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simulations.py
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simulations.py
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"""
Copright © 2023 Howard Hughes Medical Institute, Authored by Carsen Stringer and Marius Pachitariu.
"""
import numpy as np
from pathlib import Path
from scipy.ndimage import gaussian_filter1d
from scipy.stats import zscore, spearmanr
from scipy.cluster.hierarchy import fcluster, linkage
from sklearn.decomposition import TruncatedSVD
from sklearn.neighbors import NearestNeighbors
from rastermap.svd import SVD
from rastermap.utils import bin1d
from rastermap import Rastermap
from tqdm import trange, tqdm
import sys, os
from openTSNE import TSNE
#sys.path.insert(0, '/github/rastermap/paper/')
import metrics
def psth_to_spks(psth, mean_fr=1e-2):
n_neurons = psth.shape[0]
psth -= psth.min(axis=1)[:,np.newaxis]
psth /= psth.mean(axis=1)[:,np.newaxis]
fr = mean_fr * np.random.exponential(1, size=(n_neurons,))
fr = np.maximum(fr, 1e-5)
spks = np.random.poisson(psth * fr[:,np.newaxis])
spks = spks.astype("float32")
return spks
def powerlaw_module(n_neurons=1000, n_time=50000, alpha=1.5):
xi = np.random.rand(n_neurons).astype("float32")
k = np.arange(0,n_neurons)
B = np.cos(np.pi * xi[:,np.newaxis] * k )
plaw = k**(-alpha/2)
plaw[:5] = plaw[4]
B_norm = (B**2).sum(axis=0)**0.5
B = (B / B_norm[:,np.newaxis])
B *= plaw
B = B.astype("float32")
V = (np.random.rand(B.shape[1]+1, n_time) < 0.001).astype("float32")
expfilt = np.exp(-np.arange(0,200)/25)
V = np.array([np.convolve(v, expfilt, mode="full")[100:n_time+100] for v in V]).T
V -= V.mean(axis=0)
V = SVD(V, n_components=B.shape[1])
V /= (V**2).sum(axis=0)**0.5
psth = B @ V.T
psth = np.maximum(0, B @ V.T)
return psth, xi
def stim_tuning_module(n_neurons=1000, n_time=50000):
""" n_neurons must be divisible by 2, n_time divisible by 500 """
n_stims = 500
stim_types = 15
stims = np.zeros((stim_types, n_stims), "bool")
stims[np.random.randint(stim_types, size=n_stims), np.arange(0, n_stims)] = True
stim_len = n_time // n_stims
transients = np.zeros((stim_types, n_time), "float32")
transients[:,::stim_len] = stims
stim_times = np.nonzero(transients)
expfilt = np.exp(-np.arange(0,200,dtype="float32")/25)
transients = np.array([np.convolve(o, expfilt, mode="full")[:n_time]
for o in transients])
transients /= transients.max()
upsample = 100
theta_pref = np.eye(upsample*stim_types).astype("float32")
tuning_curves = gaussian_filter1d(theta_pref, 150, axis=0, mode="constant")
tuning_curves = tuning_curves[::upsample]
tuning_curves /= tuning_curves.max(axis=0)
# np.random.rand(n_neurons)
tpref = ((stim_types * upsample) * np.sort(np.random.rand(n_neurons))).astype("int")
xi = (tpref) / (stim_types * upsample)
psth = tuning_curves[:,tpref].T @ transients
return psth, xi, stim_times
def stim_sustained_module(n_neurons=1000, n_time=50000):
n_stims = n_time//500
stim_len = n_time // n_stims
stim_types = 1
t = 0
transients = np.zeros((stim_types, n_time), "float32")
while t < n_time - stim_len - 100:
if t > 0:
t += int(min(2000, np.random.exponential(750)))
if t < n_time - stim_len:
