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@@ -20,6 +20,7 @@ | |
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| _NAV = ( | ||
| ('User Guide', 'user_guide/index'), | ||
| ('Gallery', 'gallery/index'), | ||
| ('About', 'about') | ||
| ) | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,88 @@ | ||
| { | ||
| "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 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,75 @@ | ||
| { | ||
| "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 | ||
| } |
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