# Envelope for plots, 95% confidence interval
ci = 95
## Select units
duration = 2
units = exp.select_units(
min_depth=0,
max_depth=1200,
#min_spike_width=0.4,
name="cortex0-1200")
# get spike rate for left & right visual stim.
stim_left = exp.align_trials(
ActionLabels.naive_left,
Events.led_on,
'spike_rate',
units=units,
duration=duration,
)
stim_right = exp.align_trials(
ActionLabels.naive_right,
Events.led_on,
'spike_rate',
units=units,
duration=duration,
)
# side of PPC recording
sides = [
"left",
"right",
#for session in range(len(exp)):
# fig = spike_times.per_unit_histogram(stim, session, bin_ms=bin_ms, duration=duration)
# name = exp[session].name
# plt.suptitle(f'Session {name} - per-unit across-trials spike times (aligned to stim push)')
# save(f'unit_spike_histograms_stim_{duration}s_{name}_in_brain.png')
#
#for session in range(len(exp)):
# fig = spike_times.per_trial_histogram(stim, session, bin_ms=bin_ms, duration=duration)
# name = exp[session].name
# plt.suptitle(f'Session {name} - per-trial across-unit spike times (aligned to stim push)')
# save(f'trial_spike_histograms_stim_{duration}s_{name}_in_brain.png')
stim_miss = exp.align_trials(
ActionLabels.uncued_laser_nopush,
Events.laser_onset,
'spike_times',
duration=duration,
)
for session in range(len(exp)):
fig = spike_times.per_unit_histogram(stim_miss, session, bin_ms=bin_ms, duration=duration)
name = exp[session].name
plt.suptitle(f'Session {name} - per-unit across-trials spike times (aligned to nopush stim)')
utils.save(fig_dir / f'unit_spike_histograms_stim-miss_{duration}s_{name}_in_brain')
for session in range(len(exp)):
fig = spike_times.per_trial_histogram(stim_miss, session, bin_ms=bin_ms, duration=duration)
name = exp[session].name
plt.suptitle(f'Session {name} - per-trial across-unit spike times (aligned to nopush stim)')
utils.save(fig_dir / f'trial_spike_histograms_stim-miss_{duration}s_{name}_in_brain')
# Envelope for plots, 95% confidence interval
ci = 95
## Select units
duration = 2
units = exp.select_units(
min_depth=0,
max_depth=1200,
#min_spike_width=0.4,
name="cortex0-1200")
# get spike rate for left & right visual stim.
stim_left = exp.align_trials(
ActionLabels.miss_left,
Events.led_on,
'spike_rate',
units=units,
duration=duration,
)
# side of PPC recording
sides = [
"left",
"right",
]
ipsi_ppc_list = []
contra_m2_list = []
contra_ppc_list = []
for session in range(len(exp)):
exp = Experiment(
mice,
LeverPush,
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures')
duration = 4
## Spike rate plots
hits = exp.align_trials(
ActionLabels.cued_shutter_push_full,
Events.back_sensor_open,
'spike_rate',
duration=duration,
)
stim = exp.align_trials(
ActionLabels.uncued_laser_push_full,
Events.back_sensor_open,
'spike_rate',
duration=duration,
)
stim_miss = exp.align_trials(
ActionLabels.uncued_laser_nopush,
Events.laser_onset,
'spike_rate',
duration=duration,
mice,
LeverPush,
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures')
duration = 2
_, axes = plt.subplots(len(exp), 1)
hits = exp.align_trials(
ActionLabels.cued_shutter_push_full,
Events.back_sensor_open,
'spike_rate',
duration=duration,
min_depth=500,
max_depth=1200,
)
for session in range(len(exp)):
name = exp[session].name
data = hits[session].stack()
norm = StandardScaler().fit(data).transform(data)
num_components = norm.shape[1]
pca = PCA(n_components=num_components)
# probably don't need to transform
pc = pca.fit(norm).transform(norm)
axes[session].plot(np.cumsum(pca.explained_variance_ratio_))
axes[session].plot([0, num_components], [0, 1], '--', linewidth=0.4)
}
area = ["M2", "PPC"][rec_num]
## Spike rate plots for all visual stimulations
#hits = exp.align_trials(
#ActionLabels.correct_left | correct_right,
#Events.led_on,
#'spike_rate',
#duration=duration,
#)
stim_all = exp.align_trials(
ActionLabels.naive_left | ActionLabels.naive_right,
Events.led_on,
'spike_rate',
**select,
)
## Spike rate plots for short & long visual stimulation separately
#stim_short = exp.align_trials(
# ActionLabels.naive_short,
# Events.led_on,
# 'spike_rate',
# **select,
#)
#
#
#stim_long = exp.align_trials(
