medcat.utils.envsnapshot

Attributes

logger

DEP_NAME_PATTERN

Classes

AbstractSerialisable

The abstract serialisable base class.

Environment

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

Functions

get_direct_dependencies(include_extras)

Gets the direct dependencies of the current package and their versions.

get_transitive_deps(direct_deps)

Get the transitive dependencies of the direct dependencies.

get_installed_dependencies(include_extras)

Get the installed packages and their versions.

is_dependency_installed(dependency)

Checks whether a dependency is installed.

get_environment_info([include_transitive_deps, ...])

Get the current environment information.

Module Contents

class medcat.utils.envsnapshot.AbstractSerialisable

The abstract serialisable base class.

This defines some common defaults.

get_strategy()
Return type:

SerialisingStrategy

classmethod get_init_attrs()
Return type:

list[str]

classmethod ignore_attrs()
Return type:

list[str]

classmethod include_properties()
Return type:

list[str]

__eq__(other)
Parameters:

other (Any)

Return type:

bool

medcat.utils.envsnapshot.logger
medcat.utils.envsnapshot.DEP_NAME_PATTERN
medcat.utils.envsnapshot.get_direct_dependencies(include_extras)

Gets the direct dependencies of the current package and their versions.

Parameters:

include_extras (bool) – Whether to include extras (like spacy).

Return type:

list[str]

medcat.utils.envsnapshot.get_transitive_deps(direct_deps)

Get the transitive dependencies of the direct dependencies.

Parameters:

direct_deps (list[str]) – List of direct dependencies.

Returns:

dict[str, str] – The dependency names and their corresponding versions.

Return type:

dict[str, str]

medcat.utils.envsnapshot.get_installed_dependencies(include_extras)

Get the installed packages and their versions.

Parameters:

include_extras (bool) – Whether to include extras (like spacy).

Returns:

dict[str, str] – All installed packages and their versions.

Return type:

dict[str, str]

medcat.utils.envsnapshot.is_dependency_installed(dependency)

Checks whether a dependency is installed.

This takes into account changes such as ‘-’ vs ‘_’. For example, typing-extensions is a direct dependency, but its module path will be typing_extension and that’s how we can find it as an installed dependency.

Parameters:

dependency (str) – The dependency in question.

Returns:

bool – Whether the depedency has been installed.

Return type:

bool

class medcat.utils.envsnapshot.Environment(/, **data)

Bases: pydantic.BaseModel, medcat.storage.serialisables.AbstractSerialisable

Usage docs: https://docs.pydantic.dev/2.9/concepts/models/

A base class for creating Pydantic models.

Parameters:

data (Any)

__class_vars__

The names of the class variables defined on the model.

__private_attributes__

Metadata about the private attributes of the model.

__signature__

The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__

The core schema of the model.

__pydantic_custom_init__

Whether the model has a custom __init__ function.

__pydantic_decorators__

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__

Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__

The name of the post-init method for the model, if defined.

__pydantic_root_model__

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__

The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__

The names of fields explicitly set during instantiation.

__pydantic_private__

Values of private attributes set on the model instance.

dependencies: dict[str, str]
transitive_deps: dict[str, str]
os: str
cpu_arcitecture: str
python_version: str
classmethod get_init_attrs()
Return type:

list[str]

model_config: ClassVar[pydantic.config.ConfigDict]

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

model_fields: ClassVar[Dict[str, pydantic.fields.FieldInfo]]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

model_computed_fields: ClassVar[Dict[str, pydantic.fields.ComputedFieldInfo]]

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

__class_vars__: ClassVar[set[str]]

The names of the class variables defined on the model.

__private_attributes__: ClassVar[Dict[str, pydantic.fields.ModelPrivateAttr]]

Metadata about the private attributes of the model.

__signature__: ClassVar[inspect.Signature]

The synthesized __init__ [Signature][inspect.Signature] of the model.

__pydantic_complete__: ClassVar[bool] = False

Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[pydantic_core.CoreSchema]

The core schema of the model.

__pydantic_custom_init__: ClassVar[bool]

Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[pydantic._internal._decorators.DecoratorInfos]

Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_generic_metadata__: ClassVar[pydantic._internal._generics.PydanticGenericMetadata]

Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None

Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']]

The name of the post-init method for the model, if defined.

__pydantic_root_model__: ClassVar[bool] = False

Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[pydantic_core.SchemaSerializer]

The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[pydantic_core.SchemaValidator | pydantic.plugin._schema_validator.PluggableSchemaValidator]

The pydantic-core SchemaValidator used to validate instances of the model.

__pydantic_extra__: dict[str, Any] | None

A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to ‘allow’.

__pydantic_fields_set__: set[str]

The names of fields explicitly set during instantiation.

__pydantic_private__: dict[str, Any] | None

Values of private attributes set on the model instance.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')
__init__(/, **data)

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:

data (Any)

Return type:

None

property model_extra: dict[str, Any] | None

Get extra fields set during validation.

