make dimension.py xarray compatible

pysteps/converters.py

@@ -12,6 +12,7 @@
 """
 
 import numpy as np
+import numpy.typing as npt
 import pyproj
 import xarray as xr
 
@@ -67,6 +68,15 @@ def _convert_proj4_to_grid_mapping(proj4str):
     return grid_mapping_var_name, grid_mapping_name, params
 
 
+def compute_lat_lon(
+    x_r: npt.ArrayLike, y_r: npt.ArrayLike, projection: str
+) -> tuple[npt.ArrayLike, npt.ArrayLike]:
+    x_2d, y_2d = np.meshgrid(x_r, y_r)
+    pr = pyproj.Proj(projection)
+    lon, lat = pr(x_2d.flatten(), y_2d.flatten(), inverse=True)
+    return lat.reshape(x_2d.shape), lon.reshape(x_2d.shape)
+
+
 def convert_to_xarray_dataset(
     precip: np.ndarray,
     quality: np.ndarray | None,
@@ -105,9 +115,7 @@ def convert_to_xarray_dataset(
     if metadata["yorigin"] == "upper":
         y_r = np.flip(y_r)
 
-    x_2d, y_2d = np.meshgrid(x_r, y_r)
-    pr = pyproj.Proj(metadata["projection"])
-    lon, lat = pr(x_2d.flatten(), y_2d.flatten(), inverse=True)
+    lat, lon = compute_lat_lon(x_r, y_r, metadata["projection"])
 
     (
         grid_mapping_var_name,
@@ -166,7 +174,7 @@ def convert_to_xarray_dataset(
         ),
         "lon": (
             ["y", "x"],
-            lon.reshape(precip.shape),
+            lon,
             {
                 "long_name": "longitude coordinate",
                 "standard_name": "longitude",
@@ -176,7 +184,7 @@ def convert_to_xarray_dataset(
         ),
         "lat": (
             ["y", "x"],
-            lat.reshape(precip.shape),
+            lat,
             {
                 "long_name": "latitude coordinate",
                 "standard_name": "latitude",

pysteps/io/importers.py

@@ -65,6 +65,93 @@
 |   zr_b           | the Z-R exponent b in Z = a*R**b                         |
 +------------------+----------------------------------------------------------+
 
