data_sources.satellite
Satellite data sources and functions
data_sources.satellite.satellite_model
Model for output of satellite data
Satellite Objects
class Satellite(DataSourceOutput)
Class to store satellite data as a xr.Dataset with some validation
model_validation
@classmethod
def model_validation(cls, v)
Check that all values are non negative
HRVSatellite Objects
class HRVSatellite(Satellite)
Class to store HRV satellite data as a xr.Dataset with some validation
data_sources.satellite.satellite_data_source
Satellite Data Source
SatelliteDataSource Objects
@dataclass
class SatelliteDataSource(ZarrDataSource)
Satellite Data Source.
__post_init__
def __post_init__(image_size_pixels: int, meters_per_pixel: int)
Post Init
open
def open() -> None
Open Satellite data
We don't want to open_sat_data in init. If we did that, then we couldn't copy SatelliteDataSource instances into separate processes. Instead, call open() after creating separate processes.
get_data_model_for_batch
@staticmethod
def get_data_model_for_batch()
Get the model that is used in the batch
get_spatial_region_of_interest
def get_spatial_region_of_interest(data_array: xr.DataArray, x_center_osgb: Number, y_center_osgb: Number) -> xr.DataArray
Gets the satellite image as a square around the center
Ignores x and y coordinates as for the original satellite projection each pixel varies in both its x and y distance from other pixels. See Issue 401 for more details.
This results, in 'real' spatial terms, each image covering about 2x as much distance in the x direction as in the y direction.
Arguments:
data_array
- DataArray to subselect fromx_center_osgb
- Center of the image x coordinate in OSGB coordinatesy_center_osgb
- Center of image y coordinate in OSGB coordinates
Returns:
The selected data around the center
get_example
def get_example(t0_dt: pd.Timestamp, x_meters_center: Number, y_meters_center: Number) -> xr.Dataset
Get Example data
Arguments:
t0_dt
- list of timestamps for the datetime of the batches. The batch will also include data for historic and future depending onhistory_minutes
andfuture_minutes
.x_meters_center
- x center batch locations-
y_meters_center
- y center batch locations -
Returns
- Example Data
datetime_index
def datetime_index(remove_night: bool = True) -> pd.DatetimeIndex
Returns a complete list of all available datetimes
Arguments:
remove_night
- If True then remove datetimes at night. We're interested in forecasting solar power generation, so we don't care about nighttime data :)
In the UK in summer, the sun rises first in the north east, and sets last in the north west [1]. In summer, the north gets more hours of sunshine per day.
In the UK in winter, the sun rises first in the south east, and sets last in the south west [2]. In winter, the south gets more hours of sunshine per day.
Summer | Winter | |
---|---|---|
Sun rises first in | N.E. | S.E. |
Sun sets last in | N.W. | S.W. |
Most hours of sunlight | North | South |
Before training, we select timesteps which have at least some sunlight. We do this by computing the clearsky global horizontal irradiance (GHI) for the four corners of the satellite imagery, and for all the timesteps in the dataset. We only use timesteps where the maximum global horizontal irradiance across all four corners is above some threshold.
The 'clearsky solar irradiance' is the amount of sunlight we'd expect on a clear day at a specific time and location. The SI unit of irradiance is watt per square meter. The 'global horizontal irradiance' (GHI) is the total sunlight that would hit a horizontal surface on the surface of the Earth. The GHI is the sum of the direct irradiance (sunlight which takes a direct path from the Sun to the Earth's surface) and the diffuse horizontal irradiance (the sunlight scattered from the atmosphere). For more info, see: https://en.wikipedia.org/wiki/Solar_irradiance
References:
HRVSatelliteDataSource Objects
class HRVSatelliteDataSource(SatelliteDataSource)
Satellite Data Source for HRV data.
remove_acq_time_from_dataset_and_fix_time_coords
def remove_acq_time_from_dataset_and_fix_time_coords(dataset: xr.Dataset) -> xr.Dataset
Preprocess datasets by dropping acq_time
, which causes problems otherwise
Arguments:
dataset
- xr.Dataset to preprocess
Returns:
dataset with acq_time dropped
open_sat_data
def open_sat_data(zarr_path: str, consolidated: bool) -> xr.DataArray
Lazily opens the Zarr store.
Adds 1 minute to the 'time' coordinates, so the timestamps are at 00, 05, ..., 55 past the hour.
Arguments:
zarr_path
- Cloud URL or local path pattern. If GCP URL, must start with 'gs://'consolidated
- Whether or not the Zarr metadata is consolidated.