sketch of compute_tfr() API [ci skip]

refactor method_kw checking

cleaner imports

refactor _get_instance_type_string

WIP

many changes; raw.compute_tfr(multitaper, freqs) works!

add verbose to tfr_array_stockwell

add get_data() method

DRY / fixes for method=stockwell

partially handle ITC; comments

WIP [ci skip]

mne/epochs.py

@@ -2420,6 +2420,54 @@ def compute_psd(
             **method_kw,
         )
 
+    @verbose
+    def compute_tfr(
+        self,
+        method,
+        freqs,
+        *,
+        picks=None,
+        proj=False,
+        return_itc=False,
+        decim=1,
+        n_jobs=None,
+        verbose=None,
+        **method_kw,
+    ):
+        """Compute a time-frequency representation of epoched data.
+
+        Parameters
+        ----------
+        %(method_tfr)s
+        %(freqs_tfr)s
+        %(picks_good_data_noref)s
+        %(proj_psd)s
+        return_itc : bool
+            Whether to return inter-trial coherence (ITC) as well as power
+            estimates.
+        %(decim_tfr)s
+        %(n_jobs)s
+        %(verbose)s
+        %(method_kw_tfr)s
+
+        Returns
+        -------
+        tfr : instance of EpochsTFR
+            The time-frequency-resolved power estimates of the data.
+        itc : instance of EpochsTFR
+            The inter-trial coherence (ITC). Only returned if
+            ``return_itc=True``.
+
+        Notes
+        -----
+        .. versionadded:: 1.3
+
+        References
+        ----------
+        .. footbibliography::
+        """
+        pass
+
     @verbose
     def plot_psd(
         self,

mne/evoked.py

@@ -1106,6 +1106,50 @@ def compute_psd(
             **method_kw,
         )
 
+    @verbose
+    def compute_tfr(
+        self,
+        method,
+        freqs,
+        *,
+        tmin=None,
+        tmax=None,
+        picks=None,
+        proj=False,
+        decim=1,
+        n_jobs=None,
+        verbose=None,
+        **method_kw,
+    ):
+        """Compute a time-frequency representation of evoked data.
+
+        Parameters
+        ----------
+        %(method_tfr)s
+        %(freqs_tfr)s
+        %(tmin_tmax_psd)s
+        %(picks_good_data_noref)s
+        %(proj_psd)s
+        %(decim_tfr)s
+        %(n_jobs)s
+        %(verbose)s
+        %(method_kw_tfr)s
+
+        Returns
+        -------
+        tfr : instance of AverageTFR
+            The time-frequency-resolved power estimates of the data.
+
+        Notes
+        -----
+        .. versionadded:: 1.3
+
+        References
+        ----------
+        .. footbibliography::
+        """
+        pass
+
     @verbose
     def plot_psd(
         self,

mne/html_templates/repr/tfr.html.jinja

@@ -0,0 +1,54 @@
+<table class="table table-hover table-striped table-sm table-responsive small">
+    <tr>
+        <th>Data type</th>
+        <td>{{ tfr._data_type }}</td>
+    </tr>
+    {%- for unit in units %}
+    <tr>
+        {%- if loop.index == 1 %}
+        <th rowspan={{ units | length }}>Units</th>
+        {%- endif %}
+        <td class="justify">{{ unit }}</td>
+    </tr>
+    {%- endfor %}
+    <tr>
+        <th>Data source</th>
+        <td>{{ inst_type }}</td>
+    </tr>
+    {%- if inst_type == "Epochs" %}
+    <tr>
+        <th>Number of epochs</th>
+        <td>{{ tfr.shape[0] }}</td>
+    </tr>
+    {% endif -%}
+    <tr>
+        <th>Dims</th>
+        <td>{{ tfr._dims | join(", ") }}</td>
+    </tr>
+    <tr>
+        <th>Estimation method</th>
+        <td>{{ tfr.method }}</td>
+    </tr>
+    {% if "taper" in tfr._dims %}
+    <tr>
+        <th>Number of tapers</th>
+        <td>{{ tfr._mt_weights.size }}</td>
+    </tr>
+    {% endif %}
+    <tr>
+        <th>Number of channels</th>
+        <td>{{ tfr.ch_names|length }}</td>
+    </tr>
+    <tr>
+        <th>Number of timepoints</th>
+        <td>{{ tfr.times|length }}</td>
+    </tr>
+    <tr>
+        <th>Number of frequency bins</th>
+        <td>{{ tfr.freqs|length }}</td>
+    </tr>
+    <tr>
+        <th>Frequency range</th>
+        <td>{{ '%.2f'|format(tfr.freqs[0]) }} – {{ '%.2f'|format(tfr.freqs[-1]) }} Hz</td>
+    </tr>
+</table>

mne/io/base.py

@@ -111,6 +111,7 @@
 from ..defaults import _handle_default
 from ..viz import plot_raw, _RAW_CLIP_DEF
 from ..time_frequency.spectrum import Spectrum, SpectrumMixin, _validate_method
+from ..time_frequency.spectrogram import RawTFR
 
 
 @fill_doc
@@ -2195,6 +2196,65 @@ def compute_psd(
             **method_kw,
         )
 
