Multivariate Pattern Analysis in Python

# mvpa2.misc.plot.base.plot_err_line_missing

`mvpa2.misc.plot.base.``plot_err_line_missing`(data, x=None, errtype='ste', curves=None, linestyle='--', fmt='o', perc_sigchg=False, baseline=None, **kwargs)

  Make a line plot with errorbars on the data points.

  This is essentially the same function as plot_err_line(), but it expects the data transposed and tolerates an unequal number of samples, per data point.

  Parameters:

  data : sequence of sequences

  > First axis will appear as x-axis in the plot. Along the second axis are the sample. Each data point may have a different number of samples.

  x : sequence

  > Value to be used as ‘x-values’ corresponding to the elements of the 2nd axis id `data`. If `None`, a sequence of ascending integers will be generated.

  errtype : ‘ste’ or ‘std’

  > Type of error value to be computed per datapoint: ‘ste’ – standard error of the mean, ‘std’ – standard deviation.

  curves : None or list of tuple(x, y)

  > Each tuple represents an additional curve, with x and y coordinates of each point on the curve.

  linestyle : str or None

  > matplotlib linestyle argument. Applied to either the additional curve or a the line connecting the datapoints. Set to ‘None’ to disable the line completely.

  fmt : str

  > matplotlib plot style argument to be applied to the data points and errorbars.

  perc_sigchg : bool

  > If `True` the plot will show percent signal changes relative to a baseline.

  baseline : float or None

  > Baseline used for converting values into percent signal changes. If `None` and `perc_sigchg` is `True`, the absolute of the mean of the first feature (i.e. [:,0]) will be used as a baseline.

  **kwargs

  > Additional arguments are passed on to errorbar().

  Returns:

  list

  > Of lines which were plotted.

  Examples

  Make a dataset with 20 samples from a full sinus wave period, computed 100 times with individual noise pattern.

      >>> x = np.linspace(0, np.pi * 2, 20)
      >>> data = np.vstack([np.sin(x)] * 30)
      >>> data += np.random.normal(size=data.shape)

  Now, plot mean data points with error bars, plus a high-res version of the original sinus wave.

      >>> x_hd = np.linspace(0, np.pi * 2, 200)
      >>> elines = plot_err_line(data, x, curves=[(x_hd, np.sin(x_hd))])
      >>> # pl.show()