Migrate to Zarr 3

Pre-work to #6

This MR has the following:

1. Migration to Zarr 3 API while removing the deprecated yet still supported Zarr 2 API usage.
2. Essential fixes for the main-branch resulting from the learnings out of the global-hackathon exploits (The non-essential ones will be fixed in a seperate branch).

apps/hiopy/configure/configure.py

@@ -15,7 +15,9 @@ def add_time(dataset, startdate, enddate, dt, name="time"):
     time_data = (np.arange(startdate + dt, enddate + dt, dt) - startdate) // np.timedelta64(1, "s")
 
     for g in _collect_groups(dataset):
-        time = dataset.create_array(name="time", fill_value=None, shape=time_data.shape, dtype=np.longlong)
+        time = g.create_array(
+            name="time", fill_value=None, shape=time_data.shape, dtype=np.longlong
+        )
         time.append(data=time_data)
         time.attrs["_ARRAY_DIMENSIONS"] = (name,)
         time.attrs["axis"] = "T"
@@ -59,13 +61,13 @@ def add_variable(
     for g in _collect_groups(dataset):
         taxis_tuple = tuple() if taxis is None else (taxis,)
         ntime = tuple() if taxis is None else (g[taxis].shape[0],)
-        grid_mapping_name = g['crs'].attrs["grid_mapping_name"]
+        grid_mapping_name = g["crs"].attrs["grid_mapping_name"]
         spatial_attr = "point" if (grid_mapping_name == "point_cloud") else "cell"
         crs_len = 0
         if grid_mapping_name == "healpix":
-            crs_len = healpy.nside2npix(g['crs'].attrs["healpix_nside"])
+            crs_len = healpy.nside2npix(g["crs"].attrs["healpix_nside"])
         elif grid_mapping_name == "point_cloud":
-            lon_coord, lat_coord = g['crs'].attrs["coordinates"].split(" ")
+            lon_coord, lat_coord = g["crs"].attrs["coordinates"].split(" ")
             assert lon_coord in g and lat_coord in g
             assert g[lon_coord].shape[0] == g[lat_coord].shape[0]
             crs_len = g[lat_coord].shape[0]
@@ -76,18 +78,20 @@ def add_variable(
 
         if zaxis is None:
             shape = (*ntime, crs_len)
-            _chunk_shape = np.minimum(chunk_shape, shape) if chunk_shape is not None else None
             _attributes["_ARRAY_DIMENSIONS"] = (*taxis_tuple, spatial_attr)
         else:
             nheight = g[zaxis].shape[0]
             shape = (*ntime, nheight, crs_len)
-            _chunk_shape = np.minimum(chunk_shape, shape) if chunk_shape is not None else None
             _attributes["_ARRAY_DIMENSIONS"] = (*taxis_tuple, zaxis, spatial_attr)
-        _attributes["grid_mapping"] = "crs"
 
+        _attributes["grid_mapping"] = "crs"
+        _chunk_shape = "auto"
         _shard_shape = None
-        if chunks_per_shard is not None:
-            _shard_shape = tuple(i * chunks_per_shard for i in _chunk_shape)
+
+        if chunk_shape is not None:
+            _chunk_shape = tuple(min(chunk_shape, shape))
+            if chunks_per_shard is not None:
+                _shard_shape = tuple(i * chunks_per_shard for i in _chunk_shape)
 
         v = g.create_array(
             name,