Major refactor -- rm all globals

tedana/cli/run.py

@@ -1,5 +1,5 @@
 import argparse
-from tedana.interfaces import tedana
+from tedana import workflows
 
 
 def get_parser():
@@ -100,7 +100,7 @@ def get_parser():
                         dest='filecsdata',
                         help='Save component selection data',
                         action='store_true',
-                        default=False)
+                        default=True)
     parser.add_argument('--label',
                         dest='label',
                         help='Label for output directory.',
@@ -115,7 +115,7 @@ def get_parser():
 def main(argv=None):
     """Entry point"""
     options = get_parser().parse_args(argv)
-    tedana.main(**vars(options))
+    workflows.tedana.main(**vars(options))
 
 
 if __name__ == '__main__':

tedana/decomp/__init__.py

@@ -0,0 +1,11 @@
+# emacs: -*- mode: python-mode; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-
+# ex: set sts=4 ts=4 sw=4 et:
+
+from .eigendecomp import (
+    tedpca, tedica, eimask,
+)
+
+
+__all__ = [
+    'tedpca', 'tedica', 'eimask'
+    ]

tedana/decomp/eigendecomp.py

@@ -0,0 +1,255 @@
+import pickle
+import numpy as np
+import os.path as op
+from scipy import stats
+from tedana import model, utils
+
+import logging
+logging.basicConfig(format='[%(levelname)s]: %(message)s', level=logging.INFO)
+lgr = logging.getLogger(__name__)
+
+F_MAX = 500
+Z_MAX = 8
+
+
+def eimask(dd, ees=None):
+    """
+    Returns mask for data between [0.001, 5] * 98th percentile of dd
+
+    Parameters
+    ----------
+    dd : (S x E x T) array_like
+        Input data, where `S` is samples, `E` is echos, and `T` is time
+    ees : (N,) list
+        Indices of echos to assess from `dd` in calculating output
+
+    Returns
+    -------
+    imask : (S x N) np.ndarray
+        Boolean array denoting
+    """
+
+    if ees is None:
+        ees = range(dd.shape[1])
+    imask = np.zeros([dd.shape[0], len(ees)], dtype=bool)
+    for ee in ees:
+        lgr.info('++ Creating eimask for echo {}'.format(ee))
+        perc98 = stats.scoreatpercentile(dd[:, ee, :].flatten(), 98,
+                                         interpolation_method='lower')
+        lthr, hthr = 0.001 * perc98, 5 * perc98
+        lgr.info('++ Eimask threshold boundaries: '
+                 '{:.03f} {:.03f}'.format(lthr, hthr))
+        m = dd[:, ee, :].mean(axis=1)
+        imask[np.logical_and(m > lthr, m < hthr), ee] = True
+
+    return imask
+
+
+def tedpca(catd, OCcatd, combmode, mask, t2s, t2sG, stabilize,
+           ref_img, tes, kdaw, rdaw, ste=0, mlepca=True):
+    """
+    Performs PCA on `catd` and uses TE-dependence to dimensionally reduce data
+
+    Parameters
+    ----------
+    catd : (S x E x T) array_like
+        Input functional data
+    OCcatd : (S x T) array_like
+        Optimally-combined time series data
+    combmode : {'t2s', 'ste'} str
+        How optimal combination of echos should be made, where 't2s' indicates
+        using the method of Posse 1999 and 'ste' indicates using the method of
+        Poser 2006
+    mask : (S,) array_like
+        Boolean mask array
+    stabilize : bool
+        Whether to attempt to stabilize convergence of ICA by returning
+        dimensionally-reduced data from PCA and component selection.
+    ref_img : str or img_like
+        Reference image to dictate how outputs are saved to disk
+    tes : list
+        List of echo times associated with `catd`, in milliseconds
+    kdaw : float
+        Dimensionality augmentation weight for Kappa calculations
+    rdaw : float
+        Dimensionality augmentation weight for Rho calculations
+    ste : int or list-of-int, optional
+        Which echos to use in PCA. Values -1 and 0 are special, where a value
