# -*- coding: utf-8 -*-
# Authors: Mark Wronkiewicz <wronk@uw.edu>
#          Yousra Bekhti <yousra.bekhti@gmail.com>
#          Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)

from copy import deepcopy

import numpy as np

from .evoked import _generate_noise
from ..event import _get_stim_channel
from ..io.pick import pick_types, pick_info, pick_channels
from ..source_estimate import VolSourceEstimate
from ..cov import make_ad_hoc_cov, read_cov
from ..bem import fit_sphere_to_headshape, make_sphere_model, read_bem_solution
from ..io import RawArray, _BaseRaw
from ..chpi import read_head_pos, head_pos_to_trans_rot_t, _get_hpi_info
from ..io.constants import FIFF
from ..forward import (_magnetic_dipole_field_vec, _merge_meg_eeg_fwds,
                       _stc_src_sel, convert_forward_solution,
                       _prepare_for_forward, _transform_orig_meg_coils,
                       _compute_forwards, _to_forward_dict)
from ..transforms import _get_trans, transform_surface_to
from ..source_space import _ensure_src, _points_outside_surface
from ..source_estimate import _BaseSourceEstimate
from ..utils import logger, verbose, check_random_state, warn
from ..parallel import check_n_jobs
from ..externals.six import string_types


def _log_ch(start, info, ch):
    """Helper to log channel information"""
    if ch is not None:
        extra, just, ch = ' stored on channel:', 50, info['ch_names'][ch]
    else:
        extra, just, ch = ' not stored', 0, ''
    logger.info((start + extra).ljust(just) + ch)


@verbose
def simulate_raw(raw, stc, trans, src, bem, cov='simple',
                 blink=False, ecg=False, chpi=False, head_pos=None,
                 mindist=1.0, interp='cos2', iir_filter=None, n_jobs=1,
                 random_state=None, verbose=None):
    """Simulate raw data

    Head movements can optionally be simulated using the ``head_pos``
    parameter.

    Parameters
    ----------
    raw : instance of Raw
        The raw template to use for simulation. The ``info``, ``times``,
        and potentially ``first_samp`` properties will be used.
    stc : instance of SourceEstimate
        The source estimate to use to simulate data. Must have the same
        sample rate as the raw data.
    trans : dict | str | None
        Either a transformation filename (usually made using mne_analyze)
        or an info dict (usually opened using read_trans()).
        If string, an ending of `.fif` or `.fif.gz` will be assumed to
        be in FIF format, any other ending will be assumed to be a text
        file with a 4x4 transformation matrix (like the `--trans` MNE-C
        option). If trans is None, an identity transform will be used.
    src : str | instance of SourceSpaces
        Source space corresponding to the stc. If string, should be a source
        space filename. Can also be an instance of loaded or generated
        SourceSpaces.
    bem : str | dict
        BEM solution  corresponding to the stc. If string, should be a BEM
        solution filename (e.g., "sample-5120-5120-5120-bem-sol.fif").
    cov : instance of Covariance | str | None
        The sensor covariance matrix used to generate noise. If None,
        no noise will be added. If 'simple', a basic (diagonal) ad-hoc
        noise covariance will be used. If a string, then the covariance
        will be loaded.
    blink : bool
        If true, add simulated blink artifacts. See Notes for details.
    ecg : bool
        If true, add simulated ECG artifacts. See Notes for details.
    chpi : bool
        If true, simulate continuous head position indicator information.
        Valid cHPI information must encoded in ``raw.info['hpi_meas']``
        to use this option.
    head_pos : None | str | dict | tuple | array
        Name of the position estimates file. Should be in the format of
        the files produced by maxfilter. If dict, keys should
        be the time points and entries should be 4x4 ``dev_head_t``
        matrices. If None, the original head position (from
        ``info['dev_head_t']``) will be used. If tuple, should have the
        same format as data returned by `head_pos_to_trans_rot_t`.
        If array, should be of the form returned by `read_head_pos`.
    mindist : float
        Minimum distance between sources and the inner skull boundary
        to use during forward calculation.
    interp : str
        Either 'cos2', 'linear', or 'zero', the type of forward-solution
        interpolation to use between forward solutions at different
        head positions.
    iir_filter : None | array
        IIR filter coefficients (denominator) e.g. [1, -1, 0.2].
    n_jobs : int
        Number of jobs to use.
    random_state : None | int | np.random.RandomState
        The random generator state used for blink, ECG, and sensor
        noise randomization.
    verbose : bool, str, int, or None
        If not None, override default verbose level (see mne.verbose).

    Returns
    -------
    raw : instance of Raw
        The simulated raw file.

    See Also
    --------
    read_head_pos
    simulate_evoked
    simulate_stc
    simalute_sparse_stc

    Notes
    -----
    Events coded with the position number (starting at 1) will be stored
    in the trigger channel (if available) at times corresponding to t=0
    in the ``stc``.

    The resulting SNR will be determined by the structure of the noise
    covariance, the amplitudes of ``stc``, and the head position(s) provided.

    The blink and ECG artifacts are generated by 1) placing impulses at
    random times of activation, and 2) convolving with activation kernel
    functions. In both cases, the scale-factors of the activation functions
    (and for the resulting EOG and ECG channel traces) were chosen based on
    visual inspection to yield amplitudes generally consistent with those
    seen in experimental data. Noisy versions of the blink and ECG
    activations will be stored in the first EOG and ECG channel in the
    raw file, respectively, if they exist.

    For blink artifacts:

        1. Random activation times are drawn from an inhomogeneous poisson
           process whose blink rate oscillates between 4.5 blinks/minute
           and 17 blinks/minute based on the low (reading) and high (resting)
           blink rates from [1]_.
        2. The activation kernel is a 250 ms Hanning window.
        3. Two activated dipoles are located in the z=0 plane (in head
           coordinates) at ±30 degrees away from the y axis (nasion).
        4. Activations affect MEG and EEG channels.

    For ECG artifacts:

        1. Random inter-beat intervals are drawn from a uniform distribution
           of times corresponding to 40 and 80 beats per minute.
        2. The activation function is the sum of three Hanning windows with
           varying durations and scales to make a more complex waveform.
        3. The activated dipole is located one (estimated) head radius to
           the left (-x) of head center and three head radii below (+z)
           head center; this dipole is oriented in the +x direction.
        4. Activations only affect MEG channels.

    .. versionadded:: 0.10.0

    References
    ----------
    .. [1] Bentivoglio et al. "Analysis of blink rate patterns in normal
           subjects" Movement Disorders, 1997 Nov;12(6):1028-34.
    """
    if not isinstance(raw, _BaseRaw):
        raise TypeError('raw should be an instance of Raw')
    times, info, first_samp = raw.times, raw.info, raw.first_samp
    raw_verbose = raw.verbose

    # Check for common flag errors and try to override
    if not isinstance(stc, _BaseSourceEstimate):
        raise TypeError('stc must be a SourceEstimate')
    if not np.allclose(info['sfreq'], 1. / stc.tstep):
        raise ValueError('stc and info must have same sample rate')
    if len(stc.times) <= 2:  # to ensure event encoding works
        raise ValueError('stc must have at least three time points')

    stim = False if len(pick_types(info, meg=False, stim=True)) == 0 else True
    n_jobs = check_n_jobs(n_jobs)

    rng = check_random_state(random_state)
    if interp not in ('cos2', 'linear', 'zero'):
        raise ValueError('interp must be "cos2", "linear", or "zero"')

    if head_pos is None:  # use pos from file
        dev_head_ts = [info['dev_head_t']] * 2
        offsets = np.array([0, len(times)])
        interp = 'zero'
    # Use position data to simulate head movement
    else:
        if isinstance(head_pos, string_types):
            head_pos = read_head_pos(head_pos)
        if isinstance(head_pos, np.ndarray):
            head_pos = head_pos_to_trans_rot_t(head_pos)
        if isinstance(head_pos, tuple):  # can be an already-loaded pos file
            transs, rots, ts = head_pos
            ts -= first_samp / info['sfreq']  # MF files need reref
            dev_head_ts = [np.r_[np.c_[r, t[:, np.newaxis]], [[0, 0, 0, 1]]]
                           for r, t in zip(rots, transs)]
            del transs, rots
        elif isinstance(head_pos, dict):
            ts = np.array(list(head_pos.keys()), float)
            ts.sort()
            dev_head_ts = [head_pos[float(tt)] for tt in ts]
        else:
            raise TypeError('unknown head_pos type %s' % type(head_pos))
        bad = ts < 0
        if bad.any():
            raise RuntimeError('All position times must be >= 0, found %s/%s'
                               '< 0' % (bad.sum(), len(bad)))
        bad = ts > times[-1]
        if bad.any():
            raise RuntimeError('All position times must be <= t_end (%0.1f '
                               'sec), found %s/%s bad values (is this a split '
                               'file?)' % (times[-1], bad.sum(), len(bad)))
        if ts[0] > 0:
            ts = np.r_[[0.], ts]
            dev_head_ts.insert(0, info['dev_head_t']['trans'])
        dev_head_ts = [{'trans': d, 'to': info['dev_head_t']['to'],
                        'from': info['dev_head_t']['from']}
                       for d in dev_head_ts]
        if ts[-1] < times[-1]:
            dev_head_ts.append(dev_head_ts[-1])
            ts = np.r_[ts, [times[-1]]]
        offsets = raw.time_as_index(ts)
        offsets[-1] = len(times)  # fix for roundoff error
        assert offsets[-2] != offsets[-1]
        del ts

    src = _ensure_src(src, verbose=False)
    if isinstance(bem, string_types):
        bem = read_bem_solution(bem, verbose=False)
    if isinstance(cov, string_types):
        if cov == 'simple':
            cov = make_ad_hoc_cov(info, verbose=False)
        else:
            cov = read_cov(cov, verbose=False)
    assert np.array_equal(offsets, np.unique(offsets))
    assert len(offsets) == len(dev_head_ts)
    approx_events = int((len(times) / info['sfreq']) /
                        (stc.times[-1] - stc.times[0]))
    logger.info('Provided parameters will provide approximately %s event%s'
                % (approx_events, '' if approx_events == 1 else 's'))

    # Extract necessary info
    meeg_picks = pick_types(info, meg=True, eeg=True, exclude=[])  # for sim
    meg_picks = pick_types(info, meg=True, eeg=False, exclude=[])  # for CHPI
    fwd_info = pick_info(info, meeg_picks)
    fwd_info['projs'] = []  # Ensure no 'projs' applied
    logger.info('Setting up raw simulation: %s position%s, "%s" interpolation'
                % (len(dev_head_ts), 's' if len(dev_head_ts) != 1 else '',
                   interp))

    verts = stc.vertices
    verts = [verts] if isinstance(stc, VolSourceEstimate) else verts
    src = _restrict_source_space_to(src, verts)

