mvpa2.misc.stats.compute_ts_boxplot_stats¶

mvpa2.misc.stats.
compute_ts_boxplot_stats
(data, outlier_abs_minthresh=None, outlier_thresh=3.0, greedy_outlier=False, aggfx=None, *args)¶ Compute boxplotlike statistics across a set of time series.
This function can handle missing values and supports data aggregation.
Parameters: data : array
Typically a 2dimensional array (series x samples). Multifeature samples are supported (series x samples x features), but they have to be aggregated into a scalar. See
aggfx
.outlier_abs_minthresh : float or None
Absolute minimum threshold of outlier detection. Only value larger than this this threshold will ever be considered as an outlier
outlier_thresh : float or None
Outlier classification threshold in units of standard deviation.
greedy_outlier : bool
If True, an entire time series is marked as an outlier, if any of its observations matches the criterion. If False, only individual observations are marked as outlier.
aggfx : functor or None
Aggregation function used to collapse multifeature samples into a scalar value
*args : :
Additional arguments for
aggfx
.Returns: tuple :
This 2item tuple contains all computed statistics in the first item and all classified outliers in the second item. Statistics are computed for each time series observation across time series. Available information: mean value, median, standard deviation, minimum, maximum, 25% and 75% percentile, as well as number of nonoutlier data points for each sample. The outlier data points are returned a masked array of the same size as the input data. All data points classified as nonoutliers are masked.