Fixes configuration wizard not saving settings (#59):
Root cause was twofold:
1. Linear multi-step flow pattern didn't properly persist changes between steps
2. Best/peak price settings used nested sections format - values were saved
in sections (period_settings, flexibility_settings, etc.) but read from
flat structure, causing configured values to be ignored on subsequent runs
Solution:
- Replaced linear step-through flow with menu-based navigation system
- Each configuration area now has dedicated "Save & Back" buttons
- Removed nested sections from all steps except best/peak price (where they
provide better UX for grouping related settings)
- Fixed best/peak price steps to correctly extract values from sections:
period_settings, flexibility_settings, relaxation_and_target_periods
- Added reset-to-defaults functionality with confirmation dialog
UI/UX improvements:
- Menu structure: General Settings, Currency Display, Price Rating Thresholds,
Volatility, Best Price Period, Peak Price Period, Price Trend,
Chart Data Export, Reset to Defaults, Back
- Removed confusing step progress indicators ("{step_num} / {total_steps}")
- Changed all submit buttons from "Continue →" to "↩ Save & Back"
- Clear grouping of settings by functional area
Translation updates (nl.json + sv.json):
- Refined volatility threshold descriptions with CV formula explanations
- Clarified price trend thresholds (compares current vs. future N-hour average,
not "per hour increase")
- Standardized terminology (e.g., "entry" → "item", compound word consistency)
- Consistently formatted all sensor names and descriptions
- Added new data lifecycle status sensor names
Technical changes:
- Options flow refactored from linear to menu pattern with menu_options dict
- New reset_to_defaults step with confirmation and abort handlers
- Section extraction logic in best_price/peak_price steps now correctly reads
from nested structure (period_settings.*, flexibility_settings.*, etc.)
- Removed sections from general_settings, display_settings, volatility, etc.
(simpler flat structure via menu navigation)
Impact: Configuration wizard now reliably saves all settings. Users can
navigate between setting areas without restarting the flow. Reset function
enables quick recovery when experimenting with thresholds. Previously
configured best/peak price settings are now correctly applied.
Implemented comprehensive metadata calculation for chart data export service
with automatic Y-axis scaling and gradient positioning based on actual price
statistics.
Changes:
- Added 'metadata' parameter to get_chartdata service (include/only/none)
- Implemented _calculate_metadata() with per-day price statistics
* min/max/avg/median prices
* avg_position and median_position (0-1 scale for gradient stops)
* yaxis_suggested bounds (floor(min)-1, ceil(max)+1)
* time_range with day boundaries
* currency info with symbol and unit
- Integrated metadata into rolling_window modes via config-template-card
* Pre-calculated yaxis bounds (no async issues in templates)
* Dynamic gradient stops based on avg_position
* Server-side calculation ensures consistency
Visual refinements:
- Best price overlay opacity reduced to 0.05 (ultra-subtle green hint)
- Stroke width increased to 1.5 for better visibility
- Gradient opacity adjusted to 0.45 with "light" shade
- Marker configuration: size 0, hover size 2, strokeWidth 1
- Header display: Only show LOW/HIGH rating_levels (min/max prices)
* Conditional logic excludes NORMAL and level types
* Entity state shows meaningful extrema values
- NOW marker label removed for rolling_window_autozoom mode
* Static position at 120min lookback makes label misleading
Code cleanup:
- Removed redundant all_series_config (server-side data formatting)
- Currency names capitalized (Cents, Øre, Öre, Pence)
Translation updates:
- Added metadata selector translations (de, en, nb, nl, sv)
- Added metadata field description in services
- Synchronized all language files
Impact: Users get dynamic Y-axis scaling based on actual price data,
eliminating manual configuration. Rolling window charts automatically
adjust axis bounds and gradient positioning. Header shows only
meaningful extreme values (daily min/max). All data transformation
happens server-side for optimal performance and consistency.
Period data in array_of_arrays format now generates proper segment structure
for stepline charts. Each period produces 2-3 data points depending on
insert_nulls parameter:
1. Start time with price (begin period)
2. End time with price (hold price level)
3. End time with NULL (terminate segment, only if insert_nulls='segments'/'all')
This enables ApexCharts to correctly display periods as continuous blocks with
clean gaps between them. Previously only start point was generated, causing
periods to render as single points instead of continuous segments.
Changes:
- formatters.py: Updated get_period_data() to generate 2-3 points per period
- formatters.py: Added insert_nulls parameter to control NULL termination
- get_chartdata.py: Pass insert_nulls parameter to get_period_data()
- get_apexcharts_yaml.py: Set insert_nulls='segments' for period overlay
- get_apexcharts_yaml.py: Preserve NULL values in data_generator mapping
- get_apexcharts_yaml.py: Store original price for potential tooltip access
- tests: Added comprehensive period data format tests
Impact: Best price and peak price period overlays now display correctly as
continuous blocks with proper segment separation in ApexCharts cards.
Add dynamic rolling window mode to get_chartdata and get_apexcharts_yaml
services that automatically adapts to data availability.
When 'day' parameter is omitted, services return 48-hour window:
- With tomorrow data (after ~13:00): today + tomorrow
- Without tomorrow data: yesterday + today
Changes:
- Implement rolling window logic in get_chartdata using has_tomorrow_data()
- Generate config-template-card wrapper in get_apexcharts_yaml for dynamic
ApexCharts span.offset based on tomorrow_data_available binary sensor
- Update service descriptions in services.yaml
- Add rolling window descriptions to all translations (de, en, nb, nl, sv)
- Document rolling window mode in docs/user/services.md
- Add ApexCharts examples with prerequisites in docs/user/automation-examples.md
BREAKING CHANGE: get_apexcharts_yaml rolling window mode requires
config-template-card in addition to apexcharts-card for dynamic offset
calculation.
Impact: Users can create auto-adapting 48h price charts without manual day
selection. Fixed day views (day: today/yesterday/tomorrow) still work with
apexcharts-card only.
Simplifies the connect_segments implementation to use a unified bridge-point
approach for all price transitions (up/down/same). Previously used
direction-dependent logic (hold vs connect points) which was unnecessarily
complex.
Changes:
- get_chartdata.py: Bridge points now always use next interval's price at
boundary timestamp, creating smooth visual connection between segments
- get_chartdata.py: Trailing NULL removal now conditional on insert_nulls mode
('segments' removes for header fix, 'all' preserves intentional gaps)
- get_apexcharts_yaml.py: Enable connect_segments by default, activate
show_states for header min/max display
- get_apexcharts_yaml.py: Remove extrema series (not compatible with
data_generator approach - ApexCharts requires entity time-series data)
- tests: Move test_connect_segments.py to tests/services/ to mirror source
structure
Impact: ApexCharts cards now show clean visual connections between price level
segments with proper header statistics display. Trailing NULLs no longer cause
"N/A" in headers for filtered data. Test organization improved for
maintainability.
Renamed service modules for consistency with service identifiers:
- apexcharts.py → get_apexcharts_yaml.py
- chartdata.py → get_chartdata.py
- Added: get_price.py (new service module)
Naming convention: Module names now match service names directly
(tibber_prices.get_apexcharts_yaml → get_apexcharts_yaml.py)
Impact: Improved code organization, easier to locate service implementations.
No functional changes.
2025-11-25 20:44:39 +00:00
Renamed from custom_components/tibber_prices/services/chartdata.py (Browse further)