+        of -1 will indicate using all the echos and 0 will indicate using the
+        optimal combination of the echos. A list can be provided to indicate
+        a subset of echos. Default: 0
+    mlepca : bool, optional
+        Whether to use the method originally explained in Minka, NIPS 2000 for
+        guessing PCA dimensionality instead of a traditional SVD. Default: True
+
+    Returns
+    -------
+    n_components : int
+        Number of components retained from PCA decomposition
+    dd : (S x E x T) np.ndarray
+        Dimensionally-reduced functional data
+    """
+
+    n_samp, n_echos, n_vols = catd.shape
+    ste = np.array([int(ee) for ee in str(ste).split(',')])
+
+    if len(ste) == 1 and ste[0] == -1:
+        lgr.info('++ Computing PCA of optimally combined multi-echo data')
+        d = OCcatd[utils.make_min_mask(OCcatd[:, np.newaxis, :])][:, np.newaxis, :]
+    elif len(ste) == 1 and ste[0] == 0:
+        lgr.info('++ Computing PCA of spatially concatenated multi-echo data')
+        d = catd[mask].astype('float64')
+    else:
+        lgr.info('++ Computing PCA of echo #%s' % ','.join([str(ee) for ee in ste]))
+        d = np.stack([catd[mask, ee] for ee in ste - 1], axis=1).astype('float64')
+
+    eim = np.squeeze(eimask(d))
+    d = np.squeeze(d[eim])
+
+    dz = ((d.T - d.T.mean(axis=0)) / d.T.std(axis=0)).T  # var normalize ts
+    dz = (dz - dz.mean()) / dz.std()  # var normalize everything
+
+    if not op.exists('pcastate.pkl'):
+        # do PC dimension selection and get eigenvalue cutoff
+        if mlepca:
+            from sklearn.decomposition import PCA
+            ppca = PCA(n_components='mle', svd_solver='full')
+            ppca.fit(dz)
+            v = ppca.components_
+            s = ppca.explained_variance_
+            u = np.dot(np.dot(dz, v.T), np.diag(1. / s))
+        else:
+            u, s, v = np.linalg.svd(dz, full_matrices=0)
+
+        # actual variance explained (normalized)
+        sp = s / s.sum()
+        eigelb = model.getelbow_mod(sp, val=True)
+
+        spdif = np.abs(np.diff(sp))
+        spdifh = spdif[(len(spdif)//2):]
+        spdthr = np.mean([spdifh.max(), spdif.min()])
+        spmin = sp[(len(spdif)//2) + np.arange(len(spdifh))[spdifh >= spdthr][0] + 1]
+        spcum = np.cumsum(sp)
+
+        # Compute K and Rho for PCA comps
+        eimum = np.atleast_2d(eim)
+        eimum = np.transpose(eimum, np.argsort(eimum.shape)[::-1])
+        eimum = eimum.prod(axis=1)
+        o = np.zeros((mask.shape[0], *eimum.shape[1:]))
+        o[mask] = eimum
+        eimum = np.squeeze(o).astype(bool)
+
+        vTmix = v.T
+        vTmixN = ((vTmix.T - vTmix.T.mean(0)) / vTmix.T.std(0)).T
+        _, ctb, betasv, v_T = model.fitmodels_direct(catd, v.T, eimum, t2s, t2sG,
+                                                     tes, combmode, ref_img,
+                                                     mmixN=vTmixN, full_sel=False)
+        ctb = ctb[ctb[:, 0].argsort(), :]
+        ctb = np.vstack([ctb.T[:3], sp]).T
+
+        # Save state
+        lgr.info('++ Saving PCA')
+        pcastate = {'u': u, 's': s, 'v': v, 'ctb': ctb,
+                    'eigelb': eigelb, 'spmin': spmin, 'spcum': spcum}
+        try:
+            with open('pcastate.pkl', 'wb') as handle:
+                pickle.dump(pcastate, handle)
+        except TypeError:
+            lgr.warning('++ Could not save PCA solution.')
+
+    else:  # if loading existing state
+        lgr.info('++ Loading PCA')
+        with open('pcastate.pkl', 'rb') as handle:
+            pcastate = pickle.load(handle)
+        u, s, v = pcastate['u'], pcastate['s'], pcastate['v']
+        ctb, eigelb = pcastate['ctb'], pcastate['eigelb']
