diff --git a/.github/workflows/python_tests.yml b/.github/workflows/python_tests.yml index 6e41296..84b4e4d 100644 --- a/.github/workflows/python_tests.yml +++ b/.github/workflows/python_tests.yml @@ -11,8 +11,8 @@ jobs: strategy: fail-fast: false matrix: - platform: [ubuntu-latest, macos-latest, windows-latest] - python-version: ["3.8", "3.9", "3.10", "3.11"] + platform: [ubuntu-latest, windows-latest] # macos-latest + python-version: ["3.9", "3.10", "3.11"] runs-on: ${{ matrix.platform }} diff --git a/docs/api.rst b/docs/api.rst index a6a745d..a240bf2 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -43,6 +43,8 @@ Hypnogram & sleep statistics sleep_statistics Hypnogram simulate_hypnogram + EpochByEpochAgreement + SleepStatsAgreement Spectral analyses ----------------- diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo index b12a47e..83a18fd 100644 --- a/docs/build/html/.buildinfo +++ b/docs/build/html/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. -config: 489cdac284cc185cfdae105616c5af8a +config: f80ed99698c532d7a1c5d14ebdacc629 tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_images/yasa-EpochByEpochAgreement-4.png b/docs/build/html/_images/yasa-EpochByEpochAgreement-4.png new file mode 100644 index 0000000..d1106de Binary files /dev/null and b/docs/build/html/_images/yasa-EpochByEpochAgreement-4.png differ diff --git a/docs/build/html/_images/yasa-Hypnogram-2.png b/docs/build/html/_images/yasa-Hypnogram-2.png new file mode 100644 index 0000000..c766e36 Binary files /dev/null and b/docs/build/html/_images/yasa-Hypnogram-2.png differ diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html index 770b421..c97e4fd 100644 --- a/docs/build/html/_modules/index.html +++ b/docs/build/html/_modules/index.html @@ -4,7 +4,7 @@ - Overview: module code — yasa 0.6.4 documentation + Overview: module code — yasa 0.6.5 documentation @@ -43,7 +43,7 @@ yasa - 0.6.4 + 0.6.5
@@ -94,6 +94,7 @@

All modules for which code is available

@@ -101,6 +101,7 @@

Source code for yasa.detection

 - GitHub: https://github.com/raphaelvallat/yasa
 - License: BSD 3-Clause License
 """
+
 import mne
 import logging
 import numpy as np
diff --git a/docs/build/html/_modules/yasa/evaluation.html b/docs/build/html/_modules/yasa/evaluation.html
new file mode 100644
index 0000000..e9e3f38
--- /dev/null
+++ b/docs/build/html/_modules/yasa/evaluation.html
@@ -0,0 +1,1626 @@
+
+
+
+  
+    
+    
+    yasa.evaluation — yasa 0.6.5 documentation
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+
+
+
+
+
+
+
+
+
+  
+
+  
+
+

+  

+    

+      
+  
Source code for yasa.evaluation
+"""
+YASA code for evaluating the agreement between two scorers (e.g., human vs YASA), either at the
+epoch-by-epoch level or at the level of summary sleep statistics.
+
+Analyses are influenced by the standardized framework proposed in Menghini et al., 2021, SLEEP.
+See the following resources:
+- https://doi.org/10.1093/sleep/zsaa170
+- https://sri-human-sleep.github.io/sleep-trackers-performance
+- https://github.com/SRI-human-sleep/sleep-trackers-performance
+"""
+
+import logging
+
+import numpy as np
+import pandas as pd
+import scipy.stats as sps
+import sklearn.metrics as skm
+
+
+logger = logging.getLogger("yasa")
+
+__all__ = [
+    "EpochByEpochAgreement",
+    "SleepStatsAgreement",
+]
+
+
+################################################################################
+# EPOCH BY EPOCH
+################################################################################
+
+
+
[docs]class EpochByEpochAgreement:
+    """Evaluate agreement between two hypnograms or two collections of hypnograms.
+
+    Evaluation includes averaged agreement scores, one-vs-rest agreement scores, agreement scores
+    summarized across all sleep and summarized by sleep stage, and various plotting options to
+    visualize the two hypnograms simultaneously. See examples for more detail.
+
+    .. versionadded:: 0.7.0
+
+    Parameters
+    ----------
+    ref_hyps : iterable of :py:class:`yasa.Hypnogram`
+        A collection of reference hypnograms (i.e., those considered ground-truth).
+
+        Each :py:class:`yasa.Hypnogram` in ``ref_hyps`` must have the same
+        :py:attr:`~yasa.Hypnogram.scorer`.
+
+        If a ``dict``, key values are use to generate unique sleep session IDs. If any other
+        iterable (e.g., ``list`` or ``tuple``), then unique sleep session IDs are automatically
+        generated.
+    obs_hyps : iterable of :py:class:`yasa.Hypnogram`
+        A collection of observed hypnograms (i.e., those to be evaluated).
+
+        Each :py:class:`yasa.Hypnogram` in ``obs_hyps`` must have the same
+        :py:attr:`~yasa.Hypnogram.scorer`, and this scorer must be different than the scorer of
+        hypnograms in ``ref_hyps``.
+
+        If a ``dict``, key values must match those of ``ref_hyps``.
+
+    .. important::
+        It is assumed that the order of hypnograms are the same in ``ref_hyps`` and ``obs_hyps``.
+        For example, the third hypnogram in ``ref_hyps`` and ``obs_hyps`` must come from the same
+        sleep session, and they must only differ in that they have different scorers.
+
+    .. seealso:: For comparing just two hypnograms, use :py:meth:`yasa.Hynogram.evaluate`.
+
+    Notes
+    -----
+    Many steps here are influenced by guidelines proposed in Menghini et al., 2021 [Menghini2021]_.
+    See https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html
+
+    References
+    ----------
+    .. [Menghini2021] Menghini, L., Cellini, N., Goldstone, A., Baker, F. C., & de Zambotti, M.
+                      (2021). A standardized framework for testing the performance of sleep-tracking
+                      technology: step-by-step guidelines and open-source code. SLEEP, 44(2),
+                      zsaa170. https://doi.org/10.1093/sleep/zsaa170
+
+    Examples
+    --------
+    >>> import yasa
+    >>> ref_hyps = [yasa.simulate_hypnogram(tib=600, scorer="Human", seed=i) for i in range(10)]
+    >>> obs_hyps = [h.simulate_similar(scorer="YASA", seed=i) for i, h in enumerate(ref_hyps)]
+    >>> ebe = yasa.EpochByEpochAgreement(ref_hyps, obs_hyps)
+    >>> agr = ebe.get_agreement()
+    >>> agr.head(5).round(2)
+              accuracy  balanced_acc  kappa   mcc  precision  recall     f1
+    sleep_id
+    1             0.31          0.26   0.07  0.07       0.31    0.31   0.31
+    2             0.33          0.33   0.14  0.14       0.35    0.33   0.34
+    3             0.35          0.24   0.06  0.06       0.35    0.35   0.35
+    4             0.22          0.21   0.01  0.01       0.21    0.22   0.21
+    5             0.21          0.17  -0.06 -0.06       0.20    0.21   0.21
+
+    >>> ebe.get_agreement_bystage().head(12).round(3)
+                    fbeta  precision  recall  support
+    stage sleep_id
+    WAKE  1         0.391      0.371   0.413    189.0
+          2         0.299      0.276   0.326    184.0
+          3         0.234      0.204   0.275    255.0
+          4         0.268      0.285   0.252    321.0
+          5         0.228      0.230   0.227    181.0
+          6         0.407      0.384   0.433    284.0
+          7         0.362      0.296   0.467    287.0
+          8         0.298      0.519   0.209    263.0
+          9         0.210      0.191   0.233    313.0
+          10        0.369      0.420   0.329    362.0
+    N1    1         0.185      0.185   0.185    124.0
+          2         0.121      0.131   0.112    160.0
+
+    >>> ebe.get_confusion_matrix(sleep_id=1)
+    YASA   WAKE  N1   N2  N3  REM
+    Human
+    WAKE     78  24   50   3   34
+    N1       23  23   43  15   20
+    N2       60  58  183  43  139
+    N3       30  10   50   5   32
+    REM      19   9  121  50   78
+
+    .. plot::
+
+        >>> import matplotlib.pyplot as plt
+        >>> fig, ax = plt.subplots(figsize=(6, 3), constrained_layout=True)
+        >>> ebe.plot_hypnograms(sleep_id=10)
+
+    .. plot::
+
+        >>> fig, ax = plt.subplots(figsize=(6, 3))
+        >>> ebe.plot_hypnograms(
+        >>>     sleep_id=8, ax=ax, obs_kwargs={"color": "red", "lw": 2, "ls": "dotted"}
+        >>> )
+        >>> plt.tight_layout()
+
+    .. plot::
+
+        >>> session = 8
+        >>> fig, ax = plt.subplots(figsize=(6.5, 2.5), constrained_layout=True)
+        >>> style_a = dict(alpha=1, lw=2.5, ls="solid", color="gainsboro", label="Michel")
+        >>> style_b = dict(alpha=1, lw=2.5, ls="solid", color="cornflowerblue", label="Jouvet")
+        >>> legend_style = dict(
+        >>>     title="Scorer", frameon=False, ncol=2, loc="lower center", bbox_to_anchor=(0.5, 0.9)
+        >>> )
+        >>> ax = ebe.plot_hypnograms(
+        >>>     sleep_id=session, ref_kwargs=style_a, obs_kwargs=style_b, legend=legend_style, ax=ax
+        >>> )
+        >>> acc = ebe.get_agreement().multiply(100).at[session, "accuracy"]
+        >>> ax.text(
+        >>>     0.01, 1, f"Accuracy = {acc:.0f}%", ha="left", va="bottom", transform=ax.transAxes
+        >>> )
+
+    When comparing only 2 hypnograms, use the :py:meth:`~yasa.Hynogram.evaluate` method:
+
+    >>> hypno_a = yasa.simulate_hypnogram(tib=90, scorer="RaterA", seed=8)
+    >>> hypno_b = hypno_a.simulate_similar(scorer="RaterB", seed=9)
+    >>> ebe = hypno_a.evaluate(hypno_b)
+    >>> ebe.get_confusion_matrix()
+    RaterB  WAKE  N1  N2  N3
+    RaterA
+    WAKE      71   2  20   8
+    N1         1   0   9   0
+    N2        12   4  25   0
+    N3        24   0   1   3
+    """
+
+
[docs]    def __init__(self, ref_hyps, obs_hyps):
+        from yasa.hypno import Hypnogram  # Avoiding circular import, bc hypno imports this class
+
+        assert hasattr(ref_hyps, "__iter__"), "`ref_hyps` must be a an iterable"
+        assert hasattr(obs_hyps, "__iter__"), "`obs_hyps` must be a an iterable"
+        assert type(ref_hyps) is type(obs_hyps), "`ref_hyps` and `obs_hyps` must be the same type"
+        assert len(ref_hyps) == len(
+            obs_hyps
+        ), "`ref_hyps` and `obs_hyps` must have the same number of hypnograms"
+
+        if isinstance(ref_hyps, dict):
+            # If user provides dictionaries, split into sleep IDs and hypnograms
+            assert (
+                ref_hyps.keys() == obs_hyps.keys()
+            ), "keys in `ref_hyps` must be the same as keys in `obs_hyps`"
+            sleep_ids, ref_hyps = zip(*ref_hyps.items())
+            obs_hyps = tuple(obs_hyps.values())
+        else:
+            # Create hypnogram_ids
+            sleep_ids = tuple(range(1, 1 + len(ref_hyps)))
+
+        assert all(
+            isinstance(hyp, Hypnogram) for hyp in ref_hyps + obs_hyps
+        ), "`ref_hyps` and `obs_hyps` must only contain YASA hypnograms"
+        assert all(
+            h.scorer is not None for h in ref_hyps + obs_hyps
+        ), "all hypnograms in `ref_hyps` and `obs_hyps` must have a scorer name"
+        for h1, h2 in zip((ref_hyps + obs_hyps)[:-1], (ref_hyps + obs_hyps)[1:]):
+            assert h1.freq == h2.freq, "all hypnograms must have the same freq"
+            assert h1.labels == h2.labels, "all hypnograms must have the same labels"
+            assert h1.mapping == h2.mapping, "all hypnograms must have the same mapping"
+            assert h1.n_stages == h2.n_stages, "all hypnograms must have the same n_stages"
+        assert all(
+            h1.scorer == h2.scorer for h1, h2 in zip(ref_hyps[:-1], ref_hyps[1:])
+        ), "all `ref_hyps` must have the same scorer"
+        assert all(
+            h1.scorer == h2.scorer for h1, h2 in zip(obs_hyps[:-1], obs_hyps[1:])
+        ), "all `obs_hyps` must have the same scorer"
+        assert all(
+            h1.scorer != h2.scorer for h1, h2 in zip(ref_hyps, obs_hyps)
+        ), "each `ref_hyps` and `obs_hyps` pair must have unique scorers"
+        assert all(
+            h1.n_epochs == h2.n_epochs for h1, h2 in zip(ref_hyps, obs_hyps)
+        ), "each `ref_hyps` and `obs_hyps` pair must have the same n_epochs"
+        # Convert ref_hyps and obs_hyps to dictionaries with sleep_id keys and hypnogram values
+        ref_hyps = {s: h for s, h in zip(sleep_ids, ref_hyps)}
+        obs_hyps = {s: h for s, h in zip(sleep_ids, obs_hyps)}
+
+        # Merge all hypnograms into a single MultiIndexed dataframe
+        ref = pd.concat(pd.concat({s: h.as_int()}, names=["sleep_id"]) for s, h in ref_hyps.items())
+        obs = pd.concat(pd.concat({s: h.as_int()}, names=["sleep_id"]) for s, h in obs_hyps.items())
+        data = pd.concat([ref, obs], axis=1)
+
+        # Generate some mapping dictionaries to be used later in class methods
+        skm_labels = np.unique(data).tolist()  # all unique YASA integer codes in this hypno
+        skm2yasa_map = {i: l for i, l in enumerate(skm_labels)}  # skm order to YASA integers
+        yasa2yasa_map = ref_hyps[sleep_ids[0]].mapping_int.copy()  # YASA integer to YASA string
+
+        # Set attributes
+        self._data = data
+        self._sleep_ids = sleep_ids
+        self._ref_hyps = ref_hyps
+        self._obs_hyps = obs_hyps
+        self._ref_scorer = ref_hyps[sleep_ids[0]].scorer
+        self._obs_scorer = obs_hyps[sleep_ids[0]].scorer
+        self._skm_labels = skm_labels
+        self._skm2yasa_map = skm2yasa_map
+        self._yasa2yasa_map = yasa2yasa_map

+
+    def __repr__(self):
+        # TODO v0.8: Keep only the text between < and >
+        s = "s" if self.n_sleeps > 1 else ""
+        return (
+            f"<EpochByEpochAgreement | Observed hypnogram{s} scored by {self.obs_scorer} "
+            f"evaluated against reference hypnogram{s} scored by {self.ref_scorer}, "
+            f"{self.n_sleeps} sleep session{s}>\n"
+            " - Use `.get_agreement()` to get agreement measures as a pandas DataFrame or Series\n"
+            " - Use `.plot_hypnograms()` to plot two overlaid hypnograms\n"
+            "See the online documentation for more details."
+        )
+
+    def __str__(self):
+        return self.__repr__()
+
+    @property
+    def data(self):
+        """A :py:class:`pandas.DataFrame` including all hypnograms."""
+        return self._data
+
+    @property
+    def n_sleeps(self):
+        """The number of unique sleep sessions."""
+        return len(self._sleep_ids)
+
+    @property
+    def ref_scorer(self):
+        """The name of the reference scorer."""
+        return self._ref_scorer
+
+    @property
+    def obs_scorer(self):
+        """The name of the observed scorer."""
+        return self._obs_scorer
+
+
[docs]    @staticmethod
+    def multi_scorer(df, scorers):
+        """
+        Compute multiple agreement scores from a 2-column dataframe (an optional 3rd column may
+        contain sample weights).
+
+        This function offers convenience when calculating multiple agreement scores using
+        :py:meth:`pandas.DataFrame.groupby.apply`. Scikit-learn doesn't include a function that
+        returns multiple scores, and the GroupBy implementation of ``apply`` in pandas does not
+        accept multiple functions.
+
+        Parameters
+        ----------
+        df : :py:class:`pandas.DataFrame`
+            A :py:class:`~pandas.DataFrame` with 2 columns and length of *n_samples*.
+            The first column contains reference values and second column contains observed values.
+            If a third column, it must contain sample weights to be passed to underlying
+            :py:mod:`sklearn.metrics` functions as ``sample_weight`` where applicable.
+        scorers : dictionary
+            The scorers to be used for evaluating agreement. A dictionary with scorer names (str) as
+            keys and functions as values.
+
+        Returns
+        -------
+        scores : dict
+            A dictionary with scorer names (``str``) as keys and scores (``float``) as values.
+        """
+        assert isinstance(df, pd.DataFrame), "`df` must be a pandas DataFrame"
+        assert df.shape[1] in [2, 3], "`df` must have either 2 or 3 columns"
+        assert isinstance(scorers, dict), "`scorers` must be a dictionary"
+        assert all(
+            isinstance(k, str) and callable(v) for k, v in scorers.items()
+        ), "Each key of `scorers` must be a string, and each value must be a callable function"
+        if df.shape[1] == 3:
+            true, pred, weights = zip(*df.values)
+        elif df.shape[1] == 2:
+            true, pred = zip(*df.values)  # Same as (df["col1"], df["col2"]) but teensy bit faster
+            weights = None
+        scores = {s: f(true, pred, weights) for s, f in scorers.items()}
+        return scores

+
+
[docs]    def get_agreement(self, sample_weight=None, scorers=None):
+        """
+        Return a :py:class:`pandas.DataFrame` of weighted (i.e., averaged) agreement scores.
+
+        Parameters
+        ----------
+        self : :py:class:`~yasa.evaluation.EpochByEvaluation`
+            A :py:class:`~yasa.evaluation.EpochByEvaluation` instance.
+        sample_weight : None or :py:class:`pandas.Series`
+            Sample weights passed to underlying :py:mod:`sklearn.metrics` functions where possible.
+            If a :py:class:`pandas.Series`, the index must match exactly that of
+            :py:attr:`~yasa.Hypnogram.data`.
+        scorers : None, list, or dictionary
+            The scorers to be used for evaluating agreement. If None (default), default scorers are
+            used. If a list, the list must contain strings that represent metrics from the sklearn
+            metrics module (e.g., ``accuracy``, ``precision``). If more customization is desired, a
+            dictionary can be passed with scorer names (str) as keys and custom functions as values.
+            The custom functions should take 3 positional arguments (true values, predicted values,
+            and sample weights).
+
+        Returns
+        -------
+        agreement : :py:class:`pandas.DataFrame`
+            A :py:class:`~pandas.DataFrame` with agreement metrics as columns and sessions as rows.
+        """
+        assert isinstance(
+            sample_weight, (type(None), pd.Series)
+        ), "`sample_weight` must be None or pandas Series"
+        assert isinstance(scorers, (type(None), list, dict))
+        if isinstance(scorers, list):
+            assert all(isinstance(x, str) for x in scorers)
+        elif isinstance(scorers, dict):
+            assert all(isinstance(k, str) and callable(v) for k, v in scorers.items())
+        if scorers is None:
+            # Create dictionary of default scorer functions
+            scorers = {
+                "accuracy": lambda t, p, w: skm.accuracy_score(
+                    t, p, normalize=True, sample_weight=w
+                ),
+                "balanced_acc": lambda t, p, w: skm.balanced_accuracy_score(
+                    t, p, adjusted=False, sample_weight=w
+                ),
+                "kappa": lambda t, p, w: skm.cohen_kappa_score(
+                    t, p, labels=None, weights=None, sample_weight=w
+                ),
+                "mcc": lambda t, p, w: skm.matthews_corrcoef(t, p, sample_weight=w),
+                "precision": lambda t, p, w: skm.precision_score(
+                    t, p, average="weighted", sample_weight=w, zero_division=0
+                ),
+                "recall": lambda t, p, w: skm.recall_score(
+                    t, p, average="weighted", sample_weight=w, zero_division=0
+                ),
+                "f1": lambda t, p, w: skm.f1_score(
+                    t, p, average="weighted", sample_weight=w, zero_division=0
+                ),
+            }
+        elif isinstance(scorers, list):
+            # Convert the list to a dictionary of sklearn scorers
+            scorers = {s: skm.__getattribute__(f"{s}_scorer") for s in scorers}
+        # Make a copy of data since weights series might be added to it
+        df = self.data.copy()
+        if sample_weight is not None:
+            assert sample_weight.index == self.data.index, (
+                "If not `None`, `sample_weight` Series must be a pandas Series with the same index "
+                "as `self.data`"
+            )
+            # Add weights as a third column for multi_scorer to use
+            df["weights"] = sample_weight
+        # Get individual-level averaged/weighted agreement scores
+        agreement = df.groupby(level=0).apply(self.multi_scorer, scorers=scorers).apply(pd.Series)
+        # Set attribute for later access
+        self._agreement = agreement
+        # Convert to Series if just one session being evaluated
+        if self.n_sleeps == 1:
+            agreement = agreement.squeeze().rename("agreement")
+        return agreement

+
+
[docs]    def get_agreement_bystage(self, beta=1.0):
+        """
+        Return a :py:class:`pandas.DataFrame` of unweighted (i.e., one-vs-rest) agreement scores.
+
+        Parameters
+        ----------
+        self : :py:class:`~yasa.evaluation.EpochByEvaluation`
+            A :py:class:`~yasa.evaluation.EpochByEvaluation` instance.
+        beta : float
+            See :py:func:`sklearn.metrics.precision_recall_fscore_support`.
+
+        Returns
+        -------
+        agreement : :py:class:`pandas.DataFrame`
+            A :py:class:`~pandas.DataFrame` with agreement metrics as columns and a
+            :py:class:`~pandas.MultiIndex` with session and sleep stage as rows.
+        """
+
+        def scorer(df):
+            return skm.precision_recall_fscore_support(
+                *df.values.T, beta=beta, labels=self._skm_labels, average=None, zero_division=0
+            )
+
+        agreement = (
+            self.data
+            # Get precision, recall, f1, and support for each individual sleep session
+            .groupby(level=0)
+            .apply(scorer)
+            # Unpack arrays
+            .explode()
+            .apply(pd.Series)
+            # Add metric labels column and prepend it to index, creating MultiIndex
+            .assign(metric=["precision", "recall", "fbeta", "support"] * self.n_sleeps)
+            .set_index("metric", append=True)
+            # Convert stage column names to string labels
+            .rename_axis(columns="stage")
+            .rename(columns=self._skm2yasa_map)
+            .rename(columns=self._yasa2yasa_map)
+            # Remove all-zero columns (i.e., stages that were not present in the hypnogram)
+            .pipe(lambda df: df.loc[:, df.any()])
+            # Reshape so metrics are columns
+            .stack()
+            .unstack("metric")
+            .rename_axis(columns=None)
+            # Swap MultiIndex levels and sort so stages are in standard YASA order
+            .swaplevel()
+            .sort_index(
+                level="stage",
+                key=lambda x: x.map(lambda y: list(self._yasa2yasa_map.values()).index(y)),
+            )
+        )
+        # Set attribute for later access
+        self._agreement_bystage = agreement
+        # Remove the MultiIndex if just one session being evaluated
+        if self.n_sleeps == 1:
+            agreement = agreement.reset_index(level=1, drop=True)
+        return agreement

