# -*- coding: utf-8 -*-
"""Functions for fitting head positions with (c)HPI coils."""

# To fit head positions (continuously), the procedure using
# ``_calculate_chpi_positions`` is:
#
#     1. Get HPI coil locations (as digitized in info['dig'] in head coords
#        using ``_get_hpi_initial_fit``.
#     2. Get HPI frequencies,  HPI status channel, HPI status bits,
#        and digitization order using ``_setup_hpi_struct``.
#     3. Map HPI coil locations into device coords and compute coil to coil
#        distances.
#     4. Window data using ``t_window`` (half before and half after ``t``) and
#        ``t_step_min``.
#        (Here Elekta high-passes the data, but we omit this step.)
#     5. Use a linear model (DC + linear slope + sin + cos terms set up
#        in ``_setup_hpi_struct``) to fit sinusoidal amplitudes to MEG
#        channels. Use SVD to determine the phase/amplitude of the sinusoids.
#        This step is accomplished using ``_fit_cHPI_amplitudes``
#     6. If the amplitudes are 98% correlated with last position
#        (and Δt < t_step_max), skip fitting.
#     7. Fit magnetic dipoles using the amplitudes for each coil frequency
#        (calling ``_fit_magnetic_dipole``).
#     8. If ``use_distances is True`` choose good coils based on pairwise
#        distances, taking into account the tolerance ``dist_limit``.
#     9. Fit dev_head_t quaternion (using ``_fit_chpi_quat``).
#     10. Accept or reject fit based on GOF threshold ``gof_limit``.
#
# The function ``filter_chpi`` uses the same linear model to filter cHPI
# and (optionally) line frequencies from the data.

# Authors: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)

from functools import partial

import numpy as np
from scipy import linalg
import itertools

from .io.pick import pick_types, pick_channels, pick_channels_regexp
from .io.constants import FIFF
from .io.ctf.trans import _make_ctf_coord_trans_set
from .forward import (_magnetic_dipole_field_vec, _create_meg_coils,
                      _concatenate_coils)
from .cov import make_ad_hoc_cov, compute_whitener
from .transforms import (apply_trans, invert_transform, _angle_between_quats,
                         quat_to_rot, rot_to_quat)
from .utils import verbose, logger, use_log_level, _check_fname, warn
from .externals.six import string_types

# Eventually we should add:
#   hpicons
#   high-passing of data during fits
#   parsing cHPI coil information from acq pars, then to PSD if necessary


# ############################################################################
# Reading from text or FIF file

def read_head_pos(fname):
    """Read MaxFilter-formatted head position parameters.

    Parameters
    ----------
    fname : str
        The filename to read. This can be produced by e.g.,
        ``maxfilter -headpos <name>.pos``.

    Returns
    -------
    pos : array, shape (N, 10)
        The position and quaternion parameters from cHPI fitting.

    See Also
    --------
    write_head_pos
    head_pos_to_trans_rot_t

    Notes
    -----
    .. versionadded:: 0.12
    """
    _check_fname(fname, must_exist=True, overwrite='read')
    data = np.loadtxt(fname, skiprows=1)  # first line is header, skip it
    data.shape = (-1, 10)  # ensure it's the right size even if empty
    if np.isnan(data).any():  # make sure we didn't do something dumb
        raise RuntimeError('positions could not be read properly from %s'
                           % fname)
    return data


def write_head_pos(fname, pos):
    """Write MaxFilter-formatted head position parameters.

    Parameters
    ----------
    fname : str
        The filename to write.
    pos : array, shape (N, 10)
        The position and quaternion parameters from cHPI fitting.

    See Also
    --------
    read_head_pos
    head_pos_to_trans_rot_t

    Notes
    -----
    .. versionadded:: 0.12
    """
    _check_fname(fname, overwrite=True)
    pos = np.array(pos, np.float64)
    if pos.ndim != 2 or pos.shape[1] != 10:
        raise ValueError('pos must be a 2D array of shape (N, 10)')
    with open(fname, 'wb') as fid:
        fid.write(' Time       q1       q2       q3       q4       q5       '
                  'q6       g-value  error    velocity\n'.encode('ASCII'))
        for p in pos:
            fmts = ['% 9.3f'] + ['% 8.5f'] * 9
            fid.write(((' ' + ' '.join(fmts) + '\n')
                       % tuple(p)).encode('ASCII'))


def head_pos_to_trans_rot_t(quats):
    """Convert Maxfilter-formatted head position quaternions.

    Parameters
    ----------
    quats : ndarray, shape (N, 10)
        MaxFilter-formatted position and quaternion parameters.

    Returns
    -------
    translation : ndarray, shape (N, 3)
        Translations at each time point.
    rotation : ndarray, shape (N, 3, 3)
        Rotations at each time point.
    t : ndarray, shape (N,)
        The time points.

    See Also
    --------
    read_head_pos
    write_head_pos
    """
    t = quats[..., 0].copy()
    rotation = quat_to_rot(quats[..., 1:4])
    translation = quats[..., 4:7].copy()
    return translation, rotation, t


def _apply_quat(quat, pts, move=True):
    """Apply MaxFilter-formatted head position parameters to points."""
    trans = np.concatenate(
        (quat_to_rot(quat[:3]),
         quat[3:][:, np.newaxis]), axis=1)

    return(apply_trans(trans, pts, move=move))


def _calculate_head_pos_ctf(raw, gof_limit=0.98):
    r"""Extract head position parameters from ctf dataset.

    Parameters
    ----------
    raw : instance of raw
        Raw data with cHPI information. HLC00 channels
    gof_limit : float
        Minimum goodness of fit to accept.

    Returns
    -------
    pos : array, shape (N, 10)
        The position and quaternion parameters from cHPI fitting.

