Repository: spice-herald/QETpy (language: python)

Issue:
Update upgraded autocuts to be even with master
Just updating the autocuts branch so that its no longer behind master by a 17 commits


Discussion / hints:

Fix patch:
diff --git a/qetpy/__init__.py b/qetpy/__init__.py
index 234636fc..d2bc055e 100644
--- a/qetpy/__init__.py
+++ b/qetpy/__init__.py
@@ -1,7 +1,7 @@
 from . import core
 from .core import *
 from . import cut
-from .cut import autocuts
+from .cut import autocuts, IterCut
 from . import plotting
 from . import sim
 from . import utils
diff --git a/qetpy/core/didv/_didv.py b/qetpy/core/didv/_didv.py
index 5127259d..ccc02ec8 100644
--- a/qetpy/core/didv/_didv.py
+++ b/qetpy/core/didv/_didv.py
@@ -356,7 +356,7 @@ def _residual(params):
         # take matrix product of transpose of jac and jac, take the inverse
         # to get the analytic covariance matrix
         pcovinv = np.dot(res["jac"].transpose(), res["jac"])
-        pcov = np.linalg.inv(pcovinv)
+        pcov = np.linalg.pinv(pcovinv)
 
         return popt, pcov, cost
 
diff --git a/qetpy/cut/_cut.py b/qetpy/cut/_cut.py
index 74d31842..a58b176a 100644
--- a/qetpy/cut/_cut.py
+++ b/qetpy/cut/_cut.py
@@ -1,15 +1,19 @@
 import numpy as np
 import random
 from qetpy import ofamp
+from qetpy.utils import make_template
+from astropy.stats import sigma_clip
 from scipy import stats, optimize
 from scipy.stats import skew
 import warnings
+import matplotlib.pyplot as plt
 
 
 __all__ = [
     "removeoutliers",
     "iterstat",
     "itercov",
+    "IterCut",
     "autocuts",
     "get_muon_cut",
 ]
@@ -17,21 +21,24 @@
 
 def removeoutliers(x, maxiter=20, skewtarget=0.05):
     """
-    Function to return indices of inlying points, removing points by minimizing the skewness.
+    Function to return indices of inlying points, removing points
+    by minimizing the skewness.
 
     Parameters
     ----------
     x : ndarray
         Array of real-valued variables from which to remove outliers.
     maxiter : float, optional
-        Maximum number of iterations to continue to minimize skewness. Default is 20.
+        Maximum number of iterations to continue to minimize skewness.
+        Default is 20.
     skewtarget : float, optional
         Desired residual skewness of distribution. Default is 0.05.
 
     Returns
     -------
     inds : ndarray
-        Boolean indices indicating which values to select/reject, same length as x.
+        Boolean indices indicating which values to select/reject, same
+        length as `x`.
 
     """
 
@@ -57,15 +64,17 @@ class _UnbiasedEstimators(object):
     Attributes
     ----------
     mu0 : float
-        The biased estimator of the mean of the inputted and truncated data.
+        The biased estimator of the mean of the inputted and truncated
+        data.
     std0 : float
-        The biased estimator of the standard deviation of the inputted and
-        truncated data.
+        The biased estimator of the standard deviation of the inputted
+        and truncated data.
     mu : float
-        The unbiased estimator of the mean of the inputted and truncated data.
-    std : float
-        The unbiased estimator of the standard deviation of the inputted and
+        The unbiased estimator of the mean of the inputted and
         truncated data.
+    std : float
+        The unbiased estimator of the standard deviation of the
+        inputted and truncated data.
 
     """
     
@@ -76,8 +85,8 @@ def __init__(self, x, lwrbnd, uprbnd):
         Parameters
         ----------
         x : ndarray
-            A 1D array of data that has been truncated, for which the unbiased
-            estimators will be calculated.
+            A 1D array of data that has been truncated, for which the
+            unbiased estimators will be calculated.
         lwrbnd : float
             The lower bound of the truncation of the distribution.
         uprbnd : float
@@ -90,7 +99,7 @@ def __init__(self, x, lwrbnd, uprbnd):
         self._lwrbnd = lwrbnd
         self._uprbnd = uprbnd
 
-        self._x = x[inds] # make sure data is only between the specified bounds
+        self._x = x[inds] # make sure data is between the specified bounds
         self._sumx = np.sum(self._x)
         self._sumx2 = np.sum(self._x**2)
         self._lenx = len(self._x)
@@ -102,20 +111,21 @@ def __init__(self, x, lwrbnd, uprbnd):
 
     def _equations(self, p):
         """
-        Helper method for calculating the system of equations that will be
-        numerically solved for find the unbiased estimators.
+        Helper method for calculating the system of equations that will
+        be numerically solved for find the unbiased estimators.
 
         Parameters
         ----------
         p : tuple
-            A tuple of length 2 containing the current estimated values of the
-            unbiased estimators: (mu, std).
+            A tuple of length 2 containing the current estimated values
+            of the unbiased estimators: (mu, std).
 
         Returns
         -------
         (mu_eqn, std_eqn) : tuple
-            A tuple containing the two equations that will be solved to give the
-            unbiased estimators of the mean and standard deviation of the data.
+            A tuple containing the two equations that will be solved to
+            give the unbiased estimators of the mean and standard
+            deviation of the data.
 
         """
 
@@ -131,48 +141,65 @@ def _equations(self, p):
         # term due to truncation
         mu_eqn += self._lenx / (cdf_upr - cdf_lwr) * (pdf_upr - pdf_lwr)
 
-        std_eqn = self._sumx2 - 2 * mu * self._sumx + self._lenx * mu**2 - self._lenx * std**2
+        std_eqn = (
+            self._sumx2 - 2 * mu * self._sumx
+        ) + (
+            self._lenx * mu**2 - self._lenx * std**2
+        )
         # term due to truncation
-        std_eqn += self._lenx * std**2 / (cdf_upr - cdf_lwr) * ((self._uprbnd - mu) * pdf_upr - (self._lwrbnd - mu) * pdf_lwr)
+        std_eqn += self._lenx * std**2 / (cdf_upr - cdf_lwr) * (
+            (self._uprbnd - mu) * pdf_upr - (self._lwrbnd - mu) * pdf_lwr
+        )
 
         return (mu_eqn, std_eqn)
 
     def _calc_unbiased_estimators(self):
         """
-        Method for calculating the unbiased estimators of the truncated distribution.
+        Method for calculating the unbiased estimators of the truncated
+        distribution.
 
         """
 
-        self.mu, self.std = optimize.fsolve(self._equations, (self.mu0, self.std0))
+        self.mu, self.std = optimize.fsolve(
+            self._equations,
+            (self.mu0, self.std0),
+        )
 
 
-def iterstat(data, cut=3, precision=1000.0, return_unbiased_estimates=False):
+def iterstat(data, cut=3, precision=1000.0,
+             return_unbiased_estimates=False):
     """
-    Function to iteratively remove outliers based on how many standard deviations they are from the mean,
-    where the mean and standard deviation are recalculated after each cut.
+    Function to iteratively remove outliers based on how many standard
+    deviations they are from the mean, where the mean and standard
+    deviation are recalculated after each cut.
 
     Parameters
     ----------
     data : ndarray
         Array of data that we want to remove outliers from.
     cut : float, optional
-        Number of standard deviations from the mean to be used for outlier rejection
+        Number of standard deviations from the mean to be used for
+        outlier rejection
     precision : float, optional
-        Threshold for change in mean or standard deviation such that we stop iterating. The threshold is
-        determined by np.std(data)/precision. This means that a higher number for precision means a lower
-        threshold (i.e. more iterations).
+        Threshold for change in mean or standard deviation such that we
+        stop iterating. The threshold is determined by
+        np.std(data)/precision. This means that a higher number for
+        precision means a lower threshold (i.e. more iterations).
     return_unbiased_estimates : bool, optional
-        Boolean flag for whether or not to return the biased or unbiased estimates of the mean and
-        standard deviation of the data. Default is False.
+        Boolean flag for whether or not to return the biased or
+        unbiased estimates of the mean and standard deviation of the
+        data. Default is False.
 
     Returns
     -------
     datamean : float
         Mean of the data after outliers have been removed.
     datastd : float
-        Standard deviation of the data after outliers have been removed
+        Standard deviation of the data after outliers have been
+        removed.
     datamask : ndarray
-        Boolean array indicating which values to keep or reject in data, same length as data.
+        Boolean array indicating which values to keep or reject in
+        data, same length as data.
 
     """
 
@@ -200,7 +227,11 @@ def iterstat(data, cut=3, precision=1000.0, return_unbiased_estimates=False):
         meanthis = np.mean(data[mask])
         stdthis = np.std(data[mask])
 
-        if (abs(meanthis - meanlast) > stdcutoff) or (abs(stdthis - stdlast) > stdcutoff):
+        if (
+            abs(meanthis - meanlast) > stdcutoff
+        ) or (
+            abs(stdthis - stdlast) > stdcutoff
+        ):
             nstable = 0
         else:
             nstable = nstable + 1
@@ -211,46 +242,59 @@ def iterstat(data, cut=3, precision=1000.0, return_unbiased_estimates=False):
         stdlast = stdthis
 
     if return_unbiased_estimates:
-        unb = _UnbiasedEstimators(data[mask], meanthis - cut * stdthis, meanthis + cut * stdthis)
+        unb = _UnbiasedEstimators(
+            data[mask],
+            meanthis - cut * stdthis,
+            meanthis + cut * stdthis,
+        )
         return unb.mu, unb.std, mask
 
     return meanthis, stdthis, mask
 
-def itercov(*args, nsigma=2.75, threshold=None, maxiter=15, frac_err=1e-3):
+def itercov(*args, nsigma=2.75, threshold=None, maxiter=15,
+            frac_err=1e-3):
     """
-    Function for iteratively determining the estimated covariance matrix of a multidimensional
-    normal distribution.
+    Function for iteratively determining the estimated covariance
+    matrix of a multidimensional normal distribution.
 
     Parameters
     ----------
     args : array_like
-        The data to be iteratively cut on. Can be inputted as a single N-by-M array or as
-        M arguments that are 1D vectors of length N, where N is the number of data points and
-        M is the number of dimensions.
+        The data to be iteratively cut on. Can be inputted as a single
+        N-by-M array or as M arguments that are 1D vectors of length N,
+        where N is the number of data points and M is the number of
+        dimensions.
     nsigma : float, optional
-        The number of sigma that defines that maximum chi-squared each data point must be below.
-        Default is 2.75.
+        The number of sigma that defines that maximum chi-squared each
+        data point must be below. Default is 2.75.
     threshold : float, NoneType, optional
-        The threshold to cut data. If left as None, this is set to the larger of 3 sigma or
-        the number of sigma such that 95% of the data is kept if the data is normal.
+        The threshold to cut data. If left as None, this is set to the
+        larger of 3 sigma or the number of sigma such that 95% of the
+        data is kept if the data is normal.
     maxiter : int, optional
-        The maximum number of iterations to perform when cutting. Default is 15.
+        The maximum number of iterations to perform when cutting.
+        Default is 15.
     frac_err : float, optional
-        The fractional error allowed before stopping the iterations. Default is 1e-3.
+        The fractional error allowed before stopping the iterations.
+        Default is 1e-3.
 
     Returns
     -------
     datamean : ndarray
-        The estimated mean of the data points, after iteratively cutting outliers.
+        The estimated mean of the data points, after iteratively
+        cutting outliers.
     datacov : ndarray
-        The estimated covariance of the data points, after iteratively cutting outliers.
+        The estimated covariance of the data points, after iteratively
+        cutting outliers.
     datamask : ndarray
-        The boolean mask of the original data that specifies which data points were kept.
+        The boolean mask of the original data that specifies which data
+        points were kept.
 
     Raises
     ------
     ValueError
-        If the shape of the data does not match the two options specified by `args`.
+        If the shape of the data does not match the two options
+            specified by `args`.
         If the data inputted is 1-dimensional.
 
     """
@@ -269,7 +313,9 @@ def itercov(*args, nsigma=2.75, threshold=None, maxiter=15, frac_err=1e-3):
         raise ValueError("Shape of data is inconsistent.")
 
     if data.shape[0] == 1:
-        raise ValueError("The inputted data is 1-dimensional, use qetpy.cut.iterstat instead.")
+        raise ValueError(
+            "The inputted data is 1-dimensional, use qetpy.cut.iterstat instead."
+        )
 
     if threshold is None:
         sigma2 = stats.chi2.ppf(0.95**(1 / ndim), 1)**0.5
@@ -298,7 +344,10 @@ def itercov(*args, nsigma=2.75, threshold=None, maxiter=15, frac_err=1e-3):
         chi2 = np.sum(delta * np.dot(np.linalg.inv(cov_last), delta), axis=0)
         mask = chi2 < max_chi2
 
-        mask = mask & np.all(np.abs(delta) < std_last[:, np.newaxis] * threshold, axis=0)
+        mask = mask & np.all(
+            np.abs(delta) < std_last[:, np.newaxis] * threshold,
+            axis=0,
+        )
         nmask = np.sum(mask)
 
         if nmask <= 1:
@@ -316,7 +365,11 @@ def itercov(*args, nsigma=2.75, threshold=None, maxiter=15, frac_err=1e-3):
         mean_this = np.mean(data[:, mask], axis=1)
         cov_this = np.atleast_1d(np.cov(data[:, mask]))
 
-        if np.any(np.abs(mean_this - mean_last) > err_mean) or np.any(np.abs(cov_this - cov_last) > err_cov):
+        if np.any(
+            np.abs(mean_this - mean_last) > err_mean
+        ) or np.any(
+            np.abs(cov_this - cov_last) > err_cov
+        ):
             nstable = 0
         else:
             nstable += 1
@@ -333,11 +386,13 @@ def itercov(*args, nsigma=2.75, threshold=None, maxiter=15, frac_err=1e-3):
 
 def symmetrizedist(vals):
     """
-    Function to symmetrize a distribution about zero. Useful for if the distribution of some value
-    centers around a nonzero value, but should center around zero. An example of this would be when
-    most of the measured slopes are nonzero, but we want the slopes with zero values (e.g. lots of 
-    muon tails, which we want to cut out). To do this, the algorithm randomly chooses points in a histogram
-    to cut out until the histogram is symmetric about zero.
+    Function to symmetrize a distribution about zero. Useful for if the
+    distribution of some value centers around a nonzero value, but
+    should center around zero. An example of this would be when most of
+    the measured slopes are nonzero, but we want the slopes with zero
+    values (e.g. lots of muon tails, which we want to cut out). To do
+    this, the algorithm randomly chooses points in a histogram to cut
+    out until the histogram is symmetric about zero.
 
     Parameters
     ----------
@@ -355,39 +410,54 @@ def symmetrizedist(vals):
     # figure out which direction the slopes are usually
     valsmean, valsstd = iterstat(vals, cut=2, precision=10000.0)[:-1]
 
-    # if most vals are positive, flip the sign of them so we can use the same code for both negative and positive vals
+    # if most vals are positive, flip the sign of them so we can use
+    # the same code for both negative and positive vals
     if valsmean>0.0:
         vals= vals
 
-    # choose symmetric upper and lower bounds for histogram to make the middle bin centered on zero (since we want zero mean)
+    # choose symmetric upper and lower bounds for histogram to make
+    # the middle bin centered on zero (since we want zero mean)
     histupr=max(vals)
     histlwr=-histupr
 
-    # specify number of bins in histogram (should be an odd number so that we have the middle bin centered on zero)
+    # specify number of bins in histogram (should be an odd number
+    # so that we have the middle bin centered on zero)
     histbins=int(np.sqrt(nvals))
     if np.mod(histbins,2)==0:
         histbins+=1
 
     if histupr>0:
-        # create histogram, get number of events in each bin and where the bin edges are
-        hist_num, bin_edges = np.histogram(vals, bins=histbins, range=(histlwr, histupr))
+        # create histogram, get number of events in each bin and
+        # where the bin edges are
+        hist_num, bin_edges = np.histogram(
+            vals,
+            bins=histbins,
+            range=(histlwr, histupr),
+        )
 
         if len(hist_num)>2: # otherwise we cannot symmetrize the distribution
             # inititalize the cut that symmetrizes the slopes
             czeromeanvals = np.zeros(nvals, dtype=bool)
             czeromeanvals[vals>bin_edges[histbins//2]] = True
 
-            # go through each bin and remove events until the bin number is symmetric
+            # go through each bin and remove events until the bin number
+            # is symmetric
             for ibin in range(histbins//2, histbins-1):
-                cvalsinbin = np.logical_and(vals<bin_edges[histbins-ibin-1], vals>=bin_edges[histbins-ibin-2])
+                cvalsinbin = np.logical_and(
+                    vals<bin_edges[histbins-ibin-1],
+                    vals>=bin_edges[histbins-ibin-2],
+                )
                 ntracesinthisbin = hist_num[histbins-ibin-2]
                 ntracesinoppobin = hist_num[ibin+1]
                 ntracestoremove = ntracesinthisbin-ntracesinoppobin
                 if ntracestoremove>0.0:
                     cvalsinbininds = np.where(cvalsinbin)[0]
-                    crandcut = np.random.choice(cvalsinbininds, ntracestoremove, replace=False)
+                    crandcut = np.random.choice(
+                        cvalsinbininds, ntracestoremove, replace=False,
+                    )
                     cvalsinbin[crandcut] = False
-                czeromeanvals += cvalsinbin # update cut to include these events
+                # update cut to include these events
+                czeromeanvals += cvalsinbin
         else:
             # don't do anything about the shape of the distrbution
             czeromeanvals = np.ones(nvals, dtype=bool)
@@ -397,273 +467,440 @@ def symmetrizedist(vals):
 
     return czeromeanvals
 
-def pileupcut(traces, fs=625e3, outlieralgo="removeoutliers", nsig=2, removemeans=False):
+
+class _PlotCut(object):
     """
-    Function to automatically cut out outliers of the optimum filter amplitudes of the inputted traces.
+    Helper class for storing plotting functions for use in IterCut.
 
-    Parameters
-    ----------
-    traces : ndarray
-        2-dimensional array of traces to do cuts on
-    fs : float, optional
-        Digitization rate that the data was taken at
-    outlieralgo : string, optional
-        Which outlier algorithm to use. If set to "removeoutliers", uses the removeoutliers algorithm that
-        removes data based on the skewness of the dataset. If set to "iterstat", uses the iterstat algorithm
-        to remove data based on being outside a certain number of standard deviations from the mean
-    nsig : float, optional
-        If outlieralgo is "iterstat", this can be used to tune the number of standard deviations from the mean
-        to cut outliers from the data when using iterstat on the optimum filter amplitudes. Default is 2.
-    removemeans : boolean, optional
-        Boolean flag on if the mean of each trace should be removed before doing the optimal filter (True) or
-        if the means should not be removed (False). This is useful for dIdV traces, when we want to cut out
-        pulses that have smaller amplitude than the dIdV overshoot. Default is False.
+    """
 
-    Returns
-    -------
-    cpileup : ndarray
-        Boolean array giving which indices to keep or throw out based on the outlier algorithm
+    def _get_plot_inds(self, cout):
+        """
+        Hidden function for accessing the indices that specify passing
+        events and failing events given some cut on the data.
 
-    """
+        """
 
-    nbin = len(traces[0])
-    ind_trigger = round(nbin/2)
-    time = 1.0/fs*(np.arange(1, nbin+1)-ind_trigger)
-    lgc_b0 = time < 0.0
+        cinds_pre = self._cutinds
 
-    # pulse shape
-    tau_risepulse = 10.0e-6
-    tau_fallpulse = 100.0e-6
-    dummytemplate = (1.0-np.exp(-time/tau_risepulse))*np.exp(-time/tau_fallpulse)
-    dummytemplate[lgc_b0]=0.0
-    dummytemplate = dummytemplate/max(dummytemplate)
+        cpass = self._cutinds[cout]
+        npass = len(cpass)
+        cfail = np.setdiff1d(cinds_pre, cpass)
+        nfail = len(cfail)
 
-    # assume we just have white noise
-    dummypsd = np.ones(nbin)
+        fail_plot = np.random.choice(
+            cfail,
+            size=self._nplot if nfail > self._nplot else nfail,
+            replace=False,
+        )
 
-    if removemeans:
-        mean = np.mean(traces, axis=1)
-        traces -= mean[:, np.newaxis]
+        pass_plot = np.random.choice(
+            cpass,
+            size=self._nplot if npass > self._nplot else npass,
+            replace=False,
+        )
 
-    amps = np.zeros(len(traces))
+        return np.sort(fail_plot), np.sort(pass_plot)
 
-    #do optimum filter on all traces
-    for itrace in range(0,len(traces)):
-        amps[itrace] = ofamp(traces[itrace], dummytemplate, dummypsd, fs)[0]
+    def _plot_events(self, cout):
+        """
+        Hidden function for plotting passing events and failing events
+        given some cut on the data.
 
-    if outlieralgo=="removeoutliers":
-        cpileup = removeoutliers(abs(amps))
-    elif outlieralgo=="iterstat":
-        cpileup = iterstat(abs(amps), cut=nsig, precision=10000.0)[2]
-    else:
-        raise ValueError("Unknown outlier algorithm inputted.")
+        """
+
+        fail_inds, pass_inds = self._get_plot_inds(cout)
+
+        fig, axes = plt.subplots(ncols=2, sharex=True)
+        time = np.arange(self._nbin) / self.fs
+
+        for temp_trace in self.traces[fail_inds]:
+            axes[0].plot(time * 1e3, temp_trace, alpha=0.5)
+
+        for temp_trace in self.traces[pass_inds]:
+            axes[1].plot(time * 1e3, temp_trace, alpha=0.5)
+
+        axes[0].set_title('Failed Cut')
+        axes[0].set_xlim(time[0] * 1e3, time[-1] * 1e3)
+
+        axes[1].set_title('Passed Cut')
+        axes[1].yaxis.set_label_position("right")
+        axes[1].yaxis.tick_right()
+
+        for ax in axes:
+            ax.tick_params(
+                which='both',
+                direction='in',
+                right=True,
+                top=True,
+                left=True,
+            )
 
-    return cpileup
+        fig.add_subplot(111, frameon=False)
+        plt.tick_params(
+            labelcolor='none',
+            which='both',
+            top=False,
+            bottom=False,
+            left=False,
+            right=False,
+        )
+        plt.xlabel("Time [ms]")
 
-def slopecut(traces, fs=625e3, outlieralgo="removeoutliers", nsig=2, is_didv=False, symmetrizeflag=False, sgfreq=100.0):
+
+class IterCut(_PlotCut):
     """
-    Function to automatically cut out outliers of the slopes of the inputted traces. Includes a routine that 
-    attempts to symmetrize the distribution of slopes around zero, which is useful when the majority of traces 
-    have a slope.
+    Class for iteratively applying various cuts to data while being
+    able to track what events were cut between steps.
 
-    Parameters
+    Attributes
     ----------
     traces : ndarray
-        2-dimensional array of traces to do cuts on
-    fs : float, optional
-        Digitization rate that the data was taken at
-    outlieralgo : string, optional
-        Which outlier algorithm to use. If set to "removeoutliers", uses the removeoutliers algorithm that
-        removes data based on the skewness of the dataset. If set to "iterstat", uses the iterstat algorithm
-        to remove data based on being outside a certain number of standard deviations from the mean
-    nsig : float, optional
-        If outlieralgo is "iterstat", this can be used to tune the number of standard deviations from the mean
-        to cut outliers from the data when using iterstat on the slopes. Default is 2.
-    is_didv : bool, optional
-        Boolean flag on whether or not the trace is a dIdV curve
-    symmetrizeflag : bool, optional
-        Flag for whether or not the slopes should be forced to have an average value of zero.
-        Should be used if most of the traces have a slope
-    sgfreq : float, optional
-        If is_didv is True, then the sgfreq is used to know where the flat parts of the traces should be
-
-    Returns
-    -------
-    cslope : ndarray
-        Boolean array giving which indices to keep or throw out based on the outlier algorithm
+        The traces that will be cut on.
+    fs : float
+        The digitization rate of the traces.
+    cmask : ndarray
+        The current data quality cut after applying the cuts before
+        this. This is a protected attribute.
 
