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https://github.com/jpawlowski/hass.tibber_prices.git
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fix(periods): Periods are now correctly recalculated after tomorrow prices became available.
This commit is contained in:
Julian Pawlowski
parent
51a99980df
commit
284a7f4291
11 changed files with 137 additions and 107 deletions
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@ -90,7 +90,8 @@ class PeriodSummary(TypedDict, total=False):
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rating_difference_pct: float # Difference from daily average (%)
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# Price statistics (priority 3)
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price_avg: float # Average price in period (minor currency)
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price_mean: float # Arithmetic mean price in period (minor currency)
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price_median: float # Median price in period (minor currency)
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price_min: float # Minimum price in period (minor currency)
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price_max: float # Maximum price in period (minor currency)
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price_spread: float # Price spread (max - min)
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@ -122,7 +123,7 @@ class PeriodAttributes(BaseAttributes, total=False):
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Attributes follow priority ordering:
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1. Time information (timestamp, start, end, duration_minutes)
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2. Core decision attributes (level, rating_level, rating_difference_%)
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3. Price statistics (price_avg, price_min, price_max, price_spread, volatility)
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3. Price statistics (price_mean, price_median, price_min, price_max, price_spread, volatility)
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4. Price comparison (period_price_diff_from_daily_min, period_price_diff_from_daily_min_%)
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5. Detail information (period_interval_count, period_position, periods_total, periods_remaining)
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6. Relaxation information (only if period was relaxed)
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@ -140,7 +141,8 @@ class PeriodAttributes(BaseAttributes, total=False):
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rating_difference_pct: float # Difference from daily average (%)
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# Price statistics (priority 3)
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price_avg: float # Average price in current/next period (minor currency)
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price_mean: float # Arithmetic mean price in current/next period (minor currency)
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price_median: float # Median price in current/next period (minor currency)
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price_min: float # Minimum price in current/next period (minor currency)
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price_max: float # Maximum price in current/next period (minor currency)
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price_spread: float # Price spread (max - min) in current/next period
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@ -219,7 +219,7 @@ def extract_period_summaries(
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Returns sensor-ready period summaries with:
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- Timestamps and positioning (start, end, hour, minute, time)
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- Aggregated price statistics (price_avg, price_min, price_max, price_spread)
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- Aggregated price statistics (price_mean, price_median, price_min, price_max, price_spread)
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- Volatility categorization (low/moderate/high/very_high based on coefficient of variation)
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- Rating difference percentage (aggregated from intervals)
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- Period price differences (period_price_diff_from_daily_min/max)
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@ -63,7 +63,7 @@ class TibberPricesPeriodCalculator:
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Compute hash of price data and config for period calculation caching.
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Only includes data that affects period calculation:
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- Today's interval timestamps and enriched rating levels
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- All interval timestamps and enriched rating levels (yesterday/today/tomorrow)
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- Period calculation config (flex, min_distance, min_period_length)
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- Level filter overrides
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@ -71,11 +71,20 @@ class TibberPricesPeriodCalculator:
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Hash string for cache key comparison.
