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dmn_bwm.py
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from one.api import ONE
from brainbox.singlecell import bin_spikes2D
from brainwidemap import (bwm_query, load_good_units,
load_trials_and_mask, bwm_units)
from iblatlas.atlas import AllenAtlas
from iblatlas.regions import BrainRegions
import iblatlas
from iblatlas.plots import plot_swanson_vector
from brainbox.io.one import SessionLoader
import ephys_atlas.data
#from reproducible_ephys_functions import figure_style, labs
import sys
sys.path.append('Dropbox/scripts/IBL/')
from granger import get_volume, get_centroids, get_res, get_structural, get_ari
from state_space_bwm import get_cmap_bwm, pre_post
from scipy import signal
import pandas as pd
import numpy as np
from collections import Counter
from sklearn.decomposition import PCA, FastICA
from sklearn.cluster import KMeans, SpectralClustering, SpectralCoclustering
from sklearn.manifold import TSNE
from scipy.cluster.hierarchy import linkage, fcluster, dendrogram
from statsmodels.stats.multitest import multipletests
from numpy.linalg import norm
from scipy.stats import (gaussian_kde, f_oneway,
pearsonr, spearmanr, kruskal, rankdata, linregress)
from scipy.spatial import distance
from scipy.cluster import hierarchy
from scipy.spatial.distance import squareform, cdist
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import StandardScaler
from random import shuffle
from sklearn.linear_model import LogisticRegression
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import StratifiedKFold
from joblib import Parallel, delayed
from sklearn.utils import parallel_backend
import gc
from pathlib import Path
import random
from copy import deepcopy
import time, sys, math, string, os
from scipy.stats import spearmanr, zscore
import umap.umap_ as umap
from rastermap import Rastermap
from scipy.stats import wasserstein_distance
from itertools import combinations, chain
from datetime import datetime
import scipy.ndimage as ndi
import hdbscan
import subprocess
from matplotlib.axis import Axis
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
from matplotlib.gridspec import GridSpec
from matplotlib.lines import Line2D
from matplotlib.colors import to_rgba
from matplotlib.patches import Rectangle
from matplotlib.ticker import ScalarFormatter
from matplotlib.ticker import MaxNLocator
from matplotlib.pyplot import cm
from venny4py.venny4py import *
import matplotlib.colors as mcolors
from ibl_style.style import figure_style
import warnings
warnings.filterwarnings("ignore")
#mpl.use('QtAgg')
plt.ion()
np.set_printoptions(threshold=sys.maxsize)
figure_style()
plt.rcParams.update(plt.rcParamsDefault)
plt.ion()
f_size = 15 # font size
# canonical colors for left and right trial types
blue_left = [0.13850039, 0.41331206, 0.74052025]
red_right = [0.66080672, 0.21526712, 0.23069468]
T_BIN = 0.0125 # bin size [sec] for neural binning
sts = 0.002 # stride size in [sec] for overlapping bins
ntravis = 30 # #trajectories for vis, first 2 real, rest pseudo
# conversion divident to get bins in seconds
# (taking strinding into account)
c_sec = int(T_BIN // sts) / T_BIN
one = ONE()