     """
 
-    nbin = len(traces[0])
-    tracebegin = np.zeros(len(traces))
-    traceend = np.zeros(len(traces))
-
-    # number of periods and bins in period to determine where to calculate slopes and baselines
-    nperiods = np.floor((nbin/fs)*sgfreq)
-    binsinperiod = fs/sgfreq
-
-    if is_didv:
-        # try to use flat part of DIDV trace (assuming it's lined up with the start of a period)
-        # should still work if it's not lined up
-        if nperiods>1:
-            sloperangebegin = range(int(binsinperiod/4), int(3*binsinperiod/8))
-            sloperangeend = range(int((nperiods-1.0)*binsinperiod+binsinperiod/4), 
-                                  int((nperiods-1.0)*binsinperiod+3*binsinperiod/8))
+    def __init__(self, traces, fs, plotall=False, nplot=10):
+        """
+        Initialization of the IterCut class object.
+
+        Parameters
+        ----------
+        traces : ndarray
+            The traces that will be cut on.
+        fs : float
+            The digitization rate of the traces.
+        plotall : bool, optional
+            If True, the events that pass or fail each cut will be
+            plotted at each step. Default is False. To plot events that
+            pass or fail specific cuts, use the `verbose` kwarg when
+            applying those cuts to the data.
+        nplot : int, optional
+            The number of events that should be plotted from each
+            set of passing events and failing events. Default is 10.
+
+        """
+
+        self.traces = traces
+        self.fs = fs
+        self._plotall = plotall
+        self._nplot = nplot
+        self._ntraces = len(traces)
+        self._nbin = len(traces[0])
+        self._cutinds = np.arange(len(traces))
+
+    @property
+    def cmask(self):
+        _cmask = np.zeros(self._ntraces, dtype=bool)
+        _cmask[self._cutinds] = True
+        return _cmask
+
+    @cmask.setter
+    def cmask(self, value):
+        raise AttributeError("cmask is a protected attribute, can't set it")
+
+    @cmask.deleter
+    def cmask(self):
+        raise AttributeError("cmask is a protected attribute, can't delete it")
+
+    def _run_algo(self, vals, outlieralgo, verbose, **kwargs):
+        """
+        Hidden function for running the outlier algorithm on a set of
+        values.
+
+        """
+
+        if outlieralgo=="iterstat":
+            cout = iterstat(vals, **kwargs)[2]
+        elif outlieralgo=="removeoutliers": 
+            cout = removeoutliers(vals, **kwargs)
+        elif outlieralgo=="sigma_clip":
+            array = sigma_clip(vals, axis=0, masked=False, **kwargs)
+            cout = ~np.isnan(array)
         else:
-            sloperangebegin = range(int(binsinperiod/4), int(5*binsinperiod/16))
-            sloperangeend = range(int(5*binsinperiod/16), int(3*binsinperiod/8))
-    else:
-        sloperangebegin = range(0, int(nbin/10))
-        sloperangeend = range(int(9*nbin/10), nbin)
+            raise ValueError(
+                "Unrecognized outlieralgo, must be a str "
+                "of 'iterstat', 'removeoutliers', or 'sigma_clip'"
+            )
 
-    tracebegin = np.mean(traces[:, sloperangebegin], axis=1)
-    traceend = np.mean(traces[:, sloperangeend], axis=1)
+        if self._plotall or verbose:
+            self._plot_events(cout)
 
-    # now the slope cut to get rid of muon tails
-    # first, create a symmetric distribution about zero slope to get rid of the biased results, 
-    # but randomly cutting out events on biased side
-    slopes = traceend - tracebegin
+        self._cutinds = self._cutinds[cout]
 
-    if symmetrizeflag:
-        czeromeanslope = symmetrizedist(slopes)
-        czeromeanslopeinds = np.where(czeromeanslope)[0]
-    else:
-        # don't do anything about the shape of the distrbution
-        czeromeanslopeinds = np.arange(len(traces))
+    def pileupcut(self, template=None, psd=None, removemeans=False,
+                  outlieralgo="iterstat", verbose=False, **kwargs):
+        """
+        Function to automatically cut out outliers of the optimum
+        filter amplitudes of the inputted traces.
 
-    # now get rid of outliers from the symmetrized distribution
-    if outlieralgo=="removeoutliers":
-        cslope = removeoutliers(slopes[czeromeanslopeinds])
-    elif outlieralgo=="iterstat":
-        cslope = iterstat(slopes[czeromeanslopeinds], cut=nsig, precision=10000.0)[2]
-    else:
-        raise ValueError("Unknown outlier algorithm inputted.")
-    cslopeinds = czeromeanslopeinds[cslope]
+        Parameters
+        ----------
+        template : ndarray, NoneType, optional
+            The pulse template to use for the optimum filter. If
+            not passed, then a 10 us rise time and 100 us fall time
+            pulse is used.
+        psd : ndarray, NoneType, optional
+            The two-sided PSD (units of A^2/Hz) to use for the
+            optimum filter. If not passed, then all frequencies are
+            weighted equally.
+        removemeans : bool, optional
+            Boolean flag for if the mean of each trace should be
+            removed before doing the optimum filter (True) or if the
+            means should not be removed (False). This is useful for
+            dIdV traces, when we want to cut out pulses that have
+            smaller amplitude than the dIdV overshoot. Default is
+            False.
+        outlieralgo : str, optional
+            Which outlier algorithm to use: iterstat, removeoutliers,
+            or astropy's sigma_clip. Default is "iterstat".
+        verbose : bool, optional
+            If True, the events that pass or fail each cut will be
+            plotted at each step. Default is False. If `plotall` is
+            True when initializing this class, then this will be
+            ignored in favor of `plotall`.
+        **kwargs
+            Keyword arguments to pass to the outlier algorithm function
+            call.
 
-    cslopetot = np.ones(len(traces), dtype=bool)
-    cslopetot[cslopeinds] = True
+        Returns
+        -------
+        cpileup : ndarray
+            Boolean array giving which indices to keep or throw out
+            based on the outlier algorithm.
 
-    return cslopetot
+        """
 
-def baselinecut(traces, fs=625e3, outlieralgo="removeoutliers", nsig=2, is_didv=False, sgfreq=100.0):
-    """
-    Function to automatically cut out outliers of the baselines of the inputted traces.
+        if template is None:
+            time = np.arange(self._nbin) / self.fs
+            template = make_template(time, 10e-6, 100e-6)
 
-    Parameters
-    ----------
-    traces : ndarray
-        2-dimensional array of traces to do cuts on
-    fs : float, optional
-        Digitization rate that the data was taken at
-    outlieralgo : string, optional
-        Which outlier algorithm to use. If set to "removeoutliers", uses the removeoutliers algorithm that
-        removes data based on the skewness of the dataset. If set to "iterstat", uses the iterstat algorithm
-        to remove data based on being outside a certain number of standard deviations from the mean
-    nsig : float, optional
-        If outlieralgo is "iterstat", this can be used to tune the number of standard deviations from the mean
-        to cut outliers from the data when using iterstat on the baselines. Default is 2.
-    is_didv : bool, optional
-        Boolean flag on whether or not the trace is a dIdV curve
-    sgfreq : float, optional
-        If is_didv is True, then the sgfreq is used to know where the flat parts of the traces should be
+        if psd is None:
+            psd = np.ones(self._nbin)
 
-    Returns
-    -------
-    cbaseline : ndarray
-        Boolean array giving which indices to keep or throw out based on the outlier algorithm
+        temp_traces = self.traces[self._cutinds]
 
-    """
+        if removemeans:
+            mean = np.mean(temp_traces, axis=-1, keepdims=True)
+            temp_traces -= mean
 
-    nbin = len(traces[0])
-    tracebegin = np.zeros(len(traces))
+        ntemptraces = len(temp_traces)
 
-    # number of periods and bins in period to determine where to calculate slopes and baselines
-    nperiods = np.floor((nbin/fs)*sgfreq)
-    binsinperiod = fs/sgfreq
+        amps = np.zeros(ntemptraces)
 
-    if is_didv:
-        # try to use flat part of DIDV trace (assuming it's lined up with the start of a period)
-        # should still work if it's not lined up
-        if nperiods>1:
-            sloperangebegin = range(int(binsinperiod/4), int(3*binsinperiod/8))
-        else:
-            sloperangebegin = range(int(binsinperiod/4), int(5*binsinperiod/16))
-    else:
-        sloperangebegin = range(0, int(nbin/10))
+        #do optimum filter on all traces
+        for itrace in range(ntemptraces):
+            amps[itrace] = ofamp(
+                temp_traces[itrace], template, psd, self.fs,
+            )[0]
 
-    tracebegin = np.mean(traces[:, sloperangebegin], axis=1)
+        self._run_algo(np.abs(amps), outlieralgo, verbose, **kwargs)
 
-    # baseline cut
-    if outlieralgo=="removeoutliers":
-        cbaseline = removeoutliers(tracebegin)
-    elif outlieralgo=="iterstat":
-        cbaseline = iterstat(tracebegin, cut=nsig, precision=10000.0)[2]
-    else:
-        raise ValueError("Unknown outlier algorithm inputted.")
+        return self.cmask
 
-    return cbaseline
+    def baselinecut(self, endindex=None, outlieralgo="iterstat",
+                    verbose=False, **kwargs):
+        """
+        Function to automatically cut out outliers of the baselines
+        of the inputted traces.
 
-def chi2cut(traces, fs=625e3, outlieralgo="iterstat", nsig=2):
-    """
-    Function to automatically cut out outliers of the baselines of the inputted traces.
+        Parameters
+        ----------
+        endindex : int, NoneType, optional
+            The end index of the trace to average up to for the
+            basleine calculation. If not passed, the default value
+            is half of the trace length.
+        outlieralgo : str, optional
+            Which outlier algorithm to use: iterstat, removeoutliers,
+            or astropy's sigma_clip. Default is "iterstat".
+        verbose : bool, optional
+            If True, the events that pass or fail each cut will be
+            plotted at each step. Default is False. If `plotall` is
+            True when initializing this class, then this will be
+            ignored in favor of `plotall`.
+        **kwargs
+            Keyword arguments to pass to the outlier algorithm function
+            call.
 
-    Parameters
-    ----------
-    traces : ndarray
-        2-dimensional array of traces to do cuts on
-    fs : float, optional
-        Digitization rate that the data was taken at
-    outlieralgo : string, optional
-        Which outlier algorithm to use. If set to "removeoutliers", uses the removeoutliers algorithm that
-        removes data based on the skewness of the dataset. If set to "iterstat", uses the iterstat algorithm
-        to remove data based on being outside a certain number of standard deviations from the mean
-    nsig : float, optional
-        If outlieralgo is "iterstat", this can be used to tune the number of standard deviations from the mean
-        to cut outliers from the data when using iterstat on the Chi2s. Default is 2.
+        Returns
+        -------
+        cbaseline : ndarray
+            Boolean array giving which indices to keep or throw out
+            based on the outlier algorithm.
 
-    Returns
-    -------
-    cchi2 : ndarray
-        Boolean array giving which indices to keep or throw out based on the outlier algorithm
+        """
+        
+        temp_traces = self.traces[self._cutinds]
+        ntemptraces = len(temp_traces)
 
-    """
+        if endindex is None:
+            endindex = self._nbin // 2
 
-    nbin = len(traces[0])
-    ind_trigger = round(nbin/2)
-    time = 1.0/fs*(np.arange(1, nbin+1)-ind_trigger)
-    lgc_b0 = time < 0.0
+        baselines = np.mean(temp_traces[..., :endindex], axis=-1)
 
-    # pulse shape
-    tau_risepulse = 10.0e-6
-    tau_fallpulse = 100.0e-6
-    dummytemplate = (1.0-np.exp(-time/tau_risepulse))*np.exp(-time/tau_fallpulse)
-    dummytemplate[lgc_b0]=0.0
-    dummytemplate = dummytemplate/max(dummytemplate)
+        self._run_algo(baselines, outlieralgo, verbose, **kwargs)
 
-    # assume we just have white noise
-    dummypsd = np.ones(nbin)
+        return self.cmask
 
-    chi2 = np.zeros(len(traces))
+    def slopecut(self, outlieralgo="iterstat", verbose=False, **kwargs):
+        """
+        Function to automatically cut out outliers of the slopes of the
+        inputted traces. Slopes are calculated via maximum likelihood
+        and use the entire trace.
 
-    # First do optimum filter on all traces without mean subtracted
-    for itrace in range(0,len(traces)):
-        chi2[itrace] = ofamp(traces[itrace], dummytemplate, dummypsd, fs)[2]
+        Parameters
+        ----------
+        outlieralgo : str, optional
+            Which outlier algorithm to use: iterstat, removeoutliers,
+            or astropy's sigma_clip. Default is "iterstat".
+        verbose : bool, optional
+            If True, the events that pass or fail each cut will be
+            plotted at each step. Default is False. If `plotall` is
+            True when initializing this class, then this will be
+            ignored in favor of `plotall`.
+        **kwargs
+            Keyword arguments to pass to the outlier algorithm function
+            call.
 
-    if outlieralgo=="removeoutliers":
-        cchi2 = removeoutliers(chi2)
-    elif outlieralgo=="iterstat":
-        cchi2 = iterstat(chi2, cut=nsig, precision=10000.0)[2]
-    else:
-        raise ValueError("Unknown outlier algorithm inputted.")
+        Returns
+        -------
+        cslope : ndarray
+            Boolean array giving which indices to keep or throw out
+            based on the outlier algorithm.
+
+        """
 
-    return cchi2
+        temp_traces = self.traces[self._cutinds]
+        ntemptraces = len(temp_traces)
+        time = np.arange(self._nbin) / self.fs
+        ymean = np.mean(temp_traces, axis=-1, keepdims=True)
+        xmean = np.mean(time)
 
-def autocuts(traces, fs=625e3, is_didv=False, sgfreq=200.0, symmetrizeflag=False, outlieralgo="removeoutliers",
-             lgcpileup1=True, lgcslope=True, lgcbaseline=True, lgcpileup2=True, lgcchi2=True,
-             nsigpileup1=2, nsigslope=2, nsigbaseline=2, nsigpileup2=2, nsigchi2=3):
+        slopes = np.sum(
+            (time - xmean) * (temp_traces - ymean),
+            axis=-1,
+        ) / np.sum(
+            (time - xmean)**2,
+        )
+
+        self._run_algo(slopes, outlieralgo, verbose, **kwargs)
+
+        return self.cmask
+
+    def chi2cut(self, template=None, psd=None, outlieralgo="iterstat",
+                verbose=False, **kwargs):
+        """
+        Function to automatically cut out outliers of the optimum
+        filter chi-squares of the inputted traces.
+
+        Parameters
+        ----------
+        template : ndarray, NoneType, optional
+            The pulse template to use for the optimum filter. If
+            not passed, then a 10 us rise time and 100 us fall time
+            pulse is used.
+        psd : ndarray, NoneType, optional
+            The two-sided PSD (units of A^2/Hz) to use for the
+            optimum filter. If not passed, then all frequencies are
+            weighted equally.
+        outlieralgo : str, optional
+            Which outlier algorithm to use: iterstat, removeoutliers,
+            or astropy's sigma_clip. Default is "iterstat".
+        verbose : bool, optional
+            If True, the events that pass or fail each cut will be
+            plotted at each step. Default is False. If `plotall` is
+            True when initializing this class, then this will be
+            ignored in favor of `plotall`.
+        **kwargs
+            Keyword arguments to pass to the outlier algorithm function
+            call.
+
+        Returns
+        -------
+        cchi2 : ndarray
+            Boolean array giving which indices to keep or throw out
+            based on the outlier algorithm.
+
+        """
+
+        if template is None:
+            time = np.arange(self._nbin) / self.fs
+            template = make_template(time, 10e-6, 100e-6)
+
+        if psd is None:
+            psd = np.ones(self._nbin)
+
+        temp_traces = self.traces[self._cutinds]
+        ntemptraces = len(temp_traces)
+
+        chi2s = np.zeros(ntemptraces)
+
+        #do optimum filter on all traces
+        for itrace in range(ntemptraces):
+            chi2s[itrace] = ofamp(
+                temp_traces[itrace], template, psd, self.fs,
+            )[-1]
+
+        self._run_algo(chi2s, outlieralgo, verbose, **kwargs)
+
+        return self.cmask
+
+    def arbitrarycut(self, cutfunction, *args, outlieralgo="iterstat",
+                     verbose=False, **kwargs):
+        """
+        Function to automatically cut out outliers of the optimum
+        filter amplitudes of the inputted traces.
+
+        Parameters
+        ----------
+        cutfunction : FunctionType
+            A function to set cuts on. Should be able to take
+            in the traces ndarray, and any other arguments can
+            be passed before defining the kwargs.
+        *args
+            The arguments that should be passed to `cutfunction`
+            beyond the traces.
+        outlieralgo : str, optional
+            Which outlier algorithm to use: iterstat, removeoutliers,
+            or astropy's sigma_clip. Default is "iterstat".
+        verbose : bool, optional
+            If True, the events that pass or fail each cut will be
+            plotted at each step. Default is False. If `plotall` is
+            True when initializing this class, then this will be
+            ignored in favor of `plotall`.
+        **kwargs
+            Keyword arguments to pass to the outlier algorithm function
+            call.
+
+        Returns
+        -------
+        cpileup : ndarray
+            Boolean array giving which indices to keep or throw out
+            based on the outlier algorithm.
+
+        """
+
+        temp_traces = self.traces[self._cutinds]
+        vals = cutfunction(temp_traces, *args)
+
+        self._run_algo(vals, outlieralgo, verbose, **kwargs)
+
+        return self.cmask
+
+
+def autocuts(traces, fs=625e3, template=None, psd=None, is_didv=False,
+             outlieralgo="iterstat", lgcpileup1=True, lgcslope=True,
+             lgcbaseline=True, lgcpileup2=True, lgcchi2=True, nsigpileup1=2,
+             nsigslope=2, nsigbaseline=2, nsigpileup2=2, nsigchi2=3,
+             **kwargs):
     """
-    Function to automatically cut out bad traces based on the optimum filter amplitude, slope, baseline, and chi^2
-    of the traces.
+    Function to automatically cut out bad traces based on the optimum
+    filter amplitude, slope, baseline, and chi^2 of the traces.
 
     Parameters
     ----------
@@ -673,113 +910,133 @@ def autocuts(traces, fs=625e3, is_didv=False, sgfreq=200.0, symmetrizeflag=False
         Sample rate that the data was taken at
     is_didv : bool, optional
         Boolean flag on whether or not the trace is a dIdV curve
-    sgfreq : float, optional
-        If is_didv is True, then the sgfreq is used to know where the flat parts of the traces should be
-    symmetrizeflag : bool, optional
-        Flag for whether or not the slopes should be forced to have an average value of zero.
-        Should be used if most of the traces have a slope
     outlieralgo : string, optional
-        Which outlier algorithm to use. If set to "removeoutliers", uses the removeoutliers algorithm that
-        removes data based on the skewness of the dataset. If set to "iterstat", uses the iterstat algorithm
-        to remove data based on being outside a certain number of standard deviations from the mean
+        Which outlier algorithm to use. If set to "removeoutliers",
+        uses the removeoutliers algorithm that removes data based on
+        the skewness of the dataset. If set to "iterstat", uses the
+        iterstat algorithm to remove data based on being outside a
+        certain number of standard deviations from the mean. Can also
+        be set to astropy's "sigma_clip".
     lgcpileup1 : boolean, optional
-        Boolean value on whether or not do the pileup1 cut (this is the initial pileup cut
-        that is always done whether or not we have dIdV data). Default is True.
+        Boolean value on whether or not do the pileup1 cut (this is the
+        initial pileup cut that is always done whether or not we have
+        dIdV data). Default is True.
     lgcslope : boolean, optional
-        Boolean value on whether or not do the slope cut. Default is True.
+        Boolean value on whether or not do the slope cut. Default is
+        True.
     lgcbaseline : boolean, optional
-        Boolean value on whether or not do the baseline cut. Default is True.
+        Boolean value on whether or not do the baseline cut. Default is
+        True.
     lgcpileup2 : boolean, optional
-        Boolean value on whether or not do the pileup2 cut (this cut is only done when is_didv is
-        also True). Default is True.
+        Boolean value on whether or not do the pileup2 cut (this cut is
+        only done when is_didv is also True). Default is True.
     lgcchi2 : boolean, optional
-        Boolean value on whether or not do the chi2 cut. Default is True.
+        Boolean value on whether or not do the chi2 cut. Default is
+        True.
     nsigpileup1 : float, optional
-        If outlieralgo is "iterstat", this can be used to tune the number of standard deviations from the mean
-        to cut outliers from the data when using iterstat on the optimum filter amplitudes. Default is 2.
+        If outlieralgo is "iterstat", this can be used to tune the
+        number of standard deviations from the mean to cut outliers
+        from the data when using iterstat on the optimum filter
+        amplitudes. Default is 2.
     nsigslope : float, optional
-        If outlieralgo is "iterstat", this can be used to tune the number of standard deviations from the mean
-        to cut outliers from the data when using iterstat on the slopes. Default is 2.
+        If outlieralgo is "iterstat", this can be used to tune the
+        number of standard deviations from the mean to cut outliers
+        from the data when using iterstat on the slopes. Default is 2.
     nsigbaseline : float, optional
-        If outlieralgo is "iterstat", this can be used to tune the number of standard deviations from the mean
-        to cut outliers from the data when using iterstat on the baselines. Default is 2.
+        If outlieralgo is "iterstat", this can be used to tune the
+        number of standard deviations from the mean to cut outliers
+        from the data when using iterstat on the baselines. Default is
+        2.
     nsigpileup2 : float, optional
-        If outlieralgo is "iterstat", this can be used to tune the number of standard deviations from the mean
-        to cut outliers from the data when using iterstat on the optimum filter amplitudes after the mean
-        has been subtracted. (only used if is_didv is True). Default is 2.
+        If outlieralgo is "iterstat", this can be used to tune the
+        number of standard deviations from the mean to cut outliers
+        from the data when using iterstat on the optimum filter
+        amplitudes after the mean has been subtracted. (only used if
+        is_didv is True). Default is 2.
     nsigchi2 : float, optional
-        This can be used to tune the number of standard deviations from the mean to cut outliers from the data
-        when using iterstat on the chi^2 values. Default is 3. This is always used, as iterstat is always used
-        for the chi^2 cut.
+        This can be used to tune the number of standard deviations
+        from the mean to cut outliers from the data when using iterstat
+        on the chi^2 values. Default is 3.
+    **kwargs
+        Placeholder kwargs for backwards compatibility.
 
     Returns
     -------
     ctot : ndarray
-        Boolean array giving which indices to keep or throw out based on the autocuts algorithm
+        Boolean array giving which indices to keep or throw out based
+        on the autocuts algorithm.
 
