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    "result": {"data":{"site":{"siteMetadata":{"salvusDocVersions":{"current":"2026.5.0"}}},"jsonType":{"module_name":"salvus.flow.collections.event_data","module_docstring":"The EventData object which is an Event object with waveform data and/or other\ntypes of attached data.","classes":[{"qualified_name":"salvus.flow.collections.event_data.EventData","name":"EventData","init_documentation":{"qualified_name":"salvus.flow.collections.event_data.EventData.__init__","name":"__init__","signature":"class EventData(salvus.flow.collections.event.Event):\n    def __init__(\n        self,\n        event: Event,\n        data: pathlib.Path | dict | xr.Dataset | xr.DataArray,\n        data_hash: str | None = None,\n        path_to_meta_json: pathlib.Path | None = None,\n        path_to_job_info_json: pathlib.Path | None = None,\n    ): ...","description":"An event object that also contains waveform data.","parameters":[{"name":"event","description":"The event object.","type_hint":"Event","default_value":null},{"name":"data","description":"Path to the actual waveform data or a dictionary describing the data.","type_hint":"pathlib.Path | dict | xr.Dataset | xr.DataArray","default_value":null},{"name":"data_hash","description":"Uniquely identifying hash of that pieces of data. Might not always be available.","type_hint":"str | None","default_value":"None"},{"name":"path_to_meta_json","description":"Optionally specify the meta.json path of the run. Necessary if you later want to compute adjoint sources.","type_hint":"pathlib.Path | None","default_value":"None"},{"name":"path_to_job_info_json","description":"Optionally specify the job_info.json path of the run.","type_hint":"pathlib.Path | None","default_value":"None"}]},"methods":[{"qualified_name":"salvus.flow.collections.event_data.EventData.compute_data_hash","name":"compute_data_hash","signature":"def compute_data_hash(self) -> str: ...","description":"Compute a unique hash for the event data.","parameters":[]},{"qualified_name":"salvus.flow.collections.event_data.EventData.delete_associated_job_and_data","name":"delete_associated_job_and_data","signature":"def delete_associated_job_and_data(self) -> None: ...","description":"Delete the associated Salvus job, if it still exists, and any\npotentially existing remote outputs.","parameters":[]},{"qualified_name":"salvus.flow.collections.event_data.EventData.download_extra_outputs","name":"download_extra_outputs","signature":"def download_extra_outputs(\n    self,\n    data_types_to_download: (\n        typing.Collection[typing.Literal[\"volume\", \"surface\"]]\n        | typing.Literal[\"volume\", \"surface\"]\n    ) = frozenset({\"surface\", \"volume\"}),\n    localpath: pathlib.Path | str | None = None,\n) -> None: ...","description":"Download extra outputs from the simulation if they don't locally exist\nyet.","parameters":[{"name":"data_types_to_download","description":"The data types to download. Defaults to downloading everything.","type_hint":"typing.Collection[typing.Literal['volume', 'surface']] | typing.Literal['volume', 'surface']","default_value":"frozenset({'surface', 'volume'})"},{"name":"localpath","description":"By default, downloads will be stored next to the `meta.json` of the EventData object. If this is set to a folder (that must exist) it will instead download to that folder and only put symlinks in the `meta.json` file's folder.","type_hint":"pathlib.Path | str | None","default_value":"None"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.expected_components_from_receiver","name":"expected_components_from_receiver","signature":"def expected_components_from_receiver(\n    receiver: simple_config.receiver._Base | dict, receiver_field: str\n) -> list[str]: ...","description":"Get the expected components from a receiver object and field name.","parameters":[{"name":"receiver","description":"The receiver object or dictionary representation.","type_hint":"simple_config.receiver._Base | dict","default_value":null},{"name":"receiver_field","description":"The receiver field.","type_hint":"str","default_value":null}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.get_associated_salvus_job","name":"get_associated_salvus_job","signature":"def get_associated_salvus_job(self) -> SalvusJob: ...","description":"Get the associated Salvus job, if it still exists.","parameters":[]},{"qualified_name":"salvus.flow.collections.event_data.EventData.get_data_cube","name":"get_data_cube","signature":"def get_data_cube(\n    self,\n    receiver_field: str,\n    component: str,\n    sort_by: typing.Callable[[_Base], int] | None = None,\n    filter_by: typing.Callable[[_Base], bool] | None = None,\n    select_by: (\n        typing.Callable[\n            [list[tuple[_Base, obspy.core.stream.Stream]], EventData],\n            list[tuple[_Base, obspy.core.stream.Stream]],\n        ]\n        | None\n    ) = None,\n    ignore_deprecation: bool = False,\n) -> tuple[np.ndarray, np.ndarray]: ...","description":"Deprecated method, please use `get_waveform_data_xarray` for similar\nfunctionality. Get a data cube for a given field and component. Useful\nfor plotting shotgathers and wiggle plots.