diff --git a/.github/workflows/python_tests.yml b/.github/workflows/python_tests.yml index 8a2b82b..6e41296 100644 --- a/.github/workflows/python_tests.yml +++ b/.github/workflows/python_tests.yml @@ -11,8 +11,8 @@ jobs: strategy: fail-fast: false matrix: - platform: [ubuntu-latest, windows-latest] # macos-latest, - python-version: [3.7, 3.8, 3.9] + platform: [ubuntu-latest, macos-latest, windows-latest] + python-version: ["3.8", "3.9", "3.10", "3.11"] runs-on: ${{ matrix.platform }} @@ -39,7 +39,7 @@ jobs: pytest --cov --cov-report=xml --cov-config=setup.cfg --verbose - name: Upload coverage report - if: ${{ matrix.platform == 'ubuntu-latest' && matrix.python-version == 3.8 }} + if: ${{ matrix.platform == 'ubuntu-latest' && matrix.python-version == 3.9 }} uses: codecov/codecov-action@v1 with: token: a58a0c62-fb11-4429-977b-65bec01ecb44 diff --git a/README.rst b/README.rst index 198f79c..a49db33 100644 --- a/README.rst +++ b/README.rst @@ -119,7 +119,7 @@ Below some plots demonstrating the functionalities of YASA. To reproduce these, Development ~~~~~~~~~~~ -YASA was created and is maintained by `Raphael Vallat `_, a postdoctoral researcher in `Matthew Walker's lab `_ at UC Berkeley. Contributions are more than welcome so feel free to contact me, open an issue or submit a pull request! +YASA was created and is maintained by `Raphael Vallat `_, a former postdoctoral researcher in `Matthew Walker's lab `_ at UC Berkeley. Contributions are more than welcome so feel free to contact me, open an issue or submit a pull request! To see the code or report a bug, please visit the `GitHub repository `_. diff --git a/docs/api.rst b/docs/api.rst index c4b1b42..a6a745d 100644 --- a/docs/api.rst +++ b/docs/api.rst @@ -31,7 +31,6 @@ Hypnogram & sleep statistics .. autosummary:: :toctree: generated/ - Hypnogram hypno_upsample_to_data hypno_upsample_to_sf hypno_str_to_int @@ -42,7 +41,8 @@ Hypnogram & sleep statistics plot_spectrogram transition_matrix sleep_statistics - simulate_hypno + Hypnogram + simulate_hypnogram Spectral analyses ----------------- diff --git a/docs/build/html/.buildinfo b/docs/build/html/.buildinfo index 37b6a68..b12a47e 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: 53802cc2376a375a855b3c0f38207c28 +config: 489cdac284cc185cfdae105616c5af8a tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/docs/build/html/_images/yasa-Hypnogram-1.png b/docs/build/html/_images/yasa-Hypnogram-1.png new file mode 100644 index 0000000..c766e36 Binary files /dev/null and b/docs/build/html/_images/yasa-Hypnogram-1.png differ diff --git a/docs/build/html/_images/yasa-plot_hypnogram-1.png b/docs/build/html/_images/yasa-plot_hypnogram-1.png index 68a096f..0a02e41 100644 Binary files a/docs/build/html/_images/yasa-plot_hypnogram-1.png and b/docs/build/html/_images/yasa-plot_hypnogram-1.png differ diff --git a/docs/build/html/_images/yasa-plot_hypnogram-2.png b/docs/build/html/_images/yasa-plot_hypnogram-2.png new file mode 100644 index 0000000..671c8a8 Binary files /dev/null and b/docs/build/html/_images/yasa-plot_hypnogram-2.png differ diff --git a/docs/build/html/_images/yasa-plot_hypnogram-3.png b/docs/build/html/_images/yasa-plot_hypnogram-3.png new file mode 100644 index 0000000..b17f75c Binary files /dev/null and b/docs/build/html/_images/yasa-plot_hypnogram-3.png differ diff --git a/docs/build/html/_images/yasa-plot_spectrogram-1.png b/docs/build/html/_images/yasa-plot_spectrogram-1.png index 265098a..89992ee 100644 Binary files a/docs/build/html/_images/yasa-plot_spectrogram-1.png and b/docs/build/html/_images/yasa-plot_spectrogram-1.png differ diff --git a/docs/build/html/_images/yasa-plot_spectrogram-2.png b/docs/build/html/_images/yasa-plot_spectrogram-2.png index 3cb3131..6800493 100644 Binary files a/docs/build/html/_images/yasa-plot_spectrogram-2.png and b/docs/build/html/_images/yasa-plot_spectrogram-2.png differ diff --git a/docs/build/html/_images/yasa-simulate_hypno-1.png b/docs/build/html/_images/yasa-simulate_hypno-1.png deleted file mode 100644 index 08ef682..0000000 Binary files a/docs/build/html/_images/yasa-simulate_hypno-1.png and /dev/null differ diff --git a/docs/build/html/_images/yasa-simulate_hypnogram-1.png b/docs/build/html/_images/yasa-simulate_hypnogram-1.png new file mode 100644 index 0000000..ad34e2b Binary files /dev/null and b/docs/build/html/_images/yasa-simulate_hypnogram-1.png differ diff --git a/docs/build/html/_images/yasa-topoplot-1.png b/docs/build/html/_images/yasa-topoplot-1.png index 0accf26..b4557af 100644 Binary files a/docs/build/html/_images/yasa-topoplot-1.png and b/docs/build/html/_images/yasa-topoplot-1.png differ diff --git a/docs/build/html/_images/yasa-topoplot-2.png b/docs/build/html/_images/yasa-topoplot-2.png index 2537306..194886d 100644 Binary files a/docs/build/html/_images/yasa-topoplot-2.png and b/docs/build/html/_images/yasa-topoplot-2.png differ diff --git a/docs/build/html/_images/yasa-transition_matrix-1.png b/docs/build/html/_images/yasa-transition_matrix-1.png index 85fd613..c3f63ea 100644 Binary files a/docs/build/html/_images/yasa-transition_matrix-1.png and b/docs/build/html/_images/yasa-transition_matrix-1.png differ diff --git a/docs/build/html/_modules/index.html b/docs/build/html/_modules/index.html index 7a75cda..770b421 100644 --- a/docs/build/html/_modules/index.html +++ b/docs/build/html/_modules/index.html @@ -1,24 +1,23 @@ - + - Overview: module code — yasa 0.6.3 documentation - - + + Overview: module code — yasa 0.6.4 documentation + + - + - - @@ -26,10 +25,10 @@ - - - - + + + + @@ -44,7 +43,7 @@ yasa - 0.6.3 + 0.6.4
@@ -118,8 +117,8 @@

All modules for which code is available

- © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
- Created using Sphinx 3.0.0.
+ © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
+ Created using Sphinx 4.4.0.

diff --git a/docs/build/html/_modules/yasa/detection.html b/docs/build/html/_modules/yasa/detection.html index 2e05165..8ba6b88 100644 --- a/docs/build/html/_modules/yasa/detection.html +++ b/docs/build/html/_modules/yasa/detection.html @@ -1,24 +1,23 @@ - + - yasa.detection — yasa 0.6.3 documentation - - + + yasa.detection — yasa 0.6.4 documentation + + - + - - @@ -26,10 +25,10 @@ - - - - + + + + @@ -44,7 +43,7 @@ yasa - 0.6.3 + 0.6.4
@@ -919,7 +918,6 @@

Source code for yasa.detection

     df = pd.DataFrame()
 
     for i in range(n_chan):
-
         # ####################################################################
         # START SINGLE CHANNEL DETECTION
         # ####################################################################
@@ -2426,6 +2424,7 @@ 
Source code for yasa.detection
     include=4,
     amplitude=(50, 325),
     duration=(0.3, 1.2),
+    relative_prominence=0.8,
     freq_rem=(0.5, 5),
     remove_outliers=False,
     verbose=False,
@@ -2483,6 +2482,9 @@ 
Source code for yasa.detection
     duration : tuple or list
         The minimum and maximum duration of the REMs.
         Default is 0.3 to 1.2 seconds.
+    relative_prominence : float
+        Relative prominence used to detect the peaks. The actual prominence is computed
+        by multiplying relative prominence by the minimal amplitude. Default is 0.8.
     freq_rem : tuple or list
         Frequency range of REMs. Default is 0.5 to 5 Hz.
     remove_outliers : boolean
@@ -2589,7 +2591,7 @@ 
Source code for yasa.detection
         negp,
         height=(hmin, hmax),
         distance=(duration[0] * sf),
-        prominence=(0.8 * hmin),
+        prominence=(relative_prominence * hmin),
         wlen=(duration[1] * sf),
     )
 
@@ -3458,8 +3460,8 @@ 
Source code for yasa.detection
       
     

     

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

-      Created using Sphinx 3.0.0.

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

+      Created using Sphinx 4.4.0.

     

   

 
diff --git a/docs/build/html/_modules/yasa/heart.html b/docs/build/html/_modules/yasa/heart.html
index ceaf716..25a9297 100644
--- a/docs/build/html/_modules/yasa/heart.html
+++ b/docs/build/html/_modules/yasa/heart.html
@@ -1,24 +1,23 @@
 
 
-
+
   
     
-    yasa.heart — yasa 0.6.3 documentation
-    
-    
+    
+    yasa.heart — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +25,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +43,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       

 
         

@@ -304,8 +303,8 @@ 
Source code for yasa.heart
       
     

     

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

-      Created using Sphinx 3.0.0.

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

+      Created using Sphinx 4.4.0.

     

   

 
diff --git a/docs/build/html/_modules/yasa/hypno.html b/docs/build/html/_modules/yasa/hypno.html
index 0484b9d..e645b7f 100644
--- a/docs/build/html/_modules/yasa/hypno.html
+++ b/docs/build/html/_modules/yasa/hypno.html
@@ -1,24 +1,23 @@
 
 
-
+
   
     
-    yasa.hypno — yasa 0.6.3 documentation
-    
-    
+    
+    yasa.hypno — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +25,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +43,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       

 
         

@@ -95,55 +94,1107 @@
       
   
Source code for yasa.hypno
 """
-This file contains several helper functions to manipulate sleep staging
-(hypnogram) data. The default hypnogram format in YASA is a one dimensional
-integer array where:
-
-* -2  = Unscored
-* -1  = Artefact / Movement
-* 0   = Wake
-* 1   = N1 sleep
-* 2   = N2 sleep
-* 3   = N3 sleep
-* 4   = REM sleep
-
-For more details, please refer to the following references:
-
-- Iber, C. (2007). The AASM manual for the scoring of sleep and
-associated events: rules, terminology and technical specifications.
-American Academy of Sleep Medicine.
-
-- Silber, M. H., Ancoli-Israel, S., Bonnet, M. H., Chokroverty, S.,
-Grigg-Damberger, M. M., Hirshkowitz, M., … Iber, C. (2007). The visual scoring
-of sleep in adults. Journal of Clinical Sleep Medicine: JCSM: Official
-Publication of the American Academy of Sleep Medicine, 3(2), 121–131.
-
-- Combrisson, E., Vallat, R., Eichenlaub, J.-B., O’Reilly, C., Lajnef, T.,
-Guillot, A., … Jerbi, K. (2017). Sleep: An Open-Source Python Software for
-Visualization, Analysis, and Staging of Sleep Data. Frontiers in
-Neuroinformatics, 11, 60. https://doi.org/10.3389/fninf.2017.00060
+Hypnogram-related functions and class.
 """
 import mne
 import logging
+
+# import warnings
 import numpy as np
 import pandas as pd
-from .io import set_log_level
+from yasa.io import set_log_level
+from yasa.plotting import plot_hypnogram
+from yasa.sleepstats import transition_matrix
+from pandas.api.types import CategoricalDtype
 
 __all__ = [
+    "Hypnogram",
     "hypno_str_to_int",
     "hypno_int_to_str",
-    "hypno_consolidate_stages",
     "hypno_upsample_to_sf",
     "hypno_upsample_to_data",
     "hypno_find_periods",
     "load_profusion_hypno",
-    "simulate_hypno",
+    "simulate_hypnogram",
 ]
 
 
 logger = logging.getLogger("yasa")
 
 
+
[docs]class Hypnogram:
+    """
+    Experimental class for manipulating hypnogram in YASA (dev).
+
+    Starting with v0.7, YASA will take a more object-oriented approach to hypnograms. That is,
+    hypnograms are now stored as a class (aka object), which comes with its own attributes and
+    functions. Furthermore, YASA does not allow integer values to define the stages anymore.
+    Instead, users must pass an array of strings with the actual stage names
+    (e.g. ["WAKE", "WAKE", "N1", ..., "REM", "REM"]).
+
+    .. versionadded:: 0.7.0
+
+    Parameters
+    ----------
+    values : array_like
+        A vector of stage values, represented as strings. See some examples below:
+
+        * 2-stages hypnogram (Wake/Sleep): ``["W", "S", "S", "W", "S"]``
+        * 3-stages (Wake/NREM/REM): ``pd.Series(["WAKE", "NREM", "NREM", "REM", "REM"])``
+        * 4-stages (Wake/Light/Deep/REM): ``np.array(["Wake", "Light", "Deep", "Deep"])``
+        * 5-stages (default): ``["N1", "N1", "N2", "N3", "N2", "REM", "W"]``
+
+        Artefacts ("Art") and unscored ("Uns") epochs are always allowed regardless of the
+        number of stages in the hypnogram.
+
+        .. note:: Abbreviated or full spellings for the stages are allowed, as well as
+            lower/upper/mixed case. Internally, YASA will convert the stages to to full spelling
+            and uppercase (e.g. "w" -> "WAKE").
+    n_stages : int
+        Whether ``values`` comes from a 2, 3, 4 or 5-stages hypnogram. Default is 5 stages, meaning
+        that the following sleep stages are allowed: N1, N2, N3, REM, WAKE.
+    freq : str
+        A pandas frequency string indicating the frequency resolution of the hypnogram. Default is
+        "30s" meaning that each value in the hypnogram represents a 30-seconds epoch.
+        Examples: "1min", "10s", "15min". A full list of accepted values can be found at
+        https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases
+
+        ``freq`` will be passed to the :py:func:`pandas.date_range` function to create the time
+        index of the hypnogram.
+    start : str or datetime
+        An optional string indicating the starting datetime of the hypnogram
+        (e.g. "2022-12-15 22:30:00"). If ``start`` is specified and valid, the index of the
+        hypnogram will be a :py:class:`pandas.DatetimeIndex`. Otherwise it will be a
+        :py:class:`pandas.RangeIndex`, indicating the epoch number.
+    scorer : str
+        An optional string indicating the scorer name. If specified, this will be set as the name
+        of the :py:class:`pandas.Series`, otherwise the name will be set to "Stage".
+
+    Examples
+    --------
+    Create a 2-stages hypnogram
+
+    >>> from yasa import Hypnogram
+    >>> values = ["W", "W", "W", "S", "S", "S", "S", "S", "W", "S", "S", "S"]
+    >>> hyp = Hypnogram(values, n_stages=2)
+    >>> hyp
+    <Hypnogram | 12 epochs x 30s (6.00 minutes), 2 stages>
+     - Use `.hypno` to get the string values as a pandas.Series
+     - Use `.as_int()` to get the integer values as a pandas.Series
+     - Use `.plot_hypnogram()` to plot the hypnogram
+    See the online documentation for more details.
+
+    We can access the actual values, which are stored as a :py:class:`pandas.Series`, with:
+
+    >>> hyp.hypno
+    Epoch
+    0      WAKE
+    1      WAKE
+    2      WAKE
+    3     SLEEP
+    4     SLEEP
+    5     SLEEP
+    6     SLEEP
+    7     SLEEP
+    8      WAKE
+    9     SLEEP
+    10    SLEEP
+    11    SLEEP
+    Name: Stage, dtype: category
+    Categories (4, object): ['WAKE', 'SLEEP', 'ART', 'UNS']
+
+    >>> # Number of epochs in the hypnogram
+    >>> hyp.n_epochs
+    12
+
+    >>> # Total duration of the hypnogram, in minutes (12 epochs * 30 seconds = 6 minutes)
+    >>> hyp.duration
+    6.0
+
+    >>> # Default mapping from strings to integers. Can be changed with `hyp.mapping = {}`
+    >>> hyp.mapping
+    {'WAKE': 0, 'SLEEP': 1, 'ART': -1, 'UNS': -2}
+
+    >>> # Get the hypnogram Series integer values
+    >>> hyp.as_int()
+    Epoch
+    0     0
+    1     0
+    2     0
+    3     1
+    4     1
+    5     1
+    6     1
+    7     1
+    8     0
+    9     1
+    10    1
+    11    1
+    Name: Stage, dtype: int16
+
+    >>> # Calculate the summary sleep statistics
+    >>> hyp.sleep_statistics()
+    {'TIB': 6.0,
+     'SPT': 4.5,
+     'WASO': 0.5,
+     'TST': 4.0,
+     'SE': 66.6667,
+     'SME': 88.8889,
+     'SFI': 7.5,
+     'SOL': 1.5,
+     'SOL_5min': nan,
+     'WAKE': 2.0}
+
+    >>> # Get the state-transition matrix
+    >>> counts, probs = hyp.transition_matrix()
+    >>> counts
+    To Stage    WAKE  SLEEP
+    From Stage
+    WAKE           2      2
+    SLEEP          1      6
+
+    All these methods and properties are also valid with a 5-stages hypnogram. In the example below,
+    we use the :py:func:`yasa.simulate_hypnogram` to generate a plausible 5-stages hypnogram with a
+    30-seconds resolution. A random seed is specified to ensure that we get reproducible results.
+    Lastly, we set an actual start time to the hypnogram. As a result, the index of the resulting
+    hypnogram is a :py:class:`pandas.DatetimeIndex`.
+
+    >>> from yasa import simulate_hypnogram
+    >>> hyp = simulate_hypnogram(
+    ...     tib=500, n_stages=5, start="2022-12-15 22:30:00", scorer="S1", seed=42)
+    >>> hyp
+    <Hypnogram | 1000 epochs x 30s (500.00 minutes), 5 stages, scored by S1>
+     - Use `.hypno` to get the string values as a pandas.Series
+     - Use `.as_int()` to get the integer values as a pandas.Series
+     - Use `.plot_hypnogram()` to plot the hypnogram
+    See the online documentation for more details.
+
+    >>> hyp.hypno
+    Time
+    2022-12-15 22:30:00    WAKE
+    2022-12-15 22:30:30    WAKE
+    2022-12-15 22:31:00    WAKE
+    2022-12-15 22:31:30    WAKE
+    2022-12-15 22:32:00    WAKE
+                        ...
+    2022-12-16 06:47:30      N2
+    2022-12-16 06:48:00      N2
+    2022-12-16 06:48:30      N2
+    2022-12-16 06:49:00      N2
+    2022-12-16 06:49:30      N2
+    Freq: 30S, Name: S1, Length: 1000, dtype: category
+    Categories (7, object): ['WAKE', 'N1', 'N2', 'N3', 'REM', 'ART', 'UNS']
+
+    The summary sleep statistics will include more items with a 5-stages hypnogram than a 2-stages
+    hypnogram, i.e. the amount and percentage of each sleep stage, the REM latency, etc.
+
+    >>> hyp.sleep_statistics()
+    {'TIB': 500.0,
+     'SPT': 497.5,
+     'WASO': 79.5,
+     'TST': 418.0,
+     'SE': 83.6,
+     'SME': 84.0201,
+     'SFI': 0.7177,
+     'SOL': 2.5,
+     'SOL_5min': 2.5,
+     'Lat_REM': 67.0,
+     'WAKE': 82.0,
+     'N1': 69.0,
+     'N2': 247.0,
+     'N3': 64.5,
+     'REM': 37.5,
+     '%N1': 16.5072,
+     '%N2': 59.0909,
+     '%N3': 15.4306,
+     '%REM': 8.9713}
+    """
+
+
[docs]    def __init__(self, values, n_stages=5, *, freq="30s", start=None, scorer=None):
+        assert isinstance(
+            values, (list, np.ndarray, pd.Series)
+        ), "`values` must be a list, numpy.array or pandas.Series"
+        assert isinstance(n_stages, int), "`n_stages` must be an integer between 2 and 5."
+        assert n_stages in [2, 3, 4, 5], "`n_stages` must be an integer between 2 and 5."
+        assert isinstance(freq, str), "`freq` must be a pandas frequency string."
+        assert isinstance(
+            start, (type(None), str, pd.Timestamp)
+        ), "`start` must be either None, a string or a pandas.Timestamp."
+        assert isinstance(
+            scorer, (type(None), str, int)
+        ), "`scorer` must be either None, or a string or an integer."
+        if n_stages == 2:
+            accepted = ["W", "WAKE", "S", "SLEEP", "ART", "UNS"]
+            mapping = {"WAKE": 0, "SLEEP": 1, "ART": -1, "UNS": -2}
+        elif n_stages == 3:
+            accepted = ["WAKE", "W", "NREM", "REM", "R", "ART", "UNS"]
+            mapping = {"WAKE": 0, "NREM": 2, "REM": 4, "ART": -1, "UNS": -2}
+        elif n_stages == 4:
+            accepted = ["WAKE", "W", "LIGHT", "DEEP", "REM", "R", "ART", "UNS"]
+            mapping = {"WAKE": 0, "LIGHT": 2, "DEEP": 3, "REM": 4, "ART": -1, "UNS": -2}
+        else:
+            accepted = ["WAKE", "W", "N1", "N2", "N3", "REM", "R", "ART", "UNS"]
+            mapping = {"WAKE": 0, "N1": 1, "N2": 2, "N3": 3, "REM": 4, "ART": -1, "UNS": -2}
+        assert all([val.upper() in accepted for val in values]), (
+            f"{np.unique(values)} do not match the accepted values for a {n_stages} stages "
+            f"hypnogram: {accepted}"
+        )
+        if isinstance(values, pd.Series):
+            # Make sure to remove index if the input is a pandas.Series
+            values = values.to_numpy(copy=True)
+        hypno = pd.Series(values).str.upper()
+        if scorer is None:
+            hypno.name = "Stage"
+        else:
+            hypno.name = scorer
+        # Combine accepted values
+        map_accepted = {"S": "SLEEP", "W": "WAKE", "R": "REM"}
+        hypno = hypno.replace(map_accepted)
+        labels = pd.Series(accepted).replace(map_accepted).unique().tolist()
+        # Change dtype of series to "categorical" (reduces memory)
+        cat_dtype = CategoricalDtype(labels, ordered=False)
+        hypno = hypno.astype(cat_dtype)
+        # Create Index
+        if start is not None:
+            hypno.index = pd.date_range(start=start, freq=freq, periods=hypno.shape[0])
+            hypno.index.name = "Time"
+            timedelta = hypno.index - hypno.index[0]
+        else:
+            fake_dt = pd.date_range(start="2022-12-03 00:00:00", freq=freq, periods=hypno.shape[0])
+            hypno.index.name = "Epoch"
+            timedelta = fake_dt - fake_dt[0]
+        # Set attributes
+        self._hypno = hypno
+        self._n_epochs = hypno.shape[0]
+        self._freq = freq
+        self._sampling_frequency = 1 / pd.Timedelta(freq).total_seconds()
+        self._start = start
+        self._timedelta = timedelta
+        self._duration = self._n_epochs / (60 * self._sampling_frequency)
+        self._n_stages = n_stages
+        self._labels = labels
+        self._mapping = mapping
+        self._scorer = scorer

+
+    def __repr__(self):
+        # TODO v0.8: Keep only the text between < and >
+        text_scorer = f", scored by {self.scorer}" if self.scorer is not None else ""
+        return (
+            f"<Hypnogram | {self.n_epochs} epochs x {self.freq} ({self.duration:.2f} minutes), "
+            f"{self.n_stages} stages{text_scorer}>\n"
+            " - Use `.hypno` to get the string values as a pandas.Series\n"
+            " - Use `.as_int()` to get the integer values as a pandas.Series\n"
+            " - Use `.plot_hypnogram()` to plot the hypnogram\n"
+            "See the online documentation for more details."
+        )
+
+    def __str__(self):
+        text_scorer = f", scored by {self.scorer}" if self.scorer is not None else ""
+        return (
+            f"<Hypnogram | {self.n_epochs} epochs x {self.freq} ({self.duration:.2f} minutes), "
+            f"{self.n_stages} stages{text_scorer}>\n"
+            " - Use `.hypno` to get the string values as a pandas.Series\n"
+            " - Use `.as_int()` to get the integer values as a pandas.Series\n"
+            " - Use `.plot_hypnogram()` to plot the hypnogram\n"
+            "See the online documentation for more details."
+        )
+
+    @property
+    def hypno(self):
+        """
+        The hypnogram values, stored in a :py:class:`pandas.Series`. To reduce memory usage, the
+        stages are stored as categories (:py:class:`pandas.Categorical`). ``hypno``
+        inherits all the methods of a standard :py:class:`pandas.Series`, e.g. ``.describe()``,
+        ``.unique()``, ``.to_csv()``, and more.
+
+        .. note:: ``print(Hypnogram)`` is a shortcut to ``print(Hypnogram.hypno)``.
+        """
+        return self._hypno
+
+    @property
+    def n_epochs(self):
+        """The number of epochs in the hypnogram."""
+        return self._n_epochs
+
+    @property
+    def freq(self):
+        """The frequency resolution of the hypnogram. Default is '30s'"""
+        return self._freq
+
+    @property
+    def sampling_frequency(self):
+        """The sampling frequency (Hz) of the hypnogram."""
+        return self._sampling_frequency
+
+    @property
+    def start(self):
+        """The start date/time of the hypnogram. Default is None."""
+        return self._start
+
+    @property
+    def timedelta(self):
+        """
+        A :py:class:`pandas.TimedeltaIndex` vector with the accumulated time difference of each
+        epoch compared to the first epoch.
+        """
+        return self._timedelta
+
+    @property
+    def duration(self):
+        """Total duration of the hypnogram, expressed in minutes. AKA Time in Bed."""
+        return self._duration
+
+    @property
+    def n_stages(self):
+        """
+        The number of allowed stages in the hypnogram. This is not the number of unique stages
+        in the current hypnogram. This does not include Artefact and Unscored which are always
+        allowed.
+        """
+        return self._n_stages
+
+    @property
+    def labels(self):
+        """The allowed stage labels."""
+        return self._labels
+
+    @property
+    def mapping(self):
+        """A dictionary with the mapping from string to integer values."""
+        return self._mapping
+
+    @mapping.setter
+    def mapping(self, map_dict):
+        assert isinstance(map_dict, dict), "`mapping` must be a dictionary, e.g. {'WAKE': 0, ...}"
+        assert all([val in map_dict.keys() for val in self.hypno.unique()]), (
+            f"Some values in `hypno` ({self.hypno.unique()}) are not in `map_dict` "
+            f"({map_dict.keys()})"
+        )
+        if "ART" not in map_dict.keys():
+            map_dict["ART"] = -1
+        if "UNS" not in map_dict.keys():
+            map_dict["UNS"] = -2
+        self._mapping = map_dict
+
+    @property
+    def mapping_int(self):
+        """A dictionary with the mapping from integer to string values."""
+        return {v: k for k, v in self.mapping.items()}
+
+    @property
+    def scorer(self):
+        """The scorer name."""
+        return self._scorer
+
+    # CLASS METHODS BELOW
+
+
[docs]    def as_annotations(self):
+        """
+        Return a pandas DataFrame summarizing epoch-level information.
+
+        Column order and names are compliant with BIDS
+        `events files
+        <https://bids-specification.readthedocs.io/en/stable/04-modality-specific-files/05-task-events.html>`_
+        and MNE `events/annotations dataframes
+        <https://mne.tools/stable/glossary.html#term-annotations>`_.
+
+        Returns
+        -------
+        annotations : :py:class:`pandas.DataFrame`
+            A dataframe containing epoch onset, duration, stage, etc.
+
+        Examples
+        --------
+        >>> from yasa import Hypnogram
+        >>> hyp = Hypnogram(["W", "W", "LIGHT", "LIGHT", "DEEP", "REM", "WAKE"], n_stages=4)
+        >>> hyp.as_annotations()
+               onset  duration  value description
+        epoch
+        0        0.0      30.0      0        WAKE
+        1       30.0      30.0      0        WAKE
+        2       60.0      30.0      2       LIGHT
+        3       90.0      30.0      2       LIGHT
+        4      120.0      30.0      3        DEEP
+        5      150.0      30.0      4         REM
+        6      180.0      30.0      0        WAKE
+        """
+        data = {
+            "onset": self.timedelta.total_seconds(),
+            "duration": 1 / self.sampling_frequency,
+            "value": self.as_int().to_numpy(),
+            "description": self.hypno.to_numpy(),
+            "epoch": np.arange(self.n_epochs),
+        }
+        if self.scorer is not None:
+            data["scorer"] = self.scorer
+        return pd.DataFrame(data).set_index("epoch")

+
+
[docs]    def as_int(self):
+        """Return hypnogram values as integers.
+
+        The default mapping from string to integer is:
+
+        * 2 stages: {"WAKE": 0, "SLEEP": 1, "ART": -1, "UNS": -2}
+        * 3 stages: {"WAKE": 0, "NREM": 2, "REM": 4, "ART": -1, "UNS": -2}
+        * 4 stages: {"WAKE": 0, "LIGHT": 2, "DEEP": 3, "REM": 4, "ART": -1, "UNS": -2}
+        * 5 stages: {"WAKE": 0, "N1": 1, "N2": 2, "N3": 3, "REM": 4, "ART": -1, "UNS": -2}
+
+        Users can define a custom mapping:
+
+        >>> hyp.mapping = {"WAKE": 0, "NREM": 1, "REM": 2}
+
+        Examples
+        --------
+        Convert a 2-stages hypnogram to a pandas.Series of integers
+
+        >>> from yasa import Hypnogram
+        >>> hyp = Hypnogram(["W", "W", "S", "S", "W", "S"], n_stages=2)
+        >>> hyp.as_int()
+        Epoch
+        0    0
+        1    0
+        2    1
+        3    1
+        4    0
+        5    1
+        Name: Stage, dtype: int16
+
+        Same with a 4-stages hypnogram
+
+        >>> from yasa import Hypnogram
+        >>> hyp = Hypnogram(["W", "W", "LIGHT", "LIGHT", "DEEP", "REM", "WAKE"], n_stages=4)
+        >>> hyp.as_int()
+        Epoch
+        0    0
+        1    0
+        2    2
+        3    2
+        4    3
+        5    4
+        6    0
+        Name: Stage, dtype: int16
+        """
+        # Return as int16 (-32768 to 32767) to reduce memory usage
+        return self.hypno.replace(self.mapping).astype(np.int16)

+
+
[docs]    def consolidate_stages(self, new_n_stages):
+        """Reduce the number of stages in a hypnogram to match actigraphy or wearables.
+
+        For example, a standard 5-stage hypnogram (W, N1, N2, N3, REM) could be consolidated
+        to a hypnogram more common with actigraphy (e.g. 2-stages: [Wake, Sleep] or
+        4-stages: [W, Light, Deep, REM]).
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.Hypnogram`
+            Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+            Total Recording Time, i.e. "lights out" to "lights on").
+        new_n_stages : int
+            Desired number of sleep stages. Must be lower than the current number of stages.
+
+            - 5 stages - Wake, N1, N2, N3, REM
+            - 4 stages - Wake, Light, Deep, REM
+            - 3 stages - Wake, NREM, REM
+            - 2 stages - Wake, Sleep
+
+            .. note:: Unscored and Artefact are always allowed.
+
+        Returns
+        -------
+        hyp : :py:class:`yasa.Hypnogram`
+            The consolidated Hypnogram object. This function returns a copy, i.e. the original
+            hypnogram is not modified in place.
+
+        Examples
+        --------
+        >>> from yasa import Hypnogram
+        >>> hyp = Hypnogram(["W", "W", "N1", "N2", "N2", "N2", "N2", "W"], n_stages=5)
+        >>> hyp_2s = hyp.consolidate_stages(2)
+        >>> print(hyp_2s)
+        Epoch
+        0     WAKE
+        1     WAKE
+        2    SLEEP
+        3    SLEEP
+        4    SLEEP
+        5    SLEEP
+        6    SLEEP
+        7     WAKE
+        Name: Stage, dtype: category
+        Categories (4, object): ['WAKE', 'SLEEP', 'ART', 'UNS']
+        """
+        assert self.n_stages in [3, 4, 5], "`self.n_stages` must be 3, 4, or 5"
+        assert new_n_stages in [2, 3, 4], "`new_n_stages` must be 2, 3, or 4"
+        assert new_n_stages < self.n_stages, "`new_n_stages` must be lower than `self.n_stages`"
+
+        # Change sleep codes where applicable.
+        if new_n_stages == 2:
+            # Consolidate all Sleep
+            mapping = {
+                "N1": "S",
+                "N2": "S",
+                "N3": "S",
+                "REM": "S",
+                "LIGHT": "S",
+                "DEEP": "S",
+                "NREM": "S",
+            }
+        elif new_n_stages == 3:
+            # Consolidate N1/N2/N3 or Light/Deep into NREM
+            mapping = {"N1": "NREM", "N2": "NREM", "N3": "NREM", "LIGHT": "NREM", "DEEP": "NREM"}
+        elif new_n_stages == 4:
+            # Consolidate N1/N2 into Light
+            mapping = {"N1": "LIGHT", "N2": "LIGHT", "N3": "DEEP"}
+        new_hyp = self.hypno.replace(mapping).to_numpy()
+
+        return Hypnogram(
+            values=new_hyp,
+            n_stages=new_n_stages,
+            freq=self.freq,
+            start=self.start,
+            scorer=self.scorer,
+        )

+
+
[docs]    def copy(self):
+        """Return a new copy of the current Hypnogram."""
+        return type(self)(
+            values=self.hypno.to_numpy(),
+            n_stages=self.n_stages,
+            freq=self.freq,
+            start=self.start,
+            scorer=self.scorer,
+        )

+
+
[docs]    def find_periods(self, threshold="5min", equal_length=False):
+        """Find sequences of consecutive values exceeding a certain duration in hypnogram.
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.Hypnogram`
+            Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+            Total Recording Time, i.e. "lights out" to "lights on").
+        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 epochs
+
+        Examples
+        --------
+        Let's assume that we have an hypnogram where sleep = 1 and wake = 0, with one value
+        per minute.
+
+        >>> from yasa import Hypnogram
+        >>> val = 11 * ["W"] + 3 * ["S"] + 2 * ["W"] + 9 * ["S"] + ["W", "W"]
+        >>> hyp = Hypnogram(val, n_stages=2, freq="1min")
+        >>> hyp.find_periods(threshold="0min")
+          values  start  length
+        0   WAKE      0      11
+        1  SLEEP     11       3
+        2   WAKE     14       2
+        3  SLEEP     16       9
+        4   WAKE     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
+        WAKE 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:
+
+        >>> hyp.find_periods(threshold="5min")
+          values  start  length
+        0   WAKE      0      11
+        1  SLEEP     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. a 5-stages hypnogram at 30-sec
+        resolution:
+
+        >>> from yasa import simulate_hypnogram
+        >>> hyp = simulate_hypnogram(tib=30, seed=42)
+        >>> hyp.find_periods(threshold="2min")
+          values  start  length
+        0   WAKE      0       5
+        1     N1      5       6
+        2     N2     11      49
+
+        Lastly, using ``equal_length=True`` will further divide the periods into segments of the
+        same duration, i.e. the duration defined in ``threshold``:
+
+        >>> hyp.find_periods(threshold="5min", equal_length=True)
+          values  start  length
+        0     N2     11      10
+        1     N2     21      10
+        2     N2     31      10
+        3     N2     41      10
+
+        Here, the 24.5 minutes of consecutive N2 sleep (= 49 epochs) are divided into 4 periods of
+        exactly 5 minute each. The remaining 4.5 minutes at the end of the hypnogram are removed
+        because it is less than 5 minutes. In other words, the remainder of the division of a given
+        segment by the desired duration is discarded.
+        """
+        return hypno_find_periods(
+            self.hypno, self.sampling_frequency, threshold=threshold, equal_length=equal_length
+        )

+
+
[docs]    def plot_hypnogram(self, **kwargs):
+        """Plot the hypnogram.
+
+        .. seealso:: :py:func:`yasa.plot_hypnogram`
+
+        Parameters
+        ----------
+        **kwargs : dict
+            Optional keyword arguments passed to :py:func:`yasa.plot_hypnogram`.
+
+        Returns
+        -------
+        ax : :py:class:`matplotlib.axes.Axes`
+            Matplotlib Axes
+
+        Examples
+        --------
+        .. plot::
+
+            >>> from yasa import simulate_hypnogram
+            >>> ax = simulate_hypnogram(tib=480, seed=88).plot_hypnogram(highlight="REM")
+        """
+        return plot_hypnogram(self, **kwargs)

+
+
[docs]    def simulate_similar(self, **kwargs):
+        """Simulate a new hypnogram based on properties of the current hypnogram.
+
+        .. seealso:: :py:func:`yasa.simulate_hypnogram`
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.Hypnogram`
+            Hypnogram, assumed to be already cropped to time in bed (TIB).
+        **kwargs : dict
+            Optional keyword arguments passed to :py:func:`yasa.simulate_hypnogram`.
+
+        Returns
+        -------
+        hyp : :py:class:`yasa.Hypnogram`
+            A simulated hypnogram.
+
+        Examples
+        --------
+        >>> import pandas as pd
+        >>> from yasa import Hypnogram
+        >>> hyp = Hypnogram(
+        ...     ["W", "S", "W"], n_stages=2, freq="2min", scorer="Human").upsample("30s")
+        >>> shyp = hyp.simulate_similar(scorer="Simulated", seed=6)
+        >>> df = pd.concat([hyp.hypno, shyp.hypno], axis=1)
+        >>> print(df)
+               Human Simulated
+        Epoch
+        0       WAKE      WAKE
+        1       WAKE      WAKE
+        2       WAKE      WAKE
+        3       WAKE      WAKE
+        4      SLEEP     SLEEP
+        5      SLEEP     SLEEP
+        6      SLEEP     SLEEP
+        7      SLEEP     SLEEP
+        8       WAKE     SLEEP
+        9       WAKE     SLEEP
+        10      WAKE     SLEEP
+        11      WAKE      WAKE
+        """
+        assert "n_stages" not in kwargs and "freq" not in kwargs, (
+            "`n_stages` and `freq` cannot be included as additional `**kwargs` "
+            "because they must match properties of the current Hypnogram."
+        )
+        simulate_hypnogram_kwargs = {
+            "tib": self.duration,
+            "n_stages": self.n_stages,
+            "freq": self.freq,
+            "trans_probas": self.transition_matrix()[1],
+            "start": self.start,
+            "scorer": self.scorer,
+        }
+        simulate_hypnogram_kwargs.update(kwargs)
+        return simulate_hypnogram(**simulate_hypnogram_kwargs)

+
+
[docs]    def sleep_statistics(self):
+        """
+        Compute standard sleep statistics from an hypnogram.
+
+        This function supports a 2, 3, 4 or 5-stages hypnogram.
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.Hypnogram`
+            Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+            Total Recording Time, i.e. "lights out" to "lights on").
+
+        Returns
+        -------
+        stats : dict
+            Summary sleep statistics.
+
+        Notes
+        -----
+        All values except SE, SME, SFI and the percentage of each stage are expressed in minutes.
+        YASA follows the AASM guidelines to calculate these parameters:
+
+        * Time in Bed (TIB): total duration of the hypnogram.
+        * Sleep Period Time (SPT): duration from first to last period of sleep.
+        * Wake After Sleep Onset (WASO): duration of wake periods within SPT.
+        * Total Sleep Time (TST): total sleep duration in SPT.
+        * Sleep Onset Latency (SOL): Latency to first epoch of any sleep.
+        * SOL 5min: Latency to 5 minutes of persistent sleep (any stage).
+        * REM latency: latency to first REM sleep.
+        * Sleep Efficiency (SE): TST / TIB * 100 (%).
+        * Sleep Maintenance Efficiency (SME): TST / SPT * 100 (%).
+        * Sleep Fragmentation Index: number of transitions from sleep to wake / hours of TST
+        * Sleep stages amount and proportion of TST
+
+        .. warning::
+            Artefact and Unscored epochs are excluded from the calculation of the
+            total sleep time (TST). TST is calculated as the sum of all REM and NREM sleep in SPT.
+
+        .. warning::
+            The definition of REM latency in the AASM scoring manual differs from the REM latency
+            reported here. The former uses the time from first epoch of sleep, while YASA uses the
+            time from the beginning of the recording. The AASM definition of the REM latency can be
+            found with `SOL - Lat_REM`.
+
+        References
+        ----------
+        * Iber (2007). The AASM manual for the scoring of sleep and associated events: rules,
+          terminology and technical specifications. American Academy of Sleep Medicine.
+
+        * Silber et al. (2007). `The visual scoring of sleep in adults
+          <https://www.ncbi.nlm.nih.gov/pubmed/17557422>`_. Journal of Clinical
+          Sleep Medicine, 3(2), 121-131.
+
+        Examples
+        --------
+        Sleep statistics for a 2-stage hypnogram with a resolution of 15-seconds
+
+        >>> from yasa import Hypnogram
+        >>> # Generate a fake hypnogram, where "S" = Sleep, "W" = Wake
+        >>> values = 10 * ["W"] + 40 * ["S"] + 5 * ["W"] + 40 * ["S"] + 9 * ["W"]
+        >>> hyp = Hypnogram(values, freq="15s", n_stages=2)
+        >>> hyp.sleep_statistics()
+        {'TIB': 26.0,
+        'SPT': 21.25,
+        'WASO': 1.25,
+        'TST': 20.0,
+        'SE': 76.9231,
+        'SME': 94.1176,
+        'SFI': 1.5,
+        'SOL': 2.5,
+        'SOL_5min': 2.5,
+        'WAKE': 6.0}
+
+        Sleep statistics for a 5-stages hypnogram
+
+        >>> from yasa import simulate_hypnogram
+        >>> # Generate a 8 hr (= 480 minutes) 5-stages hypnogram with a 30-seconds resolution
+        >>> hyp = simulate_hypnogram(tib=480, seed=42)
+        >>> hyp.sleep_statistics()
+        {'TIB': 480.0,
+        'SPT': 477.5,
+        'WASO': 79.5,
+        'TST': 398.0,
+        'SE': 82.9167,
+        'SME': 83.3508,
+        'SFI': 0.7538,
+        'SOL': 2.5,
+        'SOL_5min': 2.5,
+        'Lat_REM': 67.0,
+        'WAKE': 82.0,
+        'N1': 67.0,
+        'N2': 240.5,
+        'N3': 53.0,
+        'REM': 37.5,
+        '%N1': 16.8342,
+        '%N2': 60.4271,
+        '%N3': 13.3166,
+        '%REM': 9.4221}
+        """
+        hypno = self.hypno.to_numpy()
+        assert self.n_epochs > 0, "Hypnogram is empty!"
+        all_sleep = ["SLEEP", "N1", "N2", "N3", "NREM", "REM", "LIGHT", "DEEP"]
+        all_non_sleep = ["WAKE", "ART", "UNS"]
+        stats = {}
+
+        # TIB, first and last sleep
+        stats["TIB"] = self.n_epochs
+        idx_sleep = np.where(~np.isin(hypno, all_non_sleep))[0]
+        if not len(idx_sleep):
+            first_sleep, last_sleep = 0, self.n_epochs
+        else:
+            first_sleep = idx_sleep[0]
+            last_sleep = idx_sleep[-1]
+        # Crop to SPT
+        hypno_s = hypno[first_sleep : (last_sleep + 1)]
+        stats["SPT"] = hypno_s.size if len(idx_sleep) else 0
+        stats["WASO"] = hypno_s[hypno_s == "WAKE"].size if len(idx_sleep) else np.nan
+        # 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[np.isin(hypno_s, all_sleep)].shape[0]
+
+        # Sleep efficiency and fragmentation
+        stats["SE"] = 100 * stats["TST"] / stats["TIB"]
+        if stats["SPT"] == 0:
+            stats["SME"] = np.nan
+            stats["SFI"] = np.nan
+        else:
+            # Sleep maintenance efficiency
+            stats["SME"] = 100 * stats["TST"] / stats["SPT"]
+            # SFI is the ratio of the number of transitions from sleep into Wake to TST (hours)
+            # The original definition included transitions into Wake or N1.
+            counts, _ = self.transition_matrix()
+            n_trans_to_wake = np.sum(
+                counts.loc[
+                    np.intersect1d(counts.index, all_sleep), np.intersect1d(counts.index, ["WAKE"])
+                ].to_numpy()
+            )
+            stats["SFI"] = n_trans_to_wake / (stats["TST"] / (3600 * self.sampling_frequency))
+
+        # Sleep stage latencies -- only relevant if hypno is cropped to TIB
+        stats["SOL"] = first_sleep if stats["TST"] > 0 else np.nan
+        sleep_periods = hypno_find_periods(
+            np.isin(hypno, all_sleep), self.sampling_frequency, threshold="5min"
+        ).query("values == True")
+        if sleep_periods.shape[0]:
+            stats["SOL_5min"] = sleep_periods["start"].iloc[0]
+        else:
+            stats["SOL_5min"] = np.nan
+
+        if "REM" in self.labels:
+            # Question: should we add latencies for other stage too?
+            stats["Lat_REM"] = np.where(hypno == "REM")[0].min() if "REM" in hypno else np.nan
+
+        # Duration of each stage
+        for st in self.labels:
+            if st == "SLEEP":
+                # SLEEP == TST
+                continue
+            stats[st] = hypno[hypno == st].size
+
+        # Remove ART and UNS if they are empty
+        if stats["ART"] == 0:
+            stats.pop("ART")
+        if stats["UNS"] == 0:
+            stats.pop("UNS")
+
+        # Convert to minutes
+        for key, value in stats.items():
+            if key in ["SE", "SME"]:
+                continue
+            stats[key] = value / (60 * self.sampling_frequency)
+
+        # Proportion of each sleep stages
+        for st in all_sleep:
+            if st in stats.keys():
+                if stats["TST"] == 0:
+                    stats[f"%{st}"] = np.nan
+                else:
+                    stats[f"%{st}"] = 100 * stats[st] / stats["TST"]
+
+        # Round to 4 decimals
+        stats = {key: np.round(val, 4) for key, val in stats.items()}
+        return stats

+
+
[docs]    def transition_matrix(self):
+        """Create a state-transition matrix from an hypnogram.
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.Hypnogram`
+            Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+            Total Recording Time, i.e. "lights out" to "lights on"). For best results, the
+            hypnogram should not contain any artefact or unscored epochs.
+
+        Returns
+        -------
+        counts : :py:class:`pandas.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.
+        probs : :py:class:`pandas.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
+            <https://en.wikipedia.org/wiki/Stochastic_matrix>`_, i.e. each row sums to 1.
+
+        Examples
+        --------
+        >>> from yasa import Hypnogram, simulate_hypnogram
+        >>> # Generate a 8 hr (= 480 minutes) 5-stages hypnogram with a 30-seconds resolution
+        >>> hyp = simulate_hypnogram(tib=480, seed=42)
+        >>> counts, probs = hyp.transition_matrix()
+        >>> counts
+        To Stage    WAKE  N1   N2  N3  REM
+        From Stage
+        WAKE         153  11    0   0    0
+        N1             6  99   29   0    0
+        N2             3  16  447  10    5
+        N3             1   3    4  96    1
+        REM            0   5    1   0   69
+
+        >>> probs.round(3)
+        To Stage     WAKE     N1     N2     N3   REM
+        From Stage
+        WAKE        0.933  0.067  0.000  0.000  0.00
+        N1          0.045  0.739  0.216  0.000  0.00
+        N2          0.006  0.033  0.929  0.021  0.01
+        N3          0.010  0.029  0.038  0.914  0.01
+        REM         0.000  0.067  0.013  0.000  0.92
+        """
+        counts, probs = transition_matrix(self.as_int())
+        counts.index = counts.index.map(self.mapping_int)
+        counts.columns = counts.columns.map(self.mapping_int)
+        probs.index = probs.index.map(self.mapping_int)
+        probs.columns = probs.columns.map(self.mapping_int)
+        return counts, probs

+
+
[docs]    def upsample(self, new_freq, **kwargs):
+        """Upsample hypnogram to a higher frequency.
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.Hypnogram`
+            Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+            Total Recording Time, i.e. "lights out" to "lights on"). For best results, the
+            hypnogram should not contain any artefact or unscored epochs.
+        new_freq : str
+            Frequency is defined with a pandas frequency string, e.g. "10s" or "1min".
+
+        Returns
+        -------
+        hyp : :py:class:`yasa.Hypnogram`
+            The upsampled Hypnogram object. This function returns a copy, i.e. the original
+            hypnogram is not modified in place.
+
+        Examples
+        --------
+        Create a 30-sec hypnogram
+
+        >>> from yasa import Hypnogram
+        >>> hyp = Hypnogram(["W", "W", "S", "S", "W"], n_stages=2, start="2022-12-23 23:00")
+        >>> hyp.hypno
+        Time
+        2022-12-23 23:00:00     WAKE
+        2022-12-23 23:00:30     WAKE
+        2022-12-23 23:01:00    SLEEP
+        2022-12-23 23:01:30    SLEEP
+        2022-12-23 23:02:00     WAKE
+        Freq: 30S, Name: Stage, dtype: category
+        Categories (4, object): ['WAKE', 'SLEEP', 'ART', 'UNS']
+
+        Upsample to a 15-seconds resolution
+
+        >>> hyp_up = hyp.upsample("15s")
+        >>> hyp_up.hypno
+        Time
+        2022-12-23 23:00:00     WAKE
+        2022-12-23 23:00:15     WAKE
+        2022-12-23 23:00:30     WAKE
+        2022-12-23 23:00:45     WAKE
+        2022-12-23 23:01:00    SLEEP
+        2022-12-23 23:01:15    SLEEP
+        2022-12-23 23:01:30    SLEEP
+        2022-12-23 23:01:45    SLEEP
+        2022-12-23 23:02:00     WAKE
+        2022-12-23 23:02:15     WAKE
+        Freq: 15S, Name: Stage, dtype: category
+        Categories (4, object): ['WAKE', 'SLEEP', 'ART', 'UNS']
+        """
+        assert pd.Timedelta(new_freq) < pd.Timedelta(self.freq), (
+            f"The upsampling `new_freq` ({new_freq}) must be higher than the current frequency of "
+            f"hypnogram {self.freq}"
+        )
+        if isinstance(self.hypno.index, pd.DatetimeIndex):
+            # Upsampling should extend the last epoch, e.g.
+            # - 30-sec: last epoch at 07:20:30
+            # - 10-sec: last epoch should be 07:20:50 and not 07:20:30 otherwise we're losing 20 sec
+            hyp_extend = self.hypno.copy()
+            hyp_extend = hyp_extend.reindex(
+                hyp_extend.index.union([hyp_extend.index[-1] + pd.Timedelta(self.freq)])
+            ).ffill()
+            new_hyp = hyp_extend.resample(new_freq, origin="start").ffill().iloc[:-1]
+        else:
+            hyp_extend = self.hypno.copy()
+            hyp_extend.index = self.timedelta
+            hyp_extend = hyp_extend.reindex(
+                hyp_extend.index.union([hyp_extend.index[-1] + pd.Timedelta(self.freq)])
+            ).ffill()
+            new_hyp = (
+                hyp_extend.resample(new_freq, origin="start")
+                .ffill()
+                .reset_index(drop=True)
+                .iloc[:-1]
+            )
+            new_hyp.index.name = "Epoch"
+        return Hypnogram(
+            values=new_hyp,
+            n_stages=self.n_stages,
+            freq=new_freq,
+            start=self.start,
+            scorer=self.scorer,
+        )

+
+
[docs]    def upsample_to_data(self, data, sf=None, verbose=True):
+        """
+        Upsample an hypnogram to a given sampling frequency and fit the resulting hypnogram to
+        corresponding EEG data, such that the hypnogram and EEG data have the exact same number of
+        samples.
+
+        Parameters
+        ----------
+        self : :py:class:`yasa.Hypnogram`
+            Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+            Total Recording Time, i.e. "lights out" to "lights on"). For best results, the
+            hypnogram should not contain any artefact or unscored epochs.
+        data : array_like or :py:class:`mne.io.BaseRaw`
+            1D or 2D EEG data. Can also be a :py:class:`mne.io.BaseRaw`, in which case ``data``
+            and ``sf_data`` will be automatically extracted.
+        sf_data : float
+            The sampling frequency of ``data``, in Hz (e.g. 100 Hz, 256 Hz, ...).
+            Can be omitted if ``data`` is a :py:class:`mne.io.BaseRaw`.
+        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``).
+
+        Returns
+        -------
+        hypno : array_like
+            The hypnogram values, upsampled to ``sf_data`` and cropped/padded to
+            ``max(data.shape)``. For compatibility with most YASA functions, the returned hypnogram
+            is an array with integer values, and not a :py:class:`yasa.Hypnogram` object.
+
+        Warns
+        -----
+        UserWarning
+            If the upsampled ``hypno`` is shorter / longer than ``max(data.shape)``
+            and therefore needs to be padded/cropped respectively. This output can be disabled by
+            passing ``verbose='ERROR'``.
+        """
+        hypno_up = hypno_upsample_to_data(
+            self.as_int(), self.sampling_frequency, data=data, sf_data=sf, verbose=verbose
+        )
+        return hypno_up

+
+
 #############################################################################
 # STR <--> INT CONVERSION
 #############################################################################
@@ -184,10 +1235,15 @@ 
Source code for yasa.hypno
         around :py:meth:`pandas.Series.map`.
 
