes are processed after specific classes (as a fallback solution)""" if is_generic: self.__generic_classes.append(cls) else: self.__thirdparty_classes.append(cls) def get(self, dset): """Return node class that matches h5 dataset""" for name in to_string(dset.name).split("/"): if name in self.__ignored_datasets: return None for cls in self.__thirdparty_classes + self.__generic_classes: try: if cls.match(dset): return cls except (UnicodeDecodeError, TypeError, ValueError, OSError) as exc: # Skip classes that can't match this dataset due to various issues print( f"Warning: Class {cls.__name__} can't match dataset " f"'{dset.name}': {exc}" ) continue if isinstance(dset, h5py.Group): return GroupNode return None def run_post_triggers(self, importer: H5Importer): """Run post-collect callbacks""" for node in importer.nodes: for nodecls, triggers in self.__post_triggers.items(): if isinstance(node, nodecls): for func in triggers: func(node, importer) NODE_FACTORY = NodeFactory() class GroupNode(BaseNode): """Object representing a HDF5 group node""" @property def icon_name(self): """Icon name associated to node""" return "h5group.svg" def collect_children(self, node_dict: dict[str, BaseNode]): """Construct tree""" for dset in self.dset.values(): try: child_cls = NODE_FACTORY.get(dset) if child_cls is not None: child = child_cls(self.h5file, dset.name) node_dict[child.id] = child self.children.append(child) if isinstance(child, GroupNode): child.collect_children(node_dict) child.collect_attributes() except (UnicodeDecodeError, TypeError, ValueError, KeyError) as exc: # Skip datasets that can't be processed due to various issues print(f"Warning: Skipping dataset '{dset.name}' due to error: {exc}") continue class RootNode(GroupNode): """Object representing a HDF5 root node""" def __init__(self, h5file): super().__init__(h5file, "/") @property def icon_name(self): """Icon name associated to node""" return "h5file.svg" @property def name(self): """Return node name, constructed from dataset name""" return osp.basename(self.h5file.filename) @property def description(self): """Return node description""" return self.h5file.filename DataLab-1.1.0/datalab/h5/generic.py000066400000000000000000000472751514014117600167120ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab Generic HDF5 format support """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... import h5py import numpy as np from guidata.utils.misc import to_string from sigima.objects import create_image, create_signal from datalab.h5 import common, utils # ============================================================================= # Encoding and Data Reading Utilities # ============================================================================= def safe_decode_bytes(data, fallback=""): """Safely decode bytes to string using multiple encoding strategies.""" if isinstance(data, str): return data if not isinstance(data, bytes): return str(data) # Try encodings in order of preference for encoding in ["utf-8", "latin1", "cp1252", "iso-8859-1", "ascii"]: try: decoded = data.decode(encoding) # For legacy encodings, validate the result looks reasonable if encoding in ["latin1", "cp1252", "iso-8859-1"]: if _is_reasonable_text(decoded): return decoded else: return decoded except (UnicodeDecodeError, UnicodeError): continue # Final fallback with replacement characters try: return data.decode("utf-8", errors="replace") except Exception: # pylint: disable=broad-except return fallback def _is_reasonable_text(text): """Check if decoded text looks reasonable (mostly printable).""" if not text: return True printable_chars = sum(1 for c in text if c.isprintable() or c.isspace()) ratio = printable_chars / len(text) # Accept if mostly printable or short enough to be likely text return ratio >= 0.8 or len(text) < 20 def safe_read_dataset(dset, fallback_data=None): """Safely read HDF5 dataset with encoding error handling.""" try: return dset[()] except UnicodeDecodeError: # Try alternative reading strategies for problematic encodings return _try_alternative_read(dset, fallback_data) except (TypeError, ValueError, OSError): return fallback_data def _try_alternative_read(dset, fallback_data): """Try alternative strategies to read datasets with encoding issues.""" strategies = [ lambda d: d.asstr()[()], # Try string conversion lambda d: d.astype("S")[()], # Try reading as bytes ] for strategy in strategies: try: return strategy(dset) except Exception: # pylint: disable=broad-except continue return fallback_data # ============================================================================= # Text Formatting Utilities # ============================================================================= def format_text_data(data): """Format various types of data for text display.""" if data is None: return "" try: return to_string(data) except (UnicodeDecodeError, UnicodeError): return _handle_encoding_issues(data) def _handle_encoding_issues(data): """Handle data with encoding issues.""" if isinstance(data, bytes): return safe_decode_bytes(data) if isinstance(data, np.ndarray): if data.dtype.kind in ["S", "a", "U"]: # String arrays return _format_string_array(data) if data.dtype.names: # Compound data return _format_compound_data(data) return f"" def _format_string_array(data): """Format string arrays with encoding handling.""" try: if data.size == 1: return safe_decode_bytes(data.item()) # Show first few elements items = [] for i, item in enumerate(data.flat): if i >= 5: items.append("...") break items.append(safe_decode_bytes(item)) return f"[{', '.join(items)}]" except Exception: # pylint: disable=broad-except return f"" def _format_compound_data(data): """Format compound data with encoding handling.""" try: result_parts = [] for field_name in data.dtype.names: field_data = data[field_name] if hasattr(field_data, "item"): field_data = field_data.item() if isinstance(field_data, bytes): field_value = safe_decode_bytes(field_data) else: field_value = str(field_data) result_parts.append(f"{field_name}: {field_value}") return f"({', '.join(result_parts)})" except Exception: # pylint: disable=broad-except return f"" # ============================================================================= # Base Node Class # ============================================================================= class BaseGenericNode(common.BaseNode): """Base class for generic HDF5 data nodes with encoding support.""" @classmethod def match(cls, dset): """Return True if h5 dataset matches this node pattern.""" return not isinstance(dset, h5py.Group) @property def icon_name(self): """Icon name associated to node.""" return "h5scalar.svg" @property def data(self): """Data associated to node, if available.""" return safe_read_dataset(self.dset, fallback_data=None) @property def dtype_str(self): """Return string representation of node data type.""" try: return str(self.dset.dtype) except (UnicodeDecodeError, TypeError, ValueError): if self.data is None: return "unknown" try: return str(self.data.dtype) except Exception: # pylint: disable=broad-except return "unknown" @property def text(self): """Return node textual representation.""" return format_text_data(self.data) # ============================================================================= # Specialized Node Classes # ============================================================================= class GenericScalarNode(BaseGenericNode): """Node for scalar HDF5 data.""" @classmethod def match(cls, dset): """Match scalar numeric data.""" if not super().match(dset): return False data = safe_read_dataset(dset) return ( data is not None and isinstance(data, np.generic) and utils.is_supported_num_dtype(data) ) class GenericTextNode(BaseGenericNode): """Node for text/string HDF5 data.""" @classmethod def match(cls, dset): """Match text or string data.""" if not super().match(dset): return False data = safe_read_dataset(dset) if data is None: # Try to match based on dtype for unreadable data try: dtype = dset.dtype return dtype.kind in ["S", "a", "U"] or "str" in str(dtype) except Exception: # pylint: disable=broad-except return False return isinstance(data, bytes) or utils.is_supported_str_dtype(data) @property def dtype_str(self): """Return simplified dtype for text data.""" return "string" @property def text(self): """Return formatted text with special handling for single arrays.""" if self.data is None: return "" try: if utils.is_single_str_array(self.data): item = self.data[0] return safe_decode_bytes(item) if isinstance(item, bytes) else str(item) return format_text_data(self.data) except (UnicodeDecodeError, UnicodeError): return _handle_text_encoding_issues(self.data) def _handle_text_encoding_issues(data): """Handle encoding issues specific to text nodes.""" if isinstance(data, bytes): return safe_decode_bytes(data) if isinstance(data, np.ndarray) and data.dtype.kind in ["S", "a"]: try: if data.size == 1: return safe_decode_bytes(data.item()) decoded = [safe_decode_bytes(item) for item in data.flat] return str(decoded[:10]) # Show first 10 elements except Exception: # pylint: disable=broad-except return f"" return "" class GenericArrayNode(BaseGenericNode): """Node for array HDF5 data, including numeric arrays from compound data.""" IS_ARRAY = True @classmethod def match(cls, dset): """Match numeric array data, including convertible compound data.""" if not super().match(dset): return False data = safe_read_dataset(dset) if data is None: return False # First check direct numeric arrays if ( utils.is_supported_num_dtype(data) and isinstance(data, np.ndarray) and len(data.shape) in (1, 2) ): return True # Then check compound data that can be converted to numeric arrays if ( isinstance(data, np.ndarray) and hasattr(data.dtype, "names") and data.dtype.names is not None ): return cls._can_convert_compound_to_numeric(data) return False @classmethod def _can_convert_compound_to_numeric(cls, data): """Check if compound data can be converted to a supported numeric array.""" try: numeric_array = cls.extract_numeric_from_compound(data) return ( numeric_array is not None and utils.is_supported_num_dtype(numeric_array) and isinstance(numeric_array, np.ndarray) and len(numeric_array.shape) in (1, 2) ) except Exception: # pylint: disable=broad-except return False @classmethod def extract_numeric_from_compound(cls, data): """Extract a numeric array from compound data.""" if not (hasattr(data.dtype, "names") and data.dtype.names): return None # Find ALL fields and check if they are numeric all_fields = list(data.dtype.names) numeric_fields = [] for field_name in all_fields: field_dtype = data.dtype.fields[field_name][0] if np.issubdtype(field_dtype, np.number): numeric_fields.append(field_name) # Only convert if ALL fields are numeric (preserve all information) # or if there's a single numeric field and no important string data if len(numeric_fields) == 0: return None if len(numeric_fields) != len(all_fields): # Mixed data - check if non-numeric fields contain meaningful data for field_name in all_fields: if field_name not in numeric_fields: field_data = data[field_name] # If there's meaningful string data, don't convert if cls._has_meaningful_string_data(field_data): return None try: # If single numeric field, extract it directly if len(numeric_fields) == 1: return data[numeric_fields[0]] # Multiple numeric fields: stack them if compatible shapes field_data = [data[field] for field in numeric_fields] # Check if all fields have the same shape shapes = [arr.shape for arr in field_data] if len(set(shapes)) == 1: # Stack along new axis to create 2D array return np.stack(field_data, axis=-1) except Exception: # pylint: disable=broad-except pass return None @classmethod def _has_meaningful_string_data(cls, field_data): """Check if string field contains meaningful data worth preserving.""" try: if hasattr(field_data, "flat"): # Check if most entries are non-empty and meaningful non_empty_count = 0 for item in field_data.flat: if isinstance(item, bytes): decoded = item.decode("utf-8", errors="ignore").strip() if decoded and len(decoded) > 0: non_empty_count += 1 elif isinstance(item, str) and item.strip(): non_empty_count += 1 # If most entries have meaningful content, preserve it return non_empty_count / field_data.size > 0.5 return True # Default to preserving unknown string data except Exception: # pylint: disable=broad-except return True # Conservative: preserve if we can't determine @property def data(self): """Data associated to node, if available.""" raw_data = safe_read_dataset(self.dset, fallback_data=None) # If this is compound data, try to extract numeric array if ( raw_data is not None and isinstance(raw_data, np.ndarray) and hasattr(raw_data.dtype, "names") and raw_data.dtype.names is not None ): numeric_data = self.extract_numeric_from_compound(raw_data) if numeric_data is not None: return numeric_data return raw_data def is_supported(self) -> bool: """Return True if node is associated to supported data""" return self.data.size > 1 @property def __is_signal(self): """Return True if array represents a signal""" shape = self.data.shape return len(shape) == 1 or shape[0] in (1, 2) or shape[1] in (1, 2) @property def icon_name(self): """Icon name associated to node""" if self.is_supported(): return "signal.svg" if self.__is_signal else "image.svg" return "h5array.svg" @property def shape_str(self): """Return string representation of node shape, if any""" return " x ".join([str(size) for size in self.data.shape]) @property def dtype_str(self): """Return string representation of node data type, if any""" return str(self.data.dtype) @property def text(self): """Return node textual representation""" return str(self.data) def create_native_object(self): """Create native object, if supported""" if self.__is_signal: obj = create_signal(self.object_title) try: self.set_signal_data(obj) except ValueError: obj = None else: obj = create_image(self.object_title) try: self.set_image_data(obj) except ValueError: obj = None return obj class GenericCompoundNode(BaseGenericNode): """Node for compound/structured HDF5 data that can't convert to numeric arrays.""" IS_ARRAY = True @classmethod def match(cls, dset): """Match compound/structured data that cannot be converted to numeric arrays.""" if not super().match(dset): return False data = safe_read_dataset(dset) if data is None: # Try to match based on dtype if we can't read the data try: return dset.dtype.names is not None except Exception: # pylint: disable=broad-except return False # Check if it's compound data (structured array) if not ( isinstance(data, np.ndarray) and hasattr(data.dtype, "names") and data.dtype.names is not None ): return False # IMPORTANT: Only match if GenericArrayNode cannot handle this data # Try to convert to a numeric array first if cls._can_convert_to_numeric_array(data): return False # Let GenericArrayNode handle it return True # We handle compound data that can't be converted @classmethod def _can_convert_to_numeric_array(cls, data): """Check if compound data can be converted to a numeric array.""" try: # Try to extract numeric fields and create a pure numeric array numeric_array = cls.extract_numeric_array(data) if numeric_array is None: return False # Check if the resulting array would be supported by GenericArrayNode return ( utils.is_supported_num_dtype(numeric_array) and isinstance(numeric_array, np.ndarray) and len(numeric_array.shape) in (1, 2) ) except Exception: # pylint: disable=broad-except return False @classmethod def extract_numeric_array(cls, data): """Try to extract a pure numeric array from compound data.""" # Use the same logic as GenericArrayNode return GenericArrayNode.extract_numeric_from_compound(data) @property def dtype_str(self): """Return detailed compound dtype information.""" try: dtype = self.dset.dtype if dtype.names: field_info = [] for name in dtype.names: field_dtype = dtype.fields[name][0] field_info.append(f"{name}: {field_dtype}") return f"compound({', '.join(field_info)})" return str(dtype) except Exception: # pylint: disable=broad-except return super().dtype_str @property def text(self): """Return formatted compound data.""" if self.data is None: return "" try: return self._format_compound_data() except Exception: # pylint: disable=broad-except return f"" def _format_compound_data(self): """Format compound data for display.""" if not (hasattr(self.data.dtype, "names") and self.data.dtype.names): return super().text if self.data.size == 1: return self._format_single_record() return self._format_multiple_records() def _format_single_record(self): """Format a single compound record.""" parts = [] for field_name in self.data.dtype.names: field_value = self.data[field_name].item() if isinstance(field_value, bytes): field_value = safe_decode_bytes(field_value) parts.append(f"{field_name}: {field_value}") return f"({', '.join(parts)})" def _format_multiple_records(self): """Format multiple compound records.""" records = [] for i, record in enumerate(self.data.flat): if i >= 3: # Show max 3 records records.append("...") break parts = [] for field_name in self.data.dtype.names: field_value = record[field_name] if isinstance(field_value, bytes): field_value = safe_decode_bytes(field_value) parts.append(f"{field_name}: {field_value}") records.append(f"({', '.join(parts)})") return f"[{', '.join(records)}]" # ============================================================================= # Node Registration # ============================================================================= # Register all node types with the factory common.NODE_FACTORY.register(GenericScalarNode, is_generic=True) common.NODE_FACTORY.register(GenericTextNode, is_generic=True) common.NODE_FACTORY.register(GenericArrayNode, is_generic=True) common.NODE_FACTORY.register(GenericCompoundNode, is_generic=True) DataLab-1.1.0/datalab/h5/native.py000066400000000000000000000016701514014117600165510ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab Native HDF5 I/O module """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from guidata.io import HDF5Reader, HDF5Writer import datalab DATALAB_VERSION_NAME = "DataLab_Version" class NativeH5Writer(HDF5Writer): """DataLab signal/image objects HDF5 guidata Dataset Writer class Args: filename (str): HDF5 file name """ def __init__(self, filename: str) -> None: super().__init__(filename) self.h5[DATALAB_VERSION_NAME] = datalab.__version__ class NativeH5Reader(HDF5Reader): """DataLab signal/image objects HDF5 guidata dataset Writer class Args: filename (str): HDF5 file name """ def __init__(self, filename: str) -> None: super().__init__(filename) self.version = self.h5[DATALAB_VERSION_NAME] DataLab-1.1.0/datalab/h5/utils.py000066400000000000000000000053131514014117600164210ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab Utilities for exogenous HDF5 format support """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... import numpy as np from guidata.utils.misc import to_string def fix_ldata(fuzzy): """Fix label data""" if fuzzy is not None: if fuzzy and isinstance(fuzzy, np.void) and len(fuzzy) > 1: # Shouldn't happen (invalid LMJ fmt) fuzzy = fuzzy[0] if isinstance(fuzzy, (np.bytes_, bytes)): fuzzy = to_string(fuzzy) if isinstance(fuzzy, str): return fuzzy return "" def fix_ndata(fuzzy): """Fix numeric data""" if fuzzy is not None: if fuzzy and isinstance(fuzzy, np.void) and len(fuzzy) > 1: # Shouldn't happen (invalid LMJ fmt) fuzzy = fuzzy[0] try: if float(fuzzy) == int(fuzzy): return int(fuzzy) return float(fuzzy) except (TypeError, ValueError): pass return None def process_scalar_value(dset, name, callback): """Process dataset numeric/str value `name`""" try: scdata = dset[name][()] if isinstance(scdata, np.ndarray): scdata = scdata[0] if scdata is not None: return callback(scdata) except (KeyError, ValueError): pass return None def process_label(dset, name): """Process dataset label `name`""" try: ldata = dset[name][()] if ldata is not None: xldata, yldata, zldata = "", "", "" if len(ldata) == 2: xldata, yldata = ldata elif len(ldata) == 3: xldata, yldata, zldata = ldata return fix_ldata(xldata), fix_ldata(yldata), fix_ldata(zldata) except KeyError: pass return "", "", "" def process_xy_values(dset, name): """Process dataset x,y values `name`""" try: ldata = dset[name][()] if ldata is not None: return fix_ndata(ldata[0]), fix_ndata(ldata[1]) except (KeyError, ValueError): pass return None, None def is_supported_num_dtype(data): """Return True if data type is a numerical type supported by DataLab""" return data.dtype.name.startswith(("int", "uint", "float", "complex")) def is_single_str_array(data): """Return True if data is a single-item string array""" return ( isinstance(data, np.generic) and data.shape == (1,) and isinstance(data[0], str) ) def is_supported_str_dtype(data): """Return True if data type is a string type supported by preview""" return data.dtype.name.startswith("string") or is_single_str_array(data) DataLab-1.1.0/datalab/locale/000077500000000000000000000000001514014117600156305ustar00rootroot00000000000000DataLab-1.1.0/datalab/locale/fr/000077500000000000000000000000001514014117600162375ustar00rootroot00000000000000DataLab-1.1.0/datalab/locale/fr/LC_MESSAGES/000077500000000000000000000000001514014117600200245ustar00rootroot00000000000000DataLab-1.1.0/datalab/locale/fr/LC_MESSAGES/datalab.po000066400000000000000000003164011514014117600217610ustar00rootroot00000000000000# French translations for datalab. # Copyright (C) 2026 DataLab Platform Developers # This file is distributed under the same license as the datalab project. # msgid "" msgstr "" "Project-Id-Version: datalab\n" "Report-Msgid-Bugs-To: p.raybaut@codra.fr\n" "PO-Revision-Date: 2025-05-22 15:46+0200\n" "Language: fr\n" "Language-Team: DataLab Platform Developers 1);\n" "MIME-Version: 1.0\n" "Content-Type: text/plain; charset=utf-8\n" "Content-Transfer-Encoding: 8bit\n" "Generated-By: Babel 2.17.0\n" msgid "Results" msgstr "Résultats" #, python-format msgid "%d more rows" msgstr "%d lignes supplémentaires" #, python-format msgid "%d more columns" msgstr "%d colonnes supplémentaires" #, python-format msgid "%s omitted" msgstr "%s non affichées" #, python-format msgid "Only %d out of %d shapes are displayed" msgstr "Seules %d sur %d formes sont affichées" msgid "DataLab is a generic signal and image processing platform" msgstr "DataLab est une plateforme générique de traitement du signal et de l'image" msgid "Remove all" msgstr "Supprimer tout" msgid "Delete all results" msgstr "Supprimer tous les résultats" #, python-format msgid "Open %s..." msgstr "Ouvrir un objet %s..." #, python-format msgid "Open one or more %s files" msgstr "Ouvrir un ou plusieurs fichiers de type %s" msgid "Open from directory..." msgstr "Ouvrir depuis le répertoire..." #, python-format msgid "Open all %s files from directory" msgstr "Ouvrir tous les fichiers de type %s dans le répertoire" #, python-format msgid "Save %s..." msgstr "Enregistrer un objet %s..." #, python-format msgid "Save selected %s" msgstr "Enregistrer l'objet %s sélectionné" msgid "Save to directory..." msgstr "Enregistrer dans un répertoire..." #, python-format msgid "Save selected %s using a filename pattern" msgstr "Enregistrer les objets %s sélectionnés en utilisant un modèle de nom de fichier" msgid "Import text file..." msgstr "Importer un fichier texte..." msgid "Recompute" msgstr "Retraiter" #, python-format msgid "Recompute selected %s with its processing parameters" msgstr "Retraiter l'objet %s sélectionné avec ses paramètres de traitement" msgid "Select source objects" msgstr "Sélectionner les objets source" #, python-format msgid "Select source object(s) used to create the selected %s" msgstr "Sélectionner le ou les objets source utilisés pour créer l'objet %s sélectionné" msgid "Rename" msgstr "Renommer" #, python-format msgid "Edit title of selected %s or group" msgstr "Modifier le titre de l'objet %s ou du groupe sélectionné" msgid "New group..." msgstr "Nouveau groupe..." msgid "Create a new group" msgstr "Créer un nouveau groupe" msgid "Move up" msgstr "Déplacer vers le haut" msgid "Move up selection (groups or objects)" msgstr "Déplacer vers le haut la sélection (groupes ou objets)" msgid "Move down" msgstr "Déplacer vers le bas" msgid "Move down selection (groups or objects)" msgstr "Déplacer vers le bas la sélection (groupes ou objets)" msgid "Duplicate" msgstr "Dupliquer" #, python-format msgid "Duplicate selected %s" msgstr "Dupliquer l'objet %s sélectionné" msgid "Remove" msgstr "Supprimer" #, python-format msgid "Remove selected %s" msgstr "Supprimer l'objet %s sélectionné" msgid "Delete all" msgstr "Supprimer tout" msgid "Delete all groups and objects" msgstr "Supprimer tous les groupes et objets" msgid "Metadata" msgstr "Métadonnées" msgid "Copy metadata" msgstr "Copier les métadonnées" #, python-format msgid "Copy metadata from selected %s" msgstr "Copier les métadonnées de l'objet %s sélectionné" msgid "Paste metadata" msgstr "Coller les métadonnées" #, python-format msgid "Paste metadata into selected %s" msgstr "Coller les métadonnées dans l'objet %s sélectionné" msgid "Add metadata" msgstr "Ajouter des métadonnées" #, python-format msgid "Add a metadata item to selected %s" msgstr "Ajouter un élément de métadonnée à l'objet %s sélectionné" msgid "Import metadata" msgstr "Importer les métadonnées" #, python-format msgid "Import metadata into %s" msgstr "Importer les métadonnées dans l'objet %s" msgid "Export metadata" msgstr "Exporter les métadonnées" #, python-format msgid "Export selected %s metadata" msgstr "Exporter les métadonnées de l'objet %s sélectionné" msgid "Delete object metadata" msgstr "Supprimer les métadonnées" msgid "Delete all that is contained in object metadata" msgstr "Supprimer toutes les métadonnées liées à l'objet" msgid "Annotations" msgstr "Annotations" msgid "Copy annotations" msgstr "Copier les annotations" #, python-format msgid "Copy annotations from selected %s" msgstr "Copier les annotations de l'objet %s sélectionné" msgid "Paste annotations" msgstr "Coller les annotations" #, python-format msgid "Paste annotations into selected %s" msgstr "Coller les annotations dans l'objet %s sélectionné" msgid "Edit annotations" msgstr "Modifier les annotations" #, python-format msgid "Edit annotations of selected %s" msgstr "Modifier les annotations de l'objet %s sélectionné" msgid "Import annotations" msgstr "Importer les annotations" #, python-format msgid "Import annotations into %s" msgstr "Importer les annotations dans l'objet %s" msgid "Export annotations" msgstr "Exporter les annotations" #, python-format msgid "Export selected %s annotations" msgstr "Exporter les annotations de l'objet %s sélectionné" msgid "Delete annotations" msgstr "Supprimer les annotations" #, python-format msgid "Delete all annotations from selected %s" msgstr "Supprimer toutes les annotations de l'objet %s sélectionné" msgid "Insert object title as annotation label" msgstr "Insérer le titre de l'objet sous forme d'annotation" msgid "Add the selected object's title as a label to the plot annotations" msgstr "Ajouter le titre de l'objet sélectionné comme étiquette aux annotations du graphique" msgid "Copy titles to clipboard" msgstr "Copier les titres dans le presse-papier" msgid "Copy titles of selected objects to clipboard" msgstr "Copier les titres des objets sélectionnés dans le presse-papier" msgid "Edit graphically" msgstr "Édition graphique" msgid "Edit regions of interest graphically" msgstr "Édition graphique des régions d'intérêt" msgid "Edit numerically" msgstr "Édition numérique" msgid "Edit regions of interest numerically" msgstr "Édition numérique des régions d'intérêt" msgid "View in a new window" msgstr "Voir dans une nouvelle fenêtre" #, python-format msgid "View selected %s in a new window" msgstr "Voir l'objet %s sélectionné dans une nouvelle fenêtre" msgid "Constant Operations" msgstr "Opérations avec une constante" msgid "Axis transformation" msgstr "Transformation des axes" msgid "Level adjustment" msgstr "Ajustement de niveau" msgid "Offset correction" msgstr "Soustraction d'offset" msgid "Evaluate and subtract the offset value from the data" msgstr "Évaluer et soustraire la valeur d'offset des données" msgid "Noise addition" msgstr "Addition de bruit" msgid "Noise reduction" msgstr "Réduction de bruit" msgid "Fourier analysis" msgstr "Analyse de Fourier" msgid "Extract" msgstr "Extraire" msgid "Copy" msgstr "Copier" #, python-format msgid "Copy regions of interest from selected %s" msgstr "Copier les régions d'intérêt de l'objet %s sélectionné" msgid "Paste" msgstr "Coller" #, python-format msgid "Paste regions of interest into selected %s" msgstr "Coller les régions d'intérêt dans l'objet %s sélectionné" msgid "Import" msgstr "Importer" #, python-format msgid "Import regions of interest into %s" msgstr "Importer les régions d'intérêt dans l'objet %s" msgid "Export" msgstr "Exporter" #, python-format msgid "Export selected %s regions of interest" msgstr "Exporter les régions d'intérêt de l'objet %s sélectionné" msgid "Show results" msgstr "Afficher les résultats" msgid "Results label" msgstr "Etiquette des résultats" msgid "Show or hide the merged result label on the plot" msgstr "Afficher ou masquer l'étiquette de résultat fusionnée sur le graphique" msgid "Plot results" msgstr "Tracer les résultats" msgid "Delete results" msgstr "Supprimer les résultats" msgid "Refresh manually" msgstr "Rafraîchir manuellement" msgid "Refresh plot, even if auto-refresh is enabled" msgstr "Rafraîchit le graphique, même si le rafraîchissement automatique est activé" msgid "signal" msgstr "signal" msgid "signals" msgstr "signaux" msgid "Zero" msgstr "Zéro" msgid "Normal distribution" msgstr "Distribution normale" msgid "Poisson distribution" msgstr "Distribution de Poisson" msgid "Uniform distribution" msgstr "Distribution uniforme" msgid "Gaussian" msgstr "Gaussienne" msgid "Lorentzian" msgstr "Lorentzienne" msgid "Voigt" msgstr "Voigt" msgid "Blackbody (Planck's law)" msgstr "Corps noir (loi de Planck)" msgid "Sine" msgstr "Sinus" msgid "Cosine" msgstr "Cosinus" msgid "Sawtooth" msgstr "Dents de scie" msgid "Triangle" msgstr "Triangle" msgid "Square" msgstr "Carré" msgid "Cardinal sine" msgstr "Sinus cardinal" msgid "Linear chirp" msgstr "Modulation linéaire de fréquence" msgid "Step" msgstr "Échelon" msgid "Exponential" msgstr "Exponentielle" msgid "Logistic" msgstr "Logistique" msgid "Pulse" msgstr "Impulsion" msgid "Step pulse" msgstr "Impulsion échelon" msgid "Square pulse" msgstr "Impulsion carrée" msgid "Polynomial" msgstr "Polynomial" msgid "Custom" msgstr "Personnalisé" #, python-format msgid "Create new %s" msgstr "Créer un nouvel objet %s" msgid "Frequency filters" msgstr "Filtres fréquentiels" msgid "Fitting" msgstr "Ajustement" msgid "Interactive fitting" msgstr "Ajustement interactif" msgid "Linear fit" msgstr "Ajustement linéaire" msgid "Polynomial fit" msgstr "Ajustement polynomial" msgid "Gaussian fit" msgstr "Ajustement gaussien" msgid "Lorentzian fit" msgstr "Ajustement lorentzien" msgid "Voigt fit" msgstr "Ajustement Voigt" msgid "Multi-Gaussian fit" msgstr "Ajustement multi-gaussien" msgid "Multi-Lorentzian fit" msgstr "Ajustement multi-lorentzien" msgid "Planckian fit" msgstr "Ajustement planckien" msgid "Planckian (blackbody radiation) fitting" msgstr "Ajustement planckien (rayonnement du corps noir)" msgid "Two half-Gaussian fit" msgstr "Ajustement de deux demi-Gaussiennes" msgid "Asymmetric peak fitting with two half-Gaussians" msgstr "Ajustement de pics asymétriques avec deux demi-Gaussiennes" msgid "Piecewise exponential (raise-decay) fit" msgstr "Ajustement exponentiel par morceaux (croissant-décroissant)" msgid "Piecewise exponential fitting with raise and decay components" msgstr "Ajustement exponentiel par morceaux avec composantes croissante et décroissante" msgid "Exponential fit" msgstr "Ajustement exponentiel" msgid "Sinusoidal fit" msgstr "Ajustement sinusoïdal" msgid "CDF fit" msgstr "Ajustement CDF" msgid "Cumulative distribution function fit, related to Error function (erf)" msgstr "Fonction de distribution cumulative (CDF), liée à la fonction d'erreur (erf)" msgid "Stability analysis" msgstr "Analyse de stabilité" msgid "All stability features" msgstr "Toutes les analyses de stabilité" msgid "Compute all stability features" msgstr "Calcule toutes les analyses de stabilité" msgid "Full width at y=..." msgstr "Largeur à y=..." msgid "Compute the full width at a given y value" msgstr "Calcule la largeur à une valeur y donnée" msgid "First abscissa at y=..." msgstr "Première abscisse à y=..." msgid "Compute the first abscissa at a given y value (linear interpolation)" msgstr "Calcule la première abscisse à une valeur y donnée (interpolation linéaire)" msgid "Ordinate at x=..." msgstr "Ordonnée à x=..." msgid "Compute the ordinate at a given x value (linear interpolation)" msgstr "Calcule l'ordonnée à une valeur x donnée (interpolation linéaire)" msgid "Peak detection" msgstr "Détection de pics" msgid "Curve anti-aliasing" msgstr "Anticrénelage des courbes" msgid "Toggle curve anti-aliasing on/off (may slow down plotting)" msgstr "Active/désactive l'anticrénelage des courbes (peut ralentir le tracé)" msgid "Reset curve styles" msgstr "Réinitialiser les styles de courbes" msgid "" "Curve styles are looped over a list of predefined styles.\n" "This action resets the list to its initial state." msgstr "" "Les styles de courbes sont parcourus dans une liste de styles prédéfinis.\n" "Cette action réinitialise la liste à son état initial." msgid "image" msgstr "image" msgid "images" msgstr "images" msgid "Geometry" msgstr "Géométrie" msgid "Distribute on a grid..." msgstr "Distribuer sur une grille..." msgid "Reset image positions" msgstr "Réinitialiser les positions des images" msgid "2D sinc" msgstr "Sinc 2D" msgid "Ring pattern" msgstr "Motif en anneau" msgid "Ramp" msgstr "Rampe" msgid "Checkerboard" msgstr "Damier" msgid "Sinusoidal grating" msgstr "Réseau sinusoïdal" msgid "Siemens star" msgstr "Étoile de Siemens" msgid "Create ROI grid" msgstr "Créer une grille de ROI" msgid "Create a grid of regions of interest" msgstr "Créer une grille de régions d'intérêt" msgid "Thresholding" msgstr "Seuillage" msgid "All thresholding methods" msgstr "Toutes les méthodes de seuillage" msgid "Apply all thresholding methods" msgstr "Appliquer toutes les méthodes de seuillage" msgid "Exposure" msgstr "Exposition" msgid "Restoration" msgstr "Restauration" msgid "All denoising methods" msgstr "Tous les débruitages" msgid "Apply all denoising methods" msgstr "Appliquer tous les débruitages" msgid "Morphology" msgstr "Morphologie" msgid "All morphological operations" msgstr "Toutes les opérations morphologiques" msgid "Apply all morphological operations" msgstr "Appliquer toutes les opérations morphologiques" msgid "Edge detection" msgstr "Détection de contours" msgid "All edge detection filters..." msgstr "Tous les filtres de contours..." msgid "Compute all edge detection filters" msgstr "Calcule tous les filtres de contours" msgid "Erase area" msgstr "Gommer une zone" msgid "Erase area in the image as defined by a region of interest" msgstr "Gomme une zone de l'image définie par une région d'intérêt (ROI)." msgid "Intensity profiles" msgstr "Profils d'intensité" msgid "Line profile..." msgstr "Profil rectiligne..." msgid "Extract horizontal or vertical profile" msgstr "Extraire un profil horizontal ou vertical" msgid "Segment profile..." msgstr "Profil le long d'un segment..." msgid "Extract profile along a segment" msgstr "Extraire un profil le long d'un segment" msgid "Average profile..." msgstr "Profil moyen..." msgid "Extract average horizontal or vertical profile" msgstr "Extraire un profil moyen horizontal ou vertical" msgid "Radial profile extraction..." msgstr "Extraire le profil radial..." msgid "Radial profile extraction around image centroid" msgstr "Extraire le profil radial autour du barycentre de l'image" msgid "2D peak detection" msgstr "Détection de pics 2D" msgid "Compute automatic 2D peak detection" msgstr "Détection automatique de pics 2D" msgid "Blob detection" msgstr "Détection de taches" msgid "Resize" msgstr "Redimensionner" msgid "Pixel binning" msgstr "Binning" msgid "View images side-by-side" msgstr "Voir les images côte à côte" msgid "View selected images side-by-side in a new window" msgstr "Voir les images sélectionnées côte à côte dans une nouvelle fenêtre" msgid "Show contrast panel" msgstr "Afficher le panneau de contraste" msgid "Show or hide contrast adjustment panel" msgstr "Afficher ou cacher le panneau de réglage du contraste" msgid "Process signal" msgstr "Traiter le signal" #, python-format msgid "Loading data from %s..." msgstr "Chargement des données depuis %s..." msgid "Clipping uint32 data to int32." msgstr "Troncature de données uint32 en int32." msgid "No supported data available in HDF5 file(s)." msgstr "Aucune donnée prise en charge dans le(s) fichier(s) HDF5." msgid "Macro simple example" msgstr "Exemple simple de macro" #, python-format msgid "# ==> Unable to run '%s' macro" msgstr "# ==> Impossible d'exécuter la macro '%s'" msgid "Macro Python interpreter failed to start!" msgstr "Le démarrage de l'interpréteur Python des macros a échoué !" #, python-format msgid "# ==> Running '%s' macro..." msgstr "# ==> Exécution de la macro '%s'..." #, python-format msgid "Terminating '%s' macro" msgstr "Arrêt de la macro '%s'" #, python-format msgid "# <== '%s' macro has finished" msgstr "# <== La macro '%s' a terminé son exécution" #, python-format msgid "Available memory is below %d MB.

    Do you want to continue?" msgstr "La mémoire disponible est inférieure à %d Mo.

    Souhaitez-vous néanmoins continuer ?" msgid "This software is in the beta stage of its release cycle. The focus of beta testing is providing a feature complete software for users interested in trying new features before the final release. However, beta software may not behave as expected and will probably have more bugs or performance issues than completed software." msgstr "Ce logiciel est en phase beta de son cycle de développement. L'objectif de la phase de beta test est de fournir une version du logiciel disposant de toutes les fonctionnalités prévues dans la version finale, à l'attention d'utilisateurs intéressés de tester en avance de phase ces fonctionnalités. Cependant, un logiciel en version beta peut ne pas fonctionner correctement et présente plus d'anomalies résiduelles et de problèmes de performance que la version finale." msgid "This software is in the alpha stage of its release cycle. The focus of alpha testing is providing an incomplete software for early testing of specific features by users. Please note that alpha software was not thoroughly tested by the developer before it is released." msgstr "Ce logiciel est en phase alpha de son cycle de développement. L'objectif de la phase d'alpha test est de fournir une version incomplète du logiciel pour des tests réalisés en amont par des utilisateurs intéressés ou concernés par de nouvelles fonctionnalités ou des corrections d'anomalies. Notez que la version alpha d'un logiciel ne fait pas l'objet de tests poussés avant sa diffusion." msgid "This is not a stable release." msgstr "Ceci n'est pas une version stable." msgid "Do you want to see available log files?" msgstr "Souhaitez-vous consulter les journaux de bord disponibles ?" msgid "DataLab v0.20 plugins detected" msgstr "Plugins DataLab v0.20 détectés" msgid "The following plugins are using the old DataLab v0.20 format:" msgstr "Les plugins suivants utilisent l'ancien format DataLab v0.20 :" msgid "These plugins will not be loaded in DataLab v1.0 because they are not compatible with the new architecture." msgstr "Ces plugins ne seront pas chargés dans DataLab v1.0 car ils ne sont pas compatibles avec la nouvelle architecture." msgid "To use these plugins with DataLab v1.0, you need to update them. Please refer to the migration guide on the DataLab website " msgstr "Pour utiliser ces plugins avec DataLab v1.0, vous devez les mettre à jour. Veuillez vous référer au guide de migration sur le site web de DataLab " msgid " or in the PDF documentation." msgstr " ou dans la documentation PDF." msgid "Choosing to ignore this message will prevent it from appearing again." msgstr "Ignorer ce message empêchera son affichage ultérieur." #, python-format msgid "Welcome to %s!" msgstr "Bienvenue dans %s !" msgid "Open HDF5 files..." msgstr "Ouvrir des fichiers HDF5..." msgid "Open one or more HDF5 files" msgstr "Ouvrir un ou plusieurs fichiers HDF5" msgid "Save to HDF5 file..." msgstr "Enregistrer dans un fichier HDF5..." msgid "Save to HDF5 file" msgstr "Enregistrer dans un fichier HDF5" msgid "Browse HDF5 file..." msgstr "Parcourir un fichier HDF5..." msgid "Browse an HDF5 file" msgstr "Parcourir un fichier HDF5" msgid "Settings..." msgstr "Préférences..." msgid "Open settings dialog" msgstr "Ouvrir la boîte de dialogue des préférences" msgid "Main Toolbar" msgstr "Barre d'outils principale" msgid "Hide window" msgstr "Cacher la fenêtre" msgid "Hide DataLab window" msgstr "Cacher la fenêtre de DataLab" msgid "Quit" msgstr "Quitter" msgid "Quit application" msgstr "Quitter l'application" msgid "Auto-refresh" msgstr "Rafraîchissement automatique" msgid "Auto-refresh plot when object is modified, added or removed" msgstr "Rafraîchir le graphique automatiquement lors de la modification, de l'ajout ou de la suppression d'un objet" msgid "Show first object only" msgstr "Afficher uniquement le premier objet" msgid "Show only the first selected object (signal or image)" msgstr "Afficher uniquement le premier objet sélectionné (signal ou image)" msgid "Show graphical object titles" msgstr "Afficher les titres des objets graphiques" msgid "Show or hide ROI and other graphical object titles or subtitles" msgstr "Afficher ou cacher les titres (et sous-titres) des régions d'intérêt et autres objets graphiques" msgid "Signal Panel Toolbar" msgstr "Barre d'outils du panneau Signal" msgid "Image Panel Toolbar" msgstr "Barre d'outils du panneau Image" msgid "Signal View" msgstr "Vue Signal" msgid "Image View" msgstr "Vue Image" msgid "Signal Panel" msgstr "Panneau Signal" msgid "Image Panel" msgstr "Panneau Image" msgid "1D Signals: Manage and process one-dimensional data" msgstr "Signaux 1D : Gérer et traiter des données unidimensionnelles" msgid "2D Images: Manage and process two-dimensional data" msgstr "Images 2D : Gérer et traiter des données bidimensionnelles" msgid "&File" msgstr "&Fichier" msgid "&Create" msgstr "&Création" msgid "&Edit" msgstr "&Édition" msgid "ROI" msgstr "ROI" msgid "Operations" msgstr "Opérations" msgid "Processing" msgstr "Traitement" msgid "Analysis" msgstr "Analyse" msgid "Plugins" msgstr "Plugins" msgid "&View" msgstr "&Affichage" msgid "Online documentation" msgstr "Documentation en ligne" msgid "PDF documentation" msgstr "Documentation PDF" msgid "Tour" msgstr "Visite guidée" msgid "Demo" msgstr "Démo" msgid "Test segfault/Python error" msgstr "Tester un plantage/une erreur Python" msgid "Log files" msgstr "Journaux de bord" msgid "Installation and configuration" msgstr "Installation et configuration" msgid "Project home page" msgstr "Page d'accueil du projet" msgid "Bug report or feature request" msgstr "Rapport d'anomalie ou demande de nouvelle fonctionnalité" msgid "About..." msgstr "À propos..." msgid "" "Welcome to DataLab console!\n" "---------------------------\n" "You can access the main window with the 'dl' variable.\n" "Example:\n" " o = dl.get_object() # returns currently selected object\n" " o = dl[1] # returns object number 1\n" " o = dl['My image'] # returns object which title is 'My image'\n" " o.data # returns object data\n" "Modules imported at startup: os, sys, os.path as osp, time, numpy as np, scipy.signal as sps, scipy.ndimage as spi" msgstr "" "Bienvenue dans la console de DataLab !\n" "--------------------------------------\n" "Vous pouvez accéder à la fenêtre principale avec la variable 'dl'.\n" "Exemples :\n" " o = dl.get_object() # retourne l'objet actuellement sélectionné\n" " o = dl[1] # retourne l'objet numéro 1\n" " o = dl['Mon image'] # retourne l'objet dont le titre est 'Mon image'\n" " o.data # retourne les données de l'objet\n" "Modules importés au démarrage : os, sys, os.path as osp, time, numpy as np, scipy.signal as sps, scipy.ndimage as spi" msgid "Console" msgstr "Console" msgid "Macro Panel" msgstr "Gestionnaire de Macros" msgid "Disable auto-refresh?" msgstr "Désactiver le rafraîchissement automatique ?" msgid "When auto-refresh is disabled, the plot view will not automatically update when objects are modified, added or removed." msgstr "Lorsque le rafraîchissement automatique est désactivé, la vue du graphique ne sera pas mise à jour automatiquement lorsque des objets sont modifiés, ajoutés ou supprimés." msgid "You will need to manually click the refresh button to update the view." msgstr "Vous devrez cliquer manuellement sur le bouton de rafraîchissement pour mettre à jour la vue." msgid "Are you sure you want to disable auto-refresh?" msgstr "Êtes-vous sûr de vouloir désactiver le rafraîchissement automatique ?" msgid "Save" msgstr "Enregistrer" msgid "Do you want to clear current workspace (signals and images) before importing data from HDF5 files?" msgstr "Souhaitez-vous vider l'espace de travail actuel (signaux et images) avant d'importer des données depuis des fichiers HDF5 ?" msgid "Note: If you choose No, when importing DataLab workspace files, objects with conflicting identifiers will have their processing history lost (features like 'Show source' and 'Recompute' will not work for those objects). Non-conflicting objects will preserve their processing history." msgstr "Note : Si vous choisissez Non, lors de l'importation de fichiers d'espace de travail DataLab, les objets ayant des identifiants en conflit perdront leur historique de traitement (les fonctionnalités telles que 'Afficher la source' et 'Retraiter' ne fonctionneront pas pour ces objets). Les objets sans conflit conserveront leur historique de traitement." #, python-format msgid "Choosing to ignore this message will prevent it from being displayed again, and will use the current setting (%s)." msgstr "Ignorer ce message empêchera son affichage ultérieur et utilisera le réglage actuel (%s)." msgid "Yes" msgstr "Oui" msgid "No" msgstr "Non" msgid "Open" msgstr "Ouvrir" msgid "HDF5 files (*.h5 *.hdf5 *.hdf *.he5);;All files (*)" msgstr "Fichiers HDF5 (*.h5 *.hdf5 *.hdf *.he5);;Tous les fichiers (*)" msgid "not started" msgstr "non démarré" #, python-format msgid "started (port %s)" msgstr "démarré (port %s)" msgid "enabled" msgstr "activé" msgid "disabled" msgstr "désactivé" msgid "Advanced configuration:" msgstr "Configuration avancée :" msgid "XML-RPC server:" msgstr "Serveur XML-RPC :" msgid "Process isolation:" msgstr "Isolation de processus :" msgid "Created by" msgstr "Créé par" #, python-format msgid "Developed and maintained by %s open-source project team" msgstr "Développé et maintenu par l'équipe du projet libre %s" msgid "About" msgstr "À propos de" msgid "Apply settings to existing results?" msgstr "Appliquer les réglages aux résultats existants ?" msgid "" "Visualization settings for annotated shapes and markers have been modified.\n" "\n" "Do you want to apply these settings to existing results in the workspace?" msgstr "" "Les réglages d'affichage par défaut pour les formes annotées et les marqueurs ont été modifiés.\n" "\n" "Souhaitez-vous appliquer ces réglages aux résultats existants dans l'espace de travail ?" msgid "Visualization settings" msgstr "Réglages d'affichage" #, python-format msgid "Default visualization settings have changed.

    Do you want to update all active %s objects?" msgstr "Les réglages d'affichage par défaut ont changé.

    Souhaitez-vous mettre à jour tous les objets %s actifs ?" msgid "Do you want to save all signals and images to an HDF5 file before quitting DataLab?" msgstr "Souhaitez-vous enregistrer tous les signaux et images dans un fichier HDF5 avant de quitter DataLab ?" msgid "Select one point then press OK to accept" msgstr "Sélectionnez un point puis appuyez sur OK pour valider" msgid "Edit curve interactively" msgstr "Modifier la courbe graphiquement" msgid "Insert point" msgstr "Insérer un point" msgid "Custom signal" msgstr "Signal personnalisé" msgid "Object metadata" msgstr "Métadonnées de l'objet" msgid "(click on Metadata button for more details)" msgstr "(cliquer sur le bouton Métadonnées pour plus de détails)" msgid "Drag files here to open" msgstr "Déposer des fichiers ici pour les ouvrir" msgid "" "Working with 2D images?\n" "Switch to the Image Panel using the tab above." msgstr "" "Vous souhaitez travailler avec des images 2D ?\n" "Passez au panneau Image en utilisant l'onglet ci-dessus." msgid "" "Working with 1D signals?\n" "Switch to the Signal Panel using the tab above." msgstr "" "Vous souhaitez travailler avec des signaux 1D ?\n" "Passez au panneau Signal en utilisant l'onglet ci-dessus." msgid "History" msgstr "Historique" msgid "Analysis parameters" msgstr "Paramètres d'analyse" msgid "Properties" msgstr "Propriétés" #, python-format msgid "Parameters for function `%s`" msgstr "Paramètres pour la fonction `%s`" msgid "Created" msgstr "Création de" msgid "Original object" msgstr "Objet original" msgid "(source deleted)" msgstr "(source supprimée)" msgid "(multiple sources)" msgstr "(sources multiples)" msgid "Creation Parameters" msgstr "Paramètres de création" msgid "Creation" msgstr "Création" msgid "Signal was modified in-place." msgstr "Le signal a été modifié sur place." msgid "Image was modified in-place." msgstr "L'image a été modifiée sur place." msgid "If computation were performed based on this object, they may need to be redone." msgstr "Si des calculs ont été effectués sur la base de cet objet, ils devront peut-être être refaits." #, python-format msgid "" "Failed to recreate object with new parameters:\n" "%s" msgstr "" "Échec de la recréation de l'objet avec les nouveaux paramètres :\n" "%s" msgid "Processing Parameters" msgstr "Paramètres de traitement" msgid "No processing object available." msgstr "Aucun objet de traitement disponible." msgid "Processing metadata is incomplete." msgstr "Les métadonnées de traitement sont incomplètes." msgid "Processing metadata is incomplete (missing source UUID)." msgstr "Les métadonnées de traitement sont incomplètes (UUID source manquant)." msgid "Source object no longer exists." msgstr "L'objet source n'existe plus." msgid "The object that was used to create this processed object has been deleted and cannot be used for reprocessing." msgstr "L'objet qui a été utilisé pour créer cet objet traité a été supprimé et ne peut pas être utilisé pour le retraitement." #, python-format msgid "" "Failed to reprocess object:\n" "%s" msgstr "" "Échec du retraitement de l'objet :\n" "%s" msgid "Processing was cancelled." msgstr "Le traitement a été annulé." msgid "Signal was reprocessed." msgstr "Le signal a été retraité." msgid "Image was reprocessed." msgstr "L'image a été retraitée." msgid "Geometry results" msgstr "Résultats géométriques" msgid "Table results" msgstr "Résultats tabulaires" msgid "Other metadata" msgstr "Autres métadonnées" msgid "Save to directory" msgstr "Enregistrer dans un répertoire" msgid "Directory" msgstr "Répertoire" msgid "Basename pattern" msgstr "Nom de base" msgid "Python format string. See description for details." msgstr "Chaîne de format Python. Voir la description pour plus de détails." msgid "Help" msgstr "Aide" msgid "Extension" msgstr "Extension" msgid "Overwrite" msgstr "Écraser" msgid "Overwrite existing files" msgstr "Écraser les fichiers existants" msgid "Preview" msgstr "Aperçu" msgid "Add a new metadata item to the selected objects.

    The metadata key will be the same for all objects, but the value can use a pattern to generate different values.
    Click the Help button for details on the pattern syntax.
    " msgstr "Ajouter un nouvel élément de métadonnée aux objets sélectionnés.

    La clé de métadonnée sera la même pour tous les objets, mais la valeur peut utiliser un modèle pour générer des valeurs différentes.
    Cliquez sur le bouton Aide pour plus de détails sur la syntaxe du modèle.
    " msgid "String" msgstr "Chaîne de caractères" msgid "Float" msgstr "Flottant" msgid "Integer" msgstr "Entier" msgid "Boolean" msgstr "Booléen" msgid "Metadata key" msgstr "Clé de métadonnées" msgid "The key name for the metadata item" msgstr "Le nom de la clé pour l'élément de métadonnées" msgid "Value pattern" msgstr "Modèle de valeur" msgid "Conversion" msgstr "Conversion" msgid "Select what to keep from the clipboard.

    Result shapes and annotations, if kept, will be merged with existing ones. All other metadata will be replaced." msgstr "Sélectionnez ce que vous souhaitez conserver dans le presse-papier.

    Les formes graphiques et les annotations, si conservées, seront fusionnées avec celles existantes. Toutes les autres métadonnées seront remplacées." msgid "Delete group(s)" msgstr "Supprimer le(s) groupe(s)" msgid "Are you sure you want to delete the selected group(s)?" msgstr "Êtes-vous sûr de vouloir supprimer le(s) groupe(s) sélectionné(s) ?" #, python-format msgid "Do you want to delete all objects (%s)?" msgstr "Souhaitez-vous supprimer tous les objets (%s) ?" msgid "Delete metadata" msgstr "Supprimer les métadonnées" msgid "Some selected objects have regions of interest.
    Do you want to delete them as well?" msgstr "Certains objets sélectionnés ont des régions d'intérêt.
    Souhaitez-vous les supprimer également ?" msgid "New group" msgstr "Nouveau groupe" msgid "Group name:" msgstr "Nom du groupe :" msgid "Rename object" msgstr "Renommer l'objet" msgid "Object name:" msgstr "Nom de l'objet :" msgid "Rename group" msgstr "Renommer le groupe" msgid "Reading objects from file" msgstr "Lecture des objets depuis le fichier" msgid "Adding objects to workspace" msgstr "Ajout d'objets à l'espace de travail" msgid "Scanning directory" msgstr "Analyse du répertoire" msgid "Save as" msgstr "Enregistrer sous" msgid "Saving..." msgstr "Enregistrement en cours..." msgid "Import ROI" msgstr "Importer une ROI" msgid "Export ROI" msgstr "Exporter une ROI" msgid "Selected object(s) do not have processing parameters that can be recomputed." msgstr "L'objet (ou les objets) sélectionné(s) n'ont pas de paramètres de traitement pouvant être retraités." msgid "Recomputing objects" msgstr "Retraitement des objets" msgid "Failed to recompute object" msgstr "Échec du retraitement de l'objet" msgid "Do you want to continue with the next object?" msgstr "Souhaitez-vous continuer avec l'objet suivant ?" msgid "Selected object does not have processing metadata." msgstr "L'objet sélectionné n'a pas de métadonnées de traitement." msgid "Selected object does not have source object references." msgstr "L'objet sélectionné n'a pas de références d'objet source." msgid "Source objects no longer exist." msgstr "Les objets source n'existent plus." msgid "Selected a single source object" msgstr "Un seul objet source sélectionné" #, python-format msgid "Selected %d source objects" msgstr "%d objets source sélectionnés" msgid "1 source object no longer exists" msgstr "1 objet source n'existe plus." #, python-format msgid "%d source objects no longer exist" msgstr "%d objets source n'existent plus." msgid "No result currently available for this object." msgstr "Aucun résultat disponible pour le moment." msgid "This feature leverages the results of previous analysis performed on the selected object(s).

    To compute results, select one or more objects and choose a feature in the Analysis menu." msgstr "Cette fonctionnalité exploite les résultats d'analyse précédents.

    Pour calculer des résultats, sélectionnez un ou plusieurs objets et choisissez une fonctionnalité dans le menu Analyse." msgid "Indices" msgstr "Indices" msgid "Plot kind" msgstr "Type de graphique" msgid "One curve per object (or ROI) and per result title" msgstr "Une courbe par objet (ou ROI) et par titre de résultat" msgid "One curve per result title" msgstr "Une courbe par titre de résultat" msgid "X axis" msgstr "Axe des X" msgid "Y axis" msgstr "Axe des Y" #, python-format msgid "Plot results obtained from previous analyses.

    This plot is based on results associated with '%s' prefix." msgstr "Tracer les résultats obtenus avec des analyses réalisées précédemment.

    Ce graphique est basé sur les résultats associés au préfixe '%s'." msgid "All objects associated with results must have the same regions of interest (ROIs) to plot results together." msgstr "Tous les objets associés aux résultats doivent avoir les mêmes ROIs pour tracer les résultats ensemble." msgid "Are you sure you want to delete all results of the selected object(s)?" msgstr "Êtes-vous sûr de vouloir supprimer tous les résultats des objets sélectionnés ?" msgid "Annotation added" msgstr "Annotation ajoutée" msgid "The label has been added as an annotation. You can edit or remove it using the annotation editing window.

    Choosing to ignore this message will prevent it from being displayed again." msgstr "L'étiquette a été ajoutée comme annotation. Vous pouvez la modifier ou la supprimer en utilisant la fenêtre d'édition des annotations.

    Ignorer ce message empêchera son affichage ultérieur." msgid "Macro panel" msgstr "Panneau des macros" msgid "Python files" msgstr "Fichiers Python" msgid "-***- Macro Console -***-" msgstr "-***- Console des macros -***-" msgid "Run macro" msgstr "Exécuter la macro" msgid "Stop macro" msgstr "Arrêter la macro" msgid "New macro" msgstr "Nouvelle macro" msgid "Rename macro" msgstr "Renommer la macro" msgid "Import macro from file" msgstr "Importer la macro depuis un fichier" msgid "Export macro to file" msgstr "Exporter la macro vers un fichier" msgid "Remove macro" msgstr "Supprimer la macro" msgid "Macro editor toolbar" msgstr "Barre d'outils des macros" msgid "New title:" msgstr "Nouveau titre :" msgid "When closed, the macro is permanently destroyed, unless it has been exported first." msgstr "Lorsqu'elle est fermée, une macro est détruite de manière définitive, sauf si elle a été exportée au préalable." msgid "Do you want to continue?" msgstr "Souhaitez-vous vraiment continuer ?" msgid "Creating geometric shapes" msgstr "Création des formes géométriques" msgid "Creating plot items" msgstr "Création des objets graphiques" msgid "Failed to deserialize processing parameters from JSON." msgstr "Échec de la désérialisation des paramètres de traitement depuis le format JSON." msgid "DataLab relies on various libraries to perform the computation. During the computation, errors may occur because of the data (e.g. division by zero, unexpected data type, etc.) or because of the libraries (e.g. memory error, etc.). If you encounter an error, before reporting it, please ensure that the computation is correct, by checking the data and the parameters." msgstr "DataLab utilise plusieurs bibliothèques pour réaliser les calculs. Lors des calculs, des erreurs peuvent survenir en raison de la nature des données (par exemple division par zéro, type de données inattendu, etc.) ou à cause de limitations ou d'erreurs dans les bibliothèques (par exemple erreur mémoire, etc.). Si vous rencontrez une erreur, avant de la signaler, assurez-vous que le calcul est correct, en vérifiant les données et les paramètres." msgid "X-array incompatibility" msgstr "Incompatibilité des tableaux des X" msgid "" "The selected signals have different X arrays.\n" "\n" "To perform the computation, signals need to be interpolated to match a common X array.\n" "\n" "Do you want to continue with automatic interpolation?" msgstr "" "Les signaux sélectionnés ont des tableaux des X différents.\n" "\n" "Pour effectuer le calcul, les signaux doivent être interpolés pour correspondre à un tableau des X commun.\n" "\n" "Souhaitez-vous continuer avec une interpolation automatique ?" msgid "Yes to All" msgstr "Oui à tout" msgid "Recomputing..." msgstr "Retraitement en cours..." msgid "Processing object with updated parameters..." msgstr "Traitement de l'objet avec les paramètres mis à jour..." msgid "Recomputing" msgstr "Retraitement" #, python-format msgid "Computing: %s" msgstr "Calcul en cours : %s" msgid "In pairwise mode, you need to select objects in at least two groups." msgstr "En mode 'pairwise', vous devez sélectionner des objets dans au moins deux groupes." msgid "In pairwise mode, you need to select the same number of objects in each group." msgstr "En mode 'pairwise', vous devez sélectionner le même nombre d'objets dans chaque groupe." msgid "Parameters" msgstr "Paramètres" #, python-format msgid "Calculating: %s" msgstr "Calcul : %s" #, python-format msgid "Select %s" msgstr "Sélectionner %s" #, python-format msgid "Note: operation mode is pairwise: %s object(s) expected (i.e. as many as in the first group)" msgstr "Note : le mode d'opération est pairwise : %s objet(s) attendu(s) (c'est-à-dire autant que dans le premier groupe)" msgid "Note: operation mode is single operand: 1 object expected" msgstr "Note : le mode d'opération est single : 1 objet attendu" msgid "Second operand" msgstr "Second opérande" msgid "Regions of Interest" msgstr "Régions d'intérêt" msgid "No ROI selected for editing." msgstr "Aucune ROI sélectionnée pour l'édition." msgid "Remove ROI" msgstr "Supprimer la ROI" #, python-format msgid "Are you sure you want to remove ROI '%s'?" msgstr "Êtes-vous sûr de vouloir supprimer la ROI '%s' ?" msgid "Sum" msgstr "Addition" msgid "Average" msgstr "Moyenne" msgid "Standard deviation" msgstr "Écart-type" msgid "Difference" msgstr "Soustraction" msgid "image to subtract" msgstr "image à soustraire" msgid "Quadratic Difference" msgstr "Différence quadratique" msgid "Product" msgstr "Produit" msgid "Division" msgstr "Division" msgid "divider" msgstr "le diviseur" msgid "Inverse" msgstr "Inverse" msgid "Arithmetic" msgstr "Arithmétique" msgid "signal to operate with" msgstr "signal opérande" msgid "Add constant" msgstr "Additionner une constante" msgid "Subtract constant" msgstr "Soustraire une constante" msgid "Multiply by constant" msgstr "Multiplier par une constante" msgid "Divide by constant" msgstr "Diviser par une constante" msgid "Absolute value" msgstr "Valeur absolue" msgid "Phase" msgstr "Phase" msgid "Combine with phase" msgstr "Combiner avec la phase" msgid "Create a complex-valued image from magnitude and phase" msgstr "Créer une image à valeurs complexes à partir de l'amplitude et de la phase" msgid "phase" msgstr "phase" msgid "Real part" msgstr "Partie réelle" msgid "Imaginary part" msgstr "Partie imaginaire" msgid "Combine with imaginary part" msgstr "Combiner avec la partie imaginaire" msgid "Create a complex-valued image from real and imaginary parts" msgstr "Créer une image à valeurs complexes à partir des parties réelle et imaginaire" msgid "imaginary part" msgstr "partie imaginaire" msgid "Convert data type" msgstr "Convertir le type de données" msgid "Logarithm (base 10)" msgstr "Logarithme (base 10)" msgid "Flat-field correction" msgstr "Correction de champ plat" msgid "flat field image" msgstr "l'image de champ plat" msgid "Flip horizontally" msgstr "Symétrie horizontale" msgid "Flip diagonally" msgstr "Symétrie diagonale" msgid "Flip vertically" msgstr "Symétrie verticale" #, python-format msgid "Rotate %s right" msgstr "Rotation directe de %s" #, python-format msgid "Rotate %s left" msgstr "Rotation inverse de %s" msgid "Rotate by" msgstr "Rotation de" msgid "Line profile" msgstr "Profil rectiligne" msgid "Segment profile" msgstr "Profil le long d'un segment" msgid "Average profile" msgstr "Profil moyen" msgid "Radial profile" msgstr "Profil radial" msgid "kernel to convolve with" msgstr "noyau de la convolution" msgid "Deconvolution" msgstr "Déconvolution" msgid "kernel to deconvolve with" msgstr "noyau de la déconvolution" msgid "Set uniform coordinates" msgstr "Convertir en coordonnées uniformes" msgid "Polynomial calibration" msgstr "Étalonnage polynomial" msgid "" "Apply polynomial calibration to the X, Y or Z axis:\n" " • x' = a0 + a1*x + a2*x^2 + ...\n" " • y' = a0 + a1*y + a2*y^2 + ...\n" " • z' = a0 + a1*z + a2*z^2 + ..." msgstr "" "Appliquer un étalonnage polynomial à l'axe X, Y ou Z :\n" " • x' = a0 + a1*x + a2*x^2 + ...\n" " • y' = a0 + a1*y + a2*y^2 + ...\n" " • z' = a0 + a1*z + a2*z^2 + ..." msgid "Swap X/Y axes" msgstr "Permuter les axes X/Y" msgid "Normalize" msgstr "Normaliser" msgid "Clipping" msgstr "Écrêtage" msgid "Add Gaussian noise" msgstr "Ajouter du bruit gaussien" msgid "Add Poisson noise" msgstr "Ajouter du bruit de Poisson" msgid "Add uniform noise" msgstr "Ajouter du bruit uniforme" msgid "Gaussian filter" msgstr "Filtre gaussien" msgid "Moving average" msgstr "Moyenne mobile" msgid "Moving median" msgstr "Médiane mobile" msgid "Wiener filter" msgstr "Filtre de Wiener" msgid "Zero padding" msgstr "Complément de zéros" msgid "Zero padding is used to increase the frequency resolution of the FFT" msgstr "Le complément de zéros est utilisé pour augmenter la résolution de la FFT" msgid "FFT" msgstr "FFT" msgid "Fast Fourier Transform (FFT) is an estimation of the Discrete Fourier Transform (DFT). Results are complex numbers, but only the real part is plotted." msgstr "La transformée de Fourier rapide (FFT) est une estimation de la transformée de Fourier discrète (DFT). Les résultats sont des nombres complexes, mais seule la partie réelle est tracée." msgid "Inverse FFT" msgstr "FFT inverse" msgid "Inverse Fast Fourier Transform (IFFT) is an estimation of the Inverse Discrete Fourier Transform (IDFT). Results are complex numbers, but only the real part is plotted." msgstr "La transformée de Fourier rapide inverse (IFFT) est une estimation de la transformée de Fourier discrète inverse (IDFT). Les résultats sont des nombres complexes, mais seule la partie réelle est tracée." msgid "Magnitude spectrum" msgstr "Spectre d'amplitude" msgid "Magnitude spectrum is the absolute value of the FFT result. It is a measure of the amplitude of the frequency components." msgstr "Le spectre d'amplitude est la valeur absolue du résultat de la transformée de Fourier rapide. Il mesure l'amplitude des composantes fréquentielles." msgid "Phase spectrum" msgstr "Spectre de phase" msgid "Phase spectrum is the angle of the FFT result. It is a measure of the phase of the frequency components." msgstr "Le spectre de phase est l'angle du résultat de la transformée de Fourier rapide. Il mesure la phase des composantes fréquentielles." msgid "Power spectral density" msgstr "Densité spectrale de puissance" msgid "Power spectral density (PSD) is the square of the magnitude spectrum. It is a measure of the power of the frequency components." msgstr "La densité spectrale de puissance est le carré du spectre d'amplitude. Elle mesure la puissance des composantes fréquentielles." msgid "Butterworth" msgstr "Butterworth" msgid "Gaussian bandpass" msgstr "Passe-bande gaussien" msgid "Parametric thresholding" msgstr "Seuillage paramétrique" msgid "Parametric thresholding allows to select a thresholding method and a threshold value." msgstr "Le seuillage paramétrique permet de sélectionner une méthode de seuillage et une valeur de seuil." msgid "ISODATA thresholding" msgstr "Seuillage ISODATA" msgid "Li thresholding" msgstr "Seuillage Li" msgid "Mean thresholding" msgstr "Seuillage moyen" msgid "Minimum thresholding" msgstr "Seuillage minimum" msgid "Otsu thresholding" msgstr "Seuillage Otsu" msgid "Triangle thresholding" msgstr "Seuillage Triangle" msgid "Yen thresholding" msgstr "Seuillage Yen" msgid "Gamma correction" msgstr "Correction gamma" msgid "Logarithmic correction" msgstr "Correction logarithmique" msgid "Sigmoid correction" msgstr "Correction sigmoïde" msgid "Histogram equalization" msgstr "Egalisation d'histogramme" msgid "Adaptive histogram equalization" msgstr "Egalisation d'histogramme adaptative" msgid "Intensity rescaling" msgstr "Ajustement des niveaux" msgid "Total variation denoising" msgstr "Filtrage variationnel (débruitage)" msgid "Bilateral filter denoising" msgstr "Filtrage bilatéral (débruitage)" msgid "Wavelet denoising" msgstr "Débruitage par ondelettes" msgid "White Top-Hat denoising" msgstr "Débruitage Top-Hat" msgid "White Top-Hat (disk)" msgstr "Top-Hat (disque)" msgid "Black Top-Hat (disk)" msgstr "Top-Hat dual (disque)" msgid "Erosion (disk)" msgstr "Erosion (disque)" msgid "Dilation (disk)" msgstr "Dilatation (disque)" msgid "Opening (disk)" msgstr "Ouverture (disque)" msgid "Closing (disk)" msgstr "Fermeture (disque)" msgid "Canny filter" msgstr "Filtre de Canny" msgid "Farid filter" msgstr "Filtre de Farid" msgid "Farid filter (horizontal)" msgstr "Filtre de Farid (horizontal)" msgid "Farid filter (vertical)" msgstr "Filtre de Farid (vertical)" msgid "Laplace filter" msgstr "Filtre de Laplace" msgid "Prewitt filter" msgstr "Filtre de Prewitt" msgid "Prewitt filter (horizontal)" msgstr "Filtre de Prewitt (horizontal)" msgid "Prewitt filter (vertical)" msgstr "Filtre de Prewitt (vertical)" msgid "Roberts filter" msgstr "Filtre de Roberts" msgid "Scharr filter" msgstr "Filtre de Scharr" msgid "Scharr filter (horizontal)" msgstr "Filtre de Scharr (horizontal)" msgid "Scharr filter (vertical)" msgstr "Filtre de Scharr (vertical)" msgid "Sobel filter" msgstr "Filtre de Sobel" msgid "Sobel filter (horizontal)" msgstr "Filtre de Sobel (horizontal)" msgid "Sobel filter (vertical)" msgstr "Filtre de Sobel (vertical)" msgid "Resampling" msgstr "Rééchantillonnage" msgid "Statistics" msgstr "Statistiques" msgid "Horizontal projection" msgstr "Projection horizontale" msgid "Compute the sum of pixel intensities along each column (projection on the x-axis)" msgstr "Calcule la somme des intensités des pixels le long de chaque colonne (projection sur l'axe des x)" msgid "Vertical projection" msgstr "Projection verticale" msgid "Compute the sum of pixel intensities along each row (projection on the y-axis)" msgstr "Calcule la somme des intensités des pixels le long de chaque ligne (projection sur l'axe des y)" msgid "Histogram" msgstr "Histogramme" msgid "Centroid" msgstr "Barycentre" msgid "Compute image centroid" msgstr "Calcule le barycentre de l'image" msgid "Minimum enclosing circle center" msgstr "Centre du cercle minimum" msgid "Compute smallest enclosing circle center" msgstr "Calcule le centre du cercle minimum" msgid "Contour detection" msgstr "Détection de contours" msgid "Compute contour shape fit" msgstr "Ajustement d'une forme géométrique à un contour" msgid "Detect peaks in the image" msgstr "Détection de pics dans l'image" msgid "Circle Hough transform" msgstr "Transformée de Hough circulaire" msgid "Detect circular shapes using circle Hough transform" msgstr "Détection de formes circulaires à partir d'une tranformée de Hough" msgid "Blob detection (DOG)" msgstr "Détection de taches (gaussien)" msgid "Detect blobs using Difference of Gaussian (DOG) method" msgstr "Détection de taches basée sur la méthode du discriminant gaussien" msgid "Blob detection (DOH)" msgstr "Détection de taches (hessien)" msgid "Detect blobs using Difference of Gaussian (DOH) method" msgstr "Détection de taches basée sur la méthode du hessien gaussien" msgid "Blob detection (LOG)" msgstr "Détection de taches (LOG)" msgid "Detect blobs using Laplacian of Gaussian (LOG) method" msgstr "Détection de taches basée sur la méthode du laplacien de gaussien (LOG)" msgid "Blob detection (OpenCV)" msgstr "Détection de taches (OpenCV)" msgid "Detect blobs using OpenCV SimpleBlobDetector" msgstr "Détection de taches basée sur SimpleBlobDetector d'OpenCV" msgid "Creating a ROI grid will overwrite any existing ROI.

    Do you want to continue?" msgstr "La création d'une grille de ROI écrasera toute ROI existante.

    Voulez-vous continuer ?" msgid "Extract ROI" msgstr "Extraire une ROI" msgid "Do you want to extract images from the defined ROI?" msgstr "Souhaitez-vous extraire les images de la ROI définie ?" msgid "Warning:" msgstr "Attention :" msgid "Selected images do not have the same size" msgstr "Les images sélectionnées n'ont pas la même taille" msgid "Destination size:" msgstr "Taille de destination :" msgid "Binning" msgstr "Binning" msgid "Profile" msgstr "Profil" msgid "Distribute on grid" msgstr "Distribuer sur grille" msgid "Denoise / Top-Hat" msgstr "Débruitage Top-Hat" msgid "Two Half-Gaussians fit" msgstr "Ajustement de deux demi-Gaussiennes" msgid "Piecewise exponential fit" msgstr "Ajustement exponentiel par morceaux" msgid "Sigmoid fit" msgstr "Ajustement sigmoïde" msgid "signal to subtract" msgstr "signal à soustraire" msgid "Create a complex-valued signal from magnitude and phase" msgstr "Créer un signal à valeurs complexes à partir de l'amplitude et de la phase" msgid "Create a complex-valued signal from real and imaginary parts" msgstr "Créer un signal à valeurs complexes à partir des parties réelle et imaginaire" msgid "Square root" msgstr "Racine carrée" msgid "Derivative" msgstr "Dérivée" msgid "Integral" msgstr "Intégrale" msgid "signal to convolve with" msgstr "signal pour la convolution" msgid "Replace X by other signal's Y" msgstr "Remplacer X par le Y d'un autre signal" msgid "" "Replace X coordinates using Y values from another signal.\n" "Useful for calibration: plot data vs wavelength scale." msgstr "" "Remplacer les coordonnées X en utilisant les valeurs Y d'un autre signal.\n" "Utile pour l'étalonnage : tracer des données en fonction d'une échelle de longueurs d'onde." msgid "signal providing Y values for X axis" msgstr "signal fournissant les valeurs Y pour l'axe X" msgid "signal to deconvolve with" msgstr "signal pour la déconvolution" msgid "Assemble signals into image" msgstr "Assembler des signaux en une image" msgid "" "Assemble multiple signals into a 2D image.\n" "Signals can be stacked as rows or columns, with optional normalization." msgstr "" "Assembler plusieurs signaux en une image 2D.\n" "Les signaux peuvent être empilés en lignes ou en colonnes, avec une normalisation optionnelle." msgid "Linear calibration" msgstr "Étalonnage linéaire" msgid "" "Apply linear calibration to the X or Y axis:\n" " • x' = ax + b\n" " • y' = ay + b" msgstr "" "Appliquer un étalonnage linéaire à l'axe X ou Y :\n" " • x' = ax + b\n" " • y' = ay + b" msgid "Reverse X-axis" msgstr "Inverser l'axe des X" msgid "Convert to polar coordinates" msgstr "Convertir en coordonnées polaires" msgid "Convert to cartesian coordinates" msgstr "Convertir en coordonnées cartésiennes" msgid "Power" msgstr "Puissance" msgid "Low-pass filter" msgstr "Filtre passe-bas" msgid "High-pass filter" msgstr "Filtre passe-haut" msgid "Band-pass filter" msgstr "Filtre passe-bande" msgid "Band-stop filter" msgstr "Filtre coupe-bande" msgid "Evaluate fit" msgstr "Évaluer l'ajustement" msgid "signal for X values" msgstr "signal pour les valeurs X" msgid "Evaluate a fitting curve on the x-axis of another signal" msgstr "Évaluer une courbe d'ajustement sur l'axe des x d'un autre signal" msgid "Windowing" msgstr "Fenêtrage" msgid "Apply a window (apodization) function: Hann, Hamming..." msgstr "Appliquer une fonction de fenêtrage (ou d'apodisation) : Hann, Hamming..." msgid "Detrending" msgstr "Elimination de tendance" msgid "Interpolation" msgstr "Interpolation" msgid "Allan variance" msgstr "Variance d'Allan" msgid "Allan deviation" msgstr "Ecart-type d'Allan" msgid "Overlapping Allan variance" msgstr "Variance d’Allan avec recouvrement" msgid "Modified Allan variance" msgstr "Variance d'Allan modifiée" msgid "Hadamard variance" msgstr "Variance de Hadamard" msgid "Total variance" msgstr "Variance totale" msgid "Time deviation" msgstr "Ecart-type temporel" msgid "X-Y mode" msgstr "Mode X-Y" msgid "Y-signal of the X-Y mode" msgstr "Signal de l'axe des ordonnées du mode X-Y" msgid "Plot one signal as a fonction of the other one" msgstr "Tracer un signal en fonction de l'autre" msgid "Full width at half-maximum" msgstr "Largeur à mi-hauteur" msgid "Full width at" msgstr "Largeur à " msgid "Extract pulse features" msgstr "Extraire les caractéristiques d'une impulsion" msgid "Extract pulse features (amplitude, rise time, fall time...)" msgstr "Extraire les caractéristiques d'une impulsion (amplitude, temps de montée, temps de descente...)" msgid "Abscissa of the minimum and maximum" msgstr "Abscisse du minimum et du maximum" msgid "Compute the smallest argument of the minima and the smallest argument of the maxima" msgstr "Calcule le plus petit argument du minimum et le plus petit argument du maximum" msgid "Sampling rate and period" msgstr "Fréquence et période d'échantillonnage" msgid "Compute sampling rate and period for a constant sampling signal" msgstr "Calcule la fréquence et la période d'échantillonnage pour un signal à pas constant" msgid "Dynamic parameters" msgstr "Paramètres dynamiques" msgid "Compute dynamic parameters: ENOB, SNR, SINAD, THD, ..." msgstr "Calcule les paramètres dynamiques : ENOB, SNR, SINAD, THD..." msgid "Bandwidth at -3dB" msgstr "Bande passante à -3dB" msgid "Compute bandwidth at -3dB assuming a low-pass filter already expressed in dB" msgstr "Calcule la bande passante à -3dB en supposant un filtre passe-bas déjà exprimé en dB" msgid "Contrast" msgstr "Contraste" msgid "Compute contrast of a signal, i.e. (max-min)/(max+min), e.g. for an image profile" msgstr "Calcule le contraste d'un signal, c'est-à-dire (max-min)/(max+min), p.ex. pour un profil d'image" msgid "Full width at y" msgstr "Largeur à y=..." msgid "Abscissa at y" msgstr "Abscisse à y=..." msgid "Ordinate at x" msgstr "Ordonnée à x=..." msgid "Edit profile parameters" msgstr "Modifier les paramètres du profil" msgid "Reset selection" msgstr "Réinitialiser la sélection" msgid "Range ROI" msgstr "Intervalle ROI" msgid "Rectangular ROI" msgstr "ROI rectangulaire" msgid "Circular ROI" msgstr "ROI circulaire" msgid "Polygonal ROI" msgstr "ROI polygonale" msgid "Regions of interest" msgstr "Régions d'intérêt" #, python-format msgid "" "Extract all ROIs\n" "into a single %s" msgstr "" "Extraire toutes les ROI\n" "dans un seul objet %s" msgid "Graphical ROI" msgstr "ROI graphique" msgid "Coordinate-based ROI" msgstr "ROI par coordonnées" msgid "Remove all ROIs" msgstr "Supprimer toutes les ROI" msgid "Are you sure you want to remove all ROIs?" msgstr "Êtes-vous sûr de vouloir supprimer toutes les ROI ?" msgid "Show mask" msgstr "Afficher le masque" msgid "Show ROI" msgstr "Afficher la ROI" msgid "Show names" msgstr "Afficher les noms" msgid "ROI grid files (*.dlabgrid);;All files (*)" msgstr "Fichiers de grille de ROI (*.dlabgrid);;Tous les fichiers (*)" msgid "Grid parameters" msgstr "Paramètres de grille" msgid "Display parameters" msgstr "Paramètres d'affichage" msgid "ROI grid" msgstr "Grille de ROI" msgid "Import/Export" msgstr "Importer/Exporter" msgid "Import grid" msgstr "Importer une grille" msgid "Export grid" msgstr "Exporter une grille" msgid "Import ROI grid" msgstr "Importer une grille de ROI" #, python-brace-format msgid "Failed to import ROI grid: {0}" msgstr "Échec de l'importation de la grille de ROI : {0}" msgid "Export ROI grid" msgstr "Exporter une grille de ROI" #, python-brace-format msgid "Failed to export ROI grid: {0}" msgstr "Échec de l'exportation de la grille de ROI : {0}" msgid "Settings for main window and general features" msgstr "Réglages de la fenêtre principale et des fonctionnalités générales" msgid "Color mode" msgstr "Thème de couleur" msgid "Color mode for the application" msgstr "Thème de couleur pour l'application" msgid "Process isolation" msgstr "Isolation de processus" msgid "With process isolation, each computation is run in a separate process,
    which prevents the application from freezing during long computations." msgstr "Avec l'isolation de processus, chaque calcul est exécuté dans un processus séparé,
    ce qui évite que l'application ne se fige pendant les calculs longs." msgid "RPC server" msgstr "Serveur XML-RPC" msgid "RPC server is used to communicate with external applications,
    like your own scripts (e.g. from Spyder or Jupyter) or other software." msgstr "Le serveur XML-RPC est utilisé pour communiquer avec des applications externes,
    comme vos propres scripts (par exemple depuis Spyder ou Jupyter) ou d'autres logiciels." msgid "Web API localhost bypass" msgstr "Contournement localhost de l'API Web" msgid "When enabled (default), connections from localhost (127.0.0.1) to the
    Web API do not require authentication. This simplifies notebook
    integration when DataLab-Kernel runs on the same machine.
    Disable for stricter security if needed." msgstr "Lorsque cette option est activée (par défaut), les connexions depuis localhost (127.0.0.1)
    vers l'API Web ne nécessitent pas d'authentification. Cela simplifie l'intégration
    des notebooks lorsque DataLab-Kernel s'exécute sur la même machine.
    Désactiver pour une sécurité plus stricte si nécessaire." msgid "Memory threshold" msgstr "Seuil de mémoire" msgid "MB" msgstr "Mo" msgid "Memory threshold below which a warning is displayed before loading any new data" msgstr "Seuil de mémoire en dessous duquel un avertissement est affiché avant de charger de nouvelles données" msgid "Third-party plugins" msgstr "Plugins tiers" msgid "Disable third-party plugins at startup" msgstr "Désactiver les plugins tiers au démarrage" msgid "Plugins path" msgstr "Chemin des plugins" msgid "Path to third-party plugins.

    DataLab will discover plugins in this path, as well as in your PYTHONPATH." msgstr "Chemin d'accès aux plugins tiers.

    DataLab découvrira les plugins dans ce chemin, ainsi que dans votre PYTHONPATH." msgid "Ignore compatibility issues warning" msgstr "Ignorer l'avertissement de compatibilité" msgid "DataLab v0.20 plugins" msgstr "Plugins DataLab v0.20" msgid "If enabled, DataLab will not warn you about v0.20 plugins that are no longer compatible with v1.0." msgstr "Si activé, DataLab ne vous avertira pas des plugins v0.20 qui ne sont plus compatibles avec v1.0." msgid "Settings for internal console, used for debugging or advanced users" msgstr "Réglages de la console interne, utilisée pour le débogage ou les utilisateurs avancés" msgid "Console enabled" msgstr "Console activée" msgid "Show console on error" msgstr "Afficher la console en cas d'erreur" msgid "If enabled, the console will be shown when an error occurs in the application. This is useful for debugging purposes as it allows to see the error traceback." msgstr "Si activé, la console sera affichée lorsqu'une erreur se produit dans l'application. Cela est utile pour le débogage car cela permet de voir la trace de l'erreur." msgid "External editor path" msgstr "Chemin de l'éditeur externe" msgid "External editor arguments" msgstr "Arguments de l'éditeur externe" msgid "Settings for I/O operations" msgstr "Réglages des opérations d'entrée/sortie" msgid "Clear workspace before loading HDF5 file" msgstr "Effacer l'espace de travail avant de charger un fichier HDF5" msgid "" "If enabled, the current workspace will be cleared before loading a new HDF5 file.\n" "If disabled, when importing DataLab workspace files, only objects with conflicting identifiers will have their processing history lost (features like 'Show source' and 'Recompute' will not work for those objects). Non-conflicting objects will preserve their processing history." msgstr "" "Si activé, l'espace de travail actuel sera effacé avant de charger un nouveau fichier HDF5.\n" "Si désactivé, lors de l'importation de fichiers de l'espace de travail DataLab, seuls les objets avec des identifiants en conflit perdront leur historique de traitement (les fonctionnalités telles que 'Afficher la source' et 'Retraiter' ne fonctionneront pas pour ces objets). Les objets non conflictuels conserveront leur historique de traitement." msgid "Ask before clearing workspace" msgstr "Demander avant d'effacer l'espace de travail" msgid "HDF5 full path in title" msgstr "Chemin complet du fichier HDF5 dans le titre" msgid "If enabled, the full path of the HDF5 data set will be used as the title for the signal/image object.
    If disabled, only the name of the data set will be used as the title." msgstr "Si activé, le chemin complet du fichier HDF5 sera utilisé comme titre de l'objet signal/image.
    Si désactivé, seul le nom du fichier HDF5 sera utilisé comme titre." msgid "HDF5 file name in title" msgstr "Nom du fichier HDF5 dans le titre" msgid "If enabled, the name of the HDF5 file will be used as a suffix in the title of the signal/image object." msgstr "Si activé, le nom du fichier HDF5 sera utilisé comme suffixe dans le titre de l'objet signal/image." msgid "Settings for computations" msgstr "Réglages des calculs" msgid "Operation mode" msgstr "Mode d'opération" #, python-format msgid "Operation mode for computations taking N inputs:
    • single: single operand mode
    • pairwise: pairwise operation mode

    Computations taking N inputs are the ones where:
    • N(>=2) objects in %s 1 object out
    • N(>=1) objects + 1 object in %s N objects out
    " msgstr "Mode d'opération pour les calculs prenant N entrées:
    • single: mode d'opération avec un seul opérande
    • pairwise: mode d'opération par paire

    Les calculs prenant N entrées sont ceux où :
    • N(>=2) objets en %s 1 objet en sortie
    • N(>=1) objets + 1 objet en %s N objets en sortie
    " msgid "Use current signal bounds" msgstr "Utiliser les bornes du signal courant" msgid "New signals" msgstr "Nouveaux signaux" msgid "If enabled, when creating a new signal, the xmin and xmax values will be initialized from the current signal's bounds.
    If disabled, the default values from the New Signal dialog will be used." msgstr "Si activé, lors de la création d'un nouveau signal, les valeurs xmin et xmax seront initialisées à partir des bornes du signal actuel.
    Si désactivé, les valeurs par défaut de la boîte de dialogue Nouveau signal seront utilisées." msgid "Use current image dimensions" msgstr "Utiliser les dimensions de l'image courante" msgid "New images" msgstr "Nouvelles images" msgid "If enabled, when creating a new image, the width and height values will be initialized from the current image's dimensions.
    If disabled, the default values from the New Image dialog will be used." msgstr "Si activé, lors de la création d'une nouvelle image, les valeurs de largeur et de hauteur seront initialisées à partir des dimensions de l'image actuelle.
    Si désactivé, les valeurs par défaut de la boîte de dialogue Nouvelle image seront utilisées." msgid "FFT shift" msgstr "Décalage FFT" msgid "Enable FFT shift to center the zero-frequency component in the frequency spectrum for easier visualization and analysis." msgstr "Activer le décalage FFT pour centrer la composante de fréquence nulle dans le spectre de fréquence pour une visualisation et une analyse plus faciles." msgid "Auto-normalize convolution/deconvolution kernel" msgstr "Normalisation automatique du noyau de convolution/déconvolution" msgid "Convolution" msgstr "Convolution" msgid "If enabled, convolution and deconvolution kernels will be automatically normalized for signal and image processing operations to preserve signal energy and image intensity." msgstr "Si activé, les noyaux de convolution et de déconvolution seront automatiquement normalisés pour les opérations de traitement de signaux et d'images afin de préserver l'énergie du signal et l'intensité de l'image." msgid "Extract multiple ROIs in a single object" msgstr "Extraire plusieurs ROI dans un seul objet" msgid "ROI extraction" msgstr "Extraction de ROI" msgid "If enabled, multiple ROIs will be extracted into a single object.
    If disabled, each ROI will be extracted into a separate object." msgstr "Si activé, plusieurs ROI seront extraites dans un seul objet.
    Si désactivé, chaque ROI sera extraite dans un objet séparé." msgid "Ignore warnings during computations" msgstr "Ignorer les avertissements lors des calculs" msgid "Warnings" msgstr "Avertissements" msgid "If enabled, warnings during computations will be ignored." msgstr "Si activé, les avertissements lors des calculs seront ignorés." msgid "X-axis" msgstr "Axe des X" msgid "Ask before interpolation" msgstr "Demander avant d'interpoler" msgid "Interpolate systematically" msgstr "Interpoler systématiquement" msgid "Behavior when X-axis arrays are incompatible in multi-signal computations:
    • Ask: display confirmation dialog (default)
    • Interpolate: automatically interpolate signals
    " msgstr "Comportement lorsque les tableaux des X sont incompatibles dans les calculs multi-signaux :
    • Demander : afficher une boîte de dialogue de confirmation (par défaut)
    • Interpoler : interpoler automatiquement les signaux
    " msgid "Settings for results management" msgstr "Réglages de la gestion des résultats" msgid "Keep results in metadata after computation" msgstr "Conserver les résultats dans les métadonnées après le calcul" msgid "Keep results" msgstr "Conserver les résultats" msgid "If enabled, the results of a previous analysis will be kept in object's metadata after the computation.
    If disabled, the results will be removed from the object's metadata.

    This option is disabled by default because keeping analysis results may be confusing as those results could be outdated following the computation." msgstr "Si activé, les résultats d'une analyse précédente seront conservés dans les métadonnées de l'objet après le calcul.
    Si désactivé, les résultats seront supprimés des métadonnées de l'objet.

    Cette option est désactivée par défaut car la conservation des résultats d'analyse peut prêter à confusion, car ces résultats pourraient être obsolètes suite au calcul." msgid "Show results dialog automatically after processing" msgstr "Afficher automatiquement la boîte de dialogue des résultats après le traitement" msgid "If enabled, the results dialog will be shown automatically after each processing operation producing results.
    If disabled, the results dialog will not be shown automatically." msgstr "Si activé, la boîte de dialogue des résultats sera affichée automatiquement après chaque opération de traitement produisant des résultats.
    Si désactivé, la boîte de dialogue des résultats ne sera pas affichée automatiquement." msgid "Default image visualization settings" msgstr "Réglages d'affichage d'images par défaut" msgid "Common" msgstr "Commun" msgid "Plot toolbar position" msgstr "Position de la barre d'outils des graphiques" msgid "Top (above plot)" msgstr "Haut (au-dessus du graphique)" msgid "Bottom (below plot)" msgstr "Bas (en-dessous du graphique)" msgid "Left (of plot)" msgstr "Gauche (à gauche du graphique)" msgid "Right (of plot)" msgstr "Droite (à droite du graphique)" msgid "Ignore title insertion message" msgstr "Ignorer le message d'insertion du titre" msgid "Ignore information message when inserting object title as annotation label" msgstr "Ignorer le message d'information lors de l'insertion du titre de l'objet en tant qu'étiquette d'annotation" msgid "Signals" msgstr "Signaux" msgid "Default line width" msgstr "Épaisseur par défaut" msgid "Default line width for curves representing signals" msgstr "Épaisseur de trait par défaut pour les courbes représentant des signaux" msgid "Performance threshold" msgstr "Seuil de performance" msgid "" "Number of points threshold for line width limitation.\n" "For signals with more than this number of points, line width is automatically limited to 1.0 for performance reasons.\n" "\n" "That's because the Qt raster engine experiences significant rendering slowdowns with thick lines on large datasets." msgstr "" "Seuil du nombre de points pour la limitation de l'épaisseur de trait.\n" "Pour les signaux comportant plus de ce nombre de points, l'épaisseur de trait est automatiquement limitée à 1,0 pour des raisons de performance.\n" "\n" "Cela est dû au fait que le moteur de rendu Qt subit des ralentissements importants lors du rendu de lignes épaisses sur de grands ensembles de données." msgid "Use auto downsampling" msgstr "Utiliser l'auto-sous-échantillonnage" msgid "Use auto downsampling for large signals" msgstr "Utiliser l'auto-sous-échantillonnage pour les signaux volumineux" msgid "Downsampling max points" msgstr "Nombre max. de points" msgid "Maximum number of points for downsampling" msgstr "Nombre maximum de points pour le sous-échantillonnage" msgid "Autoscale margin" msgstr "Marge de zoom automatique" msgid "%" msgstr "%" msgid "Percentage of margin to add around data when auto-scaling signal plots.
    A value of 0.2% adds a small margin for better visualization.
    Set to 0% for no margin (exact data bounds)." msgstr "Pourcentage de marge à ajouter autour des données lors du redimensionnement automatique des graphiques de signaux.
    Une valeur de 0,2 % ajoute une petite marge pour une meilleure visualisation.
    Réglez sur 0 % pour aucune marge (limites exactes des données)." msgid "DateTime format (s/min/h)" msgstr "Format DateTime (s/min/h)" msgid "Format string for datetime X-axis labels when using standard time units (s, min, h).
    Uses Python's strftime format codes (e.g., %H:%M:%S for hours:minutes:seconds)." msgstr "Chaîne de format pour les étiquettes de l'axe X datetime lors de l'utilisation d'unités de temps standard (s, min, h).
    Utilise les codes de format strftime de Python (par exemple, %H:%M:%S pour heures:minutes:secondes)." msgid "DateTime format (ms/μs/ns)" msgstr "Format DateTime (ms/μs/ns)" #, python-format msgid "Format string for datetime X-axis labels when using sub-second time units (ms, μs, ns).
    Uses Python's strftime format codes (e.g., %H:%M:%S.%f for hours:minutes:seconds.microseconds)." msgstr "Chaîne de format pour les étiquettes de l'axe X datetime lors de l'utilisation d'unités de temps sub-secondes (ms, μs, ns).
    Utilise les codes de format strftime de Python (par exemple, %H:%M:%S.%f pour heures:minutes:secondes.microsecondes)." msgid "Results: edit shape styles" msgstr "Résultats : modifier les styles de forme" msgid "Results: edit marker styles" msgstr "Résultats : modifier les styles de marqueur" msgid "Images" msgstr "Images" msgid "Lock aspect ratio to 1:1" msgstr "Verrouiller le ratio d'aspect à 1:1" msgid "If enabled, the aspect ratio of the image will be locked to 1:1.
    If disabled, the aspect ratio will be determined by the physical pixel size (this is the default and recommended setting)." msgstr "Si activé, le ratio d'aspect de l'image sera verrouillé à 1:1.
    Si désactivé, le ratio d'aspect sera déterminé par la taille des pixels physiques (c'est le paramètre par défaut et recommandé)." msgid "Eliminate outliers" msgstr "Eliminer les bords" msgid "Eliminate a percentage of the highest and lowest values
    of the image histogram - recommanded values are below 1%" msgstr "Eliminer un pourcentage des valeurs les plus hautes et les plus basses
    de l'histogramme de l'image - les valeurs recommandées sont inférieures à 1%" msgid "Percentage of margin to add around data when auto-scaling image plots.
    A value of 0.2% adds a small margin for better visualization.
    Set to 0% for no margin (exact data bounds)." msgstr "Pourcentage de marge à ajouter autour des données lors du redimensionnement automatique des graphiques d'images.
    Une valeur de 0,2 % ajoute une petite marge pour une meilleure visualisation.
    Réglez sur 0 % pour aucune marge (limites exactes des données)." msgid "Maximum shapes to draw" msgstr "Nombre maximum de formes à dessiner" msgid "Maximum number of geometry shapes to draw on plot." msgstr "Nombre maximum de formes géométriques à dessiner sur le graphique." msgid "Maximum cells in label" msgstr "Nombre maximum de cellules dans l'étiquette" msgid "Maximum number of table cells (rows × columns) to display in merged result label on plot." msgstr "Nombre maximum de cellules de tableau (lignes × colonnes) à afficher dans l'étiquette de résultat fusionnée sur le graphique." msgid "Maximum columns in label" msgstr "Nombre maximum de colonnes dans l'étiquette" msgid "Maximum number of columns to display in merged result label." msgstr "Nombre maximum de colonnes à afficher dans l'étiquette de résultat fusionnée." msgid "Show the merged result label by default" msgstr "Afficher le titre de résultat fusionné par défaut" msgid "Result label" msgstr "Étiquette des résultats" msgid "Show the merged result label on the plot by default. Can be toggled per-object using the checkbox in the Properties panel." msgstr "Etiquette des résultats fusionnés sur le graphique par défaut. Peut être activée ou désactivée par objet à l'aide de la case à cocher dans le panneau des propriétés." msgid "Process isolation enable status" msgstr "Etat d'activation de l'isolation de processus" msgid "RPC server enable status" msgstr "Etat d'activation du serveur XML-RPC" msgid "Console enable status" msgstr "Etat d'activation de la console" msgid "Third-party plugins support" msgstr "Prise en charge des plugins tiers" msgid "Third-party plugins path" msgstr "Chemin des plugins tiers" msgid "General" msgstr "Général" msgid "Visualization" msgstr "Visualisation" msgid "I/O" msgstr "Entrée/sortie" msgid "Settings" msgstr "Préférences" msgid "Restart required" msgstr "Redémarrage requis" #, python-format msgid "" "The following options have been changed:\n" "\n" "- %s\n" "\n" "A restart is required for these changes to take effect." msgstr "" "Les options suivantes ont été modifiées :\n" "\n" "- %s\n" "\n" "Un redémarrage est nécessaire pour que ces changements prennent effet." msgid "Start" msgstr "Démarrer" msgid "Close" msgstr "Fermer" msgid "Hit Enter to continue to the next step, or Esc to close the tour." msgstr "Taper Entrée pour passer à l'étape suivante, ou Echap pour fermer la visite guidée." msgid "Test signal, for the tour" msgstr "Signal de test, pour la visite guidée" msgid "Test image, for the tour" msgstr "Image de test, pour la visite guidée" msgid "Welcome to DataLab!" msgstr "Bienvenue dans DataLab !" msgid "This tour will guide you through the main features of DataLab." msgstr "Cette visite guidée vous présentera les principales fonctionnalités de DataLab." msgid "DataLab main window" msgstr "Fenêtre principale de DataLab" msgid "This is the main window of DataLab. It is composed of several parts that we will describe in the following steps." msgstr "Ceci est la fenêtre principale de DataLab. Elle est composée de plusieurs parties que nous décrirons dans les étapes suivantes." msgid "Menus and toolbars regroup the main actions that can be performed in DataLab. Their content is adapted to the current panel, as we will see in the following steps." msgstr "Les menus et barres d'outils regroupent les principales actions qui peuvent être réalisées dans DataLab. Leur contenu est adapté au panneau courant, comme nous le verrons dans les étapes suivantes." msgid "The main window is composed of two main panels: the Signal Panel, and the Image Panel.
    Switching between panels is done using the highlighted tabs." msgstr "La fenêtre principale est composée de deux panneaux principaux : le panneau Signal, et le panneau Image.
    Le passage d'un panneau à l'autre se fait en utilisant les onglets surlignés." msgid "The Signal Panel is used to manage 1D signals.It is composed of the elements shown in the following steps." msgstr "Le Panneau Signal est utilisé pour gérer les signaux 1D. Il est composé des éléments décrits dans les étapes suivantes." msgid "List and properties" msgstr "Liste et propriétés" msgid "In the highlighted area, signals are listed at the top, and their properties may be displayed and edited at the bottom.

    Signals are numbered (but may be reorganized) and put together in numbered groups." msgstr "Dans la zone surlignée, les signaux sont listés en haut, et leurs propriétés peuvent être affichées et éditées en bas.

    Les signaux sont numérotés (mais peuvent être réorganisés) et peuvent être regroupés dans des groupes numérotés." msgid "View" msgstr "Vue" msgid "Signals are plotted in the Signal View.

    Curves may be customized using context menus or the vertical toolbar on the left (appearance settings are saved in the signal metadata)." msgstr "Les signaux sont tracés dans la Vues Signal.

    Les courbes peuvent être personnalisées en utilisant les menus contextuels ou la barre d'outils verticale à gauche (ces réglages d'apparence sont enregistrés dans les métadonnées du signal)." msgid "File menu" msgstr "Menu Fichier" msgid "The File menu contains actions to import and export signals individually (various formats) or to save or restore the whole workspace (HDF5 files)." msgstr "Le menu Fichier contient les actions pour importer et exporter des signaux individuellement (divers formats) ou pour enregistrer ou restaurer l'ensemble de l'espace de travail (fichiers HDF5)." msgid "Create menu" msgstr "Menu Création" msgid "The Create menu contains actions to create new signals from various models (Gaussian, sine, etc.)." msgstr "Le menu Création contient des actions pour créer de nouveaux signaux à partir de différents modèles (Gaussien, sinusoïde, etc.)." msgid "Edit menu" msgstr "Menu Edition" msgid "The Edit menu contains actions to edit signals individually or in groups." msgstr "Le menu Edition contient les actions pour éditer des signaux individuellement ou en groupes." msgid "Operations menu" msgstr "Menu Opérations" msgid "The Operations menu is focused on arithmetic operations, basic mathematical functions or data type conversions." msgstr "Le menu Opérations est centré sur les opérations arithmétiques, les fonctions mathématiques de base ou les conversions de type de données." msgid "Processing menu" msgstr "Menu Traitement" msgid "The Processing menu regroups 1->1 signal processing actions." msgstr "Le menu Traitement regroupe les actions de traitement de signaux 1->1." msgid "Analysis menu" msgstr "Menu Analyse" msgid "The Analysis menu regroups 1->0 signal computing actions (that is, actions that do not modify the signals, but compute a result, e.g. scalar values), with optional ROI selection." msgstr "Le menu Analyse regroupe les actions de calculs de signaux 1->0 (c'est-à-dire les actions qui ne modifient pas les signaux, mais qui calculent un résultat, par exemple des valeurs scalaires), avec une sélection optionnelle de ROI." msgid "The Image Panel is used to manage 2D images. It is composed of the elements shown in the following steps." msgstr "Le Panneau Image est utilisé pour gérer les images 2D. Il est composé des éléments décrits dans les étapes suivantes." msgid "In the highlighted area, images are listed at the top, and their properties may be displayed and edited at the bottom.

    Images are numbered (but may be reorganized) and put together in numbered groups." msgstr "Dans la zone surlignée, les images sont listées en haut, et leurs propriétés peuvent être affichées et éditées en bas.

    Les images sont numérotées (mais peuvent être réorganisées) et peuvent être regroupées dans des groupes numérotés." msgid "Images are shown in the Image View.

    The displayed images may be customized using context menus or the vertical toolbar on the left (appearance settings are saved in the image metadata)." msgstr "Les images sont affichées dans la Vues Image.

    Les images affichées peuvent être personnalisées en utilisant les menus contextuels ou la barre d'outils verticale à gauche (ces réglages d'apparence sont enregistrés dans les métadonnées de l'image)." msgid "The File menu contains actions to import and export images individually (various formats) or to save or restore the whole workspace (HDF5 files)." msgstr "Le menu Fichier contient les actions pour importer et exporter des images individuellement (divers formats) ou pour enregistrer ou restaurer l'ensemble de l'espace de travail (fichiers HDF5)." msgid "The Create menu contains actions to create new images from various models (Gaussian, checkerboard, etc.)." msgstr "Le menu Création contient des actions pour créer de nouvelles images à partir de différents modèles (Gaussien, damier, etc.)." msgid "The Edit menu contains actions to edit images individually or in groups." msgstr "Le menu Edition contient les actions pour éditer des images individuellement ou en groupes." msgid "The Operations menu is focused on arithmetic operations, data type conversions, pixel binning resize, intensity profiles..." msgstr "Le menu Opérations est centré sur les opérations arithmétiques, les conversions de type de données, le redimensionnement par binning de pixels, les profils d'intensité..." msgid "The Processing menu regroups 1->1 image processing actions (that is, actions that modify the images)." msgstr "Le menu Traitement regroupe les actions de traitement d'images 1->1 (c'est-à-dire les actions qui modifient les images)." msgid "The Analysis menu regroups 1->0 image computing actions (that is, actions that do not modify the images, but compute a result, e.g. circle coordinates), with optional ROI selection." msgstr "Le menu Analyse regroupe les actions de calculs d'images 1->0 (c'est-à-dire les actions qui ne modifient pas les images, mais qui calculent un résultat, par exemple les coordonnées d'un cercle), avec une sélection optionnelle de ROI." msgid "Extensions" msgstr "Extensions" msgid "DataLab is designed to be easily extended with new features, by using Macros, Plugins or Remote Control.

    The common point between these mechanisms is that they are all relying on Python scripts and the DataLab API." msgstr "DataLab est conçu pour être facilement personnalisable avec de nouvelles fonctionnalités, en utilisant des Macros, des Plugins ou un Contrôle à distance.

    Le point commun entre ces mécanismes est qu'ils reposent tous sur des scripts Python et l'API DataLab." msgid "Macros" msgstr "Macros" msgid "The Macro Panel allows to create, edit and run macros.
    Macros are saved together with the DataLab workspace (HDF5 file)." msgstr "Le Gestionnaire de Macros permet de créer, éditer et exécuter des macros.
    Les macros sont sauvegardées avec l'espace de travail DataLab (fichier HDF5)." msgid "The Plugins menu regroups features that are not part of the core of DataLab, but that are provided as plugins.
    (See the documentation for more information about plugins.)" msgstr "Le menu Plugins regroupe des fonctionnalités qui ne font pas partie du coeur de DataLab, mais qui sont fournies sous forme de plugins.
    (Voir la documentation pour plus d'informations sur les plugins.)" msgid "This is the end of the tour!" msgstr "C'est la fin de la visite guidée !" msgid "You can show the tour again, or close this dialog box." msgstr "Vous pouvez afficher la visite guidée à nouveau, ou fermer cette boîte de dialogue." msgid "Registered plugins:" msgstr "Plugins enregistrés :" msgid "No plugins available" msgstr "Aucun plugin disponible" msgid "Plugins are disabled (see DataLab settings)" msgstr "Les plugins sont désactivés (voir les préférences de DataLab)" msgid "Warning" msgstr "Attention" msgid "Error" msgstr "Erreur" msgid "Information" msgstr "Information" msgid "Question" msgstr "Question" msgid "Test data" msgstr "Données de test" msgid "Testing DataLab functionalities" msgstr "Tester les fonctionnalités de DataLab" msgid "Ring" msgstr "Anneau" msgid "Grid of Gaussian Images" msgstr "Grille d'images gaussiennes" msgid "Load spectrum of paracetamol" msgstr "Charger le spectre du paracétamol" msgid "Load all test signals" msgstr "Charger tous les signaux de test" msgid "Create image with peaks" msgstr "Créer une image avec des pics" msgid "Create 2D sin cos image" msgstr "Créer une image 2D sin cos" msgid "Create 2D noisy gaussian image" msgstr "Créer une image 2D gaussienne bruitée" msgid "Create 2D multi gaussian image" msgstr "Créer une image 2D multi-gaussienne" msgid "Create annotated image" msgstr "Créer une image annotée" msgid "Create 2D step image" msgstr "Créer une image 2D échelon" msgid "Create ring image" msgstr "Créer une image avec un anneau" msgid "Create image with a grid of gaussian spots" msgstr "Créer une image avec une grille de spots gaussiens" msgid "Load all test images" msgstr "Charger toutes les images de test" msgid "Host application" msgstr "Application hôte" msgid "Raise window" msgstr "Placer la fenêtre au premier plan" msgid "Add signal objects" msgstr "Ajouter des objets signaux" msgid "Add image objects" msgstr "Ajouter des objets images" msgid "Remove all objects" msgstr "Supprimer tous les objets" msgid "Import macro from file (example)" msgstr "Importer une macro depuis un fichier (exemple)" msgid "Close DataLab" msgstr "Fermer DataLab" msgid "This the host application, which embeds DataLab." msgstr "Ceci est une application embarquant DataLab." msgid "Open DataLab" msgstr "Ouvrir DataLab" msgid "Import signal from DataLab" msgstr "Importer un signal depuis DataLab" msgid "Import image from DataLab" msgstr "Importer une image depuis DataLab" msgid "Remote client test" msgstr "Test du client distant" msgid "This the client application, which connects to DataLab." msgstr "Ceci l'application cliente, qui se connecte à DataLab." msgid "Connect to DataLab" msgstr "Se connecter à DataLab" msgid "Execute multiple commands" msgstr "Exécuter des commandes multiples" msgid "Get object titles" msgstr "Obtenir la liste des titres" msgid "Get object uuids" msgstr "Obtenir la liste des uuids" msgid "Get object" msgstr "Obtenir un objet" msgid "Raw data (2D-Gaussian)" msgstr "Image brute (gaussienne 2D)" msgid "Flat data (Uniform random)" msgstr "Image d'homogénéité (Distribution uniforme)" msgid "Random function" msgstr "Fonction gaussienne aléatoire" msgid "Multi-Gaussian" msgstr "Multi-gaussienne" msgid "2D Peaks with noise background" msgstr "Pics 2D avec fond bruité" msgid "Click OK to start the demo.

    Note:
    - Demo will cover a selection of DataLab features (for a complete list of features, please refer to the documentation).
    - It won't require any user interaction." msgstr "Cliquer sur OK pour démarrer la démo.

    Note :
    - La démo couvre une sélection des fonctionnalités de DataLab (pour une liste complète des fonctionnalités, veuillez vous référer à la documentation).
    - Elle ne nécessite aucune interaction de l'utilisateur." msgid "Click OK to end demo." msgstr "Cliquer sur OK pour terminer la démo." msgid "Cancel" msgstr "Annuler" msgid "Error:" msgstr "Erreur:" msgid "Context" msgstr "Contexte" #, python-format msgid "The file %s could not be read:" msgstr "Le fichier %s n'a pas pu être ouvert :" #, python-format msgid "The file %s could not be written:" msgstr "Le fichier %s n'a pas pu être enregistré :" msgid "in this folder" msgstr "dans ce dossier" msgid "Open tab menu" msgstr "Ouvrir le menu des onglets" msgid "Start Web API Server" msgstr "Démarrer le serveur API Web" msgid "Start the HTTP/JSON Web API server for external access" msgstr "Démarrer le serveur API Web HTTP/JSON pour l'accès externe" msgid "Stop Web API Server" msgstr "Arrêter le serveur API Web" msgid "Copy Connection Info" msgstr "Copier les infos de connexion" msgid "Copy URL and token to clipboard" msgstr "Copier l'URL et le jeton dans le presse-papier" msgid "Status: Not running" msgstr "Statut : Non démarré" msgid "Web API unavailable (install datalab-platform[webapi])" msgstr "API Web non disponible (installer datalab-platform[webapi])" msgid "Web API" msgstr "API Web" msgid "Failed to start Web API server:" msgstr "Échec du démarrage du serveur API Web :" msgid "Connection info copied to clipboard" msgstr "Infos de connexion copiées dans le presse-papier" msgid "Web API Server Started" msgstr "Serveur API Web démarré" msgid "The Web API server is now running. Use the following credentials to connect:" msgstr "Le serveur API Web est maintenant en cours d'exécution. Utilisez les identifiants suivants pour vous connecter :" msgid "URL:" msgstr "URL :" msgid "Token:" msgstr "Jeton :" msgid "Tip: Set these environment variables in your notebook:\n" msgstr "Astuce : Définissez ces variables d'environnement dans votre notebook :\n" msgid "Copy to Clipboard" msgstr "Copier dans le presse-papier" #, python-brace-format msgid "Status: Running at {}" msgstr "Statut : En cours d'exécution à {}" msgid "Web API server error:" msgstr "Erreur du serveur API Web :" msgid "" "Do you want to start the Web API server?\n" "\n" "This will allow external applications to connect to DataLab and control it remotely via HTTP/JSON." msgstr "" "Souhaitez-vous démarrer le serveur API Web ?\n" "\n" "Cela permettra aux applications externes de se connecter à DataLab et de le contrôler à distance via HTTP/JSON." msgid "Connection to DataLab" msgstr "Connexion à DataLab" msgid "Contents of file" msgstr "Contenu du fichier" msgid "Polymomial fit" msgstr "Ajustement polynomial" msgid "Amplitude" msgstr "Amplitude" msgid "Base line" msgstr "Ligne de base" msgid "Std-dev" msgstr "Ecart-type" msgid "Mean" msgstr "Moyenne" msgid "Y0" msgstr "Y0" msgid "A coefficient" msgstr "Coefficient A" msgid "B coefficient" msgstr "Coefficient B" msgid "y0 constant" msgstr "Constante y0" msgid "Frequency" msgstr "Fréquence" msgid "Continuous component" msgstr "Composante continue" msgid "Peak wavelength" msgstr "Longueur d'onde de pic" msgid "Width factor" msgstr "Facteur de largeur" msgid "Left amplitude" msgstr "Amplitude gauche" msgid "Right amplitude" msgstr "Amplitude droite" msgid "Left width" msgstr "Largeur gauche" msgid "Right width" msgstr "Largeur droite" msgid "Center" msgstr "Centre" msgid "Left baseline" msgstr "Ligne de base gauche" msgid "Right baseline" msgstr "Ligne de base droite" msgid "Center position" msgstr "Position du centre" msgid "Left time constant" msgstr "Constante de temps gauche" msgid "Right time constant" msgstr "Constante de temps droite" msgid "Collapse all" msgstr "Replier tout" msgid "Expand all" msgstr "Déplier tout" msgid "Restore" msgstr "Restaurer" msgid "Restore original tree layout" msgstr "Restaurer l'organisation initiale de l'arbre" msgid "Collapse selection" msgstr "Replier la sélection" msgid "Expand selection" msgstr "Déplier la sélection" msgid "HDF5 Browser" msgstr "Explorateur HDF5" msgid "Size" msgstr "Taille" msgid "Type" msgstr "Type" msgid "Value" msgstr "Valeur" msgid "Name" msgstr "Nom" msgid "Unsupported data" msgstr "Données non prises en charge" msgid "Group" msgstr "Groupe" msgid "Attributes" msgstr "Attributs" msgid "Show array" msgstr "Afficher le tableau" msgid "Path" msgstr "Chemin" msgid "Description" msgstr "Description" msgid "Textual preview" msgstr "Aperçu textuel" msgid "Select HDF5 file" msgstr "Sélectionner un fichier HDF5" msgid "Check all" msgstr "Cocher tout" msgid "Uncheck all" msgstr "Décocher tout" msgid "Show only supported data" msgstr "Afficher uniquement les données prises en charge" msgid "Show values" msgstr "Afficher les valeurs" msgid "Image background selection" msgstr "Sélection de l'arrière-plan de l'image" msgid "Background area" msgstr "Zone d'arrière-plan" msgid "Background value:" msgstr "Valeur de l'arrière-plan :" msgid "Installation configuration" msgstr "Configuration d'installation" msgid "User configuration" msgstr "Configuration utilisateur" msgid "Plugins and I/O features" msgstr "Plugins et fonctionnalités d'entrée/sortie" msgid "Log files were generated during current session." msgstr "Des journaux de bord ont été générés lors de la session en cours." msgid "Log files were generated during last session." msgstr "Des journaux de bord ont été générés lors de la dernière session." msgid "Log files are currently empty." msgstr "Les journaux de bord sont vides." msgid "Signal baseline selection" msgstr "Sélection de la ligne de base du signal" msgid "Select X value with cursor" msgstr "Sélectionner la valeur X avec le curseur" msgid "Select Y value with cursor" msgstr "Sélectionner la valeur Y avec le curseur" msgid "Apply" msgstr "Appliquer" msgid "Apply cursor position" msgstr "Appliquer la position du curseur" msgid "Cursor position" msgstr "Position du curseur" msgid "Minimum distance:" msgstr "Distance minimale :" msgid "Signal peak detection" msgstr "Détection de pics 1D" msgid "Peaks:" msgstr "Pics :" msgid "Internal console" msgstr "Console interne" msgid "" "Click to show the internal console.\n" "The icon will turn red if an error or warning is logged." msgstr "" "Cliquer pour afficher la console interne.\n" "L'icône deviendra rouge si une erreur ou un avertissement est enregistré." msgid "" "Click to show the internal console.\n" "An error or warning has been logged." msgstr "" "Cliquer pour afficher la console interne.\n" "Une erreur ou un avertissement a été enregistré." msgid "" "Click to show the internal console.\n" "No error or warning has been logged." msgstr "" "Cliquer pour afficher la console interne.\n" "Aucune erreur ou avertissement n'a été enregistré." msgid "Memory available:" msgstr "Mémoire disponible :" msgid "Memory used:" msgstr "Mémoire utilisée :" msgid "Alarm threshold:" msgstr "Seuil d'alarme :" msgid "Memory:" msgstr "Mémoire :" msgid "Plugins:" msgstr "Plugins :" msgid "XML-RPC:" msgstr "XML-RPC :" msgid "Web API server is not running" msgstr "Serveur API Web arrêté" msgid "Click to start" msgstr "Cliquer pour démarrer" msgid "Web API:" msgstr "API Web :" msgid "Web API server is running" msgstr "Serveur API Web démarré" msgid "Click to view connection info" msgstr "Cliquer pour voir les infos de connexion" msgid "Source" msgstr "Source" msgid "File" msgstr "Fichier" msgid "Clipboard" msgstr "Presse-papiers" msgid "Source of the data" msgstr "Source des données" msgid "File containing the data" msgstr "Fichier contenant les données" msgid "Preview parameters" msgstr "Paramètres de l'aperçu" msgid "Maximum Number of Rows" msgstr "Nombre maximum de lignes" msgid "Maximum number of rows to display" msgstr "Nombre maximum de lignes à afficher" msgid "Select the source of the data:" msgstr "Sélectionner la source des données :" msgid "Source Parameters" msgstr "Paramètres de la source" msgid "Delimiter" msgstr "Délimiteur" msgid "Tab" msgstr "Tabulation" msgid "Space" msgstr "Espace" msgid "Column delimiter character" msgstr "Caractère délimiteur de colonne" msgid "Decimal" msgstr "Décimal" msgid "Point" msgstr "Point" msgid "Comma" msgstr "Virgule" msgid "Decimal separator character" msgstr "Caractère séparateur décimal" msgid "Comments" msgstr "Commentaires" msgid "Character that indicates a comment line" msgstr "Caractère indiquant une ligne de commentaire" msgid "Rows to Skip" msgstr "Lignes à sauter" msgid "Number of rows to skip at the beginning of the file (including comments)" msgstr "Nombre de lignes à sauter au début du fichier (y compris les commentaires)" msgid "Max. nb of rows" msgstr "Nb max. de lignes" msgid "Maximum number of rows to import" msgstr "Nombre maximum de lignes à importer" msgid "Header" msgstr "En-tête" msgid "None" msgstr "Aucun" msgid "Infer" msgstr "Inférer" msgid "First row" msgstr "Première ligne" msgid "Row index to use as the column names" msgstr "Indice de ligne à utiliser comme noms de colonnes" msgid "Transpose" msgstr "Transposer" msgid "Transpose the data (swap rows and columns)" msgstr "Transposer les données (échanger les lignes et les colonnes)" msgid "Data type" msgstr "Type de données" msgid "Output signal data type." msgstr "Type de données du signal de sortie." msgid "First Column is X" msgstr "La première colonne est X" msgid "" "First column contains the X values\n" "(ignored if there is only one column)" msgstr "" "La première colonne contient les valeurs des X\n" "(ignoré s'il n'y a qu'une seule colonne)" msgid "Output image data type." msgstr "Type de données de l'image générée." msgid "Raw Data" msgstr "Données brutes" msgid "Data Preview" msgstr "Aperçu des données" msgid "Preview and modify the import settings:" msgstr "Aperçu et modification des paramètres d'importation :" msgid "Import Parameters" msgstr "Paramètres d'importation" msgid "Reading data" msgstr "Lecture des données" #, python-format msgid "" "The number of signals to import is very high (%d).\n" "This may be an error.\n" "\n" "Are you sure you want to continue?" msgstr "" "Le nombre de signaux à importer est très élevé (%d).\n" "Cela peut être une erreur.\n" "\n" "Etes-vous sûr de vouloir continuer ?" msgid "Graphical Representation" msgstr "Représentation graphique" msgid "Graphical representation of the imported data" msgstr "Représentation graphique des données importées" #, python-format msgid "Unselect the %s that you do not want to import:" msgstr "Désélectionner les %s que vous ne voulez pas importer :" msgid "Select all" msgstr "Tout sélectionner" msgid "Adding data to the plot" msgstr "Ajout des données au graphique" msgid "Title" msgstr "Titre" msgid "X label" msgstr "Titre X" msgid "Y label" msgstr "Titre Y" msgid "X unit" msgstr "Unité X" msgid "Y unit" msgstr "Unité Y" msgid "Z label" msgstr "Titre Z" msgid "Z unit" msgstr "Unité Z" msgid "Labels and units" msgstr "Titres et unités" msgid "Set the labels and units for the imported data" msgstr "Définir les titres et unités pour les données importées" msgid "The following title, labels and units will be applied to the data.

    Note: Leave empty the labels and units to keep the default values (i.e. the values which were inferred from the data)." msgstr "Les titres, étiquettes et unités suivants seront appliqués aux données.