# ActionLabels.naive_long,
'~/duguidlab/Direction_Sensitivity/Data/Neuropixel',
#'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures/DS')
duration = 4
rec_num = 0
## Spike rate plots
pushes = exp.align_trials(
ActionLabels.rewarded_push,
Events.back_sensor_open,
'spike_rate',
duration=duration,
uncurated=True,
)
pulls = exp.align_trials(
ActionLabels.rewarded_pull,
Events.front_sensor_open,
'spike_rate',
duration=duration,
uncurated=True,
)
for session in range(len(exp)):
# per unit
fig = spike_rate.per_unit_spike_rate(pushes[session][rec_num], ci='sd')
]
exp = Experiment(
mice,
LeverPush,
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures/srf_grant')
if rates_not_rasters:
firing_rates = exp.align_trials(
ActionLabels.rewarded_push,
Events.back_sensor_open,
'spike_rate',
duration=duration,
)
# IPN
ses = exp[ses_ipn_gpi].name
spike_rate.per_unit_spike_rate(firing_rates[ses_ipn_gpi][ipn], ci='sd')
plt.suptitle(f'IPN - per-unit across-trials firing rate (aligned to push)')
utils.save(fig_dir / f'IPN_unit_spike_rate_{duration}s_{ses}.png')
# GPi
spike_rate.per_unit_spike_rate(firing_rates[ses_ipn_gpi][gpi], ci='sd')
plt.suptitle(f'GPi - per-unit across-trials firing rate (aligned to push)')
utils.save(fig_dir / f'GPi_unit_spike_rate_{duration}s_{ses}.png')
# MTh
mice = [
'C57_1288723',
'C57_1288727',
]
exp = Experiment(
mice,
LeverPush,
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
hit_times = exp.align_trials(
ActionLabels.cued_shutter_push_full,
Events.back_sensor_open,
'spike_times',
duration=4,
)
stim_times = exp.align_trials(
ActionLabels.uncued_laser_push_full,
Events.back_sensor_open,
'spike_times',
duration=4,
)
hits = exp.align_trials(
ActionLabels.cued_shutter_push_full,
Events.back_sensor_open,
'spike_rate',
duration=4,
#'C57_1288723',
#'C57_1288727',
]
exp = Experiment(
mice,
LeverPush,
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
exp.set_cache(True)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures').expanduser()
duration = 2
hits = exp.align_trials(
ActionLabels.rewarded_push,
Events.back_sensor_open,
'spike',
duration=duration,
raw=True,
)
fig, axes = plt.subplots(2, 2)
for i, ai in enumerate(axes):
for j, aj in enumerate(ai):
aj.specgram(hits[0][i][j], Fs=30000, NFFT=256*8)
aj.set_ylim([0, 100])
plt.show()
# And from a session we can get continuous processed data:
spike_data = myexp[0].get_spike_data()
lfp_data = myexp[0].get_lfp_data()
behavioural_data = myexp[0].get_behavioural_data()
# Data can be loading as trial-aligned data using the Experiment.align_trials method.
# This returns a multidimensional pandas DataFrame containing the desired data organised
# by session, unit, and trial.
#
# Here are some examples of how data can be plotted:
#
# Plotting all behavioural data channels for session 1, trial 3
hits = myexp.align_trials(
ActionLabels.rewarded_push, # This selects which trials we want
Events.back_sensor_open, # This selects what event we want them aligned to
'behaviour' # And this selects what kind of data we want
)
plt.figure()
fig, axes = plt.subplots(6, 1, sharex=True)
channels = hit_trials.columns.get_level_values('unit').unique()
trial = 3
session = 0
for i in range(6):
chan_name = channels[i]
sns.lineplot(
data=hits[session][chan_name][trial],
estimator=None,
style=None,
ax=axes[i]
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures')
duration = 4
rec_num = 0
select = {
"min_depth": 500,
"max_depth": 1200,
"min_spike_width": 0.4,
}
hits = exp.align_trials(
ActionLabels.cued_shutter_push_full,
Events.back_sensor_open,
'spike_times',
duration=duration,
**select,
)
stim = exp.align_trials(
ActionLabels.uncued_laser_push_full,
Events.back_sensor_open,
'spike_times',
duration=duration,
**select,
)
hit_cis = exp.get_aligned_spike_rate_CI(
ActionLabels.cued_shutter_push_full,
Events.back_sensor_open,
#area = ["M2", "PPC"][rec_num] #for all other recordings
area = ["PPC"][rec_num] #for HFR29 session 0
## Spike rate plots for all hits
#hits = exp.align_trials(
#ActionLabels.correct_left | correct_right,
#Events.led_on,
#'spike_rate',
#duration=duration,
#)