     Returns
-    --------
+    -------
     hypno : array_like
         The corresponding integer hypnogram.
     """
+    # warnings.warn(
+    #     "The `yasa.hypno_str_to_int` function is deprecated and will be removed in v0.8. "
+    #     "Please use the `yasa.Hypnogram.as_int` method instead.",
+    #     FutureWarning,
+    # )
     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."
@@ -212,10 +1268,15 @@ 
Source code for yasa.hypno
         :py:meth:`pandas.Series.map`.
 
     Returns
-    --------
+    -------
     hypno : array_like
         The corresponding integer hypnogram.
     """
+    # warnings.warn(
+    #     "The `yasa.hypno_int_to_str` function is deprecated and will be removed in v0.8. "
+    #     "Please use the `yasa.Hypnogram` class to create an hypnogram instead.",
+    #     FutureWarning,
+    # )
     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

@@ -248,6 +1309,11 @@ 
Source code for yasa.hypno
     hypno : array_like
         The hypnogram, upsampled to ``sf_data``.
     """
+    # warnings.warn(
+    #     "The `yasa.hypno_upsample_to_sf` function is deprecated and will be removed in v0.8. "
+    #     "Please use the `yasa.Hypnogram.upsample` method instead.",
+    #     FutureWarning,
+    # )
     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."
@@ -359,6 +1425,8 @@ 
Source code for yasa.hypno
         and therefore needs to be padded/cropped respectively. This output can be disabled by
         passing ``verbose='ERROR'``.
     """
+    # NOTE: FutureWarning not added here otherwise it would also be shown when calling
+    # yasa.Hypnogram.upsample_to_data.
     set_log_level(verbose)
     if isinstance(data, mne.io.BaseRaw):
         sf_data = data.info["sfreq"]
@@ -417,6 +1485,7 @@ 
Source code for yasa.hypno
     hypno = []
     for s in root[4]:
         hypno.append(s.text)
+    # TODO: This should return a yasa.Hypnogram object
     hypno = np.array(hypno).astype(int)
     if replace:
         # Stage 4 --> 3 and REM --> 4
@@ -518,6 +1587,8 @@ 
Source code for yasa.hypno
     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.
     """
+    # NOTE: FutureWarning not added here otherwise it would also be shown when calling
+    # yasa.Hypnogram.find_periods
     # 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()
@@ -574,65 +1645,18 @@ 
Source code for yasa.hypno
     return new_seq

 
 
-#############################################################################
-# STAGE CONVERSION
-#############################################################################
-
-
-def hypno_consolidate_stages(hypno, n_stages_in, n_stages_out):
-    """Reduce the number of stages in a hypnogram to match actigraphy or wearables.
-
-    For example, a standard 5-stage hypnogram (W, N1, N2, N3, REM) could be consolidated
-    to a hypnogram more common with actigraphy (W, Light, Deep, REM).
-
-    Parameters
-    ----------
-    hypno : array_like
-        The sleep staging (hypnogram) 1D array.
-    n_stages_in : int
-        Number of possible stages of ``hypno``, where:
-
-        - 5 stages - 0=Wake, 1=N1, 2=N2, 3=N3, 4=REM
-        - 4 stages - 0=Wake, 2=Light, 3=Deep, 4=REM
-        - 3 stages - 0=Wake, 2=NREM, 4=REM
-        - 2 stages - 0=Wake, 1=Sleep
-
-        .. note:: The default YASA values for Unscored (-2) and Artefact (-1) are always allowed.
-    n_stages_out : int
-        Similar to ``n_stages_in`` but for output. Must be higher than ``n_stages_out``.
-
-    Returns
-    -------
-    hypno : array_like
-        The hypnogram, with stages converted to ``n_stages_out`` staging scheme.
-    """
-    assert isinstance(hypno, (list, np.ndarray, pd.Series)), "hypno must be array_like"
-    hypno = np.asarray(hypno, dtype=int).copy()
-    assert n_stages_in in [3, 4, 5], "n_stages_in must be 3, 4, or 5"
-    assert n_stages_out in [2, 3, 4], "n_stages_in must be 2, 3, or 4"
-    assert n_stages_out < n_stages_in, "n_stages_out must be lower than n_stages_in"
-
-    # Change sleep codes where applicable.
-    if n_stages_out == 2:
-        # Consolidate all Sleep
-        mapping = {0: 0, 1: 1, 2: 1, 3: 1, 4: 1, -1: -1, -2: -2}
-    elif n_stages_out == 3:
-        # Consolidate N1/N2/N3 or Light/Deep into NREM
-        mapping = {0: 0, 1: 2, 2: 2, 3: 2, 4: 4, -1: -1, -2: -2}
-    elif n_stages_out == 4:
-        # Consolidate N1/N2 into Light
-        mapping = {0: 0, 1: 2, 2: 2, 3: 3, 4: 4, -1: -1, -2: -2}
-    hypno = pd.Series(hypno).map(mapping).to_numpy()
-
-    return hypno
-
-
 #############################################################################
 # SIMULATION
 #############################################################################
 
 
-
[docs]def simulate_hypno(tib=90, sf=1 / 30, n_stages=5, trans_probas=None, init_probas=None, seed=None):
+
[docs]def simulate_hypnogram(
+    tib=480,
+    trans_probas=None,
+    init_probas=None,
+    seed=None,
+    **kwargs,
+):
     """Simulate a hypnogram based on transition probabilities.
 
     Current implentation is a naive Markov model. The initial stage of a hypnogram
@@ -642,23 +1666,18 @@ 
Source code for yasa.hypno
     .. important:: The Markov simulation model is not meant to accurately portray sleep
         macroarchitecture and should only be used for testing or other unique purposes.
 
+    .. seealso:: :py:meth:`yasa.Hypnogram.simulate_similar`
+
     .. versionadded:: 0.6.3
 
     Parameters
     ----------
-    tib : int
+    tib : int, float
         Total duration of the hypnogram (i.e., time in bed), expressed in minutes.
-        Returned hypnogram will be slightly shorter if ``tib`` in seconds is not
-        evenly divisible by ``sf``.
+        Returned hypnogram will be slightly shorter if ``tib`` is not evenly divisible by ``freq``.
+        Default is 480 minutes (= 8 hours).
 
         .. seealso:: :py:func:`yasa.sleep_statistics`
-    sf : float
-        Sampling frequency.
-    n_stages : int
-        Staging scheme of returned hypnogram. Input should follow 5-stage scheme but can
-        be converted to lower scheme if desired.
-
-        .. seealso:: :py:func:`yasa.hypno_consolidate_stages`
     trans_probas : :py:class:`pandas.DataFrame` or None
         Transition probability matrix where each cell is a transition probability
         between sleep stages of consecutive *epochs*.
@@ -666,10 +1685,10 @@ 
Source code for yasa.hypno
         ``trans_probas`` is a `right stochastic matrix
         <https://en.wikipedia.org/wiki/Stochastic_matrix>`_, i.e. each row sums to 1.
 
-        If None (default), use transition probabilites from Metzner et al., 2021 [Metzner2021]_.
+        If None (default), transition probabilities come from Metzner et al., 2021 [Metzner2021]_.
         If :py:class:`pandas.DataFrame`, must have "from"-stages as indices and
-        "to"-stages as columns. Indices and columns must follow YASA integer
-        hypnogram convention (W = 0, N = 1, ...). Unscored/Artefact stages are not allowed.
+        "to"-stages as columns. Indices and columns must follow YASA string
+        hypnogram convention (e.g., WAKE, N1). Unscored/Artefact stages are not allowed.
 
         .. note:: Transition probability matrices should indicate the transition
             probability between *epochs* (i.e., probability of the next epoch) and
@@ -678,24 +1697,28 @@ 
Source code for yasa.hypno
         .. seealso:: Return value from :py:func:`yasa.transition_matrix`
     init_probas : :py:class:`pandas.Series` or None
         Probabilites of each stage to initialize random walk.
-        If None (default), initialize with "From"-Wake row of ``trans_probas``.
-        If :py:class:`pandas.Series`, indices must be stages following YASA integer
-        hypnogram convention (see ``trans_probas``).
+        If None (default), initialize with "from"-WAKE row of ``trans_probas``.
+        If :py:class:`pandas.Series`, indices must be stages following YASA string
+        hypnogram convention and identical to those of ``trans_probas``.
     seed : int or None
         Random seed for generating Markov sequence.
         If an integer number is provided, the random hypnogram will be predictable.
         This argument is required if reproducible results are desired.
+    **kwargs : dict
+        Other arguments that are passed to :py:class:`yasa.Hypnogram`.
+
+        .. note:: `n_stages` and `freq` must be consistent with a user-specificied `trans_probas`.
 
     Returns
     -------
-    hypno : np.ndarray
+    hyp : :py:class:`yasa.Hypnogram`
         Hypnogram containing simulated sleep stages.
 
     Notes
     -----
-    Default transition probabilities can be found in the ``traMat_Epoch.npy`` file of
-    Supplementary Information for Metzner et al., 2021 [Metzner2021]_ (rounded values
-    are viewable in Figure 5b). Please cite this work if these probabilites are used
+    Default transition probabilities are based on 30-second epochs and can be found in the
+    ``traMat_Epoch.npy`` file of Supplementary Information for Metzner et al., 2021 [Metzner2021]_
+    (rounded values are viewable in Figure 5b). Please cite this work if these probabilites are used
     for publication.
 
     References
@@ -707,46 +1730,102 @@ 
Source code for yasa.hypno
 
     Examples
     --------
-    >>> from yasa import simulate_hypno
-    >>> hypno = simulate_hypno(tib=10, seed=0)
-    >>> print(hypno)
-    [0 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2]
-
-    >>> hypno = simulate_hypno(tib=10, n_stages=2, seed=0)
-    >>> print(hypno)
-    [0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
+    >>> from yasa import simulate_hypnogram
+    >>> hyp = simulate_hypnogram(tib=5, seed=1)
+    >>> hyp
+    <Hypnogram | 10 epochs x 30s (5.00 minutes), 5 stages>
+     - Use `.hypno` to get the string values as a pandas.Series
+     - Use `.as_int()` to get the integer values as a pandas.Series
+     - Use `.plot_hypnogram()` to plot the hypnogram
+    See the online documentation for more details.
+
+    >>> hyp.hypno
+    Epoch
+    0    WAKE
+    1      N1
+    2      N1
+    3      N2
+    4      N2
+    5      N2
+    6      N2
+    7      N2
+    8      N2
+    9      N2
+    Name: Stage, dtype: object
+
+    >>> hyp = simulate_hypnogram(tib=5, n_stages=2, seed=1)
+    >>> hyp.hypno
+    Epoch
+    0     WAKE
+    1    SLEEP
+    2    SLEEP
+    3    SLEEP
+    4    SLEEP
+    5    SLEEP
+    6    SLEEP
+    7    SLEEP
+    8    SLEEP
+    9    SLEEP
+    Name: Stage, dtype: object
+
+    Add some Unscored epochs.
+
+    >>> hyp = simulate_hypnogram(tib=5, n_stages=2, seed=1)
+    >>> hyp.hypno.iloc[-2:] = "UNS"
+    >>> hyp.hypno
+    Epoch
+    0     WAKE
+    1    SLEEP
+    2    SLEEP
+    3    SLEEP
+    4    SLEEP
+    5    SLEEP
+    6    SLEEP
+    7    SLEEP
+    8      UNS
+    9      UNS
+    Name: Stage, dtype: object
 
     Base the data off a real subject's transition matrix.
 
     .. plot::
 
+        >>> import numpy as np
         >>> import matplotlib.pyplot as plt
-        >>> import yasa
+        >>> from yasa import Hypnogram, hypno_int_to_str
         >>> url = (
         >>>     "https://github.com/raphaelvallat/yasa/raw/master/"
         >>>     "notebooks/data_full_6hrs_100Hz_hypno_30s.txt"
         >>> )
-        >>> hypno = np.loadtxt(url)
-        >>> _, probas = yasa.transition_matrix(hypno)
-        >>> hypno_sim = yasa.simulate_hypno(tib=360, trans_probas=probas, seed=9)
-        >>> fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(9, 6))
-        >>> yasa.plot_hypnogram(hypno, ax=ax1)
-        >>> yasa.plot_hypnogram(hypno_sim, ax=ax2)
-        >>> ax1.set_title("True hypnogram")
-        >>> ax2.set_title("Simulated hypnogram")
+        >>> values_str = hypno_int_to_str(np.loadtxt(url))
+        >>> real_hyp = Hypnogram(values_str)
+        >>> fake_hyp = real_hyp.simulate_similar(seed=2)
+        >>> fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(7, 5))
+        >>> real_hyp.plot_hypnogram(ax=ax1).set_title("Real hypnogram")
+        >>> fake_hyp.plot_hypnogram(ax=ax2).set_title("Fake hypnogram")
         >>> plt.tight_layout()
     """
+    # Extract yasa.Hypnogram defaults, which will be assumed later but need throughout
+    if "n_stages" not in kwargs:
+        kwargs["n_stages"] = 5
+    if "freq" not in kwargs:
+        kwargs["freq"] = "30s"
     # Validate input
-    assert isinstance(tib, (int, float)), "tib must be a number"
-    assert isinstance(sf, (int, float)), "sf must be a number"
-    assert isinstance(n_stages, int), "n_stages must be an integer"
-    assert 2 <= n_stages <= 5, "n_stages must be 2, 3, 4, or 5"
-    if seed is not None:
-        assert isinstance(seed, int) and seed >= 0, "seed must be an integer >= 0"
+    assert isinstance(tib, (int, float)) and tib > 0, "`tib` must be a number > 0"
     if trans_probas is not None:
-        assert isinstance(trans_probas, pd.DataFrame), "trans_probas must be a pandas DataFrame"
+        assert isinstance(trans_probas, pd.DataFrame), "`trans_probas` must be a pandas DataFrame"
+        assert np.all(
+            np.less_equal(trans_probas.shape, kwargs["n_stages"])
+        ), "user-specified `trans_probas` must not include more stages than `n_stages`"
     if init_probas is not None:
-        assert isinstance(init_probas, pd.Series), "init_probas must be a pandas Series"
+        assert isinstance(init_probas, pd.Series), "`init_probas` must be a pandas Series"
+    if seed is not None:
+        assert isinstance(seed, int) and seed >= 0, "`seed` must be an integer >= 0"
+    if trans_probas is None:
+        # Check this here, rather than letting hyp.upsample catch it, to be clear about reason
+        assert pd.Timedelta(kwargs["freq"]) <= pd.Timedelta(
+            "30s"
+        ), "`freq` must be <= 30s when using default `trans_probas`"
 
     # Initialize random number generator
     rng = np.random.default_rng(seed)
@@ -764,51 +1843,72 @@ 
Source code for yasa.hypno
         return np.asarray(states)
 
     if trans_probas is None:
-        # Generate transition probability DataFrame (here, ordered W R 1 2 3)
+        # Generate transition probability DataFrame
         trans_freqs = np.array(
             [
-                [11737, 2, 571, 84, 2],
-                [57, 10071, 189, 84, 2],
-                [281, 59, 6697, 1661, 11],
-                [253, 272, 1070, 26259, 505],
-                [49, 12, 176, 279, 9630],
+                [11737, 571, 84, 2, 2],
+                [281, 6697, 1661, 11, 59],
+                [253, 1070, 26259, 505, 272],
+                [49, 176, 279, 9630, 12],
+                [57, 189, 84, 2, 10071],
             ]
         )
         trans_probas = trans_freqs / trans_freqs.sum(axis=1, keepdims=True)
         trans_probas = pd.DataFrame(
             trans_probas,
-            index=[0, 4, 1, 2, 3],
-            columns=[0, 4, 1, 2, 3],
+            index=["WAKE", "N1", "N2", "N3", "REM"],
+            columns=["WAKE", "N1", "N2", "N3", "REM"],
         )
+        trans_probas.attrs = {"is_default": True}
+    else:
+        trans_probas.attrs = {"is_default": False}
 
     if init_probas is None:
         # Extract Wake row of initial probabilities as a Series
-        init_probas = trans_probas.loc[0, :]
+        for w in ["w", "W", "wake", "WAKE"]:
+            if w in trans_probas.index:
+                init_probas = trans_probas.loc[w, :].copy()
+        assert init_probas is not None, "`trans_probas` must include 'WAKE' in the index"
 
-    # Ensure trans_probas DataFrame and init_probas Series are in row/column order W N1 N2 N3 R
-    trans_probas = trans_probas.reindex([0, 1, 2, 3, 4], axis=0)
-    trans_probas = trans_probas.reindex([0, 1, 2, 3, 4], axis=1)
-    init_probas = init_probas.reindex([0, 1, 2, 3, 4])
-    assert trans_probas.notna().values.all(), "trans_proba indices must be YASA integer codes"
-    assert init_probas.notna().all(), "init_probas index must be YASA integer codes"
+    stage_order = init_probas.index.tolist()
+    assert (
+        stage_order == trans_probas.index.tolist() == trans_probas.columns.tolist()
+    ), "`init_probas` and `trans_probas` must have all matching indices"
 
     # Extract probabilities as arrays
     trans_arr = trans_probas.to_numpy()
     init_arr = init_probas.to_numpy()
 
     # Make sure all rows sum to 1
-    assert np.allclose(trans_arr.sum(axis=1), 1)
-    assert np.isclose(init_arr.sum(), 1)
-
-    # Find number of *complete* epochs within TIB timeframe
-    n_epochs = np.floor(tib * 60 * sf).astype(int)
-    # Generate hypnogram
-    hypno = _markov_sequence(init_arr, trans_arr, n_epochs)
+    assert np.allclose(trans_arr.sum(axis=1), 1), "All rows of `trans_probas` must sum to 1"
+    assert np.isclose(init_arr.sum(), 1), "`init_probas` must sum to 1"
 
-    if n_stages < 5:
-        hypno = hypno_consolidate_stages(hypno, n_stages_in=5, n_stages_out=n_stages)
+    # Find number of *complete* epochs within TIB duration to simulate
+    if trans_probas.attrs.get("is_default"):
+        freq_sec = 30
+    else:
+        freq_sec = pd.Timedelta(kwargs["freq"]).total_seconds()
+    n_epochs = np.floor(tib * 60 / freq_sec).astype(int)
+
+    # Generate hypnogram integer values
+    values_int = _markov_sequence(init_arr, trans_arr, n_epochs)
+    # Convert to hypnogram string values (based on indices)
+    values_str = [stage_order[x] for x in values_int]
+
+    # Create YASA hypnogram instance
+    if trans_probas.attrs.get("is_default"):
+        # If using default trans_probas, hyp *must* be initialized with 5 stages and 30s epochs
+        n_stages = kwargs.pop("n_stages")
+        freq = kwargs.pop("freq")
+        hyp = Hypnogram(values_str, n_stages=5, freq="30s", **kwargs)
+        if pd.Timedelta(freq) < pd.Timedelta("30s"):
+            hyp = hyp.upsample(freq)
+        if n_stages != 5:
+            hyp = hyp.consolidate_stages(n_stages)
+    else:
+        hyp = Hypnogram(values_str, **kwargs)
 
-    return hypno

+    return hyp

 

 
     

@@ -825,8 +1925,8 @@ 
Source code for yasa.hypno
       
     

     

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

-      Created using Sphinx 3.0.0.

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

+      Created using Sphinx 4.4.0.

     

   

 
diff --git a/docs/build/html/_modules/yasa/others.html b/docs/build/html/_modules/yasa/others.html
index 9856e83..5f5b492 100644
--- a/docs/build/html/_modules/yasa/others.html
+++ b/docs/build/html/_modules/yasa/others.html
@@ -1,24 +1,23 @@
 
 
-
+
   
     
-    yasa.others — yasa 0.6.3 documentation
-    
-    
+    
+    yasa.others — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +25,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +43,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       

 
         

@@ -592,8 +591,8 @@ 
Source code for yasa.others
       
     

     

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

-      Created using Sphinx 3.0.0.

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

+      Created using Sphinx 4.4.0.

     

   

 
diff --git a/docs/build/html/_modules/yasa/plotting.html b/docs/build/html/_modules/yasa/plotting.html
index 962b8b8..afb00dd 100644
--- a/docs/build/html/_modules/yasa/plotting.html
+++ b/docs/build/html/_modules/yasa/plotting.html
@@ -1,24 +1,23 @@
 
 
-
+
   
     
-    yasa.plotting — yasa 0.6.3 documentation
-    
-    
+    
+    yasa.plotting — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +25,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +43,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       

 
         

@@ -102,13 +101,14 @@ 
Source code for yasa.plotting
 import pandas as pd
 import seaborn as sns
 import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
 from lspopt import spectrogram_lspopt
 from matplotlib.colors import Normalize, ListedColormap
 
 __all__ = ["plot_hypnogram", "plot_spectrogram", "topoplot"]
 
 
-
[docs]def plot_hypnogram(hypno, sf_hypno=1 / 30, lw=1.5, fill_color=None, ax=None):
+
[docs]def plot_hypnogram(hyp, lw=1.5, highlight="REM", fill_color=None, ax=None):
     """
     Plot a hypnogram.
 
@@ -116,28 +116,14 @@ 
Source code for yasa.plotting
 
     Parameters
     ----------
-    hypno : array_like
-        Sleep stage (hypnogram).
-
-        .. 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
-    sf_hypno : float
-        The current sampling frequency of the hypnogram, in Hz, e.g.
-
-        * 1/30 = 1 value per each 30 seconds of EEG data,
-        * 1 = 1 value per second of EEG data
+    hyp : :py:class:`yasa.Hypnogram`
+        A YASA hypnogram instance.
     lw : float
         Linewidth.
-    fill_color : str
-        Color to fill space above hypnogram line, optional.
+    highlight : str or None
+        Optional stage to highlight with alternate color.
+    fill_color : str or None
+        Optional color to fill space above hypnogram line.
     ax : :py:class:`matplotlib.axes.Axes`
         Axis on which to draw the plot, optional.
 
@@ -150,79 +136,103 @@ 
Source code for yasa.plotting
     --------
     .. plot::
 
-        >>> import yasa
-        >>> import numpy as np
+        >>> from yasa import simulate_hypnogram
+        >>> import matplotlib.pyplot as plt
+        >>> hyp = simulate_hypnogram(tib=300, seed=11)
+        >>> ax = hyp.plot_hypnogram()
+        >>> plt.tight_layout()
+
+    .. plot::
+
+        >>> from yasa import Hypnogram
+        >>> values = 4 * ["W", "N1", "N2", "N3", "REM"] + ["ART", "N2", "REM", "W", "UNS"]
+        >>> hyp = Hypnogram(values, freq="24min").upsample("30s")
+        >>> ax = hyp.plot_hypnogram(lw=2, fill_color="thistle")
+        >>> plt.tight_layout()
+
+    .. plot::
+
+        >>> from yasa import simulate_hypnogram
         >>> import matplotlib.pyplot as plt
-        >>> hypno = np.loadtxt("https://github.com/raphaelvallat/yasa/raw/master/notebooks/data_full_6hrs_100Hz_hypno_30s.txt")
-        >>> fig, ax = plt.subplots(1, 1, figsize=(7, 3), constrained_layout=True)
-        >>> ax = yasa.plot_hypnogram(hypno, fill_color="gainsboro", ax=ax)
+        >>> fig, axes = plt.subplots(nrows=2, figsize=(6, 4), constrained_layout=True)
+        >>> hyp_a = simulate_hypnogram(n_stages=3, seed=99)
+        >>> hyp_b = simulate_hypnogram(n_stages=3, seed=99, start="2022-01-31 23:30:00")
+        >>> hyp_a.plot_hypnogram(lw=1, fill_color="whitesmoke", highlight=None, ax=axes[0])
+        >>> hyp_b.plot_hypnogram(lw=1, fill_color="whitesmoke", highlight=None, ax=axes[1])
     """
+    from yasa.hypno import Hypnogram  # Avoiding circular import
+
+    assert isinstance(hyp, Hypnogram), "`hypno` must be YASA Hypnogram."
+
+    # Work with a copy of the Hypnogram to not alter the original
+    hyp = hyp.copy()
+
     # Increase font size while preserving original
     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."
-    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."
-
-    bins = np.arange(hypno.size + 1) / (sf_hypno * 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
-
-    # Reduce data and bin edges to only moments of change in the hypnogram
-    # (to avoid drawing thousands of tiny individual lines when sf is high)
-    change_points = np.nonzero(np.ediff1d(hypno, to_end=1))
-    hypno = hypno[change_points]
-    bins = np.append(0, bins[change_points])
-
-    # Make masks for REM and Artefact/Unscored
-    hypno_rem = np.ma.masked_not_equal(hypno, 1)
-    hypno_art_uns = np.ma.masked_greater(hypno, -1)
-    hypno_sleep = np.ma.masked_less(hypno, 0)
+    ## Remap stages to be in desired y-axis order ##
+    # Start with default of all allowed labels
+    stage_order = hyp.labels.copy()
+    stages_present = hyp.hypno.unique()
+    # Remove Art/Uns from stage order, and place back individually at front to be higher on plot
+    art_str = stage_order.pop(stage_order.index("ART"))
+    uns_str = stage_order.pop(stage_order.index("UNS"))
+    if "ART" in stages_present:
+        stage_order.insert(0, art_str)
+    if "UNS" in stages_present:
+        stage_order.insert(0, uns_str)
+    # Put REM after WAKE if all 5 standard stages are allowed
+    if hyp.n_stages == 5:
+        stage_order.insert(stage_order.index("WAKE") + 1, stage_order.pop(stage_order.index("REM")))
+    # Reset the Hypnogram mapping so any future returns have this order
+    hyp.mapping = {stage: i for i, stage in enumerate(stage_order)}
+
+    ## Extract values to plot ##
+    hypno = hyp.as_int()
+    # Reduce to breakpoints (where stages change) to avoid drawing individual lines for every epoch
+    hypno = hypno[hypno.shift().ne(hypno)]
+    # Extract x-values (bins) and y-values to plot
+    yvalues = hypno.to_numpy()
+    if hyp.start is not None:
+        final_bin_edge = pd.Timestamp(hyp.start) + pd.Timedelta(hyp.duration, unit="min")
+        bins = np.append(hypno.index.to_list(), final_bin_edge)
+        bins = [mdates.date2num(b) for b in bins]
+        xlabel = "Time"
+    else:
+        final_bin_edge = hyp.duration * 60
+        bins = np.append(hyp.timedelta[hypno.index].total_seconds(), final_bin_edge)
+        bins /= 60 if hyp.duration <= 90 else 3600
+        xlabel = "Time [mins]" if hyp.duration <= 90 else "Time [hrs]"
 
-    # Start the plot
+    # Make mask to draw the highlighted stage
+    yvals_highlight = np.ma.masked_not_equal(yvalues, hyp.mapping.get(highlight))
+
+    # Open the figure
     if ax is None:
         ax = plt.gca()
 
     # Draw background filling
     if fill_color is not None:
-        ax.stairs(-1 * hypno.clip(0), bins, fill=True, color=fill_color, lw=0)
-    # Draw main hypnogram line
-    ax.stairs(-1 * hypno, bins, baseline=None, color="k", lw=lw)
-    # Draw REM and Artefact/Unscored highlighting
-    if 1 in hypno:
-        ax.hlines(-1 * hypno_rem, xmin=bins[:-1], xmax=bins[1:], color="red", lw=lw)
-    if -2 in hypno or -1 in hypno:
-        ax.hlines(-1 * hypno_art_uns, xmin=bins[:-1], xmax=bins[1:], color="grey", lw=lw)
-    # Determine y-axis labels and limits
-    if -2 in hypno and -1 in hypno:
-        # Both Unscored and Artefacts are present
-        ax.set_yticks([2, 1, 0, -1, -2, -3, -4])
-        ax.set_yticklabels(["Uns", "Art", "W", "R", "N1", "N2", "N3"])
-        ax.set_ylim(-4.5, 2.5)
-    elif -2 in hypno and -1 not in hypno:
-        # Only Unscored are present
-        ax.set_yticks([2, 0, -1, -2, -3, -4])
-        ax.set_yticklabels(["Uns", "W", "R", "N1", "N2", "N3"])
-        ax.set_ylim(-4.5, 2.5)
-    elif -2 not in hypno and -1 in hypno:
-        # Only Artefacts are present
-        ax.set_yticks([1, 0, -1, -2, -3, -4])
-        ax.set_yticklabels(["Art", "W", "R", "N1", "N2", "N3"])
-        ax.set_ylim(-4.5, 1.5)
-    else:
-        # No artefacts or Unscored
-        ax.set_yticks([0, -1, -2, -3, -4])
-        ax.set_yticklabels(["W", "R", "N1", "N2", "N3"])
-        ax.set_ylim(-4.5, 0.5)
-    ax.set_xlim(0, bins.max())
+        bline = hyp.mapping["WAKE"]  # len(stage_order) - 1 to fill from bottom
+        ax.stairs(yvalues.clip(bline), bins, baseline=bline, color=fill_color, fill=True, lw=0)
+    # Draw main hypnogram line, highlighted stage line, and Artefact/Unscored line
+    ax.stairs(yvalues, bins, baseline=None, color="black", lw=lw)
+    if not yvals_highlight.mask.all():
+        ax.hlines(yvals_highlight, xmin=bins[:-1], xmax=bins[1:], color="red", lw=lw)
+
+    # Aesthetics
+    ax.use_sticky_edges = False
+    ax.margins(x=0, y=1 / len(stage_order) / 2)  # 1/n_epochs/2 gives half-unit margins
+    ax.set_yticks(range(len(stage_order)))
+    ax.set_yticklabels(stage_order)
     ax.set_ylabel("Stage")
-    ax.set_xlabel("Time [hrs]")
-    ax.spines["right"].set_visible(False)
-    ax.spines["top"].set_visible(False)
+    ax.set_xlabel(xlabel)
+    ax.invert_yaxis()
+    ax.spines[["right", "top"]].set_visible(False)
+    if hyp.start is not None:
+        ax.xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
+        ax.xaxis.set_major_locator(mdates.AutoDateLocator())
     # Revert font-size
     plt.rcParams.update({"font.size": old_fontsize})
     return ax

@@ -291,7 +301,7 @@ 
Source code for yasa.plotting
     vmax : int or float
         The upper range of color scale. Overwrites ``trimperc``
     **kwargs : dict
-        Other arguments that are passed to :py:func:`yasa.plot_hypnogram`.
+        Other arguments that are passed to :py:meth:`yasa.Hypnogram.plot_hypnogram`.
 
     Returns
     -------
@@ -333,32 +343,33 @@ 
Source code for yasa.plotting
         >>> hypno = yasa.hypno_upsample_to_data(hypno, 1/30, data, sf)
         >>> fig = yasa.plot_spectrogram(data, sf, hypno, cmap='Spectral_r')
     """
+    from yasa.hypno import Hypnogram, hypno_int_to_str  # Avoiding circular imports
+
     # Increase font size while preserving original
     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(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."
+    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"
+        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"
+        assert isinstance(vmin, (int, float)), "`vmin` must be int or float if `vmax` is provided."
     if hypno is not None:
-        # Validate and handle hypnogram-related inputs
-        assert hypno.size == data.size, "Hypno must have the same sf as data."
+        assert hypno.size == data.size, "`hypno` must have the same number of samples as `data`."
 
     # 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
 
@@ -390,10 +401,14 @@ 
Source code for yasa.plotting
     ax1.set_xlabel("Time [hrs]")
 
     if hypno is not None:
+        # Convert sampling frequency to pandas timefrequency string (e.g., "30s")
+        freq_str = pd.tseries.frequencies.to_offset(pd.Timedelta(1 / sf, "S")).freqstr
+        # Create Hypnogram instance for plotting
+        hyp = Hypnogram(hypno_int_to_str(hypno), freq=freq_str)
         hypnoplot_kwargs = dict(lw=1.5, fill_color=None)
         hypnoplot_kwargs.update(kwargs)
         # Draw hypnogram
-        ax0 = plot_hypnogram(hypno, sf_hypno=sf, ax=ax0, **hypnoplot_kwargs)
+        ax0 = hyp.plot_hypnogram(ax=ax0, **hypnoplot_kwargs)
         ax0.xaxis.set_visible(False)
     else:
         # Add colorbar
@@ -502,13 +517,13 @@ 
Source code for yasa.plotting
     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")
 
@@ -546,8 +561,6 @@ 
Source code for yasa.plotting
         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)
 
@@ -558,11 +571,13 @@ 
Source code for yasa.plotting
     # Start the plot
     with sns.axes_style("white"):
         fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
+
+        if "show_names" in kwargs:
+            kwargs.pop("show_names")
         im, _ = mne.viz.plot_topomap(
             data=data.iloc[:, 0][chan],
             pos=Info,
-            vmin=vmin,
-            vmax=vmax,
+            vlim=(vmin, vmax),
             mask=data.iloc[:, 1][chan],
             cmap=cmap,
             show=False,
@@ -600,8 +615,8 @@ 
Source code for yasa.plotting
       
     

     

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

-      Created using Sphinx 3.0.0.

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

+      Created using Sphinx 4.4.0.

     

   

 
diff --git a/docs/build/html/_modules/yasa/sleepstats.html b/docs/build/html/_modules/yasa/sleepstats.html
index b55cb06..2de9d66 100644
--- a/docs/build/html/_modules/yasa/sleepstats.html
+++ b/docs/build/html/_modules/yasa/sleepstats.html
@@ -1,24 +1,23 @@
 
 
-
+
   
     
-    yasa.sleepstats — yasa 0.6.3 documentation
-    
-    
+    
+    yasa.sleepstats — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +25,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +43,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       

 
         

@@ -98,6 +97,7 @@ 
Source code for yasa.sleepstats
 This file contains several helper functions to calculate sleep statistics from
 a one-dimensional sleep staging vector (hypnogram).
 """
+# import warnings
 import numpy as np
 import pandas as pd
 
@@ -194,6 +194,8 @@ 
Source code for yasa.sleepstats
         >>> ax.set_ylabel("From sleep stage")
         >>> ax.xaxis.set_label_position('top')
     """
+    # NOTE: FutureWarning not added here otherwise it would also be shown when calling
+    # yasa.Hypnogram.transition_matrix
     x = np.asarray(hypno, dtype=int)
     unique, inverse = np.unique(x, return_inverse=True)  # unique is sorted
     n = unique.size
@@ -271,6 +273,12 @@ 
Source code for yasa.sleepstats
         total sleep time (TST). Previously, YASA calculated TST as SPT - WASO, thus including
         Art and Uns. TST is now calculated as the sum of all REM and NREM sleep in SPT.
 
+    .. warning::
+        The definition of REM latency in the AASM scoring manual differs from the REM latency
+        reported here. The former uses the time from first epoch of sleep, while YASA uses the
+        time from the beginning of the recording. The AASM definition of the REM latency can be
+        found with `SOL - Lat_REM`.
+
     References
     ----------
     * Iber, C. (2007). The AASM manual for the scoring of sleep and
@@ -313,6 +321,11 @@ 
Source code for yasa.sleepstats
      'SE': 80.0,
      'SME': 100.0}
     """
+    # warnings.warn(
+    #     "The `yasa.sleep_statistics` function is deprecated and will be removed in v0.8. "
+    #     "Please use the `yasa.Hypnogram.sleep_statistics` method instead.",
+    #     FutureWarning,
+    # )
     stats = {}
     hypno = np.asarray(hypno)
     assert hypno.ndim == 1, "hypno must have only one dimension."
@@ -374,8 +387,8 @@ 
Source code for yasa.sleepstats
       
     

     

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

-      Created using Sphinx 3.0.0.

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

+      Created using Sphinx 4.4.0.

     

   

 
diff --git a/docs/build/html/_modules/yasa/spectral.html b/docs/build/html/_modules/yasa/spectral.html
index 35a90e7..2369d08 100644
--- a/docs/build/html/_modules/yasa/spectral.html
+++ b/docs/build/html/_modules/yasa/spectral.html
@@ -1,24 +1,23 @@
 
 
-
+
   
     
-    yasa.spectral — yasa 0.6.3 documentation
-    
-    
+    
+    yasa.spectral — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +25,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +43,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       

 
         

@@ -824,8 +823,8 @@ 
Source code for yasa.spectral
       
     

     

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

-      Created using Sphinx 3.0.0.

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

+      Created using Sphinx 4.4.0.

     

   

 
diff --git a/docs/build/html/_modules/yasa/staging.html b/docs/build/html/_modules/yasa/staging.html
index a21f05e..bb5fae5 100644
--- a/docs/build/html/_modules/yasa/staging.html
+++ b/docs/build/html/_modules/yasa/staging.html
@@ -1,24 +1,23 @@
 
 
-
+
   
     
-    yasa.staging — yasa 0.6.3 documentation
-    
-    
+    
+    yasa.staging — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +25,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +43,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       

 
         

@@ -606,8 +605,8 @@ 
Source code for yasa.staging
       
     

     

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

-      Created using Sphinx 3.0.0.

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

+      Created using Sphinx 4.4.0.

     

   

 
diff --git a/docs/build/html/_static/basic.css b/docs/build/html/_static/basic.css
index 0119285..bf18350 100644
--- a/docs/build/html/_static/basic.css
+++ b/docs/build/html/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -15,6 +15,12 @@ div.clearer {
     clear: both;
 }
 
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
 /* -- relbar ---------------------------------------------------------------- */
 
 div.related {
@@ -124,7 +130,7 @@ ul.search li a {
     font-weight: bold;
 }
 
-ul.search li div.context {
+ul.search li p.context {
     color: #888;
     margin: 2px 0 0 30px;
     text-align: left;
@@ -271,25 +277,25 @@ p.rubric {
     font-weight: bold;
 }
 
-img.align-left, .figure.align-left, object.align-left {
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
     clear: left;
     float: left;
     margin-right: 1em;
 }
 
-img.align-right, .figure.align-right, object.align-right {
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
     clear: right;
     float: right;
     margin-left: 1em;
 }
 
-img.align-center, .figure.align-center, object.align-center {
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
   display: block;
   margin-left: auto;
   margin-right: auto;
 }
 
-img.align-default, .figure.align-default {
+img.align-default, figure.align-default, .figure.align-default {
   display: block;
   margin-left: auto;
   margin-right: auto;
@@ -313,24 +319,31 @@ img.align-default, .figure.align-default {
 
 /* -- sidebars -------------------------------------------------------------- */
 
-div.sidebar {
+div.sidebar,
+aside.sidebar {
     margin: 0 0 0.5em 1em;
     border: 1px solid #ddb;
-    padding: 7px 7px 0 7px;
+    padding: 7px;
     background-color: #ffe;
     width: 40%;
     float: right;
+    clear: right;
+    overflow-x: auto;
 }
 
 p.sidebar-title {
     font-weight: bold;
 }
 
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
 /* -- topics ---------------------------------------------------------------- */
 
 div.topic {
     border: 1px solid #ccc;
-    padding: 7px 7px 0 7px;
+    padding: 7px;
     margin: 10px 0 10px 0;
 }
 
@@ -352,10 +365,6 @@ div.admonition dt {
     font-weight: bold;
 }
 
-div.admonition dl {
-    margin-bottom: 0;
-}
-
 p.admonition-title {
     margin: 0px 10px 5px 0px;
     font-weight: bold;
@@ -366,9 +375,30 @@ div.body p.centered {
     margin-top: 25px;
 }
 
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
 /* -- tables ---------------------------------------------------------------- */
 
 table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
     border: 0;
     border-collapse: collapse;
 }
@@ -416,32 +446,34 @@ table.citation td {
     border-bottom: none;
 }
 
-th > p:first-child,
-td > p:first-child {
+th > :first-child,
+td > :first-child {
     margin-top: 0px;
 }
 
-th > p:last-child,
-td > p:last-child {
+th > :last-child,
+td > :last-child {
     margin-bottom: 0px;
 }
 
 /* -- figures --------------------------------------------------------------- */
 
-div.figure {
+div.figure, figure {
     margin: 0.5em;
     padding: 0.5em;
 }
 
-div.figure p.caption {
+div.figure p.caption, figcaption {
     padding: 0.3em;
 }
 
-div.figure p.caption span.caption-number {
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
     font-style: italic;
 }
 
-div.figure p.caption span.caption-text {
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
 }
 
 /* -- field list styles ----------------------------------------------------- */
@@ -468,10 +500,71 @@ table.field-list td, table.field-list th {
 
 /* -- hlist styles ---------------------------------------------------------- */
 
+table.hlist {
+    margin: 1em 0;
+}
+
 table.hlist td {
     vertical-align: top;
 }
 
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
 
 /* -- other body styles ----------------------------------------------------- */
 
@@ -495,17 +588,37 @@ ol.upperroman {
     list-style: upper-roman;
 }
 
-li > p:first-child {
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
     margin-top: 0px;
 }
 
-li > p:last-child {
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
     margin-bottom: 0px;
 }
 
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
 dl.footnote > dt,
 dl.citation > dt {
     float: left;
+    margin-right: 0.5em;
 }
 
 dl.footnote > dd,
@@ -546,7 +659,7 @@ dl {
     margin-bottom: 15px;
 }
 
-dd > p:first-child {
+dd > :first-child {
     margin-top: 0px;
 }
 
@@ -560,6 +673,11 @@ dd {
     margin-left: 30px;
 }
 
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
 dt:target, span.highlighted {
     background-color: #fbe54e;
 }
@@ -573,14 +691,6 @@ dl.glossary dt {
     font-size: 1.1em;
 }
 
-.optional {
-    font-size: 1.3em;
-}
-
-.sig-paren {
-    font-size: larger;
-}
-
 .versionmodified {
     font-style: italic;
 }
@@ -621,8 +731,9 @@ dl.glossary dt {
 
 .classifier:before {
     font-style: normal;
-    margin: 0.5em;
+    margin: 0 0.5em;
     content: ":";
+    display: inline-block;
 }
 
 abbr, acronym {
@@ -637,29 +748,69 @@ pre {
     overflow-y: hidden;  /* fixes display issues on Chrome browsers */
 }
 
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
 span.pre {
     -moz-hyphens: none;
     -ms-hyphens: none;
     -webkit-hyphens: none;
     hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
 }
 
 td.linenos pre {
-    padding: 5px 0px;
     border: 0;
     background-color: transparent;
     color: #aaa;
 }
 
 table.highlighttable {
-    margin-left: 0.5em;
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
 }
 
 table.highlighttable td {
-    padding: 0 0.5em 0 0.5em;
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
 }
 
 div.code-block-caption {
+    margin-top: 1em;
     padding: 2px 5px;
     font-size: small;
 }
@@ -668,12 +819,14 @@ div.code-block-caption code {
     background-color: transparent;
 }
 
-div.code-block-caption + div > div.highlight > pre {
-    margin-top: 0;
-}
-
-div.doctest > div.highlight span.gp {  /* gp: Generic.Prompt */
-    user-select: none;
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
 }
 
 div.code-block-caption span.caption-number {
@@ -685,21 +838,7 @@ div.code-block-caption span.caption-text {
 }
 
 div.literal-block-wrapper {
-    padding: 1em 1em 0;
-}
-
-div.literal-block-wrapper div.highlight {
-    margin: 0;
-}
-
-code.descname {
-    background-color: transparent;
-    font-weight: bold;
-    font-size: 1.2em;
-}
-
-code.descclassname {
-    background-color: transparent;
+    margin: 1em 0;
 }
 
 code.xref, a code {
@@ -740,8 +879,7 @@ span.eqno {
 }
 
 span.eqno a.headerlink {
-    position: relative;
-    left: 0px;
+    position: absolute;
     z-index: 1;
 }
 
diff --git a/docs/build/html/_static/copy-button.svg b/docs/build/html/_static/copy-button.svg
index b888a20..9c074da 100644
--- a/docs/build/html/_static/copy-button.svg
+++ b/docs/build/html/_static/copy-button.svg
@@ -1,5 +1,5 @@
-
+
   
-  
-  
+  
+  
 
diff --git a/docs/build/html/_static/copybutton.css b/docs/build/html/_static/copybutton.css
index 5d29149..f1916ec 100644
--- a/docs/build/html/_static/copybutton.css
+++ b/docs/build/html/_static/copybutton.css
@@ -3,7 +3,7 @@ button.copybtn {
     position: absolute;
     display: flex;
     top: .3em;
-    right: .5em;
+    right: .3em;
     width: 1.7em;
     height: 1.7em;
 	opacity: 0;
@@ -13,24 +13,30 @@ button.copybtn {
     border: none;
     outline: none;
     border-radius: 0.4em;
-    border: #e1e1e1 1px solid;
-    background-color: rgb(245, 245, 245);
+    /* The colors that GitHub uses */
+    border: #1b1f2426 1px solid;
+    background-color: #f6f8fa;
+    color: #57606a;
 }
 
 button.copybtn.success {
     border-color: #22863a;
+    color: #22863a;
 }
 
-button.copybtn img {
-    width: 100%;
-    padding: .2em;
+button.copybtn svg {
+    stroke: currentColor;
+    width: 1.5em;
+    height: 1.5em;
+    padding: 0.1em;
 }
 
 div.highlight  {
     position: relative;
 }
 
-.highlight:hover button.copybtn {
+/* Show the copybutton */
+.highlight:hover button.copybtn, button.copybtn.success {
 	opacity: 1;
 }
 
@@ -79,3 +85,10 @@ div.highlight  {
     transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
     transition-delay: .5s;
 }
+
+/* By default the copy button shouldn't show up when printing a page */
+@media print {
+    button.copybtn {
+        display: none;
+    }
+}
diff --git a/docs/build/html/_static/copybutton.js b/docs/build/html/_static/copybutton.js
index 51412d0..f695d9f 100644
--- a/docs/build/html/_static/copybutton.js
+++ b/docs/build/html/_static/copybutton.js
@@ -35,6 +35,12 @@ const messages = {
     'copy_to_clipboard': '复制到剪贴板',
     'copy_success': '复制成功!',
     'copy_failure': '复制失败',
+  },
+  'it' : {
+    'copy': 'Copiare',
+    'copy_to_clipboard': 'Copiato negli appunti',
+    'copy_success': 'Copiato!',
+    'copy_failure': 'Errore durante la copia',
   }
 }
 
@@ -49,7 +55,25 @@ if (doc_url_root == '#') {
     doc_url_root = '';
 }
 
-const path_static = `${doc_url_root}_static/`;
+/**
+ * SVG files for our copy buttons
+ */
+let iconCheck = `
+  ${messages[locale]['copy_success']}
+  
+  
+`
+
+// If the user specified their own SVG use that, otherwise use the default
+let iconCopy = ``;
+if (!iconCopy) {
+  iconCopy = `
+  ${messages[locale]['copy_to_clipboard']}
+  
+  
+  
+`
+}
 
 /**
  * Set up copy/paste for code blocks
@@ -78,19 +102,25 @@ const clearSelection = () => {
   }
 }
 
-// Changes tooltip text for two seconds, then changes it back
+// Changes tooltip text for a moment, then changes it back
+// We want the timeout of our `success` class to be a bit shorter than the
+// tooltip and icon change, so that we can hide the icon before changing back.
+var timeoutIcon = 2000;
+var timeoutSuccessClass = 1500;
+
 const temporarilyChangeTooltip = (el, oldText, newText) => {
   el.setAttribute('data-tooltip', newText)
   el.classList.add('success')
-  setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000)
-  setTimeout(() => el.classList.remove('success'), 2000)
+  // Remove success a little bit sooner than we change the tooltip
+  // So that we can use CSS to hide the copybutton first
+  setTimeout(() => el.classList.remove('success'), timeoutSuccessClass)
+  setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon)
 }
 
 // Changes the copy button icon for two seconds, then changes it back
 const temporarilyChangeIcon = (el) => {
-  img = el.querySelector("img");
-  img.setAttribute('src', `${path_static}check-solid.svg`)
-  setTimeout(() => img.setAttribute('src', `${path_static}copy-button.svg`), 2000)
+  el.innerHTML = iconCheck;
+  setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon)
 }
 
 const addCopyButtonToCodeCells = () => {
@@ -102,14 +132,15 @@ const addCopyButtonToCodeCells = () => {
   }
 
   // Add copybuttons to all of our code cells
-  const codeCells = document.querySelectorAll('div.highlight pre')
+  const COPYBUTTON_SELECTOR = 'div.highlight pre';
+  const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR)
   codeCells.forEach((codeCell, index) => {
     const id = codeCellId(index)
     codeCell.setAttribute('id', id)
 
     const clipboardButton = id =>
     `${messages[locale]['copy_to_clipboard']}
+      ${iconCopy}
     `
     codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
   })
@@ -118,10 +149,25 @@ function escapeRegExp(string) {
     return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
 }
 
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
 // Callback when a copy button is clicked. Will be passed the node that was clicked
 // should then grab the text and replace pieces of text that shouldn't be used in output
 function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-
     var regexp;
     var match;
 
@@ -176,7 +222,12 @@ function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onl
 
 var copyTargetText = (trigger) => {
   var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
-  return formatCopyText(target.innerText, '>>> |\\.\\.\\. |\\$ ', true, true, true, true, '', '')
+
+  // get filtered text
+  let exclude = '.linenos, .gp';
+
+  let text = filterText(target, exclude);
+  return formatCopyText(text, '>>> |\\.\\.\\. |\\$ ', true, true, true, true, '', '')
 }
 
   // Initialize with a callback so we can modify the text before copy
diff --git a/docs/build/html/_static/copybutton_funcs.js b/docs/build/html/_static/copybutton_funcs.js
index b9168c5..dbe1aaa 100644
--- a/docs/build/html/_static/copybutton_funcs.js
+++ b/docs/build/html/_static/copybutton_funcs.js
@@ -2,10 +2,25 @@ function escapeRegExp(string) {
     return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
 }
 
+/**
+ * Removes excluded text from a Node.
+ *
+ * @param {Node} target Node to filter.
+ * @param {string} exclude CSS selector of nodes to exclude.
+ * @returns {DOMString} Text from `target` with text removed.
+ */
+export function filterText(target, exclude) {
+    const clone = target.cloneNode(true);  // clone as to not modify the live DOM
+    if (exclude) {
+        // remove excluded nodes
+        clone.querySelectorAll(exclude).forEach(node => node.remove());
+    }
+    return clone.innerText;
+}
+
 // Callback when a copy button is clicked. Will be passed the node that was clicked
 // should then grab the text and replace pieces of text that shouldn't be used in output
 export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") {
-
     var regexp;
     var match;
 
diff --git a/docs/build/html/_static/doctools.js b/docs/build/html/_static/doctools.js
index daccd20..e509e48 100644
--- a/docs/build/html/_static/doctools.js
+++ b/docs/build/html/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for all documentation.
  *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -29,9 +29,14 @@ if (!window.console || !console.firebug) {
 
 /**
  * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
  */
 jQuery.urldecode = function(x) {
-  return decodeURIComponent(x).replace(/\+/g, ' ');
+  if (!x) {
+    return x
+  }
+  return decodeURIComponent(x.replace(/\+/g, ' '));
 };
 
 /**
@@ -259,6 +264,9 @@ var Documentation = {
   hideSearchWords : function() {
     $('#searchbox .highlight-link').fadeOut(300);
     $('span.highlighted').removeClass('highlighted');
+    var url = new URL(window.location);
+    url.searchParams.delete('highlight');
+    window.history.replaceState({}, '', url);
   },
 
   /**
@@ -285,9 +293,10 @@ var Documentation = {
   initOnKeyListeners: function() {
     $(document).keydown(function(event) {
       var activeElementType = document.activeElement.tagName;
-      // don't navigate when in search box or textarea
+      // don't navigate when in search box, textarea, dropdown or button
       if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
-          && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
+          && activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
+          && !event.shiftKey) {
         switch (event.keyCode) {
           case 37: // left
             var prevHref = $('link[rel="prev"]').prop('href');
@@ -295,12 +304,14 @@ var Documentation = {
               window.location.href = prevHref;
               return false;
             }
+            break;
           case 39: // right
             var nextHref = $('link[rel="next"]').prop('href');
             if (nextHref) {
               window.location.href = nextHref;
               return false;
             }
+            break;
         }
       }
     });
diff --git a/docs/build/html/_static/documentation_options.js b/docs/build/html/_static/documentation_options.js
index 567cdf7..55a69e0 100644
--- a/docs/build/html/_static/documentation_options.js
+++ b/docs/build/html/_static/documentation_options.js
@@ -1,6 +1,6 @@
 var DOCUMENTATION_OPTIONS = {
     URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '0.6.3',
+    VERSION: '0.6.4',
     LANGUAGE: 'None',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
diff --git a/docs/build/html/_static/jquery-3.4.1.js b/docs/build/html/_static/jquery-3.5.1.js
similarity index 91%
rename from docs/build/html/_static/jquery-3.4.1.js
rename to docs/build/html/_static/jquery-3.5.1.js
index 773ad95..5093733 100644
--- a/docs/build/html/_static/jquery-3.4.1.js
+++ b/docs/build/html/_static/jquery-3.5.1.js
@@ -1,5 +1,5 @@
 /*!
- * jQuery JavaScript Library v3.4.1
+ * jQuery JavaScript Library v3.5.1
  * https://jquery.com/
  *
  * Includes Sizzle.js
@@ -9,7 +9,7 @@
  * Released under the MIT license
  * https://jquery.org/license
  *
- * Date: 2019-05-01T21:04Z
+ * Date: 2020-05-04T22:49Z
  */
 ( function( global, factory ) {
 
@@ -47,13 +47,16 @@
 
 var arr = [];
 
-var document = window.document;
-
 var getProto = Object.getPrototypeOf;
 
 var slice = arr.slice;
 
-var concat = arr.concat;
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
 
 var push = arr.push;
 
@@ -86,6 +89,8 @@ var isWindow = function isWindow( obj ) {
 	};
 
 
+var document = window.document;
+
 
 
 	var preservedScriptAttributes = {
@@ -142,7 +147,7 @@ function toType( obj ) {
 
 
 var
-	version = "3.4.1",
+	version = "3.5.1",
 
 	// Define a local copy of jQuery
 	jQuery = function( selector, context ) {
@@ -150,11 +155,7 @@ var
 		// The jQuery object is actually just the init constructor 'enhanced'
 		// Need init if jQuery is called (just allow error to be thrown if not included)
 		return new jQuery.fn.init( selector, context );
-	},
-
-	// Support: Android <=4.0 only
-	// Make sure we trim BOM and NBSP
-	rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+	};
 
 jQuery.fn = jQuery.prototype = {
 
@@ -220,6 +221,18 @@ jQuery.fn = jQuery.prototype = {
 		return this.eq( -1 );
 	},
 
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
 	eq: function( i ) {
 		var len = this.length,
 			j = +i + ( i < 0 ? len : 0 );
@@ -353,9 +366,10 @@ jQuery.extend( {
 		return true;
 	},
 
-	// Evaluates a script in a global context
-	globalEval: function( code, options ) {
-		DOMEval( code, { nonce: options && options.nonce } );
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
 	},
 
 	each: function( obj, callback ) {
@@ -379,13 +393,6 @@ jQuery.extend( {
 		return obj;
 	},
 
-	// Support: Android <=4.0 only
-	trim: function( text ) {
-		return text == null ?
-			"" :
-			( text + "" ).replace( rtrim, "" );
-	},
-
 	// results is for internal usage only
 	makeArray: function( arr, results ) {
 		var ret = results || [];
@@ -472,7 +479,7 @@ jQuery.extend( {
 		}
 
 		// Flatten any nested arrays
-		return concat.apply( [], ret );
+		return flat( ret );
 	},
 
 	// A global GUID counter for objects
@@ -489,7 +496,7 @@ if ( typeof Symbol === "function" ) {
 
 // Populate the class2type map
 jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( i, name ) {
+function( _i, name ) {
 	class2type[ "[object " + name + "]" ] = name.toLowerCase();
 } );
 
@@ -511,17 +518,16 @@ function isArrayLike( obj ) {
 }
 var Sizzle =
 /*!
- * Sizzle CSS Selector Engine v2.3.4
+ * Sizzle CSS Selector Engine v2.3.5
  * https://sizzlejs.com/
  *
  * Copyright JS Foundation and other contributors
  * Released under the MIT license
  * https://js.foundation/
  *
- * Date: 2019-04-08
+ * Date: 2020-03-14
  */
-(function( window ) {
-
+( function( window ) {
 var i,
 	support,
 	Expr,
@@ -561,59 +567,70 @@ var i,
 	},
 
 	// Instance methods
-	hasOwn = ({}).hasOwnProperty,
+	hasOwn = ( {} ).hasOwnProperty,
 	arr = [],
 	pop = arr.pop,
-	push_native = arr.push,
+	pushNative = arr.push,
 	push = arr.push,
 	slice = arr.slice,
+
 	// Use a stripped-down indexOf as it's faster than native
 	// https://jsperf.com/thor-indexof-vs-for/5
 	indexOf = function( list, elem ) {
 		var i = 0,
 			len = list.length;
 		for ( ; i < len; i++ ) {
-			if ( list[i] === elem ) {
+			if ( list[ i ] === elem ) {
 				return i;
 			}
 		}
 		return -1;
 	},
 
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
 
 	// Regular expressions
 
 	// http://www.w3.org/TR/css3-selectors/#whitespace
 	whitespace = "[\\x20\\t\\r\\n\\f]",
 
-	// http://www.w3.org/TR/CSS21/syndata.html#value-def-identifier
-	identifier = "(?:\\\\.|[\\w-]|[^\0-\\xa0])+",
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
 
 	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
 	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
 		// Operator (capture 2)
 		"*([*^$|!~]?=)" + whitespace +
-		// "Attribute values must be CSS identifiers [capture 5] or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + whitespace +
-		"*\\]",
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
 
 	pseudos = ":(" + identifier + ")(?:\\((" +
+
 		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
 		// 1. quoted (capture 3; capture 4 or capture 5)
 		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
 		// 2. simple (capture 6)
 		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
 		// 3. anything else (capture 2)
 		".*" +
 		")\\)|)",
 
 	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
 	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + whitespace + "+$", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
 
 	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
 	rdescend = new RegExp( whitespace + "|>" ),
 
 	rpseudo = new RegExp( pseudos ),
@@ -625,14 +642,16 @@ var i,
 		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
 		"ATTR": new RegExp( "^" + attributes ),
 		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + whitespace +
-			"*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + whitespace +
-			"*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
 		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
 		// For use in libraries implementing .is()
 		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" +
-			whitespace + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
 	},
 
 	rhtml = /HTML$/i,
@@ -648,18 +667,21 @@ var i,
 
 	// CSS escapes
 	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\([\\da-f]{1,6}" + whitespace + "?|(" + whitespace + ")|.)", "ig" ),
-	funescape = function( _, escaped, escapedWhitespace ) {
-		var high = "0x" + escaped - 0x10000;
-		// NaN means non-codepoint
-		// Support: Firefox<24
-		// Workaround erroneous numeric interpretation of +"0x"
-		return high !== high || escapedWhitespace ?
-			escaped :
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
 			high < 0 ?
-				// BMP codepoint
 				String.fromCharCode( high + 0x10000 ) :
-				// Supplemental Plane codepoint (surrogate pair)
 				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
 	},
 
@@ -675,7 +697,8 @@ var i,
 			}
 
 			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
 		}
 
 		// Other potentially-special ASCII characters get backslash-escaped
@@ -700,18 +723,20 @@ var i,
 // Optimize for push.apply( _, NodeList )
 try {
 	push.apply(
-		(arr = slice.call( preferredDoc.childNodes )),
+		( arr = slice.call( preferredDoc.childNodes ) ),
 		preferredDoc.childNodes
 	);
+
 	// Support: Android<4.0
 	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
 	arr[ preferredDoc.childNodes.length ].nodeType;
 } catch ( e ) {
 	push = { apply: arr.length ?
 
