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src/n/i/nitime-0.4/nitime/analysis/granger.py   nitime(Download)
 
# To suppport older versions of numpy that don't have tril_indices:
from nitime.index_utils import tril_indices_from
 
def fit_model(x1, x2, order=None, max_order=10,
            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:

src/n/i/nitime-HEAD/nitime/analysis/granger.py   nitime(Download)
 
# To suppport older versions of numpy that don't have tril_indices:
from nitime.index_utils import tril_indices_from
 
def fit_model(x1, x2, order=None, max_order=10,
            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:

src/n/i/nitime-0.4/nitime/analysis/snr.py   nitime(Download)
from nitime import timeseries as ts
 
from nitime.index_utils import tril_indices_from
 
 
 
        c = np.corrcoef(self.input.data)
        c = c[tril_indices_from(c, -1)]
 
        return np.mean(c), stats.sem(c)

src/n/i/nitime-HEAD/nitime/analysis/snr.py   nitime(Download)
from nitime import timeseries as ts
 
from nitime.index_utils import tril_indices_from
 
 
 
        c = np.corrcoef(self.input.data)
        c = c[tril_indices_from(c, -1)]
 
        return np.mean(c), stats.sem(c)