points_spacing=1,
restrict_extremities=False,
apply_extra_spacing=False,
)
# %%
# load and clean complete bouts
_bouts = pd.read_hdf(paths.analysis_folder / "behavior" / "saved_data" /
f"complete_bouts.h5")
_bouts = _bouts.loc[(_bouts.start_roi == 0) & (_bouts.duration < 8)]
_bouts = _bouts.sort_values("duration") # .iloc[:5]
print(f"Kept {len(_bouts)} bouts")
bouts = []
for i, bout in track(_bouts.iterrows()):
bouts.append(LocomotionBout(bout, linearize_to=center_line))
# %%
f = plt.figure(figsize=(12, 12))
axes = f.subplot_mosaic("""
AAADDD
AAAEEE
AAAFFF
BBBGGG
CCCHHI
""")
f.tight_layout()
_ = draw.Hairpin(ax=axes["A"])
for n, bout in enumerate(bouts):
# draw 2d and linearized tracking
)
recorder.start(base_folder=folder.parent,
folder_name=folder.name,
timestamp=False)
# %%
# load and clean complete bouts
_bouts = pd.read_hdf(paths.analysis_folder / "behavior" / "saved_data" /
f"complete_bouts.h5")
_bouts = _bouts.loc[_bouts.start_roi == 0]
_bouts = _bouts.sort_values("duration").iloc[:10]
print(f"Kept {len(_bouts)} bouts")
bouts = []
for i, bout in _bouts.iterrows():
bouts.append(LocomotionBout(bout))
# %%
(
left_line,
center_line,
right_line,
left_to_track,
center_to_track,
right_to_track,
control_points,
) = track.extract_track_from_image(
points_spacing=1,
restrict_extremities=False,
apply_extra_spacing=False,
)
# %%
ROI = "T2"
bouts = pd.read_hdf(
paths.analysis_folder / "behavior" / "saved_data" / f"{ROI}_crossings.h5"
).sort_values("duration")
bouts = bouts.loc[bouts.duration < 1.5].iloc[:200]
print(f"Kept {len(bouts)} bouts")
# %%
# get clean paths
crosses = []
for i, cross in bouts.iterrows():
crosses.append(LocomotionBout(cross))
# %%
f = plt.figure(figsize=(20, 10))
axes = f.subplot_mosaic(
"""
AAABBDDFFF
AAACCEEFFF
"""
)
draw.ROI(
ROI,
ax=axes["A"],
set_ax=True,
# load and clean roi crossings
ROI = "T2"
MIN_DUR = 1.5
USE = 100
_bouts = pd.read_hdf(paths.analysis_folder / "behavior" / "saved_data" /
f"{ROI}_crossings.h5").sort_values("duration")
_l = len(_bouts)
_bouts = _bouts.loc[_bouts.duration <= MIN_DUR]
_bouts = _bouts.iloc[:USE]
print(f"Kept {len(_bouts)}/{_l} bouts")
crosses = []
for i, bout in _bouts.iterrows():
crosses.append(LocomotionBout(bout))
# %%
# ---------------------------------------------------------------------------- #
# first plot - sanity checks #
# ---------------------------------------------------------------------------- #
"""
Plot tracking over threshold crossings for sanity checks
"""
colors = dict(velocity=blue_dark, acceleration=pink)
f, axes = plt.subplots(figsize=(16, 10), ncols=2)
for ax in axes:
draw.ROI(ROI, set_ax=True, ax=ax)
f._save_name = f"vector_field_{ROI}"
& 'is_recording=1').fetch('name')
logger.info(f'Found {len(sessions)} recordings')
# filter bouts in recordings
for session in sessions:
bouts = _bouts.loc[_bouts['name'] == session]
if not bouts.empty:
print(f'{session} - Found: ', len(bouts), ' ROI crossings')
else:
print(f'{session} ---- EMPTY')
# select a session
session = 'FC_210721_AAA1110750_hairpin'
bouts = _bouts.loc[_bouts['name'] == session]
bouts = [LocomotionBout(bout) for i, bout in bouts.iterrows()]
# %%
# ----------------------------- load probe/units ----------------------------- #
logger.info('fetching units')
recording = (db_tables.Recording & f'name="{session}"').fetch(as_dict=True)[0]
cf = recording['recording_probe_configuration']
logger.info("Fetching ephys data")
units = db_tables.Unit.get_session_units(
session,
cf,
spikes=True,
firing_rate=True,
frate_window=100,
)
units['probe_configuration'] = [cf] * len(units)