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_compute_bounding_box_after_pickle():
error_count = 0
albuquerque = TerrestrialTrajectoryPoint(-106.6504, 35.0844)
albuquerque.timestamp = datetime.datetime(year=2020,
month=1,
day=1,
hour=12)
san_francisco = TerrestrialTrajectoryPoint(-122.4194, 37.7749)
san_francisco.timestamp = albuquerque.timestamp + datetime.timedelta(
hours=3)
tokyo = TerrestrialTrajectoryPoint(-221.6917, 35.6895)
tokyo.timestamp = albuquerque.timestamp + datetime.timedelta(hours=12)
trajectory_generator = TrajectoryPointSource()
trajectory_generator.start_point = albuquerque
trajectory_generator.end_point = tokyo
trajectory_generator.num_points = 20
print("DEBUG: TerrestrialTrajectory: {}".format(TerrestrialTrajectory))
albuquerque_to_tokyo = TerrestrialTrajectory.from_position_list(
list(trajectory_generator.points()))
expected_min_corner = tracktable.domain.domain_class_for_object(
albuquerque, 'BasePoint')()
expected_max_corner = tracktable.domain.domain_class_for_object(
albuquerque, 'BasePoint')()
expected_min_corner[0] = min(albuquerque[0], tokyo[0])
expected_min_corner[1] = min(albuquerque[1], tokyo[1])
expected_max_corner[0] = max(albuquerque[0], tokyo[0])
expected_max_corner[1] = max(albuquerque[1], tokyo[1])
bbox_before_pickling = geomath.compute_bounding_box(albuquerque_to_tokyo)
store = io.BytesIO()
pickle.dump(albuquerque_to_tokyo, store)
store.seek(0)
restored_trajectory = pickle.load(store)
bbox_after_pickling = geomath.compute_bounding_box(restored_trajectory)
print("Bounding box before pickling: ({} {}) - ({} {})".format(
bbox_before_pickling.min_corner[0], bbox_before_pickling.min_corner[1],
bbox_before_pickling.max_corner[0],
bbox_before_pickling.max_corner[1]))
print("Bounding box after pickling: ({} {}) - ({} {})".format(
bbox_after_pickling.min_corner[0], bbox_after_pickling.min_corner[1],
bbox_after_pickling.max_corner[0], bbox_after_pickling.max_corner[1]))
bbox_min_delta = (bbox_after_pickling.min_corner[0] -
bbox_before_pickling.min_corner[0],
bbox_after_pickling.min_corner[1] -
bbox_before_pickling.min_corner[1])
bbox_max_delta = (bbox_after_pickling.max_corner[0] -
bbox_before_pickling.max_corner[0],
bbox_after_pickling.max_corner[1] -
bbox_before_pickling.max_corner[1])
if (math.fabs(bbox_min_delta[0]) > 0.01
or math.fabs(bbox_min_delta[1]) > 0.01
or math.fabs(bbox_max_delta[0]) > 0.01
or math.fabs(bbox_max_delta[1]) > 0.01):
print(
("ERROR: Expected delta between bounding box before and after "
"pickling to be zero. Delta for minimum corner is {}. "
"Delta for maximum corner is {}.").format(bbox_min_delta,
bbox_max_delta))
error_count += 1
return error_count
def test_compute_bounding_box():
error_count = 0
bbox_type = TerrestrialTrajectoryPoint.domain_classes['BoundingBox']
expected_min_corner = TerrestrialTrajectoryPoint.domain_classes[
'BasePoint']()
expected_max_corner = TerrestrialTrajectoryPoint.domain_classes[
'BasePoint']()
albuquerque = TerrestrialTrajectoryPoint(-106.6504, 35.0844)
san_francisco = TerrestrialTrajectoryPoint(-122.4194, 37.7749)
tokyo = TerrestrialTrajectoryPoint(-221.6917, 35.6895)
small_traj = TrajectoryPointSource()
small_traj.start_point = albuquerque
small_traj.end_point = san_francisco
small_traj.num_points = 2
long_traj = TrajectoryPointSource()
long_traj.start_point = tokyo
long_traj.end_point = san_francisco
long_traj.num_points = 2
#Test smallish basic bounding box
expected_min_corner[0] = san_francisco[0]
expected_min_corner[1] = albuquerque[1]
expected_max_corner[0] = albuquerque[0]
expected_max_corner[1] = san_francisco[1]
expected = bbox_type(expected_min_corner, expected_max_corner)
map_bbox = geomath.compute_bounding_box(small_traj.points())
error_count += verify_result(expected, map_bbox, "Basic small box")
