def tierpsy_trajectories_summary(
fname, time_windows, time_units, only_abs_ventral=False,
selected_feat=None, is_manual_index=False, delta_time=1/3):
"""
Calculate the trajectory summaries for a given file fname, within a given time window
(units of start time and end time are in frame numbers).
"""
fps = read_fps(fname)
data_in = read_data(fname, time_windows, time_units, fps, is_manual_index)
if data_in is None:
return [pd.DataFrame() for iwin in range(len(time_windows))]
timeseries_data, blob_features = data_in
is_fov_tosplit = was_fov_split(timeseries_data[0])
# is_fov_tosplit = False
if is_fov_tosplit:
fovsplitter = FOVMultiWellsSplitter(fname)
good_wells_df = fovsplitter.wells[['well_name','is_good_well']].copy()
# print(good_wells_df)
# initialize list of summaries for all time windows
all_summaries_list = []
# loop over time windows
for iwin,window in enumerate(time_windows):
if timeseries_data[iwin].empty:
all_summary = pd.DataFrame([])
else:
# initialize list of trajectory summaries for given time window
all_summary = []
# loop over worm indexes (individual trajectories)
for w_ind, w_ts_data in timeseries_data[iwin].groupby('worm_index'):
w_blobs = blob_features[iwin].loc[w_ts_data.index]
w_ts_data = w_ts_data.reset_index(drop=True)
w_blobs = w_blobs.reset_index(drop=True)
worm_feats = get_summary_stats(
w_ts_data, fps, w_blobs, delta_time,
only_abs_ventral=only_abs_ventral,
selected_feat=selected_feat
) # returns empty dataframe when w_ts_data is empty
worm_feats = pd.DataFrame(worm_feats).T
worm_feats = add_trajectory_info(worm_feats, w_ind, w_ts_data, fps)
all_summary.append(worm_feats)
# concatenate all trajectories in given time window into one dataframe
all_summary = pd.concat(all_summary, ignore_index=True, sort=False)
# attach whether the wells was good or bad
if is_fov_tosplit: # but only do this if we have wells
all_summary = all_summary.merge(good_wells_df,
on='well_name',
how='left')
# add dataframe to the list of summaries for all time windows
all_summaries_list.append(all_summary)
return all_summaries_list
def tierpsy_trajectories_summary(fname,
time_windows,
time_units,
is_manual_index=False,
delta_time=1 / 3):
"""
Calculate the trajectory summaries for a given file fname, within a given time window
(units of start time and end time are in frame numbers).
"""
fps = read_fps(fname)
data_in = read_data(fname, time_windows, time_units, fps, is_manual_index)
if data_in is None:
return [pd.DataFrame() for iwin in range(len(time_windows))]
timeseries_data, blob_features = data_in
# initialize list of summaries for all time windows
all_summaries_list = []
# loop over time windows
for iwin, window in enumerate(time_windows):
if timeseries_data[iwin].empty:
all_summary = pd.DataFrame([])
else:
# initialize list of trajectory summaries for given time window
all_summary = []
# loop over worm indexes (individual trajectories)
for w_ind, w_ts_data in timeseries_data[iwin].groupby(
'worm_index'):
w_blobs = blob_features[iwin].loc[w_ts_data.index]
w_ts_data = w_ts_data.reset_index(drop=True)
w_blobs = w_blobs.reset_index(drop=True)
worm_feats = get_summary_stats(
w_ts_data, fps, w_blobs, delta_time
) # returns empty dataframe when w_ts_data is empty
worm_feats = pd.DataFrame(worm_feats).T
worm_feats = add_trajectory_info(worm_feats, w_ind, w_ts_data,
fps)
all_summary.append(worm_feats)
# concatenate all trajectories in given time window into one dataframe
all_summary = pd.concat(all_summary, ignore_index=True, sort=False)
# add dataframe to the list of summaries for all time windows
all_summaries_list.append(all_summary)
return all_summaries_list
def tierpsy_trajectories_summary(fname,
is_manual_index=False,
delta_time=1 / 3):
fps = read_fps(fname)
data_in = read_data(fname, is_manual_index)
if data_in is None:
return
timeseries_data, blob_features = data_in
all_summary = []
for w_ind, w_ts_data in timeseries_data.groupby('worm_index'):
w_blobs = blob_features.loc[w_ts_data.index]
w_ts_data = w_ts_data.reset_index(drop=True)
w_blobs = w_blobs.reset_index(drop=True)
worm_feats = get_summary_stats(w_ts_data, fps, w_blobs, delta_time)
worm_feats = pd.DataFrame(worm_feats).T
worm_feats = add_trajectory_info(worm_feats, w_ind, w_ts_data, fps)
all_summary.append(worm_feats)
all_summary = pd.concat(all_summary, ignore_index=True)
return all_summary
def ow_trajectories_summary(fname):
fps = read_fps(fname)
with pd.HDFStore(fname, 'r') as fid:
features_timeseries = fid['/features_timeseries']
all_summary = []
valid_order = None
wStats = WormStats()
for w_ind, w_ts_data in features_timeseries.groupby('worm_index'):
ll = ['worm_{}'.format(int(w_ind))]
all_feats = read_feat_events(fname, ll)
for cc in w_ts_data:
all_feats[cc] = w_ts_data[cc].values
exp_feats = wStats.getWormStats(all_feats, np.nanmean)
exp_feats = pd.DataFrame(exp_feats)
if valid_order is None:
#only calculate this the first time...
valid_order = [x for x in exp_feats.columns if x not in wStats.extra_fields]
#remove uncalculated indexes from wStats
exp_feats = exp_feats.loc[:, valid_order]
assert not 'worm_index' in exp_feats
exp_feats = add_trajectory_info(exp_feats, w_ind, w_ts_data, fps)
all_summary.append(exp_feats)
all_summary = pd.concat(all_summary, ignore_index=True)
return all_summary