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"""
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# Get relevant price data from flat interval list
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# Build minimal coordinator_data structure for get_intervals_for_day_offsets
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# Get today and tomorrow intervals for hash calculation
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# CRITICAL: Only today+tomorrow needed in hash because:
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# 1. Mitternacht: "today" startsAt changes → cache invalidates
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# 2. Tomorrow arrival: "tomorrow" startsAt changes from None → cache invalidates
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# 3. Yesterday/day-before-yesterday are static (rating_levels don't change retroactively)
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# 4. Using first startsAt as representative (changes → entire day changed)
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coordinator_data = {"priceInfo": price_info}
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today = get_intervals_for_day_offsets(coordinator_data, [0])
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today_signature = tuple((interval.get("startsAt"), interval.get("rating_level")) for interval in today)
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today_intervals = get_intervals_for_day_offsets(coordinator_data, [0])
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tomorrow_intervals = get_intervals_for_day_offsets(coordinator_data, [1])
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# Use first startsAt of each day as representative for entire day's data
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# If day is empty, use None (detects data availability changes)
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today_start = today_intervals[0].get("startsAt") if today_intervals else None
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tomorrow_start = tomorrow_intervals[0].get("startsAt") if tomorrow_intervals else None
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# Get period configs (both best and peak)
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best_config = self.get_period_config(reverse_sort=False)
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@ -88,7 +97,8 @@ class TibberPricesPeriodCalculator:
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# Compute hash from all relevant data
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hash_data = (
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today_signature,
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today_start, # Representative for today's data (changes at midnight)
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tomorrow_start, # Representative for tomorrow's data (changes when data arrives)
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tuple(best_config.items()),
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tuple(peak_config.items()),
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best_level_filter,
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@ -558,13 +568,11 @@ class TibberPricesPeriodCalculator:
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self._log("debug", "Calculating periods (cache miss or hash mismatch)")
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# Get intervals by day from flat list
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# Build minimal coordinator_data structure for get_intervals_for_day_offsets
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# Get all intervals at once (day before yesterday + yesterday + today + tomorrow)
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# CRITICAL: 4 days ensure stable historical period calculations
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# (periods calculated today for yesterday match periods calculated yesterday)
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coordinator_data = {"priceInfo": price_info}
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yesterday_prices = get_intervals_for_day_offsets(coordinator_data, [-1])
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today_prices = get_intervals_for_day_offsets(coordinator_data, [0])
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tomorrow_prices = get_intervals_for_day_offsets(coordinator_data, [1])
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all_prices = yesterday_prices + today_prices + tomorrow_prices
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all_prices = get_intervals_for_day_offsets(coordinator_data, [-2, -1, 0, 1])
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# Get rating thresholds from config
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threshold_low = self.config_entry.options.get(
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@ -54,9 +54,9 @@ def build_lifecycle_attributes(
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cache_validity = lifecycle_calculator.get_cache_validity_status()
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attributes["cache_validity"] = cache_validity
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# Use single "last_update" field instead of duplicating as "last_api_fetch" and "last_cache_update"
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if coordinator._last_price_update: # noqa: SLF001 - Internal state access for diagnostic display
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attributes["last_api_fetch"] = coordinator._last_price_update.isoformat() # noqa: SLF001
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attributes["last_cache_update"] = coordinator._last_price_update.isoformat() # noqa: SLF001
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attributes["last_update"] = coordinator._last_price_update.isoformat() # noqa: SLF001
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# Data Availability & Completeness
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data_completeness = lifecycle_calculator.get_data_completeness_status()
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@ -153,15 +153,11 @@ def add_volatility_type_attributes(
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if today_prices:
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today_vol = calculate_volatility_level(today_prices, **thresholds)
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today_spread = (max(today_prices) - min(today_prices)) * 100
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volatility_attributes["today_spread"] = round(today_spread, 2)
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volatility_attributes["today_volatility"] = today_vol
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volatility_attributes["interval_count_today"] = len(today_prices)
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if tomorrow_prices:
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tomorrow_vol = calculate_volatility_level(tomorrow_prices, **thresholds)
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tomorrow_spread = (max(tomorrow_prices) - min(tomorrow_prices)) * 100
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volatility_attributes["tomorrow_spread"] = round(tomorrow_spread, 2)
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volatility_attributes["tomorrow_volatility"] = tomorrow_vol
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volatility_attributes["interval_count_tomorrow"] = len(tomorrow_prices)
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elif volatility_type == "next_24h":
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@ -73,7 +73,8 @@ class TibberPricesVolatilityCalculator(TibberPricesBaseCalculator):
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price_min = min(prices_to_analyze)
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price_max = max(prices_to_analyze)
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spread = price_max - price_min
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price_avg = sum(prices_to_analyze) / len(prices_to_analyze)
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# Use arithmetic mean for volatility calculation (required for coefficient of variation)