     """
 
-    # pileup cut
+    if is_didv and 'sgfreq' in kwargs:
+        warnings.warn(
+            "The `sgfreq` option has been deprecated and "
+            "is now ignored when is_didv is True."
+        )
+
+    Cut = IterCut(traces, fs)
+
     if lgcpileup1:
-        cpileup1 = pileupcut(traces, fs=fs, outlieralgo=outlieralgo, nsig=nsigpileup1)
-        cpileup1inds = np.where(cpileup1)[0]
-    else:
-        cpileup1inds = np.arange(len(traces))
+        kwargs = {'cut': nsigpileup1} if outlieralgo=="iterstat" else {}
+        Cut.pileupcut(
+            template=template,
+            psd=psd,
+            outlieralgo=outlieralgo,
+            **kwargs,
+        )
 
-    #slope cut
     if lgcslope:
-        cslope = slopecut(traces[cpileup1inds], fs=fs, outlieralgo=outlieralgo, nsig=nsigslope, is_didv=is_didv, 
-                          symmetrizeflag=symmetrizeflag, sgfreq=sgfreq)
-    else:
-        cslope = np.ones(cpileup1inds.shape, dtype=bool)
-    cslopeinds = cpileup1inds[cslope]
+        kwargs = {'cut': nsigslope} if outlieralgo=="iterstat" else {}
+        Cut.slopecut(outlieralgo=outlieralgo, **kwargs)
 
-    # baseline cut
     if lgcbaseline:
-        cbaseline = baselinecut(traces[cslopeinds], fs=fs, outlieralgo=outlieralgo, nsig=nsigbaseline, is_didv=is_didv, 
-                                sgfreq=sgfreq)
-    else:
-        cbaseline = np.ones(cslopeinds.shape, dtype=bool)
-    cbaselineinds = cslopeinds[cbaseline]
+        kwargs = {'cut': nsigbaseline} if outlieralgo=="iterstat" else {}
+        Cut.baselinecut(outlieralgo=outlieralgo, **kwargs)
 
-    # do a pileup cut on the mean subtracted data if this is a dIdV, so that we remove pulses
-    # that are smaller than the dIdV peaks
+    # do a pileup cut on the mean subtracted data if this is a dIdV,
+    # so that we remove pulses that are smaller than the dIdV peaks
     if lgcpileup2 and is_didv:
-        cpileup2 = pileupcut(traces[cbaselineinds], fs=fs, outlieralgo=outlieralgo, nsig=nsigpileup2, removemeans=True)
-    else:
-        cpileup2 = np.ones(cbaselineinds.shape, dtype=bool)
-    cpileup2inds = cbaselineinds[cpileup2]
+        kwargs = {'cut': nsigpileup2} if outlieralgo=="iterstat" else {}
+        Cut.pileupcut(
+            template=template,
+            psd=psd,
+            removemeans=True,
+            outlieralgo=outlieralgo,
+            **kwargs,
+        )
 
-    #general chi2 cut, this should use iterstat, as there shouldn't be a tail
     if lgcchi2:
-        cchi2 = chi2cut(traces[cpileup2inds], fs=fs, outlieralgo="iterstat", nsig=nsigchi2)
-    else:
-        cchi2 = np.ones(cpileup2inds.shape, dtype=bool)
-    cchi2inds = cpileup2inds[cchi2]
+        kwargs = {'cut': nsigchi2} if outlieralgo=="iterstat" else {}
+        Cut.chi2cut(
+            template=template,
+            psd=psd,
+            outlieralgo=outlieralgo,
+            **kwargs,
+        )
 
-    # convert total cut to logical array
-    ctot = np.zeros(len(traces),dtype=bool)
-    ctot[cchi2inds] = True
+    return Cut.cmask
 
-    return ctot
 
-def get_muon_cut(traces, thresh_pct = 0.95, nsatbins = 600):
+def get_muon_cut(traces, thresh_pct=0.95, nsatbins=600):
     """
-    Function to help identify saturated muons from array of time series traces.
+    Function to help identify saturated muons from array of time series
+    traces.
 
     ***Traces must have POSITIVE going pulses***
 
-    Note, for best results, only large amplitude traces should based to this
-    function. The user may need to play around with the thresh_pct and nsatbins
-    parameters to achive the desired result. 
+    Note, for best results, only large amplitude traces should based to
+    this function. The user may need to play around with the thresh_pct
+    and nsatbins parameters to achive the desired result. 
 
     Parameters
     ----------
     traces: array
-        Array of time series traces of shape (#number of traces, #bins per trace).
+        Array of time series traces of shape (#number of traces, #bins
+        per trace).
     thresh_pct: float, optional
-        The percentage of the maximum amplitude that the pulse must remain above
-        for nsatbins in order to be considered `saturated'.
+        The percentage of the maximum amplitude that the pulse must
+        remain above for nsatbins in order to be considered
+        `saturated'.
     nsatbins: int, optional
         The minimum number of bins that a muon should be saturated for.
 
@@ -795,11 +1052,12 @@ def get_muon_cut(traces, thresh_pct = 0.95, nsatbins = 600):
         trace_max = np.max(trace)
         # check that the maximum value of the trace is above the threshold and
         # that the maximum is decently larger than the minimum
-
         peak_loc = np.argmax(trace)
-        # check that the peak is saturated (this should be true for muons that saturate the
-        # detector or muon that rail the amplifier) 
+
+        # check that the peak is saturated (this should be true for muons
+        # that saturate the detector or muon that rail the amplifier) 
         if ((peak_loc + int(nsatbins)) < traces.shape[-1]):
             if (trace[peak_loc+int(nsatbins)] >= trace_max*thresh_pct):
                 muon_cut[ii] = True
+
     return muon_cut
diff --git a/requirements.txt b/requirements.txt
index cd568cdc..1d50a758 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,3 +9,4 @@ sphinx-rtd-theme>=0.4.1
 sphinxcontrib-websupport>=1.0.1
 iminuit>=1.3,<2.0
 sympy>=1.4
+astropy
diff --git a/setup.py b/setup.py
index 7fcd91ca..7f5744be 100644
--- a/setup.py
+++ b/setup.py
@@ -55,5 +55,6 @@ def run(self):
         'matplotlib',
         'iminuit<2.0',
         'sympy',
+        'astropy',
     ],
 )


