meerkat.interactive.app.src.lib.component.core.MedicalImage

class MedicalImage(*, data: List[str], classes: str = '', show_toolbar: bool = False, dim: int, segmentation_column: str = '', on_fetch: meerkat.interactive.endpoint.EndpointProperty[meerkat.interactive.app.src.lib.component.core.medimage.OnFetchInterface], cell_info: Any = None, **kwargs)

A component for displaying medical images.

Parameters

• data – An array of base64 encoded images.

• classes – A string of classes to apply to the component.

• show_toolbar – Whether to show the toolbar.

• on_fetch – An endpoint to call when the component needs to fetch data.

__init__(**kwargs)

Methods

__init__(**kwargs)
append(other)
construct([_fields_set])	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
copy(*[, include, exclude, update, deep])	Duplicate a model, optionally choose which fields to include, exclude and change.
dict(*[, include, exclude, by_alias, ...])	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
from_id(id)
from_orm(obj)
get_components()
json(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, include and exclude arguments as per dict().
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
prepend_meerkat_id_prefix(id)
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
update_forward_refs(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
validate(value)

Attributes

alias
component_name
event_names
events
frontend	Returns a Pydantic model that can be should be sent to the frontend.
frontend_alias
id
identifiable_group
library
namespace
path
prop_bindings
prop_names
props
slots
slottable
virtual_props	Props, and all events (as_*) as props.
wrapper_import_style
data
classes
show_toolbar
dim
segmentation_column
on_fetch
cell_info