simstack.models package

Submodules

simstack.models.array_list module

class simstack.models.array_list.ArrayList(*, elements: list[ObjectId] = <factory>, id: ObjectId = <factory>)

Bases: Model, ObjectListMixin[ArrayStorage]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[ObjectId] = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

simstack.models.array_storage module

class simstack.models.array_storage.ArrayStorage(*, name: str | None, shape: str | None = None, field_name: str | None = None, data_json: str | None = None, id: ObjectId = <factory>)

Bases: BytesB64Mixin, Model

property array

Property getter for array

classmethod copy_name_to_field_name(values)

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data_json: str | None = <odmantic.field.FieldProxy object>

field_name: str | None = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get_array()

Retrieve the numpy array

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_encoders': {<class 'bytes'>: <function BytesB64Mixin.<lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str | None = <odmantic.field.FieldProxy object>

set_array(array)

Store a numpy array

shape: str | None = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema(**kwargs) → dict

simstack.models.artifact_models module

class simstack.models.artifact_models.ArtifactMapping(*, name: str = 'artifact', regex_pattern: str = '', function_mapping: str = '', function_code: str = '', pickle_function: FunctionPickle | None = None, id: ObjectId = <factory>)

Bases: Model

ArtifactsMapper is a mapping between the artifact and a node registry-path. The workflow executor passes a path of the type

node1.node2.node4. … .nodeN

where node is the function name of the node

Regex can maps this to the target path of the ArtifactsMapping, e.g. a path

*.parent1.node

it would map on all nodes with name node that have been directly called by a node with the name parent1.

function_code: str = <odmantic.field.FieldProxy object>

function_mapping: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

pickle_function: FunctionPickle | None = <odmantic.field.FieldProxy object>

regex_pattern: str = <odmantic.field.FieldProxy object>

set_values(other: ArtifactMapping) → ArtifactMapping

class simstack.models.artifact_models.ArtifactModel(*, name: str, description: str | None = None, data: dict[str, ~typing.Any]=<factory>, path: str | None = None, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: dict[str, Any] = <odmantic.field.FieldProxy object>

description: str | None = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': 'artifacts', 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

path: str | None = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

simstack.models.base_lists module

class simstack.models.base_lists.BooleanDataList(*, field_name: str = 'boolean_data_list', elements: list[ObjectId] = <factory>, id: ObjectId = <factory>)

Bases: Model, ObjectListMixin[StringData]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[ObjectId] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.base_lists.StringDataList(*, field_name: str = 'string_data_list', elements: list[ObjectId] = <factory>, id: ObjectId = <factory>)

Bases: Model, ObjectListMixin[StringData]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[ObjectId] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.base_lists.StringList(*, field_name: str = 'string_list', elements: list[str] = <factory>, id: ObjectId = <factory>)

Bases: Model, GenericListMixin[str]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[str] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

simstack.models.base_types module

class simstack.models.base_types.BinaryOperationInput(*, field_name: str = 'binary_operation', arg1: FloatData, arg2: FloatData, id: ObjectId = <factory>)

Bases: Model

arg1: FloatData = <odmantic.field.FieldProxy object>

arg2: FloatData = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.base_types.BooleanData(*, field_name: str = 'boolean', value: bool, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod ensure_fieldname(data)

Ensure fieldname is set for existing documents

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema(**kwargs) → dict

value: bool = <odmantic.field.FieldProxy object>

class simstack.models.base_types.FloatData(*, field_name: str = 'float', value: float, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod ensure_fieldname(data)

Ensure fieldname is set for existing documents

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema(**kwargs) → dict

value: float = <odmantic.field.FieldProxy object>

class simstack.models.base_types.IntData(*, field_name: str = 'int', value: int, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod ensure_fieldname(data)

Ensure fieldname is set for existing documents

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema(**kwargs) → dict

value: int = <odmantic.field.FieldProxy object>

class simstack.models.base_types.IteratorInput(*, start: int, stop: int, generator: str = 'range', id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

generator: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

start: int = <odmantic.field.FieldProxy object>

stop: int = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.base_types.StringData(*, field_name: str = 'text', value: str, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod ensure_fieldname(data)

Ensure fieldname is set for existing documents

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema(**kwargs) → dict

value: str = <odmantic.field.FieldProxy object>

simstack.models.charts_artifact module

class simstack.models.charts_artifact.AGAreaSeriesConfig(*, type: Literal['area'] = 'area', xKey: str, yKey: str, visible: bool | None = True, showInLegend: bool | None = True, title: str | None = None, data: list[dict[str, ~typing.Any]]=<factory>, fillOpacity: float | None = 0.8, strokeWidth: float | None = 2, marker: dict[str, ~typing.Any] | None=None, tooltip: dict[str, ~typing.Any] | None=None)

Bases: AGChartSeriesBase

AG-Charts area series configuration.

fillOpacity: float | None = <odmantic.field.FieldProxy object>

marker: dict[str, Any] | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_encoders': {<class 'bson.decimal128.Decimal128'>: <function <lambda>>, <class 'bson.objectid.ObjectId'>: <class 'str'>, <class 'bson.regex.Regex'>: <function <lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

strokeWidth: float | None = <odmantic.field.FieldProxy object>

tooltip: dict[str, Any] | None = <odmantic.field.FieldProxy object>

type: Literal['area'] = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGBarSeriesConfig(*, type: Literal['bar'] = 'bar', xKey: str, yKey: str, visible: bool | None = True, showInLegend: bool | None = True, title: str | None = None, data: list[dict[str, ~typing.Any]]=<factory>, fillOpacity: float | None = 1, strokeWidth: float | None = 0, cornerRadius: float | None = 0, tooltip: dict[str, ~typing.Any] | None=None)

Bases: AGChartSeriesBase

AG-Charts bar series configuration.

cornerRadius: float | None = <odmantic.field.FieldProxy object>

fillOpacity: float | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_encoders': {<class 'bson.decimal128.Decimal128'>: <function <lambda>>, <class 'bson.objectid.ObjectId'>: <class 'str'>, <class 'bson.regex.Regex'>: <function <lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

strokeWidth: float | None = <odmantic.field.FieldProxy object>

tooltip: dict[str, Any] | None = <odmantic.field.FieldProxy object>

type: Literal['bar'] = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGChartAxisConfig(*, type: Literal['category', 'number', 'time', 'log'], position: Literal['top', 'right', 'bottom', 'left'], title: str | None = None, min: float | None = None, max: float | None = None, tick: dict[str, Any] | None = None, label: dict[str, Any] | None = None, gridStyle: list[dict[str, Any]] | None = None)

Bases: EmbeddedModel

AG-Charts axis configuration.

gridStyle: list[dict[str, Any]] | None = <odmantic.field.FieldProxy object>

label: dict[str, Any] | None = <odmantic.field.FieldProxy object>

max: float | None = <odmantic.field.FieldProxy object>

min: float | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

position: Literal['top', 'right', 'bottom', 'left'] = <odmantic.field.FieldProxy object>

tick: dict[str, Any] | None = <odmantic.field.FieldProxy object>

title: str | None = <odmantic.field.FieldProxy object>

type: Literal['category', 'number', 'time', 'log'] = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGChartFrameConfig(*, enabled: bool = True, stroke: str = 'black', strokeWidth: float = 1, cornerRadius: float = 0, opacity: float = 1)

Bases: EmbeddedModel

AG-Charts frame (border) configuration.

cornerRadius: float = <odmantic.field.FieldProxy object>

enabled: bool = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

opacity: float = <odmantic.field.FieldProxy object>

stroke: str = <odmantic.field.FieldProxy object>

strokeWidth: float = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGChartLegendConfig(*, enabled: bool = True, position: Literal['top', 'right', 'bottom', 'left']='right', spacing: float = 20, item: dict[str, ~typing.Any]=<factory>)

Bases: EmbeddedModel

AG-Charts legend configuration.

enabled: bool = <odmantic.field.FieldProxy object>

item: dict[str, Any] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

position: Literal['top', 'right', 'bottom', 'left'] = <odmantic.field.FieldProxy object>

spacing: float = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGChartSeriesBase(*, type: str, xKey: str, yKey: str, visible: bool | None = True, showInLegend: bool | None = True, title: str | None = None, data: list[dict[str, ~typing.Any]]=<factory>)

Bases: EmbeddedModel

Base class for AG-Charts series configuration.

data: list[dict[str, Any]] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

showInLegend: bool | None = <odmantic.field.FieldProxy object>

title: str | None = <odmantic.field.FieldProxy object>

type: str = <odmantic.field.FieldProxy object>

visible: bool | None = <odmantic.field.FieldProxy object>

xKey: str = <odmantic.field.FieldProxy object>

yKey: str = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGChartSubtitleConfig(*, text: str, fontSize: int | None = 12, color: str | None = None)

Bases: EmbeddedModel

AG-Charts subtitle configuration.

color: str | None = <odmantic.field.FieldProxy object>

fontSize: int | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

text: str = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGChartTitleConfig(*, text: str = 'Chart Title')

Bases: EmbeddedModel

AG-Charts title configuration.

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

text: str = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGColumnSeriesConfig(*, type: Literal['column'] = 'column', xKey: str, yKey: str, visible: bool | None = True, showInLegend: bool | None = True, title: str | None = None, data: list[dict[str, ~typing.Any]]=<factory>, fillOpacity: float | None = 1, strokeWidth: float | None = 0, cornerRadius: float | None = 0, tooltip: dict[str, ~typing.Any] | None=None)

Bases: AGChartSeriesBase

AG-Charts column series configuration.

cornerRadius: float | None = <odmantic.field.FieldProxy object>

fillOpacity: float | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_encoders': {<class 'bson.decimal128.Decimal128'>: <function <lambda>>, <class 'bson.objectid.ObjectId'>: <class 'str'>, <class 'bson.regex.Regex'>: <function <lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

strokeWidth: float | None = <odmantic.field.FieldProxy object>

tooltip: dict[str, Any] | None = <odmantic.field.FieldProxy object>

type: Literal['column'] = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGDonutSeriesConfig(*, type: Literal['donut'] = 'donut', angleKey: str, radiusKey: str | None = None, labelKey: str | None = None, legendItemKey: str | None = None, calloutLabelKey: str | None = None, sectorLabelKey: str | None = None, innerRadiusRatio: float | None = 0.6, visible: bool | None = True, showInLegend: bool | None = True, title: str | None = None, tooltip: dict[str, Any] | None = None)

Bases: EmbeddedModel

AG-Charts donut series configuration.

angleKey: str = <odmantic.field.FieldProxy object>

calloutLabelKey: str | None = <odmantic.field.FieldProxy object>

innerRadiusRatio: float | None = <odmantic.field.FieldProxy object>

labelKey: str | None = <odmantic.field.FieldProxy object>

legendItemKey: str | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

radiusKey: str | None = <odmantic.field.FieldProxy object>

sectorLabelKey: str | None = <odmantic.field.FieldProxy object>

showInLegend: bool | None = <odmantic.field.FieldProxy object>

title: str | None = <odmantic.field.FieldProxy object>

tooltip: dict[str, Any] | None = <odmantic.field.FieldProxy object>

type: Literal['donut'] = <odmantic.field.FieldProxy object>

visible: bool | None = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGHeatmapSeriesConfig(*, type: Literal['heatmap'] = 'heatmap', xKey: str, yKey: str, visible: bool | None = True, showInLegend: bool | None = True, title: str | None = None, data: list[dict[str, ~typing.Any]]=<factory>, colorKey: str, xName: str | None = None, yName: str | None = None, colorName: str | None = None, colorRange: list[str] | None = None, colorDomain: list[float] | None = None, stroke: str | None = None, strokeWidth: float | None = None, tooltip: dict[str, ~typing.Any] | None=None)

Bases: AGChartSeriesBase

AG-Charts heatmap series configuration.

colorDomain: list[float] | None = <odmantic.field.FieldProxy object>

colorKey: str = <odmantic.field.FieldProxy object>

colorName: str | None = <odmantic.field.FieldProxy object>

colorRange: list[str] | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_encoders': {<class 'bson.decimal128.Decimal128'>: <function <lambda>>, <class 'bson.objectid.ObjectId'>: <class 'str'>, <class 'bson.regex.Regex'>: <function <lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

stroke: str | None = <odmantic.field.FieldProxy object>

strokeWidth: float | None = <odmantic.field.FieldProxy object>

tooltip: dict[str, Any] | None = <odmantic.field.FieldProxy object>

type: Literal['heatmap'] = <odmantic.field.FieldProxy object>

xName: str | None = <odmantic.field.FieldProxy object>

yName: str | None = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGLineSeriesConfig(*, type: Literal['line'] = 'line', xKey: str, yKey: str, visible: bool | None = True, showInLegend: bool | None = True, title: str | None = None, data: list[dict[str, ~typing.Any]]=<factory>, strokeWidth: float | None = 2, strokeOpacity: float | None = 1, lineDash: list[float] | None = None, marker: dict[str, ~typing.Any] | None=None, tooltip: dict[str, ~typing.Any] | None=None)

