Migrated chart_data_export from binary_sensor to sensor to enable
compatibility with dashboard integrations (ApexCharts, etc.) that
require sensor entities for data selection.
Changes:
- Moved chart_data_export from binary_sensor/ to sensor/ platform
- Changed from boolean state (ON/OFF) to ENUM states ("pending", "ready", "error")
- Maintained all functionality: service call, attribute structure, caching
- Updated translations in all 5 languages (de, en, nb, nl, sv)
- Updated user documentation (sensors.md, services.md)
- Removed all chart_data_export code from binary_sensor platform
Technical details:
- State: "pending" (before first call), "ready" (data available), "error" (service failed)
- Attributes: timestamp + error (metadata) → descriptions → service response data
- Cache (_chart_data_response) bridges async service call and sync property access
- Service call: Triggered on async_added_to_hass() and async_update()
Impact: Dashboard integrations can now select chart_data_export sensor
in their entity pickers. No breaking changes for existing users - entity ID
changes from binary_sensor.* to sensor.*, but functionality identical.
Added optional diagnostic binary sensor that exposes get_chartdata
service results as entity attributes for legacy dashboard tools.
Key features:
- Entity: binary_sensor.tibber_home_NAME_chart_data_export
- Configurable via Options Flow Step 7 (YAML parameters)
- Calls get_chartdata service with user configuration
- Exposes response as attributes for chart cards
- Disabled by default (opt-in)
- Auto-refreshes on coordinator updates
- Manual refresh via homeassistant.update_entity
Implementation details:
- Added chart_data_export entity description to definitions.py
- Implemented state/attribute logic in binary_sensor/core.py
- Added YAML configuration schema in schemas.py
- Added validation in options_flow.py (Step 7)
- Service call validation with detailed error messages
- Attribute ordering: metadata first, descriptions next, service data last
- Dynamic icon mapping (database-export/database-alert)
Translations:
- Added chart_data_export_config to all 5 languages
- Added Step 7 descriptions with legacy warning
- Added invalid_yaml_syntax/invalid_yaml_structure error messages
- Added custom_translations for sensor descriptions
Documentation:
- Added Chart Data Export section to sensors.md
- Added comprehensive service guide to services.md
- Migration path from sensor to service
- Configuration instructions via Options Flow
Impact: Provides backward compatibility for dashboard tools that can
only read entity attributes (e.g., older ApexCharts versions). New
integrations should use tibber_prices.get_chartdata service directly.
Created professional documentation structure:
**User Documentation (docs/user/):**
- README.md: Documentation hub with quick start guide
- Placeholder files for future content migration:
* installation.md, configuration.md, sensors.md
* services.md, automation-examples.md, troubleshooting.md
**Developer Documentation (docs/development/):**
- README.md: Comprehensive contributor guide with AI section
- setup.md: DevContainer and environment setup
- architecture.md: Code structure overview
- testing.md: Testing guidelines
- coding-guidelines.md: Style guide and critical patterns
- release-management.md: Complete release workflow documentation
**AI Development Disclosure:**
- README.md: "🤖 Development Note" section before license
* Honest disclosure about extensive AI assistance
* Quality assurance measures mentioned
* Invitation for bug reports with positive tone
- docs/development/README.md: Detailed AI section
* What AI handles (patterns, generation, refactoring)
* Benefits (rapid development, consistency)
* Limitations (edge cases, complex patterns)
* Quality assurance process
- CONTRIBUTING.md: Brief AI note with practical tip
**Updated:**
- README.md: Simplified to landing page with documentation links
- CONTRIBUTING.md: Modernized with new docs structure
- copilot-instructions.md: Added documentation organization section
Impact: Clear separation of user vs. developer documentation following
open-source best practices. Transparent about AI-assisted development
approach without being defensive. Scalable structure for future growth.