ist = np.random.randint(stim_types)
transients[ist, t] = 1
t += stim_len
else:
break
stim_times = np.nonzero(transients)
n_filt = 100
stim_resp = np.zeros((stim_types * n_filt, n_time), "float32")
sigma = np.stack((25*np.exp(np.arange(0, n_filt)/40),
5*np.exp(np.arange(0, n_filt)/40)), axis=1)
nt = 1400
for i in range(n_filt):
exps = [np.exp(-np.arange(0,nt)/sig) for sig in sigma[i]]
expfilt = exps[0] - exps[1]
efilt = np.zeros((nt+100), "float32")
efilt[i:i+nt] = expfilt
stim_resp[i :: n_filt] = np.array([np.convolve(o, efilt, mode="full")[:n_time]
for o in transients])
tpref = n_filt * (stim_types * np.sort(np.random.rand(n_neurons))).astype("int")#np.random.randint(stim_types, size=n_neurons)
tpref += (np.sort(np.random.rand(n_neurons)) * n_filt)[::-1].astype("int")#np.random.randint(n_filt, size=n_neurons)
xi = (tpref) / (stim_types * n_filt)
psth = stim_resp[tpref]
xi = 1 - xi
return psth, xi, stim_times
def stim_sustained_module_old(n_neurons=1000, n_time=50000):
n_stims = n_time//500
stim_len = n_time // n_stims
stim_types = 1
t = 0
transients = np.zeros((stim_types, n_time), "float32")
while t < n_time - stim_len - 100:
if t > 0:
t += int(min(2000, np.random.exponential(750)))
if t < n_time - stim_len:
ist = np.random.randint(stim_types)
transients[ist, t] = 1
t += stim_len
else:
break
stim_times = np.nonzero(transients)
n_filt = 100
stim_resp = np.zeros((stim_types * n_filt, n_time), "float32")
sigma = np.stack((10*np.exp(np.arange(0, n_filt)/40),
5*np.exp(np.arange(0, n_filt)/40)), axis=1)
for i in range(n_filt):
exps = [np.exp(-np.arange(0,500)/sig) for sig in sigma[i]]
expfilt = exps[0] - exps[1]
efilt = np.zeros((600), "float32")
efilt[i:i+500] = expfilt
stim_resp[i :: n_filt] = np.array([np.convolve(o, efilt, mode="full")[:n_time]
for o in transients])
tpref = n_filt * (stim_types * np.sort(np.random.rand(n_neurons))).astype("int")#np.random.randint(stim_types, size=n_neurons)
tpref += (np.sort(np.random.rand(n_neurons)) * n_filt)[::-1].astype("int")#np.random.randint(n_filt, size=n_neurons)
xi = (tpref) / (stim_types * n_filt)
psth = stim_resp[tpref]
xi = 1 - xi
return psth, xi, stim_times
def sequence_module(n_neurons=1000, n_time=50000):
xi = np.sort(np.random.rand(n_neurons))[::-1]
psth = np.zeros((n_neurons, n_time), "float32")
t = np.random.randint(10, 50)
nts = 3
n_seq=0
seq_times = []
while t < n_time:
seq_len = np.random.randint(350, 700)
velocity = gaussian_filter1d(np.random.randn(seq_len), 30)
velocity -= velocity.min()
velocity /= velocity.max()
velocity *= 50
velocity = velocity[(xi * seq_len).astype("int")]
#velocity = velocity[ii]
t_seq = (seq_len * xi + velocity).astype("int")
# add random breaks
if np.random.rand() > 0.5:
t_seq[xi > np.random.rand()] += np.random.randint(10,50) * seq_len // 100
for n in range(nts):
valid = t + t_seq + n < n_time
psth[np.arange(0, n_neurons)[valid], t + t_seq[valid] + n] = 1
#spks[np.arange(0, n_neurons)[valid], t + t_seq[valid]] = 1
seq_times.append(t + t_seq[valid])
t += t_seq.max()
t += np.random.randint(100, 200)
n_seq += 1
psth = gaussian_filter1d(psth, 9, axis=1)
xi = 1 - xi
return psth, xi, seq_times
def make_full_simulation(n_per_module=1000, random_state=0, add_spont=True):
