fix: support for netcdf missing values

.gitignore

@@ -120,7 +120,6 @@ tmp/
 temp/
 logs/
 _dev/
-outputs
 *tmp_data/
 
 # Project specific

src/anemoi/inference/context.py

@@ -18,16 +18,16 @@
 from typing import List
 from typing import Optional
 
-from anemoi.inference.input import Input
-from anemoi.inference.output import Output
 from anemoi.inference.processor import Processor
 from anemoi.inference.types import IntArray
 
 if TYPE_CHECKING:
+    from anemoi.inference.input import Input
+    from anemoi.inference.output import Output
+
     from .checkpoint import Checkpoint
     from .forcings import Forcings
 
-
 LOG = logging.getLogger(__name__)
 
 
@@ -64,7 +64,7 @@ def checkpoint(self) -> "Checkpoint":
     # expected to provide the forcings directly as input to the runner.
     ##################################################################
 
-    def create_input(self) -> Input:
+    def create_input(self) -> "Input":
         """Creates an input object for the inference.
 
         Returns
@@ -74,7 +74,7 @@ def create_input(self) -> Input:
         """
         raise NotImplementedError()
 
-    def create_output(self) -> Output:
+    def create_output(self) -> "Output":
         """Creates an output object for the inference.
 
         Returns

src/anemoi/inference/output.py

@@ -180,24 +180,98 @@ class ForwardOutput(Output):
     """
 
     def __init__(
-        self, context: "Context", output_frequency: Optional[int] = None, write_initial_state: Optional[bool] = None
+        self,
+        context: "Context",
+        output: dict,
+        output_frequency: Optional[int] = None,
+        write_initial_state: Optional[bool] = None,
     ):
         """Initialize the ForwardOutput object.
 
         Parameters
         ----------
         context : Context
             The context in which the output operates.
+        output : dict
+            The output configuration dictionary.
         output_frequency : Optional[int], optional
             The frequency at which to output states, by default None.
         write_initial_state : Optional[bool], optional
             Whether to write the initial state, by default None.
         """
+
+        from anemoi.inference.outputs import create_output
+
         super().__init__(context, output_frequency=None, write_initial_state=write_initial_state)
+
+        self.output = None if output is None else create_output(context, output)
+
         if self.context.output_frequency is not None:
             LOG.warning("output_frequency is ignored for '%s'", self.__class__.__name__)
 
     @cached_property
     def output_frequency(self) -> Optional[datetime.timedelta]:
         """Get the output frequency."""
         return None
+
+    def modify_state(self, state: State) -> State:
+        """Modify the state before writing.
+
+        Parameters
+        ----------
+        state : State
+            The state to modify.
+
+        Returns
+        -------
+        State
+            The modified state.
+        """
+        return state
+
+    def open(self, state) -> None:
+        """Open the output for writing.
+        Parameters
+        ----------
+        state : State
+            The initial state.
+        """
+        self.output.open(self.modify_state(state))
+
+    def close(self) -> None:
+        """Close the output."""
+
+        self.output.close()
+
+    def write_initial_step(self, state: State) -> None:
+        """Write the initial step of the state.
+
+        Parameters
+        ----------
+        state : State
+            The state dictionary.
+        """
+        state.setdefault("step", datetime.timedelta(0))
+
+        self.output.write_initial_state(self.modify_state(state))
+
+    def write_step(self, state: State) -> None:
+        """Write a step of the state.
+
+        Parameters
+        ----------
+        state : State
+            The state to write.
+        """
+        self.output.write_state(self.modify_state(state))
+
+    def print_summary(self, depth: int = 0) -> None:
+        """Print a summary of the output.
+
+        Parameters
+        ----------
+        depth : int, optional
+            The depth of the summary, by default 0.
+        """
+        super().print_summary(depth)
+        self.output.print_summary(depth + 1)

src/anemoi/inference/outputs/apply_mask.py

@@ -12,17 +12,15 @@
 
 from anemoi.inference.config import Configuration
 from anemoi.inference.context import Context
-from anemoi.inference.types import State
 
-from ..output import ForwardOutput
-from . import create_output
 from . import output_registry
+from .masked import MaskedOutput
 
 LOG = logging.getLogger(__name__)
 
 
 @output_registry.register("apply_mask")
-class ApplyMaskOutput(ForwardOutput):
+class ApplyMaskOutput(MaskedOutput):
     """Apply mask output class.
 
