def create_trajectory (self, nframes, h5filebase, start, finish):
# need to make some kalman rows!
# kalman rows = [obj_id, frame, time of acquisition, positions[3], velocities[3], other crap]
self.n_artificial_trajecs = self.n_artificial_trajecs + 1
# get extra, pretty much just for the time model
ca = core_analysis.get_global_CachingAnalyzer()
(obj_ids, use_obj_ids, is_mat_file, data_file, extra) = ca.initial_file_load(h5filebase)
start = np.array(start)
finish = np.array(finish)
mean_vel = (finish - start)
delta_t = extra['time_model'].framestamp2timestamp(2) - extra['time_model'].framestamp2timestamp(1)
kalman_rows = np.zeros([nframes, 9])
for n in range(nframes):
kalman_rows[n,0] = 1
kalman_rows[n,1] = n
kalman_rows[n,2] = 0
kalman_rows[n,6:9] = mean_vel
kalman_rows[n,3:6] = start + mean_vel*delta_t
traj_id = (str('artificial'+'_'+str(self.n_artificial_trajecs)))
self.trajecs.setdefault(traj_id, Trajectory(kalman_rows, extra, stimulus = self.stimulus) )
def load_data(self, filename, kalman_smoothing=False, dynamic_model=None, fps=None, info={}, save_covariance=False):
# use info to pass information to trajectory instances as a dictionary.
# eg. info={"post_type": "black"}
# save_covariance: set to True if you need access to the covariance data. Keep as False if this is not important for analysis (takes up lots of space)
# set up analyzer
ca = core_analysis.get_global_CachingAnalyzer()
(obj_ids, use_obj_ids, is_mat_file, data_file, extra) = ca.initial_file_load(filename)
data_file.flush()
# data set defaults
if fps is None:
fps = result_utils.get_fps(data_file)
if dynamic_model is None:
try:
dyn_model = extra['dynamic_model_name']
except:
print 'cannot find dynamic model'
print 'using EKF mamarama, units: mm'
dyn_model = 'EKF mamarama, units: mm'
if dynamic_model is not None:
dyn_model = dynamic_model
# if kalman smoothing is on, then we cannot use the EKF model - remove that from the model name
print '** Kalman Smoothing is: ', kalman_smoothing, ' **'
if kalman_smoothing is True:
dyn_model = dyn_model[4:]
print 'using dynamic model: ', dyn_model
print 'framerate: ', fps
print 'loading data.... '
self.dynamic_model = dyn_model
self.fps = fps
# load object id's and save as Trajectory instances
for obj_id in use_obj_ids:
print 'processing: ', obj_id
try:
print obj_id
kalman_rows = ca.load_data( obj_id, data_file,
dynamic_model_name = dyn_model,
use_kalman_smoothing= kalman_smoothing,
frames_per_second= fps)
except:
print 'object id failed to load (probably no data): ', obj_id
continue
# couple object ID dictionary with trajectory objects
trajec_id = str(obj_id) # this is not necessarily redundant with the obj_id, it allows for making a unique trajectory id when merging multiple datasets
tmp = Trajectory(trajec_id, kalman_rows, info=info, fps=fps, save_covariance=save_covariance, extra=extra)
self.trajecs.setdefault(trajec_id, tmp)
return
def consider_stimulus(h5file, verbose_problems=False,
fanout_name="fanout.xml"):
"""
Parses the corresponding fanout XML and finds IDs to use as well
as the stimulus.