np.random.seed(random_state)
modules = [stim_tuning_module,
stim_sustained_module,
sequence_module, sequence_module,
]
stim_times_all = []
for k, module in enumerate(modules):
psth0, xi0, stimes = module(n_neurons=n_per_module)
stim_times_all.append(stimes)
if k > 0:
psth = np.concatenate((psth, psth0), axis=0)
xi = np.hstack((xi, xi0 + k))
else:
psth = psth0
xi = xi0
psth /= psth.mean(axis=1)[:,np.newaxis]
# compute spont
n_spont = 2 * n_per_module
ntot = n_spont + psth.shape[0] if add_spont else n_spont
psth_spont, xi_spont = powerlaw_module(n_neurons=ntot)
psth_spont /= psth_spont.mean(axis=1)[:,np.newaxis]
# add shared noise (spont stats)
if add_spont:
#amp_spont = 0.25 * np.random.rand(psth.shape[0])[:,np.newaxis]
#psth_all = (1-amp_spont) * psth.copy() + amp_spont * psth_spont[:-n_spont]
psth_all = psth.copy() + 0.75 * psth_spont[:-n_spont]
else:
psth_all = psth
# concatenate with spont
psth_spont_spec = psth_spont[-n_spont:].copy()
psth_all = np.concatenate((psth_all, psth_spont_spec))
xi_all = np.hstack((xi, xi_spont[-n_spont:] + len(modules)))
# compute spks
spks = psth_to_spks(psth_all)
# independent noise
spks += np.random.poisson(0.03, size=spks.shape)
iperm = np.random.permutation(len(spks))
spks = spks[iperm]
xi_all = xi_all[iperm]
return spks, xi_all, stim_times_all, psth, psth_spont, iperm
def make_2D_simulation(filename):
n_neurons = 30000
basis = 0.1
alpha = 2.
# 2D positions for each neuron
np.random.seed(0)
xi = 1 * np.random.rand(n_neurons, 2)
# basis functions in 2D
isort0 = np.argsort(xi[:,0])
kx,ky = np.meshgrid(np.arange(0,31), np.arange(0,31))
kx = kx.flatten()
ky = ky.flatten()
kx = kx[1:]
ky = ky[1:]
B = np.cos(np.pi * xi[:,[0]] * kx ) * np.cos(np.pi * xi[:,[1]] * ky )
plaw = ((kx**2 + ky**2)**0.5)**(-alpha/2)
B *= plaw
B = B.astype(np.float32)
sv = (B**2).sum(axis=0)**0.5
B0 = B.copy()
# time components
n_time = 20000
V = np.random.randn(n_time, B0.shape[1]).astype("float32")
V -= V.mean(axis=0)
V = TruncatedSVD(n_components=B0.shape[1]).fit_transform(V)
V /= (V**2).sum(axis=0)**0.5
B = B0 @ V.T
noise = np.random.randn(*B.shape).astype("float32")
spks = B.copy() + 5e-3 * noise
np.savez(filename,
spks=spks, xi=xi)
def tuning_stats(X_embedding, spks, stim_times):
n_x = X_embedding.shape[0]
n_stims = len(np.unique(stim_times[0]))
stimes_reps = np.zeros((0,2), "int")
tcurves = np.zeros((n_x, n_stims))
for istim in range(n_stims):
tinds = np.nonzero(stim_times[0]==istim)[0]
tindst = stim_times[1][tinds]
tindst = tindst[np.newaxis,:] + np.arange(50)[:,np.newaxis]
tcurves[:,istim] = X_embedding[:, tindst].mean(axis=(-2,-1))
tinds = tinds[np.random.permutation(len(tinds))]
tinds = tinds[:(len(tinds)//2)*2]
st = stim_times[1][tinds].reshape(-1, 2)
stimes_reps = np.append(stimes_reps, st, axis=0)
sids_reps = stimes_reps[:,np.newaxis] + np.arange(50)[:,np.newaxis]
sresp = spks[:1000, sids_reps].mean(axis=-2)
sresp = zscore(sresp, axis=1)
cc =(sresp[:,:,0] * sresp[:,:,1]).sum(axis=1) / sresp.shape[1]
return tcurves, cc
def sustained_stats(X_embedding, stim_times):
n_x = X_embedding.shape[0]
n_stims = len(stim_times)
stimes_reps = np.zeros((0,2), "int")
suscurves = np.zeros((n_x, n_stims))