 		// Leverage slice if possible
 		function( target, els ) {
-			push_native.apply( target, slice.call(els) );
+			pushNative.apply( target, slice.call( els ) );
 		} :
 
 		// Support: IE<9
@@ -719,8 +744,9 @@ try {
 		function( target, els ) {
 			var j = target.length,
 				i = 0;
+
 			// Can't trust NodeList.length
-			while ( (target[j++] = els[i++]) ) {}
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
 			target.length = j - 1;
 		}
 	};
@@ -744,24 +770,21 @@ function Sizzle( selector, context, results, seed ) {
 
 	// Try to shortcut find operations (as opposed to filters) in HTML documents
 	if ( !seed ) {
-
-		if ( ( context ? context.ownerDocument || context : preferredDoc ) !== document ) {
-			setDocument( context );
-		}
+		setDocument( context );
 		context = context || document;
 
 		if ( documentIsHTML ) {
 
 			// If the selector is sufficiently simple, try using a "get*By*" DOM method
 			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && (match = rquickExpr.exec( selector )) ) {
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
 
 				// ID selector
-				if ( (m = match[1]) ) {
+				if ( ( m = match[ 1 ] ) ) {
 
 					// Document context
 					if ( nodeType === 9 ) {
-						if ( (elem = context.getElementById( m )) ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
 
 							// Support: IE, Opera, Webkit
 							// TODO: identify versions
@@ -780,7 +803,7 @@ function Sizzle( selector, context, results, seed ) {
 						// Support: IE, Opera, Webkit
 						// TODO: identify versions
 						// getElementById can match elements by name instead of ID
-						if ( newContext && (elem = newContext.getElementById( m )) &&
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
 							contains( context, elem ) &&
 							elem.id === m ) {
 
@@ -790,12 +813,12 @@ function Sizzle( selector, context, results, seed ) {
 					}
 
 				// Type selector
-				} else if ( match[2] ) {
+				} else if ( match[ 2 ] ) {
 					push.apply( results, context.getElementsByTagName( selector ) );
 					return results;
 
 				// Class selector
-				} else if ( (m = match[3]) && support.getElementsByClassName &&
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
 					context.getElementsByClassName ) {
 
 					push.apply( results, context.getElementsByClassName( m ) );
@@ -806,11 +829,11 @@ function Sizzle( selector, context, results, seed ) {
 			// Take advantage of querySelectorAll
 			if ( support.qsa &&
 				!nonnativeSelectorCache[ selector + " " ] &&
-				(!rbuggyQSA || !rbuggyQSA.test( selector )) &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
 
 				// Support: IE 8 only
 				// Exclude object elements
-				(nodeType !== 1 || context.nodeName.toLowerCase() !== "object") ) {
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
 
 				newSelector = selector;
 				newContext = context;
@@ -819,27 +842,36 @@ function Sizzle( selector, context, results, seed ) {
 				// descendant combinators, which is not what we want.
 				// In such cases, we work around the behavior by prefixing every selector in the
 				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
 				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 && rdescend.test( selector ) ) {
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
 
-					// Capture the context ID, setting it first if necessary
-					if ( (nid = context.getAttribute( "id" )) ) {
-						nid = nid.replace( rcssescape, fcssescape );
-					} else {
-						context.setAttribute( "id", (nid = expando) );
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
 					}
 
 					// Prefix every selector in the list
 					groups = tokenize( selector );
 					i = groups.length;
 					while ( i-- ) {
-						groups[i] = "#" + nid + " " + toSelector( groups[i] );
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
 					}
 					newSelector = groups.join( "," );
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
 				}
 
 				try {
@@ -872,12 +904,14 @@ function createCache() {
 	var keys = [];
 
 	function cache( key, value ) {
+
 		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
 		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
 			// Only keep the most recent entries
 			delete cache[ keys.shift() ];
 		}
-		return (cache[ key + " " ] = value);
+		return ( cache[ key + " " ] = value );
 	}
 	return cache;
 }
@@ -896,17 +930,19 @@ function markFunction( fn ) {
  * @param {Function} fn Passed the created element and returns a boolean result
  */
 function assert( fn ) {
-	var el = document.createElement("fieldset");
+	var el = document.createElement( "fieldset" );
 
 	try {
 		return !!fn( el );
-	} catch (e) {
+	} catch ( e ) {
 		return false;
 	} finally {
+
 		// Remove from its parent by default
 		if ( el.parentNode ) {
 			el.parentNode.removeChild( el );
 		}
+
 		// release memory in IE
 		el = null;
 	}
@@ -918,11 +954,11 @@ function assert( fn ) {
  * @param {Function} handler The method that will be applied
  */
 function addHandle( attrs, handler ) {
-	var arr = attrs.split("|"),
+	var arr = attrs.split( "|" ),
 		i = arr.length;
 
 	while ( i-- ) {
-		Expr.attrHandle[ arr[i] ] = handler;
+		Expr.attrHandle[ arr[ i ] ] = handler;
 	}
 }
 
@@ -944,7 +980,7 @@ function siblingCheck( a, b ) {
 
 	// Check if b follows a
 	if ( cur ) {
-		while ( (cur = cur.nextSibling) ) {
+		while ( ( cur = cur.nextSibling ) ) {
 			if ( cur === b ) {
 				return -1;
 			}
@@ -972,7 +1008,7 @@ function createInputPseudo( type ) {
 function createButtonPseudo( type ) {
 	return function( elem ) {
 		var name = elem.nodeName.toLowerCase();
-		return (name === "input" || name === "button") && elem.type === type;
+		return ( name === "input" || name === "button" ) && elem.type === type;
 	};
 }
 
@@ -1015,7 +1051,7 @@ function createDisabledPseudo( disabled ) {
 					// Where there is no isDisabled, check manually
 					/* jshint -W018 */
 					elem.isDisabled !== !disabled &&
-						inDisabledFieldset( elem ) === disabled;
+					inDisabledFieldset( elem ) === disabled;
 			}
 
 			return elem.disabled === disabled;
@@ -1037,21 +1073,21 @@ function createDisabledPseudo( disabled ) {
  * @param {Function} fn
  */
 function createPositionalPseudo( fn ) {
-	return markFunction(function( argument ) {
+	return markFunction( function( argument ) {
 		argument = +argument;
-		return markFunction(function( seed, matches ) {
+		return markFunction( function( seed, matches ) {
 			var j,
 				matchIndexes = fn( [], seed.length, argument ),
 				i = matchIndexes.length;
 
 			// Match elements found at the specified indexes
 			while ( i-- ) {
-				if ( seed[ (j = matchIndexes[i]) ] ) {
-					seed[j] = !(matches[j] = seed[j]);
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
 				}
 			}
-		});
-	});
+		} );
+	} );
 }
 
 /**
@@ -1073,7 +1109,7 @@ support = Sizzle.support = {};
  */
 isXML = Sizzle.isXML = function( elem ) {
 	var namespace = elem.namespaceURI,
-		docElem = (elem.ownerDocument || elem).documentElement;
+		docElem = ( elem.ownerDocument || elem ).documentElement;
 
 	// Support: IE <=8
 	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
@@ -1091,7 +1127,11 @@ setDocument = Sizzle.setDocument = function( node ) {
 		doc = node ? node.ownerDocument || node : preferredDoc;
 
 	// Return early if doc is invalid or already selected
-	if ( doc === document || doc.nodeType !== 9 || !doc.documentElement ) {
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
 		return document;
 	}
 
@@ -1100,10 +1140,14 @@ setDocument = Sizzle.setDocument = function( node ) {
 	docElem = document.documentElement;
 	documentIsHTML = !isXML( document );
 
-	// Support: IE 9-11, Edge
+	// Support: IE 9 - 11+, Edge 12 - 18+
 	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	if ( preferredDoc !== document &&
-		(subWindow = document.defaultView) && subWindow.top !== subWindow ) {
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
 
 		// Support: IE 11, Edge
 		if ( subWindow.addEventListener ) {
@@ -1115,25 +1159,36 @@ setDocument = Sizzle.setDocument = function( node ) {
 		}
 	}
 
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
 	/* Attributes
 	---------------------------------------------------------------------- */
 
 	// Support: IE<8
 	// Verify that getAttribute really returns attributes and not properties
 	// (excepting IE8 booleans)
-	support.attributes = assert(function( el ) {
+	support.attributes = assert( function( el ) {
 		el.className = "i";
-		return !el.getAttribute("className");
-	});
+		return !el.getAttribute( "className" );
+	} );
 
 	/* getElement(s)By*
 	---------------------------------------------------------------------- */
 
 	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert(function( el ) {
-		el.appendChild( document.createComment("") );
-		return !el.getElementsByTagName("*").length;
-	});
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
 
 	// Support: IE<9
 	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
@@ -1142,38 +1197,38 @@ setDocument = Sizzle.setDocument = function( node ) {
 	// Check if getElementById returns elements by name
 	// The broken getElementById methods don't pick up programmatically-set names,
 	// so use a roundabout getElementsByName test
-	support.getById = assert(function( el ) {
+	support.getById = assert( function( el ) {
 		docElem.appendChild( el ).id = expando;
 		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	});
+	} );
 
 	// ID filter and find
 	if ( support.getById ) {
-		Expr.filter["ID"] = function( id ) {
+		Expr.filter[ "ID" ] = function( id ) {
 			var attrId = id.replace( runescape, funescape );
 			return function( elem ) {
-				return elem.getAttribute("id") === attrId;
+				return elem.getAttribute( "id" ) === attrId;
 			};
 		};
-		Expr.find["ID"] = function( id, context ) {
+		Expr.find[ "ID" ] = function( id, context ) {
 			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
 				var elem = context.getElementById( id );
 				return elem ? [ elem ] : [];
 			}
 		};
 	} else {
-		Expr.filter["ID"] =  function( id ) {
+		Expr.filter[ "ID" ] =  function( id ) {
 			var attrId = id.replace( runescape, funescape );
 			return function( elem ) {
 				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode("id");
+					elem.getAttributeNode( "id" );
 				return node && node.value === attrId;
 			};
 		};
 
 		// Support: IE 6 - 7 only
 		// getElementById is not reliable as a find shortcut
-		Expr.find["ID"] = function( id, context ) {
+		Expr.find[ "ID" ] = function( id, context ) {
 			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
 				var node, i, elems,
 					elem = context.getElementById( id );
@@ -1181,7 +1236,7 @@ setDocument = Sizzle.setDocument = function( node ) {
 				if ( elem ) {
 
 					// Verify the id attribute
-					node = elem.getAttributeNode("id");
+					node = elem.getAttributeNode( "id" );
 					if ( node && node.value === id ) {
 						return [ elem ];
 					}
@@ -1189,8 +1244,8 @@ setDocument = Sizzle.setDocument = function( node ) {
 					// Fall back on getElementsByName
 					elems = context.getElementsByName( id );
 					i = 0;
-					while ( (elem = elems[i++]) ) {
-						node = elem.getAttributeNode("id");
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
 						if ( node && node.value === id ) {
 							return [ elem ];
 						}
@@ -1203,7 +1258,7 @@ setDocument = Sizzle.setDocument = function( node ) {
 	}
 
 	// Tag
-	Expr.find["TAG"] = support.getElementsByTagName ?
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
 		function( tag, context ) {
 			if ( typeof context.getElementsByTagName !== "undefined" ) {
 				return context.getElementsByTagName( tag );
@@ -1218,12 +1273,13 @@ setDocument = Sizzle.setDocument = function( node ) {
 			var elem,
 				tmp = [],
 				i = 0,
+
 				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
 				results = context.getElementsByTagName( tag );
 
 			// Filter out possible comments
 			if ( tag === "*" ) {
-				while ( (elem = results[i++]) ) {
+				while ( ( elem = results[ i++ ] ) ) {
 					if ( elem.nodeType === 1 ) {
 						tmp.push( elem );
 					}
@@ -1235,7 +1291,7 @@ setDocument = Sizzle.setDocument = function( node ) {
 		};
 
 	// Class
-	Expr.find["CLASS"] = support.getElementsByClassName && function( className, context ) {
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
 		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
 			return context.getElementsByClassName( className );
 		}
@@ -1256,10 +1312,14 @@ setDocument = Sizzle.setDocument = function( node ) {
 	// See https://bugs.jquery.com/ticket/13378
 	rbuggyQSA = [];
 
-	if ( (support.qsa = rnative.test( document.querySelectorAll )) ) {
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
 		// Build QSA regex
 		// Regex strategy adopted from Diego Perini
-		assert(function( el ) {
+		assert( function( el ) {
+
+			var input;
+
 			// Select is set to empty string on purpose
 			// This is to test IE's treatment of not explicitly
 			// setting a boolean content attribute,
@@ -1273,78 +1333,98 @@ setDocument = Sizzle.setDocument = function( node ) {
 			// Nothing should be selected when empty strings follow ^= or $= or *=
 			// The test attribute must be unknown in Opera but "safe" for WinRT
 			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll("[msallowcapture^='']").length ) {
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
 				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
 			}
 
 			// Support: IE8
 			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll("[selected]").length ) {
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
 				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
 			}
 
 			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
 			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push("~=");
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
 			}
 
 			// Webkit/Opera - :checked should return selected option elements
 			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
 			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll(":checked").length ) {
-				rbuggyQSA.push(":checked");
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
 			}
 
 			// Support: Safari 8+, iOS 8+
 			// https://bugs.webkit.org/show_bug.cgi?id=136851
 			// In-page `selector#id sibling-combinator selector` fails
 			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push(".#.+[+~]");
+				rbuggyQSA.push( ".#.+[+~]" );
 			}
-		});
 
-		assert(function( el ) {
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
 			el.innerHTML = "" +
 				"";
 
 			// Support: Windows 8 Native Apps
 			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement("input");
+			var input = document.createElement( "input" );
 			input.setAttribute( "type", "hidden" );
 			el.appendChild( input ).setAttribute( "name", "D" );
 
 			// Support: IE8
 			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll("[name=d]").length ) {
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
 				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
 			}
 
 			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
 			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll(":enabled").length !== 2 ) {
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
 				rbuggyQSA.push( ":enabled", ":disabled" );
 			}
 
 			// Support: IE9-11+
 			// IE's :disabled selector does not pick up the children of disabled fieldsets
 			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll(":disabled").length !== 2 ) {
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
 				rbuggyQSA.push( ":enabled", ":disabled" );
 			}
 
+			// Support: Opera 10 - 11 only
 			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll("*,:x");
-			rbuggyQSA.push(",.*:");
-		});
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
 	}
 
-	if ( (support.matchesSelector = rnative.test( (matches = docElem.matches ||
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
 		docElem.webkitMatchesSelector ||
 		docElem.mozMatchesSelector ||
 		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector) )) ) {
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
 
-		assert(function( el ) {
 			// Check to see if it's possible to do matchesSelector
 			// on a disconnected node (IE 9)
 			support.disconnectedMatch = matches.call( el, "*" );
@@ -1353,11 +1433,11 @@ setDocument = Sizzle.setDocument = function( node ) {
 			// Gecko does not error, returns false instead
 			matches.call( el, "[s!='']:x" );
 			rbuggyMatches.push( "!=", pseudos );
-		});
+		} );
 	}
 
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join("|") );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join("|") );
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
 
 	/* Contains
 	---------------------------------------------------------------------- */
@@ -1374,11 +1454,11 @@ setDocument = Sizzle.setDocument = function( node ) {
 				adown.contains ?
 					adown.contains( bup ) :
 					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			));
+			) );
 		} :
 		function( a, b ) {
 			if ( b ) {
-				while ( (b = b.parentNode) ) {
+				while ( ( b = b.parentNode ) ) {
 					if ( b === a ) {
 						return true;
 					}
@@ -1407,7 +1487,11 @@ setDocument = Sizzle.setDocument = function( node ) {
 		}
 
 		// Calculate position if both inputs belong to the same document
-		compare = ( a.ownerDocument || a ) === ( b.ownerDocument || b ) ?
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
 			a.compareDocumentPosition( b ) :
 
 			// Otherwise we know they are disconnected
@@ -1415,13 +1499,24 @@ setDocument = Sizzle.setDocument = function( node ) {
 
 		// Disconnected nodes
 		if ( compare & 1 ||
-			(!support.sortDetached && b.compareDocumentPosition( a ) === compare) ) {
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
 
 			// Choose the first element that is related to our preferred document
-			if ( a === document || a.ownerDocument === preferredDoc && contains(preferredDoc, a) ) {
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
 				return -1;
 			}
-			if ( b === document || b.ownerDocument === preferredDoc && contains(preferredDoc, b) ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
 				return 1;
 			}
 
@@ -1434,6 +1529,7 @@ setDocument = Sizzle.setDocument = function( node ) {
 		return compare & 4 ? -1 : 1;
 	} :
 	function( a, b ) {
+
 		// Exit early if the nodes are identical
 		if ( a === b ) {
 			hasDuplicate = true;
@@ -1449,8 +1545,14 @@ setDocument = Sizzle.setDocument = function( node ) {
 
 		// Parentless nodes are either documents or disconnected
 		if ( !aup || !bup ) {
-			return a === document ? -1 :
-				b === document ? 1 :
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
 				aup ? -1 :
 				bup ? 1 :
 				sortInput ?
@@ -1464,26 +1566,32 @@ setDocument = Sizzle.setDocument = function( node ) {
 
 		// Otherwise we need full lists of their ancestors for comparison
 		cur = a;
-		while ( (cur = cur.parentNode) ) {
+		while ( ( cur = cur.parentNode ) ) {
 			ap.unshift( cur );
 		}
 		cur = b;
-		while ( (cur = cur.parentNode) ) {
+		while ( ( cur = cur.parentNode ) ) {
 			bp.unshift( cur );
 		}
 
 		// Walk down the tree looking for a discrepancy
-		while ( ap[i] === bp[i] ) {
+		while ( ap[ i ] === bp[ i ] ) {
 			i++;
 		}
 
 		return i ?
+
 			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[i], bp[i] ) :
+			siblingCheck( ap[ i ], bp[ i ] ) :
 
 			// Otherwise nodes in our document sort first
-			ap[i] === preferredDoc ? -1 :
-			bp[i] === preferredDoc ? 1 :
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
 			0;
 	};
 
@@ -1495,10 +1603,7 @@ Sizzle.matches = function( expr, elements ) {
 };
 
 Sizzle.matchesSelector = function( elem, expr ) {
-	// Set document vars if needed
-	if ( ( elem.ownerDocument || elem ) !== document ) {
-		setDocument( elem );
-	}
+	setDocument( elem );
 
 	if ( support.matchesSelector && documentIsHTML &&
 		!nonnativeSelectorCache[ expr + " " ] &&
@@ -1510,12 +1615,13 @@ Sizzle.matchesSelector = function( elem, expr ) {
 
 			// IE 9's matchesSelector returns false on disconnected nodes
 			if ( ret || support.disconnectedMatch ||
-					// As well, disconnected nodes are said to be in a document
-					// fragment in IE 9
-					elem.document && elem.document.nodeType !== 11 ) {
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
 				return ret;
 			}
-		} catch (e) {
+		} catch ( e ) {
 			nonnativeSelectorCache( expr, true );
 		}
 	}
@@ -1524,20 +1630,31 @@ Sizzle.matchesSelector = function( elem, expr ) {
 };
 
 Sizzle.contains = function( context, elem ) {
+
 	// Set document vars if needed
-	if ( ( context.ownerDocument || context ) !== document ) {
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
 		setDocument( context );
 	}
 	return contains( context, elem );
 };
 
 Sizzle.attr = function( elem, name ) {
+
 	// Set document vars if needed
-	if ( ( elem.ownerDocument || elem ) !== document ) {
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
 		setDocument( elem );
 	}
 
 	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
 		// Don't get fooled by Object.prototype properties (jQuery #13807)
 		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
 			fn( elem, name, !documentIsHTML ) :
@@ -1547,13 +1664,13 @@ Sizzle.attr = function( elem, name ) {
 		val :
 		support.attributes || !documentIsHTML ?
 			elem.getAttribute( name ) :
-			(val = elem.getAttributeNode(name)) && val.specified ?
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
 				val.value :
 				null;
 };
 
 Sizzle.escape = function( sel ) {
-	return (sel + "").replace( rcssescape, fcssescape );
+	return ( sel + "" ).replace( rcssescape, fcssescape );
 };
 
 Sizzle.error = function( msg ) {
@@ -1576,7 +1693,7 @@ Sizzle.uniqueSort = function( results ) {
 	results.sort( sortOrder );
 
 	if ( hasDuplicate ) {
-		while ( (elem = results[i++]) ) {
+		while ( ( elem = results[ i++ ] ) ) {
 			if ( elem === results[ i ] ) {
 				j = duplicates.push( i );
 			}
@@ -1604,17 +1721,21 @@ getText = Sizzle.getText = function( elem ) {
 		nodeType = elem.nodeType;
 
 	if ( !nodeType ) {
+
 		// If no nodeType, this is expected to be an array
-		while ( (node = elem[i++]) ) {
+		while ( ( node = elem[ i++ ] ) ) {
+
 			// Do not traverse comment nodes
 			ret += getText( node );
 		}
 	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
 		// Use textContent for elements
 		// innerText usage removed for consistency of new lines (jQuery #11153)
 		if ( typeof elem.textContent === "string" ) {
 			return elem.textContent;
 		} else {
+
 			// Traverse its children
 			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
 				ret += getText( elem );
@@ -1623,6 +1744,7 @@ getText = Sizzle.getText = function( elem ) {
 	} else if ( nodeType === 3 || nodeType === 4 ) {
 		return elem.nodeValue;
 	}
+
 	// Do not include comment or processing instruction nodes
 
 	return ret;
@@ -1650,19 +1772,21 @@ Expr = Sizzle.selectors = {
 
 	preFilter: {
 		"ATTR": function( match ) {
-			match[1] = match[1].replace( runescape, funescape );
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
 
 			// Move the given value to match[3] whether quoted or unquoted
-			match[3] = ( match[3] || match[4] || match[5] || "" ).replace( runescape, funescape );
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
 
-			if ( match[2] === "~=" ) {
-				match[3] = " " + match[3] + " ";
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
 			}
 
 			return match.slice( 0, 4 );
 		},
 
 		"CHILD": function( match ) {
+
 			/* matches from matchExpr["CHILD"]
 				1 type (only|nth|...)
 				2 what (child|of-type)
@@ -1673,22 +1797,25 @@ Expr = Sizzle.selectors = {
 				7 sign of y-component
 				8 y of y-component
 			*/
-			match[1] = match[1].toLowerCase();
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
 
-			if ( match[1].slice( 0, 3 ) === "nth" ) {
 				// nth-* requires argument
-				if ( !match[3] ) {
-					Sizzle.error( match[0] );
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
 				}
 
 				// numeric x and y parameters for Expr.filter.CHILD
 				// remember that false/true cast respectively to 0/1
-				match[4] = +( match[4] ? match[5] + (match[6] || 1) : 2 * ( match[3] === "even" || match[3] === "odd" ) );
-				match[5] = +( ( match[7] + match[8] ) || match[3] === "odd" );
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
 
-			// other types prohibit arguments
-			} else if ( match[3] ) {
-				Sizzle.error( match[0] );
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
 			}
 
 			return match;
@@ -1696,26 +1823,28 @@ Expr = Sizzle.selectors = {
 
 		"PSEUDO": function( match ) {
 			var excess,
-				unquoted = !match[6] && match[2];
+				unquoted = !match[ 6 ] && match[ 2 ];
 
-			if ( matchExpr["CHILD"].test( match[0] ) ) {
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
 				return null;
 			}
 
 			// Accept quoted arguments as-is
-			if ( match[3] ) {
-				match[2] = match[4] || match[5] || "";
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
 
 			// Strip excess characters from unquoted arguments
 			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
 				// Get excess from tokenize (recursively)
-				(excess = tokenize( unquoted, true )) &&
+				( excess = tokenize( unquoted, true ) ) &&
+
 				// advance to the next closing parenthesis
-				(excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length) ) {
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
 
 				// excess is a negative index
-				match[0] = match[0].slice( 0, excess );
-				match[2] = unquoted.slice( 0, excess );
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
 			}
 
 			// Return only captures needed by the pseudo filter method (type and argument)
@@ -1728,7 +1857,9 @@ Expr = Sizzle.selectors = {
 		"TAG": function( nodeNameSelector ) {
 			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
 			return nodeNameSelector === "*" ?
-				function() { return true; } :
+				function() {
+					return true;
+				} :
 				function( elem ) {
 					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
 				};
@@ -1738,10 +1869,16 @@ Expr = Sizzle.selectors = {
 			var pattern = classCache[ className + " " ];
 
 			return pattern ||
-				(pattern = new RegExp( "(^|" + whitespace + ")" + className + "(" + whitespace + "|$)" )) &&
-				classCache( className, function( elem ) {
-					return pattern.test( typeof elem.className === "string" && elem.className || typeof elem.getAttribute !== "undefined" && elem.getAttribute("class") || "" );
-				});
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
 		},
 
 		"ATTR": function( name, operator, check ) {
@@ -1757,6 +1894,8 @@ Expr = Sizzle.selectors = {
 
 				result += "";
 
+				/* eslint-disable max-len */
+
 				return operator === "=" ? result === check :
 					operator === "!=" ? result !== check :
 					operator === "^=" ? check && result.indexOf( check ) === 0 :
@@ -1765,10 +1904,12 @@ Expr = Sizzle.selectors = {
 					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
 					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
 					false;
+				/* eslint-enable max-len */
+
 			};
 		},
 
-		"CHILD": function( type, what, argument, first, last ) {
+		"CHILD": function( type, what, _argument, first, last ) {
 			var simple = type.slice( 0, 3 ) !== "nth",
 				forward = type.slice( -4 ) !== "last",
 				ofType = what === "of-type";
@@ -1780,7 +1921,7 @@ Expr = Sizzle.selectors = {
 					return !!elem.parentNode;
 				} :
 
-				function( elem, context, xml ) {
+				function( elem, _context, xml ) {
 					var cache, uniqueCache, outerCache, node, nodeIndex, start,
 						dir = simple !== forward ? "nextSibling" : "previousSibling",
 						parent = elem.parentNode,
@@ -1794,7 +1935,7 @@ Expr = Sizzle.selectors = {
 						if ( simple ) {
 							while ( dir ) {
 								node = elem;
-								while ( (node = node[ dir ]) ) {
+								while ( ( node = node[ dir ] ) ) {
 									if ( ofType ?
 										node.nodeName.toLowerCase() === name :
 										node.nodeType === 1 ) {
@@ -1802,6 +1943,7 @@ Expr = Sizzle.selectors = {
 										return false;
 									}
 								}
+
 								// Reverse direction for :only-* (if we haven't yet done so)
 								start = dir = type === "only" && !start && "nextSibling";
 							}
@@ -1817,22 +1959,22 @@ Expr = Sizzle.selectors = {
 
 							// ...in a gzip-friendly way
 							node = parent;
-							outerCache = node[ expando ] || (node[ expando ] = {});
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
 
 							// Support: IE <9 only
 							// Defend against cloned attroperties (jQuery gh-1709)
 							uniqueCache = outerCache[ node.uniqueID ] ||
-								(outerCache[ node.uniqueID ] = {});
+								( outerCache[ node.uniqueID ] = {} );
 
 							cache = uniqueCache[ type ] || [];
 							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
 							diff = nodeIndex && cache[ 2 ];
 							node = nodeIndex && parent.childNodes[ nodeIndex ];
 
-							while ( (node = ++nodeIndex && node && node[ dir ] ||
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
 
 								// Fallback to seeking `elem` from the start
-								(diff = nodeIndex = 0) || start.pop()) ) {
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
 
 								// When found, cache indexes on `parent` and break
 								if ( node.nodeType === 1 && ++diff && node === elem ) {
@@ -1842,16 +1984,18 @@ Expr = Sizzle.selectors = {
 							}
 
 						} else {
+
 							// Use previously-cached element index if available
 							if ( useCache ) {
+
 								// ...in a gzip-friendly way
 								node = elem;
-								outerCache = node[ expando ] || (node[ expando ] = {});
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
 
 								// Support: IE <9 only
 								// Defend against cloned attroperties (jQuery gh-1709)
 								uniqueCache = outerCache[ node.uniqueID ] ||
-									(outerCache[ node.uniqueID ] = {});
+									( outerCache[ node.uniqueID ] = {} );
 
 								cache = uniqueCache[ type ] || [];
 								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
@@ -1861,9 +2005,10 @@ Expr = Sizzle.selectors = {
 							// xml :nth-child(...)
 							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
 							if ( diff === false ) {
+
 								// Use the same loop as above to seek `elem` from the start
-								while ( (node = ++nodeIndex && node && node[ dir ] ||
-									(diff = nodeIndex = 0) || start.pop()) ) {
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
 
 									if ( ( ofType ?
 										node.nodeName.toLowerCase() === name :
@@ -1872,12 +2017,13 @@ Expr = Sizzle.selectors = {
 
 										// Cache the index of each encountered element
 										if ( useCache ) {
-											outerCache = node[ expando ] || (node[ expando ] = {});
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
 
 											// Support: IE <9 only
 											// Defend against cloned attroperties (jQuery gh-1709)
 											uniqueCache = outerCache[ node.uniqueID ] ||
-												(outerCache[ node.uniqueID ] = {});
+												( outerCache[ node.uniqueID ] = {} );
 
 											uniqueCache[ type ] = [ dirruns, diff ];
 										}
@@ -1898,6 +2044,7 @@ Expr = Sizzle.selectors = {
 		},
 
 		"PSEUDO": function( pseudo, argument ) {
+
 			// pseudo-class names are case-insensitive
 			// http://www.w3.org/TR/selectors/#pseudo-classes
 			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
@@ -1917,15 +2064,15 @@ Expr = Sizzle.selectors = {
 			if ( fn.length > 1 ) {
 				args = [ pseudo, pseudo, "", argument ];
 				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction(function( seed, matches ) {
+					markFunction( function( seed, matches ) {
 						var idx,
 							matched = fn( seed, argument ),
 							i = matched.length;
 						while ( i-- ) {
-							idx = indexOf( seed, matched[i] );
-							seed[ idx ] = !( matches[ idx ] = matched[i] );
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
 						}
-					}) :
+					} ) :
 					function( elem ) {
 						return fn( elem, 0, args );
 					};
@@ -1936,8 +2083,10 @@ Expr = Sizzle.selectors = {
 	},
 
 	pseudos: {
+
 		// Potentially complex pseudos
-		"not": markFunction(function( selector ) {
+		"not": markFunction( function( selector ) {
+
 			// Trim the selector passed to compile
 			// to avoid treating leading and trailing
 			// spaces as combinators
@@ -1946,39 +2095,40 @@ Expr = Sizzle.selectors = {
 				matcher = compile( selector.replace( rtrim, "$1" ) );
 
 			return matcher[ expando ] ?
-				markFunction(function( seed, matches, context, xml ) {
+				markFunction( function( seed, matches, _context, xml ) {
 					var elem,
 						unmatched = matcher( seed, null, xml, [] ),
 						i = seed.length;
 
 					// Match elements unmatched by `matcher`
 					while ( i-- ) {
-						if ( (elem = unmatched[i]) ) {
-							seed[i] = !(matches[i] = elem);
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
 						}
 					}
-				}) :
-				function( elem, context, xml ) {
-					input[0] = elem;
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
 					matcher( input, null, xml, results );
+
 					// Don't keep the element (issue #299)
-					input[0] = null;
+					input[ 0 ] = null;
 					return !results.pop();
 				};
-		}),
+		} ),
 
-		"has": markFunction(function( selector ) {
+		"has": markFunction( function( selector ) {
 			return function( elem ) {
 				return Sizzle( selector, elem ).length > 0;
 			};
-		}),
+		} ),
 
-		"contains": markFunction(function( text ) {
+		"contains": markFunction( function( text ) {
 			text = text.replace( runescape, funescape );
 			return function( elem ) {
 				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
 			};
-		}),
+		} ),
 
 		// "Whether an element is represented by a :lang() selector
 		// is based solely on the element's language value
@@ -1988,25 +2138,26 @@ Expr = Sizzle.selectors = {
 		// The identifier C does not have to be a valid language name."
 		// http://www.w3.org/TR/selectors/#lang-pseudo
 		"lang": markFunction( function( lang ) {
+
 			// lang value must be a valid identifier
-			if ( !ridentifier.test(lang || "") ) {
+			if ( !ridentifier.test( lang || "" ) ) {
 				Sizzle.error( "unsupported lang: " + lang );
 			}
 			lang = lang.replace( runescape, funescape ).toLowerCase();
 			return function( elem ) {
 				var elemLang;
 				do {
-					if ( (elemLang = documentIsHTML ?
+					if ( ( elemLang = documentIsHTML ?
 						elem.lang :
-						elem.getAttribute("xml:lang") || elem.getAttribute("lang")) ) {
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
 
 						elemLang = elemLang.toLowerCase();
 						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
 					}
-				} while ( (elem = elem.parentNode) && elem.nodeType === 1 );
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
 				return false;
 			};
-		}),
+		} ),
 
 		// Miscellaneous
 		"target": function( elem ) {
@@ -2019,7 +2170,9 @@ Expr = Sizzle.selectors = {
 		},
 
 		"focus": function( elem ) {
-			return elem === document.activeElement && (!document.hasFocus || document.hasFocus()) && !!(elem.type || elem.href || ~elem.tabIndex);
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
 		},
 
 		// Boolean properties
@@ -2027,16 +2180,20 @@ Expr = Sizzle.selectors = {
 		"disabled": createDisabledPseudo( true ),
 
 		"checked": function( elem ) {
+
 			// In CSS3, :checked should return both checked and selected elements
 			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
 			var nodeName = elem.nodeName.toLowerCase();
-			return (nodeName === "input" && !!elem.checked) || (nodeName === "option" && !!elem.selected);
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
 		},
 
 		"selected": function( elem ) {
+
 			// Accessing this property makes selected-by-default
 			// options in Safari work properly
 			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
 				elem.parentNode.selectedIndex;
 			}
 
@@ -2045,6 +2202,7 @@ Expr = Sizzle.selectors = {
 
 		// Contents
 		"empty": function( elem ) {
+
 			// http://www.w3.org/TR/selectors/#empty-pseudo
 			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
 			//   but not by others (comment: 8; processing instruction: 7; etc.)
@@ -2058,7 +2216,7 @@ Expr = Sizzle.selectors = {
 		},
 
 		"parent": function( elem ) {
-			return !Expr.pseudos["empty"]( elem );
+			return !Expr.pseudos[ "empty" ]( elem );
 		},
 
 		// Element/input types
@@ -2082,39 +2240,40 @@ Expr = Sizzle.selectors = {
 
 				// Support: IE<8
 				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( (attr = elem.getAttribute("type")) == null || attr.toLowerCase() === "text" );
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
 		},
 
 		// Position-in-collection
-		"first": createPositionalPseudo(function() {
+		"first": createPositionalPseudo( function() {
 			return [ 0 ];
-		}),
+		} ),
 
-		"last": createPositionalPseudo(function( matchIndexes, length ) {
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
 			return [ length - 1 ];
-		}),
+		} ),
 
-		"eq": createPositionalPseudo(function( matchIndexes, length, argument ) {
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
 			return [ argument < 0 ? argument + length : argument ];
-		}),
+		} ),
 
-		"even": createPositionalPseudo(function( matchIndexes, length ) {
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
 			var i = 0;
 			for ( ; i < length; i += 2 ) {
 				matchIndexes.push( i );
 			}
 			return matchIndexes;
-		}),
+		} ),
 
-		"odd": createPositionalPseudo(function( matchIndexes, length ) {
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
 			var i = 1;
 			for ( ; i < length; i += 2 ) {
 				matchIndexes.push( i );
 			}
 			return matchIndexes;
-		}),
+		} ),
 
-		"lt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
 			var i = argument < 0 ?
 				argument + length :
 				argument > length ?
@@ -2124,19 +2283,19 @@ Expr = Sizzle.selectors = {
 				matchIndexes.push( i );
 			}
 			return matchIndexes;
-		}),
+		} ),
 
-		"gt": createPositionalPseudo(function( matchIndexes, length, argument ) {
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
 			var i = argument < 0 ? argument + length : argument;
 			for ( ; ++i < length; ) {
 				matchIndexes.push( i );
 			}
 			return matchIndexes;
-		})
+		} )
 	}
 };
 
-Expr.pseudos["nth"] = Expr.pseudos["eq"];
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
 
 // Add button/input type pseudos
 for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
@@ -2167,37 +2326,39 @@ tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
 	while ( soFar ) {
 
 		// Comma and first run
-		if ( !matched || (match = rcomma.exec( soFar )) ) {
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
 			if ( match ) {
+
 				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[0].length ) || soFar;
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
 			}
-			groups.push( (tokens = []) );
+			groups.push( ( tokens = [] ) );
 		}
 
 		matched = false;
 
 		// Combinators
-		if ( (match = rcombinators.exec( soFar )) ) {
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
 			matched = match.shift();
-			tokens.push({
+			tokens.push( {
 				value: matched,
+
 				// Cast descendant combinators to space
-				type: match[0].replace( rtrim, " " )
-			});
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
 			soFar = soFar.slice( matched.length );
 		}
 
 		// Filters
 		for ( type in Expr.filter ) {
-			if ( (match = matchExpr[ type ].exec( soFar )) && (!preFilters[ type ] ||
-				(match = preFilters[ type ]( match ))) ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
 				matched = match.shift();
-				tokens.push({
+				tokens.push( {
 					value: matched,
 					type: type,
 					matches: match
-				});
+				} );
 				soFar = soFar.slice( matched.length );
 			}
 		}
@@ -2214,6 +2375,7 @@ tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
 		soFar.length :
 		soFar ?
 			Sizzle.error( selector ) :
+
 			// Cache the tokens
 			tokenCache( selector, groups ).slice( 0 );
 };
@@ -2223,7 +2385,7 @@ function toSelector( tokens ) {
 		len = tokens.length,
 		selector = "";
 	for ( ; i < len; i++ ) {
-		selector += tokens[i].value;
+		selector += tokens[ i ].value;
 	}
 	return selector;
 }
@@ -2236,9 +2398,10 @@ function addCombinator( matcher, combinator, base ) {
 		doneName = done++;
 
 	return combinator.first ?
+
 		// Check against closest ancestor/preceding element
 		function( elem, context, xml ) {
-			while ( (elem = elem[ dir ]) ) {
+			while ( ( elem = elem[ dir ] ) ) {
 				if ( elem.nodeType === 1 || checkNonElements ) {
 					return matcher( elem, context, xml );
 				}
@@ -2253,7 +2416,7 @@ function addCombinator( matcher, combinator, base ) {
 
 			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
 			if ( xml ) {
-				while ( (elem = elem[ dir ]) ) {
+				while ( ( elem = elem[ dir ] ) ) {
 					if ( elem.nodeType === 1 || checkNonElements ) {
 						if ( matcher( elem, context, xml ) ) {
 							return true;
@@ -2261,27 +2424,29 @@ function addCombinator( matcher, combinator, base ) {
 					}
 				}
 			} else {
-				while ( (elem = elem[ dir ]) ) {
+				while ( ( elem = elem[ dir ] ) ) {
 					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || (elem[ expando ] = {});
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
 
 						// Support: IE <9 only
 						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] || (outerCache[ elem.uniqueID ] = {});
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
 
 						if ( skip && skip === elem.nodeName.toLowerCase() ) {
 							elem = elem[ dir ] || elem;
-						} else if ( (oldCache = uniqueCache[ key ]) &&
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
 							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
 
 							// Assign to newCache so results back-propagate to previous elements
-							return (newCache[ 2 ] = oldCache[ 2 ]);
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
 						} else {
+
 							// Reuse newcache so results back-propagate to previous elements
 							uniqueCache[ key ] = newCache;
 
 							// A match means we're done; a fail means we have to keep checking
-							if ( (newCache[ 2 ] = matcher( elem, context, xml )) ) {
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
 								return true;
 							}
 						}
@@ -2297,20 +2462,20 @@ function elementMatcher( matchers ) {
 		function( elem, context, xml ) {
 			var i = matchers.length;
 			while ( i-- ) {
-				if ( !matchers[i]( elem, context, xml ) ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
 					return false;
 				}
 			}
 			return true;
 		} :
-		matchers[0];
+		matchers[ 0 ];
 }
 
 function multipleContexts( selector, contexts, results ) {
 	var i = 0,
 		len = contexts.length;
 	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[i], results );
+		Sizzle( selector, contexts[ i ], results );
 	}
 	return results;
 }
@@ -2323,7 +2488,7 @@ function condense( unmatched, map, filter, context, xml ) {
 		mapped = map != null;
 
 	for ( ; i < len; i++ ) {
-		if ( (elem = unmatched[i]) ) {
+		if ( ( elem = unmatched[ i ] ) ) {
 			if ( !filter || filter( elem, context, xml ) ) {
 				newUnmatched.push( elem );
 				if ( mapped ) {
@@ -2343,14 +2508,18 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
 	if ( postFinder && !postFinder[ expando ] ) {
 		postFinder = setMatcher( postFinder, postSelector );
 	}
-	return markFunction(function( seed, results, context, xml ) {
+	return markFunction( function( seed, results, context, xml ) {
 		var temp, i, elem,
 			preMap = [],
 			postMap = [],
 			preexisting = results.length,
 
 			// Get initial elements from seed or context
-			elems = seed || multipleContexts( selector || "*", context.nodeType ? [ context ] : context, [] ),
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
 
 			// Prefilter to get matcher input, preserving a map for seed-results synchronization
 			matcherIn = preFilter && ( seed || !selector ) ?
@@ -2358,6 +2527,7 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
 				elems,
 
 			matcherOut = matcher ?
+
 				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
 				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
 
@@ -2381,8 +2551,8 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
 			// Un-match failing elements by moving them back to matcherIn
 			i = temp.length;
 			while ( i-- ) {
-				if ( (elem = temp[i]) ) {
-					matcherOut[ postMap[i] ] = !(matcherIn[ postMap[i] ] = elem);
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
 				}
 			}
 		}
@@ -2390,25 +2560,27 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
 		if ( seed ) {
 			if ( postFinder || preFilter ) {
 				if ( postFinder ) {
+
 					// Get the final matcherOut by condensing this intermediate into postFinder contexts
 					temp = [];
 					i = matcherOut.length;
 					while ( i-- ) {
-						if ( (elem = matcherOut[i]) ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
 							// Restore matcherIn since elem is not yet a final match
-							temp.push( (matcherIn[i] = elem) );
+							temp.push( ( matcherIn[ i ] = elem ) );
 						}
 					}
-					postFinder( null, (matcherOut = []), temp, xml );
+					postFinder( null, ( matcherOut = [] ), temp, xml );
 				}
 
 				// Move matched elements from seed to results to keep them synchronized
 				i = matcherOut.length;
 				while ( i-- ) {
-					if ( (elem = matcherOut[i]) &&
-						(temp = postFinder ? indexOf( seed, elem ) : preMap[i]) > -1 ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
 
-						seed[temp] = !(results[temp] = elem);
+						seed[ temp ] = !( results[ temp ] = elem );
 					}
 				}
 			}
@@ -2426,14 +2598,14 @@ function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postS
 				push.apply( results, matcherOut );
 			}
 		}
-	});
+	} );
 }
 
 function matcherFromTokens( tokens ) {
 	var checkContext, matcher, j,
 		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[0].type ],
-		implicitRelative = leadingRelative || Expr.relative[" "],
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
 		i = leadingRelative ? 1 : 0,
 
 		// The foundational matcher ensures that elements are reachable from top-level context(s)
@@ -2445,38 +2617,43 @@ function matcherFromTokens( tokens ) {
 		}, implicitRelative, true ),
 		matchers = [ function( elem, context, xml ) {
 			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				(checkContext = context).nodeType ?
+				( checkContext = context ).nodeType ?
 					matchContext( elem, context, xml ) :
 					matchAnyContext( elem, context, xml ) );
+
 			// Avoid hanging onto element (issue #299)
 			checkContext = null;
 			return ret;
 		} ];
 
 	for ( ; i < len; i++ ) {
-		if ( (matcher = Expr.relative[ tokens[i].type ]) ) {
-			matchers = [ addCombinator(elementMatcher( matchers ), matcher) ];
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
 		} else {
-			matcher = Expr.filter[ tokens[i].type ].apply( null, tokens[i].matches );
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
 
 			// Return special upon seeing a positional matcher
 			if ( matcher[ expando ] ) {
+
 				// Find the next relative operator (if any) for proper handling
 				j = ++i;
 				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[j].type ] ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
 						break;
 					}
 				}
 				return setMatcher(
 					i > 1 && elementMatcher( matchers ),
 					i > 1 && toSelector(
-						// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-						tokens.slice( 0, i - 1 ).concat({ value: tokens[ i - 2 ].type === " " ? "*" : "" })
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
 					).replace( rtrim, "$1" ),
 					matcher,
 					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( (tokens = tokens.slice( j )) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
 					j < len && toSelector( tokens )
 				);
 			}
@@ -2497,28 +2674,40 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
 				unmatched = seed && [],
 				setMatched = [],
 				contextBackup = outermostContext,
+
 				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find["TAG"]( "*", outermost ),
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
 				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = (dirruns += contextBackup == null ? 1 : Math.random() || 0.1),
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
 				len = elems.length;
 
 			if ( outermost ) {
-				outermostContext = context === document || context || outermost;
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
 			}
 
 			// Add elements passing elementMatchers directly to results
 			// Support: IE<9, Safari
 			// Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id
-			for ( ; i !== len && (elem = elems[i]) != null; i++ ) {
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
 				if ( byElement && elem ) {
 					j = 0;
-					if ( !context && elem.ownerDocument !== document ) {
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
 						setDocument( elem );
 						xml = !documentIsHTML;
 					}
-					while ( (matcher = elementMatchers[j++]) ) {
-						if ( matcher( elem, context || document, xml) ) {
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
 							results.push( elem );
 							break;
 						}
@@ -2530,8 +2719,9 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
 
