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domain/value_objects/report.py
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204
domain/value_objects/report.py
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"""
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Report Value Object - Strukturierte Berichte für Analytics
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"""
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from dataclasses import dataclass, field
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from datetime import datetime, timedelta
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from typing import List, Dict, Any, Optional
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from enum import Enum
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class ReportType(Enum):
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"""Typen von verfügbaren Berichten"""
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DAILY = "daily"
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WEEKLY = "weekly"
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MONTHLY = "monthly"
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CUSTOM = "custom"
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REAL_TIME = "real_time"
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class MetricType(Enum):
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"""Typen von Metriken in Berichten"""
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SUCCESS_RATE = "success_rate"
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ERROR_RATE = "error_rate"
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AVG_DURATION = "avg_duration"
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TOTAL_ACCOUNTS = "total_accounts"
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ACCOUNTS_PER_HOUR = "accounts_per_hour"
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RETRY_RATE = "retry_rate"
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RECOVERY_RATE = "recovery_rate"
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@dataclass(frozen=True)
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class Metric:
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"""Einzelne Metrik im Bericht"""
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name: str
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value: float
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unit: str
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trend: float = 0.0 # Prozentuale Veränderung zum Vorperiode
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@property
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def is_improving(self) -> bool:
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"""Prüft ob sich die Metrik verbessert"""
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positive_metrics = ["success_rate", "recovery_rate", "accounts_per_hour"]
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if self.name in positive_metrics:
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return self.trend > 0
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return self.trend < 0
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@dataclass(frozen=True)
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class PlatformStats:
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"""Statistiken für eine spezifische Plattform"""
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platform: str
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total_attempts: int
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successful_accounts: int
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failed_attempts: int
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avg_duration_seconds: float
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error_distribution: Dict[str, int]
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@property
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def success_rate(self) -> float:
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"""Berechnet die Erfolgsrate"""
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if self.total_attempts > 0:
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return self.successful_accounts / self.total_attempts
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return 0.0
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@dataclass(frozen=True)
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class TimeSeriesData:
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"""Zeitreihen-Daten für Graphen"""
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timestamps: List[datetime]
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values: List[float]
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label: str
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def get_average(self) -> float:
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"""Berechnet den Durchschnitt der Werte"""
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if self.values:
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return sum(self.values) / len(self.values)
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return 0.0
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def get_trend(self) -> float:
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"""Berechnet den Trend (vereinfacht: Vergleich erste/letzte Hälfte)"""
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if len(self.values) < 2:
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return 0.0
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mid = len(self.values) // 2
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first_half_avg = sum(self.values[:mid]) / mid
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second_half_avg = sum(self.values[mid:]) / (len(self.values) - mid)
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if first_half_avg > 0:
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return ((second_half_avg - first_half_avg) / first_half_avg) * 100
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return 0.0
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@dataclass(frozen=True)
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class Report:
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"""
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Strukturierter Bericht für Analytics.
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Frozen dataclass macht es unveränderlich (Value Object).
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"""
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report_id: str
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report_type: ReportType
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start_date: datetime
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end_date: datetime
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generated_at: datetime
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# Zusammenfassende Metriken
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total_accounts_created: int
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total_attempts: int
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overall_success_rate: float
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avg_creation_time: float # in Sekunden
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# Detaillierte Metriken
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metrics: List[Metric]
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platform_stats: List[PlatformStats]
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error_summaries: List[Dict[str, Any]] # ErrorSummary als Dict
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# Zeitreihen-Daten
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success_rate_timeline: Optional[TimeSeriesData] = None
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creation_rate_timeline: Optional[TimeSeriesData] = None
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error_rate_timeline: Optional[TimeSeriesData] = None
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# Top-Erkenntnisse
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insights: List[str] = field(default_factory=list)
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recommendations: List[str] = field(default_factory=list)
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def __post_init__(self):
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"""Validierung der Report-Daten"""
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if self.start_date > self.end_date:
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raise ValueError("Start date kann nicht nach end date liegen")
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if not 0.0 <= self.overall_success_rate <= 1.0:
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raise ValueError("Success rate muss zwischen 0.0 und 1.0 liegen")
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if self.avg_creation_time < 0:
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raise ValueError("Average creation time kann nicht negativ sein")
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@property
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def duration(self) -> timedelta:
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"""Dauer des Berichtszeitraums"""
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return self.end_date - self.start_date
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@property
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def accounts_per_day(self) -> float:
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"""Durchschnittliche Accounts pro Tag"""
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days = self.duration.days or 1
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return self.total_accounts_created / days
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def get_metric(self, metric_type: MetricType) -> Optional[Metric]:
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"""Holt eine spezifische Metrik"""
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for metric in self.metrics:
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if metric.name == metric_type.value:
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return metric
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return None
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def get_platform_stats(self, platform: str) -> Optional[PlatformStats]:
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"""Holt Statistiken für eine spezifische Plattform"""
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for stats in self.platform_stats:
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if stats.platform.lower() == platform.lower():
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return stats
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return None
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def to_dict(self) -> Dict[str, Any]:
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"""Konvertiert zu Dictionary für Serialisierung"""
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return {
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'report_id': self.report_id,
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'report_type': self.report_type.value,
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'start_date': self.start_date.isoformat(),
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'end_date': self.end_date.isoformat(),
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'generated_at': self.generated_at.isoformat(),
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'duration_days': self.duration.days,
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'total_accounts_created': self.total_accounts_created,
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'total_attempts': self.total_attempts,
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'overall_success_rate': self.overall_success_rate,
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'avg_creation_time': self.avg_creation_time,
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'accounts_per_day': self.accounts_per_day,
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'metrics': [
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{
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'name': m.name,
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'value': m.value,
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'unit': m.unit,
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'trend': m.trend,
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'is_improving': m.is_improving
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}
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for m in self.metrics
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],
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'platform_stats': [
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{
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'platform': ps.platform,
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'total_attempts': ps.total_attempts,
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'successful_accounts': ps.successful_accounts,
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'failed_attempts': ps.failed_attempts,
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'success_rate': ps.success_rate,
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'avg_duration_seconds': ps.avg_duration_seconds,
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'error_distribution': ps.error_distribution
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}
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for ps in self.platform_stats
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],
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'error_summaries': self.error_summaries,
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'success_rate_timeline': {
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'timestamps': [t.isoformat() for t in self.success_rate_timeline.timestamps],
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'values': self.success_rate_timeline.values,
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'label': self.success_rate_timeline.label,
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'average': self.success_rate_timeline.get_average(),
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'trend': self.success_rate_timeline.get_trend()
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} if self.success_rate_timeline else None,
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'insights': self.insights,
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'recommendations': self.recommendations
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}
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