+The data and metadata is then postprocessed into an xarray dataset. This dataset will
+always contain an x and y dimension, but can be extended with a time dimension and/or
+an ensemble member dimension over the course of the process.
+
+The dataset can contain the following coordinate variables:
+
+
+.. tabularcolumns:: |p{2cm}|L|
+
++---------------+-------------------------------------------------------------------------------------------+
+|  Coordinate   |                Description                                                                |
++===============+===========================================================================================+
+|   y           | y-coordinate in Cartesian system, with units determined by ``metadata["cartesian_unit"]`` |
++---------------+-------------------------------------------------------------------------------------------+
+|   x           | x-coordinate in Cartesian system, with units determined by ``metadata["cartesian_unit"]`` |
++---------------+-------------------------------------------------------------------------------------------+
+|   lat         | latitude coordinate in degrees                                                            |
++---------------+-------------------------------------------------------------------------------------------+
+|   lon         | longitude coordinate in degrees                                                           |
++---------------+-------------------------------------------------------------------------------------------+
+|   time        | forecast time in seconds since forecast start time                                        |
++---------------+-------------------------------------------------------------------------------------------+
+|   member      | ensemble member number (integer)                                                          |
++---------------+-------------------------------------------------------------------------------------------+
+
+
+The dataset can contain the following data variables:
+
+.. tabularcolumns:: |p{2cm}|L|
+
++-------------------+-----------------------------------------------------------------------------------------------------------+
+|    Variable       |                Description                                                                                |
++===================+===========================================================================================================+
+| precip_intensity, | precipitation data, based on the unit the data has it is stored in one of these 3 possible variables      |
+| precip_accum      | precip_intensity if unit is ``mm/h``, precip_accum if unit is ``mm`` and reflectivity if unit is ``dBZ``, |
+| or reflectivity   | the attributes of this variable contain metadata relevant to this attribute (see below)                   |
++-------------------+-----------------------------------------------------------------------------------------------------------+
+| quality           | value between 0 and 1 denoting the quality of the precipitation data, currently not used for anything     |
++-------------------+-----------------------------------------------------------------------------------------------------------+
+
+Some of the metadata in the metadata dictionary is not explicitely stored in the dataset,
+but is still implicitly present. For example ``x1`` can easily be found by taking the first
+value from the x coordinate variable. Metadata that is not implicitly present is explicitly
+stored either in the datasets global attributes or as attributes of the precipitation variable.
+Data that relates to the entire dataset is stored in the global attributes. The following data
+is stored in the global attributes:
+
+.. tabularcolumns:: |p{2cm}|L|
+
++------------------+----------------------------------------------------------+
+|       Key        |                Value                                     |
++==================+==========================================================+
+|   projection     | PROJ.4-compatible projection definition                  |
++------------------+----------------------------------------------------------+
+|   institution    | name of the institution who provides the data            |
++------------------+----------------------------------------------------------+
+|   precip_var     | the name of the precipitation variable in this dataset   |
++------------------+----------------------------------------------------------+
+
+The following data is stored as attributes of the precipitation variable:
+
+.. tabularcolumns:: |p{2cm}|L|
+
++------------------+----------------------------------------------------------+
+|       Key        |                Value                                     |
++==================+==========================================================+
+|   units          | the physical unit of the data: 'mm/h', 'mm' or 'dBZ'     |
++------------------+----------------------------------------------------------+
+|   transform      | the transformation of the data: None, 'dB', 'Box-Cox' or |
+|                  | others                                                   |
++------------------+----------------------------------------------------------+
+|   accutime       | the accumulation time in minutes of the data, float      |
++------------------+----------------------------------------------------------+
+|   threshold      | the rain/no rain threshold with the same unit,           |
+|                  | transformation and accutime of the data.                 |
++------------------+----------------------------------------------------------+
+|   zerovalue      | the value assigned to the no rain pixels with the same   |
+|                  | unit, transformation and accutime of the data.           |
++------------------+----------------------------------------------------------+
+|   zr_a           | the Z-R constant a in Z = a*R**b                         |
++------------------+----------------------------------------------------------+
+|   zr_b           | the Z-R exponent b in Z = a*R**b                         |
++------------------+----------------------------------------------------------+
+
+Furthermore the dataset can contain some additional metadata to make the dataset
+CF-compliant.
+
 Available Importers
 -------------------
 

pysteps/tests/helpers.py

@@ -14,6 +14,7 @@
 import pysteps as stp
 from pysteps import io, rcparams
 from pysteps.utils import aggregate_fields_space
+from pysteps.utils.dimension import clip_domain
 
 _reference_dates = dict()
 _reference_dates["bom"] = datetime(2018, 6, 16, 10, 0)
@@ -53,7 +54,6 @@ def get_precipitation_fields(
     num_prev_files=0,
     num_next_files=0,
     return_raw=False,
-    metadata=False,
     upscale=None,
     source="mch",
     log_transform=True,
@@ -100,9 +100,6 @@ def get_precipitation_fields(
         The pre-processing steps are: 1) Convert to mm/h,
         2) Mask invalid values, 3) Log-transform the data [dBR].
 
-    metadata: bool, optional
-        If True, also return file metadata.
-
     upscale: float or None, optional
         Upscale fields in space during the pre-processing steps.
         If it is None, the precipitation field is not modified.
@@ -127,8 +124,8 @@ def get_precipitation_fields(
 
     Returns
     -------
-    reference_field : array
-    metadata : dict
+    dataset: xarray.Dataset
+        As described in the documentation of :py:mod:`pysteps.io.importers`.
     """
 
     if source == "bom":
@@ -186,41 +183,34 @@ def get_precipitation_fields(
     # Read the radar composites
     importer = io.get_method(importer_name, "importer")
 
