fix: added uniform EOL into pre-comit hook.

.vscode/settings.json

@@ -1,4 +1,4 @@
-{
-  "python.analysis.typeCheckingMode": "basic",
-  "remote.autoForwardPortsFallback": 0
-}
+{
+  "python.analysis.typeCheckingMode": "basic",
+  "remote.autoForwardPortsFallback": 0
+}

data/PP/energy_use.json

@@ -35039,4 +35039,4 @@
     "1704060900000": 0.046,
     "1704061800000": 0.035,
     "1704062700000": 0.027
-}
+}

openenergyid/baseload/__init__.py

@@ -1,15 +1,15 @@
-"""Base Load analysis for Open Energy ID."""
-
-from .main import (
-    BaseLoadMetrics,
-    EnergySchema,
-    load_data,
-    calculate_base_load,
-)
-
-__all__ = [
-    "BaseLoadMetrics",
-    "EnergySchema",
-    "load_data",
-    "calculate_base_load",
-]
+"""Base Load analysis for Open Energy ID."""
+
+from .main import (
+    BaseLoadMetrics,
+    EnergySchema,
+    load_data,
+    calculate_base_load,
+)
+
+__all__ = [
+    "BaseLoadMetrics",
+    "EnergySchema",
+    "load_data",
+    "calculate_base_load",
+]

