Remove deprecations, remove crs, rename ARCOERA5

CHANGELOG.md

@@ -1,15 +1,25 @@
 # Changelog
 
-## v0.54.1 (unreleased)
+## v0.54.1
 
 ### Features
 
 - Add [CoCiP Grid notebook](https://py.contrails.org/notebooks/CoCiPGrid.html) example to documentation.
+- Implement `PSFlight.eval` on a `Fleet` source.
+
+### Breaking changes
+
+- Remove `attrs["crs"]` usage from `GeoVectorDataset` and child classes (`Flight`, `Fleet`). All spatial data is assumed to be EPSG:4326 (WGS84). This was previously assumed implicitly, but now the `crs` attribute is removed from the `attrs` dictionary.
+- Change the return type of `GeoVectorDataset.transform_crs` to a pair of numpy arrays representing `x` and `y` coordinates in the target CRS.
+- Remove deprecated `MetDataset.variables` property in favor of `MetDataset.indexes`.
+- Remove `**kwargs` in `MetDataArray` constructor.
+- Rename `ARCOERA5` to `ERA5ARCO` for consistency with the `ERA5` and `ERA5ModelLevel` interfaces.
 
 ### Fixes
 
 - Fix the integration time step in `CocipGrid.calc_evolve_one_step`. The previous implementation assumed a time interval of `params["dt_integration"]`. This may not be the case for all `source` parameters (for example, this could occur if running `CocipGrid` over a collection of ADS-B waypoints).
 - Raise an exception in constructing `MetDataset(ds, copy=False)` when `ds["level"]` has float32 dtype. Per interpolation conventions, all coordinate variables must have float64 dtype. (This was previously enforced in longitude and latitude, but was overlooked in the level coordinate.)
+- Allow `AircraftPerformance.ensure_true_airspeed_on_source` to use `eastward_wind` and `northward_wind` fields on the `source` if available. This is useful when the `source` has already been interpolated to met data.
 
 ## v0.54.0
 

docs/integrations/APCEMM.ipynb

@@ -34,7 +34,7 @@
     "\n",
     "from pycontrails import Flight, MetVariable\n",
     "from pycontrails.core.met_var import Geopotential\n",
-    "from pycontrails.datalib.ecmwf import ARCOERA5\n",
+    "from pycontrails.datalib.ecmwf import ERA5ARCO\n",
     "from pycontrails.models.apcemm.apcemm import APCEMM\n",
     "from pycontrails.models.humidity_scaling import HistogramMatching\n",
     "from pycontrails.models.issr import ISSR\n",
@@ -74,7 +74,7 @@
    "outputs": [],
    "source": [
     "variables = (v if isinstance(v, MetVariable) else Geopotential for v in APCEMM.met_variables)\n",
-    "era5ml = ARCOERA5(time=time_bounds, variables=APCEMM.met_variables, n_jobs=4)"
+    "era5ml = ERA5ARCO(time=time_bounds, variables=APCEMM.met_variables)"
    ]
   },
   {

docs/notebooks/ARCO-ERA5.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "# ARCO ERA5\n",
     "\n",
-    "This notebook demonstrates how to load [ARCO ERA5](https://cloud.google.com/storage/docs/public-datasets/era5) data from [Google Cloud Storage](https://cloud.google.com/storage) through the pycontrails `ARCOERA5` interface."
+    "This notebook demonstrates how to load [ARCO ERA5](https://cloud.google.com/storage/docs/public-datasets/era5) data from [Google Cloud Storage](https://cloud.google.com/storage) through the pycontrails `ERA5ARCO` interface."
    ]
   },
   {
@@ -22,7 +22,7 @@
     "import xarray as xr\n",
     "\n",
     "from pycontrails import Flight, MetDataset\n",
-    "from pycontrails.datalib.ecmwf import ARCOERA5\n",
+    "from pycontrails.datalib.ecmwf import ERA5ARCO\n",
     "from pycontrails.models.cocip import Cocip\n",
     "from pycontrails.models.humidity_scaling import ConstantHumidityScaling\n",
     "from pycontrails.models.issr import ISSR\n",
@@ -47,15 +47,15 @@
    "source": [
     "time = (\"2019-01-01T00\", \"2019-01-01T12\")\n",
     "\n",
-    "era5_pl = ARCOERA5(\n",
+    "era5_pl = ERA5ARCO(\n",
     "    time=time,\n",
     "    variables=(*Cocip.met_variables, *Cocip.optional_met_variables),\n",
     "    cachestore=None,\n",
     ")\n",
     "met = era5_pl.open_metdataset()\n",
     "\n",
     "\n",
-    "era5_sl = ARCOERA5(\n",
+    "era5_sl = ERA5ARCO(\n",
     "    time=time,\n",
     "    variables=Cocip.rad_variables,\n",
     "    pressure_levels=-1,\n",

