cleanup

Author: malmans2

notebooks/wp5/xch4_lev2_growth_rates.ipynb

@@ -49,7 +49,7 @@
    "outputs": [],
    "source": [
     "year_start = 2004\n",
-    "year_stop = 2022"
+    "year_stop = 2021"
    ]
   },
   {
@@ -73,7 +73,7 @@
     "    \"variable\": \"xch4\",\n",
     "    \"sensor_and_algorithm\": \"merged_emma\",\n",
     "    \"version\": \"4_5\",\n",
-    "    \"year\": [str(year) for year in range(year_start, year_stop + 1)],\n",
+    "    \"year\": [str(year) for year in range(year_start - 1, year_stop + 2)],\n",
     "    \"month\": [f\"{i:02d}\" for i in range(1, 13)],\n",
     "    \"day\": [f\"{i:02d}\" for i in range(1, 32)],\n",
     "}"
@@ -94,9 +94,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def spatial_weighted_mean(ds, dim=None):\n",
+    "def weight_dataset(ds):\n",
     "    weights = np.abs(np.cos(np.deg2rad(ds[\"latitude\"])))\n",
-    "    return ds.weighted(weights).mean(dim, keep_attrs=True)\n",
+    "    return ds.weighted(weights)\n",
+    "\n",
+    "\n",
+    "def spatial_weighted_mean(ds, dim=None):\n",
+    "    return weight_dataset(ds).mean(dim, keep_attrs=True)\n",
+    "\n",
+    "\n",
+    "def spatial_weighted_std(ds, dim=None):\n",
+    "    return weight_dataset(ds).std(dim, keep_attrs=True)\n",
     "\n",
     "\n",
     "def regrid(ds, d_lon, d_lat, lon1=180):\n",
@@ -199,8 +207,10 @@
     "dataarrays = []\n",
     "for zonal_first in [True, False]:\n",
     "    da = compute_growth_rate(ds, zonal_first=zonal_first)\n",
-    "    dataarrays.append(da.expand_dims(zonal_first=[zonal_first]))\n",
-    "da = xr.concat(dataarrays, \"zonal_first\")"
+    "    dataarrays.append(\n",
+    "        da.expand_dims(method=[\"Zonal-first\" if zonal_first else \"Standard\"])\n",
+    "    )\n",
+    "da = xr.concat(dataarrays, \"method\").sel(time=slice(str(year_start), str(year_stop)))"
    ]
   },
   {
@@ -218,7 +228,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "facet = da.plot(col=\"latitude\", col_wrap=3, hue=\"zonal_first\")\n",
+    "facet = da.plot(col=\"latitude\", col_wrap=3, hue=\"method\")\n",
     "for ax in facet.axs.flatten():\n",
     "    ax.grid()\n",
     "    for label in ax.get_xticklabels():\n",
@@ -240,11 +250,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "da_year = da.groupby(\"time.year\").map(spatial_weighted_mean, dim=[\"time\", \"latitude\"])\n",
-    "df_year = da_year.to_pandas()\n",
-    "ax = df_year.T.plot.bar()\n",
+    "grouped = da.groupby(\"time.year\")\n",
+    "da_mean = grouped.map(spatial_weighted_mean, dim=[\"time\", \"latitude\"])\n",
+    "da_std = grouped.map(spatial_weighted_std, dim=[\"time\", \"latitude\"])\n",
+    "df_mean = da_mean.to_pandas().T\n",
+    "df_std = da_std.to_pandas().T\n",
+    "ax = df_mean.plot.bar(yerr=df_std)\n",
     "ax.grid()\n",
-    "_ = ax.set_ylabel(f\"{da_year.long_name} [{da_year.units}]\")"
+    "_ = ax.set_ylabel(f\"{da_mean.long_name} [{da_mean.units}]\")"
    ]
   }
  ],