diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo index 18a90b6..a07b054 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: 7d440e1bf4ab7dd814452f500decfcbf +config: 7410ac572283522d5aaa00048d56ca4f tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html index 974f3c5..893126a 100644 --- a/docs/build/html/_modules/index.html +++ b/docs/build/html/_modules/index.html @@ -3,11 +3,14 @@ - Overview: module code — yasa 0.6.1 documentation + Overview: module code — yasa 0.6.2 documentation + + + @@ -41,7 +44,7 @@ yasa - 0.6.1 + 0.6.2
@@ -92,6 +95,7 @@

All modules for which code is available

@@ -113,31 +116,49 @@

Source code for yasa.detection

 from .spectral import stft_power
 from .numba import _detrend, _rms
 from .io import set_log_level, is_tensorpac_installed, is_pyriemann_installed
-from .others import (moving_transform, trimbothstd, get_centered_indices,
-                     sliding_window, _merge_close, _zerocrossings)
-
-
-logger = logging.getLogger('yasa')
-
-__all__ = ['art_detect', 'spindles_detect', 'SpindlesResults', 'sw_detect', 'SWResults',
-           'rem_detect', 'REMResults']
+from .others import (
+    moving_transform,
+    trimbothstd,
+    get_centered_indices,
+    sliding_window,
+    _merge_close,
+    _zerocrossings,
+)
+
+
+logger = logging.getLogger("yasa")
+
+__all__ = [
+    "art_detect",
+    "spindles_detect",
+    "SpindlesResults",
+    "sw_detect",
+    "SWResults",
+    "rem_detect",
+    "REMResults",
+    "compare_detection",
+]
 
 
 #############################################################################
 # DATA PREPROCESSING
 #############################################################################
 
+
 def _check_data_hypno(data, sf=None, ch_names=None, hypno=None, include=None, check_amp=True):
     """Helper functions for preprocessing of data and hypnogram."""
     # 1) Extract data as a 2D NumPy array
     if isinstance(data, mne.io.BaseRaw):
-        sf = data.info['sfreq']  # Extract sampling frequency
+        sf = data.info["sfreq"]  # Extract sampling frequency
         ch_names = data.ch_names  # Extract channel names
-        data = data.get_data() * 1e6  # Convert from V to uV
+        data = data.get_data(units=dict(eeg="uV", emg="uV", eog="uV", ecg="uV"))
     else:
-        assert sf is not None, 'sf must be specified if not using MNE Raw.'
+        assert sf is not None, "sf must be specified if not using MNE Raw."
+        if isinstance(sf, np.ndarray):  # Deal with sf = array(100.) --> 100
+            sf = float(sf)
+        assert isinstance(sf, (int, float)), "sf must be int or float."
     data = np.asarray(data, dtype=np.float64)
-    assert data.ndim in [1, 2], 'data must be 1D (times) or 2D (chan, times).'
+    assert data.ndim in [1, 2], "data must be 1D (times) or 2D (chan, times)."
     if data.ndim == 1:
         # Force to 2D array: (n_chan, n_samples)
         data = data[None, ...]
@@ -145,40 +166,41 @@ 
Source code for yasa.detection
 
     # 2) Check channel names
     if ch_names is None:
-        ch_names = ['CHAN' + str(i).zfill(3) for i in range(n_chan)]
+        ch_names = ["CHAN" + str(i).zfill(3) for i in range(n_chan)]
     else:
         assert len(ch_names) == n_chan
 
     # 3) Check hypnogram
     if hypno is not None:
         hypno = np.asarray(hypno, dtype=int)
-        assert hypno.ndim == 1, 'Hypno must be one dimensional.'
-        assert hypno.size == n_samples, 'Hypno must have same size as data.'
+        assert hypno.ndim == 1, "Hypno must be one dimensional."
+        assert hypno.size == n_samples, "Hypno must have same size as data."
         unique_hypno = np.unique(hypno)
-        logger.info('Number of unique values in hypno = %i', unique_hypno.size)
-        assert include is not None, 'include cannot be None if hypno is given'
+        logger.info("Number of unique values in hypno = %i", unique_hypno.size)
+        assert include is not None, "include cannot be None if hypno is given"
         include = np.atleast_1d(np.asarray(include))
-        assert include.size >= 1, '`include` must have at least one element.'
-        assert hypno.dtype.kind == include.dtype.kind, ('hypno and include must have same dtype')
-        assert np.in1d(hypno, include).any(), ('None of the stages specified '
-                                               'in `include` are present in '
-                                               'hypno.')
+        assert include.size >= 1, "`include` must have at least one element."
+        assert hypno.dtype.kind == include.dtype.kind, "hypno and include must have same dtype"
+        assert np.in1d(hypno, include).any(), (
+            "None of the stages specified " "in `include` are present in " "hypno."
+        )
 
     # 4) Check data amplitude
-    logger.info('Number of samples in data = %i', n_samples)
-    logger.info('Sampling frequency = %.2f Hz', sf)
-    logger.info('Data duration = %.2f seconds', n_samples / sf)
+    logger.info("Number of samples in data = %i", n_samples)
+    logger.info("Sampling frequency = %.2f Hz", sf)
+    logger.info("Data duration = %.2f seconds", n_samples / sf)
     all_ptp = np.ptp(data, axis=-1)
     all_trimstd = trimbothstd(data, cut=0.05)
     bad_chan = np.zeros(n_chan, dtype=bool)
     for i in range(n_chan):
-        logger.info('Trimmed standard deviation of %s = %.4f uV' % (ch_names[i], all_trimstd[i]))
-        logger.info('Peak-to-peak amplitude of %s = %.4f uV' % (ch_names[i], all_ptp[i]))
-        if check_amp and not(0.1 < all_trimstd[i] < 1e3):
-            logger.error('Wrong data amplitude for %s '
-                         '(trimmed STD = %.3f). Unit of data MUST be uV! '
-                         'Channel will be skipped.'
-                         % (ch_names[i], all_trimstd[i]))
+        logger.info("Trimmed standard deviation of %s = %.4f uV" % (ch_names[i], all_trimstd[i]))
+        logger.info("Peak-to-peak amplitude of %s = %.4f uV" % (ch_names[i], all_ptp[i]))
+        if check_amp and not (0.1 < all_trimstd[i] < 1e3):
+            logger.error(
+                "Wrong data amplitude for %s "
+                "(trimmed STD = %.3f). Unit of data MUST be uV! "
+                "Channel will be skipped." % (ch_names[i], all_trimstd[i])
+            )
             bad_chan[i] = True
 
     # 5) Create sleep stage vector mask
@@ -218,50 +240,56 @@ 
Source code for yasa.detection
             assert mask.size == n_events, "Mask.size must be the number of detected events."
         return mask
 
-    def summary(self, event_type, grp_chan=False, grp_stage=False, aggfunc='mean', sort=True,
-                mask=None):
+    def summary(
+        self, event_type, grp_chan=False, grp_stage=False, aggfunc="mean", sort=True, mask=None
+    ):
         """Summary"""
         # Check masking
         mask = self._check_mask(mask)
 
         # Define grouping
         grouper = []
-        if grp_stage is True and 'Stage' in self._events:
-            grouper.append('Stage')
-        if grp_chan is True and 'Channel' in self._events:
-            grouper.append('Channel')
+        if grp_stage is True and "Stage" in self._events:
+            grouper.append("Stage")
+        if grp_chan is True and "Channel" in self._events:
+            grouper.append("Channel")
         if not len(grouper):
             # Return a copy of self._events after masking, without grouping
             return self._events.loc[mask, :].copy()
 
-        if event_type == 'spindles':
-            aggdict = {'Start': 'count',
-                       'Duration': aggfunc,
-                       'Amplitude': aggfunc,
-                       'RMS': aggfunc,
-                       'AbsPower': aggfunc,
-                       'RelPower': aggfunc,
-                       'Frequency': aggfunc,
-                       'Oscillations': aggfunc,
-                       'Symmetry': aggfunc}
+        if event_type == "spindles":
+            aggdict = {
+                "Start": "count",
+                "Duration": aggfunc,
+                "Amplitude": aggfunc,
+                "RMS": aggfunc,
+                "AbsPower": aggfunc,
+                "RelPower": aggfunc,
+                "Frequency": aggfunc,
+                "Oscillations": aggfunc,
+                "Symmetry": aggfunc,
+            }
 
             # if 'SOPhase' in self._events:
             #     from scipy.stats import circmean
             #     aggdict['SOPhase'] = lambda x: circmean(x, low=-np.pi, high=np.pi)
 
-        elif event_type == 'sw':
-            aggdict = {'Start': 'count',
-                       'Duration': aggfunc,
-                       'ValNegPeak': aggfunc,
-                       'ValPosPeak': aggfunc,
-                       'PTP': aggfunc,
-                       'Slope': aggfunc,
-                       'Frequency': aggfunc}
-
-            if 'PhaseAtSigmaPeak' in self._events:
+        elif event_type == "sw":
+            aggdict = {
+                "Start": "count",
+                "Duration": aggfunc,
+                "ValNegPeak": aggfunc,
+                "ValPosPeak": aggfunc,
+                "PTP": aggfunc,
+                "Slope": aggfunc,
+                "Frequency": aggfunc,
+            }
+
+            if "PhaseAtSigmaPeak" in self._events:
                 from scipy.stats import circmean
-                aggdict['PhaseAtSigmaPeak'] = lambda x: circmean(x, low=-np.pi, high=np.pi)
-                aggdict['ndPAC'] = aggfunc
+
+                aggdict["PhaseAtSigmaPeak"] = lambda x: circmean(x, low=-np.pi, high=np.pi)
+                aggdict["ndPAC"] = aggfunc
 
             if "CooccurringSpindle" in self._events:
                 # We do not average "CooccurringSpindlePeak"
@@ -269,22 +297,24 @@ 
Source code for yasa.detection
                 aggdict["DistanceSpindleToSW"] = aggfunc
 
         else:  # REM
-            aggdict = {'Start': 'count',
-                       'Duration': aggfunc,
-                       'LOCAbsValPeak': aggfunc,
-                       'ROCAbsValPeak': aggfunc,
-                       'LOCAbsRiseSlope': aggfunc,
-                       'ROCAbsRiseSlope': aggfunc,
-                       'LOCAbsFallSlope': aggfunc,
-                       'ROCAbsFallSlope': aggfunc}
+            aggdict = {
+                "Start": "count",
+                "Duration": aggfunc,
+                "LOCAbsValPeak": aggfunc,
+                "ROCAbsValPeak": aggfunc,
+                "LOCAbsRiseSlope": aggfunc,
+                "ROCAbsRiseSlope": aggfunc,
+                "LOCAbsFallSlope": aggfunc,
+                "ROCAbsFallSlope": aggfunc,
+            }
 
         # Apply grouping, after masking
         df_grp = self._events.loc[mask, :].groupby(grouper, sort=sort, as_index=False).agg(aggdict)
-        df_grp = df_grp.rename(columns={'Start': 'Count'})
+        df_grp = df_grp.rename(columns={"Start": "Count"})
 
         # Calculate density (= number per min of each stage)
         if self._hypno is not None and grp_stage is True:
-            stages = np.unique(self._events['Stage'])
+            stages = np.unique(self._events["Stage"])
             dur = {}
             for st in stages:
                 # Get duration in minutes of each stage present in dataframe
@@ -292,37 +322,42 @@ 
Source code for yasa.detection
 
             # Insert new density column in grouped dataframe after count
             df_grp.insert(
-                loc=df_grp.columns.get_loc('Count') + 1, column='Density',
-                value=df_grp.apply(lambda rw: rw['Count'] / dur[rw['Stage']], axis=1))
+                loc=df_grp.columns.get_loc("Count") + 1,
+                column="Density",
+                value=df_grp.apply(lambda rw: rw["Count"] / dur[rw["Stage"]], axis=1),
+            )
 
         return df_grp.set_index(grouper)
 
     def get_mask(self):
         """get_mask"""
         from yasa.others import _index_to_events
+
         mask = np.zeros(self._data.shape, dtype=int)
-        for i in self._events['IdxChannel'].unique():
-            ev_chan = self._events[self._events['IdxChannel'] == i]
-            idx_ev = _index_to_events(
-                ev_chan[['Start', 'End']].to_numpy() * self._sf)
+        for i in self._events["IdxChannel"].unique():
+            ev_chan = self._events[self._events["IdxChannel"] == i]
+            idx_ev = _index_to_events(ev_chan[["Start", "End"]].to_numpy() * self._sf)
             mask[i, idx_ev] = 1
         return np.squeeze(mask)
 
-    def get_sync_events(self, center, time_before, time_after, filt=(None, None), mask=None,
-                        as_dataframe=True):
+    def get_sync_events(
+        self, center, time_before, time_after, filt=(None, None), mask=None, as_dataframe=True
+    ):
         """Get_sync_events (not for REM, spindles & SW only)"""
         from yasa.others import get_centered_indices
+
         assert time_before >= 0
         assert time_after >= 0
         bef = int(self._sf * time_before)
         aft = int(self._sf * time_after)
         # TODO: Step size is determined by sf: 0.01 sec at 100 Hz, 0.002 sec at
         # 500 Hz, 0.00390625 sec at 256 Hz. Should we add resample=100 (Hz) or step_size=0.01?
-        time = np.arange(-bef, aft + 1, dtype='int') / self._sf
+        time = np.arange(-bef, aft + 1, dtype="int") / self._sf
 
         if any(filt):
             data = mne.filter.filter_data(
-                self._data, self._sf, l_freq=filt[0], h_freq=filt[1], method='fir', verbose=False)
+                self._data, self._sf, l_freq=filt[0], h_freq=filt[1], method="fir", verbose=False
+            )
         else:
             data = self._data
 
@@ -332,18 +367,19 @@ 
Source code for yasa.detection
 
         output = []
 
-        for i in masked_events['IdxChannel'].unique():
+        for i in masked_events["IdxChannel"].unique():
             # Copy is required to merge with the stage later on
-            ev_chan = masked_events[masked_events['IdxChannel'] == i].copy()
-            ev_chan['Event'] = np.arange(ev_chan.shape[0])
+            ev_chan = masked_events[masked_events["IdxChannel"] == i].copy()
+            ev_chan["Event"] = np.arange(ev_chan.shape[0])
             peaks = (ev_chan[center] * self._sf).astype(int).to_numpy()
             # Get centered indices
             idx, idx_valid = get_centered_indices(data[i, :], peaks, bef, aft)
             # If no good epochs are returned raise a warning
             if len(idx_valid) == 0:
                 logger.error(
-                    'Time before and/or time after exceed data bounds, please '
-                    'lower the temporal window around center. Skipping channel.')
+                    "Time before and/or time after exceed data bounds, please "
+                    "lower the temporal window around center. Skipping channel."
+                )
                 continue
 
             # Get data at indices and time vector
@@ -356,15 +392,15 @@ 
Source code for yasa.detection
 
             # Convert to long-format dataframe
             df_chan = pd.DataFrame(amps.T)
-            df_chan['Time'] = time
+            df_chan["Time"] = time
             # Convert to long-format
-            df_chan = df_chan.melt(id_vars='Time', var_name='Event', value_name='Amplitude')
+            df_chan = df_chan.melt(id_vars="Time", var_name="Event", value_name="Amplitude")
             # Append stage
-            if 'Stage' in masked_events:
-                df_chan = df_chan.merge(ev_chan[['Event', 'Stage']].iloc[idx_valid])
+            if "Stage" in masked_events:
+                df_chan = df_chan.merge(ev_chan[["Event", "Stage"]].iloc[idx_valid])
             # Append channel name
-            df_chan['Channel'] = ev_chan['Channel'].iloc[0]
-            df_chan['IdxChannel'] = i
+            df_chan["Channel"] = ev_chan["Channel"].iloc[0]
+            df_chan["IdxChannel"] = i
             # Append to master dataframe
             output.append(df_chan)
 
@@ -386,7 +422,7 @@ 
Source code for yasa.detection
             coincidence = (x * y).sum()
             if scaled:
                 # Handle division by zero error
-                denom = (x.sum() * y.sum())
+                denom = x.sum() * y.sum()
                 if denom == 0:
                     coincidence = np.nan
                 else:
@@ -402,31 +438,144 @@ 
Source code for yasa.detection
 
         return coinc_mat
 
-    def plot_average(self, event_type, center='Peak', hue='Channel', time_before=1,
-                     time_after=1, filt=(None, None), mask=None, figsize=(6, 4.5), **kwargs):
+    def compare_channels(self, score="f1", max_distance_sec=0):
+        """
+        Compare detected events across channels.
+        See full documentation in the methods of SpindlesResults and SWResults.
+        """
+        from itertools import product
+
+        assert score in ["f1", "precision", "recall"], f"Invalid scoring metric: {score}"
+
+        # Extract events and channel
+        detected = self.summary()
+        chan = detected["Channel"].unique()
+
+        # Get indices of start in deciseconds, rounding to nearest deciseconds (100 ms).
+        # This is needed for three reasons:
+        # 1. Speed up the for loop
+        # 2. Avoid memory error in yasa.compare_detection
+        # 3. Make sure that max_distance works even when self and other have different sf.
+        # TODO: Only the Start of the event is currently supported. Add more flexibility?
+        detected["Start"] = (detected["Start"] * 10).round().astype(int)
+        max_distance = int(10 * max_distance_sec)
+
+        # Initialize output dataframe / dict
+        scores = pd.DataFrame(index=chan, columns=chan, dtype=float)
+        scores.index.name = "Channel"
+        scores.columns.name = "Channel"
+        pairs = list(product(chan, repeat=2))
+
+        # Loop across pair of channels
+        for c_index, c_col in pairs:
+            idx_chan1 = detected[detected["Channel"] == c_index]["Start"]
+            idx_chan2 = detected[detected["Channel"] == c_col]["Start"]
+            # DANGER: Note how we invert idx_chan2 and idx_chan1 here. This is because
+            # idx_chan1 (the index of the dataframe) should be the ground-truth.
+            res = compare_detection(idx_chan2, idx_chan1, max_distance)
+            scores.loc[c_index, c_col] = res[score]
+
+        return scores
+
+    def compare_detection(self, other, max_distance_sec=0, other_is_groundtruth=True):
+        """
+        Compare detected events between two detection methods, or against a ground-truth scoring.
+        See full documentation in the methods of SpindlesResults and SWResults.
+        """
+        detected = self.summary()
+        if isinstance(other, (SpindlesResults, SWResults, REMResults)):
+            groundtruth = other.summary()
+        elif isinstance(other, pd.DataFrame):
+            assert "Start" in other.columns
+            assert "Channel" in other.columns
+            groundtruth = other[["Start", "Channel"]].copy()
+        else:
+            raise ValueError(
+                f"Invalid argument other: {other}. It must be a YASA detection output or a Pandas "
+                f"DataFrame with the columns Start and Channels"
+            )
+
+        # Get indices of start in deciseconds, rounding to nearest deciseconds (100 ms).
+        # This is needed for three reasons:
+        # 1. Speed up the for loop
+        # 2. Avoid memory error in yasa.compare_detection
+        # 3. Make sure that max_distance works even when self and other have different sf.
+        detected["Start"] = (detected["Start"] * 10).round().astype(int)
+        groundtruth["Start"] = (groundtruth["Start"] * 10).round().astype(int)
+        max_distance = int(10 * max_distance_sec)
+
+        # Find channels that are present in both self and other
+        chan_detected = detected["Channel"].unique()
+        chan_groundtruth = groundtruth["Channel"].unique()
+        chan_both = np.intersect1d(chan_detected, chan_groundtruth)  # Sort
+
+        if not len(chan_both):
+            raise ValueError(
+                f"No intersecting channel between self and other:\n"
+                f"{chan_detected}\n{chan_groundtruth}"
+            )
+
+        # The output is a pandas.DataFrame (n_chan, n_metrics).
+        scores = pd.DataFrame(
+            index=chan_both, columns=["precision", "recall", "f1", "n_self", "n_other"], dtype=float
+        )
+        scores.index.name = "Channel"
+
+        # Loop on each channel
+        for c_index in chan_both:
+            idx_detected = detected[detected["Channel"] == c_index]["Start"]
+            idx_groundtruth = groundtruth[groundtruth["Channel"] == c_index]["Start"]
+            if other_is_groundtruth:
+                res = compare_detection(idx_detected, idx_groundtruth, max_distance)
+            else:
+                res = compare_detection(idx_groundtruth, idx_detected, max_distance)
+            scores.loc[c_index, "precision"] = res["precision"]
+            scores.loc[c_index, "recall"] = res["recall"]
+            scores.loc[c_index, "f1"] = res["f1"]
+            scores.loc[c_index, "n_self"] = len(idx_detected)
+            scores.loc[c_index, "n_other"] = len(idx_groundtruth)
+
+        scores["n_self"] = scores["n_self"].astype(int)
+        scores["n_other"] = scores["n_other"].astype(int)
+
+        return scores
+
+    def plot_average(
+        self,
+        event_type,
+        center="Peak",
+        hue="Channel",
+        time_before=1,
+        time_after=1,
+        filt=(None, None),
+        mask=None,
+        figsize=(6, 4.5),
+        **kwargs,
+    ):
         """Plot the average event (not for REM, spindles & SW only)"""
         import seaborn as sns
         import matplotlib.pyplot as plt
 
-        df_sync = self.get_sync_events(center=center, time_before=time_before,
-                                       time_after=time_after, filt=filt, mask=mask)
+        df_sync = self.get_sync_events(
+            center=center, time_before=time_before, time_after=time_after, filt=filt, mask=mask
+        )
         assert not df_sync.empty, "Could not calculate event-locked data."
-        assert hue in ['Stage', 'Channel'], "hue must be 'Channel' or 'Stage'"
+        assert hue in ["Stage", "Channel"], "hue must be 'Channel' or 'Stage'"
         assert hue in df_sync.columns, "%s is not present in data." % hue
 
-        if event_type == 'spindles':
+        if event_type == "spindles":
             title = "Average spindle"
         else:  # "sw":
             title = "Average SW"
 
         # Start figure
         fig, ax = plt.subplots(1, 1, figsize=figsize)
-        sns.lineplot(data=df_sync, x='Time', y='Amplitude', hue=hue, ax=ax, **kwargs)
+        sns.lineplot(data=df_sync, x="Time", y="Amplitude", hue=hue, ax=ax, **kwargs)
         # ax.legend(frameon=False, loc='lower right')
-        ax.set_xlim(df_sync['Time'].min(), df_sync['Time'].max())
+        ax.set_xlim(df_sync["Time"].min(), df_sync["Time"].max())
         ax.set_title(title)
-        ax.set_xlabel('Time (sec)')
-        ax.set_ylabel('Amplitude (uV)')
+        ax.set_xlabel("Time (sec)")
+        ax.set_ylabel("Amplitude (uV)")
         return ax
 
     def plot_detection(self):
@@ -452,19 +601,15 @@ 
Source code for yasa.detection
 
         # Plot
         fig, ax = plt.subplots(figsize=(12, 4))
-        plt.plot(times[xrng], self._data[0, xrng], 'k', lw=1)
-        plt.plot(times[xrng], highlight[0, xrng], 'indianred')
-        plt.xlabel('Time (seconds)')
-        plt.ylabel('Amplitude (uV)')
+        plt.plot(times[xrng], self._data[0, xrng], "k", lw=1)
+        plt.plot(times[xrng], highlight[0, xrng], "indianred")
+        plt.xlabel("Time (seconds)")
+        plt.ylabel("Amplitude (uV)")
         fig.canvas.header_visible = False
         fig.tight_layout()
 
         # WIDGETS
-        layout = ipy.Layout(
-            width="50%",
-            justify_content='center',
-            align_items='center'
-        )
+        layout = ipy.Layout(width="50%", justify_content="center", align_items="center")
 
         sl_ep = ipy.IntSlider(
             min=0,
@@ -481,24 +626,23 @@ 
Source code for yasa.detection
             step=25,
             value=150,
             layout=layout,
-            orientation='horizontal',
-            description="Amplitude:"
+            orientation="horizontal",
+            description="Amplitude:",
         )
 
         dd_ch = ipy.Dropdown(
-            options=self._ch_names, value=self._ch_names[0],
-            description='Channel:'
+            options=self._ch_names, value=self._ch_names[0], description="Channel:"
         )
 
         dd_win = ipy.Dropdown(
             options=[1, 5, 10, 30, 60],
             value=win_size,
-            description='Window size:',
+            description="Window size:",
         )
 
         dd_check = ipy.Checkbox(
             value=False,
-            description='Filtered',
+            description="Filtered",
         )
 
         def update(epoch, amplitude, channel, win_size, filt):
@@ -518,8 +662,9 @@ 
Source code for yasa.detection
                 pass
             ax.set_ylim([-amplitude, amplitude])
 
-        return ipy.interact(update, epoch=sl_ep, amplitude=sl_amp,
-                            channel=dd_ch, win_size=dd_win, filt=dd_check)
+        return ipy.interact(
+            update, epoch=sl_ep, amplitude=sl_amp, channel=dd_ch, win_size=dd_win, filt=dd_check
+        )
 
 
 #############################################################################
@@ -527,11 +672,21 @@ 
Source code for yasa.detection
 #############################################################################
 
 
-
[docs]def spindles_detect(data, sf=None, ch_names=None, hypno=None,
-                    include=(1, 2, 3), freq_sp=(12, 15), freq_broad=(1, 30),
-                    duration=(0.5, 2), min_distance=500,
-                    thresh={'rel_pow': 0.2, 'corr': 0.65, 'rms': 1.5},
-                    multi_only=False, remove_outliers=False, verbose=False):
+
[docs]def spindles_detect(
+    data,
+    sf=None,
+    ch_names=None,
+    hypno=None,
+    include=(1, 2, 3),
+    freq_sp=(12, 15),
+    freq_broad=(1, 30),
+    duration=(0.5, 2),
+    min_distance=500,
+    thresh={"rel_pow": 0.2, "corr": 0.65, "rms": 1.5},
+    multi_only=False,
+    remove_outliers=False,
+    verbose=False,
+):
     """Spindles detection.
 
     Parameters
@@ -704,44 +859,52 @@ 
Source code for yasa.detection
     """
     set_log_level(verbose)
 
-    (data, sf, ch_names, hypno, include, mask, n_chan, n_samples, bad_chan
-     ) = _check_data_hypno(data, sf, ch_names, hypno, include)
+    (data, sf, ch_names, hypno, include, mask, n_chan, n_samples, bad_chan) = _check_data_hypno(
+        data, sf, ch_names, hypno, include
+    )
 
     # If all channels are bad
     if sum(bad_chan) == n_chan:
-        logger.warning('All channels have bad amplitude. Returning None.')
+        logger.warning("All channels have bad amplitude. Returning None.")
         return None
 
     # Check detection thresholds
-    if 'rel_pow' not in thresh.keys():
-        thresh['rel_pow'] = 0.20
-    if 'corr' not in thresh.keys():
-        thresh['corr'] = 0.65
-    if 'rms' not in thresh.keys():
-        thresh['rms'] = 1.5
-    do_rel_pow = thresh['rel_pow'] not in [None, "none", "None"]
-    do_corr = thresh['corr'] not in [None, "none", "None"]
-    do_rms = thresh['rms'] not in [None, "none", "None"]
+    if "rel_pow" not in thresh.keys():
+        thresh["rel_pow"] = 0.20
+    if "corr" not in thresh.keys():
+        thresh["corr"] = 0.65
+    if "rms" not in thresh.keys():
+        thresh["rms"] = 1.5
+    do_rel_pow = thresh["rel_pow"] not in [None, "none", "None"]
+    do_corr = thresh["corr"] not in [None, "none", "None"]
+    do_rms = thresh["rms"] not in [None, "none", "None"]
     n_thresh = sum([do_rel_pow, do_corr, do_rms])
-    assert n_thresh >= 1, 'At least one threshold must be defined.'
+    assert n_thresh >= 1, "At least one threshold must be defined."
 
     # Filtering
     nfast = next_fast_len(n_samples)
     # 1) Broadband bandpass filter (optional -- careful of lower freq for PAC)
-    data_broad = filter_data(data, sf, freq_broad[0], freq_broad[1], method='fir', verbose=0)
+    data_broad = filter_data(data, sf, freq_broad[0], freq_broad[1], method="fir", verbose=0)
     # 2) Sigma bandpass filter
     # The width of the transition band is set to 1.5 Hz on each side,
     # meaning that for freq_sp = (12, 15 Hz), the -6 dB points are located at
     # 11.25 and 15.75 Hz.
     data_sigma = filter_data(
-        data, sf, freq_sp[0], freq_sp[1], l_trans_bandwidth=1.5, h_trans_bandwidth=1.5,
-        method='fir', verbose=0)
+        data,
+        sf,
+        freq_sp[0],
+        freq_sp[1],
+        l_trans_bandwidth=1.5,
+        h_trans_bandwidth=1.5,
+        method="fir",
+        verbose=0,
+    )
 
     # Hilbert power (to define the instantaneous frequency / power)
     analytic = signal.hilbert(data_sigma, N=nfast)[:, :n_samples]
     inst_phase = np.angle(analytic)
     inst_pow = np.square(np.abs(analytic))
-    inst_freq = (sf / (2 * np.pi) * np.diff(inst_phase, axis=-1))
+    inst_freq = sf / (2 * np.pi) * np.diff(inst_phase, axis=-1)
 
     # Extract the SO signal for coupling
     # if coupling:
@@ -770,7 +933,8 @@ 
Source code for yasa.detection
         # Note that even if the threshold is None we still need to calculate it
         # for the individual spindles parameter (RelPow).
         f, t, Sxx = stft_power(
-            data_broad[i, :], sf, window=2, step=.2, band=freq_broad, interp=False, norm=True)
+            data_broad[i, :], sf, window=2, step=0.2, band=freq_broad, interp=False, norm=True
+        )
         idx_sigma = np.logical_and(f >= freq_sp[0], f <= freq_sp[1])
         rel_pow = Sxx[idx_sigma].sum(0)
 
@@ -778,39 +942,47 @@ 
Source code for yasa.detection
         # Note that we could also have use the `interp=True` in the
         # `stft_power` function, however 2D interpolation is much slower than
         # 1D interpolation.
-        func = interp1d(t, rel_pow, kind='cubic', bounds_error=False, fill_value=0)
+        func = interp1d(t, rel_pow, kind="cubic", bounds_error=False, fill_value=0)
         t = np.arange(n_samples) / sf
         rel_pow = func(t)
 
         if do_corr:
-            _, mcorr = moving_transform(x=data_sigma[i, :], y=data_broad[i, :], sf=sf, window=.3,
-                                        step=.1, method='corr', interp=True)
+            _, mcorr = moving_transform(
+                x=data_sigma[i, :],
+                y=data_broad[i, :],
+                sf=sf,
+                window=0.3,
+                step=0.1,
+                method="corr",
+                interp=True,
+            )
         if do_rms:
-            _, mrms = moving_transform(x=data_sigma[i, :], sf=sf, window=.3, step=.1, method='rms',
-                                       interp=True)
+            _, mrms = moving_transform(
+                x=data_sigma[i, :], sf=sf, window=0.3, step=0.1, method="rms", interp=True
+            )
             # Let's define the thresholds
             if hypno is None:
-                thresh_rms = mrms.mean() + thresh['rms'] * trimbothstd(mrms, cut=0.10)
+                thresh_rms = mrms.mean() + thresh["rms"] * trimbothstd(mrms, cut=0.10)
             else:
-                thresh_rms = mrms[mask].mean() + thresh['rms'] * trimbothstd(mrms[mask], cut=0.10)
+                thresh_rms = mrms[mask].mean() + thresh["rms"] * trimbothstd(mrms[mask], cut=0.10)
             # Avoid too high threshold caused by Artefacts / Motion during Wake
             thresh_rms = min(thresh_rms, 10)
-            logger.info('Moving RMS threshold = %.3f', thresh_rms)
+            logger.info("Moving RMS threshold = %.3f", thresh_rms)
 
         # Boolean vector of supra-threshold indices
         idx_sum = np.zeros(n_samples)
         if do_rel_pow:
-            idx_rel_pow = (rel_pow >= thresh['rel_pow']).astype(int)
+            idx_rel_pow = (rel_pow >= thresh["rel_pow"]).astype(int)
             idx_sum += idx_rel_pow
-            logger.info('N supra-theshold relative power = %i', idx_rel_pow.sum())
+            logger.info("N supra-theshold relative power = %i", idx_rel_pow.sum())
         if do_corr:
-            idx_mcorr = (mcorr >= thresh['corr']).astype(int)
+            idx_mcorr = (mcorr >= thresh["corr"]).astype(int)
             idx_sum += idx_mcorr
-            logger.info('N supra-theshold moving corr = %i', idx_mcorr.sum())
+            logger.info("N supra-theshold moving corr = %i", idx_mcorr.sum())
         if do_rms:
             idx_mrms = (mrms >= thresh_rms).astype(int)
             idx_sum += idx_mrms
-            logger.info('N supra-theshold moving RMS = %i', idx_mrms.sum())
+            logger.info("N supra-theshold moving RMS = %i", idx_mrms.sum())
 
         # Make sure that we do not detect spindles outside mask
         if hypno is not None:
@@ -823,7 +995,7 @@ 
Source code for yasa.detection
         # Sampling frequecy = 256 Hz --> w = 25 samples = 97 ms
         w = int(0.1 * sf)
         # Critical bugfix March 2022, see https://github.com/raphaelvallat/yasa/pull/55
-        idx_sum = np.convolve(idx_sum, np.ones(w), mode='same') / w
+        idx_sum = np.convolve(idx_sum, np.ones(w), mode="same") / w
         # And we then find indices that are strictly greater than 2, i.e. we
         # find the 'true' beginning and 'true' end of the events by finding
         # where at least two out of the three treshold were crossed.
@@ -831,7 +1003,7 @@ 
Source code for yasa.detection
 
         # If no events are found, skip to next channel
         if not len(where_sp):
-            logger.warning('No spindle were found in channel %s.', ch_names[i])
+            logger.warning("No spindle were found in channel %s.", ch_names[i])
             continue
 