Returns:

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

Return type:

dict[str, Any] | None

property model_fields_set: set[str]

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

Returns:

A set of strings representing the fields that have been set, – i.e. that were not filled from defaults.

Return type:

set[str]

classmethod model_construct(_fields_set=None, **values)

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

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

!!! note

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

Parameters:

  • _fields_set (set[str] | None) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.

  • values (Any) – Trusted or pre-validated data dictionary.

Returns:

A new instance of the `Model` class with validated data.

Return type:

typing_extensions.Self

model_copy(*, update=None, deep=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

  • update (dict[str, Any] | None) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.

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

Returns:

New model instance.

Return type:

typing_extensions.Self

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

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

Parameters:

  • mode (Literal['json', 'python'] | str) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.

  • include (IncEx | None) – A set of fields to include in the output.

  • exclude (IncEx | None) – A set of fields to exclude from the output.

  • context (Any | None) – Additional context to pass to the serializer.

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

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

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

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

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

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

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

Returns:

A dictionary representation of the model.

Return type:

dict[str, Any]

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

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

Parameters:

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

  • include (IncEx | None) – Field(s) to include in the JSON output.

  • exclude (IncEx | None) – Field(s) to exclude from the JSON output.

  • context (Any | None) – Additional context to pass to the serializer.

  • by_alias (bool) – Whether to serialize using field aliases.

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

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

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

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

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

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

Returns:

A JSON string representation of the model.

Return type:

str

classmethod model_json_schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, schema_generator=GenerateJsonSchema, mode='validation')

Generates a JSON schema for a model class.

Parameters:

  • by_alias (bool) – Whether to use attribute aliases or not.

  • ref_template (str) – The reference template.

  • schema_generator (type[pydantic.json_schema.GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications

  • mode (pydantic.json_schema.JsonSchemaMode) – The mode in which to generate the schema.

Returns:

The JSON schema for the given model class.

Return type:

dict[str, Any]

classmethod model_parametrized_name(params)

Compute the class name for parametrizations of generic classes.

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

Parameters:

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

Returns:

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

Raises:

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

Return type:

str

model_post_init(__context)

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

Parameters:

__context (Any)

Return type:

None

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

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

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

Parameters:

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

  • raise_errors (bool) – Whether to raise errors, defaults to True.

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

  • _types_namespace (dict[str, Any] | None) – The types namespace, defaults to None.

Returns:

  • Returns `None` if the schema is already “complete” and rebuilding was not required.

  • If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.

Return type:

bool | None

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

Validate a pydantic model instance.

Parameters:

  • obj (Any) – The object to validate.

  • strict (bool | None) – Whether to enforce types strictly.

  • from_attributes (bool | None) – Whether to extract data from object attributes.

  • context (Any | None) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Returns:

The validated model instance.

Return type:

typing_extensions.Self

classmethod model_validate_json(json_data, *, strict=None, context=None)

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

  • json_data (str | bytes | bytearray) – The JSON data to validate.

  • strict (bool | None) – Whether to enforce types strictly.

  • context (Any | None) – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

Raises:

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

Return type:

typing_extensions.Self

classmethod model_validate_strings(obj, *, strict=None, context=None)

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

Parameters:

  • obj (Any) – The object containing string data to validate.

  • strict (bool | None) – Whether to enforce types strictly.

  • context (Any | None) – Extra variables to pass to the validator.

Returns:

The validated Pydantic model.

Return type:

typing_extensions.Self

classmethod __get_pydantic_core_schema__(source, handler, /)

Hook into generating the model’s CoreSchema.

Parameters:

  • source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.

  • handler (pydantic.annotated_handlers.GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Returns:

A `pydantic-core` `CoreSchema`.

Return type:

pydantic_core.CoreSchema

classmethod __get_pydantic_json_schema__(core_schema, handler, /)

Hook into generating the model’s JSON schema.

Parameters:

  • core_schema (pydantic_core.CoreSchema) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({‘type’: ‘nullable’, ‘schema’: current_schema}), or just call the handler with the original schema.

  • handler (pydantic.annotated_handlers.GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Returns:

A JSON schema, as a Python object.

Return type:

pydantic.json_schema.JsonSchemaValue

classmethod __pydantic_init_subclass__(**kwargs)

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:

**kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.

Return type:

None

classmethod __class_getitem__(typevar_values)
Parameters:

typevar_values (type[Any] | tuple[type[Any], Ellipsis])

Return type:

type[BaseModel] | pydantic._internal._forward_ref.PydanticRecursiveRef

__copy__()

Returns a shallow copy of the model.

Return type:

typing_extensions.Self

__deepcopy__(memo=None)

Returns a deep copy of the model.

Parameters:

memo (dict[int, Any] | None)

Return type:

typing_extensions.Self

__getattr__(item)
Parameters:

item (str)

Return type:

Any

_check_frozen(name, value)
Parameters:

  • name (str)

  • value (Any)

Return type:

None

__getstate__()
Return type:

dict[Any, Any]

__setstate__(state)
Parameters:

state (dict[Any, Any])

Return type:

None

__eq__(other)
Parameters:

other (Any)

Return type:

bool

classmethod __init_subclass__(**kwargs)

This signature is included purely to help type-checkers check arguments to class declaration, which provides a way to conveniently set model_config key/value pairs.

```py from pydantic import BaseModel

class MyModel(BaseModel, extra=’allow’): … ```

However, this may be deceiving, since the _actual_ calls to __init_subclass__ will not receive any of the config arguments, and will only receive any keyword arguments passed during class initialization that are _not_ expected keys in ConfigDict. (This is due to the way ModelMetaclass.__new__ works.)

Parameters:

**kwargs (typing_extensions.Unpack[pydantic.config.ConfigDict]) – Keyword arguments passed to the class definition, which set model_config

Note

You may want to override __pydantic_init_subclass__ instead, which behaves similarly but is called after the class is fully initialized.

__iter__()

So dict(model) works.

Return type:

TupleGenerator

__repr__()
Return type:

str

__repr_args__()
Return type:

pydantic._internal._repr.ReprArgs

__repr_name__
__repr_str__
__pretty__
__rich_repr__
__str__()
Return type:

str

property __fields__: dict[str, pydantic.fields.FieldInfo]
Return type:

dict[str, pydantic.fields.FieldInfo]

property __fields_set__: set[str]
Return type:

set[str]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)
Parameters:

  • include (IncEx | None)

  • exclude (IncEx | None)

  • by_alias (bool)

  • exclude_unset (bool)

  • exclude_defaults (bool)

  • exclude_none (bool)

Return type:

Dict[str, Any]

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)
Parameters:

  • include (IncEx | None)

  • exclude (IncEx | None)

  • by_alias (bool)

  • exclude_unset (bool)

  • exclude_defaults (bool)

  • exclude_none (bool)

  • encoder (Callable[[Any], Any] | None)

  • models_as_dict (bool)

  • dumps_kwargs (Any)

Return type:

str

classmethod parse_obj(obj)
Parameters:

obj (Any)

Return type:

typing_extensions.Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
Parameters:

  • b (str | bytes)

  • content_type (str | None)

  • encoding (str)

  • proto (pydantic.deprecated.parse.Protocol | None)

  • allow_pickle (bool)

Return type:

typing_extensions.Self

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)
Parameters:

  • path (str | pathlib.Path)

  • content_type (str | None)

  • encoding (str)

  • proto (pydantic.deprecated.parse.Protocol | None)

  • allow_pickle (bool)

Return type:

typing_extensions.Self

classmethod from_orm(obj)
Parameters:

obj (Any)

Return type:

typing_extensions.Self

classmethod construct(_fields_set=None, **values)
Parameters:

  • _fields_set (set[str] | None)

  • values (Any)

Return type:

typing_extensions.Self

copy(*, include=None, exclude=None, update=None, deep=False)

Returns a copy of the model.

!!! warning “Deprecated”

This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

  • include (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to include in the copied model.

  • exclude (pydantic._internal._utils.AbstractSetIntStr | pydantic._internal._utils.MappingIntStrAny | None) – Optional set or mapping specifying which fields to exclude in the copied model.

  • update (Dict[str, Any] | None) – Optional dictionary of field-value pairs to override field values in the copied model.

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

Returns:

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

Return type:

typing_extensions.Self

classmethod schema(by_alias=True, ref_template=DEFAULT_REF_TEMPLATE)
Parameters:

  • by_alias (bool)

  • ref_template (str)

Return type:

Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template=DEFAULT_REF_TEMPLATE, **dumps_kwargs)
Parameters:

  • by_alias (bool)

  • ref_template (str)

  • dumps_kwargs (Any)

Return type:

str

classmethod validate(value)
Parameters:

value (Any)

Return type:

typing_extensions.Self

classmethod update_forward_refs(**localns)
Parameters:

localns (Any)

Return type:

None

_iter(*args, **kwargs)
Parameters:

  • args (Any)

  • kwargs (Any)

Return type:

Any

_copy_and_set_values(*args, **kwargs)
Parameters:

  • args (Any)

  • kwargs (Any)

Return type:

Any

classmethod _get_value(*args, **kwargs)
Parameters:

  • args (Any)

  • kwargs (Any)

Return type:

Any

_calculate_keys(*args, **kwargs)
Parameters:

  • args (Any)

  • kwargs (Any)

Return type:

Any

get_strategy()
Return type:

SerialisingStrategy

classmethod ignore_attrs()
Return type:

list[str]

classmethod include_properties()
Return type:

list[str]

medcat.utils.envsnapshot.get_environment_info(include_transitive_deps=True, include_extras=True)

Get the current environment information.

This includes dependency versions, the OS, the CPU architecture and the

python version.

Parameters:

  • include_transitive_deps (bool) – Whether to include transitive dependencies. Defaults to True.

  • include_extras (bool) – Whether to include extras (like spacy). Defaults to True.

Returns:

Environment – The environment.

Return type:

Environment