Bases: AGChartSeriesBase

AG-Charts line series configuration.

lineDash: list[float] | None = <odmantic.field.FieldProxy object>

marker: dict[str, Any] | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_encoders': {<class 'bson.decimal128.Decimal128'>: <function <lambda>>, <class 'bson.objectid.ObjectId'>: <class 'str'>, <class 'bson.regex.Regex'>: <function <lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

strokeOpacity: float | None = <odmantic.field.FieldProxy object>

strokeWidth: float | None = <odmantic.field.FieldProxy object>

tooltip: dict[str, Any] | None = <odmantic.field.FieldProxy object>

type: Literal['line'] = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGPieSeriesConfig(*, type: Literal['pie'] = 'pie', angleKey: str, radiusKey: str | None = None, labelKey: str | None = None, legendItemKey: str | None = None, calloutLabelKey: str | None = None, sectorLabelKey: str | None = None, visible: bool | None = True, showInLegend: bool | None = True, title: str | None = None, tooltip: dict[str, ~typing.Any] | None=None, data: list[dict[str, ~typing.Any]]=<factory>)

Bases: EmbeddedModel

AG-Charts pie series configuration.

angleKey: str = <odmantic.field.FieldProxy object>

calloutLabelKey: str | None = <odmantic.field.FieldProxy object>

data: list[dict[str, Any]] = <odmantic.field.FieldProxy object>

labelKey: str | None = <odmantic.field.FieldProxy object>

legendItemKey: str | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

radiusKey: str | None = <odmantic.field.FieldProxy object>

sectorLabelKey: str | None = <odmantic.field.FieldProxy object>

showInLegend: bool | None = <odmantic.field.FieldProxy object>

title: str | None = <odmantic.field.FieldProxy object>

tooltip: dict[str, Any] | None = <odmantic.field.FieldProxy object>

type: Literal['pie'] = <odmantic.field.FieldProxy object>

visible: bool | None = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGRangeBarSeriesConfig(*, type: Literal['range-bar'] = 'range-bar', xKey: str, yLowKey: str, yHighKey: str, xName: str | None = None, yName: str | None = None, yLowName: str | None = None, yHighName: str | None = None, direction: Literal['horizontal', 'vertical'] | None=None, visible: bool | None = True, showInLegend: bool | None = True, title: str | None = None, data: list[dict[str, ~typing.Any]]=<factory>, fillOpacity: float | None = 1, strokeWidth: float | None = 0, cornerRadius: float | None = 0, tooltip: dict[str, ~typing.Any] | None=None)

Bases: EmbeddedModel

AG-Charts range-bar series configuration.

cornerRadius: float | None = <odmantic.field.FieldProxy object>

data: list[dict[str, Any]] = <odmantic.field.FieldProxy object>

direction: Literal['horizontal', 'vertical'] | None = <odmantic.field.FieldProxy object>

fillOpacity: float | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

showInLegend: bool | None = <odmantic.field.FieldProxy object>

strokeWidth: float | None = <odmantic.field.FieldProxy object>

title: str | None = <odmantic.field.FieldProxy object>

tooltip: dict[str, Any] | None = <odmantic.field.FieldProxy object>

type: Literal['range-bar'] = <odmantic.field.FieldProxy object>

visible: bool | None = <odmantic.field.FieldProxy object>

xKey: str = <odmantic.field.FieldProxy object>

xName: str | None = <odmantic.field.FieldProxy object>

yHighKey: str = <odmantic.field.FieldProxy object>

yHighName: str | None = <odmantic.field.FieldProxy object>

yLowKey: str = <odmantic.field.FieldProxy object>

yLowName: str | None = <odmantic.field.FieldProxy object>

yName: str | None = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.AGScatterSeriesConfig(*, type: Literal['scatter'] = 'scatter', xKey: str, yKey: str, visible: bool | None = True, showInLegend: bool | None = True, title: str | None = None, data: list[dict[str, ~typing.Any]]=<factory>, marker: dict[str, ~typing.Any] | None=None, tooltip: dict[str, ~typing.Any] | None=None)

Bases: AGChartSeriesBase

AG-Charts scatter series configuration.

marker: dict[str, Any] | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_encoders': {<class 'bson.decimal128.Decimal128'>: <function <lambda>>, <class 'bson.objectid.ObjectId'>: <class 'str'>, <class 'bson.regex.Regex'>: <function <lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

tooltip: dict[str, Any] | None = <odmantic.field.FieldProxy object>

type: Literal['scatter'] = <odmantic.field.FieldProxy object>

class simstack.models.charts_artifact.ChartArtifactModel(*, parent_id: ObjectId | None = None, data: list[dict[str, ~typing.Any]]=<factory>, title: AGChartTitleConfig, series: list[AGLineSeriesConfig | AGBarSeriesConfig | AGRangeBarSeriesConfig | AGColumnSeriesConfig | AGAreaSeriesConfig | AGScatterSeriesConfig | AGHeatmapSeriesConfig | AGPieSeriesConfig | AGDonutSeriesConfig] = <factory>, axes: list[AGChartAxisConfig] = <factory>, legend: AGChartLegendConfig = AGChartLegendConfig(enabled=True, position='right', spacing=20.0, item={}), width: int = 800, height: int = 400, padding: dict[str, int] | None=None, background: dict[str, ~typing.Any] | None=None, frame: AGChartFrameConfig = AGChartFrameConfig(enabled=False, stroke='black', strokeWidth=1.0, cornerRadius=0.0, opacity=1.0), animation: dict[str, ~typing.Any] | None=None, tooltip: dict[str, ~typing.Any] | None=None, theme: str | None = 'ag-default', options: dict[str, ~typing.Any] | None=<factory>, id: ObjectId = <factory>)

Bases: Model

AG-Charts configuration model.

animation: dict[str, Any] | None = <odmantic.field.FieldProxy object>

axes: list[AGChartAxisConfig] = <odmantic.field.FieldProxy object>

background: dict[str, Any] | None = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: list[dict[str, Any]] = <odmantic.field.FieldProxy object>

frame: AGChartFrameConfig = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

height: int = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

legend: AGChartLegendConfig = <odmantic.field.FieldProxy object>

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

options: dict[str, Any] | None = <odmantic.field.FieldProxy object>

padding: dict[str, int] | None = <odmantic.field.FieldProxy object>

parent_id: ObjectId | None = <odmantic.field.FieldProxy object>

series: list[AGLineSeriesConfig | AGBarSeriesConfig | AGRangeBarSeriesConfig | AGColumnSeriesConfig | AGAreaSeriesConfig | AGScatterSeriesConfig | AGHeatmapSeriesConfig | AGPieSeriesConfig | AGDonutSeriesConfig] = <odmantic.field.FieldProxy object>

theme: str | None = <odmantic.field.FieldProxy object>

title: AGChartTitleConfig = <odmantic.field.FieldProxy object>

tooltip: dict[str, Any] | None = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

width: int = <odmantic.field.FieldProxy object>

simstack.models.charts_artifact.create_multi_series_line_chart(data: List[Dict[str, Any]], x_key: str, y_keys: List[str], title: str | None = None, parent_id: ObjectId | None = None) → ChartArtifactModel

Create a line chart with multiple y-axis series.

simstack.models.charts_artifact.create_simple_area_chart(data: List[Dict[str, Any]], x_key: str, y_key: str, title: str | None = None, parent_id: ObjectId | None = None) → ChartArtifactModel

Create a simple area chart.

simstack.models.charts_artifact.create_simple_bar_chart(data: List[Dict[str, Any]], x_key: str, y_key: str, title: str | None = None, parent_id: ObjectId | None = None) → ChartArtifactModel

Create a simple bar chart.

simstack.models.charts_artifact.create_simple_donut_chart(data: List[Dict[str, Any]], angle_key: str, label_key: str, title: str | None = None, legend_item_key: str | None = None, callout_label_key: str | None = None, sector_label_key: str | None = None, inner_radius_ratio: float = 0.6, parent_id: ObjectId | None = None) → ChartArtifactModel

Create a simple donut chart.

simstack.models.charts_artifact.create_simple_heatmap_chart(data: List[Dict[str, Any]], x_key: str, y_key: str, color_key: str, title: str | None = None, parent_id: ObjectId | None = None, x_axis_type: Literal['category', 'number', 'time', 'log'] = 'category', y_axis_type: Literal['category', 'number', 'time', 'log'] = 'category') → ChartArtifactModel

Create a simple heatmap chart.

simstack.models.charts_artifact.create_simple_line_chart(data: List[Dict[str, Any]], x_key: str, y_key: str, title: str | None = None, parent_id: ObjectId = None) → ChartArtifactModel

Create a simple line chart.

simstack.models.charts_artifact.create_simple_pie_chart(data: List[Dict[str, Any]], angle_key: str, label_key: str, title: str | None = None, legend_item_key: str | None = None, callout_label_key: str | None = None, sector_label_key: str | None = None, parent_id: ObjectId | None = None) → ChartArtifactModel

Create a simple pie chart.

simstack.models.charts_artifact.create_simple_range_bar_chart(data: List[Dict[str, Any]], x_key: str, y_low_key: str, y_high_key: str, title: str | None = None, parent_id: ObjectId | None = None, direction: Literal['horizontal', 'vertical'] | None = None) → ChartArtifactModel

Create a simple range-bar chart.

simstack.models.charts_artifact.create_simple_scatter_chart(data: List[Dict[str, Any]], x_key: str, y_key: str, title: str | None = None, parent_id: ObjectId | None = None) → ChartArtifactModel

Create a simple scatter chart.

simstack.models.dataset module

class simstack.models.dataset.DataSet(*, field_name: str = 'dataset', metadata: DataSetMetadata, sections: dict[str, ~simstack.models.dataset.DataSetSection]=<factory>, id: ObjectId = <factory>)

Bases: Model

clear()

collect_structure() → Dict[str, Dict[str, str]]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

property dataset_type: str

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get(key: str, default: DataSetSection = None) → DataSetSection

id: ObjectId = <odmantic.field.FieldProxy object>

items() → ItemsView[str, DataSetSection]

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

keys() → KeysView[str]

metadata: DataSetMetadata = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

pop(key: str, default=None) → DataSetSection

async save(db)

sections: dict[str, DataSetSection] = <odmantic.field.FieldProxy object>

setdefault(key: str, default: DataSetSection = None) → DataSetSection

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema() → dict

update(other: Dict[str, DataSetSection] | DataSet = None, **kwargs) → None

values() → ValuesView[DataSetSection]

class simstack.models.dataset.DataSetSection(*, model_types: dict[str, str]=<factory>, data: dict[str, dict[str, ~odmantic.bson.ObjectId]]=<factory>, column_defs: list[dict[()]] = <factory>, table_entries: list[list[dict[()]]] = <factory>)

Bases: EmbeddedModel

Represents a section of a dataset containing dictionaries of models.

A DataSetSection is a list of dictionaries where for each key, the values are of the same model type.

Variables:

• model_types – Dictionary mapping keys to model class names.

• data – Dictionary mapping names to dictionaries mapping keys to ObjectIds.

add_row(item: Dict[str, Model | None], name: str | None = None) → None

Add a dictionary of models to this section.

Parameters:

• item – Dictionary of model instances to add.

• name – Optional name for the item. If None, a UUID will be generated.

Raises:

• ValueError – If the model types don’t match the section’s expected types.

• TypeError – If a non-None item value is not a Model instance.

clear() → None

column_defs: list[dict[()]] = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: dict[str, dict[str, ObjectId]] = <odmantic.field.FieldProxy object>

async db_find_postprocess(db: Database)

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get(name: str, default: Any = None) → Any

get_item(name: str) → Dict[str, Model]

items() → ItemsView[str, Dict[str, Model]]

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

keys() → KeysView[str]

async load_to_cache(db: Database) → None

Load all items from the database into the cache assuming that data is already loaded.

async make_column_defs()

Generate ag-grid column definitions for all model types in this section.

async make_table_entries()

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

model_types: dict[str, str] = <odmantic.field.FieldProxy object>

pop(name: str, default: Any = Ellipsis) → Any

popitem() → Tuple[str, Dict[str, Model]]

async save(db)

Save all models in the cache to the database and update data.

setdefault(name: str, default: Dict[str, Model | None] = None) → Dict[str, Model]

table_entries: list[list[dict[()]]] = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

update(other: Dict[str, Dict[str, Model | None]] | DataSetSection) → None

values() → ValuesView[Dict[str, Model]]

class simstack.models.dataset.DataSetSelection(*, field_name: str = 'dataset_selection', dataset_id: ObjectId, dataset_selection_fields: list[DataSetSelectionField] = <factory>, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

dataset_id: ObjectId = <odmantic.field.FieldProxy object>

dataset_selection_fields: list[DataSetSelectionField] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

async get_dataset()

get_selected_elements(section_name: str = None) → List[Tuple[Model, ...]]