         # Merge events that are too close
@@ -849,7 +1021,7 @@ 
Source code for yasa.detection
 
         # If no events of good duration are found, skip to next channel
         if all(~good_dur):
-            logger.warning('No spindle were found in channel %s.', ch_names[i])
+            logger.warning("No spindle were found in channel %s.", ch_names[i])
             continue
 
         # Initialize empty variables
@@ -886,7 +1058,8 @@ 
Source code for yasa.detection
 
             # Number of oscillations
             peaks, peaks_params = signal.find_peaks(
-                sp_det, distance=distance, prominence=(None, None))
+                sp_det, distance=distance, prominence=(None, None)
+            )
             sp_osc[j] = len(peaks)
 
             # For frequency and amplitude, we can also optionally use these
@@ -897,7 +1070,7 @@ 
Source code for yasa.detection
 
             # Peak location & symmetry index
             # pk is expressed in sample since the beginning of the spindle
-            pk = peaks[peaks_params['prominences'].argmax()]
+            pk = peaks[peaks_params["prominences"].argmax()]
             sp_pro[j] = sp_start[j] + pk / sf
             sp_sym[j] = pk / sp_det.size
 
@@ -910,70 +1083,82 @@ 
Source code for yasa.detection
                 sp_sta[j] = hypno[sp[j]][0]
 
         # Create a dataframe
-        sp_params = {'Start': sp_start,
-                     'Peak': sp_pro,
-                     'End': sp_end,
-                     'Duration': sp_dur,
-                     'Amplitude': sp_amp,
-                     'RMS': sp_rms,
-                     'AbsPower': sp_abs,
-                     'RelPower': sp_rel,
-                     'Frequency': sp_freq,
-                     'Oscillations': sp_osc,
-                     'Symmetry': sp_sym,
-                     # 'SOPhase': sp_cou,
-                     'Stage': sp_sta}
+        sp_params = {
+            "Start": sp_start,
+            "Peak": sp_pro,
+            "End": sp_end,
+            "Duration": sp_dur,
+            "Amplitude": sp_amp,
+            "RMS": sp_rms,
+            "AbsPower": sp_abs,
+            "RelPower": sp_rel,
+            "Frequency": sp_freq,
+            "Oscillations": sp_osc,
+            "Symmetry": sp_sym,
+            # 'SOPhase': sp_cou,
+            "Stage": sp_sta,
+        }
 
         df_chan = pd.DataFrame(sp_params)[good_dur]
 
         # We need at least 50 detected spindles to apply the Isolation Forest.
         if remove_outliers and df_chan.shape[0] >= 50:
-            col_keep = ['Duration', 'Amplitude', 'RMS', 'AbsPower', 'RelPower',
-                        'Frequency', 'Oscillations', 'Symmetry']
+            col_keep = [
+                "Duration",
+                "Amplitude",
+                "RMS",
+                "AbsPower",
+                "RelPower",
+                "Frequency",
+                "Oscillations",
+                "Symmetry",
+            ]
             ilf = IsolationForest(
-                contamination='auto', max_samples='auto', verbose=0, random_state=42)
+                contamination="auto", max_samples="auto", verbose=0, random_state=42
+            )
             good = ilf.fit_predict(df_chan[col_keep])
             good[good == -1] = 0
-            logger.info('%i outliers were removed in channel %s.'
-                        % ((good == 0).sum(), ch_names[i]))
+            logger.info(
+                "%i outliers were removed in channel %s." % ((good == 0).sum(), ch_names[i])
+            )
             # Remove outliers from DataFrame
             df_chan = df_chan[good.astype(bool)]
-            logger.info('%i spindles were found in channel %s.'
-                        % (df_chan.shape[0], ch_names[i]))
+            logger.info("%i spindles were found in channel %s." % (df_chan.shape[0], ch_names[i]))
 
         # ####################################################################
         # END SINGLE CHANNEL DETECTION
         # ####################################################################
-        df_chan['Channel'] = ch_names[i]
-        df_chan['IdxChannel'] = i
+        df_chan["Channel"] = ch_names[i]
+        df_chan["IdxChannel"] = i
         df = pd.concat([df, df_chan], axis=0, ignore_index=True)
 
     # If no spindles were detected, return None
     if df.empty:
-        logger.warning('No spindles were found in data. Returning None.')
+        logger.warning("No spindles were found in data. Returning None.")
         return None
 
     # Remove useless columns
     to_drop = []
     if hypno is None:
-        to_drop.append('Stage')
+        to_drop.append("Stage")
     else:
-        df['Stage'] = df['Stage'].astype(int)
+        df["Stage"] = df["Stage"].astype(int)
     # if not coupling:
     #     to_drop.append('SOPhase')
     if len(to_drop):
         df = df.drop(columns=to_drop)
 
     # Find spindles that are present on at least two channels
-    if multi_only and df['Channel'].nunique() > 1:
+    if multi_only and df["Channel"].nunique() > 1:
         # We round to the nearest second
         idx_good = np.logical_or(
-            df['Start'].round(0).duplicated(keep=False),
-            df['End'].round(0).duplicated(keep=False)).to_list()
+            df["Start"].round(0).duplicated(keep=False), df["End"].round(0).duplicated(keep=False)
+        ).to_list()
         df = df[idx_good].reset_index(drop=True)
 
-    return SpindlesResults(events=df, data=data, sf=sf, ch_names=ch_names,
-                           hypno=hypno, data_filt=data_sigma)

+    return SpindlesResults(
+        events=df, data=data, sf=sf, ch_names=ch_names, hypno=hypno, data_filt=data_sigma
+    )

 
 
 
[docs]class SpindlesResults(_DetectionResults):
@@ -998,7 +1183,7 @@ 
Source code for yasa.detection
 
[docs]    def __init__(self, events, data, sf, ch_names, hypno, data_filt):
         super().__init__(events, data, sf, ch_names, hypno, data_filt)

 
-
[docs]    def summary(self, grp_chan=False, grp_stage=False, mask=None, aggfunc='mean', sort=True):
+
[docs]    def summary(self, grp_chan=False, grp_stage=False, mask=None, aggfunc="mean", sort=True):
         """Return a summary of the spindles detection, optionally grouped
         across channels and/or stage.
 
@@ -1018,8 +1203,14 @@ 
Source code for yasa.detection
         sort : bool
             If True, sort group keys when grouping.
         """
-        return super().summary(event_type='spindles', grp_chan=grp_chan, grp_stage=grp_stage,
-                               aggfunc=aggfunc, sort=sort, mask=mask)

+        return super().summary(
+            event_type="spindles",
+            grp_chan=grp_chan,
+            grp_stage=grp_stage,
+            aggfunc=aggfunc,
+            sort=sort,
+            mask=mask,
+        )

 
 
[docs]    def get_coincidence_matrix(self, scaled=True):
         """Return the (scaled) coincidence matrix.
@@ -1070,14 +1261,118 @@ 
Source code for yasa.detection
         """
         return super().get_coincidence_matrix(scaled=scaled)

 
+
[docs]    def compare_channels(self, score="f1", max_distance_sec=0):
+        """
+        Compare detected spindles across channels.
+
+        This is a wrapper around the :py:func:`yasa.compare_detection` function. Please
+        refer to the documentation of this function for more details.
+
+        Parameters
+        ----------
+        score : str
+            The performance metric to compute. Accepted values are "precision", "recall"
+            (aka sensitivity) and "f1" (default). The F1-score is the harmonic mean of precision
+            and recall, and is usually the preferred metric to evaluate the agreement between
+            two channels. All three metrics are bounded by 0 and 1, where 1 indicates perfect
+            agreement.
+        max_distance_sec : float
+            The maximum distance between spindles, in seconds, to consider as the same event.
+
+            .. warning:: To reduce computation cost, YASA rounds the start time of each spindle to
+                the nearest decisecond (= 100 ms). This means that the lowest possible resolution
+                is 100 ms, regardless of the sampling frequency of the data. Two spindles starting
+                at 500 ms and 540 ms on their respective channels will therefore always be
+                considered the same event, even when max_distance_sec=0.
+
+        Returns
+        -------
+        scores : :py:class:`pandas.DataFrame`
+            A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).
+
+        Notes
+        -----
+        Some use cases of this function:
+
+        1. What proportion of spindles detected in one channel are also detected on
+           another channel (if using ``score="recall"``).
+        2. What is the overall agreement in the detected events between channels?
+        3. Is the agreement better in channels that are close to one another?
+        """
+        return super().compare_channels(score, max_distance_sec)

+
+
[docs]    def compare_detection(self, other, max_distance_sec=0, other_is_groundtruth=True):
+        """
+        Compare the detected spindles against either another YASA detection or against custom
+        annotations (e.g. ground-truth human scoring).
+
+        This function is a wrapper around the :py:func:`yasa.compare_detection` function. Please
+        refer to the documentation of this function for more details.
+
+        Parameters
+        ----------
+        other : dataframe or detection results
+            This can be either a) the output of another YASA detection, for example if you want to
+            test the impact of tweaking some parameters on the detected events or b) a pandas
+            DataFrame with custom annotations, obtained by another detection method outside
+            of YASA, or with manual labelling. If b), the dataframe must contain the "Start" and
+            "Channel" columns, with the start of each event in seconds from the beginning
+            of the recording and the channel name, respectively. The channel names should match
+            the output of the summary() method.
+        max_distance_sec : float
+            The maximum distance between spindles, in seconds, to consider as the same event.
+
+            .. warning:: To reduce computation cost, YASA rounds the start time of each spindle to
+                the nearest decisecond (= 100 ms). This means that the lowest possible resolution
+                is 100 ms, regardless of the sampling frequency of the data.
+        other_is_groundtruth : bool
+            If True (default), ``other`` will be considered as the ground-truth scoring. If False,
+            the current detection will be considered as the ground-truth, and the precision and
+            recall scores will be inverted. This parameter has no effect on the F1-score.
+
+            .. note:: when ``other`` is the ground-truth (default), the recall score is the
+                fraction of events in other that were succesfully detected by the current
+                detection, and the precision score is the proportion of detected events by the
+                current detection that are also present in other.
+
+        Returns
+        -------
+        scores : :py:class:`pandas.DataFrame`
+            A Pandas DataFrame with the channel names as index, and the following columns
+
+            * ``precision``: Precision score, aka positive predictive value
+            * ``recall``: Recall score, aka sensitivity
+            * ``f1``: F1-score
+            * ``n_self``: Number of detected events in ``self`` (current method).
+            * ``n_other``: Number of detected events in ``other``.
+
+        Notes
+        -----
+        Some use cases of this function:
+
+        1. How well does YASA events detection perform against ground-truth human annotations?
+        2. If I change the threshold(s) of the events detection, do the detected events match
+           those obtained with the default parameters?
+        3. Which detection thresholds give the highest agreement with the ground-truth scoring?
+        """
+        return super().compare_detection(other, max_distance_sec, other_is_groundtruth)

+
 
[docs]    def get_mask(self):
-        """Return a boolean array indicating for each sample in data if this
+        """
+        Return a boolean array indicating for each sample in data if this
         sample is part of a detected event (True) or not (False).
         """
         return super().get_mask()

 
-
[docs]    def get_sync_events(self, center='Peak', time_before=1, time_after=1, filt=(None, None),
-                        mask=None, as_dataframe=True):
+
[docs]    def get_sync_events(
+        self,
+        center="Peak",
+        time_before=1,
+        time_after=1,
+        filt=(None, None),
+        mask=None,
+        as_dataframe=True,
+    ):
         """
         Return the raw or filtered data of each detected event after
         centering to a specific timepoint.
@@ -1116,12 +1411,26 @@ 
Source code for yasa.detection
             'IdxChannel' : Index of channel in data
             'Stage': Sleep stage in which the events occured (if available)
         """
-        return super().get_sync_events(center=center, time_before=time_before,
-                                       time_after=time_after, filt=filt, mask=mask,
-                                       as_dataframe=as_dataframe)

-
-
[docs]    def plot_average(self, center='Peak', hue='Channel', time_before=1,
-                     time_after=1, filt=(None, None), mask=None, figsize=(6, 4.5), **kwargs):
+        return super().get_sync_events(
+            center=center,
+            time_before=time_before,
+            time_after=time_after,
+            filt=filt,
+            mask=mask,
+            as_dataframe=as_dataframe,
+        )

+
+
[docs]    def plot_average(
+        self,
+        center="Peak",
+        hue="Channel",
+        time_before=1,
+        time_after=1,
+        filt=(None, None),
+        mask=None,
+        figsize=(6, 4.5),
+        **kwargs,
+    ):
         """
         Plot the average spindle.
 
@@ -1150,10 +1459,17 @@ 
Source code for yasa.detection
         **kwargs : dict
             Optional argument that are passed to :py:func:`seaborn.lineplot`.
         """
-        return super().plot_average(event_type='spindles', center=center,
-                                    hue=hue, time_before=time_before,
-                                    time_after=time_after, filt=filt, mask=mask,
-                                    figsize=figsize, **kwargs)

+        return super().plot_average(
+            event_type="spindles",
+            center=center,
+            hue=hue,
+            time_before=time_before,
+            time_after=time_after,
+            filt=filt,
+            mask=mask,
+            figsize=figsize,
+            **kwargs,
+        )

 
 
[docs]    def plot_detection(self):
         """Plot an overlay of the detected spindles on the EEG signal.
@@ -1175,11 +1491,23 @@ 
Source code for yasa.detection
 #############################################################################
 
 
-
[docs]def sw_detect(data, sf=None, ch_names=None, hypno=None, include=(2, 3), freq_sw=(0.3, 1.5),
-              dur_neg=(0.3, 1.5), dur_pos=(0.1, 1), amp_neg=(40, 200), amp_pos=(10, 150),
-              amp_ptp=(75, 350), coupling=False,
-              coupling_params={"freq_sp": (12, 16), "time": 1, "p": 0.05},
-              remove_outliers=False, verbose=False):
+
[docs]def sw_detect(
+    data,
+    sf=None,
+    ch_names=None,
+    hypno=None,
+    include=(2, 3),
+    freq_sw=(0.3, 1.5),
+    dur_neg=(0.3, 1.5),
+    dur_pos=(0.1, 1),
+    amp_neg=(40, 200),
+    amp_pos=(10, 150),
+    amp_ptp=(75, 350),
+    coupling=False,
+    coupling_params={"freq_sp": (12, 16), "time": 1, "p": 0.05},
+    remove_outliers=False,
+    verbose=False,
+):
     """Slow-waves detection.
 
     Parameters
@@ -1387,12 +1715,13 @@ 
Source code for yasa.detection
     """
     set_log_level(verbose)
 
-    (data, sf, ch_names, hypno, include, mask, n_chan, n_samples, bad_chan
-     ) = _check_data_hypno(data, sf, ch_names, hypno, include)
+    (data, sf, ch_names, hypno, include, mask, n_chan, n_samples, bad_chan) = _check_data_hypno(
+        data, sf, ch_names, hypno, include
+    )
 
     # If all channels are bad
     if sum(bad_chan) == n_chan:
-        logger.warning('All channels have bad amplitude. Returning None.')
+        logger.warning("All channels have bad amplitude. Returning None.")
         return None
 
     # Define time vector
@@ -1402,13 +1731,21 @@ 
Source code for yasa.detection
     # Bandpass filter
     nfast = next_fast_len(n_samples)
     data_filt = filter_data(
-        data, sf, freq_sw[0], freq_sw[1], method='fir', verbose=0, l_trans_bandwidth=0.2,
-        h_trans_bandwidth=0.2)
+        data,
+        sf,
+        freq_sw[0],
+        freq_sw[1],
+        method="fir",
+        verbose=0,
+        l_trans_bandwidth=0.2,
+        h_trans_bandwidth=0.2,
+    )
 
     # Extract the spindles-related sigma signal for coupling
     if coupling:
         is_tensorpac_installed()
         import tensorpac.methods as tpm
+
         # The width of the transition band is set to 1.5 Hz on each side,
         # meaning that for freq_sp = (12, 15 Hz), the -6 dB points are located
         # at 11.25 and 15.75 Hz. The frequency band for the amplitude signal
@@ -1419,10 +1756,17 @@ 
Source code for yasa.detection
         assert "freq_sp" in coupling_params.keys()
         assert "time" in coupling_params.keys()
         assert "p" in coupling_params.keys()
-        freq_sp = coupling_params['freq_sp']
+        freq_sp = coupling_params["freq_sp"]
         data_sp = filter_data(
-            data, sf, freq_sp[0], freq_sp[1], method='fir', l_trans_bandwidth=1.5,
-            h_trans_bandwidth=1.5, verbose=0)
+            data,
+            sf,
+            freq_sp[0],
+            freq_sp[1],
+            method="fir",
+            l_trans_bandwidth=1.5,
+            h_trans_bandwidth=1.5,
+            verbose=0,
+        )
         # Now extract the instantaneous phase/amplitude using Hilbert transform
         sw_pha = np.angle(signal.hilbert(data_filt, N=nfast)[:, :n_samples])
         sp_amp = np.abs(signal.hilbert(data_sp, N=nfast)[:, :n_samples])
@@ -1449,7 +1793,7 @@ 
Source code for yasa.detection
 
         # If no peaks are detected, return None
         if len(idx_neg_peaks) == 0 or len(idx_pos_peaks) == 0:
-            logger.warning('No SW were found in channel %s.', ch_names[i])
+            logger.warning("No SW were found in channel %s.", ch_names[i])
             continue
 
         # Make sure that the last detected peak is a positive one
@@ -1464,12 +1808,12 @@ 
Source code for yasa.detection
         idx_pos_peaks = idx_neg_peaks + closest_pos_peaks
 
         # Now we compute the PTP amplitude and keep only the good peaks
-        sw_ptp = (np.abs(data_filt[i, idx_neg_peaks]) + data_filt[i, idx_pos_peaks])
+        sw_ptp = np.abs(data_filt[i, idx_neg_peaks]) + data_filt[i, idx_pos_peaks]
         good_ptp = np.logical_and(sw_ptp > amp_ptp[0], sw_ptp < amp_ptp[1])
 
         # If good_ptp is all False
         if all(~good_ptp):
-            logger.warning('No SW were found in channel %s.', ch_names[i])
+            logger.warning("No SW were found in channel %s.", ch_names[i])
             continue
 
         sw_ptp = sw_ptp[good_ptp]
@@ -1518,28 +1862,30 @@ 
Source code for yasa.detection
             sw_sta = np.zeros(sw_dur.shape)
 
         # And we apply a set of thresholds to remove bad slow waves
-        good_sw = np.logical_and.reduce((
-            # Data edges
-            previous_neg_zc != 0,
-            following_neg_zc != 0,
-            previous_pos_zc != 0,
-            following_pos_zc != 0,
-            # Duration criteria
-            sw_dur == sw_dur_both_phase,  # dur = negative + positive
-            sw_dur <= dur_neg[1] + dur_pos[1],  # dur < max(neg) + max(pos)
-            sw_dur >= dur_neg[0] + dur_pos[0],  # dur > min(neg) + min(pos)
-            neg_phase_dur > dur_neg[0],
-            neg_phase_dur < dur_neg[1],
-            pos_phase_dur > dur_pos[0],
-            pos_phase_dur < dur_pos[1],
-            # Sanity checks
-            sw_midcrossing > sw_start,
-            sw_midcrossing < sw_end,
-            sw_slope > 0,
-        ))
+        good_sw = np.logical_and.reduce(
+            (
+                # Data edges
+                previous_neg_zc != 0,
+                following_neg_zc != 0,
+                previous_pos_zc != 0,
+                following_pos_zc != 0,
+                # Duration criteria
+                sw_dur == sw_dur_both_phase,  # dur = negative + positive
+                sw_dur <= dur_neg[1] + dur_pos[1],  # dur < max(neg) + max(pos)
+                sw_dur >= dur_neg[0] + dur_pos[0],  # dur > min(neg) + min(pos)
+                neg_phase_dur > dur_neg[0],
+                neg_phase_dur < dur_neg[1],
+                pos_phase_dur > dur_pos[0],
+                pos_phase_dur < dur_pos[1],
+                # Sanity checks
+                sw_midcrossing > sw_start,
+                sw_midcrossing < sw_end,
+                sw_slope > 0,
+            )
+        )
 
         if all(~good_sw):
-            logger.warning('No SW were found in channel %s.', ch_names[i])
+            logger.warning("No SW were found in channel %s.", ch_names[i])
             continue
 
         # Filter good events
@@ -1556,28 +1902,33 @@ 
Source code for yasa.detection
         sw_sta = sw_sta[good_sw]
 
         # Create a dictionnary
-        sw_params = OrderedDict({
-            'Start': sw_start,
-            'NegPeak': sw_idx_neg,
-            'MidCrossing': sw_midcrossing,
-            'PosPeak': sw_idx_pos,
-            'End': sw_end,
-            'Duration': sw_dur,
-            'ValNegPeak': data_filt[i, idx_neg_peaks],
-            'ValPosPeak': data_filt[i, idx_pos_peaks],
-            'PTP': sw_ptp,
-            'Slope': sw_slope,
-            'Frequency': 1 / sw_dur,
-            'Stage': sw_sta,
-        })
+        sw_params = OrderedDict(
+            {
+                "Start": sw_start,
+                "NegPeak": sw_idx_neg,
+                "MidCrossing": sw_midcrossing,
+                "PosPeak": sw_idx_pos,
+                "End": sw_end,
+                "Duration": sw_dur,
+                "ValNegPeak": data_filt[i, idx_neg_peaks],
+                "ValPosPeak": data_filt[i, idx_pos_peaks],
+                "PTP": sw_ptp,
+                "Slope": sw_slope,
+                "Frequency": 1 / sw_dur,
+                "Stage": sw_sta,
+            }
+        )
 
         # Add phase (in radians) of slow-oscillation signal at maximum
         # spindles-related sigma amplitude within a XX-seconds centered epochs.
         if coupling:
             # Get phase and amplitude for each centered epoch
-            time_before = time_after = coupling_params['time']
-            assert float(sf * time_before).is_integer(), (
-                "Invalid time parameter for coupling. Must be a whole number of samples.")
+            time_before = time_after = coupling_params["time"]
+            assert float(
+                sf * time_before
+            ).is_integer(), (
+                "Invalid time parameter for coupling. Must be a whole number of samples."
+            )
             bef = int(sf * time_before)
             aft = int(sf * time_after)
             # Center of each epoch is defined as the negative peak of the SW
@@ -1591,69 +1942,74 @@ 
Source code for yasa.detection
             # Now we need to append it back to the original unmasked shape
             # to avoid error when idx.shape[0] != idx_valid.shape, i.e.
             # some epochs were out of data bounds.
-            sw_params['SigmaPeak'] = np.ones(n_peaks) * np.nan
+            sw_params["SigmaPeak"] = np.ones(n_peaks) * np.nan
             # Timestamp at sigma peak, expressed in seconds from negative peak
             # e.g. -0.39, 0.5, 1, 2 -- limits are [time_before, time_after]
             time_sigpk = (idx_max_amp - bef) / sf
             # convert to absolute time from beginning of the recording
             # time_sigpk only includes valid epoch
             time_sigpk_abs = sw_idx_neg[idx_valid] + time_sigpk
-            sw_params['SigmaPeak'][idx_valid] = time_sigpk_abs
+            sw_params["SigmaPeak"][idx_valid] = time_sigpk_abs
             # 2) PhaseAtSigmaPeak
             # Find SW phase at max sigma amplitude in epoch
             pha_at_max = np.squeeze(np.take_along_axis(sw_pha_ev, idx_max_amp[..., None], axis=1))
-            sw_params['PhaseAtSigmaPeak'] = np.ones(n_peaks) * np.nan
-            sw_params['PhaseAtSigmaPeak'][idx_valid] = pha_at_max
+            sw_params["PhaseAtSigmaPeak"] = np.ones(n_peaks) * np.nan
+            sw_params["PhaseAtSigmaPeak"][idx_valid] = pha_at_max
             # 3) Normalized Direct PAC, with thresholding
             # Unreliable values are set to 0
-            ndp = np.squeeze(tpm.norm_direct_pac(
-                sw_pha_ev[None, ...], sp_amp_ev[None, ...], p=coupling_params['p']))
-            sw_params['ndPAC'] = np.ones(n_peaks) * np.nan
-            sw_params['ndPAC'][idx_valid] = ndp
+            ndp = np.squeeze(
+                tpm.norm_direct_pac(
+                    sw_pha_ev[None, ...], sp_amp_ev[None, ...], p=coupling_params["p"]
+                )
+            )
+            sw_params["ndPAC"] = np.ones(n_peaks) * np.nan
+            sw_params["ndPAC"][idx_valid] = ndp
             # Make sure that Stage is the last column of the dataframe
-            sw_params.move_to_end('Stage')
+            sw_params.move_to_end("Stage")
 
         # Convert to dataframe, keeping only good events
         df_chan = pd.DataFrame(sw_params)
 
         # Remove all duplicates
-        df_chan = df_chan.drop_duplicates(subset=['Start'], keep=False)
-        df_chan = df_chan.drop_duplicates(subset=['End'], keep=False)
+        df_chan = df_chan.drop_duplicates(subset=["Start"], keep=False)
+        df_chan = df_chan.drop_duplicates(subset=["End"], keep=False)
 
         # We need at least 50 detected slow waves to apply the Isolation Forest
         if remove_outliers and df_chan.shape[0] >= 50:
-            col_keep = ['Duration', 'ValNegPeak', 'ValPosPeak', 'PTP', 'Slope', 'Frequency']
-            ilf = IsolationForest(contamination='auto', max_samples='auto',
-                                  verbose=0, random_state=42)
+            col_keep = ["Duration", "ValNegPeak", "ValPosPeak", "PTP", "Slope", "Frequency"]
+            ilf = IsolationForest(
+                contamination="auto", max_samples="auto", verbose=0, random_state=42
+            )
             good = ilf.fit_predict(df_chan[col_keep])
             good[good == -1] = 0
-            logger.info('%i outliers were removed in channel %s.'
-                        % ((good == 0).sum(), ch_names[i]))
+            logger.info(
+                "%i outliers were removed in channel %s." % ((good == 0).sum(), ch_names[i])
+            )
             # Remove outliers from DataFrame
             df_chan = df_chan[good.astype(bool)]
-            logger.info('%i slow-waves were found in channel %s.'
-                        % (df_chan.shape[0], ch_names[i]))
+            logger.info("%i slow-waves were found in channel %s." % (df_chan.shape[0], ch_names[i]))
 
         # ####################################################################
         # END SINGLE CHANNEL DETECTION
         # ####################################################################
 
-        df_chan['Channel'] = ch_names[i]
-        df_chan['IdxChannel'] = i
+        df_chan["Channel"] = ch_names[i]
+        df_chan["IdxChannel"] = i
         df = pd.concat([df, df_chan], axis=0, ignore_index=True)
 
     # If no SW were detected, return None
     if df.empty:
-        logger.warning('No SW were found in data. Returning None.')
+        logger.warning("No SW were found in data. Returning None.")
         return None
 
     if hypno is None:
-        df = df.drop(columns=['Stage'])
+        df = df.drop(columns=["Stage"])
     else:
-        df['Stage'] = df['Stage'].astype(int)
+        df["Stage"] = df["Stage"].astype(int)
 
-    return SWResults(events=df, data=data, sf=sf, ch_names=ch_names,
-                     hypno=hypno, data_filt=data_filt)

+    return SWResults(
+        events=df, data=data, sf=sf, ch_names=ch_names, hypno=hypno, data_filt=data_filt
+    )

 
 
 
[docs]class SWResults(_DetectionResults):
@@ -1678,7 +2034,7 @@ 
Source code for yasa.detection
 
[docs]    def __init__(self, events, data, sf, ch_names, hypno, data_filt):
         super().__init__(events, data, sf, ch_names, hypno, data_filt)

 
-
[docs]    def summary(self, grp_chan=False, grp_stage=False, mask=None, aggfunc='mean', sort=True):
+
[docs]    def summary(self, grp_chan=False, grp_stage=False, mask=None, aggfunc="mean", sort=True):
         """Return a summary of the SW detection, optionally grouped across
         channels and/or stage.
 
@@ -1696,8 +2052,14 @@ 
Source code for yasa.detection
         sort : bool
             If True, sort group keys when grouping.
         """
-        return super().summary(event_type='sw', grp_chan=grp_chan, grp_stage=grp_stage,
-                               aggfunc=aggfunc, sort=sort, mask=mask)

+        return super().summary(
+            event_type="sw",
+            grp_chan=grp_chan,
+            grp_stage=grp_stage,
+            aggfunc=aggfunc,
+            sort=sort,
+            mask=mask,
+        )

 
 
[docs]    def find_cooccurring_spindles(self, spindles, lookaround=1.2):
         """Given a spindles detection summary dataframe, find slow-waves that co-occur with
@@ -1753,13 +2115,13 @@ 
Source code for yasa.detection
         cooccurring_spindle_peaks = []
 
         # Find intersecting channels
-        common_ch = np.intersect1d(self._events['Channel'].unique(), spindles['Channel'].unique())
+        common_ch = np.intersect1d(self._events["Channel"].unique(), spindles["Channel"].unique())
         assert len(common_ch), "No common channel(s) were found."
 
         # Loop across channels
-        for chan in self._events['Channel'].unique():
+        for chan in self._events["Channel"].unique():
             sw_chan_peaks = self._events[self._events["Channel"] == chan]["NegPeak"].to_numpy()
-            sp_chan_peaks = spindles[spindles["Channel"] == chan]['Peak'].to_numpy()
+            sp_chan_peaks = spindles[spindles["Channel"] == chan]["Peak"].to_numpy()
             # Loop across individual slow-waves
             for sw_negpeak in sw_chan_peaks:
                 start = sw_negpeak - lookaround
@@ -1777,7 +2139,103 @@ 
Source code for yasa.detection
         # Add columns to self._events: IN-PLACE MODIFICATION!
         self._events["CooccurringSpindle"] = ~np.isnan(distance_sp_to_sw_peak)
         self._events["CooccurringSpindlePeak"] = cooccurring_spindle_peaks
-        self._events['DistanceSpindleToSW'] = distance_sp_to_sw_peak

+        self._events["DistanceSpindleToSW"] = distance_sp_to_sw_peak

+
+
[docs]    def compare_channels(self, score="f1", max_distance_sec=0):
+        """
+        Compare detected slow-waves across channels.
+
+        This is a wrapper around the :py:func:`yasa.compare_detection` function. Please
+        refer to the documentation of this function for more details.
+
+        Parameters
+        ----------
+        score : str
+            The performance metric to compute. Accepted values are "precision", "recall"
+            (aka sensitivity) and "f1" (default). The F1-score is the harmonic mean of precision
+            and recall, and is usually the preferred metric to evaluate the agreement between
+            two channels. All three metrics are bounded by 0 and 1, where 1 indicates perfect
+            agreement.
+        max_distance_sec : float
+            The maximum distance between slow-waves, in seconds, to consider as the same event.
+
+            .. warning:: To reduce computation cost, YASA rounds the start time of each spindle to
+                the nearest decisecond (= 100 ms). This means that the lowest possible resolution
+                is 100 ms, regardless of the sampling frequency of the data. Two slow-waves
+                starting at 500 ms and 540 ms on their respective channels will therefore always be
+                considered the same event, even when max_distance_sec=0.
+
+        Returns
+        -------
+        scores : :py:class:`pandas.DataFrame`
+            A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).
+
+        Notes
+        -----
+        Some use cases of this function:
+
+        1. What proportion of slow-waves detected in one channel are also detected on
+           another channel (if using ``score="recall"``).
+        2. What is the overall agreement in the detected events between channels?
+        3. Is the agreement better in channels that are close to one another?
+        """
+        return super().compare_channels(score, max_distance_sec)

+
+
[docs]    def compare_detection(self, other, max_distance_sec=0, other_is_groundtruth=True):
+        """
+        Compare the detected slow-waves against either another YASA detection or against custom
+        annotations (e.g. ground-truth human scoring).
+
+        This function is a wrapper around the :py:func:`yasa.compare_detection` function. Please
+        refer to the documentation of this function for more details.
+
+        Parameters
+        ----------
+        other : dataframe or detection results
+            This can be either a) the output of another YASA detection, for example if you want to
+            test the impact of tweaking some parameters on the detected events or b) a pandas
+            DataFrame with custom annotations, obtained by another detection method outside
+            of YASA, or with manual labelling. If b), the dataframe must contain the "Start" and
+            "Channel" columns, with the start of each event in seconds from the beginning
+            of the recording and the channel name, respectively. The channel names should match
+            the output of the summary() method.
+        max_distance_sec : float
+            The maximum distance between slow-waves, in seconds, to consider as the same event.
+
+            .. warning:: To reduce computation cost, YASA rounds the start time of each slow-wave
+                to the nearest decisecond (= 100 ms). This means that the lowest possible
+                resolution is 100 ms, regardless of the sampling frequency of the data.
+        other_is_groundtruth : bool
+            If True (default), ``other`` will be considered as the ground-truth scoring. If False,
+            the current detection will be considered as the ground-truth, and the precision and
+            recall scores will be inverted. This parameter has no effect on the F1-score.
+
+            .. note:: when ``other`` is the ground-truth (default), the recall score is the
+                fraction of events in other that were succesfully detected by the current
+                detection, and the precision score is the proportion of detected events by the
+                current detection that are also present in other.
+
+        Returns
+        -------
+        scores : :py:class:`pandas.DataFrame`
+            A Pandas DataFrame with the channel names as index, and the following columns
+
+            * ``precision``: Precision score, aka positive predictive value
+            * ``recall``: Recall score, aka sensitivity
+            * ``f1``: F1-score
+            * ``n_self``: Number of detected events in ``self`` (current method).
+            * ``n_other``: Number of detected events in ``other``.
+
+        Notes
+        -----
+        Some use cases of this function:
+
+        1. How well does YASA events detection perform against ground-truth human annotations?
+        2. If I change the threshold(s) of the events detection, do the detected events match
+           those obtained with the default parameters?
+        3. Which detection thresholds give the highest agreement with the ground-truth scoring?
+        """
+        return super().compare_detection(other, max_distance_sec, other_is_groundtruth)

 
 
[docs]    def get_coincidence_matrix(self, scaled=True):
         """Return the (scaled) coincidence matrix.
@@ -1834,8 +2292,15 @@ 
Source code for yasa.detection
         """
         return super().get_mask()

 
-
[docs]    def get_sync_events(self, center='NegPeak', time_before=0.4, time_after=0.8, filt=(None, None),
-                        mask=None, as_dataframe=True):
+
[docs]    def get_sync_events(
+        self,
+        center="NegPeak",
+        time_before=0.4,
+        time_after=0.8,
+        filt=(None, None),
+        mask=None,
+        as_dataframe=True,
+    ):
         """
         Return the raw data of each detected event after centering to a specific timepoint.
 