    Note : Laisser vide les étiquettes et unités pour conserver les valeurs par défaut (c'est-à-dire les valeurs qui ont été déduites des données)." msgid "clipboard" msgstr "presse-papiers" msgid "Imported from:" msgstr "Importer depuis :" msgid "DataLab Import Wizard" msgstr "Assistant d'importation de DataLab" msgid "Error message" msgstr "Message d'erreur" msgid "The following traceback may help to understand the problem:" msgstr "Le traceback suivant peut aider à comprendre le problème :" msgid "Warning message" msgstr "Message d'avertissement" msgid "Please take into account the following warning message:" msgstr "Merci de prendre en compte le message d'avertissement suivant :" msgid "An error has occured during the following context:" msgstr "Une erreur est survenue dans le contexte suivant:" msgid "Tip" msgstr "Astuce" msgid "Please click on the 'Ignore' button to ignore this warning next time." msgstr "Merci de cliquer sur le bouton 'Ignorer' pour ignorer cet avertissement la prochaine fois." msgid "Back" msgstr "Précédent" msgid "Next" msgstr "Suivant" msgid "Finish" msgstr "Terminer" msgid "Welcome to the Example Wizard" msgstr "Bienvenue dans l'assistant exemple" msgid "This wizard will guide you through the process of importing data." msgstr "Cet assistant vous guidera à travers le processus d'importation de données." msgid "Select the Source of the Data" msgstr "Sélectionner la source des données" msgid "Select the source of the data to be imported (clipboard or file)." msgstr "Sélectionner la source des données à importer (presse-papiers ou fichier)." msgid "Browse..." msgstr "Parcourir..." msgid "Select the File to Import" msgstr "Sélectionner le fichier à importer" msgid "CSV Files (*.csv);;Text Files (*.txt);;All Files (*)" msgstr "Fichiers CSV (*.csv);;Fichiers texte (*.txt);;Tous les fichiers (*)" msgid "Please select the file to import." msgstr "Merci de sélectionner le fichier à importer." msgid "Example Wizard" msgstr "Assistant exemple" DataLab-1.1.0/datalab/objectmodel.py000066400000000000000000000524211514014117600172360ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Object model ============ The :mod:`datalab.gui.objectmodel` module defines the object data model used by the GUI to store signals, images and groups. The model is based on a hierarchical tree of objects, with two levels: - The top level is a list of groups (`ObjectGroup` instances) - The second level is a list of objects (`SignalObj` or `ImageObj` instances) The model is implemented by the `ObjectModel` class. Object group ------------ The `ObjectGroup` class represents a group of objects. It is a container for `SignalObj` and `ImageObj` instances. .. autoclass:: ObjectGroup Object model ------------ The `ObjectModel` class is a container for ObjectGroup instances, as well as a container for `SignalObj` and `ImageObj` instances. .. autoclass:: ObjectModel """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import re from collections.abc import Iterator from typing import Callable from uuid import uuid4 from sigima import ImageObj, SignalObj def get_number(obj: SignalObj | ImageObj | ObjectGroup) -> int: """Get object number from metadata""" if isinstance(obj, ObjectGroup): return obj.number number = obj.get_metadata_option("number") assert isinstance(number, int) return number def set_number(obj: SignalObj | ImageObj | ObjectGroup, number: int) -> None: """Set object number in metadata""" assert isinstance(number, int) if isinstance(obj, ObjectGroup): obj.number = number else: obj.set_metadata_option("number", number) def get_uuid(obj: SignalObj | ImageObj | ObjectGroup) -> str: """Get object UUID""" if isinstance(obj, ObjectGroup): return obj.uuid return obj.get_metadata_option("uuid", str(uuid4())) def set_uuid(obj: SignalObj | ImageObj | ObjectGroup) -> None: """Set object UUID""" if isinstance(obj, ObjectGroup): obj.uuid = str(uuid4()) else: obj.set_metadata_option("uuid", str(uuid4())) def get_short_id(obj: SignalObj | ImageObj | ObjectGroup) -> str: """Short object ID""" if isinstance(obj, ObjectGroup): return f"{obj.prefix}{get_number(obj):03d}" return f"{obj.PREFIX}{get_number(obj):03d}" def patch_title_with_ids( dst_obj: SignalObj | ImageObj, src_objs: list[SignalObj] | list[ImageObj], id_func: Callable, ) -> None: """Patch object title with short IDs of source objects Destination object's title has been set to a string containing placeholders (e.g. "integral({0})"), by `sigima` computation function using a generic mecanism (see `sigima.base.dst_1_to_1` for example). Args: dst_obj: destination object src_objs: list of source objects id_func: function to get ID from object (e.g. `short_id` or `get_uuid`) """ ids = [id_func(obj) for obj in src_objs] title = dst_obj.title assert isinstance(title, str), "Title must be a string" try: dst_obj.title = title.format(*ids) except IndexError as exc: raise ValueError( f"Not enough source objects to fill title placeholders: {title}" ) from exc class ObjectGroup: """Represents a DataLab object group Args: title: group title model: object model prefix: prefix for short ID ("gs" for signal groups, "gi" for image groups) """ def __init__(self, title: str, model: ObjectModel, prefix: str) -> None: self.model = model self.prefix = prefix # Instance-specific prefix self.uuid: str = str(uuid4()) # Group uuid self.number: int = 0 # Group number (used for short ID) self.__objects: list[str] = [] # list of object uuids self.__title: str = title @property def title(self) -> str: """Return group title""" return self.__title @title.setter def title(self, title: str) -> None: """Set group title""" self.__title = title def __iter__(self) -> Iterator[SignalObj | ImageObj]: """Iterate over objects in group""" return iter(self.model.get_objects(self.__objects)) def __len__(self) -> int: """Return number of objects in group""" return len(self.__objects) def __getitem__( self, index: int | slice ) -> SignalObj | ImageObj | list[SignalObj | ImageObj]: """Return object at index""" if isinstance(index, slice): return [ self.model[self.__objects[i]] for i in range(*index.indices(len(self))) if i < len(self) ] return self.model[self.__objects[index]] def __contains__(self, obj: SignalObj | ImageObj) -> bool: """Return True if obj is in group""" return get_uuid(obj) in self.__objects def append(self, obj: SignalObj | ImageObj) -> None: """Append object to group""" self.__objects.append(get_uuid(obj)) def insert(self, index: int, obj: SignalObj | ImageObj) -> None: """Insert object at index""" self.model.replace_short_ids_by_uuids_in_titles([obj]) self.__objects.insert(index, get_uuid(obj)) self.model.reset_short_ids() self.model.replace_uuids_by_short_ids_in_titles() def remove(self, obj: SignalObj | ImageObj) -> None: """Remove object from group""" self.model.replace_short_ids_by_uuids_in_titles() self.__objects.remove(get_uuid(obj)) self.model.reset_short_ids() self.model.replace_uuids_by_short_ids_in_titles() def clear(self) -> None: """Clear group""" self.__objects.clear() def get_objects(self) -> list[SignalObj | ImageObj]: """Return objects in group""" return self.model.get_objects(self.__objects) def get_object_ids(self) -> list[str]: """Return object ids in group""" return self.__objects.copy() class ObjectModel: """Represents a DataLab object model (groups of signals/images)""" def __init__(self, group_prefix: str) -> None: """Initialize object model Args: group_prefix: prefix for group short IDs ("gs" for signal, "gi" for image) """ self._group_prefix = group_prefix # dict of objects, key is object uuid: self._objects: dict[str, SignalObj | ImageObj] = {} # list of groups: self._groups: list[ObjectGroup] = [] def reset_short_ids(self) -> None: """Reset short IDs (used for object numbering) This method is called when an object was removed from a group.""" gnb = onb = 1 for group in self._groups: set_number(group, gnb) gnb += 1 for obj in group: set_number(obj, onb) onb += 1 def __len__(self) -> int: """Return number of objects""" return len(self._objects) def __getitem__(self, uuid: str) -> SignalObj | ImageObj: """Return object with uuid""" return self._objects[uuid] def __iter__(self) -> Iterator[SignalObj | ImageObj]: """Iterate over objects""" return iter(self._objects.values()) def __repr__(self) -> str: """Return object representation""" return repr(self._objects) def __str__(self) -> str: """Return object string representation""" return str(self._objects) def __bool__(self) -> bool: """Return True if model is not empty""" return bool(self._objects) def __contains__(self, obj: SignalObj | ImageObj) -> bool: """Return True if obj is in model""" return get_uuid(obj) in self._objects def has_uuid(self, uuid: str) -> bool: """Check if an object with the given UUID exists in the model Args: uuid: UUID string to check Returns: True if an object with this UUID exists, False otherwise """ return uuid in self._objects def clear(self) -> None: """Clear model""" self._objects.clear() self._groups.clear() def get_all_objects( self, flatten: bool = True ) -> list[SignalObj | ImageObj] | list[list[SignalObj | ImageObj]]: """Return all objects, in order of appearance in groups Args: flatten: if True, return a flat list of objects, otherwise return a list of lists (one list per group) Returns: List of objects in order of appearance in groups. """ objects = [] for group in self._groups: if flatten: objects.extend(group.get_objects()) else: objects.append(group.get_objects()) return objects def get_object_or_group(self, uuid: str) -> SignalObj | ImageObj | ObjectGroup: """Return object or group with uuid""" if uuid in self._objects: return self._objects[uuid] for group in self._groups: if get_uuid(group) == uuid: return group raise KeyError(f"Object or group with uuid {uuid} not found") def get_group(self, uuid: str) -> ObjectGroup: """Return group with uuid""" for group in self._groups: if get_uuid(group) == uuid: return group raise KeyError(f"Group with uuid {uuid} not found") def get_number(self, obj_or_group: SignalObj | ImageObj | ObjectGroup) -> int: """Return number of object or group""" if isinstance(obj_or_group, ObjectGroup): try: return self._groups.index(obj_or_group) + 1 except ValueError as exc: raise KeyError( f"Group {get_uuid(obj_or_group)} not found in model" ) from exc if isinstance(obj_or_group, (SignalObj, ImageObj)): objs = self.get_all_objects() try: return objs.index(obj_or_group) + 1 except ValueError as exc: raise KeyError( f"Object {get_uuid(obj_or_group)} not found in model" ) from exc raise KeyError(f"Object or group {get_uuid(obj_or_group)} not found in model") def get_group_from_number(self, number: int) -> ObjectGroup: """Return group from its number. Args: number: group number (starts with 1) Returns: Group Raises: IndexError: if group with number not found """ if number < 1: raise IndexError(f"Group number {number} is out of range (must be >= 1)") if number > len(self._groups): raise IndexError( f"Group number {number} is out of range (max is {len(self._groups)})" ) return self._groups[number - 1] def get_group_from_title(self, title: str) -> ObjectGroup: """Return group from its title. Args: title: group title Returns: Group Raises: KeyError: if group with title not found """ for group in self._groups: if group.title == title: return group raise KeyError(f"Group with title '{title}' not found") def get_group_from_object(self, obj: SignalObj | ImageObj) -> ObjectGroup: """Return group containing object Args: obj: object to find group for Returns: Group Raises: KeyError: if object not found in any group """ for group in self._groups: if obj in group: return group raise KeyError(f"Object with uuid '{get_uuid(obj)}' not found in any group") def get_groups(self, uuids: list[str] | None = None) -> list[ObjectGroup]: """Return groups""" if uuids is None: return self._groups return [group for group in self._groups if get_uuid(group) in uuids] def add_group(self, title: str) -> ObjectGroup: """Add group to model Args: title: group title Returns: Created group object """ group = ObjectGroup(title, self, self._group_prefix) self._groups.append(group) self.reset_short_ids() return group def get_object_group_id(self, obj: SignalObj | ImageObj) -> str | None: """Return group id of object Args: obj: object to get group id from Returns: group id or None if object is not in any group """ try: return get_uuid(self.get_group_from_object(obj)) except KeyError: return None def get_group_object_ids(self, group_id: str) -> list[str]: """Return object ids in group""" for group in self._groups: if get_uuid(group) == group_id: return group.get_object_ids() raise KeyError(f"Group with uuid '{group_id}' not found") def remove_group(self, group: ObjectGroup) -> None: """Remove group from model""" self.replace_short_ids_by_uuids_in_titles() self._groups.remove(group) for obj in group: remove_obj = True for other_group in self._groups: if obj in other_group: remove_obj = False break if remove_obj: del self._objects[get_uuid(obj)] self.reset_short_ids() self.replace_uuids_by_short_ids_in_titles() def add_object(self, obj: SignalObj | ImageObj, group_id: str) -> None: """Add object to model""" self.replace_short_ids_by_uuids_in_titles([obj]) self._objects[get_uuid(obj)] = obj onb = 0 for group in self._groups: onb += len(group) if get_uuid(group) == group_id: set_number(obj, onb + 1) group.append(obj) break else: raise KeyError(f"Group with uuid '{group_id}' not found") self.reset_short_ids() self.replace_uuids_by_short_ids_in_titles() def remove_object(self, obj: SignalObj | ImageObj) -> None: """Remove object from model""" for group in self._groups: if obj in group: group.remove(obj) del self._objects[get_uuid(obj)] self.reset_short_ids() def get_object_from_number(self, number: int) -> SignalObj | ImageObj: """Return object from its number. Args: number: object number (unique in model) Returns: Object Raises: IndexError: if object with number not found """ if number < 1: raise IndexError(f"Object number {number} is out of range (must be >= 1)") objs = self.get_all_objects() if number > len(objs): raise IndexError( f"Object number {number} is out of range (max is {len(objs)})" ) return objs[number - 1] def get_objects(self, uuids: list[str]) -> list[SignalObj | ImageObj]: """Return objects with uuids""" return [self._objects[uuid] for uuid in uuids] def get_object_ids(self, flatten: bool = True) -> list[str] | list[list[str]]: """Return object ids, in order of appearance in groups Args: flatten: if True, return a flat list of object ids, otherwise return a list of lists (one list per group) Returns: List of object ids in order of appearance in groups. """ ids = [] for group in self._groups: if flatten: ids.extend(group.get_object_ids()) else: ids.append(group.get_object_ids()) return ids def get_group_titles_with_object_info( self, ) -> tuple[list[str], list[list[str]], list[list[str]]]: """Return groups titles and lists of inner objects uuids and titles. Returns: Tuple: groups titles, lists of inner objects uuids and titles """ grp_titles = [] obj_uuids = [] obj_titles = [] for group in self._groups: grp_titles.append(group.title) obj_uuids.append(group.get_object_ids()) obj_titles.append([obj.title for obj in group]) return grp_titles, obj_uuids, obj_titles def get_object_titles(self, flatten: bool = True) -> list[str] | list[list[str]]: """Return object titles, in order of appearance in groups Args: flatten: if True, return a flat list of object titles, otherwise return a list of lists (one list per group) Returns: List of object titles in order of appearance in groups. """ if flatten: return [obj.title for obj in self.get_all_objects()] return [[obj.title for obj in group] for group in self._groups] def get_object_from_title(self, title: str) -> SignalObj | ImageObj: """Return object with title. Args: title: object title Returns: object with title Raises: KeyError: if object with title not found """ for obj in self._objects.values(): if obj.title == title: return obj raise KeyError(f"Object with title '{title}' not found") def __get_group_object_mapping_to_shortid(self) -> dict[str, str]: """Return dictionary mapping group/object uuids to their short ID""" mapping = {} for group in self._groups: mapping[get_uuid(group)] = get_short_id(group) for obj in group: mapping[get_uuid(obj)] = get_short_id(obj) return mapping def replace_short_ids_by_uuids_in_titles( self, other_objects: tuple[SignalObj | ImageObj] | None = None ) -> None: """Replace short IDs by uuids in titles Args: other_objects: tuple of other objects to consider for short ID replacement .. note:: This method is called before reorganizing groups or objects. It replaces the short IDs in titles by the uuids. This is needed because the short IDs are used to reflect in the title the operation performed on the object/group, e.g. "fft(s001)" or "g001 + g002". But when reorganizing groups or objects, the short IDs may change, so we need to replace them by the uuids, which are stable. Once the reorganization is done, we will replace the uuids by the new short IDs thanks to the `__replace_uuids_by_short_ids_in_titles` method. """ mapping = self.__get_group_object_mapping_to_shortid() objs = self._objects.values() if other_objects is not None: objs = list(objs) + list(other_objects) for obj in objs: for obj_uuid, short_id in mapping.items(): obj.title = obj.title.replace(short_id, obj_uuid) for group in self._groups: for grp_uuid, short_id in mapping.items(): group.title = group.title.replace(short_id, grp_uuid) def replace_uuids_by_short_ids_in_titles(self) -> None: """Replace uuids by short IDs in titles .. note:: This method is called after reorganizing groups or objects. It replaces the uuids in titles by the short IDs. """ mapping = self.__get_group_object_mapping_to_shortid() for obj in self._objects.values(): for obj_uuid, short_id in mapping.items(): obj.title = obj.title.replace(obj_uuid, short_id) for group in self._groups: for grp_uuid, short_id in mapping.items(): group.title = group.title.replace(grp_uuid, short_id) # Replace remaining UUIDs with f"{obj.PREFIX}xxx" # (this may happen if groups or objects were removed in the meantime): pattern = re.compile(r"\b[0-9a-f]{8}-([0-9a-f]{4}-){3}[0-9a-f]{12}\b") for obj in self._objects.values(): obj.title = pattern.sub(f"{obj.PREFIX}xxx", obj.title) for group in self._groups: for obj in group: obj.title = pattern.sub(f"{obj.PREFIX}xxx", obj.title) def reorder_groups(self, group_ids: list[str]) -> None: """Reorder groups. Args: group_ids: list of group uuids """ # Replace short IDs by uuids in titles: self.replace_short_ids_by_uuids_in_titles() # Reordering groups: self._groups = [self.get_group(group_id) for group_id in group_ids] # Reset short IDs: self.reset_short_ids() # Replace uuids by short IDs in titles: self.replace_uuids_by_short_ids_in_titles() def reorder_objects(self, obj_ids: dict[str, list[str]]) -> None: """Reorder objects in groups. Args: obj_ids: dict of group uuids and list of object uuids """ # Replace short IDs by uuids in titles: self.replace_short_ids_by_uuids_in_titles() # Reordering objects in groups: for group_id, obj_uuids in obj_ids.items(): group = self.get_group(group_id) group.clear() for obj_uuid in obj_uuids: group.append(self._objects[obj_uuid]) # Reset short IDs: self.reset_short_ids() # Replace uuids by short IDs in titles: self.replace_uuids_by_short_ids_in_titles() DataLab-1.1.0/datalab/plugins.py000066400000000000000000000265131514014117600164330ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab plugin system --------------------- DataLab plugin system provides a way to extend the application with new functionalities. Plugins are Python modules that relies on two classes: - :class:`PluginInfo`, which stores information about the plugin - :class:`PluginBase`, which is the base class for all plugins Plugins may also extends DataLab I/O features by providing new image or signal formats. To do so, they must provide a subclass of :class:`ImageFormatBase` or :class:`SignalFormatBase`, in which format information is defined using the :class:`FormatInfo` class. """ from __future__ import annotations import abc import dataclasses import importlib import os import os.path as osp import pkgutil import sys from typing import TYPE_CHECKING from qtpy import QtWidgets as QW # pylint: disable=unused-import from sigima.io.base import FormatInfo # noqa: F401 from sigima.io.image.base import ImageFormatBase # noqa: F401 from sigima.io.image.formats import ClassicsImageFormat # noqa: F401 from sigima.io.signal.base import SignalFormatBase # noqa: F401 from datalab.config import MOD_NAME, OTHER_PLUGINS_PATHLIST, Conf, _ from datalab.control.proxy import LocalProxy from datalab.env import execenv if TYPE_CHECKING: from sigima.objects import NewImageParam, NewSignalParam from datalab.gui import main from datalab.gui.panel.image import ImagePanel from datalab.gui.panel.signal import SignalPanel PLUGINS_DEFAULT_PATH = Conf.get_path("plugins") if not osp.isdir(PLUGINS_DEFAULT_PATH): os.makedirs(PLUGINS_DEFAULT_PATH) # pylint: disable=bad-mcs-classmethod-argument class PluginRegistry(type): """Metaclass for registering plugins""" _plugin_classes: list[type[PluginBase]] = [] _plugin_instances: list[PluginBase] = [] def __init__(cls, name, bases, attrs): super().__init__(name, bases, attrs) if name != "PluginBase": cls._plugin_classes.append(cls) @classmethod def get_plugin_classes(cls) -> list[type[PluginBase]]: """Return plugin classes""" return cls._plugin_classes @classmethod def get_plugins(cls) -> list[PluginBase]: """Return plugin instances""" return cls._plugin_instances @classmethod def get_plugin(cls, name_or_class: str | type[PluginBase]) -> PluginBase | None: """Return plugin instance""" for plugin in cls._plugin_instances: if name_or_class in (plugin.info.name, plugin.__class__): return plugin return None @classmethod def register_plugin(cls, plugin: PluginBase): """Register plugin""" if plugin.info.name in [plug.info.name for plug in cls._plugin_instances]: raise ValueError(f"Plugin {plugin.info.name} already registered") cls._plugin_instances.append(plugin) execenv.log(cls, f"Plugin {plugin.info.name} registered") @classmethod def unregister_plugin(cls, plugin: PluginBase): """Unregister plugin""" cls._plugin_instances.remove(plugin) execenv.log(cls, f"Plugin {plugin.info.name} unregistered") execenv.log(cls, f"{len(cls._plugin_instances)} plugins left") @classmethod def unregister_all_plugins(cls): """Unregister all plugins""" for plugin in cls._plugin_instances: execenv.log(cls, f"Unregistering plugin {plugin.info.name}") plugin.unregister() cls._plugin_instances.clear() execenv.log(cls, "All plugins unregistered") @classmethod def get_plugin_info(cls, html: bool = True) -> str: """Return plugin information (names, versions, descriptions) in html format Args: html: return html formatted text (default: True) """ linesep = "
    " if html else os.linesep bullet = "• " if html else " " * 4 def italic(text: str) -> str: """Return italic text""" return f"{text}" if html else text if Conf.main.plugins_enabled.get(): plugins = cls.get_plugins() if plugins: text = italic(_("Registered plugins:")) text += linesep for plugin in plugins: text += f"{bullet}{plugin.info.name} ({plugin.info.version})" if plugin.info.description: text += f": {plugin.info.description}" text += linesep else: text = italic(_("No plugins available")) else: text = italic(_("Plugins are disabled (see DataLab settings)")) return text @dataclasses.dataclass class PluginInfo: """Plugin info""" name: str = None version: str = "0.0.0" description: str = "" icon: str = None class PluginBaseMeta(PluginRegistry, abc.ABCMeta): """Mixed metaclass to avoid conflicts""" class PluginBase(abc.ABC, metaclass=PluginBaseMeta): """Plugin base class""" PLUGIN_INFO: PluginInfo = None def __init__(self): self.main: main.DLMainWindow = None self.proxy: LocalProxy = None self._is_registered = False self.info = self.PLUGIN_INFO if self.info is None: raise ValueError(f"Plugin info not set for {self.__class__.__name__}") @property def signalpanel(self) -> SignalPanel: """Return signal panel""" return self.main.signalpanel @property def imagepanel(self) -> ImagePanel: """Return image panel""" return self.main.imagepanel def show_warning(self, message: str): """Show warning message""" QW.QMessageBox.warning(self.main, _("Warning"), message) def show_error(self, message: str): """Show error message""" QW.QMessageBox.critical(self.main, _("Error"), message) def show_info(self, message: str): """Show info message""" QW.QMessageBox.information(self.main, _("Information"), message) def ask_yesno( self, message: str, title: str | None = None, cancelable: bool = False ) -> bool: """Ask yes/no question""" if title is None: title = _("Question") buttons = QW.QMessageBox.Yes | QW.QMessageBox.No if cancelable: buttons |= QW.QMessageBox.Cancel answer = QW.QMessageBox.question(self.main, title, message, buttons) if answer == QW.QMessageBox.Yes: return True if answer == QW.QMessageBox.No: return False return None def edit_new_signal_parameters( self, title: str | None = None, size: int | None = None, ) -> NewSignalParam: """Create and edit new signal parameter dataset Args: title: title of the new signal size: size of the new signal (default: None, get from current signal) Returns: New signal parameter dataset (or None if canceled) """ newparam = self.signalpanel.get_newparam_from_current(title=title) if size is not None: newparam.size = size if newparam.edit(self.main): return newparam return None def edit_new_image_parameters( self, title: str | None = None, shape: tuple[int, int] | None = None, hide_height: bool = False, hide_width: bool = False, hide_type: bool = True, hide_dtype: bool = False, ) -> NewImageParam | None: """Create and edit new image parameter dataset Args: title: title of the new image shape: shape of the new image (default: None, get from current image) hide_height: hide image heigth parameter (default: False) hide_width: hide image width parameter (default: False) hide_type: hide image type parameter (default: True) hide_dtype: hide image data type parameter (default: False) Returns: New image parameter dataset (or None if canceled) """ newparam = self.imagepanel.get_newparam_from_current(title=title) if shape is not None: newparam.height, newparam.width = shape newparam.hide_height = hide_height newparam.hide_width = hide_width newparam.hide_type = hide_type newparam.hide_dtype = hide_dtype if newparam.edit(self.main): return newparam return None def is_registered(self): """Return True if plugin is registered""" return self._is_registered def register(self, main: main.DLMainWindow) -> None: """Register plugin""" if self._is_registered: return PluginRegistry.register_plugin(self) self._is_registered = True self.main = main self.proxy = LocalProxy(main) self.register_hooks() def unregister(self): """Unregister plugin""" if not self._is_registered: return PluginRegistry.unregister_plugin(self) self._is_registered = False self.unregister_hooks() self.main = None self.proxy = None def register_hooks(self): """Register plugin hooks""" def unregister_hooks(self): """Unregister plugin hooks""" @abc.abstractmethod def create_actions(self): """Create actions""" def discover_plugins() -> list[type[PluginBase]]: """Discover plugins using naming convention Returns: List of discovered plugins (as classes) """ if Conf.main.plugins_enabled.get(): for path in [ Conf.main.plugins_path.get(), PLUGINS_DEFAULT_PATH, ] + OTHER_PLUGINS_PATHLIST: rpath = osp.realpath(path) if rpath not in sys.path: sys.path.append(rpath) return [ importlib.import_module(name) for _finder, name, _ispkg in pkgutil.iter_modules() if name.startswith(f"{MOD_NAME}_") ] return [] def discover_v020_plugins() -> list[tuple[str, str]]: """Discover v0.20 plugins (with ``cdl_`` prefix) without importing them Returns: List of tuples (plugin_name, directory_path) for discovered v0.20 plugins """ v020_plugins = [] if Conf.main.plugins_enabled.get(): for path in [ Conf.main.plugins_path.get(), PLUGINS_DEFAULT_PATH, ] + OTHER_PLUGINS_PATHLIST: rpath = osp.realpath(path) if rpath not in sys.path: sys.path.append(rpath) for finder, name, _ispkg in pkgutil.iter_modules(): if name.startswith("cdl_"): # Get the directory path from the module finder if hasattr(finder, "path"): directory_path = finder.path v020_plugins.append((name, directory_path)) else: # Fallback if path is not available v020_plugins.append((name, "")) return v020_plugins def get_available_plugins() -> list[PluginBase]: """Instantiate and get available plugins Returns: List of available plugins (as instances) """ # Note: this function is not used by DataLab itself, but it is used by the # test suite to get a list of available plugins discover_plugins() return [plugin_class() for plugin_class in PluginRegistry.get_plugin_classes()] DataLab-1.1.0/datalab/plugins/000077500000000000000000000000001514014117600160525ustar00rootroot00000000000000DataLab-1.1.0/datalab/plugins/_readme_.txt000066400000000000000000000004161514014117600203470ustar00rootroot00000000000000Warning: -------- This folder is not a Python package. That is intentional, because it is not meant to be imported. It is distributed as data folder from a packaging point of view. It contains a collection of plugins that are automatically loaded by the plugin system.DataLab-1.1.0/datalab/plugins/datalab_imageformats.py000066400000000000000000000127551514014117600225640ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Image file formats Plugin for DataLab ------------------------------------- This plugin is an example of DataLab plugin. It provides image file formats from cameras, scanners, and other acquisition devices. """ import struct import numpy as np from sigima.io.base import FormatInfo from sigima.io.image.base import SingleImageFormatBase # ============================================================================== # Thales Pixium FXD file format # ============================================================================== class FXDFile: """Class implementing Thales Pixium FXD Image file reading feature Args: fname (str): path to FXD file debug (bool): debug mode """ HEADER = " None: self.__debug = debug self.file_format = None # long self.nbcols = None # long self.nbrows = None # long self.nbframes = None # long self.pixeltype = None # long self.quantlevels = None # long self.maxlevel = None # float self.minlevel = None # float self.comment_length = None # long self.fname = None self.data = None if fname is not None: self.load(fname) def __repr__(self) -> str: """Return a string representation of the object""" info = ( ("Image width", f"{self.nbcols:d}"), ("Image Height", f"{self.nbrows:d}"), ("Frame number", f"{self.nbframes:d}"), ("File format", f"{self.file_format:d}"), ("Pixel type", f"{self.pixeltype:d}"), ("Quantlevels", f"{self.quantlevels:d}"), ("Min. level", f"{self.minlevel:f}"), ("Max. level", f"{self.maxlevel:f}"), ("Comment length", f"{self.comment_length:d}"), ) desc_len = max(len(d) for d in list(zip(*info))[0]) + 3 res = "" for description, value in info: res += ("{:" + str(desc_len) + "}{}\n").format(description + ": ", value) res = object.__repr__(self) + "\n" + res return res def load(self, fname: str) -> None: """Load header and image pixel data Args: fname (str): path to FXD file """ with open(fname, "rb") as data_file: header_s = struct.Struct(self.HEADER) record = data_file.read(9 * 4) unpacked_rec = header_s.unpack(record) ( self.file_format, self.nbcols, self.nbrows, self.nbframes, self.pixeltype, self.quantlevels, self.maxlevel, self.minlevel, self.comment_length, ) = unpacked_rec if self.__debug: print(unpacked_rec) print(self) data_file.seek(128 + self.comment_length) if self.pixeltype == 0: size, dtype = 4, np.float32 elif self.pixeltype == 1: size, dtype = 2, np.uint16 elif self.pixeltype == 2: size, dtype = 1, np.uint8 else: raise NotImplementedError(f"Unsupported pixel type: {self.pixeltype}") block = data_file.read(self.nbrows * self.nbcols * size) data = np.frombuffer(block, dtype=dtype) self.data = data.reshape(self.nbrows, self.nbcols) class FXDImageFormat(SingleImageFormatBase): """Object representing Thales Pixium (FXD) image file type""" FORMAT_INFO = FormatInfo( name="Thales Pixium", extensions="*.fxd", readable=True, writeable=False, ) @staticmethod def read_data(filename: str) -> np.ndarray: """Read data and return it Args: filename (str): path to FXD file Returns: np.ndarray: image data """ fxd_file = FXDFile(filename) return fxd_file.data # ============================================================================== # Dürr NDT XYZ file format # ============================================================================== class XYZImageFormat(SingleImageFormatBase): """Object representing Dürr NDT XYZ image file type""" FORMAT_INFO = FormatInfo( name="Dürr NDT", extensions="*.xyz", readable=True, writeable=True, ) @staticmethod def read_data(filename: str) -> np.ndarray: """Read data and return it Args: filename (str): path to XYZ file Returns: np.ndarray: image data """ with open(filename, "rb") as fdesc: cols = int(np.fromfile(fdesc, dtype=np.uint16, count=1)[0]) rows = int(np.fromfile(fdesc, dtype=np.uint16, count=1)[0]) arr = np.fromfile(fdesc, dtype=np.uint16, count=cols * rows) arr = arr.reshape((rows, cols)) return np.fliplr(arr) @staticmethod def write_data(filename: str, data: np.ndarray) -> None: """Write data to file Args: filename: File name data: Image array data """ data = np.fliplr(data) with open(filename, "wb") as fdesc: fdesc.write(np.array(data.shape[1], dtype=np.uint16).tobytes()) fdesc.write(np.array(data.shape[0], dtype=np.uint16).tobytes()) fdesc.write(data.tobytes()) DataLab-1.1.0/datalab/plugins/datalab_testdata.py000066400000000000000000000165231514014117600217140ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Test Data Plugin for DataLab ---------------------------- This plugin is an example of DataLab plugin. It provides test data samples and some actions to test DataLab functionalities. """ from __future__ import annotations import sigima.tests.data as test_data from sigima.io.image import ImageIORegistry from sigima.io.signal import SignalIORegistry from sigima.tests import helpers from datalab.config import _ from datalab.plugins import PluginBase, PluginInfo from datalab.utils.qthelpers import create_progress_bar # ------------------------------------------------------------------------------ # All computation functions must be defined as global functions, otherwise # they cannot be pickled and sent to the worker process # ------------------------------------------------------------------------------ class PluginTestData(PluginBase): """DataLab Test Data Plugin""" PLUGIN_INFO = PluginInfo( name=_("Test data"), version="1.0.0", description=_("Testing DataLab functionalities"), ) def load_test_objs( self, registry_class: type[SignalIORegistry | ImageIORegistry], title: str ) -> None: """Load all test objects from a given registry class Args: registry_class: Registry class (SignalIORegistry or ImageIORegistry) title: Progress bar title Returns: List of (filename, object) tuples """ test_objs = list(helpers.read_test_objects(registry_class)) with create_progress_bar(self.signalpanel, title, max_=len(test_objs)) as prog: for i_obj, (_fname, obj) in enumerate(test_objs): prog.setValue(i_obj + 1) if prog.wasCanceled(): break if obj is not None: self.proxy.add_object(obj) # Signal processing features ------------------------------------------------ def create_paracetamol_signal(self) -> None: """Create paracetamol signal""" obj = test_data.create_paracetamol_signal() self.proxy.add_object(obj) # Image processing features ------------------------------------------------ def create_peak_image(self) -> None: """Create 2D peak image""" obj = self.imagepanel.new_object(add_to_panel=False) if obj is not None: param = test_data.PeakDataParam.create(size=max(obj.data.shape)) self.imagepanel.processor.update_param_defaults(param) if param.edit(self.main): obj.data, _coords = test_data.get_peak2d_data(param) self.proxy.add_object(obj) def create_sincos_image(self) -> None: """Create 2D sin cos image""" newparam = self.edit_new_image_parameters(hide_type=True) if newparam is not None: obj = test_data.create_sincos_image(newparam) self.proxy.add_object(obj) def create_noisy_gaussian_image(self) -> None: """Create 2D noisy gauss image""" newparam = self.edit_new_image_parameters(hide_height=True, hide_type=True) if newparam is not None: obj = test_data.create_noisy_gaussian_image(newparam, add_annotations=False) self.proxy.add_object(obj) def create_multigaussian_image(self) -> None: """Create 2D multi gauss image""" newparam = self.edit_new_image_parameters(hide_height=True, hide_type=True) if newparam is not None: obj = test_data.create_multigaussian_image(newparam) self.proxy.add_object(obj) def create_2dstep_image(self) -> None: """Create 2D step image""" newparam = self.edit_new_image_parameters(hide_type=True) if newparam is not None: obj = test_data.create_2dstep_image(newparam) self.proxy.add_object(obj) def create_ring_image(self) -> None: """Create 2D ring image""" param = test_data.RingParam(_("Ring")) if param.edit(self.main): obj = test_data.create_ring_image(param) self.proxy.add_object(obj) def create_annotated_image(self) -> None: """Create annotated image""" obj = test_data.create_annotated_image() self.proxy.add_object(obj) def create_grid_gaussian_image(self) -> None: """Create image with a grid of gaussian spots""" param = test_data.GridOfGaussianImages(_("Grid of Gaussian Images")) if param.edit(self.main): obj = test_data.create_grid_of_gaussian_images(param) self.proxy.add_object(obj) # Plugin menu entries ------------------------------------------------------ def create_actions(self) -> None: """Create actions""" # Signal Panel ---------------------------------------------------------- sah = self.signalpanel.acthandler with sah.new_menu(_("Test data")): sah.new_action( _("Load spectrum of paracetamol"), triggered=self.create_paracetamol_signal, select_condition="always", ) sah.new_action( _("Load all test signals"), triggered=lambda regclass=SignalIORegistry, title=_("Load all test signals"): self.load_test_objs(regclass, title), select_condition="always", separator=True, ) # Image Panel ----------------------------------------------------------- iah = self.imagepanel.acthandler with iah.new_menu(_("Test data")): iah.new_action( _("Create image with peaks"), triggered=self.create_peak_image, select_condition="always", separator=True, ) iah.new_action( _("Create 2D sin cos image"), triggered=self.create_sincos_image, select_condition="always", ) iah.new_action( _("Create 2D noisy gaussian image"), triggered=self.create_noisy_gaussian_image, select_condition="always", ) iah.new_action( _("Create 2D multi gaussian image"), triggered=self.create_multigaussian_image, select_condition="always", ) iah.new_action( _("Create annotated image"), triggered=self.create_annotated_image, select_condition="always", ) iah.new_action( _("Create 2D step image"), triggered=self.create_2dstep_image, select_condition="always", ) iah.new_action( _("Create ring image"), triggered=self.create_ring_image, select_condition="always", ) iah.new_action( _("Create image with a grid of gaussian spots"), triggered=self.create_grid_gaussian_image, select_condition="always", ) iah.new_action( _("Load all test images"), triggered=lambda regclass=ImageIORegistry, title=_("Load all test images"): self.load_test_objs(regclass, title), select_condition="always", separator=True, ) DataLab-1.1.0/datalab/start.pyw000066400000000000000000000042411514014117600162700ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab ======= Starter script for DataLab. """ import sys # -------------------------------------------------------------------------------------- # Macro command execution for the standalone version of DataLab if len(sys.argv) > 1 and sys.argv[1] == "-c": exec(sys.argv[2]) # -------------------------------------------------------------------------------------- # -------------------------------------------------------------------------------------- # Main DataLab application # # This part is executed when the script is run as a standalone application. # It is protected by using `if __name__ == '__main__':` to avoid running it again when # the multiprocessing pool is created (see datalab/gui/processor/base.py). elif __name__ == "__main__": try: from datalab.app import run run() except Exception as exc: # At this point, we intercept exceptions raised before the application is # started and write them to a log file. This could happen if there is an error # occuring during the import of one of the modules used by the application. # Last example to date: NumPy V2.0.0-2.0.1 raises an exception when imported # if the script is executed using 'pythonw.exe' instead of 'python.exe'. import datetime import traceback with open("datalab_error.log", "w") as stream: stream.write(f"DataLab failed to start:\n") stream.write(f" Date: {datetime.datetime.now()}\n") stream.write(f" Python {sys.version}\n") stream.write(f" Executable: {sys.executable}\n") comment = "" if sys.platform != "win32": comment = " - This script is intended to be run on Windows only." stream.write(f" Platform: {sys.platform}{comment}\n") stream.write(f" Python path:\n") for path in sys.path: stream.write(f" {path}\n") stream.write("\n") traceback.print_exc(file=stream) # -------------------------------------------------------------------------------------- DataLab-1.1.0/datalab/tests/000077500000000000000000000000001514014117600155335ustar00rootroot00000000000000DataLab-1.1.0/datalab/tests/__init__.py000066400000000000000000000202301514014117600176410ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Tests (:mod:`datalab.tests`) ------------------------ The DataLab test suite is based on the `pytest `_ framework. The test suite modules are organized in subpackages according to their purpose. The following subpackages are available: - :mod:`datalab.tests.backbone`: backbone tests - :mod:`datalab.tests.features`: feature tests (unit tests and application tests) - :mod:`datalab.tests.scenarios`: high-level scenarios tests .. seealso:: :ref:`validation` for more information about DataLab's testing strategy. """ from __future__ import annotations import os import os.path as osp import sys import time from contextlib import contextmanager from typing import Generator import psutil import pytest from guidata.guitest import run_testlauncher from sigima.tests import helpers import datalab.config # Loading icons from datalab.config import MOD_NAME, SHOTPATH from datalab.control.proxy import RemoteProxy, proxy_context from datalab.env import execenv from datalab.gui.main import DLMainWindow from datalab.gui.panel.image import ImagePanel from datalab.gui.panel.signal import SignalPanel from datalab.utils import qthelpers as qth # Add test data files and folders pointed by `DATALAB_DATA` environment variable: helpers.add_test_path_from_env("DATALAB_DATA") # Add test data files and folders for the DataLab module: helpers.add_test_module_path(MOD_NAME, osp.join("data", "tests")) # Set default screenshot path for tests execenv.screenshot_path = SHOTPATH @contextmanager def datalab_test_app_context( size: tuple[int, int] = None, maximized: bool = False, save: bool = False, console: bool | None = None, exec_loop: bool = True, ) -> Generator[DLMainWindow, None, None]: """Context manager handling DataLab mainwindow creation and Qt event loop with optional HDF5 file save and other options for testing purposes Args: size: mainwindow size (default: (950, 600)) maximized: whether to maximize mainwindow (default: False) save: whether to save HDF5 file (default: False) console: whether to show console (default: None) exec_loop: whether to execute Qt event loop (default: True) """ if size is None: size = 1200, 700 with qth.datalab_app_context(exec_loop=exec_loop): win: DLMainWindow | None = None try: win = DLMainWindow(console=console) if maximized: win.showMaximized() else: width, height = size win.resize(width, height) win.showNormal() win.show() win.setObjectName(helpers.get_default_test_name()) # screenshot name yield win finally: if save: path = helpers.get_output_data_path("h5") try: os.remove(path) win.save_to_h5_file(path) except (FileNotFoundError, PermissionError): pass has_exception_occurred = sys.exc_info()[0] is not None if not exec_loop or has_exception_occurred and win is not None: # Closing main window properly win.set_modified(False) win.close() def is_pid_alive(pid: int) -> bool: """Check if a process with the given PID is alive Args: Process ID to check Returns: True if the process is alive, False otherwise """ return psutil.pid_exists(pid) and psutil.Process(pid).is_running() def run_datalab_in_background(wait_until_ready: bool = True) -> None: """Run DataLab application as a service. This function starts the DataLab application in a separate process, ensuring that it runs independently of the current script. It sets the necessary environment variables to prevent the application from quitting automatically (since the script is executed in a non-interactive mode - the so-called "unattended" mode) and to avoid port conflicts. After starting the application, it waits for a short period to allow the application to initialize and then checks if the process is alive. The main use case for this function is in testing scenarios where the DataLab application needs to be running in the background while a client connects to it and performs various operations. Args: wait_until_ready: If True, waits until the DataLab application is ready to accept connections (default: True). Uses RemoteProxy's built-in retry logic with extended timeout to handle DataLab startup time. Raises: RuntimeError: If the DataLab application fails to start """ env = os.environ.copy() env[execenv.DO_NOT_QUIT_ENV] = "1" if execenv.XMLRPCPORT_ENV in env: # May happen when executing other tests before env.pop(execenv.XMLRPCPORT_ENV) proc = helpers.exec_script( "-m", args=["datalab.app"], wait=False, env=env, verbose=False ) # If the process fails to start, it will raise the `AssertionError` exception # with the message "Unable to start DataLab application". # In that case, it might be useful to set `wait=True` and `verbose=True` in the # `exec_script` call above, so that the script waits for the DataLab application # to start and prints the output to the console. This way, you can see any # error messages or logs that might help you understand why the application failed # to start. # If the script is executed within a pytest session, add the `-s` option to pytest. # Give the process a moment to actually start time.sleep(1) if not is_pid_alive(proc.pid): raise RuntimeError("DataLab process terminated immediately after start") if wait_until_ready: # Use RemoteProxy's built-in retry mechanism with extended timeout # DataLab startup: Python imports, Qt init, GUI creation, XML-RPC server try: proxy = RemoteProxy(autoconnect=False) proxy.connect(timeout=30.0) # 30 seconds max for DataLab to be ready proxy.disconnect() except ConnectionRefusedError as exc: if is_pid_alive(proc.pid): proc.kill() raise RuntimeError( "Failed to connect to DataLab application. " "Process may have started but XML-RPC server is not responding." ) from exc def close_datalab_background() -> None: """Close DataLab application running as a service. This function connects to the DataLab application running in the background (started with `run_datalab_in_background`) and sends a command to close it. It uses the `RemoteProxy` class to establish the connection and send the close command. Raises: ConnectionRefusedError: If unable to connect to the DataLab application. """ proxy = RemoteProxy(autoconnect=False) proxy.connect(timeout=5.0) # 5 seconds max to connect proxy.close_application() proxy.disconnect() @contextmanager def datalab_in_background_context() -> Generator[RemoteProxy, None, None]: """Context manager for DataLab instance with proxy connection""" run_datalab_in_background() with proxy_context("remote") as proxy: try: yield proxy except Exception as exc: # pylint: disable=broad-except proxy.close_application() raise exc # Cleanup proxy.close_application() @contextmanager def skip_if_opencv_missing() -> Generator[None, None, None]: """Skip test if OpenCV is not available""" try: yield except ImportError as exc: if "cv2" in str(exc).lower(): pytest.skip("OpenCV not available, skipping test") raise exc def take_plotwidget_screenshot(panel: SignalPanel | ImagePanel, name: str) -> None: """Eventually takes plotwidget screenshot (only in screenshot mode)""" if execenv.screenshot: prefix = panel.PARAMCLASS.PREFIX qth.grab_save_window(panel.plothandler.plotwidget, f"{prefix}_{name}") def run() -> None: """Run DataLab test launcher""" run_testlauncher(datalab) if __name__ == "__main__": run() DataLab-1.1.0/datalab/tests/backbone/000077500000000000000000000000001514014117600172775ustar00rootroot00000000000000DataLab-1.1.0/datalab/tests/backbone/__init__.py000066400000000000000000000000021514014117600214000ustar00rootroot00000000000000# DataLab-1.1.0/datalab/tests/backbone/config_unit_test.py000066400000000000000000000134001514014117600232120ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Configuration test Checking .ini configuration file management. """ # guitest: show import os import os.path as osp from qtpy import QtCore as QC from qtpy import QtWidgets as QW from datalab import app from datalab.config import Conf from datalab.env import execenv from datalab.tests import helpers from datalab.utils.conf import CONF from datalab.utils.qthelpers import datalab_app_context SEC_MAIN = Conf.main OPT_MAX = SEC_MAIN.window_maximized OPT_POS = SEC_MAIN.window_position OPT_SIZ = SEC_MAIN.window_size OPT_DIR = SEC_MAIN.base_dir SEC_CONS = Conf.console OPT_CON = SEC_CONS.console_enabled CONFIGS = ( { SEC_MAIN.get_name(): { OPT_MAX.option: False, OPT_POS.option: (250, 250), OPT_SIZ.option: (1300, 700), OPT_DIR.option: "", }, SEC_CONS.get_name(): { OPT_CON.option: False, }, }, { SEC_MAIN.get_name(): { OPT_MAX.option: False, OPT_POS.option: (100, 100), OPT_SIZ.option: (750, 600), OPT_DIR.option: osp.dirname(__file__), }, SEC_CONS.get_name(): { OPT_CON.option: False, }, }, { SEC_MAIN.get_name(): { OPT_MAX.option: True, OPT_POS.option: (10, 10), OPT_SIZ.option: (750, 600), OPT_DIR.option: "", }, SEC_CONS.get_name(): { OPT_CON.option: True, }, }, ) def apply_conf(conf, name): """Apply configuration""" execenv.print(f" Applying configuration {name}:") fname = CONF.filename() try: os.remove(fname) execenv.print(f" Removed configuration file {fname}") except FileNotFoundError: execenv.print(f" Configuration file {fname} was not found") for section, settings in conf.items(): for option, value in settings.items(): execenv.print(f" Writing [{section}][{option}] = {value}") CONF.set(section, option, value) CONF.save() def is_wsl() -> bool: """Return True if running on Windows Subsystem for Linux (WSL)""" if os.name == "posix": return "WSL" in os.uname().release # pylint: disable=no-member return False def is_offscreen() -> bool: """Return True if running in offscreen mode (e.g. in CI or pytest session)""" return os.environ.get("QT_QPA_PLATFORM", "") == "offscreen" def assert_in_interval(val1, val2, interval, context): """Raise an AssertionError if val1 is in [val2-interval/2,val2+interval/2]""" itv1, itv2 = val2 - 0.5 * interval, val2 + 0.5 * interval try: assert itv1 <= val1 <= itv2 except AssertionError as exc: if is_wsl() or is_offscreen(): # Ignore this assertion error in two cases: # 1. If running on WSL, as the position of windows is not reliable # 2. If running in offscreen mode (e.g. in CI or pytest session), # as the position of windows may not be set correctly in offscreen mode pass else: raise AssertionError(f"{context}: {itv1} <= {val1} <= {itv2}") from exc def check_conf(conf, name, win: QW.QMainWindow, h5files): """Check configuration""" execenv.print(f" Checking configuration {name}: ") sec_main_name = SEC_MAIN.get_name() sec_cons_name = SEC_CONS.get_name() sec_main = conf[sec_main_name] sec_cons = conf[sec_cons_name] execenv.print(f" Checking [{sec_main_name}][{OPT_MAX.option}]: ", end="") assert sec_main[OPT_MAX.option] == ( win.windowState() == QC.Qt.WindowState.WindowMaximized ) execenv.print("OK") execenv.print(f" Checking [{sec_main_name}][{OPT_POS.option}]: ", end="") if not sec_main[OPT_MAX.option]: # Check position/size only when not maximized # Check position, taking into account screen offset (e.g. Linux/Gnome) conf_x, conf_y = sec_main[OPT_POS.option] conf_w, conf_h = sec_main[OPT_SIZ.option] available_go = QW.QApplication.primaryScreen().availableGeometry() x_offset, y_offset = available_go.x(), available_go.y() assert_in_interval(win.x(), conf_x - x_offset, 0, "X position") assert_in_interval(win.y(), conf_y - y_offset / 2, 15 + y_offset, "Y position") # Check size assert_in_interval(win.width(), conf_w, 5, "Width") assert_in_interval(win.height(), conf_h, 5, "Height") execenv.print(f"OK {win.x(), win.y(), win.width(), win.height()}") else: execenv.print("Passed (maximized)") execenv.print(f" Checking [{sec_cons_name}][{OPT_CON.option}]: ", end="") assert sec_cons[OPT_CON.option] == (win.console is not None) execenv.print("OK") execenv.print(f" Checking [{sec_main_name}][{OPT_DIR.option}]: ", end="") if h5files is None: assert conf[SEC_MAIN.get_name()][OPT_DIR.option] == OPT_DIR.get() execenv.print("OK (written in conf file)") else: assert OPT_DIR.get() == osp.dirname(h5files[0]) execenv.print("OK (changed to HDF5 file path)") def test_config(): """Testing DataLab configuration file""" with execenv.context(unattended=True): h5files = [helpers.get_test_fnames("*.h5")[1]] execenv.print("Testing DataLab configuration settings:") for index, conf in enumerate(CONFIGS): name = f"CONFIG{index}" apply_conf(conf, name) with datalab_app_context(exec_loop=True) as qapp: win = app.create(splash=False, h5files=h5files) qapp.processEvents() check_conf(conf, name, win, h5files) h5files = None execenv.print("=> Everything is OK") if __name__ == "__main__": test_config() DataLab-1.1.0/datalab/tests/backbone/config_versioning_unit_test.py000066400000000000000000000022471514014117600254640ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Configuration versioning test. Test that the configuration folder name is versioned correctly. """ import datalab from datalab.config import get_config_app_name def test_config_app_name(): """Test configuration app name versioning.""" config_name = get_config_app_name() major_version = datalab.__version__.split(".", maxsplit=1)[0] print(f"DataLab version: {datalab.__version__}") print(f"Major version: {major_version}") print(f"Config app name: {config_name}") # For v0.x, the config name should be "DataLab" (no suffix) if major_version == "0": assert config_name == "DataLab", f"Expected 'DataLab', got '{config_name}'" print("✓ v0.x uses legacy config folder name: .DataLab") else: # For v1.x, v2.x, etc., the config name should include version suffix expected = f"DataLab_v{major_version}" assert config_name == expected, f"Expected '{expected}', got '{config_name}'" print(f"✓ v{major_version}.x uses versioned config folder: .{config_name}") if __name__ == "__main__": test_config_app_name() DataLab-1.1.0/datalab/tests/backbone/dictlistserial_app_test.py000066400000000000000000000024541514014117600245740ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Testing (de)serialization of Dictionnary/List inside object metadata """ # guitest: show import os.path as osp from sigima.tests.data import create_test_image_with_metadata from datalab.env import execenv from datalab.tests import datalab_test_app_context, helpers def test_dict_serialization(): """Dictionnary/List in metadata (de)serialization test""" with execenv.context(unattended=True): with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context(console=False) as win: panel = win.imagepanel image = create_test_image_with_metadata() panel.add_object(image) fname = osp.join(tmpdir, "test.h5") win.save_to_h5_file(fname) win.reset_all() win.open_h5_files([fname], import_all=True) execenv.print("Dictionary/List (de)serialization:") oids = panel.objmodel.get_object_ids() first_image = panel.objmodel[oids[0]] helpers.compare_metadata( image.metadata, first_image.metadata.copy(), raise_on_diff=True ) if __name__ == "__main__": test_dict_serialization() DataLab-1.1.0/datalab/tests/backbone/errorcatcher_unit_test.py000066400000000000000000000046361514014117600244430ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Catch error and warning test (during computation) Unit test for BaseProcessor.handle_output method: catching error and warning during computation. This test runs a computation function that raises an error and/or a warning, and checks that the error and/or warning are/is correctly caught and displayed in the GUI. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code # guitest: show import warnings from sigima.objects import SignalObj from sigima.tests import data as test_data from datalab.config import Conf from datalab.env import execenv from datalab.tests import datalab_test_app_context def comp_error(src: SignalObj) -> SignalObj: # pylint: disable=unused-argument """Computation function that raises an error""" raise ValueError("This is a test error") def comp_warning(src: SignalObj) -> SignalObj: # pylint: disable=unused-argument """Computation function that raises a warning""" warnings.warn("This is a test warning") return src.copy() def comp_warning_error(src: SignalObj) -> SignalObj: # pylint: disable=unused-argument """Computation function that raises a warning and an error""" warnings.warn("This is a test warning") raise ValueError("This is a test error") def comp_no_error(src: SignalObj) -> SignalObj: # pylint: disable=unused-argument """Computation function that does not raise an error""" return src.copy() def test_catcher(): """Catch error and warning test""" with execenv.context(catcher_test=True): with datalab_test_app_context() as win: panel = win.signalpanel sig = test_data.create_paracetamol_signal() panel.add_object(sig) panel.processor.compute_1_to_1(comp_no_error, title="Test no error") panel.processor.compute_1_to_1(comp_error, title="Test error") Conf.proc.ignore_warnings.set(True) panel.processor.compute_1_to_1(comp_warning, title="Test warning (ignored)") Conf.proc.ignore_warnings.set(False) panel.processor.compute_1_to_1( comp_warning, title="Test warning (not ignored)" ) panel.processor.compute_1_to_1( comp_warning_error, title="Test warning + error" ) if __name__ == "__main__": test_catcher() DataLab-1.1.0/datalab/tests/backbone/errormsgbox_unit_test.py000066400000000000000000000027721514014117600243300ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Error message box test """ # guitest: show from guidata.qthelpers import exec_dialog, qt_app_context from qtpy import QtWidgets as QW from datalab.widgets.warningerror import WarningErrorMessageBox def error_message_box(category: str): """Test error message box Args: category (str): Error category Valid values are: "error", "warning" """ with qt_app_context(): win = QW.QMainWindow() win.setWindowTitle(f"DataLab {category.capitalize()} Message Box test") win.show() if category == "error": try: raise ValueError("Test error message box") except ValueError: context = "Test_error_message_box." * 5 tip = "This error may occured when testing the error message box. " * 10 dlg = WarningErrorMessageBox(win, "error", context, tip=tip) exec_dialog(dlg) elif category == "warning": context = "Test_warning_message_box." * 5 message = "Test warning message box" * 10 dlg = WarningErrorMessageBox(win, "warning", context, message) exec_dialog(dlg) else: raise ValueError(f"Invalid category: {category}") def test_error_message_box(): """Test error message box""" error_message_box("error") error_message_box("warning") if __name__ == "__main__": test_error_message_box() DataLab-1.1.0/datalab/tests/backbone/execenv_unit.py000066400000000000000000000221301514014117600223430ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DLExecEnv test --------------- Checking DataLab execution environment management. """ # guitest: skip (only needed for regression tests) from __future__ import annotations import os import sys import datalab from datalab.env import VerbosityLevels, execenv from datalab.tests import helpers ARGV_TEST = "--execenvtest" def print_execenv() -> None: """Print DL execution environment""" sys.argv.remove(ARGV_TEST) print(str(execenv.to_dict())) def get_subprocess_execenv_dict(args: list[str], env: dict | None = None) -> dict: """Get DL execution environment dict from subprocess Args: args (list[str]): command-line arguments env (dict | None): environment variables to pass to subprocess Returns: dict[str, str | int | bool | None]: DL execution environment dict """ output = helpers.get_script_output(__file__, args=args + [ARGV_TEST], env=env) return eval(output) # pylint: disable=eval-used def assert_two_dicts_are_equal( dict1: dict, dict2: dict, exceptions: tuple[str] | None = None ) -> None: """Assert two dicts are equal Args: dict1 (dict): first dict dict2 (dict): second dict exceptions (tuple[str] | None): keys to ignore """ diff_keys = [] for key in dict1: if key in exceptions: continue if dict1[key] != dict2[key]: diff_keys.append(key) if diff_keys: assert False, f"Dictionaries differ on keys: {diff_keys}" def test_cli(): """Test DL execution environment from command-line""" remove_all_datalab_envvars() # Test default values execenv.print("Testing command-line arguments:") execenv.print(" Default values: ", end="") execenvdict = get_subprocess_execenv_dict([]) execenv.print("OK") assert execenvdict == execenv.to_dict() # Testing boolean arguments execenv.print(" Testing boolean arguments:") for argname in ("unattended", "accept_dialogs", "screenshot"): execenv.print(f" {argname}:", end="") for val in (True, False): execenv.print(f" {val}", end="") if val: args = [f"--{argname}"] else: args = [] # Default value is False execenvdict = get_subprocess_execenv_dict(args) assert_two_dicts_are_equal(execenvdict, execenv.to_dict(), (argname,)) assert execenvdict[argname] is val, ( f"execenvdict[{argname}] = {execenvdict[argname]} != {val}" ) execenv.print() # Testing integer arguments execenv.print(" Testing integer arguments:") for argname in ("delay", "xmlrpcport"): execenv.print(f" {argname}:", end="") for val in (None, 0, 1, 2): if val is None: args = [] else: args = [f"--{argname}", str(val)] execenv.print(f" {val}", end="") execenvdict = get_subprocess_execenv_dict(args) assert_two_dicts_are_equal(execenvdict, execenv.to_dict(), (argname,)) if val is None: if argname == "delay": # Default value is 0 for delay assert execenvdict[argname] == 0 else: # Default value is None for xmlrpcport assert execenvdict[argname] is None else: assert execenvdict[argname] == val execenv.print() # Testing choice arguments execenv.print(" Testing choice arguments:") for argname in ("verbose",): execenv.print(f" {argname}:", end="") choices = {"verbose": [verb.value for verb in VerbosityLevels]} defaultval = {"verbose": VerbosityLevels.NORMAL.value}[argname] for val in [None] + choices[argname]: if val is None: args = [] else: args = [f"--{argname}", val] execenv.print(f" {val}", end="") execenvdict = get_subprocess_execenv_dict(args) assert_two_dicts_are_equal(execenvdict, execenv.to_dict(), (argname,)) if val is None: assert execenvdict[argname] == defaultval else: assert execenvdict[argname] == val execenv.print() # Testing special arguments execenv.print(" Testing special arguments:") execenv.print(" version: ", end="") args = ["--version"] output = helpers.get_script_output(__file__, args=args + [ARGV_TEST]) execenv.print(output) assert datalab.__version__ in output execenv.print(" h5 positionnal argument: ", end="") for h5files in (None, ["test.h5"], ["toto.h5", "tata.h5"]): if h5files is None: args = [] else: args = [";".join(h5files)] execenv.print(f" {h5files}", end="") execenvdict = get_subprocess_execenv_dict(args) assert execenvdict["h5files"] == h5files execenv.print() execenv.print(" h5browser argument:") for argname in ("-b", "--h5browser"): execenv.print(f" {argname}", end="") for val in (None, "test.h5"): if val is None: args = [] else: args = [argname, val] execenv.print(f" {val}", end="") execenvdict = get_subprocess_execenv_dict(args) assert execenvdict["h5browser_file"] == val execenv.print() execenv.print("=> Everything is OK") def remove_all_datalab_envvars(): """Remove all DL environment variables""" for _attrname, envvar in execenv.iterate_over_attrs_envvars(): os.environ.pop(envvar, None) def get_attr_to_envvar( vartype: type, default: int | str | None | bool, values: list[int | str | None | bool] | None = None, ) -> tuple: """Get ATTR_TO_ENVVAR tuple for a given type""" if vartype is bool: if default is False: ate = ((True, ("1", "true")), (False, (None, "", "0", "false"))) else: ate = ((True, (None, "", "1", "true")), (False, ("0", "false"))) elif vartype is int: ate = [] for val in values: if val == default: ate.append((val, (None, "", str(val)))) else: ate.append((val, (str(val),))) ate = tuple(ate) if default is None: ate = ((None, (None, "")),) + ate elif vartype is str: ate = ((None, (None, "")),) elif vartype is list: ate = [] for choice in values: if choice == default: ate.append((choice, (None, "", choice))) else: ate.append((choice, (choice,))) ate = tuple(ate) else: raise ValueError(f"Unknown type {vartype}") return ate ATTR_TO_ENVVAR = { "unattended": get_attr_to_envvar(bool, False), "accept_dialogs": get_attr_to_envvar(bool, False), "screenshot": get_attr_to_envvar(bool, False), "do_not_quit": get_attr_to_envvar(bool, False), "catcher_test": get_attr_to_envvar(bool, False), "delay": get_attr_to_envvar(int, 0, [0, 10, 20]), "xmlrpcport": get_attr_to_envvar(int, None, [9854, 1020, 213]), "verbose": get_attr_to_envvar( list, VerbosityLevels.NORMAL.value, [verb.value for verb in VerbosityLevels] ), } def test_envvar(): """Testing DataLab configuration file""" assert execenv.unattended is False, "This test must be run with unattended=False" print("Testing DataLab execution environment:") for attrname, envvar in execenv.iterate_over_attrs_envvars(): attr_to_envvar = ATTR_TO_ENVVAR[attrname] print(f" Testing {attrname}:") for value, envvals in attr_to_envvar: print(f" {value}: [attr->env]", end="") remove_all_datalab_envvars() if value is not None: setattr(execenv, attrname, value) assert os.environ.get(envvar) in envvals, ( f"os.environ[{envvar}] = {os.environ.get(envvar)} != {envvals}" ) print(" [env->attr]", end="") remove_all_datalab_envvars() for envval in envvals: if envval is not None: os.environ[envvar] = envval assert getattr(execenv, attrname) == value, ( f"execenv.{attrname} = {getattr(execenv, attrname)}" f" != {value} (envval = {repr(envval)})" ) print(" [env->subprocess->attr]", end="") remove_all_datalab_envvars() for envval in envvals: if envval is not None: os.environ[envvar] = envval execenvdict = get_subprocess_execenv_dict([]) assert execenvdict[attrname] == value, ( f"execenvdict[{attrname}] = {execenvdict[attrname]}" f" != {value} (envval = {repr(envval)})" ) print() print("=> Everything is OK") if __name__ == "__main__": if ARGV_TEST in sys.argv: print_execenv() else: test_envvar() test_cli() DataLab-1.1.0/datalab/tests/backbone/loadtest_gdi.py000066400000000000000000000106071514014117600223170ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ GDI objects loading test ======================== This test is specific to Windows. It is not relevant for other platforms. This test aims at checking that all GDI objects are released when the widget are closed (i.e. when the Python object is garbage collected). """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code # guitest: skip from __future__ import annotations import ctypes import os from sigima.objects import NewImageParam from sigima.tests.data import create_sincos_image, iterate_image_creation from datalab.env import execenv from datalab.gui.main import DLMainWindow from datalab.tests import datalab_test_app_context, helpers from datalab.tests.scenarios.common import compute_common_operations if os.name == "nt": from ctypes import WinDLL def get_gdi_count() -> int: """Get the number of GDI objects for the current process. This function uses the Windows API to get the count of GDI (Graphical Device Interface) objects used by the current process. Note: This function will only work on Windows. Returns: int: The count of GDI objects for the current process. """ # Constants GR_GDIOBJECTS: int = 0 # Load the User32 DLL user32: WinDLL = ctypes.windll.user32 # Get the current process id pid: int = os.getpid() # Get handle of the process handle: int = ctypes.windll.kernel32.OpenProcess(1040, 0, pid) # Call the function and get the GDI count gdi_count: int = user32.GetGuiResources(handle, GR_GDIOBJECTS) # Close the handle ctypes.windll.kernel32.CloseHandle(handle) return gdi_count else: def get_gdi_count() -> int: """Dumb function that always returns 0.""" return 0 def test_various_image_features(win: DLMainWindow): """Run image related tests.""" win.set_current_panel("image") panel = win.imagepanel param = NewImageParam.create(height=150, width=150) for image in iterate_image_creation(param.width, non_zero=True, verbose=False): panel.add_object(create_sincos_image(param)) panel.add_object(image) compute_common_operations(panel) panel.remove_all_objects() break def test_gdi_count(win: DLMainWindow) -> int | None: """Test the GDI count. This function will create a DataSetGroup, show the widget, and then close the widget. It will then check that the GDI count has not increased. Raises: AssertionError: If the GDI count has increased. Returns: int: The GDI count after creating the widget. """ # Get the GDI count before creating the widget gdi_count_before: int = get_gdi_count() execenv.print(f" GDI count: {gdi_count_before} --> ", end="") # Create widgets during the test test_various_image_features(win) # Import HDF5 file using the HDF5 browser win.open_h5_files( helpers.get_test_fnames("*.h5")[:5], import_all=True, reset_all=False ) for panel in (win.signalpanel, win.imagepanel): panel.remove_all_objects() # Get the GDI count after creating the widget gdi_count_after: int = get_gdi_count() execenv.print(gdi_count_after) # # Check that the GDI count has not increased # assert gdi_count_before == gdi_count_after # assert gdi_count_before == gdi_count_after_close return gdi_count_after def load_test(): """Load test.""" with execenv.context(unattended=True): with datalab_test_app_context() as win: gdi_count = [] for iteration in range(4): execenv.print(f"Test iteration: {iteration}") count = test_gdi_count(win) if count is None: execenv.print("Test aborted") break gdi_count.append(count) if iteration > 0: increase = gdi_count[-1] - gdi_count[-2] assert increase <= 0, "GDI count should not increase (memory leak)" increase_pct = increase / gdi_count[0] * 100 execenv.print( f" GDI count increase: {increase:+d} ({increase_pct:.2f}%)" ) if __name__ == "__main__": load_test() DataLab-1.1.0/datalab/tests/backbone/long_callback.py000066400000000000000000000052461514014117600224330ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Long callback test ------------------ This test is not meant to be executed as part of the `pytest` suite, hence the name of the script. """ # guitest: show,skip import time from guidata.qthelpers import qt_app_context from qtpy import QtWidgets as QW from datalab.utils.qthelpers import CallbackWorker, qt_long_callback def long_computation_func(delay: float) -> str: """Simulate long computation Args: delay: Delay in seconds Returns: str: Result message """ time.sleep(delay) return "OK" def long_computation_progress_func(worker: CallbackWorker, delay: float) -> str: """Simulate long computation, with progress Args: worker: Callback worker delay: Delay in seconds Returns: str: Result message """ step_delay = 2.0 maxiter = int(delay / step_delay) for idx in range(maxiter): worker.set_progress(idx / maxiter) if worker.was_canceled(): return f"Interrupted at iteration #{idx}" time.sleep(step_delay) return "Done" class TestWindow(QW.QMainWindow): """Test window""" def __init__(self): super().__init__() self.setWindowTitle("Test window") btn1 = QW.QPushButton("Run long computation") btn1.clicked.connect(self.run_long_computation) btn2 = QW.QPushButton("Run long computation with progress bar") btn2.clicked.connect(self.run_long_computation_with_progress) mainwidget = QW.QWidget() layout = QW.QHBoxLayout() mainwidget.setLayout(layout) layout.addWidget(btn1) layout.addWidget(btn2) self.setCentralWidget(mainwidget) def __execute_worker(self, worker: CallbackWorker, progress: bool) -> None: """Execute worker""" ret = qt_long_callback(self, "Doing stuff...", worker, progress) QW.QMessageBox.information(self, "Result", f"Long computation result: {ret}") def run_long_computation(self) -> None: """Run long computation""" worker = CallbackWorker(long_computation_func, delay=5.0) self.__execute_worker(worker, progress=False) def run_long_computation_with_progress(self) -> None: """Run long computation with progress""" worker = CallbackWorker(long_computation_progress_func, delay=10.0) self.__execute_worker(worker, progress=True) def testing_long_callback() -> None: """Test long callback function""" with qt_app_context(exec_loop=True): win = TestWindow() win.resize(800, 600) win.show() if __name__ == "__main__": testing_long_callback() DataLab-1.1.0/datalab/tests/backbone/main_app_test.py000066400000000000000000000120231514014117600224720ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Application test for main window -------------------------------- Testing the features of the main window of the application that are not covered by other tests. """ # guitest: show import os import sigima.params import sigima.proc.signal as sips from sigima.tests.data import create_paracetamol_signal from datalab.env import execenv from datalab.objectmodel import get_short_id, get_uuid from datalab.tests import datalab_test_app_context def test_main_app(): """Main window test""" with datalab_test_app_context(console=False) as win: # Switch from panel to panel for panelname in ("macro", "image", "signal"): win.set_current_panel(panelname) # Switch to an unknown panel try: win.set_current_panel("unknown_panel") raise RuntimeError("Unknown panel should have raised an exception") except ValueError: pass panel = win.signalpanel # Create new groups grp1 = panel.add_group("Group 1") panel.add_group("Group 2") # Add group using different levels of the API panel.add_group("Group 3", select=True) panel.remove_object(force=True) win.add_group("Group 4", select=True) panel.remove_object(force=True) win.add_group("Group 5", panel="signal", select=True) panel.remove_object(force=True) # Rename group panel.objview.select_groups([2]) panel.rename_selected_object_or_group("Group xxx") panel.remove_object(force=True) # Add signals to signal panel sig1 = create_paracetamol_signal(500) panel.add_object(sig1) nobj0 = len(panel) win.remove_object(force=True) # Test remove object method, then add it back assert len(panel) == nobj0 - 1, "One object should have been removed" panel.add_object(sig1) panel.rename_selected_object_or_group("Paracetamol Signal 1") panel.processor.run_feature(sips.derivative) panel.processor.run_feature(sips.wiener) # Get object titles titles = win.get_object_titles() execenv.print(f"Object titles:{os.linesep}{titles}") # Get object uuids uuids = win.get_object_uuids() uuids2 = win.get_object_uuids(group=1) uuids3 = win.get_object_uuids(group="Group 1") uuids4 = win.get_object_uuids(group=get_uuid(grp1)) assert uuids == uuids2 == uuids3 == uuids4, "Group UUIDs should be the same" execenv.print(f"Object uuids:{os.linesep}{uuids}") # Testing `get_object` execenv.print("*** Testing `get_object` ***") # Get object from title obj = win.get_object(titles[-1]) execenv.print(f" Object (from title) '{get_short_id(obj)}':{os.linesep}{obj}") # Get object obj = win.get_object(1) execenv.print( f" Object (from number) '{get_short_id(obj)}':{os.linesep}{obj}" ) # Get object by uuid obj = win.get_object(uuids[-1]) execenv.print(f" Object (from uuid) '{get_short_id(obj)}':{os.linesep}{obj}") # Testing dict-like interface of main window: execenv.print("*** Testing dict-like interface of proxy ***") # Get object from title obj = win[titles[-1]] execenv.print(f" Object (from title) '{get_short_id(obj)}':{os.linesep}{obj}") # Get object obj = win[1] execenv.print( f" Object (from number) '{get_short_id(obj)}':{os.linesep}{obj}" ) # Get object by uuid obj = win[uuids[-1]] execenv.print(f" Object (from uuid) '{get_short_id(obj)}':{os.linesep}{obj}") # Use "calc" method with parameters param = sigima.params.MovingMedianParam.create(n=5) win.calc("moving_median", param) # Use "calc" method without parameters win.calc("integral") # Use "calc" and choose an unknown computation method try: win.calc("unknown_method") raise RuntimeError("Unknown method should have raised an exception") except ValueError: pass # Force application menus to pop-up for menu in ( win.file_menu, win.create_menu, win.edit_menu, win.roi_menu, win.operation_menu, win.processing_menu, win.analysis_menu, win.view_menu, win.help_menu, ): # Just access the menu to ensure it's created and initialized assert menu is not None, f"Menu {menu.title()} should exist" # Trigger aboutToShow to populate dynamic menus without showing the popup menu.aboutToShow.emit() # Verify menu has actions (optional, but good sanity check) assert len(menu.actions()) > 0, f"Menu {menu.title()} should have actions" # Open settings dialog win.settings_action.trigger() if __name__ == "__main__": test_main_app() DataLab-1.1.0/datalab/tests/backbone/memory_leak.py000066400000000000000000000020641514014117600221570ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Memory leak test DataLab application memory leak test. """ # guitest: skip import os import numpy as np import psutil from guidata.qthelpers import qt_app_context from sigima.viz import view_curves from datalab.env import execenv from datalab.tests.features.control.embedded1_unit_test import HostWindow def memory_leak_test(iterations=100): """Test for memory leak""" with qt_app_context(): proc = psutil.Process(os.getpid()) mainview = HostWindow() mainview.show() memlist = [] for i in range(iterations): mainview.init_cdl() mainview.close_datalab() memdata = proc.memory_info().vms / 1024**2 memlist.append(memdata) execenv.print(i + 1, ":", memdata, "MB") view_curves( np.array(memlist), title="Memory leak test for DataLab application", ylabel="Memory (MB)", ) if __name__ == "__main__": memory_leak_test() DataLab-1.1.0/datalab/tests/backbone/procisolation1_unit.py000066400000000000000000000105771514014117600236700ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Process isolation unit test --------------------------- """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code # guitest: skip from __future__ import annotations import time from collections.abc import Callable from multiprocessing.pool import AsyncResult, Pool from typing import Any import numpy as np import scipy.signal as sps from guidata.qthelpers import qt_app_context from plotpy.plot import PlotWindow from qtpy import QtWidgets as QW from sigima.objects import ImageDatatypes, NewImageParam from sigima.tests.data import create_2d_random, create_noisy_gaussian_image from datalab.adapters_plotpy import create_adapter_from_object from datalab.env import execenv from datalab.utils.qthelpers import create_progress_bar POOL: Pool | None = None class Worker: """Multiprocessing worker, to run long-running tasks in a separate process""" def __init__(self) -> None: self.asyncresult: AsyncResult = None self.result: Any = None @staticmethod def create_pool() -> None: """Create multiprocessing pool""" global POOL # pylint: disable=global-statement # Create a pool with one process POOL = Pool(processes=1) # pylint: disable=not-callable,consider-using-with @staticmethod def terminate_pool() -> None: """Terminate multiprocessing pool""" global POOL # pylint: disable=global-statement if POOL is not None: POOL.terminate() POOL.join() POOL = None def run(self, func: Callable, args: tuple[Any]) -> None: """Run computation""" global POOL # pylint: disable=global-statement,global-variable-not-assigned assert POOL is not None self.asyncresult = POOL.apply_async(func, args) def terminate(self) -> None: """Terminate worker""" # Terminate the process and stop the timer self.terminate_pool() execenv.print("Computation cancelled!") # Recreate the pool for the next computation self.create_pool() def is_computation_finished(self) -> bool: """Return True if computation is finished""" return self.asyncresult.ready() def get_result(self) -> Any: """Return computation result""" self.result = self.asyncresult.get() self.asyncresult = None return self.result def testfunc(data: np.ndarray, size: int, error: bool) -> np.ndarray: """Test function""" if error: raise ValueError("Test error") return sps.medfilt(data, size) + create_2d_random(data.shape[0], data.dtype) def test_multiprocessing1(iterations: int = 4) -> None: """Multiprocessing test""" Worker.create_pool() with qt_app_context(exec_loop=True): win = PlotWindow(title="Multiprocessing test", icon="datalab.svg", toolbar=True) win.resize(800, 600) win.show() param = NewImageParam.create( height=1000, width=1000, dtype=ImageDatatypes.UINT16 ) image = create_noisy_gaussian_image(param, add_annotations=True) win.get_plot().add_item(create_adapter_from_object(image).make_item()) worker = Worker() with create_progress_bar(win, "Computing", max_=iterations) as progress: for index in range(iterations): progress.setValue(index) progress.setLabelText(f"Computing {index}") test_error = index == 2 worker.run(testfunc, (image.data, 3, test_error)) while not worker.is_computation_finished(): QW.QApplication.processEvents() time.sleep(0.1) if progress.wasCanceled(): worker.terminate() break if worker.is_computation_finished(): try: image.data = worker.get_result() except Exception as exc: # pylint: disable=broad-except execenv.print(f"Intercepted exception: {exc}") win.get_plot().add_item( create_adapter_from_object(image).make_item() ) else: break worker.terminate_pool() if __name__ == "__main__": test_multiprocessing1() DataLab-1.1.0/datalab/tests/backbone/procisolation2_unit.py000066400000000000000000000134671514014117600236720ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Process isolation unit test --------------------------- """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: skip import time from multiprocessing import Pool import numpy as np import scipy.signal as sps from guidata.configtools import get_icon from guidata.qthelpers import qt_app_context from plotpy.plot import PlotOptions, PlotWidget from qtpy import QtCore as QC from qtpy import QtWidgets as QW from sigima.objects import NewImageParam from sigima.tests.data import create_2d_random, create_noisy_gaussian_image from datalab.adapters_plotpy import create_adapter_from_object from datalab.env import execenv class MainWindow(QW.QMainWindow): """Multiprocessing test main window""" SIG_COMPUTATION_FINISHED = QC.Signal() def __init__(self): super().__init__() self.setWindowTitle("DataLab Multiprocessing test") self.setWindowIcon(get_icon("datalab.svg")) # Setting up the layout and widgets self.imagewidget = PlotWidget(options=PlotOptions(type="image")) self.setCentralWidget(self.imagewidget) self.layout = QW.QVBoxLayout(self.imagewidget) self.start_button = QW.QPushButton("Start Computation", self) self.start_button.clicked.connect(self.start_computation) self.layout.addWidget(self.start_button) self.cancel_button = QW.QPushButton("Cancel Computation", self) self.cancel_button.clicked.connect(self.cancel_computation) self.layout.addWidget(self.cancel_button) # Create a test image and add it to the plot param = NewImageParam.create(height=1000, width=1000) image = create_noisy_gaussian_image(param, add_annotations=True) self.imageitem = create_adapter_from_object(image).make_item() self.imagewidget.plot.add_item(self.imageitem) self.array = image.data self.result = None self.timer = QC.QTimer() self.timer.setInterval(10) # Check every 100 ms self.timer.timeout.connect(self.check_process) self.start_time = None self.SIG_COMPUTATION_FINISHED.connect(self.update_plot) @staticmethod def long_running_task(array: np.ndarray) -> None: """ A long running task that computes a median filter on the input array and puts the result in the queue. The done_event is set when the computation is finished. Args: array (numpy.ndarray): The input data to compute the filter on. """ start_time = time.time() result = sps.medfilt(array, 1) + create_2d_random(array.shape[0], array.dtype) execenv.print(f"Computation done: delta={time.time() - start_time:.3f} s") return result def print_time(self, title: str) -> None: """ Prints the time since the last call to this method and resets the start_time. Args: title (str): The title to print before the time. """ execenv.print(f"{title}: {time.time() - self.start_time:.3f} s") self.start_time = time.time() def start_computation(self) -> None: """ Starts the computation in a separate process and starts the timer. """ global POOL # pylint: disable=global-statement,global-variable-not-assigned if self.result and not self.result.ready(): self.print_time("Computation already running!") return self.start_time = time.time() self.result = POOL.apply_async(self.long_running_task, (self.array,)) self.timer.start() self.print_time("Computation started") def cancel_computation(self) -> None: """ Cancels the computation by terminating the process and stopping the timer. """ global POOL # pylint: disable=global-statement # Terminate the process and stop the timer POOL.terminate() POOL.join() self.timer.stop() execenv.print("Computation cancelled!") # Recreate the pool for the next computation POOL = Pool(processes=1) # pylint: disable=not-callable,consider-using-with def check_process(self) -> None: """ Checks if the computation is finished. If it is, stops the timer and calls the method to handle the computation finishing. """ if self.result.ready(): self.print_time("Computation finished") self.timer.stop() self.on_computation_finished() def on_computation_finished(self) -> None: """ Retrieves the result from the queue and emits the SIG_COMPUTATION_FINISHED signal. """ self.array = self.result.get() self.SIG_COMPUTATION_FINISHED.emit() self.print_time("Computation result retrieved") def update_plot(self) -> None: """ Updates the plot with the result of the computation. """ self.imageitem.set_data(self.array) self.imagewidget.plot.replot() self.print_time("Plot updated") def closeEvent(self, event) -> None: """ Overrides the closeEvent to stop the timer and terminate the process when the window is closed. Args: event: The close event. """ self.timer.stop() super().closeEvent(event) def test_multiprocessing2() -> None: """ Creates a PyQt application context, shows the main window, and starts the event loop. """ global POOL # pylint: disable=global-statement,global-variable-not-assigned with qt_app_context(exec_loop=True): window = MainWindow() window.show() POOL.terminate() POOL.join() if __name__ == "__main__": POOL = Pool(processes=1) # pylint: disable=not-callable,consider-using-with test_multiprocessing2() DataLab-1.1.0/datalab/tests/backbone/procisolation_unit_test.py000066400000000000000000000010151514014117600246310ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Process isolation unit test --------------------------- Just test if it's possible to run the process isolation test from another module. This may be an issue with the Pool object being global. """ # guitest: show from datalab.tests.backbone import procisolation1_unit def test_procisolation(): """Test process isolation""" procisolation1_unit.test_multiprocessing1() if __name__ == "__main__": test_procisolation() DataLab-1.1.0/datalab/tests/backbone/profiling_app.py000066400000000000000000000011731514014117600225040ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Profiling """ # guitest: skip from datalab.env import execenv from datalab.tests import datalab_test_app_context def test_profiling(): """Profiling test""" with execenv.context(unattended=True): with datalab_test_app_context() as win: win.open_h5_files( [ "C:/Dev/Projets/X-GRID_data/Projets_Oasis/XGRID5/" "VS000001-blobs_doh_profiling.h5" ], import_all=True, ) if __name__ == "__main__": test_profiling() DataLab-1.1.0/datalab/tests/backbone/strings_unit_test.py000066400000000000000000000040041514014117600234360ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ String utilities unit tests for DataLab Testing the string utility functions in datalab.utils.strings module, particularly the save_html_diff function which is useful for debugging and comparing text differences. """ from __future__ import annotations import os import tempfile from pathlib import Path from unittest.mock import patch import pytest from datalab.utils.strings import save_html_diff @pytest.fixture def temp_html_file(): """Fixture providing a temporary file path for HTML diff output.""" with tempfile.TemporaryDirectory() as tmpdir: yield os.path.join(tmpdir, "test_diff.html") def test_save_html_diff_basic(temp_html_file): # pylint: disable=W0621 """Test that save_html_diff creates a valid HTML file with diff content.""" text1 = "Line 1\nLine 2\nLine 3" text2 = "Line 1\nModified Line 2\nLine 3" with patch("webbrowser.open"): save_html_diff(text1, text2, "Before", "After", temp_html_file) # Verify file creation and basic structure assert Path(temp_html_file).exists() content = Path(temp_html_file).read_text(encoding="utf-8") assert " None: """Add metadata to signals helper function.""" execenv.print("Add metadata signal application test:") panel = win.signalpanel # Add test signals to the panel for sig in create_test_signals(): panel.add_object(sig) # Select all signals panel.objview.select_objects([1, 2, 3]) # Create and configure the Add metadata dialog objs = panel.objview.get_sel_objects(include_groups=True) param = AddMetadataParam(objs) # Configure example metadata param.metadata_key = "experiment_id" param.value_pattern = "EXP_{index:03d}" param.conversion = "string" param.update_preview() execenv.print(f" Preview: {param.preview}") assert "experiment_id" in param.preview execenv.print(" ✓ Add metadata parameter configured correctly") # Edit the parameter to show the dialog execenv.print(" Opening Add metadata dialog...") with warnings.catch_warnings(): warnings.simplefilter("ignore", category=gds.DataItemValidationWarning) with execenv.context(screenshot=screenshot): param.edit(parent=win, wordwrap=False, object_name="s_add_metadata") # Run DataLab's metadata addition functionality: panel.add_metadata(param) execenv.print("==> Signal application test OK") def __add_metadata_to_images(win: DLMainWindow, screenshot: bool = False) -> None: """Add metadata to images helper function.""" execenv.print("Add metadata image test:") panel = win.imagepanel # Add test images to the panel for img in create_test_images(): panel.add_object(img) # Select all images panel.objview.select_objects([1, 2, 3]) # Create and configure the Add metadata dialog objs = panel.objview.get_sel_objects(include_groups=True) param = AddMetadataParam(objs) # Configure example metadata param.metadata_key = "sample_id" param.value_pattern = "SAMPLE_{index:04d}_{title:upper}" param.conversion = "string" param.update_preview() execenv.print(f" Preview: {param.preview}") assert "sample_id" in param.preview execenv.print(" ✓ Add metadata parameter configured correctly") # Edit the parameter to show the dialog execenv.print(" Opening Add metadata dialog...") with warnings.catch_warnings(): warnings.simplefilter("ignore", category=gds.DataItemValidationWarning) with execenv.context(screenshot=screenshot): param.edit(parent=win, wordwrap=False, object_name="i_add_metadata") # Run DataLab's metadata addition functionality: panel.add_metadata(param) execenv.print("==> Image application test OK") def test_add_metadata_to_signals() -> None: """Test Add metadata feature for signals.""" with datalab_test_app_context() as win: __add_metadata_to_signals(win) def test_add_metadata_to_images() -> None: """Test Add metadata feature for images.""" with datalab_test_app_context() as win: __add_metadata_to_images(win) def add_metadata_screenshots(): """Generate add metadata screenshots.""" with execenv.context(unattended=True): with datalab_test_app_context() as win: execenv.print("Add metadata screenshots test:") __add_metadata_to_signals(win, screenshot=True) __add_metadata_to_images(win, screenshot=True) execenv.print("==> All screenshot tests completed") if __name__ == "__main__": # test_add_metadata_to_signals() # test_add_metadata_to_images() add_metadata_screenshots() DataLab-1.1.0/datalab/tests/features/common/add_metadata_unit_test.py000066400000000000000000000214521514014117600257050ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Unit tests for Add Metadata feature. This module tests the `AddMetadataParam` class and the `add_metadata` method from `datalab.gui.panel.base`. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from typing import TYPE_CHECKING import guidata.config as gcfg import numpy as np import pytest from sigima.objects import create_image, create_signal from datalab.env import execenv from datalab.gui.panel.base import AddMetadataParam if TYPE_CHECKING: from sigima.objects import ImageObj, SignalObj def create_test_signals() -> list[SignalObj]: """Create a list of test signals for testing.""" # Signal 1: Sine wave x = np.linspace(0, 10, 100) signal1 = create_signal( title="Sine Wave", x=x, y=np.sin(x), metadata={"type": "sine", "frequency": "1 Hz"}, ) # Signal 2: Cosine wave signal2 = create_signal( title="Cosine Wave", x=x, y=np.cos(x * 2), metadata={"type": "cosine", "frequency": "2 Hz"}, ) # Signal 3: Exponential decay signal3 = create_signal( title="Exponential Decay", x=x, y=np.exp(-x / 3), metadata={"type": "exponential", "time_constant": "3 s"}, ) return [signal1, signal2, signal3] def create_test_images() -> list[ImageObj]: """Create a list of test images for testing.""" # Image 1: Random noise image1 = create_image( title="Random Noise", data=np.random.rand(100, 100), metadata={"type": "noise", "distribution": "uniform"}, ) # Image 2: Gaussian pattern x = np.linspace(-3, 3, 50) y = np.linspace(-3, 3, 50) X, Y = np.meshgrid(x, y) image2 = create_image( title="Gaussian Pattern", data=np.exp(-(X**2 + Y**2)), metadata={"type": "gaussian", "sigma": "1.0"}, ) # Image 3: Checkerboard pattern data3 = np.zeros((100, 100)) data3[::10, ::10] = 1 data3[5::10, 5::10] = 1 image3 = create_image( title="Checkerboard", data=data3, metadata={"type": "pattern", "period": "10 px"}, ) return [image1, image2, image3] class TestAddMetadata: """Test class for AddMetadataParam with proper setup/teardown.""" @pytest.fixture(autouse=True) def setup_validation(self): """Disable guidata validation during tests.""" old_mode = gcfg.get_validation_mode() gcfg.set_validation_mode(gcfg.ValidationMode.DISABLED) yield gcfg.set_validation_mode(old_mode) def test_string_values(self) -> None: """Test adding string metadata values.""" execenv.print(f"{self.test_string_values.__doc__}:") # Create test signals signals = create_test_signals() # Create parameter p = AddMetadataParam(signals) p.metadata_key = "test_string" p.value_pattern = "{title}" p.conversion = "string" # Build values values = p.build_values(signals) expected_values = ["Sine Wave", "Cosine Wave", "Exponential Decay"] assert values == expected_values, f"Expected {expected_values}, got {values}" execenv.print(f" ✓ String values: {values}") # Check preview p.update_preview() assert "test_string" in p.preview, "Preview should contain metadata key" execenv.print(" ✓ Preview generated successfully") execenv.print(f"{self.test_string_values.__doc__}: OK") def test_numeric_values(self) -> None: """Test adding numeric metadata values.""" execenv.print(f"{self.test_numeric_values.__doc__}:") # Create test signals signals = create_test_signals() # Test integer conversion p = AddMetadataParam(signals) p.metadata_key = "index" p.value_pattern = "{index}" p.conversion = "int" values = p.build_values(signals) expected_values = [1, 2, 3] assert values == expected_values, f"Expected {expected_values}, got {values}" execenv.print(f" ✓ Integer values: {values}") # Test float conversion p.conversion = "float" values = p.build_values(signals) expected_values = [1.0, 2.0, 3.0] assert values == expected_values, f"Expected {expected_values}, got {values}" execenv.print(f" ✓ Float values: {values}") execenv.print(f"{self.test_numeric_values.__doc__}: OK") def test_boolean_values(self) -> None: """Test adding boolean metadata values.""" execenv.print(f"{self.test_boolean_values.__doc__}:") # Create test signals signals = create_test_signals() # Test boolean conversion with "true" pattern p = AddMetadataParam(signals) p.metadata_key = "is_valid" p.value_pattern = "true" p.conversion = "bool" values = p.build_values(signals) expected_values = [True, True, True] assert values == expected_values, f"Expected {expected_values}, got {values}" execenv.print(f" ✓ Boolean (true) values: {values}") # Test boolean conversion with "false" pattern p.value_pattern = "false" values = p.build_values(signals) expected_values = [False, False, False] assert values == expected_values, f"Expected {expected_values}, got {values}" execenv.print(f" ✓ Boolean (false) values: {values}") execenv.print(f"{self.test_boolean_values.__doc__}: OK") def test_pattern_formatting(self) -> None: """Test various pattern formatting options.""" execenv.print(f"{self.test_pattern_formatting.__doc__}:") # Create test signals signals = create_test_signals() # Test index with zero padding p = AddMetadataParam(signals) p.metadata_key = "file_id" p.value_pattern = "file_{index:03d}" p.conversion = "string" values = p.build_values(signals) expected_values = ["file_001", "file_002", "file_003"] assert values == expected_values, f"Expected {expected_values}, got {values}" execenv.print(f" ✓ Padded index pattern: {values}") # Test uppercase modifier p.value_pattern = "{title:upper}" values = p.build_values(signals) expected_values = ["SINE WAVE", "COSINE WAVE", "EXPONENTIAL DECAY"] assert values == expected_values, f"Expected {expected_values}, got {values}" execenv.print(f" ✓ Uppercase pattern: {values}") # Test lowercase modifier p.value_pattern = "{title:lower}" values = p.build_values(signals) expected_values = ["sine wave", "cosine wave", "exponential decay"] assert values == expected_values, f"Expected {expected_values}, got {values}" execenv.print(f" ✓ Lowercase pattern: {values}") execenv.print(f"{self.test_pattern_formatting.__doc__}: OK") def test_with_images(self) -> None: """Test AddMetadataParam with image objects.""" execenv.print(f"{self.test_with_images.__doc__}:") # Create test images images = create_test_images() # Create parameter p = AddMetadataParam(images) p.metadata_key = "category" p.value_pattern = "{title:lower}" p.conversion = "string" # Build values values = p.build_values(images) expected_values = ["random noise", "gaussian pattern", "checkerboard"] assert values == expected_values, f"Expected {expected_values}, got {values}" execenv.print(f" ✓ Image metadata values: {values}") # Check preview p.update_preview() assert "category" in p.preview, "Preview should contain metadata key" # Check that all values are in the preview for expected_val in expected_values: assert expected_val in p.preview, f"Preview should contain {expected_val}" execenv.print(" ✓ Preview contains all values") execenv.print(f"{self.test_with_images.__doc__}: OK") def test_error_handling(self) -> None: """Test error handling for invalid patterns.""" execenv.print(f"{self.test_error_handling.__doc__}:") # Create test signals signals = create_test_signals() # Test with invalid pattern p = AddMetadataParam(signals) p.metadata_key = "test" p.value_pattern = "{invalid_key}" # This key doesn't exist p.conversion = "string" # Update preview should handle the error gracefully p.update_preview() assert "Invalid" in p.preview, "Preview should show invalid pattern error" execenv.print(" ✓ Invalid pattern error handled") execenv.print(f"{self.test_error_handling.__doc__}: OK") if __name__ == "__main__": pytest.main([__file__, "-v"]) DataLab-1.1.0/datalab/tests/features/common/annotations_management_unit_test.py000066400000000000000000000121531514014117600300440ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Unit tests for annotation copy/paste functionality.""" import os.path as osp from sigima.tests import data as test_data from datalab.env import execenv from datalab.tests import datalab_test_app_context, helpers def test_annotations_copy_paste(): """Test copying and pasting annotations between objects.""" with execenv.context(unattended=True): with datalab_test_app_context() as win: panel = win.signalpanel # Create two signals sig1 = test_data.create_paracetamol_signal() sig2 = test_data.create_paracetamol_signal() # Add annotations to first signal orig_annotations = [ {"type": "label", "text": "Peak 1"}, {"type": "label", "text": "Peak 2"}, ] sig1.set_annotations(orig_annotations) # Add objects to panel - sig1 will be selected after this panel.add_object(sig1) # Copy from first signal (which is currently selected) panel.copy_annotations() # Add second signal - sig2 will be selected after this panel.add_object(sig2) # Paste to second signal (which is now selected) panel.paste_annotations() # Verify annotations were copied assert sig2.get_annotations() == orig_annotations def test_annotations_import_export(): """Test importing and exporting annotations to file.""" with execenv.context(unattended=True): with helpers.WorkdirRestoringTempDir() as tmpdir: fname = osp.join(tmpdir, "test.dlabann") with datalab_test_app_context() as win: panel = win.signalpanel # Create signal with annotations sig = test_data.create_paracetamol_signal() orig_annotations = [{"type": "label", "text": "Test annotation"}] sig.set_annotations(orig_annotations) panel.add_object(sig) # Export annotations panel.export_annotations_from_file(fname) # Clear annotations to test import sig.clear_annotations() assert len(sig.get_annotations()) == 0 # Import annotations panel.import_annotations_from_file(fname) # Verify annotations were imported assert sig.get_annotations() == orig_annotations def test_annotations_delete(): """Test deleting annotations from objects.""" with execenv.context(unattended=True): with datalab_test_app_context() as win: panel = win.signalpanel # Create signal with annotations sig = test_data.create_paracetamol_signal() sig.set_annotations( [ {"type": "label", "text": "To be deleted"}, ] ) panel.add_object(sig) # Verify annotations exist assert len(sig.get_annotations()) == 1 # Delete annotations panel.delete_annotations() # Verify annotations were deleted assert len(sig.get_annotations()) == 0 def test_annotations_action_states(): """Test that action states are updated after annotation operations. This test verifies the fix for the issue where action enable states were not updated after operations like delete_annotations, requiring the user to select another object for the actions to update. Note: We can't easily test the UI action states in automated tests, but we verify that the underlying state update mechanism is called. """ with execenv.context(unattended=True): with datalab_test_app_context() as win: panel = win.signalpanel # Create signal with annotations sig = test_data.create_paracetamol_signal() orig_annotations = [{"type": "label", "text": "Test"}] sig.set_annotations(orig_annotations) panel.add_object(sig) # Verify object has annotations assert len(sig.get_annotations()) == 1 # Delete annotations - this should update action states panel.delete_annotations() # Verify annotations were deleted assert len(sig.get_annotations()) == 0 # Verify clipboard is empty assert len(panel.annotations_clipboard) == 0 # Re-add annotations and copy - this should update action states sig.set_annotations(orig_annotations) panel.selection_changed() # Refresh states panel.copy_annotations() # Verify clipboard now has annotations assert len(panel.annotations_clipboard) == 1 # Clear annotations, paste, and verify sig.clear_annotations() panel.paste_annotations() assert len(sig.get_annotations()) == 1 if __name__ == "__main__": test_annotations_copy_paste() test_annotations_import_export() test_annotations_delete() test_annotations_action_states() DataLab-1.1.0/datalab/tests/features/common/auto_analysis_recompute_unit_test.py000066400000000000000000000331611514014117600302530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Unit test for automatic recomputation of 1-to-0 analysis operations. This test verifies that analysis results (like centroid) are automatically updated when data changes through various methods: - ROI modifications (adding, deleting ROIs) - Data transformations via recompute_1_to_1 (modifying processing parameters) The tests create a Gaussian image, compute its centroid, then verify that: 1. The centroid changes when a ROI is added to restrict the calculation region 2. Two centroid rows are generated when two ROIs are added 3. One centroid row remains after deleting the first ROI 4. The centroid returns to the original value when all ROIs are deleted 5. The centroid is recomputed when processing parameters are modified """ # guitest: show from __future__ import annotations import numpy as np from sigima.objects import Gauss2DParam, create_image_from_param, create_image_roi from sigima.params import RotateParam from datalab.adapters_metadata import GeometryAdapter from datalab.config import Conf from datalab.tests import datalab_test_app_context def get_centroid_coords(obj) -> tuple[float, float] | None: """Extract centroid coordinates from object metadata. Args: obj: Image object with centroid results Returns: Tuple (x, y) of centroid coordinates, or None if no centroid found """ adapter = GeometryAdapter.from_obj(obj, "centroid") if adapter is not None: print("adapter.result.coords:", adapter.result.coords) return tuple(adapter.result.coords[0]) return None def test_analysis_recompute_after_roi_change(): """Test automatic recomputation of analysis results when ROI changes.""" with datalab_test_app_context(console=False) as win: panel = win.imagepanel # Create a 2D Gaussian image in a 200x200 image size = 200 param = Gauss2DParam.create(height=size, width=size, sigma=20) img = create_image_from_param(param) panel.add_object(img) # Compute centroid on the full image with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("centroid") centroid = get_centroid_coords(img) assert centroid is not None, "Centroid should be computed" x0, y0 = centroid print(f"\nInitial centroid (full image): ({x0:.1f}, {y0:.1f})") # Step 1: Add ROI (simulating edit_roi_graphically) # Add a rectangular ROI in the upper-left quadrant roi = create_image_roi("rectangle", [25, 25, 50, 50]) # x0, y0, width, height img.roi = roi panel.refresh_plot("selected", update_items=True) # Trigger auto-recompute by simulating ROI modification panel.processor.auto_recompute_analysis(img) # Verify centroid was updated centroid = get_centroid_coords(img) assert centroid is not None, "Centroid should still exist after ROI change" x1, y1 = centroid print(f"Centroid after adding ROI (upper-left): ({x1:.1f}, {y1:.1f})") # Centroid should have moved (different from initial) centroid_changed = not ( np.isclose(x1, x0, atol=0.1) and np.isclose(y1, y0, atol=0.1) ) assert centroid_changed, ( f"Centroid should have changed from ({x0:.1f}, {y0:.1f}) " f"to ({x1:.1f}, {y1:.1f})" ) # Step 2: Add two ROIs at once # Create a multi-ROI object with two rectangular regions roi1 = create_image_roi("rectangle", [25, 25, 50, 50]) # Upper-left roi2 = create_image_roi("rectangle", [150, 150, 40, 40]) # Lower-right roi1.add_roi(roi2) # Combine both ROIs img.roi = roi1 panel.refresh_plot("selected", update_items=True) panel.processor.auto_recompute_analysis(img) # Verify centroid now has TWO rows (one for each ROI) adapter = GeometryAdapter.from_obj(img, "centroid") assert adapter is not None, "Centroid adapter should exist" coords = adapter.result.coords print(f"Centroid coords after adding two ROIs:\n{coords}") assert len(coords) == 2, f"Should have 2 centroid rows, got {len(coords)}" x2_roi0, y2_roi0 = coords[0] x2_roi1, y2_roi1 = coords[1] print( f"Centroid ROI 0 (upper-left): ({x2_roi0:.1f}, {y2_roi0:.1f}), " f"ROI 1 (lower-right): ({x2_roi1:.1f}, {y2_roi1:.1f})" ) # Step 3: Delete the first ROI using delete_single_roi panel.processor.delete_single_roi(roi_index=0) # Verify centroid now has ONE row (for the remaining ROI) adapter = GeometryAdapter.from_obj(img, "centroid") assert adapter is not None, "Centroid adapter should exist after ROI deletion" coords = adapter.result.coords print(f"Centroid coords after deleting first ROI:\n{coords}") assert len(coords) == 1, f"Should have 1 centroid row, got {len(coords)}" x3, y3 = coords[0] print(f"Centroid after deleting first ROI: ({x3:.1f}, {y3:.1f})") # The remaining ROI should be the second one (lower-right), so centroid should # be close to what was previously in ROI 1 assert np.isclose(x3, x2_roi1, atol=1.0), ( f"X centroid should be close to {x2_roi1:.1f}, got {x3:.1f}" ) assert np.isclose(y3, y2_roi1, atol=1.0), ( f"Y centroid should be close to {y2_roi1:.1f}, got {y3:.1f}" ) # Step 4: Delete all remaining ROIs using delete_regions_of_interest panel.processor.delete_regions_of_interest() # Verify centroid was updated back to original centroid = get_centroid_coords(img) assert centroid is not None, "Centroid should exist after deleting all ROIs" x4, y4 = centroid print(f"Centroid after delete_regions_of_interest: ({x4:.1f}, {y4:.1f})") # Should return to original centroid of full image assert np.isclose(x4, x0, atol=1.0), ( f"X centroid should return to {x0:.1f}, got {x4:.1f}" ) assert np.isclose(y4, y0, atol=1.0), ( f"Y centroid should return to {y0:.1f}, got {y4:.1f}" ) print("\n✓ All ROI auto-recompute tests passed!") def test_analysis_recompute_after_recompute_1_to_1(): """Test automatic recomputation of analysis after processing parameter changes.""" with datalab_test_app_context(console=False) as win: panel = win.imagepanel # Create a Gaussian image offset from center size = 200 # In Gauss2DParam, x0 and y0 are the center coordinates with Xmin=-10.0, # Ymin=-10.0, Xmax=10.0, Ymax=10.0 by default. # The centroid position should be at (49.75, 99.5). # After a 45° rotation, it should move closer to (64.32, 134.68). # Or after a 90° rotation, it should move to (99.5, 149.2). param = Gauss2DParam.create(height=size, width=size, x0=-5.0) img = create_image_from_param(param) panel.add_object(img) # Apply a rotation transformation with 45° angle rot_param = RotateParam.create(angle=45.0) with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("rotate", rot_param) # Get the rotated image img_rotated = panel.objview.get_sel_objects()[0] print(f"\nRotated image title: {img_rotated.title}") # Compute centroid on the rotated image with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("centroid") # Get initial centroid (after 45° rotation) centroid = get_centroid_coords(img_rotated) assert centroid is not None, "Centroid should be computed" x0, y0 = centroid print(f"Initial centroid (45° rotation): ({x0:.1f}, {y0:.1f})") expected_x0 = 64.32 expected_y0 = 134.68 assert np.isclose(x0, expected_x0, atol=0.2), ( f"X centroid should be near {expected_x0:.1f}, got {x0:.1f}" ) assert np.isclose(y0, expected_y0, atol=0.2), ( f"Y centroid should be near {expected_y0:.1f}, got {y0:.1f}" ) # Now modify the rotation angle to 90° via the Processing tab # The rotated object is already selected, just ensure we're accessing it panel.objview.select_objects([img_rotated]) # Get the processing parameter editor and change the angle assert panel.objprop.processing_param_editor is not None editor = panel.objprop.processing_param_editor editor.dataset.angle = 90.0 # Change from 45° to 90° # Apply the modified parameters (this triggers recompute_1_to_1) report = panel.objprop.apply_processing_parameters(interactive=False) assert report.success, f"Recompute failed: {report.message}" print("Processing parameters recomputed with new angle (90°)") # Verify centroid was automatically recomputed centroid = get_centroid_coords(img_rotated) assert centroid is not None, "Centroid should still exist after recompute" x1, y1 = centroid print(f"Centroid after recompute (90° rotation): ({x1:.1f}, {y1:.1f})") # After 90° rotation: the Gaussian blob that was at upper-right (after 45°) # moves toward the center-right area # Empirical measurements show approximately (99.5, 149.2) expected_x1 = 99.5 expected_y1 = 149.2 assert np.isclose(x1, expected_x1, atol=0.2), ( f"X centroid should be near {expected_x1:.1f}, got {x1:.1f}" ) assert np.isclose(y1, expected_y1, atol=0.2), ( f"Y centroid should be near {expected_y1:.1f}, got {y1:.1f}" ) # Most importantly, verify the centroid changed from 90° rotation centroid_changed = not ( np.isclose(x1, x0, atol=5.0) and np.isclose(y1, y0, atol=5.0) ) assert centroid_changed, ( f"Centroid should have changed from ({x0:.1f}, {y0:.1f}) " f"to ({x1:.1f}, {y1:.1f}) after changing rotation angle" ) print("\n✓ Recompute_1_to_1 auto-analysis test passed!") def test_analysis_recompute_avoids_redundant_calculations(): """Test that auto-recompute doesn't cause O(n²) redundant calculations. This test verifies that when multiple objects have ROIs modified simultaneously, the analysis is recomputed only once per object, not once per object × number of selected objects. Regression test for bug: When N images were selected with statistics computed, adding a ROI would trigger N × N = N² calculations instead of N. """ with datalab_test_app_context(console=False) as win: panel = win.imagepanel # Create multiple images (N = 5 for this test) n_images = 5 size = 100 images = [] for i in range(n_images): param = Gauss2DParam.create(height=size, width=size, sigma=15) img = create_image_from_param(param) img.title = f"Test image {i + 1}" panel.add_object(img) images.append(img) # Select all images panel.objview.select_objects(images) selected = panel.objview.get_sel_objects() assert len(selected) == n_images, f"Should have {n_images} selected objects" # Compute statistics on all selected images with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("centroid") # Verify all images have centroid results for img in images: centroid = get_centroid_coords(img) assert centroid is not None, f"Image '{img.title}' should have centroid" print(f"\nInitial statistics computed for {n_images} images") # Track how many times compute_1_to_0 is called during auto-recompute # by counting the calls via a wrapper call_count = [0] # Use list to allow modification in closure original_compute_1_to_0 = panel.processor.compute_1_to_0 def counting_compute_1_to_0(*args, **kwargs): call_count[0] += 1 return original_compute_1_to_0(*args, **kwargs) panel.processor.compute_1_to_0 = counting_compute_1_to_0 try: # Add ROI to all selected images via edit_roi_graphically flow # Simulating what happens when user adds ROI in the editor roi = create_image_roi("rectangle", [25, 25, 50, 50]) for img in images: img.roi = roi # Simulate the auto-recompute that happens after ROI modification panel.processor.auto_recompute_analysis(img) # With the fix, compute_1_to_0 should be called exactly N times # (once per object), not N² times print(f"compute_1_to_0 was called {call_count[0]} times") assert call_count[0] == n_images, ( f"compute_1_to_0 should be called exactly {n_images} times " f"(once per object), but was called {call_count[0]} times. " f"This suggests O(n²) redundant calculations." ) # Verify the target_objs parameter is being used correctly: # Each call should process only 1 object, not all selected objects # This is verified by checking that the call count matches n_images finally: # Restore the original method panel.processor.compute_1_to_0 = original_compute_1_to_0 print(f"\n✓ Auto-recompute correctly called {n_images} times (no O(n²) issue)") if __name__ == "__main__": test_analysis_recompute_after_roi_change() test_analysis_recompute_after_recompute_1_to_1() test_analysis_recompute_avoids_redundant_calculations() DataLab-1.1.0/datalab/tests/features/common/coordutils_unit_test.py000066400000000000000000000145341514014117600255070ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Coordinate utilities unit tests """ import numpy as np import pytest from sigima.objects import ( CircularROI, PolygonalROI, RectangularROI, SegmentROI, create_image, create_signal, ) from datalab.adapters_plotpy.coordutils import ( round_image_coords, round_image_roi_param, round_signal_coords, round_signal_roi_param, ) def test_round_signal_coords(): """Test signal coordinate rounding""" # Create a signal with sampling period of 0.1 x = np.arange(0, 10, 0.1) y = np.sin(x) sig = create_signal("test", x, y) # Test basic rounding coords = [1.23456789, 5.87654321] rounded = round_signal_coords(sig, coords) # With sampling period 0.1 and precision_factor 0.1, precision = 0.01 # Should round to 2 decimal places assert rounded == [1.23, 5.88] # Test with custom precision factor rounded = round_signal_coords(sig, coords, precision_factor=1.0) # precision = 0.1, should round to 1 decimal place assert rounded == [1.2, 5.9] # Test with signal that has too few points sig_short = create_signal("test", np.array([1.0]), np.array([2.0])) coords = [1.23456789] rounded = round_signal_coords(sig_short, coords) # Should return coords as-is assert rounded == coords # Test with constant x (zero sampling period) sig_const = create_signal("test", np.ones(10), np.ones(10)) rounded = round_signal_coords(sig_const, coords) # Should return coords as-is assert rounded == coords def test_round_image_coords(): """Test image coordinate rounding""" # Create an image with dx=dy=1.0 (uniform) data = np.ones((100, 100)) img = create_image("test", data) # Test basic rounding coords = [10.123456, 20.987654, 30.555555, 40.444444] rounded = round_image_coords(img, coords) # With pixel spacing 1.0 and precision_factor 0.1, precision = 0.1 # Should round to 1 decimal place assert rounded == [10.1, 21.0, 30.6, 40.4] # Test with custom precision factor rounded = round_image_coords(img, coords, precision_factor=1.0) # precision = 1.0, should round to 0 decimal places assert rounded == [10.0, 21.0, 31.0, 40.0] # Test with empty coords assert not round_image_coords(img, []) # Test error for odd number of coordinates with pytest.raises(ValueError, match="even number of elements"): round_image_coords(img, [1.0, 2.0, 3.0]) def test_round_signal_roi_param(): """Test signal ROI parameter rounding""" # Create a signal with sampling period of 0.1 x = np.arange(0, 10, 0.1) y = np.sin(x) sig = create_signal("test", x, y) # Create a segment ROI roi = SegmentROI([1.23456789, 5.87654321], False) param = roi.to_param(sig, 0) # Round the parameter round_signal_roi_param(sig, param) # Check that coordinates are rounded assert param.xmin == 1.23 assert param.xmax == 5.88 def test_round_image_roi_param_rectangle(): """Test image ROI parameter rounding for rectangular ROI""" # Create an image with dx=dy=1.0 data = np.ones((100, 100)) img = create_image("test", data) # Create a rectangular ROI with floating-point errors roi = RectangularROI([10.0, 20.0, 50.29999999999995, 75.19999999999999], False) param = roi.to_param(img, 0) # Verify we have the floating-point errors before rounding assert param.dx == 50.29999999999995 assert param.dy == 75.19999999999999 # Round the parameter round_image_roi_param(img, param) # Check that coordinates are rounded assert param.x0 == 10.0 assert param.y0 == 20.0 assert param.dx == 50.3 assert param.dy == 75.2 def test_round_image_roi_param_circle(): """Test image ROI parameter rounding for circular ROI""" # Create an image with dx=dy=1.0 data = np.ones((100, 100)) img = create_image("test", data) # Create a circular ROI with floating-point errors roi = CircularROI([50.123456, 50.987654, 25.555555], False) param = roi.to_param(img, 0) # Round the parameter round_image_roi_param(img, param) # Check that coordinates are rounded assert param.xc == 50.1 assert param.yc == 51.0 assert param.r == 25.6 def test_round_image_roi_param_polygon(): """Test image ROI parameter rounding for polygonal ROI""" # Create an image with dx=dy=1.0 data = np.ones((100, 100)) img = create_image("test", data) # Create a polygonal ROI with floating-point errors coords = [10.123456, 20.987654, 30.555555, 40.444444, 50.111111, 60.999999] roi = PolygonalROI(coords, False) param = roi.to_param(img, 0) # Round the parameter round_image_roi_param(img, param) # Check that coordinates are rounded expected = np.array([10.1, 21.0, 30.6, 40.4, 50.1, 61.0]) np.testing.assert_array_equal(param.points, expected) def test_round_coords_non_uniform_image(): """Test coordinate rounding for non-uniform image coordinates""" # Create an image with non-uniform coordinates data = np.ones((10, 10)) img = create_image("test", data) # Set non-uniform coordinates img.xcoords = np.array([0, 1, 3, 6, 10, 15, 21, 28, 36, 45]) # varying spacing img.ycoords = np.array([0, 2, 4, 6, 8, 10, 12, 14, 16, 18]) # uniform spacing of 2 # Test rounding - should use average spacing coords = [5.123456, 7.987654, 25.555555, 13.444444] rounded = round_image_coords(img, coords) # Average dx ≈ 5.0, average dy = 2.0 # With precision_factor=0.1: precision_x=0.5, precision_y=0.2 # Should round to 1 decimal place for both assert rounded == [5.1, 8.0, 25.6, 13.4] def test_round_coords_preserves_structure(): """Test that coordinate rounding preserves the structure of coordinates""" # Create an image data = np.ones((100, 100)) img = create_image("test", data) # Test with multiple coordinate pairs coords = [ 10.111, 20.222, 30.333, 40.444, 50.555, 60.666, 70.777, 80.888, ] rounded = round_image_coords(img, coords) # Should have same length assert len(rounded) == len(coords) # Each coordinate should be rounded independently assert all(isinstance(c, (int, float)) for c in rounded) if __name__ == "__main__": pytest.main([__file__, "-v"]) DataLab-1.1.0/datalab/tests/features/common/createobject_unit_test.py000066400000000000000000000030421514014117600257420ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ New signal/image test Testing GUI functions related to signal/image creation. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code # guitest: show from __future__ import annotations import sigima.objects from guidata.qthelpers import qt_app_context from sigima.viz import view_curves, view_images from datalab.env import execenv from datalab.gui.newobject import create_image_gui, create_signal_gui def test_new_signal() -> None: """Test new signal feature""" edit = not execenv.unattended with qt_app_context(): signal = create_signal_gui(None, edit=edit) if signal is not None: data = (signal.x, signal.y) view_curves([data], name=test_new_signal.__name__, title=signal.title) def test_new_image() -> None: """Test new image feature""" # Test with no input parameter edit = not execenv.unattended with qt_app_context(): image = create_image_gui(None, edit=edit) if image is not None: view_images(image.data, name=test_new_image.__name__, title=image.title) # Test with parametered 2D-Gaussian param = sigima.objects.Gauss2DParam.create(x0=3, y0=3, sigma=5) image = create_image_gui(param, edit=edit) if image is not None: view_images(image.data, name=test_new_image.__name__, title=image.title) if __name__ == "__main__": test_new_signal() test_new_image() DataLab-1.1.0/datalab/tests/features/common/geometry_results_app_test.py000066400000000000000000000117011514014117600265260ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Result shapes application test: - Create an image with metadata shapes and ROI - Further tests to be done manually: check if copy/paste metadata works """ # guitest: show from __future__ import annotations import pandas as pd import sigima.objects import sigima.params from sigima.tests import data as test_data from datalab.adapters_metadata import GeometryAdapter from datalab.config import Conf from datalab.tests import datalab_test_app_context def __create_image_with_geometry_results() -> sigima.objects.ImageObj: """Create test image with geometry results""" param = sigima.objects.Gauss2DParam.create( height=600, width=600, title="Test image (with geometry results)", dtype=sigima.objects.ImageDatatypes.UINT16, x0=2, y0=3, ) image = sigima.objects.create_image_from_param(param) for geometry in test_data.generate_geometry_results(): GeometryAdapter(geometry).add_to(image) return image def __check_geometry_results_merge( obj1: sigima.objects.SignalObj | sigima.objects.ImageObj, obj2: sigima.objects.SignalObj | sigima.objects.ImageObj, ) -> None: """Check if geometry results merge properly: the scenario is to duplicate an object, then compute average. We thus have to check if the second object (average) has the expected geometry results (i.e. twice the number of geometry results of the original object for each geometry result type). Args: obj1: Original object obj2: Merged object """ rsl1, rsl2 = ( list(GeometryAdapter.iterate_from_obj(obj1)), list(GeometryAdapter.iterate_from_obj(obj2)), ) assert len(rsl2) == len(rsl1), ( f"Result shapes length mismatch: {len(rsl1)} != {len(rsl2)}" ) for rs1, rs2 in zip(rsl1, rsl2): df1 = rs1.to_dataframe() df2 = rs2.to_dataframe() assert len(df1) * 2 == len(df2), ( f"Result shape dataframe length mismatch: {len(df1)} * 2 != {len(df2)}" ) # Check that the second dataframe contains double the data # (original geometry result concatenated with itself) coord_cols = [col for col in df1.columns if col != "roi_index"] df1_doubled = pd.concat([df1, df1], ignore_index=True) pd.testing.assert_frame_equal( df2[coord_cols].sort_values(coord_cols[0]).reset_index(drop=True), df1_doubled[coord_cols].sort_values(coord_cols[0]).reset_index(drop=True), ) def __check_roi_merge( obj1: sigima.objects.SignalObj | sigima.objects.ImageObj, obj2: sigima.objects.SignalObj | sigima.objects.ImageObj, ) -> None: """Check if ROI merge properly: the scenario is to duplicate an object, then compute average. We thus have to check if the second object (average) has the expected ROI (i.e. the union of the original object's ROI). Args: obj1: Original object obj2: Merged object """ roi1 = obj1.roi roi2 = obj2.roi for single_roi2 in roi2: assert roi1.get_single_roi(0) == single_roi2 def test_geometry_results() -> None: """Geometry results test""" with datalab_test_app_context() as win: obj1 = test_data.create_sincos_image() obj2 = __create_image_with_geometry_results() obj2.roi = sigima.objects.create_image_roi("rectangle", [10, 10, 50, 400]) panel = win.signalpanel for noised in (False, True): sig = test_data.create_noisy_signal(noised=noised) panel.add_object(sig) panel.processor.run_feature("fwhm", sigima.params.FWHMParam()) panel.processor.run_feature("fw1e2") panel.objview.select_objects((1, 2)) panel.show_results() panel.plot_results() win.set_current_panel("image") panel = win.imagepanel for obj in (obj1, obj2): panel.add_object(obj) panel.show_results() panel.plot_results() for keep_results in (False, True): with Conf.proc.keep_results.temp(keep_results): # Test merging result shapes (duplicate obj, then compute average): for panel in (win.signalpanel, win.imagepanel): panel.objview.select_objects((2,)) panel.duplicate_object() panel.objview.select_objects((2, len(panel))) panel.processor.run_feature("average") last_obj = panel[len(panel)] if keep_results: __check_geometry_results_merge(panel[2], last_obj) if panel is win.imagepanel: __check_roi_merge(panel[2], last_obj) else: assert ( len(list(GeometryAdapter.iterate_from_obj(last_obj))) == 0 ) if __name__ == "__main__": test_geometry_results() DataLab-1.1.0/datalab/tests/features/common/interactive_processing_test.py000066400000000000000000001305031514014117600270250ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Interactive re-processing feature tests Tests the end-to-end interactive processing workflow where users can modify processing parameters and re-apply them to regenerate the result. This includes: - Parameter modification and re-application - Parameter serialization/deserialization - Processing metadata storage for 1-to-1 operations - Handling of operations with and without parameters Note: metadata_all_patterns_test.py verifies metadata storage for all processing patterns (1-to-1, 2-to-1, n-to-1). """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import numpy as np from guidata.dataset import json_to_dataset from guidata.qthelpers import qt_app_context, qt_wait from sigima.objects import Gauss2DParam, GaussParam, create_image_roi from sigima.params import ( BinningParam, ConstantParam, GaussianParam, MovingAverageParam, SignalsToImageParam, ) from sigima.proc.image import RadialProfileParam from datalab.gui.newobject import CREATION_PARAMETERS_OPTION from datalab.gui.processor.base import PROCESSING_PARAMETERS_OPTION from datalab.objectmodel import get_uuid from datalab.tests import datalab_test_app_context def test_signal_interactive_processing(): """Test interactive processing for signals""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.signalpanel processor = panel.processor # Create a test signal panel.new_object() signal = panel.objview.get_current_object() assert signal is not None # Apply a Gaussian filter (which has parameters) param = GaussianParam.create(sigma=2.0) processor.compute_1_to_1( processor.get_feature("gaussian_filter").function, param=param, title="Gaussian filter", ) # Get the filtered signal filtered_sig = panel.objview.get_current_object() assert filtered_sig is not None assert filtered_sig != signal # Check that processing metadata was stored assert PROCESSING_PARAMETERS_OPTION in filtered_sig.get_metadata_options() option_dict = filtered_sig.get_metadata_option(PROCESSING_PARAMETERS_OPTION) # Verify metadata content assert option_dict["source_uuid"] == get_uuid(signal) assert option_dict["func_name"] == "gaussian_filter" # Verify the parameter can be deserialized stored_param = json_to_dataset(option_dict["param_json"]) assert stored_param.sigma == 2.0 def test_image_interactive_processing(): """Test interactive processing for images""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.imagepanel processor = panel.processor # Create a test image panel.new_object() image = panel.objview.get_current_object() assert image is not None # Apply a moving average filter (which has parameters) param = MovingAverageParam.create(n=5) processor.compute_1_to_1( processor.get_feature("moving_average").function, param=param, title="Moving average", ) # Get the filtered image filtered_ima = panel.objview.get_current_object() assert filtered_ima is not None assert filtered_ima != image # Check that processing metadata was stored assert PROCESSING_PARAMETERS_OPTION in filtered_ima.get_metadata_options() option_dict = filtered_ima.get_metadata_option(PROCESSING_PARAMETERS_OPTION) # Verify metadata content assert option_dict["source_uuid"] == get_uuid(image) assert option_dict["func_name"] == "moving_average" # Verify the parameter can be deserialized stored_param = json_to_dataset(option_dict["param_json"]) assert stored_param.n == 5 def test_processing_without_parameters(): """Test that processing without parameters doesn't store metadata""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.signalpanel processor = panel.processor # Create a test signal panel.new_object() signal = panel.objview.get_current_object() assert signal is not None # Apply absolute value (which has NO parameters) processor.compute_1_to_1( processor.get_feature("absolute").function, title="Absolute value", ) # Get the result signal result_signal = panel.objview.get_current_object() assert result_signal is not None assert result_signal != signal # Check that processing metadata was NOT stored assert PROCESSING_PARAMETERS_OPTION in result_signal.get_metadata_options() option_dict = result_signal.get_metadata_option( PROCESSING_PARAMETERS_OPTION ) assert "param_json" not in option_dict def test_recompute(): """Test recompute feature for signals""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.signalpanel processor = panel.processor # Create a test signal panel.new_object() signal = panel.objview.get_current_object() signal_uuid = get_uuid(signal) # Apply a Gaussian filter with initial parameters param = GaussianParam.create(sigma=2.0) processor.run_feature("gaussian_filter", param=param) filtered_sig = panel.objview.get_current_object() original_data = filtered_sig.y.copy() # Recompute with different input signal data constant = 1.23098765 signal.y += constant panel.recompute_processing() assert np.allclose(filtered_sig.y, original_data + constant) # Verify metadata is correct assert PROCESSING_PARAMETERS_OPTION in filtered_sig.get_metadata_options() option_dict = filtered_sig.get_metadata_option(PROCESSING_PARAMETERS_OPTION) assert option_dict["source_uuid"] == signal_uuid assert option_dict["func_name"] == "gaussian_filter" def test_apply_creation_parameters_signal(): """Test apply_creation_parameters for signals""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.signalpanel objprop = panel.objprop # Create a signal with specific parameters param = GaussParam.create(mu=250.0, sigma=20.0, a=100.0, y0=0.0, size=500) panel.new_object(param=param, edit=False) signal = panel.objview.get_current_object() assert signal is not None signal_uuid = get_uuid(signal) # Verify the Creation tab was set up assert objprop.creation_param_editor is not None original_data = signal.y.copy() # Modify the creation parameters in the editor editor = objprop.creation_param_editor # Change the Gaussian parameters to get a predictable result editor.dataset.a = 200.0 # Double the amplitude from 100.0 to 200.0 # Apply the new creation parameters objprop.apply_creation_parameters() # Verify the signal was updated in-place (same UUID) updated_signal = panel.objview.get_current_object() assert get_uuid(updated_signal) == signal_uuid # Get the updated creation parameters from metadata creation_param_json = updated_signal.get_metadata_option( CREATION_PARAMETERS_OPTION ) updated_param = json_to_dataset(creation_param_json) # Verify the parameter was actually updated in metadata assert updated_param.a == 200.0 # Verify the data has changed # Since we're working with very small Gaussian values, # just verify they're different assert not np.array_equal(updated_signal.y, original_data) def test_apply_creation_parameters_image(): """Test apply_creation_parameters for images""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.imagepanel objprop = panel.objprop # Create an image with specific parameters (using a derived class # of NewImageParam) to ensure creation parameters are stored in metadata param = Gauss2DParam.create(x0=50.0, y0=50.0, sigma=10.0, a=100.0) panel.new_object(param=param, edit=False) image = panel.objview.get_current_object() assert image is not None # Verify the Creation tab was set up assert objprop.creation_param_editor is not None original_data = image.data.copy() # Modify the parameters in the editor to create a visibly different image editor = objprop.creation_param_editor # Change the amplitude to make it clearly different editor.dataset.a = 200.0 # Double the amplitude from 100.0 to 200.0 # Apply the new parameters objprop.apply_creation_parameters() # Verify the image was updated in-place (same UUID) updated_image = panel.objview.get_current_object() assert get_uuid(updated_image) == get_uuid(image) # Verify the data has changed (amplitude doubled) assert not np.array_equal(updated_image.data, original_data) def test_no_duplicate_creation_tabs(): """Test that applying creation parameters multiple times doesn't create duplicate tabs. This test verifies the fix for the bug where clicking "Apply" in the Creation tab would create a new Creation tab instead of reusing the existing one. It also verifies that the Creation tab remains current after applying changes. """ with qt_app_context(): with datalab_test_app_context() as win: panel = win.imagepanel objprop = panel.objprop # Create an image with creation parameters param = Gauss2DParam.create(x0=50.0, y0=50.0, sigma=10.0, a=100.0) panel.new_object(param=param, edit=False) image = panel.objview.get_current_object() assert image is not None # Verify Creation tab was set up assert objprop.creation_param_editor is not None assert objprop.creation_scroll is not None # Count how many Creation tabs exist initially using the widget reference initial_index = objprop.tabwidget.indexOf(objprop.creation_scroll) assert initial_index >= 0, "Creation tab should be present" # Count tabs by checking if they reference the same scroll widget initial_count = sum( 1 for i in range(objprop.tabwidget.count()) if objprop.tabwidget.widget(i) is objprop.creation_scroll ) assert initial_count == 1, "Should have exactly one Creation tab initially" # Apply creation parameters multiple times editor = objprop.creation_param_editor for amplitude in [150.0, 200.0, 250.0]: editor.dataset.a = amplitude objprop.apply_creation_parameters() # Wait for the deferred setup_creation_tab to complete qt_wait(0.1) # Verify that creation_scroll reference still exists assert objprop.creation_scroll is not None # Count Creation tabs again - should still be just one creation_count = sum( 1 for i in range(objprop.tabwidget.count()) if objprop.tabwidget.widget(i) is objprop.creation_scroll ) assert creation_count == 1, ( f"Should still have exactly one Creation tab after " f"applying amplitude={amplitude}" ) # Verify that the Creation tab is the current tab assert objprop.tabwidget.currentWidget() is objprop.creation_scroll, ( f"Creation tab should remain current after " f"applying amplitude={amplitude}" ) def test_no_creation_parameters_for_base_classes(): """Test that creation parameters are NOT stored for base classes This test verifies the behavior introduced by the patch that only stores creation parameters for derived classes of NewSignalParam/NewImageParam, not for the base classes themselves. """ with qt_app_context(): with datalab_test_app_context() as win: # Test with signals signal_panel = win.signalpanel signal_objprop = signal_panel.objprop # Create a signal using default new_object() (uses base NewSignalParam) signal_panel.new_object(edit=False) signal = signal_panel.objview.get_current_object() assert signal is not None # Verify the Creation tab was NOT set up (no creation parameters stored) assert signal_objprop.creation_param_editor is None # Verify that CREATION_PARAMETERS_OPTION is not in metadata assert CREATION_PARAMETERS_OPTION not in signal.get_metadata_options() # Test with images image_panel = win.imagepanel image_objprop = image_panel.objprop # Create an image using default new_object() (uses base NewImageParam) image_panel.new_object(edit=False) image = image_panel.objview.get_current_object() assert image is not None # Verify the Creation tab was NOT set up (no creation parameters stored) assert image_objprop.creation_param_editor is None # Verify that CREATION_PARAMETERS_OPTION is not in metadata assert CREATION_PARAMETERS_OPTION not in image.get_metadata_options() def test_apply_processing_parameters_signal(): """Test apply_processing_parameters for signals""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.signalpanel processor = panel.processor objprop = panel.objprop # Create a test signal with some structure param = GaussParam.create(mu=250.0, sigma=20.0, a=100.0, y0=10.0, size=500) panel.new_object(param=param, edit=False) signal = panel.objview.get_current_object() assert signal is not None signal_uuid = get_uuid(signal) original_signal_data = signal.y.copy() # Apply addition_constant with initial value v0 = 5.0 processor.run_feature("addition_constant", ConstantParam.create(value=v0)) # Get the processed signal processed_sig = panel.objview.get_current_object() assert processed_sig is not None processed_uuid = get_uuid(processed_sig) # Verify initial constant was applied: data should be original + 5.0 assert np.allclose(processed_sig.y, original_signal_data + v0) # Select the processed signal to trigger setup_processing_tab panel.objview.set_current_object(processed_sig) # Verify the Processing tab was set up assert objprop.processing_param_editor is not None # Modify the processing parameters editor = objprop.processing_param_editor # Change constant from 5.0 to 15.0 editor.dataset.value = v1 = 15.0 # Apply the new processing parameters report = objprop.apply_processing_parameters() # Verify the operation succeeded assert report.success, f"Reprocessing failed: {report.message}" assert report.obj_uuid == processed_uuid # Verify the object UUID didn't change (in-place update) assert get_uuid(processed_sig) == processed_uuid # Verify the new constant was applied: data should now be original + 15.0 assert np.allclose(processed_sig.y, original_signal_data + v1) # Verify metadata still points to the same source pp_dict = processed_sig.get_metadata_option(PROCESSING_PARAMETERS_OPTION) assert pp_dict["source_uuid"] == signal_uuid assert pp_dict["func_name"] == "addition_constant" # Verify the parameter was updated stored_param = json_to_dataset(pp_dict["param_json"]) assert stored_param.value == v1 def test_apply_processing_parameters_image(): """Test apply_processing_parameters for images""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.imagepanel processor = panel.processor objprop = panel.objprop # Create a default test image panel.new_object(edit=False) image = panel.objview.get_current_object() assert image is not None image_uuid = get_uuid(image) original_image_data = image.data.copy() # Apply addition_constant with initial value v0 = 7.0 processor.run_feature("addition_constant", ConstantParam.create(value=v0)) # Get the processed image processed_ima = panel.objview.get_current_object() assert processed_ima is not None processed_uuid = get_uuid(processed_ima) # Verify initial constant was applied: data should be original + 7.0 assert np.allclose(processed_ima.data, original_image_data + v0) # Select the processed image to trigger setup_processing_tab panel.objview.set_current_object(processed_ima) # Verify the Processing tab was set up assert objprop.processing_param_editor is not None # Modify the processing parameters editor = objprop.processing_param_editor # Change constant from 7.0 to 20.0 editor.dataset.value = v1 = 20.0 # Apply the new processing parameters report = objprop.apply_processing_parameters() # Verify the operation succeeded assert report.success, f"Reprocessing failed: {report.message}" assert report.obj_uuid == processed_uuid # Verify the object UUID didn't change (in-place update) assert get_uuid(processed_ima) == processed_uuid # Verify the new constant was applied: data should now be original + 20.0 assert np.allclose(processed_ima.data, original_image_data + v1) # Verify metadata still points to the same source pp_dict = processed_ima.get_metadata_option(PROCESSING_PARAMETERS_OPTION) assert pp_dict["source_uuid"] == image_uuid assert pp_dict["func_name"] == "addition_constant" # Verify the parameter was updated stored_param = json_to_dataset(pp_dict["param_json"]) assert stored_param.value == v1 def test_no_duplicate_processing_tabs(): """Test that applying processing parameters multiple times doesn't create duplicate tabs. This test verifies the fix for the bug where clicking "Apply" in the Processing tab would create a new Processing tab instead of reusing the existing one. It also verifies that the Processing tab remains current after applying changes. """ with qt_app_context(): with datalab_test_app_context() as win: panel = win.imagepanel processor = panel.processor objprop = panel.objprop # Create a default test image panel.new_object(edit=False) image = panel.objview.get_current_object() assert image is not None # Apply addition_constant with initial value v0 = 7.0 processor.run_feature("addition_constant", ConstantParam.create(value=v0)) # Get the processed image processed_ima = panel.objview.get_current_object() assert processed_ima is not None # Select the processed image to trigger setup_processing_tab panel.objview.set_current_object(processed_ima) # Verify Processing tab was set up assert objprop.processing_param_editor is not None assert objprop.processing_scroll is not None # Count how many Processing tabs exist initially initial_index = objprop.tabwidget.indexOf(objprop.processing_scroll) assert initial_index >= 0, "Processing tab should be present" # Count tabs by checking if they reference the same scroll widget initial_count = sum( 1 for i in range(objprop.tabwidget.count()) if objprop.tabwidget.widget(i) is objprop.processing_scroll ) assert initial_count == 1, ( "Should have exactly one Processing tab initially" ) # Apply processing parameters multiple times editor = objprop.processing_param_editor for value in [10.0, 15.0, 20.0]: editor.dataset.value = value report = objprop.apply_processing_parameters() assert report.success # Wait for the deferred setup_processing_tab to complete qt_wait(0.1) # Verify that processing_scroll reference still exists assert objprop.processing_scroll is not None # Count Processing tabs again - should still be just one processing_count = sum( 1 for i in range(objprop.tabwidget.count()) if objprop.tabwidget.widget(i) is objprop.processing_scroll ) assert processing_count == 1, ( f"Should still have exactly one Processing tab after " f"applying value={value}" ) # Verify that the Processing tab is the current tab assert objprop.tabwidget.currentWidget() is objprop.processing_scroll, ( f"Processing tab should remain current after applying value={value}" ) def test_apply_processing_parameters_missing_source(): """Test apply_processing_parameters when source object is missing""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.signalpanel processor = panel.processor objprop = panel.objprop # Create a test signal with actual data param = GaussParam.create(mu=250.0, sigma=20.0, a=100.0, y0=10.0, size=500) panel.new_object(param=param, edit=False) signal = panel.objview.get_current_object() # Apply a Gaussian filter filter_param = GaussianParam.create(sigma=2.0) processor.compute_1_to_1( processor.get_feature("gaussian_filter").function, param=filter_param, title="Gaussian filter", ) # Get the filtered signal filtered_sig = panel.objview.get_current_object() # Delete the source signal panel.objview.set_current_object(signal) panel.remove_object(force=True) # Select the filtered signal panel.objview.set_current_object(filtered_sig) # Try to apply processing parameters report = objprop.apply_processing_parameters(interactive=False) # Verify the operation failed with appropriate message assert not report.success # Check for English or French message assert ( "no longer exists" in report.message.lower() or "n'existe plus" in report.message.lower() ) def test_cross_panel_image_to_signal(): """Test cross-panel processing: Image → Signal (radial profile)""" with qt_app_context(): with datalab_test_app_context() as win: image_panel = win.imagepanel signal_panel = win.signalpanel image_processor = image_panel.processor # Create a test image with a Gaussian peak image_param = Gauss2DParam.create( x0=50.0, y0=50.0, sigma=10.0, a=100.0, height=100, width=100 ) image_panel.new_object(param=image_param, edit=False) image = image_panel.objview.get_current_object() assert image is not None image_uuid = get_uuid(image) # Apply radial_profile (Image → Signal cross-panel computation) profile_param = RadialProfileParam.create(x0=50, y0=50) image_processor.run_feature("radial_profile", param=profile_param) # Verify the result is now in the signal panel signal = signal_panel.objview.get_current_object() assert signal is not None # Check that processing metadata was stored assert PROCESSING_PARAMETERS_OPTION in signal.get_metadata_options() option_dict = signal.get_metadata_option(PROCESSING_PARAMETERS_OPTION) # Verify metadata content assert option_dict["source_uuid"] == image_uuid assert option_dict["func_name"] == "radial_profile" assert option_dict["pattern"] == "1-to-1" # Verify the parameter can be deserialized stored_param = json_to_dataset(option_dict["param_json"]) assert stored_param.x0 == 50 assert stored_param.y0 == 50 # Test that the Processing tab is set up correctly # (even though source is in a different panel) signal_panel.objview.set_current_object(signal) assert signal_panel.objprop.processing_param_editor is not None # Test modifying processing parameters editor = signal_panel.objprop.processing_param_editor # Change the center position editor.dataset.x0 = 40 editor.dataset.y0 = 40 # Apply the new processing parameters report = signal_panel.objprop.apply_processing_parameters() # Verify the operation succeeded assert report.success, f"Cross-panel reprocessing failed: {report.message}" # Verify the parameter was updated in metadata updated_dict = signal.get_metadata_option(PROCESSING_PARAMETERS_OPTION) updated_param = json_to_dataset(updated_dict["param_json"]) assert updated_param.x0 == 40 assert updated_param.y0 == 40 # Test recompute feature with modified source image image.data *= 2.0 # Double the image intensity original_signal_data = signal.y.copy() # Recompute the radial profile signal_panel.recompute_processing() # The signal should have changed (doubled intensity) assert not np.allclose(signal.y, original_signal_data) def test_cross_panel_image_to_signal_group(): """Test cross-panel processing with groups: Image group → Signal group""" with qt_app_context(): with datalab_test_app_context() as win: image_panel = win.imagepanel signal_panel = win.signalpanel image_processor = image_panel.processor # Create a group of test images group_name = "Test Images" group = image_panel.add_group(group_name) group_id = get_uuid(group) # Create multiple test images in the group n_images = 3 for i in range(n_images): image_param = Gauss2DParam.create( x0=50.0 + i * 5, y0=50.0 + i * 5, sigma=10.0, a=100.0, height=100, width=100, ) image_panel.new_object(param=image_param, edit=False) # The new objects are automatically added to the current group # Select the entire group image_panel.objview.set_current_item_id(group_id, extend=False) # Get the initial number of groups in signal panel signal_groups_before = len(signal_panel.objmodel.get_groups()) # Apply radial_profile to the entire group (Image → Signal cross-panel) profile_param = RadialProfileParam.create(x0=50, y0=50) image_processor.run_feature("radial_profile", param=profile_param) # Verify a NEW group was created in the signal panel signal_groups_after = len(signal_panel.objmodel.get_groups()) assert signal_groups_after == signal_groups_before + 1, ( "Expected a new group to be created for cross-panel computation results" ) # Verify that the new group contains the correct number of signals signal_groups = signal_panel.objmodel.get_groups() new_group = signal_groups[-1] # Get the last (newly created) group signals_in_group = new_group.get_objects() assert len(signals_in_group) == n_images, ( f"Expected {n_images} signals in new group, got {len(signals_in_group)}" ) # Verify the group name follows the expected pattern assert "radial_profile" in new_group.title.lower(), ( f"Expected group name to contain 'radial_profile', " f"got '{new_group.title}'" ) def test_cross_panel_signal_to_image(): """Test cross-panel processing: Signal → Image (combine signals into image)""" with qt_app_context(): with datalab_test_app_context() as win: signal_panel = win.signalpanel image_panel = win.imagepanel signal_processor = signal_panel.processor # Create multiple test signals n_signals = 5 signal_uuids = [] for i in range(n_signals): signal_param = GaussParam.create( mu=250.0 + i * 10, sigma=20.0, a=100.0, y0=float(i), size=500 ) signal_panel.new_object(param=signal_param, edit=False) signal = signal_panel.objview.get_current_object() signal_uuids.append(get_uuid(signal)) # Select all signals for uuid in signal_uuids: signal_panel.objview.set_current_item_id(uuid, extend=True) # Combine signals into image (Signal → Image cross-panel computation) sti_param = SignalsToImageParam.create( orientation="columns", normalize=True ) signal_processor.run_feature("signals_to_image", param=sti_param) # Verify the result is now in the image panel image = image_panel.objview.get_current_object() assert image is not None # Check that processing metadata was stored assert PROCESSING_PARAMETERS_OPTION in image.get_metadata_options() option_dict = image.get_metadata_option(PROCESSING_PARAMETERS_OPTION) # Verify metadata content for n-to-1 pattern assert option_dict["func_name"] == "signals_to_image" assert option_dict["pattern"] == "n-to-1" assert len(option_dict["source_uuids"]) == n_signals assert all(uuid in signal_uuids for uuid in option_dict["source_uuids"]) # Verify the parameter can be deserialized stored_param = json_to_dataset(option_dict["param_json"]) assert stored_param.orientation == "columns" assert stored_param.normalize is True # Test that the Processing tab is NOT set up for n-to-1 pattern # (interactive processing only works for 1-to-1 pattern) image_panel.objview.set_current_object(image) assert image_panel.objprop.processing_param_editor is None def test_select_source_objects_same_panel(): """Test selecting source objects within the same panel""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.signalpanel processor = panel.processor # Create a source signal panel.new_object(edit=False) source_signal = panel.objview.get_current_object() source_uuid = get_uuid(source_signal) # Apply a filter to create a processed signal param = GaussianParam.create(sigma=2.0) processor.run_feature("gaussian_filter", param=param) # Get the filtered signal filtered_signal = panel.objview.get_current_object() assert filtered_signal is not None # Clear selection panel.objview.clearSelection() # Select only the filtered signal panel.objview.set_current_item_id(get_uuid(filtered_signal), extend=False) # Call select_source_objects panel.select_source_objects() # Verify that the source signal is now selected selected_uuids = panel.objview.get_sel_object_uuids() assert source_uuid in selected_uuids assert len(selected_uuids) == 1 def test_select_source_objects_cross_panel(): """Test selecting source objects across panels (cross-panel computation)""" with qt_app_context(): with datalab_test_app_context() as win: image_panel = win.imagepanel signal_panel = win.signalpanel image_processor = image_panel.processor # Create a source image image_param = Gauss2DParam.create( x0=50.0, y0=50.0, sigma=10.0, a=100.0, height=100, width=100 ) image_panel.new_object(param=image_param, edit=False) image = image_panel.objview.get_current_object() image_uuid = get_uuid(image) # Apply radial_profile (Image → Signal) profile_param = RadialProfileParam.create(x0=50, y0=50) image_processor.run_feature("radial_profile", param=profile_param) # Verify we're now in the signal panel signal = signal_panel.objview.get_current_object() assert signal is not None # Clear selection in signal panel signal_panel.objview.clearSelection() # Select only the radial profile signal signal_panel.objview.set_current_item_id(get_uuid(signal), extend=False) # Call select_source_objects - should switch to image panel signal_panel.select_source_objects() # Verify that we switched to the image panel # (the current panel in the main window should now be image panel) assert win.get_current_panel() == "image" # Verify that the source image is now selected in the image panel selected_uuids = image_panel.objview.get_sel_object_uuids() assert image_uuid in selected_uuids assert len(selected_uuids) == 1 def test_select_source_objects_multiple_sources(): """Test selecting multiple source objects (n-to-1 processing)""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.signalpanel processor = panel.processor # Create multiple source signals n_signals = 3 source_uuids = [] for _i in range(n_signals): panel.new_object(edit=False) signal = panel.objview.get_current_object() source_uuids.append(get_uuid(signal)) # Select all source signals for uuid in source_uuids: panel.objview.set_current_item_id(uuid, extend=True) # Apply addition operation (n-to-1) processor.run_feature("addition") # Get the result signal result_signal = panel.objview.get_current_object() assert result_signal is not None # Clear selection panel.objview.clearSelection() # Select only the result signal panel.objview.set_current_item_id(get_uuid(result_signal), extend=False) # Call select_source_objects panel.select_source_objects() # Verify that all source signals are now selected selected_uuids = panel.objview.get_sel_object_uuids() assert len(selected_uuids) == n_signals assert all(uuid in selected_uuids for uuid in source_uuids) def test_select_source_objects_deleted_source(): """Test selecting source objects when source has been deleted""" with qt_app_context(): with datalab_test_app_context() as win: panel = win.signalpanel processor = panel.processor # Create a source signal panel.new_object(edit=False) source_signal = panel.objview.get_current_object() # Apply a filter param = GaussianParam.create(sigma=2.0) processor.run_feature("gaussian_filter", param=param) # Get the filtered signal filtered_signal = panel.objview.get_current_object() # Delete the source signal panel.objview.set_current_object(source_signal) panel.remove_object(force=True) # Select the filtered signal panel.objview.clearSelection() panel.objview.set_current_item_id(get_uuid(filtered_signal), extend=False) # Call select_source_objects - should show warning # (This won't raise an exception, just show a message box in GUI) panel.select_source_objects() # Verify that no objects are selected (source was deleted) selected_uuids = panel.objview.get_sel_object_uuids() # The filtered signal should still be selected assert get_uuid(filtered_signal) in selected_uuids def test_roi_mask_invalidation_on_size_change(): """Test that ROI masks are invalidated when image size changes. This test verifies the fix for the bug where modifying creation parameters that change the image dimensions doesn't invalidate the ROI mask cache, resulting in corrupted ROI display. Reproduction steps: 1. Create a 2D gaussian image 2. Add a rectangular ROI 3. Change the size of the image (increase width by 50%) 4. Verify that the ROI mask is properly invalidated and recomputed """ with qt_app_context(): with datalab_test_app_context() as win: panel = win.imagepanel objprop = panel.objprop # Step 1: Create a 2D gaussian image with specific dimensions param = Gauss2DParam.create( height=100, width=100, x0=50.0, y0=50.0, sigma=10.0, a=100.0 ) panel.new_object(param=param, edit=False) image = panel.objview.get_current_object() assert image is not None assert image.data.shape == (100, 100) # Step 2: Add a rectangular ROI roi = create_image_roi("rectangle", [20, 20, 40, 40]) image.roi = roi # Verify the ROI mask is created and cached mask_before = image.maskdata assert mask_before is not None assert mask_before.shape == (100, 100) # Step 3: Change the image dimensions (increase width by 50%) editor = objprop.creation_param_editor assert editor is not None editor.dataset.width = 150 # Change from 100 to 150 # Apply the new parameters objprop.apply_creation_parameters() # Step 4: Verify the image was resized updated_image = panel.objview.get_current_object() assert get_uuid(updated_image) == get_uuid(image) assert updated_image.data.shape == (100, 150) # Step 5: Verify the ROI mask was invalidated and will be recomputed # with the new dimensions mask_after = updated_image.maskdata assert mask_after is not None assert mask_after.shape == (100, 150), ( f"ROI mask shape {mask_after.shape} doesn't match " f"new image shape {updated_image.data.shape}" ) # The mask should be different from before (different shape) assert mask_before.shape != mask_after.shape def test_roi_mask_invalidation_on_processing_change(): """Test that ROI masks are invalidated when processing changes image dimensions. This test verifies the fix for the bug where reprocessing with parameters that change image dimensions doesn't invalidate the ROI mask cache, resulting in corrupted ROI display. Scenario: 1. Create a source image 2. Apply binning (reduces dimensions) 3. Add ROI to the binned image 4. Change binning factor (changes dimensions again) 5. Verify ROI mask is properly recomputed """ with qt_app_context(): with datalab_test_app_context() as win: panel = win.imagepanel objprop = panel.objprop # Step 1: Create a source image param = Gauss2DParam.create( height=100, width=100, x0=50.0, y0=50.0, sigma=10.0, a=100.0 ) panel.new_object(param=param, edit=False) source_image = panel.objview.get_current_object() assert source_image is not None # Step 2: Apply binning to reduce dimensions binning_param = BinningParam.create(sx=2, sy=2) # 100x100 -> 50x50 # Use the processor's run_feature method with edit=False panel.processor.run_feature("binning", binning_param, edit=False) # Get the binned result (last object in the list) binned = panel.objview.get_sel_objects()[-1] assert binned.data.shape == (50, 50) # Step 3: Add a rectangular ROI to the binned image roi = create_image_roi("rectangle", [10, 10, 20, 20]) binned.roi = roi # Verify the ROI mask is created and cached mask_before = binned.maskdata assert mask_before is not None assert mask_before.shape == (50, 50) # Step 4: Change binning factor via Processing tab assert objprop.setup_processing_tab(binned) editor = objprop.processing_param_editor assert editor is not None # Change binning factor from 2x2 to 4x4 (50x50 -> 25x25) editor.dataset.sx = 4 editor.dataset.sy = 4 # Apply the new processing parameters report = objprop.apply_processing_parameters(binned) assert report.success # Step 5: Verify the image was resized assert binned.data.shape == (25, 25) # Step 6: Verify the ROI mask was invalidated and will be recomputed # with the new dimensions mask_after = binned.maskdata assert mask_after is not None assert mask_after.shape == (25, 25), ( f"ROI mask shape {mask_after.shape} doesn't match " f"new image shape {binned.data.shape}" ) # The mask should be different from before (different shape) assert mask_before.shape != mask_after.shape if __name__ == "__main__": test_signal_interactive_processing() test_image_interactive_processing() test_processing_without_parameters() test_recompute() test_apply_creation_parameters_signal() test_apply_creation_parameters_image() test_no_duplicate_creation_tabs() test_no_creation_parameters_for_base_classes() test_apply_processing_parameters_signal() test_apply_processing_parameters_image() test_no_duplicate_processing_tabs() test_apply_processing_parameters_missing_source() test_cross_panel_image_to_signal() test_cross_panel_image_to_signal_group() test_cross_panel_signal_to_image() test_select_source_objects_same_panel() test_select_source_objects_cross_panel() test_select_source_objects_multiple_sources() test_select_source_objects_deleted_source() test_roi_mask_invalidation_on_size_change() test_roi_mask_invalidation_on_processing_change() DataLab-1.1.0/datalab/tests/features/common/io_app_test.py000066400000000000000000000047121514014117600235250ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ I/O application test: - Testing signals I/O - Testing images I/O """ # guitest: show from __future__ import annotations import os.path as osp from sigima.io.base import IOAction from sigima.io.image import ImageIORegistry from sigima.io.signal import SignalIORegistry from datalab.env import execenv from datalab.gui.panel.base import BaseDataPanel from datalab.tests import datalab_test_app_context, helpers def __testfunc( title: str, panel: BaseDataPanel, registry: SignalIORegistry | ImageIORegistry, pattern: str, in_folder: str, ) -> None: """Test I/O features""" execenv.print(f" {title}:") with helpers.WorkdirRestoringTempDir() as tmpdir: # os.startfile(tmpdir) fnames = helpers.get_test_fnames(pattern, in_folder) execenv.print(" Opening:") # TODO: [P3] This test does not support formats that return multiple objects # (e.g. SIF files with multiple images). As a consequence, it will not test # thoroughly the I/O functionalities for these formats (it will keep only the # first object in the list of returned objects) objs = [] for fname in fnames: execenv.print(f" {fname}: ", end="") try: registry.get_format(fname, IOAction.LOAD) except NotImplementedError: execenv.print("Skipped (not supported)") continue objs.append(panel.load_from_files([fname])[0]) execenv.print("OK") execenv.print(" Saving:") for fname, obj in zip(fnames, objs): panel.objview.set_current_object(obj) path = osp.join(tmpdir, osp.basename(fname)) execenv.print(f" {path}: ", end="") try: registry.get_format(fname, IOAction.SAVE) except NotImplementedError: execenv.print("Skipped (not supported)") continue panel.save_to_files([path]) execenv.print("OK") def test_io_app() -> None: """Run image tools test scenario""" with datalab_test_app_context() as win: execenv.print("I/O application test:") __testfunc("Signals", win.signalpanel, SignalIORegistry, "*.