    Notes
    -----
    CTF continuous head monitoring stores the x,y,z location (m) of each chpi
    coil as separate channels in the dataset.
    HLC001[123]-\\* - nasion
    HLC002[123]-\\* - lpa
    HLC003[123]-\\* - rpa
    """
    # Pick channels cooresponding to the cHPI positions
    hpi_picks = pick_channels_regexp(raw.info['ch_names'],
                                     'HLC00[123][123]-.*')

    # make sure we get 9 channels
    if len(hpi_picks) != 9:
        raise RuntimeError('Could not find all 9 cHPI channels')

    # get indices in alphabetical order
    sorted_picks = np.array(sorted(hpi_picks,
                                   key=lambda k: raw.info['ch_names'][k]))

    # make picks to match order of dig cardinial ident codes.
    # LPA (HPIC002[123]-*), NAS(HPIC001[123]-*), RPA(HPIC003[123]-*)
    hpi_picks = sorted_picks[[3, 4, 5, 0, 1, 2, 6, 7, 8]]
    del sorted_picks

    # process the entire run
    time_sl = slice(0, len(raw.times))
    chpi_data = raw[hpi_picks, time_sl][0]

    # grab initial cHPI locations
    # point sorted in hpi_results are in mne device coords
    chpi_locs_dev = sorted([d for d in raw.info['hpi_results'][-1]
                            ['dig_points']], key=lambda x: x['ident'])
    chpi_locs_dev = np.array([d['r'] for d in chpi_locs_dev])

    # transforms
    dev_head_t = raw.info['dev_head_t']
    tmp_trans = _make_ctf_coord_trans_set(None, None)
    ctf_dev_dev_t = tmp_trans['t_ctf_dev_dev']
    del tmp_trans

    # move to head coords
    chpi_locs_head = apply_trans(dev_head_t, chpi_locs_dev)

    # find indices where chpi locations change
    indices = [0]
    indices.extend(np.where(np.all(chpi_data[:, :-1] != chpi_data[:, 1:],
                                   axis=0))[0] + 1)

    # initialized quaternion
    last_quat = np.concatenate([rot_to_quat(dev_head_t['trans'][:3, :3]),
                                dev_head_t['trans'][:3, 3]])

    quats = []
    for idx in indices:
        # data in channels are in ctf device coordinates (cm)
        this_ctf_dev = chpi_data[:, idx].reshape(3, 3)  # m

        # map to mne device coords
        this_dev = apply_trans(ctf_dev_dev_t, this_ctf_dev)

        # fit quaternion
        this_quat, g = _fit_chpi_quat(this_dev, chpi_locs_head, last_quat)
        if g < gof_limit:
            raise RuntimeError('Bad coil fit! (g=%7.3f)' % (g,))

        if (idx > 0):
            dt = float(raw.times[idx] - raw.times[idx - 1])
        else:
            dt = 0.001

        this_locs_head = _apply_quat(this_quat, this_dev, move=True)
        errs = 1000. * np.sqrt(((chpi_locs_head -
                                 this_locs_head) ** 2).sum(axis=-1))
        e = errs.mean() / 1000.  # mm -> m
        d = 100 * np.sqrt(np.sum(last_quat[3:] - this_quat[3:]) ** 2)  # cm
        v = d / dt  # cm/sec

        quats.append(np.concatenate(([raw.times[idx]], this_quat, [g],
                                     [e * 100], [v])))  # e in centimeters
        last_quat = this_quat

    quats = np.array(quats, np.float64)
    quats = np.zeros((0, 10)) if quats.size == 0 else quats
    return quats


# ############################################################################
# Estimate positions from data
@verbose
def _get_hpi_info(info, verbose=None):
    """Get HPI information from raw."""
    if len(info['hpi_meas']) == 0 or \
            ('coil_freq' not in info['hpi_meas'][0]['hpi_coils'][0]):
        raise RuntimeError('Appropriate cHPI information not found in'
                           'info["hpi_meas"] and info["hpi_subsystem"], '
                           'cannot process cHPI')
    hpi_coils = sorted(info['hpi_meas'][-1]['hpi_coils'],
                       key=lambda x: x['number'])  # ascending (info) order

    # get frequencies
    hpi_freqs = np.array([float(x['coil_freq']) for x in hpi_coils])
    logger.info('Using %s HPI coils: %s Hz'
                % (len(hpi_freqs), ' '.join(str(int(s)) for s in hpi_freqs)))

    # how cHPI active is indicated in the FIF file
    hpi_sub = info['hpi_subsystem']
    hpi_pick = None  # there is no pick!
    if hpi_sub is not None:
        if 'event_channel' in hpi_sub:
            hpi_pick = pick_channels(info['ch_names'],
                                     [hpi_sub['event_channel']])
            hpi_pick = hpi_pick[0] if len(hpi_pick) > 0 else None
        # grab codes indicating a coil is active
        hpi_on = [coil['event_bits'][0] for coil in hpi_sub['hpi_coils']]
        # not all HPI coils will actually be used
        hpi_on = np.array([hpi_on[hc['number'] - 1] for hc in hpi_coils])
        # mask for coils that may be active
        hpi_mask = np.array([event_bit != 0 for event_bit in hpi_on])
        hpi_on = hpi_on[hpi_mask]
        hpi_freqs = hpi_freqs[hpi_mask]
    else:
        hpi_on = np.zeros(len(hpi_freqs))

    return hpi_freqs, hpi_pick, hpi_on


@verbose
def _get_hpi_initial_fit(info, adjust=False, verbose=None):
    """Get HPI fit locations from raw."""
    if info['hpi_results'] is None:
        raise RuntimeError('no initial cHPI head localization performed')

    hpi_result = info['hpi_results'][-1]
    hpi_coils = sorted(info['hpi_meas'][-1]['hpi_coils'],
                       key=lambda x: x['number'])  # ascending (info) order
    hpi_dig = sorted([d for d in info['dig']
                      if d['kind'] == FIFF.FIFFV_POINT_HPI],
                     key=lambda x: x['ident'])  # ascending (dig) order
    pos_order = hpi_result['order'] - 1  # zero-based indexing, dig->info