tinds = stim_times[1]
tinds = tinds[np.newaxis,:] + np.arange(300)[:,np.newaxis]
xresp = X_embedding[:, tinds].mean(axis=-1)
return xresp
def sequence_stats(X_embedding, stim_times):
n_x = X_embedding.shape[0]
n_stims = 50 # use 50 positions
n_np = len(stim_times[0]) // n_stims # neurons per position
nd = (len(stim_times[0]) // n_np) * n_np
n_seq = len(stim_times) - 1 # ignore last one (might not be full)
seqcurves = np.zeros((n_x, n_stims))
# loop over each sequence occurrence
for i in range(n_seq):
pos_times = stim_times[i][nd::-1].reshape(n_stims, n_np)
seqcurves += X_embedding[:, pos_times].mean(axis=-1)
seqcurves /= n_seq
return seqcurves
def benchmark_2D(xi, isorts):
### Benchmarks
Xdist = metrics.distance_matrix(xi)
nbrs1 = NearestNeighbors(n_neighbors=1500, metric="precomputed").fit(Xdist)
ind1 = nbrs1.kneighbors(return_distance=False)
xd = Xdist[np.tril_indices(Xdist.shape[0], -1)]
inds = []
rhos = []
for isort in isorts:
idx = np.zeros((len(isort),1))
idx[isort, 0] = np.arange(len(isort))
Zdist = metrics.distance_matrix(idx)
nbrs1 = NearestNeighbors(n_neighbors=1500, metric="precomputed").fit(Zdist)
inds.append(nbrs1.kneighbors(return_distance=False))
zd = Zdist[np.tril_indices(Xdist.shape[0], -1)]
rhos.append(spearmanr(xd[::10], zd[::10]).correlation)
knn = [10, 50, 100, 200, 400, 800, 1500]
knn_score = np.zeros((len(knn), len(inds)))
intersections = np.zeros(len(knn))
for j, ind2 in enumerate(inds):
print(j)
for k, kni in enumerate(knn):
for i in range(len(xi)):
knn_score[k,j] += len(set(ind1[i, :kni]) & set(ind2[i, :kni]))
knn_score[k,j] /= len(ind1) * kni
return knn_score, knn, rhos
def run_algos(spks, time_lag_window=0, locality=0):
embs = np.zeros((7,len(spks),1))
for k in trange(7):
if k==0:
# rastermap
model = Rastermap(n_clusters=100,
n_PCs=200,
locality=locality,
time_lag_window=time_lag_window,
grid_upsample=10,
time_bin=10,
bin_size=0,
verbose=False).fit(spks)
embs[k] = model.embedding
elif k==1:
# tsne
M = metrics.run_TSNE(model.Usv, perplexities=[30])
embs[k] = M
elif k==2:
# umap
M = metrics.run_UMAP(model.Usv)
embs[k] = M
elif k==3:
# isomap
M = metrics.run_isomap(model.Usv)
embs[k] = M
elif k==4:
# LE
M = metrics.run_LE(model.Usv)
embs[k] = M
elif k==5:
# hierarchical clustering
Z = linkage(model.Usv, metric="correlation", method="single", optimal_ordering=True)
embs[k] = fcluster(Z, t=0.01)[:,np.newaxis]
else:
# PCA
embs[k] = model.Usv[:,:1]
return embs, model
def embedding_performance(root, save=True):
# 6000 neurons in simulation with 5 modules
embs_all = np.zeros((10, 7, 6000, 1))
scores_all = np.zeros((10, 2, 8, 5))
algos = ["rastermap", "tSNE", "UMAP", "isomap", "laplacian\neigenmaps", "hierarchical\nclustering", "PCA"]
for random_state in trange(10):
path = os.path.join(root, "simulations", f"sim_{random_state}.npz")
dat = np.load(path, allow_pickle=True)
spks = dat["spks"]
embs, model = run_algos(spks, time_lag_window=10, locality=0.8)
# benchmarks
contamination_scores, triplet_scores = metrics.benchmarks(dat["xi_all"],
embs.copy())
embs_all[random_state] = embs
scores_all[random_state] = np.stack((contamination_scores, triplet_scores),
axis=0)