#base_url='https://openalyx.internationalbrainlab.org',
# password='international', silent=True
br = BrainRegions()
#units_df = bwm_units(one) # canonical set of cells
# save results here
pth_dmn = Path(one.cache_dir, 'dmn', 'res')
pth_dmn.mkdir(parents=True, exist_ok=True)
sigl = 0.05 # significance level (for stacking, plotting, fdr)
# order sensitive: must be tts__ = concat_PETHs(pid, get_tts=True).keys()
tts__ = ['inter_trial', 'blockL', 'blockR', 'block50',
'quiescence', 'stimLbLcL', 'stimLbRcL', 'stimLbRcR',
'stimLbLcR', 'stimRbLcL', 'stimRbRcL', 'stimRbRcR',
'stimRbLcR', 'motor_init', 'sLbLchoiceL', 'sLbRchoiceL',
'sLbRchoiceR', 'sLbLchoiceR', 'sRbLchoiceL', 'sRbRchoiceL',
'sRbRchoiceR', 'sRbLchoiceR', 'choiceL', 'choiceR',
'fback1', 'fback0']
peth_ila = [
r"$\mathrm{rest}$",
r"$\mathrm{L_b}$",
r"$\mathrm{R_b}$",
r"$\mathrm{50_b}$",
r"$\mathrm{quies}$",
r"$\mathrm{L_sL_cL_b, s}$",
r"$\mathrm{L_sL_cR_b, s}$",
r"$\mathrm{L_sR_cR_b, s}$",
r"$\mathrm{L_sR_cL_b, s}$",
r"$\mathrm{R_sL_cL_b, s}$",
r"$\mathrm{R_sL_cR_b, s}$",
r"$\mathrm{R_sR_cR_b, s}$",
r"$\mathrm{R_sR_cL_b, s}$",
r"$\mathrm{move}$",
r"$\mathrm{L_sL_cL_b, m}$",
r"$\mathrm{L_sL_cR_b, m}$",
r"$\mathrm{L_sR_cR_b, m}$",
r"$\mathrm{L_sR_cL_b, m}$",
r"$\mathrm{R_sL_cL_b, m}$",
r"$\mathrm{R_sL_cR_b, m}$",
r"$\mathrm{R_sR_cR_b, m}$",
r"$\mathrm{R_sR_cL_b, m}$",
r"$\mathrm{L_c}$",
r"$\mathrm{R_c}$",
r"$\mathrm{feedbk1}$",
r"$\mathrm{feedbk0}$"
]
peth_dict = dict(zip(tts__, peth_ila))
PETH_types_dict = {
'concat': [item for item in tts__],
'resting': ['inter_trial'],
'quiescence': ['quiescence'],
'pre-stim-prior': ['blockL', 'blockR'],
'block50': ['block50'],
'stim_surp_incon': ['stimLbRcL','stimRbLcR'],
'stim_surp_con': ['stimLbLcL', 'stimRbRcR'],
'stim_all': ['stimLbRcL','stimRbLcR','stimLbLcL', 'stimRbRcR'],
'motor_init': ['motor_init'],
'fback1': ['fback1'],
'fback0': ['fback0']}
# https://www.nature.com/articles/s41586-019-1716-z/figures/6
harris_hierarchy = [
"VPM", "VPL", "PCN", "LGd", "CL", "IAD",
"VISp", "MG", "AM", "IMD", "AUDp", "SSp-n",
"SSp-ll", "AUDd", "MD", "SSp-ul", "SSp-m", "PT",
"SSp-bfd", "SSs", "AIp", "VISl", "VISrl", "RSPd",
"LD", "MOp", "VISli", "PO", "VISpl", "RSPagl",
"RSPv", "VISal", "PVT", "CM", "VISpm", "AId",
"SSp-tr", "AV", "VAL", "SMT", "LP", "ORBi",
"AUDpo", "PL", "ORBm", "ILA", "FRP", "VISpor",
"ACAv", "VISam", "VISa", "MOs", "TEa", "AIv",
"ACAd", "ORBl", "PIL", "PF", "RE", "VM",
"POL"
]
def put_panel_label(ax, k):
ax.annotate(string.ascii_lowercase[k], (-0.05, 1.15),
xycoords='axes fraction',
fontsize=f_size * 1.5, va='top',
ha='right', weight='bold')
def grad(c, nobs, fr=1):
'''
color gradient for plotting trajectories
c: color map type
nobs: number of observations
'''
cmap = mpl.cm.get_cmap(c)
return [cmap(fr * (nobs - p) / nobs) for p in range(nobs)]
def eid_probe2pid(eid, probe_name):
df = bwm_query(one)
return df[np.bitwise_and(df['eid'] == eid,
df['probe_name'] == probe_name
)]['pid'].values[0]
def cosine_sim(v0, v1):
# cosine similarity
return np.inner(v0,v1)/ (norm(v0) * norm(v1))
def fn2_eid_probe_pid(u):
'''
file name u to eid, probe, pid
'''
return [u.split('_')[0], u.split('_')[1].split('.')[0],
eid_probe2pid(u.split('_')[0],
u.split('_')[1].split('.')[0])]