+
+
[docs]    def get_confusion_matrix(self, sleep_id=None, agg_func=None, **kwargs):
+        """
+        Return a ``ref_hyp``/``obs_hyp``confusion matrix from either a single session or all
+        sessions concatenated together.
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.EpochByEpochAgreement`
+            A :py:class:`yasa.EpochByEpochAgreement` instance.
+        sleep_id : None or a valid sleep ID
+            If None (default), cross-tabulation is derived from the entire group dataset.
+            If a valid sleep ID, cross-tabulation is derived using only the reference and observed
+            scored hypnograms from that sleep session.
+        agg_func : None or str
+            If None (default), group results returns a :py:class:`~pandas.DataFrame` complete with
+            all individual session results. If not None, group results returns a
+            :py:class:`~pandas.DataFrame` aggregated across sessions where ``agg_func`` is passed as
+            ``func`` parameter in :py:meth:`pandas.DataFrame.groupby.agg`. For example, set
+            ``agg_func="sum"`` to get a single confusion matrix across all epochs that does not take
+            session into account.
+        **kwargs : key, value pairs
+            Additional keyword arguments are passed to :py:func:`sklearn.metrics.confusion_matrix`.
+
+        Returns
+        -------
+        conf_matr : :py:class:`pandas.DataFrame`
+            A confusion matrix with stages from the reference scorer as indices and stages from the
+            test scorer as columns.
+
+        Examples
+        --------
+        >>> import yasa
+        >>> ref_hyps = [yasa.simulate_hypnogram(tib=90, scorer="Rater1", seed=i) for i in range(3)]
+        >>> obs_hyps = [h.simulate_similar(scorer="Rater2", seed=i) for i, h in enumerate(ref_hyps)]
+        >>> ebe = yasa.EpochByEpochAgreement(ref_hyps, obs_hyps)
+        >>> ebe.get_confusion_matrix(sleep_id=2)
+        Rater2  WAKE  N1  N2  N3  REM
+        Rater1
+        WAKE       1   2  23   0    0
+        N1         0   9  13   0    0
+        N2         0   6  71   0    0
+        N3         0  13  42   0    0
+        REM        0   0   0   0    0
+
+        >>> ebe.get_confusion_matrix()
+        Rater2           WAKE  N1  N2  N3  REM
+        sleep_id Rater1
+        1        WAKE      30   0   3   0   35
+                 N1         3   2   7   0    0
+                 N2        21  12   7   0    4
+                 N3         0   0   0   0    0
+                 REM        2   8  29   0   17
+        2        WAKE       1   2  23   0    0
+                 N1         0   9  13   0    0
+                 N2         0   6  71   0    0
+                 N3         0  13  42   0    0
+                 REM        0   0   0   0    0
+        3        WAKE      16   0   7  19   19
+                 N1         0   7   2   0    5
+                 N2         0  10  12   7    5
+                 N3         0   0  16  11    0
+                 REM        0  15  11  18    0
+
+        >>> ebe.get_confusion_matrix(agg_func="sum")
+        Rater2  WAKE  N1  N2  N3  REM
+        Rater1
+        WAKE      47   2  33  19   54
+        N1         3  18  22   0    5
+        N2        21  28  90   7    9
+        N3         0  13  58  11    0
+        REM        2  23  40  18   17
+        """
+        assert (
+            sleep_id is None or sleep_id in self._sleep_ids
+        ), "`sleep_id` must be None or a valid sleep ID"
+        assert isinstance(agg_func, (type(None), str)), "`agg_func` must be None or a str"
+        assert not (
+            (self.n_sleeps == 1 or sleep_id is not None) and agg_func is not None
+        ), "`agg_func` must be None if plotting a single session."
+        kwargs = {"labels": self._skm_labels} | kwargs
+        # Generate a DataFrame with a confusion matrix for each session
+        #   Seems easier to just generate this whole thing and then either
+        #   extract a single one or aggregate across them all, depending on user request
+        confusion_matrices = (
+            self.data
+            # Get confusion matrix for each individual sleep session
+            .groupby(level=0)
+            .apply(lambda df: skm.confusion_matrix(*df.values.T, **kwargs))
+            # Expand results matrix out from single cell
+            .explode()
+            .apply(pd.Series)
+            # Convert to MultiIndex with reference scorer as new level
+            .assign(**{self.ref_scorer: self._skm_labels * self.n_sleeps})
+            .set_index(self.ref_scorer, append=True)
+            .rename_axis(columns=self.obs_scorer)
+            # Convert sleep stage columns and indices to strings
+            .rename(columns=self._skm2yasa_map)
+            .rename(columns=self._yasa2yasa_map)
+            .rename(index=self._skm2yasa_map, level=self.ref_scorer)
+            .rename(index=self._yasa2yasa_map, level=self.ref_scorer)
+        )
+        if self.n_sleeps == 1:
+            # If just one session, use the only session ID as the key, for simplified returned df
+            sleep_id = self._sleep_ids[0]
+        if sleep_id is None:
+            if agg_func is None:
+                mat = confusion_matrices
+            else:
+                mat = confusion_matrices.groupby(self.ref_scorer, sort=False).agg(agg_func)
+        else:
+            mat = confusion_matrices.loc[sleep_id]
+        return mat

+
+
[docs]    def get_sleep_stats(self):
+        """
+        Return a :py:class:`pandas.DataFrame` of sleep statistics for each hypnogram derived from
+        both reference and observed scorers.
+
+        .. seealso:: :py:meth:`yasa.Hypnogram.sleep_statistics`
+
+        .. seealso:: :py:class:`yasa.SleepStatsAgreement`
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.EpochByEpochAgreement`
+            A :py:class:`yasa.EpochByEpochAgreement` instance.
+
+        Returns
+        -------
+        sstats : :py:class:`pandas.DataFrame`
+            A :py:class:`~pandas.DataFrame` with sleep statistics as columns and two rows for each
+            individual (one for reference scorer and another for test scorer).
+        """
+        # Get all sleep statistics
+        ref_sstats = pd.DataFrame({s: h.sleep_statistics() for s, h in self._ref_hyps.items()})
+        obs_sstats = pd.DataFrame({s: h.sleep_statistics() for s, h in self._obs_hyps.items()})
+        # Reshape and name axis
+        ref_sstats = ref_sstats.T.rename_axis("sleep_id")
+        obs_sstats = obs_sstats.T.rename_axis("sleep_id")
+        # Convert to MultiIndex with new scorer level
+        ref_sstats = pd.concat({self.ref_scorer: ref_sstats}, names=["scorer"])
+        obs_sstats = pd.concat({self.obs_scorer: obs_sstats}, names=["scorer"])
+        # Concatenate into one DataFrame
+        sstats = pd.concat([ref_sstats, obs_sstats])
+        # Remove the MultiIndex if just one session being evaluated
+        if self.n_sleeps == 1:
+            sstats = sstats.reset_index(level=1, drop=True)
+        return sstats

+
+
[docs]    def plot_hypnograms(self, sleep_id=None, legend=True, ax=None, ref_kwargs={}, obs_kwargs={}):
+        """Plot the two hypnograms of one session overlapping on the same axis.
+
+        .. seealso:: :py:func:`yasa.plot_hypnogram`
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.EpochByEpochAgreement`
+            A :py:class:`yasa.EpochByEpochAgreement` instance.
+        sleep_id : a valid sleep ID or None
+            The sleep session to plot. If multiple sessions are included in the
+            :py:class:`~yasa.EpochByEpochAgreement` instance, a ``sleep_id`` must be provided. If
+            only one session is present, ``None`` (default) will plot the two hypnograms of the
+            only session.
+        legend : bool or dict
+            If True (default) or a dictionary, a legend is added. If a dictionary, all key/value
+            pairs are passed as keyword arguments to the :py:func:`matplotlib.pyplot.legend` call.
+        ax : :py:class:`matplotlib.axes.Axes` or None
+            Axis on which to draw the plot, optional.
+        ref_kwargs : dict
+            Keyword arguments passed to :py:func:`yasa.plot_hypnogram` when plotting the reference
+            hypnogram.
+        obs_kwargs : dict
+            Keyword arguments passed to :py:func:`yasa.plot_hypnogram` when plotting the observed
+            hypnogram.
+
+        Returns
+        -------
+        ax : :py:class:`matplotlib.axes.Axes`
+            Matplotlib Axes
+
+        Examples
+        --------
+        .. plot::
+
+            >>> from yasa import simulate_hypnogram
+            >>> hyp = simulate_hypnogram(scorer="Anthony", seed=19)
+            >>> ax = hyp.evaluate(hyp.simulate_similar(scorer="Alan", seed=68)).plot_hypnograms()
+        """
+        assert (
+            sleep_id is None or sleep_id in self._sleep_ids
+        ), "`sleep_id` must be None or a valid sleep ID"
+        assert isinstance(legend, (bool, dict)), "`legend` must be True, False, or a dictionary"
+        assert isinstance(ref_kwargs, dict), "`ref_kwargs` must be a dictionary"
+        assert isinstance(obs_kwargs, dict), "`obs_kwargs` must be a dictionary"
+        assert (
+            "ax" not in ref_kwargs | obs_kwargs
+        ), "'ax' can't be supplied to `ref_kwargs` or `obs_kwargs`, use the `ax` keyword instead"
+        assert not (sleep_id is None and self.n_sleeps > 1), (
+            "Multi-session plotting is not currently supported. `sleep_id` must not be None when "
+            "multiple sessions are present"
+        )
+        # Select the session hypnograms to plot
+        if sleep_id is None and self.n_sleeps == 1:
+            ref_hyp = self._ref_hyps[self._sleep_ids[0]]
+            obs_hyp = self._obs_hyps[self._sleep_ids[0]]
+        else:
+            ref_hyp = self._ref_hyps[sleep_id]
+            obs_hyp = self._obs_hyps[sleep_id]
+        # Set default plotting kwargs and merge with user kwargs
+        plot_ref_kwargs = {
+            "label": self.ref_scorer,
+            "highlight": None,
+            "color": "black",
+            "alpha": 0.8,
+        }
+        plot_obs_kwargs = {
+            "label": self.obs_scorer,
+            "highlight": None,
+            "color": "green",
+            "alpha": 0.8,
+            "ls": "dashed",
+        }
+        plot_ref_kwargs.update(ref_kwargs)
+        plot_obs_kwargs.update(obs_kwargs)
+        # Draw the hypnograms
+        ax = ref_hyp.plot_hypnogram(ax=ax, **plot_ref_kwargs)
+        ax = obs_hyp.plot_hypnogram(ax=ax, **plot_obs_kwargs)
+        # Add legend if desired
+        if legend:
+            if isinstance(legend, dict):
+                ax.legend(**legend)
+            else:
+                ax.legend()
+        return ax

+
+
[docs]    def summary(self, by_stage=False, **kwargs):
+        """Return group-level agreement scores.
+
+        Default aggregated measures are
+
+        Parameters
+        ----------
+        self : :py:class:`~yasa.evaluation.EpochByEpochAgreement`
+            A :py:class:`~yasa.evaluation.EpochByEpochAgreement` instance.
+        by_stage : bool
+            If ``False`` (default), ``summary`` will include agreement scores derived from
+            average-based metrics. If ``True``, returned ``summary`` :py:class:`~pandas.DataFrame`
+            will include agreement scores for each sleep stage, derived from one-vs-rest metrics.
+        **kwargs : key, value pairs
+            Additional keyword arguments are passed to :py:meth:`pandas.DataFrame.groupby.agg`.
+            This can be used to customize the descriptive statistics returned.
+
+        Returns
+        -------
+        summary : :py:class:`pandas.DataFrame`
+            A :py:class:`pandas.DataFrame` summarizing agreement scores across the entire dataset
+            with descriptive statistics.
+
+            >>> ebe = yasa.EpochByEpochAgreement(...)
+            >>> agreement = ebe.get_agreement()
+            >>> ebe.summary()
+
+            This will give a :py:class:`~pandas.DataFrame` where each row is an agreement metric and
+            each column is a descriptive statistic (e.g., mean, standard deviation).
+            To control the descriptive statistics included as columns:
+
+            >>> ebe.summary(func=["count", "mean", "sem"])
+        """
+        assert self.n_sleeps > 1, "Summary scores can not be computed with only one hypnogram pair."
+        assert isinstance(by_stage, bool), "`by_stage` must be True or False"
+        if by_stage:
+            assert hasattr(
+                self, "_agreement_bystage"
+            ), "Must run `self.get_agreement_bystage` before obtaining by_stage summary results."
+        else:
+            assert hasattr(
+                self, "_agreement"
+            ), "Must run `self.get_agreement` before obtaining summary results."
+
+        # Create a function for getting mean absolute deviation
+        def mad(df):
+            return (df - df.mean()).abs().mean()
+
+        # Merge default and user kwargs
+        agg_kwargs = {"func": [mad, "mean", "std", "min", "median", "max"]} | kwargs
+        if by_stage:
+            summary = (
+                self.agreement_bystage.groupby("stage")
+                .agg(**agg_kwargs)
+                .stack(level=0)
+                .rename_axis(["stage", "metric"])
+            )
+        else:
+            summary = self._agreement.agg(**agg_kwargs).T.rename_axis("metric")
+        return summary

+
+
+################################################################################
+# SLEEP STATISTICS
+################################################################################
+
+
+
[docs]class SleepStatsAgreement:
+    """
+    Evaluate agreement between sleep statistics reported by two different scorers.
+    Evaluation includes bias and limits of agreement (as well as both their confidence intervals),
+    various plotting options, and calibration functions for correcting biased values from the
+    observed scorer.
+
+    Features include:
+    * Get summary calculations of bias, limits of agreement, and their confidence intervals.
+    * Test statistical assumptions of bias, limits of agreement, and their confidence intervals,
+    and apply corrective procedures when the assumptions are not met.
+    * Get bias and limits of agreement in a string-formatted table.
+    * Calibrate new data to correct for biases in observed data.
+    * Return individual calibration functions.
+    * Visualize discrepancies for outlier inspection.
+    * Visualize Bland-Altman plots.
+
+    .. seealso:: :py:meth:`yasa.Hypnogram.sleep_statistics`
+
+    .. versionadded:: 0.7.0
+
+    Parameters
+    ----------
+    ref_data : :py:class:`pandas.DataFrame`
+        A :py:class:`pandas.DataFrame` with sleep statistics from the reference scorer.
+        Rows are unique observations and columns are unique sleep statistics.
+    obs_data : :py:class:`pandas.DataFrame`
+        A :py:class:`pandas.DataFrame` with sleep statistics from the observed scorer.
+        Rows are unique observations and columns are unique sleep statistics.
+        Shape, index, and columns must be identical to ``ref_data``.
+    ref_scorer : str
+        Name of the reference scorer.
+    obs_scorer : str
+        Name of the observed scorer.
+    agreement : float
+        Multiple of the standard deviation to plot agreement limits. The default is 1.96, which
+        corresponds to a 95% confidence interval if the differences are normally distributed.
+
+        .. note:: ``agreement`` gets adjusted for regression-modeled limits of agreement.
+    confidence : float
+        The percentage confidence interval for the confidence intervals that are applied to bias and
+        limits of agreement. The same confidence interval percentage is applied to both standard and
+        bootstrapped confidence intervals.
+    alpha : float
+        Alpha cutoff used for all assumption tests.
+    verbose : bool or str
+        Verbose level. Default (False) will only print warning and error messages. The logging
+        levels are 'debug', 'info', 'warning', 'error', and 'critical'. For most users the choice is
+        between 'info' (or ``verbose=True``) and warning (``verbose=False``).
+
+    Notes
+    -----
+    Sleep statistics that are identical between scorers are removed from analysis.
+
+    Many steps here are influenced by guidelines proposed in Menghini et al., 2021 [Menghini2021]_.
+    See https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html
+
+    References
+    ----------
+    .. [Menghini2021] Menghini, L., Cellini, N., Goldstone, A., Baker, F. C., & de Zambotti, M.
+                      (2021). A standardized framework for testing the performance of sleep-tracking
+                      technology: step-by-step guidelines and open-source code. SLEEP, 44(2),
+                      zsaa170. https://doi.org/10.1093/sleep/zsaa170
+
+    Examples
+    --------
+    >>> import pandas as pd
+    >>> import yasa
+    >>>
+    >>> # Generate fake reference and observed datasets with similar sleep statistics
+    >>> ref_scorer = "Henri"
+    >>> obs_scorer = "Piéron"
+    >>> ref_hyps = [yasa.simulate_hypnogram(tib=600, scorer=ref_scorer, seed=i) for i in range(20)]
+    >>> obs_hyps = [h.simulate_similar(scorer=obs_scorer, seed=i) for i, h in enumerate(ref_hyps)]
+    >>> # Generate sleep statistics from hypnograms using EpochByEpochAgreement
+    >>> eea = yasa.EpochByEpochAgreement(ref_hyps, obs_hyps)
+    >>> sstats = eea.get_sleep_stats()
+    >>> ref_sstats = sstats.loc[ref_scorer]
+    >>> obs_sstats = sstats.loc[obs_scorer]
+    >>> # Create SleepStatsAgreement instance
+    >>> ssa = yasa.SleepStatsAgreement(ref_sstats, obs_sstats)
+    >>> ssa.summary().round(1).head(3)
+    variable   bias_intercept             ...   uloa_parm
+    interval           center lower upper ...      center lower upper
+    sleep_stat                            ...
+    %N1                  -5.4 -13.9   3.2 ...         6.1   3.7   8.5
+    %N2                 -27.3 -49.1  -5.6 ...        12.4   7.2  17.6
+    %N3                  -9.1 -23.8   5.5 ...        20.4  12.6  28.3
+
+    >>> ssa.get_table().head(3)[["bias", "loa"]]
+                          bias                            loa
+    sleep_stat
+    %N1                   0.25  Bias ± 2.46 * (-0.00 + 1.00x)
+    %N2         -27.34 + 0.55x   Bias ± 2.46 * (0.00 + 1.00x)
+    %N3                   1.38   Bias ± 2.46 * (0.00 + 1.00x)
+
+    >>> ssa.assumptions.head(3)
+                unbiased  normal  constant_bias  homoscedastic
+    sleep_stat
+    %N1             True    True           True          False
+    %N2             True    True          False          False
+    %N3             True    True           True          False
+
+    >>> ssa.auto_methods.head(3)
+                bias   loa    ci
+    sleep_stat
+    %N1         parm  regr  parm
+    %N2         regr  regr  parm
+    %N3         parm  regr  parm
+
+    >>> ssa.get_table(bias_method="parm", loa_method="parm").head(3)[["bias", "loa"]]
+                 bias            loa
+    sleep_stat
+    %N1          0.25    -5.55, 6.06
+    %N2         -0.23  -12.87, 12.40
+    %N3          1.38  -17.67, 20.44
+
+    >>> new_hyps = [h.simulate_similar(scorer="Kelly", seed=i) for i, h in enumerate(obs_hyps)]
+    >>> new_sstats = pd.Series(new_hyps).map(lambda h: h.sleep_statistics()).apply(pd.Series)
+    >>> new_sstats = new_sstats[["N1", "TST", "WASO"]]
+    >>> new_sstats.round(1).head(5)
+         N1    TST   WASO
+    0  42.5  439.5  147.5
+    1  84.0  550.0   38.5
+    2  53.5  489.0  103.0
+    3  57.0  469.5  120.0
+    4  71.0  531.0   69.0
+
+    >>> new_stats_calibrated = ssa.calibrate_stats(new_sstats, bias_method="auto")
+    >>> new_stats_calibrated.round(1).head(5)
+         N1    TST   WASO
+    0  42.9  433.8  150.0
+    1  84.4  544.2   41.0
+    2  53.9  483.2  105.5
+    3  57.4  463.8  122.5
+    4  71.4  525.2   71.5
+
+    .. plot::
+
+        >>> import matplotlib.pyplot as plt
+        >>> ax = ssa.plot_discrepancies_heatmap()
+        >>> ax.set_title("Sleep statistic discrepancies")
+        >>> plt.tight_layout()
+
+    .. plot::
+
+        >>> ssa.plot_blandaltman()
+    """
+
+
[docs]    def __init__(
+        self,
+        ref_data,
+        obs_data,
+        *,
+        ref_scorer="Reference",
+        obs_scorer="Observed",
+        agreement=1.96,
+        confidence=0.95,
+        alpha=0.05,
+        verbose=True,
+        bootstrap_kwargs={},
+    ):
+
+        restricted_bootstrap_kwargs = ["confidence_level", "vectorized", "paired"]
+
+        assert isinstance(ref_data, pd.DataFrame), "`ref_data` must be a pandas DataFrame"
+        assert isinstance(obs_data, pd.DataFrame), "`obs_data` must be a pandas DataFrame"
+        assert np.array_equal(
+            ref_data.index, obs_data.index
+        ), "`ref_data` and `obs_data` index values must be identical"
+        assert (
+            ref_data.index.name == obs_data.index.name
+        ), "`ref_data` and `obs_data` index names must be identical"
+        assert np.array_equal(
+            ref_data.columns, obs_data.columns
+        ), "`ref_data` and `obs_data` column values must be identical"
+        assert isinstance(ref_scorer, str), "`ref_scorer` must be a string"
+        assert isinstance(obs_scorer, str), "`obs_scorer` must be a string"
+        assert ref_scorer != obs_scorer, "`ref_scorer` and `obs_scorer` must be unique"
+        assert (
+            isinstance(agreement, (float, int)) and agreement > 0
+        ), "`agreement` must be a number greater than 0"
+        assert (
+            isinstance(confidence, (float, int)) and 0 < alpha < 1
+        ), "`confidence` must be a number between 0 and 1"
+        assert (
+            isinstance(alpha, (float, int)) and 0 <= alpha <= 1
+        ), "`alpha` must be a number between 0 and 1 inclusive"
+        assert isinstance(bootstrap_kwargs, dict), "`bootstrap_kwargs` must be a dictionary"
+        assert all(
+            k not in restricted_bootstrap_kwargs for k in bootstrap_kwargs
+        ), f"None of {restricted_bootstrap_kwargs} can be set by the user"
+
+        # If `ref_data` and `obs_data` indices are unnamed, name them
+        session_key = "session_id" if ref_data.index.name is None else ref_data.index.name
+        ref_data.index.name = obs_data.index.name = session_key
+
+        # Reshape to long format DataFrame with 2 columns (observed, reference) and MultiIndex
+        data = (
+            pd.concat([obs_data, ref_data], keys=[obs_scorer, ref_scorer], names=["scorer"])
+            .melt(var_name="sleep_stat", ignore_index=False)
+            .pivot_table(index=["sleep_stat", session_key], columns="scorer", values="value")
+            .rename_axis(columns=None)
+            .sort_index()
+        )
+
+        # Get scorer differences (i.e., observed minus reference)
+        data["difference"] = data[obs_scorer] - data[ref_scorer]
+
+        # Remove sleep statistics that have no differences between scorers
+        stats_rm = data.groupby("sleep_stat")["difference"].any().loc[lambda x: ~x].index.tolist()
+        data = data.drop(labels=stats_rm)
+        for s in stats_rm:
+            logger.warning(f"Removed {s} from evaluation because all scorings were identical.")
+
+        # Create grouper and n_sessions variables for convenience
+        grouper = data.groupby("sleep_stat")
+        n_sessions = data.index.get_level_values(session_key).nunique()
+
+        ########################################################################
+        # Generate parametric Bias and LoA for all sleep stats
+        ########################################################################
+        # Parametric Bias
+        parm_vals = grouper["difference"].mean().to_frame("bias_parm")
+        # Parametric LoA
+        parm_vals["lloa_parm"], parm_vals["uloa_parm"] = zip(
+            *grouper["difference"].apply(self._arr_to_loa, agreement=agreement)
+        )
+
+        ########################################################################
+        # Generate standard CIs for parametric Bias and LoA for all sleep stats
+        ########################################################################
+        # Get critical t and standard error used to calculate parametric CIs for parametric Bias/LoA
+        t_parm = sps.t.ppf((1 + confidence) / 2, n_sessions - 1)
+        sem = grouper["difference"].sem(ddof=1)
+        # Parametric CIs for parametric Bias and LoA
+        parm_ci = pd.DataFrame(
+            {
+                "bias_parm-lower": parm_vals["bias_parm"] - sem * t_parm,
+                "bias_parm-upper": parm_vals["bias_parm"] + sem * t_parm,
+                "lloa_parm-lower": parm_vals["lloa_parm"] - sem * t_parm * np.sqrt(3),
+                "lloa_parm-upper": parm_vals["lloa_parm"] + sem * t_parm * np.sqrt(3),
+                "uloa_parm-lower": parm_vals["uloa_parm"] - sem * t_parm * np.sqrt(3),
+                "uloa_parm-upper": parm_vals["uloa_parm"] + sem * t_parm * np.sqrt(3),
+            }
+        )
+
+        ########################################################################
+        # Generate regression/modeled (slope and intercept) Bias and LoA for all sleep stats
+        ########################################################################
+        # Run regression used to (a) model bias and (b) test for proportional/constant bias
+        bias_regr = grouper[[ref_scorer, "difference"]].apply(self._linregr_dict).apply(pd.Series)
+        # Get absolute residuals from this regression bc they are used in the next regression
+        idx = data.index.get_level_values("sleep_stat")
+        slopes = bias_regr.loc[idx, "slope"].to_numpy()
+        intercepts = bias_regr.loc[idx, "intercept"].to_numpy()
+        predicted_values = data[ref_scorer].to_numpy() * slopes + intercepts
+        data["residuals"] = data[obs_scorer].to_numpy() - predicted_values
+        data["residuals_abs"] = data["residuals"].abs()
+        # Run regression used to (a) model LoA and (b) test for heteroscedasticity/homoscedasticity
+        loa_regr = grouper[[ref_scorer, "residuals_abs"]].apply(self._linregr_dict).apply(pd.Series)
+        # Stack the two regression dataframes together
+        regr = pd.concat({"bias": bias_regr, "loa": loa_regr}, axis=0)
+
+        ########################################################################
+        # Generate parametric CIs for regression/modeled Bias and LoA for all sleep stats
+        ########################################################################
+        # Get critical t used used to calculate parametric CIs for regression Bias/LoA
+        t_regr = sps.t.ppf((1 + confidence) / 2, n_sessions - 2)  # dof=n-2 for regression
+        # Parametric CIs for modeled Bias and LoA
+        regr_ci = pd.DataFrame(
+            {
+                "intercept-lower": regr["intercept"] - regr["intercept_stderr"] * t_regr,
+                "intercept-upper": regr["intercept"] + regr["intercept_stderr"] * t_regr,
+                "slope-lower": regr["slope"] - regr["stderr"] * t_regr,
+                "slope-upper": regr["slope"] + regr["stderr"] * t_regr,
+            }
+        )
+
+        ########################################################################
+        # Test all statistical assumptions
+        ########################################################################
+        assumptions = pd.DataFrame(
+            {
+                "unbiased": (
+                    grouper["difference"].apply(lambda a: sps.ttest_1samp(a, 0).pvalue).ge(alpha)
+                ),
+                "normal": grouper["difference"].apply(lambda a: sps.shapiro(a).pvalue).ge(alpha),
+                "constant_bias": bias_regr["pvalue"].ge(alpha),
+                "homoscedastic": loa_regr["pvalue"].ge(alpha),
+            }
+        )
+
+        ########################################################################
+        # Setting attributes
+        ########################################################################
+
+        # Merge the parametric and regression values for Bias and LoA
+        regr_vals = regr.unstack(0)[["slope", "intercept"]]
+        regr_vals.columns = regr_vals.columns.swaplevel().map("_".join)
+        vals = parm_vals.join(regr_vals).rename_axis("variable", axis=1)
+
+        # Merge the two CI dataframes for easier access
+        regr_ci = regr_ci.unstack(0)
+        regr_ci.columns = regr_ci.columns.swaplevel().map("_".join)
+        ci = parm_ci.join(regr_ci)
+        ci.columns = pd.MultiIndex.from_tuples(
+            tuples=ci.columns.str.split("-", expand=True),
+            names=["variable", "interval"],
+        )
+        empty_df = pd.DataFrame().reindex_like(ci)
+        ci = pd.concat({"parm": ci, "boot": empty_df}, names=["ci_method"], axis=1)
+        ci = ci.sort_index(axis=1)  # Sort MultiIndex columns for cleanliness
+
+        # Set attributes
+        self._agreement = agreement
+        self._confidence = confidence
+        self._bootstrap_kwargs = bootstrap_kwargs
+        self._n_sessions = n_sessions
+        self._ref_scorer = ref_scorer
+        self._obs_scorer = obs_scorer
+        self._data = data
+        self._assumptions = assumptions
+        self._regr = regr
+        self._vals = vals
+        self._ci = ci
+        self._bias_method_opts = ["parm", "regr", "auto"]
+        self._loa_method_opts = ["parm", "regr", "auto"]
+        self._ci_method_opts = ["parm", "boot", "auto"]