*", "curve_formats") __testfunc("Images", win.imagepanel, ImageIORegistry, "*.*", "image_formats") if __name__ == "__main__": test_io_app() DataLab-1.1.0/datalab/tests/features/common/large_results_app_test.py000066400000000000000000000162071514014117600257730ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Large Results Application Test Module This module contains tests for verifying that DataLab handles large result datasets efficiently and correctly. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show,skip import time import numpy as np import sigima.enums import sigima.objects import sigima.params as sigima_param from sigima.tests.data import get_test_image from datalab.adapters_metadata.geometry_adapter import GeometryAdapter from datalab.config import Conf from datalab.tests import datalab_test_app_context def create_polygon_vertices( x0: float, y0: float, nb_points: int, radius_mean: float, radius_variation: float ) -> list[tuple[float, float]]: """Generate points for a polygon around (x0, y0) Args: x0: x coordinate of the polygon center y0: y coordinate of the polygon center nb_points: number of points to generate radius_mean: mean radius of the polygon radius_variation: variation in radius Returns: List of (x, y) points representing the polygon vertices """ points = [] # Calculate number of NaNs to append (random numbers between 0 and nb_points-10): num_nans = np.random.randint(0, nb_points - 10) for j in range(nb_points - num_nans): angle = j * (2 * np.pi / (nb_points - num_nans)) radius = radius_mean + radius_variation * np.random.rand() x = x0 + radius * np.cos(angle) y = y0 + radius * np.sin(angle) points.append((x, y)) for _ in range(num_nans): points.append((np.nan, np.nan)) return points def create_random_polygons( size: int, nb_polygons: int, nb_points_per_polygon: int ) -> np.ndarray: """Create random polygons Args: size: size of the area in which to create polygons nb_polygons: number of polygons to create nb_points_per_polygon: number of points per polygon Returns: Array of shape (nb_polygons, nb_points_per_polygon, 2) """ polygons = [] for _ in range(nb_polygons): x0 = size * np.random.rand() y0 = size * np.random.rand() points = create_polygon_vertices( x0, y0, nb_points_per_polygon, radius_mean=20, radius_variation=30 ) # Append the flattened points: polygons.append(points) return np.array(polygons) def test_large_results_scenario(measure_execution_time: bool = False) -> None: """Test scenario to verify result truncation limits work correctly Args: measure_execution_time: if True, measure and print the execution time, then quit immediately This scenario tests: - Contour detection on flower.npy (generates many contours) - Shape drawing truncation at max_shapes_to_draw limit - Label display truncation at max_cells_in_label limit - Performance with large polygons (many points per polygon) Performance benchmark (15 polygons × 5000 points): - Pure PlotPy: ~473ms (baseline for drawing shapes only) - DataLab: ~254ms (includes shape drawing + HTML label generation) - Note: DataLab is faster due to optimized HTML truncation before formatting """ nb_polygons = 15 nb_points_per_polygon = 5000 with datalab_test_app_context( console=False, exec_loop=not measure_execution_time ) as win: # Create an image panel panel = win.imagepanel # Load the flower test image ima = get_test_image("flower.npy") ima.title = "Test image 'flower.npy' - Contour Detection Limit Test" panel.add_object(ima) # Apply Roberts filter for edge detection panel.processor.run_feature("roberts") # Run contour detection which should produce a large set of results param = sigima_param.ContourShapeParam() param.shape = sigima.enums.ContourShape.POLYGON with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("contour_shape", param) # Create geometry results manually using many polygons (we generate results # in a manner that should be similar to what contour detection would typically # produce but in a way that we can control precisely here) vertices = create_random_polygons( size=ima.data.shape[0], nb_polygons=nb_polygons, nb_points_per_polygon=nb_points_per_polygon, ) geom_result = sigima.objects.GeometryResult( title="Polygon", kind=sigima.objects.KindShape.POLYGON, coords=vertices.reshape(-1, nb_points_per_polygon * 2), func_name="contour_detection_test", ) geom_adapter = GeometryAdapter(geom_result) ima2 = ima.copy() geom_adapter.add_to(ima2) panel.add_object(ima2) if measure_execution_time: # Now measure the execution time of switching selection between # the 3 images: # Image #1: flower.npy, as loaded # Image #2: Image #1 after Roberts filter + contour detection # Image #3: Image #1 with manually added large polygon result print("\nMeasuring image switch timings...") image_switch_times = {} for _j in range(2): # Doing multiple iterations to stabilize timings panel.objview.select_objects([1]) for i in range(2, 4): start_time = time.perf_counter() panel.objview.select_objects([i]) elapsed_time = (time.perf_counter() - start_time) * 1000 # in ms image_switch_times.setdefault(i, []).append(elapsed_time) print(f" - Switch to image #{i}: {elapsed_time:.1f} ms") print("\nImage switch timings (ms):") for i in range(2, 4): times = image_switch_times[i] avg_time = sum(times) / len(times) print( f" - Switching to image #{i}: avg {avg_time:.1f} ms " f"over {len(times)} runs" ) # Measure execution time of manual refresh of the image view: print("\nMeasuring image view refresh timings...") image_refresh_times = {} for i in range(3, 4): panel.objview.select_objects([i]) for _j in range(5): # Doing multiple iterations to stabilize timings start_time = time.perf_counter() panel.manual_refresh() elapsed_time = (time.perf_counter() - start_time) * 1000 # in ms image_refresh_times.setdefault(i, []).append(elapsed_time) print(f" - Manual refresh: {elapsed_time:.1f} ms") print("\nImage view refresh timings (ms):") for i in range(3, 4): times = image_refresh_times[i] avg_time = sum(times) / len(times) print( f" - Manual refresh: avg {avg_time:.1f} ms over {len(times)} runs" ) if __name__ == "__main__": test_large_results_scenario(measure_execution_time=False) DataLab-1.1.0/datalab/tests/features/common/metadata_all_patterns_test.py000066400000000000000000000104561514014117600266100ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Processing metadata storage tests Tests that ALL processing patterns (1-to-1, 2-to-1, n-to-1) correctly store metadata in object metadata options, regardless of whether they support interactive re-processing. This is an integration test that verifies the metadata storage infrastructure works correctly across all computation patterns. Note: interactive_processing_test.py tests the interactive re-processing UI feature. """ # guitest: show import guidata.dataset as gds from guidata.qthelpers import qt_app_context from sigima.proc.signal.filtering import GaussianParam from datalab.gui.processor.base import PROCESSING_PARAMETERS_OPTION from datalab.objectmodel import get_uuid from datalab.tests import datalab_test_app_context def test_metadata_all_patterns(): """Test that metadata is stored for all processing patterns.""" with qt_app_context(): with datalab_test_app_context() as win: # Get panels sig_panel = win.signalpanel img_panel = win.imagepanel # === Test 1: 1-to-1 pattern (with parameters) === sig_panel.new_object() sig1 = sig_panel.objmodel.get_all_objects()[-1] param = GaussianParam.create(sigma=2.0) sig_panel.processor.compute_1_to_1( sig_panel.processor.get_feature("gaussian_filter").function, param=param, title="Gaussian filter", ) filtered_sig = sig_panel.objmodel.get_all_objects()[-1] # Check metadata contains all keys including pattern type assert PROCESSING_PARAMETERS_OPTION in filtered_sig.get_metadata_options() option_dict = filtered_sig.get_metadata_option(PROCESSING_PARAMETERS_OPTION) assert option_dict["func_name"] == "gaussian_filter" assert option_dict["pattern"] == "1-to-1" assert option_dict["source_uuid"] == get_uuid(sig1) assert option_dict["param_json"] == gds.dataset_to_json(param) # === Test 2: 2-to-1 pattern (single operand) === sig_panel.new_object() sig2 = sig_panel.objmodel.get_all_objects()[-1] sig_panel.objview.select_objects([sig1]) # Select only sig1 sig_panel.processor.compute_2_to_1( obj2=sig2, obj2_name="signal to subtract", func=sig_panel.processor.get_feature("difference").function, title="Difference", ) subtracted_sig = sig_panel.objmodel.get_all_objects()[-1] # Check lightweight metadata (no params, but has pattern and sources) assert PROCESSING_PARAMETERS_OPTION in subtracted_sig.get_metadata_options() option_dict = subtracted_sig.get_metadata_option( PROCESSING_PARAMETERS_OPTION ) assert option_dict["pattern"] == "2-to-1" assert option_dict["source_uuids"] == [get_uuid(sig1), get_uuid(sig2)] assert len(option_dict["source_uuids"]) == 2 # Should NOT have full parameters stored assert "param" not in option_dict # === Test 3: n-to-1 pattern (single operand mode) === img_panel.new_object() img1 = img_panel.objmodel.get_all_objects()[-1] img_panel.new_object() img2 = img_panel.objmodel.get_all_objects()[-1] img_panel.objview.select_objects([img1, img2]) img_panel.processor.compute_n_to_1( func=img_panel.processor.get_feature("addition").function, title="Addition", ) sum_img = img_panel.objmodel.get_all_objects()[-1] # Check lightweight metadata assert PROCESSING_PARAMETERS_OPTION in sum_img.get_metadata_options() option_dict = sum_img.get_metadata_option(PROCESSING_PARAMETERS_OPTION) assert option_dict["pattern"] == "n-to-1" assert "source_uuids" in option_dict assert len(option_dict["source_uuids"]) == 2 # Should NOT have full parameters stored assert "param" not in option_dict print("✓ All processing patterns store appropriate metadata") if __name__ == "__main__": test_metadata_all_patterns() DataLab-1.1.0/datalab/tests/features/common/metadata_app_test.py000066400000000000000000000116671514014117600247050ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Metadata application test: - Create signal/image, with ROI - Compute things (adds metadata) - Test metadata delete, copy, paste """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.objects import sigima.params import sigima.proc.image as sipi import sigima.proc.signal as sips from sigima.tests.data import create_paracetamol_signal from datalab.adapters_metadata.geometry_adapter import GeometryAdapter from datalab.env import execenv from datalab.gui.panel.base import BaseDataPanel, PasteMetadataParam from datalab.gui.panel.image import ImagePanel from datalab.gui.panel.signal import SignalPanel from datalab.tests import datalab_test_app_context from datalab.tests.features.image.roi_app_test import create_test_image_with_roi def __run_signal_computations(panel: SignalPanel): """Test all signal features related to ROI""" execenv.print(" Signal features") panel.processor.run_feature(sips.fwhm, sigima.params.FWHMParam()) panel.processor.run_feature(sips.fw1e2) def __run_image_computations(panel: ImagePanel): """Test all image features related to ROI""" execenv.print(" Image features") panel.processor.run_feature(sipi.centroid) panel.processor.run_feature(sipi.enclosing_circle) panel.processor.run_feature( sipi.peak_detection, sigima.params.Peak2DDetectionParam() ) def __test_metadata_features(panel: BaseDataPanel): """Test all metadata features""" # Duplicate the first object panel.duplicate_object() # Delete metadata of the first object for keep_roi in (True, False): # Test both cases (coverage test) panel.delete_metadata(keep_roi=keep_roi) # Select and copy metadata of the second object panel.objview.select_objects([2]) source_obj = panel.objview.get_sel_objects()[0] source_metadata = source_obj.metadata.copy() # Verify source object has geometry results geometry_keys = [ k for k, v in source_metadata.items() if GeometryAdapter.match(k, v) ] execenv.print(f" Source object has {len(geometry_keys)} geometry metadata keys") assert len(geometry_keys) > 0, "Source object should have geometry results" # Copy metadata panel.copy_metadata() # Select and paste metadata to the first object panel.objview.select_objects([1]) target_obj = panel.objview.get_sel_objects()[0] # Verify target has no geometry metadata before paste target_geo_keys_before = [ k for k, v in target_obj.metadata.items() if GeometryAdapter.match(k, v) ] execenv.print( f" Target object has {len(target_geo_keys_before)} geometry keys before paste" ) # Paste metadata (with default parameters - keep everything) param = PasteMetadataParam("Test paste") param.keep_geometry = True param.keep_tables = True param.keep_other = True param.keep_roi = True panel.paste_metadata(param) # Verify the paste worked target_metadata_after = target_obj.metadata.copy() target_geo_keys_after = [ k for k, v in target_metadata_after.items() if GeometryAdapter.match(k, v) ] execenv.print( f" Target object has {len(target_geo_keys_after)} geometry keys after paste" ) # Check that geometry metadata was actually pasted assert len(target_geo_keys_after) > 0, ( "Target object should have geometry results after paste" ) # Verify that all geometry metadata uses the new unified format (_dict) for key in target_geo_keys_after: # Verify the geometry data is valid geometry_data = target_metadata_after[key] assert isinstance(geometry_data, dict), f"Geometry data should be dict: {key}" assert "title" in geometry_data, f"Missing title in geometry data: {key}" assert "coords" in geometry_data, f"Missing coords in geometry data: {key}" execenv.print(f" ✓ Valid geometry entry: {key}") execenv.print(" ✓ Metadata copy/paste verification passed") def test_metadata_app(): """Run metadata application test scenario""" size = 200 with datalab_test_app_context() as win: execenv.print("Metadata application test:") # === Signal metadata features test === panel = win.signalpanel sig = create_paracetamol_signal(size) sig.roi = sigima.objects.create_signal_roi([[26, 41], [125, 146]], indices=True) panel.add_object(sig) __run_signal_computations(panel) __test_metadata_features(panel) # === Image metadata features test === panel = win.imagepanel param = sigima.objects.NewImageParam.create(height=size, width=size) ima = create_test_image_with_roi(param) panel.add_object(ima) __run_image_computations(panel) __test_metadata_features(panel) execenv.print("==> OK") if __name__ == "__main__": test_metadata_app() DataLab-1.1.0/datalab/tests/features/common/metadata_io_unit_test.py000066400000000000000000000041271514014117600255640ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Metadata import/export unit test: - Create an image with annotations, geometry result and table results - Add the image to DataLab - Export image metadata to file (JSON) - Delete image metadata - Import image metadata from previous file - Check if image metadata is the same as the original image """ # guitest: show import os.path as osp from sigima.tests import data as test_data from datalab.adapters_metadata import GeometryAdapter, TableAdapter from datalab.env import execenv from datalab.tests import datalab_test_app_context, helpers def test_metadata_io_unit(): """Run image tools test scenario""" with execenv.context(unattended=True): with helpers.WorkdirRestoringTempDir() as tmpdir: fname = osp.join(tmpdir, "test.dlabmeta") with datalab_test_app_context() as win: panel = win.imagepanel # Create a test image with annotations ima = test_data.create_annotated_image() # Add geometry results to test their serialization for geometry in test_data.generate_geometry_results(): GeometryAdapter(geometry).add_to(ima) # Add table results to test their serialization for table in test_data.generate_table_results(): TableAdapter(table).add_to(ima) panel.add_object(ima) orig_metadata = ima.metadata.copy() panel.export_metadata_from_file(fname) panel.delete_metadata() # The +1 is for the "number" metadata option which has no default: assert len(ima.metadata) == len(ima.get_metadata_options_defaults()) + 1 panel.import_metadata_from_file(fname) execenv.print("Check metadata export <--> import features:") helpers.compare_metadata( orig_metadata, ima.metadata, raise_on_diff=True ) if __name__ == "__main__": test_metadata_io_unit() DataLab-1.1.0/datalab/tests/features/common/misc_app_test.py000066400000000000000000000160001514014117600240420ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Miscellaneous application test ------------------------------ Whenever we have a test that does not fit in any of the other test files, we put it here... """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations import os.path as osp from typing import TYPE_CHECKING, Any import sigima.params import sigima.proc.signal as sips from sigima.tests.data import ( create_2dstep_image, create_paracetamol_signal, get_test_fnames, ) from datalab.env import execenv from datalab.objectmodel import get_uuid from datalab.tests import datalab_test_app_context, helpers if TYPE_CHECKING: from datalab.gui.main import DLMainWindow def __print_test_result(title: str, result: Any = None) -> None: """Print a test result""" execenv.print(f"Testing {title}{'' if result is None else ':'}") if result is not None: execenv.print(str(result)) execenv.print("") def __misc_unit_function(win: DLMainWindow) -> None: """Run miscellaneous unit tests Args: win: DLMainWindow instance """ panel = win.signalpanel objview = panel.objview sig = create_paracetamol_signal() panel.add_object(sig) panel.processor.run_feature(sips.derivative) panel.processor.run_feature( sips.moving_average, sigima.params.MovingAverageParam.create(n=5) ) __print_test_result("`SimpleObjectTree.__str__` method", objview) # Updated metadata view settings: __print_test_result("Updated metadata view settings") panel.update_metadata_view_settings() # Double click on the first signal item in object view: __print_test_result("Double click on the first signal item in object view") tree_item = objview.currentItem() objview.itemDoubleClicked.emit(tree_item, 0) # Open context menu on current item: __print_test_result("Open context menu on current item") tree_item_pos = objview.mapToGlobal(objview.visualItemRect(tree_item).center()) objview.SIG_CONTEXT_MENU.emit(tree_item_pos) # Plot item parameters changed: __print_test_result("Plot item parameters changed") objview.select_objects([get_uuid(sig)]) item = panel.plothandler[get_uuid(sig)] panel.plot_item_parameters_changed(item) # Duplicate group: __print_test_result("Duplicate group") objview.select_groups([1]) panel.duplicate_object() # Delete group: __print_test_result("Delete group") objview.select_groups([2]) panel.remove_object() # Exec import wizard: __print_test_result("Exec signal import wizard") win.signalpanel.exec_import_wizard() __print_test_result("Exec image import wizard") win.imagepanel.exec_import_wizard() # Properties changed: __print_test_result("Properties changed") objview.select_objects([get_uuid(sig)]) panel.properties_changed() # Get object titles: __print_test_result("Get object titles") execenv.print(win.get_object_titles()) # Get object titles with info: __print_test_result("Get group titles with object info") execenv.print(win.get_group_titles_with_object_info()) # Pop up tab menu: __print_test_result("Pop up tab menu") win.tabmenu.popup(win.mapToGlobal(win.tabmenu.pos())) # Repopulate panel trees: __print_test_result("Repopulate panel trees") win.repopulate_panel_trees() # Browse HDF5 files: __print_test_result("Browse HDF5 files") win.browse_h5_files([], False) # Open object __print_test_result("Open object") fname = get_test_fnames("*.csv")[0] win.load_from_files([fname]) # Open directory __print_test_result("Open directory (signals)") path = osp.dirname(get_test_fnames("curve_formats/*.*")[0]) win.signalpanel.load_from_directory(path) # Select all signals and save them to a temporary directory __print_test_result("Select first group and save signals to a temporary directory") win.signalpanel.objview.select_groups([1]) with helpers.WorkdirRestoringTempDir() as tmpdir: param = sigima.params.SaveToDirectoryParam.create( directory=tmpdir, basename="{title}_test", extension=".csv", overwrite=True ) win.signalpanel.save_to_directory(param) # Open objects from signal panel __print_test_result("Open objects from signal panel") fnames = [f for f in get_test_fnames("curve_formats/*.*") if not f.endswith(".mca")] win.signalpanel.load_from_files(fnames) # Get version __print_test_result("Get version") execenv.print(win.get_version()) # Add signal __print_test_result("Add signal") win.add_signal( sig.title, sig.x, sig.y, sig.xunit, sig.yunit, sig.xlabel, sig.ylabel ) gp2 = win.signalpanel.add_group("group2") win.add_signal( sig.title, sig.x, sig.y, sig.xunit, sig.yunit, sig.xlabel, sig.ylabel, group_id=get_uuid(gp2), set_current=False, ) sig = win.get_object() # Get the last added signal nobj0 = len(win.signalpanel) win.remove_object(force=True) # Test the remove object method, then add it back assert len(win.signalpanel) == nobj0 - 1, "One object should have been removed" win.add_object(sig) # Add image __print_test_result("Add image") ima = create_2dstep_image() win.add_image( ima.title, ima.data, ima.xunit, ima.yunit, ima.zunit, ima.xlabel, ima.ylabel, ima.zlabel, ) gp3 = win.imagepanel.add_group("group3") win.add_image( ima.title, ima.data, ima.xunit, ima.yunit, ima.zunit, ima.xlabel, ima.ylabel, ima.zlabel, group_id=get_uuid(gp3), set_current=False, ) # Get selected object UUIDs __print_test_result("Get selected object UUIDs") uuids = win.get_sel_object_uuids() execenv.print(uuids) # Select objects by UUIDs __print_test_result("Select objects by UUIDs") win.select_objects(uuids) # Get object shapes __print_test_result("Get object shapes") win.get_object_shapes(uuids[0]) # Select groups by UUIDs __print_test_result("Select groups by UUIDs") win.select_groups([1]) uuids = win.get_sel_object_uuids() win.select_groups(uuids) # Delete metadata __print_test_result("Delete metadata") win.delete_metadata() # Close application __print_test_result("Close application") win.close_application() def test_misc_app() -> None: """Run misc. application test scenario""" with datalab_test_app_context(console=False) as win: execenv.print("Miscellaneous application test") execenv.print("==============================") execenv.print("") # We don't need to refresh the GUI during those tests (that's faster!) with win.context_no_refresh(): __misc_unit_function(win) if __name__ == "__main__": test_misc_app() DataLab-1.1.0/datalab/tests/features/common/multiple_geometry_results_unit_test.py000066400000000000000000000100121514014117600306320ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Test for multiple geometry results of the same kind ==================================================== This test verifies that multiple geometry results of the same kind (e.g., multiple segment results like FWHM and FW1e2) can coexist on the same object. """ from __future__ import annotations import numpy as np import sigima.objects as sio from sigima.objects.scalar import GeometryResult, KindShape from datalab.adapters_metadata import GeometryAdapter def test_multiple_segment_results(): """Test that multiple segment geometry results can coexist.""" # Create a simple signal object x = np.linspace(0, 10, 100) y = np.exp(-((x - 5) ** 2) / 2) obj = sio.create_signal("Test Signal", x, y) # Create two different segment results (simulating FWHM and FW1e2) fwhm_result = GeometryResult( title="fwhm", func_name="fwhm", kind=KindShape.SEGMENT, coords=np.array([[3.5, 0.6, 6.5, 0.6]]), # x0, y0, x1, y1 roi_indices=None, attrs={"method": "zero-crossing"}, ) fw1e2_result = GeometryResult( title="fw1e2", func_name="fw1e2", kind=KindShape.SEGMENT, coords=np.array([[3.0, 0.37, 7.0, 0.37]]), # Different coordinates roi_indices=None, attrs={"method": "gaussian"}, ) # Add both results to the object GeometryAdapter(fwhm_result).add_to(obj) GeometryAdapter(fw1e2_result).add_to(obj) # Verify both results are stored results = list(GeometryAdapter.iterate_from_obj(obj)) assert len(results) == 2, f"Expected 2 results, got {len(results)}" # Verify we can retrieve both results by title titles = {adapter.title for adapter in results} assert "fwhm" in titles, "FWHM result not found" assert "fw1e2" in titles, "FW1e2 result not found" # Verify the results have different coordinates for adapter in results: if adapter.title == "fwhm": assert np.allclose(adapter.result.coords[0], [3.5, 0.6, 6.5, 0.6]) elif adapter.title == "fw1e2": assert np.allclose(adapter.result.coords[0], [3.0, 0.37, 7.0, 0.37]) print("✓ Multiple segment results can coexist") def test_replace_same_title(): """Test that adding a result with the same title replaces the old one.""" # Create a simple signal object x = np.linspace(0, 10, 100) y = np.exp(-((x - 5) ** 2) / 2) obj = sio.create_signal("Test Signal", x, y) # Create first FWHM result fwhm_result_1 = GeometryResult( title="fwhm", kind=KindShape.SEGMENT, coords=np.array([[3.5, 0.6, 6.5, 0.6]]), roi_indices=None, func_name="fwhm", attrs={"method": "zero-crossing"}, ) # Create second FWHM result (same title, different coords) fwhm_result_2 = GeometryResult( title="fwhm", kind=KindShape.SEGMENT, coords=np.array([[3.6, 0.6, 6.6, 0.6]]), # Slightly different roi_indices=None, func_name="fwhm", attrs={"method": "gaussian"}, ) # Add first result GeometryAdapter(fwhm_result_1).add_to(obj) # Verify one result results = list(GeometryAdapter.iterate_from_obj(obj)) assert len(results) == 1, f"Expected 1 result after first add, got {len(results)}" # Add second result (should replace the first) GeometryAdapter(fwhm_result_2).add_to(obj) # Verify still only one result results = list(GeometryAdapter.iterate_from_obj(obj)) assert len(results) == 1, f"Expected 1 result after second add, got {len(results)}" # Verify it's the second one (with updated coordinates) adapter = results[0] assert adapter.title == "fwhm" assert np.allclose(adapter.result.coords[0], [3.6, 0.6, 6.6, 0.6]) assert adapter.result.attrs["method"] == "gaussian" print("✓ Results with same title are properly replaced") if __name__ == "__main__": test_multiple_segment_results() test_replace_same_title() print("\n✅ All tests passed!") DataLab-1.1.0/datalab/tests/features/common/multiple_table_results_unit_test.py000066400000000000000000000036751514014117600301070ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Test for multiple table results of the same kind ================================================== This test verifies that multiple table results of the same kind can coexist on the same object. """ from __future__ import annotations import numpy as np import sigima.objects as sio from sigima.objects.scalar import TableResult from datalab.adapters_metadata import TableAdapter def test_multiple_table_results(): """Test that multiple table results with different titles can coexist.""" # Create a simple signal object x = np.linspace(0, 10, 100) y = np.exp(-((x - 5) ** 2) / 2) obj = sio.create_signal("Test Signal", x, y) # Create two different table results (simulating different statistics) stats1 = TableResult( title="basic_stats", func_name="basic_stats", headers=["Mean", "Std", "Min", "Max"], data=[[5.0, 1.5, 0.0, 10.0]], roi_indices=None, attrs={"method": "basic"}, ) stats2 = TableResult( title="advanced_stats", func_name="advanced_stats", headers=["Median", "Q1", "Q3", "IQR"], data=[[5.0, 3.5, 6.5, 3.0]], roi_indices=None, attrs={"method": "advanced"}, ) # Add both results to the object TableAdapter(stats1).add_to(obj) TableAdapter(stats2).add_to(obj) # Verify both results are stored results = list(TableAdapter.iterate_from_obj(obj)) assert len(results) == 2, f"Expected 2 results, got {len(results)}" # Verify we can retrieve both results by title titles = {adapter.title for adapter in results} assert "basic_stats" in titles, "Basic stats result not found" assert "advanced_stats" in titles, "Advanced stats result not found" print("✓ Multiple table results can coexist") if __name__ == "__main__": test_multiple_table_results() print("\n✅ All tests passed!") DataLab-1.1.0/datalab/tests/features/common/operation_modes_app_test.py000066400000000000000000000372131514014117600263070ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Operation modes test -------------------- DataLab has two operation modes: - **Single operand mode**: the operation is applied to the selected objects (this is the default mode) - **Pairwise mode**: the operation is applied to the selected pairs of objects This test scenario covers the pairwise mode and the operations that can be performed in this mode: sum, difference, product, division, ... """ # guitest: show from __future__ import annotations from datalab import app from datalab.config import Conf from datalab.env import execenv from datalab.gui.processor.base import is_pairwise_mode from datalab.objectmodel import get_short_id from datalab.tests import helpers from datalab.utils.qthelpers import datalab_app_context def get_h5fname() -> str: """Get the HDF5 test filename""" fnames = helpers.get_test_fnames("reorder*") return fnames[0] def check_titles(title: str, titles: list[tuple[str, str]]) -> None: """Check that the title is one of the expected titles""" execenv.print(f"{title}:") for actual_title, expected_title in titles: execenv.print(f" {actual_title} == {expected_title}", end=" ") assert actual_title == expected_title if actual_title == expected_title: execenv.print("✓") else: execenv.print("✗") def test_single_operand_mode_compute_n_to_1() -> None: """Run single operand mode test scenario with `compute_n_to_1` operation (e.g. sum)""" h5fname = get_h5fname() original_mode = Conf.proc.operation_mode.get() Conf.proc.operation_mode.set("single") with datalab_app_context(exec_loop=True): win = app.create(h5files=[h5fname], console=False) panel = win.signalpanel view, model = panel.objview, panel.objmodel # Checking the operation mode: assert not is_pairwise_mode() # Store the number of groups before the operations n_groups = len(model.get_groups()) # Select the two first groups groups = [model.get_group_from_number(idx) for idx in (1, 2)] view.select_groups(groups) # Perform a sum operation panel.processor.run_feature("addition") # Default operation mode is single operand mode, so the sum operation # is applied to the selected groups, and we should have a new group # with two signals being the sum of signals from each group: # - signal 1: group 1 signal 1 + group 1 signal 2 # - signal 2: group 2 signal 1 + group 2 signal 2 assert len(model.get_groups()) == n_groups + 1 new_group = model.get_group_from_number(n_groups + 1) assert len(new_group) == 2 titles = [] for idx, obj in enumerate(new_group): pfx_orig = ", ".join(get_short_id(obj) for obj in groups[idx].get_objects()) titles.append((obj.title, f"Σ({pfx_orig})")) check_titles(f"Single operand mode Σ[{new_group.title}]", titles) # Remove new group view.select_groups([new_group]) panel.remove_object(force=True) # Store the number of groups before the operations n_groups = len(model.get_groups()) # Select the two first signals of the first two groups groups = [model.get_group_from_number(idx) for idx in (1, 2)] objs = groups[0][:2] + groups[1][:2] view.select_objects(objs) # Perform a sum operation panel.processor.run_feature("addition") # Default operation mode is single operand mode, so the sum operation # is applied to the selected signals, and we should have a new resulting # signal being the sum of the selected signals added in each group: # - signal added to group 1: group 1 signal 1 + group 2 signal 1 # - signal added to group 2: group 1 signal 2 + group 2 signal 2 assert len(model.get_groups()) == n_groups # no new group titles = [] for idx in range(1): pfx_orig = ", ".join(get_short_id(obj) for obj in groups[idx][:2]) titles.append((groups[idx][-1].title, f"Σ({pfx_orig})")) check_titles(f"Single operand mode Σ[{groups[1].title}]", titles) Conf.proc.operation_mode.set(original_mode) def test_pairwise_operations_mode_compute_n_to_1() -> None: """Run pairwise operations mode test scenario with `compute_n_to_1` operation (e.g. sum)""" h5fname = get_h5fname() original_mode = Conf.proc.operation_mode.get() Conf.proc.operation_mode.set("pairwise") with datalab_app_context(exec_loop=True): win = app.create(h5files=[h5fname], console=False) panel = win.signalpanel view, model = panel.objview, panel.objmodel # Checking the operation mode: assert is_pairwise_mode() # Store the number of groups before the operations n_groups = len(model.get_groups()) # Select the two first groups groups = [model.get_group_from_number(idx) for idx in (1, 2)] view.select_groups(groups) # Checking that each group contains the same number of signals (this is # required for pairwise operations - this part of the test is checking # if the data file is the one we expect) n_objects = len(groups[0]) assert all(len(group) == n_objects for group in groups) # Perform a sum operation panel.processor.run_feature("addition") # Operation mode is now pairwise, so the sum operation is applied to the # selected groups, and we should have a new group with as many signals as # the original groups, each signal being the sum of the corresponding signals: # - signal 1: group 1 signal 1 + group 2 signal 1 # - signal 2: group 1 signal 1 + group 2 signal 2 # ... assert len(model.get_groups()) == n_groups + 1 new_group = model.get_group_from_number(n_groups + 1) assert len(new_group.get_objects()) == n_objects titles = [] for idx in range(len(groups[0])): obj = new_group[idx] pfx_orig = ", ".join( get_short_id(obj) for obj in (grp[idx] for grp in groups) ) titles.append((obj.title, f"Σ({pfx_orig})")) check_titles(f"Pairwise operations mode Σ[{new_group.title}]", titles) # Remove new group view.select_groups([new_group]) panel.remove_object(force=True) # Store the number of groups before the operations n_groups = len(model.get_groups()) # Select two signals of the first two groups groups = [model.get_group_from_number(idx) for idx in (1, 2)] objs = [groups[0][0]] + [groups[0][-1]] + groups[1][-2:] view.select_objects(objs) # Perform a sum operation panel.processor.run_feature("addition") # Operation mode is now pairwise, so the sum operation is applied to the # selected signals, and we should have a new group with as many signals as # the selected signals, each signal being the sum of the corresponding signals: # - signal 1: group 1 signal 1 + group 2 signal 1 # - signal 2: group 1 signal 1 + group 2 signal 2 # ... assert len(model.get_groups()) == n_groups + 1 new_group = model.get_group_from_number(n_groups + 1) assert len(new_group) == 2 # 2 signals were selected titles = [] for idx, obj in enumerate(new_group): pfx_orig = ", ".join(get_short_id(obj) for obj in objs[idx::2]) titles.append((obj.title, f"Σ({pfx_orig})")) check_titles(f"Pairwise operations mode Σ[{new_group.title}]", titles) Conf.proc.operation_mode.set(original_mode) def test_single_operand_mode_compute_2_to_1() -> None: """Run single operand mode test scenario with `compute_2_to_1` operation (e.g. difference)""" h5fname = get_h5fname() original_mode = Conf.proc.operation_mode.get() Conf.proc.operation_mode.set("single") with datalab_app_context(exec_loop=True): win = app.create(h5files=[h5fname], console=False) panel = win.signalpanel view, model = panel.objview, panel.objmodel # Checking the operation mode: assert not is_pairwise_mode() # Store the number of groups before the operations n_groups = len(model.get_groups()) # Select the two first groups groups = [model.get_group_from_number(idx) for idx in (1, 2)] view.select_groups(groups) n_objects = [len(grp) for grp in groups] # Perform a difference operation with the first signal of the third group group3 = model.get_group_from_number(3) panel.processor.run_feature("difference", group3[0]) # Default operation mode is single operand mode, so we should have new signals # in each selected group being the difference between the original signals and # the selected signal: # - in group 1: # - signal 1: group 1 signal 1 - group 3 signal 1 # - signal 2: group 1 signal 2 - group 3 signal 1 # - in group 2: # - signal 1: group 2 signal 1 - group 3 signal 1 # - signal 2: group 2 signal 2 - group 3 signal 1 assert len(model.get_groups()) == n_groups new_objs = [] for i_group, group in enumerate(groups): titles = [] for i_obj in range(n_objects[i_group]): obj = group[i_obj + n_objects[i_group]] titles.append( ( obj.title, f"{get_short_id(group[i_obj])}-{get_short_id(group3[0])}", ) ) new_objs.append(obj) check_titles(f"Single operand mode Δ[{group.title}]", titles) # Remove new signals view.select_objects(new_objs) panel.remove_object(force=True) # Store the number of groups before the operations n_groups = len(model.get_groups()) # Select the two first signals of the first two groups groups = [model.get_group_from_number(idx) for idx in (1, 2)] objs = groups[0][:2] + groups[1][:2] view.select_objects(objs) n_objects = [2, 2] # Perform a difference operation with the first signal of the third group panel.processor.run_feature("difference", group3[0]) # Default operation mode is single operand mode, so we should have new signals # being the difference between the original signals and the selected signal: # - in group 1: # - signal 1: group 1 signal 1 - group 3 signal 1 # - signal 2: group 1 signal 2 - group 3 signal 1 # - in group 2: # - signal 1: group 2 signal 1 - group 3 signal 1 # - signal 2: group 2 signal 2 - group 3 signal 1 assert len(model.get_groups()) == n_groups # no new group for i_group, group in enumerate(groups): titles = [] for i_obj in range(n_objects[i_group]): obj = group[len(group) - n_objects[i_group] + i_obj] titles.append( ( obj.title, f"{get_short_id(group[i_obj])}-{get_short_id(group3[0])}", ) ) check_titles(f"Single operand mode Δ[{group.title}]", titles) Conf.proc.operation_mode.set(original_mode) def test_pairwise_operations_mode_compute_2_to_1() -> None: """Run pairwise operations mode test scenario with `compute_2_to_1` operation (e.g. difference)""" h5fname = get_h5fname() original_mode = Conf.proc.operation_mode.get() Conf.proc.operation_mode.set("pairwise") with datalab_app_context(exec_loop=True): win = app.create(h5files=[h5fname], console=False) panel = win.signalpanel view, model = panel.objview, panel.objmodel # Checking the operation mode: assert is_pairwise_mode() # Store the number of groups before the operations n_groups = len(model.get_groups()) # Select the two first groups groups = [model.get_group_from_number(idx) for idx in (1, 2)] view.select_groups(groups) # Checking that each group contains the same number of signals (this is # required for pairwise operations - this part of the test is checking # if the data file is the one we expect) n_objects = len(groups[0]) assert all(len(group) == n_objects for group in groups) # Perform a difference operation with the third group group3 = model.get_group_from_number(3) assert len(group3) == n_objects panel.processor.run_feature("difference", group3.get_objects()) # Operation mode is now pairwise, so the difference operation is applied to the # selected groups, and we should have a new group with as many signals as the # original groups, each signal being the difference of the corresponding # signals: # - signal 1: group 1 signal 1 - group 2 signal 1 # - signal 2: group 1 signal 1 - group 2 signal 2 # ... assert len(model.get_groups()) == n_groups + 2 new_groups = [ model.get_group_from_number(idx) for idx in (n_groups + 1, n_groups + 2) ] execenv.print("Δ|pairwise") for i_new_grp, new_grp in enumerate(new_groups): assert len(new_grp.get_objects()) == n_objects titles = [] for idx in range(n_objects): obj = new_grp[idx] obj1, obj2 = groups[i_new_grp][idx], group3[idx] titles.append((obj.title, f"{get_short_id(obj1)}-{get_short_id(obj2)}")) check_titles(f"Pairwise operations mode Δ[{new_grp.title}]", titles) # Remove new groups view.select_groups(new_groups) panel.remove_object(force=True) # Store the number of groups before the operations n_groups = len(model.get_groups()) # Select two signals of the first two groups groups = [model.get_group_from_number(idx) for idx in (1, 2)] objs = [groups[0][0]] + [groups[0][-1]] + groups[1][-2:] view.select_objects(objs) n_objects = 2 # Perform a difference operation with two signals from the third group objs2 = group3[:2] panel.processor.run_feature("difference", objs2) # Operation mode is now pairwise, so the difference operation is applied to the # selected signals, and we should have a new group with as many signals as the # selected signals, each signal being the difference of the corresponding # signals: # - signal 1: group 1 signal 1 - group 3 signal 1 # - signal 2: group 1 signal 1 - group 3 signal 2 # ... assert len(model.get_groups()) == n_groups + 2 new_groups = [ model.get_group_from_number(idx) for idx in (n_groups + 1, n_groups + 2) ] i_obj1 = 0 execenv.print("Δ|pairwise") for i_new_grp, new_grp in enumerate(new_groups): assert len(new_grp.get_objects()) == n_objects titles = [] for idx in range(n_objects): obj = new_grp[idx] obj1, obj2 = objs[i_obj1], objs2[idx] i_obj1 += 1 titles.append((obj.title, f"{get_short_id(obj1)}-{get_short_id(obj2)}")) check_titles(f"Pairwise operations mode Δ[{new_grp.title}]", titles) Conf.proc.operation_mode.set(original_mode) if __name__ == "__main__": test_single_operand_mode_compute_n_to_1() test_pairwise_operations_mode_compute_n_to_1() test_single_operand_mode_compute_2_to_1() test_pairwise_operations_mode_compute_2_to_1() DataLab-1.1.0/datalab/tests/features/common/plot_results_app_test.py000066400000000000000000000260731514014117600256610ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Plot results application test: Testing the "Plot results" feature with different options: - Different plot kinds (one curve per object vs. one curve per title) - Different X/Y axis selections - Results with and without ROIs - Both signal and image panels - Multiple result types (scalar and geometry results) - Group selection creates a new result group """ # guitest: show from __future__ import annotations from typing import Generator import sigima.objects import sigima.params from sigima.tests import data as test_data from datalab.config import _ from datalab.env import execenv from datalab.objectmodel import get_uuid from datalab.tests import datalab_test_app_context def iterate_noisy_signals( count: int, a: float, sigma: float, ) -> Generator[tuple[sigima.objects.SignalObj, float], None, None]: """Generate noisy signals for testing.""" noiseparam = sigima.objects.NormalDistribution1DParam.create(sigma=sigma, mu=0.0) param = sigima.objects.GaussParam.create(a=a) for i in range(count): param.sigma = 1.0 + (i * 0.1) ** 2 theoretical_fwhm = param.get_expected_features().fwhm sig = test_data.create_noisy_signal( noiseparam, param, f"Signal|fwhm_th={theoretical_fwhm:.2f}" ) yield sig, theoretical_fwhm def test_plot_results_signals_one_curve_per_title(): """Test plot results feature with signals, one curve per title. Create signals with single-value results (e.g., FWHM) and plot them. Verify that results are created in the "Results" group. """ with datalab_test_app_context() as win: panel = win.signalpanel with execenv.context(unattended=True): x_th = [] y_th = [] for i, (sig, theoretical_fwhm) in enumerate( iterate_noisy_signals(5, a=10.0, sigma=0.01) ): x_th.append(i) y_th.append(theoretical_fwhm) panel.add_object(sig) # Compute FWHM using the default method (zero-crossing) which introduces # a systematic ~2% error compared to the theoretical value # (trade-off for noise robustness) panel.processor.run_feature("fwhm", sigima.params.FWHMParam()) # Get number of groups before plotting groups_before = len(panel.objmodel.get_groups()) panel.objview.selectAll() panel.show_results() panel.plot_results(kind="one_curve_per_title", xaxis="indices", yaxis="Δx") # Verify a Results group was created groups_after = panel.objmodel.get_groups() assert len(groups_after) == groups_before + 1, ( f"Expected {groups_before + 1} groups, got {len(groups_after)}" ) # Verify the new group is named "Results" expected_title = _("Results") result_group = groups_after[-1] assert result_group.title == expected_title, ( f"Expected last group to be '{expected_title}', " f"got '{result_group.title}'" ) # Verify the Results group contains the result signal assert len(result_group) > 0, ( "Results group should contain at least one result signal" ) fwhm_var_th = sigima.objects.create_signal("FWHM_Theoretical", x_th, y_th) panel.add_object(fwhm_var_th) panel.objview.select_objects((6, 7)) # The observed offset should be around ~2% of the theoretical value def test_plot_results_images_one_curve_per_object(): """Test plot results feature with images, one curve per object. Create images with multi-value results (e.g., peak detection) and plot them. """ with datalab_test_app_context() as win: panel = win.imagepanel with execenv.context(unattended=True): for i in range(3): img = test_data.create_peak_image() img.title = f"Peaks_{i + 1}" panel.add_object(img) param = sigima.params.Peak2DDetectionParam.create(create_rois=False) panel.processor.run_feature("peak_detection", param) panel.objview.selectAll() panel.show_results() # Use programmatic parameters: plot y vs x for each peak panel.plot_results(kind="one_curve_per_object", xaxis="x", yaxis="y") def test_plot_results_images_with_rois(): """Test plot results feature with images containing ROIs. Create images with ROIs and single-value results (e.g., centroid) and plot them. """ with datalab_test_app_context() as win: panel = win.imagepanel with execenv.context(unattended=True): size = 512 peak_param = test_data.PeakDataParam.create(size=size) for i in range(3): data, peak_coords = test_data.get_peak2d_data(peak_param) img = sigima.objects.create_image(f"Image_ROI_{i + 1}", data) # Add rectangular ROI to image roi_coords = [] for xpeak, ypeak in peak_coords: roi_coords.append([xpeak, ypeak, 20]) img.roi = sigima.objects.create_image_roi("circle", roi_coords) panel.add_object(img) panel.processor.run_feature("centroid") panel.objview.selectAll() panel.show_results() # Plot centroid x vs indices with ROIs panel.plot_results(kind="one_curve_per_title", xaxis="indices", yaxis="x") def test_plot_results_with_group_selection(): """Test plot results with Results group. All plot results operations should create result signals in a reusable "Results" group for better organization. """ with datalab_test_app_context() as win: panel = win.signalpanel with execenv.context(unattended=True): # Create a group with signals panel.add_group("Test Group") # Add signals and compute FWHM for i, (sig, _fwhm) in enumerate( iterate_noisy_signals(3, a=10.0, sigma=0.01) ): sig.title = f"Signal_{i + 1}" panel.add_object(sig) panel.processor.run_feature("fwhm", sigima.params.FWHMParam()) # Get the number of groups before plotting results groups_before = len(panel.objmodel.get_groups()) # Select the group (not individual objects) panel.objview.select_groups([1]) # Verify the group is selected sel_groups = panel.objview.get_sel_groups() assert len(sel_groups) == 1, ( f"Expected 1 selected group, got {len(sel_groups)}" ) # Plot results - this should create or reuse a "Results" group panel.plot_results(kind="one_curve_per_title", xaxis="indices", yaxis="Δx") # Verify a new group was created groups_after = panel.objmodel.get_groups() assert len(groups_after) == groups_before + 1, ( f"Expected {groups_before + 1} groups, got {len(groups_after)}" ) # Check that the new group is named "Results" (or its translation) expected_title = _("Results") result_group = groups_after[-1] # Last group should be Results assert result_group.title == expected_title, ( f"Expected last group to be '{expected_title}', " f"got '{result_group.title}'" ) # Check that the Results group contains at least one result signal assert len(result_group) > 0, ( "Results group should contain at least one result signal" ) # Verify that the result signal title includes source object short IDs result_signal = list(result_group)[0] # Should contain all three source signal IDs: s001, s002, s003 # (s000 is the default group, so signals start at s001) assert "(s001, s002, s003)" in result_signal.title, ( f"Result signal title should include source IDs (s001, s002, s003), " f"got '{result_signal.title}'" ) # Test that the group is reused: create another group and plot results test_group_2 = panel.add_group("Test Group 2") test_group_2_id = get_uuid(test_group_2) for i, (sig, _fwhm) in enumerate( iterate_noisy_signals(2, a=10.0, sigma=0.01) ): sig.title = f"Signal2_{i + 1}" panel.add_object(sig, group_id=test_group_2_id) panel.processor.run_feature("fwhm", sigima.params.FWHMParam()) # Select the second group and plot results again panel.objview.select_groups([3]) # Plot results again num_results_before = len(result_group) panel.plot_results(kind="one_curve_per_title", xaxis="indices", yaxis="Δx") # Verify no new group was created (reused existing Results group) groups_final = panel.objmodel.get_groups() assert len(groups_final) == len(groups_after), ( "Results group should be reused, no new group created" ) # Verify more results were added to the existing Results group assert len(result_group) > num_results_before, ( "More results should be added to the existing Results group" ) # Test with many objects (more than 3) to verify "..." is used test_group_3 = panel.add_group("Test Group 3") test_group_3_id = get_uuid(test_group_3) for i, (sig, _fwhm) in enumerate( iterate_noisy_signals(5, a=10.0, sigma=0.01) ): sig.title = f"Signal3_{i + 1}" panel.add_object(sig, group_id=test_group_3_id) panel.processor.run_feature("fwhm", sigima.params.FWHMParam()) # Select the third group panel.objview.select_groups([4]) # Plot results num_results_before = len(result_group) panel.plot_results(kind="one_curve_per_title", xaxis="indices", yaxis="Δx") # Verify the result signal title uses "..." for many objects new_results = list(result_group)[num_results_before:] assert len(new_results) > 0, "Should have new results" result_signal_many = new_results[0] # With 5 source signals, should show first 2 IDs, "...", then last ID # Format: "fwhm (s..., s..., ..., s...): ..." assert ", ..., " in result_signal_many.title, ( f"Result signal title should use '...' before last ID, " f"got '{result_signal_many.title}'" ) if __name__ == "__main__": test_plot_results_signals_one_curve_per_title() test_plot_results_images_one_curve_per_object() test_plot_results_images_with_rois() test_plot_results_with_group_selection() DataLab-1.1.0/datalab/tests/features/common/reorder_app_test.py000066400000000000000000000056501514014117600245620ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Groups/signals/images reorder test: - Testing groups/signals reorder (images are not tested but the code is the same) - When executed in unattended mode, the test only covers the "move up" and "move down" actions - When executed in interactive mode, the user has to test the "drag and drop" actions manually - In unattended mode only, we take the opportunity to test other methods of the `ObjectModel` class (e.g. `get_group`, `remove_group`, ...) for maximizing the code coverage """ # guitest: show from datalab import app from datalab.env import execenv from datalab.objectmodel import get_uuid from datalab.tests import helpers from datalab.utils.qthelpers import datalab_app_context def test_reorder(): """Run signals/images reorder test scenario""" with datalab_app_context(exec_loop=True): win = app.create(h5files=[helpers.get_test_fnames("reorder*")[0]]) panel = win.signalpanel view, model = panel.objview, panel.objmodel # Select multiple signals objs = [model.get_object_from_number(idx) for idx in (2, 4, 5, 9)] view.select_objects(objs) # Move up view.move_up() # Check that the order is correct (note: objects 4 and 5 are not affected # by the move up action because they are moved up from the top of their group # to the bottom of the previous group) assert [model.get_number(obj) for obj in objs] == [1, 4, 5, 8] # Move down view.move_down() # Check that the order is correct (note: objects 4 and 5 are not affected # by the move down action because they are moved down from the bottom of their # group to the top of the next group) assert [model.get_number(obj) for obj in objs] == [2, 4, 5, 9] # Select multiple groups groups = [model.get_group_from_number(idx) for idx in (2, 3)] view.select_groups(groups) # Move up view.move_up() assert [model.get_number(group) for group in groups] == [1, 2] # Move down view.move_down() assert [model.get_number(group) for group in groups] == [2, 3] # Testing other methods of the `ObjectModel` class in unattended mode only if execenv.unattended: # Get group group = model.get_group_from_number(2) assert model.get_number(group) == 2 # Get the same group from its uuid group = model.get_group(get_uuid(group)) assert model.get_number(group) == 2 group = model.get_object_or_group(get_uuid(group)) assert model.get_number(group) == 2 # Remove group n_groups = len(model.get_groups()) model.remove_group(group) assert len(model.get_groups()) == n_groups - 1 if __name__ == "__main__": test_reorder() DataLab-1.1.0/datalab/tests/features/common/result_deletion_unit_test.py000066400000000000000000000211551514014117600265160ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Result deletion unit test -------------------------- Test the deletion of analysis results from objects. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations from sigima.objects import Gauss2DParam, create_image_from_param, create_image_roi from sigima.tests.data import create_paracetamol_signal from datalab.adapters_metadata import GeometryAdapter, TableAdapter from datalab.config import Conf from datalab.env import execenv from datalab.gui.processor.base import extract_analysis_parameters from datalab.objectmodel import get_uuid from datalab.tests import datalab_test_app_context def test_delete_results_image(): """Test deletion of analysis results from images""" with datalab_test_app_context(console=False) as win: execenv.print("Image result deletion test:") panel = win.imagepanel # Create a test image param = Gauss2DParam.create(height=200, width=200, sigma=20) img = create_image_from_param(param) panel.add_object(img) # Run centroid analysis to create results execenv.print(" Running centroid analysis...") with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("centroid") # Verify that results exist img_refreshed = panel.objmodel[get_uuid(img)] adapter_before = GeometryAdapter.from_obj(img_refreshed, "centroid") assert adapter_before is not None, ( "Centroid result should exist before deletion" ) execenv.print(" ✓ Centroid result created") # Delete all results execenv.print(" Deleting all results...") panel.objview.select_objects([get_uuid(img)]) panel.delete_results() # Verify that results were deleted img_after = panel.objmodel[get_uuid(img)] adapter_after = GeometryAdapter.from_obj(img_after, "centroid") assert adapter_after is None, "Centroid result should be deleted" execenv.print(" ✓ Centroid result deleted") def test_delete_results_signal(): """Test deletion of analysis results from signals""" with datalab_test_app_context(console=False) as win: execenv.print("Signal result deletion test:") panel = win.signalpanel # Create a test signal sig = create_paracetamol_signal() panel.add_object(sig) # Run stats analysis to create table results execenv.print(" Running stats analysis...") with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("stats") # Verify that results exist sig_refreshed = panel.objmodel[get_uuid(sig)] tables_before = list(TableAdapter.iterate_from_obj(sig_refreshed)) assert len(tables_before) > 0, "Stats result should exist before deletion" execenv.print(" ✓ Stats result created") # Delete all results execenv.print(" Deleting all results...") panel.objview.select_objects([get_uuid(sig)]) panel.delete_results() # Verify that results were deleted sig_after = panel.objmodel[get_uuid(sig)] tables_after = list(TableAdapter.iterate_from_obj(sig_after)) assert len(tables_after) == 0, "Stats result should be deleted" execenv.print(" ✓ Stats result deleted") def test_delete_results_clears_analysis_parameters(): """Test that deleting results also clears analysis parameters. This prevents auto_recompute_analysis from attempting to recompute deleted analyses when ROI changes. """ with datalab_test_app_context(console=False) as win: execenv.print("Test delete_results clears analysis parameters:") panel = win.imagepanel # Create a test image param = Gauss2DParam.create(height=200, width=200, sigma=20) img = create_image_from_param(param) panel.add_object(img) # Run centroid analysis to create results and store analysis parameters execenv.print(" Running centroid analysis...") with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("centroid") # Verify that analysis parameters exist img_refreshed = panel.objmodel[get_uuid(img)] analysis_params = extract_analysis_parameters(img_refreshed) assert analysis_params is not None, ( "Analysis parameters should exist after running centroid" ) assert analysis_params.func_name == "centroid", ( "Analysis parameters should store the centroid function name" ) execenv.print(" ✓ Analysis parameters stored") # Delete all results execenv.print(" Deleting all results...") panel.objview.select_objects([get_uuid(img)]) panel.delete_results() # Verify that analysis parameters were also cleared img_after = panel.objmodel[get_uuid(img)] analysis_params_after = extract_analysis_parameters(img_after) assert analysis_params_after is None, ( "Analysis parameters should be cleared after deleting results" ) execenv.print(" ✓ Analysis parameters cleared") # Now add a ROI and verify no auto-recompute happens (no new results) execenv.print(" Adding ROI to verify no auto-recompute...") roi = create_image_roi("rectangle", [25, 25, 100, 100]) img_after.roi = roi panel.processor.auto_recompute_analysis(img_after) # Verify that no new results were created adapter_after_roi = GeometryAdapter.from_obj(img_after, "centroid") assert adapter_after_roi is None, ( "No centroid result should be created after ROI change " "because analysis parameters were cleared" ) execenv.print( " ✓ No auto-recompute after ROI change (analysis params cleared)" ) execenv.print("\n✓ All tests passed!") def test_delete_results_after_roi_removed(): """Test that deleting results works when ROI was removed from object. This tests the fix for the bug where deleting results fails with AttributeError when results contain ROI information but the ROI was subsequently removed from the object. """ with datalab_test_app_context(console=False) as win: execenv.print("Test delete_results after ROI removed:") panel = win.imagepanel # Create a test image with ROI param = Gauss2DParam.create(height=200, width=200, sigma=20) img = create_image_from_param(param) roi = create_image_roi("rectangle", [25, 25, 100, 100]) img.roi = roi panel.add_object(img) execenv.print(" ✓ Created image with ROI") # Run centroid analysis - this stores ROI index in results execenv.print(" Running centroid analysis with ROI...") with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("centroid") # Verify that results exist and contain ROI information img_refreshed = panel.objmodel[get_uuid(img)] adapter_before = GeometryAdapter.from_obj(img_refreshed, "centroid") assert adapter_before is not None, "Centroid result should exist" df = adapter_before.to_dataframe() assert "roi_index" in df.columns, "Results should contain roi_index" execenv.print(" ✓ Centroid result created with ROI information") # Now remove the ROI from the object (simulating user action) img_refreshed.roi = None execenv.print(" ✓ Removed ROI from object") # Try to delete all results - this should NOT raise an error execenv.print(" Deleting all results (with ROI removed)...") panel.objview.select_objects([get_uuid(img)]) try: panel.delete_results() execenv.print(" ✓ Delete results succeeded (no AttributeError)") except AttributeError as e: raise AssertionError( f"delete_results should not raise AttributeError when ROI is None: {e}" ) from e # Verify that results were deleted img_after = panel.objmodel[get_uuid(img)] adapter_after = GeometryAdapter.from_obj(img_after, "centroid") assert adapter_after is None, "Centroid result should be deleted" execenv.print(" ✓ Centroid result deleted successfully") execenv.print("\n✓ Test passed!") if __name__ == "__main__": test_delete_results_image() test_delete_results_signal() test_delete_results_clears_analysis_parameters() test_delete_results_after_roi_removed() DataLab-1.1.0/datalab/tests/features/common/result_merged_label_unit_test.py000066400000000000000000000141311514014117600273110ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Merged result label test: Test that multiple results are merged into a single label that displays all results. This test verifies that: 1. Computing FWHM adds a result to the merged label 2. Computing FW1e2 adds another result to the same merged label 3. The merged label contains both results 4. The merged label is read-only (cannot be deleted to remove individual results) 5. De-selecting and re-selecting the signal maintains the merged label """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.params import sigima.proc.signal as sips from sigima.tests.data import create_paracetamol_signal from datalab.adapters_metadata.geometry_adapter import GeometryAdapter from datalab.env import execenv from datalab.tests import datalab_test_app_context def test_merged_result_label() -> None: """Test that multiple results are merged into a single label""" with datalab_test_app_context() as win: panel = win.signalpanel # Create a Gaussian curve execenv.print("Creating paracetamol signal...") sig = create_paracetamol_signal() panel.add_object(sig) execenv.print(f" Added signal: {sig.title}") # Compute the FWHM (this adds a result to metadata) execenv.print("Computing FWHM...") panel.processor.run_feature(sips.fwhm, sigima.params.FWHMParam()) # Force refresh to ensure shapes are added panel.plothandler.refresh_plot("selected", force=True) # Check that the result metadata exists sig = panel.objview.get_sel_objects()[0] geometry_results_1 = list(GeometryAdapter.iterate_from_obj(sig)) execenv.print(f" Geometry results after FWHM: {len(geometry_results_1)}") assert len(geometry_results_1) == 1, "Should have one geometry result" # Get the plot and find the merged result label item plot = panel.plothandler.plot label_items = [item for item in plot.items if hasattr(item, "labelparam")] # Find the merged result label - it should be on the plot merged_label = None for item in label_items: if hasattr(item, "is_readonly") and item.is_readonly(): merged_label = item break assert merged_label is not None, "Should find the merged result label" execenv.print(f" Found merged result label: {merged_label.title()}") # Check that the label is read-only assert merged_label.is_readonly(), "Merged label should be read-only" execenv.print(" ✓ Merged label is read-only") # Get the label text to verify it contains FWHM result label_text_1 = merged_label.text_string assert "fwhm" in label_text_1.lower(), "Label should contain FWHM result" execenv.print(" ✓ Label contains FWHM result") # Compute FW1e2 (this adds another result to metadata) execenv.print("Computing FW1e2...") panel.processor.run_feature(sips.fw1e2) # Check that we now have two results sig = panel.objview.get_sel_objects()[0] geometry_results_2 = list(GeometryAdapter.iterate_from_obj(sig)) execenv.print(f" Geometry results after FW1e2: {len(geometry_results_2)}") assert len(geometry_results_2) == 2, "Should have two geometry results" # Verify the merged label now contains both results merged_labels = [ item for item in plot.items if hasattr(item, "labelparam") and hasattr(item, "is_readonly") and item.is_readonly() ] execenv.print(f" Merged result labels on plot: {len(merged_labels)}") assert len(merged_labels) == 1, "Should have exactly one merged result label" merged_label_2 = merged_labels[0] label_text_2 = merged_label_2.text_string assert "fwhm" in label_text_2.lower(), "Label should contain FWHM result" assert "fw1e2" in label_text_2.lower(), "Label should contain FW1e2 result" assert "
    " in label_text_2, "Should contain separator between results" execenv.print(" ✓ Merged label contains both FWHM and FW1e2 results") # Create another signal and select it (to deselect the first one) execenv.print("Creating second signal to deselect first...") sig2 = create_paracetamol_signal() sig2.title = "Paracetamol 2" panel.add_object(sig2) # Re-select the first signal execenv.print("Re-selecting first signal...") panel.objview.select_objects([1]) # Check that the merged label still exists and contains both results sig = panel.objview.get_sel_objects()[0] geometry_results_after = list(GeometryAdapter.iterate_from_obj(sig)) execenv.print( f" Geometry results after reselect: {len(geometry_results_after)}" ) assert len(geometry_results_after) == 2, ( "Should still have two geometry results after re-selection" ) # Check that the merged label is still present merged_labels_after = [ item for item in plot.items if hasattr(item, "labelparam") and hasattr(item, "is_readonly") and item.is_readonly() ] execenv.print( f" Merged result labels on plot after reselect: {len(merged_labels_after)}" ) assert len(merged_labels_after) == 1, ( "Should still have one merged result label after re-selection" ) merged_label_final = merged_labels_after[0] label_text_final = merged_label_final.text_string assert "fwhm" in label_text_final.lower(), ( "Label should still contain FWHM result" ) assert "fw1e2" in label_text_final.lower(), ( "Label should still contain FW1e2 result" ) execenv.print(" ✓ Merged label persists after re-selection") execenv.print("✓ Test passed: Merged result label works correctly") if __name__ == "__main__": test_merged_result_label() DataLab-1.1.0/datalab/tests/features/common/result_shape_settings_unit_test.py000066400000000000000000000217371514014117600277410ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Unit test for visualization settings for annotated shapes and markers. This test verifies that visualization settings (configured in Settings dialog) are properly applied when creating geometry results: 1. Centroid produces a marker (MARKER kind from sigima) 2. Contour detection produces annotated shapes (CIRCLE, ELLIPSE, or POLYGON kind) The test uses the Conf.view.def*.temp() context manager to temporarily modify settings and verify they're applied to new results. """ from __future__ import annotations from plotpy.items import AnnotatedCircle, Marker from plotpy.styles import MarkerParam, ShapeParam from sigima.objects import Gauss2DParam, create_image_from_param from datalab.adapters_metadata import GeometryAdapter from datalab.adapters_plotpy import GeometryPlotPyAdapter from datalab.config import Conf from datalab.env import execenv from datalab.tests import datalab_test_app_context def test_ima_shape_param(): """Test that annotated shape settings are applied to enclosing_circle results on images.""" with datalab_test_app_context(console=False) as win: panel = win.imagepanel # Create a Gaussian image for testing (simple peak for enclosing circle) size = 200 param = Gauss2DParam.create(height=size, width=size, sigma=20) img = create_image_from_param(param) img.title = "Test Gaussian" panel.add_object(img) # Test: Verify annotated shape settings for enclosing circle # ---------------------------------------------------------- execenv.print("\n=== Test: Enclosing Circle (Annotated Shapes) ===") # Create custom annotated shape settings def_param = ShapeParam() def_param.line.width = 3 def_param.line.color = "#852727" # Temporarily set the annotated shape settings # Note: enclosing_circle is an image feature, so we use ima_shape_param with Conf.view.ima_shape_param.temp(def_param): # Compute enclosing circle with the custom settings with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("enclosing_circle") # Get the geometry adapter and create plot items adapter = GeometryAdapter.from_obj(img, "enclosing_circle") assert adapter is not None, "Enclosing circle should be computed" # Create a plotpy adapter to get the shape items plotpy_adapter = GeometryPlotPyAdapter(adapter) items = list(plotpy_adapter.iterate_shape_items("%.1f", True, "i")) # Verify we got annotated shapes assert len(items) > 0, "Should have at least one shape" shape_item = items[0] assert isinstance(shape_item, AnnotatedCircle), ( f"Expected AnnotatedCircle, got {type(shape_item)}" ) # Verify the annotation settings were applied param: ShapeParam = shape_item.shape.shapeparam execenv.print(f"Line width: {param.line.width}") execenv.print(f"Line color: {param.line.color}") assert param.line.width == def_param.line.width, ( f"Expected line.width={def_param.line.width}, got {param.line.width}" ) assert param.line.color == def_param.line.color, ( f"Expected line.color={def_param.line.color}, got {param.line.color}" ) execenv.print( "✓ Enclosing circle annotated shape settings correctly applied" ) execenv.print("\n=== Test passed ===") def test_ima_marker_param(): """Test that shape settings are applied to geometry results.""" with datalab_test_app_context(console=False) as win: panel = win.imagepanel # Create a 2D Gaussian image for testing size = 200 param = Gauss2DParam.create(height=size, width=size, sigma=20) img = create_image_from_param(param) img.title = "Test Gaussian" panel.add_object(img) # Test: Verify marker settings for centroid # ------------------------------------------ execenv.print("\n=== Test: Centroid (Marker) ===") # Create custom marker settings def_param = MarkerParam() def_symbol = def_param.symbol def_symbol.marker = "XCross" def_symbol.size = 15 def_symbol.edgecolor = "#316331" def_symbol.facecolor = "#291b6b" def_symbol.alpha = 0.87 # Temporarily set the marker settings with Conf.view.ima_marker_param.temp(def_param): # Compute centroid with the custom settings with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("centroid") # Get the geometry adapter and create plot items adapter = GeometryAdapter.from_obj(img, "centroid") assert adapter is not None, "Centroid should be computed" # Create a plotpy adapter to get the marker item plotpy_adapter = GeometryPlotPyAdapter(adapter) items = list(plotpy_adapter.iterate_shape_items("%.1f", True, "i")) # Verify we got a marker assert len(items) > 0, "Should have at least one marker" marker = items[0] assert isinstance(marker, Marker), f"Expected Marker, got {type(marker)}" # Verify the marker settings were applied symbol = marker.markerparam.symbol execenv.print(f"Marker symbol: {symbol.marker}") execenv.print(f"Marker size: {symbol.size}") execenv.print(f"Marker edge color: {symbol.edgecolor}") assert symbol.marker == def_symbol.marker, ( f"Expected marker='XCross', got '{symbol.marker}'" ) assert symbol.size == def_symbol.size, ( f"Expected size={def_symbol.size}, got {symbol.size}" ) assert symbol.edgecolor == def_symbol.edgecolor, ( f"Expected edgecolor={def_symbol.edgecolor}, got {symbol.edgecolor}" ) assert symbol.facecolor == def_symbol.facecolor, ( f"Expected facecolor={def_symbol.facecolor}, got {symbol.facecolor}" ) execenv.print("✓ Centroid marker settings correctly applied") execenv.print("\n=== Test passed ===") def test_refresh_shape_items_after_settings_change(): """Test that shape items are refreshed when settings change. This test verifies the fix for the issue where result shapes were not updated when refreshing the Image View after changing shape parameters in the Settings dialog. """ with datalab_test_app_context(console=False) as win: panel = win.imagepanel # Create a Gaussian image with a result size = 200 param = Gauss2DParam.create(height=size, width=size, sigma=20) img = create_image_from_param(param) img.title = "Test Gaussian" panel.add_object(img) # Compute enclosing circle with initial settings with Conf.proc.show_result_dialog.temp(False): panel.processor.run_feature("enclosing_circle") # Get initial shape item styling plot = panel.plothandler.plot shape_items = [item for item in plot.items if isinstance(item, AnnotatedCircle)] assert len(shape_items) > 0, "Should have at least one shape item" initial_width = shape_items[0].shape.shapeparam.line.width initial_color = shape_items[0].shape.shapeparam.line.color execenv.print(f"Initial shape: width={initial_width}, color={initial_color}") # Change temporarily the shape parameters new_param = ShapeParam() new_param.line.width = 5 new_param.line.color = "#00ff00" with Conf.view.ima_shape_param.temp(new_param): # Call refresh_all_shape_items() to apply new settings panel.plothandler.refresh_all_shape_items() # Get updated shape items shape_items_after = [ item for item in plot.items if isinstance(item, AnnotatedCircle) ] assert len(shape_items_after) > 0, ( "Should still have shape items after refresh" ) updated_width = shape_items_after[0].shape.shapeparam.line.width updated_color = shape_items_after[0].shape.shapeparam.line.color execenv.print( f"Updated shape: width={updated_width}, color={updated_color}" ) # Verify the shape was updated with new settings assert updated_width == 5, f"Expected width 5, got {updated_width}" assert updated_color == "#00ff00", ( f"Expected color #00ff00, got {updated_color}" ) execenv.print("✓ Shape items correctly refreshed after settings change\n") if __name__ == "__main__": test_ima_shape_param() test_ima_marker_param() test_refresh_shape_items_after_settings_change() DataLab-1.1.0/datalab/tests/features/common/roi_plotitem_unit_test.py000066400000000000000000000047261514014117600260300ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ ROI to plot item conversion unit tests """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations import numpy as np import sigima.objects from guidata.qthelpers import qt_app_context from sigima.tests.data import ( create_multigaussian_image, create_paracetamol_signal, create_test_image_rois, create_test_signal_rois, ) from datalab.adapters_plotpy import create_adapter_from_object, plotitem_to_singleroi from datalab.env import execenv def __conversion_methods( roi: sigima.objects.SignalROI | sigima.objects.ImageROI, obj: sigima.objects.SignalObj | sigima.objects.ImageObj, ) -> None: """Test conversion methods for single ROI objects""" execenv.print(" test `to_plot_item` and `from_plot_item` methods: ", end="") single_roi = roi.get_single_roi(0) with qt_app_context(exec_loop=False): plot_item = create_adapter_from_object(single_roi).to_plot_item(obj) sroi_new = plotitem_to_singleroi(plot_item, obj) orig_coords = [float(val) for val in single_roi.get_physical_coords(obj)] new_coords = [float(val) for val in sroi_new.get_physical_coords(obj)] execenv.print(f"{orig_coords} --> {new_coords}") # Check that coordinates are close # (allowing for rounding applied during conversion) assert np.allclose(orig_coords, new_coords, rtol=1e-5, atol=1e-10) def test_signal_roi_plotitem_conversion() -> None: """Test signal ROIs conversion to/from plot items""" execenv.print("==============================================") execenv.print("Test signal ROIs conversion to/from plot items") execenv.print("==============================================") obj = create_paracetamol_signal() for roi in create_test_signal_rois(obj): __conversion_methods(roi, obj) def test_image_roi_plotitem_conversion() -> None: """Test image ROIs conversion to/from plot items""" execenv.print("==============================================") execenv.print("Test image ROIs conversion to/from plot items") execenv.print("==============================================") obj = create_multigaussian_image() for roi in create_test_image_rois(obj): __conversion_methods(roi, obj) if __name__ == "__main__": test_signal_roi_plotitem_conversion() test_image_roi_plotitem_conversion() DataLab-1.1.0/datalab/tests/features/common/roieditor_unit_test.py000066400000000000000000000101121514014117600253040ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ ROI editor unit test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations import numpy as np from guidata.qthelpers import exec_dialog, qt_app_context from sigima.objects import ImageROI, create_image_roi, create_signal_roi from sigima.tests.data import create_multigaussian_image, create_paracetamol_signal from datalab.env import execenv from datalab.gui.roieditor import ImageROIEditor, SignalROIEditor from datalab.utils import qthelpers as qth def test_signal_roi_editor(screenshots: bool = False) -> None: """Test signal ROI editor""" cls = SignalROIEditor title = f"Testing {cls.__name__}" obj = create_paracetamol_signal() roi = create_signal_roi([36.4, 40.9], indices=False, title="Test ROI") obj.roi = roi with qt_app_context(exec_loop=False): execenv.print(title) roi_editor = cls(parent=None, obj=obj, mode="extract", size=(800, 600)) if screenshots: roi_editor.show() qth.grab_save_window(roi_editor) exec_dialog(roi_editor) def create_image_roi_example() -> ImageROI: """Create an example image ROI""" roi = create_image_roi("rectangle", [720, 720, 304, 304], title="Test ROI 1") roi.add_roi(create_image_roi("circle", [550, 650, 165], title="Test ROI 2")) roi.add_roi( create_image_roi( "polygon", [225, 75, 650, 175, 625, 475, 200, 675], title="Test ROI 3" ) ) return roi def test_image_roi_editor(screenshots: bool = False) -> None: """Test image ROI editor""" cls = ImageROIEditor title = f"Testing {cls.__name__}" obj = create_multigaussian_image() obj.roi = create_image_roi_example() with qt_app_context(exec_loop=False): execenv.print(title) for mode in ("extract", "apply"): execenv.print(f" mode={mode}") roi_editor = cls(parent=None, obj=obj, mode=mode, size=(800, 600)) if mode == "apply": # Clear the ROI roi_editor.remove_all_rois() if screenshots and mode == "extract": roi_editor.show() qth.grab_save_window(roi_editor) if exec_dialog(roi_editor): results = roi_editor.get_roieditor_results() if results is not None: edited_roi, modified = results if mode == "extract": # Test that the single ROIs are equal # pylint: disable=use-a-generator assert all( [ np.array_equal( sroi1.get_physical_coords(obj), sroi2.get_physical_coords(obj), ) for sroi1, sroi2 in zip( obj.roi.single_rois, edited_roi.single_rois ) ] ), "Single ROIs are not equal" execenv.print(" Single ROIs indice coordinates:") for sroi in edited_roi.single_rois: execenv.print( f" {sroi.title} ({sroi.__class__.__name__}):" ) c_i = [int(val) for val in sroi.get_indices_coords(obj)] c_p = [float(val) for val in sroi.get_physical_coords(obj)] execenv.print(f" Indices : {c_i}") execenv.print(f" Physical: {c_p}") else: # Test the use case where the ROI is cleared assert modified, "ROI is not modified" assert edited_roi.is_empty(), "ROI is not cleared" if __name__ == "__main__": test_signal_roi_editor(screenshots=True) test_image_roi_editor(screenshots=True) DataLab-1.1.0/datalab/tests/features/common/save_to_dir_app_test.py000066400000000000000000000140461514014117600254150ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Save to directory application test for screenshots: - Create signals and images - Open Save to directory dialog - Take screenshots for documentation """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations import warnings from typing import TYPE_CHECKING import guidata.config as gcfg import guidata.dataset as gds from datalab.env import execenv from datalab.gui.panel.base import SaveToDirectoryGUIParam from datalab.tests import datalab_test_app_context, helpers from datalab.tests.features.common.add_metadata_unit_test import ( create_test_images, create_test_signals, ) if TYPE_CHECKING: from datalab.gui.main import DLMainWindow def __save_signals_to_directory(win: DLMainWindow, screenshot: bool = False) -> None: """Save signals to directory helper function.""" execenv.print("Save to directory signal application test:") panel = win.signalpanel # Add test signals to the panel for sig in create_test_signals(): panel.add_object(sig) # Select all signals panel.objview.select_objects([1, 2, 3]) # Get selected objects objs = panel.objview.get_sel_objects(include_groups=True) extensions = ["csv", "txt", "h5sig"] with helpers.WorkdirRestoringTempDir() as tmpdir: # Temporarily disable validation to allow creating SaveToDirectoryGUIParam # with an empty directory path by default old_mode = gcfg.get_validation_mode() gcfg.set_validation_mode(gcfg.ValidationMode.DISABLED) try: # Create and configure the Save to directory dialog with warnings.catch_warnings(): warnings.simplefilter("ignore", category=gds.DataItemValidationWarning) param = SaveToDirectoryGUIParam(objs, extensions) # Configure example save parameters param.directory = "" if screenshot else tmpdir param.basename = "{index:03d}_{title}" param.extension = ".csv" param.overwrite = False param.update_preview() execenv.print(f" Preview: {param.preview}") assert "s001: 001_Sine Wave.csv" in param.preview execenv.print(" ✓ Save to directory parameter configured correctly") # Edit the parameter to show the dialog execenv.print(" Opening Save to directory dialog...") with execenv.context(screenshot=screenshot): param.edit( parent=win, wordwrap=False, object_name="s_save_to_directory" ) # Note: We don't actually call panel.save_to_directory() here as we're just # testing the dialog display for screenshots finally: gcfg.set_validation_mode(old_mode) execenv.print("==> Signal application test OK") def __save_images_to_directory(win: DLMainWindow, screenshot: bool = False) -> None: """Save images to directory helper function.""" execenv.print("Save to directory image test:") panel = win.imagepanel # Add test images to the panel for img in create_test_images(): panel.add_object(img) # Select all images panel.objview.select_objects([1, 2, 3]) # Get selected objects objs = panel.objview.get_sel_objects(include_groups=True) extensions = ["h5ima", "tiff", "png", "jpg"] with helpers.WorkdirRestoringTempDir() as tmpdir: # Temporarily disable validation to allow creating SaveToDirectoryGUIParam # with an empty directory path by default old_mode = gcfg.get_validation_mode() gcfg.set_validation_mode(gcfg.ValidationMode.DISABLED) try: # Create and configure the Save to directory dialog with warnings.catch_warnings(): warnings.simplefilter("ignore", category=gds.DataItemValidationWarning) param = SaveToDirectoryGUIParam(objs, extensions) # Configure example save parameters param.directory = "" if screenshot else tmpdir param.basename = "{title}_{index:04d}" param.extension = ".png" param.overwrite = False param.update_preview() execenv.print(f" Preview: {param.preview}") assert "i001: Random Noise_0001.png" in param.preview execenv.print(" ✓ Save to directory parameter configured correctly") # Edit the parameter to show the dialog execenv.print(" Opening Save to directory dialog...") with execenv.context(screenshot=screenshot): param.edit( parent=win, wordwrap=False, object_name="i_save_to_directory" ) # Note: We don't actually call panel.save_to_directory() here as we're just # testing the dialog display for screenshots finally: gcfg.set_validation_mode(old_mode) execenv.print("==> Image application test OK") def test_save_to_directory_signals() -> None: """Test Save to directory feature for signals.""" with datalab_test_app_context() as win: __save_signals_to_directory(win) def test_save_to_directory_images() -> None: """Test Save to directory feature for images.""" with datalab_test_app_context() as win: __save_images_to_directory(win) def save_to_directory_screenshots(): """Generate save to directory screenshots.""" with execenv.context(unattended=True): with datalab_test_app_context() as win: execenv.print("Save to directory screenshots test:") __save_signals_to_directory(win, screenshot=True) __save_images_to_directory(win, screenshot=True) execenv.print("==> All screenshot tests completed") if __name__ == "__main__": # test_save_to_directory_signals() # test_save_to_directory_images() save_to_directory_screenshots() DataLab-1.1.0/datalab/tests/features/common/save_to_dir_unit_test.py000066400000000000000000000407221514014117600256140ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Unit tests for SaveToDirectoryGUIParam class. This module thoroughly tests the `SaveToDirectoryGUIParam` class from `datalab.gui.panel.base`, including its GUI-specific features like extension choices from the IO registry, preview generation, and help dialog integration. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code from __future__ import annotations import os import os.path as osp import guidata.config as gcfg import numpy as np import pytest from sigima.objects import create_signal from datalab.env import execenv from datalab.gui.panel.base import SaveToDirectoryGUIParam from datalab.tests import helpers from datalab.tests.features.common.add_metadata_unit_test import ( create_test_images, create_test_signals, ) class TestSaveToDirectoryGUIParam: """Test class for SaveToDirectoryGUIParam with proper setup/teardown.""" @pytest.fixture(autouse=True) def setup_validation(self): """Disable guidata validation during tests. This prevents directory validation issues. """ old_mode = gcfg.get_validation_mode() gcfg.set_validation_mode(gcfg.ValidationMode.DISABLED) yield gcfg.set_validation_mode(old_mode) def test_preview_generation(self) -> None: """Test preview generation feature of SaveToDirectoryGUIParam.""" execenv.print(f"{self.test_preview_generation.__doc__}:") with helpers.WorkdirRestoringTempDir() as tmpdir: # Create test objects signals = create_test_signals() # Define extensions extensions = ["csv", "txt", "h5sig"] # Create GUI parameter instance p = SaveToDirectoryGUIParam(signals, extensions) # Set parameters p.directory = tmpdir p.basename = "{title}" p.extension = ".csv" # Update preview p.update_preview() # Check preview content expected_filenames = [ "1: Sine Wave.csv", "2: Cosine Wave.csv", "3: Exponential Decay.csv", ] preview_lines = p.preview.split("\n") assert len(preview_lines) == len(signals), ( f"Expected {len(signals)} preview lines, got {len(preview_lines)}" ) for i, expected in enumerate(expected_filenames): assert preview_lines[i] == expected, ( f"Expected '{expected}', got '{preview_lines[i]}'" ) execenv.print(f" ✓ Preview correctly shows: {preview_lines}") # Test preview with different pattern p.basename = "{index:03d}_{title}" p.update_preview() preview_lines = p.preview.split("\n") expected_filenames = [ "1: 001_Sine Wave.csv", "2: 002_Cosine Wave.csv", "3: 003_Exponential Decay.csv", ] for i, expected in enumerate(expected_filenames): assert preview_lines[i] == expected, ( f"Expected '{expected}', got '{preview_lines[i]}'" ) execenv.print(f" ✓ Preview with pattern: {preview_lines}") execenv.print(f"{self.test_preview_generation.__doc__}: OK") def test_filename_building(self) -> None: """Test filename building with various patterns via SaveToDirectoryGUIParam.""" execenv.print(f"{self.test_filename_building.__doc__}:") with helpers.WorkdirRestoringTempDir() as tmpdir: # Create test signals with specific metadata signals = create_test_signals() extensions = ["csv", "txt"] # Test different basename patterns test_cases = [ ( "{title}", ["Sine Wave.csv", "Cosine Wave.csv", "Exponential Decay.csv"], ), ( "{index:03d}_{title}", [ "001_Sine Wave.csv", "002_Cosine Wave.csv", "003_Exponential Decay.csv", ], ), ( "signal_{count}_{index}", ["signal_3_1.csv", "signal_3_2.csv", "signal_3_3.csv"], ), ( "{metadata[type]}_signal", [ "sine_signal.csv", "cosine_signal.csv", "exponential_signal.csv", ], ), ] for basename_pattern, expected_files in test_cases: execenv.print(f" Testing pattern: {basename_pattern}") # Create GUI parameter p = SaveToDirectoryGUIParam(signals, extensions) # Configure parameters p.directory = tmpdir p.basename = basename_pattern p.extension = ".csv" p.overwrite = True # Build filenames filenames = p.build_filenames(signals) execenv.print(f" Generated filenames: {filenames}") # Verify expected filenames assert filenames == expected_files, ( f"Expected {expected_files}, got {filenames}" ) execenv.print(" ✓ Pattern matched expected filenames") execenv.print(f"{self.test_filename_building.__doc__}: OK") def test_collision_handling(self) -> None: """Test collision handling in SaveToDirectoryGUIParam.""" execenv.print(f"{self.test_collision_handling.__doc__}:") with helpers.WorkdirRestoringTempDir() as tmpdir: # Create test signals with duplicate titles signals = [] x = np.linspace(0, 10, 100) for i in range(3): y = np.sin(x + i) signal = create_signal("Test Signal", x=x, y=y, metadata={"index": i}) signals.append(signal) extensions = ["h5sig"] # Create GUI parameter p = SaveToDirectoryGUIParam(signals, extensions) # Test collision handling without overwrite p.directory = tmpdir p.basename = "{title}" p.extension = ".h5sig" p.overwrite = False # Build filenames - should generate unique names filenames = p.build_filenames(signals) expected_files = [ "Test Signal.h5sig", "Test Signal_1.h5sig", "Test Signal_2.h5sig", ] assert filenames == expected_files, ( f"Expected {expected_files}, got {filenames}" ) execenv.print(f" ✓ Collision handling: {filenames}") # Create first file manually to test existing file collision first_file_path = osp.join(tmpdir, "Test Signal.h5sig") with open(first_file_path, "w", encoding="utf-8") as f: f.write("test") # Build filenames again - first file exists, should be skipped filenames = p.build_filenames(signals[:1]) expected_files = ["Test Signal_1.h5sig"] assert filenames == expected_files, ( f"Expected {expected_files} (avoiding existing file), got {filenames}" ) execenv.print(f" ✓ Existing file collision: {filenames}") # Test with overwrite enabled p.overwrite = True filenames = p.build_filenames(signals[:1]) expected_files = ["Test Signal.h5sig"] assert filenames == expected_files, ( f"Expected {expected_files} (overwrite enabled), got {filenames}" ) execenv.print(f" ✓ Overwrite mode: {filenames}") execenv.print(f"{self.test_collision_handling.__doc__}: OK") def test_metadata_access(self) -> None: """Test accessing metadata fields in basename patterns.""" execenv.print(f"{self.test_metadata_access.__doc__}:") with helpers.WorkdirRestoringTempDir() as tmpdir: # Create signals with rich metadata signals = [] x = np.linspace(0, 10, 100) metadata_list = [ {"experiment": "exp001", "sensor": "A", "temperature": "25C"}, {"experiment": "exp002", "sensor": "B", "temperature": "30C"}, {"experiment": "exp003", "sensor": "C", "temperature": "35C"}, ] for i, metadata in enumerate(metadata_list): y = np.sin(x + i) signal = create_signal(f"Signal {i + 1}", x=x, y=y, metadata=metadata) signals.append(signal) extensions = ["csv"] # Create GUI parameter p = SaveToDirectoryGUIParam(signals, extensions) # Test accessing nested metadata in basename pattern p.directory = tmpdir p.basename = ( "{metadata[experiment]}_{metadata[sensor]}_{metadata[temperature]}" ) p.extension = ".csv" p.overwrite = False # Build and verify filenames filenames = p.build_filenames(signals) expected_files = [ "exp001_A_25C.csv", "exp002_B_30C.csv", "exp003_C_35C.csv", ] assert filenames == expected_files, ( f"Expected {expected_files}, got {filenames}" ) execenv.print(f" ✓ Metadata access in basename patterns: {filenames}") execenv.print(f"{self.test_metadata_access.__doc__}: OK") def test_filepath_obj_pairs(self) -> None: """Test generate_filepath_obj_pairs method.""" execenv.print(f"{self.test_filepath_obj_pairs.__doc__}:") with helpers.WorkdirRestoringTempDir() as tmpdir: # Create test signals signals = create_test_signals() extensions = ["csv"] # Create GUI parameter p = SaveToDirectoryGUIParam(signals, extensions) p.directory = tmpdir p.basename = "{title}" p.extension = ".csv" p.overwrite = False # Generate filepath-object pairs pairs = list(p.generate_filepath_obj_pairs(signals)) # Verify we got the right number of pairs assert len(pairs) == len(signals), ( f"Expected {len(signals)} pairs, got {len(pairs)}" ) # Verify each pair expected_filenames = [ "Sine Wave.csv", "Cosine Wave.csv", "Exponential Decay.csv", ] for i, (filepath, obj) in enumerate(pairs): expected_path = osp.join(tmpdir, expected_filenames[i]) assert filepath == expected_path, ( f"Expected path '{expected_path}', got '{filepath}'" ) assert obj is signals[i], ( f"Expected object {signals[i].title}, got {obj.title}" ) execenv.print( f" ✓ Pair {i + 1}: {osp.basename(filepath)} -> {obj.title}" ) execenv.print(f"{self.test_filepath_obj_pairs.__doc__}: OK") def test_with_images(self) -> None: """Test SaveToDirectoryGUIParam with image objects.""" execenv.print(f"{self.test_with_images.__doc__}:") with helpers.WorkdirRestoringTempDir() as tmpdir: # Create test images images = create_test_images() # Image extensions extensions = ["h5ima", "tiff", "png"] # Create GUI parameter p = SaveToDirectoryGUIParam(images, extensions) p.directory = tmpdir p.basename = "{index:02d}_{title}" p.extension = ".png" p.overwrite = False # Build filenames filenames = p.build_filenames(images) expected_files = [ "01_Random Noise.png", "02_Gaussian Pattern.png", "03_Checkerboard.png", ] assert filenames == expected_files, ( f"Expected {expected_files}, got {filenames}" ) execenv.print(f" ✓ Image filenames: {filenames}") # Test preview p.update_preview() preview_lines = p.preview.split("\n") expected_preview = [ "1: 01_Random Noise.png", "2: 02_Gaussian Pattern.png", "3: 03_Checkerboard.png", ] for i, expected in enumerate(expected_preview): assert preview_lines[i] == expected, ( f"Expected '{expected}', got '{preview_lines[i]}'" ) execenv.print(f" ✓ Image preview: {preview_lines}") execenv.print(f"{self.test_with_images.__doc__}: OK") def test_edge_cases(self) -> None: """Test SaveToDirectoryGUIParam edge cases.""" execenv.print(f"{self.test_edge_cases.__doc__}:") with helpers.WorkdirRestoringTempDir() as tmpdir: # Test with empty objects list p = SaveToDirectoryGUIParam([], ["csv"]) p.directory = tmpdir p.basename = "{title}" p.extension = ".csv" # Should handle empty list gracefully filenames = p.build_filenames([]) assert not filenames, f"Expected empty list, got {filenames}" execenv.print(" ✓ Handled empty objects list") # Update preview with empty list p.update_preview() assert p.preview == "", f"Expected empty preview, got '{p.preview}'" execenv.print(" ✓ Empty preview is blank") # Test with signals having special characters in titles signals = [] x = np.linspace(0, 10, 100) y = np.sin(x) special_titles = [ "Signal with spaces", "Signal_with_underscores", "Signal-with-dashes", ] for title in special_titles: signal = create_signal(title, x=x, y=y) signals.append(signal) p = SaveToDirectoryGUIParam(signals, ["csv"]) p.directory = tmpdir p.basename = "{title}" p.extension = ".csv" # Test filename generation with special characters filenames = p.build_filenames(signals) execenv.print(f" Filenames with special chars: {filenames}") # All filenames should be valid for filename in filenames: assert "/" not in filename and "\\" not in filename, ( f"Invalid filename: {filename}" ) execenv.print(" ✓ Handled special characters in titles") # Test with very long directory path long_subdir = "a" * 50 long_dir = osp.join(tmpdir, long_subdir) os.makedirs(long_dir, exist_ok=True) p.directory = long_dir p.basename = "test" p.extension = ".csv" # Should handle long paths pairs = list(p.generate_filepath_obj_pairs(signals[:1])) expected_path = osp.join(long_dir, "test.csv") assert pairs[0][0] == expected_path, ( f"Expected path '{expected_path}', got '{pairs[0][0]}'" ) execenv.print(" ✓ Handled long directory path") execenv.print(f"{self.test_edge_cases.__doc__}: OK") def test_help_button(self) -> None: """Test help button callback of SaveToDirectoryGUIParam.""" execenv.print(f"{self.test_help_button.__doc__}:") # Create GUI parameter signals = create_test_signals() p = SaveToDirectoryGUIParam(signals, ["csv"]) # The help button callback should not raise an error # Note: We can't fully test the dialog display in automated tests, # but we can verify the callback exists and is callable assert hasattr(p, "on_button_click"), ( "GUI param should have on_button_click method" ) execenv.print(" ✓ Help button callback exists") # Verify the help callback is properly configured # (In a real GUI context, this would open a dialog) execenv.print(" ✓ Help callback is callable") execenv.print(f"{self.test_help_button.__doc__}: OK") if __name__ == "__main__": pytest.main([__file__, "-v"]) DataLab-1.1.0/datalab/tests/features/common/stat_app_test.py000066400000000000000000000022231514014117600240640ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Statistics test Testing the following: - Create a signal - Compute statistics on signal and show results - Create an image - Compute statistics on image and show results """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from sigima.tests.common.stat_unit_test import ( create_reference_image, create_reference_signal, ) from datalab.tests import datalab_test_app_context, take_plotwidget_screenshot def test_stat_app(): """Run statistics application test scenario""" with datalab_test_app_context() as win: # === Signal statistics test === panel = win.signalpanel panel.add_object(create_reference_signal()) take_plotwidget_screenshot(panel, "stat_test") panel.processor.run_feature("stats") # === Image statistics test === panel = win.imagepanel panel.add_object(create_reference_image()) take_plotwidget_screenshot(panel, "stat_test") panel.processor.run_feature("stats") if __name__ == "__main__": test_stat_app() DataLab-1.1.0/datalab/tests/features/common/stats_tools_unit_test.py000066400000000000000000000050031514014117600256650ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Custom curve and image stats tool unit test. PlotPy's statistics tools (`plotpy.tools.XCurveStatsTool`, `plotpy.tools.YCurveStatsTool`, and `plotpy.tools.ImageStatsTool` are customized in `datalab.gui.docks` module). """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations import numpy as np import plotpy from guidata.qthelpers import exec_dialog, qt_app_context from packaging.version import Version from plotpy.constants import PlotType from plotpy.tests.unit.utils import drag_mouse from plotpy.tools import CurveStatsTool, ImageStatsTool, YRangeCursorTool from qtpy import QtWidgets as QW from sigima.objects import ImageObj, SignalObj from sigima.tests.data import create_multigaussian_image, create_paracetamol_signal from datalab.adapters_plotpy import create_adapter_from_object from datalab.gui.docks import DataLabPlotWidget def simulate_stats_tool( plot_type: PlotType, obj: SignalObj | ImageObj, x_path: np.ndarray, y_path: np.ndarray, ) -> None: """Simulate stats tool with a custom signal or image.""" widget = DataLabPlotWidget(plot_type) plot = widget.get_plot() item = create_adapter_from_object(obj).make_item() plot.add_item(item) plot.set_active_item(item) item.unselect() if plot_type == PlotType.CURVE: classes = [CurveStatsTool, YRangeCursorTool] else: classes = [ImageStatsTool] for klass in classes: stattool = widget.get_manager().get_tool(klass) stattool.activate() if Version(plotpy.__version__) < Version("2.4"): qapp = QW.QApplication.instance() drag_mouse(widget, qapp, x_path, y_path) else: drag_mouse(widget, x_path, y_path) return widget def test_stats_tool() -> None: """Test `XCurveStatsTool` with a custom signal.""" sig = create_paracetamol_signal() ima = create_multigaussian_image() with qt_app_context(): dlg = QW.QDialog() dlg.setWindowTitle("Stats Tool Test") dlg.resize(1200, 600) swidget = simulate_stats_tool(PlotType.CURVE, sig, [0.01, 0.02], [0.7, 0.9]) iwidget = simulate_stats_tool(PlotType.IMAGE, ima, [0.2, 0.8], [0.9, 0.95]) hlayout = QW.QHBoxLayout() hlayout.addWidget(swidget) hlayout.addWidget(iwidget) dlg.setLayout(hlayout) exec_dialog(dlg) if __name__ == "__main__": test_stats_tool() DataLab-1.1.0/datalab/tests/features/common/table_results_app_test.py000066400000000000000000000030751514014117600257670ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Result properties application test """ # guitest: show from __future__ import annotations import numpy as np from sigima.objects import NormalDistribution1DParam from sigima.tests import data as test_data from datalab.adapters_metadata.table_adapter import TableAdapter from datalab.tests import datalab_test_app_context def create_image_with_table_results(): """Create test image with table results""" image = test_data.create_multigaussian_image() for table in test_data.generate_table_results(): TableAdapter(table).add_to(image) return image def test_table_results(): """Result properties application test""" obj1 = test_data.create_sincos_image() obj2 = create_image_with_table_results() with datalab_test_app_context() as win: panel = win.signalpanel noiseparam = NormalDistribution1DParam() for sigma in np.linspace(0.0, 0.5, 11): noiseparam.sigma = sigma sig = test_data.create_noisy_signal(noiseparam=noiseparam) panel.add_object(sig) panel.processor.run_feature("dynamic_parameters") panel.processor.run_feature("contrast") panel.objview.selectAll() panel.show_results() panel.plot_results() win.set_current_panel("image") panel = win.imagepanel for obj in (obj1, obj2): panel.add_object(obj) panel.show_results() panel.plot_results() if __name__ == "__main__": test_table_results() DataLab-1.1.0/datalab/tests/features/common/textimport_unit_test.py000066400000000000000000000124021514014117600255270ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Import wizard test """ # guitest: show import numpy as np from guidata.dataset import update_dataset from guidata.qthelpers import exec_dialog, qt_app_context from qtpy import QtWidgets as QW from sigima.objects import ImageObj, SignalObj from datalab.env import execenv from datalab.tests import helpers from datalab.widgets.textimport import ImageParam, SignalParam, TextImportWizard def file_to_clipboard(filename: str) -> None: """Copy file content to clipboard""" for encoding in ("utf-8", "latin-1"): try: with open(filename, "r", encoding=encoding) as file: text = file.read() QW.QApplication.clipboard().setText(text) return except UnicodeDecodeError: pass TEST_SIGNAL_PARAM = SignalParam.create( xlabel="X Signal axis", ylabel="Y Signal axis", xunit="X Signal unit", yunit="Y Signal unit", ) TEST_IMAGE_PARAM = ImageParam.create( xlabel="X Image axis", ylabel="Y Image axis", zlabel="Z Image axis", xunit="X Image unit", yunit="Y Image unit", zunit="Z Image unit", ) def test_import_wizard(): """Test the import wizard""" with qt_app_context(): for destination, fname, otype in ( ("image", "fiber.txt", ImageObj), ("signal", "multiple_curves.csv", SignalObj), ("signal", "paracetamol.txt", SignalObj), ("signal", "spectrum.mca", SignalObj), ): path = helpers.get_test_fnames(fname)[0] if not execenv.unattended: # Do not test clipboard in unattended mode, would fail: # - Windows: OleSetClipboard: Failed to set mime data (text/plain) # on clipboard: COM error 0xffffffff800401d0 # - Linux: QXcbClipboard: Unable to receive an event from the clipboard # manager in a reasonable time file_to_clipboard(path) wizard = TextImportWizard(destination=destination) if execenv.unattended: wizard.show() srcpge = wizard.source_page # `SourcePage` srcpge.param.path = path srcpge.param_widget.get() wizard.go_to_next_page() datapge = wizard.data_page # `DataPreviewPage` n_objs = 1 if destination == "image": datapge.param.dtype_str = "uint8" else: datapge.param.dtype_str = "float32" if fname == "fiber.txt": datapge.param.delimiter_choice = " " elif fname == "multiple_curves.csv": datapge.param.delimiter_choice = ";" datapge.param.skip_rows = 1 n_objs = 5 elif fname == "spectrum.mca": datapge.param.skip_rows = 18 datapge.param.max_rows = 2048 datapge.param.header = None datapge.param.first_col_is_x = False else: datapge.param.skip_rows = 10 datapge.param_widget.get() datapge.update_preview() wizard.go_to_next_page() # Go to `GraphicalRepresentationPage` wizard.go_to_previous_page() # For test purpose only wizard.go_to_next_page() # Go to `GraphicalRepresentationPage` labels_page = wizard.labels_page # `LabelsPage` if destination == "image": assert isinstance(labels_page.param, ImageParam) update_dataset(labels_page.param, TEST_IMAGE_PARAM) else: assert isinstance(labels_page.param, SignalParam) update_dataset(labels_page.param, TEST_SIGNAL_PARAM) wizard.go_to_next_page() # Go to `LabelsPage` wizard.accept() objs = wizard.get_objs() assert len(objs) == n_objs assert isinstance(objs[0], otype) # Check that the parameters are set correctly for obj in objs: if isinstance(obj, ImageObj): assert obj.data.dtype == np.uint8 assert obj.xlabel == TEST_IMAGE_PARAM.xlabel assert obj.ylabel == TEST_IMAGE_PARAM.ylabel assert obj.zlabel == TEST_IMAGE_PARAM.zlabel assert obj.xunit == TEST_IMAGE_PARAM.xunit assert obj.yunit == TEST_IMAGE_PARAM.yunit assert obj.zunit == TEST_IMAGE_PARAM.zunit elif isinstance(obj, SignalObj): assert obj.data.dtype == np.float32 assert obj.xlabel == TEST_SIGNAL_PARAM.xlabel assert obj.ylabel == TEST_SIGNAL_PARAM.ylabel assert obj.xunit == TEST_SIGNAL_PARAM.xunit assert obj.yunit == TEST_SIGNAL_PARAM.yunit elif exec_dialog(wizard): for obj in wizard.get_objs(): execenv.print(obj) if __name__ == "__main__": test_import_wizard() DataLab-1.1.0/datalab/tests/features/common/update_tree_robustness_test.py000066400000000000000000000045571514014117600270550ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Update tree robustness test --------------------------- This test verifies that the object tree view handles edge cases gracefully, specifically when the tree becomes out of sync with the model. This is a regression test for a bug where update_tree() crashed with AttributeError: 'NoneType' object has no attribute 'setText' when an object existed in the model but had no corresponding tree item. The fix makes update_tree() defensive by calling populate_tree() if an item is not found. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import numpy as np import sigima.objects from datalab.tests import datalab_test_app_context def test_update_tree_with_model_tree_desync(): """Test that update_tree handles model/tree desynchronization gracefully. This test simulates a scenario where an object exists in the model but doesn't have a corresponding tree item, which previously caused an AttributeError in update_tree(). The fix ensures update_tree() detects this and calls populate_tree() to restore consistency. """ with datalab_test_app_context(console=False) as win: win.set_current_panel("image") # Create and add a base image data = np.random.rand(100, 100).astype(np.float64) img1 = sigima.objects.create_image("Base Image", data) win.imagepanel.add_object(img1) # Create a second image and add it to the MODEL only (not to the tree) # This simulates a desync between model and tree data2 = np.random.rand(100, 100).astype(np.float64) img2 = sigima.objects.create_image("Second Image", data2) # Get current group ID group_id = win.imagepanel.objview.get_current_group_id() # Add to model ONLY (bypassing add_object_item which adds to tree) win.imagepanel.objmodel.add_object(img2, group_id) # Now the model has 2 objects but the tree only shows 1 # Calling update_tree() should NOT crash # With the fix, it should call populate_tree() to resync # This is the call that previously caused: # AttributeError: 'NoneType' object has no attribute 'setText' win.imagepanel.objview.update_tree() # Verify objects are now in sync assert len(win.imagepanel) == 2 DataLab-1.1.0/datalab/tests/features/common/uuid_preservation_test.py000066400000000000000000000277721514014117600260400ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Test UUID preservation when reopening workspace This test verifies that when a workspace is saved to HDF5, cleared, and reopened, the object UUIDs are preserved so that processing parameter references (source_uuid, source_uuids) remain valid. This ensures features like "Show source" and "Recompute" continue to work after reopening a workspace. Related issue: Processing tab features don't work after reopening workspace because UUIDs are regenerated. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... import os.path as osp from guidata.qthelpers import qt_app_context from sigima.params import GaussianParam from datalab.gui.processor.base import PROCESSING_PARAMETERS_OPTION from datalab.objectmodel import get_uuid from datalab.tests import datalab_test_app_context, helpers def test_uuid_preservation_signals(): """Test that signal UUIDs are preserved when reopening workspace""" with qt_app_context(): with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context(console=False) as win: panel = win.signalpanel processor = panel.processor # Step 1: Create a test signal panel.new_object() signal = panel.objview.get_current_object() assert signal is not None original_signal_uuid = get_uuid(signal) # Step 2: Apply a processing operation with parameters param = GaussianParam.create(sigma=2.0) processor.compute_1_to_1( processor.get_feature("gaussian_filter").function, param=param, title="Gaussian filter", ) # Step 3: Get the filtered signal and verify metadata filtered_sig = panel.objview.get_current_object() assert filtered_sig is not None original_filtered_uuid = get_uuid(filtered_sig) # Verify processing metadata was stored correctly assert ( PROCESSING_PARAMETERS_OPTION in filtered_sig.get_metadata_options() ) option_dict = filtered_sig.get_metadata_option( PROCESSING_PARAMETERS_OPTION ) assert option_dict["source_uuid"] == original_signal_uuid assert option_dict["func_name"] == "gaussian_filter" # Step 4: Save workspace to HDF5 fname = osp.join(tmpdir, "test_uuid_preservation.h5") win.save_to_h5_file(fname) # Step 5: Remove all objects (clear workspace) panel.remove_all_objects() assert len(panel.objmodel) == 0 # Step 6: Reopen workspace win.open_h5_files([fname], import_all=True, reset_all=True) # Step 7: Verify UUIDs are preserved signal_after = panel.objmodel.get_object_from_number(1) filtered_sig_after = panel.objmodel.get_object_from_number(2) signal_uuid_after = get_uuid(signal_after) filtered_uuid_after = get_uuid(filtered_sig_after) # CRITICAL: UUIDs must be preserved after reload assert signal_uuid_after == original_signal_uuid, ( f"Signal UUID changed after reload: " f"{original_signal_uuid} -> {signal_uuid_after}" ) assert filtered_uuid_after == original_filtered_uuid, ( f"Filtered signal UUID changed after reload: " f"{original_filtered_uuid} -> {filtered_uuid_after}" ) # Step 8: Verify processing parameters still reference correct source option_dict_after = filtered_sig_after.get_metadata_option( PROCESSING_PARAMETERS_OPTION ) assert option_dict_after["source_uuid"] == signal_uuid_after, ( f"Processing parameter source_uuid doesn't match: " f"{option_dict_after['source_uuid']} != {signal_uuid_after}" ) # Step 9: Verify "Show source" feature would work # (by checking that the source object can be found) source_obj = win.find_object_by_uuid(option_dict_after["source_uuid"]) assert source_obj is signal_after, ( "Cannot find source object by UUID - " "'Show source' feature would fail" ) def test_uuid_preservation_images(): """Test that image UUIDs are preserved when reopening workspace""" with qt_app_context(): with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context(console=False) as win: panel = win.imagepanel processor = panel.processor # Step 1: Create a test image panel.new_object() image = panel.objview.get_current_object() assert image is not None original_image_uuid = get_uuid(image) # Step 2: Apply a processing operation param = GaussianParam.create(sigma=1.5) processor.compute_1_to_1( processor.get_feature("gaussian_filter").function, param=param, title="Gaussian filter", ) # Step 3: Get the filtered image and verify metadata filtered_img = panel.objview.get_current_object() assert filtered_img is not None original_filtered_uuid = get_uuid(filtered_img) # Verify processing metadata option_dict = filtered_img.get_metadata_option( PROCESSING_PARAMETERS_OPTION ) assert option_dict["source_uuid"] == original_image_uuid # Step 4: Save, clear, and reopen workspace fname = osp.join(tmpdir, "test_uuid_preservation_images.h5") win.save_to_h5_file(fname) panel.remove_all_objects() win.open_h5_files([fname], import_all=True, reset_all=True) # Step 5: Verify UUIDs are preserved image_after = panel.objmodel.get_object_from_number(1) filtered_img_after = panel.objmodel.get_object_from_number(2) assert get_uuid(image_after) == original_image_uuid assert get_uuid(filtered_img_after) == original_filtered_uuid # Step 6: Verify processing parameters still work option_dict_after = filtered_img_after.get_metadata_option( PROCESSING_PARAMETERS_OPTION ) assert option_dict_after["source_uuid"] == get_uuid(image_after) source_obj = win.find_object_by_uuid(option_dict_after["source_uuid"]) assert source_obj is image_after def test_uuid_regeneration_on_import(): """Test that UUIDs are regenerated only when conflicts exist during import""" with qt_app_context(): with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context(console=False) as win: panel = win.signalpanel # Step 1: Create and save a signal panel.new_object() signal1 = panel.objview.get_current_object() uuid1 = get_uuid(signal1) fname = osp.join(tmpdir, "test_import.h5") win.save_to_h5_file(fname) # Step 2: Import the same file (reset_all=False, import_all=True) # This should regenerate UUID only for the conflicting object win.open_h5_files([fname], import_all=True, reset_all=False) # Step 3: Verify we now have 2 signals with different UUIDs assert len(panel.objmodel) == 2 signal1_after = panel.objmodel.get_object_from_number(1) signal2_imported = panel.objmodel.get_object_from_number(2) uuid1_after = get_uuid(signal1_after) uuid2 = get_uuid(signal2_imported) # Original signal should keep its UUID assert uuid1_after == uuid1 # Imported signal should have a NEW UUID (different from original) assert uuid2 != uuid1, ( "Imported signal should have a new UUID, " f"but got same as original: {uuid2} == {uuid1}" ) # Step 4: Import the same file again win.open_h5_files([fname], import_all=True, reset_all=False) # Step 5: Verify we now have 3 signals, all with different UUIDs assert len(panel.objmodel) == 3 signal3_imported = panel.objmodel.get_object_from_number(3) uuid3 = get_uuid(signal3_imported) # Third signal should have yet another different UUID assert uuid3 != uuid1 assert uuid3 != uuid2 assert len({uuid1, uuid2, uuid3}) == 3, ( "All three signals should have unique UUIDs" ) def test_uuid_preservation_empty_workspace(): """Test that UUIDs are preserved when opening HDF5 in empty workspace""" with qt_app_context(): with helpers.WorkdirRestoringTempDir() as tmpdir: # Create and save a workspace with processing history with datalab_test_app_context(console=False) as win1: panel = win1.signalpanel processor = panel.processor # Create signal and apply processing panel.new_object() signal = panel.objview.get_current_object() original_uuid = get_uuid(signal) param = GaussianParam.create(sigma=2.0) processor.compute_1_to_1( processor.get_feature("gaussian_filter").function, param=param, title="Gaussian filter", ) filtered_sig = panel.objview.get_current_object() original_filtered_uuid = get_uuid(filtered_sig) # Save workspace fname = osp.join(tmpdir, "test_empty_workspace.h5") win1.save_to_h5_file(fname) # Open the file in a NEW empty workspace (simulating startup) with datalab_test_app_context(console=False) as win2: panel = win2.signalpanel # Workspace is empty - UUIDs should be preserved (reset_all=True) # Note: reset_all is None, so it should auto-detect empty workspace win2.open_h5_files([fname], import_all=True, reset_all=None) # Verify UUIDs are preserved signal_loaded = panel.objmodel.get_object_from_number(1) filtered_loaded = panel.objmodel.get_object_from_number(2) assert get_uuid(signal_loaded) == original_uuid, ( "Signal UUID should be preserved when opening in empty workspace" ) assert get_uuid(filtered_loaded) == original_filtered_uuid, ( "Filtered signal UUID should be preserved " "when opening in empty workspace" ) # Verify processing parameters still work option_dict = filtered_loaded.get_metadata_option( PROCESSING_PARAMETERS_OPTION ) assert option_dict["source_uuid"] == get_uuid(signal_loaded) source_obj = win2.find_object_by_uuid(option_dict["source_uuid"]) assert source_obj is signal_loaded if __name__ == "__main__": test_uuid_preservation_signals() test_uuid_preservation_images() test_uuid_regeneration_on_import() test_uuid_preservation_empty_workspace() print("All UUID preservation tests passed!") DataLab-1.1.0/datalab/tests/features/common/worker_unit_test.py000066400000000000000000000347701514014117600246350ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Worker and WorkerStateMachine Unit Tests ======================================== This module contains comprehensive unit tests for the Worker class and WorkerStateMachine class in DataLab's processor module. Tests cover: 1. State machine functionality independently from Worker class 2. Worker class nominal operations 3. Exception handling in computations 4. Process pool restart mechanism for long-running/cancelled computations """ # pylint: disable=protected-access import time from unittest.mock import Mock, patch import pytest from guidata.qthelpers import qt_app_context from qtpy import QtWidgets as QW import datalab.gui.processor.base as base_module from datalab.gui.processor.base import Worker, WorkerState, WorkerStateMachine from datalab.gui.processor.catcher import CompOut class TestWorkerStateMachine: """Test suite for WorkerStateMachine class - independent from Worker class.""" def test_initial_state(self): """Test that state machine starts in IDLE state.""" sm = WorkerStateMachine() assert sm.current_state == WorkerState.IDLE def test_valid_transitions(self): """Test all valid state transitions.""" sm = WorkerStateMachine() # IDLE -> STARTING sm.transition_to(WorkerState.STARTING) assert sm.current_state == WorkerState.STARTING # STARTING -> RUNNING sm.transition_to(WorkerState.RUNNING) assert sm.current_state == WorkerState.RUNNING # RUNNING -> FINISHED sm.transition_to(WorkerState.FINISHED) assert sm.current_state == WorkerState.FINISHED # FINISHED -> IDLE (complete cycle) sm.transition_to(WorkerState.IDLE) assert sm.current_state == WorkerState.IDLE def test_same_state_transition_allowed(self): """Test that transitioning to the same state is a no-op.""" sm = WorkerStateMachine() # Should not raise exception sm.transition_to(WorkerState.IDLE) assert sm.current_state == WorkerState.IDLE # Same for other states sm.transition_to(WorkerState.STARTING) sm.transition_to(WorkerState.STARTING) assert sm.current_state == WorkerState.STARTING def test_invalid_transitions(self): """Test that invalid transitions raise ValueError.""" sm = WorkerStateMachine() # IDLE can only go to STARTING with pytest.raises( ValueError, match="Invalid transition from WorkerState.IDLE to WorkerState.RUNNING", ): sm.transition_to(WorkerState.RUNNING) with pytest.raises( ValueError, match="Invalid transition from WorkerState.IDLE to WorkerState.FINISHED", ): sm.transition_to(WorkerState.FINISHED) # STARTING can only go to RUNNING sm.transition_to(WorkerState.STARTING) with pytest.raises( ValueError, match="Invalid transition from WorkerState.STARTING to WorkerState.IDLE", ): sm.transition_to(WorkerState.IDLE) with pytest.raises(ValueError, match="Invalid transition.*STARTING.*FINISHED"): sm.transition_to(WorkerState.FINISHED) def test_reset_to_idle_from_any_state(self): """Test that reset_to_idle works from any state.""" sm = WorkerStateMachine() # Test from each state states_to_test = [ WorkerState.IDLE, WorkerState.STARTING, WorkerState.RUNNING, WorkerState.FINISHED, ] for state in states_to_test: # Force state (bypassing transition validation for test) sm._current_state = state sm.reset_to_idle() assert sm.current_state == WorkerState.IDLE # Module-level functions for pickle compatibility in multiprocessing def dummy_successful_computation(x: int, y: int) -> int: """Simple computation that returns sum of two numbers.""" return x + y def dummy_failing_computation() -> None: """Computation that raises an exception.""" raise ValueError("Test exception from computation") def dummy_infinite_computation() -> None: """Computation that runs forever (until process is killed).""" while True: time.sleep(0.1) # Simulate work class TestWorker: """Test suite for Worker class functionality.""" def setup_method(self): """Setup for each test method - ensure clean pool state.""" # Clean up any existing pool Worker.terminate_pool(wait=False) Worker.create_pool() def teardown_method(self): """Cleanup after each test method.""" # Clean up pool to avoid interference between tests Worker.terminate_pool(wait=False) def test_worker_initial_state(self): """Test worker starts in correct initial state.""" worker = Worker() assert worker.state_machine.current_state == WorkerState.IDLE assert worker.asyncresult is None def test_worker_pool_management(self): """Test pool creation and termination.""" # Test static methods work Worker.terminate_pool() Worker.create_pool() # Verify pool exists by checking global POOL variable assert base_module.POOL is not None # Test termination Worker.terminate_pool() # Note: POOL global variable is set to None in terminate_pool def test_nominal_computation_case(self): """Test Case 2a: Nominal case - start computation, wait, get result.""" with qt_app_context(): worker = Worker() # Start computation worker.run(dummy_successful_computation, (10, 5)) assert worker.state_machine.current_state == WorkerState.RUNNING # Wait for completion while not worker.is_computation_finished(): QW.QApplication.processEvents() time.sleep(0.001) # Small sleep to avoid busy waiting # Should be finished assert worker.state_machine.current_state == WorkerState.FINISHED assert worker.has_result_available() # Get result result = worker.get_result() assert isinstance(result, CompOut) assert result.result == 15 # 10 + 5 assert result.error_msg is None assert worker.state_machine.current_state == WorkerState.IDLE def test_computation_exception_case(self): """Test Case 2b: Computation raises an exception.""" # Note: In unattended test mode, exceptions are re-raised by wng_err_func # This test validates that the worker can handle exceptions that bubble up with qt_app_context(): worker = Worker() # Start computation that will fail worker.run(dummy_failing_computation, ()) assert worker.state_machine.current_state == WorkerState.RUNNING # In unattended mode, the exception will be raised when getting result # Wait for computation to "complete" (which means ready to raise exception) while not worker.is_computation_finished(): QW.QApplication.processEvents() time.sleep(0.001) # Should be marked as finished (even though it will raise on get_result) assert worker.state_machine.current_state == WorkerState.FINISHED assert worker.has_result_available() # In unattended mode, get_result will raise the original exception with pytest.raises(ValueError, match="Test exception from computation"): worker.get_result() # After exception, should still be back to IDLE (cleanup happened) assert worker.state_machine.current_state == WorkerState.IDLE def test_computation_cancellation_and_pool_restart(self): """Test Case 2c: Long computation with cancellation and pool restart.""" with qt_app_context(): worker = Worker() # Start infinite computation worker.run(dummy_infinite_computation, ()) assert worker.state_machine.current_state == WorkerState.RUNNING # Let it run briefly to ensure it's actually started start_time = time.time() while time.time() - start_time < 0.1: # 100ms QW.QApplication.processEvents() time.sleep(0.001) # Verify it's still running (not finished) if worker.is_computation_finished(): pytest.fail("Infinite computation finished unexpectedly") # Simulate user cancellation (like progress dialog cancel) worker.restart() assert worker.state_machine.current_state == WorkerState.IDLE assert worker.asyncresult is None # Verify pool was restarted by running a normal computation worker.run(dummy_successful_computation, (20, 22)) assert worker.state_machine.current_state == WorkerState.RUNNING # Wait for this computation to complete while not worker.is_computation_finished(): QW.QApplication.processEvents() time.sleep(0.001) # Should complete successfully, proving pool restart worked assert worker.state_machine.current_state == WorkerState.FINISHED result = worker.get_result() assert result.result == 42 # 20 + 22 assert result.error_msg is None def test_worker_restart_from_various_states(self): """Test worker.restart() behavior from different states.""" with qt_app_context(): worker = Worker() # Test restart from IDLE (should be no-op) assert worker.state_machine.current_state == WorkerState.IDLE worker.restart() assert worker.state_machine.current_state == WorkerState.IDLE # Test restart from STARTING state with patch.object(worker.state_machine, "transition_to"): # Force STARTING state worker.state_machine._current_state = WorkerState.STARTING worker.asyncresult = Mock() # Simulate asyncresult exists worker.restart() assert worker.asyncresult is None assert worker.state_machine.current_state == WorkerState.IDLE # Test restart from FINISHED state worker.state_machine._current_state = WorkerState.FINISHED worker.asyncresult = Mock() worker.restart() assert worker.asyncresult is None assert worker.state_machine.current_state == WorkerState.IDLE def test_worker_close(self): """Test worker.close() method.""" worker = Worker() # Test close with no active computation worker.close() # Should not raise # Test close with active computation (simulate) worker.asyncresult = Mock() worker.close() # Should not raise def test_error_conditions(self): """Test various error conditions.""" worker = Worker() # Test starting computation when not in IDLE state worker.state_machine._current_state = WorkerState.RUNNING with pytest.raises( ValueError, match="Cannot start computation from WorkerState.RUNNING state" ): worker.run(dummy_successful_computation, (1, 2)) # Reset to proper state worker.state_machine.reset_to_idle() # Test getting result when not in FINISHED state with pytest.raises( ValueError, match="Cannot get result from WorkerState.IDLE state" ): worker.get_result() # Test has_result_available in various states assert not worker.has_result_available() # IDLE worker.state_machine._current_state = WorkerState.RUNNING assert not worker.has_result_available() # RUNNING worker.state_machine._current_state = WorkerState.FINISHED assert worker.has_result_available() # FINISHED def test_restart_pool_method_integration(self): """Test that restart_pool is properly integrated with restart method.""" with qt_app_context(): worker = Worker() # Start an infinite computation worker.run(dummy_infinite_computation, ()) # Let it run briefly start_time = time.time() while time.time() - start_time < 0.05: # 50ms QW.QApplication.processEvents() time.sleep(0.001) # Force RUNNING state and call restart (which should use restart_pool) assert worker.state_machine.current_state == WorkerState.RUNNING worker.restart() # Should be back to IDLE assert worker.state_machine.current_state == WorkerState.IDLE # Pool should be functional - test with a simple computation worker.run(dummy_successful_computation, (100, 200)) while not worker.is_computation_finished(): QW.QApplication.processEvents() time.sleep(0.001) result = worker.get_result() assert result.result == 300 # Module-level function for pickle compatibility def simple_multiplication(a: int, b: int) -> int: """Simple computation that multiplies two numbers.""" return a * b def test_worker_and_state_machine_integration(): """Integration test ensuring Worker and WorkerStateMachine work together.""" with qt_app_context(): # Ensure clean pool Worker.terminate_pool(wait=False) Worker.create_pool() try: worker = Worker() # Test complete workflow assert worker.state_machine.current_state == WorkerState.IDLE # Start computation worker.run(simple_multiplication, (6, 7)) assert worker.state_machine.current_state == WorkerState.RUNNING # Wait and check states while not worker.is_computation_finished(): QW.QApplication.processEvents() time.sleep(0.001) assert worker.state_machine.current_state == WorkerState.FINISHED # Get result and verify state transition result = worker.get_result() assert result.result == 42 # 6 * 7 assert worker.state_machine.current_state == WorkerState.IDLE finally: # Cleanup Worker.terminate_pool(wait=False) if __name__ == "__main__": # Run specific test for debugging pytest.main([__file__, "-v"]) DataLab-1.1.0/datalab/tests/features/control/000077500000000000000000000000001514014117600210315ustar00rootroot00000000000000DataLab-1.1.0/datalab/tests/features/control/__init__.py000066400000000000000000000000021514014117600231320ustar00rootroot00000000000000# DataLab-1.1.0/datalab/tests/features/control/call_method_unit_test.py000066400000000000000000000100451514014117600257540ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Test for call_method generic proxy feature """ # guitest: show from __future__ import annotations import xmlrpc.client import numpy as np from sigima.tests.data import get_test_image, get_test_signal from datalab.tests import datalab_in_background_context def test_call_method() -> None: """Test call_method generic proxy feature""" with datalab_in_background_context() as proxy: # Test 1: Add some test data signal = get_test_signal("paracetamol.txt") proxy.add_object(signal) image = get_test_image("flower.npy") proxy.add_object(image) # Test 2: Call remove_object without panel parameter (auto-detection) # Should find method on current panel (signal panel) proxy.set_current_panel("signal") titles_before = proxy.get_object_titles("signal") assert len(titles_before) == 1, "Should have one signal" # Remove the signal object using call_method (no panel parameter) # This should auto-detect and use the current panel proxy.call_method("remove_object", force=True) titles_after = proxy.get_object_titles("signal") assert len(titles_after) == 0, "Signal should be removed" # Test 3: Call method on specific panel proxy.set_current_panel("image") titles_before = proxy.get_object_titles("image") assert len(titles_before) == 1, "Should have one image" # Remove the image object using call_method with explicit panel parameter # This tests the panel parameter is correctly passed through XML-RPC proxy.call_method("remove_object", force=True, panel="image") titles_after = proxy.get_object_titles("image") assert len(titles_after) == 0, "Image should be removed" # Test 3b: Verify panel parameter works when calling from different panel x = np.linspace(0, 10, 100) y = np.sin(x) proxy.add_signal("Test Signal X", x, y) proxy.set_current_panel("signal") assert len(proxy.get_object_titles("signal")) == 1 # Call remove_object on signal panel while being on signal panel proxy.call_method("remove_object", force=True, panel="signal") assert len(proxy.get_object_titles("signal")) == 0 # Test 4: Test method resolution order (main window -> current panel) # First verify we can call a main window method current_panel = proxy.call_method("get_current_panel") assert current_panel in ["signal", "image"], "Should return valid panel name" # Test 5: Add more test data and test other methods x = np.linspace(0, 10, 100) y = np.sin(x) proxy.add_signal("Test Signal 1", x, y) proxy.add_signal("Test Signal 2", x, y * 2) # Test calling a method with positional and keyword arguments proxy.set_current_panel("signal") proxy.select_objects([1, 2]) # Test delete_metadata through call_method proxy.call_method("delete_metadata", refresh_plot=False, keep_roi=True) # Test 5: Error handling - try to call a private method # Note: XML-RPC converts exceptions to xmlrpc.client.Fault try: proxy.call_method("__init__") assert False, "Should not allow calling private methods" except xmlrpc.client.Fault as exc: assert "private method" in exc.faultString.lower() # Test 6: Error handling - try to call non-existent method try: proxy.call_method("this_method_does_not_exist") assert False, "Should raise AttributeError for non-existent method" except xmlrpc.client.Fault as exc: assert "does not exist" in exc.faultString.lower() # Test 7: Call main window method (not panel method) panel_name = proxy.call_method("get_current_panel") assert panel_name == "signal", "Should return current panel name" print("✅ All call_method tests passed!") if __name__ == "__main__": test_call_method() DataLab-1.1.0/datalab/tests/features/control/connect_dialog.py000066400000000000000000000013721514014117600243560ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab Remote client connection dialog example """ # guitest: show,skip from guidata.qthelpers import qt_app_context from qtpy import QtWidgets as QW from datalab.control.proxy import RemoteProxy from datalab.widgets.connection import ConnectionDialog def test_dialog(): """Test connection dialog""" proxy = RemoteProxy(autoconnect=False) with qt_app_context(): dlg = ConnectionDialog(proxy.connect) if dlg.exec(): QW.QMessageBox.information(None, "Connection", "Successfully connected") else: QW.QMessageBox.critical(None, "Connection", "Connection failed") if __name__ == "__main__": test_dialog() DataLab-1.1.0/datalab/tests/features/control/embedded1_unit_test.py000066400000000000000000000250131514014117600253140ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Application embedded test 1 DataLab main window is destroyed when closing application. It is rebuilt from scratch when reopening application. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import abc from guidata.qthelpers import ( get_std_icon, qt_app_context, win32_fix_title_bar_background, ) from guidata.widgets.codeeditor import CodeEditor from qtpy import QtWidgets as QW from sigima.objects import NewImageParam from sigima.tests import data as test_data from datalab.config import _ from datalab.gui.main import DLMainWindow from datalab.tests.features.macro.macroeditor_unit_test import get_macro_example_path class HostWidget(QW.QWidget): """Host widget: menu with action buttons, log viewer""" def __init__(self, parent=None): super().__init__(parent) self.button_layout = QW.QVBoxLayout() self.logwidget = CodeEditor(self) self.logwidget.setMinimumWidth(500) grid_layout = QW.QGridLayout() grid_layout.addLayout(self.button_layout, 0, 0) grid_layout.addWidget(self.logwidget, 0, 1) self.setLayout(grid_layout) def log(self, message): """Log message""" self.logwidget.appendPlainText(message) def add_spacing(self, spacing: int) -> None: """Add spacing to button box""" self.button_layout.addSpacing(spacing) def add_label(self, text: str) -> None: """Add label to button box""" self.button_layout.addWidget(QW.QLabel(text)) def add_widget(self, obj: QW.QWidget, spacing_before: int = 0) -> None: """Add widget (QWidget) to button box""" if spacing_before > 0: self.add_spacing(spacing_before) self.button_layout.addWidget(obj) def add_button(self, title, slot, spacing_before=0, icon=None): """Add button""" btn = QW.QPushButton(title) if icon is not None: btn.setIcon(get_std_icon(icon)) btn.clicked.connect(lambda _checked=False: slot()) self.add_widget(btn, spacing_before=spacing_before) return btn def add_stretch(self): """Add stretch to button box""" self.button_layout.addStretch() class AbstractClientWindowMeta(type(QW.QMainWindow), abc.ABCMeta): """Mixed metaclass to avoid conflicts""" class AbstractClientWindow(QW.QMainWindow, metaclass=AbstractClientWindowMeta): """Abstract client window, to embed DataLab or connect to it""" PURPOSE = None INIT_BUTTON_LABEL = None SIG_TITLES = ("Oscilloscope", "Digitizer", "Radiometer", "Voltmeter", "Sensor") IMA_TITLES = ( "Camera", "Streak Camera", "Image Scanner", "Laser Beam Profiler", "Gated Imaging Camera", ) def __init__(self): super().__init__() win32_fix_title_bar_background(self) self.setWindowTitle(_("Host application")) self.setWindowIcon(get_std_icon("ComputerIcon")) self.datalab: DLMainWindow = None self.host = HostWidget(self) self.setCentralWidget(self.host) self.setup_window() self.host.add_stretch() self.index_sigtitle = -1 self.index_imatitle = -1 @property def sigtitle(self): """Return current signal title index""" self.index_sigtitle = idx = (self.index_sigtitle + 1) % len(self.SIG_TITLES) return self.SIG_TITLES[idx] @property def imatitle(self): """Return current image title index""" self.index_imatitle = idx = (self.index_imatitle + 1) % len(self.IMA_TITLES) return self.IMA_TITLES[idx] def setup_window(self): """Setup window""" self.host.add_label(self.PURPOSE) add_btn = self.host.add_button add_btn(self.INIT_BUTTON_LABEL, self.init_cdl, 10, "DialogApplyButton") add_btn(_("Raise window"), self.raise_cdl, 0, "FileDialogToParent") self.add_additional_buttons() add_btn(_("Add signal objects"), self.add_signals, 10, "CommandLink") add_btn(_("Add image objects"), self.add_images, 0, "CommandLink") add_btn(_("Remove all objects"), self.remove_all, 5, "MessageBoxWarning") add_btn(_("Run macro"), self.run_macro, 10, "CommandLink") add_btn(_("Stop macro"), self.stop_macro, 0, "CommandLink") add_btn( _("Import macro from file (example)"), self.import_macro, 0, "CommandLink" ) add_btn(_("Close DataLab"), self.close_datalab, 10, "DialogCloseButton") def add_additional_buttons(self): """Add additional buttons""" @abc.abstractmethod def init_cdl(self): """Open DataLab test""" def raise_cdl(self): """Raise DataLab window""" if self.datalab is not None: self.datalab.raise_window() self.host.log("=> Raised DataLab window") @abc.abstractmethod def close_datalab(self): """Close DataLab window""" def add_object(self, obj): """Add object to DataLab""" if self.datalab is not None: self.datalab.add_object(obj) def add_signals(self): """Add signals to DataLab""" if self.datalab is not None: for func in ( test_data.create_paracetamol_signal, test_data.create_noisy_signal, ): obj = func(title=self.sigtitle) self.add_object(obj) self.host.log(f"Added signal: {obj.title}") # Remove last added signal to test remove_object nobj0 = len(self.datalab.get_object_titles()) sig = self.datalab.get_object() # Get the last added signal self.datalab.remove_object(force=True) # Test remove object method assert len(self.datalab.get_object_titles()) == nobj0 - 1, ( "One object should have been removed" ) self.add_object(sig) # Add it back def add_images(self): """Add images to DataLab""" if self.datalab is not None: p = NewImageParam.create(height=2000, width=2000, title=self.imatitle) for func in ( test_data.create_sincos_image, test_data.create_noisy_gaussian_image, test_data.create_multigaussian_image, ): obj = func(p) self.add_object(obj) self.host.log(f"Added image: {obj.title}") @abc.abstractmethod def remove_all(self): """Remove all objects from DataLab""" def __has_macro(self): """Check if there is a macro in DataLab""" return len(self.datalab.get_object_titles("macro")) > 0 def run_macro(self): """Run macro in DataLab""" if self.datalab is not None: if self.__has_macro(): self.datalab.run_macro() self.host.log("=> Running macro") else: self.host.log("No macro to run") def stop_macro(self): """Stop macro in DataLab""" if self.datalab is not None: if self.__has_macro(): self.datalab.stop_macro() self.host.log("=> Stopping macro") else: self.host.log("No macro to stop") def import_macro(self): """Import macro in DataLab""" if self.datalab is not None: fname = get_macro_example_path() self.datalab.import_macro_from_file(fname) self.host.log(f"=> Imported macro: {fname}") class AbstractHostWindow(AbstractClientWindow): # pylint: disable=abstract-method """Abstract host window, embedding DataLab""" PURPOSE = _("This the host application, which embeds DataLab.") INIT_BUTTON_LABEL = _("Open DataLab") def remove_all(self): """Remove all objects from DataLab""" if self.datalab is not None: for panel in self.datalab.panels: panel.remove_all_objects() self.host.log(f"Removed objects from {panel.PANEL_STR}") def add_additional_buttons(self): """Add additional buttons""" add_btn = self.host.add_button add_btn(_("Import signal from DataLab"), self.import_signal, 10, "ArrowLeft") add_btn(_("Import image from DataLab"), self.import_image, 0, "ArrowLeft") def import_object(self, panel, title): """Import object from DataLab""" self.host.log(f"get_objects_with_dialog ({title}):") objs = panel.get_objects_with_dialog(title, parent=self.host) if objs is not None: for obj in objs: self.host.log(f" -> {obj.title}:") self.host.log(str(obj)) else: self.host.log(" -> canceled") def import_signal(self): """Import signal from DataLab""" if self.datalab is not None: self.import_object(self.datalab.signalpanel, self.sender().text()) def import_image(self): """Import image from DataLab""" if self.datalab is not None: self.import_object(self.datalab.imagepanel, self.sender().text()) class HostWindow(AbstractHostWindow): """Test main view""" def init_cdl(self): """Open DataLab test""" if self.datalab is None: self.datalab = DLMainWindow(console=False) self.datalab.SIG_CLOSING.connect(self.datalab_was_closed) self.datalab.show() self.host.log("✨Initialized DataLab window") else: try: self.datalab.show() self.datalab.raise_() self.host.log("=> Shown DataLab window") except RuntimeError: self.datalab = None self.init_cdl() def datalab_was_closed(self): """DataLab was closed""" self.datalab = None self.host.log("✨DataLab window was closed by user") def close_datalab(self): """Close DataLab window""" if self.datalab is not None: self.host.log("=> Closed DataLab") self.datalab.SIG_CLOSING.disconnect(self.datalab_was_closed) self.datalab.close() self.datalab.deleteLater() self.datalab = None def run_host_window(klass): """Running host window to test DataLab embedding feature""" with qt_app_context(exec_loop=True): window: HostWindow = klass() window.resize(800, 800) window.show() def test_embedded_feature(): """Testing embedded feature""" run_host_window(HostWindow) if __name__ == "__main__": test_embedded_feature() DataLab-1.1.0/datalab/tests/features/control/embedded2_unit_test.py000066400000000000000000000027471514014117600253260ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Application embedded test 2 DataLab main window is simply hidden when closing application. It is shown and raised above other windows when reopening application. """ # guitest: show from datalab.gui.main import DLMainWindow from datalab.tests.features.control import embedded1_unit_test class HostWindow(embedded1_unit_test.AbstractHostWindow): """Test main view""" def init_cdl(self) -> None: """Open DataLab test""" if self.datalab is None: self.datalab = DLMainWindow(console=False, hide_on_close=True) self.host.log("✨ Initialized DataLab window") self.datalab.show() else: self.datalab.show() self.datalab.raise_window() self.host.log("=> Shown DataLab window") def close_datalab(self) -> None: """Close DataLab window""" if self.datalab is not None: self.host.log("=> Closed DataLab") self.datalab.close() def closeEvent(self, event) -> None: # pylint: disable=invalid-name """Close event Reimplemented from QWidget.closeEvent""" if self.datalab is None or self.datalab.close_properly(): event.accept() else: event.ignore() def test_embedded_feature(): """Testing embedded feature""" embedded1_unit_test.run_host_window(HostWindow) if __name__ == "__main__": test_embedded_feature() DataLab-1.1.0/datalab/tests/features/control/remoteclient_app_test.py000066400000000000000000000177401514014117600260050ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Remote GUI-based client test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code # guitest: show from __future__ import annotations import functools from contextlib import contextmanager from guidata.qthelpers import qt_app_context, qt_wait from qtpy import QtWidgets as QW from datalab.config import _ from datalab.control.proxy import RemoteProxy from datalab.env import execenv from datalab.tests import close_datalab_background, run_datalab_in_background from datalab.tests.features.control import embedded1_unit_test from datalab.tests.features.control.remoteclient_unit import multiple_commands from datalab.utils.qthelpers import bring_to_front from datalab.widgets.connection import ConnectionDialog APP_NAME = _("Remote client test") def try_send_command(): """Try and send command to DataLab application remotely""" def try_send_command_decorator(func): """Try... except... decorator""" @functools.wraps(func) def method_wrapper(*args, **kwargs): """Decorator wrapper function""" self: HostWindow = args[0] # extracting 'self' from method arguments output = None try: output = func(*args, **kwargs) except ConnectionRefusedError as exc: self.datalab = None message = "🔥 Connection refused 🔥 (server is not ready?)" self.host.log(message) if execenv.unattended: raise ConnectionRefusedError( "Connection refused (server is not ready?)" ) from exc QW.QMessageBox.critical(self, APP_NAME, message) return output return method_wrapper return try_send_command_decorator class HostWindow(embedded1_unit_test.AbstractClientWindow): """Test main view""" PURPOSE = _("This the client application, which connects to DataLab.") INIT_BUTTON_LABEL = _("Connect to DataLab") def init_cdl(self): """Open DataLab test""" if self.datalab is None: if execenv.unattended: self.datalab = RemoteProxy() ok = True else: self.datalab = RemoteProxy(autoconnect=False) connect_dlg = ConnectionDialog(self.datalab.connect, self) ok = connect_dlg.exec() if ok: self.host.log("✨ Initialized DataLab connection ✨") self.host.log(f" Communication port: {self.datalab.port}") self.host.log(" List of exposed methods:") for name in self.datalab.get_method_list(): self.host.log(f" {name}") else: self.datalab = None error_msg = connect_dlg.get_error_message() self.host.log(f"🔥 Connection refused 🔥 ({error_msg})") if execenv.unattended: raise ConnectionRefusedError( "Connection refused (server is not ready?)" ) else: self.host.log("=> Already connected to DataLab") @try_send_command() def close_datalab(self): """Close DataLab window""" if self.datalab is not None: self.datalab.close_application() self.host.log("🎬 Closed DataLab!") self.datalab = None def add_additional_buttons(self): """Add additional buttons""" add_btn = self.host.add_button add_btn(_("Execute multiple commands"), self.exec_multiple_cmd, 10) add_btn(_("Get object titles"), self.get_object_titles, 10) add_btn(_("Get object uuids"), self.get_object_uuids, 10) add_btn(_("Get object"), self.get_object) @try_send_command() def exec_multiple_cmd(self): """Execute multiple commands in DataLab""" if self.datalab is not None: self.host.log("Starting command sequence...") multiple_commands(self.datalab) self.host.log("...end") @try_send_command() def get_object_titles(self): """Get object (signal/image) titles for current panel""" if self.datalab is not None: self.host.log("Object titles:") titles = self.datalab.get_object_titles() if titles: for name in titles: self.host.log(f" {name}") else: self.host.log(" Empty.") @try_send_command() def get_object_uuids(self): """Get object (signal/image) uuids for current panel""" if self.datalab is not None: self.host.log("Object uuids:") uuids = self.datalab.get_object_uuids() if uuids: for uuid in uuids: self.host.log(f" {uuid}") else: self.host.log(" Empty.") @try_send_command() def get_object(self): """Get object (signal/image) at index for current panel""" if self.datalab is not None: titles = self.datalab.get_object_titles() if titles: obj = self.datalab.get_object() if obj is not None: self.host.log(f"Object '{obj.title}'") self.host.log(str(obj)) else: self.host.log("🏴‍☠️ Object is None! (no selection)") else: self.host.log("🏴‍☠️ Object list is empty!") @try_send_command() def add_object(self, obj): """Add object to DataLab""" super().add_object(obj) @try_send_command() def remove_all(self): """Remove all objects from DataLab""" if self.datalab is not None: self.datalab.reset_all() self.host.log("Removed all objects") @contextmanager def qt_wait_print(dt: float, message: str, parent=None): """Wait and print message""" qt_wait(dt, show_message=True, parent=parent) execenv.print(f"{message}...", end="") yield execenv.print("OK") def test_remote_client(): """Remote client application test""" run_datalab_in_background() try: with qt_app_context(exec_loop=True): window = HostWindow() window.resize(800, 800) window.show() dt = 1 if execenv.unattended: qt_wait(8, show_message=True, parent=window) window.init_cdl() with qt_wait_print(dt, "Executing multiple commands"): window.exec_multiple_cmd() bring_to_front(window) with qt_wait_print(dt, "Raising DataLab window"): window.raise_cdl() with qt_wait_print(dt, "Import macro"): window.import_macro() with qt_wait_print(dt, "Getting object titles"): window.get_object_titles() with qt_wait_print(dt, "Getting object uuids"): window.get_object_uuids() with qt_wait_print(dt, "Getting object"): window.get_object() with qt_wait_print(dt, "Adding signals"): window.add_signals() with qt_wait_print(dt, "Adding images"): window.add_images() with qt_wait_print(dt, "Run macro"): window.run_macro() with qt_wait_print(dt * 2, "Stop macro"): window.stop_macro() with qt_wait_print(dt, "Removing all objects"): window.remove_all() with qt_wait_print(dt, "Closing DataLab"): window.close_datalab() except Exception as exc: execenv.print("❌ Remote client test failed.") close_datalab_background() # Ensure DataLab is closed in case of failure raise exc if __name__ == "__main__": test_remote_client() DataLab-1.1.0/datalab/tests/features/control/remoteclient_unit.py000066400000000000000000000056231514014117600251420ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Remote client test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code # guitest: skip import os import os.path as osp import time import numpy as np from guidata.qthelpers import qt_app_context from plotpy.builder import make from sigima.params import XYCalibrateParam from sigima.tests.data import create_2d_gaussian, create_paracetamol_signal from datalab import app from datalab.control.proxy import RemoteProxy from datalab.env import execenv from datalab.tests import helpers def multiple_commands(remote: RemoteProxy): """Execute multiple XML-RPC commands""" with helpers.WorkdirRestoringTempDir() as tmpdir: x, y = create_paracetamol_signal().get_data() remote.add_signal("tutu", x, y) z = create_2d_gaussian(2000, np.uint16) remote.add_image("toto", z) rect = make.annotated_rectangle(100, 100, 200, 200, title="Test") area = rect.get_rect() remote.add_annotations_from_items([rect]) uuid = remote.get_sel_object_uuids()[0] items = remote.get_object_shapes() assert len(items) == 1 and items[0].get_rect() == area remote.add_label_with_title(f"Image uuid: {uuid}") remote.select_groups([1]) remote.select_objects([uuid]) remote.delete_metadata() fname = osp.join(tmpdir, osp.basename("remote_test.h5")) remote.save_to_h5_file(fname) remote.reset_all() remote.open_h5_files([fname], True, False) remote.import_h5_file(fname, True) # Test new headless workspace API methods (Issue #275) fname_workspace = osp.join(tmpdir, "workspace_test.h5") remote.save_h5_workspace(fname_workspace) assert osp.exists(fname_workspace), "Workspace file was not created" remote.reset_all() remote.load_h5_workspace([fname_workspace], reset_all=True) # Verify objects were restored assert len(remote.get_object_titles()) > 0, "No objects after load_h5_workspace" remote.set_current_panel("signal") assert remote.get_current_panel() == "signal" remote.calc("log10") param = XYCalibrateParam.create(a=1.2, b=0.1) remote.calc("calibration", param) time.sleep(2) # Avoid permission error when trying to clean-up temporary files def test_remoteclient_unit(): """Remote client test""" env = os.environ.copy() env[execenv.DO_NOT_QUIT_ENV] = "1" execenv.print("Launching DataLab in a separate process") helpers.exec_script(app.__file__, wait=False, env=env) remote = RemoteProxy() execenv.print("Executing multiple commands...", end="") with qt_app_context(): # needed for building plot items multiple_commands(remote) execenv.print("OK") if __name__ == "__main__": test_remoteclient_unit() DataLab-1.1.0/datalab/tests/features/control/simpleclient_unit_test.py000066400000000000000000000311671514014117600262010ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Simple Remote client test ------------------------- This test covers the simple remote client functionalities as provided by the `sigima.client` module. In Sigima, the `sigima.tests.common.client_unit_test` module provides a set of unit tests for the client functionalities. The purpose of these tests is to ensure that the client can correctly interact with the real server, handling various scenarios and edge cases. The tests include: - A function comparing the list of methods implemented by the real server (DataLab's `RemoteServer` class) to those implemented by the stub server (Sigima's `DataLabStubServer` class). - A function comparing the list of methods implemented by the full client (DataLab's `RemoteProxy` class) to those implemented by the simple client (Sigima's `SimpleRemoteProxy` class). - A function that simply runs the `sigima.tests.common.client_unit_test` suite after having launched a real server instance (DataLab application, using the `datalab.tests.run_datalab_in_background` function), ensuring that this test passes with the stub server as well as with the real server. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: skip import inspect import warnings from guidata.env import execenv from packaging.version import Version from sigima.client.remote import SimpleRemoteProxy, __required_server_version__ from sigima.client.stub import DataLabStubServer from sigima.tests.common.client_unit_test import RemoteClientTester import datalab from datalab.control.baseproxy import AbstractDLControl from datalab.control.proxy import RemoteProxy from datalab.tests import run_datalab_in_background def test_compare_server_methods() -> None: """Compare methods implemented by RemoteServer vs DataLabStubServer. This function compares the list of methods implemented by the real server (DataLab's RemoteServer class) to those implemented by the stub server (Sigima's DataLabStubServer class). """ execenv.print("\n=== Comparing Server Methods ===") # Get methods from RemoteServer (the real DataLab server) # RemoteServer implements AbstractDLControl methods remote_server_methods = set(AbstractDLControl.get_public_methods()) # Get methods from DataLabStubServer (the stub server) stub_server_methods = { name for name, method in inspect.getmembers(DataLabStubServer, inspect.isfunction) if not name.startswith("_") } execenv.print(f"RemoteServer methods: {len(remote_server_methods)}") execenv.print(f"DataLabStubServer methods: {len(stub_server_methods)}") # Methods in RemoteServer but not in DataLabStubServer missing_in_stub = remote_server_methods - stub_server_methods if missing_in_stub: execenv.print("\n⚠️ Methods in RemoteServer but missing in DataLabStubServer:") for method in sorted(missing_in_stub): execenv.print(f" - {method}") # Methods in DataLabStubServer but not in RemoteServer extra_in_stub = stub_server_methods - remote_server_methods if extra_in_stub: execenv.print("\n🔍 Methods in DataLabStubServer but not in RemoteServer:") for method in sorted(extra_in_stub): execenv.print(f" - {method}") # Common methods common_methods = remote_server_methods & stub_server_methods execenv.print(f"\n✅ Common methods: {len(common_methods)}") # Assert that stub server implements all required methods assert not missing_in_stub, ( f"DataLabStubServer is missing {len(missing_in_stub)} methods " f"implemented by RemoteServer: {sorted(missing_in_stub)}" ) execenv.print("✨ Server method comparison completed successfully!") def test_compare_client_methods() -> None: """Compare methods implemented by RemoteProxy vs SimpleRemoteProxy. This function compares the list of methods implemented by the full client (DataLab's RemoteProxy class) to those implemented by the simple client (Sigima's SimpleRemoteProxy class). """ execenv.print("\n=== Comparing Client Methods ===") # Get public methods from RemoteProxy (the full client) remote_proxy_methods = { name for name, method in inspect.getmembers(RemoteProxy, inspect.ismethod) if not name.startswith("_") and callable(method) } # Get public methods from SimpleRemoteProxy (the simple client) simple_proxy_methods = { name for name, method in inspect.getmembers(SimpleRemoteProxy, inspect.ismethod) if not name.startswith("_") and callable(method) } execenv.print(f"RemoteProxy methods: {len(remote_proxy_methods)}") execenv.print(f"SimpleRemoteProxy methods: {len(simple_proxy_methods)}") # Methods in SimpleRemoteProxy but not in RemoteProxy extra_in_simple = simple_proxy_methods - remote_proxy_methods if extra_in_simple: execenv.print( "\n🔍 Methods in SimpleRemoteProxy but not in RemoteProxy " "(expected, as SimpleRemoteProxy is a subset):" ) for method in sorted(extra_in_simple): execenv.print(f" - {method}") # Methods in RemoteProxy but not in SimpleRemoteProxy missing_in_simple = remote_proxy_methods - simple_proxy_methods if missing_in_simple: execenv.print( "\n📝 Methods in RemoteProxy but not in SimpleRemoteProxy " "(expected, as SimpleRemoteProxy is simpler):" ) for method in sorted(missing_in_simple): execenv.print(f" - {method}") # Common methods common_methods = remote_proxy_methods & simple_proxy_methods execenv.print(f"\n✅ Common methods: {len(common_methods)}") execenv.print("✨ Client method comparison completed successfully!") def test_with_real_server() -> None: """Run sigima.tests.common.client_unit_test suite with real DataLab server. This function runs the comprehensive client unit test suite after launching a real DataLab instance in the background, ensuring that the tests pass with the real server. """ execenv.print("\n=== Testing with Real DataLab Server ===") # Launch DataLab application in the background execenv.print("Launching DataLab in background...") run_datalab_in_background() # Import and run the comprehensive test from sigima execenv.print("Running comprehensive client tests with real server...") tester = RemoteClientTester() # Initialize connection to real DataLab server using the existing port if not tester.init_cdl(): raise ConnectionRefusedError( "Failed to connect to DataLab server. " "Make sure DataLab is running and accessible." ) try: # Run all tests tester.run_comprehensive_test() execenv.print("✨ All tests passed with real DataLab server!") except Exception as exc: execenv.print("❌ Some tests failed with real DataLab server.") tester.close_datalab() raise exc # Clean up tester.close_datalab() def test_version_compatibility() -> None: """Test that DataLab version is compatible with Sigima client. This test ensures that the current version of DataLab meets the minimum requirements of the Sigima client as defined in __required_server_version__. """ execenv.print("\n=== Testing Version Compatibility ===") # Get DataLab version datalab_version = datalab.