resolve final conlficts

pysteps/io/nwp_importers.py

@@ -69,7 +69,8 @@
 .. autosummary::
     :toctree: ../generated/
 
-    import_bom_nwp
+    import_bom_nwp_xr
+    import_rmi_nwp_xr
     import_knmi_nwp
 """
 
@@ -89,7 +90,7 @@
 
 @postprocess_import()
 def import_bom_nwp_xr(filename, **kwargs):
-    """Import a NetCDF with NWP rainfall forecasts regrided to a BoM Rainfields3
+    """Import a NetCDF with NWP rainfall forecasts regridded to a BoM Rainfields3
     using xarray.
 
     Parameters
@@ -108,7 +109,7 @@ def import_bom_nwp_xr(filename, **kwargs):
 
     if not NETCDF4_IMPORTED:
         raise MissingOptionalDependency(
-            "netCDF4 package is required to import BoM NWP regrided rainfall "
+            "netCDF4 package is required to import BoM NWP regridded rainfall "
             "products but it is not installed"
         )
 
@@ -125,7 +126,7 @@ def import_bom_nwp_xr(filename, **kwargs):
     # so it needs to be disagregated by time step
     varname = kwargs.get("varname", "accum_prcp")
     if varname == "accum_prcp":
-        print("Rainfall values are accumulated. Disagreagating by time step")
+        print("Rainfall values are accumulated. Disaggregating by time step")
         accum_prcp = ds_meta[varname]
         precipitation = accum_prcp - accum_prcp.shift({varname_time: 1})
         precipitation = precipitation.dropna(varname_time, "all")
@@ -249,7 +250,165 @@ def _import_bom_nwp_geodata_xr(
             "institution": "Commonwealth of Australia, Bureau of Meteorology",
             "threshold": _get_threshold_value(da_rainfall.values),
             # TODO(_import_bom_rf3_geodata_xr): Remove before final 2.0 version
-            "yorigin": "lower",
+            "yorigin": "upper",
+            "xpixelsize": xpixelsize.values,
+            "ypixelsize": ypixelsize.values,
+        }
+    )
+
+    return ds_in
+
+
+@postprocess_import()
+def import_rmi_nwp_xr(filename, **kwargs):
+    """Import a NetCDF with NWP rainfall forecasts from RMI using xarray.
+
+    Parameters
+    ----------
+    filename: str
+        Name of the file to import.
+
+    {extra_kwargs_doc}
+
+    Returns
+    -------
+    out_da : xr.DataArray
+        A xarray DataArray containing forecast rainfall fields in mm/timestep
+        imported from a netcdf, and the metadata.
+    """
+
+    if not NETCDF4_IMPORTED:
+        raise MissingOptionalDependency(
+            "netCDF4 package is required to import RMI NWP rainfall "
+            "products but it is not installed"
+        )
+
+    ds = _import_rmi_nwp_data_xr(filename, **kwargs)
+    ds_meta = _import_rmi_nwp_geodata_xr(ds, **kwargs)
+
+    # rename varname_time (def: time) to t
+    varname_time = kwargs.get("varname_time", "time")
+    ds_meta = ds_meta.rename({varname_time: "t"})
+    varname_time = "t"
+
+    # if data variable is named accum_prcp
+    # it is assumed that NWP rainfall data is accumulated
+    # so it needs to be disagregated by time step
+    varname = kwargs.get("varname", "precipitation")
+    if varname == "accum_prcp":
+        print("Rainfall values are accumulated. Disaggregating by time step")
+        accum_prcp = ds_meta[varname]
+        precipitation = accum_prcp - accum_prcp.shift({varname_time: 1})
+        precipitation = precipitation.dropna(varname_time, "all")
+        # update/copy attributes
+        precipitation.name = "precipitation"
+        # copy attributes
+        precipitation.attrs.update({**accum_prcp.attrs})
+        # update attributes
+        precipitation.attrs.update({"standard_name": "precipitation_amount"})
+    else:
+        precipitation = ds_meta[varname]
+
+    return precipitation
+
+
+def _import_rmi_nwp_data_xr(filename, **kwargs):
+
+    varname_time = kwargs.get("varname_time", "time")