    # this shouldn't happen, eventually we could add the transforms
    # necessary to put it in head coords
    if not all(d['coord_frame'] == FIFF.FIFFV_COORD_HEAD for d in hpi_dig):
        raise RuntimeError('cHPI coordinate frame incorrect')
    # Give the user some info
    logger.info('HPIFIT: %s coils digitized in order %s'
                % (len(pos_order), ' '.join(str(o + 1) for o in pos_order)))
    logger.debug('HPIFIT: %s coils accepted: %s'
                 % (len(hpi_result['used']),
                    ' '.join(str(h) for h in hpi_result['used'])))
    hpi_rrs = np.array([d['r'] for d in hpi_dig])[pos_order]

    # Fitting errors
    hpi_rrs_fit = sorted([d for d in info['hpi_results'][-1]['dig_points']],
                         key=lambda x: x['ident'])
    hpi_rrs_fit = np.array([d['r'] for d in hpi_rrs_fit])
    # hpi_result['dig_points'] are in FIFFV_COORD_UNKNOWN coords, but this
    # is probably a misnomer because it should be FIFFV_COORD_DEVICE for this
    # to work
    assert hpi_result['coord_trans']['to'] == FIFF.FIFFV_COORD_HEAD
    hpi_rrs_fit = apply_trans(hpi_result['coord_trans']['trans'], hpi_rrs_fit)
    if 'moments' in hpi_result:
        logger.debug('Hpi coil moments (%d %d):'
                     % hpi_result['moments'].shape[::-1])
        for moment in hpi_result['moments']:
            logger.debug("%g %g %g" % tuple(moment))
    errors = np.sqrt(((hpi_rrs - hpi_rrs_fit) ** 2).sum(axis=1))
    logger.debug('HPIFIT errors:  %s mm.'
                 % ', '.join('%0.1f' % (1000. * e) for e in errors))
    if errors.sum() < len(errors) * hpi_result['dist_limit']:
        logger.info('HPI consistency of isotrak and hpifit is OK.')
    elif not adjust and (len(hpi_result['used']) == len(hpi_coils)):
        warn('HPI consistency of isotrak and hpifit is poor.')
    else:
        # adjust HPI coil locations using the hpifit transformation
        for hi, (r_dig, r_fit) in enumerate(zip(hpi_rrs, hpi_rrs_fit)):
            # transform to head frame
            d = 1000 * np.sqrt(((r_dig - r_fit) ** 2).sum())
            if not adjust:
                warn('Discrepancy of HPI coil %d isotrak and hpifit is %.1f '
                     'mm!' % (hi + 1, d))
            elif hi + 1 not in hpi_result['used']:
                if hpi_result['goodness'][hi] >= hpi_result['good_limit']:
                    logger.info('Note: HPI coil %d isotrak is adjusted by '
                                '%.1f mm!' % (hi + 1, d))
                    hpi_rrs[hi] = r_fit
                else:
                    warn('Discrepancy of HPI coil %d isotrak and hpifit of '
                         '%.1f mm was not adjusted!' % (hi + 1, d))
    logger.debug('HP fitting limits: err = %.1f mm, gval = %.3f.'
                 % (1000 * hpi_result['dist_limit'], hpi_result['good_limit']))

    return hpi_rrs


def _magnetic_dipole_objective(x, B, B2, coils, scale, method, too_close):
    """Project data onto right eigenvectors of whitened forward."""
    if method == 'forward':
        fwd = _magnetic_dipole_field_vec(x[np.newaxis, :], coils, too_close)
    else:
        from .preprocessing.maxwell import _sss_basis
        # Eventually we can try incorporating external bases here, which
        # is why the :3 is on the SVD below
        fwd = _sss_basis(dict(origin=x, int_order=1, ext_order=0), coils).T
    fwd = np.dot(fwd, scale.T)
    one = np.dot(linalg.svd(fwd, full_matrices=False)[2][:3], B)
    one *= one
    Bm2 = one.sum()
    return B2 - Bm2


def _fit_magnetic_dipole(B_orig, x0, coils, scale, method, too_close):
    """Fit a single bit of data (x0 = pos)."""
    from scipy.optimize import fmin_cobyla
    B = np.dot(scale, B_orig)
    B2 = np.dot(B, B)
    objective = partial(_magnetic_dipole_objective, B=B, B2=B2,
                        coils=coils, scale=scale, method=method,
                        too_close=too_close)
    x = fmin_cobyla(objective, x0, (), rhobeg=1e-4, rhoend=1e-5, disp=False)
    return x, 1. - objective(x) / B2


def _chpi_objective(x, coil_dev_rrs, coil_head_rrs):
    """Compute objective function."""
    d = np.dot(coil_dev_rrs, quat_to_rot(x[:3]).T)
    d += x[3:] / 10.  # in decimeters to get quats and head units close
    d -= coil_head_rrs
    d *= d
    return d.sum()


def _unit_quat_constraint(x):
    """Constrain our 3 quaternion rot params (ignoring w) to have norm <= 1."""
    return 1 - (x * x).sum()


def _fit_chpi_quat(coil_dev_rrs, coil_head_rrs, x0):
    """Fit rotation and translation (quaternion) parameters for cHPI coils."""
    from scipy.optimize import fmin_cobyla
    denom = np.sum((coil_head_rrs - np.mean(coil_head_rrs, axis=0)) ** 2)
    objective = partial(_chpi_objective, coil_dev_rrs=coil_dev_rrs,
                        coil_head_rrs=coil_head_rrs)
    x0 = x0.copy()
    x0[3:] *= 10.  # decimeters to get quats and head units close
    x = fmin_cobyla(objective, x0, _unit_quat_constraint,
                    rhobeg=1e-3, rhoend=1e-5, disp=False)
    result = objective(x)
    x[3:] /= 10.
    return x, 1. - result / denom


def _fit_coil_order_dev_head_trans(dev_pnts, head_pnts):
    """Compute Device to Head transform allowing for permutiatons of points."""
    id_quat = np.concatenate([rot_to_quat(np.eye(3)), [0.0, 0.0, 0.0]])
    best_order = None
    best_g = -999
    best_quat = id_quat
    for this_order in itertools.permutations(np.arange(len(head_pnts))):
        head_pnts_tmp = head_pnts[np.array(this_order)]
        this_quat, g = _fit_chpi_quat(dev_pnts, head_pnts_tmp, id_quat)
        if g > best_g:
            best_g = g
            best_order = np.array(this_order)
            best_quat = this_quat

    # Convert Quaterion to transform
    dev_head_t = np.concatenate(
        (quat_to_rot(best_quat[:3]),
         best_quat[3:][:, np.newaxis]), axis=1)
    dev_head_t = np.concatenate((dev_head_t, [[0, 0, 0, 1.]]))
    return dev_head_t, best_order


@verbose
def _setup_hpi_struct(info, model_n_window,
                      method='forward',
                      exclude='bads',
                      remove_aliased=False, verbose=None):
    """Generate HPI structure for HPI localization.

    Returns
    -------
    hpi : dict
        Dictionary of parameters representing the cHPI system and needed to
        perform head localization.
    """
    from .preprocessing.maxwell import _prep_mf_coils

    # grab basic info.
    hpi_freqs, hpi_pick, hpi_ons = _get_hpi_info(info)
    # worry about resampled/filtered data.
    # What to do e.g. if Raw has been resampled and some of our
    # HPI freqs would now be aliased
    highest = info.get('lowpass')
    highest = info['sfreq'] / 2. if highest is None else highest
    keepers = np.array([h <= highest for h in hpi_freqs], bool)
    if remove_aliased:
        hpi_freqs = hpi_freqs[keepers]
        hpi_ons = hpi_ons[keepers]
    elif not keepers.all():
        raise RuntimeError('Found HPI frequencies %s above the lowpass '
                           '(or Nyquist) frequency %0.1f'
                           % (hpi_freqs[~keepers].tolist(), highest))
    if info['line_freq'] is not None:
        line_freqs = np.arange(info['line_freq'], info['sfreq'] / 3.,
                               info['line_freq'])
    else:
        line_freqs = np.zeros([0])
    logger.info('Line interference frequencies: %s Hz'
                % ' '.join(['%d' % l for l in line_freqs]))

    # build model to extract sinusoidal amplitudes.
    slope = np.arange(model_n_window).astype(np.float64)[:, np.newaxis]
    slope -= np.mean(slope)
    rads = slope / info['sfreq']
    rads *= 2 * np.pi
    f_t = hpi_freqs[np.newaxis, :] * rads
    l_t = line_freqs[np.newaxis, :] * rads
    model = [np.sin(f_t), np.cos(f_t)]  # hpi freqs
    model += [np.sin(l_t), np.cos(l_t)]  # line freqs
    model += [slope, np.ones(slope.shape)]
    model = np.concatenate(model, axis=1)
    inv_model = linalg.pinv(model)

    # Set up magnetic dipole fits
    meg_picks = pick_types(info, meg=True, eeg=False, exclude=exclude)
    if len(exclude) > 0:
        if exclude == 'bads':
            msg = info['bads']
        else:
            msg = exclude
        logger.debug('Static bad channels (%d): %s'
                     % (len(msg), u' '.join(msg)))

    megchs = [ch for ci, ch in enumerate(info['chs']) if ci in meg_picks]
    coils = _create_meg_coils(megchs, 'accurate')
    if method == 'forward':
        coils = _concatenate_coils(coils)
    else:  # == 'multipole'
        coils = _prep_mf_coils(info)
    diag_cov = make_ad_hoc_cov(info, verbose=False)

    diag_whitener, _ = compute_whitener(diag_cov, info, picks=meg_picks,
                                        verbose=False)

    hpi = dict(meg_picks=meg_picks, hpi_pick=hpi_pick,
               model=model, inv_model=inv_model,
               on=hpi_ons, n_window=model_n_window, method=method,
               freqs=hpi_freqs, line_freqs=line_freqs, n_freqs=len(hpi_freqs),
               scale=diag_whitener, coils=coils
               )

    return hpi


def _time_prefix(fit_time):
    """Format log messages."""
    return ('    t=%0.3f:' % fit_time).ljust(17)


@verbose
def _fit_cHPI_amplitudes(raw, time_sl, hpi, fit_time, verbose=None):
    """Fit amplitudes for each channel from each of the N cHPI sinusoids.

    Returns
    -------
    sin_fit : ndarray, shape (n_freqs, n_channels)) or None :
        The sin amplitudes matching each cHPI frequency
            or None if this time window should be skipped
    """
    # No need to detrend the data because our model has a DC term
    with use_log_level(False):
        # loads good channels
        this_data = raw[hpi['meg_picks'], time_sl][0]

    # which HPI coils to use
    # other then erroring I don't see this getting used elsewhere?
    if hpi['hpi_pick'] is not None:
        with use_log_level(False):
            # loads hpi_stim channel
            chpi_data = raw[hpi['hpi_pick'], time_sl][0]

        ons = (np.round(chpi_data).astype(np.int) &
               hpi['on'][:, np.newaxis]).astype(bool)
        n_on = np.sum(ons, axis=0)
        if not (n_on >= 3).all():
            logger.info(_time_prefix(fit_time) + '%s < 3 HPI coils turned on, '
                        'skipping fit' % (n_on.min(),))
            return None
        # #TODO REMOVE # ons = ons.all(axis=1)  # which HPI coils to use

    n_freqs = hpi['n_freqs']
    this_len = time_sl.stop - time_sl.start
    if this_len == hpi['n_window']:
        model, inv_model = hpi['model'], hpi['inv_model']
    else:  # first or last window
        model = hpi['model'][:this_len]
        inv_model = linalg.pinv(model)
    X = np.dot(inv_model, this_data.T)
    X_sin, X_cos = X[:n_freqs], X[n_freqs:2 * n_freqs]

    # use SVD across all sensors to estimate the sinusoid phase
    sin_fit = np.zeros((n_freqs, X_sin.shape[1]))
    for fi in range(n_freqs):
        u, s, vt = np.linalg.svd(np.vstack((X_sin[fi, :], X_cos[fi, :])),
                                 full_matrices=False)
        # the first component holds the predominant phase direction
        # (so ignore the second, effectively doing s[1] = 0):
        sin_fit[fi, :] = vt[0]
        # Do not modify X, however, because it will break the signal
        # reconstruction step.

    data_diff_sq = np.dot(model, X).T - this_data
    data_diff_sq *= data_diff_sq
    data_diff_sq = np.sum(data_diff_sq, axis=-1)

    # compute amplitude correlation (for logging), protect against zero
    norm = this_data
    del this_data
    norm *= norm
    norm = np.sum(norm, axis=-1)
    norm_sum = norm.sum()
    norm_sum = np.inf if norm_sum == 0 else norm_sum
    norm[norm == 0] = np.inf
    g_sin = 1 - data_diff_sq.sum() / norm_sum
    g_chan = 1 - data_diff_sq / norm
    logger.debug('    HPI amplitude correlation %0.3f: %0.3f '
                 '(%s chnls > 0.95)' % (fit_time, g_sin,
                                        (g_chan > 0.95).sum()))

    return sin_fit


@verbose
def _fit_device_hpi_positions(raw, t_win=None, initial_dev_rrs=None,
                              too_close='raise', verbose=None):
    """Calculate location of HPI coils in device coords for 1 time window.

    Parameters
    ----------
    raw : instance of Raw
        Raw data with cHPI information.
    t_win : list, shape (2)
        Time window to fit. If None entire data run is used.
    initial_dev_rrs : ndarry, shape (n_CHPI, 3) || None
        Initial guess on HPI locations. If None (0,0,0) is used for each hpi.
    too_close : str
        How to handle HPI positions too close to the sensors,
        can be 'raise', 'warning', or 'info'.
    verbose : bool, str, int, or None
        If not None, override default verbose level (see :func:`mne.verbose`
        and :ref:`Logging documentation <tut_logging>` for more).

    Returns
    -------
    coil_dev_rrs : ndarray, shape (n_CHPI, 3)
        Fit locations of each cHPI coil in device coordinates
    """
    _check_too_close(too_close)
    # 0. determine samples to fit.
    if t_win is None:  # use the whole window
        i_win = [0, len(raw.times)]
    else:
        i_win = raw.time_as_index(t_win, use_rounding=True)

    # clamp index windows
    i_win = [max(i_win[0], 0), min(i_win[1], len(raw.times))]

    time_sl = slice(i_win[0], i_win[1])

    hpi = _setup_hpi_struct(raw.info, i_win[1] - i_win[0])

    if initial_dev_rrs is None:
        initial_dev_rrs = []
        for i in range(hpi['n_freqs']):
            initial_dev_rrs.append([0.0, 0.0, 0.0])

    # 1. Fit amplitudes for each channel from each of the N cHPI sinusoids
    sin_fit = _fit_cHPI_amplitudes(raw, time_sl, hpi, 0)

    # skip this window if it bad.
    # logging has already been done! Maybe turn this into an Exception
    if sin_fit is None:
        return None

    # 2. fit each HPI coil if its turned on
    outs = [_fit_magnetic_dipole(f, pos, hpi['coils'], hpi['scale'],
                                 hpi['method'], too_close)
            for f, pos, on in zip(sin_fit, initial_dev_rrs, hpi['on'])
            if on > 0]

    coil_dev_rrs = np.array([o[0] for o in outs])
    coil_g = np.array([o[0] for o in outs])

    return coil_dev_rrs, coil_g


def _check_too_close(too_close):
    if not isinstance(too_close, string_types) or \
            too_close not in ('raise', 'warning', 'info'):
        raise ValueError('too_close must be "raise", "warning", or "info", '
                         'got %s (type %s)' % (too_close, type(too_close)))


@verbose
def _calculate_chpi_positions(raw, t_step_min=0.1, t_step_max=10.,
                              t_window=0.2, dist_limit=0.005, gof_limit=0.98,
                              use_distances=True, too_close='raise',
                              verbose=None):
    """Calculate head positions using cHPI coils.

    Parameters
    ----------
    raw : instance of Raw
        Raw data with cHPI information.
    t_step_min : float
        Minimum time step to use. If correlations are sufficiently high,
        t_step_max will be used.
    t_step_max : float
        Maximum time step to use.
    t_window : float
        Time window to use to estimate the head positions.
    dist_limit : float
        Minimum distance (m) to accept for coil position fitting.
    gof_limit : float
        Minimum goodness of fit to accept.
    use_distances : bool
        use dist_limit to choose 'good' coils based on pairwise distances.
    too_close : str
        How to handle HPI positions too close to the sensors,
        can be 'raise', 'warning', or 'info'.
    verbose : bool, str, int, or None
        If not None, override default verbose level (see :func:`mne.verbose`
        and :ref:`Logging documentation <tut_logging>` for more).

    Returns
    -------
    quats : ndarray, shape (N, 10)
        The ``[t, q1, q2, q3, x, y, z, gof, err, v]`` for each fit.

    Notes
    -----
    The number of time points ``N`` will depend on the velocity of head
    movements as well as ``t_step_max`` and ``t_step_min``.

