code: def plot_pga(ax, data, labels, centers):
      sc = ax.scatter(
          data[:, 0], data[:, 1], c=labels, marker="P", cmap=cmap
      )
      ax.scatter(
          centers[:, 0],
          centers[:, 1],
          c=clist,
          marker="o",
          s=100,
          cmap=cmap,
      )
      ax.set(xlabel="PGA, 1st axis", ylabel="PGA, 2nd axis")
      for i in range(len(clabel)):
          ax.scatter(
              [], [], color=clist[i], marker="o", s=50, label=clabel[i]
          )
      ax.legend(loc="upper right")
      return sc
  
file_path: pyRiemann/examples/SSVEP/plot_classify_ssvep_pga.py
imports: import numpy
  import matplotlib.pyplot
  from matplotlib.animation import FuncAnimation
  from mne import find_events, Epochs, make_fixed_length_epochs
  from mne.io import Raw
  from sklearn.pipeline import make_pipeline
  from sklearn.decomposition import PCA
  from pyriemann.estimation import BlockCovariances
  from pyriemann.classification import MDM
  from pyriemann.tangentspace import TangentSpace
  from pyriemann.utils.viz import _add_alpha
  from helpers.ssvep_helpers import download_data, extend_signal
label_desc: Use image node_id 1 for calling a global function with example usage: plot_pga(ax, data, labels, centers) and returns: sc