def test_europe_map(ground_truth_dir,
test_output_dir,
image_filename='EuropeMap.png'):
pyplot.figure(figsize=(8, 6))
(mymap, artists) = mapmaker(domain='terrestrial',
map_name='region:europe',
lonlat_linewidth=0.25,
lonlat_color='#4040A0',
state_border_color='#806060',
country_border_color='#408040',
draw_coastlines=True,
draw_countries=True,
draw_states=True,
draw_lonlat=True,
fill_land=True,
fill_water=True,
coastline_linewidth=2,
coastline_color='#B0B0B0',
land_fill_color='#404040',
water_fill_color='#000030')
pyplot.savefig(os.path.join(test_output_dir, image_filename), dpi=150)
pyplot.close()
return test_utilities.compare_image_to_ground_truth(
image_filename, ground_truth_dir, test_output_dir)
def main():
print("command line:\n{}\n".format(' '.join(sys.argv)))
args = parse_args()
dpi = args.dpi
image_resolution = args.resolution
figure_dimensions = [
float(image_resolution[0]) / dpi,
float(image_resolution[1]) / dpi
]
print("STATUS: Initializing canvas")
figure = pyplot.figure(figsize=figure_dimensions)
axes = figure.add_axes([0, 0, 1, 1], frameon=False)
axes.set_frame_on(False)
print("STATUS: Initializing trajectory source")
trajectory_source = setup_trajectory_source(args.trajectory_data_file[0],
args)
# This is a little bit ugly but I don't yet know of a better way
# to do it. If we want to automatically compute the bounding box
# of the data points before we render anything we must read all the
# points at least once.
#
# That gives us a choice: read them once and keep them all in
# memory, or make one pass through the file to compute the
# bounding box and then another to read and render the points?
#
# For the moment I elect to read the points and keep them in memory.
if args.domain == 'cartesian2d' and args.map_bbox is None:
print("STATUS: Collecting points to compute bounding box")
all_trajectories = itertools.chain(list(trajectory_source))
data_bbox = geomath.compute_bounding_box(all_points)
point_source = all_points
args.map_bbox = data_bbox
print("STATUS: Creating map projection")
mapmaker_args = argument_groups.extract_arguments("mapmaker", args)
(mymap, map_actors) = mapmaker.mapmaker(**mapmaker_args)
print("STATUS: Reading trajectories and rendering data")
color_scale = matplotlib.colors.Normalize(vmin=0, vmax=1)
render_trajectories(mymap, trajectory_source, args)
print("STATUS: Saving figure to file")
pyplot.savefig(
args.image_file[0],
# facecolor=figure.get_facecolor(),
facecolor='white',
figsize=figure_dimensions,
dpi=dpi,
frameon=False)
pyplot.close()
return 0
def main():
print("command line:\n{}\n".format(' '.join(sys.argv)))
args = parse_args()
dpi = args.dpi
image_resolution = args.resolution
figure_dimensions = [ float(image_resolution[0]) / dpi, float(image_resolution[1]) / dpi ]
print("STATUS: Initializing canvas")
figure = pyplot.figure(figsize=figure_dimensions, facecolor='black', edgecolor='black')
axes = figure.add_axes([0, 0, 1, 1], frameon=False, axisbg='black')
axes.set_frame_on(False)
print("STATUS: Initializing point source")
point_source = setup_point_source(args.point_data_file[0], args)
