[DOC] Update functon-container cross-references and fix incorrect typ…

…es (#214)

mne_connectivity/base.py

@@ -888,7 +888,9 @@ class SpectralConnectivity(BaseConnectivity, SpectralMixin):
 
     See Also
     --------
+    mne_connectivity.phase_slope_index
     mne_connectivity.spectral_connectivity_epochs
+    mne_connectivity.spectral_connectivity_time
     """
 
     expected_n_dim = 2
@@ -998,6 +1000,11 @@ class SpectroTemporalConnectivity(BaseConnectivity, SpectralMixin, TimeMixin):
     %(spec_method)s
     %(n_epochs_used)s
     %(connectivity_kwargs)s
+
+    See Also
+    --------
+    mne_connectivity.phase_slope_index
+    mne_connectivity.spectral_connectivity_epochs
     """
 
     def __init__(
@@ -1045,6 +1052,10 @@ class EpochSpectralConnectivity(SpectralConnectivity):
     %(method)s
     %(spec_method)s
     %(connectivity_kwargs)s
+
+    See Also
+    --------
+    mne_connectivity.spectral_connectivity_time
     """
 
     # whether or not the connectivity occurs over epochs
@@ -1090,6 +1101,11 @@ class EpochTemporalConnectivity(TemporalConnectivity):
     %(indices)s
     %(method)s
     %(connectivity_kwargs)s
+
+    See Also
+    --------
+    mne_connectivity.envelope_correlation
+    mne_connectivity.vector_auto_regression
     """
 
     # whether or not the connectivity occurs over epochs
@@ -1179,7 +1195,6 @@ class Connectivity(BaseConnectivity):
     See Also
     --------
     mne_connectivity.vector_auto_regression
-    mne_connectivity.envelope_correlation
     """
 
     def __init__(
@@ -1224,7 +1239,6 @@ class EpochConnectivity(BaseConnectivity):
     See Also
     --------
     mne_connectivity.vector_auto_regression
-    mne_connectivity.envelope_correlation
     """
 
     # whether or not the connectivity occurs over epochs

mne_connectivity/effective.py

@@ -46,7 +46,7 @@ def phase_slope_index(
     A positive value means that time series 0 is ahead of time series 1 and
     a negative value means the opposite.
 
-    The PSI is computed from the coherency (see spectral_connectivity_epochs),
+    The PSI is computed from the coherency (see :func:`spectral_connectivity_epochs`),
     details can be found in :footcite:`NolteEtAl2008`.
 
     Parameters
@@ -103,16 +103,13 @@ def phase_slope_index(
 
     Returns
     -------
-    conn : instance of Connectivity
-        Computed connectivity measure(s). Either a
-        ``SpectralConnnectivity``, or ``SpectroTemporalConnectivity``
-        container. The shape of each array is either
-        (n_signals ** 2, n_bands) mode: 'multitaper' or 'fourier'
-        (n_signals ** 2, n_bands, n_times) mode: 'cwt_morlet'
-        when "indices" is None, or
-        (n_con, n_bands) mode: 'multitaper' or 'fourier'
-        (n_con, n_bands, n_times) mode: 'cwt_morlet'
-        when "indices" is specified and "n_con = len(indices[0])".
+    conn : instance of SpectralConnectivity or SpectroTemporalConnectivity
+        Computed connectivity measure(s). Either a :class:`SpectralConnectivity`, or
+        :class:`SpectroTemporalConnectivity` container. The shape of each array is
+        either (n_signals ** 2, n_bands) mode: 'multitaper' or 'fourier' (n_signals **
+        2, n_bands, n_times) mode: 'cwt_morlet' when "indices" is None, or (n_con,
+        n_bands) mode: 'multitaper' or 'fourier' (n_con, n_bands, n_times) mode:
+        'cwt_morlet' when "indices" is specified and "n_con = len(indices[0])".
 
     See Also
     --------

mne_connectivity/envelope.py

@@ -40,7 +40,7 @@ def envelope_correlation(
         the correlation matrix will not be returned with
         absolute values.
     log : bool
-        If True (default False), square and take the log before orthonalizing
+        If True (default False), square and take the log before orthogonalizing
         envelopes or computing correlations.
     absolute : bool
         If True (default), then take the absolute value of correlation
@@ -51,29 +51,29 @@ def envelope_correlation(
 
     Returns
     -------
-    corr : instance of EpochConnectivity
+    corr : instance of EpochTemporalConnectivity
         The pairwise orthogonal envelope correlations.
         This matrix is symmetric. The array
         will have three dimensions, the first of which is ``n_epochs``.
         The data shape would be ``(n_epochs, (n_nodes + 1) * n_nodes / 2)``.
 
