chronaxis.core.conditional_time_reduction_reference
源码基线：8904c2f744951857659abd40aaa3535d57faf53c
1"""独立标量归约有限条件极值、规范化时间均值和主BOOL持续时间。23push保留值与外条件各自的覆盖、真实更新、未知及完整来源；跨块仅保存候选与时长。4min/max保留原整数，duration累计外TRUE主TRUE的精确ns并保留主FALSE零证据。5mean首遍只查有效/选择事实，次遍以全窗TRUE有效时长D计算x*(dt/D)，不先算面积。6权重及原序求和故障与输入覆盖分开；单位仅finish转换一次。纯核不读来源、不预约7预算或保存全轴，有限临时列及误差项须由外层owner计费，推进异常后须放弃状态。8"""910# 仅使用标量及同后端原序误差核，不调用数组面积、对齐或归约。11from math import isfinite12from .calculation_output_reference import convert_result_reference13from .conditional_time_reduction import ConditionalTimeCalculationFacts, require_conditional_time_series14from .integral_sum_reference import ReferenceIntegralSum15from .moments import TIME_MOMENTS16from .moments_reference import ReferenceMoments17from .calculations import merge_ranges18from .models import Interval19from .numeric import ScalarValue, to_float_reference20from .time_reduction import TimeReductionResult, require_time_config, require_time_options21from .integral_terms import require_valid_until22from .record_intervals import clipped_record_seconds23from .types import QualityFlag as Q2425from .time_reduction import MeanTimeBlock26from .mean_weights_reference import normalized_mean_terms272829class ReferenceConditionalTimeReduction:30 """保存一个原dtype候选、精确时长与有界误差项，mean两遍实例不能混用。"""3132 def __init__(self, spec, input_unit, dtype, *, selected_duration_ns=None, partial_limit=4096,33 step_ns=None, selected_weight_seconds=None):34 """验证原配置并建立本后端有限状态。3536 参数: spec/input_unit/dtype为原配置；selected_duration_ns为mean次遍全TRUE有效分母；37 partial_limit为误差项容量；step_ns选周期，周期均值次遍传selected_weight_seconds。返回: None。异常: 配置或分配错误传播。38 """39 # 共享的静态门槛先核对算子、类型、单位及mean分母，不执行数值计算。40 self._conversion = require_time_config(spec, input_unit, dtype, selected_duration_ns, partial_limit,41 step_ns=step_ns, selected_weight_seconds=selected_weight_seconds)42 self._unit, self._factor = self._conversion.unit, self._conversion.factor43 self._moments = ReferenceMoments(spec.operation) if spec.operation in TIME_MOMENTS else None44 self._spec, self._input_unit, self._dtype = spec, input_unit, dtype45 self._step_ns = step_ns46 self._denominator = selected_duration_ns if step_ns is None else selected_weight_seconds47 # 固定状态不随块数增加；mean首遍不建立数值误差状态。48 self._identity = self._candidate = None49 self._value_duration = self._selected_duration = self._terms = 050 self._numeric_quality = Q.NONE51 self._sum = ReferenceIntegralSum(partial_limit) if spec.operation == 'time_weighted_mean' and self._denominator is not None else None52 self._weight_sum = ReferenceIntegralSum(partial_limit) if step_ns is not None and spec.operation in ('duration', 'time_weighted_mean') else None5354 @property55 def selected_weight_seconds(self):56 """读取必要周期权重。参数: 无。返回: float或不适用None。异常: 权重求和故障。"""57 if self._weight_sum is None:58 return None59 result = self._weight_sum.finish('s')60 if result.calculation_failed:61 raise OverflowError('条件周期权重累计失败')62 return 0.0 if result.value is None else result.value6364 @property65 def selected_duration_ns(self):66 """读取累计选择时长。参数: 无。返回: 原生int纳秒。异常: 无。"""67 return self._selected_duration # duration仅主TRUE；mean为全TRUE有效分母事实。6869 @property70 def value_valid_duration_ns(self):71 """读取累计值有效时长。参数: 无。返回: 原生int纳秒。异常: 无。"""72 return self._value_duration # 外FALSE/UNKNOWN不改变原值可用性。7374 def push(self, series, *, valid_until_ns=None, condition_valid_until_ns=None, retain_unknown_ranges=True, retain_mean_terms=False):75 """独立消费有限联合块，返回完整双覆盖并推进原单位候选。7677 参数: series为同单位/dtype且有显式BOOL条件的有限CalculationSeries。78 valid_until_ns/condition_valid_until_ns分别截断实际有效期；79 retain_unknown_ranges=False省去两类未知范围。80 retain_mean_terms仅均值首遍返回MeanTimeBlock，其他调用返回事实。81 返回: ConditionalTimeCalculationFacts；mean权重故障只记数值质量，不改覆盖。82 异常: 结构、身份、容量或分配错误传播；外层不得继续使用异常前缀状态。83 """84 # 输入先过明确条件/单位/dtype与交集门槛，跨块不能更换原值绑定身份。