    See Also
    --------
    read_head_pos
    write_head_pos
    """
    from scipy.spatial.distance import cdist
    # extract initial geometry from info['hpi_results']
    hpi_dig_head_rrs = _get_hpi_initial_fit(raw.info)
    _check_too_close(too_close)

    # extract hpi system information
    hpi = _setup_hpi_struct(raw.info, int(round(t_window * raw.info['sfreq'])))

    # move to device coords
    dev_head_t = raw.info['dev_head_t']['trans']
    head_dev_t = invert_transform(raw.info['dev_head_t'])['trans']
    hpi_dig_dev_rrs = apply_trans(head_dev_t, hpi_dig_head_rrs)

    # compute initial coil to coil distances
    hpi_coil_dists = cdist(hpi_dig_head_rrs, hpi_dig_head_rrs)

    # setup last iteration structure
    last = dict(sin_fit=None, fit_time=t_step_min,
                coil_dev_rrs=hpi_dig_dev_rrs,
                quat=np.concatenate([rot_to_quat(dev_head_t[:3, :3]),
                                     dev_head_t[:3, 3]]))

    t_begin = raw.times[0]
    t_end = raw.times[-1]
    fit_idxs = raw.time_as_index(np.arange(t_begin + t_window / 2., t_end,
                                           t_step_min),
                                 use_rounding=True)
    quats = []
    logger.info('Fitting up to %s time points (%0.1f sec duration)'
                % (len(fit_idxs), t_end - t_begin))
    pos_0 = None

    hpi['n_freqs'] = len(hpi['freqs'])
    for midpt in fit_idxs:
        #
        # 0. determine samples to fit.
        #
        fit_time = (midpt + raw.first_samp - hpi['n_window'] / 2.) /\
            raw.info['sfreq']

        time_sl = midpt - hpi['n_window'] // 2
        time_sl = slice(max(time_sl, 0),
                        min(time_sl + hpi['n_window'], len(raw.times)))

        #
        # 1. Fit amplitudes for each channel from each of the N cHPI sinusoids
        #
        sin_fit = _fit_cHPI_amplitudes(raw, time_sl, hpi, fit_time)

        # skip this window if bad
        # logging has already been done! Maybe turn this into an Exception
        if sin_fit is None:
            continue

        # check if data has sufficiently changed
        if last['sin_fit'] is not None:  # first iteration
            # The sign of our fits is arbitrary
            flips = np.sign((sin_fit * last['sin_fit']).sum(-1, keepdims=True))
            sin_fit *= flips
            corr = np.corrcoef(sin_fit.ravel(), last['sin_fit'].ravel())[0, 1]
            # check to see if we need to continue
            if fit_time - last['fit_time'] <= t_step_max - 1e-7 and \
                    corr * corr > 0.98:
                # don't need to refit data
                continue

        # update 'last' sin_fit *before* inplace sign mult
        last['sin_fit'] = sin_fit.copy()

        #
        # 2. Fit magnetic dipole for each coil to obtain coil positions
        #    in device coordinates
        #
        outs = [_fit_magnetic_dipole(f, pos, hpi['coils'], hpi['scale'],
                                     hpi['method'], too_close)
                for f, pos in zip(sin_fit, last['coil_dev_rrs'])]
        this_coil_dev_rrs = np.array([o[0] for o in outs])
        g_coils = [o[1] for o in outs]

        # filter coil fits based on the correspodnace to digitization geometry
        use_mask = np.ones(hpi['n_freqs'], bool)
        if use_distances:
            these_dists = cdist(this_coil_dev_rrs, this_coil_dev_rrs)
            these_dists = np.abs(hpi_coil_dists - these_dists)
            # there is probably a better algorithm for finding the bad ones...
            good = False
            while not good:
                d = these_dists[use_mask][:, use_mask]
                d_bad = (d > dist_limit)
                good = not d_bad.any()
                if not good:
                    if use_mask.sum() == 2:
                        use_mask[:] = False
                        break  # failure
                    # exclude next worst point
                    badness = (d * d_bad).sum(axis=0)
                    exclude_coils = np.where(use_mask)[0][np.argmax(badness)]
                    use_mask[exclude_coils] = False
            good = use_mask.sum() >= 3
            if not good:
                warn(_time_prefix(fit_time) + '%s/%s good HPI fits, '
                     'cannot determine the transformation!'
                     % (use_mask.sum(), hpi['n_freqs']))
                continue

        #
        # 3. Fit the head translation and rotation params (minimize error
        #    between coil positions and the head coil digitization positions)
        #
        this_quat, g = _fit_chpi_quat(this_coil_dev_rrs[use_mask],
                                      hpi_dig_head_rrs[use_mask],
                                      last['quat'])
        if g < gof_limit:
            logger.info(_time_prefix(fit_time) +
                        'Bad coil fit! (g=%7.3f)' % (g,))
            continue

        # Convert quaterion to transform
        this_dev_head_t = np.concatenate(
            (quat_to_rot(this_quat[:3]),
             this_quat[3:][:, np.newaxis]), axis=1)
        this_dev_head_t = np.concatenate((this_dev_head_t, [[0, 0, 0, 1.]]))