openenergyid/baseload/main.py

@@ -1,114 +1,114 @@
-"""
-This module provides functionality for loading, validating, and analyzing energy usage data.
-
-Classes:
-    BaseLoadMetrics: A NamedTuple container for base load analysis metrics.
-    EnergySchema: A pandera DataFrameModel for validating energy usage data.
-
-Functions:
-    load_data(path: str) -> pl.LazyFrame:
-        Loads and validates energy usage data from an NDJSON file.
-
-    calculate_base_load(lf: pl.LazyFrame) -> BaseLoadMetrics:
-        Calculates base load metrics from energy usage data.
-
-    main(file_path: str) -> BaseLoadMetrics:
-        Processes energy data and returns base load metrics.
-
-    test_energy_validation():
-        Tests various data validation scenarios using pytest.
-"""
-
-from typing import NamedTuple
-import polars as pl
-import pandera.polars as pa
-## VERY important to use pandera.polars instead of pandera to avoid pandas errors
-
-
-class BaseLoadMetrics(NamedTuple):
-    """Container for base load analysis metrics"""
-
-    base_load_watts: float  # Average base load in watts
-    daily_usage_kwh: float  # Average daily usage in kWh
-    base_percentage: float  # Base load as percentage of total
-
-
-class EnergySchema(pa.DataFrameModel):
-    """Schema for energy usage data validation"""
-
-    timestamp: pl.Datetime = pa.Field(
-        nullable=False,
-        coerce=True,
-        title="Measurement Timestamp",
-        description="Time of energy measurement in Europe/Brussels timezone",
-    )
-    total: float = pa.Field(
-        ge=0,  # Power should be non-negative
-        nullable=False,
-        title="Total Power",
-        description="Total power measurement in kW",
-    )
-
-    # Add example of pandera validation: dataframe-level validation
-    @pa.dataframe_check
-    def timestamps_are_ordered(self, data: pl.DataFrame) -> bool:
-        """Check if timestamps are in chronological order"""
-        return data["timestamp"].is_sorted()
-
-
-def load_data(path: str) -> pl.LazyFrame:
-    """Load and validate energy usage data from NDJSON file"""
-    lf = pl.scan_ndjson(
-        path,
-        schema={"timestamp": pl.Datetime(time_zone="Europe/Brussels"), "total": pl.Float64},
-    )
-    # Convert to DataFrame for data-level validation, then back to LazyFrame for processing
-    validated_df = EnergySchema.validate(lf).collect()  # type: ignore
-    return pl.LazyFrame(validated_df)
-
-
-def calculate_base_load(lf: pl.LazyFrame) -> BaseLoadMetrics:
-    """
-    Calculate base load metrics from energy usage data.
-
-    Takes lowest 10 totals per day to determine base load.
-    Returns watts, kwh, and percentage metrics.
-    """
-    metrics_df = (
-        lf.filter(pl.col("total") >= 0)
-        .sort("timestamp")
-        .group_by_dynamic("timestamp", every="1d")
-        .agg(
-            [
-                pl.col("total").sum().alias("total_daily_usage"),
-                (pl.col("total").sort().head(10).mean() * 4 * 24).alias("base_load_daily_kwh"),
-            ]
-        )
-        .with_columns(
-            [
-                (pl.col("base_load_daily_kwh") / pl.col("total_daily_usage") * 100).alias(
-                    "base_percentage"
-                )
-            ]
-        )
-        .select(
-            [
-                pl.col("base_load_daily_kwh").mean().alias("avg_daily_kwh"),
-                (pl.col("base_load_daily_kwh") * 1000 / 24).mean().alias("avg_watts"),
-                pl.col("base_percentage").mean().alias("avg_percentage"),
-            ]
-        )
-        .collect()  # TODO add validation for input data: correct format, not null, etc.
-    )
-
-    return BaseLoadMetrics(
-        base_load_watts=metrics_df[0, "avg_watts"],
-        daily_usage_kwh=metrics_df[0, "avg_daily_kwh"],
-        base_percentage=metrics_df[0, "avg_percentage"],
-    )
-
-
-def main(file_path: str) -> BaseLoadMetrics:
-    """Process energy data and return base load metrics"""
-    lf = load_data(file_path)
-    return calculate_base_load(lf)
+"""
+This module provides functionality for loading, validating, and analyzing energy usage data.
+
+Classes:
+    BaseLoadMetrics: A NamedTuple container for base load analysis metrics.
+    EnergySchema: A pandera DataFrameModel for validating energy usage data.
+
+Functions:
+    load_data(path: str) -> pl.LazyFrame:
+        Loads and validates energy usage data from an NDJSON file.
+
+    calculate_base_load(lf: pl.LazyFrame) -> BaseLoadMetrics:
+        Calculates base load metrics from energy usage data.
+
+    main(file_path: str) -> BaseLoadMetrics:
+        Processes energy data and returns base load metrics.
+
+    test_energy_validation():
+        Tests various data validation scenarios using pytest.
+"""
+
+from typing import NamedTuple
+import polars as pl
+import pandera.polars as pa
+## VERY important to use pandera.polars instead of pandera to avoid pandas errors
+
+
+class BaseLoadMetrics(NamedTuple):
+    """Container for base load analysis metrics"""
+
+    base_load_watts: float  # Average base load in watts
+    daily_usage_kwh: float  # Average daily usage in kWh
+    base_percentage: float  # Base load as percentage of total
+
+
+class EnergySchema(pa.DataFrameModel):
+    """Schema for energy usage data validation"""
+
+    timestamp: pl.Datetime = pa.Field(
+        nullable=False,
+        coerce=True,
+        title="Measurement Timestamp",
+        description="Time of energy measurement in Europe/Brussels timezone",
+    )
+    total: float = pa.Field(
+        ge=0,  # Power should be non-negative
+        nullable=False,
+        title="Total Power",
+        description="Total power measurement in kW",
+    )
+
+    # Add example of pandera validation: dataframe-level validation
+    @pa.dataframe_check
+    def timestamps_are_ordered(self, data: pl.DataFrame) -> bool:
+        """Check if timestamps are in chronological order"""
+        return data["timestamp"].is_sorted()
+
+
+def load_data(path: str) -> pl.LazyFrame:
+    """Load and validate energy usage data from NDJSON file"""
+    lf = pl.scan_ndjson(
+        path,
+        schema={"timestamp": pl.Datetime(time_zone="Europe/Brussels"), "total": pl.Float64},
+    )
+    # Convert to DataFrame for data-level validation, then back to LazyFrame for processing
+    validated_df = EnergySchema.validate(lf).collect()  # type: ignore
+    return pl.LazyFrame(validated_df)
+
+
+def calculate_base_load(lf: pl.LazyFrame) -> BaseLoadMetrics:
+    """
+    Calculate base load metrics from energy usage data.
+
+    Takes lowest 10 totals per day to determine base load.
+    Returns watts, kwh, and percentage metrics.
+    """
+    metrics_df = (
+        lf.filter(pl.col("total") >= 0)
+        .sort("timestamp")
+        .group_by_dynamic("timestamp", every="1d")
+        .agg(
+            [
+                pl.col("total").sum().alias("total_daily_usage"),
+                (pl.col("total").sort().head(10).mean() * 4 * 24).alias("base_load_daily_kwh"),
+            ]
+        )
+        .with_columns(
+            [
+                (pl.col("base_load_daily_kwh") / pl.col("total_daily_usage") * 100).alias(
+                    "base_percentage"
+                )
+            ]
+        )
+        .select(
+            [
+                pl.col("base_load_daily_kwh").mean().alias("avg_daily_kwh"),
+                (pl.col("base_load_daily_kwh") * 1000 / 24).mean().alias("avg_watts"),
+                pl.col("base_percentage").mean().alias("avg_percentage"),
+            ]
+        )
+        .collect()  # TODO add validation for input data: correct format, not null, etc.
+    )
+
+    return BaseLoadMetrics(
+        base_load_watts=metrics_df[0, "avg_watts"],
+        daily_usage_kwh=metrics_df[0, "avg_daily_kwh"],
+        base_percentage=metrics_df[0, "avg_percentage"],
+    )
+
+
+def main(file_path: str) -> BaseLoadMetrics:
+    """Process energy data and return base load metrics"""
+    lf = load_data(file_path)
+    return calculate_base_load(lf)