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price_mean = sum(prices_to_analyze) / len(prices_to_analyze)
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# Convert to minor currency units (ct/øre) for display
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spread_minor = spread * 100
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@ -87,7 +88,7 @@ class TibberPricesVolatilityCalculator(TibberPricesBaseCalculator):
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"price_volatility": volatility,
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"price_min": round(price_min * 100, 2),
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"price_max": round(price_max * 100, 2),
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"price_avg": round(price_avg * 100, 2),
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"price_mean": round(price_mean * 100, 2), # Mean used for volatility calculation
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"interval_count": len(prices_to_analyze),
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}
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@ -130,8 +130,7 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
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# Temporary/Time-Bound
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"next_api_poll",
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"next_midnight_turnover",
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"last_api_fetch",
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"last_cache_update",
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"last_update", # Lifecycle sensor last update timestamp
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"last_turnover",
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"last_error",
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"error",
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@ -139,8 +138,7 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
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"relaxation_level",
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"relaxation_threshold_original_%",
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"relaxation_threshold_applied_%",
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# Redundant/Derived
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"price_spread",
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# Redundant/Derived (removed from attributes, kept here for safety)
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"volatility",
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"diff_%",
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"rating_difference_%",
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@ -645,25 +643,12 @@ class TibberPricesSensor(TibberPricesEntity, RestoreSensor):
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if not prices_to_analyze:
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return None
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# Calculate spread and basic statistics
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price_min = min(prices_to_analyze)
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price_max = max(prices_to_analyze)
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spread = price_max - price_min
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price_avg = sum(prices_to_analyze) / len(prices_to_analyze)
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# Convert to minor currency units (ct/øre) for display
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spread_minor = spread * 100
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# Calculate volatility level with custom thresholds (pass price list, not spread)
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# Calculate volatility level with custom thresholds
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# Note: Volatility calculation (coefficient of variation) uses mean internally
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volatility = calculate_volatility_level(prices_to_analyze, **thresholds)
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# Store attributes for this sensor
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# Store minimal attributes (only unique info not available in other sensors)
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self._last_volatility_attributes = {
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"price_spread": round(spread_minor, 2),
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"price_volatility": volatility,
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"price_min": round(price_min * 100, 2),
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"price_max": round(price_max * 100, 2),
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"price_avg": round(price_avg * 100, 2),
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"interval_count": len(prices_to_analyze),
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}
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@ -20,6 +20,7 @@ Used by:
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from __future__ import annotations
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from datetime import datetime, time
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from typing import Any
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from custom_components.tibber_prices.const import (
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@ -197,7 +198,7 @@ def aggregate_hourly_exact( # noqa: PLR0913, PLR0912, PLR0915
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return hourly_data
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def get_period_data( # noqa: PLR0913, PLR0912, PLR0915
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def get_period_data( # noqa: PLR0913, PLR0912, PLR0915, C901
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*,
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coordinator: Any,
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period_filter: str,
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@ -224,7 +225,7 @@ def get_period_data( # noqa: PLR0913, PLR0912, PLR0915
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When period_filter is specified, returns the precomputed period summaries
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from the coordinator instead of filtering intervals.
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Note: Period prices (price_avg) are stored in minor currency units (ct/øre).
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Note: Period prices (price_median) are stored in minor currency units (ct/øre).
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They are converted to major currency unless minor_currency=True.
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Args:
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@ -273,11 +274,44 @@ def get_period_data( # noqa: PLR0913, PLR0912, PLR0915
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day_intervals = get_intervals_for_day_offsets(coordinator.data, offsets)
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allowed_dates = {interval["startsAt"].date() for interval in day_intervals if interval.get("startsAt")}
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# Filter periods to those within allowed dates
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for period in period_summaries:
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start = period.get("start")
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if start and start.date() in allowed_dates:
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filtered_periods.append(period)
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# Calculate day boundaries for trimming
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# Find min/max dates to determine the overall requested window
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if allowed_dates:
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min_date = min(allowed_dates)
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max_date = max(allowed_dates)
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# CRITICAL: Trim periods that span day boundaries
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# Window start = midnight of first requested day
