microbenthos.utils package

Submodules

microbenthos.utils.create module

class microbenthos.utils.create.CreateMixin

Bases: object

A Mixin class that can create instances of classes defined in schema.yml, based on the schema_key. A variety of object types are handled in create_from(), and the validated input is stored as definition_

classmethod create_from(obj, **kwargs)

Instantiate class from the given object, and if possible, save the definition that was used to create the instance.

Parameters

• obj (object, dict, str, IOBase) –

  • object: if an instance of the cls, then it is returned directly

  • dict: if a mapping, then it is passed to validate_dict()

  • str: if a string, it is converted to stream-like and passed to validate_yaml()

  • io.IOBase: if a stream, it is passed to validate_yaml()

• **kwargs – passed to validate_dict() or validate_yaml() as necessary.

Variables

`definition_` – the validated definition used to create the instance

Returns

instance of the class this is subclassed by

microbenthos.utils.loader module

class microbenthos.utils.loader.MicroBenthosSchemaValidator(*args, **kwargs)

Bases: cerberus.validator.Validator

A cereberus validator for schema.yml in MicroBenthos

logger = <Logger microbenthos.utils.loader (DEBUG)>

types_mapping = {'binary': TypeDefinition(name='binary', included_types=(<class 'bytes'>, <class 'bytearray'>), excluded_types=()), 'boolean': TypeDefinition(name='boolean', included_types=(<class 'bool'>,), excluded_types=()), 'container': TypeDefinition(name='container', included_types=(<class 'collections.abc.Container'>,), excluded_types=(<class 'str'>,)), 'date': TypeDefinition(name='date', included_types=(<class 'datetime.date'>,), excluded_types=()), 'datetime': TypeDefinition(name='datetime', included_types=(<class 'datetime.datetime'>,), excluded_types=()), 'dict': TypeDefinition(name='dict', included_types=(<class 'collections.abc.Mapping'>,), excluded_types=()), 'float': TypeDefinition(name='float', included_types=(<class 'float'>, (<class 'int'>,)), excluded_types=()), 'integer': TypeDefinition(name='integer', included_types=((<class 'int'>,),), excluded_types=()), 'list': TypeDefinition(name='list', included_types=(<class 'collections.abc.Sequence'>,), excluded_types=(<class 'str'>,)), 'number': TypeDefinition(name='number', included_types=((<class 'int'>,), <class 'float'>), excluded_types=(<class 'bool'>,)), 'physical_unit': TypeDefinition(name='physical_unit', included_types=(<class 'fipy.tools.dimensions.physicalField.PhysicalField'>,), excluded_types=()), 'set': TypeDefinition(name='set', included_types=(<class 'set'>,), excluded_types=()), 'string': TypeDefinition(name='string', included_types=<class 'str'>, excluded_types=())}

_check_with_importpath(field, value)

Validates if the value is a usable import path for an entity class

Valid examples are:

• pkg1.pkg2.mod1.class

• class_name

Invalid examples:

• .class_name

Parameters

value – A string

Returns

True if valid

_check_with_unit_name(field, value)

Checks that the string can be used as units

_validate_like_unit(unit, field, value)

Test that the given value has compatible units

Parameters

• unit – A string useful with PhysicalField

• field –

• value – An instance of a physical unit

Returns

boolean if validated

The rule’s arguments are validated against this schema: {‘type’: ‘string’}

_check_with_sympy_symbol(field, value)

String that can be run through sympify and only has one variable symbol in it.

_check_with_model_path(field, value)

Validate that the value of the field is a model store path

Value should be of type:

• domain.oxy

• env.oxy.var

• microbes.cyano.processes.oxyPS

The rule’s arguments are validated against this schema: {‘type’: ‘string’}

checkers = ('importpath', 'model_path', 'sympify', 'sympy_symbol', 'unit_name')

coercers = ('float',)

default_setters = ()

normalization_rules = {'coerce': {'oneof': [{'type': 'callable'}, {'type': 'list', 'schema': {'oneof': [{'type': 'callable'}, {'type': 'string', 'allowed': ('float',)}]}}, {'type': 'string', 'allowed': ('float',)}]}, 'default': {'nullable': True}, 'default_setter': {'oneof': [{'type': 'callable'}, {'type': 'string', 'allowed': ()}]}, 'purge_unknown': {'type': 'boolean'}, 'rename': {'type': 'hashable'}, 'rename_handler': {'oneof': [{'type': 'callable'}, {'type': 'list', 'schema': {'oneof': [{'type': 'callable'}, {'type': 'string', 'allowed': ('float',)}]}}, {'type': 'string', 'allowed': ('float',)}]}}

