Multivariate Pattern Analysis in Python

# mvpa2.misc.plot.erp.plot_erps

`mvpa2.misc.plot.erp.``plot_erps`(erps, data=None, ax=None, pre=0.2, post=None, pre_onset=None, xlabel='time (s)', ylabel='$\\mu V$', ylim=None, ymult=1.0, legend=None, xlformat='%4g', ylformat='%4g', loffset=10, alinewidth=2, **kwargs)

  Plot multiple ERPs on a new figure.

  Parameters:

  erps : list of tuples

  > List of definitions of ERPs. Each tuple should consist of (label, color, onsets) or a dictionary which defines, label, color, onsets, data. Data provided in dictionary overrides ‘common’ data provided in the next argument `data`

  data

  > Data for ERPs to be derived from 1D (samples)

  ax

  > Where to draw (e.g. subplot instance). If None, new figure is created

  pre : float, optional

  > Duration (seconds) to be plotted prior to onset

  pre_onset : None or float

  > If data is already in epochs (2D) then pre_onset provides information on how many seconds pre-stimulus were used to generate them. If None, then pre_onset = pre

  post : None or float

  > Duration (seconds) to be plotted after the onset. If any data is provided with onsets, it can’t be None. If None – plots all time points after onsets

  ymult : float, optional

  > Multiplier for the values. E.g. if negative-up ERP plot is needed: provide ymult=-1.0

  xlformat : str, optional

  > Format of the x ticks

  ylformat : str, optional

  > Format of the y ticks

  legend : None or string

  > If not None, legend will be plotted with position argument provided in this argument

  loffset : int, optional

  > Offset in voxels for axes and tick labels. Different matplotlib frontends might have different opinions, thus offset value might need to be tuned specifically per frontend

  alinewidth : int, optional

  > Axis and ticks line width

  **kwargs

  > Additional arguments provided to plot_erp()

  Returns:

  h

  > current fig handler

  Examples

      kwargs  = {'SR' : eeg.SR, 'pre_mean' : 0.2}
      fig = plot_erps((('60db', 'b', eeg.erp_onsets['60db']),
                       ('80db', 'r', eeg.erp_onsets['80db'])),
                      data[:, eeg.sensor_mapping['Cz']],
                      ax=fig.add_subplot(1,1,1,frame_on=False), pre=0.2,
                      post=0.6, **kwargs)

  or

      fig = plot_erps((('60db', 'b', eeg.erp_onsets['60db']),
                        {'color': 'r',
                         'onsets': eeg.erp_onsets['80db'],
                         'data' : data[:, eeg.sensor_mapping['Cz']]}
                       ),
                      data[:, eeg.sensor_mapping['Cz']],
                      ax=fig.add_subplot(1,1,1,frame_on=False), pre=0.2,
                      post=0.6, **kwargs)