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# Source code for spacepy.poppy

    #!/usr/bin/python
    # -*- coding: utf-8 -*-

    """PoPPy -- Point Processes in Python.

    This module contains point process class types and a variety of functions for
    association analysis. The routines given here grew from work presented by
    Morley and Freeman (Geophysical Research Letters, 34, L08104, doi:10.1029/
    2006GL028891, 2007), which were originally written in IDL. This module is
    intended for application to discrete time series of events to assess
    statistical association between the series and to calculate confidence limits.
    Any mis-application or mis-interpretation by the user is the user's own fault.

    >>> import datetime as dt
    >>> import spacepy.time as spt

    Since association analysis is rather computationally expensive, this example
    shows timing.

    >>> t0 = dt.datetime.now()
    >>> onsets = spt.Ticktock(onset_epochs, 'CDF')
    >>> ticksR1 = spt.Ticktock(tr_list, 'CDF')

    Each instance must be initialized

    >>> lags = [dt.timedelta(minutes=n) for n in range(-400,401,2)]
    >>> halfwindow = dt.timedelta(minutes=10)
    >>> pp1 = poppy.PPro(onsets.UTC, ticksR1.UTC, lags, halfwindow)

    To perform association analysis

    >>> pp1.assoc()
    Starting association analysis
    calculating association for series of length [3494, 1323] at 401 lags
    >>> t1 = dt.datetime.now()
    >>> print("Elapsed:  " + str(t1-t0))
    Elapsed:  0:35:46.927138

    Note that for calculating associations between long series at a large number of
    lags is SLOW!!

    To plot

    >>> pp1.plot(dpi=80)
    Error: No confidence intervals to plot - skipping

    To add 95% confidence limits (using 4000 bootstrap samples)

    >>> pp1.aa_ci(95, n_boots=4000)

    The plot method will then add the 95% confidence intervals as a semi-
    transparent patch.

    Authors: Steve Morley and Jon Niehof
    Institution: Los Alamos National Laboratory
    Contact: smorley@lanl.gov, jniehof@lanl.gov

    Copyright 2010 Los Alamos National Security, LLC.
    """

    import bisect
    import sys
    import warnings
    import datetime as dt

    import numpy as np
    import matplotlib.mlab

    from spacepy import help
    #Try to pull in the C version. Assumption is that if you import this module,
    #you want to do some association analysis, so the overhead in the import
    #is OK.
    from spacepy import lib
    if lib.have_libspacepy:
        import ctypes
    import spacepy.toolbox as tb

    __contact__ = 'Steve Morley, smorley@lanl.gov'

    class PPro(object):
        """PoPPy point process object

        Initialize object with series1 and series2. These should be timeseries of
        events, given as lists, arrays, or lists of datetime objects.
        Includes method to perform association analysis of input series

        Output can be nicely plotted with :py:meth:`plot`.

        .. currentmodule:: spacepy.poppy

        .. autosummary::
            ~PPro.aa_ci
            ~PPro.assoc
            ~PPro.assoc_mult
            ci
            conf_above
            ~PPro.plot
            ~PPro.plot_mult
            ~PPro.swap

        .. automethod:: aa_ci
        .. automethod:: assoc
        .. automethod:: assoc_mult

        .. attribute:: ci

           Contains the upper and lower confidence limits for the association
           number as a function of lag. The first element is the array of lower
           limits; the second, the array of upper limits. Not available until
           after calling :meth:`aa_ci`.

        .. attribute:: conf_above

           Contains the confidence that the association number, as a function
           of lag, is above the asymptotic association number. (The confidence
           of being below is 100 - ``conf_above``.)  Not available until
           after calling :meth:`aa_ci`.

