Added initial gallery examples

notebooks/gallery/bokeh/airport_graph.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import holoviews as hv\n",
+    "import geoviews as gv\n",
+    "from bokeh.sampledata.airport_routes import airports, routes\n",
+    "\n",
+    "hv.extension('bokeh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Count the number of connections from each airport\n",
+    "counts = routes.groupby('SourceID')[['Stops']].count().reset_index().rename(columns={'Stops': 'Connections'})\n",
+    "airports_df = pd.merge(airports, counts, left_on='AirportID', right_on='SourceID', how='left')\n",
+    "\n",
+    "# Select only US mainland airports & convert from Mercator to Latitudes/Longitudes\n",
+    "airport_points = gv.Points(airports_df, ['Longitude', 'Latitude']).select(Longitude=(-170, -50), Latitude=(0, 50))\n",
+    "\n",
+    "# Declare nodes, graph and tiles\n",
+    "nodes = gv.Nodes(airport_points, ['Longitude', 'Latitude', 'AirportID'],\n",
+    "                 ['Name', 'City', 'Connections'])\n",
+    "graph = gv.Graph((routes, nodes), ['SourceID', 'DestinationID'], ['Source', 'Destination'])\n",
+    "tiles = gv.WMTS('https://maps.wikimedia.org/osm-intl/{Z}/{X}/{Y}@2x.png')\n",
+    "\n",
+    "# Select 50 busiest airports\n",
+    "busiest = list(routes.groupby('SourceID').count().sort_values('Stops').iloc[-50:].index.values)\n",
+    "busiest_airports = graph.select(AirportID=busiest, selection_mode='nodes')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%opts Graph [width=800 height=600] (edge_selection_line_color='black' edge_hover_line_color='red')\n",
+    "%%opts Graph (edge_line_width=1 edge_line_alpha=0.01 edge_nonselection_line_alpha=0.01)\n",
+    "tiles * busiest_airports"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/gallery/bokeh/brexit_referendum.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import geopandas as gpd\n",
+    "import holoviews as hv\n",
+    "import geoviews as gv\n",
+    "\n",
+    "hv.extension('bokeh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Declaring data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "geometries = gpd.read_file('../../user_guide/assets/boundaries/boundaries.shp')\n",
+    "referendum = pd.read_csv('../../user_guide/assets/referendum.csv')\n",
+    "gdf = gpd.GeoDataFrame(pd.merge(geometries, referendum))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plot_opts = dict(tools=['hover'], width=550, height=700, color_index='leaveVoteshare',\n",
+    "                 colorbar=True, toolbar='above', xaxis=None, yaxis=None)\n",
+    "gv.Polygons(gdf, vdims=['name', 'leaveVoteshare'], label='Brexit Referendum Vote').opts(plot=plot_opts)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/gallery/bokeh/filled_contours.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import holoviews as hv\n",
+    "import geoviews as gv\n",
+    "import geoviews.feature as gf\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "hv.extension('bokeh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sample_data(shape=(73, 145)):\n",
+    "    \"\"\"Returns ``lons``, ``lats`` and ``data`` of some fake data.\"\"\"\n",
+    "    nlats, nlons = shape\n",
+    "    ys = np.linspace(-np.pi / 2, np.pi / 2, nlats)\n",
+    "    xs = np.linspace(0, 2 * np.pi, nlons)\n",
+    "    lons, lats = np.meshgrid(xs, ys)\n",
+    "    wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)\n",
+    "    mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)\n",
+    "\n",
+    "    lats = np.rad2deg(ys)\n",
+    "    lons = np.rad2deg(xs)\n",
+    "    data = wave + mean\n",
+    "\n",
+    "    return lons, lats, data\n",
+    "\n",
+    "lons, lats, data = sample_data()\n",
+    "contours = hv.operation.contours(gv.Image((lons, lats, data)),\n",
+    "                                 filled=True, levels=8).redim.range(z=(-1, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%opts Polygons [colorbar=True width=600 height=400] (cmap='nipy_spectral') \n",
+    "contours * gf.coastline"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/gallery/bokeh/new_york_boroughs.ipynb

@@ -0,0 +1,65 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import geopandas as gpd\n",
+    "import holoviews as hv\n",
+    "import geoviews as gv\n",
+    "import cartopy.crs as ccrs\n",
+    "\n",
+    "hv.extension('bokeh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tiles = gv.WMTS('https://maps.wikimedia.org/osm-intl/{Z}/{X}/{Y}@2x.png')\n",
+    "\n",
+    "# Project data to Web Mercator\n",
+    "nybb = gpd.read_file(gpd.datasets.get_path('nybb'))\n",
+    "poly_data = nybb.to_crs(ccrs.GOOGLE_MERCATOR.proj4_init)\n",
+    "polys = gv.Polygons(poly_data, vdims=['BoroName'], crs=ccrs.GOOGLE_MERCATOR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%opts Polygons [width=800 height=600 color_index='BoroName' tools=['hover', 'tap']] (cmap='Category20')\n",
+    "tiles * polys"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebooks/gallery/bokeh/tile_sources.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import holoviews as hv\n",
+    "import geoviews as gv\n",
+    "from bokeh.tile_providers import STAMEN_TONER\n",
+    "\n",
+    "hv.extension('bokeh')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Define data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "openmap = 'http://c.tile.openstreetmap.org/{Z}/{X}/{Y}.png'\n",
+    "esri = 'https://server.arcgisonline.com/ArcGIS/rest/services/World_Imagery/MapServer/tile/{Z}/{Y}/{X}.jpg'\n",
+    "wiki = 'https://maps.wikimedia.org/osm-intl/{Z}/{X}/{Y}@2x.png'\n",
+    "toner = STAMEN_TONER.url"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%opts WMTS [width=400 height=400 xaxis=None yaxis=None] (level='annotation')\n",
+    "hv.Layout([gv.WMTS(tiles) for tiles in [openmap, esri, wiki, toner]]).cols(2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}