Added the ability to append an InferenceData object to an existing netCDF file

CHANGELOG.md

@@ -5,6 +5,7 @@
 ### New features
 
 - Bayes Factor plot: Use arviz's kde instead of the one from scipy ([2237](https://github.com/arviz-devs/arviz/pull/2237))
+- InferenceData objects can now be appended to existing netCDF4 files and to specific groups within them ([2227](https://github.com/arviz-devs/arviz/pull/2227))
 
 ### Maintenance and fixes
 

arviz/data/inference_data.py

@@ -23,6 +23,7 @@
     Union,
     overload,
 )
+import os
 
 import numpy as np
 import xarray as xr
@@ -337,7 +338,12 @@ def items(self) -> "InferenceData.InferenceDataItemsView":
 
     @staticmethod
     def from_netcdf(
-        filename, *, engine="h5netcdf", group_kwargs=None, regex=False
+        filename,
+        *,
+        engine="h5netcdf",
+        group_kwargs=None,
+        regex=False,
+        base_group: str = "/",
     ) -> "InferenceData":
         """Initialize object from a netcdf file.
 
@@ -360,6 +366,9 @@ def from_netcdf(
         regex : bool, default False
             Specifies where regex search should be used to extend the keyword arguments.
             This feature is currently experimental.
+        base_group : str, default "/"
+            The group in the netCDF file where the InferenceData is stored. By default,
+            assumes that the file only contains an InferenceData object.
 
         Returns
         -------
@@ -380,7 +389,12 @@ def from_netcdf(
         try:
             with h5netcdf.File(filename, mode="r") if engine == "h5netcdf" else nc.Dataset(
                 filename, mode="r"
-            ) as data:
+            ) as file_handle:
+                if base_group == "/":
+                    data = file_handle
+                else:
+                    data = file_handle[base_group]
+
                 data_groups = list(data.groups)
 
             for group in data_groups:
@@ -394,13 +408,13 @@ def from_netcdf(
                         if re.search(key, group):
                             group_kws = kws
                 group_kws.setdefault("engine", engine)
-                with xr.open_dataset(filename, group=group, **group_kws) as data:
+                with xr.open_dataset(filename, group=f"{base_group}/{group}", **group_kws) as data:
                     if rcParams["data.load"] == "eager":
                         groups[group] = data.load()
                     else:
                         groups[group] = data
 
-            with xr.open_dataset(filename, engine=engine) as data:
+            with xr.open_dataset(filename, engine=engine, group=base_group) as data:
                 attrs.update(data.load().attrs)
 
             return InferenceData(attrs=attrs, **groups)
@@ -424,6 +438,8 @@ def to_netcdf(
         compress: bool = True,
         groups: Optional[List[str]] = None,
         engine: str = "h5netcdf",
+        base_group: str = "/",
+        overwrite_existing: bool = True,
     ) -> str:
         """Write InferenceData to netcdf4 file.
 
@@ -438,15 +454,29 @@ def to_netcdf(
             Write only these groups to netcdf file.
         engine : {"h5netcdf", "netcdf4"}, default "h5netcdf"
             Library used to read the netcdf file.
+        base_group : str, default "/"
+            The group in the netCDF file where the InferenceData is will be stored.
+            By default, will write to the root of the netCDF file
+        overwrite_existing : bool, default True
+            Whether to overwrite the existing file or append to it.
 
         Returns
         -------
         str
             Location of netcdf file
         """
-        mode = "w"  # overwrite first, then append
+        if base_group is None:
+            base_group = "/"
+
+        if os.path.exists(filename) and not overwrite_existing:
+            mode = "a"
+        else:
+            mode = "w"  # overwrite first, then append
+
         if self._attrs:
-            xr.Dataset(attrs=self._attrs).to_netcdf(filename, mode=mode, engine=engine)
+            xr.Dataset(attrs=self._attrs).to_netcdf(
+                filename, mode=mode, engine=engine, group=base_group
+            )
             mode = "a"
 
         if self._groups_all:  # check's whether a group is present or not.
@@ -464,7 +494,7 @@ def to_netcdf(
                         for var_name, values in data.variables.items()
                         if _compressible_dtype(values.dtype)
                     }
-                data.to_netcdf(filename, mode=mode, group=group, **kwargs)
+                data.to_netcdf(filename, mode=mode, group=f"{base_group}/{group}", **kwargs)
                 data.close()
                 mode = "a"
         elif not self._attrs:  # creates a netcdf file for an empty InferenceData object.

arviz/tests/base_tests/test_data.py

@@ -1294,10 +1294,11 @@ def test_io_function(self, data, eight_schools_params):
         os.remove(filepath)
         assert not os.path.exists(filepath)
 
+    @pytest.mark.parametrize("base_group", ["/", "test_group", "group/subgroup"])
     @pytest.mark.parametrize("groups_arg", [False, True])
     @pytest.mark.parametrize("compress", [True, False])
     @pytest.mark.parametrize("engine", ["h5netcdf", "netcdf4"])
-    def test_io_method(self, data, eight_schools_params, groups_arg, compress, engine):
+    def test_io_method(self, data, eight_schools_params, groups_arg, base_group, compress, engine):
         # create InferenceData and check it has been properly created
         inference_data = self.get_inference_data(  # pylint: disable=W0612
             data, eight_schools_params
@@ -1334,12 +1335,13 @@ def test_io_method(self, data, eight_schools_params, groups_arg, compress, engin
             filepath,
             groups=("posterior", "observed_data") if groups_arg else None,
             compress=compress,
+            base_group=base_group,
         )
 
         # assert file has been saved correctly
         assert os.path.exists(filepath)
         assert os.path.getsize(filepath) > 0
-        inference_data2 = InferenceData.from_netcdf(filepath)
+        inference_data2 = InferenceData.from_netcdf(filepath, base_group=base_group)
         if groups_arg:  # if groups arg, update test dict to contain only saved groups
             test_dict = {
                 "posterior": ["eta", "theta", "mu", "tau"],