FIX: Remove old MBCn cells in sdba.ipynb

CHANGELOG.rst

@@ -18,6 +18,7 @@ New features and enhancements
 Bug fixes
 ^^^^^^^^^
 * Fixed a small inefficiency in ``_otc_adjust``, and the `standardize` method of `OTC/dOTC` is now applied on individual variable  (:pull:`1890`, :pull:`1896`).
+* Remove deprecated cells in the tutorial notebook `sdba.ipynb` (:pull:`1895`).
 
 Breaking changes
 ^^^^^^^^^^^^^^^^

docs/notebooks/sdba.ipynb

@@ -641,52 +641,30 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "dref[\"pr\"] = dref.pr.chunk({\"location\": 1})\n",
-    "dref[\"tasmax\"] = dref.tasmax.chunk({\"location\": 1})\n",
+    "from xclim.core.units import convert_units_to\n",
+    "from xclim.testing import open_dataset\n",
     "\n",
-    "dhist[\"pr\"] = dhist.pr.chunk({\"location\": 1})\n",
-    "dhist[\"tasmax\"] = dhist.tasmax.chunk({\"location\": 1})\n",
+    "dref = open_dataset(\n",
+    "    \"sdba/ahccd_1950-2013.nc\", chunks={\"location\": 1}, drop_variables=[\"lat\", \"lon\"]\n",
+    ").sel(time=slice(\"1981\", \"2010\"))\n",
     "\n",
-    "dsim[\"pr\"] = dsim.pr.chunk({\"location\": 1})\n",
-    "dsim[\"tasmax\"] = dsim.tasmax.chunk({\"location\": 1})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Perform an initial univariate adjustment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# additive for tasmax\n",
-    "QDMtx = sdba.QuantileDeltaMapping.train(\n",
-    "    dref.tasmax, dhist.tasmax, nquantiles=20, kind=\"+\", group=\"time\"\n",
+    "# Fix the standard name of the `pr` variable.\n",
+    "# This allows the convert_units_to below to infer the correct CF transformation (precip rate to flux)\n",
+    "# see the \"Unit handling\" notebook\n",
+    "dref.pr.attrs[\"standard_name\"] = \"lwe_precipitation_rate\"\n",
+    "\n",
+    "dref = dref.assign(\n",
+    "    tasmax=convert_units_to(dref.tasmax, \"K\"),\n",
+    "    pr=convert_units_to(dref.pr, \"kg m-2 s-1\"),\n",
     ")\n",
-    "# Adjust both hist and sim, we'll feed both to the Npdf transform.\n",
-    "scenh_tx = QDMtx.adjust(dhist.tasmax)\n",
-    "scens_tx = QDMtx.adjust(dsim.tasmax)\n",
-    "\n",
-    "# remove == 0 values in pr:\n",
-    "dref[\"pr\"] = sdba.processing.jitter_under_thresh(dref.pr, \"0.01 mm d-1\")\n",
-    "dhist[\"pr\"] = sdba.processing.jitter_under_thresh(dhist.pr, \"0.01 mm d-1\")\n",
-    "dsim[\"pr\"] = sdba.processing.jitter_under_thresh(dsim.pr, \"0.01 mm d-1\")\n",
-    "\n",
-    "# multiplicative for pr\n",
-    "QDMpr = sdba.QuantileDeltaMapping.train(\n",
-    "    dref.pr, dhist.pr, nquantiles=20, kind=\"*\", group=\"time\"\n",
+    "dsim = open_dataset(\n",
+    "    \"sdba/CanESM2_1950-2100.nc\", chunks={\"location\": 1}, drop_variables=[\"lat\", \"lon\"]\n",
     ")\n",
-    "# Adjust both hist and sim, we'll feed both to the Npdf transform.\n",
-    "scenh_pr = QDMpr.adjust(dhist.pr)\n",
-    "scens_pr = QDMpr.adjust(dsim.pr)\n",
     "\n",
-    "# Stack variables : Dataset -> DataArray with `multivar` dimension\n",
-    "dref, dhist, dsim = (sdba.stack_variables(da) for da in (dref, dhist, dsim))"
+    "dhist = dsim.sel(time=slice(\"1981\", \"2010\"))\n",
+    "dsim = dsim.sel(time=slice(\"2041\", \"2070\"))\n",
+    "\n",
+    "dref"
    ]
   },
   {
@@ -702,9 +680,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Stack variables : Dataset -> DataArray with `multivar` dimension\n",
+    "ref = sdba.processing.stack_variables(dref)\n",
+    "hist = sdba.processing.stack_variables(dhist)\n",
+    "sim = sdba.processing.stack_variables(dsim)\n",
+    "\n",
     "ADJ = sdba.MBCn.train(\n",
-    "    dref,\n",
-    "    dhist,\n",
+    "    ref,\n",
+    "    hist,\n",
     "    base_kws={\"nquantiles\": 20, \"group\": \"time\"},\n",
     "    adj_kws={\"interp\": \"nearest\", \"extrapolation\": \"constant\"},\n",
     "    n_iter=20,  # perform 20 iteration\n",
@@ -714,8 +697,8 @@
     "scenh, scens = (\n",
     "    ADJ.adjust(\n",
     "        sim=ds,\n",
-    "        ref=dref,\n",
-    "        hist=dhist,\n",
+    "        ref=ref,\n",
+    "        hist=hist,\n",
     "        base=sdba.QuantileDeltaMapping,\n",
     "        base_kws_vars={\n",
     "            \"pr\": {\n",
@@ -725,9 +708,9 @@
     "            },\n",
     "            \"tasmax\": {\"kind\": \"+\"},\n",
     "        },\n",
-    "        adj_kws={\"interp\": \"nearest\", \"extrapolation\": \"constant\"},\n",
+    "        adj_kws={\"interp\": \"linear\", \"extrapolation\": \"constant\"},\n",
     "    )\n",
-    "    for ds in (dhist, dsim)\n",
+    "    for ds in (hist, sim)\n",
     ")"
    ]
   },
@@ -767,12 +750,12 @@
    "outputs": [],
    "source": [
     "fig, axs = plt.subplots(1, 2, figsize=(16, 4))\n",
-    "for da, label in zip((dref, scenh, dhist), (\"Reference\", \"Adjusted\", \"Simulated\")):\n",
+    "for da, label in zip((ref, scenh, hist), (\"Reference\", \"Adjusted\", \"Simulated\")):\n",
     "    ds = sdba.unstack_variables(da).isel(location=2)\n",
     "    # time series - tasmax\n",
     "    ds.tasmax.plot(ax=axs[0], label=label, alpha=0.65 if label == \"Adjusted\" else 1)\n",
     "    # scatter plot\n",
-    "    ds.plot.scatter(x=\"pr\", y=\"tasmax\", ax=axs[1], label=label)\n",
+    "    ds.plot.scatter(x=\"pr\", y=\"tasmax\", ax=axs[1], label=label, marker=\".\", s=40)\n",
     "axs[0].legend()\n",
     "axs[1].legend()"
    ]
@@ -808,7 +791,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.12.5"
   },
   "toc": {
    "base_numbering": 1,