Retrieve all selected model groups from the dataset.

Parameters:

section_name – Optional section name to filter results. If None, returns all sections.

Returns:

List of tuples of model instances for all selected elements

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema() → dict

class simstack.models.dataset.DataSetSelectionField(*, section_name: str = 'default', indices: list[int] = <factory>)

Bases: EmbeddedModel

indices: list[int] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

section_name: str = <odmantic.field.FieldProxy object>

simstack.models.dataset_metadata module

class simstack.models.dataset_metadata.DataSetMetadata(*, field_name: str, data: dict[str, str | int | float | bool | ~odmantic.bson._datetime]=<factory>, is_validated: bool = False, structure: dict[str, dict[str, str]]=<factory>)

Bases: EmbeddedModel

clear()

Clear data dict.

copy_data() → Dict[str, str | int | float | bool | datetime]

Return a copy of the data dict.

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: dict[str, str | int | float | bool | _datetime] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

freeze(new_structure: Dict[str, Dict[str, str]]) → bool

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get(key: str, default=None)

Get item from data dict with default.

get_json_schema()

get_key_type(key: str) → type

Get the type of a specific key.

get_schema_for_key(key: str) → dict

Get JSON schema for a specific key.

property initialized: bool

Check if the model has been fully constructed.

is_type_compatible(key: str, value) → bool

Check if a value is type-compatible with existing key.

is_validated: bool = <odmantic.field.FieldProxy object>

items()

Get items from data dict.

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

keys()

Get keys from data dict.

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

pop(key: str, *args)

Pop item from data dict.

popitem()

Pop item from data dict.

setdefault(key: str, default=None)

Set default value for key if not exists.

structure: dict[str, dict[str, str]] = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

update(*args, **kwargs)

Update data dict with validation.

async validate_dict(new_structure: Dict[str, Dict[str, str]]) → bool

values()

Get values from data dict.

class simstack.models.dataset_metadata.DataSetMetadataTemplate(*, dataset_type: str, model_json: dict[str, ~typing.Any], structure: dict[str, dict[str, str]] = <factory>, id: ~odmantic.bson.ObjectId = <factory>)

Bases: Model

dataset_type: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

model_json: dict[str, Any] = <odmantic.field.FieldProxy object>

structure: dict[str, dict[str, str]] = <odmantic.field.FieldProxy object>

simstack.models.datasettuple module

class simstack.models.datasettuple.DataSetTuple(*, field_name: str = 'dataset', metadata: DataSetMetadata, sections: dict[str, ~simstack.models.datasettuple.DataSetTupleSection]=<factory>, id: ObjectId = <factory>)

Bases: Model

clear() → None

async clone(new_field_name: str = None, exclude_sections: List[str] = None) → DataSetTuple

Clone the dataset with optionally a new field name and excluding specified sections.

Parameters:

• new_field_name – Optional new field name for the cloned dataset. If None, uses original field_name.

• exclude_sections – Optional list of section names to exclude from the clone. If None, all sections are cloned.

Returns:

A new DataSet instance that is a clone of this dataset

collect_structure() → Dict[str, Dict[str, str]]

Returns a dictionary where keys are section names and values are dictionaries mapping string indices to model types at those indices.

Returns:

Dictionary mapping section names to their model type structures

Return type:

Dict[str, Dict[str, str]]

async custom_model_dump(**kwargs) → Dict[str, str]

Returns:

dict with id

property dataset_type: str

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get(key: str, default: DataSetTupleSection = None) → DataSetTupleSection

id: ObjectId = <odmantic.field.FieldProxy object>

items() → ItemsView[str, DataSetTupleSection]

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

keys() → KeysView[str]

metadata: DataSetMetadata = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

pop(key: str, default=None) → DataSetTupleSection

popitem() → Tuple[str, DataSetTupleSection]

async save(engine)

sections: dict[str, DataSetTupleSection] = <odmantic.field.FieldProxy object>

setdefault(key: str, default: DataSetTupleSection = None) → DataSetTupleSection

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema() → dict

update(other: Dict[str, DataSetTupleSection] | DataSetTuple = None, **kwargs) → None

values() → ValuesView[DataSetTupleSection]

class simstack.models.datasettuple.DataSetTupleSection(*, model_types: list[str] = <factory>, data: list[list[ObjectId]] = <factory>, column_defs: list[dict[()]] = <factory>, table_entries: list[list[dict[()]]] = <factory>)

Bases: EmbeddedModel

Represents a section of a dataset containing tuples of models.

A DataSetSection is a list of tuples where all tuples contain the same types of models. For example, if one tuple contains (ModelA, ModelB), then all tuples in this section must contain (ModelA, ModelB) instances.

Variables:

• model_types – List of model class names that define the structure of each tuple.

• data – List of tuples, where each tuple contains model IDs corresponding to model_types.

async add_model_group(models: Model | Tuple[Model, ...]) → None

Add a tuple of models to this section.

Parameters:

models – Tuple of model instances to add

Raises:

ValueError – If the model types don’t match the section’s expected types

async append(models: Tuple[Model, ...]) → None

Append a tuple of models to the section.

Parameters:

models – Tuple of model instances to append

clear() → None

Remove all model groups from the section.

column_defs: list[dict[()]] = <odmantic.field.FieldProxy object>

count(models: Tuple[Model, ...]) → int

Return the number of occurrences of the specified tuple of models.

Parameters:

models – Tuple of model instances to count

Returns:

Number of occurrences

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: list[list[ObjectId]] = <odmantic.field.FieldProxy object>

extend(models_list: List[Tuple[Model, ...]]) → None

Extend the section with multiple tuples of models.

Parameters:

models_list – List of tuples of model instances to extend with

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

async get_all_model_groups() → List[Tuple[Model, ...]]

Retrieve all tuples in this section.

Returns:

List of tuples of model instances

async get_model_group(index: int) → Tuple[Model, ...]

Retrieve a tuple of models at the specified index.

Parameters:

index – Index of the tuple to retrieve

Returns:

Tuple of model instances

async index(models: Tuple[Model, ...], start: int = 0, stop: int = None) → int

Return the index of the first occurrence of the specified tuple of models.

Parameters:

• models – Tuple of model instances to find

• start – Start index for search

• stop – Stop index for search

Returns:

Index of the tuple

Raises:

ValueError – If the tuple is not found

async insert(index: int, models: Tuple[Model, ...]) → None

Insert a tuple of models at the specified index.

Parameters:

• index – Index to insert at

• models – Tuple of model instances to insert

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

async make_column_defs()

Generate ag-grid column definitions for all model types in this section.

Returns:

List of column definitions for ag-grid

async make_table_entries()

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

model_types: list[str] = <odmantic.field.FieldProxy object>

async pop(index: int = -1) → Tuple[Model, ...]

Remove and return a model group at the specified index (default last).

Parameters:

index – Index to pop (default -1 for last)

Returns:

Tuple of model instances that was removed

async remove(models: Tuple[Model, ...]) → None

Remove the first occurrence of the specified tuple of models.

Parameters:

models – Tuple of model instances to remove

Raises:

ValueError – If the tuple is not found

reverse() → None

Reverse the order of model groups in the section.

table_entries: list[list[dict[()]]] = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.datasettuple.DataSetTupleSelection(*, field_name: str = 'dataset_selection', dataset_id: ObjectId, dataset_selection_fields: list[DataSetTupleSelectionField] = <factory>, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

dataset_id: ObjectId = <odmantic.field.FieldProxy object>

dataset_selection_fields: list[DataSetTupleSelectionField] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

async get_dataset()

async get_selected_elements(section_name: str = None) → List[Tuple[Model, ...]]

Retrieve all selected model groups from the dataset.

Parameters:

section_name – Optional section name to filter results. If None, returns all sections.

Returns:

List of tuples of model instances for all selected elements

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema() → dict

class simstack.models.datasettuple.DataSetTupleSelectionField(*, section_name: str = 'default', indices: list[int] = <factory>)

Bases: EmbeddedModel

indices: list[int] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

section_name: str = <odmantic.field.FieldProxy object>

simstack.models.file_instance module

class simstack.models.file_instance.FileInstance(*, id: str | None = None, path: str, resource: Resource, created_at: _datetime, runner_id: str | None = None, location_type: str = 'local_path', size_bytes: int | None = None, checksum_sha256: str | None = None, last_accessed_at: _datetime | None = None, expires_at: _datetime | None = None, is_authoritative: bool = True, is_cached: bool = False, status: str = 'available')

Bases: EmbeddedModel

Represents an embedded model for a file instance.

The FileInstance class is used to encapsulate details about a file, including its path, associated resource, and creation timestamp. It provides a class method for initializing a FileInstance object from a local file path, ensuring proper handling of file-related operations.

path

Path to the file relative to the host work directory.

Type:

str

resource

Name of the resource associated with the file.

Type:

Resource

created_at

Timestamp indicating when the file instance was created.

Type:

datetime

checksum_sha256: str | None = <odmantic.field.FieldProxy object>

created_at: _datetime = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

expires_at: _datetime | None = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_local_file(path: Path | str, file_stack_id: ObjectId, make_copy: bool = True, tasks_id: str = '') → FileInstance

Creates a FileInstance object from a local file path.

This class method is responsible for initializing a FileInstance based on a local file’s path. It supports options to hash the file, make a user-specific copy, and tracks additional metadata. The method handles local file operations such as copying files to a secure directory when necessary and organizes resources under a configurable working directory.

Parameters:

• file_stack_id – the id of the filestack where the file is in

• path – The file path to the local file. Can be either a string or Path.

• make_copy – Indicates whether a secure local copy of the file should be made within the application’s working directory. Defaults to True.

Returns:

A FileInstance object initialized with file details.

Return type:

FileInstance

Raises:

ValueError – If there are issues during the creation of the FileInstance from the specified local file.

classmethod from_model(model: Model, **kwargs) → Model

id: str | None = <odmantic.field.FieldProxy object>

is_authoritative: bool = <odmantic.field.FieldProxy object>

is_cached: bool = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

last_accessed_at: _datetime | None = <odmantic.field.FieldProxy object>

location_type: str = <odmantic.field.FieldProxy object>

classmethod migration(values: Any) → Any

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

path: str = <odmantic.field.FieldProxy object>

resource: Resource = <odmantic.field.FieldProxy object>

runner_id: str | None = <odmantic.field.FieldProxy object>

size_bytes: int | None = <odmantic.field.FieldProxy object>

status: str = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

simstack.models.file_list module

class simstack.models.file_list.FileList(*, elements: list[ObjectId] = <factory>)

Bases: EmbeddedModel, ObjectListMixin[FileStack]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[ObjectId] = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.file_list.FileListIO(*, file_list: FileList = <factory>, task_status: str | None = None, error: str | None = None, message: str | None = None, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

error: str | None = <odmantic.field.FieldProxy object>

file_list: FileList = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

message: str | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

task_status: str | None = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.file_list.FileListModel(*, elements: list[ObjectId] = <factory>, id: ObjectId = <factory>)

Bases: Model, ObjectListMixin[FileStack]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[ObjectId] = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

simstack.models.files module

class simstack.models.files.FileGetterArgs(*, file_stack: FileStack, local_resource: Resource, local_dir: Path, id: ObjectId = <factory>)

Bases: Model

file_stack: FileStack = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

local_dir: Path = <odmantic.field.FieldProxy object>

local_resource: Resource = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

class simstack.models.files.FileStack(*, name: str | None = None, size: int | None = None, is_hashable: bool = False, hash: str | None = None, in_memory: bool = False, content: bytes | None = None, locations: list[FileInstance] = <factory>, id: ObjectId = <factory>)

Bases: Model

append(file_instance: FileInstance) → None

Appends a FileInstance to the file stack.

Parameters:

file_instance (FileInstance) – The FileInstance to append.

complex_hash() → str

content: bytes | None = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs: Any) → Dict[str, Any]

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_local_file(path: Path | str, is_hashable: bool = True, in_memory: bool = True, secure_source: bool = False, task_id: str = '') → FileStack

Creates a FileStack object from a local file path.

Parameters:

• task_id (str) – task_id of the task that created the file stack, used for logging and tracking

• secure_source (bool) – specifies if the source is secure (already in a directory generated within Simstack II)

• path (Union[Path, str]) – The path to the local file. Can be provided as a string or Path object.

• is_hashable (bool) – A flag indicating whether the file hash needs to be calculated.

• in_memory (bool) – Whether to store the compressed file content in memory. Defaults to True.

Returns:

A FileStack object containing FileInstances for the file.

