Review netcdf to_xarray args (#369)

* Review netcdf to_xarray args

docs/guide/sources.rst

@@ -38,6 +38,8 @@ We can get data from a given source by using :func:`from_source`:
       - read data from a stream
     * - :ref:`data-sources-memory`
       - read data from a memory buffer
+    * - :ref:`data-sources-multi`
+      - read data from multiple sources
     * - :ref:`data-sources-ads`
       - retrieve data from the `Copernicus Atmosphere Data Store <https://ads.atmosphere.copernicus.eu/>`_ (ADS)
     * - :ref:`data-sources-cds`
@@ -418,6 +420,27 @@ memory
           print(f.metadata("param"))
 
 
+.. _data-sources-multi:
+
+multi
+--------------
+
+.. py:function:: from_source("multi", *sources, merger=None, **kwargs)
+  :noindex:
+
+  The ``multi`` source reads multiple sources.
+
+  :param tuple *sources: the sources
+  :param merger: if it is None an attempt is made to merge/concatenate the sources by their classes (using the nearest common class). Otherwise the sources are merged/concatenated using the merger in a lazy way. The merger can one of the following:
+
+    - class/object implementing  the :func:`to_xarray` or :func:`to_pandas` methods
+    - callable
+    - str, describing a call either to "concat" or "merge". E.g.: "concat(concat_dim=time)"
+    - tuple with 2 elements. The fist element is a str, either "concat" or "merge", and the second element is a dict with the keyword arguments for the call. E.g.: ("concat", {"concat_dim": "time"})
+  :param dict **kwargs: other keyword arguments
+
+
+
 .. _data-sources-ads:
 
 ads

docs/howtos.rst

@@ -24,3 +24,31 @@ save the results of a :ref:`MARS retrieval <data-sources-mars>` into a file:
     )
 
     ds.save("my_data.grib")
+
+
+How to call to_xarray() with arguments for NetCDF data?
+---------------------------------------------------------
+
+When calling :func:`to_xarray` for NetCDF data it calls ``xarray.open_mfdataset`` internally. You can pass arguments to this xarray function by using the ``xarray_open_mfdataset_kwargs`` option. For example:
+
+
+.. code-block:: python
+
+    import earthkit.data
+
+    req = {
+        "format": "zip",
+        "origin": "c3s",
+        "sensor": "olci",
+        "version": "1_1",
+        "year": "2022",
+        "month": "04",
+        "nominal_day": "01",
+        "variable": "pixel_variables",
+        "region": "europe",
+    }
+
+    ds = earthkit.data.from_source("cds", "satellite-fire-burned-area", req)
+    r = ds.to_xarray(
+        xarray_open_mfdataset_kwargs=dict(decode_cf=False, decode_times=False)
+    )

earthkit/data/mergers/__init__.py

@@ -15,7 +15,7 @@
 
 LOG = logging.getLogger(__name__)
 
-FORWARDS = ("to_xarray", "to_pandas", "to_tfdataset")
+FORWARDS = ("to_xarray", "to_pandas")
 
 
 def _nearest_common_class(objects):
@@ -86,16 +86,6 @@ def to_pandas(self, **kwargs):
             **kwargs,
         )
 
-    def to_tfdataset(self, **kwargs):
-        from .tfdataset import merge
-
-        return merge(
-            sources=self.sources,
-            paths=self.paths,
-            reader_class=self.reader_class,
-            **kwargs,
-        )
-
     def to_xarray(self, **kwargs):
         from .xarray import merge
 
@@ -118,9 +108,6 @@ def to_xarray(self, *args, **kwargs):
     def to_pandas(self, *args, **kwargs):
         return self.obj.to_pandas(self.paths_or_sources, **kwargs)
 
-    def to_tfdataset(self, *args, **kwargs):
-        return self.obj.to_tfdataset(self.paths_or_sources, **kwargs)
-
 
 class CallableMerger(Merger):
     def __init__(self, func, sources, *args, **kwargs):
@@ -132,7 +119,6 @@ def _call_func(self, *args, **kwargs):
 
     to_xarray = _call_func
     to_pandas = _call_func
-    to_tfdataset = _call_func
 
 
 class XarrayGenericMerger(Merger):

earthkit/data/readers/netcdf/__init__.py

@@ -66,6 +66,8 @@ def to_xarray_multi_from_paths(cls, paths, **kwargs):
 
         options = dict()
         options.update(kwargs.get("xarray_open_mfdataset_kwargs", {}))
+        if not options:
+            options = dict(**kwargs)
 
         return xr.open_mfdataset(
             paths,

earthkit/data/readers/netcdf/fieldlist.py

@@ -188,6 +188,8 @@ def to_xarray_multi_from_paths(cls, paths, **kwargs):
 
         options = dict()
         options.update(kwargs.get("xarray_open_mfdataset_kwargs", {}))
+        if not options:
+            options = dict(**kwargs)
 
         return xr.open_mfdataset(
             paths,
@@ -248,7 +250,7 @@ def __init__(self, *args, **kwargs):
     def to_xarray(self, **kwargs):
         import xarray as xr
 
