Module madsci.common.types.datapoint_types

Types related to datapoint types

Classes¶

DataManagerDefinition(**data: Any)

Definition for a Squid Data Manager.

Attributes: manager_type: The type of the event manager. host: The hostname or IP address of the Data Manager server. port: The port number of the Data Manager server. db_url: The URL of the database used by the Data Manager.

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.

Ancestors (in MRO)¶

• madsci.common.types.manager_types.ManagerDefinition

• madsci.common.types.base_types.MadsciBaseModel

• pydantic.main.BaseModel

Class variables¶

manager_id: str :

manager_type: Literal[<ManagerType.DATA_MANAGER: 'data_manager'>] :

model_config :

name: str :

DataManagerHealth(**data: Any)

Health status for Data Manager including database and storage connectivity.

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.

Ancestors (in MRO)¶

• madsci.common.types.manager_types.ManagerHealth

• madsci.common.types.base_types.MadsciBaseModel

• pydantic.main.BaseModel

Class variables¶

db_connected: bool | None :

model_config :

storage_accessible: bool | None :

total_datapoints: int | None :

DataManagerSettings(**values: Any)

Settings for the MADSci Data Manager.

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.

Ancestors (in MRO)¶

• madsci.common.types.manager_types.ManagerSettings

• madsci.common.types.base_types.MadsciBaseSettings

• pydantic_settings.main.BaseSettings

• pydantic.main.BaseModel

Class variables¶

collection_name: str :

database_name: str :

file_storage_path: str | pathlib.Path :

manager_definition: str | pathlib.Path :

mongo_db_url: pydantic.networks.AnyUrl :

server_url: pydantic.networks.AnyUrl :

DataPoint(**data: Any)

An object to contain and locate data created during experiments.

Attributes: label: The label of this data point. step_id: The step that generated the data point. workflow_id: The workflow that generated the data point. experiment_id: The experiment that generated the data point. campaign_id: The campaign of the data point. data_type: The type of the data point, inherited from class. datapoint_id: The specific ID for this data point. data_timestamp: The time the data point was created.

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.

Ancestors (in MRO)¶

• madsci.common.types.base_types.MadsciBaseModel

• pydantic.main.BaseModel

Descendants¶

• madsci.common.types.datapoint_types.FileDataPoint

• madsci.common.types.datapoint_types.ObjectStorageDataPoint

• madsci.common.types.datapoint_types.ValueDataPoint

Class variables¶

data_timestamp: datetime.datetime

time datapoint was created

data_type: madsci.common.types.datapoint_types.DataPointTypeEnum

type of the datapoint, inherited from class

datapoint_id: str

specific id for this data point

label: str | None

Label of this data point

model_config :

ownership_info: madsci.common.types.auth_types.OwnershipInfo | None

Information about the ownership of the data point

Static methods¶

discriminate(datapoint: DataPointDataModels) ‑> madsci.common.types.datapoint_types.FileDataPoint | madsci.common.types.datapoint_types.ValueDataPoint | madsci.common.types.datapoint_types.ObjectStorageDataPoint

Return the correct data point type based on the data_type attribute.

Args: datapoint: The data point instance or dictionary to discriminate.

Returns: The appropriate DataPoint subclass instance.

object_id_to_str(v: str | bson.objectid.ObjectId) ‑> str

Cast ObjectID to string.

DataPointTypeEnum(*args, **kwds)

Enumeration for the types of data points.

Attributes: FILE: Represents a data point that contains a file. JSON: Represents a data point that contains a JSON serializable value.

Ancestors (in MRO)¶

• builtins.str

• enum.Enum

Class variables¶

FILE :

JSON :

OBJECT_STORAGE :

FileDataPoint(**data: Any)

A data point containing a file.

Attributes: data_type: The type of the data point, in this case a file. path: The path to the file.

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.

Ancestors (in MRO)¶

• madsci.common.types.datapoint_types.DataPoint

• madsci.common.types.base_types.MadsciBaseModel

• pydantic.main.BaseModel

Class variables¶

model_config :

path: str | pathlib.Path

Path to the file

ObjectStorageDataPoint(**data: Any)

A data point that references an object in S3-compatible storage (MinIO/S3).

This data point stores essential information about an object in S3-compatible storage without storing access credentials.

Attributes: url: The accessible URL for the object (can be used in frontend). storage_endpoint: The endpoint of the storage service (e.g., ‘minio.example.com:9000’). bucket_name: The name of the bucket containing the object. object_name: The path/key of the object within the bucket. content_type: The MIME type of the stored object. size_bytes: The size of the object in bytes. etag: The entity tag (typically MD5) of the object. custom_metadata: Additional user-defined metadata for the object.

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.

Ancestors (in MRO)¶

• madsci.common.types.datapoint_types.DataPoint

• madsci.common.types.base_types.MadsciBaseModel

• pydantic.main.BaseModel

Class variables¶

bucket_name: str | None :

content_type: str | None :

custom_metadata: dict[str, str] :

etag: str | None :

model_config :

object_name: str | None :

path: str | pathlib.Path

Path to the file

public_endpoint: str | None :

size_bytes: int | None :

storage_endpoint: str :

url: str | None :

ObjectStorageSettings(**values: Any)

Settings for S3-compatible object storage.

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.

Ancestors (in MRO)¶

• madsci.common.types.base_types.MadsciBaseSettings

• pydantic_settings.main.BaseSettings

• pydantic.main.BaseModel

Class variables¶

access_key: str :

default_bucket: str :

endpoint: str | None :

region: str | None :

secret_key: str :

secure: bool :

ValueDataPoint(**data: Any)

A data point corresponding to a single JSON serializable value.

Attributes: data_type: The type of the data point, in this case a value. value: The value of the data point.

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.

Ancestors (in MRO)¶

• madsci.common.types.datapoint_types.DataPoint

• madsci.common.types.base_types.MadsciBaseModel

• pydantic.main.BaseModel

Class variables¶

model_config :

value: Any

Value of the data point