+    chunks = kwargs.get("chunks", {varname_time: 1})
+
+    ds_rainfall = xr.open_mfdataset(
+        filename,
+        combine="nested",
+        concat_dim=varname_time,
+        chunks=chunks,
+        lock=False,
+        parallel=True,
+    )
+
+    return ds_rainfall
+
+
+def _import_rmi_nwp_geodata_xr(
+    ds_in,
+    **kwargs,
+):
+
+    varname = kwargs.get("varname", "precipitation")
+    varname_time = kwargs.get("varname_time", "time")
+    projdef = None
+    if "proj4string" in ds_in.attrs:
+        projdef = ds_in.proj4string
+
+    # get the accumulation period
+    time = ds_in[varname_time]
+    # shift the array to calculate the time step
+    delta_time = time - time.shift({varname_time: 1})
+    # assuming first valid delta_time is representative of all time steps
+    time_step = delta_time[1]
+    time_step = time_step.values.astype("timedelta64[m]")
+
+    # get the units of precipitation
+    units = None
+    if "units" in ds_in[varname].attrs:
+        units = ds_in[varname].units
+        if units in ("kg m-2", "mm"):
+            units = "mm"
+            ds_in[varname].attrs.update({"units": units})
+    # get spatial boundaries and pixelsize
+    # move to meters if coordinates in kilometers
+    if "units" in ds_in.x.attrs:
+        if ds_in.x.units == "km":
+            ds_in["x"] = ds_in.x * 1000.0
+            ds_in.x.attrs.update({"units": "m"})
+            ds_in["y"] = ds_in.y * 1000.0
+            ds_in.y.attrs.update({"units": "m"})
+
+    xmin = ds_in.x.min().values
+    xmax = ds_in.x.max().values
+    ymin = ds_in.y.min().values
+    ymax = ds_in.y.max().values
+    xpixelsize = abs(ds_in.x[1] - ds_in.x[0])
+    ypixelsize = abs(ds_in.y[1] - ds_in.y[0])
+
+    cartesian_unit = ds_in.x.units
+
+    # Add metadata needed by pySTEPS as attrs in X and Y variables
+
+    ds_in.x.attrs.update(
+        {
+            # TODO: Remove before final 2.0 version
+            "x1": xmin,
+            "x2": xmax,
+            "cartesian_unit": cartesian_unit,
+        }
+    )
+
+    ds_in.y.attrs.update(
+        {
+            # TODO: Remove before final 2.0 version
+            "y1": ymin,
+            "y2": ymax,
+            "cartesian_unit": cartesian_unit,
+        }
+    )
+
+    # Add metadata needed by pySTEPS as attrs in rainfall variable
+    da_rainfall = ds_in[varname].isel({varname_time: 0})
+
+    ds_in[varname].attrs.update(
+        {
+            "transform": None,
+            "unit": units,  # copy 'units' in 'unit' for legacy reasons
+            "projection": projdef,
+            "accutime": time_step,
+            "zr_a": None,
+            "zr_b": None,
+            "zerovalue": np.nanmin(da_rainfall),
+            "institution": "Royal Meteorological Institute of Belgium",
+            "threshold": _get_threshold_value(da_rainfall.values),
+            # TODO(_import_bom_rf3_geodata_xr): Remove before final 2.0 version
+            "yorigin": "upper",
             "xpixelsize": xpixelsize.values,
             "ypixelsize": ypixelsize.values,
         }
@@ -406,7 +565,7 @@ def _import_knmi_nwp_geodata_xr(
 
 def _get_threshold_value(precip):
     """
-    Get the the rain/no rain threshold with the same unit, transformation and
+    Get the rain/no rain threshold with the same unit, transformation and
     accutime of the data.
     If all the values are NaNs, the returned value is `np.nan`.
     Otherwise, np.min(precip[precip > precip.min()]) is returned.

pysteps/pystepsrc

@@ -4,8 +4,7 @@
     "silent_import": false,
     "outputs": {
         // path_outputs : path where to save results (figures, forecasts, etc)
-        "path_outputs": "./",
-        "path_workdir": "./tmp/"
+        "path_outputs": "./"
     },
     "plot": {
         // "motion_plot" : "streamplot" or "quiver"