def main():
parser = argparse.ArgumentParser()
parser.add_argument('filename', nargs='+',
help='name of flydra .hdf5 file',
)
parser.add_argument("--stim-xml",
type=str,
default=None,
help="name of XML file with stimulus info",
required=True,
)
parser.add_argument("--align-json",
type=str,
default=None,
help="previously exported json file containing s,R,T",
)
parser.add_argument("--radius", type=float,
help="radius of line (in meters)",
default=0.002,
metavar="RADIUS")
parser.add_argument("--obj-only", type=str)
parser.add_argument("--obj-filelist", type=str,
help="use object ids from list in text file",
)
parser.add_argument(
"-r", "--reconstructor", dest="reconstructor_path",
type=str,
help=("calibration/reconstructor path (if not specified, "
"defaults to FILE)"))
args = parser.parse_args()
options = args # optparse OptionParser backwards compatibility
reconstructor_path = args.reconstructor_path
fps = None
ca = core_analysis.get_global_CachingAnalyzer()
by_file = {}
for h5_filename in args.filename:
assert(tables.is_hdf5_file(h5_filename))
obj_ids, use_obj_ids, is_mat_file, data_file, extra = ca.initial_file_load(
h5_filename)
this_fps = result_utils.get_fps( data_file, fail_on_error=False )
if fps is None:
if this_fps is not None:
fps = this_fps
if reconstructor_path is None:
reconstructor_path = data_file
by_file[h5_filename] = (use_obj_ids, data_file)
del h5_filename
del obj_ids, use_obj_ids, is_mat_file, data_file, extra
if options.obj_only is not None:
obj_only = core_analysis.parse_seq(options.obj_only)
else:
obj_only = None
if reconstructor_path is None:
raise RuntimeError('must specify reconstructor from CLI if not using .h5 files')
R = reconstruct.Reconstructor(reconstructor_path)
if fps is None:
fps = 100.0
warnings.warn('Setting fps to default value of %f'%fps)
else:
fps = 1.0
if options.stim_xml is None:
raise ValueError(
'stim_xml must be specified (how else will you align the data?')
if 1:
stim_xml = xml_stimulus.xml_stimulus_from_filename(
options.stim_xml,
)
try:
fanout = xml_stimulus.xml_fanout_from_filename( options.stim_xml )
except xml_stimulus.WrongXMLTypeError:
pass
else:
include_obj_ids, exclude_obj_ids = fanout.get_obj_ids_for_timestamp(
timestamp_string=file_timestamp )
if include_obj_ids is not None:
use_obj_ids = include_obj_ids
if exclude_obj_ids is not None:
use_obj_ids = list( set(use_obj_ids).difference(
exclude_obj_ids ) )
print('using object ids specified in fanout .xml file')
if stim_xml.has_reconstructor():
stim_xml.verify_reconstructor(R)
x = []
y = []
z = []
speed = []
if options.obj_filelist is not None:
obj_filelist=options.obj_filelist
else:
obj_filelist=None
if obj_filelist is not None:
obj_only = 1
if obj_only is not None:
if len(by_file) != 1:
raise RuntimeError("specifying obj_only can only be done for a single file")
if obj_filelist is not None:
data = np.loadtxt(obj_filelist,delimiter=',')
obj_only = np.array(data[:,0], dtype='int')
print(obj_only)
use_obj_ids = numpy.array(obj_only)
h5_filename = by_file.keys()[0]
(prev_use_ob_ids, data_file) = by_file[h5_filename]
by_file[h5_filename] = (use_obj_ids, data_file)
for h5_filename in by_file:
(use_obj_ids, data_file) = by_file[h5_filename]
for obj_id_enum,obj_id in enumerate(use_obj_ids):
rows = ca.load_data( obj_id, data_file,
use_kalman_smoothing=False,
#dynamic_model_name = dynamic_model_name,
#frames_per_second=fps,
#up_dir=up_dir,
)
verts = numpy.array( [rows['x'], rows['y'], rows['z']] ).T
if len(verts)>=3:
verts_central_diff = verts[2:,:] - verts[:-2,:]
dt = 1.0/fps
vels = verts_central_diff/(2*dt)
speeds = numpy.sqrt(numpy.sum(vels**2,axis=1))
# pad end points
speeds = numpy.array([speeds[0]] + list(speeds) + [speeds[-1]])
else:
speeds = numpy.zeros( (verts.shape[0],) )
if verts.shape[0] != len(speeds):
raise ValueError('mismatch length of x data and speeds')
x.append( verts[:,0] )
y.append( verts[:,1] )
z.append( verts[:,2] )
speed.append(speeds)
data_file.close()
del h5_filename, use_obj_ids, data_file
if 0:
# debug
if stim_xml is not None:
v = None
for child in stim_xml.root:
if child.tag == 'cubic_arena':
info = stim_xml._get_info_for_cubic_arena(child)
v=info['verts4x4']
if v is not None:
for vi in v:
print('adding',vi)
x.append( [vi[0]] )
y.append( [vi[1]] )
z.append( [vi[2]] )
speed.append( [100.0] )
x = np.concatenate(x)
y = np.concatenate(y)
z = np.concatenate(z)
w = np.ones_like(x)
speed = np.concatenate(speed)
# homogeneous coords
verts = np.array([x,y,z,w])
#######################################################
# Create the MayaVi engine and start it.
e = Engine()
# start does nothing much but useful if someone is listening to
# your engine.
e.start()
# Create a new scene.
from tvtk.tools import ivtk
#viewer = ivtk.IVTK(size=(600,600))
viewer = IVTKWithCalGUI(size=(800,600))
viewer.open()
e.new_scene(viewer)
viewer.cal_align.set_data(verts,speed,R,args.align_json)
if 0:
# Do this if you need to see the MayaVi tree view UI.
ev = EngineView(engine=e)
ui = ev.edit_traits()
# view aligned data
e.add_source(viewer.cal_align.source)
v = Vectors()
v.glyph.scale_mode = 'data_scaling_off'
v.glyph.color_mode = 'color_by_scalar'
v.glyph.glyph_source.glyph_position='center'
v.glyph.glyph_source.glyph_source = tvtk.SphereSource(
radius=options.radius,
)
e.add_module(v)
if stim_xml is not None:
if 0:
stim_xml.draw_in_mayavi_scene(e)
else:
actors = stim_xml.get_tvtk_actors()
viewer.scene.add_actors(actors)
gui = GUI()
gui.start_event_loop()
del sys.path[0]
except IOError, err:
print 'not a .mat file at %s, treating as .hdf5 file' % (os.path.join(
data_path, data_filename))
ca = core_analysis.get_global_CachingAnalyzer()
obj_ids, use_obj_ids, is_mat_file, data_file, extra = ca.initial_file_load(
filename)
if obj_only is not None:
use_obj_ids = obj_only
if options.stim_xml is not None:
file_timestamp = data_file.filename[4:19]
stim_xml = xml_stimulus.xml_stimulus_from_filename(
options.stim_xml,
timestamp_string=file_timestamp,
)
try:
fanout = xml_stimulus.xml_fanout_from_filename(options.stim_xml)
except xml_stimulus.WrongXMLTypeError:
pass
else:
include_obj_ids, exclude_obj_ids = fanout.get_obj_ids_for_timestamp(
timestamp_string=file_timestamp)
if include_obj_ids is not None:
use_obj_ids = include_obj_ids
if exclude_obj_ids is not None:
use_obj_ids = list(set(use_obj_ids).difference(exclude_obj_ids))
print 'using object ids specified in fanout .xml file'
if dynamic_model_name is None:
def plot_top_and_side_views(
subplot=None, options=None, obs_mew=None, scale=1.0, units="m",
):
"""
inputs
------
subplot - a dictionary of matplotlib axes instances with keys 'xy' and/or 'xz'
fps - the framerate of the data
"""
assert subplot is not None
assert options is not None
if not hasattr(options, "show_track_ends"):
options.show_track_ends = False
if not hasattr(options, "unicolor"):
options.unicolor = False
if not hasattr(options, "show_landing"):
options.show_landing = False
kalman_filename = options.kalman_filename
fps = options.fps
dynamic_model = options.dynamic_model
use_kalman_smoothing = options.use_kalman_smoothing
if not hasattr(options, "ellipsoids"):
options.ellipsoids = False
if not hasattr(options, "show_observations"):
options.show_observations = False
if not hasattr(options, "markersize"):
options.markersize = 0.5
if options.ellipsoids and use_kalman_smoothing:
warnings.warn(
"plotting ellipsoids while using Kalman smoothing does not reveal original error estimates"
)
assert kalman_filename is not None
start = options.start
stop = options.stop
obj_only = options.obj_only
if not use_kalman_smoothing:
if dynamic_model is not None:
print(
"ERROR: disabling Kalman smoothing (--disable-kalman-smoothing) is incompatable with setting dynamic model options (--dynamic-model)",
file=sys.stderr,
)
sys.exit(1)
ca = core_analysis.get_global_CachingAnalyzer()
if kalman_filename is not None:
obj_ids, use_obj_ids, is_mat_file, data_file, extra = ca.initial_file_load(
kalman_filename
)
if not is_mat_file:
mat_data = None
if fps is None:
fps = result_utils.get_fps(data_file, fail_on_error=False)
if fps is None:
fps = 100.0
warnings.warn("Setting fps to default value of %f" % fps)
reconstructor = reconstruct.Reconstructor(data_file)
else:
reconstructor = None
if options.stim_xml:
file_timestamp = data_file.filename[4:19]
stim_xml = xml_stimulus.xml_stimulus_from_filename(
options.stim_xml, timestamp_string=file_timestamp,
)
try:
fanout = xml_stimulus.xml_fanout_from_filename(options.stim_xml)
except xml_stimulus.WrongXMLTypeError:
walking_start_stops = []
else:
include_obj_ids, exclude_obj_ids = fanout.get_obj_ids_for_timestamp(
timestamp_string=file_timestamp
)
walking_start_stops = fanout.get_walking_start_stops_for_timestamp(
timestamp_string=file_timestamp
)
if include_obj_ids is not None:
use_obj_ids = include_obj_ids
if exclude_obj_ids is not None:
use_obj_ids = list(set(use_obj_ids).difference(exclude_obj_ids))
stim_xml = fanout.get_stimulus_for_timestamp(
timestamp_string=file_timestamp
)
if stim_xml.has_reconstructor():
stim_xml.verify_reconstructor(reconstructor)
else:
walking_start_stops = []
if dynamic_model is None:
dynamic_model = extra.get("dynamic_model_name", None)
if dynamic_model is None:
if use_kalman_smoothing:
warnings.warn(
"no kalman smoothing will be performed because no "
"dynamic model specified or found."
)
use_kalman_smoothing = False
else:
print('detected file loaded with dynamic model "%s"' % dynamic_model)
if use_kalman_smoothing:
if dynamic_model.startswith("EKF "):
dynamic_model = dynamic_model[4:]
print(' for smoothing, will use dynamic model "%s"' % dynamic_model)
subplots = subplot.keys()
subplots.sort() # ensure consistency across runs
dt = 1.0 / fps
if obj_only is not None:
use_obj_ids = [i for i in use_obj_ids if i in obj_only]
subplot["xy"].set_aspect("equal")
subplot["xz"].set_aspect("equal")
subplot["xy"].set_xlabel("x (%s)" % units)
subplot["xy"].set_ylabel("y (%s)" % units)
subplot["xz"].set_xlabel("x (%s)" % units)
subplot["xz"].set_ylabel("z (%s)" % units)
if options.stim_xml:
stim_xml.plot_stim(
subplot["xy"], projection=xml_stimulus.SimpleOrthographicXYProjection()
)
stim_xml.plot_stim(
subplot["xz"], projection=xml_stimulus.SimpleOrthographicXZProjection()
)
allX = {}
frame0 = None
results = collections.defaultdict(list)
for obj_id in use_obj_ids:
line = None
ellipse_lines = []
MLE_line = None
try:
kalman_rows = ca.load_data(
obj_id,
data_file,
use_kalman_smoothing=use_kalman_smoothing,
dynamic_model_name=dynamic_model,
frames_per_second=fps,
up_dir=options.up_dir,
)
except core_analysis.ObjectIDDataError:
continue
if options.show_observations:
kobs_rows = ca.load_dynamics_free_MLE_position(obj_id, data_file)
frame = kalman_rows["frame"]
if options.show_observations:
frame_obs = kobs_rows["frame"]
if (start is not None) or (stop is not None):
valid_cond = numpy.ones(frame.shape, dtype=numpy.bool)