# This is a little bit ugly but I don't yet know of a better way
# to do it. If we want to automatically compute the bounding box
# of the data points before we render anything we must read all the
# points at least once.
#
# That gives us a choice: read them once and keep them all in
# memory, or make one pass through the file to compute the
# bounding box and then another to read and render the points?
#
# For the moment I elect to read the points and keep them in memory.
if args.domain == 'cartesian2d' and args.map_bbox is None:
print("STATUS: Collecting points to compute bounding box")
all_points = [ point for point in point_source ] # list(point_source)
data_bbox = geomath.compute_bounding_box(all_points)
point_source = all_points
args.map_bbox = data_bbox
print("STATUS: Creating map projection")
mapmaker_args = argument_groups.extract_arguments("mapmaker", args)
(mymap, map_actors) = mapmaker.mapmaker(**mapmaker_args)
print("STATUS: Initializing trajectory source")
trajectory_source = setup_trajectory_source(point_source, args)
print("STATUS: Reading points, assembling trajectories and rendering data")
color_scale = matplotlib.colors.Normalize(vmin=0, vmax=1)
render_trajectories(mymap,
trajectory_source,
args)
print("STATUS: Saving figure to file")
pyplot.savefig(args.image_file[0],
facecolor=figure.get_facecolor(),
figsize=figure_dimensions,
dpi=dpi,
frameon=False)
pyplot.close()
return 0
def test_conus_map(ground_truth_dir,
test_output_dir,
image_filename='ConusMap.png'):
pyplot.figure(figsize=(8, 6))
(mymap, artists) = mapmaker(map_name='conus', domain='terrestrial')
pyplot.savefig(os.path.join(test_output_dir, image_filename), dpi=150)
pyplot.close()
return test_utilities.compare_image_to_ground_truth(
image_filename, ground_truth_dir, test_output_dir)
def test_conus_map(ground_truth_dir,
test_output_dir,
image_filename='ConusMap.png'):
pyplot.figure(figsize=(8, 6))
(mymap, artists) = mapmaker(map_name='conus', domain='terrestrial')
pyplot.savefig(os.path.join(test_output_dir, image_filename),
dpi=150)
pyplot.close()
return test_utilities.compare_image_to_ground_truth(image_filename,
ground_truth_dir,
test_output_dir)
def test_europe_map(ground_truth_dir,
test_output_dir,
image_filename='EuropeMap.png'):
pyplot.figure(figsize=(8, 6))
(mymap, artists) = mapmaker(domain='terrestrial',
map_name='europe',
lonlat_linewidth=0.25,
lonlat_color='#6060FF')
pyplot.savefig(os.path.join(test_output_dir, image_filename), dpi=150)
pyplot.close()
return test_utilities.compare_image_to_ground_truth(
image_filename, ground_truth_dir, test_output_dir)
def test_north_america_map(ground_truth_dir,
test_output_dir,
image_filename='NorthAmericaMap.png'):
pyplot.figure(figsize=(8, 6))
(mymap, artists) = mapmaker(domain='terrestrial',
map_name='region:north_america',
state_color='#FF8080',
country_color='#80FF80',
lonlat_color='#0000FF',
lonlat_linewidth=0.5)
pyplot.savefig(os.path.join(test_output_dir, image_filename), dpi=150)
pyplot.close()
return test_utilities.compare_image_to_ground_truth(
image_filename, ground_truth_dir, test_output_dir)
def test_europe_map(ground_truth_dir,
test_output_dir,
image_filename='EuropeMap.png'):
pyplot.figure(figsize=(8, 6))
(mymap, artists) = mapmaker(domain='terrestrial',
map_name='europe',
lonlat_linewidth=0.25,
lonlat_color='#6060FF')
pyplot.savefig(os.path.join(test_output_dir, image_filename),
dpi=150)
pyplot.close()
return test_utilities.compare_image_to_ground_truth(image_filename,
ground_truth_dir,
test_output_dir)
def test_map_for_airport(ground_truth_dir,
test_output_dir,
image_filename='MapForAirport.png'):
pyplot.figure(figsize=(8, 6))
(mymap, artists) = mapmaker(map_name='airport:ORD',
domain='terrestrial',
region_size=(400, 400),
land_color='#505050',
draw_coastlines=True)
pyplot.savefig(os.path.join(test_output_dir, image_filename),
dpi=150)
pyplot.close()
return test_utilities.compare_image_to_ground_truth(image_filename,
ground_truth_dir,
test_output_dir)
def test_north_america_map(ground_truth_dir,
test_output_dir,
image_filename='NorthAmericaMap.png'):
pyplot.figure(figsize=(8, 6))
(mymap, artists) = mapmaker(domain='terrestrial',
map_name='north_america',
state_color='#FF8080',
country_color='#80FF80',
lonlat_color='#0000FF',
lonlat_linewidth=0.5)
pyplot.savefig(os.path.join(test_output_dir, image_filename),
dpi=150)
pyplot.close()
return test_utilities.compare_image_to_ground_truth(image_filename,
ground_truth_dir,
test_output_dir)
def test_florida_map(ground_truth_dir,
test_output_dir,
image_filename='CustomBboxMap.png'):
pyplot.figure(figsize=(8, 8))
# This is approximately Florida
bounding_box = terrestrial.BoundingBox((-88, 24), (-79.5, 31))
(mymap, artists) = mapmaker(domain='terrestrial',
map_name='custom',
map_bbox=bounding_box,
state_color='#FF8080',
country_color='#80FF80',
lonlat_color='#0000FF',
lonlat_linewidth=0.5)
pyplot.savefig(os.path.join(test_output_dir, image_filename), dpi=150)
pyplot.close()
return test_utilities.compare_image_to_ground_truth(
image_filename, ground_truth_dir, test_output_dir)
def test_geographic_histogram(outfilename):
min_corner = BasePoint(-180, -23.4378)
max_corner = BasePoint(180, 23.4378)
bbox = BoundingBox(min_corner, max_corner)
num_points = 100000
points_in_tropics = random_box_uniform(min_corner, max_corner, num_points)
pyplot.figure()
pyplot.subplot(111, aspect='equal')
mymap = mapmaker(domain='terrestrial', map_name='region:world')
histogram2d.render_histogram(map_projection=mymap,
point_source=points_in_tropics,
bounding_box=bbox,
bin_size=0.5,
colormap='gist_heat',
colorscale=matplotlib.colors.LogNorm())
pyplot.savefig(outfilename, figsize=(4, 4), dpi=150)
return True
def example_heatmap_rendering():
