def load_tracksets(data, trackids=None, min_length=10, verbose=False,
run_remove_dupes=False, run_fill_gaps=False, run_track_orient=False):
""" Returns a dict of slices into data based on trackid
"""
if trackids is None:
# copy actually speeds it up by a factor of two
trackids = data['t'].copy()
elif not trackids.flags.owndata:
# copy in case called as ...(data, data['t'])
trackids = trackids.copy()
lengths = np.bincount(trackids+1)[1:]
if min_length > 1:
lengths = lengths >= min_length
longtracks = np.where(lengths)[0]
tracksets = {track: data[trackids==track] for track in longtracks}
if run_remove_dupes:
from tracks import remove_duplicates
remove_duplicates(tracksets=tracksets, inplace=True, verbose=verbose)
if run_fill_gaps:
from tracks import fill_gaps
fill_gaps(tracksets=tracksets, inplace=True, verbose=verbose)
if run_track_orient:
from orientation import track_orient
for track in tracksets:
tracksets[track]['o'] = track_orient(tracksets[track]['o'])
return tracksets
def merge_data(datasets, savename=None, dupes=False, do_orient=False):
""" returns (and optionally saves) new `data` array merged from list or
tuple of individual `data` arrays or path prefixes.
parameters
----------
datasets : list of arrays, prefixes, or single prefix with wildcards
savename : path prefix at which to save the merged data,
saved as "<savename>_MERGED_<TRACKS|ORIENTATION>.npz"
do_orient : True or False, whether to merge the orientation data as
well. default is False, NOT IMPLEMENTED
returns
-------
merged : always returned, the main merged data array
if orientational data is to be merged, then a list of filenames or
prefixes must be given instead of data objects.
only data is returned if array objects are given.
"""
if do_orient:
raise ValueError, 'do_orient is not possible yet'
if isinstance(datasets, str):
if '*' in datasets or '?' in datasets:
from glob import glob
suf = '_TRACKS.npz'
datasets = [ s[:-len(suf)] for s in glob(datasets+suf) ]
elif datasets.endsith('.npz'):
raise ValueError, "please give only the prefix"
else:
raise ValueError, "only one file given"
if isinstance(datasets[0], str):
if datasets[0].endswith('.npz'):
raise ValueError, "please only give the prefix"
print 'Merging'
print '\t\n'.join(datasets)
datasets = map(load_data, datasets)
track_increment = 0
for dataset in datasets:
ts = quick_field_view(dataset, 't', False)
if dupes:
from tracks import remove_duplicates
ts[:] = remove_duplicates(ts, dataset)
ts[ts >= 0] += track_increment
track_increment = ts.max() + 1
merged = np.concatenate(datasets)
if savename:
fulldir = os.path.abspath(os.path.dirname(savename))
if not os.path.exists(fulldir):
print "Creating new directory", fulldir
os.makedirs(fulldir)
savename += '_MERGED_TRACKS.npz'
np.savez_compressed(savename, data=merged)
print "saved to", savename
return merged
def convol_track_config(self, pdata, odata, startframe):
pfsets = helpy.load_framesets(pdata)
pftrees = {f: KDTree(helpy.consecutive_fields_view(pfset, 'xy'),
leafsize=32) for f, pfset in pfsets.iteritems()}
trackids = tracks.find_tracks(pdata, maxdist= self.max_dist, giveup = 10, n = 0, stub = 20, \
pftrees = pftrees, pfsets = pfsets, startframe = startframe)
trackids = tracks.remove_duplicates(trackids, data = pdata)
return_data = helpy.initialize_tdata(pdata, trackids, odata)
return_data = helpy.add_self_view(return_data, ('x','y'),'xy')
return return_data
def compile_noise(prefixes, vs, width=3, side=1, fps=1, cat=True,
do_orientation=True, do_translation=True, subtract=True,
minlen=10, torient=True, interp=True, dupes=False, **ignored):
if np.isscalar(prefixes):
prefixes = [prefixes]
for prefix in prefixes:
if args.verbose:
print "Loading data for", prefix
data = helpy.load_data(prefix, 'tracks')
if dupes:
data['t'] = tracks.remove_duplicates(data['t'], data)
tracksets = helpy.load_tracksets(data, min_length=minlen,
run_track_orient=torient, run_fill_gaps=interp)
for track in tracksets:
tdata = tracksets[track]
velocities = noise_derivatives(tdata, width=width,
side=side, fps=fps, do_orientation=do_orientation,
do_translation=do_translation, subtract=subtract)
for v in velocities:
vs[v].append(velocities[v])
if cat:
for v in vs:
vs[v] = np.concatenate(vs[v], -1)
return len(tracksets)