+        spmin, spcum = pcastate['spmin'], pcastate['spcum']
+
+    np.savetxt('comp_table_pca.txt', ctb[ctb[:, 1].argsort(), :][::-1])
+    np.savetxt('mepca_mix.1D', v[ctb[:, 1].argsort()[::-1], :].T)
+
+    kappas = ctb[ctb[:, 1].argsort(), 1]
+    rhos = ctb[ctb[:, 2].argsort(), 2]
+    fmin, fmid, fmax = utils.getfbounds(n_echos)
+    kappa_thr = np.average(sorted([fmin, model.getelbow_mod(kappas, val=True)/2, fmid]),
+                           weights=[kdaw, 1, 1])
+    rho_thr = np.average(sorted([fmin, model.getelbow_cons(rhos, val=True)/2, fmid]),
+                         weights=[rdaw, 1, 1])
+    if int(kdaw) == -1:
+        kappas_lim = kappas[utils.andb([kappas < fmid, kappas > fmin]) == 2]
+        kappa_thr = kappas_lim[model.getelbow_mod(kappas_lim)]
+        rhos_lim = rhos[utils.andb([rhos < fmid, rhos > fmin]) == 2]
+        rho_thr = rhos_lim[model.getelbow_mod(rhos_lim)]
+        stabilize = True
+    if int(kdaw) != -1 and int(rdaw) == -1:
+        rhos_lim = rhos[utils.andb([rhos < fmid, rhos > fmin]) == 2]
+        rho_thr = rhos_lim[model.getelbow_mod(rhos_lim)]
+
+    is_hik = np.array(ctb[:, 1] > kappa_thr, dtype=np.int)
+    is_hir = np.array(ctb[:, 2] > rho_thr, dtype=np.int)
+    is_hie = np.array(ctb[:, 3] > eigelb, dtype=np.int)
+    is_his = np.array(ctb[:, 3] > spmin, dtype=np.int)
+    is_not_fmax1 = np.array(ctb[:, 1] != F_MAX, dtype=np.int)
+    is_not_fmax2 = np.array(ctb[:, 2] != F_MAX, dtype=np.int)
+    pcscore = (is_hik + is_hir + is_hie) * is_his * is_not_fmax1 * is_not_fmax2
+    if stabilize:
+        temp7 = np.array(spcum < 0.95, dtype=np.int)
+        temp8 = np.array(ctb[:, 2] > fmin, dtype=np.int)
+        temp9 = np.array(ctb[:, 1] > fmin, dtype=np.int)
+        pcscore = pcscore * temp7 * temp8 * temp9
+
+    pcsel = pcscore > 0
+    dd = u.dot(np.diag(s*np.array(pcsel, dtype=np.int))).dot(v)
+
+    n_components = s[pcsel].shape[0]
+    lgr.info('++ Selected {0} components. Kappa threshold: {1:.02f}, '
+             'Rho threshold: {2:.02f}'.format(n_components, kappa_thr, rho_thr))
+
+    dd = stats.zscore(dd.T, axis=0).T  # variance normalize timeseries
+    dd = stats.zscore(dd, axis=None)  # variance normalize everything
+
+    return n_components, dd
+
+
+def tedica(n_components, dd, conv, fixed_seed, cost, final_cost):
+    """
+    Performs ICA on `dd` and returns mixing matrix
+
+    Parameters
+    ----------
+    n_components : int
+        Number of components retained from PCA decomposition
+    dd : (S x E x T) np.ndarray
+        Dimensionally-reduced functional data, where `S` is samples, `E` is
+        echos, and `T` is time
+    conv : float
+        Convergence limit for ICA
+    fixed_seed : int
+        Seed for ensuring reproducibility of ICA results
+    initcost : {'tanh', 'pow3', 'gaus', 'skew'} str, optional
+        Initial cost function for ICA
+    finalcost : {'tanh', 'pow3', 'gaus', 'skew'} str, optional
+        Final cost function for ICA
+
+    Returns
+    -------
+    mmix : (C x T) np.ndarray
+        Mixing matrix for converting input data to component space, where `C`
+        is components and `T` is the same as in `dd`
+
+    Notes
+    -----
+    Uses `mdp` implementation of FastICA for decomposition
+    """
+
+    import mdp
+    climit = float(conv)
+    mdp.numx_rand.seed(fixed_seed)
+    icanode = mdp.nodes.FastICANode(white_comp=n_components, approach='symm', g=cost,
+                                    fine_g=final_cost, coarse_limit=climit*100,
+                                    limit=climit, verbose=True)
+    icanode.train(dd)
+    smaps = icanode.execute(dd)  # noqa
+    mmix = icanode.get_recmatrix().T
+    mmix = stats.zscore(mmix, axis=0)
+    return mmix