## Spike rate plots for all miss trials
miss_all = exp.align_trials(
ActionLabels.miss_left | ActionLabels.miss_right,
Events.led_on,
'spike_rate',
units=units,
duration=duration,
)
#plot firing rate per unit & per trial
for session in range(len(exp)):
# per unit
# fig = spike_rate.per_unit_spike_rate(hits[session][rec_num])
# name = exp[session].name
# plt.suptitle(f'Session {name} - per-unit across-trials firing rate (aligned to cued reach)')
# utils.save(fig_dir / f'unit_spike_rate_cued_reach_{duration}s_{area}_{name}')
# m2 = stim_all[session][0]
# ppc = stim_all[session][1]
exp = Experiment(
mice,
PushPull,
'~/duguidlab/Direction_Sensitivity/Data/Neuropixel',
)
units = exp.select_units(
min_depth=550,
max_depth=1200,
name="550-1200",
)
pushes = exp.align_trials(
ActionLabels.rewarded_push_good_mi,
Events.motion_index_onset,
'spike_rate',
duration=duration,
units=units,
)
pulls = exp.align_trials(
ActionLabels.rewarded_pull_good_mi,
Events.motion_index_onset,
'spike_rate',
duration=duration,
units=units,
)
# Only one recording
rec_num = 0
"C57_1350951", # MI done
#"C57_1350952", # MI done
#"C57_1350953", # MI done
#"C57_1350954", # MI done
#"C57_1350955", # no ROIs drawn
]
exp = Experiment(
mice,
PushPull,
'~/duguidlab/Direction_Sensitivity/Data/Neuropixel',
)
hits = exp.align_trials(
ActionLabels.rewarded_push,
Events.back_sensor_open,
'motion_index',
duration=duration,
)
for i, session in enumerate(exp):
fig, axes = plt.subplots(10, 1, sharex=True)
for t in range(10):
sns.lineplot(
data=hits[i][rec_num][0][t],
estimator=None,
style=None,
ax=axes[t]
)
axes[t].set_title(f"Trial {t}")
mice,
LeverPush,
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures')
duration = 4
rec_num = 0
## Spike rate plots
hits = exp.align_trials(
ActionLabels.cued_shutter_push_full,
Events.back_sensor_open,
'spike_rate',
duration=duration,
)
stim = exp.align_trials(
ActionLabels.uncued_laser_push_full,
Events.back_sensor_open,
'spike_rate',
duration=duration,
)
stim_miss = exp.align_trials(
ActionLabels.uncued_laser_nopush,
Events.laser_onset,
'spike_rate',
duration=duration,
# This performs spike sorting, and ...
# myexp.extract_spikes()
# This...
# myexp.process_motion_tracking()
# Step 3: Run analyses
#
# Once all the data has been processed and converted into forms that are compatible with
# the rest of the data, we are ready to extract data organised by trials.
#
myexp.trial_duration = 60
# # behaviour
hit_trials = myexp.align_trials(ActionLabels.rewarded_push,
Events.back_sensor_open, 'behaviour')[0]
#
# fig, axes = plt.subplots(6, 1, sharex=True)
# tmp=hit_trials.columns.get_level_values('unit').unique()
# for i in range(6):
# sns.lineplot(data=hit_trials[tmp[i]][1], estimator=None, style=None, ax=axes[i])
# plt.show()
# lfp
hit_trials_lfp = myexp.align_trials(ActionLabels.rewarded_push,
Events.back_sensor_open, 'lfp')[0]
fig, axes = plt.subplots(10, 1, sharex=True)
for i in range(10):
sns.lineplot(data=hit_trials_lfp[100 + i][8],
estimator=None,
exp = Experiment(
mice,
LeverPush,
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
exp.set_cache(False)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures')
exp.process_behaviour()
hits = exp.align_trials(
ActionLabels.rewarded_push,
Events.back_sensor_open,
'behavioural',
duration=duration,
)
fig, axes = plt.subplots(5, 1, sharex=True)
channels = hits.columns.get_level_values('unit').unique()
for i, session in enumerate(exp):
for c in range(5):
chan_name = channels[c]
sns.lineplot(
data=hits[i][rec_num][chan_name][trial],
estimator=None,
style=None,
ax=axes[c]
)
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
fig_dir = Path('~/duguidlab/visuomotor_control/figures')
sns.set(font_scale=0.4)
align_args = {
"duration": 1,
"min_depth": 500,
"max_depth": 1200,
}
hits = exp.align_trials(
ActionLabels.cued_shutter_push_full,
Events.back_sensor_open,
'spike_rate',
**align_args,
)
stim = exp.align_trials(
ActionLabels.uncued_laser_push_full,
Events.back_sensor_open,
'spike_rate',
**align_args,
)
stim_miss = exp.align_trials(
ActionLabels.uncued_laser_nopush,
Events.laser_onset,
'spike_rate',
**align_args,