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# Window end = midnight of day AFTER last requested day (exclusive boundary)
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window_start = datetime.combine(min_date, time.min)
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window_end = datetime.combine(max_date, time.max).replace(microsecond=999999)
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# Make timezone-aware using coordinator's time service
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window_start = coordinator.time.as_local(window_start)
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window_end = coordinator.time.as_local(window_end)
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# Filter and trim periods to window
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for period in period_summaries:
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start = period.get("start")
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end = period.get("end")
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if not start:
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continue
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# Skip periods that end before window or start after window
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if end and end <= window_start:
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continue
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if start >= window_end:
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continue
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# Trim period to window boundaries
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trimmed_period = period.copy()
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if start < window_start:
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trimmed_period["start"] = window_start
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if end and end > window_end:
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trimmed_period["end"] = window_end
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filtered_periods.append(trimmed_period)
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else:
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filtered_periods = period_summaries
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@ -298,7 +332,7 @@ def get_period_data( # noqa: PLR0913, PLR0912, PLR0915
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# Build data point based on output format
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if output_format == "array_of_objects":
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# Map period fields to custom field names
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# Period has: start, end, level, rating_level, price_avg, price_min, price_max
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# Period has: start, end, level, rating_level, price_mean, price_median, price_min, price_max
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data_point = {}
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# Start time
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@ -309,14 +343,16 @@ def get_period_data( # noqa: PLR0913, PLR0912, PLR0915
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end = period.get("end")
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data_point[end_time_field] = end.isoformat() if end and hasattr(end, "isoformat") else end
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# Price (use price_avg from period, stored in minor units)
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price_avg = period.get("price_avg", 0.0)
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# Price (use price_median from period for visual consistency with sensor states)
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# Median is more representative than mean for periods with gap tolerance
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# (single "normal" intervals between cheap/expensive ones don't skew the display)
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price_median = period.get("price_median", 0.0)
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# Convert to major currency unless minor_currency=True
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if not minor_currency:
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price_avg = price_avg / 100
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price_median = price_median / 100
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if round_decimals is not None:
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price_avg = round(price_avg, round_decimals)
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data_point[price_field] = price_avg
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price_median = round(price_median, round_decimals)
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data_point[price_field] = price_median
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# Level (only if requested and present)
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if include_level and "level" in period:
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@ -335,21 +371,22 @@ def get_period_data( # noqa: PLR0913, PLR0912, PLR0915
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# 2. End time with price (hold price until end)
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# If insert_nulls='segments' or 'all':
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# 3. End time with NULL (cleanly terminate segment for ApexCharts)
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price_avg = period.get("price_avg", 0.0)
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# Use price_median for consistency with sensor states (more representative for periods)
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price_median = period.get("price_median", 0.0)
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# Convert to major currency unless minor_currency=True
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if not minor_currency:
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price_avg = price_avg / 100
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price_median = price_median / 100
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if round_decimals is not None:
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price_avg = round(price_avg, round_decimals)
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price_median = round(price_median, round_decimals)
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start = period["start"]
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end = period.get("end")
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start_serialized = start.isoformat() if hasattr(start, "isoformat") else start
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end_serialized = end.isoformat() if end and hasattr(end, "isoformat") else end
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# Add data points per period
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chart_data.append([start_serialized, price_avg]) # 1. Start with price
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chart_data.append([start_serialized, price_median]) # 1. Start with price
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if end_serialized:
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chart_data.append([end_serialized, price_avg]) # 2. End with price (hold level)
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chart_data.append([end_serialized, price_median]) # 2. End with price (hold level)
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# 3. Add NULL terminator only if insert_nulls is enabled
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if insert_nulls in ("segments", "all"):
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chart_data.append([end_serialized, None]) # 3. End with NULL (terminate segment)
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@ -18,7 +18,7 @@ Both `TibberPricesSensor` and `TibberPricesBinarySensor` implement `_unrecorded_
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```python
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class TibberPricesSensor(TibberPricesEntity, SensorEntity):
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"""tibber_prices Sensor class."""
