Interactive Rendering Trajectories ExampleΒΆ

Tutorial showing many of the options available for rendering trajectories.

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from tracktable.domain.terrestrial import TrajectoryPointReader
from tracktable.analysis.assemble_trajectories import AssembleTrajectoryFromPoints
from tracktable.render.render_trajectories import render_trajectories, render_trajectories_separate
from tracktable.core import data_directory
import os.path

#Load sample data for rendering exmples

#Read in points and assemble trajectories
inFile = open(os.path.join(data_directory(), 'SampleFlightsUS.csv'))
reader = TrajectoryPointReader()
reader.input = inFile
reader.comment_character = '#'
reader.field_delimiter = ','
# Set columns for data we care about
reader.object_id_column = 0
reader.timestamp_column = 1
reader.coordinates[0] = 2
reader.coordinates[1] = 3
reader.set_real_field_column('altitude',4) #could be ints
reader.set_real_field_column('heading',5)
reader.set_real_field_column('speed',6)

builder = AssembleTrajectoryFromPoints()
builder.input = reader
builder.minimum_length = 3

trajs = list(builder.trajectories())

few_trajs = [traj for traj in trajs if traj[0].object_id == 'SSS019' or traj[0].object_id == 'TTT020']

/home/docs/checkouts/readthedocs.org/user_builds/tracktable/conda/v1.5.0/lib/python3.8/site-packages/traitlets/traitlets.py:3030: FutureWarning: --rc={'figure.dpi': 96} for dict-traits is deprecated in traitlets 5.0. You can pass --rc <key=value> ... multiple times to add items to a dict.
  warn(
INFO:tracktable.analysis.assemble_trajectoriesAssembleTrajectoryFromPoints:New trajectories will be declared after a separation of None distance units between two points or a time lapse of at least 0:30:00 (hours, minutes, seconds).
INFO:tracktable.analysis.assemble_trajectoriesAssembleTrajectoryFromPoints:Trajectories with fewer than 3 points will be discarded.
INFO:tracktable.analysis.assemble_trajectoriesAssembleTrajectoryFromPoints:Done assembling trajectories. 29 trajectories produced and 0 discarded for having fewer than 3 points.

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from tracktable.render.render_trajectories import render_trajectories

# The only required parameter is a list of trajectories.  It's that simple.
render_trajectories(few_trajs)
# The default rendering assigns each object ID a hue which transitions from dark to light as the trajectory
# progresses.  In addition, by default, a white dot shows the point with the latest timestamp in the trajectory.
# Hovering over a trajectory reveals its object_id, and clicking on a trajectory give the object_id and start and stop
# time for the entire trajectory

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# Can use a different backend (default in Jupyter Notebooks is folium, default otherwise is cartopy)
# render_trajectories(few_trajs, backend='folium')
render_trajectories(few_trajs, backend='cartopy')

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<GeoAxesSubplot:>
../_images/examples_Interactive_Trajectory_Rendering_5_1.svg
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# Can render a single trajectory
render_trajectories(trajs[3])

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# Can render a set of trajectories separately (each with their own map)
render_trajectories_separate(few_trajs)
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# You can change map tiles
render_trajectories(trajs[3], tiles='CartoDBPositron')
# Options include:
# OpenStreetMaps
# StamenTerrain
# StamenToner
# StamenWatercolor
# CartoDBPositron
# CartoDBDark_Matter

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# You can specify a map tile server by URL # must include attribution(attr) string
render_trajectories(trajs[3], tiles='http://server.arcgisonline.com/ArcGIS/rest/services/NatGeo_World_Map/MapServer/tile/{z}/{y}/{x}', attr='ESRI')

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# Can specify a bounding box (default extent shows all input trajectories)
# format of map_bbox is  [minLon, minLat, maxLon, maxLat]
render_trajectories(few_trajs, map_bbox=[-108.081, 39.3078, -104.811, 41.27])

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# Can specify specific object_ids to render as a string...
render_trajectories(trajs, obj_ids="VVV022")

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# ... or a list of strings
render_trajectories(trajs, obj_ids=["JJJ010", "LLL012"])

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# Can specify a solid color for all trajectories ...
render_trajectories([trajs[0], trajs[12]], line_color = 'red')
#Other color strings include: β€˜red’, β€˜blue’, β€˜green’, β€˜purple’, β€˜orange’, β€˜darkred’,’lightred’, β€˜beige’,
#β€˜darkblue’, β€˜darkgreen’, β€˜cadetblue’, β€˜darkpurple’, β€˜white’, β€˜pink’, β€˜lightblue’, β€˜lightgreen’, β€˜gray’,
#β€˜black’, β€˜lightgray’

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# ... or a list of colors.  Note you can use hex string notation for the colors as well.
render_trajectories([trajs[0], trajs[12]], line_color = ['red', '#0000FF'])
# Hex string notation is of the format #RRGGBBAA,
# Red Green Blue values from 0 to 255 as 2 hex digits each,
# and an OPTIONAL alpha (opacity) value with same range and format.