        .. automethod:: plot
        .. automethod:: plot_mult
        .. automethod:: swap
        """
        #NB: P2 is the "master" timescale, P1 gets shifted by lags
        #Add lag to p1 to reach p2's timescale, subtract lag from p2 to reach p1's

        def __init__(self, process1, process2, lags=None, winhalf=None, verbose=False):
            self.process1 = process1
            self.process2 = process2
            self.lags = lags
            self.winhalf = winhalf
            self.verbose = verbose

        def __str__(self):
            """String Representation of PoPPy object"""

            try:
                pk = max(self.assoc_total)
            except AttributeError:
                pk = 'N/A'

            try:
                asy = self.asympt_assoc
            except:
                asy = 'N/A'

            # TODO this is broken not enough values for the format statement
            return """Point Process Object:
            Points in process #1 - %d ; Points in process #2 - %d
            Peak association number - %s ; Asymptotic association - %s
            """ % (len(self.process1), len(self.process2), pk, asy)

        __repr__ = __str__

        def __len__(self):
            """Calling len(obj) will return the number of points in process 1"""

            return len(self.process1)

        def swap(self):
            """Swaps process 1 and process 2"""
            self.process1, self.process2 = self.process2, self.process1
            return None

        def assoc(self, u=None, h=None):
            """Perform association analysis on input series

            Parameters
            ==========
            u : list, optional
                the time lags to use
            h :
                association window half-width, same type as process1
            """

            #check for existence of lags and winhalf
            try:
                if u:
                    self.lags = u
                assert self.lags
                if h != None:
                    self.winhalf = h
                assert self.winhalf != None
                if self.verbose:
                    print('calculating association for series of length %s at %d lags' \
                        % ([len(self.process1), len(self.process2)], len(self.lags)))
            except:
                if self.verbose:
                    return 'assoc error: attributes lags and winhalf must be populated'
                else:
                    return None

            import matplotlib as mpl
            import matplotlib.dates as mpd
            if lib.have_libspacepy == False:
                dtype = 'int64'
            else:
                dtype = 'int' + str(ctypes.sizeof(ctypes.c_long) * 8)

            ##Method 1 - use tb.tOverlap
            #create list for association number
            self.n_assoc = np.empty((len(self.lags), len(self.process1)),
                                    order='C', dtype=dtype)
            p2 = sorted(self.process2)
            p1 = sorted(self.process1)

            if lib.have_libspacepy == False:
                winhalf = self.winhalf
                lags = self.lags
                starts = [t - self.winhalf for t in p1]
                stops = [t + self.winhalf for t in p1]
                nss_list = list(range(len(self.process1)))
                for ilag in range(len(self.lags)):
                    last_idx = [bisect.bisect_right(p2, stops[nss] + self.lags[ilag])
                                for nss in nss_list]
                    first_idx = [bisect.bisect_left(p2, starts[nss] + self.lags[ilag])
                                 for nss in nss_list]
                    self.n_assoc[ilag, :] = [last_idx[i] - first_idx[i] for i in nss_list]
                pul = matplotlib.mlab.prctile_rank(lags, p=(20,80))
            else:
                def fracday(dd):
                    '''turn a timedelta into a fractional day'''
                    #NOTE: If 2.6 compatibility is ever dropped, can use dd.total_seconds()/86400
                    return dd.days + dd.seconds/86400.0 + dd.microseconds/(86400.0 * 10**6)
                #test for datetime input and try to convert to numeric
                if isinstance(p1[0], dt.datetime):
                    p1 = [mpd.date2num(pp) for pp in p1]
                if isinstance(p2[0], dt.datetime):
                    p2 = [mpd.date2num(pp) for pp in p2]
                #TODO: convert lags and winhalf from timedelta to fractional days
                if isinstance(self.lags[0], dt.timedelta):
                    lags = [fracday(dd) for dd in self.lags]
                else:
                    lags = self.lags
                if isinstance(self.winhalf, dt.timedelta):
                    winhalf = fracday(self.winhalf)
                else:
                    winhalf = self.winhalf
                p2 = (ctypes.c_double * len(p2))(*p2)
                p1 = (ctypes.c_double * len(p1))(*p1)
                #prctile_rank dies on some versions if this is a ctypes array
                lags = np.require(lags, dtype=ctypes.c_double, requirements='C')
                n_assoc = self.n_assoc.ctypes.data_as(lib.lptr)
                lib.assoc(p2, p1, lags.ctypes.data_as(lib.dptr), n_assoc,
                          winhalf, len(p2), len(p1), len(self.lags))
                pul = matplotlib.mlab.prctile_rank(lags, p=(20,80))
            self.assoc_total = np.sum(self.n_assoc, axis=1)
            valsL = self.assoc_total[pul==0]
            valsR = self.assoc_total[pul==2]
            self.asympt_assoc = np.mean([np.mean(valsL), np.mean(valsR)])