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_unrecorded_attributes = frozenset(
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{
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"description",
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@ -73,7 +73,8 @@ class TibberPricesSensor(TibberPricesEntity, SensorEntity):
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"start": "2025-12-07T06:00:00+01:00",
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"end": "2025-12-07T08:00:00+01:00",
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"duration_minutes": 120,
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"price_avg": 18.5,
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"price_mean": 18.5,
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"price_median": 18.3,
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"price_min": 17.2,
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"price_max": 19.8,
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// ... 10+ more attributes × 10-20 periods
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@ -164,7 +165,7 @@ These attributes **remain in history** because they provide essential analytical
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### Period Data
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- `start`, `end`, `duration_minutes` - Core period timing
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- `price_avg`, `price_min`, `price_max` - Core price statistics
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- `price_mean`, `price_median`, `price_min`, `price_max` - Core price statistics
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### High-Level Status
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- `relaxation_active` - Whether relaxation was used (boolean, useful for analyzing when periods needed relaxation)
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@ -265,12 +266,12 @@ After modifying `_unrecorded_attributes`:
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**SQL Query to check attribute presence:**
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```sql
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SELECT
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SELECT
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state_id,
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attributes
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FROM states
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FROM states
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WHERE entity_id = 'sensor.tibber_home_current_interval_price'
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ORDER BY last_updated DESC
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||||
ORDER BY last_updated DESC
|
||||
LIMIT 5;
|
||||
```
|
||||
|
||||
|
|
|
|||
|
|
@ -16,20 +16,20 @@ def test_period_array_of_arrays_with_insert_nulls() -> None:
|
|||
period = {
|
||||
"start": datetime(2025, 12, 3, 10, 0, tzinfo=UTC),
|
||||
"end": datetime(2025, 12, 3, 12, 0, tzinfo=UTC),
|
||||
"price_avg": 1250, # Stored in minor units (12.50 EUR/ct)
|
||||
"price_median": 1250, # Stored in minor units (12.50 EUR/ct)
|
||||
"level": "CHEAP",
|
||||
"rating_level": "LOW",
|
||||
}
|
||||
|
||||
# Test with insert_nulls='segments' (should add NULL terminator)
|
||||
chart_data = []
|
||||
price_avg = period["price_avg"]
|
||||
price_median = period["price_median"]
|
||||
start_serialized = period["start"].isoformat()
|
||||
end_serialized = period["end"].isoformat()
|
||||
insert_nulls = "segments"
|
||||
|
||||
chart_data.append([start_serialized, price_avg]) # 1. Start with price
|
||||
chart_data.append([end_serialized, price_avg]) # 2. End with price (hold level)
|
||||
chart_data.append([start_serialized, price_median]) # 1. Start with price
|
||||
chart_data.append([end_serialized, price_median]) # 2. End with price (hold level)
|
||||
# 3. Add NULL terminator only if insert_nulls is enabled
|
||||
if insert_nulls in ("segments", "all"):
|
||||
chart_data.append([end_serialized, None]) # 3. End with NULL (terminate segment)
|
||||
|
|
