cleanup

notebooks/wp5/ocean_color_timeseries.ipynb

@@ -192,16 +192,16 @@
     "        \"marker\": \".\",\n",
     "    }\n",
     "    kwargs_running = {\n",
+    "        \"label\": \"48-month running\",\n",
     "        \"color\": \"tab:red\",\n",
     "        \"ls\": \"--\",\n",
     "        \"marker\": \" \",\n",
-    "        \"label\": \"48-month running\",\n",
     "    }\n",
     "    kwargs_slope = {\n",
+    "        \"label\": \"yearly slope\",\n",
     "        \"color\": \"tab:blue\",\n",
     "        \"ls\": \"-\",\n",
     "        \"marker\": \" \",\n",
-    "        \"label\": \"yearly slope\",\n",
     "    }\n",
     "\n",
     "    da_daily = da.resample(time=\"D\").mean()\n",
@@ -219,7 +219,7 @@
     "    if not col:\n",
     "        *_, slope, intercept = mk.original_test(da_yearly.squeeze())\n",
     "\n",
-    "        da_running.plot(**kwargs_running)\n",
+    "        da_running.plot(add_legend=False, **kwargs_running)\n",
     "        plt.plot(\n",
     "            da_yearly[\"time\"],\n",
     "            np.arange(da_yearly.sizes[\"time\"]) * slope + intercept,\n",
@@ -232,7 +232,7 @@
     "    for i, (ax, sel_dict) in enumerate(\n",
     "        zip(plt_obj.axs.flatten(), plt_obj.name_dicts.flatten())\n",
     "    ):\n",
-    "        da_running.sel(sel_dict).plot(ax=ax, **kwargs_running)\n",
+    "        da_running.sel(sel_dict).plot(ax=ax, add_legend=False, **kwargs_running)\n",
     "\n",
     "        *_, slope, intercept = mk.original_test(da_yearly.sel(sel_dict))\n",
     "        ax.plot(\n",
@@ -241,10 +241,11 @@
     "            **kwargs_slope,\n",
     "        )\n",
     "\n",
+    "        ax.grid()\n",
     "        if i:\n",
     "            ax.set_ylabel(\"\")\n",
-    "        ax.legend()\n",
-    "        ax.grid()\n",
+    "\n",
+    "    ax.legend(bbox_to_anchor=(1, 1), loc=\"upper left\")\n",
     "    return plt_obj"
    ]
   },