varunayan.convenience module

Convenience wrapper functions for common climate data workflows.

These functions combine data download with derived metric calculations, reducing user code from ~30 lines to a single function call.

varunayan.convenience.get_era5_daily_temperature(request_id: str, start_date: str, end_date: str, north: float, south: float, east: float, west: float, resolution: float = 0.25, verbosity: int = 0) → DataFrame[source]

Download ERA5 daily temperature data with automatic unit conversion.

Parameters:

  • request_id – Unique identifier for the request

  • start_date – Start date in ‘YYYY-MM-DD’ format

  • end_date – End date in ‘YYYY-MM-DD’ format

  • north – Northern latitude bound

  • south – Southern latitude bound

  • east – Eastern longitude bound

  • west – Western longitude bound

  • resolution – Spatial resolution in degrees (default 0.25)

  • verbosity – Logging verbosity level (0-2)

Returns:

latitude, longitude, date, temp_c

Return type:

DataFrame with columns

varunayan.convenience.get_era5_daily_humidity(request_id: str, start_date: str, end_date: str, north: float, south: float, east: float, west: float, resolution: float = 0.25, verbosity: int = 0) → DataFrame[source]

Download ERA5 daily humidity data with automatic RH calculation.

Parameters:

  • request_id – Unique identifier for the request

  • start_date – Start date in ‘YYYY-MM-DD’ format

  • end_date – End date in ‘YYYY-MM-DD’ format

  • north – Northern latitude bound

  • south – Southern latitude bound

  • east – Eastern longitude bound

  • west – Western longitude bound

  • resolution – Spatial resolution in degrees (default 0.25)

  • verbosity – Logging verbosity level (0-2)

Returns:

latitude, longitude, date, temp_c, dewpoint_c, rh

Return type:

DataFrame with columns

varunayan.convenience.get_era5_daily_wind(request_id: str, start_date: str, end_date: str, north: float, south: float, east: float, west: float, resolution: float = 0.25, verbosity: int = 0) → DataFrame[source]

Download ERA5 daily wind data with automatic wind speed calculation.

Parameters:

  • request_id – Unique identifier for the request

  • start_date – Start date in ‘YYYY-MM-DD’ format

  • end_date – End date in ‘YYYY-MM-DD’ format

  • north – Northern latitude bound

  • south – Southern latitude bound

  • east – Eastern longitude bound

  • west – Western longitude bound

  • resolution – Spatial resolution in degrees (default 0.25)

  • verbosity – Logging verbosity level (0-2)

Returns:

latitude, longitude, date, u10, v10, wind_speed

Return type:

DataFrame with columns

varunayan.convenience.get_era5_daily_heat_index_data(request_id: str, start_date: str, end_date: str, north: float, south: float, east: float, west: float, solar_load: bool = False, resolution: float = 0.25, verbosity: int = 0) → DataFrame[source]

Download ERA5 daily data and calculate comprehensive heat stress indices.

This convenience function downloads temperature, humidity, and wind data, then calculates all common heat stress indices in one call.

Parameters:

  • request_id – Unique identifier for the request

  • start_date – Start date in ‘YYYY-MM-DD’ format

  • end_date – End date in ‘YYYY-MM-DD’ format

  • north – Northern latitude bound

  • south – Southern latitude bound

  • east – Eastern longitude bound

  • west – Western longitude bound

  • solar_load – If True, also download solar radiation for outdoor UTCI

  • resolution – Spatial resolution in degrees (default 0.25)

  • verbosity – Logging verbosity level (0-2)

Returns:

  • latitude, longitude, date (or year, month, day depending on aggregation)

  • temp_c: Temperature in Celsius

  • dewpoint_c: Dewpoint temperature in Celsius

  • rh: Relative humidity (%)

  • wind_speed: Wind speed in m/s

  • wet_bulb: Wet bulb temperature (°C)

  • heat_index: Heat index (°C)

  • wbgt: Wet Bulb Globe Temperature (°C)

  • humidex: Humidex value

  • utci: Universal Thermal Climate Index (°C)

  • heat_index_risk: NWS risk category

  • wbgt_risk: Occupational risk category

  • utci_stress: Thermal stress category

  • If solar_load=True: ssrd (solar radiation) and mrt (mean radiant temp)

Return type:

DataFrame with columns

varunayan.convenience.get_era5_monthly_heat_index_data(request_id: str, start_date: str, end_date: str, north: float, south: float, east: float, west: float, solar_load: bool = False, resolution: float = 0.25, verbosity: int = 0) → DataFrame[source]

Download ERA5 monthly data and calculate comprehensive heat stress indices.

Same as get_era5_daily_heat_index_data but aggregated to monthly frequency.

Parameters:

  • request_id – Unique identifier for the request

  • start_date – Start date in ‘YYYY-MM-DD’ format

  • end_date – End date in ‘YYYY-MM-DD’ format

  • north – Northern latitude bound

  • south – Southern latitude bound

  • east – Eastern longitude bound

  • west – Western longitude bound

  • solar_load – If True, also download solar radiation for outdoor UTCI

  • resolution – Spatial resolution in degrees (default 0.25)

  • verbosity – Logging verbosity level (0-2)

Returns:

DataFrame with same columns as get_era5_daily_heat_index_data, aggregated to monthly means.

varunayan.convenience.get_era5_daily_heat_index_geojson(request_id: str, start_date: str, end_date: str, geojson_file: str, dist_features: List[str] | None = None, solar_load: bool = False, resolution: float = 0.25, verbosity: int = 0) → DataFrame[source]

Download ERA5 daily data for a GeoJSON region and calculate heat stress indices.

Parameters:

  • request_id – Unique identifier for the request

  • start_date – Start date in ‘YYYY-MM-DD’ format

  • end_date – End date in ‘YYYY-MM-DD’ format

  • geojson_file – Path to GeoJSON file defining the region

  • dist_features – List of GeoJSON properties to distinguish features

  • solar_load – If True, also download solar radiation for outdoor UTCI

  • resolution – Spatial resolution in degrees (default 0.25)

  • verbosity – Logging verbosity level (0-2)

Returns:

DataFrame with heat stress indices for points within the GeoJSON region

varunayan.convenience.get_era5_daily_heat_index_point(request_id: str, start_date: str, end_date: str, latitude: float, longitude: float, solar_load: bool = False, verbosity: int = 0) → DataFrame[source]

Download ERA5 daily data for a single point and calculate heat stress indices.

Parameters:

  • request_id – Unique identifier for the request

  • start_date – Start date in ‘YYYY-MM-DD’ format

  • end_date – End date in ‘YYYY-MM-DD’ format

  • latitude – Latitude of the point

  • longitude – Longitude of the point

  • solar_load – If True, also download solar radiation for outdoor UTCI

  • verbosity – Logging verbosity level (0-2)

Returns:

DataFrame with heat stress indices for the specified point