Returns 3 values: valid, use_objs_ids, stimulus.
valid is false if something was wrong
"""
try:
dirname = os.path.dirname(h5file)
fanout_xml = os.path.join(dirname, fanout_name)
if not(os.path.exists(fanout_xml)):
if verbose_problems:
logger.error("Stim_xml path not found '%s' for file '%s'" %
(h5file, fanout_xml))
return False, None, None
ca = core_analysis.get_global_CachingAnalyzer()
(_, use_obj_ids, _, _, _) = ca.initial_file_load(h5file)
file_timestamp = timestamp_string_from_filename(h5file)
fanout = xml_stimulus.xml_fanout_from_filename(fanout_xml)
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))
episode = fanout._get_episode_for_timestamp(
timestamp_string=file_timestamp)
(_, _, stim_fname) = episode
return True, use_obj_ids, stim_fname
except xml_stimulus.WrongXMLTypeError:
if verbose_problems:
logger.error("Caught WrongXMLTypeError for '%s'" % file_timestamp)
return False, None, None
except ValueError, ex:
if verbose_problems:
logger.error("Caught ValueError for '%s': %s" %
(file_timestamp, ex))
return False, None, None
def __init__(self,kalman_filename):
import flydra.a2.core_analysis as core_analysis
import flydra.analysis.result_utils as result_utils
self.ca = core_analysis.get_global_CachingAnalyzer()
(obj_ids, use_obj_ids, is_mat_file, data_file,
extra) = self.ca.initial_file_load(kalman_filename)
self.data_file = data_file
self.up_dir = None
if 1:
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
self.dynamic_model = dynamic_model
self.fps = result_utils.get_fps( data_file )
self.use_kalman_smoothing = True
self.up_dir = (0,0,1)
def test(self, filename, kalman_smoothing=False, dynamic_model=None, fps=None, info={}, save_covariance=False):
ca = core_analysis.get_global_CachingAnalyzer()
(obj_ids, use_obj_ids, is_mat_file, data_file, extra) = ca.initial_file_load(filename)
return ca, obj_ids, use_obj_ids, is_mat_file, data_file, extra
def __init__(self, h5):
self._ca = core_analysis.get_global_CachingAnalyzer()
obj_ids, use_obj_ids, is_mat_file, data_file, extra = self._ca.initial_file_load(
h5)
x = []
y = []
z = []
for obj_id_enum, obj_id in enumerate(use_obj_ids):
rows = self._ca.load_data(obj_id,
data_file,
use_kalman_smoothing=False)
verts = np.array([rows['x'], rows['y'], rows['z']]).T
x.append(verts[:, 0])
y.append(verts[:, 1])
z.append(verts[:, 2])
x = np.concatenate(x)
y = np.concatenate(y)
z = np.concatenate(z)
coords = np.array([x, y, z]).T
self.pubcyl = None
self.pubpt = None
self.p = pcl.PointCloud()
self.p.from_array(coords)
self.p.to_file('/tmp/fuckit.pcd')
self.np = len(coords)
self._cx, self._cy, self._cz, self._ax, self._ay, self._az, self._radius = (
0, 0, 0, 0, 0, 0, 0)
self.pubpts, pts = calib.visualization.create_point_cloud_message_publisher(
coords,
topic_name='/flydracalib/points',
publish_now=True,
latch=True)
self.pubptsinliers = calib.visualization.create_point_cloud_message_publisher(
[(0, 0, 0)],
topic_name='/flydracalib/pointsinliers',
publish_now=False,
latch=True)
self.pubcyl = calib.visualization.create_cylinder_publisher(
self._cx,
self._cy,
self._cz,
self._ax,
self._ay,
self._az,
self._radius,
topic_name='/flydracalib/cyl',
publish_now=False,
latch=True,
length=5,
color=(0, 1, 1, 0.4))
self.pubpt = calib.visualization.create_point_publisher(
self._cx,
self._cy,
self._cz,
r=0.1,
topic_name='/flydracalib/cylcenter',
publish_now=False,
latch=True,
color=(1, 0, 0, 0.5))
thisdir = os.path.dirname(os.path.abspath(__file__))
self.ui = Gtk.Builder()
self.ui.add_from_file(os.path.join(thisdir, "tune.ui"))
self._ks = -1.0
self.ui.get_object("ks_adjustment").props.value = self._ks
self._sr = 0.5
self.ui.get_object("sr_adjustment").props.value = self._sr
self._nd = 0.5
self.ui.get_object("nd_adjustment").props.value = self._nd
self._dt = 0.10
self.ui.get_object("dt_adjustment").props.value = self._dt
self._minr = 0.5
self.ui.get_object("minr_adjustment").props.value = self._minr
self._maxr = 1.5
self.ui.get_object("maxr_adjustment").props.value = self._maxr
self._exbtn = self.ui.get_object("execute_btn")
self._exbtn.connect("clicked", self._calculate)