\n\nReturns a 2-D array with dimensions of (number of receivers, npts).\nSorting, filtering, and composed operations are available through\n`sort_by()`, `filter_by()` and `select_by()`. In absence of these,\nreceivers will be sorted as in the event object, unless sort_by or\nselect_by are altering the sort order.","parameters":[{"name":"receiver_field","description":"Receiver field to retrieve.","type_hint":"str","default_value":null},{"name":"component","description":"The component to retrieve. Note that for legacy reasons, this is always converted to an uppercase string, to mirror ObsPy behaviour, which was case-insensitive.","type_hint":"str","default_value":null},{"name":"sort_by","description":"Callback function called with the receiver objects to determine the sorting. Works like the `key` callback for the standard Python `sorted()` method.","type_hint":"typing.Callable[[_Base], int] | None","default_value":"None"},{"name":"filter_by","description":"Callback function called with the receiver objects in internal Salvus format to determine the filtering. Works like the callback for the standard Python `filter()` method. For complex filter operations requiring all receivers, defer to the `select_by` method.","type_hint":"typing.Callable[[_Base], bool] | None","default_value":"None"},{"name":"select_by","description":"Callback function called with a list of tuples in the receiver objects in both internal Salvus and ObsPy Stream format, as well as the EventData object.","type_hint":"typing.Callable[[list[tuple[_Base, obspy.core.stream.Stream]], EventData], list[tuple[_Base, obspy.core.stream.Stream]]] | None","default_value":"None"},{"name":"ignore_deprecation","description":"Hide the deprecation warning.","type_hint":"bool","default_value":"False"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.get_receiver","name":"get_receiver","signature":"def get_receiver(self, receiver_name: str) -> simple_config.receiver._Base: ...","description":"Returns a receiver object by name.\n\nThe receiver name should be NET.STA.LOC - if LOC is omitted it will\nbe assumed to be an empty string.","parameters":[{"name":"receiver_name","description":"The name of the receiver.","type_hint":"str","default_value":null}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.get_receiver_channel","name":"get_receiver_channel","signature":"def get_receiver_channel(\n    self, receiver_channel_name: str\n) -> ReceiverChannel: ...","description":"Returns a receiver channel object by its identifier.","parameters":[{"name":"receiver_channel_name","description":"The name of the receiver channel.","type_hint":"str","default_value":null}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.get_receiver_data","name":"get_receiver_data","signature":"def get_receiver_data(\n    self,\n    receiver_name: str,\n    receiver_field: str,\n    header: typing.Any | None = None,\n    _ignore_temporal_weights: bool = False,\n    _use_cache: bool = False,\n) -> obspy.Stream: ...","description":"Please use `.get_waveform_data()` instead.","parameters":[{"name":"receiver_name","description":"The name of the receiver","type_hint":"str","default_value":null},{"name":"receiver_field","description":"The field to get.","type_hint":"str","default_value":null},{"name":"header","description":"Manually set the tr.stats.header to that value. Sometimes useful to have access to extra meta data in the processing and data selection functions.","type_hint":"typing.Any | None","default_value":"None"},{"name":"_ignore_temporal_weights","description":"Ignore temporal weights, even if they are set. Useful mainly for plotting and debugging purposes. Don't set this if you are not aware of the consequences.","type_hint":"bool","default_value":"False"},{"name":"_use_cache","description":"Utilize a cache for this function. Useful for certain use cases with repeated per-station access.","type_hint":"bool","default_value":"False"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.get_remote_extra_output_filenames","name":"get_remote_extra_output_filenames","signature":"def get_remote_extra_output_filenames(\n    self,\n) -> dict[tuple[str, ...], pathlib.Path]: ...","description":"Get a dictionary of the remote paths of extra output files.\n\nThese files might no longer exist and might already have been\ntransferred to the local site or deleted. These are the paths of the\nextra output files of the solver.","parameters":[]},{"qualified_name":"salvus.flow.collections.event_data.EventData.get_time_axis_from_meta_json","name":"get_time_axis_from_meta_json","signature":"def get_time_axis_from_meta_json(\n    self,\n    output_type: typing.Literal[\n        \"simulation\", \"point_data\", \"surface_data\", \"volume_data\"\n    ] = \"simulation\",\n) -> TimeAxis: ...","description":"Return the start time, end time, sampling rate and number of samples by\nparsing the meta.json file.