     Parameters
@@ -48,75 +46,10 @@ def __init__(
         output_frequency: Optional[int] = None,
         write_initial_state: Optional[bool] = None,
     ) -> None:
-        super().__init__(context, output_frequency=output_frequency, write_initial_state=write_initial_state)
-        self.mask = self.checkpoint.load_supporting_array(mask)
-        self.output = create_output(context, output)
-
-    def __repr__(self) -> str:
-        """Return a string representation of the ApplyMaskOutput object."""
-        return f"ApplyMaskOutput({self.mask}, {self.output})"
-
-    def write_initial_step(self, state: State) -> None:
-        """Write the initial step of the state.
-
-        Parameters
-        ----------
-        state : State
-            The state dictionary.
-        """
-        # Note: we foreward to 'state', so we write-up options again
-        self.output.write_initial_state(self._apply_mask(state))
-
-    def write_step(self, state: State) -> None:
-        """Write a step of the state.
-
-        Parameters
-        ----------
-        state : State
-            The state dictionary.
-        """
-        # Note: we foreward to 'state', so we write-up options again
-        self.output.write_state(self._apply_mask(state))
-
-    def _apply_mask(self, state: State) -> State:
-        """Apply the mask to the state.
-
-        Parameters
-        ----------
-        state : State
-            The state dictionary.
-
-        Returns
-        -------
-        State
-            The masked state dictionary.
-        """
-        state = state.copy()
-        state["fields"] = state["fields"].copy()
-        state["latitudes"] = state["latitudes"][self.mask]
-        state["longitudes"] = state["longitudes"][self.mask]
-
-        for field in state["fields"]:
-            data = state["fields"][field]
-            if data.ndim == 1:
-                data = data[self.mask]
-            else:
-                data = data[..., self.mask]
-            state["fields"][field] = data
-
-        return state
-
-    def close(self) -> None:
-        """Close the output."""
-        self.output.close()
-
-    def print_summary(self, depth: int = 0) -> None:
-        """Print the summary of the output.
-
-        Parameters
-        ----------
-        depth : int, optional
-            The depth of the summary, by default 0.
-        """
-        super().print_summary(depth)
-        self.output.print_summary(depth + 1)
+        super().__init__(
+            context,
+            mask=self.checkpoint.load_supporting_array(mask),
+            output=output,
+            output_frequency=output_frequency,
+            write_initial_state=write_initial_state,
+        )

src/anemoi/inference/outputs/extract_lam.py

@@ -14,17 +14,15 @@
 
 from anemoi.inference.config import Configuration
 from anemoi.inference.context import Context
-from anemoi.inference.types import State
 
-from ..output import ForwardOutput
-from . import create_output
 from . import output_registry
+from .masked import MaskedOutput
 
 LOG = logging.getLogger(__name__)
 
 
 @output_registry.register("extract_lam")
-class ExtractLamOutput(ForwardOutput):
+class ExtractLamOutput(MaskedOutput):
     """Extract LAM output class.
 
     Parameters
@@ -50,91 +48,27 @@ def __init__(
         output_frequency: Optional[int] = None,
         write_initial_state: Optional[bool] = None,
     ) -> None:
-        super().__init__(context, output_frequency=output_frequency, write_initial_state=write_initial_state)
 
-        if "cutout_mask" in self.checkpoint.supporting_arrays:
+        if "cutout_mask" in context.checkpoint.supporting_arrays:
             # Backwards compatibility
-            mask = self.checkpoint.load_supporting_array("cutout_mask")
+            mask = context.checkpoint.load_supporting_array("cutout_mask")
             points = slice(None, -np.sum(mask))
         else:
             if "lam_0" not in lam:
                 raise NotImplementedError("Only lam_0 is supported")
 
-            if "lam_1/cutout_mask" in self.checkpoint.supporting_arrays:
+            if "lam_1/cutout_mask" in context.checkpoint.supporting_arrays:
                 raise NotImplementedError("Only lam_0 is supported")
 
-            mask = self.checkpoint.load_supporting_array(f"{lam}/cutout_mask")
+            mask = context.checkpoint.load_supporting_array(f"{lam}/cutout_mask")
+
             assert len(mask) == np.sum(mask)
             points = slice(None, np.sum(mask))
 
-        self.points = points
-        self.output = create_output(context, output)
-
-    def __repr__(self) -> str:
-        """Return a string representation of the ExtractLamOutput object."""
-        return f"ExtractLamOutput({self.points}, {self.output})"
-
-    def write_initial_state(self, state: State) -> None:
-        """Write the initial step of the state.
-
-        Parameters
-        ----------
-        state : State
-            The state dictionary.
-        """
-        # Note: we foreward to 'state', so we write-up options again
-        self.output.write_initial_state(self._apply_mask(state))
-
-    def write_step(self, state: State) -> None:
-        """Write a step of the state.
-
-        Parameters
-        ----------
-        state : State
-            The state dictionary.
-        """
-        # Note: we foreward to 'state', so we write-up options again
-        self.output.write_state(self._apply_mask(state))
-
-    def _apply_mask(self, state: State) -> State:
-        """Apply the mask to the state.
-
-        Parameters
-        ----------
-        state : State
-            The state dictionary.
-
-        Returns
-        -------
-        State
-            The masked state dictionary.
-        """
-        state = state.copy()
-        state["fields"] = state["fields"].copy()
-        state["latitudes"] = state["latitudes"][self.points]
-        state["longitudes"] = state["longitudes"][self.points]
-
-        for field in state["fields"]:
-            data = state["fields"][field]
-            if data.ndim == 1:
-                data = data[self.points]
-            else:
-                data = data[..., self.points]
-            state["fields"][field] = data
-
-        return state
-
-    def close(self) -> None:
-        """Close the output."""
-        self.output.close()
-
-    def print_summary(self, depth: int = 0) -> None:
-        """Print the summary of the output.
-
-        Parameters
-        ----------
-        depth : int, optional
-            The depth of the summary, by default 0.
-        """
-        super().print_summary(depth)
-        self.output.print_summary(depth + 1)
+        super().__init__(
+            context,
+            mask=points,
+            output=output,
+            output_frequency=output_frequency,
+            write_initial_state=write_initial_state,
+        )