if options.show_observations:
valid_obs_cond = np.ones(frame_obs.shape, dtype=numpy.bool)
if start is not None:
valid_cond = valid_cond & (frame >= start)
if options.show_observations:
valid_obs_cond = valid_obs_cond & (frame_obs >= start)
if stop is not None:
valid_cond = valid_cond & (frame <= stop)
if options.show_observations:
valid_obs_cond = valid_obs_cond & (frame_obs <= stop)
kalman_rows = kalman_rows[valid_cond]
if options.show_observations:
kobs_rows = kobs_rows[valid_obs_cond]
if not len(kalman_rows):
continue
frame = kalman_rows["frame"]
Xx = kalman_rows["x"]
Xy = kalman_rows["y"]
Xz = kalman_rows["z"]
if options.max_z is not None:
cond = Xz <= options.max_z
frame = numpy.ma.masked_where(~cond, frame)
Xx = numpy.ma.masked_where(~cond, Xx)
Xy = numpy.ma.masked_where(~cond, Xy)
Xz = numpy.ma.masked_where(~cond, Xz)
with keep_axes_dimensions_if(subplot["xz"], options.stim_xml):
subplot["xz"].axhline(options.max_z)
kws = {"markersize": options.markersize}
if options.unicolor:
kws["color"] = "k"
landing_idxs = []
for walkstart, walkstop in walking_start_stops:
if walkstart in frame:
tmp_idx = numpy.nonzero(frame == walkstart)[0][0]
landing_idxs.append(tmp_idx)
with keep_axes_dimensions_if(subplot["xy"], options.stim_xml):
(line,) = subplot["xy"].plot(
Xx * scale, Xy * scale, ".", label="obj %d" % obj_id, **kws
)
kws["color"] = line.get_color()
if options.ellipsoids:
for i in range(len(Xx)):
rowi = kalman_rows[i]
mu = [rowi["x"], rowi["y"], rowi["z"]]
va = get_covariance(rowi)
ellx, elly = densities.gauss_ell(mu, va, [0, 1], 30, 0.39)
(ellipse_line,) = subplot["xy"].plot(
ellx * scale, elly * scale, color=kws["color"]
)
ellipse_lines.append(ellipse_line)
if options.show_track_ends:
subplot["xy"].plot(
[Xx[0] * scale, Xx[-1] * scale],
[Xy[0] * scale, Xy[-1] * scale],
"cd",
ms=6,
label="track end",
)
if options.show_obj_id:
subplot["xy"].text(Xx[0] * scale, Xy[0] * scale, str(obj_id))
if options.show_landing:
for landing_idx in landing_idxs:
subplot["xy"].plot(
[Xx[landing_idx] * scale],
[Xy[landing_idx] * scale],
"rD",
ms=10,
label="landing",
)
if options.show_observations:
mykw = {}
mykw.update(kws)
mykw["markersize"] *= 5
mykw["mew"] = obs_mew
badcond = np.isnan(kobs_rows["x"])
Xox = np.ma.masked_where(badcond, kobs_rows["x"])
Xoy = np.ma.masked_where(badcond, kobs_rows["y"])
(MLE_line,) = subplot["xy"].plot(
Xox * scale, Xoy * scale, "x", label="obj %d" % obj_id, **mykw
)
with keep_axes_dimensions_if(subplot["xz"], options.stim_xml):
(line,) = subplot["xz"].plot(
Xx * scale, Xz * scale, ".", label="obj %d" % obj_id, **kws
)
kws["color"] = line.get_color()
if options.ellipsoids:
for i in range(len(Xx)):
rowi = kalman_rows[i]
mu = [rowi["x"], rowi["y"], rowi["z"]]
va = get_covariance(rowi)
ellx, ellz = densities.gauss_ell(mu, va, [0, 2], 30, 0.39)
(ellipse_line,) = subplot["xz"].plot(
ellx * scale, ellz * scale, color=kws["color"]
)
ellipse_lines.append(ellipse_line)
if options.show_track_ends:
subplot["xz"].plot(
[Xx[0] * scale, Xx[-1] * scale],
[Xz[0] * scale, Xz[-1] * scale],
"cd",
ms=6,
label="track end",
)
if options.show_obj_id:
subplot["xz"].text(Xx[0] * scale, Xz[0] * scale, str(obj_id))
if options.show_landing:
for landing_idx in landing_idxs:
subplot["xz"].plot(
[Xx[landing_idx] * scale],
[Xz[landing_idx] * scale],
"rD",
ms=10,
label="landing",
)
if options.show_observations:
mykw = {}
mykw.update(kws)
mykw["markersize"] *= 5
mykw["mew"] = obs_mew
badcond = np.isnan(kobs_rows["x"])
Xox = np.ma.masked_where(badcond, kobs_rows["x"])
Xoz = np.ma.masked_where(badcond, kobs_rows["z"])
(MLE_line,) = subplot["xz"].plot(
Xox * scale, Xoz * scale, "x", label="obj %d" % obj_id, **mykw
)
results["lines"].append(line)
results["ellipse_lines"].extend(ellipse_lines)
results["MLE_line"].append(MLE_line)
return results
def doit(
filename,
obj_only=None,
min_length=10,
use_kalman_smoothing=True,
data_fps=100.0,
save_fps=25,
vertical_scale=False,
max_vel="auto",
draw_stim_func_str=None,
floor=True,
animation_path_fname=None,
output_dir=".",
cam_only_move_duration=5.0,
options=None,
):
if not use_kalman_smoothing:
if dynamic_model_name is not None:
print(
"ERROR: disabling Kalman smoothing (--disable-kalman-smoothing) is incompatable with setting dynamic model option (--dynamic-model)",
file=sys.stderr,
)
sys.exit(1)
dynamic_model_name = options.dynamic_model
if animation_path_fname is None:
animation_path_fname = pkg_resources.resource_filename(
__name__, "kdmovie_saver_default_path.kmp"
)
camera_animation_path = AnimationPath(animation_path_fname)
mat_data = None
try:
try:
data_path, data_filename = os.path.split(filename)
data_path = os.path.expanduser(data_path)
sys.path.insert(0, data_path)
mat_data = scipy.io.mio.loadmat(data_filename)
finally:
del sys.path[0]
except IOError as err:
print(
"not a .mat file at %s, treating as .hdf5 file"
% (os.path.join(data_path, data_filename))
)
ca = core_analysis.get_global_CachingAnalyzer()
obj_ids, use_obj_ids, is_mat_file, data_file, extra = ca.initial_file_load(filename)
if obj_only is not None:
use_obj_ids = obj_only
if options.stim_xml is not None:
file_timestamp = data_file.filename[4:19]
stim_xml = xml_stimulus.xml_stimulus_from_filename(
options.stim_xml, timestamp_string=file_timestamp,
)
try:
fanout = xml_stimulus.xml_fanout_from_filename(options.stim_xml)
except xml_stimulus.WrongXMLTypeError:
pass
else:
include_obj_ids, exclude_obj_ids = fanout.get_obj_ids_for_timestamp(
timestamp_string=file_timestamp
)
if include_obj_ids is not None:
use_obj_ids = include_obj_ids
if exclude_obj_ids is not None:
use_obj_ids = list(set(use_obj_ids).difference(exclude_obj_ids))
print("using object ids specified in fanout .xml file")
if dynamic_model_name is None:
if "dynamic_model_name" in extra:
dynamic_model_name = extra["dynamic_model_name"]
print('detected file loaded with dynamic model "%s"' % dynamic_model_name)
if dynamic_model_name.startswith("EKF "):
dynamic_model_name = dynamic_model_name[4:]