@@ -1874,11 +2339,25 @@ 
Source code for yasa.detection
             'Stage': Sleep stage in which the events occured (if available)
         """
         return super().get_sync_events(
-            center=center, time_before=time_before, time_after=time_after, filt=filt, mask=mask,
-            as_dataframe=as_dataframe)

-
-
[docs]    def plot_average(self, center='NegPeak', hue='Channel', time_before=0.4, time_after=0.8,
-                     filt=(None, None), mask=None, figsize=(6, 4.5), **kwargs):
+            center=center,
+            time_before=time_before,
+            time_after=time_after,
+            filt=filt,
+            mask=mask,
+            as_dataframe=as_dataframe,
+        )

+
+
[docs]    def plot_average(
+        self,
+        center="NegPeak",
+        hue="Channel",
+        time_before=0.4,
+        time_after=0.8,
+        filt=(None, None),
+        mask=None,
+        figsize=(6, 4.5),
+        **kwargs,
+    ):
         """
         Plot the average slow-wave.
 
@@ -1908,8 +2387,16 @@ 
Source code for yasa.detection
             Optional argument that are passed to :py:func:`seaborn.lineplot`.
         """
         return super().plot_average(
-            event_type='sw', center=center, hue=hue, time_before=time_before,
-            time_after=time_after, filt=filt, mask=mask, figsize=figsize, **kwargs)

+            event_type="sw",
+            center=center,
+            hue=hue,
+            time_before=time_before,
+            time_after=time_after,
+            filt=filt,
+            mask=mask,
+            figsize=figsize,
+            **kwargs,
+        )

 
 
[docs]    def plot_detection(self):
         """Plot an overlay of the detected slow-waves on the EEG signal.
@@ -1931,8 +2418,18 @@ 
Source code for yasa.detection
 #############################################################################
 
 
-
[docs]def rem_detect(loc, roc, sf, hypno=None, include=4, amplitude=(50, 325), duration=(0.3, 1.2),
-               freq_rem=(0.5, 5), remove_outliers=False, verbose=False):
+
[docs]def rem_detect(
+    loc,
+    roc,
+    sf,
+    hypno=None,
+    include=4,
+    amplitude=(50, 325),
+    duration=(0.3, 1.2),
+    freq_rem=(0.5, 5),
+    remove_outliers=False,
+    verbose=False,
+):
     """Rapid eye movements (REMs) detection.
 
     This detection requires both the left EOG (LOC) and right EOG (LOC).
@@ -1951,10 +2448,9 @@ 
Source code for yasa.detection
         .. warning::
             The default unit of :py:class:`mne.io.BaseRaw` is Volts.
             Therefore, if passing data from a :py:class:`mne.io.BaseRaw`,
-            you need to multiply the data by 1e6 to convert to micro-Volts
-            (1 V = 1,000,000 uV), e.g.:
+            make sure to use units="uV" to get the data in micro-Volts, e.g.:
 
-            >>> data = raw.get_data() * 1e6  # Make sure that data is in uV
+            >>> data = raw.get_data(units="uV")  # Make sure that data is in uV
     sf : float
         Sampling frequency of the data, in Hz.
     hypno : array_like
@@ -2063,18 +2559,18 @@ 
Source code for yasa.detection
     # Safety checks
     loc = np.squeeze(np.asarray(loc, dtype=np.float64))
     roc = np.squeeze(np.asarray(roc, dtype=np.float64))
-    assert loc.ndim == 1, 'LOC must be 1D.'
-    assert roc.ndim == 1, 'ROC must be 1D.'
-    assert loc.size == roc.size, 'LOC and ROC must have the same size.'
+    assert loc.ndim == 1, "LOC must be 1D."
+    assert roc.ndim == 1, "ROC must be 1D."
+    assert loc.size == roc.size, "LOC and ROC must have the same size."
     data = np.vstack((loc, roc))
 
-    (data, sf, ch_names, hypno, include, mask, n_chan, n_samples, bad_chan
-     ) = _check_data_hypno(data, sf, ['LOC', 'ROC'], hypno, include)
+    (data, sf, ch_names, hypno, include, mask, n_chan, n_samples, bad_chan) = _check_data_hypno(
+        data, sf, ["LOC", "ROC"], hypno, include
+    )
 
     # If all channels are bad
     if any(bad_chan):
-        logger.warning('At least one channel has bad amplitude. '
-                       'Returning None.')
+        logger.warning("At least one channel has bad amplitude. " "Returning None.")
         return None
 
     # Bandpass filter
@@ -2088,9 +2584,14 @@ 
Source code for yasa.detection
     # - distance: required distance in samples between neighboring peaks.
     # - prominence: required prominence of peaks.
     # - wlen: limit search for bases to a specific window.
-    hmin, hmax = amplitude[0]**2, amplitude[1]**2
-    pks, pks_params = signal.find_peaks(negp, height=(hmin, hmax), distance=(duration[0] * sf),
-                                        prominence=(0.8 * hmin), wlen=(duration[1] * sf))
+    hmin, hmax = amplitude[0] ** 2, amplitude[1] ** 2
+    pks, pks_params = signal.find_peaks(
+        negp,
+        height=(hmin, hmax),
+        distance=(duration[0] * sf),
+        prominence=(0.8 * hmin),
+        wlen=(duration[1] * sf),
+    )
 
     # Intersect with sleep stage vector
     # We do that before calculating the features in order to gain some time
@@ -2101,78 +2602,98 @@ 
Source code for yasa.detection
 
     # If no peaks are detected, return None
     if len(pks) == 0:
-        logger.warning('No REMs were found in data. Returning None.')
+        logger.warning("No REMs were found in data. Returning None.")
         return None
 
     # Hypnogram
     if hypno is not None:
         # The sleep stage at the beginning of the REM is considered.
-        rem_sta = hypno[pks_params['left_bases']]
+        rem_sta = hypno[pks_params["left_bases"]]
     else:
         rem_sta = np.zeros(pks.shape)
 
     # Calculate time features
-    pks_params['Start'] = pks_params['left_bases'] / sf
-    pks_params['Peak'] = pks / sf
-    pks_params['End'] = pks_params['right_bases'] / sf
-    pks_params['Duration'] = pks_params['End'] - pks_params['Start']
+    pks_params["Start"] = pks_params["left_bases"] / sf
+    pks_params["Peak"] = pks / sf
+    pks_params["End"] = pks_params["right_bases"] / sf
+    pks_params["Duration"] = pks_params["End"] - pks_params["Start"]
     # Time points in minutes (HH:MM:SS)
     # pks_params['StartMin'] = pd.to_timedelta(pks_params['Start'], unit='s').dt.round('s')  # noqa
     # pks_params['PeakMin'] = pd.to_timedelta(pks_params['Peak'], unit='s').dt.round('s')  # noqa
     # pks_params['EndMin'] = pd.to_timedelta(pks_params['End'], unit='s').dt.round('s')  # noqa
     # Absolute LOC / ROC value at peak (filtered)
-    pks_params['LOCAbsValPeak'] = abs(data_filt[0, pks])
-    pks_params['ROCAbsValPeak'] = abs(data_filt[1, pks])
+    pks_params["LOCAbsValPeak"] = abs(data_filt[0, pks])
+    pks_params["ROCAbsValPeak"] = abs(data_filt[1, pks])
     # Absolute rising and falling slope
-    dist_pk_left = (pks - pks_params['left_bases']) / sf
-    dist_pk_right = (pks_params['right_bases'] - pks) / sf
-    locrs = (data_filt[0, pks] - data_filt[0, pks_params['left_bases']]) / dist_pk_left
-    rocrs = (data_filt[1, pks] - data_filt[1, pks_params['left_bases']]) / dist_pk_left
-    locfs = (data_filt[0, pks_params['right_bases']] - data_filt[0, pks]) / dist_pk_right
-    rocfs = (data_filt[1, pks_params['right_bases']] - data_filt[1, pks]) / dist_pk_right
-    pks_params['LOCAbsRiseSlope'] = abs(locrs)
-    pks_params['ROCAbsRiseSlope'] = abs(rocrs)
-    pks_params['LOCAbsFallSlope'] = abs(locfs)
-    pks_params['ROCAbsFallSlope'] = abs(rocfs)
-    pks_params['Stage'] = rem_sta  # Sleep stage
+    dist_pk_left = (pks - pks_params["left_bases"]) / sf
+    dist_pk_right = (pks_params["right_bases"] - pks) / sf
+    locrs = (data_filt[0, pks] - data_filt[0, pks_params["left_bases"]]) / dist_pk_left
+    rocrs = (data_filt[1, pks] - data_filt[1, pks_params["left_bases"]]) / dist_pk_left
+    locfs = (data_filt[0, pks_params["right_bases"]] - data_filt[0, pks]) / dist_pk_right
+    rocfs = (data_filt[1, pks_params["right_bases"]] - data_filt[1, pks]) / dist_pk_right
+    pks_params["LOCAbsRiseSlope"] = abs(locrs)
+    pks_params["ROCAbsRiseSlope"] = abs(rocrs)
+    pks_params["LOCAbsFallSlope"] = abs(locfs)
+    pks_params["ROCAbsFallSlope"] = abs(rocfs)
+    pks_params["Stage"] = rem_sta  # Sleep stage
 
     # Convert to Pandas DataFrame
     df = pd.DataFrame(pks_params)
 
     # Make sure that the sign of ROC and LOC is opposite
-    df['IsOppositeSign'] = (np.sign(data_filt[1, pks]) != np.sign(data_filt[0, pks]))
+    df["IsOppositeSign"] = np.sign(data_filt[1, pks]) != np.sign(data_filt[0, pks])
     df = df[np.sign(data_filt[1, pks]) != np.sign(data_filt[0, pks])]
 
     # Remove bad duration
     tmin, tmax = duration
-    good_dur = np.logical_and(pks_params['Duration'] >= tmin, pks_params['Duration'] < tmax)
+    good_dur = np.logical_and(pks_params["Duration"] >= tmin, pks_params["Duration"] < tmax)
     df = df[good_dur]
 
     # Keep only useful channels
-    df = df[['Start', 'Peak', 'End', 'Duration', 'LOCAbsValPeak', 'ROCAbsValPeak',
-             'LOCAbsRiseSlope', 'ROCAbsRiseSlope', 'LOCAbsFallSlope', 'ROCAbsFallSlope', 'Stage']]
+    df = df[
+        [
+            "Start",
+            "Peak",
+            "End",
+            "Duration",
+            "LOCAbsValPeak",
+            "ROCAbsValPeak",
+            "LOCAbsRiseSlope",
+            "ROCAbsRiseSlope",
+            "LOCAbsFallSlope",
+            "ROCAbsFallSlope",
+            "Stage",
+        ]
+    ]
 
     if hypno is None:
-        df = df.drop(columns=['Stage'])
+        df = df.drop(columns=["Stage"])
     else:
-        df['Stage'] = df['Stage'].astype(int)
+        df["Stage"] = df["Stage"].astype(int)
 
     # We need at least 50 detected REMs to apply the Isolation Forest.
     if remove_outliers and df.shape[0] >= 50:
-        col_keep = ['Duration', 'LOCAbsValPeak', 'ROCAbsValPeak', 'LOCAbsRiseSlope',
-                    'ROCAbsRiseSlope', 'LOCAbsFallSlope', 'ROCAbsFallSlope']
-        ilf = IsolationForest(contamination='auto', max_samples='auto',
-                              verbose=0, random_state=42)
+        col_keep = [
+            "Duration",
+            "LOCAbsValPeak",
+            "ROCAbsValPeak",
+            "LOCAbsRiseSlope",
+            "ROCAbsRiseSlope",
+            "LOCAbsFallSlope",
+            "ROCAbsFallSlope",
+        ]
+        ilf = IsolationForest(contamination="auto", max_samples="auto", verbose=0, random_state=42)
         good = ilf.fit_predict(df[col_keep])
         good[good == -1] = 0
-        logger.info('%i outliers were removed.', (good == 0).sum())
+        logger.info("%i outliers were removed.", (good == 0).sum())
         # Remove outliers from DataFrame
         df = df[good.astype(bool)]
 
-    logger.info('%i REMs were found in data.', df.shape[0])
+    logger.info("%i REMs were found in data.", df.shape[0])
     df = df.reset_index(drop=True)
-    return REMResults(events=df, data=data, sf=sf, ch_names=ch_names,
-                      hypno=hypno, data_filt=data_filt)

+    return REMResults(
+        events=df, data=data, sf=sf, ch_names=ch_names, hypno=hypno, data_filt=data_filt
+    )

 
 
 
[docs]class REMResults(_DetectionResults):
@@ -2199,7 +2720,7 @@ 
Source code for yasa.detection
 
[docs]    def __init__(self, events, data, sf, ch_names, hypno, data_filt):
         super().__init__(events, data, sf, ch_names, hypno, data_filt)

 
-
[docs]    def summary(self, grp_stage=False, mask=None, aggfunc='mean', sort=True):
+
[docs]    def summary(self, grp_stage=False, mask=None, aggfunc="mean", sort=True):
         """Return a summary of the REM detection, optionally grouped across stage.
 
         Parameters
@@ -2217,8 +2738,14 @@ 
Source code for yasa.detection
         """
         # ``grp_chan`` is always False for REM detection because the
         # REMs are always detected on a combination of LOC and ROC.
-        return super().summary(event_type='rem', grp_chan=False, grp_stage=grp_stage,
-                               aggfunc=aggfunc, sort=sort, mask=mask)

+        return super().summary(
+            event_type="rem",
+            grp_chan=False,
+            grp_stage=grp_stage,
+            aggfunc=aggfunc,
+            sort=sort,
+            mask=mask,
+        )

 
 
[docs]    def get_mask(self):
         """Return a boolean array indicating for each sample in data if this
@@ -2226,14 +2753,15 @@ 
Source code for yasa.detection
         """
         # We cannot use super() because "Channel" is not present in _events.
         from yasa.others import _index_to_events
+
         mask = np.zeros(self._data.shape, dtype=int)
-        idx_ev = _index_to_events(
-            self._events[['Start', 'End']].to_numpy() * self._sf)
+        idx_ev = _index_to_events(self._events[["Start", "End"]].to_numpy() * self._sf)
         mask[:, idx_ev] = 1
         return mask

 
-
[docs]    def get_sync_events(self, center='Peak', time_before=0.4, time_after=0.4,
-                        filt=(None, None), mask=None):
+
[docs]    def get_sync_events(
+        self, center="Peak", time_before=0.4, time_after=0.4, filt=(None, None), mask=None
+    ):
         """
         Return the raw or filtered data of each detected event after centering to a specific
         timepoint.
@@ -2268,6 +2796,7 @@ 
Source code for yasa.detection
             'IdxChannel' : Index of channel in data
         """
         from yasa.others import get_centered_indices
+
         assert time_before >= 0
         assert time_after >= 0
         bef = int(self._sf * time_before)
@@ -2275,7 +2804,8 @@ 
Source code for yasa.detection
 
         if any(filt):
             data = mne.filter.filter_data(
-                self._data, self._sf, l_freq=filt[0], h_freq=filt[1], method='fir', verbose=False)
+                self._data, self._sf, l_freq=filt[0], h_freq=filt[1], method="fir", verbose=False
+            )
         else:
             data = self._data
 
@@ -2283,15 +2813,16 @@ 
Source code for yasa.detection
         mask = self._check_mask(mask)
         masked_events = self._events.loc[mask, :]
 
-        time = np.arange(-bef, aft + 1, dtype='int') / self._sf
+        time = np.arange(-bef, aft + 1, dtype="int") / self._sf
         # Get location of peaks in data
         peaks = (masked_events[center] * self._sf).astype(int).to_numpy()
         # Get centered indices (here we could use second channel as well).
         idx, idx_valid = get_centered_indices(data[0, :], peaks, bef, aft)
         # If no good epochs are returned raise a warning
         assert len(idx_valid), (
-            'Time before and/or time after exceed data bounds, please '
-            'lower the temporal window around center.')
+            "Time before and/or time after exceed data bounds, please "
+            "lower the temporal window around center."
+        )
 
         # Initialize empty dataframe
         df_sync = pd.DataFrame()
@@ -2300,16 +2831,24 @@ 
Source code for yasa.detection
         for i, ch in enumerate(self._ch_names):
             amps = data[i, idx]
             df_chan = pd.DataFrame(amps.T)
-            df_chan['Time'] = time
-            df_chan = df_chan.melt(id_vars='Time', var_name='Event', value_name='Amplitude')
-            df_chan['Channel'] = ch
-            df_chan['IdxChannel'] = i
+            df_chan["Time"] = time
+            df_chan = df_chan.melt(id_vars="Time", var_name="Event", value_name="Amplitude")
+            df_chan["Channel"] = ch
+            df_chan["IdxChannel"] = i
             df_sync = pd.concat([df_sync, df_chan], axis=0, ignore_index=True)
 
         return df_sync

 
-
[docs]    def plot_average(self, center='Peak', time_before=0.4, time_after=0.4, filt=(None, None),
-                     mask=None, figsize=(6, 4.5), **kwargs):
+
[docs]    def plot_average(
+        self,
+        center="Peak",
+        time_before=0.4,
+        time_after=0.4,
+        filt=(None, None),
+        mask=None,
+        figsize=(6, 4.5),
+        **kwargs,
+    ):
         """
         Plot the average REM.
 
@@ -2338,17 +2877,18 @@ 
Source code for yasa.detection
         import seaborn as sns
         import matplotlib.pyplot as plt
 
-        df_sync = self.get_sync_events(center=center, time_before=time_before,
-                                       time_after=time_after, filt=filt, mask=mask)
+        df_sync = self.get_sync_events(
+            center=center, time_before=time_before, time_after=time_after, filt=filt, mask=mask
+        )
 
         # Start figure
         fig, ax = plt.subplots(1, 1, figsize=figsize)
-        sns.lineplot(data=df_sync, x='Time', y='Amplitude', hue='Channel', ax=ax, **kwargs)
+        sns.lineplot(data=df_sync, x="Time", y="Amplitude", hue="Channel", ax=ax, **kwargs)
         # ax.legend(frameon=False, loc='lower right')
-        ax.set_xlim(df_sync['Time'].min(), df_sync['Time'].max())
+        ax.set_xlim(df_sync["Time"].min(), df_sync["Time"].max())
         ax.set_title("Average REM")
-        ax.set_xlabel('Time (sec)')
-        ax.set_ylabel('Amplitude (uV)')
+        ax.set_xlabel("Time (sec)")
+        ax.set_ylabel("Amplitude (uV)")
         return ax

 
 
@@ -2357,8 +2897,17 @@ 
Source code for yasa.detection
 #############################################################################
 
 
-
[docs]def art_detect(data, sf=None, window=5, hypno=None, include=(1, 2, 3, 4),
-               method='covar', threshold=3, n_chan_reject=1, verbose=False):
+
[docs]def art_detect(
+    data,
+    sf=None,
+    window=5,
+    hypno=None,
+    include=(1, 2, 3, 4),
+    method="covar",
+    threshold=3,
+    n_chan_reject=1,
+    verbose=False,
+):
     r"""
     Automatic artifact rejection.
 
@@ -2537,23 +3086,24 @@ 
Source code for yasa.detection
     ###########################################################################
     set_log_level(verbose)
 
-    (data, sf, _, hypno, include, _, n_chan, n_samples, _
-     ) = _check_data_hypno(data, sf, ch_names=None, hypno=hypno, include=include, check_amp=False)
+    (data, sf, _, hypno, include, _, n_chan, n_samples, _) = _check_data_hypno(
+        data, sf, ch_names=None, hypno=hypno, include=include, check_amp=False
+    )
 
-    assert isinstance(n_chan_reject, int), 'n_chan_reject must be int.'
-    assert n_chan_reject >= 1, 'n_chan_reject must be >= 1.'
-    assert n_chan_reject <= n_chan, 'n_chan_reject must be <= n_chan.'
+    assert isinstance(n_chan_reject, int), "n_chan_reject must be int."
+    assert n_chan_reject >= 1, "n_chan_reject must be >= 1."
+    assert n_chan_reject <= n_chan, "n_chan_reject must be <= n_chan."
 
     # Safety check: sampling frequency and window
-    assert isinstance(sf, (int, float)), 'sf must be int or float'
-    assert isinstance(window, (int, float)), 'window must be int or float'
+    assert isinstance(sf, (int, float)), "sf must be int or float"
+    assert isinstance(window, (int, float)), "window must be int or float"
     if isinstance(sf, float):
-        assert sf.is_integer(), 'sf must be a whole number.'
+        assert sf.is_integer(), "sf must be a whole number."
         sf = int(sf)
     win_sec = window
     window = win_sec * sf  # Convert window to samples
     if isinstance(window, float):
-        assert window.is_integer(), 'window * sf must be a whole number.'
+        assert window.is_integer(), "window * sf must be a whole number."
         window = int(window)
 
     # Safety check: hypnogram
@@ -2565,24 +3115,27 @@ 
Source code for yasa.detection
     # Safety checks: methods
     assert isinstance(method, str), "method must be a string."
     method = method.lower()
-    if method in ['cov', 'covar', 'covariance', 'riemann', 'potato']:
-        method = 'covar'
+    if method in ["cov", "covar", "covariance", "riemann", "potato"]:
+        method = "covar"
         is_pyriemann_installed()
         from pyriemann.estimation import Covariances, Shrinkage
         from pyriemann.clustering import Potato
+
         # Must have at least 4 channels to use method='covar'
         if n_chan <= 4:
-            logger.warning("Must have at least 4 channels for method='covar'. "
-                           "Automatically switching to method='std'.")
-            method = 'std'
+            logger.warning(
+                "Must have at least 4 channels for method='covar'. "
+                "Automatically switching to method='std'."
+            )
+            method = "std"
 
     ###########################################################################
     # START THE REJECTION
     ###########################################################################
     # Remove flat channels
-    isflat = (np.nanstd(data, axis=-1) == 0)
+    isflat = np.nanstd(data, axis=-1) == 0
     if isflat.any():
-        logger.warning('Flat channel(s) were found and removed in data.')
+        logger.warning("Flat channel(s) were found and removed in data.")
         data = data[~isflat]
         n_chan = data.shape[0]
 
@@ -2600,13 +3153,13 @@ 
Source code for yasa.detection
     n_flat_epochs = where_flat_epochs.size
 
     # Now let's make sure that we have an hypnogram and an include variable
-    if 'hypno_win' not in locals():
+    if "hypno_win" not in locals():
         # [-2, -2, -2, -2, ...], where -2 stands for unscored
-        hypno_win = -2 * np.ones(n_epochs, dtype='float')
-        include = np.array([-2], dtype='float')
+        hypno_win = -2 * np.ones(n_epochs, dtype="float")
+        include = np.array([-2], dtype="float")
 
     # We want to make sure that hypno-win and n_epochs have EXACTLY same shape
-    assert n_epochs == hypno_win.shape[-1], 'Hypno and epochs do not match.'
+    assert n_epochs == hypno_win.shape[-1], "Hypno and epochs do not match."
 
     # Finally, we make sure not to include any flat epochs in calculation
     # just using a random number that is unlikely to be picked by users
@@ -2614,19 +3167,19 @@ 
Source code for yasa.detection
         hypno_win[where_flat_epochs] = -111991
 
     # Add logger info
-    logger.info('Number of channels in data = %i', n_chan)
-    logger.info('Number of samples in data = %i', n_samples)
-    logger.info('Sampling frequency = %.2f Hz', sf)
-    logger.info('Data duration = %.2f seconds', n_samples / sf)
-    logger.info('Number of epochs = %i' % n_epochs)
-    logger.info('Artifact window = %.2f seconds' % win_sec)
-    logger.info('Method = %s' % method)
-    logger.info('Threshold = %.2f standard deviations' % threshold)
+    logger.info("Number of channels in data = %i", n_chan)
+    logger.info("Number of samples in data = %i", n_samples)
+    logger.info("Sampling frequency = %.2f Hz", sf)
+    logger.info("Data duration = %.2f seconds", n_samples / sf)
+    logger.info("Number of epochs = %i" % n_epochs)
+    logger.info("Artifact window = %.2f seconds" % win_sec)
+    logger.info("Method = %s" % method)
+    logger.info("Threshold = %.2f standard deviations" % threshold)
 
     # Create empty `hypno_art` vector (1 sample = 1 epoch)
-    epoch_is_art = np.zeros(n_epochs, dtype='int')
+    epoch_is_art = np.zeros(n_epochs, dtype="int")
 
-    if method == 'covar':
+    if method == "covar":
         # Calculate the covariance matrices,
         # shape (n_epochs, n_chan, n_chan)
         covmats = Covariances().fit_transform(epochs)
@@ -2634,10 +3187,11 @@ 
Source code for yasa.detection
         covmats = Shrinkage().fit_transform(covmats)
         # Define Potato instance: 0 = clean, 1 = art
         # To increase speed we set the max number of iterations from 10 to 100
-        potato = Potato(metric='riemann', threshold=threshold, pos_label=0,
-                        neg_label=1, n_iter_max=10)
+        potato = Potato(
+            metric="riemann", threshold=threshold, pos_label=0, neg_label=1, n_iter_max=10
+        )
         # Create empty z-scores output (n_epochs)
-        zscores = np.zeros(n_epochs, dtype='float') * np.nan
+        zscores = np.zeros(n_epochs, dtype="float") * np.nan
 
         for stage in include:
             where_stage = np.where(hypno_win == stage)[0]
@@ -2646,9 +3200,11 @@ 
Source code for yasa.detection
             if where_stage.size < 30:
                 if hypno is not None:
                     # Only show warnig if user actually pass an hypnogram
-                    logger.warning(f"At least 30 epochs are required to "
-                                   f"calculate z-score. Skipping "
-                                   f"stage {stage}")
+                    logger.warning(
+                        f"At least 30 epochs are required to "
+                        f"calculate z-score. Skipping "
+                        f"stage {stage}"
+                    )
                 continue
             # Apply Potato algorithm, extract z-scores and labels
             zs = potato.fit_transform(covmats[where_stage])
@@ -2656,20 +3212,22 @@ 
Source code for yasa.detection
             if hypno is not None:
                 # Only shows if user actually pass an hypnogram
                 perc_reject = 100 * (art.sum() / art.size)
-                text = (f"Stage {stage}: {art.sum()} / {art.size} "
-                        f"epochs rejected ({perc_reject:.2f}%)")
+                text = (
+                    f"Stage {stage}: {art.sum()} / {art.size} "
+                    f"epochs rejected ({perc_reject:.2f}%)"
+                )
                 logger.info(text)
             # Append to global vector
             epoch_is_art[where_stage] = art
             zscores[where_stage] = zs
 
-    elif method in ['std', 'sd']:
+    elif method in ["std", "sd"]:
         # Calculate log-transformed standard dev in each epoch
         # We add 1 to avoid log warning id std is zero (e.g. flat line)
         # (n_epochs, n_chan)
         std_epochs = np.log(np.nanstd(epochs, axis=-1) + 1)
         # Create empty zscores output (n_epochs, n_chan)
-        zscores = np.zeros((n_epochs, n_chan), dtype='float') * np.nan
+        zscores = np.zeros((n_epochs, n_chan), dtype="float") * np.nan
         for stage in include:
             where_stage = np.where(hypno_win == stage)[0]
             # At least 30 epochs are required to calculate z-scores
@@ -2677,9 +3235,11 @@ 
Source code for yasa.detection
             if where_stage.size < 30:
                 if hypno is not None:
                     # Only show warnig if user actually pass an hypnogram
-                    logger.warning(f"At least 30 epochs are required to "
-                                   f"calculate z-score. Skipping "
-                                   f"stage {stage}")
+                    logger.warning(
+                        f"At least 30 epochs are required to "
+                        f"calculate z-score. Skipping "
+                        f"stage {stage}"
+                    )
                 continue
             # Calculate z-scores of STD for each channel x stage
             c_mean = np.nanmean(std_epochs[where_stage], axis=0, keepdims=True)
@@ -2691,8 +3251,10 @@ 
Source code for yasa.detection
             if hypno is not None:
                 # Only shows if user actually pass an hypnogram
                 perc_reject = 100 * (art.sum() / art.size)
-                text = (f"Stage {stage}: {art.sum()} / {art.size} "
-                        f"epochs rejected ({perc_reject:.2f}%)")
+                text = (
+                    f"Stage {stage}: {art.sum()} / {art.size} "
+                    f"epochs rejected ({perc_reject:.2f}%)"
+                )
                 logger.info(text)
             # Append to global vector
             epoch_is_art[where_stage] = art
@@ -2700,18 +3262,186 @@ 
Source code for yasa.detection
 
     # Mark flat epochs as artefacts
     if n_flat_epochs > 0:
-        logger.info(f"Rejecting {n_flat_epochs} epochs with >=50% of channels "
-                    f"that are flat. Z-scores set to np.nan for these epochs.")
+        logger.info(
+            f"Rejecting {n_flat_epochs} epochs with >=50% of channels "
+            f"that are flat. Z-scores set to np.nan for these epochs."
+        )
         epoch_is_art[where_flat_epochs] = 1
 
     # Log total percentage of epochs rejected
     perc_reject = 100 * (epoch_is_art.sum() / n_epochs)
-    text = (f"TOTAL: {epoch_is_art.sum()} / {n_epochs} epochs rejected ({perc_reject:.2f}%)")
+    text = f"TOTAL: {epoch_is_art.sum()} / {n_epochs} epochs rejected ({perc_reject:.2f}%)"
     logger.info(text)
 
     # Convert epoch_is_art to boolean [0, 0, 1] -- > [False, False, True]
     epoch_is_art = epoch_is_art.astype(bool)
     return epoch_is_art, zscores

+
+
+#############################################################################
+# COMPARE DETECTION
+#############################################################################
+
+
+
[docs]def compare_detection(indices_detection, indices_groundtruth, max_distance=0):
+    """
+    Determine correctness of detected events against ground-truth events.
+
+    Parameters
+    ----------
+    indices_detection : array_like
+        Indices of the detected events. For example, this could be the indices of the
+        start of the spindles, or the negative peak of the slow-waves. The indices must be in
+        samples, and not in seconds.
+    indices_groundtruth : array_like
+        Indices of the ground-truth events, in samples.
+    max_distance : int, optional
+        Maximum distance between indices, in samples, to consider as the same event (default = 0).
+        For example, if the sampling frequency of the data is 100 Hz, using `max_distance=100` will
+        search for a matching event 1 second before or after the current event.
+
+    Returns
+    -------
+    results : dict
+        A dictionary with the comparison results:
+
+        * ``tp``: True positives, i.e. actual events detected as events.
+        * ``fp``: False positives, i.e. non-events detected as events.
+        * ``fn``: False negatives, i.e. actual events not detected as events.
+        * ``precision``: Precision score, aka positive predictive value (see Notes)
+        * ``recall``: Recall score, aka sensitivity (see Notes)
+        * ``f1``: F1-score (see Notes)
+
+    Notes
+    -----`
+    * The precision score is calculated as TP / (TP + FP).
+    * The recall score is calculated as TP / (TP + FN).
+    * The F1-score is calculated as TP / (TP + 0.5 * (FP + FN)).
+
+    This function is inspired by the `sleepecg.compare_heartbeats
+    <https://sleepecg.readthedocs.io/en/stable/generated/sleepecg.compare_heartbeats.html>`_
+    function.
+
+    Examples
+    --------
+    A simple example. Here, `detected` refers to the indices (in the data) of the detected events.
+    These could be for example the index of the onset of each detected spindle. `grndtrth` refers
+    to the ground-truth (e.g. human-annotated) events.
+
+    >>> from yasa import compare_detection
+    >>> detected = [5, 12, 20, 34, 41, 57, 63]
+    >>> grndtrth = [5, 12, 18, 26, 34, 41, 55, 63, 68]
+    >>> compare_detection(detected, grndtrth)
+    {'tp': array([ 5, 12, 34, 41, 63]),
+     'fp': array([20, 57]),
+     'fn': array([18, 26, 55, 68]),
+     'precision': 0.7142857142857143,
+     'recall': 0.5555555555555556,
+     'f1': 0.625}
+
+    There are 4 true positives, 2 false positives and 4 false negatives. This gives a precision
+    score of 0.71 (= 5 / (5 + 2)), a recall score of 0.55 (= 5 / (5 + 4)) and a F1-score of 0.625.
+    The F1-score is the harmonic average of precision and recall, and should be the preferred
+    metric when comparing the performance of a detection against a ground-truth.
+
+    Order matters! If we set `detected` as the ground-truth, FP and FN are inverted, and same for
+    precision and recall. The TP and F1-score remain the same though. Therefore, when comparing two
+    detections (and not a detection against a ground-truth), the F1-score is the preferred metric
+    because it is independent of the order.
+
+    >>> compare_detection(grndtrth, detected)
+    {'tp': array([ 5, 12, 34, 41, 63]),
+     'fp': array([18, 26, 55, 68]),
+     'fn': array([20, 57]),
+     'precision': 0.7142857142857143,
+     'recall': 0.7142857142857143,
+     'f1': 0.625}
+
+    There might be some events that are very close to each other, and we would like to count them
+    as true positive even though they do not occur exactly at the same index. This is possible
+    with the `max_distance` argument, which defines the lookaround window (in samples) for
+    each event.
+
+    >>> compare_detection(detected, grndtrth, max_distance=2)
+    {'tp': array([ 5, 12, 20, 34, 41, 57, 63]),
+     'fp': array([], dtype=int64),
+     'fn': array([26, 68]),
+     'precision': 1.0,
+     'recall': 0.7777777777777778,
+     'f1': 0.875}
+
+    Finally, if detected is empty, all performance metrics will be set to zero, and a copy of
+    the groundtruth array will be returned as false negatives.
+
+    >>> compare_detection([], grndtrth)
+    {'tp': array([], dtype=int64),
+     'fp': array([], dtype=int64),
+     'fn': array([ 5, 12, 18, 26, 34, 41, 55, 63, 68]),
+     'precision': 0,
+     'recall': 0,
+     'f1': 0}
+    """
+    # Safety check
+    assert all([float(i).is_integer() for i in indices_detection])  # all([]) == True
+    assert all([float(i).is_integer() for i in indices_groundtruth])
+    indices_detection = np.array(indices_detection, dtype=int)  # Force copy
+    indices_groundtruth = np.array(indices_groundtruth, dtype=int)
+    assert indices_detection.ndim == 1, "detection indices must be a 1D list or array."
+    assert indices_groundtruth.ndim == 1, "groundtruth indices must be a 1D list or array."
+    assert max_distance >= 0, "max_distance must be 0 or a positive integer."
+    assert isinstance(max_distance, int), "max_distance must be 0 or a positive integer."
+
+    # Handle cases where indices_detection or indices_groundtruth is empty
+    if indices_detection.size == 0:
+        results = dict(
+            tp=np.array([], dtype=int),
+            fp=np.array([], dtype=int),
+            fn=indices_groundtruth.copy(),
+            precision=0,
+            recall=0,
+            f1=0,
+        )
+        return results
+
+    if indices_groundtruth.size == 0:
+        results = dict(
+            tp=np.array([], dtype=int),
+            fp=indices_detection.copy(),
+            fn=np.array([], dtype=int),
+            precision=0,
+            recall=0,
+            f1=0,
+        )
+        return results
+
+    # Create boolean masks
+    max_len = max(max(indices_detection), max(indices_groundtruth)) + 1
+    detection_mask = np.zeros(max_len, dtype=bool)
+    detection_mask[indices_detection] = 1
+    true_mask = np.zeros(max_len, dtype=bool)
+    true_mask[indices_groundtruth] = 1
+
+    # Create smoothed masks
+    fuzzy_filter = np.ones(max_distance * 2 + 1, dtype=bool)
+    if len(fuzzy_filter) >= max_len:
+        raise ValueError(
+            f"The convolution window is larger than the signal. `max_distance` should be between "
+            f"0 and {int(max_len / 2 - 1)} samples."
+        )
+    detection_mask_fuzzy = np.convolve(detection_mask, fuzzy_filter, mode="same")
+    true_mask_fuzzy = np.convolve(true_mask, fuzzy_filter, mode="same")
+
+    # Confusion matrix and performance metrics
+    results = {}
+    results["tp"] = np.where(detection_mask & true_mask_fuzzy)[0]
+    results["fp"] = np.where(detection_mask & ~true_mask_fuzzy)[0]
+    results["fn"] = np.where(~detection_mask_fuzzy & true_mask)[0]
+
+    n_tp, n_fp, n_fn = len(results["tp"]), len(results["fp"]), len(results["fn"])
+    results["precision"] = n_tp / (n_tp + n_fp)
+    results["recall"] = n_tp / (n_tp + n_fn)
+    results["f1"] = n_tp / (n_tp + 0.5 * (n_fp + n_fn))
+    return results

