swarmpal.experimental._local_magnetic_model

swarmpal.experimental._local_magnetic_model#

Attributes#

`LATEST_CHAOS`
`CHAOS_REGISTRY`
`CHAOS_BASE_URL`

Classes#

LocalForwardMagneticModel

Compute a magnetic model locally and append it to a datatree

Functions#

`fetch_chaos_file`(filename)
`fetch_latest_chaos_files`()
`evaluate_chaos`(longitude, latitude, radius, time)

Module Contents#

swarmpal.experimental._local_magnetic_model.LATEST_CHAOS#

swarmpal.experimental._local_magnetic_model.CHAOS_REGISTRY#

swarmpal.experimental._local_magnetic_model.CHAOS_BASE_URL = 'https://zenodo.org/records/14893049/files/'#

swarmpal.experimental._local_magnetic_model.fetch_chaos_file(filename)[source]#

swarmpal.experimental._local_magnetic_model.fetch_latest_chaos_files()[source]#

swarmpal.experimental._local_magnetic_model.evaluate_chaos(longitude: numpy.typing.ArrayLike, latitude: numpy.typing.ArrayLike, radius: numpy.typing.ArrayLike, time: numpy.typing.ArrayLike)[source]#

class swarmpal.experimental._local_magnetic_model.LocalForwardMagneticModel(config: dict | None = None, active_tree: str = '/', inplace: bool = True)[source]#

Bases: swarmpal.io.PalProcess

Compute a magnetic model locally and append it to a datatree

property process_name#

set_config(dataset='SW_OPER_MAGA_LR_1B', model_descriptor='CHAOS-Core')[source]#

_call(datatree)[source]#