+
+    @property
+    def ref_scorer(self):
+        """The name of the reference scorer."""
+        return self._ref_scorer
+
+    @property
+    def obs_scorer(self):
+        """The name of the observed scorer."""
+        return self._obs_scorer
+
+    @property
+    def n_sessions(self):
+        """The number of sessions."""
+        return self._n_sessions
+
+    @property
+    def data(self):
+        """A long-format :py:class:`pandas.DataFrame` containing all raw sleep statistics from
+        ``ref_data`` and ``obs_data``.
+        """
+        return self._data.drop(columns=["difference", "residuals", "residuals_abs"])
+
+    @property
+    def sleep_statistics(self):
+        """Return a list of all sleep statistics included in the agreement analyses."""
+        return self.data.index.get_level_values("sleep_stat").unique().to_list()
+
+    @property
+    def assumptions(self):
+        """A :py:class:`pandas.DataFrame` containing boolean values indicating the pass/fail status
+        of all statistical tests performed to test assumptions.
+        """
+        return self._assumptions
+
+    @property
+    def auto_methods(self):
+        """
+        A :py:class:`pandas.DataFrame` containing the methods applied when ``'auto'`` is selected.
+        """
+        return pd.concat(
+            [
+                self.assumptions["constant_bias"].map({True: "parm", False: "regr"}).rename("bias"),
+                self.assumptions["homoscedastic"].map({True: "parm", False: "regr"}).rename("loa"),
+                self.assumptions["normal"].map({True: "parm", False: "boot"}).rename("ci"),
+            ],
+            axis=1,
+        )
+
+    def __repr__(self):
+        # TODO v0.8: Keep only the text between < and >
+        return (
+            f"<SleepStatsAgreement | Observed scorer ('{self.obs_scorer}') evaluated against "
+            f"reference scorer ('{self.ref_scorer}'), {self.n_sessions} sleep sessions>\n"
+            " - Use `.summary()` to get a dataframe of bias and limits of agreement for each sleep "
+            "statistic\n"
+            " - Use `.plot_blandaltman()` to get a Bland-Altman-plot grid for sleep statistics\n"
+            "See the online documentation for more details."
+        )
+
+    def __str__(self):
+        return self.__repr__()
+
+    ############################################################################
+    # Define some utility functions, mostly to aid with the use of df.apply and stats.bootstrap
+    ############################################################################
+
+    @staticmethod
+    def _arr_to_loa(x, agreement):
+        """Return a tuple with lower and upper limits of agreement."""
+        mean = np.mean(x)
+        bound = agreement * np.std(x, ddof=1)
+        return mean - bound, mean + bound
+
+    @staticmethod
+    def _linregr_dict(*args, **kwargs):
+        """
+        A wrapper around :py:func:`scipy.stats.linregress` that returns a dictionary instead of a
+        named tuple. In the normally returned object, ``intercept_stderr`` is an extra field that is
+        not included when converting the named tuple, so this allows it to be included when using
+        something like groupby.
+        """
+        regr = sps.linregress(*args, **kwargs)
+        return {
+            "slope": regr.slope,
+            "intercept": regr.intercept,
+            "rvalue": regr.rvalue,
+            "pvalue": regr.pvalue,
+            "stderr": regr.stderr,
+            "intercept_stderr": regr.intercept_stderr,
+        }
+
+    def _generate_bootstrap_ci(self, sleep_stats):
+        """
+        Internal method to generate bootstrapped confidence intervals for bias and LoA.
+        This operates in-place by concatenating bootstrapped CIs to existing parametric CIs.
+        Note that parametric CIs are generated by default during init (bc they are quicker).
+
+        Parameters
+        ----------
+        sleep_stats : list
+            A list of sleep statistics to bootstrap confidence intervals for.
+        """
+        assert isinstance(sleep_stats, list), "`sleep_stats` must be a list"
+        assert len(sleep_stats) == len(set(sleep_stats)), "elements of `sleep_stats` must be unique"
+        assert all(
+            isinstance(ss, str) for ss in sleep_stats
+        ), "all elements of `sleep_stats` must be strings"
+        assert all(
+            ss in self.sleep_statistics for ss in sleep_stats
+        ), f"all elements of `sleep_stats` must be one of {self.sleep_statistics}"
+        # Update bootstrap keyword arguments with defaults
+        bs_kwargs = {
+            "n_resamples": 1000,
+            "method": "BCa",
+            "confidence_level": self._confidence,  # should not change from parametric level
+            "vectorized": False,  # should stay False, bc of how the custom get_vars function works
+            "paired": True,  # should stay True, especially if method is BCa
+        } | self._bootstrap_kwargs
+
+        def get_vars(ref_arr, diff_arr, rabs_arr):
+            """A function to get all variables at once and avoid redundant stats.bootstrap calls."""
+            bias_parm = np.mean(diff_arr)
+            lloa_parm, uloa_parm = self._arr_to_loa(diff_arr, self._agreement)
+            bias_slope, bias_inter = sps.linregress(ref_arr, diff_arr)[:2]
+            # Note this is NOT recalculating residuals each time for the next regression
+            loa_slope, loa_inter = sps.linregress(ref_arr, rabs_arr)[:2]
+            return bias_parm, lloa_parm, uloa_parm, bias_inter, bias_slope, loa_inter, loa_slope
+
+        # !! Column order MUST match the order of arrays boot_stats expects as INPUT
+        # !! Variable order MUST match the order of floats boot_stats returns as OUTPUT
+        interval_order = ["lower", "upper"]
+        column_order = ["Reference", "difference", "residuals_abs"]
+        variable_order = [
+            "bias_parm",
+            "lloa_parm",
+            "uloa_parm",
+            "bias_intercept",
+            "bias_slope",
+            "loa_intercept",
+            "loa_slope",
+        ]
+        boot_ci = (
+            self._data.loc[
+                sleep_stats, column_order
+            ]  # Extract the relevant sleep stats and columns
+            .groupby("sleep_stat")  # Group so the bootstrapping is applied once to each sleep stat
+            # Apply the bootstrap function, where tuple(df.to_numpy().T) convert the 3 columns
+            # of the passed dataframe to a tuple of 3 1D arrays
+            .apply(lambda df: sps.bootstrap(tuple(df.to_numpy().T), get_vars, **bs_kwargs))
+            .map(lambda res: res.confidence_interval)  # Pull high/low CIs out of the results object
+            .explode()  # Break high and low CIs into separate rows
+            .to_frame("value")  # Convert to dataframe and name column
+            .assign(interval=interval_order * len(sleep_stats))  # Add a column indicating interval
+            .explode("value")  # Break low CI variables and high CI variables out of arrays
+            .assign(variable=variable_order * len(sleep_stats) * 2)  # Add column indicating variabl
+            .pivot(columns=["variable", "interval"], values="value")  # Go long to wide format
+            .sort_index(axis=1)  # Sort MultiIndex columns for cleanliness
+        )
+        # Merge with existing CI dataframe
+        self._ci["boot"] = self._ci["boot"].fillna(boot_ci)
+
+
[docs]    def get_table(self, bias_method="auto", loa_method="auto", ci_method="auto", fstrings={}):
+        """
+        Return a :py:class:`~pandas.DataFrame` with bias, loa, bias_ci, loa_ci as string equations.
+
+        Parameters
+        ----------
+        bias_method : str
+            If ``'parm'`` (i.e., parametric), bias is always represented as the mean difference
+            (observed minus reference).
+            If ``'regr'`` (i.e., regression), bias is always represented as a regression equation.
+            If ``'auto'`` (default), bias is represented as a regression equation for sleep
+            statistics where the score differences are proportionally biased and as the mean
+            difference otherwise.
+        loa_method : str
+            If ``'parm'`` (i.e., parametric), limits of agreement are always represented as
+            bias +/- 1.96 standard deviations (where 1.96 can be adjusted through the ``agreement``
+            parameter).
+            If ``'regr'`` (i.e., regression), limits of agreement are always represented as a
+            regression equation.
+            If ``'auto'`` (default), limits of agreement are represented as a regression equation
+            for sleep statistics where the score differences are proportionally biased and as
+            bias +/- 1.96 standard deviation otherwise.
+        ci_method : str
+            If ``'parm'`` (i.e., parametric), confidence intervals are always represented using a
+            standard t-distribution.
+            If ``'boot'`` (i.e., bootstrap), confidence intervals are always represented using a
+            bootstrap resampling procedure.
+            If  ``'auto'`` (default), confidence intervals are represented using a bootstrap
+            resampling procedure for sleep statistics where the distribution of score differences is
+            non-normal and using a standard t-distribution otherwise.
+        fstrings : dict
+            Optional custom strings for formatting cells.
+
+        Returns
+        -------
+        table : :py:class:`pandas.DataFrame`
+            A :py:class:`~pandas.DataFrame` of string representations for bias, limits of agreement,
+            and their confidence intervals for all sleep statistics.
+        """
+        assert isinstance(bias_method, str), "`bias_method` must be a string"
+        assert (
+            bias_method in self._bias_method_opts
+        ), f"`bias_method` must be one of {self._bias_method_opts}"
+        assert isinstance(loa_method, str), "`loa_method` must be a string"
+        assert (
+            loa_method in self._loa_method_opts
+        ), f"`loa_method` must be one of {self._loa_method_opts}"
+        assert isinstance(fstrings, dict), "`fstrings` must be a dictionary"
+        # Agreement gets adjusted when LoA is modeled
+        loa_regr_agreement = self._agreement * np.sqrt(np.pi / 2)
+        if not fstrings:
+            fstrings = {
+                "bias_parm": "{bias_parm_center:.2f}",
+                "bias_regr": "{bias_intercept_center:.2f} + {bias_slope_center:.2f}x",
+                "loa_parm": "{lloa_parm_center:.2f}, {uloa_parm_center:.2f}",
+                "loa_regr": (
+                    "Bias \u00B1 {loa_regr_agreement:.2f} "
+                    "* ({loa_intercept_center:.2f} + {loa_slope_center:.2f}x)"
+                ),
+                "bias_parm_ci": ("[{bias_parm_lower:.2f}, {bias_parm_upper:.2f}]"),
+                "bias_regr_ci": (
+                    "[{bias_intercept_lower:.2f}, {bias_intercept_upper:.2f}], "
+                    "[{bias_slope_lower:.2f}, {bias_slope_upper:.2f}]"
+                ),
+                "loa_parm_ci": (
+                    "[{lloa_parm_lower:.2f}, {lloa_parm_upper:.2f}], "
+                    "[{uloa_parm_lower:.2f}, {uloa_parm_upper:.2f}]"
+                ),
+                "loa_regr_ci": (
+                    "[{loa_intercept_lower:.2f}, {loa_intercept_upper:.2f}], "
+                    "[{loa_slope_lower:.2f}, {loa_slope_upper:.2f}]"
+                ),
+            }
+        values = self.summary(ci_method=ci_method)
+        values.columns = values.columns.map("_".join)  # Convert MultiIndex columns to Index
+        # Add a column of regr agreement so it can be used as variable
+        values["loa_regr_agreement"] = loa_regr_agreement
+
+        def format_all_str(row, fstrings_dict):
+            return {var: fstr.format(**row) for var, fstr in fstrings_dict.items()}
+
+        all_strings = values.apply(format_all_str, fstrings_dict=fstrings, axis=1).apply(pd.Series)
+        if bias_method == "auto":
+            bias_parm_idx = self.auto_methods.query("bias == 'parm'").index.tolist()
+        elif bias_method == "parm":
+            bias_parm_idx = self.sleep_statistics
+        elif bias_method == "regr":
+            bias_parm_idx = []
+        if loa_method == "auto":
+            loa_parm_idx = self.auto_methods.query("loa == 'parm'").index.tolist()
+        elif loa_method == "parm":
+            loa_parm_idx = self.sleep_statistics
+        elif loa_method == "regr":
+            loa_parm_idx = []
+        bias_regr_idx = [ss for ss in self.sleep_statistics if ss not in bias_parm_idx]
+        loa_regr_idx = [ss for ss in self.sleep_statistics if ss not in loa_parm_idx]
+        bias_parm = all_strings.loc[bias_parm_idx, ["bias_parm", "bias_parm_ci"]]
+        bias_regr = all_strings.loc[bias_regr_idx, ["bias_regr", "bias_regr_ci"]]
+        bias_parm.columns = bias_parm.columns.str.replace("_parm", "")
+        bias_regr.columns = bias_parm.columns.str.replace("_regr", "")
+        bias = pd.concat([bias_parm, bias_regr])
+        loa_parm = all_strings.loc[loa_parm_idx, ["loa_parm", "loa_parm_ci"]]
+        loa_regr = all_strings.loc[loa_regr_idx, ["loa_regr", "loa_regr_ci"]]
+        loa_parm.columns = loa_parm.columns.str.replace("_parm", "")
+        loa_regr.columns = loa_regr.columns.str.replace("_regr", "")
+        loa = pd.concat([loa_parm, loa_regr])
+        table = bias.join(loa, validate="1:1").sort_index(axis=0)
+        return table

+
+
[docs]    def summary(self, ci_method="auto"):
+        """
+        Return a :py:class:`~pandas.DataFrame` that includes all calculated metrics:
+        * Parametric bias
+        * Parametric lower and upper limits of agreement
+        * Regression intercept and slope for modeled bias
+        * Regression intercept and slope for modeled limits of agreement
+        * Lower and upper confidence intervals for all metrics
+
+        Parameters
+        ----------
+        ci_method : str
+            If ``'parm'`` (i.e., parametric), confidence intervals are always represented using a
+            standard t-distribution.
+            If ``'boot'`` (i.e., bootstrap), confidence intervals are always represented using a
+            bootstrap resampling procedure.
+            If  ``'auto'`` (default), confidence intervals are represented using a bootstrap
+            resampling procedure for sleep statistics where the distribution of score differences is
+            non-normal and using a standard t-distribution otherwise.
+
+        Returns
+        -------
+        summary : :py:class:`pandas.DataFrame`
+            A :py:class:`~pandas.DataFrame` of string representations for bias, limits of agreement,
+            and their confidence intervals for all sleep statistics.
+        """
+        assert isinstance(ci_method, str), "`ci_method` must be a string"
+        assert ci_method in self._ci_method_opts, f"`ci_method` must be in {self._ci_method_opts}"
+        # Make sure relevant sleep statistics have bootstrapped CIs, and generate them if not
+        if ci_method in ["boot", "auto"]:
+            if ci_method == "boot":
+                sleep_stats_to_boot = self.sleep_statistics
+            elif ci_method == "auto":
+                sleep_stats_to_boot = self.auto_methods.query("ci == 'boot'").index.tolist()
+            # Remove any sleep stats already bootstrapped CIs (eg if "boot" is callaed after "auto")
+            sleep_stats_booted = self._ci["boot"].dropna().index
+            sleep_stats_to_boot = [s for s in sleep_stats_to_boot if s not in sleep_stats_booted]
+            if sleep_stats_to_boot:
+                self._generate_bootstrap_ci(sleep_stats=sleep_stats_to_boot)
+        if ci_method == "auto":
+            parm_idx = self.auto_methods.query("ci == 'parm'").index.to_list()
+            boot_idx = [ss for ss in self.sleep_statistics if ss not in parm_idx]
+            parm_vals = self._ci.loc[parm_idx, "parm"]
+            boot_vals = self._ci.loc[boot_idx, "boot"]
+            ci_vals = pd.concat([parm_vals, boot_vals])
+        else:
+            ci_vals = self._ci[ci_method]
+        # Add an extra level to values columns, indicating they are the center interval
+        centr_vals = pd.concat({"center": self._vals}, names=["interval"], axis=1).swaplevel(axis=1)
+        summary = centr_vals.join(ci_vals, how="left", validate="1:1").astype(float)
+        return summary.sort_index(axis=1)

+
+
[docs]    def calibrate(self, data, bias_method="auto", adjust_all=False):
+        """
+        Calibrate a :py:class:`~pandas.DataFrame` of sleep statistics from a new scorer based on
+        observed biases in ``obs_data``/``obs_scorer``.
+
+        Parameters
+        ----------
+        data : :py:class:`pandas.DataFrame`
+            A :py:class:`pandas.DataFrame` with sleep statistics from an observed scorer.
+            Rows are unique observations and columns are unique sleep statistics.
+        bias_method : str
+            If ``'parm'``, sleep statistics are always adjusted based on parametric bias.
+            If ``'regr'``, sleep statistics are always adjusted based on regression-modeled bias.
+            If ``'auto'`` (default), bias sleep statistics are adjusted by either ``'parm'`` or
+            ``'regr'``, depending on assumption violations.
+
+            .. seealso:: :py:meth:`~yasa.SleepStatsAgreement.summary`
+
+        adjust_all: bool
+            If False (default), only adjust values for sleep statistics that showed a statistically
+            significant bias in the ``obs_data``. If True, adjust values for all sleep statistics.
+
+        Returns
+        -------
+        calibrated_data : :py:class:`pandas.DataFrame`
+            A :py:class:`~pandas.DataFrame` with calibrated sleep statistics.
+
+        .. seealso:: :py:meth:`~yasa.SleepStatsAgreement.calibrate`
+        """
+        assert isinstance(data, pd.DataFrame), "`data` must be a pandas DataFrame"
+        assert all(
+            col in self.sleep_statistics for col in data
+        ), f"all columns of `data` must be valid sleep statistics: {self.sleep_statistics}"
+        assert isinstance(bias_method, str), "`bias_method` must be a string"
+        assert (
+            bias_method in self._bias_method_opts
+        ), f"`bias_method` must be one of {self._bias_method_opts}"
+        assert isinstance(adjust_all, bool), "`adjust_all` must be True or False"
+        parm_adjusted = data + self._vals["bias_parm"]
+        regr_adjusted = data * self._vals["bias_slope"] + self._vals["bias_intercept"]
+        if bias_method == "parm":
+            calibrated_data = parm_adjusted
+        elif bias_method == "regr":
+            calibrated_data = regr_adjusted
+        elif bias_method == "auto":
+            parm_idx = self.auto_methods.query("bias == 'parm'").index.to_list()
+            regr_idx = [ss for ss in self.sleep_statistics if ss not in parm_idx]
+            calibrated_data = parm_adjusted[parm_idx].join(regr_adjusted[regr_idx]).dropna(axis=1)
+        if not adjust_all:
+            # Put the raw values back for sleep stats that don't show statistical bias
+            unbiased_sstats = self.assumptions.query("unbiased == True").index.to_list()
+            calibrated_data[unbiased_sstats] = data[unbiased_sstats]
+        return calibrated_data

+
+
[docs]    def get_calibration_func(self, sleep_stat):
+        """
+        Return a function for calibrating a specific sleep statistic, based on observed biases in
+        ``obs_data``/``obs_scorer``.
+
+        .. seealso:: :py:meth:`~yasa.SleepStatsAgreement.calibrate`
+
+        Examples
+        --------
+        >>> ssa = yasa.SleepStatsAgreement(...)
+        >>> calibrate_rem = ssa.get_calibration_func("REM")
+        >>> new_obs_rem_vals = np.array([50, 40, 30, 20])
+        >>> calibrate_rem(new_obs_rem_vals)
+        >>> calibrate_rem(new_obs_rem_vals)
+        array([50, 40, 30, 20])
+        >>> calibrate_rem(new_obs_rem_vals, bias_test=False)
+        array([42.825, 32.825, 22.825, 12.825])
+        >>> calibrate_rem(new_obs_rem_vals, bias_test=False, method="regr")
+        array([ -9.33878878,  -9.86815607, -10.39752335, -10.92689064])
+        """
+        assert isinstance(sleep_stat, str), "`sleep_stat` must be a string"
+        assert sleep_stat in self.sleep_statistics, "`sleep_stat` must be a valid sleep statistic"
+        columns = ["bias_parm", "bias_slope", "bias_intercept"]
+        parm, slope, intercept = self._vals.loc[sleep_stat, columns]
+        auto_method = self.auto_methods.at[sleep_stat, "bias"]
+        not_biased = self.assumptions.at[sleep_stat, "unbiased"]
+
+        def calibration_func(x, method="auto", adjust_all=False):
+            """Calibrate values for sleep statistic.
+
+            Parameters
+            ----------
+            x : array
+                Values to be calibrated
+            method: str
+                Method of bias calculation for calibration (``'parm'``, ``'regr'``, or ``'auto'``).
+            adjust_all : bool
+                If False, only adjust sleep stat if observed bias was statistically significant.
+
+            Returns
+            -------
+            x_calibrated : :py:class:`numpy.array`
+                An array of calibrated x values.
+            """
+            x = np.asarray(x)
+            method = auto_method if method == "auto" else method
+            if not_biased and not adjust_all:  # Return input if sleep stat is not statstclly biased
+                return x
+            elif method == "parm":
+                return x + parm
+            elif method == "regr":
+                return x * slope + intercept
+
+        return calibration_func

+

+
+    

+      
+  

+

+

+  

+    

+      Back to top
+      
+        

+        
+      
+    

+    

+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.

+      Created using Sphinx 4.4.0.