Return type:

FileStack

classmethod from_model(model: Model, **kwargs) → Model

classmethod from_string(data_string: str, file_name: str) → FileStack

get(local_dir: Path | None = None) → Path

Copies the file stack to a local directory. This is the version to be used in applications

Parameters:

local_dir (Path) – The local directory to copy the file stack to.

get_raw(local_resource: Resource, local_dir: Path | None = None) → Path

Copies the file stack to a local directory, assumes no context.

Parameters:

• local_resource – the local resource to copy the file stack to. Defaults to the current resource.

• local_dir (Path) – The local directory to copy the file stack to.

hash: str | None = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

in_memory: bool = <odmantic.field.FieldProxy object>

is_hashable: bool = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

locations: list[FileInstance] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str | None = <odmantic.field.FieldProxy object>

size: int | None = <odmantic.field.FieldProxy object>

str() → str

classmethod ui_base_schema(**kwargs: Any) → Dict[str, Any]

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

async simstack.models.files.main() → None

simstack.models.fire_and_forget_result module

class simstack.models.fire_and_forget_result.FireAndForgetResult(*, call_path: str, models: dict[str, ~typing.Any], success: bool, next_step: bool = False, id: ~odmantic.bson.ObjectId = <factory>)

Bases: Model

Model representing the result of a fire-and-forget task.

call_path

The full call path of the node.

Type:

str

models

A dictionary mapping argument/result names to their values.

Type:

dict[str, Any]

success

Whether the task was successful.

Type:

bool

next_step

Whether this result triggers a next step.

Type:

bool

call_path: str = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

models: dict[str, Any] = <odmantic.field.FieldProxy object>

next_step: bool = <odmantic.field.FieldProxy object>

success: bool = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

simstack.models.images2d module

class simstack.models.images2d.Image2DArtifactModel(*, parent_id: ~odmantic.bson.ObjectId | None = None, name: str, description: str | None = None, format: ~typing.Literal['png', 'jpg', 'jpeg', 'svg', 'gif', 'bmp', 'webp'], data: bytes, width: int | None = None, height: int | None = None, metadata: dict[str, ~typing.Any] = <factory>, id: ~odmantic.bson.ObjectId = <factory>)

Bases: Model

Model for storing 2D image artifacts in MongoDB.

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: bytes = <odmantic.field.FieldProxy object>

description: str | None = <odmantic.field.FieldProxy object>

format: Literal['png', 'jpg', 'jpeg', 'svg', 'gif', 'bmp', 'webp'] = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get_data_uri() → str

Get data URI for the image.

height: int | None = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

metadata: dict[str, Any] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

parent_id: ObjectId | None = <odmantic.field.FieldProxy object>

to_base64() → str

Convert binary data to base64 string.

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

width: int | None = <odmantic.field.FieldProxy object>

simstack.models.images2d.create_image_artifact(name: str, data: bytes, format: str, description: str | None = None, parent_id: ObjectId | None = None, width: int | None = None, height: int | None = None, metadata: Dict[str, Any] | None = None) → Image2DArtifactModel

Create an Image2DArtifactModel instance.

simstack.models.images2d.create_image_artifact_from_file(path: str, name: str | None = None, description: str | None = None, parent_id: ObjectId | None = None) → Image2DArtifactModel

Create an Image2DArtifactModel instance from a file path.

simstack.models.log_entry_model module

class simstack.models.log_entry_model.LogEntry(*, timestamp: _datetime = <factory>, level: LogLevel, logger_name: str, message: str, module: str, function: str, line: int, task_id: str | None = None, resource: str | None = None, thread_name: str | None = None, process_name: str | None = None, exception_type: str | None = None, exception_message: str | None = None, exception_traceback: str | None = None, parameters: Parameters | None = None, id: ObjectId = <factory>)

Bases: Model

exception_message: str | None = <odmantic.field.FieldProxy object>

exception_traceback: str | None = <odmantic.field.FieldProxy object>

exception_type: str | None = <odmantic.field.FieldProxy object>

function: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

level: LogLevel = <odmantic.field.FieldProxy object>

line: int = <odmantic.field.FieldProxy object>

logger_name: str = <odmantic.field.FieldProxy object>

message: str = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': 'logs', 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

module: str = <odmantic.field.FieldProxy object>

parameters: Parameters | None = <odmantic.field.FieldProxy object>

process_name: str | None = <odmantic.field.FieldProxy object>

resource: str | None = <odmantic.field.FieldProxy object>

task_id: str | None = <odmantic.field.FieldProxy object>

thread_name: str | None = <odmantic.field.FieldProxy object>

timestamp: _datetime = <odmantic.field.FieldProxy object>

class simstack.models.log_entry_model.LogLevel(*values)

Bases: str, Enum

CRITICAL = 'CRITICAL'

DEBUG = 'DEBUG'

ERROR = 'ERROR'

INFO = 'INFO'

WARNING = 'WARNING'

simstack.models.models module

class simstack.models.models.DataMapping(*, name: str, mapping: str, description: str | None = '', version: str | None = None)

Bases: EmbeddedModel

description: str | None = <odmantic.field.FieldProxy object>

mapping: str = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

version: str | None = <odmantic.field.FieldProxy object>

class simstack.models.models.ModelMapping(*, name: str, mapping: str, version: str | None = None, collection_name: str, json_schema: str | None = None, ui_schema: str | None = None, id: ObjectId = <factory>)

Bases: Model

name: shorthand - must be unique mapping: full name - path relative to project root in module.module.class/function format

collection_name: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

json_schema: str | None = <odmantic.field.FieldProxy object>

mapping: str = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

ui_schema: str | None = <odmantic.field.FieldProxy object>

version: str | None = <odmantic.field.FieldProxy object>

class simstack.models.models.NodeModel(*, name: str, function_mapping: str, version: str | None = None, input_mappings: list[~simstack.models.models.DataMapping], result_mappings: list[~simstack.models.models.DataMapping] = <factory>, called_nodes: list[str] = <factory>, description: str | None = '', favorite: bool = False, default_parameters: ~simstack.models.parameters.Parameters, pickle_function: ~simstack.models.pickle_models.FunctionPickle | None = None, id: ~odmantic.bson.ObjectId = <factory>)

Bases: Model

called_nodes: list[str] = <odmantic.field.FieldProxy object>

default_parameters: Parameters = <odmantic.field.FieldProxy object>

description: str | None = <odmantic.field.FieldProxy object>

favorite: bool = <odmantic.field.FieldProxy object>

function_mapping: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

input_mappings: list[DataMapping] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': 'node_model', 'extra': None, 'indexes': None, 'json_encoders': {<class 'bytes'>: <function NodeModel.<lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

pickle_function: FunctionPickle | None = <odmantic.field.FieldProxy object>

result_mappings: list[DataMapping] = <odmantic.field.FieldProxy object>

version: str | None = <odmantic.field.FieldProxy object>

simstack.models.named_data_reference module

class simstack.models.named_data_reference.NamedDataReference(*, variable_name: str, variable_mapping: str, reference: ObjectId)

Bases: EmbeddedModel

classmethod from_variable(variable: Model, variable_name: str | None = None, task_id: str | None = None)

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

reference: ObjectId = <odmantic.field.FieldProxy object>

variable_mapping: str = <odmantic.field.FieldProxy object>

variable_name: str = <odmantic.field.FieldProxy object>

simstack.models.node_registry module

class simstack.models.node_registry.NodeRegistry(*, name: str, status: TaskStatus, custom_name: str | None = None, version: str | None = None, project: ObjectId | None = None, category: str | None = None, description: str | None = None, call_path: str | None = None, assignment_rule_id: str | None = None, assignment_rule_name: str | None = None, assignment_pattern: str | None = None, error: str | None = None, message: str | None = None, input_references: list[NamedDataReference] = <factory>, results_references: list[NamedDataReference] = <factory>, info_files: FileList = <factory>, parent_ids: list[ObjectId] = <factory>, artifact_ids: list[ObjectId] = <factory>, created_at: _datetime = <factory>, started_at: _datetime | None = None, completed_at: _datetime | None = None, job_id: str | None = None, function_hash: str, arg_hash: str, func_mapping: str, is_async: bool = False, parameters: Parameters = <odmantic.reference.ODMReferenceInfo object>, id: ObjectId = <factory>)

Bases: Model

Represents a registry for nodes with associated metadata, configurations, and status information. It allows tracking the state and attributes of a workflow node, including its execution parameters, process status, and relationship to other nodes.

This class is designed for managing workflow instances and their lifecycle, with capabilities to monitor execution states, inputs, outputs, and associated timestamps.

Variables:

• name – The name of the node.

• custom_name – An optional custom name for the node.

• status – TheTaskStatus of the node in string format.

• category – An optional category classification for the node.

• description – An optional description providing details about the node.

• input_references – List of references to input data.

• results_references – List of references to result data.

• parent_ids – A list of identifiers representing parent nodes associated with this node.

• created_at – The timestamp when the node was created.

• started_at – An optional timestamp indicating when the execution of the node started.

• completed_at – An optional timestamp indicating when the execution of the node was completed.

• function_hash – A hash value representing the unique function executed by this node.

• arg_hash – A hash value representing the unique arguments passed to the function of this node.

• func_mapping – A mapping identifier associated with the function executed by this node.

• is_async – A boolean value indicating whether the node execution is asynchronous.

• parameters – Parameters associated with the node execution.

• call_path – An optional path indicating where the function is called from by concatenating the name of all nodes in the call stack.

arg_hash: str = <odmantic.field.FieldProxy object>

artifact_ids: list[ObjectId] = <odmantic.field.FieldProxy object>

assignment_pattern: str | None = <odmantic.field.FieldProxy object>

assignment_rule_id: str | None = <odmantic.field.FieldProxy object>

assignment_rule_name: str | None = <odmantic.field.FieldProxy object>

call_path: str | None = <odmantic.field.FieldProxy object>

category: str | None = <odmantic.field.FieldProxy object>

completed_at: _datetime | None = <odmantic.field.FieldProxy object>

created_at: _datetime = <odmantic.field.FieldProxy object>

custom_name: str | None = <odmantic.field.FieldProxy object>

description: str | None = <odmantic.field.FieldProxy object>

error: str | None = <odmantic.field.FieldProxy object>

func_mapping: str = <odmantic.field.FieldProxy object>

function_hash: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

info_files: FileList = <odmantic.field.FieldProxy object>

input_references: list[NamedDataReference] = <odmantic.field.FieldProxy object>

is_async: bool = <odmantic.field.FieldProxy object>

job_id: str | None = <odmantic.field.FieldProxy object>

message: str | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

parameters: Parameters = <odmantic.field.FieldProxy object>

parent_ids: list[ObjectId] = <odmantic.field.FieldProxy object>

project: ObjectId | None = <odmantic.field.FieldProxy object>

results_references: list[NamedDataReference] = <odmantic.field.FieldProxy object>

started_at: _datetime | None = <odmantic.field.FieldProxy object>

status: TaskStatus = <odmantic.field.FieldProxy object>

version: str | None = <odmantic.field.FieldProxy object>

async simstack.models.node_registry.find_child_nodes(task_id: str) → List[NodeRegistry]

simstack.models.pandas_model module

class simstack.models.pandas_model.PandasModel(*, field_name: str = 'pandas_model', content_: bytes = b'', file_stack: FileStack | None = None, id: ObjectId = <factory>)

Bases: Model

content_: bytes = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs) → Dict[str, Any]

field_name: str = <odmantic.field.FieldProxy object>

file_stack: FileStack | None = <odmantic.field.FieldProxy object>

classmethod from_data_frame(df)

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': [('field_name', {'unique': True})], 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

property table

to_react_data(orient='records')

Convert the DataFrame to a Python object suitable for conversion to JSON. This can be used in API responses.

Returns: - List/Dict: Python object ready for json.dumps()

to_react_json(orient='records')

Convert the DataFrame to a JSON string suitable for React visualization libraries.

Parameters: - orient: Determines the JSON string layout:

‘records’ - list like [{column -> value}, … , {column -> value}] (default) ‘columns’ - {column -> [values, …]} ‘index’ - {column -> value}} ‘split’ - {index -> [index], columns -> [columns], data -> [values]} ‘table’ - {‘schema’: {schema}, ‘data’: {data}}

Returns: - String: JSON formatted string ready for React

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

async simstack.models.pandas_model.main()

simstack.models.parameters module

class simstack.models.parameters.Parameters(*, force_rerun: bool = False, resource: Resource = <factory>, queue: str = 'default', recompute_artifacts: bool | None = False, docker_image: str | None = None, other_value: str = 'other', test_dict: dict[str, ~typing.Any]=<factory>, slurm_parameters: SlurmParameters = None)

Bases: EmbeddedModel

docker_image: str | None = <odmantic.field.FieldProxy object>

force_rerun: bool = <odmantic.field.FieldProxy object>

classmethod migrate_slurm_parameters(data)

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': {'description': 'Parameters for running a simulation', 'examples': [{'queue': 'default', 'resource': 'self', 'slurm_parameters': {'nodes': 2}}], 'title': 'Parameters'}, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

other_value: str = <odmantic.field.FieldProxy object>

queue: str = <odmantic.field.FieldProxy object>

recompute_artifacts: bool | None = <odmantic.field.FieldProxy object>

resource: Resource = <odmantic.field.FieldProxy object>

slurm_parameters: SlurmParameters = <odmantic.field.FieldProxy object>

test_dict: dict[str, Any] = <odmantic.field.FieldProxy object>

classmethod validate_resource(v)

Validate and convert resource input to a Resource object.

