def __init__(self,
input=None,
ij=None,
order=None,
max_order=10,
criterion=utils.bayesian_information_criterion,
n_freqs=1024):
"""
Initializer for the GrangerAnalyzer.
Parameters
----------
input: nitime TimeSeries object
ij: List of tuples of the form: [(0, 1), (0, 2)], etc.
These are the indices of pairs of time-series for which the
analysis will be done. Defaults to all vs. all.
order: int (optional)
The order of the process. If this is not known, it will be
estimated from the data, using the information criterion
max_order: if the order is estimated, this is the maximal order to
estimate for.
n_freqs: int (optional)
The size of the sampling grid in the frequency domain.
Defaults to 1024
criterion:
XXX
"""
self.data = input.data
self.sampling_rate = input.sampling_rate
self._n_process = input.shape[0]
self._n_freqs = n_freqs
self._order = order
self._criterion = criterion
self._max_order = max_order
if ij is None:
# The following gets the full list of combinations of
# non-same i's and j's:
x, y = np.meshgrid(np.arange(self._n_process),
np.arange(self._n_process))
self.ij = zip(x[tril_indices_from(x, -1)],
y[tril_indices_from(y, -1)])
else:
self.ij = ij
def __init__(self, input=None, ij=None, order=None, max_order=10,
criterion=utils.bayesian_information_criterion, n_freqs=1024):
"""
Initializer for the GrangerAnalyzer.
Parameters
----------
input: nitime TimeSeries object
ij: List of tuples of the form: [(0, 1), (0, 2)], etc.
These are the indices of pairs of time-series for which the
analysis will be done. Defaults to all vs. all.
order: int (optional)
The order of the process. If this is not known, it will be
estimated from the data, using the information criterion
max_order: if the order is estimated, this is the maximal order to
estimate for.
n_freqs: int (optional)
The size of the sampling grid in the frequency domain.
Defaults to 1024
criterion:
XXX
"""
self.data = input.data
self.sampling_rate = input.sampling_rate
self._n_process = input.shape[0]
self._n_freqs = n_freqs
self._order = order
self._criterion = criterion
self._max_order = max_order
if ij is None:
# The following gets the full list of combinations of
# non-same i's and j's:
x, y = np.meshgrid(np.arange(self._n_process),
np.arange(self._n_process))
self.ij = list(zip(x[tril_indices_from(x, -1)],
y[tril_indices_from(y, -1)]))
else:
self.ij = ij
def correlation(self):
"""
The correlation between all combinations of trials
Returns
-------
(r,e) : tuple
r is the mean correlation and e is the mean error of the correlation
(with df = n_trials - 1)
"""
c = np.corrcoef(self.input.data)
c = c[tril_indices_from(c, -1)]
return np.mean(c), stats.sem(c)
def correlation(self):
"""
The correlation between all combinations of trials
Returns
-------
(r,e) : tuple
r is the mean correlation and e is the mean error of the correlation
(with df = n_trials - 1)
"""
c = np.corrcoef(self.input.data)
c = c[tril_indices_from(c, -1)]
return np.mean(c), stats.sem(c)
