mvpa.misc.plot.base

errLinePlot(data, errtype='ste', curves=None, linestyle='--', fmt='o')

Make a line plot with errorbars on the data points.

Parameters:

data, sequence, of, sequences - First axis separates samples and second axis will appear as x-axis in the plot.
errtype, 'ste', |, 'std' -

Type of error value to be computed per datapoint.

'ste': standard error of the mean 'std': standard deviation
curves, None, |, ndarrayb - Each row of the array is plotted as an additional curve. The curves might have a different sampling frequency (i.e. number of samples) than the data array, as it will be scaled (along x-axis) to the range of the data points.
linestyle, str - 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.

plotFeatureHist(dataset, xlim=None, noticks=True, perchunk=False, **kwargs)

Plot histograms of feature values for each labels.

:Parameters:
  dataset: Dataset
  xlim: None | 2-tuple
    Common x-axis limits for all histograms.
  noticks: boolean
    If True, no axis ticks will be plotted. This is useful to save
    space in large plots.
  perchunk: boolean
    If True, one histogramm will be plotted per each label and each
    chunk, resulting is a histogram grid (labels x chunks).
  **kwargs:
    Any additional arguments are passed to matplotlib's hist().

plotSamplesDistance(dataset, sortbyattr=None)

source code

Plot the euclidean distances between all samples of a dataset.

Parameters:

dataset, Dataset - Providing the samples.
sortbyattr, None, |, str - If None, the samples distances will be in the same order as their appearance in the dataset. Alternatively, the name of a samples attribute can be given, which wil then be used to sort/group the samples, e.g. to investigate the similarity samples by label or by chunks.

plotBars(data, labels=None, title=None, ylim=None, ylabel=None, width=0.2, offset=0.2, color='0.6', distance=1.0, yerr='ste', **kwargs)

source code

Make bar plots with automatically computed error bars.

Candlestick plot (multiple interleaved barplots) can be done, by calling this function multiple time with appropriatly modified offset argument.

Parameters:

data: array (nbars x nobservations) | other sequence type: Source data for the barplot. Error measure is computed along the second axis.
labels: list | None: If not None, a label from this list is placed on each bar.
title: str: An optional title of the barplot.
ylim: 2-tuple: Y-axis range.
ylabel: str: An optional label for the y-axis.
width: float: Width of a bar. The value should be in a reasonable relation to distance.
offset: float: Constant offset of all bar along the x-axis. Can be used to create candlestick plots.
color: matplotlib color spec: Color of the bars.
distance: float: Distance of two adjacent bars.
yerr: 'ste' | 'std' | None: Type of error for the errorbars. If None no errorbars are plotted.
**kwargs:: Any additional arguments are passed to matplotlib's bar() function.

Module base

errLinePlot(data, errtype='ste', curves=None, linestyle='--', fmt='o')

plotFeatureHist(dataset, xlim=None, noticks=True, perchunk=False, **kwargs)

plotSamplesDistance(dataset, sortbyattr=None)

plotBars(data, labels=None, title=None, ylim=None, ylabel=None, width=0.2, offset=0.2, color='0.6', distance=1.0, yerr='ste', **kwargs)

plotDatasetChunks(ds, clf_labels=None)