def __init__(self, src_h5, dst_h5):
self.ca = core_analysis.get_global_CachingAnalyzer()
self.src_h5 = src_h5
self.dst_h5 = dst_h5
self.ca.initial_file_load(self.src_h5)
tmp = self.ca.initial_file_load(self.dst_h5)
obj_ids, unique_obj_ids, is_mat_file, data_file, extra = tmp
self.dst_frames = extra["frames"]
self.dst_obj_ids = obj_ids
self.dst_unique_obj_ids = unique_obj_ids
self.ff = utils.FastFinder(self.dst_frames)
def convert(
infilename,
outfilename,
frames_per_second=None,
save_timestamps=True,
file_time_data=None,
do_nothing=False, # set to true to test for file existance
start_obj_id=None,
stop_obj_id=None,
obj_only=None,
dynamic_model_name=None,
hdf5=False,
show_progress=False,
show_progress_json=False,
**kwargs):
if start_obj_id is None:
start_obj_id = -numpy.inf
if stop_obj_id is None:
stop_obj_id = numpy.inf
smoothed_data_filename = os.path.split(infilename)[1]
raw_data_filename = smoothed_data_filename
ca = core_analysis.get_global_CachingAnalyzer()
with ca.kalman_analysis_context(infilename,
data2d_fname=file_time_data) as h5_context:
extra_vars = {}
tzname = None
if save_timestamps:
print 'STAGE 1: finding timestamps'
table_kobs = h5_context.get_pytable_node('ML_estimates')
tzname = h5_context.get_tzname0()
fps = h5_context.get_fps()
try:
table_data2d = h5_context.get_pytable_node('data2d_distorted',
from_2d_file=True)
except tables.exceptions.NoSuchNodeError, err:
print >> sys.stderr, "No timestamps in file. Either specify not to save timestamps ('--no-timestamps') or specify the original .h5 file with the timestamps ('--time-data=FILE2D')"
sys.exit(1)
print 'caching raw 2D data...',
sys.stdout.flush()
table_data2d_frames = table_data2d.read(field='frame')
assert numpy.max(table_data2d_frames) < 2**63
table_data2d_frames = table_data2d_frames.astype(numpy.int64)
#table_data2d_frames_find = fastsearch.binarysearch.BinarySearcher( table_data2d_frames )
table_data2d_frames_find = utils.FastFinder(table_data2d_frames)
table_data2d_camns = table_data2d.read(field='camn')
table_data2d_timestamps = table_data2d.read(field='timestamp')
print 'done'
print '(cached index of %d frame values of dtype %s)' % (
len(table_data2d_frames), str(table_data2d_frames.dtype))
drift_estimates = h5_context.get_drift_estimates()
camn2cam_id, cam_id2camns = h5_context.get_caminfo_dicts()
gain = {}
offset = {}
print 'hostname time_gain time_offset'
print '-------- --------- -----------'
for i, hostname in enumerate(drift_estimates.get('hostnames', [])):
tgain, toffset = result_utils.model_remote_to_local(
drift_estimates['remote_timestamp'][hostname][::10],
drift_estimates['local_timestamp'][hostname][::10])
gain[hostname] = tgain
offset[hostname] = toffset
print ' ', repr(hostname), tgain, toffset
print
if do_nothing:
return
print 'caching Kalman obj_ids...'
obs_obj_ids = table_kobs.read(field='obj_id')
fast_obs_obj_ids = utils.FastFinder(obs_obj_ids)
print 'finding unique obj_ids...'
unique_obj_ids = numpy.unique(obs_obj_ids)
print '(found %d)' % (len(unique_obj_ids), )
unique_obj_ids = unique_obj_ids[unique_obj_ids >= start_obj_id]
unique_obj_ids = unique_obj_ids[unique_obj_ids <= stop_obj_id]
if obj_only is not None:
unique_obj_ids = numpy.array(
[oid for oid in unique_obj_ids if oid in obj_only])
print 'filtered to obj_only', obj_only
print '(will export %d)' % (len(unique_obj_ids), )
print 'finding 2d data for each obj_id...'