\n\nNaturally only works for synthetic data.","parameters":[{"name":"output_type","description":"Optionally define for which output type the time axis is constructed. The output_type \"simulation\" will always output the time axis as used by SalvusCompute. The other variations will adhere to the sampling rates of the output data for the relevant type.","type_hint":"typing.Literal['simulation', 'point_data', 'surface_data', 'volume_data']","default_value":"'simulation'"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.get_wavefield_output","name":"get_wavefield_output","signature":"def get_wavefield_output(\n    self,\n    output_type: typing.Literal[\"volume\", \"surface\"],\n    field: str,\n    time_steps: slice = slice(None, None, None),\n    download_if_not_exists: bool = False,\n    delete_remote_data_after_download: bool = False,\n) -> salvus.toolbox.helpers.wavefield_output.WavefieldOutput: ...","description":"Get a surface or volume wavefield output object for the given\nsimulation.\n\nThis will read the full volume or surface output to memory so please\nuse this with caution.","parameters":[{"name":"output_type","description":"The output type to get, `\"surface\"` or `\"volume\"`.","type_hint":"typing.Literal['volume', 'surface']","default_value":null},{"name":"field","description":"The field to get.","type_hint":"str","default_value":null},{"name":"time_steps","description":"The time step indices to read expressed as a Python slice object.","type_hint":"slice","default_value":"slice(None, None, None)"},{"name":"download_if_not_exists","description":"The data might not yet exist locally and only on a remote Salvus site. If that is the case, try to automatically download it. It will be stored in the same folder as the meta.json file for the simulation.","type_hint":"bool","default_value":"False"},{"name":"delete_remote_data_after_download","description":"Delete the remote data and associated Salvus job after the download has succeeded. If set, this will delete all remote files for the current event, even if only a subset has been downloaded.","type_hint":"bool","default_value":"False"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.get_waveform_data","name":"get_waveform_data","signature":"def get_waveform_data(\n    self,\n    receiver_name: str,\n    receiver_field: str,\n    header: typing.Any | None = None,\n    enforce_regular_time_grid: bool = True,\n    time_axis: TimeAxis | None = None,\n    _ignore_temporal_weights: bool = False,\n    _use_cache: bool = False,\n) -> obspy.Stream: ...","description":"Get the waveform data for a given receiver and field in a consistent\nmanner. The receiver name can also correspond to a receiver channel.\n\nThis is the central waveform retrieval function in SalvusProject and\nthus will return fully usable waveforms - this means fully filtered\nand selected/windowed waveforms, if so requested.\n\nMight contain multiple traces per component, but **only for data with\na data selection configuration**.\n\nThe order of the traces is always in the order of the expected output\ncomponents.\n\nIt uses ObsPy's Stream/Trace classes as they are just very suitable\ncontainers for waveform data.","parameters":[{"name":"receiver_name","description":"The name of the receiver or receiver channel","type_hint":"str","default_value":null},{"name":"receiver_field","description":"The field to get.","type_hint":"str","default_value":null},{"name":"header","description":"Manually set the tr.stats.header to that value. Sometimes useful to have access to extra meta data in the processing and data selection functions.","type_hint":"typing.Any | None","default_value":"None"},{"name":"enforce_regular_time_grid","description":"Whether to check if a synthetic dataset that is subsampled during the simulation has an appropriate time axis. By default, SalvusCompute will always output the last time step of a simulation, even when the subsampling chosen wouldn't match that point. Setting this parameter to True will check the `meta.json` specifications and ensure that the final point is removed again if it doesn't match the regular grid of the time axis.","type_hint":"bool","default_value":"True"},{"name":"time_axis","description":"Optionally specify a time axis for the data. Can be useful when dealing with heterogeneous data sources, such as seismological data, where the time axis may not be uniform across different stations.","type_hint":"TimeAxis | None","default_value":"None"},{"name":"_ignore_temporal_weights","description":"Ignore temporal weights, even if they are set. Useful mainly for plotting and debugging purposes. Don't set this if you are not aware of the consequences.","type_hint":"bool","default_value":"False"},{"name":"_use_cache","description":"Utilize a cache for this function. Useful for certain use cases with repeated per-station access.","type_hint":"bool","default_value":"False"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.get_waveform_data_xarray","name":"get_waveform_data_xarray","signature":"def get_waveform_data_xarray(\n    self,\n    receiver_field: str,\n    enforce_regular_time_grid: bool = True,\n    include_receiver_channel_inputs: bool = False,\n    receiver_ids: list[str] | None = None,\n    time_axis: TimeAxis | None = None,\n    _ignore_temporal_weights: bool = False,\n    _header_template: xr.DataArray | None = None,\n    _use_cache: bool = False,\n    _extra_output: ExtraOutput | None = None,\n    _extra_output_force_is_observed: bool | None = None,\n) -> xr.DataArray: ...","description":"Get the waveform data for a given field for all receivers or receiver\nchannels contained in the Event.