How can I use yasa.plot_spectrogram(), to plot an all-night multi-taper sound spectrogram and add a hypnogram (of the three sleep stages: WAKE, NREM, REM) at the top, which defaults to five stages?Or any other way？

[Fisher-Ding]

I code to implement it:

What I want: this one is a PS realization

My code: using the quickstart dataset but with a change in sleep staging.This code uses a different dataset than the graph I showed, but the code logic is the same

Clear all variables from the workspace

%reset -f

import mne
import pandas as pd
import yasa
import numpy as np
import matplotlib.pyplot as plt

Load the EDF file containing EEG data

raw = mne.io.read_raw_edf("/Users/ding/Desktop/yasa_example_night_young.edf", preload=True)
print("Original channel names:", raw.ch_names) # Display the original channel names

Drop unnecessary channels

try:
raw.drop_channels(["ROC-A1", "LOC-A2", "EMG1-EMG2", "EKG-R-EKG-L"])
except ValueError as e:
print(f"Error while dropping channels: {e}")
remaining_channels = raw.ch_names
print("Remaining channel names:", remaining_channels) # Display the remaining channel names

Get the original sampling frequency and resample to 100 Hz

original_sampling_rate = raw.info["sfreq"]
print("Original sampling rate:", original_sampling_rate) # Display the original sampling rate
raw.resample(100)
updated_sampling_rate = raw.info["sfreq"] # Update the sampling rate after resampling
print("Updated sampling rate:", updated_sampling_rate) # Display the updated sampling rate

Apply a band-pass filter (0.3 - 45 Hz)

raw.filter(0.3, 45)

Extract the data and convert it to microvolts (µV)

data = raw.get_data(units="uV") # data is a NumPy array
print("Data shape:", data.shape) # Display the shape of the data array

Load the sleep stage labels from a CSV file

hypnogram_file_path = "/Users/ding/Desktop/yasa_exa
yasa_example_night_young_hypno.csv
yasa_example_night_young_hypno.csv
mple_night_young_hypno.csv"

Read the CSV file, assuming the first column contains the sleep stage labels

try:
hypnogram_labels = pd.read_csv(hypnogram_file_path, header=None)[0] # Read the first column
except Exception as e:
print(f"Error while reading the sleep stage labels file: {e}")
raise

Create a Hypnogram object, assuming there are 3 sleep stages in the labels

from yasa import Hypnogram

try:
hypnogram = Hypnogram(hypnogram_labels, n_stages=3)
except Exception as e:
print(f"Error while creating the Hypnogram object: {e}")
raise

Visualize the hypnogram

try:
yasa.plot_hypnogram(hypnogram)
plt.show()
except Exception as e:
print(f"Error while visualizing the Hypnogram: {e}")

Map sleep stages to integer values and convert the Hypnogram to a NumPy array

hypnogram.mapping = {'WAKE': 0, 'NREM': 1, 'REM': 4} # Map sleep stages to integers
hypnogram_int_values = hypnogram.as_int()
hypnogram_np_array = hypnogram_int_values.to_numpy()

Upsample the Hypnogram to match the resolution of the EEG data

try:
upsampled_hypnogram = yasa.hypno_upsample_to_data(
hypno=hypnogram_np_array,
sf_hypno=1 / 30, # Hypnogram sampling rate (1 epoch every 30 seconds)
data=raw, # EEG data
sf_data=updated_sampling_rate # EEG data sampling rate
)
print("Length after upsampling:", len(upsampled_hypnogram)) # Display the length of the upsampled Hypnogram
except Exception as e:
print(f"Error during upsampling: {e}")

Plot the spectrogram for the 'C4-A1' channel with the Hypnogram overlay

if "C4-A1" in remaining_channels:
try:
yasa.plot_spectrogram(
data[remaining_channels.index("C4-A1")], # Use data from the 'C4-A1' channel
updated_sampling_rate, # Sampling rate of the EEG data
hypno=upsampled_hypnogram # Use the upsampled Hypnogram
)
plt.show()
except Exception as e:
print(f"Error while plotting the spectrogram: {e}")
else:
print("The channel 'C4-A1' is not present in the data.")

[Fisher-Ding]

yasa_example_night_young.edf
https://drive.usercontent.google.com/download?id=13YFQrvAGvcz77Qm8kFXu1nyRKhTti3VB&authuser=0

[remrama]

Duplicate of #212 and now converted to an active Issue in #215.