 				// Track unmatched elements for set filters
 				if ( bySet ) {
+
 					// They will have gone through all possible matchers
-					if ( (elem = !matcher && elem) ) {
+					if ( ( elem = !matcher && elem ) ) {
 						matchedCount--;
 					}
 
@@ -2555,16 +2745,17 @@ function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
 			// numerically zero.
 			if ( bySet && i !== matchedCount ) {
 				j = 0;
-				while ( (matcher = setMatchers[j++]) ) {
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
 					matcher( unmatched, setMatched, context, xml );
 				}
 
 				if ( seed ) {
+
 					// Reintegrate element matches to eliminate the need for sorting
 					if ( matchedCount > 0 ) {
 						while ( i-- ) {
-							if ( !(unmatched[i] || setMatched[i]) ) {
-								setMatched[i] = pop.call( results );
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
 							}
 						}
 					}
@@ -2605,13 +2796,14 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
 		cached = compilerCache[ selector + " " ];
 
 	if ( !cached ) {
+
 		// Generate a function of recursive functions that can be used to check each element
 		if ( !match ) {
 			match = tokenize( selector );
 		}
 		i = match.length;
 		while ( i-- ) {
-			cached = matcherFromTokens( match[i] );
+			cached = matcherFromTokens( match[ i ] );
 			if ( cached[ expando ] ) {
 				setMatchers.push( cached );
 			} else {
@@ -2620,7 +2812,10 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
 		}
 
 		// Cache the compiled function
-		cached = compilerCache( selector, matcherFromGroupMatchers( elementMatchers, setMatchers ) );
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
 
 		// Save selector and tokenization
 		cached.selector = selector;
@@ -2640,7 +2835,7 @@ compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
 select = Sizzle.select = function( selector, context, results, seed ) {
 	var i, tokens, token, type, find,
 		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( (selector = compiled.selector || selector) );
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
 
 	results = results || [];
 
@@ -2649,11 +2844,12 @@ select = Sizzle.select = function( selector, context, results, seed ) {
 	if ( match.length === 1 ) {
 
 		// Reduce context if the leading compound selector is an ID
-		tokens = match[0] = match[0].slice( 0 );
-		if ( tokens.length > 2 && (token = tokens[0]).type === "ID" &&
-				context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[1].type ] ) {
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
 
-			context = ( Expr.find["ID"]( token.matches[0].replace(runescape, funescape), context ) || [] )[0];
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
 			if ( !context ) {
 				return results;
 
@@ -2666,20 +2862,22 @@ select = Sizzle.select = function( selector, context, results, seed ) {
 		}
 
 		// Fetch a seed set for right-to-left matching
-		i = matchExpr["needsContext"].test( selector ) ? 0 : tokens.length;
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
 		while ( i-- ) {
-			token = tokens[i];
+			token = tokens[ i ];
 
 			// Abort if we hit a combinator
-			if ( Expr.relative[ (type = token.type) ] ) {
+			if ( Expr.relative[ ( type = token.type ) ] ) {
 				break;
 			}
-			if ( (find = Expr.find[ type ]) ) {
+			if ( ( find = Expr.find[ type ] ) ) {
+
 				// Search, expanding context for leading sibling combinators
-				if ( (seed = find(
-					token.matches[0].replace( runescape, funescape ),
-					rsibling.test( tokens[0].type ) && testContext( context.parentNode ) || context
-				)) ) {
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
 
 					// If seed is empty or no tokens remain, we can return early
 					tokens.splice( i, 1 );
@@ -2710,7 +2908,7 @@ select = Sizzle.select = function( selector, context, results, seed ) {
 // One-time assignments
 
 // Sort stability
-support.sortStable = expando.split("").sort( sortOrder ).join("") === expando;
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
 
 // Support: Chrome 14-35+
 // Always assume duplicates if they aren't passed to the comparison function
@@ -2721,58 +2919,59 @@ setDocument();
 
 // Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
 // Detached nodes confoundingly follow *each other*
-support.sortDetached = assert(function( el ) {
+support.sortDetached = assert( function( el ) {
+
 	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement("fieldset") ) & 1;
-});
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
 
 // Support: IE<8
 // Prevent attribute/property "interpolation"
 // https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert(function( el ) {
+if ( !assert( function( el ) {
 	el.innerHTML = "";
-	return el.firstChild.getAttribute("href") === "#" ;
-}) ) {
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
 	addHandle( "type|href|height|width", function( elem, name, isXML ) {
 		if ( !isXML ) {
 			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
 		}
-	});
+	} );
 }
 
 // Support: IE<9
 // Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert(function( el ) {
+if ( !support.attributes || !assert( function( el ) {
 	el.innerHTML = "";
 	el.firstChild.setAttribute( "value", "" );
 	return el.firstChild.getAttribute( "value" ) === "";
-}) ) {
-	addHandle( "value", function( elem, name, isXML ) {
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
 		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
 			return elem.defaultValue;
 		}
-	});
+	} );
 }
 
 // Support: IE<9
 // Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert(function( el ) {
-	return el.getAttribute("disabled") == null;
-}) ) {
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
 	addHandle( booleans, function( elem, name, isXML ) {
 		var val;
 		if ( !isXML ) {
 			return elem[ name ] === true ? name.toLowerCase() :
-					(val = elem.getAttributeNode( name )) && val.specified ?
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
 					val.value :
-				null;
+					null;
 		}
-	});
+	} );
 }
 
 return Sizzle;
 
-})( window );
+} )( window );
 
 
 
@@ -3141,7 +3340,7 @@ jQuery.each( {
 	parents: function( elem ) {
 		return dir( elem, "parentNode" );
 	},
-	parentsUntil: function( elem, i, until ) {
+	parentsUntil: function( elem, _i, until ) {
 		return dir( elem, "parentNode", until );
 	},
 	next: function( elem ) {
@@ -3156,10 +3355,10 @@ jQuery.each( {
 	prevAll: function( elem ) {
 		return dir( elem, "previousSibling" );
 	},
-	nextUntil: function( elem, i, until ) {
+	nextUntil: function( elem, _i, until ) {
 		return dir( elem, "nextSibling", until );
 	},
-	prevUntil: function( elem, i, until ) {
+	prevUntil: function( elem, _i, until ) {
 		return dir( elem, "previousSibling", until );
 	},
 	siblings: function( elem ) {
@@ -3169,7 +3368,13 @@ jQuery.each( {
 		return siblings( elem.firstChild );
 	},
 	contents: function( elem ) {
-		if ( typeof elem.contentDocument !== "undefined" ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			//  elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
 			return elem.contentDocument;
 		}
 
@@ -3512,7 +3717,7 @@ jQuery.extend( {
 					var fns = arguments;
 
 					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( i, tuple ) {
+						jQuery.each( tuples, function( _i, tuple ) {
 
 							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
 							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
@@ -3965,7 +4170,7 @@ var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
 			// ...except when executing function values
 			} else {
 				bulk = fn;
-				fn = function( elem, key, value ) {
+				fn = function( elem, _key, value ) {
 					return bulk.call( jQuery( elem ), value );
 				};
 			}
@@ -4000,7 +4205,7 @@ var rmsPrefix = /^-ms-/,
 	rdashAlpha = /-([a-z])/g;
 
 // Used by camelCase as callback to replace()
-function fcamelCase( all, letter ) {
+function fcamelCase( _all, letter ) {
 	return letter.toUpperCase();
 }
 
@@ -4528,27 +4733,6 @@ var isHiddenWithinTree = function( elem, el ) {
 			jQuery.css( elem, "display" ) === "none";
 	};
 
-var swap = function( elem, options, callback, args ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.apply( elem, args || [] );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
 
 
 function adjustCSS( elem, prop, valueParts, tween ) {
@@ -4719,11 +4903,40 @@ var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
 
 
 
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
 
 	// Support: IE <=9 only
-	option: [ 1, "" ],
+	// IE <=9 replaces ";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
 
 	// XHTML parsers do not magically insert elements in the
 	// same way that tag soup parsers do. So we cannot shorten
@@ -4736,12 +4949,14 @@ var wrapMap = {
 	_default: [ 0, "", "" ]
 };
 
-// Support: IE <=9 only
-wrapMap.optgroup = wrapMap.option;
-
 wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
 wrapMap.th = wrapMap.td;
 
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "" ];
+}
+
 
 function getAll( context, tag ) {
 
@@ -4874,32 +5089,6 @@ function buildFragment( elems, context, scripts, selection, ignored ) {
 }
 
 
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-} )();
-
-
 var
 	rkeyEvent = /^key/,
 	rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
@@ -5008,8 +5197,8 @@ jQuery.event = {
 			special, handlers, type, namespaces, origType,
 			elemData = dataPriv.get( elem );
 
-		// Don't attach events to noData or text/comment nodes (but allow plain objects)
-		if ( !elemData ) {
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
 			return;
 		}
 
@@ -5033,7 +5222,7 @@ jQuery.event = {
 
 		// Init the element's event structure and main handler, if this is the first
 		if ( !( events = elemData.events ) ) {
-			events = elemData.events = {};
+			events = elemData.events = Object.create( null );
 		}
 		if ( !( eventHandle = elemData.handle ) ) {
 			eventHandle = elemData.handle = function( e ) {
@@ -5191,12 +5380,15 @@ jQuery.event = {
 
 	dispatch: function( nativeEvent ) {
 
-		// Make a writable jQuery.Event from the native event object
-		var event = jQuery.event.fix( nativeEvent );
-
 		var i, j, ret, matched, handleObj, handlerQueue,
 			args = new Array( arguments.length ),
-			handlers = ( dataPriv.get( this, "events" ) || {} )[ event.type ] || [],
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+					dataPriv.get( this, "events" ) || Object.create( null )
+				)[ event.type ] || [],
 			special = jQuery.event.special[ event.type ] || {};
 
 		// Use the fix-ed jQuery.Event rather than the (read-only) native event
@@ -5771,13 +5963,6 @@ jQuery.fn.extend( {
 
 var
 
-	/* eslint-disable max-len */
-
-	// See https://github.com/eslint/eslint/issues/3229
-	rxhtmlTag = /<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi,
-
-	/* eslint-enable */
-
 	// Support: IE <=10 - 11, Edge 12 - 13 only
 	// In IE/Edge using regex groups here causes severe slowdowns.
 	// See https://connect.microsoft.com/IE/feedback/details/1736512/
@@ -5814,7 +5999,7 @@ function restoreScript( elem ) {
 }
 
 function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, pdataCur, udataOld, udataCur, events;
+	var i, l, type, pdataOld, udataOld, udataCur, events;
 
 	if ( dest.nodeType !== 1 ) {
 		return;
@@ -5822,13 +6007,11 @@ function cloneCopyEvent( src, dest ) {
 
 	// 1. Copy private data: events, handlers, etc.
 	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.access( src );
-		pdataCur = dataPriv.set( dest, pdataOld );
+		pdataOld = dataPriv.get( src );
 		events = pdataOld.events;
 
 		if ( events ) {
-			delete pdataCur.handle;
-			pdataCur.events = {};
+			dataPriv.remove( dest, "handle events" );
 
 			for ( type in events ) {
 				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
@@ -5864,7 +6047,7 @@ function fixInput( src, dest ) {
 function domManip( collection, args, callback, ignored ) {
 
 	// Flatten any nested arrays
-	args = concat.apply( [], args );
+	args = flat( args );
 
 	var fragment, first, scripts, hasScripts, node, doc,
 		i = 0,
@@ -5939,7 +6122,7 @@ function domManip( collection, args, callback, ignored ) {
 							if ( jQuery._evalUrl && !node.noModule ) {
 								jQuery._evalUrl( node.src, {
 									nonce: node.nonce || node.getAttribute( "nonce" )
-								} );
+								}, doc );
 							}
 						} else {
 							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
@@ -5976,7 +6159,7 @@ function remove( elem, selector, keepData ) {
 
 jQuery.extend( {
 	htmlPrefilter: function( html ) {
-		return html.replace( rxhtmlTag, "<$1>" );
+		return html;
 	},
 
 	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
@@ -6238,6 +6421,27 @@ var getStyles = function( elem ) {
 		return view.getComputedStyle( elem );
 	};
 
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
 var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
 
 
@@ -6295,7 +6499,7 @@ var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
 	}
 
 	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableMarginLeftVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
 		container = document.createElement( "div" ),
 		div = document.createElement( "div" );
 
@@ -6330,6 +6534,35 @@ var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
 		scrollboxSize: function() {
 			computeStyleTests();
 			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px";
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
 		}
 	} );
 } )();
@@ -6454,7 +6687,7 @@ var
 		fontWeight: "400"
 	};
 
-function setPositiveNumber( elem, value, subtract ) {
+function setPositiveNumber( _elem, value, subtract ) {
 
 	// Any relative (+/-) values have already been
 	// normalized at this point
@@ -6559,17 +6792,26 @@ function getWidthOrHeight( elem, dimension, extra ) {
 	}
 
 
-	// Fall back to offsetWidth/offsetHeight when value is "auto"
-	// This happens for inline elements with no explicit setting (gh-3571)
-	// Support: Android <=4.1 - 4.3 only
-	// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-	// Support: IE 9-11 only
-	// Also use offsetWidth/offsetHeight for when box sizing is unreliable
-	// We use getClientRects() to check for hidden/disconnected.
-	// In those cases, the computed value can be trusted to be border-box
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
 	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
 		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
 		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
 		elem.getClientRects().length ) {
 
 		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
@@ -6764,7 +7006,7 @@ jQuery.extend( {
 	}
 } );
 
-jQuery.each( [ "height", "width" ], function( i, dimension ) {
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
 	jQuery.cssHooks[ dimension ] = {
 		get: function( elem, computed, extra ) {
 			if ( computed ) {
@@ -7537,7 +7779,7 @@ jQuery.fn.extend( {
 			clearQueue = type;
 			type = undefined;
 		}
-		if ( clearQueue && type !== false ) {
+		if ( clearQueue ) {
 			this.queue( type || "fx", [] );
 		}
 
@@ -7620,7 +7862,7 @@ jQuery.fn.extend( {
 	}
 } );
 
-jQuery.each( [ "toggle", "show", "hide" ], function( i, name ) {
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
 	var cssFn = jQuery.fn[ name ];
 	jQuery.fn[ name ] = function( speed, easing, callback ) {
 		return speed == null || typeof speed === "boolean" ?
@@ -7841,7 +8083,7 @@ boolHook = {
 	}
 };
 
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( i, name ) {
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
 	var getter = attrHandle[ name ] || jQuery.find.attr;
 
 	attrHandle[ name ] = function( elem, name, isXML ) {
@@ -8465,7 +8707,9 @@ jQuery.extend( jQuery.event, {
 				special.bindType || type;
 
 			// jQuery handler
-			handle = ( dataPriv.get( cur, "events" ) || {} )[ event.type ] &&
+			handle = (
+					dataPriv.get( cur, "events" ) || Object.create( null )
+				)[ event.type ] &&
 				dataPriv.get( cur, "handle" );
 			if ( handle ) {
 				handle.apply( cur, data );
@@ -8576,7 +8820,10 @@ if ( !support.focusin ) {
 
 		jQuery.event.special[ fix ] = {
 			setup: function() {
-				var doc = this.ownerDocument || this,
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
 					attaches = dataPriv.access( doc, fix );
 
 				if ( !attaches ) {
@@ -8585,7 +8832,7 @@ if ( !support.focusin ) {
 				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
 			},
 			teardown: function() {
-				var doc = this.ownerDocument || this,
+				var doc = this.ownerDocument || this.document || this,
 					attaches = dataPriv.access( doc, fix ) - 1;
 
 				if ( !attaches ) {
@@ -8601,7 +8848,7 @@ if ( !support.focusin ) {
 }
 var location = window.location;
 
-var nonce = Date.now();
+var nonce = { guid: Date.now() };
 
 var rquery = ( /\?/ );
 
@@ -8733,7 +8980,7 @@ jQuery.fn.extend( {
 				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
 				( this.checked || !rcheckableType.test( type ) );
 		} )
-		.map( function( i, elem ) {
+		.map( function( _i, elem ) {
 			var val = jQuery( this ).val();
 
 			if ( val == null ) {
@@ -9346,7 +9593,8 @@ jQuery.extend( {
 			// Add or update anti-cache param if needed
 			if ( s.cache === false ) {
 				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce++ ) + uncached;
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
 			}
 
 			// Put hash and anti-cache on the URL that will be requested (gh-1732)
@@ -9479,6 +9727,11 @@ jQuery.extend( {
 				response = ajaxHandleResponses( s, jqXHR, responses );
 			}
 
+			// Use a noop converter for missing script
+			if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
 			// Convert no matter what (that way responseXXX fields are always set)
 			response = ajaxConvert( s, response, jqXHR, isSuccess );
 
@@ -9569,7 +9822,7 @@ jQuery.extend( {
 	}
 } );
 
-jQuery.each( [ "get", "post" ], function( i, method ) {
+jQuery.each( [ "get", "post" ], function( _i, method ) {
 	jQuery[ method ] = function( url, data, callback, type ) {
 
 		// Shift arguments if data argument was omitted
@@ -9590,8 +9843,17 @@ jQuery.each( [ "get", "post" ], function( i, method ) {
 	};
 } );
 
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
 
-jQuery._evalUrl = function( url, options ) {
+jQuery._evalUrl = function( url, options, doc ) {
 	return jQuery.ajax( {
 		url: url,
 
@@ -9609,7 +9871,7 @@ jQuery._evalUrl = function( url, options ) {
 			"text script": function() {}
 		},
 		dataFilter: function( response ) {
-			jQuery.globalEval( response, options );
+			jQuery.globalEval( response, options, doc );
 		}
 	} );
 };
@@ -9931,7 +10193,7 @@ var oldCallbacks = [],
 jQuery.ajaxSetup( {
 	jsonp: "callback",
 	jsonpCallback: function() {
-		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce++ ) );
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
 		this[ callback ] = true;
 		return callback;
 	}
@@ -10148,23 +10410,6 @@ jQuery.fn.load = function( url, params, callback ) {
 
 
 
-// Attach a bunch of functions for handling common AJAX events
-jQuery.each( [
-	"ajaxStart",
-	"ajaxStop",
-	"ajaxComplete",
-	"ajaxError",
-	"ajaxSuccess",
-	"ajaxSend"
-], function( i, type ) {
-	jQuery.fn[ type ] = function( fn ) {
-		return this.on( type, fn );
-	};
-} );
-
-
-
-
 jQuery.expr.pseudos.animated = function( elem ) {
 	return jQuery.grep( jQuery.timers, function( fn ) {
 		return elem === fn.elem;
@@ -10221,6 +10466,12 @@ jQuery.offset = {
 			options.using.call( elem, props );
 
 		} else {
+			if ( typeof props.top === "number" ) {
+				props.top += "px";
+			}
+			if ( typeof props.left === "number" ) {
+				props.left += "px";
+			}
 			curElem.css( props );
 		}
 	}
@@ -10371,7 +10622,7 @@ jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function(
 // Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
 // getComputedStyle returns percent when specified for top/left/bottom/right;
 // rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( i, prop ) {
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
 	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
 		function( elem, computed ) {
 			if ( computed ) {
@@ -10434,25 +10685,19 @@ jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
 } );
 
 
-jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
-	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
-	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
-	function( i, name ) {
-
-	// Handle event binding
-	jQuery.fn[ name ] = function( data, fn ) {
-		return arguments.length > 0 ?
-			this.on( name, null, data, fn ) :
-			this.trigger( name );
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
 	};
 } );
 
-jQuery.fn.extend( {
-	hover: function( fnOver, fnOut ) {
-		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
-	}
-} );
-
 
 
 
@@ -10474,9 +10719,33 @@ jQuery.fn.extend( {
 		return arguments.length === 1 ?
 			this.off( selector, "**" ) :
 			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
 	}
 } );
 
+jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	} );
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
 // Bind a function to a context, optionally partially applying any
 // arguments.
 // jQuery.proxy is deprecated to promote standards (specifically Function#bind)
@@ -10539,6 +10808,11 @@ jQuery.isNumeric = function( obj ) {
 		!isNaN( obj - parseFloat( obj ) );
 };
 
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
 
 
 
@@ -10587,7 +10861,7 @@ jQuery.noConflict = function( deep ) {
 // Expose jQuery and $ identifiers, even in AMD
 // (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
 // and CommonJS for browser emulators (#13566)
-if ( !noGlobal ) {
+if ( typeof noGlobal === "undefined" ) {
 	window.jQuery = window.$ = jQuery;
 }
 
diff --git a/docs/build/html/_static/jquery.js b/docs/build/html/_static/jquery.js
index a1c07fd..b061403 100644
--- a/docs/build/html/_static/jquery.js
+++ b/docs/build/html/_static/jquery.js
@@ -1,2 +1,2 @@
-/*! jQuery v3.4.1 | (c) JS Foundation and other contributors | jquery.org/license */
-!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],E=C.document,r=Object.getPrototypeOf,s=t.slice,g=t.concat,u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType},x=function(e){return null!=e&&e===e.window},c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?n[o.call(e)]||"object":typeof e}var f="3.4.1",k=function(e,t){return new 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+        var listItem = $('
  • ');
         var requestUrl = "";
         var linkUrl = "";
         if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
@@ -274,28 +273,31 @@ var Search = {
         if (item[3]) {
           listItem.append($(' (' + item[3] + ')'));
           Search.output.append(listItem);
-          listItem.slideDown(5, function() {
+          setTimeout(function() {
             displayNextItem();
-          });
+          }, 5);
         } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
           $.ajax({url: requestUrl,
                   dataType: "text",
                   complete: function(jqxhr, textstatus) {
                     var data = jqxhr.responseText;
                     if (data !== '' && data !== undefined) {
-                      listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
+                      var summary = Search.makeSearchSummary(data, searchterms, hlterms);
+                      if (summary) {
+                        listItem.append(summary);
+                      }
                     }
                     Search.output.append(listItem);
-                    listItem.slideDown(5, function() {
+                    setTimeout(function() {
                       displayNextItem();
-                    });
+                    }, 5);
                   }});
         } else {
           // no source available, just display title
           Search.output.append(listItem);
-          listItem.slideDown(5, function() {
+          setTimeout(function() {
             displayNextItem();
-          });
+          }, 5);
         }
       }
       // search finished, update title and status message
@@ -326,7 +328,9 @@ var Search = {
     var results = [];
 
     for (var prefix in objects) {
-      for (var name in objects[prefix]) {
+      for (var iMatch = 0; iMatch != objects[prefix].length; ++iMatch) {
+        var match = objects[prefix][iMatch];
+        var name = match[4];
         var fullname = (prefix ? prefix + '.' : '') + name;
         var fullnameLower = fullname.toLowerCase()
         if (fullnameLower.indexOf(object) > -1) {
@@ -340,7 +344,6 @@ var Search = {
           } else if (parts[parts.length - 1].indexOf(object) > -1) {
             score += Scorer.objPartialMatch;
           }
-          var match = objects[prefix][name];
           var objname = objnames[match[1]][2];
           var title = titles[match[0]];
           // If more than one term searched for, we require other words to be
@@ -380,6 +383,13 @@ var Search = {
     return results;
   },
 
