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 pandas as pd
import seaborn as sns
from pixels import Experiment
from pixels.behaviours.leverpush import LeverPush, ActionLabels, Events
from pixtools import spike_rate, responsiveness, utils
mice = [
'C57_724',
'C57_1288723',
'C57_1288727',
]
exp = Experiment(
mice,
LeverPush,
'~/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,
from matplotlib_venn import venn2
from pixels import Experiment
from pixels.behaviours.leverpush import LeverPush, ActionLabels, Events
from pixtools import spike_rate
mice = [
'C57_724',
'C57_1288723',
'C57_1288727',
]
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
rec_num = 0
## Spike rate plots
hits = exp.align_trials(
ActionLabels.cued_shutter_push_full,
Events.back_sensor_open,
'spike_rate',
duration=duration,
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')
import numpy as np
import pandas as pd
from pixels import Experiment
from pixels.behaviours.reach import ActionLabels, Events, Reach
from pixtools import spike_rate, utils
mice = [
"HFR25",
#"HFR29",
]
exp = Experiment(
mice,
Reach,
'~/duguidlab/visuomotor_control/neuropixels',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
fig_dir = Path('~/duguidlab/visuomotor_control/AZ_notes/npx-plots/expert')
duration = 2
# select units
units = exp.select_units(
min_depth=0,
max_depth=1200,
#min_spike_width=0.4,
name="cortex0-1200",
)
import seaborn as sns
from pixels import Experiment
from pixels.behaviours.leverpush import LeverPush, ActionLabels, Events
from pixtools import spike_times, utils
mice = [
'C57_724',
'C57_1288723',
'C57_1288727',
]
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
bin_ms = 100
#hits = exp.align_trials(
# ActionLabels.cued_shutter_push_full,
# Events.back_sensor_open,
# 'spike_times',
# duration=duration,
#)
#
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)
#
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",
import seaborn as sns
from pixels import Experiment
from pixels.behaviours.pushpull import PushPull, ActionLabels, Events
from pixtools import spike_rate, utils
mice = [
#"C57_1319786",
"C57_1319781",
#"C57_1319784",
#"C57_1319783",
]
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')
duration = 4
rec_num = 0
hits = exp.align_trials(ActionLabels.rewarded_push, Events.back_sensor_open,
'behavioural')
_, axes = plt.subplots(6, 1, sharex=True)
channels = hits.columns.get_level_values('unit').unique()
trial = 8
session = 0
import seaborn as sns
from pixels import Experiment
from pixels.behaviours.leverpush import LeverPush, ActionLabels, Events
from pixtools import spike_times, utils
mice = [
'C57_724',
'C57_1288723',
'C57_1288727',
'C57_1313404',
]
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
rec_num = 0
select = {
"min_depth": 500,
"max_depth": 1200,
"min_spike_width": 0.4,
}
hits = exp.align_trials(
import seaborn as sns
from pixels import Experiment
from pixels.behaviours.leverpush import LeverPush
from pixtools import utils
mice = [
'C57_724',
'C57_1288723',
'C57_1288727',
'C57_1313404',
]
exp = Experiment(
mice,
LeverPush,
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
widths = exp.get_spike_widths(
min_depth=500,
max_depth=1200,
)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/figures')
fig, axes = plt.subplots(len(exp) + 1, 1, sharex=True)
plt.tight_layout()
counts = []
pool = True
from pathlib import Path
import matplotlib.pyplot as plt
import seaborn as sns
from pixels import Experiment
from pixels.behaviours.reach import Reach, ActionLabels, Events
from pixtools import spike_rate, utils
mice = [
'HFR29', #first session only on right PPC
]
exp = Experiment(
mice,
Reach,
'~/duguidlab/visuomotor_control/neuropixels',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
sns.set(font_scale=0.4)
fig_dir = Path('~/duguidlab/visuomotor_control/AZ_notes')
# Envelope for plots
ci = "sd"
## Select units
rec_num = 0
duration = 2
units = exp.select_units(
min_depth=200,
max_depth=1400,
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from pixels import Experiment
from pixels.behaviours.leverpush import LeverPush, ActionLabels, Events
from pixtools import utils
mice = [
'C57_724',
'C57_1288723',
'C57_1288727',
]
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 = 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,
from pixels import Experiment
from pixels.behaviours.reach import VisualOnly, ActionLabels, Events
from pixtools import spike_rate, utils
mice = [
'HFR19',
'HFR20',
#'HFR21', # poor quality session
'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')
# Envelope for plots
ci = "sd"
## Select units
rec_num = 0
duration = 2
select = {
from pixels import Experiment
from pixels.behaviours.reach import VisualOnly, ActionLabels, Events
from pixtools import spike_rate, utils
mice = [
#'HFR19',
'HFR20',
#'HFR21', # poor quality session
'HFR22',
'HFR23',
]
exp = Experiment(
mice,
VisualOnly,