#Sample code to render heatmap of points
# In this example, data points are filtered and only a set number of cities are represented.
num_cities = 40
num_points_per_city = 1000
cities = gen_sample.n_largest_cities(num_cities)
all_sources = [ gen_sample.points_near_city(city, num_points_per_city)
for city in cities ]
all_points = list(itertools.chain(*all_sources))
#The type of map, colors, scaling can be customised depending the on the desired look and feel of the finished map.
(figure, axes) = initialize_matplotlib_figure([20, 15])
(mymap, map_actors) = mapmaker.mapmaker(domain='terrestrial',
map_name='region:world')
render_histogram(mymap,
domain='terrestrial',
point_source=all_points,
bin_size=0.25,
color_map='gist_heat',
scale_type='logarithmic')
print("STATUS: Saving figure to file")
savefig_kwargs = {
}
pyplot.savefig('Example_Heatmap_Rendering.png',
figsize=[800,600],
dpi=72,
frameon=False,
facecolor='black'
)
pyplot.close()
return 0
def main():
args = parse_args()
dpi = args.dpi
image_resolution = args.resolution
if image_resolution is None:
image_resolution = [ 800, 600 ]
figure_dimensions = [ float(image_resolution[0]) / dpi, float(image_resolution[1]) / dpi ]
print("STATUS: Initializing image")
figure = pyplot.figure(figsize=figure_dimensions, facecolor='black', edgecolor='black')
axes = figure.add_axes([0, 0, 1, 1], frameon=False, axisbg='black')
axes.set_frame_on(False)
mapmaker_kwargs = argument_groups.extract_arguments("mapmaker", args)
(mymap, base_artists) = mapmaker.mapmaker(**mapmaker_kwargs)
print("STATUS: Initializing point source for main data file")
main_point_source = setup_point_source(args.point_data_file[0], args)
print("STATUS: Initializing trajectory source for main data file")
main_trajectory_source = setup_trajectory_source(main_point_source, args)
print("STATUS: Collecting all trajectories from main source")
all_trajectories = list(main_trajectory_source)
highlight_trajectories = []
if len(args.highlight) > 0:
print("STATUS: Initializing point source for highlight data file")
highlight_point_source = setup_point_source(args.highlight[0], args)
print("STATUS: Initializing trajectory source for highlight data file")
highlight_trajectory_source = setup_trajectory_source(highlight_point_source, args)
print("STATUS: Collecting trajectories to highlight")
highlight_trajectories = list(highlight_trajectory_source)
movie_kwargs = argument_groups.extract_arguments("movie_rendering", args)
movie_writer = example_movie_rendering.setup_encoder(**movie_kwargs)
# This set of arguments will be passed to the savefig() call that
# grabs the latest movie frame. This is the place to put things
# like background color, tight layout and friends.
savefig_kwargs = { 'facecolor': figure.get_facecolor(),
'figsize': figure_dimensions,
'frameon': False }
trajectory_kwargs = argument_groups.extract_arguments("trajectory_rendering", args)
example_movie_rendering.render_trajectory_movie(
movie_writer,
basemap=mymap,
trajectories=all_trajectories,
dpi=args.dpi,
figure=figure,
filename=args.movie_file[0],
num_frames=movie_kwargs['fps'] * movie_kwargs['duration'],
start_time=movie_kwargs['start_time'],
end_time=movie_kwargs['end_time'],
trail_duration = datetime.timedelta(seconds=args.trail_duration),
savefig_kwargs=savefig_kwargs,
axes=axes,
trajectory_rendering_args=trajectory_kwargs,
highlight_trajectories=highlight_trajectories
)
pyplot.close()
return 0
def main():
args = parse_args()
dpi = args.dpi
image_resolution = args.resolution
if image_resolution is None:
image_resolution = [ 800, 600 ]
figure_dimensions = [ float(image_resolution[0]) / dpi, float(image_resolution[1]) / dpi ]
print("STATUS: Initializing canvas")
figure = pyplot.figure(figsize=figure_dimensions, facecolor='black', edgecolor='black')
axes = figure.add_axes([0, 0, 1, 1], frameon=False, axisbg='black')
axes.set_frame_on(False)
print("STATUS: Initializing point source")
point_source = setup_point_source(args.point_data_file[0], args)
