Merge v0.1.18

openenergyid/__init__.py

@@ -1,6 +1,6 @@
 """Open Energy ID Python SDK."""
 
-__version__ = "0.1.17"
+__version__ = "0.1.18"
 
 from .enums import Granularity
 from .models import TimeDataFrame, TimeSeries

openenergyid/dyntar/const.py

@@ -16,3 +16,7 @@
 HEATMAP_DELIVERED = "heatmap_delivered"
 HEATMAP_EXPORTED = "heatmap_exported"
 HEATMAP_TOTAL = "heatmap_total"
+
+HEATMAP_DELIVERED_DESCRIPTION = "heatmap_delivered_description"
+HEATMAP_EXPORTED_DESCRIPTION = "heatmap_exported_description"
+HEATMAP_TOTAL_DESCRIPTION = "heatmap_total_description"

openenergyid/dyntar/main.py

@@ -26,6 +26,9 @@
     HEATMAP_DELIVERED,
     HEATMAP_EXPORTED,
     HEATMAP_TOTAL,
+    HEATMAP_DELIVERED_DESCRIPTION,
+    HEATMAP_EXPORTED_DESCRIPTION,
+    HEATMAP_TOTAL_DESCRIPTION,
 )
 
 
@@ -147,8 +150,7 @@ def extend_dataframe_with_heatmap(df: pd.DataFrame, inplace: bool = False) -> pd
 
     # Invert scores so that positive values indicate a positive impact
     heatmap_score_delivered = -heatmap_score_delivered
-    heatmap_score_exported = -heatmap_score_exported
-    heatmap_score_combined = heatmap_score_delivered - heatmap_score_exported
+    heatmap_score_combined = heatmap_score_delivered + heatmap_score_exported
 
     df[HEATMAP_DELIVERED] = heatmap_score_delivered
     df[HEATMAP_EXPORTED] = heatmap_score_exported
@@ -159,6 +161,116 @@ def extend_dataframe_with_heatmap(df: pd.DataFrame, inplace: bool = False) -> pd
     return None
 
