particle_tracking_manager.config_ocean_model

Defines OceanModelSimulation and ocean_model_simulation_mapper, a dict with key, value pairs of ocean model name and OceanModelSimulation class. Each OceanModelSimulation is built using information from an OceanModelConfig instance.

Functions needed to work with ocean model output are defined here. Also register on-the-fly instances of ocean models here.

Functions

create_ocean_model_simulation(ocean_model)

Create an ocean model simulation object.

generate_enum_from_registry()

Generate an Enum class from the ocean model registry.

loc_local(name, kerchunk_func_str, ...)

This sets up a short kerchunk file for reading in just enough model output.

register_on_the_fly(ds_info[, ocean_model])

Update an ocean model in the registry and rebuild its Pydantic model.

update_TXLA_with_download_location()

The user-defined TXLA model is missing the download location

Classes

OceanModelEnum(*values)

OceanModelSimulation(*, ocean_model_local)

Ocean model simulation configuration.
class particle_tracking_manager.config_ocean_model.OceanModelEnum(*values)

Bases: Enum

CIOFS = 'CIOFS'
CIOFS3 = 'CIOFS3'
CIOFSFRESH = 'CIOFSFRESH'
CIOFSOP = 'CIOFSOP'
NWGOA = 'NWGOA'
ONTHEFLY = 'ONTHEFLY'
TXLA = 'TXLA'
class particle_tracking_manager.config_ocean_model.OceanModelSimulation(*, ocean_model_local)[source]

Bases: BaseModel

Ocean model simulation configuration.

Attributes:
model_extra

Get extra fields set during validation.

model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Methods

check_config_oceanmodel_lon0_360()

Check if the ocean model is using 0-360 longitude convention.

copy(*[, include, exclude, update, deep])

Returns a copy of the model.

model_construct([_fields_set])

Creates a new instance of the Model class with validated data.

model_copy(*[, update, deep])

!!! abstract "Usage Documentation"

model_dump(*[, mode, include, exclude, ...])

!!! abstract "Usage Documentation"

model_dump_json(*[, indent, ensure_ascii, ...])

!!! abstract "Usage Documentation"

model_json_schema([by_alias, ref_template, ...])

Generates a JSON schema for a model class.

model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct.

model_rebuild(*[, force, raise_errors, ...])

Try to rebuild the pydantic-core schema for the model.

model_validate(obj, *[, strict, extra, ...])

Validate a pydantic model instance.

model_validate_json(json_data, *[, strict, ...])

!!! abstract "Usage Documentation"

model_validate_strings(obj, *[, strict, ...])

Validate the given object with string data against the Pydantic model.

open_dataset(drop_vars)

Open an xarray dataset

construct

dict

from_orm

json

parse_file

parse_obj

parse_raw

schema

schema_json

update_forward_refs

validate
_abc_impl = <_abc._abc_data object>
_calculate_keys(*args, **kwargs)
_copy_and_set_values(*args, **kwargs)
classmethod _get_value(*args, **kwargs)
_iter(*args, **kwargs)
_setattr_handler(name, value)

Get a handler for setting an attribute on the model instance.

Returns:

A handler for setting an attribute on the model instance. Used for memoization of the handler. Memoizing the handlers leads to a dramatic performance improvement in __setattr__ Returns None when memoization is not safe, then the attribute is set directly.

check_config_oceanmodel_lon0_360()[source]

Check if the ocean model is using 0-360 longitude convention.

classmethod construct(_fields_set=None, **values)
copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`python {test="skip" lint="skip"} data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

  • include – Optional set or mapping specifying which fields to include in the copied model.

  • exclude – Optional set or mapping specifying which fields to exclude in the copied model.

  • update – Optional dictionary of field-value pairs to override field values in the copied model.

  • deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
classmethod from_orm(obj)
json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
model_computed_fields = {}
model_config = {'extra': 'forbid', 'use_enum_values': True, 'validate_defaults': True}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note

model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == ‘allow’, then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == ‘ignore’ (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == ‘forbid’ does not result in an error if extra values are passed, but they will be ignored.

Parameters:

  • _fields_set – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

  • values – Trusted or pre-validated data dictionary.

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)
!!! abstract “Usage Documentation”

[model_copy](../concepts/models.md#model-copy)

Returns a copy of the model.

!!! note

The underlying instance’s [__dict__][object.__dict__] attribute is copied. This might have unexpected side effects if you store anything in it, on top of the model fields (e.g. the value of [cached properties][functools.cached_property]).

Parameters:

  • update – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

  • deep – Set to True to make a deep copy of the model.

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
!!! abstract “Usage Documentation”

[model_dump](../concepts/serialization.md#python-mode)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

  • mode – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

  • include – A set of fields to include in the output.

  • exclude – A set of fields to exclude from the output.

  • context – Additional context to pass to the serializer.

  • by_alias – Whether to use the field’s alias in the dictionary key if defined.

  • exclude_unset – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults – Whether to exclude fields that are set to their default value.

  • exclude_none – Whether to exclude fields that have a value of None.

  • exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

  • round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

  • warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

  • fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

  • serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, ensure_ascii=False, include=None, exclude=None, context=None, by_alias=None, exclude_unset=False, exclude_defaults=False, exclude_none=False, exclude_computed_fields=False, round_trip=False, warnings=True, fallback=None, serialize_as_any=False)
!!! abstract “Usage Documentation”

[model_dump_json](../concepts/serialization.md#json-mode)

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

  • indent – Indentation to use in the JSON output. If None is passed, the output will be compact.

  • ensure_ascii – If True, the output is guaranteed to have all incoming non-ASCII characters escaped. If False (the default), these characters will be output as-is.

  • include – Field(s) to include in the JSON output.

  • exclude – Field(s) to exclude from the JSON output.

  • context – Additional context to pass to the serializer.

  • by_alias – Whether to serialize using field aliases.

  • exclude_unset – Whether to exclude fields that have not been explicitly set.

  • exclude_defaults – Whether to exclude fields that are set to their default value.

  • exclude_none – Whether to exclude fields that have a value of None.

  • exclude_computed_fields – Whether to exclude computed fields. While this can be useful for round-tripping, it is usually recommended to use the dedicated round_trip parameter instead.

  • round_trip – If True, dumped values should be valid as input for non-idempotent types such as Json[T].

  • warnings – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].

  • fallback – A function to call when an unknown value is encountered. If not provided, a [PydanticSerializationError][pydantic_core.PydanticSerializationError] error is raised.

  • serialize_as_any – Whether to serialize fields with duck-typing serialization behavior.

Returns:

A JSON string representation of the model.

property model_extra

Get extra fields set during validation.

Returns:

A dictionary of extra fields, or None if config.extra is not set to “allow”.

model_fields = {'ocean_model_local': FieldInfo(annotation=bool, required=True)}
property model_fields_set

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,

i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation', *, union_format='any_of')

Generates a JSON schema for a model class.

Parameters:

  • by_alias – Whether to use attribute aliases or not.

  • ref_template – The reference template.

  • union_format –

    The format to use when combining schemas from unions together. Can be one of:

    keyword to combine schemas (the default). - ‘primitive_type_array’: Use the [type](https://json-schema.org/understanding-json-schema/reference/type) keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive type (string, boolean, null, integer or number) or contains constraints/metadata, falls back to any_of.

  • schema_generator – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

  • mode – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:

params – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.

Returns:

String representing the new class where params are passed to cls as type variables.

Raises:

TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(context, /)

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

  • force – Whether to force the rebuilding of the model schema, defaults to False.

  • raise_errors – Whether to raise errors, defaults to True.

  • _parent_namespace_depth – The depth level of the parent namespace, defaults to 2.

  • _types_namespace – The types namespace, defaults to None.

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, extra=None, from_attributes=None, context=None, by_alias=None, by_name=None)

Validate a pydantic model instance.

Parameters:

  • obj – The object to validate.

  • strict – Whether to enforce types strictly.

  • extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

  • from_attributes – Whether to extract data from object attributes.

  • context – Additional context to pass to the validator.

  • by_alias – Whether to use the field’s alias when validating against the provided input data.

  • by_name – Whether to use the field’s name when validating against the provided input data.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)
!!! abstract “Usage Documentation”

[JSON Parsing](../concepts/json.md#json-parsing)

Validate the given JSON data against the Pydantic model.

Parameters:

  • json_data – The JSON data to validate.

  • strict – Whether to enforce types strictly.

  • extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

  • context – Extra variables to pass to the validator.

  • by_alias – Whether to use the field’s alias when validating against the provided input data.

  • by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, extra=None, context=None, by_alias=None, by_name=None)

Validate the given object with string data against the Pydantic model.

Parameters:

  • obj – The object containing string data to validate.

  • strict – Whether to enforce types strictly.

  • extra – Whether to ignore, allow, or forbid extra data during model validation. See the [extra configuration value][pydantic.ConfigDict.extra] for details.

  • context – Extra variables to pass to the validator.

  • by_alias – Whether to use the field’s alias when validating against the provided input data.

  • by_name – Whether to use the field’s name when validating against the provided input data.

Returns:

The validated Pydantic model.

ocean_model_local
open_dataset(drop_vars)[source]

Open an xarray dataset

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
classmethod parse_obj(obj)
classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
classmethod schema(by_alias=True, ref_template='#/$defs/{model}')
classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)
classmethod update_forward_refs(**localns)
classmethod validate(value)
particle_tracking_manager.config_ocean_model.create_ocean_model_simulation(ocean_model)[source]

Create an ocean model simulation object.

particle_tracking_manager.config_ocean_model.generate_enum_from_registry()[source]

Generate an Enum class from the ocean model registry.

particle_tracking_manager.config_ocean_model.loc_local(name, kerchunk_func_str, start_sim, end_sim)[source]

This sets up a short kerchunk file for reading in just enough model output.

particle_tracking_manager.config_ocean_model.register_on_the_fly(ds_info, ocean_model='ONTHEFLY')[source]

Update an ocean model in the registry and rebuild its Pydantic model.

The default model to register is “ONTHEFLY”, which is a placeholder for user-defined models. However, alternations could also be made to any existing model in the registry.

ds_info can contain any of the OceanModelConfig fields.

particle_tracking_manager.config_ocean_model.update_TXLA_with_download_location()[source]

The user-defined TXLA model is missing the download location

because it depends on the user’s operating system and setup. Run this function to update the TXLA model with the location of the downloaded file.