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src/n/i/nipy-0.3.0/nipy/algorithms/diagnostics/screens.py   nipy(Download)
    screen_res['min'] = Image(np.min(data, axis=-1), cmap_3d)
    # PCA
    screen_res['pca_res'] = pca.pca(data,
                                    axis=-1,
                                    standardize=False,

src/n/i/nipy-0.3.0/nipy/algorithms/diagnostics/tests/test_screen.py   nipy(Download)
from ..screens import screen
from ..timediff import time_slice_diffs
from ...utils.pca import pca
from ...utils.tests.test_pca import res2pos1
 
    assert_array_equal(np.min(data, axis=-1), res['min'].get_data())
    assert_array_equal(np.std(data, axis=-1), res['std'].get_data())
    pca_res = pca(data, axis=-1, standardize=False, ncomp=10)
    # On windows, there seems to be some randomness in the PCA output vector
    # signs; this routine sets the basis vectors to have first value positive,