# compute stats for example sim
if random_state == 0:
xi_all = dat["xi_all"]
stim_times_all = dat["stim_times_all"]
# superneurons and correlation matrices
spks_norm = zscore(spks, axis=1)
X_embs = [zscore(bin1d(spks_norm[emb[:,0].argsort()], 30, axis=0), axis=1)
for emb in embs_all[random_state].copy()]
X_embs_bin = [zscore(bin1d(X_emb, 10, axis=1), axis=1) for X_emb in X_embs]
cc_embs = [(X_emb @ X_emb.T) / X_emb.shape[1] for X_emb in X_embs_bin]
tshifts = np.arange(0, 11)
nn, nt = X_embs_bin[0].shape
cc_embs_max = []
for X_emb in X_embs_bin:
cc_emb_max = -1 * np.ones((nn, nn))
for i,tshift in enumerate(tshifts):
cc_emb_max = np.maximum(cc_emb_max,
(X_emb[:, tshift:] @ X_emb[:, :nt-tshift].T) / (nt-tshift))
cc_embs_max.append(cc_emb_max)
# tuning of rastermap superneurons
tcurves, csig = tuning_stats(X_embs[0], spks, stim_times_all[0])
xresp = sustained_stats(X_embs[0], stim_times_all[1])
seqcurves0 = sequence_stats(X_embs[0], stim_times_all[2])
seqcurves1 = sequence_stats(X_embs[0], stim_times_all[3])
# grab intermediate rastermap steps
X_nodes = zscore(model.X_nodes, axis=1)
n_nodes, nt = X_nodes.shape
time_lag_window=10
symmetric=True
tshifts = np.arange(-time_lag_window*symmetric, time_lag_window+1)
cc_tdelay = np.zeros((n_nodes, n_nodes, len(tshifts)), np.float32)
for i,tshift in enumerate(tshifts):
if tshift < 0:
cc_tdelay[:,:,i] = (X_nodes[:, :nt+tshift] @ X_nodes[:, -tshift:].T) / (nt-tshift)
else:
cc_tdelay[:,:,i] = (X_nodes[:, tshift:] @ X_nodes[:, :nt-tshift].T) / (nt-tshift)
cc_tdelay[np.arange(0, n_nodes), np.arange(0, n_nodes)] = 0
# get matching matrix
from rastermap.sort import compute_BBt
x = np.arange(0, 1.0, 1.0/n_nodes)[:n_nodes]
BBt_travel = compute_BBt(x, x, locality=1.0)
BBt_travel = np.triu(BBt_travel, 1)
BBt_log = compute_BBt(x, x, locality=0)
BBt_log = np.triu(BBt_log, 1)
# get U_nodes and U_upsampled
U_nodes = model.U_nodes
U_upsampled = model.U_upsampled
if save:
np.savez(os.path.join(root, "simulations", "sim_results.npz"),
xi_all=xi_all, cc_tdelay=cc_tdelay, tshifts=tshifts,
BBt_log=BBt_log, BBt_travel=BBt_travel,
U_nodes=U_nodes, U_upsampled=U_upsampled, cc_embs=cc_embs,
X_embs=X_embs, cc_embs_max=cc_embs_max,
tcurves=tcurves, csig=csig, xresp=xresp, seqcurves0=seqcurves0,
seqcurves1=seqcurves1)
if save:
np.savez(os.path.join(root, "simulations", "sim_performance.npz"),
scores_all=scores_all,
embs_all=embs_all)
def repro_algs(root):
random_state = 0
path = Path(root) / "simulations" / f"sim_{random_state}.npz"
dat = np.load(path, allow_pickle=True)
spks = dat["spks"]
xi_all = dat["xi_all"]
embs_all = np.zeros((2, 20, 6000, 1))
for rs in trange(20):
model = Rastermap(n_clusters=100, n_PCs=200, locality=0.8,
time_lag_window=10, grid_upsample=10, time_bin=10,
bin_size=0, verbose=False, random_state=rs).fit(spks)
if rs==0:
Usv = model.Usv.copy()
embs_all[0,rs] = model.embedding
for rs in trange(20):
tsne = TSNE(
perplexity=30,
metric="cosine",
n_jobs=16,
random_state=rs,
verbose=False,
n_components = 1,
initialization = .0001 * Usv[:,:1],
)
emb = tsne.fit(Usv)
embs_all[1,rs] = emb
contamination_scores, triplet_scores = metrics.benchmarks(dat["xi_all"], embs_all.reshape(-1, 6000, 1))