+    

+  

+

+  
+
\ No newline at end of file
diff --git a/docs/build/html/_modules/yasa/heart.html b/docs/build/html/_modules/yasa/heart.html
index 25a9297..23f0386 100644
--- a/docs/build/html/_modules/yasa/heart.html
+++ b/docs/build/html/_modules/yasa/heart.html
@@ -4,7 +4,7 @@
   
     
     
-    yasa.heart — yasa 0.6.4 documentation
+    yasa.heart — yasa 0.6.5 documentation
     
     
     
@@ -43,7 +43,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
@@ -99,6 +99,7 @@

Source code for yasa.heart

 Author: Dr Raphael Vallat <raphaelvallat@berkeley.edu>, UC Berkeley.
 Date: May 2022
 """
+
 import logging
 import numpy as np
 import pandas as pd
diff --git a/docs/build/html/_modules/yasa/hypno.html b/docs/build/html/_modules/yasa/hypno.html
index e645b7f..9f7c112 100644
--- a/docs/build/html/_modules/yasa/hypno.html
+++ b/docs/build/html/_modules/yasa/hypno.html
@@ -4,7 +4,7 @@
   
     
     
-    yasa.hypno — yasa 0.6.4 documentation
+    yasa.hypno — yasa 0.6.5 documentation
     
     
     
@@ -43,7 +43,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
@@ -96,6 +96,7 @@

Source code for yasa.hypno

 """
 Hypnogram-related functions and class.
 """
+
 import mne
 import logging
 
@@ -105,6 +106,7 @@ 
Source code for yasa.hypno
 from yasa.io import set_log_level
 from yasa.plotting import plot_hypnogram
 from yasa.sleepstats import transition_matrix
+from yasa.evaluation import EpochByEpochAgreement
 from pandas.api.types import CategoricalDtype
 
 __all__ = [
@@ -575,7 +577,7 @@ 
Source code for yasa.hypno
         Name: Stage, dtype: int16
         """
         # Return as int16 (-32768 to 32767) to reduce memory usage
-        return self.hypno.replace(self.mapping).astype(np.int16)

+        return self.hypno.cat.rename_categories(self.mapping).astype(np.int16)

 
 
[docs]    def consolidate_stages(self, new_n_stages):
         """Reduce the number of stages in a hypnogram to match actigraphy or wearables.
@@ -665,6 +667,43 @@ 
Source code for yasa.hypno
             scorer=self.scorer,
         )

 
+
[docs]    def evaluate(self, obs_hyp):
+        """Evaluate agreement between two hypnograms of the same sleep session.
+
+        For example, the reference hypnogram (i.e., ``self``) might be a manually-scored hypnogram
+        and the reference hypnogram (i.e., ``ref_hyp``) might be a hypnogram from actigraphy, a
+        wearable device, or an automated scorer (e.g., :py:meth:`yasa.SleepStaging.predict`).
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.Hypnogram`
+            Reference or ground-truth hypnogram.
+        obs_hyp : :py:class:`yasa.Hypnogram`
+            The observed or to-be-evaluated hypnogram.
+
+        Returns
+        -------
+        ebe : :py:class:`yasa.EpochByEpochAgreement`
+            See :py:class:`~yasa.EpochByEpochAgreement` documentation for more detail.
+
+        Examples
+        --------
+        >>> from yasa import simulate_hypnogram
+        >>> hyp_a = simulate_hypnogram(tib=90, scorer="AASM", seed=8)
+        >>> hyp_b = hyp_a.simulate_similar(scorer="YASA", seed=9)
+        >>> ebe = hyp_a.evaluate(hyp_b)
+        >>> ebe.get_agreement().round(3)
+        accuracy        0.550
+        balanced_acc    0.355
+        kappa           0.227
+        mcc             0.231
+        precision       0.515
+        recall          0.550
+        fbeta           0.524
+        Name: agreement, dtype: float64
+        """
+        return EpochByEpochAgreement([self], [obs_hyp])

+
 
[docs]    def find_periods(self, threshold="5min", equal_length=False):
         """Find sequences of consecutive values exceeding a certain duration in hypnogram.
 
diff --git a/docs/build/html/_modules/yasa/others.html b/docs/build/html/_modules/yasa/others.html
index 5f5b492..336ad68 100644
--- a/docs/build/html/_modules/yasa/others.html
+++ b/docs/build/html/_modules/yasa/others.html
@@ -4,7 +4,7 @@
   
     
     
-    yasa.others — yasa 0.6.4 documentation
+    yasa.others — yasa 0.6.5 documentation
     
     
     
@@ -43,7 +43,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       

 
         

@@ -96,6 +96,7 @@ 
Source code for yasa.others
 """
 This file contains several helper functions to manipulate 1D and 2D EEG data.
 """
+
 import logging
 import numpy as np
 from scipy.interpolate import interp1d
diff --git a/docs/build/html/_modules/yasa/plotting.html b/docs/build/html/_modules/yasa/plotting.html
index afb00dd..0edeed6 100644
--- a/docs/build/html/_modules/yasa/plotting.html
+++ b/docs/build/html/_modules/yasa/plotting.html
@@ -4,7 +4,7 @@
   
     
     
-    yasa.plotting — yasa 0.6.4 documentation
+    yasa.plotting — yasa 0.6.5 documentation
     
     
     
@@ -43,7 +43,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       

 
         

@@ -96,6 +96,7 @@ 
Source code for yasa.plotting
 """
 Plotting functions of YASA.
 """
+
 import mne
 import numpy as np
 import pandas as pd
@@ -108,7 +109,7 @@ 
Source code for yasa.plotting
 __all__ = ["plot_hypnogram", "plot_spectrogram", "topoplot"]
 
 
-
[docs]def plot_hypnogram(hyp, lw=1.5, highlight="REM", fill_color=None, ax=None):
+
[docs]def plot_hypnogram(hyp, sf_hypno=1 / 30, highlight="REM", fill_color=None, ax=None, **kwargs):
     """
     Plot a hypnogram.
 
@@ -116,16 +117,32 @@ 
Source code for yasa.plotting
 
     Parameters
     ----------
-    hyp : :py:class:`yasa.Hypnogram`
-        A YASA hypnogram instance.
-    lw : float
-        Linewidth.
+    hyp : :py:class:`yasa.Hypnogram` or array_like
+        A YASA hypnogram instance, or a 1D integer array where:
+
+            * -2 = Unscored
+            * -1 = Artefact / Movement
+            * 0 = Wake
+            * 1 = N1 sleep
+            * 2 = N2 sleep
+            * 3 = N3 sleep
+            * 4 = REM sleep
+    sf_hypno : float
+        The current sampling frequency of the hypnogram, in Hz, e.g.
+
+        * 1/30 = 1 value per each 30 seconds of EEG data,
+        * 1 = 1 value per second of EEG data
+
+        This has no impact if `hyp` is a :py:class:`yasa.Hypnogram`.
     highlight : str or None
         Optional stage to highlight with alternate color.
     fill_color : str or None
         Optional color to fill space above hypnogram line.
     ax : :py:class:`matplotlib.axes.Axes`
         Axis on which to draw the plot, optional.
+    **kwargs : dict
+        Keyword arguments controlling hypnogram line display (e.g., ``lw``, ``linestyle``).
+        Passed to :py:func:`matplotlib.pyplot.stairs` and py:func:`matplotlib.pyplot.hlines`.
 
     Returns
     -------
@@ -160,9 +177,13 @@ 
Source code for yasa.plotting
         >>> hyp_a.plot_hypnogram(lw=1, fill_color="whitesmoke", highlight=None, ax=axes[0])
         >>> hyp_b.plot_hypnogram(lw=1, fill_color="whitesmoke", highlight=None, ax=axes[1])
     """
-    from yasa.hypno import Hypnogram  # Avoiding circular import
+    from yasa.hypno import Hypnogram, hypno_int_to_str  # Avoiding circular imports
 
-    assert isinstance(hyp, Hypnogram), "`hypno` must be YASA Hypnogram."
+    if not isinstance(hyp, Hypnogram):
+        # Convert sampling frequency to pandas timefrequency string (e.g., "30s")
+        freq_str = pd.tseries.frequencies.to_offset(pd.Timedelta(1 / sf_hypno, "S")).freqstr
+        # Create Hypnogram instance for plotting
+        hyp = Hypnogram(hypno_int_to_str(hyp), freq=freq_str)
 
     # Work with a copy of the Hypnogram to not alter the original
     hyp = hyp.copy()
@@ -171,20 +192,25 @@ 
Source code for yasa.plotting
     old_fontsize = plt.rcParams["font.size"]
     plt.rcParams.update({"font.size": 18})
 
+    # Open the figure
+    if ax is None:
+        ax = plt.gca()
+
     ## Remap stages to be in desired y-axis order ##
     # Start with default of all allowed labels
     stage_order = hyp.labels.copy()
-    stages_present = hyp.hypno.unique()
-    # Remove Art/Uns from stage order, and place back individually at front to be higher on plot
-    art_str = stage_order.pop(stage_order.index("ART"))
-    uns_str = stage_order.pop(stage_order.index("UNS"))
-    if "ART" in stages_present:
-        stage_order.insert(0, art_str)
-    if "UNS" in stages_present:
-        stage_order.insert(0, uns_str)
+    stages_present = hyp.hypno.unique().tolist()
+    # Reverse order so WAKE is highest, and exclude ART/UNS which are always last
+    stage_order = stage_order[:-2][::-1]
+    # Add ART/UNS back above WAKE if they're present in the current hypnogram or existing axis
+    gca_ylabels = [x.get_text() for x in ax.get_yticklabels()]
+    if "ART" in stages_present or "ART" in gca_ylabels:
+        stage_order += ["ART"]
+    if "UNS" in stages_present or "UNS" in gca_ylabels:
+        stage_order += ["UNS"]
     # Put REM after WAKE if all 5 standard stages are allowed
     if hyp.n_stages == 5:
-        stage_order.insert(stage_order.index("WAKE") + 1, stage_order.pop(stage_order.index("REM")))
+        stage_order.insert(stage_order.index("WAKE") - 1, stage_order.pop(stage_order.index("REM")))
     # Reset the Hypnogram mapping so any future returns have this order
     hyp.mapping = {stage: i for i, stage in enumerate(stage_order)}
 
@@ -208,18 +234,17 @@ 
Source code for yasa.plotting
     # Make mask to draw the highlighted stage
     yvals_highlight = np.ma.masked_not_equal(yvalues, hyp.mapping.get(highlight))
 
-    # Open the figure
-    if ax is None:
-        ax = plt.gca()
-
     # Draw background filling
     if fill_color is not None:
-        bline = hyp.mapping["WAKE"]  # len(stage_order) - 1 to fill from bottom
-        ax.stairs(yvalues.clip(bline), bins, baseline=bline, color=fill_color, fill=True, lw=0)
-    # Draw main hypnogram line, highlighted stage line, and Artefact/Unscored line
-    ax.stairs(yvalues, bins, baseline=None, color="black", lw=lw)
+        bline = hyp.mapping["WAKE"]
+        ax.stairs(yvalues.clip(max=bline), bins, baseline=bline, color=fill_color, fill=True, lw=0)
+    # Draw main hypnogram line and highlighted stage line
+    line_kwargs = {"color": "black", "linewidth": 1.5, "label": hyp.scorer}
+    line_kwargs.update(kwargs)
+    ax.stairs(yvalues, bins, baseline=None, **line_kwargs)
     if not yvals_highlight.mask.all():
-        ax.hlines(yvals_highlight, xmin=bins[:-1], xmax=bins[1:], color="red", lw=lw)
+        line_kwargs.update({"color": "red", "label": None})
+        ax.hlines(yvals_highlight, xmin=bins[:-1], xmax=bins[1:], **line_kwargs)
 
     # Aesthetics
     ax.use_sticky_edges = False
@@ -228,7 +253,6 @@ 
Source code for yasa.plotting
     ax.set_yticklabels(stage_order)
     ax.set_ylabel("Stage")
     ax.set_xlabel(xlabel)
-    ax.invert_yaxis()
     ax.spines[["right", "top"]].set_visible(False)
     if hyp.start is not None:
         ax.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
@@ -402,7 +426,7 @@ 
Source code for yasa.plotting
 
     if hypno is not None:
         # Convert sampling frequency to pandas timefrequency string (e.g., "30s")
-        freq_str = pd.tseries.frequencies.to_offset(pd.Timedelta(1 / sf, "S")).freqstr
+        freq_str = pd.tseries.frequencies.to_offset(pd.Timedelta(1 / sf, "s")).freqstr
         # Create Hypnogram instance for plotting
         hyp = Hypnogram(hypno_int_to_str(hypno), freq=freq_str)
         hypnoplot_kwargs = dict(lw=1.5, fill_color=None)
diff --git a/docs/build/html/_modules/yasa/sleepstats.html b/docs/build/html/_modules/yasa/sleepstats.html
index 2de9d66..d648edc 100644
--- a/docs/build/html/_modules/yasa/sleepstats.html
+++ b/docs/build/html/_modules/yasa/sleepstats.html
@@ -4,7 +4,7 @@
   
     
     
-    yasa.sleepstats — yasa 0.6.4 documentation
+    yasa.sleepstats — yasa 0.6.5 documentation
     
     
     
@@ -43,7 +43,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       

 
         

@@ -97,6 +97,7 @@ 
Source code for yasa.sleepstats
 This file contains several helper functions to calculate sleep statistics from
 a one-dimensional sleep staging vector (hypnogram).
 """
+
 # import warnings
 import numpy as np
 import pandas as pd
diff --git a/docs/build/html/_modules/yasa/spectral.html b/docs/build/html/_modules/yasa/spectral.html
index 2369d08..1d496fb 100644
--- a/docs/build/html/_modules/yasa/spectral.html
+++ b/docs/build/html/_modules/yasa/spectral.html
@@ -4,7 +4,7 @@
   
     
     
-    yasa.spectral — yasa 0.6.4 documentation
+    yasa.spectral — yasa 0.6.5 documentation
     
     
     
@@ -43,7 +43,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       

 
         

@@ -97,12 +97,13 @@ 
Source code for yasa.spectral
 This file contains several helper functions to calculate spectral power from
 1D and 2D EEG data.
 """
+
 import mne
 import logging
 import numpy as np
 import pandas as pd
 from scipy import signal
-from scipy.integrate import simps
+from scipy.integrate import simpson
 from scipy.interpolate import RectBivariateSpline
 from .io import set_log_level
 
@@ -326,7 +327,7 @@ 
Source code for yasa.spectral
         ch_names = ["CHAN" + str(i).zfill(3) for i in range(nchan)]
     bp = np.zeros((nchan, len(bands)), dtype=np.float64)
     psd = psd[:, idx_good_freq]
-    total_power = simps(psd, dx=res)
+    total_power = simpson(psd, dx=res)
     total_power = total_power[..., np.newaxis]
 
     # Check if there are negative values in PSD
@@ -345,7 +346,7 @@ 
Source code for yasa.spectral
         b0, b1, la = band
         labels.append(la)
         idx_band = np.logical_and(freqs >= b0, freqs <= b1)
-        bp[:, i] = simps(psd[:, idx_band], dx=res)
+        bp[:, i] = simpson(psd[:, idx_band], dx=res)
 
     if relative:
         bp /= total_power
@@ -434,7 +435,7 @@ 
Source code for yasa.spectral
         logger.warning(msg)
 
     # Calculate total power
-    total_power = simps(psd, dx=res, axis=-1)
+    total_power = simpson(psd, dx=res, axis=-1)
     total_power = total_power[np.newaxis, ...]
 
     # Initialize empty array
@@ -446,7 +447,7 @@ 
Source code for yasa.spectral
         b0, b1, la = band
         labels.append(la)
         idx_band = np.logical_and(freqs >= b0, freqs <= b1)
-        bp[i] = simps(psd[..., idx_band], dx=res, axis=-1)
+        bp[i] = simpson(psd[..., idx_band], dx=res, axis=-1)
 
     if relative:
         bp /= total_power
diff --git a/docs/build/html/_modules/yasa/staging.html b/docs/build/html/_modules/yasa/staging.html
index bb5fae5..06ab869 100644
--- a/docs/build/html/_modules/yasa/staging.html
+++ b/docs/build/html/_modules/yasa/staging.html
@@ -4,7 +4,7 @@
   
     
     
-    yasa.staging — yasa 0.6.4 documentation
+    yasa.staging — yasa 0.6.5 documentation
     
     
     
@@ -43,7 +43,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       

 
         

@@ -94,6 +94,7 @@
       
   
Source code for yasa.staging
 """Automatic sleep staging of polysomnography data."""
+
 import os
 import mne
 import glob
@@ -283,7 +284,7 @@ 
Source code for yasa.staging
         ch_names = ch_names[keep_chan].tolist()
         ch_types = ch_types[keep_chan].tolist()
         # Keep only selected channels (creating a copy of Raw)
-        raw_pick = raw.copy().pick_channels(ch_names, ordered=True)
+        raw_pick = raw.copy().pick(ch_names)
 
         # Downsample if sf != 100
         assert sf > 80, "Sampling frequency must be at least 80 Hz."
@@ -296,7 +297,12 @@ 
Source code for yasa.staging
 
         # Extract duration of recording in minutes
         duration_minutes = data.shape[1] / sf / 60
-        assert duration_minutes >= 5, "At least 5 minutes of data is required."
+        if duration_minutes < 5:
+            msg = (
+                "Insufficient data. A minimum of 5 minutes of data is recommended "
+                "otherwise results may be unreliable."
+            )
+            logger.warning(msg)
 
         # Add to self
         self.sf = sf
diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js
index 55a69e0..ccfea1c 100644
--- a/docs/build/html/_static/documentation_options.js
+++ b/docs/build/html/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.6.4',
+    VERSION: '0.6.5',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/docs/build/html/api.html b/docs/build/html/api.html
index 16e1e4b..5fb84a2 100644
--- a/docs/build/html/api.html
+++ b/docs/build/html/api.html
@@ -5,7 +5,7 @@
     
     
 
-    API reference — yasa 0.6.4 documentation
+    API reference — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       

 
         

@@ -159,7 +159,7 @@ 
Hypnogram & sleep statistics
load_profusion_hypno(fname[, replace])

 
Load a Compumedics Profusion hypnogram (.xml).

 
-
plot_hypnogram(hyp[, lw, highlight, ...])

+
plot_hypnogram(hyp[, sf_hypno, highlight, ...])

 
Plot a hypnogram.

 
 
plot_spectrogram(data, sf[, hypno, win_sec, ...])

@@ -177,6 +177,12 @@ 
Hypnogram & sleep statistics
simulate_hypnogram([tib, trans_probas, ...])

 
Simulate a hypnogram based on transition probabilities.

 
+
EpochByEpochAgreement(ref_hyps, obs_hyps)

+
Evaluate agreement between two hypnograms or two collections of hypnograms.

+
+
SleepStatsAgreement(ref_data, obs_data, *[, ...])

+
Evaluate agreement between sleep statistics reported by two different scorers.

+
 
 
 
diff --git a/docs/build/html/changelog.html b/docs/build/html/changelog.html
index 4a2610a..e6b8ab1 100644
--- a/docs/build/html/changelog.html
+++ b/docs/build/html/changelog.html
@@ -5,7 +5,7 @@
     
     
 
-    What’s new — yasa 0.6.4 documentation
+    What’s new — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       

 
         

@@ -101,17 +101,18 @@
 
  • What’s new
    
 
 
    • 
 
@@ -144,14 +145,23 @@ 
    v0.7.devImportant
    
 
    The adoption of object-oriented yasa.Hypnogram usage brings along critical changes to several YASA function, for example:
    
 
 
    
 
 
    
+
    
+
    v0.6.5 (July 2024)
    
+
    Minor release with a brand new Evaluation module and several minor bugfixes
    
+
+
    
+
    
 
    
-
    v0.6.4 (January 2024)
    
+
    v0.6.4 (January 2024)
    
 
    Minor release with one bugfix and one improvement to an existing function.
    
 
      
 
    • Fix MNE plot_topomap deprecation (PR 119)
      • 
@@ -163,7 +173,7 @@ 
      v0.6.4 (January 2024)
 
      
-
      v0.6.3 (December 2022)
      
+
      v0.6.3 (December 2022)
      
 
      This is a minor release with one bugfix and one new function. Big shoutout to @remrama for his huge help on this new version!
      
 
      Bugfix
      
 
      Solved a bug in the spindles detection which resulted in no spindle being detected on some systems. See issue 107 for detail. PR 115
      
@@ -171,13 +181,13 @@ 
      v0.6.3 (December 2022)Figure 5b).
      
 
      Improvements
      
 
 
      
 
      
 
      
-
      v0.6.2 (August 2022)
      
+
      v0.6.2 (August 2022)
      
 
      ECG analysis - PR 68
      
 
        
 
      1. Added the yasa.hypno_find_periods() function to find sequences of consecutive values in hypnogram that are longer than a certain duration. This is a flexible function that can be used to detect NREM/REM periods.
        2. 
@@ -204,10 +214,10 @@ 
        v0.6.2 (August 2022)
 
        
-
        v0.6.1 (March 2022)
        
+
        v0.6.1 (March 2022)
        
 
        This release fixes a CRITICAL BUG with the spindles detection. Specifically, the yasa.spindles_detect() could return different results depending on the sampling rate of the data.
 For example, downsampling the data from 256 Hz to 128 Hz may have significantly reduced the number of detected spindles. As explained in issue 54, this bug was caused by a floating-point error
-in numpy.convolve() when calculating the soft spindle threshold. Tests seem to indicate that only certain sampling frequencies were impacted, such as 200 Hz, 256 Hz or 400 Hz. Other sampling frequencies such as 100 Hz and 500 Hz were seemingly not affected by this bug. Please double-check any results obtained with yasa.spindles_detect()!
        
+in numpy.convolve() when calculating the soft spindle threshold. Tests seem to indicate that only certain sampling frequencies were impacted, such as 200 Hz, 256 Hz or 400 Hz. Other sampling frequencies such as 100 Hz and 500 Hz were seemingly not affected by this bug. Please double-check any results obtained with yasa.spindles_detect()!
        
 
        
 
        Warning
        
 
        We recommend all users to upgrade to this new version ASAP and check any results obtained with the yasa.spindles_detect() function!
        
@@ -215,7 +225,7 @@ 
        v0.6.1 (March 2022)
 
        
-
        v0.6.0 (February 2022)
        
+
        v0.6.0 (February 2022)
        
 
        This is a MAJOR release with several API-breaking changes, new functions, bugfixes and a new section in the documentation.
        
 
        Documentation
        
 
          
@@ -254,12 +264,12 @@ 
          v0.6.0 (February 2022)
 
          
-
          v0.5.1 (August 2021)
          
+
          v0.5.1 (August 2021)
          
 
          This is a bugfix release. The latest pre-trained classifiers for yasa.SleepStaging were accidentally missing from the previous release. They have now been included in this release.
          
 
          
 
          
 
          
-
          v0.5.0 (August 2021)
          
+
          v0.5.0 (August 2021)
          
 
          This is a major release with an important bugfix for the slow-waves detection as well as API-breaking changes in the automatic sleep staging module. We recommend all users to upgrade to this version with pip install –upgrade yasa.
          
 
          Slow-waves detection
          
 
          We have fixed a critical bug in yasa.sw_detect() in which the detection could keep slow-waves with invalid duration (e.g. several tens of seconds). We have now added extra safety checks to make sure that the total duration of the slow-waves does not exceed the maximum duration allowed by the dur_neg and dur_pos parameters (default = 2.5 seconds).
          
@@ -288,10 +298,10 @@ 
          v0.5.0 (August 2021)
 
          
-
          v0.4.1 (March 2021)
          
+
          v0.4.1 (March 2021)
          
 
          New functions
          
 
            
-
          1. Added yasa.topoplot(), a wrapper around mne.viz.plot_topomap(). See 15_topoplot.ipynb
            2. 
+
          2. Added yasa.topoplot(), a wrapper around mne.viz.plot_topomap(). See 15_topoplot.ipynb
            3. 
 
          
 
          Enhancements
          
 
            
@@ -304,7 +314,7 @@ 
            v0.4.1 (March 2021)
 
            
-
            v0.4.0 (November 2020)
            
+
            v0.4.0 (November 2020)
            
 
            This is a major release with several new functions, the biggest of which is the addition of an automatic sleep staging module (yasa.SleepStaging). This means that YASA can now automatically score the sleep stages of your raw EEG data. The classifier was trained and validated on more than 3000 nights from the National Sleep Research Resource (NSRR) website.
            
 
            Briefly, the algorithm works by calculating a set of features for each 30-sec epochs from a central EEG channel (required), as well as an EOG channel (optional) and an EMG channel (optional). For best performance, users can also specify the age and the sex of the participants. Pre-trained classifiers are already included in YASA. The automatic sleep staging algorithm requires the LightGBM and antropy package.
            
 
            Other changes
            
@@ -322,7 +332,7 @@ 
            v0.4.0 (November 2020)
 
            
-
            v0.3.0 (May 2020)
            
+
            v0.3.0 (May 2020)
            
 
            This is a major release with several API-breaking changes in the spindles, slow-waves and REMs detection.
            