 

 
     

diff --git a/docs/build/html/_modules/yasa/heart.html b/docs/build/html/_modules/yasa/heart.html
new file mode 100644
index 0000000..c419b1e
--- /dev/null
+++ b/docs/build/html/_modules/yasa/heart.html
@@ -0,0 +1,313 @@
+
+
+
+  
+    
+    yasa.heart — yasa 0.6.2 documentation
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+
+
+
+
+
+
+
+
+
+  
+
+  
+
+

+  

+    

+      
+  
Source code for yasa.heart
+"""
+ECG-based functions.
+
+Author: Dr Raphael Vallat <raphaelvallat@berkeley.edu>, UC Berkeley.
+Date: May 2022
+"""
+import logging
+import numpy as np
+import pandas as pd
+
+from .hypno import hypno_find_periods
+from .detection import _check_data_hypno
+from .io import set_log_level, is_sleepecg_installed
+
+logger = logging.getLogger("yasa")
+
+__all__ = ["hrv_stage"]
+
+
+
[docs]def hrv_stage(
+    data,
+    sf,
+    *,
+    hypno=None,
+    include=(2, 3, 4),
+    threshold="2min",
+    equal_length=False,
+    rr_limit=(400, 2000),
+    verbose=False,
+):
+    """Calculate heart rate and heart rate variability (HRV) features from an ECG.
+
+    By default, the cardiac features are calculated for each period of N2, N3 or REM sleep that
+    are longer than 2 minutes.
+
+    .. versionadded:: 0.6.2
+
+    Parameters
+    ----------
+    data : :py:class:`numpy.ndarray`
+        Single-channel ECG data. Must be a 1D NumPy array.
+    sf : float
+        The sampling frequency of the data.
+    hypno : array_like
+        Sleep stage (hypnogram). The heart rate calculation will be applied for each sleep stage
+        defined in ``include`` (default = N2, N3 and REM sleep separately).
+
+        The hypnogram must have the same number of samples as ``data``.
+        To upsample your hypnogram, please refer to
+        :py:func:`yasa.hypno_upsample_to_data`.
+
+        .. note::
+            The default hypnogram format in YASA is a 1D integer
+            vector where:
+
+            - -2 = Unscored
+            - -1 = Artefact / Movement
+            - 0 = Wake
+            - 1 = N1 sleep
+            - 2 = N2 sleep
+            - 3 = N3 sleep
+            - 4 = REM sleep
+    include : tuple, list or int
+        Values in ``hypno`` that will be included in the mask. The default is
+        (2, 3, 4), meaning that the detection is applied on N2, N3 and REM
+        sleep separately.
+    threshold : str
+        Only periods of a given stage that exceed the duration defined in ``threshold`` will be
+        kept in subsequent analysis. The default is 2 minutes ('2min'). Other possible values
+        include: '5min', '15min', '30sec', '1hour', etc. To disable thresholding, use '0min'.
+    equal_length : bool
+        If True, the periods will all have the exact duration defined in ``threshold``.
+        That is, periods that are longer than the duration threshold will be divided into
+        sub-periods of exactly the length of threshold.
+    rr_limit : tuple
+        Lower and upper limit for the RR interval. Default is 400 to 2000 ms, corresponding to a
+        heart rate of 30 to 150 bpm. RR intervals outside this range will be set to NaN and
+        filled with linear interpolation. Use ``rr_limit=(0, np.inf)`` to disable RR correction.
+    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``). Set this to True if you are getting
+        invalid results and want to better understand what is happening.
+
+    Returns
+    -------
+    epochs : :py:class:`pandas.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.
+        * ``duration`` : The duration of the epoch, in seconds.
+        * ``hr_mean``: The mean heart rate (HR) across the epoch, in beats per minute (bpm).
+        * ``hr_std``: The standard deviation of the HR across the epoch, in bpm
+        * ``hrv_rmssd``: Heart rate variability across the epoch (RMSSD), in milliseconds.
+    rpeaks : dict
+        A dictionary with the detected heartbeats (R-peaks) indices for each epoch of each stage.
+        Indices are expressed as samples from the beginning of the epoch. This can be used to
+        manually recalculate the RR intervals, apply a custom preprocessing on the RR intervals,
+        and/or calculate more advanced HRV metrics.
+
+    Notes
+    -----
+    This function returns three cardiac features for each epoch: the mean and standard deviation of
+    the heart rate, and the root mean square of successive differences between normal heartbeats
+    (RMSSD). The RMSSD reflects the beat-to-beat variance in HR and is the primary time-domain
+    measure used to estimate the vagally mediated changes reflected in heart rate variability.
+
+    Heartbeat detection is performed with the SleepECG library: https://github.com/cbrnr/sleepecg
+
+    For an example of this function, please see the `Jupyter notebook
+    <https://github.com/raphaelvallat/yasa/blob/master/notebooks/16_EEG-HRV_coupling.ipynb>`_
+
+    References
+    ----------
+    * Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and
+      norms. Frontiers in public health, 258.
+    """
+    set_log_level(verbose)
+    is_sleepecg_installed()
+    from sleepecg import detect_heartbeats
+
+    if isinstance(hypno, type(None)):
+        logger.warning(
+            "No hypnogram was passed. The entire recording will be used, i.e. "
+            "hypno will be set to np.zeros(data.size) and include will be set to 0."
+        )
+        data = np.asarray(data, dtype=np.float64)
+        hypno = np.zeros(max(data.shape), dtype=int)
+        include = 0
+
+    # Safety check
+    (data, sf, _, hypno, include, _, n_chan, n_samples, _) = _check_data_hypno(
+        data, sf, None, hypno, include, check_amp=False
+    )
+    assert n_chan == 1, "data must be a 1D ECG array."
+    data = np.squeeze(data)
+
+    # Find periods of equal duration
+    epochs = hypno_find_periods(hypno, sf, threshold=threshold, equal_length=equal_length)
+    assert epochs.shape[0] > 0, f"No epochs longer than {threshold} found in hypnogram."
+    epochs = epochs[epochs["values"].isin(include)].reset_index(drop=True)
+    # Sort by stage and add epoch number
+    epochs = epochs.sort_values(by=["values", "start"])
+    epochs["epoch"] = epochs.groupby("values")["start"].transform(lambda x: range(len(x)))
+    epochs = epochs.set_index(["values", "epoch"])
+
+    # Loop over epochs
+    rpeaks = {}
+    for idx in epochs.index:
+        start = epochs.loc[idx, "start"]
+        duration = epochs.loc[idx, "length"]
+        end = int(epochs.loc[idx, "start"] + duration)
+        # Detect R-peaks
+        try:
+            pks = detect_heartbeats(data[start:end], fs=sf)
+        except Exception as e:
+            logger.info(f"Heartbeat detection failed for epoch {idx[1]} of stage {idx[0]}: {e}")
+            continue
+
+        # Save rpeaks to dict
+        rpeaks[idx] = pks
+
+        # If not enough R-peaks were detected, skip epochs and return NaN
+        # Here, we assume a minimal HR of 30 bpm
+        constant_hr = 60 * (pks.size / (duration / sf))
+        if constant_hr < 30:
+            logger.info(f"Too few detected heartbeats in epoch {idx[1]} of stage {idx[0]}.")
+            continue
+
+        # Find and correct RR intervals. Default is 400 ms (150 bpm) to 2000 ms (30 bpm)
+        rri = 1000 * np.diff(pks) / sf
+        rri = np.ma.masked_outside(rri, rr_limit[0], rr_limit[1]).filled(np.nan)
+        # Interpolate NaN values, but no more than 10 consecutive values
+        if np.isnan(rri).any():
+            rri = pd.Series(rri).interpolate(limit_direction="both", limit=10).to_numpy()
+        if np.isnan(rri).any():
+            # If there are still NaN present, skip current epoch
+            logger.info(f"Invalid RR intervals in epoch {idx[1]} of stage {idx[0]}.")
+            continue
+
+        # Heart rate
+        hr = 60000 / rri
+        epochs.loc[idx, "hr_mean"] = np.mean(hr)
+        epochs.loc[idx, "hr_std"] = np.std(hr, ddof=1)
+        epochs.loc[idx, "hrv_rmssd"] = np.sqrt(np.mean(np.diff(rri) ** 2))
+
+    # Convert start and duration to seconds
+    epochs["start"] /= sf
+    epochs["length"] /= sf
+    epochs = epochs.rename(columns={"length": "duration"})
+
+    return epochs, rpeaks

+

+
+    

+      
+  

+

+

+  

+    

+      Back to top
+      
+        

+        
+      
+    

+    

+        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.

+      Created using Sphinx 3.0.0.

+    

+  

+

+  
+
\ No newline at end of file
diff --git a/docs/build/html/_modules/yasa/hypno.html b/docs/build/html/_modules/yasa/hypno.html
index 91488ae..71e2c5f 100644
--- a/docs/build/html/_modules/yasa/hypno.html
+++ b/docs/build/html/_modules/yasa/hypno.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.hypno — yasa 0.6.1 documentation
+    yasa.hypno — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -126,21 +129,44 @@ 
Source code for yasa.hypno
 import pandas as pd
 from .io import set_log_level
 
-__all__ = ['hypno_str_to_int', 'hypno_int_to_str', 'hypno_upsample_to_sf',
-           'hypno_upsample_to_data', 'load_profusion_hypno']
+__all__ = [
+    "hypno_str_to_int",
+    "hypno_int_to_str",
+    "hypno_upsample_to_sf",
+    "hypno_upsample_to_data",
+    "hypno_find_periods",
+    "load_profusion_hypno",
+]
 
 
-logger = logging.getLogger('yasa')
+logger = logging.getLogger("yasa")
 
 
 #############################################################################
 # STR <--> INT CONVERSION
 #############################################################################
 
-
[docs]def hypno_str_to_int(hypno, mapping_dict={'w': 0, 'wake': 0, 'n1': 1, 's1': 1,
-                                          'n2': 2, 's2': 2, 'n3': 3, 's3': 3,
-                                          's4': 3, 'r': 4, 'rem': 4, 'art': -1,
-                                          'mt': -1, 'uns': -2, 'nd': -2}):
+
+
[docs]def hypno_str_to_int(
+    hypno,
+    mapping_dict={
+        "w": 0,
+        "wake": 0,
+        "n1": 1,
+        "s1": 1,
+        "n2": 2,
+        "s2": 2,
+        "n3": 3,
+        "s3": 3,
+        "s4": 3,
+        "r": 4,
+        "rem": 4,
+        "art": -1,
+        "mt": -1,
+        "uns": -2,
+        "nd": -2,
+    },
+):
     """Convert a string hypnogram array to integer.
 
     ['W', 'N2', 'N2', 'N3', 'R'] ==> [0, 2, 2, 3, 4]
@@ -160,14 +186,15 @@ 
Source code for yasa.hypno
     hypno : array_like
         The corresponding integer hypnogram.
     """
-    assert isinstance(hypno, (list, np.ndarray, pd.Series)), 'Not an array.'
+    assert isinstance(hypno, (list, np.ndarray, pd.Series)), "Not an array."
     hypno = pd.Series(np.asarray(hypno, dtype=str))
-    assert not hypno.str.isnumeric().any(), 'Hypno contains numeric values.'
+    assert not hypno.str.isnumeric().any(), "Hypno contains numeric values."
     return hypno.str.lower().map(mapping_dict).values

 
 
-
[docs]def hypno_int_to_str(hypno, mapping_dict={0: 'W', 1: 'N1', 2: 'N2', 3: 'N3',
-                                          4: 'R', -1: 'Art', -2: 'Uns'}):
+
[docs]def hypno_int_to_str(
+    hypno, mapping_dict={0: "W", 1: "N1", 2: "N2", 3: "N3", 4: "R", -1: "Art", -2: "Uns"}
+):
     """Convert an integer hypnogram array to a string array.
 
     [0, 2, 2, 3, 4] ==> ['W', 'N2', 'N2', 'N3', 'R']
@@ -187,10 +214,11 @@ 
Source code for yasa.hypno
     hypno : array_like
         The corresponding integer hypnogram.
     """
-    assert isinstance(hypno, (list, np.ndarray, pd.Series)), 'Not an array.'
+    assert isinstance(hypno, (list, np.ndarray, pd.Series)), "Not an array."
     hypno = pd.Series(np.asarray(hypno, dtype=int))
     return hypno.map(mapping_dict).values

 
+
 #############################################################################
 # UPSAMPLING
 #############################################################################
@@ -219,8 +247,8 @@ 
Source code for yasa.hypno
         The hypnogram, upsampled to ``sf_data``.
     """
     repeats = sf_data / sf_hypno
-    assert sf_hypno <= sf_data, 'sf_hypno must be less than sf_data.'
-    assert repeats.is_integer(), 'sf_hypno / sf_data must be a whole number.'
+    assert sf_hypno <= sf_data, "sf_hypno must be less than sf_data."
+    assert repeats.is_integer(), "sf_hypno / sf_data must be a whole number."
     assert isinstance(hypno, (list, np.ndarray, pd.Series))
     return np.repeat(np.asarray(hypno), repeats)

 
@@ -249,11 +277,11 @@ 
Source code for yasa.hypno
     """
     # Check if data is an MNE raw object
     if isinstance(data, mne.io.BaseRaw):
-        sf = data.info['sfreq']
+        sf = data.info["sfreq"]
         data = data.times  # 1D array and does not require to preload data
     data = np.asarray(data)
     hypno = np.asarray(hypno)
-    assert hypno.ndim == 1, 'Hypno must be 1D.'
+    assert hypno.ndim == 1, "Hypno must be 1D."
     npts_hyp = hypno.size
     npts_data = max(data.shape)  # Support for 2D data
     if npts_hyp < npts_data:
@@ -261,22 +289,30 @@ 
Source code for yasa.hypno
         npts_diff = npts_data - npts_hyp
         if sf is not None:
             dur_diff = npts_diff / sf
-            logger.warning('Hypnogram is SHORTER than data by %.2f seconds. '
-                           'Padding hypnogram with last value to match data.size.' % dur_diff)
+            logger.warning(
+                "Hypnogram is SHORTER than data by %.2f seconds. "
+                "Padding hypnogram with last value to match data.size." % dur_diff
+            )
         else:
-            logger.warning('Hypnogram is SHORTER than data by %i samples. '
-                           'Padding hypnogram with last value to match data.size.' % npts_diff)
-        hypno = np.pad(hypno, (0, npts_diff), mode='edge')
+            logger.warning(
+                "Hypnogram is SHORTER than data by %i samples. "
+                "Padding hypnogram with last value to match data.size." % npts_diff
+            )
+        hypno = np.pad(hypno, (0, npts_diff), mode="edge")
     elif npts_hyp > npts_data:
         # Hypnogram is longer than data
         npts_diff = npts_hyp - npts_data
         if sf is not None:
             dur_diff = npts_diff / sf
-            logger.warning('Hypnogram is LONGER than data by %.2f seconds. '
-                           'Cropping hypnogram to match data.size.' % dur_diff)
+            logger.warning(
+                "Hypnogram is LONGER than data by %.2f seconds. "
+                "Cropping hypnogram to match data.size." % dur_diff
+            )
         else:
-            logger.warning('Hypnogram is LONGER than data by %i samples. '
-                           'Cropping hypnogram to match data.size.' % npts_diff)
+            logger.warning(
+                "Hypnogram is LONGER than data by %i samples. "
+                "Cropping hypnogram to match data.size." % npts_diff
+            )
         hypno = hypno[0:npts_data]
     return hypno
 
@@ -323,7 +359,7 @@ 
Source code for yasa.hypno
     """
     set_log_level(verbose)
     if isinstance(data, mne.io.BaseRaw):
-        sf_data = data.info['sfreq']
+        sf_data = data.info["sfreq"]
         data = data.times
     hypno_up = hypno_upsample_to_sf(hypno=hypno, sf_hypno=sf_hypno, sf_data=sf_data)
     return hypno_fit_to_data(hypno=hypno_up, data=data, sf=sf_data)

@@ -333,6 +369,7 @@ 
Source code for yasa.hypno
 # HYPNO LOADING
 #############################################################################
 
+
 
[docs]def load_profusion_hypno(fname, replace=True):  # pragma: no cover
     """
     Load a Compumedics Profusion hypnogram (.xml).
@@ -370,6 +407,7 @@ 
Source code for yasa.hypno
     # >>> annotations["Start"] = annotations["Start"].astype(float)
     # >>> annotations["Duration"] = annotations["Duration"].astype(float)
     import xml.etree.ElementTree as ET
+
     tree = ET.parse(fname)
     root = tree.getroot()
     epoch_length = float(root[0].text)
@@ -382,6 +420,156 @@ 
Source code for yasa.hypno
         # Stage 4 --> 3 and REM --> 4
         hypno = pd.Series(hypno).replace({4: 3, 5: 4}).to_numpy()
     return hypno, sf_hyp

+
+
+#############################################################################
+# PERIODS & CYCLES
+#############################################################################
+
+
+
[docs]def hypno_find_periods(hypno, sf_hypno, threshold="5min", equal_length=False):
+    """Find sequences of consecutive values exceeding a certain duration in hypnogram.
+
+    .. versionadded:: 0.6.2
+
+    Parameters
+    ----------
+    hypno : array_like
+        A 1D array with the sleep stages (= hypnogram). The dtype can be anything (int, bool, str).
+        More generally, this can be any vector for which you wish to find runs of
+        consecutive items.
+    sf_hypno : float
+        The current sampling frequency of ``hypno``, in Hz, e.g. 1/30 = 1 value per each 30 seconds
+        of EEG data, 1 = 1 value per second of EEG data.
+    threshold : str
+        This function will only keep periods that exceed a certain duration (default '5min'), e.g.
+        '5min', '15min', '30sec', '1hour'. To disable thresholding, use '0sec'.
+    equal_length : bool
+        If True, the periods will all have the exact duration defined
+        in threshold. That is, periods that are longer than the duration threshold will be divided
+        into sub-periods of exactly the length of ``threshold``.
+
+    Returns
+    -------
+    periods : :py:class:`pandas.DataFrame`
+        Output dataframe
+
+        * ``values`` : The value in hypno of the current period
+        * ``start`` : The index of the start of the period in hypno
+        * ``length`` : The duration of the period, in number of samples
+
+    Examples
+    --------
+    Let's assume that we have an hypnogram where sleep = 1 and wake = 0. There is one value per
+    minute, and therefore the sampling frequency of the hypnogram is 1 / 60 sec (~0.016 Hz).
+
+    >>> import yasa
+    >>> hypno = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]
+    >>> yasa.hypno_find_periods(hypno, sf_hypno=1/60, threshold="0min")
+       values  start  length
+    0       0      0      11
+    1       1     11       3
+    2       0     14       2
+    3       1     16       9
+    4       0     25       2
+
+    This gives us the start and duration of each sequence of consecutive values in the hypnogram.
+    For example, the first row tells us that there is a sequence of 11 consecutive 0 starting at
+    the first index of hypno.
+
+    Now, we may want to keep only periods that are longer than a specific threshold,
+    for example 5 minutes:
+
+    >>> yasa.hypno_find_periods(hypno, sf_hypno=1/60, threshold="5min")
+       values  start  length
+    0       0      0      11
+    1       1     16       9
+
+    Only the two sequences that are longer than 5 minutes (11 minutes and 9 minutes respectively)
+    are kept. Feel free to play around with different values of threshold!
+
+    This function is not limited to binary arrays, e.g.
+
+    >>> hypno = [0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 0, 1]
+    >>> yasa.hypno_find_periods(hypno, sf_hypno=1/60, threshold="2min")
+       values  start  length
+    0       0      0       4
+    1       2      5       6
+    2       0     11       3
+
+    Lastly, using ``equal_length=True`` will further divide the periods into segments of the
+    same duration, i.e. the duration defined in ``threshold``:
+
+    >>> hypno = [0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 0, 1]
+    >>> yasa.hypno_find_periods(hypno, sf_hypno=1/60, threshold="2min", equal_length=True)
+       values  start  length
+    0       0      0       2
+    1       0      2       2
+    2       2      5       2
+    3       2      7       2
+    4       2      9       2
+    5       0     11       2
+
+    Here, the first period of 4 minutes of consecutive 0 is further divided into 2 periods of
+    exactly 2 minutes. Next, the sequence of 6 consecutive 2 is further divided into 3 periods of
+    2 minutes. Lastly, the last value in the sequence of 3 consecutive 0 at the end of the array is
+    removed to keep only a segment of 2 exactly minutes. In other words, the remainder of the
+    division of a given segment by the desired duration is discarded.
+    """
+    # Convert the threshold to number of samples
+    assert isinstance(threshold, str), "Threshold must be a string, e.g. '5min', '30sec', '15min'"
+    thr_sec = pd.Timedelta(threshold).total_seconds()
+    thr_samp = sf_hypno * thr_sec
+    if float(thr_samp).is_integer():
+        thr_samp = int(thr_samp)
+    else:
+        raise ValueError(
+            f"The selected threshold does not result in an whole number of samples ("
+            f"{thr_sec:.3f} seconds * {sf_hypno:.3f} Hz = {thr_samp:.3f} samples)"
+        )
+
+    # Find run starts
+    # https://gist.github.com/alimanfoo/c5977e87111abe8127453b21204c1065
+    assert isinstance(hypno, (list, np.ndarray, pd.Series)), "hypno must be an array."
+    x = np.asarray(hypno)
+    n = x.shape[0]
+    loc_run_start = np.empty(n, dtype=bool)
+    loc_run_start[0] = True
+    loc_run_start[1:] = x[:-1] != x[1:]
+    run_starts = np.nonzero(loc_run_start)[0]
+    # Find run values
+    run_values = x[loc_run_start]
+    # Find run lengths
+    run_lengths = np.diff(np.append(run_starts, n))
+    seq = pd.DataFrame({"values": run_values, "start": run_starts, "length": run_lengths})
+
+    # Remove runs that are shorter than threshold
+    seq = seq[seq["length"] >= thr_samp].reset_index(drop=True)
+
+    if not equal_length:
+        return seq
+
+    # Divide into epochs of equal length
+    assert thr_samp > 0, "Threshold must be non-zero if using equal_length=True."
+    new_seq = {"values": [], "start": [], "length": []}
+
+    for i, row in seq.iterrows():
+        quotient, remainder = np.divmod(row["length"], thr_samp)
+        new_start = row["start"]
+        if quotient > 0:
+            while quotient != 0:
+                new_seq["values"].append(row["values"])
+                new_seq["start"].append(new_start)
+                new_seq["length"].append(thr_samp)
+                new_start += thr_samp
+                quotient -= 1
+        else:
+            new_seq["values"].append(row["values"])
+            new_seq["start"].append(row["start"])
+            new_seq["length"].append(row["length"])
+
+    new_seq = pd.DataFrame(new_seq)
+    return new_seq

 

 
     

diff --git a/docs/build/html/_modules/yasa/others.html b/docs/build/html/_modules/yasa/others.html
index 5900879..3c529fe 100644
--- a/docs/build/html/_modules/yasa/others.html
+++ b/docs/build/html/_modules/yasa/others.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.others — yasa 0.6.1 documentation
+    yasa.others — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -99,10 +102,9 @@ 
Source code for yasa.others
 from scipy.interpolate import interp1d
 from .numba import _slope_lstsq, _covar, _corr, _rms
 
-logger = logging.getLogger('yasa')
+logger = logging.getLogger("yasa")
 
-__all__ = ['moving_transform', 'trimbothstd', 'sliding_window',
-           'get_centered_indices']
+__all__ = ["moving_transform", "trimbothstd", "sliding_window", "get_centered_indices"]
 
 
 def _merge_close(index, min_distance_ms, sf):
@@ -128,7 +130,7 @@ 
Source code for yasa.others
     Original code imported from the Visbrain package.
     """
     # Convert min_distance_ms
-    min_distance = min_distance_ms / 1000. * sf
+    min_distance = min_distance_ms / 1000.0 * sf
     idx_diff = np.diff(index)
     condition = idx_diff > 1
     idx_distance = np.where(condition)[0]
@@ -136,8 +138,7 @@ 
Source code for yasa.others
     bad = idx_distance[np.where(distance < min_distance)[0]]
     # Fill gap between events separated with less than min_distance_ms
     if len(bad) > 0:
-        fill = np.hstack([np.arange(index[j] + 1, index[j + 1])
-                          for i, j in enumerate(bad)])
+        fill = np.hstack([np.arange(index[j] + 1, index[j + 1]) for i, j in enumerate(bad)])
         f_index = np.sort(np.append(index, fill))
         return f_index
     else:
@@ -170,8 +171,7 @@ 
Source code for yasa.others
     return index
 
 
-
[docs]def moving_transform(x, y=None, sf=100, window=.3, step=.1, method='corr',
-                     interp=False):
+
[docs]def moving_transform(x, y=None, sf=100, window=0.3, step=0.1, method="corr", interp=False):
     """Moving transformation of one or two time-series.
 
     Parameters
@@ -220,8 +220,17 @@ 
Source code for yasa.others
     Wonambi package (https://github.com/wonambi-python/wonambi).
     """
     # Safety checks
-    assert method in ['mean', 'min', 'max', 'ptp', 'rms',
-                      'prop_above_zero', 'slope', 'covar', 'corr']
+    assert method in [
+        "mean",
+        "min",
+        "max",
+        "ptp",
+        "rms",
+        "prop_above_zero",
+        "slope",
+        "covar",
+        "corr",
+    ]
     x = np.asarray(x, dtype=np.float64)
     if y is not None:
         y = np.asarray(y, dtype=np.float64)
@@ -247,55 +256,63 @@ 
Source code for yasa.others
     # beg, end = beg[mask], end[mask]
     t = np.column_stack((beg, end)).mean(1) / sf
 
-    if method == 'mean':
+    if method == "mean":
+
         def func(x):
             return np.mean(x)
 
-    elif method == 'min':
+    elif method == "min":
+
         def func(x):
             return np.min(x)
 
-    elif method == 'max':
+    elif method == "max":
+
         def func(x):
             return np.max(x)
 
-    elif method == 'ptp':
+    elif method == "ptp":
+
         def func(x):
             return np.ptp(x)
 
-    elif method == 'prop_above_zero':
+    elif method == "prop_above_zero":
+
         def func(x):
             return np.count_nonzero(x >= 0) / x.size
 
-    elif method == 'slope':
+    elif method == "slope":
+
         def func(x):
             times = np.arange(x.size, dtype=np.float64) / sf
             return _slope_lstsq(times, x)
 
-    elif method == 'covar':
+    elif method == "covar":
+
         def func(x, y):
             return _covar(x, y)
 
-    elif method == 'corr':
+    elif method == "corr":
+
         def func(x, y):
             return _corr(x, y)
 
     else:
+
         def func(x):
             return _rms(x)
 
     # Now loop over successive epochs
-    if method in ['covar', 'corr']:
+    if method in ["covar", "corr"]:
         for i in range(idx.size):
-            out[i] = func(x[beg[i]:end[i]], y[beg[i]:end[i]])
+            out[i] = func(x[beg[i] : end[i]], y[beg[i] : end[i]])
     else:
         for i in range(idx.size):
-            out[i] = func(x[beg[i]:end[i]])
+            out[i] = func(x[beg[i] : end[i]])
 
     # Finally interpolate
     if interp and step != 1 / sf:
-        f = interp1d(t, out, kind='cubic', bounds_error=False,
-                     fill_value=0, assume_sorted=True)
+        f = interp1d(t, out, kind="cubic", bounds_error=False, fill_value=0, assume_sorted=True)
         t = np.arange(n) / sf
         out = f(t)
 
@@ -446,36 +463,34 @@ 
Source code for yasa.others
             [-51,   3,  31, -99,  33, -47,   5, -97, -47,  90]]])
     """
     from numpy.lib.stride_tricks import as_strided
+
     assert axis <= data.ndim, "Axis value out of range."
-    assert isinstance(sf, (int, float)), 'sf must be int or float'
-    assert isinstance(window, (int, float)), 'window must be int or float'
-    assert isinstance(step, (int, float, type(None))), ('step must be int, '
-                                                        'float or None.')
+    assert isinstance(sf, (int, float)), "sf must be int or float"
+    assert isinstance(window, (int, float)), "window must be int or float"
+    assert isinstance(step, (int, float, type(None))), "step must be int, " "float or None."
     if isinstance(sf, float):
-        assert sf.is_integer(), 'sf must be a whole number.'
+        assert sf.is_integer(), "sf must be a whole number."
         sf = int(sf)
-    assert isinstance(axis, int), 'axis must be int.'
+    assert isinstance(axis, int), "axis must be int."
 
     # window and step in samples instead of points
     window *= sf
     step = window if step is None else step * sf
 
     if isinstance(window, float):
-        assert window.is_integer(), 'window * sf must be a whole number.'
+        assert window.is_integer(), "window * sf must be a whole number."
         window = int(window)
 
     if isinstance(step, float):
-        assert step.is_integer(), 'step * sf must be a whole number.'
+        assert step.is_integer(), "step * sf must be a whole number."
         step = int(step)
 
     assert step >= 1, "Stepsize may not be zero or negative."
-    assert window < data.shape[axis], ("Sliding window size may not exceed "
-                                       "size of selected axis")
+    assert window < data.shape[axis], "Sliding window size may not exceed " "size of selected axis"
 
     # Define output shape
     shape = list(data.shape)
-    shape[axis] = np.floor(data.shape[axis] / step - window / step + 1
-                           ).astype(int)
+    shape[axis] = np.floor(data.shape[axis] / step - window / step + 1).astype(int)
     shape.append(window)
 
     # Calculate strides and time vector
@@ -546,13 +561,13 @@ 
Source code for yasa.others
     npts_before = int(npts_before)
     npts_after = int(npts_after)
     data = np.asarray(data)
-    idx = np.asarray(idx, dtype='int')
+    idx = np.asarray(idx, dtype="int")
     assert idx.ndim == 1, "idx must be 1D."
     assert data.ndim == 1, "data must be 1D."
 
     def rng(x):
         """Create a range before and after a given value."""
-        return np.arange(x - npts_before, x + npts_after + 1, dtype='int')
+        return np.arange(x - npts_before, x + npts_after + 1, dtype="int")
 
     idx_ep = np.apply_along_axis(rng, 1, idx[..., np.newaxis])
     # We drop the events for which the indices exceed data
diff --git a/docs/build/html/_modules/yasa/plotting.html b/docs/build/html/_modules/yasa/plotting.html
index a773b95..a12daa2 100644
--- a/docs/build/html/_modules/yasa/plotting.html
+++ b/docs/build/html/_modules/yasa/plotting.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.plotting — yasa 0.6.1 documentation
+    yasa.plotting — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -102,10 +105,10 @@ 
Source code for yasa.plotting
 from lspopt import spectrogram_lspopt
 from matplotlib.colors import Normalize, ListedColormap
 
-__all__ = ['plot_hypnogram', 'plot_spectrogram', 'topoplot']
+__all__ = ["plot_hypnogram", "plot_spectrogram", "topoplot"]
 
 
-
[docs]def plot_hypnogram(hypno, sf_hypno=1/30, lw=1.5, figsize=(9, 3)):
+
[docs]def plot_hypnogram(hypno, sf_hypno=1 / 30, lw=1.5, figsize=(9, 3)):
     """
     Plot a hypnogram.
 
@@ -151,15 +154,15 @@ 
Source code for yasa.plotting
         >>> ax = yasa.plot_hypnogram(hypno)
     """
     # Increase font size while preserving original
-    old_fontsize = plt.rcParams['font.size']
-    plt.rcParams.update({'font.size': 18})
+    old_fontsize = plt.rcParams["font.size"]
+    plt.rcParams.update({"font.size": 18})
 
     # Safety checks
-    assert isinstance(hypno, (np.ndarray, pd.Series, list)), 'hypno must be an array.'
+    assert isinstance(hypno, (np.ndarray, pd.Series, list)), "hypno must be an array."
     hypno = np.asarray(hypno).astype(int)
     assert (hypno >= -2).all() and (hypno <= 4).all(), "hypno values must be between -2 to 4."
-    assert hypno.ndim == 1, 'hypno must be a 1D array.'
-    assert isinstance(sf_hypno, (int, float)), 'sf must be int or float.'
+    assert hypno.ndim == 1, "hypno must be a 1D array."
+    assert isinstance(sf_hypno, (int, float)), "sf must be int or float."
 
     t_hyp = np.arange(hypno.size) / (sf_hypno * 3600)
     # Make sure that REM is displayed after Wake
@@ -170,40 +173,51 @@ 
Source code for yasa.plotting
     fig, ax0 = plt.subplots(nrows=1, figsize=figsize)
 
     # Hypnogram (top axis)
-    ax0.step(t_hyp, -1 * hypno, color='k', lw=lw)
-    ax0.step(t_hyp, -1 * hypno_rem, color='red', lw=lw)
-    ax0.step(t_hyp, -1 * hypno_art_uns, color='grey', lw=lw)
+    ax0.step(t_hyp, -1 * hypno, color="k", lw=lw)
+    ax0.step(t_hyp, -1 * hypno_rem, color="red", lw=lw)
+    ax0.step(t_hyp, -1 * hypno_art_uns, color="grey", lw=lw)
     if -2 in hypno and -1 in hypno:
         # Both Unscored and Artefacts are present
         ax0.set_yticks([2, 1, 0, -1, -2, -3, -4])
-        ax0.set_yticklabels(['Uns', 'Art', 'W', 'R', 'N1', 'N2', 'N3'])
+        ax0.set_yticklabels(["Uns", "Art", "W", "R", "N1", "N2", "N3"])
         ax0.set_ylim(-4.5, 2.5)
     elif -2 in hypno and -1 not in hypno:
         # Only Unscored are present
         ax0.set_yticks([2, 0, -1, -2, -3, -4])
-        ax0.set_yticklabels(['Uns', 'W', 'R', 'N1', 'N2', 'N3'])
+        ax0.set_yticklabels(["Uns", "W", "R", "N1", "N2", "N3"])
         ax0.set_ylim(-4.5, 2.5)
     elif -2 not in hypno and -1 in hypno:
         # Only Artefacts are present
         ax0.set_yticks([1, 0, -1, -2, -3, -4])
-        ax0.set_yticklabels(['Art', 'W', 'R', 'N1', 'N2', 'N3'])
+        ax0.set_yticklabels(["Art", "W", "R", "N1", "N2", "N3"])
         ax0.set_ylim(-4.5, 1.5)
     else:
         # No artefacts or Unscored
         ax0.set_yticks([0, -1, -2, -3, -4])
-        ax0.set_yticklabels(['W', 'R', 'N1', 'N2', 'N3'])
+        ax0.set_yticklabels(["W", "R", "N1", "N2", "N3"])
         ax0.set_ylim(-4.5, 0.5)
     ax0.set_xlim(0, t_hyp.max())
-    ax0.set_ylabel('Stage')
-    ax0.set_xlabel('Time [hrs]')
-    ax0.spines['right'].set_visible(False)
-    ax0.spines['top'].set_visible(False)
+    ax0.set_ylabel("Stage")
+    ax0.set_xlabel("Time [hrs]")
+    ax0.spines["right"].set_visible(False)
+    ax0.spines["top"].set_visible(False)
     # Revert font-size
-    plt.rcParams.update({'font.size': old_fontsize})
+    plt.rcParams.update({"font.size": old_fontsize})
     return ax0

 
-
[docs]def plot_spectrogram(data, sf, hypno=None, win_sec=30, fmin=0.5, fmax=25,
-                     trimperc=2.5, cmap='RdBu_r'):
+
+
[docs]def plot_spectrogram(
+    data,
+    sf,
+    hypno=None,
+    win_sec=30,
+    fmin=0.5,
+    fmax=25,
+    trimperc=2.5,
+    cmap="RdBu_r",
+    vmin=None,
+    vmax=None,
+):
     """
     Plot a full-night multi-taper spectrogram, optionally with the hypnogram on top.
 
@@ -249,6 +263,10 @@ 
Source code for yasa.plotting
         are defined as the 2.5 and 97.5 percentiles of the spectrogram.
     cmap : str
         Colormap. Default to 'RdBu_r'.
+    vmin : int or float
+        The lower range of color scale. Overwrites ``trimperc``
+    vmax : int or float
+        The upper range of color scale. Overwrites ``trimperc``
 
     Returns
     -------
@@ -291,22 +309,28 @@ 
Source code for yasa.plotting
         >>> fig = yasa.plot_spectrogram(data, sf, hypno, cmap='Spectral_r')
     """
     # Increase font size while preserving original
-    old_fontsize = plt.rcParams['font.size']
-    plt.rcParams.update({'font.size': 18})
+    old_fontsize = plt.rcParams["font.size"]
+    plt.rcParams.update({"font.size": 18})
 
     # Safety checks
-    assert isinstance(data, np.ndarray), 'Data must be a 1D NumPy array.'
-    assert isinstance(sf, (int, float)), 'sf must be int or float.'
-    assert data.ndim == 1, 'Data must be a 1D (single-channel) NumPy array.'
-    assert isinstance(win_sec, (int, float)), 'win_sec must be int or float.'
-    assert isinstance(fmin, (int, float)), 'fmin must be int or float.'
-    assert isinstance(fmax, (int, float)), 'fmax must be int or float.'
-    assert fmin < fmax, 'fmin must be strictly inferior to fmax.'
-    assert fmax < sf / 2, 'fmax must be less than Nyquist (sf / 2).'
+    assert isinstance(data, np.ndarray), "Data must be a 1D NumPy array."
+    assert isinstance(sf, (int, float)), "sf must be int or float."
+    assert data.ndim == 1, "Data must be a 1D (single-channel) NumPy array."
+    assert isinstance(win_sec, (int, float)), "win_sec must be int or float."
+    assert isinstance(fmin, (int, float)), "fmin must be int or float."
+    assert isinstance(fmax, (int, float)), "fmax must be int or float."
+    assert fmin < fmax, "fmin must be strictly inferior to fmax."
+    assert fmax < sf / 2, "fmax must be less than Nyquist (sf / 2)."
+    assert isinstance(vmin, (int, float, type(None))), "vmin must be int, float, or None."
+    assert isinstance(vmax, (int, float, type(None))), "vmax must be int, float, or None."
+    if vmin is not None:
+        assert isinstance(vmax, (int, float)), "vmax must be int or float if vmin is provided"
+    if vmax is not None:
+        assert isinstance(vmin, (int, float)), "vmin must be int or float if vmax is provided"
 
     # Calculate multi-taper spectrogram
     nperseg = int(win_sec * sf)
-    assert data.size > 2 * nperseg, 'Data length must be at least 2 * win_sec.'
+    assert data.size > 2 * nperseg, "Data length must be at least 2 * win_sec."
     f, t, Sxx = spectrogram_lspopt(data, sf, nperseg=nperseg, noverlap=0)
     Sxx = 10 * np.log10(Sxx)  # Convert uV^2 / Hz --> dB / Hz
 
@@ -317,77 +341,94 @@ 
Source code for yasa.plotting
     t /= 3600  # Convert t to hours
 
     # Normalization
-    vmin, vmax = np.percentile(Sxx, [0 + trimperc, 100 - trimperc])
-    norm = Normalize(vmin=vmin, vmax=vmax)
+    if vmin is None:
+        vmin, vmax = np.percentile(Sxx, [0 + trimperc, 100 - trimperc])
+        norm = Normalize(vmin=vmin, vmax=vmax)
+    else:
+        norm = Normalize(vmin=vmin, vmax=vmax)
 
     if hypno is None:
         fig, ax = plt.subplots(nrows=1, figsize=(12, 4))
         im = ax.pcolormesh(t, f, Sxx, norm=norm, cmap=cmap, antialiased=True, shading="auto")
         ax.set_xlim(0, t.max())
-        ax.set_ylabel('Frequency [Hz]')
-        ax.set_xlabel('Time [hrs]')
+        ax.set_ylabel("Frequency [Hz]")
+        ax.set_xlabel("Time [hrs]")
 
         # Add colorbar
         cbar = fig.colorbar(im, ax=ax, shrink=0.95, fraction=0.1, aspect=25)
-        cbar.ax.set_ylabel('Log Power (dB / Hz)', rotation=270, labelpad=20)
+        cbar.ax.set_ylabel("Log Power (dB / Hz)", rotation=270, labelpad=20)
         return fig
     else:
         hypno = np.asarray(hypno).astype(int)
-        assert hypno.ndim == 1, 'Hypno must be 1D.'
-        assert hypno.size == data.size, 'Hypno must have the same sf as data.'
+        assert hypno.ndim == 1, "Hypno must be 1D."
+        assert hypno.size == data.size, "Hypno must have the same sf as data."
         t_hyp = np.arange(hypno.size) / (sf * 3600)
         # Make sure that REM is displayed after Wake
         hypno = pd.Series(hypno).map({-2: -2, -1: -1, 0: 0, 1: 2, 2: 3, 3: 4, 4: 1}).values
         hypno_rem = np.ma.masked_not_equal(hypno, 1)
 
         fig, (ax0, ax1) = plt.subplots(
-            nrows=2, figsize=(12, 6), gridspec_kw={'height_ratios': [1, 2]})
+            nrows=2, figsize=(12, 6), gridspec_kw={"height_ratios": [1, 2]}
+        )
         plt.subplots_adjust(hspace=0.1)
 
         # Hypnogram (top axis)
-        ax0.step(t_hyp, -1 * hypno, color='k')
-        ax0.step(t_hyp, -1 * hypno_rem, color='r')
+        ax0.step(t_hyp, -1 * hypno, color="k")
+        ax0.step(t_hyp, -1 * hypno_rem, color="r")
         if -2 in hypno and -1 in hypno:
             # Both Unscored and Artefacts are present
             ax0.set_yticks([2, 1, 0, -1, -2, -3, -4])
-            ax0.set_yticklabels(['Uns', 'Art', 'W', 'R', 'N1', 'N2', 'N3'])
+            ax0.set_yticklabels(["Uns", "Art", "W", "R", "N1", "N2", "N3"])
             ax0.set_ylim(-4.5, 2.5)
         elif -2 in hypno and -1 not in hypno:
             # Only Unscored are present
             ax0.set_yticks([2, 0, -1, -2, -3, -4])
-            ax0.set_yticklabels(['Uns', 'W', 'R', 'N1', 'N2', 'N3'])
+            ax0.set_yticklabels(["Uns", "W", "R", "N1", "N2", "N3"])
             ax0.set_ylim(-4.5, 2.5)
 
         elif -2 not in hypno and -1 in hypno:
             # Only Artefacts are present
             ax0.set_yticks([1, 0, -1, -2, -3, -4])
-            ax0.set_yticklabels(['Art', 'W', 'R', 'N1', 'N2', 'N3'])
+            ax0.set_yticklabels(["Art", "W", "R", "N1", "N2", "N3"])
             ax0.set_ylim(-4.5, 1.5)
         else:
             # No artefacts or Unscored
             ax0.set_yticks([0, -1, -2, -3, -4])
-            ax0.set_yticklabels(['W', 'R', 'N1', 'N2', 'N3'])
+            ax0.set_yticklabels(["W", "R", "N1", "N2", "N3"])
             ax0.set_ylim(-4.5, 0.5)
         ax0.set_xlim(0, t_hyp.max())
-        ax0.set_ylabel('Stage')
+        ax0.set_ylabel("Stage")
         ax0.xaxis.set_visible(False)
-        ax0.spines['right'].set_visible(False)
-        ax0.spines['top'].set_visible(False)
+        ax0.spines["right"].set_visible(False)
+        ax0.spines["top"].set_visible(False)
 
         # Spectrogram (bottom axis)
         im = ax1.pcolormesh(t, f, Sxx, norm=norm, cmap=cmap, antialiased=True, shading="auto")
         ax1.set_xlim(0, t.max())
-        ax1.set_ylabel('Frequency [Hz]')
-        ax1.set_xlabel('Time [hrs]')
+        ax1.set_ylabel("Frequency [Hz]")
+        ax1.set_xlabel("Time [hrs]")
 
         # Revert font-size
-        plt.rcParams.update({'font.size': old_fontsize})
+        plt.rcParams.update({"font.size": old_fontsize})
         return fig

 
 
-
[docs]def 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):
+
[docs]def 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
+):
     """
     Topoplot.
 
@@ -463,19 +504,19 @@ 
Source code for yasa.plotting
         ...                     cbar_title="Pearson correlation")
     """
     # Increase font size while preserving original
-    old_fontsize = plt.rcParams['font.size']
-    plt.rcParams.update({'font.size': fontsize})
-    plt.rcParams.update({'savefig.bbox': 'tight'})
-    plt.rcParams.update({'savefig.transparent': 'True'})
+    old_fontsize = plt.rcParams["font.size"]
+    plt.rcParams.update({"font.size": fontsize})
+    plt.rcParams.update({"savefig.bbox": "tight"})
+    plt.rcParams.update({"savefig.transparent": "True"})
 
     # Make sure we don't do any in-place modification
-    assert isinstance(data, pd.Series), 'Data must be a Pandas Series'
+    assert isinstance(data, pd.Series), "Data must be a Pandas Series"
     data = data.copy()
 
     # Add mask, if present
     if mask is not None:
-        assert isinstance(mask, pd.Series), 'mask must be a Pandas Series'
-        assert mask.dtype.kind in 'bi', "mask must be True/False or 0/1."
+        assert isinstance(mask, pd.Series), "mask must be a Pandas Series"
+        assert mask.dtype.kind in "bi", "mask must be True/False or 0/1."
     else:
         mask = pd.Series(1, index=data.index, name="mask")
 
@@ -483,11 +524,11 @@ 
Source code for yasa.plotting
     data = data.to_frame().join(mask, how="left")
 
     # Preprocess channel names: C4-M1 --> C4
-    data.index = data.index.str.split('-').str.get(0)
+    data.index = data.index.str.split("-").str.get(0)
 
     # Define electrodes coordinates
-    Info = mne.create_info(data.index.tolist(), sfreq=100, ch_types='eeg')
-    Info.set_montage(montage, match_case=False, on_missing='ignore')
+    Info = mne.create_info(data.index.tolist(), sfreq=100, ch_types="eeg")
+    Info.set_montage(montage, match_case=False, on_missing="ignore")
     chan = Info.ch_names
 
     # Define vmin and vmax
@@ -499,36 +540,43 @@ 
Source code for yasa.plotting
     # Choose and discretize colormap
     if cmap is None:
         if vmin < 0 and vmax <= 0:
-            cmap = 'mako'
+            cmap = "mako"
         elif vmin < 0 and vmax > 0:
-            cmap = 'Spectral_r'
+            cmap = "Spectral_r"
         elif vmin >= 0 and vmax > 0:
-            cmap = 'rocket_r'
+            cmap = "rocket_r"
 
     cmap = ListedColormap(sns.color_palette(cmap, n_colors).as_hex())
 
-    if 'sensors' not in kwargs:
-        kwargs['sensors'] = False
-    if 'res' not in kwargs:
-        kwargs['res'] = 256
-    if 'names' not in kwargs:
-        kwargs['names'] = chan
-    if 'show_names' not in kwargs:
-        kwargs['show_names'] = True
-    if 'mask_params' not in kwargs:
-        kwargs['mask_params'] = dict(marker=None)
+    if "sensors" not in kwargs:
+        kwargs["sensors"] = False
+    if "res" not in kwargs:
+        kwargs["res"] = 256
+    if "names" not in kwargs:
+        kwargs["names"] = chan
+    if "show_names" not in kwargs:
+        kwargs["show_names"] = True
+    if "mask_params" not in kwargs:
+        kwargs["mask_params"] = dict(marker=None)
 
     # Hidden feature: if names='values', show the actual values.
-    if kwargs['names'] == 'values':
-        kwargs['names'] = data.iloc[:, 0][chan].round(2).to_numpy()
+    if kwargs["names"] == "values":
+        kwargs["names"] = data.iloc[:, 0][chan].round(2).to_numpy()
 
     # Start the plot
     with sns.axes_style("white"):
         fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
         im, _ = mne.viz.plot_topomap(
-            data=data.iloc[:, 0][chan], pos=Info, vmin=vmin, vmax=vmax,
-            mask=data.iloc[:, 1][chan], cmap=cmap, show=False, axes=ax,
-            **kwargs)
+            data=data.iloc[:, 0][chan],
+            pos=Info,
+            vmin=vmin,
+            vmax=vmax,
+            mask=data.iloc[:, 1][chan],
+            cmap=cmap,
+            show=False,
+            axes=ax,
+            **kwargs
+        )
 
         if title is not None:
             ax.set_title(title)
@@ -542,7 +590,7 @@ 
Source code for yasa.plotting
         cbar.set_label(cbar_title)
 
         # Revert font-size
-        plt.rcParams.update({'font.size': old_fontsize})
+        plt.rcParams.update({"font.size": old_fontsize})
     return fig

 

 
diff --git a/docs/build/html/_modules/yasa/sleepstats.html b/docs/build/html/_modules/yasa/sleepstats.html
index 7be9caf..e32babb 100644
--- a/docs/build/html/_modules/yasa/sleepstats.html
+++ b/docs/build/html/_modules/yasa/sleepstats.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.sleepstats — yasa 0.6.1 documentation
+    yasa.sleepstats — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -98,13 +101,14 @@ 
Source code for yasa.sleepstats
 import numpy as np
 import pandas as pd
 
-__all__ = ['transition_matrix', 'sleep_statistics']
+__all__ = ["transition_matrix", "sleep_statistics"]
 
 
 #############################################################################
 # TRANSITION MATRIX
 #############################################################################
 
+
 
[docs]def transition_matrix(hypno):
     """Create a state-transition matrix from an hypnogram.
 
@@ -201,12 +205,13 @@ 
Source code for yasa.sleepstats
     # Convert to a Pandas DataFrame
     counts = pd.DataFrame(counts, index=unique, columns=unique)
     probs = pd.DataFrame(probs, index=unique, columns=unique)
-    counts.index.name = 'From Stage'
-    probs.index.name = 'From Stage'
-    counts.columns.name = 'To Stage'
-    probs.columns.name = 'To Stage'
+    counts.index.name = "From Stage"
+    probs.index.name = "From Stage"
+    counts.columns.name = "To Stage"
+    probs.columns.name = "To Stage"
     return counts, probs

 
+
 #############################################################################
 # SLEEP STATISTICS
 #############################################################################
@@ -310,48 +315,48 @@ 
Source code for yasa.sleepstats
     """
     stats = {}
     hypno = np.asarray(hypno)
-    assert hypno.ndim == 1, 'hypno must have only one dimension.'
-    assert hypno.size > 1, 'hypno must have at least two elements.'
+    assert hypno.ndim == 1, "hypno must have only one dimension."
+    assert hypno.size > 1, "hypno must have at least two elements."
 
     # TIB, first and last sleep
-    stats['TIB'] = len(hypno)
+    stats["TIB"] = len(hypno)
     first_sleep = np.where(hypno > 0)[0][0]
     last_sleep = np.where(hypno > 0)[0][-1]
 
     # Crop to SPT
-    hypno_s = hypno[first_sleep:(last_sleep + 1)]
-    stats['SPT'] = hypno_s.size
-    stats['WASO'] = hypno_s[hypno_s == 0].size
+    hypno_s = hypno[first_sleep : (last_sleep + 1)]
+    stats["SPT"] = hypno_s.size
+    stats["WASO"] = hypno_s[hypno_s == 0].size
     # Before YASA v0.5.0, TST was calculated as SPT - WASO, meaning that Art
     # and Unscored epochs were included. TST is now restrained to sleep stages.
-    stats['TST'] = hypno_s[hypno_s > 0].size
+    stats["TST"] = hypno_s[hypno_s > 0].size
 
     # Duration of each sleep stages
-    stats['N1'] = hypno[hypno == 1].size
-    stats['N2'] = hypno[hypno == 2].size
-    stats['N3'] = hypno[hypno == 3].size
-    stats['REM'] = hypno[hypno == 4].size
-    stats['NREM'] = stats['N1'] + stats['N2'] + stats['N3']
+    stats["N1"] = hypno[hypno == 1].size
+    stats["N2"] = hypno[hypno == 2].size
+    stats["N3"] = hypno[hypno == 3].size
+    stats["REM"] = hypno[hypno == 4].size
+    stats["NREM"] = stats["N1"] + stats["N2"] + stats["N3"]
 
     # Sleep stage latencies -- only relevant if hypno is cropped to TIB
-    stats['SOL'] = first_sleep
-    stats['Lat_N1'] = np.where(hypno == 1)[0].min() if 1 in hypno else np.nan
-    stats['Lat_N2'] = np.where(hypno == 2)[0].min() if 2 in hypno else np.nan
-    stats['Lat_N3'] = np.where(hypno == 3)[0].min() if 3 in hypno else np.nan
-    stats['Lat_REM'] = np.where(hypno == 4)[0].min() if 4 in hypno else np.nan
+    stats["SOL"] = first_sleep
+    stats["Lat_N1"] = np.where(hypno == 1)[0].min() if 1 in hypno else np.nan
+    stats["Lat_N2"] = np.where(hypno == 2)[0].min() if 2 in hypno else np.nan
+    stats["Lat_N3"] = np.where(hypno == 3)[0].min() if 3 in hypno else np.nan
+    stats["Lat_REM"] = np.where(hypno == 4)[0].min() if 4 in hypno else np.nan
 
     # Convert to minutes
     for key, value in stats.items():
         stats[key] = value / (60 * sf_hyp)
 
     # Percentage
-    stats['%N1'] = 100 * stats['N1'] / stats['TST']
-    stats['%N2'] = 100 * stats['N2'] / stats['TST']
-    stats['%N3'] = 100 * stats['N3'] / stats['TST']
-    stats['%REM'] = 100 * stats['REM'] / stats['TST']
-    stats['%NREM'] = 100 * stats['NREM'] / stats['TST']
-    stats['SE'] = 100 * stats['TST'] / stats['TIB']
-    stats['SME'] = 100 * stats['TST'] / stats['SPT']
+    stats["%N1"] = 100 * stats["N1"] / stats["TST"]
+    stats["%N2"] = 100 * stats["N2"] / stats["TST"]
+    stats["%N3"] = 100 * stats["N3"] / stats["TST"]
+    stats["%REM"] = 100 * stats["REM"] / stats["TST"]
+    stats["%NREM"] = 100 * stats["NREM"] / stats["TST"]
+    stats["SE"] = 100 * stats["TST"] / stats["TIB"]
+    stats["SME"] = 100 * stats["TST"] / stats["SPT"]
     return stats

 

 
diff --git a/docs/build/html/_modules/yasa/spectral.html b/docs/build/html/_modules/yasa/spectral.html
index 37fc0e5..490f497 100644
--- a/docs/build/html/_modules/yasa/spectral.html
+++ b/docs/build/html/_modules/yasa/spectral.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.spectral — yasa 0.6.1 documentation
+    yasa.spectral — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -104,17 +107,30 @@ 
Source code for yasa.spectral
 from scipy.interpolate import RectBivariateSpline
 from .io import set_log_level
 
-logger = logging.getLogger('yasa')
-
-__all__ = ['bandpower', 'bandpower_from_psd', 'bandpower_from_psd_ndarray',
-           'irasa', 'stft_power']
-
-
-
[docs]def bandpower(data, sf=None, ch_names=None, hypno=None, include=(2, 3),
-              win_sec=4, relative=True, bandpass=False,
-              bands=[(0.5, 4, 'Delta'), (4, 8, 'Theta'), (8, 12, 'Alpha'),
-                     (12, 16, 'Sigma'), (16, 30, 'Beta'), (30, 40, 'Gamma')],
-              kwargs_welch=dict(average='median', window='hamming')):
+logger = logging.getLogger("yasa")
+
+__all__ = ["bandpower", "bandpower_from_psd", "bandpower_from_psd_ndarray", "irasa", "stft_power"]
+
+
+
[docs]def bandpower(
+    data,
+    sf=None,
+    ch_names=None,
+    hypno=None,
+    include=(2, 3),
+    win_sec=4,
+    relative=True,
+    bandpass=False,
+    bands=[
+        (0.5, 4, "Delta"),
+        (4, 8, "Theta"),
+        (8, 12, "Alpha"),
+        (12, 16, "Sigma"),
+        (16, 30, "Beta"),
+        (30, 40, "Gamma"),
+    ],
+    kwargs_welch=dict(average="median", window="hamming"),
+):
     """
     Calculate the Welch bandpower for each channel and, if specified, for each sleep stage.
 
@@ -181,74 +197,85 @@ 
Source code for yasa.spectral
     https://github.com/raphaelvallat/yasa/blob/master/notebooks/08_bandpower.ipynb
     """
     # Type checks
-    assert isinstance(bands, list), 'bands must be a list of tuple(s)'
-    assert isinstance(relative, bool), 'relative must be a boolean'
-    assert isinstance(bandpass, bool), 'bandpass must be a boolean'
+    assert isinstance(bands, list), "bands must be a list of tuple(s)"
+    assert isinstance(relative, bool), "relative must be a boolean"
+    assert isinstance(bandpass, bool), "bandpass must be a boolean"
 
     # Check if input data is a MNE Raw object
     if isinstance(data, mne.io.BaseRaw):
-        sf = data.info['sfreq']  # Extract sampling frequency
+        sf = data.info["sfreq"]  # Extract sampling frequency
         ch_names = data.ch_names  # Extract channel names
-        data = data.get_data() * 1e6  # Convert from V to uV
+        data = data.get_data(units=dict(eeg="uV", emg="uV", eog="uV", ecg="uV"))
         _, npts = data.shape
     else:
         # Safety checks
-        assert isinstance(data, np.ndarray), 'Data must be a numpy array.'
+        assert isinstance(data, np.ndarray), "Data must be a numpy array."
         data = np.atleast_2d(data)
-        assert data.ndim == 2, 'Data must be of shape (nchan, n_samples).'
+        assert data.ndim == 2, "Data must be of shape (nchan, n_samples)."
         nchan, npts = data.shape
         # assert nchan < npts, 'Data must be of shape (nchan, n_samples).'
-        assert sf is not None, 'sf must be specified if passing a numpy array.'
+        assert sf is not None, "sf must be specified if passing a numpy array."
         assert isinstance(sf, (int, float))
         if ch_names is None:
-            ch_names = ['CHAN' + str(i).zfill(3) for i in range(nchan)]
+            ch_names = ["CHAN" + str(i).zfill(3) for i in range(nchan)]
         else:
             ch_names = np.atleast_1d(np.asarray(ch_names, dtype=str))
-            assert ch_names.ndim == 1, 'ch_names must be 1D.'
-            assert len(ch_names) == nchan, 'ch_names must match data.shape[0].'
+            assert ch_names.ndim == 1, "ch_names must be 1D."
+            assert len(ch_names) == nchan, "ch_names must match data.shape[0]."
 
     if bandpass:
         # Apply FIR bandpass filter
         all_freqs = np.hstack([[b[0], b[1]] for b in bands])
         fmin, fmax = min(all_freqs), max(all_freqs)
-        data = mne.filter.filter_data(data.astype('float64'), sf, fmin, fmax, verbose=0)
+        data = mne.filter.filter_data(data.astype("float64"), sf, fmin, fmax, verbose=0)
 
     win = int(win_sec * sf)  # nperseg
 
     if hypno is None:
         # Calculate the PSD over the whole data
         freqs, psd = signal.welch(data, sf, nperseg=win, **kwargs_welch)
-        return bandpower_from_psd(
-            psd, freqs, ch_names, bands=bands, relative=relative).set_index('Chan')
+        return bandpower_from_psd(psd, freqs, ch_names, bands=bands, relative=relative).set_index(
+            "Chan"
+        )
     else:
         # Per each sleep stage defined in ``include``.
         hypno = np.asarray(hypno)
-        assert include is not None, 'include cannot be None if hypno is given'
+        assert include is not None, "include cannot be None if hypno is given"
         include = np.atleast_1d(np.asarray(include))
-        assert hypno.ndim == 1, 'Hypno must be a 1D array.'
-        assert hypno.size == npts, 'Hypno must have same size as data.shape[1]'
-        assert include.size >= 1, '`include` must have at least one element.'
-        assert hypno.dtype.kind == include.dtype.kind, 'hypno and include must have same dtype'
-        assert np.in1d(hypno, include).any(), (
-            'None of the stages specified in `include` are present in hypno.')
+        assert hypno.ndim == 1, "Hypno must be a 1D array."
+        assert hypno.size == npts, "Hypno must have same size as data.shape[1]"
+        assert include.size >= 1, "`include` must have at least one element."
+        assert hypno.dtype.kind == include.dtype.kind, "hypno and include must have same dtype"
+        assert np.in1d(
+            hypno, include
+        ).any(), "None of the stages specified in `include` are present in hypno."
         # Initialize empty dataframe and loop over stages
         df_bp = pd.DataFrame([])
         for stage in include:
             if stage not in hypno:
                 continue
             data_stage = data[:, hypno == stage]
-            freqs, psd = signal.welch(data_stage, sf, nperseg=win,
-                                      **kwargs_welch)
-            bp_stage = bandpower_from_psd(psd, freqs, ch_names, bands=bands,
-                                          relative=relative)
-            bp_stage['Stage'] = stage
+            freqs, psd = signal.welch(data_stage, sf, nperseg=win, **kwargs_welch)
+            bp_stage = bandpower_from_psd(psd, freqs, ch_names, bands=bands, relative=relative)
+            bp_stage["Stage"] = stage
             df_bp = pd.concat([df_bp, bp_stage], axis=0)
-        return df_bp.set_index(['Stage', 'Chan'])

-
-
-
[docs]def bandpower_from_psd(psd, freqs, ch_names=None, bands=[(0.5, 4, 'Delta'),
-                       (4, 8, 'Theta'), (8, 12, 'Alpha'), (12, 16, 'Sigma'),
-                       (16, 30, 'Beta'), (30, 40, 'Gamma')], relative=True):
+        return df_bp.set_index(["Stage", "Chan"])

+
+
+
[docs]def bandpower_from_psd(
+    psd,
+    freqs,
+    ch_names=None,
+    bands=[
+        (0.5, 4, "Delta"),
+        (4, 8, "Theta"),
+        (8, 12, "Alpha"),
+        (12, 16, "Sigma"),
+        (16, 30, "Beta"),
+        (30, 40, "Gamma"),
+    ],
+    relative=True,
+):
     """Compute the average power of the EEG in specified frequency band(s)
     given a pre-computed PSD.
 
@@ -277,27 +304,27 @@ 
Source code for yasa.spectral
         Bandpower dataframe, in which each row is a channel and each column a spectral band.
     """
     # Type checks
-    assert isinstance(bands, list), 'bands must be a list of tuple(s)'
-    assert isinstance(relative, bool), 'relative must be a boolean'
+    assert isinstance(bands, list), "bands must be a list of tuple(s)"
+    assert isinstance(relative, bool), "relative must be a boolean"
 
     # Safety checks
     freqs = np.asarray(freqs)
     assert freqs.ndim == 1
     psd = np.atleast_2d(psd)
-    assert psd.ndim == 2, 'PSD must be of shape (n_channels, n_freqs).'
+    assert psd.ndim == 2, "PSD must be of shape (n_channels, n_freqs)."
     all_freqs = np.hstack([[b[0], b[1]] for b in bands])
     fmin, fmax = min(all_freqs), max(all_freqs)
     idx_good_freq = np.logical_and(freqs >= fmin, freqs <= fmax)
     freqs = freqs[idx_good_freq]
     res = freqs[1] - freqs[0]
     nchan = psd.shape[0]
-    assert nchan < psd.shape[1], 'PSD must be of shape (n_channels, n_freqs).'
+    assert nchan < psd.shape[1], "PSD must be of shape (n_channels, n_freqs)."
     if ch_names is not None:
         ch_names = np.atleast_1d(np.asarray(ch_names, dtype=str))
-        assert ch_names.ndim == 1, 'ch_names must be 1D.'
-        assert len(ch_names) == nchan, 'ch_names must match psd.shape[0].'
+        assert ch_names.ndim == 1, "ch_names must be 1D."
+        assert len(ch_names) == nchan, "ch_names must match psd.shape[0]."
     else:
-        ch_names = ['CHAN' + str(i).zfill(3) for i in range(nchan)]
+        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)
@@ -309,7 +336,8 @@ 
Source code for yasa.spectral
             "There are negative values in PSD. This will result in incorrect "
             "bandpower values. We highly recommend working with an "
             "all-positive PSD. For more details, please refer to: "
-            "https://github.com/raphaelvallat/yasa/issues/29")
+            "https://github.com/raphaelvallat/yasa/issues/29"
+        )
         logger.warning(msg)
 
     # Enumerate over the frequency bands
@@ -325,21 +353,30 @@ 
Source code for yasa.spectral
 
     # Convert to DataFrame
     bp = pd.DataFrame(bp, columns=labels)
-    bp['TotalAbsPow'] = np.squeeze(total_power)
-    bp['FreqRes'] = res
+    bp["TotalAbsPow"] = np.squeeze(total_power)
+    bp["FreqRes"] = res
     # bp['WindowSec'] = 1 / res
-    bp['Relative'] = relative
-    bp['Chan'] = ch_names
-    bp = bp.set_index('Chan').reset_index()
+    bp["Relative"] = relative
+    bp["Chan"] = ch_names
+    bp = bp.set_index("Chan").reset_index()
     # Add hidden attributes
     bp.bands_ = str(bands)
     return bp

 
 
-
[docs]def bandpower_from_psd_ndarray(psd, freqs, bands=[(0.5, 4, 'Delta'),
-                               (4, 8, 'Theta'), (8, 12, 'Alpha'),
-                               (12, 16, 'Sigma'), (16, 30, 'Beta'),
-                               (30, 40, 'Gamma')], relative=True):
+
[docs]def bandpower_from_psd_ndarray(
+    psd,
+    freqs,
+    bands=[
+        (0.5, 4, "Delta"),
+        (4, 8, "Theta"),
+        (8, 12, "Alpha"),
+        (12, 16, "Sigma"),
+        (16, 30, "Beta"),
+        (30, 40, "Gamma"),
+    ],
+    relative=True,
+):
     """Compute bandpowers in N-dimensional PSD.
 
     This is a NumPy-only implementation of the :py:func:`yasa.bandpower_from_psd` function,
@@ -368,14 +405,14 @@ 
Source code for yasa.spectral
         Bandpower array of shape *(n_bands, ...)*.
     """
     # Type checks
-    assert isinstance(bands, list), 'bands must be a list of tuple(s)'
-    assert isinstance(relative, bool), 'relative must be a boolean'
+    assert isinstance(bands, list), "bands must be a list of tuple(s)"
+    assert isinstance(relative, bool), "relative must be a boolean"
 
     # Safety checks
     freqs = np.asarray(freqs)
     psd = np.asarray(psd)
-    assert freqs.ndim == 1, 'freqs must be a 1-D array of shape (n_freqs,)'
-    assert psd.shape[-1] == freqs.shape[-1], 'n_freqs must be last axis of psd'
+    assert freqs.ndim == 1, "freqs must be a 1-D array of shape (n_freqs,)"
+    assert psd.shape[-1] == freqs.shape[-1], "n_freqs must be last axis of psd"
 
     # Extract frequencies of interest
     all_freqs = np.hstack([[b[0], b[1]] for b in bands])
@@ -393,7 +430,8 @@ 
Source code for yasa.spectral
             "There are negative values in PSD. This will result in incorrect "
             "bandpower values. We highly recommend working with an "
             "all-positive PSD. For more details, please refer to: "
-            "https://github.com/raphaelvallat/yasa/issues/29")
+            "https://github.com/raphaelvallat/yasa/issues/29"
+        )
         logger.warning(msg)
 
     # Calculate total power
@@ -416,11 +454,35 @@ 
Source code for yasa.spectral
     return bp

 
 
-
[docs]def irasa(data, sf=None, ch_names=None, band=(1, 30),
-          hset=[1.1, 1.15, 1.2, 1.25, 1.3, 1.35, 1.4, 1.45, 1.5, 1.55, 1.6,
-          1.65, 1.7, 1.75, 1.8, 1.85, 1.9], return_fit=True, win_sec=4,
-          kwargs_welch=dict(average='median', window='hamming'),
-          verbose=True):
+
[docs]def irasa(
+    data,
+    sf=None,
+    ch_names=None,
+    band=(1, 30),
+    hset=[
+        1.1,
+        1.15,
+        1.2,
+        1.25,
+        1.3,
+        1.35,
+        1.4,
+        1.45,
+        1.5,
+        1.55,
+        1.6,
+        1.65,
+        1.7,
+        1.75,
+        1.8,
+        1.85,
+        1.9,
+    ],
+    return_fit=True,
+    win_sec=4,
+    kwargs_welch=dict(average="median", window="hamming"),
+    verbose=True,
+):
     r"""
     Separate the aperiodic (= fractal, or 1/f) and oscillatory component
     of the power spectra of EEG data using the IRASA method.
@@ -533,40 +595,41 @@ 
Source code for yasa.spectral
     [5] https://doi.org/10.1101/2021.10.15.464483
     """
     import fractions
+
     set_log_level(verbose)
     # Check if input data is a MNE Raw object
     if isinstance(data, mne.io.BaseRaw):
-        sf = data.info['sfreq']  # Extract sampling frequency
+        sf = data.info["sfreq"]  # Extract sampling frequency
         ch_names = data.ch_names  # Extract channel names
-        hp = data.info['highpass']  # Extract highpass filter
-        lp = data.info['lowpass']  # Extract lowpass filter
-        data = data.get_data() * 1e6  # Convert from V to uV
+        hp = data.info["highpass"]  # Extract highpass filter
+        lp = data.info["lowpass"]  # Extract lowpass filter
+        data = data.get_data(units=dict(eeg="uV", emg="uV", eog="uV", ecg="uV"))
     else:
         # Safety checks
-        assert isinstance(data, np.ndarray), 'Data must be a numpy array.'
+        assert isinstance(data, np.ndarray), "Data must be a numpy array."
         data = np.atleast_2d(data)
-        assert data.ndim == 2, 'Data must be of shape (nchan, n_samples).'
+        assert data.ndim == 2, "Data must be of shape (nchan, n_samples)."
         nchan, npts = data.shape
-        assert nchan < npts, 'Data must be of shape (nchan, n_samples).'
-        assert sf is not None, 'sf must be specified if passing a numpy array.'
+        assert nchan < npts, "Data must be of shape (nchan, n_samples)."
+        assert sf is not None, "sf must be specified if passing a numpy array."
         assert isinstance(sf, (int, float))
         if ch_names is None:
-            ch_names = ['CHAN' + str(i).zfill(3) for i in range(nchan)]
+            ch_names = ["CHAN" + str(i).zfill(3) for i in range(nchan)]
         else:
             ch_names = np.atleast_1d(np.asarray(ch_names, dtype=str))
-            assert ch_names.ndim == 1, 'ch_names must be 1D.'
-            assert len(ch_names) == nchan, 'ch_names must match data.shape[0].'
+            assert ch_names.ndim == 1, "ch_names must be 1D."
+            assert len(ch_names) == nchan, "ch_names must match data.shape[0]."
         hp = 0  # Highpass filter unknown -> set to 0 Hz
         lp = sf / 2  # Lowpass filter unknown -> set to Nyquist
 
     # Check the other arguments
     hset = np.asarray(hset)
-    assert hset.ndim == 1, 'hset must be 1D.'
-    assert hset.size > 1, '2 or more resampling fators are required.'
+    assert hset.ndim == 1, "hset must be 1D."
+    assert hset.size > 1, "2 or more resampling fators are required."
     hset = np.round(hset, 4)  # avoid float precision error with np.arange.
     band = sorted(band)
-    assert band[0] > 0, 'first element of band must be > 0.'
-    assert band[1] < (sf / 2), 'second element of band must be < (sf / 2).'
+    assert band[0] > 0, "first element of band must be > 0."
+    assert band[1] < (sf / 2), "second element of band must be < (sf / 2)."
     win = int(win_sec * sf)  # nperseg
 
     # Inform about maximum resampled fitting range
@@ -577,21 +640,27 @@ 
Source code for yasa.spectral
     logging.info(f"Fitting range: {band[0]:.2f}Hz-{band[1]:.2f}Hz")
     logging.info(f"Evaluated frequency range: {band_evaluated[0]:.2f}Hz-{band_evaluated[1]:.2f}Hz")
     if band_evaluated[0] < hp:
-        logging.warning("The evaluated frequency range starts below the "
-                        f"highpass filter ({hp:.2f}Hz). Increase the lower band"
-                        f" ({band[0]:.2f}Hz) or decrease the maximum value of "
-                        f"the hset ({h_max:.2f}).")
+        logging.warning(
+            "The evaluated frequency range starts below the "
+            f"highpass filter ({hp:.2f}Hz). Increase the lower band"
+            f" ({band[0]:.2f}Hz) or decrease the maximum value of "
+            f"the hset ({h_max:.2f})."
+        )
     if band_evaluated[1] > lp and lp < freq_Nyq_res:
-        logging.warning("The evaluated frequency range ends after the "
-                        f"lowpass filter ({lp:.2f}Hz). Decrease the upper band"
-                        f" ({band[1]:.2f}Hz) or decrease the maximum value of "
-                        f"the hset ({h_max:.2f}).")
+        logging.warning(
+            "The evaluated frequency range ends after the "
+            f"lowpass filter ({lp:.2f}Hz). Decrease the upper band"
+            f" ({band[1]:.2f}Hz) or decrease the maximum value of "
+            f"the hset ({h_max:.2f})."
+        )
     if band_evaluated[1] > freq_Nyq_res:
-        logging.warning("The evaluated frequency range ends after the "
-                        "resampled Nyquist frequency "
-                        f"({freq_Nyq_res:.2f}Hz). Decrease the upper band "
-                        f"({band[1]:.2f}Hz) or decrease the maximum value "
-                        f"of the hset ({h_max:.2f}).")
+        logging.warning(
+            "The evaluated frequency range ends after the "
+            "resampled Nyquist frequency "
+            f"({freq_Nyq_res:.2f}Hz). Decrease the upper band "
+            f"({band[1]:.2f}Hz) or decrease the maximum value "
+            f"of the hset ({h_max:.2f})."
+        )
 
     # Calculate the original PSD over the whole data
     freqs, psd = signal.welch(data, sf, nperseg=win, **kwargs_welch)
@@ -628,6 +697,7 @@ 
Source code for yasa.spectral
     if return_fit:
         # Aperiodic fit in semilog space for each channel
         from scipy.optimize import curve_fit
+
         intercepts, slopes, r_squared = [], [], []
 
         def func(t, a, b):
@@ -638,26 +708,31 @@ 
Source code for yasa.spectral
             y_log = np.log(y)
             # Note that here we define bounds for the slope but not for the
             # intercept.
-            popt, pcov = curve_fit(func, freqs, y_log, p0=(2, -1),
-                                   bounds=((-np.inf, -10), (np.inf, 2)))
+            popt, pcov = curve_fit(
+                func, freqs, y_log, p0=(2, -1), bounds=((-np.inf, -10), (np.inf, 2))
+            )
             intercepts.append(popt[0])
             slopes.append(popt[1])
             # Calculate R^2: https://stackoverflow.com/q/19189362/10581531
             residuals = y_log - func(freqs, *popt)
             ss_res = np.sum(residuals**2)
-            ss_tot = np.sum((y_log - np.mean(y_log))**2)
+            ss_tot = np.sum((y_log - np.mean(y_log)) ** 2)
             r_squared.append(1 - (ss_res / ss_tot))
 
         # Create fit parameters dataframe
-        fit_params = {'Chan': ch_names, 'Intercept': intercepts,
-                      'Slope': slopes, 'R^2': r_squared,
-                      'std(osc)': np.std(psd_osc, axis=-1, ddof=1)}
+        fit_params = {
+            "Chan": ch_names,
+            "Intercept": intercepts,
+            "Slope": slopes,
+            "R^2": r_squared,
+            "std(osc)": np.std(psd_osc, axis=-1, ddof=1),
+        }
         return freqs, psd_aperiodic, psd_osc, pd.DataFrame(fit_params)
     else:
         return freqs, psd_aperiodic, psd_osc

 
 
-
[docs]def stft_power(data, sf, window=2, step=.2, band=(1, 30), interp=True, norm=False):
+
[docs]def stft_power(data, sf, window=2, step=0.2, band=(1, 30), interp=True, norm=False):
     """Compute the pointwise power via STFT and interpolation.
 
     Parameters
@@ -712,7 +787,8 @@ 
Source code for yasa.spectral
 
     # Compute STFT and remove the last epoch
     f, t, Sxx = signal.stft(
-        data, sf, nperseg=nperseg, noverlap=noverlap, detrend=False, padded=True)
+        data, sf, nperseg=nperseg, noverlap=noverlap, detrend=False, padded=True
+    )
 
     # Let's keep only the frequency of interest
     if band is not None:
diff --git a/docs/build/html/_modules/yasa/staging.html b/docs/build/html/_modules/yasa/staging.html
index 10a445b..9dcef5b 100644
--- a/docs/build/html/_modules/yasa/staging.html
+++ b/docs/build/html/_modules/yasa/staging.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.staging — yasa 0.6.1 documentation
+    yasa.staging — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -109,7 +112,7 @@ 
Source code for yasa.staging
 from .others import sliding_window
 from .spectral import bandpower_from_psd_ndarray
 
-logger = logging.getLogger('yasa')
+logger = logging.getLogger("yasa")
 
 
 
[docs]class SleepStaging:
@@ -259,21 +262,21 @@ 
Source code for yasa.staging
 
         # Validate metadata
         if isinstance(metadata, dict):
-            if 'age' in metadata.keys():
-                assert 0 < metadata['age'] < 120, 'age must be between 0 and 120.'
-            if 'male' in metadata.keys():
-                metadata['male'] = int(metadata['male'])
-                assert metadata['male'] in [0, 1], 'male must be 0 or 1.'
+            if "age" in metadata.keys():
+                assert 0 < metadata["age"] < 120, "age must be between 0 and 120."
+            if "male" in metadata.keys():
+                metadata["male"] = int(metadata["male"])
+                assert metadata["male"] in [0, 1], "male must be 0 or 1."
 
         # Validate Raw instance and load data
-        assert isinstance(raw, mne.io.BaseRaw), 'raw must be a MNE Raw object.'
-        sf = raw.info['sfreq']
+        assert isinstance(raw, mne.io.BaseRaw), "raw must be a MNE Raw object."
+        sf = raw.info["sfreq"]
         ch_names = np.array([eeg_name, eog_name, emg_name])
-        ch_types = np.array(['eeg', 'eog', 'emg'])
+        ch_types = np.array(["eeg", "eog", "emg"])
         keep_chan = []
         for c in ch_names:
             if c is not None:
-                assert c in raw.ch_names, '%s does not exist' % c
+                assert c in raw.ch_names, "%s does not exist" % c
                 keep_chan.append(True)
             else:
                 keep_chan.append(False)
@@ -284,17 +287,17 @@ 
Source code for yasa.staging
         raw_pick = raw.copy().pick_channels(ch_names, ordered=True)
 
         # Downsample if sf != 100
-        assert sf > 80, 'Sampling frequency must be at least 80 Hz.'
+        assert sf > 80, "Sampling frequency must be at least 80 Hz."
         if sf != 100:
             raw_pick.resample(100, npad="auto")
-            sf = raw_pick.info['sfreq']
+            sf = raw_pick.info["sfreq"]
 
         # Get data and convert to microVolts
-        data = raw_pick.get_data() * 1e6
+        data = raw_pick.get_data(units=dict(eeg="uV", emg="uV", eog="uV", ecg="uV"))
 
         # 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.'
+        assert duration_minutes >= 5, "At least 5 minutes of data is required."
 
         # Add to self
         self.sf = sf
@@ -320,10 +323,14 @@ 
Source code for yasa.staging
         win_sec = 5  # = 2 / freq_broad[0]
         sf = self.sf
         win = int(win_sec * sf)
-        kwargs_welch = dict(window='hamming', nperseg=win, average='median')
+        kwargs_welch = dict(window="hamming", nperseg=win, average="median")
         bands = [
-            (0.4, 1, 'sdelta'), (1, 4, 'fdelta'), (4, 8, 'theta'),
-            (8, 12, 'alpha'), (12, 16, 'sigma'), (16, 30, 'beta')
+            (0.4, 1, "sdelta"),
+            (1, 4, "fdelta"),
+            (4, 8, "theta"),
+            (8, 12, "alpha"),
+            (12, 16, "sigma"),
+            (16, 30, "beta"),
         ]
 
         #######################################################################
@@ -336,7 +343,8 @@ 
Source code for yasa.staging
             # Preprocessing
             # - Filter the data
             dt_filt = filter_data(
-                self.data[i, :], sf, l_freq=freq_broad[0], h_freq=freq_broad[1], verbose=False)
+                self.data[i, :], sf, l_freq=freq_broad[0], h_freq=freq_broad[1], verbose=False
+            )
             # - Extract epochs. Data is now of shape (n_epochs, n_samples).
             times, epochs = sliding_window(dt_filt, sf=sf, window=30)
 
@@ -344,44 +352,42 @@ 
Source code for yasa.staging
             hmob, hcomp = ant.hjorth_params(epochs, axis=1)
 
             feat = {
-                'std': np.std(epochs, ddof=1, axis=1),
-                'iqr': sp_stats.iqr(epochs, rng=(25, 75), axis=1),
-                'skew': sp_stats.skew(epochs, axis=1),
-                'kurt': sp_stats.kurtosis(epochs, axis=1),
-                'nzc': ant.num_zerocross(epochs, axis=1),
-                'hmob': hmob,
-                'hcomp': hcomp
+                "std": np.std(epochs, ddof=1, axis=1),
+                "iqr": sp_stats.iqr(epochs, rng=(25, 75), axis=1),
+                "skew": sp_stats.skew(epochs, axis=1),
+                "kurt": sp_stats.kurtosis(epochs, axis=1),
+                "nzc": ant.num_zerocross(epochs, axis=1),
+                "hmob": hmob,
+                "hcomp": hcomp,
             }
 
             # Calculate spectral power features (for EEG + EOG)
             freqs, psd = sp_sig.welch(epochs, sf, **kwargs_welch)
-            if c != 'emg':
+            if c != "emg":
                 bp = bandpower_from_psd_ndarray(psd, freqs, bands=bands)
                 for j, (_, _, b) in enumerate(bands):
                     feat[b] = bp[j]
 
             # Add power ratios for EEG
-            if c == 'eeg':
-                delta = feat['sdelta'] + feat['fdelta']
-                feat['dt'] = delta / feat['theta']
-                feat['ds'] = delta / feat['sigma']
-                feat['db'] = delta / feat['beta']
-                feat['at'] = feat['alpha'] / feat['theta']
+            if c == "eeg":
+                delta = feat["sdelta"] + feat["fdelta"]
+                feat["dt"] = delta / feat["theta"]
+                feat["ds"] = delta / feat["sigma"]
+                feat["db"] = delta / feat["beta"]
+                feat["at"] = feat["alpha"] / feat["theta"]
 
             # Add total power
             idx_broad = np.logical_and(freqs >= freq_broad[0], freqs <= freq_broad[1])
             dx = freqs[1] - freqs[0]
-            feat['abspow'] = np.trapz(psd[:, idx_broad], dx=dx)
+            feat["abspow"] = np.trapz(psd[:, idx_broad], dx=dx)
 
             # Calculate entropy and fractal dimension features
-            feat['perm'] = np.apply_along_axis(
-                ant.perm_entropy, axis=1, arr=epochs, normalize=True)
-            feat['higuchi'] = np.apply_along_axis(
-                ant.higuchi_fd, axis=1, arr=epochs)
-            feat['petrosian'] = ant.petrosian_fd(epochs, axis=1)
+            feat["perm"] = np.apply_along_axis(ant.perm_entropy, axis=1, arr=epochs, normalize=True)
+            feat["higuchi"] = np.apply_along_axis(ant.higuchi_fd, axis=1, arr=epochs)
+            feat["petrosian"] = ant.petrosian_fd(epochs, axis=1)
 
             # Convert to dataframe
-            feat = pd.DataFrame(feat).add_prefix(c + '_')
+            feat = pd.DataFrame(feat).add_prefix(c + "_")
             features.append(feat)
 
         #######################################################################
@@ -390,20 +396,19 @@ 
Source code for yasa.staging
 
         # Save features to dataframe
         features = pd.concat(features, axis=1)
-        features.index.name = 'epoch'
+        features.index.name = "epoch"
 
         # Apply centered rolling average (15 epochs = 7 min 30)
         # Triang: [0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.,
         #          0.875, 0.75, 0.625, 0.5, 0.375, 0.25, 0.125]
-        rollc = features.rolling(
-            window=15, center=True, min_periods=1, win_type='triang').mean()
+        rollc = features.rolling(window=15, center=True, min_periods=1, win_type="triang").mean()
         rollc[rollc.columns] = robust_scale(rollc, quantile_range=(5, 95))
-        rollc = rollc.add_suffix('_c7min_norm')
+        rollc = rollc.add_suffix("_c7min_norm")
 
         # Now look at the past 2 minutes
         rollp = features.rolling(window=4, min_periods=1).mean()
         rollp[rollp.columns] = robust_scale(rollp, quantile_range=(5, 95))
-        rollp = rollp.add_suffix('_p2min_norm')
+        rollp = rollp.add_suffix("_p2min_norm")
 
         # Add to current set of features
         features = features.join(rollc).join(rollp)
@@ -413,8 +418,8 @@ 
Source code for yasa.staging
         #######################################################################
 
         # Add temporal features
-        features['time_hour'] = times / 3600
-        features['time_norm'] = times / times[-1]
+        features["time_hour"] = times / 3600
+        features["time_norm"] = times / times[-1]
 
         # Add metadata if present
         if self.metadata is not None:
@@ -425,10 +430,10 @@ 
Source code for yasa.staging
         cols_float = features.select_dtypes(np.float64).columns.tolist()
         features[cols_float] = features[cols_float].astype(np.float32)
         # Make sure that age and sex are encoded as int
-        if 'age' in features.columns:
-            features['age'] = features['age'].astype(int)
-        if 'male' in features.columns:
-            features['male'] = features['male'].astype(int)
+        if "age" in features.columns:
+            features["age"] = features["age"].astype(int)
+        if "male" in features.columns:
+            features["male"] = features["male"].astype(int)
 
         # Sort the column names here (same behavior as lightGBM)
         features.sort_index(axis=1, inplace=True)
@@ -445,7 +450,7 @@ 
Source code for yasa.staging
         features : :py:class:`pandas.DataFrame`
             Feature dataframe.
         """
-        if not hasattr(self, '_features'):
+        if not hasattr(self, "_features"):
             self.fit()
         return self._features.copy()

 
@@ -455,22 +460,32 @@ 
Source code for yasa.staging
         # Note that clf.feature_name_ is only available in lightgbm>=3.0
         f_diff = np.setdiff1d(clf.feature_name_, self.feature_name_)
         if len(f_diff):
-            raise ValueError("The following features are present in the "
-                             "classifier but not in the current features set:", f_diff)
-        f_diff = np.setdiff1d(self.feature_name_, clf.feature_name_, )
+            raise ValueError(
+                "The following features are present in the "
+                "classifier but not in the current features set:",
+                f_diff,
+            )
+        f_diff = np.setdiff1d(
+            self.feature_name_,
+            clf.feature_name_,
+        )
         if len(f_diff):
-            raise ValueError("The following features are present in the "
-                             "current feature set but not in the classifier:", f_diff)
+            raise ValueError(
+                "The following features are present in the "
+                "current feature set but not in the classifier:",
+                f_diff,
+            )
 
     def _load_model(self, path_to_model):
         """Load the relevant trained classifier."""
         if path_to_model == "auto":
             from pathlib import Path
-            clf_dir = os.path.join(str(Path(__file__).parent), 'classifiers/')
-            name = 'clf_eeg'
-            name = name + '+eog' if 'eog' in self.ch_types else name
-            name = name + '+emg' if 'emg' in self.ch_types else name
-            name = name + '+demo' if self.metadata is not None else name
+
+            clf_dir = os.path.join(str(Path(__file__).parent), "classifiers/")
+            name = "clf_eeg"
+            name = name + "+eog" if "eog" in self.ch_types else name
+            name = name + "+emg" if "emg" in self.ch_types else name
+            name = name + "+demo" if self.metadata is not None else name
             # e.g. clf_eeg+eog+emg+demo_lgb_0.4.0.joblib
             all_matching_files = glob.glob(clf_dir + name + "*.joblib")
             # Find the latest file
@@ -503,7 +518,7 @@ 
Source code for yasa.staging
         pred : :py:class:`numpy.ndarray`
             The predicted sleep stages.
         """
-        if not hasattr(self, '_features'):
+        if not hasattr(self, "_features"):
             self.fit()
         # Load and validate pre-trained classifier
         clf = self._load_model(path_to_model)
@@ -512,7 +527,7 @@ 
Source code for yasa.staging
         # Predict the sleep stages and probabilities
         self._predicted = clf.predict(X)
         proba = pd.DataFrame(clf.predict_proba(X), columns=clf.classes_)
-        proba.index.name = 'epoch'
+        proba.index.name = "epoch"
         self._proba = proba
         return self._predicted.copy()

 
@@ -535,13 +550,16 @@ 
Source code for yasa.staging
         proba : :py:class:`pandas.DataFrame`
             The predicted probability for each sleep stage for each 30-sec epoch of data.
         """
-        if not hasattr(self, '_proba'):
+        if not hasattr(self, "_proba"):
             self.predict(path_to_model)
         return self._proba.copy()

 
-
[docs]    def plot_predict_proba(self, proba=None, majority_only=False,
-                           palette=['#99d7f1', '#009DDC', 'xkcd:twilight blue',
-                                    'xkcd:rich purple', 'xkcd:sunflower']):
+
[docs]    def plot_predict_proba(
+        self,
+        proba=None,
+        majority_only=False,
+        palette=["#99d7f1", "#009DDC", "xkcd:twilight blue", "xkcd:rich purple", "xkcd:sunflower"],
+    ):
         """
         Plot the predicted probability for each sleep stage for each 30-sec epoch of data.
 
@@ -552,16 +570,16 @@ 
Source code for yasa.staging
         majority_only : boolean
             If True, probabilities of the non-majority classes will be set to 0.
         """
-        if proba is None and not hasattr(self, '_features'):
+        if proba is None and not hasattr(self, "_features"):
             raise ValueError("Must call .predict_proba before this function")
         if proba is None:
             proba = self._proba.copy()
         else:
-            assert isinstance(proba, pd.DataFrame), 'proba must be a dataframe'
+            assert isinstance(proba, pd.DataFrame), "proba must be a dataframe"
         if majority_only:
             cond = proba.apply(lambda x: x == x.max(), axis=1)
             proba = proba.where(cond, other=0)
-        ax = proba.plot(kind='area', color=palette, figsize=(10, 5), alpha=.8, stacked=True, lw=0)
+        ax = proba.plot(kind="area", color=palette, figsize=(10, 5), alpha=0.8, stacked=True, lw=0)
         # Add confidence
         # confidence = proba.max(1)
         # ax.plot(confidence, lw=1, color='k', ls='-', alpha=0.5,
diff --git a/docs/build/html/_panels_static/panels-bootstrap.5fd3999ee7762ccc51105388f4a9d115.css b/docs/build/html/_panels_static/panels-bootstrap.5fd3999ee7762ccc51105388f4a9d115.css
new file mode 100644
index 0000000..1b057df
--- /dev/null
+++ b/docs/build/html/_panels_static/panels-bootstrap.5fd3999ee7762ccc51105388f4a9d115.css
@@ -0,0 +1 @@
+.badge{border-radius:.25rem;display:inline-block;font-size:75%;font-weight:700;line-height:1;padding:.25em .4em;text-align:center;vertical-align:baseline;white-space:nowrap}.badge:empty{display:none}.btn 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diff --git a/docs/build/html/_panels_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css b/docs/build/html/_panels_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css
new file mode 100644
index 0000000..fc14abc
--- /dev/null
+++ b/docs/build/html/_panels_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css
@@ -0,0 +1 @@
+details.dropdown .summary-title{padding-right:3em !important;-moz-user-select:none;-ms-user-select:none;-webkit-user-select:none;user-select:none}details.dropdown:hover{cursor:pointer}details.dropdown .summary-content{cursor:default}details.dropdown summary{list-style:none;padding:1em}details.dropdown summary .octicon.no-title{vertical-align:middle}details.dropdown[open] summary .octicon.no-title{visibility:hidden}details.dropdown summary::-webkit-details-marker{display:none}details.dropdown summary:focus{outline:none}details.dropdown summary:hover .summary-up svg,details.dropdown summary:hover .summary-down svg{opacity:1}details.dropdown .summary-up svg,details.dropdown .summary-down svg{display:block;opacity:.6}details.dropdown .summary-up,details.dropdown .summary-down{pointer-events:none;position:absolute;right:1em;top:.75em}details.dropdown[open] .summary-down{visibility:hidden}details.dropdown:not([open]) .summary-up{visibility:hidden}details.dropdown.fade-in[open] summary~*{-moz-animation:panels-fade-in .5s ease-in-out;-webkit-animation:panels-fade-in .5s ease-in-out;animation:panels-fade-in .5s ease-in-out}details.dropdown.fade-in-slide-down[open] summary~*{-moz-animation:panels-fade-in .5s ease-in-out, panels-slide-down .5s ease-in-out;-webkit-animation:panels-fade-in .5s ease-in-out, panels-slide-down .5s ease-in-out;animation:panels-fade-in .5s ease-in-out, panels-slide-down .5s ease-in-out}@keyframes panels-fade-in{0%{opacity:0}100%{opacity:1}}@keyframes panels-slide-down{0%{transform:translate(0, -10px)}100%{transform:translate(0, 0)}}.octicon{display:inline-block;fill:currentColor;vertical-align:text-top}.tabbed-content{box-shadow:0 -.0625rem var(--tabs-color-overline),0 .0625rem var(--tabs-color-underline);display:none;order:99;padding-bottom:.75rem;padding-top:.75rem;width:100%}.tabbed-content>:first-child{margin-top:0 !important}.tabbed-content>:last-child{margin-bottom:0 !important}.tabbed-content>.tabbed-set{margin:0}.tabbed-set{border-radius:.125rem;display:flex;flex-wrap:wrap;margin:1em 0;position:relative}.tabbed-set>input{opacity:0;position:absolute}.tabbed-set>input:checked+label{border-color:var(--tabs-color-label-active);color:var(--tabs-color-label-active)}.tabbed-set>input:checked+label+.tabbed-content{display:block}.tabbed-set>input:focus+label{outline-style:auto}.tabbed-set>input:not(.focus-visible)+label{outline:none;-webkit-tap-highlight-color:transparent}.tabbed-set>label{border-bottom:.125rem solid transparent;color:var(--tabs-color-label-inactive);cursor:pointer;font-size:var(--tabs-size-label);font-weight:700;padding:1em 1.25em .5em;transition:color 250ms;width:auto;z-index:1}html .tabbed-set>label:hover{color:var(--tabs-color-label-active)}
diff --git a/docs/build/html/_panels_static/panels-variables.06eb56fa6e07937060861dad626602ad.css b/docs/build/html/_panels_static/panels-variables.06eb56fa6e07937060861dad626602ad.css
new file mode 100644
index 0000000..adc6166
--- /dev/null
+++ b/docs/build/html/_panels_static/panels-variables.06eb56fa6e07937060861dad626602ad.css
@@ -0,0 +1,7 @@
+:root {
+--tabs-color-label-active: hsla(231, 99%, 66%, 1);
+--tabs-color-label-inactive: rgba(178, 206, 245, 0.62);
+--tabs-color-overline: rgb(207, 236, 238);
+--tabs-color-underline: rgb(207, 236, 238);
+--tabs-size-label: 1rem;
+}
\ No newline at end of file
diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js
index 1be82db..977fc65 100644
--- a/docs/build/html/_static/documentation_options.js
+++ b/docs/build/html/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
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+    VERSION: '0.6.2',
     LANGUAGE: 'None',
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     BUILDER: 'html',
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new file mode 100644
index 0000000..1b057df
--- /dev/null
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diff --git a/docs/build/html/_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css b/docs/build/html/_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css
new file mode 100644
index 0000000..fc14abc
--- /dev/null
+++ b/docs/build/html/_static/panels-main.c949a650a448cc0ae9fd3441c0e17fb0.css
@@ -0,0 +1 @@
+details.dropdown .summary-title{padding-right:3em !important;-moz-user-select:none;-ms-user-select:none;-webkit-user-select:none;user-select:none}details.dropdown:hover{cursor:pointer}details.dropdown .summary-content{cursor:default}details.dropdown summary{list-style:none;padding:1em}details.dropdown summary .octicon.no-title{vertical-align:middle}details.dropdown[open] summary .octicon.no-title{visibility:hidden}details.dropdown summary::-webkit-details-marker{display:none}details.dropdown summary:focus{outline:none}details.dropdown summary:hover .summary-up svg,details.dropdown summary:hover .summary-down svg{opacity:1}details.dropdown .summary-up svg,details.dropdown .summary-down svg{display:block;opacity:.6}details.dropdown .summary-up,details.dropdown .summary-down{pointer-events:none;position:absolute;right:1em;top:.75em}details.dropdown[open] .summary-down{visibility:hidden}details.dropdown:not([open]) .summary-up{visibility:hidden}details.dropdown.fade-in[open] summary~*{-moz-animation:panels-fade-in .5s ease-in-out;-webkit-animation:panels-fade-in .5s ease-in-out;animation:panels-fade-in .5s ease-in-out}details.dropdown.fade-in-slide-down[open] summary~*{-moz-animation:panels-fade-in .5s ease-in-out, panels-slide-down .5s ease-in-out;-webkit-animation:panels-fade-in .5s ease-in-out, panels-slide-down .5s ease-in-out;animation:panels-fade-in .5s ease-in-out, panels-slide-down .5s ease-in-out}@keyframes panels-fade-in{0%{opacity:0}100%{opacity:1}}@keyframes panels-slide-down{0%{transform:translate(0, -10px)}100%{transform:translate(0, 0)}}.octicon{display:inline-block;fill:currentColor;vertical-align:text-top}.tabbed-content{box-shadow:0 -.0625rem var(--tabs-color-overline),0 .0625rem var(--tabs-color-underline);display:none;order:99;padding-bottom:.75rem;padding-top:.75rem;width:100%}.tabbed-content>:first-child{margin-top:0 !important}.tabbed-content>:last-child{margin-bottom:0 !important}.tabbed-content>.tabbed-set{margin:0}.tabbed-set{border-radius:.125rem;display:flex;flex-wrap:wrap;margin:1em 0;position:relative}.tabbed-set>input{opacity:0;position:absolute}.tabbed-set>input:checked+label{border-color:var(--tabs-color-label-active);color:var(--tabs-color-label-active)}.tabbed-set>input:checked+label+.tabbed-content{display:block}.tabbed-set>input:focus+label{outline-style:auto}.tabbed-set>input:not(.focus-visible)+label{outline:none;-webkit-tap-highlight-color:transparent}.tabbed-set>label{border-bottom:.125rem solid transparent;color:var(--tabs-color-label-inactive);cursor:pointer;font-size:var(--tabs-size-label);font-weight:700;padding:1em 1.25em .5em;transition:color 250ms;width:auto;z-index:1}html .tabbed-set>label:hover{color:var(--tabs-color-label-active)}
diff --git a/docs/build/html/_static/panels-variables.06eb56fa6e07937060861dad626602ad.css b/docs/build/html/_static/panels-variables.06eb56fa6e07937060861dad626602ad.css
new file mode 100644
index 0000000..adc6166
--- /dev/null
+++ b/docs/build/html/_static/panels-variables.06eb56fa6e07937060861dad626602ad.css
@@ -0,0 +1,7 @@
+:root {
+--tabs-color-label-active: hsla(231, 99%, 66%, 1);
+--tabs-color-label-inactive: rgba(178, 206, 245, 0.62);
+--tabs-color-overline: rgb(207, 236, 238);
+--tabs-color-underline: rgb(207, 236, 238);
+--tabs-size-label: 1rem;
+}
\ No newline at end of file
diff --git a/docs/build/html/api.html b/docs/build/html/api.html
index cdfad3c..4aa1539 100644
--- a/docs/build/html/api.html
+++ b/docs/build/html/api.html
@@ -3,11 +3,14 @@
 
   
     
-    API reference — yasa 0.6.1 documentation
+    API reference — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -124,6 +127,9 @@ 
Automatic sleep staging & events detection
SWResults(events, data, sf, ch_names, hypno, …)

 
Output class for slow-waves detection.

 
+
compare_detection(indices_detection, …[, …])

+
Determine correctness of detected events against ground-truth events.

+
 
 
 

@@ -147,19 +153,22 @@ 
Hypnogram & sleep statistics
hypno_int_to_str(hypno[, mapping_dict])

 
Convert an integer hypnogram array to a string array.

 
-
load_profusion_hypno(fname[, replace])

+
hypno_find_periods(hypno, sf_hypno[, …])

+
Find sequences of consecutive values exceeding a certain duration in hypnogram.

+
+
load_profusion_hypno(fname[, replace])

 
Load a Compumedics Profusion hypnogram (.xml).

 
-
plot_hypnogram(hypno[, sf_hypno, lw, figsize])

+
plot_hypnogram(hypno[, sf_hypno, lw, figsize])

 
Plot a hypnogram.

 
-
plot_spectrogram(data, sf[, hypno, win_sec, …])

+
plot_spectrogram(data, sf[, hypno, win_sec, …])

 
Plot a full-night multi-taper spectrogram, optionally with the hypnogram on top.

 
-
transition_matrix(hypno)

+
transition_matrix(hypno)

 
Create a state-transition matrix from an hypnogram.

 
-
sleep_statistics(hypno, sf_hyp)

+
sleep_statistics(hypno, sf_hyp)

 
Compute standard sleep statistics from an hypnogram.

 
 
@@ -203,6 +212,20 @@ 
Spectral analyses
+
Heart rate analysis

+
++++
+
+
+
+
+
+




hrv_stage(data, sf, *[, hypno, include, …])
Calculate heart rate and heart rate variability (HRV) features from an ECG.

+

 

 
 
diff --git a/docs/build/html/changelog.html b/docs/build/html/changelog.html
index da16766..23776e1 100644
--- a/docs/build/html/changelog.html
+++ b/docs/build/html/changelog.html
@@ -3,11 +3,14 @@
 
   
     
-    What’s new — yasa 0.6.1 documentation
+    What’s new — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -91,47 +94,66 @@
     

       
   

-
What’s new

+
What’s new

 
 

+

+
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. 
+
  2. Added the yasa.hrv_stage() function, which calculates heart rate (HR) and heart rate variability (HRV) by stage and periods.
    3. 
+
  3. Added a new dataset containing 8 hours of ECG data. The dataset is in compressed NumPy format and can be found in notebooks/data_ECG_8hrs_200Hz.npz. The dataset also includes an upsampled hypnogram.
    4. 
+
  4. Added a new Jupyter notebook to calculate EEG-HRV overnight coupling based on the yasa.hrv_stage() function. https://github.com/raphaelvallat/yasa/blob/master/notebooks/16_EEG-HRV_coupling.ipynb
    5. 
+

+
Spindles & slow-waves detection - PR 71

+
    
+
  1. Added the yasa.compare_detection() function to determine the correctness of detected events against ground-truth events. It calculates the true positive, false positives and false negatives, and from those, the precision, recall and F1-scores. The input should be the indices of the onset of the event, in samples. It includes a max_distance argument which specifies the tolerance window (in number of samples) for two events to be considered the same.
    2. 
+
  2. Added the yasa.SpindlesResults.compare_detection() and yasa.SWResults.compare_detection() method. This is a powerful and flexible function that allows to calculate the performance of the current detection against a) another detection or b) ground-truth annotations. For example, we can compare the output of the spindles detection with different thresholds.
    3. 
+
  3. Added the yasa.SpindlesResults.compare_channels() and yasa.SWResults.compare_channels() methods to compare the overlap of the detected events between channels. Agreement is calculated using the F1-score (default), precision or recall.
    4. 
+
  4. Add vmin and vmax parameters to yasa.plot_spectrogram(). PR 75
    5. 
+
  5. Better handling of flat data in yasa.spindles_detect(). The function previously returned a division by zero error if part of the data was flat. See issue 85
    6. 
+
  6. When using an MNE.Raw object, conversion of the data from Volts to micro-Volts is now performed within MNE. PR 70
    7. 
+
  7. Use black code formatting.
    8. 
+

+
Others

+
    
+
  1. When using an MNE.Raw object, conversion of the data from Volts to micro-Volts is now performed within MNE. PR 70
    2. 
+
  2. Added SleepECG to the dependencies. SleepECG is used for the heartbeats detection in yasa.hrv_stage().
    3. 
+
  3. YASA now requires MNE>0.23
    4. 
+

+

+

 

-
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!

 

 

+

 

-
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

 
    
@@ -170,11 +192,12 @@ 
    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).
    
@@ -203,10 +226,10 @@ 
    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
    
 
      
@@ -219,7 +242,7 @@ 
      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
      
@@ -237,7 +260,7 @@ 
      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.
      
@@ -265,147 +288,195 @@ 
      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. 
 
    
 
    
 
    
-
    
-
    v0.2.0 (April 2020)
    
-
    This is a major release with several new functions, bugfixes and miscellaneous enhancements in existing functions.
    
-
    Bugfixes
    
+
    
+
    Older versions
    
+
    
+
+v0.2.0 (April 2020)
    
+
    
+
    
+
    
+
    
+
    This is a major release with several new functions, bugfixes and miscellaneous enhancements in existing functions.
    
+
    Bugfixes
    
 
      
-
    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. 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).
      4. 
+
    4. 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.
      5. 
 
    
-
    New functions
    
+
    New functions
    
 
      
-
    1. Added yasa.art_detect() to automatically detect artefacts on single or multi-channel EEG data.
      2. 
-
    2. Added yasa.bandpower_from_psd_ndarray() to calculate band power from a multi-dimensional PSD. This is a NumPy-only implementation and this function will return a np.array and not a pandas DataFrame. This function is useful if you need to calculate the bandpower from a 3-D PSD array, e.g. of shape (n_epochs, n_chan, n_freqs).
      3. 
-
    3. Added yasa.get_centered_indices() to extract indices in data centered around specific events or peaks.
      4. 
-
    4. Added yasa.load_profusion_hypno() to load a Compumedics Profusion hypnogram (.xml), as found on the National Sleep Research Resource (NSRR) website.
      5. 
+
    5. Added yasa.art_detect() to automatically detect artefacts on single or multi-channel EEG data.
      6. 
+
    6. Added yasa.bandpower_from_psd_ndarray() to calculate band power from a multi-dimensional PSD. This is a NumPy-only implementation and this function will return a np.array and not a pandas DataFrame. This function is useful if you need to calculate the bandpower from a 3-D PSD array, e.g. of shape (n_epochs, n_chan, n_freqs).
      7. 
+
    7. Added yasa.get_centered_indices() to extract indices in data centered around specific events or peaks.
      8. 
+
    8. Added yasa.load_profusion_hypno() to load a Compumedics Profusion hypnogram (.xml), as found on the National Sleep Research Resource (NSRR) website.
      9. 
 
    
-
    Enhancements
    
+
    Enhancements
    
 
      
-
    1. yasa.sleep_statistics() now also returns the sleep onset latency, i.e. the latency to the first epoch of any sleep.
      2. 
-
    2. Added the bandpass argument to yasa.bandpower() to apply a FIR bandpass filter using the lowest and highest frequencies defined in bands. This is useful if you work with absolute power and want to remove contributions from frequency bands of non-interests.
      3. 
-
    3. The yasa.bandpower_from_psd() now always return the total absolute physical power (TotalAbsPow) of the signal, in units of uV^2 / Hz. This allows to quickly calculate the absolute bandpower from the relative bandpower.
      4. 
-
    4. Added sigma (12-16Hz) to the default frequency bands (bands) in yasa.bandpower() and yasa.bandpower_from_psd().
      5. 
-
    5. 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).
      6. 
-
    6. 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.
      7. 
-
    7. 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.
      8. 
-
    8. Stage is no longer taken into account when finding outliers with sklearn.ensemble.IsolationForest in yasa.spindles_detect().
      9. 
-
    9. 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().
      10. 
-
    10. Added the verbose parameter to all detection functions.
      11. 
-
    11. Added -2 to the default hypnogram format to denote unscored data.
      12. 
+
    12. yasa.sleep_statistics() now also returns the sleep onset latency, i.e. the latency to the first epoch of any sleep.
      13. 
+
    13. Added the bandpass argument to yasa.bandpower() to apply a FIR bandpass filter using the lowest and highest frequencies defined in bands. This is useful if you work with absolute power and want to remove contributions from frequency bands of non-interests.
      14. 
+
    14. The yasa.bandpower_from_psd() now always return the total absolute physical power (TotalAbsPow) of the signal, in units of uV^2 / Hz. This allows to quickly calculate the absolute bandpower from the relative bandpower.
      15. 
+
    15. Added sigma (12-16Hz) to the default frequency bands (bands) in yasa.bandpower() and yasa.bandpower_from_psd().
      16. 
+
    16. 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).
      17. 
+
    17. 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.
      18. 
+
    18. 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.
      19. 
+
    19. Stage is no longer taken into account when finding outliers with sklearn.ensemble.IsolationForest in yasa.spindles_detect().
      20. 
+
    20. 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().
      21. 
+
    21. Added the verbose parameter to all detection functions.
      22. 
+
    22. Added -2 to the default hypnogram format to denote unscored data.
      23. 
 
    
-
    Dependencies
    
+
    Dependencies
    
 
      
-
    1. Removed deprecated behavior argument to avoid warning when calling sklearn.ensemble.IsolationForest.
      2. 
-
    2. Added TensorPAC and pyRiemann to dependencies.
      3. 
-
    3. Updated dependencies version for MNE and scikit-learn.
      4. 
+
    4. Removed deprecated behavior argument to avoid warning when calling sklearn.ensemble.IsolationForest.
      5. 
+
    5. Added TensorPAC and pyRiemann to dependencies.
      6. 
+
    6. Updated dependencies version for MNE and scikit-learn.
      7. 
 
    
 
    
-
    
-
    
-
    v0.1.9 (February 2020)
    
-
    New functions
    
+
    
+
+v0.1.9 (February 2020)
    
+
    
+
    
+
    
+
    
+
    New functions
    
 
      
-
    1. Added yasa.transition_matrix() to calculate the state-transition matrix of an hypnogram.
      2. 
-
    2. Added yasa.sleep_statistics() to extract the sleep statistics from an hypnogram.
      3. 
-
    3. Added the coupling and freq_so keyword-arguments to the yasa.spindles_detect() function. If coupling=True, the function will also returns the phase of the slow-waves (in radians) at the most prominent peak of the spindles. This can be used to perform spindles-SO coupling, as explained in the new Jupyter notebooks on PAC and spindles-SO coupling.
      4. 
+
    4. Added yasa.transition_matrix() to calculate the state-transition matrix of an hypnogram.
      5. 
+
    5. Added yasa.sleep_statistics() to extract the sleep statistics from an hypnogram.
      6. 
+
    6. Added the coupling and freq_so keyword-arguments to the yasa.spindles_detect() function. If coupling=True, the function will also returns the phase of the slow-waves (in radians) at the most prominent peak of the spindles. This can be used to perform spindles-SO coupling, as explained in the new Jupyter notebooks on PAC and spindles-SO coupling.
      7. 
 
    
-
    Enhancements
    
+
    Enhancements
    
 
      
-
    1. It is now possible to disable one or two out of the three thresholds in the yasa.spindles_detect(). This allows the users to run a simpler detection (for example focusing exclusively on the moving root mean square signal).
      2. 
-
    2. The yasa.spindles_detect() now returns the timing (in seconds) of the most prominent peak of each spindles (Peak).
      3. 
-
    3. The yasa.get_sync_sw has been renamed to yasa.get_sync_events() and is now compatible with spindles detection. This can be used for instance to plot the peak-locked grand averaged spindle.
      4. 
+
    4. It is now possible to disable one or two out of the three thresholds in the yasa.spindles_detect(). This allows the users to run a simpler detection (for example focusing exclusively on the moving root mean square signal).
      5. 
+
    5. The yasa.spindles_detect() now returns the timing (in seconds) of the most prominent peak of each spindles (Peak).
      6. 
+
    6. The yasa.get_sync_sw has been renamed to yasa.get_sync_events() and is now compatible with spindles detection. This can be used for instance to plot the peak-locked grand averaged spindle.
      7. 
 
    
-
    Code testing
    
+
    Code testing
    
 
      
-
    1. Removed Travis and AppVeyor testing for Python 3.5.
      2. 
+
    2. Removed Travis and AppVeyor testing for Python 3.5.
      3. 
 
    
 
    
-
    
-
    
+
+v0.1.8 (October 2019)
    
+
    
+
    
+
    
+
    
 
      
-
    1. Added yasa.plot_spectrogram() function.
      2. 
-
    2. Added lspopt in the dependencies.
      3. 
-
    3. YASA now requires MNE>0.19.
      4. 
-
    4. Added a notebook on non-linear features.
      5. 
+
    5. Added yasa.plot_spectrogram() function.
      6. 
+
    6. Added lspopt in the dependencies.
      7. 
+
    7. YASA now requires MNE>0.19.
      8. 
+
    8. Added a notebook on non-linear features.
      9. 
 
    
 
    
-
    
-
    
+
+v0.1.7 (August 2019)
    
+
    
+
    
+
    
+
    
 
      
-
    1. Added yasa.sliding_window() function.
      2. 
-
    2. Added yasa.irasa() function.
      3. 
-
    3. Reorganized code into several sub-files for readability (internal changes with no effect on user experience).
      4. 
+
    4. Added yasa.sliding_window() function.
      5. 
+
    5. Added yasa.irasa() function.
      6. 
+
    6. Reorganized code into several sub-files for readability (internal changes with no effect on user experience).
      7. 
 
    
 
    
-
    
-
    
+
+v0.1.6 (August 2019)
    
+
    
+
    
+
    
+
    
 
      
-
    1. Added bandpower function
      2. 
-
    2. One can now directly pass a raw MNE object in several multi-channel functions of YASA, instead of manually passing data, sf, and ch_names. YASA will automatically convert MNE data from Volts to uV, and extract the sampling frequency and channel names. Examples of this can be found in the Jupyter notebooks examples.
      3. 
+
    3. Added bandpower function
      4. 
+
    4. One can now directly pass a raw MNE object in several multi-channel functions of YASA, instead of manually passing data, sf, and ch_names. YASA will automatically convert MNE data from Volts to uV, and extract the sampling frequency and channel names. Examples of this can be found in the Jupyter notebooks examples.
      5. 
 
    
 
    
-
    
-
    
+
+v0.1.5 (August 2019)
    
+
    
+
    
+
    
+
    
 
      
-
    1. Added REM detection (rem_detect) on LOC and ROC EOG channels + example notebook
      2. 
-
    2. Added yasa/hypno.py file, with several functions to load and upsample sleep stage vector (hypnogram).
      3. 
-
    3. Added yasa/spectral.py file, which includes the bandpower_from_psd function to calculate the single or multi-channel spectral power in specified bands from a pre-computed PSD (see example notebook at notebooks/10_bandpower.ipynb)
      4. 
+
    4. Added REM detection (rem_detect) on LOC and ROC EOG channels + example notebook
      5. 
+
    5. Added yasa/hypno.py file, with several functions to load and upsample sleep stage vector (hypnogram).
      6. 
+
    6. Added yasa/spectral.py file, which includes the bandpower_from_psd function to calculate the single or multi-channel spectral power in specified bands from a pre-computed PSD (see example notebook at notebooks/10_bandpower.ipynb)
      7. 
 
    
 
    
-
    
-
    
+
+v0.1.4 (May 2019)
    
+
    
+
    
+
    
+
    
 
      
-
    1. Added get_sync_sw function to get the synchronized timings of landmarks timepoints in slow-wave sleep. This can be used in combination with seaborn.lineplot to plot an average template of the detected slow-wave, per channel.
      2. 
+
    2. Added get_sync_sw function to get the synchronized timings of landmarks timepoints in slow-wave sleep. This can be used in combination with seaborn.lineplot to plot an average template of the detected slow-wave, per channel.
      3. 
 
    
 
    
-
    
-
    
+
+v0.1.3 (March 2019)
    
+
    
+
    
+
    
+
    
 
      
-
    1. Added slow-waves detection for single and multi channel
      2. 
-
    2. Added include argument to select which values of hypno should be used as a mask.
      3. 
-
    3. New examples notebooks + changes in README
      4. 
-
    4. Minor improvements in performance (e.g. faster detrending)
      5. 
-
    5. Added html API (/html)
      6. 
-
    6. Travis and AppVeyor test for Python 3.5, 3.6 and 3.7
      7. 
+
    7. Added slow-waves detection for single and multi channel
      8. 
+
    8. Added include argument to select which values of hypno should be used as a mask.
      9. 
+
    9. New examples notebooks + changes in README
      10. 
+
    10. Minor improvements in performance (e.g. faster detrending)
      11. 
+
    11. Added html API (/html)
      12. 
+
    12. Travis and AppVeyor test for Python 3.5, 3.6 and 3.7
      13. 
 
    
 
    
-
    
-
    
+
+v0.1.2 (February 2019)
    
+
    
+
    
+
    
+
    
 
      
-
    1. Added support for multi-channel detection via spindles_detect_multi function.
      2. 
-
    2. Added support for hypnogram mask
      3. 
-
    3. Added several notebook examples
      4. 
-
    4. Changed some default parameters to optimize behavior
      5. 
+
    5. Added support for multi-channel detection via spindles_detect_multi function.
      6. 
+
    6. Added support for hypnogram mask
      7. 
+
    7. Added several notebook examples
      8. 
+
    8. Changed some default parameters to optimize behavior
      9. 
 
    
 
    
-
    
-
    
+
+v0.1.1 (January 2019)
    
+
    
+
    
+
    
+
    
 
      
-
    1. Added post-processing Isolation Forest
      2. 
-
    2. Updated Readme and added support with Visbrain
      3. 
-
    3. Added Cz full night in notebooks/
      4. 
+
    4. Added post-processing Isolation Forest
      5. 
+
    5. Updated Readme and added support with Visbrain
      6. 
+
    6. Added Cz full night in notebooks/
      7. 
 
    
 
    
-
    
-
    
-
    v0.1 (December 2018)
    
-
    Initial release of YASA: basic spindles detection.
    
+
    
+
+v0.1 (December 2018)
    
+
    
+
    
+
    
+
    
+
    Initial release of YASA: basic spindles detection.
    
 
    
+
    
 
    
 
 
diff --git a/docs/build/html/contributing.html b/docs/build/html/contributing.html
index 18e2b41..e138dae 100644
--- a/docs/build/html/contributing.html
+++ b/docs/build/html/contributing.html
@@ -3,11 +3,14 @@
 
   
     
-    Contribute to YASA — yasa 0.6.1 documentation
+    Contribute to YASA — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
diff --git a/docs/build/html/faq.html b/docs/build/html/faq.html
index 38c0f85..5f9f25e 100644
--- a/docs/build/html/faq.html
+++ b/docs/build/html/faq.html
@@ -3,11 +3,14 @@
 
   
     
-    FAQ — yasa 0.6.1 documentation
+    FAQ — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
diff --git a/docs/build/html/generated/yasa.REMResults.html b/docs/build/html/generated/yasa.REMResults.html
index 7840876..c0f4849 100644
--- a/docs/build/html/generated/yasa.REMResults.html
+++ b/docs/build/html/generated/yasa.REMResults.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.REMResults — yasa 0.6.1 documentation
+    yasa.REMResults — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -99,7 +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.
    
@@ -132,6 +135,12 @@ 
    yasa.REMResults
    __init__(events, data, sf, ch_names, hypno, …)
    
 
    Initialize self.
    
 
+
    compare_channels([score, max_distance_sec])
    
+
    Compare detected events across channels.
    
+
+
    compare_detection(other[, max_distance_sec, …])
    
+
    Compare detected events between two detection methods, or against a ground-truth scoring.
    
+
 
    get_coincidence_matrix([scaled])
    
 
    
 
@@ -177,7 +186,7 @@ 
    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).
    
@@ -186,7 +195,7 @@ 
    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
@@ -217,7 +226,7 @@ 
    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).
    
diff --git a/docs/build/html/generated/yasa.SWResults.html b/docs/build/html/generated/yasa.SWResults.html
index f07b152..f3ec694 100644
--- a/docs/build/html/generated/yasa.SWResults.html
+++ b/docs/build/html/generated/yasa.SWResults.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.SWResults — yasa 0.6.1 documentation
+    yasa.SWResults — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -99,7 +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).
    
 
    
@@ -130,6 +133,12 @@ 
    yasa.SWResults
    __init__(events, data, sf, ch_names, hypno, …)
    
 
    Initialize self.
    
 
+
    compare_channels([score, max_distance_sec])
    
+
    Compare detected slow-waves across channels.
    
+
+
    compare_detection(other[, max_distance_sec, …])
    
+
    Compare the detected slow-waves against either another YASA detection or against custom annotations (e.g.
    
+
 
    find_cooccurring_spindles(spindles[, lookaround])
    
 
    Given a spindles detection summary dataframe, find slow-waves that co-occur with sleep spindles.
    
 
@@ -153,6 +162,113 @@ 
    yasa.SWResults
+
    
+compare_channels(score='f1', max_distance_sec=0)[source]
    
+
    Compare detected slow-waves across channels.
    
+
    This is a wrapper around the yasa.compare_detection() function. Please
+refer to the documentation of this function for more details.
    
+
    
+
    Parameters
    
+
    
+
    scorestr
    The performance metric to compute. Accepted values are “precision”, “recall”
+(aka sensitivity) and “f1” (default). The F1-score is the harmonic mean of precision
+and recall, and is usually the preferred metric to evaluate the agreement between
+two channels. All three metrics are bounded by 0 and 1, where 1 indicates perfect
+agreement.
    
+
    
+
    max_distance_secfloat
    The maximum distance between slow-waves, in seconds, to consider as the same event.
    
+
    
+
    Warning
    
+
    To reduce computation cost, YASA rounds the start time of each spindle to
+the nearest decisecond (= 100 ms). This means that the lowest possible resolution
+is 100 ms, regardless of the sampling frequency of the data. Two slow-waves
+starting at 500 ms and 540 ms on their respective channels will therefore always be
+considered the same event, even when max_distance_sec=0.
    
+
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    scorespandas.DataFrame
    A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).
    
+
    
+
    
+
    
+
    
+
    Notes
    
+
    Some use cases of this function:
    
+
      
+
    1. What proportion of slow-waves detected in one channel are also detected on
+another channel (if using score="recall").
      2. 
+
    2. What is the overall agreement in the detected events between channels?
      3. 
+
    3. Is the agreement better in channels that are close to one another?
      4. 
+
    
+
    
+
+
    
+
    
+compare_detection(other, max_distance_sec=0, other_is_groundtruth=True)[source]
    
+
    Compare the detected slow-waves against either another YASA detection or against custom
+annotations (e.g. ground-truth human scoring).
    
+
    This function is a wrapper around the yasa.compare_detection() function. Please
+refer to the documentation of this function for more details.
    
+
    
+
    Parameters
    
+
    
+
    otherdataframe or detection results
    This can be either a) the output of another YASA detection, for example if you want to
+test the impact of tweaking some parameters on the detected events or b) a pandas
+DataFrame with custom annotations, obtained by another detection method outside
+of YASA, or with manual labelling. If b), the dataframe must contain the “Start” and
+“Channel” columns, with the start of each event in seconds from the beginning
+of the recording and the channel name, respectively. The channel names should match
+the output of the summary() method.
    
+
    
+
    max_distance_secfloat
    The maximum distance between slow-waves, in seconds, to consider as the same event.
    
+
    
+
    Warning
    
+
    To reduce computation cost, YASA rounds the start time of each slow-wave
+to the nearest decisecond (= 100 ms). This means that the lowest possible
+resolution is 100 ms, regardless of the sampling frequency of the data.
    
+
    
+
    
+
    other_is_groundtruthbool
    If True (default), other will be considered as the ground-truth scoring. If False,
+the current detection will be considered as the ground-truth, and the precision and
+recall scores will be inverted. This parameter has no effect on the F1-score.
    
+
    
+
    Note
    
+
    when other is the ground-truth (default), the recall score is the
+fraction of events in other that were succesfully detected by the current
+detection, and the precision score is the proportion of detected events by the
+current detection that are also present in other.
    
+
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    scorespandas.DataFrame
    A Pandas DataFrame with the channel names as index, and the following columns
    
+
      
+
    • precision: Precision score, aka positive predictive value
      • 
+
    • recall: Recall score, aka sensitivity
      • 
+
    • f1: F1-score
      • 
+
    • n_self: Number of detected events in self (current method).
      • 
+
    • n_other: Number of detected events in other.
      • 
+
    
+
    
+
    
+
    
+
    
+
    Notes
    
+
    Some use cases of this function:
    
+
      
+
    1. How well does YASA events detection perform against ground-truth human annotations?
      2. 
+
    2. If I change the threshold(s) of the events detection, do the detected events match
+those obtained with the default parameters?
      3. 
+
    3. Which detection thresholds give the highest agreement with the ground-truth scoring?
      4. 
+
    
+
    
+
 
    
 
    
 find_cooccurring_spindles(spindles, lookaround=1.2)[source]
    
@@ -164,7 +280,7 @@ 
    yasa.SWResults
 
    Parameters
    
 
    
-
    spindlespandas.DataFrame
    Output dataframe of yasa.SpindlesResults.summary().
    
+
    spindlespandas.DataFrame
    Output dataframe of yasa.SpindlesResults.summary().
    
 
    
 
    lookaroundfloat
    Lookaround window, in seconds. The default is +/- 1.2 seconds around the
 negative peak of the slow-wave, as in [1]. This means that YASA will look for a
@@ -174,7 +290,7 @@ 
    yasa.SWResultsReturns
 
    
-
    _eventspandas.DataFrame
    The slow-wave detection is modified IN-PLACE (see Notes). To see the updated dataframe,
+
    _eventspandas.DataFrame
    The slow-wave detection is modified IN-PLACE (see Notes). To see the updated dataframe,
 call the yasa.SWResults.summary() method.
    
 
    
 
    
@@ -285,7 +401,7 @@ 
    yasa.SWResultstuple
    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).
    
@@ -298,7 +414,7 @@ 
    yasa.SWResultsReturns
 
    
-
    df_syncpandas.DataFrame or list
    Ouput long-format dataframe (if as_dataframe=True):
    
+
    df_syncpandas.DataFrame or list
    Ouput long-format dataframe (if as_dataframe=True):
    
 
    'Event' : Event number
 'Time' : Timing of the events (in seconds)
 'Amplitude' : Raw or filtered data for event
@@ -333,7 +449,7 @@ 
    yasa.SWResultstuple
    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
 plotted. Default is None, i.e. no masking (all events are included).
    
diff --git a/docs/build/html/generated/yasa.SleepStaging.html b/docs/build/html/generated/yasa.SleepStaging.html
index d093921..7fd6237 100644
--- a/docs/build/html/generated/yasa.SleepStaging.html
+++ b/docs/build/html/generated/yasa.SleepStaging.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.SleepStaging — yasa 0.6.1 documentation
+    yasa.SleepStaging — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -105,7 +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
@@ -279,7 +282,7 @@ 
    yasa.SleepStaging
 
    Returns
    
 
    
-
    featurespandas.DataFrame
    Feature dataframe.
    
+
    featurespandas.DataFrame
    Feature dataframe.
    
 
    
 
    
 
    
@@ -319,7 +322,7 @@ 
    yasa.SleepStagingReturns
 
    
-
    prednumpy.ndarray
    The predicted sleep stages.
    
+
    prednumpy.ndarray
    The predicted sleep stages.
    
 
    
 
    
 
    
@@ -343,7 +346,7 @@ 
    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.SpindlesResults.html b/docs/build/html/generated/yasa.SpindlesResults.html
index 0771fca..449c374 100644
--- a/docs/build/html/generated/yasa.SpindlesResults.html
+++ b/docs/build/html/generated/yasa.SpindlesResults.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.SpindlesResults — yasa 0.6.1 documentation
+    yasa.SpindlesResults — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -99,7 +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).
    
 
    
@@ -130,6 +133,12 @@ 
    yasa.SpindlesResults
    __init__(events, data, sf, ch_names, hypno, …)
    
 
    Initialize self.
    
 
+
    compare_channels([score, max_distance_sec])
    
+
    Compare detected spindles across channels.
    
+
+
    compare_detection(other[, max_distance_sec, …])
    
+
    Compare the detected spindles against either another YASA detection or against custom annotations (e.g.
    
+
 
    get_coincidence_matrix([scaled])
    
 
    Return the (scaled) coincidence matrix.
    
 
@@ -150,6 +159,113 @@ 
    yasa.SpindlesResults
 
 
+
    
+
    
+compare_channels(score='f1', max_distance_sec=0)[source]
    
+
    Compare detected spindles across channels.
    
+
    This is a wrapper around the yasa.compare_detection() function. Please
+refer to the documentation of this function for more details.
    
+
    
+
    Parameters
    
+
    
+
    scorestr
    The performance metric to compute. Accepted values are “precision”, “recall”
+(aka sensitivity) and “f1” (default). The F1-score is the harmonic mean of precision
+and recall, and is usually the preferred metric to evaluate the agreement between
+two channels. All three metrics are bounded by 0 and 1, where 1 indicates perfect
+agreement.
    
+
    
+
    max_distance_secfloat
    The maximum distance between spindles, in seconds, to consider as the same event.
    
+
    
+
    Warning
    
+
    To reduce computation cost, YASA rounds the start time of each spindle to
+the nearest decisecond (= 100 ms). This means that the lowest possible resolution
+is 100 ms, regardless of the sampling frequency of the data. Two spindles starting
+at 500 ms and 540 ms on their respective channels will therefore always be
+considered the same event, even when max_distance_sec=0.
    
+
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    scorespandas.DataFrame
    A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).
    
+
    
+
    
+
    
+
    
+
    Notes
    
+
    Some use cases of this function:
    
+
      
+
    1. What proportion of spindles detected in one channel are also detected on
+another channel (if using score="recall").
      2. 
+
    2. What is the overall agreement in the detected events between channels?
      3. 
+
    3. Is the agreement better in channels that are close to one another?
      4. 
+
    
+
    
+
+
    
+
    
+compare_detection(other, max_distance_sec=0, other_is_groundtruth=True)[source]
    
+
    Compare the detected spindles against either another YASA detection or against custom
+annotations (e.g. ground-truth human scoring).
    
+
    This function is a wrapper around the yasa.compare_detection() function. Please
+refer to the documentation of this function for more details.
    
+
    
+
    Parameters
    
+
    
+
    otherdataframe or detection results
    This can be either a) the output of another YASA detection, for example if you want to
+test the impact of tweaking some parameters on the detected events or b) a pandas
+DataFrame with custom annotations, obtained by another detection method outside
+of YASA, or with manual labelling. If b), the dataframe must contain the “Start” and
+“Channel” columns, with the start of each event in seconds from the beginning
+of the recording and the channel name, respectively. The channel names should match
+the output of the summary() method.
    
+
    
+
    max_distance_secfloat
    The maximum distance between spindles, in seconds, to consider as the same event.
    
+
    
+
    Warning
    
+
    To reduce computation cost, YASA rounds the start time of each spindle to
+the nearest decisecond (= 100 ms). This means that the lowest possible resolution
+is 100 ms, regardless of the sampling frequency of the data.
    
+
    
+
    
+
    other_is_groundtruthbool
    If True (default), other will be considered as the ground-truth scoring. If False,
+the current detection will be considered as the ground-truth, and the precision and
+recall scores will be inverted. This parameter has no effect on the F1-score.
    
+
    
+
    Note
    
+
    when other is the ground-truth (default), the recall score is the
+fraction of events in other that were succesfully detected by the current
+detection, and the precision score is the proportion of detected events by the
+current detection that are also present in other.
    
+
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    scorespandas.DataFrame
    A Pandas DataFrame with the channel names as index, and the following columns
    
+
      
+
    • precision: Precision score, aka positive predictive value
      • 
+
    • recall: Recall score, aka sensitivity
      • 
+
    • f1: F1-score
      • 
+
    • n_self: Number of detected events in self (current method).
      • 
+
    • n_other: Number of detected events in other.
      • 
+
    
+
    
+
    
+
    
+
    
+
    Notes
    
+
    Some use cases of this function:
    
+
      
+
    1. How well does YASA events detection perform against ground-truth human annotations?
      2. 
+
    2. If I change the threshold(s) of the events detection, do the detected events match
+those obtained with the default parameters?
      3. 
+
    3. Which detection thresholds give the highest agreement with the ground-truth scoring?
      4. 
+
    
+
    
+
 
    
 
    
 get_coincidence_matrix(scaled=True)[source]
    
@@ -227,7 +343,7 @@ 
    yasa.SpindlesResultsfilttuple
    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).
    