rules = {'allof': {'logical': 'allof', 'type': 'list'}, 'allow_unknown': {'oneof': [{'type': 'boolean'}, {'type': ['dict', 'string'], 'check_with': 'bulk_schema'}]}, 'allowed': {'type': 'container'}, 'anyof': {'logical': 'anyof', 'type': 'list'}, 'check_with': {'oneof': [{'type': 'callable'}, {'type': 'list', 'schema': {'oneof': [{'type': 'callable'}, {'type': 'string', 'allowed': ('importpath', 'model_path', 'sympify', 'sympy_symbol', 'unit_name')}]}}, {'type': 'string', 'allowed': ('importpath', 'model_path', 'sympify', 'sympy_symbol', 'unit_name')}]}, 'coerce': {'oneof': [{'type': 'callable'}, {'type': 'list', 'schema': {'oneof': [{'type': 'callable'}, {'type': 'string', 'allowed': ('float',)}]}}, {'type': 'string', 'allowed': ('float',)}]}, 'contains': {'empty': False}, 'default': {'nullable': True}, 'default_setter': {'oneof': [{'type': 'callable'}, {'type': 'string', 'allowed': ()}]}, 'dependencies': {'check_with': 'dependencies', 'type': ('dict', 'hashable', 'list')}, 'empty': {'type': 'boolean'}, 'excludes': {'schema': {'type': 'hashable'}, 'type': ('hashable', 'list')}, 'forbidden': {'type': 'list'}, 'items': {'check_with': 'items', 'type': 'list'}, 'keysrules': {'check_with': 'bulk_schema', 'forbidden': ['rename', 'rename_handler'], 'type': ['dict', 'string']}, 'like_unit': {'type': 'string'}, 'max': {'nullable': False}, 'maxlength': {'type': 'integer'}, 'meta': {}, 'min': {'nullable': False}, 'minlength': {'type': 'integer'}, 'noneof': {'logical': 'noneof', 'type': 'list'}, 'nullable': {'type': 'boolean'}, 'oneof': {'logical': 'oneof', 'type': 'list'}, 'purge_unknown': {'type': 'boolean'}, 'readonly': {'type': 'boolean'}, 'regex': {'type': 'string'}, 'rename': {'type': 'hashable'}, 'rename_handler': {'oneof': [{'type': 'callable'}, {'type': 'list', 'schema': {'oneof': [{'type': 'callable'}, {'type': 'string', 'allowed': ('float',)}]}}, {'type': 'string', 'allowed': ('float',)}]}, 'require_all': {'type': 'boolean'}, 'required': {'type': 'boolean'}, 'schema': {'anyof': [{'check_with': 'schema'}, {'check_with': 'bulk_schema'}], 'type': ['dict', 'string']}, 'type': {'check_with': 'type', 'type': ['string', 'list']}, 'valuesrules': {'check_with': 'bulk_schema', 'forbidden': ['rename', 'rename_handler'], 'type': ['dict', 'string']}}

validation_rules = {'allof': {'logical': 'allof', 'type': 'list'}, 'allow_unknown': {'oneof': [{'type': 'boolean'}, {'type': ['dict', 'string'], 'check_with': 'bulk_schema'}]}, 'allowed': {'type': 'container'}, 'anyof': {'logical': 'anyof', 'type': 'list'}, 'check_with': {'oneof': [{'type': 'callable'}, {'type': 'list', 'schema': {'oneof': [{'type': 'callable'}, {'type': 'string', 'allowed': ('importpath', 'model_path', 'sympify', 'sympy_symbol', 'unit_name')}]}}, {'type': 'string', 'allowed': ('importpath', 'model_path', 'sympify', 'sympy_symbol', 'unit_name')}]}, 'contains': {'empty': False}, 'dependencies': {'check_with': 'dependencies', 'type': ('dict', 'hashable', 'list')}, 'empty': {'type': 'boolean'}, 'excludes': {'schema': {'type': 'hashable'}, 'type': ('hashable', 'list')}, 'forbidden': {'type': 'list'}, 'items': {'check_with': 'items', 'type': 'list'}, 'keysrules': {'check_with': 'bulk_schema', 'forbidden': ['rename', 'rename_handler'], 'type': ['dict', 'string']}, 'like_unit': {'type': 'string'}, 'max': {'nullable': False}, 'maxlength': {'type': 'integer'}, 'meta': {}, 'min': {'nullable': False}, 'minlength': {'type': 'integer'}, 'noneof': {'logical': 'noneof', 'type': 'list'}, 'nullable': {'type': 'boolean'}, 'oneof': {'logical': 'oneof', 'type': 'list'}, 'readonly': {'type': 'boolean'}, 'regex': {'type': 'string'}, 'require_all': {'type': 'boolean'}, 'required': {'type': 'boolean'}, 'schema': {'anyof': [{'check_with': 'schema'}, {'check_with': 'bulk_schema'}], 'type': ['dict', 'string']}, 'type': {'check_with': 'type', 'type': ['string', 'list']}, 'valuesrules': {'check_with': 'bulk_schema', 'forbidden': ['rename', 'rename_handler'], 'type': ['dict', 'string']}}

microbenthos.utils.loader.validate_yaml(stream, key=None, schema=None, schema_stream=None)

microbenthos.utils.loader.validate_dict(inp_dict, key, schema=None, schema_stream=None)

microbenthos.utils.loader.get_schema(schema_stream=None)

Returns the inbuilt model schema

microbenthos.utils.loader.find_subclasses_recursive(baseclass, subclasses=None)

Find subclasses recursively. subclasses should be a set into which to add the subclasses

microbenthos.utils.log module

microbenthos.utils.snapshotters module

microbenthos.utils.snapshotters.snapshot_var(V, base=False, to_unit=None)

Utility to express a variable array as its numeric value and corresponding units

Parameters

• V (PhysicalField, Variable, CellVariable, binOp, np.ndarray, int, float) – The variable

• base (bool) – Whether to express in base units

• to_unit (str) – Return array in these units. Ignored if base = True

Returns

Tuple of (array, dict(unit=unit))

microbenthos.utils.snapshotters.restore_var(input, tidx)

This is the inverse operation of snapshot_var(). It takes the output of that function and returns a PhysicalField quantity

Returns

PhysicalField

microbenthos.utils.yaml_setup module

Imports yaml from PyYaml and adds serialization options for fipy.PhysicalField.

microbenthos.utils.yaml_setup.unit_constructor(loader, node)

microbenthos.utils.yaml_setup.unit_representer(dumper, data)

Module contents