 
            First, the yasa.spindles_detect_multi() and yasa.sw_detect_multi() have been removed. Instead, the yasa.spindles_detect() and yasa.sw_detect() functions can now handle both single and multi-channel data.
            
 
            Second, I was getting some feedback that it was difficult to get summary statistics from the detection dataframe. For instance, how can you get the average duration of the detected spindles, per channel and/or per stage? Similarly, how can you get the slow-waves count and density per stage and channel? To address these issues, I’ve now modified the output of the yasa.spindles_detect(), yasa.sw_detect() and yasa.rem_detect() functions, which is now a class (= object) and not a simple Pandas DataFrame. The advantage is that the new output allows you to quickly get the raw data or summary statistics grouped by channel and/or sleep stage using the .summary() method.
            
@@ -350,14 +360,14 @@ 
            v0.3.0 (May 2020)
 
          1. The coupling argument has been removed from the yasa.spindles_detect() function. Instead, slow-oscillations / sigma coupling can only be calculated from the slow-waves detection, which is 1) the most standard way, 2) better because PAC assumptions require a strong oscillatory component in the lower frequency range (slow-oscillations). This also avoids unecessary confusion between spindles-derived coupling and slow-waves-derived coupling. For more details, refer to the Jupyter notebooks.
            2. 
-
          2. Downsampling of data in detection functions has been removed. In other words, YASA will no longer downsample the data to 100 / 128 Hz before applying the events detection. If the detection is too slow, we recommend that you manually downsample your data before applying the detection. See for example mne.filter.resample().
            3. 
+
          3. Downsampling of data in detection functions has been removed. In other words, YASA will no longer downsample the data to 100 / 128 Hz before applying the events detection. If the detection is too slow, we recommend that you manually downsample your data before applying the detection. See for example mne.filter.resample().
            4. 
 
          4. yasa.trimbothstd() can now work with multi-dimensional arrays. The trimmed standard deviation will always be calculated on the last axis of the array.
            5. 
 
          5. Filtering and Hilbert transform are now applied at once on all channels (instead of looping across individual channels) in the yasa.spindles_detect() and yasa.sw_detect() functions. This should lead to some improvements in computation time.
            6. 
 
          
 
          
 
          
 
          
-
          Older versions
          
+
          Older versions
          
 
          
 
 v0.2.0 (April 2020)
          
@@ -369,7 +379,7 @@ 
          Older versionsBugfixes
          
 
            
 
          1. Sleep efficiency in the yasa.sleep_statistics() is now calculated using time in bed (TIB) as the denominator instead of sleep period time (SPT), in agreement with the AASM guidelines. The old way of computing the efficiency (TST / SPT) has now been renamed Sleep Maintenance Efficiency (SME).
            2. 
-
          2. The yasa.sliding_window() now always return an array of shape (n_epochs, …, n_samples), i.e. the epochs are now always the first dimension of the epoched array. This is consistent with MNE default shape of mne.Epochs objects.
            3. 
+
          3. The yasa.sliding_window() now always return an array of shape (n_epochs, …, n_samples), i.e. the epochs are now always the first dimension of the epoched array. This is consistent with MNE default shape of mne.Epochs objects.
            4. 
 
          
 
          New functions
          
 
            
@@ -387,14 +397,14 @@ 
            Older versions
            Added the coupling and freq_sp keyword-arguments to the yasa.sw_detect() function. If coupling=True, the function will return the phase of the slow-waves (in radians) at the most prominent peak of sigma-filtered band (PhaseAtSigmaPeak), as well as the normalized mean vector length (ndPAC).
            
 
          1. Added an section in the 06_sw_detection.ipynb notebooks on how to use relative amplitude thresholds (e.g. z-scores or percentiles) instead of absolute thresholds in slow-waves detection.
            2. 
 
          2. The upper frequency band for yasa.sw_detect() has been changed from freq_sw=(0.3, 3.5) to freq_sw=(0.3, 2) Hz to comply with AASM guidelines.
            3. 
-
          3. Stage is no longer taken into account when finding outliers with sklearn.ensemble.IsolationForest in yasa.spindles_detect().
            4. 
+
          4. Stage is no longer taken into account when finding outliers with sklearn.ensemble.IsolationForest in yasa.spindles_detect().
            5. 
 
          5. To be consistent with yasa.spindles_detect(), automatic outlier removal now requires at least 50 (instead of 100) detected events in yasa.sw_detect() and yasa.rem_detect().
            6. 
 
          6. Added the verbose parameter to all detection functions.
            7. 
 
          7. Added -2 to the default hypnogram format to denote unscored data.
            8. 
 
          
 
          Dependencies
          
 
            
-
          1. Removed deprecated behavior argument to avoid warning when calling sklearn.ensemble.IsolationForest.
            2. 
+
          2. Removed deprecated behavior argument to avoid warning when calling sklearn.ensemble.IsolationForest.
            3. 
 
          3. Added TensorPAC and pyRiemann to dependencies.
            4. 
 
          4. Updated dependencies version for MNE and scikit-learn.
            5. 
 
          
diff --git a/docs/build/html/contributing.html b/docs/build/html/contributing.html
index 99f7d4c..e6e6fd4 100644
--- a/docs/build/html/contributing.html
+++ b/docs/build/html/contributing.html
@@ -5,7 +5,7 @@
     
     
 
-    Contribute to YASA — yasa 0.6.4 documentation
+    Contribute to YASA — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
          
 
         
          
diff --git a/docs/build/html/faq.html b/docs/build/html/faq.html
index 36dc595..df00b97 100644
--- a/docs/build/html/faq.html
+++ b/docs/build/html/faq.html
@@ -5,7 +5,7 @@
     
     
 
-    FAQ — yasa 0.6.4 documentation
+    FAQ — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
          
 
         
          
@@ -114,7 +114,7 @@ 
          
 # Apply a bandpass filter from 0.1 to 40 Hz
 raw.filter(0.1, 40)
 # Select a subset of EEG channels
-raw.pick_channels(['C4-A1', 'C3-A2'])
+raw.pick(['C4-A1', 'C3-A2'])
 
    
 
    
       
    
diff --git a/docs/build/html/generated/yasa.EpochByEpochAgreement.html b/docs/build/html/generated/yasa.EpochByEpochAgreement.html
new file mode 100644
index 0000000..2addd22
--- /dev/null
+++ b/docs/build/html/generated/yasa.EpochByEpochAgreement.html
@@ -0,0 +1,620 @@
+
+
+
+  
+    
+    
+
+    yasa.EpochByEpochAgreement — yasa 0.6.5 documentation
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+
+
+
+
+
+
+
+
+
+  
+
+  
+
+
    
+  
    
+    
    
+      
+  
    
+
    yasa.EpochByEpochAgreement
    
+
    
+
    
+class yasa.EpochByEpochAgreement(ref_hyps, obs_hyps)[source]
    
+
    Evaluate agreement between two hypnograms or two collections of hypnograms.
    
+
    Evaluation includes averaged agreement scores, one-vs-rest agreement scores, agreement scores
+summarized across all sleep and summarized by sleep stage, and various plotting options to
+visualize the two hypnograms simultaneously. See examples for more detail.
    
+
    
+
    New in version 0.7.0.
    
+
    
+
    
+
    Parameters
    
+
    
+
    ref_hypsiterable of yasa.Hypnogram
    A collection of reference hypnograms (i.e., those considered ground-truth).
    
+
    Each yasa.Hypnogram in ref_hyps must have the same
+scorer.
    
+
    If a dict, key values are use to generate unique sleep session IDs. If any other
+iterable (e.g., list or tuple), then unique sleep session IDs are automatically
+generated.
    
+
    
+
    obs_hypsiterable of yasa.Hypnogram
    A collection of observed hypnograms (i.e., those to be evaluated).
    
+
    Each yasa.Hypnogram in obs_hyps must have the same
+scorer, and this scorer must be different than the scorer of
+hypnograms in ref_hyps.
    
+
    If a dict, key values must match those of ref_hyps.
    
+
    
+
    .. important::
    It is assumed that the order of hypnograms are the same in ref_hyps and obs_hyps.
+For example, the third hypnogram in ref_hyps and obs_hyps must come from the same
+sleep session, and they must only differ in that they have different scorers.
    
+
    
+
    .. seealso:: For comparing just two hypnograms, use :py:meth:`yasa.Hynogram.evaluate`.
    
+
    
+
    
+
    
+
    Notes
    
+
    Many steps here are influenced by guidelines proposed in Menghini et al., 2021 [Menghini2021].
+See https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html
    
+
    References
    
+
    
+
    Menghini2021
    
+
    Menghini, L., Cellini, N., Goldstone, A., Baker, F. C., & de Zambotti, M.
+(2021). A standardized framework for testing the performance of sleep-tracking
+technology: step-by-step guidelines and open-source code. SLEEP, 44(2),
+zsaa170. https://doi.org/10.1093/sleep/zsaa170
    
+
    
+
    
+
    Examples
    
+
    >>> import yasa
+>>> ref_hyps = [yasa.simulate_hypnogram(tib=600, scorer="Human", seed=i) for i in range(10)]
+>>> obs_hyps = [h.simulate_similar(scorer="YASA", seed=i) for i, h in enumerate(ref_hyps)]
+>>> ebe = yasa.EpochByEpochAgreement(ref_hyps, obs_hyps)
+>>> agr = ebe.get_agreement()
+>>> agr.head(5).round(2)
+          accuracy  balanced_acc  kappa   mcc  precision  recall     f1
+sleep_id
+1             0.31          0.26   0.07  0.07       0.31    0.31   0.31
+2             0.33          0.33   0.14  0.14       0.35    0.33   0.34
+3             0.35          0.24   0.06  0.06       0.35    0.35   0.35
+4             0.22          0.21   0.01  0.01       0.21    0.22   0.21
+5             0.21          0.17  -0.06 -0.06       0.20    0.21   0.21
+
    
+
    
+
    >>> ebe.get_agreement_bystage().head(12).round(3)
+                fbeta  precision  recall  support
+stage sleep_id
+WAKE  1         0.391      0.371   0.413    189.0
+      2         0.299      0.276   0.326    184.0
+      3         0.234      0.204   0.275    255.0
+      4         0.268      0.285   0.252    321.0
+      5         0.228      0.230   0.227    181.0
+      6         0.407      0.384   0.433    284.0
+      7         0.362      0.296   0.467    287.0
+      8         0.298      0.519   0.209    263.0
+      9         0.210      0.191   0.233    313.0
+      10        0.369      0.420   0.329    362.0
+N1    1         0.185      0.185   0.185    124.0
+      2         0.121      0.131   0.112    160.0
+
    
+
    
+
    >>> ebe.get_confusion_matrix(sleep_id=1)
+YASA   WAKE  N1   N2  N3  REM
+Human
+WAKE     78  24   50   3   34
+N1       23  23   43  15   20
+N2       60  58  183  43  139
+N3       30  10   50   5   32
+REM      19   9  121  50   78
+
    
+
    
+
    >>> import matplotlib.pyplot as plt
+>>> fig, ax = plt.subplots(figsize=(6, 3), constrained_layout=True)
+>>> ebe.plot_hypnograms(sleep_id=10)
+
    
+
    
+
    >>> fig, ax = plt.subplots(figsize=(6, 3))
+>>> ebe.plot_hypnograms(
+>>>     sleep_id=8, ax=ax, obs_kwargs={"color": "red", "lw": 2, "ls": "dotted"}
+>>> )
+>>> plt.tight_layout()
+
    
+
    
+
    >>> session = 8
+>>> fig, ax = plt.subplots(figsize=(6.5, 2.5), constrained_layout=True)
+>>> style_a = dict(alpha=1, lw=2.5, ls="solid", color="gainsboro", label="Michel")
+>>> style_b = dict(alpha=1, lw=2.5, ls="solid", color="cornflowerblue", label="Jouvet")
+>>> legend_style = dict(
+>>>     title="Scorer", frameon=False, ncol=2, loc="lower center", bbox_to_anchor=(0.5, 0.9)
+>>> )
+>>> ax = ebe.plot_hypnograms(
+>>>     sleep_id=session, ref_kwargs=style_a, obs_kwargs=style_b, legend=legend_style, ax=ax
+>>> )
+>>> acc = ebe.get_agreement().multiply(100).at[session, "accuracy"]
+>>> ax.text(
+>>>     0.01, 1, f"Accuracy = {acc:.0f}%", ha="left", va="bottom", transform=ax.transAxes
+>>> )
+
    
+
    
+
    When comparing only 2 hypnograms, use the evaluate() method:
    
+
    >>> hypno_a = yasa.simulate_hypnogram(tib=90, scorer="RaterA", seed=8)
+>>> hypno_b = hypno_a.simulate_similar(scorer="RaterB", seed=9)
+>>> ebe = hypno_a.evaluate(hypno_b)
+>>> ebe.get_confusion_matrix()
+RaterB  WAKE  N1  N2  N3
+RaterA
+WAKE      71   2  20   8
+N1         1   0   9   0
+N2        12   4  25   0
+N3        24   0   1   3
+
    
+
    
+
    
+
    
+__init__(ref_hyps, obs_hyps)[source]
    
+
    
+
+
    Methods
    
+
++++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    



    __init__(ref_hyps, obs_hyps)
    get_agreement([sample_weight, scorers])
    Return a pandas.DataFrame of weighted (i.e., averaged) agreement scores.
    get_agreement_bystage([beta])
    Return a pandas.DataFrame of unweighted (i.e., one-vs-rest) agreement scores.
    get_confusion_matrix([sleep_id, agg_func])
    Return a ref_hyp/``obs_hyp``confusion matrix from either a single session or all sessions concatenated together.
    get_sleep_stats()
    Return a pandas.DataFrame of sleep statistics for each hypnogram derived from both reference and observed scorers.
    multi_scorer(df, scorers)
    Compute multiple agreement scores from a 2-column dataframe (an optional 3rd column may contain sample weights).
    plot_hypnograms([sleep_id, legend, ax, ...])
    Plot the two hypnograms of one session overlapping on the same axis.
    summary([by_stage])
    Return group-level agreement scores.
    
+
    Attributes
    
+
++++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    



    data
    A pandas.DataFrame including all hypnograms.
    n_sleeps
    The number of unique sleep sessions.
    obs_scorer
    The name of the observed scorer.
    ref_scorer
    The name of the reference scorer.
    
+
    
+
    
+get_agreement(sample_weight=None, scorers=None)[source]
    
+
    Return a pandas.DataFrame of weighted (i.e., averaged) agreement scores.
    
+
    
+
    Parameters
    
+
    
+
    selfEpochByEvaluation
    A EpochByEvaluation instance.
    
+
    
+
    sample_weightNone or pandas.Series
    Sample weights passed to underlying sklearn.metrics functions where possible.
+If a pandas.Series, the index must match exactly that of
+data.
    
+
    
+
    scorersNone, list, or dictionary
    The scorers to be used for evaluating agreement. If None (default), default scorers are
+used. If a list, the list must contain strings that represent metrics from the sklearn
+metrics module (e.g., accuracy, precision). If more customization is desired, a
+dictionary can be passed with scorer names (str) as keys and custom functions as values.
+The custom functions should take 3 positional arguments (true values, predicted values,
+and sample weights).
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    agreementpandas.DataFrame
    A DataFrame with agreement metrics as columns and sessions as rows.
    
+
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+get_agreement_bystage(beta=1.0)[source]
    
+
    Return a pandas.DataFrame of unweighted (i.e., one-vs-rest) agreement scores.
    
+
    
+
    Parameters
    
+
    
+
    selfEpochByEvaluation
    A EpochByEvaluation instance.
    
+
    
+
    betafloat
    See sklearn.metrics.precision_recall_fscore_support().
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    agreementpandas.DataFrame
    A DataFrame with agreement metrics as columns and a
+MultiIndex with session and sleep stage as rows.
    
+
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+get_confusion_matrix(sleep_id=None, agg_func=None, **kwargs)[source]
    
+
    Return a ref_hyp/``obs_hyp``confusion matrix from either a single session or all
+sessions concatenated together.
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.EpochByEpochAgreement
    A yasa.EpochByEpochAgreement instance.
    
+
    
+
    sleep_idNone or a valid sleep ID
    If None (default), cross-tabulation is derived from the entire group dataset.
+If a valid sleep ID, cross-tabulation is derived using only the reference and observed
+scored hypnograms from that sleep session.
    
+
    
+
    agg_funcNone or str
    If None (default), group results returns a DataFrame complete with
+all individual session results. If not None, group results returns a
+DataFrame aggregated across sessions where agg_func is passed as
+func parameter in pandas.DataFrame.groupby.agg(). For example, set
+agg_func="sum" to get a single confusion matrix across all epochs that does not take
+session into account.
    
+
    
+
    **kwargskey, value pairs
    Additional keyword arguments are passed to sklearn.metrics.confusion_matrix().
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    conf_matrpandas.DataFrame
    A confusion matrix with stages from the reference scorer as indices and stages from the
+test scorer as columns.
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    >>> import yasa
+>>> ref_hyps = [yasa.simulate_hypnogram(tib=90, scorer="Rater1", seed=i) for i in range(3)]
+>>> obs_hyps = [h.simulate_similar(scorer="Rater2", seed=i) for i, h in enumerate(ref_hyps)]
+>>> ebe = yasa.EpochByEpochAgreement(ref_hyps, obs_hyps)
+>>> ebe.get_confusion_matrix(sleep_id=2)
+Rater2  WAKE  N1  N2  N3  REM
+Rater1
+WAKE       1   2  23   0    0
+N1         0   9  13   0    0
+N2         0   6  71   0    0
+N3         0  13  42   0    0
+REM        0   0   0   0    0
+
    
+
    
+
    >>> ebe.get_confusion_matrix()
+Rater2           WAKE  N1  N2  N3  REM
+sleep_id Rater1
+1        WAKE      30   0   3   0   35
+         N1         3   2   7   0    0
+         N2        21  12   7   0    4
+         N3         0   0   0   0    0
+         REM        2   8  29   0   17
+2        WAKE       1   2  23   0    0
+         N1         0   9  13   0    0
+         N2         0   6  71   0    0
+         N3         0  13  42   0    0
+         REM        0   0   0   0    0
+3        WAKE      16   0   7  19   19
+         N1         0   7   2   0    5
+         N2         0  10  12   7    5
+         N3         0   0  16  11    0
+         REM        0  15  11  18    0
+
    
+
    
+
    >>> ebe.get_confusion_matrix(agg_func="sum")
+Rater2  WAKE  N1  N2  N3  REM
+Rater1
+WAKE      47   2  33  19   54
+N1         3  18  22   0    5
+N2        21  28  90   7    9
+N3         0  13  58  11    0
+REM        2  23  40  18   17
+
    
+
    
+
    
+
+
    
+
    
+get_sleep_stats()[source]
    
+
    Return a pandas.DataFrame of sleep statistics for each hypnogram derived from
+both reference and observed scorers.
    
+
    
+
    See also
    
+
    yasa.Hypnogram.sleep_statistics()
    
+
    
+
    
+
    See also
    
+
    yasa.SleepStatsAgreement
    
+
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.EpochByEpochAgreement
    A yasa.EpochByEpochAgreement instance.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    sstatspandas.DataFrame
    A DataFrame with sleep statistics as columns and two rows for each
+individual (one for reference scorer and another for test scorer).
    
+
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+static multi_scorer(df, scorers)[source]
    
+
    Compute multiple agreement scores from a 2-column dataframe (an optional 3rd column may
+contain sample weights).
    
+
    This function offers convenience when calculating multiple agreement scores using
+pandas.DataFrame.groupby.apply(). Scikit-learn doesn’t include a function that
+returns multiple scores, and the GroupBy implementation of apply in pandas does not
+accept multiple functions.
    
+
    
+
    Parameters
    
+
    
+
    dfpandas.DataFrame
    A DataFrame with 2 columns and length of n_samples.
+The first column contains reference values and second column contains observed values.
+If a third column, it must contain sample weights to be passed to underlying
+sklearn.metrics functions as sample_weight where applicable.
    
+
    
+
    scorersdictionary
    The scorers to be used for evaluating agreement. A dictionary with scorer names (str) as
+keys and functions as values.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    scoresdict
    A dictionary with scorer names (str) as keys and scores (float) as values.
    
+
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+plot_hypnograms(sleep_id=None, legend=True, ax=None, ref_kwargs={}, obs_kwargs={})[source]
    
+
    Plot the two hypnograms of one session overlapping on the same axis.
    
+
    
+
    See also
    
+
    yasa.plot_hypnogram()
    
+
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.EpochByEpochAgreement
    A yasa.EpochByEpochAgreement instance.
    
+
    
+
    sleep_ida valid sleep ID or None
    The sleep session to plot. If multiple sessions are included in the
+EpochByEpochAgreement instance, a sleep_id must be provided. If
+only one session is present, None (default) will plot the two hypnograms of the
+only session.
    
+
    
+
    legendbool or dict
    If True (default) or a dictionary, a legend is added. If a dictionary, all key/value
+pairs are passed as keyword arguments to the matplotlib.pyplot.legend() call.
    
+
    
+
    axmatplotlib.axes.Axes or None
    Axis on which to draw the plot, optional.
    
+
    
+
    ref_kwargsdict
    Keyword arguments passed to yasa.plot_hypnogram() when plotting the reference
+hypnogram.
    
+
    
+
    obs_kwargsdict
    Keyword arguments passed to yasa.plot_hypnogram() when plotting the observed
+hypnogram.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    axmatplotlib.axes.Axes
    Matplotlib Axes
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    >>> from yasa import simulate_hypnogram
+>>> hyp = simulate_hypnogram(scorer="Anthony", seed=19)
+>>> ax = hyp.evaluate(hyp.simulate_similar(scorer="Alan", seed=68)).plot_hypnograms()
+
    
+
    
+
    
+../_images/yasa-EpochByEpochAgreement-4.png
+
    
+
    
+
+
    
+
    
+summary(by_stage=False, **kwargs)[source]
    
+
    Return group-level agreement scores.
    
+
    Default aggregated measures are
    
+
    
+
    Parameters
    
+
    
+
    selfEpochByEpochAgreement
    A EpochByEpochAgreement instance.
    
+
    
+
    by_stagebool
    If False (default), summary will include agreement scores derived from
+average-based metrics. If True, returned summary DataFrame
+will include agreement scores for each sleep stage, derived from one-vs-rest metrics.
    
+
    
+
    **kwargskey, value pairs
    Additional keyword arguments are passed to pandas.DataFrame.groupby.agg().
+This can be used to customize the descriptive statistics returned.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    summarypandas.DataFrame
    A pandas.DataFrame summarizing agreement scores across the entire dataset
+with descriptive statistics.
    
+
    >>> ebe = yasa.EpochByEpochAgreement(...)
+>>> agreement = ebe.get_agreement()
+>>> ebe.summary()
+
    
+
    
+
    This will give a DataFrame where each row is an agreement metric and
+each column is a descriptive statistic (e.g., mean, standard deviation).
+To control the descriptive statistics included as columns:
    
+
    >>> ebe.summary(func=["count", "mean", "sem"])
+
    
+
    
+
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+property data
    
+
    A pandas.DataFrame including all hypnograms.
    
+
    
+
+
    
+
    
+property n_sleeps
    
+
    The number of unique sleep sessions.
    
+
    
+
+
    
+
    
+property obs_scorer
    
+
    The name of the observed scorer.
    
+
    
+
+
    
+
    
+property ref_scorer
    
+
    The name of the reference scorer.
    
+
    
+
+
    
+
+
    
+
+
+    
    
+      
+  
    
+
    
+
    
+  
    
+    
    
+      Back to top
+      
+        
    
+        
+      
+    
    
+    
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
+    
    
+  
    
+
    
+  
+
\ No newline at end of file
diff --git a/docs/build/html/generated/yasa.Hypnogram.html b/docs/build/html/generated/yasa.Hypnogram.html
index 86a444b..69b7604 100644
--- a/docs/build/html/generated/yasa.Hypnogram.html
+++ b/docs/build/html/generated/yasa.Hypnogram.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.Hypnogram — yasa 0.6.4 documentation
+    yasa.Hypnogram — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -133,16 +133,16 @@ 
    yasa.Hypnogramhttps://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases
    
-
    freq will be passed to the pandas.date_range() function to create the time
+
    freq will be passed to the pandas.date_range() function to create the time
 index of the hypnogram.
    
 
 
    startstr or datetime
    An optional string indicating the starting datetime of the hypnogram
 (e.g. “2022-12-15 22:30:00”). If start is specified and valid, the index of the
-hypnogram will be a pandas.DatetimeIndex. Otherwise it will be a
-pandas.RangeIndex, indicating the epoch number.
    
+hypnogram will be a pandas.DatetimeIndex. Otherwise it will be a
+pandas.RangeIndex, indicating the epoch number.
    
 
    
 
    scorerstr
    An optional string indicating the scorer name. If specified, this will be set as the name
-of the pandas.Series, otherwise the name will be set to “Stage”.
    
+of the pandas.Series, otherwise the name will be set to “Stage”.
    
 
    
 
 
@@ -160,7 +160,7 @@ 
    yasa.HypnogramSee the online documentation for more details.
 
    
 
    
-
    We can access the actual values, which are stored as a pandas.Series, with:
    
+
    We can access the actual values, which are stored as a pandas.Series, with:
    
 
    >>> hyp.hypno
 Epoch
 0      WAKE
@@ -239,7 +239,7 @@ 
    yasa.Hypnogramyasa.simulate_hypnogram() to generate a plausible 5-stages hypnogram with a
 30-seconds resolution. A random seed is specified to ensure that we get reproducible results.
 Lastly, we set an actual start time to the hypnogram. As a result, the index of the resulting
-hypnogram is a pandas.DatetimeIndex.
    
+hypnogram is a pandas.DatetimeIndex.
    
 
    >>> from yasa import simulate_hypnogram
 >>> hyp = simulate_hypnogram(
 ...     tib=500, n_stages=5, start="2022-12-15 22:30:00", scorer="S1", seed=42)
@@ -319,25 +319,28 @@ 
    yasa.Hypnogram
    copy()
    
 
    Return a new copy of the current Hypnogram.
    
 
-
    find_periods([threshold, equal_length])
    
+
    evaluate(obs_hyp)
    
+
    Evaluate agreement between two hypnograms of the same sleep session.
    
+
+
    find_periods([threshold, equal_length])
    
 
    Find sequences of consecutive values exceeding a certain duration in hypnogram.
    
 
-
    plot_hypnogram(**kwargs)
    
+
    plot_hypnogram(**kwargs)
    
 
    Plot the hypnogram.
    
 
-
    simulate_similar(**kwargs)
    
+
    simulate_similar(**kwargs)
    
 
    Simulate a new hypnogram based on properties of the current hypnogram.
    
 
-
    sleep_statistics()
    
+
    sleep_statistics()
    
 
    Compute standard sleep statistics from an hypnogram.
    
 
-
    transition_matrix()
    
+
    transition_matrix()
    
 
    Create a state-transition matrix from an hypnogram.
    
 
-
    upsample(new_freq, **kwargs)
    
+
    upsample(new_freq, **kwargs)
    
 
    Upsample hypnogram to a higher frequency.
    
 
-
    upsample_to_data(data[, sf, verbose])
    
+
    upsample_to_data(data[, sf, verbose])
    
 
    Upsample an hypnogram to a given sampling frequency and fit the resulting hypnogram to corresponding EEG data, such that the hypnogram and EEG data have the exact same number of samples.
    
 
 
@@ -356,7 +359,7 @@ 
    yasa.Hypnogram
    hypno
    
-
    The hypnogram values, stored in a pandas.Series.
    
+
    The hypnogram values, stored in a pandas.Series.
    
 
 
    labels
    
 
    The allowed stage labels.
    
@@ -383,7 +386,7 @@ 
    yasa.Hypnogram
    timedelta
    
-
    A pandas.TimedeltaIndex vector with the accumulated time difference of each epoch compared to the first epoch.
    
+
    A pandas.TimedeltaIndex vector with the accumulated time difference of each epoch compared to the first epoch.
    
 
 
 
@@ -397,7 +400,7 @@ 
    yasa.Hypnogram
 
    Returns
    
 
    
-
    annotationspandas.DataFrame
    A dataframe containing epoch onset, duration, stage, etc.
    
+
    annotationspandas.DataFrame
    A dataframe containing epoch onset, duration, stage, etc.
    
 
    
 
    
 
    
@@ -527,6 +530,47 @@ 
    yasa.Hypnogram
+
    
+evaluate(obs_hyp)[source]
    
+
    Evaluate agreement between two hypnograms of the same sleep session.
    
+
    For example, the reference hypnogram (i.e., self) might be a manually-scored hypnogram
+and the reference hypnogram (i.e., ref_hyp) might be a hypnogram from actigraphy, a
+wearable device, or an automated scorer (e.g., yasa.SleepStaging.predict()).
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.Hypnogram
    Reference or ground-truth hypnogram.
    
+
    
+
    obs_hypyasa.Hypnogram
    The observed or to-be-evaluated hypnogram.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    ebeyasa.EpochByEpochAgreement
    See EpochByEpochAgreement documentation for more detail.
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    >>> from yasa import simulate_hypnogram
+>>> hyp_a = simulate_hypnogram(tib=90, scorer="AASM", seed=8)
+>>> hyp_b = hyp_a.simulate_similar(scorer="YASA", seed=9)
+>>> ebe = hyp_a.evaluate(hyp_b)
+>>> ebe.get_agreement().round(3)
+accuracy        0.550
+balanced_acc    0.355
+kappa           0.227
+mcc             0.231
+precision       0.515
+recall          0.550
+fbeta           0.524
+Name: agreement, dtype: float64
+
    
+
    
+
    
+
 
    
 
    
 find_periods(threshold='5min', equal_length=False)[source]
    
@@ -548,7 +592,7 @@ 
    yasa.HypnogramReturns
 
    
-
    periodspandas.DataFrame
    Output dataframe
    
+
    periodspandas.DataFrame
    Output dataframe
    
 
      
 
    • values : The value in hypno of the current period
      • 
 
    • start : The index of the start of the period in hypno
      • 
@@ -630,7 +674,7 @@ 
      yasa.HypnogramReturns
 
      
-
      axmatplotlib.axes.Axes
      Matplotlib Axes
      
+
      axmatplotlib.axes.Axes
      Matplotlib Axes
      
 
      
 
      
 
      
@@ -812,10 +856,10 @@ 
      yasa.HypnogramReturns
 
      
-
      countspandas.DataFrame
      Counts transition matrix (number of transitions from stage A to stage B). The
+
      countspandas.DataFrame
      Counts transition matrix (number of transitions from stage A to stage B). The
 pre-transition states are the rows and the post-transition states are the columns.
      
 
      
-
      probspandas.DataFrame
      Conditional probability transition matrix, i.e. given that current state is A, what is
+
      probspandas.DataFrame
      Conditional probability transition matrix, i.e. given that current state is A, what is
 the probability that the next state is B. probs is a right stochastic matrix, i.e. each row sums to 1.
      
 
      
 
      
@@ -919,11 +963,11 @@ 
      yasa.Hypnogramarray_like or mne.io.BaseRaw
      1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which case data
+
      dataarray_like or mne.io.BaseRaw
      1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which case data
 and sf_data will be automatically extracted.
      
 
      
 
      sf_datafloat
      The sampling frequency of data, in Hz (e.g. 100 Hz, 256 Hz, …).
-Can be omitted if data is a mne.io.BaseRaw.
      
+Can be omitted if data is a mne.io.BaseRaw.
      
 
      
 
      verbosebool or str
      Verbose level. Default (False) will only print warning and error messages. The logging
 levels are ‘debug’, ‘info’, ‘warning’, ‘error’, and ‘critical’. For most users the
@@ -965,9 +1009,9 @@ 
      yasa.Hypnogram
 
      
 property hypno
      
-
      The hypnogram values, stored in a pandas.Series. To reduce memory usage, the
-stages are stored as categories (pandas.Categorical). hypno
-inherits all the methods of a standard pandas.Series, e.g. .describe(),
+
      The hypnogram values, stored in a pandas.Series. To reduce memory usage, the
+stages are stored as categories (pandas.Categorical). hypno
+inherits all the methods of a standard pandas.Series, e.g. .describe(),
 .unique(), .to_csv(), and more.
      
 
      
 
      Note
      
@@ -1028,7 +1072,7 @@ 
      yasa.Hypnogram
 
      
 property timedelta
      
-
      A pandas.TimedeltaIndex vector with the accumulated time difference of each
+
      A pandas.TimedeltaIndex vector with the accumulated time difference of each
 epoch compared to the first epoch.
      
 
    
 
diff --git a/docs/build/html/generated/yasa.REMResults.html b/docs/build/html/generated/yasa.REMResults.html
index 2a3b5c1..578453d 100644
--- a/docs/build/html/generated/yasa.REMResults.html
+++ b/docs/build/html/generated/yasa.REMResults.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.REMResults — yasa 0.6.4 documentation
+    yasa.REMResults — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -102,7 +102,7 @@ 
    yasa.REMResults
 
    Attributes
    
 
    
-
    _eventspandas.DataFrame
    Output detection dataframe
    
+
    _eventspandas.DataFrame
    Output detection dataframe
    
 
    
 
    _dataarray_like
    EOG data of shape (n_chan, n_samples), where the two channels are
 LOC and ROC.
    
@@ -185,7 +185,7 @@ 
    yasa.REMResultstuple
    Optional filtering to apply to data. For instance, filt=(1, 30)
 will apply a 1 to 30 Hz bandpass filter, and filt=(None, 40)
 will apply a 40 Hz lowpass filter. Filtering is done using default
-parameters in the mne.filter.filter_data() function.
    
+parameters in the mne.filter.filter_data() function.
    
 
    
 
    maskarray_like or None
    Custom boolean mask. Only the detected events for which mask is True will be
 included. Default is None, i.e. no masking (all events are included).
    
@@ -194,7 +194,7 @@ 
    yasa.REMResultsReturns
 
    
-
    df_syncpandas.DataFrame
    Ouput long-format dataframe:
    
+
    df_syncpandas.DataFrame
    Ouput long-format dataframe:
    
 
    'Event' : Event number
 'Time' : Timing of the events (in seconds)
 'Amplitude' : Raw or filtered data for event
@@ -225,14 +225,14 @@ 
    yasa.REMResultstuple
    Optional filtering to apply to data. For instance, filt=(1, 30)
 will apply a 1 to 30 Hz bandpass filter, and filt=(None, 40)
 will apply a 40 Hz lowpass filter. Filtering is done using default
-parameters in the mne.filter.filter_data() function.
    
+parameters in the mne.filter.filter_data() function.
    
 
    
 
    maskarray_like or None
    Custom boolean mask. Only the detected events for which mask is True will be
 included. Default is None, i.e. no masking (all events are included).
    
 
    
 
    figsizetuple
    Figure size in inches.
    
 
    
-
    **kwargsdict
    Optional argument that are passed to seaborn.lineplot().
    
+
    **kwargsdict
    Optional argument that are passed to seaborn.lineplot().
    
 
    
 
    
 
    
diff --git a/docs/build/html/generated/yasa.SWResults.html b/docs/build/html/generated/yasa.SWResults.html
index 352f95c..ec2168d 100644
--- a/docs/build/html/generated/yasa.SWResults.html
+++ b/docs/build/html/generated/yasa.SWResults.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.SWResults — yasa 0.6.4 documentation
+    yasa.SWResults — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -102,7 +102,7 @@ 
    yasa.SWResults
 
    Attributes
    
 
    
-
    _eventspandas.DataFrame
    Output detection dataframe
    
+
    _eventspandas.DataFrame
    Output detection dataframe
    
 
    
 
    _dataarray_like
    EEG data of shape (n_chan, n_samples).
    
 
    
@@ -190,7 +190,7 @@ 
    yasa.SWResultsReturns
 
    
-
    scorespandas.DataFrame
    A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).
    
+
    scorespandas.DataFrame
    A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).
    
 
    
 
    
 
    
@@ -246,7 +246,7 @@ 
    yasa.SWResultsReturns
 
    
-
    scorespandas.DataFrame
    A Pandas DataFrame with the channel names as index, and the following columns
    
+
    scorespandas.DataFrame
    A Pandas DataFrame with the channel names as index, and the following columns
    
 
    
 
         
    
@@ -108,7 +108,7 @@ 
    yasa.SleepStaging
 
    Parameters
    
 
    
-
    rawmne.io.BaseRaw
    An MNE Raw instance.
    
+
    rawmne.io.BaseRaw
    An MNE Raw instance.
    
 
    
 
    eeg_namestr
    The name of the EEG channel in raw. Preferentially a central
 electrode referenced either to the mastoids (C4-M1, C3-M2) or to the
@@ -281,7 +281,7 @@ 
    yasa.SleepStaging
 
    Returns
    
 
    
-
    featurespandas.DataFrame
    Feature dataframe.
    
+
    featurespandas.DataFrame
    Feature dataframe.
    
 
    
 
    
 
    
@@ -321,7 +321,7 @@ 
    yasa.SleepStagingReturns
 
    
-
    prednumpy.ndarray
    The predicted sleep stages.
    
+
    prednumpy.ndarray
    The predicted sleep stages.
    
 
    
 
    
 
    
@@ -345,7 +345,7 @@ 
    yasa.SleepStagingReturns
 
    
-
    probapandas.DataFrame
    The predicted probability for each sleep stage for each 30-sec epoch of data.
    
+
    probapandas.DataFrame
    The predicted probability for each sleep stage for each 30-sec epoch of data.
    
 
    
 
    
 
    
diff --git a/docs/build/html/generated/yasa.SleepStatsAgreement.html b/docs/build/html/generated/yasa.SleepStatsAgreement.html
new file mode 100644
index 0000000..3582c29
--- /dev/null
+++ b/docs/build/html/generated/yasa.SleepStatsAgreement.html
@@ -0,0 +1,524 @@
+
+
+
+  
+    
+    
+
+    yasa.SleepStatsAgreement — yasa 0.6.5 documentation
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+
+
+
+
+
+
+
+
+
+  
+
+  
+
+
    
+  
    
+    
    
+      
+  
    
+
    yasa.SleepStatsAgreement
    
+
    
+
    
+class yasa.SleepStatsAgreement(ref_data, obs_data, *, ref_scorer='Reference', obs_scorer='Observed', agreement=1.96, confidence=0.95, alpha=0.05, verbose=True, bootstrap_kwargs={})[source]
    
+
    Evaluate agreement between sleep statistics reported by two different scorers.
+Evaluation includes bias and limits of agreement (as well as both their confidence intervals),
+various plotting options, and calibration functions for correcting biased values from the
+observed scorer.
    
+
    Features include:
+* Get summary calculations of bias, limits of agreement, and their confidence intervals.
+* Test statistical assumptions of bias, limits of agreement, and their confidence intervals,
+and apply corrective procedures when the assumptions are not met.
+* Get bias and limits of agreement in a string-formatted table.
+* Calibrate new data to correct for biases in observed data.
+* Return individual calibration functions.
+* Visualize discrepancies for outlier inspection.
+* Visualize Bland-Altman plots.
    
+
    
+
    See also
    
+
    yasa.Hypnogram.sleep_statistics()
    
+
    
+
    
+
    New in version 0.7.0.
    
+
    
+
    
+
    Parameters
    
+
    
+
    ref_datapandas.DataFrame
    A pandas.DataFrame with sleep statistics from the reference scorer.
+Rows are unique observations and columns are unique sleep statistics.
    
+
    
+
    obs_datapandas.DataFrame
    A pandas.DataFrame with sleep statistics from the observed scorer.
+Rows are unique observations and columns are unique sleep statistics.
+Shape, index, and columns must be identical to ref_data.
    
+
    
+
    ref_scorerstr
    Name of the reference scorer.
    
+
    
+
    obs_scorerstr
    Name of the observed scorer.
    
+
    
+
    agreementfloat
    Multiple of the standard deviation to plot agreement limits. The default is 1.96, which
+corresponds to a 95% confidence interval if the differences are normally distributed.
    
+
    
+
    Note
    
+
    agreement gets adjusted for regression-modeled limits of agreement.
    
+
    
+
    
+
    confidencefloat
    The percentage confidence interval for the confidence intervals that are applied to bias and
+limits of agreement. The same confidence interval percentage is applied to both standard and
+bootstrapped confidence intervals.
    
+
    
+
    alphafloat
    Alpha cutoff used for all assumption tests.
    
+
    
+
    verbosebool or str
    Verbose level. Default (False) will only print warning and error messages. The logging
+levels are ‘debug’, ‘info’, ‘warning’, ‘error’, and ‘critical’. For most users the choice is
+between ‘info’ (or verbose=True) and warning (verbose=False).
    
+
    
+
    
+
    
+
    
+
    Notes
    
+
    Sleep statistics that are identical between scorers are removed from analysis.
    
+
    Many steps here are influenced by guidelines proposed in Menghini et al., 2021 [Menghini2021].
+See https://sri-human-sleep.github.io/sleep-trackers-performance/AnalyticalPipeline_v1.0.0.html
    
+
    References
    
+
    
+
    Menghini2021
    
+
    Menghini, L., Cellini, N., Goldstone, A., Baker, F. C., & de Zambotti, M.
+(2021). A standardized framework for testing the performance of sleep-tracking
+technology: step-by-step guidelines and open-source code. SLEEP, 44(2),
+zsaa170. https://doi.org/10.1093/sleep/zsaa170
    
+
    
+
    
+
    Examples
    
+
    >>> import pandas as pd
+>>> import yasa
+>>>
+>>> # Generate fake reference and observed datasets with similar sleep statistics
+>>> ref_scorer = "Henri"
+>>> obs_scorer = "Piéron"
+>>> ref_hyps = [yasa.simulate_hypnogram(tib=600, scorer=ref_scorer, seed=i) for i in range(20)]
+>>> obs_hyps = [h.simulate_similar(scorer=obs_scorer, seed=i) for i, h in enumerate(ref_hyps)]
+>>> # Generate sleep statistics from hypnograms using EpochByEpochAgreement
+>>> eea = yasa.EpochByEpochAgreement(ref_hyps, obs_hyps)
+>>> sstats = eea.get_sleep_stats()
+>>> ref_sstats = sstats.loc[ref_scorer]
+>>> obs_sstats = sstats.loc[obs_scorer]
+>>> # Create SleepStatsAgreement instance
+>>> ssa = yasa.SleepStatsAgreement(ref_sstats, obs_sstats)
+>>> ssa.summary().round(1).head(3)
+variable   bias_intercept             ...   uloa_parm
+interval           center lower upper ...      center lower upper
+sleep_stat                            ...
+%N1                  -5.4 -13.9   3.2 ...         6.1   3.7   8.5
+%N2                 -27.3 -49.1  -5.6 ...        12.4   7.2  17.6
+%N3                  -9.1 -23.8   5.5 ...        20.4  12.6  28.3
+
    
+
    
+
    >>> ssa.get_table().head(3)[["bias", "loa"]]
+                      bias                            loa
+sleep_stat
+%N1                   0.25  Bias ± 2.46 * (-0.00 + 1.00x)
+%N2         -27.34 + 0.55x   Bias ± 2.46 * (0.00 + 1.00x)
+%N3                   1.38   Bias ± 2.46 * (0.00 + 1.00x)
+
    
+
    
+
    >>> ssa.assumptions.head(3)
+            unbiased  normal  constant_bias  homoscedastic
+sleep_stat
+%N1             True    True           True          False
+%N2             True    True          False          False
+%N3             True    True           True          False
+
    
+
    
+
    >>> ssa.auto_methods.head(3)
+            bias   loa    ci
+sleep_stat
+%N1         parm  regr  parm
+%N2         regr  regr  parm
+%N3         parm  regr  parm
+
    
+
    
+
    >>> ssa.get_table(bias_method="parm", loa_method="parm").head(3)[["bias", "loa"]]
+             bias            loa
+sleep_stat
+%N1          0.25    -5.55, 6.06
+%N2         -0.23  -12.87, 12.40
+%N3          1.38  -17.67, 20.44
+
    
+
    
+
    >>> new_hyps = [h.simulate_similar(scorer="Kelly", seed=i) for i, h in enumerate(obs_hyps)]
+>>> new_sstats = pd.Series(new_hyps).map(lambda h: h.sleep_statistics()).apply(pd.Series)
+>>> new_sstats = new_sstats[["N1", "TST", "WASO"]]
+>>> new_sstats.round(1).head(5)
+     N1    TST   WASO
+0  42.5  439.5  147.5
+1  84.0  550.0   38.5
+2  53.5  489.0  103.0
+3  57.0  469.5  120.0
+4  71.0  531.0   69.0
+
    
+
    
+
    >>> new_stats_calibrated = ssa.calibrate_stats(new_sstats, bias_method="auto")
+>>> new_stats_calibrated.round(1).head(5)
+     N1    TST   WASO
+0  42.9  433.8  150.0
+1  84.4  544.2   41.0
+2  53.9  483.2  105.5
+3  57.4  463.8  122.5
+4  71.4  525.2   71.5
+
    
+
    
+
    >>> import matplotlib.pyplot as plt
+>>> ax = ssa.plot_discrepancies_heatmap()
+>>> ax.set_title("Sleep statistic discrepancies")
+>>> plt.tight_layout()
+
    
+
    
+
    >>> ssa.plot_blandaltman()
+
    
+
    
+
    
+
    
+__init__(ref_data, obs_data, *, ref_scorer='Reference', obs_scorer='Observed', agreement=1.96, confidence=0.95, alpha=0.05, verbose=True, bootstrap_kwargs={})[source]
    
+
    
+
+
    Methods
    
+
++++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    



    __init__(ref_data, obs_data, *[, ...])
    calibrate(data[, bias_method, adjust_all])
    Calibrate a DataFrame of sleep statistics from a new scorer based on observed biases in obs_data/obs_scorer.
    get_calibration_func(sleep_stat)
    Return a function for calibrating a specific sleep statistic, based on observed biases in obs_data/obs_scorer.
    get_table([bias_method, loa_method, ...])
    Return a DataFrame with bias, loa, bias_ci, loa_ci as string equations.
    summary([ci_method])
    Return a DataFrame that includes all calculated metrics: * Parametric bias * Parametric lower and upper limits of agreement * Regression intercept and slope for modeled bias * Regression intercept and slope for modeled limits of agreement * Lower and upper confidence intervals for all metrics
    
+
    Attributes
    
+
++++
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
    



    assumptions
    A pandas.DataFrame containing boolean values indicating the pass/fail status of all statistical tests performed to test assumptions.
    auto_methods
    A pandas.DataFrame containing the methods applied when 'auto' is selected.
    data
    A long-format pandas.DataFrame containing all raw sleep statistics from ref_data and obs_data.
    n_sessions
    The number of sessions.
    obs_scorer
    The name of the observed scorer.
    ref_scorer
    The name of the reference scorer.
    sleep_statistics
    Return a list of all sleep statistics included in the agreement analyses.
    
+
    
+
    
+calibrate(data, bias_method='auto', adjust_all=False)[source]
    
+
    Calibrate a DataFrame of sleep statistics from a new scorer based on
+observed biases in obs_data/obs_scorer.
    
+
    
+
    Parameters
    
+
    
+
    datapandas.DataFrame
    A pandas.DataFrame with sleep statistics from an observed scorer.
+Rows are unique observations and columns are unique sleep statistics.
    
+
    
+
    bias_methodstr
    If 'parm', sleep statistics are always adjusted based on parametric bias.
+If 'regr', sleep statistics are always adjusted based on regression-modeled bias.
+If 'auto' (default), bias sleep statistics are adjusted by either 'parm' or
+'regr', depending on assumption violations.
    
+
    
+
    See also
    
+
    summary()
    
+
    
+
    
+
    adjust_all: bool
    If False (default), only adjust values for sleep statistics that showed a statistically
+significant bias in the obs_data. If True, adjust values for all sleep statistics.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    calibrated_datapandas.DataFrame
    A DataFrame with calibrated sleep statistics.
    
+
    
+
    
+
    
+
    See also
    
+
    calibrate()
+..
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+get_calibration_func(sleep_stat)[source]
    
+
    Return a function for calibrating a specific sleep statistic, based on observed biases in
+obs_data/obs_scorer.
    
+
    
+
    See also
    
+
    calibrate()
    
+
    
+
    Examples
    
+
    >>> ssa = yasa.SleepStatsAgreement(...)
+>>> calibrate_rem = ssa.get_calibration_func("REM")
+>>> new_obs_rem_vals = np.array([50, 40, 30, 20])
+>>> calibrate_rem(new_obs_rem_vals)
+>>> calibrate_rem(new_obs_rem_vals)
+array([50, 40, 30, 20])
+>>> calibrate_rem(new_obs_rem_vals, bias_test=False)
+array([42.825, 32.825, 22.825, 12.825])
+>>> calibrate_rem(new_obs_rem_vals, bias_test=False, method="regr")
+array([ -9.33878878,  -9.86815607, -10.39752335, -10.92689064])
+
    
+
    
+
    
+
+
    
+
    
+get_table(bias_method='auto', loa_method='auto', ci_method='auto', fstrings={})[source]
    
+
    Return a DataFrame with bias, loa, bias_ci, loa_ci as string equations.
    
+
    
+
    Parameters
    
+
    
+
    bias_methodstr
    If 'parm' (i.e., parametric), bias is always represented as the mean difference
+(observed minus reference).
+If 'regr' (i.e., regression), bias is always represented as a regression equation.
+If 'auto' (default), bias is represented as a regression equation for sleep
+statistics where the score differences are proportionally biased and as the mean
+difference otherwise.
    
+
    
+
    loa_methodstr
    If 'parm' (i.e., parametric), limits of agreement are always represented as
+bias +/- 1.96 standard deviations (where 1.96 can be adjusted through the agreement
+parameter).
+If 'regr' (i.e., regression), limits of agreement are always represented as a
+regression equation.
+If 'auto' (default), limits of agreement are represented as a regression equation
+for sleep statistics where the score differences are proportionally biased and as
+bias +/- 1.96 standard deviation otherwise.
    
+
    
+
    ci_methodstr
    If 'parm' (i.e., parametric), confidence intervals are always represented using a
+standard t-distribution.
+If 'boot' (i.e., bootstrap), confidence intervals are always represented using a
+bootstrap resampling procedure.
+If  'auto' (default), confidence intervals are represented using a bootstrap
+resampling procedure for sleep statistics where the distribution of score differences is
+non-normal and using a standard t-distribution otherwise.
    
+
    
+
    fstringsdict
    Optional custom strings for formatting cells.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    tablepandas.DataFrame
    A DataFrame of string representations for bias, limits of agreement,
+and their confidence intervals for all sleep statistics.
    
+
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+summary(ci_method='auto')[source]
    
+
    Return a DataFrame that includes all calculated metrics:
+* Parametric bias
+* Parametric lower and upper limits of agreement
+* Regression intercept and slope for modeled bias
+* Regression intercept and slope for modeled limits of agreement
+* Lower and upper confidence intervals for all metrics
    
+
    
+
    Parameters
    
+
    
+
    ci_methodstr
    If 'parm' (i.e., parametric), confidence intervals are always represented using a
+standard t-distribution.
+If 'boot' (i.e., bootstrap), confidence intervals are always represented using a
+bootstrap resampling procedure.
+If  'auto' (default), confidence intervals are represented using a bootstrap
+resampling procedure for sleep statistics where the distribution of score differences is
+non-normal and using a standard t-distribution otherwise.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    summarypandas.DataFrame
    A DataFrame of string representations for bias, limits of agreement,
+and their confidence intervals for all sleep statistics.
    
+
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+property assumptions
    
+
    A pandas.DataFrame containing boolean values indicating the pass/fail status
+of all statistical tests performed to test assumptions.
    
+
    
+
+
    
+
    
+property auto_methods
    
+
    A pandas.DataFrame containing the methods applied when 'auto' is selected.
    
+
    
+
+
    
+
    
+property data
    
+
    A long-format pandas.DataFrame containing all raw sleep statistics from
+ref_data and obs_data.
    
+
    
+
+
    
+
    
+property n_sessions
    
+
    The number of sessions.
    
+
    
+
+
    
+
    
+property obs_scorer
    
+
    The name of the observed scorer.
    
+
    
+
+
    
+
    
+property ref_scorer
    
+
    The name of the reference scorer.
    
+
    
+
+
    
+
    
+property sleep_statistics
    
+
    Return a list of all sleep statistics included in the agreement analyses.
    
+
    
+
+
    
+
+
    
+
+
+    
    
+      
+  
    
+
    
+
    
+  
    
+    
    
+      Back to top
+      
+        
    
+        
+      
+    
    
+    
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
+    
    
+  
    
+
    
+  
+
\ No newline at end of file
diff --git a/docs/build/html/generated/yasa.SpindlesResults.html b/docs/build/html/generated/yasa.SpindlesResults.html
index 55ab5dd..af08e68 100644
--- a/docs/build/html/generated/yasa.SpindlesResults.html
+++ b/docs/build/html/generated/yasa.SpindlesResults.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.SpindlesResults — yasa 0.6.4 documentation
+    yasa.SpindlesResults — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -102,7 +102,7 @@ 
    yasa.SpindlesResults
 
    Attributes
    
 
    
-
    _eventspandas.DataFrame
    Output detection dataframe
    
+
    _eventspandas.DataFrame
    Output detection dataframe
    
 
    
 
    _dataarray_like
    Original EEG data of shape (n_chan, n_samples).
    
 
    
@@ -187,7 +187,7 @@ 
    yasa.SpindlesResults
 
    Returns
    
 
    
-
    scorespandas.DataFrame
    A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).
    
+
    scorespandas.DataFrame
    A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).
    
 
    
 
    
 
    
@@ -243,7 +243,7 @@ 
    yasa.SpindlesResults
 
    Returns
    
 
    
-
    scorespandas.DataFrame
    A Pandas DataFrame with the channel names as index, and the following columns
    
+
    scorespandas.DataFrame
    A Pandas DataFrame with the channel names as index, and the following columns
    
 
    
 
         
    
@@ -108,7 +108,7 @@ 
    yasa.art_detect
    
 
    dataarray_like
    Single or multi-channel EEG data.
 Unit must be uV and shape (n_chan, n_samples).
-Can also be a mne.io.BaseRaw, in which case data
+Can also be a mne.io.BaseRaw, in which case data
 and sf will be automatically extracted,
 and data will also be automatically converted from Volts (MNE)
 to micro-Volts (YASA).
    
@@ -119,7 +119,7 @@ 
    yasa.art_detectfloat
    Sampling frequency of the data in Hz.
-Can be omitted if data is a mne.io.BaseRaw object.
    
+Can be omitted if data is a mne.io.BaseRaw object.
    
 
    
 
    windowfloat
    The window length (= resolution) for artifact rejection, in seconds.
 Default to 5 seconds. Shorter windows (e.g. 1 or 2-seconds) will
@@ -217,7 +217,7 @@ 
    yasa.art_detectmethod='covar' is essentially a wrapper around the
-pyriemann.clustering.Potato class implemented in the
+pyriemann.clustering.Potato class implemented in the
 pyRiemann package.
    
 
    The main idea of this approach is to estimate a reference covariance
 matrix \(\bar{C}\) (for each sleep stage separately if hypno is
diff --git a/docs/build/html/generated/yasa.bandpower.html b/docs/build/html/generated/yasa.bandpower.html
index ac4bd27..1bdb66c 100644
--- a/docs/build/html/generated/yasa.bandpower.html
+++ b/docs/build/html/generated/yasa.bandpower.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.bandpower — yasa 0.6.4 documentation
+    yasa.bandpower — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -105,15 +105,15 @@ 
    yasa.bandpower
 
    Parameters
    
 
    
-
    datanp.array_like or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which case data,
+
    datanp.array_like or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which case data,
 sf, and ch_names will be automatically extracted, and data will also be
 converted from Volts (MNE default) to micro-Volts (YASA).
    
 
    
 
    sffloat
    The sampling frequency of data AND the hypnogram. Can be omitted if data is a
-mne.io.BaseRaw.
    
+mne.io.BaseRaw.
    
 
    
 
    ch_nameslist
    List of channel names, e.g. [‘Cz’, ‘F3’, ‘F4’, …]. If None, channels will be labelled
-[‘CHAN000’, ‘CHAN001’, …]. Can be omitted if data is a mne.io.BaseRaw.
    
+[‘CHAN000’, ‘CHAN001’, …]. Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    hypnoarray_like
    Sleep stage (hypnogram). If the hypnogram is loaded, the bandpower will be extracted for
 each sleep stage defined in include.
    
@@ -146,18 +146,18 @@ 
    yasa.bandpowerband.
    
 
    
 
    bandpassboolean
    If True, apply a standard FIR bandpass filter using the minimum and maximum frequencies
-in bands. Fore more details, refer to mne.filter.filter_data().
    
+in bands. Fore more details, refer to mne.filter.filter_data().
    
 
    
 
    bandslist of tuples
    List of frequency bands of interests. Each tuple must contain the lower and upper
 frequencies, as well as the band name (e.g. (0.5, 4, ‘Delta’)).
    
 
    
-
    kwargs_welchdict
    Optional keywords arguments that are passed to the scipy.signal.welch() function.
    
+
    kwargs_welchdict
    Optional keywords arguments that are passed to the scipy.signal.welch() function.
    
 
    
 
    
 
    
 
    Returns
    
 
    
-
    bandpowerspandas.DataFrame
    Bandpower dataframe, in which each row is a channel and each column a spectral band.
    
+
    bandpowerspandas.DataFrame
    Bandpower dataframe, in which each row is a channel and each column a spectral band.
    
 
    
 
    
 
    
diff --git a/docs/build/html/generated/yasa.bandpower_from_psd.html b/docs/build/html/generated/yasa.bandpower_from_psd.html
index ce238b1..86b5a0f 100644
--- a/docs/build/html/generated/yasa.bandpower_from_psd.html
+++ b/docs/build/html/generated/yasa.bandpower_from_psd.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.bandpower_from_psd — yasa 0.6.4 documentation
+    yasa.bandpower_from_psd — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -107,7 +107,7 @@ 
    yasa.bandpower_from_psdParameters
 
    
 
    psdarray_like
    Power spectral density of data, in uV^2/Hz. Must be of shape (n_channels, n_freqs).
-See scipy.signal.welch() for more details.
    
+See scipy.signal.welch() for more details.
    
 
    
 
    freqsarray_like
    Array of frequencies.
    
 
    
@@ -124,7 +124,7 @@ 
    yasa.bandpower_from_psd
 
    Returns
    
 
    
-
    bandpowerspandas.DataFrame
    Bandpower dataframe, in which each row is a channel and each column a spectral band.
    
+
    bandpowerspandas.DataFrame
    Bandpower dataframe, in which each row is a channel and each column a spectral band.
    
 
    
 
    
 
    
diff --git a/docs/build/html/generated/yasa.bandpower_from_psd_ndarray.html b/docs/build/html/generated/yasa.bandpower_from_psd_ndarray.html
index 2f42433..827cda8 100644
--- a/docs/build/html/generated/yasa.bandpower_from_psd_ndarray.html
+++ b/docs/build/html/generated/yasa.bandpower_from_psd_ndarray.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.bandpower_from_psd_ndarray — yasa 0.6.4 documentation
+    yasa.bandpower_from_psd_ndarray — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -108,10 +108,10 @@ 
    yasa.bandpower_from_psd_ndarray
 
    Parameters
    
 
    
-
    psdnumpy.ndarray
    Power spectral density of data, in uV^2/Hz. Must be a N-D array of shape (…, n_freqs).
-See scipy.signal.welch() for more details.
    
+
    psdnumpy.ndarray
    Power spectral density of data, in uV^2/Hz. Must be a N-D array of shape (…, n_freqs).
+See scipy.signal.welch() for more details.
    
 
    
-
    freqsnumpy.ndarray
    Array of frequencies. Must be a 1-D array of shape (n_freqs,)
    
+
    freqsnumpy.ndarray
    Array of frequencies. Must be a 1-D array of shape (n_freqs,)
    
 
    
 
    bandslist of tuples
    List of frequency bands of interests. Each tuple must contain the lower and upper
 frequencies, as well as the band name (e.g. (0.5, 4, ‘Delta’)).
    
@@ -123,7 +123,7 @@ 
    yasa.bandpower_from_psd_ndarrayReturns
 
    
-
    bandpowersnumpy.ndarray
    Bandpower array of shape (n_bands, …).
    
+
    bandpowersnumpy.ndarray
    Bandpower array of shape (n_bands, …).
    
 
    
 
    
 
    
diff --git a/docs/build/html/generated/yasa.compare_detection.html b/docs/build/html/generated/yasa.compare_detection.html
index 2c10b11..3340a7e 100644
--- a/docs/build/html/generated/yasa.compare_detection.html
+++ b/docs/build/html/generated/yasa.compare_detection.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.compare_detection — yasa 0.6.4 documentation
+    yasa.compare_detection — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
diff --git a/docs/build/html/generated/yasa.hrv_stage.html b/docs/build/html/generated/yasa.hrv_stage.html
index 3106a9b..5201f65 100644
--- a/docs/build/html/generated/yasa.hrv_stage.html
+++ b/docs/build/html/generated/yasa.hrv_stage.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.hrv_stage — yasa 0.6.4 documentation
+    yasa.hrv_stage — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -107,7 +107,7 @@ 
    yasa.hrv_stage
 
    Parameters
    
 
    
-
    datanumpy.ndarray
    Single-channel ECG data. Must be a 1D NumPy array.
    
+
    datanumpy.ndarray
    Single-channel ECG data. Must be a 1D NumPy array.
    
 
    
 
    sffloat
    The sampling frequency of the data.
    
 
    
@@ -157,7 +157,7 @@ 
    yasa.hrv_stageReturns
 
    
-
    epochspandas.DataFrame
    Output dataframe with values (= the sleep stages defined in include) and
+
    epochspandas.DataFrame
    Output dataframe with values (= the sleep stages defined in include) and
 epoch number as index. The columns are
    
 
      
 
    • start : The start of the epoch, in seconds from the beginning of the recording.
      • 
diff --git a/docs/build/html/generated/yasa.hypno_find_periods.html b/docs/build/html/generated/yasa.hypno_find_periods.html
index 2e6b44f..2b28705 100644
--- a/docs/build/html/generated/yasa.hypno_find_periods.html
+++ b/docs/build/html/generated/yasa.hypno_find_periods.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.hypno_find_periods — yasa 0.6.4 documentation
+    yasa.hypno_find_periods — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -123,7 +123,7 @@ 
    yasa.hypno_find_periods
 
    Returns
    
 
    
-
    periodspandas.DataFrame
    Output dataframe
    
+
    periodspandas.DataFrame
    Output dataframe
    
 
      
 
    • values : The value in hypno of the current period
      • 
 
    • start : The index of the start of the period in hypno
      • 
diff --git a/docs/build/html/generated/yasa.hypno_int_to_str.html b/docs/build/html/generated/yasa.hypno_int_to_str.html
index ab65fa1..096a99a 100644
--- a/docs/build/html/generated/yasa.hypno_int_to_str.html
+++ b/docs/build/html/generated/yasa.hypno_int_to_str.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.hypno_int_to_str — yasa 0.6.4 documentation
+    yasa.hypno_int_to_str — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -109,7 +109,7 @@ 
    yasa.hypno_int_to_strhypnoarray_like
    The sleep staging (hypnogram) 1D array.
    
 
    
 
    mapping_dictdict
    The mapping dictionnary. Note that this function is essentially a wrapper around
-pandas.Series.map().
    
+pandas.Series.map().
    
 
    
 
 
diff --git a/docs/build/html/generated/yasa.hypno_str_to_int.html b/docs/build/html/generated/yasa.hypno_str_to_int.html
index da041b6..cf41cdb 100644
--- a/docs/build/html/generated/yasa.hypno_str_to_int.html
+++ b/docs/build/html/generated/yasa.hypno_str_to_int.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.hypno_str_to_int — yasa 0.6.4 documentation
+    yasa.hypno_str_to_int — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -109,7 +109,7 @@ 
    yasa.hypno_str_to_inthypnoarray_like
    The sleep staging (hypnogram) 1D array.
    
 
    
 
    mapping_dictdict
    The mapping dictionnary, in lowercase. Note that this function is essentially a wrapper
-around pandas.Series.map().
    
+around pandas.Series.map().
    
 
    
 
 
diff --git a/docs/build/html/generated/yasa.hypno_upsample_to_data.html b/docs/build/html/generated/yasa.hypno_upsample_to_data.html
index f450ddc..cce8381 100644
--- a/docs/build/html/generated/yasa.hypno_upsample_to_data.html
+++ b/docs/build/html/generated/yasa.hypno_upsample_to_data.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.hypno_upsample_to_data — yasa 0.6.4 documentation
+    yasa.hypno_upsample_to_data — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -115,11 +115,11 @@ 
    yasa.hypno_upsample_to_dataarray_like or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which
+
    dataarray_like or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which
 case data and sf_data will be automatically extracted.
    
 
    
 
    sf_datafloat
    The sampling frequency of data, in Hz (e.g. 100 Hz, 256 Hz, …).
-Can be omitted if data is a mne.io.BaseRaw.
    
+Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    verbosebool or str
    Verbose level. Default (False) will only print warning and error
 messages. The logging levels are ‘debug’, ‘info’, ‘warning’, ‘error’,
diff --git a/docs/build/html/generated/yasa.hypno_upsample_to_sf.html b/docs/build/html/generated/yasa.hypno_upsample_to_sf.html
index 7c768b7..bc0b7e0 100644
--- a/docs/build/html/generated/yasa.hypno_upsample_to_sf.html
+++ b/docs/build/html/generated/yasa.hypno_upsample_to_sf.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.hypno_upsample_to_sf — yasa 0.6.4 documentation
+    yasa.hypno_upsample_to_sf — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
diff --git a/docs/build/html/generated/yasa.irasa.html b/docs/build/html/generated/yasa.irasa.html
index 944ee35..b0d91e0 100644
--- a/docs/build/html/generated/yasa.irasa.html
+++ b/docs/build/html/generated/yasa.irasa.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.irasa — yasa 0.6.4 documentation
+    yasa.irasa — yasa 0.6.5 documentation
     
     
     
@@ -45,7 +45,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -107,22 +107,22 @@ 
    yasa.irasa
 
    Parameters
    
 
    
-
    datanumpy.ndarray or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which
+
    datanumpy.ndarray or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which
 case data, sf, and ch_names will be automatically
 extracted, and data will also be converted from Volts (MNE default)
 to micro-Volts (YASA).
    
 
    
 
    sffloat
    The sampling frequency of data AND the hypnogram.
-Can be omitted if data is a mne.io.BaseRaw.
    
+Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    ch_nameslist
    List of channel names, e.g. [‘Cz’, ‘F3’, ‘F4’, …]. If None,
 channels will be labelled [‘CHAN000’, ‘CHAN001’, …].
-Can be omitted if data is a mne.io.BaseRaw.
    
+Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    bandtuple or None
    Broad band frequency range.
 Default is 1 to 30 Hz.
    
 
    
-
    hsetlist or numpy.ndarray
    Resampling factors used in IRASA calculation. Default is to use a range
+
    hsetlist or numpy.ndarray
    Resampling factors used in IRASA calculation. Default is to use a range
 of values from 1.1 to 1.9 with an increment of 0.05.
    
 
    
 
    return_fitboolean
    If True (default), fit an exponential function to the aperiodic PSD
@@ -142,7 +142,7 @@ 
    yasa.irasadict
    Optional keywords arguments that are passed to the
-scipy.signal.welch() function.
    
+scipy.signal.welch() function.
    
 
    
 
    verbosebool or str
    Verbose level. Default (False) will only print warning and error
 messages. The logging levels are ‘debug’, ‘info’, ‘warning’, ‘error’,
@@ -153,13 +153,13 @@ 
    yasa.irasaReturns
 
    
-
    freqsnumpy.ndarray
    Frequency vector.
    
+
    freqsnumpy.ndarray
    Frequency vector.
    
 
    
-
    psd_aperiodicnumpy.ndarray
    The fractal (= aperiodic) component of the PSD.
    
+
    psd_aperiodicnumpy.ndarray
    The fractal (= aperiodic) component of the PSD.
    
 
    
-
    psd_oscillatorynumpy.ndarray
    The oscillatory (= periodic) component of the PSD.
    
+
    psd_oscillatorynumpy.ndarray
    The oscillatory (= periodic) component of the PSD.
    
 
    
-
    fit_paramspandas.DataFrame (optional)
    Dataframe of fit parameters. Only if return_fit=True.
    
+
    fit_paramspandas.DataFrame (optional)
    Dataframe of fit parameters. Only if return_fit=True.
    
 
    
 
    
 
    
diff --git a/docs/build/html/generated/yasa.load_profusion_hypno.html b/docs/build/html/generated/yasa.load_profusion_hypno.html
index a484547..6de6dc2 100644
--- a/docs/build/html/generated/yasa.load_profusion_hypno.html
+++ b/docs/build/html/generated/yasa.load_profusion_hypno.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.load_profusion_hypno — yasa 0.6.4 documentation
+    yasa.load_profusion_hypno — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
diff --git a/docs/build/html/generated/yasa.moving_transform.html b/docs/build/html/generated/yasa.moving_transform.html
index 1eeb54f..7fae300 100644
--- a/docs/build/html/generated/yasa.moving_transform.html
+++ b/docs/build/html/generated/yasa.moving_transform.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.moving_transform — yasa 0.6.4 documentation
+    yasa.moving_transform — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
diff --git a/docs/build/html/generated/yasa.plot_hypnogram.html b/docs/build/html/generated/yasa.plot_hypnogram.html
index 7990d5d..b02b0c6 100644
--- a/docs/build/html/generated/yasa.plot_hypnogram.html
+++ b/docs/build/html/generated/yasa.plot_hypnogram.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.plot_hypnogram — yasa 0.6.4 documentation
+    yasa.plot_hypnogram — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -97,7 +97,7 @@
 
    yasa.plot_hypnogram
    
 
    
 
    
-yasa.plot_hypnogram(hyp, lw=1.5, highlight='REM', fill_color=None, ax=None)[source]
    
+yasa.plot_hypnogram(hyp, sf_hypno=0.03333333333333333, highlight='REM', fill_color=None, ax=None, **kwargs)[source]
 
    Plot a hypnogram.
    
 
    
 
    New in version 0.6.0.
    
@@ -105,21 +105,40 @@ 
    yasa.plot_hypnogram
 
    Parameters
    
 
    
-
    hypyasa.Hypnogram
    A YASA hypnogram instance.
    
+
    hypyasa.Hypnogram or array_like
    A YASA hypnogram instance, or a 1D integer array where:
    
+
    
+
      
+
    • -2 = Unscored
      • 
+
    • -1 = Artefact / Movement
      • 
+
    • 0 = Wake
      • 
+
    • 1 = N1 sleep
      • 
+
    • 2 = N2 sleep
      • 
+
    • 3 = N3 sleep
      • 
+
    • 4 = REM sleep
      • 
+
    
+
    
 
    
-
    lwfloat
    Linewidth.
    
+
    sf_hypnofloat
    The current sampling frequency of the hypnogram, in Hz, e.g.
    
+
      
+
    • 1/30 = 1 value per each 30 seconds of EEG data,
      • 
+
    • 1 = 1 value per second of EEG data
      • 
+
    
+
    This has no impact if hyp is a yasa.Hypnogram.
    
 
    
 
    highlightstr or None
    Optional stage to highlight with alternate color.
    
 
    
 
    fill_colorstr or None
    Optional color to fill space above hypnogram line.
    
 
    
-
    axmatplotlib.axes.Axes
    Axis on which to draw the plot, optional.
    
+
    axmatplotlib.axes.Axes
    Axis on which to draw the plot, optional.
    
+
    
+
    **kwargsdict
    Keyword arguments controlling hypnogram line display (e.g., lw, linestyle).
+Passed to matplotlib.pyplot.stairs() and py:func:matplotlib.pyplot.hlines.
    
 
    
 
    
 
    
 
    Returns
    
 
    
-
    axmatplotlib.axes.Axes
    Matplotlib Axes
    
+
    axmatplotlib.axes.Axes
    Matplotlib Axes
    
 
    
 
    
 
    
diff --git a/docs/build/html/generated/yasa.plot_spectrogram.html b/docs/build/html/generated/yasa.plot_spectrogram.html
index 387c51f..867b6c6 100644
--- a/docs/build/html/generated/yasa.plot_spectrogram.html
+++ b/docs/build/html/generated/yasa.plot_spectrogram.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.plot_spectrogram — yasa 0.6.4 documentation
+    yasa.plot_spectrogram — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -106,7 +106,7 @@ 
    yasa.plot_spectrogram
 
    Parameters
    
 
    
-
    datanumpy.ndarray
    Single-channel EEG data. Must be a 1D NumPy array.
    
+
    datanumpy.ndarray
    Single-channel EEG data. Must be a 1D NumPy array.
    
 
    
 
    sffloat
    The sampling frequency of data AND the hypnogram.
    