            self.expected = np.empty([len(self.lags)], dtype='float64')
            for i in range(len(self.lags)):
                start_time = max([p1[0] + lags[i], p2[0]]) - winhalf
                stop_time = min([p1[-1] + lags[i], p2[-1]]) + winhalf
                if start_time != stop_time:
                    n1 = bisect.bisect_right(p1, stop_time - lags[i]) - \
                         bisect.bisect_left(p1, start_time - lags[i])
                    n2 = bisect.bisect_right(p2, stop_time) - \
                         bisect.bisect_left(p2, start_time)
                    self.expected[i] = 2.0 * n1 * n2 * winhalf / \
                                           (stop_time - start_time)
                else:
                    self.expected[i] = 0.0
            return None

        def assoc_mult(self, windows, inter=95, n_boots=1000, seed=None):
            """Association analysis w/confidence interval on multiple windows

            Using the time sequence and lags stored in this object, perform
            full association analysis, including bootstrapping of confidence
            intervals, for every listed window half-size

            Parameters
            ==========
            windows : sequence
                window half-size for each analysis
            inter : float, optional
                desired confidence interval, default 95
            n_boots : int, optional
                number of bootstrap iterations, default 1000
            seed : int, optional
                Random number generator seed. It is STRONGLY
                recommended not to specify (i.e. leave None) to permit
                multithreading.

            Warnings
            ========
            This function is likely to take a LOT of time.

            Returns
            =======
            out : three numpy array
                Three numpy arrays, (windows x lags), containing (in order)
                low values of confidence interval, high values of ci,
                percentage confidence above the asymptotic association number
            """
            n_lags = len(self.lags)
            ci_low = np.empty([len(windows), n_lags])
            ci_high = np.empty([len(windows), n_lags])
            percentiles = np.empty([len(windows), n_lags])
            for i in range(len(windows)):
                window = windows[i]
                self.assoc(h=window)
                self.aa_ci(inter, n_boots, seed)
                ci_low[i, :] = self.ci[0]
                ci_high[i, :] = self.ci[1]
                percentiles[i, :] = self.conf_above
            return (ci_low, ci_high, percentiles)

        def plot_mult(self, windows, data, min=None, max=None, cbar_label=None,
                      figsize=None, dpi=80,
                      xlabel='Lag', ylabel='Window Size'):
            """Plots a 2D function of window size and lag

            Parameters
            ==========
            windows : list
                list of window sizes (y axis)
            data : list
                list of data, dimensioned (windows x lags)
            min : float, optional
                clip L{data} to this minimum value
            max : float, optional
                clip L{data} to this maximum value
            """
            import matplotlib.pyplot as plt

            x = np.array(tb.bin_center_to_edges(self.lags))
            y = np.array(tb.bin_center_to_edges(windows))

            fig = plt.figure(figsize=figsize, dpi=dpi)
            ax0 = fig.add_subplot(111)
            cax = ax0.pcolormesh(x, y, data, vmin=min, vmax=max,
                                 shading='flat')
            ax0.set_xlim((x[0], x[-1]))
            ax0.set_ylim((y[0], y[-1]))
            plt.xlabel(xlabel)
            plt.ylabel(ylabel)
            if cbar_label == None:
                if plt.rcParams['text.usetex']:
                    cbar_label = r'\% confident above asymptotic association'
                else:
                    cbar_label = r'% confident above asymptotic association'
            plt.colorbar(cax, fraction=0.05).set_label(cbar_label)
            return fig

        def plot(self, figsize=None, dpi=80, asympt=True, show=True, norm=True,
                 xlabel='Time lag', xscale=None, ylabel=None, title=None, transparent=True):
            """Create basic plot of association analysis.

            Uses object attributes created by :py:meth:`assoc` and,
            optionally, :py:meth:`aa_ci`.

