spacepy.toolbox.bootHisto¶
-
spacepy.toolbox.
bootHisto
(data, inter=90.0, n=1000, seed=None, plot=False, target=None, figsize=None, loc=None, **kwargs)[source]¶ Bootstrap confidence intervals for a histogram.
All other keyword arguments are passed to
numpy.histogram()
ormatplotlib.pyplot.bar()
.Parameters: data : array_like
list/array of data values
inter : float (optional; default 90)
percentage confidence interval to return. Default 90% (i.e. lower CI will be 5% and upper will be 95%)
n : int (optional; default 100)
number of bootstrap iterations
seed : int (optional)
Optional seed for the random number generator. If not specified; numpy generator will not be reseeded.
plot : bool (optional)
Plot the result. Plots if True or
target
,figsize
, orloc
specified.target : (optional)
Target on which to plot the figure (figure or axes). See
spacepy.plot.utils.set_target()
for details.figsize : tuple (optional)
Passed to
spacepy.plot.utils.set_target()
.loc : int (optional)
Passed to
spacepy.plot.utils.set_target()
.Returns: out : tuple
tuple of bin_edges, low, high, sample[, bars]. Where
bin_edges
is the edges of the bins used;low
is the histogram with the value for each bin from the bottom of that bin’s confidence interval;high
similarly for the top;sample
is the histogram of the input sample without resampling. If plotting, also returned isbars
, the container object returned from matplotlib.See also
binHisto
,plot.utils.set_target
,numpy.histogram
,matplotlib.pyplot.hist
Notes
The confidence intervals are calculated for each bin individually and thus the resulting low/high histograms may not have actually occurred in the calculation from the surrogates. If using a probability density histogram, this can have “interesting” implications for interpretation.
Examples
>>> import numpy.random >>> import spacepy.toolbox >>> numpy.random.seed(0) >>> data = numpy.random.randn(1000) >>> bin_edges, ci_low, ci_high, sample, bars = spacepy.toolbox.bootHisto( data, plot=True)
(Source code, png, hires.png, pdf)