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.


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.


Additional arguments are passed on to errorbar().



Of lines which were plotted.


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))])
>>> #