# Flight graph example

An example showing a graph display between cities positioned on the Earth surface.

This graph displays the longest flight routes operated by Boing 777. The two main interests of this example are that it shows how to build a graph of arbitrary connectivity, and that it shows how to position data on the surface of the Earth.

The graph is created by first building a scalar scatter dataset with the mlab.points3d command, and adding line information to it. One of the difficulties is that the lines are specified using the indexing number of the points, so we must ‘massage’ our data when loading it. A similar technique to plot the graph is done in the Protein example. Another example of graph plotting, showing a different technique to plot the graph, can be seen on Delaunay graph example.

To simplify things we do not plot the connection on the surface of the Earth, but as straight lines going through the Earth. As a result must use transparency to show the connection.

Data source: http://www.777fleetpage.com/777fleetpage3.htm

Python source code: `flight_graph.py`

    ###############################################################################
    # The data. This could be loaded from a file, or scraped from a website

    routes_data = """
    Bombay,Atlanta
    Johannesburg,Atlanta
    Dubai,Los Angeles
    Dubai,Houston
    Dubai,San Francisco
    New York,Hong Kong
    Newark,Hong Kong
    Doha,Houston
    Toronto,Hong Kong
    Bombay,Newark
    Bombay,New York
    Vancouver,Hong Kong
    Dubai,Sao Paulo
    Los Angeles,Sydney
    Chicago,Delhi
    """

    cities_data = """
    Toronto,-79.38,43.65
    Chicago,-87.68,41.84
    Houston,-95.39,29.77
    New York,-73.94,40.67
    Vancouver,-123.13,49.28
    Los Angeles,-118.41,34.11
    San Francisco,-122.45,37.77
    Atlanta,-84.42,33.76
    Dubai,55.33,25.27
    Sydney,151.21,-33.87
    Hong Kong,114.19,22.38
    Bombay,72.82,18.96
    Delhi,77.21,28.67
    Newark,-82.43,40.04
    Johannesburg,28.04,-26.19
    Doha,51.53,25.29
    Sao Paulo,-46.63,-23.53
    """

    ###############################################################################
    # Load the data, and put it in data structures we can use
    import csv
    routes_table = [i for i in csv.reader(routes_data.split('\n')[1:-1])]

    # Build a dictionnary returning GPS coordinates for each city
    cities_coord = dict()
    for line in list(csv.reader(cities_data.split('\n')))[1:-1]:
        name, long_, lat = line
        cities_coord[name] = (float(long_), float(lat))

    # Store all the coordinates of connected cities in a list also keep
    # track of which city corresponds to a given index in the list. The
    # connectivity information is specified as connecting the i-th point
    # with the j-th.
    cities = dict()
    coords = list()
    connections = list()
    for city1, city2 in routes_table[1:-1]:
        if not city1 in cities:
            cities[city1] = len(coords)
            coords.append(cities_coord[city1])
        if not city2 in cities:
            cities[city2] = len(coords)
            coords.append(cities_coord[city2])
        connections.append((cities[city1], cities[city2]))

    ###############################################################################
    from mayavi import mlab
    mlab.figure(1, bgcolor=(0.48, 0.48, 0.48), fgcolor=(0, 0, 0),
                   size=(400, 400))
    mlab.clf()

    ###############################################################################
    # Display points at city positions
    import numpy as np
    coords = np.array(coords)
    # First we have to convert latitude/longitude information to 3D
    # positioning.
    lat, long = coords.T * np.pi / 180
    x = np.cos(long) * np.cos(lat)
    y = np.cos(long) * np.sin(lat)
    z = np.sin(long)

    points = mlab.points3d(x, y, z,
                         scale_mode='none',
                         scale_factor=0.03,
                         color=(0, 0, 1))

    ###############################################################################
    # Display connections between cities
    connections = np.array(connections)
    # We add lines between the points that we have previously created by
    # directly modifying the VTK dataset.
    points.mlab_source.dataset.lines = connections
    points.mlab_source.reset()
    # To represent the lines, we use the surface module. Using a wireframe
    # representation allows to control the line-width.
    mlab.pipeline.surface(points, color=(1, 1, 1),
                                  representation='wireframe',
                                  line_width=4,
                                  name='Connections')

    ###############################################################################
    # Display city names
    for city, index in cities.items():
        label = mlab.text(x[index], y[index], city, z=z[index],
                          width=0.016 * len(city), name=city)
        label.property.shadow = True

    ###############################################################################
    # Display continents outline, using the VTK Builtin surface 'Earth'
    from mayavi.sources.builtin_surface import BuiltinSurface
    continents_src = BuiltinSurface(source='earth', name='Continents')
    # The on_ratio of the Earth source controls the level of detail of the
    # continents outline.
    continents_src.data_source.on_ratio = 2
    continents = mlab.pipeline.surface(continents_src, color=(0, 0, 0))

    ###############################################################################
    # Display a semi-transparent sphere, for the surface of the Earth

    # We use a sphere Glyph, through the points3d mlab function, rather than
    # building the mesh ourselves, because it gives a better transparent
    # rendering.
    sphere = mlab.points3d(0, 0, 0, scale_mode='none',
                                    scale_factor=2,
                                    color=(0.67, 0.77, 0.93),
                                    resolution=50,
                                    opacity=0.7,
                                    name='Earth')

    # These parameters, as well as the color, where tweaked through the GUI,
    # with the record mode to produce lines of code usable in a script.
    sphere.actor.property.specular = 0.45
    sphere.actor.property.specular_power = 5
    # Backface culling is necessary for more a beautiful transparent
    # rendering.
    sphere.actor.property.backface_culling = True

    ###############################################################################
    # Plot the equator and the tropiques
    theta = np.linspace(0, 2 * np.pi, 100)
    for angle in (- np.pi / 6, 0, np.pi / 6):
        x = np.cos(theta) * np.cos(angle)
        y = np.sin(theta) * np.cos(angle)
        z = np.ones_like(theta) * np.sin(angle)

        mlab.plot3d(x, y, z, color=(1, 1, 1),
                            opacity=0.2, tube_radius=None)

    mlab.view(63.4, 73.8, 4, [-0.05, 0, 0])
    mlab.show()