def get_name(brainregion):
'''
get verbose name for brain region acronym
'''
regid = br.id[np.argwhere(br.acronym == brainregion)][0, 0]
return br.name[np.argwhere(br.id == regid)[0, 0]]
def get_eid_info(eid):
'''
return counter of regions for a given session
'''
units_df = bwm_units(one)
return Counter(units_df[units_df['eid']==eid]['Beryl'])
def get_sess_per(reg, t='pid'):
'''
return bwm insertions that have this region
'''
units_df = bwm_units(one)
print(f'listing {t} per region')
return Counter(units_df[units_df['Beryl'] == reg][t])
def eid2pids(eid):
'''
return pids for a given eid
'''
units_df = bwm_units(one)
return Counter(units_df[units_df['eid'] == eid]['pid'])
def deep_in_block(trials, pleft, depth=10):
'''
get mask for trials object of pleft trials that are
"depth" trials into the block
'''
# pleft trial indices
ar = np.arange(len(trials))[trials['probabilityLeft'] == pleft]
# pleft trial indices shifted by depth earlier
ar_shift = ar - depth
# trial indices where shifted ones are in block
ar_ = ar[trials['probabilityLeft'][ar_shift] == pleft]
# transform into mask for all trials
bool_array = np.full(len(trials), False, dtype=bool)
bool_array[ar_] = True
return bool_array
def concat_PETHs(pid, get_tts=False, vers='concat'):
'''
for each cell concat all possible PETHs
vers: different PETH set
vers == 'contrast': extra analyiss for BWM reviewer;
check for zero contrast effects
have PETHs aligned to main events; irrespective of type
'''
eid, probe = one.pid2eid(pid)
# Load in trials data and mask bad trials (False if bad)
trials, mask = load_trials_and_mask(one, eid,
saturation_intervals=['saturation_stim_plus04',
'saturation_feedback_plus04',
'saturation_move_minus02',
'saturation_stim_minus04_minus01',
'saturation_stim_plus06',
'saturation_stim_minus06_plus06'])
if vers == 'concat':
# define align, trial type, window length
# For the 'inter_trial' mask trials with too short iti
idcs = [0]+ list(np.where((trials['stimOn_times'].values[1:]
- trials['intervals_1'].values[:-1])>1.15)[0]+1)
mask_iti = [True if i in idcs else False
for i in range(len(trials['stimOn_times']))]
# all sorts of PETHs, some for the surprise conditions
# need to be 10 trials into a block, see - 10
tts = {
'inter_trial': ['stimOn_times',
np.bitwise_and.reduce([mask, mask_iti]),
[1.15, -1]],
'blockL': ['stimOn_times',
np.bitwise_and.reduce([mask,
trials['probabilityLeft'] == 0.8]),
[0.4, -0.1]],
'blockR': ['stimOn_times',
np.bitwise_and.reduce([mask,
trials['probabilityLeft'] == 0.2]),
[0.4, -0.1]],
'block50': ['stimOn_times',
np.bitwise_and.reduce([mask,
trials['probabilityLeft'] == 0.5]),
[0.4, -0.1]],
'quiescence': ['stimOn_times', mask,
[0.4, -0.1]],
'stimLbLcL': ['stimOn_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastLeft']),
trials['probabilityLeft'] == 0.8,
deep_in_block(trials, 0.8),
trials['choice'] == 1]),
[0, 0.2]],
'stimLbRcL': ['stimOn_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastLeft']),
trials['probabilityLeft'] == 0.2,
deep_in_block(trials, 0.2),
trials['choice'] == 1]), [0, 0.2]],
'stimLbRcR': ['stimOn_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastLeft']),
trials['probabilityLeft'] == 0.2,
deep_in_block(trials, 0.2),