scores_all = np.stack((contamination_scores, triplet_scores), axis=0)
random_state = 0
path = Path(root) / "simulations" / f"sim_spont_{random_state}.npz"
dat = np.load(path, allow_pickle=True)
spks = dat["spks"]
xi_all = dat["xi_all"]
corrs_all = np.zeros((2, 20))
for rs in trange(20):
model = Rastermap(n_clusters=100, n_PCs=200, locality=0.,
time_lag_window=0, grid_upsample=10, time_bin=10,
bin_size=0, verbose=False, random_state=rs).fit(spks)
if rs==0:
Usv = model.Usv.copy()
corrs = (zscore(model.embedding[:,0]) * zscore(dat["xi_all"])).mean(axis=-1)
corrs = np.abs(corrs)
print(rs, corrs)
corrs_all[0, rs] = corrs
for rs in trange(20):
tsne = TSNE(
perplexity=30,
metric="cosine",
n_jobs=16,
random_state=rs,
verbose=False,
n_components = 1,
initialization = .0001 * Usv[:,:1],
)
emb = tsne.fit(Usv)
corrs = (zscore(emb[:,0]) * zscore(dat["xi_all"])).mean(axis=-1)
corrs = np.abs(corrs)
print(rs, corrs)
corrs_all[1, rs] = corrs
np.savez(os.path.join(root, "simulations", "sim_0_reproducibility.npz"),
corrs_all=corrs_all, embs_all=embs_all, scores_all=scores_all)
def spont_simulations(root):
""" create power-law only simulation + benchmark """
for random_state in trange(10):
np.random.seed(random_state)
psth_spont, xi_spont = powerlaw_module(n_neurons=6000, alpha=1.0)
psth_spont /= psth_spont.mean(axis=1)[:,np.newaxis]
# poisson firing
spks = psth_to_spks(psth_spont)
# remove neurons with no firing
igood = spks.sum(axis=1) > 0
spks = spks[igood]
xi_spont = xi_spont[igood]
iperm = np.random.permutation(len(spks))
spks = spks[iperm]
xi_spont = xi_spont[iperm]
np.savez(Path(root) / "simulations" / f"sim_spont_{random_state}.npz", spks=spks, xi_all=xi_spont)
corrs_all = np.zeros((10, 7))
for random_state in range(10):
path = Path(root) / "simulations" / f"sim_spont_{random_state}.npz"
dat = np.load(path, allow_pickle=True)
spks = dat["spks"]
embs, model = run_algos(spks)
# abs correlation with sim axis
corrs = (zscore(embs.squeeze(), axis=1) * zscore(dat["xi_all"])).mean(axis=-1)
corrs = np.abs(corrs)
print(corrs)
corrs_all[random_state] = corrs
if random_state==0:
embs0 = embs.copy()
xi_all = dat["xi_all"].copy()
# superneurons and correlation matrices
spks_norm = zscore(spks, axis=1)
X_embs = [zscore(bin1d(spks_norm[emb[:,0].argsort()], 30, axis=0), axis=1)
for emb in embs.copy()]
X_embs_bin = [zscore(bin1d(X_emb, 10, axis=1), axis=1) for X_emb in X_embs]
cc_embs = [(X_emb @ X_emb.T) / X_emb.shape[1] for X_emb in X_embs_bin]
np.savez(os.path.join(root, "simulations", "sim_spont_performance.npz"),
corrs_all=corrs_all, embs=embs0, cc_embs=cc_embs, X_embs=X_embs, xi_all=xi_all)
def params_rastermap(root):
loc, tl = np.meshgrid(np.array([0, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0]),
np.array([0, 1, 2, 5, 10, 20]))
tl = tl.flatten()
loc = loc.flatten()
embs_all = []
scores_all = np.zeros((10, 2, len(tl)+1, 5))
for random_state in range(10):
print(random_state)
embs = []
dat = np.load(os.path.join(root, "simulations", f"sim_{random_state}.npz"), allow_pickle=True)
spks = dat["spks"]
for k, (tli, loci) in tqdm(enumerate(zip(tl, loc))):
if k==0:
model = Rastermap(n_clusters=100, n_PCs=200, locality=loci,