Test patch:
diff --git a/demos/cuts/test_autocuts.ipynb b/demos/cuts/test_autocuts.ipynb
index 5c7724ff..485fcea3 100644
--- a/demos/cuts/test_autocuts.ipynb
+++ b/demos/cuts/test_autocuts.ipynb
@@ -1,26 +1,16 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "toc": true
-   },
-   "source": [
-    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
-    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Using the `autocuts` Algorithm\n",
+    "# Using the `autocuts` and `IterCut` Algorithms\n",
     "_____\n",
-    "This is a quick look at how to use the `autocuts` algorithm.\n",
+    "This is a quick look at how to use the `autocuts` and the `IterCut` algorithms. The `autocuts` function acts as a black box (the user cannot see what is going on under the hood), while `IterCut` allows the user to understand each cut being applied to data. For quick results, `autocuts` is usually sufficient, but `IterCut` is very useful to actually understand what is happening.\n",
     "\n",
-    "Note that there are many more optional arguments than what are shown here. For example, you can choose which cuts to do, change the outlier algorithm used, or indicate that you are cutting on dIdV data.\n",
+    "Note that there are many more optional arguments than what are shown here in the notebook. As always, we recommend reading the docstrings!\n",
     "\n",
-    "First, let's import the function."
+    "First, let's import our functions."
    ]
   },
   {
@@ -31,7 +21,10 @@
    "source": [
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "from qetpy import autocuts, calc_psd"
+    "from qetpy import autocuts, calc_psd, IterCut\n",
+    "\n",
+    "# ensure that the notebook is repeatable by using the same random seed\n",
+    "np.random.seed(1)"
    ]
   },
   {
@@ -75,12 +68,14 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
@@ -89,8 +84,7 @@
     "ax.loglog(f, np.sqrt(psd), label=\"Before autocuts\")\n",
     "ax.set_ylim([1e-12,1e-7])\n",
     "ax.set_xlabel('Frequency (Hz)')\n",
-    "ax.set_ylabel('PSD (A/$\\sqrt{\\mathrm{Hz}}$)')\n",
-    "ax.set_title(\"PSD before autocuts\")\n",
+    "ax.set_ylabel(r'PSD (A/$\\sqrt{\\mathrm{Hz}}$)')\n",
     "ax.legend(loc=\"upper right\")\n",
     "ax.grid(linestyle='dotted')\n",
     "ax.tick_params(which='both',direction='in',right=True,top=True)"
@@ -100,14 +94,116 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "## Using `autocuts`\n",
+    "\n",
     "Apply the autocuts function."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m\n",
+       "\u001b[0mautocuts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mtraces\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mfs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m625000.0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mtemplate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mpsd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mis_didv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0moutlieralgo\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'iterstat'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mlgcpileup1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mlgcslope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mlgcbaseline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mlgcpileup2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mlgcchi2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mnsigpileup1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mnsigslope\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mnsigbaseline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mnsigpileup2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mnsigchi2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m\n",
+       "Function to automatically cut out bad traces based on the optimum\n",
+       "filter amplitude, slope, baseline, and chi^2 of the traces.\n",
+       "\n",
+       "Parameters\n",
+       "----------\n",
+       "traces : ndarray\n",
+       "    2-dimensional array of traces to do cuts on\n",
+       "fs : float, optional\n",
+       "    Sample rate that the data was taken at\n",
+       "is_didv : bool, optional\n",
+       "    Boolean flag on whether or not the trace is a dIdV curve\n",
+       "outlieralgo : string, optional\n",
+       "    Which outlier algorithm to use. If set to \"removeoutliers\",\n",
+       "    uses the removeoutliers algorithm that removes data based on\n",
+       "    the skewness of the dataset. If set to \"iterstat\", uses the\n",
+       "    iterstat algorithm to remove data based on being outside a\n",
+       "    certain number of standard deviations from the mean. Can also\n",
+       "    be set to astropy's \"sigma_clip\".\n",
+       "lgcpileup1 : boolean, optional\n",
+       "    Boolean value on whether or not do the pileup1 cut (this is the\n",
+       "    initial pileup cut that is always done whether or not we have\n",
+       "    dIdV data). Default is True.\n",
+       "lgcslope : boolean, optional\n",
+       "    Boolean value on whether or not do the slope cut. Default is\n",
+       "    True.\n",
+       "lgcbaseline : boolean, optional\n",
+       "    Boolean value on whether or not do the baseline cut. Default is\n",
+       "    True.\n",
+       "lgcpileup2 : boolean, optional\n",
+       "    Boolean value on whether or not do the pileup2 cut (this cut is\n",
+       "    only done when is_didv is also True). Default is True.\n",
+       "lgcchi2 : boolean, optional\n",
+       "    Boolean value on whether or not do the chi2 cut. Default is\n",
+       "    True.\n",
+       "nsigpileup1 : float, optional\n",
+       "    If outlieralgo is \"iterstat\", this can be used to tune the\n",
+       "    number of standard deviations from the mean to cut outliers\n",
+       "    from the data when using iterstat on the optimum filter\n",
+       "    amplitudes. Default is 2.\n",
+       "nsigslope : float, optional\n",
+       "    If outlieralgo is \"iterstat\", this can be used to tune the\n",
+       "    number of standard deviations from the mean to cut outliers\n",
+       "    from the data when using iterstat on the slopes. Default is 2.\n",
+       "nsigbaseline : float, optional\n",
+       "    If outlieralgo is \"iterstat\", this can be used to tune the\n",
+       "    number of standard deviations from the mean to cut outliers\n",
+       "    from the data when using iterstat on the baselines. Default is\n",
+       "    2.\n",
+       "nsigpileup2 : float, optional\n",
+       "    If outlieralgo is \"iterstat\", this can be used to tune the\n",
+       "    number of standard deviations from the mean to cut outliers\n",
+       "    from the data when using iterstat on the optimum filter\n",
+       "    amplitudes after the mean has been subtracted. (only used if\n",
+       "    is_didv is True). Default is 2.\n",
+       "nsigchi2 : float, optional\n",
+       "    This can be used to tune the number of standard deviations\n",
+       "    from the mean to cut outliers from the data when using iterstat\n",
+       "    on the chi^2 values. Default is 3.\n",
+       "**kwargs\n",
+       "    Placeholder kwargs for backwards compatibility.\n",
+       "\n",
+       "Returns\n",
+       "-------\n",
+       "ctot : ndarray\n",
+       "    Boolean array giving which indices to keep or throw out based\n",
+       "    on the autocuts algorithm.\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/Library/Python/3.10/lib/python/site-packages/qetpy/cut/_cut.py\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "?autocuts"
    ]
@@ -123,13 +219,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The cut efficiency is 0.488.\n"
+      "The cut efficiency is 0.428.\n"
      ]
     }
    ],
    "source": [
-    "cut = autocuts(traces, fs=fs)\n",
-    "print(f\"The cut efficiency is {np.sum(cut)/len(traces)}.\")"
+    "cut = autocuts(\n",
+    "    traces,\n",
+    "    fs=fs,\n",
+    ")\n",
+    "print(f\"The cut efficiency is {np.sum(cut)/len(traces):.3f}.\")"
    ]
   },
   {
@@ -155,23 +254,397 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 720x432 with 1 Axes>"
       ]
      },
-     "metadata": {},
+     "metadata": {
+      "needs_background": "light"
+     },
      "output_type": "display_data"
     }
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(10,6))\n",
     "ax.loglog(f, np.sqrt(psd), label=\"Before autocuts\")\n",
-    "ax.loglog(f, np.sqrt(psd_cut), label=\"Before autocuts\")\n",
+    "ax.loglog(f, np.sqrt(psd_cut), label=\"After autocuts\")\n",
+    "ax.set_ylim([1e-12,1e-7])\n",
+    "ax.set_xlabel('Frequency (Hz)')\n",
+    "ax.set_ylabel(r'PSD (A/$\\sqrt{\\mathrm{Hz}}$)')\n",
+    "ax.legend(loc=\"upper right\")\n",
+    "ax.grid(linestyle='dotted')\n",
+    "ax.tick_params(which='both',direction='in',right=True,top=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The change is huge! Which makes sense, as we have removed many of the pulses, muon tails, etc. Please note that there may still be \"bad\" traces in the data, as the autocuts function is not perfect. There may be more cuts that one would like to do that are more specific to the dataset.\n",
+    "\n",
+    "## Using IterCut for better cut control\n",
+    "\n",
+    "A good way of understanding the cuts further than using the black box that is `autocuts` is to use the object-oriented version `IterCut`. This class allows the user freedom in cut order, which cuts are used, which algorithms are used for outlier removal, and more.\n",
+    "\n",
+    "Below, we match the default parameters and outlier algorithm (`iterstat`) to show that the cut efficiency is the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IC = IterCut(traces, fs)\n",
+    "IC.pileupcut(cut=2)\n",
+    "IC.slopecut(cut=2)\n",
+    "IC.baselinecut(cut=2)\n",
+    "IC.chi2cut(cut=3)\n",
+    "cut_ic = IC.cmask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The cut efficiency is 0.428.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"The cut efficiency is {np.sum(cut_ic)/len(traces):.3f}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psd_cut = calc_psd(traces[cut_ic], fs=fs, folded_over=True)[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "ax.loglog(f, np.sqrt(psd), label=\"Before IterCut\")\n",
+    "ax.loglog(f, np.sqrt(psd_cut), label=\"After IterCut\")\n",
+    "ax.set_ylim([1e-12,1e-7])\n",
+    "ax.set_xlabel('Frequency (Hz)')\n",
+    "ax.set_ylabel(r'PSD (A/$\\sqrt{\\mathrm{Hz}}$)')\n",
+    "ax.legend(loc=\"upper right\")\n",
+    "ax.grid(linestyle='dotted')\n",
+    "ax.tick_params(which='both',direction='in',right=True,top=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With `IterCut` we can also access the cuts at each step as they have been iteratively applied, and there is a verbose option for plotting the passing event and failing events for each cut."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FJE0gEO5V1bYwsm4lsTodq6rnVNXXYOXzAVrKbMUhEShrhiRsOEFneEb5H6fhM7Kvvu3iCV/T8ro+8Isu1cqZxme/xPT0/Zw5+4k1PY+qXrI6WPeemeGeE1O4p4uUHp/CPRWaIjt8lZeiknRY6uqDwLOq+tsdxsSWxOp2rIjsjHz8YVrKbMUhEShrjS2+Qk+c8vE44wTlTqbI17et1JexVHhuTWkypsqxVz5IpXJ6Tefted41Zv7z89/mlVc+cMk0H0XrCZKm6te3Bmg2rc3Pr66i+sJ9r+BOLzup8U7gPcBd0RBfEXl/rSwWQUmsp0TkceD3aJTEij02POb/CrvzPgG8BfjXvQhJfChrjURDAWDXQLwteEujixlfVXtWGZg6/zkGB65d9olahXuh8HzT5/PFs9wzM873mId588F3dpzteLnKXNXhQMv2z07Nc11/noN9gY/ELzickb9uGtNMgcRsu3CUSoEz3POLpFKDazz7yrCWlSL8xSrpiXzPcar6AF2frs4lsbodq6rvWR6lDSQaylqhrpkkK3RIfCm+X2Fq+ov1XhieV6RUOlbfXz2+iClHy4Cs5f2Kb/3bWvJj2g32v1zt3sPmw2dm+WyhPcrs6UKZj5ybrX8ufPMMhmYfgj8fbTgXspvIomu1z0mh+CInT/31BnrOVkeH7xXbZ1KfCqculKB1QSJQ1hob5gFPsJ6Yn3+QpcWnWFoKzM5RYQJhNNT5zqYfx5lmcfGJ1Z08XCW7Znl5Lr0W1XPzD1J0XgCCrPuVIDq+cZ6V/UaOn/izNtPR+fP/EGb/G+IaiV1q9DL1roS02dmvMyP3AfBQeYRnC4Hpy3FmLsjpfymQCJQ1Q+KUj2Kr+1Jarz/ubpS8lzuOOHnqr5mevn9V567xbaPLD8VVVVy3NdK0Ac8P9rlnL9z/EdfBMNiuHHtyGrdFaHnuIrOzX1/xeSom+C1OOZeyIOSFm7yiCa+Pl4b49NQ8qj4nT/4VJ0/de8HzX0wkAmXNsbUZaQ0mEawhujMYU/bina8dlrSLSz0DbXqes36KWll7oFB4hhMn/uwiOOi70xK9ytnTRc4fW+TE07MdxwOUfMOS37vvTk2gfPL8HN+c750Yulxo1ccUW4TiSrWjygJ4vQW+t1jF+FVmZr+GKnxpYWN3RE0EylpjizPSmk17q2soy2UwzpkC/uLys8oLXSr51vqUVI8s4E6tTJOohOG9ta6Oa4eVPAfLe3b+6Ph5PrwYqQ5SM/F5Cx2PeWDuwiLA5ua+heME+YTOqQLuudb7q020lEpHOX36I50nPPsEnHwodlf58ccxpYZvZWHxMVxnDg/hBWeF9dwuMRKBslaoZ8qvLxkbBRvHWbq+mJn5CkeO/PdljOx8v9Qo7rliWPak931VH/ylhq19obIQqzEaA6boos76LYKmKy6m0LmD5NIDp6gebzbFmQ73YOr85zrO4zgznJUPoaw8L8QYh7m5B7sLiBacO/9ZKpXVOdbdc+fw5+brn9VTXlic4i8W9nU+aIMgEShrjq3NSGury0SgNMNxTc8s8xpa752/5OAXXby5al2TWC4qfoWThROcLjabslx3HnV9jOPjdcl3UM/UH2m/9dnWIHJsOXXGOuGDT53EPV9qiXhrwJS9SO2z1aNcOoZPpS0KbSWIE0aqyuLSU3VneWvxR292BlNeXj7JM9VB/tvRMxjgSHqcP5drAPCXqnyuGF/OZaMhEShrhqR8fRRmnbO0Nxoef34Jf7Hacb/nLXHkyH+nUHyRavVsz/lUfQrfPk31WJyZp+G3qGkmrt+sBRj16uLhFXL8xXSeOHm3eP8JvPkKpuzRSr3nLVKpnGZq6r6e9DrONOdjNIi6sDCrX4DIKh3hjjONqnKi4vDfjp7hXDVyj7wqzLwce1yJl+r5NlXvNNNTX6wXeTx//u+BwGDx4cXdaLmCP9vdJ1TDQ5Wg9qLr5fl63xWruqb1RiJQElwUbHUNpTXk1+/BMOfmgjpO5899lnL5la5jK5UzHD36+xxf/AALL8a36V0WIlbaoi9UtN3RbXBbh9frejW2+1Seew5/sV241Qpgnj33d3UfT9P8pTDjvNXJ3QMXGktVKh3l5Ml7KRSe5aViBddd4Fsv/xVemBdy5OF/xfzjvweV9sg3j0Jd04nenyh8YMl0zht3sXCIDyxQv3te0EZGIlASrCl0mY7Vyxsr186izvC5uUb18I+eneWRmb5g1pDpnj79t/X9s3I/L5cqeBEm37xi7/w9OCdDU5VR/IKDqbabnc5II0y1NlOcVrR03xcov7CcCLR4uBWXZ5bOx+47zgB/Mr+fBbdT+G938eK6C22VnWuhuY4TdLKsVs81veKUmOUUmO4hx47XTLMqsb1dWrfcy+v4S+4EAuXsgdI4rgbXUbA2diRXN6xKoIjIsbDGy2Mi0lasRgL8noi8FNbSv/XCSd3g2OIr8lZ4PX6IWwmqihfzePgdVuUPlMZ52QmEyJFyle8ERWFje5GcJ8fHzs3xlaYopnYGG1cSxJQb5zdFF+dMEd9vz9xeDtRP4WbbEymXmwh5pnSG3z/9PJ79II73YtO+l2UIgLMrzCdR42KMQ6Hw3IrrpqnCwxyg2KP/y2L1O/gKzzsDqMIXi6Pc/ciXcf3utHqhdqIoLy36PFvtD8/rU7RiNJQLMAleSlyIhvIWVb1ZVQ/H7Ps+glr7VwF3E9TVT7AFUDN1JXkoDUwdX2KhcHzZ459zBri/NBm7r1Xzq2LjugucWWxv3hQe0AW12loeDoGGZDw/KAtTaWeIteiqT8t+XnD6m/dV4mtomWUKFD/0PfrW8xTdr/QcX7sPpdIxfNPu9C6XTzC/8Ciu0+y/8JhfFj3HGOMx9vH5ucbcqu2+KIDvVEb4Wmmco24fT5RgyVliqhSvbbXipSVhbm6WSqUSniP+fnkL1U0hVC6WyeuHgL/UAN8CRlpKIV+G2Phf9qVEIlAacKs+xlpdA6lqtZkxuSfbI6oKhecoFJ6nenwx6JbIIrOVWSp+5yAAAJXgmfVp0GZKXlOTrCieC6Ocpsnx1VLHpoGxaHWctyZQikJaAg3Hq/hNYc/dcO7cp3Gdds2oZrpqrUBcpXeUnKrhixpEWFUilodp1+Lx849QdAMtzhAI3VnX43ThNKVQK8mY+WXRDnBfeSdz9LX5peLpWva064bVChQFPi8ij4jI3TH7dwPRSnQnaelT3KlNaoLLA+vhQ1lum9QNDbcc/LpMbQXe7Mg2js+Mn27qU15DTRC4xqPqVeqr5KPs4Lx/4VV4v+JX8SOmo3q4cP2rjv/OVX1ct9nvcvr0/2r6bPtCmsABrgsVCt86w9fPL/A/rIaGsDZ1fDvQ6PqY0KQ2N/et+na/JWrT1nLdH+MTCJbT1UD4Ft1AeFkrKHkDtDnnaxS6xqXsLbuE/YbAasvXv0FVT4nINuA+EXlOVb+6kgmW2yY1webCeuahLKdN6qXBKlifCMWlWXTmJXyzB5UUjMUP/fhS0BrgnzLTMcqoBhfhKQ7wktPHa5Zh7pl+6cux2z2jLBYc7k8JhLKpXAnWjJXKKYo7nyZVHQX62o/t0KekqaaXNN5L6HD6+lz3/JYPLe7mFmuGawYqvOz0M2B59Aq27eRL8eaqeFqGq5T5hcfr2+eXHqNlLdyGadMPlJpDIbS3v0dVUF95ge3UvCaV8knc7DywjeoySrNsNKxKQ1HVU+HreeDjtLeGPAXsjXzeE267fLEZ9NFLiK1t8mo8Cw+UxvmYfXXLXmWqNIXjR8w6qvxN4QAf4rWo8TFLSxS//e36bi/CrtRd/r09LZ01E42Re50eYxPuOB5J7qt4FdxQKKjl47Q45cvYTXQDmEoFbzbw1xw79oeUOUGVdn/DSf6sQRMSq1gslYX7p4YxJZf7SxP8XWFH2xjftDLlzr/TRTLMzX2Lrxf7UROwxsXKFHOV+cio5uvx5ir1Kath2LWlHgXNMFeZo+B2Fop+JYMpNS8Iqs4UsonN5ysWKCLSLxI8pSLSD3wP7a0hPwX8szDa6w5gQVVXluKbYFMjSWwM8JwzgNfyM/OMh2dcFpyGGaiWGAc08Tz1gpXudyIWPL/goK6hSIqnZLztnB+2ruG+zA1t20+6Ob5VHm3MXWn+jpajVx3PnKu/n63MMlWa6jj2w3KI+6SxrvTmKxQfPkL56bMUHnyB0lMncJmhymlaGb1T8qhUT3WNzqpFvXXLpG9tDTyD4Lo+BR/+ZH4/z5Qa383zMoLjTPNCIY9fsVHfZm5ploc/9QVOF063mXFr98vyQAx8p7C9vu9zlaspe+WumnqnEjIdUZqFYrviLSJ7ReR+EXlGRJ4WkV+MGfNmEVmIdGX89ci+t4vI82FU7r+LbD8oIg+G2z8sIj0TZFajoWwHHghbSX4b+Iyq/kNLu8nPAkeAlwia2//sKs6zyRDlApt3hXHBqCXLbeV7EINud8Oo8sx0OXhsOgxsS4Izyv2ym3NhS2ETud8ONotS6/TX2P4Pxe08VR2KTNIsQr7NNsodku3qp5UpjO9xcq5M1Yv4FzRgJe6pU2iloRWci7Q89mYr9d+GOi7+XOdy+QCV6ilc5iitUWPZMjYftUb41FeOMLMQaAbPV2wU8GgI91qgVdH3yFUypFyLU4VTzFfCiDFV3FOn8BdC+hVylTSUDdNLVfyYGHHj+lSOzNdzfSrqUTJeEF6/3N+KVwI3tuinB/yyql4P3AH8nIhcHzPua2Fk7s2q+psAImIDf0AQmXs98E8ix/4W8DuqegiYA366F4kr/qZU9QhwU8z2P468V+DnVjr3ZQM1IL3La1/uWE5b28sRs+7yy+8YNTxXqvAl+wB5y8OuZCEFvvr4ETu8FSNppsk1PsSYwWzHghTYXoPZSy4HI+B7hbYZj8kgxxjkn+nzWET7pTfgVytgG6x8nqpvyKbCfIqI/cwvFDifaziAov09Vg7DNDlSBNUHioxT99P04sNGUV+RdHD9DhagnMZwZbFhbjxbjVYDUNTz8TCU1WMYoSZ4TxSOA1ciYcDE1PzHKFccrG37MRrEUWSMkKmmINtMih9WBHBOFbD70njhAKMGUzUQWfuvdCkWWn/OhO+XRORZAsfPcsoovAZ4KeTriMiHgB8K57gL+Ilw3F8Av0GPFJCkp/zFgBrosdK7XBE1Cxg12FtQsM4XivzJBz4AwCNX/jA7x/dxm1linuG2sdOlOQomTGoL+beieMZjtjzbqIq+CjosA2XSpLyGIaKmPZw9+0kq7I89bokMoxhoSeqzzBS4Ltg2+arPUqozVV+Xhj/jxeP3IiKI+hg1WGLhYWGiV2V1L7dfWpznoYXn8PcMQ/pdXcfW4Bdc1DekRnJNN9BlhoXKo2AF1+9FNATXF1wPHOM3fsJSBb8K0ocVqWjmpQvMMFgXMABpE5xIWrQOjRDgl1yQQKB4xkWWwSsKhQKV4rlankrHCEYROQDcAjwYs/t1oWXpNPArqvo08RG5rwXGgXltRBe0RerGIREoa4XoA7SlHdINGAz2FhSsuZzNd7/1n7GDMozdxNJ0mexSfIFAz/fqzM6EvErDEiMGU/cTLEq689LVd8CNy3AXHrF3gVfFiknrKMtRoD097DgDWFpmpCWT37aOBgxNbVLlIrm0i2VbPLGY42AH0gCem30OgIliP9/MHWA/Hi9kJilbmYbN3Q7uzyfsq3iTdZS90uxM95wqTzPKicJ2RkdZHlpyO3wsHGYoyVnMogMjte0Eza7U1H1MTUcOPABLZyHtk8qW6uY9gPsGrm06h20k8Pq0sIBXTD/7CYRmgRTiptrMwuor1fklvpS/um0BMTAwwPDAdiTIHYqNYBSRAeCjwC+paqs98VFgv6oWROQdwCcIEs/XFEktr4uBxH8QYIveBuO5lCNrNWsZUVmm7IJxUcdDnZCRK2glUFs6FRIEAmZXPNe2+aOZm+tfgRhQXd7P/RGZ5KPVfZSXOoW+BrNmyg7ZgsUD59pDhTvhXHqEx3O7KceUFxEjONg8lZ7EDVlqt2jB2rWZit/WGsCY9qTOT8mBxrERYeOrBtWFfTe2QGZFU7xsXYnvN5sAp+3+trG1Wa0K9BXja3J9XvailRREbm/Vr+IVqvgVF28VFctFJE0gTO5V1Y+10aW6qKqF8P1ngbSITNA5IneGICE91bK9KxINZc2QaCjQYvLawpFebbJUTdD2NdNgQg/aBzhqj/La6kuxz0yG4YBRWtLmQ1nOmsVg1VeM4gImhYqPqVRwThfQGBNcFNPk6KdzPoVKuBqPpSUQCL5RTs6V2DGUixvUhFw1VfclfD21h6pxKJdnyOXiaSgXXCzf4KuDL1VSo8E57j8/xzdPOBhjMJ4insFONwvTwlyVas7Dyc6SjpA2bzIQCY2esQb4TO5mStY0tiipYgXLCYj8TP8N4ffSrE+IUVQV9U09WCJ6ixxsTE2gaZAbU6ouYVu6Kn1eAkflB4FnVfW3O4zZAZxTVRWR1xAoEzPAPHCViBwkEBg/DvxEOO5+4N3Ah4CfAj7Zi5ZEoFwMbGGBEsXWzkVpQdjf3C0X0UzAsI7bYwRNSOKlQ0rzmGIRq78faVEuNKbWFkDRKeOlQpOZ+rT9xNVua19r1FBwSgxk+rAiJ/q87KUbfCuFE1N7SrWxlHBDk13VM6hjYSKaieM7iF8la4ce7MhtmJY8nu8CaXz1CZi2Bv3c68242u9byTf89bOfp+oZRso+loCzWGVwPN821i04eP0utjbsgYsmDR26OvrY5EoOkMNXwTMWtrQ/4+IZjDjg+/i10Oa4pJ9gR3AdvoOaCm3e/NbhJnaeO4H3AE+KyGPhtn8P7AtOoX9MIBh+RkQ8oAz8eBg85YnIzwOfI/Aa/WnoWwH4VeBDIvJ/AN8hEFpdkQiUiwHTPXt5q2Arhw5ry6rVFwvfV4wBt1SpG5vVr+JXFyG0jqg2hwADqOv24jN1zJUXKGZLpO3Oa11FWSBdZ8dOWIPKMz4ZuyFQzFJYSmSwPTky+t26kcrSRg1L1YUg4dFqsBcFnqS5ha2ieL7TEChNkICvp2aADLZr46c9/JKLWypCBz/KH7xyru3+dYLlGfwFDzO8/CrGRjMYUvimVlizhcHHhM0HArFxX6OJi2oUsaT54JbpfKPYlqC+jXFjSu6oPkCPuA1V/X3g9zvs+yxBqkfr9iO0J613RSJQ1gpNTvktzEgj155oKEH7XlHl4exu7NApGzULOsajElnlO/jBanaF4dZl0rDM9rYPmHGeZ4RdsrzxahS/4rcxz1rBRy/yPfvqg4TtC0Ieur3cz1IfvJDdjhUGHLi+hcb4K6DBpK1K9DcVfduhXXDRpTq3EBtgqXH9AwiE6EtPPI+mlFTrPVeps2lRC5XQ9yJmRYulkltqMi8KcaIjfqPjG9R4DOXSEK+dbCgkTvmLgS0sUKLYyj6UGuKyuJ9MNZcIeSLfKM0S9+io62JC08kXJT5yc7pbiZWWzy+4hrJbako67AZTd3g3MzS77q9tOYPvNCXg9XlpqBiq2nCUK0KbxyDGLKRG8crNEV9LLYU5PiZBBS+nWm2UgjEenjFUw/sfLXFSL77oK8WqC57BjVn8pEzEqa6hGAi/oE8PBpUIVE1T865OZVOWFqbqhSaFZWbJq246VpIIlDVDdAm1dfvKNznlzdYVKKraseLyk6kdzEkjMsqV7h36fB+cssdieZETDNTnr5/LWKhv4SN8KndL+wRVF7/q4LpljO+x1GI/U6MRodEbbuvY1kONTxGbalg+XxSk0GW4p6hjx6/AI7kw0rI/rL7PYmgvrLhlpkpTGM9B3UpTtJRRQ8ltCKaClcWoUumWhKrg+D5GFS0btNz4RhftfEiehx9JjPQ7aE+l0ydYmAmCpIoxhqGUnwnmVtNYVbRKk42voCQC5aIgMfUAW1tDOV+c4cR8hzpXfpUvpa9p2+zWGUjweiY1En4SXN+l4BRDe3zrdGmMZwfFGFu99yGM72EwOL6DV3abmLrvmqYwWuMrTtmL15ZUY809z2W2oTQEXdCMykE9ZcGbwKo2mKhvmmn0I0LDq1bakgJrsPzm46RDDxENHd0WjbImVa+KZ7x6J8VvZ4OKzTNWP6Tic4QMgSDyTBgv52sgXNpD+Bo0dejwaJFGPY/ZDs4wy9hBd0vfBePHayaqGz4UPxEoFwOJQAG2tg/F9wzFqt/EqGvrW+N6ENMitrUX+VcGG+WYplLtOQ/LQqsyoWD1YEo1mtsczkBjmRxtmaU8lN/P8dQojmn2y6ivfGPg6iZCTItpSz2/IURqDDnu1CoYY9DyQpA30gUp4yJqkF6LGgEn3V5eXxyvnj9k1JDVPlIxwqAqPlX1AMU3ihjTpJlmZYA0AzzaH3yXj0mHJHcTLCgMisHCNXabCdA4HqayPL/XeiERKGuF6I90CzPSKLZylJejNq+ktzXz85bbYWo29Rih03rr1nJxGtUAvFIJ47uYHj3Q24mSQOAY8MOCh1Wrc4yP1SlsFsBzEc8nuPrgt+N0YE2lSgXHNziz7ZUBFNBiCg0TEP1laMhKo797FOIrVotpz1a7rfOkEQ18KL7THF2miuX6SIvG6PpOELBA8L1bfk1oKT5QxcQKcsd38L2Nb0pPorwuBhKBAhBrntkSUHgyux/VSGyPceur7ia27BOsRENTimoWwUFVA43FDpmNUR6vbEczFejrbz3dioSNYMB4+JUKXrUSLISleYaa+Uqb3kcFn0U9eTFkpK1l+qPIm95rV0fd4Bxi4Xpe7FV5XhnPMkg11x7NpQJUUddDBHy0zuCiP8kl0kTT1CueoX+Z/omsFQ1kaI5CM5Finp7vYWkYrBfmPhYkS9EtUrECn48YRTBo2g6HROp9NTXoUhy/iqhHfoOz7I1N3aZC9OHaugKlyVm8RTUUbfn+FXDVJyOK0SDMtsY8VK06GzGmVtm2xSQU3saj2W1kXBdf/XrRTXVdCsahapRFLYC1vVlZ7ihqtL5SjkPZL2PUIW/SOG4R3xhUXLJx5VtCAl1jYzRNdRWlQ1ppNV4Y2dbK6ONydFIG0mks10f7jq/onOJpUJXYDjXDuDI5TYqHhhHdiq8+itYjvH31sdQgKAY71OEk0FwU3JhCqapaz5rXUPB0Or9sAoPSxqdwM2ILC5QotrJTPgrjB05wTw2ucXuGjPqmc7iopx7lMCTXVCq4pQKYwFm8UF4KfDN+I+rI9WNs7jHJd+1DtGmMj4+K4ou2MfU6bcbCNxlmU+0hzE5TJJtiNMiv6OT4Nx0c7q3XocbgFopQKWJ5fuAUbzEPdptJtJmH+8WY+xUXrWj82PvQrdui7cQL2l4LL0eDvvIW3aMBNwISgXIxsEVX5q3Ysk55rZksapw7uA9+F30hCqFzyDFQL+Ph+U5gVgyHHtOhesi6VXWxKoFgEa+5FL3ta6uFawVQJOpvUQIhA8yn8pxJD2NU2uj/xsCrmz6bWiST6Z0kKK6HVmulmNv3G7VB/Ubxy8gYASg3m4+Cl/B+1KPSJLivxgv+okeEh1imVXvsfROjl+bG+ECMgh8KrPbvfPPxkcTktVZIytcDzT+KLWvyQlETIz663Y6oqbDrsBS4VlspFjHKc+m9QbkSyzSFr4q2axV2xYNMmrL4+EjnWlOACTWmwB3QWb86lhnn5dQQB0rHwrIpjX2e2B3TKOq1umomH98N6K2Vj9HQGeFV6XZ3NLI+tkzDI+GbwJfi+FUUn2HpB9/FUqtetcsYC1dtMlJEHS+cDwgTI8VXLI2PGTNdAiaiVqxOJWFME+W1Aw3x9q+NjURDuRjYwgIlii3rlO+EbpIi9DsoGjrpw81qMMYNMrIJQm59Yyg6xSYfiGVoa4jVEyYUDvVF+2oXAM3HzaYHmzWgblED4apdjVf/3Zy3I45v09AkjAHpIvh8DB4+KRMwfyU+0bCRcNleZNLzFROpbtCJ7OZ8lIAm1/fwumgZKhJcYw/+UF+IRNeoTmXFX+96IBEoa4ZEQ2nFVtVQotBSuc40W/YA7WvQiniBoxcNmajB4OPiUJHGEd38U82ZELXTtX8Xvuc0jfQiWeN+PRdF2/JjuqE2n+WaMBjBwfY9LGNauHP7nM2tD5SycYPWutQCG7RFoCgm4pNyxceV5nvt0GyeU6M4xsMPf6NCmDviB8mdvi7veltHuOH9qiXzWxFTZA2eWNhLxZ7FYxuHSZ3GDgWQNxwSk9fFwBYWKElxyIAxilEQRd0yaIq2OlgVH800r55rjFElEsVrfBzLhPFC8bDcRjRUjSm7GDJYbZnbtWmrEuQ9BN4aE0QQKRjPqxtgtB7K3E3FaL7uiuUxEKHFwwtjo0GMwQ9TDY2YrgmWNb+MowbLMmRMCgcPCyHl2xjjQcYK6FdD1W1xh8c5+30TSKdMCjUmKA1kZ0CD61ff4AU57T2u1LQZ/oyxqBvQOhz+1aEbsaSzfcwyoLX9AngWpDaJJAmRCJSLgS3KSFuxVaO8lrugF8cBy0bV4BtQsTs4y7vb0iVmTG0aq+rG2/3bjg8Ys1utUi+ECHgSsE7LxAsVCbmfUal/314YHms5tfGN43xRVDyM+GRDIdYKR/3AShZxmDsRzcPyFR+DcUp1E4tRu+Wqoqeu+ZBCZm2IhHZr/X/FBE7yWPHd7BuMLwYd3Dfbr81Z0yZr99jCYKPGAH58mHD9moNWy5ZvUFvqu1zaO11uJKzK5CUitoh8R0Q+HbPvvSIyJSKPhX//4sLJ3ARInPJAi9liC9+HGqrYlC23Q9TWMh3xNGe3K9RrUjUgXV0VzbBQ7Cb/SW1m3zNBaZjQhOPHNJBqnLEBzzQiuxbtvuBdjGUnbpGhLb+dqnHwl1ePt45OZiqDwTdVPKdMvdxAUyvf5ufV8prL9IsfhCIbBaeXj7yDf6dqGRyJSA9jN12zaKjRNpnNA79Sc7Z+fFCyiOwVkftF5BkReVpEfrETiSJyu4h4IvLu8PNbIrz6MRGpiMi7wn1/LiJHI/tu7nEHVq2h/CLwLDDUYf+HVfXnVzn35kfCSIFEoAB4IWNU4itj1WBqYVR12ESDh6P8yMcHo02Z4NBcEr1254MILdMS7BqTsk9QQNIiZHYriitWfONStRp+ksAhbupMNiC/oXE0r/hrHRmX53fzRfHEJWWswKeiimv89uVxqEmI+rhuNJIsRa2nihWThApZFJ+q5dbJ9CU4r4hix5XZjyE7epWm5X4atfEsHwsC/1Icat9571viAb+sqo+KyCDwiIjcp6rPNNEjYgO/BXy+QbfeD9wc7h8DXoruB/6tqn6kJwUhVqyhiMge4PuBP1npsZc3Eg2lBpHlM4fLHau7A0GOdcVSSpYfP0uviaMyo87kUyipSJX4eJ3GRLYHx7cwQ6CtRUNLaRZXAnNZa1Jgx0ciJrS5QUAzlV6MvammSQUuCEXCMvBiIteB1H0zZfGpRrSvVn2obEXp1g7KR6/feZQnNE/gisEIeK2CJvJeQjNdXNh301lUz6jqo+H7JYLFflzjnF8APgqc7zDVu4G/V9VSh/09sRqT1+8C/xvd7+Y/FpEnROQjIvGNqaempjh8+HD975577lkFKRsUW5iRKlovC3KpNZR77rmn/jwBHcq6XhqoKr4xQUa41jSVZvNFp5+fY2md8cUZxWrmL4Wgl4l2y8+Gqq24EggU1zK4dU1iec+pRF4NQRpMazSVTzPTaxZV8f6d4ENr9FfzmNhS9pEy/64YvBbvvmLaTGt1TSF6R0XrAq/dwCYQ6SgZ9yTXFkyi2rHkfty1dbWcaeQpUcVSQ7rqIV4QheYFVRA6PtsicgC4BXiwZftu4IeBP+py9h8H/qZl238OefnviEjPRtQrMnmJyA8A51X1ERF5c4dhfwf8japWReRfAn8B3NU6aHJykocffnglp9882OIaihVWWL3UTvm7776bu+++GwARmb6kJ2+DBL3CQ0uWarM4UdIE0VRefXwUXm2FHLc0Nj6KAbF6KypqUAFPIK2N6Tp5dHprlWFkVYvIcKyG2ao3Vr/gCkxHWq9r5dcFW4twVqvzebTTxxih0uFz8N1Fnm9tHm6ikXq1ITUh7vqRwpbxNGr0jWqg9YvUrjv22RaRAQIN5JdUdbFl9+8Cv6qqRmI0PBHZCbwa+Fxk868BZ4EMcA/wq8BvxhIcYqUayp3AO0XkGPAh4C4R+avoAFWdUa33+vwT4LYVnmNzInHK11EXKFv2PjS4S+2xaF3BtuVGa5Dtri1C2HRi0oF6Er5tLygJUJb28NZmRGhQwDcYDU1sPXh+q09g1fBNXRtrw6pOEWgLXUvXdNjnxtIhse97tXd3LMWxAOxQuNWP7Hpc+/qh5ncKP3UQ+CKSJhAm96rqx2KGHAY+FPLudwN/WHO+h/hR4OOqWg+lCE1pGvLzPwNe05V4VihQVPXXVHWPqh4gUI++pKr/tOXCdkY+vpPAnre1sGUZKaAE3e3YygIlgNSZQPBq+YqrYcTPqvmxNpzbppZ1La1DMGEn2V6ML7Y2VVBkq8PZW01xnWA1je+EqhCY4LqcLVab6qVJRYWKBuNrQt3reG1xaL+BRhRH/HhB2MFt0j0kozOWc5QEKscHgWdV9bfjxqjqQVU9EPLujwA/q6qfiAz5J7SYu2q8PJz/XcBTvWhZkzwUEflN4GFV/RTwr0TknQS6/Czw3rU4x8ZHoqHUkGgo7QgYGo3IUfWxWsqZN5hklwKELaaV9hMRMfe0M7x2Bh8/WSd2XRNQnUnQhvYSez2tkOZ3HWVt+9aYWKuYzxKGODT2tfYaadzUbjdXmo4I7mTz2MDH322O4EinSyg2QNX2sVWCKLYWF5MTL3zvBN4DPCkij4Xb/j2wD0BV/7jb+UK/y17gKy277hWRSYILegx4f1fCuQCBoqpfBr4cvv/1yPZfI7C9bV2ssh/E5QBFscQKfsZbODihGZ3W2XHjuu1uLvjYzQ+8lmUF4x3OrSa8xv/LPXc0wskEjiaWP4G2X2hNGLUcHxXkgU8rLG4jUJV4H1bLDMshKETUdxMv8oz0/oZ8CTKFWpcWcaZGVX1gJUSq6ntbPh8jJipMVdt8372Q1PK6GEhW5ohIUhzyQixbcZMBUVOUJ3EFz+MYffi+63PZhdILvohY7zR+ZLW9vML+zahay3y+okmE9dc4bab5kJXT1Gl8jHa1jKkdu1FvLMDGZ9cbn8LNgvrqytryAkVEsMTa8hpK4GDvXsRxpTOudGzDYd97ARv4+buz0U4Byr2scNFPK2XUvY1Q3dHBTNSCVppMZFuHCLwOc7SLr8Z9a/hdlncPKrZiZC0XJhcXiUBZa1gWHW0RWwD1arNibdlaXsuFs9wVdgTNzDgIG25NjquP7RHtFDe7LNc6F3MsdBY4UXQfs9rfTlQAtIiwFUekrUYARXw0HSSdY5kg2XOFcCylaq9Oi7vUSIpDrhkSDSUKS6zEKd+CuhIb6SEeX2SwM6qW31SNuGq12OO1/h/QHOXVLHi6Ryh1w3KExkoRO+MqT9M9iK77pEa0axXkzmgc5F2UZfrmUFMSDWWt4Tlw6pH1pmLdUDNzWWxlk9dyjTS929+u+MxKRw25NbWyfX/t/9UFuS7vmHjaotnujVIjukLhpU0vK0VQBcB0MWktV9tZbUhEp+oJYU7TWkZaXCQkAiXBRUHilI8iDBe1okVMLmS61XHMlR21mnNciHBsPrZLsZY1o6Et2VRa/RztaNq3VsmdjQmXuW3jIjF5rRW27Go8HolTPkYDqDMguYBM827aQ6c5gyN6nXPtWVerQ2Zl69fV0RP6curP3sUzu3YTPHFYuS9n8yHRUBKsKYIIf0mc8j3QOTt8tVDaGXgrS14eQ+skeNrKxSyLpsinS7bA2DiMey0d6SuPCrz0SATKmmHjf9mXEolTvhPWWg9Ye71i7YXdVkTAWmu5JL3RO7HSX120wCVFYvJKsOYQEeYqc8xV5tablC2AS2VjX3U88RbG1rtfiYaSYE2xGWLlLw3W7z40i5i1+omvxfVstWdjcznU1wKJhrJWqCUVjOwHr7ze1CTYsFidX+NSQEWDNr4JLiFqAn/jPAcXgkRDWWtYW1xGa1DfNcHmQyJKElwoEoGypgi6qiUhxNCf7ieuM1yCBAkuX2zx5fRaolZXQ5LSK8C2vm0sVlu7kCZIsJWw9RaWiYay5tjaq/JaHoqIJHkomwztiXe1yK7ke7z4WD3fEJG9InK/iDwjIk+LyC92GXu7iHgi8u7INl9EHgv/PhXZflBEHhSRl0TkwyKS6UVLIlDWCtFKf77bfewWgEWSh7L5kYQKbxJ4wC+r6vXAHcDPicj1rYNExAZ+C/h8y66yqt4c/r0zsv23gN9R1UPAHPDTvQhJBMpaY+p5qCysNxXriqQfSoIElw6qekZVHw3fLwHPEtOBEfgF4KPA+V5zhn3k7yLoPw/wFwR95bsiEShriq1t7oJGeQ0R2dIaSlwhlAQJLgSR52mi05iwP/wtwIMt23cDPwz8UcxhORF5WES+JSLvCreNA/OqWuuRfJJ4IdWExCm/ZkhYRxQW1pZOckyWFgnWGpFnajp2v8gAgQbyS6raGhHzu8CvqqqJib7cr6qnROQK4Esi8iSwKjNLIlDWGoPbYencelOxbqgJkRNLJyi6xXWmJkGCrQERSRMIk3tV9WMxQw4DHwqFyQTwDhHxVPUTqnoKQFWPiMiXCTScjwIjIpIKtZQ9wKledKzK5CUitoh8R0Q+HbMvG0YEvBRGCBxYzTk2HVSDJcT4oSQXBSi4BeBSVphNkGBrIvR3fBB4VlV/O26Mqh5U1QOqeoDAL/KzqvoJERkVkWw4zwRwJ/CMBj/c+4FaNNhPAZ/sRctqfSi/SOD4icNPA3NhZMDvEEQKbB1I2FN+CzNSEeHasWsBtrQfJUGCS4Q7gfcAd0XCf98hIu8Xkff3OPY64GEReZxAgPxXVX0m3PerwL8RkZcIfCof7EXIik1eIrIH+H7gPwP/JmbIDwG/Eb7/CPD7IiK6JZaqAmIHb9VnK8Y81ExeI9mRps8JEiS4OFDVB1iB205V3xt5/w3g1R3GHQFesxJaVsPxfhf43+ic7bQbOBES5BE4d8ZbB01NTXH48OH63z333LMKUjYSQsZphQLFbO32tzXH36VcR9xzzz3154kukTAJEiS4OFiRhiIiPwCcV9VHROTNF3LiyclJHn744QuZYmOiSUPZelANM+XDBdOl1FDuvvtu7r77bgBEJDYS5nKFmhRieb0HXiJ46pGSJOZnq2GlGsqdwDtF5BjwIQKb3V+1jDkF7AUQkRQwDMxcIJ0bH0rgjLfCW7rFfQc130niQ7k0cAojPccYP33xCUmwahh/8wvgFQkUVf01Vd0TRgr8OPAlVf2nLcM+RRARAEGEwJe2hv8khIS3dIubvL515lsAnCr0jDTcknAWJ1Bjr9l86veeyzjZNTtfggRxWBOvsYj8pojUasB8EBgPIwP+DfDv1uIcmwNRp/zWXZlH+6FU/Mo6UrI+UC+NWxrqOsZ4GXwnv4YnXTvhlCDBarFqHUtVvwx8OXz/65HtFeBHLpSwzYfEKR8HNymU2RHGyUKu0LTNLQ2T7ltZknJlbgcAXmWAVMt8lxJGDZZsvcjGiwm3OEy6f/PUBky+/bVGoqE0NdZ6fOrxdaRk88Gv9K9ovKoFaq3q2LWGv0UDUVaK5fqyVAW/ur7f6UqRCJS1Qq18fW2FtkV/XK1RXXsG9qwTJeuLtWbuzlJb5H0b1NhUZnet6XkTbAxkzObQ9BOBstawEqc8wM2TNwNBTa+tid55ZsYs3+Ksxoq87+4vUY3/WXc6butEzGwM+E5umSMbz5C9SRaoiUBZU0ijuVahZ8uByxL1PJTQ7LUVnfLLxjKivIyXobqwDfXTeJV+nMVxjLu6aK2O5pMuQZjV+W2rOtdmh3sRNQK/PBi/vTLQ9LkWubeZwr0TgbJmCH+Uc8eC12NfWzdKNgLqmfLJ+veC4CxOoGF+glcaxnhZvCjjWcbtVbWozG1v2lad317PezBdJtEVaFEJLgxRLdRZGqubOf1K33qRtGIkAmWtkR9tft2isMJHayulIHWCv0qNohNWmr/ilQbbwopVBePk8NTDXGRziurl3x3Gr64sBLz2TDiFeD5h3Fw92OLq+TMXRtwlRCJQ1go1p/z4oeDz7lvXl551Qk0jcYyzzpRsLlTnt7VpERcfsiKBb9TgGjepfhADXSErVS8TvC7DnCWqDPilVdF1qZEIlLWGJKVXAAYz8XbirQavxS7eCWpSsVrERkJNkMSZMVcqZLa63uqVB6nOb6ubMwEIv2+vxdc16Ja5pfDcpSRv1UgEyppCGomNW1igiAiT+cn1JmPdUZndhVcaanO2LhsbKPvdU6+nP8ytLt+0txwBtNamwouJlZq8oN0/ZUyK6uIEXrFRZeHK6RnGqkXSSZTXVkP4Y6vX8tqaAiXxmbTDuNlNnR/Syc7fC556+B0Ex3L8NnqRiiWa0Ny0FlC1qMzuqpuwVosJZy6Yz8sQDRfeO795suQhEShrj1qm/KnLsDT/CjCcHV5vEhJ0wzLMaUrA+M0qa44FiwvFXaVAuliIRlMtB5eiCvCriy/Gbl/O8kxE9orI/SLyjIg8LSK/2GXs7SLiici7w883i8g3w+OeEJEfi4z9cxE5GukCeXMvWpKYwLVCa6Z8aXZ96VlHCEI+tYaFDy8DXOxIqijUWIjdS0O++P6Z4twoaizydnMi32Zz6nulYTKD8R04nKWxFc3VqSBon6nC6i2cHvDLqvqoiAwCj4jIfZFWvgCIiE3Qkv3zkc0l4J+p6osisis89nOqOh/u/7eq+pHlEpJoKGuNVKj67njV+tKxzpBLwLA2OlbLOGthwcbtYEZpWra232evdPG1wz2Vcz3HGN+ODSzo5YtZS5PUxYJbHG4zdflurrd5cIWBFssZrapnVPXR8P0S8CxB59xW/ALwUeB85NgXVPXF8P3pcN+qHaCJQFlThF9/KgNbeIUuIk0FIrciVKHql+tJ6J18CXGoMdTOTunu9zaOifctta+we2pNEb7vrcDhfqGoreKXE1LbC1eXXrmg49VYbUJAiS/a6C6NYZw8Ny09f0HnbIVXDBYI+2bnuHXpGejS3lpEDgC3AA+2bN8N/DDwR12OfQ2QAV6ObP7PoSnsd0Sk50OQCJQ1Q+TXZ6XAbJx2rJcSSWZ8AJHgv5pc9XVtn4dava7llmHZf+rImp6/Fa3fe2Wpt5YUPcKNaFV+pR9nabyt5lWvMOq4asej3mLPc/dCnMZUK81SjSktZLE6zbTsFdu2KWC8LPMvHWb4+Xm2lwoAse2tRWSAQAP5JVVtvfDfBX5VNX5lIyI7gf8H+P9ExvwacC1wOzAG/Gqva0gEysWA2Fu22nCCC4cXViruaPKCoKaXl8EtLs+8lfXialOtnRZp1KC+hQlVMj9Wo2lm436kXlar8IgTlFHnfrT67hvnH6nPfrHqXlVmd9b7zkCg3ZW84iUt2e+6wvzR62P3iUiaQJjcq6ofixlyGPhQ2L793cAfisi7wmOHgM8A/19V/VbtgNCUpqpaBf4MeE0vGhOBslaoOeVha2soYXHIBKuHepkgzLhLHor6aZzFCS5EKHSKdrpj4QlGvKUVzVVeHMJzevk+tMunuOEtpqZIyZm+cmOhPRjJIm8VwmunL8uK/R9xWMu2zzVIYF/+IPCsqv527HlVD6rqgbB9+0eAn1XVT4hIBvg48JetzvdQa6nN/y7gqV60JALlYsCyt6xAiSJtpelLbZ7CdhcTaxHZVF7j8ht+jAakKgz5Ba4uHet6bJ9pNvX0FiarwcVbmLRafpzCyqK1akitsE1FnBDfVT0f0rQcdhwrIu8E3gPcFQnxfYeIvF9E3t9jwh8F3gi8NyY8+F4ReRJ4ksBv83/0oi4JG15TRH4AXlLLakf/Dqp+db3JWDfYxjSFgior096qfoVsJOR2uUmj6qVRY6NqYdnusviyEgxzFidYeuUGuPJ47Lg+v8T1pSPs8gq80Le/bb9RH5FUT4d/YXYUyzIMj611jSrp8ukiYA0SeYtnr2T+bD8cbKe2dcuc0x5coaoPxAztCFV9b+T9XwF/1WHcXcuds4ZEQ1kzRB6s4nSjjP0WQ9Q5a4m16XIO1hL9lfIFHW9W6dwFCcrT1zWQ4DtpdSB72vBDeDWfhArGz2B1+N4EZZsz05FxKIpn3C6mprAemLHxvd7+jqjfYjXYHCEiQhwrvv34CSpufFDBRkUiUC4GsoOBH2WL42zxLNPl2ICULYFrT3YOWbUjpXnsVQjdyirMX7c/+50mAd8tIm/MW8TqwY6/b+aByKflse4xd4WlREIzkO+l8NQJo546mde0yaT0ttlvruxcq0HoN33DkWNsWyrUNq5qqtYFyFDVWdX3vJ5YkUARkZyIfFtEHg9T9f9jzJj3ishUxB73L9aO3A2MqFN+aBfkR9aVnPVELQdlK5u7AFJ+565/b3r5aN0sdNfcgx3HdYJZhamll4CIQoCD5ZP1z3Ehu83zLU8o9nL2762cbdtWXZhkcS7f0KKiZ41Gg7VcXmYFodrGvbDoMKvD9+EaF089RkNNI85PUjvy+uMvc/vxzd0ye6UaShW4S1VvAm4G3i4id8SM+7Cq3hz+/cmFErnpsJWjvDaJkeFSQFGqfpWK32760rC3CARmpDj4a2U/WGl0Ui0Zs9xI3hs451MtXXiARVqbhcJyTKLqpyOMuPleeeWgkvPrFx7D95YbQRV3PxrbWkOBGzQu9z42aCwtBJWDrysdwSmMdq2LljY+Q1WHQ9MzvOpMu2DdDFjRIxvGJNf0unT4l3CQOpKw4ShunryZ1BY3/fnq1RlSZS7oz27UMOtM4atP2njoOoVZ59z2wJGAcQsLR2+iMtto+LXv/BROjECRFZrrriif5LpCfCHEnqHK4W26Zqk9YGDUW6K0MNyzbMstS882fXZLQ01+mrfMfRtVpTDbiPqqRhYERrXjAqCNUJo1kuUW2dw/N8/2QnuS42bAitdAImKLyGMENV/uU9U4ff0fh+n6HxGRvXHzTE1Ncfjw4frfPffcs1JSNhgiD5lYUC10HnoZI5qHYlmX1il/zz331J8nupSnWC/UchD2lo4hwBVnT/L6hcc6H9BBztSivS40LeKqczGRXCEDVD+NbXqvFd88/21SxkdorOr7utj9BcP+ysmO+2vo9txkYkxfEJjlepWR3+VMs7s03axJR5h+1jhtwQIXvGLuYZ5UkSahtZmxYoGiqr6q3gzsAV4jIq1VEP8OOKCqNwL3AX8RN8/k5CQPP/xw/e/uu+9eKSkbF6e/E7xu8dDholsMMqgvUY+Uu+++u/480aE8xUZAzQm/Y36GAdOZkeyamWrbVvaLDUftcuulSfv9H6r08m8pfmUegCumZ+vnKs8PNTH7PlNhzJsDDCZ0iN85txbO8Haa804FFAa9zgLLX4Zl4NDsU10FVnlhpP6+LxIZN1hpjpK75tSxrufR8D8FKpF5avlEOxcCv4pVnqe4wkTSjYpVW2nD8sb3A29v2T4TpuoD/Alw26qp20yIOuVrlYa3cMgswPOzQZG82crWLeW/eyZIWptYnK9vi7JKr4vjOOO179OQQQ2VCmwrFFBrdWpKyhjyle5a9NjiHNedOcP+ubn6NreS7xjOXNPAVkLRrmrvqsU17Fw4z9tmH2A4IlCumms//o6Fx3nz3ENN2/J+ta4NdqurZqFsX5jjmlNHAbhzPjim6lcYnj7Wlb7addfKyESDCKIakWoQWt3nBvsd49T3L7kLm1q4rDTKa1JERsL3eeBtwHMtY3ZGPr6ToJTy1oJTbH7dYmhN3nM7mCi2AuyYTOrKYqP+lmOqlP2456T5HrY69lO+zw3nzvGWl1/mrS++vKp7bHVJPpTQs7NjaSmWSZT8IsWzV6z4nDXccPxlXv3Ki9y0zF7pOddhvDCHCVf3XnkQgP4CzL98KwvHbqyPHfcW6W/J5O/3S4yGjLpTq+KasfaqMyeZKMzz/TNfq7fe9dUnU60QpzlJ/fju8CLaUzTx00bww3gCx1TaQoWtTVTKaKUe053AX4SNWizgb1X10yLym8DDqvop4F+JyDsJmr7MAu9dS4I3NsIvfjZY3VA4B/3j60fOOuOq0at4ce5F0tbFKdi3UWHQjma+yuwu/JkSsLLFRquJRli+JrCjNB+7vVOoa4DAXOuqS4pMc/6KBFY0NyxX0ttJ3Y7hUrx21Mkxf+vLz5LJu/WVvF/ppzI7gfqn8fwSZWf5+RodOzZGbqhKfMSirz5KaySYxLxrh2OqOKbadWx+oYCfSUFE5hXdBVw2R7HZFQkUVX2CoNZ+6/Zfj7z/NYKyx1sMMT+qLVpxuJaHcsXwFbw49+KWCyVWNLbuVu0+bJ+fWV5r15Dhx7hA6vuWg7jEyclCd3NXjdmVvAIVv4zJpMPtwXmNFfiyncUxGKwdZfDdNAtnt0OHBPeMEaK6VF3whhEGe6tnSb1ynK/v390+JoRrXBx1gHz42Wli1CuBmmYWaETx1EMItEL1vbZOiq1lZTwTCN/V6hEqgooN+Ihp/64cU8VbddWES4skU/5iIB3WX9piK3No/vGfKpwC4PGpx9eLnHVHLSPeUoPjV5DKErrMhcZoYZH9s3Mcmm7EF1x1upZ937jPK2Fk/V4gSCam53qMbCCuLpdaglpC+fw+SueCml55p4xO5SjNd+5aGNDaECmuhv4DFQjNbH2ui2McfPUp+8W6cHYqOarFPs6fHsLpEszQRGePu2NayuZXcHCyFsa3KS/1M3Oyc+mXvmpgVmuYG7vntzTTpU1rUMXC8v1NZNyKRyJQ1gpNmfJ7gtfpte3ctllRcjdX+YgLRc6pctPR4LvfPjfD/qkz7J45H5pK2n0dafW49twUab/BuJXAtn5oZpZU06pVIv93hmucWL9KLlzJ9zmmLe74Qn1dB86f5JYjzT4RwW0I1fp1NK+2PeNR9mapcdgFZxajhqpfaY64VYvZE/vwqrlw7ki+hwSaHwQ+FbdDG+Sc3zn58friyxjfDu2JwtypPTjlaO5NcL6sqXDHc0+Qd6pceeYEeaeK5xZC6qVOl++myJccyouDLE43m74FYffCAjsXl9gzHQRurETr3KhIBMrFQG1FV55fVzLWA9GKujdOBI7Sq0evXk+SLjlSxqc/XL1aKLtnzvfwV8DuxUWuPR8JExYQsVhwZvGLp+qb6zJAu2sornHx/WCM5zVG3Lj0DDcvPU+/4+C7aaqlvoCJoqS6RD/VV/oxl7GtUEAE8tX27oVgePPLR8NDe+RjhOfw1F3VSv3Ksyd58wvPg1ptDbq8apBRb8ck79SKZkZDuE2X6DmZS9fLzowvLXDbkecoeUtoi6CsFAewjMFzskEocsuUKVWuP3ee9ApL4G9kJALlYmDymuB1ZN/60rHOqGXJdwuNTdDAtkKRt774ctM2T136Sw1BU1vtZzyvzp6jK3XPuHUG6ZTzFBb68WYbjD6jHrud8/UjnVIfpkuNl9rMNT9MJqbz467FBV7//EPk3e5+DAFyPnSOh1qdGS+K5mfNkAoXd1mvM9O2vCo3njnDhNswA3by2y9NT1AtDcTu275UAIRhr9k/ZVlpVFonbL8H1ZxNpc+O9aNsFiQCZc0QeUCGQ0GyVQtEhtzAtgLzgn8ZrcBWj5WxyIFqIylWIivfsaUFrjh7kr3T8bWedszPRnweglNNMVCKdDT07bauLLX8Ed/rHKOTd6pcdfo4h04H2fVpLBbMIipCCqtHxWQlZSxsE+ggVqXxW6ksDVJeGgypbfwfxauPxZdqaTpDyMmiIdgWQU/5Vxde5PqFV7BC7aS8ONh0rK3KtmKh+Z7EaCi1EPCsEy84J4pF3vrSUQZMtc4NglO2x+S1BhqkJFMfZjsrW4CJyF4RuV9EngmL9v5il7G3i4gnIu+ObPspEXkx/PupyPbbRORJEXlJRH5PpHcmbSJQLgbc8KF+/h/Wl451QNSskZKAQZ0tbs5Cd2sKWX7r19cfPc6tJ09FtjSv3HfMz3Q0oR06fyp2ew1nX7yShZduoeA1+mzUKglf/8oRRqZPkoowxCgmF+fq5hlBqWhD8+lrCUA5dKZR1mW2OoUbSUb0Cg2/xNyp3XhhXohrHMotFapf99zjDFZKGLHQCD9rjhUDP9XCyjQYJcB+5zzipMm7aVIqoYmvGbVIrVYIS9x47AWuOv0K/dUK1548yhVnu99joN4RUkXw0u2B1Z2zWValnXnAL6vq9cAdwM+JSFvz+TDd47eAz0e2jQH/AXgtQc/4/yAitaiKPwLeB1wV/jUlscchEShrhahTfgXM43JGTUN5af6ldaZk/aEr+KnlPY/UChy0alnLnt1Xn5nqFCW/PWx42FnginMvoloLg41x0gu41RxSj2UOXi0EO8xdEYGhUkNT0PBfnVV2uLbCXB9njhxoPV39XXAPl1fOyJSHSdVPI1QjBSBFaPMXLXmLuOpSdS1sJ6od+gxUikyGlQ7GCosdtbFaUEPFLzblqdS0nbUoAuqn2udQ1TOq+mj4fokgmXx320D4BeCjBHUYa/hegpqMs6o6R1Au6+1hgvqQqn5LA3XqLwn6yndFIlAuBga29x5zmaKpOGSb3XjrwKip/60kD8eylt+bfcEOHf+p5aeTdSsoaVElLYW6w9mihBXDwKde2c/M8SvwM0uA4q8wB6RVmzh4LljxS9Gq97kfX4rvVFgz/11x9hW2LxUYrlSa/B01/4NXHMGvBOX3K1MHMFZjkO37vGH2G7yqECx0ts/P4fvC6VPbmTu9D7V8Uj26bdoxwqHgLbHgzuN7Fm+c/RqvefkJIFhMtI2OMal1EziKYtTgBcKsY+FTETlAkCv4YMv23cAPE2gdUewGok1YTobbdofvW7d3xdauLb7mqJWvTzSUrY5ewlRF2sJE00aY9mcZtgexWrPpYlARFwjzKJZbKLIFr3vucR697VDH/ZaCZ4M0LcoFy0qBqTLrnqTft0jb3c/vlTNkchZg46abhebOuWlsXWB8aY4gRTzNNaePs7/LnHmnyr6z5/BbhLXterV8R9TNQL6E8VNM6wx7IxmiWXXZXz3D0EtHmA2d8ZXSADlJYfwClkl1XG6rCEoagxcIOK1tU3x8Fs/sQvoWsMp2fXwcmsSMgJHOeWuCICLYwT2JLXwqIgMEGsgvqWqrRP5d4FdV1SzDFbJqJALlYuAifmEJLl+kTRpj2VHjUAgFgdGlRYwNVkyMg1oNO70gK9KKYpoI1mEjpNXG61D6IwiVbeRetMJSgxiDs9CPukUcrVDWdi1sx/wUOWwK4uOlM+RcJdMlMqs+f8t5O+Vy+C30C0GAQlDoUgPNLXSgt3s8Wr8NK4jaUkGx8awUGprQBEH9FM7CKFFPiQLDxQKLff2xs2oqS2VwnL6l+eYzS9M0ZDWeZYtImkCY3KuqH4sZchj4UChMJoB3iIgHnALeHBm3B/hyuH1Py/aezqNEoCRYU0TzULY2fGo1O0yorUTXGdrCuKp+Gc8rYTKNn6QV/jyNJdz+0hOkXEWXofxaeBhswCBdrdoerSxg2+LLlMcaLYzivkkNuVzVHiSVXYRScG1tYwWyroto8FQ4ZTh9qsKSvatF/3JDukNfQ4w5SMSuVxhIRaKmBEhhkQ6v17QcKkiT0BGqoDBz7ACOm8IzM1gtPs8hzVAGKhkhG1r8UqkBVDK43gIAXjZHqtoIQrE8H5OySdkCXi0coCHiLTvHdSeOYqnPN6+9KbhO20L8UPVLD9BW4yUGsbn4gZT4IPCsqv523HGqejAy/s+BT6vqJ0Kn/H+JOOK/B/g1VZ0VkcWwI++DwD8D/kcv+hKBslaIOuUTbHkIHlpnEAJSYzFSFzAmYhYreIt4YpOWSIZ3+DxpeGzLYjWcuebwDWts4WPhY+GipBGxMBE+pVggiq1TwP769n3lk4jlcvb1kww/157Y52UEFcHS5hyNKD2t2oICYluRn4VQnu4ntb2v5Tqa0uGxRbCkuTRJ87ULmYgQCBLb02hMEEG7uSnQ9oIy/BrLofOkSGNTtiS41rBKcMqu4NZ8+ZaFl80F5VI8h0yhgjPUF7+YEgAL2/VIe07jPoXJqzm7HzczDLLqpnx3Au8BngybHwL8e2AfgKr+cacDQ8Hxn4Bavf/fVNVav4mfBf6cwIj49+FfVyQCJcGa42LaaDcbmgJ+JUWwGhfSdhnXjyQkpgXPsklHeWsYcurlLCzHJ8i7jxMrQY8PX/166XMBMAbseEe8FVY7FuCNJ79B//gcxYE87oBwbmCRIW+Ugg0Tsy5uNlcXMGpJxyitjFh4KgGjDmGyFq1cW6Wh5bQWvhTpbKyzxK1felTelbWMkXz9LCIWqGKqeWAR9TNAu5PdDr+TTMWlmm2cNZMt1cvjV/M22WKQdzPhZHmFMABBLLAEI4LtOcG5u1oZDbnieUxqqGW7YImNCBgrvkWw1aTsxEZ5PcAKoo1V9b0tn/8U+NOYcQ8DrQ0Uu2LrhuFcFCSMtBMuVdfGjQRT7/LQDhFFJNKAKa7UhwgmnUJFqORTeGmJDRutD/cqgUkHsLGwO/68gzlyhQK5pWK4wreQ/mB8JVVlWs4wQJpctRpzZDxSlkWqHk3lI5jAJCVLSKRcv4qFl7LxMu30WQK5aoWUajvvlPhwXbUlvNfNPhRvdgeFk9fhVwLhMDvrseCcqktYCf+lfR/Rav18qVTwvdiug7EVN58ijdW0UErnmqtgRKPIgrhkC4toDTufbiHPggVi4Vo5fGle51siODkLN2utuqHapUIiUNYMW49hrgRz1eVXt72cYFddsoslUo6P1UVzy3ZyjoTOCUtSGDt4HzXjGCxstbj96PPcfvRF4ta4cXWpbISU62J7PrbrBOcZb45GTWOTau0aKWB1YmqhHyUjNhYF3GxgUsp4SwjN8/gpCxVozTFMWzm2SYaU3738yEA0Uiya8Ni0cBH8ymC9Odn0sWFmn+qjVSxaWhst5CJZ6rbrIlqgTzJoNk0m4qBJ5/cig3tALDSVoTzQR7amvKQsbMtBcDh09igThUWGqhXAxe9WgVwEZ+QgKhY7Kvm2e9Yt5HujIBEoCdYU0TyUKFx/a3ZttB0PyyhpJ2CQEvpN0m7zajVtu7Ecw/LbI52iSZIiKSzLI+N7ZIy2OKBbnBC1twZGoh2c3MC3o1aaXNoGBc8KK/qqIe02GFtaLPKRFbRpDe0NhUrW91ARthVmyVaXsKNCKPLWTzezIFtSpJZhMs3ZqViXpeV6LLqzgWGw5mdRwwg2aReM05BgJryOlMJgdYlctdRW88vCQhDyuX6u2jYYSV5OQVirzqQz5DOTRNNLa/c+5y9wYHYKwSLX3y5MLLEb2hUWmukjndtLvwySNootm6vrayJQ1gqdnPLFmUtPywbEy/Mv9x50mUHExjZziFjYvpL1GsI24zbb9G3bAxTfDn6SNS0kWHE3ooXqc1u1hlcWtuWTsh3S+YZ5Kl8tNgRKNLrMkrbig4XpERbmd1LyxkhZAnZDmxSgvxiZV1KkxSIThslGQ45r57PFZ0Qs3j53gisWg57vVhMNPdiO1RCLbjbKpIW+ufbfk2U1BFzaygUlf+wUKQ3ukcFi3DOkI+wuaGplYVvCUDZFxq3GmvPSWFhhXtlKrU0Zp1aaJjhwW+Ya0tLcf0WtDK4dKZEfOuprYRiySTo11pAIlIuNLdYLpBPy6XiH4+UIQYIw1hqjC/l62u7NHLxU1AYkmP5G1JcvaXzJoAK2la0XO8yLhW17ZHGwQxNV4OzW8PQNTuj0wVCl1ma31i3RYrZ4kBo7cLa1Zr43NJ10yieX8cjGRFQJSk7KZKWMJcL2vuHQhxIIsEo+TTWbxslm8dMVjB2fYd8cXt1AJ4aezg1CqCmlM6NkveA6UmqxqzzAoBP4UGyELDZB2EA4xrKatCc3dIwLwhBZJjQPAjP9nZM/25AKhMaOwiKjlRKDMkzG6sOWdNuis60KcS3oAMG2IJ8y5NP9bBYkAmVNEfPEn3/20pOxjmjNQ3nnle8EYCLXsVrEZYmmJyFkIqmYXr6CS7Rmlu1nG87fVBbqq3lBPB8EnGya7OAAgwR+hFT4M/ZT/VgoV519hTc+8yApv9kGb8QmnRunsu9QOGOjuKNGVvnYgpeJDwDNTUySTjUEo58XNG3T75TpcyvY+HVNpW9iF+mcxWDWxxKo5jKc2zUGloWx/Vgnu3R4D5CrlEAs7Ja8EREbrMZ9cPtTlP0g2CCtNlm/cS2elUHtVBMjt8KwXgDde1d9eyo0eAXaY/uCIJc90PTZ9pS0ryAWTmYACxgWwbdzVMZ3c/J1303Kab7mTjXezlfPsuieQaRmdguQyQ7Gjt8oSATKmqGFWfSHDNRZdWz5ZYFcuFor+8tr2Xq5wMKE/hIPqxYqKlo392SzNSbnh3+Qkb5Qs5Dgl5luDTENIdA/sBS/D9g9f55+p0x/pB6VSVm4Amm74TtpOH2l5TXwL5QyY03zquWDHZiR6qUhM4AI45VFJsoLzb4ayyI/ksG2OqdoaTbQCNIEmpQ/PNp8oZH3ooqdGyFrN1bsed9mWPrqGgqAGc2SrlQY8tp9Fr6kUdsOI9sgk/IYyDiQykCmHy/Tfs8t28fsH8C2moVBre6ahufuHx5koGLAEpzMAN6uUTSfRsVGUykqo5OkpcJtR57j8NHnWs9C3h6pfyqZEp5WqdUuqwVCDPVt7LbiiUC5WDj01uB1fAWq8uWCCB9Ih7b+L7zyhXUi5tJDAAsXbzCHiItlD1CrQF0L+0zHBHXZREuSaNd6YLbt09e3iKYbNztlNXPuKDuuhRtbkTpzxi5S7Z/Dy8Rze18ylNMjKBbGrmLS3R3Ek1mLsQ6azVC5gwBMZUjbGbDTOHY/ms3Gybe61pu1raZoue2VLLmWGlgiQs5rDw5JWVI3m6Vsi4xtk7YMIkpfOs9Y/7Y28hbye5mbvKmeODqkafJe4x5q3mZoJMuukTz5iTHSYyNIGIFW6NuJnW8WUCmpkncccm5DK51gkFxqJ0P2jroPraaTuP0prEwK7Cy2JeRiQq03ElZMnYjkROTbIvJ42MzlP8aMyYrIh8PGLA+GFTAvb7Q65Wsrwec+sz70rBNa803S3cIkL2MUJg6AXSu5YjFsbWe4WubK2TN4mXJMjoVgqTCQzRDmfjMxEGh3JhMkv+EbMMH9LaVHKWYn64cPjuQZyKbIhqXnrUiJdct3sSNNoWpawUCun+KYQ6fsEhXBtlxStoPavcvG78g21+CtPQuihrecfpRDZwLzbz27P1zxz++oBR8E28eHS9jik7YraKrZz9KXseuFKGX+LDPVk8RhW38RjYTd7tx1DZYlDOXDYIaWL2Aw00/ezqF9gUDMpy2WsjtxrnkjEt4v+013MepZ9FcimorAUD7NzisCf5GVayRYzuf2MLr/xh53DfpNDkvSiAhuqLn2E9BZ3J7D7s9GeMvGjh1ejbirAnep6k3AzQS18+9oGfPTwJyqHgJ+h6Cpy9bCFnJCd0Nfuq/3oMsMqXQaf7DZXGQpvOn4C4xUSlT7sli7Alu4hmFSI2YJSy360ilEUoidIZfrwxLwB4SU5AKG5XigMNt3kGIuWFFHM6n7q0Um5860CBQf2/cob7sKuea1mJvuwt47TnZkmK4QobBrH9aOFG0p7SuAjSGHR78T0XAUEJ/q4Gn8TIlcZF8u4zNgLSGWoNIQCv1pYXu+4Ysb8GYpegu0mZtD+OqSSY+3XhS5qkO6U5pLKKwaCZqhUESQKw/RytDTqRzbBvtJddAc0rVS/V1uX5yIEATTnwUrDakM+VSO7Cbos7RigaIBao6BNDXzZzN+CPiL8P1HgLcup33kZYW+sd5jLkMkxSF7w7eU2buuQyzhzMsHOX1sH5M7Brn2ulsA2FMdR+wMmb4U1eH4FTgA2X4kP0g6YmYSwFKlkNnGSCGoYJ72HGxL0FQGGRhFUhk0l17WYtfpHwqLGJ5F/DmGctneB4VQVciFJh+7Yc6zoiaqUFBlwn70dsgm7LAQpFo+JlUhJVX6MwMMRCKeBq2GMJH8ANRMhONBUdyitwBiYdSwc3KOrFRBwMZnwDFYogznB9g33qV/UUTjjiv2cHBigKF8rn3HcKsgC5DL9GNFwrazmg6+hv52VjwwPMHte94A/dtIS4qs2JHGZhsTqzLIiYgdFiE7T9Dt68GWIfWmLRrUdV4Amu7w1NQUhw8frv/dc889qyFlA6G96HiCS4t77rmn/jzRpQnReiCVCxiqH8nwFgE1Nq6kyeXTjG+/goyVqZdMmdw7iJ+fb5pHRXEyjc6Is2N9WENTAGTClG/H7sdIihteeZHXP/00o66DnS9xftd5uqPx/GrEyR0Y4BShEgmx7RDbi2BlUkjaDtrg2jENw0Ym2zbVpkjZQiZjs208jZWxQKDa55HK9jM2MMRYLgxgCMuY9LEQlLEZnsTO7AgKC4yeY3JohEUzxXFnDjebZWK0wLA1S38qj4hPykrRLy578n2MDcQHP1gTNyB2Q/iZmkQJL/3QtgF2jTRbIkSEwYk85PuR3dew59ob2T7UEDiWWAwtlEh5Pkv+EqWhSfqzKUYnhhkf6CCsQyGbSXuMDW3s4JZVFYfUoI70zSIyAnxcRF6lqk+tZI7JyUkefvjh1Zw+wQZHJw3FN369LfDFwN13383dd98d0CAS24ToUsASC/qGoDBHOhP0N8n099F33Y3o0XOEgTuICQs+Nt2u5hVors+lXCCIHMo6VHMOKWkwMd8WcvkSB22f89Y1VFMncTVgTE5qlP6yRXUAyFQ4NPnWyMzdFz9uPhNbemool2NRlDQO0Bdmm0dotjPYIf/M5Ppi5/AOXgdnm9lFzX7RN7mD23ZX+ObDJyA7gDcLlrpUR7cxbjkMSpVJ+2wjE12KjOw+ykvVG0GkruHsHp3kxJzFvKuUd+0Dgh73GSvDtvx2ymWYcDyuzHbWuKRvEmj4cIzC8MAeKu5JsFIYhYHRcXzfo7y40HxsOovsOMiBm67kwe8cRfPNa/f+soOnBRbvuorDt+1j4XyJbz10up2I3BCWHQQ0ZNM+tn0Zaig1qOo8cD/tzetPAXsBRCQFDAOXd8p4Ur6+J6bKU+tNwiWDpNLIrkPYqXYBmk0FobH58hzie/jpzs/NSBgmamHjDVbrjuy280mQS1LMTARGx/qUFlauD2t3npzVyGE4PvIaZvqujJ1r+2C2YxHCXVcc4vohl0BMKqX0KJXMEGT6sEUY6Q9MvbYt5AYGOl6XtkialCWk+0bID4/VHeYT+fEwzHcEas0AsoNs6x+jL6avu3QodqWWhWUJud2jgJLa1h7NFYVlR3JUcv3h6wCg5LMjDA9chYigCjd+99u55Xt/oONc6azN7qtHIVfLtm88D7Yq2MLwZGOBsC1zNX32OCJCNhXohiPb+ugbWb6pcT2xmiivyVAzQUTywNuA1qDqTwE/Fb5/N/Al3YrlZmvYwpcexalCz4Zvlw1q0UQ1SM30k0qT3R00WHritiuYG0nhWlme3fkuIK7fSQOjfRl2j+brW6/b2TDVSMQUJSL1iUQs7LFtWP3Nxgh/YBLPrtXraj6nVeuWNRE07DMRNnHVa1/PLa/aCUAmWw1yLCQNfRNkv/u93PJjPxbMETq1M1a8EaRqGXyx6zXDnn7DtQzfeisTEbOP1MOmmwXFweFtvGrnNTC0M6QvRWZkB0OVASwR9qXbk/9s2+LwHQd467//FSQdF3nYOMeufaPcefsk/hUDWNl+sntfxauv2MWB8UC4ZMP6Y32ZeG17+9AAmR1X1T/feOONZLZdER7bx2C63Ro7MBrcB1vSWGLTn0kx2hc8M/0jWezUxg4XrmE1VO4E7heRJwiastynqp8Wkd8UkXeGYz4IjIvIS8C/Af7d2pC7SeF0j9+/nKCqHfuhPHx2C5k460w6dDLbWV711u+Fyf1IOmCavm3hpZSq5VAOI7aGMoMcr55jsUMYUvTW9mdS9Wgk27bY+Zo3M7DtSgZyI5HxHcK2hyZaGtQCVoy/A3BSgabhRBIKD4z11bWnGuyxcVJjzcEoQ3aj533KCq5bEBCLsqWMDQTRa1k7z67R/PKVfCsFVhovM8zT23+QVN8IE/k8e9MDfF/f/thD8rk0lt3Z5JrOBYLIsi22Tzh1rULE4ntu2EHKrgmSFLfsH2Ew1ywstx8cZseVwwzmsliZhtYxPj4eajiBj2VC2itvj+1afXkVEdkrIveLyDNhKscvxoz5IRF5QkQeE5GHReQN4fa3hNtqfxUReVe4789F5Ghk3829aFmxD0VVnwBuidn+65H3FeBHVjr35kYXp3zhHGQ7q/9bBb5urkJ3q0UQPZRmseLSVAcrk+XVe4bJ9A3RqVTmWG6MRWsR3/I6jIiu3OG2/aNwNHifHR4LCyUKqZSFFy231aGXyHbtp0qaipUHiuzIjXKyNBs7Vluf7/BjXcPpspjos3MsHTpIpTLFYOTAzKgHi3BF6qom39uuHUO419zA46c6R7ltHx7jpLGppEfYnvPoz9qIK9jS0CDmXeqaGABDu2LnevW+Q5y98k6OlcpQpK2Xe/2SLQv1fTK2RTYiUA//wA8jlsVDn/poR3oB8hmbET1BtFvmsjG4HUJNpwUe8Muq+qiIDAKPiMh9qvpMZMwXgU+pqorIjcDfAteq6v0E6R+E7YBfAj4fOe7fqupHlkvi5tCjNjue+Nv1pmBdsXtg93qTcEmRTvmhaSqCMBhhIJtix3DA4FpX+HWIdG0v3m+HAZPS2W3n778i2N9pkdMXmMuGybJt4tp6yO2hq9+J5obq7YmzEVNLaz8XESgOQNbvYtJVxRkIrtPvy1MdyvODN+3EkhQjfWkyQz7+yFlSdvO9OLB3hPzISNv5AOyxwAe1c2ScnSM7ADg40U/rgu76XUOMpE/hXDXG0uv+Lbzl1yCdZ2x3u++oL2245W0H0LGwPlqHisi5668js2c3fcM5rv+uhnDK5PtIZ2PCh1uwe2T5+Wm5bPOiIjM4GpuOoKpnVPXR8P0S8CxBpG10TCHidugnPjPm3cDfq+qqK9omAmWtEOeUHz2wLqSsJ+LyUL5rz3etEzXrA8ep8oEPfIBHHn2UU6dP4zgO3q72lfH+8X7G0gcYTe9t2j6cOkG3HLZcWLclZQnXXXddx3GWuEGFW2A8leefv+FgfZ9kGsyv9m1ZCAztIr37Zrx04IuxQzNPjhRjA+0mMTcXYSKx0k05c3iM6e+5gRoP2z6U53sOHea2nTfUKzLHNZ5S1fq1glDLw8/fdHPHa44iZVlcu7OPdF+KiQjt2694NXH8NJNP9QyssfJ50rt20TeUId3Bh9IN+biaOy0o7NhHZu9YEynni2X+7gtf4gMf+AB0CYkPq5LcArSmciAiPywizwGfAf55zOE/DvxNy7b/HJrKfkdEekYGrJtAMaaM71/mpd0nrl5vCjYEBjMbu0LqWmOoP8v73vc+brv1Vnbv2kUmk4EY270IZKx+slagLey+Olh5Twym2TXSvtqt1ZOaGMhyxxXtiXOtrHDncI5tgykODB/kroGDDLcECqSuuoWRif563aj0eGP/0phFdnutDp2wRwe5afAgrdg33sf+8S7VEBQ0ZeEP5OrBKYKQT2cYyg7x46/9t1w9/KNM98XXvLMtQcXQlxui37JI790Ta1lu7USfuz4QtCP5DD9/11VkI9F2k3uD57FXO4F//ba1//3214qCxgiuWiCHOziElWsW3rfd/hr+xfvex/ve9z6A2JB4ERkAPgr8kqoutu5X1Y+r6rXAu4D/1HLsTuDVwOcim38NuBa4HRgDfrXX9a2bQHHdJU6e+uv1Ov2lwfYb1puCdUGrHT1az6uwBaovS0v/ipRtsXO4t6lj9zWj3HyDcNW2waYkN1EgvfKw0bRtkU+nmNh5M/k7frZtvzO8l6GRPkQEK7uL9Gi6o4nMQkhHIrb6wgik7SN5+lsKQg5E/T/5QEgSCRSIYqx/G/NDh5sZ7E0/DrsCN20tkTIVRsnZg8tbnAy+9a0d9w1N5HnVwSHsDlnnT29/J1z/Q7H7rDDhc2T76koKZfbvC+fJYLf4ycb6MnXHP8BIbvlpCBJEX3wUuFdVP9ZtrKp+FbhCRKKazo8CH1dVNzLuTFgZpQr8GfCaXnSsq8nL94rMzHyNI0f+O5632SOhYpzy6d421csNrSvFVvzlM395iShZR+SaM6+DgoZxP7XeDONHt72GKxgJPoS3du9o9xpcTbN2M+GIxZWT/YyNn2JxXyMD+yeu/QmuH3gH0FQVvglXXTGOfeNeJKa68PZo46y+ETjwBsgOkrYCQZsO/SV9QyPxk48dhGsaqW2xty6Ed33v4otx2LtjNzfvvyp231J2B2y/PnZf31Ag2DP5+Him277/Xdz0tnd0PnGoqV61t8Sh69vNfLYl+Hv66BsfCO69CNt65M2EZa0+CDyrqr/dYcyhWvkrEbkVyNKcG/hPaDF3hVpLbf53AT2T11eVKb+WWFh4FID5+W8zMfGWtv1VZ5p0aqSpzeemRLWQRHptaXQXtOk6525u9ztxxy9w7tmPwfSp+p5sOsXI9u2cO3eubR6Tzgbj6mXyO2s2V28fZNtMjpdzM3hhSRMRYSQ3Qs4aRJnjqu2DHHX7iPTiAgKty4rRurL9/YgF24Yi5w2F2r7cYUZSu5nIT9D3vT9AJnfhBVS9W26HIxG+OLgDXv1aAIbe/r1I/tIWac329ZPt6weak3j/0a27cX0DD70IBIEbe7b380LEDPmqN+3hyW/5IMqBt72NE89vg4JTz+npgjuB9wBPhiWxAP49sA9AVf8Y+MfAPxMRFygDP1Zz0od+l73AV1rmvVdEJgkexseA9/ciZMNw6cXFJ1hcfAKAvXvfC0C1eo7z5/+ewcEbmJz87nWkbhnolSl/9Ctw7fdfOnrWCVs5f7UXBsLM8V27dgW1JCJ4752BfyJ79dVUnn4Gayg07dgpRgZyzC6zkMwVP/g2nvx74dAOIV2YJp3pnN/Ql+3tIM6mAi1mdqHRNZCbfixoz3D0kwBce8NNTId95+1Umtcf3se3TnqUgRtvu5FXjr8S7JM0o+nA5NM31KPSMc3PUtSMavUF5iZ7KGL+CpmuNTACE4E/JntVvAbSOvfFwuEDo/X3+8OkyGiBlt0jeX46EijRN5QJElBLLtgZJDcEhemetKrqA/RQd1X1t+hQ9V1Vj9ESFRZuv6t9dHdsGIESxezs1ygWG5H6S0tPMzx8C5lMfAXPTYEzT2wJgdIJ/el+im5g1vSMR2qza5wrhpDNZnnLW0ItvEWgDITO2syePUz+ws9j3X9/5Mjl445rd3HVwFt4/ptfJZ3KEJ99At9zw3au2T4I9fJRDYd5K8b6sqSHcg1CxppzIcaufxX28y+S2dMeHp4Lc0B2DewiplJVV+T6cpBNESTUtNNlR5Mo831kD12JPdShy2UL0jt34s/Mkt61C/f0SinrjU4OfatWO6y31tGGyclJxsc3Ng/ckGHDUWFSw8mTf0XVaV+mGVNlZuZr6EZNmhtrj4zZirh+vGGT/tOn/nQdKdlYuGnvCDfvHek6phadlepQX2s1uGHXcJMDuI4Op7Bti9GJ+JYMmT27mfjZnyG9u12g9Kf7+cnrfpI7d9+5YhoHhwfJDfnttWE6IDU2hqSWt1Cx+kJ/zu74RMeLhf43vhH3wARmZPlO/aiGsnPnzotB1pph3QSKnVp5qYFTJ+/l5Ml76+HGJ0/9NceO/TELC49y5uzH8byldTS5dMiUPxQx1S1c/rWsOvVDObz9cP29ZzpngV9OGOvPMDHYPTqrL2OTj8lnOHz4MIcOBaab73vz67nt4I5Gs6YWGKmVCGnZsQL5k5aAweVTEZ9DOGEqbfPaW/dw3c3tOS8Hh4MFk3QpaTKcHe7azvhNV09GT1eHLTaWHbyWR4OApNz1zc7ymmZXe105gpMOvCFe4L3ndft59217Vjl3O6xMBn/3WEfz+PfesIP9430M5dMdqw5sZKyb3SFl93PgwM9x7NgfrOg4x5nmlVc+QF/fAZxqw/FVKZ/i+PE/ZXT0DkZHX7vW5K4efREV9dG/DLJ1tyA244/jQnH19kGqaZuU9TxX3Lyy9iyDg4MMhiGyw8PDbB8Z4rkO7Uwe2fWTWOrzulXSmU1Z7Mhcx9sP3MTBoYhG3TfMgQO3Msk5OPXNtuP+5Y3/squgWC5u2TfKLftGuf/+5gXX/qH9XDlyJS9PlfDy/Qy9/2dJ96WpHj1aH/Oq3UPkMxZXTg7w0HfiS8b0wuQv/HzHfROdepSsBa7+nrZNu0by/KNb106AXWqsqyHbslLs3v0TFArPUK2ep1JZvi2zVDoWu31u7ltUKqfYseOHQjOYoOphWRnm5h6kWHwB113giiva6qctG6oGVdMcedbJKb8FGelyhEe3IpKXG2yrwtju1Rf/i8OBAwcolUqMj4/j24t0Nfj20NpfvXuYva89yGSLNiUi7Lr6OjgWz6hX2tvm3bftqVfqXQ5EhH2D+zgqL3Ydc2jb2iXO3nXtNmaK1d4DLwC37B0J2MLu27qOO3jwINVqldHRUc6f79UcbWNg3T2j2ewk2eybgIDJLC09iapPofA8jjMVdH1bIcrlExw9+vtdxywuPgko2ex2stmgBajrLlCtnkXVJ50ZI5fdgecV8LwlcrmG7fLs2Y9TLp9k2/Z3MDf3TcZGX0+ftpXOA8AYD6MuqVrVV+PX6zptNUzkJ5guB36wp6af4tWTr15nijYv+vr6ap0pLxhp22oTJheKbEoo07y42Du28mTAbQevhO+8CNnVJRKuFDf18GetBXJpG4Z717fr7+/ntttu2zTCBDaAQIlCRBgaChKVhodvQdVw9Oj/uCjnmp7+Uv39vn0/TSo1wIkTf95xfFSjKZeDCqjnz30WgHPnPgPV73Aw831tQuXsuU9QGZmH+eNk6GPyO/eQve1nYs/hOLNMT3+JHTveidWhlPhmxht2v4FPvPQJAL526mtbQqDsuvn1HHvhofjV/BoraHGKiC2Kz6VXlG+YtJi97jryK8gDueOOO9p8oNsPXkn+te/ALbsdjrq0GN3ZT2GuQrZDYuOycOe/im+N3AGpMNAgl9v4idIbSqC0QsTi4MGfp1Q6xrlzn75o5zl+/IM9xywVnsOSFH0dutwBHC19kSv4saZtlfIpGNwJ88dxKHFq4Ytc4b8P7PZbPzv7AJXKKWZmvszg4KubtKLNAtV4pzwEoaNbDTe++33cyPvid64ifkTGB7jmdfHFNn3TPGHf7bezMzVA+cAE/RfS8a+W+Z/rnTtSQ8YWduzYsaLTLFf41LWeNfDfrBQ7rhhi275B7BWY7trQJTcoDqOjo9xwww0bPmQYNrhAARCx6e+/kiuu+EUcZwaRNCdO/Nklp2Pq/Od6DwKmpu5jePg2MpkxCoXng40tq5Gpr/wco2/8v0mFjYuM8VD1KZUCZ+PS0rMsLT3L+Ph3MTh4A5a1Odp/rhRzlTlGc6O9Byaow94/zviefbH7WsOA7eFhxu5804WfdPurIN3Xln+yXkjv20f+phvJ39rsgxgcz7PtwMUtRCoi2F1aNl+sc/Yqv7JRsOEFCoD6PtWXXyZ7VdDLedeuH8G2+0inR6hUzpBKDbCw+DiFpafx/UrvCS8OlSDC0tIzLC090757ZD/MBxnDS8yw9Mx/gYHt7Nz5bs6cie9fMzPzNWZmvtZkbvP9KkuFp5if+zbGOGzb9naMcRgcfNWGcXJ37MEB/OR1P8m9z94LwN889zfcfePdy05yNMZFxEK61XbfILjtttsoFjvXp9s/tB/cEsw5HccsFzfsGuLp04tkuhW9uhCIwHhnzfxSQyyLgTe+sW37da/ffBr95YZ1FShTf/AH2AMD+ItLjN/9vkYWaQtKDz1M6aGHqDzxBCPvfje5XMN0UjMLjY+9gfGxN6Dq93TIrwtG9tUFCgDTL8D8K5xZxqFHjvx3AIaGb2Jx4fGmfefP/0Mw3fSX2LPnPWQyzclnqj6Li49TLL7M6Ojryed3Y4zy4NFZvnVkhh+8aSeT2ZcZHLwey8pQqZwhk5ng9ILLM6cXuXHPCEP5FEsVj+1DgQ3XcWbwvEX6+tqTNlU9VJVq9Tzp9BiqHq+88j+BwA81nG02m9zzxD386DU/ylh2iGPH/giAAwd+ps2HVC6f4MyZj5HJjDM6egeut8DI8G185okzvHBuiV+46xC2RT2ib70xNDTEUJes7e+/4vuZyZ7g+WNfveBzXayFRFx+TIIE3bBuAsU7fx6M4i8uATBzzwcY/pmfJWUJViQj2BSLlB56CADn9Bke+quPM3rNIQ7cekNspm/pW99m+/CbWMy8xMTEWzgx+1cA7N//fgrF5yiXXqFaPYvnFfHOnye1bfsF/SDtVD/+Kiolz5YcLHEZKZwPurB1WKWXXZ+ppSr7xvrahEkrXj725yyUXbYN5sjldwf+mwjOnPkI4+Nv5P4nPs/JyhsY4BW+/sQ53nCFMDPzFfr6DtbNbp9++XWok+f5Iyfwc1ksHO66yrBjoFivuTaYvwH9yNNk7rqZkRvehKoyWvoGfnqeU057//ilwnP09x1i98BuThVOIeoByhe/80vsH9pP2k4zmh3lxSN/ymePvon944P1mPwzZ4KK3I4zEwRBAILFC+f6AOHvnzrLa3c+RaHwbFOL3I2M/uGVm/usS5SL/MO37I5tqLUs9E9AhzbCCS5vbAiTl1HlqVMLPPvpR9l/cBc/eFNDA1n4xjc5v1RhcjDLo8fncI/OcvaRJ/jS81N8z9tfy4GJhoPLOX6c0sOPAFCquJxP38uBn/sZ1FJsO8vw0E0MDd5I5fHHqZw6zsmlh9FKhcz+A/U5du36ESqVMwz234B77gynnU+10au+wT1T5NCt/4bUQB+mWuXoqfch4bUYVTxfWSi7jPZnGqaI4b2wcAKAYjXIHBiefj4wEE1chfZNohWDFWkN+/iJeQBOz5e5df8oKUsoVD2Gco0xviqn58ucmgtKkB+ZKgIzjPaluWZH8yr5Mw99HIAxafiEHj9hcdPeEUqloxhVimfOcfVL/yfqpZGUy+xVQTTWsZMp+nY1NIz5c9+gMvk8PPltznz1s7wyvh/dA+rOQK49zPP4sU9wvuAwvVjkXG4716cere97ZTHQ3m7fcTtPnjjLdvkbCrMjfO6J7+GqgS+2zTVfcpgrfYEhxinozRw7P8vS1APctHeEfHtV8A2JVCbs8ZFaHsHXj1/PTVe3l0avRUatpaIS/V2tGLf/i7UjBHjrtdv48vPnGchtCHaVoAvW9RsqOR7TBYfT8wEj3P+Vz3A09cP8zvkC//ptV2MqFR747NeBGpNsYMejD/Dx7Xt4z+v2U33uOUZ2TlL4xCcpVj2yaZtnTgcNy275nf+H4wvTFF73Bt72/a/HOXKExa9+jTPzZbaNHMY5baiOvINDN+4GbGw7S/FDn2d2KcgMtm9wcPdamPkF3EwWzeZITVcwjo97qoK1T5j5kw+SOulTeOMix088w9zwJJMLgcAoAFbG5i0/8B/RXQ4vffRHWfTL0J/DMikWCj45dwRmzwPng8iVbJrxs1dS+C6PocopjJWikNnOY0dnSJkyTqqffWN97BrJ4549y9HHn8PYKezdB/CzjUiZuZLL0vPPYy3Mw823kTp7msFT51nafRDUIMagdoqKa3jwyCyvvWKMpx58itxCUA5cUkGoZqpcxMv0sThX4ZVFnz2HhrBsO9AwVTk7XwZegJkXmBvLsL1fMKr1HuS+5/HEy2fpP/YSALmrXsXQ0gOcBNIpi+2DgSnN9Q1fOPoN+vVqBCHFPLPnPoyfGsHKWvjz86RGx6i4Ps+dDTTbffMzePaLzA0FTPnxE/O85mB8zamNBjuVAoQDN93ac2wqk6E/3c9Att3pfEHFhuw0HFjjFs1rbII7MNHPeyeSmnibASsSKCKyF/hLYDvBc3yPqv73ljFvBj4J1OojfExVf7N1Lsc3PHFyoXUzB7/4cV56xz/hj/7267zrzCOdaVFl9KWn+eZnW1sgN5CVPGfOp/F0gOoXv8DZlx8hZVnMFh2Ozlf4gtXHG+aHsO/9Jn/7eZ/v/Uffy9hVuyjOLlCouEwO5nji74pg+0zc8BILlQrHt/0Mr+/7GiKw+PmHEfMS6vZzbsbgfTKN2WczOX4ca2gCEIydxszAI393jAnX4eyjb6fPfIrMFQ4yPIZTFhzxsUVwSzZ9usTYkRuZmf8qS+khJkSo9p9luHwSu7wLNWXOjw6wcCIN35nGq1QYdM9QSo+z66Vv4k0MMZfehzfQj5/Oc+bYaUbMAu5XPkXJHyYtNrZXYezlp/GtDJXhMSzfQzyP+bMvkFtqzhJWhL4TpygO7MCkM5zFw3ztNNv6CpQ9w2yhxak8vcgxv8Ape5Zb9w6TtuDlLz5AdL07+sJzpP0icxMWhaE8J+dK7MTgThexLaFoP8Re62rSpKlS5dhMlYI7xYDzCrnRCeyDr8Zyq4jvI6UlxKTZtpRjNmvjTWT5xpGzHZ+JjQSxLF7/Iz+xrLFXv+4NzJw8Hlv2vZa6UePj+VrJlu3LCNt9468s6/yXG+yR4abXBGuDlWooHvDLqvqoiAwCj4jIfaraGtb0NVX9gW4TuV4jA15VmSucJZ8ZYCCd4VAoJFqt8G52hHxlAVd9VISBl5/mhe2HuHV6noIfZGC7lkXaGBzL4ktX30Te8ymntjM5m8YcOwP5DFbZ4bnJbRwZHeHU5Cg3zBa5aarIy//zC3xuTx9DJ5Yw+CyWB8AVLLLMz2QZGMhx7TcKzMmNiGVwrAoTfROcXaww2Hcbc5UnGJp9O2YW7jtk8+ZTc6T9SWynSoWnOQn0WUNY8qOMvHyW6mCR8vgUbp8gviF18jqqpWEK3gKz5irGHh8G8Vi63kOqLqoLnM9BZnYOdX3STooMKVygzw20iszULNuZJZ3aSXH7AL5XZUGU/nKVAc6jCIPPnQMMrpVn8PwZljI76HPOsWDZ2PYwfe4MVStPwc7h63Zm89uZXJpCgPLAIMVSmbmlU1QyQyDpoEujgqODjE6lsKcXsXNljr64iKCk/RKqhs+MfxfXl08w4iyRkR2MTZ/A6TPsOXEOT/tJC/hWBsvOkzFTCB6SGUO9Ofo9nwWjuP45qqfPk8GC7BhlVXwrB5pnZCnDzNI5qhs//2vFyOTy7Dx0TYe9zaXn+4ZHOPyD/4h09jK8EWuE7NVXYw8MkNq19XKjLiZWJFBU9QwEgUmquiQizxI0ZomJk+05G2cnhpkb2M+VT/wtlsnwzR17yaIcni7juRWcwUn2zTzP3958J/vmDSd3Xke6Oscbj53giwf3BeGmIpwcneC7nptlKdXP16/Ywfed9fjmjm0oUE4FkSpTY/u4b3SCCg63nD7D8eE+xoyNaIHHRyf49tgQFbvIsMny+tFryM8+w7mzs4z2XcmxYWHWv5k9Z5eYNBY2oH6aeX+OQtmjbOepyA7ODu9gXOEbu/pZsFJ8cu8A735hDgO4tk0xZbHNyWFJCuP3U104gL8k+NkKfeVMnSEUZBuZ/DZKnk8pt8jU8Z3s2P4F9NxtLOGSnXiFTG4eVJHFSWQoQ9U9xVJ2lMFCiv7QfdF/vsJ8epKS5ZBOuUyp8rWxN/C9C0+Qq56jRJ68BQuWxUJuBxldxHhTYA9i/AUGUvs5kR3joaGD3GyluaF8huzCOQw+RdlGzpujkurjVG4Xjw5chwW89cw/YEQ5o5Psd4q80D+BlXF4vu8QivDI0C1818JDfG3sZm6e8Rk54fNM/gAj7izbnJOksmNYmsInzbcHrmbcmWZSlxgTm1F7Pwu6BBkLFRC1cJGgnI0pskSBDMLg7OYwea01opamteiGeDlDRGLL7W9GLNNy9EPAfwIMgWLwS2FjLkTEB54Mhx5X1XeG2w8CHwLGgUeA96hq1zj3VftQwraRtwAPxux+nYg8TtC+51dU9enWAbPZDF8Z3U0GePmmd5D3LUw6x8mMz+7qcSrZHTw/PkBubISZ9ADlXXkGTQVX4Yv7d4NRXPVBDRkRPrFvH1cXBxgwwhe3gW8cLB+y4lGwfMq2YdKzqaRyPLj3IGMeTFs2KbXImyqlVAnbGJZY4rM7M4wNX005M0BGbapSIWuUFweHcXcarpmp8MSEjaeDfNfRkzy+M8tiXz/jbh8mErLqiMuDYws8sWOCtK94eHzv6X6KtvLsxARGPSyxqVo+RcthxMvz6vNz2M48I36OT145Sp+ZxMFlz8zb2OeUeXZ8GyU9wG2pr/JYfhdjUzsxfU/zSOa7cUsjpIYM/0j+F87CdtzFHaT2PcWCPUzKrfLVsTsoW1U+PnYL75z9Fg8PXEXVynDH4pM8OPhqptMjqLG5tfgyz/TvR7Ho9+dxxePZviGuKZ2kmhnmc2O3cKh8mrSWebrvIL6AKy5pA5/b8Vb2uAVOpwd5iZvwnJ3M5RbJF7Kkh8+i6vLFkVsR4KGJ1yFY2E4/z5KFtIWaFBPDLzDkFTiVHedIPsgO3lkt86ri48xO34ZTGUItiwO7voJBuW/0Dq4onybvlxj1FniY2wFWVt53EyNpkrnlsRzL0ReBT6mqisiNwN8C14b7yqp6c8y8vwX8jqp+SET+GPhp4I+6ESKr6R8iIgME/Yf/s6p+rGXfEGBUtSAi7wD+u6q29eIcP3BI3/6//1/1z4oiJnhthVqChOv3AS+NGh8UiimPvB/IxLQRPFsppPxg5R5Hd22rSFgiJAg9NWGtVqHW1UTqNNWOEwWVYG/Q88NCW/rhBdsIE++CORxTISV2Y26x6hxACDdawfw1OgIdKDy/SGzzsNzYGfxqHrc4gtge+JnwmBpNwv75RU5ut/GdwPTRbwR3ZArfyeOXB8iNnUH9FNWFbVi2D5Zi3DRWpgRqYaUcjJNDEdS3SfUtYmeqVOe3kR6cwS8PYPw0udFmn4VbHCFd6SPtp1CglDLBPbNdMsPNvbYBsuf2sZj26vfbSlfJDM60jasuTKJ+IyJqLHOa0kDzGOOncBa28Tf/8kceUdW1qZ64Qhw+fFgffrg9bPpi4bNPnuH5s0t836t3cO2O7h0Lf+e+F4DOHQUTbHyISNdnW0Q+Cfy+qt7XYf/rgD9V1evCzwVVHWgZI8AUsENVvfCY31DV7+1G24o1FBFJAx8F7m0VJgCquhh5/1kR+UMRmVDVpnaLimJahUeN68ZAQ3azmAo0LgnrFpVSjWZNajUEBiZGMKGohOeR8FRq6g3hNDIu9rjIqFZhUtumYbl8gLJfQlFSdn9jbjV1YacQmm606ZwmWoy8w/2ozDaygtWkoI0e5ZWRQYj42IsCLDRKOFRmG/Zj49sEp1WMk0ctwbjNiaZeaRgv6G2GuzTeNI+VriCWwXfyoIIRoZpqXIcA+GmcuV1Y6TLqp0gPzuIsjlNNe+GYQFgbN0t1fhuohdZuvGXANCfazTq7YM6QGZhDbA9nYRLV9c9BmZqaaqoEfPfdd3P33XdftPPVnfJrXW0ywYbBPffcwz333FP72FH77mY5EpEfBv5PYBsQ7UeeE5GHCTSd/6qqnyAwc81rjZnBSWL6zrdipVFeAnwQeFZVf7vDmB3AuVC1eg1BV8j25WYMVMIfhYbstVv4odUsNLRlbE24SOuYmK52iiAdNLXoMXUhFiYy9qX6G/tNQysK5GJDSFT8Mjk7X99X/y+iSVWNg1FD3s41zdJ4D3HSpSFEwxelyQai0fNJpJvqMq63fv9VEfzw7M1MXQEswffziBc/r4g01fEzTnAvnPntbVdU1+BMun4fg4Nazivhd6YW1cWx4BwXFkC7ZpicnORSaig1bJDKOwkuAqKLEhFp74VO3XL0UQL/yGLrflX9OPBxEXkjgT+l1k52v6qeEpErgC+JyJNAewjuMrDS5dydwHuAu0TksfDvHSLyfhF5fzjm3cBToQ/l94Af1xXY1VRCJmlJXZOIHQfBL0gkGB8zbmzhPFfNznQdA3DF/BxqSeMvujNyjFqCIx7YgqaEspbxa9qEJQEzDhl8xZSxRJCUBVLBNYX6VEXLQS2oWC4lqVBlAT/vYdLCglWhIoYlqQY5KZZF1XKpWC6eehg1geYjgrZWChBp3D9q99EK71Pk/kbvQ1R4WFbz9dbGWkogdhXBI1jIRL6nyPgaV7Mw9a+vNXFd7MYxdQEbHmtZYNmCCFT8Eo5fwkKwQqOnRL5LEaHfT+HLmZAuP3zdWmiYZhNsVfSyHEWhql8FrhCRifDzqfD1CPBlAg1nBhgRkZrSsQfo2cN8pVFeD9DjuVXV3wdWXkxLanao5TXUGqpW6XNdzg5ETH8SrPr3LixwYPosCzoHwKvnX2JQhvj6lde3zXPXyy8zrVPs0n3sKRm+vWdPIBxaxvl4OOpy1eyzvLDtICAYL02VCijkJEcVB1sy+OJii4OfyqIZULIYBK9SCu5eSihJCVwLUuClQke+raT4f9u789g47uuA4983szcvHaQOSrJIWYIOS9Gt6rAT146RBjasIjEUF01aNEABA02bGk3aJOgfBpoCbRo0aYM2NRBfQIzGtuwktpv4aOIaid0oriW1smxLsmlJpMRDFG/uPfP6xwx1mIqsOLs7tPZ9AELkaDlvyH3Lt/Ob3/weeBRxgHyhFA4NBr8Tz1VEPdR1yUkBwSEl71r/LPwj6+MzLDnixHDVIVi8MvxXFXWEspZoJE1cYgjCmBS4uKIEcdVRcBwmnQkciZEsJ5GpqhTus+hk8SmR1AxlVRp8By+8PhQICltjMc9EPI7jEtwcqR6eLyAxmDq7FhefcWKuh0cMXz1iIvjiII7ghE91UvIkkzkWOBmklMYrZBlgkpTzrgsrV7l334di6ssVjhwtB94OR442AUngrIjMBrKqWggLzC7ga+HjXiA4Qfge8IcE9xdeVoR3ygfZv61/gFfa28m6JeJpoeSB5sbIeM3BGluOCyhtY2MkvGFaij6a8EjmCqg/zurBVl7oXArArpOnKXgjlBIFRl0XJAlecAZR8M+vQrztnTdJOxnyboxBhiCVoHWin2KqmRtOdlFwE7zUPp8FExOUHOhrzPBb77yBZLJIKcv2E0N0LVzNQLoFvBJz8/0MJ+fhksR3imgcypKguTiJm/cYTgcXSjUt4DrghUUzMb14Oo6P7zts6jvCiNdCS3mcA9ecn9OQcJN4vocnRfx8jCy5c/+XcVMkgOHkUDC2VXYpxYoUPId0PMX6UwdpLCToSjXQNjJM74Kl9M4pnBsHa0vNwx9yaJocobchTTY+SdpLouKSdXO4MRffF9ZMjDPix/HiMVb1D5BtbqDodHOweSm5ZIl0phGv4KOeRylXpDOX43RrK4sK43wkXmD0bIEDs+cx7kDWTZwrXJ7jEPfLZJrS3FYWnvXKlMpKgRQNmSacAhTyRUric1ffWf67tZ+No93Mnd/GYFsrj3ktzMslLzoDqgczY6DPRGhq5OiQiBwMt30FuAZAVf8N+CTwByJSAnLAp8KisRq4V0R8ghGrv7tgdthfAd8Tka8CBwiK1mVFVlAa/SJrB46wbvFK1i9I8z+DR2lc0sjEaCfzm2EwB68WHGblJ9g60E1z22a68z6lawbRoodbSBBvm8+sE3PYPTHEnJYW8m2NnFjnkdQG8idypFbPZuLZMRJlj4YFOW7vGedUIkdecnhLhHgmg+vOIT4nhn+mzIb2zRwYOsic8WV8ddcq/vPRH+KvENYMvIYucPDb5rJwrB2kQPMml8mzYygemdESE5M9ZAuQLhe4fs0O9h97GZ0/h+LpPLF4jpeal9JQTnFbLM/rYz3M6i9wCp+Y75FKz+K2ndfTdfQoj5azuAubWJFLMzSeZ8XmnXx0+QoeeaubuSODOPEEa3LDPDI7RWoMfrvk0TOZYzLmsnDslxTcOK+0XofTPpt5mVYGx/uJS5x18z/Ep1Yu5eD+V9HsMdzf7eSOZdfzg+FGxnNjfGHxQrKlMsef+i80Cfu2r+Lnr/2CG0t53l67jvzgforxDr60eStjA0foe/kA+XyRhY2L2c9+ijsXsWtyglzvEMvn3YB3+ACOCNliCQSWD/Wwaf0G5jY10LpkKR8eHeOnP3wK31Vu+fit/Py558hqmTs/+8ckEgl836f80osMnB1h6EPb2Tq7ia1ugocOHOH6WB53cYwtJ3tp2rybNTcHE09WnzzOsX3BUj0zcL3pqjk/olxfhdQErnDk6O8JpgG/e/vLwCVbp4ZDYNt+nWN5X9OGK2HLli1634/vYzI3wc5rdlH0iiQuaES1b98+Mo2NLO55klTLVlLbbgaB/mw/R4ePsnHeRpoSTfi5Mlr2cZuC7/XV55Ejj1AoF9jevp3xviyp/lFW7thOLJFisK+fx3+yl8KCMresuYXO2Z1kMhlOnDhBV1cXnZ2ddHR0XHSso4VRSl6J1kwwucLzPe79v3vZs3IPrenWc4+ZKE7Q3tiOiFD2y/RO9nK87zjlnjKrN20kncqQQuka76KzqZN8Iccvn3yCdCLFDXt+n5cfe5ih4gRDGxpY/qZLQ8ss1t30MRwnuAjh+x7Z0VHiySSPjeTxyyWW7/sJqsr2T97J2e6TxBJJHup/ir5yA59bu4efnR3gZEHYNquZj7Sk2ff9R3FjcTbv/gQxJ8aJXAFPYVkmeS6GIIjjoL6PVy6fW8TwQv/7/I+YHBlm7Y230Nw2vfnP2Z5uvHKJt48dozw0wJJV101bs6q39zStrW3E4+9vNcdCNksskQjXxLrYe02trKZaTxueWsJ/94Z2lrVdfrjvG88fpbUxwWd2dNTm4EzFRZnb7yXSghLFTBgIik73eHfQ5CjkeR7Hjx+no6MD161dH4jzK8UKZ3tOkmpsomHW7Cv+/uzoCJ5XpmnO+ZmEfZN9NCWaaIhPXzG2kM0STyVxLtXj/Nc5bt+nmM+TzExfVfiix6nS9/ZR5ncuD66Z1Eg9FZRc0WP/yWF2LJt7UeuHSymUg3XjLtX6wXwwzOSCUpfrQTviXFRMAFzX5dpra9+V7sJeLL+qtevlZFpmTdu2oOFXLwr4XgXgSonjXNG+ROQya1CZSkgnXHYtv7KFAZIxa5plqsfephhjjKkIKyjGGGMqwgqKMcaYirCCYowxpiKsoBhjjKmIyArKmTPTlzGvlQtW7bS4V3HsqESV2/X4PNdb3JkusoIyOHjJBTNrot6SsB7/0EQpqtyux+e53uLOdDbkZYwxpiIiu1NeRMaBI5EEDxrURPE2st7iRhl7qaq2RRA3ytyux+e53uJChLn9XiIrKMYYY64uNuRljDGmIqygGGOMqYhICoqI/I6IHBGRt0TkSzWKuUREXhCR10XksIh8vhZxL4jvisgBEXm6xnFnicheEXlTRN4QkR01int3+Ht+TUT+XURStYgbpSjyOoxruW25PSPUvKCIiAv8C/BxYA3weyIyvTdv5ZWBv1DVNcB24E9qFHfK54E3ahhvyj8Bz6jqKmB9LY5BRBYBfwZsUdW1gAvcWe24UYowr8Fy23J7hojiDGUb8JaqdqlqkaBf8e5qB1XVXlXdH34+TpB8i6odF0BEFgO3At+pRbwL4rYAHyZs3amqRVUdqVH4GJAWkRiQAU7XKG5UIslrsNwGy+2ZIoqCsgjovuDrHmqU/FNEpAPYCOyrUchvAn8JTG8iX12dwBnggXBI4jsiMr3rVoWp6ing68BJoBcYVdXnqh03YpHnNVhuVztoneb2Fau7i/Ii0gg8Dvy5qo7VIN5twICqvlrtWJcQAzYB31bVjcAkUPWxfRGZTfDuvBNoBxpE5NPVjlvvLLctt6MWRUE5BSy54OvF4baqE5E4wQvuYVV9ohYxgV3A7SJynGAY5CYR+W6NYvcAPao69W51L8GLsNo+CryjqmdUtQQ8AeysQdwoRZbXYLmN5faMEEVBeQVYISKdIpIguKD1ZLWDStBr9z7gDVX9x2rHm6KqX1bVxaraQfCz/lRVa/KORlX7gG4RmerBezPweg1CnwS2i0gm/L3fTDQXbWspkrwGy+1wk+X2DFDznvKqWhaRzwHPEsyQuF9VD9cg9C7gM8AhETkYbvuKqv6oBrGj9KfAw+EfuS7gj6odUFX3icheYD/BDKQDwFW9ml6EeQ2W25bbM4QtvWKMMaYi6u6ivDHGmOqwgmKMMaYirKAYY4ypCCsoxhhjKsIKijHGmIqwgmKMMaYirKAYY4ypCCsoxhhjKsIKijHGmIqwgmKMMaYirKCYDxQRmSsiB8OPPhE5FX4+ISL/WoV4D4rIOyJyVwX29Q/hMX+hEsdmzExT88UhjflNqOpZYAOAiNwDTKjq16sc9ouquvc33YmqflFEJitxQMbMRHaGYq4KInKjiDwdfn6PiDwkIj8TkRMi8gkR+ZqIHBKRZ8LeIYjIZhF5UUReFZFnRWThFcR5UES+LSK/EJGuMO79IvKGiDwYPsYNH/daGPPuqv7wxswQVlDM1epa4CbgduC7wAuqug7IAbeGReVbwB2quhm4H/jbK9z3bGAHcDdBz5NvANcB60RkA8EZ1CJVXRvGfKBSP5QxM5kNeZmr1Y9VtSQihwj6kzwTbj8EdAArgbXA80GfJFyCHuFX4ilV1XDf/ap6CEBEDof7fhFYJiLfAv4DsJ7jpi5YQTFXqwKAqvoiUtLzjX98grwX4LCq7ni/+w73Vbhguw/EVHVYRNYDHwPuAvYAn30fcYz5QLEhL1OvjgBtIrIDgp7sInJdJXYsIq2Ao6qPA39NbXqdGxM5O0MxdUlViyJyB/DPItJC8Fr4JlCJtr2LgAdEZOoN25crsE9jZjxrAWzMZYQzt56uxLThcH/3UJupzsbUnA15GXN5o8DfVOrGRuDTgN2LYq5KdoZijDGmIuwMxRhjTEVYQTHGGFMRVlCMMcZUhBUUY4wxFfH/wyDCfV1vKOUAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "IC = IterCut(traces, fs, plotall=True, nplot=10)\n",
+    "cpileup = IC.pileupcut(cut=2)\n",
+    "cpileup1 = IC.cmask\n",
+    "cslope = IC.slopecut(cut=2)\n",
+    "cbaseline = IC.baselinecut(cut=2)\n",
+    "cchi2 = IC.chi2cut(cut=3)\n",
+    "cut_ic = IC.cmask"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This allows to calculate the efficiency of each cut, and we can see what cuts are going the heavy lifting. Note the importance of the denominator being the number of events that passed the previous cuts when calculating these efficiencies. If we were to divide by the number of traces each time, then this would be the total cut efficiency up to that cut. Below, we show the individual performance of each cut."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The pileup cut efficiency is 0.679.\n",
+      "The slope cut efficiency is 0.719.\n",
+      "The baseline cut efficiency is 0.889.\n",
+      "The chi2 cut efficiency is 0.986.\n",
+      "-------------\n",
+      "The total cut efficiency is 0.428.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"The pileup cut efficiency is {np.sum(cpileup)/len(traces):.3f}.\")\n",
+    "print(f\"The slope cut efficiency is {np.sum(cslope)/np.sum(cpileup):.3f}.\")\n",
+    "print(f\"The baseline cut efficiency is {np.sum(cbaseline)/np.sum(cslope):.3f}.\")\n",
+    "print(f\"The chi2 cut efficiency is {np.sum(cchi2)/np.sum(cbaseline):.3f}.\")\n",
+    "print(\"-------------\")\n",
+    "print(f\"The total cut efficiency is {np.sum(cut_ic)/len(traces):.3f}.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Thus, we see that the pileup cut is has the lowest efficiency, with the slope cut as a close second. If we were to remove the baseline and chi-squared cuts, then we would expect the PSD to not change noticeably. Let's test this expectation.\n",
+    "\n",
+    "Note that we can also plot the events passing/failing a specific cut by passing `verbose=True`, as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "IC = IterCut(traces, fs)\n",
+    "cpileup = IC.pileupcut(cut=2, verbose=True)\n",
+    "cslope = IC.slopecut(cut=2)\n",
+    "cut_ic = IC.cmask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The pileup cut efficiency is 0.679.\n",
+      "The slope cut efficiency is 0.719.\n",
+      "-------------\n",
+      "The total cut efficiency is 0.488.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"The pileup cut efficiency is {np.sum(cpileup)/len(traces):.3f}.\")\n",
+    "print(f\"The slope cut efficiency is {np.sum(cslope)/np.sum(cpileup):.3f}.\")\n",
+    "print(\"-------------\")\n",
+    "print(f\"The total cut efficiency is {np.sum(cut_ic)/len(traces):.3f}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psd_cut = calc_psd(traces[cut_ic], fs=fs, folded_over=True)[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "ax.loglog(f, np.sqrt(psd), label=\"Before IterCut\")\n",
+    "ax.loglog(f, np.sqrt(psd_cut), label=\"After IterCut\")\n",
+    "ax.set_ylim([1e-12,1e-7])\n",
+    "ax.set_xlabel('Frequency (Hz)')\n",
+    "ax.set_ylabel(r'PSD (A/$\\sqrt{\\mathrm{Hz}}$)')\n",
+    "ax.legend(loc=\"upper right\")\n",
+    "ax.grid(linestyle='dotted')\n",
+    "ax.tick_params(which='both',direction='in',right=True,top=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What if we reversed the cut order? How does this affect the cut efficiencies?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "IC = IterCut(traces, fs, plotall=False)\n",
+    "cchi2 = IC.chi2cut(cut=3)\n",
+    "cbaseline = IC.baselinecut(cut=2)\n",
+    "cslope = IC.slopecut(cut=2)\n",
+    "cpileup = IC.pileupcut(cut=2)\n",
+    "cut_ic = IC.cmask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The chi2 cut efficiency is 0.840.\n",
+      "The baseline cut efficiency is 0.739.\n",
+      "The slope cut efficiency is 0.718.\n",
+      "The pileup cut efficiency is 0.706.\n",
+      "-------------\n",
+      "The total cut efficiency is 0.315.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"The chi2 cut efficiency is {np.sum(cchi2)/len(traces):.3f}.\")\n",
+    "print(f\"The baseline cut efficiency is {np.sum(cbaseline)/np.sum(cchi2):.3f}.\")\n",
+    "print(f\"The slope cut efficiency is {np.sum(cslope)/np.sum(cbaseline):.3f}.\")\n",
+    "print(f\"The pileup cut efficiency is {np.sum(cpileup)/np.sum(cslope):.3f}.\")\n",
+    "print(\"-------------\")\n",
+    "print(f\"The total cut efficiency is {np.sum(cut_ic)/len(traces):.3f}.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "psd_cut = calc_psd(traces[cut_ic], fs=fs, folded_over=True)[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(10,6))\n",
+    "ax.loglog(f, np.sqrt(psd), label=\"Before IterCut\")\n",
+    "ax.loglog(f, np.sqrt(psd_cut), label=\"After IterCut\")\n",
     "ax.set_ylim([1e-12,1e-7])\n",
     "ax.set_xlabel('Frequency (Hz)')\n",
-    "ax.set_ylabel('PSD (A/$\\sqrt{\\mathrm{Hz}}$)')\n",
-    "ax.set_title(\"PSD before autocuts\")\n",
+    "ax.set_ylabel(r'PSD (A/$\\sqrt{\\mathrm{Hz}}$)')\n",
     "ax.legend(loc=\"upper right\")\n",
     "ax.grid(linestyle='dotted')\n",
     "ax.tick_params(which='both',direction='in',right=True,top=True)"
@@ -181,7 +654,422 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The change is huge! Which makes sense, as we have removed many of the pulses, muon tails, etc. Please note that there may still be \"bad\" traces in the data, as the autocuts function is not perfect. There may be more cuts that one would like to do that are more specific to the dataset."
+    "The PSD is essentially the same, but the pileup cut is no longer doing much, as we did it last (the previous three cuts ended cutting out a lot of pileup!). Thus, this shows that order does matter, and its worth thinking about what order makes the most sense in one's application.\n",
+    "\n",
+    "## Advanced Usage: Arbitrary Cuts\n",
+    "\n",
+    "For advanced users, `IterCut` includes an option to apply some arbitrary cut based on some function that isn't included by default (or some one-off user-defined function). As an example, let's add a maximum cut via `numpy.max`, but only finding the maximum up to some specified bin number in the trace."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "maximum = lambda traces, end_index: np.max(traces[..., :end_index], axis=-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "IC = IterCut(traces, fs, plotall=True)\n",
