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src/n/i/nitime-0.4/nitime/algorithms/spectral.py   nitime(Download)
 
# To support older versions of numpy that don't have tril_indices:
from nitime.index_utils import tril_indices, triu_indices
 
 
 
    upper_idc = triu_indices(M, k=1)
    lower_idc = tril_indices(M, k=-1)
    csd_mat[upper_idc] = csd_mat[lower_idc].conj()
    return freqs, csd_mat
 
    upper_idc = triu_indices(M, k=1)
    lower_idc = tril_indices(M, k=-1)
    csdfs[upper_idc] = csdfs[lower_idc].conj()
 

src/n/i/nitime-0.4/nitime/analysis/coherence.py   nitime(Download)
 
# To suppport older versions of numpy that don't have tril_indices:
from nitime.index_utils import tril_indices, triu_indices
 
from .base import BaseAnalyzer
                                                     self.spectrum[j][j])
 
        idx = tril_indices(tseries_length, -1)
        coherency[idx[0], idx[1], ...] = coherency[idx[1], idx[0], ...].conj()
 
                                                     self.spectrum[j][j])
 
        idx = tril_indices(tseries_length, -1)
        coherence[idx[0], idx[1], ...] = coherence[idx[1], idx[0], ...].conj()
 
                            self.spectrum[k][k])
 
        idx = tril_indices(tseries_length, -1)
        p_coherence[idx[0], idx[1], ...] =\
                            p_coherence[idx[1], idx[0], ...].conj()

src/n/i/nitime-0.4/nitime/algorithms/cohere.py   nitime(Download)
 
# To suppport older versions of numpy that don't have tril_indices:
from nitime.index_utils import tril_indices
 
 
            c[i][j] = coherency_spec(fxy[i][j], fxy[i][i], fxy[j][j])
 
    idx = tril_indices(time_series.shape[0], -1)
    c[idx[0], idx[1], ...] = c[idx[1], idx[0], ...].conj()  # Make it symmetric
 
            c[i][j] = coherence_spec(fxy[i][j], fxy[i][i], fxy[j][j])
 
    idx = tril_indices(time_series.shape[0], -1)
    c[idx[0], idx[1], ...] = c[idx[1], idx[0], ...].conj()  # Make it symmetric
 
                                             fxy[j][j], epsilon, alpha)
 
    idx = tril_indices(time_series.shape[0], -1)
    c[idx[0], idx[1], ...] = c[idx[1], idx[0], ...].conj()  # Make it symmetric
 
                                             fxy[j][j], epsilon, alpha)
 
    idx = tril_indices(time_series.shape[0], -1)
    c[idx[0], idx[1], ...] = c[idx[1], idx[0], ...].conj()  # Make it symmetric
 

src/n/i/nitime-HEAD/nitime/analysis/coherence.py   nitime(Download)
 
# To suppport older versions of numpy that don't have tril_indices:
from nitime.index_utils import tril_indices, triu_indices
 
from .base import BaseAnalyzer
                                                     self.spectrum[j][j])
 
        idx = tril_indices(tseries_length, -1)
        coherency[idx[0], idx[1], ...] = coherency[idx[1], idx[0], ...].conj()
 
                                                     self.spectrum[j][j])
 
        idx = tril_indices(tseries_length, -1)
        coherence[idx[0], idx[1], ...] = coherence[idx[1], idx[0], ...].conj()
 
                            self.spectrum[k][k])
 
        idx = tril_indices(tseries_length, -1)
        p_coherence[idx[0], idx[1], ...] =\
                            p_coherence[idx[1], idx[0], ...].conj()

src/n/i/nitime-HEAD/nitime/algorithms/cohere.py   nitime(Download)
 
# To suppport older versions of numpy that don't have tril_indices:
from nitime.index_utils import tril_indices
 
 
            c[i][j] = coherency_spec(fxy[i][j], fxy[i][i], fxy[j][j])
 
    idx = tril_indices(time_series.shape[0], -1)
    c[idx[0], idx[1], ...] = c[idx[1], idx[0], ...].conj()  # Make it symmetric
 
            c[i][j] = coherence_spec(fxy[i][j], fxy[i][i], fxy[j][j])
 
    idx = tril_indices(time_series.shape[0], -1)
    c[idx[0], idx[1], ...] = c[idx[1], idx[0], ...].conj()  # Make it symmetric
 
                                             fxy[j][j], epsilon, alpha)
 
    idx = tril_indices(time_series.shape[0], -1)
    c[idx[0], idx[1], ...] = c[idx[1], idx[0], ...].conj()  # Make it symmetric
 
                                             fxy[j][j], epsilon, alpha)
 
    idx = tril_indices(time_series.shape[0], -1)
    c[idx[0], idx[1], ...] = c[idx[1], idx[0], ...].conj()  # Make it symmetric
 

src/n/i/nitime-0.4/nitime/analysis/correlation.py   nitime(Download)
 
# To suppport older versions of numpy that don't have tril_indices:
from nitime.index_utils import tril_indices
 
from .base import BaseAnalyzer
                                          mode='full')
 
        idx = tril_indices(tseries_length, -1)
        xcorr[idx[0], idx[1], ...] = xcorr[idx[1], idx[0], ...]
 
                xcorr[i, j] *= self.corrcoef[i, j]
 
        idx = tril_indices(tseries_length, -1)
        xcorr[idx[0], idx[1], ...] = xcorr[idx[1], idx[0], ...]
 

src/n/i/nitime-HEAD/nitime/analysis/correlation.py   nitime(Download)
 
# To suppport older versions of numpy that don't have tril_indices:
from nitime.index_utils import tril_indices
 
from .base import BaseAnalyzer
                                          mode='full')
 
        idx = tril_indices(tseries_length, -1)
        xcorr[idx[0], idx[1], ...] = xcorr[idx[1], idx[0], ...]
 
                xcorr[i, j] *= self.corrcoef[i, j]
 
        idx = tril_indices(tseries_length, -1)
        xcorr[idx[0], idx[1], ...] = xcorr[idx[1], idx[0], ...]
 

src/n/i/nitime-HEAD/nitime/algorithms/spectral.py   nitime(Download)
 
# To support older versions of numpy that don't have tril_indices:
from nitime.index_utils import tril_indices, triu_indices