deploy: 14f5059

_preview/11/_sources/notebooks/03-uxarray-vis/03-polygons.ipynb

@@ -60,9 +60,10 @@
    "execution_count": null,
    "outputs": [],
    "source": [
+    "fig_size = 400\n",
     "plot_kwargs = {\"backend\": \"matplotlib\",\n",
     "               \"aspect\": 2,\n",
-    "               \"fig_size\": 250}"
+    "               \"fig_size\": fig_size}"
    ],
    "metadata": {
     "collapsed": false
@@ -232,7 +233,7 @@
    "outputs": [],
    "source": [
     "(uxds_mpas[\"bottomDepth\"].plot.rasterize(xlim=(-20, 0), ylim=(-5, 5), pixel_ratio=2.0, **plot_kwargs) +\n",
-    "uxds_mpas[\"bottomDepth\"].plot.rasterize(xlim=(-20, 0), ylim=(-5, 5), pixel_ratio=4.0, **plot_kwargs)).opts(fig_size=250)"
+    "uxds_mpas[\"bottomDepth\"].plot.rasterize(xlim=(-20, 0), ylim=(-5, 5), pixel_ratio=4.0, **plot_kwargs)).opts(fig_size=fig_size).cols(1)"
    ],
    "metadata": {
     "collapsed": false
@@ -258,7 +259,7 @@
    "outputs": [],
    "source": [
     "(uxds_mpas[\"bottomDepth\"].plot.polygons(xlim=(-185, -175), ylim=(-5, 5), exclude_antimeridian=False, **plot_kwargs) +\n",
-    "uxds_mpas[\"bottomDepth\"].plot.polygons(xlim=(175, 185), ylim=(-5, 5), exclude_antimeridian=False, **plot_kwargs)).opts(fig_size=250)"
+    "uxds_mpas[\"bottomDepth\"].plot.polygons(xlim=(175, 185), ylim=(-5, 5), exclude_antimeridian=False, **plot_kwargs)).opts(fig_size=fig_size).cols(1)"
    ],
    "metadata": {
     "collapsed": false
@@ -270,7 +271,7 @@
    "outputs": [],
    "source": [
     "(uxds_mpas[\"bottomDepth\"].plot.polygons(xlim=(-185, -175), ylim=(-5, 5), exclude_antimeridian=True, **plot_kwargs) +\n",
-    "uxds_mpas[\"bottomDepth\"].plot.polygons(xlim=(175, 185), ylim=(-5, 5), exclude_antimeridian=True, **plot_kwargs)).opts(fig_size=250)"
+    "uxds_mpas[\"bottomDepth\"].plot.polygons(xlim=(175, 185), ylim=(-5, 5), exclude_antimeridian=True, **plot_kwargs)).opts(fig_size=fig_size).cols(1)"
    ],
    "metadata": {
     "collapsed": false
@@ -387,118 +388,6 @@
    "metadata": {
     "collapsed": false
    }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "uxds_mpas[\"bottomDepth\"].plot.polygons(width=900, height=400)"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "outputs": [],
-   "source": [
-    "uxds_geoflow[\"v1\"].nodal_average()[0][0].plot.polygons(\n",
-    "    cmap=\"coolwarm\", width=900, height=400\n",
-    ")"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    ":::{attention}\n",
-    "\n",
-    ":::\n",
-    "\n",
-    "<div class=\"admonition alert alert-warning\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Attention!</p>\n",
-    "    For moderately to high resolution grids, it's not recommended to directly plot polygons. <br>\n",
-    "    Plotting each polygon is extremely computationally expensive to render and may not provide the most visually-appealing plots.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "## Rasterized Polygon Plots\n",
-    "\n",
-    "Instead of plotting the geometry of each polygon directly, we can rasterize our set of polygons to obtain a raster plot.\n",
-    "\n",
-    "<div class=\"admonition alert alert-info\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Hint</p>\n",
-    "    A raster plot of any set of geometric elements (in this case Polygons) renders each data into a regularly shaped array as opposed to rendering each\n",
-    "    shape directly.\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "uxds_mpas[\"bottomDepth\"].plot.rasterize(method=\"polygon\", width=900, height=400)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "outputs": [],
-   "source": [
-    "uxds_geoflow[\"v1\"].nodal_average()[0][0].plot.rasterize(\n",
-    "    method=\"polygon\", cmap=\"coolwarm\", width=900, height=400\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": false,
-    "jupyter": {
-     "outputs_hidden": false
-    }
-   },
-   "source": [
-    "You can select whether to include or exclude these antimeridian polygons by using the `exclude_antimeridian` parameter. \n",
-    "\n",
-    "<div class=\"admonition alert alert-warning\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Attention!</p>\n",
-    "    For larger, higher-resolution, grids, it's suggested to keep `exclude_antimeridian=True` to decrease the time needed to process the grid for\n",
-    "    visualization.\n",
-    "</div>"
-   ]
   }
  ],
  "metadata": {

_preview/11/notebooks/01-intro/01-unstructured-grid-overview.html

@@ -504,7 +504,7 @@ <h2>Structured Grids<a class="headerlink" href="#structured-grids" title="Link t
   warnings.warn(f&#39;Downloading: {url}&#39;, DownloadWarning)
 </pre></div>
 </div>
-<img alt="../../_images/c1a63bf430c34a612fe4af23b2824805b102757be3c22081d319c1a5be8af258.png" src="../../_images/c1a63bf430c34a612fe4af23b2824805b102757be3c22081d319c1a5be8af258.png" />
+<img alt="../../_images/1b796ff42a875abbb0b4b3e493ab2e49cf39db685d1ff85baf66bc424887ed09.png" src="../../_images/1b796ff42a875abbb0b4b3e493ab2e49cf39db685d1ff85baf66bc424887ed09.png" />
 </div>
 </div>
 </section>