 
+def extend_dataframe_with_heatmap_description(
+    df: pd.DataFrame, inplace: bool = False
+) -> pd.DataFrame | None:
+    """Extend a DataFrame with the heatmap description columns."""
+    if not inplace:
+        df = df.copy()
+
+    # Delivered
+
+    # Where Heatmap is 0, we put a desription of 0 (No impact)
+    df[HEATMAP_DELIVERED_DESCRIPTION] = df[HEATMAP_DELIVERED].apply(
+        lambda x: 0 if x == 0 else float("NaN")
+    )
+    # When the energy delta is positive, and the price delta is positive, we put a description of 1 (high consumption, high price)
+    df[HEATMAP_DELIVERED_DESCRIPTION] = df.apply(
+        lambda x: 1
+        if x[PRICE_ELECTRICITY_DELIVERED] > x[RLP_WEIGHTED_PRICE_DELIVERED]
+        and x[ELECTRICITY_DELIVERED_SMR3] > x[ELECTRICITY_DELIVERED_SMR2]
+        else x[HEATMAP_DELIVERED_DESCRIPTION],
+        axis=1,
+    )
+    # When the energy delta is negative, and the price delta is positive, we put a description of 2 (low consumption, high price)
+    df[HEATMAP_DELIVERED_DESCRIPTION] = df.apply(
+        lambda x: 2
+        if x[PRICE_ELECTRICITY_DELIVERED] > x[RLP_WEIGHTED_PRICE_DELIVERED]
+        and x[ELECTRICITY_DELIVERED_SMR3] < x[ELECTRICITY_DELIVERED_SMR2]
+        else x[HEATMAP_DELIVERED_DESCRIPTION],
+        axis=1,
+    )
+    # When the energy delta is positive, and the price delta is negative, we put a description of 3 (high consumption, low price)
+    df[HEATMAP_DELIVERED_DESCRIPTION] = df.apply(
+        lambda x: 3
+        if x[PRICE_ELECTRICITY_DELIVERED] < x[RLP_WEIGHTED_PRICE_DELIVERED]
+        and x[ELECTRICITY_DELIVERED_SMR3] > x[ELECTRICITY_DELIVERED_SMR2]
+        else x[HEATMAP_DELIVERED_DESCRIPTION],
+        axis=1,
+    )
+    # When the energy delta is negative, and the price delta is negative, we put a description of 4 (low consumption, low price)
+    df[HEATMAP_DELIVERED_DESCRIPTION] = df.apply(
+        lambda x: 4
+        if x[PRICE_ELECTRICITY_DELIVERED] < x[RLP_WEIGHTED_PRICE_DELIVERED]
+        and x[ELECTRICITY_DELIVERED_SMR3] < x[ELECTRICITY_DELIVERED_SMR2]
+        else x[HEATMAP_DELIVERED_DESCRIPTION],
+        axis=1,
+    )
+    # All other cases are put as 0
+    df[HEATMAP_DELIVERED_DESCRIPTION] = df[HEATMAP_DELIVERED_DESCRIPTION].replace(np.nan, 0)
+
+    # Exported
+
+    # Where Heatmap is 0, we put a desription of 0 (No impact)
+    df[HEATMAP_EXPORTED_DESCRIPTION] = df[HEATMAP_EXPORTED].apply(
+        lambda x: 0 if x == 0 else float("NaN")
+    )
+    # When the energy delta is positive, and the price delta is positive, we put a description of 5 (high injection, high price)
+    df[HEATMAP_EXPORTED_DESCRIPTION] = df.apply(
+        lambda x: 5
+        if x[PRICE_ELECTRICITY_EXPORTED] > x[SPP_WEIGHTED_PRICE_EXPORTED]
+        and x[ELECTRICITY_EXPORTED_SMR3] > x[ELECTRICITY_EXPORTED_SMR2]
+        else x[HEATMAP_EXPORTED_DESCRIPTION],
+        axis=1,
+    )
+    # When the energy delta is negative, and the price delta is positive, we put a description of 6 (low injection, high price)
+    df[HEATMAP_EXPORTED_DESCRIPTION] = df.apply(
+        lambda x: 6
+        if x[PRICE_ELECTRICITY_EXPORTED] > x[SPP_WEIGHTED_PRICE_EXPORTED]
+        and x[ELECTRICITY_EXPORTED_SMR3] < x[ELECTRICITY_EXPORTED_SMR2]
+        else x[HEATMAP_EXPORTED_DESCRIPTION],
+        axis=1,
+    )
+    # When the energy delta is positive, and the price delta is negative, we put a description of 7 (high injection, low price)
+    df[HEATMAP_EXPORTED_DESCRIPTION] = df.apply(
+        lambda x: 7
+        if x[PRICE_ELECTRICITY_EXPORTED] < x[SPP_WEIGHTED_PRICE_EXPORTED]
+        and x[ELECTRICITY_EXPORTED_SMR3] > x[ELECTRICITY_EXPORTED_SMR2]
+        else x[HEATMAP_EXPORTED_DESCRIPTION],
+        axis=1,
+    )
+    # When the energy delta is negative, and the price delta is negative, we put a description of 8 (low injection, low price)
+    df[HEATMAP_EXPORTED_DESCRIPTION] = df.apply(
+        lambda x: 8
+        if x[PRICE_ELECTRICITY_EXPORTED] < x[SPP_WEIGHTED_PRICE_EXPORTED]
+        and x[ELECTRICITY_EXPORTED_SMR3] < x[ELECTRICITY_EXPORTED_SMR2]
+        else x[HEATMAP_EXPORTED_DESCRIPTION],
+        axis=1,
+    )
+    # All other cases are put as 0
+    df[HEATMAP_EXPORTED_DESCRIPTION] = df[HEATMAP_EXPORTED_DESCRIPTION].replace(np.nan, 0)
+
+    # Total
+
+    # We see which of the individual heatmaps has the highest absolute value
+    # We put the description of the highest absolute value
+    df[HEATMAP_TOTAL_DESCRIPTION] = df.apply(
+        lambda x: x[HEATMAP_DELIVERED_DESCRIPTION]
+        if abs(x[HEATMAP_DELIVERED]) > abs(x[HEATMAP_EXPORTED])
+        else x[HEATMAP_EXPORTED_DESCRIPTION],
+        axis=1,
+    )
+    # Where Heatmap is 0, we put a desription of 0 (No impact)
+    df[HEATMAP_TOTAL_DESCRIPTION] = df.apply(
+        lambda x: 0 if x[HEATMAP_TOTAL] == 0 else x[HEATMAP_TOTAL_DESCRIPTION], axis=1
+    )
+    # All other cases are put as 0
+    df[HEATMAP_TOTAL_DESCRIPTION] = df[HEATMAP_TOTAL_DESCRIPTION].replace(np.nan, 0)
+
+    if not inplace:
+        return df
+
+
 def calculate_dyntar_columns(df: pd.DataFrame, inplace: bool = False) -> pd.DataFrame | None:
     """Calculate all columns required for the dynamic tariff analysis."""
     if not inplace:
@@ -168,6 +280,7 @@ def calculate_dyntar_columns(df: pd.DataFrame, inplace: bool = False) -> pd.Data
     extend_dataframe_with_costs(df, inplace=True)
     extend_dataframe_with_weighted_prices(df, inplace=True)
     extend_dataframe_with_heatmap(df, inplace=True)
+    extend_dataframe_with_heatmap_description(df, inplace=True)
 