docs/notebooks/specific-humidity-interpolation.ipynb

@@ -19,7 +19,7 @@
    "source": [
     "## ERA5 data\n",
     "\n",
-    "In this notebook, we use the `ARCOERA5` interface to the publicly available [ARCO ERA5](https://cloud.google.com/storage/docs/public-datasets/era5) \"model-level\" data."
+    "In this notebook, we use the `ERA5ARCO` interface to the publicly available [ARCO ERA5](https://cloud.google.com/storage/docs/public-datasets/era5) \"model-level\" data."
    ]
   },
   {
@@ -35,7 +35,7 @@
     "from scipy.interpolate import PchipInterpolator\n",
     "\n",
     "from pycontrails.core import models\n",
-    "from pycontrails.datalib.ecmwf import ARCOERA5\n",
+    "from pycontrails.datalib.ecmwf import ERA5ARCO\n",
     "from pycontrails.physics import thermo"
    ]
   },
@@ -65,11 +65,11 @@
     "time = \"2021-03-14T15\"\n",
     "variables = [\"t\", \"q\", \"ciwc\"]\n",
     "\n",
-    "arco_coarse = ARCOERA5(time, variables, pl_coarse)\n",
+    "arco_coarse = ERA5ARCO(time, variables, pl_coarse)\n",
     "ds_coarse = arco_coarse.open_metdataset().data\n",
     "ds_coarse.load()\n",
     "\n",
-    "arco_fine = ARCOERA5(time, variables, pl_fine)\n",
+    "arco_fine = ERA5ARCO(time, variables, pl_fine)\n",
     "ds_fine = arco_fine.open_metdataset().data\n",
     "ds_fine.load();"
    ]

pycontrails/core/aircraft_performance.py

@@ -12,6 +12,7 @@
 from overrides import overrides
 
 from pycontrails.core import flight, fuel
+from pycontrails.core.fleet import Fleet
 from pycontrails.core.flight import Flight
 from pycontrails.core.met import MetDataset
 from pycontrails.core.models import Model, ModelParams, interpolate_met
@@ -76,6 +77,10 @@ class AircraftPerformance(Model):
 
     source: Flight
 
+    @abc.abstractmethod
+    @overload
+    def eval(self, source: Fleet, **params: Any) -> Fleet: ...
+
     @abc.abstractmethod
     @overload
     def eval(self, source: Flight, **params: Any) -> Flight: ...
@@ -467,10 +472,11 @@ def ensure_true_airspeed_on_source(self) -> npt.NDArray[np.float64]:
             tas[cond] = self.source.segment_groundspeed()[cond]
             return tas
 
-        met_incomplete = (
-            self.met is None or "eastward_wind" not in self.met or "northward_wind" not in self.met
+        wind_available = ("eastward_wind" in self.source and "northward_wind" in self.source) or (
+            self.met is not None and "eastward_wind" in self.met and "northward_wind" in self.met
         )
-        if met_incomplete:
+
+        if not wind_available:
             if fill_with_groundspeed:
                 tas = self.source.segment_groundspeed()
                 self.source["true_airspeed"] = tas

pycontrails/core/fleet.py

@@ -196,17 +196,15 @@ def _maybe_warn(fl: Flight) -> Flight:
 
         fl_attrs: dict[str, Any] = {}
 
-        # Pluck from the first flight to get fuel, data_keys, and crs
+        # Pluck from the first flight to get fuel and data_keys
         fuel = seq[0].fuel
         data_keys = set(seq[0])  # convert to a new instance to because we mutate seq[0]
-        crs = seq[0].attrs["crs"]
 
         for fl in seq:
             _validate_fl(
                 fl,
                 fl_attrs=fl_attrs,
                 data_keys=data_keys,
-                crs=crs,
                 fuel=fuel,
                 broadcast_numeric=broadcast_numeric,
             )
@@ -318,10 +316,9 @@ def calc_tas(fl: Flight) -> npt.NDArray[np.float64]:
 
     @overrides
     def segment_groundspeed(self, *args: Any, **kwargs: Any) -> npt.NDArray[np.float64]:
-        # Implement if we have a usecase for this.
-        # Because the super() method uses a smoothing pattern, it will not reliably
-        # work on Fleet.
-        raise NotImplementedError
+        fls = self.to_flight_list(copy=False)
+        gs = [fl.segment_groundspeed(*args, **kwargs) for fl in fls]
+        return np.concatenate(gs)
 
     @overrides
     def resample_and_fill(self, *args: Any, **kwargs: Any) -> Fleet:
@@ -336,10 +333,6 @@ def segment_length(self) -> npt.NDArray[np.float64]:
     @property
     @overrides
     def max_distance_gap(self) -> float:
-        if self.attrs["crs"] != "EPSG:4326":
-            msg = "Only implemented for EPSG:4326 CRS."
-            raise NotImplementedError(msg)
-
         return np.nanmax(self.segment_length()).item()
 
     @overrides
@@ -400,7 +393,6 @@ def _validate_fl(
     *,
     fl_attrs: dict[str, Any],
     data_keys: set[str],
-    crs: str,
     fuel: Fuel,
     broadcast_numeric: bool,
 ) -> None:
@@ -419,8 +411,6 @@ def _validate_fl(
         Set of data keys expected in each flight.
     fuel : Fuel
         Fuel used all flights
-    crs : str
-        CRS to use all flights
     broadcast_numeric : bool
         If True, broadcast numeric attributes to data variables.
 
@@ -429,7 +419,7 @@ def _validate_fl(
     KeyError
         ``fl`` does not have a ``flight_id`` key in :attr:`attrs`.
     ValueError
-        If ``flight_id`` is duplicated or incompatible CRS found.
+        If ``flight_id`` is duplicated or if ``fuel`` or ``data_keys`` are inconsistent.
     """
     flight_id = _extract_flight_id(fl)
 
@@ -446,13 +436,6 @@ def _validate_fl(
             "The 'fuel' attributes must be consistent between flights in a Fleet."
         )
         raise ValueError(msg)
-    if fl.attrs["crs"] != crs:
-        msg = (
-            f"CRS on Flight {flight_id} ({fl.attrs['crs']}) "
-            f"is not inconsistent with previous flights ({crs}). "
-            "The 'crs' attributes must be consistent between flights in a Fleet."
-        )
-        raise ValueError(msg)
     if fl.data.keys() != data_keys:
         msg = (
             f"Data keys on Flight {flight_id} ({fl.data.keys()}) "