mtpy.processing.aurora package

Submodules

mtpy.processing.aurora.process_aurora module

Created on Tue Jul 30 17:11:42 2024

@author: jpeacock

class mtpy.processing.aurora.process_aurora.AuroraProcessing(**kwargs)[source]

Bases: BaseProcessing

Convenience class to process with Aurora

from mtpy.processing.aurora.process_aurora import AuroraProcessing

ap = AuroraProcessing()

# set local station and path to MTH5
ap.local_station_id = "mt01"
ap.local_mth5_path = "/path/to/local_mth5.h5"

# set remote station and path to MTH5
ap.remote_station_id = "rr01"
ap.remote_mth5_path = "/path/to/remote_mth5.h5"

# process single sample rate
tf_obj = ap.process_single_sample_rate(sample_rate=1)

# process multiple sample rates, merge them all together and
# save transfer functions to the local MTH5
tf_processed_dict = ap.process(
    sample_rates=[4096, 1],
    merge=True,
    save_to_mth5=True
    ).
add_simple_coherence_weights(channel_list=[('ex', ['ex', 'hy']), ('ey', ['ey', 'hx']), ('hz', ['hx', 'hx'])], **kwargs) → list[ChannelWeightSpec][source]

Add coherence weights using the channel weight spec.

Parameters:

**kwargs (dict) – Additional keyword arguments (currently unused).

Returns:

List of channel weight specifications with coherence features.

Return type:

list[ChannelWeightSpec]

create_config(kernel_dataset: KernelDataset | None = None, decimation_kwargs: dict = {}, add_coherence_weights: bool = False, **kwargs) → Processing[source]

Create Aurora processing configuration.

Parameters:

  • kernel_dataset (KernelDataset or None, optional) – Kernel dataset defining processing runs, by default None.

  • decimation_kwargs (dict, optional) – Decimation parameters including window settings, by default {}.

  • add_coherence_weights (bool, optional) – Whether to add coherence-based weights, by default True.

  • **kwargs (dict) – Additional configuration parameters.

Returns:

Aurora configuration object.

Return type:

Processing

Raises:

ValueError – If kernel_dataset is None and no kernel dataset exists.

create_kernel_dataset(run_summary: RunSummary | None = None, local_station_id: str | None = None, remote_station_id: str | None = None, sample_rate: float | None = None) → KernelDataset[source]

Build and return a KernelDataset.

Parameters:

  • run_summary (RunSummary or None, optional) – Run summary to use, by default None (creates from MTH5).

  • local_station_id (str or None, optional) – Local station identifier, by default None.

  • remote_station_id (str or None, optional) – Remote reference station identifier, by default None.

  • sample_rate (float or None, optional) – Sample rate to filter runs, by default None.

Returns:

Kernel dataset defining processing configuration.

Return type:

KernelDataset

merge_transfer_functions(tf_dict: dict[float, dict[str, bool | MT]]) → MT[source]

Merge multiple transfer functions according to merge_dict.

Parameters:

tf_dict (dict) – Dictionary of transfer functions to merge.

Returns:

Merged transfer function combining all sample rates.

Return type:

MT

process(sample_rates: float | list[float] | None = None, processing_dict: dict[float, dict[str, Processing | KernelDataset]] | None = None, merge: bool = True, save_to_mth5: bool = True, plot: bool = False) → dict[float | str, dict[str, bool | MT]][source]

Process magnetotelluric data at multiple sample rates.

Parameters:

  • sample_rates (float, list of float, or None, optional) – Sample rate(s) to process, by default None.

  • processing_dict (dict or None, optional) – Dictionary mapping sample rates to config and kernel_dataset. Format: {sample_rate: {‘config’: Processing, ‘kernel_dataset’: KernelDataset}} By default None.

  • merge (bool, optional) – Whether to merge all sample rates into a single transfer function according to merge_dict, by default True.

  • save_to_mth5 (bool, optional) – Whether to save transfer functions to local MTH5 file, by default True.

Returns:

Dictionary with sample rates and ‘combined’ as keys, each containing {‘processed’: bool, ‘tf’: MT or None}.

Return type:

dict[float or str, dict[str, bool or MT]]

Raises:

  • ValueError – If neither sample_rates nor processing_dict is provided.

  • TypeError – If sample_rates or processing_dict is not the correct format.

Notes

If merge is True and multiple sample rates are processed, a ‘combined’ key is added with the merged transfer function.

Examples

>>> ap = AuroraProcessing()
>>> ap.local_station_id = "mt01"
>>> ap.local_mth5_path = "data.h5"
>>> results = ap.process(sample_rates=[1, 4], merge=True)
process_single_sample_rate(sample_rate: float, config: Processing | None = None, kernel_dataset: KernelDataset | None = None, plot: bool = False) → MT | None[source]

Process a single sample rate to generate transfer functions.

Parameters:

  • sample_rate (float) – Sample rate of time series data to process.

  • config (Processing or None, optional) – Aurora configuration object, by default None (creates from kernel_dataset).

  • kernel_dataset (KernelDataset or None, optional) – Kernel dataset defining processing runs, by default None (creates from run summary).

Returns:

Transfer function object, or None if processing fails.

Return type:

MT or None

Module contents