            Parameters
            ==========
            figsize : , optional
                passed through to matplotlib.pyplot.figure
            dpi : int, optional
                passed through to matplotlib.pyplot.figure
            asympt : boolean, optional
                True to overplot the line of asymptotic association number
            show : boolean, optional
                Show the plot? (if false, will create without showing)
            norm : boolean, optional
                Normalize plot to the asymptotic association number
            title : string, optional
                label/title for the plot
            xlabel : string, optional
                label to put on the X axis of the resulting plot
            xscale : float, optional
                scale x-axis by this factor (e.g. 60.0 to convert seconds to minutes)
            ylabel : string, optional
                label to put on the Y axis of the resulting plot
            transparent : boolean, optional
                make c.i. patch transparent (default)
            """
            try:
                dum = self.n_assoc
            except AttributeError:
                return 'Error: No association analysis results to plot'

            import datetime as dt
            import matplotlib as mpl
            import matplotlib.pyplot as plt

            fig = plt.figure(figsize=figsize, dpi=dpi)
            ax0 = fig.add_subplot(111)

            #fix such that self.lags (a timedelta) are converted to a time
            if type(self.lags[0]) == dt.timedelta:
                x = [i.seconds/60 + i.days*1440. for i in self.lags]
            else:
                x = self.lags
            if xscale != None:
                x = [i / xscale for i in x]
            ax0.set_xlim((min(x), max(x)))

            ci = None
            if norm:
                if hasattr(self, 'ci'):
                    ci = [[j / self.asympt_assoc for j in self.ci[i]]
                        for i in [0,1]]
                asympt_assoc = 1.0
                assoc_total = [assoc / self.asympt_assoc
                               for assoc in self.assoc_total]
            else:
                try:
                    ci = self.ci
                except AttributeError:
                    pass
                asympt_assoc = self.asympt_assoc
                assoc_total = self.assoc_total

            if ci != None:
                if transparent:
                    ax0.fill_between(x, ci[0], ci[1],
                                     edgecolor='none', facecolor='blue', alpha=0.5)
                else:
                    ax0.fill_between(x, ci[0], ci[1],
                                     edgecolor='none', facecolor='#ABABFF')
            else:
                print('Error: No confidence intervals to plot - skipping')

            ax0.plot(x, assoc_total, 'b-', lw=1.0)
            if asympt:
                ax0.plot([x[0], x[-1]], [asympt_assoc]*2, 'r--', lw=1.0)
            if ylabel == None:
                if norm:
                    plt.ylabel(
                        'Normalized Association Number n(u, h={0}) / n({1}, h={0})'.format(
                        self.winhalf,
                        '$\mathrm{u\rightarrow\infty}$'
                        if plt.rcParams['text.usetex'] else 'u->Inf'))
                else:
                    plt.ylabel('Association Number n(u, h={0})'.format(
                        self.winhalf))
            else:
                plt.ylabel(ylabel)
            plt.xlabel(xlabel)
            if title != None:
                plt.title(title)

            if show:
                plt.show()
                return None
            else:
                return fig

        def aa_ci(self, inter, n_boots=1000, seed=None):
            """Get bootstrap confidence intervals for association number

            Requires input of desired confidence interval, e.g.:
                >>> obj.aa_ci(95)

            Upper and lower confidence limits are added to :attr:`~PPro.ci`.

            After calling, :attr:`~PPro.conf_above` will contain the confidence
            (in percent) that
            the association number at that lag is *above* the asymptotic
            association number. (The confidence of being below is 100 - conf_above)
            For minor variations in conf_above to be meaningful, a *large* number
            of bootstraps is required. (Rougly, 1000 to be meaningful to the
            nearest percent; 10000 to be meaningful to a tenth of a percent.) A
            conf_above of 100 usually indicates an insufficient sample size to
            resolve, *not* perfect certainty.

            Note also that a 95% chance of being above indicates an exclusion
            from the *90%* confidence interval!

            Parameters
            ==========
            inter : float
                percentage confidence interval to calculate
            n_boots : int, optional
                number of bootstrap iterations to run
            seed : int, optional
                seed for the random number generator. If not specified,
                Python code will use numpy's RNG and its current seed;
                C code will seed from the clock.