@@ -240,7 +356,7 @@ 
    yasa.SpindlesResults
 
    Returns
    
 
    
-
    df_syncpandas.DataFrame
    Ouput long-format dataframe (if as_dataframe=True):
    
+
    df_syncpandas.DataFrame
    Ouput long-format dataframe (if as_dataframe=True):
    
 
    'Event' : Event number
 'Time' : Timing of the events (in seconds)
 'Amplitude' : Raw or filtered data for event
@@ -275,7 +391,7 @@ 
    yasa.SpindlesResultsfilttuple
    Optional filtering to apply to data. For instance, filt=(12, 16)
 will apply a 12 to 16 Hz bandpass filter, and filt=(None, 40)
 will apply a 40 Hz lowpass filter. Filtering is done using the 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
 plotted. Default is None, i.e. no masking (all events are included).
    
diff --git a/docs/build/html/generated/yasa.art_detect.html b/docs/build/html/generated/yasa.art_detect.html
index 0eebaf7..8c47675 100644
--- a/docs/build/html/generated/yasa.art_detect.html
+++ b/docs/build/html/generated/yasa.art_detect.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.art_detect — yasa 0.6.1 documentation
+    yasa.art_detect — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -104,7 +107,7 @@ 
    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).
    
@@ -115,7 +118,7 @@ 
    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
@@ -213,7 +216,7 @@ 
    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 406e63a..20863b9 100644
--- a/docs/build/html/generated/yasa.bandpower.html
+++ b/docs/build/html/generated/yasa.bandpower.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.bandpower — yasa 0.6.1 documentation
+    yasa.bandpower — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -102,15 +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.
    
@@ -143,18 +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 4c942e3..38fd9d5 100644
--- a/docs/build/html/generated/yasa.bandpower_from_psd.html
+++ b/docs/build/html/generated/yasa.bandpower_from_psd.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.bandpower_from_psd — yasa 0.6.1 documentation
+    yasa.bandpower_from_psd — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -104,7 +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.
    
 
    
@@ -121,7 +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 c939ebd..4377126 100644
--- a/docs/build/html/generated/yasa.bandpower_from_psd_ndarray.html
+++ b/docs/build/html/generated/yasa.bandpower_from_psd_ndarray.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.bandpower_from_psd_ndarray — yasa 0.6.1 documentation
+    yasa.bandpower_from_psd_ndarray — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -105,10 +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’)).
    
@@ -120,7 +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
new file mode 100644
index 0000000..85cacb0
--- /dev/null
+++ b/docs/build/html/generated/yasa.compare_detection.html
@@ -0,0 +1,222 @@
+
+
+
+  
+    
+    yasa.compare_detection — yasa 0.6.2 documentation
+    
+    
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+    
    
+      
+  
    
+
    yasa.compare_detection
    
+
    
+
    
+yasa.compare_detection(indices_detection, indices_groundtruth, max_distance=0)[source]
    
+
    Determine correctness of detected events against ground-truth events.
    
+
    
+
    Parameters
    
+
    
+
    indices_detectionarray_like
    Indices of the detected events. For example, this could be the indices of the
+start of the spindles, or the negative peak of the slow-waves. The indices must be in
+samples, and not in seconds.
    
+
    
+
    indices_groundtrutharray_like
    Indices of the ground-truth events, in samples.
    
+
    
+
    max_distanceint, optional
    Maximum distance between indices, in samples, to consider as the same event (default = 0).
+For example, if the sampling frequency of the data is 100 Hz, using max_distance=100 will
+search for a matching event 1 second before or after the current event.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    resultsdict
    A dictionary with the comparison results:
    
+
      
+
    • tp: True positives, i.e. actual events detected as events.
      • 
+
    • fp: False positives, i.e. non-events detected as events.
      • 
+
    • fn: False negatives, i.e. actual events not detected as events.
      • 
+
    • precision: Precision score, aka positive predictive value (see Notes)
      • 
+
    • recall: Recall score, aka sensitivity (see Notes)
      • 
+
    • f1: F1-score (see Notes)
      • 
+
    
+
    
+
    
+
    
+
    
+
    Notes
    
+
      
+
    • The precision score is calculated as TP / (TP + FP).
      • 
+
    • The recall score is calculated as TP / (TP + FN).
      • 
+
    • The F1-score is calculated as TP / (TP + 0.5 * (FP + FN)).
      • 
+
    
+
    This function is inspired by the sleepecg.compare_heartbeats
+function.
    
+
    Examples
    
+
    A simple example. Here, detected refers to the indices (in the data) of the detected events.
+These could be for example the index of the onset of each detected spindle. grndtrth refers
+to the ground-truth (e.g. human-annotated) events.
    
+
    >>> from yasa import compare_detection
+>>> detected = [5, 12, 20, 34, 41, 57, 63]
+>>> grndtrth = [5, 12, 18, 26, 34, 41, 55, 63, 68]
+>>> compare_detection(detected, grndtrth)
+{'tp': array([ 5, 12, 34, 41, 63]),
+ 'fp': array([20, 57]),
+ 'fn': array([18, 26, 55, 68]),
+ 'precision': 0.7142857142857143,
+ 'recall': 0.5555555555555556,
+ 'f1': 0.625}
+
    
+
    
+
    There are 4 true positives, 2 false positives and 4 false negatives. This gives a precision
+score of 0.71 (= 5 / (5 + 2)), a recall score of 0.55 (= 5 / (5 + 4)) and a F1-score of 0.625.
+The F1-score is the harmonic average of precision and recall, and should be the preferred
+metric when comparing the performance of a detection against a ground-truth.
    
+
    Order matters! If we set detected as the ground-truth, FP and FN are inverted, and same for
+precision and recall. The TP and F1-score remain the same though. Therefore, when comparing two
+detections (and not a detection against a ground-truth), the F1-score is the preferred metric
+because it is independent of the order.
    
+
    >>> compare_detection(grndtrth, detected)
+{'tp': array([ 5, 12, 34, 41, 63]),
+ 'fp': array([18, 26, 55, 68]),
+ 'fn': array([20, 57]),
+ 'precision': 0.7142857142857143,
+ 'recall': 0.7142857142857143,
+ 'f1': 0.625}
+
    
+
    
+
    There might be some events that are very close to each other, and we would like to count them
+as true positive even though they do not occur exactly at the same index. This is possible
+with the max_distance argument, which defines the lookaround window (in samples) for
+each event.
    
+
    >>> compare_detection(detected, grndtrth, max_distance=2)
+{'tp': array([ 5, 12, 20, 34, 41, 57, 63]),
+ 'fp': array([], dtype=int64),
+ 'fn': array([26, 68]),
+ 'precision': 1.0,
+ 'recall': 0.7777777777777778,
+ 'f1': 0.875}
+
    
+
    
+
    Finally, if detected is empty, all performance metrics will be set to zero, and a copy of
+the groundtruth array will be returned as false negatives.
    
+
    >>> compare_detection([], grndtrth)
+{'tp': array([], dtype=int64),
+ 'fp': array([], dtype=int64),
+ 'fn': array([ 5, 12, 18, 26, 34, 41, 55, 63, 68]),
+ 'precision': 0,
+ 'recall': 0,
+ 'f1': 0}
+
    
+
    
+
    
+
+
    
+
+
+    
    
+      
+  
    
+
    
+
    
+  
    
+    
    
+      Back to top
+      
+        
    
+        
+      
+    
    
+    
    
+        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 3.0.0.
    
+    
    
+  
    
+
    
+  
+
\ No newline at end of file
diff --git a/docs/build/html/generated/yasa.hrv_stage.html b/docs/build/html/generated/yasa.hrv_stage.html
new file mode 100644
index 0000000..1b5ba62
--- /dev/null
+++ b/docs/build/html/generated/yasa.hrv_stage.html
@@ -0,0 +1,215 @@
+
+
+
+  
+    
+    yasa.hrv_stage — yasa 0.6.2 documentation
+    
+    
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+
    yasa.hrv_stage
    
+
    
+
    
+yasa.hrv_stage(data, sf, *, hypno=None, include=2, 3, 4, threshold='2min', equal_length=False, rr_limit=400, 2000, verbose=False)[source]
    
+
    Calculate heart rate and heart rate variability (HRV) features from an ECG.
    
+
    By default, the cardiac features are calculated for each period of N2, N3 or REM sleep that
+are longer than 2 minutes.
    
+
    
+
    New in version 0.6.2.
    
+
    
+
    
+
    Parameters
    
+
    
+
    datanumpy.ndarray
    Single-channel ECG data. Must be a 1D NumPy array.
    
+
    
+
    sffloat
    The sampling frequency of the data.
    
+
    
+
    hypnoarray_like
    Sleep stage (hypnogram). The heart rate calculation will be applied for each sleep stage
+defined in include (default = N2, N3 and REM sleep separately).
    
+
    The hypnogram must have the same number of samples as data.
+To upsample your hypnogram, please refer to
+yasa.hypno_upsample_to_data().
    
+
    
+
    Note
    
+
    The default hypnogram format in YASA is a 1D integer
+vector where:
    
+
      
+
    • -2 = Unscored
      • 
+
    • -1 = Artefact / Movement
      • 
+
    • 0 = Wake
      • 
+
    • 1 = N1 sleep
      • 
+
    • 2 = N2 sleep
      • 
+
    • 3 = N3 sleep
      • 
+
    • 4 = REM sleep
      • 
+
    
+
    
+
    
+
    includetuple, list or int
    Values in hypno that will be included in the mask. The default is
+(2, 3, 4), meaning that the detection is applied on N2, N3 and REM
+sleep separately.
    
+
    
+
    thresholdstr
    Only periods of a given stage that exceed the duration defined in threshold will be
+kept in subsequent analysis. The default is 2 minutes (‘2min’). Other possible values
+include: ‘5min’, ‘15min’, ’30sec’, ‘1hour’, etc. To disable thresholding, use ‘0min’.
    
+
    
+
    equal_lengthbool
    If True, the periods will all have the exact duration defined in threshold.
+That is, periods that are longer than the duration threshold will be divided into
+sub-periods of exactly the length of threshold.
    
+
    
+
    rr_limittuple
    Lower and upper limit for the RR interval. Default is 400 to 2000 ms, corresponding to a
+heart rate of 30 to 150 bpm. RR intervals outside this range will be set to NaN and
+filled with linear interpolation. Use rr_limit=(0, np.inf) to disable RR correction.
    
+
    
+
    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). Set this to True if you are getting
+invalid results and want to better understand what is happening.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    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.
      • 
+
    • duration : The duration of the epoch, in seconds.
      • 
+
    • hr_mean: The mean heart rate (HR) across the epoch, in beats per minute (bpm).
      • 
+
    • hr_std: The standard deviation of the HR across the epoch, in bpm
      • 
+
    • hrv_rmssd: Heart rate variability across the epoch (RMSSD), in milliseconds.
      • 
+
    
+
    
+
    rpeaksdict
    A dictionary with the detected heartbeats (R-peaks) indices for each epoch of each stage.
+Indices are expressed as samples from the beginning of the epoch. This can be used to
+manually recalculate the RR intervals, apply a custom preprocessing on the RR intervals,
+and/or calculate more advanced HRV metrics.
    
+
    
+
    
+
    
+
    
+
    Notes
    
+
    This function returns three cardiac features for each epoch: the mean and standard deviation of
+the heart rate, and the root mean square of successive differences between normal heartbeats
+(RMSSD). The RMSSD reflects the beat-to-beat variance in HR and is the primary time-domain
+measure used to estimate the vagally mediated changes reflected in heart rate variability.
    
+
    Heartbeat detection is performed with the SleepECG library: https://github.com/cbrnr/sleepecg
    
+
    For an example of this function, please see the Jupyter notebook
    
+
    References
    
+
      
+
    • Shaffer, F., & Ginsberg, J. P. (2017). An overview of heart rate variability metrics and
+norms. Frontiers in public health, 258.
      • 
+
    
+
    
+
+
    
+
+
+    
    
+      
+  
    
+
    
+
    
+  
    
+    
    
+      Back to top
+      
+        
    
+        
+      
+    
    
+    
    
+        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 3.0.0.
    
+    
    
+  
    
+
    
+  
+
\ No newline at end of file
diff --git a/docs/build/html/generated/yasa.hypno_find_periods.html b/docs/build/html/generated/yasa.hypno_find_periods.html
new file mode 100644
index 0000000..3ff7aa6
--- /dev/null
+++ b/docs/build/html/generated/yasa.hypno_find_periods.html
@@ -0,0 +1,215 @@
+
+
+
+  
+    
+    yasa.hypno_find_periods — yasa 0.6.2 documentation
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
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+  
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+      
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+
    yasa.hypno_find_periods
    
+
    
+
    
+yasa.hypno_find_periods(hypno, sf_hypno, threshold='5min', equal_length=False)[source]
    
+
    Find sequences of consecutive values exceeding a certain duration in hypnogram.
    
+
    
+
    New in version 0.6.2.
    
+
    
+
    
+
    Parameters
    
+
    
+
    hypnoarray_like
    A 1D array with the sleep stages (= hypnogram). The dtype can be anything (int, bool, str).
+More generally, this can be any vector for which you wish to find runs of
+consecutive items.
    
+
    
+
    sf_hypnofloat
    The current sampling frequency of hypno, in Hz, e.g. 1/30 = 1 value per each 30 seconds
+of EEG data, 1 = 1 value per second of EEG data.
    
+
    
+
    thresholdstr
    This function will only keep periods that exceed a certain duration (default ‘5min’), e.g.
+‘5min’, ‘15min’, ’30sec’, ‘1hour’. To disable thresholding, use ‘0sec’.
    
+
    
+
    equal_lengthbool
    If True, the periods will all have the exact duration defined
+in threshold. That is, periods that are longer than the duration threshold will be divided
+into sub-periods of exactly the length of threshold.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    periodspandas.DataFrame
    Output dataframe
    
+
      
+
    • values : The value in hypno of the current period
      • 
+
    • start : The index of the start of the period in hypno
      • 
+
    • length : The duration of the period, in number of samples
      • 
+
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    Let’s assume that we have an hypnogram where sleep = 1 and wake = 0. There is one value per
+minute, and therefore the sampling frequency of the hypnogram is 1 / 60 sec (~0.016 Hz).
    
+
    >>> import yasa
+>>> hypno = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]
+>>> yasa.hypno_find_periods(hypno, sf_hypno=1/60, threshold="0min")
+   values  start  length
+0       0      0      11
+1       1     11       3
+2       0     14       2
+3       1     16       9
+4       0     25       2
+
    
+
    
+
    This gives us the start and duration of each sequence of consecutive values in the hypnogram.
+For example, the first row tells us that there is a sequence of 11 consecutive 0 starting at
+the first index of hypno.
    
+
    Now, we may want to keep only periods that are longer than a specific threshold,
+for example 5 minutes:
    
+
    >>> yasa.hypno_find_periods(hypno, sf_hypno=1/60, threshold="5min")
+   values  start  length
+0       0      0      11
+1       1     16       9
+
    
+
    
+
    Only the two sequences that are longer than 5 minutes (11 minutes and 9 minutes respectively)
+are kept. Feel free to play around with different values of threshold!
    
+
    This function is not limited to binary arrays, e.g.
    
+
    >>> hypno = [0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 0, 1]
+>>> yasa.hypno_find_periods(hypno, sf_hypno=1/60, threshold="2min")
+   values  start  length
+0       0      0       4
+1       2      5       6
+2       0     11       3
+
    
+
    
+
    Lastly, using equal_length=True will further divide the periods into segments of the
+same duration, i.e. the duration defined in threshold:
    
+
    >>> hypno = [0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 2, 0, 0, 0, 1, 0, 1]
+>>> yasa.hypno_find_periods(hypno, sf_hypno=1/60, threshold="2min", equal_length=True)
+   values  start  length
+0       0      0       2
+1       0      2       2
+2       2      5       2
+3       2      7       2
+4       2      9       2
+5       0     11       2
+
    
+
    
+
    Here, the first period of 4 minutes of consecutive 0 is further divided into 2 periods of
+exactly 2 minutes. Next, the sequence of 6 consecutive 2 is further divided into 3 periods of
+2 minutes. Lastly, the last value in the sequence of 3 consecutive 0 at the end of the array is
+removed to keep only a segment of 2 exactly minutes. In other words, the remainder of the
+division of a given segment by the desired duration is discarded.
    
+
    
+
+
    
+
+
+    
    
+      
+  
    
+
    
+
    
+  
    
+    
    
+      Back to top
+      
+        
    
+        
+      
+    
    
+    
    
+        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 3.0.0.
    
+    
    
+  
    
+
    
+  
+
\ No newline at end of file
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 4a3d068..eedb06f 100644
--- a/docs/build/html/generated/yasa.hypno_int_to_str.html
+++ b/docs/build/html/generated/yasa.hypno_int_to_str.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.hypno_int_to_str — yasa 0.6.1 documentation
+    yasa.hypno_int_to_str — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -106,7 +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 8096ee8..17f1328 100644
--- a/docs/build/html/generated/yasa.hypno_str_to_int.html
+++ b/docs/build/html/generated/yasa.hypno_str_to_int.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.hypno_str_to_int — yasa 0.6.1 documentation
+    yasa.hypno_str_to_int — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -106,7 +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 74d49ef..421131d 100644
--- a/docs/build/html/generated/yasa.hypno_upsample_to_data.html
+++ b/docs/build/html/generated/yasa.hypno_upsample_to_data.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.hypno_upsample_to_data — yasa 0.6.1 documentation
+    yasa.hypno_upsample_to_data — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -112,11 +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 7abc7b6..85627c0 100644
--- a/docs/build/html/generated/yasa.hypno_upsample_to_sf.html
+++ b/docs/build/html/generated/yasa.hypno_upsample_to_sf.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.hypno_upsample_to_sf — yasa 0.6.1 documentation
+    yasa.hypno_upsample_to_sf — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
diff --git a/docs/build/html/generated/yasa.irasa.html b/docs/build/html/generated/yasa.irasa.html
index d0d78d2..5fbea0c 100644
--- a/docs/build/html/generated/yasa.irasa.html
+++ b/docs/build/html/generated/yasa.irasa.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.irasa — yasa 0.6.1 documentation
+    yasa.irasa — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -103,22 +106,22 @@ 
    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
@@ -138,7 +141,7 @@ 
    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’,
@@ -149,13 +152,13 @@ 
    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 87abe15..0f81774 100644
--- a/docs/build/html/generated/yasa.load_profusion_hypno.html
+++ b/docs/build/html/generated/yasa.load_profusion_hypno.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.load_profusion_hypno — yasa 0.6.1 documentation
+    yasa.load_profusion_hypno — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
diff --git a/docs/build/html/generated/yasa.moving_transform.html b/docs/build/html/generated/yasa.moving_transform.html
index 052b229..aa6dd38 100644
--- a/docs/build/html/generated/yasa.moving_transform.html
+++ b/docs/build/html/generated/yasa.moving_transform.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.moving_transform — yasa 0.6.1 documentation
+    yasa.moving_transform — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
diff --git a/docs/build/html/generated/yasa.plot_hypnogram.html b/docs/build/html/generated/yasa.plot_hypnogram.html
index bace3a2..4152510 100644
--- a/docs/build/html/generated/yasa.plot_hypnogram.html
+++ b/docs/build/html/generated/yasa.plot_hypnogram.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.plot_hypnogram — yasa 0.6.1 documentation
+    yasa.plot_hypnogram — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -131,7 +134,7 @@ 
    yasa.plot_hypnogramReturns
 
    
-
    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 1d16ce0..64567ba 100644
--- a/docs/build/html/generated/yasa.plot_spectrogram.html
+++ b/docs/build/html/generated/yasa.plot_spectrogram.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.plot_spectrogram — yasa 0.6.1 documentation
+    yasa.plot_spectrogram — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -94,7 +97,7 @@
 
    yasa.plot_spectrogram
    
 
    
 
    
-yasa.plot_spectrogram(data, sf, hypno=None, win_sec=30, fmin=0.5, fmax=25, trimperc=2.5, cmap='RdBu_r')[source]
    
+yasa.plot_spectrogram(data, sf, hypno=None, win_sec=30, fmin=0.5, fmax=25, trimperc=2.5, cmap='RdBu_r', vmin=None, vmax=None)[source]
 
    Plot a full-night multi-taper spectrogram, optionally with the hypnogram on top.
    
 
    For more details, please refer to the Jupyter notebook
    
 
    
@@ -103,7 +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.
    
 
    
@@ -139,11 +142,15 @@ 
    yasa.plot_spectrogram
 
    cmapstr
    Colormap. Default to ‘RdBu_r’.
    
 
    
+
    vminint or float
    The lower range of color scale. Overwrites trimperc
    
+
    
+
    vmaxint or float
    The upper range of color scale. Overwrites trimperc
    
+
    
 
    
 
    
 
    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 a96edff..c51154f 100644
--- a/docs/build/html/generated/yasa.rem_detect.html
+++ b/docs/build/html/generated/yasa.rem_detect.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.rem_detect — yasa 0.6.1 documentation
+    yasa.rem_detect — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
    
 
         
    
@@ -110,11 +113,10 @@ 
    yasa.rem_detect
 
    Warning
    
-
    The default unit of mne.io.BaseRaw is Volts.
-Therefore, if passing data from a mne.io.BaseRaw,
-you need to multiply the data by 1e6 to convert to micro-Volts
-(1 V = 1,000,000 uV), e.g.:
    
-
    >>> data = raw.get_data() * 1e6  # Make sure that data is in uV
+
    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
 
    
 
    
 
    
@@ -155,7 +157,7 @@ 
    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.
    
@@ -177,7 +179,7 @@ 
    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.sleep_statistics.html b/docs/build/html/generated/yasa.sleep_statistics.html
index 4c07133..97c24c5 100644
--- a/docs/build/html/generated/yasa.sleep_statistics.html
+++ b/docs/build/html/generated/yasa.sleep_statistics.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.sleep_statistics — yasa 0.6.1 documentation
+    yasa.sleep_statistics — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

diff --git a/docs/build/html/generated/yasa.sliding_window.html b/docs/build/html/generated/yasa.sliding_window.html
index cefec58..195d671 100644
--- a/docs/build/html/generated/yasa.sliding_window.html
+++ b/docs/build/html/generated/yasa.sliding_window.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.sliding_window — yasa 0.6.1 documentation
+    yasa.sliding_window — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -129,7 +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 a3a839f..425cdef 100644
--- a/docs/build/html/generated/yasa.spindles_detect.html
+++ b/docs/build/html/generated/yasa.spindles_detect.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.spindles_detect — yasa 0.6.1 documentation
+    yasa.spindles_detect — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -100,22 +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
@@ -175,7 +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.
@@ -200,7 +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 1d729df..45945c9 100644
--- a/docs/build/html/generated/yasa.stft_power.html
+++ b/docs/build/html/generated/yasa.stft_power.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.stft_power — yasa 0.6.1 documentation
+    yasa.stft_power — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -126,18 +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 c0415a1..88b0563 100644
--- a/docs/build/html/generated/yasa.sw_detect.html
+++ b/docs/build/html/generated/yasa.sw_detect.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.sw_detect — yasa 0.6.1 documentation
+    yasa.sw_detect — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -100,22 +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
@@ -216,7 +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
@@ -240,7 +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 256f1a8..46620ef 100644
--- a/docs/build/html/generated/yasa.topoplot.html
+++ b/docs/build/html/generated/yasa.topoplot.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.topoplot — yasa 0.6.1 documentation
+    yasa.topoplot — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -96,7 +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.

@@ -104,16 +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.

@@ -133,13 +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 6d90af2..4a8a262 100644
--- a/docs/build/html/generated/yasa.transition_matrix.html
+++ b/docs/build/html/generated/yasa.transition_matrix.html
@@ -3,11 +3,14 @@
 
   
     
-    yasa.transition_matrix — yasa 0.6.1 documentation
+    yasa.transition_matrix — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -115,11 +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,
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index 4ef4bc2..9910de7 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -4,11 +4,14 @@
 
   
     
-    Index — yasa 0.6.1 documentation
+    Index — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -42,7 +45,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       

 
         

@@ -98,6 +101,7 @@ 
Index

  _
  | A
  | B
+ | C
  | F
  | G
  | H
@@ -148,6 +152,28 @@ 
B

   
 
 
+
C

+
+  
+  
+

+
 
F

 

   
    
@@ -195,12 +221,16 @@ 
    G
    
 
    H
    
 
    
   
   
      
+      
    • hypno_str_to_int() (in module yasa)
+
      • 
       
    • hypno_upsample_to_data() (in module yasa)
 
      • 
       
    • hypno_upsample_to_sf() (in module yasa)
diff --git a/docs/build/html/index.html b/docs/build/html/index.html
index bd266fd..f6d4d87 100644
--- a/docs/build/html/index.html
+++ b/docs/build/html/index.html
@@ -3,11 +3,14 @@
 
   
     
-    Installation — yasa 0.6.1 documentation
+    Installation — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
 
         
      
@@ -103,11 +106,11 @@
 
      
 
      YASA (Yet Another Spindle Algorithm) is a command-line sleep analysis toolbox in Python. The main functions of YASA are:
      
 
        
-
      • Automatic sleep staging of polysomnography data (see preprint article).
        • 
+
      • Automatic sleep staging of polysomnography data (see eLife article).
        • 
 
      • Event detection: sleep spindles, slow-waves and rapid eye movements, on single or multi-channel EEG data.
        • 
 
      • Artefact rejection, on single or multi-channel EEG data.
        • 
 
      • Spectral analyses: bandpower, phase-amplitude coupling, 1/f slope, and more!
        • 
-
      • Hypnogram analysis: sleep statistics and stage tranisitions.
        • 
+
      • Hypnogram analysis: sleep statistics and stage transitions.
        • 
 
      
 
      For more details, try the quickstart or read the FAQ.
      
 
      
@@ -117,6 +120,12 @@ 
      Installation
      pip install --upgrade yasa
 
      
 
+
      Alternatively, YASA can be installed with conda:
      
+
      conda config --add channels conda-forge
+conda config --set channel_priority strict
+conda install yasa
+
      
+
      
 
      What are the prerequisites for using YASA?
      
 
      To use YASA, all you need is:
      
       
 
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
index 5563ffa..932964b 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 4a03e16..7d566aa 100644
--- a/docs/build/html/quickstart.html
+++ b/docs/build/html/quickstart.html
@@ -3,11 +3,14 @@
 
   
     
-    Quickstart — yasa 0.6.1 documentation
+    Quickstart — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -41,7 +44,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
 
         
      
@@ -183,19 +186,18 @@ 
      Polysomnography data
      >>> data = raw.get_data() * 1e6
+
      >>> data = raw.get_data(units="uV")
 >>> print(data.shape)
 (19, 2892000)
 
      
 
      
 
      In this example, data is a two-dimensional NumPy array where the rows represent the channels (19 EEG channels) and the columns represent the data samples (~3 million samples per channel).
      
-
      Note that we have also multiplied the data by a million (=1e6). This is because MNE converts the data from microVolts (the standard EEG unit) to Volts when loading the EDF file. We therefore revert this operation, i.e. convert from Volts to microVolts.
      
 
      
 
      
 
      Hypnogram
      
 
      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
@@ -311,11 +313,11 @@ 
      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 9bab7e0..7548a57 100644
--- a/docs/build/html/search.html
+++ b/docs/build/html/search.html
@@ -3,11 +3,14 @@
 
   
     
-    Search — yasa 0.6.1 documentation
+    Search — yasa 0.6.2 documentation
     
     
     
     
+    
+    
+    
     
     
     
@@ -47,7 +50,7 @@
         
         
           yasa
-        0.6.1
+        0.6.2
       
      
 
         
      
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index 4cceea8..12f7989 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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ample_night_young_hypno:33,year:[3,6,29,33],yet:32,yetton:24,ylorrd:31,you:[1,2,3,5,6,7,8,13,14,27,29,32,33],your:[1,2,3,6,8,9,13,23,24,27,29,32,33],zero:[1,6,12,21,29],zhang:6,zink:5,zsaa290:5,zscore:8},titles:["API reference","What\u2019s new","Contribute to YASA","FAQ","yasa.REMResults","yasa.SWResults","yasa.SleepStaging","yasa.SpindlesResults","yasa.art_detect","yasa.bandpower","yasa.bandpower_from_psd","yasa.bandpower_from_psd_ndarray","yasa.compare_detection","yasa.hrv_stage","yasa.hypno_find_periods","yasa.hypno_int_to_str","yasa.hypno_str_to_int","yasa.hypno_upsample_to_data","yasa.hypno_upsample_to_sf","yasa.irasa","yasa.load_profusion_hypno","yasa.moving_transform","yasa.plot_hypnogram","yasa.plot_spectrogram","yasa.rem_detect","yasa.sleep_statistics","yasa.sliding_window","yasa.spindles_detect","yasa.stft_power","yasa.sw_detect","yasa.topoplot","yasa.transition_matrix","Installation","Quickstart"],titleterms:{"new":1,analys:[0,33],analysi:0,api:0,art_detect:8,august:1,automat:[0,33],band:33,bandpow:9,bandpower_from_psd:10,bandpower_from_psd_ndarrai:11,build:2,check:2,citat:32,code:2,compare_detect:12,content:[1,33],contribut:2,data:[3,33],detect:[0,3,33],develop:32,document:2,eeg:33,event:[0,3,33],faq:3,februari:1,frequenc:33,full:33,galleri:32,get:32,guidelin:2,heart:0,how:32,hrv_stage:13,hypno_find_period:14,hypno_int_to_str:15,hypno_str_to_int:16,hypno_upsample_to_data:17,hypno_upsample_to_sf:18,hypnogram:[0,33],instal:32,irasa:19,load:[3,33],load_profusion_hypno:20,mai:1,march:1,matrix:33,moving_transform:21,night:33,novemb:1,older:1,other:3,plot:33,plot_hypnogram:22,plot_spectrogram:23,polysomnographi:[3,33],power:33,preprocess:33,prerequisit:33,quickstart:33,rate:0,refer:0,rem_detect:24,remresult:4,sleep:[0,3,33],sleep_statist:25,sleepstag:6,sliding_window:26,slow:33,specif:33,spectral:[0,33],spectrogram:33,spindl:33,spindles_detect:27,spindlesresult:7,stage:[0,3,33],start:32,statist:[0,33],stft_power:28,sw_detect:29,swresult:5,tabl:1,topoplot:30,transit:33,transition_matrix:31,version:1,visual:3,wave:33,what:1,yasa:[2,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]}})
\ No newline at end of file
diff --git a/docs/changelog.rst b/docs/changelog.rst
index 15f7e5d..7e42a10 100644
--- a/docs/changelog.rst
+++ b/docs/changelog.rst
@@ -8,8 +8,8 @@ What's new
 
 ----------------------------------------------------------------------------------------
 
-v0.6.2.dev
-----------
+v0.6.2 (August 2022)
+--------------------
 
 **ECG analysis** - `PR 68 `_
 
diff --git a/docs/conf.py b/docs/conf.py
index f02cafe..d622f17 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -20,6 +20,7 @@
     "sphinx.ext.intersphinx",
     "matplotlib.sphinxext.plot_directive",
     "sphinx_copybutton",
+    "sphinx_panels",
     "numpydoc",
 ]
 
diff --git a/docs/index.rst b/docs/index.rst
index 007aadf..e7a80ab 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -21,11 +21,11 @@
 
 **YASA** (*Yet Another Spindle Algorithm*) is a command-line sleep analysis toolbox in Python. The main functions of YASA are:
 
-* Automatic sleep staging of polysomnography data (see `preprint article `_).
+* Automatic sleep staging of polysomnography data (see `eLife article `_).
 * Event detection: sleep spindles, slow-waves and rapid eye movements, on single or multi-channel EEG data.
 * Artefact rejection, on single or multi-channel EEG data.
 * Spectral analyses: bandpower, phase-amplitude coupling, 1/f slope, and more!
-* Hypnogram analysis: sleep statistics and stage tranisitions.
+* Hypnogram analysis: sleep statistics and stage transitions.
 
 For more details, try the `quickstart `_ or read the `FAQ `_.
 
diff --git a/setup.py b/setup.py
index 3e6ef15..51268f9 100644
--- a/setup.py
+++ b/setup.py
@@ -2,8 +2,8 @@
 #
 # Copyright (C) 2018 Raphael Vallat
 
-DESCRIPTION = "Yet Another Spindle Algorithm"
-LONG_DESCRIPTION = """YASA (Yet Another Spindle Algorithm) : fast and robust detection of spindles, slow-waves, and rapid eye movements from sleep EEG recordings..
+DESCRIPTION = "YASA: Analysis of polysomnography recordings."
+LONG_DESCRIPTION = """YASA (Yet Another Spindle Algorithm) : an open-source Python package to analyze polysomnographic sleep recordings.
 """
 
 DISTNAME = "yasa"
@@ -12,16 +12,16 @@
 URL = "https://github.com/raphaelvallat/yasa/"
 LICENSE = "BSD (3-clause)"
 DOWNLOAD_URL = "https://github.com/raphaelvallat/yasa/"
-VERSION = "0.6.1"
+VERSION = "0.6.2"
 PACKAGE_DATA = {"yasa.data.icons": ["*.svg"]}
 
 INSTALL_REQUIRES = [
-    "numpy",
+    "numpy>=1.16.5",
     "scipy",
     "pandas",
     "matplotlib",
     "seaborn",
-    "mne>=0.20.0",
+    "mne>=0.23",
     "numba",
     "outdated",
     "antropy",
diff --git a/yasa/__init__.py b/yasa/__init__.py
index bdbb327..bf3f925 100644
--- a/yasa/__init__.py
+++ b/yasa/__init__.py
@@ -15,7 +15,7 @@
 logging.basicConfig(format="%(asctime)s | %(levelname)s | %(message)s", datefmt="%d-%b-%y %H:%M:%S")
 
 __author__ = "Raphael Vallat "
-__version__ = "0.6.1"
+__version__ = "0.6.2"
 
 # Warn if a newer version of YASA is available
 warn_if_outdated("yasa", __version__)