print(' for smoothing, will use dynamic model "%s"' % dynamic_model_name)
else:
print(
"no dynamic model name specified, and it could not be determined from the file, either"
)
filename_trimmed = os.path.split(os.path.splitext(filename)[0])[-1]
assert use_obj_ids is not None
#################
rw = tvtk.RenderWindow(size=(1024, 768))
ren = tvtk.Renderer(background=(1.0, 1.0, 1.0))
camera = ren.active_camera
rw.add_renderer(ren)
lut = tvtk.LookupTable(hue_range=(0.667, 0.0))
#################
if not os.path.exists(output_dir):
os.mkdir(output_dir)
if len(use_obj_ids) == 1:
animate_path = True
# allow path to grow during trajectory
else:
animate_path = False
obj_verts = []
speeds = []
for obj_id in use_obj_ids:
print("loading %d" % obj_id)
results = ca.calculate_trajectory_metrics(
obj_id,
data_file,
use_kalman_smoothing=use_kalman_smoothing,
frames_per_second=data_fps,
dynamic_model_name=dynamic_model_name,
# method='position based',
method_params={"downsample": 1,},
)
if len(use_obj_ids) == 1:
obj_verts = results["X_kalmanized"]
speeds = results["speed_kalmanized"]
real_frames = results["frame"]
else:
obj_verts.append(results["X_kalmanized"])
speeds.append(results["speed_kalmanized"])
real_frames.append(results["frame"])
if options.start is not None:
good_cond = real_frames >= options.start
obj_verts = obj_verts[good_cond]
speeds = speeds[good_cond]
real_frames = real_frames[good_cond]
if not len(use_obj_ids) == 1:
obj_verts = numpy.concatenate(obj_verts, axis=0)
speeds = numpy.concatenate(speeds, axis=0)
real_frames = numpy.concatenate(real_frames, axis=0)
####################### start draw permanently on stuff ############################
if options.stim_xml is not None:
if not is_mat_file:
R = reconstruct.Reconstructor(data_file)
stim_xml.verify_reconstructor(R)
if not is_mat_file:
assert data_file.filename.startswith("DATA") and (
data_file.filename.endswith(".h5")
or data_file.filename.endswith(".kh5")
)
file_timestamp = data_file.filename[4:19]
actors = stim_xml.get_tvtk_actors()
for actor in actors:
ren.add_actor(actor)
if 1:
if 0:
# Inspired by pyface.tvtk.decorated_scene
marker = tvtk.OrientationMarkerWidget()
axes = tvtk.AxesActor()
axes.set(
# normalized_tip_length=(0.04, 0.4, 0.4),
# normalized_shaft_length=(0.6, 0.6, 0.6),
shaft_type="cylinder",
total_length=(0.15, 0.15, 0.15),
)
if 1:
axes.x_axis_label_text = ""
axes.y_axis_label_text = ""
axes.z_axis_label_text = ""
else:
p = axes.x_axis_caption_actor2d.caption_text_property
axes.y_axis_caption_actor2d.caption_text_property = p
axes.z_axis_caption_actor2d.caption_text_property = p
p.color = 0.0, 0.0, 0.0 # black
# axes.camera = camera
# axes.attachment_point_coordinate = (0,0,0)
axes.origin = (-0.5, 1, 0)
if 0:
rwi = rw.interactor
print("rwi", rwi)
marker.orientation_marker = axes
# marker.interactive = False
marker.interactor = rwi
marker.enabled = True
ren.add_actor(axes)
#######################
if max_vel == "auto":
max_vel = speeds.max()
else:
max_vel = float(max_vel)
vel_mapper = tvtk.PolyDataMapper()
vel_mapper.lookup_table = lut
vel_mapper.scalar_range = 0.0, max_vel
if 1:
# Create a scalar bar
if vertical_scale:
scalar_bar = tvtk.ScalarBarActor(
orientation="vertical", width=0.08, height=0.4
)
else:
scalar_bar = tvtk.ScalarBarActor(
orientation="horizontal", width=0.4, height=0.08
)
scalar_bar.title = "Speed (m/s)"
scalar_bar.lookup_table = vel_mapper.lookup_table
scalar_bar.property.color = 0.0, 0.0, 0.0 # black
scalar_bar.title_text_property.color = 0.0, 0.0, 0.0
scalar_bar.title_text_property.shadow = False
scalar_bar.label_text_property.color = 0.0, 0.0, 0.0
scalar_bar.label_text_property.shadow = False
scalar_bar.position_coordinate.coordinate_system = "normalized_viewport"
if vertical_scale:
scalar_bar.position_coordinate.value = 0.01, 0.01, 0.0
else:
scalar_bar.position_coordinate.value = 0.1, 0.01, 0.0
ren.add_actor(scalar_bar)
imf = tvtk.WindowToImageFilter(input=rw, read_front_buffer="off")
writer = tvtk.PNGWriter()
####################### end draw permanently on stuff ############################
save_dt = 1.0 / save_fps
if animate_path:
data_dt = 1.0 / data_fps
n_frames = len(obj_verts)
dur = n_frames * data_dt
else:
data_dt = 0.0
dur = 0.0
print("data_fps", data_fps)
print("data_dt", data_dt)
print("save_fps", save_fps)
t_now = 0.0
frame_number = 0
while t_now <= dur:
frame_number += 1
t_now += save_dt
print("t_now", t_now)
pos, ori = camera_animation_path.get_pos_ori(t_now)
focal_point, view_up = pos_ori2fu(pos, ori)
camera.position = tuple(pos)
# camera.focal_point = (focal_point[0], focal_point[1], focal_point[2])
# camera.view_up = (view_up[0], view_up[1], view_up[2])
camera.focal_point = tuple(focal_point)
camera.view_up = tuple(view_up)
if data_dt != 0.0:
draw_n_frames = int(round(t_now / data_dt))
else:
draw_n_frames = len(obj_verts)
print("frame_number, draw_n_frames", frame_number, draw_n_frames)
#################
pd = tvtk.PolyData()
pd.points = obj_verts[:draw_n_frames]
real_frame_number = real_frames[:draw_n_frames][-1]
pd.point_data.scalars = speeds
if numpy.any(speeds > max_vel):
print(
"WARNING: maximum speed (%.3f m/s) exceeds color map max"
% (speeds.max(),)
)
g = tvtk.Glyph3D(
scale_mode="data_scaling_off", vector_mode="use_vector", input=pd
)
vel_mapper.input = g.output
ss = tvtk.SphereSource(radius=options.radius)
g.source = ss.output
a = tvtk.Actor(mapper=vel_mapper)
##################
ren.add_actor(a)
if 1:
imf.update()
imf.modified()
writer.input = imf.output
# fname = 'movie_%s_%03d_frame%05d.png'%(filename_trimmed,obj_id,frame_number)
fname = "movie_%s_%03d_frame%05d.png" % (
filename_trimmed,
obj_id,
real_frame_number,
)
print("saving", fname)
full_fname = os.path.join(output_dir, fname)
writer.file_name = full_fname
writer.write()
ren.remove_actor(a)
ren.add_actor(a) # restore actors removed
dur = dur + cam_only_move_duration
while t_now < dur:
frame_number += 1
t_now += save_dt
print("t_now", t_now)