            Warnings
            ========
            If ``seed`` is specified on numpy 1.5 and earlier, the available
            entropy is reduced to work around a random number generator bug.
            Upgrade to numpy 1.6 to avoid this limitation.
            Because of this workaround, if a seed is specified, results
            from numpy 1.5 are not reproducible with numpy 1.6
            """
            lags = self.lags

            ci_low = np.empty([len(lags)])
            ci_high = np.empty([len(lags)])
            conf_above = np.empty([len(lags)])

            if seed != None:
                np.random.seed(seed)
                #TODO: This is a bit ugly and we potentially lose entropy
                #should be unsigned long, but numpy forces signed int...
                minseed = -sys.maxsize - 1
                maxseed = sys.maxsize
                (maj, minor) = np.__version__.split('.')[0:2]
                if int(maj) < 2 and int(minor) < 6:
                    warnings.warn('Upgrade to numpy 1.6 to avoid reduced entropy.')
                    maxseed = sys.maxsize
                    minseed = 0
                lag_seeds = np.random.randint(minseed, maxseed, [len(lags)])
                newtype = np.dtype('u' + str(lag_seeds.dtype))
                lag_seeds = np.require(lag_seeds, dtype=newtype)
            if lib.have_libspacepy == False:
                for i in range(len(lags)):
                    if seed != None:
                        np.random.seed(lag_seeds[i])
                    ci_low[i], ci_high[i], conf_above[i]= boots_ci(
                        self.n_assoc[i, :],
                        n_boots, inter, np.add.reduce,
                        seed=None, target=self.asympt_assoc)
            else:
                perc_low = (100.-inter)/2. #set confidence interval
                perc_high = inter + perc_low
                dtype = 'int' + str(ctypes.sizeof(ctypes.c_long) * 8)
                assoc_totals = np.empty([len(lags), n_boots],
                                        dtype=dtype, order='C')
                if seed == None:
                    clock_seed = ctypes.c_int(1)
                    lag_seeds = np.empty([len(lags)], dtype=dtype)
                else:
                    clock_seed = ctypes.c_int(0)
                def thread_targ(start, size):
                    lib.aa_ci(
                        self.n_assoc[start:start+size].ctypes.data_as(lib.ulptr),
                        assoc_totals[start:start+size].ctypes.data_as(lib.ulptr),
                        size, len(self.process1), n_boots,
                        lag_seeds[start:start+size].ctypes.data_as(lib.ulptr),
                        clock_seed)
                tb.thread_job(len(lags), 0, thread_targ)
                for i in range(len(lags)):
                    assoc_totals[i, :].sort()
                    ci_low[i], ci_high[i] = matplotlib.mlab.prctile(
                        assoc_totals[i, :], p=(perc_low,perc_high))
                    conf_above[i] = 100.0 - value_percentile(assoc_totals[i, :],
                                                             self.asympt_assoc)
            self.ci = [ci_low, ci_high]
            self.conf_above = conf_above
            return None

    #Functions outside class

    def plot_two_ppro(pprodata, pproref, ratio=None, norm=False,
                      title=None, xscale=None, figsize=None, dpi=80,
                      ylim=[None, None], log=False, xticks=None, yticks=None):
        """Overplots two PPro objects

