aurora.config.metadata package

Submodules

aurora.config.metadata.processing module

Extend the mt_metadata.transfer_functions.processing.aurora.processing.Processing class with some aurora-specific methods.

class aurora.config.metadata.processing.EMTFTFHeader(**kwargs)

Bases: ListDict

Convenience class for storing metadata for a TF estimate. Based on Gary Egbert’s TFHeader.m originally in iris_mt_scratch/egbert_codes-20210121T193218Z-001/egbert_codes/matlabPrototype_10-13-20/TF/classes

It completely depends on the Processing class

Methods

append(obj)	Append an object
copy()	Copy object
extend(other[, skip_keys])	extend the dictionary from another ListDict object
pop(key)	pop item off of dictionary.
remove(key)	remove an item based on key or index
sort([inplace])	sort the dictionary keys into alphabetical order
update(other)	Update from another ListDict

items
keys
values

class aurora.config.metadata.processing.Processing(**kwargs)

Bases: Processing

Attributes:

band_edges_dict

changed

decimations

decimations_dict

need to have a dictionary, but it can’t be an attribute cause that

num_decimation_levels

Methods

add_base_attribute(name, value, value_dict)	Add an attribute to _attr_dict so it will be included in the output dictionary
add_decimation_level(decimation_level)	add a decimation level
assign_bands(band_edges_dict, sample_rate, ...)	Warning: This does not actually tell us how many samples we are decimating down at each level.
assign_decimation_level_data_emtf(sample_rate)	Warning: This does not actually tell us how many samples we are decimating down at each level.
attribute_information([name])	return a descriptive string of the attribute if none returns for all
copy()	Copy object
decimation_info()	Zips decimation level ids to the Decimation objects adn returns as a dict
emtf_tf_header(dec_level_id)	Returns a ListDict object that has the information that was in the old EMTF TF
from_dict(meta_dict[, skip_none])	fill attributes from a dictionary
from_json(json_str)	read in a json string and update attributes of an object
from_series(pd_series)	Fill attributes from a Pandas series
from_xml(xml_element)	param xml_element: XML element
get_attr_from_name(name)	Access attribute from the given name.
get_attribute_list()	return a list of the attributes
get_decimation_level(level)	Get a decimation level for easy access
make_tf_level(dec_level_id)	Initialize container for a single decimation level -- "flat" transfer function.
save_as_json([filename, nested, required])	Exports self to a JSON
set_attr_from_name(name, value)	Helper function to set attribute from the given name.
to_dict([nested, single, required])	make a dictionary from attributes, makes dictionary from _attr_list.
to_json([nested, indent, required])	Write a json string from a given object, taking into account other class objects contained within the given object.
to_series([required])	Convert attribute list to a pandas.Series
to_xml([string, required])	make an xml element for the attribute that will add types and units.
update(other[, match])	Update attribute values from another like element, skipping None
validate_processing(kernel_dataset)	Placeholder.
window_scheme([as_type])	Make a dataframe of processing parameters one row per decimation level.

drop_reference_channels
json_fn
set_default_input_output_channels
set_default_reference_channels
set_input_channels
set_output_channels
set_reference_channels

decimation_info()

Zips decimation level ids to the Decimation objects adn returns as a dict

Returns:

decimation_info – The decimation objects keyed by decimation level id.

Return type:

dict

emtf_tf_header(dec_level_id: int) → ListDict

Returns a ListDict object that has the information that was in the old EMTF TF

Header object. This may be deprecated in future – it is an artefact of the old matlab implementation.

Parameters:

dec_level_id (int) – This may tolerate strings in the future, but keep as int for now

Returns:

• tfh (ListDict)

• Object with the properties of the old EMTF TransferFunctionHeader class.

make_tf_level(dec_level_id: int)

Initialize container for a single decimation level – “flat” transfer function.

Parameters:

dec_level_id (int) – This may tolerate strings in the future, but keep as int for now

Returns:

tf_obj

Return type:

aurora.transfer_function.TTFZ.TTFZ

save_as_json(filename: str | Path | None = None, nested: bool | None = True, required: bool | None = False) → None

Exports self to a JSON

Parameters:

• filename (Optional[Union[str, pathlib.Path, None]) – Where to write the json

• nested (Optional[bool] = True,) – An mt_metadata argument

• required (Optional[bool] = False,) – An mt_metadata argument

window_scheme(as_type='df')

Make a dataframe of processing parameters one row per decimation level.