\n\nIf the Event contains receiver channels. Only the data of the\nreceiver channels will be returned and not of the individual receivers\nthat form the channel.","parameters":[{"name":"receiver_field","description":"The field to get.","type_hint":"str","default_value":null},{"name":"enforce_regular_time_grid","description":"Whether to check if a synthetic dataset that is subsampled during the simulation has an appropriate time axis. By default, SalvusCompute will always output the last time step of a simulation, even when the subsampling chosen wouldn't match that point. Setting this parameter to True will check the `meta.json` specifications and ensure that the final point is removed again if it doesn't match the regular grid of the time axis.","type_hint":"bool","default_value":"True"},{"name":"include_receiver_channel_inputs","description":"Include the input receivers for receiver channels in the returned data array. By default, only the final receiver channel data will be included.","type_hint":"bool","default_value":"False"},{"name":"receiver_ids","description":"Optionally specify a list of receiver IDs to filter the data by. If this is set, only the data for the specified receivers will be returned. If this is `None`, all receivers will be returned.","type_hint":"list[str] | None","default_value":"None"},{"name":"time_axis","description":"An optional time axis to resample the data to. This will overwrite any potentially set temporal interpolation settings of the event data object.","type_hint":"TimeAxis | None","default_value":"None"},{"name":"_ignore_temporal_weights","description":"Ignore temporal weights, even if they are set. Useful mainly for plotting and debugging purposes. Don't set this if you are not aware of the consequences.","type_hint":"bool","default_value":"False"},{"name":"_header_template","description":"A data set to use as a template for the header.","type_hint":"xr.DataArray | None","default_value":"None"},{"name":"_use_cache","description":"Utilize a cache for this function. Useful for certain use cases with repeated per-station access.","type_hint":"bool","default_value":"False"},{"name":"_extra_output","description":"Optionally write intermediate data to disk.","type_hint":"ExtraOutput | None","default_value":"None"},{"name":"_extra_output_force_is_observed","description":"Force the waveform data to be considered observed data for the purpose of the extra output. Salvus considers all data that has been computed with it as synthetic but for the synthetic inversions the \"observed\" data should be written as observed extra output.","type_hint":"bool | None","default_value":"None"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.has_receiver","name":"has_receiver","signature":"def has_receiver(self, receiver_name: str) -> bool: ...","description":"Figure out if the given receiver is available.","parameters":[{"name":"receiver_name","description":"Name of the receiver.","type_hint":"str","default_value":null}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.has_receiver_nsl","name":"has_receiver_nsl","signature":"def has_receiver_nsl(\n    self, network_code: str, station_code: str, location_code: str\n) -> bool: ...","description":"Figure out if the given receiver is available.","parameters":[{"name":"network_code","description":"The network code.","type_hint":"str","default_value":null},{"name":"station_code","description":"The station code.","type_hint":"str","default_value":null},{"name":"location_code","description":"The location code.","type_hint":"str","default_value":null}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.plot","name":"plot","signature":"def plot(\n    self,\n    plot_types: typing.Iterable[str] = (\"wiggles\",),\n    receiver_field: str,\n    component: str,\n    sort_by: typing.Callable | None = None,\n) -> None: ...","description":"Plot the waveforms.","parameters":[{"name":"plot_types","description":"How to plot the data.","type_hint":"typing.Iterable[str]","default_value":"('wiggles',)"},{"name":"receiver_field","description":"The receiver field to plot.","type_hint":"str","default_value":null},{"name":"component","description":"The component to plot.","type_hint":"str","default_value":null},{"name":"sort_by","description":"Callback function called with the receiver objects to determine the sorting. Works like the `key` callback for the standard Python `sorted()` method.","type_hint":"typing.Callable | None","default_value":"None"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.register_processing_function","name":"register_processing_function","signature":"def register_processing_function(\n    self,\n    function: typing.Callable | None,\n    chain_to_existing_function: bool = False,\n) -> None: ...","description":"Register a function to process data on the fly.