timestamp_time = numpy.zeros(unique_obj_ids.shape,
dtype=numpy.float64)
table_kobs_frame = table_kobs.read(field='frame')
if len(table_kobs_frame) == 0:
raise ValueError('no 3D data, cannot convert')
assert numpy.max(table_kobs_frame) < 2**63
table_kobs_frame = table_kobs_frame.astype(numpy.int64)
assert table_kobs_frame.dtype == table_data2d_frames.dtype # otherwise very slow
all_idxs = fast_obs_obj_ids.get_idx_of_equal(unique_obj_ids)
for obj_id_enum, obj_id in enumerate(unique_obj_ids):
idx0 = all_idxs[obj_id_enum]
framenumber = table_kobs_frame[idx0]
remote_timestamp = numpy.nan
this_camn = None
frame_idxs = table_data2d_frames_find.get_idxs_of_equal(
framenumber)
if len(frame_idxs):
frame_idx = frame_idxs[0]
this_camn = table_data2d_camns[frame_idx]
remote_timestamp = table_data2d_timestamps[frame_idx]
if this_camn is None:
print 'skipping frame %d (obj %d): no data2d_distorted data' % (
framenumber, obj_id)
continue
cam_id = camn2cam_id[this_camn]
try:
remote_hostname = cam_id2hostname(cam_id, h5_context)
except ValueError, e:
print 'error getting hostname of cam: %s' % e.message
continue
if remote_hostname not in gain:
warnings.warn('no host %s in timestamp data. making up '
'data.' % remote_hostname)
gain[remote_hostname] = 1.0
offset[remote_hostname] = 0.0
mainbrain_timestamp = remote_timestamp * gain[
remote_hostname] + offset[
remote_hostname] # find mainbrain timestamp
timestamp_time[obj_id_enum] = mainbrain_timestamp
extra_vars['obj_ids'] = unique_obj_ids
extra_vars['timestamps'] = timestamp_time
print 'STAGE 2: running Kalman smoothing operation'
def find_equiv(self, src_obj_id, mean_distance_maximum=None):
"""find the obj_id in the dst file that corresponds to src_obj_id
arguments
---------
src_obj_id : int
The obj_id of the object in src_h5 to find.
mean_distance_maximum : float or None
The maximum average distance between points in dst and src.
returns
-------
dst_obj_id : int
The obj_id in dst_h5 that corresponds to the src_obj_id
"""
# get information from source to identify trace in dest
src_rows = self.ca.load_data(src_obj_id,
self.src_h5,
use_kalman_smoothing=False)
src_frame = src_rows["frame"]
if len(src_frame) < 2:
raise ValueError("Can only find equivalent obj_id if "
"2 or more frames present")
src_X = np.vstack((src_rows["x"], src_rows["y"], src_rows["z"]))
src_timestamp = src_rows["timestamp"]
candidate_obj_id = set()
for f in src_frame:
idxs = self.ff.get_idxs_of_equal(f)
for obj_id in self.dst_obj_ids[idxs]:
candidate_obj_id.add(obj_id)
candidate_obj_id = list(candidate_obj_id)
## print 'candidate_obj_id',candidate_obj_id
error = []
for obj_id in candidate_obj_id:
# get array for each candidation obj_id in destination
dst_rows = self.ca.load_data(obj_id,
self.dst_h5,
use_kalman_smoothing=False)
dst_frame = dst_rows["frame"]
dst_X = np.vstack((dst_rows["x"], dst_rows["y"], dst_rows["z"]))
dst_ff = utils.FastFinder(dst_frame)
# get indices into destination array for each frame of source
dst_idxs = dst_ff.get_idx_of_equal(src_frame, missing_ok=1)
assert len(dst_idxs) == len(src_frame)
missing_cond = dst_idxs == -1 # these points are in source but not dest
n_missing = np.sum(missing_cond)
n_total = len(src_frame)
present_cond = ~missing_cond
final_dst_idxs = dst_idxs[present_cond]
final_src_idxs = np.arange(len(src_frame))[present_cond]
src_X_i = src_X[:, final_src_idxs]
dst_X_i = dst_X[:, final_dst_idxs]
diff = src_X_i - dst_X_i
dist = np.sqrt(np.sum(diff**2, axis=0))
av_dist = np.mean(dist)
frac_missing = n_missing / float(
n_total) # 0 = none missing, 1 = all
## print 'candidate dst obj_id %d: %s dist, %s missing'%(
## obj_id, av_dist, frac_missing)
if frac_missing > 0.1:
this_error = np.inf
else:
this_error = av_dist
error.append(this_error)
idx = np.argmin(error)
best_error = error[idx]
if not np.isfinite(best_error):
return None # could not find answer
else:
if (mean_distance_maximum is None) or (best_error <=
mean_distance_maximum):
return candidate_obj_id[idx]
else:
return None
def iterate_frames(h5_filename,
ufmf_fnames, # or fmfs
white_background=False,
max_n_frames = None,
start = None,
stop = None,
rgb8_if_color=False,
movie_cam_ids=None,
camn2cam_id = None,
):
"""yield frame-by-frame data"""
# First pass over .ufmf files: get intersection of timestamps
first_ufmf_ts = -np.inf
last_ufmf_ts = np.inf
ufmfs = {}
cam_ids = []
global_data = {'width_heights': {}}
for movie_idx,ufmf_fname in enumerate(ufmf_fnames):
if movie_cam_ids is not None:
cam_id = movie_cam_ids[movie_idx]
else:
cam_id = get_cam_id_from_ufmf_fname(ufmf_fname)
cam_ids.append( cam_id )
kwargs = {}
extra = {}
if ufmf_fname.lower().endswith('.fmf'):
ufmf = fmf_mod.FlyMovie(ufmf_fname)
bg_fmf_filename = os.path.splitext(ufmf_fname)[0] + '_mean.fmf'
if os.path.exists(bg_fmf_filename):
extra['bg_fmf'] = fmf_mod.FlyMovie(bg_fmf_filename)
extra['bg_tss'] = extra['bg_fmf'].get_all_timestamps()
extra['bg_fmf'].seek(0)
else:
ufmf = ufmf_mod.FlyMovieEmulator(ufmf_fname,
white_background=white_background,
**kwargs)
global_data['width_heights'][cam_id] = ( ufmf.get_width(), ufmf.get_height() )
tss = ufmf.get_all_timestamps()
ufmf.seek(0)
ufmfs[ufmf_fname] = (ufmf, cam_id, tss, extra)
min_ts = np.min(tss)
max_ts = np.max(tss)
if min_ts > first_ufmf_ts:
first_ufmf_ts = min_ts
if max_ts < last_ufmf_ts:
last_ufmf_ts = max_ts
assert first_ufmf_ts < last_ufmf_ts, ".ufmf files don't all overlap in time"
ufmf_fnames.sort()
cam_ids.sort()
with open_file_safe( h5_filename, mode='r' ) as h5:
if camn2cam_id is None:
camn2cam_id, cam_id2camns = result_utils.get_caminfo_dicts(h5)
parsed = result_utils.read_textlog_header(h5)
flydra_version = parsed.get('flydra_version',None)
if flydra_version is not None and flydra_version >= '0.4.45':
# camnode.py saved timestamps into .ufmf file given by
# time.time() (camn_receive_timestamp). Compare with
# mainbrain's data2d_distorted column
# 'cam_received_timestamp'.
old_camera_timestamp_source = False
timestamp_name = 'cam_received_timestamp'
else:
# camnode.py saved timestamps into .ufmf file given by
# camera driver. Compare with mainbrain's data2d_distorted
# column 'timestamp'.
old_camera_timestamp_source = True
timestamp_name = 'timestamp'
h5_data = h5.root.data2d_distorted[:]
if 1:
# narrow search to local region of .h5
cond = ((first_ufmf_ts <= h5_data[timestamp_name]) &
(h5_data[timestamp_name] <= last_ufmf_ts))
narrow_h5_data = h5_data[cond]
narrow_camns = narrow_h5_data['camn']
narrow_timestamps = narrow_h5_data[timestamp_name]
# Find the camn for each .ufmf file
cam_id2camn = {}
for cam_id in cam_ids:
cam_id_camn_already_found = False
for ufmf_fname in ufmfs.keys():
(ufmf, test_cam_id, tss, extra) = ufmfs[ufmf_fname]
if cam_id != test_cam_id:
continue
assert not cam_id_camn_already_found
cam_id_camn_already_found = True
umin=np.min(tss)
umax=np.max(tss)
cond = (umin<=narrow_timestamps) & (narrow_timestamps<=umax)
ucamns = narrow_camns[cond]
ucamns = np.unique(ucamns)
camns = []
for camn in ucamns:
if camn2cam_id[camn]==cam_id:
camns.append(camn)
assert len(camns)<2, "can't handle multiple camns per cam_id"
if len(camns):
cam_id2camn[cam_id] = camns[0]
ff = utils.FastFinder(narrow_h5_data['frame'])
unique_frames = list(np.unique(narrow_h5_data['frame']))
unique_frames.sort()
unique_frames = np.array( unique_frames )
if start is not None:
unique_frames = unique_frames[ unique_frames >= start ]
if stop is not None:
unique_frames = unique_frames[ unique_frames <= stop ]
if max_n_frames is not None:
unique_frames = unique_frames[:max_n_frames]
for frame_enum,frame in enumerate(unique_frames):
narrow_idxs = ff.get_idxs_of_equal(frame)
# trim data under consideration to just this frame
this_h5_data = narrow_h5_data[narrow_idxs]
this_camns = this_h5_data['camn']
this_tss = this_h5_data[timestamp_name]
# a couple more checks
if np.any( this_tss < first_ufmf_ts):
continue
if np.any( this_tss >= last_ufmf_ts):
break
per_frame_dict = {}
for ufmf_fname in ufmf_fnames:
ufmf, cam_id, tss, extra = ufmfs[ufmf_fname]
if cam_id not in cam_id2camn:
continue
camn = cam_id2camn[cam_id]
this_camn_cond = this_camns == camn
this_cam_h5_data = this_h5_data[this_camn_cond]
this_camn_tss = this_cam_h5_data[timestamp_name]
if not len(this_camn_tss):
# no h5 data for this cam_id at this frame
continue
this_camn_ts=np.unique(this_camn_tss)
assert len(this_camn_ts)==1
this_camn_ts = this_camn_ts[0]
if isinstance(ufmf, ufmf_mod.FlyMovieEmulator):
is_real_ufmf = True
else:
is_real_ufmf = False
# optimistic: get next frame. it's probably the one we want
try:
if is_real_ufmf:
image,image_ts,more = ufmf.get_next_frame(_return_more=True)
else:
image,image_ts = ufmf.get_next_frame()
more = fill_more_for( extra, image_ts )
except ufmf_mod.NoMoreFramesException:
image_ts = None
if this_camn_ts != image_ts:
# It was not the frame we wanted. Find it.
ufmf_frame_idxs = np.nonzero(tss == this_camn_ts)[0]
if (len(ufmf_frame_idxs)==0 and
old_camera_timestamp_source):
warnings.warn(
'low-precision timestamp comparison in '
'use due to outdated .ufmf timestamp '
'saving.')
# 2.5 msec precision required
ufmf_frame_idxs = np.nonzero(
abs( tss - this_camn_ts ) < 0.0025)[0]
assert len(ufmf_frame_idxs)==1
ufmf_frame_no = ufmf_frame_idxs[0]
if is_real_ufmf:
image,image_ts,more = ufmf.get_frame(ufmf_frame_no,
_return_more=True)
else:
image,image_ts = ufmf.get_frame(ufmf_frame_no)
more = fill_more_for( extra, image_ts )
del ufmf_frame_no, ufmf_frame_idxs
coding = ufmf.get_format()
if imops.is_coding_color(coding):
if rgb8_if_color:
image = imops.to_rgb8(coding,image)
else:
warnings.warn('color image not converted to color')
per_frame_dict[ufmf_fname] = {
'image':image,
'cam_id':cam_id,
'camn':camn,
'timestamp':this_cam_h5_data['timestamp'][0],
'cam_received_timestamp':
this_cam_h5_data['cam_received_timestamp'][0],
'ufmf_frame_timestamp':this_cam_h5_data[timestamp_name][0],
}
if more is not None:
per_frame_dict[ufmf_fname].update(more)
per_frame_dict['tracker_data']=this_h5_data
per_frame_dict['global_data']=global_data # on every iteration, pass our global data
yield (per_frame_dict,frame)
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
def convert(
infilename,
outfilename,
frames_per_second=None,
save_timestamps=True,
file_time_data=None,
do_nothing=False, # set to true to test for file existance
start_obj_id=None,
stop_obj_id=None,
obj_only=None,
dynamic_model_name=None,
hdf5=False,
show_progress=False,
show_progress_json=False,
**kwargs
):
if start_obj_id is None:
start_obj_id = -numpy.inf
if stop_obj_id is None:
stop_obj_id = numpy.inf
smoothed_data_filename = os.path.split(infilename)[1]
raw_data_filename = smoothed_data_filename
ca = core_analysis.get_global_CachingAnalyzer()
with ca.kalman_analysis_context(
infilename, data2d_fname=file_time_data
) as h5_context:
extra_vars = {}
tzname = None
if save_timestamps:
print("STAGE 1: finding timestamps")
table_kobs = h5_context.get_pytable_node("ML_estimates")
tzname = h5_context.get_tzname0()
fps = h5_context.get_fps()
try:
table_data2d = h5_context.get_pytable_node(
"data2d_distorted", from_2d_file=True
)
except tables.exceptions.NoSuchNodeError as err:
print(
"No timestamps in file. Either specify not to save timestamps ('--no-timestamps') or specify the original .h5 file with the timestamps ('--time-data=FILE2D')",
file=sys.stderr,
)
sys.exit(1)
print("caching raw 2D data...", end=" ")
sys.stdout.flush()
table_data2d_frames = table_data2d.read(field="frame")
assert numpy.max(table_data2d_frames) < 2 ** 63
table_data2d_frames = table_data2d_frames.astype(numpy.int64)
# table_data2d_frames_find = fastsearch.binarysearch.BinarySearcher( table_data2d_frames )
table_data2d_frames_find = utils.FastFinder(table_data2d_frames)
table_data2d_camns = table_data2d.read(field="camn")
table_data2d_timestamps = table_data2d.read(field="timestamp")
print("done")
print(
"(cached index of %d frame values of dtype %s)"
% (len(table_data2d_frames), str(table_data2d_frames.dtype))
)
drift_estimates = h5_context.get_drift_estimates()
camn2cam_id, cam_id2camns = h5_context.get_caminfo_dicts()
gain = {}
offset = {}
print("hostname time_gain time_offset")
print("-------- --------- -----------")
for i, hostname in enumerate(drift_estimates.get("hostnames", [])):
tgain, toffset = result_utils.model_remote_to_local(
drift_estimates["remote_timestamp"][hostname][::10],
drift_estimates["local_timestamp"][hostname][::10],
)
gain[hostname] = tgain
offset[hostname] = toffset
print(" ", repr(hostname), tgain, toffset)
print()
if do_nothing:
return
print("caching Kalman obj_ids...")
obs_obj_ids = table_kobs.read(field="obj_id")
fast_obs_obj_ids = utils.FastFinder(obs_obj_ids)
print("finding unique obj_ids...")
unique_obj_ids = numpy.unique(obs_obj_ids)
print("(found %d)" % (len(unique_obj_ids),))
unique_obj_ids = unique_obj_ids[unique_obj_ids >= start_obj_id]
unique_obj_ids = unique_obj_ids[unique_obj_ids <= stop_obj_id]
if obj_only is not None:
unique_obj_ids = numpy.array(
[oid for oid in unique_obj_ids if oid in obj_only]
)
print("filtered to obj_only", obj_only)
print("(will export %d)" % (len(unique_obj_ids),))
print("finding 2d data for each obj_id...")
timestamp_time = numpy.zeros(unique_obj_ids.shape, dtype=numpy.float64)
table_kobs_frame = table_kobs.read(field="frame")
if len(table_kobs_frame) == 0:
raise ValueError("no 3D data, cannot convert")
assert numpy.max(table_kobs_frame) < 2 ** 63
table_kobs_frame = table_kobs_frame.astype(numpy.int64)
assert (
table_kobs_frame.dtype == table_data2d_frames.dtype
) # otherwise very slow
all_idxs = fast_obs_obj_ids.get_idx_of_equal(unique_obj_ids)
for obj_id_enum, obj_id in enumerate(unique_obj_ids):
idx0 = all_idxs[obj_id_enum]
framenumber = table_kobs_frame[idx0]
remote_timestamp = numpy.nan
this_camn = None
frame_idxs = table_data2d_frames_find.get_idxs_of_equal(framenumber)
if len(frame_idxs):
frame_idx = frame_idxs[0]
this_camn = table_data2d_camns[frame_idx]
remote_timestamp = table_data2d_timestamps[frame_idx]
if this_camn is None:
print(
"skipping frame %d (obj %d): no data2d_distorted data"
% (framenumber, obj_id)
)
continue
cam_id = camn2cam_id[this_camn]
try:
remote_hostname = cam_id2hostname(cam_id, h5_context)
except ValueError as e:
print("error getting hostname of cam: %s" % e.message)
continue
if remote_hostname not in gain:
warnings.warn(
"no host %s in timestamp data. making up "
"data." % remote_hostname
)
gain[remote_hostname] = 1.0
offset[remote_hostname] = 0.0
mainbrain_timestamp = (
remote_timestamp * gain[remote_hostname] + offset[remote_hostname]
) # find mainbrain timestamp
timestamp_time[obj_id_enum] = mainbrain_timestamp
extra_vars["obj_ids"] = unique_obj_ids
extra_vars["timestamps"] = timestamp_time