-        return xr.merge([x.ds for x in self._indexes], **kwargs)
+        return xr.merge([x._ds for x in self._indexes], **kwargs)
 
 
 class NetCDFFieldList(XArrayFieldListCore):
@@ -268,10 +270,8 @@ def new_mask_index(cls, *args, **kwargs):
         return NetCDFMaskFieldList(*args, **kwargs)
 
     def to_xarray(self, **kwargs):
-        import xarray as xr
-
-        if self.path.startswith("http"):
-            return xr.open_dataset(self.path, **kwargs)
+        # if self.path.startswith("http"):
+        #     return xr.open_dataset(self.path, **kwargs)
         return type(self).to_xarray_multi_from_paths([self.path], **kwargs)
 
     def write(self, *args, **kwargs):

tests/netcdf/test_netcdf_concat.py

@@ -74,6 +74,55 @@ def test_netcdf_concat(mode):
         ],
     }
 
+    import xarray as xr
+
+    target = xr.merge([ds1.to_xarray(), ds2.to_xarray()])
+    merged = ds.to_xarray()
+    assert target.identical(merged)
+
+
+def test_netcdf_read_multiple_files():
+    ds = from_source(
+        "file",
+        [
+            earthkit_test_data_file("era5_2t_1.nc"),
+            earthkit_test_data_file("era5_2t_2.nc"),
+        ],
+    )
+
+    assert len(ds) == 2
+    assert ds.metadata("variable") == ["t2m", "t2m"]
+
+    assert ds[0].datetime() == {
+        "base_time": datetime.datetime(2021, 3, 1, 12, 0),
+        "valid_time": datetime.datetime(2021, 3, 1, 12, 0),
+    }
+    assert ds[1].datetime() == {
+        "base_time": datetime.datetime(2021, 3, 2, 12, 0),
+        "valid_time": datetime.datetime(2021, 3, 2, 12, 0),
+    }
+    assert ds.datetime() == {
+        "base_time": [
+            datetime.datetime(2021, 3, 1, 12, 0),
+            datetime.datetime(2021, 3, 2, 12, 0),
+        ],
+        "valid_time": [
+            datetime.datetime(2021, 3, 1, 12, 0),
+            datetime.datetime(2021, 3, 2, 12, 0),
+        ],
+    }
+
+    import xarray as xr
+
+    target = xr.merge(
+        [
+            xr.open_dataset(earthkit_test_data_file("era5_2t_1.nc")),
+            xr.open_dataset(earthkit_test_data_file("era5_2t_2.nc")),
+        ]
+    )
+    merged = ds.to_xarray()
+    assert target.identical(merged)
+
 
 @pytest.mark.parametrize("custom_merger", (merger_func, Merger_obj()))
 def test_netdcf_merge_custom(custom_merger):

tests/netcdf/test_netcdf_convert.py

@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+
+# (C) Copyright 2020 ECMWF.
+#
+# This software is licensed under the terms of the Apache Licence Version 2.0
+# which can be obtained at http://www.apache.org/licenses/LICENSE-2.0.
+# In applying this licence, ECMWF does not waive the privileges and immunities
+# granted to it by virtue of its status as an intergovernmental organisation
+# nor does it submit to any jurisdiction.
+#
+
+import numpy as np
+import pytest
+
+from earthkit.data import from_source
+from earthkit.data.testing import earthkit_remote_test_data_file
+
+
+@pytest.mark.long_test
+@pytest.mark.download
+def test_netcdf_to_xarray_args():
+    # The JD variable in the NetCDF is defined as follows:
+    #
+    # short JD(time, lat, lon) ;
+    #   string JD:long_name = "Date of the first detection" ;
+    #   string JD:units = "days since 2022-01-01" ;
+    #   string JD:comment = "Possible values: 0  when the pixel is not burned; 1 to 366 day of
+    #       the first detection when the pixel is burned; -1 when the pixel is not observed
+    #       in the month; -2 when pixel is not burnable: water bodies, bare areas, urban areas,
+    #       and permanent snow and ice.
+    #
+    # when loaded with xarray.open_dataset/xarray.open_mdataset without any kwargs the
+    # type of the JD variable is datetime64[ns], which is wrong. The correct type should
+    # be int16.
+
+    ds = from_source(
+        "url",
+        earthkit_remote_test_data_file(
+            "test-data", "20220401-C3S-L3S_FIRE-BA-OLCI-AREA_3-fv1.1.nc"
+        ),
+    )
+
+    r = ds.to_xarray(
+        xarray_open_mfdataset_kwargs=dict(decode_cf=False, decode_times=False)
+    )
+    assert r["JD"].dtype == "int16"
+    r["JD"].shape == (1, 20880, 28440)
+    assert np.isclose(r["JD"].values.min(), -2)
+    assert np.isclose(r["JD"].values.max(), 120)
+
+    r = ds.to_xarray(decode_cf=False, decode_times=False)
+    assert r["JD"].dtype == "int16"
+    r["JD"].shape == (1, 20880, 28440)
+    assert np.isclose(r["JD"].values.min(), -2)
+    assert np.isclose(r["JD"].values.max(), 120)
+
+    r = ds.to_xarray()
+    assert r["JD"].dtype == "<M8[ns]"
+    r["JD"].shape == (1, 20880, 28440)
+
+
+if __name__ == "__main__":
+    from earthkit.data.testing import main
+
+    # test_datetime()
+    main(__file__)