varunayan.processing.data_aggregator module#
- varunayan.processing.data_aggregator.aggregate_by_frequency(df: DataFrame, frequency: str, keep_original_time: bool = False) tuple[DataFrame, DataFrame][source]#
Aggregate ERA5 data by the specified frequency for multiple points within a polygon.
The function first aggregates data spatially (across points) for each timestamp, then performs temporal aggregation based on the specified frequency.
- Parameters:
df – DataFrame containing ERA5 data with latitude/longitude points and timestamps
frequency – One of ‘hourly’, ‘daily’, ‘weekly’, ‘monthly’, ‘yearly’
keep_original_time – Whether to keep the original valid_time column (default: False)
- Returns:
Tuple of (aggregated DataFrame, unique lat/lon DataFrame)
- varunayan.processing.data_aggregator.aggregate_pressure_levels(df: DataFrame, frequency: str = 'hourly', keep_original_time: bool = False) tuple[DataFrame, DataFrame][source]#
Aggregate ERA5 pressure level data by the specified frequency.
All variables are aggregated using mean values (both spatially and temporally).
- Parameters:
df – DataFrame containing ERA5 pressure level data with columns: - latitude, longitude: Spatial coordinates - valid_time or time: Timestamps - pressure_level: Pressure level in hPa - Other columns: Meteorological variables
frequency – One of ‘hourly’, ‘daily’, ‘weekly’, ‘monthly’, ‘yearly’
keep_original_time – Whether to keep the original time column
- Returns:
Tuple of (aggregated DataFrame, unique lat/lon DataFrame)