85 actual = require_conditional_time_series(series, self._spec, self._input_unit, self._dtype)86 require_time_options(series, self._step_ns, valid_until_ns, retain_unknown_ranges)87 require_valid_until(series.times_ns, condition_valid_until_ns)88 if type(retain_mean_terms) is not bool or retain_mean_terms and (self._spec.operation != 'time_weighted_mean' or self._denominator is not None):89 raise ValueError('仅均值首遍可请求必要回读项')90 if self._identity is not None and self._identity != series.binding_identity:91 raise ValueError('条件时间归约不能跨绑定身份累计')92 self._identity = series.binding_identity93 # 只有mean检查to_float精度；极值和主BOOL不被提升成浮点类型。94 values = tuple(to_float_reference(value) for value in series.values) if self._spec.operation == 'time_weighted_mean' or self._moments is not None else series.values95 start, end = (actual.start_ns, actual.end_ns) if actual is not None else (0, 0)96 value_duration = known_duration = selected_duration = selected_unknown = false_duration = selected_false = samples = terms = 097 value_unknown, condition_unknown, weighted = [], [], []98 period_weights = [] if self._weight_sum is not None else None99 mean_values = [] if self._moments is not None or (self._spec.operation == 'time_weighted_mean' and100 (self._denominator is not None or retain_mean_terms)) else None101 mean_weights = [] if mean_values is not None else None102 quality, weight_quality = Q.NONE, Q.NONE103 # 每个正区间先核对双覆盖，再决定TRUE有效数值是否推进，末点没有区间。104 for index in range(len(series.times_ns) - 1):105 left, right = max(series.times_ns[index], start), min(series.times_ns[index+1], end)106 if left >= right:107 continue108 value, condition = values[index], series.conditions[index]109 quality |= series.diagnostics[index] | value.quality | condition.quality110 deadline = None if valid_until_ns is None else valid_until_ns[index]111 condition_deadline = None if condition_valid_until_ns is None else condition_valid_until_ns[index]112 value_end = max(left, min(right, deadline)) if deadline is not None else right113 condition_end = max(left, min(right, condition_deadline)) if condition_deadline is not None else right114 value_end = value_end if value.valid else left115 condition_end = condition_end if condition.valid else left116 value_duration += value_end - left117 known_duration += condition_end - left118 if value_end < right:119 if retain_unknown_ranges:120 value_unknown.append(Interval(value_end, right))121 if value.valid:122 quality |= Q.STALE123 if condition_end < right:124 if retain_unknown_ranges:125 condition_unknown.append(Interval(condition_end, right))126 if condition.valid:127 quality |= Q.STALE128 if condition_end <= left:129 continue130 if not condition.value:131 false_duration += condition_end - left132 continue133 chosen_end = min(value_end, condition_end)134 selected_unknown += condition_end - chosen_end135 if chosen_end <= left:136 continue137 dt = chosen_end - left138 # 外TRUE主FALSE仍是duration正常0证据，但不增加已选时长与真实样本。139 terms += 1140 if self._spec.operation == 'duration' and not value.value:141 selected_false += dt142 else:143 selected_duration += dt144 measure = dt145 if (period_weights is not None or (self._step_ns is not None and self._moments is not None)) and (self._spec.operation != 'duration' or value.value):146 measure = clipped_record_seconds(series.times_ns[index], series.times_ns[index+1],147 Interval(left, chosen_end), step_ns=self._step_ns)148 if period_weights is not None:149 period_weights.append(measure)150 if self._spec.operation in ('min_time', 'max_time'):151 # 每次只比较一个原dtype候选，跨块不转float，保留大整数的精确极值。152 self._candidate = value.value if self._candidate is None else (min if self._spec.operation == 'min_time' else max)(self._candidate, value.value)153 elif mean_values is not None:154 mean_values.append(value.value)155 mean_weights.append(measure)156 # 原值真实更新才计样本，外条件TRUE且duration另须主TRUE；右端与条件更新不计。