def plotTCs(activity_tensor, targets, R, title=''):
U = tt.cp_als(activity_tensor, rank=R, verbose=False)
tt.plot_factors(U.factors, targets, plots=['scatter', 'line', 'bar'])
plt.title(title)
#plt.show()
# plt.savefig('tensorFactors.pdf')
#return neuron factors
return U.factors[2][:,0]
def tca(X,
non_neg=True,
R=10,
prefix="",
max_iter=500,
epoc="all",
effect="all"):
# Fit CP tensor decomposition (two times).
U = None
V = None
opti_str = 'ncp_bcd_'
if non_neg:
U = tt.ncp_bcd(X, rank=R, verbose=True, max_iter=max_iter, tol=1e-6)
V = tt.ncp_bcd(X, rank=R, verbose=True, max_iter=max_iter, tol=1e-6)
else:
U = tt.cp_als(X, rank=R, verbose=True, max_iter=max_iter, tol=1e-6)
V = tt.cp_als(X, rank=R, verbose=True, max_iter=max_iter, tol=1e-6)
opti_str = 'cp_als_'
# Compare the low-dimensional factors from the two fits.
# fig, ax, po = tt.plot_factors(U.factors)
# tt.plot_factors(V.factors, fig=fig)
# fig.suptitle("raw models")
# fig.tight_layout()
# Align the two fits and print a similarity score.
sim = tt.kruskal_align(U.factors,
V.factors,
permute_U=True,
permute_V=True)
print(sim)
# Plot the results again to see alignment.
fig, ax, po = tt.plot_factors(U.factors,
plots=["scatter", "scatter", "line"])
tt.plot_factors(V.factors, plots=["scatter", "scatter", "line"], fig=fig)
[x.set_xticks([11.5, 15.5, 27.5, 31.5]) for x in ax[:, 2]]
ax[-1, 0].set_xlabel("SU #")
ax[-1, 1].set_xlabel("Trial #")
ax[-1, 2].set_xlabel("Time (s)")
ax[-1, 2].set_xticklabels(["S", "+1", "T", "+1"])
fig.suptitle("aligned models")
fig.tight_layout()
# Show plots.
plt.show()
fig.set_size_inches(40, 40)
fig.set_dpi(300)
fig.savefig(
opti_str + prefix + "tca_trial_" + epoc + "_" + effect + "_" +
str(X.shape[1]) + "_R" + str(R) + ".png",
dpi=300,
bbox_inches="tight",
)
return (U, V, sim)
def cp_als_test():
# Create synthetic dataset.
I, J, K, R = 25, 25, 25, 3 # dimensions and rank parameters
# Create a random tensor consisting of a low-rank component and noise.
X = tensortools.randn_ktensor((I, J, K), rank=R).full()
X += np.random.randn(I, J, K) # add some random noise
# Perform CP tensor decomposition.
U = tensortools.cp_als(X, rank=R, verbose=True)
V = tensortools.cp_als(X, rank=R, verbose=True)
# Compare the low-dimensional factors from the two fits.
fig, _, _ = tensortools.plot_factors(U.factors)
tensortools.plot_factors(V.factors, fig=fig)
# Align the two fits and print a similarity score.
similarity_score = tensortools.kruskal_align(U.factors,
V.factors,
permute_U=True,
permute_V=True)
print(similarity_score)
# Plot the results to see alignment.
fig, ax, po = tensortools.plot_factors(U.factors)
tensortools.plot_factors(V.factors, fig=fig)
plt.show()
for x in dir_list:
file_list = file_list + glob.glob(x + '/**/' + '*.h5',recursive=True)
file = 4
this_dir = file_list[file].split(sep='/')[-2]
data_dir = os.path.dirname(file_list[file])
data = ephys_data(data_dir = data_dir ,file_id = file, use_chosen_units = True)
data.firing_rate_params = dict(zip(['step_size','window_size','total_time','calc_type','baks_len'],
[25,250,7000,'conv',700]))
data.get_data()
data.get_firing_rates()
data.get_normalized_firing()
data.firing_overview('off')
all_firing_array = np.asarray(data.all_normal_off_firing)
#taste = 2
X = all_firing_array.swapaxes(1,2) #data.all_normal_off_firing[:,:,100:200].swapaxes(1,2) #
rank = 7
# Fit CP tensor decomposition (two times).
U = tt.cp_als(X, rank=rank, verbose=True)
# Compare the low-dimensional factors from the two fits.
fig, _, _ = tt.plot_factors(U.factors)
## We should be able to see differences in tastes by using distance matrices on trial factors
trial_factors = U.factors.factors[-1]
trial_distances = dist_mat(trial_factors,trial_factors)
plt.figure();plt.imshow(trial_distances)
import tensortools as tt import numpy as np import matplotlib.pyplot as plt # Make synthetic dataset. I, J, K, R = 25, 25, 25, 4 # dimensions and rank X = tt.randn_ktensor((I, J, K), rank=R).full() X += np.random.randn(I, J, K) # add noise # Fit CP tensor decomposition (two times). U = tt.cp_als(X, rank=R, verbose=True) V = tt.cp_als(X, rank=R, verbose=True) # Compare the low-dimensional factors from the two fits. fig, _, _ = tt.plot_factors(U.factors) tt.plot_factors(V.factors, fig=fig) # Align the two fits and print a similarity score. sim = tt.kruskal_align(U.factors, V.factors, permute_U=True, permute_V=True) print(sim) # Plot the results again to see alignment. fig, ax, po = tt.plot_factors(U.factors) tt.plot_factors(V.factors, fig=fig) # Show plots. plt.show() # ============================================================================= # =============================================================================
@author: Yongjie Zhu
"""
import tensortools as tt
import matplotlib.pyplot as plt
import scipy.io as scio
import numpy as np
from mne.viz import plot_connectivity_circle
Sim = scio.loadmat("simulatedData.mat")
X = Sim[
'Sim'] # X[i,j,k] represents the connectivity value in time points k, freq bin j and paires (nodes) i
freq_index = np.linspace(3, 45, 169)