# This is a little bit ugly but I don't yet know of a better way
# to do it. If we want to automatically compute the bounding box
# of the data points before we render anything we must read all the
# points at least once.
#
# That gives us a choice: read them once and keep them all in
# memory, or make one pass through the file to compute the
# bounding box and then another to read and render the points?
#
# For the moment I elect to read the points and keep them in memory.
data_bbox = None
if args.domain == 'cartesian2d' and args.map_bbox is None:
print("STATUS: Collecting points to compute bounding box")
all_points = list(point_source)
data_bbox = geomath.compute_bounding_box(all_points)
point_source = all_points
print("STATUS: Initializing map projection")
mapmaker_kwargs = argument_groups.extract_arguments("mapmaker", args)
(mymap, base_artists) = mapmaker.mapmaker(computed_bbox=data_bbox, **mapmaker_kwargs)
print("STATUS: Initializing trajectory source")
trajectory_source = setup_trajectory_source(point_source, args)
print("STATUS: Collecting all trajectories")
print("DEBUG: Trajectory source is a {}".format(type(trajectory_source)))
all_trajectories = list(trajectory_source)
print("STATUS: Done collecting trajectories")
movie_kwargs = argument_groups.extract_arguments("movie_rendering", args)
movie_writer = example_movie_rendering.setup_encoder(**movie_kwargs)
# This set of arguments will be passed to the savefig() call that
# grabs the latest movie frame. This is the place to put things
# like background color, tight layout and friends.
savefig_kwargs = { 'facecolor': figure.get_facecolor(),
'figsize': figure_dimensions,
'frameon': False }
trajectory_kwargs = argument_groups.extract_arguments("trajectory_rendering", args)
example_movie_rendering.render_trajectory_movie(
movie_writer,
map_projection=mymap,
trajectories=all_trajectories,
dpi=args.dpi,
figure=figure,
filename=args.movie_file[0],
num_frames=movie_kwargs['fps'] * movie_kwargs['duration'],
start_time=movie_kwargs['start_time'],
end_time=movie_kwargs['end_time'],
trail_duration = datetime.timedelta(seconds=args.trail_duration),
savefig_kwargs=savefig_kwargs,
axes=axes,
trajectory_rendering_args=trajectory_kwargs
)
pyplot.close()
return 0
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.
# Set up the canvas and map projection
# Set up a bounding box based off of a default
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')
heatmap.render_histogram(mymap,
point_source=points, # Our list of points we created above
bounding_box = bbox, # Bounding box is generated from mymap
bin_size=0.25,
color_map='gist_heat')
def example_trajectory_rendering():
'''Sample code to render trajectories from points
In some cases, you may wish to read in trajectories with certain constraints. For example, we can have trajectories with a minimum number of points. Or we acknowledge that the points in the
trajectory should be within a certain time and/or distance threshold to belong to the same trajectory. The Trajectory Builder does this.'''
# First, We will need data points built into trajectories. Replace the following with your own code to build the trajectories or use the provided example.
fileName = './tracktable/examples/data/SampleTrajectories.csv'
trajectories = example_trajectory_builder(fileName)
# Set up the canvas and map projection
dpi = 160
figure = pyplot.figure(figsize=[20, 15])
axes = figure.add_subplot(1, 1, 1)
#(figure, axes) = initialize_matplotlib_figure([10, 7.5])
(mymap, map_actors) = mapmaker.mapmaker(domain='terrestrial',
map_name='region:conus',
draw_coastlines=True,
draw_countries=True,
draw_states=True,
draw_lonlat=True,
fill_land=True,
fill_water=True,
land_fill_color='#101010',
water_fill_color='#222222',
land_zorder=0,
water_zorder=0,
lonlat_spacing=90,
lonlat_color='#A0A0A0',
lonlat_linewidth=0.2,
lonlat_zorder=2,
coastline_color='#808080',
coastline_linewidth=1,
coastline_zorder=5,
country_border_color='#606060',
country_fill_color='#FFFF80',
country_linewidth=0.5,
country_zorder=5,
state_border_color='#404040',
state_fill_color='none',
state_linewidth=0.3,
state_zorder=2,
draw_largest_cities=50,
draw_cities_larger_than=None,
city_label_size=12,
city_dot_size=2,
city_dot_color='white',
city_label_color='white',
city_zorder=6,
border_resolution='110m',
axes=axes,
map_projection='PlateCarree')
color_scale = matplotlib.colors.Normalize(vmin=0, vmax=1)
render_trajectories(mymap, trajectories, trajectory_linewidth=2)
print("STATUS: Saving figure to file")
pyplot.savefig('./Example_Trajectory_Rendering_CONUS.png',
figsize=[800, 600],
dpi=dpi,
frameon=False,
facecolor='black')
pyplot.close()
def main():
args = parse_args()
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
dpi = args.dpi
image_resolution = args.resolution
figure_dimensions = [
float(image_resolution[0]) / dpi,
float(image_resolution[1]) / dpi
]
logger.info("Initializing image canvas.")