Parameters:

as_type (Optional[str]) – if “df” return a dataframe, if “dict” return dict

Returns:

windowing – return type depends on as_type argument.

Return type:

Union[dict, pd.DataFrame]

Module contents

class aurora.config.metadata.Processing(**kwargs)

Bases: Processing

Attributes:

band_edges_dict

changed

decimations

decimations_dict

need to have a dictionary, but it can’t be an attribute cause that

num_decimation_levels

Methods

add_base_attribute(name, value, value_dict)	Add an attribute to _attr_dict so it will be included in the output dictionary
add_decimation_level(decimation_level)	add a decimation level
assign_bands(band_edges_dict, sample_rate, ...)	Warning: This does not actually tell us how many samples we are decimating down at each level.
assign_decimation_level_data_emtf(sample_rate)	Warning: This does not actually tell us how many samples we are decimating down at each level.
attribute_information([name])	return a descriptive string of the attribute if none returns for all
copy()	Copy object
decimation_info()	Zips decimation level ids to the Decimation objects adn returns as a dict
emtf_tf_header(dec_level_id)	Returns a ListDict object that has the information that was in the old EMTF TF
from_dict(meta_dict[, skip_none])	fill attributes from a dictionary
from_json(json_str)	read in a json string and update attributes of an object
from_series(pd_series)	Fill attributes from a Pandas series
from_xml(xml_element)	param xml_element: XML element
get_attr_from_name(name)	Access attribute from the given name.
get_attribute_list()	return a list of the attributes
get_decimation_level(level)	Get a decimation level for easy access
make_tf_level(dec_level_id)	Initialize container for a single decimation level -- "flat" transfer function.
save_as_json([filename, nested, required])	Exports self to a JSON
set_attr_from_name(name, value)	Helper function to set attribute from the given name.
to_dict([nested, single, required])	make a dictionary from attributes, makes dictionary from _attr_list.
to_json([nested, indent, required])	Write a json string from a given object, taking into account other class objects contained within the given object.
to_series([required])	Convert attribute list to a pandas.Series
to_xml([string, required])	make an xml element for the attribute that will add types and units.
update(other[, match])	Update attribute values from another like element, skipping None
validate_processing(kernel_dataset)	Placeholder.
window_scheme([as_type])	Make a dataframe of processing parameters one row per decimation level.

drop_reference_channels
json_fn
set_default_input_output_channels
set_default_reference_channels
set_input_channels
set_output_channels
set_reference_channels

decimation_info()

Zips decimation level ids to the Decimation objects adn returns as a dict

Returns:

decimation_info – The decimation objects keyed by decimation level id.

Return type:

dict

emtf_tf_header(dec_level_id: int) → ListDict

Returns a ListDict object that has the information that was in the old EMTF TF

Header object. This may be deprecated in future – it is an artefact of the old matlab implementation.

Parameters:

dec_level_id (int) – This may tolerate strings in the future, but keep as int for now

Returns:

• tfh (ListDict)

• Object with the properties of the old EMTF TransferFunctionHeader class.

make_tf_level(dec_level_id: int)

Initialize container for a single decimation level – “flat” transfer function.

Parameters:

dec_level_id (int) – This may tolerate strings in the future, but keep as int for now

Returns:

tf_obj

Return type:

aurora.transfer_function.TTFZ.TTFZ

save_as_json(filename: str | Path | None = None, nested: bool | None = True, required: bool | None = False) → None

Exports self to a JSON

Parameters:

• filename (Optional[Union[str, pathlib.Path, None]) – Where to write the json

• nested (Optional[bool] = True,) – An mt_metadata argument

• required (Optional[bool] = False,) – An mt_metadata argument

window_scheme(as_type='df')

Make a dataframe of processing parameters one row per decimation level.

Parameters:

as_type (Optional[str]) – if “df” return a dataframe, if “dict” return dict

Returns:

windowing – return type depends on as_type argument.

Return type:

Union[dict, pd.DataFrame]