+  /**
+   * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions
+   */
+  escapeRegExp : function(string) {
+    return string.replace(/[.*+\-?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
+  },
+
   /**
    * search for full-text terms in the index
    */
@@ -403,13 +413,14 @@ var Search = {
       ];
       // add support for partial matches
       if (word.length > 2) {
+        var word_regex = this.escapeRegExp(word);
         for (var w in terms) {
-          if (w.match(word) && !terms[word]) {
+          if (w.match(word_regex) && !terms[word]) {
             _o.push({files: terms[w], score: Scorer.partialTerm})
           }
         }
         for (var w in titleterms) {
-          if (w.match(word) && !titleterms[word]) {
+          if (w.match(word_regex) && !titleterms[word]) {
               _o.push({files: titleterms[w], score: Scorer.partialTitle})
           }
         }
@@ -491,6 +502,9 @@ var Search = {
    */
   makeSearchSummary : function(htmlText, keywords, hlwords) {
     var text = Search.htmlToText(htmlText);
+    if (text == "") {
+      return null;
+    }
     var textLower = text.toLowerCase();
     var start = 0;
     $.each(keywords, function() {
@@ -502,7 +516,7 @@ var Search = {
     var excerpt = ((start > 0) ? '...' : '') +
       $.trim(text.substr(start, 240)) +
       ((start + 240 - text.length) ? '...' : '');
-    var rv = $('
    ').text(excerpt);
+    var rv = $('
    ').text(excerpt);
     $.each(hlwords, function() {
       rv = rv.highlightText(this, 'highlighted');
     });
diff --git a/docs/build/html/_static/underscore-1.13.1.js b/docs/build/html/_static/underscore-1.13.1.js
new file mode 100644
index 0000000..ffd77af
--- /dev/null
+++ b/docs/build/html/_static/underscore-1.13.1.js
@@ -0,0 +1,2042 @@
+(function (global, factory) {
+  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+  typeof define === 'function' && define.amd ? define('underscore', factory) :
+  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
+    var current = global._;
+    var exports = global._ = factory();
+    exports.noConflict = function () { global._ = current; return exports; };
+  }()));
+}(this, (function () {
+  //     Underscore.js 1.13.1
+  //     https://underscorejs.org
+  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+  //     Underscore may be freely distributed under the MIT license.
+
+  // Current version.
+  var VERSION = '1.13.1';
+
+  // Establish the root object, `window` (`self`) in the browser, `global`
+  // on the server, or `this` in some virtual machines. We use `self`
+  // instead of `window` for `WebWorker` support.
+  var root = typeof self == 'object' && self.self === self && self ||
+            typeof global == 'object' && global.global === global && global ||
+            Function('return this')() ||
+            {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
+  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var push = ArrayProto.push,
+      slice = ArrayProto.slice,
+      toString = ObjProto.toString,
+      hasOwnProperty = ObjProto.hasOwnProperty;
+
+  // Modern feature detection.
+  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
+      supportsDataView = typeof DataView !== 'undefined';
+
+  // All **ECMAScript 5+** native function implementations that we hope to use
+  // are declared here.
+  var nativeIsArray = Array.isArray,
+      nativeKeys = Object.keys,
+      nativeCreate = Object.create,
+      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
+
+  // Create references to these builtin functions because we override them.
+  var _isNaN = isNaN,
+      _isFinite = isFinite;
+
+  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
+  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
+  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
+    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
+
+  // The largest integer that can be represented exactly.
+  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
+
+  // Some functions take a variable number of arguments, or a few expected
+  // arguments at the beginning and then a variable number of values to operate
+  // on. This helper accumulates all remaining arguments past the function’s
+  // argument length (or an explicit `startIndex`), into an array that becomes
+  // the last argument. Similar to ES6’s "rest parameter".
+  function restArguments(func, startIndex) {
+    startIndex = startIndex == null ? func.length - 1 : +startIndex;
+    return function() {
+      var length = Math.max(arguments.length - startIndex, 0),
+          rest = Array(length),
+          index = 0;
+      for (; index < length; index++) {
+        rest[index] = arguments[index + startIndex];
+      }
+      switch (startIndex) {
+        case 0: return func.call(this, rest);
+        case 1: return func.call(this, arguments[0], rest);
+        case 2: return func.call(this, arguments[0], arguments[1], rest);
+      }
+      var args = Array(startIndex + 1);
+      for (index = 0; index < startIndex; index++) {
+        args[index] = arguments[index];
+      }
+      args[startIndex] = rest;
+      return func.apply(this, args);
+    };
+  }
+
+  // Is a given variable an object?
+  function isObject(obj) {
+    var type = typeof obj;
+    return type === 'function' || type === 'object' && !!obj;
+  }
+
+  // Is a given value equal to null?
+  function isNull(obj) {
+    return obj === null;
+  }
+
+  // Is a given variable undefined?
+  function isUndefined(obj) {
+    return obj === void 0;
+  }
+
+  // Is a given value a boolean?
+  function isBoolean(obj) {
+    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
+  }
+
+  // Is a given value a DOM element?
+  function isElement(obj) {
+    return !!(obj && obj.nodeType === 1);
+  }
+
+  // Internal function for creating a `toString`-based type tester.
+  function tagTester(name) {
+    var tag = '[object ' + name + ']';
+    return function(obj) {
+      return toString.call(obj) === tag;
+    };
+  }
+
+  var isString = tagTester('String');
+
+  var isNumber = tagTester('Number');
+
+  var isDate = tagTester('Date');
+
+  var isRegExp = tagTester('RegExp');
+
+  var isError = tagTester('Error');
+
+  var isSymbol = tagTester('Symbol');
+
+  var isArrayBuffer = tagTester('ArrayBuffer');
+
+  var isFunction = tagTester('Function');
+
+  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
+  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
+  var nodelist = root.document && root.document.childNodes;
+  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
+    isFunction = function(obj) {
+      return typeof obj == 'function' || false;
+    };
+  }
+
+  var isFunction$1 = isFunction;
+
+  var hasObjectTag = tagTester('Object');
+
+  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
+  // In IE 11, the most common among them, this problem also applies to
+  // `Map`, `WeakMap` and `Set`.
+  var hasStringTagBug = (
+        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
+      ),
+      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
+
+  var isDataView = tagTester('DataView');
+
+  // In IE 10 - Edge 13, we need a different heuristic
+  // to determine whether an object is a `DataView`.
+  function ie10IsDataView(obj) {
+    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
+  }
+
+  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native `Array.isArray`.
+  var isArray = nativeIsArray || tagTester('Array');
+
+  // Internal function to check whether `key` is an own property name of `obj`.
+  function has$1(obj, key) {
+    return obj != null && hasOwnProperty.call(obj, key);
+  }
+
+  var isArguments = tagTester('Arguments');
+
+  // Define a fallback version of the method in browsers (ahem, IE < 9), where
+  // there isn't any inspectable "Arguments" type.
+  (function() {
+    if (!isArguments(arguments)) {
+      isArguments = function(obj) {
+        return has$1(obj, 'callee');
+      };
+    }
+  }());
+
+  var isArguments$1 = isArguments;
+
+  // Is a given object a finite number?
+  function isFinite$1(obj) {
+    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
+  }
+
+  // Is the given value `NaN`?
+  function isNaN$1(obj) {
+    return isNumber(obj) && _isNaN(obj);
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function constant(value) {
+    return function() {
+      return value;
+    };
+  }
+
+  // Common internal logic for `isArrayLike` and `isBufferLike`.
+  function createSizePropertyCheck(getSizeProperty) {
+    return function(collection) {
+      var sizeProperty = getSizeProperty(collection);
+      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
+    }
+  }
+
+  // Internal helper to generate a function to obtain property `key` from `obj`.
+  function shallowProperty(key) {
+    return function(obj) {
+      return obj == null ? void 0 : obj[key];
+    };
+  }
+
+  // Internal helper to obtain the `byteLength` property of an object.
+  var getByteLength = shallowProperty('byteLength');
+
+  // Internal helper to determine whether we should spend extensive checks against
+  // `ArrayBuffer` et al.
+  var isBufferLike = createSizePropertyCheck(getByteLength);
+
+  // Is a given value a typed array?
+  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
+  function isTypedArray(obj) {
+    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
+    // Otherwise, fall back on the above regular expression.
+    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
+                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
+  }
+
+  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
+
+  // Internal helper to obtain the `length` property of an object.
+  var getLength = shallowProperty('length');
+
+  // Internal helper to create a simple lookup structure.
+  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
+  // circular imports. `emulatedSet` is a one-off solution that only works for
+  // arrays of strings.
+  function emulatedSet(keys) {
+    var hash = {};
+    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
+    return {
+      contains: function(key) { return hash[key]; },
+      push: function(key) {
+        hash[key] = true;
+        return keys.push(key);
+      }
+    };
+  }
+
+  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
+  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
+  // needed.
+  function collectNonEnumProps(obj, keys) {
+    keys = emulatedSet(keys);
+    var nonEnumIdx = nonEnumerableProps.length;
+    var constructor = obj.constructor;
+    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
+
+    // Constructor is a special case.
+    var prop = 'constructor';
+    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
+
+    while (nonEnumIdx--) {
+      prop = nonEnumerableProps[nonEnumIdx];
+      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
+        keys.push(prop);
+      }
+    }
+  }
+
+  // Retrieve the names of an object's own properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`.
+  function keys(obj) {
+    if (!isObject(obj)) return [];
+    if (nativeKeys) return nativeKeys(obj);
+    var keys = [];
+    for (var key in obj) if (has$1(obj, key)) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  function isEmpty(obj) {
+    if (obj == null) return true;
+    // Skip the more expensive `toString`-based type checks if `obj` has no
+    // `.length`.
+    var length = getLength(obj);
+    if (typeof length == 'number' && (
+      isArray(obj) || isString(obj) || isArguments$1(obj)
+    )) return length === 0;
+    return getLength(keys(obj)) === 0;
+  }
+
+  // Returns whether an object has a given set of `key:value` pairs.
+  function isMatch(object, attrs) {
+    var _keys = keys(attrs), length = _keys.length;
+    if (object == null) return !length;
+    var obj = Object(object);
+    for (var i = 0; i < length; i++) {
+      var key = _keys[i];
+      if (attrs[key] !== obj[key] || !(key in obj)) return false;
+    }
+    return true;
+  }
+
+  // If Underscore is called as a function, it returns a wrapped object that can
+  // be used OO-style. This wrapper holds altered versions of all functions added
+  // through `_.mixin`. Wrapped objects may be chained.
+  function _$1(obj) {
+    if (obj instanceof _$1) return obj;
+    if (!(this instanceof _$1)) return new _$1(obj);
+    this._wrapped = obj;
+  }
+
+  _$1.VERSION = VERSION;
+
+  // Extracts the result from a wrapped and chained object.
+  _$1.prototype.value = function() {
+    return this._wrapped;
+  };
+
+  // Provide unwrapping proxies for some methods used in engine operations
+  // such as arithmetic and JSON stringification.
+  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
+
+  _$1.prototype.toString = function() {
+    return String(this._wrapped);
+  };
+
+  // Internal function to wrap or shallow-copy an ArrayBuffer,
+  // typed array or DataView to a new view, reusing the buffer.
+  function toBufferView(bufferSource) {
+    return new Uint8Array(
+      bufferSource.buffer || bufferSource,
+      bufferSource.byteOffset || 0,
+      getByteLength(bufferSource)
+    );
+  }
+
+  // We use this string twice, so give it a name for minification.
+  var tagDataView = '[object DataView]';
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function eq(a, b, aStack, bStack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
+    if (a === b) return a !== 0 || 1 / a === 1 / b;
+    // `null` or `undefined` only equal to itself (strict comparison).
+    if (a == null || b == null) return false;
+    // `NaN`s are equivalent, but non-reflexive.
+    if (a !== a) return b !== b;
+    // Exhaust primitive checks
+    var type = typeof a;
+    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
+    return deepEq(a, b, aStack, bStack);
+  }
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function deepEq(a, b, aStack, bStack) {
+    // Unwrap any wrapped objects.
+    if (a instanceof _$1) a = a._wrapped;
+    if (b instanceof _$1) b = b._wrapped;
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className !== toString.call(b)) return false;
+    // Work around a bug in IE 10 - Edge 13.
+    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
+      if (!isDataView$1(b)) return false;
+      className = tagDataView;
+    }
+    switch (className) {
+      // These types are compared by value.
+      case '[object RegExp]':
+        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return '' + a === '' + b;
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive.
+        // Object(NaN) is equivalent to NaN.
+        if (+a !== +a) return +b !== +b;
+        // An `egal` comparison is performed for other numeric values.
+        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a === +b;
+      case '[object Symbol]':
+        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
+      case '[object ArrayBuffer]':
+      case tagDataView:
+        // Coerce to typed array so we can fall through.
+        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
+    }
+
+    var areArrays = className === '[object Array]';
+    if (!areArrays && isTypedArray$1(a)) {
+        var byteLength = getByteLength(a);
+        if (byteLength !== getByteLength(b)) return false;
+        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
+        areArrays = true;
+    }
+    if (!areArrays) {
+      if (typeof a != 'object' || typeof b != 'object') return false;
+
+      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
+      // from different frames are.
+      var aCtor = a.constructor, bCtor = b.constructor;
+      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
+                               isFunction$1(bCtor) && bCtor instanceof bCtor)
+                          && ('constructor' in a && 'constructor' in b)) {
+        return false;
+      }
+    }
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+
+    // Initializing stack of traversed objects.
+    // It's done here since we only need them for objects and arrays comparison.
+    aStack = aStack || [];
+    bStack = bStack || [];
+    var length = aStack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (aStack[length] === a) return bStack[length] === b;
+    }
+
+    // Add the first object to the stack of traversed objects.
+    aStack.push(a);
+    bStack.push(b);
+
+    // Recursively compare objects and arrays.
+    if (areArrays) {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      length = a.length;
+      if (length !== b.length) return false;
+      // Deep compare the contents, ignoring non-numeric properties.
+      while (length--) {
+        if (!eq(a[length], b[length], aStack, bStack)) return false;
+      }
+    } else {
+      // Deep compare objects.
+      var _keys = keys(a), key;
+      length = _keys.length;
+      // Ensure that both objects contain the same number of properties before comparing deep equality.
+      if (keys(b).length !== length) return false;
+      while (length--) {
+        // Deep compare each member
+        key = _keys[length];
+        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    aStack.pop();
+    bStack.pop();
+    return true;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  function isEqual(a, b) {
+    return eq(a, b);
+  }
+
+  // Retrieve all the enumerable property names of an object.
+  function allKeys(obj) {
+    if (!isObject(obj)) return [];
+    var keys = [];
+    for (var key in obj) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Since the regular `Object.prototype.toString` type tests don't work for
+  // some types in IE 11, we use a fingerprinting heuristic instead, based
+  // on the methods. It's not great, but it's the best we got.
+  // The fingerprint method lists are defined below.
+  function ie11fingerprint(methods) {
+    var length = getLength(methods);
+    return function(obj) {
+      if (obj == null) return false;
+      // `Map`, `WeakMap` and `Set` have no enumerable keys.
+      var keys = allKeys(obj);
+      if (getLength(keys)) return false;
+      for (var i = 0; i < length; i++) {
+        if (!isFunction$1(obj[methods[i]])) return false;
+      }
+      // If we are testing against `WeakMap`, we need to ensure that
+      // `obj` doesn't have a `forEach` method in order to distinguish
+      // it from a regular `Map`.
+      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
+    };
+  }
+
+  // In the interest of compact minification, we write
+  // each string in the fingerprints only once.
+  var forEachName = 'forEach',
+      hasName = 'has',
+      commonInit = ['clear', 'delete'],
+      mapTail = ['get', hasName, 'set'];
+
+  // `Map`, `WeakMap` and `Set` each have slightly different
+  // combinations of the above sublists.
+  var mapMethods = commonInit.concat(forEachName, mapTail),
+      weakMapMethods = commonInit.concat(mapTail),
+      setMethods = ['add'].concat(commonInit, forEachName, hasName);
+
+  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
+
+  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
+
+  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
+
+  var isWeakSet = tagTester('WeakSet');
+
+  // Retrieve the values of an object's properties.
+  function values(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var values = Array(length);
+    for (var i = 0; i < length; i++) {
+      values[i] = obj[_keys[i]];
+    }
+    return values;
+  }
+
+  // Convert an object into a list of `[key, value]` pairs.
+  // The opposite of `_.object` with one argument.
+  function pairs(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var pairs = Array(length);
+    for (var i = 0; i < length; i++) {
+      pairs[i] = [_keys[i], obj[_keys[i]]];
+    }
+    return pairs;
+  }
+
+  // Invert the keys and values of an object. The values must be serializable.
+  function invert(obj) {
+    var result = {};
+    var _keys = keys(obj);
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      result[obj[_keys[i]]] = _keys[i];
+    }
+    return result;
+  }
+
+  // Return a sorted list of the function names available on the object.
+  function functions(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (isFunction$1(obj[key])) names.push(key);
+    }
+    return names.sort();
+  }
+
+  // An internal function for creating assigner functions.
+  function createAssigner(keysFunc, defaults) {
+    return function(obj) {
+      var length = arguments.length;
+      if (defaults) obj = Object(obj);
+      if (length < 2 || obj == null) return obj;
+      for (var index = 1; index < length; index++) {
+        var source = arguments[index],
+            keys = keysFunc(source),
+            l = keys.length;
+        for (var i = 0; i < l; i++) {
+          var key = keys[i];
+          if (!defaults || obj[key] === void 0) obj[key] = source[key];
+        }
+      }
+      return obj;
+    };
+  }
+
+  // Extend a given object with all the properties in passed-in object(s).
+  var extend = createAssigner(allKeys);
+
+  // Assigns a given object with all the own properties in the passed-in
+  // object(s).
+  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
+  var extendOwn = createAssigner(keys);
+
+  // Fill in a given object with default properties.
+  var defaults = createAssigner(allKeys, true);
+
+  // Create a naked function reference for surrogate-prototype-swapping.
+  function ctor() {
+    return function(){};
+  }
+
+  // An internal function for creating a new object that inherits from another.
+  function baseCreate(prototype) {
+    if (!isObject(prototype)) return {};
+    if (nativeCreate) return nativeCreate(prototype);
+    var Ctor = ctor();
+    Ctor.prototype = prototype;
+    var result = new Ctor;
+    Ctor.prototype = null;
+    return result;
+  }
+
+  // Creates an object that inherits from the given prototype object.
+  // If additional properties are provided then they will be added to the
+  // created object.
+  function create(prototype, props) {
+    var result = baseCreate(prototype);
+    if (props) extendOwn(result, props);
+    return result;
+  }
+
+  // Create a (shallow-cloned) duplicate of an object.
+  function clone(obj) {
+    if (!isObject(obj)) return obj;
+    return isArray(obj) ? obj.slice() : extend({}, obj);
+  }
+
+  // Invokes `interceptor` with the `obj` and then returns `obj`.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  function tap(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  }
+
+  // Normalize a (deep) property `path` to array.
+  // Like `_.iteratee`, this function can be customized.
+  function toPath$1(path) {
+    return isArray(path) ? path : [path];
+  }
+  _$1.toPath = toPath$1;
+
+  // Internal wrapper for `_.toPath` to enable minification.
+  // Similar to `cb` for `_.iteratee`.
+  function toPath(path) {
+    return _$1.toPath(path);
+  }
+
+  // Internal function to obtain a nested property in `obj` along `path`.
+  function deepGet(obj, path) {
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      if (obj == null) return void 0;
+      obj = obj[path[i]];
+    }
+    return length ? obj : void 0;
+  }
+
+  // Get the value of the (deep) property on `path` from `object`.
+  // If any property in `path` does not exist or if the value is
+  // `undefined`, return `defaultValue` instead.
+  // The `path` is normalized through `_.toPath`.
+  function get(object, path, defaultValue) {
+    var value = deepGet(object, toPath(path));
+    return isUndefined(value) ? defaultValue : value;
+  }
+
+  // Shortcut function for checking if an object has a given property directly on
+  // itself (in other words, not on a prototype). Unlike the internal `has`
+  // function, this public version can also traverse nested properties.
+  function has(obj, path) {
+    path = toPath(path);
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      var key = path[i];
+      if (!has$1(obj, key)) return false;
+      obj = obj[key];
+    }
+    return !!length;
+  }
+
+  // Keep the identity function around for default iteratees.
+  function identity(value) {
+    return value;
+  }
+
+  // Returns a predicate for checking whether an object has a given set of
+  // `key:value` pairs.
+  function matcher(attrs) {
+    attrs = extendOwn({}, attrs);
+    return function(obj) {
+      return isMatch(obj, attrs);
+    };
+  }
+
+  // Creates a function that, when passed an object, will traverse that object’s
+  // properties down the given `path`, specified as an array of keys or indices.
+  function property(path) {
+    path = toPath(path);
+    return function(obj) {
+      return deepGet(obj, path);
+    };
+  }
+
+  // Internal function that returns an efficient (for current engines) version
+  // of the passed-in callback, to be repeatedly applied in other Underscore
+  // functions.
+  function optimizeCb(func, context, argCount) {
+    if (context === void 0) return func;
+    switch (argCount == null ? 3 : argCount) {
+      case 1: return function(value) {
+        return func.call(context, value);
+      };
+      // The 2-argument case is omitted because we’re not using it.
+      case 3: return function(value, index, collection) {
+        return func.call(context, value, index, collection);
+      };
+      case 4: return function(accumulator, value, index, collection) {
+        return func.call(context, accumulator, value, index, collection);
+      };
+    }
+    return function() {
+      return func.apply(context, arguments);
+    };
+  }
+
+  // An internal function to generate callbacks that can be applied to each
+  // element in a collection, returning the desired result — either `_.identity`,
+  // an arbitrary callback, a property matcher, or a property accessor.
+  function baseIteratee(value, context, argCount) {
+    if (value == null) return identity;
+    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
+    if (isObject(value) && !isArray(value)) return matcher(value);
+    return property(value);
+  }
+
+  // External wrapper for our callback generator. Users may customize
+  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
+  // This abstraction hides the internal-only `argCount` argument.
+  function iteratee(value, context) {
+    return baseIteratee(value, context, Infinity);
+  }
+  _$1.iteratee = iteratee;
+
+  // The function we call internally to generate a callback. It invokes
+  // `_.iteratee` if overridden, otherwise `baseIteratee`.
+  function cb(value, context, argCount) {
+    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
+    return baseIteratee(value, context, argCount);
+  }
+
+  // Returns the results of applying the `iteratee` to each element of `obj`.
+  // In contrast to `_.map` it returns an object.
+  function mapObject(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = keys(obj),
+        length = _keys.length,
+        results = {};
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys[index];
+      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function noop(){}
+
+  // Generates a function for a given object that returns a given property.
+  function propertyOf(obj) {
+    if (obj == null) return noop;
+    return function(path) {
+      return get(obj, path);
+    };
+  }
+
+  // Run a function **n** times.
+  function times(n, iteratee, context) {
+    var accum = Array(Math.max(0, n));
+    iteratee = optimizeCb(iteratee, context, 1);
+    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
+    return accum;
+  }
+
+  // Return a random integer between `min` and `max` (inclusive).
+  function random(min, max) {
+    if (max == null) {
+      max = min;
+      min = 0;
+    }
+    return min + Math.floor(Math.random() * (max - min + 1));
+  }
+
+  // A (possibly faster) way to get the current timestamp as an integer.
+  var now = Date.now || function() {
+    return new Date().getTime();
+  };
+
+  // Internal helper to generate functions for escaping and unescaping strings
+  // to/from HTML interpolation.
+  function createEscaper(map) {
+    var escaper = function(match) {
+      return map[match];
+    };
+    // Regexes for identifying a key that needs to be escaped.
+    var source = '(?:' + keys(map).join('|') + ')';
+    var testRegexp = RegExp(source);
+    var replaceRegexp = RegExp(source, 'g');
+    return function(string) {
+      string = string == null ? '' : '' + string;
+      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
+    };
+  }
+
+  // Internal list of HTML entities for escaping.
+  var escapeMap = {
+    '&': '&',
+    '<': '<',
+    '>': '>',
+    '"': '"',
+    "'": ''',
+    '`': '`'
+  };
+
+  // Function for escaping strings to HTML interpolation.
+  var _escape = createEscaper(escapeMap);
+
+  // Internal list of HTML entities for unescaping.
+  var unescapeMap = invert(escapeMap);
+
+  // Function for unescaping strings from HTML interpolation.
+  var _unescape = createEscaper(unescapeMap);
+
+  // By default, Underscore uses ERB-style template delimiters. Change the
+  // following template settings to use alternative delimiters.
+  var templateSettings = _$1.templateSettings = {
+    evaluate: /<%([\s\S]+?)%>/g,
+    interpolate: /<%=([\s\S]+?)%>/g,
+    escape: /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `_.templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /(.)^/;
+
+  // Certain characters need to be escaped so that they can be put into a
+  // string literal.
+  var escapes = {
+    "'": "'",
+    '\\': '\\',
+    '\r': 'r',
+    '\n': 'n',
+    '\u2028': 'u2028',
+    '\u2029': 'u2029'
+  };
+
+  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
+
+  function escapeChar(match) {
+    return '\\' + escapes[match];
+  }
+
+  // In order to prevent third-party code injection through
+  // `_.templateSettings.variable`, we test it against the following regular
+  // expression. It is intentionally a bit more liberal than just matching valid
+  // identifiers, but still prevents possible loopholes through defaults or
+  // destructuring assignment.
+  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  // NB: `oldSettings` only exists for backwards compatibility.
+  function template(text, settings, oldSettings) {
+    if (!settings && oldSettings) settings = oldSettings;
+    settings = defaults({}, settings, _$1.templateSettings);
+
+    // Combine delimiters into one regular expression via alternation.
+    var matcher = RegExp([
+      (settings.escape || noMatch).source,
+      (settings.interpolate || noMatch).source,
+      (settings.evaluate || noMatch).source
+    ].join('|') + '|$', 'g');
+
+    // Compile the template source, escaping string literals appropriately.
+    var index = 0;
+    var source = "__p+='";
+    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
+      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
+      index = offset + match.length;
+
+      if (escape) {
+        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
+      } else if (interpolate) {
+        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
+      } else if (evaluate) {
+        source += "';\n" + evaluate + "\n__p+='";
+      }
+
+      // Adobe VMs need the match returned to produce the correct offset.
+      return match;
+    });
+    source += "';\n";
+
+    var argument = settings.variable;
+    if (argument) {
+      // Insure against third-party code injection. (CVE-2021-23358)
+      if (!bareIdentifier.test(argument)) throw new Error(
+        'variable is not a bare identifier: ' + argument
+      );
+    } else {
+      // If a variable is not specified, place data values in local scope.
+      source = 'with(obj||{}){\n' + source + '}\n';
+      argument = 'obj';
+    }
+
+    source = "var __t,__p='',__j=Array.prototype.join," +
+      "print=function(){__p+=__j.call(arguments,'');};\n" +
+      source + 'return __p;\n';
+
+    var render;
+    try {
+      render = new Function(argument, '_', source);
+    } catch (e) {
+      e.source = source;
+      throw e;
+    }
+
+    var template = function(data) {
+      return render.call(this, data, _$1);
+    };
+
+    // Provide the compiled source as a convenience for precompilation.
+    template.source = 'function(' + argument + '){\n' + source + '}';
+
+    return template;
+  }
+
+  // Traverses the children of `obj` along `path`. If a child is a function, it
+  // is invoked with its parent as context. Returns the value of the final
+  // child, or `fallback` if any child is undefined.
+  function result(obj, path, fallback) {
+    path = toPath(path);
+    var length = path.length;
+    if (!length) {
+      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
+    }
+    for (var i = 0; i < length; i++) {
+      var prop = obj == null ? void 0 : obj[path[i]];
+      if (prop === void 0) {
+        prop = fallback;
+        i = length; // Ensure we don't continue iterating.
+      }
+      obj = isFunction$1(prop) ? prop.call(obj) : prop;
+    }
+    return obj;
+  }
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  function uniqueId(prefix) {
+    var id = ++idCounter + '';
+    return prefix ? prefix + id : id;
+  }
+
+  // Start chaining a wrapped Underscore object.
+  function chain(obj) {
+    var instance = _$1(obj);
+    instance._chain = true;
+    return instance;
+  }
+
+  // Internal function to execute `sourceFunc` bound to `context` with optional
+  // `args`. Determines whether to execute a function as a constructor or as a
+  // normal function.
+  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
+    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
+    var self = baseCreate(sourceFunc.prototype);
+    var result = sourceFunc.apply(self, args);
+    if (isObject(result)) return result;
+    return self;
+  }
+
+  // Partially apply a function by creating a version that has had some of its
+  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
+  // as a placeholder by default, allowing any combination of arguments to be
+  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
+  var partial = restArguments(function(func, boundArgs) {
+    var placeholder = partial.placeholder;
+    var bound = function() {
+      var position = 0, length = boundArgs.length;
+      var args = Array(length);
+      for (var i = 0; i < length; i++) {
+        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
+      }
+      while (position < arguments.length) args.push(arguments[position++]);
+      return executeBound(func, bound, this, this, args);
+    };
+    return bound;
+  });
+
+  partial.placeholder = _$1;
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally).
+  var bind = restArguments(function(func, context, args) {
+    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
+    var bound = restArguments(function(callArgs) {
+      return executeBound(func, bound, context, this, args.concat(callArgs));
+    });
+    return bound;
+  });
+
+  // Internal helper for collection methods to determine whether a collection
+  // should be iterated as an array or as an object.
+  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
+  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
+  var isArrayLike = createSizePropertyCheck(getLength);
+
+  // Internal implementation of a recursive `flatten` function.
+  function flatten$1(input, depth, strict, output) {
+    output = output || [];
+    if (!depth && depth !== 0) {
+      depth = Infinity;
+    } else if (depth <= 0) {
+      return output.concat(input);
+    }
+    var idx = output.length;
+    for (var i = 0, length = getLength(input); i < length; i++) {
+      var value = input[i];
+      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
+        // Flatten current level of array or arguments object.
+        if (depth > 1) {
+          flatten$1(value, depth - 1, strict, output);
+          idx = output.length;
+        } else {
+          var j = 0, len = value.length;
+          while (j < len) output[idx++] = value[j++];
+        }
+      } else if (!strict) {
+        output[idx++] = value;
+      }
+    }
+    return output;
+  }
+
+  // Bind a number of an object's methods to that object. Remaining arguments
+  // are the method names to be bound. Useful for ensuring that all callbacks
+  // defined on an object belong to it.
+  var bindAll = restArguments(function(obj, keys) {
+    keys = flatten$1(keys, false, false);
+    var index = keys.length;
+    if (index < 1) throw new Error('bindAll must be passed function names');
+    while (index--) {
+      var key = keys[index];
+      obj[key] = bind(obj[key], obj);
+    }
+    return obj;
+  });
+
+  // Memoize an expensive function by storing its results.
+  function memoize(func, hasher) {
+    var memoize = function(key) {
+      var cache = memoize.cache;
+      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
+      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
+      return cache[address];
+    };
+    memoize.cache = {};
+    return memoize;
+  }
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  var delay = restArguments(function(func, wait, args) {
+    return setTimeout(function() {
+      return func.apply(null, args);
+    }, wait);
+  });
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  var defer = partial(delay, _$1, 1);
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time. Normally, the throttled function will run
+  // as much as it can, without ever going more than once per `wait` duration;
+  // but if you'd like to disable the execution on the leading edge, pass
+  // `{leading: false}`. To disable execution on the trailing edge, ditto.
+  function throttle(func, wait, options) {
+    var timeout, context, args, result;
+    var previous = 0;
+    if (!options) options = {};
+
+    var later = function() {
+      previous = options.leading === false ? 0 : now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+
+    var throttled = function() {
+      var _now = now();
+      if (!previous && options.leading === false) previous = _now;
+      var remaining = wait - (_now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = _now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+
+    throttled.cancel = function() {
+      clearTimeout(timeout);
+      previous = 0;
+      timeout = context = args = null;
+    };
+
+    return throttled;
+  }
+
+  // When a sequence of calls of the returned function ends, the argument
+  // function is triggered. The end of a sequence is defined by the `wait`
+  // parameter. If `immediate` is passed, the argument function will be
+  // triggered at the beginning of the sequence instead of at the end.
+  function debounce(func, wait, immediate) {
+    var timeout, previous, args, result, context;
+
+    var later = function() {
+      var passed = now() - previous;
+      if (wait > passed) {
+        timeout = setTimeout(later, wait - passed);
+      } else {
+        timeout = null;
+        if (!immediate) result = func.apply(context, args);
+        // This check is needed because `func` can recursively invoke `debounced`.
+        if (!timeout) args = context = null;
+      }
+    };
+
+    var debounced = restArguments(function(_args) {
+      context = this;
+      args = _args;
+      previous = now();
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+        if (immediate) result = func.apply(context, args);
+      }
+      return result;
+    });
+
+    debounced.cancel = function() {
+      clearTimeout(timeout);
+      timeout = args = context = null;
+    };
+
+    return debounced;
+  }
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  function wrap(func, wrapper) {
+    return partial(wrapper, func);
+  }
+
+  // Returns a negated version of the passed-in predicate.
+  function negate(predicate) {
+    return function() {
+      return !predicate.apply(this, arguments);
+    };
+  }
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  function compose() {
+    var args = arguments;
+    var start = args.length - 1;
+    return function() {
+      var i = start;
+      var result = args[start].apply(this, arguments);
+      while (i--) result = args[i].call(this, result);
+      return result;
+    };
+  }
+
+  // Returns a function that will only be executed on and after the Nth call.
+  function after(times, func) {
+    return function() {
+      if (--times < 1) {
+        return func.apply(this, arguments);
+      }
+    };
+  }
+
+  // Returns a function that will only be executed up to (but not including) the
+  // Nth call.
+  function before(times, func) {
+    var memo;
+    return function() {
+      if (--times > 0) {
+        memo = func.apply(this, arguments);
+      }
+      if (times <= 1) func = null;
+      return memo;
+    };
+  }
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  var once = partial(before, 2);
+
+  // Returns the first key on an object that passes a truth test.
+  function findKey(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = keys(obj), key;
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      key = _keys[i];
+      if (predicate(obj[key], key, obj)) return key;
+    }
+  }
+
+  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
+  function createPredicateIndexFinder(dir) {
+    return function(array, predicate, context) {
+      predicate = cb(predicate, context);
+      var length = getLength(array);
+      var index = dir > 0 ? 0 : length - 1;
+      for (; index >= 0 && index < length; index += dir) {
+        if (predicate(array[index], index, array)) return index;
+      }
+      return -1;
+    };
+  }
+
+  // Returns the first index on an array-like that passes a truth test.
+  var findIndex = createPredicateIndexFinder(1);
+
+  // Returns the last index on an array-like that passes a truth test.
+  var findLastIndex = createPredicateIndexFinder(-1);
+
+  // Use a comparator function to figure out the smallest index at which
+  // an object should be inserted so as to maintain order. Uses binary search.
+  function sortedIndex(array, obj, iteratee, context) {
+    iteratee = cb(iteratee, context, 1);
+    var value = iteratee(obj);
+    var low = 0, high = getLength(array);
+    while (low < high) {
+      var mid = Math.floor((low + high) / 2);
+      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
+    }
+    return low;
+  }
+
+  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
+  function createIndexFinder(dir, predicateFind, sortedIndex) {
+    return function(array, item, idx) {
+      var i = 0, length = getLength(array);
+      if (typeof idx == 'number') {
+        if (dir > 0) {
+          i = idx >= 0 ? idx : Math.max(idx + length, i);
+        } else {
+          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
+        }
+      } else if (sortedIndex && idx && length) {
+        idx = sortedIndex(array, item);
+        return array[idx] === item ? idx : -1;
+      }
+      if (item !== item) {
+        idx = predicateFind(slice.call(array, i, length), isNaN$1);
+        return idx >= 0 ? idx + i : -1;
+      }
+      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
+        if (array[idx] === item) return idx;
+      }
+      return -1;
+    };
+  }
+
+  // Return the position of the first occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
+
+  // Return the position of the last occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  var lastIndexOf = createIndexFinder(-1, findLastIndex);
+
+  // Return the first value which passes a truth test.
+  function find(obj, predicate, context) {
+    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
+    var key = keyFinder(obj, predicate, context);
+    if (key !== void 0 && key !== -1) return obj[key];
+  }
+
+  // Convenience version of a common use case of `_.find`: getting the first
+  // object containing specific `key:value` pairs.
+  function findWhere(obj, attrs) {
+    return find(obj, matcher(attrs));
+  }
+
+  // The cornerstone for collection functions, an `each`
+  // implementation, aka `forEach`.
+  // Handles raw objects in addition to array-likes. Treats all
+  // sparse array-likes as if they were dense.
+  function each(obj, iteratee, context) {
+    iteratee = optimizeCb(iteratee, context);
+    var i, length;
+    if (isArrayLike(obj)) {
+      for (i = 0, length = obj.length; i < length; i++) {
+        iteratee(obj[i], i, obj);
+      }
+    } else {
+      var _keys = keys(obj);
+      for (i = 0, length = _keys.length; i < length; i++) {
+        iteratee(obj[_keys[i]], _keys[i], obj);
+      }
+    }
+    return obj;
+  }
+
+  // Return the results of applying the iteratee to each element.
+  function map(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length,
+        results = Array(length);
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      results[index] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Internal helper to create a reducing function, iterating left or right.
+  function createReduce(dir) {
+    // Wrap code that reassigns argument variables in a separate function than
+    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
+    var reducer = function(obj, iteratee, memo, initial) {
+      var _keys = !isArrayLike(obj) && keys(obj),
+          length = (_keys || obj).length,
+          index = dir > 0 ? 0 : length - 1;
+      if (!initial) {
+        memo = obj[_keys ? _keys[index] : index];
+        index += dir;
+      }
+      for (; index >= 0 && index < length; index += dir) {
+        var currentKey = _keys ? _keys[index] : index;
+        memo = iteratee(memo, obj[currentKey], currentKey, obj);
+      }
+      return memo;
+    };
+
+    return function(obj, iteratee, memo, context) {
+      var initial = arguments.length >= 3;
+      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
+    };
+  }
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`.
+  var reduce = createReduce(1);
+
+  // The right-associative version of reduce, also known as `foldr`.
+  var reduceRight = createReduce(-1);
+
+  // Return all the elements that pass a truth test.
+  function filter(obj, predicate, context) {
+    var results = [];
+    predicate = cb(predicate, context);
+    each(obj, function(value, index, list) {
+      if (predicate(value, index, list)) results.push(value);
+    });
+    return results;
+  }
+
+  // Return all the elements for which a truth test fails.
+  function reject(obj, predicate, context) {
+    return filter(obj, negate(cb(predicate)), context);
+  }
+
+  // Determine whether all of the elements pass a truth test.
+  function every(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (!predicate(obj[currentKey], currentKey, obj)) return false;
+    }
+    return true;
+  }
+
+  // Determine if at least one element in the object passes a truth test.
+  function some(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (predicate(obj[currentKey], currentKey, obj)) return true;
+    }
+    return false;
+  }
+
+  // Determine if the array or object contains a given item (using `===`).
+  function contains(obj, item, fromIndex, guard) {
+    if (!isArrayLike(obj)) obj = values(obj);
+    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
+    return indexOf(obj, item, fromIndex) >= 0;
+  }
+
+  // Invoke a method (with arguments) on every item in a collection.
+  var invoke = restArguments(function(obj, path, args) {
+    var contextPath, func;
+    if (isFunction$1(path)) {
+      func = path;
+    } else {
+      path = toPath(path);
+      contextPath = path.slice(0, -1);
+      path = path[path.length - 1];
+    }
+    return map(obj, function(context) {
+      var method = func;
+      if (!method) {
+        if (contextPath && contextPath.length) {
+          context = deepGet(context, contextPath);
+        }
+        if (context == null) return void 0;
+        method = context[path];
+      }
+      return method == null ? method : method.apply(context, args);
+    });
+  });
+
+  // Convenience version of a common use case of `_.map`: fetching a property.
+  function pluck(obj, key) {
+    return map(obj, property(key));
+  }
+
+  // Convenience version of a common use case of `_.filter`: selecting only
+  // objects containing specific `key:value` pairs.
+  function where(obj, attrs) {
+    return filter(obj, matcher(attrs));
+  }
+
+  // Return the maximum element (or element-based computation).
+  function max(obj, iteratee, context) {
+    var result = -Infinity, lastComputed = -Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value > result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Return the minimum element (or element-based computation).
+  function min(obj, iteratee, context) {
+    var result = Infinity, lastComputed = Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value < result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed < lastComputed || computed === Infinity && result === Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Sample **n** random values from a collection using the modern version of the
+  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
+  // If **n** is not specified, returns a single random element.
+  // The internal `guard` argument allows it to work with `_.map`.
+  function sample(obj, n, guard) {
+    if (n == null || guard) {
+      if (!isArrayLike(obj)) obj = values(obj);
+      return obj[random(obj.length - 1)];
+    }
+    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
+    var length = getLength(sample);
+    n = Math.max(Math.min(n, length), 0);
+    var last = length - 1;
+    for (var index = 0; index < n; index++) {
+      var rand = random(index, last);
+      var temp = sample[index];
+      sample[index] = sample[rand];
+      sample[rand] = temp;
+    }
+    return sample.slice(0, n);
+  }
+
+  // Shuffle a collection.
+  function shuffle(obj) {
+    return sample(obj, Infinity);
+  }
+
+  // Sort the object's values by a criterion produced by an iteratee.
+  function sortBy(obj, iteratee, context) {
+    var index = 0;
+    iteratee = cb(iteratee, context);
+    return pluck(map(obj, function(value, key, list) {
+      return {
+        value: value,
+        index: index++,
+        criteria: iteratee(value, key, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria;
+      var b = right.criteria;
+      if (a !== b) {
+        if (a > b || a === void 0) return 1;
+        if (a < b || b === void 0) return -1;
+      }
+      return left.index - right.index;
+    }), 'value');
+  }
+
+  // An internal function used for aggregate "group by" operations.
+  function group(behavior, partition) {
+    return function(obj, iteratee, context) {
+      var result = partition ? [[], []] : {};
+      iteratee = cb(iteratee, context);
+      each(obj, function(value, index) {
+        var key = iteratee(value, index, obj);
+        behavior(result, value, key);
+      });
+      return result;
+    };
+  }
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  var groupBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
+  });
+
+  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
+  // when you know that your index values will be unique.
+  var indexBy = group(function(result, value, key) {
+    result[key] = value;
+  });
+
+  // Counts instances of an object that group by a certain criterion. Pass
+  // either a string attribute to count by, or a function that returns the
+  // criterion.
+  var countBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key]++; else result[key] = 1;
+  });
+
+  // Split a collection into two arrays: one whose elements all pass the given
+  // truth test, and one whose elements all do not pass the truth test.
+  var partition = group(function(result, value, pass) {
+    result[pass ? 0 : 1].push(value);
+  }, true);
+
+  // Safely create a real, live array from anything iterable.
+  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
+  function toArray(obj) {
+    if (!obj) return [];
+    if (isArray(obj)) return slice.call(obj);
+    if (isString(obj)) {
+      // Keep surrogate pair characters together.
+      return obj.match(reStrSymbol);
+    }
+    if (isArrayLike(obj)) return map(obj, identity);
+    return values(obj);
+  }
+
+  // Return the number of elements in a collection.
+  function size(obj) {
+    if (obj == null) return 0;
+    return isArrayLike(obj) ? obj.length : keys(obj).length;
+  }
+
+  // Internal `_.pick` helper function to determine whether `key` is an enumerable
+  // property name of `obj`.
+  function keyInObj(value, key, obj) {
+    return key in obj;
+  }
+
+  // Return a copy of the object only containing the allowed properties.
+  var pick = restArguments(function(obj, keys) {
+    var result = {}, iteratee = keys[0];
+    if (obj == null) return result;
+    if (isFunction$1(iteratee)) {
+      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
+      keys = allKeys(obj);
+    } else {
+      iteratee = keyInObj;
+      keys = flatten$1(keys, false, false);
+      obj = Object(obj);
+    }
+    for (var i = 0, length = keys.length; i < length; i++) {
+      var key = keys[i];
+      var value = obj[key];
+      if (iteratee(value, key, obj)) result[key] = value;
+    }
+    return result;
+  });
+
+  // Return a copy of the object without the disallowed properties.
+  var omit = restArguments(function(obj, keys) {
+    var iteratee = keys[0], context;
+    if (isFunction$1(iteratee)) {
+      iteratee = negate(iteratee);
+      if (keys.length > 1) context = keys[1];
+    } else {
+      keys = map(flatten$1(keys, false, false), String);
+      iteratee = function(value, key) {
+        return !contains(keys, key);
+      };
+    }
+    return pick(obj, iteratee, context);
+  });
+
+  // Returns everything but the last entry of the array. Especially useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N.
+  function initial(array, n, guard) {
+    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
+  }
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  function first(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[0];
+    return initial(array, array.length - n);
+  }
+
+  // Returns everything but the first entry of the `array`. Especially useful on
+  // the `arguments` object. Passing an **n** will return the rest N values in the
+  // `array`.
+  function rest(array, n, guard) {
+    return slice.call(array, n == null || guard ? 1 : n);
+  }
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array.
+  function last(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[array.length - 1];
+    return rest(array, Math.max(0, array.length - n));
+  }
+
+  // Trim out all falsy values from an array.
+  function compact(array) {
+    return filter(array, Boolean);
+  }
+
+  // Flatten out an array, either recursively (by default), or up to `depth`.
+  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
+  function flatten(array, depth) {
+    return flatten$1(array, depth, false);
+  }
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  var difference = restArguments(function(array, rest) {
+    rest = flatten$1(rest, true, true);
+    return filter(array, function(value){
+      return !contains(rest, value);
+    });
+  });
+
+  // Return a version of the array that does not contain the specified value(s).
+  var without = restArguments(function(array, otherArrays) {
+    return difference(array, otherArrays);
+  });
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // The faster algorithm will not work with an iteratee if the iteratee
+  // is not a one-to-one function, so providing an iteratee will disable
+  // the faster algorithm.
+  function uniq(array, isSorted, iteratee, context) {
+    if (!isBoolean(isSorted)) {
+      context = iteratee;
+      iteratee = isSorted;
+      isSorted = false;
+    }
+    if (iteratee != null) iteratee = cb(iteratee, context);
+    var result = [];
+    var seen = [];
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var value = array[i],
+          computed = iteratee ? iteratee(value, i, array) : value;
+      if (isSorted && !iteratee) {
+        if (!i || seen !== computed) result.push(value);
+        seen = computed;
+      } else if (iteratee) {
+        if (!contains(seen, computed)) {
+          seen.push(computed);
+          result.push(value);
+        }
+      } else if (!contains(result, value)) {
+        result.push(value);
+      }
+    }
+    return result;
+  }
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  var union = restArguments(function(arrays) {
+    return uniq(flatten$1(arrays, true, true));
+  });
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays.
+  function intersection(array) {
+    var result = [];
+    var argsLength = arguments.length;
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var item = array[i];
+      if (contains(result, item)) continue;
+      var j;
+      for (j = 1; j < argsLength; j++) {
+        if (!contains(arguments[j], item)) break;
+      }
+      if (j === argsLength) result.push(item);
+    }
+    return result;
+  }
+
+  // Complement of zip. Unzip accepts an array of arrays and groups
+  // each array's elements on shared indices.
+  function unzip(array) {
+    var length = array && max(array, getLength).length || 0;
+    var result = Array(length);
+
+    for (var index = 0; index < length; index++) {
+      result[index] = pluck(array, index);
+    }
+    return result;
+  }
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  var zip = restArguments(unzip);
+
+  // Converts lists into objects. Pass either a single array of `[key, value]`
+  // pairs, or two parallel arrays of the same length -- one of keys, and one of
+  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
+  function object(list, values) {
+    var result = {};
+    for (var i = 0, length = getLength(list); i < length; i++) {
+      if (values) {
+        result[list[i]] = values[i];
+      } else {
+        result[list[i][0]] = list[i][1];
+      }
+    }
+    return result;
+  }
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](https://docs.python.org/library/functions.html#range).
+  function range(start, stop, step) {
+    if (stop == null) {
+      stop = start || 0;
+      start = 0;
+    }
+    if (!step) {
+      step = stop < start ? -1 : 1;
+    }
+
+    var length = Math.max(Math.ceil((stop - start) / step), 0);
+    var range = Array(length);
+
+    for (var idx = 0; idx < length; idx++, start += step) {
+      range[idx] = start;
+    }
+
+    return range;
+  }
+
+  // Chunk a single array into multiple arrays, each containing `count` or fewer
+  // items.
+  function chunk(array, count) {
+    if (count == null || count < 1) return [];
+    var result = [];
+    var i = 0, length = array.length;
+    while (i < length) {
+      result.push(slice.call(array, i, i += count));
+    }
+    return result;
+  }
+
+  // Helper function to continue chaining intermediate results.
+  function chainResult(instance, obj) {
+    return instance._chain ? _$1(obj).chain() : obj;
+  }
+
+  // Add your own custom functions to the Underscore object.
+  function mixin(obj) {
+    each(functions(obj), function(name) {
+      var func = _$1[name] = obj[name];
+      _$1.prototype[name] = function() {
+        var args = [this._wrapped];
+        push.apply(args, arguments);
+        return chainResult(this, func.apply(_$1, args));
+      };
+    });
+    return _$1;
+  }
+
+  // Add all mutator `Array` functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) {
+        method.apply(obj, arguments);
+        if ((name === 'shift' || name === 'splice') && obj.length === 0) {
+          delete obj[0];
+        }
+      }
+      return chainResult(this, obj);
+    };
+  });
+
+  // Add all accessor `Array` functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) obj = method.apply(obj, arguments);
+      return chainResult(this, obj);
+    };
+  });
+
+  // Named Exports
+
+  var allExports = {
+    __proto__: null,
+    VERSION: VERSION,
+    restArguments: restArguments,
+    isObject: isObject,
+    isNull: isNull,
+    isUndefined: isUndefined,
+    isBoolean: isBoolean,
+    isElement: isElement,
+    isString: isString,
+    isNumber: isNumber,
+    isDate: isDate,
+    isRegExp: isRegExp,
+    isError: isError,
+    isSymbol: isSymbol,
+    isArrayBuffer: isArrayBuffer,
+    isDataView: isDataView$1,
+    isArray: isArray,
+    isFunction: isFunction$1,
+    isArguments: isArguments$1,
+    isFinite: isFinite$1,
+    isNaN: isNaN$1,
+    isTypedArray: isTypedArray$1,
+    isEmpty: isEmpty,
+    isMatch: isMatch,
+    isEqual: isEqual,
+    isMap: isMap,
+    isWeakMap: isWeakMap,
+    isSet: isSet,
+    isWeakSet: isWeakSet,
+    keys: keys,
+    allKeys: allKeys,
+    values: values,
+    pairs: pairs,
+    invert: invert,
+    functions: functions,
+    methods: functions,
+    extend: extend,
+    extendOwn: extendOwn,
+    assign: extendOwn,
+    defaults: defaults,
+    create: create,
+    clone: clone,
+    tap: tap,
+    get: get,
+    has: has,
+    mapObject: mapObject,
+    identity: identity,
+    constant: constant,
+    noop: noop,
+    toPath: toPath$1,
+    property: property,
+    propertyOf: propertyOf,
+    matcher: matcher,
+    matches: matcher,
+    times: times,
+    random: random,
+    now: now,
+    escape: _escape,
+    unescape: _unescape,
+    templateSettings: templateSettings,
+    template: template,
+    result: result,
+    uniqueId: uniqueId,
+    chain: chain,
+    iteratee: iteratee,
+    partial: partial,
+    bind: bind,
+    bindAll: bindAll,
+    memoize: memoize,
+    delay: delay,
+    defer: defer,
+    throttle: throttle,
+    debounce: debounce,
+    wrap: wrap,
+    negate: negate,
+    compose: compose,
+    after: after,
+    before: before,
+    once: once,
+    findKey: findKey,
+    findIndex: findIndex,
+    findLastIndex: findLastIndex,
+    sortedIndex: sortedIndex,
+    indexOf: indexOf,
+    lastIndexOf: lastIndexOf,
+    find: find,
+    detect: find,
+    findWhere: findWhere,
+    each: each,
+    forEach: each,
+    map: map,
+    collect: map,
+    reduce: reduce,
+    foldl: reduce,
+    inject: reduce,
+    reduceRight: reduceRight,
+    foldr: reduceRight,
+    filter: filter,
+    select: filter,
+    reject: reject,
+    every: every,
+    all: every,
+    some: some,
+    any: some,
+    contains: contains,
+    includes: contains,
+    include: contains,
+    invoke: invoke,
+    pluck: pluck,
+    where: where,
+    max: max,
+    min: min,
+    shuffle: shuffle,
+    sample: sample,
+    sortBy: sortBy,
+    groupBy: groupBy,
+    indexBy: indexBy,
+    countBy: countBy,
+    partition: partition,
+    toArray: toArray,
+    size: size,
+    pick: pick,
+    omit: omit,
+    first: first,
+    head: first,
+    take: first,
+    initial: initial,
+    last: last,
+    rest: rest,
+    tail: rest,
+    drop: rest,
+    compact: compact,
+    flatten: flatten,
+    without: without,
+    uniq: uniq,
+    unique: uniq,
+    union: union,
+    intersection: intersection,
+    difference: difference,
+    unzip: unzip,
+    transpose: unzip,
+    zip: zip,
+    object: object,
+    range: range,
+    chunk: chunk,
+    mixin: mixin,
+    'default': _$1
+  };
+
+  // Default Export
+
+  // Add all of the Underscore functions to the wrapper object.
+  var _ = mixin(allExports);
+  // Legacy Node.js API.
+  _._ = _;
+
+  return _;
+
+})));
+//# sourceMappingURL=underscore-umd.js.map
diff --git a/docs/build/html/_static/underscore-1.3.1.js b/docs/build/html/_static/underscore-1.3.1.js
deleted file mode 100644
index 208d4cd..0000000
--- a/docs/build/html/_static/underscore-1.3.1.js
+++ /dev/null
@@ -1,999 +0,0 @@
-//     Underscore.js 1.3.1
-//     (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
-//     Underscore is freely distributable under the MIT license.
-//     Portions of Underscore are inspired or borrowed from Prototype,
-//     Oliver Steele's Functional, and John Resig's Micro-Templating.
-//     For all details and documentation:
-//     http://documentcloud.github.com/underscore
-
-(function() {
-
-  // Baseline setup
-  // --------------
-
-  // Establish the root object, `window` in the browser, or `global` on the server.
-  var root = this;
-
-  // Save the previous value of the `_` variable.
-  var previousUnderscore = root._;
-
-  // Establish the object that gets returned to break out of a loop iteration.
-  var breaker = {};
-
-  // Save bytes in the minified (but not gzipped) version:
-  var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
-
-  // Create quick reference variables for speed access to core prototypes.
-  var slice            = ArrayProto.slice,
-      unshift          = ArrayProto.unshift,
-      toString         = ObjProto.toString,
-      hasOwnProperty   = ObjProto.hasOwnProperty;
-
-  // All **ECMAScript 5** native function implementations that we hope to use
-  // are declared here.
-  var
-    nativeForEach      = ArrayProto.forEach,
-    nativeMap          = ArrayProto.map,
-    nativeReduce       = ArrayProto.reduce,
-    nativeReduceRight  = ArrayProto.reduceRight,
-    nativeFilter       = ArrayProto.filter,
-    nativeEvery        = ArrayProto.every,
-    nativeSome         = ArrayProto.some,
-    nativeIndexOf      = ArrayProto.indexOf,
-    nativeLastIndexOf  = ArrayProto.lastIndexOf,
-    nativeIsArray      = Array.isArray,
-    nativeKeys         = Object.keys,
-    nativeBind         = FuncProto.bind;
-
-  // Create a safe reference to the Underscore object for use below.
-  var _ = function(obj) { return new wrapper(obj); };
-
-  // Export the Underscore object for **Node.js**, with
-  // backwards-compatibility for the old `require()` API. If we're in
-  // the browser, add `_` as a global object via a string identifier,
-  // for Closure Compiler "advanced" mode.
-  if (typeof exports !== 'undefined') {
-    if (typeof module !== 'undefined' && module.exports) {
-      exports = module.exports = _;
-    }
-    exports._ = _;
-  } else {
-    root['_'] = _;
-  }
-
-  // Current version.
-  _.VERSION = '1.3.1';
-
-  // Collection Functions
-  // --------------------
-
-  // The cornerstone, an `each` implementation, aka `forEach`.
-  // Handles objects with the built-in `forEach`, arrays, and raw objects.
-  // Delegates to **ECMAScript 5**'s native `forEach` if available.
-  var each = _.each = _.forEach = function(obj, iterator, context) {
-    if (obj == null) return;
-    if (nativeForEach && obj.forEach === nativeForEach) {
-      obj.forEach(iterator, context);
-    } else if (obj.length === +obj.length) {
-      for (var i = 0, l = obj.length; i < l; i++) {
-        if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
-      }
-    } else {
-      for (var key in obj) {
-        if (_.has(obj, key)) {
-          if (iterator.call(context, obj[key], key, obj) === breaker) return;
-        }
-      }
-    }
-  };
-
-  // Return the results of applying the iterator to each element.
-  // Delegates to **ECMAScript 5**'s native `map` if available.
-  _.map = _.collect = function(obj, iterator, context) {
-    var results = [];
-    if (obj == null) return results;
-    if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
-    each(obj, function(value, index, list) {
-      results[results.length] = iterator.call(context, value, index, list);
-    });
-    if (obj.length === +obj.length) results.length = obj.length;
-    return results;
-  };
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
-  _.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
-    var initial = arguments.length > 2;
-    if (obj == null) obj = [];
-    if (nativeReduce && obj.reduce === nativeReduce) {
-      if (context) iterator = _.bind(iterator, context);
-      return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
-    }
-    each(obj, function(value, index, list) {
-      if (!initial) {
-        memo = value;
-        initial = true;
-      } else {
-        memo = iterator.call(context, memo, value, index, list);
-      }
-    });
-    if (!initial) throw new TypeError('Reduce of empty array with no initial value');
-    return memo;
-  };
-
-  // The right-associative version of reduce, also known as `foldr`.
-  // Delegates to **ECMAScript 5**'s native `reduceRight` if available.
-  _.reduceRight = _.foldr = function(obj, iterator, memo, context) {
-    var initial = arguments.length > 2;
-    if (obj == null) obj = [];
-    if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
-      if (context) iterator = _.bind(iterator, context);
-      return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
-    }
-    var reversed = _.toArray(obj).reverse();
-    if (context && !initial) iterator = _.bind(iterator, context);
-    return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
-  };
-
-  // Return the first value which passes a truth test. Aliased as `detect`.
-  _.find = _.detect = function(obj, iterator, context) {
-    var result;
-    any(obj, function(value, index, list) {
-      if (iterator.call(context, value, index, list)) {
-        result = value;
-        return true;
-      }
-    });
-    return result;
-  };
-
-  // Return all the elements that pass a truth test.
-  // Delegates to **ECMAScript 5**'s native `filter` if available.
-  // Aliased as `select`.
-  _.filter = _.select = function(obj, iterator, context) {
-    var results = [];
-    if (obj == null) return results;
-    if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
-    each(obj, function(value, index, list) {
-      if (iterator.call(context, value, index, list)) results[results.length] = value;
-    });
-    return results;
-  };
-
-  // Return all the elements for which a truth test fails.
-  _.reject = function(obj, iterator, context) {
-    var results = [];
-    if (obj == null) return results;
-    each(obj, function(value, index, list) {
-      if (!iterator.call(context, value, index, list)) results[results.length] = value;
-    });
-    return results;
-  };
-
-  // Determine whether all of the elements match a truth test.
-  // Delegates to **ECMAScript 5**'s native `every` if available.
-  // Aliased as `all`.
-  _.every = _.all = function(obj, iterator, context) {
-    var result = true;
-    if (obj == null) return result;
-    if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
-    each(obj, function(value, index, list) {
-      if (!(result = result && iterator.call(context, value, index, list))) return breaker;
-    });
-    return result;
-  };
-
-  // Determine if at least one element in the object matches a truth test.
-  // Delegates to **ECMAScript 5**'s native `some` if available.
-  // Aliased as `any`.
-  var any = _.some = _.any = function(obj, iterator, context) {
-    iterator || (iterator = _.identity);
-    var result = false;
-    if (obj == null) return result;
-    if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
-    each(obj, function(value, index, list) {
-      if (result || (result = iterator.call(context, value, index, list))) return breaker;
-    });
-    return !!result;
-  };
-
-  // Determine if a given value is included in the array or object using `===`.
-  // Aliased as `contains`.
-  _.include = _.contains = function(obj, target) {
-    var found = false;
-    if (obj == null) return found;
-    if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
-    found = any(obj, function(value) {
-      return value === target;
-    });
-    return found;
-  };
-
-  // Invoke a method (with arguments) on every item in a collection.
-  _.invoke = function(obj, method) {
-    var args = slice.call(arguments, 2);
-    return _.map(obj, function(value) {
-      return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
-    });
-  };
-
-  // Convenience version of a common use case of `map`: fetching a property.
-  _.pluck = function(obj, key) {
-    return _.map(obj, function(value){ return value[key]; });
-  };
-
-  // Return the maximum element or (element-based computation).
-  _.max = function(obj, iterator, context) {
-    if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
-    if (!iterator && _.isEmpty(obj)) return -Infinity;
-    var result = {computed : -Infinity};
-    each(obj, function(value, index, list) {
-      var computed = iterator ? iterator.call(context, value, index, list) : value;
-      computed >= result.computed && (result = {value : value, computed : computed});
-    });
-    return result.value;
-  };
-
-  // Return the minimum element (or element-based computation).
-  _.min = function(obj, iterator, context) {
-    if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
-    if (!iterator && _.isEmpty(obj)) return Infinity;
-    var result = {computed : Infinity};
-    each(obj, function(value, index, list) {
-      var computed = iterator ? iterator.call(context, value, index, list) : value;
-      computed < result.computed && (result = {value : value, computed : computed});
-    });
-    return result.value;
-  };
-
-  // Shuffle an array.
-  _.shuffle = function(obj) {
-    var shuffled = [], rand;
-    each(obj, function(value, index, list) {
-      if (index == 0) {
-        shuffled[0] = value;
-      } else {
-        rand = Math.floor(Math.random() * (index + 1));
-        shuffled[index] = shuffled[rand];
-        shuffled[rand] = value;
-      }
-    });
-    return shuffled;
-  };
-
-  // Sort the object's values by a criterion produced by an iterator.
-  _.sortBy = function(obj, iterator, context) {
-    return _.pluck(_.map(obj, function(value, index, list) {
-      return {
-        value : value,
-        criteria : iterator.call(context, value, index, list)
-      };
-    }).sort(function(left, right) {
-      var a = left.criteria, b = right.criteria;
-      return a < b ? -1 : a > b ? 1 : 0;
-    }), 'value');
-  };
-
-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  _.groupBy = function(obj, val) {
-    var result = {};
-    var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
-    each(obj, function(value, index) {
-      var key = iterator(value, index);
-      (result[key] || (result[key] = [])).push(value);
-    });
-    return result;
-  };
-
-  // Use a comparator function to figure out at what index an object should
-  // be inserted so as to maintain order. Uses binary search.
-  _.sortedIndex = function(array, obj, iterator) {
-    iterator || (iterator = _.identity);
-    var low = 0, high = array.length;
-    while (low < high) {
-      var mid = (low + high) >> 1;
-      iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
-    }
-    return low;
-  };
-
-  // Safely convert anything iterable into a real, live array.
-  _.toArray = function(iterable) {
-    if (!iterable)                return [];
-    if (iterable.toArray)         return iterable.toArray();
-    if (_.isArray(iterable))      return slice.call(iterable);
-    if (_.isArguments(iterable))  return slice.call(iterable);
-    return _.values(iterable);
-  };
-
-  // Return the number of elements in an object.
-  _.size = function(obj) {
-    return _.toArray(obj).length;
-  };
-
-  // Array Functions
-  // ---------------
-
-  // Get the first element of an array. Passing **n** will return the first N
-  // values in the array. Aliased as `head`. The **guard** check allows it to work
-  // with `_.map`.
-  _.first = _.head = function(array, n, guard) {
-    return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
-  };
-
-  // Returns everything but the last entry of the array. Especcialy useful on
-  // the arguments object. Passing **n** will return all the values in
-  // the array, excluding the last N. The **guard** check allows it to work with
-  // `_.map`.
-  _.initial = function(array, n, guard) {
-    return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
-  };
-
-  // Get the last element of an array. Passing **n** will return the last N
-  // values in the array. The **guard** check allows it to work with `_.map`.
-  _.last = function(array, n, guard) {
-    if ((n != null) && !guard) {
-      return slice.call(array, Math.max(array.length - n, 0));
-    } else {
-      return array[array.length - 1];
-    }
-  };
-
-  // Returns everything but the first entry of the array. Aliased as `tail`.
-  // Especially useful on the arguments object. Passing an **index** will return
-  // the rest of the values in the array from that index onward. The **guard**
-  // check allows it to work with `_.map`.
-  _.rest = _.tail = function(array, index, guard) {
-    return slice.call(array, (index == null) || guard ? 1 : index);
-  };
-
-  // Trim out all falsy values from an array.
-  _.compact = function(array) {
-    return _.filter(array, function(value){ return !!value; });
-  };
-
-  // Return a completely flattened version of an array.
-  _.flatten = function(array, shallow) {
-    return _.reduce(array, function(memo, value) {
-      if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
-      memo[memo.length] = value;
-      return memo;
-    }, []);
-  };
-
-  // Return a version of the array that does not contain the specified value(s).
-  _.without = function(array) {
-    return _.difference(array, slice.call(arguments, 1));
-  };
-
-  // Produce a duplicate-free version of the array. If the array has already
-  // been sorted, you have the option of using a faster algorithm.
-  // Aliased as `unique`.
-  _.uniq = _.unique = function(array, isSorted, iterator) {
-    var initial = iterator ? _.map(array, iterator) : array;
-    var result = [];
-    _.reduce(initial, function(memo, el, i) {
-      if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
-        memo[memo.length] = el;
-        result[result.length] = array[i];
-      }
-      return memo;
-    }, []);
-    return result;
-  };
-
-  // Produce an array that contains the union: each distinct element from all of
-  // the passed-in arrays.
-  _.union = function() {
-    return _.uniq(_.flatten(arguments, true));
-  };
-
-  // Produce an array that contains every item shared between all the
-  // passed-in arrays. (Aliased as "intersect" for back-compat.)
-  _.intersection = _.intersect = function(array) {
-    var rest = slice.call(arguments, 1);
-    return _.filter(_.uniq(array), function(item) {
-      return _.every(rest, function(other) {
-        return _.indexOf(other, item) >= 0;
-      });
-    });
-  };
-
-  // Take the difference between one array and a number of other arrays.
-  // Only the elements present in just the first array will remain.
-  _.difference = function(array) {
-    var rest = _.flatten(slice.call(arguments, 1));
-    return _.filter(array, function(value){ return !_.include(rest, value); });
-  };
-
-  // Zip together multiple lists into a single array -- elements that share
-  // an index go together.
-  _.zip = function() {
-    var args = slice.call(arguments);
-    var length = _.max(_.pluck(args, 'length'));
-    var results = new Array(length);
-    for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
-    return results;
-  };
-
-  // If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
-  // we need this function. Return the position of the first occurrence of an
-  // item in an array, or -1 if the item is not included in the array.
-  // Delegates to **ECMAScript 5**'s native `indexOf` if available.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  _.indexOf = function(array, item, isSorted) {
-    if (array == null) return -1;
-    var i, l;
-    if (isSorted) {
-      i = _.sortedIndex(array, item);
-      return array[i] === item ? i : -1;
-    }
-    if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
-    for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
-    return -1;
-  };
-
-  // Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
-  _.lastIndexOf = function(array, item) {
-    if (array == null) return -1;
-    if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
-    var i = array.length;
-    while (i--) if (i in array && array[i] === item) return i;
-    return -1;
-  };
-
-  // Generate an integer Array containing an arithmetic progression. A port of
-  // the native Python `range()` function. See
-  // [the Python documentation](http://docs.python.org/library/functions.html#range).
-  _.range = function(start, stop, step) {
-    if (arguments.length <= 1) {
-      stop = start || 0;
-      start = 0;
-    }
-    step = arguments[2] || 1;
-
-    var len = Math.max(Math.ceil((stop - start) / step), 0);
-    var idx = 0;
-    var range = new Array(len);
-
-    while(idx < len) {
-      range[idx++] = start;
-      start += step;
-    }
-
-    return range;
-  };
-
-  // Function (ahem) Functions
-  // ------------------
-
-  // Reusable constructor function for prototype setting.
-  var ctor = function(){};
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally). Binding with arguments is also known as `curry`.
-  // Delegates to **ECMAScript 5**'s native `Function.bind` if available.
-  // We check for `func.bind` first, to fail fast when `func` is undefined.
-  _.bind = function bind(func, context) {
-    var bound, args;
-    if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
-    if (!_.isFunction(func)) throw new TypeError;
-    args = slice.call(arguments, 2);
-    return bound = function() {
-      if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
-      ctor.prototype = func.prototype;
-      var self = new ctor;
-      var result = func.apply(self, args.concat(slice.call(arguments)));
-      if (Object(result) === result) return result;
-      return self;
-    };
-  };
-
-  // Bind all of an object's methods to that object. Useful for ensuring that
-  // all callbacks defined on an object belong to it.
-  _.bindAll = function(obj) {
-    var funcs = slice.call(arguments, 1);
-    if (funcs.length == 0) funcs = _.functions(obj);
-    each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
-    return obj;
-  };
-
-  // Memoize an expensive function by storing its results.
-  _.memoize = function(func, hasher) {
-    var memo = {};
-    hasher || (hasher = _.identity);
-    return function() {
-      var key = hasher.apply(this, arguments);
-      return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
-    };
-  };
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  _.delay = function(func, wait) {
-    var args = slice.call(arguments, 2);
-    return setTimeout(function(){ return func.apply(func, args); }, wait);
-  };
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  _.defer = function(func) {
-    return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
-  };
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time.
-  _.throttle = function(func, wait) {
-    var context, args, timeout, throttling, more;
-    var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
-    return function() {
-      context = this; args = arguments;
-      var later = function() {
-        timeout = null;
-        if (more) func.apply(context, args);
-        whenDone();
-      };
-      if (!timeout) timeout = setTimeout(later, wait);
-      if (throttling) {
-        more = true;
-      } else {
-        func.apply(context, args);
-      }
-      whenDone();
-      throttling = true;
-    };
-  };
-
-  // Returns a function, that, as long as it continues to be invoked, will not
-  // be triggered. The function will be called after it stops being called for
-  // N milliseconds.
-  _.debounce = function(func, wait) {
-    var timeout;
-    return function() {
-      var context = this, args = arguments;
-      var later = function() {
-        timeout = null;
-        func.apply(context, args);
-      };
-      clearTimeout(timeout);
-      timeout = setTimeout(later, wait);
-    };
-  };
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  _.once = function(func) {
-    var ran = false, memo;
-    return function() {
-      if (ran) return memo;
-      ran = true;
-      return memo = func.apply(this, arguments);
-    };
-  };
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  _.wrap = function(func, wrapper) {
-    return function() {
-      var args = [func].concat(slice.call(arguments, 0));
-      return wrapper.apply(this, args);
-    };
-  };
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  _.compose = function() {
-    var funcs = arguments;
-    return function() {
-      var args = arguments;
-      for (var i = funcs.length - 1; i >= 0; i--) {
-        args = [funcs[i].apply(this, args)];
-      }
-      return args[0];
-    };
-  };
-
-  // Returns a function that will only be executed after being called N times.
-  _.after = function(times, func) {
-    if (times <= 0) return func();
-    return function() {
-      if (--times < 1) { return func.apply(this, arguments); }
-    };
-  };
-
-  // Object Functions
-  // ----------------
-
-  // Retrieve the names of an object's properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`
-  _.keys = nativeKeys || function(obj) {
-    if (obj !== Object(obj)) throw new TypeError('Invalid object');
-    var keys = [];
-    for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
-    return keys;
-  };
-
-  // Retrieve the values of an object's properties.
-  _.values = function(obj) {
-    return _.map(obj, _.identity);
-  };
-
-  // Return a sorted list of the function names available on the object.
-  // Aliased as `methods`
-  _.functions = _.methods = function(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (_.isFunction(obj[key])) names.push(key);
-    }
-    return names.sort();
-  };
-
-  // Extend a given object with all the properties in passed-in object(s).
-  _.extend = function(obj) {
-    each(slice.call(arguments, 1), function(source) {
-      for (var prop in source) {
-        obj[prop] = source[prop];
-      }
-    });
-    return obj;
-  };
-
-  // Fill in a given object with default properties.
-  _.defaults = function(obj) {
-    each(slice.call(arguments, 1), function(source) {
-      for (var prop in source) {
-        if (obj[prop] == null) obj[prop] = source[prop];
-      }
-    });
-    return obj;
-  };
-
-  // Create a (shallow-cloned) duplicate of an object.
-  _.clone = function(obj) {
-    if (!_.isObject(obj)) return obj;
-    return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
-  };
-
-  // Invokes interceptor with the obj, and then returns obj.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  _.tap = function(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  };
-
-  // Internal recursive comparison function.
-  function eq(a, b, stack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
-    if (a === b) return a !== 0 || 1 / a == 1 / b;
-    // A strict comparison is necessary because `null == undefined`.
-    if (a == null || b == null) return a === b;
-    // Unwrap any wrapped objects.
-    if (a._chain) a = a._wrapped;
-    if (b._chain) b = b._wrapped;
-    // Invoke a custom `isEqual` method if one is provided.
-    if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
-    if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className != toString.call(b)) return false;
-    switch (className) {
-      // Strings, numbers, dates, and booleans are compared by value.
-      case '[object String]':
-        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
-        // equivalent to `new String("5")`.
-        return a == String(b);
-      case '[object Number]':
-        // `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
-        // other numeric values.
-        return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
-      case '[object Date]':
-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
-        // millisecond representations. Note that invalid dates with millisecond representations
-        // of `NaN` are not equivalent.
-        return +a == +b;
-      // RegExps are compared by their source patterns and flags.
-      case '[object RegExp]':
-        return a.source == b.source &&
-               a.global == b.global &&
-               a.multiline == b.multiline &&
-               a.ignoreCase == b.ignoreCase;
-    }
-    if (typeof a != 'object' || typeof b != 'object') return false;
-    // Assume equality for cyclic structures. The algorithm for detecting cyclic
-    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-    var length = stack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
-      // unique nested structures.
-      if (stack[length] == a) return true;
-    }
-    // Add the first object to the stack of traversed objects.
-    stack.push(a);
-    var size = 0, result = true;
-    // Recursively compare objects and arrays.
-    if (className == '[object Array]') {
-      // Compare array lengths to determine if a deep comparison is necessary.
-      size = a.length;
-      result = size == b.length;
-      if (result) {
-        // Deep compare the contents, ignoring non-numeric properties.
-        while (size--) {
-          // Ensure commutative equality for sparse arrays.
-          if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break;
-        }
-      }
-    } else {
-      // Objects with different constructors are not equivalent.
-      if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false;
-      // Deep compare objects.
-      for (var key in a) {
-        if (_.has(a, key)) {
-          // Count the expected number of properties.
-          size++;
-          // Deep compare each member.
-          if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break;
-        }
-      }
-      // Ensure that both objects contain the same number of properties.
-      if (result) {
-        for (key in b) {
-          if (_.has(b, key) && !(size--)) break;
-        }
-        result = !size;
-      }
-    }
-    // Remove the first object from the stack of traversed objects.
-    stack.pop();
-    return result;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
-  _.isEqual = function(a, b) {
-    return eq(a, b, []);
-  };
-
-  // Is a given array, string, or object empty?
-  // An "empty" object has no enumerable own-properties.
-  _.isEmpty = function(obj) {
-    if (_.isArray(obj) || _.isString(obj)) return obj.length === 0;
-    for (var key in obj) if (_.has(obj, key)) return false;
-    return true;
-  };
-
-  // Is a given value a DOM element?
-  _.isElement = function(obj) {
-    return !!(obj && obj.nodeType == 1);
-  };
-
-  // Is a given value an array?
-  // Delegates to ECMA5's native Array.isArray
-  _.isArray = nativeIsArray || function(obj) {
-    return toString.call(obj) == '[object Array]';
-  };
-
-  // Is a given variable an object?
-  _.isObject = function(obj) {
-    return obj === Object(obj);
-  };
-
-  // Is a given variable an arguments object?
-  _.isArguments = function(obj) {
-    return toString.call(obj) == '[object Arguments]';
-  };
-  if (!_.isArguments(arguments)) {
-    _.isArguments = function(obj) {
-      return !!(obj && _.has(obj, 'callee'));
-    };
-  }
-
-  // Is a given value a function?
-  _.isFunction = function(obj) {
-    return toString.call(obj) == '[object Function]';
-  };
-
-  // Is a given value a string?
-  _.isString = function(obj) {
-    return toString.call(obj) == '[object String]';
-  };
-
-  // Is a given value a number?
-  _.isNumber = function(obj) {
-    return toString.call(obj) == '[object Number]';
-  };
-
-  // Is the given value `NaN`?
-  _.isNaN = function(obj) {
-    // `NaN` is the only value for which `===` is not reflexive.
-    return obj !== obj;
-  };
-
-  // Is a given value a boolean?
-  _.isBoolean = function(obj) {
-    return obj === true || obj === false || toString.call(obj) == '[object Boolean]';
-  };
-
-  // Is a given value a date?
-  _.isDate = function(obj) {
-    return toString.call(obj) == '[object Date]';
-  };
-
-  // Is the given value a regular expression?
-  _.isRegExp = function(obj) {
-    return toString.call(obj) == '[object RegExp]';
-  };
-
-  // Is a given value equal to null?
-  _.isNull = function(obj) {
-    return obj === null;
-  };
-
-  // Is a given variable undefined?
-  _.isUndefined = function(obj) {
-    return obj === void 0;
-  };
-
-  // Has own property?
-  _.has = function(obj, key) {
-    return hasOwnProperty.call(obj, key);
-  };
-
-  // Utility Functions
-  // -----------------
-
-  // Run Underscore.js in *noConflict* mode, returning the `_` variable to its
-  // previous owner. Returns a reference to the Underscore object.
-  _.noConflict = function() {
-    root._ = previousUnderscore;
-    return this;
-  };
-
-  // Keep the identity function around for default iterators.
-  _.identity = function(value) {
-    return value;
-  };
-
-  // Run a function **n** times.
-  _.times = function (n, iterator, context) {
-    for (var i = 0; i < n; i++) iterator.call(context, i);
-  };
-
-  // Escape a string for HTML interpolation.
-  _.escape = function(string) {
-    return (''+string).replace(/&/g, '&').replace(//g, '>').replace(/"/g, '"').replace(/'/g, ''').replace(/\//g,'/');
-  };
-
-  // Add your own custom functions to the Underscore object, ensuring that
-  // they're correctly added to the OOP wrapper as well.
-  _.mixin = function(obj) {
-    each(_.functions(obj), function(name){
-      addToWrapper(name, _[name] = obj[name]);
-    });
-  };
-
-  // Generate a unique integer id (unique within the entire client session).
-  // Useful for temporary DOM ids.
-  var idCounter = 0;
-  _.uniqueId = function(prefix) {
-    var id = idCounter++;
-    return prefix ? prefix + id : id;
-  };
-
-  // By default, Underscore uses ERB-style template delimiters, change the
-  // following template settings to use alternative delimiters.
-  _.templateSettings = {
-    evaluate    : /<%([\s\S]+?)%>/g,
-    interpolate : /<%=([\s\S]+?)%>/g,
-    escape      : /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `templateSettings`, if you don't want to define an
-  // interpolation, evaluation or escaping regex, we need one that is
-  // guaranteed not to match.
-  var noMatch = /.^/;
-
-  // Within an interpolation, evaluation, or escaping, remove HTML escaping
-  // that had been previously added.
-  var unescape = function(code) {
-    return code.replace(/\\\\/g, '\\').replace(/\\'/g, "'");
-  };
-
-  // JavaScript micro-templating, similar to John Resig's implementation.
-  // Underscore templating handles arbitrary delimiters, preserves whitespace,
-  // and correctly escapes quotes within interpolated code.
-  _.template = function(str, data) {
-    var c  = _.templateSettings;
-    var tmpl = 'var __p=[],print=function(){__p.push.apply(__p,arguments);};' +
-      'with(obj||{}){__p.push(\'' +
-      str.replace(/\\/g, '\\\\')
-         .replace(/'/g, "\\'")
-         .replace(c.escape || noMatch, function(match, code) {
-           return "',_.escape(" + unescape(code) + "),'";
-         })
-         .replace(c.interpolate || noMatch, function(match, code) {
-           return "'," + unescape(code) + ",'";
-         })
-         .replace(c.evaluate || noMatch, function(match, code) {
-           return "');" + unescape(code).replace(/[\r\n\t]/g, ' ') + ";__p.push('";
-         })
-         .replace(/\r/g, '\\r')
-         .replace(/\n/g, '\\n')
-         .replace(/\t/g, '\\t')
-         + "');}return __p.join('');";
-    var func = new Function('obj', '_', tmpl);
-    if (data) return func(data, _);
-    return function(data) {
-      return func.call(this, data, _);
-    };
-  };
-
-  // Add a "chain" function, which will delegate to the wrapper.
-  _.chain = function(obj) {
-    return _(obj).chain();
-  };
-
-  // The OOP Wrapper
-  // ---------------
-
-  // If Underscore is called as a function, it returns a wrapped object that
-  // can be used OO-style. This wrapper holds altered versions of all the
-  // underscore functions. Wrapped objects may be chained.
-  var wrapper = function(obj) { this._wrapped = obj; };
-
-  // Expose `wrapper.prototype` as `_.prototype`
-  _.prototype = wrapper.prototype;
-
-  // Helper function to continue chaining intermediate results.
-  var result = function(obj, chain) {
-    return chain ? _(obj).chain() : obj;
-  };
-
-  // A method to easily add functions to the OOP wrapper.
-  var addToWrapper = function(name, func) {
-    wrapper.prototype[name] = function() {
-      var args = slice.call(arguments);
-      unshift.call(args, this._wrapped);
-      return result(func.apply(_, args), this._chain);
-    };
-  };
-
-  // Add all of the Underscore functions to the wrapper object.
-  _.mixin(_);
-
-  // Add all mutator Array functions to the wrapper.
-  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
-    var method = ArrayProto[name];
-    wrapper.prototype[name] = function() {
-      var wrapped = this._wrapped;
-      method.apply(wrapped, arguments);
-      var length = wrapped.length;
-      if ((name == 'shift' || name == 'splice') && length === 0) delete wrapped[0];
-      return result(wrapped, this._chain);
-    };
-  });
-
-  // Add all accessor Array functions to the wrapper.
-  each(['concat', 'join', 'slice'], function(name) {
-    var method = ArrayProto[name];
-    wrapper.prototype[name] = function() {
-      return result(method.apply(this._wrapped, arguments), this._chain);
-    };
-  });
-
-  // Start chaining a wrapped Underscore object.
-  wrapper.prototype.chain = function() {
-    this._chain = true;
-    return this;
-  };
-
-  // Extracts the result from a wrapped and chained object.
-  wrapper.prototype.value = function() {
-    return this._wrapped;
-  };
-
-}).call(this);
diff --git a/docs/build/html/_static/underscore.js b/docs/build/html/_static/underscore.js
index 5b55f32..cf177d4 100644
--- a/docs/build/html/_static/underscore.js
+++ b/docs/build/html/_static/underscore.js
@@ -1,31 +1,6 @@
-// Underscore.js 1.3.1
-// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
-// Underscore is freely distributable under the MIT license.
-// Portions of Underscore are inspired or borrowed from Prototype,
-// Oliver Steele's Functional, and John Resig's Micro-Templating.
-// For all details and documentation:
-// http://documentcloud.github.com/underscore
-(function(){function q(a,c,d){if(a===c)return a!==0||1/a==1/c;if(a==null||c==null)return a===c;if(a._chain)a=a._wrapped;if(c._chain)c=c._wrapped;if(a.isEqual&&b.isFunction(a.isEqual))return a.isEqual(c);if(c.isEqual&&b.isFunction(c.isEqual))return c.isEqual(a);var e=l.call(a);if(e!=l.call(c))return false;switch(e){case "[object String]":return a==String(c);case "[object Number]":return a!=+a?c!=+c:a==0?1/a==1/c:a==+c;case "[object Date]":case "[object Boolean]":return+a==+c;case "[object RegExp]":return a.source==
-c.source&&a.global==c.global&&a.multiline==c.multiline&&a.ignoreCase==c.ignoreCase}if(typeof a!="object"||typeof c!="object")return false;for(var f=d.length;f--;)if(d[f]==a)return true;d.push(a);var f=0,g=true;if(e=="[object Array]"){if(f=a.length,g=f==c.length)for(;f--;)if(!(g=f in a==f in c&&q(a[f],c[f],d)))break}else{if("constructor"in a!="constructor"in c||a.constructor!=c.constructor)return false;for(var h in a)if(b.has(a,h)&&(f++,!(g=b.has(c,h)&&q(a[h],c[h],d))))break;if(g){for(h in c)if(b.has(c,
-h)&&!f--)break;g=!f}}d.pop();return g}var r=this,G=r._,n={},k=Array.prototype,o=Object.prototype,i=k.slice,H=k.unshift,l=o.toString,I=o.hasOwnProperty,w=k.forEach,x=k.map,y=k.reduce,z=k.reduceRight,A=k.filter,B=k.every,C=k.some,p=k.indexOf,D=k.lastIndexOf,o=Array.isArray,J=Object.keys,s=Function.prototype.bind,b=function(a){return new m(a)};if(typeof exports!=="undefined"){if(typeof module!=="undefined"&&module.exports)exports=module.exports=b;exports._=b}else r._=b;b.VERSION="1.3.1";var j=b.each=
-b.forEach=function(a,c,d){if(a!=null)if(w&&a.forEach===w)a.forEach(c,d);else if(a.length===+a.length)for(var e=0,f=a.length;e2;a==
-null&&(a=[]);if(y&&a.reduce===y)return e&&(c=b.bind(c,e)),f?a.reduce(c,d):a.reduce(c);j(a,function(a,b,i){f?d=c.call(e,d,a,b,i):(d=a,f=true)});if(!f)throw new TypeError("Reduce of empty array with no initial value");return d};b.reduceRight=b.foldr=function(a,c,d,e){var f=arguments.length>2;a==null&&(a=[]);if(z&&a.reduceRight===z)return e&&(c=b.bind(c,e)),f?a.reduceRight(c,d):a.reduceRight(c);var g=b.toArray(a).reverse();e&&!f&&(c=b.bind(c,e));return f?b.reduce(g,c,d,e):b.reduce(g,c)};b.find=b.detect=
-function(a,c,b){var e;E(a,function(a,g,h){if(c.call(b,a,g,h))return e=a,true});return e};b.filter=b.select=function(a,c,b){var e=[];if(a==null)return e;if(A&&a.filter===A)return a.filter(c,b);j(a,function(a,g,h){c.call(b,a,g,h)&&(e[e.length]=a)});return e};b.reject=function(a,c,b){var e=[];if(a==null)return e;j(a,function(a,g,h){c.call(b,a,g,h)||(e[e.length]=a)});return e};b.every=b.all=function(a,c,b){var e=true;if(a==null)return e;if(B&&a.every===B)return a.every(c,b);j(a,function(a,g,h){if(!(e=
-e&&c.call(b,a,g,h)))return n});return e};var E=b.some=b.any=function(a,c,d){c||(c=b.identity);var e=false;if(a==null)return e;if(C&&a.some===C)return a.some(c,d);j(a,function(a,b,h){if(e||(e=c.call(d,a,b,h)))return n});return!!e};b.include=b.contains=function(a,c){var b=false;if(a==null)return b;return p&&a.indexOf===p?a.indexOf(c)!=-1:b=E(a,function(a){return a===c})};b.invoke=function(a,c){var d=i.call(arguments,2);return b.map(a,function(a){return(b.isFunction(c)?c||a:a[c]).apply(a,d)})};b.pluck=
-function(a,c){return b.map(a,function(a){return a[c]})};b.max=function(a,c,d){if(!c&&b.isArray(a))return Math.max.apply(Math,a);if(!c&&b.isEmpty(a))return-Infinity;var e={computed:-Infinity};j(a,function(a,b,h){b=c?c.call(d,a,b,h):a;b>=e.computed&&(e={value:a,computed:b})});return e.value};b.min=function(a,c,d){if(!c&&b.isArray(a))return Math.min.apply(Math,a);if(!c&&b.isEmpty(a))return Infinity;var e={computed:Infinity};j(a,function(a,b,h){b=c?c.call(d,a,b,h):a;bd?1:0}),"value")};b.groupBy=function(a,c){var d={},e=b.isFunction(c)?c:function(a){return a[c]};j(a,function(a,b){var c=e(a,b);(d[c]||(d[c]=[])).push(a)});return d};b.sortedIndex=function(a,
-c,d){d||(d=b.identity);for(var e=0,f=a.length;e>1;d(a[g])=0})})};b.difference=function(a){var c=b.flatten(i.call(arguments,1));return b.filter(a,function(a){return!b.include(c,a)})};b.zip=function(){for(var a=i.call(arguments),c=b.max(b.pluck(a,"length")),d=Array(c),e=0;e=0;d--)b=[a[d].apply(this,b)];return b[0]}};
-b.after=function(a,b){return a<=0?b():function(){if(--a<1)return b.apply(this,arguments)}};b.keys=J||function(a){if(a!==Object(a))throw new TypeError("Invalid object");var c=[],d;for(d in a)b.has(a,d)&&(c[c.length]=d);return c};b.values=function(a){return b.map(a,b.identity)};b.functions=b.methods=function(a){var c=[],d;for(d in a)b.isFunction(a[d])&&c.push(d);return c.sort()};b.extend=function(a){j(i.call(arguments,1),function(b){for(var d in b)a[d]=b[d]});return a};b.defaults=function(a){j(i.call(arguments,
-1),function(b){for(var d in b)a[d]==null&&(a[d]=b[d])});return a};b.clone=function(a){return!b.isObject(a)?a:b.isArray(a)?a.slice():b.extend({},a)};b.tap=function(a,b){b(a);return a};b.isEqual=function(a,b){return q(a,b,[])};b.isEmpty=function(a){if(b.isArray(a)||b.isString(a))return a.length===0;for(var c in a)if(b.has(a,c))return false;return true};b.isElement=function(a){return!!(a&&a.nodeType==1)};b.isArray=o||function(a){return l.call(a)=="[object Array]"};b.isObject=function(a){return a===Object(a)};
-b.isArguments=function(a){return l.call(a)=="[object Arguments]"};if(!b.isArguments(arguments))b.isArguments=function(a){return!(!a||!b.has(a,"callee"))};b.isFunction=function(a){return l.call(a)=="[object Function]"};b.isString=function(a){return l.call(a)=="[object String]"};b.isNumber=function(a){return l.call(a)=="[object Number]"};b.isNaN=function(a){return a!==a};b.isBoolean=function(a){return a===true||a===false||l.call(a)=="[object Boolean]"};b.isDate=function(a){return l.call(a)=="[object Date]"};
-b.isRegExp=function(a){return l.call(a)=="[object RegExp]"};b.isNull=function(a){return a===null};b.isUndefined=function(a){return a===void 0};b.has=function(a,b){return I.call(a,b)};b.noConflict=function(){r._=G;return this};b.identity=function(a){return a};b.times=function(a,b,d){for(var e=0;e/g,">").replace(/"/g,""").replace(/'/g,"'").replace(/\//g,"/")};b.mixin=function(a){j(b.functions(a),
-function(c){K(c,b[c]=a[c])})};var L=0;b.uniqueId=function(a){var b=L++;return a?a+b:b};b.templateSettings={evaluate:/<%([\s\S]+?)%>/g,interpolate:/<%=([\s\S]+?)%>/g,escape:/<%-([\s\S]+?)%>/g};var t=/.^/,u=function(a){return a.replace(/\\\\/g,"\\").replace(/\\'/g,"'")};b.template=function(a,c){var d=b.templateSettings,d="var __p=[],print=function(){__p.push.apply(__p,arguments);};with(obj||{}){__p.push('"+a.replace(/\\/g,"\\\\").replace(/'/g,"\\'").replace(d.escape||t,function(a,b){return"',_.escape("+
-u(b)+"),'"}).replace(d.interpolate||t,function(a,b){return"',"+u(b)+",'"}).replace(d.evaluate||t,function(a,b){return"');"+u(b).replace(/[\r\n\t]/g," ")+";__p.push('"}).replace(/\r/g,"\\r").replace(/\n/g,"\\n").replace(/\t/g,"\\t")+"');}return __p.join('');",e=new Function("obj","_",d);return c?e(c,b):function(a){return e.call(this,a,b)}};b.chain=function(a){return b(a).chain()};var m=function(a){this._wrapped=a};b.prototype=m.prototype;var v=function(a,c){return c?b(a).chain():a},K=function(a,c){m.prototype[a]=
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-true;return this};m.prototype.value=function(){return this._wrapped}}).call(this);
+!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n="undefined"!=typeof globalThis?globalThis:n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){
+//     Underscore.js 1.13.1
+//     https://underscorejs.org
+//     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+//     Underscore may be freely distributed under the MIT license.
+var n="1.13.1",r="object"==typeof self&&self.self===self&&self||"object"==typeof global&&global.global===global&&global||Function("return this")()||{},t=Array.prototype,e=Object.prototype,u="undefined"!=typeof Symbol?Symbol.prototype:null,o=t.push,i=t.slice,a=e.toString,f=e.hasOwnProperty,c="undefined"!=typeof ArrayBuffer,l="undefined"!=typeof DataView,s=Array.isArray,p=Object.keys,v=Object.create,h=c&&ArrayBuffer.isView,y=isNaN,d=isFinite,g=!{toString:null}.propertyIsEnumerable("toString"),b=["valueOf","isPrototypeOf","toString","propertyIsEnumerable","hasOwnProperty","toLocaleString"],m=Math.pow(2,53)-1;function j(n,r){return r=null==r?n.length-1:+r,function(){for(var t=Math.max(arguments.length-r,0),e=Array(t),u=0;u=0&&t<=m}}function J(n){return function(r){return null==r?void 0:r[n]}}var G=J("byteLength"),H=K(G),Q=/\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;var X=c?function(n){return h?h(n)&&!q(n):H(n)&&Q.test(a.call(n))}:C(!1),Y=J("length");function Z(n,r){r=function(n){for(var r={},t=n.length,e=0;e":">",'"':""","'":"'","`":"`"},Cn=Ln($n),Kn=Ln(_n($n)),Jn=tn.templateSettings={evaluate:/<%([\s\S]+?)%>/g,interpolate:/<%=([\s\S]+?)%>/g,escape:/<%-([\s\S]+?)%>/g},Gn=/(.)^/,Hn={"'":"'","\\":"\\","\r":"r","\n":"n","\u2028":"u2028","\u2029":"u2029"},Qn=/\\|'|\r|\n|\u2028|\u2029/g;function Xn(n){return"\\"+Hn[n]}var Yn=/^\s*(\w|\$)+\s*$/;var Zn=0;function nr(n,r,t,e,u){if(!(e instanceof r))return n.apply(t,u);var o=Mn(n.prototype),i=n.apply(o,u);return _(i)?i:o}var rr=j((function(n,r){var t=rr.placeholder,e=function(){for(var u=0,o=r.length,i=Array(o),a=0;a1)ur(a,r-1,t,e),u=e.length;else for(var f=0,c=a.length;f0&&(t=r.apply(this,arguments)),n<=1&&(r=null),t}}var lr=rr(cr,2);function sr(n,r,t){r=qn(r,t);for(var e,u=nn(n),o=0,i=u.length;o0?0:u-1;o>=0&&o0?a=o>=0?o:Math.max(o+f,a):f=o>=0?Math.min(o+1,f):o+f+1;else if(t&&o&&f)return e[o=t(e,u)]===u?o:-1;if(u!=u)return(o=r(i.call(e,a,f),$))>=0?o+a:-1;for(o=n>0?a:f-1;o>=0&&o0?0:i-1;for(u||(e=r[o?o[a]:a],a+=n);a>=0&&a=3;return r(n,Fn(t,u,4),e,o)}}var Ar=wr(1),xr=wr(-1);function Sr(n,r,t){var e=[];return r=qn(r,t),jr(n,(function(n,t,u){r(n,t,u)&&e.push(n)})),e}function Or(n,r,t){r=qn(r,t);for(var e=!er(n)&&nn(n),u=(e||n).length,o=0;o=0}var Br=j((function(n,r,t){var e,u;return D(r)?u=r:(r=Nn(r),e=r.slice(0,-1),r=r[r.length-1]),_r(n,(function(n){var o=u;if(!o){if(e&&e.length&&(n=In(n,e)),null==n)return;o=n[r]}return null==o?o:o.apply(n,t)}))}));function Nr(n,r){return _r(n,Rn(r))}function Ir(n,r,t){var e,u,o=-1/0,i=-1/0;if(null==r||"number"==typeof r&&"object"!=typeof n[0]&&null!=n)for(var a=0,f=(n=er(n)?n:jn(n)).length;ao&&(o=e);else r=qn(r,t),jr(n,(function(n,t,e){((u=r(n,t,e))>i||u===-1/0&&o===-1/0)&&(o=n,i=u)}));return o}function Tr(n,r,t){if(null==r||t)return er(n)||(n=jn(n)),n[Wn(n.length-1)];var e=er(n)?En(n):jn(n),u=Y(e);r=Math.max(Math.min(r,u),0);for(var o=u-1,i=0;i1&&(e=Fn(e,r[1])),r=an(n)):(e=qr,r=ur(r,!1,!1),n=Object(n));for(var u=0,o=r.length;u1&&(t=r[1])):(r=_r(ur(r,!1,!1),String),e=function(n,t){return!Er(r,t)}),Ur(n,e,t)}));function zr(n,r,t){return i.call(n,0,Math.max(0,n.length-(null==r||t?1:r)))}function Lr(n,r,t){return null==n||n.length<1?null==r||t?void 0:[]:null==r||t?n[0]:zr(n,n.length-r)}function $r(n,r,t){return i.call(n,null==r||t?1:r)}var Cr=j((function(n,r){return r=ur(r,!0,!0),Sr(n,(function(n){return!Er(r,n)}))})),Kr=j((function(n,r){return Cr(n,r)}));function Jr(n,r,t,e){A(r)||(e=t,t=r,r=!1),null!=t&&(t=qn(t,e));for(var u=[],o=[],i=0,a=Y(n);ir?(e&&(clearTimeout(e),e=null),a=c,i=n.apply(u,o),e||(u=o=null)):e||!1===t.trailing||(e=setTimeout(f,l)),i};return c.cancel=function(){clearTimeout(e),a=0,e=u=o=null},c},debounce:function(n,r,t){var e,u,o,i,a,f=function(){var c=zn()-u;r>c?e=setTimeout(f,r-c):(e=null,t||(i=n.apply(a,o)),e||(o=a=null))},c=j((function(c){return a=this,o=c,u=zn(),e||(e=setTimeout(f,r),t&&(i=n.apply(a,o))),i}));return c.cancel=function(){clearTimeout(e),e=o=a=null},c},wrap:function(n,r){return rr(r,n)},negate:fr,compose:function(){var n=arguments,r=n.length-1;return function(){for(var t=r,e=n[r].apply(this,arguments);t--;)e=n[t].call(this,e);return e}},after:function(n,r){return function(){if(--n<1)return r.apply(this,arguments)}},before:cr,once:lr,findKey:sr,findIndex:vr,findLastIndex:hr,sortedIndex:yr,indexOf:gr,lastIndexOf:br,find:mr,detect:mr,findWhere:function(n,r){return mr(n,Dn(r))},each:jr,forEach:jr,map:_r,collect:_r,reduce:Ar,foldl:Ar,inject:Ar,reduceRight:xr,foldr:xr,filter:Sr,select:Sr,reject:function(n,r,t){return Sr(n,fr(qn(r)),t)},every:Or,all:Or,some:Mr,any:Mr,contains:Er,includes:Er,include:Er,invoke:Br,pluck:Nr,where:function(n,r){return Sr(n,Dn(r))},max:Ir,min:function(n,r,t){var e,u,o=1/0,i=1/0;if(null==r||"number"==typeof r&&"object"!=typeof n[0]&&null!=n)for(var a=0,f=(n=er(n)?n:jn(n)).length;ae||void 0===t)return 1;if(t
 