Accepts string, Resource objects, and dictionary representations. If a string is provided, converts it to a Resource object. If a dictionary is provided (e.g., during deserialization), extracts the value and creates a Resource object.

Parameters:

v – The value to validate (str, Resource, or dict)

Returns:

The validated Resource object

Return type:

Resource

class simstack.models.parameters.Queue(*values)

Bases: str, Enum

DEFAULT = 'default'

DOCKER = 'docker'

SLURM_DOCKER = 'slurm-docker'

SLURM_QUEUE = 'slurm-queue'

class simstack.models.parameters.Resource(*, value: str)

Bases: EmbeddedModel

Resource whose value is validated against the allowed resources only when the value is read, not when it is set or constructed.

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

value: str = <odmantic.field.FieldProxy object>

class simstack.models.parameters.SlurmParameters(*, nodes: Annotated[int | None, ~annotated_types.Ge(ge=1)] = 1, tasks: Annotated[int | None, ~annotated_types.Ge(ge=1)] = 1, tasks_per_node: Annotated[int | None, ~annotated_types.Ge(ge=1)] = 1, cpus_per_task: Annotated[int | None, ~annotated_types.Ge(ge=1)] = 1, mem: str | None = '1G', mem_per_cpu: str | None = None, time: str | None = '1:00:00', begin: str | None = None, partition: str | None = None, qos: str | None = None, job_name: str | None = 'simstack', output: str | None = None, error: str | None = None, mail_type: str | None = None, mail_user: str | None = None, gres: str | None = None, account: str | None = None, priority: int | None = None, reservation: str | None = None, constraint: str | None = None, exclusive: bool | None = None, nice: int | None = None, dependency: str | None = None, array: str | None = None, startup_commands: list[str] = <factory>, chdir: str | None = None, export: str | None = None, signal: str | None = None, requeue: bool | None = None, no_requeue: bool | None = None)

Bases: EmbeddedModel

account: str | None = <odmantic.field.FieldProxy object>

array: str | None = <odmantic.field.FieldProxy object>

begin: str | None = <odmantic.field.FieldProxy object>

chdir: str | None = <odmantic.field.FieldProxy object>

constraint: str | None = <odmantic.field.FieldProxy object>

cpus_per_task: int | None = <odmantic.field.FieldProxy object>

dependency: str | None = <odmantic.field.FieldProxy object>

error: str | None = <odmantic.field.FieldProxy object>

exclusive: bool | None = <odmantic.field.FieldProxy object>

export: str | None = <odmantic.field.FieldProxy object>

gres: str | None = <odmantic.field.FieldProxy object>

job_name: str | None = <odmantic.field.FieldProxy object>

mail_type: str | None = <odmantic.field.FieldProxy object>

mail_user: str | None = <odmantic.field.FieldProxy object>

mem: str | None = <odmantic.field.FieldProxy object>

mem_per_cpu: str | None = <odmantic.field.FieldProxy object>

model_config: ClassVar[Dict[str, Any]] = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': {'description': 'Comprehensive parameters for Slurm job submission', 'examples': [{'cpus_per_task': 4, 'error': 'job_%j.err', 'gres': 'gpu:2', 'job_name': 'my_simulation', 'mail_type': 'END,FAIL', 'mail_user': 'user@institution.edu', 'mem': '64G', 'nodes': 2, 'ntasks_per_node': 8, 'output': 'job_%j.out', 'partition': 'compute', 'time': '12:00:00'}], 'title': 'SlurmParameters'}, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

nice: int | None = <odmantic.field.FieldProxy object>

no_requeue: bool | None = <odmantic.field.FieldProxy object>

nodes: int | None = <odmantic.field.FieldProxy object>

output: str | None = <odmantic.field.FieldProxy object>

partition: str | None = <odmantic.field.FieldProxy object>

priority: int | None = <odmantic.field.FieldProxy object>

qos: str | None = <odmantic.field.FieldProxy object>

requeue: bool | None = <odmantic.field.FieldProxy object>

reservation: str | None = <odmantic.field.FieldProxy object>

signal: str | None = <odmantic.field.FieldProxy object>

startup_commands: list[str] = <odmantic.field.FieldProxy object>

tasks: int | None = <odmantic.field.FieldProxy object>

tasks_per_node: int | None = <odmantic.field.FieldProxy object>

time: str | None = <odmantic.field.FieldProxy object>

to_sbatch_args() → List[str]

Convert parameters to SBATCH arguments list.

to_sbatch_header() → str

Convert parameters to SBATCH header string for script files.

simstack.models.pickle_models module

class simstack.models.pickle_models.ClassPickle(*, name: str, module_path: str, pickle_data: bytes | None = None, id: ObjectId = <factory>)

Bases: BytesB64Mixin, Model

Persist an arbitrary Python class in MongoDB.

Fields

name – class __name__ (for reference / debugging) module_path – original module path (dotted) pickle_data – base64-encoded pickled bytes of the class

store_class(cls)

Serialise and save the given class into pickle_data.

load_class()

Reconstruct the class object from pickle_data.

id: ObjectId = <odmantic.field.FieldProxy object>

load_class() → Type[Any]

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_encoders': {<class 'bytes'>: <function BytesB64Mixin.<lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

module_path: str = <odmantic.field.FieldProxy object>

name: str = <odmantic.field.FieldProxy object>

pickle_data: bytes | None = <odmantic.field.FieldProxy object>

store_class(cls: Type[Any]) → None

class simstack.models.pickle_models.FunctionPickle(*, name: str, module_path: str, pickle_data: bytes | None = None, id: ObjectId = <factory>)

Bases: BytesB64Mixin, Model

Persist an arbitrary Python function in MongoDB.

Fields

name – function __name__ module_path – original module path pickle_data – base64 pickled bytes of the function

id: ObjectId = <odmantic.field.FieldProxy object>

load_function() → Callable

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_encoders': {<class 'bytes'>: <function BytesB64Mixin.<lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

module_path: str = <odmantic.field.FieldProxy object>

name: str = <odmantic.field.FieldProxy object>

pickle_data: bytes | None = <odmantic.field.FieldProxy object>

store_function(func: Callable) → None

simstack.models.project module

class simstack.models.project.Project(*, field_name: str, description: str | None = None, tag_ids: list[ObjectId] = <factory>, id: ObjectId = <factory>)

Bases: Model

description: str | None = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': 'projects', 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

tag_ids: list[ObjectId] = <odmantic.field.FieldProxy object>

simstack.models.resource_assignment module

class simstack.models.resource_assignment.ResourceAssignmentRule(*, name: str, regex_pattern: str, priority: int = 0, enabled: bool = True, resource_str: str | None = None, queue: str | None = None, slurm_parameters_patch: dict[str, ~typing.Any]=<factory>, description: str | None = '', id: ObjectId = <factory>)

Bases: Model

description: str | None = <odmantic.field.FieldProxy object>

enabled: bool = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod matches_call_path(pattern: str, normalized_call_path: str) → bool

model_config = {'arbitrary_types_allowed': False, 'collection': 'resource_assignment_rule', 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

static normalize_pattern(pattern: str) → str

classmethod pattern_specificity_score(pattern: str) → int

classmethod pattern_to_regex(pattern: str) → str

priority: int = <odmantic.field.FieldProxy object>

queue: str | None = <odmantic.field.FieldProxy object>

regex_pattern: str = <odmantic.field.FieldProxy object>

resource_str: str | None = <odmantic.field.FieldProxy object>

slurm_parameters_patch: dict[str, Any] = <odmantic.field.FieldProxy object>

class simstack.models.resource_assignment.SlurmParametersPatch(*, nodes: Annotated[int | None, Ge(ge=1)] = None, tasks: Annotated[int | None, Ge(ge=1)] = None, tasks_per_node: Annotated[int | None, Ge(ge=1)] = None, cpus_per_task: Annotated[int | None, Ge(ge=1)] = None, mem: str | None = None, mem_per_cpu: str | None = None, time: str | None = None, begin: str | None = None, partition: str | None = None, qos: str | None = None, job_name: str | None = None, output: str | None = None, error: str | None = None, mail_type: str | None = None, mail_user: str | None = None, gres: str | None = None, account: str | None = None, priority: int | None = None, reservation: str | None = None, constraint: str | None = None, exclusive: bool | None = None, nice: int | None = None, dependency: str | None = None, array: str | None = None, startup_commands: list[str] | None = None, chdir: str | None = None, export: str | None = None, signal: str | None = None, requeue: bool | None = None, no_requeue: bool | None = None)

Bases: EmbeddedModel

account: str | None = <odmantic.field.FieldProxy object>

array: str | None = <odmantic.field.FieldProxy object>

begin: str | None = <odmantic.field.FieldProxy object>

chdir: str | None = <odmantic.field.FieldProxy object>

constraint: str | None = <odmantic.field.FieldProxy object>

cpus_per_task: int | None = <odmantic.field.FieldProxy object>

dependency: str | None = <odmantic.field.FieldProxy object>

error: str | None = <odmantic.field.FieldProxy object>

exclusive: bool | None = <odmantic.field.FieldProxy object>

export: str | None = <odmantic.field.FieldProxy object>

gres: str | None = <odmantic.field.FieldProxy object>

job_name: str | None = <odmantic.field.FieldProxy object>

mail_type: str | None = <odmantic.field.FieldProxy object>

mail_user: str | None = <odmantic.field.FieldProxy object>

mem: str | None = <odmantic.field.FieldProxy object>

mem_per_cpu: str | None = <odmantic.field.FieldProxy object>

model_config: ClassVar[Dict[str, Any]] = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

nice: int | None = <odmantic.field.FieldProxy object>

no_requeue: bool | None = <odmantic.field.FieldProxy object>

nodes: int | None = <odmantic.field.FieldProxy object>

output: str | None = <odmantic.field.FieldProxy object>

partition: str | None = <odmantic.field.FieldProxy object>

priority: int | None = <odmantic.field.FieldProxy object>

qos: str | None = <odmantic.field.FieldProxy object>

requeue: bool | None = <odmantic.field.FieldProxy object>

reservation: str | None = <odmantic.field.FieldProxy object>

signal: str | None = <odmantic.field.FieldProxy object>

startup_commands: list[str] | None = <odmantic.field.FieldProxy object>

tasks: int | None = <odmantic.field.FieldProxy object>

tasks_per_node: int | None = <odmantic.field.FieldProxy object>

time: str | None = <odmantic.field.FieldProxy object>

simstack.models.resource_definition module

class simstack.models.resource_definition.GitRepo(*, url: str, branch: str | None, is_submodule: bool = False, id: ObjectId = <factory>)

Bases: Model

Represents a Git repository with relevant attributes such as its URL, branch, and whether it is a submodule. Ensures that the URL provided is valid.

This class is used to model information about a Git repository, including its URL, the branch being used, and whether it is included as a submodule within another repository. It provides validation for the URL to ensure that it is in the correct format.

In the user database there is a list of Git repositories for the user

Variables:

• url – The URL of the Git repository.

• branch – The branch of the Git repository. Optional.