trials['choice'] == -1]),
[0, 0.2]],
'stimLbLcR': ['stimOn_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastLeft']),
trials['probabilityLeft'] == 0.8,
deep_in_block(trials, 0.8),
trials['choice'] == -1]),
[0, 0.2]],
'stimRbLcL': ['stimOn_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastRight']),
trials['probabilityLeft'] == 0.8,
deep_in_block(trials, 0.8),
trials['choice'] == 1]),
[0, 0.2]],
'stimRbRcL': ['stimOn_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastRight']),
trials['probabilityLeft'] == 0.2,
deep_in_block(trials, 0.2),
trials['choice'] == 1]),
[0, 0.2]],
'stimRbRcR': ['stimOn_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastRight']),
trials['probabilityLeft'] == 0.2,
deep_in_block(trials, 0.2),
trials['choice'] == -1]),
[0, 0.2]],
'stimRbLcR': ['stimOn_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastRight']),
trials['probabilityLeft'] == 0.8,
deep_in_block(trials, 0.8),
trials['choice'] == -1]),
[0, 0.2]],
'motor_init': ['firstMovement_times', mask,
[0.15, 0]],
'sLbLchoiceL': ['firstMovement_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastLeft']),
trials['probabilityLeft'] == 0.8,
trials['choice'] == 1]),
[0.15, 0]],
'sLbRchoiceL': ['firstMovement_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastLeft']),
trials['probabilityLeft'] == 0.2,
trials['choice'] == 1]),
[0.15, 0]],
'sLbRchoiceR': ['firstMovement_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastLeft']),
trials['probabilityLeft'] == 0.2,
trials['choice'] == -1]),
[0.15, 0]],
'sLbLchoiceR': ['firstMovement_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastLeft']),
trials['probabilityLeft'] == 0.8,
trials['choice'] == -1]),
[0.15, 0]],
'sRbLchoiceL': ['firstMovement_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastRight']),
trials['probabilityLeft'] == 0.8,
trials['choice'] == 1]),
[0.15, 0]],
'sRbRchoiceL': ['firstMovement_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastRight']),
trials['probabilityLeft'] == 0.2,
trials['choice'] == 1]),
[0.15, 0]],
'sRbRchoiceR': ['firstMovement_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastRight']),
trials['probabilityLeft'] == 0.2,
trials['choice'] == -1]),
[0.15, 0]],
'sRbLchoiceR': ['firstMovement_times',
np.bitwise_and.reduce([mask,
~np.isnan(trials[f'contrastRight']),
trials['probabilityLeft'] == 0.8,
trials['choice'] == -1]),
[0.15, 0]],
'choiceL': ['firstMovement_times',
np.bitwise_and.reduce([mask,
trials['choice'] == 1]),
[0, 0.15]],
'choiceR': ['firstMovement_times',
np.bitwise_and.reduce([mask,
trials['choice'] == -1]),
[0, 0.15]],
'fback1': ['feedback_times',
np.bitwise_and.reduce([mask,
trials['feedbackType'] == 1]),
[0, 0.3]],
'fback0': ['feedback_times',
np.bitwise_and.reduce([mask,
trials['feedbackType'] == -1]),
[0, 0.3]]}
elif vers == 'contrast':
# for latency plot based on coarse PETHs
# and extra control for auditory signal
tts = {'stim': ['stimOn_times', mask, [0, .15]],
'choice': ['firstMovement_times', mask, [0, 0.15]],
'fback': ['feedback_times', mask, [0, 0.15]],
'stim0': ['stimOn_times', np.bitwise_and(mask,
np.bitwise_or(trials[f'contrastRight'] == 0,
trials[f'contrastLeft'] == 0)),
[0, .15]]}
else:
print('what set of PETHs??')