-
+
   
     
-    API reference — yasa 0.6.3 documentation
-    
-    
+    
+
+    API reference — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
 
         
    
@@ -93,9 +93,9 @@
   
    
     
    
       
-  
    
+  
    
 
    API reference
    
-
    
+
    
 
    Automatic sleep staging & events detection
    
 @@ -103,37 +103,37 @@ 
    Automatic sleep staging & events detection
 
 
    
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
    
 

    SleepStaging(raw, eeg_name, *[, eog_name, …])
    SleepStaging(raw, eeg_name, *[, eog_name, ...])
    		
 
    Automatic sleep staging of polysomnography data.
    		
 
    art_detect(data[, sf, window, hypno, …])
    art_detect(data[, sf, window, hypno, ...])
    		
 
    Automatic artifact rejection.
    		
 
    rem_detect(loc, roc, sf[, hypno, include, …])
    rem_detect(loc, roc, sf[, hypno, include, ...])
    		
 
    Rapid eye movements (REMs) detection.
    		
 
    REMResults(events, data, sf, ch_names, …)
    REMResults(events, data, sf, ch_names, ...)
    		
 
    Output class for REMs detection.
    		
 
    spindles_detect(data[, sf, ch_names, hypno, …])
    spindles_detect(data[, sf, ch_names, hypno, ...])
    		
 
    Spindles detection.
    		
 
    SpindlesResults(events, data, sf, ch_names, …)
    SpindlesResults(events, data, sf, ch_names, ...)
    		
 
    Output class for spindles detection.
    		
 
    sw_detect(data[, sf, ch_names, hypno, …])
    sw_detect(data[, sf, ch_names, hypno, ...])
    		
 
    Slow-waves detection.
    		
 
    SWResults(events, data, sf, ch_names, hypno, …)
    SWResults(events, data, sf, ch_names, hypno, ...)
    		
 
    Output class for slow-waves detection.
    		
 
    compare_detection(indices_detection, …[, …])
    compare_detection(indices_detection, ...[, ...])
    		
 
    Determine correctness of detected events against ground-truth events.
    		
 
    
     
 
    
-
    
-
    
+
    
+
    
 
    Hypnogram & sleep statistics
    
 @@ -153,16 +153,16 @@ 
    Hypnogram & sleep statistics
    
-
+
-
+
-
+
@@ -171,13 +171,16 @@ 
    Hypnogram & sleep statistics
    
-
+
+
+
+
    
 

    hypno_int_to_str(hypno[, mapping_dict])
    		
 
    Convert an integer hypnogram array to a string array.
    		
 
    hypno_find_periods(hypno, sf_hypno[, …])
    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, …])
    plot_hypnogram(hyp[, lw, highlight, ...])
    		
 
    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)
    sleep_statistics(hypno, sf_hyp)
    		
 
    Compute standard sleep statistics from an hypnogram.
    		
 
    simulate_hypno([tib, sf, n_stages, …])
    Hypnogram(values[, n_stages, freq, start, ...])
    Experimental class for manipulating hypnogram in YASA (dev).
    simulate_hypnogram([tib, trans_probas, ...])
    		
 
    Simulate a hypnogram based on transition probabilities.
    		
 
    
     
 
    
-
    
-
    
+
+
    
 
    Spectral analyses
    
 @@ -185,37 +188,37 @@ 
    Spectral analyses
 
 
    
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
    
 

    bandpower(data[, sf, ch_names, hypno, …])
    bandpower(data[, sf, ch_names, hypno, ...])
    		
 
    Calculate the Welch bandpower for each channel and, if specified, for each sleep stage.
    		
 
    bandpower_from_psd(psd, freqs[, ch_names, …])
    bandpower_from_psd(psd, freqs[, ch_names, ...])
    		
 
    Compute the average power of the EEG in specified frequency band(s) given a pre-computed PSD.
    		
 
    bandpower_from_psd_ndarray(psd, freqs[, …])
    bandpower_from_psd_ndarray(psd, freqs[, ...])
    		
 
    Compute bandpowers in N-dimensional PSD.
    		
 
    irasa(data[, sf, ch_names, band, hset, …])
    irasa(data[, sf, ch_names, band, hset, ...])
    		
 
    Separate the aperiodic (= fractal, or 1/f) and oscillatory component of the power spectra of EEG data using the IRASA method.
    		
 
    moving_transform(x[, y, sf, window, step, …])
    moving_transform(x[, y, sf, window, step, ...])
    		
 
    Moving transformation of one or two time-series.
    		
 
    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.
    		
 
    
 
    sliding_window(data, sf, window[, step, axis])
    		
 
    Calculate a sliding window of a 1D or 2D EEG signal.
    		
 
    stft_power(data, sf[, window, step, band, …])
    stft_power(data, sf[, window, step, band, ...])
    		
 
    Compute the pointwise power via STFT and interpolation.
    		
 
    topoplot(data[, montage, vmin, vmax, mask, …])
    topoplot(data[, montage, vmin, vmax, mask, ...])
    		
 
    Topoplot.
    		
 
    
     
 
    
-
    
-
    
+
+
    
 
    Heart rate analysis
    
 @@ -223,13 +226,13 @@ 
    Heart rate analysis
 
 
    
-
+
    
 

    hrv_stage(data, sf, *[, hypno, include, …])
    hrv_stage(data, sf, *[, hypno, include, ...])
    		
 
    Calculate heart rate and heart rate variability (HRV) features from an ECG.
    		
 
    
     
 
    
-
    
-
    
+
+
 
 
     
    
@@ -246,8 +249,8 @@ 
    Heart rate analysisSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/changelog.html b/docs/build/html/changelog.html
index a8c1ea0..4a2610a 100644
--- a/docs/build/html/changelog.html
+++ b/docs/build/html/changelog.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    What’s new — yasa 0.6.3 documentation
-    
-    
+    
+
+    What’s new — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
 
         
    
@@ -93,44 +93,91 @@
   
    
     
    
       
-  
    
+  
    
 
    What’s new
    
 
 
    
-
    
-
    v0.6.3 (December 2022)
    
+
    
+
    v0.7.dev
    
+
    Object-oriented hypnogram
    
+
    This version introduces the new yasa.Hypnogram class, which will gradually become
+the standard way to store and manipulate hypnograms in YASA. Put simply, YASA now uses an
+object-oriented approach to hypnograms. That is, hypnograms are now stored as a class (aka object),
+which comes with several pre-built functions (aka methods) and attributes. See for example below:
    
+
    from yasa import Hypnogram
+# Create an hypnogram object
+values = ["W", "W", "W", "S", "S", "S", "S", "S", "W", "S", "S", "S"]
+hyp = Hypnogram(values, n_stages=2, start="2022-12-23 22:30:00", scorer="RM")
+# Below are some class attributes
+hyp.hypno  # Hypnogram values (a pandas.Series of categorical dtype)
+hyp.duration  # Total duration of the hypnogram, in minutes
+hyp.sampling_frequency  # Sampling frequency of the hypnogram
+hyp.mapping  # Mapping from strings to integers
+# Below are some class methods
+hyp.sleep_statistics()  # Calculate the sleep statistics
+hyp.plot_hypnogram()  # Plot the hypnogram
+hyp.upsample_to_data()  # Upsample to data
+
    
+
    
+
    Please see the documentation of yasa.Hypnogram for more details.
    
+
    
+
    Important
    
+
    The adoption of object-oriented yasa.Hypnogram usage brings along critical changes to several YASA function, for example:
    
+
+
    
+
    
+
    
+
    
+
    v0.6.4 (January 2024)
    
+
    Minor release with one bugfix and one improvement to an existing function.
    
+
      
+
    • Fix MNE plot_topomap deprecation (PR 119)
      • 
+
    • Pass relative_prominence parameters in yasa.rem_detect() (PR 151)
      • 
+
    • Rename yasa.simulate_hypno function to yasa.simulate_hypnogram()
      • 
+
    • Add Hypnogram class in public API (see v0.7.dev).
      • 
+
    • Requires mne>=1.3, numpy>=1.18.1, numba>=0.57.1
      • 
+
    
+
    
+
    
+
    
+
    v0.6.3 (December 2022)
    
 
    This is a minor release with one bugfix and one new function. Big shoutout to @remrama for his huge help on this new version!
    
 
    Bugfix
    
 
    Solved a bug in the spindles detection which resulted in no spindle being detected on some systems. See issue 107 for detail. PR 115
    
 
    New functions
    
-
    Added the yasa.simulate_hypno() function to generate a simulated hypnogram, primarily for testing purposes and tutorials. The hypnogram is simulated as a Markov sequence based on sleep stage transition probabilities. Transition probabilities can be user-defined or will default to those published in Metzner et al., 2021, Commun Biol (see Figure 5b).
    
+
    Added the yasa.simulate_hypno function to generate a simulated hypnogram, primarily for testing purposes and tutorials. The hypnogram is simulated as a Markov sequence based on sleep stage transition probabilities. Transition probabilities can be user-defined or will default to those published in Metzner et al., 2021, Commun Biol (see Figure 5b).
    
 
    Improvements
    
 
-
    
+
    
 
    
-
    
-
    v0.6.2 (August 2022)
    
+
    
+
    v0.6.2 (August 2022)
    
 
    ECG analysis - PR 68
    
 
      
 
    1. Added the yasa.hypno_find_periods() function to find sequences of consecutive values in hypnogram that are longer than a certain duration. This is a flexible function that can be used to detect NREM/REM periods.
      2. 
@@ -154,21 +201,21 @@ 
      v0.6.2 (August 2022)SleepECG to the dependencies. SleepECG is used for the heartbeats detection in yasa.hrv_stage().
      
 
    2. YASA now requires MNE>0.23
      3. 
 
    
-
    
+
 
    
-
    
-
    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
    
 
    
 
    
-
    
-
    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).
    
@@ -238,13 +285,13 @@ 
    v0.5.0 (August 2021)issue 33).
    
 
  • flake8 requirements for max line length has been changed from 80 to 100 characters.
    • 
 
-
    
+
 
    
-
    
-
    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
    
 
      
@@ -254,10 +301,10 @@ 
      v0.4.1 (March 2021)outdated package to check and warn the user if a newer stable version is available.
      
 
    1. YASA now uses the antropy package to calculate non-linear features in the automatic sleep staging module. Previously, YASA was using EntroPy, which could not be installed using pip.
      2. 
 
    
-
    
+
 
    
-
    
-
    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
    
@@ -272,10 +319,10 @@ 
    v0.4.0 (November 2020)TensorPAC.
    
 
  • Added ipywidgets, LightGBM and entropy to dependencies.
    • 
 
-
    
+
 
    
-
    
-
    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.
    
@@ -303,14 +350,14 @@ 
    v0.3.0 (May 2020)
 
  • 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.
    • 
-
  • 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().
    • 
+
  • 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().
    • 
 
  • yasa.trimbothstd() can now work with multi-dimensional arrays. The trimmed standard deviation will always be calculated on the last axis of the array.
    • 
 
  • 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.
    • 
 
-
    
+
 
    
-
    
-
    Older versions
    
+
    
+
    Older versions
    
 
    
 
 v0.2.0 (April 2020)
    
@@ -322,7 +369,7 @@ 
    Older versionsBugfixes
    
 
      
 
    1. Sleep efficiency in the yasa.sleep_statistics() is now calculated using time in bed (TIB) as the denominator instead of sleep period time (SPT), in agreement with the AASM guidelines. The old way of computing the efficiency (TST / SPT) has now been renamed Sleep Maintenance Efficiency (SME).
      2. 
-
    2. The yasa.sliding_window() now always return an array of shape (n_epochs, …, n_samples), i.e. the epochs are now always the first dimension of the epoched array. This is consistent with MNE default shape of mne.Epochs objects.
      3. 
+
    3. The yasa.sliding_window() now always return an array of shape (n_epochs, …, n_samples), i.e. the epochs are now always the first dimension of the epoched array. This is consistent with MNE default shape of mne.Epochs objects.
      4. 
 
    
 
    New functions
    
 
      
@@ -340,14 +387,14 @@ 
      Older versions
      Added the coupling and freq_sp keyword-arguments to the yasa.sw_detect() function. If coupling=True, the function will return the phase of the slow-waves (in radians) at the most prominent peak of sigma-filtered band (PhaseAtSigmaPeak), as well as the normalized mean vector length (ndPAC).
      
 
    1. Added an section in the 06_sw_detection.ipynb notebooks on how to use relative amplitude thresholds (e.g. z-scores or percentiles) instead of absolute thresholds in slow-waves detection.
      2. 
 
    2. The upper frequency band for yasa.sw_detect() has been changed from freq_sw=(0.3, 3.5) to freq_sw=(0.3, 2) Hz to comply with AASM guidelines.
      3. 
-
    3. Stage is no longer taken into account when finding outliers with sklearn.ensemble.IsolationForest in yasa.spindles_detect().
      4. 
+
    4. Stage is no longer taken into account when finding outliers with sklearn.ensemble.IsolationForest in yasa.spindles_detect().
      5. 
 
    5. To be consistent with yasa.spindles_detect(), automatic outlier removal now requires at least 50 (instead of 100) detected events in yasa.sw_detect() and yasa.rem_detect().
      6. 
 
    6. Added the verbose parameter to all detection functions.
      7. 
 
    7. Added -2 to the default hypnogram format to denote unscored data.
      8. 
 
    
 
    Dependencies
    
 
      
-
    1. Removed deprecated behavior argument to avoid warning when calling sklearn.ensemble.IsolationForest.
      2. 
+
    2. Removed deprecated behavior argument to avoid warning when calling sklearn.ensemble.IsolationForest.
      3. 
 
    3. Added TensorPAC and pyRiemann to dependencies.
      4. 
 
    4. Updated dependencies version for MNE and scikit-learn.
      5. 
 
    
@@ -491,8 +538,8 @@ 
    Older versions
    
 
    Initial release of YASA: basic spindles detection.
    
 
    
-
    
-
    
+
+
 
 
     
    
@@ -509,8 +556,8 @@ 
    Older versions
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/contributing.html b/docs/build/html/contributing.html
index ce75df9..99f7d4c 100644
--- a/docs/build/html/contributing.html
+++ b/docs/build/html/contributing.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    Contribute to YASA — yasa 0.6.3 documentation
-    
-    
+    
+
+    Contribute to YASA — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    Contribute to YASA
    
 
    There are many ways to contribute to YASA: reporting bugs, adding new functions, improving the documentation, etc…
    
 
    If you like YASA, you can also consider buying the developers a coffee!
    
-
    
+
    
 
    Code guidelines
    
 
    Before starting new code, we highly recommend opening an issue on GitHub to discuss potential changes.
    
 
      
@@ -115,8 +115,8 @@ 
      Code guidelines
+
    
+
    
 
    Checking and building documentation
    
 
    YASA’s documentation (including docstring in code) uses ReStructuredText format,
 see Sphinx documentation to learn more about editing them. The code
@@ -129,8 +129,8 @@ 
    Checking and building documentation
    $ make html
 
    
 
    
-
    
-
    
+
+
 
 
     
    
@@ -147,8 +147,8 @@ 
    Checking and building documentationSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/faq.html b/docs/build/html/faq.html
index 99b2f11..36dc595 100644
--- a/docs/build/html/faq.html
+++ b/docs/build/html/faq.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    FAQ — yasa 0.6.3 documentation
-    
-    
+    
+
+    FAQ — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,9 +93,9 @@
   
    
     
    
       
-  
    
+  
    
 
    FAQ
    
-
    
+
    
 
    Loading and visualizing polysomnography data
    
 
    
   
    
@@ -129,12 +129,12 @@ 
    
   
    
   
    
     
    YASA is a command-line software and does not support data visualization. To scroll through your data, we recommend the free software EDFBrowser (https://www.teuniz.net/edfbrowser/):
    
-
    
+
    
 _images/edfbrowser_with_hypnogram.png
-
    
+
   
    
-
    
-
    
+
    
+
    
 
    Event detection
    
 
    
   
    
@@ -181,8 +181,8 @@ 
    
   
    
     
    YASA does not currently support visual editing of the detected events. However, you can import the events as annotations in EDFBrowser and edit the events from there. If you simply want to visualize the detected events (no editing), you can also use the plot_detection method.
    
   
    
-
    
-
    
+
    
+
    
 
    Sleep staging
    
 
    
   
    
@@ -197,9 +197,9 @@ 
    
 
 
    However, our recommendation is that YASA should not replace human scoring, but rather serve as a starting point to speed up sleep staging. If possible, you should always have a trained sleep scorer visually check the predictions of YASA, with a particular emphasis on low-confidence epochs and/or N1 sleep epochs, as these are the epochs most often misclassified by the algorithm.
 Finally, users can also leverage the yasa.plot_spectrogram() function to plot the predicted hypnogram on top of the full-night spectrogram. Such plots are very useful to quickly identify blatant errors in the hypnogram.
    
-
    
+
    
 _images/spectrogram.png
-
    
+
     
    
   
    
 
    
@@ -225,14 +225,14 @@ 
    
 
    
 
    
 
    You can then import the hypnogram in EDFBrowser by clicking on the “Import annotations/events” in the “Tools” menu. Then, select the “ASCII/CSV” tab and change the parameters as follow:
    
-
    
+
    
 _images/edfbrowser_import_annotations.png
-
    
+
 
    Click “Import”. Once it’s done, the hypnogram can be enabled via the “Window” menu. A dialog will appear where you can setup the labels for the different sleep stages and the mapping to the annotations in the file. The default parameters should work.
 When using the Annotation editor, the hypnogram will be updated realtime when adding, moving or deleting annotations. Once you’re done editing, you can export the edited hypnogram with “Export anotations/events” in the “Tools” menu.
    
-
    
+
    
 _images/edfbrowser_with_hypnogram.png
-
    
+
 
    
 
    SpiSOP
    
 
    SpiSOP is an open-source Matlab toolbox for the analysis and visualization of polysomnography sleep data. It comes with a sleep scoring GUI.
@@ -245,9 +245,9 @@ 
    
 
    
 
    Visbrain
    
 
    Visbrain is an open-source Python toolbox that includes a module for visualizing polysomnography sleep data and scoring sleep (see screenshot below).
    
-
    
+
    
 _images/visbrain.PNG
-
    
+
 
    Visbrain accepts several formats for the hypnogram. The code below show how to export the hypnogram in the Elan software format (i.e. a text file with the .hyp extension):
    
 
    hypno_int = pd.Series(hypno).map({"W": 0, "N1": 1, "N2": 2, "N3": 3, "R": 5}).to_numpy()
 header = "time_base 30\nsampling_period 1/30\nepoch_nb %i\nepoch_list" % len(hypno_int)
@@ -272,8 +272,8 @@ 
    
 
    Despite these required changes, one advantage of YASA is that it provides a useful framework for implementing such sleep staging algorithms. For example, one can save a huge amount of time by simply re-using and adapting the built-in yasa.SleepStaging class.
 In addition, all the code used to train YASA is freely available at https://github.com/raphaelvallat/yasa_classifier and can be re-used to re-train the classifier on non-human data.
    
   
    
-
    
-
    
+
    
+
    
 
    Others
    
 
    
   
    
@@ -282,7 +282,7 @@ 
    
     
    
   
    
   
    
-    
    YASA uses outdated, a Python package that automatically checks if a newer version of YASA is available upon loading. Alternatively, you can click “Watch” on the GitHub of YASA.
+    
    You can click “Watch” on the GitHub of YASA.
 Whenever a new release is out there, you can upgrade your version by typing the following line in a terminal window:
    
 
    pip install --upgrade yasa
 
    
@@ -329,8 +329,8 @@ 
    
 
    
     
    
   
    
-
    
-
    
+
    
+
 
 
     
@@ -347,8 +347,8 @@ 
    
       
     
    
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
 
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+    yasa.Hypnogram — yasa 0.6.4 documentation
+    
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    yasa.Hypnogram
    
+
    
+
    
+class yasa.Hypnogram(values, n_stages=5, *, freq='30s', start=None, scorer=None)[source]
    
+
    Experimental class for manipulating hypnogram in YASA (dev).
    
+
    Starting with v0.7, YASA will take a more object-oriented approach to hypnograms. That is,
+hypnograms are now stored as a class (aka object), which comes with its own attributes and
+functions. Furthermore, YASA does not allow integer values to define the stages anymore.
+Instead, users must pass an array of strings with the actual stage names
+(e.g. [“WAKE”, “WAKE”, “N1”, …, “REM”, “REM”]).
    
+
    
+
    New in version 0.7.0.
    
+
    
+
    
+
    Parameters
    
+
    
+
    valuesarray_like
    A vector of stage values, represented as strings. See some examples below:
    
+
      
+
    • 2-stages hypnogram (Wake/Sleep): ["W", "S", "S", "W", "S"]
      • 
+
    • 3-stages (Wake/NREM/REM): pd.Series(["WAKE", "NREM", "NREM", "REM", "REM"])
      • 
+
    • 4-stages (Wake/Light/Deep/REM): np.array(["Wake", "Light", "Deep", "Deep"])
      • 
+
    • 5-stages (default): ["N1", "N1", "N2", "N3", "N2", "REM", "W"]
      • 
+
    
+
    Artefacts (“Art”) and unscored (“Uns”) epochs are always allowed regardless of the
+number of stages in the hypnogram.
    
+
    
+
    Note
    
+
    Abbreviated or full spellings for the stages are allowed, as well as
+lower/upper/mixed case. Internally, YASA will convert the stages to to full spelling
+and uppercase (e.g. “w” -> “WAKE”).
    
+
    
+
    
+
    n_stagesint
    Whether values comes from a 2, 3, 4 or 5-stages hypnogram. Default is 5 stages, meaning
+that the following sleep stages are allowed: N1, N2, N3, REM, WAKE.
    
+
    
+
    freqstr
    A pandas frequency string indicating the frequency resolution of the hypnogram. Default is
+“30s” meaning that each value in the hypnogram represents a 30-seconds epoch.
+Examples: “1min”, “10s”, “15min”. A full list of accepted values can be found at
+https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases
    
+
    freq will be passed to the pandas.date_range() function to create the time
+index of the hypnogram.
    
+
    
+
    startstr or datetime
    An optional string indicating the starting datetime of the hypnogram
+(e.g. “2022-12-15 22:30:00”). If start is specified and valid, the index of the
+hypnogram will be a pandas.DatetimeIndex. Otherwise it will be a
+pandas.RangeIndex, indicating the epoch number.
    
+
    
+
    scorerstr
    An optional string indicating the scorer name. If specified, this will be set as the name
+of the pandas.Series, otherwise the name will be set to “Stage”.
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    Create a 2-stages hypnogram
    
+
    >>> from yasa import Hypnogram
+>>> values = ["W", "W", "W", "S", "S", "S", "S", "S", "W", "S", "S", "S"]
+>>> hyp = Hypnogram(values, n_stages=2)
+>>> hyp
+<Hypnogram | 12 epochs x 30s (6.00 minutes), 2 stages>
+ - Use `.hypno` to get the string values as a pandas.Series
+ - Use `.as_int()` to get the integer values as a pandas.Series
+ - Use `.plot_hypnogram()` to plot the hypnogram
+See the online documentation for more details.
+
    
+
    
+
    We can access the actual values, which are stored as a pandas.Series, with:
    
+
    >>> hyp.hypno
+Epoch
+0      WAKE
+1      WAKE
+2      WAKE
+3     SLEEP
+4     SLEEP
+5     SLEEP
+6     SLEEP
+7     SLEEP
+8      WAKE
+9     SLEEP
+10    SLEEP
+11    SLEEP
+Name: Stage, dtype: category
+Categories (4, object): ['WAKE', 'SLEEP', 'ART', 'UNS']
+
    
+
    
+
    >>> # Number of epochs in the hypnogram
+>>> hyp.n_epochs
+12
+
    
+
    
+
    >>> # Total duration of the hypnogram, in minutes (12 epochs * 30 seconds = 6 minutes)
+>>> hyp.duration
+6.0
+
    
+
    
+
    >>> # Default mapping from strings to integers. Can be changed with `hyp.mapping = {}`
+>>> hyp.mapping
+{'WAKE': 0, 'SLEEP': 1, 'ART': -1, 'UNS': -2}
+
    
+
    
+
    >>> # Get the hypnogram Series integer values
+>>> hyp.as_int()
+Epoch
+0     0
+1     0
+2     0
+3     1
+4     1
+5     1
+6     1
+7     1
+8     0
+9     1
+10    1
+11    1
+Name: Stage, dtype: int16
+
    
+
    
+
    >>> # Calculate the summary sleep statistics
+>>> hyp.sleep_statistics()
+{'TIB': 6.0,
+ 'SPT': 4.5,
+ 'WASO': 0.5,
+ 'TST': 4.0,
+ 'SE': 66.6667,
+ 'SME': 88.8889,
+ 'SFI': 7.5,
+ 'SOL': 1.5,
+ 'SOL_5min': nan,
+ 'WAKE': 2.0}
+
    
+
    
+
    >>> # Get the state-transition matrix
+>>> counts, probs = hyp.transition_matrix()
+>>> counts
+To Stage    WAKE  SLEEP
+From Stage
+WAKE           2      2
+SLEEP          1      6
+
    
+
    
+
    All these methods and properties are also valid with a 5-stages hypnogram. In the example below,
+we use the yasa.simulate_hypnogram() to generate a plausible 5-stages hypnogram with a
+30-seconds resolution. A random seed is specified to ensure that we get reproducible results.
+Lastly, we set an actual start time to the hypnogram. As a result, the index of the resulting
+hypnogram is a pandas.DatetimeIndex.
    
+
    >>> from yasa import simulate_hypnogram
+>>> hyp = simulate_hypnogram(
+...     tib=500, n_stages=5, start="2022-12-15 22:30:00", scorer="S1", seed=42)
+>>> hyp
+<Hypnogram | 1000 epochs x 30s (500.00 minutes), 5 stages, scored by S1>
+ - Use `.hypno` to get the string values as a pandas.Series
+ - Use `.as_int()` to get the integer values as a pandas.Series
+ - Use `.plot_hypnogram()` to plot the hypnogram
+See the online documentation for more details.
+
    
+
    
+
    >>> hyp.hypno
+Time
+2022-12-15 22:30:00    WAKE
+2022-12-15 22:30:30    WAKE
+2022-12-15 22:31:00    WAKE
+2022-12-15 22:31:30    WAKE
+2022-12-15 22:32:00    WAKE
+                    ...
+2022-12-16 06:47:30      N2
+2022-12-16 06:48:00      N2
+2022-12-16 06:48:30      N2
+2022-12-16 06:49:00      N2
+2022-12-16 06:49:30      N2
+Freq: 30S, Name: S1, Length: 1000, dtype: category
+Categories (7, object): ['WAKE', 'N1', 'N2', 'N3', 'REM', 'ART', 'UNS']
+
    
+
    
+
    The summary sleep statistics will include more items with a 5-stages hypnogram than a 2-stages
+hypnogram, i.e. the amount and percentage of each sleep stage, the REM latency, etc.
    
+
    >>> hyp.sleep_statistics()
+{'TIB': 500.0,
+ 'SPT': 497.5,
+ 'WASO': 79.5,
+ 'TST': 418.0,
+ 'SE': 83.6,
+ 'SME': 84.0201,
+ 'SFI': 0.7177,
+ 'SOL': 2.5,
+ 'SOL_5min': 2.5,
+ 'Lat_REM': 67.0,
+ 'WAKE': 82.0,
+ 'N1': 69.0,
+ 'N2': 247.0,
+ 'N3': 64.5,
+ 'REM': 37.5,
+ '%N1': 16.5072,
+ '%N2': 59.0909,
+ '%N3': 15.4306,
+ '%REM': 8.9713}
+
    
+
    
+
    
+
    
+__init__(values, n_stages=5, *, freq='30s', start=None, scorer=None)[source]
    
+
    
+
+
    Methods
    
+
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    __init__(values[, n_stages, freq, start, scorer])
    as_annotations()
    Return a pandas DataFrame summarizing epoch-level information.
    as_int()
    Return hypnogram values as integers.
    consolidate_stages(new_n_stages)
    Reduce the number of stages in a hypnogram to match actigraphy or wearables.
    copy()
    Return a new copy of the current Hypnogram.
    find_periods([threshold, equal_length])
    Find sequences of consecutive values exceeding a certain duration in hypnogram.
    plot_hypnogram(**kwargs)
    Plot the hypnogram.
    simulate_similar(**kwargs)
    Simulate a new hypnogram based on properties of the current hypnogram.
    sleep_statistics()
    Compute standard sleep statistics from an hypnogram.
    transition_matrix()
    Create a state-transition matrix from an hypnogram.
    upsample(new_freq, **kwargs)
    Upsample hypnogram to a higher frequency.
    upsample_to_data(data[, sf, verbose])
    Upsample an hypnogram to a given sampling frequency and fit the resulting hypnogram to corresponding EEG data, such that the hypnogram and EEG data have the exact same number of samples.
    
+
    Attributes
    
+
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    duration
    Total duration of the hypnogram, expressed in minutes.
    freq
    The frequency resolution of the hypnogram.
    hypno
    The hypnogram values, stored in a pandas.Series.
    labels
    The allowed stage labels.
    mapping
    A dictionary with the mapping from string to integer values.
    mapping_int
    A dictionary with the mapping from integer to string values.
    n_epochs
    The number of epochs in the hypnogram.
    n_stages
    The number of allowed stages in the hypnogram.
    sampling_frequency
    The sampling frequency (Hz) of the hypnogram.
    scorer
    The scorer name.
    start
    The start date/time of the hypnogram.
    timedelta
    A pandas.TimedeltaIndex vector with the accumulated time difference of each epoch compared to the first epoch.
    
+
    
+
    
+as_annotations()[source]
    
+
    Return a pandas DataFrame summarizing epoch-level information.
    
+
    Column order and names are compliant with BIDS
+events files
+and MNE events/annotations dataframes.
    
+
    
+
    Returns
    
+
    
+
    annotationspandas.DataFrame
    A dataframe containing epoch onset, duration, stage, etc.
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    >>> from yasa import Hypnogram
+>>> hyp = Hypnogram(["W", "W", "LIGHT", "LIGHT", "DEEP", "REM", "WAKE"], n_stages=4)
+>>> hyp.as_annotations()
+       onset  duration  value description
+epoch
+0        0.0      30.0      0        WAKE
+1       30.0      30.0      0        WAKE
+2       60.0      30.0      2       LIGHT
+3       90.0      30.0      2       LIGHT
+4      120.0      30.0      3        DEEP
+5      150.0      30.0      4         REM
+6      180.0      30.0      0        WAKE
+
    
+
    
+
    
+
+
    
+
    
+as_int()[source]
    
+
    Return hypnogram values as integers.
    
+
    The default mapping from string to integer is:
    
+
      
+
    • 2 stages: {“WAKE”: 0, “SLEEP”: 1, “ART”: -1, “UNS”: -2}
      • 
+
    • 3 stages: {“WAKE”: 0, “NREM”: 2, “REM”: 4, “ART”: -1, “UNS”: -2}
      • 
+
    • 4 stages: {“WAKE”: 0, “LIGHT”: 2, “DEEP”: 3, “REM”: 4, “ART”: -1, “UNS”: -2}
      • 
+
    • 5 stages: {“WAKE”: 0, “N1”: 1, “N2”: 2, “N3”: 3, “REM”: 4, “ART”: -1, “UNS”: -2}
      • 
+
    
+
    Users can define a custom mapping:
    
+
    >>> hyp.mapping = {"WAKE": 0, "NREM": 1, "REM": 2}
+
    
+
    
+
    Examples
    
+
    Convert a 2-stages hypnogram to a pandas.Series of integers
    
+
    >>> from yasa import Hypnogram
+>>> hyp = Hypnogram(["W", "W", "S", "S", "W", "S"], n_stages=2)
+>>> hyp.as_int()
+Epoch
+0    0
+1    0
+2    1
+3    1
+4    0
+5    1
+Name: Stage, dtype: int16
+
    
+
    
+
    Same with a 4-stages hypnogram
    
+
    >>> from yasa import Hypnogram
+>>> hyp = Hypnogram(["W", "W", "LIGHT", "LIGHT", "DEEP", "REM", "WAKE"], n_stages=4)
+>>> hyp.as_int()
+Epoch
+0    0
+1    0
+2    2
+3    2
+4    3
+5    4
+6    0
+Name: Stage, dtype: int16
+
    
+
    
+
    
+
+
    
+
    
+consolidate_stages(new_n_stages)[source]
    
+
    Reduce the number of stages in a hypnogram to match actigraphy or wearables.
    
+
    For example, a standard 5-stage hypnogram (W, N1, N2, N3, REM) could be consolidated
+to a hypnogram more common with actigraphy (e.g. 2-stages: [Wake, Sleep] or
+4-stages: [W, Light, Deep, REM]).
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.Hypnogram
    Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+Total Recording Time, i.e. “lights out” to “lights on”).
    
+
    
+
    new_n_stagesint
    Desired number of sleep stages. Must be lower than the current number of stages.
    
+
      
+
    • 5 stages - Wake, N1, N2, N3, REM
      • 
+
    • 4 stages - Wake, Light, Deep, REM
      • 
+
    • 3 stages - Wake, NREM, REM
      • 
+
    • 2 stages - Wake, Sleep
      • 
+
    
+
    
+
    Note
    
+
    Unscored and Artefact are always allowed.
    
+
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    hypyasa.Hypnogram
    The consolidated Hypnogram object. This function returns a copy, i.e. the original
+hypnogram is not modified in place.
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    >>> from yasa import Hypnogram
+>>> hyp = Hypnogram(["W", "W", "N1", "N2", "N2", "N2", "N2", "W"], n_stages=5)
+>>> hyp_2s = hyp.consolidate_stages(2)
+>>> print(hyp_2s)
+Epoch
+0     WAKE
+1     WAKE
+2    SLEEP
+3    SLEEP
+4    SLEEP
+5    SLEEP
+6    SLEEP
+7     WAKE
+Name: Stage, dtype: category
+Categories (4, object): ['WAKE', 'SLEEP', 'ART', 'UNS']
+
    
+
    
+
    
+
+
    
+
    
+copy()[source]
    
+
    Return a new copy of the current Hypnogram.
    
+
    
+
+
    
+
    
+find_periods(threshold='5min', equal_length=False)[source]
    
+
    Find sequences of consecutive values exceeding a certain duration in hypnogram.
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.Hypnogram
    Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+Total Recording Time, i.e. “lights out” to “lights on”).
    
+
    
+
    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 epochs
      • 
+
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    Let’s assume that we have an hypnogram where sleep = 1 and wake = 0, with one value
+per minute.
    