• is_submodule – Indicates whether the repository is a submodule. Defaults to False.

branch: str | None = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

is_submodule: bool = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

url: str = <odmantic.field.FieldProxy object>

classmethod validate_url(v)

class simstack.models.resource_definition.ResourceDefinition(*, resource_str: str, workdir: str, hostname: str, python_paths: list[str] = <factory>, environment_start: str | None = None, ssh_key: str | None = None, routes: list[str] | None = [], queue: str = 'default', is_default: bool = False, git_branch: str = 'main', id: ObjectId = <factory>)

Bases: Model

classmethod convert_python_paths(v)

classmethod convert_ssh_key(v)

classmethod convert_workdir(v)

environment_start: str | None = <odmantic.field.FieldProxy object>

classmethod from_resource_definition(resource_definition: dict)

get_python_path()

get_ssh_key_path()

git_branch: str = <odmantic.field.FieldProxy object>

hostname: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

is_default: bool = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod normalize_queue(v)

python_paths: list[str] = <odmantic.field.FieldProxy object>

queue: str = <odmantic.field.FieldProxy object>

resource_str: str = <odmantic.field.FieldProxy object>

routes: list[str] | None = <odmantic.field.FieldProxy object>

ssh_key: str | None = <odmantic.field.FieldProxy object>

validate_hostname()

validate_python_path()

validate_ssh_key()

workdir: str = <odmantic.field.FieldProxy object>

simstack.models.runner_model module

class simstack.models.runner_model.RunnerEvent(*, timestamp: _datetime = <factory>, event: RunnerEventEnum, resource: Resource, runner_type: RunnerType, hostname: str | None = None, user: str | None = None, pid: int | None = None, node_id: ObjectId | None = None, message: str | None = None, git_status: list[str] = <factory>, id: ObjectId = <factory>)

Bases: Model

event: RunnerEventEnum = <odmantic.field.FieldProxy object>

git_status: list[str] = <odmantic.field.FieldProxy object>

hostname: str | None = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

message: str | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': True, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

node_id: ObjectId | None = <odmantic.field.FieldProxy object>

pid: int | None = <odmantic.field.FieldProxy object>

resource: Resource = <odmantic.field.FieldProxy object>

runner_type: RunnerType = <odmantic.field.FieldProxy object>

timestamp: _datetime = <odmantic.field.FieldProxy object>

user: str | None = <odmantic.field.FieldProxy object>

class simstack.models.runner_model.RunnerEventEnum(*values)

Bases: str, Enum

ALIVE = 'alive'

CRONTAB_GONE = 'crontab_gone'

CRONTAB_OK = 'crontab_ok'

NODE_STARTED = 'node_started'

NODE_SUBMIT = 'node_submit'

RESTART = 'restart'

RUNNER_STARTED = 'runner_started'

SHUTDOWN = 'shutdown'

class simstack.models.runner_model.RunnerType(*values)

Bases: str, Enum

NODE_RUNNER = 'node_runner'

RESOURCE_RUNNER = 'resource_runner'

simstack.models.simple_table module

class simstack.models.simple_table.SimpleTable(*, name: str = 'SimpleTable', heading: list[str] = <factory>, row: list[dict[str, ~typing.Any]]=<factory>, type: list[SimpleTableColumnType] = <factory>, id: ObjectId = <factory>)

Bases: Model

A simple table model to display tabular data using ag-grid

add_column(column_name: str, column_type: SimpleTableColumnType | str) → None

add_row(row: Dict[str, Any]) → None

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

heading: list[str] = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

classmethod normalize_column_types(value: Any) → Any

row: list[dict[str, Any]] = <odmantic.field.FieldProxy object>

type: list[SimpleTableColumnType] = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema() → Dict[str, str]

class simstack.models.simple_table.SimpleTableColumnType(*values)

Bases: str, Enum

NUMBER = 'number'

STRING = 'string'

classmethod from_value(value: SimpleTableColumnType | str) → SimpleTableColumnType

simstack.models.simstack_model module

simstack.models.simstack_model.is_simstack_model(cls: Type) → bool

Check if a class has been decorated with @simstack_model.

Parameters:

cls – The class to check

Returns:

True if the class was decorated with @simstack_model, False otherwise

Return type:

bool

simstack.models.simstack_model.simstack_model(cls: Type[T]) → Type[T]

Decorates a given class to equip it with default implementations of utility methods for handling operations such as dictionary conversion, schema generation, and UI schema generation.

simstack.models.slurm_info module

class simstack.models.slurm_info.SlurmInfo(*, node_registry: ~odmantic.bson.ObjectId, updated: ~odmantic.bson._datetime, resource: ~simstack.models.parameters.Resource, job_id: str, name: str, user: str, code: str, time: str, nodes: list[str], id: ~odmantic.bson.ObjectId = <factory>)

Bases: Model

code: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

job_id: str = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

node_registry: ObjectId = <odmantic.field.FieldProxy object>

nodes: list[str] = <odmantic.field.FieldProxy object>

resource: Resource = <odmantic.field.FieldProxy object>

time: str = <odmantic.field.FieldProxy object>

updated: _datetime = <odmantic.field.FieldProxy object>

user: str = <odmantic.field.FieldProxy object>

simstack.models.table_artifact module

class simstack.models.table_artifact.AGGridColumnDef(*, field: str, headerName: str, width: int | None = None, minWidth: int | None = None, maxWidth: int | None = None, flex: int | None = None, hide: bool | None = None, sortable: bool | None = None, resizable: bool | None = None, editable: bool | None = None)

Bases: EmbeddedModel

AG-Grid column definition with support for nested tables.

editable: bool | None = <odmantic.field.FieldProxy object>

field: str = <odmantic.field.FieldProxy object>

flex: int | None = <odmantic.field.FieldProxy object>

headerName: str = <odmantic.field.FieldProxy object>

hide: bool | None = <odmantic.field.FieldProxy object>

maxWidth: int | None = <odmantic.field.FieldProxy object>

minWidth: int | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

resizable: bool | None = <odmantic.field.FieldProxy object>

sortable: bool | None = <odmantic.field.FieldProxy object>

width: int | None = <odmantic.field.FieldProxy object>

class simstack.models.table_artifact.TableArtifactModel(*, parent_id: ObjectId = None, columns_defs: list[AGGridColumnDef] = <factory>, row_data: list[dict[str, ~typing.Any]]=<factory>, master_detail: bool | None = False, detail_cell_renderer: str | None = None, detail_cell_renderer_params: dict[str, ~typing.Any] | None=None, id: ObjectId = <factory>)

Bases: Model

columns_defs: list[AGGridColumnDef] = <odmantic.field.FieldProxy object>

detail_cell_renderer: str | None = <odmantic.field.FieldProxy object>

detail_cell_renderer_params: dict[str, Any] | None = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

master_detail: bool | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

parent_id: ObjectId = <odmantic.field.FieldProxy object>

row_data: list[dict[str, Any]] = <odmantic.field.FieldProxy object>

simstack.models.tag module

class simstack.models.tag.Tag(*, name: str, description: str | None = None, id: ObjectId = <factory>)

Bases: Model

description: str | None = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': 'tags', 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

simstack.models.unused_test_file module

Module contents

class simstack.models.ArrayList(*, elements: list[ObjectId] = <factory>, id: ObjectId = <factory>)

Bases: Model, ObjectListMixin[ArrayStorage]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[ObjectId] = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.ArtifactMapping(*, name: str = 'artifact', regex_pattern: str = '', function_mapping: str = '', function_code: str = '', pickle_function: FunctionPickle | None = None, id: ObjectId = <factory>)

Bases: Model

ArtifactsMapper is a mapping between the artifact and a node registry-path. The workflow executor passes a path of the type

node1.node2.node4. … .nodeN

where node is the function name of the node

Regex can maps this to the target path of the ArtifactsMapping, e.g. a path

*.parent1.node

it would map on all nodes with name node that have been directly called by a node with the name parent1.

function_code: str = <odmantic.field.FieldProxy object>

function_mapping: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

pickle_function: FunctionPickle | None = <odmantic.field.FieldProxy object>

regex_pattern: str = <odmantic.field.FieldProxy object>

set_values(other: ArtifactMapping) → ArtifactMapping

class simstack.models.ArtifactModel(*, name: str, description: str | None = None, data: dict[str, ~typing.Any]=<factory>, path: str | None = None, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: dict[str, Any] = <odmantic.field.FieldProxy object>

description: str | None = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': 'artifacts', 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

path: str | None = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.BinaryOperationInput(*, field_name: str = 'binary_operation', arg1: FloatData, arg2: FloatData, id: ObjectId = <factory>)

Bases: Model

arg1: FloatData = <odmantic.field.FieldProxy object>

arg2: FloatData = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.BooleanData(*, field_name: str = 'boolean', value: bool, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod ensure_fieldname(data)

Ensure fieldname is set for existing documents

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema(**kwargs) → dict

value: bool = <odmantic.field.FieldProxy object>

class simstack.models.DataSet(*, field_name: str = 'dataset', metadata: DataSetMetadata, sections: dict[str, ~simstack.models.dataset.DataSetSection]=<factory>, id: ObjectId = <factory>)

Bases: Model

clear()

collect_structure() → Dict[str, Dict[str, str]]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

property dataset_type: str

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get(key: str, default: DataSetSection = None) → DataSetSection

id: ObjectId = <odmantic.field.FieldProxy object>

items() → ItemsView[str, DataSetSection]

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

keys() → KeysView[str]

metadata: DataSetMetadata = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

pop(key: str, default=None) → DataSetSection

async save(db)

sections: dict[str, DataSetSection] = <odmantic.field.FieldProxy object>

setdefault(key: str, default: DataSetSection = None) → DataSetSection

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema() → dict

update(other: Dict[str, DataSetSection] | DataSet = None, **kwargs) → None

values() → ValuesView[DataSetSection]

class simstack.models.DataSetMetadata(*, field_name: str, data: dict[str, str | int | float | bool | ~odmantic.bson._datetime]=<factory>, is_validated: bool = False, structure: dict[str, dict[str, str]]=<factory>)

Bases: EmbeddedModel

clear()

Clear data dict.

copy_data() → Dict[str, str | int | float | bool | datetime]

Return a copy of the data dict.

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: dict[str, str | int | float | bool | _datetime] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

freeze(new_structure: Dict[str, Dict[str, str]]) → bool

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get(key: str, default=None)

Get item from data dict with default.

get_json_schema()

get_key_type(key: str) → type

Get the type of a specific key.

get_schema_for_key(key: str) → dict

Get JSON schema for a specific key.

property initialized: bool

Check if the model has been fully constructed.

is_type_compatible(key: str, value) → bool

Check if a value is type-compatible with existing key.

is_validated: bool = <odmantic.field.FieldProxy object>

items()

Get items from data dict.

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

keys()

Get keys from data dict.

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

pop(key: str, *args)

Pop item from data dict.

popitem()

Pop item from data dict.

setdefault(key: str, default=None)

Set default value for key if not exists.

structure: dict[str, dict[str, str]] = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

update(*args, **kwargs)

Update data dict with validation.

async validate_dict(new_structure: Dict[str, Dict[str, str]]) → bool

values()

Get values from data dict.

class simstack.models.DataSetMetadataTemplate(*, dataset_type: str, model_json: dict[str, ~typing.Any], structure: dict[str, dict[str, str]] = <factory>, id: ~odmantic.bson.ObjectId = <factory>)

Bases: Model

dataset_type: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

model_json: dict[str, Any] = <odmantic.field.FieldProxy object>

structure: dict[str, dict[str, str]] = <odmantic.field.FieldProxy object>

class simstack.models.DataSetSection(*, model_types: dict[str, str]=<factory>, data: dict[str, dict[str, ~odmantic.bson.ObjectId]]=<factory>, column_defs: list[dict[()]] = <factory>, table_entries: list[list[dict[()]]] = <factory>)

Bases: EmbeddedModel

Represents a section of a dataset containing dictionaries of models.

A DataSetSection is a list of dictionaries where for each key, the values are of the same model type.

Variables:

• model_types – Dictionary mapping keys to model class names.

• data – Dictionary mapping names to dictionaries mapping keys to ObjectIds.

add_row(item: Dict[str, Model | None], name: str | None = None) → None

Add a dictionary of models to this section.

Parameters:

• item – Dictionary of model instances to add.

• name – Optional name for the item. If None, a UUID will be generated.

Raises:

• ValueError – If the model types don’t match the section’s expected types.

• TypeError – If a non-None item value is not a Model instance.

clear() → None

column_defs: list[dict[()]] = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: dict[str, dict[str, ObjectId]] = <odmantic.field.FieldProxy object>

async db_find_postprocess(db: Database)

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get(name: str, default: Any = None) → Any

get_item(name: str) → Dict[str, Model]

items() → ItemsView[str, Dict[str, Model]]

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

keys() → KeysView[str]

async load_to_cache(db: Database) → None

Load all items from the database into the cache assuming that data is already loaded.

async make_column_defs()

Generate ag-grid column definitions for all model types in this section.

async make_table_entries()

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

model_types: dict[str, str] = <odmantic.field.FieldProxy object>

pop(name: str, default: Any = Ellipsis) → Any

popitem() → Tuple[str, Dict[str, Model]]

async save(db)

Save all models in the cache to the database and update data.

setdefault(name: str, default: Dict[str, Model | None] = None) → Dict[str, Model]

table_entries: list[list[dict[()]]] = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

update(other: Dict[str, Dict[str, Model | None]] | DataSetSection) → None

values() → ValuesView[Dict[str, Model]]

class simstack.models.DataSetSelection(*, field_name: str = 'dataset_selection', dataset_id: ObjectId, dataset_selection_fields: list[DataSetSelectionField] = <factory>, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

dataset_id: ObjectId = <odmantic.field.FieldProxy object>

dataset_selection_fields: list[DataSetSelectionField] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

async get_dataset()

get_selected_elements(section_name: str = None) → List[Tuple[Model, ...]]

