annotations

openenergyid/__init__.py

@@ -1,6 +1,6 @@
 """Open Energy ID Python SDK."""
 
-__version__ = "0.1.1"
+__version__ = "0.1.3"
 
 from .enums import Granularity
 from .models import TimeSeries

openenergyid/mvlr/helpers.py

@@ -28,3 +28,20 @@ def resample_input_data(
     return data.resample(rule=pandas_granularity_map[granularity]).agg(
         aggregation_methods,
     )
+
+
+def temperature_equivalent_to_degree_days(
+    temperature_equivalent: pd.Series, base_temperature: float, cooling: bool = False
+) -> pd.Series:
+    """
+    Convert temperature equivalent to degree days.
+
+    Use cooling=True to convert cooling degree days.
+    """
+
+    if cooling:
+        result = temperature_equivalent - base_temperature
+    else:
+        result = base_temperature - temperature_equivalent
+
+    return result.clip(lower=0)

openenergyid/mvlr/models.py

@@ -1,7 +1,7 @@
 """Models for multivariable linear regression."""
 from typing import Optional
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field, ConfigDict
 import statsmodels.formula.api as fm
 
 from openenergyid.enums import Granularity
@@ -13,20 +13,24 @@
 class ConfidenceInterval(BaseModel):
     """Confidence interval for a coefficient."""
 
-    confidence: float
-    lower: float
-    upper: float
+    confidence: float = Field(ge=0, le=1)
+    lower: float = Field(ge=0, le=1)
+    upper: float = Field(ge=0, le=1)
 
 
 class IndependentVariable(BaseModel):
     """Independent variable for a multivariable linear regression model."""
 
     name: str
     coef: float
-    t_stat: Optional[float] = None
-    p_value: Optional[float] = None
-    std_err: Optional[float] = None
-    confidence_interval: Optional[ConfidenceInterval] = None
+    t_stat: Optional[float] = Field(ge=0, le=1, default=None, alias="tStat")
+    p_value: Optional[float] = Field(ge=0, le=1, default=None, alias="pValue")
+    std_err: Optional[float] = Field(ge=0, le=1, default=None, alias="stdErr")
+    confidence_interval: Optional[ConfidenceInterval] = Field(
+        default=None, alias="confidenceInterval"
+    )
+
+    model_config = ConfigDict(populate_by_name=True)
 
     @classmethod
     def from_fit(cls, fit: fm.ols, name: str) -> "IndependentVariable":
@@ -48,12 +52,12 @@ def from_fit(cls, fit: fm.ols, name: str) -> "IndependentVariable":
 class MultiVariableRegressionResult(BaseModel):
     """Result of a multivariable regression model."""
 
-    dependent_variable: str
-    independent_variables: list[IndependentVariable]
-    r2: float
-    r2_adj: float
-    f_stat: float
-    prob_f_stat: float
+    dependent_variable: str = Field(alias="dependentVariable")
+    independent_variables: list[IndependentVariable] = Field(alias="independentVariables")
+    r2: float = Field(ge=0, le=1, alias="rSquared")
+    r2_adj: float = Field(ge=0, le=1, alias="rSquaredAdjusted")
+    f_stat: float = Field(ge=0, alias="fStat")
+    prob_f_stat: float = Field(ge=0, le=1, alias="probFStat")
     intercept: IndependentVariable
     granularity: Granularity
     frame: TimeSeries