print("STAGE 2: running Kalman smoothing operation")
# also save the experiment data if present
uuid = None
try:
table_experiment = h5_context.get_pytable_node(
"experiment_info", from_2d_file=True
)
except tables.exceptions.NoSuchNodeError:
pass
else:
try:
uuid = table_experiment.read(field="uuid")
except (KeyError, tables.exceptions.HDF5ExtError):
pass
else:
extra_vars["experiment_uuid"] = uuid
recording_header = h5_context.read_textlog_header_2d()
recording_flydra_version = recording_header["flydra_version"]
# -----------------------------------------------
obj_ids = h5_context.get_unique_obj_ids()
smoothing_flydra_version = h5_context.get_extra_info()["header"][
"flydra_version"
]
obj_ids = obj_ids[obj_ids >= start_obj_id]
obj_ids = obj_ids[obj_ids <= stop_obj_id]
if obj_only is not None:
obj_ids = numpy.array(obj_only)
print("filtered to obj_only", obj_ids)
if frames_per_second is None:
frames_per_second = h5_context.get_fps()
if dynamic_model_name is None:
extra = h5_context.get_extra_info()
orig_dynamic_model_name = extra.get("dynamic_model_name", None)
dynamic_model_name = orig_dynamic_model_name
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)
allrows = []
allqualrows = []
failed_quality = False
if show_progress:
import progressbar
class StringWidget(progressbar.Widget):
def set_string(self, ts):
self.ts = ts
def update(self, pbar):
if hasattr(self, "ts"):
return self.ts
else:
return ""
string_widget = StringWidget()
objs_per_sec_widget = progressbar.FileTransferSpeed(unit="obj_ids ")
widgets = [
string_widget,
objs_per_sec_widget,
progressbar.Percentage(),
progressbar.Bar(),
progressbar.ETA(),
]
pbar = progressbar.ProgressBar(widgets=widgets, maxval=len(obj_ids)).start()
for i, obj_id in enumerate(obj_ids):
if obj_id > stop_obj_id:
break
if show_progress:
string_widget.set_string("[obj_id: % 5d]" % obj_id)
pbar.update(i)
if show_progress_json and i % 100 == 0:
rough_percent_done = float(i) / len(obj_ids) * 100.0
result_utils.do_json_progress(rough_percent_done)
try:
rows = h5_context.load_data(
obj_id,
dynamic_model_name=dynamic_model_name,
frames_per_second=frames_per_second,
**kwargs
)
except core_analysis.DiscontiguousFramesError:
warnings.warn(
"discontiguous frames smoothing obj_id %d, skipping." % (obj_id,)
)
continue
except core_analysis.NotEnoughDataToSmoothError:
# warnings.warn('not enough data to smooth obj_id %d, skipping.'%(obj_id,))
continue
except numpy.linalg.linalg.LinAlgError:
warnings.warn(
"linear algebra error smoothing obj_id %d, skipping." % (obj_id,)
)
continue
except core_analysis.CouldNotCalculateOrientationError:
warnings.warn(
"orientation error smoothing obj_id %d, skipping." % (obj_id,)
)
continue
allrows.append(rows)
try:
qualrows = compute_ori_quality(
h5_context, rows["frame"], obj_id, smooth_len=0
)
allqualrows.append(qualrows)
except ValueError:
failed_quality = True
if show_progress:
pbar.finish()
allrows = numpy.concatenate(allrows)
if not failed_quality:
allqualrows = numpy.concatenate(allqualrows)
else:
allqualrows = None
recarray = numpy.rec.array(allrows)
smoothed_source = "kalman_estimates"
flydra_analysis.analysis.flydra_analysis_convert_to_mat.do_it(
rows=recarray,
ignore_observations=True,
newfilename=outfilename,
extra_vars=extra_vars,
orientation_quality=allqualrows,
hdf5=hdf5,
tzname=tzname,
fps=fps,
smoothed_source=smoothed_source,
smoothed_data_filename=smoothed_data_filename,
raw_data_filename=raw_data_filename,
dynamic_model_name=orig_dynamic_model_name,
recording_flydra_version=recording_flydra_version,
smoothing_flydra_version=smoothing_flydra_version,
)
if show_progress_json:
result_utils.do_json_progress(100)