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docs/notebooks/partitioning.ipynb

@@ -20,11 +20,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import matplotlib as mpl\n",
     "import numpy as np\n",
-    "import xarray as xr\n",
     "import pandas as pd\n",
-    "import matplotlib as mpl\n",
+    "import xarray as xr\n",
     "from matplotlib import pyplot as plt\n",
+    "\n",
     "import xclim.ensembles\n",
     "\n",
     "# The directory in the Atlas repo where the data is stored\n",
@@ -44,10 +47,10 @@
    "source": [
     "# Here we'll download data only for a very small demo sample of models and scenarios.\n",
     "\n",
-    "# Download data for a few models and scenarios. \n",
+    "# Download data for a few models and scenarios.\n",
     "models = [\"ACCESS-CM2\", \"CMCC-CM2-SR5\", \"CanESM5\"]\n",
     "\n",
-    "# The variant label is not always r1i1p1f1, so we need to match it to the model. \n",
+    "# The variant label is not always r1i1p1f1, so we need to match it to the model.\n",
     "members = [\"r1i1p1f1\", \"r1i1p1f1\", \"r1i1p1f1\"]\n",
     "\n",
     "# GHG concentrations and land-use scenarios\n",
@@ -57,11 +60,15 @@
     "for model, member in zip(models, members):\n",
     "    for scenario in scenarios:\n",
     "        url = host + pat.format(model=model, scenario=scenario, member=member)\n",
-    "        \n",
+    "\n",
     "        # Fetch data using pandas\n",
-    "        df = pd.read_csv(url, index_col=0, comment='#', parse_dates=True)[\"world\"]\n",
+    "        df = pd.read_csv(url, index_col=0, comment=\"#\", parse_dates=True)[\"world\"]\n",
     "        # Convert to a DataArray, complete with coordinates.\n",
-    "        da = xr.DataArray(df).expand_dims(model=[model], scenario=[scenario]).rename(date=\"time\")\n",
+    "        da = (\n",
+    "            xr.DataArray(df)\n",
+    "            .expand_dims(model=[model], scenario=[scenario])\n",
+    "            .rename(date=\"time\")\n",
+    "        )\n",
     "        data.append(da)"
    ]
   },
@@ -93,16 +100,23 @@
     }
    ],
    "source": [
-    "# Combine DataArrays from the different models and scenarios into one. \n",
+    "# Combine DataArrays from the different models and scenarios into one.\n",
     "ens_mon = xr.combine_by_coords(data)[\"world\"]\n",
     "\n",
     "# Then resample the monthly time series at the annual frequency\n",
     "ens = ens_mon.resample(time=\"Y\").mean()\n",
-    "ssp_col = {'ssp126': '#1d3354', 'ssp245': '#eadd3d', 'ssp370': '#f21111', 'ssp585': '#840b22'}\n",
+    "ssp_col = {\n",
+    "    \"ssp126\": \"#1d3354\",\n",
+    "    \"ssp245\": \"#eadd3d\",\n",
+    "    \"ssp370\": \"#f21111\",\n",
+    "    \"ssp585\": \"#840b22\",\n",
+    "}\n",
     "plt.figure(figsize=(10, 4))\n",
     "for scenario in ens.scenario:\n",
     "    for model in ens.model:\n",
-    "        ens.sel(scenario=scenario, model=model).plot(color=ssp_col[str(scenario.data)], alpha=.8, lw=1)\n",
+    "        ens.sel(scenario=scenario, model=model).plot(\n",
+    "            color=ssp_col[str(scenario.data)], alpha=0.8, lw=1\n",
+    "        )\n",
     "plt.title(\"Projected mean global temperature\");"
    ]
   },
@@ -155,9 +169,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "colors = {\"variability\": \"#DC551A\", \"model\": \"#2B2B8B\", \"scenario\": \"#275620\", \"total\": \"k\"}\n",
+    "colors = {\n",
+    "    \"variability\": \"#DC551A\",\n",
+    "    \"model\": \"#2B2B8B\",\n",
+    "    \"scenario\": \"#275620\",\n",
+    "    \"total\": \"k\",\n",
+    "}\n",
     "names = {\"variability\": \"Internal variability\"}\n",
     "\n",
+    "\n",
     "def graph_fraction_of_total_variance(variance, ref=\"2000\", ax=None):\n",
     "    \"\"\"Figure from Hawkins and Sutton (2009) showing fraction of total variance.\n",
     "    Parameters\n",