Restructure im-calc onto dask and annotate its output

pyproject.toml

@@ -11,7 +11,7 @@ requires-python = ">=3.12"
 dynamic = ["version"]
 dependencies = [
   # UCGMSim Dependencies
-  "im-calculation>=2025.12.5",
+  "im-calculation @ git+https://github.com/ucgmsim/IM_calculation@no_parallel",
   "velocity-modelling>=2026.8.1",
   "nshmdb>=2026.09.1",
   "oq_wrapper>=2025.12.3",
@@ -37,6 +37,7 @@ dependencies = [
   "h5py>=3.15.1",
   "parse>=1.21.0",
   "rich>=14.3.2",
+  "dask>=2026.6.0",
 ]
 
 [project.optional-dependencies]
@@ -171,3 +172,6 @@ exclude = [ # don't report on objects that match any of these regex
   '\.undocumented_method$',
   '\.__repr__$',
 ]
+
+[tool.uv.sources]
+im-calculation = { git = "https://github.com/ucgmsim/im_calculation", branch = "no_parallel" }

workflow/realisations.py

@@ -672,6 +672,23 @@ class Rakes(RealisationConfiguration):
     rakes: dict[str, float]
     """A map from faults to their rake angles."""
 
+    def as_vectors(self) -> dict[str, npt.NDArray[np.float64]]:
+        """Represent each rake angle as a unit vector.
+
+        Rakes are angles, so they cannot be averaged directly (the mean of
+        -179 and 179 degrees is 0, not 180). Averaging the unit vectors and
+        recovering the angle with `arctan2` avoids this.
+
+        Returns
+        -------
+        dict
+            A map from faults to the unit vector of their rake angle.
+        """
+        return {
+            k: np.array([np.cos(np.radians(r)), np.sin(np.radians(r))])
+            for k, r in self.rakes.items()
+        }
+
     def __getitem__(self, key: str) -> float:
         """Get the rake for a fault name.
 
@@ -713,6 +730,38 @@ def __getitem__(self, key: str) -> BoldM:
         """
         return self.magnitudes[key]
 
+    @property
+    def moments(self) -> dict[str, float]:  # numpydoc ignore=RT01
+        """dict: a map from faults to their moment."""
+        return {
+            k: moment.magnitude_to_moment(mag, bold_m=True)
+            for k, mag in self.magnitudes.items()
+        }
+
+    def moment_averaged(self, values: dict[str, Any]) -> Any:
+        """Average per-fault quantities, weighted by fault moment.
+
+        Parameters
+        ----------
+        values : dict
+            A map from faults to the quantity to average. Every fault in
+            this realisation must be present. Values may be scalars or
+            arrays, provided they all share the same shape.
+
+        Returns
+        -------
+        Any
+            The moment-weighted average of `values`, with the same shape as
+            the individual values.
+        """
+        keys = list(self.magnitudes)
+        moments = self.moments
+        return np.average(
+            [values[key] for key in keys],
+            weights=[moments[key] for key in keys],
+            axis=0,
+        )
+
     @property
     def total_moment(self) -> float:  # numpydoc ignore=RT01
         """float: total moment of realisation"""