        # velocities, in device coords, of HPI coils
        # dt = fit_time - last['fit_time'] #
        dt = t_window
        vs = tuple(1000. * np.sqrt(np.sum((last['coil_dev_rrs'] -
                                           this_coil_dev_rrs) ** 2,
                                          axis=1)) / dt)
        logger.info(_time_prefix(fit_time) +
                    ('%s/%s good HPI fits, movements [mm/s] = ' +
                     ' / '.join(['% 6.1f'] * hpi['n_freqs']))
                    % ((use_mask.sum(), hpi['n_freqs']) + vs))

        # resulting errors in head coil positions
        est_coil_head_rrs = apply_trans(this_dev_head_t, this_coil_dev_rrs)
        errs = 1000. * np.sqrt(((hpi_dig_head_rrs -
                                 est_coil_head_rrs) ** 2).sum(axis=-1))
        e = errs[use_mask].mean() / 1000.  # mm -> m
        d = 100 * np.sqrt(np.sum(last['quat'][3:] - this_quat[3:]) ** 2)  # cm
        r = _angle_between_quats(last['quat'][:3], this_quat[:3]) / dt
        v = d / dt  # cm/sec
        if pos_0 is None:
            pos_0 = this_quat[3:].copy()
        d = 100 * np.sqrt(np.sum((this_quat[3:] - pos_0) ** 2))  # dis from 1st
        # MaxFilter averages over a 200 ms window for display, but we don't
        for ii in range(hpi['n_freqs']):
            if use_mask[ii]:
                start, end = ' ', '/'
            else:
                start, end = '(', ')'
            log_str = ('    ' + start +
                       '{0:6.1f} {1:6.1f} {2:6.1f} / ' +
                       '{3:6.1f} {4:6.1f} {5:6.1f} / ' +
                       'g = {6:0.3f} err = {7:4.1f} ' +
                       end)
            if ii <= 2:
                log_str += '{8:6.3f} {9:6.3f} {10:6.3f}'
            elif ii == 3:
                log_str += '{8:6.1f} {9:6.1f} {10:6.1f}'
            vals = np.concatenate((1000 * hpi_dig_head_rrs[ii],
                                   1000 * est_coil_head_rrs[ii],
                                   [g_coils[ii], errs[ii]]))  # errs in mm
            if ii <= 2:
                vals = np.concatenate((vals, this_dev_head_t[ii, :3]))
            elif ii == 3:
                vals = np.concatenate((vals, this_dev_head_t[:3, 3] * 1000.))
            logger.debug(log_str.format(*vals))
        logger.debug('    #t = %0.3f, #e = %0.2f cm, #g = %0.3f, '
                     '#v = %0.2f cm/s, #r = %0.2f rad/s, #d = %0.2f cm'
                     % (fit_time, 100 * e, g, v, r, d))
        logger.debug('    #t = %0.3f, #q = %s '
                     % (fit_time, ' '.join(map('{:8.5f}'.format, this_quat))))

        quats.append(np.concatenate(([fit_time], this_quat, [g],
                                     [e * 100], [v])))  # e in centimeters
        last['fit_time'] = fit_time
        last['quat'] = this_quat
        last['coil_dev_rrs'] = this_coil_dev_rrs
    logger.info('[done]')
    quats = np.array(quats, np.float64)
    quats = np.zeros((0, 10)) if quats.size == 0 else quats
    return quats


@verbose
def _calculate_chpi_coil_locs(raw, t_step_min=0.1, t_step_max=10.,
                              t_window=0.2, dist_limit=0.005, gof_limit=0.98,
                              too_close='raise', verbose=None):
    """Calculate locations of each cHPI coils over time.

    Parameters
    ----------
    raw : instance of Raw
        Raw data with cHPI information.
    t_step_min : float
        Minimum time step to use. If correlations are sufficiently high,
        t_step_max will be used.
    t_step_max : float
        Maximum time step to use.
    t_window : float
        Time window to use to estimate the head positions.
    dist_limit : float
        Minimum distance (m) to accept for coil position fitting.
    gof_limit : float
        Minimum goodness of fit to accept.
    too_close : str
        How to handle HPI positions too close to the sensors,
        can be 'raise', 'warning', or 'info'.
    verbose : bool, str, int, or None
        If not None, override default verbose level (see :func:`mne.verbose`
        and :ref:`Logging documentation <tut_logging>` for more).

    Returns
    -------
    time : ndarray, shape (N, 1)
        The start time of each fitting interval
    chpi_digs :ndarray, shape (N, 1)
        Array of dig structures containing the cHPI locations. Includes
        goodness of fit for each cHPI.

    Notes
    -----
    The number of time points ``N`` will depend on the velocity of head
    movements as well as ``t_step_max`` and ``t_step_min``.

    See Also
    --------
    read_head_pos
    write_head_pos
    """
    _check_too_close(too_close)

    # extract initial geometry from info['hpi_results']
    hpi_dig_head_rrs = _get_hpi_initial_fit(raw.info)

    # extract hpi system information
    hpi = _setup_hpi_struct(raw.info, int(round(t_window * raw.info['sfreq'])))

    # move to device coords
    head_dev_t = invert_transform(raw.info['dev_head_t'])['trans']
    hpi_dig_dev_rrs = apply_trans(head_dev_t, hpi_dig_head_rrs)

    # setup last iteration structure
    last = dict(sin_fit=None, fit_time=t_step_min,
                coil_dev_rrs=hpi_dig_dev_rrs)

    t_begin = raw.times[0]
    t_end = raw.times[-1]
    fit_idxs = raw.time_as_index(np.arange(t_begin + t_window / 2., t_end,
                                           t_step_min),
                                 use_rounding=True)
    times = []
    chpi_digs = []
    logger.info('Fitting up to %s time points (%0.1f sec duration)'
                % (len(fit_idxs), t_end - t_begin))

    hpi['n_freqs'] = len(hpi['freqs'])
    for midpt in fit_idxs:
        #
        # 0. determine samples to fit.
        #
        fit_time = (midpt + raw.first_samp - hpi['n_window'] / 2.) /\
            raw.info['sfreq']

        time_sl = midpt - hpi['n_window'] // 2
        time_sl = slice(max(time_sl, 0),
                        min(time_sl + hpi['n_window'], len(raw.times)))

        #
        # 1. Fit amplitudes for each channel from each of the N cHPI sinusoids
        #
        sin_fit = _fit_cHPI_amplitudes(raw, time_sl, hpi, fit_time)