figure = pyplot.figure(figsize=figure_dimensions,
facecolor='black',
edgecolor='black')
axes = figure.add_axes([0, 0, 1, 1], frameon=False, facecolor='black')
axes.set_frame_on(False)
logger.info("Initializing point source.")
point_filename = args.point_data_file[0]
with open(point_filename, 'r') as infile:
point_source = points_from_file(
infile,
args.coordinate0,
args.coordinate1,
comment_character=args.comment_character,
field_delimiter=args.delimiter,
domain=args.domain)
# This is a little bit ugly but I don't yet know of a better way
# to do it. If we want to automatically compute the bounding box
# of the data points before we render anything we must read all the
# points at least once.
#
# That gives us a choice: read them once and keep them all in
# memory, or make one pass through the file to compute the
# bounding box and then another to read and render the points?
#
# For the moment I elect to read the points and keep them in memory.
if args.domain == 'cartesian2d':
if args.map_bbox is None:
logger.info(('Collecting points to compute Cartesian '
'bounding box.'))
point_source = list(point_source)
data_bbox = geomath.compute_bounding_box(point_source)
else:
# The bounding box on the command line is
# [x_min, y_min, x_max, y_max]
data_bbox = cartesian2d.BoundingBox(
(args.map_bbox[0], args.map_bbox[1]),
(args.map_bbox[2], args.map_bbox[3])
)
else:
# Default to taking the histogram bounds from the map extent.
data_bbox = None
if args.map_bbox is not None:
# The bounding box on the command line is
# [x_min, y_min, x_max, y_max]
data_bbox = terrestrial.BoundingBox(
(args.map_bbox[0], args.map_bbox[1]),
(args.map_bbox[2], args.map_bbox[3])
)
logger.info("Creating map projection.")
# There are a lot of keyword arguments for the map -- see
# tracktable.script_helpers.argument_groups.mapmaker --
# so rather than pull them out individually like we did for
# the point reader we extract the whole dict using
# tracktable.script_helpers.argument_groups.extract_arguments().
mapmaker_kwargs = argument_groups.extract_arguments("mapmaker", args)
(mymap, artists) = mapmaker.mapmaker(**mapmaker_kwargs)
logger.info("Rendering histogram.")
render_histogram(mymap,
domain=args.domain,
bounding_box=data_bbox,
point_source=point_source,
bin_size=args.histogram_bin_size,
color_map=args.colormap,
scale_type=args.scale)
# We're done with the points so we exit the with: block where we held
# the input file open.
if args.title is not None:
logger.info("Setting title: {}".format(args.title))
figure.suptitle(args.title, color='white')
logger.info("STATUS: Saving figure to file")
savefig_kwargs = {'figsize': figure_dimensions,
'dpi': dpi,
'facecolor': args.bgcolor
}
pyplot.savefig(args.image_file[0],
**savefig_kwargs)
pyplot.close()
return 0
def main():
args = parse_args()
dpi = args.dpi
image_resolution = args.resolution
if image_resolution is None:
image_resolution = [800, 600]
figure_dimensions = [
float(image_resolution[0]) / dpi,
float(image_resolution[1]) / dpi
]
print("STATUS: Initializing canvas")
figure = pyplot.figure(figsize=figure_dimensions,
facecolor='black',
edgecolor='black')
axes = figure.add_axes([0, 0, 1, 1], frameon=False, axisbg='black')
axes.set_frame_on(False)
print("STATUS: Initializing point source")
point_source = setup_point_source(args.point_data_file[0], args)
