from pixels.behaviours.leverpush import LeverPush, ActionLabels, Events
from pixtools import spike_rate, spike_times, utils
# This plots the behavioural channels for a given trial for each session
# Settings
duration = 4
trial = 3
mice = [
#'C57_1343253', # has no behaviour
'C57_1343255',
]
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,
)
import matplotlib.pyplot as plt
import seaborn as sns
from pixels import Experiment
from pixels.behaviours.reach import VisualOnly
from pixtools import clusters, utils
mice = [
## 'HFR19',
'HFR20',
## 'HFR21',
## 'HFR22',
## 'HFR23',
]
exp = Experiment(
mice,
VisualOnly,
'~/duguidlab/visuomotor_control/neuropixels',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/AZ_notes')
fig = clusters.depth_profile(exp, curated=True, in_brain=True)
plt.ylim([1750, -250])
plt.suptitle('Cluster depth profile')
utils.save(fig_dir / f'cluster_depth_profile')
from pixels import Experiment
from pixels.behaviours.pushpull import PushPull
from pixtools import clusters, utils
mice = [
"C57_1319786",
"C57_1319781",
"C57_1319784",
"C57_1319783",
"C57_1319782",
"C57_1319785",
]
exp = Experiment(
mice,
PushPull,
'~/duguidlab/Direction_Sensitivity/Data/Neuropixel',
#'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures/DS')
# all clusters
fig = clusters.depth_profile(exp, curated=False)
plt.ylim([2000, -250])
plt.suptitle('Cluster depth profile uncurated')
utils.save(fig_dir / f'cluster_depth_profile_uncurated')
#fig = clusters.depth_profile(exp, curated=True)
#plt.ylim([-250, 2000])
#plt.suptitle('Cluster depth profile curated')
duration = 4
rec_num = 0
fig_dir = Path('~/duguidlab/Direction_Sensitivity/neuropixels_figures')
mice = [
#"C57_1350950", # no ROIs drawn
"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',
)
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,
import seaborn as sns
from pixels import Experiment
from pixels.behaviours.leverpush import LeverPush
from pixels.behaviours.pushpull import PushPull
from pixels.behaviours.reach import Reach
mthal = Experiment(
[
# paper behaviour + opto
'C57_724',
'C57_1288723',
'C57_1288727',
'C57_1313404',
# muscimol spread test
'C57_1318495',
'C57_1318496',
'C57_1318497',
'C57_1318498',
],
LeverPush,
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
dirsens = Experiment(
[
"C57_1319786",
"C57_1319781",
"C57_1319784",
"C57_1319783",
from pixels import Experiment
from pixels.behaviours.pushpull import ActionLabels, Events, PushPull
from pixtools import spike_rate, utils
fig_dir = Path('~/duguidlab/Direction_Sensitivity/neuropixels_figures')
mice = [
"HFR20",
"HFR22",
"HFR23",
]
exp = Experiment(
mice,
VisualOnly,
'~/duguidlab/visuomotor_control/neuropixles',
)
rec_num = 0
duration = 2
# select units
units = exp.select_units(
min_depth=0,
max_depth=1200,
#min_spike_width=0.4,
name="cortex0-1200",
)
start = 0.000
from pixels import Experiment
from pixels.behaviours.leverpush import LeverPush, ActionLabels, Events
# Step 1: Load an experiment
#
# An experiment handles a group of mice that were trained in the same behaviour. It
# stores data and metadata for all included sessions belonging to the list of mice
# provided. The Experiment class is passed the mouse or list of mice, the class
# definition for the behaviour they were trained in (imported from pixels.behaviours),
# and the paths where it can find recording data (the folder containing 'raw', 'interim'
# and 'processed' folders) and training metadata.
#
myexp = Experiment(
'MCos1497', # This can be a list
LeverPush,
'~/path/to/data',
'~/path/to/metadata',
)
# Step 2: Process raw data
#
# These methods each process a different type of raw data and save the output into the
# 'processed' folder. The outputs are all resampled to 1kHz, and are saved as:
#
# - action labels (.npy)
# - behavioural data (.h5)
# - LFP data (.h5)
# - spike data (.h5)
# - sorted spikes (TODO)
#