__version__ execenv.print(f"DataLab version: {datalab_version}") execenv.print(f"Required version: {__required_server_version__}") # Test version comparison using Version class with edge cases execenv.print("\nTesting Version comparison:") test_cases = [ ("1.0.0", "1.0.0", True, "Same version"), ("1.0.1", "1.0.0", True, "Newer patch version"), ("1.1.0", "1.0.0", True, "Newer minor version"), ("2.0.0", "1.0.0", True, "Newer major version"), ("0.9.9", "1.0.0", False, "Older version"), ("1.0.0a1", "1.0.0", False, "Alpha is pre-release (< 1.0.0)"), ("1.0.0b2", "1.0.0", False, "Beta is pre-release (< 1.0.0)"), ("1.0.0rc1", "1.0.0", False, "RC is pre-release (< 1.0.0)"), ("1.0.0", "1.0.0a1", True, "Release is newer than alpha"), ("1.0.1", "1.0.0a1", True, "Newer patch > alpha"), ("1.0.0a10", "1.0.0a2", True, "Later alpha version"), ] for ver1, ver2, expected, description in test_cases: result = Version(ver1) >= Version(ver2) status = "✓" if result == expected else "✗" execenv.print( f" {status} Version('{ver1}') >= Version('{ver2}') = {result} " f"[expected: {expected}] - {description}" ) assert result == expected, ( f"Version comparison failed for {ver1} vs {ver2}: " f"expected {expected}, got {result}" ) # Check if current DataLab version is compatible execenv.print(f"\nChecking DataLab {datalab_version} compatibility...") is_compatible = Version(datalab_version) >= Version(__required_server_version__) if is_compatible: execenv.print( f"✅ DataLab version {datalab_version} is compatible " f"(>= {__required_server_version__})" ) else: execenv.print( f"⚠️ DataLab version {datalab_version} is NOT compatible " f"(< {__required_server_version__})" ) # Note: During development (alpha/beta/rc versions), we allow the test to pass # even if the version comparison indicates incompatibility. In production, # both DataLab and Sigima should be at release versions. vdatalab = Version(datalab_version) # Allow pre-release versions if they're the same base version if vdatalab.is_prerelease and not is_compatible: # Check if the base version (without pre-release) would be compatible base_ver = f"{vdatalab.major}.{vdatalab.minor}.{vdatalab.micro}" if Version(base_ver) >= Version(__required_server_version__): execenv.print( f"ℹ️ Pre-release version {datalab_version} allowed for testing " f"(base version {base_ver} >= {__required_server_version__})" ) is_compatible = True # This assertion ensures DataLab's version meets Sigima's requirements assert is_compatible, ( f"DataLab version {datalab_version} is not compatible with " f"Sigima client requirements (>= {__required_server_version__}). " f"Please upgrade DataLab or downgrade the required version in Sigima." ) # Test that warning is issued when connecting to incompatible version execenv.print("\nTesting version compatibility warning with stub server...") with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") # Create a stub server that reports an old version stub = DataLabStubServer(port=0, verbose=False) # Temporarily patch get_version to return an old version original_get_version = stub.get_version def mock_old_version(): return "0.9.0" # Old version that should trigger warning stub.get_version = mock_old_version try: port = stub.start() # Try to connect - should trigger warning proxy = SimpleRemoteProxy(autoconnect=False) proxy.connect(port=str(port)) # Check that a warning was issued version_warnings = [ warning for warning in w if "may not be fully compatible" in str(warning.message) ] if version_warnings: execenv.print( f"✅ Warning correctly issued for incompatible version: " f"{version_warnings[0].message}" ) else: execenv.print("⚠️ No warning issued for incompatible version") # Print all warnings for debugging if w: execenv.print(f" All warnings captured ({len(w)}):") for warning in w: execenv.print(f" - {warning.message}") assert len(version_warnings) > 0, ( "Expected warning for incompatible version but none was issued" ) finally: # Restore original method and stop server stub.get_version = original_get_version stub.stop() execenv.print("✨ Version compatibility test completed successfully!") if __name__ == "__main__": test_compare_server_methods() test_compare_client_methods() test_version_compatibility() test_with_real_server() DataLab-1.1.0/datalab/tests/features/hdf5/000077500000000000000000000000001514014117600201775ustar00rootroot00000000000000DataLab-1.1.0/datalab/tests/features/hdf5/__init__.py000066400000000000000000000000021514014117600223000ustar00rootroot00000000000000# DataLab-1.1.0/datalab/tests/features/hdf5/h5browser1_unit_test.py000066400000000000000000000016371514014117600246570ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ HDF5 browser unit tests 1 ------------------------- Try and open all HDF5 test data available. """ # guitest: show from __future__ import annotations from guidata.qthelpers import exec_dialog, qt_app_context from datalab.tests import helpers from datalab.tests.features.hdf5.h5browser_app_test import create_h5browser_dialog def test_h5browser_all_files(pattern=None): """HDF5 browser unit test for all available .h5 test files""" with qt_app_context(): fnames = helpers.get_test_fnames("*.h5" if pattern is None else pattern) for index, fname in enumerate(fnames): dlg = create_h5browser_dialog([fname], toggle_all=True, select_all=True) dlg.setObjectName(dlg.objectName() + f"_{index:02d}") exec_dialog(dlg) if __name__ == "__main__": test_h5browser_all_files() DataLab-1.1.0/datalab/tests/features/hdf5/h5browser2_unit.py000066400000000000000000000030541514014117600236140ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ HDF5 browser unit tests 2 ------------------------- Testing for memory leak """ # guitest: show,skip import os import time import numpy as np import psutil from guidata.qthelpers import qt_app_context from sigima.viz import view_curves from datalab.env import execenv from datalab.tests import helpers from datalab.widgets.h5browser import H5BrowserDialog def test_memoryleak(fname, iterations=20): """Memory leak test""" with qt_app_context(): proc = psutil.Process(os.getpid()) fname = helpers.get_test_fnames(fname)[0] dlg = H5BrowserDialog(None) memlist = [] for i in range(iterations): t0 = time.time() dlg.open_file(fname) memdata = proc.memory_info().vms / 1024**2 memlist.append(memdata) execenv.print(i + 1, ":", memdata, "MB") dlg.browser.tree.select_all(True) dlg.browser.tree.toggle_all(True) execenv.print(i + 1, ":", proc.memory_info().vms / 1024**2, "MB") dlg.show() dlg.accept() dlg.close() execenv.print(i + 1, ":", proc.memory_info().vms / 1024**2, "MB") dlg.cleanup() execenv.print(i + 1, ":", f"{(time.time() - t0):.1f} s") view_curves( np.array(memlist), title="Memory leak test for HDF5 browser dialog", ylabel="Memory (MB)", ) if __name__ == "__main__": test_memoryleak("scenario*.h5") DataLab-1.1.0/datalab/tests/features/hdf5/h5browser_app_test.py000066400000000000000000000041271514014117600243740ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ HDF5 Browser Application test ----------------------------- """ # guitest: show from __future__ import annotations from guidata.qthelpers import exec_dialog, qt_app_context from qtpy import QtWidgets as QW from datalab.env import execenv from datalab.tests import helpers from datalab.widgets.h5browser import H5BrowserDialog def create_h5browser_dialog( fnames: list[str], toggle_all: bool = False, select_all: bool = False ) -> H5BrowserDialog: """Create HDF5 browser dialog with all nodes expanded and selected Args: fnames: HDF5 file names Returns: H5BrowserDialog instance """ execenv.print(f"Opening: {fnames}") dlg = H5BrowserDialog(None) dlg.open_files(fnames) dlg.browser.tree.toggle_all(toggle_all) dlg.browser.tree.select_all(select_all) return dlg def test_h5browser() -> None: """Test HDF5 browser""" fnames = helpers.get_test_fnames("*.h5")[-2:] with qt_app_context(): dlg = create_h5browser_dialog(fnames) if execenv.unattended: # Test all buttons: dlg.show() for index in range(dlg.button_layout.count()): widget = dlg.button_layout.itemAt(index).widget() if isinstance(widget, QW.QCheckBox): widget.setChecked(True) widget.setChecked(False) elif isinstance(widget, QW.QPushButton): widget.click() # Test various features: tree = dlg.browser.tree tree.update_menu() tree.expandAll() tree.collapseAll() tree.restore() # Removing file, adding file from browser: dlg.browser.close_file(fnames[0]) dlg.browser.open_file(fnames[0]) # Removing file, adding file from file selector: dlg.browser.selector.remove_file(fnames[0]) dlg.browser.selector.add_file(fnames[0]) exec_dialog(dlg) if __name__ == "__main__": test_h5browser() DataLab-1.1.0/datalab/tests/features/hdf5/h5import_app_test.py000066400000000000000000000011671514014117600242240ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ HDF5 import application test """ # guitest: show from datalab import app from datalab.env import execenv from datalab.tests import helpers from datalab.utils.qthelpers import datalab_app_context def test_hdf5_import(): """Testing DataLab app launcher""" with datalab_app_context(exec_loop=True): win = app.create(console=False) fname = helpers.get_test_fnames("*.h5")[-1] execenv.print(f"Importing HDF5 file: {fname}") win.import_h5_file(fname) if __name__ == "__main__": test_hdf5_import() DataLab-1.1.0/datalab/tests/features/hdf5/h5importer_app_test.py000066400000000000000000000020501514014117600245430ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ HDF5 Importer Application test ------------------------------ Running application a few times in a row with different entry parameters. """ # guitest: show from datalab import app from datalab.env import execenv from datalab.tests import helpers from datalab.utils.qthelpers import datalab_app_context def test_h5importer_app(pattern=None): """Testing DataLab app launcher""" if pattern is None: pattern = "*.h5" execenv.print("HDF5 import test scenario:") execenv.print("[1] Loading all h5 files at once (only the first 5 files)") with datalab_app_context(exec_loop=True): app.create(h5files=helpers.get_test_fnames(pattern)[:5]) execenv.print("[2] Loading h5 files one by one") for fname in helpers.get_test_fnames(pattern): with datalab_app_context(exec_loop=True): execenv.print(f" Opening: {fname}") app.create(h5files=[fname]) if __name__ == "__main__": test_h5importer_app() DataLab-1.1.0/datalab/tests/features/hdf5/h5workspace_unit_test.py000066400000000000000000000114531514014117600251060ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ HDF5 workspace API unit tests ----------------------------- Tests for the headless HDF5 workspace API methods: - load_h5_workspace: Load native DataLab HDF5 files without GUI elements - save_h5_workspace: Save workspace to native DataLab HDF5 file without GUI elements These methods are designed for use from the internal console where Qt GUI elements would cause thread-safety issues. """ # guitest: show import os.path as osp import h5py import pytest from sigima.tests.data import create_noisy_gaussian_image, create_paracetamol_signal from datalab.tests import datalab_test_app_context, helpers def test_save_and_load_h5_workspace(): """Test save_h5_workspace and load_h5_workspace methods""" with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context(console=False) as win: # === Create test objects sig1 = create_paracetamol_signal() win.signalpanel.add_object(sig1) ima1 = create_noisy_gaussian_image() win.imagepanel.add_object(ima1) # Store object counts and titles for verification sig_count_before = len(win.signalpanel.objmodel) ima_count_before = len(win.imagepanel.objmodel) sig_title = sig1.title ima_title = ima1.title # === Test save_h5_workspace fname = osp.join(tmpdir, "test_workspace.h5") win.save_h5_workspace(fname) assert osp.exists(fname), "HDF5 file was not created" # === Clear workspace for panel in win.panels: panel.remove_all_objects() assert len(win.signalpanel.objmodel) == 0 assert len(win.imagepanel.objmodel) == 0 # === Test load_h5_workspace win.load_h5_workspace([fname], reset_all=True) # Verify objects were restored assert len(win.signalpanel.objmodel) == sig_count_before assert len(win.imagepanel.objmodel) == ima_count_before # Verify titles (get objects in order from groups) loaded_sig = win.signalpanel.objmodel.get_all_objects()[0] loaded_ima = win.imagepanel.objmodel.get_all_objects()[0] assert loaded_sig.title == sig_title assert loaded_ima.title == ima_title def test_load_h5_workspace_invalid_file(): """Test load_h5_workspace raises ValueError for non-native HDF5 files""" with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context(console=False) as win: # Create a non-native HDF5 file (just an empty HDF5) fname = osp.join(tmpdir, "not_native.h5") with h5py.File(fname, "w") as f: f.create_dataset("dummy", data=[1, 2, 3]) # Should raise ValueError for non-native file with pytest.raises(ValueError, match="not a native DataLab HDF5 file"): win.load_h5_workspace([fname]) def test_load_h5_workspace_append(): """Test load_h5_workspace with reset_all=False (append mode)""" with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context(console=False) as win: # Create and save first signal sig1 = create_paracetamol_signal() sig1.title = "Signal 1" win.signalpanel.add_object(sig1) fname = osp.join(tmpdir, "workspace1.h5") win.save_h5_workspace(fname) # Clear and create second signal win.signalpanel.remove_all_objects() sig2 = create_paracetamol_signal() sig2.title = "Signal 2" win.signalpanel.add_object(sig2) assert len(win.signalpanel.objmodel) == 1 # Load first workspace with reset_all=False (append) win.load_h5_workspace([fname], reset_all=False) # Should now have both signals (appended) assert len(win.signalpanel.objmodel) == 2 def test_save_h5_workspace_modified_flag(): """Test that save_h5_workspace clears the modified flag""" with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context(console=False) as win: # Create an object (this sets modified flag) sig1 = create_paracetamol_signal() win.signalpanel.add_object(sig1) # Workspace should be modified assert win.is_modified() # Save workspace fname = osp.join(tmpdir, "test.h5") win.save_h5_workspace(fname) # Modified flag should be cleared assert not win.is_modified() if __name__ == "__main__": test_save_and_load_h5_workspace() test_load_h5_workspace_invalid_file() test_load_h5_workspace_append() test_save_h5_workspace_modified_flag() DataLab-1.1.0/datalab/tests/features/image/000077500000000000000000000000001514014117600204335ustar00rootroot00000000000000DataLab-1.1.0/datalab/tests/features/image/__init__.py000066400000000000000000000000021514014117600225340ustar00rootroot00000000000000# DataLab-1.1.0/datalab/tests/features/image/annotations_app_test.py000066400000000000000000000014171514014117600252440ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Annotations application test: - Create an image with annotations and ROI - Further tests to be done manually: edit "Annotations" and check that modifications are taken into account, without affecting the existing ROI """ # guitest: show from sigima.objects import create_image_roi from sigima.tests import data as test_data from datalab.app import run def test_annotations_app(): """Annotations test""" obj1 = test_data.create_sincos_image() obj2 = test_data.create_annotated_image() obj2.roi = create_image_roi("rectangle", [10, 10, 60, 400]) run(console=False, objects=(obj1, obj2), size=(1200, 550)) if __name__ == "__main__": test_annotations_app() DataLab-1.1.0/datalab/tests/features/image/annotations_unit_test.py000066400000000000000000000057011514014117600254430ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Annotations unit test: - Create an image with annotations - Open dialog (equivalent to click on button "Annotations") - Accept dialog without modifying shapes - Check if image annotations are still the same """ # guitest: show from plotpy.builder import make from plotpy.items import AnnotatedShape, PolygonShape from plotpy.plot import BasePlot from qtpy import QtWidgets as QW from sigima.tests import data as test_data from datalab.adapters_plotpy import create_adapter_from_object from datalab.env import execenv from datalab.tests import datalab_test_app_context def set_annotation_color(annotation: AnnotatedShape, color: str) -> None: """Set annotation color""" shape: PolygonShape = annotation.shape param = shape.shapeparam param.line.color = param.fill.color = color param.fill.alpha = 0.3 param.fill.style = "SolidPattern" param.update_item(shape) plot: BasePlot = annotation.plot() if plot is not None: plot.replot() def test_annotations_unit(): """Run image tools test scenario""" with datalab_test_app_context() as win: panel = win.imagepanel # Create image with annotations ima1 = test_data.create_multigaussian_image() ima1.title = "Annotations from items" rect = make.annotated_rectangle(100, 100, 200, 200, title="Test") set_annotation_color(rect, "#2222ff") circ = make.annotated_circle(300, 300, 400, 400, title="Test") set_annotation_color(circ, "#22ff22") elli = make.annotated_ellipse( 500, 500, 800, 500, 650, 400, 650, 600, title="Test" ) segm = make.annotated_segment(700, 700, 800, 800, title="Test") label = make.label("Test", (1000, 1000), (0, 0), "BR") adapter = create_adapter_from_object(ima1) adapter.add_annotations_from_items([rect, circ, elli, segm, label]) panel.add_object(ima1) # Create another image with annotations ima2 = test_data.create_annotated_image(title="Annotations from JSON") panel.add_object(ima2) execenv.print("Check [geometric shapes] <--> [plot items] conversion:") execenv.print("(comparing annotations)") for ima in (ima1, ima2): execenv.print(f" Checking image '{ima.title}': ", end="") panel.objview.select_objects([ima]) # Open separate view panel.open_separate_view().done(QW.QDialog.DialogCode.Accepted) orig_ann = ima.annotations panel.open_separate_view().done(QW.QDialog.DialogCode.Accepted) # Check if annotations are still the same # open("before.json", mode="wb").write(orig_ann.encode()) # open("after.json", mode="wb").write(ima.annotations.encode()) assert orig_ann == ima.annotations execenv.print("OK") if __name__ == "__main__": test_annotations_unit() DataLab-1.1.0/datalab/tests/features/image/average_app_test.py000066400000000000000000000030051514014117600243140ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Average application test ======================== The purpose of this test is to check that we can average a set of images without overflowing the data type. This test was written following a regression where the average of 10 images of size 256x256 with a Gaussian distribution of values was overflowing the data type uint8. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code # guitest: show import numpy as np import sigima.objects import sigima.tests.data as ctd from datalab.tests import datalab_test_app_context, helpers def test_image_average() -> None: """Average application test.""" with datalab_test_app_context() as win: panel = win.imagepanel N, size = 10, 256 dtype = sigima.objects.ImageDatatypes.UINT8 p = sigima.objects.NewImageParam.create(height=size, width=size, dtype=dtype) data = ctd.create_2d_gaussian(size, dtype.to_numpy_dtype()) for _idx in range(N): obj = sigima.objects.create_image_from_param(p) obj.data = data panel.add_object(obj) panel.objview.select_groups([0]) panel.processor.run_feature("average") res_data = panel.objview.get_sel_objects(include_groups=True)[0].data exp_data = np.array(data, dtype=float) helpers.check_array_result("Average", res_data, exp_data) if __name__ == "__main__": test_image_average() DataLab-1.1.0/datalab/tests/features/image/background_dialog_test.py000066400000000000000000000052271514014117600255100ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Image background dialog unit test. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code # guitest: show from __future__ import annotations import numpy as np import sigima.objects import sigima.params import sigima.proc.image as sipi from guidata.qthelpers import exec_dialog, qt_app_context from sigima import viz from sigima.tests.data import create_noisy_gaussian_image from datalab.env import execenv from datalab.widgets.imagebackground import ImageBackgroundDialog def test_image_background_selection() -> None: """Image background selection test.""" with qt_app_context(): img = create_noisy_gaussian_image() # Switch to non-uniform coordinates to test the background dialog handling: xcoords = np.linspace(0, 10, img.data.shape[1]) img.set_coords(xcoords, 0.02 * xcoords**3) dlg = ImageBackgroundDialog(img) dlg.resize(640, 480) dlg.setObjectName(dlg.objectName() + "_00") # to avoid timestamp suffix exec_dialog(dlg) if execenv.unattended: dlg.test_compute_background() execenv.print(f"background: {dlg.get_background()}") execenv.print(f"rect coords: {dlg.get_rect_coords()}") # Check background value: x0, y0, x1, y1 = dlg.get_rect_coords() ix0, iy0, ix1, iy1 = dlg.imageitem.get_closest_index_rect(x0, y0, x1, y1) assert np.isclose(img.data[iy0:iy1, ix0:ix1].mean(), dlg.get_background()) def test_image_offset_correction_with_background_dialog() -> None: """Image offset correction interactive test using the background dialog.""" with qt_app_context(): i1 = create_noisy_gaussian_image() dlg = ImageBackgroundDialog(i1) ok = exec_dialog(dlg) if ok: if execenv.unattended: dlg.test_compute_background() param = sigima.objects.ROI2DParam() # pylint: disable=unbalanced-tuple-unpacking ix0, iy0, ix1, iy1 = i1.physical_to_indices(dlg.get_rect_coords()) param.x0, param.y0, param.dx, param.dy = ix0, iy0, ix1 - ix0, iy1 - iy0 i2 = sipi.offset_correction(i1, param) i3 = sipi.clip(i2, sigima.params.ClipParam.create(lower=0)) viz.view_images_side_by_side( [i1, i3], titles=["Original image", "Corrected image"], title="Image offset correction and thresholding", ) if __name__ == "__main__": test_image_background_selection() test_image_offset_correction_with_background_dialog() DataLab-1.1.0/datalab/tests/features/image/blobs_app_test.py000066400000000000000000000033371514014117600240130ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Blob detection application test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.params from sigima.objects import create_image from sigima.tests.data import get_test_image from datalab.adapters_plotpy import create_adapter_from_object from datalab.tests import datalab_test_app_context, skip_if_opencv_missing def test_blobs(): """Run blob detection application test scenario""" with datalab_test_app_context() as win: panel = win.imagepanel proc = panel.processor data = get_test_image("flower.npy").data # Testing blob detection # ====================== for paramclass, compute_method, name in ( (sigima.params.BlobDOGParam, "blob_dog", "BlobDOG"), (sigima.params.BlobDOHParam, "blob_doh", "BlobDOH"), (sigima.params.BlobLOGParam, "blob_log", "BlobLOG"), (sigima.params.BlobOpenCVParam, "blob_opencv", "BlobOpenCV"), ): param = paramclass() param.min_sigma = 10.0 param.max_sigma = 30.0 image = create_image(name, data) create_adapter_from_object(image).add_label_with_title() panel.add_object(image) with skip_if_opencv_missing(): proc.run_feature(compute_method, param) # Testing distribute_on_grid and reset_positions # ============================================== panel.objview.selectAll() param = sigima.params.GridParam.create(cols=2, colspac=10, rowspac=10) proc.distribute_on_grid(param) if __name__ == "__main__": test_blobs() DataLab-1.1.0/datalab/tests/features/image/contour_app_test.py000066400000000000000000000030051514014117600243730ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Contour finding application test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.enums import sigima.params from sigima.objects import NewImageParam from sigima.tests.data import create_multigaussian_image from datalab.env import execenv from datalab.tests import datalab_test_app_context, take_plotwidget_screenshot from datalab.tests.features.image.roi_app_test import create_test_image_with_roi def test_contour_app(): """Run contour finding application test scenario""" newparam = NewImageParam.create(height=200, width=200) with datalab_test_app_context() as win: panel = win.imagepanel for shape in sigima.enums.ContourShape: ima1 = create_multigaussian_image(newparam) ima1.set_metadata_option("colormap", "gray") panel.add_object(ima1) param = sigima.params.ContourShapeParam.create(shape=shape) panel.processor.run_feature("contour_shape", param) take_plotwidget_screenshot(panel, "contour_test") ima2 = create_test_image_with_roi(newparam) ima2.set_metadata_option("colormap", "gray") panel.add_object(ima2) panel.processor.edit_roi_graphically() panel.processor.run_feature("contour_shape", param) if not execenv.unattended: break if __name__ == "__main__": test_contour_app() DataLab-1.1.0/datalab/tests/features/image/contour_fabryperot_app_test.py000066400000000000000000000032651514014117600266400ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Contour finding application test with Fabry-Perot images """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.enums import sigima.objects import sigima.params from sigima.tests.data import get_test_image from datalab.tests import datalab_test_app_context, take_plotwidget_screenshot def test_contour_app() -> None: """Run contour finding application test scenario""" with datalab_test_app_context() as win: panel = win.imagepanel shape = sigima.enums.ContourShape.CIRCLE ima1 = get_test_image("fabry-perot1.jpg") ima1.set_metadata_option("colormap", "gray") xc, yc, r = 601.0, 556.0, 457.0 roi = sigima.objects.create_image_roi("circle", [xc, yc, r]) ima1.roi = roi panel.add_object(ima1) param = sigima.params.ContourShapeParam.create(shape=shape) panel.processor.run_feature("contour_shape", param) take_plotwidget_screenshot(panel, "contour_fabryperot_test") ima2 = get_test_image("fabry-perot2.jpg") ima2.set_metadata_option("colormap", "gray") ima2.roi = roi panel.add_object(ima2) panel.processor.run_feature("contour_shape", param) param = sigima.params.LineProfileParam.create(direction="horizontal", row=554) panel.processor.run_feature("line_profile", param) param = sigima.params.AverageProfileParam.create( direction="horizontal", row1=550, row2=560 ) panel.processor.run_feature("average_profile", param) if __name__ == "__main__": test_contour_app() DataLab-1.1.0/datalab/tests/features/image/denoise_app_test.py000066400000000000000000000015271514014117600243370ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Denoise processing application test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.params from sigima.tests.data import get_test_image from datalab.tests import datalab_test_app_context def test_denoise(): """Run denoise application test scenario""" with datalab_test_app_context() as win: win.showMaximized() panel = win.imagepanel panel.add_object(get_test_image("flower.npy")) proc = panel.processor proc.compute_all_denoise() panel.objview.select_groups() param = sigima.params.GridParam.create(cols=3) proc.distribute_on_grid(param) panel.add_label_with_title() if __name__ == "__main__": test_denoise() DataLab-1.1.0/datalab/tests/features/image/distribute_on_grid_app_test.py000066400000000000000000000071531514014117600265710ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Distribute on grid application test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import numpy as np import sigima.params from sigima.tests.data import get_test_image from datalab.env import execenv from datalab.tests import datalab_test_app_context def get_image_origin(obj): """Get image origin (x0, y0) handling both uniform and non-uniform coordinates. Args: obj: Image object Returns: Tuple (x0, y0) """ if obj.is_uniform_coords: return obj.x0, obj.y0 return obj.xcoords[0], obj.ycoords[0] def test_distribute_on_grid(): """Run distribute on grid application test scenario""" with execenv.context(unattended=True): with datalab_test_app_context(console=False) as win: panel = win.imagepanel proc = panel.processor panel.add_object(get_test_image("flower.npy")) proc.compute_all_morphology(sigima.params.MorphologyParam.create(radius=10)) panel.objview.select_groups() # Distribute on grid params = [ sigima.params.GridParam.create(cols=4), sigima.params.GridParam.create( rows=3, colspac=20, rowspac=20, direction="row" ), sigima.params.GridParam.create(cols=2, colspac=10, rowspac=10), ] origins = [] for param in params: proc.distribute_on_grid(param) objs = panel.objview.get_sel_objects(include_groups=True) origins.append([get_image_origin(obj) for obj in objs]) # Reset positions proc.reset_positions() objs = panel.objview.get_sel_objects(include_groups=True) origins.append([get_image_origin(obj) for obj in objs]) # Verify results with tolerance for floating point comparisons tol = 1e-10 # First distribution (cols=4) assert np.allclose(origins[0][0], (0.0, 0.0), atol=tol) assert np.allclose(origins[0][1], (512.0, 0.0), atol=tol) assert np.allclose(origins[0][2], (1024.0, 0.0), atol=tol) assert np.allclose(origins[0][3], (1536.0, 0.0), atol=tol) assert np.allclose(origins[0][4], (0.0, 512.0), atol=tol) assert np.allclose(origins[0][5], (512.0, 512.0), atol=tol) assert np.allclose(origins[0][6], (1024.0, 512.0), atol=tol) # Second distribution (rows=3, colspac=20, rowspac=20, direction="row") assert np.allclose(origins[1][0], (0.0, 0.0), atol=tol) assert np.allclose(origins[1][1], (0.0, 532.0), atol=tol) assert np.allclose(origins[1][2], (0.0, 1064.0), atol=tol) assert np.allclose(origins[1][3], (532.0, 0.0), atol=tol) assert np.allclose(origins[1][4], (532.0, 532.0), atol=tol) assert np.allclose(origins[1][5], (532.0, 1064.0), atol=tol) # Third distribution (cols=2, colspac=10, rowspac=10) assert np.allclose(origins[2][0], (0.0, 0.0), atol=tol) assert np.allclose(origins[2][1], (522.0, 0.0), atol=tol) assert np.allclose(origins[2][2], (0.0, 522.0), atol=tol) assert np.allclose(origins[2][3], (522.0, 522.0), atol=tol) assert np.allclose(origins[2][4], (0.0, 1044.0), atol=tol) assert np.allclose(origins[2][5], (522.0, 1044.0), atol=tol) # After reset, all should be at origin for x0, y0 in origins[3]: assert np.allclose([x0, y0], [0.0, 0.0], atol=tol) if __name__ == "__main__": test_distribute_on_grid() DataLab-1.1.0/datalab/tests/features/image/edges_app_test.py000066400000000000000000000015301514014117600237720ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Edges processing application test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.params from sigima.tests.data import get_test_image from datalab.tests import datalab_test_app_context def test_edges(): """Run edges processing application test scenario""" with datalab_test_app_context() as win: win.showMaximized() panel = win.imagepanel panel.add_object(get_test_image("flower.npy")) proc = panel.processor proc.compute_all_edges() panel.objview.select_groups() param = sigima.params.GridParam.create(cols=4) proc.distribute_on_grid(param) panel.add_label_with_title() if __name__ == "__main__": test_edges() DataLab-1.1.0/datalab/tests/features/image/erase_app_test.py000066400000000000000000000011071514014117600240020ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Image erase application test""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from sigima.tests.data import get_test_image from datalab.tests import datalab_test_app_context def test_erase(): """Run erase application test scenario""" with datalab_test_app_context() as win: win.imagepanel.add_object(get_test_image("flower.npy")) win.imagepanel.processor.compute_erase() if __name__ == "__main__": test_erase() DataLab-1.1.0/datalab/tests/features/image/fft2d_app_test.py000066400000000000000000000013321514014117600237100ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Image FFT application test. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.objects from datalab.tests import datalab_test_app_context def test_fft2d_app() -> None: """FFT application test.""" with datalab_test_app_context() as win: panel = win.imagepanel param = sigima.objects.Gauss2DParam.create(width=100, height=100) i1 = sigima.objects.create_image_from_param(param) panel.add_object(i1) panel.processor.run_feature("fft") panel.processor.run_feature("ifft") if __name__ == "__main__": test_fft2d_app() DataLab-1.1.0/datalab/tests/features/image/flatfield_app_test.py000066400000000000000000000021301514014117600246320ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Flat field test Testing the following: - Create a gaussian image (raw data) - Create a random noised image (flat data) - Compute the flat field image """ # guitest: show from sigima.objects import Gauss2DParam, UniformDistribution2DParam from sigima.proc.image import FlatFieldParam from datalab.config import _ from datalab.tests import datalab_test_app_context def test_flatfield(): """Run flat field test scenario""" with datalab_test_app_context() as win: panel = win.imagepanel param0 = Gauss2DParam.create(title=_("Raw data (2D-Gaussian)")) ima0 = panel.new_object(param0, edit=False) param1 = UniformDistribution2DParam.create( title=_("Flat data (Uniform random)"), vmax=5 ) ima1 = panel.new_object(param1, edit=False) panel.objview.select_objects([ima0]) ffp = FlatFieldParam() ffp.threshold = 80 panel.processor.run_feature("flatfield", ima1, ffp) if __name__ == "__main__": test_flatfield() DataLab-1.1.0/datalab/tests/features/image/geometry_transform_unit_test.py000066400000000000000000000342141514014117600270350ustar00rootroot00000000000000#!/usr/bin/env python3 # -*- coding: utf-8 -*- """Unit test for geometry transformation operations.""" from __future__ import annotations from typing import Callable import numpy as np import sigima.objects as sio import sigima.params as sip import sigima.proc.image as sipi from sigima.tests import data as test_data from datalab.adapters_metadata.geometry_adapter import GeometryAdapter from datalab.gui.processor.image import apply_geometry_transform from datalab.tests import datalab_test_app_context def __create_test_image() -> sio.ImageObj: """Create a test image with geometry results for testing.""" param = sio.Gauss2DParam.create( height=600, width=600, x0=2.5, y0=7.5, title="Test image (with geometry results)", dtype=sio.ImageDatatypes.UINT16, ) obj = sio.create_image_from_param(param) for geometry in test_data.generate_geometry_results(): GeometryAdapter(geometry).add_to(obj) obj.roi = sio.create_image_roi("rectangle", [10, 10, 50, 400]) return obj def __get_geometries(obj: sio.ImageObj) -> list[sio.GeometryResult]: """Get geometries from an image object.""" return [ga.result for ga in GeometryAdapter.iterate_from_obj(obj)] def __get_expected_geometry_result( orig_geom: sio.GeometryResult, operation: str ) -> sio.GeometryResult: """Get expected geometry result for a given operation. Args: orig_geom: Original geometry result operation: Operation name (rotate90, rotate270, etc.) Returns: Expected geometry result after transformation """ # For image transformations, rotations should be around the image center # (300, 300 for our 600x600 test image) xc, yc = (300.0, 300.0) if operation == "rotate90": exp_geom = sipi.transformer.rotate(orig_geom, -np.pi / 2, (xc, yc)) elif operation == "rotate270": exp_geom = sipi.transformer.rotate(orig_geom, np.pi / 2, (xc, yc)) elif operation == "fliph": exp_geom = sipi.transformer.fliph(orig_geom, xc) elif operation == "flipv": exp_geom = sipi.transformer.flipv(orig_geom, yc) elif operation == "transpose": exp_geom = sipi.transformer.transpose(orig_geom) elif operation == "resize": # Resize changes pixel resolution but keeps physical coordinates unchanged exp_geom = orig_geom elif operation == "binning": # Binning changes pixel resolution but keeps physical coordinates unchanged exp_geom = orig_geom else: raise ValueError(f"Unknown operation: {operation}") return exp_geom def __validate_geometry_transformation( tr_geometries: list[sio.GeometryResult], or_geometries: list[sio.GeometryResult], operation: str, test_context: str = "geometry", ) -> None: """Validate that geometry transformation was applied correctly. Args: tr_geometries: List of transformed geometry results or_geometries: List of original geometry results operation: Operation name (rotate90, rotate270, etc.) test_context: Context string for error messages (e.g., "App", "Unit") """ for i, tr_geom in enumerate(tr_geometries): original_geom = or_geometries[i] expected_geom = __get_expected_geometry_result(original_geom, operation) # Compare the actual transformation result with expected np.testing.assert_allclose( tr_geom.coords, expected_geom.coords, rtol=1e-10, err_msg=f"{test_context} geometry result {i} " f"({tr_geom.title}) coordinates do not match expected for {operation}. " f"Got: {tr_geom.coords}, Expected: {expected_geom.coords}", ) # Verify other properties are preserved assert tr_geom.title == expected_geom.title, ( f"Title should be preserved for geometry {i} in {operation}" ) assert tr_geom.kind == expected_geom.kind, ( f"Kind should be preserved for geometry {i} in {operation}" ) np.testing.assert_array_equal( tr_geom.roi_indices, expected_geom.roi_indices, err_msg=f"ROI indices should be preserved for geometry {i} in {operation}", ) def __validate_basic_transformation(tr_obj: sio.ImageObj, or_obj: sio.ImageObj) -> None: """Validate basic transformation requirements. Args: tr_obj: The transformed image object or_obj: The original image object """ assert tr_obj is not or_obj, "Transformation should create a new object" assert len(tr_obj.metadata) == len(or_obj.metadata), ( "Number of geometry results should be preserved" ) def __validate_roi_transformation( tr_obj: sio.ImageObj, or_obj: sio.ImageObj, operation: str, ) -> None: """Validate that ROI is properly transformed. Args: tr_obj: The transformed image object or_obj: Original image object operation: Operation name for expected transformation """ tr_roi = tr_obj.roi assert tr_roi is not None, "ROI should not be removed after transformation" assert len(tr_roi.single_rois) == len(or_obj.roi.single_rois), ( "Number of ROI objects should be preserved" ) # Validate that the ROI coordinates were properly transformed for i, roi in enumerate(tr_roi.single_rois): # Temporary transform the ROI into a geometry result: or_roi = or_obj.roi.get_single_roi(i) temp_geom = sio.GeometryResult( "temp_geom", sio.KindShape.RECTANGLE, coords=np.asarray([or_roi.get_physical_coords(or_obj)]), ) expected_geom = __get_expected_geometry_result(temp_geom, operation) exp_coords = expected_geom.coords[0] np.testing.assert_allclose( roi.get_physical_coords(tr_obj), exp_coords, rtol=1e-10, atol=1e-10, err_msg=f"ROI {i} coordinates do not match expected for {operation}.", ) def __validate_image_data_transformation( tr_obj: sio.ImageObj, or_obj: sio.ImageObj, operation: str ) -> None: """Validate that image data was properly transformed. Args: tr_obj: The transformed image object or_obj: Original image object operation: Operation name for expected transformation """ if operation == "rotate90": expected_data = np.rot90(or_obj.data) elif operation == "rotate270": expected_data = np.rot90(or_obj.data, k=-1) elif operation == "fliph": expected_data = np.fliplr(or_obj.data) elif operation == "flipv": expected_data = np.flipud(or_obj.data) elif operation == "transpose": expected_data = np.transpose(or_obj.data) elif operation in ("resize", "binning"): # These operations are more complex and use Sigima's functions # We'll just verify the operation was applied (shape may change) return else: raise ValueError(f"Unknown operation for data validation: {operation}") np.testing.assert_array_equal( tr_obj.data, expected_data, err_msg=f"Image data not properly transformed for {operation}", ) # ============================================================================= # Unit Tests # ============================================================================= def __create_unit_test( operation: str, param_creator: Callable[ [], sip.BinningParam | sip.ResizeParam | sip.RotateParam ] = None, ) -> Callable[[], None]: """Create a unit test function for a geometry transformation operation. Args: operation: Operation name (e.g., "rotate90", "fliph") param_creator: Function to create parameter object, if needed Returns: Test function """ def test_func() -> None: """Test geometry transformation at unit level.""" obj = __create_test_image() or_geometries = __get_geometries(obj) # Store original data for comparison # Apply transformation (same as DataLab processor does) param = param_creator() if param_creator else None compfunc = getattr(sipi, operation, None) if compfunc is None: raise ValueError(f"Unknown operation: {operation}") if param is None: tr_obj = compfunc(obj) else: tr_obj = compfunc(obj, param) # Apply the geometry transformation to the result # (skip geometry transformation for resize and binning as they preserve # physical coordinates) if operation not in ["resize", "binning"]: apply_geometry_transform(tr_obj, operation) # Validate basic transformation requirements __validate_basic_transformation(tr_obj, obj) # Validate geometry transformation tr_geometries = __get_geometries(tr_obj) __validate_geometry_transformation( tr_geometries, or_geometries, operation, "Unit" ) # Validate ROI transformation (only for simple operations) simple_ops = ["fliph", "flipv", "transpose"] if operation in simple_ops: __validate_roi_transformation(tr_obj, obj, operation) # Set proper function name and docstring test_func.__name__ = f"test_geometry_transform_{operation}_unit" test_func.__doc__ = ( f"Test geometry transformation for {operation} at unit level.\n\n" f"This test verifies that the DataLab geometry transformation properly " f"applies Sigima's {operation} function to geometry results." ) return test_func def __create_app_test( operation: str, param_creator: Callable[ [], sip.BinningParam | sip.ResizeParam | sip.RotateParam ] = None, ) -> Callable[[], None]: """Create an app test function for a geometry transformation operation. Args: operation: Operation name (e.g., "rotate90", "fliph") param_creator: Function to create parameter object, if needed Returns: Test function """ def test_func() -> None: """Test operation at application level through direct processing.""" with datalab_test_app_context() as win: panel = win.imagepanel proc = panel.processor obj = __create_test_image() panel.add_object(obj) panel.objview.select_objects((1,)) # Apply operation using app workflow param = param_creator() if param_creator else None if param is None: proc.run_feature(operation) else: proc.run_feature(operation, param=param) tr_obj = panel[len(panel)] # # Validate basic transformation requirements # __validate_basic_transformation(tr_obj, obj) # # Validate geometry transformation # tr_geometries = __get_geometries(tr_obj) # __validate_geometry_transformation( # tr_geometries, or_geometries, operation, "App", param # ) # Verify that image data is properly transformed __validate_image_data_transformation(tr_obj, obj, operation) # For simple transformations, validate ROI transformation # Skip ROI validation for rotation operations due to inconsistency in Sigima # library between image data rotation (around image center) and ROI coordinate # rotation (around ROI center) simple_ops = ["fliph", "flipv", "transpose"] if operation in simple_ops: __validate_roi_transformation(tr_obj, obj, operation) # Set proper function name and docstring test_func.__name__ = f"test_geometry_transform_{operation}_app" test_func.__doc__ = ( f"Test {operation} operation at application level.\n\n" f"This test verifies the complete transformation workflow including " f"image processing and geometry transformation pipeline." ) return test_func # ============================================================================= # Parameter Creators # ============================================================================= def __resize_param() -> sip.ResizeParam: """Create resize parameter.""" return sip.ResizeParam.create(zoom=1.5) def __binning_param() -> sip.BinningParam: """Create binning parameter.""" return sip.BinningParam.create(sx=2, sy=3) # ============================================================================= # Generate Test Functions # ============================================================================= # Simple transformations (no parameters) test_geometry_transform_rotate90_unit = __create_unit_test("rotate90") test_geometry_transform_rotate90_app = __create_app_test("rotate90") test_geometry_transform_rotate270_unit = __create_unit_test("rotate270") test_geometry_transform_rotate270_app = __create_app_test("rotate270") test_geometry_transform_fliph_unit = __create_unit_test("fliph") test_geometry_transform_fliph_app = __create_app_test("fliph") test_geometry_transform_flipv_unit = __create_unit_test("flipv") test_geometry_transform_flipv_app = __create_app_test("flipv") test_geometry_transform_transpose_unit = __create_unit_test("transpose") test_geometry_transform_transpose_app = __create_app_test("transpose") # Parametric transformations test_geometry_transform_resize_unit = __create_unit_test("resize", __resize_param) test_geometry_transform_resize_app = __create_app_test("resize", __resize_param) test_geometry_transform_binning_unit = __create_unit_test("binning", __binning_param) test_geometry_transform_binning_app = __create_app_test("binning", __binning_param) if __name__ == "__main__": # test_geometry_transform_rotate90_unit() test_geometry_transform_rotate90_app() test_geometry_transform_rotate270_unit() test_geometry_transform_rotate270_app() test_geometry_transform_fliph_unit() test_geometry_transform_fliph_app() test_geometry_transform_flipv_unit() test_geometry_transform_flipv_app() test_geometry_transform_transpose_unit() test_geometry_transform_transpose_app() test_geometry_transform_resize_unit() test_geometry_transform_resize_app() test_geometry_transform_binning_unit() test_geometry_transform_binning_app() print("✅ All geometry transformation tests passed!") DataLab-1.1.0/datalab/tests/features/image/imagetools_app_test.py000066400000000000000000000034161514014117600250530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Image tools application test: - Testing `ZAxisLogTool` - Testing `profile_to_signal` function (image cross section -> curve) """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import os.path as osp from plotpy.coords import axes_to_canvas from plotpy.tools import CrossSectionTool from qtpy import QtCore as QC from sigima.objects import NewImageParam from sigima.tests.data import create_multigaussian_image from datalab.gui.docks import profile_to_signal from datalab.tests import datalab_test_app_context def test_image_tools_app(): """Run image tools test scenario""" with datalab_test_app_context() as win: panel = win.imagepanel newparam = NewImageParam.create(height=200, width=200) ima = create_multigaussian_image(newparam) panel.add_object(ima) panel.set_current_object_title(f"Test image for {osp.basename(__file__)}") plotwidget = panel.plothandler.plotwidget mgr = plotwidget.get_manager() plot = plotwidget.get_plot() # === Testing "profile_to_signal" ---------------------------------------------- cstool: CrossSectionTool = mgr.get_tool(CrossSectionTool) xcs_panel, ycs_panel = mgr.get_xcs_panel(), mgr.get_ycs_panel() xcs_panel.setVisible(True) ycs_panel.setVisible(True) x, y = newparam.width // 2, newparam.height // 2 active_item = plot.get_active_item() pos = QC.QPointF(*axes_to_canvas(active_item, x, y)) cstool.add_shape_to_plot(plot, pos, pos) for panel in (xcs_panel, ycs_panel): profile_to_signal(plot, panel) if __name__ == "__main__": test_image_tools_app() DataLab-1.1.0/datalab/tests/features/image/imagetools_unit_test.py000066400000000000000000000013701514014117600252470ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Image tools test Simple image dialog for testing all image tools available in DataLab """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from guidata.qthelpers import qt_app_context from plotpy.builder import make from sigima.tests.data import create_noisy_gaussian_image from sigima.viz import view_image_items def test_image_tools_unit(): """Image tools test""" with qt_app_context(): data = create_noisy_gaussian_image().data items = [make.image(data, interpolation="nearest", eliminate_outliers=2.0)] view_image_items(items) if __name__ == "__main__": test_image_tools_unit() DataLab-1.1.0/datalab/tests/features/image/load_app_test.py000066400000000000000000000041241514014117600236240ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Load application test: high number of images """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations import numpy as np import pytest import sigima.objects import sigima.params from datalab.env import execenv from datalab.tests import datalab_test_app_context def create_random_test_data( size: tuple[int, int] | None = None, ) -> sigima.objects.ImageObj: """Create a test image, based on a fast algorithm, to be able to generate a high number of images. Args: size: Size of the image. """ if size is None: size = (2048, 2048) # Create a base image with low frequency shapes: x = np.linspace(-1, 1, size[0]) y = np.linspace(-1, 1, size[1]) xx, yy = np.meshgrid(x, y) a1, a2, f1, f2 = np.random.rand(4) data = a1 * np.sin(2 * np.pi * xx * (1 + f1 * 0.2)) + a2 * np.cos( 2 * np.pi * yy * (1 + f2 * 0.2) ) # Add some random noise: data += 0.1 * np.random.randn(*data.shape) image = sigima.objects.create_image("Random test image", data) return image @pytest.mark.skip("This a load test, not a functional test") def test_high_number_of_images() -> None: """Run a test with a high number of images.""" nb = 30 execenv.print("Creating images", end="") images = [] for idx in range(nb): execenv.print(".", end="") ima = create_random_test_data() ima.title += f" {idx}" images.append(ima) execenv.print(" done") with datalab_test_app_context() as win: panel = win.imagepanel for ima in images: panel.add_object(ima) panel.objview.select_groups() # Comment the two following lines to check if DataLab only shows the last image # when they are all superposed param = sigima.params.GridParam.create(cols=10) panel.processor.distribute_on_grid(param) panel.duplicate_object() if __name__ == "__main__": test_high_number_of_images() DataLab-1.1.0/datalab/tests/features/image/morph_app_test.py000066400000000000000000000016531514014117600240360ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Morphology processing application test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.params from sigima.tests.data import get_test_image from datalab.tests import datalab_test_app_context def test_morphology(): """Run morphology application test scenario""" with datalab_test_app_context() as win: win.showMaximized() panel = win.imagepanel panel.add_object(get_test_image("flower.npy")) proc = panel.processor param = sigima.params.MorphologyParam.create(radius=10) proc.compute_all_morphology(param) panel.objview.select_groups() param = sigima.params.GridParam.create(cols=4) proc.distribute_on_grid(param) panel.add_label_with_title() if __name__ == "__main__": test_morphology() DataLab-1.1.0/datalab/tests/features/image/offsetcorrection_app_test.py000066400000000000000000000021051514014117600262600ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Image offset correction application test An application test for the image offset correction feature is necessary to ensure that the feature works correctly with the GUI and the image processing pipeline, as it involves user interaction and a specific dialog for selecting the offset region. Experience has shown that this feature can be prone to issues, especially with dialog interactions, so a dedicated test is essential to catch any regressions or unexpected behavior in the future. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from sigima.tests.data import get_test_image from datalab.tests import datalab_test_app_context def test_offset_correction(): """Run offset correction application test scenario""" with datalab_test_app_context() as win: win.imagepanel.add_object(get_test_image("flower.npy")) win.imagepanel.processor.compute_offset_correction() if __name__ == "__main__": test_offset_correction() DataLab-1.1.0/datalab/tests/features/image/peak2d_app_test.py000066400000000000000000000033131514014117600240520ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ 2D peak detection test Testing the following: - Create a test image with multiple peaks - Compute 2D peak detection and show points on image """ # guitest: show import sigima.params from sigima.tests.data import create_peak_image from datalab.tests import datalab_test_app_context, take_plotwidget_screenshot def test_peak2d(screenshot: bool = False) -> None: """Run 2D peak detection scenario""" with datalab_test_app_context() as win: panel = win.imagepanel ima = create_peak_image() panel.add_object(ima) # Test with ROI creation enabled (default) param = sigima.params.Peak2DDetectionParam.create(create_rois=True) results = panel.processor.compute_peak_detection(param) assert results is not None, "Peak detection should return results" # Get the processed image object obj = panel.objview.get_current_object() assert obj.roi is not None, "ROI should be created when create_rois=True" assert not obj.roi.is_empty(), "ROI should not be empty" # Test with ROI creation disabled ima2 = create_peak_image() panel.add_object(ima2) param2 = sigima.params.Peak2DDetectionParam.create(create_rois=False) panel.processor.compute_peak_detection(param2) obj2 = panel.objview.get_current_object() assert obj2.roi is None or obj2.roi.is_empty(), ( "ROI should not be created when create_rois=False" ) win.toggle_show_titles(False) if screenshot: take_plotwidget_screenshot(panel, "peak2d_test") if __name__ == "__main__": test_peak2d() DataLab-1.1.0/datalab/tests/features/image/profile_app_test.py000066400000000000000000000046441514014117600243540ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Profile extraction test ======================= Testing the profile extraction features of the image panel: - Compute a profile along a horizontal line - Compute a profile along a vertical line - Compute an average profile between two points along a horizontal line - Compute an average profile between two points along a vertical line - Compute a radial profile """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.params from sigima.tests.data import create_noisy_gaussian_image, get_test_image from datalab.tests import datalab_test_app_context def test_profile(): """Run profile extraction test scenario""" with datalab_test_app_context() as win: panel = win.imagepanel panel.add_object(get_test_image("flower.npy")) proc = panel.processor for direction, row, col in ( ("horizontal", 102, 131), ("vertical", 102, 131), ): profparam = sigima.params.LineProfileParam.create( direction=direction, row=row, col=col ) proc.compute_line_profile(profparam) for direction, row1, col1, row2, col2 in ( ("horizontal", 10, 10, 102, 131), ("vertical", 10, 10, 102, 131), ): avgprofparam = sigima.params.AverageProfileParam.create( direction=direction, row1=row1, col1=col1, row2=row2, col2=col2, ) proc.compute_average_profile(avgprofparam) segprofparam = sigima.params.SegmentProfileParam.create( row1=10, col1=10, row2=102, col2=131 ) proc.compute_segment_profile(segprofparam) image2 = create_noisy_gaussian_image(center=(0.0, 0.0), add_annotations=False) panel.add_object(image2) for center, x0, y0 in ( (None, 0.0, 0.0), ("centroid", 0.0, 0.0), ("center", 0.0, 0.0), ("user", 800.0, 900.0), ): if center is None: proc.compute_radial_profile() else: param = sigima.params.RadialProfileParam.create( center=center, x0=x0, y0=y0 ) proc.compute_radial_profile(param) if __name__ == "__main__": test_profile() DataLab-1.1.0/datalab/tests/features/image/profile_dialog_test.py000066400000000000000000000042171514014117600250270ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Profile extraction unit test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.params from guidata.qthelpers import exec_dialog, qt_app_context from sigima.tests.data import create_noisy_gaussian_image from datalab.env import execenv from datalab.gui.profiledialog import ProfileExtractionDialog def test_profile_unit(): """Run profile extraction test""" with qt_app_context(): obj = create_noisy_gaussian_image(center=(0.0, 0.0), add_annotations=False) for mode in ("line", "segment", "rectangle"): for initial_param in (True, False): if initial_param: if mode == "line": param = sigima.params.LineProfileParam.create(row=100, col=200) elif mode == "segment": param = sigima.params.SegmentProfileParam.create( row1=10, col1=20, row2=200, col2=300 ) else: param = sigima.params.AverageProfileParam.create( row1=10, col1=20, row2=200, col2=300 ) else: if mode == "line": param = sigima.params.LineProfileParam() elif mode == "segment": param = sigima.params.SegmentProfileParam() else: param = sigima.params.AverageProfileParam() execenv.print("-" * 80) execenv.print(f"Testing mode: {mode} - initial_param: {initial_param}") dialog = ProfileExtractionDialog( mode, param, add_initial_shape=initial_param ) dialog.set_obj(obj) if initial_param: dialog.edit_values() ok = exec_dialog(dialog) execenv.print(f"Returned code: {ok}") execenv.print(f"Param: {param}") if __name__ == "__main__": test_profile_unit() DataLab-1.1.0/datalab/tests/features/image/roi_app_test.py000066400000000000000000000065271514014117600235070ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Image ROI application test""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations from typing import TYPE_CHECKING import pytest import sigima.params as sigima_param from sigima.objects import ImageObj, ImageROI, NewImageParam, create_image_roi from sigima.tests.data import create_multigaussian_image from datalab.config import Conf from datalab.env import execenv from datalab.tests import datalab_test_app_context, helpers if TYPE_CHECKING: from datalab.gui.panel.image import ImagePanel SIZE = 200 # Image ROIs: IROI1 = [100, 100, 75, 100] # Rectangle IROI2 = [66, 100, 50] # Circle # Polygon (triangle, that is intentionally inside the rectangle, so that this ROI # has no impact on the mask calculations in the tests) IROI3 = [100, 100, 100, 150, 150, 133] def __run_image_computations(panel: ImagePanel): """Test all image features related to ROI""" panel.processor.run_feature("centroid") panel.processor.run_feature("histogram", sigima_param.HistogramParam()) panel.processor.run_feature( "peak_detection", sigima_param.Peak2DDetectionParam.create(create_rois=False) ) roi = ImageROI() panel.processor.compute_roi_extraction(roi) def create_test_image_with_roi(newimageparam: NewImageParam) -> ImageObj: """Create test image with ROIs Args: newimageparam (sigima.NewImageParam): Image parameters Returns: sigima.ImageObj: Image object with ROIs """ ima = create_multigaussian_image(newimageparam) ima.data += 1 # Ensure that the image has non-zero values (for ROI check tests) roi = create_image_roi("rectangle", IROI1) roi.add_roi(create_image_roi("circle", IROI2)) roi.add_roi(create_image_roi("polygon", IROI3)) ima.roi = roi return ima def test_image_roi_app(screenshots: bool = False): """Run Image ROI application test scenario""" with datalab_test_app_context(console=False) as win: execenv.print("Image ROI application test:") panel = win.imagepanel param = NewImageParam.create(height=SIZE, width=SIZE) ima1 = create_multigaussian_image(param) panel.add_object(ima1) __run_image_computations(panel) ima2 = create_test_image_with_roi(param) for singleobj in (False, True): with Conf.proc.extract_roi_singleobj.temp(singleobj): ima2_i = ima2.copy() panel.add_object(ima2_i) helpers.print_obj_data_dimensions(ima2_i) panel.processor.edit_roi_graphically() if screenshots: win.statusBar().hide() win.take_screenshot("i_roi_image") __run_image_computations(panel) @pytest.mark.skip(reason="This test is only for manual testing") def test_image_roi_basic_app(): """Run Image ROI basic application test scenario""" with datalab_test_app_context(console=False) as win: panel = win.imagepanel param = NewImageParam.create(height=SIZE, width=SIZE) ima1 = create_multigaussian_image(param) panel.add_object(ima1) panel.processor.edit_roi_graphically() if __name__ == "__main__": test_image_roi_app(screenshots=True) test_image_roi_basic_app() DataLab-1.1.0/datalab/tests/features/image/roi_circ_app_test.py000066400000000000000000000037431514014117600245040ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Circular ROI test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations from typing import Literal import numpy as np import sigima.objects from skimage import draw from datalab.env import execenv from datalab.tests import datalab_test_app_context, helpers def create_test_image_with_roi( geometry: Literal["rectangle", "circle", "polygon"], ) -> sigima.objects.ImageObj: """Create test image with ROIs""" data = np.zeros((500, 750), dtype=np.uint16) xc, yc, r = 500, 200, 100 rr, cc = draw.disk((yc, xc), r) data[rr, cc] = 10000 data[yc + r - 20 : yc + r, xc + r - 30 : xc + r - 10] = 50000 if geometry == "rectangle": coords = [xc - r, yc - r, 2 * r, 2 * r] elif geometry == "circle": coords = [xc, yc, r] else: raise NotImplementedError(f"Geometry {geometry} not implemented") ima = sigima.objects.create_image(f"Test image with ROI/{geometry}", data) ima.roi = sigima.objects.create_image_roi(geometry, coords, indices=True) return ima def test_roi_circ() -> None: """Run circular ROI application test scenario""" with datalab_test_app_context() as win: execenv.print("Circular ROI test:") panel = win.imagepanel for geometry in ("rectangle", "circle"): # model.ROI2DParam.geometries: ima = create_test_image_with_roi(geometry) panel.add_object(ima) helpers.print_obj_data_dimensions(ima) panel.processor.run_feature("stats") panel.processor.run_feature("centroid") # Extracting ROIs: for obj_nb in (1, 2): obj = panel[obj_nb] panel.objview.set_current_object(obj) params = obj.roi.to_params(obj) panel.processor.run_feature("extract_roi", params=params) if __name__ == "__main__": test_roi_circ() DataLab-1.1.0/datalab/tests/features/image/roi_manipulation_app_test.py000066400000000000000000000250241514014117600262600ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Image ROI manipulation application test (copy/paste, import/export)""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations import os import tempfile from typing import TYPE_CHECKING from sigima.io import read_roi from sigima.objects import NewImageParam, create_image_roi from sigima.tests.data import create_multigaussian_image from datalab.env import execenv from datalab.objectmodel import get_uuid from datalab.tests import datalab_test_app_context if TYPE_CHECKING: from datalab.gui.panel.image import ImagePanel SIZE = 200 # Image ROIs: IROI1 = [100, 100, 75, 100] # Rectangle IROI2 = [66, 100, 50] # Circle IROI3 = [100, 100, 100, 150, 150, 133] # Polygon def test_image_roi_copy_paste(): """Test image ROI copy and paste functionality""" with datalab_test_app_context(console=False) as win: execenv.print("Image ROI Copy/Paste test:") panel: ImagePanel = win.imagepanel param = NewImageParam.create(height=SIZE, width=SIZE) # Create first image with ROI ima1 = create_multigaussian_image(param) ima1.title = "Image with ROI" roi1 = create_image_roi("rectangle", IROI1) roi1.add_roi(create_image_roi("circle", IROI2)) ima1.roi = roi1 panel.add_object(ima1) # Create second image without ROI ima2 = create_multigaussian_image(param) ima2.title = "Image without ROI" panel.add_object(ima2) # Create third image without ROI ima3 = create_multigaussian_image(param) ima3.title = "Image without ROI 2" panel.add_object(ima3) execenv.print(" Initial state:") execenv.print(f" Image 1 ROI: {ima1.roi is not None}") execenv.print(f" Image 2 ROI: {ima2.roi is not None}") execenv.print(f" Image 3 ROI: {ima3.roi is not None}") # Select first image and copy its ROI panel.objview.set_current_item_id(get_uuid(ima1)) panel.copy_roi() execenv.print(" Copied ROI from Image 1") # Select second image and paste ROI panel.objview.set_current_item_id(get_uuid(ima2)) panel.paste_roi() execenv.print(" Pasted ROI to Image 2") # Verify that ima2 now has the same ROI as ima1 assert ima2.roi is not None, "Image 2 should have ROI after paste" assert len(ima2.roi) == len(ima1.roi), "ROI should have same number of regions" execenv.print(f" Image 2 now has {len(ima2.roi)} ROI regions") # Select third image and paste ROI (should create new ROI) panel.objview.set_current_item_id(get_uuid(ima3)) panel.paste_roi() execenv.print(" Pasted ROI to Image 3") assert ima3.roi is not None, "Image 3 should have ROI after paste" assert len(ima3.roi) == len(ima1.roi), "ROI should have same number of regions" execenv.print(f" Image 3 now has {len(ima3.roi)} ROI regions") # Test pasting to image that already has ROI (should combine) panel.objview.set_current_item_id(get_uuid(ima2)) panel.copy_roi() execenv.print(" Copied ROI from Image 2") # Add a different ROI to ima1 roi_new = create_image_roi("polygon", IROI3) ima1.roi.add_roi(roi_new) original_roi_count = len(ima1.roi) execenv.print(f" Image 1 now has {original_roi_count} ROI regions") # Paste the ROI from ima2 into ima1 (should combine) panel.objview.set_current_item_id(get_uuid(ima1)) panel.paste_roi() execenv.print(" Pasted ROI to Image 1 (should combine)") # Get fresh reference to ima1 from panel ima1_updated = panel.objmodel[get_uuid(ima1)] assert ima1_updated.roi is not None, "Image 1 should still have ROI" # After combining, ima1 should have more regions than before assert len(ima1_updated.roi) >= original_roi_count, ( f"Expected at least {original_roi_count} ROI regions, " f"got {len(ima1_updated.roi)}" ) execenv.print( f" Image 1 now has {len(ima1_updated.roi)} ROI regions (combined)" ) execenv.print(" ✓ Image ROI copy/paste test passed") def test_image_roi_copy_paste_multiple_selection(): """Test image ROI paste to multiple selected images""" with datalab_test_app_context(console=False) as win: execenv.print("Image ROI Copy/Paste with multiple selection test:") panel: ImagePanel = win.imagepanel param = NewImageParam.create(height=SIZE, width=SIZE) # Create source image with ROI ima_src = create_multigaussian_image(param) ima_src.title = "Source with ROI" roi = create_image_roi("rectangle", IROI1) roi.add_roi(create_image_roi("circle", IROI2)) ima_src.roi = roi panel.add_object(ima_src) # Create multiple target images without ROI target_images = [] for i in range(3): ima = create_multigaussian_image(param) ima.title = f"Target image {i + 1}" panel.add_object(ima) target_images.append(ima) execenv.print(f" Created {len(target_images)} target images") # Copy ROI from source panel.objview.set_current_item_id(get_uuid(ima_src)) panel.copy_roi() execenv.print(" Copied ROI from source image") # Select all target images target_uuids = [get_uuid(img) for img in target_images] panel.objview.set_current_item_id(target_uuids[0]) for uuid in target_uuids[1:]: panel.objview.set_current_item_id(uuid, extend=True) execenv.print(f" Selected {len(target_uuids)} target images") # Paste ROI to all selected images panel.paste_roi() execenv.print(" Pasted ROI to all selected images") # Verify all target images have ROI for i, img in enumerate(target_images): assert img.roi is not None, f"Target image {i + 1} should have ROI" assert len(img.roi) == len(ima_src.roi), ( f"Target image {i + 1} should have {len(ima_src.roi)} ROI regions" ) execenv.print(f" Target image {i + 1}: {len(img.roi)} ROI regions ✓") execenv.print(" ✓ Multiple selection paste test passed") def test_image_roi_import_export(): """Test image ROI import and export to/from file functionality""" with datalab_test_app_context(console=False) as win: execenv.print("Image ROI Import/Export test:") panel: ImagePanel = win.imagepanel param = NewImageParam.create(height=SIZE, width=SIZE) # Create first image with ROI ima1 = create_multigaussian_image(param) ima1.title = "Image with ROI" roi1 = create_image_roi("rectangle", IROI1) roi1.add_roi(create_image_roi("circle", IROI2)) roi1.add_roi(create_image_roi("polygon", IROI3)) ima1.roi = roi1 panel.add_object(ima1) original_roi_count = len(ima1.roi) execenv.print(f" Image 1 has {original_roi_count} ROI regions") # Export ROI to file roi_file = tempfile.mktemp(suffix=".dlabroi") try: execenv.print(" Exporting ROI to temporary file") # Select first image and export its ROI panel.objview.set_current_item_id(get_uuid(ima1)) panel.export_roi_to_file(roi_file) execenv.print(" ✓ ROI exported") # Verify file was created assert os.path.exists(roi_file), "ROI file should have been created" # Read the exported ROI directly to verify content exported_roi = read_roi(roi_file) assert len(exported_roi) == original_roi_count, ( f"Exported ROI should have {original_roi_count} regions" ) execenv.print(f" ✓ Exported ROI has {len(exported_roi)} regions") # Create second image without ROI ima2 = create_multigaussian_image(param) ima2.title = "Image without ROI" panel.add_object(ima2) assert ima2.roi is None, "Image 2 should not have ROI initially" # Import ROI from file to second image panel.objview.set_current_item_id(get_uuid(ima2)) panel.import_roi_from_file(roi_file) execenv.print(" Imported ROI to Image 2") # Get fresh reference to ima2 from panel ima2_updated = panel.objmodel[get_uuid(ima2)] assert ima2_updated.roi is not None, "Image 2 should have ROI after import" assert len(ima2_updated.roi) == original_roi_count, ( f"Imported ROI should have {original_roi_count} regions" ) execenv.print(f" ✓ Image 2 now has {len(ima2_updated.roi)} ROI regions") # Test importing ROI to image that already has ROI (should combine) ima3 = create_multigaussian_image(param) ima3.title = "Image with existing ROI" roi3 = create_image_roi("circle", [150, 150, 40]) ima3.roi = roi3 panel.add_object(ima3) initial_roi_count = len(ima3.roi) execenv.print(f" Image 3 has {initial_roi_count} ROI region initially") # Import ROI (should combine with existing) panel.objview.set_current_item_id(get_uuid(ima3)) panel.import_roi_from_file(roi_file) execenv.print(" Imported ROI to Image 3 (should combine)") # Get fresh reference to ima3 from panel ima3_updated = panel.objmodel[get_uuid(ima3)] assert ima3_updated.roi is not None, "Image 3 should still have ROI" # After combining, should have more regions assert len(ima3_updated.roi) >= initial_roi_count, ( f"Expected at least {initial_roi_count} ROI regions, " f"got {len(ima3_updated.roi)}" ) execenv.print( f" ✓ Image 3 now has {len(ima3_updated.roi)} ROI regions (combined)" ) finally: # Clean up temporary file if os.path.exists(roi_file): try: os.unlink(roi_file) except (PermissionError, OSError): pass # Ignore cleanup errors on Windows execenv.print(" ✓ Image ROI import/export test passed") if __name__ == "__main__": test_image_roi_copy_paste() test_image_roi_copy_paste_multiple_selection() test_image_roi_import_export() DataLab-1.1.0/datalab/tests/features/image/roigrid_unit_test.py000066400000000000000000000117371514014117600245530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ROI grid unit test.""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations from guidata.qthelpers import exec_dialog, qt_app_context from sigima.proc.image import Direction, ROIGridParam from sigima.tests.data import create_grid_of_gaussian_images from datalab.gui.roigrideditor import ImageGridROIEditor from datalab.utils import qthelpers as qth def test_roi_grid(screenshots: bool = False) -> None: """ROI grid test.""" with qt_app_context(): roi_editor = ImageGridROIEditor( parent=None, obj=create_grid_of_gaussian_images() ) if screenshots: roi_editor.show() qth.grab_save_window(roi_editor) exec_dialog(roi_editor) def test_roi_grid_geometry_headless() -> None: """Test ROI grid geometry in headless mode.""" img = create_grid_of_gaussian_images() # Create grid parameters gp = ROIGridParam() gp.nx, gp.ny = 2, 2 gp.xsize = gp.ysize = 50 gp.xtranslation = gp.ytranslation = 50 gp.xdirection = gp.ydirection = Direction.INCREASING with qt_app_context(): dlg = ImageGridROIEditor(parent=None, obj=img, gridparam=gp) # Set a small grid and sizes dlg.update_obj(update_item=False) roi = dlg.get_roi() assert roi is not None # 4 ROIs, centered in each cell assert len(list(roi)) == 4 titles = [r.title for r in roi] assert "ROI(1,1)" in titles and "ROI(2,2)" in titles # Check one ROI position approximately r00 = next(r for r in roi if r.title == "ROI(1,1)") _x0, _y0, dx, dy = r00.get_physical_coords(img) assert dx == img.width / 2 * 0.5 assert dy == img.height / 2 * 0.5 def test_roi_grid_custom_step() -> None: """Test ROI grid with custom xstep/ystep parameters. This test verifies the bug fix for Issue #XXX where grid ROI extraction was not working correctly for images with non-uniformly distributed features (e.g., laser spot arrays with gaps between spots). The bug was that xstep and ystep parameters were missing, so users couldn't adjust the spacing between ROIs when spots don't fill the entire image. """ img = create_grid_of_gaussian_images() # Test Case 1: Custom step to simulate tighter spacing (e.g., laser spots) gp = ROIGridParam() gp.nx, gp.ny = 3, 3 gp.xsize = gp.ysize = 20 # Small ROI size (20% of cell) gp.xtranslation = gp.ytranslation = 50 # Centered gp.xstep = gp.ystep = 75 # Tighter spacing (75% instead of 100%) gp.xdirection = gp.ydirection = Direction.INCREASING with qt_app_context(): dlg = ImageGridROIEditor(parent=None, obj=img, gridparam=gp) dlg.update_obj(update_item=False) roi = dlg.get_roi() assert roi is not None # 9 ROIs for 3x3 grid assert len(list(roi)) == 9 # Verify spacing is correctly applied r11 = next(r for r in roi if r.title == "ROI(1,1)") r12 = next(r for r in roi if r.title == "ROI(1,2)") x0_r11, _, _, _ = r11.get_physical_coords(img) x0_r12, _, _, _ = r12.get_physical_coords(img) # Expected spacing: (width / nx) * (xstep / 100) expected_spacing = (img.width / gp.nx) * (gp.xstep / 100.0) actual_spacing = x0_r12 - x0_r11 # Allow 1% tolerance for numerical precision assert abs(actual_spacing - expected_spacing) / expected_spacing < 0.01 # Test Case 2: Different X and Y steps gp2 = ROIGridParam() gp2.nx, gp2.ny = 2, 2 gp2.xsize = gp2.ysize = 30 gp2.xtranslation = gp2.ytranslation = 50 gp2.xstep = 80 # Tighter X spacing gp2.ystep = 120 # Wider Y spacing gp2.xdirection = gp2.ydirection = Direction.INCREASING with qt_app_context(): dlg2 = ImageGridROIEditor(parent=None, obj=img, gridparam=gp2) dlg2.update_obj(update_item=False) roi2 = dlg2.get_roi() assert roi2 is not None assert len(list(roi2)) == 4 # Verify X spacing (80%) r11 = next(r for r in roi2 if r.title == "ROI(1,1)") r12 = next(r for r in roi2 if r.title == "ROI(1,2)") x0_r11, y0_r11, _, _ = r11.get_physical_coords(img) x0_r12, _, _, _ = r12.get_physical_coords(img) expected_x_spacing = (img.width / gp2.nx) * 0.8 actual_x_spacing = x0_r12 - x0_r11 assert abs(actual_x_spacing - expected_x_spacing) / expected_x_spacing < 0.01 # Verify Y spacing (120%) r21 = next(r for r in roi2 if r.title == "ROI(2,1)") _, y0_r21, _, _ = r21.get_physical_coords(img) expected_y_spacing = (img.height / gp2.ny) * 1.2 actual_y_spacing = y0_r21 - y0_r11 assert abs(actual_y_spacing - expected_y_spacing) / expected_y_spacing < 0.01 if __name__ == "__main__": test_roi_grid_geometry_headless() test_roi_grid_custom_step() test_roi_grid(screenshots=True) DataLab-1.1.0/datalab/tests/features/image/side_by_side_app_test.py000066400000000000000000000027201514014117600253270ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ View images side-by-side test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations import numpy as np import sigima.objects from datalab.tests import datalab_test_app_context def test_view_images_side_by_side() -> None: """Test viewing multiple images side-by-side.""" with datalab_test_app_context() as win: panel = win.imagepanel # Create several test images with different content for i in range(5): x = np.linspace(-5, 5, 100) y = np.linspace(-5, 5, 100) xx, yy = np.meshgrid(x, y) # Different patterns for each image if i == 0: data = np.exp(-(xx**2 + yy**2) / (2 * (i + 1))) elif i == 1: data = np.sin(xx) * np.cos(yy) elif i == 2: data = np.abs(xx) + np.abs(yy) elif i == 3: data = xx**2 - yy**2 else: data = np.random.randn(100, 100) image = sigima.objects.create_image(f"Test Image {i + 1}", data) panel.add_object(image) # Select all images (1 to 5) panel.objview.select_objects(list(range(1, 6))) # Open side-by-side view panel.view_images_side_by_side() if __name__ == "__main__": test_view_images_side_by_side() DataLab-1.1.0/datalab/tests/features/macro/000077500000000000000000000000001514014117600204525ustar00rootroot00000000000000DataLab-1.1.0/datalab/tests/features/macro/__init__.py000066400000000000000000000000021514014117600225530ustar00rootroot00000000000000# DataLab-1.1.0/datalab/tests/features/macro/macro_app_test.py000066400000000000000000000014101514014117600240200ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Macro Panel application test (essentially for the screenshot...) """ # guitest: show from datalab import config from datalab.gui.main import DLMainWindow from datalab.utils import qthelpers as qth def test_macro(screenshots: bool = False) -> None: """Run image tools test scenario""" config.reset() # Reset configuration (remove configuration file and initialize it) with qth.datalab_app_context(exec_loop=True): win = DLMainWindow() win.show() win.set_current_panel("macro") win.macropanel.add_macro() if screenshots: qth.grab_save_window(win.macropanel, "macro_panel") if __name__ == "__main__": test_macro() DataLab-1.1.0/datalab/tests/features/macro/macroeditor_unit_test.py000066400000000000000000000167661514014117600254520ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Macro Panel unit tests ---------------------- The objective of this test is to check all the functionalities of the Macro Panel widget, by calling all its methods and checking the results. Some methods are not tested here, as they are tested in remote control tests (see datalab/tests/features/control/remoteclient_app.py): - `run_macro` - `stop_macro` All other methods should be tested here. """ # guitest: show import os.path as osp import time from guidata.qthelpers import qt_app_context from qtpy import QtWidgets as QW from datalab.env import execenv from datalab.gui.macroeditor import Macro from datalab.gui.panel.macro import MacroPanel from datalab.tests import datalab_test_app_context, helpers def get_macro_example_path() -> str: """Return macro example path""" path = helpers.get_temporary_directory() contents = """ # Simple DataLab macro example import numpy as np from datalab.control.proxy import RemoteProxy proxy = RemoteProxy() z = np.random.rand(20, 20) proxy.add_image("toto", z) proxy.calc("fft") print("All done! :)") """ filename = osp.join(path, "macro_example.py") with open(filename, "w", encoding="utf-8") as f: f.write(contents) return filename def test_macro_editor(): """Test dep viewer window""" with qt_app_context(exec_loop=True): widget = MacroPanel(None) widget.resize(800, 600) widget.show() # Create a new macro new_macro = widget.add_macro() assert new_macro is widget.get_macro() # Check out the macro title, serializable name, number, ... execenv.print("Macro title:", new_macro.title) execenv.print("Serializable name:", widget.get_serializable_name(new_macro)) nb1 = widget.get_number_from_macro(new_macro) nb2 = widget.get_number_from_title(new_macro.title) assert nb1 == nb2 and nb1 == 1 execenv.print("Macro number:", nb1) titles = widget.get_macro_titles() assert titles[0] == new_macro.title and len(titles) == 1 new_title = "New title" widget.rename_macro(1, new_title) assert widget.get_macro_titles()[0] == new_title with helpers.WorkdirRestoringTempDir() as tmpdir: fname = osp.join(tmpdir, "macro.py") widget.export_macro_to_file(1, fname) widget.import_macro_from_file(fname) imported_macro: Macro = widget.get_macro(2) assert imported_macro.title == new_macro.title assert imported_macro.get_code() == new_macro.get_code() widget.rename_macro(1, "Other title") widget.remove_macro(1) assert len(widget.get_macro_titles()) == 1 assert widget.get_macro_titles()[0] == imported_macro.title # Remove all macros widget.remove_all_objects() assert len(widget.get_macro_titles()) == 0 # Load a macro from file macro_path = get_macro_example_path() widget.import_macro_from_file(macro_path) assert len(widget.get_macro_titles()) == 1 assert widget.get_macro_titles()[0] == osp.basename(macro_path) def test_macro_unicode_encoding(): """Test that macros can print Unicode characters without encoding errors. This test verifies the fix for the UnicodeEncodeError that occurred on Windows systems with locales like cp1252 when macros printed Unicode characters. The test creates and runs a macro that prints various Unicode characters, simulating the scenario where RemoteProxy connection messages (which contain arrows →) would cause encoding errors on Windows with cp1252 locale. Without the UTF-8 encoding fix in Macro.run(), this test would fail with: UnicodeEncodeError: 'charmap' codec can't encode character '\u2192' """ with helpers.WorkdirRestoringTempDir(): with datalab_test_app_context(console=False) as win: win.set_current_panel("macro") # Create a macro that prints various Unicode characters macro = win.macropanel.add_macro() macro.title = "Unicode Test Macro" # This test verifies that Unicode characters can be printed successfully. # The macro prints Unicode characters without any encoding manipulation. # With the UTF-8 fix in Macro.run(), these print statements work correctly. # Without the fix, on systems with cp1252 locale, these would fail. # # Note: We cannot reliably simulate cp1252 locale in the test because: # 1. Modern Python often defaults to UTF-8 # 2. If we manually reconfigure to cp1252 in the macro, it overrides # any fix done before the macro code runs # 3. The PYTHONIOENCODING env var might be set system-wide # # This test serves as a regression test - it will catch if the fix # is removed, but only on systems that actually default to cp1252. unicode_code = """ import sys # Print encoding info for debugging print(f"stdout encoding: {sys.stdout.encoding}") print(f"stderr encoding: {sys.stderr.encoding}") # Print various Unicode characters that are not in cp1252 # On systems with cp1252 default locale, without the UTF-8 fix, # these would cause UnicodeEncodeError print("Testing Unicode output:") print(" → Arrow character (U+2192)") print(" ✓ Check mark (U+2713)") print(" • Bullet point (U+2022)") print(" … Ellipsis (U+2026)") print(" Emoji: 🎉 🚀 ⚡") # Simulate RemoteProxy connection message format print("Setting XML-RPC port... [input:None] →[execenv.xmlrpcport:None] OK") print("All Unicode tests passed! ✓") """ macro.set_code(unicode_code) # Run the macro and wait for completion execenv.print("Running Unicode test macro...") win.macropanel.run_macro() # Wait for macro to complete (with timeout) # We need to process Qt events for the QProcess signals to be delivered max_wait = 10 # seconds elapsed = 0 while macro.is_running() and elapsed < max_wait: QW.QApplication.processEvents() time.sleep(0.1) elapsed += 0.1 # Verify the macro completed (not still running) # If there was an encoding error, the process would have crashed assert not macro.is_running(), ( "Macro did not complete within timeout - " "likely failed with encoding error" ) # Check the exit code - should be 0 for success # With the UTF-8 fix, the macro completes successfully (exit code 0) # Without the fix, it crashes with UnicodeEncodeError (exit code 1) exit_code = macro.get_exit_code() assert exit_code == 0, ( f"Macro exited with error code {exit_code} - " f"likely UnicodeEncodeError when trying to print Unicode characters" ) execenv.print("✓ Unicode test macro completed successfully!") execenv.print( "Note: This test verifies Unicode support works. On systems with " "UTF-8 as default encoding, it may pass even without the fix. " "The fix is critical for Windows systems with cp1252 locale." ) # Clean up win.macropanel.remove_all_objects() if __name__ == "__main__": test_macro_editor() test_macro_unicode_encoding() DataLab-1.1.0/datalab/tests/features/signal/000077500000000000000000000000001514014117600206265ustar00rootroot00000000000000DataLab-1.1.0/datalab/tests/features/signal/__init__.py000066400000000000000000000000021514014117600227270ustar00rootroot00000000000000# DataLab-1.1.0/datalab/tests/features/signal/baseline_dialog_test.py000066400000000000000000000033141514014117600253410ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Baseline dialog test """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code # guitest: show from __future__ import annotations import numpy as np import sigima.objects import sigima.proc.signal as sips from guidata.qthelpers import exec_dialog, qt_app_context from sigima.tests.data import create_paracetamol_signal from sigima.viz import view_curves from datalab.env import execenv from datalab.widgets.signalbaseline import SignalBaselineDialog def test_signal_baseline_selection(): """Signal baseline selection dialog test""" sig = create_paracetamol_signal() with qt_app_context(): dlg = SignalBaselineDialog(sig) dlg.resize(640, 480) dlg.setObjectName(dlg.objectName() + "_00") # to avoid timestamp suffix exec_dialog(dlg) execenv.print(f"baseline: {dlg.get_baseline()}") execenv.print(f"X range: {dlg.get_x_range()}") # Check baseline value: i0, i1 = np.searchsorted(sig.x, dlg.get_x_range()) assert dlg.get_baseline() == sig.data[i0:i1].mean() def test_signal_baseline_dialog() -> None: """Test the signal baseline dialog for offset correction.""" with qt_app_context(): s1 = create_paracetamol_signal() dlg = SignalBaselineDialog(s1) if exec_dialog(dlg): param = sigima.objects.ROI1DParam() param.xmin, param.xmax = dlg.get_x_range() s2 = sips.offset_correction(s1, param) view_curves([s1, s2], title="Signal offset correction") if __name__ == "__main__": test_signal_baseline_selection() test_signal_baseline_dialog() DataLab-1.1.0/datalab/tests/features/signal/custom_signal_bug_unit_test.py000066400000000000000000000066711514014117600270140ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Test for Custom Signal creation bug fix ======================================== This test verifies that creating a Custom signal doesn't crash with AttributeError when xyarray is None. Bug report: "Créer un signal Custom déclenche immédiatement l'erreur AttributeError: 'NoneType' object has no attribute 'T'." """ import pytest from guidata.qthelpers import qt_app_context from datalab.gui.newobject import CustomSignalParam, create_signal_gui def test_custom_signal_param_initialization(): """Test that CustomSignalParam initializes properly""" with qt_app_context(): param = CustomSignalParam() # Initially, xyarray should be None assert param.xyarray is None # But other defaults should exist assert param.size == 10 assert param.xmin == 0.0 assert param.xmax == 1.0 def test_custom_signal_param_setup_array(): """Test that setup_array creates the xyarray properly""" with qt_app_context(): param = CustomSignalParam() assert param.xyarray is None # Call setup_array to initialize param.setup_array(size=10, xmin=0.0, xmax=1.0) # Now xyarray should exist and be transposable assert param.xyarray is not None assert param.xyarray.shape == (10, 2) # Should be able to transpose without error transposed = param.xyarray.T assert transposed.shape == (2, 10) def test_custom_signal_param_generate_1d_data(): """Test that generate_1d_data works even with None xyarray""" with qt_app_context(): param = CustomSignalParam() assert param.xyarray is None # generate_1d_data should call setup_array internally x, y = param.generate_1d_data() # Should succeed without AttributeError assert x is not None assert y is not None assert len(x) == param.size assert len(y) == param.size def test_custom_signal_creation_forces_edit_mode(): """Test that creating a custom signal with edit=False forces edit=True This test verifies the bug fix for: "Créer un signal Custom déclenche immédiatement l'erreur AttributeError: 'NoneType' object has no attribute 'T'." The bug occurred when edit=False was passed (the default), which caused setup_array to not be called, leaving xyarray as None, which then crashed when trying to access xyarray.T. The fix forces edit=True for CustomSignalParam to ensure the user is always prompted to set up the array properly. """ # This test can't actually test the dialog interaction, but we can verify # that the code doesn't crash immediately with AttributeError due to None xyarray # The test framework will handle the unattended mode appropriately with qt_app_context(): # In unattended mode, the dialogs will be auto-canceled, so this should # return None rather than crashing param = CustomSignalParam() # In actual usage with GUI, this would show dialogs # In unattended test mode, dialogs are auto-canceled # The important thing is it doesn't crash with AttributeError _signal = create_signal_gui(param, edit=False, parent=None) # In unattended mode, this will be None because dialogs are canceled # But it shouldn't have crashed with AttributeError if __name__ == "__main__": pytest.main([__file__, "-v"]) DataLab-1.1.0/datalab/tests/features/signal/deltax_dialog_unit_test.py000066400000000000000000000021541514014117600261000ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Signal delta x dialog unit test. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from guidata.qthelpers import exec_dialog, qt_app_context from sigima.tests.data import create_paracetamol_signal from sigima.tools.signal.pulse import full_width_at_y from datalab.widgets.signaldeltax import SignalDeltaXDialog def test_signal_delta_x_dialog(): """Test the SignalDeltaXDialog widget.""" sig = create_paracetamol_signal() with qt_app_context(): dlg = SignalDeltaXDialog(signal=sig) dlg.resize(640, 480) dlg.setObjectName(dlg.objectName() + "_00") # to avoid timestamp suffix exec_dialog(dlg) y = dlg.get_y_value() x0, y0, x1, y1 = dlg.get_coords() exp_x0, exp_y0, exp_x1, exp_y1 = full_width_at_y(sig.x, sig.y, y) assert (x0, y0, x1, y1) == (exp_x0, exp_y0, exp_x1, exp_y1), ( f"Expected: {(exp_x0, exp_y0, exp_x1, exp_y1)} but got: {(x0, y0, x1, y1)}" ) if __name__ == "__main__": test_signal_delta_x_dialog() DataLab-1.1.0/datalab/tests/features/signal/fft1d_app_test.py000066400000000000000000000013001514014117600240750ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Signal FFT application test. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from sigima.objects import CosineParam, create_signal_from_param from datalab.tests import datalab_test_app_context def test_fft1d_app(): """FFT application test.""" with datalab_test_app_context() as win: panel = win.signalpanel s1 = create_signal_from_param(CosineParam.create(size=10000)) panel.add_object(s1) panel.processor.run_feature("fft") panel.processor.run_feature("ifft") if __name__ == "__main__": test_fft1d_app() DataLab-1.1.0/datalab/tests/features/signal/filter_app_test.py000066400000000000000000000031231514014117600243630ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Frequential filtering application test. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.params from sigima.objects import create_signal_from_param from sigima.tests.helpers import check_array_result from sigima.tests.signal.pulse.pulse_unit_test import create_test_square_params from datalab.tests import datalab_test_app_context def test_signal_freq_filter_app(): """Signal frequency filtering application test.""" with datalab_test_app_context(console=False) as win: panel = win.signalpanel s1 = create_signal_from_param(create_test_square_params()) panel.add_object(s1) for feature, paramclass in ( ("lowpass", sigima.params.LowPassFilterParam), ("highpass", sigima.params.HighPassFilterParam), ("bandstop", sigima.params.BandStopFilterParam), ): for zero_padding in (True, False): panel.objview.select_objects([1]) # Select the first signal param = paramclass.create(method="brickwall", zero_padding=zero_padding) param.update_from_obj(s1) panel.processor.run_feature(feature, param) s2 = panel.objview.get_sel_objects()[0] check_array_result( f"{feature} filter output X data (zero_padding={zero_padding})", s2.x, s1.x, ) if __name__ == "__main__": test_signal_freq_filter_app() DataLab-1.1.0/datalab/tests/features/signal/fitdialog_unit_test.py000066400000000000000000000035751514014117600252520ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Curve fitting dialog test Testing fit dialogs: Gaussian, Lorentzian, Voigt, etc. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from guidata.qthelpers import qt_app_context from sigima.objects import NormalDistribution1DParam from sigima.tests.data import create_noisy_signal, get_test_signal from sigima.tools.signal.peakdetection import peak_indices from datalab.env import execenv from datalab.tests import helpers from datalab.widgets import fitdialog as fdlg def test_fit_dialog(): """Test function""" with qt_app_context(): # Multi-gaussian curve fitting test s1 = get_test_signal("paracetamol.txt") peakidx = peak_indices(s1.y) s2 = create_noisy_signal(NormalDistribution1DParam.create(sigma=5.0)) s3 = get_test_signal("gaussian_fit.txt") s4 = get_test_signal("piecewiseexponential_fit.txt") ep = execenv.print tn = helpers.get_default_test_name ep(fdlg.polynomial_fit(s2.x, s2.y, 4, name=tn("00"))) ep(fdlg.linear_fit(s2.x, s2.y, name=tn("01"))) ep(fdlg.gaussian_fit(s3.x, s3.y, name=tn("02"))) ep(fdlg.lorentzian_fit(s3.x, s3.y, name=tn("03"))) ep(fdlg.multigaussian_fit(s1.x, s1.y, peakidx, name=tn("04"))) ep(fdlg.multilorentzian_fit(s1.x, s1.y, peakidx, name=tn("05"))) ep(fdlg.voigt_fit(s3.x, s3.y, name=tn("06"))) ep(fdlg.exponential_fit(s2.x, s2.y, name=tn("07"))) ep(fdlg.sinusoidal_fit(s2.x, s2.y, name=tn("08"))) ep(fdlg.cdf_fit(s2.x, s2.y, name=tn("09"))) ep(fdlg.planckian_fit(s3.x, s3.y, name=tn("10"))) ep(fdlg.twohalfgaussian_fit(s3.x, s3.y, name=tn("11"))) ep(fdlg.piecewiseexponential_fit(s4.x, s4.y, name=tn("12"))) if __name__ == "__main__": test_fit_dialog() DataLab-1.1.0/datalab/tests/features/signal/interpolation_app_test.py000066400000000000000000000101631514014117600257670ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Interpolation application test and X-array compatibility behavior. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import numpy as np import sigima.params from sigima.objects import SignalObj, create_signal from datalab.tests import datalab_test_app_context def test_interpolation_app(): """Test interpolation feature in DataLab application. This test ensures that interpolation works correctly when signals have different X arrays (which is the whole point of interpolation). """ with datalab_test_app_context() as win: panel = win.signalpanel # Create interpolation parameters param = sigima.params.InterpolationParam.create(method="linear") # Create X arrays x_dense = np.linspace(0, 10, 100) x_sparse = np.linspace(1, 9, 20) for x1, x2 in [(x_dense, x_sparse), (x_sparse, x_dense)]: context = f"Source X {'dense' if len(x1) > len(x2) else 'sparse'} → " context += f"Target X {'sparse' if len(x1) > len(x2) else 'dense'}" # Create source signal with dense sampling y1 = np.sin(x1) sig1 = create_signal("Source signal", x1, y1) panel.add_object(sig1) # Create target signal with sparse sampling and different range y2 = np.zeros_like(x2) # Y values don't matter for interpolation target sig2 = create_signal("Target X values", x2, y2) panel.add_object(sig2) # This is the key test - interpolation should work even when X arrays differ # The X-array compatibility check should NOT interfere panel.objview.select_objects([sig1]) panel.processor.run_feature("interpolate", sig2, param) # Verify the result result_objects = panel.objview.get_sel_objects() assert len(result_objects) == 1, ( f"[{context}] Should have one interpolated result" ) result = result_objects[0] assert isinstance(result, SignalObj), ( f"[{context}] Result should be a SignalObj" ) assert result.x.shape == sig2.x.shape, ( f"[{context}] Result X should match target X shape" ) assert np.allclose(result.x, sig2.x), ( f"[{context}] Result X should match target X" ) expected_y = np.interp(x2, x1, y1) assert np.allclose(result.y, expected_y, atol=1e-10), ( f"[{context}] Result Y should be properly interpolated" ) def test_xarray_compatibility_still_works_for_other_operations(): """Test that X-array compatibility still works for operations that need it. This test ensures that the fix for interpolation doesn't break X-array compatibility for other operations like addition. """ with datalab_test_app_context() as win: panel = win.signalpanel # Create signals with different X arrays (should trigger compatibility check) x1 = np.linspace(0, 10, 100) y1 = np.ones_like(x1) sig1 = create_signal("Signal 1", x1, y1) panel.add_object(sig1) x2 = np.linspace(0, 10, 50) # Different sampling y2 = np.ones_like(x2) sig2 = create_signal("Signal 2", x2, y2) panel.add_object(sig2) # Test addition (should still use X-array compatibility) panel.objview.select_objects([sig1, sig2]) panel.processor.run_feature("addition") # Get the result result_objects = panel.objview.get_sel_objects() assert len(result_objects) == 1, "Should have one addition result" result = result_objects[0] # For addition, the X-array compatibility should have handled the different # arrays. The result should have consistent X values and proper Y values (2.0) assert np.allclose(result.y, 2.0, atol=1e-10), ( "Addition result should be 2.0 (1.0 + 1.0)" ) if __name__ == "__main__": test_interpolation_app() DataLab-1.1.0/datalab/tests/features/signal/loadbigsignal_app_test.py000066400000000000000000000053311514014117600257000ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Load big signal application test: - Generate a big signal (open an existing CSV file and create a dummy CSV file by duplicating the data several times) - Load the big signal in the signal panel """ # guitest: show import os.path as osp import numpy as np import pandas as pd import pytest from datalab.env import execenv from datalab.tests import datalab_test_app_context, helpers def create_large_random_dataframe(nrows: int, ncols: int) -> pd.DataFrame: """Create a large DataFrame with random data. Args: nrows: The number of rows to generate. ncols: The number of columns to generate. """ # The number of initial random rows to generate initial_rows = min(nrows, 1000) # Generate the initial "SignalXX" data with random values initial_signal_data = np.random.rand(initial_rows, ncols) # Duplicate the "SignalXX" data to fill the desired number of rows full_signal_data = np.tile( initial_signal_data, (nrows // initial_rows + (1 if nrows % initial_rows > 0 else 0), 1), ) # If nrows is not an exact multiple of initial_rows, trim the excess rows full_signal_data = full_signal_data[:nrows, :] # Generate the "Time" column as a sequential array from 0 to nrows-1 time_data = np.arange(nrows).reshape(-1, 1) # Combine the "Time" column with the "SignalXX" data full_data = np.hstack((time_data, full_signal_data)) # Prepare the column names column_names = ["Time"] + [f"Signal{str(i).zfill(2)}" for i in range(1, ncols + 1)] # Create and return the DataFrame df = pd.DataFrame(full_data, columns=column_names) return df @pytest.mark.parametrize("nrows, ncols", [(10000, 16)]) def test_loadbigsignal_app(nrows: int, ncols: int) -> None: """Load big signal application test""" with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context() as win: execenv.print("Loading big signal application test:") execenv.print(" - Working in temporary directory:", tmpdir) execenv.print(f" - Creating a big dataset ({nrows} rows, {ncols} columns)") df = create_large_random_dataframe(nrows, ncols) big_csv = osp.join(tmpdir, "big.csv") df.to_csv(big_csv, index=False) execenv.print(" - Loading the big signal in the signal panel") panel = win.signalpanel panel.load_from_files([big_csv]) execenv.print(" - Saving the big signal") panel.save_to_files([big_csv.replace(".csv", "_copy.csv")]) execenv.print("OK") if __name__ == "__main__": test_loadbigsignal_app(1000000, 16) DataLab-1.1.0/datalab/tests/features/signal/multiple_rois_unit_test.py000066400000000000000000000112151514014117600261650ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Test for multiple geometry results with multiple ROIs ====================================================== This test verifies that multiple geometry results (e.g., FWHM and FW1e2) can coexist when computed on signals with multiple ROIs (e.g., multiple pulse peaks). """ from __future__ import annotations import numpy as np import sigima.objects as sio import sigima.params import sigima.proc.signal as sig_proc from datalab.adapters_metadata import GeometryAdapter def test_multiple_results_with_multiple_rois(): """Test that FWHM and FW1e2 work correctly with multiple ROIs.""" # Create a signal with three Gaussian pulses x = np.linspace(0, 30, 3000) # Three pulses at different positions pulse1 = 1.0 * np.exp(-((x - 5) ** 2) / 2) # Peak at x=5, sigma=1 pulse2 = 0.8 * np.exp(-((x - 15) ** 2) / 4) # Peak at x=15, sigma=2 pulse3 = 1.2 * np.exp(-((x - 25) ** 2) / 1) # Peak at x=25, sigma=0.707 y = pulse1 + pulse2 + pulse3 signal = sio.create_signal("Triple Pulse Signal", x, y) # Create three ROIs around each pulse (pass all coordinates at once) signal.roi = sio.create_signal_roi( [[0.0, 10.0], [10.0, 20.0], [20.0, 30.0]] # Three ROI ranges ) roi_count = len(list(signal.iterate_roi_indices())) print(f"Signal has {roi_count} ROIs") fwhm_param = sigima.params.FWHMParam() fwhm_param.method = "zero-crossing" fwhm_result = sig_proc.fwhm(signal, fwhm_param) assert fwhm_result is not None, "FWHM computation failed" print(f"FWHM result has {len(fwhm_result.coords)} rows") assert len(fwhm_result.coords) == 3, "Expected 3 FWHM measurements (one per ROI)" GeometryAdapter(fwhm_result).add_to(signal) # Compute FW1e2 (should also get one result per ROI) fw1e2_result = sig_proc.fw1e2(signal) assert fw1e2_result is not None, "FW1e2 computation failed" print(f"FW1e² result has {len(fw1e2_result.coords)} rows") assert len(fw1e2_result.coords) == 3, "Expected 3 FW1e2 measurements (one per ROI)" GeometryAdapter(fw1e2_result).add_to(signal) # Verify both results are stored separately results = list(GeometryAdapter.iterate_from_obj(signal)) assert len(results) == 2, f"Expected 2 distinct results, got {len(results)}" # Verify the titles titles = {adapter.title for adapter in results} assert "fwhm" in titles, "FWHM result not found" assert "fw1e2" in titles, "FW1e2 result not found" # Verify each result has measurements for all 3 ROIs for adapter in results: assert len(adapter.result.coords) == 3, ( f"{adapter.title} should have 3 measurements" ) # Verify ROI indices are correct roi_indices = adapter.result.roi_indices assert roi_indices is not None, f"{adapter.title} should have ROI indices" assert len(roi_indices) == 3, f"{adapter.title} should have 3 ROI indices" assert set(roi_indices) == {0, 1, 2}, ( f"{adapter.title} ROI indices should be [0, 1, 2]" ) print(f"\n{adapter.title.upper()} measurements:") for coords, roi_idx in zip(adapter.result.coords, roi_indices): width = abs(coords[2] - coords[0]) print(f" ROI {roi_idx}: width = {width:.4f}") print("\n✓ Multiple results with multiple ROIs work correctly") def test_roi_filtering(): """Test that we can filter results by ROI index.""" # Create a signal with two pulses x = np.linspace(0, 20, 2000) pulse1 = np.exp(-((x - 5) ** 2) / 2) pulse2 = np.exp(-((x - 15) ** 2) / 2) y = pulse1 + pulse2 signal = sio.create_signal("Dual Pulse Signal", x, y) # Create two ROIs (pass all coordinates at once) signal.roi = sio.create_signal_roi([[0.0, 10.0], [10.0, 20.0]]) # Compute FWHM fwhm_param = sigima.params.FWHMParam() fwhm_param.method = "zero-crossing" fwhm_result = sig_proc.fwhm(signal, fwhm_param) assert fwhm_result is not None assert len(fwhm_result.coords) == 2 GeometryAdapter(fwhm_result).add_to(signal) # Retrieve and filter by ROI adapters = list(GeometryAdapter.iterate_from_obj(signal)) assert len(adapters) == 1 adapter = adapters[0] # Get data for specific ROI roi0_df = adapter.get_roi_data(0) roi1_df = adapter.get_roi_data(1) assert len(roi0_df) == 1, "ROI 0 should have 1 measurement" assert len(roi1_df) == 1, "ROI 1 should have 1 measurement" print("\n✓ ROI filtering works correctly") if __name__ == "__main__": test_multiple_results_with_multiple_rois() test_roi_filtering() print("\n✅ All multi-ROI tests passed!") DataLab-1.1.0/datalab/tests/features/signal/pulse_features_app_test.py000066400000000000000000000074631514014117600261370ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Pulse features application test. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from sigima.objects import ( SignalObj, TableKind, TableResult, create_signal_from_param, create_signal_roi, ) from sigima.params import PulseFeaturesParam from sigima.tests.helpers import check_scalar_result from sigima.tests.signal.pulse.pulse_unit_test import ( create_test_square_params, create_test_step_params, ) from datalab.adapters_metadata import TableAdapter from datalab.gui.panel.signal import SignalPanel from datalab.tests import datalab_test_app_context from datalab.utils import qthelpers as qth def __check_table(obj: SignalObj) -> TableResult: """Check that the object has a pulse features table.""" tables = list(TableAdapter.iterate_from_obj(obj)) assert len(tables) == 1 table = tables[0].result assert table.kind == TableKind.PULSE_FEATURES return table def __add_signal_and_check_pulse_features( panel: SignalPanel, obj: SignalObj, assertions: dict[str, float] ) -> None: """Add signal to the application and check that pulse features are extracted.""" panel.add_object(obj) param = PulseFeaturesParam() param.update_from_obj(obj) panel.processor.run_feature("extract_pulse_features", param) table = __check_table(obj) for name, expected_value in assertions.items(): if isinstance(expected_value, str): assert table[name][0] == expected_value else: check_scalar_result(name, table[name][0], expected_value, rtol=1e-2) def test_pulse_features_app(): """Pulse features application test.""" with datalab_test_app_context(console=False) as win: panel = win.signalpanel # Add first signal and extract features s1 = create_signal_from_param(create_test_step_params()) __add_signal_and_check_pulse_features( panel, s1, { "signal_shape": "step", "polarity": 1.0, "amplitude": 4.9, "rise_time": 1.5, }, ) # Add second signal and extract features s2 = create_signal_from_param(create_test_square_params()) __add_signal_and_check_pulse_features( panel, s2, { "signal_shape": "square", "polarity": 1.0, "amplitude": 4.94, "rise_time": 1.60, "fall_time": 3.95, "fwhm": 5.49, }, ) # Select the two signals and show the results table panel.objview.select_objects([s1, s2]) panel.show_results() # Define a ROI, just to test that it works (⚠️ it makes no sense here): # it tests that the "comparison rows" features work correctly with ROIs. # TODO: Maybe we should test this part in a more appropriate place, like in # a test for the "statistics" feature. roi = create_signal_roi([[0.650227, 5.8], [1.1596, 9.09509]]) s1.roi = roi s2.roi = roi.copy() panel.processor.run_feature("stats") panel.show_results() def pulse_features_screenshot(): """Generate pulse features screenshot.""" with datalab_test_app_context(size=(1400, 600), console=False) as win: panel = win.signalpanel s1 = create_signal_from_param(create_test_step_params()) panel.add_object(s1) param = PulseFeaturesParam() param.update_from_obj(s1) panel.processor.run_feature("extract_pulse_features", param) qth.grab_save_window(win.signalview, "s_pulse_features") if __name__ == "__main__": # test_pulse_features_app() pulse_features_screenshot() DataLab-1.1.0/datalab/tests/features/signal/pulse_features_roi_app_test.py000066400000000000000000000035051514014117600270010ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Pulse features with ROIs application test. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations from sigima.objects import SignalObj, TableResult from sigima.params import PulseFeaturesParam from sigima.tests.signal.pulse.pulse_features_roi_unit_test import ( check_results_equal, generate_source_signal, ) from datalab.gui.panel.signal import SignalPanel from datalab.tests import datalab_test_app_context def __extract_pulse_features(panel: SignalPanel, obj: SignalObj) -> TableResult: """Extract pulse features.""" panel.objview.select_objects([obj]) param = PulseFeaturesParam() param.update_from_obj(obj) rdata = panel.processor.run_feature("extract_pulse_features", param) assert len(rdata.results) == 1 return rdata.results[0] def test_pulse_features_roi_app(): """Pulse features with ROIs application test.""" with datalab_test_app_context(console=False) as win: panel = win.signalpanel # Test signal with multiple ROIs defined around peaks of the spectrum sig = generate_source_signal() panel.add_object(sig) pf_sig = __extract_pulse_features(panel, sig) # Extract ROIs in separate signals and test each one panel.processor.run_feature("extract_roi", params=sig.roi.to_params(sig)) pf_extracted_sigs = [] for nb_sig in (2, 3, 4): extracted_sig = panel.objmodel.get_object_from_number(nb_sig) pf_extracted_sig = __extract_pulse_features(panel, extracted_sig) pf_extracted_sigs.append(pf_extracted_sig) check_results_equal(pf_sig, pf_extracted_sigs) if __name__ == "__main__": test_pulse_features_roi_app() DataLab-1.1.0/datalab/tests/features/signal/roi_app_test.py000066400000000000000000000050451514014117600236740ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Signal ROI application test""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations from typing import TYPE_CHECKING import pytest import sigima.params as sigima_param from sigima.objects import SignalROI, create_signal_roi from sigima.tests.data import create_paracetamol_signal from datalab.config import Conf from datalab.env import execenv from datalab.tests import datalab_test_app_context, helpers if TYPE_CHECKING: from datalab.gui.panel.signal import SignalPanel SIZE = 200 # Signal ROIs: SROI1 = [26, 41] SROI2 = [125, 146] def __run_signal_computations(panel: SignalPanel): """Test all signal features related to ROI""" panel.processor.run_feature("fwhm", sigima_param.FWHMParam()) panel.processor.run_feature("fw1e2") panel.processor.run_feature("histogram", sigima_param.HistogramParam()) roi = SignalROI() panel.processor.compute_roi_extraction(roi) def test_signal_roi_app(screenshots: bool = False) -> None: """Run Signal ROI application test scenario Args: screenshots: If True, take screenshots during the test. """ with datalab_test_app_context(console=False) as win: execenv.print("Signal ROI application test:") panel = win.signalpanel sig1 = create_paracetamol_signal(SIZE) panel.add_object(sig1) __run_signal_computations(panel) sig2 = create_paracetamol_signal(SIZE) sig2.roi = create_signal_roi([SROI1, SROI2], indices=True) for singleobj in (False, True): with Conf.proc.extract_roi_singleobj.temp(singleobj): sig2_i = sig2.copy() panel.add_object(sig2_i) helpers.print_obj_data_dimensions(sig2_i, indent=1) panel.processor.edit_roi_graphically() if screenshots: win.statusBar().hide() win.take_screenshot("s_roi_signal") __run_signal_computations(panel) @pytest.mark.skip(reason="This test is only for manual testing") def test_signal_roi_basic_app(): """Run Signal ROI basic application test scenario""" with datalab_test_app_context(console=False) as win: panel = win.signalpanel sig1 = create_paracetamol_signal(SIZE) panel.add_object(sig1) panel.processor.edit_roi_graphically() if __name__ == "__main__": test_signal_roi_basic_app() test_signal_roi_app(screenshots=False) DataLab-1.1.0/datalab/tests/features/signal/roi_manipulation_app_test.py000066400000000000000000000242521514014117600264550ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Signal ROI manipulation application test (copy/paste, import/export)""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations import os import tempfile from typing import TYPE_CHECKING from sigima.io import read_roi from sigima.objects import create_signal_roi from sigima.tests.data import create_paracetamol_signal from datalab.env import execenv from datalab.objectmodel import get_uuid from datalab.tests import datalab_test_app_context if TYPE_CHECKING: from datalab.gui.panel.signal import SignalPanel SIZE = 200 # Signal ROIs: SROI1 = [26, 41] SROI2 = [125, 146] SROI3 = [60, 80] def test_signal_roi_copy_paste(): """Test signal ROI copy and paste functionality""" with datalab_test_app_context(console=False) as win: execenv.print("Signal ROI Copy/Paste test:") panel: SignalPanel = win.signalpanel # Create first signal with ROI sig1 = create_paracetamol_signal(SIZE) sig1.title = "Signal with ROI" roi1 = create_signal_roi([SROI1, SROI2], indices=True) sig1.roi = roi1 panel.add_object(sig1) # Create second signal without ROI sig2 = create_paracetamol_signal(SIZE) sig2.title = "Signal without ROI" panel.add_object(sig2) # Create third signal without ROI sig3 = create_paracetamol_signal(SIZE) sig3.title = "Signal without ROI 2" panel.add_object(sig3) execenv.print(" Initial state:") execenv.print(f" Signal 1 ROI: {sig1.roi is not None}") execenv.print(f" Signal 2 ROI: {sig2.roi is not None}") execenv.print(f" Signal 3 ROI: {sig3.roi is not None}") # Select first signal and copy its ROI panel.objview.set_current_item_id(get_uuid(sig1)) panel.copy_roi() execenv.print(" Copied ROI from Signal 1") # Select second signal and paste ROI panel.objview.set_current_item_id(get_uuid(sig2)) panel.paste_roi() execenv.print(" Pasted ROI to Signal 2") # Verify that sig2 now has the same ROI as sig1 assert sig2.roi is not None, "Signal 2 should have ROI after paste" assert len(sig2.roi) == len(sig1.roi), "ROI should have same number of regions" execenv.print(f" Signal 2 now has {len(sig2.roi)} ROI regions") # Select third signal and paste ROI (should create new ROI) panel.objview.set_current_item_id(get_uuid(sig3)) panel.paste_roi() execenv.print(" Pasted ROI to Signal 3") assert sig3.roi is not None, "Signal 3 should have ROI after paste" assert len(sig3.roi) == len(sig1.roi), "ROI should have same number of regions" execenv.print(f" Signal 3 now has {len(sig3.roi)} ROI regions") # Test pasting to signal that already has ROI (should combine) panel.objview.set_current_item_id(get_uuid(sig2)) panel.copy_roi() execenv.print(" Copied ROI from Signal 2") # Add a different ROI to sig1 roi_new = create_signal_roi([SROI3], indices=True) sig1.roi = sig1.roi.combine_with(roi_new) original_roi_count = len(sig1.roi) execenv.print(f" Signal 1 now has {original_roi_count} ROI regions") # Paste the ROI from sig2 into sig1 (should combine) panel.objview.set_current_item_id(get_uuid(sig1)) panel.paste_roi() execenv.print(" Pasted ROI to Signal 1 (should combine)") # Get fresh reference to sig1 from panel sig1_updated = panel.objmodel[get_uuid(sig1)] assert sig1_updated.roi is not None, "Signal 1 should still have ROI" # After combining, sig1 should have more regions than before assert len(sig1_updated.roi) >= original_roi_count, ( f"Expected at least {original_roi_count} ROI regions, " f"got {len(sig1_updated.roi)}" ) execenv.print( f" Signal 1 now has {len(sig1_updated.roi)} ROI regions (combined)" ) execenv.print(" ✓ Signal ROI copy/paste test passed") def test_signal_roi_copy_paste_multiple_selection(): """Test signal ROI paste to multiple selected signals""" with datalab_test_app_context(console=False) as win: execenv.print("Signal ROI Copy/Paste with multiple selection test:") panel: SignalPanel = win.signalpanel # Create source signal with ROI sig_src = create_paracetamol_signal(SIZE) sig_src.title = "Source with ROI" roi = create_signal_roi([SROI1, SROI2], indices=True) sig_src.roi = roi panel.add_object(sig_src) # Create multiple target signals without ROI target_signals = [] for i in range(3): sig = create_paracetamol_signal(SIZE) sig.title = f"Target signal {i + 1}" panel.add_object(sig) target_signals.append(sig) execenv.print(f" Created {len(target_signals)} target signals") # Copy ROI from source panel.objview.set_current_item_id(get_uuid(sig_src)) panel.copy_roi() execenv.print(" Copied ROI from source signal") # Select all target signals target_uuids = [get_uuid(sig) for sig in target_signals] panel.objview.set_current_item_id(target_uuids[0]) for uuid in target_uuids[1:]: panel.objview.set_current_item_id(uuid, extend=True) execenv.print(f" Selected {len(target_uuids)} target signals") # Paste ROI to all selected signals panel.paste_roi() execenv.print(" Pasted ROI to all selected signals") # Verify all target signals have ROI for i, sig in enumerate(target_signals): assert sig.roi is not None, f"Target signal {i + 1} should have ROI" assert len(sig.roi) == len(sig_src.roi), ( f"Target signal {i + 1} should have {len(sig_src.roi)} ROI regions" ) execenv.print(f" Target signal {i + 1}: {len(sig.roi)} ROI regions ✓") execenv.print(" ✓ Multiple selection paste test passed") def test_signal_roi_import_export(): """Test signal ROI import and export to/from file functionality""" with datalab_test_app_context(console=False) as win: execenv.print("Signal ROI Import/Export test:") panel: SignalPanel = win.signalpanel # Create first signal with ROI sig1 = create_paracetamol_signal(SIZE) sig1.title = "Signal with ROI" roi1 = create_signal_roi([SROI1, SROI2, SROI3], indices=True) sig1.roi = roi1 panel.add_object(sig1) original_roi_count = len(sig1.roi) execenv.print(f" Signal 1 has {original_roi_count} ROI regions") # Export ROI to file roi_file = tempfile.mktemp(suffix=".dlabroi") try: execenv.print(" Exporting ROI to temporary file") # Select first signal and export its ROI panel.objview.set_current_item_id(get_uuid(sig1)) panel.export_roi_to_file(roi_file) execenv.print(" ✓ ROI exported") # Verify file was created assert os.path.exists(roi_file), "ROI file should have been created" # Read the exported ROI directly to verify content exported_roi = read_roi(roi_file) assert len(exported_roi) == original_roi_count, ( f"Exported ROI should have {original_roi_count} regions" ) execenv.print(f" ✓ Exported ROI has {len(exported_roi)} regions") # Create second signal without ROI sig2 = create_paracetamol_signal(SIZE) sig2.title = "Signal without ROI" panel.add_object(sig2) assert sig2.roi is None, "Signal 2 should not have ROI initially" # Import ROI from file to second signal panel.objview.set_current_item_id(get_uuid(sig2)) panel.import_roi_from_file(roi_file) execenv.print(" Imported ROI to Signal 2") # Get fresh reference to sig2 from panel sig2_updated = panel.objmodel[get_uuid(sig2)] assert sig2_updated.roi is not None, "Signal 2 should have ROI after import" assert len(sig2_updated.roi) == original_roi_count, ( f"Imported ROI should have {original_roi_count} regions" ) execenv.print(f" ✓ Signal 2 now has {len(sig2_updated.roi)} ROI regions") # Test importing ROI to signal that already has ROI (should combine) sig3 = create_paracetamol_signal(SIZE) sig3.title = "Signal with existing ROI" roi3 = create_signal_roi([[90, 110]], indices=True) sig3.roi = roi3 panel.add_object(sig3) initial_roi_count = len(sig3.roi) execenv.print(f" Signal 3 has {initial_roi_count} ROI region initially") # Import ROI (should combine with existing) panel.objview.set_current_item_id(get_uuid(sig3)) panel.import_roi_from_file(roi_file) execenv.print(" Imported ROI to Signal 3 (should combine)") # Get fresh reference to sig3 from panel sig3_updated = panel.objmodel[get_uuid(sig3)] assert sig3_updated.roi is not None, "Signal 3 should still have ROI" # After combining, should have more regions assert len(sig3_updated.roi) >= initial_roi_count, ( f"Expected at least {initial_roi_count} ROI regions, " f"got {len(sig3_updated.roi)}" ) execenv.print( f" ✓ Signal 3 now has {len(sig3_updated.roi)} ROI regions (combined)" ) finally: # Clean up temporary file if os.path.exists(roi_file): try: os.unlink(roi_file) except (PermissionError, OSError): pass # Ignore cleanup errors on Windows execenv.print(" ✓ Signal ROI import/export test passed") if __name__ == "__main__": test_signal_roi_copy_paste() test_signal_roi_copy_paste_multiple_selection() test_signal_roi_import_export() DataLab-1.1.0/datalab/tests/features/signal/select_xy_cursor_unit_test.py000066400000000000000000000031521514014117600266730ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Signal horizontal or vertical cursor selection unit test. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from typing import Literal import numpy as np import pytest from guidata.qthelpers import exec_dialog, qt_app_context from sigima.tests.data import create_paracetamol_signal from sigima.tools.signal.features import find_x_values_at_y from datalab.env import execenv from datalab.widgets.signalcursor import SignalCursorDialog @pytest.mark.parametrize("cursor_orientation", ["horizontal", "vertical"]) def test_signal_cursor_selection( cursor_orientation: Literal["horizontal", "vertical"], ) -> None: """Parametrized signal cursor selection unit test.""" sig = create_paracetamol_signal() with qt_app_context(): dlg = SignalCursorDialog(signal=sig, cursor_orientation=cursor_orientation) dlg.resize(640, 480) dlg.setObjectName(dlg.objectName() + "_00") # to avoid timestamp suffix exec_dialog(dlg) x, y = dlg.get_cursor_position() if cursor_orientation == "horizontal": execenv.print(f"X value: {x}") x_sig = find_x_values_at_y(sig.x, sig.y, y)[0] assert x == x_sig, f"Expected {x_sig}, got {x}" else: execenv.print(f"Y value: {y}") y_sig = sig.y[np.searchsorted(sig.x, x)] assert y == y_sig, f"Expected {y_sig}, got {y}" if __name__ == "__main__": test_signal_cursor_selection(cursor_orientation="horizontal") test_signal_cursor_selection(cursor_orientation="vertical") DataLab-1.1.0/datalab/tests/features/signal/signalpeakdetection_dialog_test.py000066400000000000000000000017231514014117600275760ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Signal peak detection dialog test. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from guidata.qthelpers import exec_dialog, qt_app_context from sigima.tests.data import get_test_signal from datalab.env import execenv from datalab.widgets.signalpeak import SignalPeakDetectionDialog def test_peak1d_dialog(): """Signal peak dialog test""" with qt_app_context(): s = get_test_signal("paracetamol.txt") dlg = SignalPeakDetectionDialog(s) dlg.resize(640, 300) plot = dlg.get_plot() plot.set_axis_limits(plot.xBottom, 16, 30) dlg.setObjectName(dlg.objectName() + "_00") # to avoid timestamp suffix exec_dialog(dlg) execenv.print("peaks:") execenv.pprint(dlg.get_peaks()) execenv.pprint(dlg.get_min_dist()) if __name__ == "__main__": test_peak1d_dialog() DataLab-1.1.0/datalab/tests/features/signal/signals_to_image_app_test.py000066400000000000000000000066761514014117600264220ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Signals to image conversion application test""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show from __future__ import annotations import sigima.params from sigima.enums import NormalizationMethod, SignalsToImageOrientation from sigima.objects import SignalTypes from sigima.tests.data import create_periodic_signal from datalab.env import execenv from datalab.tests import datalab_test_app_context SIZE = 100 N_SIGNALS = 10 def test_signals_to_image_app(screenshots: bool = False) -> None: """Run signals to image conversion application test scenario Args: screenshots: If True, take screenshots during the test. """ with datalab_test_app_context(console=False) as win: execenv.print("Signals to image conversion application test:") # Create multiple test signals for i in range(N_SIGNALS): sig = create_periodic_signal( SignalTypes.SINE, freq=50.0 + i * 10.0, size=SIZE, a=(i + 1) * 0.1 ) sig.title = f"Signal {i + 1}" win.signalpanel.add_object(sig) # Select all signals win.signalpanel.objview.select_objects(list(range(1, N_SIGNALS + 1))) # Test without normalization, as rows p = sigima.params.SignalsToImageParam() p.orientation = SignalsToImageOrientation.ROWS p.normalize = False win.signalpanel.processor.run_feature("signals_to_image", p) if screenshots: win.statusBar().hide() win.take_screenshot("s_signals_to_image_rows") # Select all signals again win.set_current_panel("signal") win.signalpanel.objview.select_objects(list(range(1, N_SIGNALS + 1))) # Test without normalization, as columns p.orientation = SignalsToImageOrientation.COLUMNS win.signalpanel.processor.run_feature("signals_to_image", p) if screenshots: win.take_screenshot("s_signals_to_image_columns") # Select all signals again win.set_current_panel("signal") win.signalpanel.objview.select_objects(list(range(1, N_SIGNALS + 1))) # Test with normalization, as rows p.orientation = SignalsToImageOrientation.ROWS p.normalize = True p.normalize_method = NormalizationMethod.MAXIMUM win.signalpanel.processor.run_feature("signals_to_image", p) if screenshots: win.take_screenshot("s_signals_to_image_normalized") # Verify that images were created assert len(win.imagepanel) == 3, f"Expected 3 images, got {len(win.imagepanel)}" # Switch to image panel to verify dimensions win.set_current_panel("image") # Verify image dimensions - get objects directly from object model images = win.imagepanel.objmodel.get_all_objects() assert len(images) == 3, f"Expected 3 images in object model, got {len(images)}" assert images[0].data.shape == ( N_SIGNALS, SIZE, ), f"Expected shape ({N_SIGNALS}, {SIZE}), got {images[0].data.shape}" assert images[1].data.shape == ( SIZE, N_SIGNALS, ), f"Expected shape ({SIZE}, {N_SIGNALS}), got {images[1].data.shape}" execenv.print(" ✓ Signals to image conversion test passed") if __name__ == "__main__": test_signals_to_image_app(screenshots=False) DataLab-1.1.0/datalab/tests/features/signal/xarray_compat_app_test.py000066400000000000000000000121331514014117600257500ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ X-array compatibility application test. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import numpy as np from sigima.objects import GaussParam, SignalObj, create_signal_from_param from datalab.config import Conf from datalab.tests import datalab_test_app_context def __check_addition_result( result: SignalObj, s_ref: SignalObj, context: str, coeff: float ) -> None: """Check that the Y data of the result is the same as the reference signal multiplied by coeff.""" assert len(result.x) == len(s_ref.x), f"[{context}] Addition result length mismatch" assert np.allclose(result.y, coeff * s_ref.y, atol=1e-4), ( f"[{context}] Addition result value mismatch" ) def __check_difference_result( result: SignalObj, s_ref: SignalObj, context: str ) -> None: """Check that the Y data of the result is zero.""" assert len(result.x) == len(s_ref.x), ( f"[{context}] Difference result length mismatch" ) assert np.allclose(result.y, 0.0, atol=1e-4), ( f"[{context}] Difference result value mismatch" ) def test_xarray_compatibility_app(): """X-array compatibility application test. This test aims at verifying that the X-array compatibility feature works as expected in the application context. """ with datalab_test_app_context() as win: panel = win.signalpanel # Reference parameters: p_ref = GaussParam.create(size=500, xmin=-10, xmax=10) # Parameters with the same number of points but different X ranges: p_same_nbp = GaussParam.create(size=500, xmin=-5, xmax=5) # Parameters with different number of points but same X ranges: p_same_range = GaussParam.create(size=1000, xmin=-10, xmax=10) # Parameters with different number of points and different X ranges: p_different = GaussParam.create(size=1000, xmin=-5, xmax=5) panel.add_object(s_ref := create_signal_from_param(p_ref)) panel.add_object(s_same_nbp := create_signal_from_param(p_same_nbp)) panel.add_object(s_same_range := create_signal_from_param(p_same_range)) panel.add_object(s_different := create_signal_from_param(p_different)) # Test with addition operation: with Conf.proc.xarray_compat_behavior.temp("interpolate"): # Select signals with the same number of points but different X ranges: panel.objview.select_objects([s_ref, s_same_nbp]) panel.processor.run_feature("addition") __check_addition_result( panel.objview.get_sel_objects()[0], s_ref, "same_nbp", 2.0 ) # Select signals with different number of points but same X ranges: panel.objview.select_objects([s_ref, s_same_range]) panel.processor.run_feature("addition") __check_addition_result( panel.objview.get_sel_objects()[0], s_ref, "same_range", 2.0 ) # Select signals with different number of points and different X ranges: panel.objview.select_objects([s_ref, s_different]) panel.processor.run_feature("addition") __check_addition_result( panel.objview.get_sel_objects()[0], s_ref, "different", 2.0 ) # Select all signals and add them: panel.objview.select_objects([s_ref, s_same_nbp, s_same_range, s_different]) panel.processor.run_feature("addition") __check_addition_result( panel.objview.get_sel_objects()[0], s_ref, "all", 4.0 ) # Select all signals and average them: panel.objview.select_objects([s_ref, s_same_nbp, s_same_range, s_different]) panel.processor.run_feature("average") __check_addition_result( panel.objview.get_sel_objects()[0], s_ref, "all", 1.0 ) # Test with subtraction operation: with Conf.proc.xarray_compat_behavior.temp("interpolate"): # Select signals with the same number of points but different X ranges: panel.objview.select_objects([s_ref]) panel.processor.run_feature("difference", obj2=s_same_nbp) __check_difference_result( panel.objview.get_sel_objects()[0], s_ref, "same_nbp" ) # Select signals with different number of points but same X ranges: panel.objview.select_objects([s_ref]) panel.processor.run_feature("difference", obj2=s_same_range) __check_difference_result( panel.objview.get_sel_objects()[0], s_ref, "same_range" ) # Select signals with different number of points and different X ranges: panel.objview.select_objects([s_ref]) panel.processor.run_feature("difference", obj2=s_different) __check_difference_result( panel.objview.get_sel_objects()[0], s_ref, "different" ) if __name__ == "__main__": test_xarray_compatibility_app() DataLab-1.1.0/datalab/tests/features/tour_unit_test.py000066400000000000000000000006161514014117600230150ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Tour test """ # guitest: show from datalab.gui.tour import start from datalab.tests import datalab_test_app_context def test_tour() -> None: """ Test the tour of DataLab features. """ with datalab_test_app_context() as win: start(win) if __name__ == "__main__": test_tour() DataLab-1.1.0/datalab/tests/features/utilities/000077500000000000000000000000001514014117600213645ustar00rootroot00000000000000DataLab-1.1.0/datalab/tests/features/utilities/__init__.py000066400000000000000000000000021514014117600234650ustar00rootroot00000000000000# DataLab-1.1.0/datalab/tests/features/utilities/installconf_unit_test.py000066400000000000000000000006701514014117600263530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Dependencies viewer test """ # guitest: show from guidata.qthelpers import qt_app_context from datalab.widgets.instconfviewer import exec_datalab_installconfig_dialog def test_dep_viewer(): """Test dep viewer window""" with qt_app_context(): exec_datalab_installconfig_dialog() if __name__ == "__main__": test_dep_viewer() DataLab-1.1.0/datalab/tests/features/utilities/logview_app_test.py000066400000000000000000000006371514014117600253170ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Log viewer test """ # guitest: show from datalab.app import run from datalab.tests import helpers from datalab.tests.features.utilities import logview_error def test_logviewer_app(): """Test log viewer""" helpers.exec_script(logview_error.__file__) run() if __name__ == "__main__": test_logviewer_app() DataLab-1.1.0/datalab/tests/features/utilities/logview_error.py000066400000000000000000000011041514014117600246170ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Log viewer test: raise an exception and create a seg fault in DataLab """ # guitest: skip from guidata.qthelpers import qt_app_context from datalab.env import execenv from datalab.gui.main import DLMainWindow def error(): """Raise an exception and create a seg fault in DataLab""" with execenv.context(unattended=True): with qt_app_context(exec_loop=True): win = DLMainWindow() win.test_segfault_error() if __name__ == "__main__": error() DataLab-1.1.0/datalab/tests/features/utilities/logview_unit_test.py000066400000000000000000000006561514014117600255170ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Log viewer test """ # guitest: show from guidata.qthelpers import qt_app_context from datalab.widgets.logviewer import exec_datalab_logviewer_dialog def test_logviewer_dialog(): """Test log viewer window""" with qt_app_context(): exec_datalab_logviewer_dialog() if __name__ == "__main__": test_logviewer_dialog() DataLab-1.1.0/datalab/tests/features/utilities/memstatus_app_test.py000066400000000000000000000023641514014117600256640ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Memory status widget application test """ # guitest: show import numpy as np import psutil from sigima.objects import Gauss2DParam from datalab import config from datalab.env import execenv from datalab.tests import datalab_test_app_context def memory_alarm(threshold): """Memory alarm test""" config.Conf.main.available_memory_threshold.set(threshold) rng = np.random.default_rng() with datalab_test_app_context() as win: panel = win.imagepanel win.memorystatus.update_status() # Force memory status update param = Gauss2DParam.create( x0=rng.integers(-9, 9), y0=rng.integers(-9, 9), sigma=rng.integers(1, 20) ) panel.new_object(param, edit=False) def test_mem_status(): """Memory alarm test""" mem_available = psutil.virtual_memory().available // (1024**2) execenv.print(f"Memory status widget test (memory available: {mem_available} MB):") for index, threshold in enumerate((mem_available * 2, mem_available - 100)): execenv.print(f" Threshold {index}: {threshold} MB") memory_alarm(threshold) config.reset() if __name__ == "__main__": test_mem_status() DataLab-1.1.0/datalab/tests/features/utilities/settings_unit_test.py000066400000000000000000000064751514014117600257100ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab settings test """ # guitest: show import guidata.dataset as gds from guidata.dataset.qtwidgets import DataSetGroupEditDialog from guidata.qthelpers import qt_app_context from qtpy import QtWidgets as QW from datalab.config import _ from datalab.env import execenv from datalab.gui.settings import create_dataset_dict, edit_settings from datalab.utils import qthelpers as qth def test_edit_settings(): """Test edit settings""" with qt_app_context(): changed = edit_settings(None) execenv.print(changed) def capture_settings_screenshots(): """Capture screenshots of each settings tab Note: Screenshot filenames are language-independent (use English names) to ensure consistency across different language builds. """ with qt_app_context(exec_loop=False): paramdict = create_dataset_dict() params = gds.DataSetGroup(paramdict.values(), title=_("Settings")) names = list(paramdict.keys()) # Define fixed sub-tab names for View settings (language-independent) # These correspond to the order of BeginGroup items in ViewSettings view_subtab_names = ["common", "signals", "images", "results"] # Create the dialog manually so we can access the tab widget dialog = DataSetGroupEditDialog( instance=params, icon="", parent=None, apply=None, wordwrap=True, size=None ) # Find the main QTabWidget in the dialog main_tab_widget = dialog.findChild(QW.QTabWidget) if main_tab_widget is not None: # Take a screenshot of each main tab for i in range(main_tab_widget.count()): main_tab_widget.setCurrentIndex(i) dialog.show() tab_name = names[i] # Check if this tab contains a nested tab widget (like View settings) current_widget = main_tab_widget.currentWidget() nested_tab_widgets = current_widget.findChildren(QW.QTabWidget) if nested_tab_widgets: # Handle nested tabs (e.g., Common, Signals, Images, Results) nested_tab_widget = nested_tab_widgets[0] for j in range(nested_tab_widget.count()): nested_tab_widget.setCurrentIndex(j) # Use predefined names instead of translated tab text if tab_name == "view" and j < len(view_subtab_names): nested_tab_name = view_subtab_names[j] else: # Fallback for any other potential nested tabs nested_tab_name = f"tab{j}" qth.grab_save_window( dialog, f"settings_{tab_name}_{nested_tab_name}", add_timestamp=False, ) else: # No nested tabs, just take a screenshot of the main tab qth.grab_save_window( dialog, f"settings_{tab_name}", add_timestamp=False ) # Don't execute the dialog, just close it dialog.close() if __name__ == "__main__": capture_settings_screenshots() DataLab-1.1.0/datalab/tests/scenarios/000077500000000000000000000000001514014117600175215ustar00rootroot00000000000000DataLab-1.1.0/datalab/tests/scenarios/__init__.py000066400000000000000000000000021514014117600216220ustar00rootroot00000000000000# DataLab-1.1.0/datalab/tests/scenarios/beautiful_app.py000066400000000000000000000111111514014117600227060ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ beautiful_app ------------- A high-level test scenario producing beautiful screenshots. .. note:: This scenario is used to produce screenshots for the documentation. Thus, it is not run by default when running all tests. .. warning:: When modifying this scenario, please update the script "update_screenshots.py" in the "doc" folder. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show,skip import sigima.objects import sigima.params from sigima.tests.data import get_test_image from datalab.config import Conf from datalab.tests import datalab_test_app_context def run_beautiful_scenario(screenshots: bool = False) -> None: """High-level test scenario producing beautiful screenshots""" data_size = 500 with datalab_test_app_context(console=False, exec_loop=not screenshots) as win: # Beautiful screenshot of a signal panel = win.signalpanel sig = sigima.objects.create_signal_from_param(sigima.objects.LorentzParam()) panel.add_object(sig) panel.processor.run_feature("fft") panel.processor.run_feature("wiener") panel.processor.run_feature("derivative") panel.processor.run_feature("integral") panel.processor.run_feature("gaussian_filter", sigima.params.GaussianParam()) panel.processor.run_feature("fft") panel.processor.run_feature("derivative") # Beautiful screenshot of an image panel = win.imagepanel param = sigima.objects.Gauss2DParam.create(height=data_size, width=data_size) ima = sigima.objects.create_image_from_param(param) ima.set_metadata_option("colormap", "jet") panel.add_object(ima) panel.processor.run_feature("equalize_hist", sigima.params.EqualizeHistParam()) panel.processor.run_feature( "equalize_adapthist", sigima.params.EqualizeAdaptHistParam() ) panel.processor.run_feature("denoise_tv", sigima.params.DenoiseTVParam()) panel.processor.run_feature( "denoise_wavelet", sigima.params.DenoiseWaveletParam() ) panel.processor.run_feature("white_tophat", sigima.params.MorphologyParam()) panel.processor.run_feature("denoise_tv", sigima.params.DenoiseTVParam()) n = data_size // 3 roi = sigima.objects.create_image_roi( "rectangle", [n, n, data_size - 2 * n, data_size - 2 * n] ) panel.processor.compute_roi_extraction(roi) if screenshots: win.statusBar().hide() win.take_screenshot("i_beautiful") win.take_menu_screenshots() win.set_current_panel(win.signalpanel) win.take_screenshot("s_beautiful") def run_blob_detection_on_flower_image(screenshots: bool = False) -> None: """High-level test scenario for flower image with ROI extraction This scenario creates: - A flower test image - Roberts edge detection filter applied - A rectangular ROI extraction - A closing morphological filter to clean up the result - Blob detection using OpenCV algorithm """ with datalab_test_app_context(console=False, exec_loop=not screenshots) as win: # Create an image panel panel = win.imagepanel # Load the flower test image ima = get_test_image("flower.npy") ima.title = "Test image 'flower.npy'" panel.add_object(ima) # Apply Roberts filter for edge detection panel.processor.run_feature("roberts") # Extract a rectangular ROI roi = sigima.objects.create_image_roi("rectangle", [32, 64, 448, 384]) panel.processor.compute_roi_extraction(roi) # Apply a closing morphological filter to clean up the result closing_param = sigima.params.MorphologyParam.create(radius=10) panel.processor.run_feature("closing", closing_param) # Detect blobs using OpenCV algorithm param = sigima.params.BlobOpenCVParam() param.filter_by_color = False param.min_area = 400 param.max_area = 1000 param.filter_by_circularity = True param.min_circularity = 0.7 with Conf.proc.show_result_dialog.temp(False): with Conf.view.show_result_label.temp(False): panel.processor.run_feature("blob_opencv", param) if screenshots: win.statusBar().hide() win.take_screenshot("i_blob_detection_flower") if __name__ == "__main__": run_blob_detection_on_flower_image(screenshots=False) DataLab-1.1.0/datalab/tests/scenarios/common.py000066400000000000000000000406371514014117600213750ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Scenarios common functions """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: skip from __future__ import annotations import numpy as np import sigima.enums import sigima.objects import sigima.params from sigima.tests.data import ( create_noisy_signal, create_paracetamol_signal, create_peak_image, create_sincos_image, iterate_image_creation, iterate_signal_creation, ) from datalab.config import _ from datalab.gui.main import DLMainWindow from datalab.gui.panel.image import ImagePanel from datalab.gui.panel.signal import SignalPanel def __compute_1_to_1_operations(panel: SignalPanel | ImagePanel, number: int) -> None: """Test `compute_1_to_1` type operations on a signal or image Requires that one signal or image has been added at index.""" assert len(panel) >= number - 1 panel.objview.select_objects((number,)) panel.processor.run_feature("gaussian_filter", sigima.params.GaussianParam()) panel.processor.run_feature("moving_average", sigima.params.MovingAverageParam()) panel.processor.run_feature("moving_median", sigima.params.MovingMedianParam()) panel.processor.run_feature("wiener") panel.processor.run_feature("fft") panel.processor.run_feature("ifft") panel.processor.run_feature("absolute") panel.processor.run_feature("magnitude_spectrum") panel.processor.run_feature("phase_spectrum") panel.processor.run_feature("psd") panel.remove_object() panel.processor.run_feature("real") panel.remove_object() panel.processor.run_feature("imag") panel.remove_object() panel.processor.run_feature( "astype", sigima.params.DataTypeIParam.create(dtype_str="float64"), ) panel.processor.run_feature("log10") panel.processor.run_feature("exp") panel.processor.run_feature("transpose") panel.processor.run_feature("transpose") def compute_common_operations(panel: SignalPanel | ImagePanel) -> None: """Test operations common to signal/image Requires that two (and only two) signals/images are created/added to panel First signal/image is supposed to be always the same (reference) Second signal/image is the tested object """ assert len(panel) == 2 panel.objview.select_objects((2,)) panel.processor.run_feature("difference", panel[1]) # difference with obj #1 panel.remove_object() panel.objview.select_objects((2,)) panel.processor.run_feature("quadratic_difference", panel[2]) panel.delete_metadata() const_oper_param = sigima.params.ConstantParam.create(value=2.0) for const_oper in ( "addition_constant", "difference_constant", "product_constant", "division_constant", ): panel.objview.select_objects((3,)) panel.processor.run_feature(const_oper, const_oper_param) panel.objview.select_objects((3,)) panel.remove_object() panel.objview.select_objects((1, 2)) panel.processor.run_feature("addition") panel.objview.select_objects((1, 2)) panel.processor.run_feature("addition") panel.objview.select_objects((1, 2)) panel.processor.run_feature("product") param = sigima.params.ConstantParam.create(value=2.0) panel.processor.run_feature("addition_constant", param) panel.processor.run_feature("difference_constant", param) panel.processor.run_feature("product_constant", param) panel.processor.run_feature("division_constant", param) obj = panel.objmodel.get_groups()[0][-1] param = sigima.params.ClipParam() # Clipping before division... param.upper = (obj.data.max() - obj.data.min()) * 0.8 + obj.data.min() panel.processor.run_feature("clip", param) param = sigima.params.NormalizeParam() for method in sigima.enums.NormalizationMethod: param.method = method panel.processor.run_feature("normalize", param) panel.objview.select_objects((3, 7)) panel.processor.run_feature("division") for feature_name in ("average", "standard_deviation"): panel.objview.select_objects((1, 2, 3)) panel.processor.run_feature(feature_name) panel.add_label_with_title() __compute_1_to_1_operations(panel, 2) def run_signal_computations( win: DLMainWindow, data_size: int = 500, all_types: bool = True ) -> None: """Testing signal features""" panel = win.signalpanel win.set_current_panel("signal") if all_types: for signal in iterate_signal_creation(data_size, non_zero=True): panel.add_object(create_paracetamol_signal(data_size)) panel.add_object(signal) compute_common_operations(panel) panel.remove_all_objects() sig1 = create_paracetamol_signal(data_size) win.add_object(sig1) # Add new signal based on s0 panel.objview.set_current_object(sig1) param = sigima.objects.UniformDistribution1DParam.create( title=_("Random function"), vmin=0, vmax=sig1.y.max() * 0.2 ) noiseobj1 = panel.new_object(param, edit=False) compute_common_operations(panel) # Signal specific operations panel.processor.run_feature("sqrt") panel.processor.run_feature("power", sigima.params.PowerParam.create(power=2)) panel.processor.run_feature("reverse_x") panel.processor.run_feature("reverse_x") # Test filter methods for filter_func_name, paramclass in ( ("lowpass", sigima.params.LowPassFilterParam), ("highpass", sigima.params.HighPassFilterParam), ("bandpass", sigima.params.BandPassFilterParam), ("bandstop", sigima.params.BandStopFilterParam), ): for method in sigima.enums.FrequencyFilterMethod: panel.objview.set_current_object(sig1) param = paramclass.create(method=method) param.update_from_obj(sig1) # Use default cut-off frequencies panel.processor.run_feature(filter_func_name, param) # Test windowing methods noiseobj2 = noiseobj1.copy() win.add_object(noiseobj2) param = sigima.params.WindowingParam() for method in sigima.enums.WindowingMethod: panel.objview.set_current_object(noiseobj2) param.method = method panel.processor.run_feature("apply_window", param) win.add_object(sig1.copy()) param = sigima.params.XYCalibrateParam.create(a=1.2, b=0.1) panel.processor.run_feature("calibration", param) panel.processor.run_feature("derivative") panel.processor.run_feature("integral") param = sigima.params.PeakDetectionParam() panel.processor.compute_peak_detection(param) panel.processor.compute_multigaussianfit() panel.objview.select_objects([-3]) sig = panel.objview.get_sel_objects()[0] i1 = data_size // 10 i2 = len(sig.y) - i1 roi = sigima.objects.create_signal_roi([i1, i2], indices=True) panel.processor.compute_roi_extraction(roi) sig = create_noisy_signal( sigima.objects.NormalDistribution1DParam.create(sigma=5.0) ) panel.add_object(sig) for _name, fitfunc in panel.processor.FIT_FUNCTIONS: panel.objview.set_current_object(sig) panel.processor.run_feature(fitfunc) # Test evaluate_fit: evaluate a fit on another signal's x-axis # First, perform a linear fit on sig panel.objview.set_current_object(sig) panel.processor.run_feature("linear_fit") fitted_signal = panel.objview.get_current_object() # Create a signal with different x-axis x_new = np.linspace(sig.x.min() - 10, sig.x.max() + 10, 150) y_new = np.zeros_like(x_new) sig_new_x = sigima.objects.create_signal( "New X values for fit evaluation", x_new, y_new ) panel.add_object(sig_new_x) # Evaluate the fit on the new signal's x-axis panel.objview.set_current_object(fitted_signal) panel.processor.run_feature("evaluate_fit", sig_new_x) param = sigima.objects.GaussParam.create(title=_("Gaussian")) sig = sigima.objects.create_signal_from_param(param) panel.add_object(sig) param = sigima.params.FWHMParam() for method_value, _method_name in param.methods: param.method = method_value panel.processor.run_feature("fwhm", param) panel.processor.run_feature("fw1e2") # Create a new signal which X values are a subset of sig1 x = np.linspace(sig1.x.min(), sig1.x.max(), data_size // 2)[: data_size // 4] y = x * 0.0 sig2 = sigima.objects.create_signal("X values for interpolation", x, y) panel.add_object(sig2) # Test interpolation # pylint: disable=protected-access for method in sigima.enums.Interpolation1DMethod: for fill_value in (None, 0.0): panel.objview.set_current_object(sig1) param = sigima.params.InterpolationParam.create( method=method, fill_value=fill_value ) panel.processor.run_feature("interpolate", sig2, param) # Test resampling xmin, xmax = x[0], x[-1] for mode, dx, nbpts in (("dx", 0.1, 10), ("nbpts", 0.0, 100)): panel.objview.set_current_object(sig1) param = sigima.params.Resampling1DParam.create( xmin=xmin, xmax=xmax, mode=mode, dx=dx, nbpts=nbpts ) panel.processor.run_feature("resampling", param) # Test convolution panel.objview.set_current_object(sig1) panel.processor.run_feature("derivative") panel.processor.run_feature("convolution", sig1) # Test detrending panel.objview.set_current_object(sig1) # pylint: disable=protected-access for method_choice_tuple in sigima.params.DetrendingParam.methods: param = sigima.params.DetrendingParam.create(method=method_choice_tuple[0]) panel.processor.run_feature("detrending", param) # Test histogram panel.objview.set_current_object(sig1) param = sigima.params.HistogramParam.create(bins=100) panel.processor.run_feature("histogram", param) # Test bandwidth and dynamic parameters panel.processor.run_feature("bandwidth_3db") panel.processor.run_feature("dynamic_parameters") def run_image_computations( win: DLMainWindow, data_size: int = 150, all_types: bool = True ) -> None: """Test image features""" win.set_current_panel("image") panel = win.imagepanel newparam = sigima.objects.NewImageParam.create(height=data_size, width=data_size) if all_types: for image in iterate_image_creation(data_size, non_zero=True): panel.add_object(create_sincos_image(newparam)) panel.add_object(image) compute_common_operations(panel) panel.remove_all_objects() ima1 = create_sincos_image(newparam) panel.add_object(ima1) # Add new image based on i0 panel.objview.set_current_object(ima1) unifparam = sigima.objects.UniformDistribution2DParam() unifparam.set_from_datatype(ima1.data.dtype) unifparam.vmax = int(ima1.data.max() * 0.2) panel.new_object(unifparam, edit=False) compute_common_operations(panel) # Test resampling w, h = ima1.data.shape[1], ima1.data.shape[0] for method, mode, dx, dy, width_param, height_param in ( (sigima.enums.Interpolation2DMethod.NEAREST, "dxy", 0.5, 0.5, 10, 10), (sigima.enums.Interpolation2DMethod.LINEAR, "shape", 0.0, 0.0, w // 2, h // 2), (sigima.enums.Interpolation2DMethod.CUBIC, "shape", 0.0, 0.0, w * 2, h // 2), ): panel.objview.set_current_object(ima1) param = sigima.params.Resampling2DParam.create( method=method, mode=mode, dx=dx, dy=dy, width=width_param, height=height_param, xmin=ima1.x0, xmax=ima1.x0 + ima1.width, ymin=ima1.y0, ymax=ima1.y0 + ima1.height, ) panel.processor.run_feature("resampling", param) # Test denoising methods param = sigima.params.XYZCalibrateParam.create(axis="z", a1=1.2, a0=0.1) panel.processor.run_feature("calibration", param) param = sigima.params.DenoiseTVParam() panel.processor.run_feature("denoise_tv", param) param = sigima.params.DenoiseBilateralParam() panel.processor.run_feature("denoise_bilateral", param) param = sigima.params.DenoiseWaveletParam() panel.processor.run_feature("denoise_wavelet", param) # Test exposure methods ima2 = create_sincos_image(newparam) panel.add_object(ima2) panel.processor.run_feature( "absolute" ) # Avoid neg. values for skimage correction methods param = sigima.params.AdjustGammaParam.create(gamma=0.5) panel.processor.run_feature("adjust_gamma", param) param = sigima.params.AdjustLogParam.create(gain=0.5) panel.processor.run_feature("adjust_log", param) param = sigima.params.AdjustSigmoidParam.create(gain=0.5) panel.processor.run_feature("adjust_sigmoid", param) param = sigima.params.EqualizeHistParam() panel.processor.run_feature("equalize_hist", param) param = sigima.params.EqualizeAdaptHistParam() panel.processor.run_feature("equalize_adapthist", param) param = sigima.params.RescaleIntensityParam() panel.processor.run_feature("rescale_intensity", param) # Test morphology methods param = sigima.params.MorphologyParam.create(radius=10) panel.processor.run_feature("denoise_tophat", param) panel.processor.run_feature("white_tophat", param) panel.processor.run_feature("black_tophat", param) param.radius = 1 panel.processor.run_feature("erosion", param) panel.processor.run_feature("dilation", param) panel.processor.run_feature("opening", param) panel.processor.run_feature("closing", param) param = sigima.params.ButterworthParam.create(order=2, cut_off=0.5) panel.processor.run_feature("butterworth", param) param = sigima.params.CannyParam() panel.processor.run_feature("canny", param) # Test threshold methods ima2 = create_sincos_image(newparam) panel.add_object(ima2) param = sigima.params.ThresholdParam() for method_value, _method_name in param.methods: panel.objview.set_current_object(ima2) param = sigima.params.ThresholdParam.create(method=method_value) if method_value == "manual": param.value = (ima2.data.max() - ima2.data.min()) * 0.5 + ima2.data.min() panel.processor.run_feature("threshold", param) for func_name in ( "threshold_isodata", "threshold_li", "threshold_mean", "threshold_minimum", "threshold_otsu", "threshold_triangle", "threshold_yen", ): panel.objview.set_current_object(ima2) panel.processor.run_feature(func_name) # Test edge detection methods ima2 = create_sincos_image(newparam) panel.add_object(ima2) for func_name in ( "roberts", "prewitt", "prewitt_h", "prewitt_v", "sobel", "sobel_h", "sobel_v", "scharr", "scharr_h", "scharr_v", "farid", "farid_h", "farid_v", "laplace", ): panel.processor.run_feature(func_name) param = sigima.params.Log10ZPlusNParam.create(n=1) panel.processor.run_feature("log10_z_plus_n", param) panel.processor.run_feature("rotate90") panel.processor.run_feature("rotate270") panel.processor.run_feature("fliph") panel.processor.run_feature("flipv") param = sigima.params.RotateParam.create(angle=5.0) for boundary in sigima.enums.BorderMode: if boundary is sigima.enums.BorderMode.MIRROR: continue param.mode = boundary panel.processor.run_feature("rotate", param) param = sigima.params.ResizeParam.create(zoom=1.3) panel.processor.run_feature("resize", param) n = data_size // 10 roi = sigima.objects.create_image_roi( "rectangle", [n, n, data_size - 2 * n, data_size - 2 * n] ) panel.processor.compute_roi_extraction(roi) panel.processor.run_feature("centroid") panel.processor.run_feature("enclosing_circle") ima = create_peak_image(newparam) panel.add_object(ima) param = sigima.params.Peak2DDetectionParam.create(create_rois=True) panel.processor.compute_peak_detection(param) param = sigima.params.ContourShapeParam() panel.processor.run_feature("contour_shape", param) param = sigima.params.BinningParam.create(sx=2, sy=2, operation="average") panel.processor.run_feature("binning", param) # Test histogram panel.objview.set_current_object(ima) param = sigima.params.HistogramParam.create(bins=100) panel.processor.run_feature("histogram", param) DataLab-1.1.0/datalab/tests/scenarios/demo.py000066400000000000000000000135441514014117600210260ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab Demo """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # pylint: disable=duplicate-code # guitest: show,skip from __future__ import annotations from typing import TYPE_CHECKING import sigima.objects import sigima.params import sigima.proc.image as sipi from guidata.qthelpers import qt_wait from qtpy import QtWidgets as QW from sigima.enums import BorderMode from sigima.tests.data import ( create_multigaussian_image, create_paracetamol_signal, create_peak_image, create_sincos_image, get_test_image, ) from datalab.config import _, reset from datalab.env import execenv from datalab.tests import datalab_test_app_context if TYPE_CHECKING: from datalab.gui.main import DLMainWindow DELAY1, DELAY2, DELAY3 = 1, 2, 3 # DELAY1, DELAY2, DELAY3 = 0, 0, 0 def test_signal_features(win: DLMainWindow, data_size: int = 500) -> None: """Testing signal features""" panel = win.signalpanel win.set_current_panel("signal") qt_wait(DELAY2) sig1 = create_paracetamol_signal(data_size) win.add_object(sig1) qt_wait(DELAY1) panel.objview.set_current_object(sig1) param = sigima.objects.UniformDistribution1DParam.create( title=_("Random function"), vmin=0, vmax=sig1.y.max() * 0.2 ) sig2 = sigima.objects.create_signal_from_param(param) win.add_object(sig2) # compute_common_operations(panel) panel.objview.select_objects((1, 2)) qt_wait(DELAY1) panel.processor.run_feature("addition") qt_wait(DELAY1) panel.processor.run_feature("normalize") panel.processor.run_feature("derivative") panel.processor.run_feature("integral") panel.objview.set_current_object(sig1) qt_wait(DELAY1) panel.processor.run_feature("detrending") sig3 = panel.objview.get_current_object() param = sigima.params.PeakDetectionParam() panel.processor.run_feature("peak_detection", param) sig4 = panel.objview.get_current_object() panel.objview.select_objects([sig3, sig4]) qt_wait(DELAY2) panel.objview.set_current_object(sig3) panel.processor.compute_multigaussianfit() sig = sigima.objects.create_signal_from_param(sigima.objects.StepPulseParam()) panel.add_object(sig) panel.processor.run_feature("extract_pulse_features") qt_wait(DELAY2) def test_image_features(win: DLMainWindow, data_size: int = 512) -> None: """Testing signal features""" win.set_current_panel("image") panel = win.imagepanel panel.add_object(get_test_image("flower.npy")) ima1 = panel.objview.get_current_object() qt_wait(DELAY2) panel.objview.set_current_object(ima1) param = sigima.objects.UniformDistribution2DParam.create( width=data_size, height=data_size ) param.set_from_datatype(ima1.data.dtype) param.vmax = int(ima1.data.max() * 0.2) ima2 = sigima.objects.create_image_from_param(param) panel.add_object(ima2) panel.objview.select_objects((1, 2)) panel.processor.run_feature("addition") qt_wait(DELAY2) # compute_common_operations(panel) panel.processor.run_feature("histogram") qt_wait(DELAY2) param = sigima.objects.NewImageParam.create(width=data_size, height=data_size) ima1 = create_sincos_image(param) panel.add_object(ima1) qt_wait(DELAY3) panel.processor.run_feature("rotate90") panel.processor.run_feature("rotate270") panel.processor.run_feature("fliph") panel.processor.run_feature("flipv") rotparam = sipi.RotateParam.create(angle=5.0) for boundary in BorderMode: if boundary is BorderMode.MIRROR: continue rotparam.mode = boundary panel.processor.run_feature("rotate", rotparam) param.title = _("Multi-Gaussian") ima1 = create_multigaussian_image(param) s = data_size roi = sigima.objects.create_image_roi( "rectangle", [s // 2, s // 2, s - 25 - s // 2, s - s // 2] ) roi.add_roi(sigima.objects.create_image_roi("circle", [s // 3, s // 2, s // 4])) ima1.roi = roi panel.add_object(ima1) qt_wait(DELAY2) panel.processor.run_feature("centroid") qt_wait(DELAY1) panel.processor.run_feature("enclosing_circle") qt_wait(DELAY2) param.title = _("2D Peaks with noise background") ima = create_peak_image(param) panel.add_object(ima) param = sigima.params.Peak2DDetectionParam.create(create_rois=True) panel.processor.run_feature("peak_detection", param) qt_wait(DELAY2) param = sigima.params.ContourShapeParam() panel.processor.run_feature("contour_shape", param) qt_wait(DELAY2) n = data_size // 10 roi = sigima.objects.create_image_roi( "rectangle", [n, n, data_size - 2 * n, data_size - 2 * n] ) panel.processor.compute_roi_extraction(roi) def play_demo(win: DLMainWindow) -> None: """Play demo Args: win: DLMainWindow instance """ ret = QW.QMessageBox.information( win, _("Demo"), _( "Click OK to start the demo.

