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extract_section.py
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extract_section.py
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__author__ = 'ruben'
__doc__ = 'Extract and creates a npy and txt files with the spikes events in the section of interest'
import scipy.io as sio
import numpy as np
import re
from sklearn.decomposition import PCA, FactorAnalysis
from sklearn.cross_validation import cross_val_score
import matplotlib.pyplot as plt
files = {'rat005': '/media/bigdata/i01_maze05.005/i01_maze05_MS.005_BehavElectrData.mat',
'rat006': '/media/bigdata/i01_maze06.002/i01_maze06_MS.002_BehavElectrData.mat'}
def get_cells(path, section=None, only_pyr=None, verbose=False):
"""
Extract the spikes events from the MAT file of the HC-5 DB.
if section is provided, then spikes are split accordingly
:param path: Path to the processed MAT file
:param section: Name of the section to extract (default: None)
Run, Wheel, or Other
:param only_pyr: return only pyramidal cells
:return neuron: list with the spikes for each cell and lap is section
provided
"""
# TODO: implement section wheel and other
data = sio.loadmat(path)
clusters = np.squeeze(data['Spike']['totclu'][0, 0])
spikes = np.squeeze(data['Spike']['res'][0, 0])
isIntern = np.squeeze(data['Clu']['isIntern'][0, 0]) == 1
sections = np.squeeze(data['Par']['MazeSectEnterLeft'][0, 0])
# Separate spikes by neuron number
neuron = list()
for n in range(1, max(clusters) + 1):
if only_pyr and isIntern[n - 1]:
continue
spk = spikes[clusters == n]
if verbose: print 'neuron {}-th with {} spks'.format(n, len(spk))
if section == 'Run':
# Get intervals of interest: Section 2 to 6 that correspond to the running sections.
# Section 1 seems to be between the running wheel and the central arm of the Maze.
# It is not clear the boundary thus it is avoided
if verbose: print 'neuron {}-th is pyramidal with {} spks'.format(n, len(spk))
num_laps = len(sections)
laps = list()
for i in range(num_laps):
start_run, end_run = sections[i][1, 0], sum(sections[i][4:6, 1])
idx = np.where(np.logical_and(spk >= start_run, spk <= end_run))
# save spike events aligned to the entering to sect 2.
laps.append(spk[idx] - start_run)
neuron.append(laps)
else:
neuron.append(spk)
print '{} cells extracted'.format(len(neuron))
print 'Loading completed'
return neuron
def raster(cells, title=''):
"""
Creates a raster plot of spikes events
:param cells: list (N x 1) containing spk times
"""
import matplotlib.pyplot as plt
space = 0
fig = plt.figure(frameon=False, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
for n in range(len(cells)):
plt.plot(cells[n], np.ones(len(cells[n])) + space, '|')
space += 1
plt.xlabel('Samples')
plt.ylabel('Cell Num.')
plt.title(title)
def spike_train(spks, length=1000, threshold=0.):
"""
Conver spk events to matrix of spike trains (1's and 0's)
:param spks: spike events
:param length: maximum length to extract
:return: trains: matrix of spike trains (N x length) or (N x lenght x laps)
"""
n, l = np.shape(spks)
trains = np.zeros([n, length, l])
for icell, cell in enumerate(spks):
for ilap, lap in enumerate(cell):
inside = lap[lap < length]
trains[icell, inside, ilap] = 1.
if threshold != 0.:
m = np.mean(trains.reshape([n, -1]), axis=1) * 1250.
keep = m >= threshold
# print '{} Neurons removed with firing rate below {}'.format(sum(~keep), threshold)
return trains[keep]
return trains
def smooth_spk(train, width=0.1, plot=False, normalize=False):
"""
Gaussian convolution of spike trains, averaged across trials
:param train: spikes trains (N x Length x Trials)
:param width: width of the gaussian kernel
:return: smo: smoothed trains
"""
import scipy.ndimage.filters as fil
ave = np.mean(train, axis=2)
smo = list()
for n in range(len(train)):
y = fil.gaussian_filter1d(ave[n, :], sigma=width)
if normalize:
den = np.max(y) - np.min(y)
y = (y - np.min(y)) / den if den != 0. else y
smo.append(y)
if plot:
import matplotlib.pyplot as plt
space = 0.