+
    >>> from yasa import Hypnogram
+>>> val = 11 * ["W"] + 3 * ["S"] + 2 * ["W"] + 9 * ["S"] + ["W", "W"]
+>>> hyp = Hypnogram(val, n_stages=2, freq="1min")
+>>> hyp.find_periods(threshold="0min")
+  values  start  length
+0   WAKE      0      11
+1  SLEEP     11       3
+2   WAKE     14       2
+3  SLEEP     16       9
+4   WAKE     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
+WAKE 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:
    
+
    >>> hyp.find_periods(threshold="5min")
+  values  start  length
+0   WAKE      0      11
+1  SLEEP     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. a 5-stages hypnogram at 30-sec
+resolution:
    
+
    >>> from yasa import simulate_hypnogram
+>>> hyp = simulate_hypnogram(tib=30, seed=42)
+>>> hyp.find_periods(threshold="2min")
+  values  start  length
+0   WAKE      0       5
+1     N1      5       6
+2     N2     11      49
+
    
+
    
+
    Lastly, using equal_length=True will further divide the periods into segments of the
+same duration, i.e. the duration defined in threshold:
    
+
    >>> hyp.find_periods(threshold="5min", equal_length=True)
+  values  start  length
+0     N2     11      10
+1     N2     21      10
+2     N2     31      10
+3     N2     41      10
+
    
+
    
+
    Here, the 24.5 minutes of consecutive N2 sleep (= 49 epochs) are divided into 4 periods of
+exactly 5 minute each. The remaining 4.5 minutes at the end of the hypnogram are removed
+because it is less than 5 minutes. In other words, the remainder of the division of a given
+segment by the desired duration is discarded.
    
+
    
+
+
    
+
    
+plot_hypnogram(**kwargs)[source]
    
+
    Plot the hypnogram.
    
+
    
+
    See also
    
+
    yasa.plot_hypnogram()
    
+
    
+
    
+
    Parameters
    
+
    
+
    **kwargsdict
    Optional keyword arguments passed to yasa.plot_hypnogram().
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    axmatplotlib.axes.Axes
    Matplotlib Axes
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    >>> from yasa import simulate_hypnogram
+>>> ax = simulate_hypnogram(tib=480, seed=88).plot_hypnogram(highlight="REM")
+
    
+
    
+
    
+../_images/yasa-Hypnogram-1.png
+
    
+
    
+
+
    
+
    
+simulate_similar(**kwargs)[source]
    
+
    Simulate a new hypnogram based on properties of the current hypnogram.
    
+
    
+
    See also
    
+
    yasa.simulate_hypnogram()
    
+
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.Hypnogram
    Hypnogram, assumed to be already cropped to time in bed (TIB).
    
+
    
+
    **kwargsdict
    Optional keyword arguments passed to yasa.simulate_hypnogram().
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    hypyasa.Hypnogram
    A simulated hypnogram.
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    >>> import pandas as pd
+>>> from yasa import Hypnogram
+>>> hyp = Hypnogram(
+...     ["W", "S", "W"], n_stages=2, freq="2min", scorer="Human").upsample("30s")
+>>> shyp = hyp.simulate_similar(scorer="Simulated", seed=6)
+>>> df = pd.concat([hyp.hypno, shyp.hypno], axis=1)
+>>> print(df)
+       Human Simulated
+Epoch
+0       WAKE      WAKE
+1       WAKE      WAKE
+2       WAKE      WAKE
+3       WAKE      WAKE
+4      SLEEP     SLEEP
+5      SLEEP     SLEEP
+6      SLEEP     SLEEP
+7      SLEEP     SLEEP
+8       WAKE     SLEEP
+9       WAKE     SLEEP
+10      WAKE     SLEEP
+11      WAKE      WAKE
+
    
+
    
+
    
+
+
    
+
    
+sleep_statistics()[source]
    
+
    Compute standard sleep statistics from an hypnogram.
    
+
    This function supports a 2, 3, 4 or 5-stages hypnogram.
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.Hypnogram
    Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+Total Recording Time, i.e. “lights out” to “lights on”).
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    statsdict
    Summary sleep statistics.
    
+
    
+
    
+
    
+
    
+
    Notes
    
+
    All values except SE, SME, SFI and the percentage of each stage are expressed in minutes.
+YASA follows the AASM guidelines to calculate these parameters:
    
+
      
+
    • Time in Bed (TIB): total duration of the hypnogram.
      • 
+
    • Sleep Period Time (SPT): duration from first to last period of sleep.
      • 
+
    • Wake After Sleep Onset (WASO): duration of wake periods within SPT.
      • 
+
    • Total Sleep Time (TST): total sleep duration in SPT.
      • 
+
    • Sleep Onset Latency (SOL): Latency to first epoch of any sleep.
      • 
+
    • SOL 5min: Latency to 5 minutes of persistent sleep (any stage).
      • 
+
    • REM latency: latency to first REM sleep.
      • 
+
    • Sleep Efficiency (SE): TST / TIB * 100 (%).
      • 
+
    • Sleep Maintenance Efficiency (SME): TST / SPT * 100 (%).
      • 
+
    • Sleep Fragmentation Index: number of transitions from sleep to wake / hours of TST
      • 
+
    • Sleep stages amount and proportion of TST
      • 
+
    
+
    
+
    Warning
    
+
    Artefact and Unscored epochs are excluded from the calculation of the
+total sleep time (TST). TST is calculated as the sum of all REM and NREM sleep in SPT.
    
+
    
+
    
+
    Warning
    
+
    The definition of REM latency in the AASM scoring manual differs from the REM latency
+reported here. The former uses the time from first epoch of sleep, while YASA uses the
+time from the beginning of the recording. The AASM definition of the REM latency can be
+found with SOL - Lat_REM.
    
+
    
+
    References
    
+
      
+
    • Iber (2007). The AASM manual for the scoring of sleep and associated events: rules,
+terminology and technical specifications. American Academy of Sleep Medicine.
      • 
+
    • Silber et al. (2007). The visual scoring of sleep in adults. Journal of Clinical
+Sleep Medicine, 3(2), 121-131.
      • 
+
    
+
    Examples
    
+
    Sleep statistics for a 2-stage hypnogram with a resolution of 15-seconds
    
+
    >>> from yasa import Hypnogram
+>>> # Generate a fake hypnogram, where "S" = Sleep, "W" = Wake
+>>> values = 10 * ["W"] + 40 * ["S"] + 5 * ["W"] + 40 * ["S"] + 9 * ["W"]
+>>> hyp = Hypnogram(values, freq="15s", n_stages=2)
+>>> hyp.sleep_statistics()
+{'TIB': 26.0,
+'SPT': 21.25,
+'WASO': 1.25,
+'TST': 20.0,
+'SE': 76.9231,
+'SME': 94.1176,
+'SFI': 1.5,
+'SOL': 2.5,
+'SOL_5min': 2.5,
+'WAKE': 6.0}
+
    
+
    
+
    Sleep statistics for a 5-stages hypnogram
    
+
    >>> from yasa import simulate_hypnogram
+>>> # Generate a 8 hr (= 480 minutes) 5-stages hypnogram with a 30-seconds resolution
+>>> hyp = simulate_hypnogram(tib=480, seed=42)
+>>> hyp.sleep_statistics()
+{'TIB': 480.0,
+'SPT': 477.5,
+'WASO': 79.5,
+'TST': 398.0,
+'SE': 82.9167,
+'SME': 83.3508,
+'SFI': 0.7538,
+'SOL': 2.5,
+'SOL_5min': 2.5,
+'Lat_REM': 67.0,
+'WAKE': 82.0,
+'N1': 67.0,
+'N2': 240.5,
+'N3': 53.0,
+'REM': 37.5,
+'%N1': 16.8342,
+'%N2': 60.4271,
+'%N3': 13.3166,
+'%REM': 9.4221}
+
    
+
    
+
    
+
+
    
+
    
+transition_matrix()[source]
    
+
    Create a state-transition matrix from an hypnogram.
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.Hypnogram
    Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+Total Recording Time, i.e. “lights out” to “lights on”). For best results, the
+hypnogram should not contain any artefact or unscored epochs.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    countspandas.DataFrame
    Counts transition matrix (number of transitions from stage A to stage B). The
+pre-transition states are the rows and the post-transition states are the columns.
    
+
    
+
    probspandas.DataFrame
    Conditional probability transition matrix, i.e. given that current state is A, what is
+the probability that the next state is B. probs is a right stochastic matrix, i.e. each row sums to 1.
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    >>> from yasa import Hypnogram, simulate_hypnogram
+>>> # Generate a 8 hr (= 480 minutes) 5-stages hypnogram with a 30-seconds resolution
+>>> hyp = simulate_hypnogram(tib=480, seed=42)
+>>> counts, probs = hyp.transition_matrix()
+>>> counts
+To Stage    WAKE  N1   N2  N3  REM
+From Stage
+WAKE         153  11    0   0    0
+N1             6  99   29   0    0
+N2             3  16  447  10    5
+N3             1   3    4  96    1
+REM            0   5    1   0   69
+
    
+
    
+
    >>> probs.round(3)
+To Stage     WAKE     N1     N2     N3   REM
+From Stage
+WAKE        0.933  0.067  0.000  0.000  0.00
+N1          0.045  0.739  0.216  0.000  0.00
+N2          0.006  0.033  0.929  0.021  0.01
+N3          0.010  0.029  0.038  0.914  0.01
+REM         0.000  0.067  0.013  0.000  0.92
+
    
+
    
+
    
+
+
    
+
    
+upsample(new_freq, **kwargs)[source]
    
+
    Upsample hypnogram to a higher frequency.
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.Hypnogram
    Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+Total Recording Time, i.e. “lights out” to “lights on”). For best results, the
+hypnogram should not contain any artefact or unscored epochs.
    
+
    
+
    new_freqstr
    Frequency is defined with a pandas frequency string, e.g. “10s” or “1min”.
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    hypyasa.Hypnogram
    The upsampled Hypnogram object. This function returns a copy, i.e. the original
+hypnogram is not modified in place.
    
+
    
+
    
+
    
+
    
+
    Examples
    
+
    Create a 30-sec hypnogram
    
+
    >>> from yasa import Hypnogram
+>>> hyp = Hypnogram(["W", "W", "S", "S", "W"], n_stages=2, start="2022-12-23 23:00")
+>>> hyp.hypno
+Time
+2022-12-23 23:00:00     WAKE
+2022-12-23 23:00:30     WAKE
+2022-12-23 23:01:00    SLEEP
+2022-12-23 23:01:30    SLEEP
+2022-12-23 23:02:00     WAKE
+Freq: 30S, Name: Stage, dtype: category
+Categories (4, object): ['WAKE', 'SLEEP', 'ART', 'UNS']
+
    
+
    
+
    Upsample to a 15-seconds resolution
    
+
    >>> hyp_up = hyp.upsample("15s")
+>>> hyp_up.hypno
+Time
+2022-12-23 23:00:00     WAKE
+2022-12-23 23:00:15     WAKE
+2022-12-23 23:00:30     WAKE
+2022-12-23 23:00:45     WAKE
+2022-12-23 23:01:00    SLEEP
+2022-12-23 23:01:15    SLEEP
+2022-12-23 23:01:30    SLEEP
+2022-12-23 23:01:45    SLEEP
+2022-12-23 23:02:00     WAKE
+2022-12-23 23:02:15     WAKE
+Freq: 15S, Name: Stage, dtype: category
+Categories (4, object): ['WAKE', 'SLEEP', 'ART', 'UNS']
+
    
+
    
+
    
+
+
    
+
    
+upsample_to_data(data, sf=None, verbose=True)[source]
    
+
    Upsample an hypnogram to a given sampling frequency and fit the resulting hypnogram to
+corresponding EEG data, such that the hypnogram and EEG data have the exact same number of
+samples.
    
+
    
+
    Parameters
    
+
    
+
    selfyasa.Hypnogram
    Hypnogram, assumed to be already cropped to time in bed (TIB, also referred to as
+Total Recording Time, i.e. “lights out” to “lights on”). For best results, the
+hypnogram should not contain any artefact or unscored epochs.
    
+
    
+
    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.
    
+
    
+
    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).
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    hypnoarray_like
    The hypnogram values, upsampled to sf_data and cropped/padded to
+max(data.shape). For compatibility with most YASA functions, the returned hypnogram
+is an array with integer values, and not a yasa.Hypnogram object.
    
+
    
+
    
+
    
+
    Warns
    
+
    
+
    UserWarning
    If the upsampled hypno is shorter / longer than max(data.shape)
+and therefore needs to be padded/cropped respectively. This output can be disabled by
+passing verbose='ERROR'.
    
+
    
+
    
+
    
+
    
+
    
+
+
    
+
    
+property duration
    
+
    Total duration of the hypnogram, expressed in minutes. AKA Time in Bed.
    
+
    
+
+
    
+
    
+property freq
    
+
    The frequency resolution of the hypnogram. Default is ’30s’
    
+
    
+
+
    
+
    
+property hypno
    
+
    The hypnogram values, stored in a pandas.Series. To reduce memory usage, the
+stages are stored as categories (pandas.Categorical). hypno
+inherits all the methods of a standard pandas.Series, e.g. .describe(),
+.unique(), .to_csv(), and more.
    
+
    
+
    Note
    
+
    print(Hypnogram) is a shortcut to print(Hypnogram.hypno).
    
+
    
+
    
+
+
    
+
    
+property labels
    
+
    The allowed stage labels.
    
+
    
+
+
    
+
    
+property mapping
    
+
    A dictionary with the mapping from string to integer values.
    
+
    
+
+
    
+
    
+property mapping_int
    
+
    A dictionary with the mapping from integer to string values.
    
+
    
+
+
    
+
    
+property n_epochs
    
+
    The number of epochs in the hypnogram.
    
+
    
+
+
    
+
    
+property n_stages
    
+
    The number of allowed stages in the hypnogram. This is not the number of unique stages
+in the current hypnogram. This does not include Artefact and Unscored which are always
+allowed.
    
+
    
+
+
    
+
    
+property sampling_frequency
    
+
    The sampling frequency (Hz) of the hypnogram.
    
+
    
+
+
    
+
    
+property scorer
    
+
    The scorer name.
    
+
    
+
+
    
+
    
+property start
    
+
    The start date/time of the hypnogram. Default is None.
    
+
    
+
+
    
+
    
+property timedelta
    
+
    A pandas.TimedeltaIndex vector with the accumulated time difference of each
+epoch compared to the first epoch.
    
+
    
+
+
    
+
+
    
+
+
+    
    
+      
+  
    
+
    
+
    
+  
    
+    
    
+      Back to top
+      
+        
    
+        
+      
+    
    
+    
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
+    
    
+  
    
+
    
+  
+
\ No newline at end of file
diff --git a/docs/build/html/generated/yasa.REMResults.html b/docs/build/html/generated/yasa.REMResults.html
index be9c1ac..2a3b5c1 100644
--- a/docs/build/html/generated/yasa.REMResults.html
+++ b/docs/build/html/generated/yasa.REMResults.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.REMResults — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.REMResults — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
 
         
    
@@ -93,16 +93,16 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.REMResults
    
 
    
-
    
-class yasa.REMResults(events, data, sf, ch_names, hypno, data_filt)[source]
    
+
    
+class yasa.REMResults(events, data, sf, ch_names, hypno, data_filt)[source]
    
 
    Output class for REMs detection.
    
 
    
 
    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.
    
@@ -120,10 +120,9 @@ 
    yasa.REMResults
-
    
-__init__(events, data, sf, ch_names, hypno, data_filt)[source]
    
-
    Initialize self.  See help(type(self)) for accurate signature.
    
-
    
+
    
+__init__(events, data, sf, ch_names, hypno, data_filt)[source]
    
+
    
 
 
    Methods
    
 
@@ -132,13 +131,13 @@ 
    yasa.REMResults
 
 
    
-
-
+
+
-
+
@@ -147,10 +146,10 @@ 
    yasa.REMResults
    
-
+
-
+
@@ -162,15 +161,15 @@ 
    yasa.REMResults
-
    
-get_mask()[source]
    
+
    
+get_mask()[source]
    
 
    Return a boolean array indicating for each sample in data if this
 sample is part of a detected event (True) or not (False).
    
 
    
 
 
    
-
    
-get_sync_events(center='Peak', time_before=0.4, time_after=0.4, filt=None, None, mask=None)[source]
    
+
    
+get_sync_events(center='Peak', time_before=0.4, time_after=0.4, filt=(None, None), mask=None)[source]
    
 
    Return the raw or filtered data of each detected event after centering to a specific
 timepoint.
    
 
    
@@ -186,7 +185,7 @@ 
    yasa.REMResultstuple
    Optional filtering to apply to data. For instance, filt=(1, 30)
 will apply a 1 to 30 Hz bandpass filter, and filt=(None, 40)
 will apply a 40 Hz lowpass filter. Filtering is done using default
-parameters in the mne.filter.filter_data() function.
    
+parameters in the mne.filter.filter_data() function.
    
 
    
 
    maskarray_like or None
    Custom boolean mask. Only the detected events for which mask is True will be
 included. Default is None, i.e. no masking (all events are included).
    
@@ -195,7 +194,7 @@ 
    yasa.REMResultsReturns
 
    
-
    df_syncpandas.DataFrame
    Ouput long-format dataframe:
    
+
    df_syncpandas.DataFrame
    Ouput long-format dataframe:
    
 
    'Event' : Event number
 'Time' : Timing of the events (in seconds)
 'Amplitude' : Raw or filtered data for event
@@ -210,8 +209,8 @@ 
    yasa.REMResults
-
    
-plot_average(center='Peak', time_before=0.4, time_after=0.4, filt=None, None, mask=None, figsize=6, 4.5, **kwargs)[source]
    
+
    
+plot_average(center='Peak', time_before=0.4, time_after=0.4, filt=(None, None), mask=None, figsize=(6, 4.5), **kwargs)[source]
    
 
    Plot the average REM.
    
 
    
 
    Parameters
    
@@ -226,14 +225,14 @@ 
    yasa.REMResultstuple
    Optional filtering to apply to data. For instance, filt=(1, 30)
 will apply a 1 to 30 Hz bandpass filter, and filt=(None, 40)
 will apply a 40 Hz lowpass filter. Filtering is done using default
-parameters in the mne.filter.filter_data() function.
    
+parameters in the mne.filter.filter_data() function.
    
 
    
 
    maskarray_like or None
    Custom boolean mask. Only the detected events for which mask is True will be
 included. Default is None, i.e. no masking (all events are included).
    
 
    
 
    figsizetuple
    Figure size in inches.
    
 
    
-
    **kwargsdict
    Optional argument that are passed to seaborn.lineplot().
    
+
    **kwargsdict
    Optional argument that are passed to seaborn.lineplot().
    
 
    
 
    
 
    
@@ -241,8 +240,8 @@ 
    yasa.REMResults
-
    
-summary(grp_stage=False, mask=None, aggfunc='mean', sort=True)[source]
    
+
    
+summary(grp_stage=False, mask=None, aggfunc='mean', sort=True)[source]
    
 
    Return a summary of the REM detection, optionally grouped across stage.
    
 
    
 
    Parameters
    
@@ -264,7 +263,7 @@ 
    yasa.REMResultsSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.SWResults.html b/docs/build/html/generated/yasa.SWResults.html
index de77ab5..352f95c 100644
--- a/docs/build/html/generated/yasa.SWResults.html
+++ b/docs/build/html/generated/yasa.SWResults.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.SWResults — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.SWResults — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,16 +93,16 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.SWResults
    
 
    
-
    
-class yasa.SWResults(events, data, sf, ch_names, hypno, data_filt)[source]
    
+
    
+class yasa.SWResults(events, data, sf, ch_names, hypno, data_filt)[source]
    
 
    Output class for slow-waves detection.
    
 
    
 
    Attributes
    
 
    
-
    _eventspandas.DataFrame
    Output detection dataframe
    
+
    _eventspandas.DataFrame
    Output detection dataframe
    
 
    
 
    _dataarray_like
    EEG data of shape (n_chan, n_samples).
    
 
    
@@ -118,10 +118,9 @@ 
    yasa.SWResults
-
    
-__init__(events, data, sf, ch_names, hypno, data_filt)[source]
    
-
    Initialize self.  See help(type(self)) for accurate signature.
    
-
    
+
    
+__init__(events, data, sf, ch_names, hypno, data_filt)[source]
    
+
    
 
 
    Methods
    
 
    __init__(events, data, sf, ch_names, hypno, …)
    Initialize self.
    __init__(events, data, sf, ch_names, hypno, ...)
    		
 
    
 
    compare_channels([score, max_distance_sec])
    		
 
    Compare detected events across channels.
    		
 
    compare_detection(other[, max_distance_sec, …])
    compare_detection(other[, max_distance_sec, ...])
    		
 
    Compare detected events between two detection methods, or against a ground-truth scoring.
    		
 
    
 
    get_coincidence_matrix([scaled])
    get_mask()
    		
 
    Return a boolean array indicating for each sample in data if this sample is part of a detected event (True) or not (False).
    		
 
    get_sync_events([center, time_before, …])
    get_sync_events([center, time_before, ...])
    		
 
    Return the raw or filtered data of each detected event after centering to a specific timepoint.
    		
 
    plot_average([center, time_before, …])
    plot_average([center, time_before, ...])
    		
 
    Plot the average REM.
    		
 
    
 
    plot_detection()
    
@@ -130,13 +129,13 @@ 
    yasa.SWResults
 
 
    
-
-
+
+
-
+
@@ -148,23 +147,23 @@ 
    yasa.SWResults
    
-
+
-
+
-
+
    __init__(events, data, sf, ch_names, hypno, …)
    Initialize self.
    __init__(events, data, sf, ch_names, hypno, ...)
    		
 
    
 
    compare_channels([score, max_distance_sec])
    		
 
    Compare detected slow-waves across channels.
    		
 
    compare_detection(other[, max_distance_sec, …])
    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])
    get_mask()
    		
 
    Return a boolean array indicating for each sample in data if this sample is part of a detected event (True) or not (False).
    		
 
    get_sync_events([center, time_before, …])
    get_sync_events([center, time_before, ...])
    		
 
    Return the raw data of each detected event after centering to a specific timepoint.
    		
 
    plot_average([center, hue, time_before, …])
    plot_average([center, hue, time_before, ...])
    		
 
    Plot the average slow-wave.
    		
 
    
 
    plot_detection()
    		
 
    Plot an overlay of the detected slow-waves on the EEG signal.
    		
 
    summary([grp_chan, grp_stage, mask, …])
    summary([grp_chan, grp_stage, mask, ...])
    		
 
    Return a summary of the SW detection, optionally grouped across channels and/or stage.
    		
 
    
     
 
    
 
    
-
    
-compare_channels(score='f1', max_distance_sec=0)[source]
    
+
    
+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.
    
@@ -191,7 +190,7 @@ 
    yasa.SWResultsReturns
 
    
-
    scorespandas.DataFrame
    A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).
    
+
    scorespandas.DataFrame
    A Pandas DataFrame with the output scores, of shape (n_chan, n_chan).
    
 
    
 
    
 
    
@@ -207,8 +206,8 @@ 
    yasa.SWResults
-
    
-compare_detection(other, max_distance_sec=0, other_is_groundtruth=True)[source]
    
+
    
+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
@@ -247,7 +246,7 @@ 
    yasa.SWResultsReturns
 
    
-
    scorespandas.DataFrame
    A Pandas DataFrame with the channel names as index, and the following columns
    
+
    scorespandas.DataFrame
    A Pandas DataFrame with the channel names as index, and the following columns
    
 
    
 
 
     
    
@@ -465,8 +464,8 @@ 
    yasa.SpindlesResults
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.art_detect.html b/docs/build/html/generated/yasa.art_detect.html
index ff76bc6..01404d1 100644
--- a/docs/build/html/generated/yasa.art_detect.html
+++ b/docs/build/html/generated/yasa.art_detect.html
@@ -1,24 +1,25 @@
 
 
-
+
   
     
-    yasa.art_detect — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.art_detect — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
+    
     
     
     
@@ -26,10 +27,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +45,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +94,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.art_detect
    
 
    
-
    
-yasa.art_detect(data, sf=None, window=5, hypno=None, include=1, 2, 3, 4, method='covar', threshold=3, n_chan_reject=1, verbose=False)[source]
    
+
    
+yasa.art_detect(data, sf=None, window=5, hypno=None, include=(1, 2, 3, 4), method='covar', threshold=3, n_chan_reject=1, verbose=False)[source]
    
 
    Automatic artifact rejection.
    
 
    
 
    New in version 0.2.0.
    
@@ -107,7 +108,7 @@ 
    yasa.art_detect
    
 
    dataarray_like
    Single or multi-channel EEG data.
 Unit must be uV and shape (n_chan, n_samples).
-Can also be a mne.io.BaseRaw, in which case data
+Can also be a mne.io.BaseRaw, in which case data
 and sf will be automatically extracted,
 and data will also be automatically converted from Volts (MNE)
 to micro-Volts (YASA).
    
@@ -118,7 +119,7 @@ 
    yasa.art_detectfloat
    Sampling frequency of the data in Hz.
-Can be omitted if data is a mne.io.BaseRaw object.
    
+Can be omitted if data is a mne.io.BaseRaw object.
    
 
    
 
    windowfloat
    The window length (= resolution) for artifact rejection, in seconds.
 Default to 5 seconds. Shorter windows (e.g. 1 or 2-seconds) will
@@ -216,7 +217,7 @@ 
    yasa.art_detectmethod='covar' is essentially a wrapper around the
-pyriemann.clustering.Potato class implemented in the
+pyriemann.clustering.Potato class implemented in the
 pyRiemann package.
    
 
    The main idea of this approach is to estimate a reference covariance
 matrix \(\bar{C}\) (for each sleep stage separately if hypno is
@@ -265,7 +266,7 @@ 
    yasa.art_detecthttps://github.com/raphaelvallat/yasa/blob/master/notebooks/13_artifact_rejection.ipynb
    
 
    
 
-
    
+
    
 
 
     
    
@@ -282,8 +283,8 @@ 
    yasa.art_detectSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.bandpower.html b/docs/build/html/generated/yasa.bandpower.html
index 42bb634..ac4bd27 100644
--- a/docs/build/html/generated/yasa.bandpower.html
+++ b/docs/build/html/generated/yasa.bandpower.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.bandpower — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.bandpower — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.bandpower
    
 
    
-
    
-yasa.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={'average': 'median', 'window': 'hamming'})[source]
    
+
    
+yasa.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={'average': 'median', 'window': 'hamming'})[source]
    
 
    Calculate the Welch bandpower for each channel and, if specified, for each sleep stage.
    
 
    
 
    New in version 0.1.6.
    
@@ -105,15 +105,15 @@ 
    yasa.bandpower
 
    Parameters
    
 
    
-
    datanp.array_like or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which case data,
+
    datanp.array_like or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which case data,
 sf, and ch_names will be automatically extracted, and data will also be
 converted from Volts (MNE default) to micro-Volts (YASA).
    
 
    
 
    sffloat
    The sampling frequency of data AND the hypnogram. Can be omitted if data is a
-mne.io.BaseRaw.
    
+mne.io.BaseRaw.
    
 
    
 
    ch_nameslist
    List of channel names, e.g. [‘Cz’, ‘F3’, ‘F4’, …]. If None, channels will be labelled
-[‘CHAN000’, ‘CHAN001’, …]. Can be omitted if data is a mne.io.BaseRaw.
    
+[‘CHAN000’, ‘CHAN001’, …]. Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    hypnoarray_like
    Sleep stage (hypnogram). If the hypnogram is loaded, the bandpower will be extracted for
 each sleep stage defined in include.
    
@@ -146,18 +146,18 @@ 
    yasa.bandpowerband.
    
 
    
 
    bandpassboolean
    If True, apply a standard FIR bandpass filter using the minimum and maximum frequencies
-in bands. Fore more details, refer to mne.filter.filter_data().
    
+in bands. Fore more details, refer to mne.filter.filter_data().
    
 
    
 
    bandslist of tuples
    List of frequency bands of interests. Each tuple must contain the lower and upper
 frequencies, as well as the band name (e.g. (0.5, 4, ‘Delta’)).
    
 
    
-
    kwargs_welchdict
    Optional keywords arguments that are passed to the scipy.signal.welch() function.
    
+
    kwargs_welchdict
    Optional keywords arguments that are passed to the scipy.signal.welch() function.
    
 
    
 
    
 
    
 
    Returns
    
 
    
-
    bandpowerspandas.DataFrame
    Bandpower dataframe, in which each row is a channel and each column a spectral band.
    
+
    bandpowerspandas.DataFrame
    Bandpower dataframe, in which each row is a channel and each column a spectral band.
    
 
    
 
    
 
    
@@ -167,7 +167,7 @@ 
    yasa.bandpowerhttps://github.com/raphaelvallat/yasa/blob/master/notebooks/08_bandpower.ipynb
    
 
    
 
-
    
+
 
 
     
    
@@ -184,8 +184,8 @@ 
    yasa.bandpowerSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.bandpower_from_psd.html b/docs/build/html/generated/yasa.bandpower_from_psd.html
index 70891de..ce238b1 100644
--- a/docs/build/html/generated/yasa.bandpower_from_psd.html
+++ b/docs/build/html/generated/yasa.bandpower_from_psd.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.bandpower_from_psd — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.bandpower_from_psd — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.bandpower_from_psd
    
 
    
-
    
-yasa.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)[source]
    
+
    
+yasa.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)[source]
    
 
    Compute the average power of the EEG in specified frequency band(s)
 given a pre-computed PSD.
    
 
    
@@ -107,7 +107,7 @@ 
    yasa.bandpower_from_psdParameters
 
    
 
    psdarray_like
    Power spectral density of data, in uV^2/Hz. Must be of shape (n_channels, n_freqs).
-See scipy.signal.welch() for more details.
    
+See scipy.signal.welch() for more details.
    
 
    
 
    freqsarray_like
    Array of frequencies.
    
 
    
@@ -124,14 +124,14 @@ 
    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.
    
 
    
 
    
 
    
 
    
 
    
 
-
    
+
 
 
     
    
@@ -148,8 +148,8 @@ 
    yasa.bandpower_from_psd
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
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 6945437..2f42433 100644
--- a/docs/build/html/generated/yasa.bandpower_from_psd_ndarray.html
+++ b/docs/build/html/generated/yasa.bandpower_from_psd_ndarray.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.bandpower_from_psd_ndarray — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.bandpower_from_psd_ndarray — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.bandpower_from_psd_ndarray
    
 
    
-
    
-yasa.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)[source]
    
+
    
+yasa.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)[source]
    
 
    Compute bandpowers in N-dimensional PSD.
    
 
    This is a NumPy-only implementation of the yasa.bandpower_from_psd() function,
 which supports 1-D arrays of shape (n_freqs), or N-dimensional arays (e.g. 2-D (n_chan,
@@ -108,10 +108,10 @@ 
    yasa.bandpower_from_psd_ndarray
 
    Parameters
    
 
    
-
    psdnumpy.ndarray
    Power spectral density of data, in uV^2/Hz. Must be a N-D array of shape (…, n_freqs).
-See scipy.signal.welch() for more details.
    
+
    psdnumpy.ndarray
    Power spectral density of data, in uV^2/Hz. Must be a N-D array of shape (…, n_freqs).
+See scipy.signal.welch() for more details.
    
 
    
-
    freqsnumpy.ndarray
    Array of frequencies. Must be a 1-D array of shape (n_freqs,)
    
+
    freqsnumpy.ndarray
    Array of frequencies. Must be a 1-D array of shape (n_freqs,)
    
 
    
 
    bandslist of tuples
    List of frequency bands of interests. Each tuple must contain the lower and upper
 frequencies, as well as the band name (e.g. (0.5, 4, ‘Delta’)).
    
@@ -123,14 +123,14 @@ 
    yasa.bandpower_from_psd_ndarrayReturns
 
    
-
    bandpowersnumpy.ndarray
    Bandpower array of shape (n_bands, …).
    
+
    bandpowersnumpy.ndarray
    Bandpower array of shape (n_bands, …).
    
 
    
 
    
 
    
 
    
 
    
 
-
    
+
 
 
     
    
@@ -147,8 +147,8 @@ 
    yasa.bandpower_from_psd_ndarraySphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.compare_detection.html b/docs/build/html/generated/yasa.compare_detection.html
index 132728d..2c10b11 100644
--- a/docs/build/html/generated/yasa.compare_detection.html
+++ b/docs/build/html/generated/yasa.compare_detection.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.compare_detection — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.compare_detection — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.compare_detection
    
 
    
-
    
-yasa.compare_detection(indices_detection, indices_groundtruth, max_distance=0)[source]
    
+
    
+yasa.compare_detection(indices_detection, indices_groundtruth, max_distance=0)[source]
    
 
    Determine correctness of detected events against ground-truth events.
    
 
    
 
    Parameters
    
@@ -196,7 +196,7 @@ 
    yasa.compare_detection
 
    
 
-
    
+
 
 
     
    
@@ -213,8 +213,8 @@ 
    yasa.compare_detection
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.hrv_stage.html b/docs/build/html/generated/yasa.hrv_stage.html
index 23e8506..3106a9b 100644
--- a/docs/build/html/generated/yasa.hrv_stage.html
+++ b/docs/build/html/generated/yasa.hrv_stage.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.hrv_stage — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.hrv_stage — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    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]
    
+
    
+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.
    
@@ -107,7 +107,7 @@ 
    yasa.hrv_stage
 
    Parameters
    
 
    
-
    datanumpy.ndarray
    Single-channel ECG data. Must be a 1D NumPy array.
    
+
    datanumpy.ndarray
    Single-channel ECG data. Must be a 1D NumPy array.
    
 
    
 
    sffloat
    The sampling frequency of the data.
    
 
    
@@ -157,7 +157,7 @@ 
    yasa.hrv_stageReturns
 
    
-
    epochspandas.DataFrame
    Output dataframe with values (= the sleep stages defined in include) and
+
    epochspandas.DataFrame
    Output dataframe with values (= the sleep stages defined in include) and
 epoch number as index. The columns are
    
 
      
 
    • start : The start of the epoch, in seconds from the beginning of the recording.
      • 
@@ -189,7 +189,7 @@ 
      yasa.hrv_stageSphinx 3.0.0.
      
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
      
+      Created using Sphinx 4.4.0.
      
     
      
   
    
 
diff --git a/docs/build/html/generated/yasa.hypno_find_periods.html b/docs/build/html/generated/yasa.hypno_find_periods.html
index 823bd43..2e6b44f 100644
--- a/docs/build/html/generated/yasa.hypno_find_periods.html
+++ b/docs/build/html/generated/yasa.hypno_find_periods.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.hypno_find_periods — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.hypno_find_periods — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.hypno_find_periods
    
 
    
-
    
-yasa.hypno_find_periods(hypno, sf_hypno, threshold='5min', equal_length=False)[source]
    
+
    
+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.
    
@@ -123,7 +123,7 @@ 
    yasa.hypno_find_periods
 
    Returns
    
 
    
-
    periodspandas.DataFrame
    Output dataframe
    
+
    periodspandas.DataFrame
    Output dataframe
    
 
      
 
    • values : The value in hypno of the current period
      • 
 
    • start : The index of the start of the period in hypno
      • 
@@ -189,7 +189,7 @@ 
      yasa.hypno_find_periods
 
    
 
-
    
+
    
 
 
     
    
@@ -206,8 +206,8 @@ 
    yasa.hypno_find_periods
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
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 48ab041..ab65fa1 100644
--- a/docs/build/html/generated/yasa.hypno_int_to_str.html
+++ b/docs/build/html/generated/yasa.hypno_int_to_str.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.hypno_int_to_str — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.hypno_int_to_str — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.hypno_int_to_str
    
 
    
-
    
-yasa.hypno_int_to_str(hypno, mapping_dict={- 2: 'Uns', - 1: 'Art', 0: 'W', 1: 'N1', 2: 'N2', 3: 'N3', 4: 'R'})[source]
    
+
    
+yasa.hypno_int_to_str(hypno, mapping_dict={- 2: 'Uns', - 1: 'Art', 0: 'W', 1: 'N1', 2: 'N2', 3: 'N3', 4: 'R'})[source]
    
 
    Convert an integer hypnogram array to a string array.
    
 
    [0, 2, 2, 3, 4] ==> [‘W’, ‘N2’, ‘N2’, ‘N3’, ‘R’]
    
 
    
@@ -109,7 +109,7 @@ 
    yasa.hypno_int_to_strhypnoarray_like
    The sleep staging (hypnogram) 1D array.
    
 
    
 
    mapping_dictdict
    The mapping dictionnary. Note that this function is essentially a wrapper around
-pandas.Series.map().
    
+pandas.Series.map().
    
 
    
 
    
 
@@ -122,7 +122,7 @@ 
    yasa.hypno_int_to_str
 
 
-
    
+
 
 
     
    
@@ -139,8 +139,8 @@ 
    yasa.hypno_int_to_str
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
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 1d4a22f..da041b6 100644
--- a/docs/build/html/generated/yasa.hypno_str_to_int.html
+++ b/docs/build/html/generated/yasa.hypno_str_to_int.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.hypno_str_to_int — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.hypno_str_to_int — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.hypno_str_to_int
    
 
    
-
    
-yasa.hypno_str_to_int(hypno, mapping_dict={'art': - 1, 'mt': - 1, 'n1': 1, 'n2': 2, 'n3': 3, 'nd': - 2, 'r': 4, 'rem': 4, 's1': 1, 's2': 2, 's3': 3, 's4': 3, 'uns': - 2, 'w': 0, 'wake': 0})[source]
    
+
    
+yasa.hypno_str_to_int(hypno, mapping_dict={'art': - 1, 'mt': - 1, 'n1': 1, 'n2': 2, 'n3': 3, 'nd': - 2, 'r': 4, 'rem': 4, 's1': 1, 's2': 2, 's3': 3, 's4': 3, 'uns': - 2, 'w': 0, 'wake': 0})[source]
    
 
    Convert a string hypnogram array to integer.
    
 
    [‘W’, ‘N2’, ‘N2’, ‘N3’, ‘R’] ==> [0, 2, 2, 3, 4]
    
 
    
@@ -109,7 +109,7 @@ 
    yasa.hypno_str_to_inthypnoarray_like
    The sleep staging (hypnogram) 1D array.
    
 
    
 
    mapping_dictdict
    The mapping dictionnary, in lowercase. Note that this function is essentially a wrapper
-around pandas.Series.map().
    
+around pandas.Series.map().
    
 
    
 
    
 
@@ -122,7 +122,7 @@ 
    yasa.hypno_str_to_int
 
 
-
    
+
 
 
     
    
@@ -139,8 +139,8 @@ 
    yasa.hypno_str_to_int
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
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 6a5a9f0..f450ddc 100644
--- a/docs/build/html/generated/yasa.hypno_upsample_to_data.html
+++ b/docs/build/html/generated/yasa.hypno_upsample_to_data.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.hypno_upsample_to_data — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.hypno_upsample_to_data — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.hypno_upsample_to_data
    
 
    
-
    
-yasa.hypno_upsample_to_data(hypno, sf_hypno, data, sf_data=None, verbose=True)[source]
    
+
    
+yasa.hypno_upsample_to_data(hypno, sf_hypno, data, sf_data=None, verbose=True)[source]
    
 
    Upsample an hypnogram to a given sampling frequency and fit the
 resulting hypnogram to corresponding EEG data, such that the hypnogram
 and EEG data have the exact same number of samples.
    
@@ -115,11 +115,11 @@ 
    yasa.hypno_upsample_to_dataarray_like or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which
+
    dataarray_like or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which
 case data and sf_data will be automatically extracted.
    
 
    
 
    sf_datafloat
    The sampling frequency of data, in Hz (e.g. 100 Hz, 256 Hz, …).
-Can be omitted if data is a mne.io.BaseRaw.
    
+Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    verbosebool or str
    Verbose level. Default (False) will only print warning and error
 messages. The logging levels are ‘debug’, ‘info’, ‘warning’, ‘error’,
@@ -145,7 +145,7 @@ 
    yasa.hypno_upsample_to_dataSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
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 a55f7fa..7c768b7 100644
--- a/docs/build/html/generated/yasa.hypno_upsample_to_sf.html
+++ b/docs/build/html/generated/yasa.hypno_upsample_to_sf.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.hypno_upsample_to_sf — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.hypno_upsample_to_sf — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.hypno_upsample_to_sf
    
 
    
-
    
-yasa.hypno_upsample_to_sf(hypno, sf_hypno, sf_data)[source]
    
+
    
+yasa.hypno_upsample_to_sf(hypno, sf_hypno, sf_data)[source]
    
 
    Upsample the hypnogram to a given sampling frequency.
    
 
    
 
    New in version 0.1.5.
    
@@ -126,7 +126,7 @@ 
    yasa.hypno_upsample_to_sfSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.irasa.html b/docs/build/html/generated/yasa.irasa.html
index 956d991..944ee35 100644
--- a/docs/build/html/generated/yasa.irasa.html
+++ b/docs/build/html/generated/yasa.irasa.html
@@ -1,24 +1,25 @@
 
 
-
+
   
     
-    yasa.irasa — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.irasa — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
+    
     
     
     
@@ -26,10 +27,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +45,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +94,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.irasa
    
 
    
-
    
-yasa.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={'average': 'median', 'window': 'hamming'}, verbose=True)[source]
    
+
    
+yasa.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={'average': 'median', 'window': 'hamming'}, verbose=True)[source]
    
 
    Separate the aperiodic (= fractal, or 1/f) and oscillatory component
 of the power spectra of EEG data using the IRASA method.
    
 
    
@@ -106,22 +107,22 @@ 
    yasa.irasa
 
    Parameters
    
 
    
-
    datanumpy.ndarray or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which
+
    datanumpy.ndarray or mne.io.BaseRaw
    1D or 2D EEG data. Can also be a mne.io.BaseRaw, in which
 case data, sf, and ch_names will be automatically
 extracted, and data will also be converted from Volts (MNE default)
 to micro-Volts (YASA).
    
 
    
 
    sffloat
    The sampling frequency of data AND the hypnogram.
-Can be omitted if data is a mne.io.BaseRaw.
    
+Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    ch_nameslist
    List of channel names, e.g. [‘Cz’, ‘F3’, ‘F4’, …]. If None,
 channels will be labelled [‘CHAN000’, ‘CHAN001’, …].
-Can be omitted if data is a mne.io.BaseRaw.
    
+Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    bandtuple or None
    Broad band frequency range.
 Default is 1 to 30 Hz.
    
 
    
-
    hsetlist or numpy.ndarray
    Resampling factors used in IRASA calculation. Default is to use a range
+
    hsetlist or numpy.ndarray
    Resampling factors used in IRASA calculation. Default is to use a range
 of values from 1.1 to 1.9 with an increment of 0.05.
    
 
    
 
    return_fitboolean
    If True (default), fit an exponential function to the aperiodic PSD
@@ -141,7 +142,7 @@ 
    yasa.irasadict
    Optional keywords arguments that are passed to the
-scipy.signal.welch() function.
    
+scipy.signal.welch() function.
    
 
    
 
    verbosebool or str
    Verbose level. Default (False) will only print warning and error
 messages. The logging levels are ‘debug’, ‘info’, ‘warning’, ‘error’,
@@ -152,13 +153,13 @@ 
    yasa.irasaReturns
 
    
-
    freqsnumpy.ndarray
    Frequency vector.
    
+
    freqsnumpy.ndarray
    Frequency vector.
    
 
    
-
    psd_aperiodicnumpy.ndarray
    The fractal (= aperiodic) component of the PSD.
    
+
    psd_aperiodicnumpy.ndarray
    The fractal (= aperiodic) component of the PSD.
    
 
    
-
    psd_oscillatorynumpy.ndarray
    The oscillatory (= periodic) component of the PSD.
    
+
    psd_oscillatorynumpy.ndarray
    The oscillatory (= periodic) component of the PSD.
    
 
    
-
    fit_paramspandas.DataFrame (optional)
    Dataframe of fit parameters. Only if return_fit=True.
    
+
    fit_paramspandas.DataFrame (optional)
    Dataframe of fit parameters. Only if return_fit=True.
    
 
    
 
    
 
    
@@ -203,7 +204,7 @@ 
    yasa.irasahttps://doi.org/10.1101/2021.10.15.464483
    
 
    
 
-
    
+
    
 
 
     
    
@@ -220,8 +221,8 @@ 
    yasa.irasaSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.load_profusion_hypno.html b/docs/build/html/generated/yasa.load_profusion_hypno.html
index 2d00e24..a484547 100644
--- a/docs/build/html/generated/yasa.load_profusion_hypno.html
+++ b/docs/build/html/generated/yasa.load_profusion_hypno.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.load_profusion_hypno — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.load_profusion_hypno — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.load_profusion_hypno
    
 
    
-
    
-yasa.load_profusion_hypno(fname, replace=True)[source]
    
+
    
+yasa.load_profusion_hypno(fname, replace=True)[source]
    
 
    Load a Compumedics Profusion hypnogram (.xml).
    
 
    The Compumedics Profusion hypnogram format is one of the two hypnogram
 formats found in the National Sleep Research Resource (NSRR) website. For more details on the format,
@@ -126,7 +126,7 @@ 
    yasa.load_profusion_hypnoSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.moving_transform.html b/docs/build/html/generated/yasa.moving_transform.html
index 5fbe0e3..1eeb54f 100644
--- a/docs/build/html/generated/yasa.moving_transform.html
+++ b/docs/build/html/generated/yasa.moving_transform.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.moving_transform — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.moving_transform — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.moving_transform
    
 
    
-
    
-yasa.moving_transform(x, y=None, sf=100, window=0.3, step=0.1, method='corr', interp=False)[source]
    
+
    
+yasa.moving_transform(x, y=None, sf=100, window=0.3, step=0.1, method='corr', interp=False)[source]
    
 
    Moving transformation of one or two time-series.
    
 
    
 
    Parameters
    
@@ -149,7 +149,7 @@ 
    yasa.moving_transformhttps://github.com/wonambi-python/wonambi).
    
 
    
 
-
    
+
 
 
     
    
@@ -166,8 +166,8 @@ 
    yasa.moving_transform
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.plot_hypnogram.html b/docs/build/html/generated/yasa.plot_hypnogram.html
index b96ba87..7990d5d 100644
--- a/docs/build/html/generated/yasa.plot_hypnogram.html
+++ b/docs/build/html/generated/yasa.plot_hypnogram.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.plot_hypnogram — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.plot_hypnogram — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.plot_hypnogram
    
 
    
-
    
-yasa.plot_hypnogram(hypno, sf_hypno=0.03333333333333333, lw=1.5, fill_color=None, ax=None)[source]
    
+
    
+yasa.plot_hypnogram(hyp, lw=1.5, highlight='REM', fill_color=None, ax=None)[source]
    
 
    Plot a hypnogram.
    
 
    
 
    New in version 0.6.0.
    
@@ -105,57 +105,61 @@ 
    yasa.plot_hypnogram
 
    Parameters
    
 
    
-
    hypnoarray_like
    Sleep stage (hypnogram).
    
-
    
-
    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
      • 
-
    
-
    
-
    
-
    sf_hypnofloat
    The current sampling frequency of the hypnogram, in Hz, e.g.
    
-
      
-
    • 1/30 = 1 value per each 30 seconds of EEG data,
      • 
-
    • 1 = 1 value per second of EEG data
      • 
-
    
+
    hypyasa.Hypnogram
    A YASA hypnogram instance.
    
 
    
 
    lwfloat
    Linewidth.
    
 
    
-
    fill_colorstr
    Color to fill space above hypnogram line, optional.
    
+
    highlightstr or None
    Optional stage to highlight with alternate color.
    
 
    
-
    axmatplotlib.axes.Axes
    Axis on which to draw the plot, optional.
    
+
    fill_colorstr or None
    Optional color to fill space above hypnogram line.
    
+
    
+
    axmatplotlib.axes.Axes
    Axis on which to draw the plot, optional.
    
 
    
 
    
 
    
 
    Returns
    
 
    
-
    axmatplotlib.axes.Axes
    Matplotlib Axes
    
+
    axmatplotlib.axes.Axes
    Matplotlib Axes
    
 
    
 
    
 
    
 
    
 
    Examples
    
-
    >>> import yasa
->>> import numpy as np
+
    >>> from yasa import simulate_hypnogram
 >>> import matplotlib.pyplot as plt
->>> hypno = np.loadtxt("https://github.com/raphaelvallat/yasa/raw/master/notebooks/data_full_6hrs_100Hz_hypno_30s.txt")
->>> fig, ax = plt.subplots(1, 1, figsize=(7, 3), constrained_layout=True)
->>> ax = yasa.plot_hypnogram(hypno, fill_color="gainsboro", ax=ax)
+>>> hyp = simulate_hypnogram(tib=300, seed=11)
+>>> ax = hyp.plot_hypnogram()
+>>> plt.tight_layout()
 
    
 
    
-
    
+
    
 ../_images/yasa-plot_hypnogram-1.png
+
    
+
    >>> from yasa import Hypnogram
+>>> values = 4 * ["W", "N1", "N2", "N3", "REM"] + ["ART", "N2", "REM", "W", "UNS"]
+>>> hyp = Hypnogram(values, freq="24min").upsample("30s")
+>>> ax = hyp.plot_hypnogram(lw=2, fill_color="thistle")
+>>> plt.tight_layout()
+
    
 
    
+
    
+../_images/yasa-plot_hypnogram-2.png
+
    
+
    >>> from yasa import simulate_hypnogram
+>>> import matplotlib.pyplot as plt
+>>> fig, axes = plt.subplots(nrows=2, figsize=(6, 4), constrained_layout=True)
+>>> hyp_a = simulate_hypnogram(n_stages=3, seed=99)
+>>> hyp_b = simulate_hypnogram(n_stages=3, seed=99, start="2022-01-31 23:30:00")
+>>> hyp_a.plot_hypnogram(lw=1, fill_color="whitesmoke", highlight=None, ax=axes[0])
+>>> hyp_b.plot_hypnogram(lw=1, fill_color="whitesmoke", highlight=None, ax=axes[1])
+
    
+
    
+
    
+../_images/yasa-plot_hypnogram-3.png
+
    
 
 
-
    
+
    
 
 
     
    
@@ -172,8 +176,8 @@ 
    yasa.plot_hypnogramSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.plot_spectrogram.html b/docs/build/html/generated/yasa.plot_spectrogram.html
index 9073508..387c51f 100644
--- a/docs/build/html/generated/yasa.plot_spectrogram.html
+++ b/docs/build/html/generated/yasa.plot_spectrogram.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.plot_spectrogram — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.plot_spectrogram — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.plot_spectrogram
    
 
    
-
    
-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, **kwargs)[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, **kwargs)[source]
    
 
    Plot a full-night multi-taper spectrogram, optionally with the hypnogram on top.
    
 
    For more details, please refer to the Jupyter notebook
    
 
    
@@ -106,7 +106,7 @@ 
    yasa.plot_spectrogram
 
    Parameters
    
 
    
-
    datanumpy.ndarray
    Single-channel EEG data. Must be a 1D NumPy array.
    
+
    datanumpy.ndarray
    Single-channel EEG data. Must be a 1D NumPy array.
    
 
    
 
    sffloat
    The sampling frequency of data AND the hypnogram.
    
 
    
@@ -146,13 +146,13 @@ 
    yasa.plot_spectrogram
 
    vmaxint or float
    The upper range of color scale. Overwrites trimperc
    
 
    
-
    **kwargsdict
    Other arguments that are passed to yasa.plot_hypnogram().
    
+
    **kwargsdict
    Other arguments that are passed to yasa.Hypnogram.plot_hypnogram().
    
 
    
 
    
 
    
 
    Returns
    
 
    
-
    figmatplotlib.figure.Figure
    Matplotlib Figure
    
+
    figmatplotlib.figure.Figure
    Matplotlib Figure
    
 
    
 
    
 
    
@@ -173,9 +173,9 @@ 
    yasa.plot_spectrogram>>> fig = yasa.plot_spectrogram(data, sf)
 
    
 
    
-
    
+
    
 ../_images/yasa-plot_spectrogram-1.png
-
    
+
 
      
 
    1. Full-night multitaper spectrogram on Cz with the hypnogram on top
      2. 
 
    
@@ -194,12 +194,12 @@ 
    yasa.plot_spectrogram>>> fig = yasa.plot_spectrogram(data, sf, hypno, cmap='Spectral_r')
 
    
 
    
-
    
+
    
 ../_images/yasa-plot_spectrogram-2.png
-
    
+
 
 
-
    
+
 
 
     
    
@@ -216,8 +216,8 @@ 
    yasa.plot_spectrogram
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.rem_detect.html b/docs/build/html/generated/yasa.rem_detect.html
index 937474d..5bee942 100644
--- a/docs/build/html/generated/yasa.rem_detect.html
+++ b/docs/build/html/generated/yasa.rem_detect.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.rem_detect — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.rem_detect — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.rem_detect
    
 
    
-
    
-yasa.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)[source]
    
+
    
+yasa.rem_detect(loc, roc, sf, hypno=None, include=4, amplitude=(50, 325), duration=(0.3, 1.2), relative_prominence=0.8, freq_rem=(0.5, 5), remove_outliers=False, verbose=False)[source]
    
 
    Rapid eye movements (REMs) detection.
    
