Add introduction to quickstart documentation

bin/build_all.sh

@@ -3,7 +3,7 @@
 #PBS -P tm70
 #PBS -l storage=gdata/tm70+gdata/xp65+gdata/ik11+gdata/cj50+gdata/hh5+gdata/p73+gdata/dk92
 #PBS -q normal
-#PBS -l walltime=02:00:00
+#PBS -l walltime=03:00:00
 #PBS -l mem=192gb
 #PBS -l ncpus=48
 #PBS -l wd

docs/usage/chunking.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Chunking tutorial\n",
     "\n",
-    "The end goal when using the ACCESS-NRI catalog is to open some data as an xarray dataset for analysis. The Intake-ESM datastore methods `to_dask` and `to_dataset_dict` return xarray datasets backed by dask arrays.\n",
+    "The end goal when using the ACCESS-NRI catalog is to open some data as an xarray dataset(s) for analysis. The Intake-ESM datastore methods `to_dask` and `to_dataset_dict` return xarray datasets backed by dask arrays.\n",
     "\n",
     "```{note}\n",
     "   If you don’t know what a dask array is, check out the dask array documentation [here](https://docs.dask.org/en/stable/array.html)\n",
@@ -57,7 +57,7 @@
    "source": [
     "import intake\n",
     "\n",
-    "cat = intake.cat.access_nri"
+    "catalog = intake.cat.access_nri"
    ]
   },
   {
@@ -154,7 +154,7 @@
     }
    ],
    "source": [
-    "esm_datastore = cat[\"01deg_jra55v13_ryf9091\"]\n",
+    "esm_datastore = catalog[\"01deg_jra55v13_ryf9091\"]\n",
     "\n",
     "esm_datastore"
    ]
@@ -213,11 +213,11 @@
    },
    "outputs": [],
    "source": [
-    "esm_datastore2 = esm_datastore.search(\n",
+    "esm_datastore_filtered = esm_datastore.search(\n",
     "    variable=[\"temp\", \"average_DT\"], frequency=\"1day\", require_all_on=\"path\"\n",
     ")\n",
     "\n",
-    "ds = esm_datastore2.to_dask()"
+    "ds = esm_datastore_filtered.to_dask()"
    ]
   },
   {
@@ -931,7 +931,7 @@
    "source": [
     "xarray_open_kwargs = {\"chunks\": {\"st_ocean\": 7, \"xt_ocean\": 400, \"yt_ocean\": 300}}\n",
     "\n",
-    "ds = esm_datastore2.to_dask(xarray_open_kwargs=xarray_open_kwargs)"
+    "ds = esm_datastore_filtered.to_dask(xarray_open_kwargs=xarray_open_kwargs)"
    ]
   },
   {

docs/usage/what.rst

@@ -77,8 +77,10 @@ moment these include:
 A simple search API allows users to filter the entries in the catalog based on these metadata 
 attributes. The idea is that users will:
 
-#. search the ACCESS-NRI catalog for data products containing the models, variables etc that are of 
-   interest to them.
-#. open the Intake-ESM datastore(s) for the filtered product(s). 
-#. possibly query further on the files within the datastores(s) and eventually open some data as 
-   xarray Dataset(s) to analyse.
+#. search the ACCESS-NRI catalog for data products, e.g. products containing the models, variables etc 
+   that are of interest to them.
+#. open the Intake-ESM datastore(s) for the product(s) of interest. 
+#. search the Intake-ESM datastore(s) for the datasets within each product that are of interest to them.
+   A "dataset" here is a set of files that can be readily opened and combined for analysis.
+#. open the datasets of interest as xarray Dataset(s).
+#. perform some analysis on the xarray Dataset(s).

src/access_nri_intake/cli.py

@@ -193,7 +193,7 @@ def _get_project(path):
     yaml_dict["sources"]["access_nri"]["args"]["mode"] = "r"
     yaml_dict["sources"]["access_nri"]["metadata"] = {
         "version": "{{version}}",
-        "storage": "+".join(list(storage_flags)),
+        "storage": storage_flags,
     }
     yaml_dict["sources"]["access_nri"]["parameters"] = {
         "version": {"description": "Catalog version", "type": "str", "default": version}