     if not inplace:
         return df

openenergyid/dyntar/models.py

@@ -1,7 +1,7 @@
 """Models for dynamic tariff analysis."""
 
 from typing import Literal
-from pydantic import Field, conlist
+from pydantic import Field, conlist, confloat
 
 from openenergyid.models import TimeDataFrame
 
@@ -33,6 +33,9 @@
     "heatmap_delivered",
     "heatmap_exported",
     "heatmap_total",
+    "heatmap_delivered_description",
+    "heatmap_exported_description",
+    "heatmap_total_description",
 ]
 
 
@@ -46,11 +49,11 @@ class DynamicTariffAnalysisInput(TimeDataFrame):
     )
     data: list[
         conlist(
-            item_type=float,
+            item_type=confloat(allow_inf_nan=True),
             min_length=len(RequiredColumns.__args__),
             max_length=len(RequiredColumns.__args__),
         )  # type: ignore
-    ] = Field(examples=[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
+    ] = Field(examples=[[0.0] * len(RequiredColumns.__args__)])
 
 
 class DynamicTariffAnalysisOutput(TimeDataFrame):
@@ -62,5 +65,9 @@ class DynamicTariffAnalysisOutput(TimeDataFrame):
         examples=[OutputColumns.__args__],
     )
     data: list[
-        conlist(item_type=float, min_length=1, max_length=len(OutputColumns.__args__))  # type: ignore
-    ] = Field(examples=[[0.0, 0.0, 0.0, 0.0, 0.0, 0.0]])
+        conlist(
+            item_type=confloat(allow_inf_nan=True),
+            min_length=1,
+            max_length=len(OutputColumns.__args__),
+        )  # type: ignore
+    ] = Field(examples=[[0.0] * len(OutputColumns.__args__)])

openenergyid/models.py

@@ -6,7 +6,7 @@
 from typing import Self
 
 import pandas as pd
-from pydantic import BaseModel, field_validator
+from pydantic import BaseModel
 
 
 class TimeSeriesBase(BaseModel):
@@ -78,13 +78,7 @@ class TimeSeries(TimeSeriesBase):
     """
 
     name: str | None = None
-    data: list[float | None]
-
-    @field_validator("data")
-    @classmethod
-    def replace_nan_with_none(cls, data: list[float]) -> list[float | None]:
-        """Replace NaN values with None."""
-        return [None if pd.isna(value) else value for value in data]
+    data: list[float]
 
     @classmethod
     def from_pandas(cls, data: pd.Series) -> Self:
@@ -102,13 +96,7 @@ class TimeDataFrame(TimeSeriesBase):
     """Time series data with multiple columns."""
 
     columns: list[str]
-    data: list[list[float | None]]
-
-    @field_validator("data")
-    @classmethod
-    def replace_nan_with_none(cls, data: list[list[float]]) -> list[list[float | None]]:
-        """Replace NaN values with None."""
-        return [[None if pd.isna(value) else value for value in row] for row in data]
+    data: list[list[float]]
 
     @classmethod
     def from_pandas(cls, data: pd.DataFrame) -> Self: