How to use the mne.utils.warn function in mne

def _read_talxfm(subject, subjects_dir, mode=None, verbose=None):
    """Compute MNI transform from FreeSurfer talairach.xfm file.

    Adapted from freesurfer m-files. Altered to deal with Norig
    and Torig correctly.
    """
    if mode is not None:
        warn('mode is deprecated and will be removed in 0.21, do not set it')
    # Setup the RAS to MNI transform
    ras_mni_t = read_ras_mni_t(subject, subjects_dir)

    # We want to get from Freesurfer surface RAS ('mri') to MNI ('mni_tal').
    # This file only gives us RAS (non-zero origin) ('ras') to MNI ('mni_tal').
    # Se we need to get the ras->mri transform from the MRI headers.

    # To do this, we get Norig and Torig
    # (i.e. vox_ras_t and vox_mri_t, respectively)
    path = op.join(subjects_dir, subject, 'mri', 'orig.mgz')
    if not op.isfile(path):
        path = op.join(subjects_dir, subject, 'mri', 'T1.mgz')
    if not op.isfile(path):
        raise IOError('mri not found: %s' % path)
    _, _, mri_ras_t, _, _ = _read_mri_info(path, units='mm')
    mri_mni_t = combine_transforms(mri_ras_t, ras_mni_t, 'mri', 'mni_tal')

from ..cov import whiten_evoked

    if not type(evoked) in (tuple, list):
        evoked = [evoked]

    if type(color) in (tuple, list):
        if len(color) != len(evoked):
            raise ValueError('Lists of evoked objects and colors'
                             ' must have the same length')
    elif color is None:
        colors = ['w'] + _get_color_list
        stop = (slice(len(evoked)) if len(evoked) < len(colors)
                else slice(len(colors)))
        color = cycle(colors[stop])
        if len(evoked) > len(colors):
            warn('More evoked objects than colors available. You should pass '
                 'a list of unique colors.')
    else:
        color = cycle([color])

    noise_cov = _check_cov(noise_cov, evoked[0].info)
    if noise_cov is not None:
        evoked = [whiten_evoked(e, noise_cov) for e in evoked]
    else:
        evoked = [e.copy() for e in evoked]
    info = evoked[0].info
    ch_names = evoked[0].ch_names
    scalings = _handle_default('scalings', scalings)
    if not all(e.ch_names == ch_names for e in evoked):
        raise ValueError('All evoked.picks must be the same')
    ch_names = _clean_names(ch_names)
    if merge_grads:

def __init__(self, info, data, times, nave, aspect_kind,
                 first=None, last=None, comment='',
                 verbose=None):  # noqa: D102
        self.info = info
        self.nave = nave
        self._aspect_kind = aspect_kind
        self.kind = _aspect_rev.get(aspect_kind, 'unknown')
        self.comment = comment
        self.times = times
        if first is not None or last is not None:
            warn(DeprecationWarning, 'first and last are deprecated, '
                 'do not pass them')
        self.data = data
        self.verbose = verbose
        self.preload = True
        self._update_first_last()

iter_n_components = np.arange(5, data.shape[1], 5)
    from sklearn.decomposition import PCA, FactorAnalysis
    if mode == 'factor_analysis':
        est = FactorAnalysis
    else:
        assert mode == 'pca'
        est = PCA
    est = est(**method_params)
    est.n_components = 1
    scores = np.empty_like(iter_n_components, dtype=np.float64)
    scores.fill(np.nan)

    # make sure we don't empty the thing if it's a generator
    max_n = max(list(deepcopy(iter_n_components)))
    if max_n > data.shape[1]:
        warn('You are trying to estimate %i components on matrix '
             'with %i features.' % (max_n, data.shape[1]))

    for ii, n in enumerate(iter_n_components):
        est.n_components = n
        try:  # this may fail depending on rank and split
            score = _cross_val(data=data, est=est, cv=cv, n_jobs=n_jobs)
        except ValueError:
            score = np.inf
        if np.isinf(score) or score > 0:
            logger.info('... infinite values encountered. stopping estimation')
            break
        logger.info('... rank: %i - loglik: %0.3f' % (n, score))
        if score != -np.inf:
            scores[ii] = score

        if (ii >= 3 and np.all(np.diff(scores[ii - 3:ii]) < 0) and stop_early):

for (t, pos) in zip(times[np.logical_not(inuse)],
                            pos[np.logical_not(inuse)]):
            msg += '    t={:.0f} ms, pos=({:.0f}, {:.0f}, {:.0f}) mm\n'.\
                format(t * 1000., pos[0] * 1000.,
                       pos[1] * 1000., pos[2] * 1000.)
        msg += len(head) * '#'
        logger.error(msg)
        raise ValueError('One or more dipoles outside the inner skull.')