    Note:
    " "- Demo will cover a selection of DataLab features " "(for a complete list of features, please refer to the documentation).
    " "- It won't require any user interaction." ), QW.QMessageBox.Ok | QW.QMessageBox.Cancel, ) if ret == QW.QMessageBox.Ok: execenv.enable_demo_mode(int(DELAY1 * 1000)) test_signal_features(win) test_image_features(win) qt_wait(DELAY3) execenv.disable_demo_mode() QW.QMessageBox.information(win, _("Demo"), _("Click OK to end demo.")) def run(): """Run demo""" reset() # Reset configuration (remove configuration file and initialize it) with datalab_test_app_context(console=False) as win: play_demo(win) if __name__ == "__main__": run() DataLab-1.1.0/datalab/tests/scenarios/example_app_test.py000066400000000000000000000027031514014117600234270ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Example of high-level test scenario Create an image object from Scikit-image human mitosis sample, then open DataLab to show it. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import sigima.objects import sigima.params from sigima.tests.data import get_test_image from datalab.control.proxy import proxy_context from datalab.tests import skip_if_opencv_missing def test_example_app() -> None: """Example of high-level test scenario using proxy interface""" with proxy_context("local") as proxy: data = get_test_image("flower.npy").data image = sigima.objects.create_image("Test image with peaks", data) proxy.add_object(image) proxy.calc("roberts") data_size = data.shape[0] n = data_size // 5 roi = sigima.objects.create_image_roi( "rectangle", [n, n, data_size - 2 * n, data_size - 2 * n] ) proxy.calc("compute_roi_extraction", roi) param = sigima.params.BlobOpenCVParam.create( min_dist_between_blobs=0.1, filter_by_color=False, min_area=500, max_area=2000, filter_by_circularity=True, min_circularity=0.2, ) with skip_if_opencv_missing(): proxy.calc("blob_opencv", param) if __name__ == "__main__": test_example_app() DataLab-1.1.0/datalab/tests/scenarios/scenario_h5_app_test.py000066400000000000000000000054761514014117600242050ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab mainwindow automation test ---------------------------------- This test scenario shows how to use the DataLab application in a test scenario. It also validates the HDF5 file format. This scenario executes the following steps: - Creating two test signals - Creating two test images - Creating a macro - Saving current project (h5 file) - Removing all objects - Opening another project (h5 file) - Access current image metadata - Checking macro code """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import os.path as osp from sigima.params import ClipParam from sigima.tests.data import create_noisy_gaussian_image, create_paracetamol_signal from datalab.env import execenv from datalab.tests import datalab_test_app_context, helpers from datalab.tests.scenarios import scenario_mac_app_test from datalab.utils.strings import save_html_diff def test_scenario_h5(): """Example of high-level test scenario with HDF5 file""" with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context(console=False) as win: # === Creating two test signals panel = win.signalpanel sig1 = create_paracetamol_signal() panel.add_object(sig1) panel.processor.run_feature("derivative") # === Creating two test images panel = win.imagepanel ima1 = create_noisy_gaussian_image(add_annotations=True) panel.add_object(ima1) param = ClipParam.create(upper=ima1.data.mean()) panel.processor.run_feature("clip", param) # === Creating a macro scode = scenario_mac_app_test.add_macro_sample(win, 0).get_code() scenario_mac_app_test.add_macro_sample(win, 1) # === Saving project fname = osp.join(tmpdir, "test.h5") win.save_to_h5_file(fname) # === Removing all objects for panel in win.panels: panel.remove_all_objects() # === Reopening previously saved project win.open_h5_files([fname], import_all=True, reset_all=True) # === Accessing object metadata obj = win.imagepanel.objmodel.get_groups()[0][0] execenv.print(f"Image '{obj.title}':") for key, value in obj.metadata.items(): execenv.print(f' metadata["{key}"] = {value}') # === Checking macro code lcode = win.macropanel.get_macro(1).get_code() if lcode != scode: save_html_diff(scode, lcode, "Saved code", "Loaded code", "macro0.html") raise AssertionError("Macro code is not the same as saved") if __name__ == "__main__": test_scenario_h5() DataLab-1.1.0/datalab/tests/scenarios/scenario_ima1_app_test.py000066400000000000000000000020271514014117600245050ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Image processing test scenario ------------------------------ Testing all the image processing features, without process isolation. """ # pylint: disable=duplicate-code # guitest: show from datalab.config import Conf from datalab.env import execenv from datalab.tests import datalab_test_app_context from datalab.tests.scenarios import common def test_scenario_image() -> None: """Run image unit test scenario 1 (no process isolation)""" assert Conf.main.process_isolation_enabled.get(), ( "Process isolation must be enabled" ) with datalab_test_app_context(save=True) as win: with win.context_no_refresh(): execenv.print("Testing image features without process isolation...") win.set_process_isolation_enabled(False) common.run_image_computations(win) win.imagepanel.remove_all_objects() execenv.print("==> OK") if __name__ == "__main__": test_scenario_image() DataLab-1.1.0/datalab/tests/scenarios/scenario_ima2_app_test.py000066400000000000000000000020001514014117600244750ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Image processing test scenario ------------------------------ Testing all the image processing features, with process isolation. """ # pylint: disable=duplicate-code # guitest: show from datalab.config import Conf from datalab.env import execenv from datalab.tests import datalab_test_app_context from datalab.tests.scenarios import common def test_scenario_image2() -> None: """Run image unit test scenario 2 (process isolation)""" assert Conf.main.process_isolation_enabled.get(), ( "Process isolation must be enabled" ) with datalab_test_app_context(save=True) as win: execenv.print("Testing image features *with* process isolation...") common.run_image_computations(win, all_types=False) oids = win.imagepanel.objmodel.get_object_ids() win.imagepanel.open_separate_view(oids[:4]) execenv.print("==> OK") if __name__ == "__main__": test_scenario_image2() DataLab-1.1.0/datalab/tests/scenarios/scenario_mac_app_test.py000066400000000000000000000035021514014117600244150ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Macro-commands test scenario ---------------------------- Testing all the macro-commands features. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... # guitest: show import os.path as osp import time from datalab.gui.macroeditor import Macro from datalab.gui.main import DLMainWindow from datalab.tests import datalab_test_app_context, helpers def add_macro_sample(win: DLMainWindow, index: int) -> Macro: """Add a macro sample to the macro panel Args: win: DLMainWindow index: index of the macro sample to add """ macro = win.macropanel.add_macro() macro.title = f"Test macro {index}" samples = [ "import time; [print(f'{i}:{time.sleep(.1)}') for i in range(50)]", "print('Hello world')", ] assert index < len(samples), f"index={index} is out of range" macro.set_code(samples[index]) return macro def test_scenario_macro() -> None: """Example of high-level test scenario with HDF5 file""" with helpers.WorkdirRestoringTempDir() as tmpdir: with datalab_test_app_context(console=False) as win: win.set_current_panel("macro") add_macro_sample(win, 0) win.macropanel.run_macro() time.sleep(1) win.macropanel.stop_macro() code2 = add_macro_sample(win, 1).get_code() fname = osp.join(tmpdir, "test.macro") win.macropanel.export_macro_to_file(2, fname) win.macropanel.remove_macro() macro_nb = win.macropanel.import_macro_from_file(fname) macro2 = win.macropanel.get_macro(macro_nb) assert macro2.get_code() == code2, "Macro code is not the same" if __name__ == "__main__": test_scenario_macro() DataLab-1.1.0/datalab/tests/scenarios/scenario_sig1_app_test.py000066400000000000000000000021251514014117600245200ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Signal processing test scenario ------------------------------- Testing all the signal processing features, without process isolation. """ # pylint: disable=duplicate-code # guitest: show from __future__ import annotations from datalab.config import Conf from datalab.env import execenv from datalab.tests import datalab_test_app_context from datalab.tests.scenarios import common def test_scenario_signal1() -> None: """Run signal unit test scenario 1 (no process isolation)""" assert Conf.main.process_isolation_enabled.get(), ( "Process isolation must be enabled" ) with datalab_test_app_context(save=True) as win: with win.context_no_refresh(): execenv.print("Testing signal features (process isolation: off)...") win.set_process_isolation_enabled(False) common.run_signal_computations(win, all_types=True) win.signalpanel.remove_all_objects() execenv.print("==> OK") if __name__ == "__main__": test_scenario_signal1() DataLab-1.1.0/datalab/tests/scenarios/scenario_sig2_app_test.py000066400000000000000000000020541514014117600245220ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Signal processing test scenario ------------------------------- Testing all the signal processing features, with process isolation. """ # pylint: disable=duplicate-code # guitest: show from __future__ import annotations from datalab.config import Conf from datalab.env import execenv from datalab.tests import datalab_test_app_context from datalab.tests.scenarios import common def test_scenario_signal2() -> None: """Run signal unit test scenario 2 (process isolation)""" assert Conf.main.process_isolation_enabled.get(), ( "Process isolation must be enabled" ) with datalab_test_app_context(save=True) as win: execenv.print("Testing signal features (process isolation: on)...") common.run_signal_computations(win, all_types=False) oids = win.signalpanel.objmodel.get_object_ids() win.signalpanel.open_separate_view(oids[:3]) execenv.print("==> OK") if __name__ == "__main__": test_scenario_signal2() DataLab-1.1.0/datalab/tests/webapi_test.py000066400000000000000000000300611514014117600204130ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause License # See LICENSE file for details """ Web API Tests ============= Unit and integration tests for the DataLab Web API. """ from __future__ import annotations import io import zipfile import numpy as np import pytest from fastapi import FastAPI from fastapi.testclient import TestClient from sigima import ImageObj, SignalObj from datalab.webapi.routes import ( generate_auth_token, router, set_adapter, set_auth_token, set_server_url, ) from datalab.webapi.schema import ( MetadataPatchRequest, ObjectListResponse, ObjectMetadata, ObjectType, ) from datalab.webapi.serialization import ( deserialize_object_from_npz, object_to_metadata, serialize_object_to_npz, ) class TestNPZSerialization: """Tests for NPZ serialization module.""" def test_signal_round_trip(self): """Test serializing and deserializing a SignalObj.""" # Create a signal x = np.linspace(0, 10, 100) y = np.sin(x) obj = SignalObj() obj.set_xydata(x, y) obj.title = "Test Signal" obj.xlabel = "Time" obj.ylabel = "Amplitude" obj.xunit = "s" obj.yunit = "V" # Serialize data = serialize_object_to_npz(obj) assert isinstance(data, bytes) assert len(data) > 0 # Verify it's a valid zip buffer = io.BytesIO(data) with zipfile.ZipFile(buffer, "r") as zf: assert "x.npy" in zf.namelist() assert "y.npy" in zf.namelist() assert "metadata.json" in zf.namelist() # Deserialize result = deserialize_object_from_npz(data) # Verify assert type(result).__name__ == "SignalObj" np.testing.assert_array_equal(result.x, x) np.testing.assert_array_equal(result.y, y) assert result.title == "Test Signal" assert result.xlabel == "Time" assert result.ylabel == "Amplitude" assert result.xunit == "s" assert result.yunit == "V" def test_signal_with_uncertainties(self): """Test signal with dx/dy uncertainties.""" x = np.linspace(0, 10, 50) y = np.cos(x) dx = np.ones_like(x) * 0.01 dy = np.abs(y) * 0.05 obj = SignalObj() obj.set_xydata(x, y, dx=dx, dy=dy) obj.title = "Signal with Errors" data = serialize_object_to_npz(obj) result = deserialize_object_from_npz(data) np.testing.assert_array_equal(result.dx, dx) np.testing.assert_array_equal(result.dy, dy) def test_image_round_trip(self): """Test serializing and deserializing an ImageObj.""" # Create an image data = np.random.rand(128, 128).astype(np.float32) obj = ImageObj() obj.data = data obj.title = "Test Image" obj.xlabel = "X" obj.ylabel = "Y" obj.zlabel = "Intensity" obj.x0 = 10.0 obj.y0 = 20.0 obj.dx = 0.5 obj.dy = 0.5 # Serialize npz_data = serialize_object_to_npz(obj) assert isinstance(npz_data, bytes) # Verify structure buffer = io.BytesIO(npz_data) with zipfile.ZipFile(buffer, "r") as zf: assert "data.npy" in zf.namelist() assert "metadata.json" in zf.namelist() # Deserialize result = deserialize_object_from_npz(npz_data) # Verify assert type(result).__name__ == "ImageObj" np.testing.assert_array_equal(result.data, data) assert result.title == "Test Image" assert result.x0 == 10.0 assert result.y0 == 20.0 assert result.dx == 0.5 assert result.dy == 0.5 def test_image_preserves_dtype(self): """Test that image dtype is preserved through serialization.""" for dtype in [np.uint8, np.uint16, np.float32, np.float64]: obj = ImageObj() obj.data = np.random.randint(0, 255, (64, 64)).astype(dtype) obj.title = f"Image {dtype.__name__}" data = serialize_object_to_npz(obj) result = deserialize_object_from_npz(data) assert result.data.dtype == dtype, ( f"Expected {dtype}, got {result.data.dtype}" ) class TestObjectMetadata: """Tests for object_to_metadata helper.""" def test_signal_metadata(self): """Test extracting metadata from a SignalObj.""" obj = SignalObj() obj.set_xydata(np.linspace(0, 10, 100), np.sin(np.linspace(0, 10, 100))) obj.title = "My Signal" obj.xlabel = "Time" meta = object_to_metadata(obj, "my_signal") assert meta["name"] == "my_signal" assert meta["type"] == "signal" assert meta["shape"] == [100] assert meta["dtype"] == "float64" assert meta["title"] == "My Signal" assert meta["xlabel"] == "Time" def test_image_metadata(self): """Test extracting metadata from an ImageObj.""" obj = ImageObj() obj.data = np.zeros((256, 512), dtype=np.uint16) obj.title = "My Image" obj.x0 = 5.0 obj.dx = 0.1 meta = object_to_metadata(obj, "my_image") assert meta["name"] == "my_image" assert meta["type"] == "image" assert meta["shape"] == [256, 512] assert meta["dtype"] == "uint16" assert meta["title"] == "My Image" assert meta["attributes"]["x0"] == 5.0 assert meta["attributes"]["dx"] == 0.1 class TestSchemaModels: """Tests for Pydantic schema models.""" def test_object_metadata_validation(self): """Test ObjectMetadata model validation.""" # Valid metadata meta = ObjectMetadata( name="test", type=ObjectType.SIGNAL, shape=[100], dtype="float64", ) assert meta.name == "test" assert meta.type == ObjectType.SIGNAL # With optional fields meta_full = ObjectMetadata( name="test2", type=ObjectType.IMAGE, shape=[256, 256], dtype="uint8", title="Test Image", xlabel="X", ylabel="Y", attributes={"custom": "value"}, ) assert meta_full.attributes == {"custom": "value"} def test_object_list_response(self): """Test ObjectListResponse model.""" objs = [ ObjectMetadata( name="s1", type=ObjectType.SIGNAL, shape=[50], dtype="float64" ), ObjectMetadata( name="i1", type=ObjectType.IMAGE, shape=[64, 64], dtype="uint8" ), ] response = ObjectListResponse(objects=objs, count=2) assert len(response.objects) == 2 assert response.count == 2 def test_metadata_patch_request(self): """Test MetadataPatchRequest model.""" patch = MetadataPatchRequest(title="New Title", xlabel="Updated X") # model_dump should exclude None values data = patch.model_dump(exclude_none=True) assert "title" in data assert "xlabel" in data assert "ylabel" not in data class TestAuthToken: """Tests for authentication token handling.""" def test_generate_token(self): """Test token generation.""" token1 = generate_auth_token() token2 = generate_auth_token() # Tokens should be non-empty strings assert isinstance(token1, str) assert len(token1) > 20 # Each token should be unique assert token1 != token2 # Integration tests would require a running DataLab instance # They are marked for opt-in execution class MockWorkspaceAdapter: """Mock workspace adapter for testing.""" def __init__(self): self._objects: dict[str, SignalObj | ImageObj] = {} def add_object(self, name: str, obj: SignalObj | ImageObj) -> None: """Add an object to the mock workspace.""" self._objects[name] = obj def list_objects(self) -> list[tuple[str, str]]: """List all objects in the mock workspace.""" result = [] for name, obj in self._objects.items(): panel = "signal" if type(obj).__name__ == "SignalObj" else "image" result.append((name, panel)) return result def get_object(self, name: str) -> SignalObj | ImageObj: """Get an object by name.""" if name not in self._objects: raise KeyError(f"Object '{name}' not found") return self._objects[name] class TestAPIEndpointsWithMock: """Integration tests using a mock workspace adapter.""" @pytest.fixture def test_client(self): """Create a test client with mock adapter.""" # Create a fresh app with the router app = FastAPI() app.include_router(router) # Set up mock adapter and auth mock_adapter = MockWorkspaceAdapter() test_token = "test-token-12345" set_adapter(mock_adapter) set_auth_token(test_token) set_server_url("http://localhost:8000") client = TestClient(app) return client, test_token, mock_adapter def test_status_endpoint(self, test_client): """Test the /api/v1/status endpoint (no auth required).""" client, _token, _adapter = test_client response = client.get("/api/v1/status") assert response.status_code == 200 data = response.json() assert data["running"] is True assert "version" in data assert data["api_version"] == "v1" assert data["url"] == "http://localhost:8000" assert data["workspace_mode"] == "live" def test_list_objects_requires_auth(self, test_client): """Test that /api/v1/objects requires authentication.""" client, _token, _adapter = test_client # Request without Authorization header should return 401 response = client.get("/api/v1/objects") assert response.status_code == 401 assert "WWW-Authenticate" in response.headers assert response.headers["WWW-Authenticate"] == "Bearer" def test_list_objects_with_invalid_token(self, test_client): """Test that /api/v1/objects rejects invalid tokens.""" client, _token, _adapter = test_client response = client.get( "/api/v1/objects", headers={"Authorization": "Bearer wrong-token"} ) assert response.status_code == 401 def test_list_objects_with_valid_token(self, test_client): """Test that /api/v1/objects works with valid token.""" client, token, adapter = test_client # Add a test signal to the mock adapter x = np.linspace(0, 10, 50) y = np.sin(x) signal = SignalObj() signal.set_xydata(x, y) signal.title = "Test Signal" adapter.add_object("Test Signal", signal) response = client.get( "/api/v1/objects", headers={"Authorization": f"Bearer {token}"} ) assert response.status_code == 200 data = response.json() assert data["count"] == 1 assert len(data["objects"]) == 1 assert data["objects"][0]["name"] == "Test Signal" assert data["objects"][0]["type"] == "signal" def test_list_objects_empty_workspace(self, test_client): """Test listing objects in an empty workspace.""" client, token, _adapter = test_client response = client.get( "/api/v1/objects", headers={"Authorization": f"Bearer {token}"} ) assert response.status_code == 200 data = response.json() assert data["count"] == 0 assert data["objects"] == [] def test_invalid_auth_header_format(self, test_client): """Test that malformed Authorization headers are rejected.""" client, _token, _adapter = test_client # Missing "Bearer" prefix response = client.get( "/api/v1/objects", headers={"Authorization": "test-token-12345"} ) assert response.status_code == 401 # Wrong prefix response = client.get( "/api/v1/objects", headers={"Authorization": "Basic test-token-12345"} ) assert response.status_code == 401 DataLab-1.1.0/datalab/utils/000077500000000000000000000000001514014117600155315ustar00rootroot00000000000000DataLab-1.1.0/datalab/utils/__init__.py000066400000000000000000000000021514014117600176320ustar00rootroot00000000000000# DataLab-1.1.0/datalab/utils/conf.py000066400000000000000000000235521514014117600170370ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab Configuration utilities """ from __future__ import annotations import os import os.path as osp import warnings from collections.abc import Generator from contextlib import contextmanager from typing import TYPE_CHECKING, Any import guidata.dataset as gds from guidata.configtools import MONOSPACE, get_family from guidata.userconfig import NoDefault, UserConfig if TYPE_CHECKING: import qtpy.QtGui as QG class AppUserConfig(UserConfig): """Application user configuration""" def to_dict(self) -> dict: """Return configuration as a dictionary""" confdict = {} for section in self.sections(): secdict = {} for option, value in self.items(section, raw=self.raw): secdict[option] = value confdict[section] = secdict return confdict CONF = AppUserConfig({}) class Configuration: """Configuration file""" @classmethod def initialize(cls, name: str, version: str, load: bool) -> None: """Initialize configuration""" CONF.set_application(name, version, load=load) @classmethod def reset(cls) -> None: """Reset configuration""" global CONF # pylint: disable=global-statement CONF.cleanup() # Remove configuration file CONF = AppUserConfig({}) @classmethod def get_filename(cls) -> str: """Return configuration file name""" return CONF.filename() @classmethod def get_path(cls, basename: str) -> str: """Return filename path inside configuration directory""" return CONF.get_path(basename) @classmethod def to_dict(cls) -> dict: """Return configuration as a dictionary""" return CONF.to_dict() class Section: """Configuration section""" @classmethod def set_name(cls, section: str) -> None: """Set section name""" cls._name = section @classmethod def get_name(cls) -> str: """Return section name""" return cls._name class Option: """Configuration option handler""" def __init__(self) -> None: self.section = None self.option = None def get(self, default=NoDefault) -> Any: """Get configuration option value""" return CONF.get(self.section, self.option, default) def set(self, value: Any) -> None: """Set configuration option value""" CONF.set(self.section, self.option, value) def remove(self) -> None: """Remove configuration option""" # No use case for this method yet (quite dangerous!) CONF.remove_option(self.section, self.option) @contextmanager def temp(self, value: Any) -> Generator[None, None, None]: """Temporarily set configuration option value Args: value: new value """ old_value = self.get() self.set(value) yield self.set(old_value) class FontOption(Option): """Font configuration option handler""" def get(self, default=NoDefault) -> str: """Get font name from configuration""" if default is NoDefault: default = (MONOSPACE, 9, False) family = CONF.get(self.section, self.option + "_family", default[0]) size = CONF.get(self.section, self.option + "_size", default[1]) bold = CONF.get(self.section, self.option + "_bold", default[2]) return family, size, bold def set(self, value: tuple[str | list[str], int, bool]) -> None: """Set font name in configuration""" assert isinstance(value, tuple), ( "Font value must be a tuple (family, size, bold)" ) CONF.set(self.section, self.option + "_family", value[0]) CONF.set(self.section, self.option + "_size", value[1]) CONF.set(self.section, self.option + "_bold", value[2]) def get_font(self) -> QG.QFont: """Get QFont from configuration""" # Import here to avoid having to create a Qt application when # just manipulating configuration files from qtpy import QtGui as QG # pylint: disable=import-outside-toplevel family, size, bold = self.get() if isinstance(family, (list, tuple)): family = get_family(family) return QG.QFont(family, size, QG.QFont.Bold if bold else QG.QFont.Normal) class ConfigPathOption(Option): """Configuration file path configuration option handler""" def get(self, default=NoDefault) -> str: """Get configuration file path from configuration""" if default is NoDefault: default = "" fname = super().get(default) if osp.basename(fname) != fname: raise ValueError(f"Invalid configuration file name {fname}") return CONF.get_path(osp.basename(fname)) class WorkingDirOption(Option): """Working directory configuration option handler""" def get(self, default=NoDefault) -> str: """Get working directory from configuration""" if default is NoDefault: default = "" path = super().get(default) if osp.isdir(path): return path return "" def set(self, value: str) -> None: """Set working directory in configuration""" if not osp.isdir(value): value = osp.dirname(value) if not osp.isdir(value): raise FileNotFoundError(f"Invalid working directory name {value}") os.chdir(value) super().set(value) class EnumOption(Option): """Enumeration option handler""" def __init__(self, values: list[Any], default: Any = NoDefault) -> None: super().__init__() if default is NoDefault: default = values[0] self.values = values self.default = default def get(self, default: Any = NoDefault) -> Any: """Get configuration option value""" value = super().get(default) if value not in self.values: # Only show a warning here, as the configuration file may be edited manually warnings.warn( f"Invalid value {value} for option {self.option}, " f"expected {self.values}" ) return self.default return value def set(self, value: Any) -> None: """Set configuration option value""" if value not in self.values: raise ValueError( f"Invalid value {value} for option {self.option}, " f"expected {self.values}" ) super().set(value) class DataSetOption(Option): """DataSet option handler""" def __init__(self, default_instance: gds.DataSet | None = None) -> None: super().__init__() self.default_instance = default_instance def set_default_instance(self, default_instance: gds.DataSet) -> None: """Set the default instance (for lazy initialization)""" self.default_instance = default_instance def get(self, default: Any = NoDefault) -> gds.DataSet: """Get configuration option value""" # Use provided default or fall back to default_instance effective_default = ( default if default is not NoDefault else self.default_instance ) try: json_str = super().get(default) except RuntimeError as exc: # Option not yet registered in CONF - register it with default if effective_default is None: raise ValueError( f"No default instance set for option '{self.option}' " f"in section '{self.section}'" ) from exc # Register the default value in CONF (writes to config file) self.set(effective_default) return effective_default # If the option doesn't exist, super().get() returns the default parameter if not isinstance(json_str, str): return json_str if json_str is NoDefault or not json_str: if effective_default is None: raise ValueError( f"No default instance set for option '{self.option}' " f"in section '{self.section}'" ) return effective_default # Unescape percent signs that were escaped for ConfigParser json_str = json_str.replace("%%", "%") try: return gds.json_to_dataset(json_str) except Exception as exc: # pylint: disable=broad-except # Corrupted JSON: remove the option and return default self.remove() if effective_default is None: raise ValueError( f"No default instance set for option '{self.option}' " f"in section '{self.section}'" ) from exc return effective_default def set(self, value: gds.DataSet) -> None: """Set configuration option value""" json_str = gds.dataset_to_json(value) # Escape percent signs for ConfigParser (which uses % for interpolation) json_str = json_str.replace("%", "%%") super().set(json_str) class SectionMeta(type): """Configuration metaclass""" # pylint: disable=bad-mcs-classmethod-argument def __new__(cls, name, bases, dct): optlist = [] for attrname, obj in list(dct.items()): if isinstance(obj, Option): obj.option = attrname optlist.append(obj) dct["_options"] = optlist return type.__new__(cls, name, bases, dct) class ConfMeta(type): """Configuration metaclass""" # pylint: disable=bad-mcs-classmethod-argument def __new__(cls, name, bases, dct): for attrname, obj in list(dct.items()): if isinstance(obj, Section): obj.set_name(attrname) for option in obj._options: option.section = attrname return type.__new__(cls, name, bases, dct) DataLab-1.1.0/datalab/utils/dephash.py000066400000000000000000000067631514014117600175330ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Module checking dependencies with respect to a reference """ import hashlib import os import os.path as osp import sys from guidata.utils.misc import get_module_path BUFFER_SIZE = 65536 def get_file_hash(filename, dhash=None, bufsize=None): """Return SHA256 hash for file""" if dhash is None: dhash = hashlib.sha256() buffer = bytearray(128 * 1024 if bufsize is None else bufsize) # using a memoryview so that we can slice the buffer without copying it buffer_view = memoryview(buffer) with open(filename, "rb", buffering=0) as fobj: while True: n_chunk = fobj.readinto(buffer_view) if not n_chunk: break dhash.update(buffer_view[:n_chunk]) return dhash def get_directory_hash(directory, extensions=None, verbose=False): """Return SHA256 hash for whole directory""" dhash = hashlib.sha1() if not os.path.exists(directory): return -1 assert extensions is None or isinstance(extensions, (tuple, list)) extlist = [] if extensions is not None: for ext in extensions: if not ext.startswith("."): ext = "." + ext extlist.append(ext) filenb = 0 for root, _dirs, files in os.walk(directory): for name in files: if extensions is None or osp.splitext(name)[1] in extlist: filepath = os.path.join(root, name) if verbose: filenb += 1 print(f"[{filenb:07d}] Hashing: {filepath}") dhash = get_file_hash(filepath, dhash) return dhash.hexdigest() def get_module_hash(module_name, verbose=False): """Return SHA256 hash for Python module""" path = get_module_path(module_name) return get_directory_hash(path, extensions=(".py",), verbose=verbose) def get_dependencies_hash(dependencies): """Return SHA256 hash for Python module dependencies""" return {name: get_module_hash(name) for name in dependencies} DEPFILENAME = f"dependencies-py{sys.version_info.major}-{sys.platform}.txt" def check_dependencies_hash(datapath, hash_dict=None): """Check dependencies hash dictionary""" if hash_dict is None: hash_dict = {} with open(osp.join(datapath, DEPFILENAME), "rb") as filehandle: for line in filehandle.readlines(): key, value = line.decode("utf-8").strip().split(":") hash_dict[key] = value return {name: get_module_hash(name) == hash_dict[name] for name in hash_dict} def create_dependencies_file(datapath, dependencies): """Create dependencies.txt file""" hdict = get_dependencies_hash(dependencies) path = osp.join(datapath, DEPFILENAME) print("Hash file for Python module dependencies:") with open(path, "wb") as filehandle: print(f" Opening {path}") for name in hdict: line = name + ":" + hdict[name] + os.linesep print(f" Writing: {line}", end="") filehandle.write(line.encode("utf-8")) print(" => All done.") if __name__ == "__main__": # datapath = osp.join(osp.dirname(__file__), os.pardir, "data") # hdict = get_dependencies_hash(("guidata", "plotpy")) # print(hdict) # print(check_dependencies_hash(datapath, hdict)) create_dependencies_file( osp.join(osp.dirname(__file__), os.pardir, "data"), ("guidata", "plotpy") ) # print(check_dependencies_hash(datapath)) DataLab-1.1.0/datalab/utils/qthelpers.py000066400000000000000000000524021514014117600201150ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab Qt utilities """ from __future__ import annotations import faulthandler import functools import inspect import logging import os import os.path as osp import shutil import sys import time import traceback from collections.abc import Callable, Generator from contextlib import contextmanager from typing import Any import guidata from guidata.configtools import get_icon from guidata.qthelpers import grab_save_window as guidata_grab_save_window from guidata.utils.misc import to_string from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from datalab.config import ( APP_NAME, DATETIME_FORMAT, SHOTPATH, Conf, _, get_old_log_fname, ) from datalab.env import execenv def close_widgets_and_quit(screenshot=False) -> None: """Close Qt top level widgets and quit Qt event loop""" for widget in QW.QApplication.instance().topLevelWidgets(): try: wname = widget.objectName() except RuntimeError: # Object has been deleted continue if screenshot and wname and widget.isVisible(): # pragma: no cover grab_save_window(widget, wname.lower()) assert widget.close() QW.QApplication.instance().quit() QAPP_INSTANCE = None def get_log_contents(fname: str) -> str | None: """Return True if file exists and something was logged in it""" if osp.exists(fname): with open(fname, "rb") as fdesc: return to_string(fdesc.read()).strip() return None def initialize_log_file(fname: str) -> bool: """Eventually keep the previous log file Returns True if there was a previous log file""" contents = get_log_contents(fname) if contents: try: shutil.move(fname, get_old_log_fname(fname)) except Exception: # pylint: disable=broad-except pass return True return False def remove_empty_log_file(fname: str) -> None: """Eventually remove empty log files""" if not get_log_contents(fname): try: os.remove(fname) except Exception: # pylint: disable=broad-except pass @contextmanager def datalab_app_context( exec_loop=False, enable_logs=True ) -> Generator[QW.QApplication, None, None]: """DataLab Qt application context manager, handling Qt application creation and persistance, faulthandler/traceback logging features, screenshot mode and unattended mode. Args: exec_loop: whether to execute Qt event loop (default: False) enable_logs: whether to enable logs (default: True) """ global QAPP_INSTANCE # pylint: disable=global-statement if QAPP_INSTANCE is None: QAPP_INSTANCE = guidata.qapplication() # === Set application name and version --------------------------------------------- # pylint: disable=import-outside-toplevel import datalab QAPP_INSTANCE.setApplicationName(APP_NAME) QAPP_INSTANCE.setApplicationVersion(datalab.__version__) QAPP_INSTANCE.setOrganizationName(APP_NAME + " project") if enable_logs: # === Create a logger for standard exceptions ---------------------------------- tb_log_fname = Conf.main.traceback_log_path.get() Conf.main.traceback_log_available.set(initialize_log_file(tb_log_fname)) logger = logging.getLogger(__name__) fmt = "[%(asctime)s] {%(pathname)s:%(lineno)d} %(levelname)s - %(message)s" logging.basicConfig( filename=tb_log_fname, filemode="w", level=logging.ERROR, format=fmt, datefmt=DATETIME_FORMAT, ) def custom_excepthook(exc_type, exc_value, exc_traceback): "Custom exception hook" logger.critical( "Unhandled exception", exc_info=(exc_type, exc_value, exc_traceback) ) return sys.__excepthook__(exc_type, exc_value, exc_traceback) sys.excepthook = custom_excepthook # === Use faulthandler for other exceptions ------------------------------------ fh_log_fname = Conf.main.faulthandler_log_path.get() Conf.main.faulthandler_log_available.set(initialize_log_file(fh_log_fname)) with open(fh_log_fname, "w", encoding="utf-8") as fh_log_fn: if enable_logs and Conf.main.faulthandler_enabled.get(True): faulthandler.enable(file=fh_log_fn) exception_occured = False try: yield QAPP_INSTANCE except Exception: # pylint: disable=broad-except exception_occured = True finally: if ( execenv.unattended or execenv.screenshot ) and not execenv.do_not_quit: # pragma: no cover if execenv.delay > 0: mode = "Screenshot" if execenv.screenshot else "Unattended" message = f"{mode} mode (delay: {execenv.delay}ms)" msec = execenv.delay - 200 for widget in QW.QApplication.instance().topLevelWidgets(): if isinstance(widget, QW.QMainWindow): widget.statusBar().showMessage(message, msec) QC.QTimer.singleShot( execenv.delay, lambda: close_widgets_and_quit(screenshot=execenv.screenshot), ) if exec_loop and not exception_occured: QAPP_INSTANCE.exec() if exception_occured: raise # pylint: disable=misplaced-bare-raise if enable_logs and Conf.main.faulthandler_enabled.get(): faulthandler.disable() remove_empty_log_file(fh_log_fname) if enable_logs: logging.shutdown() remove_empty_log_file(tb_log_fname) def is_running_tests() -> bool: """Check if code is running during test execution""" return "pytest" in sys.modules @contextmanager def try_or_log_error(context: str) -> Generator[None, None, None]: """Try to execute a function and log an error message if it fails""" try: yield except Exception: # pylint: disable=broad-except if is_running_tests(): # If we are running tests, we want to raise the exception raise traceback.print_exc() logger = logging.getLogger(__name__) logger.error("Error in %s", context, exc_info=traceback.format_exc()) Conf.main.traceback_log_available.set(True) finally: pass @contextmanager def create_progress_bar( parent: QW.QWidget, label: str, max_: int, show_after: int = 1000 ) -> Generator[QW.QProgressDialog, None, None]: """Create modal progress bar Args: parent: Parent widget label: Progress dialog title max_: Maximum progress value show_after: Delay before showing the progress dialog (ms, default: 1000) """ prog = QW.QProgressDialog(label, _("Cancel"), 0, max_, parent, QC.Qt.SplashScreen) prog.setWindowModality(QC.Qt.WindowModal) prog.setMinimumDuration(show_after) try: yield prog finally: prog.close() prog.deleteLater() class CallbackWorker(QC.QThread): """Worker for executing long operations in a separate thread (this must not be confused with the :py:class:`datalab.gui.processor.base.Worker` class, which handles the execution of computations in a another process) Implements `CallbackWorkerProtocol` from `sigima.worker`, used for computations that support cancellation and progress reporting. Args: callback: The function to be executed in a separate thread, that takes optionnally 'worker' as argument (instance of this class), and any other argument passed with **kwargs kwargs: Callback keyword arguments """ SIG_PROGRESS_UPDATE = QC.Signal(int) def __init__(self, callback: Callable, **kwargs) -> None: super().__init__() self.callback = callback if "worker" in inspect.signature(callback).parameters: kwargs["worker"] = self self.kwargs = kwargs self.result: Any | None = None self.__canceled = False self.__exc = None def run(self) -> None: """Start thread""" # Initialize progress bar: setting progress to 0.0 has the effect of # showing the progress dialog after the `minimumDuration` time has elapsed. # If we don't set the progress to 0.0, the progress dialog will be shown only # after the first call to `set_progress` method even if the `minimumDuration` # time has elapsed. self.set_progress(0.0) try: self.result = self.callback(**self.kwargs) except Exception as exc: # pylint: disable=broad-except self.__exc = exc def cancel(self) -> None: """Progress bar was canceled""" self.__canceled = True def was_canceled(self) -> bool: """Return whether the progress dialog was canceled by user""" return self.__canceled def set_progress(self, value: float) -> None: """Set progress bar value Args: value: float between 0.0 and 1.0 """ self.SIG_PROGRESS_UPDATE.emit(int(100 * value)) def get_result(self) -> Any: """Return callback result""" if self.__exc is not None: raise self.__exc return self.result def qt_long_callback( parent: QW.QWidget, label: str, worker: CallbackWorker, progress: bool, show_after: int = 500, ) -> Any: """Handle long callbacks: run in a separate thread while showing a busy bar Args: parent: Parent widget label: Progress dialog title worker: Callback worker handling the function execution in a separate thread progress: Whether the progress feature is handled or not. If True, a progress bar and a 'Cancel' button are shown on the progress dialog. The progress value is updated by the `worker.set_progress` method (which takes a float between 0.0 and 1.0). Moreover, if `progress` is True, we wait for the callback function to return (it means that the callback function must implement a mechanism to return an intermediate result or `None` if the `worker.was_canceled` method returns True). show_after: Delay before showing the progress dialog (ms, default: 1000) Returns: Callback result """ if progress: prog = QW.QProgressDialog( label, _("Cancel"), 0, 100, parent, QC.Qt.SplashScreen ) prog.setMinimumDuration(show_after) worker.SIG_PROGRESS_UPDATE.connect(prog.setValue) prog.canceled.connect(worker.cancel) else: prog = QW.QProgressDialog(label, None, 0, 0, parent, QC.Qt.SplashScreen) prog.setMinimumDuration(0) prog.setCancelButton(None) prog.setRange(0, 0) prog.show() prog.setWindowModality(QC.Qt.WindowModal) worker.start() while worker.isRunning() and not worker.was_canceled(): QW.QApplication.processEvents() time.sleep(0.005) if progress: worker.SIG_PROGRESS_UPDATE.disconnect(prog.setValue) worker.wait() try: result = worker.get_result() except Exception as exc: # pylint: disable=broad-except prog.close() prog.deleteLater() raise exc prog.close() prog.deleteLater() return result def qt_handle_error_message(widget: QW.QWidget, message: str, context: str = None): """Handles application (QWidget) error message""" traceback.print_exc() txt = str(message) msglines = txt.splitlines() firstline = _("Error:") if context is None else f"%s: {context}" % _("Context") msglines.insert(0, firstline) if len(msglines) > 10: msglines = msglines[:10] + ["..."] title = widget.window().objectName() QW.QMessageBox.critical(widget, title, os.linesep.join(msglines)) def qt_try_except(message=None, context=None): """Try...except Qt widget method decorator""" def qt_try_except_decorator(func): """Try...except Qt widget method decorator""" @functools.wraps(func) def method_wrapper(*args, **kwargs): """Decorator wrapper function""" self = args[0] # extracting 'self' from method arguments # If "self" is a BaseProcessor, then we need to get the panel instance panel = getattr(self, "panel", self) if message is not None: panel.SIG_STATUS_MESSAGE.emit(message, 0) QW.QApplication.setOverrideCursor(QG.QCursor(QC.Qt.WaitCursor)) panel.repaint() output = None try: output = func(*args, **kwargs) except Exception as msg: # pylint: disable=broad-except if is_running_tests(): # If we are running tests, we want to raise the exception raise qt_handle_error_message(panel.parentWidget(), msg, context) finally: if message is not None: panel.SIG_STATUS_MESSAGE.emit("", 0) QW.QApplication.restoreOverrideCursor() return output return method_wrapper return qt_try_except_decorator @contextmanager def qt_try_loadsave_file( parent: QW.QWidget, filename: str, operation: str ) -> Generator[str, None, None]: """Try and open file (operation: "load" or "save")""" if operation not in ("load", "save"): raise ValueError("operation argument must be 'load' or 'save'") try: yield filename except Exception as msg: # pylint: disable=broad-except if is_running_tests(): # If we are running tests, we want to raise the exception raise traceback.print_exc() url = osp.dirname(filename).replace("\\", "/") if operation == "load": text = _("The file %s could not be read:") else: text = _("The file %s could not be written:") in_folder = _("in this folder") message = text % ( f"{osp.basename(filename)}" f" ({in_folder})" ) QW.QMessageBox.critical(parent, APP_NAME, f"{message}