# This is a little bit ugly but I don't yet know of a better way
# to do it. If we want to automatically compute the bounding box
# of the data points before we render anything we must read all the
# points at least once.
#
# That gives us a choice: read them once and keep them all in
# memory, or make one pass through the file to compute the
# bounding box and then another to read and render the points?
#
# For the moment I elect to read the points and keep them in memory.
data_bbox = None
if args.domain == 'cartesian2d' and args.map_bbox is None:
print("STATUS: Collecting points to compute bounding box")
all_points = list(point_source)
data_bbox = geomath.compute_bounding_box(all_points)
point_source = all_points
print("STATUS: Initializing map projection")
mapmaker_kwargs = argument_groups.extract_arguments("mapmaker", args)
(mymap, base_artists) = mapmaker.mapmaker(computed_bbox=data_bbox,
**mapmaker_kwargs)
print("STATUS: Initializing trajectory source")
trajectory_source = setup_trajectory_source(point_source, args)
print("STATUS: Collecting all trajectories")
print("DEBUG: Trajectory source is a {}".format(type(trajectory_source)))
all_trajectories = list(trajectory_source)
print("STATUS: Done collecting trajectories")
movie_kwargs = argument_groups.extract_arguments("movie_rendering", args)
movie_writer = example_movie_rendering.setup_encoder(**movie_kwargs)
# This set of arguments will be passed to the savefig() call that
# grabs the latest movie frame. This is the place to put things
# like background color, tight layout and friends.
savefig_kwargs = {
'facecolor': figure.get_facecolor(),
'figsize': figure_dimensions,
'frameon': False
}
trajectory_kwargs = argument_groups.extract_arguments(
"trajectory_rendering", args)
example_movie_rendering.render_trajectory_movie(
movie_writer,
map_projection=mymap,
trajectories=all_trajectories,
dpi=args.dpi,
figure=figure,
filename=args.movie_file[0],
num_frames=movie_kwargs['fps'] * movie_kwargs['duration'],
start_time=movie_kwargs['start_time'],
end_time=movie_kwargs['end_time'],
trail_duration=datetime.timedelta(seconds=args.trail_duration),
savefig_kwargs=savefig_kwargs,
axes=axes,
trajectory_rendering_args=trajectory_kwargs)
pyplot.close()
return 0
def main():
logger = logging.getLogger(__name__)
args = parse_args()
mapmaker_kwargs = argument_groups.extract_arguments("mapmaker", args)
# Some of the argument names for trajectory rendering are out of
# sync with their command-line parameter names. We extract those
# arguments manually at the render_annotated_trajectories
# call instead of using extract_arguments("trajectory_rendering")
# here.
# Step 1: Load all the trajectories into memory.
point_filename = args.point_data_file[0]
field_assignments = extract_field_assignments(vars(args))
with open(point_filename, 'r') as infile:
logger.info('Loading points and building trajectories.')
trajectories = list(
trajectories_from_point_file(
infile,
object_id_column=args.object_id_column,
timestamp_column=args.timestamp_column,
coordinate0_column=args.coordinate0,
coordinate1_column=args.coordinate1,
string_fields=field_assignments['string'],
real_fields=field_assignments['real'],
time_fields=field_assignments['time'],
comment_character=args.comment_character,
field_delimiter=args.delimiter,
separation_distance=args.separation_distance,
separation_time=datetime.timedelta(
minutes=args.separation_time),
minimum_length=args.minimum_length,
domain=args.domain))
# Add the 'progress' annotation to all of our trajectories so
# we have some way to color them
trajectories = [annotations.progress(t) for t in trajectories]
# We can compute the bounding box for Cartesian data automatically.
# We don't need to do so for terrestrial data because the map will
# default to the whole world.
if (args.domain == 'cartesian2d'
and (args.map_bbox is None or len(args.map_bbox) == 0)):
args.map_bbox = geomath.compute_bounding_box(
itertools.chain(*trajectories))
#
# Step 3: Set up the map.
#
# There are a lot of keyword arguments for the map -- see
# tracktable.script_helpers.argument_groups.mapmaker --
# so rather than pull them out individually like we did for
# the point reader we extract the whole dict using
# tracktable.script_helpers.argument_groups.extract_arguments().
logger.info('Initializing map canvas for rendering.')
(figure, axes) = initialize_canvas(args.resolution, args.dpi)
(mymap, map_artists) = mapmaker.mapmaker(**mapmaker_kwargs)
if args.trajectory_linewidth == 'taper':
linewidth_style = 'taper'
linewidth = args.trajectory_initial_linewidth
final_linewidth = args.trajectory_final_linewidth
else:
linewidth_style = 'constant'