# Fit CP tensor decomposition .
U = tt.ncp_hals(X, rank=3, verbose=True) # _bcd or _als for options
fig, ax, po = tt.plot_factors(U.factors)
plt.show()
# visulazation
Nroi = 64 # number of rois
R = 3 # number of components
for index in range(R):
# temporal courses
plt.subplot(R, R, index * R + 1)
plt.plot(U.factors.factors[2][:, index])
if not index:
plt.title('Temporal courses')
plt.xlabel('Time/s')
# spectra
plt.subplot(R, R, index * R + 2)
plt.plot(freq_index, U.factors.factors[1][:, index])
if not index:
# colors = range(np.int(X.shape[1]))/np.max(range(np.int(X.shape[1])))
# for time in (trial_num+1)*np.arange(np.int(X.shape[1])):
# ax.scatter(X_embedded[time,0],X_embedded[time,1],c='red')
# =============================================================================
# Fit CP tensor decomposition (two times).
rank = 5
repeats = 30
all_models = []
all_obj = []
for repeat in tqdm(range(repeats)):
U = tt.cp_als(X, rank=rank, verbose=False)
all_models.append(U)
all_obj.append(U.obj)
U = all_models[np.argmin(all_obj)]
tt.plot_factors(U.factors)
## We should be able to see differences in tastes by using distance matrices on trial factors
trial_factors = U.factors.factors[-1]
trial_distances = dist_mat(trial_factors, trial_factors)
plt.figure()
plt.imshow(exposure.equalize_hist(trial_distances))
## Cluster trials using tsne
trial_labels = np.sort([0, 1, 2, 3] * 15)
X_embedded = tsne(n_components=2, perplexity=40).fit_transform(trial_factors)
plt.figure()
plt.scatter(X_embedded[:, 0], X_embedded[:, 1], c=trial_labels)
def run_pipeline(filename):
data = lm.loadmat(filename)
session_name = os.path.basename(filename)[0:-4]
(good_cells, pos_edges, trial_idx, spikelocations, spike_idx,
location_vec) = prepareData(data)
n_trials = 30
n_cells = len(good_cells)
shape = (n_cells, len(pos_edges) - 1, n_trials)
counts = np.zeros(shape, dtype=float)
_fast_bin(counts, trial_idx, spikelocations, spike_idx)
occupancy = np.zeros((len(pos_edges) - 1, n_trials), dtype=float)
_fast_occ(occupancy, data['trial'] - 1, location_vec)
for iT in range(n_trials):
tmp = occupancy[:, iT]
idx_v = np.flatnonzero(tmp)
idx_n = np.flatnonzero(tmp == 0)
tmp[idx_n] = np.interp(idx_n, idx_v, tmp[idx_v])
occupancy[:, iT] = tmp
spMapN = np.zeros(counts.shape)
for iC in range(n_cells):
spMapN[iC, :, :] = np.divide(counts[iC, :, :], occupancy)
spMapN = spi.gaussian_filter(spMapN, (0, 2, 0))
n_cells = len(good_cells)
n_bins = len(pos_edges) - 1
spFlat = np.zeros((n_cells, n_trials * n_bins))
for iC in range(n_cells):
spFlat[iC, :] = spMapN[iC, :, :].ravel(order='F')
#spFlat = spFlat-spFlat.mean(axis=1)[:,np.newaxis]
spFlat = normalize(spFlat, axis=0, norm='l2')
for iC in range(n_cells):
for iT in range(n_trials):
start = iT * n_bins
stop = (iT + 1) * n_bins
trial_idx = np.arange(start, stop)
tmp = spFlat[iC, trial_idx]
spMapN[iC, :, iT] = tmp
R = 5
# Fit CP tensor decomposition (two times).
U = tt.ncp_bcd(spMapN, rank=R, verbose=False)
V = tt.ncp_bcd(spMapN, rank=R, verbose=False)
# Align the two fits and print a similarity score.
sim = tt.kruskal_align(U.factors,
V.factors,
permute_U=True,
permute_V=True)
#print(sim)
# Plot the results again to see alignment.
fig, ax, po = tt.plot_factors(U.factors)
tt.plot_factors(V.factors, fig=fig)
fig.suptitle("aligned models")
fig.tight_layout()
fig.savefig('C:\\temp\\try3\\' + session_name + '_tca.png')
ff = np.matmul(np.transpose(spFlat), spFlat)
plt.figure()
ax = plt.imshow(ff)
plt.colorbar()
plt.axvline(x=n_bins * 20, color='red', ls='--', linewidth=1)
plt.axvline(x=n_bins * 21, color='green', ls='--', linewidth=1)
plt.axhline(y=n_bins * 20, color='red', ls='--', linewidth=1)
plt.axhline(y=n_bins * 21, color='green', ls='--', linewidth=1)
plt.savefig('C:\\temp\\try3\\' + session_name + '_cov.png')
plt.close('all')