Heat Map Rendering Example

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Purpose: Sample code to render heatmap of points

Imports

from tracktable.domain.terrestrial import TrajectoryPointReader
from tracktable.render import mapmaker
from tracktable.render.histogram2d import render_histogram
from tracktable.render import maps
from tracktable.core import data_directory

from matplotlib import pyplot

import os.path

First we set up our point source by reading points from a file. Then we dump the points to a list. We do not care about extra data in this example, so we leave all the column fields as default.

The data file we use here is bundled with Tracktable.

points = []
data_filename = os.path.join(data_directory(), 'SampleHeatmapPoints.csv')
with open(data_filename, 'r') as inFile:
    reader = TrajectoryPointReader()
    reader.input = inFile
    reader.comment_character = '#'
    reader.field_delimiter = ','
    for point in reader:
        points.append(point)

Now we generate a map and create a heatmap from the points we generated.

The type of map, colors, scaling can be customised depending the on the desired look and feel of the finished map.

# JUPYTER NOTE: Jupyter will show you the state of the figure when you exit
# the cell in which you created it.  You cannot apply different effects in
# different cells as far as I know.  To work around this, just put all your
# different things in functions, then call those functions one after another
# in a single cell.

# Set up the canvas and map projection
# Grab default bounding box
def get_bbox(area, domain):
    coords = []
    location = maps.CONVENIENCE_MAPS[area]
    coords.append(location['min_corner'][0])
    coords.append(location['min_corner'][1])
    coords.append(location['max_corner'][0])
    coords.append(location['max_corner'][1])
    return mapmaker._make_bounding_box(coords, domain)

# 100 DPI * (8, 6) gives an 800x600-pixel image
figure = pyplot.figure(dpi=100, figsize=(8, 6))
(mymap, map_actors) = mapmaker.mapmaker(domain='terrestrial',
                                        map_name='region:conus')
bbox = get_bbox('conus', 'terrestrial')
render_histogram(map_projection=mymap,
                 point_source=points,       # Our list of points we created above
                 bounding_box = bbox,       # Bounding box is generated from mymap
                 bin_size=0.25,
                colormap='gist_heat')

[<matplotlib.collections.QuadMesh at 0x7fb9b52f8e50>]