'~/duguidlab/visuomotor_control/neuropixels',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
sns.set(font_scale=1)
fig_dir = Path('~/duguidlab/visuomotor_control/AZ_notes')
# Envelope for plots
ci = "sd"
## Select units
rec_num = 0
duration = 2
units = exp.select_units(
import tensorflow as tf
from pathlib import Path
from pixels import Experiment
from pixels.behaviours.leverpush import LeverPush, ActionLabels, Events
mice = [
'C57_724',
'C57_1288723',
'C57_1288727',
]
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')
def save(name):
plt.gcf().savefig(fig_dir / name, bbox_inches='tight', dpi=300)
## run settings
ses = exp[0]
rec_num = 0
from pixels.behaviours.pushpull import ActionLabels, Events, PushPull
from pixtools import utils
fig_dir = Path('~/duguidlab/Direction_Sensitivity/neuropixels_figures')
mice = [
"C57_1350950",
"C57_1350951",
"C57_1350952",
#"C57_1350953",
"C57_1350954",
]
exp = Experiment(
mice,
PushPull,
'~/duguidlab/Direction_Sensitivity/Data/Neuropixel',
)
rec_num = 0
duration = 4
sns.set_context("paper")
units = exp.select_units(
min_depth=550,
max_depth=900,
name="550-900",
)
pushes = exp.get_aligned_spike_rate_CI(
ActionLabels.rewarded_push_good_mi,
import pandas as pd
import numpy as np
from pixels import Experiment
from pixels.behaviours.reach import VisualOnly, ActionLabels, Events
from pixtools import spike_rate, utils
mice = [
'HFR20',
'HFR22',
'HFR23',
]
exp = Experiment(
mice,
VisualOnly,
'~/duguidlab/visuomotor_control/neuropixels',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
fig_dir = Path('~/duguidlab/visuomotor_control/AZ_notes/npx-plots/naive')
# 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")
from pixtools import spike_rate, 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')
duration = 4
rec_num = 0
## Spike rate plots
pushes = exp.align_trials(
ActionLabels.rewarded_push,
Events.back_sensor_open,
'spike_rate',
from pixels import Experiment
from pixels.behaviours.pushpull import PushPull
from pixtools import clusters, utils
mice = [
"C57_1350950",
"C57_1350951",
"C57_1350952",
#"C57_1350953",
"C57_1350954",
]
exp = Experiment(
mice,
PushPull,
'~/duguidlab/Direction_Sensitivity/Data/Neuropixel',
)
fig_dir = Path('~/duguidlab/Direction_Sensitivity/neuropixels_figures')
sns.set(font_scale=0.4)
rec_num = 0
pyramidals_units = exp.select_units(
min_depth=550,
max_depth=900,
min_spike_width=0.4,
name="550-900-pyramidals",
)
pyramidals = exp.get_spike_waveforms(units=pyramidals_units)
duration = 8
rec_num = 0
fig_dir = Path('~/duguidlab/visuomotor_control/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',
)
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):
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,
from pixels.behaviours.pushpull import ActionLabels, Events, PushPull
from pixtools import utils
fig_dir = Path('~/duguidlab/Direction_Sensitivity/neuropixels_figures')
mice = [
"C57_1350950",
"C57_1350951",
"C57_1350952",
#"C57_1350953", # MI done, needs curation
"C57_1350954",
]
exp = Experiment(
mice,
PushPull,
'~/duguidlab/Direction_Sensitivity/Data/Neuropixel',
)
rec_num = 0
duration = 4
pyramidals = exp.select_units(
min_depth=550,
max_depth=900,
min_spike_width=0.4,
name="550-900-pyramidals",
)
interneurons = exp.select_units(
min_depth=550,
import pandas as pd
from pixels import Experiment
from pixels.behaviours.leverpush import LeverPush, ActionLabels, Events
from pixtools import utils
mice = [
'C57_724',
'C57_1288723',
'C57_1288727',
'C57_1313404',
]
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')
rec_num = 0
select = {
"min_depth": 500,
"max_depth": 1200,
"min_spike_width": 0.4,
"sigma": 200,
}
hits = exp.get_aligned_spike_rate_CI(
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
ses_m1_mth = 0
m1 = 0 # session 0
mth = 1 # session 0
ses_ipn_gpi = 1
ipn = 0 # session 1
gpi = 1 # session 1
mice = [
#'C57_1343253', # has no behaviour
'C57_1343255',
]
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
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(
'MCos1371',
LeverPush,
'~/duguidlab/thalamus_paper/Npx_data',
'~/duguidlab/CuedBehaviourAnalysis/Data/TrainingJSON',
)
# 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)
# - spike data (.h5)
# - lfp data (.h5)
#
# This aligns, crops and downsamples behavioural data.
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')
import seaborn as sns
from pixels import Experiment
from pixels.behaviours.pushpull import PushPull, ActionLabels, Events
mice = [
"C57_1350950",
"C57_1350951",
"C57_1350952",
#"C57_1350953",
"C57_1350954",
]
exp = Experiment(
mice,
PushPull,
'~/duguidlab/Direction_Sensitivity/Data/Neuropixel',
)
rec_num = 0
all_push_rts = []
med_push_rts = []
all_pull_rts = []
med_pull_rts = []
for i, ses in enumerate(exp):
action_labels = ses.get_action_labels()
# Just confirm each session has only one recording
assert len(action_labels) == 1
actions = action_labels[rec_num][:, 0]
