- MNE version: e.g. 0.24.0
- operating system: Windows 11
“The script creates two plots: one using Matplotlib with mouse wheel scrolling left-right, and the other using self.raw.plot() which doesn’t work (it functions when using arrows). I aim to scroll horizontally using the mouse wheel with MNE-Python, instead of Matplotlib.”
import mne
import matplotlib.pyplot as plt
import numpy as npclass EEGAnalyzer:
def init(self):
self.fig_home = None # Figure for the EEG plot
self.raw = None # Variable to store raw data
self.current_start = 0 # Track the current start position for plotting
self.duration = 10 # Duration (window size) in seconds
self.sampling_rate = None # To store the sampling rate
self.n_channels = None # Number of channels in the data
self.data_range = None # Range for y-axis to simulate channel separation
self.ax = None # Store Axes object for plottingdef on_scroll(self, event): """Handle mouse wheel scroll events to move the plot left or right.""" if self.raw is None or self.ax is None: return # Adjust the start time based on scroll direction if event.button == 'up': self.current_start += 1 # Scroll right elif event.button == 'down': self.current_start = max(0, self.current_start - 1) # Scroll left, ensure it's non-negative else: return # Update the plot with the new start time self.update_plot() def update_plot(self): """Efficiently update the plot based on the current start time.""" if self.ax is None: return # Clear the current axes without destroying the figure self.ax.cla() # Get the current data window (convert time to sample indices) start_sample = int(self.current_start * self.sampling_rate) stop_sample = int((self.current_start + self.duration) * self.sampling_rate) # Extract data and times from the raw object data, times = self.raw[:, start_sample:stop_sample] # Normalize and offset each channel for visibility for i in range(self.n_channels): self.ax.plot(times, data[i] + i * self.data_range, color='black') # Black traces # Add the channel names on the right side of each trace self.ax.text(times[-1], data[i][-1] + i * self.data_range, self.raw.ch_names[i], verticalalignment='center') # Set limits and labels self.ax.set_xlim(times[0], times[-1]) self.ax.set_ylim(np.min(data) - self.data_range, np.max(data) + self.n_channels * self.data_range) self.ax.set_xlabel('Time (s)') self.ax.set_ylabel('Amplitude (µV)') self.ax.set_title('EEG Channels') # Redraw the canvas self.fig_home.canvas.draw_idle() def plot_raw_data(self, raw): """ Plots raw EEG data using MNE-Python. Args: raw: The MNE Raw object containing the EEG data. """ self.raw = raw # Store the raw data self.current_start = 0 # Reset the starting point when loading new data self.sampling_rate = raw.info['sfreq'] # Get the sampling frequency self.n_channels = len(raw.ch_names) # Get the number of channels # Define the data range for channel separation self.data_range = np.max(np.abs(self.raw.get_data())) # Max amplitude for scaling # Create the plot without showing it immediately self.fig_home = plt.figure() self.ax = self.fig_home.add_subplot(111) # Create a single subplot self.update_plot() # Initial plot # Connect the scroll event to the handler self.cid_scroll = self.fig_home.canvas.mpl_connect('scroll_event', self.on_scroll) # Use raw.plot() to display EEG data with additional options self.raw.plot( events=None, duration=self.duration, start=self.current_start, n_channels=self.n_channels, bgcolor='w', color=None, bad_color=(0.8, 0.8, 0.8), event_color='cyan', remove_dc=True, order=None, show_options=True, title=None, show=False, # Prevents showing the plot yet block=False, highpass=None, lowpass=None, filtorder=4, clipping=1.5, show_first_samp=False, proj=True, group_by='type', butterfly=False, decim='auto', noise_cov=None, event_id=None, show_scrollbars=True, show_scalebars=True, verbose=None ) plt.show()analyzer = EEGAnalyzer()
raw = mne.io.read_raw(“C:\000_tmp\fif\3r_eeg.fif”, preload=True)
analyzer.plot_raw_data(raw)
