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tonic_stuff.py
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tonic_stuff.py
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import matplotlib.pyplot as plt
import numpy as np
from pathlib import Path
import pandas as pd
from brainrender._colors import map_color
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from pyinspect import install_traceback
install_traceback()
from fcutils.plotting.utils import calc_nrows_ncols, clean_axes, save_figure
from fcutils.maths.utils import rolling_mean, derivative
from vgatPAG.database.db_tables import ManualBehaviourTags, Roi, Sessions
from Analysis import get_session_data, get_session_tags, get_tags_sequences, get_active_rois, seq_type
# %%
pre_pos_s = 1.5
M = int(pre_pos_s*30)
lbls = ('stim', 'start', 'run', 'shelter', 'stop')
xlbl = dict(
xlabel='time from tag\n(s)',
xticks=[0, M, 2*M],
xticklabels=[-pre_pos_s, 0, pre_pos_s]
)
fld = Path('D:\\Dropbox (UCL)\\Project_vgatPAG\\analysis\\doric\\Fede\\\ddf_tag_aligned_all_tags_V2')
def dff(sig):
th = np.nanpercentile(sig[:(3 * 30)], 30)
return rolling_mean((sig - th)/th, 3)
# %%
f, axarr = plt.subplots(ncols=3, nrows = 2, figsize=(16, 12))
axarr = axarr.flatten()
outcomes = dict(complete=[], failed=[], aborted=[], incomplete=[])
S = len(Sessions.fetch())
for nsess, sess in enumerate(Sessions.fetch(as_dict=True)):
print(sess['mouse'], sess['date'])
color = map_color(nsess, 'viridis', 0, S)
data, rois = get_session_data(sess['mouse'], sess['date'], roi_data_type='raw')
if data is None:
continue
tags = get_session_tags(sess['mouse'], sess['date'],
etypes=('visual', 'audio', 'audio_visual'),
ttypes=('A' , 'H', 'B', 'C', 'E', 'D'))
# get tags sequences
sequences = get_tags_sequences(tags)
colors = [map_color(n, 'viridis', 0, len(sequences)) for n in range(len(sequences))]
# Get which ROIs show escape-related acivity
active_rois = get_active_rois(rois, sequences, sess)
# fit pca
signals = active_rois.values[data.is_rec==1, :].astype(np.float32)
scaler = StandardScaler().fit(signals)
pca = PCA(n_components=1).fit(scaler.transform(signals))
pc = pca.transform(scaler.transform(active_rois.values))
# iterate sequences
prev_stim = 0
for seq in sequences:
if seq.STIM - prev_stim < 60*30:
continue
else:
pre_stim = seq.STIM
start = seq.STIM - int(3 * 30)
if data.is_rec[start] != 0:
if seq_type(seq) != 'complete':
continue
# Compute mean DFF threshold
ths, maxes = [], []
for roi in active_rois.columns:
th = np.nanpercentile(active_rois[roi][start:seq.STIM], 30)
ths.append(th)
maxes.append(dff(active_rois[roi][start:seq.E]))
TH = np.mean(ths)
# TH = np.mean(pc[start:seq.STIM])
outcomes[seq_type(seq)].append(TH)
if seq_type(seq) == 'complete':
color = [.4 , .4, .4]
elif seq_type(seq) == 'failed':
color = 'r'
elif seq_type(seq) == 'incomplete':
continue
else:
color = 'b'
if seq_type(seq) not in ('failed', 'incomplete'):
if np.max(data.s[seq.STIM:seq.E]) > 200:
continue
axarr[0].scatter(TH, (seq.A-seq.STIM)/30, s=100, color=color, alpha=.5, lw=1, edgecolors=[.4, .4, .4])
axarr[1].scatter(TH, (seq.H-seq.STIM)/30, s=100, color=color, alpha=.5, lw=1, edgecolors=[.4, .4, .4])
axarr[2].scatter(TH, (seq.B-seq.STIM)/30, s=100, color=color, alpha=.5, lw=1, edgecolors=[.4, .4, .4])
axarr[3].scatter(np.mean(maxes), np.max(data.s[seq.STIM:seq.E]), s=100, color=color, alpha=.5, lw=1, edgecolors=[.4, .4, .4])
axarr[5].scatter(seq.STIM, TH, color=color, alpha=.5)
for outcome, ths in outcomes.items():
if outcome in ('aborted', 'incomplete'): continue
axarr[4].hist(ths, label=outcome, alpha=.4, density=True)
axarr[4].legend()
axarr[4].set(ylabel='density', xlabel='Mean DFF TH')
axarr[0].set(ylabel='A tag RT', xlabel='Mean DFF TH (per FOV)')
axarr[1].set(ylabel='H tag RT', xlabel='Mean DFF TH (per FOV)')
axarr[2].set(ylabel='B tag RT', xlabel='Mean DFF TH (per FOV)')
_ = axarr[3].set(ylabel='Max escape speed', xlabel='Max DFF during escape (per FOV)')
# %%
# Plt THs difference for escapes and contro runs
thresholds, control_thresholds = [], []
for nsess, sess in enumerate(Sessions.fetch(as_dict=True)):
print(sess['mouse'], sess['date'])
data, rois = get_session_data(sess['mouse'], sess['date'], roi_data_type='raw')
# Get which ROIs show escape-related acivity
active_rois = get_active_rois(rois, sequences, sess)
tags = get_session_tags(sess['mouse'], sess['date'],
etypes=('visual', 'audio', 'audio_visual'),
ttypes=('A' , 'H', 'B', 'C', 'E'))
control_tags = get_session_tags(sess['mouse'], sess['date'],
etypes=('control',),
ttypes=('A', 'H', 'B', 'C', 'E'))
# get tags sequences
sequences = get_tags_sequences(tags)
control_sequences = get_tags_sequences(control_tags)
lengths = len([s for s in sequences if seq_type(s)=='complete']), len(control_sequences)
print(f'Escape and control number of sequences: {lengths}')
for tp, sink, seqs in zip(('escape', 'control'), (thresholds, control_thresholds), (sequences, control_sequences)):
for seq in seqs:
if tp == 'escape' and seq_type(seq) != 'complete':
continue
start = seq.STIM - int(3 * 30)
if not data['is_rec'][start]: continue
ths = []
for roi in active_rois.columns:
ths.append(np.nanpercentile(active_rois[roi][start:seq.STIM], 30))
if np.nanmean(ths) < .1:
raise ValueError
sink.append(np.nanmean(ths))
# %%
f, ax = plt.subplots(figsize=(12, 12))
ax.hist(thresholds, bins=np.linspace(0, 20, 20), color='blue', alpha=.5, density=True, label='escape')
ax.hist(control_thresholds, bins=np.linspace(0, 20, 20), color='salmon', alpha=.5, density=True, label='control')
ax.legend()
# %%
# %%
from scipy.stats import ttest_ind
ttest_ind(thresholds, control_thresholds, equal_var=False)
# %%
pvals = []
for i in range(10000):
t = np.random.choice(thresholds, len(thresholds))
c = np.random.choice(control_thresholds, len(thresholds))
pvals.append(ttest_ind(t, c).pvalue)
_ = plt.hist(pvals, bins=np.linspace(0, .6, 100))
x = np.mean(pvals)
plt.axvline(x, color='red')
# %%