fig = plt.figure(frameon=False, figsize=(9, 7), dpi=80, facecolor='w', edgecolor='k')
ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])
for n in range(len(train)):
plt.plot(smo[n] + space)
space += 1.
plt.xlabel('Samples')
plt.ylabel('Cell Num.')
return np.array(smo)
def binned(train, bin_size=0.1):
"""
Binned and square root transformed spike trains
:param train: spike trains
:param bin_size: in ms
:return: y : spike trains binned
"""
# q x (Bins * Laps)
q, t, laps = np.shape(train)
bin_width = int(bin_size * 1250)
T = int(t / bin_width)
y = np.zeros([q, T, laps])
for ilap in range(laps):
for ibin in range(T):
bin_start, bin_end = ibin * bin_width, (ibin + 1) * bin_width - 1
y[:, ibin, ilap] = np.sum(train[:, bin_start:bin_end, ilap], axis=1)
return y.reshape([q, -1])
def compute_scores(X, n_components):
pca = PCA()
fa = FactorAnalysis()
pca_scores, fa_scores = [], []
for n in n_components:
print 'Processing dimension {}'.format(n)
pca.n_components = n
fa.n_components = n
pca_scores.append(np.mean(cross_val_score(pca, X)))
fa_scores.append(np.mean(cross_val_score(fa, X)))
return pca_scores, fa_scores
def zscore(X):
return (X - np.mean(X, axis=1)[:, np.newaxis]) / np.std(X, axis=1)[:, np.newaxis]
def squareT(X):
return np.sqrt(X)
def minmax(X):
return (X - np.min(X, axis=1)[:, np.newaxis]) / (np.max(X, axis=1) - np.min(X, axis=1))[:, np.newaxis]
max_dims = 40
n_components = np.arange(0, max_dims, 2) # options for n_components
for k, source in files.iteritems():
data = get_cells(source, only_pyr=True, section='Run')
# sanity check: raster one lap
# raster([x[0] for x in data], title='{} Lap 0'.format(k))
y = spike_train(data, length=int(3.0 * 1250), threshold=0.2)
X = squareT(binned(y, 0.1))
# y_smo = smooth_spk(y, width=int(0.05 * 1250), plot=True, normalize=True)
name_file = re.findall(r'(i0\w+.\d+)', source)[0] + '_firings.npy'
# np.save(name_file, y_bin)
# ###########################EID with PCA and FA#################
pca_scores, fa_scores = compute_scores(X.T, n_components)
n_components_pca = n_components[np.argmax(pca_scores)]
n_components_fa = n_components[np.argmax(fa_scores)]
pca = PCA(n_components='mle')
pca.fit(X.T)
n_components_pca_mle = pca.n_components_
print("best n_components by PCA CV = %d" % n_components_pca)
print("best n_components by FactorAnalysis CV = %d" % n_components_fa)
print("best n_components by PCA MLE = %d" % n_components_pca_mle)
plt.figure()
plt.plot(n_components, pca_scores, 'b', label='PCA scores')
plt.plot(n_components, fa_scores, 'r', label='FA scores')
plt.axvline(n_components_pca, color='b',
label='PCA CV: %d' % n_components_pca, linestyle='--')
plt.axvline(n_components_fa, color='r',
label='FactorAnalysis CV: %d' % n_components_fa, linestyle='--')
plt.axvline(n_components_pca_mle, color='k',
label='PCA MLE: %d' % n_components_pca_mle, linestyle='--')
plt.legend(loc='lower right')
plt.xlabel('num. of components')
plt.ylabel('CV scores')
plt.title(k)
plt.show()