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# The trajectories can be colored using a colormap ...
render_trajectories([trajs[0], trajs[12]], color_map = 'BrBG')

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# ... or a list of color maps
render_trajectories([trajs[0], trajs[12]], color_map = ['BrBG', 'plasma'])

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# You can even define your own color maps
import matplotlib.cm
import numpy as np
blues_map = matplotlib.cm.get_cmap('Blues', 256)
newcolors = blues_map(np.linspace(0, 1, 256))
pink = np.array([248/256, 24/256, 148/256, 1])
newcolors[:25, :] = pink # pink for takeoff (first ~10% of trajectory)
render_trajectories([trajs[0], trajs[12]], color_map = matplotlib.colors.ListedColormap(newcolors))

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# You can specify a hue as a float between 0 and 1 which will be used to create a gradient that transitions form dark
# to light as the trajectory progresses
render_trajectories([trajs[0], trajs[12]], gradient_hue = .5)

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# As with other color options, a list of hues can be used as well.
render_trajectories([trajs[0], trajs[12]], gradient_hue = [.25, .66])

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# Hues can be derived from an rgb color specified as a color name or hex string color
render_trajectories([trajs[0], trajs[12]], gradient_hue = ['#00FF00', 'orange'])

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#You can specify custom scalar mappings.  In this case the color gets lighter as altitude increases.
import matplotlib.colors
def altitude_generator(trajectory):
    #N-1 segments show altitude at beginning point
    return [point.properties['altitude'] for point in trajectory[:-1]]
#Note: be sure to include the generator, and the scale for mapping scalars to the color map.
render_trajectories([trajs[16]], trajectory_scalar_generator = altitude_generator,
                    color_scale = matplotlib.colors.Normalize(vmin=0, vmax=35000))

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#The linewidth of the trajectories can be adjusted.  Default is 2.5 in folium
render_trajectories(trajs[26], linewidth=5)

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# You can also adjust the width of the trajectory by some scalar.  In this case the trajectory starts out very narrow
# and gets wider at each point it passes (as it progresses), but the color remains green throughout.
from tracktable.render.render_trajectories import progress_linewidth_generator
render_trajectories(trajs[28], line_color='green', trajectory_linewidth_generator=progress_linewidth_generator)

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# You can also show the sample points along the trajectory. By default the points are colored consistent with line
# segments
render_trajectories(trajs[15], show_points=True)
# Hovering over a point gives the timestamp of that point and by default, clicking on a point reveals the object_id,
# timestamp, Latitude, and Longitude of that point.

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# You can specify any set of properties to view when clicking on a point:
render_trajectories(trajs[15], show_points=True, point_popup_properties = ['altitude', 'heading', 'speed'])
# Clicking on a point now additionaly reveals the altitude, heading, and speed.

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# The colors of all points can be specified
render_trajectories(trajs[15], point_color='red', show_points=True)

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# The color and radius of the dot at the close of the trajectory can be changed
render_trajectories(trajs[24], dot_color='red', dot_size=5)

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# To not show the dot at the close of the trajectory:
render_trajectories(trajs[24], show_dot=False)

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# In folium the distance geometry calculations can be shown.  Default depth is 4
render_trajectories(trajs[25], show_distance_geometry=True, distance_geometry_depth=4)
#red are level 1 lines
#blue are leve 2 lines
#yellow are level 3 lines
# and purple are level 4 lines
#hovering over the lines gives the normalized length of each line

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# Save trajectories (as html file) to a default unique filename (trajs+datetimestamp+.html)
render_trajectories(trajs[20:22], save=True)

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# Save trajectories (as html file) to a given filename/path and don't render to the notebook
# (; at end of line (or assigning output to a variable) causes map to not be rendered in the notebook)
render_trajectories(trajs[23], save=True, filename='tmp_my_tracks.html')

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# If render_trajectories is not the last function in a cell the map won't render
render_trajectories(trajs[24])
print('A nice trajectory')

A nice trajectory

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# To overcome this, set show=True to force rendering to the notebook
render_trajectories(trajs[24], show=True)
print('A nice trajectory')
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A nice trajectory

In [32]:

# or, assign the returned map to a variable, and list the variable name alone on the last line of the cell
m=render_trajectories(trajs[24])
print('A nice trajectory')
m

A nice trajectory

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# Can use cartopy as backend and save imgaes
render_trajectories(trajs[23], backend='cartopy', save=True, filename='tmp_my_tracks.png')

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<GeoAxesSubplot:>
../_images/examples_Interactive_Trajectory_Rendering_35_1.svg
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