    # multiple dipoles (rr and nn) per time instant allowed
    # uneven sampling in time returns list
    timepoints = np.unique(times)
    if len(timepoints) > 1:
        tdiff = np.diff(timepoints)
        if not np.allclose(tdiff, tdiff[0]):
            warn('Unique time points of dipoles unevenly spaced: returned '
                 'stc will be a list, one for each time point.')
            tstep = -1.0
        else:
            tstep = tdiff[0]
    elif len(timepoints) == 1:
        tstep = 0.001

    # Build the data matrix, essentially a block-diagonal with
    # n_rows: number of dipoles in total (dipole.amplitudes)
    # n_cols: number of unique time points in dipole.times
    # amplitude with identical value of times go together in one col (others=0)
    data = np.zeros((len(amplitude), len(timepoints)))  # (n_d, n_t)
    row = 0
    for tpind, tp in enumerate(timepoints):
        amp = amplitude[np.in1d(times, tp)]
        data[row:row + len(amp), tpind] = amp

def _find_unique_events(events):
    """Uniquify events (ie remove duplicated rows."""
    e = np.ascontiguousarray(events).view(
        np.dtype((np.void, events.dtype.itemsize * events.shape[1])))
    _, idx = np.unique(e, return_index=True)
    n_dupes = len(events) - len(idx)
    if n_dupes > 0:
        warn("Some events are duplicated in your different stim channels."
             " %d events were ignored during deduplication." % n_dupes)
    return events[idx]

ecg_idx = pick_types(inst.info, meg=False, eeg=False, stim=False,
                             eog=False, ecg=True, emg=False, ref_meg=False,
                             exclude='bads')
    else:
        if ch_name not in inst.ch_names:
            raise ValueError('%s not in channel list (%s)' %
                             (ch_name, inst.ch_names))
        ecg_idx = pick_channels(inst.ch_names, include=[ch_name])

    if len(ecg_idx) == 0:
        return None
        # raise RuntimeError('No ECG channel found. Please specify ch_name '
        #                    'parameter e.g. MEG 1531')

    if len(ecg_idx) > 1:
        warn('More than one ECG channel found. Using only %s.'
             % inst.ch_names[ecg_idx[0]])

    return ecg_idx[0]

info = deepcopy(info)
        evoked = EvokedArray(data, info, tmin=self.times[0], comment=comment,
                             nave=n_events, kind=kind, verbose=self.verbose)
        # XXX: above constructor doesn't recreate the times object precisely
        evoked.times = self.times.copy()

        # pick channels
        picks = _picks_to_idx(self.info, picks, 'data_or_ica', ())
        ch_names = [evoked.ch_names[p] for p in picks]
        evoked.pick_channels(ch_names)

        if len(evoked.info['ch_names']) == 0:
            raise ValueError('No data channel found when averaging.')

        if evoked.nave < 1:
            warn('evoked object is empty (based on less than 1 epoch)')

        return evoked

value_pattern = r"\-?\d+\.?\d*e?\-?\d*"
        coord_pattern = r"({0})\s+({0})\s+({0})\s*$".format(value_pattern)
        if ext == '.hsp':
            coord_pattern = '^' + coord_pattern
        points_str = [m.groups() for m in re.finditer(coord_pattern, file_str,
                                                      re.MULTILINE)]
        dig_points = np.array(points_str, dtype=float)
    elif ext == '.mat':  # like FastScan II
        from scipy.io import loadmat
        dig_points = loadmat(fname)['Points'].T
    else:
        dig_points = np.loadtxt(fname, comments=comments, ndmin=2)
        if unit == 'auto':
            unit = 'mm'
        if dig_points.shape[1] > 3:
            warn('Found %d columns instead of 3, using first 3 for XYZ '
                 'coordinates' % (dig_points.shape[1],))
            dig_points = dig_points[:, :3]

    if dig_points.shape[-1] != 3:
        raise ValueError(
            'Data must be of shape (n, 3) instead of %s' % (dig_points.shape,))

    if unit == 'mm':
        dig_points /= 1000.
    elif unit == 'cm':
        dig_points /= 100.

    return dig_points

def _fit_dipole_fixed(min_dist_to_inner_skull, B_orig, t, guess_rrs,
                      guess_data, fwd_data, whitener,
                      fmin_cobyla, ori, rank):
    """Fit a data using a fixed position."""
    B = np.dot(whitener, B_orig)
    B2 = np.dot(B, B)
    if B2 == 0:
        warn('Zero field found for time %s' % t)
        return np.zeros(3), 0, np.zeros(3), 0, np.zeros(6)
    # Compute the dipole moment
    Q, gof, residual_noproj = _fit_Q(guess_data, whitener, B, B2, B_orig,
                                     rd=None, ori=ori)[:3]
    if ori is None:
        amp = np.sqrt(np.dot(Q, Q))
        norm = 1. if amp == 0. else amp
        ori = Q / norm
    else:
        amp = np.dot(Q, ori)
    rd_final = guess_rrs[0]
    # This will be slow, and we don't use it anyway, so omit it for now:
    # conf = _fit_confidence(rd_final, Q, ori, whitener, fwd_data)
    conf = khi2 = nfree = None
    # No corresponding 'logger' message here because it should go *very* fast
    return rd_final, amp, ori, gof, conf, khi2, nfree, residual_noproj