self._axadj = self.ui.get_object("ax_adjustment")
self._ayadj = self.ui.get_object("ay_adjustment")
self._azadj = self.ui.get_object("az_adjustment")
self._cxadj = self.ui.get_object("cx_adjustment")
self._cyadj = self.ui.get_object("cy_adjustment")
self._czadj = self.ui.get_object("cz_adjustment")
self._radj = self.ui.get_object("r_adjustment")
self._in = self.ui.get_object("inliersentry")
self.ui.connect_signals(self)
w = self.ui.get_object("window1")
w.connect("delete-event", rosgobject.main_quit)
w.show_all()
def load_data(self, filename, kalman_smoothing=False, dynamic_model=None, fps=None, info={}, save_covariance=False, experiment_length=19*60*60):
# use info to pass information to trajectory instances as a dictionary.
# eg. info={"post_type": "black"}
# save_covariance: set to True if you need access to the covariance data. Keep as False if this is not important for analysis (takes up lots of space)
# set up analyzer
ca = core_analysis.get_global_CachingAnalyzer()
(obj_ids, use_obj_ids, is_mat_file, data_file, extra) = ca.initial_file_load(filename)
data_file.flush()
# data set defaults
if fps is None:
fps = result_utils.get_fps(data_file)
if dynamic_model is None:
try:
dyn_model = extra['dynamic_model_name']
except:
print 'cannot find dynamic model'
print 'using EKF mamarama, units: mm'
dyn_model = 'EKF mamarama, units: mm'
if dynamic_model is not None:
dyn_model = dynamic_model
# if kalman smoothing is on, then we cannot use the EKF model - remove that from the model name
print '** Kalman Smoothing is: ', kalman_smoothing, ' **'
if kalman_smoothing is True:
dyn_model = dyn_model[4:]
print 'using dynamic model: ', dyn_model
print 'framerate: ', fps
print 'loading data.... '
self.dynamic_model = dyn_model
self.fps = fps
time_start = None
# load object id's and save as Trajectory instances
for obj_id in use_obj_ids:
print 'processing: ', obj_id
try:
print obj_id
kalman_rows = ca.load_data( obj_id, data_file,
dynamic_model_name = dyn_model,
use_kalman_smoothing= kalman_smoothing,
frames_per_second= fps)
except:
print 'object id failed to load (probably no data): ', obj_id
continue
# couple object ID dictionary with trajectory objects
filenamebase = os.path.basename(filename)
trajecbase = filenamebase.rstrip('5').rstrip('.h').lstrip('DATA').rstrip('.kalmanized')
trajec_id = trajecbase + '_' + str(obj_id) # filename details + original object id - this is unique
tmp = Trajectory(trajec_id, kalman_rows, info=info, fps=fps, save_covariance=save_covariance, extra=extra)
tmp.h5 = os.path.basename(filename)
if time_start is None:
time_start = tmp.timestamp_epoch
else:
if tmp.timestamp_epoch - time_start > experiment_length:
break
else:
self.trajecs.setdefault(trajec_id, tmp)
self.h5_files_loaded.append(os.path.basename(filename))
return
def load_data (self, filename,
calibration_file = None,
objs = None,
obj_filelist = None,
kalman_smoothing = True,
fps = None,
dynamic_model = None,
load_2d=True,):
self.datasets.append(len(self.datasets)+1)
# raw h5 - this is the 2d raw camera data:
if load_2d is True:
h5 = PT.openFile( filename, mode='r' )
camn2cam_id, cam_id2camns = result_utils.get_caminfo_dicts(h5)
cam_ids = cam_id2camns.keys()
cams2d = []
for cam_id in cam_ids:
cams2d.append( Cam2d(cam_id, h5) )
self.cams2d.append(cams2d)
# set up analyzer
ca = core_analysis.get_global_CachingAnalyzer()
(obj_ids, use_obj_ids, is_mat_file, data_file, extra) = ca.initial_file_load(filename)
# data set defaults
if fps is None:
fps = result_utils.get_fps(data_file)
if dynamic_model is None:
try:
dyn_model = extra['dynamic_model_name']
except:
dyn_model = 'EKF mamarama, units: mm'
if dynamic_model is not None:
dyn_model = dynamic_model
# if kalman smoothing is on, then we cannot use the EKF model - remove that from the model name
print '**Kalman Smoothing is: ', kalman_smoothing, ' **'
if kalman_smoothing is True:
dyn_model = dyn_model[4:]
print 'using dynamic model: ', dyn_model
if objs is None and obj_filelist is None:
print "running through all object id's, this might take a while..."