return
if get_tts:
return tts
# load in spikes
spikes, clusters = load_good_units(one, pid)
assert len(
spikes['times']) == len(
spikes['clusters']), 'spikes != clusters'
D = {}
D['ids'] = np.array(clusters['atlas_id'])
D['xyz'] = np.array(clusters[['x','y','z']])
D['channels'] = np.array(clusters['channels'])
D['axial_um'] = np.array(clusters['axial_um'])
D['lateral_um'] = np.array(clusters['lateral_um'])
D['uuids'] = np.array(clusters['uuids'])
tls = {} # trial numbers for each type
ws = [] # list of binned data
for tt in tts:
event = trials[tts[tt][0]][np.bitwise_and.reduce([mask, tts[tt][1]])]
tls[tt] = len(event)
# bin and cut into trials
# overlapping time bins, bin size = T_BIN, stride = sts
# tts[key][-1][pre-event time, post-event time]
bis = []
st = int(T_BIN // sts)
for ts in range(st):
bi, _ = bin_spikes2D(
spikes['times'],
clusters['cluster_id'][spikes['clusters']],
clusters['cluster_id'],
np.array(event) + ts * sts,
tts[tt][-1][0], tts[tt][-1][1],
T_BIN)
bis.append(bi)
ntr, nn, nbin = bi.shape
ar = np.zeros((ntr, nn, st * nbin))
for ts in range(st):
ar[:, :, ts::st] = bis[ts]
# average squared firing rates across trials
ws.append(np.mean(ar**2, axis=0))
D['tls'] = tls
D['trial_names'] = list(tts.keys())
D['ws'] = ws
return D
def load_atlas_data():
LOCAL_DATA_PATH = Path(one.cache_dir, 'ephys_atlas_data')
D = {}
(D['df_raw_features'],
D['df_clusters'],
D['df_channels'],
D['df_probes']) = ephys_atlas.data.download_tables(
label='2024_W50',
local_path=LOCAL_DATA_PATH,
one=one)
merged_df0 = D['df_raw_features'].merge(D['df_channels'],
on=['pid','channel'])
merged_df0.reset_index(inplace=True)
merged_df = merged_df0.merge(
D['df_clusters'],
on=['pid', 'axial_um', 'lateral_um'])
return merged_df
def get_allen_info(rerun=False):
'''
Function to load Allen atlas info, like region colors
'''
pth_dmna = Path(one.cache_dir, 'dmn', 'alleninfo.npy')
if (not pth_dmna.is_file() or rerun):
p = (Path(iblatlas.__file__).parent /
'allen_structure_tree.csv')
dfa = pd.read_csv(p)
# replace yellow by brown #767a3a
cosmos = []
cht = []
for i in range(len(dfa)):
try:
ind = dfa.iloc[i]['structure_id_path'].split('/')[4]
cr = br.id2acronym(ind, mapping='Cosmos')[0]
cosmos.append(cr)
if cr == 'CB':
cht.append('767A3A')
else:
cht.append(dfa.iloc[i]['color_hex_triplet'])
except:
cosmos.append('void')
cht.append('FFFFFF')
dfa['Cosmos'] = cosmos
dfa['color_hex_triplet2'] = cht
# get colors per acronym and transfomr into RGB
dfa['color_hex_triplet2'] = dfa['color_hex_triplet2'].fillna('FFFFFF')
dfa['color_hex_triplet2'] = dfa['color_hex_triplet2'
].replace('19399', '19399a')
dfa['color_hex_triplet2'] = dfa['color_hex_triplet2'].replace(
'0', 'FFFFFF')
dfa['color_hex_triplet2'] = '#' + dfa['color_hex_triplet2'].astype(str)
dfa['color_hex_triplet2'] = dfa['color_hex_triplet2'
].apply(lambda x:
mpl.colors.to_rgba(x))
palette = dict(zip(dfa.acronym, dfa.color_hex_triplet2))
palette['CB'] = (0.4627450980392157, 0.47843137254901963,
0.22745098039215686, 1.0)
palette['void'] = (0, 0, 0, 1)
palette['root'] = (0, 0, 0, 1)
#add layer colors
bc = ['b', 'g', 'r', 'c', 'm', 'y', 'brown', 'pink']
for i in range(7):
palette[str(i)] = mcolors.to_rgba(bc[i])
palette['thal'] = mcolors.to_rgba('k')
palette['~layer'] = (0, 0, 0, 0)
r = {}
r['dfa'] = dfa