linewidth = args.trajectory_linewidth
final_linewidth = linewidth
# Eventually we will be able to use argument_groups.extract_arguments() for
# this, but right now it's broken. Not all of the parameters in the
# trajectory rendering argument group are supported and some of the names
# have changed.
#
trajectory_rendering_kwargs = {
'decorate_head': args.decorate_trajectory_head,
'head_color': args.trajectory_head_color,
'head_size': args.trajectory_head_dot_size,
'color_map': args.trajectory_colormap,
'scalar': args.trajectory_color,
'scalar_min': args.scalar_min,
'scalar_max': args.scalar_max,
'linewidth_style': linewidth_style,
'linewidth': linewidth,
'final_linewidth': final_linewidth
}
render_annotated_trajectories(trajectories, mymap,
**trajectory_rendering_kwargs)
print("STATUS: Saving figure to file")
pyplot.savefig(args.image_file[0],
facecolor=figure.get_facecolor(),
figsize=compute_figure_dimensions(args.resolution,
args.dpi),
dpi=args.dpi)
pyplot.close()
return 0
def main():
args = parse_args()
dpi = args.dpi
image_resolution = args.resolution
figure_dimensions = [
float(image_resolution[0]) / dpi,
float(image_resolution[1]) / dpi
]
print("STATUS: Initializing image")
figure = pyplot.figure(figsize=figure_dimensions,
facecolor='black',
edgecolor='black')
axes = figure.add_axes([0, 0, 1, 1], frameon=False, axisbg='black')
axes.set_frame_on(False)
print("STATUS: Initializing point source")
point_source = setup_point_source(args.point_data_file[0], args)
# This is a little bit ugly but I don't yet know of a better way
# to do it. If we want to automatically compute the bounding box
# of the data points before we render anything we must read all the
# points at least once.
#
# That gives us a choice: read them once and keep them all in
# memory, or make one pass through the file to compute the
# bounding box and then another to read and render the points?
#
# For the moment I elect to read the points and keep them in memory.
if args.domain == 'cartesian2d' and args.map_bbox is None:
print("STATUS: Collecting points to compute bounding box")
all_points = [point for point in point_source] # list(point_source)
data_bbox = geomath.compute_bounding_box(all_points)
point_source = all_points
args.map_bbox = data_bbox
print("STATUS: Creating map projection")
mapmaker_kwargs = argument_groups.extract_arguments("mapmaker", args)
(mymap, artists) = mapmaker.mapmaker(**mapmaker_kwargs)
print("STATUS: Rendering histogram")
render_histogram(mymap,
domain=args.domain,
bounding_box=args.map_bbox,
point_source=point_source,
bin_size=args.histogram_bin_size,
color_map=args.colormap,
scale_type=args.scale)
if args.title is not None:
print("Setting title: {}".format(args.title))
figure.suptitle(args.title, color='white')
print("STATUS: Saving figure to file")
savefig_kwargs = {
'figsize': figure_dimensions,
'dpi': dpi,
'frameon': False,
'facecolor': args.bgcolor
}
pyplot.savefig(args.image_file[0], **savefig_kwargs)
pyplot.close()
return 0
def main():
args = parse_args()
dpi = args.dpi
image_resolution = args.resolution
figure_dimensions = [
float(image_resolution[0]) / dpi,
float(image_resolution[1]) / dpi
]
print("STATUS: Initializing image")
figure = pyplot.figure(figsize=figure_dimensions, facecolor='black', edgecolor='black')
axes = figure.add_axes([0, 0, 1, 1], frameon=False, axisbg='black')
axes.set_frame_on(False)
print("STATUS: Initializing point source")
point_source = setup_point_source(args.point_data_file[0], args)
# This is a little bit ugly but I don't yet know of a better way
# to do it. If we want to automatically compute the bounding box
# of the data points before we render anything we must read all the
# points at least once.
#
# That gives us a choice: read them once and keep them all in
# memory, or make one pass through the file to compute the
# bounding box and then another to read and render the points?
#
# For the moment I elect to read the points and keep them in memory.
if args.domain == 'cartesian2d' and args.map_bbox is None:
print("STATUS: Collecting points to compute bounding box")
all_points = [ point for point in point_source ] # list(point_source)
data_bbox = geomath.compute_bounding_box(all_points)
point_source = all_points
args.map_bbox = data_bbox
print("STATUS: Creating map projection")
mapmaker_kwargs = argument_groups.extract_arguments("mapmaker", args)
(mymap, artists) = mapmaker.mapmaker(**mapmaker_kwargs)
print("STATUS: Rendering histogram")
render_histogram(mymap,
domain=args.domain,
bounding_box=args.map_bbox,
point_source=point_source,
bin_size=args.histogram_bin_size,
color_map=args.colormap,
scale_type=args.scale)
if args.title is not None:
print("Setting title: {}".format(args.title))
figure.suptitle(args.title, color='white')
print("STATUS: Saving figure to file")
savefig_kwargs = { 'figsize': figure_dimensions,
'dpi': dpi,
'frameon': False,
'facecolor': args.bgcolor
}
pyplot.savefig(args.image_file[0],
**savefig_kwargs)
pyplot.close()
return 0
def main():
logger = logging.getLogger(__name__)
# Step 0: Parse the command line arguments and grab sets we will need
# later.
args = parse_args()
mapmaker_kwargs = argument_groups.extract_arguments("mapmaker", args)
movie_kwargs = argument_groups.extract_arguments("movie_rendering", args)