\n\nThe function must take the following arguments\nif it is supposed to operate on structured data:\n\n* `da`: An `xarray.DataArray` object with the data.\n* `sources`: The source objects of this event\n\nIf the function should operate on obspy.Stream objects\nper receiver, the function must take the following\narguments:\n\n* `st`: An `obspy.Stream` object with the data.\n* `receiver`: The receiver object for the receiver.\n* `sources`: All sources for the event.\n\nIn the case of ASDF data it can also optionally take:\n\n* `inv`: An `obspy.Inventory` object for the station in case it is\n  stored in an ASDF file. Might be None.\n\nThe function must return the processed `xarray.DataArray` or\n`obspy.Stream`. Both must contain exactly the same number of traces,\ncomponents and time steps as the input data.","parameters":[{"name":"function","description":"Function to process data on the fly. Or `None` to unset it.","type_hint":"typing.Callable | None","default_value":null},{"name":"chain_to_existing_function","description":"If `True` the new function will be chained to a possibly already pre-existing processing function. If this is `False` and there is an already pre-existing function it will raise.","type_hint":"bool","default_value":"False"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.register_temporal_weights_function","name":"register_temporal_weights_function","signature":"def register_temporal_weights_function(\n    self, function: typing.Callable | None, extra_kwargs: dict | None = None\n) -> None: ...","description":"Register a function to weight data on the fly.\n\nThe function must take two arguments if operating on\nstructured xarray.DataArray objects:\n\n* `da`: An `xarray.DataArray` object with the data.\n* `sources`: All sources for the event.\n\nIf the function should operate on obspy.Stream objects\nper receiver, the function must take the following\narguments:\n\n* `st`: An `obspy.Stream` object with the data.\n* `receiver`: The receiver object for the receiver.\n* `sources`: All sources for the event.\n\nIt furthermore must return a dictionary with a list of dictionaries\nfor each component, e.g.\n\n```python\ndef compute_temporal_weights(\n    da: xr.DataArray,\n    sources: list[simple_config.source._Base],\n) -> dict[str, list[dict]]:\n    ...\n    # Omit components which you want to ignore for the adjoint source\n    # computation.\n    weights = {\n        \"X\": [\n            {\"values\": np.array([...]), \"misfit_weight\": 0.5},\n            {\"values\": np.array([...]), \"misfit_weight\": 0.5},\n        ],\n        \"Y\": [\n            # If no \"misfit_weight\" is given, it defaults to 1.0.\n            {\"values\": np.array([...])},\n        ]}\n    return weights\n```","parameters":[{"name":"function","description":"Function to compute windows on the fly. Or `None` to unset it.","type_hint":"typing.Callable | None","default_value":null},{"name":"extra_kwargs","description":"Extra keyword arguments to pass to the function.","type_hint":"dict | None","default_value":"None"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.set_temporal_interpolation","name":"set_temporal_interpolation","signature":"def set_temporal_interpolation(\n    self,\n    start_time_in_seconds: float,\n    sampling_rate_in_hertz: float,\n    npts: int,\n    lanczos_window_width: int = 12,\n    taper_in_samples: int = 5,\n    reference_time: float | None = None,\n) -> None: ...","description":"Set the desired temporal interpolation. Once this is set, all\nreturned waveform data will be resampled in time using a sinc-based\nLanczos interpolator.\n\nThe interpolation will taper and pad with zeros if necessary. It will\nraise if it would result in more than 50 % of padded new samples.","parameters":[{"name":"start_time_in_seconds","description":"The new start time in seconds. Can be interpreted as a UNIX timestamp if so desired.","type_hint":"float","default_value":null},{"name":"sampling_rate_in_hertz","description":"The new sampling rate in Hertz.","type_hint":"float","default_value":null},{"name":"npts","description":"The new number of samples.","type_hint":"int","default_value":null},{"name":"lanczos_window_width","description":"The width in samples on either side to use when interpolating onto a new sample.","type_hint":"int","default_value":"12"},{"name":"taper_in_samples","description":"In case padding with zeros is necessary each traces will be tapered to zero using this many samples at each end it is being padded at. Necessary to avoid introducing unresolved frequencies.","type_hint":"int","default_value":"5"},{"name":"reference_time","description":"Reference time as a UTC timestamp in seconds. Needs to be set for seismological data.","type_hint":"float | None","default_value":"None"}]},{"qualified_name":"salvus.flow.collections.event_data.EventData.shallow_copy","name":"shallow_copy","signature":"def shallow_copy(self) -> EventData: ...","description":"Create a shallow copy of the object - things like data and sources and\nreceivers are just references. 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