+    @verbose
+    def compute_tfr(
+        self,
+        method,
+        freqs,
+        *,
+        tmin=None,
+        tmax=None,
+        picks=None,
+        proj=False,
+        reject_by_annotation=True,
+        decim=1,
+        n_jobs=None,
+        verbose=None,
+        **method_kw,
+    ):
+        """Compute a time-frequency representation of sensor data.
+
+        Parameters
+        ----------
+        %(method_tfr)s
+        %(freqs_tfr)s
+        %(tmin_tmax_psd)s
+        %(picks_good_data_noref)s
+        %(proj_psd)s
+        %(reject_by_annotation_tfr)s
+        %(decim_tfr)s
+        %(n_jobs)s
+        %(verbose)s
+        %(method_kw_tfr)s
+
+        Returns
+        -------
+        tfr : instance of RawTFR
+            The time-frequency-resolved power estimates of the data.
+
+        Notes
+        -----
+        .. versionadded:: 1.3
+
+        References
+        ----------
+        .. footbibliography::
+        """
+        return RawTFR(
+            self,
+            method=method,
+            freqs=freqs,
+            tmin=tmin,
+            tmax=tmax,
+            picks=picks,
+            proj=proj,
+            reject_by_annotation=reject_by_annotation,
+            decim=decim,
+            n_jobs=n_jobs,
+            verbose=verbose,
+            **method_kw,
+        )
+
     @verbose
     def to_data_frame(
         self,

mne/time_frequency/_stockwell.py

@@ -9,7 +9,7 @@
 from scipy.fft import fft, fftfreq, ifft
 
 from .._fiff.pick import _pick_data_channels, pick_info
-from ..utils import verbose, logger, fill_doc, _validate_type
+from ..utils import verbose, logger, _validate_type
 from ..parallel import parallel_func
 from .tfr import AverageTFR, _get_data
 
@@ -104,7 +104,22 @@ def _st_power_itc(x, start_f, compute_itc, zero_pad, decim, W):
     return psd, itc
 
 
-@fill_doc
+def _compute_freqs_st(fmin, fmax, n_fft, sfreq):
+    from scipy.fft import fftfreq
+
+    freqs = fftfreq(n_fft, 1.0 / sfreq)
+    if fmin is None:
+        fmin = freqs[freqs > 0][0]
+    if fmax is None:
+        fmax = freqs.max()
+
+    start_f = np.abs(freqs - fmin).argmin()
+    stop_f = np.abs(freqs - fmax).argmin()
+    freqs = freqs[start_f:stop_f]
+    return start_f, stop_f, freqs
+
+
+@verbose
 def tfr_array_stockwell(
     data,
     sfreq,
@@ -115,6 +130,7 @@ def tfr_array_stockwell(
     decim=1,
     return_itc=False,
     n_jobs=None,
+    verbose=None,
 ):
     """Compute power and intertrial coherence using Stockwell (S) transform.
 
@@ -147,6 +163,7 @@ def tfr_array_stockwell(
     return_itc : bool
         Return intertrial coherence (ITC) as well as averaged power.
     %(n_jobs)s
+    %(verbose)s
 
     Returns
     -------
@@ -179,23 +196,14 @@ def tfr_array_stockwell(
     n_epochs, n_channels = data.shape[:2]
     n_out = data.shape[2] // decim + bool(data.shape[-1] % decim)
     data, n_fft_, zero_pad = _check_input_st(data, n_fft)
-
-    freqs = fftfreq(n_fft_, 1.0 / sfreq)
-    if fmin is None:
-        fmin = freqs[freqs > 0][0]
-    if fmax is None:
-        fmax = freqs.max()
-
-    start_f = np.abs(freqs - fmin).argmin()
-    stop_f = np.abs(freqs - fmax).argmin()
-    freqs = freqs[start_f:stop_f]
+    start_f, stop_f, freqs = _compute_freqs_st(fmin, fmax, n_fft_, sfreq)
 
     W = _precompute_st_windows(data.shape[-1], start_f, stop_f, sfreq, width)
     n_freq = stop_f - start_f
     psd = np.empty((n_channels, n_freq, n_out))
     itc = np.empty((n_channels, n_freq, n_out)) if return_itc else None
 
-    parallel, my_st, n_jobs = parallel_func(_st_power_itc, n_jobs)
+    parallel, my_st, n_jobs = parallel_func(_st_power_itc, n_jobs, verbose=verbose)
     tfrs = parallel(
         my_st(data[:, c, :], start_f, return_itc, zero_pad, decim, W)
         for c in range(n_channels)

mne/time_frequency/multitaper.py

@@ -475,6 +475,7 @@ def tfr_array_multitaper(
     output="complex",
     n_jobs=None,
     *,
+    return_mt_weights=False,
     verbose=None,
 ):
     """Compute Time-Frequency Representation (TFR) using DPSS tapers.
@@ -507,6 +508,11 @@ def tfr_array_multitaper(
         * ``'avg_power_itc'`` : average of single trial power and inter-trial
           coherence across trials.
     %(n_jobs)s
+        The number of epochs to process at the same time. The parallelization
+        is implemented across channels. Defaults to 1.
+    return_mt_weights : bool
+        Whether to return taper weights alongside the complex taper
+        coefficients. Ignored if output is not ``'complex'``.
     %(verbose)s
 
     Returns
@@ -553,5 +559,6 @@ def tfr_array_multitaper(
         decim=decim,
         output=output,
         n_jobs=n_jobs,
+        return_mt_weights=return_mt_weights,
         verbose=verbose,
     )