Package mvpa :: Package misc :: Package plot :: Module base

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# Module base

source code

Misc. plotting helpers.

Functions | [hide private]

plotErrLine(data, x=None, errtype='ste', curves=None, linestyle='--', fmt='o', perc_sigchg=False, baseline=None) Make a line plot with errorbars on the data points. | source code

plotFeatureHist(dataset, xlim=None, noticks=True, perchunk=False, **kwargs) Plot histograms of feature values for each labels. | source code

plotSamplesDistance(dataset, sortbyattr=None) Plot the euclidean distances between all samples of a dataset. | source code

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) Make bar plots with automatically computed error bars. | source code

inverseCmap(cmap_name) Create a new colormap from the named colormap, where it got reversed | source code

plotDatasetChunks(ds, clf_labels=None) Quick plot to see chunk sctructure in dataset with 2 features | source code

Imports: P, N, NFoldSplitter, squared_euclidean_distance

Function Details | [hide private]

### plotErrLine(data, x=None, errtype='ste', curves=None, linestyle='--', fmt='o', perc_sigchg=False, baseline=None)

source code

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.
  - `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'`, `|`, `'std'` -
    Type of error value to be computed per datapoint.
      'ste': standard error of the mean 'std': standard deviation
  - `curves`, `None`, `|`, `list`, `of`, `tuple(x`, `y)` - Each tuple represents an additional curve, with x and y coordinates of each point on the curve.
  - `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.
  - `perc_sigchg`, `bool` - If `True` the plot will show percent signal changes relative to a baseline.
  - `baseline`, `float`, `|`, `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.

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

source code

    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.

### plotDatasetChunks(ds, clf_labels=None)

source code

Quick plot to see chunk sctructure in dataset with 2 features

if clf_labels is provided for the predicted labels, then incorrectly labeled samples will have 'x' in them