obj_only = use_obj_ids # this is all the unique object id's
if obj_filelist is not None:
tmp = np.loadtxt(obj_filelist,delimiter=',')
obj_only = np.array(tmp[:,0], dtype='int')
elif objs is not None:
obj_only = np.array(objs)
print 'loading data.... '
for obj_id in obj_only:
try:
kalman_rows = ca.load_data( obj_id, data_file,
dynamic_model_name = dyn_model,
use_kalman_smoothing= kalman_smoothing,
frames_per_second= fps)
except:
print 'object id failed to load (probably no data): ', obj_id
continue
# couple object ID dictionary with trajectory objects
traj_id = (str(self.datasets[-1])+'_'+str(obj_id))
self.trajecs.setdefault(traj_id, ffa.Trajectory(kalman_rows, extra, stimulus = self.stimulus, fps = fps) )
def __init__(self, h5):
self._ca = core_analysis.get_global_CachingAnalyzer()
obj_ids, use_obj_ids, is_mat_file, data_file, extra = self._ca.initial_file_load(h5)
x = []
y = []
z = []
for obj_id_enum,obj_id in enumerate(use_obj_ids):
rows = self._ca.load_data(obj_id,
data_file,
use_kalman_smoothing=False)
verts = np.array( [rows['x'], rows['y'], rows['z']] ).T
x.append( verts[:,0] )
y.append( verts[:,1] )
z.append( verts[:,2] )
x = np.concatenate(x)
y = np.concatenate(y)
z = np.concatenate(z)
coords = np.array([x,y,z]).T
self.pubcyl = None
self.pubpt = None
self.p = pcl.PointCloud()
self.p.from_array(coords)
self.p.to_file('/tmp/fuckit.pcd')
self.np = len(coords)
self._cx,self._cy,self._cz,self._ax,self._ay,self._az,self._radius = (0,0,0,0,0,0,0)
self.pubpts, pts = calib.visualization.create_point_cloud_message_publisher(
coords,
topic_name='/flydracalib/points',
publish_now=True,
latch=True)
self.pubptsinliers = calib.visualization.create_point_cloud_message_publisher(
[(0,0,0)],
topic_name='/flydracalib/pointsinliers',
publish_now=False,
latch=True)
self.pubcyl = calib.visualization.create_cylinder_publisher(
self._cx,self._cy,self._cz,self._ax,self._ay,self._az,self._radius,
topic_name='/flydracalib/cyl',
publish_now=False,
latch=True,
length=5,
color=(0,1,1,0.4))
self.pubpt = calib.visualization.create_point_publisher(
self._cx,self._cy,self._cz,
r=0.1,
topic_name='/flydracalib/cylcenter',
publish_now=False,
latch=True,
color=(1,0,0,0.5))
thisdir = os.path.dirname(os.path.abspath(__file__))
self.ui = Gtk.Builder()
self.ui.add_from_file(os.path.join(thisdir,"tune.ui"))
self._ks = -1.0
self.ui.get_object("ks_adjustment").props.value = self._ks
self._sr = 0.5
self.ui.get_object("sr_adjustment").props.value = self._sr
self._nd = 0.5
self.ui.get_object("nd_adjustment").props.value = self._nd
self._dt = 0.10
self.ui.get_object("dt_adjustment").props.value = self._dt
self._minr = 0.5
self.ui.get_object("minr_adjustment").props.value = self._minr
self._maxr = 1.5
self.ui.get_object("maxr_adjustment").props.value = self._maxr
self._exbtn = self.ui.get_object("execute_btn")