157 for index, time in enumerate(series.times_ns):158 if not start <= time < end or not series.actual_value_updates[index]:159 continue160 value, condition = values[index], series.conditions[index]161 quality |= series.diagnostics[index] | value.quality | condition.quality162 deadline = None if valid_until_ns is None else valid_until_ns[index]163 condition_deadline = None if condition_valid_until_ns is None else condition_valid_until_ns[index]164 if (value.valid and condition.valid and condition.value165 and (deadline is None or time < deadline) and (condition_deadline is None or time < condition_deadline)166 and (self._spec.operation != 'duration' or value.value)):167 samples += 1168 # 任意权重故障使整次数值失败，不清除前述可核实覆盖；正常权重按原序推进。169 if self._denominator is not None:170 weighted, weight_quality = normalized_mean_terms(mean_values, mean_weights, self._denominator)171 if self._moments is not None:172 self._moments.push(mean_values, mean_weights)173 self._numeric_quality |= weight_quality174 if period_weights is not None:175 self._weight_sum.push_values(tuple(period_weights), (True,)*len(period_weights))176 if self._sum is not None and self._numeric_quality == Q.NONE:177 self._sum.push_values(tuple(weighted), (True,)*len(weighted))178 self._value_duration += value_duration179 self._selected_duration += selected_duration180 self._terms += terms181 facts = ConditionalTimeCalculationFacts(self._spec, actual, value_duration, known_duration, selected_duration,182 selected_unknown, false_duration, selected_false, samples, merge_ranges(value_unknown),183 merge_ranges(condition_unknown), quality, self._input_unit, self._dtype, series.binding_identity, series.source_refs, retain_unknown_ranges)184 return MeanTimeBlock(facts, tuple(mean_values), tuple(mean_weights)) if retain_mean_terms else facts185186 def finish(self):187 """生成数值快照并只转换一次，覆盖与全外FALSE分类由外层处理。188189 参数: 无。返回: 既有TimeReductionResult。异常: mean首遍RuntimeError、次遍分母不符ValueError；190 配置/分配错误传播，数值故障保留None和质量。191 """192 # mean首遍没有候选；次遍只有累计选择时长与原确认分母一致才能交付。193 if self._moments is not None:194 value, quality = self._moments.finish(self._spec.ddof, self._conversion.factor)195 return TimeReductionResult(value, self._unit, quality != Q.NONE, quality, self._terms)196 if self._spec.operation == 'time_weighted_mean':197 if self._denominator is None:198 raise RuntimeError('条件时间均值首遍只收事实，不能生成数值')199 measured = self._selected_duration if self._step_ns is None else self.selected_weight_seconds200 if measured != self._denominator:201 raise ValueError('条件均值次遍选择时长与全窗分母不同')202 if self._numeric_quality != Q.NONE:203 return TimeReductionResult(None, self._unit, True, self._numeric_quality, self._terms)204 # 同后端原序核负责一次最终单位转换，不在映射结果时再次乘因子。205 number = self._sum.finish(self._input_unit,206 1.0)207 if not number.calculation_failed:208 value, quality = convert_result_reference(number.value, self._dtype, self._conversion)209 return TimeReductionResult(value, self._unit, quality != Q.NONE, quality, self._terms)210 return TimeReductionResult(number.value, self._unit, number.calculation_failed, number.quality, self._terms)211 # 外FALSE/UNKNOWN或值全未知没有有效项，空证据分类由外层负责。212 if self._terms == 0:213 return TimeReductionResult(None, self._unit, False, Q.NONE, 0)214 value = ((self._selected_duration / 1_000_000_000 if self._step_ns is None else self.selected_weight_seconds)215 if self._spec.operation == 'duration' else self._candidate)216 quality = Q.NONE217 # 极值factor=1保留精确整数；显式换单位先检查整数浮点可表示性。218 # v4换算包含偏移；输入/归约故障不能被正常单位结果覆盖。219 if quality == Q.NONE:220 value, quality = convert_result_reference(value, self._dtype, self._conversion)221 return TimeReductionResult(None if quality != Q.NONE else value, self._unit, quality != Q.NONE, quality, self._terms)222