 
    
@@ -152,7 +152,7 @@ 
    yasa.plot_spectrogram
 
    Returns
    
 
    
-
    figmatplotlib.figure.Figure
    Matplotlib Figure
    
+
    figmatplotlib.figure.Figure
    Matplotlib Figure
    
 
    
 
    
 
    
diff --git a/docs/build/html/generated/yasa.rem_detect.html b/docs/build/html/generated/yasa.rem_detect.html
index 5bee942..ae2c751 100644
--- a/docs/build/html/generated/yasa.rem_detect.html
+++ b/docs/build/html/generated/yasa.rem_detect.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.rem_detect — yasa 0.6.4 documentation
+    yasa.rem_detect — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -113,8 +113,8 @@ 
    yasa.rem_detect
 
    Warning
    
-
    The default unit of mne.io.BaseRaw is Volts.
-Therefore, if passing data from a mne.io.BaseRaw,
+
    The default unit of mne.io.BaseRaw is Volts.
+Therefore, if passing data from a mne.io.BaseRaw,
 make sure to use units=”uV” to get the data in micro-Volts, e.g.:
    
 
    >>> data = raw.get_data(units="uV")  # Make sure that data is in uV
 
    
@@ -160,7 +160,7 @@ 
    yasa.rem_detecttuple or list
    Frequency range of REMs. Default is 0.5 to 5 Hz.
    
 
    
 
    remove_outliersboolean
    If True, YASA will automatically detect and remove outliers REMs
-using sklearn.ensemble.IsolationForest.
+using sklearn.ensemble.IsolationForest.
 YASA uses a random seed (42) to ensure reproducible results.
 Note that this step will only be applied if there are more than
 50 detected REMs in the first place. Default to False.
    
@@ -182,7 +182,7 @@ 
    yasa.rem_detect>>> rem.summary()
 
    
 
    
-
    This will give a pandas.DataFrame where each row is a
+
    This will give a pandas.DataFrame where each row is a
 detected REM and each column is a parameter (= property).
 To get the average parameters sleep stage:
    
 
    >>> rem.summary(grp_stage=True)
diff --git a/docs/build/html/generated/yasa.simulate_hypnogram.html b/docs/build/html/generated/yasa.simulate_hypnogram.html
index 9128e4e..41478c4 100644
--- a/docs/build/html/generated/yasa.simulate_hypnogram.html
+++ b/docs/build/html/generated/yasa.simulate_hypnogram.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.simulate_hypnogram — yasa 0.6.4 documentation
+    yasa.simulate_hypnogram — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -125,11 +125,11 @@ 
    yasa.simulate_hypnogramyasa.sleep_statistics()
    
 
    
 
-
    trans_probaspandas.DataFrame or None
    Transition probability matrix where each cell is a transition probability
+
    trans_probaspandas.DataFrame or None
    Transition probability matrix where each cell is a transition probability
 between sleep stages of consecutive epochs.
    
 
    trans_probas is a right stochastic matrix, i.e. each row sums to 1.
    
 
    If None (default), transition probabilities come from Metzner et al., 2021 [Metzner2021].
-If pandas.DataFrame, must have “from”-stages as indices and
+If pandas.DataFrame, must have “from”-stages as indices and
 “to”-stages as columns. Indices and columns must follow YASA string
 hypnogram convention (e.g., WAKE, N1). Unscored/Artefact stages are not allowed.
    
 
    
@@ -143,9 +143,9 @@ 
    yasa.simulate_hypnogramReturn value from yasa.transition_matrix()
    
 
    
 
    
-
    init_probaspandas.Series or None
    Probabilites of each stage to initialize random walk.
+
    init_probaspandas.Series or None
    Probabilites of each stage to initialize random walk.
 If None (default), initialize with “from”-WAKE row of trans_probas.
-If pandas.Series, indices must be stages following YASA string
+If pandas.Series, indices must be stages following YASA string
 hypnogram convention and identical to those of trans_probas.
    
 
    
 
    seedint or None
    Random seed for generating Markov sequence.
diff --git a/docs/build/html/generated/yasa.sleep_statistics.html b/docs/build/html/generated/yasa.sleep_statistics.html
index 827329f..3d75201 100644
--- a/docs/build/html/generated/yasa.sleep_statistics.html
+++ b/docs/build/html/generated/yasa.sleep_statistics.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.sleep_statistics — yasa 0.6.4 documentation
+    yasa.sleep_statistics — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
diff --git a/docs/build/html/generated/yasa.sliding_window.html b/docs/build/html/generated/yasa.sliding_window.html
index 9548467..7346380 100644
--- a/docs/build/html/generated/yasa.sliding_window.html
+++ b/docs/build/html/generated/yasa.sliding_window.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.sliding_window — yasa 0.6.4 documentation
+    yasa.sliding_window — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -132,7 +132,7 @@ 
    yasa.sliding_windowNotes
    
 
    This is a wrapper around the
-numpy.lib.stride_tricks.as_strided() function.
    
+numpy.lib.stride_tricks.as_strided() function.
    
 
    Examples
    
 
    With a 1-D array
    
 
    >>> import numpy as np
diff --git a/docs/build/html/generated/yasa.spindles_detect.html b/docs/build/html/generated/yasa.spindles_detect.html
index 8484909..8d042fb 100644
--- a/docs/build/html/generated/yasa.spindles_detect.html
+++ b/docs/build/html/generated/yasa.spindles_detect.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.spindles_detect — yasa 0.6.4 documentation
+    yasa.spindles_detect — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -103,22 +103,22 @@ 
    yasa.spindles_detectParameters
 
    
 
    dataarray_like
    Single or multi-channel data. Unit must be uV and shape (n_samples) or
-(n_chan, n_samples). Can also be a mne.io.BaseRaw,
+(n_chan, n_samples). Can also be a mne.io.BaseRaw,
 in which case data, sf, and ch_names will be automatically
 extracted, and data will also be automatically converted from
 Volts (MNE) to micro-Volts (YASA).
    
 
    
 
    sffloat
    Sampling frequency of the data in Hz.
-Can be omitted if data is a mne.io.BaseRaw.
    
+Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    Tip
    
 
    If the detection is taking too long, make sure to downsample
 your data to 100 Hz (or 128 Hz). For more details, please refer to
-mne.filter.resample().
    
+mne.filter.resample().
    
 
    
 
    
 
    ch_nameslist of str
    Channel names. Can be omitted if data is a
-mne.io.BaseRaw.
    
+mne.io.BaseRaw.
    
 
    
 
    hypnoarray_like
    Sleep stage (hypnogram). If the hypnogram is loaded, the
 detection will only be applied to the value defined in
@@ -178,7 +178,7 @@ 
    yasa.spindles_detect
 
    
 
    remove_outliersboolean
    If True, YASA will automatically detect and remove outliers spindles
-using sklearn.ensemble.IsolationForest.
+using sklearn.ensemble.IsolationForest.
 The outliers detection is performed on all the spindles
 parameters with the exception of the Start, Peak, End,
 Stage, and SOPhase columns.
@@ -203,7 +203,7 @@ 
    yasa.spindles_detect>>> sp.summary()
 
    
 
    
-
    This will give a pandas.DataFrame where each row is a
+
    This will give a pandas.DataFrame where each row is a
 detected spindle and each column is a parameter (= feature or property)
 of this spindle. To get the average spindles parameters per channel and
 sleep stage:
    
diff --git a/docs/build/html/generated/yasa.stft_power.html b/docs/build/html/generated/yasa.stft_power.html
index e40be06..71d08a7 100644
--- a/docs/build/html/generated/yasa.stft_power.html
+++ b/docs/build/html/generated/yasa.stft_power.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.stft_power — yasa 0.6.4 documentation
+    yasa.stft_power — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -129,18 +129,18 @@ 
    yasa.stft_powerReturns
 
    
-
    fnumpy.ndarray
    Frequency vector
    
+
    fnumpy.ndarray
    Frequency vector
    
 
    
-
    tnumpy.ndarray
    Time vector
    
+
    tnumpy.ndarray
    Time vector
    
 
    
-
    Sxxnumpy.ndarray
    Power in the specified frequency bins of shape (f, t)
    
+
    Sxxnumpy.ndarray
    Power in the specified frequency bins of shape (f, t)
    
 
    
 
    
 
    
 
 
    Notes
    
-
    2D Interpolation is done using scipy.interpolate.RectBivariateSpline
-which is much faster than scipy.interpolate.interp2d for a rectangular grid.
+
    2D Interpolation is done using scipy.interpolate.RectBivariateSpline
+which is much faster than scipy.interpolate.interp2d for a rectangular grid.
 The default is to use a bivariate spline with 3 degrees.
    
 
 
diff --git a/docs/build/html/generated/yasa.sw_detect.html b/docs/build/html/generated/yasa.sw_detect.html
index c467497..f84e8e0 100644
--- a/docs/build/html/generated/yasa.sw_detect.html
+++ b/docs/build/html/generated/yasa.sw_detect.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.sw_detect — yasa 0.6.4 documentation
+    yasa.sw_detect — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -103,22 +103,22 @@ 
    yasa.sw_detectParameters
 
    
 
    dataarray_like
    Single or multi-channel data. Unit must be uV and shape (n_samples) or
-(n_chan, n_samples). Can also be a mne.io.BaseRaw,
+(n_chan, n_samples). Can also be a mne.io.BaseRaw,
 in which case data, sf, and ch_names will be automatically
 extracted, and data will also be automatically converted from
 Volts (MNE) to micro-Volts (YASA).
    
 
    
 
    sffloat
    Sampling frequency of the data in Hz.
-Can be omitted if data is a mne.io.BaseRaw.
    
+Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    Tip
    
 
    If the detection is taking too long, make sure to downsample
 your data to 100 Hz (or 128 Hz). For more details, please refer to
-mne.filter.resample().
    
+mne.filter.resample().
    
 
    
 
    
 
    ch_nameslist of str
    Channel names. Can be omitted if data is a
-mne.io.BaseRaw.
    
+mne.io.BaseRaw.
    
 
    
 
    hypnoarray_like
    Sleep stage (hypnogram). If the hypnogram is loaded, the
 detection will only be applied to the value defined in
@@ -219,7 +219,7 @@ 
    yasa.sw_detectboolean
    If True, YASA will automatically detect and remove outliers slow-waves
-using sklearn.ensemble.IsolationForest.
+using sklearn.ensemble.IsolationForest.
 The outliers detection is performed on the frequency, amplitude and
 duration parameters of the detected slow-waves. YASA uses a random seed
 (42) to ensure reproducible results. Note that this step will only be
@@ -243,7 +243,7 @@ 
    yasa.sw_detect>>> sw.summary()
 
    
 
    
-
    This will give a pandas.DataFrame where each row is a
+
    This will give a pandas.DataFrame where each row is a
 detected slow-wave and each column is a parameter (= property).
 To get the average SW parameters per channel and sleep stage:
    
 
    >>> sw.summary(grp_chan=True, grp_stage=True)
diff --git a/docs/build/html/generated/yasa.topoplot.html b/docs/build/html/generated/yasa.topoplot.html
index cab4099..b253a65 100644
--- a/docs/build/html/generated/yasa.topoplot.html
+++ b/docs/build/html/generated/yasa.topoplot.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.topoplot — yasa 0.6.4 documentation
+    yasa.topoplot — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -99,7 +99,7 @@ 
    yasa.topoplot
 yasa.topoplot(data, montage='standard_1020', vmin=None, vmax=None, mask=None, title=None, cmap=None, n_colors=100, cbar_title=None, cbar_ticks=None, figsize=(4, 4), dpi=80, fontsize=14, **kwargs)[source]
 
    Topoplot.
    
-
    This is a wrapper around mne.viz.plot_topomap().
    
+
    This is a wrapper around mne.viz.plot_topomap().
    
 
    For more details, please refer to this example notebook.
    
 
    
 
    New in version 0.4.1.
    
@@ -107,16 +107,16 @@ 
    yasa.topoplot
 
    Parameters
    
 
    
-
    datapandas.Series
    A pandas Series with the values to plot. The index MUST be the channel
+
    datapandas.Series
    A pandas Series with the values to plot. The index MUST be the channel
 names (e.g. [‘C4’, ‘F4’] or [‘C4-M1’, ‘C3-M2’]).
    
 
    
 
    montagestr
    The name of the montage to use. Valid montages can be found at
-mne.channels.make_standard_montage().
    
+mne.channels.make_standard_montage().
    
 
    
 
    vmin, vmaxfloat
    The minimum and maximum values of the colormap. If None, these will be
 defined based on the min / max values of data.
    
 
    
-
    maskpandas.Series
    A pandas Series indicating the significant electrodes. The index MUST
+
    maskpandas.Series
    A pandas Series indicating the significant electrodes. The index MUST
 be the channel names (e.g. [‘C4’, ‘F4’] or [‘C4-M1’, ‘C3-M2’]).
    
 
    
 
    titlestr
    The plot title.
    
@@ -136,13 +136,13 @@ 
    yasa.topoplotint
    Global font size of all the elements of the plot.
    
 
    
-
    **kwargsdict
    Other arguments that are passed to mne.viz.plot_topomap().
    
+
    **kwargsdict
    Other arguments that are passed to mne.viz.plot_topomap().
    
 
    
 
    
 
    
 
    Returns
    
 
    
-
    figmatplotlib.figure.Figure
    Matplotlib Figure
    
+
    figmatplotlib.figure.Figure
    Matplotlib Figure
    
 
    
 
    
 
    
diff --git a/docs/build/html/generated/yasa.transition_matrix.html b/docs/build/html/generated/yasa.transition_matrix.html
index eb32a11..d9f6781 100644
--- a/docs/build/html/generated/yasa.transition_matrix.html
+++ b/docs/build/html/generated/yasa.transition_matrix.html
@@ -5,7 +5,7 @@
     
     
 
-    yasa.transition_matrix — yasa 0.6.4 documentation
+    yasa.transition_matrix — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -118,11 +118,11 @@ 
    yasa.transition_matrix
 
    Returns
    
 
    
-
    countspandas.DataFrame
    Counts transition matrix (number of transitions from stage A to
+
    countspandas.DataFrame
    Counts transition matrix (number of transitions from stage A to
 stage B). The pre-transition states are the rows and the
 post-transition states are the columns.
    
 
    
-
    probspandas.DataFrame
    Conditional probability transition matrix, i.e.
+
    probspandas.DataFrame
    Conditional probability transition matrix, i.e.
 given that current state is A, what is the probability that
 the next state is B.
 probs is a right stochastic matrix,
@@ -162,7 +162,7 @@ 
    yasa.transition_matrix0.514
 
    
 
    
-
    Finally, we can plot the transition matrix using seaborn.heatmap()
    
+
    Finally, we can plot the transition matrix using seaborn.heatmap()
    
 
    >>> import numpy as np
 >>> import seaborn as sns
 >>> import matplotlib.pyplot as plt
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index f8e6982..6b1b271 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -4,7 +4,7 @@
   
     
     
-    Index — yasa 0.6.4 documentation
+    Index — yasa 0.6.5 documentation
     
     
     
@@ -43,7 +43,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
    
 
         
    
@@ -101,6 +101,7 @@ 
    Index
    
  | B
  | C
  | D
+ | E
  | F
  | G
  | H
@@ -108,6 +109,7 @@ 
    Index
    
  | L
  | M
  | N
+ | O
  | P
  | R
  | S
@@ -118,12 +120,16 @@ 
    Index
    
 
    _
    
 
-  
+  
    
   
 
    
@@ -164,6 +174,8 @@ 
    B
    
 
    C
    
 
    
   
      
+      
    • calibrate() (yasa.SleepStatsAgreement method)
+
      • 
       
    • compare_channels() (yasa.SpindlesResults method)
 
       
        
@@ -189,12 +201,32 @@ 
        C
        
 
 
        D
        
 
+  
        
   
 
        
 
+
        E
        
+
+  
+  
+
        
+
 
        F
        
 
-  
+  
        
   
 
        
@@ -305,14 +351,30 @@ 
        N
        
 
        
   
   
 
        
 
+
        O
        
+
+  
+
        
+
 
        P
        
 
-  
+  
        
   
-  
          
+      
        • plot_hypnograms() (yasa.EpochByEpochAgreement method)
+
          • 
       
        • plot_predict_proba() (yasa.SleepStaging method)
 
          • 
       
        • plot_spectrogram() (in module yasa)
@@ -352,10 +416,16 @@ 
          P
          
 
          R
          
 
@@ -371,6 +441,8 @@ 
          S
        • simulate_hypnogram() (in module yasa)
 
        • simulate_similar() (yasa.Hypnogram method)
+
          • 
+      
        • sleep_statistics (yasa.SleepStatsAgreement property)
 
        • sleep_statistics() (in module yasa)
 
@@ -379,6 +451,8 @@ 
          S
          
 
        • SleepStaging (class in yasa)
+
          • 
+      
        • SleepStatsAgreement (class in yasa)
 
        • sliding_window() (in module yasa)
 
          • 
@@ -392,9 +466,13 @@ 
          S
        • stft_power() (in module yasa)
 
          • 
-      
        • summary() (yasa.REMResults method)
+      
        • summary() (yasa.EpochByEpochAgreement method)
 
       
            
+        
          • (yasa.REMResults method)
+
            • 
+        
          • (yasa.SleepStatsAgreement method)
+
            • 
         
          • (yasa.SpindlesResults method)
 
            • 
         
          • (yasa.SWResults method)
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index 6f8cb34..7640c9d 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -5,7 +5,7 @@
     
     
 
-    Installation — yasa 0.6.4 documentation
+    Installation — yasa 0.6.5 documentation
     
     
     
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
 
         
            
@@ -143,7 +143,7 @@ 
            Installation# Apply a bandpass filter from 0.1 to 40 Hz
 raw.filter(0.1, 40)
 # Select a subset of EEG channels
-raw.pick_channels(['C4-A1', 'C3-A2'])
+raw.pick(['C4-A1', 'C3-A2'])
 
            
 
 
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
index 8d565ea..6a32e24 100644
Binary files a/docs/build/html/objects.inv and b/docs/build/html/objects.inv differ
diff --git a/docs/build/html/quickstart.html b/docs/build/html/quickstart.html
index eb75630..56cbb39 100644
--- a/docs/build/html/quickstart.html
+++ b/docs/build/html/quickstart.html
@@ -5,7 +5,7 @@
     
     
 
-    Quickstart — yasa 0.6.4 documentation
+    Quickstart — yasa 0.6.5 documentation
     
     
     
@@ -45,7 +45,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
 
         
            
@@ -198,7 +198,7 @@ 
            Polysomnography dataHypnogram
            
 
            Sleep staging (aka hypnogram) for this example night was performed by a trained technician following the standard rules of the American Academy of Sleep Medicine (AASM).
 The output is saved in a .csv file, where each row represents 30 seconds of data. The stages are mapped to integers such that 0 = Wake, 1 = N1 sleep, 2 = N2 sleep, 3 = N3 sleep and 4 = REM sleep.
-We can load this file using the pandas.read_csv() function:
            
+We can load this file using the pandas.read_csv() function:
            
 
            >>> import pandas as pd
 >>> hypno = pd.read_csv("yasa_example_night_young_hypno.csv", squeeze=True)
 >>> hypno
@@ -314,11 +314,11 @@ 
            EEG power in specific frequency bands
            >>> bandpower = yasa.bandpower(raw, hypno=hypno_up, include=(2, 3, 4))
 
            
 
            
-
            If desired, we can then export the bandpower dataframe to a CSV file using pandas.DataFrame.to_csv():
            
+
            If desired, we can then export the bandpower dataframe to a CSV file using pandas.DataFrame.to_csv():
            
 
            >>> bandpower.to_csv("bandpower.csv")
 
            
 
            
-
            Finally, we can use the yasa.topoplot() function to visualize the spectral powers across all electrodes. In the example below, we only plot the spectral values of stage N3, using the pandas.DataFrame.xs() function. As expected, the relative delta power is higher in frontal channels.
            
+
            Finally, we can use the yasa.topoplot() function to visualize the spectral powers across all electrodes. In the example below, we only plot the spectral values of stage N3, using the pandas.DataFrame.xs() function. As expected, the relative delta power is higher in frontal channels.
            
 
            >>> fig = yasa.topoplot(bandpower.xs(3)['Delta'])
 
            
 
            
diff --git a/docs/build/html/search.html b/docs/build/html/search.html
index 40909bb..76bcc1e 100644
--- a/docs/build/html/search.html
+++ b/docs/build/html/search.html
@@ -4,7 +4,7 @@
   
     
     
-    Search — yasa 0.6.4 documentation
+    Search — yasa 0.6.5 documentation
     
     
     
@@ -50,7 +50,7 @@
         
         
           yasa
-        0.6.4
+        0.6.5
       
            
 
         
            
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index fad0663..ba2e394 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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reference","What\u2019s new","Contribute to 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\ No newline at end of file
diff --git a/docs/build/plot_directive/generated/yasa-EpochByEpochAgreement-4.png b/docs/build/plot_directive/generated/yasa-EpochByEpochAgreement-4.png
new file mode 100644
index 0000000..d1106de
Binary files /dev/null and b/docs/build/plot_directive/generated/yasa-EpochByEpochAgreement-4.png differ
diff --git a/docs/build/plot_directive/generated/yasa-Hypnogram-2.png b/docs/build/plot_directive/generated/yasa-Hypnogram-2.png
new file mode 100644
index 0000000..c766e36
Binary files /dev/null and b/docs/build/plot_directive/generated/yasa-Hypnogram-2.png differ
diff --git a/docs/changelog.rst b/docs/changelog.rst
index c89a363..cb78872 100644
--- a/docs/changelog.rst
+++ b/docs/changelog.rst
@@ -44,6 +44,16 @@ Please see the documentation of :py:class:`yasa.Hypnogram` for more details.
 
 ----------------------------------------------------------------------------------------
 
+v0.6.5 (July 2024)
+---------------------
+
+Minor release with a brand new Evaluation module and several minor bugfixes
+
+- `PR 130 `_: Add new :py:class:`yasa.EpochByEpochAgreement` and :py:class:`yasa.SleepStatsAgreement` modules for evaluating the agreement between two scorers (e.g. human vs YASA), either at the epoch-by-epoch level or at the level of summary sleep statistics. This is an experimental feature. Remaining tasks to complete the Evaluation pipeline are described in: https://github.com/raphaelvallat/yasa/issues/166
+- `PR 171 `_: Fix deprecated `scipy.integrate.simps``.
+
+----------------------------------------------------------------------------------------
+
 v0.6.4 (January 2024)
 ---------------------
 
diff --git a/setup.py b/setup.py
index 5780c24..307a047 100644
--- a/setup.py
+++ b/setup.py
@@ -12,7 +12,7 @@
 URL = "https://github.com/raphaelvallat/yasa/"
 LICENSE = "BSD (3-clause)"
 DOWNLOAD_URL = "https://github.com/raphaelvallat/yasa/"
-VERSION = "0.6.4"
+VERSION = "0.6.5"
 PACKAGE_DATA = {"yasa.data.icons": ["*.svg"]}
 
 INSTALL_REQUIRES = [
diff --git a/yasa/__init__.py b/yasa/__init__.py
index 7177a2f..a750a04 100644
--- a/yasa/__init__.py
+++ b/yasa/__init__.py
@@ -15,4 +15,4 @@
 logging.basicConfig(format="%(asctime)s | %(levelname)s | %(message)s", datefmt="%d-%b-%y %H:%M:%S")
 
 __author__ = "Raphael Vallat "
-__version__ = "0.6.4"
+__version__ = "0.6.5"
diff --git a/yasa/hypno.py b/yasa/hypno.py
index 4789eb1..e782361 100644
--- a/yasa/hypno.py
+++ b/yasa/hypno.py
@@ -606,10 +606,6 @@ def evaluate(self, obs_hyp):
         recall          0.550
         fbeta           0.524
         Name: agreement, dtype: float64
-
-        .. plot::
-
-            >>> ebe.plot_hypnograms()
         """
         return EpochByEpochAgreement([self], [obs_hyp])
 
@@ -821,7 +817,7 @@ def sleep_statistics(self):
             The definition of REM latency in the AASM scoring manual differs from the REM latency
             reported here. The former uses the time from first epoch of sleep, while YASA uses the
             time from the beginning of the recording. The AASM definition of the REM latency can be
-            found with `SOL - Lat_REM`.
+            found with `Lat_REM - SOL`.
 
         References
         ----------
diff --git a/yasa/sleepstats.py b/yasa/sleepstats.py
index 5988350..99643cf 100644
--- a/yasa/sleepstats.py
+++ b/yasa/sleepstats.py
@@ -183,7 +183,7 @@ def sleep_statistics(hypno, sf_hyp):
         The definition of REM latency in the AASM scoring manual differs from the REM latency
         reported here. The former uses the time from first epoch of sleep, while YASA uses the
         time from the beginning of the recording. The AASM definition of the REM latency can be
-        found with `SOL - Lat_REM`.
+        found with `Lat_REM - SOL`.
 
     References
     ----------

          •