r['palette'] = palette
np.save(pth_dmna, r, allow_pickle=True)
r = np.load(pth_dmna, allow_pickle=True).flat[0]
return r['dfa'], r['palette']
_,pal = get_allen_info()
def regional_group(mapping, vers='concat', ephys=False,
nclus = 13, rerun=False):
'''
mapping: how to color 2d points, say Beryl, layers, kmeans
find group labels for all cells
mapping: [Allen, Beryl, Cosmos, layers, clusters, clusters_xyz, 'in tts__']
'''
if mapping == 'kmeans':
pth__ = Path(one.cache_dir, 'dmn', f'kmeans_group_ephys{ephys}.npy')
if ((pth__.is_file()) and (not rerun)):
return np.load(pth__,allow_pickle=True).flat[0]
r = np.load(Path(pth_dmn, f'{vers}_ephys{ephys}.npy'),
allow_pickle=True).flat[0]
# add point names to dict
r['nums'] = range(len(r['xyz'][:,0]))
if mapping == 'kmeans':
feat = 'concat_z'
nclus = nclus
kmeans = KMeans(n_clusters=nclus, random_state=0)
kmeans.fit(r[feat]) # kmeans in feature space
clusters = kmeans.labels_
cmap = mpl.cm.get_cmap('Spectral')
cols = cmap(clusters/nclus)
acs = clusters
regs = np.unique(clusters)
color_map = dict(zip(list(acs), list(cols)))
r['els'] = [Line2D([0], [0], color=color_map[reg],
lw=4, label=f'{reg + 1}')
for reg in regs]
av = None
elif mapping == 'cocluster':
feat = 'concat_z'
clusterer = SpectralCoclustering(n_clusters=nclus, random_state=0)
clusterer.fit(r[feat])
labels = clusterer.row_labels_
unique_labels = np.unique(labels)
mapping = {old_label: new_label
for new_label, old_label in
enumerate(unique_labels)}
clusters = np.array([mapping[label] for label in labels])
cmap = mpl.cm.get_cmap('Spectral')
cols = cmap(clusters/len(unique_labels))
acs = clusters
elif mapping == 'layers':
acs = np.array(br.id2acronym(r['ids'],
mapping='Allen'))
regs0 = Counter(acs)
# get regs with number at and of acronym
regs = [reg for reg in regs0
if reg[-1].isdigit()]
for reg in regs:
acs[acs == reg] = reg[-1]
# extra class of thalamic (and hypothalamic) regions
names = dict(zip(regs0,[get_name(reg) for reg in regs0]))
thal = {x:names[x] for x in names if 'thala' in names[x]}
for reg in thal:
acs[acs == reg] = 'thal'
mask = np.array([(x.isdigit() or x == 'thal') for x in acs])
acs[~mask] = '~layer'
remove_0 = False
if remove_0:
# also remove layer 6, as there are only 20 neurons
zeros = np.arange(len(acs))[
np.bitwise_or(acs == '~layer', acs == '6')]
for key in r:
if len(r[key]) == len(acs):
r[key] = np.delete(r[key], zeros, axis=0)
acs = np.delete(acs, zeros)
_,pa = get_allen_info()
cols = np.array([pa[reg] for reg in acs])
regs = Counter(acs)
r['els'] = [Line2D([0], [0], color=pa[reg],
lw=4, label=f'{reg} {regs[reg]}')
for reg in regs]
elif mapping == 'clusters_xyz':
# use clusters from hierarchical clustering to color
nclus = 1000
clusters = fcluster(r['linked_xyz'], t=nclus,
criterion='maxclust')
cmap = mpl.cm.get_cmap('Spectral')
cols = cmap(clusters/nclus)
acs = clusters
elif mapping in PETH_types_dict:
# For a group of PETH types defined in PETH_types_dict,
# concatenate the defined PETH segments and rank cells by mean score
feat = 'concat_z' # was _bd
assert vers == 'concat', 'vers needs to be concat for this'
assert feat == 'concat_z'
# Retrieve the list of PETH types to concatenate from PETH_types_dict
peth_types_to_concat = PETH_types_dict[mapping]
# Initialize a list to store the concatenated segments
concatenated_data = []