@ -61,18 +61,18 @@ def test_period_array_of_arrays_without_insert_nulls() -> None:
|
|||
period = {
|
||||
"start": datetime(2025, 12, 3, 10, 0, tzinfo=UTC),
|
||||
"end": datetime(2025, 12, 3, 12, 0, tzinfo=UTC),
|
||||
"price_avg": 1250,
|
||||
"price_median": 1250,
|
||||
}
|
||||
|
||||
# Test with insert_nulls='none' (should NOT add NULL terminator)
|
||||
chart_data = []
|
||||
price_avg = period["price_avg"]
|
||||
price_median = period["price_median"]
|
||||
start_serialized = period["start"].isoformat()
|
||||
end_serialized = period["end"].isoformat()
|
||||
insert_nulls = "none"
|
||||
|
||||
chart_data.append([start_serialized, price_avg])
|
||||
chart_data.append([end_serialized, price_avg])
|
||||
chart_data.append([start_serialized, price_median])
|
||||
chart_data.append([end_serialized, price_median])
|
||||
if insert_nulls in ("segments", "all"):
|
||||
chart_data.append([end_serialized, None])
|
||||
|
||||
|
|
@ -92,24 +92,24 @@ def test_multiple_periods_separated_by_nulls() -> None:
|
|||
{
|
||||
"start": datetime(2025, 12, 3, 10, 0, tzinfo=UTC),
|
||||
"end": datetime(2025, 12, 3, 12, 0, tzinfo=UTC),
|
||||
"price_avg": 1250,
|
||||
"price_median": 1250,
|
||||
},
|
||||
{
|
||||
"start": datetime(2025, 12, 3, 15, 0, tzinfo=UTC),
|
||||
"end": datetime(2025, 12, 3, 17, 0, tzinfo=UTC),
|
||||
"price_avg": 1850,
|
||||
"price_median": 1850,
|
||||
},
|
||||
]
|
||||
|
||||
chart_data = []
|
||||
insert_nulls = "segments"
|
||||
for period in periods:
|
||||
price_avg = period["price_avg"]
|
||||
price_median = period["price_median"]
|
||||
start_serialized = period["start"].isoformat()
|
||||
end_serialized = period["end"].isoformat()
|
||||
|
||||
chart_data.append([start_serialized, price_avg])
|
||||
chart_data.append([end_serialized, price_avg])
|
||||
chart_data.append([start_serialized, price_median])
|
||||
chart_data.append([end_serialized, price_median])
|
||||
if insert_nulls in ("segments", "all"):
|
||||
chart_data.append([end_serialized, None])
|
||||
|
||||
|
|
@ -137,24 +137,24 @@ def test_multiple_periods_without_nulls() -> None:
|
|||
{
|
||||
"start": datetime(2025, 12, 3, 10, 0, tzinfo=UTC),
|
||||
"end": datetime(2025, 12, 3, 12, 0, tzinfo=UTC),
|
||||
"price_avg": 1250,
|
||||
"price_median": 1250,
|
||||
},
|
||||
{
|
||||
"start": datetime(2025, 12, 3, 15, 0, tzinfo=UTC),
|
||||
"end": datetime(2025, 12, 3, 17, 0, tzinfo=UTC),
|
||||
"price_avg": 1850,
|
||||
"price_median": 1850,
|
||||
},
|
||||
]
|
||||
|
||||
chart_data = []
|
||||
insert_nulls = "none"
|
||||
for period in periods:
|
||||
price_avg = period["price_avg"]
|
||||
price_median = period["price_median"]
|
||||
start_serialized = period["start"].isoformat()
|
||||
end_serialized = period["end"].isoformat()
|
||||
|
||||
chart_data.append([start_serialized, price_avg])
|
||||
chart_data.append([end_serialized, price_avg])
|
||||
chart_data.append([start_serialized, price_median])
|
||||
chart_data.append([end_serialized, price_median])
|
||||
if insert_nulls in ("segments", "all"):
|
||||
chart_data.append([end_serialized, None])
|
||||
|
||||
|
|
@ -174,15 +174,15 @@ def test_period_currency_conversion() -> None:
|
|||
period = {
|
||||
"start": datetime(2025, 12, 3, 10, 0, tzinfo=UTC),
|
||||
"end": datetime(2025, 12, 3, 12, 0, tzinfo=UTC),
|
||||
"price_avg": 1250, # 12.50 ct/øre
|
||||
"price_median": 1250, # 12.50 ct/øre
|
||||
}
|
||||
|
||||
# Test 1: Keep minor currency (for ApexCharts internal use)
|
||||
price_minor = period["price_avg"]
|
||||
price_minor = period["price_median"]
|
||||
assert price_minor == 1250, "Should keep minor units"
|
||||
|
||||
# Test 2: Convert to major currency (for display)
|
||||
price_major = period["price_avg"] / 100
|
||||
price_major = period["price_median"] / 100
|
||||
assert price_major == 12.50, "Should convert to major units (EUR)"
|
||||
|
||||
|
||||
|
|
@ -195,22 +195,22 @@ def test_period_with_missing_end_time() -> None:
|
|||
period = {
|
||||
"start": datetime(2025, 12, 3, 10, 0, tzinfo=UTC),
|
||||
"end": None, # No end time
|
||||
"price_avg": 1250,
|
||||
"price_median": 1250,
|
||||
}
|
||||
|
||||
chart_data = []
|
||||
price_avg = period["price_avg"]
