def plot_grasp_duration_distro(LogDf, SessionDf, bin_width, max_grasp_dur,
percentile):
" Plots the distribution of reach durations split by chosen side and outcome"
" Works for allowing mistakes but doesnt plot text or percentile due to Nan's "
sides = ['LEFT', 'RIGHT']
no_bins = round(max_grasp_dur / bin_width)
kwargs = dict(bins=no_bins, range=(0, max_grasp_dur), edgecolor='none')
fig, axes = plt.subplots(ncols=len(sides),
figsize=[6, 3],
sharex=True,
sharey=True)
colors = sns.color_palette('hls', n_colors=len(sides))
for ax, side, color in zip(axes, sides, colors):
# For all reaches
event_on, event_off = 'REACH_' + str(side) + '_ON', 'REACH_' + str(
side) + '_OFF' # event names
grasp_spansDf = bhv.get_spans_from_names(LogDf, event_on, event_off)
grasp_durs = np.array(grasp_spansDf['dt'].values, dtype=object)
ax.hist(grasp_durs,
**kwargs,
alpha=0.25,
color=color,
label='All grasps') # Histogram
# For choice-inducing reaches
choiceDf = bhv.groupby_dict(
SessionDf, dict(has_choice=True, chosen_side=side.lower()))
if len(choiceDf) != 0:
grasp_durs = choiceDf[~choiceDf['grasp_dur'].isna(
)]['grasp_dur'].values # filter out Nans
ax.hist(grasp_durs,
**kwargs,
alpha=1,
color=color,
label='Choice grasps')
perc = np.percentile(grasp_durs, percentile)
ax.axvline(perc, color=color, alpha=1) # perc line
ax.text(perc / max_grasp_dur, 0.9, perc,
transform=ax.transAxes) # perc number shifted a bit
for ax, side in zip(axes, sides):
ax.legend(frameon=False, fontsize='small')
ax.set_title(side)
ax.set_xlabel('Time (ms)')
axes[0].set_ylabel('No. of occurrences')
fig.suptitle("Histogram of grasps' duration, vertical bar is " +
str(percentile) + "th percentile")
fig.tight_layout()
return axes
def filter_trials_by(SessionDf, TrialDfs, filter_dict):
"""
This function filters input TrialDfs given filter_pair tuple (or list of tuples)
Example: given dict(outcome='correct', chosen_side='left') it will only output trials which are correct to left side
"""
if len(filter_dict) == 1: # in case its only one pair
groupby_keys = list(filter_dict.keys())
getgroup_keys = list(filter_dict.values())[0]
try:
SDf = SessionDf.groupby(groupby_keys).get_group(getgroup_keys)
except utils.NoFiltPairError:
error('No trials with given input filter_pair combination')
else: # more than 1 pair
try:
SDf = bhv.groupby_dict(SessionDf, filter_dict)
except utils.NoFiltPairError:
error('No trials with given input filter_pair combination')
TrialDfs_filt = [TrialDfs[i] for i in SDf.index.values.astype(int)]
return TrialDfs_filt
def plot_psychometric(SessionDf, N=1000, axes=None, discrete=False):
if axes is None:
fig, axes = plt.subplots()
# Get subset of timing trials with choices outside correction loops
try:
SDf = bhv.groupby_dict(SessionDf,
dict(has_choice=True, timing_trial=True))
except KeyError:
print('No trials fulfil criteria')
y = SDf['chosen_side'].values == 'right'
x = SDf['this_interval'].values
# plot the choices
if not discrete:
axes.plot(x, y, '.', color='k', alpha=0.5)
axx = plt.twinx(axes)
axx.set_yticks([0, 1])
axx.set_yticklabels(['short', 'long'])
axx.set_ylabel('choice')
w = 0.05
axx.set_ylim(0 - w, 1 + w)
axes.set_ylim(0 - w, 1 + w)
axes.set_ylabel('p')
axes.axvline(1500, linestyle=':', alpha=0.5, lw=1, color='k')
axes.axhline(0.5, linestyle=':', alpha=0.5, lw=1, color='k')
# plot the fit
x_fit = np.linspace(0, 3000, 100)
axes.plot(x_fit,
bhv.log_reg(x, y, x_fit),
color='red',
linewidth=2,
alpha=0.75)
# plot the random models based on the choice bias
bias = (SDf['chosen_side'] == 'right').sum() / SDf.shape[0]
