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]

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

For more details, please refer to the Jupyter notebook

New in version 0.1.8.

Parameters
datanumpy.ndarray

Single-channel EEG data. Must be a 1D NumPy array.

sffloat

The sampling frequency of data AND the hypnogram.

hypnoarray_like

Sleep stage (hypnogram), optional.

The hypnogram must have the exact same number of samples as data. To upsample your hypnogram, please refer to yasa.hypno_upsample_to_data().

Note

The default hypnogram format in YASA is a 1D integer vector where:

  • -2 = Unscored

  • -1 = Artefact / Movement

  • 0 = Wake

  • 1 = N1 sleep

  • 2 = N2 sleep

  • 3 = N3 sleep

  • 4 = REM sleep

win_secint or float

The length of the sliding window, in seconds, used for multitaper PSD calculation. Default is 30 seconds. Note that data must be at least twice longer than win_sec (e.g. 60 seconds).

fmin, fmaxint or float

The lower and upper frequency of the spectrogram. Default 0.5 to 25 Hz.

trimpercint or float

The amount of data to trim on both ends of the distribution when normalizing the colormap. This parameter directly impacts the contrast of the spectrogram plot (higher values = higher contrast). Default is 2.5, meaning that the min and max of the colormap are defined as the 2.5 and 97.5 percentiles of the spectrogram.

cmapstr

Colormap. Default to ‘RdBu_r’.

vminint or float

The lower range of color scale. Overwrites trimperc

vmaxint or float

The upper range of color scale. Overwrites trimperc

**kwargsdict

Other arguments that are passed to yasa.Hypnogram.plot_hypnogram().

Returns
figmatplotlib.figure.Figure

Matplotlib Figure

Examples

  1. Full-night multitaper spectrogram on Cz, no hypnogram

>>> import yasa
>>> import numpy as np
>>> # In the next 5 lines, we're loading the data from GitHub.
>>> import requests
>>> from io import BytesIO
>>> r = requests.get('https://github.com/raphaelvallat/yasa/raw/master/notebooks/data_full_6hrs_100Hz_Cz%2BFz%2BPz.npz', stream=True)
>>> npz = np.load(BytesIO(r.raw.read()))
>>> data = npz.get('data')[0, :]
>>> sf = 100
>>> fig = yasa.plot_spectrogram(data, sf)
../_images/yasa-plot_spectrogram-1.png

  1. Full-night multitaper spectrogram on Cz with the hypnogram on top

>>> import yasa
>>> import numpy as np
>>> # In the next lines, we're loading the data from GitHub.
>>> import requests
>>> from io import BytesIO
>>> r = requests.get('https://github.com/raphaelvallat/yasa/raw/master/notebooks/data_full_6hrs_100Hz_Cz%2BFz%2BPz.npz', stream=True)
>>> npz = np.load(BytesIO(r.raw.read()))
>>> data = npz.get('data')[0, :]
>>> sf = 100
>>> # Load the 30-sec hypnogram and upsample to data
>>> hypno = np.loadtxt('https://raw.githubusercontent.com/raphaelvallat/yasa/master/notebooks/data_full_6hrs_100Hz_hypno_30s.txt')
>>> hypno = yasa.hypno_upsample_to_data(hypno, 1/30, data, sf)
>>> fig = yasa.plot_spectrogram(data, sf, hypno, cmap='Spectral_r')
../_images/yasa-plot_spectrogram-2.png