        Parameters
        ==========
        pprodata : PPro
            first point process to plot (in blue)
        pproref : PPro
            second process to plot (in red)
        ratio : float
            multiply L{pprodata} by this ratio before plotting,
            useful for comparing processes of different magnitude
        norm : boolean
            normalize everything to L{pproref}, i.e. the association
            number for L{pproref} will always plot as 1.
        title : string
            title to put on the plot
        xscale : float
            scale x-axis by this factor (e.g. 60.0 to convert
            seconds to minutes)
        figsize :
            passed through to matplotlib.pyplot.figure
        dpi : int
            passed through to matplotlib.pyplot.figure
        ylim : list
            [minimum, maximum] values of y for the axis
        log : bollean
            True for a log plot
        xticks : sequence or float
            if provided, a list of tickmarks for the X axis
        yticks : sequance or float
            if provided, a list of tickmarks for the Y axis
        """
        import matplotlib.pyplot as plt
        if ratio == None:
            ratio = float(pproref.asympt_assoc) / pprodata.asympt_assoc
        lags = pproref.lags
        nlags = len(lags)
        assert lags[:] == pprodata.lags[:]
        if xscale != None:
            lags = [float(i) / xscale for i in lags]
        fig = plt.figure(figsize=figsize, dpi=dpi)
        plt.subplots_adjust(wspace=0.0, hspace=0.0)
        ax0 = fig.add_subplot(111)
        ax0.set_xlim((lags[0], lags[-1]))
        if norm:
            scaleddata = [ratio *
                          float(pprodata.assoc_total[i]) / pproref.assoc_total[i]
                          for i in range(nlags)]
            scaledhi = [ratio *
                        float(pprodata.ci[1][i]) / pproref.assoc_total[i]
                        for i in range(nlags)]
            scaledlo = [ratio *
                        float(pprodata.ci[0][i]) / pproref.assoc_total[i]
                        for i in range(nlags)]
            scaledref = [1.0 for i in range(nlags)]
            refhi = [float(pproref.ci[1][i])  / pproref.assoc_total[i]
                     for i in range(nlags)]
            reflo = [float(pproref.ci[0][i])  / pproref.assoc_total[i]
                     for i in range(nlags)]
        else:
            scaleddata = [ratio * float(pprodata.assoc_total[i])
                          for i in range(nlags)]
            scaledhi = [ratio * float(pprodata.ci[1][i])
                        for i in range(nlags)]
            scaledlo = [ratio * float(pprodata.ci[0][i])
                        for i in range(nlags)]
            scaledref = pproref.assoc_total
            refhi = pproref.ci[1]
            reflo = pproref.ci[0]
        ax0.fill_between(lags, reflo, refhi,
                         edgecolor='none', facecolor='#FFABAB', interpolate=True)
        ax0.fill_between(lags, scaledlo, scaledhi,
                         edgecolor='none', facecolor='#ABABFF', interpolate=True)
        bottom = np.fromiter((max([scaledlo[i], reflo[i]]) for i in range(nlags)),
                             np.float64, count=-1)
        top = np.fromiter((min([scaledhi[i], refhi[i]]) for i in range(nlags)),
                          np.float64, count=-1)
        ax0.fill_between(lags, bottom, top, where=(bottom <= top),
                         edgecolor='none', facecolor='#AB81D5',
                         interpolate=True)
        ax0.plot(lags, scaleddata, lw=1.0)
        ax0.plot(lags, scaledref, 'r--', lw=1.0)
        if ylim[0] == None:
            ax0.set_ylim(bottom=0)
        else:
            ax0.set_ylim(bottom=ylim[0])
        if ylim[1] != None:
            ax0.set_ylim(top=ylim[1])
        if log:
            ax0.set_yscale('log', nonposy='clip')
        if xticks:
            ax0.set_xticks(xticks)
        if yticks:
            ax0.set_yticks(yticks)
        if title:
            plt.title(title)

    def boots_ci(data, n, inter, func, seed=None, target=None, sample_size=None):
        """Construct bootstrap confidence interval

        The bootstrap is a statistical tool that uses multiple samples derived from
        the original data (called surrogates) to estimate a parameter of the
        population from which the sample was drawn. This assumes that the sample is
        randomly drawn and hence is representative of the underlying distribution.
        The benefit of the bootstrap is that it is non-parametric and can be
        applied in situations where there is reasonable doubt about the
        characteristics of the underlying distribution. This routine uses the boot-
        strap for its most common application - the estimation of confidence
        intervals.

        Examples
        ========
        >>> data, n = numpy.random.lognormal(mean=5.1, sigma=0.3, size=3000), 4000.
        >>> myfunc = lambda x: numpy.median(x)
        >>> ci_low, ci_high = poppy.boots_ci(data, n, 95, myfunc)
        >>> ci_low, numpy.median(data), ci_high
        (163.96354196633686, 165.2393331896551, 166.60491435416566) iter. 1
        ... repeat
        (162.50379144492726, 164.15218265100233, 165.42840588032755) iter. 2

        For comparison

        >>> data = numpy.random.lognormal(mean=5.1, sigma=0.3, size=90000)
        >>> numpy.median(data)
        163.83888237895815

        Note that the true value of the desired quantity may lie outside the
        95% confidence interval one time in 20 realizations. This occurred
        for the first iteration here.