    {str(msg)}") finally: pass def grab_save_window( widget: QW.QWidget, name: str | None = None, add_timestamp: bool = True ) -> None: # pragma: no cover """Grab window screenshot and save it. Args: widget: Widget to grab name: Screenshot name (if None, uses widget.objectName()) add_timestamp: Whether to add a timestamp to the screenshot name (default: True) """ if name is None: name = widget.objectName() # DataLab-specific logic: determine if timestamp should be added # based on name patterns and DataLab conventions if name.endswith("_"): # Name ending with underscore always gets timestamp add_timestamp = True elif name[-1].isdigit() or name.startswith(("s_", "i_")): # DataLab screenshot names or numbered items don't get timestamp add_timestamp = False # Use guidata's grab_save_window with DataLab-specific configuration guidata_grab_save_window( widget=widget, name=name, save_dir=SHOTPATH, add_timestamp=add_timestamp ) @contextmanager def save_restore_stds() -> Generator[None, None, None]: """Save/restore standard I/O before/after doing some things (e.g. calling Qt open/save dialogs)""" saved_in, saved_out, saved_err = sys.stdin, sys.stdout, sys.stderr sys.stdout = None try: yield finally: sys.stdin, sys.stdout, sys.stderr = saved_in, saved_out, saved_err @contextmanager def block_signals( widget: QW.QWidget, enable: bool = True, children: bool = False ) -> Generator[None, None, None]: """Eventually block/unblock widget Qt signals before/after doing some things Args: widget: Widget to block/unblock signals enable: Whether to block/unblock signals (default: True). This is useful to avoid blocking signals when not needed without having to handle it by adding an `if` statement which would require to duplicate the code that is inside the `with` statement in the `else` branch. children: Whether to block/unblock signals for child widgets (default: False). Returns: Context manager """ if enable: widget.blockSignals(True) if children: for child in widget.findChildren(QW.QWidget): child.blockSignals(True) try: yield finally: if enable: widget.blockSignals(False) if children: for child in widget.findChildren(QW.QWidget): child.blockSignals(False) def create_menu_button( parent: QW.QWidget | None = None, menu: QW.QMenu | None = None ) -> QW.QPushButton: """Create a menu button Args: parent (QWidget): Parent widget menu (QMenu): Menu to attach to the button Returns: QW.QPushButton: Menu button """ button = QW.QPushButton(get_icon("libre-gui-menu.svg"), "", parent) button.setFlat(True) if menu is not None: button.setMenu(menu) return button def bring_to_front(window: QW.QWidget) -> None: """Bring window to front Args: window: Window to bring to front """ # Show window on top of others eflags = window.windowFlags() window.setWindowFlags(eflags | QC.Qt.WindowStaysOnTopHint) window.show() window.setWindowFlags(eflags) window.show() # If window is minimized, restore it if window.isMinimized(): window.showNormal() def configure_menu_about_to_show(menu: QW.QMenu, slot: Callable) -> None: """Configure menu about to show. This method is only used to connect the "aboutToShow" signal of menus, and more importantly to fix Issue #15 (Part 2) which is the fact that dynamic menus are not supported on MacOS unless an action is added to the menu before it is displayed. Args: menu: menu slot: slot """ # On MacOS, add an empty action to the menu before connecting the # "aboutToShow" signal to the slot. This is required to fix Issue #15 (Part 2) if sys.platform == "darwin": menu.addAction(QW.QAction(menu)) menu.aboutToShow.connect(slot) def resize_widget_to_parent( widget: QW.QWidget, parent: QW.QWidget | None = None, ratio: float = 0.95, aspect_ratio: float = 1.0, min_size: int = 500, ) -> None: """Resize widget based on parent widget's dimensions Args: widget: Widget to resize parent: Parent widget (if None, uses widget.parentWidget()) ratio: Ratio of parent size to use (0.0 to 1.0, default: 0.95 for 95%). This represents the percentage of the maximum dimension with respect to the widget. aspect_ratio: Width/height ratio (1.0 for square, >1.0 for landscape, <1.0 for portrait, default: 1.0) min_size: Minimum size in pixels (default: 500) """ if parent is None: parent = widget.parentWidget() if parent is not None: parent_size = parent.size() parent_width = parent_size.width() parent_height = parent_size.height() # Calculate maximum available dimensions max_width = parent_width * ratio max_height = parent_height * ratio # Determine which dimension is limiting based on aspect ratio # For aspect_ratio = w/h, we have: w = aspect_ratio * h # Check which constraint is more restrictive width_from_height = max_height * aspect_ratio height_from_width = max_width / aspect_ratio if width_from_height <= max_width: # Height is the limiting factor height = int(max_height) width = int(width_from_height) else: # Width is the limiting factor width = int(max_width) height = int(height_from_width) # Ensure minimum size while preserving aspect ratio if width < min_size or height < min_size: if aspect_ratio >= 1.0: # Landscape or square: scale up from minimum width width = max(width, min_size) height = max(height, int(min_size / aspect_ratio)) else: # Portrait: scale up from minimum height height = max(height, min_size) width = max(width, int(min_size * aspect_ratio)) # Final check: ensure we don't exceed parent dimensions width = min(width, parent_width) height = min(height, parent_height) widget.resize(width, height) else: # Fallback: use square with min_size if no parent widget.resize(min_size, min_size) def add_corner_menu( tabwidget: QW.QTabWidget, corner: QC.Qt.Corner | None = None ) -> QW.QMenu: """Add menu as corner widget to tab widget Args: tabwidget: Tab widget corner: Corner Returns: Menu """ if corner is None: corner = QC.Qt.TopRightCorner menu = QW.QMenu(tabwidget) btn = QW.QToolButton(tabwidget) btn.setMenu(menu) btn.setPopupMode(QW.QToolButton.InstantPopup) btn.setIcon(get_icon("menu.svg")) btn.setToolTip(_("Open tab menu")) tabwidget.setCornerWidget(btn, corner) return menu DataLab-1.1.0/datalab/utils/strings.py000066400000000000000000000017711514014117600176020ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab strings utilities """ from __future__ import annotations import difflib import os.path as osp import webbrowser def save_html_diff( text1: str, text2: str, desc1: str, desc2: str, filename: str ) -> None: """Generates HTML diff between two strings, saves it to a file, and opens it in a web browser (Windows only). Args: text1 (str): The first string to compare. text2 (str): The second string to compare. desc1 (str): Description of the first string. desc2 (str): Description of the second string. filename (str): The name of the file to save the HTML diff to. Returns: None """ differ = difflib.HtmlDiff() diff_html = differ.make_file(text1.splitlines(), text2.splitlines(), desc1, desc2) with open(filename, "w", encoding="utf-8") as file: file.write(diff_html) webbrowser.open("file://" + osp.realpath(filename)) DataLab-1.1.0/datalab/utils/tests.py000066400000000000000000000000001514014117600172330ustar00rootroot00000000000000DataLab-1.1.0/datalab/webapi/000077500000000000000000000000001514014117600156405ustar00rootroot00000000000000DataLab-1.1.0/datalab/webapi/__init__.py000066400000000000000000000046541514014117600177620ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause License # See LICENSE file for details """ DataLab Web API =============== This package provides a web-native HTTP/JSON API for DataLab, enabling: - **DataLab-Kernel integration**: Jupyter notebooks can connect to a running DataLab instance via HTTP instead of XML-RPC - **WASM/Pyodide compatibility**: The HTTP-based protocol works in WebAssembly environments where XML-RPC is not available - **External tool integration**: Any HTTP client can interact with the DataLab workspace Architecture ------------ The Web API follows a layered design: - **Control plane (JSON)**: Metadata operations via standard REST endpoints - **Data plane (binary)**: Efficient NumPy array transfer using NPZ format - **Events (WebSocket)**: Optional real-time notifications (future) Security -------- By default, the API: - Binds to localhost only (127.0.0.1) - Requires a bearer token for authentication - Token is generated at startup and displayed in the UI Usage ----- Enable the Web API via: - **UI**: Tools → Web API → Start - **CLI**: ``datalab --webapi`` - **Environment**: ``DATALAB_WEBAPI_ENABLED=1`` See Also -------- - :mod:`datalab.webapi.controller`: Server lifecycle management - :mod:`datalab.webapi.routes`: API endpoint definitions - :mod:`datalab.webapi.adapter`: Thread-safe workspace access """ from __future__ import annotations __all__ = [ "WEBAPI_AVAILABLE", "get_webapi_controller", ] # Check if webapi dependencies are available try: import fastapi # noqa: F401 import uvicorn # noqa: F401 WEBAPI_AVAILABLE = True except ImportError: WEBAPI_AVAILABLE = False _CONTROLLER_INSTANCE = None def get_webapi_controller(): """Get the singleton WebAPI controller instance. Returns: WebApiController instance, or None if webapi dependencies not installed. Raises: ImportError: If webapi dependencies are not available. """ # pylint: disable=global-statement global _CONTROLLER_INSTANCE # noqa: PLW0603 if not WEBAPI_AVAILABLE: raise ImportError( "Web API dependencies not installed. " "Install with: pip install datalab-platform[webapi]" ) if _CONTROLLER_INSTANCE is None: # pylint: disable=import-outside-toplevel from datalab.webapi.controller import WebApiController _CONTROLLER_INSTANCE = WebApiController() return _CONTROLLER_INSTANCE DataLab-1.1.0/datalab/webapi/actions.py000066400000000000000000000246321514014117600176610ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause License # See LICENSE file for details """ Web API GUI Actions =================== GUI actions for controlling the DataLab Web API server. This module provides menu actions and status display for the Web API feature. It integrates with the DataLab main window to provide UI controls for: - Starting/stopping the Web API server - Viewing connection information (URL, token) - Copying connection info to clipboard """ from __future__ import annotations from typing import TYPE_CHECKING from guidata.configtools import get_icon from guidata.qthelpers import add_actions, create_action from qtpy import QtWidgets as QW from datalab.config import APP_NAME, _ if TYPE_CHECKING: from datalab.gui.main import DLMainWindow class WebApiActions: """Manager for Web API GUI actions. This class creates and manages the menu actions for the Web API feature. It handles the server lifecycle through the WebApiController. Attributes: main_window: Reference to the DataLab main window. """ def __init__(self, main_window: DLMainWindow) -> None: """Initialize Web API actions. Args: main_window: The DataLab main window. """ self._main_window = main_window self._controller = None self._menu: QW.QMenu | None = None self._start_action: QW.QAction | None = None self._stop_action: QW.QAction | None = None self._copy_action: QW.QAction | None = None self._status_action: QW.QAction | None = None self._init_controller() self._create_actions() def _init_controller(self) -> None: """Initialize the Web API controller if available.""" try: # pylint: disable=import-outside-toplevel from datalab.webapi import WEBAPI_AVAILABLE, get_webapi_controller if WEBAPI_AVAILABLE: self._controller = get_webapi_controller() self._controller.set_main_window(self._main_window) self._controller.server_started.connect(self._on_server_started) self._controller.server_stopped.connect(self._on_server_stopped) self._controller.server_error.connect(self._on_server_error) except ImportError: self._controller = None def _create_actions(self) -> None: """Create menu actions.""" available = self._controller is not None # Start action self._start_action = create_action( self._main_window, _("Start Web API Server"), icon=get_icon("start_webapi_server.svg"), triggered=self._start_server, tip=_("Start the HTTP/JSON Web API server for external access"), ) self._start_action.setEnabled(available) # Stop action self._stop_action = create_action( self._main_window, _("Stop Web API Server"), icon=get_icon("stop_webapi_server.svg"), triggered=self._stop_server, ) self._stop_action.setEnabled(False) # Copy connection info action self._copy_action = create_action( self._main_window, _("Copy Connection Info"), icon=get_icon("copy_connection_info.svg"), triggered=self._copy_connection_info, tip=_("Copy URL and token to clipboard"), ) self._copy_action.setEnabled(False) # Status indicator (not clickable) self._status_action = create_action( self._main_window, _("Status: Not running"), ) self._status_action.setEnabled(False) if not available: self._status_action.setText( _("Web API unavailable (install datalab-platform[webapi])") ) def create_menu(self, parent_menu: QW.QMenu) -> QW.QMenu: """Create the Web API submenu. Args: parent_menu: Parent menu to add submenu to. Returns: The created submenu. """ self._menu = parent_menu.addMenu(_("Web API")) add_actions( self._menu, [ self._start_action, self._stop_action, None, self._copy_action, None, self._status_action, ], ) return self._menu def _start_server(self) -> None: """Start the Web API server.""" if self._controller is None: return try: url, token = self._controller.start() self._show_connection_dialog(url, token) except Exception as e: # pylint: disable=broad-exception-caught QW.QMessageBox.critical( self._main_window, APP_NAME, _("Failed to start Web API server:") + f"\n{e}", ) def _stop_server(self) -> None: """Stop the Web API server.""" if self._controller is None: return self._controller.stop() def _copy_connection_info(self) -> None: """Copy connection info to clipboard.""" if self._controller is None or not self._controller.is_running: return info = self._controller.get_connection_info() text = f"URL: {info['url']}\nToken: {info['token']}" clipboard = QW.QApplication.clipboard() clipboard.setText(text) # Show brief notification in status bar self._main_window.statusBar().showMessage( _("Connection info copied to clipboard"), 3000 ) def _show_connection_dialog(self, url: str, token: str) -> None: """Show dialog with connection information. Args: url: Server URL. token: Authentication token. """ dialog = QW.QDialog(self._main_window) dialog.setWindowTitle(_("Web API Server Started")) dialog.setMinimumWidth(450) layout = QW.QVBoxLayout(dialog) # Info label info_label = QW.QLabel( _( "The Web API server is now running. " "Use the following credentials to connect:" ) ) info_label.setWordWrap(True) layout.addWidget(info_label) # URL field url_layout = QW.QHBoxLayout() url_layout.addWidget(QW.QLabel(_("URL:"))) url_edit = QW.QLineEdit(url) url_edit.setReadOnly(True) url_layout.addWidget(url_edit) layout.addLayout(url_layout) # Token field token_layout = QW.QHBoxLayout() token_layout.addWidget(QW.QLabel(_("Token:"))) token_edit = QW.QLineEdit(token) token_edit.setReadOnly(True) token_layout.addWidget(token_edit) layout.addLayout(token_layout) # Environment variable hint hint_label = QW.QLabel( _("Tip: Set these environment variables in your notebook:\n") + f"DATALAB_WORKSPACE_URL={url}\n" + f"DATALAB_WORKSPACE_TOKEN={token}" ) hint_label.setStyleSheet("color: gray; font-size: 10px;") layout.addWidget(hint_label) # Buttons button_box = QW.QDialogButtonBox() copy_btn = button_box.addButton( _("Copy to Clipboard"), QW.QDialogButtonBox.ActionRole ) copy_btn.clicked.connect(self._copy_connection_info) close_btn = button_box.addButton(QW.QDialogButtonBox.Close) close_btn.clicked.connect(dialog.accept) layout.addWidget(button_box) dialog.exec_() def _on_server_started(self, url: str, _token: str) -> None: """Handle server started signal.""" self._start_action.setEnabled(False) self._stop_action.setEnabled(True) self._copy_action.setEnabled(True) self._status_action.setText(_("Status: Running at {}").format(url)) # Update status bar widget if self._main_window.webapistatus is not None: # Extract port from URL try: # pylint: disable=import-outside-toplevel from urllib.parse import urlparse parsed = urlparse(url) port = parsed.port except Exception: # pylint: disable=broad-exception-caught port = None self._main_window.webapistatus.set_status(url, port) def _on_server_stopped(self) -> None: """Handle server stopped signal.""" self._start_action.setEnabled(True) self._stop_action.setEnabled(False) self._copy_action.setEnabled(False) self._status_action.setText(_("Status: Not running")) # Update status bar widget if self._main_window.webapistatus is not None: self._main_window.webapistatus.set_status(None, None) def _on_server_error(self, message: str) -> None: """Handle server error signal.""" QW.QMessageBox.warning( self._main_window, APP_NAME, _("Web API server error:") + f"\n{message}", ) self._on_server_stopped() def cleanup(self) -> None: """Clean up resources on shutdown.""" if self._controller is not None and self._controller.is_running: self._controller.stop() def show_connection_info(self) -> None: """Show connection info dialog or copy to clipboard. This is called when the status widget is clicked while server is running. """ if self._controller is None or not self._controller.is_running: return # Show the connection info dialog info = self._controller.get_connection_info() self._show_connection_dialog(info["url"], info["token"]) def start_server_from_status_widget(self) -> None: """Start server from status widget click. This is called when the status widget is clicked while server is not running. Shows a confirmation dialog before starting the server. """ # Show confirmation dialog answer = QW.QMessageBox.question( self._main_window, _("Start Web API Server"), _( "Do you want to start the Web API server?\n\n" "This will allow external applications to connect to DataLab " "and control it remotely via HTTP/JSON." ), QW.QMessageBox.Yes | QW.QMessageBox.No, QW.QMessageBox.No, ) if answer == QW.QMessageBox.Yes: self._start_server() DataLab-1.1.0/datalab/webapi/adapter.py000066400000000000000000000527761514014117600176530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause License # See LICENSE file for details """ Web API Adapter =============== Thread-safe adapter for accessing the DataLab workspace from Web API handlers. The Web API server runs in a separate thread from the Qt GUI. This adapter provides safe access to workspace operations by using Qt's signal/slot mechanism to marshal calls to the main thread when necessary. Design ------ The adapter provides the same interface as the workspace but ensures: 1. Read operations are safe (DataLab's data model is mostly immutable) 2. Write operations are marshaled to the Qt main thread 3. Errors are properly propagated back to the calling thread Usage ----- The adapter is instantiated once when the Web API starts and passed to route handlers via FastAPI dependency injection. """ from __future__ import annotations import threading from typing import TYPE_CHECKING, Any, Union from qtpy.QtCore import QCoreApplication, QObject, QThread, Signal, Slot from datalab.objectmodel import get_uuid if TYPE_CHECKING: from sigima.objects import ImageObj, SignalObj DataObject = Union[SignalObj, ImageObj] class MainThreadExecutor(QObject): """Helper to execute functions on the main thread using Qt signals. This class uses Qt's signal/slot mechanism to safely marshal function calls from worker threads (like the Uvicorn server thread) to the Qt main thread. This is essential because Qt GUI operations must be performed on the main thread. The implementation uses a signal connected with Qt.QueuedConnection to post work to the main thread's event loop, and a threading.Event to synchronize the calling thread with the result. Important: This class must be instantiated on the main thread! """ # Signal to request execution on main thread _execute_signal = Signal(object, object) # (func, result_holder) def __init__(self) -> None: super().__init__() # Connect signal to slot - this connection will queue calls to main thread self._execute_signal.connect(self._execute_on_main_thread) # Store the main thread for comparison self._main_thread = QThread.currentThread() @Slot(object, object) def _execute_on_main_thread(self, func, result_holder: dict) -> None: """Slot that executes the function on the main thread. Args: func: Zero-argument callable to execute. result_holder: Dict to store result/exception and signal completion. """ try: result_holder["result"] = func() result_holder["exception"] = None except Exception as e: # pylint: disable=broad-exception-caught result_holder["result"] = None result_holder["exception"] = e finally: # Signal that execution is complete result_holder["event"].set() def run_on_main_thread(self, func) -> Any: """Run a function on the Qt main thread and wait for the result. If already on the main thread, executes directly. Otherwise, uses Qt's signal/slot mechanism to marshal the call to the main thread. Args: func: Zero-argument callable to execute. Returns: The result of the function. Raises: Any exception raised by the function. """ # Check if we're already on the main thread if QThread.currentThread() == self._main_thread: return func() # Create result holder with synchronization event result_holder = { "result": None, "exception": None, "event": threading.Event(), } # Emit signal to queue execution on main thread self._execute_signal.emit(func, result_holder) # Wait for execution to complete # Use a timeout to avoid hanging forever if something goes wrong if not result_holder["event"].wait(timeout=30.0): raise TimeoutError("Main thread execution timed out after 30 seconds") # Process pending events to ensure UI updates are applied QCoreApplication.processEvents() # Re-raise any exception from the main thread if result_holder["exception"] is not None: raise result_holder["exception"] # pylint: disable=raising-bad-type return result_holder["result"] class WorkspaceAdapter(QObject): """Thread-safe adapter for workspace access. This class wraps access to DataLab's workspace, ensuring that all modifying operations are executed on the Qt main thread. Attributes: main_window: Reference to the DataLab main window. """ def __init__(self, main_window=None) -> None: """Initialize the adapter. This should be called from the main thread to ensure the executor is properly initialized with working Qt signals. Args: main_window: The DataLab main window. If None, operations will fail. """ super().__init__() self._main_window = main_window # Create executor on main thread to ensure proper Qt signal connection self._executor = MainThreadExecutor() def set_main_window(self, main_window) -> None: """Set the main window reference. Args: main_window: The DataLab main window. """ self._main_window = main_window def _ensure_main_window(self) -> None: """Ensure main window is available.""" if self._main_window is None: raise RuntimeError("DataLab main window not available") # ========================================================================= # Read operations (marshaled to Qt main thread for thread safety) # ========================================================================= def list_objects(self) -> list[tuple[str, str]]: """List all objects in the workspace. This operation is marshaled to the Qt main thread for thread safety. Returns: List of (name, panel) tuples for all objects. """ self._ensure_main_window() def do_list(): result = [] # Access signal panel sig_panel = self._main_window.signalpanel if sig_panel is not None: for obj in sig_panel.objmodel: result.append((obj.title, "signal")) # Access image panel img_panel = self._main_window.imagepanel if img_panel is not None: for obj in img_panel.objmodel: result.append((obj.title, "image")) return result return self._executor.run_on_main_thread(do_list) def get_object(self, name: str) -> DataObject: """Get an object by name. This operation is marshaled to the Qt main thread for thread safety. Args: name: Object name/title. Returns: The requested object. Raises: KeyError: If object not found. """ self._ensure_main_window() def do_get(): # Search in signal panel sig_panel = self._main_window.signalpanel if sig_panel is not None: for obj in sig_panel.objmodel: if obj.title == name: return obj.copy() # Search in image panel img_panel = self._main_window.imagepanel if img_panel is not None: for obj in img_panel.objmodel: if obj.title == name: return obj.copy() raise KeyError(f"Object '{name}' not found") return self._executor.run_on_main_thread(do_get) def object_exists(self, name: str) -> bool: """Check if an object exists. Args: name: Object name/title. Returns: True if object exists. """ try: self.get_object(name) return True except KeyError: return False def get_object_panel(self, name: str) -> str | None: """Get the panel containing an object. This operation is marshaled to the Qt main thread for thread safety. Args: name: Object name/title. Returns: "signal" or "image", or None if not found. """ self._ensure_main_window() def do_lookup(): sig_panel = self._main_window.signalpanel if sig_panel is not None: for obj in sig_panel.objmodel: if obj.title == name: return "signal" img_panel = self._main_window.imagepanel if img_panel is not None: for obj in img_panel.objmodel: if obj.title == name: return "image" return None return self._executor.run_on_main_thread(do_lookup) # ========================================================================= # Write operations (must be marshaled to Qt main thread) # ========================================================================= def add_object(self, obj: DataObject, overwrite: bool = False) -> None: """Add an object to the workspace. This operation is marshaled to the Qt main thread as a single atomic operation to ensure thread safety. Args: obj: Object to add. overwrite: If True, replace existing object with same name. Raises: ValueError: If object exists and overwrite is False. """ self._ensure_main_window() name = obj.title obj_type = type(obj).__name__ if obj_type not in ("SignalObj", "ImageObj"): raise TypeError(f"Unsupported object type: {obj_type}") # Check if object exists if self.object_exists(name): if not overwrite: raise ValueError(f"Object '{name}' already exists") # Remove existing object first using the working remove method self._remove_object_sync(name) # Add the new object self._add_object_sync(obj) def _add_object_sync(self, obj: DataObject) -> None: """Add object (called from main thread or marshaled via executor).""" obj_type = type(obj).__name__ if obj_type == "SignalObj": panel = self._main_window.signalpanel elif obj_type == "ImageObj": panel = self._main_window.imagepanel else: raise TypeError(f"Unsupported object type: {obj_type}") # Use executor to run on main thread if necessary self._executor.run_on_main_thread(lambda: panel.add_object(obj)) def remove_object(self, name: str) -> None: """Remove an object from the workspace. This operation is marshaled to the Qt main thread. Args: name: Object name/title. Raises: KeyError: If object not found. """ self._ensure_main_window() if not self.object_exists(name): raise KeyError(f"Object '{name}' not found") self._remove_object_sync(name) def _remove_object_sync(self, name: str) -> None: """Remove object (called from main thread or marshaled via executor).""" panel_name = self.get_object_panel(name) if panel_name is None: return main_window = self._main_window # Use executor to run on main thread, including all panel access def do_remove(): # All panel access happens inside the executor if panel_name == "signal": panel = main_window.signalpanel else: panel = main_window.imagepanel # Find the object target_obj = None for obj in panel.objmodel: if obj.title == name: target_obj = obj break if target_obj is None: return obj_uuid = get_uuid(target_obj) # Remove using the same approach as remove_all_objects but for single object # Remove from plot handler panel.plothandler.remove_item(obj_uuid) # Remove from tree view panel.objview.remove_item(obj_uuid, refresh=False) # Remove from object model panel.objmodel.remove_object(target_obj) # Update tree panel.objview.update_tree() # Emit signal panel.SIG_OBJECT_REMOVED.emit() self._executor.run_on_main_thread(do_remove) def update_metadata(self, name: str, metadata: dict) -> None: """Update object metadata. This operation modifies Qt objects and should be marshaled to the Qt main thread for thread safety. Args: name: Object name/title. metadata: Dictionary of metadata fields to update. Raises: KeyError: If object not found. """ self._ensure_main_window() panel_name = self.get_object_panel(name) if panel_name is None: raise KeyError(f"Object '{name}' not found") if panel_name == "signal": panel = self._main_window.signalpanel else: panel = self._main_window.imagepanel def do_update(): # Find and update object for obj in panel.objmodel: if obj.title == name: for key, value in metadata.items(): if value is not None and hasattr(obj, key): setattr(obj, key, value) # Refresh display panel.SIG_REFRESH_PLOT.emit("selected", True) break self._executor.run_on_main_thread(do_update) def clear(self) -> None: """Clear all objects from the workspace. This operation is marshaled to the Qt main thread. """ self._ensure_main_window() def do_clear(): # Clear both panels using remove_all_objects (no confirmation dialog) for panel in [self._main_window.signalpanel, self._main_window.imagepanel]: if panel is not None: panel.remove_all_objects() self._executor.run_on_main_thread(do_clear) # ========================================================================= # Computation operations (for calc API) # ========================================================================= def select_objects( self, names: list[str], panel: str | None = None ) -> tuple[list[str], str]: """Select objects by name in a panel. This operation is marshaled to the Qt main thread for thread safety. Args: names: List of object names/titles to select. panel: Panel name ("signal" or "image"). None = auto-detect or current. Returns: Tuple of (list of selected names, panel name). Raises: KeyError: If any object not found. ValueError: If objects span multiple panels. """ self._ensure_main_window() def do_select(): # Determine panel for each object panels_found = set() obj_indices = [] for name in names: obj_panel = self.get_object_panel(name) if obj_panel is None: raise KeyError(f"Object '{name}' not found") panels_found.add(obj_panel) if len(panels_found) > 1: raise ValueError( "Cannot select objects from multiple panels. " f"Found objects in: {panels_found}" ) if panel is not None: target_panel = panel elif panels_found: target_panel = panels_found.pop() else: target_panel = "signal" # Get the panel widget if target_panel == "signal": panel_widget = self._main_window.signalpanel else: panel_widget = self._main_window.imagepanel # Find object indices (1-based) by name for name in names: for idx, obj in enumerate(panel_widget.objmodel): if obj.title == name: obj_indices.append(idx + 1) # 1-based indexing break # Select the objects using the panel's method if obj_indices: panel_widget.objview.select_objects(obj_indices) return names, target_panel return self._executor.run_on_main_thread(do_select) def get_selected_objects(self, panel: str | None = None) -> list[str]: """Get names of currently selected objects. Args: panel: Panel name. None = current panel. Returns: List of selected object names. """ self._ensure_main_window() def do_get_selected(): if panel == "signal": panel_widget = self._main_window.signalpanel elif panel == "image": panel_widget = self._main_window.imagepanel else: # Use current panel panel_widget = self._main_window.tabwidget.currentWidget() if not hasattr(panel_widget, "objmodel"): return [] return [obj.title for obj in panel_widget.objview.get_sel_objects()] return self._executor.run_on_main_thread(do_get_selected) def calc(self, name: str, param: dict | None = None) -> tuple[bool, list[str]]: """Call a computation function on currently selected objects. This operation is marshaled to the Qt main thread for thread safety. Args: name: Computation function name (e.g., "normalize", "fft"). param: Optional parameters as a dictionary. Returns: Tuple of (success, list of new object names created). Raises: ValueError: If computation function not found. """ self._ensure_main_window() def do_calc(): # Get objects before calc to track new ones before_names = set(self._get_all_object_names()) # Convert param dict to DataSet if provided param_dataset = None if param is not None: param_dataset = self._dict_to_dataset(name, param) # Call the main window's calc method with edit=False to prevent # blocking modal dialogs when called from the API try: self._main_window.calc(name, param_dataset, edit=False) success = True except ValueError: raise except Exception as e: # pylint: disable=broad-exception-caught raise RuntimeError(f"Computation '{name}' failed: {e}") from e # Get objects after calc to find new ones after_names = set(self._get_all_object_names()) new_names = list(after_names - before_names) return success, new_names return self._executor.run_on_main_thread(do_calc) def _get_all_object_names(self) -> list[str]: """Get all object names from all panels.""" names = [] for panel in [self._main_window.signalpanel, self._main_window.imagepanel]: if panel is not None: for obj in panel.objmodel: names.append(obj.title) return names def _dict_to_dataset(self, func_name: str, param_dict: dict): """Convert a parameter dictionary to a DataSet object. This looks up the parameter class for the given function and creates an instance with the provided values. Args: func_name: Computation function name. param_dict: Dictionary of parameter values. Returns: DataSet instance, or None if no parameters needed. """ import guidata.dataset as gds # pylint: disable=import-outside-toplevel # Try to find the parameter class from the processor # First, look in the current panel's processor panel = self._main_window.tabwidget.currentWidget() if hasattr(panel, "processor"): try: feature = panel.processor.get_feature(func_name) if feature.paramclass is not None: # Create instance and set values param_obj = feature.paramclass() for key, value in param_dict.items(): if hasattr(param_obj, key): setattr(param_obj, key, value) return param_obj except ValueError: pass # Fallback: try to import common parameter classes from sigima try: import sigima.params # pylint: disable=import-outside-toplevel # Try to find matching param class (e.g., "normalize" -> NormalizeParam) param_class_name = func_name.title().replace("_", "") + "Param" if hasattr(sigima.params, param_class_name): param_class = getattr(sigima.params, param_class_name) return param_class.create(**param_dict) except ImportError: pass # If we can't find a param class, create a simple DataSet if param_dict: class DynamicParam(gds.DataSet): """Dynamic parameter class created at runtime.""" param_obj = DynamicParam() for key, value in param_dict.items(): setattr(param_obj, key, value) return param_obj return None DataLab-1.1.0/datalab/webapi/controller.py000066400000000000000000000302541514014117600204010ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause License # See LICENSE file for details """ Web API Controller ================== Manages the lifecycle of the Web API server embedded in DataLab. The controller is responsible for: - Starting and stopping the Uvicorn server - Token generation and management - Port selection - Thread-safe server lifecycle Usage ----- :: from datalab.webapi import get_webapi_controller controller = get_webapi_controller() controller.set_main_window(main_window) controller.start() # Later... controller.stop() """ from __future__ import annotations import json import os import socket import threading from pathlib import Path from typing import TYPE_CHECKING from qtpy.QtCore import QObject, Signal if TYPE_CHECKING: from datalab.gui.main import DLMainWindow # Default port for Web API (predictable for auto-discovery) DEFAULT_WEBAPI_PORT = 18080 def get_connection_file_path() -> Path: """Get the path to the connection info file. The file is stored in a platform-specific location: - Windows: %APPDATA%/DataLab/webapi_connection.json - Linux/Mac: ~/.config/datalab/webapi_connection.json Returns: Path to the connection file. """ if os.name == "nt": # Windows: use APPDATA base = Path(os.environ.get("APPDATA", Path.home() / "AppData" / "Roaming")) else: # Linux/Mac: use XDG_CONFIG_HOME or ~/.config base = Path(os.environ.get("XDG_CONFIG_HOME", Path.home() / ".config")) config_dir = base / "datalab" config_dir.mkdir(parents=True, exist_ok=True) return config_dir / "webapi_connection.json" class WebApiController(QObject): """Controller for the DataLab Web API server. This class manages the lifecycle of an embedded Uvicorn server running FastAPI routes for the Web API. Signals: server_started: Emitted when server starts (url, token) server_stopped: Emitted when server stops server_error: Emitted on server error (message) """ # Qt signals for status updates server_started = Signal(str, str) # url, token server_stopped = Signal() server_error = Signal(str) def __init__(self) -> None: """Initialize the controller.""" super().__init__() self._main_window: DLMainWindow | None = None self._server_thread: threading.Thread | None = None self._uvicorn_server = None self._adapter = None self._token: str | None = None self._url: str | None = None self._running = False self._lock = threading.Lock() def set_main_window(self, main_window: DLMainWindow) -> None: """Set the DataLab main window reference. Args: main_window: The DataLab main window. """ self._main_window = main_window @property def is_running(self) -> bool: """Check if the server is running.""" with self._lock: return self._running @property def url(self) -> str | None: """Get the server URL.""" return self._url @property def token(self) -> str | None: """Get the authentication token.""" return self._token def _find_available_port( self, start_port: int = DEFAULT_WEBAPI_PORT, max_attempts: int = 100 ) -> int: """Find an available port. Args: start_port: Port to start searching from. max_attempts: Maximum number of ports to try. Returns: Available port number. Raises: RuntimeError: If no available port found. """ for port in range(start_port, start_port + max_attempts): try: with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s: s.bind(("127.0.0.1", port)) return port except OSError: continue raise RuntimeError( f"No available port found in range {start_port}-{start_port + max_attempts}" ) def start( self, host: str | None = None, port: int | None = None, token: str | None = None, ) -> tuple[str, str]: """Start the Web API server. Args: host: Host to bind to. Defaults to DATALAB_WEBAPI_HOST or "127.0.0.1". port: Port to bind to. Defaults to DATALAB_WEBAPI_PORT or 18080. token: Authentication token. Defaults to DATALAB_WEBAPI_TOKEN or generated. Returns: Tuple of (url, token). Raises: RuntimeError: If server already running or main window not set. """ if self._main_window is None: raise RuntimeError("Main window not set. Call set_main_window() first.") with self._lock: if self._running: raise RuntimeError("Server already running") self._running = True try: # Import here to allow graceful failure if deps not installed # pylint: disable=import-outside-toplevel import uvicorn from fastapi import FastAPI from fastapi.middleware.cors import CORSMiddleware from datalab.webapi.adapter import WorkspaceAdapter from datalab.webapi.routes import ( generate_auth_token, router, set_adapter, set_auth_token, set_localhost_no_token, set_server_url, ) # Resolve configuration host = host or os.environ.get("DATALAB_WEBAPI_HOST", "127.0.0.1") if port is None: env_port = os.environ.get("DATALAB_WEBAPI_PORT") port = int(env_port) if env_port else self._find_available_port() token = ( token or os.environ.get("DATALAB_WEBAPI_TOKEN") or generate_auth_token() ) # Check localhost bypass setting from datalab.config import Conf localhost_no_token = Conf.main.webapi_localhost_no_token.get() # Create adapter self._adapter = WorkspaceAdapter(self._main_window) # Configure routes set_adapter(self._adapter) set_auth_token(token) set_localhost_no_token(localhost_no_token) self._url = f"http://{host}:{port}" set_server_url(self._url) self._token = token # Create FastAPI app app = FastAPI( title="DataLab Web API", description="HTTP/JSON API for DataLab workspace access", version="1.0.0", ) # Add CORS middleware for JupyterLite and other browser-based clients app.add_middleware( CORSMiddleware, allow_origins=["*"], # Allow all origins for JupyterLite allow_credentials=True, allow_methods=["*"], allow_headers=["*"], ) # Add Private Network Access headers for browser-to-localhost requests # This allows JupyterLite (HTTPS public origin) to reach local DataLab # See: https://wicg.github.io/private-network-access/ @app.middleware("http") async def add_private_network_access_headers(request, call_next): # Handle preflight OPTIONS requests for Private Network Access if request.method == "OPTIONS": # Check if this is a Private Network Access preflight if request.headers.get("Access-Control-Request-Private-Network"): # Return a proper preflight response with PNA header from starlette.responses import Response response = Response( status_code=204, headers={ "Access-Control-Allow-Origin": request.headers.get( "Origin", "*" ), "Access-Control-Allow-Methods": ( "GET, POST, PUT, DELETE, PATCH, OPTIONS" ), "Access-Control-Allow-Headers": request.headers.get( "Access-Control-Request-Headers", "*" ), "Access-Control-Allow-Credentials": "true", "Access-Control-Allow-Private-Network": "true", "Access-Control-Max-Age": "86400", }, ) return response response = await call_next(request) # Add PNA header to all responses (for non-preflight requests) response.headers["Access-Control-Allow-Private-Network"] = "true" return response app.include_router(router) # Configure Uvicorn config = uvicorn.Config( app, host=host, port=port, log_level="warning", access_log=False, log_config=None, # Disable Uvicorn's logging config ) self._uvicorn_server = uvicorn.Server(config) # Write connection file for client auto-discovery self._write_connection_file() # Start server in thread self._server_thread = threading.Thread( target=self._run_server, name="DataLab-WebAPI", daemon=True, ) self._server_thread.start() # Emit signal self.server_started.emit(self._url, self._token) return self._url, self._token except Exception as e: with self._lock: self._running = False self.server_error.emit(str(e)) raise def _run_server(self) -> None: """Run the Uvicorn server (called in thread).""" try: self._uvicorn_server.run() except Exception as e: # pylint: disable=broad-exception-caught self.server_error.emit(str(e)) finally: with self._lock: self._running = False self.server_stopped.emit() def stop(self) -> None: """Stop the Web API server.""" with self._lock: if not self._running: return self._running = False # Remove connection file self._remove_connection_file() if self._uvicorn_server is not None: self._uvicorn_server.should_exit = True if self._server_thread is not None: self._server_thread.join(timeout=5.0) self._server_thread = None self._uvicorn_server = None self._adapter = None self._url = None self._token = None self.server_stopped.emit() def _write_connection_file(self) -> None: """Write connection info to file for client auto-discovery.""" try: connection_info = { "url": self._url, "token": self._token, "pid": os.getpid(), } connection_file = get_connection_file_path() connection_file.write_text(json.dumps(connection_info, indent=2)) except Exception: # pylint: disable=broad-exception-caught # Non-critical: don't fail server start if file write fails pass def _remove_connection_file(self) -> None: """Remove the connection file.""" try: connection_file = get_connection_file_path() if connection_file.exists(): connection_file.unlink() except Exception: # pylint: disable=broad-exception-caught # Non-critical: ignore errors during cleanup pass def get_connection_info(self) -> dict: """Get connection information for clients. Returns: Dictionary with url, token, and running status. """ return { "running": self.is_running, "url": self._url, "token": self._token, } DataLab-1.1.0/datalab/webapi/routes.py000066400000000000000000000423051514014117600175370ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause License # See LICENSE file for details """ Web API Routes ============== FastAPI route definitions for the DataLab Web API. This module defines all HTTP endpoints for the API. Routes are organized by function: - ``/api/v1/status`` - Server status - ``/api/v1/objects`` - Workspace object operations - ``/api/v1/objects/{name}/data`` - Binary data transfer Security -------- All routes (except ``/api/v1/status``) require bearer token authentication. The token is generated when the server starts and must be included in the ``Authorization`` header. """ from __future__ import annotations import secrets import traceback from typing import Annotated, Optional from urllib.parse import quote from fastapi import APIRouter, Depends, Header, HTTPException, Request, Response, status from datalab import __version__ from datalab.webapi.adapter import WorkspaceAdapter from datalab.webapi.schema import ( ApiStatus, CalcRequest, CalcResponse, ErrorResponse, MetadataPatchRequest, ObjectListResponse, ObjectMetadata, SelectObjectsRequest, SelectObjectsResponse, ) from datalab.webapi.serialization import ( deserialize_object_from_npz, object_to_metadata, serialize_object_to_npz, ) # Router for the API router = APIRouter(prefix="/api/v1", tags=["workspace"]) # Global references (set by controller at startup) _ADAPTER: WorkspaceAdapter | None = None _AUTH_TOKEN: str | None = None _SERVER_URL: str | None = None _LOCALHOST_NO_TOKEN: bool = False def set_adapter(adapter: WorkspaceAdapter) -> None: """Set the workspace adapter for route handlers.""" global _ADAPTER # noqa: PLW0603 # pylint: disable=global-statement _ADAPTER = adapter def set_auth_token(token: str) -> None: """Set the authentication token.""" global _AUTH_TOKEN # noqa: PLW0603 # pylint: disable=global-statement _AUTH_TOKEN = token def set_server_url(url: str) -> None: """Set the server URL.""" global _SERVER_URL # noqa: PLW0603 # pylint: disable=global-statement _SERVER_URL = url def set_localhost_no_token(enabled: bool) -> None: """Set whether localhost connections can bypass authentication.""" global _LOCALHOST_NO_TOKEN # noqa: PLW0603 # pylint: disable=global-statement _LOCALHOST_NO_TOKEN = enabled def generate_auth_token() -> str: """Generate a secure random authentication token.""" return secrets.token_urlsafe(32) def get_adapter() -> WorkspaceAdapter: """Dependency: Get the workspace adapter.""" if _ADAPTER is None: raise HTTPException( status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail="Workspace adapter not initialized", ) return _ADAPTER def verify_token( request: Request, authorization: Annotated[Optional[str], Header()] = None ) -> Optional[str]: """Dependency: Verify the bearer token. Args: request: The incoming request (for client IP check). authorization: Authorization header value. Returns: The validated token, or None if localhost bypass is enabled. Raises: HTTPException: If token is missing or invalid. """ # Check for localhost bypass if _LOCALHOST_NO_TOKEN: client_ip = request.client.host if request.client else None if client_ip in ("127.0.0.1", "::1", "localhost"): # Localhost bypass enabled and request is from localhost return None if _AUTH_TOKEN is None: raise HTTPException( status_code=status.HTTP_503_SERVICE_UNAVAILABLE, detail="Authentication not configured", ) if authorization is None: raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="Missing Authorization header", headers={"WWW-Authenticate": "Bearer"}, ) # Parse "Bearer " parts = authorization.split() if len(parts) != 2 or parts[0].lower() != "bearer": raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid Authorization header format", headers={"WWW-Authenticate": "Bearer"}, ) if not secrets.compare_digest(parts[1], _AUTH_TOKEN): raise HTTPException( status_code=status.HTTP_401_UNAUTHORIZED, detail="Invalid token", headers={"WWW-Authenticate": "Bearer"}, ) return parts[1] # ============================================================================= # Status endpoint (no auth required) # ============================================================================= @router.get("/status", response_model=ApiStatus, tags=["status"]) async def get_status() -> ApiStatus: """Get API server status. This endpoint does not require authentication and can be used to check if the server is running and to get the API version. """ return ApiStatus( running=True, version=__version__, api_version="v1", url=_SERVER_URL, workspace_mode="live", localhost_no_token=_LOCALHOST_NO_TOKEN, ) # ============================================================================= # Object listing and metadata (JSON control plane) # ============================================================================= @router.get( "/objects", response_model=ObjectListResponse, responses={401: {"model": ErrorResponse}}, ) async def list_objects( _token: str = Depends(verify_token), adapter: WorkspaceAdapter = Depends(get_adapter), ) -> ObjectListResponse: """List all objects in the workspace. Returns metadata for all signals and images currently in DataLab. """ try: objects_list = adapter.list_objects() result = [] for name, _panel in objects_list: try: obj = adapter.get_object(name) meta = object_to_metadata(obj, name) result.append(ObjectMetadata(**meta)) except Exception: # pylint: disable=broad-exception-caught # Skip objects that can't be serialized continue return ObjectListResponse(objects=result, count=len(result)) except Exception as e: # pylint: disable=broad-exception-caught raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e), ) from e @router.get( "/objects/{name}", response_model=ObjectMetadata, responses={ 401: {"model": ErrorResponse}, 404: {"model": ErrorResponse}, 500: {"model": ErrorResponse}, }, ) async def get_object_metadata( name: str, _token: str = Depends(verify_token), adapter: WorkspaceAdapter = Depends(get_adapter), ) -> ObjectMetadata: """Get metadata for a specific object. Args: name: Object name/title. Returns: Object metadata (without data arrays). """ try: obj = adapter.get_object(name) meta = object_to_metadata(obj, name) return ObjectMetadata(**meta) except KeyError as e: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, detail=f"Object '{name}' not found", ) from e except Exception as e: # pylint: disable=broad-exception-caught raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Error retrieving metadata for '{name}': {e}", ) from e @router.delete( "/objects/{name}", status_code=status.HTTP_204_NO_CONTENT, responses={ 401: {"model": ErrorResponse}, 404: {"model": ErrorResponse}, 500: {"model": ErrorResponse}, }, ) async def delete_object( name: str, _token: str = Depends(verify_token), adapter: WorkspaceAdapter = Depends(get_adapter), ) -> None: """Delete an object from the workspace. Args: name: Object name/title. """ try: adapter.remove_object(name) except KeyError as e: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, detail=f"Object '{name}' not found", ) from e except Exception as e: # pylint: disable=broad-exception-caught raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Error deleting '{name}': {e}", ) from e @router.patch( "/objects/{name}/metadata", response_model=ObjectMetadata, responses={401: {"model": ErrorResponse}, 404: {"model": ErrorResponse}}, ) async def update_object_metadata( name: str, patch: MetadataPatchRequest, _token: str = Depends(verify_token), adapter: WorkspaceAdapter = Depends(get_adapter), ) -> ObjectMetadata: """Update object metadata. Args: name: Object name/title. patch: Metadata fields to update. Returns: Updated object metadata. """ try: # Convert patch to dict, excluding None values updates = patch.model_dump(exclude_none=True) adapter.update_metadata(name, updates) # Return updated metadata obj = adapter.get_object(name) meta = object_to_metadata(obj, name) return ObjectMetadata(**meta) except KeyError as e: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, detail=f"Object '{name}' not found", ) from e # ============================================================================= # Binary data transfer (NPZ format) # ============================================================================= @router.get( "/objects/{name}/data", responses={ 200: {"content": {"application/x-npz": {}}}, 401: {"model": ErrorResponse}, 404: {"model": ErrorResponse}, 500: {"model": ErrorResponse}, }, ) async def get_object_data( name: str, compress: bool = True, _token: str = Depends(verify_token), adapter: WorkspaceAdapter = Depends(get_adapter), ) -> Response: """Get object data in NPZ format. Returns the object's numerical arrays (x/y for signals, data for images) plus metadata in a NumPy NPZ archive. Args: name: Object name/title. compress: If True (default), use ZIP deflate compression. Set to False for faster serialization (10-30x) at the cost of larger size (~10% increase for typical image data). Recommended: False for large images and fast local connections. Returns: Binary NPZ archive. """ try: obj = adapter.get_object(name) npz_data = serialize_object_to_npz(obj, compress=compress) # Build Content-Disposition header with safe filename # Use RFC 5987 encoding for non-ASCII characters, or fallback to ASCII try: # Try ASCII first (will fail for Unicode chars) name.encode("ascii") content_disposition = f'attachment; filename="{name}.npz"' except UnicodeEncodeError: # Use RFC 5987 encoding for Unicode filenames encoded_name = quote(name, safe="") content_disposition = f"attachment; filename*=UTF-8''{encoded_name}.npz" return Response( content=npz_data, media_type="application/x-npz", headers={ "Content-Disposition": content_disposition, }, ) except KeyError as e: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, detail=f"Object '{name}' not found", ) from e except Exception as e: # pylint: disable=broad-exception-caught tb = traceback.format_exc() raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Error retrieving object '{name}': {e}\n\nTraceback:\n{tb}", ) from e @router.put( "/objects/{name}/data", status_code=status.HTTP_201_CREATED, response_model=ObjectMetadata, responses={ 401: {"model": ErrorResponse}, 409: {"model": ErrorResponse}, 422: {"model": ErrorResponse}, }, ) async def put_object_data( name: str, request: Request, overwrite: bool = False, _token: str = Depends(verify_token), adapter: WorkspaceAdapter = Depends(get_adapter), ) -> ObjectMetadata: """Create or replace an object from NPZ data. The request body must be a NumPy NPZ archive containing the object data (x.npy/y.npy for signals, data.npy for images) and metadata.json. Args: name: Object name/title. request: FastAPI request (body contains NPZ archive bytes). overwrite: If True, replace existing object. Returns: Metadata of the created object. """ try: # Read body from request body = await request.body() # Check if exists if adapter.object_exists(name) and not overwrite: raise HTTPException( status_code=status.HTTP_409_CONFLICT, detail=f"Object '{name}' already exists. Use overwrite=true.", ) # Deserialize obj = deserialize_object_from_npz(body) obj.title = name # Add to workspace adapter.add_object(obj, overwrite=overwrite) # Return metadata meta = object_to_metadata(obj, name) return ObjectMetadata(**meta) except HTTPException: raise # Re-raise HTTPException without wrapping except ValueError as e: raise HTTPException( status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, detail=f"Invalid NPZ format: {e}", ) from e except Exception as e: # pylint: disable=broad-exception-caught raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Error adding object '{name}': {e}", ) from e # ============================================================================= # Computation endpoints # ============================================================================= @router.post( "/select", response_model=SelectObjectsResponse, responses={ 401: {"model": ErrorResponse}, 404: {"model": ErrorResponse}, 422: {"model": ErrorResponse}, }, ) async def select_objects( request: SelectObjectsRequest, _token: str = Depends(verify_token), adapter: WorkspaceAdapter = Depends(get_adapter), ) -> SelectObjectsResponse: """Select objects in a panel. Selects the specified objects by name, making them the active selection for subsequent operations like ``calc``. Args: request: Selection request with object names and optional panel. Returns: List of selected object names and the panel. """ try: panel_str = request.panel.value if request.panel else None selected, panel = adapter.select_objects(request.selection, panel_str) return SelectObjectsResponse(selected=selected, panel=panel) except KeyError as e: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, detail=str(e), ) from e except ValueError as e: raise HTTPException( status_code=status.HTTP_422_UNPROCESSABLE_ENTITY, detail=str(e), ) from e except Exception as e: # pylint: disable=broad-exception-caught raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Error selecting objects: {e}", ) from e @router.post( "/calc", response_model=CalcResponse, responses={ 401: {"model": ErrorResponse}, 404: {"model": ErrorResponse}, 422: {"model": ErrorResponse}, 500: {"model": ErrorResponse}, }, ) async def calc( request: CalcRequest, _token: str = Depends(verify_token), adapter: WorkspaceAdapter = Depends(get_adapter), ) -> CalcResponse: """Call a computation function. Executes a DataLab computation function on the currently selected objects. Use the ``/select`` endpoint first to select the objects to process. Common computation functions include: - Signal: ``normalize``, ``fft``, ``ifft``, ``moving_average``, ``derivative`` - Image: ``normalize``, ``rotate``, ``flip``, ``denoise``, ``threshold`` Args: request: Computation request with function name and optional parameters. Returns: Success status and names of any newly created result objects. Example request:: { "name": "normalize", "param": {"method": "maximum"} } """ try: success, new_names = adapter.calc(request.name, request.param) return CalcResponse( success=success, function=request.name, result_names=new_names, ) except ValueError as e: raise HTTPException( status_code=status.HTTP_404_NOT_FOUND, detail=f"Unknown computation function '{request.name}': {e}", ) from e except RuntimeError as e: raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=str(e), ) from e except Exception as e: # pylint: disable=broad-exception-caught raise HTTPException( status_code=status.HTTP_500_INTERNAL_SERVER_ERROR, detail=f"Computation error: {e}", ) from e DataLab-1.1.0/datalab/webapi/schema.py000066400000000000000000000157761514014117600174720ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause License # See LICENSE file for details """ Web API Schema Definitions ========================== Pydantic models defining the API contract for DataLab Web API. These schemas define: - Object metadata (signals and images) - API request/response payloads - Event messages Design Principles ----------------- 1. **Minimal but complete**: Include only essential metadata for workspace operations 2. **Type-safe**: Full type annotations for all fields 3. **Serializable**: All models can be serialized to JSON 4. **Extensible**: Designed for future additions without breaking changes """ from __future__ import annotations from enum import Enum from typing import Any, Optional from pydantic import BaseModel, Field class ObjectType(str, Enum): """Type of data object in the workspace.""" SIGNAL = "signal" IMAGE = "image" class ObjectMetadata(BaseModel): """Metadata for a workspace object. This is the core representation of an object in the API. The actual data is transferred separately via the binary data plane. """ name: str = Field(..., description="Unique object identifier/title") type: ObjectType = Field(..., description="Object type (signal or image)") shape: list[int] = Field(..., description="Array shape (e.g., [100] or [512, 512])") dtype: str = Field(..., description="NumPy dtype string (e.g., 'float64')") # Optional metadata title: Optional[str] = Field(None, description="Display title") xlabel: Optional[str] = Field(None, description="X-axis label") ylabel: Optional[str] = Field(None, description="Y-axis label") zlabel: Optional[str] = Field(None, description="Z-axis label (images only)") xunit: Optional[str] = Field(None, description="X-axis unit") yunit: Optional[str] = Field(None, description="Y-axis unit") zunit: Optional[str] = Field(None, description="Z-axis unit (images only)") # Extended attributes attributes: dict[str, Any] = Field( default_factory=dict, description="Additional key/value metadata" ) model_config = {"extra": "ignore"} class ObjectListResponse(BaseModel): """Response for listing workspace objects.""" objects: list[ObjectMetadata] = Field(..., description="List of object metadata") count: int = Field(..., description="Total number of objects") class ObjectCreateRequest(BaseModel): """Request to create a new object (metadata only, data sent separately).""" name: str = Field(..., description="Object name/title") type: ObjectType = Field(..., description="Object type") overwrite: bool = Field(False, description="Replace existing object if present") # Optional metadata to set title: Optional[str] = Field(None, description="Display title") xlabel: Optional[str] = Field(None, description="X-axis label") ylabel: Optional[str] = Field(None, description="Y-axis label") zlabel: Optional[str] = Field(None, description="Z-axis label (images only)") xunit: Optional[str] = Field(None, description="X-axis unit") yunit: Optional[str] = Field(None, description="Y-axis unit") zunit: Optional[str] = Field(None, description="Z-axis unit (images only)") attributes: dict[str, Any] = Field( default_factory=dict, description="Additional metadata" ) class MetadataPatchRequest(BaseModel): """Request to update object metadata.""" title: Optional[str] = Field(None, description="New display title") xlabel: Optional[str] = Field(None, description="New X-axis label") ylabel: Optional[str] = Field(None, description="New Y-axis label") zlabel: Optional[str] = Field(None, description="New Z-axis label") xunit: Optional[str] = Field(None, description="New X-axis unit") yunit: Optional[str] = Field(None, description="New Y-axis unit") zunit: Optional[str] = Field(None, description="New Z-axis unit") attributes: Optional[dict[str, Any]] = Field( None, description="Attributes to merge (not replace)" ) class ApiStatus(BaseModel): """API server status information.""" running: bool = Field(..., description="Whether the API server is running") version: str = Field(..., description="DataLab version") api_version: str = Field("v1", description="API version") url: Optional[str] = Field(None, description="Base URL when running") workspace_mode: str = Field(..., description="Current workspace mode") localhost_no_token: bool = Field( False, description="Whether localhost connections can bypass authentication" ) class ErrorResponse(BaseModel): """Standard error response.""" error: str = Field(..., description="Error type/code") message: str = Field(..., description="Human-readable error message") detail: Optional[str] = Field(None, description="Additional detail") # Event types for WebSocket (future) class EventType(str, Enum): """Types of workspace events.""" OBJECT_ADDED = "object_added" OBJECT_UPDATED = "object_updated" OBJECT_REMOVED = "object_removed" OBJECT_RENAMED = "object_renamed" WORKSPACE_CLEARED = "workspace_cleared" class WorkspaceEvent(BaseModel): """A workspace change event (for WebSocket notifications).""" event: EventType = Field(..., description="Event type") object_name: Optional[str] = Field(None, description="Affected object name") old_name: Optional[str] = Field(None, description="Old name (for rename events)") timestamp: float = Field(..., description="Unix timestamp") # ============================================================================= # Computation API schemas # ============================================================================= class SelectObjectsRequest(BaseModel): """Request to select objects in a panel.""" selection: list[str] = Field( ..., description="List of object names/titles to select" ) panel: Optional[ObjectType] = Field( None, description="Panel to select in (signal/image). None = current panel." ) class SelectObjectsResponse(BaseModel): """Response for object selection.""" selected: list[str] = Field(..., description="List of selected object names") panel: str = Field(..., description="Panel where selection was made") class CalcRequest(BaseModel): """Request to call a computation function. The computation is applied to the currently selected objects in DataLab. """ name: str = Field( ..., description="Computation function name (e.g., 'normalize', 'fft')" ) param: Optional[dict[str, Any]] = Field( None, description="Computation parameters as a dictionary. " "Keys are parameter names, values are parameter values.", ) class CalcResponse(BaseModel): """Response from a computation.""" success: bool = Field(..., description="Whether computation succeeded") function: str = Field(..., description="Name of the computation function called") result_names: list[str] = Field( default_factory=list, description="Names of newly created result objects" ) DataLab-1.1.0/datalab/webapi/serialization.py000066400000000000000000000305571514014117600211010ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause License # See LICENSE file for details """ Web API Serialization ===================== Binary serialization of DataLab objects using NumPy's NPZ format. This module handles the data plane of the Web API, efficiently transferring large numerical arrays between DataLab and clients. Format Specification (V1) ------------------------- The NPZ archive contains: For SignalObj: - ``x.npy``: X coordinates (1D float64 array) - ``y.npy``: Y data (1D float64 array) - ``dx.npy``: X uncertainties (optional) - ``dy.npy``: Y uncertainties (optional) - ``metadata.json``: JSON-encoded metadata For ImageObj: - ``data.npy``: Image data (2D array, preserves dtype) - ``metadata.json``: JSON-encoded metadata Metadata JSON includes: - ``type``: "signal" or "image" - ``title``, ``xlabel``, ``ylabel``, etc. - ``x0``, ``y0``, ``dx``, ``dy`` (for images) """ from __future__ import annotations import io import json import zipfile from typing import TYPE_CHECKING, Union import numpy as np from sigima.objects import ImageObj, SignalObj if TYPE_CHECKING: DataObject = Union[SignalObj, ImageObj] def _to_python_scalar(value): """Convert numpy scalar types to Python native types for JSON serialization. Args: value: Any value, possibly a numpy scalar. Returns: Python native type if input was a numpy scalar, otherwise unchanged. """ if isinstance(value, np.integer): return int(value) if isinstance(value, np.floating): return float(value) if isinstance(value, np.bool_): return bool(value) return value def _make_json_serializable(value): # pylint: disable=too-many-return-statements """Recursively convert a value to be JSON serializable. Handles numpy arrays, numpy scalars, nested dicts, and lists. Args: value: Any value that may contain numpy types. Returns: JSON-serializable value. """ if isinstance(value, dict): return {k: _make_json_serializable(v) for k, v in value.items()} if isinstance(value, list): return [_make_json_serializable(item) for item in value] if isinstance(value, tuple): return [_make_json_serializable(item) for item in value] if isinstance(value, np.ndarray): return value.tolist() if isinstance(value, np.integer): return int(value) if isinstance(value, np.floating): return float(value) if isinstance(value, np.bool_): return bool(value) return value def _serialize_obj_metadata(obj_metadata: dict) -> dict: """Convert object metadata to JSON-serializable format. Handles numpy arrays (from GeometryResult.coords, TableResult, etc.) by converting them to nested lists, and numpy scalar types (int64, float64) to Python native types. Args: obj_metadata: The object's metadata dictionary. Returns: JSON-serializable dictionary. """ return _make_json_serializable(obj_metadata) def _deserialize_obj_metadata(serialized: dict) -> dict: """Convert deserialized metadata back to original types. Converts nested lists back to numpy arrays where appropriate (for coords fields in geometry results). Args: serialized: The JSON-deserialized metadata. Returns: Metadata with numpy arrays restored. """ result = {} for key, value in serialized.items(): if isinstance(value, dict): # Recursively handle nested dicts result[key] = _deserialize_obj_metadata(value) elif isinstance(value, list) and key == "coords": # Convert coords back to numpy array result[key] = np.array(value) else: result[key] = value return result def serialize_object_to_npz(obj: DataObject, *, compress: bool = True) -> bytes: """Serialize a SignalObj or ImageObj to NPZ format. Args: obj: The object to serialize. compress: If True (default), use ZIP deflate compression. Set to False for faster serialization at the cost of larger size. For incompressible data (random images), False can be 10-30x faster. Returns: Bytes containing the NPZ archive. Raises: TypeError: If object type is not supported. """ buffer = io.BytesIO() # Detect object type obj_type = type(obj).__name__ if obj_type == "SignalObj": _serialize_signal(obj, buffer, compress=compress) elif obj_type == "ImageObj": _serialize_image(obj, buffer, compress=compress) else: raise TypeError(f"Unsupported object type: {obj_type}") return buffer.getvalue() def _serialize_signal(obj, buffer: io.BytesIO, *, compress: bool = True) -> None: """Serialize a SignalObj to NPZ format.""" # Build metadata dict metadata = { "type": "signal", "title": getattr(obj, "title", None), "xlabel": getattr(obj, "xlabel", None), "ylabel": getattr(obj, "ylabel", None), "xunit": getattr(obj, "xunit", None), "yunit": getattr(obj, "yunit", None), } # Include object metadata (contains Geometry_*, Table_* results) obj_metadata = getattr(obj, "metadata", None) if obj_metadata: metadata["obj_metadata"] = _serialize_obj_metadata(obj_metadata) # Create zip archive compression = zipfile.ZIP_DEFLATED if compress else zipfile.ZIP_STORED with zipfile.ZipFile(buffer, "w", compression) as zf: # Write arrays _write_array_to_zip(zf, "x.npy", obj.x) _write_array_to_zip(zf, "y.npy", obj.y) if obj.dx is not None: _write_array_to_zip(zf, "dx.npy", obj.dx) if obj.dy is not None: _write_array_to_zip(zf, "dy.npy", obj.dy) # Write metadata zf.writestr("metadata.json", json.dumps(metadata, ensure_ascii=False)) def _serialize_image(obj, buffer: io.BytesIO, *, compress: bool = True) -> None: """Serialize an ImageObj to NPZ format.""" # Build metadata dict - use _to_python_scalar for numeric values that may be # numpy scalars after HDF5 deserialization metadata = { "type": "image", "title": getattr(obj, "title", None), "xlabel": getattr(obj, "xlabel", None), "ylabel": getattr(obj, "ylabel", None), "zlabel": getattr(obj, "zlabel", None), "xunit": getattr(obj, "xunit", None), "yunit": getattr(obj, "yunit", None), "zunit": getattr(obj, "zunit", None), "x0": _to_python_scalar(getattr(obj, "x0", 0.0)), "y0": _to_python_scalar(getattr(obj, "y0", 0.0)), "dx": _to_python_scalar(getattr(obj, "dx", 1.0)), "dy": _to_python_scalar(getattr(obj, "dy", 1.0)), } # Include object metadata (contains Geometry_*, Table_* results) obj_metadata = getattr(obj, "metadata", None) if obj_metadata: metadata["obj_metadata"] = _serialize_obj_metadata(obj_metadata) # Create zip archive compression = zipfile.ZIP_DEFLATED if compress else zipfile.ZIP_STORED with zipfile.ZipFile(buffer, "w", compression) as zf: _write_array_to_zip(zf, "data.npy", obj.data) zf.writestr("metadata.json", json.dumps(metadata, ensure_ascii=False)) def _write_array_to_zip(zf: zipfile.ZipFile, name: str, arr: np.ndarray) -> None: """Write a numpy array to a zip file.""" arr_buffer = io.BytesIO() np.save(arr_buffer, arr, allow_pickle=False) zf.writestr(name, arr_buffer.getvalue()) def deserialize_object_from_npz(data: bytes) -> DataObject: """Deserialize a SignalObj or ImageObj from NPZ format. Args: data: Bytes containing the NPZ archive. Returns: SignalObj or ImageObj. Raises: ValueError: If the archive format is invalid. """ buffer = io.BytesIO(data) with zipfile.ZipFile(buffer, "r") as zf: # Read metadata if "metadata.json" not in zf.namelist(): raise ValueError("Invalid NPZ format: missing metadata.json") metadata = json.loads(zf.read("metadata.json")) obj_type = metadata.get("type") if obj_type == "signal": return _deserialize_signal(zf, metadata) if obj_type == "image": return _deserialize_image(zf, metadata) raise ValueError(f"Unknown object type in NPZ: {obj_type}") def _read_array_from_zip(zf: zipfile.ZipFile, name: str) -> np.ndarray | None: """Read a numpy array from a zip file.""" if name not in zf.namelist(): return None arr_buffer = io.BytesIO(zf.read(name)) return np.load(arr_buffer, allow_pickle=False) def _deserialize_signal(zf: zipfile.ZipFile, metadata: dict) -> DataObject: """Deserialize a SignalObj from NPZ archive.""" x = _read_array_from_zip(zf, "x.npy") y = _read_array_from_zip(zf, "y.npy") dx = _read_array_from_zip(zf, "dx.npy") dy = _read_array_from_zip(zf, "dy.npy") if x is None or y is None: raise ValueError("Invalid signal NPZ: missing x.npy or y.npy") obj = SignalObj() obj.set_xydata(x, y, dx=dx, dy=dy) # Set metadata if metadata.get("title"): obj.title = metadata["title"] if metadata.get("xlabel"): obj.xlabel = metadata["xlabel"] if metadata.get("ylabel"): obj.ylabel = metadata["ylabel"] if metadata.get("xunit"): obj.xunit = metadata["xunit"] if metadata.get("yunit"): obj.yunit = metadata["yunit"] # Restore object metadata (Geometry_*, Table_* results) if metadata.get("obj_metadata"): obj.metadata = _deserialize_obj_metadata(metadata["obj_metadata"]) return obj def _deserialize_image(zf: zipfile.ZipFile, metadata: dict) -> DataObject: """Deserialize an ImageObj from NPZ archive.""" data = _read_array_from_zip(zf, "data.npy") if data is None: raise ValueError("Invalid image NPZ: missing data.npy") obj = ImageObj() obj.data = data # Set metadata if metadata.get("title"): obj.title = metadata["title"] if metadata.get("xlabel"): obj.xlabel = metadata["xlabel"] if metadata.get("ylabel"): obj.ylabel = metadata["ylabel"] if metadata.get("zlabel"): obj.zlabel = metadata["zlabel"] if metadata.get("xunit"): obj.xunit = metadata["xunit"] if metadata.get("yunit"): obj.yunit = metadata["yunit"] if metadata.get("zunit"): obj.zunit = metadata["zunit"] # Set coordinate info obj.x0 = metadata.get("x0", 0.0) obj.y0 = metadata.get("y0", 0.0) obj.dx = metadata.get("dx", 1.0) obj.dy = metadata.get("dy", 1.0) # Restore object metadata (Geometry_*, Table_* results) if metadata.get("obj_metadata"): obj.metadata = _deserialize_obj_metadata(metadata["obj_metadata"]) return obj def object_to_metadata(obj: DataObject, name: str) -> dict: """Extract metadata from an object for API responses. Args: obj: The object to extract metadata from. name: The object name in the workspace. Returns: Dictionary suitable for ObjectMetadata schema. """ obj_type = type(obj).__name__ if obj_type == "SignalObj": return { "name": name, "type": "signal", "shape": list(obj.y.shape), "dtype": str(obj.y.dtype), "title": getattr(obj, "title", None), "xlabel": getattr(obj, "xlabel", None), "ylabel": getattr(obj, "ylabel", None), "xunit": getattr(obj, "xunit", None), "yunit": getattr(obj, "yunit", None), "attributes": {}, } if obj_type == "ImageObj": return { "name": name, "type": "image", "shape": list(obj.data.shape), "dtype": str(obj.data.dtype), "title": getattr(obj, "title", None), "xlabel": getattr(obj, "xlabel", None), "ylabel": getattr(obj, "ylabel", None), "zlabel": getattr(obj, "zlabel", None), "xunit": getattr(obj, "xunit", None), "yunit": getattr(obj, "yunit", None), "zunit": getattr(obj, "zunit", None), "attributes": { "x0": _to_python_scalar(getattr(obj, "x0", 0.0)), "y0": _to_python_scalar(getattr(obj, "y0", 0.0)), "dx": _to_python_scalar(getattr(obj, "dx", 1.0)), "dy": _to_python_scalar(getattr(obj, "dy", 1.0)), }, } raise TypeError(f"Unsupported object type: {obj_type}") DataLab-1.1.0/datalab/widgets/000077500000000000000000000000001514014117600160375ustar00rootroot00000000000000DataLab-1.1.0/datalab/widgets/__init__.py000066400000000000000000000000021514014117600201400ustar00rootroot00000000000000# DataLab-1.1.0/datalab/widgets/connection.py000066400000000000000000000111551514014117600205530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Module providing a connection dialog box for the DataLab proxy client """ from __future__ import annotations import time from collections.abc import Callable from guidata.configtools import get_icon, get_image_file_path from guidata.qthelpers import ( get_std_icon, qt_app_context, win32_fix_title_bar_background, ) from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from datalab.config import _ class ConnectionThread(QC.QThread): """DataLab Connection thread""" SIG_CONNECTION_OK = QC.Signal() SIG_CONNECTION_KO = QC.Signal(str) def __init__(self, connect_callback: Callable, parent: QC.QObject = None) -> None: super().__init__(parent) self.connect_callback = connect_callback def run(self) -> None: """Run thread""" try: self.connect_callback() self.SIG_CONNECTION_OK.emit() except ConnectionRefusedError as exc: self.SIG_CONNECTION_KO.emit(str(exc)) class ConnectionDialog(QW.QDialog): """DataLab Connection dialog Args: connect_callback: Callback function to connect to DataLab server parent: Parent widget. Defaults to None. """ def __init__(self, connect_callback: Callable, parent: QW.QWidget = None) -> None: super().__init__(parent) win32_fix_title_bar_background(self) self.setWindowTitle(_("Connection to DataLab")) self.setWindowIcon(get_icon("DataLab.svg")) self.resize(300, 50) self.__error_message = "" self.host_label = QW.QLabel() pixmap = QG.QPixmap(get_image_file_path("DataLab-Banner-200.png")) self.host_label.setPixmap(pixmap) self.host_label.setAlignment(QC.Qt.AlignCenter) self.progress_bar = QW.QProgressBar() self.progress_bar.setRange(0, 0) status = QW.QWidget() status_layout = QW.QHBoxLayout() status.setLayout(status_layout) self.status_label = QW.QLabel("Waiting for connection...") self.status_icon = QW.QLabel() status_layout.addWidget(self.status_icon) status_layout.addWidget(self.status_label) status_layout.setContentsMargins(0, 0, 0, 0) status_layout.addStretch() layout = QW.QVBoxLayout() layout.addWidget(self.host_label) layout.addWidget(self.progress_bar) layout.addWidget(status) self.setLayout(layout) self.thread = ConnectionThread(connect_callback) self.thread.SIG_CONNECTION_OK.connect(self.__on_connection_successful) self.thread.SIG_CONNECTION_KO.connect(self.__on_connection_failed) button_box = QW.QDialogButtonBox(QW.QDialogButtonBox.Cancel) button_box.rejected.connect(self.reject) layout.addWidget(button_box) def __set_status_icon(self, name: str) -> None: """Set status icon with standard Qt icon name""" self.status_icon.setPixmap(QG.QPixmap(get_std_icon(name).pixmap(24))) def __connect_to_server(self) -> None: """Connect to server""" self.progress_bar.setRange(0, 0) self.__set_status_icon("BrowserReload") self.status_label.setText("Connecting to server...") self.thread.start() def __on_connection_successful(self) -> None: """Connection successful""" self.progress_bar.setRange(0, 1) self.progress_bar.setValue(1) self.__set_status_icon("DialogApplyButton") self.status_label.setText("Connection successful!") QC.QTimer.singleShot(1000, self.accept) def __on_connection_failed(self, error_message: str) -> None: """Connection failed""" self.__error_message = error_message self.progress_bar.setRange(0, 1) self.progress_bar.setValue(1) self.__set_status_icon("MessageBoxCritical") self.status_label.setText(f"Connection failed: {error_message}") QC.QTimer.singleShot(2000, self.reject) def get_error_message(self) -> str: """Get error message if connection failed""" return self.__error_message def exec(self) -> int: """Execute dialog""" self.__connect_to_server() return super().exec() def reject(self) -> None: """Reject dialog""" super().reject() self.__error_message = "Cancelled by user" if __name__ == "__main__": def fake_connect(): """Fake connection""" time.sleep(5) # raise ConnectionRefusedError("Connection refused") with qt_app_context(): dlg = ConnectionDialog(fake_connect) dlg.exec() DataLab-1.1.0/datalab/widgets/filedialog.py000066400000000000000000000060711514014117600205140ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Module providing a file dialog widget based on Qt's QFileDialog.getOpenFileNames but supporting multiple file preselection (Qt original dialog only supports single file selection). """ from __future__ import annotations import os import os.path as osp from guidata.qthelpers import qt_app_context from qtpy.QtCore import QItemSelectionModel from qtpy.QtWidgets import QAbstractItemView, QFileDialog, QListView, QWidget def get_open_file_names( parent: QWidget | None = None, caption: str = "", basedir: str | list[str] = "", filters: str = "", selectedfilter: str = "", options: QFileDialog.Options = None, ) -> tuple[list[str], str]: """Wrapper around QtGui.QFileDialog.getOpenFileNames static method Returns a tuple (filenames, selectedfilter) -- when dialog box is canceled, returns a tuple (empty list, empty string) Args: parent: Parent widget for the dialog. caption: Dialog title. basedir: Initial directory to open the dialog in, or preselected files (single string or list of strings). filters: File filters for the dialog. selectedfilter: Default filter to be selected. options: Additional options for the dialog. Returns: A tuple containing a list of selected filenames and the selected filter. """ if isinstance(basedir, str): if osp.isfile(basedir): sel_files = [basedir] basedir = osp.dirname(basedir) else: sel_files = [] else: assert isinstance(basedir, list) sel_files = basedir basedir = osp.dirname(sel_files[0]) if sel_files else "" dlg = QFileDialog( parent, caption, basedir, filters, options=QFileDialog.DontUseNativeDialog ) if options is not None: dlg.setOptions(options | QFileDialog.DontUseNativeDialog) file_view = dlg.findChild(QListView, "listView") sel_model = file_view.selectionModel() for fname in sel_files: idx = sel_model.model().index(fname) sel_model.select(idx, QItemSelectionModel.Select | QItemSelectionModel.Rows) file_view.setSelectionMode(QAbstractItemView.ExtendedSelection) file_view.setSelectionBehavior(QAbstractItemView.SelectRows) if dlg.exec(): filenames = dlg.selectedFiles() selectedfilter = dlg.selectedNameFilter() else: filenames = [] selectedfilter = "" return filenames, selectedfilter def test_get_open_file_names(): """Test get_open_file_names function""" datalab_widgets_path = osp.dirname(__file__) sel_files = [ osp.join(datalab_widgets_path, fname) for fname in os.listdir(datalab_widgets_path)[:2] ] with qt_app_context(): filenames, selectedfilter = get_open_file_names( filters="Python files (*.py);;All files (*)", caption="Select Python files", basedir=sel_files, ) print(filenames, selectedfilter) if __name__ == "__main__": test_get_open_file_names() DataLab-1.1.0/datalab/widgets/fileviewer.py000066400000000000000000000043131514014117600205530ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Module providing a file viewer widget """ from __future__ import annotations from pathlib import Path from guidata.widgets.codeeditor import CodeEditor from qtpy import QtWidgets as QW from datalab.config import _ def read_text_file(path: str) -> str: """Read text file using multiple encodings Args: path (str): path to file Raises: UnicodeDecodeError: if unable to read file using any of the encodings Returns: str: file contents """ encodings = ["utf-8", "latin1", "cp1252", "utf-16", "utf-32", "ascii"] for encoding in encodings: try: with open(path, "r", encoding=encoding) as fdesc: return fdesc.read() except UnicodeDecodeError: pass raise UnicodeDecodeError( f"Unable to read file using the following encodings: {encodings}" ) def get_title_contents(path: str) -> tuple[str, str]: """Get title and contents for log filename Args: path (str): path to file Returns: tuple[str, str]: title and contents """ contents = read_text_file(path) pathobj = Path(path) uri_path = pathobj.absolute().as_uri() prefix = _("Contents of file") text = f'{prefix} {path}:' return text, contents class FileViewerWidget(QW.QWidget): """File viewer widget Args: parent (QW.QWidget | None): parent widget. Defaults to None. """ def __init__(self, language: str | None = None, parent: QW.QWidget = None) -> None: super().__init__(parent) self.editor = CodeEditor(language=language) self.editor.setReadOnly(True) layout = QW.QVBoxLayout() self.label = QW.QLabel("") layout.addWidget(self.label) layout.addWidget(self.editor) self.setLayout(layout) def set_data(self, text: str, contents: str) -> None: """Set log data Args: text (str): text to display contents (str): contents to display """ self.label.setText(text) self.label.setOpenExternalLinks(True) self.editor.setPlainText(contents) DataLab-1.1.0/datalab/widgets/fitdialog.py000066400000000000000000000600261514014117600203570ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Curve fitting dialog widgets""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... import numpy as np from guidata.configtools import get_icon from guidata.qthelpers import exec_dialog from plotpy.plot import PlotOptions from plotpy.widgets.fit import FitDialog, FitParam from scipy.special import erf # pylint: disable=no-name-in-module from sigima.tools.checks import check_1d_arrays from sigima.tools.signal import fitting, fourier, pulse from datalab.config import Conf, _ DEFAULT_FORMAT = "%g" def guifit( x, y, fitfunc, fitparams, fitargs=None, fitkwargs=None, wintitle=None, title=None, xlabel=None, ylabel=None, param_cols=1, auto_fit=True, winsize=None, winpos=None, parent=None, name=None, ): # pylint: disable=too-many-positional-arguments """GUI-based curve fitting tool""" win = FitDialog( edit=True, title=wintitle, icon=None, toolbar=True, options=PlotOptions( title=title, xlabel=xlabel, ylabel=ylabel, curve_antialiasing=True, show_axes_tab=False, autoscale_margin_percent=Conf.view.sig_autoscale_margin_percent.get(), ), parent=parent, param_cols=param_cols, auto_fit=auto_fit, ) win.setObjectName(name) win.set_data(x, y, fitfunc, fitparams, fitargs, fitkwargs) try: win.autofit() # TODO: [P3] make this optional except ValueError: pass if parent is None: win.setWindowIcon(get_icon("DataLab.svg")) if winsize is not None: win.resize(*winsize) if winpos is not None: win.move(*winpos) win.get_plot().do_autoscale() if exec_dialog(win): return win.get_values() return None # --- Polynomial fitting curve ------------------------------------------------- def polynomial_fit(x, y, degree, parent=None, name=None): """Compute polynomial fit Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" ivals = np.polyfit(x, y, degree) params = [] for index in range(degree + 1): val = ivals[index] vmax = max(1.0, np.abs(val)) param = FitParam( f"c{(len(ivals) - index - 1):d}", val, -2 * vmax, 2 * vmax, format=DEFAULT_FORMAT, ) params.append(param) def fitfunc(x, params): return np.polyval(params, x) values = guifit( x, y, fitfunc, params, parent=parent, wintitle=_("Polymomial fit"), name=name ) if values: return fitfunc(x, values), params def linear_fit(x: np.ndarray, y: np.ndarray, parent=None, name=None): """Compute linear fit using polynomialfit. Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters """ return polynomial_fit(x, y, 1, parent=parent, name=name) # --- Gaussian fitting curve --------------------------------------------------- def gaussian_fit(x, y, parent=None, name=None): """Compute Gaussian fit Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima GaussianFitComputer computer = fitting.GaussianFitComputer(x, y) initial_params = computer.compute_initial_params() amp_guess = initial_params["amp"] sigma_guess = initial_params["sigma"] mu_guess = initial_params["x0"] b_guess = initial_params["y0"] dy = np.max(y) - np.min(y) max_amp = amp_guess * 2.0 if amp_guess > 0 else dy a = FitParam(_("Amplitude"), amp_guess, 0.0, max_amp, format=DEFAULT_FORMAT) b = FitParam( _("Base line"), b_guess, np.min(y) - 0.1 * dy, np.max(y), format=DEFAULT_FORMAT ) sigma = FitParam( _("Std-dev") + " (σ)", sigma_guess, sigma_guess * 0.1, sigma_guess * 10, format=DEFAULT_FORMAT, ) mu = FitParam( _("Mean") + " (μ)", mu_guess, np.min(x), np.max(x), format=DEFAULT_FORMAT ) params = [a, sigma, mu, b] def fitfunc(x, params): return pulse.GaussianModel.func(x, *params) values = guifit( x, y, fitfunc, params, parent=parent, wintitle=_("Gaussian fit"), name=name ) if values: return fitfunc(x, values), params # --- Lorentzian fitting curve ------------------------------------------------- def lorentzian_fit(x, y, parent=None, name=None): """Compute Lorentzian fit Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima LorentzianFitComputer computer = fitting.LorentzianFitComputer(x, y) initial_params = computer.compute_initial_params() amp_guess = initial_params["amp"] sigma_guess = initial_params["sigma"] mu_guess = initial_params["x0"] b_guess = initial_params["y0"] # Create parameter bounds dy = np.max(y) - np.min(y) max_amp = amp_guess * 2.0 if amp_guess > 0 else dy a = FitParam(_("Amplitude"), amp_guess, 0.0, max_amp, format=DEFAULT_FORMAT) b = FitParam( _("Base line"), b_guess, np.min(y) - 0.1 * dy, np.max(y), format=DEFAULT_FORMAT ) sigma = FitParam( _("Std-dev") + " (σ)", sigma_guess, sigma_guess * 0.1, sigma_guess * 10, format=DEFAULT_FORMAT, ) mu = FitParam( _("Mean") + " (μ)", mu_guess, np.min(x), np.max(x), format=DEFAULT_FORMAT ) params = [a, sigma, mu, b] def fitfunc(x, params): return pulse.LorentzianModel.func(x, *params) values = guifit( x, y, fitfunc, params, parent=parent, wintitle=_("Lorentzian fit"), name=name ) if values: return fitfunc(x, values), params # --- Voigt fitting curve ------------------------------------------------------ def voigt_fit(x, y, parent=None, name=None): """Compute Voigt fit Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima VoigtFitComputer computer = fitting.VoigtFitComputer(x, y) initial_params = computer.compute_initial_params() amp_guess = initial_params["amp"] sigma_guess = initial_params["sigma"] mu_guess = initial_params["x0"] b_guess = initial_params["y0"] # Create parameter bounds dy = np.max(y) - np.min(y) max_amp = amp_guess * 2.0 if amp_guess > 0 else dy a = FitParam(_("Amplitude"), amp_guess, 0.0, max_amp, format=DEFAULT_FORMAT) b = FitParam( _("Base line"), b_guess, np.min(y) - 0.1 * dy, np.max(y), format=DEFAULT_FORMAT ) sigma = FitParam( _("Std-dev") + " (σ)", sigma_guess, sigma_guess * 0.1, sigma_guess * 10, format=DEFAULT_FORMAT, ) mu = FitParam( _("Mean") + " (μ)", mu_guess, np.min(x), np.max(x), format=DEFAULT_FORMAT ) params = [a, sigma, mu, b] def fitfunc(x, params): return pulse.VoigtModel.func(x, *params) values = guifit( x, y, fitfunc, params, parent=parent, wintitle=_("Voigt fit"), name=name ) if values: return fitfunc(x, values), params # --- Multi-Gaussian fitting curve --------------------------------------------- def multigaussian(x, *values, **kwargs): """Return a 1-dimensional multi-Gaussian function.""" a_amp = values[0::2] a_sigma = values[1::2] y0 = values[-1] a_x0 = kwargs["a_x0"] y = np.zeros_like(x) + y0 for amp, sigma, x0 in zip(a_amp, a_sigma, a_x0): y += amp * np.exp(-0.5 * ((x - x0) / sigma) ** 2) return y def multigaussian_fit(x, y, peak_indices, parent=None, name=None): """Compute Multi-Gaussian fit Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima MultiGaussianFitComputer computer = fitting.MultiGaussianFitComputer(x, y, peak_indices) initial_params = computer.compute_initial_params() # Use Sigima parameters to populate DataLab params params = [] for index, i0 in enumerate(peak_indices): stri = f"{index + 1:02d}" amp_key = f"amp_{index + 1}" sigma_key = f"sigma_{index + 1}" amp_val = initial_params[amp_key] if amp_key in initial_params else y[i0] sigma_val = ( initial_params[sigma_key] if sigma_key in initial_params else (x.max() - x.min()) / 100 ) # Calculate bounds based on local data istart = 0 iend = len(x) - 1 if index > 0: istart = (peak_indices[index - 1] + i0) // 2 if index < len(peak_indices) - 1: iend = (peak_indices[index + 1] + i0) // 2 dx = 0.5 * (x[iend] - x[istart]) dy = np.max(y[istart:iend]) - np.min(y[istart:iend]) params += [ FitParam( ("A") + stri, amp_val, 0.0, max(dy * 2, amp_val * 2), format=DEFAULT_FORMAT, ), FitParam("σ" + stri, sigma_val, dx / 100, dx, format=DEFAULT_FORMAT), ] y0_val = initial_params.get("y0", np.min(y)) params.append( FitParam( _("Y0"), y0_val, np.min(y) - 0.1 * (np.max(y) - np.min(y)), np.max(y), format=DEFAULT_FORMAT, ) ) kwargs = {"a_x0": x[peak_indices]} def fitfunc(xi, params): return multigaussian(xi, *params, **kwargs) param_cols = 1 if len(params) > 8: param_cols = 4 values = guifit( x, y, fitfunc, params, param_cols=param_cols, winsize=(900, 600), parent=parent, name=name, wintitle=_("Multi-Gaussian fit"), ) if values: return fitfunc(x, values), params # --- Multi-Lorentzian fitting curve ------------------------------------------- def multilorentzian(x, *values, **kwargs): """Return a 1-dimensional multi-Lorentzian function.""" a_amp = values[0::2] a_sigma = values[1::2] y0 = values[-1] a_x0 = kwargs["a_x0"] y = np.zeros_like(x) + y0 for amp, sigma, x0 in zip(a_amp, a_sigma, a_x0): y += pulse.LorentzianModel.func(x, amp, sigma, x0, 0) return y def multilorentzian_fit( x: np.ndarray, y: np.ndarray, peak_indices, parent=None, name=None ): """Compute Multi-Lorentzian fit Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima MultiLorentzianFitComputer computer = fitting.MultiLorentzianFitComputer(x, y, peak_indices) initial_params = computer.compute_initial_params() # Use Sigima parameters to populate DataLab params params = [] for index, i0 in enumerate(peak_indices): stri = f"{index + 1:02d}" amp_key = f"amp_{index + 1}" sigma_key = f"sigma_{index + 1}" amp_val = ( initial_params[amp_key] if amp_key in initial_params else pulse.LorentzianModel.get_amp_from_amplitude( y[i0] - np.min(y), (x.max() - x.min()) / 100 ) ) sigma_val = ( initial_params[sigma_key] if sigma_key in initial_params else (x.max() - x.min()) / 100 ) params += [ FitParam( ("A") + stri, amp_val, 0.0, max(amp_val * 1.2, y[i0] * 1.2), format=DEFAULT_FORMAT, ), FitParam( "σ" + stri, sigma_val, sigma_val * 0.2, sigma_val * 10, format=DEFAULT_FORMAT, ), ] y0_val = initial_params.get("y0", np.min(y)) params.append( FitParam( _("Y0"), y0_val, np.min(y) - 0.1 * (np.max(y) - np.min(y)), np.max(y), format=DEFAULT_FORMAT, ) ) kwargs = {"a_x0": x[peak_indices]} def fitfunc(xi, params): return multilorentzian(xi, *params, **kwargs) param_cols = 1 if len(params) > 8: param_cols = 4 values = guifit( x, y, fitfunc, params, param_cols=param_cols, winsize=(900, 600), parent=parent, name=name, wintitle=_("Multi-Lorentzian fit"), ) if values: return fitfunc(x, values), params # --- Exponential fitting curve ------------------------------------------------ def exponential_fit(x: np.ndarray, y: np.ndarray, parent=None, name=None): """Compute exponential fit Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima ExponentialFitComputer computer = fitting.ExponentialFitComputer(x, y) initial_params = computer.compute_initial_params() oa = initial_params["a"] ob = initial_params["b"] oc = initial_params["y0"] # Create parameter bounds moa, mob, moc = np.maximum(1, [abs(oa), abs(ob), abs(oc)]) a_p = FitParam( _("A coefficient"), oa, -2 * moa, 2 * moa, logscale=True, format=DEFAULT_FORMAT ) b_p = FitParam(_("B coefficient"), ob, 0.5 * mob, 1.5 * mob, format=DEFAULT_FORMAT) c_p = FitParam(_("y0 constant"), oc, -2 * moc, 2 * moc, format=DEFAULT_FORMAT) params = [a_p, b_p, c_p] def modelfunc(x, a, b, c): return a * np.exp(b * x) + c def fitfunc(x, params): return modelfunc(x, *params) values = guifit( x, y, fitfunc, params, parent=parent, wintitle=_("Exponential fit"), name=name ) if values: return fitfunc(x, values), params # --- Sinusoidal fitting curve ------------------------------------------------ @check_1d_arrays(x_evenly_spaced=True) def dominant_frequency(x: np.ndarray, y: np.ndarray) -> np.floating: """Find the dominant frequency. Args: x: 1-D x values. y: 1-D y values. Returns: Dominant frequency. """ f, spectrum = fourier.magnitude_spectrum(x, y) return np.abs(f[np.argmax(spectrum)]) def sinusoidal_fit(x: np.ndarray, y: np.ndarray, parent=None, name=None): """Compute sinusoidal fit Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima SinusoidalFitComputer computer = fitting.SinusoidalFitComputer(x, y) initial_params = computer.compute_initial_params() guess_a = initial_params["amplitude"] guess_f = initial_params["frequency"] guess_ph = np.rad2deg(initial_params["phase"]) # Convert to degrees guess_c = initial_params["offset"] # Create parameter bounds abs_values = [abs(guess_a), abs(guess_f), abs(guess_ph), abs(guess_c)] moa, mof, _mop, moc = np.maximum(1, abs_values) a_p = FitParam(_("Amplitude"), guess_a, -2 * moa, 2 * moa, format=DEFAULT_FORMAT) f_p = FitParam(_("Frequency"), guess_f, 0, 2 * mof, format=DEFAULT_FORMAT) p_p = FitParam(_("Phase"), guess_ph, -360, 360, format=DEFAULT_FORMAT) c_p = FitParam( _("Continuous component"), guess_c, -2 * moc, 2 * moc, format=DEFAULT_FORMAT ) params = [a_p, f_p, p_p, c_p] def modelfunc(x, a, f, p, c): return a * np.sin(2 * np.pi * f * x + np.deg2rad(p)) + c def fitfunc(x, params): return modelfunc(x, *params) values = guifit( x, y, fitfunc, params, parent=parent, wintitle=_("Sinusoidal fit"), name=name ) if values: return fitfunc(x, values), params # --- Cumulative distribution function fitting curve ----------------------------------- def cdf_fit(x: np.ndarray, y: np.ndarray, parent=None, name=None): """Compute Cumulative Distribution Function (CDF) fit Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima CDFFitComputer computer = fitting.CDFFitComputer(x, y) initial_params = computer.compute_initial_params() a_guess = initial_params["amplitude"] mu_guess = initial_params["mu"] sigma_guess = initial_params["sigma"] b_guess = initial_params["baseline"] # Create parameter bounds dy = np.max(y) - np.min(y) abs_values = [abs(a_guess), abs(mu_guess), abs(sigma_guess), abs(b_guess)] iamp, ix0, islope, _ioff = np.maximum(1, abs_values) a = FitParam(_("Amplitude"), a_guess, 0, iamp * 2.0, format=DEFAULT_FORMAT) b = FitParam( _("Base line"), b_guess, np.min(y) - 0.1 * dy, np.max(y), format=DEFAULT_FORMAT ) sigma = FitParam( _("Std-dev") + " (σ)", sigma_guess, islope * 0.1, islope * 2, format=DEFAULT_FORMAT, ) mu = FitParam( _("Mean") + " (μ)", mu_guess, ix0 * 0.2, ix0 * 2, format=DEFAULT_FORMAT ) params = [a, mu, sigma, b] def modelfunc(x, a, mu, sigma, b): return a * erf((x - mu) / (sigma * np.sqrt(2))) + b def fitfunc(x, params): return modelfunc(x, *params) values = guifit( x, y, fitfunc, params, parent=parent, wintitle=_("CDF fit"), name=name, ) if values: return fitfunc(x, values), params # --- Planckian fitting curve -------------------------------------------------- def planckian_fit(x: np.ndarray, y: np.ndarray, parent=None, name=None): """Compute Planckian (blackbody radiation) fit Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima PlanckianFitComputer computer = fitting.PlanckianFitComputer(x, y) initial_params = computer.compute_initial_params() amp_guess = initial_params["amp"] x0_guess = initial_params["x0"] sigma_guess = initial_params["sigma"] y0_guess = initial_params["y0"] # Create parameter bounds dy = np.max(y) - np.min(y) # Parameter bounds with appropriate ranges for Planckian fitting amp = FitParam( _("Amplitude"), amp_guess, amp_guess * 0.01, amp_guess * 100, format=DEFAULT_FORMAT, ) x0 = FitParam( _("Peak wavelength"), x0_guess, np.min(x), np.max(x), format=DEFAULT_FORMAT ) sigma = FitParam(_("Width factor"), sigma_guess, 0.1, 5.0, format=DEFAULT_FORMAT) y0 = FitParam( _("Base line"), y0_guess, y0_guess - 0.2 * dy, y0_guess + 0.2 * dy, format=DEFAULT_FORMAT, ) params = [amp, x0, sigma, y0] def fitfunc(x, params: list[float]) -> np.ndarray: """Evaluate Planckian function with given parameters.""" return fitting.PlanckianFitComputer.evaluate(x, *params) values = guifit( x, y, fitfunc, params, parent=parent, wintitle=_("Planckian fit"), name=name ) if values: return fitfunc(x, values), params # --- Two half-Gaussian fitting curve ------------------------------------------ def twohalfgaussian_fit(x: np.ndarray, y: np.ndarray, parent=None, name=None): """Compute two half-Gaussian fit for asymmetric peaks Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima TwoHalfGaussianFitComputer computer = fitting.TwoHalfGaussianFitComputer(x, y) initial_params = computer.compute_initial_params() amp_left_guess = initial_params["amp_left"] amp_right_guess = initial_params["amp_right"] sigma_left_guess = initial_params["sigma_left"] sigma_right_guess = initial_params["sigma_right"] x0_guess = initial_params["x0"] y0_left_guess = initial_params["y0_left"] y0_right_guess = initial_params["y0_right"] # Create parameter bounds dx = np.max(x) - np.min(x) dy = np.max(y) - np.min(y) # Parameter bounds with better ranges # New model signature: func(x, amp_left, amp_right, sigma_left, # sigma_right, x0, y0_left, y0_right) amp_left = FitParam( _("Left amplitude"), amp_left_guess, dy * 0.1, dy * 3, format=DEFAULT_FORMAT ) amp_right = FitParam( _("Right amplitude"), amp_right_guess, dy * 0.1, dy * 3, format=DEFAULT_FORMAT ) sigma_left = FitParam( _("Left width") + " (σL)", sigma_left_guess, dx * 0.001, # Very small minimum dx * 0.5, # Reasonable maximum format=DEFAULT_FORMAT, ) sigma_right = FitParam( _("Right width") + " (σR)", sigma_right_guess, dx * 0.001, # Very small minimum dx * 0.5, # Reasonable maximum format=DEFAULT_FORMAT, ) x0 = FitParam( _("Center") + " (x₀)", x0_guess, np.min(x), np.max(x), format=DEFAULT_FORMAT ) y0_left = FitParam( _("Left baseline"), y0_left_guess, y0_left_guess - 0.2 * dy, y0_left_guess + 0.2 * dy, format=DEFAULT_FORMAT, ) y0_right = FitParam( _("Right baseline"), y0_right_guess, y0_right_guess - 0.2 * dy, y0_right_guess + 0.2 * dy, format=DEFAULT_FORMAT, ) params = [amp_left, amp_right, sigma_left, sigma_right, x0, y0_left, y0_right] def fitfunc(x, params): return fitting.TwoHalfGaussianFitComputer.evaluate(x, *params) values = guifit( x, y, fitfunc, params, parent=parent, wintitle=_("Two half-Gaussian fit"), name=name, ) if values: return fitfunc(x, values), params # --- Piecewise exponential (raise-decay) fitting curve ------------------------ def piecewiseexponential_fit(x: np.ndarray, y: np.ndarray, parent=None, name=None): """Compute piecewise exponential fit (raise-decay) Returns (yfit, params), where yfit is the fitted curve and params are the fitting parameters""" # Get initial parameter estimates from Sigima DoubleExponentialFitComputer computer = fitting.DoubleExponentialFitComputer(x, y) initial_params = computer.compute_initial_params() x_center_guess = initial_params["x_center"] a_left_guess = initial_params["a_left"] b_left_guess = initial_params["b_left"] a_right_guess = initial_params["a_right"] b_right_guess = initial_params["b_right"] y0_guess = initial_params["y0"] # Create parameter bounds x_min, x_max = float(x.min()), float(x.max()) y_min, y_max = float(y.min()), float(y.max()) y_range = y_max - y_min x_range = x_max - x_min # Parameter bounds with more realistic ranges # New model signature: func(x, x_center, a_left, b_left, a_right, b_right, y0) x_center = FitParam( _("Center position"), x_center_guess, x_min, x_max, format=DEFAULT_FORMAT ) a_left = FitParam( _("Left amplitude"), a_left_guess, 0.0, a_left_guess * 10.0, format=DEFAULT_FORMAT, ) b_left = FitParam( _("Left time constant") + " (bL)", b_left_guess, # Already in coefficient form 0.001 / x_range, # Slow decay 100.0 / x_range, # Fast decay format=DEFAULT_FORMAT, ) a_right = FitParam( _("Right amplitude"), a_right_guess, 0.0, a_right_guess * 10.0, format=DEFAULT_FORMAT, ) b_right = FitParam( _("Right time constant") + " (bR)", b_right_guess, # Already in coefficient form -100.0 / x_range, # Fast decay -0.001 / x_range, # Slow decay format=DEFAULT_FORMAT, ) y0 = FitParam( _("Base line"), y0_guess, y0_guess - 0.2 * y_range, y0_guess + 0.2 * y_range, format=DEFAULT_FORMAT, ) params = [x_center, a_left, b_left, a_right, b_right, y0] def fitfunc(x, params): return fitting.DoubleExponentialFitComputer.evaluate(x, *params) values = guifit( x, y, fitfunc, params, parent=parent, wintitle=_("Piecewise exponential (raise-decay) fit"), name=name, ) if values: return fitfunc(x, values), params DataLab-1.1.0/datalab/widgets/h5browser.py000066400000000000000000001024771514014117600203440ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab HDF5 browser module """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import abc import os import os.path as osp from typing import TYPE_CHECKING, Any, Callable from guidata.qthelpers import ( add_actions, create_action, create_toolbutton, exec_dialog, get_icon, get_std_icon, win32_fix_title_bar_background, ) from guidata.utils.misc import to_string from guidata.widgets.arrayeditor import ArrayEditor from plotpy.builder import make from plotpy.plot import PlotOptions, PlotWidget from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from qtpy.compat import getopenfilename from sigima import ImageObj, SignalObj from datalab.adapters_plotpy import CURVESTYLES, create_adapter_from_object from datalab.config import Conf, _ from datalab.h5 import H5Importer from datalab.utils.qthelpers import block_signals, qt_handle_error_message if TYPE_CHECKING: from plotpy.plot import BasePlot from datalab.h5.common import BaseNode class AbstractTreeWidgetMeta(type(QW.QTreeWidget), abc.ABCMeta): """Mixed metaclass to avoid conflicts""" class AbstractTreeWidget(QW.QTreeWidget, metaclass=AbstractTreeWidgetMeta): """One-column tree widget with context menu, ...""" def __init__(self, parent: QW.QWidget) -> None: super().__init__(parent) self.setItemsExpandable(True) self.itemActivated.connect(self.activated) self.itemClicked.connect(self.clicked) # Setup context menu self.menu = QW.QMenu(self) self.collapse_all_action = None self.collapse_selection_action = None self.expand_all_action = None self.expand_selection_action = None self.common_actions = self.setup_common_actions() self.itemSelectionChanged.connect(self.item_selection_changed) self.item_selection_changed() @abc.abstractmethod def activated(self, item: QW.QTreeWidgetItem) -> None: """Double-click event""" @abc.abstractmethod def clicked(self, item: QW.QTreeWidgetItem) -> None: """Item was clicked""" @abc.abstractmethod def get_actions_from_items( self, items: list[QW.QTreeWidgetItem] ) -> list[QW.QAction]: """Get actions from item""" # Right here: add other actions if necessary (reimplement this method) return [] def setup_common_actions(self) -> list[QW.QAction]: """Setup context menu common actions""" self.collapse_all_action = create_action( self, _("Collapse all"), icon=get_icon("collapse.svg"), triggered=self.collapseAll, ) self.expand_all_action = create_action( self, _("Expand all"), icon=get_icon("expand.svg"), triggered=self.expandAll ) self.restore_action = create_action( self, _("Restore"), tip=_("Restore original tree layout"), icon=get_icon("restore.svg"), triggered=self.restore, ) self.collapse_selection_action = create_action( self, _("Collapse selection"), icon=get_icon("collapse_selection.svg"), triggered=self.collapse_selection, ) self.expand_selection_action = create_action( self, _("Expand selection"), icon=get_icon("expand_selection.svg"), triggered=self.expand_selection, ) return [ self.collapse_all_action, self.expand_all_action, self.restore_action, None, self.collapse_selection_action, self.expand_selection_action, ] def update_menu(self) -> None: """Update context menu""" self.menu.clear() items = self.selectedItems() actions = self.get_actions_from_items(items) if actions: actions.append(None) actions += self.common_actions add_actions(self.menu, actions) def restore(self) -> None: """Restore tree state""" self.collapseAll() for item in self.get_top_level_items(): self.expandItem(item) def __expand_item(self, item: QW.QTreeWidgetItem) -> None: # pragma: no cover """Expand item tree branch""" self.expandItem(item) for index in range(item.childCount()): child = item.child(index) self.__expand_item(child) def expand_selection(self) -> None: # pragma: no cover """Expand selection""" items = self.selectedItems() if not items: items = self.get_top_level_items() for item in items: self.__expand_item(item) if items: self.scrollToItem(items[0]) def __collapse_item(self, item: QW.QTreeWidgetItem) -> None: # pragma: no cover """Collapse item tree branch""" self.collapseItem(item) for index in range(item.childCount()): child = item.child(index) self.__collapse_item(child) def collapse_selection(self) -> None: # pragma: no cover """Collapse selection""" items = self.selectedItems() if not items: items = self.get_top_level_items() for item in items: self.__collapse_item(item) if items: self.scrollToItem(items[0]) def item_selection_changed(self) -> None: """Item selection has changed""" is_selection = len(self.selectedItems()) > 0 self.expand_selection_action.setEnabled(is_selection) self.collapse_selection_action.setEnabled(is_selection) def get_top_level_items(self) -> list[QW.QTreeWidgetItem]: """Iterate over top level items""" return [self.topLevelItem(_i) for _i in range(self.topLevelItemCount())] def find_all_items(self): """Find all items""" return self.findItems("", QC.Qt.MatchContains | QC.Qt.MatchRecursive) def contextMenuEvent(self, event: QG.QContextMenuEvent) -> None: """Override Qt method""" self.update_menu() self.menu.popup(event.globalPos()) class H5TreeWidget(AbstractTreeWidget): """HDF5 Browser Tree Widget Args: parent: Parent widget """ SIG_SELECTED = QC.Signal(QW.QTreeWidgetItem) def __init__(self, parent: QW.QWidget) -> None: super().__init__(parent) title = _("HDF5 Browser") self.setColumnCount(4) self.setWindowTitle(title) self.setHeaderLabels([_("Name"), _("Size"), _("Type"), _("Value")]) self.header().setSectionResizeMode(0, QW.QHeaderView.Stretch) self.header().setStretchLastSection(False) self.fnames: list[str] = [] self.h5importers: list[H5Importer] = [] def add_root(self, fname: str) -> None: """Add HDF5 root (new file) Args: fname: HDF5 file name """ self.fnames.append(osp.abspath(fname)) importer = H5Importer(fname) self.h5importers.append(importer) self.add_root_to_tree(importer) # Temporarily expand all items to calculate proper column widths rootitem = self.topLevelItem(self.topLevelItemCount() - 1) self.expand_all_children(rootitem) for col in range(4): self.resizeColumnToContents(col) # Restore to default state (only root and its immediate children expanded) for index in range(rootitem.childCount()): child = rootitem.child(index) self.collapseItem(child) def remove_root(self, fname: str) -> None: """Remove HDF5 root Args: fname: HDF5 file name """ index = self.fnames.index(osp.abspath(fname)) self.fnames.pop(index) importer = self.h5importers.pop(index) importer.close() # Remove root item associated with file item = self.topLevelItem(index) self.takeTopLevelItem(index) del item def cleanup(self) -> None: """Clean up widget""" for importer in self.h5importers: importer.close() self.fnames: list[str] = [] self.h5importers: list[H5Importer] = [] self.clear() def __get_top_level_item(self, item: QW.QTreeWidgetItem) -> QW.QTreeWidgetItem: """Get top level item associated to item Args: item: Tree item Returns: Top level item """ while item.parent(): item = item.parent() return item def get_node(self, item: QW.QTreeWidgetItem) -> BaseNode: """Get HDF5 dataset associated to item Args: item: Tree item Returns: HDF5 node """ toplevel_item = self.__get_top_level_item(item) toplevel_index = self.indexOfTopLevelItem(toplevel_item) node_id = item.data(0, QC.Qt.UserRole) if node_id: importer = self.h5importers[toplevel_index] return importer.get(node_id) return None def get_nodes(self, only_checked_items: bool = True) -> list[BaseNode]: """Get all nodes associated to checked items Args: only_checked_items: If True, only checked items are returned Returns: List of HDF5 nodes """ datasets = [] for item in self.find_all_items(): if item.flags() & QC.Qt.ItemIsUserCheckable: if only_checked_items and item.checkState(0) == 0: continue if item is not self.topLevelItem(0): node = self.get_node(item) datasets.append(node) return datasets def activated(self, item: QW.QTreeWidgetItem) -> None: """Double-click event""" if item is not self.topLevelItem(0): self.SIG_SELECTED.emit(item) def clicked(self, item: QW.QTreeWidgetItem) -> None: """Click event""" self.activated(item) def get_actions_from_items(self, items): # pylint: disable=W0613 """Get actions from item""" return [] def is_empty(self) -> bool: """Return True if tree is empty""" return len(self.find_all_items()) == 1 def is_any_item_checked(self) -> bool: """Return True if any item is checked""" for item in self.find_all_items(): if item.checkState(0) > 0: return True return False def select_all(self, state: bool) -> None: """Select all items Args: state: If True, all items are selected """ for item in self.find_all_items(): if item.flags() & QC.Qt.ItemIsUserCheckable: item.setSelected(state) if state: self.clicked(item) def toggle_all(self, state: bool) -> None: """Toggle all item state from 'unchecked' to 'checked' (or vice-versa) Args: state: If True, all items are checked """ for item in self.find_all_items(): if item.flags() & QC.Qt.ItemIsUserCheckable: item.setCheckState(0, QC.Qt.Checked if state else QC.Qt.Unchecked) @staticmethod def __create_node(node: BaseNode) -> QW.QTreeWidgetItem: """Create tree node from HDF5 node Args: node: HDF5 node Returns: Tree widget node """ text = to_string(node.text) if len(text) > 30: text = text[:30] + "..." treeitem = QW.QTreeWidgetItem([node.name, node.shape_str, node.dtype_str, text]) treeitem.setData(0, QC.Qt.UserRole, node.id) if node.description: for col in range(treeitem.columnCount()): treeitem.setToolTip(col, node.description) return treeitem @staticmethod def __recursive_popfunc(parent_item: QW.QTreeWidgetItem, node: BaseNode) -> None: """Recursive HDF5 analysis Args: parent_item: Parent tree item node: HDF5 node """ tree_item = H5TreeWidget.__create_node(node) if node.is_supported(): tree_item.setCheckState(0, QC.Qt.Unchecked) else: tree_item.setFlags(QC.Qt.ItemIsEnabled) tree_item.setIcon(0, get_icon(node.icon_name)) parent_item.addChild(tree_item) for child in node.children: H5TreeWidget.__recursive_popfunc(tree_item, child) def expand_all_children(self, item: QW.QTreeWidgetItem) -> None: """Expand all children (recursively) Args: item: Tree item """ self.expandItem(item) for index in range(item.childCount()): child = item.child(index) self.expand_all_children(child) def add_root_to_tree(self, importer: H5Importer) -> None: """Add root to tree Args: importer: HDF5 importer """ root = importer.root rootitem = QW.QTreeWidgetItem([root.name]) rootitem.setToolTip(0, root.description) rootitem.setData(0, QC.Qt.UserRole, root.id) rootitem.setFlags(QC.Qt.ItemIsEnabled) rootitem.setIcon(0, get_icon(root.icon_name)) self.addTopLevelItem(rootitem) for node in root.children: self.__recursive_popfunc(rootitem, node) self.expandItem(rootitem) def toggle_show_only_checkable_items(self, state: bool) -> None: """Show only checkable items Args: state: If True, only checkable items are shown """ for item in self.find_all_items(): item.setHidden(state) if state: # Iterate over checkable items and show them (and their parents) for item in self.find_all_items(): if item.flags() & QC.Qt.ItemIsUserCheckable: item.setHidden(False) parent = item.parent() while parent: parent.setHidden(False) parent = parent.parent() def toggle_show_values(self, state: bool) -> None: """Show values Args: state: If True, values are shown """ # Hide or show the "Value" column self.setColumnHidden(3, not state) def set_current_file(self, fname: str) -> None: """Set current file Args: fname: HDF5 file name """ index = self.fnames.index(osp.abspath(fname)) item = self.topLevelItem(index) self.setCurrentItem(item) self.scrollToItem(item, QW.QAbstractItemView.PositionAtTop) class PlotPreview(QW.QStackedWidget): """Plot preview""" def __init__(self, parent: QW.QWidget) -> None: super().__init__(parent) self.curvewidget = PlotWidget( self, options=PlotOptions( type="curve", curve_antialiasing=True, show_axes_tab=False, autoscale_margin_percent=Conf.view.sig_autoscale_margin_percent.get( 0.2 ), ), ) self.addWidget(self.curvewidget) self.imagewidget = PlotWidget( self, options=PlotOptions( type="image", show_contrast=True, show_axes_tab=False, autoscale_margin_percent=Conf.view.ima_autoscale_margin_percent.get( 0.2 ), ), ) self.addWidget(self.imagewidget) def cleanup(self) -> None: """Clean up widget""" for widget in (self.imagewidget, self.curvewidget): widget.get_plot().del_all_items() def update_plot_preview(self, node: BaseNode) -> None: """Update plot preview widget""" try: obj = node.get_native_object() except Exception as msg: # pylint: disable=broad-except qt_handle_error_message(self, msg) return if obj is None: # An error occurred while creating the object (invalid data, ...) label = make.label(_("Unsupported data"), "C", (0, 0), "C") plot: BasePlot = self.currentWidget().get_plot() plot.del_all_items() plot.add_item(label) plot.replot() return if isinstance(obj, SignalObj): obj: SignalObj widget = self.curvewidget else: obj: ImageObj widget = self.imagewidget with CURVESTYLES.suspend(): item = create_adapter_from_object(obj).make_item() plot = widget.get_plot() plot.del_all_items() plot.add_item(item) plot.set_active_item(item) item.unselect() plot.do_autoscale() self.setCurrentWidget(widget) class TablePreview(QW.QWidget): """Table preview Args: title: Group title parent: Parent widget """ def __init__(self, parent: QW.QWidget) -> None: super().__init__(parent) self.setLayout(QW.QVBoxLayout()) self.table = QW.QTableWidget(self) self.table.setEditTriggers(QW.QAbstractItemView.NoEditTriggers) self.table.horizontalHeader().setStretchLastSection(True) self.layout().addWidget(self.table) def clear(self) -> None: """Clear table""" self.table.clear() def update_table_preview(self, data: dict[str, Any]) -> None: """Update table preview widget Args: node: HDF5 node """ self.clear() self.table.setRowCount(len(data)) self.table.setColumnCount(1) self.table.setHorizontalHeaderLabels([_("Value")]) self.table.setVerticalHeaderLabels(list(data.keys())) for row, value in enumerate(data.values()): self.table.setItem(row, 0, QW.QTableWidgetItem(str(value))) self.table.resizeRowsToContents() class GroupAndAttributes(QW.QTabWidget): """Group and attributes Args: parent: Parent widget show_array_callback: Callback to show array """ def __init__(self, parent: QW.QWidget, show_array_callback: Callable) -> None: super().__init__(parent) self.group = TablePreview(self) self.addTab(self.group, get_icon("h5group.svg"), _("Group")) self.attrs = TablePreview(self) self.addTab(self.attrs, get_icon("h5attrs.svg"), _("Attributes")) # Add a button as corner widget to show the array (if any): self.__show_array_btn = create_toolbutton( self, icon=get_icon("show_results.svg"), text=_("Show array"), autoraise=False, triggered=show_array_callback, ) self.__show_array_btn.setEnabled(False) self.setCornerWidget(self.__show_array_btn, QC.Qt.TopRightCorner) def cleanup(self) -> None: """Clean up widget""" self.group.clear() self.attrs.clear() def update_from_node(self, node: BaseNode) -> None: """Update widget from node Args: node: HDF5 node """ # Update group ================================================================= text = to_string(node.text) if text: lines = text.splitlines()[:5] if len(lines) == 5: lines += ["[...]"] text = os.linesep.join(lines) data = { _("Path"): node.id, _("Name"): node.name, _("Description"): node.description, _("Textual preview"): text, # "Raw": repr(node.data), } self.group.update_table_preview(data) # Update attributes ============================================================ self.attrs.update_table_preview(node.metadata) # Update show array button ===================================================== self.__show_array_btn.setEnabled(node.IS_ARRAY) class H5FileSelector(QW.QWidget): """HDF5 file selector Args: parent: Parent widget """ SIG_ADD_FILENAME = QC.Signal(str) SIG_REMOVE_FILENAME = QC.Signal(str) SIG_CURRENT_CHANGED = QC.Signal(str) def __init__(self, parent: QW.QWidget) -> None: super().__init__(parent) self.setLayout(QW.QHBoxLayout()) self.layout().setContentsMargins(0, 0, 0, 0) self.combo = QW.QComboBox(self) self.combo.currentTextChanged.connect(self.current_file_changed) self.layout().addWidget(self.combo) self.btn_add = create_toolbutton( self, icon=get_std_icon("DirOpenIcon"), text=_("Open") + " ...", autoraise=False, triggered=lambda _checked=False: self.add_file(), ) self.btn_add.setSizePolicy(QW.QSizePolicy.Fixed, QW.QSizePolicy.Fixed) self.layout().addWidget(self.btn_add) self.btn_rmv = create_toolbutton( self, icon=get_std_icon("DialogCloseButton"), text=_("Close"), autoraise=False, triggered=self.remove_file, ) self.btn_rmv.setSizePolicy(QW.QSizePolicy.Fixed, QW.QSizePolicy.Fixed) self.layout().addWidget(self.btn_rmv) self.btn_rmv.setEnabled(False) def set_current_fname(self, fname: str) -> None: """Set current file name Args: fname: HDF5 file name """ index = self.combo.findText(fname) if index >= 0: self.combo.setCurrentIndex(index) def get_current_fname(self) -> str: """Return current file name Returns: HDF5 file name """ return self.combo.currentText() def current_file_changed(self, fname: str) -> None: """Current file changed Args: fname: HDF5 file name """ self.SIG_CURRENT_CHANGED.emit(fname) def add_fname(self, fname: str) -> None: """Add file name Args: fname: HDF5 file name """ self.combo.addItem(get_icon("h5file.svg"), fname) self.btn_rmv.setEnabled(True) def remove_fname(self, fname: str) -> None: """Remove file name Args: fname: HDF5 file name """ index = self.combo.findText(fname) if index >= 0: self.combo.removeItem(index) if self.combo.count() == 0: self.btn_rmv.setEnabled(False) def add_file(self, fname: str | None = None) -> None: """Browse file Args: fname: HDF5 file name. Default is None. (this is used for testing only) """ if fname is None: fname = getopenfilename( self, _("Select HDF5 file"), "", _("HDF5 files (*.h5 *.hdf5 *.hdf *.he5);;All files (*)"), )[0] if fname: self.SIG_ADD_FILENAME.emit(osp.abspath(fname)) def remove_file(self, fname: str | None = None) -> None: """Remove file name Args: fname: HDF5 file name """ if fname is None: fname = self.combo.currentText() self.SIG_REMOVE_FILENAME.emit(fname) class H5Browser(QW.QSplitter): """HDF5 Browser Widget Args: parent: Parent widget """ SIG_SELECT_NEW_FILE = QC.Signal(str) SIG_REMOVE_FILE = QC.Signal(str) def __init__(self, parent: QW.QWidget | None = None) -> None: super().__init__(parent) self.selector = H5FileSelector(self) self.selector.SIG_ADD_FILENAME.connect(self.__add_new_file) self.selector.SIG_REMOVE_FILENAME.connect(self.__remove_file) self.selector.SIG_CURRENT_CHANGED.connect(self.__selector_current_file_changed) self.tree = H5TreeWidget(self) self.tree.SIG_SELECTED.connect(self.__item_selected_on_tree) selectorandtree = QW.QFrame(self) selectorandtree.setLayout(QW.QVBoxLayout()) selectorandtree.layout().addWidget(self.selector) # Add toolbar with tree actions toolbar = self.__create_toolbar() selectorandtree.layout().addWidget(toolbar) selectorandtree.layout().addWidget(self.tree) selectorandtree.layout().setContentsMargins(0, 0, 0, 0) self.addWidget(selectorandtree) preview = QW.QSplitter(self) preview.setOrientation(QC.Qt.Vertical) self.addWidget(preview) self.plotpreview = PlotPreview(self) preview.addWidget(self.plotpreview) self.groupandattrs = GroupAndAttributes(self, self.show_array) preview.addWidget(self.groupandattrs) preview.setSizes([int(self.size().height() / 2)] * 2) def __create_toolbar(self) -> QW.QToolBar: """Create toolbar with tree actions Returns: Toolbar widget """ toolbar = QW.QToolBar(self) toolbar.setToolButtonStyle(QC.Qt.ToolButtonTextBesideIcon) toolbar.setIconSize(QC.QSize(16, 16)) toolbar.setStyleSheet("QToolBar { padding: 2px; spacing: 2px; }") toolbar.addAction(self.tree.expand_all_action) toolbar.addAction(self.tree.collapse_all_action) toolbar.addAction(self.tree.restore_action) toolbar.addSeparator() toolbar.addAction(self.tree.expand_selection_action) toolbar.addAction(self.tree.collapse_selection_action) return toolbar def open_file(self, fname: str) -> None: """Open HDF5 file Args: fname: HDF5 file name """ self.tree.add_root(fname) self.selector.add_fname(fname) def close_file(self, fname: str) -> None: """Close HDF5 file Args: fname: HDF5 file name """ self.tree.remove_root(fname) self.selector.remove_fname(fname) def __add_new_file(self, fname: str) -> None: """Add new file Args: fname: HDF5 file name """ self.open_file(fname) self.selector.set_current_fname(fname) self.SIG_SELECT_NEW_FILE.emit(fname) def __remove_file(self, fname: str) -> None: """Remove file Args: fname: HDF5 file name """ self.close_file(fname) self.SIG_REMOVE_FILE.emit(fname) def cleanup(self) -> None: """Clean up widget""" self.tree.cleanup() self.plotpreview.cleanup() def get_node(self, item: QW.QTreeWidgetItem | None = None) -> BaseNode: """Return (selected) dataset Args: item: Tree item Returns: HDF5 node """ if item is None: item = self.tree.currentItem() return self.tree.get_node(item) def __item_selected_on_tree(self, item: QW.QTreeWidgetItem) -> None: """Item selected on tree Args: item: Tree item """ # View the selected item node = self.get_node(item) if node.is_supported(): self.plotpreview.update_plot_preview(node) self.groupandattrs.update_from_node(node) # Update the file selector combo box with block_signals(self.selector.combo): # Avoid triggering current file changed signal, which would result in # loosing the current selection on the tree (side effect: "Show array" # button would still be enabled if the previous node was an array, except # that now the current node is not an array, thus causing an error if # the user clicks on the button). self.selector.set_current_fname(node.h5file.filename) def __selector_current_file_changed(self, fname: str) -> None: """Selector current file changed Args: fname: HDF5 file name """ if fname: self.tree.set_current_file(fname) def show_array(self) -> None: """Show array""" node = self.get_node() assert node.IS_ARRAY arrayeditor = ArrayEditor(self) arrayeditor.setup_and_check( node.data, title=node.name, readonly=True, add_title_suffix=False ) exec_dialog(arrayeditor) class H5BrowserDialog(QW.QDialog): """HDF5 Browser Dialog Args: parent: Parent widget size: Dialog size """ def __init__( self, parent: QW.QWidget | None = None, size: tuple[int, int] = (1150, 700) ) -> None: super().__init__(parent) self.setWindowFlags(QC.Qt.Window) self.setObjectName("h5browser") self.setWindowTitle(_("HDF5 Browser")) self.setWindowIcon(get_icon("h5browser.svg")) win32_fix_title_bar_background(self) vlayout = QW.QVBoxLayout() self.setLayout(vlayout) self.button_layout: QW.QHBoxLayout | None = None self.bbox: QW.QDialogButtonBox | None = None self.nodes: list[BaseNode] = [] self.checkbox_show_only: QW.QCheckBox | None = None self.checkbox_show_values: QW.QCheckBox | None = None self.browser = H5Browser(self) self.browser.SIG_SELECT_NEW_FILE.connect(self.select_new_file) self.browser.SIG_REMOVE_FILE.connect(self.remove_file) vlayout.addWidget(self.browser) self.browser.tree.itemChanged.connect(lambda item: self.refresh_buttons()) self.install_button_layout() self.setMinimumSize(QC.QSize(900, 500)) self.resize(QC.QSize(*size)) self.browser.setSizes([int(self.size().height() / 2)] * 2) self.refresh_buttons() def accept(self) -> None: """Accept changes""" self.nodes = self.browser.tree.get_nodes() QW.QDialog.accept(self) def is_empty(self) -> bool: """Return True if tree is empty""" return self.browser.tree.is_empty() def cleanup(self) -> None: """Cleanup dialog""" self.browser.cleanup() def refresh_buttons(self) -> None: """Refresh buttons""" state = self.browser.tree.is_any_item_checked() self.bbox.button(QW.QDialogButtonBox.Ok).setEnabled(state) def show_only_checkable_items(self, state: int) -> None: """Show only checkable items Args: state: If True, only checkable items are shown """ self.browser.tree.toggle_show_only_checkable_items(state) fname = self.browser.selector.get_current_fname() if fname: self.browser.tree.set_current_file(fname) def __finalize_setup(self) -> None: """Finalize setup""" tree = self.browser.tree tree.toggle_show_only_checkable_items(self.checkbox_show_only.isChecked()) tree.toggle_show_values(self.checkbox_show_values.isChecked()) def open_file(self, fname: str) -> None: """Open file Args: fname: HDF5 file name """ self.browser.open_file(fname) self.__finalize_setup() def open_files(self, fnames: list[str]) -> None: """Open files Args: fnames: HDF5 file names """ for fname in fnames: self.browser.open_file(fname) self.__finalize_setup() def select_new_file(self, fname: str) -> None: # pylint:disable=unused-argument """Select new file Args: fname: HDF5 file name """ self.__finalize_setup() self.refresh_buttons() def remove_file(self, fname: str) -> None: # pylint:disable=unused-argument """Remove file Args: fname: HDF5 file name """ self.refresh_buttons() def get_all_nodes(self) -> list[BaseNode]: """Return all supported datasets Returns: List of HDF5 nodes """ return self.browser.tree.get_nodes(only_checked_items=False) def get_nodes(self) -> list[BaseNode]: """Return datasets Returns: List of HDF5 nodes """ return self.nodes def install_button_layout(self) -> None: """Install button layout""" bbox = QW.QDialogButtonBox(QW.QDialogButtonBox.Ok | QW.QDialogButtonBox.Cancel) bbox.accepted.connect(self.accept) bbox.rejected.connect(self.reject) btn_check_all = create_toolbutton( self, icon=get_icon("check_all.svg"), text=_("Check all"), autoraise=False, shortcut=QG.QKeySequence.SelectAll, triggered=lambda checked=True: self.browser.tree.toggle_all(checked), ) btn_uncheck_all = create_toolbutton( self, icon=get_icon("uncheck_all.svg"), text=_("Uncheck all"), autoraise=False, triggered=lambda checked=False: self.browser.tree.toggle_all(checked), ) self.checkbox_show_only = QW.QCheckBox(_("Show only supported data")) self.checkbox_show_only.stateChanged.connect(self.show_only_checkable_items) self.checkbox_show_values = QW.QCheckBox(_("Show values")) self.checkbox_show_values.stateChanged.connect( self.browser.tree.toggle_show_values ) self.button_layout = QW.QHBoxLayout() self.button_layout.addWidget(self.checkbox_show_only) self.button_layout.addWidget(self.checkbox_show_values) self.button_layout.addSpacing(10) self.button_layout.addWidget(btn_check_all) self.button_layout.addWidget(btn_uncheck_all) self.button_layout.addStretch() self.button_layout.addWidget(bbox) self.bbox = bbox vlayout: QW.QVBoxLayout = self.layout() vlayout.addSpacing(10) vlayout.addLayout(self.button_layout) DataLab-1.1.0/datalab/widgets/imagebackground.py000066400000000000000000000100451514014117600215330ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Image background selection dialog.""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from typing import TYPE_CHECKING, Any import numpy as np from guidata.configtools import get_icon from plotpy.builder import make from plotpy.plot import PlotDialog, PlotOptions from datalab.adapters_plotpy import create_adapter_from_object from datalab.config import _ from datalab.utils.qthelpers import resize_widget_to_parent if TYPE_CHECKING: from plotpy.items import MaskedXYImageItem, RangeComputation2d, RectangleShape from qtpy.QtWidgets import QWidget from sigima.objects import ImageObj class ImageBackgroundDialog(PlotDialog): """Image background selection dialog. Args: image: image object parent: parent widget. Defaults to None. options: plot options. Defaults to None. """ def __init__( self, image: ImageObj, parent: QWidget | None = None, options: PlotOptions | dict[str, Any] | None = None, ) -> None: self.__background: float | None = None self.__rect_coords: tuple[float, float, float, float] | None = None self.imageitem: MaskedXYImageItem | None = None self.rectarea: RectangleShape | None = None self.comput2d: RangeComputation2d | None = None super().__init__( title=_("Image background selection"), edit=True, parent=parent, options=options, ) self.setObjectName("backgroundselection") if parent is None: self.setWindowIcon(get_icon("DataLab.svg")) else: resize_widget_to_parent(self, aspect_ratio=1.0) self.__image = image.copy() self.__setup_dialog() def test_compute_background(self) -> None: """Method to test background computation.""" # Instead of waiting for Qt events, directly test the computation method # by simulating what RangeComputation2d would do x0, y0, x1, y1 = self.rectarea.get_rect() x, y, z = self.imageitem.get_data(x0, y0, x1, y1) self.__compute_background(x, y, z) def __compute_background( self, x: np.ndarray, # pylint: disable=unused-argument y: np.ndarray, # pylint: disable=unused-argument z: np.ndarray, ) -> float: """Compute background value""" self.__rect_coords = self.rectarea.get_rect() self.__background = z.mean() return self.__background def __setup_dialog(self) -> None: """Setup dialog box""" obj = self.__image self.imageitem = create_adapter_from_object(obj).make_item() plot = self.get_plot() if obj.is_uniform_coords: x0, y0 = obj.x0, obj.y0 x1, y1 = obj.xc + obj.dx, obj.yc + obj.dy else: x0, y0 = obj.xcoords[0], obj.ycoords[0] xc = (obj.xcoords[0] + obj.xcoords[-1]) / 2 yc = (obj.ycoords[0] + obj.ycoords[-1]) / 2 x1, y1 = xc, yc self.rectarea = make.rectangle(x0, y0, x1, y1, _("Background area")) self.comput2d = make.computation2d( self.rectarea, "TL", _("Background value:") + " %g", self.imageitem, self.__compute_background, ) for item in (self.imageitem, self.rectarea, self.comput2d): plot.add_item(item) plot.replot() plot.set_active_item(self.rectarea) def get_background(self) -> float: """Get background value""" return self.__background def get_rect_coords(self) -> tuple[float, float, float, float]: """Get rectangle coordinates Returns: tuple: rectangle coordinates (x0, y0, x1, y1) Raises: ValueError: if rectangle coordinates are not set """ if self.__rect_coords is None: raise ValueError("Rectangle coordinates not set") return self.__rect_coords DataLab-1.1.0/datalab/widgets/instconfviewer.py000066400000000000000000000136021514014117600214600ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Module providing DataLab Installation configuration widget """ from __future__ import annotations import json import locale import os import platform import sys from importlib.metadata import distributions from pathlib import Path from guidata.configtools import get_icon from guidata.qthelpers import exec_dialog from qtpy import QtWidgets as QW from sigima.io.image import ImageIORegistry from sigima.io.signal import SignalIORegistry import datalab from datalab.config import APP_NAME, IS_FROZEN, Conf, _ from datalab.plugins import PluginRegistry from datalab.widgets.fileviewer import FileViewerWidget, get_title_contents def decode_fs_string(string: bytes) -> str: """Convert string from file system charset to unicode""" charset = sys.getfilesystemencoding() if charset is None: charset = locale.getpreferredencoding() return string.decode(charset) def get_installed_package_info() -> str: """Get the list of installed packages with their versions""" packages = [(dist.metadata["Name"], dist.version) for dist in distributions()] # Sort alphabetically by package name packages.sort(key=lambda x: x[0].lower()) # Determine column widths name_width = max(len(name) for name, _ in packages) version_width = max(len(version) for _, version in packages) header = f"{'Package':{name_width}} {'Version':{version_width}}" separator = f"{'-' * name_width} {'-' * version_width}" result_lines = [header, separator] for name, version in packages: result_lines.append(f"{name:{name_width}} {version:{version_width}}") return os.linesep.join(result_lines) def get_manifest_package_info(manifest_path: Path) -> str: """Get the list of packages from the build manifest file Args: manifest_path: Path to the manifest.json file Returns: Formatted string with package list and build information """ try: with open(manifest_path, "r", encoding="utf-8") as f: manifest = json.load(f) packages = list(manifest["packages"].items()) packages.sort(key=lambda x: x[0].lower()) # Determine column widths name_width = max(len(name) for name, _ in packages) version_width = max(len(version) for _, version in packages) header = f"{'Package':{name_width}} {'Version':{version_width}}" separator = f"{'-' * name_width} {'-' * version_width}" result_lines = [ f"Build time: {manifest['build_time']}", f"Python version: {manifest['python_version']}", f"Build system: {manifest['system']} {manifest['release']}", f"Architecture: {manifest['architecture']}", "", header, separator, ] for name, version in packages: result_lines.append(f"{name:{name_width}} {version:{version_width}}") return os.linesep.join(result_lines) except Exception as exc: # pylint: disable=broad-except return f"Error reading manifest file: {exc}" def get_install_info() -> str: """Get DataLab installation informations Returns: str: installation informations """ if IS_FROZEN: # Stand-alone version more_info = "This is the Stand-alone version of DataLab." more_info += os.linesep * 2 # Try to read manifest file from executable root directory manifest_path = Path(sys.executable).parent / "manifest.json" if manifest_path.exists(): more_info += get_manifest_package_info(manifest_path) else: more_info += "Manifest file not found." else: more_info = get_installed_package_info() info = os.linesep.join( [ f"DataLab v{datalab.__version__}", "", f"Machine type: {platform.machine()}", f"Platform: {platform.platform()}", f"Python v{sys.version}", "", more_info, ] ) return info class InstallConfigViewerWindow(QW.QDialog): """Installation configuration window""" def __init__(self, parent: QW.QWidget | None = None) -> None: super().__init__(parent) self.setObjectName("instviewer") self.setWindowTitle(APP_NAME + " - " + _("Installation and configuration")) self.setWindowIcon(get_icon("DataLab.svg")) self.tabs = QW.QTabWidget(self) uc_title, uc_contents = get_title_contents(Conf.get_filename()) plugins_io_contents = PluginRegistry.get_plugin_info(html=False) for registry in (SignalIORegistry, ImageIORegistry): plugins_io_contents += os.linesep + ("_" * 80) + os.linesep * 2 plugins_io_contents += registry.get_format_info(mode="text") for title, contents, tab_icon, tab_title in ( ( _("Installation configuration"), get_install_info(), get_icon("libre-toolbox.svg"), _("Installation configuration"), ), ( uc_title, uc_contents, get_icon("libre-gui-settings.svg"), _("User configuration"), ), ( _("Plugins and I/O features"), plugins_io_contents, get_icon("libre-gui-plugin.svg"), _("Plugins and I/O features"), ), ): viewer = FileViewerWidget() viewer.set_data(title, contents) self.tabs.addTab(viewer, tab_icon, tab_title) layout = QW.QVBoxLayout() layout.addWidget(self.tabs) self.setLayout(layout) self.resize(800, 500) def exec_datalab_installconfig_dialog(parent: QW.QWidget | None = None) -> None: """View DataLab installation configuration""" dlg = InstallConfigViewerWindow(parent=parent) exec_dialog(dlg) DataLab-1.1.0/datalab/widgets/logviewer.py000066400000000000000000000054141514014117600204200ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Module providing a log viewer widget, a log viewer window and DataLab's log viewer """ from __future__ import annotations import os.path as osp from guidata.configtools import get_icon from guidata.qthelpers import exec_dialog from qtpy import QtWidgets as QW from datalab.config import APP_NAME, Conf, _, get_old_log_fname from datalab.env import execenv from datalab.widgets.fileviewer import FileViewerWidget, get_title_contents class LogViewerWindow(QW.QDialog): """Log viewer window""" def __init__(self, fnames: list[str], parent: QW.QWidget | None = None) -> None: super().__init__(parent) self.setObjectName("logviewer") self.setWindowTitle(APP_NAME + " - " + _("Log files")) self.setWindowIcon(get_icon("DataLab.svg")) self.tabs = QW.QTabWidget(self) for fname in fnames: if osp.isfile(fname): title, contents = get_title_contents(fname) if not contents.strip(): continue viewer = FileViewerWidget(language="Python") viewer.set_data(title, contents) self.tabs.addTab(viewer, get_icon("logs.svg"), osp.basename(fname)) layout = QW.QVBoxLayout() layout.addWidget(self.tabs) self.setLayout(layout) self.resize(900, 400) @property def is_empty(self) -> bool: """Return True if there is no log available""" return self.tabs.count() == 0 def get_log_filenames() -> list[str]: """Return log filenames""" return [ Conf.main.traceback_log_path.get(), Conf.main.faulthandler_log_path.get(), get_old_log_fname(Conf.main.traceback_log_path.get()), get_old_log_fname(Conf.main.faulthandler_log_path.get()), ] def get_log_prompt_message() -> str | None: """Return prompt message for log files, i.e. a message informing the user whether log files were generated during last session or current session.""" avail = [osp.isfile(fname) for fname in get_log_filenames()] if avail[0] or avail[1]: return _("Log files were generated during current session.") if avail[2] or avail[3]: return _("Log files were generated during last session.") return None def exec_datalab_logviewer_dialog(parent: QW.QWidget | None = None) -> None: """View DataLab logs""" fnames = [osp.normpath(fname) for fname in get_log_filenames() if osp.isfile(fname)] dlg = LogViewerWindow(fnames, parent=parent) if dlg.is_empty: if not execenv.unattended: QW.QMessageBox.information( dlg, APP_NAME, _("Log files are currently empty.") ) dlg.close() else: exec_dialog(dlg) DataLab-1.1.0/datalab/widgets/signalbaseline.py000066400000000000000000000063511514014117600213760ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Signal base line selection dialog.""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from typing import TYPE_CHECKING import numpy as np from guidata.configtools import get_icon from plotpy.builder import make from plotpy.plot import PlotDialog from datalab.adapters_plotpy import CURVESTYLES, create_adapter_from_object from datalab.config import _ from datalab.utils.qthelpers import resize_widget_to_parent if TYPE_CHECKING: from plotpy.items import CurveItem, Marker, XRangeSelection from qtpy.QtWidgets import QWidget from sigima.objects import SignalObj class SignalBaselineDialog(PlotDialog): """Signal baseline selection dialog. Args: signal: signal object parent: parent widget. Defaults to None. """ def __init__(self, signal: SignalObj, parent: QWidget | None = None) -> None: self.__curve_styles = CURVESTYLES.style_generator() self.__baseline: float | None = None self.__x_range: tuple[float, float] = [np.nan, np.nan] self.curve: CurveItem | None = None self.cursor: Marker | None = None self.xrange: XRangeSelection | None = None super().__init__(title=_("Signal baseline selection"), edit=True, parent=parent) self.setObjectName("baselineselection") if parent is None: self.setWindowIcon(get_icon("DataLab.svg")) legend = make.legend("TR") self.get_plot().add_item(legend) self.__signal = signal.copy() self.__setup_dialog() resize_widget_to_parent(self, aspect_ratio=1.0) def __setup_dialog(self) -> None: """Setup dialog box""" obj = self.__signal with CURVESTYLES.alternative(self.__curve_styles): self.curve = create_adapter_from_object(obj).make_item() plot = self.get_plot() plot.set_antialiasing(True) plot.SIG_RANGE_CHANGED.connect(self.xrange_changed) plot.SIG_MARKER_CHANGED.connect(self.cursor_changed) self.cursor = make.hcursor(0.0, _("Base line") + " = %g") self.cursor.set_movable(False) self.xrange = make.xrange(obj.x[0], obj.x[int(0.2 * len(obj.x))]) for item in (self.curve, self.cursor, self.xrange): plot.add_item(item) plot.replot() plot.set_active_item(self.xrange) self.xrange_changed(self.xrange, *self.xrange.get_range()) # pylint: disable=unused-argument def xrange_changed(self, item: XRangeSelection, xmin: float, xmax: float) -> None: """X range changed""" self.__x_range = sorted([xmin, xmax]) imin, imax = np.searchsorted(self.__signal.x, self.__x_range) if imin == imax: return self.cursor.set_pos(0, np.mean(self.__signal.y[imin:imax])) plot = self.get_plot() plot.replot() def cursor_changed(self, item: Marker) -> None: """Cursor changed""" _x, self.__baseline = item.get_pos() def get_baseline(self) -> float: """Get baseline""" return self.__baseline def get_x_range(self) -> tuple[float, float]: """Get x range""" return self.__x_range DataLab-1.1.0/datalab/widgets/signalcursor.py000066400000000000000000000202721514014117600211270ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Signal horizontal or vertical cursor selection dialog.""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from typing import TYPE_CHECKING, Literal import numpy as np from guidata.configtools import get_icon from plotpy.builder import make from plotpy.plot import PlotDialog from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from sigima.tools.signal.features import find_x_values_at_y from datalab.adapters_plotpy import CURVESTYLES, create_adapter_from_object from datalab.config import _ from datalab.utils.qthelpers import block_signals, resize_widget_to_parent if TYPE_CHECKING: from plotpy.items import CurveItem, Marker from qtpy.QtWidgets import QWidget from sigima.objects import SignalObj class SignalCursorDialog(PlotDialog): """Signal horizontal or vertical cursor selection dialog. Args: signal: signal object parent: parent widget. Defaults to None. """ def __init__( self, signal: SignalObj, cursor_orientation: Literal["horizontal", "vertical"], parent: QWidget | None = None, ) -> None: assert cursor_orientation in ( "horizontal", "vertical", ), "cursor_orientation must be 'horizontal' or 'vertical'" self.__curve_styles = CURVESTYLES.style_generator() self.__cursor_orientation = cursor_orientation self.__signal = signal self.__x_value: float | None = None self.__y_value: float | None = None self.curve: CurveItem | None = None self.hcursor: Marker | None = None self.vcursor: Marker | None = None self.xlineedit: QW.QLineEdit | None = None self.ylineedit: QW.QLineEdit | None = None if cursor_orientation == "horizontal": title = _("Select X value with cursor") else: title = _("Select Y value with cursor") super().__init__(title=title, edit=True, parent=parent) self.setObjectName("SignalCursorDialog") if parent is None: self.setWindowIcon(get_icon("DataLab.svg")) legend = make.legend("TR") self.get_plot().add_item(legend) self.__setup_dialog() resize_widget_to_parent(self, aspect_ratio=1.0) def __setup_dialog(self) -> None: """Setup dialog box""" apply_button = QW.QPushButton(_("Apply")) apply_button.setIcon(get_icon("apply.svg")) apply_button.setToolTip(_("Apply cursor position")) xlabel = QW.QLabel("X=") ylabel = QW.QLabel("Y=") self.xlineedit = QW.QLineEdit() self.xlineedit.editingFinished.connect(self.xlineedit_editing_finished) x_validator = QG.QDoubleValidator() x_validator.setLocale(QC.QLocale("C")) self.xlineedit.setValidator(x_validator) self.ylineedit = QW.QLineEdit() self.ylineedit.editingFinished.connect(self.ylineedit_editing_finished) y_validator = QG.QDoubleValidator() y_validator.setLocale(QC.QLocale("C")) self.ylineedit.setValidator(y_validator) self.xlineedit.setReadOnly(self.__cursor_orientation == "horizontal") self.xlineedit.setDisabled(self.__cursor_orientation == "horizontal") self.ylineedit.setReadOnly(self.__cursor_orientation == "vertical") self.ylineedit.setDisabled(self.__cursor_orientation == "vertical") xygroup = QW.QGroupBox(_("Cursor position")) xylayout = QW.QHBoxLayout() xylayout.addWidget(xlabel) xylayout.addWidget(self.xlineedit) if self.__cursor_orientation == "vertical": xylayout.addWidget(apply_button) apply_button.clicked.connect(self.xlineedit_editing_finished) xylayout.addStretch() xylayout.addSpacing(10) xylayout.addWidget(ylabel) xylayout.addWidget(self.ylineedit) if self.__cursor_orientation == "horizontal": xylayout.addWidget(apply_button) apply_button.clicked.connect(self.ylineedit_editing_finished) xygroup.setLayout(xylayout) self.button_layout.insertWidget(0, xygroup) obj = self.__signal with CURVESTYLES.alternative(self.__curve_styles): self.curve = create_adapter_from_object(obj).make_item() plot = self.get_plot() plot.set_antialiasing(True) xcursor = make.xcursor(np.mean(obj.x), np.mean(obj.y), "X = %g, Y = %g") xcursor.set_selectable(False) param = xcursor.markerparam param.symbol.facecolor = "blue" param.symbol.edgecolor = "cyan" param.symbol.size = 9 param.line.style = "DotLine" param.line.color = "blue" param.line.width = 2.0 param.update_item(xcursor) plot.SIG_MARKER_CHANGED.connect(self.cursor_changed) if self.__cursor_orientation == "horizontal": self.hcursor = make.hcursor(np.mean(obj.y), "Y = %g") self.vcursor = xcursor self.vcursor.setVisible(False) else: self.vcursor = make.vcursor(np.mean(obj.x), "X = %g") self.hcursor = xcursor self.hcursor.setVisible(False) for item in (self.curve, self.vcursor, self.hcursor): plot.add_item(item) plot.replot() if self.__cursor_orientation == "horizontal": plot.set_active_item(self.hcursor) self.cursor_changed(self.hcursor) else: plot.set_active_item(self.vcursor) self.cursor_changed(self.vcursor) def cursor_changed(self, item: Marker) -> None: """Cursor changed""" sig = self.__signal plot = self.get_plot() if self.__cursor_orientation == "horizontal" and item is self.hcursor: _x, y = item.get_pos() x = None x_values = find_x_values_at_y(sig.x, sig.y, y) if len(x_values) > 0: x = x_values[0] with block_signals(plot): self.vcursor.set_pos(x, y) self.vcursor.setVisible(x is not None) self.button_box.button(QW.QDialogButtonBox.Ok).setEnabled(x is not None) elif self.__cursor_orientation == "vertical" and item is self.vcursor: x, _y = item.get_pos() y_index = np.searchsorted(self.__signal.x, x) if x < self.__signal.x[0] or y_index >= len(self.__signal.y): y = None else: y = self.__signal.y[y_index] with block_signals(plot): self.hcursor.set_pos(x, y) self.hcursor.setVisible(True) self.hcursor.setVisible(y is not None) self.button_box.button(QW.QDialogButtonBox.Ok).setEnabled(y is not None) self.xlineedit.setText(f"{x:g}" if x is not None else "") self.ylineedit.setText(f"{y:g}" if y is not None else "") self.__x_value, self.__y_value = x, y def xlineedit_editing_finished(self) -> None: """X line edit editing finished""" try: x = float(self.xlineedit.text()) _x, y = self.vcursor.get_pos() if self.__cursor_orientation == "horizontal": self.hcursor.set_pos(x, y) else: self.vcursor.set_pos(x, y) except ValueError: pass plot = self.get_plot() plot.replot() def ylineedit_editing_finished(self) -> None: """Y line edit editing finished""" try: y = float(self.ylineedit.text()) x, _y = self.hcursor.get_pos() if self.__cursor_orientation == "horizontal": self.hcursor.set_pos(x, y) else: self.vcursor.set_pos(x, y) except ValueError: pass plot = self.get_plot() plot.replot() def get_cursor_position(self) -> tuple[float, float]: """Get cursor position""" return self.__x_value, self.__y_value def get_x_value(self) -> float: """Get cursor x value""" return self.__x_value def get_y_value(self) -> float: """Get cursor y value""" return self.__y_value DataLab-1.1.0/datalab/widgets/signaldeltax.py000066400000000000000000000132261514014117600210740ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ GUI dialog for analyzing signals and calculating full width at Y. """ # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations import warnings from typing import TYPE_CHECKING import numpy as np from guidata.configtools import get_icon from plotpy.builder import make from plotpy.plot import PlotDialog from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from sigima.tools.signal.pulse import full_width_at_y from datalab.adapters_plotpy import CURVESTYLES, create_adapter_from_object from datalab.config import _ from datalab.utils.qthelpers import resize_widget_to_parent if TYPE_CHECKING: from plotpy.items import CurveItem, Marker, XRangeSelection from qtpy.QtWidgets import QWidget from sigima.objects import SignalObj class SignalDeltaXDialog(PlotDialog): """Signal Delta X dialog. Args: signal: signal object parent: parent widget. Defaults to None. """ def __init__(self, signal: SignalObj, parent: QWidget | None = None) -> None: self.__curve_styles = CURVESTYLES.style_generator() self.__signal = signal self.__coords: list[float, float, float, float] | None = None self.curve: CurveItem | None = None self.hcursor: Marker | None = None self.delta_xrange: XRangeSelection | None = None self.deltaxlineedit: QW.QLineEdit | None = None self.ylineedit: QW.QLineEdit | None = None title = _("Select Y value with cursor") super().__init__(title=title, edit=True, parent=parent) self.setObjectName("SignalCursorDialog") if parent is None: self.setWindowIcon(get_icon("DataLab.svg")) legend = make.legend("TR") self.get_plot().add_item(legend) self.__setup_dialog() resize_widget_to_parent(self, aspect_ratio=1.0) def __setup_dialog(self) -> None: """Setup dialog box""" apply_button = QW.QPushButton(_("Apply")) apply_button.setIcon(get_icon("apply.svg")) apply_button.setToolTip(_("Apply cursor position")) xlabel = QW.QLabel("∆X=") ylabel = QW.QLabel("Y=") self.deltaxlineedit = QW.QLineEdit() self.deltaxlineedit.setReadOnly(True) self.deltaxlineedit.setDisabled(True) self.ylineedit = QW.QLineEdit() self.ylineedit.editingFinished.connect(self.ylineedit_editing_finished) y_validator = QG.QDoubleValidator() y_validator.setLocale(QC.QLocale("C")) self.ylineedit.setValidator(y_validator) xygroup = QW.QGroupBox(_("Cursor position")) xylayout = QW.QHBoxLayout() xylayout.addWidget(xlabel) xylayout.addWidget(self.deltaxlineedit) xylayout.addWidget(ylabel) xylayout.addWidget(self.ylineedit) xylayout.addWidget(apply_button) vlayout = QW.QVBoxLayout() vlayout.addLayout(xylayout) self.warning_label = QW.QLabel() vlayout.addWidget(self.warning_label) apply_button.clicked.connect(self.ylineedit_editing_finished) xygroup.setLayout(vlayout) self.button_layout.insertWidget(0, xygroup) obj = self.__signal with CURVESTYLES.alternative(self.__curve_styles): self.curve = create_adapter_from_object(obj).make_item() plot = self.get_plot() plot.set_antialiasing(True) self.delta_xrange = make.xrange(0.0, 1.0) self.delta_xrange.setVisible(False) self.delta_xrange.set_style("roi", "s/readonly") self.delta_xrange.set_selectable(False) plot.SIG_MARKER_CHANGED.connect(self.cursor_changed) self.hcursor = make.hcursor(np.mean(obj.y), "Y = %g") for item in (self.curve, self.delta_xrange, self.hcursor): plot.add_item(item) plot.replot() plot.set_active_item(self.hcursor) self.cursor_changed(self.hcursor) def cursor_changed(self, item: Marker) -> None: """Cursor changed""" sig = self.__signal _x, y = item.get_pos() try: with warnings.catch_warnings(record=True) as w: self.__coords = full_width_at_y(sig.x, sig.y, y) if np.nan in self.__coords: raise ValueError("Invalid coordinates") delta_str = f"{self.__coords[2] - self.__coords[0]:g}" ok = True if len(w) > 0: self.warning_label.setText("⚠️ " + str(w[-1].message)) else: self.warning_label.setText("") self.delta_xrange.setVisible(True) self.delta_xrange.set_range(self.__coords[0], self.__coords[2]) except ValueError: delta_str = "" ok = False self.delta_xrange.setVisible(False) self.button_box.button(QW.QDialogButtonBox.Ok).setEnabled(ok) self.deltaxlineedit.setText(delta_str) self.ylineedit.setText(f"{y:g}" if y is not None else "") def ylineedit_editing_finished(self) -> None: """Y line edit editing finished""" try: y = float(self.ylineedit.text()) x, _y = self.hcursor.get_pos() self.hcursor.set_pos(x, y) except ValueError: pass plot = self.get_plot() plot.replot() def get_coords(self) -> tuple[float, float, float, float]: """Return coordinates of segment associated to the width at Y""" return self.__coords def get_y_value(self) -> float: """Get cursor y value""" _x, y = self.hcursor.get_pos() return y DataLab-1.1.0/datalab/widgets/signalpeak.py000066400000000000000000000147541514014117600205420ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """Signal peak detection feature""" # pylint: disable=invalid-name # Allows short reference names like x, y, ... from __future__ import annotations from typing import TYPE_CHECKING import numpy as np from guidata.configtools import get_icon from plotpy.builder import make from plotpy.plot import PlotDialog from qtpy import QtCore as QC from qtpy import QtWidgets as QW from sigima.tools.signal.peakdetection import peak_indices from datalab.adapters_plotpy import CURVESTYLES, create_adapter_from_object from datalab.config import _ from datalab.utils.qthelpers import resize_widget_to_parent if TYPE_CHECKING: from plotpy.items import Marker from qtpy.QtWidgets import QWidget from sigima.objects import SignalObj class DistanceSlider(QW.QWidget): """Minimum distance slider Args: parent: parent widget. Defaults to None. """ TITLE = _("Minimum distance:") SIG_VALUE_CHANGED = QC.Signal(int) def __init__(self, parent: QWidget | None = None) -> None: super().__init__(parent) self.slider = QW.QSlider(QC.Qt.Horizontal) self.label = QW.QLabel() layout = QW.QHBoxLayout() layout.addWidget(self.label) layout.addWidget(self.slider) self.setLayout(layout) def value_changed(self, value: int) -> None: """Slider value has changed Args: value: slider value """ plural = "s" if value > 1 else "" self.label.setText(f"{self.TITLE} {value} point{plural}") self.SIG_VALUE_CHANGED.emit(value) def setup_slider(self, value: int, maxval: int) -> None: """Setup slider Args: value: initial value maxval: maximum value """ self.slider.setMinimum(1) self.slider.setMaximum(maxval) self.slider.setValue(value) self.slider.setTickPosition(QW.QSlider.TicksBothSides) self.value_changed(value) self.slider.valueChanged.connect(self.value_changed) class SignalPeakDetectionDialog(PlotDialog): """Signal Peak detection dialog Args: signal: signal object parent: parent widget. Defaults to None. """ def __init__(self, signal: SignalObj, parent: QWidget | None = None) -> None: self.__curve_styles = CURVESTYLES.style_generator() self.peaks = None self.peak_indices = None self.in_curve = None self.in_threshold = None self.in_threshold_cursor = None self.co_results = None self.co_positions = None self.co_markers = None self.min_distance = None self.distance_slider: DistanceSlider | None = None super().__init__(title=_("Signal peak detection"), edit=True, parent=parent) self.setObjectName("peakdetection") if parent is None: self.setWindowIcon(get_icon("DataLab.svg")) legend = make.legend("TR") self.get_plot().add_item(legend) self.__signal = signal.copy() self.__setup_dialog() resize_widget_to_parent(self, parent, aspect_ratio=1.0) def populate_plot_layout(self) -> None: # Reimplement PlotDialog method """Populate the plot layout""" super().populate_plot_layout() self.distance_slider = DistanceSlider(self) self.add_widget(self.distance_slider, 1, 0, 1, 1) def __setup_dialog(self) -> None: """Setup dialog box""" obj = self.__signal with CURVESTYLES.alternative(self.__curve_styles): self.in_curve = create_adapter_from_object(obj).make_item() plot = self.get_plot() plot.set_antialiasing(True) plot.add_item(self.in_curve) self.in_threshold = 0.5 * (np.max(obj.y) - np.min(obj.y)) + np.min(obj.y) cursor = make.hcursor(self.in_threshold) self.in_threshold_cursor = cursor plot.add_item(self.in_threshold_cursor) self.co_results = make.label("", "TL", (0, 0), "TL") plot.add_item(self.co_results) plot.SIG_MARKER_CHANGED.connect(self.hcursor_changed) self.min_distance = 1 self.distance_slider.setup_slider(self.min_distance, len(obj.y) // 4) self.distance_slider.SIG_VALUE_CHANGED.connect(self.minimum_distance_changed) self.compute_peaks() # Replot, otherwise, it's not possible to set active item: plot.replot() plot.set_active_item(cursor) def get_peaks(self) -> list[tuple[float, float]]: """Return peaks coordinates""" return self.peaks def get_peak_indices(self) -> list[int]: """Return peak indices""" return self.peak_indices def get_threshold(self) -> float: """Return relative threshold""" y = self.__signal.y return (self.in_threshold - np.min(y)) / (np.max(y) - np.min(y)) def get_min_dist(self) -> int: """Return minimum distance""" return self.min_distance def compute_peaks(self) -> None: """Compute peak detection""" x, y = self.__signal.xydata plot = self.get_plot() self.peak_indices = peak_indices( y, thres=self.in_threshold, min_dist=self.min_distance, thres_abs=True, ) self.peaks = [(x[index], y[index]) for index in self.peak_indices] markers = [ make.marker( pos, movable=False, color="orange", markerstyle="|", linewidth=1, marker="NoSymbol", linestyle="DashLine", ) for pos in self.peaks ] if self.co_markers is not None: plot.del_items(self.co_markers) self.co_markers = markers for item in self.co_markers: plot.add_item(item) positions = [str(marker.get_pos()[0]) for marker in markers] prefix = f"{_('Peaks:')}
    " self.co_results.set_text(prefix + "
    ".join(positions)) def hcursor_changed(self, marker: Marker) -> None: """Horizontal cursor position has changed Args: marker: marker item """ _x, y = marker.get_pos() self.in_threshold = y self.compute_peaks() def minimum_distance_changed(self, value: int) -> None: """Minimum distance changed Args: value: minimum distance value """ self.min_distance = value self.compute_peaks() self.get_plot().replot() DataLab-1.1.0/datalab/widgets/status.py000066400000000000000000000232111514014117600177330ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab main window status bar widgets """ from __future__ import annotations import os import psutil from guidata.configtools import get_icon from guidata.qthelpers import get_std_icon from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from datalab.config import DEBUG, Conf, _ from datalab.env import execenv from datalab.plugins import PluginRegistry class BaseStatus(QW.QWidget): """Base status widget. Args: delay (int | None): update interval (s). If None, widget will not be updated. parent (QWidget): parent widget """ def __init__( self, delay: int | None = None, parent: QW.QWidget | None = None ) -> None: super().__init__(parent) layout = QW.QHBoxLayout() layout.setContentsMargins(0, 0, 0, 0) self.setLayout(layout) self.icon = QW.QLabel() self.label = QW.QLabel() layout.addWidget(self.icon) layout.addWidget(self.label) if delay is not None: self.timer = QC.QTimer() self.timer.timeout.connect(self.update_status) self.timer.start(delay * 1000) def set_icon(self, icon: QG.QIcon | str | None) -> None: """Set icon. Args: icon (QIcon | None): icon """ size = self.label.sizeHint().height() if isinstance(icon, str): icon = get_icon(icon) pixmap = QG.QPixmap() if icon is None else icon.pixmap(size, size) self.icon.setPixmap(pixmap) def update_status(self) -> None: """Update status widget""" raise NotImplementedError class ConsoleStatus(BaseStatus): """Console status widget. Shows a message if an error or warning has been logged to the console. Shows a button to show the console, only if the console is hidden. Args: parent (QWidget): parent widget """ SIG_SHOW_CONSOLE = QC.Signal() def __init__(self, parent: QW.QWidget | None = None) -> None: super().__init__(None, parent) self.label.setText(_("Internal console")) self.label.setToolTip( _( "Click to show the internal console.\n" "The icon will turn red if an error or warning is logged." ) ) self.label.setCursor(QG.QCursor(QC.Qt.PointingHandCursor)) self.label.mouseReleaseEvent = self.on_click self.ok_icon = get_std_icon("MessageBoxInformation") self.ko_icon = get_std_icon("MessageBoxWarning") self.has_errors = False self.update_status() def on_click(self, event: QG.QMouseEvent) -> None: """Handle mouse click event on label. Args: event: mouse event """ if event.button() == QC.Qt.LeftButton: self.SIG_SHOW_CONSOLE.emit() def console_visibility_changed(self, visible: bool) -> None: """Handle console visibility changed event. Args: visible (bool): console visibility """ if visible: # Hide this status widget when console is visible self.hide() else: self.show() self.update_status() def exception_occurred(self) -> None: """Handle exception occurred event""" self.has_errors = True self.update_status() def update_status(self) -> None: """Update status widget""" if self.has_errors: self.set_icon(self.ko_icon) self.label.setStyleSheet("color: red") self.label.setToolTip( _( "Click to show the internal console.\n" "An error or warning has been logged." ) ) else: self.set_icon(self.ok_icon) self.label.setStyleSheet("") self.label.setToolTip( _( "Click to show the internal console.\n" "No error or warning has been logged." ) ) class MemoryStatus(BaseStatus): """Memory status widget. Args: threshold (int): available memory thresold (MB) delay (int): update interval (s) parent (QWidget): parent widget """ SIG_MEMORY_ALARM = QC.Signal(bool) def __init__( self, threshold: int = 500, delay: int = 2, parent: QW.QWidget | None = None ) -> None: super().__init__(delay, parent) self.demo_mode = False self.ko_icon = get_std_icon("MessageBoxWarning") self.__threshold = threshold * (1024**2) self.label.setMinimumWidth(self.label.fontMetrics().width("000%")) self.update_status() def set_demo_mode(self, state: bool) -> None: """Set demo mode state (used when taking screenshots). The demo mode allows to take screenshots which always look the same. (this will set memory usage to a constant value). If demo mode is set to False, memory usage will be set to actual value. Args: state (bool): demo mode state """ self.demo_mode = state self.update_status() def update_status(self) -> None: """Update status widget""" mem = psutil.virtual_memory() memok = mem.available > self.__threshold self.SIG_MEMORY_ALARM.emit(not memok) txtlist = [ f"%s {mem.available // (1024**2)} MB" % _("Memory available:"), f"%s {mem.used // (1024**2)} MB" % _("Memory used:"), f"%s {self.__threshold // (1024**2)} MB" % _("Alarm threshold:"), ] txt = os.linesep.join(txtlist) self.setToolTip(txt) if DEBUG and not memok: execenv.log(self, txt) self.label.setStyleSheet("" if memok else "color: red") self.set_icon("libre-tech-ram.svg" if memok else self.ko_icon) mem_percent = 65 if self.demo_mode else int(mem.percent) self.label.setText(_("Memory:") + f" {mem_percent}%") class PluginStatus(BaseStatus): """Plugin status widget. Shows the number of plugins loaded. Args: parent (QWidget): parent widget """ def __init__(self, parent: QW.QWidget | None = None) -> None: super().__init__(None, parent) self.set_icon("libre-gui-plugin.svg") self.update_status() def update_status(self) -> None: """Update status widget""" text = _("Plugins:") + " " if Conf.main.plugins_enabled.get(): nplugins = len(PluginRegistry.get_plugins()) text += str(nplugins) else: text += "-" self.label.setText(text) self.setToolTip(PluginRegistry.get_plugin_info()) class XMLRPCStatus(BaseStatus): """XML-RPC status widget. Shows the XML-RPC server status and port number. Args: parent (QWidget): parent widget """ def __init__(self, parent: QW.QWidget | None = None) -> None: super().__init__(None, parent) self.port: int | None = None def set_port(self, port: int | None): """Set XML-RPC server port number. Args: port (int | None): XML-RPC server port number. If None, XML-RPC server is disabled. """ self.port = port self.update_status() def update_status(self) -> None: """Update status widget""" text = _("XML-RPC:") + " " if self.port is None: self.label.setText(text + "-") self.set_icon("libre-gui-unlink.svg") else: self.label.setText(text + str(self.port)) self.set_icon("libre-gui-link.svg") class WebAPIStatus(BaseStatus): """Web API status widget. Shows the Web API server status and port number. Args: parent (QWidget): parent widget """ SIG_SHOW_INFO = QC.Signal() # Signal to show connection info SIG_START_SERVER = QC.Signal() # Signal to propose starting server def __init__(self, parent: QW.QWidget | None = None) -> None: super().__init__(None, parent) self.port: int | None = None self.url: str | None = None self.label.setCursor(QG.QCursor(QC.Qt.PointingHandCursor)) self.label.mouseReleaseEvent = self.on_click self.update_status() # Initialize widget state def on_click(self, event: QG.QMouseEvent) -> None: """Handle mouse click event on label. Args: event: mouse event """ if event.button() == QC.Qt.LeftButton: if self.port is None: self.SIG_START_SERVER.emit() else: self.SIG_SHOW_INFO.emit() def set_status(self, url: str | None, port: int | None) -> None: """Set Web API server status. Args: url: Web API server URL (e.g. "http://127.0.0.1:8080"). If None, Web API server is not running. port: Web API server port number. """ self.url = url self.port = port self.update_status() def update_status(self) -> None: """Update status widget""" if self.port is None: self.label.setText(_("Web API")) self.set_icon("libre-gui-unlink.svg") self.setToolTip( _("Web API server is not running") + "\n" + _("Click to start") ) else: self.label.setText(_("Web API:") + " " + str(self.port)) self.set_icon("libre-gui-link.svg") tooltip = _("Web API server is running") + "\n" if self.url: tooltip += f"URL: {self.url}\n" tooltip += _("Click to view connection info") self.setToolTip(tooltip) DataLab-1.1.0/datalab/widgets/textimport.py000066400000000000000000000651131514014117600206360ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab Text Import Wizard -------------------------- The DataLab Import Wizard is a tool for importing data from a text source: - Clipboard - File (csv, txt, etc.) It is based on the DataLab Wizard widget, which is a widget that guides the user through a series of steps to complete a task. It is implemented as a series of pages, each of which is a separate widget. """ from __future__ import annotations import io import os.path as osp from typing import TYPE_CHECKING, Generator import guidata.dataset as gds import guidata.dataset.qtwidgets as gdq import numpy as np import pandas as pd from guidata.configtools import get_icon from guidata.widgets.codeeditor import CodeEditor from plotpy.plot import PlotOptions, PlotWidget from qtpy import QtCore as QC from qtpy import QtGui as QG from qtpy import QtWidgets as QW from sigima import ImageObj, SignalObj, create_image, create_signal from sigima.io.common.textreader import count_lines, read_first_n_lines from sigima.io.signal.funcs import get_labels_units_from_dataframe, read_csv_by_chunks from datalab.adapters_plotpy import CURVESTYLES, create_adapter_from_object from datalab.config import Conf, _ from datalab.utils.qthelpers import ( CallbackWorker, create_progress_bar, qt_long_callback, ) from datalab.widgets.wizard import Wizard, WizardPage if TYPE_CHECKING: from plotpy.items import CurveItem, MaskedXYImageItem from plotpy.plot import BasePlot from qtpy.QtWidgets import QWidget class SourceParam(gds.DataSet): """Source parameters dataset""" def __init__(self, source_page: SourcePage) -> None: super().__init__() self.source_page = source_page # pylint: disable=unused-argument def source_callback(self, item: gds.ChoiceItem, value: str) -> None: """Source callback""" if value == "clipboard": is_valid = bool(QW.QApplication.clipboard().text()) else: is_valid = self.path is not None and osp.isfile(self.path) self.source_page.set_valid(is_valid) def file_callback(self, item: gds.FileOpenItem, value: str | None) -> None: """File callback""" self.source_page.set_valid(value is not None and osp.isfile(value)) _prop = gds.GetAttrProp("choice") source = ( gds.ChoiceItem( _("Source"), [("clipboard", _("Clipboard")), ("file", _("File"))], default="file", help=_("Source of the data"), radio=True, ) .set_prop("display", store=_prop) .set_prop("display", callback=source_callback) ) path = ( gds.FileOpenItem( _("File"), formats=("txt", "csv", "dat", "asc", "mca"), check=False, help=_("File containing the data"), ) .set_prop("display", active=gds.FuncProp(_prop, lambda x: x == "file")) .set_prop("display", callback=file_callback) ) _bg = gds.BeginGroup(_("Preview parameters")) preview_max_rows = gds.IntItem( _("Maximum Number of Rows"), default=100000, min=1, check=False, help=_("Maximum number of rows to display"), ).set_prop("display", active=gds.FuncProp(_prop, lambda x: x == "file")) _eg = gds.EndGroup(_("Preview parameters")) class SourcePage(WizardPage): """Source page""" def __init__(self) -> None: super().__init__() self.__text = "" self.__path = "" self.__loaded_partially = True self.set_title(_("Source")) self.set_subtitle(_("Select the source of the data:")) self.param_widget = gdq.DataSetEditGroupBox( _("Source Parameters"), SourceParam, show_button=False, source_page=self ) self.param = self.param_widget.dataset self.add_to_layout(self.param_widget) self.add_stretch() self.set_valid(False) def get_source_path(self) -> str | None: """Return the selected source path, or None if clipboard is selected""" return self.param.path if self.param.source == "file" else None def get_source_text(self, preview: bool) -> str: """Return the source text""" if not self.__loaded_partially or preview: return self.__text with open(self.__path, "r", encoding="utf-8") as file: self.__text = file.read() self.__loaded_partially = False return self.__text def validate_page(self) -> bool: """Validate the page""" self.__text = "" self.param_widget.set() if self.param.source == "file": self.__path = self.get_source_path() if self.__path is not None and osp.isfile(self.__path): try: self.__text = read_first_n_lines( self.__path, n=self.param.preview_max_rows ) except Exception: # pylint:disable=broad-except return False self.__loaded_partially = ( count_lines(self.__path) > self.param.preview_max_rows ) else: return False else: self.__text = QW.QApplication.clipboard().text() self.__loaded_partially = False return bool(self.__text) class BaseImportParam(gds.DataSet): """Import parameters dataset""" delimiter_choice = gds.ChoiceItem( _("Delimiter"), [ (",", ","), (";", ";"), ("\t", _("Tab")), (" ", _("Space")), ], help=_("Column delimiter character"), ) decimal_choice = gds.ChoiceItem( _("Decimal"), [(".", _("Point")), (",", _("Comma"))], help=_("Decimal separator character"), default=".", ).set_pos(col=1) comment_char = gds.StringItem( _("Comments"), default="#", help=_("Character that indicates a comment line"), ).set_pos(col=2) skip_rows = gds.IntItem( _("Rows to Skip"), default=0, help=_( "Number of rows to skip at the beginning of the file (including comments)" ), ) max_rows = gds.IntItem( _("Max. nb of rows"), default=None, min=1, check=False, help=_("Maximum number of rows to import"), ).set_pos(col=1) header = gds.ChoiceItem( _("Header"), ((None, _("None")), ("infer", _("Infer")), (0, _("First row"))), default="infer", help=_("Row index to use as the column names"), ).set_pos(col=2) transpose = gds.BoolItem( _("Transpose"), default=False, help=_("Transpose the data (swap rows and columns)"), ) class SignalImportParam(BaseImportParam): """Signal import parameters dataset""" VALID_DTYPES_STRLIST = SignalObj.get_valid_dtypenames() dtype_str = gds.ChoiceItem( _("Data type"), list(zip(VALID_DTYPES_STRLIST, VALID_DTYPES_STRLIST)), help=_("Output signal data type."), default="float64", ).set_pos(col=1) first_col_is_x = gds.BoolItem( _("First Column is X"), default=True, help=_( "First column contains the X values\n(ignored if there is only one column)" ), ).set_pos(col=2) class ImageImportParam(BaseImportParam): """Image import parameters dataset""" VALID_DTYPES_STRLIST = ImageObj.get_valid_dtypenames() dtype_str = gds.ChoiceItem( _("Data type"), list(zip(VALID_DTYPES_STRLIST, VALID_DTYPES_STRLIST)), help=_("Output image data type."), default="uint16", ).set_pos(col=1) class ArrayModel(QC.QAbstractTableModel): """Array model Args: parent: Parent widget data: Data array horizontal_headers: Horizontal headers """ # pylint: disable=invalid-name def __init__( self, parent: QWidget, data: np.ndarray, horizontal_headers: list[str] | None = None, ) -> None: super().__init__(parent) self.__data = data self.__horizontal_headers = horizontal_headers def rowCount(self, _parent: QC.QModelIndex) -> int: """Return the row count (reimplement the `QC.QAbstractTableModel` method)""" return self.__data.shape[0] def columnCount(self, _parent: QC.QModelIndex) -> int: """Return the column count (reimplement the `QC.QAbstractTableModel` method)""" return self.__data.shape[1] def data(self, index: QC.QModelIndex, role: int) -> str | None: """Return the data (reimplement the `QC.QAbstractTableModel` method)""" if role == QC.Qt.DisplayRole: return str(self.__data[index.row(), index.column()]) return None def headerData(self, section: int, orientation: int, role: int) -> str | None: """Return the header data (reimplement the `QC.QAbstractTableModel` method)""" if ( role == QC.Qt.DisplayRole and orientation == QC.Qt.Horizontal and self.__horizontal_headers is not None ): return self.__horizontal_headers[section] return super().headerData(section, orientation, role) class ArrayView(QW.QTableView): """Array view Args: parent: Parent widget """ def __init__(self, parent: QWidget) -> None: super().__init__(parent) self.setShowGrid(True) self.setAlternatingRowColors(True) def set_data( self, data: np.ndarray, horizontal_headers: list[str] | None = None ) -> None: """Set the data Args: data: Data array horizontal_headers: Horizontal headers """ model = ArrayModel(self, data, horizontal_headers) self.setModel(model) class PreviewWidget(QW.QWidget): """Widget showing the raw data, the prefiltered data and a preview of the data""" def __init__(self, parent: QWidget, destination: str) -> None: super().__init__(parent) self.destination = destination main_layout = QW.QVBoxLayout() self.setLayout(main_layout) self.tabwidget = tw = QW.QTabWidget() self._raw_text_edit = self.create_editor() tw.addTab( self._raw_text_edit, get_icon("libre-gui-questions.svg"), _("Raw Data") ) self._preview_table = ArrayView(self) self._preview_table.setFont(self._raw_text_edit.font()) self._preview_table.setEditTriggers(QW.QAbstractItemView.NoEditTriggers) tw.addTab(self._preview_table, get_icon("table.svg"), _("Preview")) main_layout.addWidget(tw) def create_editor(self) -> CodeEditor: """Create a code editor""" editor = CodeEditor() font = editor.font() font.setPointSize(font.pointSize() - 2) editor.setFont(font) editor.setReadOnly(True) editor.setWordWrapMode(QG.QTextOption.NoWrap) return editor def set_raw_data(self, data: str) -> None: """Set the raw data""" self._raw_text_edit.setPlainText(data) def __clear_preview_table(self, enable: bool) -> None: """Clear the preview table""" self._preview_table.setModel(None) self._preview_table.setEnabled(enable) idx = self.tabwidget.indexOf(self._preview_table) icon_name = "table.svg" if enable else "table_unavailable.svg" self.tabwidget.setTabIcon(idx, get_icon(icon_name)) if enable: self.tabwidget.setCurrentIndex(idx) def set_preview_data( self, df: pd.DataFrame | None, param: SignalImportParam | ImageImportParam ) -> None: """Set the preview data""" data = df.to_numpy(np.dtype(param.dtype_str)) if df is not None else None if data is None or len(data.shape) not in (1, 2) or data.size == 0: self.__clear_preview_table(False) else: self.__clear_preview_table(True) if self.destination == "signal": assert param.first_col_is_x is not None h_headers = df.columns.tolist() else: h_headers = None self._preview_table.set_data(data, horizontal_headers=h_headers) def str_to_dataframe( raw_data: str, param: SignalImportParam | ImageImportParam, worker: CallbackWorker | None = None, ) -> pd.DataFrame | None: """Convert raw data to a DataFrame Args: raw_data: Raw data param: Import parameters worker: Callback worker object parent: Parent widget Returns: The DataFrame, or None if the conversion failed """ if not raw_data: return None textstream = io.StringIO(raw_data) nlines = raw_data.count("\n") + (not raw_data.endswith("\n")) try: df = read_csv_by_chunks( textstream, nlines, worker=worker, decimal=param.decimal_choice, delimiter=param.delimiter_choice, skiprows=param.skip_rows, nrows=param.max_rows, comment=param.comment_char, header=param.header, ) df.to_numpy(np.dtype(param.dtype_str)) # To eventually raise ValueError except Exception: # pylint:disable=broad-except return None # Remove rows and columns where all values are NaN in the DataFrame: df = df.dropna(axis=0, how="all").dropna(axis=1, how="all") if param.transpose: return df.T return df class DataPreviewPage(WizardPage): """Data preview page Args: destination: Destination type ('signal' or 'image') """ def __init__(self, source_page: SourcePage, destination: str) -> None: super().__init__() self.__quick_update = False self.source_page = source_page self.destination = destination self.__preview_df: pd.DataFrame | None = None self.set_title(_("Data Preview")) self.set_subtitle(_("Preview and modify the import settings:")) self.param_widget = gdq.DataSetEditGroupBox( _("Import Parameters"), SignalImportParam if destination == "signal" else ImageImportParam, ) self.param_widget.SIG_APPLY_BUTTON_CLICKED.connect(self.update_preview) self.param_widget.set_apply_button_state(False) self.param = self.param_widget.dataset self.add_to_layout(self.param_widget) self.preview_widget = PreviewWidget(self, destination) self.preview_widget.setSizePolicy( QW.QSizePolicy(QW.QSizePolicy.Expanding, QW.QSizePolicy.Expanding) ) self.add_to_layout(self.preview_widget) def read_data_callback(self, worker: CallbackWorker) -> pd.DataFrame: """Read data callback Args: worker: Callback worker object Returns: The DataFrame """ source_text = self.source_page.get_source_text(preview=False) df = str_to_dataframe(source_text, self.param, worker) return df def get_dataframe(self) -> pd.DataFrame | None: """Return the data Returns: The data frame, or None if the data could not be converted """ worker = CallbackWorker(self.read_data_callback) df = qt_long_callback(self, _("Reading data"), worker, 100) return df def update_preview(self) -> None: """Update the preview""" # Raw data raw_data = self.source_page.get_source_text(preview=True) self.preview_widget.set_raw_data(raw_data) # Preview df = str_to_dataframe(raw_data, self.param) if not self.__quick_update: self.preview_widget.set_preview_data(df, param=self.param) self.__preview_df = df self.set_valid(df is not None) def initialize_page(self) -> None: """Initialize the page""" super().initialize_page() self.param_widget.get() self.update_preview() def validate_page(self) -> bool: """Validate the page""" self.__quick_update = True self.param_widget.set() self.__quick_update = False if self.destination == "signal": nb_sig = len(self.__preview_df.columns) if self.param.first_col_is_x: nb_sig -= 1 if ( nb_sig > 20 and QW.QMessageBox.warning( self, _("Warning"), _( "The number of signals to import is very high (%d).\n" "This may be an error.\n\n" "Are you sure you want to continue?" ) % nb_sig, QW.QMessageBox.Yes | QW.QMessageBox.No, ) == QW.QMessageBox.No ): return False return super().validate_page() class GraphicalRepresentationPage(WizardPage): """Graphical representation page""" def __init__(self, data_page: DataPreviewPage, destination: str) -> None: super().__init__() self.__curve_styles: Generator[tuple[str, str], None, None] | None = None self.__objitmlist: list[ tuple[SignalObj | ImageObj, CurveItem | MaskedXYImageItem] ] = [] self.set_title(_("Graphical Representation")) self.set_subtitle(_("Graphical representation of the imported data")) self.data_page = data_page self.destination = destination layout = QW.QVBoxLayout() instruction = QW.QLabel( _("Unselect the %s that you do not want to import:") % (_("signals") if destination == "signal" else _("images")) ) layout.addWidget(instruction) cb_all = QW.QCheckBox(_("Select all")) cb_all.setChecked(True) cb_all.stateChanged.connect(self.toggle_all) layout.addWidget(cb_all) plot_type = "curve" if destination == "signal" else "image" # Get appropriate autoscale margin from configuration if plot_type == "curve": autoscale_margin = Conf.view.sig_autoscale_margin_percent.get() else: autoscale_margin = Conf.view.ima_autoscale_margin_percent.get() self.plot_widget = PlotWidget( self, toolbar=True, options=PlotOptions( type=plot_type, show_itemlist=True, show_contrast=True, curve_antialiasing=True, show_axes_tab=False, autoscale_margin_percent=autoscale_margin, ), ) plot = self.plot_widget.get_plot() plot.SIG_ITEMS_CHANGED.connect(self.items_changed) layout.addWidget(self.plot_widget) self.add_to_layout(layout) def toggle_all(self, state: int) -> None: """Toggle all items selection""" plot = self.plot_widget.get_plot() for _obj, item in self.__objitmlist: plot.set_item_visible(item, state == QC.Qt.Checked, replot=False) plot.replot() def items_changed(self, _plot: BasePlot) -> None: """Item selection changed""" objs = self.get_objs() self.set_valid(len(objs) > 0) def get_objs(self) -> list[SignalObj | ImageObj]: """Return the objects""" return [obj for obj, item in self.__objitmlist if item.isVisible()] def __plot_signal( self, x: np.ndarray, y: np.ndarray, title: str, labels: tuple[str, str] | None = None, units: tuple[str, str] | None = None, zorder: int | None = None, ) -> None: """Plot signals""" obj = create_signal(title, x=x, y=y, labels=labels, units=units) with CURVESTYLES.alternative(self.__curve_styles): item = create_adapter_from_object(obj).make_item() plot = self.plot_widget.get_plot() plot.add_item(item, z=zorder) maxpoints = 20000 if len(x) >= 2 * maxpoints: item.param.use_dsamp = True item.param.dsamp_factor = len(x) // maxpoints self.__objitmlist.append((obj, item)) def __show_image(self, data: np.ndarray) -> None: """Show image""" obj = create_image("", data) item = create_adapter_from_object(obj).make_item() plot = self.plot_widget.get_plot() plot.add_item(item) self.__objitmlist.append((obj, item)) plot.set_active_item(item) item.unselect() def initialize_page(self) -> None: """Initialize the page""" self.__curve_styles = CURVESTYLES.style_generator() df = self.data_page.get_dataframe() assert df is not None data = df.to_numpy(np.dtype(self.data_page.param.dtype_str)) param = self.data_page.param plot = self.plot_widget.get_plot() plot.del_all_items() self.__objitmlist = [] if self.destination == "signal": xydata = data.T x = np.arange(len(xydata[0]), dtype=self.data_page.param.dtype_str) if len(xydata) == 1: self.__plot_signal(x, xydata[0], "") else: xlabel, ylabels, xunit, yunits = get_labels_units_from_dataframe(df) if param.first_col_is_x: x = xydata[0] plot.set_axis_title("bottom", xlabel) zorder = 1000 with create_progress_bar( self, _("Adding data to the plot"), max_=len(xydata) - 1 ) as progress: for ycol in range(1 if param.first_col_is_x else 0, len(xydata)): progress.setValue(ycol - 1) if progress.wasCanceled(): break yidx = ycol if param.first_col_is_x else ycol - 1 self.__plot_signal( x, xydata[yidx], ylabels[ycol - 1], labels=(xlabel, ylabels[ycol - 1]), units=(xunit, yunits[ycol - 1]), zorder=zorder, ) zorder -= 1 QW.QApplication.processEvents() else: self.__show_image(data) plot.do_autoscale() self.items_changed(plot) return super().initialize_page() def validate_page(self) -> bool: """Validate the page""" objs = self.get_objs() if len(objs) == 0: return False return super().validate_page() class SignalParam(gds.DataSet): """Signal parameters dataset""" title = gds.StringItem(_("Title"), default="").set_pos(col=0, colspan=2) xlabel = gds.StringItem(_("X label"), default="") ylabel = gds.StringItem(_("Y label"), default="").set_pos(col=1) xunit = gds.StringItem(_("X unit"), default="") yunit = gds.StringItem(_("Y unit"), default="").set_pos(col=1) class ImageParam(gds.DataSet): """Image parameters dataset""" title = gds.StringItem(_("Title"), default="").set_pos(col=0, colspan=3) xlabel = gds.StringItem(_("X label"), default="") ylabel = gds.StringItem(_("Y label"), default="").set_pos(col=1) zlabel = gds.StringItem(_("Z label"), default="").set_pos(col=2) xunit = gds.StringItem(_("X unit"), default="") yunit = gds.StringItem(_("Y unit"), default="").set_pos(col=1) zunit = gds.StringItem(_("Z unit"), default="").set_pos(col=2) def update_dataset_non_empty_values(dest: gds.DataSet, source: gds.DataSet) -> None: """Update the destination dataset from the non-empty values of the source dataset Args: dest: Destination dataset source: Source dataset """ for item in source.get_items(): key = item.get_name() value = getattr(source, key) if value not in (None, "") and hasattr(dest, key): setattr(dest, key, value) class LabelsPage(WizardPage): """Labels page""" def __init__( self, source_page: SourcePage, plot_page: GraphicalRepresentationPage, destination: str, ) -> None: super().__init__() self.set_title(_("Labels and units")) self.set_subtitle(_("Set the labels and units for the imported data")) self.source_page = source_page self.plot_page = plot_page self.param_widget = gdq.DataSetEditGroupBox( _("Parameters"), SignalParam if destination == "signal" else ImageParam, show_button=False, ) self.param = self.param_widget.dataset label = QW.QLabel( _( "The following title, labels and units will be applied to the data." "

    Note: " "Leave empty the labels and units to keep the default values " "(i.e. the values which were inferred from the data)." ) ) self.add_to_layout(label) self.add_to_layout(self.param_widget) self.add_stretch() def get_objs(self) -> list[SignalObj | ImageObj]: """Return the objects""" objs = self.plot_page.get_objs() for idx, obj in enumerate(objs): update_dataset_non_empty_values(obj, self.param) if len(objs) > 1: obj.title = f"{obj.title} {idx + 1:02d}" return objs def initialize_page(self) -> None: """Initialize the page""" path = self.source_page.get_source_path() if path is None: path = _("clipboard") else: path = osp.basename(path) self.param.title = _("Imported from:") + " " + path self.param_widget.get() return super().initialize_page() def validate_page(self) -> bool: """Validate the page""" self.param_widget.set() return super().validate_page() class TextImportWizard(Wizard): """Text data import wizard Args: destination: Destination type ('signal' or 'image') """ def __init__(self, destination: str, parent: QWidget | None = None) -> None: super().__init__(parent) assert destination in ("signal", "image") self.setWindowTitle(_("DataLab Import Wizard")) self.source_page = SourcePage() self.add_page(self.source_page) self.data_page = DataPreviewPage(self.source_page, destination) self.add_page(self.data_page) self.plot_page = GraphicalRepresentationPage(self.data_page, destination) self.add_page(self.plot_page) self.labels_page = LabelsPage(self.source_page, self.plot_page, destination) self.add_page(self.labels_page, last_page=True) def get_objs(self) -> list[SignalObj | ImageObj]: """Return the objects""" return self.labels_page.get_objs() DataLab-1.1.0/datalab/widgets/warningerror.py000066400000000000000000000175431514014117600211420ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ Module providing a warning/error message box """ import os.path as osp import re import subprocess import traceback from guidata.config import CONF from guidata.configtools import get_font from guidata.qthelpers import exec_dialog, get_std_icon from guidata.widgets.console.shell import PythonShellWidget from qtpy import QtCore as QC from qtpy import QtWidgets as QW from datalab.config import Conf, _, get_mod_source_dir def go_to_error(text: str) -> None: """Go to error: open file with external editor, and go to line number Args: text (str): Error text """ pattern = r'File "(.+)", line (\d+),' match = re.search(pattern, text) if match: path = match.group(1) line_number = match.group(2) mod_src_dir = get_mod_source_dir() if not osp.isfile(path) and mod_src_dir is not None: otherpath = osp.join(mod_src_dir, path) if not osp.isfile(otherpath): # TODO: [P3] For frozen app, go to error is implemented only when the # source code is available locally (development mode). # How about using a web browser to open the source code on github? return path = otherpath if not osp.isfile(path): return # File not found (unhandled case) fdict = {"path": path, "line_number": line_number} args = Conf.console.external_editor_args.get().format(**fdict).split(" ") editor_path = Conf.console.external_editor_path.get() subprocess.run([editor_path] + args, shell=True, check=False) def insert_spaces(text: str, nbchars: int) -> str: """ Inserts spaces regularly in a string, every nbchars characters, after certain characters (",", ";", "-", "+", "*", ")"), and keeps searching until detecting one of those characters. Args: text (str): The input string. nbchars (int): The number of characters after which a space should be inserted. Returns: str: The modified string with spaces inserted. """ special_chars = (",", ";", "-", "+", "*", ")", "_") new_text = "" index = 0 while index < len(text): if ( index + nbchars < len(text) and text[index + nbchars] not in special_chars and not any(c in special_chars for c in text[index : index + nbchars + 1]) ): new_text += text[index : index + nbchars] # Append characters index += nbchars else: new_text += text[index : index + nbchars] + " " # Insert space index += nbchars return new_text class WarningErrorMessageBox(QW.QDialog): """Warning/Error message box Args: parent (QW.QWidget): parent widget category (str): message category ("error" or "warning") context (str | None): context. Defaults to None. message (str | None): message. Defaults to None. tip (str | None): tip. Defaults to None. """ def __init__( self, parent: QW.QWidget, category: str, context: str = None, message: str = None, tip: str = None, ) -> None: super().__init__(parent) assert category in ("error", "warning") self.setWindowTitle(parent.window().objectName()) self.shell = PythonShellWidget(self, read_only=True) self.shell.go_to_error.connect(go_to_error) font = get_font(CONF, "console") font.setPointSize(9) self.shell.set_font(font) message = traceback.format_exc() if message is None else message self.shell.insert_text(message, at_end=True, error=True) bbox = QW.QDialogButtonBox(QW.QDialogButtonBox.Ok) bbox.accepted.connect(self.accept) if category == "warning": bbox.addButton(QW.QDialogButtonBox.Ignore).clicked.connect(self.ignore) layout = QW.QVBoxLayout() if category == "error": width, height = 725, 400 icon = "MessageBoxCritical" tb_title = _("Error message") tb_text = _("The following traceback may help to understand the problem:") else: width, height = 725, 200 icon = "MessageBoxWarning" tb_title = _("Warning message") tb_text = _("Please take into account the following warning message:") if context is not None: context = insert_spaces(context, 80) msgprefix = _("An error has occured during the following context:") text = "
    ".join([msgprefix, f"{context}"]) ct_groupbox = QW.QGroupBox(_("Context"), self) ct_layout = QW.QHBoxLayout() ct_image_layout = QW.QVBoxLayout() ct_image = QW.QLabel() ct_image.setPixmap(get_std_icon(icon).pixmap(24, 24)) ct_image.setSizePolicy(QW.QSizePolicy.Fixed, QW.QSizePolicy.Fixed) ct_image_layout.addWidget(ct_image) ct_image_layout.addStretch() ct_layout.addLayout(ct_image_layout) ct_label = QW.QLabel(text) ct_label.setWordWrap(True) ct_label.setAlignment(QC.Qt.AlignLeft | QC.Qt.AlignTop) ct_layout.addWidget(ct_label) ct_groupbox.setLayout(ct_layout) ct_groupbox.setSizePolicy( QW.QSizePolicy.MinimumExpanding, QW.QSizePolicy.Fixed ) layout.addWidget(ct_groupbox) tb_groupbox = QW.QGroupBox(tb_title, self) tb_layout = QW.QVBoxLayout() tb_layout.addWidget(QW.QLabel(tb_text)) tb_layout.addWidget(self.shell) tb_groupbox.setLayout(tb_layout) layout.addWidget(tb_groupbox) if tip is not None: tip_groupbox = QW.QGroupBox(_("Tip"), self) tip_layout = QW.QHBoxLayout() tip_image_layout = QW.QVBoxLayout() tip_image = QW.QLabel() tip_image.setPixmap(get_std_icon("MessageBoxInformation").pixmap(24, 24)) tip_image.setSizePolicy(QW.QSizePolicy.Fixed, QW.QSizePolicy.Fixed) tip_image_layout.addWidget(tip_image) tip_image_layout.addStretch() tip_layout.addLayout(tip_image_layout) tip_label = QW.QLabel(tip) tip_label.setWordWrap(True) tip_label.setAlignment(QC.Qt.AlignLeft | QC.Qt.AlignTop) tip_layout.addWidget(tip_label) tip_groupbox.setLayout(tip_layout) tip_groupbox.setSizePolicy( QW.QSizePolicy.MinimumExpanding, QW.QSizePolicy.Fixed ) layout.addWidget(tip_groupbox) layout.addSpacing(10) if category == "warning": layout.addWidget( QW.QLabel( _( "Please click on the 'Ignore' button to " "ignore this warning next time." ) ) ) layout.addSpacing(10) layout.addWidget(bbox) self.setLayout(layout) self.resize(width, height) bbox.button(QW.QDialogButtonBox.Ok).setFocus() def ignore(self): """Ignore warning next time""" Conf.proc.ignore_warnings.set(True) self.accept() def show_warning_error( parent: QW.QWidget, category: str, context: str = None, message: str = None, tip: str = None, ) -> None: """Show error message Args: parent (QW.QWidget): parent widget category (str): message category ("error" or "warning") context (str | None): context. Defaults to None. message (str | None): message. Defaults to None. tip (str | None): tip. Defaults to None. """ if category == "warning" and Conf.proc.ignore_warnings.get(): return dlg = WarningErrorMessageBox(parent, category, context, message, tip) exec_dialog(dlg) DataLab-1.1.0/datalab/widgets/wizard.py000066400000000000000000000226221514014117600177150ustar00rootroot00000000000000# Copyright (c) DataLab Platform Developers, BSD 3-Clause license, see LICENSE file. """ DataLab Wizard Widget --------------------- The DataLab Wizard is a widget that guides the user through a series of steps to complete a task. It is implemented as a series of pages, each of which is a separate widget. The `Wizard` class is the main widget that contains the pages. The `WizardPage` class is the base class for the pages. This module is strongly inspired from Qt's `QWizard` and `QWizardPage` classes. .. note:: The only motivation for reimplementing the wizard widget is to support complete styling with `QPalette` and `QStyle` (e.g. `guidata`'s dark mode is not supported on Windows). """ from __future__ import annotations from qtpy import QtCore as QC from qtpy import QtWidgets as QW from qtpy.compat import getopenfilename from qtpy.QtWidgets import QWidget from datalab.config import _ class WizardPage(QW.QWidget): """Wizard page base class We create our own wizard page class instead of using QWizardPage because the latter does not support complete styling with `QPalette` and `QStyle` (e.g. `guidata`'s dark mode is not supported on Windows). This class reimplements the `QWizardPage` features. """ SIG_INITIALIZE_PAGE = QC.Signal() SIG_VALID_STATE_CHANGED = QC.Signal() def __init__(self, parent: QW.QWidget | None = None) -> None: super().__init__(parent) self.__is_valid: bool = True self.wizard: Wizard | None = None self._main_layout = QW.QVBoxLayout() self._user_layout = QW.QVBoxLayout() self._title_label = QW.QLabel("") font = self._title_label.font() font.setPointSize(font.pointSize() + 4) font.setBold(True) self._title_label.setFont(font) self._title_label.setStyleSheet("color: #1E90FF") horiz_line = QW.QFrame() horiz_line.setFrameShape(QW.QFrame.HLine) horiz_line.setFrameShadow(QW.QFrame.Sunken) self._subtitle_label = QW.QLabel("") self._main_layout.addWidget(self._title_label) self._main_layout.addWidget(self._subtitle_label) self._main_layout.addWidget(horiz_line) self._main_layout.addLayout(self._user_layout) self.setLayout(self._main_layout) def set_wizard(self, wizard: Wizard) -> None: """Set the wizard""" self.wizard = wizard def get_wizard(self) -> Wizard: """Return the wizard""" return self.wizard def set_title(self, title: str) -> None: """Set the title of the page""" self._title_label.setText(title) def set_subtitle(self, subtitle: str) -> None: """Set the subtitle of the page""" self._subtitle_label.setText(subtitle) def set_valid(self, is_valid: bool) -> None: """Set the page as valid""" self.__is_valid = is_valid self.SIG_VALID_STATE_CHANGED.emit() def is_valid(self) -> bool: """Return whether the page is valid""" return self.__is_valid def add_to_layout(self, layout: QW.QLayout | QW.QWidget) -> None: """Add a layout to the user layout""" if isinstance(layout, QW.QWidget): self._user_layout.addWidget(layout) else: self._user_layout.addLayout(layout) def add_stretch(self) -> None: """Add a stretch to the user layout""" self._user_layout.addStretch() def initialize_page(self) -> None: """Initialize the page""" self.SIG_INITIALIZE_PAGE.emit() def validate_page(self) -> bool: """Validate the page""" return self.is_valid() class Wizard(QW.QDialog): """Wizard base class""" def __init__(self, parent: QWidget | None = None) -> None: super().__init__(parent) _main_layout = QW.QVBoxLayout() self.setLayout(_main_layout) self._pages_widget = QW.QStackedWidget() _main_layout.addWidget(self._pages_widget) btn_layout = QW.QHBoxLayout() self._back_btn = QW.QPushButton(_("Back")) self._back_btn.clicked.connect(self.go_to_previous_page) self._next_btn = QW.QPushButton(_("Next")) self._next_btn.clicked.connect(self.go_to_next_page) self._finish_btn = QW.QPushButton(_("Finish")) self._finish_btn.clicked.connect(self.accept) self._cancel_btn = QW.QPushButton(_("Cancel")) self._cancel_btn.clicked.connect(self.reject) btn_layout.addWidget(self._back_btn) btn_layout.addWidget(self._next_btn) btn_layout.addWidget(self._finish_btn) btn_layout.addWidget(self._cancel_btn) _main_layout.addLayout(btn_layout) self.setSizePolicy( QW.QSizePolicy(QW.QSizePolicy.Minimum, QW.QSizePolicy.Minimum) ) def add_page(self, page: WizardPage, last_page: bool = False) -> None: """Add a page to the wizard""" page.set_wizard(self) page.SIG_INITIALIZE_PAGE.connect(self.__update_button_states) page.SIG_VALID_STATE_CHANGED.connect(self.__update_button_states) self._pages_widget.addWidget(page) if last_page: self._pages_widget.widget(0).initialize_page() def __update_button_states(self, index: int | None = None) -> None: """Update button states""" if index is None: index = self._pages_widget.currentIndex() self._back_btn.setEnabled(index > 0) not_last_page = index < self._pages_widget.count() - 1 page_valid = self._pages_widget.currentWidget().is_valid() self._next_btn.setEnabled(not_last_page and page_valid) is_last_page = index == self._pages_widget.count() - 1 self._finish_btn.setEnabled(is_last_page and page_valid) def go_to_previous_page(self) -> None: """Go to the previous page""" self._pages_widget.setCurrentIndex(self._pages_widget.currentIndex() - 1) self.__update_button_states() def go_to_next_page(self) -> None: """Go to the next page""" if self.validate_page(): self._pages_widget.setCurrentIndex(self._pages_widget.currentIndex() + 1) self.initialize_page() def initialize_page(self) -> None: """Initialize the page""" self._pages_widget.currentWidget().initialize_page() def validate_page(self) -> bool: """Validate the page""" return self._pages_widget.currentWidget().validate_page() def accept(self) -> None: """Accept the wizard""" if self.validate_page(): super().accept() class ExamplePage1(WizardPage): """Example wizard page 1""" def __init__(self) -> None: super().__init__() self.set_title(_("Welcome to the Example Wizard")) self.set_subtitle( _("This wizard will guide you through the process of importing data.") ) def initialize_page(self) -> None: """Initialize the page""" print("ExamplePage1 initialized") super().initialize_page() def validate_page(self) -> bool: """Validate the page""" print("ExamplePage1 validated") return super().validate_page() class ExamplePage2(WizardPage): """Example wizard page 2""" def __init__(self) -> None: super().__init__() self.set_title(_("Select the Source of the Data")) self.set_subtitle( _("Select the source of the data to be imported (clipboard or file).") ) self._clipboard_rb = QW.QRadioButton(_("Clipboard")) self._file_rb = QW.QRadioButton(_("File")) self._file_rb.toggled.connect(self.file_rb_toggled) self._file_le = QW.QLineEdit() self._file_btn = QW.QPushButton(_("Browse...")) self._file_btn.clicked.connect(self.browse_file) self.add_to_layout(self._clipboard_rb) self.add_to_layout(self._file_rb) self.add_to_layout(self._file_le) self.add_to_layout(self._file_btn) def initialize_page(self) -> None: """Initialize the page""" print("ExamplePage2 initialized") super().initialize_page() def file_rb_toggled(self, checked: bool) -> None: """File radio button toggled""" self._file_le.setEnabled(checked) self._file_btn.setEnabled(checked) def browse_file(self) -> None: """Browse file""" file_name, _filt = getopenfilename( self, _("Select the File to Import"), "", _("CSV Files (*.csv);;Text Files (*.txt);;All Files (*)"), ) if file_name: self._file_le.setText(file_name) def validate_page(self) -> bool: """Validate the page""" if self._file_rb.isChecked() and not self._file_le.text(): QW.QMessageBox.critical( self, _("Error"), _("Please select the file to import."), QW.QMessageBox.Ok, ) return False return True class ExampleWizard(Wizard): """Example wizard widget""" def __init__(self) -> None: super().__init__() self.setWindowTitle(_("Example Wizard")) self.add_page(ExamplePage1()) self.add_page(ExamplePage2(), last_page=True) def test_example_wizard(): """Test the import wizard""" # pylint: disable=import-outside-toplevel from guidata.qthelpers import qt_app_context with qt_app_context(): wizard = ExampleWizard() wizard.exec() if __name__ == "__main__": test_example_wizard() 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pylint: skip-file import os import os.path as osp import sys import zipfile import guidata.config as gcfg sys.path.insert(0, os.path.abspath("..")) # Importing sigima to avoid re-enabling guidata validation mode import sigima # noqa import datalab os.environ["DATALAB_DOC"] = "1" # Turn off validation of guidata config # (documentation build is not the right place for validation) gcfg.set_validation_mode(gcfg.ValidationMode.DISABLED) def compress_tutorials_data(app): """Compress tutorials data folders to zip files in doc/_download directory.""" docpath = osp.abspath(osp.dirname(__file__)) tutorials_path = osp.join(docpath, "..", "datalab", "data", "tutorials") download_dir = osp.join(docpath, "_download") if not osp.exists(tutorials_path): print(f"Warning: tutorials directory not found: {tutorials_path}") return # Create download directory if it doesn't exist os.makedirs(download_dir, exist_ok=True) # Look for folders in tutorials directory try: for item in os.listdir(tutorials_path): item_path = osp.join(tutorials_path, item) if osp.isdir(item_path): zip_filename = osp.join(download_dir, f"{item}.zip") print(f"Compressing tutorial folder: {item} -> {zip_filename}") # Create zip file with zipfile.ZipFile(zip_filename, "w", zipfile.ZIP_DEFLATED) as zipf: for root, dirs, files in os.walk(item_path): for file in files: file_path = osp.join(root, file) arcname = osp.join(item, osp.relpath(file_path, item_path)) zipf.write(file_path, arcname) print(f"Created: {zip_filename}") except Exception as exc: print(f"Warning: failed to compress tutorials: {exc}") def setup(app): """Setup function for Sphinx.""" app.connect("builder-inited", compress_tutorials_data) # Exclude outreach directory from LaTeX/PDF builds def exclude_outreach_from_latex(app): """Exclude outreach directory from LaTeX builds to keep it HTML-only.""" if app.builder.format == "latex": # Exclude the entire outreach directory for PDF builds patterns_to_exclude = ["outreach/*"] for pattern in patterns_to_exclude: if pattern not in app.config.exclude_patterns: app.config.exclude_patterns.append(pattern) # Suppress warnings about excluded outreach documents during latex builds warnings_to_suppress = ["toc.excluded", "ref.doc"] for warning_type in warnings_to_suppress: if warning_type not in app.config.suppress_warnings: app.config.suppress_warnings.append(warning_type) # Exclude detailed API documentation from gettext extraction, but keep api/index.rst def exclude_api_from_gettext(app): if app.builder.name == "gettext": # Get all RST files in the api directory api_rel_dir = "features/advanced/api" api_dir = osp.join(app.srcdir, *api_rel_dir.split("/")) if osp.exists(api_dir): for filename in os.listdir(api_dir): if filename.endswith(".rst") and filename != "index.rst": # Remove .rst extension and add wildcard pattern = f"{api_rel_dir}/{filename[:-4]}*" if pattern not in app.config.exclude_patterns: app.config.exclude_patterns.append(pattern) # Also check subdirectories like api/gui/ for dirname in os.listdir(api_dir): subdir_path = osp.join(api_dir, dirname) if osp.isdir(subdir_path): # Exclude entire subdirectories except their index files pattern = f"{api_rel_dir}/{dirname}/*" if pattern not in app.config.exclude_patterns: app.config.exclude_patterns.append(pattern) # Suppress warnings about excluded API documents during gettext builds app.config.suppress_warnings.extend(["toc.excluded", "ref.doc"]) app.connect("builder-inited", exclude_outreach_from_latex) app.connect("builder-inited", exclude_api_from_gettext) # -- Project information ----------------------------------------------------- project = "DataLab" author = "" copyright = "2023, DataLab Platform Developers" release = datalab.__version__ rst_prolog = f""" .. |download_link1| raw:: html DataLab {release} | Windows 7 SP1, 8, 10, 11 """ # noqa: E501 # -- General configuration --------------------------------------------------- extensions = [ "sphinx.ext.intersphinx", "sphinx.ext.napoleon", "sphinx.ext.mathjax", "sphinx.ext.githubpages", "sphinx.ext.viewcode", "sphinx_sitemap", "myst_parser", "sphinx_design", "sphinx_copybutton", "guidata.dataset.autodoc", ] templates_path = ["_templates"] exclude_patterns = [] # -- Options for sitemap extension ------------------------------------------- html_baseurl = datalab.__homeurl__ # for sitemap extension sitemap_locales = ["en", "fr"] sitemap_filename = "../sitemap.xml" # -- Options for HTML output ------------------------------------------------- html_theme = "pydata_sphinx_theme" html_logo = "_static/DataLab-Title.svg" html_title = project html_favicon = "_static/favicon.ico" html_show_sourcelink = False templates_path = ["_templates"] if "language=fr" in sys.argv: ann = "DataLab a été présenté à SciPy 2024 🐍, PyData Paris 2024, OSXP 2024 et OSXP 2025 🚀 — En savoir plus" # noqa: E501 else: ann = "DataLab has been presented at SciPy 2024 🐍, PyData Paris 2024, OSXP 2024, and OSXP 2025 🚀 — Learn more" # noqa: E501 html_theme_options = { "show_toc_level": 2, "github_url": "https://github.com/DataLab-Platform/DataLab/", "logo": { "text": f"v{datalab.__version__}", }, "icon_links": [ { "name": "PyPI", "url": "https://pypi.org/project/datalab-platform", "icon": "_static/pypi.svg", "type": "local", "attributes": {"target": "_blank"}, }, { "name": "CODRA", "url": "https://codra.net", "icon": "_static/codra.png", "type": "local", "attributes": {"target": "_blank"}, }, ], "announcement": ann, } html_static_path = ["_static"] # -- Options for LaTeX output ------------------------------------------------ latex_logo = "_static/DataLab-Frontpage.png" # -- Options for sphinx-intl package ----------------------------------------- locale_dirs = ["locale/"] # path is example but recommended. gettext_compact = False gettext_location = False # -- Options for autodoc extension ------------------------------------------- autodoc_default_options = { "members": True, "member-order": "bysource", } # -- Options for intersphinx extension --------------------------------------- intersphinx_mapping = { "python": ("https://docs.python.org/3", None), "qwt": ("https://pythonqwt.readthedocs.io/en/latest/", None), "numpy": ("https://numpy.org/doc/stable/", None), "scipy": ("https://docs.scipy.org/doc/scipy/", None), "scikit-image": ("https://scikit-image.org/docs/stable/", None), "h5py": ("https://docs.h5py.org/en/stable/", None), "guidata": ("https://guidata.readthedocs.io/en/latest/", None), "plotpy": ("https://plotpy.readthedocs.io/en/latest/", None), } # -- Latex macros for math in docstrings ------------------------------------- macros = { "FFT": r"\operatorname{FFT}", "PSD": r"\operatorname{PSD}", "sgn": r"\operatorname{sgn}", "sinc": r"\operatorname{sinc}", "sawtooth": r"\operatorname{sawtooth}", "erfc": r"\operatorname{erfc}", "erf": r"\operatorname{erf}", } latex_elements = { "preamble": r""" \usepackage{amsmath} \usepackage{amssymb} \usepackage{mathrsfs}""" + "\n".join(f"\\newcommand{{\\{cmd}}}{{{defn}}}" for cmd, defn in macros.items()), } # -- MathJax configuration for HTML output ----------------------------------- mathjax3_config = { "loader": {"load": ["[tex]/ams"]}, "tex": { "macros": macros, }, } DataLab-1.1.0/doc/contributing/000077500000000000000000000000001514014117600162555ustar00rootroot00000000000000DataLab-1.1.0/doc/contributing/contribute_code.rst000066400000000000000000000026011514014117600221560ustar00rootroot00000000000000.. _contribute_code: Contribute code =============== This page explains how you can contribute code to the project. .. seealso:: The :ref:`guidelines` page presents the coding guidelines that you should follow when contributing to the project. Fork the project ---------------- The first step is to fork the project on GitHub. You can do that by visiting `DataLab GitHub project `_ and clicking on the "Fork" button on the top right corner of the page. Once you have forked the project, you will have a copy of the project in your own GitHub account. Then you can clone the project on your computer and start working on it. Submit a pull request --------------------- Once you have made some changes, you can submit a pull request to the original project. To do that, go to your forked project on GitHub and click on the "Pull request" button on the top right corner of the page. Then you will have to fill a form to describe your pull request. Once you have submitted the pull request, the project maintainers will review your changes and merge them if they are satisfied. During the review process, the project maintainers will check that your code follows the coding guidelines and that it does not break the existing tests. If your code does not follow the coding guidelines, you will have to fix it before your pull request can be merged. DataLab-1.1.0/doc/contributing/dependencies.rst000066400000000000000000000071261514014117600214430ustar00rootroot00000000000000.. _dependencies: About Dependencies ================== Dependencies are an important part of any software project, and they can significantly affect the development process. In this section, we will discuss the dependencies used in our project, how to manage them, and best practices for keeping them up to date. Managing Dependencies --------------------- Dependencies in DataLab Project are defined in the `pyproject.toml` file, as officially recommended by the Python packaging authority (see `Writing your pyproject.toml `_). The `pyproject.toml` file contains many sections, but the most relevant for dependencies are: - `[project.dependencies]`: This section lists the direct dependencies of the application. These are the packages that the application needs to run. - `[project.optional-dependencies]`: This section lists optional dependencies that can be installed to enable additional features. These dependencies are not required for the core functionality of the application but can enhance its capabilities. Among the optional dependencies, we have the following groups: - `qt`: Qt-based graphical user interface (currently, PyQt5). - `opencv`: computer vision tasks requiring OpenCV. - `dev`: development and testing (linters, formatters, etc.). - `doc`: building the documentation (Sphinx, etc.). - `test`: running the tests (pytest, etc.). Deploying Dependencies ---------------------- Production ^^^^^^^^^^ In production, we recommend using the package manager that is most suitable for your environment and that you are most comfortable with. All package managers (e.g., `pip`, `conda`) are directly or indirectly using the information from the `pyproject.toml` file to install the dependencies. See :ref:`installation` for more information on how to install DataLab and its dependencies. Development ^^^^^^^^^^^ In development, you may also use the requirements text file to make it easier to install the dependencies in a virtual environment or container. The `requirements.txt` file lists all the dependencies needed for the project, including both direct and optional dependencies. This is the exact list of dependencies that are defined in the `pyproject.toml` file, but formatted for use with `pip` or other package managers that support requirements files. .. note:: The requirements file is generated from the `pyproject.toml` file using a tool provided by the `guidata` package. .. code-block:: console python -m guidata.utils.genreqs txt # to generate requirements.txt Adding New Dependencies ----------------------- When adding new dependencies to the project, please follow these rules: 1. If it is a direct dependency, that is a package that the application needs to run, add it to the `[project.dependencies]` section in the `pyproject.toml` file, and specify the version range that is compatible with the application, in order to avoid breaking changes. 2. If it is an optional dependency, that is a package that can be installed to enable additional features, add it to the appropriate section in the `[project.optional-dependencies]` section in the `pyproject.toml` file. - For the `dev` dependencies, unless it's absolutely necessary, do not specify a version range, as it may limit the ability to use the latest version of the package. - For the other optional dependencies, specify the version range that is compatible with the application, in order to avoid breaking changes. .. note:: In other words, except for the `dev` dependencies, always specify a version range that is compatible with the application.DataLab-1.1.0/doc/contributing/environment.md000066400000000000000000000137371514014117600211560ustar00rootroot00000000000000Setting up Development Environment ================================== Getting started with DataLab development is easy. Here is what you will need: 1. An integrated development environment (IDE) for Python. We recommend [Spyder](https://www.spyder-ide.org/) or [Visual Studio Code](https://code.visualstudio.com/), but any IDE will do. 2. A Python distribution. We recommend [WinPython](https://winpython.github.io/), on Windows, or [Anaconda](https://www.anaconda.com/), on Linux or Mac. But, again, any Python distribution will do. 3. A clean project structure (see below). 4. Test data (see below). 5. Environment variables (see below). 6. Third-party software (see below). Development Environment ----------------------- If you are using [Spyder](https://www.spyder-ide.org/), thank you for supporting the scientific open-source Python community! If you are using Visual Studio Code, that's also an excellent choice (for other reasons). We recommend installing the following extensions: | Extension | Description | | --------- | ----------- | | [gettext](https://marketplace.visualstudio.com/items?itemName=mrorz.language-gettext) | Gettext syntax highlighting | | [Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance) | Python language server | | [Python](https://marketplace.visualstudio.com/items?itemName=ms-python.python) | Python extension | | [reStructuredText Syntax highlighting](https://marketplace.visualstudio.com/items?itemName=trond-snekvik.simple-rst) | reStructuredText syntax highlighting | | [Ruff](https://marketplace.visualstudio.com/items?itemName=charliermarsh.ruff) | Extremely fast Python linter and code formatter | | [Todo Tree](https://marketplace.visualstudio.com/items?itemName=Gruntfuggly.todo-tree) | Todo tree | | [Insert GUID](https://marketplace.visualstudio.com/items?itemName=heaths.vscode-guid) | Insert GUID | | [XML Tools](https://marketplace.visualstudio.com/items?itemName=DotJoshJohnson.xml) | XML Tools | Python Environment ------------------ DataLab requires the following : * Python (e.g. WinPython) * Additional Python packages Installing all required packages : pip install --upgrade -r requirements.txt ℹ️ See [Installation](https://datalab-platform.com/en/intro/installation.html) for more details on reference Python and Qt versions. ⚠️ If you are contributing to DataLab's processing features, you will need to work on both DataLab's main repository and sigima's repository. To install `sigima` in editable mode, use the following command: pip install -e ../sigima # Adjust the path to your local sigima repository If you are using [WinPython](https://winpython.github.io/), thank you for supporting the scientific open-source Python community! The following table lists the currently officially used Python distributions: | Python version | Status | WinPython version | | -------------- | ------------ | ----------------- | | 3.9 | OK | 3.9.10.0 | | 3.10 | OK | 3.10.11.1 | | 3.11 | OK | 3.11.5.0 | | 3.12 | OK | 3.12.3.0 | | 3.13 | OK | 3.13.2.0 | ⚠ We strongly recommend using the `.dot` versions of WinPython which are lightweight and can be customized to your needs (using `pip install -r requirements.txt`). ✅ We also recommend using a dedicated WinPython instance for DataLab. Test data --------- DataLab test data are located in different folders, depending on their nature or origin. Required data for unit tests are located in "datalab\data\tests" (public data). A second folder %DATALAB_DATA% (optional) may be defined for additional tests which are still under development (or for confidential data). Specific environment variables ------------------------------ Enable the "debug" mode (no stdin/stdout redirection towards internal console): @REM Mode DEBUG set DEBUG=1 Building PDF documentation requires LaTeX. On Windows, the following environment: @REM LaTeX executable must be in Windows PATH, for mathematical equations rendering @REM Example with MiKTeX : set PATH=C:\\Apps\\miktex-portable\\texmfs\\install\\miktex\\bin\\x64;%PATH% Visual Studio Code configuration used in `launch.json` and/or `tasks.json` (examples) : @REM Folder containing additional working test data set DATALAB_DATA=C:\Dev\Projets\DATALAB_data Visual Studio Code `.env` file: * This file is used to set environment variables for the application. * It is used to set the `PYTHONPATH` environment variable to the root of the project. * This is required to be able to import the project modules from within VS Code. * To create this file, copy the `.env.template` file to `.env` (and eventually add your own paths). Windows installer ----------------- The Windows installer is built using [WiX Toolset](https://wixtoolset.org/) V4.0.5. Using the WiX Toolset requires [.NET SDK](https://dotnet.microsoft.com/download/dotnet/) V6.0 minimum. You may install .NET SDK using `winget`: winget install Microsoft.DotNet.SDK.8 Once .NET SDK is installed, the WiX Toolset can be installed and configured using the following commands: dotnet tool install --global wix --version 4.0.5 wix extension add WixToolset.UI.wixext/4.0.5 First, you need to generate the installer script from a generic template: python wix/makewxs.py Building the installer is done using the following command: wix build .\wix\DataLab.wxs -ext WixToolset.UI.wixext Third-party Software -------------------- The following software may be required for maintaining the project: | Software | Description | | -------- | ----------- | | [gettext](https://mlocati.github.io/articles/gettext-iconv-windows.html) | Translations | | [Git](https://git-scm.com/) | Version control system | | [ImageMagick](https://imagemagick.org/) | Image manipulation utilities | | [Inkscape](https://inkscape.org/) | Vector graphics editor | | [MikTeX](https://miktex.org/) | LaTeX distribution on Windows | DataLab-1.1.0/doc/contributing/gitworkflow.rst000066400000000000000000000207661514014117600214000ustar00rootroot00000000000000.. _gitworkflow: Git Workflow ============ This document describes the Git workflow used in the DataLab project, based on a ``main`` branch, a ``develop`` branch, and feature-specific branches. It also defines how bug fixes are managed. .. note:: This workflow is a simplified version of the `Gitflow Workflow `_. It has been adapted to suit the needs of the DataLab project at the current stage of development. In the near future, we may consider adopting a more complex workflow, e.g. by adding release branches. Branching Model --------------- Main Branches ^^^^^^^^^^^^^ - ``main``: Represents the stable, production-ready version of the project. - ``develop``: Used for ongoing development and integration of new features. Feature Branches ^^^^^^^^^^^^^^^^ - ``feature/feature_name``: Used for the development of new features. - Created from ``develop``. - Merged back into ``develop`` once completed. - Deleted after merging. Release Branch ^^^^^^^^^^^^^^ - ``release``: Represents the current maintenance line for patch releases. - Created from ``main`` after a stable release when the first patch is needed. - Accepts ``fix/xxx`` and ``hotfix/xxx`` branch merges. - Merged back into ``main`` for each patch release tag (e.g., 1.0.1, 1.0.2). - Reset or recreated when starting a new minor/major release cycle. .. note:: The ``release`` branch is not versioned (no ``release/1.0.x``). It always represents "the current maintenance line." When a new minor version is released (e.g., 1.2.0), the old ``release`` branch is deleted and a new one is created from the fresh tag when the first patch for 1.2.1 is needed. Bug Fix Branches ^^^^^^^^^^^^^^^^ - ``fix/xxx``: Used for general bug fixes that are not urgent. - Created from ``develop`` (for next feature release) or ``release`` (for patch release). - Merged back into the originating branch once completed. - Deleted after merging. - ``hotfix/xxx``: Used for urgent production-critical fixes. - Created from ``release`` (if exists) or ``main`` (if no ``release`` branch yet). - Merged back into ``release`` or ``main``. - The fix is then cherry-picked into ``develop``. - Deleted after merging. .. note:: Hotfixes (high-priority fixes) will be integrated in the next maintenance release (X.Y.Z -> Z+1), while fixes (low-priority fixes) will be integrated in the next feature release (X.Y -> Y+1). Documentation Branches ---------------------- When working on documentation that is not related to source code (e.g. training materials, user guides), branches should be named using the ``doc/`` prefix. Examples: - ``doc/training-materials`` - ``doc/user-guide`` This naming convention improves clarity by clearly separating documentation efforts from code-related development (features, fixes, etc.). Workflow for New Features ------------------------- 1. Create a new feature branch from ``develop``: .. code-block:: sh git checkout develop git checkout -b develop/feature_name 2. Develop the feature and commit changes. 3. Merge the feature branch back into ``develop``: .. code-block:: sh git checkout develop git merge --no-ff develop/feature_name 4. Delete the feature branch: .. code-block:: sh git branch -d develop/feature_name .. warning:: Do not leave feature branches unmerged for too long. Regularly rebase them on ``develop`` to minimize conflicts. Workflow for Regular Bug Fixes ------------------------------ For next feature release (target: ``develop``): 1. Create a bug fix branch from ``develop``: .. code-block:: sh git checkout develop git checkout -b fix/bug_description 2. Apply the fix and commit changes. 3. Merge the fix branch back into ``develop``: .. code-block:: sh git checkout develop git merge --no-ff fix/bug_description 4. Delete the fix branch: .. code-block:: sh git branch -d fix/bug_description For current maintenance release (target: ``release``): 1. Create a bug fix branch from ``release``: .. code-block:: sh git checkout release git checkout -b fix/bug_description 2. Apply the fix and commit changes. 3. Merge the fix branch back into ``release``: .. code-block:: sh git checkout release git merge --no-ff fix/bug_description 4. Delete the fix branch: .. code-block:: sh git branch -d fix/bug_description .. warning:: Do not create a ``fix/xxx`` branch from a ``develop/feature_name`` branch. Always branch from ``develop`` or ``release`` to ensure fixes are correctly propagated. .. code-block:: sh # Incorrect: git checkout develop/feature_name git checkout -b fix/wrong_branch .. code-block:: sh # Correct: git checkout develop git checkout -b fix/correct_branch Workflow for Critical Hotfixes ------------------------------ 1. Create a hotfix branch from ``release`` (or ``main`` if no ``release`` branch exists): .. code-block:: sh git checkout release # or: git checkout main git checkout -b hotfix/critical_bug 2. Apply the fix and commit changes. 3. Merge the fix back into ``release`` (or ``main``): .. code-block:: sh git checkout release # or: git checkout main git merge --no-ff hotfix/critical_bug 4. Cherry-pick the fix into ``develop``: .. code-block:: sh git checkout develop git cherry-pick 5. Delete the hotfix branch: .. code-block:: sh git branch -d hotfix/critical_bug .. warning:: Do not merge ``fix/xxx`` or ``hotfix/xxx`` directly into ``main`` without following the workflow. Ensure hotfixes are cherry-picked into ``develop`` to avoid losing fixes in future releases. Workflow for Patch Releases ---------------------------- When ready to release a patch version (e.g., 1.0.1, 1.0.2): 1. Ensure the ``release`` branch contains all desired fixes. 2. Merge ``release`` into ``main``: .. code-block:: sh git checkout main git merge --no-ff release 3. Tag the release on ``main``: .. code-block:: sh git tag -a v1.0.1 -m "Release version 1.0.1" git push origin main --tags 4. Keep the ``release`` branch for additional patches in the same minor version series. Workflow for Minor/Major Releases ---------------------------------- When ready to release a new minor or major version (e.g., 1.1.0, 2.0.0): 1. Merge ``develop`` into ``main``: .. code-block:: sh git checkout main git merge --no-ff develop 2. Tag the release on ``main``: .. code-block:: sh git tag -a v1.1.0 -m "Release version 1.1.0" git push origin main --tags 3. Delete the old ``release`` branch (if exists): .. code-block:: sh git branch -d release git push origin --delete release 4. Create a new ``release`` branch from ``main`` when the first patch for 1.1.1 is needed: .. code-block:: sh git checkout main git checkout -b release git push -u origin release Best Practices -------------- - Regularly **rebase feature branches** on ``develop`` to stay up to date: .. code-block:: sh git checkout develop/feature_name git rebase develop - Avoid long-lived branches to minimize merge conflicts. - Ensure bug fixes in ``release`` or ``main`` are **always cherry-picked** to ``develop``. - Clearly differentiate between ``fix/xxx`` (non-urgent fixes) and ``hotfix/xxx`` (critical production fixes). - When creating the ``release`` branch, update release notes to indicate which version it targets (e.g., add a comment in the merge commit: "Create release branch for v1.0.x maintenance"). - The ``release`` branch always represents the current maintenance line. To know which version it targets, check the most recent tag on ``main`` or the release notes. Takeaway -------- This workflow ensures a structured yet flexible development process while keeping ``main`` stable and ``develop`` always updated with the latest changes. The ``release`` branch provides a dedicated maintenance line for patch releases, allowing ``develop`` to proceed with new features without interference. This solves the problem of coordinating unreleased changes across the PlotPyStack ecosystem (DataLab, Sigima, PlotPy, guidata, PythonQwt) by providing a stable branch for CI testing during maintenance cycles. DataLab-1.1.0/doc/contributing/guidelines.rst000066400000000000000000000047111514014117600211420ustar00rootroot00000000000000.. _guidelines: Coding guidelines ================= Generic coding guidelines ------------------------- We follow the `PEP 8 `_ coding style. In particular, we are especially strict about the following guidelines: - Limit all lines to a maximum of 79 characters. - Respect the naming conventions (classes, functions, variables, etc.). - Use specific exceptions instead of the generic :class:`Exception`. To enforce these guidelines, the following tools are mandatory: - `ruff `_ for code formatting and static code analysis. - `pylint `_ for static code analysis. ruff ^^^^ If you are using `Visual Studio Code `_, the project settings will automatically format your code with `ruff` on save (you may also run the task "Run Ruff" to run `ruff` on the project). To run `ruff`, run the following command:: ruff check pylint ^^^^^^ To run `pylint`, run the following command:: pylint datalab If you are using `Visual Studio Code `_ on Windows, you may run the task "Run Pylint" to run `pylint` on the project. .. note:: A `pylint` rating greater than 9/10 is required to merge a pull request. Specific coding guidelines -------------------------- In addition to the generic coding guidelines, we have the following specific guidelines: - Write docstrings for all classes, methods and functions. The docstrings should follow the `Google style `_. - Add typing annotations for all functions and methods. The annotations should use the future syntax (``from __future__ import annotations``) - Try to keep the code as simple as possible. If you have to write a complex piece of code, try to split it into several functions or classes. - Add as many comments as possible. The code should be self-explanatory, but it is always useful to add some comments to explain the general idea of the code, or to explain some tricky parts. - Do not use ``from module import *`` statements, even in the ``__init__`` module of a package. - Avoid using mixins (multiple inheritance) when possible. It is often possible to use composition instead of inheritance. - Avoid using ``__getattr__`` and ``__setattr__`` methods. They are often used to implement lazy initialization, but this can be done in a more explicit way. DataLab-1.1.0/doc/contributing/index.rst000066400000000000000000000106041514014117600201170ustar00rootroot00000000000000Contributing ============ .. meta:: :description: Contribute to DataLab project, the open-source scientific data analysis platform :keywords: DataLab, contribute, open-source, scientific, data, analysis, platform, signal processing, image processing DataLab is **your** platform. If you want it to be improved, you **can** contribute to the project, whether you are a developer or not. There are many ways to contribute to DataLab project, depending on how much time you have, your experience with open source projects, and your skills. Share your ideas and experiences -------------------------------- .. only:: html and not latex :octicon:`info;1em;sd-text-info` :bdg-success-line:`No coding required` Besides the classic bug reports and feature requests, you can share your ideas and experiences for improving DataLab. In particular, we are very interested in your feedback on the documentation and tutorials. Moreover, if you have a use case that you would like to share with the community, please let us know. .. only:: html and not latex .. grid:: 2 :gutter: 1 2 3 4 .. grid-item-card:: :octicon:`bug;1em;sd-text-info` Bugs :link: https://github.com/DataLab-Platform/DataLab/issues/new?assignees=&labels=bug&projects=&template=bug_report.md&title= Reporting a bug .. grid-item-card:: :octicon:`light-bulb;1em;sd-text-info` Enhancements :link: https://github.com/DataLab-Platform/DataLab/issues/new?assignees=&labels=enhancement&projects=&template=feature_request.md&title= Suggesting an enhancement .. grid-item-card:: :octicon:`book;1em;sd-text-info` Documentation :link: https://github.com/DataLab-Platform/DataLab/issues/new?assignees=&labels=documentation&projects=&template=doc_request.md&title= Suggesting a documentation topic .. grid-item-card:: :octicon:`mortar-board;1em;sd-text-info` Tutorial :link: https://github.com/DataLab-Platform/DataLab/issues/new?assignees=&labels=documentation&projects=&template=tutorial_request.md&title= Suggesting a tutorial topic .. only:: latex and not html Without coding, you can contribute to DataLab project by: - `Reporting a bug `_ - `Suggesting an enhancement `_ - `Suggesting a documentation topic `_ - `Suggesting a tutorial topic `_ Share your scientific/technical knowledge ----------------------------------------- .. only:: html and not latex :octicon:`info;1em;sd-text-info` :bdg-success-line:`No coding required` Your technical or scientific knowledge is also very valuable to us, as you may directly contribute to the documentation or tutorials. Or, better, if you want to write a tutorial, we will be happy to help you. Again without coding, you can contribute to DataLab project by: - Writing documentation - Writing a tutorial Contribute to new features -------------------------- .. only:: html and not latex :octicon:`info;1em;sd-text-info` :bdg-info-line:`Coding (beginner)` :bdg-warning-line:`Coding (advanced)` Even if you are not a developer, you can contribute to the project by: - Testing new features - Writing and submitting new Plugins - Writing and submitting macro-commands Develop new features -------------------- .. only:: html and not latex :octicon:`info;1em;sd-text-info` :bdg-warning-line:`Coding (advanced)` If you are a developer, you can contribute to the core of the project by fixing bugs or implementing new features. .. only:: html and not latex .. grid:: 1 :gutter: 1 2 3 4 .. grid-item-card:: :octicon:`terminal;1em;sd-text-info` Code :link: contribute_code :link-type: doc Contributing to the code .. only:: latex and not html See :ref:`contribute_code` section for more information. .. toctree:: :maxdepth: 2 :hidden: contribute_code guidelines gitworkflow dependencies environment roadmap DataLab-1.1.0/doc/contributing/roadmap.md000066400000000000000000000445171514014117600202350ustar00rootroot00000000000000# Roadmap This document outlines the current and future development plans for DataLab: * It includes information about [funding sources](#funding), and [future evolution](#future-milestones). * It also provides a summary of [past milestones](#past-milestones) to give context to the project's evolution. (funding)= ## 📈 Funding > ℹ️ As an open-source project, DataLab relies on the support of various organizations and grants to fund its development and maintenance. The project's roadmap is shaped by the needs and priorities of its users, as well as the availability of resources. Funded work is prioritized and scheduled accordingly, while community contributions are welcomed and encouraged. From the project’s inception in 2023, the development of DataLab has been funded by the following grants and organizations: | Funding | Description | |---------|-----------------------------------------------------------------------------| | CEA | [CEA](https://www.cea.fr/) - French Alternative Energies and Atomic Energy Commission:
    • DataLab was initially created for analyzing data from CEA's Laser Megajoule (LMJ) facility
    • Interfaced with the LMJ control system, DataLab is used to process and visualize signals and images acquired with plasma diagnostics (devices such as cameras, digitizers, spectrometers, etc.)
    • It is also used for R&D activities around the LMJ facility (e.g., metrology, data analysis, etc.)
    • CEA is the major investor in DataLab and the main contributor to the project: CEA scientists and engineers are actively involved in the roadmap | | CODRA | [CODRA](https://codra.net/), a software engineering company and software publisher, has supported DataLab’s open-source journey since its inception:
    • Open-source project management and communication (social media, website, etc.)
    • Conferences and events: [SciPy 2024](https://cfp.scipy.org/2024/talk/G3MC9L/), [PyData Paris 2024](https://www.youtube.com/watch?v=lBEu-DeHyz0&list=PLJjbbmRgu6RqGMOhahm2iE6NUkIYIaEDK), [Open Source Experience 2024](https://pretalx.com/pydata-paris-2024/talk/WTDVCC/), etc.
    • Documentation: tutorials, videos, and more | | NLnet | [NLnet Foundation](https://nlnet.nl/), as part of the NGI0 Commons Fund backed by the European Commission, funded the [redesign of DataLab’s core architecture](https://nlnet.nl/project/DataLab/) — a major overhaul included in the 1.0 release (November 2025):
    • The goal was to decouple the data model, computation, and I/O from the UI
    • This led to the creation of the [Sigima](https://sigima.readthedocs.io/en/latest/) library, which now handles data processing independently of DataLab’s GUI
    • This redesign improves maintainability, testability, and extensibility of the codebase
    • This also enables DataLab to be used as a library (through Sigima) in other software projects, fostering integration and reuse | (future-milestones)= ## 🏗️ Future Milestones > ℹ️ The following tasks are long-term goals for DataLab. They are not scheduled for any specific release and may evolve over time based on user needs and project direction. The following table summarizes the future evolutions and maintenance plans for DataLab that are detailed in the sections below. | Milestone type | Description | |----------------------|----------------------------------------------| | 🔄 Future Evolutions | • Support for data acquisition
    • Web frontend
    • Support for time series
    • Database connectors
    • Jupyter plugin for interactive data analysis
    • Spyder plugin for interactive data analysis
    • Jupyter kernel interface for DataLab | | 🛠️ Maintenance | • Transition to gRPC for remote control
    • Drop Qt5 support and migrate to Qt6 | | 🧱 Other Tasks | • Create a DataLab plugin template | ### 🔄 Support for Data Acquisition Adding support for data acquisition would be a major step forward in making DataLab not only a platform for signal and image processing, but also a **versatile tool for real-time experimental workflows**. The idea would be to allow users to acquire data directly from various hardware devices (e.g., cameras, digitizers, spectrometers, etc.) **within DataLab itself**. Such a feature would enable **seamless integration between acquisition and analysis**, allowing users to process and visualize data immediately after it is captured, without needing to switch tools or export/import files. While no formal design has been established yet, a viable approach could be to: * Introduce a **new plugin family dedicated to data acquisition**, following the modular architecture of DataLab; * Define a **generic API for acquisition plugins**, ensuring flexibility and compatibility across device types; * **Leverage existing solutions** such as [PyMoDAQ](https://pymodaq.cnrs.fr/), a Python-based data acquisition framework already compatible with a wide range of laboratory instruments. A potential **collaboration with the PyMoDAQ development team** could be explored, to benefit from their ecosystem and avoid duplicating efforts. This would also help foster interoperability and promote open standards in the Python scientific instrumentation community. ### 🌐 Web Frontend As DataLab's modular architecture evolves, a natural next step is to provide a **web-based frontend** to complement the existing desktop application. A web frontend would allow users to: * **Run DataLab remotely** (e.g. from a server) and access it via a browser; * Perform processing and visualization tasks without needing a local Python environment; * Facilitate **collaborative data analysis**, sharing sessions or results with colleagues; * Integrate with JupyterHub, dashboards, or lab management tools for centralized usage. This frontend could be built on top of the [Sigima](https://sigima.readthedocs.io/en/latest/) library (that was recently created from the externalization of DataLab's processing functionalities), exposing its features through a web interface — possibly leveraging tools like **JupyterLab extensions**, **Panel**, or **Dash**, depending on the chosen stack. While still exploratory, this direction would increase **accessibility and portability** of DataLab, especially in academic and industrial environments. > ℹ️ The web frontend shall not replace the desktop application, but rather complement it by providing a different access mode. DataLab's philosophy is to remain a **local application** that does not require a server to run, ensuring data privacy and security. More precisely, the web frontend will empower users to run DataLab on a server and access it remotely - typically on a local network, but it will not be a cloud-based solution or a first step towards a fully cloud-based application which would be against the project's philosophy. ### ⏱️ Support for Time Series DataLab currently focuses on generic signal and image processing, but many use cases — especially in scientific instrumentation and experimental physics — involve **time series data**. Adding dedicated support for time series would make it easier to: * Handle signals with associated timestamps or non-uniform sampling; * Perform time-aware processing and visualization (e.g., event alignment, time-based filtering, resampling); * Integrate with external systems generating time-indexed data. This feature is tracked in [Issue #27](https://github.com/DataLab-Platform/DataLab/issues/27), where potential use cases and design considerations are discussed. Introducing robust time series handling would broaden DataLab’s applicability in domains such as data logging, slow control, and real-time monitoring. ### 🗃️ Database Connectors Currently, DataLab operates primarily on files and in-memory data structures. Adding **connectors to databases** would significantly extend its capabilities, especially for users dealing with large, structured, or historical datasets. This feature would allow DataLab to: * **Query and load data** from SQL or NoSQL databases (e.g. PostgreSQL, SQLite, MongoDB, etc.); * Support metadata-driven workflows, where experiments or datasets are referenced from a database; * **Store analysis results** or annotations back into a database for traceability and reproducibility; * Facilitate integration into lab information management systems (LIMS) or enterprise data infrastructures. The design could include: * A **plugin-based system** for supporting various database backends; * A simple configuration interface for connection settings and query management; * Integration with pandas or SQLAlchemy for flexible data exchange. This evolution would help bridge the gap between **data acquisition, analysis, and long-term storage**, enabling more robust scientific data workflows. ### 📓 Jupyter Plugin for Interactive Data Analysis Although DataLab can already be remotely controlled from a Jupyter notebook — thanks to its existing remote control capabilities (see {ref}`ref-to-remote-control`) — the user experience could be greatly enhanced by developing a **dedicated Jupyter plugin**. This plugin would provide a **tighter integration** between Jupyter and DataLab, offering the following features: * Use DataLab as a **Jupyter kernel**, enabling direct access to its processing capabilities from within a notebook; * **Display numerical results from DataLab in Jupyter**, and vice versa — for example, importing results computed in a Jupyter notebook into the DataLab interface; * Allow **interactive data manipulation**: use DataLab for efficient signal and image operations, and Jupyter for custom or home-made data analysis routines; * Bridge scripting and GUI workflows, making DataLab more attractive for scientific users familiar with Jupyter environments. Technically, this plugin could rely on the **Jupyter kernel interface** to expose DataLab's capabilities in an interactive programming context. Such integration would reinforce DataLab’s role in the scientific Python ecosystem and facilitate reproducible, notebook-driven analysis workflows. ### 🧩 Spyder Plugin for Interactive Data Analysis A plugin for the [Spyder IDE](https://www.spyder-ide.org/) would address the **same use cases as the Jupyter plugin**, providing seamless integration between DataLab and Spyder’s interactive environment. This plugin would allow users to: * Interact with DataLab directly from Spyder; * Visualize or send data between the DataLab GUI and the Spyder console; * Use DataLab’s processing capabilities in real time while scripting custom analysis workflows in Spyder. As with the Jupyter integration, this plugin could be implemented by leveraging the **Jupyter kernel interface** (see {ref}`ref-to-remote-control`), which Spyder already supports internally. Such a plugin would enhance DataLab's usability for scientists and engineers who prefer Spyder’s integrated development environment for exploratory analysis. ### 🧠 Jupyter Kernel Interface for DataLab Implementing a native **Jupyter kernel interface** for DataLab would provide a more integrated way to use it from other environments such as **Jupyter notebooks, Spyder, or Visual Studio Code**. This interface would allow: * Direct control of DataLab from third-party tools that support Jupyter kernels; * **Two-way data exchange**: e.g., displaying DataLab results inside a notebook, or visualizing Jupyter-generated data inside the DataLab GUI; * Tighter integration into scripting workflows and IDEs beyond simple remote control. However, based on initial exploration, implementing a full Jupyter kernel may be **non-trivial and potentially time-consuming**. Given that remote control already enables communication between DataLab and Jupyter (see {ref}`ref-to-remote-control`), the added value of a full kernel integration should be carefully evaluated against its complexity and maintenance cost. This option remains open for discussion depending on user demand and development resources. ### 🛠️ Maintenance Plan #### 🔄 2026: Transition to gRPC for Remote Control DataLab currently relies on XML-RPC for remote control, which may become a limitation for more advanced or high-performance use cases. If the need arises for a more **efficient, robust, and extensible communication protocol**, a switch to **gRPC** is under consideration. This improvement is tracked in [Issue #18](https://github.com/DataLab-Platform/DataLab/issues/18). #### 🚫 2025: Drop Qt5 Support and Migrate to Qt6 With the **end-of-life of Qt5 scheduled for mid-2025**, DataLab will fully migrate to **Qt6**. This transition is expected to be **straightforward**, thanks to: * The use of the `qtpy` abstraction layer; * The fact that the `PlotPyStack` library is already compatible with Qt6. This change will ensure compatibility with future versions of Qt and modern Python environments. ### 🧱 Other Tasks #### 🧩 Create a DataLab Plugin Template To encourage community contributions and facilitate the development of extensions, a **template for creating DataLab plugins** is planned. This template would: * Provide a ready-to-use scaffold with best practices; * Help new developers quickly understand the plugin architecture; * Promote consistency and modularity across third-party plugins. The task is tracked in [Issue #26](https://github.com/DataLab-Platform/DataLab/issues/26). (past-milestones)= ## 🏆 Past Milestones From version 0.9 to 1.0, DataLab has undergone significant development and enhancements. The project has evolved from a simple data analysis tool to a powerful platform for processing and visualizing signals and images. Those enhancements have been made possible thanks to the support of the following organizations (see [Funding](#funding) for more details): * [CEA](https://www.cea.fr/): French Alternative Energies and Atomic Energy Commission * [CODRA](https://codra.net/en/): software engineering company and software publisher * [NLnet](https://nlnet.nl/): NLnet Foundation, as part of the NGI0 Commons Fund The following table summarizes the major past milestones of DataLab, including the release dates and a brief description of the features or enhancements introduced in each version. | Milestone | Description | |-----------|-----------------------------------------------------------------------------| | [1.0](https://github.com/DataLab-Platform/DataLab/releases/tag/v1.0.0)
    2025/11 | • Interactive object editing: modify creation/processing parameters and recompute objects
    • New object creation: parametric signal/image generators, Poisson noise, complex-valued objects
    • Enhanced processing: pulse features, ideal filters, 2D resampling, convolution, projections
    • ROI & annotations: copy/paste, import/export, grid creation, inverse ROI logic
    • Visualization: merged result labels, datetime support, performance optimizations
    • File formats: FT-Lab (.sig, .ima), coordinated text files, improved HDF5 support
    • Non-uniform coordinates and polynomial calibration
    • Public API enhancements for programmability | | [0.20](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.20.0)
    2025/04 | • ANDOR SIF images: add support for background images
    • Array editor: new copy all, export, and paste features
    • Fourier analysis: new zero padding feature for signals and images
    • ROI editor: ROI selection tool now active by default
    • Signal processing: X-Y mode, abscissa/ordinate find features, full width at y=...
    • Public API: add `group_id` and `set_current` arguments to add methods | | [0.19](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.19.0)
    2025/04 | • Open all signals or images from a folder (recursively)
    • ROI editor: add options to create ROIs from coordinates | | [0.18](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.18.0)
    2024/11 | • New pairwise operand mode (the operation is done on each pair of signals/images)
    • New polygonal ROI feature
    • Support Windows 7 SP1 to Windows 11 with a single installer | | [0.17](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.17.0)
    2024/08 | • Introduce ROI support across all processing features
    • Add arithmetic operations on signals and images
    • New Ubuntu package for native installation on Linux
    • New Conda package for all platforms (Windows, Linux, MacOS) | | [0.16](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.16.0)
    2024/06 | • New validation process for signal and image features
    • Add support for binary images (unlocking new processing features) | | [0.15](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.15.0)
    2024/04 | • New MSI installer for the stand-alone version on Windows
    • Add support for large text/CSV files (> 1 GB)
    • Add auto downsampling feature | | [0.14](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.14.0)
    2024/03 | • HDF5 browser: multiple file support, detailed info on groups and attributes
    • New Debian package for native installation on Linux | | [0.12](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.12.0)
    2024/02 | • Add a tour and demo feature
    • Add tutorials to the documentation
    • Add a Text file import assistant | | [0.11](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.11.0)
    2024/01 | • Add features for reordering signals and images (e.g. drag-and-drop)
    • Add 1D convolution, interpolation, resampling and detrending features | | [0.10](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.10.0)
    2023-12 | • Develop a very simple DataLab plugin to demonstrate the plugin system
    • Allow to disable the automatic refresh when doing multiple processing steps
    • Serialize curve and image styles in HDF5 files
    • Improve curve readability | | [0.9](https://github.com/DataLab-Platform/DataLab/releases/tag/v0.9.0)
    2023-11 | • Run computations in a separate process
    • Add a plugin system: API for third-party extensions
    • Add a macro-command system
    • Add an XML-RPC server to allow DataLab remote control | DataLab-1.1.0/doc/features/000077500000000000000000000000001514014117600153645ustar00rootroot00000000000000DataLab-1.1.0/doc/features/advanced/000077500000000000000000000000001514014117600171315ustar00rootroot00000000000000DataLab-1.1.0/doc/features/advanced/api.rst000066400000000000000000000013341514014117600204350ustar00rootroot00000000000000.. _api: API === The public Application Programming Interface (API) of DataLab offers a set of functions to access the DataLab features. .. note:: For more details about the `sigima` package, please refer to the `Sigima documentation `_. The Sigima API is especially useful to write scripts that can be run in DataLab (see :ref:`about_macros`) or to develop plugins (see :ref:`about_plugins`). .. toctree:: :maxdepth: 2 :caption: Public features: proxy .. toctree:: :maxdepth: 1 :caption: Internal features: api/index api/main api/panel api/actionhandler api/objectview api/plothandler api/roieditor api/processor api/docks api/h5io DataLab-1.1.0/doc/features/advanced/api/000077500000000000000000000000001514014117600177025ustar00rootroot00000000000000DataLab-1.1.0/doc/features/advanced/api/actionhandler.rst000066400000000000000000000000731514014117600232470ustar00rootroot00000000000000.. automodule:: datalab.gui.actionhandler :no-members: DataLab-1.1.0/doc/features/advanced/api/docks.rst000066400000000000000000000000631514014117600215360ustar00rootroot00000000000000.. automodule:: datalab.gui.docks :no-members: DataLab-1.1.0/doc/features/advanced/api/h5io.rst000066400000000000000000000000621514014117600212760ustar00rootroot00000000000000.. automodule:: datalab.gui.h5io :no-members: DataLab-1.1.0/doc/features/advanced/api/index.rst000066400000000000000000000000551514014117600215430ustar00rootroot00000000000000.. automodule:: datalab.gui :no-members: DataLab-1.1.0/doc/features/advanced/api/main.rst000066400000000000000000000000621514014117600213560ustar00rootroot00000000000000.. automodule:: datalab.gui.main :no-members: DataLab-1.1.0/doc/features/advanced/api/objectview.rst000066400000000000000000000000701514014117600225720ustar00rootroot00000000000000.. automodule:: datalab.gui.objectview :no-members: DataLab-1.1.0/doc/features/advanced/api/panel.rst000066400000000000000000000000421514014117600215270ustar00rootroot00000000000000.. automodule:: datalab.gui.panel DataLab-1.1.0/doc/features/advanced/api/plothandler.rst000066400000000000000000000000711514014117600227460ustar00rootroot00000000000000.. automodule:: datalab.gui.plothandler :no-members: DataLab-1.1.0/doc/features/advanced/api/processor.rst000066400000000000000000000000461514014117600224530ustar00rootroot00000000000000.. automodule:: datalab.gui.processor DataLab-1.1.0/doc/features/advanced/api/roieditor.rst000066400000000000000000000000671514014117600224370ustar00rootroot00000000000000.. automodule:: datalab.gui.roieditor :no-members: DataLab-1.1.0/doc/features/advanced/command.rst000066400000000000000000000054571514014117600213140ustar00rootroot00000000000000.. _ref-to-command-line-features: Command line features ===================== .. meta:: :description: DataLab command line features :keywords: command line, datalab, run, test, demo Run DataLab ----------- To run DataLab from the command line, type the following:: $ datalab To show help on command line usage, simply run:: $ datalab --help usage: app.py [-h] [-b path] [-v] [--unattended] [--screenshot] [--delay DELAY] [--xmlrpcport PORT] [--verbose {quiet,minimal,normal}] [h5] Run DataLab positional arguments: h5 HDF5 file names (separated by ';'), optionally with dataset name (separated by ',') options: -h, --help show this help message and exit -b path, --h5browser path path to open with HDF5 browser -v, --version show DataLab version --reset reset DataLab configuration --unattended non-interactive mode --accept_dialogs accept dialogs in unattended mode --screenshot automatic screenshots --delay DELAY delay (ms) before quitting application in unattended mode --xmlrpcport XMLRPCPORT XML-RPC port number --verbose {quiet,normal,debug} verbosity level: for debugging/testing purpose Open HDF5 file at startup ------------------------- To open HDF5 files, or even import only a specified HDF5 dataset, use the following:: $ datalab /path/to/file1.h5 $ datalab /path/to/file1.h5,/path/to/dataset1 $ datalab /path/to/file1.h5,/path/to/dataset1;/path/to/file2.h5,/path/to/dataset2 Open HDF5 browser at startup ---------------------------- To open the HDF5 browser at startup, use one of the following commands:: $ datalab -b /path/to/file1.h5 $ datalab --h5browser /path/to/file1.h5 Run DataLab demo ---------------- To execute DataLab demo, run the following:: $ datalab-demo .. _run_scientific_validation_tests: Run technical validation tests ------------------------------- .. note:: Technical validation tests are directly included in individual unit tests and are disseminated throughout the code. The test functions including validation tests are marked with the ``@pytest.mark.validation`` decorator. To execute DataLab technical validation tests, run the following:: $ pytest -m validation .. seealso:: See section :ref:`validation` for more information on DataLab's validation strategy. .. _run_functional_validation_tests: Run complete test suite ------------------------ To execute all DataLab unit tests, simply run:: $ pytest Run interactive tests --------------------- To execute DataLab interactive tests, run the following:: $ datalab-tests .. image:: /images/interactive_tests.png DataLab-1.1.0/doc/features/advanced/instviewer.rst000066400000000000000000000055701514014117600220710ustar00rootroot00000000000000.. _ref-to-instviewer: Installation and Configuration ============================== The **Installation and Configuration** dialog box is a diagnostic tool available from DataLab’s graphical interface. It provides a structured overview of the current installation, user-specific settings, and available plugins and I/O features. This tool is primarily designed for troubleshooting and verification purposes — whether you are a user trying to understand how DataLab is set up on your system, or a developer validating your plugin integration. To open the dialog box, go to the "?" menu in the main DataLab window and select **Installation and configuration**. This will open a dialog window that displays all relevant information about your DataLab installation. The dialog window is divided into **three tabs**, each covering a specific aspect of the environment. 1. Installation Configuration ----------------------------- .. figure:: /images/shots/instviewer.png Installation and configuration (see "?" menu), tab 1 This tab displays system-level information about the DataLab installation, including: - Application version; - Platform and operating system; - Whether DataLab is running in frozen (standalone) mode or as a standard Python package; - Internal paths used for data storage and plugin discovery; - Python interpreter details (version, architecture, paths); - Location of the `datalab` package. This section is especially useful for debugging path-related issues or confirming the installation context (e.g., standalone vs. pip-installed). 2. User Configuration --------------------- .. figure:: /images/shots/instviewer2.png Installation and configuration (see "?" menu), tab 2 This tab lists user-specific configuration details, through the contents of DataLab's configuration file. It helps users verify their personalized settings and ensures that DataLab is reading the correct directories for user-defined plugins or preferences. 3. Plugins and I/O Features --------------------------- .. figure:: /images/shots/instviewer3.png Installation and configuration (see "?" menu), tab 3 This tab lists all plugins and I/O handlers currently available in the application. This view is ideal for verifying that all expected plugins and I/O features are detected, loaded, and functional. Usage Tips ---------- - You can copy text from any of the tabs for diagnostic or support purposes. - If a plugin or I/O feature does not appear, check that its filename starts with `datalab_` and that it is located in a recognized plugin directory (see the *User Configuration* tab). .. note:: Reporting unexpected behavior or any other bug on `GitHub Issues`_ will be greatly appreciated, especially if the contents of this viewer are attached to the report (as well as log files, see :ref:`ref-to-logviewer`). .. _GitHub Issues: https://github.com/DataLab-Platform/DataLab/issues/new/choose DataLab-1.1.0/doc/features/advanced/logviewer.rst000066400000000000000000000022531514014117600216700ustar00rootroot00000000000000.. _ref-to-logviewer: Log viewer ========== .. meta:: :description: DataLab's log viewer :keywords: DataLab, log, viewer, crash, bug, report Despite countless efforts (unit testing, test coverage...), DataLab might crash or behave unexpectedly. For those situations, DataLab provides two logs (located in your home directory): - "Traceback log", for Python exceptions - "Faulthandler log", for system failures (e.g. Qt-related crash) .. note:: The log viewer is accessible from the "?" menu in the main window. .. figure:: /images/shots/logviewer.png DataLab log viewer (see "?" menu) If DataLab crashed or if any Python exception is raised during its execution, those log files will be updated accordingly. DataLab will even notify that new informations are available in log files at next startup. This is an invitation to submit a bug report. Reporting unexpected behavior or any other bug on `GitHub Issues`_ will be greatly appreciated, especially if those log file contents are attached to the report (as information on your installation configuration, see :ref:`ref-to-instviewer`). .. _GitHub Issues: https://github.com/DataLab-Platform/DataLab/issues/new/choose DataLab-1.1.0/doc/features/advanced/macros.rst000066400000000000000000000067711514014117600211620ustar00rootroot00000000000000.. _about_macros: Macros ====== .. meta:: :description: How to use macros in DataLab, the open-source data analysis and visualization platform :keywords: DataLab, data analysis, data visualization, open-source, Python, macros Overview -------- There are many ways to extend DataLab with new functionality (see :ref:`about_plugins` or :ref:`ref-to-remote-control`). The easiest way to do so is by using macros. Macros are small Python scripts that can be executed from the "Macro Panel" in DataLab. .. figure:: ../../images/shots/macro_panel.png :align: center :alt: Macro Panel The Macro Panel in DataLab. Macros can be used to automate repetitive tasks, or to create new functionality. As the plugin and remote control system, macros rely on the DataLab high-level API to interact with the application. This means that you can reuse the same code snippets in macros, plugins, and remote control scripts. .. warning:: DataLab handles macros as Python scripts. This means that you can use the full power of Python to create your macros. Even though this is a powerful feature, it also means that you should be careful when running macros from unknown sources, as they can potentially harm your system. .. seealso:: The DataLab high-level API is documented in the :ref:`api` section. The plugin system is documented in the :ref:`about_plugins` section, and the remote control system is documented in the :ref:`ref-to-remote-control` section. Main features ------------- The Macro Panel is a simple interface to: - Create new macros, using the "New macro" |libre-gui-add| button. - Rename existing macros, using the "Rename macro" |libre-gui-pencil| button. - Import/export macros from/to files, using the "Import macro" |fileopen_py| and "Export macro" |filesave_py| buttons. - Execute macros, using the "Run macro" |libre-camera-flash-on| button. - Stop the execution of a macro, using the "Stop macro" |libre-camera-flash-off| button. .. |libre-gui-add| image:: ../../../datalab/data/icons/libre-gui-add.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |libre-gui-pencil| image:: ../../../datalab/data/icons/libre-gui-pencil.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |fileopen_py| image:: ../../../datalab/data/icons/io/fileopen_py.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |filesave_py| image:: ../../../datalab/data/icons/io/filesave_py.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |libre-camera-flash-on| image:: ../../../datalab/data/icons/libre-camera-flash-on.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |libre-camera-flash-off| image:: ../../../datalab/data/icons/libre-camera-flash-off.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Macros are embedded in the DataLab workspace, so they are saved together with the rest of the data (i.e. with signals and images) when exporting the workspace to a HDF5 file. This means that you can share your macros with other users simply by sharing the workspace file. .. note:: Macro are executed in a separate process, so they won't block the main DataLab application. This means that you can continue working with DataLab while a macro is running and that *you can stop a macro at any time* using the |libre-camera-flash-off| button. Example ------- For a detailed example of how to create a macro, see the :ref:`tutorial_custom_func` tutorial. DataLab-1.1.0/doc/features/advanced/migration_v020_to_v100.rst000066400000000000000000000630321514014117600236770ustar00rootroot00000000000000.. _migration_v020_to_v100: Migrating from DataLab v0.20 to v1.0 ==================================== .. meta:: :description: Guide for migrating DataLab plugins from v0.20 to v1.0 :keywords: DataLab, plugin, migration, v0.20, v1.0, Sigima, API changes .. warning:: **Critical compatibility notice:** DataLab v1.0 introduces **major breaking changes** that are **not backward compatible** with v0.20. - **Plugins** developed for v0.20 **will not work** and must be updated - **API changes** require code modifications for all custom integrations Please read this guide carefully before upgrading to v1.0. DataLab v1.0 introduces significant architectural changes compared to v0.20. The most important change is the **externalization of signal and image processing features** into a separate library called **Sigima**. This architectural change improves modularity, testability, and enables the reuse of processing functions in other projects. However, it requires updates to existing plugins to adapt to the new API. This guide describes the steps to migrate plugins from DataLab v0.20 to v1.0. .. contents:: :local: :depth: 2 What changed in v1.0? --------------------- Major architectural changes ^^^^^^^^^^^^^^^^^^^^^^^^^^^ The most significant change in DataLab v1.0 is the creation of the **Sigima library**, which now contains: - **Signal and image objects** (``SignalObj``, ``ImageObj``, ROI classes) - **Processing parameters** (all ``*Param`` classes) - **Processing functions** (``sigima.proc.signal`` and ``sigima.proc.image`` modules) - **Algorithms** for signal and image processing, or data type conversion, coordinates transformation, ... (``sigima.tools`` module) - **I/O functions** (reading and writing signals/images) - **Test data utilities** (test signal and image generation) - **Result objects** (``TableResult`` and ``GeometryResult``, replacing ``ResultProperties`` and ``ResultShape``) What remains in DataLab: - **GUI components** (panels, widgets, plot integration) - **Plugin system** (``datalab.plugins`` module) - **Application logic** (main window, configuration, project management) - **Result metadata adapters** (``TableAdapter``, ``GeometryAdapter`` for storing results in object metadata) Package renaming ^^^^^^^^^^^^^^^^ The DataLab package was renamed from ``cdl`` to ``datalab`` for clarity: .. code-block:: python # v0.20 import cdl.obj import cdl.param from cdl.plugins import PluginBase # v1.0 import sigima.objects import sigima.params from datalab.plugins import PluginBase Plugin file naming convention ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Plugin filenames must follow the new naming convention: .. important:: Plugin files must be renamed from ``cdl_*.py`` to ``datalab_*.py``. **Examples:** - ``cdl_myplugin.py`` → ``datalab_myplugin.py`` - ``cdl_custom_filters.py`` → ``datalab_custom_filters.py`` - ``cdl_example.py`` → ``datalab_example.py`` This naming convention is required for DataLab to automatically discover and load plugins at startup. Plugins that don't follow this naming pattern will not be loaded. .. note:: The plugin discovery mechanism looks for Python files matching the pattern ``datalab_*.py`` in the plugins directory. Make sure to update your plugin filenames accordingly. New result objects ^^^^^^^^^^^^^^^^^^ DataLab v1.0 introduces two new immutable result types: - **TableResult**: Replaces ``ResultProperties`` for tabular scalar results (e.g., statistics, measurements) - **GeometryResult**: Replaces ``ResultShape`` for geometric results (e.g., detected features, contours) These new result types are computation-oriented and free of application-specific logic (e.g., Qt, metadata). All metadata-related behaviors have been migrated to the DataLab application layer using **adapter classes** (``TableAdapter``, ``GeometryAdapter``). Object UUID access ^^^^^^^^^^^^^^^^^^ The way to access an object's UUID has changed: .. code-block:: python # v0.20 - Direct attribute access obj_uuid = obj.uuid # v1.0 - Use helper function from datalab.objectmodel from datalab.objectmodel import get_uuid obj_uuid = get_uuid(obj) # Alternative in v1.0 - Access metadata directly obj_uuid = obj.get_metadata_option("uuid") This change applies to both ``SignalObj`` and ``ImageObj`` objects. The UUID is now stored in the object's metadata rather than as a direct attribute. .. note:: The ``get_uuid()`` function is defined in ``datalab.objectmodel``, not as a method on the object itself. Import it before use: ``from datalab.objectmodel import get_uuid``. Unified processor interface ^^^^^^^^^^^^^^^^^^^^^^^^^^^ The processor methods have been unified and renamed: - Most specific ``compute_*`` methods now use the generic ``run_feature()`` method - Naming conventions updated: ``compute_11`` → ``compute_1_to_1``, ``compute_10`` → ``compute_1_to_0``, etc. Updating the imports -------------------- Import path changes ^^^^^^^^^^^^^^^^^^^ The following table gives the equivalence between DataLab v0.20 and v1.0 imports. The table below shows the mapping between DataLab v0.20 and v1.0 API. For most entries, only the import statement needs to be updated. .. csv-table:: DataLab v0.20 to v1.0 Compatibility Table :file: v020_to_v100.csv :header-rows: 1 :widths: 50, 50 :delim: ; Migrating plugin code --------------------- Example 1: Simple processing plugin ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Here's how a simple custom filter plugin needs to be updated: **DataLab v0.20:** .. code-block:: python import numpy as np import scipy.ndimage as spi import cdl.computation.image as cpi import cdl.obj import cdl.param import cdl.plugins def weighted_average_denoise(data: np.ndarray) -> np.ndarray: """Apply a custom denoising filter to an image.""" def filter_func(values: np.ndarray) -> float: central_pixel = values[len(values) // 2] differences = np.abs(values - central_pixel) weights = np.exp(-differences / np.mean(differences)) return np.average(values, weights=weights) return spi.generic_filter(data, filter_func, size=5) class CustomFilters(cdl.plugins.PluginBase): """DataLab Custom Filters Plugin""" PLUGIN_INFO = cdl.plugins.PluginInfo( name="My custom filters", version="1.0.0", description="This is an example plugin", ) def create_actions(self) -> None: """Create actions""" acth = self.imagepanel.acthandler proc = self.imagepanel.processor with acth.new_menu(self.PLUGIN_INFO.name): for name, func in (("Weighted average denoise", weighted_average_denoise),): # Wrap function to handle ImageObj objects wrapped_func = cpi.Wrap11Func(func) acth.new_action( name, triggered=lambda: proc.compute_11(wrapped_func, title=name) ) **DataLab v1.0:** .. code-block:: python import numpy as np import scipy.ndimage as spi import sigima.proc.image as sipi import datalab.plugins def weighted_average_denoise(data: np.ndarray) -> np.ndarray: """Apply a custom denoising filter to an image.""" def filter_func(values: np.ndarray) -> float: central_pixel = values[len(values) // 2] differences = np.abs(values - central_pixel) weights = np.exp(-differences / np.mean(differences)) return np.average(values, weights=weights) return spi.generic_filter(data, filter_func, size=5) class CustomFilters(datalab.plugins.PluginBase): """DataLab Custom Filters Plugin""" PLUGIN_INFO = datalab.plugins.PluginInfo( name="My custom filters", version="1.0.0", description="This is an example plugin", ) def create_actions(self) -> None: """Create actions""" acth = self.imagepanel.acthandler proc = self.imagepanel.processor with acth.new_menu(self.PLUGIN_INFO.name): for name, func in (("Weighted average denoise", weighted_average_denoise),): # Wrap function to handle ImageObj objects wrapped_func = sipi.Wrap1to1Func(func) acth.new_action( name, triggered=lambda: proc.compute_1_to_1(wrapped_func, title=name), ) **Key changes:** 1. ``cdl.computation.image`` → ``sigima.proc.image`` 2. ``cdl.plugins`` → ``datalab.plugins`` 3. ``Wrap11Func`` → ``Wrap1to1Func`` 4. ``compute_11`` → ``compute_1_to_1`` Example 2: Plugin using built-in features ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ **DataLab v0.20:** .. code-block:: python import cdl.obj import cdl.param import cdl.plugins class ExtractBlobs(cdl.plugins.PluginBase): """DataLab Example Plugin""" PLUGIN_INFO = cdl.plugins.PluginInfo( name="Extract blobs (example)", version="1.0.0", description="This is an example plugin", ) def preprocess(self) -> None: """Preprocess image""" panel = self.imagepanel param = cdl.param.BinningParam.create(sx=2, sy=2) panel.processor.compute_binning(param) panel.processor.compute_moving_median(cdl.param.MovingMedianParam.create(n=5)) def detect_blobs(self) -> None: """Detect circular blobs""" panel = self.imagepanel param = cdl.param.BlobOpenCVParam() param.filter_by_color = False param.min_area = 600.0 param.max_area = 6000.0 param.filter_by_circularity = True param.min_circularity = 0.8 param.max_circularity = 1.0 panel.processor.compute_blob_opencv(param) def create_actions(self) -> None: """Create actions""" acth = self.imagepanel.acthandler with acth.new_menu(self.PLUGIN_INFO.name): acth.new_action("Preprocess image", triggered=self.preprocess) acth.new_action("Detect circular blobs", triggered=self.detect_blobs) **DataLab v1.0:** .. code-block:: python import sigima.objects import sigima.params import datalab.plugins class ExtractBlobs(datalab.plugins.PluginBase): """DataLab Example Plugin""" PLUGIN_INFO = datalab.plugins.PluginInfo( name="Extract blobs (example)", version="1.0.0", description="This is an example plugin", ) def preprocess(self) -> None: """Preprocess image""" panel = self.imagepanel param = sigima.params.BinningParam.create(sx=2, sy=2) panel.processor.run_feature("binning", param) panel.processor.run_feature( "moving_median", sigima.params.MovingMedianParam.create(n=5) ) def detect_blobs(self) -> None: """Detect circular blobs""" panel = self.imagepanel param = sigima.params.BlobOpenCVParam() param.filter_by_color = False param.min_area = 600.0 param.max_area = 6000.0 param.filter_by_circularity = True param.min_circularity = 0.8 param.max_circularity = 1.0 panel.processor.run_feature("blob_opencv", param) def create_actions(self) -> None: """Create actions""" acth = self.imagepanel.acthandler with acth.new_menu(self.PLUGIN_INFO.name): acth.new_action("Preprocess image", triggered=self.preprocess) acth.new_action("Detect circular blobs", triggered=self.detect_blobs) **Key changes:** 1. ``cdl.param`` → ``sigima.params`` 2. ``cdl.plugins`` → ``datalab.plugins`` 3. ``compute_binning(param)`` → ``run_feature("binning", param)`` 4. ``compute_moving_median(param)`` → ``run_feature("moving_median", param)`` 5. ``compute_blob_opencv(param)`` → ``run_feature("blob_opencv", param)`` Example 3: Plugin creating objects and handling results ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ **DataLab v0.20:** .. code-block:: python import numpy as np import cdl.obj as dlo import cdl.tests.data as test_data from cdl.computation import image as cpima from cdl.config import _ from cdl.plugins import PluginBase, PluginInfo def add_noise_to_image(src: dlo.ImageObj, p: dlo.NormalRandomParam) -> dlo.ImageObj: """Add gaussian noise to image""" dst = cpima.dst_11(src, "add_gaussian_noise", f"mu={p.mu},sigma={p.sigma}") test_data.add_gaussian_noise_to_image(dst, p) return dst class PluginTestData(PluginBase): """DataLab Test Data Plugin""" PLUGIN_INFO = PluginInfo( name=_("Test data"), version="1.0.0", description=_("Testing DataLab functionalities"), ) def add_noise_to_image(self) -> None: """Add noise to image""" self.imagepanel.processor.compute_11( add_noise_to_image, paramclass=dlo.NormalRandomParam, title=_("Add noise"), ) def create_noisygauss_image(self) -> None: """Create 2D noisy gauss image""" newparam = self.edit_new_image_parameters( hide_image_height=True, hide_image_type=True ) if newparam is not None: obj = test_data.create_noisygauss_image(newparam, add_annotations=False) self.proxy.add_object(obj) def create_actions(self) -> None: """Create actions""" iah = self.imagepanel.acthandler with iah.new_menu(_("Test data")): iah.new_action(_("Add noise to image"), triggered=self.add_noise_to_image) iah.new_action( _("Create 2D noisy gauss image"), triggered=self.create_noisygauss_image, select_condition="always", ) **DataLab v1.0:** .. code-block:: python import numpy as np import sigima.objects import sigima.tests.data as test_data from sigima.proc import image as sipi from datalab.config import _ from datalab.plugins import PluginBase, PluginInfo def add_noise_to_image( src: sigima.objects.ImageObj, p: sigima.objects.NormalDistribution2DParam ) -> sigima.objects.ImageObj: """Add gaussian noise to image""" dst = sipi.dst_1to1(src, "add_gaussian_noise", f"mu={p.mu},sigma={p.sigma}") test_data.add_gaussian_noise_to_image(dst, p) return dst class PluginTestData(PluginBase): """DataLab Test Data Plugin""" PLUGIN_INFO = PluginInfo( name=_("Test data"), version="1.0.0", description=_("Testing DataLab functionalities"), ) def add_noise_to_image(self) -> None: """Add noise to image""" self.imagepanel.processor.compute_1_to_1( add_noise_to_image, paramclass=sigima.objects.NormalDistribution2DParam, title=_("Add noise"), ) def create_noisy_gaussian_image(self) -> None: """Create 2D noisy gauss image""" newparam = self.edit_new_image_parameters( hide_height=True, hide_type=True ) if newparam is not None: obj = test_data.create_noisy_gaussian_image(newparam, add_annotations=False) self.proxy.add_object(obj) def create_actions(self) -> None: """Create actions""" iah = self.imagepanel.acthandler with iah.new_menu(_("Test data")): iah.new_action(_("Add noise to image"), triggered=self.add_noise_to_image) iah.new_action( _("Create 2D noisy gaussian image"), triggered=self.create_noisy_gaussian_image, select_condition="always", ) **Key changes:** 1. ``cdl.obj`` → ``sigima.objects`` 2. ``cdl.tests.data`` → ``sigima.tests.data`` 3. ``cdl.computation.image`` → ``sigima.proc.image`` 4. ``dst_11`` → ``dst_1to1`` 5. ``compute_11`` → ``compute_1_to_1`` 6. ``dlo.NormalRandomParam`` → ``sigima.objects.NormalDistribution2DParam`` 7. ``create_noisygauss_image`` → ``create_noisy_gaussian_image`` 8. ``hide_image_height`` → ``hide_height``, ``hide_image_type`` → ``hide_type`` Working with result objects --------------------------- Changes to result objects ^^^^^^^^^^^^^^^^^^^^^^^^^ The result object architecture has been completely redesigned in v1.0: **v0.20 Result Objects:** - ``ResultProperties``: For tabular results (statistics, etc.) - ``ResultShape``: For geometric results (detected features, etc.) - These objects contained Qt-specific code and metadata handling logic - Methods like ``add_to(obj)`` and ``from_metadata_entry()`` were part of the result class **v1.0 Result Objects:** - ``TableResult``: Immutable, computation-oriented table results - ``GeometryResult``: Immutable, computation-oriented geometry results - No Qt or metadata dependencies - Metadata handling moved to DataLab adapter classes Using TableResult and GeometryResult ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In v1.0, when your plugin receives results from ``compute_1_to_0`` operations, you'll work with ``TableResult`` or ``GeometryResult`` objects. **Example 1: Computing signal dynamic parameters (TableResult)** This example shows how to compute dynamic parameters (ENOB, SINAD, THD, etc.) and work with the resulting ``TableResult``: .. code-block:: python import sigima.params import sigima.proc.signal from datalab.adapters_metadata import TableAdapter # Get the current signal from the panel panel = self.signalpanel obj = panel.objview.get_current_object() # Compute dynamic parameters param = sigima.params.DynamicParam.create(full_scale=1.0) table = sigima.proc.signal.dynamic_parameters(obj, param) # Access specific values from the table enob = table["enob"][0] # Effective Number of Bits sinad = table["sinad"][0] # Signal-to-Noise and Distortion Ratio thd = table["thd"][0] # Total Harmonic Distortion # Convert to dictionary for easy access result_dict = table.as_dict() print(f"ENOB: {result_dict['enob']}") print(f"SINAD: {result_dict['sinad']} dB") # Store result in object metadata using adapter adapter = TableAdapter(table) adapter.add_to(obj) # Convert to pandas DataFrame for further processing df = table.to_dataframe() print(df) **Example 2: Detecting image peaks (GeometryResult)** This example shows how to detect peaks in an image and work with the resulting ``GeometryResult``: .. code-block:: python import sigima.params import sigima.proc.image from datalab.adapters_metadata import GeometryAdapter # Get the current image from the panel panel = self.imagepanel obj = panel.objview.get_current_object() # Configure peak detection parameters param = sigima.params.Peak2DDetectionParam.create( size=50, # Neighborhood size threshold=0.5, # Relative threshold create_rois=True # Create ROI for each peak ) # Compute peak detection geometry = sigima.proc.image.peak_detection(obj, param) # Access geometry data coords = geometry.coords # numpy array of shape (n_peaks, 2) n_peaks = len(coords) print(f"Detected {n_peaks} peaks") # Iterate over detected peaks for i, (x, y) in enumerate(coords): print(f"Peak {i+1}: x={x:.2f}, y={y:.2f}") # Check geometry kind from sigima.objects import KindShape assert geometry.kind == KindShape.POINT # Store geometry in object metadata using adapter adapter = GeometryAdapter(geometry) adapter.add_to(obj) **Example 3: Computing bandwidth (GeometryResult with segments)** This example shows how to compute signal bandwidth, which returns a segment geometry: .. code-block:: python import sigima.proc.signal from datalab.adapters_metadata import GeometryAdapter # Get the current signal from the panel panel = self.signalpanel obj = panel.objview.get_current_object() # Compute -3dB bandwidth geometry = sigima.proc.signal.bandwidth_3db(obj) # Access segment coordinates [x0, y0, x1, y1] x0, y0, x1, y1 = geometry.coords[0] print(f"Bandwidth segment: ({x0}, {y0}) to ({x1}, {y1})") # Calculate bandwidth length bandwidth = geometry.segments_lengths()[0] print(f"Bandwidth@-3dB: {bandwidth:.2f}") # Store in metadata adapter = GeometryAdapter(geometry) adapter.add_to(obj) Retrieving results from metadata ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To retrieve results stored in object metadata: .. code-block:: python from datalab.adapters_metadata import TableAdapter, GeometryAdapter obj = ... # Some signal or image object # Iterate over all table results for adapter in TableAdapter.iterate_from_obj(obj): result = adapter.result # TableResult instance title = adapter.title df = result.to_dataframe() # Iterate over all geometry results for adapter in GeometryAdapter.iterate_from_obj(obj): result = adapter.result # GeometryResult instance title = adapter.title coords = result.coords Processor method changes ------------------------ The processor interface has been unified in v1.0. Most specific ``compute_*`` methods now use the generic ``run_feature()`` method. Using run_feature() ^^^^^^^^^^^^^^^^^^^ The ``run_feature()`` method is a unified interface for running processing features: .. code-block:: python # v1.0 - Generic interface proc = panel.processor # Simple features (no parameters) proc.run_feature("normalize") proc.run_feature("fft") # Features with parameters param = sigima.params.GaussianParam.create(sigma=2.0) proc.run_feature("gaussian_filter", param) # Features with direct function reference import sigima.proc.image as sipi proc.run_feature(sipi.normalize) proc.run_feature(sipi.gaussian_filter, param) Method renaming ^^^^^^^^^^^^^^^ Several processor methods have been renamed for consistency: .. list-table:: :header-rows: 1 :widths: 50 50 * - v0.20 - v1.0 * - ``compute_11(func, ...)`` - ``compute_1_to_1(func, ...)`` * - ``compute_10(func, ...)`` - ``compute_1_to_0(func, ...)`` * - ``compute_n1(func, ...)`` - ``compute_n_to_1(func, ...)`` * - ``compute_1n(func, ...)`` - ``compute_1_to_n(func, ...)`` * - ``compute_21(func, ...)`` - ``compute_2_to_1(func, ...)`` Built-in features using run_feature ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Most built-in processing features that previously had dedicated ``compute_*`` methods now use ``run_feature()``: .. code-block:: python # v0.20 proc.compute_binning(param) proc.compute_moving_median(param) proc.compute_blob_opencv(param) proc.compute_gaussian_filter(param) # v1.0 proc.run_feature("binning", param) proc.run_feature("moving_median", param) proc.run_feature("blob_opencv", param) proc.run_feature("gaussian_filter", param) Some complex features still have dedicated methods: .. code-block:: python # These still use dedicated methods in v1.0 proc.compute_roi_extraction(roi) proc.compute_multigaussianfit() proc.compute_peak_detection(param) Testing your plugin ------------------- After migration, thoroughly test your plugin: 1. **Import checks**: Verify all imports work correctly 2. **Parameter creation**: Check that parameter classes are correctly instantiated 3. **Object creation**: Test signal/image object creation functions 4. **Processing operations**: Verify all processing features work as expected 5. **Result handling**: Check that results are correctly generated and stored 6. **GUI integration**: Test that menus and actions appear correctly Common issues and solutions --------------------------- Import errors ^^^^^^^^^^^^^ **Problem**: ``ModuleNotFoundError: No module named 'cdl'`` **Solution**: Update all imports from ``cdl.*`` to either ``datalab.*`` or ``sigima.*`` depending on the module. Attribute errors on processor ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ **Problem**: ``AttributeError: 'ImageProcessor' object has no attribute 'compute_binning'`` **Solution**: Replace ``compute_*`` method calls with ``run_feature()`` calls: .. code-block:: python # Wrong (v0.20 style) proc.compute_binning(param) # Correct (v1.0 style) proc.run_feature("binning", param) Result object incompatibility ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ **Problem**: ``AttributeError: 'TableResult' object has no attribute 'add_to'`` **Solution**: Use adapter classes for metadata operations: .. code-block:: python # Wrong (v0.20 style) result.add_to(obj) # Correct (v1.0 style) from datalab.adapters_metadata import TableAdapter TableAdapter(result).add_to(obj) Missing parameters ^^^^^^^^^^^^^^^^^^ **Problem**: ``ImportError: cannot import name 'SomeParam' from 'datalab'`` **Solution**: Import parameters from ``sigima.params`` instead of ``cdl.param``: .. code-block:: python # Wrong from cdl.param import GaussianParam # Correct from sigima.params import GaussianParam Additional resources -------------------- - `DataLab v1.0 documentation `_ - `Sigima documentation `_ - `Plugin examples `_ - :doc:`DataLab API reference ` - `Sigima API reference `_ Getting help ------------ If you encounter issues during migration: 1. Check the `GitHub issue tracker `_ 2. Consult the built-in plugin examples in the ``datalab/plugins`` directory DataLab-1.1.0/doc/features/advanced/model.rst000066400000000000000000000016511514014117600207660ustar00rootroot00000000000000.. _ref-to-model: Internal data model =================== .. meta:: :description: Internal model of DataLab, the open-source scientific data analysis and visualisation platform :keywords: DataLab, internal model, data model, signal, image, dataset, parameter, computation, scientific data analysis, visualisation, platform In its internal data model, DataLab stores data using two main classes: * :class:`sigima.objects.SignalObj`, which represents a signal object, and * :class:`sigima.objects.ImageObj`, which represents an image object. These classes are defined in the :mod:`sigima.objects` package. Also, DataLab uses many different datasets (based on guidata's ``DataSet`` class) to store the parameters of the computations. These datasets are defined in different modules but are exposed publicly in the :mod:`sigima.params` package. .. seealso:: The :ref:`api` section for more information on the public API. DataLab-1.1.0/doc/features/advanced/plugins.rst000066400000000000000000000124231514014117600213460ustar00rootroot00000000000000.. _about_plugins: Plugins ======= .. meta:: :description: Plugin system for DataLab, the open-source scientific data analysis and visualization platform :keywords: DataLab, plugin, processing, input/output, HDF5, file format, data analysis, visualization, scientific, open-source, platform DataLab supports a robust plugin architecture, allowing users to extend the application’s features without modifying its core. Plugins can introduce new processing tools, data import/export formats, or custom GUI elements — all seamlessly integrated into the platform. What is a plugin? ----------------- A plugin is a Python module that is automatically loaded by DataLab at startup. It can define new features or modify existing ones. To be recognized as a plugin, the file must: - Be a Python module whose name **starts with** ``datalab_`` (e.g. ``datalab_myplugin.py``), - Contain a class that **inherits from** :class:`datalab.plugins.PluginBase`, - Include a class attribute named ``PLUGIN_INFO``, which must be an instance of :class:`datalab.plugins.PluginInfo`. This `PLUGIN_INFO` object is used by DataLab to retrieve metadata such as the plugin name, type, and menu integration. .. note:: Only Python files whose names start with ``datalab_`` will be scanned for plugins. DataLab supports three categories of plugins, each with its own purpose and registration mechanism: - **Processing and visualization plugins** Add custom actions for signal or image processing. These may include new computation functions, data visualization tools, or interactive dialogs. Integrated into a dedicated submenu of the “Plugins” menu. - **Input/Output plugins** Define new file formats (read and/or write) handled transparently by DataLab's I/O framework. These plugins extend compatibility with custom or third-party data formats. - **HDF5 plugins** Special plugins that support HDF5 files with domain-specific tree structures. These allow DataLab to interpret signals or images organized in non-standard ways. Where to put a plugin? ---------------------- Plugins are automatically discovered at startup from multiple locations: - The user plugin directory: Typically `~/.DataLab/plugins` on Linux/macOS or `C:/Users/YourName/.DataLab/plugins` on Windows. - A custom plugin directory: Configurable in DataLab's preferences. - The standalone distribution directory: If using a frozen (standalone) build, the `plugins` folder located next to the executable is scanned. - The internal `datalab/plugins` folder (not recommended for user plugins): This location is reserved for built-in or bundled plugins and should not be modified manually. How to develop a plugin? ------------------------ The recommended approach to developing a plugin is to derive from an existing example and adapt it to your needs. You can explore the source code in the `datalab/plugins` folder or refer to community-contributed examples. .. note:: Most of DataLab's signal and image processing functionalities have been externalized into a dedicated library called **Sigima** (`https://sigima.readthedocs.io/en/latest/ `_). When developing DataLab plugins, you will typically import and use many Sigima functions and features to perform data processing, analysis, and visualization tasks. Sigima provides a comprehensive set of tools for scientific data manipulation that can be leveraged directly in your plugins. To develop in your usual Python environment (e.g., with an IDE like `Spyder `_), you can: 1. **Install DataLab in your Python environment**, using one of the following methods: - :ref:`install_conda` - :ref:`install_pip` - :ref:`install_wheel` - :ref:`install_source` 2. **Or add the `datalab` package manually to your Python path**: - Download the source from the `PyPI page `_, - Unzip the archive, - Add the `datalab` directory to your PYTHONPATH (e.g., using the *PYTHONPATH Manager* in Spyder). .. note:: Even if you’ve installed `datalab` in your environment, you cannot run the full DataLab application directly from an IDE. You must launch DataLab via the command line or using the installer-created shortcut to properly test your plugin. Example: processing plugin -------------------------- Here is a minimal example of a plugin that prints a message when activated: .. literalinclude:: ../../../datalab/plugins/datalab_testdata.py Example: input/output plugin ---------------------------- Here is a simple example of a plugin that adds a new file formats to DataLab. .. literalinclude:: ../../../datalab/plugins/datalab_imageformats.py Other examples -------------- Other examples of plugins can be found in the `plugins/examples` directory of the DataLab source code (explore `here on GitHub `_). Migrating from v0.20 to v1.0 ---------------------------- If you have existing plugins written for DataLab v0.20, please refer to the :ref:`migration guide ` for detailed instructions on updating your plugins to work with DataLab v1.0. Public API ---------- .. automodule:: datalab.plugins :members: PluginInfo, PluginBase, FormatInfo, ImageFormatBase, ClassicsImageFormat, SignalFormatBase DataLab-1.1.0/doc/features/advanced/proxy.rst000066400000000000000000000000671514014117600210470ustar00rootroot00000000000000.. automodule:: datalab.control.proxy :no-members: DataLab-1.1.0/doc/features/advanced/remote.rst000066400000000000000000000114641514014117600211640ustar00rootroot00000000000000.. _ref-to-remote-control: Remote controlling (XML-RPC) ============================ .. meta:: :description: How to remote control DataLab using XML-RPC :keywords: DataLab, remote control, XML-RPC, Python, IDE, Spyder, Jupyter, third-party application, signal, image, HDF5, calculation, processor .. note:: **For new projects, we recommend using the** :ref:`Web API ` **instead of XML-RPC.** The Web API provides better performance for large arrays, token-based authentication, and compatibility with browser environments (WASM/Pyodide). XML-RPC remains available for backward compatibility with existing integrations. DataLab may be controlled remotely using the `XML-RPC`_ protocol which is natively supported by Python (and many other languages). Remote controlling allows to access DataLab main features from a separate process. .. note:: If you are looking for a lighweight alternative solution to remote control DataLab (i.e. without having to install the whole DataLab package and its dependencies on your environment), you may use the `Sigima`_ package that provides a simple remote client for DataLab. To install it, just run: ``pip install sigima``. .. _Sigima: https://github.com/DataLab-Platform/Sigima From an IDE ^^^^^^^^^^^ DataLab may be controlled remotely from an IDE (e.g. `Spyder`_ or any other IDE, or even a Jupyter Notebook) that runs a Python script. It allows to connect to a running DataLab instance, adds a signal and an image, and then runs calculations. This feature is exposed by the `RemoteProxy` class that is provided in module ``datalab.control.proxy``. From a third-party application ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ DataLab may also be controlled remotely from a third-party application, for the same purpose. If the third-party application is written in Python 3, it may directly use the `RemoteProxy` class as mentioned above. From another language, it is also achievable, but it requires to implement a XML-RPC client in this language using the same methods of proxy server as in the `RemoteProxy` class. Data (signals and images) may also be exchanged between DataLab and the remote client application, in both directions. The remote client application may be written in any language that supports XML-RPC. For example, it is possible to write a remote client application in Python, Java, C++, C#, etc. The remote client application may be a graphical application or a command line application. The remote client application may be run on the same computer as DataLab or on a different computer. In the latter case, the remote client application must know the IP address of the computer running DataLab. The remote client application may be run before or after DataLab. In the latter case, the remote client application must try to connect to DataLab until it succeeds. Supported features ^^^^^^^^^^^^^^^^^^ Supported features are the following: - Switch to signal or image panel - Remove all signals and images - Save current session to a HDF5 file - Open HDF5 files into current session - Browse HDF5 file - Open a signal or an image from file - Add a signal - Add an image - Get object list - Run calculation with parameters .. note:: The signal and image objects are described on this section: :ref:`ref-to-model`. Some examples are provided to help implementing such a communication between your application and DataLab: - See module: ``datalab.tests.features.control.remoteclient_app_test`` - See module: ``datalab.tests.features.control.remoteclient_unit`` .. figure:: /images/shots/remote_control_test.png Screenshot of remote client application test (``datalab.tests.features.control.remoteclient_app_test``) Examples ^^^^^^^^ When using Python 3, you may directly use the `RemoteProxy` class as in examples cited above or below. Here is an example in Python 3 of a script that connects to a running DataLab instance, adds a signal and an image, and then runs calculations (the cell structure of the script make it convenient to be used in `Spyder`_ IDE): .. literalinclude:: ../../remote_example.py Here is a Python 2.7 reimplementation of this class: .. literalinclude:: ../../remotecontrol_py27.py Connection dialog ^^^^^^^^^^^^^^^^^ The DataLab package also provides a connection dialog that may be used to connect to a running DataLab instance. It is exposed by the :py:class:`datalab.widgets.connection.ConnectionDialog` class. .. figure:: ../../images/shots/connect_dialog.png Screenshot of connection dialog (``datalab.widgets.connection.ConnectionDialog``) Example of use: .. literalinclude:: ../../../datalab/tests/features/control/connect_dialog.py Public API ^^^^^^^^^^ .. autoclass:: datalab.control.remote.RemoteClient :inherited-members: .. _XML-RPC: https://docs.python.org/3/library/xmlrpc.html .. _Spyder: https://www.spyder-ide.org/DataLab-1.1.0/doc/features/advanced/v020_to_v100.csv000066400000000000000000000070761514014117600216170ustar00rootroot00000000000000DataLab v0.20;DataLab v1.0 **Module/package renames**; ``cdl``;``datalab`` ``cdl.obj``;``sigima.objects`` ``cdl.param``;``sigima.params`` ``cdl.computation.image``;``sigima.proc.image`` ``cdl.computation.signal``;``sigima.proc.signal`` ``cdl.plugins``;``datalab.plugins`` **Object creation and manipulation**; ``cdl.obj.create_image()``;``sigima.objects.create_image()`` ``cdl.obj.create_signal()``;``sigima.objects.create_signal()`` ``cdl.obj.ImageObj``;``sigima.objects.ImageObj`` ``cdl.obj.SignalObj``;``sigima.objects.SignalObj`` ``cdl.obj.create_image_roi()``;``sigima.objects.create_image_roi()`` ``cdl.obj.create_signal_roi()``;``sigima.objects.create_signal_roi()`` ``cdl.obj.ImageROI``;``sigima.objects.ImageROI`` ``cdl.obj.SignalROI``;``sigima.objects.SignalROI`` **Parameter classes**; ``cdl.param.BinningParam``;``sigima.params.BinningParam`` ``cdl.param.MovingMedianParam``;``sigima.params.MovingMedianParam`` ``cdl.param.BlobOpenCVParam``;``sigima.params.BlobOpenCVParam`` ``cdl.param.GaussianParam``;``sigima.params.GaussianParam`` ``cdl.param.*Param``;``sigima.params.*Param`` **Computation function wrappers**; ``cdl.computation.image.Wrap11Func``;``sigima.proc.image.Wrap1to1Func`` ``cdl.computation.signal.Wrap11Func``;``sigima.proc.signal.Wrap1to1Func`` ``cdl.computation.image.dst_11()``;``sigima.proc.image.dst_1to1()`` ``cdl.computation.signal.dst_11()``;``sigima.proc.signal.dst_1to1()`` **Processor methods** (`p` is a signal or image processor instance); ``p.compute_11(...)``;``p.compute_1_to_1(...)`` ``p.compute_10(...)``;``p.compute_1_to_0(...)`` ``p.compute_n1(...)``;``p.compute_n_to_1(...)`` ``p.compute_binning(param)``;``p.run_feature("binning", param)`` ``p.compute_moving_median(param)``;``p.run_feature("moving_median", param)`` ``p.compute_blob_opencv(param)``;``p.run_feature("blob_opencv", param)`` ``p.compute_*(param)``;``p.run_feature("feature_name", param)`` **Result objects**; ``cdl.obj.ResultProperties``;``sigima.objects.TableResult`` ``cdl.obj.ResultShape``;``sigima.objects.GeometryResult`` ``result.add_to(obj)``;``TableAdapter(result).add_to(obj)`` ``ResultShape.from_metadata_entry()``;``GeometryAdapter.from_metadata_entry()`` ``result.to_html(obj)``;``ResultHtmlGenerator.generate_html(result, obj)`` **Test data functions**; ``cdl.tests.data.*``;``sigima.tests.data.*`` ``cdl.tests.data.create_paracetamol_signal()``;``sigima.tests.data.create_paracetamol_signal()`` ``cdl.tests.data.add_gaussian_noise_to_signal()``;``sigima.tests.data.add_gaussian_noise_to_signal()`` ``cdl.tests.data.create_noisygauss_image()``;``sigima.tests.data.create_noisy_gaussian_image()`` ``cdl.tests.data.create_multigauss_image()``;``sigima.tests.data.create_multigaussian_image()`` **New image parameters**; ``edit_new_image_parameters(hide_image_dtype=True)``;``edit_new_image_parameters(hide_dtype=True)`` ``edit_new_image_parameters(hide_image_type=True)``;``edit_new_image_parameters(hide_type=True)`` ``edit_new_image_parameters(hide_image_height=True)``;``edit_new_image_parameters(hide_height=True)`` **I/O functions**; ``cdl.io.image.read_image()``;``sigima.io.image.read_image()`` ``cdl.io.image.write_image()``;``sigima.io.image.write_image()`` ``cdl.io.signal.read_signal()``;``sigima.io.signal.read_signal()`` ``cdl.io.signal.write_signal()``;``sigima.io.signal.write_signal()`` **Enumerations**; ``cdl.obj.ImageDatatypes``;``sigima.objects.ImageDatatypes`` ``cdl.obj.ImageTypes``;``sigima.objects.ImageTypes`` **DataLab-specific features**; ``cdl.config._``;``datalab.config._`` ``cdl.plugins.PluginBase``;``datalab.plugins.PluginBase`` ``cdl.plugins.PluginInfo``;``datalab.plugins.PluginInfo`` DataLab-1.1.0/doc/features/advanced/webapi.rst000066400000000000000000000237031514014117600211370ustar00rootroot00000000000000.. _ref-to-webapi: Web API ======= .. meta:: :description: How to use the DataLab Web API for HTTP/JSON integration :keywords: DataLab, Web API, REST, HTTP, JSON, Jupyter, notebook, integration The DataLab Web API provides a modern **HTTP/JSON interface** for external access to the DataLab workspace. It is the recommended integration path for new projects, especially for Jupyter notebooks and web-based tools. .. note:: The Web API is available starting from DataLab 1.1. The required dependencies (FastAPI, Uvicorn, Pydantic) are included in the standard DataLab installation. Overview -------- The Web API enables: - **Jupyter notebook integration** via `DataLab-Kernel `_ - **WASM/Pyodide compatibility** (no XML-RPC dependency) - **External tool integration** (any HTTP client) Unlike the XML-RPC interface, the Web API: - Uses **JSON** for metadata and **NPZ** for binary data (efficient large array transfer) - Provides **bearer token authentication** for security - Follows **REST conventions** with OpenAPI documentation - Works in **browser environments** (WASM/Pyodide) .. list-table:: Comparison with XML-RPC :header-rows: 1 * - Feature - XML-RPC - Web API * - Protocol - XML - JSON + Binary (NPZ) * - WASM Support - ❌ - ✅ * - Large Arrays - Slow (base64) - Fast (NPZ) * - Authentication - None - Bearer Token * - Standards - XML-RPC - REST + OpenAPI Quick Start ----------- Enabling the Web API ^^^^^^^^^^^^^^^^^^^^ There are several ways to enable the Web API server: 1. **Via UI**: File → Web API → Start Web API Server 2. **Via environment variable**: Set ``DATALAB_WEBAPI_ENABLED=1`` before starting DataLab When started, DataLab displays the server URL and authentication token in a dialog. The status bar also shows the Web API port when the server is running. The default port is **18080** (or the next available port if busy). .. TODO: Add screenshot when available .. figure:: /images/shots/webapi_started.png Web API connection dialog showing URL and token Auto-Discovery ^^^^^^^^^^^^^^ DataLab-Kernel can automatically discover and connect to a running DataLab instance without any manual configuration. When the Web API starts, DataLab writes connection information to a file that DataLab-Kernel reads automatically. **Just load the extension** — no environment variables or explicit connection needed: .. code-block:: python # In your notebook (JupyterLab, VS Code, etc.) %load_ext datalab_kernel # DataLab-Kernel automatically finds and connects to DataLab workspace.list() # Already connected! The auto-discovery mechanism tries the following methods in order: 1. **Environment variables** (``DATALAB_WORKSPACE_URL``, ``DATALAB_WORKSPACE_TOKEN``) 2. **Connection file** written by DataLab (native Python only) 3. **URL query parameters** (JupyterLite: ``?datalab_url=...&datalab_token=...``) 4. **Well-known port probing** (``http://127.0.0.1:18080``) If discovery fails, DataLab-Kernel starts in standalone mode and you can connect later using ``workspace.connect()``. Manual Connection (Legacy) ^^^^^^^^^^^^^^^^^^^^^^^^^^ If auto-discovery doesn't work (e.g., running on a different machine), you can set environment variables before starting your notebook kernel: .. code-block:: bash export DATALAB_WORKSPACE_URL=http://127.0.0.1:18080 export DATALAB_WORKSPACE_TOKEN= Then in your notebook using DataLab-Kernel: .. code-block:: python from datalab_kernel import workspace # List objects in DataLab workspace.list() # Retrieve an object signal = workspace.get("my_signal") # Add a processed result back to DataLab workspace.add("processed", result) API Reference ------------- Base URL: ``http://127.0.0.1:/api/v1`` Status Endpoint (No Auth Required) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. list-table:: :header-rows: 1 * - Method - Endpoint - Description * - GET - ``/status`` - Server status and version information Object Operations (Auth Required) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ All endpoints below require the ``Authorization: Bearer `` header. .. list-table:: :header-rows: 1 * - Method - Endpoint - Description * - GET - ``/objects`` - List all objects with metadata (JSON) * - GET - ``/objects/{name}`` - Get object metadata (JSON) * - DELETE - ``/objects/{name}`` - Delete an object * - PATCH - ``/objects/{name}/metadata`` - Update object metadata Binary Data Transfer (Auth Required) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. list-table:: :header-rows: 1 * - Method - Endpoint - Description * - GET - ``/objects/{name}/data`` - Download object as NPZ archive * - PUT - ``/objects/{name}/data`` - Upload object from NPZ archive Data Format ----------- Objects are transferred using NumPy's NPZ format for efficiency. SignalObj NPZ Structure ^^^^^^^^^^^^^^^^^^^^^^^ .. code-block:: text my_signal.npz ├── x.npy # X coordinates (float64) ├── y.npy # Y data (float64) ├── dx.npy # X uncertainties (optional) ├── dy.npy # Y uncertainties (optional) └── metadata.json # Labels, units, title ImageObj NPZ Structure ^^^^^^^^^^^^^^^^^^^^^^ .. code-block:: text my_image.npz ├── data.npy # 2D image array (preserves dtype) └── metadata.json # Labels, units, coordinates (x0, y0, dx, dy) Authentication -------------- All API endpoints except ``/status`` require a bearer token: .. code-block:: text Authorization: Bearer The token is generated when the server starts and displayed in the connection dialog. Environment Variables ^^^^^^^^^^^^^^^^^^^^^ .. list-table:: :header-rows: 1 * - Variable - Description - Default * - ``DATALAB_WEBAPI_HOST`` - Bind address - ``127.0.0.1`` * - ``DATALAB_WEBAPI_PORT`` - Server port - ``18080`` (or next available) * - ``DATALAB_WEBAPI_TOKEN`` - Auth token - Generated Security Model -------------- The Web API implements the following security measures: 1. **Localhost binding**: By default, the server only accepts connections from the local machine (127.0.0.1). 2. **Token authentication**: Every request (except status) must include a valid bearer token. 3. **Explicit opt-in**: Remote binding (0.0.0.0) requires explicit configuration. Localhost Token Bypass ^^^^^^^^^^^^^^^^^^^^^^ For simplified local development, you can disable token verification for localhost connections in **Edit → Settings → Web API localhost bypass**. When enabled, clients connecting from ``127.0.0.1`` do not need to provide a token. This makes auto-discovery work seamlessly even when the connection file cannot be read (e.g., in JupyterLite or sandboxed environments). .. warning:: Only enable localhost bypass when you control all applications running on your machine. Malicious local software could access your data without a token. .. warning:: Exposing the API to the network allows anyone with the token to access your data. Use with caution on trusted networks only. Architecture ------------ .. code-block:: text ┌─────────────────────────────────────────┐ │ DataLab Main Window │ │ (Qt) │ ├─────────────────────────────────────────┤ │ WebApiController │ │ (Server Lifecycle) │ ├─────────────────────────────────────────┤ │ WorkspaceAdapter │ │ (Thread-safe access) │ ├─────────────────────────────────────────┤ │ FastAPI + Uvicorn │ │ (Separate thread) │ └─────────────────────────────────────────┘ ↑ │ HTTP/JSON ↓ ┌─────────────────────────────────────────┐ │ DataLab-Kernel / Client │ └─────────────────────────────────────────┘ The Web API runs in a separate thread from the Qt GUI. The ``WorkspaceAdapter`` ensures thread-safe access to DataLab objects by marshalling calls to the main thread using Qt's signal/slot mechanism. Installation ------------ The Web API dependencies are included in the standard DataLab installation: - FastAPI (web framework) - Uvicorn (ASGI server) - Pydantic (data validation) No additional installation steps are required. Programmatic Control -------------------- The Web API can also be started and controlled programmatically via the XML-RPC interface, which is useful for automated workflows: .. code-block:: python from datalab.control.remote import RemoteClient proxy = RemoteClient() proxy.connect() # Start the Web API server info = proxy.start_webapi_server() print(f"URL: {info['url']}") print(f"Token: {info['token']}") # Check status status = proxy.get_webapi_status() print(f"Running: {status['running']}") # Stop the server proxy.stop_webapi_server() See Also -------- - :ref:`ref-to-remote-control` for the XML-RPC interface (legacy) - `DataLab-Kernel documentation `_ for Jupyter integration DataLab-1.1.0/doc/features/common/000077500000000000000000000000001514014117600166545ustar00rootroot00000000000000DataLab-1.1.0/doc/features/common/h5browser.rst000066400000000000000000000023451514014117600213320ustar00rootroot00000000000000.. _h5browser: HDF5 Browser ============ .. meta:: :description: HDF5 Browser in DataLab, the open-source scientific data analysis and visualization platform :keywords: DataLab, workspace, HDF5, browser, scientific, data, analysis, visualization, platform The "HDF5 Browser" is a modal dialog box allowing to import almost any 1D and 2D data into DataLab workspace (and eventually metadata). .. image:: /images/h5browser.png Compatible curve or image data are displayed in a hierarchical view on the left panel, as well as other scalar data (scalar values are just shown for context purpose and may not be imported into DataLab workspace). The right panel displays the selected curve or image data. It also shows information on "Group" (path, description, etc.) and "Attributes" (names and values). The HDF5 browser is fairly simple to use: * On the left panel, select the curve or image data you want to import * Selected data is plotted on the right panel * Click on "Check all" if you want to import all compatible data * Then validate by clicking on "OK" .. note:: The HDF5 browser may be used to explore multiple HDF5 files at once, thus allowing to import data from different files into the same DataLab workspace. DataLab-1.1.0/doc/features/common/overview.rst000066400000000000000000000147661514014117600212720ustar00rootroot00000000000000.. _overview: Overview ======== .. meta:: :description: Overview and basic concepts of DataLab, the open-source scientific data analysis and visualization platform :keywords: DataLab, workspace, scientific, data, analysis, visualization, platform Basic concepts -------------- Working with DataLab is very easy. The user interface is intuitive and self-explanatory. The main window is divided into two main areas: - The left area shows the list of data sets which are currently loaded in DataLab, distibuted over two tabs: **Signals** and **Images**. The user can switch between the two tabs by clicking on the corresponding tab: this switches the main window to the corresponding panel, as well as the menu and toolbar contents. Below the list of data sets, a **Properties** view shows information about the currently selected data set. - The right area shows the visualization of the currently selected data set. The visualization is updated automatically when the user selects a new data set in the list of data sets. .. figure:: /images/shots/s_app_at_startup.png DataLab main window, at startup. Internal data model and workspace --------------------------------- DataLab has its own internal data model, in which data sets are organized around a tree structure. Each panel in the main window corresponds to a branch of the tree. Each data set shown in the panels corresponds to a leaf of the tree. Inside the data set, the data is organized in an object-oriented way, with a set of attributes and methods. The data model is described in more details in the API section (see :mod:`sigima.objects`). For each data set (1D signal or 2D image), not only the data itself is stored, but also a set of metadata, which describes the data or the way it has to be displayed. The metadata is stored in a dictionary, which is accessible through the ``metadata`` attribute of the data set (and may also be browsed in the **Properties** view, with the **Metadata** button). The DataLab **Workspace** is defined as the collection of all data sets which are currently loaded in DataLab, in both the **Signals** and **Images** panels. Loading and saving the workspace -------------------------------- The following actions are available to manage the workspace from the **File** menu: - **Open HDF5 file**: load a workspace from an HDF5 file. - **Save to HDF5 file**: save the current workspace to an HDF5 file. - **Browse HDF5 file**: open the :ref:`h5browser` to explore the content of an HDF5 file and import data sets into the workspace. .. note:: Data sets may also be saved or loaded individually, using data formats such as `.txt` or `.npy` for 1D signals (see :ref:`open_signal` for the list of supported formats), , or `.tiff` or `.dcm` for 2D images (see :ref:`open_image` for the list of supported formats). Interactive object creation and processing ------------------------------------------ DataLab provides an interactive workflow for creating objects and adjusting processing parameters, allowing you to fine-tune results without creating multiple objects. Interactive object creation ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ When creating a new signal or image using the creation functions (e.g., Gaussian signal, 2D peak image, etc.), DataLab stores the creation parameters in the object's metadata. This enables interactive parameter adjustment after creation: 1. Create a signal or image using **Operations** > **Create** menu 2. Select the created object in the list 3. A **Creation** tab appears in the Properties panel (bottom-left) 4. Modify any creation parameter (amplitude, frequency, size, etc.) 5. Click **Apply** to regenerate the object with new parameters The object is updated in-place, preserving any subsequent processing or analysis results. This is particularly useful for: - Exploring different parameter values without cluttering the workspace - Fine-tuning object characteristics while observing the results - Educational purposes to demonstrate parameter effects .. note:: Interactive creation is only available for objects created with parameter classes (not for imported data from files). Interactive 1-to-1 processing ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ When applying a 1-to-1 processing operation that has configurable parameters (e.g., Gaussian filter, threshold, morphological operations), DataLab stores the processing metadata, enabling parameter adjustment and re-processing: 1. Apply a processing operation with parameters (e.g., **Processing** > **Filtering** > **Gaussian filter**) 2. The result object contains processing metadata (parameters, source object, function name) 3. Select the processed object in the list 4. A **Processing** tab appears in the Properties panel 5. Modify processing parameters (e.g., filter sigma value) 6. Click **Apply** to re-process with updated parameters The processed object is updated in-place with the new results. This workflow is ideal for: - Iteratively tuning filter parameters while observing results in real-time - Adjusting threshold values without creating multiple intermediate objects - Experimenting with different morphological structure element sizes - Educational demonstrations of parameter effects on processing results .. note:: **Processing metadata requirements:** - Only works for 1-to-1 processing functions with parameter classes - Processing functions without parameters (e.g., absolute value, inverse) work as before - Source object must still exist for re-processing (error shown if deleted) **Not supported for:** - 2-to-1 processing (operations combining two objects) - n-to-1 processing (operations aggregating multiple objects) - These patterns don't benefit significantly from interactive parameter adjustment Example workflow ^^^^^^^^^^^^^^^^ Here's a typical workflow using interactive processing: 1. **Create a test signal**: Operations > Create > Gaussian signal - Initial parameters: amplitude=1.0, mu=50, sigma=10 2. **Adjust creation parameters**: In the Creation tab, change sigma to 20, click Apply - Signal is regenerated with new width 3. **Apply Gaussian filter**: Processing > Filtering > Gaussian filter - Initial sigma=2.0 4. **Fine-tune filtering**: In the Processing tab, try sigma=1.0, then sigma=5.0 - Each Apply updates the filtered result - Compare different smoothing levels without creating multiple objects This interactive approach provides immediate visual feedback and keeps your workspace organized by avoiding proliferation of test objects with slightly different parameters. DataLab-1.1.0/doc/features/common/settings.rst000066400000000000000000000314731514014117600212560ustar00rootroot00000000000000.. _settings: Settings ======== .. meta:: :description: Settings of DataLab, the open-source scientific data analysis and visualization platform :keywords: DataLab, settings, scientific, data, analysis, visualization, platform DataLab provides a comprehensive settings dialog to customize the application behavior, visualization defaults, and I/O operations. The settings are organized into five tabs: General, Processing, Visualization, I/O, and Console. General ------- The General settings tab contains main window and general feature settings: .. figure:: /images/shots/settings_main.png General settings tab **Color mode** Choose the color mode for the application interface (e.g., light, dark, or auto). **Process isolation** When enabled, each computation runs in a separate process, preventing the application from freezing during long computations. This is the recommended setting for better responsiveness. **RPC server** Enable the RPC (Remote Procedure Call) server to communicate with external applications, such as your own scripts running in Spyder, Jupyter, or other software. This allows programmatic control of DataLab. **Web API localhost bypass** When enabled (default), connections from localhost (127.0.0.1) to the Web API do not require authentication. This simplifies notebook integration when DataLab-Kernel runs on the same machine. Disable for stricter security if needed. .. note:: When this option is enabled, any application running on your local machine can access the DataLab Web API without a token. Disable this option if you need stricter security. **Memory threshold** Set a threshold (in MB) below which a warning is displayed before loading new data. This helps prevent out-of-memory errors when working with large datasets. Set to 0 to disable the warning. **Third-party plugins** Enable or disable third-party plugins at startup. **Plugins path** Specify the directory path where DataLab should look for third-party plugins. DataLab will also discover plugins in your PYTHONPATH. Processing ---------- The Processing settings tab controls computation behavior and default parameters: .. figure:: /images/shots/settings_proc.png Processing settings tab **Operation mode** Choose the operation mode for computations taking N inputs: - **Single**: single operand mode - **Pairwise**: pairwise operation mode .. note:: These operation modes determine how DataLab handles computations involving multiple objects. They apply to two types of operations: - **N→1 operations**: Combine N (≥2) objects into 1 output (e.g. sum, average) - **N+1→N operations**: Apply an operation between N (≥1) objects and 1 operand to produce N outputs (e.g. difference, division) **Single operand mode** (default): Operations are applied independently within each group. - For **N→1 operations**: All objects in each group are combined into one result per group. Example with groups G1={A, B} and G2={C, D}, sum operation: - Result: Σ(A,B) and Σ(C,D) (one per group) - For **N+1→N operations**: Each selected object is combined with a single reference operand. Example with groups G1={A, B} and G2={C, D}, difference with reference R: - In G1: A-R, B-R - In G2: C-R, D-R **Pairwise operation mode**: Objects from different groups are combined at matching positions (all groups must have the same number of objects). - For **N→1 operations**: Objects at the same position in each group are combined. Example with groups G1={A, B} and G2={C, D}, sum operation: - Result: A+C and B+D (pairing by position) - For **N+1→N operations**: Objects at matching positions are combined pairwise. Example with groups G1={A, B}, G2={C, D}, difference with group G3={E, F}: - New group 1: A-E, B-F - New group 2: C-E, D-F **Use signal bounds for new signals** When enabled, the xmin and xmax values for new signals are initialized from the current signal's bounds. When disabled, default values are used. **Use image dimensions for new images** When enabled, the width and height values for new images are initialized from the current image's dimensions. When disabled, default values are used. **FFT shift** Enable FFT shift to center the zero-frequency component in the frequency spectrum for easier visualization and analysis. **Extract multiple ROIs in a single object** When enabled, multiple ROIs (Regions of Interest) are extracted into a single object. When disabled, each ROI is extracted into a separate object. **Ignore warnings** Suppress warning messages during computations. **X-array compatibility behavior** Choose the behavior when X arrays are incompatible in multi-signal computations: - **Ask**: display a confirmation dialog (default) - **Interpolate**: automatically interpolate signals Result management ^^^^^^^^^^^^^^^^^ **Keep results in metadata after computation** When enabled, results from previous analyses are kept in the object's metadata after computation. When disabled, results are removed. This option is disabled by default to avoid confusion from outdated results. **Show results dialog automatically after processing** When enabled, the results dialog is shown automatically after each processing operation producing results. When disabled, the results dialog is not shown automatically but results can still be viewed using the dedicated button or menu option. Visualization ------------- The Visualization settings tab controls how data is displayed. This tab is organized into four sub-tabs: Common, Signals, Images, and Results. Common settings ^^^^^^^^^^^^^^^ The Common sub-tab contains settings that apply to all visualizations: .. figure:: /images/shots/settings_view_common.png Visualization settings - Common sub-tab **Plot toolbar position** Choose where to position the plot toolbar (top, bottom, left, or right of the plot). **Ignore title insertion message** Suppress the information message when inserting an object title as an annotation label. Signals ^^^^^^^ The Signals sub-tab contains settings specific to signal visualizations: .. figure:: /images/shots/settings_view_signals.png Visualization settings - Signals sub-tab **Default line width** Default line width for curves representing signals. This setting affects all signal visualizations unless overridden individually. Note: for signals exceeding the line width performance threshold (see below), the line width is automatically clamped to 1.0 for optimal rendering performance. **Line width performance threshold** For signals with more than this number of points (default: 1,000), line width is automatically limited to 1.0 for performance reasons. This prevents the ~10x rendering slowdown caused by Qt's raster engine when drawing thick lines (width > 1.0) on large datasets. For smaller signals, the configured default line width applies normally. This optimization is transparent and requires no user intervention. **Use auto downsampling** Enable automatic downsampling for large signals to improve performance and visualization clarity. **Downsampling max points** Maximum number of points to display when downsampling is enabled (default: 10,000). **Autoscale margin** Percentage of margin to add around data when auto-scaling signal plots. A value of 0.2% adds a small margin for better visualization. Set to 0% for no margin (exact data bounds). **DateTime format (s/min/h)** Format string for datetime X-axis labels when using standard time units (s, min, h). Uses Python's strftime format codes (e.g., %H:%M:%S for hours:minutes:seconds). **DateTime format (ms/μs/ns)** Format string for datetime X-axis labels when using sub-second time units (ms, μs, ns). Uses Python's strftime format codes (e.g., %H:%M:%S.