#Test larger basic bounding box
expected_min_corner[0] = tokyo[0]
expected_min_corner[1] = tokyo[1]
expected_max_corner[0] = san_francisco[0]
expected_max_corner[1] = san_francisco[1]
expected = bbox_type(expected_min_corner, expected_max_corner)
map_bbox = geomath.compute_bounding_box(long_traj.points())
error_count += verify_result(expected, map_bbox, "Basic large box")
#Test smallish bounding box with buffer
lon_buffer = .2
lat_buffer = .5
expected_min_corner[0] = san_francisco[0] - (
(albuquerque[0] - san_francisco[0]) * lon_buffer)
expected_min_corner[1] = albuquerque[1] - (
(san_francisco[1] - albuquerque[1]) * lat_buffer)
expected_max_corner[0] = albuquerque[0] + (
(albuquerque[0] - san_francisco[0]) * lon_buffer)
expected_max_corner[1] = san_francisco[1] + (
(san_francisco[1] - albuquerque[1]) * lat_buffer)
expected = bbox_type(expected_min_corner, expected_max_corner)
map_bbox = geomath.compute_bounding_box(small_traj.points(),
(lon_buffer, lat_buffer))
error_count += verify_result(expected, map_bbox, "Buffered small box")
#Test larger basic bounding box with buffer
lon_buffer = .2
lat_buffer = .1
expected_min_corner[0] = tokyo[0] - (
(san_francisco[0] - tokyo[0]) * lon_buffer)
expected_min_corner[1] = tokyo[1] - (
(san_francisco[1] - tokyo[1]) * lat_buffer)
expected_max_corner[0] = san_francisco[0] + (
(san_francisco[0] - tokyo[0]) * lon_buffer)
expected_max_corner[1] = san_francisco[1] + (
(san_francisco[1] - tokyo[1]) * lat_buffer)
expected = bbox_type(expected_min_corner, expected_max_corner)
map_bbox = geomath.compute_bounding_box(long_traj.points(),
(lon_buffer, lat_buffer))
error_count += verify_result(expected, map_bbox, "Buffered large box")
#Test cartesian based boxes
c_bbox_type = Cartesian2dTrajectoryPoint.domain_classes['BoundingBox']
c_expected_min_corner = Cartesian2dTrajectoryPoint.domain_classes[
'BasePoint']()
c_expected_max_corner = Cartesian2dTrajectoryPoint.domain_classes[
'BasePoint']()
point0 = Cartesian2dTrajectoryPoint(0, 0)
point1 = Cartesian2dTrajectoryPoint(1, 1)
point2 = Cartesian2dTrajectoryPoint(2, 2)
c_expected_min_corner = Cartesian2dTrajectoryPoint(0, 0)
c_expected_max_corner = Cartesian2dTrajectoryPoint(2, 2)
expected = c_bbox_type(c_expected_min_corner, c_expected_max_corner)
map_bbox = geomath.compute_bounding_box([point0, point1, point2])
error_count += verify_result(expected, map_bbox, "Basic cartesian box")
#Test cartesian box with buffer
x_buffer = .5
y_buffer = 1.
c_expected_min_corner[0] = point0[0] - ((point2[0] - point0[0]) * x_buffer)
c_expected_min_corner[1] = point0[1] - ((point2[1] - point0[1]) * y_buffer)
c_expected_max_corner[0] = point2[0] + ((point2[0] - point0[0]) * x_buffer)
c_expected_max_corner[1] = point2[1] + ((point2[1] - point0[1]) * y_buffer)
expected = c_bbox_type(c_expected_min_corner, c_expected_max_corner)
map_bbox = geomath.compute_bounding_box([point0, point1, point2],
(x_buffer, y_buffer))
error_count += verify_result(expected, map_bbox, "Buffered cartesian box")
# Test error conditions
map_bbox = geomath.compute_bounding_box([])
if (map_bbox != None):
sys.stderr.write('ERROR: Empty point sequence did not return None')
error_count += 1
map_bbox = geomath.compute_bounding_box(long_traj.points(), (1.0, ))
if (map_bbox != None):
sys.stderr.write('ERROR: Buffer tuple length of 1 did not return None')
error_count += 1
map_bbox = geomath.compute_bounding_box(long_traj.points(),
(1.0, 2.0, 3.0))
if (map_bbox != None):
sys.stderr.write('ERROR: Buffer tuple length of 3 did not return None')
error_count += 1
return error_count
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():
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():
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
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