# Some of the keyword arguments for trajectory rendering have been renamed.
# For now, we'll extract them by hand farther down in this function.
# trajectory_rendering_kwargs = argument_groups.extract_arguments(
# "trajectory_rendering", args)
# Step 1: Load all the trajectories into memory.
point_filename = args.point_data_file[0]
field_assignments = extract_field_assignments(vars(args))
with open(point_filename, 'r') as infile:
logger.info('Loading points and building trajectories.')
trajectories = list(
trajectories_from_point_file(
infile,
object_id_column=args.object_id_column,
timestamp_column=args.timestamp_column,
coordinate0_column=args.coordinate0,
coordinate1_column=args.coordinate1,
string_fields=field_assignments['string'],
real_fields=field_assignments['real'],
time_fields=field_assignments['time'],
comment_character=args.comment_character,
field_delimiter=args.delimiter,
separation_distance=args.separation_distance,
separation_time=datetime.timedelta(minutes=args.separation_time),
minimum_length=args.minimum_length,
domain=args.domain)
)
# Add the 'progress' annotation to all of our trajectories so
# we have some way to color them
trajectories = [annotations.progress(t) for t in trajectories]
# We can compute the bounding box for Cartesian data automatically.
# We don't need to do so for terrestrial data because the map will
# default to the whole world.
if (args.domain == 'cartesian2d' and
(args.map_bbox is None or
len(args.map_bbox) == 0)):
args.map_bbox = geomath.compute_bounding_box(
itertools.chain(*trajectories)
)
#
# Step 3: Set up the map.
#
# There are a lot of keyword arguments for the map -- see
# tracktable.script_helpers.argument_groups.mapmaker --
# so rather than pull them out individually like we did for
# the point reader we extract the whole dict using
# tracktable.script_helpers.argument_groups.extract_arguments().
logger.info('Initializing map canvas for rendering.')
(figure, axes) = initialize_canvas(args.resolution,
args.dpi)
(mymap, map_artists) = mapmaker.mapmaker(**mapmaker_kwargs)
#
# Step 4: Set up the video encoder.
#
#
movie_kwargs = argument_groups.extract_arguments("movie_rendering", args)
movie_writer = setup_encoder(**movie_kwargs)
# This set of arguments will be passed to the savefig() call that
# grabs the latest movie frame. This is the place to put things
# like background color, tight layout and friends.
savefig_kwargs = {'facecolor': figure.get_facecolor(),
'figsize': compute_figure_dimensions(args.resolution,
args.dpi),
'frameon': False}
#
# Lights! Camera! Action!
#
if args.trajectory_linewidth == 'taper':
linewidth_style = 'taper'
linewidth = args.trajectory_initial_linewidth
final_linewidth = args.trajectory_final_linewidth
else:
linewidth_style = 'constant'
linewidth = args.trajectory_linewidth
final_linewidth = linewidth
# Eventually we will be able to use argument_groups.extract_arguments() for
# this, but right now it's broken. Not all of the parameters in the
# trajectory rendering argument group are supported and some of the names
# have changed.
#
trajectory_rendering_kwargs = {
'decorate_head': args.decorate_trajectory_head,
'head_color': args.trajectory_head_color,
'head_size': args.trajectory_head_dot_size,
'color_map': args.trajectory_colormap,
'scalar': args.trajectory_color,
'scalar_min': args.scalar_min,
'scalar_max': args.scalar_max,
'linewidth_style': linewidth_style,
'linewidth': linewidth,
'final_linewidth': final_linewidth
}
render_trajectory_movie(
movie_writer,
axes=mymap,
trajectories=trajectories,
dpi=args.dpi,
figure=figure,
filename=args.movie_file[0],
num_frames=movie_kwargs['fps'] * movie_kwargs['duration'],
start_time=movie_kwargs['start_time'],
end_time=movie_kwargs['end_time'],
trail_duration=datetime.timedelta(seconds=args.trail_duration),
savefig_kwargs=savefig_kwargs,
trajectory_rendering_kwargs=trajectory_rendering_kwargs,
domain=args.domain
)
pyplot.close()
logger.info("Movie render complete. File saved to {}".format(args.movie_file[0]))
return 0