        For the lognormal distribution, the median is found exactly by taking
        the exponential of the "mean" parameter. Thus here, the theoretical
        median is 164.022 (6 s.f.) and this is well captured by the above
        bootstrap confidence interval.

        Parameters
        ==========
        data : array like
            data to bootstrap
        n : int
            number of surrogate series to select, i.e. number of bootstrap iterations.
        inter : numerical
            desired percentage confidence interval
        func : callable
            Function to apply to each surrogate series
        sample_size : int
            number of samples in the surrogate series, default
            length of L{data}. This will change the statistical
            properties of the bootstrap and should only be used
            for good reason!
        seed : int
            Optional seed for the random number generator. If not
            specified, numpy generator will not be reseeded;
            C generator will be seeded from the clock.
        target : same as data
            a 'target' value. If specified, will also calculate
            percentage confidence of being at or above this value.

        Returns
        =======
        out : sequence of float
            inter percent confidence interval on value derived from
            func applied to the population sampled by data.
            If target is specified, also the percentage confidence of
            being above that value.
        """
        perc_low = (100.-inter)/2. #set confidence interval
        perc_high = inter + perc_low

        n_els = len(data)
        if n_els <= 2:
            if target == None:
                return np.nan, np.nan
            else:
                return np.nan, np.nan, np.nan
        if sample_size == None:
            sample_size = n_els
        if lib.have_libspacepy == False:
            surr_quan = np.empty([n])
            if seed != None:
                np.random.seed(seed)
            ran_el = np.random.randint(n_els, size=[n, sample_size])
            for i in range(int(n)): #compute n bootstrapped series
                surr_ser = [data[rec] for rec in ran_el[i, :]] #resample w/ replace
                surr_quan[i] = func(surr_ser) #get desired quantity from surrogates
            surr_quan.sort()
        else:
            n = int(n)
            data = (ctypes.c_double * n_els)(*data)
            surr_ser = (ctypes.c_double * (n * sample_size))()
            if seed == None:
                seed = 0
                clock_seed = ctypes.c_int(1)
            else:
                clock_seed= ctypes.c_int(0)
            lib.boots(surr_ser, data,
                      ctypes.c_ulong(n), ctypes.c_ulong(n_els),
                      ctypes.c_ulong(sample_size),
                      ctypes.c_ulong(seed), clock_seed)
            surr_quan = sorted(
                (func(surr_ser[i * sample_size:(i  + 1) * sample_size])
                 for i in range(n)))
        #get confidence interval
        pul = matplotlib.mlab.prctile(surr_quan, p=(perc_low,perc_high))
        if target == None:
            return pul[0], pul[1]
        else:
            vp = value_percentile(surr_quan, target)
            return pul[0], pul[1], 100.0 - vp

    def value_percentile(sequence, target):
        """Find the percentile of a particular value in a sequence

        Parameters
        ==========
        sequence : sequence
            a sequence of values, sorted in ascending order
        target : same type as sequence
             a target value

        Returns
        =======
        out : float
            the percentile of target in sequence
        """
        min = bisect.bisect_left(sequence, target) #first value >= x
        max = bisect.bisect_right(sequence, target, lo=min) #first value > x
        #min:max, in Python syntax, will include all values that are target
        count = len(sequence)
        if max == 0: #everything is bigger
            return 0.0
        if min == count: #everything is smaller
            return 100.0
        #Index 0 is 0th percentile (by definition); count-1 is 100th
        #so index n is (n * 100.0) / (count - 1)
        #find a 'virtual' index
        if min == max: #Not equal to any, between min-1 and min
            #interpolate between two points based on value.
            idx = min - 1 + float(target - sequence[min - 1]) / \
                  (sequence[min] - sequence[min - 1])
        else: #Equal to one or more values (min...max-1), average them
            idx = (min + max - 1) / 2
        return idx * 100.0 / (count - 1)

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