fix seasonal

Author: malmans2

notebooks/wp3/seasonal_forecast_vs_oras5.ipynb

@@ -48,7 +48,7 @@
    "outputs": [],
    "source": [
     "# Time\n",
-    "start = \"1993-05\"\n",
+    "start = \"1993-10\"\n",
     "stop = \"2025-01\"\n",
     "freq = \"12MS\"\n",
     "leadtimes = [0, 1, 2, 3, 4]\n",
@@ -95,7 +95,9 @@
     "        date_reanalysis = date + pd.DateOffset(months=leadtime)\n",
     "        requests_reanalysis[leadtime].append(\n",
     "            {\n",
-    "                \"product_type\": [\"operational\" if date.year > 2014 else \"consolidated\"],\n",
+    "                \"product_type\": [\n",
+    "                    \"operational\" if date_reanalysis.year > 2014 else \"consolidated\"\n",
+    "                ],\n",
     "                \"vertical_resolution\": \"single_level\",\n",
     "                \"variable\": [\"ocean_heat_content_for_the_upper_300m\"],\n",
     "                \"year\": date_reanalysis.strftime(\"%Y\"),\n",
@@ -177,6 +179,8 @@
     "\n",
     "# Seasonal\n",
     "def preprocess_seasonal(ds):\n",
+    "    ds[\"leadtime\"] = ds[\"leadtime\"] // np.timedelta64(31, \"D\")\n",
+    "\n",
     "    # TODO: How to combine? Use first leadtime only for now\n",
     "    ds = ds.set_coords([var for var, da in ds.data_vars.items() if \"bnds\" in da.dims])\n",
     "    ds[\"realization\"] = ds[\"realization\"].str.replace(\" \", \"\").astype(str)\n",
@@ -188,6 +192,7 @@
     "        *grid_request,\n",
     "        preprocess=preprocess_seasonal,\n",
     "        invalidate_cache=False,\n",
+    "        decode_timedelta=True,\n",
     "    )\n",
     "    mask_out = (\n",
     "        ds_seasonal[\"thetaot300\"]\n",
@@ -268,9 +273,10 @@
     "        \"grid_request\": None,\n",
     "    },\n",
     "    transform_chunks=False,\n",
+    "    decode_timedelta=True,\n",
     ")\n",
     "datasets = []\n",
-    "for leadtime, ds in ds_seasonal.isel(leadtime=leadtimes).groupby(\"leadtime\"):\n",
+    "for leadtime, ds in ds_seasonal.sel(leadtime=leadtimes).groupby(\"leadtime\"):\n",
     "    ds = ds.squeeze(\"leadtime\").swap_dims(forecast_reference_time=\"time\")\n",
     "    ds = reindex(ds)\n",
     "    ds[\"leadtime\"] = ds[\"leadtime\"].expand_dims(\"month\")\n",