R = []
for i in tqdm(range(N)):
rand_choices = np.random.rand(SDf.shape[0]) < bias
try:
R.append(bhv.log_reg(x, rand_choices, x_fit))
except ValueError:
# thrown when all samples are true or false
print("all true or false")
pass
R = np.array(R)
# Several statistical boundaries
alphas = [5, 0.5, 0.05]
opacities = [0.2, 0.2, 0.2]
for alpha, a in zip(alphas, opacities):
R_pc = sp.percentile(R, (alpha, 100 - alpha), 0)
axes.fill_between(x_fit,
R_pc[0],
R_pc[1],
color='blue',
alpha=a,
linewidth=0)
axes.set_xlabel('time (ms)')
if discrete:
intervals = list(SessionDf.groupby('this_interval').groups.keys())
correct_sides = ['right'] * len(intervals)
for i, interval in enumerate(intervals):
SDf = bhv.groupby_dict(
SessionDf,
dict(this_interval=interval,
has_choice=True,
in_corr_loop=False,
timing_trial=True))
f = (SDf['chosen_side'] == correct_sides[i]).sum() / SDf.shape[0]
axes.plot(interval, f, 'o', color='r')
return axes
def grasp_dur_across_sess(animal_fd_path, tasks_names):
SessionsDf = utils.get_sessions(animal_fd_path)
animal_meta = pd.read_csv(animal_fd_path / 'animal_meta.csv')
# Filter sessions to the ones of the task we want to see
FilteredSessionsDf = pd.concat(
[SessionsDf.groupby('task').get_group(name) for name in tasks_names])
log_paths = [
Path(path) / 'arduino_log.txt' for path in FilteredSessionsDf['path']
]
fig, axes = plt.subplots(ncols=2, figsize=[8, 4], sharey=True, sharex=True)
sides = ['LEFT', 'RIGHT']
Df = []
# gather data
for day, log_path in enumerate(log_paths):
LogDf = bhv.get_LogDf_from_path(log_path)
TrialSpans = bhv.get_spans_from_names(LogDf, "TRIAL_ENTRY_STATE",
"ITI_STATE")
TrialDfs = []
for i, row in tqdm(TrialSpans.iterrows(), position=0, leave=True):
TrialDfs.append(bhv.time_slice(LogDf, row['t_on'], row['t_off']))
metrics = (met.get_start, met.get_stop, met.get_correct_side,
met.get_outcome, met.get_interval_category,
met.get_chosen_side, met.has_reach_left,
met.has_reach_right)
SessionDf = bhv.parse_trials(TrialDfs, metrics)
for side in sides:
event_on, event_off = 'REACH_' + str(side) + '_ON', 'REACH_' + str(
side) + '_OFF' # event names
# reaches
grasp_spansDf = bhv.get_spans_from_names(LogDf, event_on,
event_off)
reach_durs = np.array(grasp_spansDf['dt'].values, dtype=object)
Df.append([reach_durs], columns=['durs'], ignore_index=True)
# grasps
choiceDf = bhv.groupby_dict(
SessionDf, dict(has_choice=True, chosen_side=side.lower()))
grasp_durs = choiceDf[~choiceDf['grasp_dur'].isna(
)]['grasp_dur'].values # filter out Nans
Df.append([grasp_durs, 'g', side],
columns=['durs', 'type', 'side'],
ignore_index=True)
sns.violinplot(data=Df[Df['type'] == 'r'],
x=day,
y='durs',
hue='side',
split=True,
cut=0,
legend='reaches')
sns.violinplot(data=Df[Df['type'] == 'g'],
x=day,
y='durs',
hue='side',
split=True,
cut=0,
legend='grasps')
fig.suptitle('CDF of first reach split on trial type \n' +
animal_meta['value'][5] + '-' + animal_meta['value'][0])
axes.legend
return axes
fig, axes = plt.subplots()
frame_ix = Sync.convert(t_on, 'arduino', 'dlc')
frame = dlc.get_frame(Vid, frame_ix)
dlc.plot_frame(frame, axes=axes)
dlc.plot_bodyparts(bodyparts, DlcDf, frame_ix, colors=bp_cols, axes=axes)
# trajectory
DlcDfSlice = bhv.time_slice(DlcDf, t_on, t_off)
dlc.plot_trajectories(DlcDfSlice, bodyparts, axes=axes, colors=bp_cols, lw=1, p=0.99)
# %% plot all of the selected trial type
# SDf = bhv.groupby_dict(SessionDf, dict(outcome='correct', correct_side='right', paw_resting=False))
# trial selection
SDf = bhv.groupby_dict(SessionDf, dict(has_choice=True, correct_side='left', outcome='correct'))