self._exbtn.connect("clicked", self._calculate)
self._axadj = self.ui.get_object("ax_adjustment")
self._ayadj = self.ui.get_object("ay_adjustment")
self._azadj = self.ui.get_object("az_adjustment")
self._cxadj = self.ui.get_object("cx_adjustment")
self._cyadj = self.ui.get_object("cy_adjustment")
self._czadj = self.ui.get_object("cz_adjustment")
self._radj = self.ui.get_object("r_adjustment")
self._in = self.ui.get_object("inliersentry")
self.ui.connect_signals(self)
w = self.ui.get_object("window1")
w.connect("delete-event", rosgobject.main_quit)
w.show_all()
def get_good_smoothed_tracks(filename, obj_ids,
min_frames_per_track,
use_smoothing,
dynamic_model_name):
''' Yields (obj_id, rows) for each track in obj_ids in the file
that has the given minimum number of frames. '''
frames_per_second = 60.0
dt = 1 / frames_per_second
ca = core_analysis.get_global_CachingAnalyzer()
#warned = False
#obj_ids, unique_obj_ids, is_mat_file, data_file, extra = \
# ca.initial_file_load(filename)
data_file = filename
for obj_id in obj_ids:
try:
frows = ca.load_data(obj_id, data_file, use_kalman_smoothing=False)
# don't consider tracks too small
if len(frows) < min_frames_per_track:
continue
# write timestamp entry
# The 'timestamp' field returned by flydra is the time
# when the computation was made, not the actual data timestamp.
# For computing the actual timestamp, use the frame number
# and multiply by dt
global warned_fixed_dt
if not warned_fixed_dt:
warned_fixed_dt = True
logger.info('Warning: We are assuming that the data is ' \
'equally spaced, and fps = %s.' % frames_per_second)
for i in range(len(frows)):
frows['timestamp'][i] = frows['frame'][i] * dt
for i in range(len(frows) - 1):
if frows['obj_id'][i] == frows['obj_id'][i + 1]:
if not (frows['timestamp'][i] < frows['timestamp'][i + 1]):
print("fishy behavior at index %d" % i)
# return raw data if smoothing is not requested
if not use_smoothing:
yield obj_id, frows
continue
# otherwise, run the smoothing
srows = ca.load_data(obj_id, data_file, use_kalman_smoothing=True,
frames_per_second=frames_per_second,
dynamic_model_name=dynamic_model_name);
# make a copy, just in case
srows = srows.copy()
for i in range(len(srows)):
srows['timestamp'][i] = srows['frame'][i] * dt
# From Andrew:
# I'm pretty sure there is an inconsistency in some of this
# unit stuff. Basically, I used to do the camera calibrations
# all in mm (so that the 3D coords would come out in mm). Then,
# I started doing analyses in meters... And I think some of
# the calibration and dynamic model stuff got defaulted to meters.
# And basically there are inconsistencies in there.
# Anyhow, I think the extent of the issue is that you'll be off
# by 1000, so hopefully you can just determine that by looking at the data.
# quick fix
if dynamic_model_name == "mamarama, units: mm":
#and not warned:
#warned = True
# logger.info("Warning: Implementing simple workaround for flydra's " \
# "units inconsistencies (multiplying xvel,yvel by 1000).")
#srows['xvel'] *= 1000
#srows['yvel'] *= 1000
#srows['xvel'] *= 1000
v = numpy.hypot(srows['xvel'], srows['yvel'], srows['zvel'])
perc = [1, 5, 50, 95, 99]
scores = map(lambda x: scipy.stats.scoreatpercentile(v, x), perc)
print "scores: %s" % map(lambda x: "%f " % x, scores)
score95 = scipy.stats.scoreatpercentile(v, 95)
if score95 < 100.0:
logger.debug(" score95 = %f, assuming m" % score95)
else:
logger.debug(" score95 = %f, assuming mm" % score95)
srows['xvel'] *= 0.001
srows['yvel'] *= 0.001
srows['xvel'] *= 0.001
v = numpy.hypot(srows['xvel'], srows['yvel'], srows['zvel'])
final_score95 = scipy.stats.scoreatpercentile(v, 95)
logger.info('After much deliberation, 95%% score is %f.' %
final_score95)
yield obj_id, srows
except core_analysis.NotEnoughDataToSmoothError:
logger.warning('not enough data to smooth obj_id %d, skipping.' % (obj_id,))
continue
ca.close()
def main():
np.seterr(all='raise')
parser = OptionParser()
parser.add_option("--output_dir", default='saccade_detect_output',
help="Output directory")
parser.add_option("--min_frames_per_track", default=400,
help="Minimum number of frames per track [= %default]")
parser.add_option("--confirm_problems",
help="Stop interactively on problems with log files'\
'(e.g.: cannot find valid obj_ids) [default: %default]",
default=False, action="store_true")
parser.add_option("--dynamic_model_name",
help="Smoothing dynamical model [default: %default]",
default="mamarama, units: mm")
parser.add_option("--debug_output", help="Creates debug figures.",
default=False, action="store_true")
parser.add_option("--nocache", help="Ignores already computed results.",
default=False, action="store_true")
parser.add_option("--smoothing", help="Uses Kalman-smoothed data.",
default=False, action="store_true")
# detection parameters
dt = 1.0 / 60
parser.add_option("--deltaT_inner_sec", default=4 * dt, type='float',
help="Inner interval [= %default]")
parser.add_option("--deltaT_outer_sec", default=10 * dt, type='float',
help="Outer interval [= %default]")
parser.add_option("--min_amplitude_deg", default=25, type='float',
help="Minimum saccade amplitude (deg) [= %default]")
parser.add_option("--min_linear_velocity", default=0.1, type='float',
help="Minimum linear velocity when saccading (m/s) "
"[= %default]")
parser.add_option("--max_linear_acceleration", default=20, type='float',
help="Maximum linear acceleration when saccading "
"(m/s^2) [= %default]")
parser.add_option("--max_angular_velocity", default=8000, type='float',
help="Maximum angular velocity when saccading (deg/s) "
"[= %default]")
parser.add_option("--max_orientation_dispersion_deg", default=15,
type='float',
help="Maximum dispersion (deg) [= %default]")
parser.add_option("--minimum_interval_sec", default=10 * dt, type='float',
help="Minimum interval between saccades. [= %default]")
(options, args) = parser.parse_args()
if not args:
logger.error('No files or directories specified.')
sys.exit(-1)
# Create processed string
processed = 'geometric_saccade_detector %s %s %s@%s Python %s' % \
(__version__, datetime.now().strftime("%Y%m%d_%H%M%S"),
get_user(), platform.node(), platform.python_version())
if not os.path.exists(options.output_dir):
os.makedirs(options.output_dir)
good_files = get_good_files(where=args, pattern="*.kh5",
confirm_problems=options.confirm_problems)
if len(good_files) == 0:
logger.error("No good files to process.")