    # array used to store result
    raw_data = np.zeros((len(info['ch_names']), len(times)))

    # figure out our cHPI, ECG, and blink dipoles
    ecg_rr = blink_rrs = exg_bem = hpi_rrs = None
    ecg = ecg and len(meg_picks) > 0
    chpi = chpi and len(meg_picks) > 0
    if chpi:
        hpi_freqs, hpi_rrs, hpi_pick, hpi_ons = _get_hpi_info(info)[:4]
        hpi_nns = hpi_rrs / np.sqrt(np.sum(hpi_rrs * hpi_rrs,
                                           axis=1))[:, np.newaxis]
        # turn on cHPI in file
        raw_data[hpi_pick, :] = hpi_ons.sum()
        _log_ch('cHPI status bits enbled and', info, hpi_pick)
    if blink or ecg:
        R, r0 = fit_sphere_to_headshape(info, units='m', verbose=False)[:2]
        exg_bem = make_sphere_model(r0, head_radius=R,
                                    relative_radii=(0.97, 0.98, 0.99, 1.),
                                    sigmas=(0.33, 1.0, 0.004, 0.33),
                                    verbose=False)
    if blink:
        # place dipoles at 45 degree angles in z=0 plane
        blink_rrs = np.array([[np.cos(np.pi / 3.), np.sin(np.pi / 3.), 0.],
                              [-np.cos(np.pi / 3.), np.sin(np.pi / 3), 0.]])
        blink_rrs /= np.sqrt(np.sum(blink_rrs *
                                    blink_rrs, axis=1))[:, np.newaxis]
        blink_rrs *= 0.96 * R
        blink_rrs += r0
        # oriented upward
        blink_nns = np.array([[0., 0., 1.], [0., 0., 1.]])
        # Blink times drawn from an inhomogeneous poisson process
        # by 1) creating the rate and 2) pulling random numbers
        blink_rate = (1 + np.cos(2 * np.pi * 1. / 60. * times)) / 2.
        blink_rate *= 12.5 / 60.
        blink_rate += 4.5 / 60.
        blink_data = rng.rand(len(times)) < blink_rate / info['sfreq']
        blink_data = blink_data * (rng.rand(len(times)) + 0.5)  # varying amps
        # Activation kernel is a simple hanning window
        blink_kernel = np.hanning(int(0.25 * info['sfreq']))
        blink_data = np.convolve(blink_data, blink_kernel,
                                 'same')[np.newaxis, :] * 1e-7
        # Add rescaled noisy data to EOG ch
        ch = pick_types(info, meg=False, eeg=False, eog=True)
        noise = rng.randn(blink_data.shape[1]) * 5e-6
        if len(ch) >= 1:
            ch = ch[-1]
            raw_data[ch, :] = blink_data * 1e3 + noise
        else:
            ch = None
        _log_ch('Blinks simulated and trace', info, ch)
        del blink_kernel, blink_rate, noise
    if ecg:
        ecg_rr = np.array([[-R, 0, -3 * R]])
        max_beats = int(np.ceil(times[-1] * 80. / 60.))
        # activation times with intervals drawn from a uniform distribution
        # based on activation rates between 40 and 80 beats per minute
        cardiac_idx = np.cumsum(rng.uniform(60. / 80., 60. / 40., max_beats) *
                                info['sfreq']).astype(int)
        cardiac_idx = cardiac_idx[cardiac_idx < len(times)]
        cardiac_data = np.zeros(len(times))
        cardiac_data[cardiac_idx] = 1
        # kernel is the sum of three hanning windows
        cardiac_kernel = np.concatenate([
            2 * np.hanning(int(0.04 * info['sfreq'])),
            -0.3 * np.hanning(int(0.05 * info['sfreq'])),
            0.2 * np.hanning(int(0.26 * info['sfreq']))], axis=-1)
        ecg_data = np.convolve(cardiac_data, cardiac_kernel,
                               'same')[np.newaxis, :] * 15e-8
        # Add rescaled noisy data to ECG ch
        ch = pick_types(info, meg=False, eeg=False, ecg=True)
        noise = rng.randn(ecg_data.shape[1]) * 1.5e-5
        if len(ch) >= 1:
            ch = ch[-1]
            raw_data[ch, :] = ecg_data * 2e3 + noise
        else:
            ch = None
        _log_ch('ECG simulated and trace', info, ch)
        del cardiac_data, cardiac_kernel, max_beats, cardiac_idx

    stc_event_idx = np.argmin(np.abs(stc.times))
    if stim:
        event_ch = pick_channels(info['ch_names'],
                                 _get_stim_channel(None, info))[0]
        raw_data[event_ch, :] = 0.
    else:
        event_ch = None
    _log_ch('Event information', info, event_ch)
    used = np.zeros(len(times), bool)
    stc_indices = np.arange(len(times)) % len(stc.times)
    raw_data[meeg_picks, :] = 0.
    hpi_mag = 70e-9
    last_fwd = last_fwd_chpi = last_fwd_blink = last_fwd_ecg = src_sel = None
    zf = None  # final filter conditions for the noise
    # don't process these any more if no MEG present
    for fi, (fwd, fwd_blink, fwd_ecg, fwd_chpi) in \
        enumerate(_iter_forward_solutions(
            fwd_info, trans, src, bem, exg_bem, dev_head_ts, mindist,
            hpi_rrs, blink_rrs, ecg_rr, n_jobs)):
        # must be fixed orientation
        # XXX eventually we could speed this up by allowing the forward
        # solution code to only compute the normal direction
        fwd = convert_forward_solution(fwd, surf_ori=True,
                                       force_fixed=True, verbose=False)
        if blink:
            fwd_blink = fwd_blink['sol']['data']
            for ii in range(len(blink_rrs)):
                fwd_blink[:, ii] = np.dot(fwd_blink[:, 3 * ii:3 * (ii + 1)],
                                          blink_nns[ii])
            fwd_blink = fwd_blink[:, :len(blink_rrs)]
            fwd_blink = fwd_blink.sum(axis=1)[:, np.newaxis]
        # just use one arbitrary direction
        if ecg:
            fwd_ecg = fwd_ecg['sol']['data'][:, [0]]

        # align cHPI magnetic dipoles in approx. radial direction
        if chpi:
            for ii in range(len(hpi_rrs)):
                fwd_chpi[:, ii] = np.dot(fwd_chpi[:, 3 * ii:3 * (ii + 1)],
                                         hpi_nns[ii])
            fwd_chpi = fwd_chpi[:, :len(hpi_rrs)].copy()

        if src_sel is None:
            src_sel = _stc_src_sel(fwd['src'], stc)
            verts = stc.vertices
            verts = [verts] if isinstance(stc, VolSourceEstimate) else verts
            diff_ = sum([len(v) for v in verts]) - len(src_sel)
            if diff_ != 0:
                warn('%s STC vertices omitted due to fwd calculation' % diff_)
        if last_fwd is None:
            last_fwd, last_fwd_blink, last_fwd_ecg, last_fwd_chpi = \
                fwd, fwd_blink, fwd_ecg, fwd_chpi
            continue

        # set up interpolation
        n_pts = offsets[fi] - offsets[fi - 1]
        if interp == 'zero':
            interps = None
        else:
            if interp == 'linear':
                interps = np.linspace(1, 0, n_pts, endpoint=False)
            else:  # interp == 'cos2':
                interps = np.cos(0.5 * np.pi * np.arange(n_pts)) ** 2
            interps = np.array([interps, 1 - interps])

        assert not used[offsets[fi - 1]:offsets[fi]].any()
        event_idxs = np.where(stc_indices[offsets[fi - 1]:offsets[fi]] ==
                              stc_event_idx)[0] + offsets[fi - 1]
        if stim:
            raw_data[event_ch, event_idxs] = fi

        logger.info('  Simulating data for %0.3f-%0.3f sec with %s event%s'
                    % (tuple(offsets[fi - 1:fi + 1] / info['sfreq']) +
                       (len(event_idxs), '' if len(event_idxs) == 1 else 's')))

        # Process data in large chunks to save on memory
        chunk_size = 10000
        chunks = np.concatenate((np.arange(offsets[fi - 1], offsets[fi],
                                           chunk_size), [offsets[fi]]))
        for start, stop in zip(chunks[:-1], chunks[1:]):
            assert stop - start <= chunk_size

            used[start:stop] = True
            if interp == 'zero':
                this_interp = None
            else:
                this_interp = interps[:, start - chunks[0]:stop - chunks[0]]
            time_sl = slice(start, stop)
            this_t = np.arange(start, stop) / info['sfreq']
            stc_idxs = stc_indices[time_sl]

            # simulate brain data
            raw_data[meeg_picks, time_sl] = \
                _interp(last_fwd['sol']['data'], fwd['sol']['data'],
                        stc.data[:, stc_idxs][src_sel], this_interp)

            # add sensor noise, ECG, blink, cHPI
            if cov is not None:
                noise, zf = _generate_noise(fwd_info, cov, iir_filter, rng,
                                            len(stc_idxs), zi=zf)
                raw_data[meeg_picks, time_sl] += noise
            if blink:
                raw_data[meeg_picks, time_sl] += \
                    _interp(last_fwd_blink, fwd_blink, blink_data[:, time_sl],
                            this_interp)
            if ecg:
                raw_data[meg_picks, time_sl] += \
                    _interp(last_fwd_ecg, fwd_ecg, ecg_data[:, time_sl],
                            this_interp)
            if chpi:
                sinusoids = np.zeros((len(hpi_freqs), len(stc_idxs)))
                for fidx, freq in enumerate(hpi_freqs):
                    sinusoids[fidx] = 2 * np.pi * freq * this_t
                    sinusoids[fidx] = hpi_mag * np.sin(sinusoids[fidx])
                raw_data[meg_picks, time_sl] += \
                    _interp(last_fwd_chpi, fwd_chpi, sinusoids, this_interp)

        assert used[offsets[fi - 1]:offsets[fi]].all()

        # prepare for next iteration
        last_fwd, last_fwd_blink, last_fwd_ecg, last_fwd_chpi = \
            fwd, fwd_blink, fwd_ecg, fwd_chpi
    assert used.all()
    raw = RawArray(raw_data, info, first_samp=first_samp, verbose=False)
    raw.verbose = raw_verbose
    logger.info('Done')
    return raw


def _iter_forward_solutions(info, trans, src, bem, exg_bem, dev_head_ts,
                            mindist, hpi_rrs, blink_rrs, ecg_rrs, n_jobs):
    """Calculate a forward solution for a subject"""
    mri_head_t, trans = _get_trans(trans)
    logger.info('Setting up forward solutions')
    megcoils, meg_info, compcoils, megnames, eegels, eegnames, rr, info, \
        update_kwargs, bem = _prepare_for_forward(
            src, mri_head_t, info, bem, mindist, n_jobs, verbose=False)
    del (src, mindist)

    eegfwd = _compute_forwards(rr, bem, [eegels], [None],
                               [None], ['eeg'], n_jobs, verbose=False)[0]
    eegfwd = _to_forward_dict(eegfwd, eegnames)
    if blink_rrs is not None:
        eegblink = _compute_forwards(blink_rrs, exg_bem, [eegels], [None],
                                     [None], ['eeg'], n_jobs,
                                     verbose=False)[0]
        eegblink = _to_forward_dict(eegblink, eegnames)

    # short circuit here if there are no MEG channels (don't need to iterate)
    if len(pick_types(info, meg=True)) == 0:
        eegfwd.update(**update_kwargs)
        for _ in dev_head_ts:
            yield eegfwd, eegblink, None, None
        return

    coord_frame = FIFF.FIFFV_COORD_HEAD
    if not bem['is_sphere']:
        idx = np.where(np.array([s['id'] for s in bem['surfs']]) ==
                       FIFF.FIFFV_BEM_SURF_ID_BRAIN)[0]
        assert len(idx) == 1
        bem_surf = transform_surface_to(bem['surfs'][idx[0]], coord_frame,
                                        mri_head_t)
    for ti, dev_head_t in enumerate(dev_head_ts):
        # Could be *slightly* more efficient not to do this N times,
        # but the cost here is tiny compared to actual fwd calculation
        logger.info('Computing gain matrix for transform #%s/%s'
                    % (ti + 1, len(dev_head_ts)))
        _transform_orig_meg_coils(megcoils, dev_head_t)
        _transform_orig_meg_coils(compcoils, dev_head_t)

        # Make sure our sensors are all outside our BEM
        coil_rr = [coil['r0'] for coil in megcoils]
        if not bem['is_sphere']:
            outside = _points_outside_surface(coil_rr, bem_surf, n_jobs,
                                              verbose=False)
        else:
            d = coil_rr - bem['r0']
            outside = np.sqrt(np.sum(d * d, axis=1)) > bem.radius
        if not outside.all():
            raise RuntimeError('%s MEG sensors collided with inner skull '
                               'surface for transform %s'
                               % (np.sum(~outside), ti))

        # Compute forward
        megfwd = _compute_forwards(rr, bem, [megcoils], [compcoils],
                                   [meg_info], ['meg'], n_jobs,
                                   verbose=False)[0]
        megfwd = _to_forward_dict(megfwd, megnames)
        fwd = _merge_meg_eeg_fwds(megfwd, eegfwd, verbose=False)
        fwd.update(**update_kwargs)

        fwd_blink = fwd_ecg = fwd_chpi = None
        if blink_rrs is not None:
            megblink = _compute_forwards(blink_rrs, exg_bem, [megcoils],
                                         [compcoils], [meg_info], ['meg'],
                                         n_jobs, verbose=False)[0]
            megblink = _to_forward_dict(megblink, megnames)
            fwd_blink = _merge_meg_eeg_fwds(megblink, eegblink, verbose=False)
        if ecg_rrs is not None:
            megecg = _compute_forwards(ecg_rrs, exg_bem, [megcoils],
                                       [compcoils], [meg_info], ['meg'],
                                       n_jobs, verbose=False)[0]
            fwd_ecg = _to_forward_dict(megecg, megnames)
        if hpi_rrs is not None:
            fwd_chpi = _magnetic_dipole_field_vec(hpi_rrs, megcoils).T
        yield fwd, fwd_blink, fwd_ecg, fwd_chpi


def _restrict_source_space_to(src, vertices):
    """Helper to trim down a source space"""
    assert len(src) == len(vertices)
    src = deepcopy(src)
    for s, v in zip(src, vertices):
        s['inuse'].fill(0)
        s['nuse'] = len(v)
        s['vertno'] = v
        s['inuse'][s['vertno']] = 1
        del s['pinfo']
        del s['nuse_tri']
        del s['use_tris']
        del s['patch_inds']
    return src


def _interp(data_1, data_2, stc_data, interps):
    """Helper to interpolate"""
    out_data = np.dot(data_1, stc_data)
    if interps is not None:
        out_data *= interps[0]
        data_1 = np.dot(data_1, stc_data)
        data_1 *= interps[1]
        out_data += data_1
        del data_1
    return out_data