# Iterate through each PETH type in the list and concatenate the data
for peth_type in peth_types_to_concat:
# Extract relevant information from the data
# Get the start and end indices of the PETH segment
segment_names = list(r['len'].keys())
segment_lengths = list(r['len'].values())
start_idx = sum(segment_lengths[:segment_names.index(peth_type)])
end_idx = start_idx + r['len'][peth_type]
# Extract the segment of interest and append to concatenated_data
segment_data = r[feat][:, start_idx:end_idx]
concatenated_data.append(segment_data)
# Concatenate all the segments along the column axis
concatenated_data = np.concatenate(concatenated_data, axis=1)
# Compute the mean (max) for each neuron across the concatenated segment
means = np.mean(abs(concatenated_data), axis=1)
# Rank neurons based on their mean values
df = pd.DataFrame({'means': means})
df['rankings'] = df['means'].rank(method='min', ascending=False).astype(int)
r['rankings'] = df['rankings'].values
# Map neuron IDs to brain region acronyms (if needed)
acs = np.array(br.id2acronym(r['ids'], mapping='Beryl'))
# Apply a color map based on the rankings
cmap = mpl.cm.get_cmap('Spectral')
cols = cmap(r['rankings'] / max(r['rankings']))
# Count the occurrence of each brain region acronym
regs = Counter(acs)
elif mapping in tts__:
# for a specific PETH type,
# rank cells by mean score
feat = 'concat_z' # was _bd
assert vers == 'concat', 'vers needs to be concat for this'
assert feat == 'concat_z'
# Extract relevant information from the data
# Get the start and end indices of the PETH segment
segment_names = list(r['len'].keys())
segment_lengths = list(r['len'].values())
start_idx = sum(segment_lengths[:segment_names.index(mapping)])
end_idx = start_idx + r['len'][mapping]
# Extract the segment of interest
segment_data = r[feat][:, start_idx:end_idx]
# Compute the mean for each neuron across the segment
means = np.mean(abs(segment_data), axis=1)
# Get the ranking of neurons based on their mean values
df = pd.DataFrame({'means': means})
df['rankings'] = df['means'].rank(method='min',
ascending=False).astype(int)
r['rankings'] = df['rankings'].values
acs = np.array(br.id2acronym(r['ids'],
mapping='Beryl'))
cmap = mpl.cm.get_cmap('Spectral')
cols = cmap(r['rankings']/max(r['rankings']))
regs = Counter(acs)
elif mapping == 'functional':
funct = {
"FRP": "Prefrontal", "ACAd": "Prefrontal", "ACAv": "Prefrontal", "PL": "Prefrontal", "ILA": "Prefrontal",
"ORBl": "Prefrontal", "ORBm": "Prefrontal", "ORBvl": "Prefrontal",
"AId": "Lateral", "AIv": "Lateral", "AIp": "Lateral", "GU": "Lateral", "VISc": "Lateral",
"TEa": "Lateral", "PERI": "Lateral", "ECT": "Lateral",
"SSs": "Somatomotor", "SSp-bfd": "Somatomotor", "SSp-tr": "Somatomotor", "SSp-ll": "Somatomotor",
"SSp-ul": "Somatomotor", "SSp-un": "Somatomotor", "SSp-n": "Somatomotor", "SSp-m": "Somatomotor",
"MOp": "Somatomotor", "MOs": "Somatomotor",
"VISal": "Visual", "VISl": "Visual", "VISp": "Visual", "VISpl": "Visual",
"VISli": "Visual", "VISpor": "Visual", "VISrl": "Visual",
"VISa": "Medial", "VISam": "Medial", "VISpm": "Medial",
"RSPagl": "Medial", "RSPd": "Medial", "RSPv": "Medial",
"AUDd": "Auditory", "AUDp": "Auditory", "AUDpo": "Auditory", "AUDv": "Auditory"
}
cols0 = {
"Prefrontal": (0.78, 0.16, 0.16, 1.0), # Deep Red
