Summary statistics

The examples below describe spm1d’s basic summary statistic functionality, which is housed in spm1d.plot.

Means and SD clouds


Load data:

>>> dataset =
>>> Y,A = dataset.get_data()
>>> Y1      = Y[A==1]
>>> Y2      = Y[A==2]
>>> Y3      = Y[A==3]

Each variable (Y1, Y2 and Y3) is a (20 x 100) data array. Mean and standard deviation clouds can be plotted as follows:

>>> spm1d.plot.plot_mean_sd(Y0, linecolor='b', facecolor=(0.7,0.7,1), edgecolor='b', label='Slow')
>>> spm1d.plot.plot_mean_sd(Y1, label='Normal')
>>> spm1d.plot.plot_mean_sd(Y2, linecolor='r', facecolor=(1,0.7,0.7), edgecolor='r', label='Fast')

(Source code, png, hires.png, pdf)


Arbitrary variance clouds


>>> Y0,Y1,Y2 = spm1d.util.get_dataset('speed-kinematics-categorical')
>>> datum    = Y1.mean(axis=0)   #arbitrary datum
>>> err      = np.linspace(0.1, 2.5, datum.size)**2   #arbitrary error cloud
>>> pyplot.plot(datum, 'b', lw=3)
>>> spm1d.plot.plot_errorcloud(datum, err, facecolor='r', edgecolor='r')

(Source code, png, hires.png, pdf)