Array Lists

SimStack II provides a specialized way to handle collections of arrays (typically numpy arrays) through the ArrayList and ArrayStorage models.

ArrayStorage

ArrayStorage is the model used to store an individual array. It handles compression and serialization of the array data.

Key Features:

  • Compression: Array data is automatically compressed before being stored in the database.

  • Shape Preservation: Automatically stores and restores the shape of the array.

  • Property Access: Use the array property to get or set the numpy array directly.

Usage:

import numpy as np
from simstack.models.array_storage import ArrayStorage

storage = ArrayStorage(name="my_array")
storage.array = np.random.rand(10, 10)

ArrayList

ArrayList is a top-level Model that stores a collection of ArrayStorage objects. Like FileListModel, it inherits from ObjectListMixin, providing a familiar list-like interface.

Usage:

from simstack.models.array_list import ArrayList
from simstack.models.array_storage import ArrayStorage
import numpy as np

array_list = ArrayList()

storage = ArrayStorage(name="first")
storage.array = np.zeros((5, 5))

array_list.append(storage)

API Reference

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

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.array_storage.ArrayStorage(*, name: str | None, shape: str | None = None, field_name: str | None = None, data_json: str | None = None, id: ObjectId = <factory>)[source]

Bases: BytesB64Mixin, Model

property array

Property getter for array

classmethod copy_name_to_field_name(values)[source]
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()[source]

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

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[source]