Merge pull request #73 from maresb/fix-notebook

Fix sample_output_analyses notebook

Author: dgilford

notebooks/sample_output_analyses.ipynb

@@ -105,18 +105,23 @@
     "        output_core_dims=[\n",
     "            [], [], [], [], []\n",
     "        ],\n",
-    "        vectorize=True\n",
+    "        vectorize=True,\n",
+    "        keep_attrs=\"drop_conflicts\",\n",
     "    )\n",
     "\n",
     "    vmax, pmin, ifl, t0, otl = result\n",
     "    \n",
-    "    out_ds=xr.Dataset({\n",
-    "        'vmax': vmax, \n",
-    "        'pmin': pmin,\n",
-    "        'ifl': ifl,\n",
-    "        't0': t0,\n",
-    "        'otl': otl\n",
-    "    })\n",
+    "    out_ds=xr.Dataset(\n",
+    "        {\n",
+    "            'vmax': vmax, \n",
+    "            'pmin': pmin,\n",
+    "            'ifl': ifl,\n",
+    "            't0': t0,\n",
+    "            'otl': otl,\n",
+    "        },\n",
+    "        coords=ds.coords,\n",
+    "        attrs=ds.attrs,\n",
+    "    )\n",
     "    \n",
     "    # add names and units\n",
     "    out_ds.vmax.attrs['standard_name'],out_ds.vmax.attrs['units']='Maximum Potential Intensity','m/s'\n",
@@ -286,7 +291,8 @@
     "        output_core_dims=[\n",
     "            [],\n",
     "        ],\n",
-    "        vectorize=True\n",
+    "        vectorize=True,\n",
+    "        keep_attrs=\"drop_conflicts\",\n",
     "    )\n",
     "    \n",
     "    diseq = xr.apply_ufunc(\n",
@@ -299,7 +305,8 @@
     "        output_core_dims=[\n",
     "            [],\n",
     "        ],\n",
-    "        vectorize=True\n",
+    "        vectorize=True,\n",
+    "        keep_attrs=\"drop_conflicts\",\n",
     "    )\n",
     "    \n",
     "    result1 = xr.apply_ufunc(\n",
@@ -312,19 +319,24 @@
     "        output_core_dims=[\n",
     "            [], [], [], [],\n",
     "        ],\n",
-    "        vectorize=True\n",
+    "        vectorize=True,\n",
+    "        keep_attrs=\"drop_conflicts\",\n",
     "    )\n",
     "\n",
     "    lnpi, lneff, lndiseq, lnCKCD = result1\n",
     "    \n",
-    "    out_ds = xr.Dataset({\n",
-    "                'eff': efficiency, \n",
-    "                'diseq': diseq,\n",
-    "                'lnpi': lnpi,\n",
-    "                'lneff': lneff,\n",
-    "                'lndiseq': lndiseq,\n",
-    "                'lnCKCD': lnCKCD[0,0,0]\n",
-    "            })\n",
+    "    out_ds = xr.Dataset(\n",
+    "        {\n",
+    "            'eff': efficiency, \n",
+    "            'diseq': diseq,\n",
+    "            'lnpi': lnpi,\n",
+    "            'lneff': lneff,\n",
+    "            'lndiseq': lndiseq,\n",
+    "            'lnCKCD': lnCKCD[0,0,0],\n",
+    "        },\n",
+    "        coords=pi_ds.coords,\n",
+    "        attrs=pi_ds.attrs,\n",
+    "    )\n",
     "    \n",
     "    # add names and units (where applicable)\n",
     "    out_ds.eff.attrs['standard_name'],out_ds.eff.attrs['units']='Tropical Cyclone Efficiency','unitless fraction'\n",
@@ -379,7 +391,10 @@
     "        'lneff': diag_ds.lneff,\n",
     "        'lndiseq': diag_ds.lndiseq,\n",
     "        'lnCKCD': diag_ds.lnCKCD\n",
-    "    })\n",
+    "    },\n",
+    "    coords=diag_ds.coords,\n",
+    "    attrs=diag_ds.attrs,\n",
+    ")\n",
     "del pi_ds, ds, diag_ds"
    ]
   },
@@ -703,8 +718,11 @@
     "    weights = np.cos(np.deg2rad(dat_array.lat))\n",
     "    \n",
     "    # find the basin locations in the data structure\n",
-    "    var_slice=dat_array.sel(lon=slice(basin_dict.bounds.values[1,0], basin_dict.bounds.values[1,1]), \\\n",
-    "                         lat=slice(basin_dict.bounds.values[0,1], basin_dict.bounds.values[0,0]))\n",
+    "    var_slice=dat_array.sel(\n",
+    "        lon=slice(basin_dict[\"lon_min\"], basin_dict[\"lon_max\"]),\n",
+    "        # Warning: latitude is decreasing from 90 to -90, so the slice is reversed\n",
+    "        lat=slice(basin_dict[\"lat_max\"], basin_dict[\"lat_min\"]),\n",
+    "    )\n",
     "    \n",
     "    # take latitude weighted basin average\n",
     "    basin_mean=var_slice.weighted(weights).mean((\"lon\", \"lat\"))\n",
@@ -766,11 +784,11 @@
    "outputs": [],
    "source": [
     "# define the function to plot the seasonal cycle of a variable\n",
-    "def plot_pi_seascycle(var_want,meanb,basin_dict):\n",
+    "def plot_pi_seascycle(var_want,meanb,basins):\n",
     "    plt.figure()\n",
     "    mongrid=np.linspace(1,12,12)\n",
     "    for k in basins.keys():\n",
-    "        plt.plot(mongrid,meanb[var_want][k],lw=4,color=str(basins[k].color.values),label=str(basins[k].shortname.values))\n",
+    "        plt.plot(mongrid,meanb[var_want][k],lw=4,color=str(basins[k][\"color\"]),label=str(basins[k][\"shortname\"]))\n",
     "    plt.xlim([0.75,12.25])\n",
     "    plt.xticks(mongrid,labels=monlabels)\n",
     "    plt.xlabel('Month')\n",