Retrieve all selected model groups from the dataset.

Parameters:

section_name – Optional section name to filter results. If None, returns all sections.

Returns:

List of tuples of model instances for all selected elements

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema() → dict

class simstack.models.DataSetSelectionField(*, section_name: str = 'default', indices: list[int] = <factory>)

Bases: EmbeddedModel

indices: list[int] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

section_name: str = <odmantic.field.FieldProxy object>

class simstack.models.DataSetTuple(*, field_name: str = 'dataset', metadata: DataSetMetadata, sections: dict[str, ~simstack.models.datasettuple.DataSetTupleSection]=<factory>, id: ObjectId = <factory>)

Bases: Model

clear() → None

async clone(new_field_name: str = None, exclude_sections: List[str] = None) → DataSetTuple

Clone the dataset with optionally a new field name and excluding specified sections.

Parameters:

• new_field_name – Optional new field name for the cloned dataset. If None, uses original field_name.

• exclude_sections – Optional list of section names to exclude from the clone. If None, all sections are cloned.

Returns:

A new DataSet instance that is a clone of this dataset

collect_structure() → Dict[str, Dict[str, str]]

Returns a dictionary where keys are section names and values are dictionaries mapping string indices to model types at those indices.

Returns:

Dictionary mapping section names to their model type structures

Return type:

Dict[str, Dict[str, str]]

async custom_model_dump(**kwargs) → Dict[str, str]

Returns:

dict with id

property dataset_type: str

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get(key: str, default: DataSetTupleSection = None) → DataSetTupleSection

id: ObjectId = <odmantic.field.FieldProxy object>

items() → ItemsView[str, DataSetTupleSection]

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

keys() → KeysView[str]

metadata: DataSetMetadata = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

pop(key: str, default=None) → DataSetTupleSection

popitem() → Tuple[str, DataSetTupleSection]

async save(engine)

sections: dict[str, DataSetTupleSection] = <odmantic.field.FieldProxy object>

setdefault(key: str, default: DataSetTupleSection = None) → DataSetTupleSection

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema() → dict

update(other: Dict[str, DataSetTupleSection] | DataSetTuple = None, **kwargs) → None

values() → ValuesView[DataSetTupleSection]

class simstack.models.DataSetTupleSection(*, model_types: list[str] = <factory>, data: list[list[ObjectId]] = <factory>, column_defs: list[dict[()]] = <factory>, table_entries: list[list[dict[()]]] = <factory>)

Bases: EmbeddedModel

Represents a section of a dataset containing tuples of models.

A DataSetSection is a list of tuples where all tuples contain the same types of models. For example, if one tuple contains (ModelA, ModelB), then all tuples in this section must contain (ModelA, ModelB) instances.

Variables:

• model_types – List of model class names that define the structure of each tuple.

• data – List of tuples, where each tuple contains model IDs corresponding to model_types.

async add_model_group(models: Model | Tuple[Model, ...]) → None

Add a tuple of models to this section.

Parameters:

models – Tuple of model instances to add

Raises:

ValueError – If the model types don’t match the section’s expected types

async append(models: Tuple[Model, ...]) → None

Append a tuple of models to the section.

Parameters:

models – Tuple of model instances to append

clear() → None

Remove all model groups from the section.

column_defs: list[dict[()]] = <odmantic.field.FieldProxy object>

count(models: Tuple[Model, ...]) → int

Return the number of occurrences of the specified tuple of models.

Parameters:

models – Tuple of model instances to count

Returns:

Number of occurrences

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: list[list[ObjectId]] = <odmantic.field.FieldProxy object>

extend(models_list: List[Tuple[Model, ...]]) → None

Extend the section with multiple tuples of models.

Parameters:

models_list – List of tuples of model instances to extend with

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

async get_all_model_groups() → List[Tuple[Model, ...]]

Retrieve all tuples in this section.

Returns:

List of tuples of model instances

async get_model_group(index: int) → Tuple[Model, ...]

Retrieve a tuple of models at the specified index.

Parameters:

index – Index of the tuple to retrieve

Returns:

Tuple of model instances

async index(models: Tuple[Model, ...], start: int = 0, stop: int = None) → int

Return the index of the first occurrence of the specified tuple of models.

Parameters:

• models – Tuple of model instances to find

• start – Start index for search

• stop – Stop index for search

Returns:

Index of the tuple

Raises:

ValueError – If the tuple is not found

async insert(index: int, models: Tuple[Model, ...]) → None

Insert a tuple of models at the specified index.

Parameters:

• index – Index to insert at

• models – Tuple of model instances to insert

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

async make_column_defs()

Generate ag-grid column definitions for all model types in this section.

Returns:

List of column definitions for ag-grid

async make_table_entries()

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

model_types: list[str] = <odmantic.field.FieldProxy object>

async pop(index: int = -1) → Tuple[Model, ...]

Remove and return a model group at the specified index (default last).

Parameters:

index – Index to pop (default -1 for last)

Returns:

Tuple of model instances that was removed

async remove(models: Tuple[Model, ...]) → None

Remove the first occurrence of the specified tuple of models.

Parameters:

models – Tuple of model instances to remove

Raises:

ValueError – If the tuple is not found

reverse() → None

Reverse the order of model groups in the section.

table_entries: list[list[dict[()]]] = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.DataSetTupleSelection(*, field_name: str = 'dataset_selection', dataset_id: ObjectId, dataset_selection_fields: list[DataSetTupleSelectionField] = <factory>, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

dataset_id: ObjectId = <odmantic.field.FieldProxy object>

dataset_selection_fields: list[DataSetTupleSelectionField] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

async get_dataset()

async get_selected_elements(section_name: str = None) → List[Tuple[Model, ...]]

Retrieve all selected model groups from the dataset.

Parameters:

section_name – Optional section name to filter results. If None, returns all sections.

Returns:

List of tuples of model instances for all selected elements

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema() → dict

class simstack.models.DataSetTupleSelectionField(*, section_name: str = 'default', indices: list[int] = <factory>)

Bases: EmbeddedModel

indices: list[int] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

section_name: str = <odmantic.field.FieldProxy object>

class simstack.models.FileList(*, elements: list[ObjectId] = <factory>)

Bases: EmbeddedModel, ObjectListMixin[FileStack]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[ObjectId] = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.FileListModel(*, elements: list[ObjectId] = <factory>, id: ObjectId = <factory>)

Bases: Model, ObjectListMixin[FileStack]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[ObjectId] = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.FileStack(*, name: str | None = None, size: int | None = None, is_hashable: bool = False, hash: str | None = None, in_memory: bool = False, content: bytes | None = None, locations: list[FileInstance] = <factory>, id: ObjectId = <factory>)

Bases: Model

append(file_instance: FileInstance) → None

Appends a FileInstance to the file stack.

Parameters:

file_instance (FileInstance) – The FileInstance to append.

complex_hash() → str

content: bytes | None = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs: Any) → Dict[str, Any]

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_local_file(path: Path | str, is_hashable: bool = True, in_memory: bool = True, secure_source: bool = False, task_id: str = '') → FileStack

Creates a FileStack object from a local file path.

Parameters:

• task_id (str) – task_id of the task that created the file stack, used for logging and tracking

• secure_source (bool) – specifies if the source is secure (already in a directory generated within Simstack II)

• path (Union[Path, str]) – The path to the local file. Can be provided as a string or Path object.

• is_hashable (bool) – A flag indicating whether the file hash needs to be calculated.

• in_memory (bool) – Whether to store the compressed file content in memory. Defaults to True.

Returns:

A FileStack object containing FileInstances for the file.

Return type:

FileStack

classmethod from_model(model: Model, **kwargs) → Model

classmethod from_string(data_string: str, file_name: str) → FileStack

get(local_dir: Path | None = None) → Path

Copies the file stack to a local directory. This is the version to be used in applications

Parameters:

local_dir (Path) – The local directory to copy the file stack to.

get_raw(local_resource: Resource, local_dir: Path | None = None) → Path

Copies the file stack to a local directory, assumes no context.

Parameters:

• local_resource – the local resource to copy the file stack to. Defaults to the current resource.

• local_dir (Path) – The local directory to copy the file stack to.

hash: str | None = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

in_memory: bool = <odmantic.field.FieldProxy object>

is_hashable: bool = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

locations: list[FileInstance] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str | None = <odmantic.field.FieldProxy object>

size: int | None = <odmantic.field.FieldProxy object>

str() → str

classmethod ui_base_schema(**kwargs: Any) → Dict[str, Any]

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.FireAndForgetResult(*, call_path: str, models: dict[str, ~typing.Any], success: bool, next_step: bool = False, id: ~odmantic.bson.ObjectId = <factory>)

Bases: Model

Model representing the result of a fire-and-forget task.

call_path

The full call path of the node.

Type:

str

models

A dictionary mapping argument/result names to their values.

Type:

dict[str, Any]

success

Whether the task was successful.

Type:

bool

next_step

Whether this result triggers a next step.

Type:

bool

call_path: str = <odmantic.field.FieldProxy object>

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

models: dict[str, Any] = <odmantic.field.FieldProxy object>

next_step: bool = <odmantic.field.FieldProxy object>

success: bool = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.FloatData(*, field_name: str = 'float', value: float, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod ensure_fieldname(data)

Ensure fieldname is set for existing documents

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema(**kwargs) → dict

value: float = <odmantic.field.FieldProxy object>

class simstack.models.Image2DArtifactModel(*, parent_id: ~odmantic.bson.ObjectId | None = None, name: str, description: str | None = None, format: ~typing.Literal['png', 'jpg', 'jpeg', 'svg', 'gif', 'bmp', 'webp'], data: bytes, width: int | None = None, height: int | None = None, metadata: dict[str, ~typing.Any] = <factory>, id: ~odmantic.bson.ObjectId = <factory>)

Bases: Model

Model for storing 2D image artifacts in MongoDB.

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

data: bytes = <odmantic.field.FieldProxy object>

description: str | None = <odmantic.field.FieldProxy object>

format: Literal['png', 'jpg', 'jpeg', 'svg', 'gif', 'bmp', 'webp'] = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

get_data_uri() → str

Get data URI for the image.

height: int | None = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

metadata: dict[str, Any] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

parent_id: ObjectId | None = <odmantic.field.FieldProxy object>

to_base64() → str

Convert binary data to base64 string.

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

width: int | None = <odmantic.field.FieldProxy object>

class simstack.models.IntData(*, field_name: str = 'int', value: int, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod ensure_fieldname(data)

Ensure fieldname is set for existing documents

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema(**kwargs) → dict

value: int = <odmantic.field.FieldProxy object>

class simstack.models.IteratorInput(*, start: int, stop: int, generator: str = 'range', id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

generator: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

start: int = <odmantic.field.FieldProxy object>

stop: int = <odmantic.field.FieldProxy object>

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.ModelMapping(*, name: str, mapping: str, version: str | None = None, collection_name: str, json_schema: str | None = None, ui_schema: str | None = None, id: ObjectId = <factory>)

Bases: Model

name: shorthand - must be unique mapping: full name - path relative to project root in module.module.class/function format

collection_name: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

json_schema: str | None = <odmantic.field.FieldProxy object>

mapping: str = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

ui_schema: str | None = <odmantic.field.FieldProxy object>

version: str | None = <odmantic.field.FieldProxy object>

class simstack.models.NamedDataReference(*, variable_name: str, variable_mapping: str, reference: ObjectId)

Bases: EmbeddedModel

classmethod from_variable(variable: Model, variable_name: str | None = None, task_id: str | None = None)

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

reference: ObjectId = <odmantic.field.FieldProxy object>

variable_mapping: str = <odmantic.field.FieldProxy object>

variable_name: str = <odmantic.field.FieldProxy object>

class simstack.models.NodeModel(*, name: str, function_mapping: str, version: str | None = None, input_mappings: list[~simstack.models.models.DataMapping], result_mappings: list[~simstack.models.models.DataMapping] = <factory>, called_nodes: list[str] = <factory>, description: str | None = '', favorite: bool = False, default_parameters: ~simstack.models.parameters.Parameters, pickle_function: ~simstack.models.pickle_models.FunctionPickle | None = None, id: ~odmantic.bson.ObjectId = <factory>)

Bases: Model

called_nodes: list[str] = <odmantic.field.FieldProxy object>

default_parameters: Parameters = <odmantic.field.FieldProxy object>

description: str | None = <odmantic.field.FieldProxy object>

favorite: bool = <odmantic.field.FieldProxy object>

function_mapping: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

input_mappings: list[DataMapping] = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': 'node_model', 'extra': None, 'indexes': None, 'json_encoders': {<class 'bytes'>: <function NodeModel.<lambda>>}, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

pickle_function: FunctionPickle | None = <odmantic.field.FieldProxy object>

result_mappings: list[DataMapping] = <odmantic.field.FieldProxy object>

version: str | None = <odmantic.field.FieldProxy object>

class simstack.models.NodeRegistry(*, name: str, status: TaskStatus, custom_name: str | None = None, version: str | None = None, project: ObjectId | None = None, category: str | None = None, description: str | None = None, call_path: str | None = None, assignment_rule_id: str | None = None, assignment_rule_name: str | None = None, assignment_pattern: str | None = None, error: str | None = None, message: str | None = None, input_references: list[NamedDataReference] = <factory>, results_references: list[NamedDataReference] = <factory>, info_files: FileList = <factory>, parent_ids: list[ObjectId] = <factory>, artifact_ids: list[ObjectId] = <factory>, created_at: _datetime = <factory>, started_at: _datetime | None = None, completed_at: _datetime | None = None, job_id: str | None = None, function_hash: str, arg_hash: str, func_mapping: str, is_async: bool = False, parameters: Parameters = <odmantic.reference.ODMReferenceInfo object>, id: ObjectId = <factory>)

Bases: Model

Represents a registry for nodes with associated metadata, configurations, and status information. It allows tracking the state and attributes of a workflow node, including its execution parameters, process status, and relationship to other nodes.

This class is designed for managing workflow instances and their lifecycle, with capabilities to monitor execution states, inputs, outputs, and associated timestamps.

Variables:

• name – The name of the node.

• custom_name – An optional custom name for the node.

• status – TheTaskStatus of the node in string format.

• category – An optional category classification for the node.

• description – An optional description providing details about the node.

• input_references – List of references to input data.

• results_references – List of references to result data.

• parent_ids – A list of identifiers representing parent nodes associated with this node.

• created_at – The timestamp when the node was created.

• started_at – An optional timestamp indicating when the execution of the node started.

• completed_at – An optional timestamp indicating when the execution of the node was completed.

• function_hash – A hash value representing the unique function executed by this node.

• arg_hash – A hash value representing the unique arguments passed to the function of this node.

• func_mapping – A mapping identifier associated with the function executed by this node.

• is_async – A boolean value indicating whether the node execution is asynchronous.

• parameters – Parameters associated with the node execution.

• call_path – An optional path indicating where the function is called from by concatenating the name of all nodes in the call stack.

arg_hash: str = <odmantic.field.FieldProxy object>

artifact_ids: list[ObjectId] = <odmantic.field.FieldProxy object>

assignment_pattern: str | None = <odmantic.field.FieldProxy object>

assignment_rule_id: str | None = <odmantic.field.FieldProxy object>

assignment_rule_name: str | None = <odmantic.field.FieldProxy object>

call_path: str | None = <odmantic.field.FieldProxy object>

category: str | None = <odmantic.field.FieldProxy object>

completed_at: _datetime | None = <odmantic.field.FieldProxy object>

created_at: _datetime = <odmantic.field.FieldProxy object>

custom_name: str | None = <odmantic.field.FieldProxy object>

description: str | None = <odmantic.field.FieldProxy object>

error: str | None = <odmantic.field.FieldProxy object>

func_mapping: str = <odmantic.field.FieldProxy object>

function_hash: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

info_files: FileList = <odmantic.field.FieldProxy object>

input_references: list[NamedDataReference] = <odmantic.field.FieldProxy object>

is_async: bool = <odmantic.field.FieldProxy object>

job_id: str | None = <odmantic.field.FieldProxy object>

message: str | None = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

parameters: Parameters = <odmantic.field.FieldProxy object>

parent_ids: list[ObjectId] = <odmantic.field.FieldProxy object>

project: ObjectId | None = <odmantic.field.FieldProxy object>

results_references: list[NamedDataReference] = <odmantic.field.FieldProxy object>

started_at: _datetime | None = <odmantic.field.FieldProxy object>

status: TaskStatus = <odmantic.field.FieldProxy object>

version: str | None = <odmantic.field.FieldProxy object>

class simstack.models.Parameters(*, force_rerun: bool = False, resource: Resource = <factory>, queue: str = 'default', recompute_artifacts: bool | None = False, docker_image: str | None = None, other_value: str = 'other', test_dict: dict[str, ~typing.Any]=<factory>, slurm_parameters: SlurmParameters = None)

Bases: EmbeddedModel

docker_image: str | None = <odmantic.field.FieldProxy object>

force_rerun: bool = <odmantic.field.FieldProxy object>

classmethod migrate_slurm_parameters(data)

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': {'description': 'Parameters for running a simulation', 'examples': [{'queue': 'default', 'resource': 'self', 'slurm_parameters': {'nodes': 2}}], 'title': 'Parameters'}, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

other_value: str = <odmantic.field.FieldProxy object>

queue: str = <odmantic.field.FieldProxy object>

recompute_artifacts: bool | None = <odmantic.field.FieldProxy object>

resource: Resource = <odmantic.field.FieldProxy object>

slurm_parameters: SlurmParameters = <odmantic.field.FieldProxy object>

test_dict: dict[str, Any] = <odmantic.field.FieldProxy object>

classmethod validate_resource(v)

Validate and convert resource input to a Resource object.

Accepts string, Resource objects, and dictionary representations. If a string is provided, converts it to a Resource object. If a dictionary is provided (e.g., during deserialization), extracts the value and creates a Resource object.

Parameters:

v – The value to validate (str, Resource, or dict)

Returns:

The validated Resource object

Return type:

Resource

class simstack.models.Project(*, field_name: str, description: str | None = None, tag_ids: list[ObjectId] = <factory>, id: ObjectId = <factory>)

Bases: Model

description: str | None = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': 'projects', 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

tag_ids: list[ObjectId] = <odmantic.field.FieldProxy object>

class simstack.models.ResourceAssignmentRule(*, name: str, regex_pattern: str, priority: int = 0, enabled: bool = True, resource_str: str | None = None, queue: str | None = None, slurm_parameters_patch: dict[str, ~typing.Any]=<factory>, description: str | None = '', id: ObjectId = <factory>)

Bases: Model

description: str | None = <odmantic.field.FieldProxy object>

enabled: bool = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod matches_call_path(pattern: str, normalized_call_path: str) → bool

model_config = {'arbitrary_types_allowed': False, 'collection': 'resource_assignment_rule', 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

static normalize_pattern(pattern: str) → str

classmethod pattern_specificity_score(pattern: str) → int

classmethod pattern_to_regex(pattern: str) → str

priority: int = <odmantic.field.FieldProxy object>

queue: str | None = <odmantic.field.FieldProxy object>

regex_pattern: str = <odmantic.field.FieldProxy object>

resource_str: str | None = <odmantic.field.FieldProxy object>

slurm_parameters_patch: dict[str, Any] = <odmantic.field.FieldProxy object>

class simstack.models.SlurmParametersPatch(*, nodes: Annotated[int | None, Ge(ge=1)] = None, tasks: Annotated[int | None, Ge(ge=1)] = None, tasks_per_node: Annotated[int | None, Ge(ge=1)] = None, cpus_per_task: Annotated[int | None, Ge(ge=1)] = None, mem: str | None = None, mem_per_cpu: str | None = None, time: str | None = None, begin: str | None = None, partition: str | None = None, qos: str | None = None, job_name: str | None = None, output: str | None = None, error: str | None = None, mail_type: str | None = None, mail_user: str | None = None, gres: str | None = None, account: str | None = None, priority: int | None = None, reservation: str | None = None, constraint: str | None = None, exclusive: bool | None = None, nice: int | None = None, dependency: str | None = None, array: str | None = None, startup_commands: list[str] | None = None, chdir: str | None = None, export: str | None = None, signal: str | None = None, requeue: bool | None = None, no_requeue: bool | None = None)

Bases: EmbeddedModel

account: str | None = <odmantic.field.FieldProxy object>

array: str | None = <odmantic.field.FieldProxy object>

begin: str | None = <odmantic.field.FieldProxy object>

chdir: str | None = <odmantic.field.FieldProxy object>

constraint: str | None = <odmantic.field.FieldProxy object>

cpus_per_task: int | None = <odmantic.field.FieldProxy object>

dependency: str | None = <odmantic.field.FieldProxy object>

error: str | None = <odmantic.field.FieldProxy object>

exclusive: bool | None = <odmantic.field.FieldProxy object>

export: str | None = <odmantic.field.FieldProxy object>

gres: str | None = <odmantic.field.FieldProxy object>

job_name: str | None = <odmantic.field.FieldProxy object>

mail_type: str | None = <odmantic.field.FieldProxy object>

mail_user: str | None = <odmantic.field.FieldProxy object>

mem: str | None = <odmantic.field.FieldProxy object>

mem_per_cpu: str | None = <odmantic.field.FieldProxy object>

model_config: ClassVar[Dict[str, Any]] = {'arbitrary_types_allowed': False, 'collection': None, 'extra': 'forbid', 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

nice: int | None = <odmantic.field.FieldProxy object>

no_requeue: bool | None = <odmantic.field.FieldProxy object>

nodes: int | None = <odmantic.field.FieldProxy object>

output: str | None = <odmantic.field.FieldProxy object>

partition: str | None = <odmantic.field.FieldProxy object>

priority: int | None = <odmantic.field.FieldProxy object>

qos: str | None = <odmantic.field.FieldProxy object>

requeue: bool | None = <odmantic.field.FieldProxy object>

reservation: str | None = <odmantic.field.FieldProxy object>

signal: str | None = <odmantic.field.FieldProxy object>

startup_commands: list[str] | None = <odmantic.field.FieldProxy object>

tasks: int | None = <odmantic.field.FieldProxy object>

tasks_per_node: int | None = <odmantic.field.FieldProxy object>

time: str | None = <odmantic.field.FieldProxy object>

class simstack.models.StringData(*, field_name: str = 'text', value: str, id: ObjectId = <factory>)

Bases: Model

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

classmethod ensure_fieldname(data)

Ensure fieldname is set for existing documents

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

make_column_defs_instance(table_name=None, max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

make_table_entries(max_recursion_level=1, drop_id=True, current_level=0, visited=None, field_prefix='')

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema(**kwargs) → dict

value: str = <odmantic.field.FieldProxy object>

class simstack.models.StringDataList(*, field_name: str = 'string_data_list', elements: list[ObjectId] = <factory>, id: ObjectId = <factory>)

Bases: Model, ObjectListMixin[StringData]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[ObjectId] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.StringList(*, field_name: str = 'string_list', elements: list[str] = <factory>, id: ObjectId = <factory>)

Bases: Model, GenericListMixin[str]

async custom_model_dump(**kwargs) → Dict[str, Any]

Custom model dump method to handle the conversion of model instances to dictionaries. This method recursively traverses dictionaries and lists to convert any nested model instances to their dictionary representation.

Parameters:

• self – The model instance

• kwargs – Additional keyword arguments

Returns:

A dictionary representation of the model instance

elements: list[str] = <odmantic.field.FieldProxy object>

field_name: str = <odmantic.field.FieldProxy object>

classmethod from_dict(data: dict, **kwargs) → Any

Create an instance of the model from a dictionary. Handles nested models and enum values.

classmethod from_model(model: Model, **kwargs) → Model

id: ObjectId = <odmantic.field.FieldProxy object>

classmethod json_schema()

Generates a JSON schema for the given class and its fields, but eliminates all fields which are models, embedded models, or references to models.

Parameters:

cls

Returns:

model_config = {'arbitrary_types_allowed': False, 'collection': None, 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

classmethod ui_make_title(ui_schema: Dict[str, Any], field: str, title: str) → dict

Adds a title to the JSON schema.

Parameters:

• cls – The class to which the JSON schema belongs

• ui_schema – The original ui_schema schema

• title – Title to be added

Returns:

Modified JSON schema with title

classmethod ui_schema()

Generates a UI schema that uses GenericForm for fields with ui_schema function. Also preserves any existing UI schema configurations from the class.

Parameters:

cls – The model class to generate UI schema for

Returns:

The generated UI schema

Return type:

dict

class simstack.models.Tag(*, name: str, description: str | None = None, id: ObjectId = <factory>)

Bases: Model

description: str | None = <odmantic.field.FieldProxy object>

id: ObjectId = <odmantic.field.FieldProxy object>

model_config = {'arbitrary_types_allowed': False, 'collection': 'tags', 'extra': None, 'indexes': None, 'json_schema_extra': None, 'parse_doc_with_default_factories': False, 'str_strip_whitespace': False, 'title': None, 'validate_assignment': True, 'validate_default': True}

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

name: str = <odmantic.field.FieldProxy object>

simstack.models.simstack_model(cls: Type[T]) → Type[T]

Decorates a given class to equip it with default implementations of utility methods for handling operations such as dictionary conversion, schema generation, and UI schema generation.