+    "cmaximum = IC.arbitrarycut(maximum, 200, cut=2)\n",
+    "cpileup = IC.pileupcut(cut=2)\n",
+    "cslope = IC.slopecut(cut=2)\n",
+    "cut_ic = IC.cmask"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The maximum cut efficiency is 0.721.\n",
+      "The pileup cut efficiency is 0.742.\n",
+      "The slope cut efficiency is 0.763.\n",
+      "-------------\n",
+      "The total cut efficiency is 0.408.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"The maximum cut efficiency is {np.sum(cmaximum)/len(traces):.3f}.\")\n",
+    "print(f\"The pileup cut efficiency is {np.sum(cpileup)/np.sum(cmaximum):.3f}.\")\n",
+    "print(f\"The slope cut efficiency is {np.sum(cslope)/np.sum(cpileup):.3f}.\")\n",
+    "print(\"-------------\")\n",
+    "print(f\"The total cut efficiency is {np.sum(cut_ic)/len(traces):.3f}.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Looking at the events that passed, we see that the maximum cut we applied allowed a \"bad\" trace (a trace with a pulse). This makes sense since we only looked at a portion of the trace for that cut, so it was not a very good cut. Fortunately, our pileup and slope cuts did a good job of removing the bad traces that passed the maximum cut.\n",
+    "\n",
+    "Lastly, it's worth simply printing out the docstrings of the three different supported outlier algorithms. The `kwargs` vary considerably between each of them, so to specify them in `IterCut`, one must know which ones to use! For example, `iterstat` has the `cut` `kwarg`, which we were using in the above examples (because `iterstat` is the default outlier algorithm for these automated cut routines)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from qetpy.cut import iterstat, removeoutliers\n",
+    "from astropy.stats import sigma_clip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0miterstat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprecision\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_unbiased_estimates\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m\n",
+       "Function to iteratively remove outliers based on how many standard\n",
+       "deviations they are from the mean, where the mean and standard\n",
+       "deviation are recalculated after each cut.\n",
+       "\n",
+       "Parameters\n",
+       "----------\n",
+       "data : ndarray\n",
+       "    Array of data that we want to remove outliers from.\n",
+       "cut : float, optional\n",
+       "    Number of standard deviations from the mean to be used for\n",
+       "    outlier rejection\n",
+       "precision : float, optional\n",
+       "    Threshold for change in mean or standard deviation such that we\n",
+       "    stop iterating. The threshold is determined by\n",
+       "    np.std(data)/precision. This means that a higher number for\n",
+       "    precision means a lower threshold (i.e. more iterations).\n",
+       "return_unbiased_estimates : bool, optional\n",
+       "    Boolean flag for whether or not to return the biased or\n",
+       "    unbiased estimates of the mean and standard deviation of the\n",
+       "    data. Default is False.\n",
+       "\n",
+       "Returns\n",
+       "-------\n",
+       "datamean : float\n",
+       "    Mean of the data after outliers have been removed.\n",
+       "datastd : float\n",
+       "    Standard deviation of the data after outliers have been\n",
+       "    removed.\n",
+       "datamask : ndarray\n",
+       "    Boolean array indicating which values to keep or reject in\n",
+       "    data, same length as data.\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/Library/Python/3.10/lib/python/site-packages/qetpy/cut/_cut.py\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "?iterstat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0mremoveoutliers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmaxiter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskewtarget\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.05\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m\n",
+       "Function to return indices of inlying points, removing points\n",
+       "by minimizing the skewness.\n",
+       "\n",
+       "Parameters\n",
+       "----------\n",
+       "x : ndarray\n",
+       "    Array of real-valued variables from which to remove outliers.\n",
+       "maxiter : float, optional\n",
+       "    Maximum number of iterations to continue to minimize skewness.\n",
+       "    Default is 20.\n",
+       "skewtarget : float, optional\n",
+       "    Desired residual skewness of distribution. Default is 0.05.\n",
+       "\n",
+       "Returns\n",
+       "-------\n",
+       "inds : ndarray\n",
+       "    Boolean indices indicating which values to select/reject, same\n",
+       "    length as `x`.\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/Library/Python/3.10/lib/python/site-packages/qetpy/cut/_cut.py\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "?removeoutliers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m\n",
+       "\u001b[0msigma_clip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0msigma\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0msigma_lower\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0msigma_upper\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mmaxiters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mcenfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'median'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mstdfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'std'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mmasked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mreturn_bounds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mgrow\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m\n",
+       "Perform sigma-clipping on the provided data.\n",
+       "\n",
+       "The data will be iterated over, each time rejecting values that are\n",
+       "less or more than a specified number of standard deviations from a\n",
+       "center value.\n",
+       "\n",
+       "Clipped (rejected) pixels are those where::\n",
+       "\n",
+       "    data < center - (sigma_lower * std)\n",
+       "    data > center + (sigma_upper * std)\n",
+       "\n",
+       "where::\n",
+       "\n",
+       "    center = cenfunc(data [, axis=])\n",
+       "    std = stdfunc(data [, axis=])\n",
+       "\n",
+       "Invalid data values (i.e., NaN or inf) are automatically clipped.\n",
+       "\n",
+       "For an object-oriented interface to sigma clipping, see\n",
+       ":class:`SigmaClip`.\n",
+       "\n",
+       ".. note::\n",
+       "    `scipy.stats.sigmaclip` provides a subset of the functionality\n",
+       "    in this class. Also, its input data cannot be a masked array\n",
+       "    and it does not handle data that contains invalid values (i.e.,\n",
+       "    NaN or inf). Also note that it uses the mean as the centering\n",
+       "    function. The equivalent settings to `scipy.stats.sigmaclip`\n",
+       "    are::\n",
+       "\n",
+       "        sigma_clip(sigma=4., cenfunc='mean', maxiters=None, axis=None,\n",
+       "        ...        masked=False, return_bounds=True)\n",
+       "\n",
+       "Parameters\n",
+       "----------\n",
+       "data : array-like or `~numpy.ma.MaskedArray`\n",
+       "    The data to be sigma clipped.\n",
+       "\n",
+       "sigma : float, optional\n",
+       "    The number of standard deviations to use for both the lower\n",
+       "    and upper clipping limit. These limits are overridden by\n",
+       "    ``sigma_lower`` and ``sigma_upper``, if input. The default is 3.\n",
+       "\n",
+       "sigma_lower : float or None, optional\n",
+       "    The number of standard deviations to use as the lower bound for\n",
+       "    the clipping limit. If `None` then the value of ``sigma`` is\n",
+       "    used. The default is `None`.\n",
+       "\n",
+       "sigma_upper : float or None, optional\n",
+       "    The number of standard deviations to use as the upper bound for\n",
+       "    the clipping limit. If `None` then the value of ``sigma`` is\n",
+       "    used. The default is `None`.\n",
+       "\n",
+       "maxiters : int or None, optional\n",
+       "    The maximum number of sigma-clipping iterations to perform or\n",
+       "    `None` to clip until convergence is achieved (i.e., iterate\n",
+       "    until the last iteration clips nothing). If convergence is\n",
+       "    achieved prior to ``maxiters`` iterations, the clipping\n",
+       "    iterations will stop. The default is 5.\n",
+       "\n",
+       "cenfunc : {'median', 'mean'} or callable, optional\n",
+       "    The statistic or callable function/object used to compute\n",
+       "    the center value for the clipping. If using a callable\n",
+       "    function/object and the ``axis`` keyword is used, then it must\n",
+       "    be able to ignore NaNs (e.g., `numpy.nanmean`) and it must have\n",
+       "    an ``axis`` keyword to return an array with axis dimension(s)\n",
+       "    removed. The default is ``'median'``.\n",
+       "\n",
+       "stdfunc : {'std', 'mad_std'} or callable, optional\n",
+       "    The statistic or callable function/object used to compute the\n",
+       "    standard deviation about the center value. If using a callable\n",
+       "    function/object and the ``axis`` keyword is used, then it must\n",
+       "    be able to ignore NaNs (e.g., `numpy.nanstd`) and it must have\n",
+       "    an ``axis`` keyword to return an array with axis dimension(s)\n",
+       "    removed. The default is ``'std'``.\n",
+       "\n",
+       "axis : None or int or tuple of int, optional\n",
+       "    The axis or axes along which to sigma clip the data. If `None`,\n",
+       "    then the flattened data will be used. ``axis`` is passed to the\n",
+       "    ``cenfunc`` and ``stdfunc``. The default is `None`.\n",
+       "\n",
+       "masked : bool, optional\n",
+       "    If `True`, then a `~numpy.ma.MaskedArray` is returned, where\n",
+       "    the mask is `True` for clipped values. If `False`, then a\n",
+       "    `~numpy.ndarray` and the minimum and maximum clipping thresholds\n",
+       "    are returned. The default is `True`.\n",
+       "\n",
+       "return_bounds : bool, optional\n",
+       "    If `True`, then the minimum and maximum clipping bounds are also\n",
+       "    returned.\n",
+       "\n",
+       "copy : bool, optional\n",
+       "    If `True`, then the ``data`` array will be copied. If `False`\n",
+       "    and ``masked=True``, then the returned masked array data will\n",
+       "    contain the same array as the input ``data`` (if ``data`` is a\n",
+       "    `~numpy.ndarray` or `~numpy.ma.MaskedArray`). If `False` and\n",
+       "    ``masked=False``, the input data is modified in-place. The\n",
+       "    default is `True`.\n",
+       "\n",
+       "grow : float or `False`, optional\n",
+       "    Radius within which to mask the neighbouring pixels of those\n",
+       "    that fall outwith the clipping limits (only applied along\n",
+       "    ``axis``, if specified). As an example, for a 2D image a value\n",
+       "    of 1 will mask the nearest pixels in a cross pattern around each\n",
+       "    deviant pixel, while 1.5 will also reject the nearest diagonal\n",
+       "    neighbours and so on.\n",
+       "\n",
+       "Returns\n",
+       "-------\n",
+       "result : array-like\n",
+       "    If ``masked=True``, then a `~numpy.ma.MaskedArray` is returned,\n",
+       "    where the mask is `True` for clipped values and where the input\n",
+       "    mask was `True`.\n",
+       "\n",
+       "    If ``masked=False``, then a `~numpy.ndarray` is returned.\n",
+       "\n",
+       "    If ``return_bounds=True``, then in addition to the masked array\n",
+       "    or array above, the minimum and maximum clipping bounds are\n",
+       "    returned.\n",
+       "\n",
+       "    If ``masked=False`` and ``axis=None``, then the output array\n",
+       "    is a flattened 1D `~numpy.ndarray` where the clipped values\n",
+       "    have been removed. If ``return_bounds=True`` then the returned\n",
+       "    minimum and maximum thresholds are scalars.\n",
+       "\n",
+       "    If ``masked=False`` and ``axis`` is specified, then the output\n",
+       "    `~numpy.ndarray` will have the same shape as the input ``data``\n",
+       "    and contain ``np.nan`` where values were clipped. If the input\n",
+       "    ``data`` was a masked array, then the output `~numpy.ndarray`\n",
+       "    will also contain ``np.nan`` where the input mask was `True`.\n",
+       "    If ``return_bounds=True`` then the returned minimum and maximum\n",
+       "    clipping thresholds will be be `~numpy.ndarray`\\s.\n",
+       "\n",
+       "See Also\n",
+       "--------\n",
+       "SigmaClip, sigma_clipped_stats\n",
+       "\n",
+       "Notes\n",
+       "-----\n",
+       "The best performance will typically be obtained by setting\n",
+       "``cenfunc`` and ``stdfunc`` to one of the built-in functions\n",
+       "specified as as string. If one of the options is set to a string\n",
+       "while the other has a custom callable, you may in some cases see\n",
+       "better performance if you have the `bottleneck`_ package installed.\n",
+       "\n",
+       ".. _bottleneck:  https://github.com/pydata/bottleneck\n",
+       "\n",
+       "Examples\n",
+       "--------\n",
+       "This example uses a data array of random variates from a Gaussian\n",
+       "distribution. We clip all points that are more than 2 sample\n",
+       "standard deviations from the median. The result is a masked array,\n",
+       "where the mask is `True` for clipped data::\n",
+       "\n",
+       "    >>> from astropy.stats import sigma_clip\n",
+       "    >>> from numpy.random import randn\n",
+       "    >>> randvar = randn(10000)\n",
+       "    >>> filtered_data = sigma_clip(randvar, sigma=2, maxiters=5)\n",
+       "\n",
+       "This example clips all points that are more than 3 sigma relative\n",
+       "to the sample *mean*, clips until convergence, returns an unmasked\n",
+       "`~numpy.ndarray`, and does not copy the data::\n",
+       "\n",
+       "    >>> from astropy.stats import sigma_clip\n",
+       "    >>> from numpy.random import randn\n",
+       "    >>> from numpy import mean\n",
+       "    >>> randvar = randn(10000)\n",
+       "    >>> filtered_data = sigma_clip(randvar, sigma=3, maxiters=None,\n",
+       "    ...                            cenfunc=mean, masked=False, copy=False)\n",
+       "\n",
+       "This example sigma clips along one axis::\n",
+       "\n",
+       "    >>> from astropy.stats import sigma_clip\n",
+       "    >>> from numpy.random import normal\n",
+       "    >>> from numpy import arange, diag, ones\n",
+       "    >>> data = arange(5) + normal(0., 0.05, (5, 5)) + diag(ones(5))\n",
+       "    >>> filtered_data = sigma_clip(data, sigma=2.3, axis=0)\n",
+       "\n",
+       "Note that along the other axis, no points would be clipped, as the\n",
+       "standard deviation is higher.\n",
+       "\u001b[0;31mFile:\u001b[0m      /Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/astropy/stats/sigma_clipping.py\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "?sigma_clip"
    ]
   },
   {
@@ -195,7 +1083,7 @@
  "metadata": {
   "hide_input": false,
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -209,7 +1097,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.10.1"
   },
   "latex_envs": {
    "LaTeX_envs_menu_present": true,
@@ -244,5 +1132,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }