feat: add direct from ground truth

src/microsim/schema/sample/direct.py

@@ -0,0 +1,24 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING, Any, Literal
+
+from microsim.schema._base_model import SimBaseModel
+from microsim.schema.backend import NumpyAPI
+
+if TYPE_CHECKING:
+    from microsim._data_array import DataArray
+
+
+class FixedArrayTruth(SimBaseModel):
+    type: Literal["fixed-array"] = "fixed-array"
+    array: Any
+
+    def render(self, space: DataArray, xp: NumpyAPI | None = None) -> DataArray:
+        if space.shape != self.array.shape:
+            raise ValueError(
+                "This GroundTruth may only be used with simulation space of shape: "
+                f"{self.array.shape}. Got: {space.shape}"
+            )
+
+        xp = xp or NumpyAPI()
+        return space + xp.asarray(self.array).astype(space.dtype)

src/microsim/schema/sample/sample.py

@@ -2,14 +2,15 @@
 
 from pydantic import Field, model_validator
 
-from microsim._data_array import DataArray
+from microsim._data_array import ArrayProtocol, DataArray
 from microsim.schema._base_model import SimBaseModel
 from microsim.schema.backend import NumpyAPI
 
+from .direct import FixedArrayTruth
 from .fluorophore import Fluorophore
 from .matslines import MatsLines
 
-Distribution = MatsLines
+Distribution = MatsLines | FixedArrayTruth
 
 
 class FluorophoreDistribution(SimBaseModel):
@@ -21,13 +22,17 @@ def render(self, space: DataArray, xp: NumpyAPI | None = None) -> DataArray:
 
     @model_validator(mode="before")
     def _vmodel(cls, value: Any) -> Any:
-        if isinstance(value, Distribution):
+        if isinstance(value, (MatsLines | FixedArrayTruth)):
             return {"distribution": value}
         if isinstance(value, dict):
             if "distribution" not in value and "type" in value:
                 return {"distribution": value}
         return value
 
+    @classmethod
+    def from_array(cls, array: ArrayProtocol) -> "FluorophoreDistribution":
+        return cls(distribution=FixedArrayTruth(array=array))
+
 
 class Sample(SimBaseModel):
     labels: list[FluorophoreDistribution]

src/microsim/schema/simulation.py

@@ -2,22 +2,34 @@
 from typing import TYPE_CHECKING, Annotated
 
 if TYPE_CHECKING:
-    from typing import Self
+    from typing import Self, Unpack
 
     from .backend import NumpyAPI
 
 from pydantic import AfterValidator, Field, model_validator
 
-from microsim._data_array import DataArray
+from microsim._data_array import ArrayProtocol, DataArray
 
 from ._base_model import SimBaseModel
 from .detectors import Detector
 from .lens import ObjectiveLens
 from .modality import Modality, Widefield
 from .optical_config import FITC, OpticalConfig
-from .sample import Sample
+from .sample import FluorophoreDistribution, Sample
 from .settings import Settings
-from .space import Space, _RelativeSpace
+from .space import ShapeScaleSpace, Space, _RelativeSpace
+
+if TYPE_CHECKING:
+    from typing import TypedDict
+
+    class SimluationKwargs(TypedDict, total=False):
+        output_space: Space | dict | None
+        objective_lens: ObjectiveLens
+        channels: list[OpticalConfig]
+        detector: Detector | None
+        modality: Modality
+        settings: Settings
+        output_path: "OutPath" | None
 
 
 def _check_extensions(path: Path) -> Path:
@@ -42,6 +54,25 @@ class Simulation(SimBaseModel):
     settings: Settings = Field(default_factory=Settings)
     output_path: OutPath | None = None
 
+    @classmethod
+    def from_ground_truth(
+        self,
+        ground_truth: ArrayProtocol,
+        scale: tuple[float, ...],
+        **kwargs: "Unpack[SimluationKwargs]",
+    ) -> "Self":
+        """Shortcut to create a simulation directly from a ground truth array.
+
+        In this case, we bypass derive the `truth_space` and `sample` objects directly
+        from a pre-calculated ground truth array.  `scale` must also be provided as a
+        tuple of floats, one for each dimension of the ground truth array.
+        """
+        return self(
+            truth_space=ShapeScaleSpace(shape=ground_truth.shape, scale=scale),
+            sample=Sample(labels=[FluorophoreDistribution.from_array(ground_truth)]),
+            **kwargs,
+        )
+
     @model_validator(mode="after")
     def _resolve_spaces(self) -> "Self":
         if isinstance(self.truth_space, _RelativeSpace):

tests/test_schema.py

@@ -103,3 +103,11 @@ def test_sim_from_json() -> None:
     """
 
     ms.Simulation.model_validate_json(json_string)
+
+
+def test_simulation_from_ground_truth() -> None:
+    ground_truth = np.random.rand(64, 128, 128)
+    scale = (0.04, 0.02, 0.02)
+    sim = ms.Simulation.from_ground_truth(ground_truth=ground_truth, scale=scale)
+    assert sim.truth_space.scale == scale
+    np.testing.assert_array_almost_equal(sim.ground_truth(), ground_truth)