     See Also
     --------
-    mne_connectivity.EpochConnectivity
+    mne_connectivity.EpochTemporalConnectivity
 
     Notes
     -----
     This function computes the power envelope correlation between
     orthogonalized signals :footcite:`HippEtAl2012,KhanEtAl2018`.
 
-    If you would like to combine Epochs after the fact using some
-    function over the Epochs axis, see the ``combine`` function from
-    `EpochConnectivity` classes.
+    If you would like to combine Epochs after the fact using some function over the
+    Epochs axis, see the :meth:`~EpochTemporalConnectivity.combine` method of the
+    :class:`EpochTemporalConnectivity` class.
 
     References
     ----------
     .. footbibliography::
-    """  # noqa
+    """
     _check_option("orthogonalize", orthogonalize, (False, "pairwise"))
     from scipy.signal import hilbert
 

mne_connectivity/spectral/epochs.py

@@ -728,10 +728,11 @@ def spectral_connectivity_epochs(
 
     Returns
     -------
-    con : array | list of array
-        Computed connectivity measure(s). Either an instance of
-        ``SpectralConnectivity`` or ``SpectroTemporalConnectivity``.
-        The shape of the connectivity result will be:
+    con : instance of SpectralConnectivity or SpectroTemporalConnectivity | list
+        Computed connectivity measure(s). An instance of :class:`SpectralConnectivity`,
+        :class:`SpectroTemporalConnectivity`, or a list of instances corresponding to
+        connectivity measures if several connectivity measures are specified. The shape
+        of the connectivity result will be:
 
         - ``(n_cons, n_freqs)`` for multitaper or fourier modes
         - ``(n_cons, n_freqs, n_times)`` for cwt_morlet mode
@@ -759,8 +760,8 @@ def spectral_connectivity_epochs(
     measure is stationary. The spectral measures implemented in this function
     are computed across Epochs. **Thus, spectral measures computed with only
     one Epoch will result in errorful values and spectral measures computed
-    with few Epochs will be unreliable.** Please see
-    ``spectral_connectivity_time`` for time-resolved connectivity estimation.
+    with few Epochs will be unreliable.** Please see :func:`spectral_connectivity_time`
+    for time-resolved connectivity estimation.
 
     The spectral densities can be estimated using a multitaper method with
     digital prolate spheroidal sequence (DPSS) windows, a discrete Fourier

mne_connectivity/spectral/time.py

@@ -165,16 +165,15 @@ def spectral_connectivity_time(
 
     Returns
     -------
-    con : instance of Connectivity | list
+    con : instance of EpochSpectralConnectivity or SpectralConnectivity | list
         Computed connectivity measure(s). An instance of
-        :class:`EpochSpectralConnectivity`, :class:`SpectralConnectivity`
-        or a list of instances corresponding to connectivity measures if
-        several connectivity measures are specified.
-        The shape of each connectivity dataset is (n_epochs, n_cons, n_freqs).
-        When "indices" is None and a bivariate method is called,
-        "n_cons = n_signals ** 2", or if a multivariate method is called
-        "n_cons = 1". When "indices" is specified, "n_con = len(indices[0])"
-        for bivariate and multivariate methods.
+        :class:`EpochSpectralConnectivity`, :class:`SpectralConnectivity`, or a list of
+        instances corresponding to connectivity measures if several connectivity
+        measures are specified. The shape of each connectivity dataset is ([n_epochs,]
+        n_cons, n_freqs). When "indices" is None and a bivariate method is called,
+        "n_cons = n_signals ** 2", or if a multivariate method is called "n_cons = 1".
+        When "indices" is specified, "n_con = len(indices[0])" for bivariate and
+        multivariate methods.
 
     See Also
     --------

mne_connectivity/vector_ar/var.py

@@ -54,13 +54,14 @@ def vector_auto_regression(
 
     Returns
     -------
-    conn : Connectivity | TemporalConnectivity | EpochConnectivity
+    conn : Connectivity | EpochConnectivity | EpochTemporalConnectivity
         The connectivity data estimated.
 
     See Also
     --------
     mne_connectivity.Connectivity
     mne_connectivity.EpochConnectivity
+    mne_connectivity.EpochTemporalConnectivity
 
     Notes
     -----
@@ -126,7 +127,7 @@ def vector_auto_regression(
     References
     ----------
     .. footbibliography::
-    """  # noqa
+    """
     if model not in ["avg-epochs", "dynamic"]:
         raise ValueError(
             f'"model" parameter must be one of ' f"(avg-epochs, dynamic), not {model}."