-    ref_dataset = io.read_timeseries(fns, importer, **_importer_kwargs)
+    dataset = io.read_timeseries(fns, importer, **_importer_kwargs)
 
     if not return_raw:
-        if (num_prev_files == 0) and (num_next_files == 0):
-            # Remove time dimension
-            reference_field = np.squeeze(reference_field)
+        precip_var = dataset.attrs["precip_var"]
 
         # Convert to mm/h
-        ref_dataset = stp.utils.to_rainrate(ref_dataset)
+        dataset = stp.utils.to_rainrate(dataset)
+        precip_var = dataset.attrs["precip_var"]
 
         # Clip domain
-        ref_dataset = stp.utils.clip_domain(ref_dataset, clip)
+        dataset = clip_domain(dataset, clip)
 
         # Upscale data
-        reference_field, ref_metadata = aggregate_fields_space(
-            reference_field, ref_metadata, upscale
-        )
+        dataset = aggregate_fields_space(dataset, upscale)
 
         # Mask invalid values
-        reference_field = np.ma.masked_invalid(reference_field)
+        valid_mask = np.isfinite(dataset[precip_var].values)
 
         if log_transform:
             # Log-transform the data [dBR]
-            reference_field, ref_metadata = stp.utils.dB_transform(
-                reference_field, ref_metadata, threshold=0.1, zerovalue=-15.0
-            )
+            dataset = stp.utils.dB_transform(dataset, threshold=0.1, zerovalue=-15.0)
 
         # Set missing values with the fill value
-        np.ma.set_fill_value(reference_field, ref_metadata["zerovalue"])
-        reference_field.data[reference_field.mask] = ref_metadata["zerovalue"]
-
-    if metadata:
-        return reference_field, ref_metadata
+        metadata = dataset[precip_var].attrs
+        zerovalue = metadata["zerovalue"]
+        dataset[precip_var].data[~valid_mask] = zerovalue
 
-    return reference_field
+    return dataset
 
 
 def smart_assert(actual_value, expected, tolerance=None):

pysteps/tests/test_nowcasts_steps.py

@@ -7,7 +7,6 @@
 from pysteps import io, motion, nowcasts, verification
 from pysteps.tests.helpers import get_precipitation_fields
 
-
 steps_arg_names = (
     "n_ens_members",
     "n_cascade_levels",
@@ -44,28 +43,29 @@ def test_steps_skill(
 ):
     """Tests STEPS nowcast skill."""
     # inputs
-    precip_input, metadata = get_precipitation_fields(
+    dataset_input = get_precipitation_fields(
         num_prev_files=2,
         num_next_files=0,
         return_raw=False,
         metadata=True,
         upscale=2000,
     )
-    precip_input = precip_input.filled()
 
-    precip_obs = get_precipitation_fields(
+    dataset_obs = get_precipitation_fields(
         num_prev_files=0, num_next_files=3, return_raw=False, upscale=2000
-    )[1:, :, :]
-    precip_obs = precip_obs.filled()
+    ).isel(time=slice(1, None, None))
+    precip_var = dataset_input.attrs["precip_var"]
+    metadata = dataset_input[precip_var].attrs
+    precip_data = dataset_input[precip_var].values
 
     pytest.importorskip("cv2")
     oflow_method = motion.get_method("LK")
-    retrieved_motion = oflow_method(precip_input)
+    retrieved_motion = oflow_method(precip_data)
 
     nowcast_method = nowcasts.get_method("steps")
 
     precip_forecast = nowcast_method(
-        precip_input,
+        precip_data,
         retrieved_motion,
         timesteps=timesteps,
         precip_thr=metadata["threshold"],
@@ -86,7 +86,9 @@ def test_steps_skill(
         timesteps if isinstance(timesteps, int) else len(timesteps)
     )
 
-    crps = verification.probscores.CRPS(precip_forecast[:, -1], precip_obs[-1])
+    crps = verification.probscores.CRPS(
+        precip_forecast[:, -1], dataset_obs[precip_var].values[-1]
+    )
     assert crps < max_crps, f"CRPS={crps:.2f}, required < {max_crps:.2f}"