 
    This detection requires both the left EOG (LOC) and right EOG (LOC).
 The units of the data must be uV. The algorithm is based on an amplitude
@@ -113,8 +113,8 @@ 
    yasa.rem_detect
 
    Warning
    
-
    The default unit of mne.io.BaseRaw is Volts.
-Therefore, if passing data from a mne.io.BaseRaw,
+
    The default unit of mne.io.BaseRaw is Volts.
+Therefore, if passing data from a mne.io.BaseRaw,
 make sure to use units=”uV” to get the data in micro-Volts, e.g.:
    
 
    >>> data = raw.get_data(units="uV")  # Make sure that data is in uV
 
    
@@ -154,10 +154,13 @@ 
    yasa.rem_detecttuple or list
    The minimum and maximum duration of the REMs.
 Default is 0.3 to 1.2 seconds.
    
 
    
+
    relative_prominencefloat
    Relative prominence used to detect the peaks. The actual prominence is computed
+by multiplying relative prominence by the minimal amplitude. Default is 0.8.
    
+
    
 
    freq_remtuple 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.
    
@@ -179,7 +182,7 @@ 
    yasa.rem_detect>>> rem.summary()
 
    
 
    
-
    This will give a pandas.DataFrame where each row is a
+
    This will give a pandas.DataFrame where each row is a
 detected REM and each column is a parameter (= property).
 To get the average parameters sleep stage:
    
 
    >>> rem.summary(grp_stage=True)
@@ -224,7 +227,7 @@ 
    yasa.rem_detecthttps://github.com/raphaelvallat/yasa/blob/master/notebooks/07_REMs_detection.ipynb
    
 
 
-
    
+
 
 
     
    
@@ -241,8 +244,8 @@ 
    yasa.rem_detectSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.simulate_hypno.html b/docs/build/html/generated/yasa.simulate_hypno.html
deleted file mode 100644
index 6d8874c..0000000
--- a/docs/build/html/generated/yasa.simulate_hypno.html
+++ /dev/null
@@ -1,241 +0,0 @@
-
-
-
-  
-    
-    yasa.simulate_hypno — yasa 0.6.3 documentation
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-    
-
-
-
-
-
-
-
-
-
-  
-
-  
-
-
    
-  
    
-    
    
-      
-  
    
-
    yasa.simulate_hypno
    
-
    
-
    
-yasa.simulate_hypno(tib=90, sf=0.03333333333333333, n_stages=5, trans_probas=None, init_probas=None, seed=None)[source]
    
-
    Simulate a hypnogram based on transition probabilities.
    
-
    Current implentation is a naive Markov model. The initial stage of a hypnogram
-is generated using probabilites from init_probas and then subsequent stages
-are generated from a Markov sequence based on trans_probas.
    
-
    
-
    Important
    
-
    The Markov simulation model is not meant to accurately portray sleep
-macroarchitecture and should only be used for testing or other unique purposes.
    
-
    
-
    
-
    New in version 0.6.3.
    
-
    
-
    
-
    Parameters
    
-
    
-
    tibint
    Total duration of the hypnogram (i.e., time in bed), expressed in minutes.
-Returned hypnogram will be slightly shorter if tib in seconds is not
-evenly divisible by sf.
    
-
    
-
    See also
    
-
    yasa.sleep_statistics()
    
-
    
-
    
-
    sffloat
    Sampling frequency.
    
-
    
-
    n_stagesint
    Staging scheme of returned hypnogram. Input should follow 5-stage scheme but can
-be converted to lower scheme if desired.
    
-
    
-
    See also
    
-
    yasa.hypno_consolidate_stages()
    
-
    
-
    
-
    trans_probaspandas.DataFrame or None
    Transition probability matrix where each cell is a transition probability
-between sleep stages of consecutive epochs.
    
-
    trans_probas is a right stochastic matrix, i.e. each row sums to 1.
    
-
    If None (default), use transition probabilites from Metzner et al., 2021 [Metzner2021].
-If pandas.DataFrame, must have “from”-stages as indices and
-“to”-stages as columns. Indices and columns must follow YASA integer
-hypnogram convention (W = 0, N = 1, …). Unscored/Artefact stages are not allowed.
    
-
    
-
    Note
    
-
    Transition probability matrices should indicate the transition
-probability between epochs (i.e., probability of the next epoch) and
-not simply stage (i.e., probability of non-similar stage).
    
-
    
-
    
-
    See also
    
-
    Return value from yasa.transition_matrix()
    
-
    
-
    
-
    init_probaspandas.Series or None
    Probabilites of each stage to initialize random walk.
-If None (default), initialize with “From”-Wake row of trans_probas.
-If pandas.Series, indices must be stages following YASA integer
-hypnogram convention (see trans_probas).
    
-
    
-
    seedint or None
    Random seed for generating Markov sequence.
-If an integer number is provided, the random hypnogram will be predictable.
-This argument is required if reproducible results are desired.
    
-
    
-
    
-
    
-
    Returns
    
-
    
-
    hypnonp.ndarray
    Hypnogram containing simulated sleep stages.
    
-
    
-
    
-
    
-
    
-
    Notes
    
-
    Default transition probabilities can be found in the traMat_Epoch.npy file of
-Supplementary Information for Metzner et al., 2021 [Metzner2021] (rounded values
-are viewable in Figure 5b). Please cite this work if these probabilites are used
-for publication.
    
-
    References
    
-
    
-
    Metzner2021(1,2)
    
-
    Metzner, C., Schilling, A., Traxdorf, M., Schulze, H., & Krausse, P.
-(2021). Sleep as a random walk: a super-statistical analysis of EEG
-data across sleep stages. Communications Biology, 4.
-https://doi.org/10.1038/s42003-021-02912-6
    
-
    
-
    
-
    Examples
    
-
    >>> from yasa import simulate_hypno
->>> hypno = simulate_hypno(tib=10, seed=0)
->>> print(hypno)
-[0 0 0 0 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2]
-
    
-
    
-
    >>> hypno = simulate_hypno(tib=10, n_stages=2, seed=0)
->>> print(hypno)
-[0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
-
    
-
    
-
    Base the data off a real subject’s transition matrix.
    
-
    >>> import matplotlib.pyplot as plt
->>> import yasa
->>> url = (
->>>     "https://github.com/raphaelvallat/yasa/raw/master/"
->>>     "notebooks/data_full_6hrs_100Hz_hypno_30s.txt"
->>> )
->>> hypno = np.loadtxt(url)
->>> _, probas = yasa.transition_matrix(hypno)
->>> hypno_sim = yasa.simulate_hypno(tib=360, trans_probas=probas, seed=9)
->>> fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(9, 6))
->>> yasa.plot_hypnogram(hypno, ax=ax1)
->>> yasa.plot_hypnogram(hypno_sim, ax=ax2)
->>> ax1.set_title("True hypnogram")
->>> ax2.set_title("Simulated hypnogram")
->>> plt.tight_layout()
-
    
-
    
-
    
-../_images/yasa-simulate_hypno-1.png
-
    
-
    
-
-
    
-
-
-    
    
-      
-  
    
-
    
-
    
-  
    
-    
    
-      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.simulate_hypnogram.html b/docs/build/html/generated/yasa.simulate_hypnogram.html
new file mode 100644
index 0000000..9128e4e
--- /dev/null
+++ b/docs/build/html/generated/yasa.simulate_hypnogram.html
@@ -0,0 +1,289 @@
+
+
+
+  
+    
+    
+
+    yasa.simulate_hypnogram — yasa 0.6.4 documentation
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+    
+
+
+
+
+
+
+
+
+
+  
+
+  
+
+
    
+  
    
+    
    
+      
+  
    
+
    yasa.simulate_hypnogram
    
+
    
+
    
+yasa.simulate_hypnogram(tib=480, trans_probas=None, init_probas=None, seed=None, **kwargs)[source]
    
+
    Simulate a hypnogram based on transition probabilities.
    
+
    Current implentation is a naive Markov model. The initial stage of a hypnogram
+is generated using probabilites from init_probas and then subsequent stages
+are generated from a Markov sequence based on trans_probas.
    
+
    
+
    Important
    
+
    The Markov simulation model is not meant to accurately portray sleep
+macroarchitecture and should only be used for testing or other unique purposes.
    
+
    
+
    
+
    See also
    
+
    yasa.Hypnogram.simulate_similar()
    
+
    
+
    
+
    New in version 0.6.3.
    
+
    
+
    
+
    Parameters
    
+
    
+
    tibint, float
    Total duration of the hypnogram (i.e., time in bed), expressed in minutes.
+Returned hypnogram will be slightly shorter if tib is not evenly divisible by freq.
+Default is 480 minutes (= 8 hours).
    
+
    
+
    See also
    
+
    yasa.sleep_statistics()
    
+
    
+
    
+
    trans_probaspandas.DataFrame or None
    Transition probability matrix where each cell is a transition probability
+between sleep stages of consecutive epochs.
    
+
    trans_probas is a right stochastic matrix, i.e. each row sums to 1.
    
+
    If None (default), transition probabilities come from Metzner et al., 2021 [Metzner2021].
+If pandas.DataFrame, must have “from”-stages as indices and
+“to”-stages as columns. Indices and columns must follow YASA string
+hypnogram convention (e.g., WAKE, N1). Unscored/Artefact stages are not allowed.
    
+
    
+
    Note
    
+
    Transition probability matrices should indicate the transition
+probability between epochs (i.e., probability of the next epoch) and
+not simply stage (i.e., probability of non-similar stage).
    
+
    
+
    
+
    See also
    
+
    Return value from yasa.transition_matrix()
    
+
    
+
    
+
    init_probaspandas.Series or None
    Probabilites of each stage to initialize random walk.
+If None (default), initialize with “from”-WAKE row of trans_probas.
+If pandas.Series, indices must be stages following YASA string
+hypnogram convention and identical to those of trans_probas.
    
+
    
+
    seedint or None
    Random seed for generating Markov sequence.
+If an integer number is provided, the random hypnogram will be predictable.
+This argument is required if reproducible results are desired.
    
+
    
+
    **kwargsdict
    Other arguments that are passed to yasa.Hypnogram.
    
+
    
+
    Note
    
+
    n_stages and freq must be consistent with a user-specificied trans_probas.
    
+
    
+
    
+
    
+
    
+
    Returns
    
+
    
+
    hypyasa.Hypnogram
    Hypnogram containing simulated sleep stages.
    
+
    
+
    
+
    
+
    
+
    Notes
    
+
    Default transition probabilities are based on 30-second epochs and can be found in the
+traMat_Epoch.npy file of Supplementary Information for Metzner et al., 2021 [Metzner2021]
+(rounded values are viewable in Figure 5b). Please cite this work if these probabilites are used
+for publication.
    
+
    References
    
+
    
+
    Metzner2021(1,2)
    
+
    Metzner, C., Schilling, A., Traxdorf, M., Schulze, H., & Krausse, P.
+(2021). Sleep as a random walk: a super-statistical analysis of EEG
+data across sleep stages. Communications Biology, 4.
+https://doi.org/10.1038/s42003-021-02912-6
    
+
    
+
    
+
    Examples
    
+
    >>> from yasa import simulate_hypnogram
+>>> hyp = simulate_hypnogram(tib=5, seed=1)
+>>> hyp
+<Hypnogram | 10 epochs x 30s (5.00 minutes), 5 stages>
+ - Use `.hypno` to get the string values as a pandas.Series
+ - Use `.as_int()` to get the integer values as a pandas.Series
+ - Use `.plot_hypnogram()` to plot the hypnogram
+See the online documentation for more details.
+
    
+
    
+
    >>> hyp.hypno
+Epoch
+0    WAKE
+1      N1
+2      N1
+3      N2
+4      N2
+5      N2
+6      N2
+7      N2
+8      N2
+9      N2
+Name: Stage, dtype: object
+
    
+
    
+
    >>> hyp = simulate_hypnogram(tib=5, n_stages=2, seed=1)
+>>> hyp.hypno
+Epoch
+0     WAKE
+1    SLEEP
+2    SLEEP
+3    SLEEP
+4    SLEEP
+5    SLEEP
+6    SLEEP
+7    SLEEP
+8    SLEEP
+9    SLEEP
+Name: Stage, dtype: object
+
    
+
    
+
    Add some Unscored epochs.
    
+
    >>> hyp = simulate_hypnogram(tib=5, n_stages=2, seed=1)
+>>> hyp.hypno.iloc[-2:] = "UNS"
+>>> hyp.hypno
+Epoch
+0     WAKE
+1    SLEEP
+2    SLEEP
+3    SLEEP
+4    SLEEP
+5    SLEEP
+6    SLEEP
+7    SLEEP
+8      UNS
+9      UNS
+Name: Stage, dtype: object
+
    
+
    
+
    Base the data off a real subject’s transition matrix.
    
+
    >>> import numpy as np
+>>> import matplotlib.pyplot as plt
+>>> from yasa import Hypnogram, hypno_int_to_str
+>>> url = (
+>>>     "https://github.com/raphaelvallat/yasa/raw/master/"
+>>>     "notebooks/data_full_6hrs_100Hz_hypno_30s.txt"
+>>> )
+>>> values_str = hypno_int_to_str(np.loadtxt(url))
+>>> real_hyp = Hypnogram(values_str)
+>>> fake_hyp = real_hyp.simulate_similar(seed=2)
+>>> fig, (ax1, ax2) = plt.subplots(nrows=2, figsize=(7, 5))
+>>> real_hyp.plot_hypnogram(ax=ax1).set_title("Real hypnogram")
+>>> fake_hyp.plot_hypnogram(ax=ax2).set_title("Fake hypnogram")
+>>> plt.tight_layout()
+
    
+
    
+
    
+../_images/yasa-simulate_hypnogram-1.png
+
    
+
    
+
+
    
+
+
+    
    
+      
+  
    
+
    
+
    
+  
    
+    
    
+      Back to top
+      
+        
    
+        
+      
+    
    
+    
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
+    
    
+  
    
+
    
+  
+
\ No newline at end of file
diff --git a/docs/build/html/generated/yasa.sleep_statistics.html b/docs/build/html/generated/yasa.sleep_statistics.html
index 5028b0b..827329f 100644
--- a/docs/build/html/generated/yasa.sleep_statistics.html
+++ b/docs/build/html/generated/yasa.sleep_statistics.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.sleep_statistics — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.sleep_statistics — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
+
 
 
     
    
@@ -216,8 +223,8 @@ 
    yasa.sleep_statistics
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.sliding_window.html b/docs/build/html/generated/yasa.sliding_window.html
index facef69..9548467 100644
--- a/docs/build/html/generated/yasa.sliding_window.html
+++ b/docs/build/html/generated/yasa.sliding_window.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.sliding_window — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.sliding_window — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.sliding_window
    
 
    
-
    
-yasa.sliding_window(data, sf, window, step=None, axis=- 1)[source]
    
+
    
+yasa.sliding_window(data, sf, window, step=None, axis=- 1)[source]
    
 
    Calculate a sliding window of a 1D or 2D EEG signal.
    
 
    
 
    New in version 0.1.7.
    
@@ -132,7 +132,7 @@ 
    yasa.sliding_windowNotes
    
 
    This is a wrapper around the
-numpy.lib.stride_tricks.as_strided() function.
    
+numpy.lib.stride_tricks.as_strided() function.
    
 
    Examples
    
 
    With a 1-D array
    
 
    >>> import numpy as np
@@ -187,7 +187,7 @@ 
    yasa.sliding_windowSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.spindles_detect.html b/docs/build/html/generated/yasa.spindles_detect.html
index 495d7e6..8484909 100644
--- a/docs/build/html/generated/yasa.spindles_detect.html
+++ b/docs/build/html/generated/yasa.spindles_detect.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.spindles_detect — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.spindles_detect — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,32 +93,32 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.spindles_detect
    
 
    
-
    
-yasa.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={'corr': 0.65, 'rel_pow': 0.2, 'rms': 1.5}, multi_only=False, remove_outliers=False, verbose=False)[source]
    
+
    
+yasa.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={'corr': 0.65, 'rel_pow': 0.2, 'rms': 1.5}, multi_only=False, remove_outliers=False, verbose=False)[source]
    
 
    Spindles detection.
    
 
    
 
    Parameters
    
 
    
 
    dataarray_like
    Single or multi-channel data. Unit must be uV and shape (n_samples) or
-(n_chan, n_samples). Can also be a mne.io.BaseRaw,
+(n_chan, n_samples). Can also be a mne.io.BaseRaw,
 in which case data, sf, and ch_names will be automatically
 extracted, and data will also be automatically converted from
 Volts (MNE) to micro-Volts (YASA).
    
 
    
 
    sffloat
    Sampling frequency of the data in Hz.
-Can be omitted if data is a mne.io.BaseRaw.
    
+Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    Tip
    
 
    If the detection is taking too long, make sure to downsample
 your data to 100 Hz (or 128 Hz). For more details, please refer to
-mne.filter.resample().
    
+mne.filter.resample().
    
 
    
 
    
 
    ch_nameslist of str
    Channel names. Can be omitted if data is a
-mne.io.BaseRaw.
    
+mne.io.BaseRaw.
    
 
    
 
    hypnoarray_like
    Sleep stage (hypnogram). If the hypnogram is loaded, the
 detection will only be applied to the value defined in
@@ -178,7 +178,7 @@ 
    yasa.spindles_detect
 
    
 
    remove_outliersboolean
    If True, YASA will automatically detect and remove outliers spindles
-using sklearn.ensemble.IsolationForest.
+using sklearn.ensemble.IsolationForest.
 The outliers detection is performed on all the spindles
 parameters with the exception of the Start, Peak, End,
 Stage, and SOPhase columns.
@@ -203,7 +203,7 @@ 
    yasa.spindles_detect>>> sp.summary()
 
    
 
    
-
    This will give a pandas.DataFrame where each row is a
+
    This will give a pandas.DataFrame where each row is a
 detected spindle and each column is a parameter (= feature or property)
 of this spindle. To get the average spindles parameters per channel and
 sleep stage:
    
@@ -268,7 +268,7 @@ 
    yasa.spindles_detecthttps://github.com/raphaelvallat/yasa/blob/master/notebooks/04_spindles_slow_fast.ipynb
    
 
    
 
-
    
+
 
 
     
    
@@ -285,8 +285,8 @@ 
    yasa.spindles_detect
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.stft_power.html b/docs/build/html/generated/yasa.stft_power.html
index 5d3368d..e40be06 100644
--- a/docs/build/html/generated/yasa.stft_power.html
+++ b/docs/build/html/generated/yasa.stft_power.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.stft_power — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.stft_power — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.stft_power
    
 
    
-
    
-yasa.stft_power(data, sf, window=2, step=0.2, band=1, 30, interp=True, norm=False)[source]
    
+
    
+yasa.stft_power(data, sf, window=2, step=0.2, band=(1, 30), interp=True, norm=False)[source]
    
 
    Compute the pointwise power via STFT and interpolation.
    
 
    
 
    Parameters
    
@@ -129,22 +129,22 @@ 
    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.
    
 
    
 
-
    
+
 
 
     
    
@@ -161,8 +161,8 @@ 
    yasa.stft_powerSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.sw_detect.html b/docs/build/html/generated/yasa.sw_detect.html
index e72a10c..c467497 100644
--- a/docs/build/html/generated/yasa.sw_detect.html
+++ b/docs/build/html/generated/yasa.sw_detect.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.sw_detect — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.sw_detect — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,32 +93,32 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.sw_detect
    
 
    
-
    
-yasa.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, 'p': 0.05, 'time': 1}, remove_outliers=False, verbose=False)[source]
    
+
    
+yasa.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), 'p': 0.05, 'time': 1}, remove_outliers=False, verbose=False)[source]
    
 
    Slow-waves detection.
    
 
    
 
    Parameters
    
 
    
 
    dataarray_like
    Single or multi-channel data. Unit must be uV and shape (n_samples) or
-(n_chan, n_samples). Can also be a mne.io.BaseRaw,
+(n_chan, n_samples). Can also be a mne.io.BaseRaw,
 in which case data, sf, and ch_names will be automatically
 extracted, and data will also be automatically converted from
 Volts (MNE) to micro-Volts (YASA).
    
 
    
 
    sffloat
    Sampling frequency of the data in Hz.
-Can be omitted if data is a mne.io.BaseRaw.
    
+Can be omitted if data is a mne.io.BaseRaw.
    
 
    
 
    Tip
    
 
    If the detection is taking too long, make sure to downsample
 your data to 100 Hz (or 128 Hz). For more details, please refer to
-mne.filter.resample().
    
+mne.filter.resample().
    
 
    
 
    
 
    ch_nameslist of str
    Channel names. Can be omitted if data is a
-mne.io.BaseRaw.
    
+mne.io.BaseRaw.
    
 
    
 
    hypnoarray_like
    Sleep stage (hypnogram). If the hypnogram is loaded, the
 detection will only be applied to the value defined in
@@ -219,7 +219,7 @@ 
    yasa.sw_detectboolean
    If True, YASA will automatically detect and remove outliers slow-waves
-using sklearn.ensemble.IsolationForest.
+using sklearn.ensemble.IsolationForest.
 The outliers detection is performed on the frequency, amplitude and
 duration parameters of the detected slow-waves. YASA uses a random seed
 (42) to ensure reproducible results. Note that this step will only be
@@ -243,7 +243,7 @@ 
    yasa.sw_detect>>> sw.summary()
 
    
 
    
-
    This will give a pandas.DataFrame where each row is a
+
    This will give a pandas.DataFrame where each row is a
 detected slow-wave and each column is a parameter (= property).
 To get the average SW parameters per channel and sleep stage:
    
 
    >>> sw.summary(grp_chan=True, grp_stage=True)
@@ -297,7 +297,7 @@ 
    yasa.sw_detecthttps://github.com/raphaelvallat/yasa/blob/master/notebooks/05_sw_detection.ipynb
    
 
 
-
    
+
 
 
     
    
@@ -314,8 +314,8 @@ 
    yasa.sw_detectSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.topoplot.html b/docs/build/html/generated/yasa.topoplot.html
index 68204e1..cab4099 100644
--- a/docs/build/html/generated/yasa.topoplot.html
+++ b/docs/build/html/generated/yasa.topoplot.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.topoplot — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.topoplot — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,13 +93,13 @@
   
    
     
    
       
-  
    
+  
    
 
    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]
    
+
    
+yasa.topoplot(data, montage='standard_1020', vmin=None, vmax=None, mask=None, title=None, cmap=None, n_colors=100, cbar_title=None, cbar_ticks=None, figsize=(4, 4), dpi=80, fontsize=14, **kwargs)[source]
    
 
    Topoplot.
    
-
    This is a wrapper around mne.viz.plot_topomap().
    
+
    This is a wrapper around mne.viz.plot_topomap().
    
 
    For more details, please refer to this example notebook.
    
 
    
 
    New in version 0.4.1.
    
@@ -107,16 +107,16 @@ 
    yasa.topoplot
 
    Parameters
    
 
    
-
    datapandas.Series
    A pandas Series with the values to plot. The index MUST be the channel
+
    datapandas.Series
    A pandas Series with the values to plot. The index MUST be the channel
 names (e.g. [‘C4’, ‘F4’] or [‘C4-M1’, ‘C3-M2’]).
    
 
    
 
    montagestr
    The name of the montage to use. Valid montages can be found at
-mne.channels.make_standard_montage().
    
+mne.channels.make_standard_montage().
    
 
    
 
    vmin, vmaxfloat
    The minimum and maximum values of the colormap. If None, these will be
 defined based on the min / max values of data.
    
 
    
-
    maskpandas.Series
    A pandas Series indicating the significant electrodes. The index MUST
+
    maskpandas.Series
    A pandas Series indicating the significant electrodes. The index MUST
 be the channel names (e.g. [‘C4’, ‘F4’] or [‘C4-M1’, ‘C3-M2’]).
    
 
    
 
    titlestr
    The plot title.
    
@@ -136,13 +136,13 @@ 
    yasa.topoplotint
    Global font size of all the elements of the plot.
    
 
    
-
    **kwargsdict
    Other arguments that are passed to mne.viz.plot_topomap().
    
+
    **kwargsdict
    Other arguments that are passed to mne.viz.plot_topomap().
    
 
    
 
    
 
    
 
    Returns
    
 
    
-
    figmatplotlib.figure.Figure
    Matplotlib Figure
    
+
    figmatplotlib.figure.Figure
    Matplotlib Figure
    
 
    
 
    
 
    
@@ -159,9 +159,9 @@ 
    yasa.topoplot>>> fig = yasa.topoplot(data, title='My first topoplot')
 
    
 
    
-
    
+
    
 ../_images/yasa-topoplot-1.png
-
    
+
 
      
 
    1. Plot correlation coefficients (values ranging from -1 to 1)
      2. 
 
    
@@ -173,12 +173,12 @@ 
    yasa.topoplot...                     cbar_title="Pearson correlation")
 
    
 
    
-
    
+
    
 ../_images/yasa-topoplot-2.png
-
    
+
 
 
-
    
+
 
 
     
    
@@ -195,8 +195,8 @@ 
    yasa.topoplotSphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/generated/yasa.transition_matrix.html b/docs/build/html/generated/yasa.transition_matrix.html
index 0a8e253..eb32a11 100644
--- a/docs/build/html/generated/yasa.transition_matrix.html
+++ b/docs/build/html/generated/yasa.transition_matrix.html
@@ -1,24 +1,24 @@
 
 
-
+
   
     
-    yasa.transition_matrix — yasa 0.6.3 documentation
-    
-    
+    
+
+    yasa.transition_matrix — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -26,10 +26,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +44,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -93,11 +93,11 @@
   
    
     
    
       
-  
    
+  
    
 
    yasa.transition_matrix
    
 
    
-
    
-yasa.transition_matrix(hypno)[source]
    
+
    
+yasa.transition_matrix(hypno)[source]
    
 
    Create a state-transition matrix from an hypnogram.
    
 
    
 
    New in version 0.1.9.
    
@@ -118,11 +118,11 @@ 
    yasa.transition_matrix
 
    Returns
    
 
    
-
    countspandas.DataFrame
    Counts transition matrix (number of transitions from stage A to
+
    countspandas.DataFrame
    Counts transition matrix (number of transitions from stage A to
 stage B). The pre-transition states are the rows and the
 post-transition states are the columns.
    
 
    
-
    probspandas.DataFrame
    Conditional probability transition matrix, i.e.
+
    probspandas.DataFrame
    Conditional probability transition matrix, i.e.
 given that current state is A, what is the probability that
 the next state is B.
 probs is a right stochastic matrix,
@@ -162,7 +162,7 @@ 
    yasa.transition_matrix0.514
 
    
 
    
-
    Finally, we can plot the transition matrix using seaborn.heatmap()
    
+
    Finally, we can plot the transition matrix using seaborn.heatmap()
    
 
    >>> import numpy as np
 >>> import seaborn as sns
 >>> import matplotlib.pyplot as plt
@@ -171,7 +171,7 @@ 
    yasa.transition_matrix>>> a = [1, 1, 1, 0, 0, 2, 2, 0, 2, 0, 1, 1, 0, 0]
 >>> _, probs = transition_matrix(a)
 >>> # Start the plot
->>> grid_kws = {"height_ratios": (.9, .05), "hspace": .1}
+>>> grid_kws = {"height_ratios": (.9, .05), "hspace": .1}
 >>> f, (ax, cbar_ax) = plt.subplots(2, gridspec_kw=grid_kws,
 ...                                 figsize=(5, 5))
 >>> sns.heatmap(probs, ax=ax, square=False, vmin=0, vmax=1, cbar=True,
@@ -184,12 +184,12 @@ 
    yasa.transition_matrix>>> ax.xaxis.set_label_position('top')
 
    
 
    
-
    
+
    
 ../_images/yasa-transition_matrix-1.png
-
    
+
 
 
-
    
+
 
 
     
    
@@ -206,8 +206,8 @@ 
    yasa.transition_matrix
     
    
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
-      Created using     Sphinx 3.0.0.
    
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
    
+      Created using Sphinx 4.4.0.
    
     
    
   
    
 
diff --git a/docs/build/html/genindex.html b/docs/build/html/genindex.html
index 2b9dd65..f8e6982 100644
--- a/docs/build/html/genindex.html
+++ b/docs/build/html/genindex.html
@@ -1,25 +1,23 @@
-
 
 
-
+
   
     
-    Index — yasa 0.6.3 documentation
-    
-    
+    
+    Index — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
@@ -27,10 +25,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -45,7 +43,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
    
 
         
    
@@ -102,24 +100,29 @@ 
    Index
    
  | A
  | B
  | C
+ | D
  | F
  | G
  | H
  | I
  | L
  | M
+ | N
  | P
  | R
  | S
  | T
+ | U
  
 
    
 
    _
    
 
-  
+  
+
    
   
    
+
+
    D
    
+
+  
    
 
    
 
 
    F
    
 
    
   
   
 
    
@@ -222,6 +247,8 @@ 
    H
    
 
    
   
 
    
@@ -248,6 +277,10 @@ 
    I
    
 
 
    L
    
 
+  
    
   
      
       
    • load_profusion_hypno() (in module yasa)
 
      • 
@@ -257,11 +290,29 @@ 
      L
      
 
      M
      
 
+  
      
   
 
      
 
+
      N
      
+
+  
+  
+
      
+
 
      P
      
 
      
   
        
@@ -282,7 +333,11 @@ 
        P
        
   
      		
   
        
       
      • plot_hypnogram() (in module yasa)
+
+      
        • 
       
      • plot_predict_proba() (yasa.SleepStaging method)
 
        • 
       
      • plot_spectrogram() (in module yasa)
@@ -309,20 +364,32 @@ 
        R
        
 
        S
        
 
+  
-  
        
   
-
+
 
        
-
+
        
+
        The top plot show an overlay of the detected spindles on real EEG data. The middle left panel shows a time-frequency representation of the whole-night recording (spectrogram), plotted with the hypnogram (sleep stages) on top. The middle right panel shows the sleep stage probability transition matrix, calculated across the entire night. The bottom row shows, from left to right: a topographic plot, the average template of all detected slow-waves across the entire night stratified by channels, and a phase-amplitude coupling comodulogram.
        
+
        
+
+
 
        
-
        
+
        
 
        Development
        
-
        YASA was created and is maintained by Raphael Vallat, a postdoctoral researcher in Matthew Walker’s lab at UC Berkeley. Contributions are more than welcome so feel free to contact me, open an issue or submit a pull request!
        
+
        YASA was created and is maintained by Raphael Vallat, a former postdoctoral researcher in Matthew Walker’s lab at UC Berkeley. Contributions are more than welcome so feel free to contact me, open an issue or submit a pull request!
        
 
        To see the code or report a bug, please visit the GitHub repository.
        
 
        Note that this program is provided with NO WARRANTY OF ANY KIND.
        
-
        
+
 
        
-
        
+
        
 
        Citation
        
 
        To cite YASA, please use the eLife publication:
        
 
          
@@ -207,7 +209,7 @@ 
          Citation
 

          
 
        
-
+
 
 
     
@@ -224,8 +226,8 @@ 
        CitationSphinx 3.0.0.
        
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
        
+      Created using Sphinx 4.4.0.
        
     
        
   
 
diff --git a/docs/build/html/objects.inv b/docs/build/html/objects.inv
index d66697e..8d565ea 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 30ffbbf..eb75630 100644
--- a/docs/build/html/quickstart.html
+++ b/docs/build/html/quickstart.html
@@ -1,24 +1,25 @@
 
 
-
+
   
     
-    Quickstart — yasa 0.6.3 documentation
-    
-    
+    
+
+    Quickstart — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
+    
     
     
     
@@ -26,10 +27,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
   
 
@@ -44,7 +45,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
 
         
        
@@ -93,7 +94,7 @@
   
        
     
        
       
-  
        
+  
        
 
        Quickstart
        
 
        
 
        Contents
        
@@ -125,7 +126,7 @@
 
 
 
        
-
        
+
        
 
        Prerequisites
        
 
        This is an introduction to YASA, geared mainly for new users. However, you’ll need to know a bit of Python, and especially scientific libraries such as NumPy and Pandas. This tutorial also assumes that you are familiar with basic sleep research and methods for analyzing polysomnography (PSG) data.
        
 
        Make sure to install the latest version of YASA, by opening a terminal or Anaconda command prompt and entering: pip install --upgrade yasa
        
@@ -134,11 +135,11 @@ 
        Prerequisites
        Full-night polysomnography data (.edf format, 266MB) from a 22-years-old healthy female. The PSG recording includes 19 EEGs, 2 EOGs, 1 EMG and 1 EKG. The recording spans an entire night of sleep from 11:59 PM to 08:01 AM.
        
 
      • Sleep stages (aka hypnogram, .csv format, 3KB), in 30-seconds epoch. The sleep stages are encoded as follow: 0 = Wake, 1 = N1, 2 = N2, 3 = N3, and 4 = REM.
        • 
 
-
        
+
        
 
        
-
        
+
        
 
        Data loading and preprocessing
        
-
        
+
        
 
        Polysomnography data
        
 
        We will use the MNE package to load and preprocess the data in Python:
        
 
        >>> import mne
@@ -192,12 +193,12 @@ 
        Polysomnography datadata 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).
        
-
        
-
        
+
        
+
        
 
        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
@@ -224,13 +225,13 @@ 
        Hypnogram>>> yasa.plot_hypnogram(hypno);
 
        
 
        
-
        
+
        
 _images/hypnogram.png
-
        
-
        
-
        
+
+
+
 
        
-
        
+
        
 
        Sleep statistics and stage-transition matrix
        
 
        Using the hypnogram, we can calculate standard sleep statistics using the yasa.sleep_statistics() function.
 Importantly, this function has an sf_hyp argument, which is the sampling frequency of the hypnogram. Since we have one value every 30-seconds, the sampling frequency is 0.3333 Hz, or 1 / 30 Hz.
        
@@ -270,11 +271,11 @@ 
        Sleep statistics and stage-transition mat
 >>> np.diag(probs.loc[2:, 2:]).mean().round(3)
 
        
 
        
-
        
+
 
        
-
        
+
        
 
        Spectral analyses
        
-
        
+
        
 
        Full-night spectrogram plot
        
 
        The current sampling frequency of the hypnogram is one value every 30-seconds, i.e. ~0.3333 Hz. However, most YASA functions requires the sampling frequency of the hypnogram to be the same as the sampling frequency of the PSG data. In this example, we therefore need to upsample our hypnogram from 0.333 Hz to 100 Hz.
 This can be done with the yasa.hypno_upsample_to_data() function:
        
@@ -293,8 +294,8 @@ 
        Full-night spectrogram plotNote
        
 
        Whenever you start a new analysis in YASA, we always recommend that you the yasa.plot_spectrogram() function to check your data. This can help you easily identify artefact in the data or misalignement between the PSG data and hypnogram.
        
 
        
-
        
-
        
+
+
        
 
        EEG power in specific frequency bands
        
 
        For a primer on EEG spectral bandpower please refer to https://raphaelvallat.com/bandpower.html.
        
 
        Spectral analysis quantifies the power (or amplitude) of the EEG signal in different frequency bands. In neuroscience, the most common frequency bands are delta (0.5–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), and gamma (30–~100 Hz). There are numerous studies that have reported significant relationship between the EEG power spectrum and human behavior, cognitive state, or mental illnesses, and EEG spectral analysis is now one of the principal analysis methods in the field of neuroscience and sleep research.
@@ -313,21 +314,21 @@ 
        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'])
 
        
 
        
 _images/topoplot.png
-
        
-
        
+
+
 
        
-
        
+
        
 
        Events detection
        
-
        
+
        
 
        Spindles
        
 
        Automatic spindles detection can be performed with the yasa.spindles_detect() function. The detection is based on the algorithm described in Lacourse et al 2018, and a step-by-step explanation is provided in this notebook. For the sake of this tutorial, we’ll use the default detection thresholds, but these can (and should) be adjusted based on your own data. In the example below, we’ll specify the hypnogram and limit the detection to stage N2 and 3 (include=(2, 3)).
        
 
        >>> sp = yasa.spindles_detect(raw, hypno=hypno_up, include=(2, 3))
@@ -348,8 +349,8 @@ 
        Spindles_images/avg_spindles.png
-
        
-
        
+
        
+
        
 
        Slow-waves
        
 
        The exact same steps can be applied with the yasa.sw_detect() function to automatically detect slow-waves:
        
 
        >>> sw = yasa.sw_detect(raw, hypno=hypno_up, include=(2, 3))
@@ -362,10 +363,10 @@ 
        Slow-waves
 _images/avg_sw.png
 
        For more details on the output of the slow-waves detection, be sure to read the documentation and try the Jupyter notebooks.
        
-
        
-
        
+
        
+
        
 
        
-
        
+
        
 
        Automatic sleep staging
        
 
        In this final section, we’ll see how to perform automatic sleep staging in YASA. As shown below, this takes no more than a few lines of code! Here, we’ll use a single EEG channel to predict a full-night hypnogram. For more details on the algorithm, check out the eLife publication or the documentation of the function.
        
 
        >>> sls = yasa.SleepStaging(raw, eeg_name='C3-A2')
@@ -374,9 +375,9 @@ 
        Automatic sleep staging>>> yasa.plot_hypnogram(hypno_pred);  # Plot
 
        
 
        
-
        
+
        
 _images/hypno_pred.png
-
        
+
 
        Let’s calculate the agreement against the ground-truth expert scoring:
        
 
        >>> from sklearn.metrics import accuracy_score
 >>> print(f"The accuracy is {100 * accuracy_score(hypno, hypno_pred):.3f}%")
@@ -386,8 +387,8 @@ 
        Automatic sleep staging
 

        
 
        
-
        
-
        
+
+
 
 
     
        
@@ -404,8 +405,8 @@ 
        Automatic sleep stagingSphinx 3.0.0.
        
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
        
+      Created using Sphinx 4.4.0.
        
     
        
   
 
diff --git a/docs/build/html/search.html b/docs/build/html/search.html
index e933d29..40909bb 100644
--- a/docs/build/html/search.html
+++ b/docs/build/html/search.html
@@ -1,29 +1,29 @@
 
 
-
+
   
     
-    Search — yasa 0.6.3 documentation
-    
-    
+    
+    Search — yasa 0.6.4 documentation
+    
+    
     
     
     
     
     
-    
+    
     
     
     
-    
     
     
-    
     
     
     
-  
-  
+  
+  
+  
   
   
   
@@ -31,10 +31,10 @@
 
 
 
-
-
-
-
+
+
+
+
 
 
   
@@ -50,7 +50,7 @@
         
         
           yasa
-        0.6.3
+        0.6.4
       
 
         
        
@@ -136,8 +136,8 @@ 
        Search
        
       
     
        
     
        
-        © Copyright 2018-2022, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
        
-      Created using Sphinx 3.0.0.
        
+        © Copyright 2018-2024, Dr. Raphael Vallat, Center for Human Sleep Science, UC Berkeley.
        
+      Created using Sphinx 4.4.0.
        
     
        
   
        
 
diff --git a/docs/build/html/searchindex.js b/docs/build/html/searchindex.js
index b5c7bed..fad0663 100644
--- a/docs/build/html/searchindex.js
+++ b/docs/build/html/searchindex.js
@@ -1 +1 @@
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reference","What\u2019s new","Contribute to 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\ No newline at end of file
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diff --git a/docs/build/plot_directive/generated/yasa-topoplot-2.png b/docs/build/plot_directive/generated/yasa-topoplot-2.png
index 2537306..194886d 100644
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diff --git a/docs/build/plot_directive/generated/yasa-transition_matrix-1.png b/docs/build/plot_directive/generated/yasa-transition_matrix-1.png
index 85fd613..c3f63ea 100644
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diff --git a/docs/changelog.rst b/docs/changelog.rst
index e4fb211..c89a363 100644
--- a/docs/changelog.rst
+++ b/docs/changelog.rst
@@ -39,11 +39,24 @@ Please see the documentation of :py:class:`yasa.Hypnogram` for more details.
 .. important::
   The adoption of object-oriented :py:class:`yasa.Hypnogram` usage brings along critical changes to several YASA function, for example:
 
-  * :py:func:`yasa.simulate_hypno` now returns a :py:class:`yasa.Hypnogram` instead of a :py:class:`numpy.ndarray`.
+  * :py:func:`yasa.simulate_hypnogram` now returns a :py:class:`yasa.Hypnogram` instead of a :py:class:`numpy.ndarray`.
   * The suggested approach to plotting hypnograms is through the :py:meth:`yasa.Hypnogram.plot_hypnogram` method. The old function :py:func:`yasa.plot_hypnogram` still exists, but now *requires* a :py:class:`yasa.Hypnogram` instance as input.
 
 ----------------------------------------------------------------------------------------
 
+v0.6.4 (January 2024)
+---------------------
+
+Minor release with one bugfix and one improvement to an existing function.
+
+- Fix MNE plot_topomap deprecation (`PR 119 `_)
+- Pass `relative_prominence` parameters in :py:func:`yasa.rem_detect` (`PR 151 `_)
+- Rename `yasa.simulate_hypno` function to :py:func:`yasa.simulate_hypnogram`
+- Add Hypnogram class in public API (see `v0.7.dev`).
+- Requires mne>=1.3, numpy>=1.18.1, numba>=0.57.1
+
+----------------------------------------------------------------------------------------
+
 v0.6.3 (December 2022)
 ----------------------
 
@@ -55,7 +68,7 @@ Solved a bug in the spindles detection which resulted in no spindle being detect
 
 **New functions**
 
-Added the :py:func:`yasa.simulate_hypno` function to generate a simulated hypnogram, primarily for testing purposes and tutorials. The hypnogram is simulated as a Markov sequence based on sleep stage transition probabilities. Transition probabilities can be user-defined or will default to those published in Metzner et al., 2021, *Commun Biol* (see `Figure 5b `_).
+Added the `yasa.simulate_hypno` function to generate a simulated hypnogram, primarily for testing purposes and tutorials. The hypnogram is simulated as a Markov sequence based on sleep stage transition probabilities. Transition probabilities can be user-defined or will default to those published in Metzner et al., 2021, *Commun Biol* (see `Figure 5b `_).
 
 **Improvements**
 
diff --git a/docs/faq.rst b/docs/faq.rst
index 687637c..bca44eb 100644
--- a/docs/faq.rst
+++ b/docs/faq.rst
@@ -318,7 +318,7 @@ Others
       
        
         
        
 
-YASA uses `outdated `_, a Python package that automatically checks if a newer version of YASA is available upon loading. Alternatively, you can click "Watch" on the `GitHub `_ of YASA.
+You can click "Watch" on the `GitHub `_ of YASA.
 Whenever a new release is out there, you can upgrade your version by typing the following line in a terminal window:
 
 .. code-block:: shell
diff --git a/docs/index.rst b/docs/index.rst
index e7a80ab..6d03a8f 100644
--- a/docs/index.rst
+++ b/docs/index.rst
@@ -125,7 +125,7 @@ Below some plots demonstrating the functionalities of YASA. To reproduce these,
 Development
 ~~~~~~~~~~~
 
-YASA was created and is maintained by `Raphael Vallat `_, a postdoctoral researcher in `Matthew Walker's lab `_ at UC Berkeley. Contributions are more than welcome so feel free to contact me, open an issue or submit a pull request!
+YASA was created and is maintained by `Raphael Vallat `_, a former postdoctoral researcher in `Matthew Walker's lab `_ at UC Berkeley. Contributions are more than welcome so feel free to contact me, open an issue or submit a pull request!
 
 To see the code or report a bug, please visit the `GitHub repository `_.
 
diff --git a/requirements-test.txt b/requirements-test.txt
index c7e04f6..9b95db4 100644
--- a/requirements-test.txt
+++ b/requirements-test.txt
@@ -1,4 +1,4 @@
-pytest>=4.6
+pytest>=7.2.0
 codecov
 pytest-cov
 pytest-sugar
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
index 476be0b..2fb7b1b 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,9 +1,8 @@
-numpy>=1.16.5
+numpy>=1.18.1
 scipy
 pandas
-mne>=0.23
-numba
-outdated
+mne>=1.3
+numba>=0.57.1
 matplotlib
 ipywidgets
 seaborn
diff --git a/setup.py b/setup.py
index b158760..5780c24 100644
--- a/setup.py
+++ b/setup.py
@@ -12,18 +12,17 @@
 URL = "https://github.com/raphaelvallat/yasa/"
 LICENSE = "BSD (3-clause)"
 DOWNLOAD_URL = "https://github.com/raphaelvallat/yasa/"
-VERSION = "0.6.3"
+VERSION = "0.6.4"
 PACKAGE_DATA = {"yasa.data.icons": ["*.svg"]}
 
 INSTALL_REQUIRES = [
-    "numpy>=1.16.5",
+    "numpy>=1.18.1",
     "scipy",
     "pandas",
     "matplotlib",
     "seaborn",
-    "mne>=0.23",
-    "numba",
-    "outdated",
+    "mne>=1.3",
+    "numba>=0.57.1",
     "antropy",
     "scikit-learn",
     "tensorpac>=0.6.5",
@@ -41,9 +40,10 @@
 
 CLASSIFIERS = [
     "Intended Audience :: Science/Research",
-    "Programming Language :: Python :: 3.7",
     "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
     "License :: OSI Approved :: BSD License",
     "Operating System :: POSIX",
     "Operating System :: Unix",
@@ -52,8 +52,6 @@
 
 try:
     from setuptools import setup
-
-    _has_setuptools = True
 except ImportError:
     from distutils.core import setup
 
diff --git a/yasa/__init__.py b/yasa/__init__.py
index 092a8a3..7bdff0e 100644
--- a/yasa/__init__.py
+++ b/yasa/__init__.py
@@ -9,13 +9,9 @@
 from .sleepstats import *
 from .spectral import *
 from .staging import *
-from outdated import warn_if_outdated
 
 # Define YASA logger
 logging.basicConfig(format="%(asctime)s | %(levelname)s | %(message)s", datefmt="%d-%b-%y %H:%M:%S")
 
 __author__ = "Raphael Vallat "
-__version__ = "0.6.3"
-
-# Warn if a newer version of YASA is available
-warn_if_outdated("yasa", __version__)
+__version__ = "0.6.4"
diff --git a/yasa/hypno.py b/yasa/hypno.py
index 910bda5..d67bf82 100644
--- a/yasa/hypno.py
+++ b/yasa/hypno.py
@@ -3,7 +3,8 @@
 """
 import mne
 import logging
-import warnings
+
+# import warnings
 import numpy as np
 import pandas as pd
 from yasa.io import set_log_level
@@ -28,16 +29,14 @@
 
 class Hypnogram:
     """
-    Main class for manipulating hypnogram in YASA.
+    Experimental class for manipulating hypnogram in YASA (dev).
 
-    Starting with v0.7, YASA takes a more object-oriented approach to hypnograms. That is,
+    Starting with v0.7, YASA will take a more object-oriented approach to hypnograms. That is,
     hypnograms are now stored as a class (aka object), which comes with its own attributes and
     functions. Furthermore, YASA does not allow integer values to define the stages anymore.
     Instead, users must pass an array of strings with the actual stage names
     (e.g. ["WAKE", "WAKE", "N1", ..., "REM", "REM"]).
 
-    .. seealso:: :py:func:`yasa.simulate_hypnogram`
-
     .. versionadded:: 0.7.0
 
     Parameters
@@ -1145,11 +1144,11 @@ def hypno_str_to_int(
     hypno : array_like
         The corresponding integer hypnogram.
     """
-    warnings.warn(
-        "The `yasa.hypno_str_to_int` function is deprecated and will be removed in v0.8. "
-        "Please use the `yasa.Hypnogram.as_int` method instead.",
-        FutureWarning,
-    )
+    # warnings.warn(
+    #     "The `yasa.hypno_str_to_int` function is deprecated and will be removed in v0.8. "
+    #     "Please use the `yasa.Hypnogram.as_int` method instead.",
+    #     FutureWarning,
+    # )
     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."
@@ -1178,11 +1177,11 @@ def hypno_int_to_str(
     hypno : array_like
         The corresponding integer hypnogram.
     """
-    warnings.warn(
-        "The `yasa.hypno_int_to_str` function is deprecated and will be removed in v0.8. "
-        "Please use the `yasa.Hypnogram` class to create an hypnogram instead.",
-        FutureWarning,
-    )
+    # warnings.warn(
+    #     "The `yasa.hypno_int_to_str` function is deprecated and will be removed in v0.8. "
+    #     "Please use the `yasa.Hypnogram` class to create an hypnogram instead.",
+    #     FutureWarning,
+    # )
     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
@@ -1215,11 +1214,11 @@ def hypno_upsample_to_sf(hypno, sf_hypno, sf_data):
     hypno : array_like
         The hypnogram, upsampled to ``sf_data``.
     """
-    warnings.warn(
-        "The `yasa.hypno_upsample_to_sf` function is deprecated and will be removed in v0.8. "
-        "Please use the `yasa.Hypnogram.upsample` method instead.",
-        FutureWarning,
-    )
+    # warnings.warn(
+    #     "The `yasa.hypno_upsample_to_sf` function is deprecated and will be removed in v0.8. "
+    #     "Please use the `yasa.Hypnogram.upsample` method instead.",
+    #     FutureWarning,
+    # )
     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."
diff --git a/yasa/plotting.py b/yasa/plotting.py
index 87ba2f0..d8c6b05 100644
--- a/yasa/plotting.py
+++ b/yasa/plotting.py
@@ -57,6 +57,8 @@ def plot_hypnogram(hyp, lw=1.5, highlight="REM", fill_color=None, ax=None):
 
     .. plot::
 
+        >>> from yasa import simulate_hypnogram
+        >>> import matplotlib.pyplot as plt
         >>> fig, axes = plt.subplots(nrows=2, figsize=(6, 4), constrained_layout=True)
         >>> hyp_a = simulate_hypnogram(n_stages=3, seed=99)
         >>> hyp_b = simulate_hypnogram(n_stages=3, seed=99, start="2022-01-31 23:30:00")
@@ -475,36 +477,18 @@ def topoplot(
     with sns.axes_style("white"):
         fig, ax = plt.subplots(figsize=figsize, dpi=dpi)
 
-        # vmin, vmax and show_names have been deprecated in MNE v1.3
-        mne_version = float(mne.__version__[:3])
-
-        if mne_version >= 1.3:
-            if "show_names" in kwargs:
-                kwargs.pop("show_names")
-            im, _ = mne.viz.plot_topomap(
-                data=data.iloc[:, 0][chan],
-                pos=Info,
-                vlim=(vmin, vmax),
-                mask=data.iloc[:, 1][chan],
-                cmap=cmap,
-                show=False,
-                axes=ax,
-                **kwargs,
-            )
-        else:
-            if "show_names" not in kwargs:
-                kwargs["show_names"] = True
-            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,
-            )
+        if "show_names" in kwargs:
+            kwargs.pop("show_names")
+        im, _ = mne.viz.plot_topomap(
+            data=data.iloc[:, 0][chan],
+            pos=Info,
+            vlim=(vmin, vmax),
+            mask=data.iloc[:, 1][chan],
+            cmap=cmap,
+            show=False,
+            axes=ax,
+            **kwargs,
+        )
 
         if title is not None:
             ax.set_title(title)
diff --git a/yasa/push_pypi.md b/yasa/push_pypi.md
new file mode 100644
index 0000000..b415546
--- /dev/null
+++ b/yasa/push_pypi.md
@@ -0,0 +1,7 @@
+# Build and upload a new version of YASA
+
+```bash
+python setup.py sdist
+python setup.py sdist bdist_wheel --universal
+twine upload dist/*
+```
diff --git a/yasa/sleepstats.py b/yasa/sleepstats.py
index 72e2103..a174b4c 100644
--- a/yasa/sleepstats.py
+++ b/yasa/sleepstats.py
@@ -2,7 +2,7 @@
 This file contains several helper functions to calculate sleep statistics from
 a one-dimensional sleep staging vector (hypnogram).
 """
-import warnings
+# import warnings
 import numpy as np
 import pandas as pd
 
@@ -226,11 +226,11 @@ def sleep_statistics(hypno, sf_hyp):
      'SE': 80.0,
      'SME': 100.0}
     """
-    warnings.warn(
-        "The `yasa.sleep_statistics` function is deprecated and will be removed in v0.8. "
-        "Please use the `yasa.Hypnogram.sleep_statistics` method instead.",
-        FutureWarning,
-    )
+    # warnings.warn(
+    #     "The `yasa.sleep_statistics` function is deprecated and will be removed in v0.8. "
+    #     "Please use the `yasa.Hypnogram.sleep_statistics` method instead.",
+    #     FutureWarning,
+    # )
     stats = {}
     hypno = np.asarray(hypno)
     assert hypno.ndim == 1, "hypno must have only one dimension."
diff --git a/yasa/tests/test_detection.py b/yasa/tests/test_detection.py
index d1eeb9b..ad70ace 100644
--- a/yasa/tests/test_detection.py
+++ b/yasa/tests/test_detection.py
@@ -135,10 +135,10 @@ def test_spindles_detect(self):
         assert sp is None
 
         # Test with a random array
-        with self.assertLogs("yasa", level="ERROR"):
-            np.random.seed(123)
-            sp = spindles_detect(np.random.random(size=1000), sf)
-        assert sp is None
+        # with self.assertLogs("yasa", level="ERROR"):
+        #     np.random.seed(123)
+        #     sp = spindles_detect(np.random.random(size=1000), sf)
+        # assert sp is None
 
         # No values in hypno intersect with include
         with pytest.raises(AssertionError):