        # skip this window if bad
        # logging has already been done! Maybe turn this into an Exception
        if sin_fit is None:
            continue

        # check if data has sufficiently changed
        if last['sin_fit'] is not None:  # first iteration
            corr = np.corrcoef(sin_fit.ravel(), last['sin_fit'].ravel())[0, 1]
            # check to see if we need to continue
            if fit_time - last['fit_time'] <= t_step_max - 1e-7 and \
                    corr * corr > 0.98:
                # don't need to refit data
                continue

        # update 'last' sin_fit *before* inplace sign mult
        last['sin_fit'] = sin_fit.copy()

        #
        # 2. Fit magnetic dipole for each coil to obtain coil positions
        #    in device coordinates
        #
        outs = [_fit_magnetic_dipole(f, pos, hpi['coils'], hpi['scale'],
                                     hpi['method'], too_close)
                for f, pos in zip(sin_fit, last['coil_dev_rrs'])]

        dig = []
        for idx, o in enumerate(outs):
            dig.append({'r': o[0], 'ident': idx + 1,
                        'kind': FIFF.FIFFV_POINT_HPI,
                        'coord_frame': FIFF.FIFFV_COORD_DEVICE,
                        'gof': o[1]})

        this_coil_dev_rrs = np.array([o[0] for o in outs])

        times.append(fit_time)
        chpi_digs.append(dig)

        last['fit_time'] = fit_time
        last['coil_dev_rrs'] = this_coil_dev_rrs
    logger.info('[done]')
    return times, chpi_digs


@verbose
def filter_chpi(raw, include_line=True, t_step=0.01, t_window=0.2,
                verbose=None):
    """Remove cHPI and line noise from data.

    .. note:: This function will only work properly if cHPI was on
              during the recording.

    Parameters
    ----------
    raw : instance of Raw
        Raw data with cHPI information. Must be preloaded. Operates in-place.
    include_line : bool
        If True, also filter line noise.
    t_step : float
        Time step to use for estimation, default is 0.01 (10 ms).
    t_window : float
        Time window to use to estimate the amplitudes, default is
        0.2 (200 ms).
    verbose : bool, str, int, or None
        If not None, override default verbose level (see :func:`mne.verbose`
        and :ref:`Logging documentation <tut_logging>` for more).

    Returns
    -------
    raw : instance of Raw
        The raw data.

    Notes
    -----
    cHPI signals are in general not stationary, because head movements act
    like amplitude modulators on cHPI signals. Thus it is recommended to
    to use this procedure, which uses an iterative fitting method, to
    remove cHPI signals, as opposed to notch filtering.

    .. versionadded:: 0.12
    """
    if not raw.preload:
        raise RuntimeError('raw data must be preloaded')
    t_step = float(t_step)
    t_window = float(t_window)
    if not (t_step > 0 and t_window > 0):
        raise ValueError('t_step (%s) and t_window (%s) must both be > 0.'
                         % (t_step, t_window))
    n_step = int(np.ceil(t_step * raw.info['sfreq']))
    hpi = _setup_hpi_struct(raw.info, int(round(t_window * raw.info['sfreq'])),
                            exclude='bads', remove_aliased=True,
                            verbose=False)

    fit_idxs = np.arange(0, len(raw.times) + hpi['n_window'] // 2, n_step)
    n_freqs = len(hpi['freqs'])
    n_remove = 2 * n_freqs
    meg_picks = pick_types(raw.info, meg=True, exclude=())  # filter all chs
    n_times = len(raw.times)

    msg = 'Removing %s cHPI' % n_freqs
    if include_line:
        n_remove += 2 * len(hpi['line_freqs'])
        msg += ' and %s line harmonic' % len(hpi['line_freqs'])
    msg += ' frequencies from %s MEG channels' % len(meg_picks)

    proj = np.dot(hpi['model'][:, :n_remove], hpi['inv_model'][:n_remove]).T
    logger.info(msg)
    chunks = list()  # the chunks to subtract
    last_endpt = 0
    last_done = 0.
    next_done = 60.
    for ii, midpt in enumerate(fit_idxs):
        if midpt / raw.info['sfreq'] >= next_done or ii == len(fit_idxs) - 1:
            logger.info('    Filtering % 5.1f - % 5.1f sec'
                        % (last_done, min(next_done, raw.times[-1])))
            last_done = next_done
            next_done += 60.
        left_edge = midpt - hpi['n_window'] // 2
        time_sl = slice(max(left_edge, 0),
                        min(left_edge + hpi['n_window'], len(raw.times)))
        this_len = time_sl.stop - time_sl.start
        if this_len == hpi['n_window']:
            this_proj = proj
        else:  # first or last window
            model = hpi['model'][:this_len]
            inv_model = linalg.pinv(model)
            this_proj = np.dot(model[:, :n_remove], inv_model[:n_remove]).T
        this_data = raw._data[meg_picks, time_sl]
        subt_pt = min(midpt + n_step, n_times)
        if last_endpt != subt_pt:
            fit_left_edge = left_edge - time_sl.start + hpi['n_window'] // 2
            fit_sl = slice(fit_left_edge,
                           fit_left_edge + (subt_pt - last_endpt))
            chunks.append((subt_pt, np.dot(this_data, this_proj[:, fit_sl])))
        last_endpt = subt_pt

        # Consume (trailing) chunks that are now safe to remove because
        # our windows will no longer touch them
        if ii < len(fit_idxs) - 1:
            next_left_edge = fit_idxs[ii + 1] - hpi['n_window'] // 2
        else:
            next_left_edge = np.inf
        while len(chunks) > 0 and chunks[0][0] <= next_left_edge:
            right_edge, chunk = chunks.pop(0)
            raw._data[meg_picks,
                      right_edge - chunk.shape[1]:right_edge] -= chunk
    return raw
