def save_xcorr_h5py(datafilename, netfilename, outfilename, node='Vm', numcells=-1):
"""Same as save_xcorr_pytables using h5py. For comparing h5py with
pytables."""
print 'save_xcorr_h5py'
t_start = datetime.now()
datafile = h5py.File(datafilename, 'r')
netfile = h5py.File(netfilename, 'r')
outfile = h5py.File(outfilename, 'w')
simtime = None
plotdt = None
timestamp = None
try:
simtime = datafile.attrs['simtime']
except KeyError:
pass
try:
plotdt = datafile.attrs['plotdt']
except KeyError:
pass
try:
timestamp = datafile.attrs['timestamp']
except KeyError:
pass
outfile.attrs['TITLE'] = 'Correlation of data with time stamp: %s' % (timestamp)
datanode = datafile['/%s' % (node)]
cell = []
vmdata = []
for data in datanode:
if node == 'Vm':
cellname = data[:len(data) - 3] # remove trailing '_Vm'
cell.append(cellname)
nparray = numpy.zeros(shape=datanode[data].shape, dtype=datanode[data].dtype)
nparray[:] = datanode[data][:]
vmdata.append(nparray)
t_end = datetime.now()
t_delta = t_end - t_start
print 'Finished reading data in %g s' % (t_delta.days * 86400 + t_delta.seconds + t_delta.microseconds * 1e-6)
if simtime is None:
simtime = 1.0 * len(vmdata[0])
if plotdt is None:
plotdt = 1.0
if numcells > 0:
indices = random.sample(range(len(cell)), numcells)
cell = [cell[index] for index in indices]
vmdata = [vmdata[index] for index in indices]
t_start = datetime.now()
lags = 2 * int(simtime/plotdt) - 1
time = numpy.fft.fftshift(numpy.fft.fftfreq(lags, 1/(plotdt*lags)))
corrgroup = outfile.create_group('/%s' % (node))
timearray = corrgroup.create_dataset('t', data=time, compression='gzip')
for ii in range(len(cell)):
left = vmdata[ii]
for jj in range(len(cell)):
right = vmdata[jj]
corr = ncc(left, right)
corrarray = corrgroup.create_dataset('%s-%s' % (cell[ii], cell[jj]), data=corr, chunks=corr.shape)
outfile.close()
datafile.close()
netfile.close()
t_end = datetime.now()
t_delta = t_end - t_start
print 'Computed and saved correlatioons in %g seconds' % (t_delta.days * 86400 + t_delta.seconds + t_delta.microseconds * 1e-6)
def save_xcorr_pytables(datafilename, netfilename, outfilename, node='Vm', numcells=-1):
"""Save the cross correlation. I am hoping that pytables will give
better performance in disk space.
datafilename -- path of file containing data.
netfilename -- path of file containing network structure.
outfilename -- path of output file.
node -- what node to look into for datasets. Default is /Vm
numcells -- number of cells among which cross correlation is to be
computed. if -1, all cells in datafile are used.
"""
print 'save_xcorr_pytables'
t_start = datetime.now()
datafile = tables.openFile(datafilename, mode='r')
netfile = tables.openFile(netfilename, mode='r')
outfilter = tables.Filters(complevel=4, complib='zlib', fletcher32=True)
outfile = tables.openFile(outfilename, mode='w', filters=outfilter)
simtime = None
plotdt = None
timestamp = None
if hasattr(datafile.root._v_attrs, 'simtime'):
simtime = datafile.root._v_attrs.simtime
if hasattr(datafile.root._v_attrs, 'plotdt'):
plotdt = datafile.root._v_attrs.plotdt
if hasattr(datafile.root._v_attrs, 'timestamp'):
timestamp = datafile.root._v_attrs.timestamp
outfile.title = 'Correlation of data with time stamp: %s' % (timestamp)
datanode = datafile.getNode('/', node)
cell = []
vmdata = []
for data in datanode:
if node == 'Vm':
cellname = data.name[:len(data.name) - 3] # remove trailing '_Vm'
cell.append(cellname)
vmdata.append(data.read())
t_end = datetime.now()
t_delta = t_end - t_start
print 'Finished reading data in %g s' % (t_delta.days * 86400 + t_delta.seconds + t_delta.microseconds * 1e-6)
if simtime is None:
simtime = 1.0 * len(vmdata[0])
if plotdt is None:
plotdt = 1.0
if numcells > 0:
indices = random.sample(range(len(cell)), numcells)
cell = [cell[index] for index in indices]
vmdata = [vmdata[index] for index in indices]
t_start = datetime.now()
lags = 2*int(simtime/plotdt)-1
time = numpy.fft.fftshift(numpy.fft.fftfreq(lags, 1/(plotdt*lags)))
corrgroup = outfile.createGroup(outfile.root, node, 'Correlation between %s series' % (node))
timearray = outfile.createCArray(corrgroup, 't', tables.FloatAtom(), time.shape)
timearray[:] = time[:]
for ii in range(len(cell)):
left = vmdata[ii]
for jj in range(len(cell)):
right = vmdata[jj]
corr = ncc(left, right)
corrarray = outfile.createCArray(corrgroup, '%s__%s' % (cell[ii], cell[jj]), tables.FloatAtom(), corr.shape)
corrarray[:] = corr[:]
outfile.close()
datafile.close()
netfile.close()
t_end = datetime.now()
t_delta = t_end - t_start
print 'Computed and saved correlatioons in %g seconds' % (t_delta.days * 86400 + t_delta.seconds + t_delta.microseconds * 1e-6)