sys.exit(1)
try:
n = len(good_files)
for i in range(n):
(filename, obj_ids, stim_fname) = good_files[i]
# only maintain basename
stim_fname = os.path.splitext(os.path.basename(stim_fname))[0]
basename = os.path.splitext(os.path.basename(filename))[0]
output_basename = os.path.join(options.output_dir,
basename + '-saccades')
output_saccades_hdf = output_basename + '.h5'
if os.path.exists(output_saccades_hdf) and not options.nocache:
logger.info('File %r exists; skipping. '
'(use --nocache to ignore)' %
output_saccades_hdf)
continue
logger.info("File %d/%d %s %s %s " %
(i, n, str(filename), str(obj_ids), stim_fname))
# concatenate all in one track
all_data = None
for _, rows in get_good_smoothed_tracks(
filename=filename,
obj_ids=obj_ids,
min_frames_per_track=options.min_frames_per_track,
dynamic_model_name=options.dynamic_model_name,
use_smoothing=options.smoothing):
all_data = rows.copy() if all_data is None \
else np.concatenate((all_data, rows))
if all_data is None:
logger.info('Not enough data found for %s; skipping.' %
filename)
continue
params = {
'deltaT_inner_sec': options.deltaT_inner_sec,
'deltaT_outer_sec': options. deltaT_outer_sec,
'min_amplitude_deg': options.min_amplitude_deg,
'max_orientation_dispersion_deg':
options.max_orientation_dispersion_deg,
'minimum_interval_sec': options.minimum_interval_sec,
'max_linear_acceleration': options.max_linear_acceleration,
'min_linear_velocity': options.min_linear_velocity,
'max_angular_velocity': options.max_angular_velocity,
}
saccades, annotated_data = geometric_saccade_detect(all_data,
params)
for saccade in saccades:
check_saccade_is_well_formed(saccade)
# other fields used for managing different samples,
# used in the analysis
saccades['species'] = 'Dmelanogaster'
saccades['stimulus'] = stim_fname
sample_name = 'DATA' + timestamp_string_from_filename(filename)
saccades['sample'] = sample_name
saccades['sample_num'] = -1 # will be filled in by someone else
saccades['processed'] = processed
logger.info("Writing to %s {h5,mat,pickle}" % output_basename)
saccades_write_all(output_basename, saccades)
# Write debug figures
if options.debug_output:
debug_output_dir = os.path.join(options.output_dir, basename)
logger.info("Writing HTML+png to %s" % debug_output_dir)
write_debug_output(debug_output_dir, basename,
annotated_data, saccades)
except Exception as e:
logger.error('Error while processing. Exception and traceback follow.')
logger.error(str(e))
logger.error(traceback.format_exc())
sys.exit(-2)
finally:
print('Closing flydra cache')
ca = core_analysis.get_global_CachingAnalyzer()
ca.close()
sys.exit(0)
def get_good_smoothed_tracks(filename, obj_ids,
min_frames_per_track,
use_smoothing,
dynamic_model_name):
''' Yields (obj_id, rows) for each track in obj_ids in the file
that has the given minimum number of frames. '''
frames_per_second = 60.0
dt = 1 / frames_per_second
ca = core_analysis.get_global_CachingAnalyzer()
warned = False
#obj_ids, unique_obj_ids, is_mat_file, data_file, extra = \
# ca.initial_file_load(filename)
data_file = filename
for obj_id in obj_ids:
try:
frows = ca.load_data(obj_id, data_file, use_kalman_smoothing=False)
# don't consider tracks too small
if len(frows) < min_frames_per_track:
continue
# write timestamp entry
# The 'timestamp' field returned by flydra is the time
# when the computation was made, not the actual data timestamp.
# For computing the actual timestamp, use the frame number
# and multiply by dt
global warned_fixed_dt
if not warned_fixed_dt:
warned_fixed_dt = True
logger.info('Warning: We are assuming that the data is ' \
'equally spaced, and fps = %s.' % frames_per_second)
for i in range(len(frows)):
frows['timestamp'][i] = frows['frame'][i] * dt
for i in range(len(frows) - 1):
if frows['obj_id'][i] == frows['obj_id'][i + 1]:
assert frows['timestamp'][i] < frows['timestamp'][i + 1]
# return raw data if smoothing is not requested
if not use_smoothing:
yield (obj_id,
extract_interesting_fields(frows,
np.dtype(rows_dtype)))
continue
# otherwise, run the smoothing
srows = ca.load_data(obj_id, data_file, use_kalman_smoothing=True,
frames_per_second=frames_per_second,
dynamic_model_name=dynamic_model_name)
# make a copy, just in case
srows = srows.copy()
for i in range(len(srows)):
srows['timestamp'][i] = srows['frame'][i] * dt
# From Andrew:
# I'm pretty sure there is an inconsistency in some of this
# unit stuff. Basically, I used to do the camera calibrations
# all in mm (so that the 3D coords would come out in mm). Then,
# I started doing analyses in meters... And I think some of
# the calibration and dynamic model stuff got defaulted to meters.
# And basically there are inconsistencies in there.
# Anyhow, I think the extent of the issue is that you'll be off
# by 1000, so hopefully you can just determine that by looking
# at the data.
# quick fix
if dynamic_model_name == "mamarama, units: mm" and not warned:
warned = True
logger.info("Warning: Implementing simple workaround"
" for flydra's "
"units inconsistencies "
"(multiplying xvel,yvel by 1000).")
srows['xvel'] *= 1000
srows['yvel'] *= 1000
yield obj_id, extract_interesting_fields(srows,
np.dtype(rows_dtype))
except core_analysis.NotEnoughDataToSmoothError:
#logger.warning('not enough data to
# smooth obj_id %d, skipping.'%(obj_id,))
continue
ca.close()
def load_data (self, filename, calibration_file = None, objs = None, obj_filelist = None, kalman_smoothing = True, fps = None, dynamic_model = None, gender = None, post_type=None):
self.datasets.append(len(self.datasets)+1)
self.filename.append(filename)
if calibration_file is not None:
print "Calibration Files not yet supported!!!"
# set up analyzer
ca = core_analysis.get_global_CachingAnalyzer()
(obj_ids, use_obj_ids, is_mat_file, data_file, extra) = ca.initial_file_load(filename)
# data set defaults
if fps is None:
fps = result_utils.get_fps(data_file)
if dynamic_model is None:
try:
dyn_model = extra['dynamic_model_name']
except:
dyn_model = 'EKF mamarama, units: mm'
if dynamic_model is not None:
dyn_model = dynamic_model
# if kalman smoothing is on, then we cannot use the EKF model - remove that from the model name
print '**Kalman Smoothing is: ', kalman_smoothing, ' **'
if kalman_smoothing is True:
dyn_model = dyn_model[4:]
print 'using dynamic model: ', dyn_model
if objs is None and obj_filelist is None:
print "running through all object id's, this might take a while..."
obj_only = use_obj_ids # this is all the unique object id's
if obj_filelist is not None:
tmp = np.loadtxt(obj_filelist,delimiter=',')
obj_only = np.array(tmp[:,0], dtype='int')
elif objs is not None:
if type(objs) is not list:
objs = [objs]
obj_only = np.array(objs)
print fps
print 'loading data.... '
for obj_id in obj_only:
print 'processing: ', obj_id
try:
print obj_id
# kalman rows = [
kalman_rows = ca.load_data( obj_id, data_file,
dynamic_model_name = dyn_model,
use_kalman_smoothing= kalman_smoothing,
frames_per_second= fps)
except:
print 'object id failed to load (probably no data): ', obj_id
continue
#print kalman_rows[0]
# couple object ID dictionary with trajectory objects
traj_id = (str(self.datasets[-1])+'_'+str(obj_id))
self.trajecs.setdefault(traj_id, Trajectory(kalman_rows, extra, stimulus = self.stimulus, fps = fps, post_type=post_type) )
