mvpa.misc.plot.erp

_offset(ax, x, y)

source code

Provide offset in pixels

Idea borrowed from: http://www.scipy.org/Cookbook/Matplotlib/Transformations

but then heavily extended to be compatible with many reincarnations of matplotlib

Parameters:

x (int) - Offset in pixels for x
y (int) - Offset in pixels for y

_make_centeredaxis(ax, loc, offset=5, ai=0, mult=1.0, format='%4g', label=None, **props)

source code

Plot an axis which is centered at loc (e.g. 0)

Idea borrowed from: http://www.mail-archive.com/matplotlib-users@lists.sourceforge.net /msg05669.html

It sustained heavy refactoring/extension

Parameters:

ax: Axes from the figure
loc: Value to center at
offset: Relative offset (in pixels) for the labels
ai : int: Axis index: 0 for x, 1 for y
mult: Multiplier for the axis labels. ERPs for instance need to be inverted, thus labels too manually here since there is no easy way in matplotlib to invert an axis
label : basestring or None: If not -- put a label outside of the axis
**props: Given to underlying plotting functions

plotERP(data, SR=500, onsets=None, pre=0.2, pre_onset=None, post=None, pre_mean=None, color='r', errcolor=None, errtype=None, ax=P, ymult=1.0, *args, **kwargs)

source code

Plot single ERP on existing canvas

Returns:

array: Mean ERP timeseries.

Parameters:

data: 1D or 2D ndarray

The data array can either be 1D (samples over time) or 2D (trials x samples). In the first case a boxcar mapper is used to extract the respective trial timecourses given a list of trial onsets. In the latter case, each row of the data array is taken as the EEG signal timecourse of a particular trial.

onsets: list(int)

List of onsets (in samples not in seconds).

SR: int

Sampling rate (1/s) of the signal.

pre: float

Duration (in seconds) to be plotted prior to onset.

pre_onset : float or None

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: float

Duration (in seconds) to be plotted after the onset.

pre_mean: float

Duration (in seconds) at the beginning of the window which is used for deriving the mean of the signal. If None, pre_mean = pre

errtype: None | 'ste' | 'std' | 'ci95' | list of previous three

Type of error value to be computed per datapoint.: 'ste': standard error of the mean 'std': standard deviation 'ci95': 95% confidence interval (1.96 * ste) None: no error margin is plotted (default)

Optionally, multiple error types can be specified in a list. In that case all of them will be plotted.

color: matplotlib color code

Color to be used for plotting the mean signal timecourse.

errcolor: matplotlib color code

Color to be used for plotting the error margin. If None, use main color but with weak alpha level

ax:

Target where to draw.

ymult: float

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

*args, **kwargs

Additional arguments to plot().

plotERPs(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)

source code

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
    Duration (seconds) to be plotted prior to onset
  pre_onset : float or None
    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 : float or None
    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
    Multiplier for the values. E.g. if negative-up ERP plot is needed:
    provide ymult=-1.0
  xlformat : basestring
    Format of the x ticks
  ylformat : basestring
    Format of the y ticks
  legend : basestring or None
    If not None, legend will be plotted with position argument
    provided in this argument
  loffset : int
    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
    Axis and ticks line width
  **kwargs
    Additional arguments provided to plotERP()


:Examples:
    kwargs  = {'SR' : eeg.SR, 'pre_mean' : 0.2}
    fig = plotERPs((('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 = plotERPs((('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)

:Returns: current fig handler

Module erp

_offset(ax, x, y)

_make_centeredaxis(ax, loc, offset=5, ai=0, mult=1.0, format='%4g', label=None, **props)

plotERP(data, SR=500, onsets=None, pre=0.2, pre_onset=None, post=None, pre_mean=None, color='r', errcolor=None, errtype=None, ax=P, ymult=1.0, *args, **kwargs)

plotERPs(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)