pos, ori = camera_animation_path.get_pos_ori(t_now)
focal_point, view_up = pos_ori2fu(pos, ori)
camera.position = tuple(pos)
camera.focal_point = tuple(focal_point)
camera.view_up = tuple(view_up)
if 1:
imf.update()
imf.modified()
writer.input = imf.output
if len(use_obj_ids) == 1:
fname = "movie_%s_%03d_frame%05d.png" % (
filename_trimmed,
obj_id,
frame_number,
)
else:
fname = "movie_%s_many_frame%05d.png" % (filename_trimmed, frame_number)
full_fname = os.path.join(output_dir, fname)
writer.file_name = full_fname
writer.write()
if not is_mat_file:
data_file.close()
def show_it(
self,
fig,
filename,
kalman_filename=None,
frame_start=None,
frame_stop=None,
show_nth_frame=None,
obj_only=None,
reconstructor_filename=None,
options=None,
):
if show_nth_frame == 0:
show_nth_frame = None
results = result_utils.get_results(filename, mode="r")
opened_kresults = False
kresults = None
if kalman_filename is not None:
if os.path.abspath(kalman_filename) == os.path.abspath(filename):
kresults = results
else:
kresults = PT.open_file(kalman_filename, mode="r")
opened_kresults = True
# copied from plot_timeseries_2d_3d.py
ca = core_analysis.get_global_CachingAnalyzer()
(
xxobj_ids,
xxuse_obj_ids,
xxis_mat_file,
xxdata_file,
extra,
) = ca.initial_file_load(kalman_filename)
fps = extra["frames_per_second"]
dynamic_model_name = None
if dynamic_model_name is None:
dynamic_model_name = extra.get("dynamic_model_name", None)
if dynamic_model_name is None:
dynamic_model_name = dynamic_models.DEFAULT_MODEL
warnings.warn('no dynamic model specified, using "%s"' %
dynamic_model_name)
else:
print('detected file loaded with dynamic model "%s"' %
dynamic_model_name)
if dynamic_model_name.startswith("EKF "):
dynamic_model_name = dynamic_model_name[4:]
print(' for smoothing, will use dynamic model "%s"' %
dynamic_model_name)
if hasattr(results.root, "images"):
img_table = results.root.images
else:
img_table = None
reconstructor_source = None
if reconstructor_filename is None:
if kresults is not None:
reconstructor_source = kresults
elif hasattr(results.root, "calibration"):
reconstructor_source = results
else:
reconstructor_source = None
else:
if os.path.abspath(reconstructor_filename) == os.path.abspath(
filename):
reconstructor_source = results
elif (kalman_filename
is not None) and (os.path.abspath(reconstructor_filename)
== os.path.abspath(kalman_filename)):
reconstructor_source = kresults
else:
reconstructor_source = reconstructor_filename
if reconstructor_source is not None:
self.reconstructor = flydra_core.reconstruct.Reconstructor(
reconstructor_source)
if options.stim_xml:
file_timestamp = results.filename[4:19]
stim_xml = xml_stimulus.xml_stimulus_from_filename(
options.stim_xml, timestamp_string=file_timestamp)
if self.reconstructor is not None:
stim_xml.verify_reconstructor(self.reconstructor)
if self.reconstructor is not None:
self.reconstructor = self.reconstructor.get_scaled()
camn2cam_id, cam_id2camns = result_utils.get_caminfo_dicts(results)
data2d = results.root.data2d_distorted # make sure we have 2d data table
print("reading frames...")
frames = data2d.read(field="frame")
print("OK")
if frame_start is not None:
print("selecting frames after start")
# after_start = data2d.get_where_list( 'frame>=frame_start')
after_start = numpy.nonzero(frames >= frame_start)[0]
else:
after_start = None
if frame_stop is not None:
print("selecting frames before stop")
# before_stop = data2d.get_where_list( 'frame<=frame_stop')
before_stop = numpy.nonzero(frames <= frame_stop)[0]
else:
before_stop = None
print("finding all frames")
if after_start is not None and before_stop is not None:
use_idxs = numpy.intersect1d(after_start, before_stop)
elif after_start is not None:
use_idxs = after_start
elif before_stop is not None:
use_idxs = before_stop
else:
use_idxs = numpy.arange(data2d.nrows)
# OK, we have data coords, plot
print("reading cameras")
frames = frames[
use_idxs] # data2d.read_coordinates( use_idxs, field='frame')
if len(frames):
print("frame range: %d - %d (%d frames total)" %
(frames[0], frames[-1], len(frames)))
camns = data2d.read(field="camn")
camns = camns[use_idxs]
# camns = data2d.read_coordinates( use_idxs, field='camn')
unique_camns = numpy.unique(camns)
unique_cam_ids = list(set([camn2cam_id[camn]
for camn in unique_camns]))
unique_cam_ids.sort()
print("%d cameras with data" % (len(unique_cam_ids), ))
# plot all cameras, not just those with data
all_cam_ids = cam_id2camns.keys()
all_cam_ids.sort()
unique_cam_ids = all_cam_ids
if len(unique_cam_ids) == 1:
n_rows = 1
n_cols = 1
elif len(unique_cam_ids) <= 6:
n_rows = 2
n_cols = 3
elif len(unique_cam_ids) <= 12:
n_rows = 3
n_cols = 4
else:
n_rows = 4
n_cols = int(math.ceil(len(unique_cam_ids) / n_rows))
for i, cam_id in enumerate(unique_cam_ids):
ax = auto_subplot(fig, i, n_rows=n_rows, n_cols=n_cols)
ax.set_title("%s: %s" % (cam_id, str(cam_id2camns[cam_id])))
## ax.set_xticks([])
## ax.set_yticks([])
ax.this_minx = np.inf
ax.this_maxx = -np.inf
ax.this_miny = np.inf
ax.this_maxy = -np.inf
self.subplot_by_cam_id[cam_id] = ax
for cam_id in unique_cam_ids:
ax = self.subplot_by_cam_id[cam_id]
if img_table is not None:
bg_arr_h5 = getattr(img_table, cam_id)
bg_arr = bg_arr_h5.read()
ax.imshow(bg_arr.squeeze(), origin="lower", cmap=cm.pink)
ax.set_autoscale_on(True)
ax.autoscale_view()
pylab.draw()
ax.set_autoscale_on(False)
if self.reconstructor is not None:
if cam_id in self.reconstructor.get_cam_ids():
res = self.reconstructor.get_resolution(cam_id)
ax.set_xlim([0, res[0]])
ax.set_ylim([res[1], 0])
if options.stim_xml is not None:
stim_xml.plot_stim_over_distorted_image(ax, cam_id)
for camn in unique_camns:
cam_id = camn2cam_id[camn]
ax = self.subplot_by_cam_id[cam_id]
this_camn_idxs = use_idxs[camns == camn]
xs = data2d.read_coordinates(this_camn_idxs, field="x")
valid_idx = numpy.nonzero(~numpy.isnan(xs))[0]
if not len(valid_idx):
continue
ys = data2d.read_coordinates(this_camn_idxs, field="y")