|
||||
price_median = period["price_median"]
|
||||
start_serialized = period["start"].isoformat()
|
||||
end = period.get("end")
|
||||
end_serialized = end.isoformat() if end else None
|
||||
insert_nulls = "segments"
|
||||
|
||||
# Add start point
|
||||
chart_data.append([start_serialized, price_avg])
|
||||
chart_data.append([start_serialized, price_median])
|
||||
|
||||
# Only add end points if end_serialized exists
|
||||
if end_serialized:
|
||||
chart_data.append([end_serialized, price_avg])
|
||||
chart_data.append([end_serialized, price_median])
|
||||
if insert_nulls in ("segments", "all"):
|
||||
chart_data.append([end_serialized, None])
|
||||
|
||||
|
|
@ -252,17 +252,17 @@ def test_insert_nulls_all_mode() -> None:
|
|||
period = {
|
||||
"start": datetime(2025, 12, 3, 10, 0, tzinfo=UTC),
|
||||
"end": datetime(2025, 12, 3, 12, 0, tzinfo=UTC),
|
||||
"price_avg": 1250,
|
||||
"price_median": 1250,
|
||||
}
|
||||
|
||||
chart_data = []
|
||||
price_avg = period["price_avg"]
|
||||
price_median = period["price_median"]
|
||||
start_serialized = period["start"].isoformat()
|
||||
end_serialized = period["end"].isoformat()
|
||||
insert_nulls = "all"
|
||||
|
||||
chart_data.append([start_serialized, price_avg])
|
||||
chart_data.append([end_serialized, price_avg])
|
||||
chart_data.append([start_serialized, price_median])
|
||||
chart_data.append([end_serialized, price_median])
|
||||
if insert_nulls in ("segments", "all"):
|
||||
chart_data.append([end_serialized, None])
|
||||
|
||||
|
|
@ -285,7 +285,7 @@ def test_insert_nulls_and_add_trailing_null_both_enabled() -> None:
|
|||
{
|
||||
"start": datetime(2025, 12, 3, 10, 0, tzinfo=UTC),
|
||||
"end": datetime(2025, 12, 3, 12, 0, tzinfo=UTC),
|
||||
"price_avg": 1250,
|
||||
"price_median": 1250,
|
||||
},
|
||||
]
|
||||
|
||||
|
|
@ -294,12 +294,12 @@ def test_insert_nulls_and_add_trailing_null_both_enabled() -> None:
|
|||
add_trailing_null = True
|
||||
|
||||
for period in periods:
|
||||
price_avg = period["price_avg"]
|
||||
price_median = period["price_median"]
|
||||
start_serialized = period["start"].isoformat()
|
||||
end_serialized = period["end"].isoformat()
|
||||
|
||||
chart_data.append([start_serialized, price_avg])
|
||||
chart_data.append([end_serialized, price_avg])
|
||||
chart_data.append([start_serialized, price_median])
|
||||
chart_data.append([end_serialized, price_median])
|
||||
if insert_nulls in ("segments", "all"):
|
||||
chart_data.append([end_serialized, None])
|
||||
|
||||
|
|
@ -328,18 +328,18 @@ def test_neither_insert_nulls_nor_add_trailing_null() -> None:
|
|||
period = {
|
||||
"start": datetime(2025, 12, 3, 10, 0, tzinfo=UTC),
|
||||
"end": datetime(2025, 12, 3, 12, 0, tzinfo=UTC),
|
||||
"price_avg": 1250,
|
||||
"price_median": 1250,
|
||||
}
|
||||
|
||||
chart_data = []
|
||||
price_avg = period["price_avg"]
|
||||
price_median = period["price_median"]
|
||||
start_serialized = period["start"].isoformat()
|
||||
end_serialized = period["end"].isoformat()
|
||||
insert_nulls = "none"
|
||||
add_trailing_null = False
|
||||
|
||||
chart_data.append([start_serialized, price_avg])
|
||||
chart_data.append([end_serialized, price_avg])
|
||||
chart_data.append([start_serialized, price_median])
|
||||
chart_data.append([end_serialized, price_median])
|
||||
if insert_nulls in ("segments", "all"):
|
||||
chart_data.append([end_serialized, None])
|
||||
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ def create_test_period(start_hour: int, end_hour: int, base_date: datetime) -> d
|
|||
"start": start,
|
||||
"end": end,
|
||||
"duration_minutes": int((end - start).total_seconds() / 60),
|
||||
"price_avg": 25.5,
|
||||
"price_median": 25.5,
|
||||
}
|
||||
|
||||
|
||||
|
|
@ -95,7 +95,7 @@ def test_period_spanning_three_days(base_date: datetime) -> None:
|
|||
"start": day1.replace(hour=22, minute=0),
|
||||
"end": day3.replace(hour=2, minute=0),
|
||||
"duration_minutes": int((day3.replace(hour=2) - day1.replace(hour=22)).total_seconds() / 60),
|
||||
"price_avg": 25.5,
|
||||
"price_median": 25.5,
|
||||
}
|
||||
|
||||
periods = [period]
|
||||
|
|
|
|||
Loading…
Reference in a new issue