merge/rebase from main

Author: scrrlt

codecarbon/core/monte_carlo.py

@@ -160,12 +160,18 @@ def compute_confidence_interval(
     n = len(samples)
     if n >= 100:
         try:
-            # Use optimized quantiles function from Python 3.8+
+            # Use 40 quantiles for proper 95% CI bounds (1/0.025 = 40)
+            # This ensures we get exact 2.5% and 97.5% percentiles
+            n_quantiles = int(1.0 / (alpha / 2.0))  # For α=0.05: 1/0.025 = 40
             quantiles = statistics.quantiles(
-                samples, n=int(1.0/alpha), method='inclusive'
+                samples, n=n_quantiles, method='inclusive'
             )
+            # Map to confidence interval bounds
             lower_idx = int(len(quantiles) * alpha / 2)
             upper_idx = int(len(quantiles) * (1 - alpha / 2)) - 1
+            # Ensure bounds are within quantiles array
+            lower_idx = max(0, min(lower_idx, len(quantiles) - 1))
+            upper_idx = max(0, min(upper_idx, len(quantiles) - 1))
             return (quantiles[lower_idx], quantiles[upper_idx])
         except (AttributeError, ValueError):
             # Fall back to manual sorting for older Python or edge cases

codecarbon/core/uncertainty_emissions.py

@@ -6,6 +6,7 @@
 precision assessments for carbon footprint estimates.
 """
 
+import math
 from typing import Optional
 
 from codecarbon.core.emissions import Emissions
@@ -248,6 +249,19 @@ def _extract_carbon_intensity_from_emissions(
             ValueError: If energy is zero or calculation is invalid
             ZeroDivisionError: If energy or PUE is zero
         """
+        # Guard against NaN/Infinity inputs
+        if not math.isfinite(energy_kwh) or not math.isfinite(emissions_kg) or not math.isfinite(pue):
+            logger.warning(
+                "Non-finite input detected (energy_kwh=%s, emissions_kg=%s, pue=%s). "
+                "Using world average fallback.",
+                energy_kwh, emissions_kg, pue
+            )
+            try:
+                carbon_intensity_per_source = self._data_source.get_carbon_intensity_per_source_data()
+                return carbon_intensity_per_source.get("world_average", 475.0)
+            except Exception:
+                return 475.0
+        
         if energy_kwh <= 1e-6:  # Below 1 milliwatt-hour threshold
             logger.warning(
                 f"Energy consumption too small for reliable reverse calculation: {energy_kwh} kWh. "
@@ -267,6 +281,17 @@ def _extract_carbon_intensity_from_emissions(
         # Therefore: carbon_intensity = emissions * 1000 / (energy * pue)
         carbon_intensity_gco2_kwh = (emissions_kg * 1000) / (energy_kwh * pue)
 
+        # Guard against non-finite result from calculation
+        if not math.isfinite(carbon_intensity_gco2_kwh):
+            logger.warning(
+                "Calculated carbon intensity is non-finite. Using world average fallback."
+            )
+            try:
+                carbon_intensity_per_source = self._data_source.get_carbon_intensity_per_source_data()
+                return carbon_intensity_per_source.get("world_average", 475.0)
+            except Exception:
+                return 475.0
+        
         if carbon_intensity_gco2_kwh < 0 or carbon_intensity_gco2_kwh > 2000:
             logger.warning(
                 f"Calculated carbon intensity ({carbon_intensity_gco2_kwh:.1f} g CO₂/kWh) "

codecarbon/output_methods/uncertainty_emissions_data.py

@@ -14,16 +14,9 @@
 from codecarbon.output_methods.emissions_data import EmissionsData, TaskEmissionsData
 
 
-@dataclass
-class UncertaintyAwareEmissionsData(EmissionsData):
-    """
-    Enhanced emissions data with uncertainty quantification.
+class UncertaintyMixin:
+    """Mixin for uncertainty quantification fields and methods."""
 
-    Extends the base EmissionsData to include confidence intervals
-    and uncertainty metadata from Monte Carlo analysis.
-    """
-    
-    # Uncertainty analysis results
     uncertainty_enabled: bool = False
     uncertainty_method: Optional[str] = None
     emissions_ci_lower_kg: Optional[float] = None
@@ -64,6 +57,16 @@ def set_uncertainty_data(self, uncertainty_summary: UncertaintySummary) -> None:
             else:
                 self.uncertainty_quality = "very_low_precision"
 
+
+@dataclass
+class UncertaintyAwareEmissionsData(EmissionsData, UncertaintyMixin):
+    """
+    Enhanced emissions data with uncertainty quantification.
+    
+    Extends the base EmissionsData to include confidence intervals
+    and uncertainty metadata from Monte Carlo analysis.
+    """
+
     @property
     def values(self) -> OrderedDict:
         """Extended values property including uncertainty fields."""
@@ -161,39 +164,11 @@ def toJSON(self):
 
 
 @dataclass
-class UncertaintyAwareTaskEmissionsData(TaskEmissionsData):
+class UncertaintyAwareTaskEmissionsData(TaskEmissionsData, UncertaintyMixin):
     """
     Task-level emissions data with uncertainty quantification.
     
     Inherits all base task fields from TaskEmissionsData and adds
-    uncertainty-specific metadata and confidence intervals.
+    uncertainty-specific metadata and confidence intervals via UncertaintyMixin.
     """
-    
-    # Uncertainty fields - only declare new fields, inherit the rest
-    uncertainty_enabled: bool = False
-    uncertainty_method: Optional[str] = None
-    emissions_ci_lower_kg: Optional[float] = None
-    emissions_ci_upper_kg: Optional[float] = None
-    confidence_level_pct: Optional[float] = None
-    relative_uncertainty_pct: Optional[float] = None
-    uncertainty_quality: Optional[str] = None
-
-    def set_uncertainty_data(self, uncertainty_summary: UncertaintySummary) -> None:
-        """Populate uncertainty fields from analysis results."""
-        self.uncertainty_enabled = True
-        self.uncertainty_method = uncertainty_summary.method
-        self.emissions_ci_lower_kg = uncertainty_summary.ci_lower_kg
-        self.emissions_ci_upper_kg = uncertainty_summary.ci_upper_kg
-        self.confidence_level_pct = uncertainty_summary.confidence_level_pct
-        self.relative_uncertainty_pct = uncertainty_summary.relative_uncertainty_pct
-        
-        # Quality assessment
-        if self.relative_uncertainty_pct is not None:
-            if self.relative_uncertainty_pct <= 5.0:
-                self.uncertainty_quality = "high_precision"
-            elif self.relative_uncertainty_pct <= 15.0:
-                self.uncertainty_quality = "moderate_precision"
-            elif self.relative_uncertainty_pct <= 25.0:
-                self.uncertainty_quality = "low_precision"
-            else:
-                self.uncertainty_quality = "very_low_precision"
+    pass  # All uncertainty functionality provided by UncertaintyMixin