"Lateral": (0.83, 0.79, 0.36, 1.0), # Yellow-green
"Somatomotor": (0.89, 0.63, 0.33, 1.0), # Light Orange
"Visual": (0.49, 0.73, 0.50, 1.0), # Light Green
"Medial": (0.53, 0.63, 0.83, 1.0), # Light Blue
"Auditory": (0.65, 0.51, 0.84, 1.0), # Purple
"Other": (0.,0.,0.,1.) # Black
}
acs0 = np.array(br.id2acronym(r['ids'],
mapping='Beryl'))
acs = [funct[reg] if (reg in funct) else 'Other' for reg in acs0]
cols = np.array([cols0[reg] for reg in acs])
# get average points and color per region
regs = Counter(acs)
else:
acs = np.array(br.id2acronym(r['ids'],
mapping=mapping))
_,pa = get_allen_info()
cols = np.array([pa[reg] for reg in acs])
# get average points and color per region
regs = Counter(acs)
rmv_void_rt = True
if 'end' in r['len']:
del r['len']['end']
r['acs'] = np.array(acs)
r['cols'] = np.array(cols)
if mapping == 'kmeans':
pth__ = Path(one.cache_dir, 'dmn', 'kmeans_group.npy')
np.save(pth__, r, allow_pickle=True)
return r
def get_umap_dist(rerun=False, algo='umap_z',
mapping='Beryl', vers='concat'):
pth_ = Path(one.cache_dir, 'dmn',
f'{algo}_{mapping}_{vers}_smooth.npy')
if (not pth_.is_file() or rerun):
res, regs = smooth_dist(algo=algo, mapping=mapping, vers=vers)
d = {'res': res, 'regs' : regs}
np.save(pth_, d, allow_pickle=True)
else:
d = np.load(pth_, allow_pickle=True).flat[0]
return d
def get_pw_dist(rerun=False, mapping='Beryl', vers='concat',
nclus=7, norm_=False, zscore_=True, nmin=20):
'''
get distance for all region pairs by computing
the Euclidean distance of the feature vectors
for all pairs of neurons in the two regions and then
average that score
'''
pth_ = Path(one.cache_dir, 'dmn',
f'{mapping}_{vers}_zscore_{zscore_}_pw.npy')
if (not pth_.is_file() or rerun):
r = np.load(Path(pth_dmn, f'{vers}_norm{norm_}.npy'),
allow_pickle=True).flat[0]
vecs = 'concat_z' if zscore_ else 'concat'
if mapping == 'kmeans':
# use kmeans to cluster high-dim points
nclus = nclus
kmeans = KMeans(n_clusters=nclus, random_state=0)
kmeans.fit(r[vecs])
acs = kmeans.labels_
print('kmeans done')
else:
acs = np.array(br.id2acronym(r['ids'],
mapping=mapping))
assert len(r[vecs]) == len(acs), 'mismatch, data != acs'
regs0 = Counter(acs)
regs = [x for x in regs0 if regs0[x] > nmin]
res = np.zeros((len(regs),len(regs)))
print(len(regs), 'regions')
k = 0
for i in range(len(regs)):
for j in range(i, len(regs)):
# group of cells a and b
g_a = r[vecs][acs == regs[i]]
g_b = r[vecs][acs == regs[j]]
# compute pairwise distance
M = cdist(g_a, g_b)
rows, cols = M.shape
# remove duplicate counts
mask = np.ones_like(M, dtype=bool)
min_dim = min(rows, cols)
mask[:min_dim, :min_dim] = np.triu(
np.ones((min_dim, min_dim), dtype=bool), k=1)
# average across all pairwise scores
res[i,j] = np.mean(M[mask])
res[j,i] = res[i,j]
if np.isnan(res[i,j]):
print(regs[i], regs[j], len(g_a), len(g_b))
return
#res[i,j] = np.mean(M)
k += 1
print(k, 'of', 0.5*(len(regs)**2), 'done')
d = {'res': res, 'regs' : regs}
np.save(pth_, d, allow_pickle=True)
else:
d = np.load(pth_, allow_pickle=True).flat[0]
return d
def NN(x, y, decoder='LDA', CC=1.0, confusion=False,
return_weights=False, shuf=False, verb=True):
'''
Decode region label y from activity x with parallelized cross-validation.
'''
nclasses = len(Counter(y))
startTime = datetime.now()