%f for hours:minutes:seconds.microseconds). **Results: edit shape styles** Click this button to configure the visual style for annotation shapes (rectangles, circles, segments, etc.) displayed on signal plots. This includes: - Line style, color, and width - Fill pattern, color, and transparency - Symbol shape, size, and colors These settings apply to all result shapes drawn on signal plots (e.g., peak markers, FWHM indicators, feature detection results). **Results: edit marker styles** Click this button to configure the visual style for cursor markers on signal plots. This includes: - Line style, color, and width - Symbol appearance - Text label formatting and positioning - Background transparency These settings apply to cursor-type markers used in signal analysis results. Images ^^^^^^ The Images sub-tab contains settings specific to image visualizations: .. figure:: /images/shots/settings_view_images.png Visualization settings - Images sub-tab **Lock aspect ratio to 1:1** When enabled, the aspect ratio of images is locked to 1:1. When disabled, the aspect ratio is determined by the physical pixel size (default and recommended setting). **Eliminate outliers** Percentage of the highest and lowest values to eliminate from the image histogram. Recommended values are below 1%. **Autoscale margin** Percentage of margin to add around data when auto-scaling image plots. A value of 0.2% adds a small margin for better visualization. Set to 0% for no margin (exact data bounds). **Default image visualization settings** Click this button to configure default visualization settings for images (colormap, interpolation, contrast, etc.). **Results: edit shape styles** Click this button to configure the visual style for annotation shapes displayed on image plots. Parameters are similar to signal shapes but optimized for image visualization (e.g., different colors for better visibility on images). **Results: edit marker styles** Click this button to configure the visual style for cursor markers on image plots. Results ^^^^^^^ The Results sub-tab contains settings for displaying analysis results on plots: .. figure:: /images/shots/settings_view_results.png Visualization settings - Results sub-tab These settings control how analysis results are displayed on plots to prevent performance issues with large datasets: **Maximum shapes to draw** Maximum number of geometry shapes to draw on the plot (default: 1,000). When the number of shapes exceeds this limit, only the first N shapes are drawn and a warning label is displayed. **Maximum cells in label** Maximum number of table cells (rows × columns) to display in merged result labels on plots (default: 100). When the number of cells exceeds this limit, the table is truncated. **Maximum columns in label** Maximum number of columns to display in merged result labels (default: 15). When the number of columns exceeds this limit, only the first N columns are displayed. **Show the merged result label by default** When enabled, the merged result label is shown on the plot by default for new objects. This setting can be toggled per-object using the checkbox in the Properties panel. .. note:: These settings affect only the visualization of results on plots. They do not affect the actual computation or storage of results in metadata. I/O --- The I/O settings tab controls input/output operations: .. figure:: /images/shots/settings_io.png I/O settings tab **Clear workspace before loading HDF5 file** When enabled, the workspace is cleared before loading an HDF5 file. **Ask before clearing workspace** When enabled, a confirmation dialog is displayed before clearing the workspace. **HDF5 full path in title** When enabled, the full path of the HDF5 dataset is used as the title for the signal/image object. When disabled, only the dataset name is used. **HDF5 file name in title** When enabled, the HDF5 file name is appended as a suffix to the title of the signal/image object. Console ------- The Console settings tab configures the internal console for debugging and advanced users: .. figure:: /images/shots/settings_console.png Console settings tab **Console enabled** Enable the internal Python console for debugging and advanced scripting. **Show console on error** When enabled, the console is automatically shown when an error occurs in the application. This is useful for debugging as it allows you to see the error traceback. **External editor path** Path to an external text editor to use for editing Python code from the console. **External editor arguments** Command-line arguments to pass to the external editor. DataLab-1.1.0/doc/features/image/000077500000000000000000000000001514014117600164465ustar00rootroot00000000000000DataLab-1.1.0/doc/features/image/2d_peak_detection.rst000066400000000000000000000052431514014117600225470ustar00rootroot00000000000000.. _ref-to-2d-peak-detection: 2D Peak Detection ================= DataLab provides a "2D Peak Detection" feature which is based on a minimum-maximum filter algorithm. .. figure:: /images/2d_peak_detection/peak2d_app_param.png 2D peak detection parameters. How to use the feature: - Create or open an image in DataLab workspace - Select "2d peak detection" in "Analysis" menu - Enter parameters "Neighborhoods size" and "Relative threhold" - Optionally, enable "Create regions of interest" to automatically create ROIs around each detected peak: * Choose ROI geometry: "Rectangle" or "Circle" * ROI size is automatically calculated based on the minimum distance between detected peaks (to avoid overlap) * This feature requires at least 2 detected peaks * Created ROIs can be useful for subsequent processing on each peak area (e.g., contour detection, measurements, etc.) .. figure:: /images/2d_peak_detection/peak2d_app_results.png 2d peak detection results (see test "peak2d_app.py") Results are shown in a table: - Each row is associated to a detected peak - First column shows the ROI index (0 if no ROI is defined on input image) - Second and third columns show peak coordinates .. figure:: /images/2d_peak_detection/peak2d_app_zoom.png Example of 2D peak detection. The 2d peak detection algorithm works in the following way: - First, the minimum and maximum filtered images are computed using a sliding window algorithm with a user-defined size (implementation based on `scipy.ndimage.minimum_filter `_ and `scipy.ndimage.maximum_filter `_) - Then, the difference between the maximum and minimum filtered images is clipped at a user-defined threshold - Resulting image features are labeled using `scipy.ndimage.label `_ - Peak coordinates are then obtained from labels center - Duplicates are eventually removed The 2d peak detection parameters are the following: - "Neighborhoods size": size of the sliding window (see above) - "Relative threshold": detection threshold - "Create regions of interest": if enabled, automatically creates ROIs around each detected peak (requires at least 2 peaks) - "ROI geometry": shape of the ROIs ("Rectangle" or "Circle") Feature is based on ``get_2d_peaks_coords`` function from ``sigima.tools`` module: .. literalinclude:: ../../../../Sigima/sigima/tools/image/detection.py :pyobject: get_2d_peaks_coords DataLab-1.1.0/doc/features/image/contour_detection.rst000066400000000000000000000037741514014117600227420ustar00rootroot00000000000000.. _ref-to-contour-detection: Contour Detection ================= DataLab provides a "Contour Detection" feature which is based on `the marching cubes algorithm `_. .. figure:: /images/contour_detection/contour_app_param.png Contour detection parameters. How to use the feature: - Create or open an image in DataLab workspace - Eventually create a ROI around the target area - Select "Contour detection" in "Analysis" menu - Enter parameter "Shape" ("Ellipse", "Circle" or "Polygon") - Optionally, enable "Create regions of interest" to automatically create ROIs around each detected contour: * Choose ROI geometry: "Rectangle" or "Circle" * ROI size is automatically calculated based on the minimum distance between detected contours (to avoid overlap) * This feature requires at least 2 detected contours * Created ROIs can be useful for subsequent processing on each contour area .. figure:: /images/contour_detection/contour_app_results.png Contour detection results (see test "contour_app.py") Results are shown in a table: - Each row is associated to a contour - First column shows the ROI index (0 if no ROI is defined on input image) - Other columns show contour coordinates: 4 columns for circles (coordinates of diameter), 8 columns for ellipses (coordinates of diameters) .. figure:: /images/contour_detection/contour_app.png Example of contour detection. The contour detection algorithm works in the following way: - First, iso-valued contours are computed (implementation based on `skimage.measure.find_contours.find_contours `_) - Then, each contour is fitted to the closest ellipse (or circle) Feature is based on ``get_contour_shapes`` function from ``sigima.tools`` module: .. literalinclude:: ../../../../Sigima/sigima/tools/image/detection.py :pyobject: get_contour_shapes DataLab-1.1.0/doc/features/image/menu_analysis.rst000066400000000000000000000175231514014117600220570ustar00rootroot00000000000000.. _ima-menu-analysis: Analysis features on Images ============================ This section describes the image analysis features available in DataLab. .. seealso:: :ref:`ima-menu-operations` for more information on operations that can be performed on images, or :ref:`ima-menu-processing` for information on processing features on images. .. figure:: /images/shots/i_analysis.png Screenshot of the "Analysis" menu. When the "Image Panel" is selected, the menus and toolbars are updated to provide image-related actions. The "Analysis" menu allows you to perform various computations on the current image or group of images. It also allows you to compute statistics, to compute the centroid, to detect peaks, to detect contours, and so on. .. note:: In DataLab vocabulary, an "analysis" is a feature that computes a scalar result from an image. This result is stored as metadata, and thus attached to image. This is different from a "processing" which creates a new image from an existing one. Statistics ^^^^^^^^^^ Compute statistics on selected image and show a summary table. .. figure:: /images/shots/i_stats.png Example of statistical summary table: each row is associated to an ROI (the first row gives the statistics for the whole data). Histogram ^^^^^^^^^ Compute histogram of selected image and show it in the Signal Panel. Parameters are: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - Bins - Number of bins * - Lower limit - Lower limit of the histogram * - Upper limit - Upper limit of the histogram .. figure:: /images/shots/i_histogram.png Example of histogram. Intensity profiles ^^^^^^^^^^^^^^^^^^ Line profile ~~~~~~~~~~~~ Extract an horizontal or vertical profile from each selected image, and create new signals from these profiles. .. figure:: /images/shots/i_profile.png Line profile dialog. Parameters may also be set manually ("Edit profile parameters" button). Segment profile ~~~~~~~~~~~~~~~ Extract a segment profile from each selected image, and create new signals from these profiles. Average profile ~~~~~~~~~~~~~~~ Extract an horizontal or vertical profile averaged over a rectangular area, from each selected image, and create new signals from these profiles. .. figure:: /images/shots/i_profile_average.png Average profile dialog: the area is defined by a rectangle shape. Parameters may also be set manually ("Edit profile parameters" button). Radial profile extraction ~~~~~~~~~~~~~~~~~~~~~~~~~ Extract a radial profile from each selected image, and create new signals from these profiles. The following parameters are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - Center - Center around which the radial profile is computed: centroid, image center, or user-defined * - X - X coordinate of the center (if user-defined), in pixels * - Y - Y coordinate of the center (if user-defined), in pixels Horizontal and vertical projections ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Compute horizontal and vertical projection profiles: - Horizontal projection: Sum the pixel values along each row (projection on the x-axis). - Vertical projection: Sum the pixel values along each column (projection on the y-axis). Centroid ^^^^^^^^ Compute the centroid of the image using a robust adaptive algorithm. DataLab uses the :py:func:`sigima.tools.image.get_centroid_auto` function to estimate the image centroid. This function combines the robustness of a Fourier-based approach (as discussed by `Weisshaar et al. `_) with fallback mechanisms designed to handle pathological cases, such as: - truncated or asymmetric shapes, - off-center objects, - strong background noise. Internally, :py:func:`sigima.tools.image.get_centroid_auto` compares the Fourier result with two alternative estimators (a projected profile-based method :py:func:`sigima.tools.image.get_projected_profile_centroid` and a standard centroid from `scikit-image`), and selects the most consistent one. This strategy ensures accurate and stable results across a wide range of image types — from clean laboratory data to noisy or partial acquisitions. Minimum enclosing circle center ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Compute the circle contour enclosing image values above a threshold level defined as the half-maximum value. 2D peak detection ^^^^^^^^^^^^^^^^^ Automatically find peaks on image using a minimum-maximum filter algorithm. .. figure:: /images/shots/i_peak2d_test.png Example of 2D peak detection. .. seealso:: See :ref:`ref-to-2d-peak-detection` for more details on algorithm and associated parameters. Contour detection ^^^^^^^^^^^^^^^^^ Automatically extract contours and fit them using a circle or an ellipse, or directly represent them as a polygon. .. figure:: /images/shots/i_contour_test.png Example of contour detection. .. seealso:: See :ref:`ref-to-contour-detection` for more details on algorithm and associated parameters. .. note:: Computed scalar results are systematically stored as metadata. Metadata is attached to image and serialized with it when exporting current session in a HDF5 file. Circle Hough transform ^^^^^^^^^^^^^^^^^^^^^^ Detect circular shapes using circle Hough transform (implementation based on `skimage.transform.hough_circle_peaks `_). .. _ima-blob-detection: Blob detection ^^^^^^^^^^^^^^ Blob detection (DOG) Detect blobs using Difference of Gaussian (DOG) method (implementation based on `skimage.feature.blob_dog `_). Blob detection (DOH) Detect blobs using Determinant of Hessian (DOH) method (implementation based on `skimage.feature.blob_doh `_). Blob detection (LOG) Detect blobs using Laplacian of Gaussian (LOG) method (implementation based on `skimage.feature.blob_log `_). Blob detection (OpenCV) Detect blobs using OpenCV implementation of `SimpleBlobDetector `_. .. note:: **Automatic ROI creation for detection features** All detection features (2D peak detection, contour detection, circle Hough transform, and blob detection) support automatic ROI creation around detected objects. This feature: - Creates rectangular or circular ROIs around each detected feature - Automatically sizes ROIs based on minimum distance between detections - Requires at least 2 detected objects to determine appropriate ROI size - Enables subsequent processing on individual detected regions To use this feature, enable "Create regions of interest" in the detection parameters dialog and choose the desired ROI geometry. Show results ^^^^^^^^^^^^ Show the results of all analyses performed on the selected images. This shows the same table as the one shown after having performed a computation. Results label ^^^^^^^^^^^^^ Toggle the visibility of result labels on the plot. When enabled, this checkable menu item displays result annotations (such as centroid markers, detected contours, blob circles, or other analysis shapes) directly on the image plot. This option is synchronized between Signal and Image panels and persists across sessions. It is only enabled when results are available for the selected image. Plot results ^^^^^^^^^^^^ Plot the results of analyses performed on the selected images, with user-defined X and Y axes (e.g. plot the contour circle radius as a function of the image number). DataLab-1.1.0/doc/features/image/menu_create.rst000066400000000000000000000052211514014117600214670ustar00rootroot00000000000000.. _ima-menu-create: Create Images ============= This section describes how to create new images from various mathematical models. .. figure:: /images/shots/i_create.png Screenshot of the "Create" menu. When the "Image Panel" is selected, the menus and toolbars are updated to provide image-related actions. The "Create" menu allows you to create new images from various models (see below). New image ^^^^^^^^^ Create a new image from various models (supported datatypes: uint8, uint16, int16, float32, float64): .. list-table:: :header-rows: 1 :widths: 10, 20, 70 :class: longtable * - Icon - Model - Equation * - .. image:: ../../../datalab/data/icons/create/2d-zero.svg :width: 30px - Zero - :math:`z[i] = 0` * - .. image:: ../../../datalab/data/icons/create/2d-normal.svg :width: 30px - Normal distribution - :math:`z[i]` is normally distributed with configurable mean and standard deviation * - .. image:: ../../../datalab/data/icons/create/2d-poisson.svg :width: 30px - Poisson distribution - :math:`z[i]` is Poisson distributed with configurable mean * - .. image:: ../../../datalab/data/icons/create/2d-uniform.svg :width: 30px - Uniform distribution - :math:`z[i]` is uniformly distributed between two configurable bounds * - .. image:: ../../../datalab/data/icons/create/2d-gaussian.svg :width: 30px - 2D Gaussian - :math:`z = A \cdot \exp\left(-\dfrac{\left(\sqrt{\left(x-x_0\right)^2+\left(y-y_0\right)^2}-\mu\right)^2}{2\sigma^2}\right)` * - .. image:: ../../../datalab/data/icons/create/2d-ramp.svg :width: 30px - 2D Ramp - :math:`z = A (x - x_0) + B (y - y_0) + C` * - .. image:: ../../../datalab/data/icons/create/checkerboard.svg :width: 30px - Checkerboard - Alternating square pattern for calibration and spatial frequency analysis * - .. image:: ../../../datalab/data/icons/create/grating.svg :width: 30px - Sinusoidal grating - :math:`z = A \sin(2\pi(f_x \cdot x + f_y \cdot y) + \varphi) + C` * - .. image:: ../../../datalab/data/icons/create/ring.svg :width: 30px - Ring pattern - Concentric circular rings for radial analysis * - .. image:: ../../../datalab/data/icons/create/siemens.svg :width: 30px - Siemens star - Radial spoke pattern for resolution testing * - .. image:: ../../../datalab/data/icons/create/2d-sinc.svg :width: 30px - 2D sinc - :math:`z = A \cdot \mathrm{sinc}\left(\dfrac{\sqrt{(x-x_0)^2+(y-y_0)^2}}{\sigma}\right) + C` DataLab-1.1.0/doc/features/image/menu_edit.rst000066400000000000000000000255441514014117600211630ustar00rootroot00000000000000.. _ima-menu-edit: Manipulate metadata and annotations =================================== This section describes how to manipulate metadata and annotations in DataLab. Image metadata contains various information about the image or its representation, such as view settings, Regions Of Interest (ROIs), processing chain history, analysis results, and any other information that you may have added to the metadata of an image (or that comes from the image file itself). .. seealso:: For more information about Regions Of Interest (ROIs), see the :ref:`ima-menu-roi` section. .. figure:: /images/shots/i_edit.png Screenshot of the "Edit" menu. The "Edit" menu allows you to perform classic editing operations on the current image or group of images (create/rename group, move up/down, delete image/group of images, etc.). As detailed below, it also allows you to: - Navigate and utilize the processing chain history through actions like "Recompute" and "Select source objects". - Manipulate metadata and annotations associated with the current image, thanks to the "Metadata" and "Annotations" submenus which provide the following features. Recompute --------- The "Recompute" |recompute| action allows you to recompute the selected image(s) using their original processing parameters. This is useful when you want to re-execute the processing chain that was used to create an image, for example after modifying global settings or dependencies. .. |recompute| image:: ../../../datalab/data/icons/edit/recompute.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. note:: This action is only available for images that were created through processing operations and have stored processing parameters. Select source objects --------------------- The "Select source objects" |goto_source| action allows you to select the source object(s) that were used to create the currently selected image. This helps trace back the processing history and understand which original images were used as input for the current result. .. |goto_source| image:: ../../../datalab/data/icons/edit/goto_source.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. note:: This action is only available when exactly one image is selected and that image has source object references. Metadata -------- .. figure:: /images/shots/i_edit_metadata.png Screenshot of the "Metadata" submenu. Copy/paste metadata ^^^^^^^^^^^^^^^^^^^ As metadata contains useful information about the image, it can be copied and pasted from one image to another by selecting the "Copy metadata" |metadata_copy| and "Paste metadata" |metadata_paste| actions in the "Edit" menu. .. |metadata_copy| image:: ../../../datalab/data/icons/edit/metadata_copy.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |metadata_paste| image:: ../../../datalab/data/icons/edit/metadata_paste.svg :width: 24px :height: 24px :class: dark-light no-scaled-link This feature allows you to tranfer those information from one image to another: - :ref:`Regions Of Interest (ROIs) `: that is a very efficient way to reuse the same ROI on different images and easily compare the results of the analysis on those images - Analyze results, such as a centroid position or a contour detection (the relevance of transferring such information depends on the context and is up to the user to decide) - Any other information that you may have added to the metadata of an image .. note:: Copying metadata from an image to another will overwrite the metadata of the destination image (for the metadata keys that are common to both images) or simply add the metadata keys that are not present in the destination image. Import/export metadata ^^^^^^^^^^^^^^^^^^^^^^ Metadata can also be imported and exported from/to a JSON file using the "Import metadata" |metadata_import| and "Export metadata" |metadata_export| actions in the "Edit" menu. This is exactly the same as the copy/paste metadata feature (see above for more details on the use cases of this feature), but it allows you to save the metadata to a file and then import it back later. .. |metadata_import| image:: ../../../datalab/data/icons/edit/metadata_import.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |metadata_export| image:: ../../../datalab/data/icons/edit/metadata_export.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Delete metadata ^^^^^^^^^^^^^^^ When deleting metadata using the "Delete metadata" |metadata_delete| action in the "Edit" menu, you will be prompted to confirm the deletion of Region of Interests (ROIs) if they are present in the metadata. After this eventual confirmation, the metadata will be deleted, meaning that analysis results, ROIs, and any other information associated with the image will be lost. .. |metadata_delete| image:: ../../../datalab/data/icons/edit/metadata_delete.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Add metadata ^^^^^^^^^^^^ The "Add metadata" |metadata_add| action allows you to add custom metadata items to one or more selected images. This is useful for tagging images with experiment IDs, sample names, processing steps, or any other custom information. .. |metadata_add| image:: ../../../datalab/data/icons/edit/metadata_add.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. figure:: /images/shots/i_add_metadata.png Add metadata dialog. When you select "Add metadata..." from the Edit menu, a dialog appears where you can: - **Metadata key**: Enter the name of the metadata field to add - **Value pattern**: Define a pattern for the metadata value using Python format strings - **Conversion**: Choose how to store the value (string, float, integer, or boolean) - **Preview**: See how the metadata will be added to each selected image The value pattern supports the following placeholders: - ``{title}``: Image title - ``{index}``: 1-based index of the image in the selection - ``{count}``: Total number of selected images - ``{xlabel}``, ``{xunit}``, ``{ylabel}``, ``{yunit}``: Axis labels and units - ``{metadata[key]}``: Access existing metadata values You can also use format modifiers: - ``{title:upper}``: Convert to uppercase - ``{title:lower}``: Convert to lowercase - ``{index:03d}``: Format as 3-digit number with leading zeros **Examples:** - Add acquisition number: key=``acquisition``, pattern=``ACQ_{index:04d}``, conversion=string → Creates metadata like ``acquisition="ACQ_0001"`` - Add exposure time: key=``exposure_ms``, pattern=``{metadata[exposure]}``, conversion=float → Copies exposure from existing metadata and converts to float - Flag calibrated images: key=``is_calibrated``, pattern=``true``, conversion=bool → Sets ``is_calibrated=True`` for all selected images Annotations ----------- Annotations are visual elements that can be added to images to highlight specific features, mark regions of interest, or add explanatory notes. DataLab provides a dedicated submenu in the "Edit" menu for managing annotations. .. figure:: /images/shots/i_edit_annotations.png Screenshot of the "Annotations" submenu. Copy/paste annotations ^^^^^^^^^^^^^^^^^^^^^^ Annotations can be copied from one image and pasted to one or more other images using the "Copy annotations" |annotations_copy| and "Paste annotations" |annotations_paste| actions. This is useful when you want to apply the same visual markers across multiple images. .. |annotations_copy| image:: ../../../datalab/data/icons/edit/annotations_copy.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |annotations_paste| image:: ../../../datalab/data/icons/edit/annotations_paste.svg :width: 24px :height: 24px :class: dark-light no-scaled-link The "Paste annotations" action is only enabled when there are annotations in the clipboard (i.e., after using "Copy annotations"). Edit annotations ^^^^^^^^^^^^^^^^ The "Edit annotations" |annotations_edit| action opens a dialog where you can view, add, modify, or remove annotations from the current image. This provides a visual way to manage all annotations on an image. .. |annotations_edit| image:: ../../../datalab/data/icons/edit/annotations_edit.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Import/export annotations ^^^^^^^^^^^^^^^^^^^^^^^^^^ Annotations can be saved to and loaded from JSON files (.dlabann extension) using the "Import annotations" |annotations_import| and "Export annotations" |annotations_export| actions. This allows you to: - Save annotation sets for later reuse - Share annotations with colleagues - Archive annotations separately from image data - Apply the same annotations to different images across sessions .. |annotations_import| image:: ../../../datalab/data/icons/edit/annotations_import.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |annotations_export| image:: ../../../datalab/data/icons/edit/annotations_export.svg :width: 24px :height: 24px :class: dark-light no-scaled-link The "Export annotations" action is only available when the selected image has annotations. Delete annotations ^^^^^^^^^^^^^^^^^^ The "Delete annotations" |annotations_delete| action removes all annotations from the selected image(s). This action is only enabled when the selected image(s) have annotations. .. |annotations_delete| image:: ../../../datalab/data/icons/edit/annotations_delete.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. note:: Annotations are stored separately from metadata and analysis results. Deleting annotations does not affect ROIs or other metadata items. Image titles ------------ Image titles may be considered as metadata from a user point of view, even if they are not stored in the metadata of the image (but in an attribute of the image object). The "Edit" menu allows you to: - "Add object title to plot": this action will add a label on top of the image with its title, which might be useful in combination with the "Distribute on a grid" operation (see :ref:`ima-menu-processing`) to easily identify the images. - "Copy titles to clipboard" |copy_titles|: this action will copy the titles of the selected images to the clipboard, which might be useful to paste them in a text editor or in a spreadsheet. Example of the content of the clipboard: .. code-block:: text g001: s001: lorentz(a=1,sigma=1,mu=0,ymin=0) s002: derivative(s001) s003: wiener(s002) g002: derivative(g001) s004: derivative(s001) s005: derivative(s002) s006: derivative(s003) g003: fft(g002) s007: fft(s004) s008: fft(s005) s009: fft(s006) .. |copy_titles| image:: ../../../datalab/data/icons/edit/copy_titles.svg :width: 24px :height: 24px :class: dark-light no-scaled-link DataLab-1.1.0/doc/features/image/menu_file.rst000066400000000000000000000130331514014117600211430ustar00rootroot00000000000000.. _ima-menu-file: Open and save Images ==================== This section describes how to open and save images (and workspaces). .. note:: For creating new images from mathematical models, see :ref:`ima-menu-create`. .. figure:: /images/shots/i_file.png Screenshot of the "File" menu. When the "Image Panel" is selected, the menus and toolbars are updated to provide image-related actions. The "File" menu allows you to: - Open, save and close images (see below). - Save and restore the current workspace or browse HDF5 files (see :ref:`overview`). - Edit DataLab preferences (see :ref:`settings`). .. _open_image: Open image ^^^^^^^^^^ Create a new image from the following supported filetypes: .. list-table:: :header-rows: 1 * - File type - Extensions * - PNG files - .png * - TIFF files - .tif, .tiff * - 8-bit images - .jpg, .gif * - NumPy arrays - .npy * - MAT-Files - .mat * - Text files - .txt, .csv, .asc * - Andor SIF files - .sif * - Princeton Instruments SPE files - .spe * - Opticks GEL files - .gel * - Hammamatsu NDPI files - .ndpi * - PCO Camera REC files - .rec * - SPIRICON files - .scor-data * - FXD files - .fxd * - Bitmap images - .bmp * - FT-Lab files - .ima .. note:: DataLab also supports any image format that can be read by the `imageio` library, provided that the associated plugin(s) are installed (see `imageio documentation `_) and that the output NumPy array data type and shape are supported by DataLab. To add a new file format, you may use the `imageio_formats` entry of DataLab configuration file. This entry is a formatted like the `IMAGEIO_FORMATS` object which represents the natively supported formats: .. autodata:: sigima.config.IMAGEIO_FORMATS Open from directory ^^^^^^^^^^^^^^^^^^^ Open multiple images from a specified directory. Save image ^^^^^^^^^^ Save current image (see "Open image" supported filetypes). Save images to directory ^^^^^^^^^^^^^^^^^^^^^^^^ Save all selected images to a specified directory, with configurable filename pattern and image format. .. figure:: /images/shots/i_save_to_directory.png Save images to directory dialog. When you select "Save to directory..." from the File menu, a dialog appears where you can: - **Directory**: Choose the target directory where images will be saved - **Filename pattern**: Define a pattern for the filenames using Python format strings - **File extension**: Select the output format (.png, .tiff, .h5ima, etc.) - **Overwrite**: Choose whether to overwrite existing files - **Preview**: See the list of files that will be created (with object IDs) The filename pattern supports the following placeholders: - ``{title}``: Image title - ``{index}``: 1-based index of the image in the selection (with zero-padding) - ``{count}``: Total number of selected images - ``{xlabel}``, ``{xunit}``, ``{ylabel}``, ``{yunit}``, ``{zlabel}``, ``{zunit}``: Axis labels and units - ``{metadata[key]}``: Access metadata values You can also use format modifiers, for example ``{index:03d}`` will format the index with 3 digits zero-padding (001, 002, 003, etc.). Import text file ^^^^^^^^^^^^^^^^ DataLab can natively import many types of image files (e.g. TIFF, JPEG, PNG, etc.). However some specific text file formats may not be supported. In this case, you can use the `Import text file` feature, which allows you to import a text file and convert it to an image. This feature is accessible from the `File` menu, under the `Import text file` option. It opens an import wizard that guides you through the process of importing the text file. Step 1: Select the source ------------------------- The first step is to select the source of the text file. You can either select a file from your computer or the clipboard if you have copied the text from another application. .. figure:: ../../images/import_text_file/i_01.png :alt: Step 1: Select the source :align: center Step 1: Select the source Step 2: Preview and configure the import ----------------------------------------- The second step consists of configuring the import and previewing the result. You can configure the following options: - **Delimiter**: The character used to separate the values in the text file. - **Comments**: The character used to indicate that the line is a comment and should be ignored. - **Rows to Skip**: The number of rows to skip at the beginning of the file. - **Maximum Number of Rows**: The maximum number of rows to import. If the file contains more rows, they will be ignored. - **Transpose**: If checked, the rows and columns will be transposed. - **Data type**: The destination data type of the imported data. When you are done configuring the import, click the `Apply` button to see the result. .. figure:: ../../images/import_text_file/i_02.png :alt: Step 2: Configure the import :align: center Step 2: Configure the import .. figure:: ../../images/import_text_file/i_03.png :alt: Step 2: Preview the result :align: center Step 2: Preview the result Step 3: Show graphical representation ------------------------------------- The third step shows a graphical representation of the imported data. You can use the `Finish` button to import the data into DataLab workspace. .. figure:: ../../images/import_text_file/i_04.png :alt: Step 3: Show graphical representation :align: center Step 3: Show graphical representationDataLab-1.1.0/doc/features/image/menu_operations.rst000066400000000000000000000225641514014117600224200ustar00rootroot00000000000000.. _ima-menu-operations: Operations on Images ==================== This section describes the operations that can be performed on images. .. seealso:: :ref:`ima-menu-processing` for more information on image processing features, or :ref:`ima-menu-analysis` for information on analysis features on images. .. figure:: /images/shots/i_operation.png Screenshot of the "Operations" menu. When the "Image Panel" is selected, the menus and toolbars are updated to provide image-related actions. The "Operations" menu allows you to perform various operations on the current image or group of images. It also allows you to extract profiles, distribute images on a grid, or resize images. Operations with a constant -------------------------- Create a new image which is the result of a constant operation on each selected image: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Operation - Equation * - |constant_add| Add - :math:`z_{k} = z_{k-1} + conv(c)` * - |constant_subtract| Subtract - :math:`z_{k} = z_{k-1} - conv(c)` * - |constant_multiply| Multiply - :math:`z_{k} = conv(z_{k-1} \times c)` * - |constant_divide| Divide - :math:`z_{k} = conv(\dfrac{z_{k-1}}{c})` .. |constant_add| image:: ../../../datalab/data/icons/operations/constant_add.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |constant_subtract| image:: ../../../datalab/data/icons/operations/constant_subtract.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |constant_multiply| image:: ../../../datalab/data/icons/operations/constant_multiply.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |constant_divide| image:: ../../../datalab/data/icons/operations/constant_divide.svg :width: 24px :height: 24px :class: dark-light no-scaled-link where :math:`c` is the constant value and :math:`conv` is the conversion function which handles data type conversion (keeping the same data type as the input image). Basic arithmetic operations --------------------------- Arithmetic operations are performed pixel by pixel between the selected images. .. list-table:: :header-rows: 1 :widths: 40, 60 * - Operation - Description * - |sum| Sum - :math:`z_{M} = \sum_{k=0}^{M-1}{z_{k}}` * - |difference| Difference - :math:`z_{2} = z_{1} - z_{0}` * - |product| Product - :math:`z_{M} = \prod_{k=0}^{M-1}{z_{k}}` * - |division| Division - :math:`z_{2} = \dfrac{z_{1}}{z_{0}}` * - |inverse| Inverse - :math:`z_{2} = \dfrac{1}{z_{1}}` * - |exponential| Exponential - :math:`z_{2} = \exp(z_{1})` * - |logarithm| Logarithm (base 10) - :math:`z_{2} = \log_{10}(z_{1})` .. |sum| image:: ../../../datalab/data/icons/operations/sum.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |difference| image:: ../../../datalab/data/icons/operations/difference.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |product| image:: ../../../datalab/data/icons/operations/product.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |division| image:: ../../../datalab/data/icons/operations/division.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |inverse| image:: ../../../datalab/data/icons/operations/inverse.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |exponential| image:: ../../../datalab/data/icons/operations/exp.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |logarithm| image:: ../../../datalab/data/icons/operations/log10.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Basic mathematical functions ---------------------------- .. list-table:: :header-rows: 1 :widths: 40, 60 * - Function - Description * - |exp| Exponential - :math:`z_{k} = \exp(z_{k})` * - |log10| Logarithm (base 10) - :math:`z_{k} = \log_{10}(z_{k})` * - Log10(z+10) - :math:`z_{k} = \log_{10}(z_{k}+n)` (useful if image background is zero) .. |exp| image:: ../../../datalab/data/icons/operations/exp.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |log10| image:: ../../../datalab/data/icons/operations/log10.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Convolution and Deconvolution ------------------------------ .. list-table:: :header-rows: 1 :widths: 40, 60 * - Operation - Implementation * - |convolution| Convolution - Based on `scipy.signal.convolve `_ * - |deconvolution| Deconvolution - Frequency domain deconvolution .. |convolution| image:: ../../../datalab/data/icons/operations/convolution.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |deconvolution| image:: ../../../datalab/data/icons/operations/deconvolution.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Absolute value and complex image operations ------------------------------------------- .. list-table:: :header-rows: 1 :widths: 40, 60 * - Operation - Description * - |abs| Absolute value - :math:`z_{k} = |z_{k-1}|` * - |phase| Phase (argument) - :py:func:`sigima.proc.image.phase` * - |complex_from_magnitude_phase| Combine with phase - Consider current image as the module and allow to select a image representing the phase to merge them in a complex image :py:func:`sigima.proc.image.complex_from_magnitude_phase` * - |re| Real part - :math:`z_{k} = \Re(z_{k-1})` * - |im| Imaginary part - :math:`z_{k} = \Im(z_{k-1})` * - |complex_from_real_imag| Combine with imaginary part - Consider current image as the real part and allow to select a image representing the imaginary part to merge them in a complex image :py:func:`sigima.proc.image.complex_from_real_imag` .. |abs| image:: ../../../datalab/data/icons/operations/abs.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |phase| image:: ../../../datalab/data/icons/operations/phase.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |complex_from_magnitude_phase| image:: ../../../datalab/data/icons/operations/phase.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |re| image:: ../../../datalab/data/icons/operations/re.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |im| image:: ../../../datalab/data/icons/operations/im.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |complex_from_real_imag| image:: ../../../datalab/data/icons/operations/im.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Data type conversion -------------------- The "Convert data type" |convert_dtype| action allows you to convert the data type of the selected images. For integer data types, the conversion is done by clipping the values to the new data type range before effectively converting the data type. For floating point data types, the conversion is straightforward. .. |convert_dtype| image:: ../../../datalab/data/icons/operations/convert_dtype.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. note:: Data type conversion uses the :py:func:`sigima.tools.datatypes.clip_astype` function which relies on :py:func:`numpy.ndarray.astype` function with the default parameters (`casting='unsafe'`). Statistics between images ^^^^^^^^^^^^^^^^^^^^^^^^^^^ Create a new image which is the result of a statistical operation on each pixel of the selected images: .. list-table:: :header-rows: 1 :widths: 40, 60 * - Operation - Description * - |average| Average - :math:`z_{M} = \dfrac{1}{M}\sum_{k=0}^{M-1}{z_{k}}` * - |standard_deviation| Standard Deviation - :math:`z_{M} = \sqrt{\dfrac{1}{M}\sum_{k=0}^{M-1}{(y_{k} - \bar{y})^{2}}}` * - |quadratic_difference| Quadratic difference - :math:`z_{2} = \dfrac{z_{1} - z_{0}}{\sqrt{2}}` .. |average| image:: ../../../datalab/data/icons/operations/average.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |standard_deviation| image:: ../../../datalab/data/icons/operations/std.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |quadratic_difference| image:: ../../../datalab/data/icons/operations/quadratic_difference.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Flat-field correction ^^^^^^^^^^^^^^^^^^^^^ Create a new image which is flat-field correction of the **two** selected images: .. math:: z_{1} = \begin{cases} \dfrac{z_{0}}{z_{f}}.\overline{z_{f}} & \text{if } z_{0} > z_{threshold} \\ z_{0} & \text{otherwise} \end{cases}` where :math:`z_{0}` is the raw image, :math:`z_{f}` is the flat field image, :math:`z_{threshold}` is an adjustable threshold and :math:`\overline{z_{f}}` is the flat field image average value: .. math:: \overline{z_{f}}= \dfrac{1}{N_{row}.N_{col}}.\sum_{i=0}^{N_{row}}\sum_{j=0}^{N_{col}}{z_{f}(i,j)} .. note:: Raw image and flat field image are supposedly already corrected by performing a dark frame subtraction. DataLab-1.1.0/doc/features/image/menu_processing.rst000066400000000000000000000451011514014117600224010ustar00rootroot00000000000000.. _ima-menu-processing: Processing Images ================= This section describes the image processing features available in DataLab. .. seealso:: :ref:`ima-menu-operations` for more information on operations that can be performed on images, or :ref:`ima-menu-analysis` for information on analysis features on images. .. figure:: /images/shots/i_processing.png Screenshot of the "Processing" menu. When the "Image Panel" is selected, the menus and toolbars are updated to provide image-related actions. The "Processing" menu allows you to perform various processing on the current image or group of images: it allows you to apply filters, to perform exposure correction, to perform denoising, to perform morphological operations, and so on. Geometry ^^^^^^^^ Flip and rotation ~~~~~~~~~~~~~~~~~ Create a new image by flipping or rotating the data of the selected image. The image may be flipped horizontally, vertically, or diagonally (transposition). It may be rotated by 90°, 270° or any user-defined value. .. list-table:: :header-rows: 1 :widths: 40, 60 * - Operation - Description * - Flip horizontally - Mirror image along the vertical axis * - Flip diagonally - Transpose the image (swap X/Y axes) * - Flip vertically - Mirror image along the horizontal axis * - Rotate 90° right - Rotate image 90° clockwise * - Rotate 90° left - Rotate image 90° counter-clockwise * - Rotate by... - Rotate image by any angle (user-defined) Distribute images along a grid ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ .. list-table:: :header-rows: 1 :widths: 40, 60 * - Feature - Description * - |distribute_on_grid| Distribute on a grid - Distribute selected images on a regular grid * - |reset_positions| Reset image positions - Reset the positions of the selected images to first image (x0, y0) coordinates .. |distribute_on_grid| image:: ../../../datalab/data/icons/processing/distribute_on_grid.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |reset_positions| image:: ../../../datalab/data/icons/processing/reset_positions.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Axis transformation ^^^^^^^^^^^^^^^^^^^ Set uniform coordinates ~~~~~~~~~~~~~~~~~~~~~~~ Create a new image with uniform coordinates (i.e., with a constant pixel size in both X and Y directions). Polynomial calibration ~~~~~~~~~~~~~~~~~~~~~~ Create a new image which is a polynomial calibration of each selected image with respect to Z axis. .. note:: Linear calibration was removed from the UI as it is a special case of polynomial calibration with degree 1. Level adjustment ^^^^^^^^^^^^^^^^ Normalize ~~~~~~~~~ Create a new image which is the normalized version of each selected image by maximum, amplitude, sum, energy or RMS: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Normalization - Equation * - Maximum - :math:`z_{1} = \dfrac{z_{0}}{z_{\max}}` * - Amplitude - :math:`z_{1} = \dfrac{z_{0}}{z_{\max}-z_{\min}}` * - Area - :math:`z_{1} = \dfrac{z_{0}}{\sum_{i=0}^{N-1}{z_{i}}}` * - Energy - :math:`z_{1}= \dfrac{z_{0}}{\sqrt{\sum_{n=0}^{N}\left|z_{0}[n]\right|^2}}` * - RMS - :math:`z_{1}= \dfrac{z_{0}}{\sqrt{\dfrac{1}{N}\sum_{n=0}^{N}\left|z_{0}[n]\right|^2}}` Clipping ~~~~~~~~ Apply the clipping to each selected image. Offset correction ~~~~~~~~~~~~~~~~~ Create a new image which is the result of offset correction on each selected image. This operation is performed by subtracting the image background value which is estimated by the mean value of a user-defined rectangular area. Noise addition ^^^^^^^^^^^^^^ Generate new images by adding the same noise to each selected image. The available noise types are: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Noise - Description * - Gaussian - Normal distribution * - Uniform - Uniform distribution * - Poisson - Poisson distribution Noise reduction ^^^^^^^^^^^^^^^ Create a new image which is the result of noise reduction on each selected image. The following filters are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Filter - Formula/implementation * - Gaussian filter - `scipy.ndimage.gaussian_filter `_ * - Moving average - `scipy.ndimage.uniform_filter `_ * - Moving median - `scipy.ndimage.median_filter `_ * - Wiener filter - `scipy.signal.wiener `_ Fourier analysis ^^^^^^^^^^^^^^^^ Zero padding ~~~~~~~~~~~~ Create a new image which is the result of zero padding on each selected image. The following parameters are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - Strategy - Zero padding strategy (see below) * - Rows - Number of rows to add (if `strategy` is 'custom') * - Columns - Number of columns to add (if `strategy` is 'custom') * - Position - Position of the added zeros: 'bottom-right', 'centered' Zero padding strategy refers to the method used to add zeros to the image, and it can be one of the following: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Strategy - Description * - next_pow2 - Next power of 2 (e.g. 512, 1024, ...) * - multiple_of_64 - Next multiple of 64 (e.g. 512, 576, ...) * - custom - Custom size (user-defined) FFT related functions ~~~~~~~~~~~~~~~~~~~~~ Create a new image which is the result of a Fourier analysis on each selected image. The following functions are available: .. list-table:: :header-rows: 1 :widths: 20, 30, 50 * - Function - Description - Formula/implementation * - FFT - Fast Fourier Transform - `numpy.fft.fft2 `_ * - Inverse FFT - Inverse Fast Fourier Transform - `numpy.fft.ifft2 `_ * - Magnitude spectrum - Optional: output in decibels (dB) - | :math:`z_{1} = \left|\FFT\left(z_{0}\right)\right|` or | :math:`z_{1} = 20 \log_{10} \left(\left|\FFT\left(z_{0}\right)\right|\right)` (dB) * - Phase spectrum - Phase of the FFT expressed in degrees, using `numpy.angle `_ - :math:`z_{1} = \angle\left(\FFT\left(z_{0}\right)\right)` * - Power spectral density - Optional: output in decibels (dB) - | :math:`z_{1} = \left|\FFT\left(z_{0}\right)\right|^2` or | :math:`z_{1} = 10 \log_{10} \left(\left|\FFT\left(z_{0}\right)\right|^2\right)` (dB) .. note:: FFT and inverse FFT are performed using frequency shifting if the option is enabled in DataLab settings (see :ref:`settings`). Frequency-domain filters ^^^^^^^^^^^^^^^^^^^^^^^^ The following frequency-domain filters are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Method - Description * - Butterworth - Butterworth filter, based on `skimage.filters.butterworth `_ * - Gaussian filter - Gaussian filter Thresholding ^^^^^^^^^^^^ Create a new image which is the result of thresholding on each selected image, eventually based on user-defined parameters ("Parametric thresholding"). The following parameters are available when selecting "Parametric thresholding": .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - Threshold method - The thresholding method to use (see table below) * - Bins - Number of bins for histogram calculation * - Value - Threshold value * - Operation - Operation to apply (> or <) The following thresholding methods are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Method - Implementation * - Manual - Manual thresholding (user-defined parameters) * - ISODATA - `skimage.filters.threshold_isodata `_ * - Li - `skimage.filters.threshold_li `_ * - Mean - `skimage.filters.threshold_mean `_ * - Minimum - `skimage.filters.threshold_minimum `_ * - Otsu - `skimage.filters.threshold_otsu `_ * - Triangle - `skimage.filters.threshold_triangle `_ * - Yen - `skimage.filters.threshold_yen `_ .. note:: The "All thresholding methods" option allows to perform all thresholding methods on the same image. Combined with the "distribute on a grid" option, this allows to compare the different thresholding methods on the same image. Exposure ^^^^^^^^ Create a new image which is the result of exposure correction on each selected image. The following functions are available: .. list-table:: :header-rows: 1 :widths: 25, 25, 50 * - Function - Implementation - Comments * - Gamma correction - `skimage.exposure.adjust_gamma `_ - * - Logarithmic correction - `skimage.exposure.adjust_log `_ - * - Sigmoid correction - `skimage.exposure.adjust_sigmoid `_ - * - Histogram equalization - `skimage.exposure.equalize_hist `_ - * - Adaptive histogram equalization - `skimage.exposure.equalize_adapthist `_ - Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm * - Intensity rescaling - `skimage.exposure.rescale_intensity `_ - Stretch or shrink image intensity levels Restoration ^^^^^^^^^^^ Create a new image which is the result of restoration on each selected image. The following functions are available: .. list-table:: :header-rows: 1 :widths: 25, 25, 50 * - Function - Implementation - Comments * - Total variation denoising - `skimage.restoration.denoise_tv_chambolle `_ - * - Bilateral filter denoising - `skimage.restoration.denoise_bilateral `_ - * - Wavelet denoising - `skimage.restoration.denoise_wavelet `_ - * - White Top-Hat denoising - `skimage.morphology.white_tophat `_ - Denoise image by subtracting its white top hat transform .. note:: The "All denoising methods" option allows to perform all denoising methods on the same image. Combined with the "distribute on a grid" option, this allows to compare the different denoising methods on the same image. Morphology ^^^^^^^^^^ Create a new image which is the result of morphological operations on each selected image, using a disk footprint. The following functions are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Function - Implementation * - White Top-Hat (disk) - `skimage.morphology.white_tophat `_ * - Black Top-Hat (disk) - `skimage.morphology.black_tophat `_ * - Erosion (disk) - `skimage.morphology.erosion `_ * - Dilation (disk) - `skimage.morphology.dilation `_ * - Opening (disk) - `skimage.morphology.opening `_ * - Closing (disk) - `skimage.morphology.closing `_ .. note:: The "All morphological operations" option allows to perform all morphological operations on the same image. Combined with the "distribute on a grid" option, this allows to compare the different morphological operations on the same image. Edges ^^^^^ Create a new image which is the result of edge filtering on each selected image. The following functions are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Function - Implementation * - Canny filter - `skimage.feature.canny `_ * - Farid filter - `skimage.filters.farid `_ * - Farid filter (horizontal) - `skimage.filters.farid_h `_ * - Farid filter (vertical) - `skimage.filters.farid_v `_ * - Laplace filter - `skimage.filters.laplace `_ * - Prewitt filter - `skimage.filters.prewitt `_ * - Prewitt filter (horizontal) - `skimage.filters.prewitt_h `_ * - Prewitt filter (vertical) - `skimage.filters.prewitt_v `_ * - Roberts filter - `skimage.filters.roberts `_ * - Scharr filter - `skimage.filters.scharr `_ * - Scharr filter (horizontal) - `skimage.filters.scharr_h `_ * - Scharr filter (vertical) - `skimage.filters.scharr_v `_ * - Sobel filter - `skimage.filters.sobel `_ * - Sobel filter (horizontal) - `skimage.filters.sobel_h `_ * - Sobel filter (vertical) - `skimage.filters.sobel_v `_ .. note:: The "All edges filters" option allows to perform all edge filtering algorithms on the same image. Combined with the "distribute on a grid" option, this allows to compare the different edge filters on the same image. Erase area ^^^^^^^^^^ Erase an area in the image as defined by a region of interest (ROI). .. note:: The region to erase is defined by the user through a dialog box. It can consist of a single ROI or multiple ROIs with various shapes (rectangular, circular, etc.). Note that the ROI defined here is not bound to any object; it is used solely to specify the area to erase in the image. In particular, it is independent of the image’s ROI, if any (the latter is shown in the dialog box as a masked area). Resize ^^^^^^ Create a new image which is a resized version of each selected image. Pixel binning ^^^^^^^^^^^^^ Combine clusters of adjacent pixels, throughout the image, into single pixels. The result can be the sum, average, median, minimum, or maximum value of the cluster. Resampling ^^^^^^^^^^ Generate new images by resampling each selected image. The following parameters are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - math:`x_{min}` - Minimum x-coordinate of the output image * - :math:`x_{max}` - Maximum x-coordinate of the output image * - :math:`y_{min}` - Minimum y-coordinate of the output image * - :math:`y_{max}` - Maximum y-coordinate of the output image * - Mode - Image size definition mode: 'Pixel size' or 'Output shape'. The 'Pixel size' mode allows to define the pixel size of the new image, while the 'Output shape' mode allows to define the number of pixels of the new image. * - ΔX - Pixel size in x-direction (if 'Pixel size' mode is selected) * - ΔY - Pixel size in y-direction (if 'Pixel size' mode is selected) * - Width - Output image width in pixels (if 'Output shape' mode is selected) * - Height - Output image height in pixels (if 'Output shape' mode is selected) * - Interpolation method - Interpolation method to use: 'nearest', 'linear', 'cubic' * - Fill value - Value to use for points outside the input image domain (if None, function uses NaN for extrapolation) DataLab-1.1.0/doc/features/image/menu_roi.rst000066400000000000000000000101031514014117600210100ustar00rootroot00000000000000.. _ima-menu-roi: Regions Of Interest (ROI) ========================= This section describes how to manipulate Regions Of Interest (ROIs) for images in DataLab. .. figure:: /images/shots/i_roi.png Screenshot of the "ROI" menu. The "ROI" menu allows you to manage Regions Of Interest (ROIs) associated with the current image. .. seealso:: For more information about image metadata, see the :ref:`ima-menu-edit` section. The Regions Of Interest (ROI) are image areas that are defined by the user to perform specific operations, processing, or analysis on them. ROI are taken into account almost in all computing features in DataLab: - The "Operations" menu features are done only on the ROI if one is defined (except if the operation changes the data shape - like the resize operation - or the pixel size - like the binning operation). - The "Processing" menu actions are performed only on the ROI if one is defined (except if the destination signal data type is different from the source's, like in the Fourier analysis features or like the thresholding operations). - The "Analysis" menu actions are done only on the ROI if one is defined. .. note:: ROI are stored as metadata, and thus attached to image. The "ROI" menu allows you to: - "Edit graphically" |edit_roi|: open a dialog box to manage ROI associated with the selected image (add, remove, move, resize, etc.). The ROI definition dialog is exactly the same as ROI extraction (see below). .. figure:: /images/shots/i_roi_editor.png An image with an ROI. - "Edit numerically": open a dialog box to edit the parameters of the selected ROIs numerically (i.e. using a simple form). This allows you to define or modify ROIs based on numerical values. - "Create ROI grid" |roi_grid|: open a dialog box to create a grid of ROIs on the selected image. This allows you to define multiple ROIs distributed evenly across the image. - "Extract" |image_roi|: extract the defined ROI from the selected images. This will create a new image for each ROI (or a single image, if the "Extract all ROIs into a single image" option is selected in the dialog), with the same metadata as the original image, but with the data corresponding to the ROI only. The new images will be added to the current workspace. - "Copy" |roi_copy|: copy the defined ROI from the selected images to the clipboard. This allows you to paste the ROI into another image or use it in other operations. - "Paste" |roi_paste|: paste the copied ROI from the clipboard to the selected images. This will add the ROI to the images, allowing you to define or modify ROIs based on previously copied ones. - "Import" |roi_import|: import ROIs from a file. This allows you to load previously saved ROIs into the current image. - "Export" |roi_export|: export the defined ROIs to a file. This allows you to save the ROIs for later use or to share them with others. - "Remove all" |roi_delete|: remove all defined ROI for the selected images. .. figure:: /images/shots/i_roi_dialog.png ROI extraction dialog: the ROI is defined by moving the position and adjusting the size of a rectangle shape. .. |edit_roi| image:: ../../../datalab/data/icons/roi/roi.svg :width: 24px :height: 24px :class: dark-light .. |image_roi| image:: ../../../datalab/data/icons/roi/roi_ima.svg :width: 24px :height: 24px :class: dark-light .. |roi_grid| image:: ../../../datalab/data/icons/roi/roi_grid.svg :width: 24px :height: 24px :class: dark-light .. |roi_copy| image:: ../../../datalab/data/icons/roi/roi_copy.svg :width: 24px :height: 24px :class: dark-light .. |roi_paste| image:: ../../../datalab/data/icons/roi/roi_paste.svg :width: 24px :height: 24px :class: dark-light .. |roi_import| image:: ../../../datalab/data/icons/roi/roi_import.svg :width: 24px :height: 24px :class: dark-light .. |roi_export| image:: ../../../datalab/data/icons/roi/roi_export.svg :width: 24px :height: 24px :class: dark-light .. |roi_delete| image:: ../../../datalab/data/icons/roi/roi_delete.svg :width: 24px :height: 24px :class: dark-light DataLab-1.1.0/doc/features/image/menu_view.rst000066400000000000000000000106441514014117600212030ustar00rootroot00000000000000.. _ima-menu-view: View options for Images ======================= .. figure:: /images/shots/i_view.png Screenshot of the "View" menu. When the "Image Panel" is selected, the menus and toolbars are updated to provide image-related actions. The "View" menu allows you to visualize the current image or group of images. It also allows you to show/hide titles, to show/hide the contrast panel, to refresh the visualization, and so on. View in a new window ^^^^^^^^^^^^^^^^^^^^ Open a new window to visualize the selected images. This option allows you to show the selected images in a separate window, in which you can visualize the data more comfortably (e.g., by maximizing the window) as well as to add or edit annotations. When you click on the button "Annotations" in the toolbar of the new window, the annotation editing mode is activated (see the section "Edit annotations" below). View images side-by-side ^^^^^^^^^^^^^^^^^^^^^^^^ Open a new window to visualize the selected images side-by-side. This option allows you to show the selected images in a separate window, arranged in a grid layout, with synchronized zooming and panning. This is useful to compare multiple images simultaneously. Edit annotations ^^^^^^^^^^^^^^^^ Open a new window to edit annotations. This option allows you to edit the annotations of the selected images in a separate window. This is equivalent to select the "View in a new window" option and then click on the "Annotations" button in the toolbar of the new window. Annotations are used to add text, lines, rectangles, ellipses, and other geometrical shapes to the images. They are useful to highlight regions of interest, to add comments, to mark points, and so on. .. note:: The annotations are saved in the metadata of the image, so they are persistent and will be displayed every time you visualize the image. .. figure:: /images/annotations/image_annotations1.png Annotations may be added in the separate view. The typical workflow to edit annotations is as follows: 1. Select the "Edit annotations" option. 2. In the new window, add annotations by clicking on the corresponding buttons at the bottom of the window. 3. Eventually, customize the annotations by changing their properties (e.g., the text, the color, the position, etc.) using the "Parameters" option in the context menu of the annotations. 4. When you are done, click on the "OK" button to save the annotations. This will close the window and the annotations will be saved in the metadata of the image and will be displayed in the main window. .. figure:: /images/annotations/image_annotations2.png Annotations are now part of the image metadata. .. note:: Annotations may be copied from an image to another by using the "copy/paste metadata" features. Auto-refresh ^^^^^^^^^^^^ Automatically refresh the visualization when the data changes: - When enabled (default), the plot view is automatically refreshed when the data changes. - When disabled, the plot view is not refreshed until you manually refresh it by using the "Refresh manually" menu entry. Even though the refresh algorithm is optimized, it may still take some time to refresh the plot view when the data changes, especially when the data set is large. Therefore, you may want to disable the auto-refresh feature when you are working with large data sets, and enable it again when you are done. This will avoid unnecessary refreshes. Show only first selected image ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Toggle between showing all selected images or only the first one. When this option is enabled, only the first selected image is displayed in the plot view. This may be useful when multiple images are selected and focusing on a single image is (temporarily) preferred. Refresh manually ^^^^^^^^^^^^^^^^ Refresh the visualization manually. This triggers a refresh of the plot view. It is useful when the auto-refresh feature is disabled, or when you want to force a refresh of the plot view. Show contrast panel ^^^^^^^^^^^^^^^^^^^ Show/hide contrast adjustment panel. Show graphical object titles ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Show/hide titles of analysis results or annotations. This option allows you to show or hide the titles of the graphical objects (e.g., the titles of the analysis results or annotations). Hiding the titles can be useful when you want to visualize the data without any distractions, or if there are too many titles and they are overlapping.DataLab-1.1.0/doc/features/index.rst000066400000000000000000000071361514014117600172340ustar00rootroot00000000000000Features ======== .. meta:: :description: DataLab features :keywords: DataLab, features, validation, signal processing, image processing The following sections describe the features of DataLab, the open-source scientific data analysis and visualization platform. .. note:: Before jumping into the details of other parts of the documentation, it is recommended to start with the :ref:`overview` page, which describes the basic concepts of DataLab. .. seealso:: For a synthetic overview of the features of DataLab, please refer to the :ref:`key_features` page. .. _validation: Overview & Common features -------------------------- .. figure:: /images/shots/i_blob_detection_flower.png DataLab main window .. toctree:: :maxdepth: 1 :caption: Overview & Common features: common/overview common/settings common/h5browser .. raw:: latex \newpage Signal processing ----------------- This section describes the features specific to the signal processing panel. The signal processing panel is the default panel when DataLab is started. .. figure:: /images/shots/s_beautiful.png DataLab main window: Signal processing view .. toctree:: :maxdepth: 1 :caption: Signal processing: signal/menu_file signal/menu_create signal/menu_edit signal/menu_roi signal/menu_operations signal/menu_processing signal/menu_analysis signal/menu_view .. raw:: latex \newpage Image processing ---------------- This section describes the features specific to the image processing panel. The image processing panel can be selected by clicking on the "Images" tab at the bottom-right of the DataLab main window. .. figure:: /images/shots/i_beautiful.png DataLab main window: Image processing view .. toctree:: :maxdepth: 1 :caption: Image processing: image/menu_file image/menu_create image/menu_edit image/menu_roi image/menu_operations image/menu_processing image/menu_analysis image/menu_view image/2d_peak_detection image/contour_detection Validation ---------- DataLab is a platform for scientific data analysis and visualization. It may be used in a variety of scientific disciplines, including biology, physics, and astronomy. The common ground for all these disciplines is the need to validate the results of computational analysis against ground-truth data. This is a critical step in the scientific method, and it is essential for reproducibility and trust in the results. This is what we call **technical validation**. DataLab is also used in industrial applications, where the validation of the results is essential for guaranteeing the quality of the process but a wider range of validation is required to ensure that a maximum of use cases are covered from a functional point of view. This is what we call **functional validation**. Thus, DataLab validation may be categorized into two types: - **Technical validation**: ensures that the results of computational analysis are accurate and reliable. - **Functional validation**: checks that the software behaves as expected in a variety of use cases (classic automated unit test suite). .. toctree:: :maxdepth: 1 :caption: Validation: validation/technical validation/functional validation/status Advanced features ----------------- This section describes the advanced features of DataLab. .. toctree:: :maxdepth: 1 :caption: Advanced features: advanced/plugins advanced/migration_v020_to_v100 advanced/macros advanced/webapi advanced/remote advanced/model advanced/logviewer advanced/instviewer advanced/command advanced/api DataLab-1.1.0/doc/features/signal/000077500000000000000000000000001514014117600166415ustar00rootroot00000000000000DataLab-1.1.0/doc/features/signal/menu_analysis.rst000066400000000000000000000211541514014117600222450ustar00rootroot00000000000000.. _sig-menu-analysis: Analysis features on Signals ============================= This section describes the signal analysis features available in DataLab. .. seealso:: :ref:`sig-menu-operations` for more information on operations that can be performed on signals, or :ref:`sig-menu-processing` for information on processing features on signals. .. figure:: /images/shots/s_analysis.png Screenshot of the "Analysis" menu. When the "Signal Panel" is selected, the menus and toolbars are updated to provide signal-related actions. The "Analysis" menu allows you to perform various computations on the selected signals, such as statistics, full width at half-maximum, or full width at 1/e². .. note:: In DataLab vocabulary, an "analysis" is a feature that computes a scalar result from a signal. This result is stored as metadata, and thus attached to signal. This is different from a "processing" which creates a new signal from an existing one. Statistics ^^^^^^^^^^ Compute statistics on selected signal and show a summary table. .. figure:: /images/shots/s_stats.png Example of statistical summary table: each row is associated to an ROI (the first row gives the statistics for the whole data). Histogram ^^^^^^^^^ Compute histogram of selected signal and show it. Parameters are: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - Bins - Number of bins * - Lower limit - Lower limit of the histogram * - Upper limit - Upper limit of the histogram .. figure:: /images/shots/s_histogram.png Example of histogram. Full width at half-maximum ^^^^^^^^^^^^^^^^^^^^^^^^^^ Compute the Full Width at Half-Maximum (FWHM) of selected signal, using one of the following methods: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Method - Description * - Zero-crossing - Find the zero-crossings of the signal after having centered its amplitude around zero * - Gauss - Fit data to a Gaussian model using least-square method * - Lorentz - Fit data to a Lorentzian model using least-square method * - Voigt - Fit data to a Voigt model using least-square method .. figure:: /images/shots/s_fwhm.png The computed result is displayed as an annotated segment. Full width at 1/e² ^^^^^^^^^^^^^^^^^^ Fit data to a Gaussian model using least-square method. Then, compute the full width at 1/e². .. note:: Computed scalar results are systematically stored as metadata. Metadata is attached to signal and serialized with it when exporting current session in a HDF5 file. Arguments of the min and max ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Compute the smallest argument of the minima and the smallest argument of the maxima of the selected signal. Abscissa at y=... ^^^^^^^^^^^^^^^^^ Compute the abscissa at a given ordinate value for the selected signal. If there is no solution, the displayed result is NaN. If there are multiple solutions, the displayed result is the smallest value. Peak detection ^^^^^^^^^^^^^^ Create a new signal from semi-automatic peak detection of each selected signal. .. figure:: /images/shots/s_peak_detection.png Peak detection dialog: threshold is adjustable by moving the horizontal marker, peaks are detected automatically (see vertical markers with labels indicating peak position) Sampling rate and period ^^^^^^^^^^^^^^^^^^^^^^^^ Compute the sampling rate and period of selected signal. .. warning:: This feature assumes that the X values are regularly spaced. Dynamic parameters ^^^^^^^^^^^^^^^^^^ Compute the following dynamic parameters on selected signal: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - f - Frequency (sinusoidal fit) * - ENOB - Effective Number Of Bits * - SNR - Signal-to-Noise Ratio * - SINAD - Signal-to-Noise And Distortion Ratio * - THD - Total Harmonic Distortion * - SFDR - Spurious-Free Dynamic Range Bandwidth at -3 dB ^^^^^^^^^^^^^^^^^^ Determine the bandwidth at -3 dB by identifying the range of abscissa values where the signal remains greater than its maximum value minus 3 dB. .. warning:: This feature requires that the signal is expressed in decibels (dB). Contrast ^^^^^^^^ Compute the contrast of selected signal. The contrast is defined as the ratio of the difference and the sum of the maximum and minimum values: .. math:: \text{Contrast} = \dfrac{\text{max}(y) - \text{min}(y)}{\text{max}(y) + \text{min}(y)} .. note:: This feature assumes that the signal is a profile from an image, where the contrast is meaningful. This justifies the optical definition of contrast. Extract pulse features ^^^^^^^^^^^^^^^^^^^^^^ Perform comprehensive pulse analysis on selected signals, automatically extracting timing and amplitude characteristics for step and square pulse signals. This feature provides automated pulse characterization with intelligent signal type recognition and robust parameter extraction for digital signal analysis, oscilloscope measurements, and pulse timing validation. .. figure:: /images/shots/s_pulse_features.png Pulse features results as displayed in the Signal View. **Key Capabilities:** * **Automated signal recognition**: Heuristically identifies signal type (step, square, or other) for optimal analysis * **Polarity detection**: Automatically determines positive/negative pulse polarity using baseline comparison * **Comprehensive timing measurements**: Extracts rise time, fall time, FWHM, and timing parameters at specific amplitude fractions * **Baseline characterization**: Analyzes start and end baseline regions for accurate feature computation * **Robust algorithms**: Uses advanced statistical methods with noise tolerance and error handling **Parameters:** .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - Signal shape - Signal type selection: Auto (automatic detection), Step, or Square * - Start baseline min - Lower X boundary for the start (initial) baseline region * - Start baseline max - Upper X boundary for the start (initial) baseline region * - End baseline min - Lower X boundary for the end (final) baseline region * - End baseline max - Upper X boundary for the end (final) baseline region * - Rise/Fall time - Reference levels for rise/fall time measurement with predefined choices: 5%-95% (High precision), 10%-90% (IEEE standard), 20%-80% (Noisy signals), 25%-75% (Alternative) **Extracted Features:** The analysis computes the following pulse characteristics: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Feature - Description * - Signal shape - Detected signal type (STEP, SQUARE, or OTHER) * - Polarity - Signal polarity (+1 for positive, -1 for negative pulses) * - Amplitude - Peak-to-peak amplitude of the pulse * - Offset - DC offset (baseline level) * - Rise time - Time from the lower to upper reference levels during rising edge * - Fall time - Time from the lower to upper reference levels during falling edge (square pulses only) * - FWHM - Full Width at Half Maximum (square pulses only) * - x50 - Time at which signal reaches 50% of maximum amplitude * - x100 - Time at which signal reaches 100% of maximum amplitude (plateau start) * - Foot duration - Duration of flat region before pulse rise * - Baseline ranges - Extracted start and end baseline boundary coordinates .. note:: Results are displayed in a comprehensive table showing all extracted parameters, and as visual annotations on the signal plot. For step signals, fall-related parameters (fall_time, fwhm) are not applicable and show as None. The feature works best with well-defined pulse signals that have clear baseline regions. Show results ^^^^^^^^^^^^ Show the results of all analyses performed on the selected signals. This shows the same table as the one shown after having performed a computation. Results label ^^^^^^^^^^^^^ Toggle the visibility of result labels on the plot. When enabled, this checkable menu item displays result annotations (such as FWHM, peak positions, or other analysis markers) directly on the signal plot. This option is synchronized between Signal and Image panels and persists across sessions. It is only enabled when results are available for the selected signal. Plot results ^^^^^^^^^^^^ Plot the results of analyses performed on the selected signals, with user-defined X and Y axes (e.g. plot the FWHM as a function of the signal index). DataLab-1.1.0/doc/features/signal/menu_create.rst000066400000000000000000000142511514014117600216650ustar00rootroot00000000000000.. _sig-menu-create: Create Signals ============== This section describes how to create new signals from various mathematical models. .. figure:: /images/shots/s_create.png Screenshot of the "Create" menu. When the "Signal Panel" is selected, the menus and toolbars are updated to provide signal-related actions. The "Create" menu allows you to create new signals from various models (see below). New signal ^^^^^^^^^^ Create a new signal from various models: .. list-table:: :header-rows: 1 :widths: 10, 20, 70 :class: longtable * - Icon - Model - Equation * - .. image:: ../../../datalab/data/icons/create/1d-zero.svg :width: 30px - Zero - :math:`y[i] = 0` * - .. image:: ../../../datalab/data/icons/create/1d-normal.svg :width: 30px - Normal distribution - :math:`y[i]` is normally distributed with configurable mean and standard deviation * - .. image:: ../../../datalab/data/icons/create/1d-poisson.svg :width: 30px - Poisson distribution - :math:`y[i]` is Poisson distributed with configurable mean * - .. image:: ../../../datalab/data/icons/create/1d-uniform.svg :width: 30px - Uniform distribution - :math:`y[i]` is uniformly distributed between two configurable bounds * - .. image:: ../../../datalab/data/icons/create/gaussian.svg :width: 30px - Gaussian - :math:`y = y_{0}+\dfrac{A}{\sqrt{2\pi} \cdot \sigma} \cdot \exp\left(-\dfrac{1}{2} \cdot \left(\dfrac{x-x_{0}}{\sigma}\right)^2\right)` * - .. image:: ../../../datalab/data/icons/create/lorentzian.svg :width: 30px - Lorentzian - :math:`y = y_{0}+\dfrac{A}{\sigma \cdot \pi} \cdot \dfrac{1}{1+\left(\dfrac{x-x_{0}}{\sigma}\right)^2}` * - .. image:: ../../../datalab/data/icons/create/voigt.svg :width: 30px - Voigt - :math:`y = y_{0}+A \cdot \dfrac{\Re\left(\exp\left(-z^2\right) \cdot \erfc(-j \cdot z)\right)}{\sqrt{2\pi} \cdot \sigma}` with :math:`z = \dfrac{x-x_{0}-j \cdot \sigma}{\sqrt{2} \cdot \sigma}` * - .. image:: ../../../datalab/data/icons/create/planck.svg :width: 30px - Blackbody (Planck's law) - :math:`y = \dfrac{2 h c^2}{\lambda^5 \left(\exp\left(\dfrac{h c}{\lambda k T}\right)-1\right)}` * - .. image:: ../../../datalab/data/icons/create/sine.svg :width: 30px - Sine - :math:`y = y_{0}+A\sin\left(2\pi \cdot f \cdot x+\phi\right)` * - .. image:: ../../../datalab/data/icons/create/cosine.svg :width: 30px - Cosine - :math:`y = y_{0}+A\cos\left(2\pi \cdot f \cdot x+\phi\right)` * - .. image:: ../../../datalab/data/icons/create/sawtooth.svg :width: 30px - Sawtooth - :math:`y = y_{0}+A \left( 2 \left( f x + \frac{\phi}{2\pi} - \left\lfloor f x + \frac{\phi}{2\pi} + \frac{1}{2} \right\rfloor \right) \right)` * - .. image:: ../../../datalab/data/icons/create/triangle.svg :width: 30px - Triangle - :math:`y = y_{0}+A \sawtooth\left(2 \pi f x + \phi, \text{width} = 0.5\right)` * - .. image:: ../../../datalab/data/icons/create/square.svg :width: 30px - Square - :math:`y = y_0 + A \sgn\left( \sin\left( 2\pi f x + \phi \right) \right)` * - .. image:: ../../../datalab/data/icons/create/sinc.svg :width: 30px - Cardinal sine - :math:`y = y_0 + A \sinc\left(2\pi f x + \phi\right)` * - .. image:: ../../../datalab/data/icons/create/linear_chirp.svg :width: 30px - Linear chirp - :math:`y = y_{0} + A \sin\left(\phi_{0} + 2\pi \left(f_{0}\, x + \frac{1}{2} c\, x^{2}\right)\right)` * - .. image:: ../../../datalab/data/icons/create/step.svg :width: 30px - Step - :math:`y = y_{0}+A \left\{\begin{array}{ll}1 & \text{if } x > x_{0} \\ 0 & \text{otherwise}\end{array}\right.` * - .. image:: ../../../datalab/data/icons/create/exponential.svg :width: 30px - Exponential - :math:`y = y_{0}+A \exp\left(B \cdot x\right)` * - .. image:: ../../../datalab/data/icons/create/logistic.svg :width: 30px - Logistic - :math:`y = y_{0} + \dfrac{A}{1 + \exp\left(-k \left(x - x_{0}\right)\right)}` * - .. image:: ../../../datalab/data/icons/create/pulse.svg :width: 30px - Pulse - :math:`y = y_{0}+A \left\{\begin{array}{ll}1 & \text{if } x_{0} < x < x_{1} \\ 0 & \text{otherwise}\end{array}\right.` * - .. image:: ../../../datalab/data/icons/create/step_pulse.svg :width: 30px - Step Pulse - | :math:`y = \left( \begin{cases} y_0 & \text{if } x < t_0 \\ y_0 + A \cdot \dfrac{x - t_0}{t_r} & \text{if } t_0 \leq x < t_0 + t_r \\ y_0 + A & \text{if } x \geq t_0 + t_r \end{cases} \right) + \mathcal{N}\left(0, \sigma_n\right)` | where: * :math:`t_0` is the pulse start time, * :math:`t_r` is the rise time, * :math:`\sigma_n` is the noise amplitude * - .. image:: ../../../datalab/data/icons/create/square_pulse.svg :width: 30px - Square Pulse - | :math:`y(x) = \left(\begin{cases} y_0 & \text{if } x < t_0 \\ y_0 + A \cdot \dfrac{x - t_0}{t_r} & \text{if } t_0 \leq x < t_0 + t_r \\ y_0 + A & \text{if } t_0 + t_r \leq x < t_1 \\ y_0 + A - A \cdot \dfrac{x - t_1}{t_f} & \text{if } t_1 \leq x < t_1 + t_f \\ y_0 & \text{if } x \geq t_1 + t_f \end{cases} \right) + \mathcal{N}(0, \sigma_n)` | where: * :math:`t_0` is the pulse start time, * :math:`t_r` is the rise time, * :math:`t_f` is the fall time, * :math:`t_1 = t_0 + t_r + d` is the time at which the decay starts, * :math:`\sigma_n` is the noise amplitude * the duration of the plateau :math:`d` is computed as :math:`d = t_{\mathrm{FWHM}} - \dfrac{t_r + t_f}{2}` from the full width at half maximum :math:`t_{\mathrm{FWHM}}` .. warning:: The duration of the plateau :math:`d` should not be negative. * - .. image:: ../../../datalab/data/icons/create/polynomial.svg :width: 30px - Polynomial - :math:`y = y_{0}+A_{0}+A_{1} \cdot x+A_{2} \cdot x^2+\ldots+A_{n} \cdot x^n` * - - Custom - Manual input of X and Y values DataLab-1.1.0/doc/features/signal/menu_edit.rst000066400000000000000000000252101514014117600213440ustar00rootroot00000000000000.. _sig-menu-edit: Manipulate metadata and annotations =================================== This section describes how to manipulate metadata and annotations in DataLab. Signal metadata contains various information about the signal or its representation, such as view settings, Regions Of Interest (ROIs), processing chain history, analysis results, and any other information that you may have added to the metadata of a signal (or that comes from the signal file itself). .. figure:: /images/shots/s_edit.png Screenshot of the "Edit" menu. The "Edit" menu allows you to perform classic editing operations on the current signal or group of signals (create/rename group, move up/down, delete signal/group of signals, etc.). As detailed below, it also allows you to: - Navigate and utilize the processing chain history through actions like "Recompute" and "Select source objects". - Manipulate metadata and annotations associated with the current signal, thanks to the "Metadata" and "Annotations" submenus which provide the following features. Recompute --------- The "Recompute" |recompute| action allows you to recompute the selected signal(s) using their original processing parameters. This is useful when you want to re-execute the processing chain that was used to create a signal, for example after modifying global settings or dependencies. .. |recompute| image:: ../../../datalab/data/icons/edit/recompute.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. note:: This action is only available for signals that were created through processing operations and have stored processing parameters. Select source objects --------------------- The "Select source objects" |goto_source| action allows you to select the source object(s) that were used to create the currently selected signal. This helps trace back the processing history and understand which original signals were used as input for the current result. .. |goto_source| image:: ../../../datalab/data/icons/edit/goto_source.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. note:: This action is only available when exactly one signal is selected and that signal has source object references. Metadata -------- .. figure:: /images/shots/s_edit_metadata.png Screenshot of the "Metadata" submenu. Copy/paste metadata ^^^^^^^^^^^^^^^^^^^ As metadata contains useful information about the signal, it can be copied and pasted from one signal to another by selecting the "Copy metadata" |metadata_copy| and "Paste metadata" |metadata_paste| actions in the "Edit" menu. .. |metadata_copy| image:: ../../../datalab/data/icons/edit/metadata_copy.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |metadata_paste| image:: ../../../datalab/data/icons/edit/metadata_paste.svg :width: 24px :height: 24px :class: dark-light no-scaled-link This feature allows you to tranfer those information from one signal to another: - :ref:`Regions Of Interest (ROIs) `: that is a very efficient way to reuse the same ROI on different signals and easily compare the results of the analysis on those signals - Analyze results, such as peak positions or FHWM intervals (the relevance of transferring such information depends on the context and is up to the user to decide) - Any other information that you may have added to the metadata of a signal .. note:: Copying metadata from a signal to another will overwrite the metadata of the destination signal (for the metadata keys that are common to both signals) or simply add the metadata keys that are not present in the destination signal. Import/export metadata ^^^^^^^^^^^^^^^^^^^^^^ Metadata can also be imported and exported from/to a JSON file using the "Import metadata" |metadata_import| and "Export metadata" |metadata_export| actions in the "Edit" menu. This is exactly the same as the copy/paste metadata feature (see above for more details on the use cases of this feature), but it allows you to save the metadata to a file and then import it back later. .. |metadata_import| image:: ../../../datalab/data/icons/edit/metadata_import.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |metadata_export| image:: ../../../datalab/data/icons/edit/metadata_export.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Delete metadata ^^^^^^^^^^^^^^^ When deleting metadata using the "Delete metadata" |metadata_delete| action in the "Edit" menu, you will be prompted to confirm the deletion of Region of Interests (ROIs) if they are present in the metadata. After this eventual confirmation, the metadata will be deleted, meaning that analysis results, ROIs, and any other information associated with the signal will be lost. .. |metadata_delete| image:: ../../../datalab/data/icons/edit/metadata_delete.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Add metadata ------------ The "Add metadata" |metadata_add| action allows you to add custom metadata items to one or more selected signals. This is useful for tagging signals with experiment IDs, sample names, processing steps, or any other custom information. .. |metadata_add| image:: ../../../datalab/data/icons/edit/metadata_add.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. figure:: /images/shots/s_add_metadata.png Add metadata dialog. When you select "Add metadata..." from the Edit menu, a dialog appears where you can: - **Metadata key**: Enter the name of the metadata field to add - **Value pattern**: Define a pattern for the metadata value using Python format strings - **Conversion**: Choose how to store the value (string, float, integer, or boolean) - **Preview**: See how the metadata will be added to each selected signal The value pattern supports the following placeholders: - ``{title}``: Signal title - ``{index}``: 1-based index of the signal in the selection - ``{count}``: Total number of selected signals - ``{xlabel}``, ``{xunit}``, ``{ylabel}``, ``{yunit}``: Axis labels and units - ``{metadata[key]}``: Access existing metadata values You can also use format modifiers: - ``{title:upper}``: Convert to uppercase - ``{title:lower}``: Convert to lowercase - ``{index:03d}``: Format as 3-digit number with leading zeros **Examples:** - Add experiment ID: key=``experiment_id``, pattern=``EXP_{index:03d}``, conversion=string → Creates metadata like ``experiment_id="EXP_001"`` - Add sample temperature: key=``temperature``, pattern=``{metadata[temp]}``, conversion=float → Copies temperature from existing metadata and converts to float - Mark processed signals: key=``is_processed``, pattern=``true``, conversion=bool → Sets ``is_processed=True`` for all selected signals Annotations ----------- Annotations are visual elements that can be added to signals to highlight specific features, mark regions of interest, or add explanatory notes. DataLab provides a dedicated submenu in the "Edit" menu for managing annotations. .. figure:: /images/shots/s_edit_annotations.png Screenshot of the "Annotations" submenu. Copy/paste annotations ^^^^^^^^^^^^^^^^^^^^^^ Annotations can be copied from one signal and pasted to one or more other signals using the "Copy annotations" |annotations_copy| and "Paste annotations" |annotations_paste| actions. This is useful when you want to apply the same visual markers across multiple signals. .. |annotations_copy| image:: ../../../datalab/data/icons/edit/annotations_copy.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |annotations_paste| image:: ../../../datalab/data/icons/edit/annotations_paste.svg :width: 24px :height: 24px :class: dark-light no-scaled-link The "Paste annotations" action is only enabled when there are annotations in the clipboard (i.e., after using "Copy annotations"). Edit annotations ^^^^^^^^^^^^^^^^ The "Edit annotations" |annotations_edit| action opens a dialog where you can view, add, modify, or remove annotations from the current signal. This provides a visual way to manage all annotations on a signal. .. |annotations_edit| image:: ../../../datalab/data/icons/edit/annotations_edit.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Import/export annotations ^^^^^^^^^^^^^^^^^^^^^^^^^^ Annotations can be saved to and loaded from JSON files (.dlabann extension) using the "Import annotations" |annotations_import| and "Export annotations" |annotations_export| actions. This allows you to: - Save annotation sets for later reuse - Share annotations with colleagues - Archive annotations separately from signal data - Apply the same annotations to different signals across sessions .. |annotations_import| image:: ../../../datalab/data/icons/edit/annotations_import.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |annotations_export| image:: ../../../datalab/data/icons/edit/annotations_export.svg :width: 24px :height: 24px :class: dark-light no-scaled-link The "Export annotations" action is only available when the selected signal has annotations. Delete annotations ^^^^^^^^^^^^^^^^^^ The "Delete annotations" |annotations_delete| action removes all annotations from the selected signal(s). This action is only enabled when the selected signal(s) have annotations. .. |annotations_delete| image:: ../../../datalab/data/icons/edit/annotations_delete.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. note:: Annotations are stored separately from metadata and analysis results. Deleting annotations does not affect ROIs or other metadata items. Signal titles ------------- Signal titles may be considered as metadata from a user point of view, even if they are not stored in the metadata of the signal (but in an attribute of the signal object). The "Edit" menu allows you to: - "Add object title to plot": this action will add a label on top of the signal with its title. - "Copy titles to clipboard" |copy_titles|: this action will copy the titles of the selected signals to the clipboard, which might be useful to paste them in a text editor or in a spreadsheet. Example of the content of the clipboard: .. code-block:: text g001: s001: lorentz(a=1,sigma=1,mu=0,ymin=0) s002: derivative(s001) s003: wiener(s002) g002: derivative(g001) s004: derivative(s001) s005: derivative(s002) s006: derivative(s003) g003: fft(g002) s007: fft(s004) s008: fft(s005) s009: fft(s006) .. |copy_titles| image:: ../../../datalab/data/icons/edit/copy_titles.svg :width: 24px :height: 24px :class: dark-light no-scaled-link DataLab-1.1.0/doc/features/signal/menu_file.rst000066400000000000000000000112551514014117600213420ustar00rootroot00000000000000.. _sig-menu-file: Open and save Signals ===================== This section describes how to open and save signals (and workspaces). .. note:: For creating new signals from mathematical models, see :ref:`sig-menu-create`. .. figure:: /images/shots/s_file.png Screenshot of the "File" menu. When the "Signal Panel" is selected, the menus and toolbars are updated to provide signal-related actions. The "File" menu allows you to: - Open, save and close signals (see below). - Save and restore the current workspace or browse HDF5 files (see :ref:`overview`). - Edit DataLab preferences (see :ref:`settings`). .. _open_signal: Open signal ^^^^^^^^^^^ Create a new signal from the following supported filetypes: .. list-table:: :header-rows: 1 * - File type - Extensions * - Text files - .txt, .csv * - NumPy arrays - .npy * - MAT-Files - .mat * - FT-Lab files - .sig Open from directory ^^^^^^^^^^^^^^^^^^^ Open multiple signals from a specified directory. Save signal ^^^^^^^^^^^ Save current signal to the following supported filetypes: .. list-table:: :header-rows: 1 * - File type - Extensions * - Text files - .csv Save signals to directory ^^^^^^^^^^^^^^^^^^^^^^^^^ Save all selected signals to a specified directory, with configurable filename pattern and signal format. .. figure:: /images/shots/s_save_to_directory.png Save signals to directory dialog. When you select "Save to directory..." from the File menu, a dialog appears where you can: - **Directory**: Choose the target directory where signals will be saved - **Filename pattern**: Define a pattern for the filenames using Python format strings - **File extension**: Select the output format (.csv, .txt, etc.) - **Overwrite**: Choose whether to overwrite existing files - **Preview**: See the list of files that will be created (with object IDs) The filename pattern supports the following placeholders: - ``{title}``: Signal title - ``{index}``: 1-based index of the signal in the selection (with zero-padding) - ``{count}``: Total number of selected signals - ``{xlabel}``, ``{xunit}``, ``{ylabel}``, ``{yunit}``: Axis labels and units - ``{metadata[key]}``: Access metadata values You can also use format modifiers, for example ``{index:03d}`` will format the index with 3 digits zero-padding (001, 002, 003, etc.). Import text file ^^^^^^^^^^^^^^^^ DataLab can natively import signal files (e.g. CSV, NPY, etc.). However some specific text file formats may not be supported. In this case, you can use the `Import text file` feature, which allows you to import a text file and convert it to a signal. This feature is accessible from the `File` menu, under the `Import text file` option. It opens an import wizard that guides you through the process of importing the text file. Step 1: Select the source ------------------------- The first step is to select the source of the text file. You can either select a file from your computer or the clipboard if you have copied the text from another application. .. figure:: ../../images/import_text_file/s_01.png :alt: Step 1: Select the source :align: center Step 1: Select the source Step 2: Preview and configure the import ----------------------------------------- The second step consists of configuring the import and previewing the result. You can configure the following options: - **Delimiter**: The character used to separate the values in the text file. - **Comments**: The character used to indicate that the line is a comment and should be ignored. - **Rows to Skip**: The number of rows to skip at the beginning of the file. - **Maximum Number of Rows**: The maximum number of rows to import. If the file contains more rows, they will be ignored. - **Transpose**: If checked, the rows and columns will be transposed. - **Data type**: The destination data type of the imported data. - **First Column is X**: If checked, the first column will be used as the X axis. When you are done configuring the import, click the `Apply` button to see the result. .. figure:: ../../images/import_text_file/s_02.png :alt: Step 2: Configure the import :align: center Step 2: Configure the import .. figure:: ../../images/import_text_file/s_03.png :alt: Step 2: Preview the result :align: center Step 2: Preview the result Step 3: Show graphical representation ------------------------------------- The third step shows a graphical representation of the imported data. You can use the `Finish` button to import the data into DataLab workspace. .. figure:: ../../images/import_text_file/s_04.png :alt: Step 3: Show graphical representation :align: center Step 3: Show graphical representationDataLab-1.1.0/doc/features/signal/menu_operations.rst000066400000000000000000000211731514014117600226060ustar00rootroot00000000000000.. _sig-menu-operations: Operations on Signals ===================== This section describes the operations that can be performed on signals. .. seealso:: :ref:`sig-menu-processing` for more information on signal processing features, or :ref:`sig-menu-analysis` for information on analysis features on signals. .. figure:: /images/shots/s_operation.png Screenshot of the "Operations" menu. When the "Signal Panel" is selected, the menus and toolbars are updated to provide signal-related actions. The "Operations" menu allows you to perform various operations on the selected signals, such as arithmetic operations, peak detection, or convolution. Operations with a constant -------------------------- Create a new signal which is the result of a constant operation on each selected signal: .. list-table:: :header-rows: 1 :widths: 40, 60 * - Operation - Description * - |constant_add| Addition - :math:`y_{k} = y_{k-1} + c` * - |constant_subtract| Subtraction - :math:`y_{k} = y_{k-1} - c` * - |constant_multiply| Multiplication - :math:`y_{k} = y_{k-1} \times c` * - |constant_divide| Division - :math:`y_{k} = \dfrac{y_{k-1}}{c}` .. |constant_add| image:: ../../../datalab/data/icons/operations/constant_add.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |constant_subtract| image:: ../../../datalab/data/icons/operations/constant_subtract.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |constant_multiply| image:: ../../../datalab/data/icons/operations/constant_multiply.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |constant_divide| image:: ../../../datalab/data/icons/operations/constant_divide.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Basic arithmetic operations --------------------------- .. list-table:: :header-rows: 1 :widths: 40, 60 * - Operation - Description * - |sum| Sum - :math:`y_{M} = \sum_{k=0}^{M-1}{y_{k}}` * - |difference| Difference - :math:`y_{2} = y_{1} - y_{0}` * - |product| Product - :math:`y_{M} = \prod_{k=0}^{M-1}{y_{k}}` * - |division| Division - :math:`y_{2} = \dfrac{y_{1}}{y_{0}}` * - |inverse| Inverse - :math:`y_{2} = \dfrac{1}{y_{1}}` * - |exponential| Exponential - :math:`y_{2} = \exp(y_{1})` * - |logarithm| Logarithm (base 10) - :math:`y_{2} = \log_{10}(y_{1})` .. |sum| image:: ../../../datalab/data/icons/operations/sum.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |difference| image:: ../../../datalab/data/icons/operations/difference.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |product| image:: ../../../datalab/data/icons/operations/product.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |division| image:: ../../../datalab/data/icons/operations/division.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |inverse| image:: ../../../datalab/data/icons/operations/inverse.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |exponential| image:: ../../../datalab/data/icons/operations/exp.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |logarithm| image:: ../../../datalab/data/icons/operations/log10.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Convolution and Deconvolution ------------------------------ .. list-table:: :header-rows: 1 :widths: 40, 60 * - Operation - Implementation * - |convolution| Convolution - Based on `scipy.signal.convolve `_ * - |deconvolution| Deconvolution - Frequency domain deconvolution .. |convolution| image:: ../../../datalab/data/icons/operations/convolution.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |deconvolution| image:: ../../../datalab/data/icons/operations/deconvolution.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Absolute value and complex signal operations -------------------------------------------- .. list-table:: :header-rows: 1 :widths: 40, 60 * - Operation - Description * - |abs| Absolute value - :math:`y_{k} = |y_{k-1}|` * - |phase| Phase (argument) - :py:func:`sigima.proc.signal.phase` * - |complex_from_magnitude_phase| Combine with phase - Consider current signal as the module and allow to select a signal representing the phase to merge them in a complex signal :py:func:`sigima.proc.signal.complex_from_magnitude_phase` * - |re| Real part - :math:`y_{k} = \Re(y_{k-1})` * - |im| Imaginary part - :math:`y_{k} = \Im(y_{k-1})` * - |complex_from_real_imag| Combine with imaginary part - Consider current signal as the real part and allow to select a signal representing the imaginary part to merge them in a complex signal :py:func:`sigima.proc.signal.complex_from_real_imag` .. |abs| image:: ../../../datalab/data/icons/operations/abs.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |phase| image:: ../../../datalab/data/icons/operations/phase.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |complex_from_magnitude_phase| image:: ../../../datalab/data/icons/operations/phase.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |re| image:: ../../../datalab/data/icons/operations/re.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |im| image:: ../../../datalab/data/icons/operations/im.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |complex_from_real_imag| image:: ../../../datalab/data/icons/operations/im.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Data type conversion -------------------- The "Convert data type" |convert_dtype| action allows you to convert the data type of the selected signals. .. |convert_dtype| image:: ../../../datalab/data/icons/operations/convert_dtype.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. note:: Data type conversion relies on :py:func:`numpy.ndarray.astype` function with the default parameters (`casting='unsafe'`). Statistics between signals ^^^^^^^^^^^^^^^^^^^^^^^^^^^ Create a new signal which is the result of a statistical operation on each point of the selected signals: .. list-table:: :header-rows: 1 :widths: 40, 60 * - Operation - Description * - |average| Average - :math:`y_{M} = \dfrac{1}{M}\sum_{k=0}^{M-1}{y_{k}}` * - |standard_deviation| Standard Deviation - :math:`y_{M} = \sqrt{\dfrac{1}{M}\sum_{k=0}^{M-1}{(y_{k} - \bar{y})^{2}}}` .. |average| image:: ../../../datalab/data/icons/operations/average.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |standard_deviation| image:: ../../../datalab/data/icons/operations/std.svg :width: 24px :height: 24px :class: dark-light no-scaled-link Other mathematical functions ---------------------------- .. list-table:: :header-rows: 1 :widths: 40, 60 * - Function - Description * - |power| Power - :math:`y_{k} = y_{k}^{n}` * - |sqrt| Square root - :math:`y_{k} = \sqrt{y_{k}}` * - |derivative| Derivative - Based on `numpy.gradient `_ * - |integral| Integral - Based on `scipy.integrate.cumulative_trapezoid `_ * - |signal_to_image| Assemble signals into image - Create a 2D image by assembling selected 1D signals as rows or columns, with optional normalization. .. |power| image:: ../../../datalab/data/icons/operations/power.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |sqrt| image:: ../../../datalab/data/icons/operations/sqrt.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |derivative| image:: ../../../datalab/data/icons/operations/derivative.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |integral| image:: ../../../datalab/data/icons/operations/integral.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |signal_to_image| image:: ../../../datalab/data/icons/operations/signals_to_image.svg :width: 24px :height: 24px :class: dark-light no-scaled-link DataLab-1.1.0/doc/features/signal/menu_processing.rst000066400000000000000000000425021514014117600225760ustar00rootroot00000000000000.. _sig-menu-processing: Processing Signals ================== This section describes the signal processing features available in DataLab. .. seealso:: :ref:`sig-menu-operations` for more information on operations that can be performed on signals, or :ref:`sig-menu-analysis` for information on analysis features on signals. .. figure:: /images/shots/s_processing.png Screenshot of the "Processing" menu. When the "Signal Panel" is selected, the menus and toolbars are updated to provide signal-related actions. The "Processing" menu allows you to perform various processing on the selected signals, such as smoothing, normalization, or interpolation. Axis transformation ^^^^^^^^^^^^^^^^^^^ Linear calibration ~~~~~~~~~~~~~~~~~~ Create a new signal which is a linear calibration of each selected signal with respect to X or Y axis: .. list-table:: :header-rows: 1 :widths: 40, 60 * - Parameter - Linear calibration * - X-axis - :math:`x_{1} = a.x_{0} + b` * - Y-axis - :math:`y_{1} = a.y_{0} + b` Swap X/Y axes ~~~~~~~~~~~~~ Create a new signal which is the result of swapping X/Y data. Reverse X-axis ~~~~~~~~~~~~~~ Create a new signal which is the result of reversing X data. Replace X by other signal's Y ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Create a new signal by combining two signals, using Y values from the second signal as X coordinates for the first signal's Y values. This feature is particularly useful for calibration scenarios, such as: - Applying wavelength calibration to spectroscopy data - Using a calibrated reference signal to rescale measurement data - Combining signals where one contains calibration points The two signals must have the same number of points. The operation directly uses the Y arrays without interpolation. .. list-table:: :header-rows: 1 :widths: 40, 60 * - Input - Description * - Signal 1 - Provides the Y data for the output signal * - Signal 2 - Provides the Y data that becomes the X coordinates of the output signal X-Y mode ~~~~~~~~ Create a new signal by simulating the X-Y mode of an oscilloscope. This operation plots one signal as a function of another by: 1. Finding the overlapping X range between both signals 2. Resampling both signals onto a common X grid within that overlap 3. Using the resampled Y values as coordinates (first signal's Y as X, second signal's Y as Y) .. note:: This is different from "Replace X by other signal's Y" as it performs interpolation to handle signals with different X arrays. Convert to Cartesian coordinates ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Create a new signal which is the result of converting polar coordinates to Cartesian coordinates. This function assumes that the x-axis represents the radius and the y-axis the angle. .. warning:: A radius cannot be negative. Any negative value is clipped to 0. Convert to polar coordinates ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Create a new signal which is the result of converting Cartesian coordinates to polar coordinates. Level adjustment ^^^^^^^^^^^^^^^^ Normalize ~~~~~~~~~ Create a new signal which is the normalization of each selected signal by maximum, amplitude, sum, energy or RMS: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Normalization * - Maximum - :math:`y_{1}= \dfrac{y_{0}}{\max\left(y_{0}\right)}` * - Amplitude - :math:`y_{1}= \dfrac{y_{0}'}{\max\left(y_{0}'\right)}` with :math:`y_{0}'=y_{0}-\min\left(y_{0}\right)` * - Area - :math:`y_{1}= \dfrac{y_{0}}{\sum_{n=0}^{N}y_{0}[n]}` * - Energy - :math:`y_{1}= \dfrac{y_{0}}{\sqrt{\sum_{n=0}^{N}\left|y_{0}[n]\right|^2}}` * - RMS - :math:`y_{1}= \dfrac{y_{0}}{\sqrt{\dfrac{1}{N}\sum_{n=0}^{N}\left|y_{0}[n]\right|^2}}` Clipping ~~~~~~~~ Create a new signal which is the result of clipping each selected signal. Offset correction ~~~~~~~~~~~~~~~~~ Create a new signal which is the result of offset correction of each selected signal. This operation is performed by subtracting the signal baseline which is estimated by the mean value of a user-defined range. Noise addition ^^^^^^^^^^^^^^ Generate new signals by adding the same noise to each selected signal. The available noise types are: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Noise - Description * - Gaussian - Normal distribution * - Uniform - Uniform distribution * - Poisson - Poisson distribution Noise reduction ^^^^^^^^^^^^^^^ Create a new signal which is the result of noise reduction of each selected signal. The following filters are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Filter - Formula/implementation * - Gaussian filter - `scipy.ndimage.gaussian_filter `_ * - Moving average - `scipy.ndimage.uniform_filter `_ * - Moving median - `scipy.ndimage.median_filter `_ * - Wiener filter - `scipy.signal.wiener `_ Fourier analysis ^^^^^^^^^^^^^^^^ Zero padding ~~~~~~~~~~~~ Create a new signal which is the result of zero padding of each selected signal. The following parameters are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - Strategy - Zero padding strategy (see below) * - Number of points - Custom length (if `strategy` is "custom") Zero padding strategy refers to the method used to increase the length of the signal, and it can be one of the following: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Strategy - Description * - next_pow2 - Next power of 2 (e.g. 1024 → 2048) * - double - Double the length (e.g. 1024 → 2048) * - triple - Triple the length (e.g. 1024 → 3072) * - custom - Custom length (user-defined) FFT related functions ~~~~~~~~~~~~~~~~~~~~~ Create a new signal which is the result of a Fourier analysis of each selected signal. The following functions are available: .. list-table:: :header-rows: 1 :widths: 20, 30, 50 * - Function - Description - Formula/implementation * - FFT - Fast Fourier Transform - `numpy.fft.fft `_ * - Inverse FFT - Inverse Fast Fourier Transform - `numpy.fft.ifft `_ * - Magnitude spectrum - Optional: output in decibels (dB) - | :math:`y_{1} = \left|\FFT\left(y_{0}\right)\right|` or | :math:`y_{1} = 20 \log_{10} \left(\left|\FFT\left(y_{0}\right)\right|\right)` (dB) * - Phase spectrum - Phase of the FFT expressed in degrees, using `numpy.angle `_ - :math:`y_{1} = \angle \FFT\left(y_{0}\right)` * - Power spectral density (PSD) - Optional: output in decibels (dB). PSD is estimated using Welch's method (see `scipy.signal.welch `_) - | :math:`y_{1} = \PSD\left(y_{0}\right)` or | :math:`y_{1} = 10 \log_{10} \left(\PSD\left(y_{0}\right)\right)` (dB) .. note:: FFT and inverse FFT are performed using frequency shifting if the option is enabled in DataLab settings (see :ref:`settings`). Frequency filters ^^^^^^^^^^^^^^^^^ Create a new signal which is the result of applying a frequency filter to each selected signal. The following filters are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Filter - Description * - |lowpass| Low-pass - Filter out high frequencies, above a cutoff frequency * - |highpass| High-pass - Filter out low frequencies, below a cutoff frequency * - |bandpass| Band-pass - Filter out frequencies outside a range * - |bandstop| Band-stop - Filter out frequencies inside a range .. |lowpass| image:: ../../../datalab/data/icons/processing/lowpass.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |highpass| image:: ../../../datalab/data/icons/processing/highpass.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |bandpass| image:: ../../../datalab/data/icons/processing/bandpass.svg :width: 24px :height: 24px :class: dark-light no-scaled-link .. |bandstop| image:: ../../../datalab/data/icons/processing/bandstop.svg :width: 24px :height: 24px :class: dark-light no-scaled-link For each filter, the following methods are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Method - Description * - Bessel - Bessel filter, using SciPy's `scipy.signal.bessel `_ function * - Brick wall - Ideal (rectangular) filter in the frequency domain * - Butterworth - Butterworth filter, using SciPy's `scipy.signal.butter `_ function * - Chebyshev I - Chebyshev type I filter, using SciPy's `scipy.signal.cheby1 `_ function * - Chebyshev II - Chebyshev type II filter, using SciPy's `scipy.signal.cheby2 `_ function * - Elliptic - Elliptic filter, using SciPy's `scipy.signal.ellip `_ function Fitting ^^^^^^^ Fit a model to each selected signal. This can be done automatically or through an interactive curve fitting dialog. .. list-table:: :header-rows: 1 :widths: 20, 80 * - Model - Equation * - Linear - :math:`y = c_{0} + c_{1} \cdot x` * - Polynomial - :math:`y = c_{0} + c_{1} \cdot x + c_{2} \cdot x^2 + ... + c_{n} \cdot x^n` * - Gaussian - :math:`y = y_{0} + \dfrac{A}{\sqrt{2 \pi} \sigma} \exp\left(-\dfrac{1}{2} \left(\dfrac{x - x_{0}}{\sigma}\right)^2\right)` * - Lorentzian - :math:`y = y_{0} + \dfrac{A}{\pi \sigma} \cdot \dfrac{1}{1+\left(\dfrac{x-x_{0}}{\sigma}\right)^2}` * - Voigt - :math:`y = y_{0} + A \cdot \dfrac{\Re\left(\exp(-z^2) \cdot \operatorname{erfc}(-j \cdot z)\right)}{\sqrt{2 \pi} \sigma}` with :math:`z = \dfrac{x - x_{0} - j \cdot \sigma}{\sqrt{2} \sigma}` * - Multi-Gaussian - :math:`y = y_{0} + \sum_{i=0}^{N}\dfrac{A_{i}}{\sqrt{2 \pi} \sigma_{i}} \exp\left(-\dfrac{1}{2} \left(\dfrac{x - x_{0,i}}{\sigma_{i}}\right)^2\right)` * - Multi-Lorentzian - :math:`y = y_{0} + \sum_{i=0}^{N}\dfrac{A_{i}}{\pi \sigma_{i}} \cdot \dfrac{1}{1 + \left(\dfrac{x - x_{0,i}}{\sigma_{i}}\right)^2}` * - Planck - :math:`y = y_{0} + A\cdot \dfrac{2h \cdot c^2}{\lambda^5} \cdot \left(\exp\left(\dfrac{h \cdot c}{\lambda \cdot k \cdot T}\right)-1\right)^{-1}` * - Two half Gaussians - | :math:`y = y_{0} + A \cdot \dfrac{1}{\sqrt{2 \pi} \sigma_{0}} \cdot \exp\left(-\dfrac{1}{2}\left(\dfrac{x - x_{0}}{\sigma_{0}}\right)^2\right)` if :math:`x < x_{0}` | :math:`y = y_{0} + A \cdot \dfrac{1}{\sqrt{2 \pi} \sigma_{1}} \cdot \exp\left(-\dfrac{1}{2}\left(\dfrac{x - x_{1}}{\sigma_{1}}\right)^2\right)` otherwise * - Two back-to-back exponentials - | :math:`y = y_{0} + A_{1} \cdot \exp\left(-x/\tau_{1}\right)` if :math:`x < 0` | :math:`y = y_{0} + A_{2} \cdot \exp(-x/\tau_{2})` otherwise * - Exponential - :math:`y = y_{0} + A \exp\left(B \cdot x\right)` * - Sinusoidal - :math:`y = y_{0} + A \sin\left(2 \pi f \cdot x + \phi\right)` * - Cumulative Distribution Function (CDF) - :math:`y = y_{0} + A \erf\left(\dfrac{x - x_{0}}{\sqrt{2} \sigma}\right)` Windowing ^^^^^^^^^ Create a new signal which is the result of applying a window function to each selected signal. The following window functions are available: .. list-table:: :header-rows: 1 :widths: 20, 80 * - Window function - Reference * - Barthann - :py:func:`scipy.signal.windows.barthann` * - Bartlett - :py:func:`numpy.bartlett` * - Blackman - :py:func:`scipy.signal.windows.blackman` * - Blackman-Harris - :py:func:`scipy.signal.windows.blackmanharris` * - Bohman - :py:func:`scipy.signal.windows.bohman` * - Boxcar (rectangular) - :py:func:`scipy.signal.windows.boxcar` * - Cosine - :py:func:`scipy.signal.windows.cosine` * - Exponential - :py:func:`scipy.signal.windows.exponential` * - Flat top - :py:func:`scipy.signal.windows.flattop` * - Gaussian - :py:func:`scipy.signal.windows.gaussian` * - Hamming - :py:func:`numpy.hamming` * - Hann - :py:func:`numpy.hanning` * - Kaiser - :py:func:`scipy.signal.windows.kaiser` * - Lanczos - :py:func:`scipy.signal.windows.lanczos` * - Nuttall - :py:func:`scipy.signal.windows.nuttall` * - Parzen - :py:func:`scipy.signal.windows.parzen` * - Taylor - :py:func:`scipy.signal.windows.taylor` * - Tukey - :py:func:`scipy.signal.windows.tukey` Detrending ^^^^^^^^^^ Create a new signal which is the detrending of each selected signal. This features is based on SciPy's `scipy.signal.detrend `_ function. The following parameters are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - Method - Detrending method: 'linear' or 'constant'. See SciPy's `scipy.signal.detrend `_ function. Interpolation ^^^^^^^^^^^^^ Create a new signal which is the interpolation of each selected signal with respect to a second signal X-axis (which might be the same as one of the selected signals). The following interpolation methods are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Method - Description * - Linear - Linear interpolation, using using NumPy's `interp `_ function * - Spline - Cubic spline interpolation, using using SciPy's `scipy.interpolate.splev `_ function * - Quadratic - Quadratic interpolation, using using NumPy's `polyval `_ function * - Cubic - Cubic interpolation, using using SciPy's `Akima1DInterpolator `_ class * - Barycentric - Barycentric interpolation, using using SciPy's `BarycentricInterpolator `_ class * - PCHIP - Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) interpolation, using using SciPy's `PchipInterpolator `_ class Resampling ^^^^^^^^^^ Create a new signal which is the resampling of each selected signal. The following parameters are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Parameter - Description * - Method - Interpolation method (see previous section) * - Fill value - Interpolation fill value (see previous section) * - Xmin - Minimum X value * - Xmax - Maximum X value * - Mode - Resampling mode: step size or number of points * - Step size - Resampling step size * - Number of points - Resampling number of points Stability analysis ^^^^^^^^^^^^^^^^^^ Create a new signal which is the result of a stability analysis of each selected signal. The following stability analysis methods are available: .. list-table:: :header-rows: 1 :widths: 25, 75 * - Function - Description * - Allan variance - Measure of the stability of a signal: defined as the variance of the difference between two successive measurements as a function of the time interval between them. * - Allan deviation - Square root of the Allan variance. * - Overlapping Allan deviation - A more robust version of the Allan variance that overlaps successive segments to improve statistical confidence. * - Modified Allan variance - A variation of the Allan variance that accounts for phase noise by introducing a filtering operation. * - Hadamard variance - An alternative to Allan variance, more robust to linear frequency drift in the signal * - Total variance - Extends the Allan variance concept to cover all possible averaging intervals. * - Time deviation - Derived from Allan deviation, quantifies stability in terms of time rather than frequency. .. note:: The "All stability features" option allows to compute all stability analysis methods at once. DataLab-1.1.0/doc/features/signal/menu_roi.rst000066400000000000000000000074751514014117600212250ustar00rootroot00000000000000.. _sig-menu-roi: Regions Of Interest (ROI) ========================= This section describes how to manipulate Regions Of Interest (ROIs) for signals in DataLab. .. figure:: /images/shots/s_roi.png Screenshot of the "ROI" menu. The "ROI" menu allows you to manage Regions Of Interest (ROIs) associated with the current signal. .. seealso:: For more information about signal metadata, see the :ref:`sig-menu-edit` section. The Regions Of Interest (ROI) are signal areas that are defined by the user to perform specific operations, processing, or analysis on them. ROI are taken into account almost in all computing features in DataLab: - The "Operations" menu features are done only on the ROI if one is defined (except if the operation changes the number of points, like interpolation or resampling). - The "Processing" menu actions are performed only on the ROI if one is defined (except if the destination signal data type is different from the source's, like in the Fourier analysis features). - The "Analysis" menu actions are done only on the ROI if one is defined. .. note:: ROI are stored as metadata, and thus attached to signal. The "ROI" menu allows you to: - "Edit graphically" |edit_roi|: open a dialog box to manage ROI associated with the selected signal (add, remove, move, resize, etc.). The ROI definition dialog is exactly the same as ROI extraction (see below): the ROI is defined by moving the position and adjusting the width of an horizontal range. .. figure:: /images/shots/s_roi_editor.png A signal with an ROI. - "Edit numerically": open a dialog box to edit the parameters of the selected ROIs numerically (i.e. using a simple form). This allows you to define or modify ROIs based on numerical values. - "Extract" |signal_roi|: extract the defined ROI from the selected signals. This will create a new signal for each ROI (or a single signal, if the "Extract all ROIs into a single signal" option is selected in the dialog), with the same metadata as the original signal, but with the data corresponding to the ROI only. The new signals will be added to the current workspace. - "Copy" |roi_copy|: copy the defined ROI from the selected signals to the clipboard. This allows you to paste the ROI into another signal or use it in other operations. - "Paste" |roi_paste|: paste the copied ROI from the clipboard to the selected signals. This will add the ROI to the signals, allowing you to define or modify ROIs based on previously copied ones. - "Import" |roi_import|: import ROIs from a file. This allows you to load previously saved ROIs into the current signal. - "Export" |roi_export|: export the defined ROIs to a file. This allows you to save the ROIs for later use or to share them with others. - "Remove all" |roi_delete|: remove all defined ROI for the selected signals. .. figure:: /images/shots/s_roi_dialog.png ROI extraction dialog: the ROI is defined by moving the position and adjusting the width of an horizontal range. .. |edit_roi| image:: ../../../datalab/data/icons/roi/roi.svg :width: 24px :height: 24px :class: dark-light .. |signal_roi| image:: ../../../datalab/data/icons/roi/roi_sig.svg :width: 24px :height: 24px :class: dark-light .. |roi_copy| image:: ../../../datalab/data/icons/roi/roi_copy.svg :width: 24px :height: 24px :class: dark-light .. |roi_paste| image:: ../../../datalab/data/icons/roi/roi_paste.svg :width: 24px :height: 24px :class: dark-light .. |roi_import| image:: ../../../datalab/data/icons/roi/roi_import.svg :width: 24px :height: 24px :class: dark-light .. |roi_export| image:: ../../../datalab/data/icons/roi/roi_export.svg :width: 24px :height: 24px :class: dark-light .. |roi_delete| image:: ../../../datalab/data/icons/roi/roi_delete.svg :width: 24px :height: 24px :class: dark-light DataLab-1.1.0/doc/features/signal/menu_view.rst000066400000000000000000000114551514014117600213770ustar00rootroot00000000000000.. _sig-menu-view: View options for Signals ======================== .. figure:: /images/shots/s_view.png Screenshot of the "View" menu. When the "Signal Panel" is selected, the menus and toolbars are updated to provide signal-related actions. The "View" menu allows you to visualize the current signal or group of signals. It also allows you to show/hide titles, to enable/disable anti-aliasing, or to refresh the visualization. View in a new window ^^^^^^^^^^^^^^^^^^^^ Open a new window to visualize the selected signals. This option allows you to visualize the selected signals in a separate window, in which you can visualize the data more comfortably (e.g., by maximizing the window) and you can also annotate the data. When you click on the button "Annotations" in the toolbar of the new window, the annotation editing mode is activated (see the section "Edit annotations" below). Edit annotations ^^^^^^^^^^^^^^^^ Open a new window to edit annotations. This option allows you to show the selected signals in a separate window, in which you can visualize the data more comfortably (e.g., by maximizing the window) as well as to add or edit annotations. Annotations are used to add comments or labels to the data, and they can be used to highlight specific events or regions of interest. .. note:: The annotations are saved in the metadata of the signal, so they are persistent and will be displayed every time you visualize the signal. .. figure:: /images/annotations/signal_annotations1.png Annotations may be added in the separate view. The typical workflow to edit annotations is as follows: 1. Select the "Edit annotations" option. 2. In the new window, add annotations by clicking on the corresponding buttons at the bottom of the window. 3. Eventually, customize the annotations by changing their properties (e.g., the text, the color, the position, etc.) using the "Parameters" option in the context menu of the annotations. 4. When you are done, click on the "OK" button to save the annotations. This will close the window and the annotations will be saved in the metadata of the signal and will be displayed in the main window. .. figure:: /images/annotations/signal_annotations2.png Annotations are now part of the signal metadata. .. note:: Annotations may be copied from a signal to another by using the "copy/paste metadata" features. Auto-refresh ^^^^^^^^^^^^ Automatically refresh the visualization when the data changes: - When enabled (default), the plot view is automatically refreshed when the data changes. - When disabled, the plot view is not refreshed until you manually refresh it by using the "Refresh manually" menu entry. Even though the refresh algorithm is optimized, it may still take some time to refresh the plot view when the data changes, especially when the data set is large. Therefore, you may want to disable the auto-refresh feature when you are working with large data sets, and enable it again when you are done. This will avoid unnecessary refreshes. Show only first selected signal ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Toggle between showing all selected signals or only the first one. When this option is enabled, only the first selected signal is displayed in the plot view. This may be useful when multiple signals are selected and focusing on a single signal is (temporarily) preferred. Refresh manually ^^^^^^^^^^^^^^^^ Refresh the visualization manually. This triggers a refresh of the plot view. It is useful when the auto-refresh feature is disabled, or when you want to force a refresh of the plot view. Show graphical object titles ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Show/hide titles of analysis results or annotations. This option allows you to show or hide the titles of the graphical objects (e.g., the titles of the analysis results or annotations). Hiding the titles can be useful when you want to visualize the data without any distractions, or if there are too many titles and they are overlapping. Curve anti-aliasing ^^^^^^^^^^^^^^^^^^^ Enable/disable anti-aliasing of curves. Anti-aliasing makes the curves look smoother, but it may also make them look less sharp. .. note:: Anti-aliasing is enabled by default. .. warning:: Anti-aliasing may slow down the visualization, especially when working with large data sets. Reset curve styles ^^^^^^^^^^^^^^^^^^ Reset the curve style cycle to the beginning. When plotting curves, DataLab automatically assigns a color and a line style to each curve. Both parameters are chosen from a predefined list of colors and line styles, and are assigned in a round-robin fashion. This menu entry allows you to reset the curve styles, so that the next time you plot curves, the first curve will be assigned the first color and the first line style of the predefined lists, and the loop will start again from there.DataLab-1.1.0/doc/features/validation/000077500000000000000000000000001514014117600175165ustar00rootroot00000000000000DataLab-1.1.0/doc/features/validation/functional.rst000066400000000000000000000040111514014117600224060ustar00rootroot00000000000000.. _functional_validation: Functional validation ===================== .. meta:: :description: Validation in DataLab, the open-source scientific data analysis and visualization platform :keywords: DataLab, scientific, data, analysis Strategy -------- DataLab functional validation is based a classic test strategy, with a strong emphasis on automated testing. Apart from one or two manual tests (e.g. load test), all tests are automated (more than 99% of the tests are automated). Writing tests follows the TDD (Test-Driven Development) principle: - When *a new feature is developed*, the developer writes the tests first. The tests are then executed to ensure that they fail. The developer then implements the feature, and the tests are executed again to ensure that they pass. - When *a bug is reported*, the developer writes a test that reproduces the bug. The test is executed to ensure that it fails. The developer then fixes the bug, and the test is executed again to ensure that it passes. Depending on the abstraction level, unit tests and/or application tests are written. When writing both types of tests, the developer starts with the unit tests and then writes the application tests. Types of tests -------------- The functional validation of DataLab is based on two main types of tests: - **Unit tests** (test scripts named ``*_unit_test.py``): Test individual functions or methods. All unit tests are automated. - **Application tests** (test scripts named ``*_app_test.py``): Test the interaction between components (integration tests), or the application as a whole. All application tests are automated. Implementation -------------- The tests are implemented using the `pytest `_ framework. Many existing tests may be derived from to create new tests. Executing tests --------------- To execute the tests, the developer uses the command line interface. See section :ref:`run_functional_validation_tests` for more information on how to run functional validation tests.DataLab-1.1.0/doc/features/validation/status.rst000066400000000000000000000046341514014117600216020ustar00rootroot00000000000000.. _validation_status: Validation Status of DataLab ============================ .. meta:: :description: Validation in DataLab, the open-source scientific data analysis and visualization platform :keywords: DataLab, scientific, data, analysis, validation, ground-truth, analytical Functional validation --------------------- In DataLab, functional validation is based on a classic test strategy (see :ref:`functional_validation`). .. list-table:: :header-rows: 1 :align: left * - Package - Coverage - Number of tests * - DataLab - ~82% - >130 * - Sigima - ~90% - >570 Technical validation --------------------- This paragraph provides the validation status of compute functions in DataLab (this is what we call technical validation, see :ref:`scientific_validation`). .. note:: This is a work in progress: the tables below are updated continuously as new functions are validated or test code is adapted (the tables are generated from the test code). Some functions are already validated but do not appear in the list below yet, while others are still in the validation process. .. warning:: The validation status must not be confused with the test coverage. The validation status indicates whether the function has been validated against ground-truth data or analytical models. The test coverage indicates the percentage of the code that is executed by the test suite, but it does not necessarily take into account the correctness of the results (DataLab's test coverage is around 90%). .. csv-table:: Validation Statistics :file: ../../validation_statistics.csv :header: Category, Signal, Image, Total Signal Compute Functions ^^^^^^^^^^^^^^^^^^^^^^^^ The table below shows the validation status of signal compute functions in DataLab. It is automatically generated from the source code. .. csv-table:: Validation status of signal compute functions :file: ../../validation_status_signal.csv :header: Compute function, Description, Test function :widths: 30, 40, 30 Image Compute Functions ^^^^^^^^^^^^^^^^^^^^^^^ The table below shows the validation status of image compute functions in DataLab. It is automatically generated from the source code. .. csv-table:: Validation status of image compute functions :file: ../../validation_status_image.csv :header: Compute function, Description, Test function :widths: 30, 40, 30 DataLab-1.1.0/doc/features/validation/technical.rst000066400000000000000000000061261514014117600222070ustar00rootroot00000000000000.. _scientific_validation: Technical Validation ===================== .. meta:: :description: Validation in DataLab, the open-source scientific data analysis and visualization platform :keywords: DataLab, scientific, data, analysis, validation, ground-truth, analytical DataLab technical validation is based on two key concepts: **ground-truth data** and **analytical validation**. Ground-truth data ----------------- Ground-truth data is data that is known to be correct. It is used to validate the results of computational analysis. In DataLab, ground-truth data may be obtained from a variety of sources, including: - Experimental data - Simulated data - Synthetic data - Data from a trusted source Analytical validation --------------------- Analytical validation is the process of comparing the results of computational analysis to ground-truth data. This is done to ensure that the results are accurate and reliable. In DataLab, analytical validation is implemented using a variety of techniques, including: - Cross-validation with an analytical model (from a trusted source, e.g. `SciPy `_ or `NumPy `_) - Statistical analysis - Visual inspection - Expert review Scope ----- The scope of technical validation in DataLab includes all compute functions that operate on DataLab's signal and image objects (i.e. :class:`sigima.objects.SignalObj` and :class:`sigima.objects.ImageObj`). This includes functions for (all functions are named ``compute_``): - Signal processing (:mod:`sigima.proc.signal`) - Image processing (:mod:`sigima.proc.image`) Implementation -------------- The tests are implemented using the `pytest `_ framework. When writing a new technical validation test, the following rules should be followed regarding the test function: - The test function should be named: - ``test_signal_`` for signal compute functions - ``test_image_`` for image compute functions .. note:: The ``signal`` or ``image`` prefix is used to indicate the type of object that the function operates on. It may be omitted if the function operates exclusively on one type of object (e.g. ``test_adjust_gamma`` is the test function for the ``compute_adjust_gamma`` function, which operates on images). - The test function should be marked with the ``@pytest.mark.validation`` decorator. Following those rules ensures that: - The tests are easily identified as technical validation tests. - The tests can be executed separately using the command line interface (see :ref:`run_scientific_validation_tests`). - The tests are automatically discovered for synthetizing the validation status of the compute functions (see :ref:`validation_status`). 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