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misc.plot.base
Module: misc.plot.base
Misc. plotting helpers.
Functions
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mvpa.misc.plot.base.inverseCmap(cmap_name)
Create a new colormap from the named colormap, where it got reversed
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mvpa.misc.plot.base.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.
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.
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mvpa.misc.plot.base.plotDatasetChunks(ds, clf_labels=None)
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
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mvpa.misc.plot.base.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.
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.
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Make dataset with 20 samples from a full sinus wave period,
computed 100 times with individual noise pattern.
>>> x = N.linspace(0, N.pi * 2, 20)
>>> data = N.vstack([N.sin(x)] * 30)
>>> data += N.random.normal(size=data.shape)
Now, plot mean data points with error bars, plus a high-res
version of the original sinus wave.
>>> x = N.linspace(0, N.pi * 2, 200)
>>> plotErrLine(data, curves=[(x, N.sin(x))])
>>> #P.show()
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mvpa.misc.plot.base.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().
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mvpa.misc.plot.base.plotSamplesDistance(dataset, sortbyattr=None)
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.
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