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- import mne
- import pandas as pd
- import numpy as np
- import matplotlib.pyplot as plt
- from matplotlib.colors import ListedColormap
- # lay object to plot
- lay = mne.channels.read_layout("biosemi")
- # info object to plot with
- info = mne.create_info(lay.names, ch_types=['eeg']*len(lay.names), sfreq=256)
- info.set_montage('biosemi64', match_case=False)
- # get 3d locations
- chs = info["chs"]
- locs3d = np.array([ch["loc"][:3] for ch in chs])
- # get associated colours from colour wheel
- x, y, z = locs3d.T
- colors = mne.viz.evoked._rgb(x, y, z)
- # get inputs for plot_sensors
- ch_grps = [[i] for i in range(len(info.ch_names))]
- cmap = ListedColormap(colors)
- fig, ax = plt.subplots()
- pl = mne.viz.utils.plot_sensors(info, ch_groups=ch_grps, cmap=cmap, linewidth=0, pointsize=250, axes=ax)
- topo_cols = pd.DataFrame(colors).rename(columns={0:'r', 1:'g', 2:'b'})
- topo_cols['ch_name'] = info.ch_names
- topo_cols = topo_cols[['ch_name', 'r', 'g', 'b']]
- topo_cols.to_csv('Biosemi64_fiducial.csv', index=False)
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