# plot some random frame
fig, axes = plt.subplots()
frame_ix = 1000
frame = dlc.get_frame(Vid, frame_ix)
dlc.plot_frame(frame, axes=axes)
# plot all traj in selection
for i in tqdm(SDf.index):
TrialDf = TrialDfs[i]
Df = bhv.event_slice(TrialDf,'PRESENT_INTERVAL_STATE','CHOICE_EVENT')
# Df = bhv.time_slice(Df, Df.iloc[-1]['t']-500, Df.iloc[-1]['t'])
t_on = Df.iloc[0]['t']
t_off = Df.iloc[-1]['t']
bp_cols = make_bodypart_colors(bodyparts)
fig, axes = plt.subplots()
frame_ix = Sync.convert(t_on, 'arduino', 'dlc')
frame = dlc.get_frame(Vid, frame_ix)
dlc.plot_frame(frame, axes=axes)
dlc.plot_bodyparts(bodyparts, DlcDf, frame_ix, colors=bp_cols, axes=axes)
# trajectory
DlcDfSlice = bhv.time_slice(DlcDf, t_on, t_off)
dlc.plot_trajectories(DlcDfSlice, bodyparts, axes=axes, colors=bp_cols, lw=1, p=0.99)
# %% plot all of the selected trial type
# trial selection
SDf = bhv.groupby_dict(SessionDf, dict(correct_side='left',outcome='incorrect'))
# SDf = SessionDf.groupby('correct_side').get_group('right')
# plot some random frame
fig, axes = plt.subplots()
frame_ix = 1000
frame = dlc.get_frame(Vid, frame_ix)
dlc.plot_frame(frame, axes=axes)
# plot all traj in selection
for i in tqdm(SDf.index):
TrialDf = TrialDfs[i]
Df = bhv.event_slice(TrialDf,'PRESENT_INTERVAL_STATE','ITI_STATE')
t_on = Df.iloc[0]['t']
t_off = Df.iloc[-1]['t']
date_abbr.append(month_abbr+'-'+str(date[2]))
# Getting metrics
TrialSpans = bhv.get_spans_from_names(LogDf, "TRIAL_ENTRY_STATE", "ITI_STATE")
TrialDfs = []
for i, row in tqdm(TrialSpans.iterrows(),position=0, leave=True):
TrialDfs.append(bhv.time_slice(LogDf, row['t_on'], row['t_off']))
metrics = (met.get_start, met.get_stop, met.get_correct_side, met.get_outcome, met.get_chosen_side, met.has_choice)
SessionDf = bhv.parse_trials(TrialDfs, metrics)
# Session metrics
MissedDf = SessionDf[SessionDf['outcome'] == 'missed']
choiceDf = SessionDf[SessionDf['has_choice'] == True]
left_trials_missedDf = bhv.groupby_dict(SessionDf, dict(outcome='missed', correct_side='left'))
right_trials_missedDf = bhv.groupby_dict(SessionDf, dict(outcome='missed', correct_side='right'))
corr_leftDf = bhv.groupby_dict(SessionDf, dict(outcome='correct', correct_side='left'))
left_trials_with_choiceDf = bhv.groupby_dict(SessionDf, dict(has_choice=True, correct_side='left'))
corr_rightDf = bhv.groupby_dict(SessionDf, dict(outcome='correct', correct_side='right'))
right_trials_with_choiceDf = bhv.groupby_dict(SessionDf, dict(has_choice=True, correct_side='right'))
# Metrics of evolution
try:
perc_corr_left.append(len(corr_leftDf)/len(left_trials_with_choiceDf)*100)
except:
perc_corr_left.append(np.NaN)
try:
perc_corr_right.append(len(corr_rightDf)/len(right_trials_with_choiceDf)*100)
outcomes = SessionDf['outcome'].unique()
for outcome in outcomes:
SessionDf['is_'+outcome] = SessionDf['outcome'] == outcome
# setup general filter
SessionDf['exclude'] = False
# %%
N = 100
# def plot_psychometric(SessionDf, N=1000, axes=None, discrete=False):
fig, axes = plt.subplots(figsize=[4,3])
# get only the subset with choices - excludes missed
SDf = bhv.groupby_dict(SessionDf, dict(has_choice=True, exclude=False,
in_corr_loop=False, is_premature=False,
timing_trial=True))
try:
SDf = SDf.groupby('timing_trial').get_group(True)
except KeyError:
print("no timing trials in session")
y = SDf['chosen_side'].values == 'right'
x = SDf['this_interval'].values
# axx = plt.twinx(axes)
# axx.set_yticks([0,1])
# axx.set_yticklabels(['short','long'])
# axx.set_ylabel('choice')
# axx.set_ylim(0-w, 1+w)