if options.show_orientation:
slope = data2d.read_coordinates(this_camn_idxs, field="slope")
idx_first_valid = valid_idx[0]
idx_last_valid = valid_idx[-1]
tmp_frames = data2d.read_coordinates(this_camn_idxs, field="frame")
ax.plot([xs[idx_first_valid]], [ys[idx_first_valid]],
"ro",
label="first point")
ax.this_minx = min(np.min(xs[valid_idx]), ax.this_minx)
ax.this_maxx = max(np.max(xs[valid_idx]), ax.this_maxx)
ax.this_miny = min(np.min(ys[valid_idx]), ax.this_miny)
ax.this_maxy = max(np.max(ys[valid_idx]), ax.this_maxy)
ax.plot(xs[valid_idx], ys[valid_idx], "g.", label="all points")
if options.show_orientation:
angle = np.arctan(slope)
r = 20.0
dx = r * np.cos(angle)
dy = r * np.sin(angle)
x0 = xs - dx
x1 = xs + dx
y0 = ys - dy
y1 = ys + dy
segs = []
for i in valid_idx:
segs.append(((x0[i], y0[i]), (x1[i], y1[i])))
line_segments = collections.LineCollection(
segs,
linewidths=[1],
colors=[(0, 1, 0)],
)
ax.add_collection(line_segments)
ax.plot([xs[idx_last_valid]], [ys[idx_last_valid]],
"bo",
label="first point")
if show_nth_frame is not None:
for i, f in enumerate(tmp_frames):
if f % show_nth_frame == 0:
ax.text(xs[i], ys[i], "%d" % (f, ))
if 0:
for x, y, frame in zip(xs[::5], ys[::5], tmp_frames[::5]):
ax.text(x, y, "%d" % (frame, ))
fig.canvas.mpl_connect("key_press_event", self.on_key_press)
if options.autozoom:
for cam_id in self.subplot_by_cam_id.keys():
ax = self.subplot_by_cam_id[cam_id]
ax.set_xlim((ax.this_minx - 10, ax.this_maxx + 10))
ax.set_ylim((ax.this_miny - 10, ax.this_maxy + 10))
if options.save_fig:
for cam_id in self.subplot_by_cam_id.keys():
ax = self.subplot_by_cam_id[cam_id]
ax.set_xticks([])
ax.set_yticks([])
if kalman_filename is None:
return
if 0:
# Do same as above for Kalman-filtered data
kobs = kresults.root.ML_estimates
kframes = kobs.read(field="frame")
if frame_start is not None:
k_after_start = numpy.nonzero(kframes >= frame_start)[0]
else:
k_after_start = None
if frame_stop is not None:
k_before_stop = numpy.nonzero(kframes <= frame_stop)[0]
else:
k_before_stop = None
if k_after_start is not None and k_before_stop is not None:
k_use_idxs = numpy.intersect1d(k_after_start, k_before_stop)
elif k_after_start is not None:
k_use_idxs = k_after_start
elif k_before_stop is not None:
k_use_idxs = k_before_stop
else:
k_use_idxs = numpy.arange(kobs.nrows)
obj_ids = kobs.read(field="obj_id")[k_use_idxs]
obs_2d_idxs = kobs.read(field="obs_2d_idx")[k_use_idxs]
kframes = kframes[k_use_idxs]
kobs_2d = kresults.root.ML_estimates_2d_idxs
xys_by_obj_id = {}
for obj_id, kframe, obs_2d_idx in zip(obj_ids, kframes,
obs_2d_idxs):
if obj_only is not None:
if obj_id not in obj_only:
continue
obs_2d_idx_find = int(
obs_2d_idx) # XXX grr, why can't pytables do this?
obj_id_save = int(obj_id) # convert from possible numpy scalar
xys_by_cam_id = xys_by_obj_id.setdefault(obj_id_save, {})
kobs_2d_data = kobs_2d.read(start=obs_2d_idx_find,
stop=obs_2d_idx_find + 1)
assert len(kobs_2d_data) == 1
kobs_2d_data = kobs_2d_data[0]
this_camns = kobs_2d_data[0::2]
this_camn_idxs = kobs_2d_data[1::2]
this_use_idxs = use_idxs[frames == kframe]
d2d = data2d.read_coordinates(this_use_idxs)
for this_camn, this_camn_idx in zip(this_camns,
this_camn_idxs):
this_idxs_tmp = numpy.nonzero(d2d["camn"] == this_camn)[0]
this_camn_d2d = d2d[d2d["camn"] == this_camn]
found = False
for this_row in this_camn_d2d: # XXX could be sped up
if this_row["frame_pt_idx"] == this_camn_idx:
found = True
break
if not found:
if 1:
print(
"WARNING:point not found in data -- 3D data starts before 2D I guess."
)
continue
else:
raise RuntimeError("point not found in data!?")
this_cam_id = camn2cam_id[this_camn]
xys = xys_by_cam_id.setdefault(this_cam_id, ([], []))
xys[0].append(this_row["x"])
xys[1].append(this_row["y"])
for obj_id in xys_by_obj_id:
xys_by_cam_id = xys_by_obj_id[obj_id]
for cam_id, (xs, ys) in xys_by_cam_id.iteritems():
ax = self.subplot_by_cam_id[cam_id]
ax.plot(xs, ys, "x-", label="obs: %d" % obj_id)
ax.text(xs[0], ys[0], "%d:" % (obj_id, ))
ax.text(xs[-1], ys[-1], ":%d" % (obj_id, ))
if 1:
# do for core_analysis smoothed (or not) data
for obj_id in xxuse_obj_ids:
try:
rows = ca.load_data(
obj_id,
kalman_filename,
use_kalman_smoothing=True,
frames_per_second=fps,
dynamic_model_name=dynamic_model_name,
)
except core_analysis.NotEnoughDataToSmoothError:
warnings.warn(
"not enough data to smooth obj_id %d, skipping." %
(obj_id, ))
if frame_start is not None:
c1 = rows["frame"] >= frame_start
else:
c1 = np.ones((len(rows), ), dtype=np.bool)
if frame_stop is not None:
c2 = rows["frame"] <= frame_stop
else:
c2 = np.ones((len(rows), ), dtype=np.bool)
valid = c1 & c2
rows = rows[valid]
if len(rows) > 1:
X3d = np.array((rows["x"], rows["y"], rows["z"],
np.ones_like(rows["z"]))).T
for cam_id in self.subplot_by_cam_id.keys():
ax = self.subplot_by_cam_id[cam_id]
newx, newy = self.reconstructor.find2d(cam_id,
X3d,
distorted=True)
ax.plot(newx, newy, "-", label="k: %d" % obj_id)
results.close()
if opened_kresults:
kresults.close()
def plot_timeseries(subplot=None, options=None):
kalman_filename = options.kalman_filename
if not hasattr(options, 'frames'):
options.frames = False
if not hasattr(options, 'show_landing'):
options.show_landing = False
if not hasattr(options, 'unicolor'):
options.unicolor = False
if not hasattr(options, 'show_obj_id'):
options.show_obj_id = True
if not hasattr(options, 'show_track_ends'):
options.show_track_ends = False
start = options.start
stop = options.stop
obj_only = options.obj_only
fps = options.fps
dynamic_model = options.dynamic_model
use_kalman_smoothing = options.use_kalman_smoothing
if not use_kalman_smoothing:
if (dynamic_model is not None):
print >> sys.stderr, (
'WARNING: disabling Kalman smoothing '
'(--disable-kalman-smoothing) is incompatable '
'with setting dynamic model options (--dynamic-model)')
ca = core_analysis.get_global_CachingAnalyzer()
if kalman_filename is None:
raise ValueError('No kalman_filename given. Nothing to do.')
m = hashlib.md5()
m.update(open(kalman_filename, mode='rb').read())
actual_md5 = m.hexdigest()
(obj_ids, use_obj_ids, is_mat_file, data_file,
extra) = ca.initial_file_load(kalman_filename)
print 'opened kalman file %s %s, %d obj_ids' % (
kalman_filename, actual_md5, len(use_obj_ids))
if 'frames' in extra:
if (start is not None) or (stop is not None):
valid_frames = np.ones((len(extra['frames']), ), dtype=np.bool)
if start is not None:
valid_frames &= extra['frames'] >= start
if stop is not None:
valid_frames &= extra['frames'] <= stop
this_use_obj_ids = np.unique(obj_ids[valid_frames])
use_obj_ids = list(set(use_obj_ids).intersection(this_use_obj_ids))
include_obj_ids = None
exclude_obj_ids = None
do_fuse = False
if options.stim_xml:
file_timestamp = data_file.filename[4:19]
fanout = xml_stimulus.xml_fanout_from_filename(options.stim_xml)
include_obj_ids, exclude_obj_ids = fanout.get_obj_ids_for_timestamp(
timestamp_string=file_timestamp)
walking_start_stops = fanout.get_walking_start_stops_for_timestamp(
timestamp_string=file_timestamp)
if include_obj_ids is not None:
use_obj_ids = include_obj_ids
if exclude_obj_ids is not None:
use_obj_ids = list(set(use_obj_ids).difference(exclude_obj_ids))
if options.fuse:
do_fuse = True
else:
walking_start_stops = []
if dynamic_model is None:
dynamic_model = extra['dynamic_model_name']
print 'detected file loaded with dynamic model "%s"' % dynamic_model
if dynamic_model.startswith('EKF '):
dynamic_model = dynamic_model[4:]
print ' for smoothing, will use dynamic model "%s"' % dynamic_model
if not is_mat_file:
mat_data = None
if fps is None:
fps = result_utils.get_fps(data_file, fail_on_error=False)
if fps is None:
fps = 100.0
import warnings
warnings.warn('Setting fps to default value of %f' % fps)
tz = result_utils.get_tz(data_file)
dt = 1.0 / fps
all_vels = []
if obj_only is not None:
use_obj_ids = [i for i in use_obj_ids if i in obj_only]
allX = {}
frame0 = None
line2obj_id = {}
Xz_all = []
fuse_did_once = False
if not hasattr(options, 'timestamp_file'):
options.timestamp_file = None
if not hasattr(options, 'ori_qual'):
options.ori_qual = None
if options.timestamp_file is not None:
h5 = tables.open_file(options.timestamp_file, mode='r')
print 'reading timestamps and frames'
table_data2d_frames = h5.root.data2d_distorted.read(field='frame')
table_data2d_timestamps = h5.root.data2d_distorted.read(
field='timestamp')
print 'done'
h5.close()
table_data2d_frames_find = utils.FastFinder(table_data2d_frames)
if len(use_obj_ids) == 0:
print 'No obj_ids to plot, quitting'
sys.exit(0)
time0 = 0.0 # set default value
for obj_id in use_obj_ids:
if not do_fuse:
try:
kalman_rows = ca.load_data(
obj_id,
data_file,
use_kalman_smoothing=use_kalman_smoothing,
dynamic_model_name=dynamic_model,
return_smoothed_directions=options.smooth_orientations,
frames_per_second=fps,
up_dir=options.up_dir,
min_ori_quality_required=options.ori_qual,
)
except core_analysis.ObjectIDDataError:
continue
#kobs_rows = ca.load_dynamics_free_MLE_position( obj_id, data_file )
else:
if options.show_3d_orientations:
raise NotImplementedError('orientation data is not supported '
'when fusing obj_ids')
if fuse_did_once:
break
fuse_did_once = True
kalman_rows = flydra_analysis.a2.flypos.fuse_obj_ids(
use_obj_ids,
data_file,
dynamic_model_name=dynamic_model,
frames_per_second=fps)
frame = kalman_rows['frame']
if (start is not None) or (stop is not None):
valid_cond = numpy.ones(frame.shape, dtype=numpy.bool)
if start is not None:
valid_cond = valid_cond & (frame >= start)
if stop is not None:
valid_cond = valid_cond & (frame <= stop)
kalman_rows = kalman_rows[valid_cond]
if not len(kalman_rows):
continue
walking_and_flying_kalman_rows = kalman_rows # preserve original data
for flystate in ['flying', 'walking']:
frame = walking_and_flying_kalman_rows['frame'] # restore
if flystate == 'flying':
# assume flying unless we're told it's walking
state_cond = numpy.ones(frame.shape, dtype=numpy.bool)
else:
state_cond = numpy.zeros(frame.shape, dtype=numpy.bool)
if len(walking_start_stops):
for walkstart, walkstop in walking_start_stops:
frame = walking_and_flying_kalman_rows['frame'] # restore
# handle each bout of walking
walking_bout = numpy.ones(frame.shape, dtype=numpy.bool)
if walkstart is not None:
walking_bout &= (frame >= walkstart)
if walkstop is not None:
walking_bout &= (frame <= walkstop)
if flystate == 'flying':
state_cond &= ~walking_bout
else:
state_cond |= walking_bout
kalman_rows = np.take(walking_and_flying_kalman_rows,
np.nonzero(state_cond)[0])
assert len(kalman_rows) == np.sum(state_cond)
frame = kalman_rows['frame']
if frame0 is None:
frame0 = int(frame[0])
time0 = 0.0
if options.timestamp_file is not None:
frame_idxs = table_data2d_frames_find.get_idxs_of_equal(frame0)
if len(frame_idxs):
time0 = table_data2d_timestamps[frame_idxs[0]]
else:
raise ValueError(
'could not fine frame %d in timestamp file' % frame0)
Xx = kalman_rows['x']
Xy = kalman_rows['y']
Xz = kalman_rows['z']
Dx = Dy = Dz = None
if options.smooth_orientations:
Dx = kalman_rows['dir_x']
Dy = kalman_rows['dir_y']
Dz = kalman_rows['dir_z']
elif 'rawdir_x' in kalman_rows.dtype.fields:
Dx = kalman_rows['rawdir_x']
Dy = kalman_rows['rawdir_y']
Dz = kalman_rows['rawdir_z']
if not options.frames:
f2t = Frames2Time(frame0, fps, time0)
else:
def identity(x):
return x
f2t = identity
kws = {
'linewidth': 2,
'picker': 5,
}
if options.unicolor:
kws['color'] = 'k'
line = None
if 'frame' in subplot:
subplot['frame'].plot(f2t(frame), frame)
if 'P55' in subplot:
subplot['P55'].plot(f2t(frame), kalman_rows['P55'])
if 'x' in subplot:
line, = subplot['x'].plot(f2t(frame),
Xx,
label='obj %d (%s)' %
(obj_id, flystate),
**kws)
line2obj_id[line] = obj_id
kws['color'] = line.get_color()
if 'y' in subplot:
line, = subplot['y'].plot(f2t(frame),
Xy,
label='obj %d (%s)' %
(obj_id, flystate),
**kws)
line2obj_id[line] = obj_id
kws['color'] = line.get_color()
if 'z' in subplot:
frame_data = numpy.ma.getdata(
frame) # works if frame is masked or not
# plot landing time
if options.show_landing:
if flystate == 'flying': # only do this once
for walkstart, walkstop in walking_start_stops:
if walkstart in frame_data:
landing_dix = numpy.nonzero(
frame_data == walkstart)[0][0]
subplot['z'].plot([f2t(walkstart)],
[Xz.data[landing_dix]],
'rD',
ms=10,
label='landing')
if options.show_track_ends:
if flystate == 'flying': # only do this once
subplot['z'].plot(f2t([frame_data[0], frame_data[-1]]),
[
numpy.ma.getdata(Xz)[0],
numpy.ma.getdata(Xz)[-1]
],
'cd',
ms=6,
label='track end')
line, = subplot['z'].plot(f2t(frame),
Xz,
label='obj %d (%s)' %
(obj_id, flystate),
**kws)
kws['color'] = line.get_color()
line2obj_id[line] = obj_id
if flystate == 'flying':
# only do this once
if options.show_obj_id:
subplot['z'].text(f2t(frame_data[0]),
numpy.ma.getdata(Xz)[0],
'%d' % (obj_id, ))
line2obj_id[line] = obj_id
if flystate == 'flying':
Xz_all.append(np.ma.array(Xz).compressed())
#bins = np.linspace(0,.8,30)
#print 'Xz.shape',Xz.shape
#pylab.hist(Xz, bins=bins)
for (dir_var, Dd) in [('dx', Dx), ('dy', Dy), ('dz', Dz)]:
if dir_var in subplot:
line, = subplot[dir_var].plot(f2t(frame),
Dd,
label='obj %d (%s)' %
(obj_id, flystate),
**kws)
line2obj_id[line] = obj_id
kws['color'] = line.get_color()
if numpy.__version__ >= '1.2.0':
X = numpy.ma.array((Xx, Xy, Xz))
else:
# See http://scipy.org/scipy/numpy/ticket/820
X = numpy.ma.vstack(
(Xx[numpy.newaxis, :], Xy[numpy.newaxis, :],
Xz[numpy.newaxis, :]))
dist_central_diff = (X[:, 2:] - X[:, :-2])
vel_central_diff = dist_central_diff / (2 * dt)
vel2mag = numpy.ma.sqrt(numpy.ma.sum(vel_central_diff**2, axis=0))
xy_vel2mag = numpy.ma.sqrt(
numpy.ma.sum(vel_central_diff[:2, :]**2, axis=0))
frames2 = frame[1:-1]
accel4mag = (vel2mag[2:] - vel2mag[:-2]) / (2 * dt)
frames4 = frames2[1:-1]
if 'vel' in subplot:
line, = subplot['vel'].plot(f2t(frames2),
vel2mag,
label='obj %d (%s)' %
(obj_id, flystate),
**kws)
line2obj_id[line] = obj_id
kws['color'] = line.get_color()
if 'xy_vel' in subplot:
line, = subplot['xy_vel'].plot(f2t(frames2),
xy_vel2mag,
label='obj %d (%s)' %
(obj_id, flystate),
**kws)
line2obj_id[line] = obj_id
kws['color'] = line.get_color()
if len(accel4mag.compressed()) and 'accel' in subplot:
line, = subplot['accel'].plot(f2t(frames4),
accel4mag,
label='obj %d (%s)' %
(obj_id, flystate),
**kws)
line2obj_id[line] = obj_id
kws['color'] = line.get_color()
if flystate == 'flying':
valid_vel2mag = vel2mag.compressed()
all_vels.append(valid_vel2mag)
if len(all_vels):
all_vels = numpy.hstack(all_vels)
else:
all_vels = numpy.array([], dtype=float)
if 1:
cond = all_vels < 2.0
if numpy.ma.sum(cond) != len(all_vels):
all_vels = all_vels[cond]
import warnings
warnings.warn('clipping all velocities > 2.0 m/s')
if not options.frames:
xlabel = 'time (s)'
else:
xlabel = 'frame'
for ax in subplot.itervalues():
ax.xaxis.set_major_formatter(ticker.FormatStrFormatter("%d"))
ax.yaxis.set_major_formatter(ticker.FormatStrFormatter("%s"))
fixup_ax = FixupAxesWithTimeZone(tz).fixup_ax
if 'frame' in subplot:
if time0 != 0.0:
fixup_ax(subplot['frame'])
else:
subplot['frame'].set_xlabel(xlabel)
if 'x' in subplot:
subplot['x'].set_ylim([-1, 1])
subplot['x'].set_ylabel(r'x (m)')
if time0 != 0.0:
fixup_ax(subplot['x'])
else:
subplot['x'].set_xlabel(xlabel)
if 'y' in subplot:
subplot['y'].set_ylim([-0.5, 1.5])
subplot['y'].set_ylabel(r'y (m)')
if time0 != 0.0:
fixup_ax(subplot['y'])
else:
subplot['y'].set_xlabel(xlabel)
max_z = None
if options.stim_xml:
file_timestamp = options.kalman_filename[4:19]
stim_xml = xml_stimulus.xml_stimulus_from_filename(
options.stim_xml, timestamp_string=file_timestamp)
post_max_zs = []
for post_num, post in enumerate(stim_xml.iterate_posts()):
post_max_zs.append(max(post['verts'][0][2],
post['verts'][1][2])) # max post height
if len(post_max_zs):
max_z = min(post_max_zs) # take shortest of posts
if 'z' in subplot:
subplot['z'].set_ylim([0, 1])
subplot['z'].set_ylabel(r'z (m)')
if max_z is not None:
subplot['z'].axhline(max_z, color='m')
if time0 != 0.0:
fixup_ax(subplot['z'])
else:
subplot['z'].set_xlabel(xlabel)
for dir_var in ['dx', 'dy', 'dz']:
if dir_var in subplot:
subplot[dir_var].set_ylabel(dir_var)
if time0 != 0.0:
fixup_ax(subplot[dir_var])
else:
subplot[dir_var].set_xlabel(xlabel)
if 'z_hist' in subplot: # and flystate=='flying':
Xz_all = np.hstack(Xz_all)
bins = np.linspace(0, .8, 30)
ax = subplot['z_hist']
ax.hist(Xz_all, bins=bins, orientation='horizontal')
ax.set_xticks([])
ax.set_yticks([])
xlim = tuple(ax.get_xlim()) # matplotlib 0.98.3 returned np.array view
ax.set_xlim((xlim[1], xlim[0]))
ax.axhline(max_z, color='m')
if 'vel' in subplot:
subplot['vel'].set_ylim([0, 2])
subplot['vel'].set_ylabel(r'vel (m/s)')
subplot['vel'].set_xlabel(xlabel)
if time0 != 0.0:
fixup_ax(subplot['vel'])
else:
subplot['vel'].set_xlabel(xlabel)
if 'xy_vel' in subplot:
#subplot['xy_vel'].set_ylim([0,2])
subplot['xy_vel'].set_ylabel(r'horiz vel (m/s)')
subplot['xy_vel'].set_xlabel(xlabel)
if time0 != 0.0:
fixup_ax(subplot['xy_vel'])
else:
subplot['xy_vel'].set_xlabel(xlabel)
if 'accel' in subplot:
subplot['accel'].set_ylabel(r'acceleration (m/(s^2))')
subplot['accel'].set_xlabel(xlabel)
if time0 != 0.0:
fixup_ax(subplot['accel'])
else:
subplot['accel'].set_xlabel(xlabel)
if 'vel_hist' in subplot:
ax = subplot['vel_hist']
bins = numpy.linspace(0, 2, 50)
ax.set_title('excluding walking')
pdf, bins, patches = ax.hist(all_vels, bins=bins, normed=True)
ax.set_xlim(0, 2)
ax.set_ylabel('probability density')
ax.set_xlabel('velocity (m/s)')
return line2obj_id