time_lag_window=tli, time_bin=10, verbose=False).fit(spks)
X = model.X
Usv = model.Usv
Vsv = model.Vsv
else:
model = Rastermap(n_clusters=100, n_PCs=200, locality=loci, normalize=False, mean_time=False,
time_lag_window=tli, verbose=False).fit(data=X, Usv=Usv, Vsv=Vsv)
cc_nodes = model.cc.copy()
isort = model.isort
embs.append(model.embedding)
embs_all.append(embs)
contamination_scores, triplet_scores = metrics.benchmarks(dat["xi_all"], embs) #, mids)
scores_all[random_state] = np.stack((contamination_scores, triplet_scores),
axis=0)
embs_all = np.array(embs_all)
np.savez(Path(root) / "simulations" / "sim_performance_tl_loc.npz", scores_all=scores_all, embs_all=embs_all,
loc=loc, tl=tl)
nclust = np.array([25, 50, 75, 100, 125, 150])
scores_all = np.zeros((10, 2, len(nclust)+1, 5))
embs_all = []
for random_state in range(10):
print(random_state)
embs = []
dat = np.load(os.path.join(root, "simulations", f"sim_{random_state}.npz"), allow_pickle=True)
spks = dat["spks"]
for k, nc in tqdm(enumerate(nclust)):
if k==0:
model = Rastermap(n_clusters=nc, n_PCs=200, locality=0.8,
time_lag_window=10, time_bin=10, verbose=False).fit(spks)
X = model.X
Usv = model.Usv
Vsv = model.Vsv
else:
model = Rastermap(n_clusters=nc, n_PCs=200, locality=0.8,
time_lag_window=10,normalize=False, mean_time=False,
verbose=False).fit(data=X, Usv=Usv, Vsv=Vsv)
cc_nodes = model.cc.copy()
isort = model.isort
embs.append(model.embedding)
embs_all.append(embs)
contamination_scores, triplet_scores = metrics.benchmarks(dat["xi_all"], embs) #, mids)
scores_all[random_state] = np.stack((contamination_scores, triplet_scores),
axis=0)
embs_all = np.array(embs_all)
np.savez(Path(root) / "simulations" / "sim_performance_nclust.npz", scores_all=scores_all, embs_all=embs_all,
nclust=nclust)
try:
import scanpy as sc
except:
raise ImportError("install scanpy for leiden")
nclust_leiden = []
embs_all = []
scores_all = np.zeros((10, 2, 3, 5))
for random_state in range(10):
print(random_state)
embs = []
dat = np.load(os.path.join(root, "simulations", f"sim_{random_state}.npz"), allow_pickle=True)
spks = dat["spks"]
for k in range(2):
if k==0:
model = Rastermap(n_clusters=100, n_PCs=200, locality=0.8,
time_lag_window=10, time_bin=10, verbose=False).fit(spks)
X = model.X
Usv = model.Usv
Vsv = model.Vsv
else:
adata = sc.AnnData(Usv)
sc.pp.neighbors(adata, n_neighbors=100, use_rep='X')
sc.tl.leiden(adata, resolution=3)
leiden_labels = np.array(adata.obs['leiden'].astype(int))
nc = leiden_labels.max()+1
U_nodes = np.array([Usv[leiden_labels==l].mean(axis=0) for l in range(nc)])
model = Rastermap(n_clusters=nc, n_PCs=200, locality=0.8,
time_lag_window=10,normalize=False, mean_time=False,
verbose=False).fit(data=X, Usv=Usv, Vsv=Vsv, U_nodes=U_nodes)
nclust_leiden.append(nc)
embs.append(model.embedding)
embs_all.append(embs)
contamination_scores, triplet_scores = metrics.benchmarks(dat["xi_all"], embs) #, mids)
scores_all[random_state] = np.stack((contamination_scores, triplet_scores),
axis=0)
embs_all = np.array(embs_all)
nclust_leiden = np.array(nclust_leiden)
np.savez(Path(root) / "simulations" / "sim_performance_leiden.npz", scores_all=scores_all, embs_all=embs_all,
nclust_leiden=nclust_leiden)